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Graduate School

Research Interest Statement Samples That Worked

A good research interest statement sample can be hard to find. Still, it can also be a beneficial tool for writing one and preparing for a grad school application or post-graduate position. Your research interest statement is one of the key components of your application to get into grad school . In a few cases, admissions committees have used it instead of an interview, so it is important to write a strong essay. We’ve provided research interest statement samples for you in this blog post. We have also included several tips that will help you write a strong statement to help improve your chances of getting accepted into your dream program.

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Article Contents 13 min read

What is a research interest statement.

A research interest statement is essential for most graduate school, post-graduate, and academic job applications. Sometimes, it may be referred to it as a " statement of intent " or "description of research interests." While they are similar, research interest statement may require some additional information. Generally, your statement will pride a brief overview of your research background, including your past research experience, the current state of your research, and the future research you'd like to complete, including any required equipment and collaborations. It is usually written in the form of a short essay. Still, of course, different graduate programs can have specific requirements, so make sure to check the program you are applying to and read the particular instructions that they give to ensure your research interest statement meets their requirements.

Your research statement plays a big role in the committee's decision. Ultimately, they are trying to figure out if you, as a person, and your research, would be a good fit for their program. A strong statement can help you convince them of this by showing your passion for research, your research interests and experience, the connection between your interests and the program, and the extent of your writing skills which is really important for paper and grant writing, and thus for earning money for your research!

Undergraduate programs are centered around classes, but graduate and post-graduate programs are all about your research and what your research contributes to your discipline of choice. That is why a research interest statement is so important, because it is essentially a way for you to share this information with the program that you have chosen.

Writing a strong statement can be helpful to you, as well. Having to explain your research and talk about your goals coherently will give you a chance to define your future research and career plans, as well as academic interests.

What Should Your Research Interest Statement Include?

The exact requirements of the research interest statement can vary depending on where you are applying and for what position. Most faculty positions will need you to produce a separate file for your statement, and most of the time, for an academic program, you can simply include your statement within your CV for graduate school .

Need to prepare your grad school CV? This video has helpful advice for you:

Unless otherwise stated by the program or faculty that you are applying to, your statement should be one to two pages long or between 600 and 1000 words. If you are including your description of interest statements on your resume, then it would be ideal to keep it between 400 and 600 words. Most programs will give you guidelines for the research interest statement so make sure you follow those. They rarely include a specific question or prompt but they might ask for a particular detail to be included in your interest statement. For example, a university’s requirements may look something like this: “In your statement of interest, you should detail your study and/or research interests and reasons for seeking admission. You must identify a faculty member from the Anthropology of Department with whom you are interested in being your advisor. The length of a statement of intent should be 2 pages in length (single-spaced, Times New Roman font size 12 point)”

Your statement should include a brief history of your past research. It should tell the committee what you have previously set out to answer with your research projects, what you found, and if it led to any academic publications or collaborations. It should also address your current research. What questions are you actively trying to solve? You will need to tell the committee if you’ve made any progress, what you have found, if you are connecting your research to the larger academic conversation and what the larger implications of your work actually are. Finally, you want to talk about the future of your research. What further questions do you want to solve? How do you intend to find answers to these questions? What are the broader implications of your potential results, and how can the institution you are applying to help you?

Before we show you some examples, let's go over a few essential things that you need to keep in mind while writing your research interest statement to make sure it is strong.

Preparation

Give Yourself Ample Time: Much like with other components of your application, like your CV or a graduate school interview question , preparation is the key to success. You should give yourself enough time to thoroughly research the program or faculty you are applying to, gather all the information or documents that can aid you in writing, and then write and rewrite as many times as you need to. Give yourself at least 6 weeks to draft, redraft, and finalize your statement. You may also want to consider investing in a graduate school admissions consultant as they have more experience writing these types of essays and may see things that you can’t.

Research the Program/Faculty: The purpose of your research interest statement is to tell the committee all about your research plans, how it will contribute to the field and convince them that not only is their institution is the best place for it, but that you will be an asset to them as a candidate. To do this, you need to know what kind of candidate they are looking for, what kind of research they have been interested in in the past, and if there is anything particular that they require in the research interest statement. Remember, expectations for research statements can vary among disciplines and universities, so it is essential that you write for the right audience.

The Format / Writing Style

Your research statement should be in an academic essay format. It needs to be concise, well-organized, and easy to read. For graduate school, PhD or post-doc positions, your research interest statement will usually be a part of your resume. We recommend that you stick to the following things when it comes to the format:

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The Content

Introduction: This is a functional academic document, unlike college essays or personal statements, so you want to go straight to the point and focus on the key information that needs to be conveyed. You want to use this paragraph to tell the committee why you are writing this statement. In other words, you should clearly state what kind of research you are interested in pursuing at the institution in question and explain why you are drawn to the subject.

Body: This is your “why and how” paragraphs. In 2 or 3 paragraphs, you should expand on your interest, background, accomplishments, and plans in the field of research. Depending on your level of experience, you may use this time to talk about your previous or current research. If you do not have much experience, then you may use this paragraph to talk about any skills or academic achievements that could be relevant.

Conclusion: To conclude, you should restate your interest and tie it back to the research you intend to continue at the university. Be specific about the direction you’d like to take the research in, who you’d like to work with, and what the institution has that would help you. We also suggest including a concise statement that reiterates your unique suitability for the program, and what you can contribute to it and your chosen field.

Common Pitfalls to Avoid

Being Too Personal: Often, students will confuse the statement of purpose and the research interest statement or letter of intent. It is essential to understand the difference between these two documents because some programs will ask for both of these documents. There is quite a bit of overlap between the two essays, so they are very easy to mix up. Both documents ask applicants to focus on their research interests, relevant past academic & professional experiences, and their long-term goals in the field. However, a statement of purpose is more of a personal statement that describes your journey and overall suitability for a program. In contrast, a research interest statement is a more formal academic document specific to the research you intend to pursue in a program. It will include many details such as the faculty members you want to work with, the program facilities and resources you wish to use, etc.

Not Following Guidelines: As mentioned earlier, these statements can vary depending on the discipline and the faculty. It is crucial that you review all the institution's guidelines and follow them. Some schools will have a specific word count, others may simply give you a maximum and minimum word count. Others may even have a specific prompt or question that you will need to answer with your essay. You want to make sure that you are following the instructions provided by the program.

Using Too Much Jargon: Your statement will be read by people who are most likely knowledgeable, but they might not be from your specific field or specialty. We understand that it may not be possible to be clear about your research without using a few niche words, but try to keep them at a minimum and avoid using acronyms that are not well known outside of your specialty.

Having One Generic Statement: The requirements of your research statement are different from one school to another, and you should tailor your letter to the program you are writing to. We know that the research and experience you are talking about are still the same, but the qualities and aspects of that experience you play up should help you appeal to the school you are applying to. For example, if you are applying to a very collaborative program, you should highlight your collaborations and your experience working as part of a team.

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Research of Interest Statement Samples

Below are sample research interest statements for reference:

Research Statement of Interest 1

Jennifer Doe

As the child of an immigrant, I have always been fascinated by the relationship between identity, geographic territory, and economic development. With the rise of globalization, there is a broader effort in the social sciences to study the link between cultural identity, human mobility, and economic development in the contemporary world. I hope that my research will contribute to this as well. I am applying to the X University Global Anthropology program, as it is the best place for me to explore my research interests and channel them towards my long-term goals. I believe that my undergraduate education and the research experience it gave me have prepared me to undertake advanced research projects, thus making me an excellent candidate for this program.

I spent the first two years of undergraduate studies taking psychology courses. I went to university knowing that I wanted to learn about human behavior and culture. I was thirsty for information, but I did not know what kind of information just yet. It wasn’t until I took an elective anthropology class in my second year and started discussing identity in anthropology that something clicked. Unlike many other social sciences, anthropology explores the different ways that cultures affect human behavior and that connected right away with my experience as an immigrant. I have been passionate about the subject ever since, and I intend on spending my career exploring this topic further.

In the long run, I am interested in understanding how geography affects the construction of one’s cultural identity, especially when it comes to immigrants. Literature already exists on the topic, but most of it examines the upper levels of this process of social reproduction, concentrating on the roles of governments and associations in promoting ties between migrants and their homelands. Prof. Jane Doe Smith is one of the anthropologists researching the transnational migration experience, and I hope to have the opportunity to work with her at X University.

I was fortunate to be part of a summer research experience as an undergraduate, which took place in several west African countries, including Mali, Senegal, and Nigeria. Dr. Sam Smith was leading the research, and my time on his team allowed me to gain hands-on experience in research while living abroad. One of the things that I did almost daily was interview the subjects in a controlled environment, and sometimes I got to be a part of traditional ceremonies. I learnt how to observe without being intrusive and how to interact with clinical subjects. The experience only strengthened my curiosity and conviction that today more than ever, we need to understand what identity is and the different factors that can affect it.

I enrolled in several challenging research-oriented courses such as Applied Statistical Inference for the Behavioral Sciences, Principles of Measurement, and more throughout my degree. I was also able to work as a research lab assistant for one of my mentors, Mr. Jonathan Smith. I worked with him while he studied the relationship between identity, culture and “self.” My main duties were to assist in the creating of surveys and other assessment materials, administer written and verbal tests to participants, create literature reviews for potential resources, create summaries of findings for analysis and other office duties such as reserving testing rooms. This particular experience allowed me to get some hands-on experience with data collection, data analysis, report preparation and the creation of data summaries.

I know that there is a lot more that I can learn from the X University. I have seen the exemplary work in anthropology and other social studies done by the staff and alumni of this school. It has inspired and convinced me beyond the shadow of a doubt that pursuing my graduate studies in your program meets my personal, academic, and professional goals objectives.

My advanced research skills, passion for anthropology and clinical research, as well as my academic proficiency make me the ideal candidate for X University's Clinical Global Anthropology Master’s program. I believe that X University’s rigorous curriculum and facilities make it the perfect place for me, my long-term career goals and my research commitments.

Jamie Medicine

I am applying to the brain and development master's program of X university because it is one of the few universities that not only has a program that combines the two disciplines that I majored in my undergraduate studies: Psychology and Linguistics; but also because it is a program that I know would allow me to grow as a researcher, contribute to my chosen fields and achieve my long-term career goals. My research is motivated by two of my favorite things: language and music. To be more specific, hip-hop music. In 20xx, Rollingstone magazine published an article stating that hip hop was now more popular than rock and roll. The rise in popularity of this initially very niche genre has sparked a conversation in specific academic fields such as psychology, sociology, linguistics, and English about the use of language within it but also the effects that it can have on those who listen to it. I hope to one day contribute to that conversation by studying the relationship between hip-hop music and vocabulary development, and I believe that pursuing this particular research interest at X university is the best way for me to do that.

There are many potential places this research may lead me and many potential topics I may explore. Furthermore, there are many things that it would allow us to learn about the effect that music has on our brains and society at large.

I was fortunate enough to work under Dr. Jane D. Smith at the University of X for two years while conducting her recently published study on vocabulary instruction for children with a developmental language disorder. During my time in her lab, I interviewed participants and put together evaluation materials for them. I was also responsible for data entry, analysis, and summarizing. This experience gave me the skills and the knowledge that allowed me to exceed expectations for my final research project in undergraduate school.

One of my undergraduate degree requirements was to complete a small independent study under the supervision of a professor. I chose to study music's effect on children's vocabulary development. Several studies look for ways to decrease the million-word gap, and I wanted to see if this thing that I am so passionate about, music, had any effect at all. I compiled multiple literature reviews and analyzed their results, and I found that there is indeed a correlation between the number of words that a child spoke and the amount of music that they were exposed to.

This research is currently being explored on a larger scale by Prof. John Doe at X university and learning from him is one of the many reasons I have applied to this program. I took several research methodology courses throughout my degree, and I would love to enroll in the Applied Statistics for Psychology course he is currently teaching to build upon the foundational knowledge I already have. There are several other faculty members in the brain and language department with whom learning from would be a dream come true. In addition to that, working with them is a real possibility because the research they are currently doing and the research I hope to pursue are greatly matched.

I genuinely believe that X university has the curriculum and facilities that I need to meet my long-term goals and research commitments. I also believe that my academic achievements, eagerness to learn, and passion make me the perfect candidate for your program.

Interested in some tips to help you manage grad school once you're there? Check out this video :

It is essentially an essay that provides a brief overview of your research experience and goals. This includes your past research experience, the current state of your research, and the future research you'd like to complete. It is also sometimes referred to as a "statement of intent" or "description of research interests."

This statement tells the admissions committee more about you as an applicant. It gives you the opportunity to tell them more about your research (past, present, and future) and show them that you are a good fit for their institution.

No. Some graduate school programs might ask for a statement of purpose and a writing sample instead, or they could ask for none of the above. You should always check the requirements of the specific program that you’re applying to.

Generally, your statement should be 400 to 1000 words or about two pages long. That said, most programs will give you guidelines so make sure you check those and follow them.

You certainly can but we do not recommend it. You should always tailor your statement to the program you are applying to. Remember that the aim is to convince the admissions committee that you are a good fit for their school so make sure you highlight the qualities and values that they care about.

We recommend that you doublecheck the information provided by your chosen program as they often have specific instructions for the format of the letter. If none exist, make sure that the format of your document is pleasing to the eye. Stick to easily legible fonts, a decent font size, spacing, margins, etc. Also, it is best to keep the content of the letter concise and professional.

We recommend giving yourself at least 6 weeks to write your statement. This will give you ample time to brainstorm, write a strong letter, read it again and edit it as many times as necessary. It also gives you enough time to get expert eyes on your letter and work with them to improve it if you wish.

No. Research interest statements are often required for post-graduate school applications and for other positions in academic faculties.

Absolutely! You can always reach out to admissions professionals, such as graduate school admissions consultants or grad school essays tutors .

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Thank you for your excellent site

BeMo Academic Consulting

You are very welcome, Rasool!

Sadia Sultana

hello, thanks for providing guide line for Research Interest statement, the important aspect of scholarship application. Kindly guide me, What should be the title of the Research Statement. Thanks

Hi Sadia! Check the requirements of your school first. They might provide some info on whether a title is even needed.

Sadia Tasnim Epa

I'm very pleased that you have mentioned every detail of research interest which helped me to clear all of my doubts.... Thank you very much.

Hi Sadia! Glad you found this helpful!

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December 8, 2023

How to Write About Your Research Interests

The most common challenge that my master’s and PhD applicant clients face when writing a statement of research interests or a statement of purpose (SOP) is how to describe in concrete terms what their research interests and goals are. This is understandable. Their ideas are still evolving, and some worry that they’ll later be held to the ideas they stated in their applications, as though they were chiseled in stone. Others simply haven’t yet thought those ideas through very much.

Take a deep breath! By the time you begin writing your thesis, I promise that no one will pop up and wave your SOP or research interests statement around, saying, “But that’s not what you said here!” Everyone knows that your knowledge and ideas will develop throughout your grad program.

Here are the two things that a great statement of research interests or SOP will do:

It will clearly illustrate to the admissions committee that you possess a depth of interest and comprehension in your field and that you understand what goes into research. You will sound naïve if you talk about ideas that are too vague or nebulous, or ones that cannot be addressed adequately through your discipline.
It will explain any relevant background you have in this field, why you find it compelling, and why you are well suited for this career track .

Four questions to help you find your statement focus

To narrow your interests into something that is concrete enough for you to be able to write about convincingly, without being overly general, ask yourself these questions:

What are the broad research questions/issues that interest you? Create a summary of your interests that you can work with, and describe your interests in a sentence – or a paragraph, at most.
Within those broad areas of interest, can you begin to focus on more specific questions? If you’re not sure what the current questions/problems are in your field, now is the time to start catching up. Read recent journal publications, and go to conferences if you can. Reading the literature in your field will also give you a sense of how to frame your ideas in the language of your field.
Have you done any research in this field already? If so, do you intend to build on your previous work in grad school or go in a new direction?
How will your research contribute to the field?

Understanding how to present your goals

Some projects described in SOPs are achievable in the short term, while others are big enough to last a career. If your interests/goals fall into this latter category, acknowledge your ambitions, and try to identify some element of your interests that you can pursue as a first step.

Once you have demonstrated your skills (and past experience) in your field, you will be better equipped to define your next steps.

Focusing your interests will also involve doing more detailed research about the programs to which you plan to apply. For example, consider the following questions:

Who might be your research supervisor?
How do your interests relate to the work this scholar or these scholars are doing now?
How would you contribute to the department and to the discipline?

Your SOP will also address your post-degree, longer-term goals. Consider this: do you envision yourself pursuing a career in research/academia? (For many PhD programs, this remains the department’s formal expectation, even though many PhDs find employment outside the academy.) If you’re applying for a master’s degree, be prepared to discuss what your future plans are and how the degree will help you.

Working on your SOP or statement of research interests?

Your SOP needs to be direct, informative, and… well… purposeful! When you choose Accepted, we match you with a dedicated advisor who will help you create an SOP that best reflects your experiences, goals, and intense desire to attend your target graduate school program. And did you know that Accepted’s clients have received millions of dollars in scholarship offers? Don’t delay – get started now by checking out our Graduate School Application Services .

For 25 years, Accepted has helped applicants gain acceptance to top undergraduate and graduate programs. Our expert team of admissions consultants features former admissions directors, PhDs, and professional writers who have advised clients to acceptance at top programs worldwide, including Harvard, Stanford, Yale, Princeton, Penn, Columbia, Oxford, Cambridge, INSEAD, MIT, Caltech, UC Berkeley, and Northwestern. Want an admissions expert to help you get Accepted? Click here to get in touch!

Related Resources:

STEM Applicants: Why Your Statement of Purpose is So Important
Three Must-Have Elements of a Good Statement of Purpose
Writing Your Career Goals Essay

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Research interests statement

How to write your statement of research interests

Eleanor C Sayre

A statement of research interests is a way for you to articulate what you are interested in, your relevant past experience, and your concise future plans for research. You can think of it like a teaching philosophy, but for research; a future-oriented bio statement; or a narrative account of your research activity and plans.

Why write a statement of research interests?

Broadly speaking, statements of research interest are used in three ways:

As part of your application package for graduate school or for a faculty job which includes research (1-2pp)
As generative writing to clarify to yourself and your immediate (prospective) collaborators what you want to do. (1p)
As part of an advertisement for you and your work, such as in a bio statement or on your website. (0.5p)

Let’s focus on the middle way right now, as it’s a good place to start. Your goal in this statement is to clarify to yourself about what kind of (research) work you want to be doing, and how it connects to the work you’ve already done.

Getting started

What are you curious about.

Research is fundamentally about creating new knowledge. It is a creative, inventive process. If you’re new to research, it can be a bit intimidating to start. Some options:

Spend some time working through the research design exercises to familiarize yourself with questions, access, methods, and theories. Instead of planning a specific research project, though, your goal is to design an ideal project.

When you read a paper, particularly a paper published in the last 18 months, ask yourself what is interesting or cool about this paper. It might not be their conclusions; it might be the clever way they connected hypotheses or the surprising population they worked with.

I am curious about how people develop professional identity as scientists. I’m not particularly interested in student learning of specific topics in physics, except inasmuch as they are indicative of student learning across multiple topics.

Don’t worry if someone else might have already done the research you want to do. If there’s already a large body of literature around your chosen topics, that means you have a lot of opportunity to look for nuance and compare other people’s ideas against each other.

Conversely, if nobody has ever done the research you want to do and you don’t know of anyone doing anything similar, then your interests are probably too esoteric and/or your keywords are too narrow. That’s ok eventually, but right now you need to describe your interests in more general terms.

Some people have a hard time imagining what they’re curious about. They want someone else to tell them what project to work on, how to move forward, and which topics to focus on. If that’s you, now is a good time for introspection: why do you want to do research?

How would you like to change the world?

This is a really big question about the intended impact of your research. Some people want the knowledge they generate to have practical, immediate applications. For example, you might be curious about how first generation college students fare in your program because you want increase their completion rate. Or you might be curious about how students understand topic X because you want to teach it better. The world is a really big place; you don’t have to change all of it. How would you like to change your teaching practice, your department, your town, etc?

I would like academic science to be a more equitable and just place, which means that some of my research is about how marginalized students navigate occasionally hostile pathways through undergraduate degrees. Separately, I want to help emerging researchers learn how to do research in education, so I do research on the best ways to teach graduate students and faculty about how to do education research. These two interests are not the same, but I can pursue both of them in the same project.

It’s ok if you want to change the world in multiple different ways at different scales. For example, you might want to do research on how physics students in general operate in lab classes because you want to develop a vision of undergraduate labs that better prepare students for research, while at the same time you want to improve the learning of students in the classes at your institution.

Who do you want to work with, and in what capacity?

For some researchers, this is a highly constrained topic; for others, it is quite open. Think about the following questions:

Do you want local or remote collaborators on the same project?
Do you want to be part of a research group of people on related projects?
Do you want to be the sole PI with many students? One of a few PIs? Not a PI?
How much time, realistically, can you devote to research endeavors?
How many projects do you want to keep going at the same time?
How much money do you have access to? Do you need to be externally funded? Who should be responsible for acquiring your funding?

I thrive when I have a large collaborative research group to talk to. Some of the people in it should be working on the same projects as me, but some of them can be working on different things in similar ways. I thoroughly enjoy being one PI of many, though I’m ok being a sole-PI or occasional consultant. I need to have several projects going at the same time, and it’s ok with me if that means engaging substantially in multiple research groups.

Some of my collaborators thrive when they can focus on one main project and keep some other things on the back burner. Other collaborators are primarily interested in advising projects that their students are interested in, while still others only want to work on projects that closely align with their own interests.

The best options are the ones that make you happy. There’s no right answer that works for everyone.

What experience do you have?

Even though these are called statements of research interests, they’re often used as to link your past experience with your future plans. Past experience is a pretty good indicator of future plans, so think about what you’ve already done. You can start with just talking about each project: the major goals, the work you personally performed, the products that have (or are planned to) come out of it.

You can use your past experience to teach you about what you like about the research process, and also to teach you what you don’t want your future work to look like. Did you learn that you strongly dislike sitting alone in front of a computer? love working closely with one person? Rather like the idea of observational astronomy but not that particular project? Love computational work but find computational biophysics not as appealing as you previously thought?

Be reflective here, and honest. You are learning about you. In the next stage you’ll work on refining your reflections into a statement for a particular audience.

Write your statement

Generative writing.

Write about one page for each of these questions. It’s ok to leave out questions you’re not sure about the answers for, but strive to be thorough. If you have multiple interests or past projects, it’s ok to write a paragraph about each of them. Look for similarities across projects and experiences to help you synthesize across projects.

Using the ideas in the flow handout , reverse outline your generative writing. A common structure for research statements is:

Big idea about interests and changing the world
Your experience & past work on this topic
Future plans for this topic
Another topic? Link and repeat.
Closing thoughts about who you want to work with and in what capacity.

Most statements of research interests are 1-2 pages long. Your generative writing is a lot longer than that! Use the refining process to make your statement more concise.

Many students’ statements of research interests start with a paragraph about how much they have always loved this topic. Something like “ever since I was a young child, I have loved science.” Don’t do this. Our narratives about what “has always been true” are constructed in the present, and they are generally only selectively accurate renditions of the past.

Another common opening is to quote some famous scientist, usually Einstein or Feynman, about the wonder of the natural world or the majesty of science. Don’t do this. It’s trite and boring.

Think about audience

If you have a lot of ideas or interests, the audience for your research statement can help you decide what to focus on.

For example, if you’re writing an application essay to graduate school, your future plans probably aren’t very detailed. You can still have a big idea for changing the world, but it might be difficult to link your prior experience to your research interests. Many undergraduate research experiences teach participants that they enjoy research, just not that kind of research. In this statement, you need to name potential advisors in the department, and link their work to your interests. For help with that linking, I very strongly encourage you to email with and have an informational interview with each prospective advisor after your generative writing, but before you polish your statement. Receiving emails from prospective grad students is a totally normal part of being a research advisor, and I do it pretty much every week in application season. As an advisor and member of my department’s grad admissions committee, I look more favorably on applications which clearly fit the kinds of research we do in the department.

Alternately, if you are applying to faculty jobs , linking your past experience and future plans is very important. You will need to adjust your future plans so that they fit well into the kind of job you’re applying for, and specifically into the interests and resources of the department. Depending on the department, you might need to emphasize your goals around working with undergraduate students, attracting external funding, working with k12 teachers, or developing lab materials. In my department, to get tenure you need to demonstrate intellectual independence from your grad/postdoc work, so it is important that applicants’ research plans are not merely a continuation of their dissertations.

If you’re writing your statement of research interests for internal purposes only, to clarify what you’re looking for in your research life, then you should focus on whatever parts of the statement you need to work through to bring clarity to yourself. At different times in my life, I’ve focused on how to make my different projects sound like a coherent whole, how to finesse bad research experiences as learning opportunities, particular funding opportunities, and who I want to work with (both number and names).

Make it pretty

With your audience in mind, go through the last two exercises on the flow handout . You’re looking to make your statement feel like a cohesive whole that best shows off your goals, experience, and future plans, as moderated by the resources available in a particular context.

When it feels reasonably ok – not perfect! – send it to a trusted beta-reader to get feedback on your writing. This could be your advisor, a mentor in the field, or someone you know that knows a lot about the kind of position you’re looking for. You can also visit with your university writing center or career center (even after graduation!) for help with flow. They’re not usually specialized into statements of research interests, but they are good at general writing help.

Sometimes people ask me if I would be willing to read their statements ahead of time. For my current and former students (& collaborators), the answer is always yes. I will always help you do the thing you want to do next in your professional life. For prospective students, prospective collaborators, or other community members this is a little more complicated. Among these groups, I prioritize statements from BIPOC, women, and people whose research interests are aligned with my own. My availability for this kind of service to the community is limited, especially during application season. You should contact me to ask before you send your statement.

Additional topics to consider

Generative writing.

How to make the first draft of your research paper.

How to decide who is an author, in what order, and why.

Writing better papers

How to make a coherent and easy-to-read research paper.

This article was first written on June 1, 2018, and last modified on May 30, 2024.

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Research statement, what is a research statement.

The research statement (or statement of research interests) is a common component of academic job applications. It is a summary of your research accomplishments, current work, and future direction and potential of your work.

The statement can discuss specific issues such as:

funding history and potential
requirements for laboratory equipment and space and other resources
potential research and industrial collaborations
how your research contributes to your field
future direction of your research

The research statement should be technical, but should be intelligible to all members of the department, including those outside your subdiscipline. So keep the “big picture” in mind. The strongest research statements present a readable, compelling, and realistic research agenda that fits well with the needs, facilities, and goals of the department.

Research statements can be weakened by:

overly ambitious proposals
lack of clear direction
lack of big-picture focus
inadequate attention to the needs and facilities of the department or position

Why a Research Statement?

It conveys to search committees the pieces of your professional identity and charts the course of your scholarly journey.
It communicates a sense that your research will follow logically from what you have done and that it will be different, important, and innovative.
It gives a context for your research interests—Why does your research matter? The so what?
It combines your achievements and current work with the proposal for upcoming research.
areas of specialty and expertise
potential to get funding
academic strengths and abilities
compatibility with the department or school
ability to think and communicate like a serious scholar and/or scientist

Formatting of Research Statements

The goal of the research statement is to introduce yourself to a search committee, which will probably contain scientists both in and outside your field, and get them excited about your research. To encourage people to read it:

make it one or two pages, three at most
use informative section headings and subheadings
use bullets
use an easily readable font size
make the margins a reasonable size

Organization of Research Statements

Think of the overarching theme guiding your main research subject area. Write an essay that lays out:

The main theme(s) and why it is important and what specific skills you use to attack the problem.
A few specific examples of problems you have already solved with success to build credibility and inform people outside your field about what you do.
A discussion of the future direction of your research. This section should be really exciting to people both in and outside your field. Don’t sell yourself short; if you think your research could lead to answers for big important questions, say so!
A final paragraph that gives a good overall impression of your research.

Writing Research Statements

Avoid jargon. Make sure that you describe your research in language that many people outside your specific subject area can understand. Ask people both in and outside your field to read it before you send your application. A search committee won’t get excited about something they can’t understand.
Write as clearly, concisely, and concretely as you can.
Keep it at a summary level; give more detail in the job talk.
Ask others to proofread it. Be sure there are no spelling errors.
Convince the search committee not only that you are knowledgeable, but that you are the right person to carry out the research.
Include information that sets you apart (e.g., publication in Science, Nature, or a prestigious journal in your field).
What excites you about your research? Sound fresh.
Include preliminary results and how to build on results.
Point out how current faculty may become future partners.
Acknowledge the work of others.
Use language that shows you are an independent researcher.
BUT focus on your research work, not yourself.
Include potential funding partners and industrial collaborations. Be creative!
Provide a summary of your research.
Put in background material to give the context/relevance/significance of your research.
List major findings, outcomes, and implications.
Describe both current and planned (future) research.
Communicate a sense that your research will follow logically from what you have done and that it will be unique, significant, and innovative (and easy to fund).

Describe Your Future Goals or Research Plans

Major problem(s) you want to focus on in your research.
The problem’s relevance and significance to the field.
Your specific goals for the next three to five years, including potential impact and outcomes.
If you know what a particular agency funds, you can name the agency and briefly outline a proposal.
Give broad enough goals so that if one area doesn’t get funded, you can pursue other research goals and funding.

Identify Potential Funding Sources

Almost every institution wants to know whether you’ll be able to get external funding for research.
Try to provide some possible sources of funding for the research, such as NIH, NSF, foundations, private agencies.
Mention past funding, if appropriate.

Be Realistic

There is a delicate balance between a realistic research statement where you promise to work on problems you really think you can solve and over-reaching or dabbling in too many subject areas. Select an over-arching theme for your research statement and leave miscellaneous ideas or projects out. Everyone knows that you will work on more than what you mention in this statement.

Consider Also Preparing a Longer Version

A longer version (five–15 pages) can be brought to your interview. (Check with your advisor to see if this is necessary.)
You may be asked to describe research plans and budget in detail at the campus interview. Be prepared.
Include laboratory needs (how much budget you need for equipment, how many grad assistants, etc.) to start up the research.

Samples of Research Statements

To find sample research statements with content specific to your discipline, search on the internet for your discipline + “Research Statement.”

University of Pennsylvania Sample Research Statement
Advice on writing a Research Statement (Plan) from the journal Science

How to Write a Statement of Interest for Research

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If you are applying for a research program, one of the key components of your application package is a statement of interest. Your statement of interest or statement of purpose is an important document that allows you to showcase your skills, achievements, and passion for research. In this article, we will provide you with a comprehensive guide on how to write a statement of interest for research.

Understanding the Purpose of a Statement of Interest

Before you start writing your statement of interest, it is important to understand its purpose. Your statement of interest should provide the admissions committee with an understanding of your research interests, qualifications, and motivation. It is an opportunity for you to demonstrate your passion for research and convince the committee that you are the right candidate for the program.

A statement of interest is a crucial component of your graduate school application. It is your chance to showcase your research interests and explain why you are the ideal candidate for the program. The statement of interest is often the first thing the admissions committee will read, so it is essential to make a good first impression.

Importance of a well-crafted statement

A well-crafted statement of interest can make a big difference in the outcome of your application. It can help you stand out among other applicants, showing the admissions committee that you are a dedicated and passionate researcher. Therefore, investing time in writing a high-quality statement of interest is crucial to the success of your application.

When crafting your statement of interest, it is important to keep in mind that the admissions committee is looking for candidates who are passionate about their research interests. They want to see that you have a clear understanding of your field and that you are committed to advancing knowledge in that area. A well-crafted statement of interest can help you demonstrate these qualities.

Differentiating between a statement of interest and a personal statement

It is important to recognize that a statement of interest is different from a personal statement. While a personal statement is more general and can focus on various aspects of your personality, a statement of interest should solely focus on your research interests and goals.

When writing your statement of interest, you should avoid discussing personal details that are not relevant to your research interests. Instead, focus on your academic achievements, research experience, and future goals. This will help you demonstrate to the admissions committee that you are a serious candidate who is committed to advancing knowledge in your field.

Keep in mind that the statement of interest is not just a summary of your academic achievements. It is an opportunity for you to explain why you are passionate about your research interests and how you plan to contribute to your field in the future. A well-crafted statement of interest can help you stand out from other applicants and increase your chances of being accepted into your desired graduate program.

Preparing to Write Your Statement of Interest

Before you start writing, it is essential to prepare and conduct thorough research. Here are some tips to help you get started:

Researching the institution and program

Research the institution and program you're applying to. Look into the research interests of faculty members and research projects they're currently working on. This information will help you tailor your statement of interest to the specific program and demonstrate your alignment with the program's research goals.

Identifying your research interests and goals

Reflect on your research interests and goals. Think about what you want to achieve through the research program. Carefully consider your past research experiences and how they have contributed to your goals.

Reflecting on your relevant experiences and skills

Identify your relevant experiences and skills by reflecting on your academic and professional achievements. This will help you highlight your strengths, qualifications, and potential contributions to the program.

Structuring Your Statement of Interest

The following structure can help you organize your statement of interest:

Introduction: Grabbing the reader's attention

Your introduction should be compelling, engaging, and concise. Aim to grab the reader's attention and make them want to continue reading. Introduce your research interests and motivation for applying to the program. Explain what inspired you to pursue further studies in this field.

Body: Showcasing your research interests and qualifications

In the body of your statement, elaborate on your research interests and qualifications. Demonstrate your knowledge of the program and its research goals. Provide specific examples of your academic and professional achievements that relate to your research interests. Make sure that the body is well-structured, easy to read, and clearly expresses your goals and motivation.

Conclusion: Summarizing your goals and motivation

Your concluding paragraph should summarize your key points. Reiterate your research interests and goals and their alignment with the program. Highlight your passion for research and your potential contributions to the program. End on a positive note, showing enthusiasm for the opportunity to join the program.

Tips for Writing an Effective Statement of Interest

Be concise and clear.

Avoid wordiness and ensure your statement is concise and clear. Focus on expressing your ideas effectively in a manner that is easy to understand. Keep your sentences short and to the point, avoiding jargon and technical language that might confuse the reader.

Tailor your statement to the specific program

Your statement of interest should be tailored to the specific research program. Remember to highlight how your research interests and goals align with the program's research goals and demonstrate that you have a thorough understanding of the program and its faculty members.

Demonstrate your passion for research

Your statement should be a reflection of your passion for research. Show the admissions committee that you are committed to your field of study and are dedicated to advancing knowledge in your area of interest.

Proofread and revise

Ensure your statement is error-free by proofreading and revising it after writing. Read it out loud to ensure it flows smoothly and makes sense. Have someone else read your statement and provide feedback on its clarity, structure, grammar, and punctuation.

ChatGPT Prompt for Writing a Statement of Interest for Research

Use the following prompt in an AI chatbot . Below each prompt, be sure to provide additional details about your situation. These could be scratch notes, what you'd like to say or anything else that guides the AI model to write a certain way.

Please compose an in-depth and well-articulated description of your interest in conducting research, highlighting the specific topic or area you intend to investigate and the significance of this research. Your statement should demonstrate your understanding of the research process and your ability to contribute meaningfully to the field.

[ADD ADDITIONAL CONTEXT. CAN USE BULLET POINTS.]

Writing an effective statement of interest requires research, planning, and careful consideration of your qualifications, experience, and goals. By following the tips and guidelines provided in this article, you can create a compelling statement that showcases your passion for research and convinces the admissions committee that you are the right candidate for the program.

September 2023

Top tip: how to write a strong statement of interest.

A statement of interest, also known as statement of intent and description of research interests, is an important component of most graduate school applications. According to one of our faculty members, “The statement of interest is your opportunity to provide more evidence that you will succeed in your program.”

So how to get it right?

Read the instructions. Visit the website of the graduate program you’re interested in and check what their guidelines might be. These may include page and word count limitations, document upload specifications and specific questions.

Demonstrate fit. Show an understanding of the type of work done in the department, and provide an explanation of what you want to study, which should match up with some of the research interests in the faculty.

Be specific. Why UBC? Why this program? Be clear about what you want to do in the program and how the program can support you.

Be flexible. Indicate your well thought out and informed ideas, but allow them to be malleable. Sketch out a potential research agenda with room for further developmentand show interest in both a particular research area as well as alternative projects.

Be clear. Avoid repetition. Watch out for spelling mistakes and typos, irrelevant personal information, information already contained in other parts of your application, as well as general statements of enthusiasm, empty loyalty, and vague references without any details. Most importantly, don’t forget to proofread.

And if you feel stuck, start with these questions:

Why are you interested in this field of study?
What is your background and how does it relate?
Can you describe your previous research experience and how it has formed your current interests?
What is your motivation for proposing a particular research path?
Are you able to connect your area of interest to work being done in the program?
Is there anything the admissions committee should be aware of that is not addressed in other parts of your application?
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How to Write an Effective Research Interest Statement

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Writing a statement of academic research interest

Your ‘statement of research interests’ contains a proposal for future academic research and shows how that builds on your current expertise and achievements. It forms the basis for discussions and your presentation if you are invited for interview.

Tailor it for each academic position you apply for. Your research interests are likely to be broad enough to be tailored to the local interests and expertise. Make sure that there is palpable synergy between the research you are proposing and what the employing department carries out. This is worth the substantial time investment.

In preparing your statement, read your colleagues' statements. ask for feedback from your supervisor/principal investigator or colleagues.

Previous research experience

Consider structuring your research experience by project, tailored as far as possible to your proposed research, as follows:

achievements
relevant techniques
your responsibilities.

Research proposal

If at all possible, talk with people in the department you are applying to. This will raise your profile with potential future colleagues as well as inform your thinking. They are likely enjoy the opportunity to explore exciting new research avenues and will appreciate being asked. Getting to know them will also make the application process seem less daunting to you.

If you are asked for a research proposal, a word limit is normally specified: this can vary enormously.

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Graduate School Applications: Writing a Research Statement

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What is a Research Statement?

A research statement is a short document that provides a brief history of your past research experience, the current state of your research, and the future work you intend to complete.

The research statement is a common component of a potential candidate’s application for post-undergraduate study. This may include applications for graduate programs, post-doctoral fellowships, or faculty positions. The research statement is often the primary way that a committee determines if a candidate’s interests and past experience make them a good fit for their program/institution.

What Should It Look Like?

Research statements are generally one to two single-spaced pages. You should be sure to thoroughly read and follow the length and content requirements for each individual application.

Your research statement should situate your work within the larger context of your field and show how your works contributes to, complicates, or counters other work being done. It should be written for an audience of other professionals in your field.

What Should It Include?

Your statement should start by articulating the broader field that you are working within and the larger question or questions that you are interested in answering. It should then move to articulate your specific interest.

The body of your statement should include a brief history of your past research . What questions did you initially set out to answer in your research project? What did you find? How did it contribute to your field? (i.e. did it lead to academic publications, conferences, or collaborations?). How did your past research propel you forward?

It should also address your present research . What questions are you actively trying to solve? What have you found so far? How are you connecting your research to the larger academic conversation? (i.e. do you have any publications under review, upcoming conferences, or other professional engagements?) What are the larger implications of your work?

Finally, it should describe the future trajectory on which you intend to take your research. What further questions do you want to solve? How do you intend to find answers to these questions? How can the institution to which you are applying help you in that process? What are the broader implications of your potential results?

Note: Make sure that the research project that you propose can be completed at the institution to which you are applying.

Other Considerations:

What is the primary question that you have tried to address over the course of your academic career? Why is this question important to the field? How has each stage of your work related to that question?
Include a few specific examples that show your success. What tangible solutions have you found to the question that you were trying to answer? How have your solutions impacted the larger field? Examples can include references to published findings, conference presentations, or other professional involvement.
Be confident about your skills and abilities. The research statement is your opportunity to sell yourself to an institution. Show that you are self-motivated and passionate about your project.

Tips For A Winning PhD Research Statement

The process of creating a winning PhD research statement can be daunting and grueling, but if you take the time to put together an outstanding research statement there’s no telling how far you could get in graduate school!

That’s why we’ve pulled together some top tips to help make you stand out from the crowd. From understanding what a PhD admission search committee looks for to presenting your skills professionally, these tips will ensure that your application documents truly shine.

So, whether you’re new to the world of academia or already know it like the back of your hand; our advice is sure to give you an edge in this highly competitive scheme. Read on and get ready for success in your academic career!

What Is A Research Interest Statement?

A research interest statement is an important part of a PhD application and provides insight into your future research interests, research accomplishments, knowledge, writing skills, and research work experience. Not only is it useful to make a persuasive case for yourself but it is a great opportunity to explain your goals and objectives, the proposed approach, how the results of your research could contribute to the field, and any other relevant information that could help demonstrate to faculty members that you are an excellent candidate for the PhD.

This essay should be written in an easy-to-understand manner and clearly articulate your research projects, provide evidence of your understanding of your research topic in a broader context, and demonstrate your commitment to producing academic publications.

What Is The Difference Between A Research Interest Statement And A Statement of Purpose?

A research statement is a document that outlines an individual’s research agenda and accomplishments. It typically includes details about their past research experience, current research interests, and how they envision their future trajectory. A statement of purpose is a document that outlines an individual’s academic and professional goals. It typically includes information about the applicant’s educational background, career objectives, and desired area of study.

While both documents are important in applications to graduate programs, a research statement is more focused on current and future research plans, while a statement of purpose is more focused on career aspirations. Both essays require careful thought and consideration to effectively communicate an individual’s goals and objectives. It is important to keep these differences in mind when crafting either document.

Is This an Essay That Is Always Required in PhD Applications?

Although not a common component of the application in the U.S., the research interest statement is often required from graduate students by many universities in Europe as part of the PhD application process. The exact content and format of the essay will vary depending on the target institution but typically requires you to briefly describe the research that you plan to pursue during your PhD program.

This document can help graduate school admissions committees get a better understanding of your research goals, verify if you are aware of the most current trends and developments in your field, and quickly determine if you are PhD material or not.

Regardless of the requirements, it is important to keep in mind that your research interest statement should clearly explain why you are applying for the PhD program and what makes you a good candidate for it. Additionally, it should be well-structured and your ideas and questions should be thoughtfully laid out.

What Is The Difference Between a Research Statement and a Research Proposal?

A research statement and a research proposal are two different documents that serve distinct purposes. A research statement is typically a document that provides a brief history of your research interests and experience. It is used to demonstrate your knowledge and skills within a specific field of study, as well as the commitment you have to pursue a research project.

On the other hand, a research proposal is much longer and more formal. It includes an introduction, objectives, methodology, timeline, budget, and potential funding sources for a research project. It is used to provide detailed information about the project, as well as to convince others that the proposed research is worth pursuing.

Ultimately, the research interest statement is more of a personal document used to market your skills and experience in a specific field, while the research proposal is a formal document used to outline the scope and methodology of a project to potential future colleagues in research.

Both documents are important for different purposes, so it is important to understand the differences between research statements and research proposals.

Each should be tailored to fit its intended purpose, so researchers need to understand the differences and be able to create documents with detailed information for each.

What Should A Research Statement Include?

It should include your previous work, such as any prior publications, presentations you have made, or any other completed work. It should also include your current areas of interest, the types of questions you would like to find answers to, and any plans for future faculty collaborations.

Furthermore, it should explain how your past experiences have shaped your academic goals and how it all ties in with the work of current faculty in your target program.

Most programs will provide you with guidelines for crafting an effective essay. Although rarely containing a direct question, these guidelines may ask for specific details to be included. Be sure to adhere strictly to the program’s instructions; you must follow them!

How Long Should It Be?

A research statement should generally be around one to two pages long, although the exact length can vary depending on the institution. Keep in mind that your document is meant to showcase your capabilities as a researcher and should be written in a way that is both concise and comprehensive. Make sure to organize ideas and concepts with rigor!

Focus on the research project, your qualifications, and the contributions that you can make to the field of study. Be sure to explain why you are the best person to carry out this research and how you plan to go about achieving your goals.

How to Write an Outstanding Research Statement for PhD Applications?

It is important to be as specific as possible when discussing your interests and to explain how your work fits into a larger academic conversation. Additionally, it is important to highlight any accomplishments you have achieved in your research, as these will demonstrate the depth and breadth of your knowledge.

Finally, be sure to provide an outline of how you plan to accomplish your research goals with a few specific examples, as this will demonstrate your commitment and dedication to achieving them. With a well-written statement, you can make an impactful impression on the admissions committee and increase your chances of acceptance into the program.

What Are PhD Admissions Committees Looking For In a Research Statement?

Admissions committees are looking for research statements that demonstrate a passion for research and the ability to think critically. They want to see that you have the ability to develop and execute a research idea from conception to completion. You should be able to explain why your research topic is important, highlight the larger implications of your domain of inquiry, show how this research contributes to the field, and how you plan to go about achieving your goals.

Additionally, admissions committees are looking for evidence of creativity in developing novel approaches and evidence of dedication to pursuing your work. Your research statement should also make clear that you can work collaboratively with other researchers and provide thoughtful feedback on the work of your peers.

Finally, admissions committees want to see that you are able to communicate effectively about your research in both written and oral formats.

What Are Common Mistakes to Avoid When Writing a Research Statement?

When writing a research statement, it is important to avoid making broad generalizations or assumptions about the research field. It is also important to be clear and concise. Avoid jargon and overly technical language that could confuse readers.

Additionally, it is important to avoid making unsubstantiated claims or exaggerating results. Finally, it is important to be sure to focus on the research topic that you are proposing and avoid any tangential topics or unrelated information. By avoiding these pitfalls, you will be better able to create a clear and concise research statement that effectively conveys your research ideas.

The last mistake is not proofreading your statement. Even if you think you have written a perfect statement, it is important to review and ensure that there are no mistakes in grammar or spelling; these small errors can easily lead to an unsuccessful application!

With careful attention and consideration of these mistakes, you can create an effective research statement for your PhD application.

How Much Time Should You Spend Writing Your Research Interest Statement?

You should dedicate a significant amount of time to writing your statement. This document serves as an introduction to your research interests, motivations, and goals.

It is an important factor in determining whether you are an ideal candidate for the program or not. It should be given the same care and attention as an undergraduate or graduate thesis. It should be thoroughly composed and edited with the help of advisors and mentors to create a clear, concise, yet comprehensive presentation of who you are and who you will be as a future researcher.

Finally…

Now that you know all there is to about research statements, it’s time to get writing! This document can be daunting but spending the necessary time on it will be worth it when you eventually submit your flawless application. If you need help getting started or even just want someone to review what you’ve written so far, our essay service can assist you in ensuring that your research statement perfectly reflects everything the admissions committee wants to see. Good luck and happy writing! Got questions? Sign up for a consultation or send us a copy of your draft for an assessment, it’s FREE!

With a Master’s from McGill University and a Ph.D. from New York University, Dr. Philippe Barr is the founder of The Admit Lab . As a tenure-track professor, Dr. Barr spent a decade teaching and serving on several graduate admission committees at UNC-Chapel Hill before turning to full-time consulting. With more than seven years of experience as a graduate school admissions consultant, Dr. Barr has stewarded the candidate journey across multiple master’s and Ph.D. programs and helped hundreds of students get admitted to top-tier graduate programs all over the world .

Subscribe to my YouTube Channel for weekly tutorials on navigating the PhD application process and live Q&A sessions!

How to Write a Research Statement

Last Updated: April 25, 2024 Fact Checked

This article was co-authored by Christopher Taylor, PhD . Christopher Taylor is an Adjunct Assistant Professor of English at Austin Community College in Texas. He received his PhD in English Literature and Medieval Studies from the University of Texas at Austin in 2014. There are 7 references cited in this article, which can be found at the bottom of the page. This article has been fact-checked, ensuring the accuracy of any cited facts and confirming the authority of its sources. This article has been viewed 65,489 times.

The research statement is a very common component of job applications in academia. The statement provides a summary of your research experience, interests, and agenda for reviewers to use to assess your candidacy for a position. Because the research statement introduces you as a researcher to the people reviewing your job application, it’s important to make the statement as impressive as possible. After you’ve planned out what you want to say, all you have to do is write your research statement with the right structure, style, and formatting!

Research Statement Outline and Example

Planning Your Research Statement

Step 1 Ask yourself what the major themes or questions in your research are.

For example, some of the major themes of your research might be slavery and race in the 18th century, the efficacy of cancer treatments, or the reproductive cycles of different species of crab.
You may have several small questions that guide specific aspects of your research. Write all of these questions out, then see if you can formulate a broader question that encapsulates all of these smaller questions.

Step 2 Identify why your research is important.

For example, if your work is on x-ray technology, describe how your research has filled any knowledge gaps in your field, as well as how it could be applied to x-ray machines in hospitals.
It’s important to be able to articulate why your research should matter to people who don’t study what you study to generate interest in your research outside your field. This is very helpful when you go to apply for grants for future research.

Step 3 Describe what your future research interests are.

Explain why these are the things you want to research next. Do your best to link your prior research to what you hope to study in the future. This will help give your reviewer a deeper sense of what motivates your research and why it matters.

Step 4 Think of examples of challenges or problems you’ve solved.

For example, if your research was historical and the documents you needed to answer your question didn’t exist, describe how you managed to pursue your research agenda using other types of documents.

Step 5 List the relevant skills you can use at the institution you’re applying to.

Some skills you might be able to highlight include experience working with digital archives, knowledge of a foreign language, or the ability to work collaboratively. When you're describing your skills, use specific, action-oriented words, rather than just personality traits. For example, you might write "speak Spanish" or "handled digital files."
Don’t be modest about describing your skills. You want your research statement to impress whoever is reading it.

Structuring and Writing the Statement

Step 1 Put an executive summary in the first section.

Because this section summarizes the rest of your research statement, you may want to write the executive summary after you’ve written the other sections first.
Write your executive summary so that if the reviewer chooses to only read this section instead of your whole statement, they will still learn everything they need to know about you as an applicant.
Make sure that you only include factual information that you can prove or demonstrate. Don't embellish or editorialize your experience to make it seem like it's more than it is.

Step 2 Describe your graduate research in the second section.

If you received a postdoctoral fellowship, describe your postdoc research in this section as well.
If at all possible, include research in this section that goes beyond just your thesis or dissertation. Your application will be much stronger if reviewers see you as a researcher in a more general sense than as just a student.

Step 3 Discuss your current research projects in the third section.

Again, as with the section on your graduate research, be sure to include a description of why this research matters and what relevant skills you bring to bear on it.
If you’re still in graduate school, you can omit this section.

Step 4 Write about your future research interests in the fourth section.

Be realistic in describing your future research projects. Don’t describe potential projects or interests that are extremely different from your current projects. If all of your research to this point has been on the American civil war, future research projects in microbiology will sound very farfetched.

Step 5 Acknowledge how your work complements others’ research.

For example, add a sentence that says “Dr. Jameson’s work on the study of slavery in colonial Georgia has served as an inspiration for my own work on slavery in South Carolina. I would welcome the opportunity to be able to collaborate with her on future research projects.”

Step 6 Discuss potential funding partners in your research statement.

For example, if your research focuses on the history of Philadelphia, add a sentence to the paragraph on your future research projects that says, “I believe based on my work that I would be a very strong candidate to receive a Balch Fellowship from the Historical Society of Pennsylvania.”
If you’ve received funding for your research in the past, mention this as well.

Step 7 Aim to keep your research statement to about 2 pages.

Typically, your research statement should be about 1-2 pages long if you're applying for a humanities or social sciences position. For a position in psychology or the hard sciences, your research statement may be 3-4 pages long.
Although you may think that having a longer research statement makes you seem more impressive, it’s more important that the reviewer actually read the statement. If it seems too long, they may just skip it, which will hurt your application.

Formatting and Editing

Step 1 Maintain a polite and formal tone throughout the statement.

For example, instead of saying, “This part of my research was super hard,” say, “I found this obstacle to be particularly challenging.”

Step 2 Avoid using technical jargon when writing the statement.

For example, if your research is primarily in anthropology, refrain from using phrases like “Gini coefficient” or “moiety.” Only use phrases that someone in a different field would probably be familiar with, such as “cultural construct,” “egalitarian,” or “social division.”
If you have trusted friends or colleagues in fields other than your own, ask them to read your statement for you to make sure you don’t use any words or concepts that they can’t understand.

Step 3 Write in present tense, except when you’re describing your past work.

For example, when describing your dissertation, say, “I hypothesized that…” When describing your future research projects, say, “I intend to…” or “My aim is to research…”

Step 4 Use single spacing and 11- or 12-point font.

At the same time, don’t make your font too big. If you write your research statement in a font larger than 12, you run the risk of appearing unprofessional.

Step 5 Use section headings to organize your statement.

For instance, if you completed a postdoc, use subheadings in the section on previous research experience to delineate the research you did in graduate school and the research you did during your fellowship.

Step 6 Proofread your research statement thoroughly before submitting it.

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Princeton Correspondents on Undergraduate Research

Tips for Writing about Your Research Experience (Even if You Don’t Think You Have Any)

If you’re someone who hasn’t yet done formal research in a university setting, one of the most intimidating parts of the process can be simply getting your foot in the door. Just like the way your options can seem very limited when applying for your first job, asking for a research position when you have no “experience” can seem discouraging — maybe even to the point of causing you to question whether you should apply in the first place. With that being said, there are some simple tips you can employ when applying for research positions to highlight the link between your existing interests and the work of the position for which you are applying.

Illustrated resume on a desk being held by anthropomorphic tiger paws/hands. Tiger is wearing a suit. Desk is covered in writing/working items like pens, reading glasses, and coffee.

First things first: tailor not just your cover letter (for applications that ask for it) but your resume to the position for which you are applying. Even if you’re just sending a casual email to a professor to ask about the research that they’re doing, as a rule, it never hurts to attach your resume. I also like to think that submitting a resume even without being asked to shows that you’re serious about doing research, and have taken the time to put together a thoughtful inquiry into a position. If you’ve never written a cover letter or resume before, don’t fret. The Center for Career Development has some great online resources to help you create one from scratch. If you are looking for more individualized help, you can also schedule an appointment to get one-on-one feedback on your application at any stage in the writing process.

One of the things that I’ve found, however, is that the single-page format of a resume often isn’t enough space to include all of the information about every single thing you’ve ever done. Rather than trying to jam as many impressive accomplishments as you can onto a page, your goal should be to create a resume that gives a cumulative sense of your interests and experiences as they relate to the position for which you are applying. One of my favorite ways to do this is to create a “Research” section. “But Kate, what if I don’t have any research experience?,” you ask. Remember that paper you wrote about a painting by Monet in your favorite class last semester? Write the title down, or even a sentence or two that summarizes your main argument. The art museum you’re hoping to do research at will love knowing that your interest in their current exhibition on Impressionism is rooted in classes you’ve taken and the projects you’ve done in them, no matter how new you may be to a topic. Your interest in a specific research position has to come from somewhere, and your resume is an important part of demonstrating this to others.

What I would like to reassure you of is that it’s normal to be an undergraduate with very little research experience. The people reading your application —whether it be for an official program or even if it’s just a friendly email with a few questions— know that you are a student and will probably be excited to offer you guidance on how to get involved with more specific research projects even if all you have to offer at this point is enthusiasm for the topic. Working in a lab or with a professor on a research project is an opportunity designed to help you learn above all else, so it’s ok if you don’t know what you’re doing! It goes without saying that having little experience will make the final result of your research experience all the more worthwhile because of the potential to gain knowledge in ways you haven’t even imagined.

— Kate Weseley-Jones, Humanities Correspondent

How to: Statement of Research Interests

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A statement of research interests, along with a resume and a cover letter, is a crucial part of applying for a research-oriented job in academia or with a private research and development firm. This statement summarizes previous and current research, highlights your accomplishments and describes the future direction your work will take. Your research interests list helps hiring committees assess your expertise, your compatibility with the organization and your potential to attract research funding.

Getting Started

Open your statement of research interests with a section that gives the "big picture" of your research interests, summarizing your past work and describing your future research goals, as explained by the University of Washington Career Center . Consider the major themes or issues that unite the research projects on which you have worked in the past and describe these in this opening section. The research statement introduces the topics you are passionate about exploring.

Significance of Research

Expand on your past and present research projects by including a section that describes this work in more detail. Like the opening section that summarizes your overall research interests, think of a central theme that unites your body of work. The Cornell University Graduate School suggests describing the main theme of your research, why it is important and what skills you use to approach the issue. Summarize your previous and current research activities, remembering to address the “So what?” question. This means pointing out how your research has contributed to the field or solved specific problems. Describe the connection among past projects and highlight any recognition your work has received, such as publications, awards or grants.

Potential Contribution to the Field

Start a new section of the research statement that explains your future research aspirations and goals. This section should excite potential employers. Cornell advises not selling yourself short. If you believe your research could point the way to resolving major issues or problems, say it. The Career Center at the University of Washington suggests discussing two or three research ideas that interest you, as well as explaining how your goals build on past and current work. In addition, describe your ideas about potential funding sources, such as the National Science Foundation, the National Institutes of Health or private foundations.

Research Statement Format

A research statement is generally between one and three pages in length, according to Cornell. It should contain sufficient detail to demonstrate the depth of your knowledge but should not overwhelm readers with minor details. Organized content for readability using headings, subheadings and bullets. In addition, a research statement should be written in concise language that is free of technical jargon. A compelling statement is intriguing, substantial, ambitious but realistic, and achievable within the stated time frame.

Career Center, University of Washington: Research Statements
Cornell Graduate School: Research Statements
University of Pennsylvania: Research Statements

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Postdoctoral Fellowship Research Statements: What I Wish I Knew Before Writing

Written by Andrew Feldman

Photo of Andrew outside, with trees in the background. He wears glasses and a gray t-shirt.

Of course, the odds of receiving postdoctoral fellowships are not high (typically single digit percentages). Knowing these odds, I applied for eight fellowships: four through university departments and four through government agencies. I initially felt like I had no idea how to be successful, especially since I received none of the 12 doctoral fellowships I had previously applied for. I also had a rough start: my first postdoctoral fellowship application was rejected a month after submission for being slightly out of scope. It certainly required mental fortitude to continue through this application process.

After speaking with colleagues in my field, common themes emerged in how they approach proposals, especially in how to write a stand-out research statement. At this point starting the fifth year of my PhD, I understood the importance of conveying a strong vision in my research statement: it is essential for getting and staying funded regardless of how stellar one’s publication record is. While I knew the motivation and methodology well, my colleagues taught me that conveying my vision in a convincing, focused, and exciting way for other scientists is a different matter. I believe their collective advice was pivotal to improving my research statement and ultimately getting me on the “funded” pile for three of the eight fellowships. I share some of these insights here.

1) Why now? Why me? When formulating your idea, focus on ensuring that your proposal answers why this research should be completed right now, as opposed to anytime. Many committees strongly weigh how much of a priority your research question is. The best introductions will extend beyond an informative literature review and directly state why answering your question is necessary and urgent.

They also want to know: why are you the best person to address this problem as opposed to someone else? Explicitly sell your fit to your research problem and your vision. Lean on your PI choice here – PIs can fill in any technical knowledge gaps and provide complementary tools to those learned during your PhD.

Most surprising to me is how much focus you need place on “why now? why me?” in your motivation. There is no fixed number, but be sure you spend more real estate motivating why the problem and approach is so amazing rather than on addressing every pitfall with your research question and approach.

2) Your audience is broader than you think. Many proposal writers will incorrectly assume (like I initially did) that their committee will include that harsh reviewer of their journal articles who can identify all methodological shortcomings. Rather than trying to defend against this omniscient and unlikely reader, keep the focus on convincing a researcher of an adjacent field that your questions and approach are spectacular. An excellent research statement will ultimately excite any researcher enough to fund the work.

Another nuance to consider: postdoctoral fellowships are mainly offered through federal government agencies (i.e., NSF, NIH, etc.) and specific university departments. Government-based fellowships will be reviewed by researchers closer to your field (but not quite as close as that of a journal article review). In this case, lean slightly towards convincing them that you understand the limitations of the approach and that your background fits the problem. By contrast, university departmental fellowships will typically have committees of professors that will not be in your exact field. For this audience, lean towards exciting them with an accessible, clear problem motivation, provide only a broad overview of the methods you would use, and be very brief.

3) Spend time just thinking: resist the urge to open Microsoft Word and start typing. Spend time purely thinking and schematically charting out your research problem and anticipated results. If you sufficiently plan, the statement will write itself.

4) Less is more: your reviewers are just as busy as you are. They want to see your main idea fast. You may see a ten page limit and feel an urge to cram in as much material as possible. I did this initially, but the statement will quickly become noisy. Instead, prioritize reader friendliness. This means more pictures and less walls of text. Reviewers are thankful for 1.5 spacing, 12 point font, and schematic figures with question marks and arrows that clearly convey your research questions. Use parsimony in discussing methods – mention only the essential methods and main anticipated challenges.

5) Start early: I started formulating my research statement in June 2020. My first deadline was in early August 2020. While this seems early to start, it was not! Give yourself at least two months before your first fellowship deadline to formulate a problem with your prospective PI (or any co-PIs) and write your statements. Provide adequate time for your PI(s) to provide feedback on your ideas and statements. If applying to multiple fellowships with different PIs and/or different project topics, start even earlier.

Lastly, I encourage asking your colleagues for help. Folks around you regardless of career stage have likely spent a significant portion of their time writing research statements. The MIT Communication Lab was a great source of help for me that I used multiple times! Don’t be afraid to ask for help. I was always glad I did.

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How to Develop a Personal Statement for Research

To share your interest and enthusiasm for the specific work you are applying to do
To demonstrate what you can contribute to the program to which you are applying
To state the specific lab you want to work in and why
To state your professional goals and what or how you hope to contribute to this program

First Steps

Read the personal statement question carefully and analyze what it is asking for
Visualize your audience: will this be read by a scientist? A physician? An administrator?
Make yourself as desirable to the selector as possible while being honest about yourself
Your research interests as they relate to the work you are applying for
Year of study and current major, related academic and career goals, impressive academic credentials

Experience in the Field

Any special connection to this work such as prior experience or family background
Something unique about your research interests or an idea that fuels your own research interests.

Your Proposed Contributions to the Program and Benefits of the Program to You

Personal qualities that would benefit the program, demonstrated through examples
What you can do for them; what you seek to gain from the opportunity
How this specific work fits into your academic and research goals

Writing and Mechanics

Correct usage conveys your attention to detail

Use strong word choices, particularly verbs and adjectives
Use the more powerful "I am," rather than "I have always been"
Make positive statements: "I have experience in…" not "I don't have experience in x, but do have…”
Craft clear, engaging opening and closing sentences
Check that the opening statement is supported in the body and consistent with the closing statement
Organize the statement so it flows from sentence to sentence and paragraph to paragraph
Proofread for grammar, spelling, paragraph breaks, and correct punctuation

Ask Yourself

Does this statement show my interest in this specific program , or could it be sent to any program?
Does this statement describe me specifically, or could any good student in my field use this?

Additional Suggestions:

Reread the personal statement multiple times out loud for clarity, logic, and flow
Have someone else read the statement. Ask someone at the Center for Career Opportunities .
Share your finished personal statement with the faculty member writing your recommendations
Limit the statement to one and a half to two pages with at least one and a half spacing
Include a header with your name on each page, which will be numbered as well
Restating the question/topic
Rewriting your transcript or resume
Clichés such as "to make the world a better place"; instead, explain exactly how such a lofty goal will be achieved
Providing unrelated information, e.g., explaining when you learned you were not interested in computers
Using phrases like "this opportunity will be fun and interesting for me"; focus on what you can contribute
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1. biology research, 2. chemistry research, 3. economic research, 4. computer science research, 5. psychology research, step 1: create an executive summary or introduction, step 2: define the purpose, step 3: explain the recent, current, and future research, step 4: be realistic and practical, step 5: organize the format, step 6: finalize your research interest statement, share this post on your network, file formats, word templates, google docs templates, excel templates, powerpoint templates, google sheets templates, google slides templates, pdf templates, publisher templates, psd templates, indesign templates, illustrator templates, pages templates, keynote templates, numbers templates, outlook templates, you may also like these articles, 20+ sample nursing personal statements in pdf | ms word.

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eFigure. Flowchart: nationwide population-matched cohort and sibling-matched cohort, 2001-2018

eTable 1. Codes for identification of premenstrual disorders, psychiatric comorbidities, causes of death

eTable 2. Hazard ratio (HR) and 95% confidence intervals (CI) of all-cause mortality among women with premenstrual disorders, stratified by age and comorbidities: population-based cohort 2001-2018

eTable 3. Incidence rates of death and distribution of underlying causes of death: population-matched cohort 2001-2018

eTable 4. Underlying cause of death by age at diagnosis: population-matched cohort 2001-2018, n=2,325

eTable 5. Distribution of the underlying causes of death for women younger than 25 years old: population-matched cohort 2001-2018, n=196

eTable 6. Sensitivity analysis: hazard ratio (HR) and 95% confidence interval (CI) of mortality for women with premenstrual disorders, overall and by age: nationwide population-matched cohort 2001-2018, PMDs defined with at least 2 diagnoses ≥28 days, N=192,618

eTable 7. Sensitivity analyses: hazard ratio (HR) and 95% confidence interval (CI) of cause-specific mortality: population-matched cohort 2001-2018

eTable 8. Hazard ratio (HR) and 95% confidence intervals (CI) of mortality among women with premenstrual disorders overall, by age at diagnosis/matching and by specific causes: nationwide population-matched cohort, 2001-2018

eTable 9. Hazard ratio (HR) and 95% confidence intervals (CI) of mortality among women with premenstrual disorders by hormone replacement therapy and selective serotonin inhibitor: nationwide population-matched cohort, 2005-2018

eTable 10. The most frequent comorbidities at matching, among individuals with at least one comorbidity, population-matched cohort 2001-2018

eTable 11. Distribution of the underlying causes of death for women diagnosed at age 45 or over, population-matched cohort 2001-2018

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Opatowski M , Valdimarsdóttir UA , Oberg AS , Bertone-Johnson ER , Lu D. Mortality Risk Among Women With Premenstrual Disorders in Sweden. JAMA Netw Open. 2024;7(5):e2413394. doi:10.1001/jamanetworkopen.2024.13394

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Mortality Risk Among Women With Premenstrual Disorders in Sweden

1 Unit of Integrative Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
2 Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
3 Center of Public Health Sciences, Faculty of Medicine, University of Iceland, Reykjavík, Iceland
4 Department of Biostatistics and Epidemiology, School of Public Health and Health Sciences, University of Massachusetts Amherst, Amherst
5 Department of Health Promotion and Policy, School of Public Health and Health Sciences, University of Massachusetts Amherst, Amherst

Question Do women with premenstrual disorders (PMDs) have higher risks of all-cause and cause-specific mortality compared with women without PMDs?

Findings This nationwide matched cohort study of 406 488 women in Sweden during a mean (SD) follow-up of 6.2 (4.6) years (range, 1-18 years) revealed that overall, women with diagnosed PMDs did not have an increased risk of all-cause premature death. However, there was an increased mortality risk among women with PMDs diagnosed before age 25 years, as well as increased risk of death due to suicide, irrespective of age at diagnosis.

Meaning These findings suggest that women with PMDs are not at increased risk of all-cause mortality, but active surveillance might be needed among young patients and for suicide prevention for all ages.

Importance Premenstrual disorders (PMDs) adversely affect the quality of life of millions of women worldwide, yet research on the long-term consequences of PMDs is limited, and the risk of mortality has not been explored.

Objective To estimate the associations of PMDs with overall and cause-specific mortality.

Design, Setting, and Participants This nationwide, population-based, matched cohort study used data from population and health registers in Sweden. Participants included women of reproductive age with a first diagnosis of PMDs between January 1, 2001, and December 31, 2018. Data analysis was performed from September 2022 to April 2023.

Exposures PMDs were identified through inpatient and outpatient diagnoses and drug dispensing.

Main Outcomes and Measures Dates of death and underlying causes were ascertained from the National Cause of Death Register. Conditional Cox regression was used to estimate the hazard ratios (HRs) of overall and cause-specific death (eg, death due to natural or nonnatural cause, suicide, or cardiovascular events), adjusting for age, socioeconomic status, and somatic and psychiatric comorbidities; in a separate sibling comparison, models were also adjusted for all factors that sisters share.

Results A total of 67 748 women with clinically diagnosed PMDs and 338 740 matched unaffected women were included, for a total of 406 488 women. Women with PMDs received a diagnosis at a mean (SD) age of 35.8 (8.2) years. During a mean (SD) follow-up of 6.2 (4.6) years (range, 1-18 years), 367 deaths were observed among women with PMDs (rate, 8.4 deaths per 10 000 person-years; 95% CI, 7.6-9.3 deaths per 10 000 person-years), and 1958 deaths were observed among women without PMDs (rate, 9.1 deaths per 10 000 person-years; 95% CI, 8.7-9.6 deaths per 10 000 person-years). Compared with unaffected women, women with PMDs had increased risk of death due to nonnatural causes (HR, 1.59; 95% CI, 1.25-2.04), particularly suicide (HR, 1.92; 95% CI, 1.43-2.60), but they did not have increased risk of overall mortality (adjusted HR, 0.91; 95% CI, 0.82-1.02). Notably, women who received a diagnosis before the age of 25 years experienced higher all-cause mortality (HR, 2.51; 95% CI, 1.42-4.42) and death from both suicide (HR, 3.84; 95% CI, 1.18-12.45) and natural causes (HR, 2.59; 95% CI, 1.21-5.54).

Conclusions and Relevance The findings of this matched cohort study suggest that women with PMDs are not at increased risk of early death overall. However, the risk was elevated among young women and for death by suicide. This supports the importance of careful follow-up for young patients and highlights the need to develop suicide prevention strategies for all women with PMDs.

Premenstrual disorders (PMDs) are characterized by a range of mental and physical symptoms occurring during the week before menstruation. 1 These disorders are classified into premenstrual syndrome, which affects 20% to 30% of women of reproductive age, and premenstrual dysphoric disorders, with a prevalence ranging from 2% to 6%. 1 , 2 Numerous studies have highlighted the impairments related to PMDs, which can affect women’s daily activities, relationships, and professional performance. 3 , 4

Despite the dearth of research on long-term consequences of PMDs, there are some indications of an association between PMDs and mortality. For instance, women with PMDs present with elevated blood pressure levels and a 40% higher risk of developing hypertension. 5 , 6 Women with diabetes with PMDs have been found to have a greater risk of uncontrolled blood glucose levels. 7 In addition, PMDs are highly comorbid with psychiatric disorders, 8 , 9 which are associated with elevated risk of both nonnatural and natural-cause mortality. 10 - 12 Furthermore, we recently showed that, even after accounting for psychiatric comorbidities, 13 , 14 women with PMDs were at risk of suicidal behavior and accidents, which are the leading causes of death in young women. 13 To further our understanding of the risk of death associated with PMDs, this study aimed to evaluate the risks of all-cause and cause-specific mortality among women with clinically diagnosed PMDs and the risks stratified by age groups whenever possible, by leveraging the national health registers in Sweden.

This cohort study was approved by the Swedish Ethical Review Authority. Written consent from the participants is not required for register-based studies under Swedish law. This study followed the Strengthening the Reporting of Observational Studies in Epidemiology ( STROBE ) reporting guidelines for cohort studies. 15

The study was based on Swedish national health and population registers 16 : (1) the National Patient Register collects all hospital discharge diagnoses in Sweden from 1987 and 80% of hospital-based outpatient visits from 2001, (2) the National Prescribed Drug Register captures redeemed drug prescriptions from all pharmacies since 2005, (3) the National Cause of Death Register comprises death certificates since 1952, (4) the Longitudinal Integration Database for Health Insurance and Labor Market Studies integrates sociodemographic information for all residents aged 16 years and older since 1990, (5) the Total Population Register includes country of birth and migrations, and (6) the Multi-Generation Register documents parental information on residents since 1961. Registers were linked using the personal identification number assigned to all residents at birth or migration to Sweden.

We conducted a nationwide, population-based matched cohort study. Using incidence density sampling, women of reproductive age with a first diagnosis of PMDs between January 1, 2001, and December 31, 2018, were randomly matched by year of birth to 5 women who were free of PMD at that date. Reproductive age was defined as the period between age 15 years (96% of Swedish women had menarche by then 17 , 18 ) and 52 years (the average age of menopause in Sweden 17 , 18 ). Women who had undergone hysterectomy or bilateral oophorectomy before matching were excluded from the analyses. Individuals were followed-up until death, emigration, or December 31, 2018, whichever came first. If matched unaffected women received a diagnosis of PMD during the study period, their follow-up was censored at that time (eFigure in Supplement 1 ).

We also conducted a sibling comparison to address unmeasured confounding from factors shared by sisters, such as early family environment and genetics. In brief, full siblings were identified as sharing both biological parents recorded in the Multi-Generation Register. Women with PMDs were matched to their full sisters on the basis of age at diagnosis such that the unaffected sister(s) had no PMD diagnosis at the same age when the affected sister received a diagnosis.

As described elsewhere, 13 PMDs were identified through inpatient and outpatient diagnoses, along with treatments. In brief, inpatient and outpatient diagnoses were identified using the Swedish version of International Classification of Diseases, Ninth Revision (ICD-9) or International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10) . The National Patient Register has nationwide coverage on inpatient care from 1987 onward and includes information on more than 80% of specialist-based outpatient visits from 2001 onward with high validity (positive predictive value of 85%-95% across diseases). 19 To compensate for a lack of information on PMDs diagnosed in primary care, we identified prescriptions for antidepressant and oral contraceptives with a written indication for PMD treatment. Diagnoses and treatment codes are provided in eTable 1 in Supplement 1 .

Date and cause of death were obtained from the National Cause of Death Register. The underlying cause of death was classified according to ICD-10 codes as death due to nonnatural and natural causes (eTable 1 in Supplement 1 ). We further identified deaths due to neoplasms, cardiovascular diseases, and nervous system diseases as deaths from natural causes, whereas suicide and death due to accidents were classified as nonnatural causes. The register has a satisfactory accuracy rate for the studied causes of death (eg, 90% for malignant neoplasms, 87% for ischemic heart disease, and 96% for suicide). 20 - 22 All deaths were considered as premature deaths because they occurred before the age of 70 years. 23

Age at diagnosis or matching was calculated from date of birth, and country of birth was classified as born in Sweden or not. Region of residence, educational level, and income at the time of matching were derived from Longitudinal Integration Database for Health Insurance and Labor Market Studies, with regions grouped into south, center, or north of Sweden. Educational level was classified as 9 years or less, 10 to 12 years, more than 12 years, and unknown; personal income was divided into less than the 20th percentile, 20th to 80th percentile, and greater than the 80th percentile of the income distribution of the study population, and unknown.

Some psychiatric and somatic conditions are associated with both PMDs and premature death 8 , 10 - 12 , 24 - 26 and were considered as confounders in this study. Psychiatric comorbidities were defined as any psychiatric diagnoses recorded for inpatient or outpatient specialist care visits by the time of matching. The Charlson Comorbidity Index, adapted to Swedish ICD-9 or ICD-10 codes, 27 was used to assess somatic comorbidity. Only psychiatric and somatic comorbidities diagnosed before the matching date were included. Codes used to identify covariates are available in eTable 1 in Supplement 1 .

Descriptive statistics were performed to summarize the baseline characteristics between women with and without PMDs. Next, hazard ratios (HRs) of all-cause mortality associated with PMDs and corresponding 95% CIs were estimated using Cox regression models conditional on the matching set for the population-matched cohort and the sibling set for the sibling-matched cohort. The underlying timescales were time since matching for the population-matched cohort and age at follow-up for the sibling-matched cohorts. The Schoenfeld residual-based test was used to confirm that there were no violations to the proportional-hazard assumption. HRs were adjusted for attained age (through matching or underlying timescale), educational level, residence, country of birth, and personal income and were additionally adjusted for somatic and psychiatric comorbidities. Stratification by age at diagnosis was conducted because early onset of PMDs may have a longer impact throughout reproductive ages. We also performed stratification by psychiatric and somatic comorbidities to examine potential risk modification.

Associations with mortality were further estimated for natural and nonnatural causes and for the major causes observed in the population. In corresponding analyses, individuals were censored for deaths due to causes other than the ones under study. Stratification by age at diagnosis was also conducted. Having seen comparable but underpowered results from sibling comparison in the main analysis, these analyses were performed in the population-matched cohort only.

Prospective symptom charting over 2 consecutive menstrual cycles is recommend when diagnosing PMDs. 28 , 29 Because such information is not recorded in registers, we conducted a sensitivity analysis limited to women with a minimum of 2 recorded diagnoses of PMDs 28 days apart or longer. Owing to the challenges of determining intent in clinical practice, our main evaluation of suicide included deaths with undetermined intent, 30 - 32 whereas in a sensitivity analysis we restricted to intentional self-harm ( ICD-10 codes X60-X84). Additional sensitivity analyses for deaths attributed to neoplasms or cardiovascular disease were restricted to individuals without a history of the corresponding disease. Because receiving a diagnosis of PMD can take several years, 33 somatic and psychiatric comorbidities identified in the study could have occurred after PMDs onset and mediated the association with mortality. Allowing this possibility, we repeated the analyses without these comorbidities in the models. Finally, because treatment may influence natural mortality risk, we conducted stratifications by selective serotonin reuptake inhibitor (SSRI; Anatomical Therapeutic Chemical Classification N06AB) and hormonal replacement therapy (HRT; Anatomical Therapeutic Chemical Classification G03C and G03F) prescriptions, assessed as time-varying variables.

For all analyses, we used a 2-sided P < .05 to define statistical significance. Analyses were conducted from September 2022 to April 2023. Data were prepared in SAS statistical software version 9.4 (SAS Institute) and analyzed with Stata statistical software version 17.0 (StataCorp).

A total of 3 700 275 women were identified in the registers (eFigure in Supplement 1 ). Among them, 67 748 received a diagnosis of PMDs between 2001 and 2018 and were free from bilateral oophorectomy or hysterectomy at the time of PMD diagnosis. These women were matched at the time of diagnosis to 5 women fulfilling the same inclusion criteria and free from PMD (338 740 women), for a total population-matched cohort of 406 488 individuals. More than one-third of the women with PMDs had at least 1 full sister, and matching to up to 7 siblings resulted in a sibling-matched cohort of 55 801 individuals.

The mean (SD) age at diagnosis or matching was 35.8 (8.2) years ( Table 1 ). More than one-half of the women in the population-matched cohort resided in the middle of the country, reaching 65.4% (44 285 women) for women with PMDs. Somatic comorbidities were similarly distributed (6718 women with PMD [9.9%] and 30 870 women without PMD [9.1%] had ≥1 somatic comorbidity), whereas psychiatric disorders were more frequent among women with PMDs (16 160 women with PMD [23.8%] vs 47 919 women without PMD [14.1%]). Similar patterns were noted in the sibling-matched cohort.

During a mean (SD) follow-up of 6.2 (4.6) years (range, 1-18 years), 367 deaths were observed among women with PMDs (rate, 8.4 deaths per 10 000 person-years; 95% CI, 7.6-9.3 deaths per 10 000 person-years), and 1958 deaths were observed among women without PMDs (rate, 9.1 deaths per 10 000 person-years; 95% CI, 8.7-9.6 deaths per 10 000 person-years). Overall, women with PMDs did not have a higher risk of all-cause mortality compared with women without PMDs (age-adjusted HR, 0.91; 95% CI, 0.82-1.02) ( Table 2 ). A lower mortality risk was found when accounting for demographics and comorbidities (HR, 0.88; 95% CI, 0.77-0.99). This lower risk was mainly seen among women with PMDs diagnosed at ages 45 to 51 years (HR, 0.79; 95% CI, 0.64-0.97), whereas women with PMDs diagnosed before the age of 25 years had more than doubled risk of death (HR, 2.51; 95% CI, 1.42-4.42). Largely comparable results were found in the sibling comparison (HR, 0.84; 95% CI, 0.67-1.12), although statistical power was limited, particularly in age-stratified analysis.

The association of PMDs with all-cause mortality was comparable between women with and without psychiatric comorbidity ( P for interaction = .58) ( Table 3 ). However, the inverse association was greater among women with somatic comorbidities (HR, 0.47; 95% CI, 0.35-0.62). Again, this finding appeared to be more prevalent among women whose PMD was diagnosed at ages 45 to 51 years (eTable 2 in Supplement 1 ), whereas among women who received a diagnosis before age 25 years, there was no significant interaction with either comorbidity.

The 5 most common causes of death were neoplasms (150 deaths [40.9%]), suicide (100 deaths [27.2%]), cardiovascular diseases (29 deaths [7.9%]), accident (29 deaths [7.9%]), and nervous system disease (18 deaths [4.9%]) (eTable 3 in Supplement 1 ). The PMD-associated risk of death due to natural causes followed the same pattern observed for all-cause mortality, with lower risk overall (HR, 0.73; 95% CI, 0.62-0.84) but elevated risk among women who received a diagnosis before the age of 25 years (HR, 2.59; 95% CI, 1.21-5.54) ( Table 4 ). The lower risk was largely associated with cardiovascular-specific mortality (HR, 0.53; 95% CI, 0.34-0.84). There were 257 deaths attributed to cardiovascular disease, with 133 (51.8%) occurring in women who were included in the cohort at age 45 years or older (eTable 4 in Supplement 1 ). In contrast, mortality due to nonnatural causes was higher among women with PMDs (HR, 1.59; 95% CI, 1.24-2.04) and was primarily explained by suicide (HR, 1.92; 95% CI, 1.42-2.60). Notably, an elevated risk of suicide was observed regardless of the age at diagnosis ( P for interaction = .68) ( Table 5 ), although it was more pronounced among women who received a diagnosis before age 25 years (HR, 3.84; 95% CI, 1.18-12.45). Among women younger than 25 years who died, approximately one-third of the deaths (19 deaths) were due to suicide (eTables 4 and 5 in Supplement 1 ).

Comparable results were observed when the analysis was limited to PMDs with 2 diagnoses 28 days or more apart (eTable 6 in Supplement 1 ). Similarly, applying a stricter definition of suicide resulted in comparable estimates (eTable 7 in Supplement 1 ). Excluding individuals with a history of cancer or cardiovascular disease did not change the HR for the corresponding cause-specific mortality. Removing psychiatric or somatic comorbidities from regression models did not noticeably change the estimates (eTable 8 in Supplement 1 ). In addition, the inverse association between PMDs and natural mortality risk remained among individuals who did not use HRT or SSRI before or during the follow-up (eTable 9 in Supplement 1 ). SSRIs were prescribed to more than 80% of patients with PMD (55 552 women); 43% of women who received a diagnosis of PMDs at age 45 years or older were prescribed HRT (4110 women) compared with 23% of unaffected women (10 829 women). Finally, PMD-free women had a higher prevalence of diabetes or breast cancer (eTables 10 and 11 in Supplement 1 ).

This nationwide, population-based, matched cohort study with follow-up for up to 18 years fills an important gap in the understanding of all-cause and cause-specific mortality among women with PMDs. The use of national registers enabled complete follow-up and comprehensive information on death and its causes. Although no increased risk of all-cause death was observed overall, an elevated risk was noted among women who received a diagnosis before age 25 years. Women with PMDs were also found to have an elevated risk for suicide, regardless of age at diagnosis.

Our study revealed a consistently elevated risk of suicide among women with PMDs across all age groups. This is in line with previous research 14 , 34 showing that individuals with PMDs have a higher prevalence of suicidality. Our recent study 13 showed that women with PMDs were at increased risk of suicide attempt. However, to our knowledge, the current study is the first report to illustrate the increased risk of completed suicide.

Our findings further revealed that young women with diagnosed PMDs had an increased risk of all-cause mortality. Although suicidal behavior is more common in young women, this finding is not completely explained by suicide, which accounted for one-third of the deaths (eTables 4 and 5 in Supplement 1 ). Indeed, we also observed an increased risk of deaths due to natural causes in this group. Future studies with larger sample size are needed to examine the cause-specific mortality for these women.

In contrast, women who received a diagnosis at age 45 years or older had a lower risk of mortality than women without PMDs. These women may present a late-onset PMD or an exacerbation of mild symptoms, with a shorter cumulative impact of PMDs as symptoms are expected to end after menopause. We cannot exclude a potential healthy survivor effect because women who died before receiving a diagnosis or being identified in the registers were not included. Also, we must consider the potential for misclassification, as perimenopausal symptoms and depression may mirror PMDs symptoms, resulting in potential diagnostic errors. Moreover, women with premature menopause are not at risk of PMDs and might be oversampled in the unaffected group. Given that early menopause is associated with elevated risk of cardiovascular events 35 , 36 and premature mortality, 37 this may have influenced and partly explains the inverse associations. Specific information on menopause is lacking from registers; the age of first HRT prescription or diagnosis of early menopause were similar between the studied groups, although diagnoses and treatment rates are low in Sweden. 18

The unexpected finding of a lower risk of death due to natural causes associated with PMDs, particularly from cardiovascular events, warrants further investigation. This association may have been influenced by the reduced mortality in women who received a diagnosis at 45 years or older, because 51.8% of the cardiovascular-specific deaths occurred among this group (eTable 4 in Supplement 1 ). Because PMDs are often underdiagnosed, women identified with PMDs may have a higher level of self-awareness and maintain closer contact with the health care system. They may be more inclined to behavior changes, receiving a diagnosis early, and/or receiving treatment for comorbidities. The lower risk of cardiovascular-specific death could also be related to the use of SSRIs, which were prescribed to more than 80% of the patients with PMD. The cardioprotective effect of SSRI has been reported, although they can confer adverse impact on specific cardiovascular diseases. 38 , 39 A similar hypothesis can be formulated for HRT 40 : 43% of women who received a diagnosis of PMD at age 45 years or older were prescribed HRT compared with 23% for unaffected women. However, the inverse association remained among individuals not exposed to HRT or SSRIs. We also acknowledge the possibility of unexplored biases that could have influenced these unexpected findings, despite our concerted efforts to address them thoroughly.

PMDs are highly comorbid with psychiatric disorders, 8 , 9 , 12 , 41 which are associated with a higher mortality risk. 12 , 40 Although psychiatric comorbidities could have explained our findings among young women, we observed comparable associations in the presence or absence of psychiatric comorbidity. Notably, some women may develop major depression later in life, 9 and such a mediation pathway warrants future research. Regarding somatic comorbidity, we found greater inverse associations between PMDs and mortality in the presence of 1 or more condition. This could be partially attributed a closer contact of affected women with the health care system. Moreover, although the prevalence for 1 or more comorbidity diagnosed before the matching date was comparable between women, the conditions and their severity may differ. For instance, some somatic comorbidities (eg, terminal stage of cancer) or treatments (eg, chemotherapy for breast cancer) can lead to amenorrhea or oligomenorrhea, 7 , 42 - 44 reducing the occurrence of PMD symptoms. Indeed, in our data, PMD-free women had a higher prevalence of diabetes or breast cancer (eTables 10 and 11 in Supplement 1 ). The limited information on disease severity did not allow further investigation. The main strength of this study is the use of the Swedish register data, which allowed us to conduct a nationwide study with complete follow-up and comprehensive information on death and its causes.

This study has several limitations. First, although guidelines suggest diagnosis confirmation by a prospective evaluation of symptoms for 2 menstrual cycles, 29 the registers lacked such information. However, restricting PMDs to 2 diagnoses registered separately yielded comparable results. Moreover, delayed diagnosis is frequent for PMDs, 45 and most are diagnosed in primary care and not treated with medication, leading to potential misclassification of affected women as unaffected. However, such potential misclassification should not be related to the risk of death but may have attenuated the association toward the null.

Second, this study relied on recordings in the National Cause of Death Register, resulting in potential misclassification on the cause of death. 21 A study 20 showed an overall satisfactory accuracy rate of 77%, with variation depending on the disease (90% for malignant neoplasms, 87% for ischemic heart disease) and age (98% and 91% agreement in groups 0-44 and 45-64 years, respectively). We used ICD-9 or ICD-10 chapters to identify and group causes of death, minimizing the potential misclassification. 46

Third, some potential confounding factors were not available in the registers (eg, smoking status 47 , 48 or body mass index 25 , 49 ). Comparable results were observed in the sibling comparison, which allowed us to address factors shared between sisters including familial environment, genetics, and possibly some lifestyle factors.

Fourth, the population under study was relatively young, with a mean (SD) age of 35.8 (8.2) years, and the mean (SD) duration of follow-up was 6.2 (4.6) years. As a result, the study primarily captured deaths due to nonnatural causes, and only short-term mortality could be assessed. The small number of events also limits the generalization of the results. Future studies with longer follow-up are needed to capture long-term consequences.

Our findings suggest that women with clinically diagnosed PMDs were not at elevated risk of all-cause short-term death overall. However, women who received a diagnosis of PMD at an early age showed excess mortality, and the risk of suicide was elevated regardless of age. This supports the importance of careful follow-up for young women with PMDs and highlights the need to develop suicide prevention strategies for all women with PMDs.

Accepted for Publication: March 25, 2024.

Published: May 28, 2024. doi:10.1001/jamanetworkopen.2024.13394

Corresponding Author: Marion Opatowski, PhD, Unit of Integrative Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Nobels väg 13, Stockholm 171 65, Sweden ( [email protected] ).

Author Contributions: Dr Opatowski had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Opatowski, Lu.

Acquisition, analysis, or interpretation of data: All authors.

Drafting of the manuscript: Opatowski, Lu.

Critical review of the manuscript for important intellectual content: Valdimarsdóttir, Oberg, Bertone-Johnson, Lu.

Statistical analysis: Opatowski, Valdimarsdóttir.

Obtained funding: Opatowski, Valdimarsdóttir, Lu.

Administrative, technical, or material support: Oberg, Lu.

Supervision: Lu.

Conflict of Interest Disclosures: None reported.

Funding/Support: This work was supported by the Swedish Research Council for Health, Working Life and Welfare (FORTE) (grant No. 2020-00971 to Dr Lu), the Swedish Research Council (Vetenskapsrådet) (grant No. 2020-01003 to Dr Lu), the Karolinska Institutet Strategic Research Area in Epidemiology and Biostatistics (to Dr Lu), the Icelandic Research Fund (grant No. 218274-051 to Dr Valdimarsdóttir), and the Mental Health Foundation (Fonden för Psykisk Hälsa, to Dr Opatowski).

Role of the Funder/Sponsor: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Meeting Presentation: A poster with preliminary data was presented at the Society for Epidemiologic Research conference; June 15, 2023; Portland, Oregon.

Data Sharing Statement: See Supplement 2 .

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Open access
Published: 28 May 2024

Gut microbiome remodeling and metabolomic profile improves in response to protein pacing with intermittent fasting versus continuous caloric restriction

Alex E. Mohr ORCID: orcid.org/0000-0001-5401-3702 1 , 2 ,
Karen L. Sweazea 1 , 2 , 3 ,
Devin A. Bowes ORCID: orcid.org/0000-0001-9819-2503 2 ,
Paniz Jasbi 4 , 5 ,
Corrie M. Whisner ORCID: orcid.org/0000-0003-3888-6348 1 , 2 ,
Dorothy D. Sears ORCID: orcid.org/0000-0002-9260-3540 1 ,
Rosa Krajmalnik-Brown ORCID: orcid.org/0000-0001-6064-3524 2 ,
Yan Jin 6 ,
Haiwei Gu 1 , 6 ,
Judith Klein-Seetharaman ORCID: orcid.org/0000-0002-4892-6828 1 , 4 ,
Karen M. Arciero 7 ,
Eric Gumpricht 8 &
Paul J. Arciero ORCID: orcid.org/0000-0001-7445-6164 7 , 9

Nature Communications volume 15 , Article number: 4155 ( 2024 ) Cite this article

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Metabolomics
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The gut microbiome (GM) modulates body weight/composition and gastrointestinal functioning; therefore, approaches targeting resident gut microbes have attracted considerable interest. Intermittent fasting (IF) and protein pacing (P) regimens are effective in facilitating weight loss (WL) and enhancing body composition. However, the interrelationships between IF- and P-induced WL and the GM are unknown. The current randomized controlled study describes distinct fecal microbial and plasma metabolomic signatures between combined IF-P ( n = 21) versus a heart-healthy, calorie-restricted (CR, n = 20) diet matched for overall energy intake in free-living human participants (women = 27; men = 14) with overweight/obesity for 8 weeks. Gut symptomatology improves and abundance of Christensenellaceae microbes and circulating cytokines and amino acid metabolites favoring fat oxidation increase with IF-P (p < 0.05), whereas metabolites associated with a longevity-related metabolic pathway increase with CR (p < 0.05). Differences indicate GM and metabolomic factors play a role in WL maintenance and body composition. This novel work provides insight into the GM and metabolomic profile of participants following an IF-P or CR diet and highlights important differences in microbial assembly associated with WL and body composition responsiveness. These data may inform future GM-focused precision nutrition recommendations using larger sample sizes of longer duration. Trial registration, March 6, 2020 (ClinicalTrials.gov as NCT04327141), based on a previous randomized intervention trial.

Protein supplementation during an energy-restricted diet induces visceral fat loss and gut microbiota amino acid metabolism activation: a randomized trial

Gut microbiota plasticity is correlated with sustained weight loss on a low-carb or low-fat dietary intervention

Intermittent fasting modulates the intestinal microbiota and improves obesity and host energy metabolism

Introduction.

As a principal modulator of the gut microbiome (GM) and weight status, nutritional input holds great therapeutic promise for addressing a wide range of metabolic dysregulation 1 . Dependent on the host for nutrients and fluid, one of the main processes by which the GM affects host physiology is producing bioactive metabolites from the gastrointestinal (GI) contents. Nutrient composition, feeding frequency, and meal timing impact this dependency 2 , 3 . To maintain a stable community and ecosystem, the GM must regulate its growth rate and diversity in response to nutrient availability and population density 4 . Such maintenance is affected by caloric restriction (CR) coupled with periods of feeding and intermittent fasting (IF) 5 . Moreover, we’ve recently shown the nutritional composition and meal frequency during these periods alter the metabolizable energy for the host 6 . The current study incorporates protein pacing (P), defined as four meals/day consumed evenly spaced every 4 h, consisting of 25–50 g of protein/meal 7 , 8 , 9 . Indeed, we have previously characterized a dietary approach of calorie-restricted IF-P combined and P alone 7 , 8 . These studies included nutrient-dense meal replacement shakes, along with whole foods, to quantitatively examine beneficial changes in body composition and cardiometabolic, inflammatory, and toxin-related outcomes in healthy and overweight individuals 7 , 8 , 10 , 11 , 12 . Further, recent preclinical work in mice has identified dietary protein as having anti-obesity effects after CR that are partially modulated through the GM 13 . Thus, the need to examine this in humans is warranted.

In this current work, we compare the effects of two low-calorie dietary interventions matched for weekly energy intake and expenditure; continuous caloric restriction on a heart-healthy diet (CR) aligned with current United States (US) dietary recommendations 14 versus our calorie-restricted IF-P diet 8 , 15 , in forty-one individuals with overweight or obesity, over an 8-week intervention. We hypothesize an IF-P diet may favorably influence the GM and metabolome to a greater extent than a calorie-matched CR alone. This exploratory investigation utilizes data and samples from a randomized controlled trial (NCT04327141) that compares the effects of the CR versus IF-P diet on anthropometric and cardiometabolic outcomes, as previously published 15 . As an additional analysis, we select “high” and “low” responders based on relative weight loss (WL) for a subgroup examination of the IF-P diet to better elucidate potential differential responses to intermittent fasting and protein pacing. Of special note, one individual lost 15% of their initial body weight over the 8-week intervention; this individual is followed longitudinally for a year to explore the dynamics of their GM and fecal metabolome. Novel findings from the current study shows an IF-P regimen results in improved gut symptomatology, a more pronounced community shift, and greater divergence of the gut microbiome, including microbial families and genera, such as Christensenellaceae , Rikenellaceae , and Marvinbryantia , associated with favorable metabolic profiles, compared to CR. Furthermore, IF-P significantly increases cytokines linked to lipolysis, weight loss, inflammation, and immune response. These findings shed light on the differential effects of IF-P as a promising dietary intervention for obesity management and microbiotic and metabolic health.

Intermittent fasting - protein pacing (IF-P) significantly influences gut microbiome (GM) dynamics compared to calorie restriction (CR)

We compared an IF-P vs. a CR per-protocol dietary intervention (matched for total energy intake and expenditure) over eight weeks to compare changes in weight, cardiometabolic outcomes, and the GM in men and women with overweight/obesity (IF-P: n = 21; CR: n = 20). One participant in each group were lost to follow-up due to non-compliance with dietary intervention (Fig. 1a ; CONSORT flow diagram: Supplementary Fig. S1a ). The primary outcomes of dietary intake, body weight and composition responses, cardiometabolic outcomes, and hunger ratings after both dietary interventions are provided in our companion paper 15 . Briefly, after a one-week run-in period consuming their usual dietary intake (baseline diet), with no differences between groups at baseline for any dietary intake variable 15 , both dietary interventions significantly reduced total fat, carbohydrate, sodium, sugar, and energy intake by approximately 40% (~1000 kcals/day) from baseline levels (Fig. 1b ; Supplementary Data 1 ). By design, IF-P increased protein intake greater than CR during the intervention. The IF-P regimen consisted of 35% carbohydrate, 30% fat, and 35% protein for five to six days per week and a weekly extended modified fasting period (36–60 h) consisting of 350–550 kcals per day using randomization, as detailed previously 7 , 8 , 9 , 10 , 15 . In comparison, the CR regimen consisted of 41% carbohydrate, 38% fat, and 21% protein in accordance with current US dietary recommendations (Supplementary Table S1 ) 14 , 16 . Using two-way factorial mixed model analysis of variance (ANOVA), significant macronutrient decreases drove energy reduction from dietary fat and carbohydrate ( p < 0.001), with increased protein in the IF-P compared to CR ( p < 0.001; Supplementary Fig. S1b ; Supplementary Data 1 ). Regarding GI functioning and GM modulation, IF-P significantly decreased sugar and increased dietary fiber relative to CR (IF-P; pre, 20 ± 2 vs. post, 26 ± 2: CR; pre, 24 ± 3 vs. 24 ± 2 g/day; p < 0.05). Despite similar average weekly energy intake (~9000 kcals/week) and physical activity energy expenditure (~350 kcals/day; p = 0.260) during the intervention, participants following the IF-P regimen lost significantly more body weight (−8.81 ± 0.71% vs. −5.40 ± 0.67%; p = 0.003; Fig. 1c ; Supplementary Data 1 ) and total, abdominal, and visceral fat mass and increased fat-free mass percentage (~2×; p ≤ 0.030), as previously reported 15 . In addition, within-group analyses revealed a significant decrease in the reported frequency of total and lower-moderate GI symptoms (GI symptom rating score [GSRS] ≥4) over time for both IF-P and CR participants. However, when comparing the two dietary interventions at each time point, a more substantial reduction was observed in IF-P participants compared to CR participants (i.e., −9.3% vs. −5.4% and −13.2% vs. −3.9%, respectively; Table 1 ). The increased protein and lower sugar intake in IF-P compared to CR may have favorably mediated the GM and symptomatology.

a Study design with baseline participant characteristics. A registered dietitian counseled individuals from both groups each week. Time points with data collection are shown for both IF-P and CR participants. Icons created using BioRender.com. b Total daily caloric intake at each time point was not significantly different between IF-P and CR diet groups (two-sided Student’s t -test, p < 0.05). Adjusted values are displayed by dividing total weekly intake by seven, to account for the fasting periods of IF-P. c IF-P participants lost significantly more weight over time versus CR participants. Points connected by line represent percent of weight compared to baseline weight for each participant. d Overall gut microbial colonization, as demonstrated by qPCR-based quantification of 16S rRNA gene copies per gram wet weight was unaffected by time or intervention (linear-mixed effects [LME] model, two-sided p > 0.05). Alpha diversity metrics, e observed amplicon sequence variants (ASVs), and f Phylogenetic diversity at the ASV level significantly increased over time, independent of the intervention. g Intra-individual changes in GM community structure from baseline to weeks four and eight in IF-P participants shifted significantly throughout the IF-P intervention compared to CR as measured by the Bray-Curtis dissimilarity index (two-sided Wilcoxon rank-sum test). All box and whiskers plots display the box ranging from the first to the third quartile, and the center the median value, while the whiskers extend from each quartile to the minimum or maximum values. Heatmap of significant changes in h family- and i genus-level bacter i a by intervention. Colors indicate the within-group change beta coefficients over time for each cell, and asterisks denote significance. Black-white annotations on the bottom denote the significance of between-group change difference (by MaAsLin2 group × time interactions; p -values were corrected to produce adjusted values [ p .adj] using the Benjamini–Hochberg method). For all panels, IF-P: n = 20, CR: n = 19. Source data are provided as a Source Data file.

The substantial reduction in calorie intake of both groups (~40% from baseline) led us to investigate its potential impact on transient microbial colonization in the gut, as estimated by 16S rRNA gene copies (linear-mixed effects model [LME] time effect, p = 0.114; Fig. 1d ; Supplementary Data 2 ). While it might be expected that a significant reduction in calorie intake could influence gut microbial colonization, our findings indicate that this reduction did not reach statistical significance within the timeframe of our study. This result contrasts with previous research that imposed more substantial energy restriction, such as a four-week regimen of ~800 kcal/day in participants with overweight/obesity, where overall gut microbial colonization notably decreased 4 . In addition to assessing microbial colonization, we also investigated whether the calorie reduction significantly influenced principal stool characteristics, including wet stool weight, Bristol stool scale (BSS), and fecal pH ( p ≥ 0.066; Table 1 ). However, we did not observe statistically significant changes in these parameters over the course of the study. Moreover, there were no significant differences between the two dietary intervention groups over time (interaction effect, p ≥ 0.051). In contrast, there were significant time effects for observed amplicon sequence variants (ASVs) and phylogenetic diversity (LME time effect, p ≤ 0.023; Fig. 1e, f ; Supplementary Data 2 ), with values increasing at weeks four and eight compared to baseline (pairwise comparisons, p ≤ 0.048); however, no interaction was observed for either alpha diversity metric (group × time effect, p ≥ 0.925). To rule out the potential confounding effects of GI transit time 17 , BSS (as a surrogate marker) and stool pH were not significantly correlated with alpha diversity (Spearman correlations, p ≥ 0.210). In relation to community composition, much of the intervention variance could be attributed to individual response upon testing nested permutational analysis of variance (PERMANOVA; R 2 = 0.749, p = 0.001; Supplementary Table S2 ), showcasing the highly individualistic landscape of the human GM in response to dietary intervention. However, a significant 1.8% of the variance was accounted for by the group × time interaction ( p = 0.001). Moreover, individual responses over time showed variance between the two dietary interventions (PERMANOVA, R 2 = 0.123, p = 0.003). This variability was apparent by assessing intra-individual differences, where a pronounced increase in Bray-Curtis dissimilarity was observed in the IF-P compared to the CR group after four (median Bray-Curtis dissimilarity, 0.53 [IQR: 0.47–0.61] vs. 0.38 [IQR: 0.33–0.47]) and eight weeks (0.50 [IQR: 0.41–0.55] vs. 0.39 [IQR: 0.33–0.45]; Fig. 1g ; Wilcoxon rank-sum test, p ≤ 0.005).

To understand the taxa driving this GM variation from baseline to weeks four and eight between the two dietary interventions, we constructed MaAsLin2 linear-mixed models with the individual participant as a random factor 18 . We observed differential abundance patterns at the family and genus level in response to the IF-P but not the CR intervention. Of the 28 family and 69 genus-level features captured after filtering, a respective total of six and 18 taxa displayed significant interaction effects, with all significant time effects occurring from IF-P ( p .adj ≤ 0.10; Fig. 1h, i ; Supplementary Data 3 , 4 ). Notably, the changes observed at the four-week mark were more pronounced compared to those at eight weeks. These early alterations may signify an initial adaptation phase during which microbial populations respond to the modified substrate availability and nutrient composition, suggesting a degree of community resilience 19 . Increases were sustained to the third fecal collection for the family Christensenellaceae and the genera Incertae Sedis ( Ruminococcaceae family), Christensenellaceae R-7 group , and UBA1819 ( Ruminococcaceae family) (effect size > 2.0). Christensenellaceae is well regarded as a marker of a lean (anti-obesity) phenotype 20 and is associated with higher protein intake 21 . Other notable increases included Rikenellaceae , which, like Christensenellaceae , has been linked to reduced visceral adipose tissue and healthy metabolic profiles 22 , and Marvinbryantia , a candidate marker for predicting long-term weight loss success in individuals with obesity 23 . In addition, IF-P increased Ruminococcaceae , which has been noted to have an increased proteolytic and lipolytic capacity 24 . This shift in IF-P participants likely represents a change in GM substrate fermentation preferences as the diet regimen (relative protein and carbohydrate) and energy restriction is expected to increase the proteolytic: saccharolytic potential ratio 25 . In contrast, all taxa that decreased in IF-P participants were butyrate producers. These included the family Butyricicoccaceae and several genera such as Butyricicoccus (week four), Eubacterium ventriosum group (weeks four and eight), and Agathobacter (week four) (effect size < −2.0). When comparing monozygotic twin pairs, Eubacterium ventriosum group and another reduced genus, Roseburia , were more abundant in the higher body mass index (BMI) siblings 26 . Others, such as the mucosa-associated Butyricicoccus and Erysipelotricaceae UCG-003, have been positively correlated with insulin resistance and speculated to contribute to impaired glycolipid metabolism 27 .

Despite these changes in GM composition and increased fiber intake (+30% vs. baseline) of the IF-P participants 15 , we did not detect a significant shift in the abundance of the principal fecal short-chain fatty acids (SCFAs), acetate, propionate, butyrate, or valerate, as assessed by gas chromatography-mass spectrometry (GC–MS) (LME, p ≥ 0.470; Supplementary Fig. S1c ; Supplementary Data 5 ). Several factors likely contribute to this finding. For example, the distinct physical-chemical properties of fiber sources between IF-P and CR are inherently different. Participants adhering to the IF-P diet consumed most of their dietary fiber as liquid meal replacements (shakes) that are rich in non-digestible, oligosaccharide dietary-resistant starch 5 (RS5). In contrast, subjects on the CR regimen consumed their fiber from whole food sources such as vegetables, whole grains, and legumes. These fiber sources provided a mixture of soluble and insoluble fibers and a more complex fiber profile than IF-P participants. Moreover, even similar fiber profiles may function differently due to differences in food matrices and/or food preparation (cooking, raw consumption, etc.). Also of relevance is the timing of their fiber consumption. IF-P participants’ fiber intake was concentrated in fiber-rich shakes, offering immediate availability of fiber to the GI tract. In contrast, CR participants consumed fiber through whole foods, leading to a slower digestion and absorption process influenced by individual digestive transit times and enzymatic profiles. Interestingly, our results parallel recent work where participants more than doubled their fiber intake without affecting fecal SCFAs 28 . The disparate findings may be due to the type of dietary-resistant starch (RS) as a component of the nutrition regimen. In the current study, RS5 was included in the meal replacement shakes (eight grams/shake, two shakes/day, 16 g/day total). Prior research supports resistant starch intakes of >20 g/day favorably modulate SCFA production, primarily butyrate, over four to 12-week interventions 29 , 30 . Moreover, this lack of response in fecal SCFAs in both groups may have been further compounded by the significant reduction in energy intake in both groups, where the epithelia of the GI tract may have absorbed any potential increase in SCFAs from the dietary shift. It is worth noting that stool analysis may not be the most reliable biological surrogate for capturing SCFA flux over time 28 . Nevertheless, the changes in nutrient quality, timing, ratios, and the observed shift toward proteolytic activity suggest that the luminal matrix of digesta in the IF-P group impacted substrate availability for GM. This effect appears to be an influencing force in driving the observed beneficial shifts in microbial communities, such as Christensenellaceae and Incertae Sedis , as well as improvements in GI symptomatology in IF-P compared to CR. These results underscore the complexity of dietary influences on GM and highlight the need for further research to explore the impact of liquid meal replacements versus whole food sources on GM changes and SCFA status.

IF-P modulates circulating cytokines and gut microbiome taxa compared to CR

Caloric restriction and WL have been well known to positively influence inflammatory cytokine expression, with GM now emerging as an important modulator 31 . Surveying a panel of 14 plasma cytokines, we noted significant interaction (group × time) effects for IL-4, IL-6, IL-8, and IL-13 (LME, p ≤ 0.034; Fig. 2a–d ; Supplementary Table S3 ; Supplementary Data 6 ). These cytokines exhibited increases at weeks four and/or eight compared to baseline exclusively in the IF-P group (pairwise comparisons, p .adj ≤ 0.098), while no significant changes were observed in the CR group ( p .adj ≥ 0.562). Notably, IL-4 has been reported to display lipolytic effects 32 , and IL-8 has been positively associated with weight loss and maintenance 33 . Regarded as a proinflammatory myokine, IL-6 can acutely increase lipid mobilization in adipose tissue under fasting or exercise conditions 34 , 35 , 36 . IL-13 may be important for gut mucosal immune responses and is a stimulator of mucus production from goblet cells 37 , which has been recently reported to be influenced during a two-day-a-week fasting regimen in mice 38 . These results were of note considering the significant total body weight, fat, and visceral fat loss in the IF-P compared to the CR group. Surprisingly, correlational analysis with change (post – pre) in anthropometric and select plasma biomarker values with the cytokine profile did not reveal any significant associations after correcting for multiple testing effects ( p .adj ≥ 0.476; Supplementary Data 7 ). Plasma cytokines were, however, correlated with microbial composition for samples collected in the IF-P group during the intervention period (weeks four and eight) using graph-guided fused least absolute shrinkage and selection operator (GFLASSO) regression, revealing associations between cytokine-taxa pairs (Supplementary Fig. S2a ). Of the four cytokines that increased in IF-P participants, we identified multiple significant correlations: Colidextribacter (rho = −0.55, p .adj = 0.015), Ruminococcus gauvreauii group (rho = 0.50, p .adj = 0.036), and Intestinibacter (rho = 0.45, p .adj = 0.086) with IL-4 (Supplementary Fig. S2b ) and an unclassified genus from Oscillospiraceae (rho = −0.53, p .adj = 0.019), Colidextribacter (rho = −0.52, p .adj = 0.019), and Ruminoccus gauvreauii group (rho = 0.51, p .adj = 0.019) with IL-13 (Supplementary Fig. S2c ).

a IL-4, b IL-6, c IL-8, and d IL-13: Each panel shows the cytokine concentration levels. Significant time effects and interaction effects (group × time) were detected using linear-mixed effects models (LME, two-sided p < 0.05), indicating differential changes over the intervention period. IF-P participants exhibited significant increases in cytokine levels compared to baseline, as evidenced by pairwise comparisons adjusted for multiple testing using the Benjamini–Hochberg method (two-sided p .adj < 0.10). All box and whiskers plots display the box ranging from the first to the third quartile, and the center the median value, while the whiskers extend from each quartile to the minimum or maximum values. For all panels, IF-P: n = 20, CR: n = 19. Source data are provided as a Source Data file.

Displaying negative correlations for IL-4 and IL-13, Colidextribacter has been shown to be positively correlated to fat accumulation, insulin, and triglyceride levels in mice fed a high-fat diet 39 and positively correlated with products of lipid peroxidation, suggesting its potential role in promoting oxidative stress 40 . Conversely, Ruminoccus gauvreauii group was positively correlated with IL-4 and IL-13. Although limited information is available regarding the host interactions of this microbe, this genus is considered a commensal part of the core human GM and able to convert complex polysaccharides into a variety of nutrients for their hosts 41 . While these findings highlight the potential interplay between specific microbes and cytokine profiles, the directional influence—whether microbial changes drive cytokine alterations or vice versa—cannot be determined in this study setting. Furthermore, despite the change in cytokine profiles in the IF-P group, we did not detect any significant time or group × time effects when measuring lipopolysaccharide-binding protein (LBP; Δ pre/post, IF-P: 0.24 ± 0.31 vs CR: −0.93 ± 0.49 μg/mL; p ≥ 0.254), a surrogate marker for gut permeability 42 . While the GM plays a crucial role in modulating the gut-immune axis, the observed cytokine fluctuations and microbial associations might also involve other factors. These include the production of specific metabolites due to shifts in microbial composition as well as the influence of the dietary regimen itself, which may have a central role in shaping these interactions.

IF-P and CR yield distinct circulating metabolite signatures and convergence of multiple metabolic pathways

To understand the potential differential impact of IF-P versus CR on the host, we surveyed the plasma metabolome, reliably detecting 136 plasma metabolites across 117 samples (i.e., QC CV < 20% and relative abundance > 1000 in 80% of samples). Based on outlier examination (random forest [RF] and principal component analysis [PCA]), no samples were categorized as outliers, and all data were retained for subsequent analysis. Metabolomic profile shifts were observed in both IF-P and CR groups compared with baseline (Canberra distance), however, these did not differ significantly by group or time (weeks four and eight; Wilcoxon rank-sum test, p ≥ 0.087; Supplementary Fig. S3a ). We prepared a general linear model (GLM) with age, sex, and time as covariates and corrected for false discovery rate (FDR). When controlling for these relevant covariates, we observed significant differences between IF-P and CR for 15 metabolites (Fig. 3a , Supplementary Table S4 ): 2,3-dihydroxybenzoic acid, malonic acid, choline, agmatine, protocatechuic acid, myoinositol, oxaloacetic acid, xylitol, dulcitol, asparagine, n-acetylglutamine, sorbitol, cytidine, acetylcarnitine, and urate ( p .adj ≤ 0.089). To estimate the univariate classification performance of the 15 significant metabolites, we performed a receiver operating characteristic (ROC) analysis. Ten metabolites demonstrated a moderate area under the curve (AUC) (0.718–0.819), while five metabolites had an AUC < 0.70. Therefore, to improve classification performance, we constructed a supervised PLS-DA model using levels of the 15 significant metabolites ( p .adj ≤ 0.089) and analyzed variable importance in projection (VIP) scores (Supplementary Fig. S3b ). Five metabolites with a VIP > 1.0 (2,3-dihydroxybenzoic acid, malonic acid, protocatechuic acid, agmatine, and myoinositol) were retained to construct an enhanced orthogonal projection to latent structures discriminant analysis (OPLS-DA) model. In contrast, the model fit was assessed with 100-fold leave-one-out cross-validation (LOOCV; see “Methods” section). Permutation testing showed the refined OPLS-DA model to have an acceptable fit to data ( Q 2 = 0.460, p < 0.001), with appreciable explanatory capacity ( R 2 = 0.506, p < 0.001; Supplementary Fig. S3c ). The ROC analysis produced an area under the curve (AUC) of 0.929 (95% CI: 0.868–0.973, sensitivity = 0.8, specificity = 0.9; Supplementary Fig. S3d ) between the CR and IF-P groups showing good accuracy of the GLM and providing strong support for the differential expression of these 15 metabolites between groups.

a Abundance and log fold-change of significant plasma metabolites between IF-P and CR groups as determined by a general linear model (GLM) adjusted for age, sex, and time. All GLM analyses utilized two-sided p -values, with multiple testing corrections applied using the Benjamini–Hochberg method ( p .adj). Metabolome pathway analysis was conducted for b IF-P and c CR using all reliably detected metabolites showing significantly altered pathways ( p .adj < 0.10) with moderate and above impact (>0.10). Impact scores were calculated using a hypergeometric test, while significance was assessed via a test of relative betweenness centrality, emphasizing the changes in metabolic network connectivity. For all panels, IF-P: n = 20, CR: n = 19. Source data are provided as a Source Data file.

Two metabolites, malonic acid, and acetylcarnitine, increased compared to the CR intervention. Several other investigators have noted the increase in acetylcarnitine via fasting protocols 43 , 44 . This increase is consistent with free fatty acid mobilization and increased transportation of these fatty acids via carnitine acylation into the mitochondria for fatty acid oxidation. These results would also be consistent with the expected ketogenesis, although not documented in our study, but noted by similar fasting interventions 44 . Relatedly, malonic acid, a naturally occurring organic acid, is a key regulatory molecule in fatty acid synthesis via its conversion to acetoacetate; hence, our results may reflect this increased synthesis in response to the mobilization and oxidation of fatty acids occurring during fasting. Other metabolites that decreased with IF-P include several sugar alcohols (myoinositol, dulcitol, and xylitol). Dulcitol (galactitol) is a sugar alcohol derived from galactose. It is possible that during fasting, levels of dulcitol decrease as glucose (initially) and free fatty acids (after 24–36 h of fasting) are preferentially utilized as energy substrates. One amino acid (asparagine) and one amino acid analog (N-acetylglutamine, associated with consumption of a Mediterranean diet 45 ) also decreased with IF-P relative to CR. Finally, 2,3-dihydroxybenzoic acid significantly decreased with IF-P. This metabolite is formed during the metabolism of flavonoids, as it is found abundantly in fruits, vegetables, and some spices. At the cellular level, this hydroxybenzoic acid functions as a cell signaling agent and has been speculated as a potentially protective molecule in various cancers 46 . It is unclear whether this metabolite decreased due to either dietary intake or metabolic processes related to high-protein intake or the fasting protocol. Collectively, the metabolic responses to these dietary regimens reflect the interrelationships of several anabolic and catabolic physiologic responses to three key components of these interventions: (a) the WL process itself, (b) changes in amount (and type) of macronutrient distribution (i.e., meal replacement shakes vs. whole food diet approach; higher vs. normal protein intakes), and (c) the adherence to fasting (IF-P only).

To determine the significantly impacted pathways of the dietary interventions, we grouped participant samples according to baseline or intervention period (weeks four and eight), with IF-P and CR assessed separately. A total of 14 pathways were significant in the IF-P group ( p .adj < 0.10; Fig. 3b ), with three displaying large impact coefficients (>0.5): (1) Glycine, serine, and threonine metabolism, (2) alanine, aspartate, and glutamate metabolism, and (3) ascorbate and aldarate metabolism. In comparison, 24 pathways were significant for the CR group (Fig. 3c ), with four showing large impact coefficients (>0.5): (1) Phenylalanine, tyrosine, and tryptophan biosynthesis, (2) alanine, aspartate, and glutamate metabolism, (3) citrate cycle (TCA cycle), and (4) glycine, serine and threonine metabolism. Notably, the glycine, serine, and threonine pathway has recently been found in preclinical models to play a pivotal role in longevity and related life-sustaining mechanisms independent of diet, though heavily impacted by fasting time and caloric restriction 47 . This may be partially related to the ability of glycine to increase tissue glutathione 48 , 49 and protect against oxidative stress 50 . In our analysis, this pathway was significant in both diet groups and is biochemically and topologically related to the additionally captured amino acid pathway, alanine, aspartate, and glutamate metabolism, as well as the energy-releasing pathway, the citrate cycle (TCA cycle). Notably, in the CR group, phenylalanine, tyrosine, and tryptophan biosynthesis, are important for neurotransmitter production and reported to be suppressed (tryptophan) in obesity 51 . This representation may have also been attributed to the differences in protein intake 52 or differences in dietary diversity 53 , yet to be determined. Regardless, we noted similar representations of pathway impact between IF-P and CR, with metabolic response centered on utilization of amino acids in addition to lipid turnover and energy pathways.

Gut microbiome and plasma metabolome latent factors indicate differential multi-omic signatures between IF-P and CR regimens

As the plasma metabolome has been suggested as a bidirectional mediator of GM influence on the host 54 , we performed a multi-omics factor analysis (MOFA) 55 to identify potential patterns of covariation and co-occurrence between the microbiome and circulating metabolites. Operating in a probabilistic Bayesian framework, MOFA simultaneously performs unsupervised matrix factorization to obtain overall sources of variability via a limited number of inferred factors and identifies shared versus exclusive variation across multiple omic data sets 55 . Eight latent factors were identified (minimum explained variance ≥2%; see “Methods” section), with the plasma metabolome and GM explaining 37.12% and 17.49% of the overall sample variability, respectively (Fig. 4a ). Based on significance and the proportion of total variance explained by individual factors for each omic assay, Factors 1 ( R 2 = 11.98) and 6 ( R 2 = 5.28) captured the greatest covariation between the two omic layers (Fig. 4a ; Supplementary Table S5 ). In contrast, Factors 2 and 5 were nearly exclusive to the metabolome, and factors 3 and 4 to the GM. Interestingly, Factor 1 was significantly negatively correlated to dietary protein intake (Spearman rho = −0.270, p.adj = 0.021; Fig. 4b ) and captured the variation associated with the CR diet (Wilcoxon rank-sum test, p .adj = 3.2e-04; Fig. 4c ). Factor 6 had the greatest number of significant correlations, including negative associations with visceral adipose tissue, waist circumference, body weight, BMI, fat mass, android fat, subcutaneous adipose tissue, dietary sodium, carbohydrate, fat, energy intake (kcal), and sugar (Spearman rho ≤ −0.220, p .adj ≤ 0.075) and captured the variation associated with IF-P (Wilcoxon rank-sum test, p .adj = 0.007).

a The cumulative proportion of total variance explained ( R 2 ) and proportion of total variance explained by eight individual latent factors for each omic layer. b Spearman correlation matrix of the eight latent factors and clinical anthropometric and dietary covariates. Each circle represents a separate association, with the size indicating the significance (-log10 ( p -values)) and the color representing the effect size (hue) with its direction (red: positive; blue: negative). All correlations are calculated using two-sided tests. Asterisks within a circle denote significance after adjustment with the Benjamini–Hochberg method. c Scatter plot of Factors 1 and 6, with each dot representing a sample colored by intervention. Box and whisker plots illustrate significant differences between groups after adjusting for multiple testing using the Benjamini–Hochberg method (Wilcoxon rank-sum test; top = Factor 1, p .adj = 3.2e-04; right = Factor 6, p .adj = 0.007). The plots show boxes ranging from the first to the third quartile and the median at the center, with whiskers extending to the minimum and maximum values. d Factor 1 and 6 loadings of genera and metabolites with the largest weights annotated. Symbols: * p .adj < 0.10, ** p .adj < 0.01, *** p .adj < 0.001, **** p .adj < 1.0e-04. For all panels, IF-P: n = 20, CR: n = 19. Source data are provided as a Source Data file.

Assessing the positive weights (feature importance) of Factor 1 revealed a microbial and metabolomic signature linked with CR, including the taxa Faecalibacterium , Romboutsia , and Roseburia , and the plasma metabolites myoinositol, agmatine, N-acetylglutamine, erythrose, and mucic acid (Fig. 4d ). Previous dietary restriction studies have reported co-occurrence of gut microbial taxa and plasma metabolites that span a wide variety of applications and investigations 56 . The specific co-occurrences observed in Factor 1 exhibited an abundance of butyrate-producing bacterial taxa that utilize carbohydrates as their predominant substrate and plasma metabolites that are generally involved in carbohydrate metabolism, such as erythrose, an intermediate in the pentose phosphate pathway (PPP), and mucic acid which is derived from galactose and/or galactose-containing compounds (i.e., lactose). These co-occurrence patterns biologically cohere considering the nutritional profile of the CR group and the large contribution of fiber-rich, unrefined carbohydrates and reduction in sugar (~50% kcal from sugar). Indeed, these nutritional changes may have influenced the GM to accommodate changes in dietary substrate more efficiently. One interesting co-occurrence was the genus Romboutsia and metabolite N-acetylglutamine. Romboutsia has been shown to produce several SCFAs and ferment certain amino acids, including glutamate 57 . N-acetylglutamine is biosynthesized from glutamate; thus, its co-occurrence with the abundance of Romboutsia encourages further exploration into this interaction 58 .

Factor 6 captured the signature associated with IF-P, with positive contributions from the taxa Incertae Sedis ( Ruminococcaceae family), Erysipelatoclostridium , Christensenellaceae R-7 group , Oscillospiraceae UCG-002, and Alistipes , and the plasma metabolites malonic acid, adipic acid, succinate, methylmalonic acid, and mucic acid (Fig. 4d ). Prior work has established that Alistipes increases from diets rich in protein and fat, and contributes to the highest number of putrefaction pathways (i.e., fermentation of undigested proteins in the GI tract) over the other commensals 59 . This could explain the co-occurrence of plasma metabolites from protein catabolism, such as 2-aminoadipid acid, adipic acid, and glutamic acid 22 , 59 . Oscillospiraceae has recently been viewed with next-generation probiotic potential, harboring positive regulatory effects in areas related to obesity and chronic inflammation 60 . Mentioned prior, recent studies have reported on the role of Christensenellaceae on human health, participating in host amino acid and lipid metabolism as well as fiber fermentation 20 , with Christensenellaceae R-7 group notably evidenced to correlate with visceral adipose tissue reduction 22 . As such, the elevated abundance of microbes in the GM of IF-P participants observed in this study in tandem with the co-occurrence of metabolites indicative of protein degradation and mobilization and oxidation of fatty acids, such as methylmalonic acid, malonic acid, and succinate, presents a nascent multi-omic signature of IF-P. In addition, and more pronounced in the IF-P vs CR group, participants decreased sugar intake by ~75% (kcals) compared to baseline levels. Considering the other regimental components of IF-P, the differences in multi-omic signatures likely display the selective pressures of these two interventions.

Gut microbiome (GM) composition is associated with weight loss (WL) responsiveness to IF-P diet

The IF-P intervention produced a microbiome and metabolomic response; however, the loss in body weight and fat across individuals varied (Fig. 5a ). To provide deeper characterization and explore differential features of WL responsiveness, we performed a GM-focused subgroup analysis by employing shotgun metagenomic and untargeted fecal metabolomic surveys in 10 individuals that either achieved ≥10% loss in body weight or bordered on clinically important WL (i.e., >5% BW; herein, ‘High’ and ‘Low’ responders) 61 . Importantly, baseline characteristics between WL responder classification did not differ significantly (baseline body weight: High, 108.9 ± 30.8 vs. Low, 81.9 ± 18.1 kg, p = 0.117; Supplementary Table S6 ). Assessing the GM at the fundamental taxonomic rank, species composition showed significant separation by weight loss response evaluated by Bray-Curtis dissimilarity (group × time: R 2 = 0.114, p = 0.001; Fig. 5b ; Supplementary Table S7 ), with most of the variation explained by the individual ( R 2 = 0.711, p = 0.001). In comparison, species level alpha diversity did not differ significantly between classifications (group × time: p ≥ 0.674; Fig. 5c, d ). Identifying 212 species after filtering, we noted significant differences in bacterial abundances between groups over time (Fig. 5e ; Supplementary Data 8 ). A total of 10 features increased in the High-responder group relative to the Low-response group over the eight-week study period, including Collinsella SGB14861 , Clostridium leptum , Blautia hydrogenotrophica , and less typified species; GGB74510 SGB47635 (unclassified Firmicutes), GGB3511 SGB4688 (unclassified Firmicutes), Faecalicatena contorta , Lachnospiraceae bacterium NSJ-29 , Phascolarctobacterium SGB4573 , GGB38744 SGB14842 (unclassified Oscillospiraceae ), and Massiliimalia timonensis (effect size ≥ 1.163, p .adj ≤ 0.092). The increase in Collinsella , a less characterized anaerobic pathobiont that produces lactate and has been associated with low-fiber intakes 62 , 63 and lipid metabolism 64 , may have been related to the periods of CR and IF, in conjunction with the greater influx of host-released fatty acids in the High-responder group. Relatedly, Clostridium leptum growth has been linked with increases in monounsaturated fat intake, reductions in blood cholesterol 65 , and stimulation of Treg induction (i.e., anti-inflammatory) 66 . The latter association is relevant to the SCFA-promoting (primarily butyrate) qualities of Clostridium leptum 67 . Blautia hydrogenotrophica , an acetogen with bidirectional metabolic cross-feeding properties (e.g., transfer of hydrogen and acetate), is also important for butyrate formation 68 . Taxa that decreased relative to the Low-responder group; Eubacterium ventriosum , Streptococcus salivarius , Eubacterium rectale , Anaerostipes hadrus , Roseburia inulinivorans , Mediterraneibacter glycyrrhizinilyticus , and Blautia massiliensis (effect size ≤ −1.690, p .adj ≤ 0.078), included butyrate producers, Eubacterium ventriosum , Eubacterium rectale , Roseburia inulinivorans , and others, such as Streptococcus salivarius , a nuclear factor kappa B (NF-κB) activity repressor 69 and Peroxisome proliferator-activated receptor gamma (PPARγ) inhibitor potentially influencing lipid and glucose metabolism 70 . Investigating monozygotic (MZ) twin pairs, Eubacterium ventriosum was more abundant in the higher BMI siblings 26 , with enhanced scavenging fermentation capabilities 71 . Roseburia inulinivorans is a mobile firmicute (flagella) that harbors a wide-ranging enzymatic repertoire able to act on various dietary polysaccharide substrates suggestive of the ability to respond to the availability of alternative dietary substrates 72 . While we noted a more variable shift in fecal total SCFAs, acetate, propionate, butyrate, or valerate (via targeted GC–MS), in the Low weight loss responders, there was no significant difference when compared to High weight loss responders (Wilcoxon rank-sum test, p ≥ 0.210; Supplementaryl Fig. S4a ; Supplementary Data 9 ).

a Relative weight loss over the eight-week intervention for each participant in the IF-P group. b NMDS ordination showed the personalized trajectories of participants’ microbiomes over time. Dotted lines connect the same individual and point toward the final sample collection. No significant time or group × time interaction effects for alpha diversity metrics, c observed species, and d the Shannon index. Box and whiskers plots display the box ranging from the first to the third quartile, and the center the median value, while the whiskers extend from each quartile to the minimum or maximum values. Volcano plots displaying differential abundance between High and Low weight loss responders for e microbial species and f functional pathways. Significant features were more enriched in High and Low weight loss responders colored orange and light blue, respectively. g Alluvial plot displaying the fecal metabolite profile at the subclass level (Human Microbiome Database). Most abundant metabolite subclasses displayed (i.e., ≥1%). Metabolome pathway analysis for h High and i Low weight loss responders using all reliably detected fecal metabolites showing altered pathways with moderate and above impact (>0.10). Impact was calculated using a hypergeometric test, while significance was determined using a test of relative betweenness centrality. j Grid-fused least absolute shrinkage and selection operator (GFLASSO) regression of species from differential abundance analysis displayed correlative relationships with fecal metabolites. Species with greater abundance in High (High > Low) and Low (Low > High) weight loss responders are separate‘. For all panels, High: n = 5, Low: n = 5. Source data are provided as a Source Data file.

Less affected compared to taxonomic features were the 275 microbial-affiliated metabolic pathways identified after filtering, of which gluconeogenesis III and guanosine ribonucleotides de novo biosynthesis were increased (effect size ≥ 0.108, p .adj = 0.079), while super pathway of L-alanine biosynthesis, sucrose degradation IV (sucrose phosphorylase), sucrose degradation III (sucrose invertase), super pathway of thiamine diphosphate biosynthesis III, and flavin biosynthesis I (bacteria and plants) were decreased in the High relative to the Low weight loss responder group (effect size ≤ −0.247, p .adj ≤ 0.079; Fig. 5f ; Supplementary Data 10 )

As the difference in microbial shifts versus function is well established, we also tracked the fecal metabolome to better understand metabolic modification/production and identify potential microbial metabolic targets for future weight loss interventions. Overall, we reliably detected (QC relative standard deviation > 20% and mean intensity value > 1000 in 80% of samples) and annotated 607 (Human Metabolome Database) compounds across fecal samples. Notably, we found the fecal metabolite profile of both subgroups abundant in amino acids, peptides, and analogs, with decreases in sulfates, furanones, and quaternary ammonium salts and increases in cholestane steroids, carboxylic acid derivatives, and imidazoles (Fig. 5g ). Assessing metabolite changes between groups did not yield significance when comparing logFC values (Wilcoxon rank-sum test, p .adj > 0.10; Supplementary Fig. S4b ). Pathway analysis of High weight loss responders revealed prominent metabolic signatures relevant to lipid metabolism (glycerolipid and arachidonic metabolism), nucleotide turnover (pyrimidine metabolism), and aromatic amino acid formation (phenylalanine, tyrosine, and tryptophan biosynthesis; Fig. 5h , Supplementary Data 11 ). In comparison, the more prominent enriched pathways for Low weight loss responders included those related to amino acid and peptide metabolism (glycine, serine, and threonine, d-glutamine and d-glutamate, and tyrosine metabolism and arginine biosynthesis; Fig. 5i , Supplementary Data 12 ).

Finally, species captured by our differential abundance analysis were channeled into a GFLASSO model with the fecal metabolome library to select metabolically relevant compounds best predicted by microbial abundances. Restricting taxa and metabolites displaying stronger co-occurrence signals (GFLASSO coefficients > 0.02), we noted several patterns (Fig. 5j ). This included positive associations between GGB3511 SGB4688 (unclassified Firmicute) and malonic acid (important to fatty acid metabolism), as well as Roseburia inulinivorans and 3-Hydroxy-2-oxo-1H-indole-3-acetic acid. Negative associations included Phascolarctobacterium SGB4573 with the fatty acid ester, methyl sorbate, and Streptococcus salivarius (anti-inflammatory) with leukotriene B4 dimethylamide.

Differences detected in our subgroup analysis suggest that the GM composition plays a role in WL responsiveness during IF-P interventions. Notable differences in taxa and fecal metabolites suggest differing substrate utilization capabilities and nutrient-acquiring pathways between High and Low responders, despite being on the same dietary regimen. Although differences between High and Low responders were statistically significant for the microbiome data, the magnitude of differences varied, suggesting further research is needed to clarify these differences.

Long-term IF-P remodels the gut microbiome after substantial weight loss – A case study

Considering the microbiomic and metabolic importance of sustained WL, we additionally performed a longitudinal, exploratory case study analysis on the participant who lost the most body weight during the eight-week WL period (−15.3% BW, −24.9 kg). Under rigorous clinical supervision, this individual was guided through and comprehensively tracked over 52 weeks, strictly adhering to an IF-P regimen, including WL (0–16 weeks) and maintenance (16–52 weeks) periods, which included adjusting the calorie intake to maintain energy balance. Microbial richness and evenness at the species level displayed a general inverse trend with body weight reduction, although they converged at 52 weeks (Fig. 6a, b ). Species dissimilarity peaked at weeks four and 16, after which it plateaued, but remained consistently higher in comparison to baseline over the 52-week period (Fig. 6c ). Examining positive linear coefficients of a PERMANOVA model, constructed to detect variation between community compositions over time, dominant influences included several species within the Lachnospiraceae family such as Fusicatenibacter saccharivorans , Blautia wexlerae , Blautia massillensis , Anaerostipes hadrus , and Coprococcus comes and others like Akkermansia muciniphila (Fig. 6d ). Negative contributions included species from the Oscillospiraceae family, such as Ruminococcus bromii and Ruminococcus torques . Indeed, visualizing community composition over the sampling time points suggested specific GM remodeling (Fig. 6e ; Supplementary Data 13 ). Many keystone taxa prominent over time in the microbiome are highly relevant to the significant reduction in body weight and metabolic improvement of the case-study participant. For example, Blautia wexlerae , a commensal bacterium recently reported to confer anti-adipogenesis and anti-inflammatory properties to adipocytes 73 became visually more prominent over time. This association was also the case for the health-associated microbe, Anaerostipes hadrus , which converts inositol stereoisomers (including myoinositol) to propionate and acetate, apt to improve insulin sensitivity and reduce serum triglyceride levels 74 , translating to reduced host metabolic disease risk 75 . Other elevated taxa, like the mucin-degrading Akkermansia muciniphila and Bacteroides faecis , are negatively correlated with markers for insulin resistance 76 . There was also a notable bloom of Collinsella SGB14861 (anaerobic pathobiont producing lactate) 63 and suppression of Eubacterium rectale , Ruminococcus torques (associated with circadian rhythm disruption in mice) 77 , and Ruminococcus bromii (an exceptional starch degrader) 78 .

Change in alpha diversity metrics a observed species and b Shannon index with percentage of baseline body weight. c Bray-Curtis dissimilarity at the species level with d top PERMANOVA model coefficients (analysis: species~time). e Alluvial plot displaying the variation in abundance of the 20 most prevalent bacteria over time. For visual clarity, the less abundant taxa are not displayed. f Canberra distance of fecal metabolome with g top PERMANOVA model coefficients (analysis: pathway~time). h Pathway analysis of fecal metabolites comparing baseline to subsequent sample collections. Data are plotted as -log10(p) versus pathway impact. Node size corresponds to the proportion of metabolites captured in each pathway set, while node color signifies significance. Impact was calculated using a hypergeometric test, while significance was determined using a test of relative betweenness centrality. No p -value adjustments were made. Source data are provided as a Source Data file.

Compared to the more pronounced shifts in the GM, an inspection of Bray-Curtis dissimilarity at the microbial metabolic pathway level was much less affected (Supplementary Fig. S5a ). Though positive contributions in multiple biosynthesis pathways were noted, as well as reductions in the superpathway of UDP-glucose-derived O-antigen building blocks biosynthesis and glucose and glucose-1-phosphate degradation (Supplementary Fig. S5b ; Supplementary Data 14 ). We also tracked the fecal metabolome concordance with the GM to corroborate potential metabolic output. Shifts in metabolites captured by calculating the Canberra distance were prominent (Fig. 6f ), with positive influences from agrocybin (possessing antifungal activity 79 ), nicotinic acid (nicotinamide adenine dinucleotide precursor), and sulfate, and reductions in cadaverine (involved in the inhibition of intestinal motility 80 ), maltitol, acetohydroxamic acid (a urease inhibitor), and hypoxanthine, after removing the dominant amino acid subclass (Fig. 6g ; Supplementary Fig. S5c ). At the chemical class level, we observed apparent shifts in chemical subclasses; cholestane steroids, amines, purines, and purine derivatives, and amino acids, peptides, and analogs (Supplementary Fig. S5d ). Given our case-study approach, we performed a pathway analysis using all reliably detected fecal metabolites at each collection point over 52 weeks. Pathway analysis (Fig. 6h ) identified primary bile acid biosynthesis ( p = 0.014) and cysteine and methionine metabolism ( p = 0.096) as having the greatest significance, while the greatest impact (I) was observed in phenylalanine, tyrosine, and tryptophan biosynthesis and linoleic acid metabolism ( I = 1.0). Alanine, aspartate, and glutamate metabolism ( I = 0.756), vitamin B6 metabolism ( I = 0.647), sulfur metabolism ( I = 0.532), phenylalanine metabolism (I = 0.357), and nicotinate and nicotinamide metabolism ( I = 0.194) also displayed marked pathway impacts (Supplementary Fig. S5e ; Supplementary Data 15 ). Together, these integrated findings from the group comparisons (IF-P vs. CR), high vs. low responders, and the case study, suggest that the remodeling of the gut microbiome through sustained weight loss on an IF-P regimen not only alters the microbial composition but also influences key metabolic pathways and output, reflective of fat mobilization and metabolic improvement.

Our study demonstrates distinct effects of IF-P on gut symptomatology and microbiome, as well as circulating metabolites compared to continuous CR. We observed significant changes in the GM response to both interventions; however, the IF-P group exhibited a more pronounced community shift and greater divergence from baseline (i.e., intra-individual Bray-Curtis dissimilarities). This shift was characterized by increased specific microbial families and genera, such as Christensenellaceae , Rikenellaceae , and Marvinbryantia , associated with favorable metabolic profiles. Furthermore, IF-P significantly increased circulating cytokine concentrations of IL-4, IL-6, IL-8, and IL-13. These cytokines have been linked to lipolysis, WL, inflammation, and immune response. The plasma metabolome analysis revealed distinct metabolite signatures in IF-P and CR groups, with the convergence of multiple metabolic pathways. These findings shed light on the differential effects of IF regimens, including IF-P as a promising dietary intervention for obesity management and microbiotic and metabolic health.

While acknowledging individual contributions of WL, protein pacing, and IF, we propose that the beneficial shifts observed may be best characterized as the culmination of features inherent in our IF-P approach. For example, it is possible that microbial competition is leveraged during reduced and intermittent nutritional input periods, emphasizing nutrient composition and food matrix type (combination of whole food and meal replacements vs. primarily whole food), affecting available substrates for gut microbes. IF-P participants’ fiber intake was concentrated in fiber-rich (RS5 type) shakes, offering immediate availability of fiber to the GI tract. In contrast, CR participants consumed fiber through whole foods, leading to a slower digestion and absorption process influenced by individual digestive transit times and enzymatic profiles. This nutritional environment may create ecological niches that support symbiont microbial communities. In this investigation, we provide support of such remodeling, with intentional fasting and increased relative protein (protein pacing) consumption well-validated to improve body composition and metabolism during weight loss 7 , 8 , 15 . Our results align with previous studies on CR, where greater relative protein intake was associated with an increased abundance of Christensenella 81 . This increase is likely a result of increased amino acid-derived metabolites 21 . We also observed increased signatures of amino acid metabolism in the GM of IF-P participants, which may be attributed to increased nitrogen availability, prompting de novo amino acid biosynthesis. The liquid format of two of the daily meals and precise timing of high-quality protein consumption (Protein Pacing) in the IF-P regimen may have influenced these results, as amino acids play essential roles in microbial communities, acting as energy and nitrogen sources and essential nutrients for amino acid auxotrophs.

In addition to the differences in nutrient composition, the IF-P group exhibited a profound reduction (33%) in visceral fat 15 . This reduction is significant because visceral fat is highly correlated with GM. While the specific influence of GM on fat depots in our study remains unclear, the shift in cytokine profile and metabolic pathways suggests an interaction between GM and fat metabolism. Regarding GM-host interaction, we did not detect changes in gut permeability assaying LBP. However, correlations were found with cytokines IL-4 and IL-13 and microbes Colidextribacter (negative association) and Ruminoccus gauveauii group (positive association). These associations may reflect the direct impact of the dietary intervention, yet they also hint at a deeper crosstalk within the gut-immune axis. This crosstalk is known to play a pivotal role in modulating host inflammation and influencing adipose tissue signaling pathways 42 . Furthermore, the observed microbial shifts, including changes in populations of Christensenella , suggest a nuanced role for certain microbes in regulating metabolic health. Notably, certain strains of Christensenella have been implicated in the regulation of key metabolic markers, such as glycemia and leptin levels, and in promoting hepatic fat oxidation 82 .

Our findings also underscore that GM composition plays a role in WL responsiveness during IF-P interventions. Subgroup analysis based on WL responsiveness revealed significant differences in species composition at the taxonomic level. The High-responder group showed an increased abundance of certain bacteria associated with metabolic benefits and anti-inflammatory effects. In contrast, the Low-responder group exhibited an increased abundance of butyrate-producing and nutritionally adaptive species (e.g., Eubacterium ventriosum 71 and Roseburia inulinivorans 72 ). Fecal metabolome analysis further highlighted differences between the two subgroups, with distinct metabolic signatures and enrichment in specific metabolic pathways. Notably, the High WL responders displayed enrichment of fecal metabolites involved in lipid metabolism. In contrast, Low responders were more prominent in pathways related to the metabolism of amino acids and peptides, including glycine, serine, and threonine, d-glutamine, and d-glutamate, as well as tyrosine metabolism and arginine biosynthesis. The latter metabolic signature has been reported in individuals with severe obesity undergoing high-protein, low-calorie diets 83 . As both High and Low WL responders were consuming the same diet, our results suggest differences in GM composition and metabolism, which could play a role in determining the success of an IF-P regimen. Though, as these enrichment analyses were performed in an exploratory manner, we acknowledge the need for a more systematic approach to validate these findings.

Finally, we provide evidence of long-term GM stabilization from these changes by following one individual over 12 months. Dietary restriction is widely used to reduce fat mass and weight in individuals with or without obesity; however, weight regain after such periods presents a critical challenge, and the underlying homeostatic mechanisms remain largely elusive. Notably, keystone taxa that became more prominent over time were associated with anti-adipogenesis, improved insulin sensitivity, and reduced metabolic disease risk. The microbial shifts were accompanied by noticeable changes in the fecal metabolome, with shifts in various metabolites and chemical subclasses. Pathway analysis identified impacts on primary bile acid biosynthesis, cysteine and methionine metabolism, and other fat mobilization and metabolic improvement pathways. These shifts were accompanied by noticeable changes in the fecal metabolome, particularly in metabolites and chemical subclasses related to lipid metabolism, nucleotide turnover, and aromatic amino acid formation.

Despite the valuable insights from our study on the complex interactions between intermittent fasting, higher protein intake using protein pacing, the GM, and circulating metabolites in obese individuals, several limitations should be acknowledged. First, our reliance on fecal samples to represent the GM may have overlooked potential microbial populations in the upper GI tract. Including samples from proximal regions in future studies would provide a more comprehensive understanding of the gut microbiome’s response to IF-P and CR. In addition, the sample size for our study was determined based on the primary outcomes related to body weight and composition from the parent study 15 . This sample size may have reduced statistical power and potentially amplified individual variability among participants. However, it is important to note that the smaller RCT design allowed for more precise control over diet and lifestyle factors, minimizing potential confounding influences on the study outcomes. Furthermore, the study’s duration was limited to eight weeks, which prevented potential insights into the differential long-term effects between the two interventions. However, we were able to extend the follow-up duration and conduct periodic assessments for a year in our case-study participant, offering a more comprehensive understanding of the sustainability of the observed changes and the potential for weight regain for IF-P. The current study compared a combination of whole food and supplements (shakes and bars; IF-P) versus primarily whole food (CR), which together with variations in protein and fiber content and type may have influenced the gut symptomatology and nutrient absorption between groups. Additionally, study participants self-reported dietary intake daily, although there was close monitoring of intake through the return of empty food packaging/containers of consumed food and daily monitoring by investigators and weekly meetings with a registered dietitian. Overall, knowledge gaps are present in this research, including how the microbiome is rebuilt after food reintroduction and how overall caloric restriction and specific macronutrients contribute to this process. However, considering the multifactorial nature of weight loss and metabolic health, our work represents an important precedent for future work. Future investigators should consider integrating these factors to provide a more comprehensive understanding of the underlying mechanisms. Additional research is warranted to characterize the metabolic signature of IF-P, the time relationship between these fasting periods, and the analysis of these metabolic changes. A strength of our High-Low-responder and case-study analyses is the hypothesis-driving nature of the findings, from which targeted microbiome and/or precision nutrition interventions can be designed and tested.

In conclusion, our study provides valuable insights into the complex interactions among intermittent fasting and protein pacing, the GM, and circulating metabolites in individuals with obesity. Specifically, intermittent fasting - protein pacing significantly reduces gut symptomatology and increases gut microbes associated with a lean phenotype ( Christensenella ) and circulating cytokines mediating total body weight and fat loss. These findings highlight the importance of personalized approaches in tailoring dietary interventions for optimal weight management and metabolic health outcomes. Further research is necessary to elucidate the underlying mechanisms driving these associations and to explore the therapeutic implications for developing personalized strategies in obesity management. Additionally, future studies should consider investigating microbial populations in upper GI sections and potential intestinal tissue remodeling to gain a more comprehensive understanding of the gut microbiome’s role in these interventions.

Study design and participants

The protocol of the clinical trial was registered on March 6, 2020 (Clinicaltrials.gov; NCT04327141), and the results of the primary analysis have been published previously 15 . Briefly, participants were recruited from Saratoga Springs, NY, and were provided informed written consent in accordance with the Skidmore College Human Subjects Institutional Review Board before participation (IRB#: 1911-859), including consent for the use of samples and data from the current study. Each procedure performed was in adherence with New York state regulations and the Federal Wide Assurance, which follows the National Commission for the Protection of Human Subjects of Biomedical and Behavioral Research, and in agreement with the Helsinki Declaration (revised in 1983). Their physicians performed a comprehensive medical examination/history assessment to rule out any current cardiovascular or metabolic disease. For at least six months before the start of the study, all eligible participants were either sedentary or lightly active (<30 min, two days/week of organized physical activity), with overweight or obesity (BMI > 27.5 kg/m2; % body fat > 30%), weight stable (±2 kg), and middle-aged (30–65 years). In addition, participants taking antibiotics, antifungals, or probiotics within the previous two months were excluded. Enrolled participants were matched for body weight, BMI, and body fat and randomly assigned to one of two groups: (a) IF-P ( n = 21; 14 women; 7 men) or (b) CR ( n = 20; 12 women; 8 men) for eight weeks. During a one-week run-in period, subjects maintained a stable body weight by consuming a similar caloric intake as their pre-enrollment caloric intake while maintaining their sedentary lifestyle. This was confirmed by matching their pre-enrollment dietary intake to the one-week run-in diet period 15 . Following baseline testing, participants were provided detailed instructions on their weight loss dietary regimen (Supplementary Table S1 ) and received weekly dietary counseling and compliance/adherence monitoring from the research team via daily food records, and weekly registered dietitian meetings, along with weekly visits to the Human Nutrition and Metabolism laboratory at Skidmore College (Saratoga Springs, NY) for meal distribution and empty packet/container returns. All outcome variables were assessed pre (week 0), mid (week 4), and post (week 8). All participants were compensated $100 for successful completion of the study and received an additional monthly stipend of $75 for groceries (CR group only) or up to two meals per day of food supplements and meal replacements (IF-P only).

IF days consisted of ~350–550 kcals per day, in which participants were provided a variety of supplements and snacks. Protein pacing (P) days for IF-P consisted of four and five meals/day for women and men, respectively, two of which (breakfast and one other meal) were liquid meal replacement shakes with added whole foods (Whole Blend IsaLean® Shakes, 350/400 kcals, 30/36 g of protein/meal, 9 g of fiber); a whole food evening dinner meal (450/500 kcals men), an afternoon snack (200 kcals, men only), and an evening protein snack (IsaLean® or IsaPro® Shake or IsaLean Whole Blend® Bar; 200–250 kcals). This dietary regimen provided 1350–1500 and 1700–1850 kcals/day for women and men, respectively, and a macronutrient distribution targeting 35% protein, 35% carbohydrate, 20–30 g/day of fiber, and 30% fat. Isagenix International, LLC (Gilbert, AZ, USA) provided all meal replacement shakes, bars, beverages, and supplements. In comparison, participants assigned to the CR diet followed specific guidelines of the National Cholesterol Education Program Therapeutics Lifestyle Changes (TLC) diet of the American Heart Association with a strong Mediterranean diet influence of a variety of fresh vegetables, fruits, nuts, and legumes. The specific macronutrient distribution recommended was <35% of kcal as fat; 50%–60% of kcal as carbohydrates; 15% kcal as protein; <200 mg/dL of dietary cholesterol; and 20–30 g/day of fiber. The total calorie intake was 1200 and 1500 calories per day for women and men, respectively, during the 8-week weight loss intervention. In addition to weekly meetings with the registered dietitian and daily contact with research team members, subjects were provided detailed written instructions for their meal plans. They were closely monitored through daily participant-researcher communication (e.g., email, text, and mobile phone), two-day food diary analysis, weekly dietary intake journal inspections, weekly meal/supplement container distribution, and returning empty packets and containers.

Gastrointestinal (GI) symptom rating scale

Participants completed the 15-question GI symptom rating scale (GSRS) 84 at baseline, week four, and week eight. Briefly, each question is rated on a 7-point Likert scale (1 = absent; 2 = minor; 3 = mild; 4 = moderate; 5 = moderately severe; 6 = severe and 7 = very severe) and recalled from the previous week. Questions include symptoms related to upper abdominal pain, heartburn, regurgitation (acid reflux), empty feeling in the stomach, nausea, abdominal rumbling, bloating, belching, flatulence, and questions on defecation. The GSRS questionnaire provides explanations of each symptom, is understandable, and has reproducibility for measuring the presence of GI symptoms 85 . In our analysis, a score of ≥2 (minor) was defined as symptom presence, and a score ≥ 4 (moderate) was defined as moderate symptom presence. Furthermore, to better categorize symptom location, bloating, flatulence, constipation, diarrhea, stool consistency, defecation urgency, and sensation of not completely emptying bowels were classified as lower GI symptoms, and nausea, heartburn, regurgitation, upper abdominal pain, empty feeling in the stomach, stomach rumbling, and belching was classified as upper GI symptoms. Total scores were also generated for overall symptom and moderate symptom presence.

Fecal sample collection and DNA extraction

Participants were instructed to provide stool samples at baseline, week four, and week eight of the intervention. The case-study participant additionally provided samples at weeks 12, 16, 32, and 52. The entire bowel movement was collected and transported within 24 h of defecation to the Skidmore College Human Nutrition and Metabolism (Saratoga Springs, NY) laboratory using a cooler and ice packs and frozen at −80 °C. Samples were then sent to ASU (Phoenix, AZ) overnight on dry ice for analysis, where they were thawed at 4 °C and processed. Wet weight was recorded to the nearest 0.01 g after subtracting the weight of fecal collection materials. Stool samples were then rated according to the BSS 86 , homogenized in a stomacher bag, and the pH was measured (Symphony SB70P, VWR International, LLC., Radnor, PA, USA). Next, the extraction of DNA was performed using the DNeasy PowerSoil Pro Kit (Cat. No. 47016, Qiagen, Germantown, MD) per the manufacturer’s instructions. DNA concentration and quality were quantified using the NanoDrop™ OneC Microvolume UV-Vis Spectrophotometer (Thermo Scientific™, Waltham, MA) according to manufacturer instructions. The OD 260 /OD 280 ratio of all samples was ≥1.80 (demonstrating DNA purity).

Quantification of bacterial 16S rRNA genes

To estimate total bacterial biomass per sample (16S rRNA gene copies per gram of wet stool), DNA extracted from the fecal collections was assessed via quantitative polymerase chain reaction (qPCR) based on previously published methods 87 , 88 . Briefly, all 20 μL qPCR reactions contained 10 uL of 2X SYBR Premix Ex Taq ™ (Tli RNase H Plus) (Takara Bio USA, Inc., San Jose, CA, USA), 0.3 μM (0.6 μL) of each primer (926 F: AAACTCAAAKGAATTGACGG; 1062 R: CTCACRRCACGAGCTGAC), 2 μL DNA template (or PCR-grade water as negative control), and 6.8 μL nuclease-free water (Thermo Fisher Scientific, Waltham, MA, USA). PCR thermal cycling conditions were as follows: 95 °C for 5 min, followed by 35 cycles of 95 °C for 15 s, 61.5 °C for 15 s, and 72 °C for 20 s, then hold at 72 °C for 5 min, along with a melt curve of 95 °C for 15 s, 60 °C for 1 min, then 95 °C for 1 s. Quantification was performed using a QuantStudio3™ Real-Time PCR System by Applied Biosystems with QuantStudio Design and Analysis Software 1.2 from Thermo Fisher Scientific (Waltham, MA, USA). All samples were analyzed in technical replicates. For quality assurance and quality control, molecular negative template controls (NTC) consisting of PCR-grade water (Invitrogen, Waltham, MA, USA) and positive controls created by linearized plasmids were run on every qPCR plate. Standard curves were run-in triplicate and used for sample quantification, ranging from 10 7 to 10 1 copies/μL with a cycle threshold (CT) detection limit cutoff of 33. Reaction efficiency was approximately 101%, with a slope of −3.29 and R 2 ≥ 0.99.

Fecal microbiome analysis

Amplification of the 16S rRNA gene sequence was completed in triplicate PCRs using 96-well plates. Barcoded universal forward 515 F primers and 806 R reverse primers containing Illumina adapter sequences, which target the highly conserved V4 region, were used to amplify microbial DNA 89 , 90 . PCR, amplicon cleaning, and quantification were performed as previously outlined 90 . Equimolar ratios of amplicons from individual samples were pooled together before sequencing on the Illumina platform (Illumina MiSeq instrument, Illumina, Inc., San Diego, CA). Raw Illumina microbial data were cleaned by removing short and long sequences, sequences with primer mismatches, uncorrectable barcodes, and ambiguous bases using the Quantitative Insights into Microbial Ecology 2 (QIIME2) software, version 2021.8 91 .

16S rRNA sequencing produced 7,366,128 reads with a median of 53,776 per sample (range: 9512–470,848). Paired-end, demultiplexed data were imported and analyzed using QIIME2 software. Upon examination of sequence quality plots, base pairs were trimmed at position 20 and truncated at position 240 and were run through DADA2 to remove low-quality regions and construct a feature table using ASVs. Next, the ASV feature table was passed through the feature-classifier plugin 92 , which was implemented using a naive Bayes machine-learning classifier, pre-trained to discern taxonomy mapped to the latest version of the rRNA database SILVA (138.1; 99% ASVs from 515 F/806 R region of sequences) 93 . Based on an assessment of alpha rarefaction, a threshold of 6500 sequences/sample was established, retaining all samples for downstream analysis. A phylogenic tree was then constructed using the fragment-insertion plugin with SILVA at a p-sampling depth of the rarefaction threshold to impute high-quality reads and normalize for uneven sequencing depth between samples 94 . Alpha diversity (intra-community diversity) was measured using observed ASVs and the Phylogenetic diversity index. Additionally, the Shannon index was calculated for the subgroup and case-study analyses to capture richness and evenness at the species level. Beta diversity (inter-community diversity) was measured using Bray-Curtis dissimilarity.

For shotgun metagenomics, DNA was sequenced on the Illumina NextSeq 500 platform (Illumina, CA, USA) to generate 2 × 150 bp paired-end reads at greater sequencing depth with a minimum of 10 million reads. Raw Illumina sequencing reads underwent standard quality control with FastQC. Adapters were trimmed using TrimGalore. DNA sequences were aligned to Hg38 using bowtie2 95 . DNA sequences were then analyzed via the bio bakery pipeline 96 for taxonomic composition and potential functional content with MetaPhlAn4 and HUMAnN 3.0 (UniRef90 gene-families and MetaCyc metabolic pathways), using standard parameters. Functional profiling resulted in 8528 distinct Kyoto Encyclopedia of Genes and Genomes Orthology (KO) groups and 511 metabolic pathways, which align with previous human gut microbiome studies 96 .

Blood sample collection and biochemical analyses

All participants were tested between the hours of 6:00 a.m. and 9:00 a.m., after an overnight fast for body composition assessments (height, body weight, and total body composition) at weeks 0, 4, and 8. 12-h fasted venous blood samples (~20 mL) were collected into EDTA-coated vacutainer tubes and centrifuged (Hettich Rotina 46R5) for 15 min at 4000 × g at −4 °C. After separation, plasma was stored at −80 °C until analyzed. Undiluted plasma samples were sent to Eve Technologies (Calgary, Alberta, Canada) for assessment of inflammatory cytokines [Granulocyte-macrophage colony-stimulating factor [GM-CSF], interferon-γ (IFNγ), interleukin (IL)-β, IL-2, IL-4, IL-5, IL-6, IL-8, IL-10, IL-12p70, IL-13, IL-17A, IL-23, and Tumor necrosis factor-α (TNFα)] using a high human sensitivity 14-plex cytokine assay (Millipore, Burlington, MA). Circulating LBP concentrations were quantified in duplicate using 1000x diluted plasma samples. A commercially available kit was used per the manufacturer’s protocol (Cat No. EH297RB, Thermo Fisher Scientific, Inc, Waltham, MA; intra-assay coefficient variation [CV] <10%).

Targeted plasma metabolomic analysis

For the plasma metabolomic analysis, a 12-h fasted venous blood sample (~20 mL) was collected into EDTA-coated vacutainer tubes and centrifuged (Hettich Rotina 46R5) for 15 min at 4000 × g at 4 °C. After separation, 2 mL of plasma was aliquoted and stored at −80 °C at the Biochemistry Laboratory at Skidmore College (Saratoga Springs, NY, USA). Samples were then sent to the Arizona Metabolomics Laboratory at ASU (Phoenix, AZ, USA) overnight on dry ice for analysis, where they were thawed at 4 °C and processed. Briefly, 50 μL of plasma from each sample was processed to precipitate proteins and extract metabolites by adding 500 μL MeOH and 50 μL internal standard solution (containing 1810.5 μM 13 C 3 -lactate and 142 μM 13 C 5 -glutamic acid). The mixture was vortexed (10 s) and stored for 30 min at –20 °C, then centrifuged at 224,000 × g for 10 min at 4 °C. Supernatants (450 μL) were extracted, transferred to new Eppendorf vials, and dried (CentriVap Concentrator; Labconco, Fort Scott, KS, USA). Samples were then reconstituted in 150 μL of 40% phosphate-buffered saline (PBS)/60% acetonitrile (ACN) and centrifuged again at 22,000 × g at 4 °C for 10 min. Supernatants (100 µL) were transferred to an LC autosampler vial for subsequent analysis. Quality control (QC) was performed by creating a pooled sample from all plasma samples and injecting once every ten experimental samples to monitor system performance.

The highly-reproducible targeted LC–MS/MS method used in the current investigation was modeled after previous studies 97 , 98 , 99 . The specific metabolites included in our targeted detection panel are representative of more than 35 biological pathways most essential to biological metabolism and have been successfully leveraged for the sensitive and broad detection of effects related to diet 100 , diseases 101 , drug treatment 102 , environmental contamination 103 , and lifestyle factors 104 . Briefly, LC–MS/MS experiments were performed on an Agilent 1290 UPLC-6490 QQQ-MS system (Santa Clara, CA, USA). Each sample was injected twice for analysis, 10 µL using negative and 4 µL using positive ionization modes. Chromatographic separations were performed in hydrophilic interaction chromatography (HILIC) mode on a Waters Xbridge BEH Amide column (150 × 2.1 mm, 2.5 µm particle size, Waters Corporation, Milford, MA, USA). The flow rate was 0.3 mL/min, the autosampler temperature was maintained at 4 °C, and the column compartment was set at 40 °C. The mobile phase system was composed of Solvents A (10 mM ammonium acetate, 10 mM ammonium hydroxide in 95% H 2 O/5% ACN) and B (10 mM ammonium acetate, 10 mM ammonium hydroxide in 95% ACN/5% H 2 O). After the initial 1 min isocratic elution of 90% Solvent B, the percentage of Solvent B decreased to 40% at t = 11 min. The composition of Solvent B was maintained at 40% for 4 min ( t = 15 min).

The mass spectrometer was equipped with an electrospray ionization (ESI) source. Targeted data acquisition was performed in multiple-reaction monitoring (MRM) mode. The LC–MS system was controlled by Agilent MassHunter Workstation software (Santa Clara, CA, USA), and extracted MRM peaks were integrated using Agilent MassHunter Quantitative Data Analysis software (Santa Clara, CA, USA).

GC–MS fecal short-chain fatty acid analysis

Before GC–MS analysis of SCFAs, frozen fecal samples were first thawed overnight under 4 °C. Then, 20 mg of each sample was homogenized with 5 μL hexanoic acid—6,6,6-d 3 (internal standard; 200 µM in H 2 O), 15 μL sodium hydroxide (NaOH [0.5 M]), and 500 μL MeOH. Samples were stored at −20 °C for 20 min and centrifuged at 22,000 × g for 10 min afterward. Next, 450 μL of supernatant was collected, and the sample pH was adjusted to 10 by adding 30 μL of NaOH:H 2 O (1:4, v-v). Samples were then dried, and the residues were initially derivatized with 40 µL of 20 mg/mL MeOX solution in pyridine under 60 °C for 90 min. Subsequently, 60 µL of MTBSTFA containing d 27 -mysristic acid was added, and the mixture was incubated at 60 °C for 30 min. The samples were then vortexed for 30 s and centrifuged at 22,000 × g for 10 min. Finally, 70 µL of supernatant was collected from each sample and injected into new glass vials for GC–MS analysis.

GC–MS conditions used here were adopted from a previously published protocol 105 . Briefly, GC–MS experiments were performed on an Agilent 7820 A GC-5977B MSD system (Santa Clara, CA); all samples were analyzed by injecting 1 µL of prepared samples. Helium was the carrier gas with a constant flow rate of 1.2 mL/min. Separation of metabolites was achieved using an Agilent HP-5 ms capillary column (30 m × 250 µm × 0.25 µm). Ramping parameters were as follows: column temperature was maintained at 60 °C for 1 min, increased at a rate of 10 °C/min to 325 °C, and then held at this temperature for 10 min. Mass spectral signals were recorded at an m/z range of 50–600, and data extraction was performed using Agilent Quantitative Analysis software. Following peak integration, metabolites were filtered for reliability. Only those with QC CV < 20% and a relative abundance of 1000 in > 80% of samples were retained for statistical analysis.

Untargeted fecal metabolomic analysis

Briefly, each fecal sample (~20 mg) was homogenized in 200 µL MeOH:PBS (4:1, v-v, containing 1810.5 μM 13 C 3 -lactate and 142 μM 13 C 5 -glutamic Acid) in an Eppendorf tube using a Bullet Blender homogenizer (Next Advance, Averill Park, NY). Then 800 µL MeOH:PBS (4:1, v-v, containing 1810.5 μM 13 C 3 -lactate and 142 μM 13 C 5 -glutamic Acid) was added, and after vortexing for 10 s, the samples were stored at −20 °C for 30 min. The samples were then sonicated in an ice bath for 30 min. The samples were centrifuged at 22,000 × g for 10 min (4 °C), and 800 µL supernatant was transferred to a new Eppendorf tube. The samples were then dried under vacuum using a CentriVap Concentrator (Labconco, Fort Scott, KS). Prior to MS analysis, the obtained residue was reconstituted in 150 μL 40% PBS/60% ACN. A quality control (QC) sample was pooled from all the study samples.

The untargeted LC–MS metabolomics method used here was modeled after that developed and used in a growing number of studies 106 , 107 , 108 . Briefly, all LC–MS experiments were performed on a Thermo Vanquish UPLC-Exploris 240 Orbitrap MS instrument (Waltham, MA). Each sample was injected twice, 10 µL for analysis using negative ionization mode and 4 µL for analysis using positive ionization mode. Both chromatographic separations were performed in hydrophilic interaction chromatography (HILIC) mode on a Waters XBridge BEH Amide column (150 × 2.1 mm, 2.5 µm particle size, Waters Corporation, Milford, MA). The flow rate was 0.3 mL/min, autosampler temperature was kept at 4 °C, and the column compartment was set at 40 °C. The mobile phase was composed of Solvents A (10 mM ammonium acetate, 10 mM ammonium hydroxide in 95% H 2 O/5% ACN) and B (10 mM ammonium acetate, 10 mM ammonium hydroxide in 95% ACN/5% H 2 O). After the initial 1 min isocratic elution of 90% B, the percentage of Solvent B decreased to 40% at t = 11 min. The composition of Solvent B maintained at 40% for 4 min ( t = 15 min), and then the percentage of B gradually went back to 90%, to prepare for the next injection. Using mass spectrometer equipped with an electrospray ionization (ESI) source, we collected untargeted data from 70 to 1050 m/z.

To identify peaks from the MS spectra, we made extensive use of the in-house chemical standards (~600 aqueous metabolites), and in addition, we searched the resulting MS spectra against the HMDB library, Lipidmap database, METLIN database, as well as commercial databases including mzCloud, Metabolika, and ChemSpider. The absolute intensity threshold for the MS data extraction was 1000, and the mass accuracy limit was set to 5 ppm. Identifications and annotations used available data for retention time (RT), exact mass (MS), MS/MS fragmentation pattern, and isotopic pattern. We used the Thermo Compound Discoverer 3.3 software for aqueous metabolomics data processing. The untargeted data were processed by the software for peak picking, alignment, and normalization. To improve rigor, only the signals/peaks with CV < 20% across quality control (QC) pools, and the signals showing up in >80% of all the samples were included for further analysis. To ensure the robustness of our model validation, we employed an enhanced validation approach by repeating the LOOCV process 100 times. Each iteration involves excluding one sample from the dataset to serve as the test set, with the model being trained on the remaining samples. This approach, referred to as ‘repeated LOOCV’, was adopted to mitigate bias and provide a thorough validation of our model’s predictive capability. The method signifies the number of repetitions of the LOOCV process, rather than splitting the dataset into 100 equal parts.

Multi-omics data analysis

For MOFA, bacterial 16S rRNA ASVs and plasma metabolites were integrated using the MOFA2 package 55 . Before integration, ASV sequences were filtered (minimum of 5 ASV in greater than 10% of all samples), collapsed to the genus level, and scaled using a centralized-log-ratio, as described previously 109 . Plasma metabolites were scaled and normalized as described in the metabolome analysis. The inputs for MOFA model training comprised 53 taxa and 138 metabolites. The latent factors and feature loadings were extracted from the best-trained model with the built-in functions of MOFA2. After model fitting, the number of factors was estimated by requiring a minimum of 2% variance explained across all microbiome modalities.

Integrating microbial taxa with the same filtration as stated above (at the genus level from 16S amplicon sequencing and species level from metagenomic sequencing) and cytokine data and fecal metabolomic data, respectively, was conducted with GFLASSO (R package: GFLASSO, v0.0.0.9000). This correlation-based network solution can handle multiple response variables for a given set of predictors (in this case: 1. cytokine abundances predicted by microbial taxa response; and 2. fecal metabolite response predicted by microbial taxa). Solution parsimony was determined by an unweighted (i.e., presence or absence of association by imposing a correlation threshold) network structure. The regularization and fusion parameters were determined from the smallest root mean squared error (RMSE) estimate via cross-validation, accounting for interdependencies among microbial features. The tested parameters encompassed all combinations between λ and γ with values ranging from 0 to 1 (inclusive) in step increments of 0.1. GFLASSO coefficient matrices were constructed using a threshold coefficient of >0.02 to discern the strongest associative signals.

Statistical analysis

Gastrointestinal symptom scores were on the low end of the GSRS scale and not normally distributed; therefore, nonparametric statistical tests were applied. Symptom prevalence (number of scores ≥ 2) and moderate symptom prevalence (≥4) for total, upper, and lower GI GSRS clusters were analyzed using contingency tables. Specifically, differences between IF-P and CR GI symptoms at baseline were compared using a Fisher’s Exact test, whereas baseline vs. weeks four and eight values were compared with McNemar’s test. Stool weight, BSS, fecal pH, plasma cytokines and LBP, and SCFAs were assessed for normality with Q-Q plots and Shapiro-Wilk tests and log-transformed where appropriate. These were then tested for time and interaction (group × time) effects using linear-mixed effect (LME) models, with each participant included as a random effect.

For analysis and visualization of the microbiome data, artifacts generated in QIIME2 were imported into the R environment (v4.2.2) using the phyloseq package (v1.42.0) 110 . Before conducting downstream analyses, sequences were filtered to remove all non-bacterial sequences, including archaea, mitochondria, and chloroplasts. After assessing normality (Shapiro-Wilk’s tests), LME models were used to test the effect of time and the interaction of group and time with the covariates of age and sex with each participant included as a random effect on the alpha diversity metrics using the nLME package (v3.1.160). For beta diversity, a nested permutational analysis of variance (PERMANOVA) was conducted on Bray-Curtis dissimilarities using the Adonis test in the vegan package (v2.6.2) with 999 permutations. The PERMANOVA model incorporated the factors of time, individual, interaction (group × time), and participant (nested factor). A permutation test for homogeneity in multivariate dispersion (PERMDISP) was conducted using the ‘betadisper’ function in the vegan package to compare dispersion. To support the Adonis analysis, intra-individual differences were also compared between groups, as previously described 111 , by calculating the within-subject distance for paired samples (baseline vs. weeks four and eight) and testing for group distances (Wilcoxon rank-sum test). Differential abundance analysis was performed using MaAsLin2 (v1.12.0) 18 . To detect changes in microbial features between groups over time, we built linear-mixed models that include group, time, and their interaction, with age and sex as covariates and the participant as a random factor. Before analysis, raw counts from the ASV table were filtered for any sequence not present five times in at least 30% of all samples. A significant p-value for the product term indicates that changes in microbial features differed over time between groups. The Benjamini–Hochberg (BH) procedure was used to correct for multiple testing at ≤0.10. To assess the correlation between changes in specific taxa and biomarkers over the eight-week intervention, Spearman correlation tests were performed.

Univariate and multivariate analyses of plasma metabolites and metabolic ontology analysis were performed, and results were visualized using the MetaboAnalystR 5.0 112 . Human metabolomic data were mapped to the Kyoto Encyclopedia of Genes and Genomes (KEGG) human pathway library to analyze predicted states 113 . The data were log 10 -transformed, and Pareto scaled to approximate normality before all analyses. A GLM was constructed with age, sex, and time as covariates to determine significantly affected metabolites by group intervention. Levene’s test was performed to detect significant homogeneity. The BH procedure was used to correct for multiple testing at ≤0.10. Fecal metabolomic analysis for the subgroup comparison was performed by assessing logFC values between groups with a Wilcoxon rank-sum test with BH adjustment. For pathway analysis, the impact was calculated using a hypergeometric test, while significance was determined using a test of relative betweenness centrality. Importantly, the BH procedure was not applied to pathway and enzyme enrichment analyses for the subgroup assessment since these analyses involve testing the significance of multiple related hypotheses rather than independent hypotheses, which is too conservative, resulting in false negative results.

For MOFA, latent factors explaining ≥2.0% of model variance from the plasma metabolomic and amplicon microbiome data were used to perform Spearman correlations on anthropometric and nutritional data and compared between IF-P and CR groups using Wilcoxon rank-sum tests. The highest beta coefficients (>0.3) detected from GFLASSO models were further assessed by performing Spearman correlations of select microbial features with the response variables (i.e., cytokines and fecal metabolites). All statistical tests were performed with a significance level of p < 0.05 and BH correction of p .adj < 0.10. In addition, we present data in this study in accordance with the ‘Strengthening The Organization and Reporting of Microbiome Studies’ (STORMS) guidelines for human microbiome research 114 .

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

The microbiome sequencing data generated in this study have been deposited in the BioProject Database of National Centre for Biotechnology Information database under accession code PRJNA847971 . The metadata data linking the microbiome sequences with the appropriate sample ID and intervention in this study are provided in Supplementary Data 1 . The processed data are available at https://github.com/Alex-E-Mohr/GM-Remodeling-IF-ProteinPacing-vs-CaloricRestriction . Source data are provided with this paper.

Code availability

The R code used for analysis and figure generation for reproducibility purposes are available at: https://github.com/Alex-E-Mohr/GM-Remodeling-IF-ProteinPacing-vs-CaloricRestriction . 115

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Acknowledgements

We thank the trial volunteers for their dedication and commitment to the study protocol. We are grateful for the research assistants from Skidmore College who provided valuable assistance with study protocol design, scheduling, recruitment, data testing, collection, entry, and statistical analysis, and preparation of manuscripts: Molly Boyce, Jenny Zhang, Melissa Haas, Olivia Furlong, Emma Valdez, Jessica Centore, Annika Smith, Kaitlyn Judd, Aaliyah Yarde, Katy Ehnstrom, Dakembay Hoyte, Sheriden Beard, Heather Mak, and Monique Dudar. We are grateful for the extensive guidance and counseling provided by the registered dietitian Jaime Martin. We thank research coordinator Michelle Poe for her superior dedication to all aspects of the study. This study was primarily funded by an unrestricted grant from Isagenix International LLC to P.J.A. (grant #:1911-859), with secondary funding provided to K.L.S.

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College of Health Solutions, Arizona State University, Phoenix, AZ, USA

Alex E. Mohr, Karen L. Sweazea, Corrie M. Whisner, Dorothy D. Sears, Haiwei Gu & Judith Klein-Seetharaman

Biodesign Institute Center for Health Through Microbiomes, Arizona State University, Tempe, AZ, USA

Alex E. Mohr, Karen L. Sweazea, Devin A. Bowes, Corrie M. Whisner & Rosa Krajmalnik-Brown

Center for Evolution and Medicine, College of Liberal Arts and Sciences, Arizona State University, Tempe, AZ, USA

Karen L. Sweazea

School of Molecular Sciences, Arizona State University, Tempe, AZ, USA

Paniz Jasbi & Judith Klein-Seetharaman

Systems Precision Engineering and Advanced Research (SPEAR), Theriome Inc., Phoenix, AZ, USA

Paniz Jasbi

Center of Translational Science, Florida International University, Port St. Lucie, FL, USA

Yan Jin & Haiwei Gu

Human Nutrition and Metabolism Laboratory, Department of Health and Human Physiological Sciences, Skidmore College, Saratoga Springs, NY, USA

Karen M. Arciero & Paul J. Arciero

Isagenix International, LLC, Gilbert, AZ, USA

Eric Gumpricht

School of Health and Rehabilitation Sciences, Department of Sports Medicine and Nutrition, University of Pittsburgh, Pittsburgh, PA, USA

Paul J. Arciero

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Contributions

Study conceived and designed: P.J.A. Manuscript preparation with input from all authors: A.E.M., K.L.S., D.A.B., P.J., C.M.W., D.D.S., R.K.-B., H.G., J.K.-S., K.M.A., E.G., and P.J.A. Randomized study design and execution: K.M.A., and P.J.A. Microbiome analysis: A.E.M., D.A.B., C.M.W., and R.K.-B. Blood analyte analysis: A.E.M., K.L.S., and P.J.A. Metabolomic analysis: A.E.M., Y.J., H.G., and P.J. Statistical analysis and data presentation: A.E.M., C.M.W., D.D.S., R.K.-B., and P.J.A. Supervision and funding: K.L.S., E.G., and P.J.A.

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Correspondence to Paul J. Arciero .

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Competing interests.

P.J.A. is a consultant for Isagenix International LLC, the study’s sponsor, he is an advisory board member of the International Protein Board (iPB), and he receives financial compensation for books and keynote presentations on protein pacing ( www.paularciero.com ). Eric Gumpricht is employed by Isagenix International, LLC, the funding source for this research. Isagenix International, LLC had no role in the study design, data collection, analysis, or decision to publish. No authors have financial interests regarding the outcomes of this investigation. The other authors declare no competing interests.

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Mohr, A.E., Sweazea, K.L., Bowes, D.A. et al. Gut microbiome remodeling and metabolomic profile improves in response to protein pacing with intermittent fasting versus continuous caloric restriction. Nat Commun 15 , 4155 (2024). https://doi.org/10.1038/s41467-024-48355-5

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Received : 13 September 2023

Accepted : 26 April 2024

Published : 28 May 2024

DOI : https://doi.org/10.1038/s41467-024-48355-5

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Writing as Research and Research through Writing

This project reflects on the processes and products of designing a pilot course in academic and research writing in English for PhD research scholars in Sree Sankara University of Sanskrit (SSUS), Kalady, Kerala, India. The one-week (40 hours) intensive course was designed for my pedagogy-based Capstone Project for the IWT CLASP Fellows Program to investigate through a pilot designing of an Academic Writing in English Course for doctoral researchers from various disciplines in the humanities and social sciences, ranging from classical Indian music to European Philosophy and Sanskrit. The students are all non-native speakers of English who had their school education in the regional language of Kerala, Malayalam. They had not attended any academic writing course in their previous education. My goal was to test the relevance and appropriateness of some of the Bard College Institute for Writing & Thinking (IWT) Writing practices in such a multilingual and interdisciplinary classroom of doctoral researchers and reflect on the larger implications for designing academic writing courses based on practice for multilingual students in India. My primary conclusion is that Bard IWT practices, especially private freewriting, focused freewriting and writing to read in the zones are effective in generative writing inside and outside the classroom, facilitating discussion of academic writing processes, and significantly increasing the learner’s ability to generate more academic research-based text, as evident from the different kinds of feedback. Finally, I recommend that a skills-based (adapting IWT writing practices) content course can be a positive intervention in improving academic literacies for multilingual and inter-disciplinary learners in a setting like the state of Kerala, India.

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