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Trends in incidence of total or type 2 diabetes: systematic review

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Showing the turning point in diabetes incidence in 61 populations

Linked editorial

Trends in type 2 diabetes

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• Peer review
• Rakibul M Islam , postdoctoral research fellow 1 2 ,
• Elizabeth L M Barr , postdoctoral research fellow 1 ,
• Edward W Gregg , chair in diabetes and cardiovascular disease epidemiology 3 4 ,
• Meda E Pavkov , physician scientist 3 ,
• Jessica L Harding , research fellow 3 ,
• Maryam Tabesh , research study coordinator 1 2 ,
• Digsu N Koye , postdoctoral research fellow 1 2 ,
• Jonathan E Shaw , deputy director of Baker Heart and Diabetes Institute 1 2
• 1 Baker Heart and Diabetes Institute, Melbourne, VIC 3004, Australia
• 2 School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia
• 3 Centres for Diseases Control and Prevention, Division of Diabetes Translation, Atlanta, GA, USA
• 4 School of Public Health, Epidemiology and Biostatistics, Imperial College London, London, UK
• Correspondence to: D J Magliano dianna.magliano{at}baker.edu.au
• Accepted 16 July 2019

Objective To assess what proportions of studies reported increasing, stable, or declining trends in the incidence of diagnosed diabetes.

Design Systematic review of studies reporting trends of diabetes incidence in adults from 1980 to 2017 according to PRISMA guidelines.

Data sources Medline, Embase, CINAHL, and reference lists of relevant publications.

Eligibility criteria Studies of open population based cohorts, diabetes registries, and administrative and health insurance databases on secular trends in the incidence of total diabetes or type 2 diabetes in adults were included. Poisson regression was used to model data by age group and year.

Results Among the 22 833 screened abstracts, 47 studies were included, providing data on 121 separate sex specific or ethnicity specific populations; 42 (89%) of the included studies reported on diagnosed diabetes. In 1960-89, 36% (8/22) of the populations studied had increasing trends in incidence of diabetes, 55% (12/22) had stable trends, and 9% (2/22) had decreasing trends. In 1990-2005, diabetes incidence increased in 66% (33/50) of populations, was stable in 32% (16/50), and decreased in 2% (1/50). In 2006-14, increasing trends were reported in only 33% (11/33) of populations, whereas 30% (10/33) and 36% (12/33) had stable or declining incidence, respectively.

Conclusions The incidence of clinically diagnosed diabetes has continued to rise in only a minority of populations studied since 2006, with over a third of populations having a fall in incidence in this time period. Preventive strategies could have contributed to the fall in diabetes incidence in recent years. Data are limited in low and middle income countries, where trends in diabetes incidence could be different.

Systematic review registration Prospero CRD42018092287.

Introduction

Over the past few decades, the prevalence of diabetes in developed and developing countries has risen substantially, making diabetes a key health priority globally. 1 Examination of trends in total burden of diabetes is an essential part of the monitoring of this health priority area, but, to date, it has consisted primarily of studies looking at diabetes prevalence. 1 2 3 4 5 Prevalence estimates suggest that the diabetes burden is still rising in most countries, and this is often interpreted as evidence of increasing risk in the population. However, selective incidence studies 6 7 and some accompanying risk factor data 8 suggest otherwise. Prevalence can be a crude and misleading metric of the trajectory of an epidemic, because increasing prevalence of a disease might be due to either increasing incidence or to improved survival. Furthermore, prevalence cannot be reliably used to study the effects of changes in population risk factors, because their effects are detected earlier with incidence trends than with prevalence trends, and incidence is not affected by changes in survival.

Incidence measures the proportion of people who develop diabetes over a period of time among the population at risk. It is the appropriate measure of population risk, and a valuable way of assessing whether public health campaigns for diabetes prevention are succeeding. While prevalence can rise simply because mortality falls, incidence of diagnosed diabetes is affected only by the risk of the population and the amount of screening undertaken. Changes in prevalence might be an inadequate guide to the effects of prevention activities, and could lead to the inappropriate rejection of effective interventions. It is only by measuring both incidence and prevalence that a better understanding of the extent of diabetes can be achieved.

Among existing diabetes incidence data, a few studies suggest that diabetes incidence could be falling despite rising or stable prevalence, 6 7 9 but not all data are consistently showing the same trends. For example, studies from England and Wales (1994-98), 10 Portugal (1992-2015), 11 and Canada (1995-2007) 12 are reporting increases in diabetes incidence. To understand what is happening at a global level over time, a systematic approach to review all incidence trend data should be undertaken to study patterns and distributions of incidence trends by time, age, and sex. So far, no systematic reviews have reported on trends in the incidence of diabetes. Therefore, we conducted a systematic review of the literature reporting diabetes incidence trends.

Data sources and searches

We conducted a systematic review in accordance with PRISMA guidelines. 13 We searched Medline, Embase, and CINAHL from January 1980 to December 2017 without language restrictions. The full search strategy is available in supplementary table 1.

Study selection

Inclusion and exclusion criteria.

Eligible studies needed to report diabetes incidence in two or more time periods. Study populations derived from open, population based cohort studies (that is, with ongoing recruitment over time), diabetes registries, or administrative or health insurance databases based mainly or wholly in primary care (electronic medical records, health insurance databases, or health maintenance organisations). We also included serial, cross sectional, population based studies where incidence was defined as a person reporting the development of diabetes in the 12 months before the survey. Studies were required to report on the incidence of either total diabetes or type 2 diabetes. We excluded studies reporting incidence restricted to select groups (eg, people with heart failure) and studies reporting only on children or youth.

Each title and abstract was screened by at least two authors (DJM, JES, DNK, JLH, and MT) and discrepancies were resolved by discussion. We aimed to avoid overlap of populations between studies. Therefore, if national data and regional data were available from the same country over the same time period, we only included the national data. If multiple publications used the same data source, over the same time period, we chose the publication that covered the longest time period.

Outcome measure

Our outcome was diabetes incidence using various methods of diabetes ascertainment including: blood glucose, glycated haemoglobin (HbA1c), linkage to drug treatment or reimbursement registries, clinical diagnosis by physicians, administrative data (ICD codes (international classification of diseases)), or self report. Several studies developed algorithms based on several of these elements to define diabetes. We categorised the definition of diabetes into one of five groups: clinical diagnosis, diabetes treatment, algorithm derived, glycaemia defined (blood glucose or HbA1c, with or without treatment), and self report.

Data extraction and quality of studies

We extracted crude and standardised incidence by year (including counts and denominators) and the reported pattern of the trends (increasing, decreasing, or stable, (that is, no statistically significant change)) in each time period as well as study and population characteristics. Age specific data were also extracted if available. Data reported only in graphs were extracted by DigitizeIt software (European Organisation for Nuclear Research, Germany). We assessed study quality using a modified Newcastle-Ottawa scale for assessing the risk of bias of cohort studies 14 (supplementary material).

Statistical methods

Data were reported as incidence density (per person year) or yearly rates (percentage per year). From every study, we extracted data from every subpopulation reported, such that a study reporting incidence in men and women separately contributed two populations to this analysis. If studies reported two different trends over different time periods, we considered these as two populations. Further, if the study was over 10 years in duration, we treated these as two separate time periods. To avoid double counting, when the data were reported in the total population as well as by sex and ethnic groups, we only included data once and prioritised ethnicity specific data over sex specific data.

We extracted the age specific incidence data reported for every individual calendar year. These data were then categorised into four age bands (<40, 40-54, 55-69, and ≥70), and were plotted against calendar year. In studies where counts and denominators were reported by smaller age groups than we used, we recalculated incidence across our specified larger age groups. If we found multiple age groups within any of our broader age groups, but with insufficient information to combine the data into a new category, only data from one age group were used. To limit overcrowding on plots, if data were available for men, women, and the total population, only total population data were plotted. Data from populations with high diabetes incidence such as Mauritians 15 and First Nation populations from Canada 16 were plotted separately to allow the examination of most of the data more easily on a common scale (supplementary material). Furthermore, studies reporting data before 1991 or populations with fewer than three data points were not plotted. We also categorised studies into European and non-European populations on the basis of the predominant ethnicity of the population in which they were conducted. Studies conducted in Israel, Canada, and the United States were assigned to the European category.

We took two approaches to analyse trends of diabetes incidence over time. Firstly, we allocated the reported trend (increasing, decreasing, or stable (that is, no statistically significant change)) of each population to the mid-point of each study’s observational period, and then assigned this trend into one of five time periods (1960-79, 1980-89, 1990-99, 2000-05, and 2006-14). Where a test of significance of trends was not reported or when a time period was longer than 10 years, we performed Joinpoint trend analyses 17 18 to observe any significant trends in the data (assuming a constant standard deviation). Joinpoint Trend Analysis Software (version 4.5.0.1) uses permutation tests to identify points where linear trends change significantly in direction or in magnitude, and calculates an annual percentage change for each time period identified. In sensitivity analyses we also tested different cut points in the last two time periods.

The second approach was used to more accurately allocate trends to the prespecified time periods. Among the studies that reported raw counts of diabetes cases and denominators, we examined the association between calendar year and incidence, using Poisson models with the log person years as offset. The midpoints of age and calendar period were used as continuous covariates, and the effects of these were taken as linear functions. We analysed each study separately by prespecified time periods, and reported annual percentage change when the number of data points in the time period was at least four. For studies that did not provide raw data but did report a sufficient number of points, we analysed the relation between year and incidence using Joinpoint regression across the time periods specified above and reported annual percentage change. Analyses were conducted with Stata software version 14.0 (Stata Corporation, College Station, TX, USA), and Joinpoint (Joinpoint Desktop Software Version 4.5.0.1). 17 18

Patient and public involvement

No patients or members of the public were involved in setting the research question or the outcome measures for this study. No patients were asked to advise on interpretation or writing up of results. We intend to disseminate this research through press releases and at research meetings.

We found 22 833 unique abstracts from 1 January 1980 to the end of 2017. Among these, 80 described trends of diabetes incidence, of which 47 met all inclusion criteria. Articles describing trends were excluded for the following reasons: duplicated data (n=21), closed cohorts (n=5), populations included youth only (n=1), occupational cohorts (n=2), or no usable data presented (n=4; fig 1 ).

Flowchart of study selection

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Table 1 and supplementary material table 2 describe the characteristics of the included studies. Only 19% (9/47) of studies were from predominantly non-Europid populations and 4% (2/47) of studies were from low or middle income countries (China 25 and Mauritius 15 ). Administrative datasets, health insurance data, registry data, survey data, and cohort studies accounted for 38% (n=18), 21% (n=10), 19% (n=9), 11% (n=5), and 11% (n=5) of the 47 data sources, respectively. Among the 47 studies, diabetes was defined by a clinical diagnosis, diabetes treatment (via linkage to drug treatment registers), an algorithm, blood glucose, and self report in 28% (n=13), 9% (n=4), 47% (n=22), 11% (n=5), and 6% (n=3) of studies, respectively. Sample sizes of the populations were greater than 10 000 in every year in 85% (n=40) of the studies, and greater than 130 000 per year in 70% (n=33) of the studies. A total of 62% (n=29) of the 47 included studies exclusively reported on type 2 diabetes, and 38% (n=18) reported on total diabetes.

Characteristics of 47 included studies reporting on diabetes incidence trends, by country

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Summary of patterns of diabetes incidence trends based on analyses reported in publications in 1960-99

Trends of diabetes incidence

Among the 47 studies, 16 provided information on incidence by age group. Of these 16 studies, 14 were plotted in figure 2 , with those from high incidence countries plotted in supplementary figure 1. In these figures, incidence in most studies increased progressively until the mid-2000s in all age groups. Thereafter, most studies showed a stable or decreasing trend, apart from studies in Denmark 26 27 and Germany 31 and in a US health insurance population 9 where the incidence inflected upwards in the later years for some age groups.

Incidence of diabetes over time for populations aged under 40, 40-54, 55-69, and 70 or more, among studies reporting age specific data. Only populations with at least three points were plotted. NHIS=National Health Interview Survey

Using the first approach to analyse trends of diabetes incidence over time, we separated the data into populations based on sex and ethnicity, and allocated a time period to each population, generating 105 populations for analysis. Seventy four and 31 populations were predominantly Europid and non-Europid, respectively. Table 2 and table 3 show the reported trend for each population. Table 4 summarises the findings in table 2 and table 3 , and shows that the proportion of populations reporting increasing trends peaked in 1990-99 and fell progressively in the two later time periods. Between 1960 and 1989, 36% (8/22) of the populations studied had increasing trends in incidence of diabetes, 55% (12/22) had stable trends, and 9% (2/22) had decreasing trends. In 1990-2005, diabetes incidence increased in 66% (33/50) of populations, was stable in 32% (16/50), and decreased in 2% (1/50). In 2006-14, increasing trends were reported in 33% (11/33) of populations, whereas 30% (10/33) and 36% (12/33) had stable or declining incidence, respectively.

Summary of patterns of diabetes incidence trends based on analyses reported in publications in 2000-14

Summary of incidence trends over time of total or type 2 diabetes

Populations that reported a decrease in incidence after 2005 came from the US, 6 9 Israel, 34 Switzerland, 46 Hong Kong, 32 Sweden, 43 and Korea. 36 Populations reporting increasing incidence after 2005 included Portugal, 11 Denmark, 26 27 and Germany, 31 while populations from Canada, 19 Italy, 35 Scotland, 40 Norway, 39 US (non-Hispanic white), 56 and the United Kingdom 50 showed stable incidence. For two studies (16 populations), 16 29 we could not determine a direction of a trend (increasing, decreasing, or stable), because they showed three phases of change with the trend of the middle phase differing from the trend of the first and last phase. Across the total time period, we observed a higher proportion of populations reporting stable or decreasing trends in predominantly Europid than in non-Europid populations (52% v 41%).

Using the second approach to analyse trends of diabetes incidence over time, we modelled 21 studies (62 populations) that reported diabetes counts and denominators specifically within each time period ( table 5 ). The percentage of populations with a decreased or stable incidence was highest in 1980-89 (88%; 7/8), but this proportion was based on only eight populations in three studies. From 1990 onwards, the percentage with decreasing or stable incidence increased progressively, reaching 83% (19/23) of populations in 2006-14. Eight studies (21 populations) that were analysed by Joinpoint had no data on counts or denominators (supplementary table 3). When these data were considered with the data in table 5 , the percentage of populations in 2006-14 with decreasing or stable incidence fell to 70% (19/27), but this proportion was still the highest of all the time periods, whereas the percentage for 1990-99 remained the lowest at 31% (5/16).

Annual percentage change in diabetes incidence in men (M), women (W), or total population (T) among studies that provided counts and denominators, by time period

In a sensitivity analysis, we tested whether our selection of time periods was driving our results. When we defined the final time periods to be 2000-07 and 2008-14, our results were not altered, with 66% (21/32) of the populations in the last time period showing decreasing or stable trends. We also repeated the analysis in table 4 and excluded cohort studies and surveys, and found that the results were not materially altered, with 65% (20/31) of populations in the last time period (from 2006 onwards) showing decreasing or stable incidence of diabetes.

Quality of studies

The median score for study quality was 10 (interquartile range 8-11; supplementary table 4). We repeated the analyses reported in table 4 after excluding studies that had quality scores in the lowest quarter, and observed similar results to the main findings. For example, in 1960-89, 67% (10/15) of populations reported stable or decreasing incidence, while in the final time period, 67% (18/27) of populations reported stable or decreasing incidence of diagnosed diabetes.

Principal findings

In this systematic review of population based studies on diabetes incidence, we show evidence that the incidence of diagnosed diabetes increased in most populations from the 1960s to the early 2000s, after which a pattern emerged of levelling trends in 30% and declining trends in 36% of the reported populations. Although the lack of data for non-Europid populations leaves global trends in incidence unclear, these findings suggest that trends in the diabetes epidemic in some high income countries have turned in a more encouraging direction compared with previous decades. It is important to note that these results apply predominantly to type 2 diabetes, as even though many studies did not accurately define diabetes type, the incidence of type 2 diabetes in adults is an order of magnitude greater than that of type 1 diabetes.

The countries that showed stable or decreasing trends in the last time period were from Europe and east Asia, with no obvious clustering or commonalities. For the countries showing decreasing or stable diabetes trends, if the prevalence data were used to understand the diabetes epidemic in that country, a different message would be obtained. For example, national data from Korea showed that the prevalence of diabetes increased from 2000 to 2010. 59 Similarly in Sweden, the prevalence of pharmacologically treated diabetes increased moderately from 2006 to 2014. 43 In the US, the prevalence of diabetes reached a plateau when incidence began to decrease. However, we lacked incidence data from many areas of the world where the most steady and substantial increases in prevalence have been reported, including the Pacific Islands, Middle East, and south Asia. Large increases in incidence could still be occurring in these areas. The lack of incidence data for much of the world, combined with the common observation of discordance between incidence and prevalence rates where such data exist, both underscore the importance of using incidence data to understand the direction of the diabetes epidemic.

Incidence could be starting to fall for several reasons. Firstly, we might be starting to benefit from prevention activities of type 2 diabetes, including increased awareness, education, and risk factor modification. These activities have involved both targeted prevention among high risk individuals, similar to that conducted in the Diabetes Prevention study 60 and Diabetes Prevention Programme 61 62 in many countries, 63 and less intensive interventions with broader reach such as telephone counselling in the general community. 64 65 67 Secondly, health awareness and education programmes have also been implemented in schools and work places, and many changes to the physical environment, such as the introduction of bike tracks and exercise parks, have occurred. 68 Thirdly, favourable trends in selected risk factors of type 2 diabetes in some countries provide indirect evidence of positive changes to reduce diabetes incidence. Finally, in the US, there is some evidence in recent years of improved diets and related behaviours, which include reductions in intake of sugar sweetened beverages 69 and fat, 70 small declines in overall energy intake, and declines in some food purchases. 8 71

Similar reduction in consumptions of sugar sweetened beverages have occurred in Norway 72 and Australia 73 and fast food intake has decreased in Korea. 74 Some of these changes could be linked to a fall in diabetes incidence. Some places such as Scotland 75 have also had a plateauing of obesity prevalence, but this is not universal. In the US, despite earlier studies suggesting that the rate of increase in obesity might be slowing down, 76 77 more recent data show a small increase. 78 79 While some evidence supports the hypothesis that these prevention activities for type 2 diabetes and an improved environment could trigger sufficient behaviour change to have an effect on diabetes incidence, other data, such as the continuing rising obesity prevalence in the US, 79 casts some doubt over the explanations underpinning our findings on diabetes incidence trends.

Other factors might have also influenced reported diabetes incidence. Only 11% (n=5) of the studies reported here screened for undiagnosed diabetes, and therefore trends could have been influenced by secular changes in diagnostic behaviour. In 1997, the threshold for fasting plasma glucose for diagnosis of diabetes was reduced from 7.8 to 7.0 mmol/L, which could increase diagnosis of new cases of type 2 diabetes. In 2009-10, HbA1c was then introduced as an alternative way to diagnose diabetes. 80 Evidence from some studies suggests that the HbA1c diagnostic threshold detects fewer people with diabetes than do the thresholds for fasting plasma blood glucose, 80 81 potentially leading to a lowering of incidence estimates. However, across multiple studies, prevalence estimates based on fasting plasma glucose only versus HbA1c definitions are similar. 82 Furthermore, because HbA1c can be measured in the non-fasting state (unlike the fasting blood glucose or oral glucose tolerance test), the number of people who actually undergo diagnostic testing could be higher with HbA1c. Nichols and colleagues 56 reported that among seven million insured US adults, despite a shift towards HbA1c as the diagnostic test in 2010, the incidence of diabetes did not change from 2010 to 2011.

Another potential explanation for declining or stable diabetes incidence after the mid-2000s is a reduction in the pool of undiagnosed diabetes 83 through the intensification of diagnostic and screening activities 83 84 and changing diagnostic criteria during the previous decade. 80 Data from Read and colleagues provide some evidence to support this notion. 41

Among the included studies, two studies specifically examined clinical screening patterns in parallel with incidence trends. These studies reported that the proportion of the population screened for diabetes increased over time, and the incidence of diabetes remained stable 56 or fell. 34 While the Karpati study 34 combined data for glucose testing with HbA1c testing, the study by Nichols and colleagues 56 separated the two, and showed that both glucose testing and HbA1c testing increased over time. A third study, in Korea, 36 also noted that the incidence of diabetes decreased in the setting of an increase in the uptake of the national health screening programme. Despite the introduction of HbA1c for diagnosis of diabetes by the World Health Organization, this practice has not been adopted everywhere. For example, neither Scotland nor Hong Kong have introduced the use of HbA1c for screening or diagnosis of diabetes, and studies in these areas showed a levelling of diabetes incidence trends and decreasing trends, respectively.

Our findings appear to contrast with data showing increasing global prevalence of diabetes. 1 3 However, increasing prevalence could be influenced by improved survival of people with diabetes, because this increases the length of time that each individual remains within the diabetes population. As is shown in several studies in this review, 23 41 mortality from diabetes and incidence of diabetes might both be falling but as long as mortality is lower than incidence, prevalence will rise. Therefore, we argue that prevalence alone is an insufficient measure to track the epidemic of diabetes and other non-communicable diseases.

Strengths and weaknesses of this study

A key strength of this work was the systematic approach and robust methodology to describe trends in diagnosed diabetes incidence. We also presented the reported trends allocated to approximate time periods, as well as conducting our own regression within exact time periods. The following limitations should also be considered. Firstly, we did not formally search the grey literature, because a preliminary grey literature search revealed only low quality studies, with inadequate methodological detail to provide confidence in any observed incidence trends, and thus review could be subject to publication bias. Secondly, we were not able to source age or sex specific data on all populations. Thirdly, it was not possible to adjust for different methods of diabetes diagnosis or ascertain trends by different definitions of diabetes. Fourthly, most data sources reported only on clinically diagnosed diabetes and so were subject to influence from diagnostic behaviour and coding practices. Fifthly, study type changed over time, with large administrative datasets becoming more common and cohort studies becoming less common over time. Nevertheless, the size and absence of volunteer bias in administrative datasets likely make them less biased. Finally, data were limited in low and middle income countries.

Conclusions and unanswered questions

This systematic review shows that in most countries for which data are available, the incidence of diagnosed diabetes was rising from the 1990s to the mid-2000s, but has been stable or falling since. Preventive strategies and public health education and awareness campaigns could have contributed to this recent trend. Data are limited in low and middle income countries where trends in diabetes incidence might be different. Improvement of the collection, availability, and analysis of incidence data will be important to effectively monitor the epidemic and guide prevention efforts into the future.

What is already known on this topic

Monitoring of the diabetes epidemic has mainly focused on reporting diabetes prevalence, which continues to rise; however, increasing prevalence is partly driven by improved medical treatment and declining mortality

Studies on diabetes incidence are scarce, but among those that exist, some report a fall or stabilisation of diabetes incidence;

Whether the proportion of studies reporting falling incidence has changed over time is not known

What this study adds

This systematic review of published data reporting diabetes incidence trends over time shows that in most countries with available data, incidence of diabetes (mainly diagnosed diabetes) increased from the 1990s to the mid-2000s, and has been stable or falling since

Preventive strategies and public health education and awareness campaigns could have contributed to this flattening of rates, suggesting that worldwide efforts to curb the diabetes epidemic over the past decade might have been effective

Published data were very limited in low and middle income countries, where trends in diabetes incidence might be different

Acknowledgments

The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the US Centers for Disease Control and Prevention (CDC).

Contributors: MT, DNK, JLH, and RMI are postdoctoral fellows who screened abstracts for selection into the systematic review. JES and DJM also screened abstracts. ELMB applied the quality criteria to the selected articles. RMI extracted data, applied quality criteria to selected articles, and contributed to preparing the manuscript. DJM conceived the project, screened abstracts, extracted the data, analysed the data, and wrote the manuscript. JES, MEP, and EWG conceived the project, edited the manuscript, and provided intellectual input throughout the process. The funder of the study (CDC) was part of the study group and contributed to data collection, data analysis, data interpretation, and writing of the report. The corresponding author had full access to all the data in the study and had final responsibility for the decision to submit for publication. DJM is guarantor. The corresponding author attests that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted.

Funding: Funded by the CDC. The researchers were independent from the funders.

Competing interests: All authors have completed the ICMJE uniform disclosure form at www.icmje.org/coi_disclosure.pdf and declare: support from the CDC for the submitted work; no financial relationships with any organisations that might have an interest in the submitted work in the previous three years; no other relationships or activities that could appear to have influenced the submitted work.

Ethical approval: Not required because this work was a systematic review.

Data sharing: Data are available from the corresponding author ([email protected]).

The lead author affirms that the manuscript is an honest, accurate, and transparent account of the study being reported; that no important aspects of the study have been omitted; and that any discrepancies from the study as planned (and, if relevant, registered) have been explained.

This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/ .

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Type 2 diabetes mellitus in older adults: clinical considerations and management

• Srikanth Bellary   ORCID: orcid.org/0000-0002-5924-5278 1 , 2 , 3 ,
• Ioannis Kyrou   ORCID: orcid.org/0000-0002-6997-3439 4 , 5 , 6 , 7 ,
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Nature Reviews Endocrinology volume  17 ,  pages 534–548 ( 2021 ) Cite this article

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• Endocrine system and metabolic diseases
• Type 2 diabetes

The past 50 years have seen a growing ageing population with an increasing prevalence of type 2 diabetes mellitus (T2DM); now, nearly half of all individuals with diabetes mellitus are older adults (aged ≥65 years). Older adults with T2DM present particularly difficult challenges. For example, the accentuated heterogeneity of these patients, the potential presence of multiple comorbidities, the increased susceptibility to hypoglycaemia, the increased dependence on care and the effect of frailty all add to the complexity of managing diabetes mellitus in this age group. In this Review, we offer an update on the key pathophysiological mechanisms associated with T2DM in older people. We then evaluate new evidence relating particularly to the effects of frailty and sarcopenia, the clinical difficulties of age-associated comorbidities, and the implications for existing guidelines and therapeutic options. Our conclusions will focus on the effect of T2DM on an ageing society.

Older adults (≥65 years of age) with type 2 diabetes mellitus (T2DM) account for nearly half of all individuals with diabetes mellitus.

T2DM in older adults is highly heterogeneous but is generally associated with various degrees of underlying insulin resistance, excess adiposity, β-cell dysfunction and sarcopenia.

The management of T2DM in older adults is complicated by the frequent occurrence of multimorbidity, necessitating highly individualized approaches.

The presence of frailty, cognitive decline and functional impairments in older adults with T2DM highlights the importance of liaison with carers and social support.

Targets for glycaemic control in older adults with T2DM are often less stringent than in younger adults to avoid hypoglycaemia and minimize unbeneficial interventions.

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Literature searches were conducted using Google Scholar, MEDLINE and Embase with the following terms: ‘elderly’, ‘frail’, ‘older people’ and ‘aged’ combined with ‘diabetes’, ‘type 2 diabetes’, ‘prediabetes’ and ‘glucose control’. The selection was made by at least two of the present authors and was limited to English language articles from 2000 to 2020 and relevant references cited in the publications selected. Priority was given to prospective randomized studies and meta-analyses, though these were scarce, and observational and descriptive studies, guidelines, and policy documents were also consulted.

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Bellary, S., Kyrou, I., Brown, J.E. et al. Type 2 diabetes mellitus in older adults: clinical considerations and management. Nat Rev Endocrinol 17 , 534–548 (2021). https://doi.org/10.1038/s41574-021-00512-2

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Article Contents

Materials and methods, meta-analysis, conclusions, acknowledgments, author contributions, disclosures, data availability, abbreviations.

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Association Between Visceral Obesity Index and Diabetes: A Systematic Review and Meta-analysis

Ruixue Deng and Weijie Chen contributed equally to this work and should be considered as co-first authors.

Jia Mi and Xiangyan Li contributed equally to this work and should be considered as co-corresponding authors.

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Ruixue Deng, Weijie Chen, Zepeng Zhang, Jingzhou Zhang, Ying Wang, Baichuan Sun, Kai Yin, Jingsi Cao, Xuechun Fan, Yuan Zhang, Huan Liu, Jinxu Fang, Jiamei Song, Bin Yu, Jia Mi, Xiangyan Li, Association Between Visceral Obesity Index and Diabetes: A Systematic Review and Meta-analysis, The Journal of Clinical Endocrinology & Metabolism , Volume 109, Issue 10, October 2024, Pages 2692–2707, https://doi.org/10.1210/clinem/dgae303

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The correlation between visceral obesity index (VAI) and diabetes and accuracy of early prediction of diabetes are still controversial.

This study aims to review the relationship between high level of VAI and diabetes and early predictive value of diabetes.

The databases of PubMed, Cochrane, Embase, and Web of Science were searched until October 17, 2023.

After adjusting for confounding factors, the original study on the association between VAI and diabetes was analyzed.

We extracted odds ratio (OR) between VAI and diabetes management after controlling for mixed factors, and the sensitivity, specificity, and diagnostic 4-grid table for early prediction of diabetes.

Fifty-three studies comprising 595 946 participants were included. The findings of the meta-analysis elucidated that in cohort studies, a high VAI significantly increased the risk of diabetes mellitus in males (OR = 2.83 [95% CI, 2.30-3.49]) and females (OR = 3.32 [95% CI, 2.48-4.45]). The receiver operating characteristic, sensitivity, and specificity of VAI for early prediction of diabetes in males were 0.64 (95% CI, .62–.66), 0.57 (95% CI, .53–.61), and 0.65 (95% CI, .61–.69), respectively, and 0.67 (95% CI, .65–.69), 0.66 (95% CI, .60–.71), and 0.61 (95% CI, .57–.66) in females, respectively.

VAI is an independent predictor of the risk of diabetes, yet its predictive accuracy remains limited. In future studies, determine whether VAI can be used in conjunction with other related indicators to early predict the risk of diabetes, to enhance the accuracy of prediction of the risk of diabetes.

Diabetes mellitus (DM) has emerged as a prominent chronic disease, affecting approximately 463 million individuals worldwide, which imposes substantial public health and economic burdens ( 1 ). Type 2 diabetes mellitus (T2DM) is the predominant form of DM, accounting for approximately 90% of all diabetes cases globally ( 2 ). The occurrence of DM is often closely related to health status indicators. Therefore, research on the early risk prediction based on DM-related indicators is if clinical significance, which holds the potential to reverse the outcomes of DM ( 3 ). Among related measurement indicators such as body mass index (BMI), waist circumference (WC), visceral adiposity index (VAI), fasting blood glucose, glycated hemoglobin, triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), and total cholesterol ( 4 ), VAI is a clinically available metabolic parameter with a predictive value for DM and cardiovascular disease ( 5 ). Previous reviews and studies have shown a significant correlation between VAI and a series of chronic diseases, such as nonalcoholic fatty liver disease and liver fibrosis ( 6 ), chronic kidney disease ( 7 ), and obstructive sleep apnea ( 8 ).

The hallmark of DM is insulin resistance ( 9 ). Obesity has been recognized as a risk factor for T2DM and its complications, even all-cause mortality ( 10 ). VAI is a novel marker of fat distribution and function in obese patients ( 11 ) and has been recognized as an important indicator of insulin-related alterations in fat function ( 12 ). Increased VAI is significantly associated with decreased insulin sensitivity ( 13 ). Some studies have explored and discussed the correlation between VAI and DM.

Currently, because of the absence of long-term prospective studies, we cannot assess the predictive ability of VAI for the risk of DM. There is a deficiency of comprehensive, systematic evidence-based arguments regarding the association between VAI and DM. Furthermore, comprehensive and systematic studies on the relevance and early predictive value of VAI for DM in different sexes are scarce. It remains unclear whether it is reasonable to use VAI as a screening tool for DM to broaden an effective method for early identification of diabetes risk. Therefore, this systematic review and meta-analysis aims to elucidate the correlation between VAI and DM, clarify its early predictive accuracy for DM, and provide a reliable evidence-based argument for subsequent DM prevention efforts.

Study Registration

This study adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines (2020) and as duly registered in PROSPERO (ID: CRD42023480506) in a prospective manner.

Eligibility Criteria

Inclusion criteria.

Population (P): Adults aged ≥ 18 years were included

Exposure (E): High VAI was defined as the exposure factor in this systematic review according to original studies.

Comparison (C): Low VAI in various cohort, case-control, as well as cross-sectional studies was deemed as the control group. The formula for calculating VAI is as follows ( 14 ):

Outcome (O):

The outcome indicators of this systematic review are the odds ratios (ORs) and hazard ratios (HRs) associated with high VAI and DM, adjusted for other confounding factors using logistic regression or Cox regression analysis.

The ORs associated with high VAI and DM after grouping by propensity score matching in case-control studies.

The area under the receiver operating characteristic (ROC) curve (ROC area under the curve [AUC]) reflecting the predictive value of VAI for the occurrence of DM in cohort studies.

The sensitivity, as well as the specificity of VAI for the prediction accuracy of DM.

Study design (S): This systematic review incorporated case-control studies, cross-sectional studies, and cohort studies.

Exclusion criteria

Population (P): Individuals aged <18 years and patients with other underlying diseases.

Exposure (E): None.

Comparison (C): None.

Outcome (O): Cohort studies that only conducted univariate analyses to explore the correlation between VAI and DM.

Study design (S): The following studies were excluded:

Abstracts of meetings published without peer review

For repeatedly published studies of the same dataset, the earliest published article was selected

Since multivariate logistic regression or Cox regression models were used when discussing the correlation between VAI and DM, the robustness of the regression coefficient was challenging when the number of cases is small, so studies with <30 cases were excluded.

Data Sources and Search Strategy

We systematically searched the PubMed, Cochrane, Embase, and Web of Science databases for all data available up to Oct. 17, 2023. The search was conducted using MeSH + free text terms without restrictions on region or year of publication. (For the detailed search strategy, see Table 1 ).

Literature search strategy

Study Selection and Data Extraction

All obtained articles were imported into EndNote, where titles and abstracts were read to eliminate duplicates or studies that did not meet the criteria. Studies that matched the criteria were selected, and their full texts were downloaded for thorough review. Ultimately, original study data that conformed to the criteria for this systematic review were included. Before extracting data, a standard data extraction spreadsheet was rigorously prepared. The extracted content included serial number, title, first author, year of publication, PMID/DOI, study type (cohort study, case-control study, cross-sectional study, nested case-control study, case-cohort study), follow-up duration, author's country, patient source, population, outcome events (prediabetes, T2DM), number of outcome events, total case number, gender (male/female), age, biomarker cutoff points, and adjusted confounders.

The screening of literature and extraction of data were independently conducted by 2 researchers in the field of DM: M.J. (a doctor with 10 years of clinical experience in DM) and Z.J.Z. (a doctor with 5 years of clinical experience in gastroenterological diseases). After completion, a cross-check was performed, and any disagreements were resolved with the assistance of a third researcher, C.W.J. (a doctor with 5 years of clinical experience in nephrology).

Assessment of Study Quality

The studies included case-control, cohort, as well as cross-sectional studies. Therefore, 2 assessment tools are needed for bias risk evaluation or quality assessment. For cohort and case-control studies, Newcastle-Ottawa Scale (NOS) (NOS) is a commonly used quality assessment tool for case-control and cohort studies) ( 15 ) was employed, and for cross-sectional studies, the The Journal of Biomedical Informatics (JBI) (JBI) document quality Assessment scale is a tool for assessing the quality of biomedical literature) scale ( 16 ) was adopted. Two researchers assessed the quality of original data from the included cohort, case-control, as well as cross-sectional studies using NOS and JBI to evaluate the quality of the literature. Afterward, an interactive check was conducted. A third researcher was invited to make decisions in case of disagreements.

Synthesis Methods

The data was meta-analyzed using Stata 15.0. The heterogeneity among the results of the included studies was analyzed via the Q test and quantitatively determined by the heterogeneity index, I 2 . In the meta-analysis elucidating the correlation between VAI and DM because the original studies used different cutoffs for VAI, we summarized the highest ORs or HRs in a dose-response meta-analytic approach ( 17 ). During the meta-analysis, a fixed-effects model was employed when I 2 < 50%, and a random-effects model was adopted when I 2 > 50%. Subsequent subgroup analysis, sensitivity analysis, and meta-regression analysis were systematically undertaken to delve into potential sources of heterogeneity. Additionally, when discussing the predictive value of the VAI for DM, we only conducted a meta-analysis of cohort studies via a bivariate mixed-effects model.

Funnel plots were drawn based on the obtained data to visually display publication bias. Concurrently, statistical tests for publication bias, namely Egger's and Begg's tests, were executed. P < .05 indicated publication bias, and the trim-and-fill method was implemented to assess the impact of such bias on the meta-analysis. In this study, a P value less than .05 was deemed statistically significant.

Study Selection

A total of 6007 articles were retrieved, including 950 duplicates. Following the exclusion process, 64 relevant articles were screened through the reading of titles and abstracts.

Three articles that could not be downloaded were therefore deleted, 2 articles were removed because the included population was already diagnosed with DM, 2 articles were excluded for involving populations with metabolic syndrome, insulin resistance, and other diseases, and 4 conference abstracts with incomplete information were also excluded. Ultimately, 53 studies ( 4 , 5 , 14 , 18-26 ) were included in the study ( Fig. 1 ).

Literature screening flowchart.

Study Characteristics

Among the 53 articles included, there were 31 cohort studies ( 4 , 5 , 14 , 20 , 23 , 24 , 26-31 ), 19 cross-sectional studies ( 18 , 21 , 22 , 32-47 ), 2 case-control studies ( 19 , 48 ), and 1 nested case-control study (which is a part of cohort study) ( 25 ).

The total number of cases across the included cohort, cross-sectional, case-control, as well as nested case-control studies, were 225 530, 240 381, 15 004, and 2556, respectively, amounting to 594 939 cases in total. The follow-up duration in cohort studies ranged from 3 to 24 years, covering 14 countries, with 34 studies originating from China ( 4 , 5 , 14 , 18-23 , 28 , 29 , 33 , 34 ). The top 3 countries with the highest number of cases were Poland, Brazil, and Iran ( 29 , 32 , 40 , 43 , 44 , 49 , 50 ). The included patients mainly came from communities, hospitals, health examination centers, and public research databases. Confounders such as age, gender, race, residence, drinking and smoking status, systolic blood pressure, diastolic blood pressure, BMI, WC, waist-to-hip ratio, total cholesterol, TG, HDL-C, low-density lipoprotein cholesterol, fasting plasma glucose, 2-hour postprandial glucose, and oral glucose tolerance test were adjusted. Among these, 37 articles divided VAI into quartiles ( 4 , 5 , 14 , 18-22 , 25 , 27 , 28 , 32 , 34-36 , 38 , 40 , 42 , 43 , 45-48 , 50-64 ).

In 31 cohort studies, NOS was adopted for analysis. Among these, 10 articles ( 5 , 20 , 24 , 28 , 52 , 53 , 57 , 60 , 63 , 65 ) had insufficient follow-up time (for DM follow-up duration, a duration less than 5 years was considered insufficient in this study; however, it is noteworthy that there is currently a lack of universally recognized standards to precisely define this item), but all were high-quality studies with total scores between 7 and 9 points. In the 2 case-control studies, 1 article ( 48 ) had insufficient control source communities/hospitals, but all were high-quality studies with total scores between 7 and 9 points. In the cross-sectional studies, the JBI scale was employed for evaluation. The assessment results showed that 6 articles ( 18 , 32 , 34 , 37 , 40 , 44 ) had insufficient sample size (a sample size less than 2000 was considered insufficient in this study; however, it is noteworthy that there is currently a lack of universally recognized standards to precisely define this item); additionally, 2 articles ( 18 , 37 ) had insufficient response rate ( Tables 2 and 3 ).

Results of quality assessment for cohort studies and case-control studies employing the NOS

1 = The maximum score is 9, with higher scores indicating higher study quality. Each item can score up to 1 point, except for comparability, which can score up to 2 points.

2 = When using the NOS for quality assessment of cohort studies, V1 = representativeness of the exposed cohort; V2 = selection of the nonexposed cohort; V3 = ascertainment of exposure; V4 = demonstration that the outcome of interest was not present at the start of the study; V5 = comparability of cohorts based on the design or analysis; V6 = assessment of outcome; V7 = was follow-up long enough for outcomes to occur? (eg, 5 years); V8 = adequacy of follow-up.

3 = When using the NOS for quality assessment of case-control studies, V1 = adequate definition of cases; V2 = representativeness of cases; V3 = selection of controls; V4 = definition of controls; V5 = comparability of cases and controls based on the design or analysis; V6 = ascertainment of exposure; V7 = same method of exposure ascertainment for cases and controls; V8 = nonresponse rate.

Abbreviation: NOS, Newcastle-Ottawa Scale.

Results of quality assessment for cross-sectional studies employing the JBI scale

1 = The maximum score is 9, with higher scores indicating higher research quality. Each item can score up to 1 point.

2 = When using the JBI scale for quality assessment of cross-sectional studies, V1 = Was the sample frame appropriate to address the target population? V2 = Were study participants sampled in an appropriate way? V3 = Was the sample size adequate? V4 = Were the study subjects and the setting described in detail? V5 = Was the data analysis conducted with sufficient coverage of the identified sample? V6 = Were valid methods used for the identification of the condition? V7 = Was the condition measured in a standard, reliable way for all participants? V8 = Was there appropriate statistical analysis? V9 = Was the response rate adequate, and if not, was the low response rate managed appropriately?

Abbreviation: JBI, Journal of Biomedical Informatics.

Correlation Analysis

Synthesized results and sensitivity analysis and publication bias of cohort studies.

Six studies ( 4 , 20 , 52 , 54 , 58 , 66 ) encompassing 7 cohorts reported the correlation between VAI and DM occurrence risk in males. The results of the random-effects model ( I 2 = 57.9%) suggested that an increase in VAI is correlated with an increased DM occurrence risk in males (OR = 2.83 [95% CI, 2.30-3.49]). Five studies ( 20 , 52 , 54 , 58 , 66 ) encompassing 6 cohorts reported the correlation between VAI and DM occurrence risk in females. The random-effects model results ( I 2 = 65.1%) showed that an increase in VAI is associated with an increased DM occurrence risk in females (OR = 3.32 [95% CI, 2.48-4.45]). Thirteen studies ( 14 , 23 , 26 , 27-29 , 51 , 57-62 ) encompassing 19 cohorts expounded on the correlation between VAI and DM occurrence risk without gender distinction. The results of the random-effects model ( I 2 = 90.6%) showed that an increase in VAI is correlated with an increased risk of DM irrespective of gender (OR = 2.52 [95% CI, 2.01-3.15]) ( Fig. 2 ).

Forest plot of the meta-analysis of ORs for the correlation between high VAI and DM risk in cohort studies.

Sensitivity analysis of all cohort studies indicated that the meta-analysis results had no significant changes on the sequential exclusion of individual studies, underscoring the stability of our meta-analysis. The evaluation of publication bias through funnel plots revealed indications of bias, further supported by Egger's test results ( P = .025), as shown in Fig. 3 .

Sensitivity analysis and funnel plot for publication bias of the relationship between high level of VAI and diabetes in cohort studies.

Synthesized Results and Sensitivity Analysis and Publication Bias of Cross-sectional or Case-control Studies

Five studies ( 33 , 36 , 38 , 41 , 47 ) encompassing 7 cohorts reported the correlation between VAI and DM occurrence risk in males. The results of the random-effects model ( I 2 = 81.9%) suggested that an increase in VAI is correlated with an increased risk of DM in males (OR = 2.66 [95% CI, 1.91-3.70]). Five studies ( 33 , 36 , 38 , 41 , 47 ) encompassing 7 cohorts reported the correlation between VAI and DM occurrence risk in females. The random-effects model results ( I 2 = 77.6%) showed that an increase in VAI is correlated with an increased DM occurrence risk in females (OR = 2.32 [95% CI, 1.77-3.04]). Eight studies ( 18 , 21 , 22 , 34 , 38 , 41 , 42 , 46 ) encompassing 10 cohorts reported the correlation between VAI and DM occurrence risk without gender distinction. The results of the random-effects model ( I 2 = 95%) indicated that an increase in VAI is correlated with an increased risk of DM irrespective of gender (OR = 1.99 [95% CI, 1.57-2.54]) ( Fig. 4 ).

Forest plot of the meta-analysis of ORs for the correlation between high VAI and DM risk in cross-sectional studies and case-control studies.

The sensitivity analysis of all studies without gender distinction indicated that the meta-analysis results had no significant changes on the sequential exclusion of individual studies, underscoring the stability of our meta-analysis. The evaluation of publication bias through funnel plots revealed indications of bias, further supported by Egger's test results ( P = .336) ( Fig. 5 ).

Sensitivity analysis and funnel plot for publication bias of the relationship between high level of VAI and diabetes in cross-sectional or case-control studies.

Area Under ROC Curve

In cohort studies, 18 studies ( 4 , 5 , 14 , 20 , 24 , 27 , 31 , 51-54 ) encompassing 20 cohorts reported on the correlation between VAI and DM occurrence risk in males. The results of the random-effects model ( I 2 =93.8%) showed that the VAI had a predictive value for DM in males of [AUC = 0.64 (95% CI: .62-.66)]. Fifteen studies ( 4 , 20 , 27 , 31 , 47 , 51-54 , 56-58 , 63 , 65 , 66 ) encompassing 17 cohorts reported the correlation between VAI and DM occurrence risk in females. The random-effects model results ( I 2 = 97.3%) showed that the VAI had a predictive value for DM in females of (AUC = 0.67 [95% CI, .64-.69]). Eight studies ( 29 , 31 , 51 , 53 , 58 , 60 , 62 , 63 ) encompassing 9 cohorts reported the correlation between VAI and DM occurrence risk without gender distinction. The results of the random-effects model ( I 2 = 98.5%) indicated that the VAI had a predictive value for DM irrespective of gender of (AUC = 0.67 [95% CI, .62-.71]) ( Figs. 6 - 8 ).

Forest plot of ROC AUC for the correlation between high VAI and DM in males in cohort studies.

Forest plot of ROC AUC for the correlation between high VAI and DM in females in cohort studies.

Forest plot of ROC AUC for the correlation between high VAI and DM without distinguishing by gender in cohort studies.

Sensitivity and Specificity

A meta-analysis of multiple different trials of the same index can be expressed by a ROC curve according to their OR weight, called SROC, from which the specificity and sensitivity of the group of studies can be obtained. Sixteen studies ( 4 , 5 , 20 , 24 , 27 , 30 , 31 , 51-54 , 57 , 58 , 63 , 65 , 66 ) encompassing 19 cohorts reported the 2×2 tables for predicting DM in males using VAI, the results of the meta-analysis via a bivariate mixed-effects model showed the following for the prediction of DM in males using VAI: sensitivity 0.57 (95% CI, .53-.61), specificity 0.65 (95% CI, .61-.69), positive likelihood ratio 1.6 (95% CI, 1.5-1.8), negative likelihood ratio 0.66 (95% CI, .62-.70), diagnostic OR 2 (95% CI, 2-3), and SROC curve 0.64 (95% CI, .15-.95).

Fifteen studies ( 4 , 20 , 24 , 27 , 30 , 31 , 51-54 , 58 , 60 , 63 , 65 , 66 ) encompassing 18 cohorts reported the 2×2 tables for predicting DM in females using VAI, and the results of the meta-analysis by a bivariate mixed-effects model showed the following for the prediction of DM in females using VAI: sensitivity 0.66 (95% CI, .60-.71), specificity 0.61 (95% CI, .57-.66), positive likelihood ratio 1.7 (95% CI, 1.5-1.9), negative likelihood ratio 0.56 (95% CI, .49–.64), diagnostic odds ratio 3 (95% CI, 2-4), and SROC curve 0.67 (95% CI, .16-.96).

Six studies ( 29 , 31 , 51 , 60 , 62 , 63 ) encompassing 6 cohorts reported the 2×2 tables for predicting DM without distinguishing gender using VAI, and the results of the meta-analysis through a bivariate mixed-effects model showed the following for the prediction of DM without distinguishing gender using VAI: sensitivity 0.60 (95% CI, .51-.69), specificity 0.63 (95% CI, .56-.69), positive likelihood ratio 1.6 (95% CI, 1.4-1.9), negative likelihood ratio 0.63 (95% CI, .51-.78), diagnostic odds ratio 3 (95% CI, 2-4), and SROC curve 0.65 (95% CI, .45-.81) ( Figs. 9 - 11 ).

Forest plot of sensitivity and specificity meta-analysis for the correlation between high VAI and DM in males in cohort studies.

Forest plot of sensitivity and specificity meta-analysis for the correlation between high VAI and DM in females in cohort studies.

Forest plot of sensitivity and specificity meta-analysis for the correlation between high VAI and DM without distinguishing by gender in cohort studies.

Summary of the Main Findings

In cohort studies, we found that high VAI was associated with an augmented risk of DM in both males (OR = 2.83 [95% CI, 2.30-3.49]) and females (OR = 3.32 [95% CI, 2.48-4.45]) with DM. In cross-sectional or case-control studies, a high VAI was correlated with an augmented risk of DM in both males (OR = 2.66 [95% CI, 1.91-3.70]) and females (OR = 2.32 [95% CI, 1.77-3.04]). Although there is an independent correlation, the predictive value for the risk of DM occurrence remains limited. In cohort studies, the predictive value of VAI for the occurrence of DM in males was determined through the ROC curve, resulting in a C-index of 0.64 (95% CI, .62-.66), with a sensitivity of 0.57 (95% CI, .53-.60) and a specificity of 0.66 (95% CI, .62-.69), respectively; for females, the predictive value of VAI for the occurrence of DM was also determined through the ROC curve, resulting in a C-index of 0.67 (95% CI, .65-.69), with a sensitivity of 0.66 (95% CI, .61-.71) and a specificity of 0.61 (95% CI, .57-.65), respectively; for DM occurrence without distinguishing by gender, the predictive value of VAI was calculated through the ROC curve, resulting in a C-index of 0.67 (95% CI, .62-.71), with sensitivity and specificity of 0.60 (95% CI, .51-.69) and 0.63 (95% CI, .56-.69).

Comparison With Previous Other Reviews

We have noticed that other researchers have also provided evidence-based arguments for the correlation between VAI and human health and diseases. Early systematic reviews have found a significant correlation between VAI and the occurrence of prediabetes ( 67 ); other related measurement indicators besides VAI are also worthy of attention, such as fasting blood glucose ( 68 ), glycated hemoglobin ( 69 ), BMI ( 70 ), WC ( 71 ), TG ( 72 ), and HDL ( 73 ). Unfortunately, the composite indicator we constructed based on these indicators did not significantly improve the predictive value for the risk of DM occurrence. As mentioned previously, VAI is composed of the related indicators mentioned; however, there are significant differences in weight, waist circumference, etc., across different regions or countries, ethnicities, and economic levels ( 74 , 75 ). In our study, 5 cohort studies and 1 cross-sectional study ( 5 , 27 , 47 , 57 , 58 , 60 ) developed the correlation between the Chinese VAI and the risk of DM occurrence (OR) on the basis of VAI, with its predictive value for the risk of DM occurrence being (AUC = 0.62 [95% CI, .52-.71]). Hence, the contemplation of the predictive efficacy of VAI for the occurrence of DM risk across diverse populations is imperative.

Given the differences in VAI calculations between males and females, we believe that males and females should not be integrated to discuss the correlation between VAI and the risk of DM occurrence. However, in the original studies we included, a notable number did not distinguish between the correlation of VAI with the risk of DM occurrence in males and females ( 14 , 18 , 21-23 , 26 , 27-29 , 34 , 38 , 41 , 42 , 46 , 50 , 51 , 57-59 , 61 , 62 ). Therefore, we consider there to be certain limitations in their studies regarding the correlation analysis, and achieving comprehensiveness in considering related factors after correcting for confounders remains a challenge.

Limitations of the Study

Limitations are presented in this study. First, in our registered protocol, we planned to discuss the variability of VAI cutoff values used in different studies and attempted to explore its predictive accuracy for early DM. However, the limited number of original studies included did not provide detailed results on the correlation between the VAI and the risk of DM at various levels. Additionally, very few previous original studies examined the VAI across multiple groups; therefore, an effective dose-response analysis was not performed. Variations exist among different ethnicities and countries, resulting in a lack of consensus on the definition of VAI cutoffs, which prevents us from providing effective cutoffs to offer useful insights for clinicians. Comprehensive, large-scale studies involving multiple ethnicities are needed to further discuss the cutoffs for VAI. Furthermore, we summarized the association of high-level VAI with DM obtained from cohort studies. However, very few studies used HRs; thus, effective subgroup analyses were not performed.

This systematic review and meta-analysis aimed to discuss the association between elevated level of VAI and the risk of DM occurrence and to confirm its early predictive accuracy for the risk of DM. VAI appears to be an effective factor for early prediction of DM when combined with other relevant indicators, although challenges in the prediction persist. Specialized screening regimens should be established in future for obese DM populations. Additionally, we consider combining VAI with other indicators as a scoring tool for assessing the risk of DM, thereby enhancing the predictive accuracy of DM in the general population.

Not applicable.

This study was supported by The National Natural Science Foundation of China (No: 82205039), Chinese Medicine Innovation Team talent Support Program of the State Administration of Traditional Chinese Medicine (No: ZYYCXTD-D-202001).

Conceptualization: J.M., X.L.; methodology: J.F., B.Y., J.M., X.L.; formal analysis: J.S., K.Y., J.F.; investigation: J.S., K.Y., J.F.; funding acquisition: J.M.; resources: J.S., K.Y., J.F., B.Y.; data curation: X.F., Y.Z., H.L.; writing—original draft preparation: R.D., W.C.; writing—review and editing: Z.Z., J.Z., Y.W., B.S., J.C., J.M., X.L.; visualization: R.D., W.C.; supervision: J.M., X.L.; project administration: J.F., B.Y. All authors have read and agreed to the published version of the manuscript.

The authors have nothing to disclose.

Original data generated and analyzed during this study are included in this published article.

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area under the curve

body mass index

diabetes mellitus

high-density lipoprotein cholesterol

hazard ratio

receiver operating characteristic

triglyceride

visceral adiposity index

waist circumference

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Type 2 diabetes mellitus negatively affects the functional performance of 6-min step test in chronic heart failure: a 3-year follow-up study

• Aldair Darlan Santos-de-Araújo 1 ,
• Daniela Bassi-Dibai 2 ,
• Izadora Moraes Dourado 1 ,
• Cássia da Luz Goulart 3 ,
• Renan Shida Marinho 4 ,
• Jaqueline de Almeida Mantovani 1 ,
• Gabriela Silva de Souza 1 ,
• Polliana Batista dos Santos 5 ,
• Meliza Goi Roscani 6 ,
• Shane A. Phillips 7 &
• Audrey Borghi-Silva 1  

Diabetology & Metabolic Syndrome volume  16 , Article number:  229 ( 2024 ) Cite this article

Metrics details

Type 2 diabetes mellitus (T2DM) and chronic heart failure (CHF) present a decrease in functional capacity due to the intrinsic nature of both pathologies. It is not known about the potential impact of T2DM on functional capacity when assessed by 6-min step test (6MST) and its effect as a prognostic marker for fatal and non-fatal events in patients with CHF.

to evaluate the coexistence of T2DM and CHF in functional capacity through 6MST when compared to CHF non-T2DM, as well as to investigate the different cardiovascular responses to 6MST and the risk of mortality, decompensation of CHF and acute myocardial infarction (AMI) over 36 months.

This is a prospective cohort study with 36 months of follow-up in individuals with T2DM and CHF. All participants completed a clinical assessment, followed by pulmonary function testing, echocardiography, and 6MST. The 6MST was performed on a 20 cm high step and cardiovascular responses were collected: heart rate, systemic blood pressure, oxygen saturation, BORG dyspnea and fatigue. The risk of mortality, acute myocardial infarction and decompensation of CHF was evaluated.

Eighty-six participants were included. The CHF-T2DM group had a significantly lower functional capacity than the CHF non-T2DM group (p < 0.05). Forced Expiratory Volume in one second (L), ejection fraction (%), gender and T2DM influence and are predictors of functional capacity (p < 0.05; adjusted R squared: 0.419). CHF-T2DM group presented a higher risk of mortality and acute myocardial infarction over the 36 months of follow-up (p < 0.05), but not to the risk of decompensation (p > 0.05).

T2DM negatively affects the functional performance of 6MST in patients with CHF. Gender, ejection fraction (%), FEV1 (L) and T2DM itself negatively influence exercise performance.

Introduction

Common, highly prevalent, closely related and frequently associated, CHF and T2DM have a bidirectional relationship, that is, the origin and evolution of each pathology can be mutually influenced [ 1 , 2 , 3 , 4 ]. The interactions between both diseases is widely known [ 5 , 6 ], however, the treatment approach continues to be a challenge, from screening to optimizing therapeutic decision-making that addresses aspects of rehabilitation of these individuals due to the high rate of morbidity and mortality and the heterogeneous clinical presentation of both conditions [ 1 , 6 , 7 ].

In both diseases, exercise capacity has been adopted as an important outcome and the main guidelines and international campaigns have drawn attention to the importance of this assessment and the inclusion of this outcome in therapeutic optimization [ 8 , 9 , 10 , 11 ]. Since the results that reflect exercise capacity have discriminative prognostic value for mortality risk, risk of unfavorable outcomes, the assessment of treatment efficacy in both conditions is highly desirable [ 12 , 13 , 14 ].

When it comes to exercise capacity, cardiopulmonary exercise testing (CPET) has established itself as the most effective tool for assessing this outcome [ 10 , 15 ], however, the arsenal of equipment used and the need for a team that involves trained professionals led scientists to develop options that could reflect the CPET's ability to exercise in a more economically accessible and simple, but not replaceable [ 16 , 17 ]. The 6-min step test (6MST) has gained notoriety and clinical and scientific popularity due to its practicality and simple, low-cost and easily available alternative option, especially in environments where the most sophisticated resources and equipment for achieving the gold standard are not available, in addition to having an important correlation with CPET [ 16 , 18 ]. In both individuals with CHF [ 16 ] and individuals with T2DM [ 18 ], the reliability and validity of the 6MST has already been scientifically proven and the test has strong concurrent validity when compared to the CPET.

Undoubtedly, both disease negatively impact on exercise capacity, affecting the cardiovascular, respiratory and metabolic dynamic for the supply of oxygen to peripheral muscles [ 19 , 20 ]. However, although the coexistence of T2DM and CHF has been growingly reported, description of fatal and nonfatal events and its relation with functional (in)capacity considering the presence of both conditions is still scarce. Therefore, the objective of this investigation is to evaluate whether individuals with CHF with T2DM have worse functional capacity when compared to a group with CHF without T2DM. Secondarily, we aimed to investigate the different cardiovascular responses presented in both groups and the risk of mortality, decompensation of heart failure and acute myocardial infarction over 36 months. Our hypothesis is, based on all the previously described aspects, that T2DM not only negatively affects the functional performance of individuals with CHF, but also presents itself as an independent factor for reducing functional capacity in the 6MST, worse cardiovascular responses and presents greater mortality, decompensation of CHF and acute myocardial infarction risk.

Methodology

Study design.

This is a prospective longitudinal investigation with a follow-up of 3 years (36 months) carried out by the Cardiopulmonary Physiotherapy Laboratory (LACAP) of the Federal University of São Carlos—UFSCar, located in São Carlos, SP, Brazil. The participant recruitment process took place between December 2017 and November 2020. The university's ethics committee previously approved the development of the investigation under protocol number 5.188.654 and the research followed the principles of the Declaration of Helsinki. The STrengthening the Reporting of OBservational Studies in Epidemiology (STROBE) guideline was used to conduct the study [ 21 ]. All participants were informed about the research objectives and gave their informed consent before being evaluated .

Participants

The Cardiology Outpatient Clinics of the Medical Specialties Center (CEME) and the São Carlos University Hospital (HU-UFSCar) were used to actively search for participants eligible for the investigation. We included patients over 40 years of age, confirmed diagnosis of heart failure with left ventricular ejection fraction below 50% by echocardiography, with or without clinical diagnosis of T2DM, clinical stability and absence of medication changes in the last 3 months. Those aged over 80 years, diagnosed with heart failure with preserved ejection fraction, history of cardiovascular events in the last 6 months, decompensation of the disease in the last 3 months, presence of any implantable cardiac pacemaker, unstable angina, diagnosis of any neoplasms, uncontrolled systemic arterial hypertension, cognitive impairment or lack of understanding of the study proposal were excluded of study.

Initial assessment

Initially, a prior anamnesis was carried out using an assessment form developed by the laboratory and researchers involved so that personal information, associated pathologies and medications used were collected. The medical records of the included patients were also used as a tool to search for important information.

Anthropometric variables

To estimate the height of participants, a stadiometer (Welmy R-110, Santa Bárbara do Oeste, São Paulo, Brazil) was used. Body mass in kilograms (kg), body fat mass (kg), body fat percentage (%) and skeletal muscle mass (kg) were determined through bioelectrical impedance analysis, using the InBody 720 device. Participants were instructed to fast for at least 4 h, wear light clothing, remove all metallic objects in contact with the body, urinate before the exam, avoid drinking alcoholic beverages for 12 h and not perform strenuous physical exercise the day before the evaluation. During the examination, participants were positioned in an upright position, barefoot, with their shoulders slightly abducted and their elbows flexed at approximately 15°, as recommended by the manufacturer (BIOSPACE, 2004). The Body Mass Index (BMI) was calculated by dividing body mass (kg) by height squared in meters (kg/m 2 ). The BMI classification was established as follows: low weight (15–19.9 kg/m 2 ); normal weight (20–24.9 kg/m 2 ); overweight (25–29.9 kg/m 2 ); obesity I (30–34.9 kg/m 2 ); obesity II (35–39.9 kg/m 2 ); and obesity III (≥ 40 kg/m 2 )[ 22 ].

Minnesota Questionnaire

Previously validated for the Brazilian population [ 23 ], this questionnaire consists of 21 questions relating to the limitations associated with heart failure considering the last month. The answers to each question range from 0 to 5, where 0 represents no limitations and 5 the maximum limitation. These questions involve a physical dimension (1–7, 12 and 13), which are highly related to dyspnea, fatigue; emotional dimension (17- 21); and other issues (number 8, 9, 10, 11, 14, 15 and 16) which, together with the previous dimensions, form the total score.

New York Heart Association—NYHA

The New York Heart Association (NYHA) functional classification was used to assess the severity of functional limitations resulting from the CHF condition based on the symptoms experienced by the participant during physical activity. It allows stratifying the degree of limitation imposed by it: class I—absence of symptoms during daily activities, with limitation in efforts similar to that expected in healthy individuals; class II—symptoms triggered by daily activities; class III—symptoms triggered by activities less intense than everyday activities; class IV—symptoms at rest [ 24 ].

Pulmonary function—spirometry

The assessment of lung function was conducted using spirometry (Masterscreen Body, Mijnhardt/Jäger, Würzburg, Germany) by a previously trained researcher, following conventional techniques and the acceptability and reproducibility guidelines of the American Thoracic and European Respiratory Societies (ATS/ERS). At least three slow and forced maneuvers considered acceptable and reproducible were performed, as recommended, and repeated 20 min after administration of 400 µg of Albuterol Sulfate. Participants with overlapping chronic obstructive pulmonary disease were diagnosed according to the GOLD criteria (post-bronchodilator forced expiratory volume in one second (FEV1)/forced vital capacity (FVC) ratio < 0.70) [ 25 , 26 ].

Transthoracic echocardiogram

The transthoracic echocardiogram was performed by a cardiologist, using an ultrasound device with a 3 MHz transducer (Phillips, HD11 XE, Bothell, Washington, United States) according to recommendations [ 27 ]. The end systolic and diastolic diameter of the left ventricle, early diastolic mitral filling velocities (E wave), early diastolic velocity of the mitral annulus (E' wave) and left ventricular ejection fraction (LFEV) were obtained using the Simpson method [ 28 ].

6-min step test—6MST

The 6MST has been previously validated for individuals with CHF [ 16 ]. Prior to the test, upon arrival at the laboratory, participants were informed about the nature and dynamics of the test so that any doubts regarding carrying it out could be clarified. Then, they underwent a period of 4 min of rest (2 min sitting and 2 min standing) so that vital signs could be collected (resting heart rate [HR], peripheral oxygen saturation [SpO 2 ] and blood pressure systemic) in addition to perceived exertion for dyspnea and fatigue of the lower limbs using the BORG 10 scale in each position. At the end of the 4 min, they were instructed to go up and down a single step with a height of 20 cm (cm) in a self-paced manner, being allowed to slow down, if necessary, and even interrupt the test to rest. Verbal encouragement commands were used for each minute of testing and the time remaining until completion. The step numbers were counted from the beginning to the end of the 6-min time and recorded. Vital signs collected prior to the test, as well as feelings of lower limb fatigue and dyspnea were obtained immediately at the end of the test and the 6th min of recovery.

Despite being considered a test of a submaximal nature, some criteria for interrupting the exam were adopted so that the integrity of the patient's health was guaranteed: reaching 85% of maximum HR, arterial oxygen saturation ≤ 87%, systolic blood pressure (SBP) greater than 170 mmHg and DBP greater than 110 mmHg, BORG score greater than 7 for dyspnea and lower limb fatigue, anginal pain > 2, dizziness, vertigo and nausea. The prediction of functional performance of participants in the 6MST for the Brazilian population was made using the equations proposed by Arcuri et al.[ 29 ] 6MST = 209 – (1.05 × age) for men and 6MST = 174 – (1.05 × age) for women, where age is expressed in years; and Albuquerque et al.[ 30 ] 6MST = 106 + (17.02 × [0:woman; 1:man]) + (− 1.24 × age) + (0.8 × height) + (− 0.39 × weight) where 6MST is expressed in number of steps; age, in years; height, in cm; and weight, in kg.

Participants follow-up

Information on mortality, AMI and acute decompensated heart failure was collected through periodic telephone calls every 6 months and/or through hospital records from the date of the patient's initial evaluation in the laboratory. According to the European Society of Cardiology [ 28 ], acute decompensation of heart failure was understood as a rapid or gradual clinical presentation of the signs and symptoms of heart failure at rest, severe enough to cause unplanned office visits, emergency room visits or hospitalization requiring urgent assessment and subsequent initiation or intensification of treatment that includes therapies or procedures.

Statistical analyses

Data are presented as mean and standard deviation or absolute values and percentages of occurrence when appropriate. The Kolmogorov–Smirnov test was used to verify the normality of the data. For the analysis between the groups test T for independent samples was used when the data presented a normal distribution. When the data presented a non-parametric distribution, the Mann–Whitney test was used. The χ 2 test was used to compare categorical variables. Kaplan–Meier analysis was used to test the risk of all-cause mortality, acute decompensated heart failure, and acute myocardial infarction over 36 months of follow-up. Differences between curves were evaluated using the Log-rank test, Breslow and Tarone-Ware.

The covariates included in the present analysis constitute a broad spectrum of factors associated with unfavorable outcomes (mortality, decompensation of heart failure and AMI). Univariate linear regression analyses were performed to verify the association between the independent variables and the dependent variable (steps in the 6MST) [ 31 ]. For the multiple linear regression model, variables that presented a p-value < 0.20 in the univariate analysis were selected as covariates [ 32 ]. Comparisons of 6MST performance and cardiovascular responses between groups were expressed as mean, standard deviation (SD), mean difference (MD), and effect size calculated using Cohen’s d, with the categorization based on the values established by Cohen [ 33 ]. The effect size was calculated based on the Cohen d, according to the website: < https://www.psychometrica.de/effect_size.html >. It was considered the following interpretation of the d value: 0.2 (weak), 0.5 (moderate) and > 0.8 (large effect size) [ 33 ]. Raincloud plots were produced using the JASP 0.18.2 software [ 34 ] for data visualization of the step test performance and predictive values < https://jasp-stats.org/ >. All analyzes were performed using GraphPad Software, Inc. (2019). GraphPad Prism (versão 8.0.1). San Diego, CA < https://www.graphpad.com >. The probability of type 1 error occurrence was established at 5% for all tests (p < 0.05).

Initially, one hundred and twenty-one participants were recruited, however thirty-five were not included. Finally, eighty-six participants were included: 34 CHF-T2DM group and 52 in CHF non-T2DM group (Fig.  1 ). Information about the characteristics of the sample included in the study can be viewed in Table  1 . The groups did not differ in terms of age (years), sex distribution and height (m). However, the CHF-T2DM group had higher body weight and BMI when compared to the CHF non-T2DM and, consequently, a greater number of participants with obesity (65%) (class I [40%], class II [18%] and class III [5%]). Additionally, this same group had a higher prevalence of coronary artery disease (12%), dyslipidemia (72%), use of beta-blockers (85%) and lower LVEF (%). No statistically significant differences were observed in the outcomes of quality of life (Minnesotta questionnaire), functional classification (NYHA) and lung function (spirometry). In total, 14 individuals died over the 36 months of follow-up (9 in the CHF-T2DM group and 5 in the CHF non-T2DM). Furthermore, 13 individuals progressed to acute decompensation of heart failure and 10 to AMI.

In Fig.  2 , when we evaluated functional performance comparing to 6MST in both groups, we observed that the CHF-T2DM group had a significantly lower functional capacity than the CHF non-T2DM group (60 ± 29 versus 87 ± 31; Cohen’s d = 0.875) and that they achieved an average percentage of 45 ± 20 versus 64 ± 22 when considering the prediction equation by Arcuri et al., and 43 ± 19 versus 60 ± 19 when considering the prediction equation by Albuquerque et al. Regarding cardiovascular responses (Table  2 ), we only found a lower heart rate chronotropic response by heart rate in beats per minute in the CHF-T2DM (bpm) at peak exercise (97 ± 26 versus 108 ± 21; Cohen’s d: 0.476).

Raincloud plots for functional capacity by 6MST and predicted values in CHF non-T2DM and CHF-T2DM. CHF non-T2DM chronic heart failure without type 2 diabetes mellitus, CHF-T2DM chronic heart failure with type 2 diabetes mellitus, % percentage, n number in absolute value, p < 0.05: statistical significance

The univariate linear regression model (Table  3 ) revealed that FEV1 (L), ejection fraction (%), gender and T2DM influence and are predictors of approximately 42% functional capacity (p < 0.05; adjusted R squared: 0.419). Secondarily, when we analyzed the Kaplan–Meier curves, we observed that the CHF-T2DM presented a higher risk of mortality (Fig.  3 ) and acute myocardial infarction (Fig.  4 ) over the 36 months of follow-up (p < 0.05 to Log-rank, Brelow and Tarone-ware), however, regarding the risk of heart failure decompensation (Fig.  5 ), there was no statistically significant difference between the groups (p > 0.05 to Log-rank, Brelow and Tarone-ware).

Kaplan–Meier curve for mortality over a period of 36 months. CHF chronic heart failure, %: percentage

Kaplan–Meier curve for acute myocardial infarction over a period of 36 months. CHF chronic heart failure, % percentage

Kaplan–Meier curve for acute decompensation over a period of 36 months. CHF chronic heart failure, % percentage

The main results of this investigation are associated with some important aspects: (1) for the first time, the impact of T2DM on CHF was investigated considering the performance and cardiovascular variables of 6MST; (2) we confirmed our hypothesis that the association of T2DM and CHF presents worse functional capacity compared to the CHF non-T2DM group; (3) secondarily, we observed a higher risk of mortality and AMI in the CHF-T2DM over 36 months of follow-up.

The heterogeneous presentation of CHF, that is, concomitant with other risk factors that contribute to the increase in unfavorable outcomes, with consequent development of disabling functional limitations [ 28 , 35 ]. Particularly, in individuals affected by CHF, the decrease in functional capacity is linked to multifactorial mechanisms that involve, above all, early anaerobic metabolism resulting from a combination of reduced blood flow in skeletal muscle, decreased aerobic enzymes in skeletal muscle, morphological and functional changes of musculoskeletal fibers and inefficiency of the cardiovascular and respiratory system [ 36 , 37 , 38 ]. T2DM, in turn, presents peculiar characteristics that compromise exercise capacity in this population, mainly associated with ineffective glucose uptake, mitochondrial imbalance and the transition from oxidative to glycolytic fiber type [ 39 , 40 ].

Paradoxically to the physiological limitations mentioned above, the effort required to perform the 6MST requires vertical displacement and the involvement of large muscle groups that demand greater cardiovascular stress when compared, for example, to the 6-min walk test, leading to an increase extraction oxygen [ 16 , 29 ]. Considering oxygen uptake, it is nothing new that, individually, both diseases present a decrease in functional capacity when evaluated by field and laboratory tests. In individuals with CHF, whether with a reduced ejection fraction or with its preservation, different methods that reflect this outcome indicate functional impairment over time [ 41 , 42 ]. The same reasoning can be observed in patients with T2DM [ 40 , 43 ].

Previously, and in an unprecedented way, an investigation proved that in individuals with CHF the addition of T2DM is associated with a reduction in the distance covered during the 6-min walk test (6MWT) in addition to being an independent determinant of worse performance in the group with coexistence of both pathologies [ 44 ]. Still considering the phenotypic nature of CHF presentation, recently, a group of researchers observed that T2DM demonstrated to be the strongest predictor of limited exercise capacity in CHF and preserved ejection fraction when also assessed by the 6MWT [ 45 ].

In healthy individuals, variables such as weight (kg), height (cm), age (years) and gender influence 6MST performance and explained at least 42% of the variability in functional capacity [ 30 ]. Parallel to this, our results point to an influence of gender, T2DM, ejection fraction (%) and FEV1 (L) and, undoubtedly, we need to recognize how much each variable makes sense in our regression model since current literature has demonstrated the influence of each of them on exercise capacity. The influence of gender is associated with the nature of the physiological difference that men and women present in the cardiovascular, respiratory and musculoskeletal systems, both in healthy individuals and in individuals affected by heart failure [ 46 , 47 ]. In turn, airflow limitation, more specifically when assessed by FEV1 (L), contributes to functional performance in this population also being compromised. The contribution of lung function to exercise capacity in patients with CHF has been previously discussed and accounts for approximately 30% of maximal exercise capacity during CPET [ 48 ].

Our sample presented some important characteristics that deserve discussion. At first, we must keep in mind that the majority of people affected by T2DM are overweight or obese [ 3 ]. Although there is a paradoxical relationship, that is, inversely proportional, between weight gain and 6MST performance, in practical terms, when we talk about T2DM it is practically utopian to disregard overweight or obesity in this population due to the close relationship between these two outcomes [ 30 ]. We minimally understand the importance of controlling the variable that reflects obesity in both groups so that this bias is minimized, but this may reflect a small portion of the population affected by CHF-T2DM since the coexistence of both pathologies is highly prevalent [ 49 ] and that obesity is strongly connected to T2DM [ 3 ]. Nevertheless, the impact of T2DM culminates in structural and functional changes in the heart muscle that lead to exercise intolerance in patients with CHF and, not surprisingly, the CHF-T2DM group showed a lower left ventricular ejection fraction that may be a reflection of coexistence of both diseases [ 20 ].

When we considered the characterization of our sample using the NYHA scale, we observed that most participants were categorized as NYHA I and II. Curiously, there was a significant risk of mortality and AMI in our sample, revealing a true paradox, as these functional classes typically reflect better functional capacity. Since our patients were followed during a pandemic period, we hypothesize several possible explanations for these unfavorable outcomes: (a) COVID-19 infection and its deleterious effects, leading to an increased risk of AMI and mortality [ 50 ]; (b) the impact of lockdown on increasing the risks associated with these outcomes [ 51 ]; (c) limited discrimination of the NYHA classification [ 52 ].

Clinically, our results contribute not only to recognizing the impact of T2DM in individuals with CHF on 6MST performance, but mainly so that the results can be used in more precise therapies that consider the nature of the coexistence of both pathologies once the evaluation of this outcome. It is routinely used for prognostic, diagnostic, pharmacological optimization, monitoring of disease progression and investigation of functional decline.

Limitations

This is a study with some limitations that deserve to be described. It was not possible to characterize the sample according to metabolic outcomes such as fasting blood glucose or glycated hemoglobin and we also do not have information about the time of diagnosis of T2DM.

T2DM negatively affects the functional performance of 6MST in patients with CHF. Sex, ejection fraction (%), FEV1 (L) and T2DM itself negatively influence this outcome and must be considered within the evaluation.

Availability of data and materials

The set of data generated and/or analyzed during the present study are available through the corresponding author upon reasonable request.

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Acknowledgements

To the patients who participated in the research. To the Coordination for the Improvement of Higher Education Personnel (CAPES), National Council for Scientific and Technological Development (CNPq) and São Paulo Research Foundation (FAPESP) for maintaining the postgraduate programs in Brazil. University Hospital of Federal University of São Carlos—SP-Brazil (HU-UFSCar) Brazilian Company of Hospital Services (EBSERH). Professor Ph.D. Audrey Borghi-Silva is CNPq Research Productivity Scholarship—Level 1B.

This work was supported by São Paulo Research Foundation (FAPESP Process 15/26501-1).

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Study design: ADS, DB, IMD, CLG, RSM, JAM, GSS, PBS, MGR, SAP, AB. Conceptualization: ADS, DB, IMD, CLG, RSM, JAM, GSS, PBS, MGR, SAP, AB. Methodology: ADS, DB, IMD, CLG, RSM, JAM, GSS, PBS, MGR, SAP, AB. Data collection: ADS, DB, IMD, CLG, RSM, JAM, GSS, PBS, MGR, SAP, AB. Data analysis and interpretation: ADS, DB, IMD, CLG, RSM, JAM, GSS, PBS, MGR, SAP, AB. Initial manuscript writing: ADS, DB, IMD, CLG, RSM, JAM, GSS, PBS, MGR, SAP, AB. All the authors read and approved the final manuscript.

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Santos-de-Araújo, A.D., Bassi-Dibai, D., Dourado, I.M. et al. Type 2 diabetes mellitus negatively affects the functional performance of 6-min step test in chronic heart failure: a 3-year follow-up study. Diabetol Metab Syndr 16 , 229 (2024). https://doi.org/10.1186/s13098-024-01464-z

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Insulin Delivery Technology for Treatment of Infants with Neonatal Diabetes Mellitus: A Systematic Review

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• Raffaella Panza   ORCID: orcid.org/0000-0003-2489-9500 1 ,
• Valentina Cattivera 2   na1 ,
• Jacopo Colella 2   na1 ,
• Maria Elisabetta Baldassarre   ORCID: orcid.org/0000-0003-0590-6664 1 ,
• Manuela Capozza   ORCID: orcid.org/0000-0003-2194-6711 1 ,
• Luca Zagaroli   ORCID: orcid.org/0000-0003-0202-9547 3 ,
• Maria Laura Iezzi   ORCID: orcid.org/0000-0003-4089-7273 3 ,
• Nicola Laforgia   ORCID: orcid.org/0000-0002-4610-1216 1 &
• Maurizio Delvecchio   ORCID: orcid.org/0000-0002-1528-0012 2  

Neonatal diabetes mellitus is a rare disorder of glucose metabolism with onset within the first 6 months of life. The initial treatment is based on insulin infusion. The technologies for diabetes treatment can be very helpful, even if guidelines are still lacking. The current study aimed to provide a comprehensive review of the literature about the safety and efficacy of insulin treatment with technology for diabetes to support clinicians in the management of infants with neonatal diabetes mellitus. A total of 22 papers were included, most of them case reports or case series. The first infants with neonatal diabetes mellitus treated with insulin pumps were described nearly two decades ago. Over the years, continuous glucose monitoring systems were added to treat these individuals, allowing for a better customization of insulin administration. Insulin was diluted in some cases to further minimize the doses. Improvement in technology for diabetes prompted clinicians to use new devices and algorithms for insulin delivery in infants with neonatal diabetes as well. These systems are safe and effective, may shorten hospital stay, and help clinicians weaning insulin during the remission phase in the transient forms or switching from insulin to sulfonylurea when suggested by the molecular diagnosis. New technologies for insulin delivery in infants with neonatal diabetes can be used safely and closed-loop algorithms can work properly in these situations, optimizing blood glucose control.

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Introduction

Neonatal diabetes mellitus (NDM) is a rare disease defined by the presence of severe hyperglycemia requiring treatment mainly before 6 months of age and less frequently between 6 months and 1 year [ 1 ]. NDM incidence is between 1:21,000 and 1:350,000 live births, with the highest incidence in those countries with high rates of consanguinity [ 2 , 3 , 4 , 5 ]. It can be classified into transient NDM (TNDM) and permanent NDM (PNDM) [ 6 , 7 ] and insulin therapy must be started if necessary. Genetic testing is suggested in all patients and a switch from insulin to sulfonylureas is recommended for carriers of KCNJ11 and ABCC8 abnormalities [ 1 , 8 , 9 ].

Continuous subcutaneous insulin infusion (CSII) should be taken into consideration in these patients, as it permits the administration of very small insulin doses and avoids severe hypoglycemic episodes due to the unpredictable feeding patterns of neonates [ 10 , 11 ]. Technology for insulin delivery is recommended and appropriate for youth with diabetes, regardless of age. Indeed, standard insulin pump therapy is recommended for all youth with diabetes if access to more advanced diabetes technologies, including sensor-augmented pump therapy (SAP), low glucose suspend (LGS) or predicted low glucose suspend (PLGS) system, and automated insulin delivery (AID), is limited [ 12 ].

This paper presents a systematic review of the significant literature on the use of technology for insulin treatment in patients with NDM.

Materials and Methods

The literature search was carried out according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines [ 13 ] and provides a review of the current literature about technologies for insulin delivery in the management of NDM. The search for eligible studies was performed from inception to 20 July 2024 in the PubMed database. The keywords used for the query strings were “Neonatal diabetes mellitus” AND “insulin pump” and “Neonatal diabetes mellitus” AND “Closed loop”. Only “insulin pump” is indexed in the Medical Subject Headings (MeSH) thesaurus, while “Neonatal diabetes mellitus” is indexed as supplementary concept in association with other terms (permanent, hypothyroidism, and so on).

Non-English language papers were excluded. To be as comprehensive as possible, only guidelines and review papers were not included in the Literature Review section. Commentaries and editorials were taken into consideration only if they reported original data. Additional studies were searched from the reference lists of the selected papers. The literature search was carried out by two authors (R.P. and V.C.) and identified 162 manuscripts with the first query string and 23 with the second. Once the criteria had been applied, most of them were excluded for not meeting the predefined criteria and 25 papers were selected from the first search. Only 1 paper was added from the second search, and thus 26 papers were retrieved to assess for eligibility, five articles were excluded either for not meeting the prespecified study treatment or due to the inconsistency of the study population (i.e., other than neonates). Thus, 21 articles were elected for this review and underwent a review of the full text (PRISMA flowchart; Fig.  1 ). One additional paper was added because it reported the insulin treatment of an infant with NDM with advanced hybrid closed loop (AHCL) even if not retrieved by the literature search [ 14 ]. A final number of 22 papers were included in this review paper. The papers were considered irrespective of study setting (hospital or home), subtype of NDM, duration of insulin requirement, and genetic defect. The Population, Intervention, Professions, Outcomes and Healthcare system (PIPOH) summary of this review is presented in Table  1 .

PRISMA flowchart

This article is based on previously conducted studies and does not contain any new studies with human participants or animals performed by any of the authors.

Data regarding the treatment of NDM with technologies for diabetes are available from observational studies, case reports, and case series. The main features of selected studies are summarized in Table  2 .

The first description of treatment with CSII in an individual with NDM was done by Olinder et al. [ 15 ]. They reported on two infants with NDM who were treated with CSII (Minimed 507C and 508 and a Disetronic H-tron V100). No episodes of diabetic ketoacidosis, severe hypoglycemia, and technical issues were reported. Diluted Lispro insulin (10 IU/mL) was used in the infusion sets. The authors concluded that CSII was effective and safe, suggesting that it could be an alternative treatment for these individuals. The same conclusion was drawn by Park et al. [ 16 ], who treated an infant with TNDM and showed wide serum glucose fluctuation. Both groups suggested that CSII may shorten the length of hospital stays in these children. In addition to the evidence about safety and effectiveness, Tubiana-Rufi [ 17 ] described her experience over 18 years with 17 children and adolescents (8 with PNDM). She confirmed previous data and showed that CSII allows for easy adaptation of insulin dosing, more flexible based on the current feeding regimen. She suggested that very small insulin doses could be administered after insulin dilution (5–10 IU/ml).

Interestingly, Ortolani et al. [ 18 ] described the first infant with NDM treated with CSII integrated with a continuous glucose monitoring system (CGMS). In their paper, they described three patients with PNDM due to INS gene mutation, all of them treated with CSII. Insulin was not diluted, and the authors stated that both treatment strategies are feasible and safe not only in the hospital setting, but also at home. Moreover, they suggest that CSII–CGM integrated system may be superior to CSII only. The same conclusion was achieved with the use of the MiniMed 530G system, the first-generation artificial pancreas system, which allowed for fine insulin dosing with automation algorithms preventing hypoglycemia [ 19 ]. In contrast, in addition to the advantage of CSII, the CGM system could be also technically difficult, as reported in a 6q24-related TNMD with severe intrauterine growth retardation [ 20 ].

The postnatal growth and HbA1c levels in NDM were evaluated by Alyafie et al. [ 21 ] in five patients treated with a pump and in four patients treated with multiple daily injections (MDI). Even if no statistical significance was reached, the authors suggest that insulin pumps could be associated with better glucose control and catch-up growth at 24 months of age.

A Swedish study reported data from six neonates with TNDM treated with an insulin pump (Medtronic MiniMed Paradigm Real-Time and Paradigm VEO) from day of life 5–54 to day of life 17–145 [ 22 ]. The authors diluted insulin 1:10 (final concentration 10 IU/ml) as described by the manufacturer. The meal boluses represented 70–80% of the total daily dose of insulin and they were personalized for each patient on the basis of CGM values and amount of milk. The authors concluded that these devices are safe for neonates with NDM and effective in improving blood glucose control. However, they suggested that the staff should have good technical skills to use this technology properly. Support for the staff involved in NDM management comes from the video and the paper by Zanfardino et al. [ 23 , 24 ], who reported technical information about the management of the insulin pump infusion set in these patients who usually present low birth weight and scarce subcutaneous adipose tissue.

A few different and interesting clinical aspects are pointed out in some case reports. Passanisi et al. concluded that both insulin pumps and long-acting insulin injections alone are effective, but proper expertise is mandatory [ 25 ]. The use of an insulin pump was reported also in very rare situations such as homozygous PTF1A enhancer mutation (two neonates) [ 26 ], GLIS3 mutation (two twins) [ 27 ], and Donohue syndrome [ 28 ]. Interestingly, Huggard et al. suggested that the decision to start an insulin pump in NDM should be shared with caregivers. Even though their patient died at 4 months, the patient’s mother declared her satisfaction with the choice of CSII with a sensor-augmented pump, since it protected her son from hyper- and hypoglycemia and improved his quality of life [ 28 ]. An insulin pump was also very helpful for two patients who switched to sulfonylurea after genetic testing, allowing for a safe and progressive decrease in the daily insulin infusion together with the introduction of sulfonylurea [ 29 ]. In all these papers, the authors confirmed that insulin pumps, with or without CGM, are safe and effective in blood glucose management.

Consistent support to start insulin pumps in neonates with NDM is provided by Kapellen et al. [ 11 ]. In their large cohort of 67 neonates treated with a pump, they report data about the insulin requirement, but no information about gene mutation and transient/permanent form is provided. The median insulin dose at pump start was 0.74 IU/kg (median age 1.8 weeks), with a median basal insulin requirement of 0.56 IU/kg and a median requirement of pre-meal insulin of 0.4 IU/10 g of carbohydrates (I:C ratio 1:25 IU/g of CHO). The dose was adjusted on the basis of blood glucose values, and it was 0.64 IU/kg at discharge (basal insulin requirement 0.43 IU/kg in 63 patients). At the follow-up visit, 35 weeks later, the median insulin requirement was 0.52 IU/kg (median basal insulin requirement was 0.38 IU/kg in 51 patients). The median age at follow-up visit (44 weeks) suggests that most of them had a PNDM. Some data about insulin requirements had been reported previously, but the cohorts were small.

Sensor-augmented pump (SAP) therapy with the MiniMed™ 640G system was described in three patients [ 30 ]. The SmartGuard Technology embedded in this system was reported as particularly useful for these infants to prevent hypoglycemia, to manage insulin delivery more accurately, and to reduce the burden of diabetes for the caregivers. Confirmatory data were shown by Sakai et al. in a small for gestational age infant with TNDM [ 31 ]. He was treated with continuous intravenous insulin infusion initiated on day of life 4. At 39 days, SAP was started successfully without any hypoglycemia. After discontinuation of insulin at 58 days of age, the infant was discharged. The authors suggested that CGM may help physicians to decide when discontinue insulin in SAP therapy.

The usefulness of patient monitoring technologies was described in a proband with NDM due to the beta cell potassium ATP channel gene KCNJ11 mutation. CGM and insulin pumps with automated insulin delivery (AID) and remote monitoring technologies allowed for an easy switch from insulin to oral sulfonylurea [ 32 ]. This report further supports the safety and effectiveness of technologies for infants with diabetes. Similar data and conclusions about switching from insulin to sulfonylurea under CGM were reported in two cases by Mancioppi et al. [ 33 ]. They described a 51-day-old infant started on CSII therapy (MiniMedTM 780G) and CGM (Guardian Sensor 4) with only PLGS function on. Insulin dilution was not necessary. After the detection of a de novo heterozygous pathogenic mutation in the KCNJ11 gene, insulin administration was safely and progressively reduced, with a simultaneous switch to glibenclamide. The safety and effectiveness of AID systems in infants with NDM were also confirmed by Wanaguru et al. [ 34 ]. In their case series of four patients younger than 2 years of age, one infant was genetically confirmed to have 6q24 TND on day of life 23. Due to a very low insulin dose (0.8 IU/day), he commenced on AHCL with SmartGuard technology (blood glucose target 6.7 mmol/l with 0.9% normal saline and auto-corrections turned on) on day of life 26 with 1:10 diluted Aspart, and after 18 days he went into complete remission. We reported similar findings in an infant with 6q24-related TNDM treated. He started on MiniMed™ 780G and CGM with only PLGS function on and insulin Lispro 100 IU/ml. The patient was discharged home, he did not experience any hypoglycemia, and discontinued insulin treatment safely when he went into remission [ 14 ].

NDM is a heterogeneous rare disease. Treatment may be very challenging because of the rarity of the disorder, small insulin doses, and unpredictable feeding. The development of technologies for diabetes treatment provided useful tools for clinicians involved in diabetes care management. Gene mutations can be detected in more than 80% of individuals with NDM [ 1 , 35 ], less frequently in preterm (66%) than in full-term neonates (83%) [ 36 ], prompting the physician to try sulfonylurea in most cases, but even in this situation the first-line treatment remains insulin infusion. The lack of genetic testing is a good point to not try sulfonylurea treatment [ 1 ]. At hyperglycemia onset, a differential diagnosis with other causes of neonatal hyperglycemia, such as infection, low birth weight, beta-cell immaturity, parental nutrition, and hypoxia, may be very challenging overall in preterm babies, and a short course of insulin treatment may be required. If hyperglycemia disappears after a few days, NDM can be reasonably excluded from the diagnostic workup. While waiting for genetic testing results, insulin treatment is necessary to normalize blood glucose as much as possible and to allow for appropriate development and weight growth. The remission rate in newborns with TNDM is close to 100% and insulin therapy may be unnecessary if hyperglycemia is mild. On the contrary, if the initial presentation is severe, insulin treatment has to be promptly started and optimized. In infants with PNDM, the initial insulin treatment can be switched to sulfonylurea tablets once sulfonylurea-responsive KCNJ11 or ABCC8 mutations are determined, since sulfonylurea therapy may be critical to improving long-term neurocognitive and neuromuscular outcomes [ 37 ]. Management of intravenous infusion can be complicated by infections and displacement, and thus CSII may also be a plausible alternative way to manage newborns with the lowest birth weight. Data from the literature suggest that these devices allow for safer and earlier discharge of infants to home [ 15 , 16 ]. There are no clinical research studies aiming to evaluate the best way to deliver insulin to infants with NDM and optimal setting. Reasonably, such trials will never be run in consideration of clinical and genetic heterogeneity, and thus our knowledge about the optimal treatment of these individuals is based on case reports and observational studies.

Given the rarity of the condition, awareness and clinical practice of such conditions can be limited, and thus a review reflects advances in methods to identify, select, appraise, and synthesize findings from available studies. The difficulties in the management and treatment of NMD were pointed out by a recent survey of pediatricians practicing in the Arab Society for Pediatric Endocrinology and Diabetes (ASPED) countries. This survey highlighted that almost all participants (93%) start insulin treatment, preferably after dilution (80%). Interestingly, basal-bolus and insulin pumps were used similarly (36% each), suggesting that the insulin pump was not the most preferred route of administration. The authors concluded that established guidelines for this condition are needed, in consideration of the inhomogeneous treatment despite the good knowledge of this condition [ 38 ]. A similar survey was recently closed by the International Society for Pediatric and Adolescent Diabetes, and we hope that the results will be helpful for future treatment.

The advances in technology for diabetes treatment give physicians a wide range of devices and clinically relevant solutions to treat patients. The first reports about treatment with CSII in infants with NDM showed that this route of administration was safe [ 15 , 16 , 17 ]. As these papers are case reports or case series, the only data that aimed to provide clinically useful information to set the insulin pump were reported by Kapellen et al. from 67 infants with NDM [ 11 ]. They reported detailed information about insulin dose and treatment management irrespective of genetic diagnosis and clinical course, unfortunately.

A significant step toward the improvement of care was represented by the integration of CSII with CGMS [ 18 , 19 , 20 , 29 ], which is helpful to avoid hypoglycemia episodes [ 39 ]. Despite technical difficulties and lack of randomized trials investigating the real benefits, the CGMS allows for 24-h monitoring with retrospective or real-time evaluation of glucose levels, avoiding blood samplings, and thus fine dosing of insulin and reduction of blood glucose fluctuations [ 40 ].

Over the last decade, technology for diabetes treatment has been greatly improved and AID systems have become more widely available [ 12 ]. In the case of very low doses, insulin dilution may be helpful in administering minimal insulin doses [ 18 , 20 , 25 ] and allowing the AID systems to work in automode [ 34 ]. The use of AID systems with automode function has been reported in some infants more recently [ 30 , 31 , 33 , 34 ]. In all these papers, AHCL technology is feasible and safe, allowing for optimization of blood glucose control, early discharge of the patient to home, and supporting the clinicians both in switching from insulin to sulfonylurea and in weaning insulin during the transition to complete remission in infants with TNDM. Despite promising results, delivery of very small doses of insulin in a neonate with NDM and intrauterine growth retardation may be very challenging, and the insertion of needles for insulin infusion and glucose sensing may appear very difficult or nearly impossible. However, it should be noted that the last generation of AID systems cannot be prescribed in infants below 1 year of age and most of them cannot be used under the age of 7. Furthermore, available data suggest that technologies for insulin delivery in infants with NDM can face successfully all the critical points in clinical management, and practical suggestions to manage the infusion sites are provided to support the staff in the management of the devices [ 23 , 24 ]. Furthermore, the use of an AHCL system has the pros of a lower number of injections to deliver the daily insulin. Technology for diabetes treatment allows normal feeding with normal growth [ 15 , 16 , 31 ], even in newborns with severe intrauterine growth retardation [ 17 ]. Finally, unanimously, these devices allow for shortened stay in the hospital, discharging the infants in safe conditions home [ 14 , 15 , 17 ].

This paper has some limitations. First, selected papers are not homogeneous regarding the clinical course, molecular diagnosis, and devices. Second, almost all papers are case reports or case series and there is a limited number of infants. The current study presents data as clearly as possible to provide clinicians useful data for clinical practice. We cannot exclude that some papers could have been missed through our search strategy because of inappropriate keywords. However, the literature on the topic is limited, and retrieving possible papers by reading the references of each paper reduces this bias as much as possible. Finally, we think that the quality of the evidence is quite poor in most of the papers, as the metabolic outcomes are incomplete in most of them, the AID device is often not specified, and the diluent used with the insulin is often not reported.

In conclusion, the evidence from the current study suggests that AID systems with AHCL technology are helpful for the treatment of infants with NDM. Even in preterm and low birth weight infants, CGMS and insulin catheters can be inserted safely. Treatment goals should be defined with the family members or caregivers for a therapeutic alliance that can be the key to treatment success. The staff of the diabetes team and/or the neonatal intensive care unit should have good technical skills to use this technology properly. AID systems can be used to treat these infants, reducing blood glucose fluctuations and preventing hypoglycemia. In the case of a total daily dose lower than the minimal requirement for the AHCL algorithm, insulin can be diluted to start the automode functionality, otherwise, the algorithm can properly work with the PLGS function on.

Data Availability

Data sharing is not applicable to this article as no datasets were generated or analyzed during the current study.

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Valentina Cattivera and Jacopo Colella contributed equally to this work.

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Section of Neonatology and NICU, Interdisciplinary Department of Medicine, University of Bari “Aldo Moro”, 70124, Bari, Italy

Raffaella Panza, Maria Elisabetta Baldassarre, Manuela Capozza & Nicola Laforgia

Department of Biotechnological and Applied Clinical Sciences, University of L’Aquila, 67100, L’Aquila, Italy

Valentina Cattivera, Jacopo Colella & Maurizio Delvecchio

Unit of Pediatrics, San Salvatore Hospital, ASL 1 Abruzzo, 67100, L’Aquila, Italy

Luca Zagaroli & Maria Laura Iezzi

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All authors (Raffaella Panza, Valentina Cattivera, Jacopo Colella, Maria Elisabetta Baldassare, Manuela Capozza, Luca Zagaroli, Maria Laura Iezzi, Nicola Laforgia, Maurizio Delvecchio) contributed to the study conception and design. All authors read and approved the final manuscript.

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All the authors (Raffaella Panza, Valentina Cattivera, Jacopo Colella, Maria Elisabetta Baldassare, Manuela Capozza, Luca Zagaroli, Maria Laura Iezzi, Nicola Laforgia, Maurizio Delvecchio) do not declare any competing interests. Maurizio Delvecchio is an Editorial Board member of Diabetes Therapy . Maurizio Delvecchio was not involved in the selection of peer reviewers for the manuscript nor any of the subsequent editorial decisions.

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Panza, R., Cattivera, V., Colella, J. et al. Insulin Delivery Technology for Treatment of Infants with Neonatal Diabetes Mellitus: A Systematic Review. Diabetes Ther (2024). https://doi.org/10.1007/s13300-024-01653-z

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Examining the quality of life among pregnant women diagnosed with gestational diabetes mellitus: A systematic review and meta-analysis for women's health promotion

Affiliations.

• 1 Endocrine Research Center, Tabriz University of Medical Sciences, Tabriz, Iran.
• 2 Student Research Committee, Tabriz University of Medical Sciences, Tabriz, Iran.
• 3 School of Nursing and Midwifery, Iran University of Medical Science, Tehran, Iran.
• 4 Research Center for Evidence-based Medicine, Iranian EBM Centre: A JBI Centre of Excellence, Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran.
• PMID: 39291040
• PMCID: PMC11403342
• DOI: 10.34172/hpp.2024.05

Background: Quality of life (QoL) of women with gestational diabetes mellitus (GDM) is one of the fundamental issues and public health challenges. This study examines the QoL among pregnant women with GDM through a systematic review and meta-analysis.

Methods: A search was conducted in Scopus, PubMed, and the Web of Science databases for articles published until Jan 30, 2024. Manual searches of gray literature, Google Scholar, reference checks, and citation checks were conducted. The JBI's Critical Appraisal Checklist for Analytical Cross-Sectional Studies was utilized to assess the quality of the articles' reporting. The random model implemented in Stata software (version 16; Stata Corp.) was utilized to conduct the meta-analysis.

Results: Among the 516 studies obtained from the literature, only 15 were deemed suitable for inclusion. Most studies (73.3%) were conducted in nations with high-income levels. Additionally, general QoL was assessed in most studies (11 studies). The SF-36 and WHOQOLBREF questionnaires were the most often utilized. Based on the SF-36 measure, there was no statistically significant difference in the QoL of patients with GDM compared to the control group in most of dimensions. The WHOQOL-BREF instrument was utilized to estimate the QoL score at 49.69. The EQ-5D-5L tool revealed a difference in QoL scores between the GDM and control groups (MD=-7.40). The research findings were highly heterogeneous. The median evaluation score for the reporting quality of the articles was calculated to be 5, with a mean of 4.8 out of 7.

Conclusion: The results of the present study showed that GDM reduces the QoL of pregnant women, especially in terms of mental and social health. Therefore, interventions and support programs should be designed and implemented to improve these women's QoL.

Keywords: Gestational diabetes mellitus; Pregnant women; Quality of life; Systematic review and meta-analysis; Women’s health.

© 2024 The Author(s).

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Conflict of interest statement

The authors declare that they have no competing interests.

• Zimmet PZ, Magliano DJ, Herman WH, Shaw JE. Diabetes: a 21st century challenge. Lancet Diabetes Endocrinol. 2014;2(1):56–64. doi: 10.1016/s2213-8587(13)70112-8. - DOI - PubMed
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Obesity differs from diabetes mellitus in antibody and T-cell responses post-COVID-19 recovery

Mohammad ali.

Peter Medawar Building for Pathogen Research, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK

Centre for Global Health Research, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK

Mahidol-Oxford Tropical Medicine Research Unit, Mahidol University, Bangkok, Thailand

Directorate General of Health Services, Dhaka, Bangladesh

Department of Biochemistry and Molecular Biology, Bangabandhu Sheikh Mujib Medical University, Dhaka, Bangladesh

Stephanie Longet

Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK

Isabel Neale

Patpong rongkard, forhad uddin hassan chowdhury.

Department of Internal Medicine, Dhaka Medical College, Dhaka, Bangladesh

Jennifer Hill

Anthony brown, stephen laidlaw, ashraful hoque.

Department of Transfusion Medicine, Sheikh Hasina National Burn & Plastics Surgery Institute, Dhaka, Bangladesh

Nazia Hassan

Department of Internal Medicine, Bangabandhu Sheikh Mujib Medical University, Dhaka, Bangladesh

Carl-Philipp Hackstein

Translational Gastroenterology Unit, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK

Sandra Adele

Hossain delowar akther, priyanka abraham, shrebash paul, md matiur rahman, md masum alam, shamima parvin.

Department of Biochemistry and Molecular Biology, Mugda Medical College, Dhaka, Bangladesh

Forhadul Hoque Mollah

Md mozammel hoque, shona c moore.

Tropical and Infectious Disease Unit, Liverpool University Hospitals NHS Foundation Trust, Member of Liverpool Health Partners, Liverpool, UK

Subrata K Biswas

Department of Molecular and Cell Biology, University of Connecticut, Storrs, Connecticut, USA

Lance Turtle

Thushan i de silva.

Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK

John Frater

Eleanor barnes.

NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, UK

Adriana Tomic

National Emerging Infectious Diseases Laboratories, Boston University, Boston, MA, USA

Department of Microbiology, Boston University School of Medicine, Boston, MA, USA

Department of Biomedical Engineering, Boston University, Boston, MA, USA

Miles W Carroll

Paul klenerman, barbara kronsteiner, fazle rabbi chowdhury, susanna j dunachie, associated data.

All data reported in this paper is available in the manuscript and supplementary files. Additional information is available from the lead contact upon request.

Objective: Obesity and type 2 diabetes (DM) are risk factors for severe coronavirus disease 2019 (COVID-19) outcomes, which disproportionately affect South Asian populations. This study aims to investigate the humoral and cellular immune responses to SARS-CoV-2 in adult COVID-19 survivors with overweight/obesity (Ov/Ob, BMI ≥ 23 kg/m 2 ) and DM in Bangladesh. Methods: In this cross-sectional study, SARS-CoV-2-specific antibody and T-cell responses were investigated in 63 healthy and 75 PCR-confirmed COVID-19 recovered individuals in Bangladesh, during the pre-vaccination first wave of the COVID-19 pandemic in 2020. Results: In COVID-19 survivors, SARS-CoV-2 infection induced robust antibody and T-cell responses, which correlated with disease severity. After adjusting for age, sex, DM status, disease severity, and time since onset of symptoms, Ov/Ob was associated with decreased neutralizing antibody titers, and increased SARS-CoV-2 spike-specific IFN-γ response along with increased proliferation and IL-2 production by CD8 + T cells. In contrast, DM was not associated with SARS-CoV-2-specific antibody and T-cell responses after adjustment for obesity and other confounders. Conclusion: Ov/Ob is associated with lower neutralizing antibody levels and higher T-cell responses to SARS-CoV-2 post-COVID-19 recovery, while antibody or T-cell responses remain unaltered in DM.

Despite similar IgG antibody levels, adults with overweight/obesity (BMI ≥ 23 kg/m 2 ) have lower neutralizing antibody capacity and higher T-cell responses to SARS-CoV-2 following COVID-19 recovery. In contrast, antigen-specific antibody and T-cell responses are preserved in individuals with diabetes mellitus who survive SARS-CoV-2 infection.

Graphical Abstract

Introduction

Since the onset of the coronavirus disease 2019 (COVID-19) pandemic, obesity and type 2 diabetes (DM) have emerged as clinically significant risk factors for disease severity, hospitalization, and mortality due to COVID-19 [ 1–5 ]. Both comorbidities are associated with increased susceptibility, severity, and poor prognosis in bacterial and other viral infections [ 6–9 ]. Obesity and DM exhibit similar clinical characteristics including low-grade chronic inflammation [ 10 , 11 ], impaired energy homeostasis [ 11 , 12 ], oxidative stress [ 11 , 13 ], altered cytokine profile [ 14 ], and impaired cellular immunity [ 15 , 16 ], which can significantly impact the body’s ability to fight against pathogens. However, the impact of obesity and DM on COVID-19 remains unclear. Cellular stress, systemic inflammation, and endothelial damage present in metabolic diseases are thought to be aggravated by SARS-CoV-2 infection, increasing the chance of thromboembolism and damage to vital organs [ 17 ]. Higher expression levels of angiotensin-converting enzyme 2 (ACE2), used for entry of SARS-CoV-2 into cells, are observed in adipose tissue from individuals with obesity and DM [ 18 ]. Consequently, adipose tissue may act as a reservoir for the virus, promoting a persistent inflammatory response and poor prognosis [ 19 ].

Ethnicity has also been linked with COVID-19 severity and mortality. The Office for National Statistics in England reported that COVID-19 mortality in the UK was up to five times higher for people of Bangladeshi ethnic background compared to people with White British background, and rate of deaths involving COVID-19 has remained highest for the Bangladeshi ethnic group since the second wave of the pandemic [ 20 , 21 ]. Multiple contributing factors have been proposed to explain worse COVID-19 outcomes in certain ethnicities, with differences related to metabolism and metabolic health among these [ 22 ] alongside socio-economic factors. Emerging data show ethnicity may modify the association between body mass index (BMI) and COVID-19 outcomes [ 23 , 24 ]. South Asian ethnicities with a BMI of 27 kg/m 2 have the same risk of COVID-19 mortality as white ethnicities at a BMI of 40 kg/m 2 [ 25 ].

An in-depth understanding of adaptive immune responses to SARS-CoV-2 in South Asian ethnicities, particularly in the Bangladeshi population, is critical to understand the mechanisms associated with disease severity, while they are disproportionately burdened with obesity and DM. This observational clinical study presents our comprehensive analysis of antibody, B-cell, and T-cell responses to SARS-CoV-2 in COVID-19 survivors in Bangladesh, exploring potential alterations in adaptive immunity related to obesity and DM.

Methods and materials

Study design and sample collection.

Adult participants aged 18 or over were recruited from Bangabandhu Sheikh Mujib Medical University (BSMMU) Hospital, Dhaka Medical College (DMC) Hospital, and Sheikh Hasina National Institute of Burn and Plastic Surgery (SHNIBPS) in Dhaka, Bangladesh, from September 2020 to November 2020, during Bangladesh’s first wave of COVID-19 pandemic and prior to the global introduction of vaccines. Participants with prior PCR-confirmed SARS-CoV-2 infection with one or more symptoms were recruited at least 28 days after the onset of symptoms. Among them, 31 individuals had recovered from mild/moderate disease (without oxygen support), while 44 individuals had recovered from severe disease (requiring oxygen support). Additionally, 63 healthy control individuals were enrolled who had reported no COVID-19 symptoms since the onset of pandemic. These controls were further categorized as healthy seronegative ( n  = 35, presumed infection-naive) and healthy seropositive ( n  = 28, presumed asymptomatic infection) based on anti-spike seropositivity (MSD IgG binding assay—see below).

After obtaining written informed consent, we collected clinical information including age, sex, body mass index (BMI), comorbidities, the date of any SARS-CoV-2 infection (defined by a positive PCR test), presence of symptoms, lowest recorded SpO 2 during infection (measured by pulse oximeter), and time since onset of symptoms. HbA1c levels were measured by ion-exchange liquid chromatography in a Bio-Rad D-10™ analyzer (Bio-Rad Laboratories Inc., Hercules, CA, USA) to assess glycaemic status. Diabetes was defined as HbA1c ≥ 6.5%, while any previous history of diabetes and HbA1c less than 6.5% was included as controlled diabetes. Type 1 and type 2 diabetes were not formally typed, but type 2 diabetes accounts for 90–95% of diabetes cases in Bangladesh [ 26 ]. Key demographic information is shown in Table 1 . For healthy pre-pandemic controls, we used cryopreserved samples ( n  = 40) from a previous unrelated study conducted in Dhaka, Bangladesh in 2017.

Demographic characteristics of participants enrolled from September 2020 to November 2020

Peripheral blood mononuclear cells (PBMCs) and plasma were separated as previously described [ 27 ] and cryopreserved at −80°C then shipped to Oxford, UK on dry ice and stored in liquid nitrogen for later use.

We performed the multiplexed MSD IgG binding assay on all donors, as outlined in Table 1 . This allowed us to classify healthy individuals into seronegative and seropositive controls based on anti-spike seropositivity (see MSD IgG binding assay below). Our study’s aim was to evaluate the impact of overweight/obesity and diabetes on adaptive immune responses, particularly in individuals who recovered from PCR-confirmed SARS-CoV-2 infection with symptoms. Consequently, when selecting recovered donors for other assays, we made efforts to match the age, sex, disease severity, and days post-symptom onset across the recovered lean and overweight/obese, and DM and Non-DM groups.

Supplementary Table S1a–d presents the demographic characteristics of recovered participants with or without overweight/obesity and diabetes status, selected for each specific assay. For the pre-pandemic and healthy control cohorts, representative samples were included, taking into account age and sex considerations, as shown in Supplementary Table S4a–e .

Meso scale discovery (MSD) IgG binding assay

IgG responses to SARS-CoV-2 spike (S), receptor binding domain (RBD), and nucleocapsid (N) antigens were measured using a multiplexed MSD immunoassay: The V-PLEX COVID-19 Coronavirus Panel 3 (IgG) (Meso Scale Discovery, Rockville, MD USA) as previously described [ 28 ]. Briefly, plasma samples were diluted 1:1000-30 000 in diluent buffer and added to the MULTI-SPOT ® 96-well plates, priorly coated with SARS-CoV-2 antigens (S, RBD, N) at 200 − 400 μg/ml, along with MSD standard and undiluted internal MSD controls. After 2-h incubation, detection antibody was added. Following washing and addition of read buffer, and plates were read using a MESO ® SECTOR S 600 reader. Concentrations are expressed in arbitrary units/ml (AU/ml). Cutoffs for each SARS-CoV-2 antigen were as defined previously [ 27 ]: S, 1160 AU/ml; RBD, 1169 AU/ml; and N, 3874 AU/ml.

Focus reduction neutralization test (FRNT)

Plasma was serially diluted in DMEM with 1% FBS from 1:10 to 1:10 000, then combined with equal volume of 100 foci forming units (FFU) of SARS-CoV-2 Victoria virus and incubated for 30 min. Vero E6 cells (4.5 × 10^5/ml) were added 100 µl/well and incubated for 2 h at 37°C, 5% CO 2 . Carboxymethyl cellulose (1.5%) was then added (100 µl/well) and incubated at 37°C, 5% CO 2 for 20 h. Assays were done in duplicate. Cells were washed with DPBS, fixed with 4% paraformaldehyde for 30 min at room temperature, and then permeabilized with 1% TritonX100 in PBS, followed by staining with a human monoclonal antibody (FB9B) [ 29 ]. Bound antibody was detected by incubating with goat anti-human IgG HRP conjugate (Sigma, UK) and followed by TrueBlue™ Peroxidase substrate (Insight Biotechnology, UK), then imaged with an ELISPOT reader. The half-maximal inhibitory concentration (IC50) was defined as the concentration of plasma that reduced the FFU by 50% compared to the control wells.

Memory B-cell fluorospot assay

Cryopreserved PBMCs were thawed and cultured for 72 h at 37°C, 5% CO 2 , with polyclonal stimulation containing 1 μg/ml R848 and 10 ng/ml IL-2 from the Human IgA/IgG FluoroSpotFLEX kit (Mabtech) as previously described [ 30 ]. Stimulated PBMCs were added at 2 × 10 5 cells/well to fluorospot plates coated with 10 μg/ml SARS-CoV-2 spike glycoprotein (S) and nucleocapsid protein (both from The Native Antigen Company, UK) diluted in PBS (Gibco). Plates were incubated for 18 h in a humidified incubator at 37°C, 5% CO 2 , and developed according to the manufacturer’s instructions (Mabtech). Analysis was carried out with AID ELISpot software 8.0 (Autoimmun Diagnostika). Memory B-cell IgG response was measured as antibody spot-forming units (SFU) per million.

T-cell interferon-gamma ELISpot assay

The standard operating procedure for T-cell interferon-gamma (IFN-γ) ELISpot Assay has been published previously [ 28 ]. In brief, cryopreserved PBMCs were thawed and plated at 200 000 cells/well in a MultiScreen-IP filter plate (Millipore, MAIPS4510) previously coated with capture antibody (clone 1-D1K). PBMCs were then incubated with overlapping SARS-CoV-2 peptide pools (18-mers with 10 amino acid overlap, Mimotopes), representing the S1, S2, membrane (M), nucleocapsid (N), open reading frame (ORF) 3 and 6, ORF 7 and 8 (2 µg/ml) for 16–18 h in a humidified incubator at 37°C, 5% CO 2 . Positive controls included the cytomegalovirus pp65 protein (Miltenyi Biotec) and concanavalin A, DMSO served as the negative control. After incubation, the plates were developed following manufacturers protocol (Mabtech 3420-2A) and analyzed with the ImmunoSpot ® S6 Alfa Analyser (Cellular Technology Limited LLC, Germany). Antigen-specific responses were quantified as IFN-γ spot-forming units (SFU)/million PBMC. A positive response was defined as greater than 37 SFU/million PBMC based on the mean + 2 SD of the negative control wells.

Proliferation assay

T-cell proliferation assay was carried out as previously described [ 27 ]. In brief, cryopreserved PBMCs were thawed and stained with CellTrace® Violet (Life Technologies). PBMCs were then plated in 96-well plates and stimulated with SARS-CoV-2 peptide pools (S1, S2, M, N, ORF3 and 6, ORF 7 and 8) at 1 μg/ml. DMSO and PHA-L were used as negative and positive controls, respectively. Cells were incubated at 37°C, 5% CO 2 for 7 days with media change on Day 4. Flow cytometry was used to analyze the relative frequency of proliferating CD4 + and CD8 + T cells. Responses above 1% were considered true positive.

Intracellular cytokine stimulation assay

In a subset of donors ( n  = 36), selected from healthy seronegative ( n  = 5), healthy seropositive ( n  = 13), and recovered patients ( n  = 18), T-cell responses were characterized by intracellular cytokine staining (ICS) as described previously [ 30 ]. In brief, PBMCs were stimulated with SARS-CoV-2 peptide pools (spike, M, and N) at 2 μg/ml along with anti-CD28 and anti-CD49d (both from BD). DMSO and a cell-activation cocktail (PMA and ionomycin) were used as negative and positive controls, respectively. After 1 h incubation, Brefeldin A was added and the samples were further incubated for 15 h at 37°C and 5% CO 2 . Flow cytometry was used to analyze the relative frequency of IFN-γ, TNF, and IL-2-producing CD4 + and CD8 + T cells. Details of antibodies used for proliferation assay and ICS are listed in Supplementary Table S3 .

Statistical analysis

Statistical analyses were conducted using R version 4.2.1 ( https://www.R-project.org/ ). Unpaired comparisons between two or more groups were assessed using the two-tailed Wilcoxon rank-sum test or Kruskal–Wallis test with Dunn’s multiple comparisons test as appropriate. Correlation analysis was performed using Spearman’s rank correlation coefficient. SIMON software version 0.2.1 ( https://genular.org ) [ 31 ] was used to perform correlation and principal component analysis. A significance level of P  < 0.05 was set.

The detailed descriptions of generalized linear models (GLMs) are provided in Supporting Information Methods , and summary tables are reported in Supplementary Table S2a–f . Cases with missing data were eliminated in GLMs.

SARS-CoV-2 infection induces robust antibody responses, which are more pronounced in recovery from severe illness

We first analyzed SARS-CoV-2-specific IgG responses among our study cohorts ( Fig. 1A ) using the MSD IgG binding assay. Participants who recovered from severe disease had significantly higher IgG responses to SARS-CoV-2 spike, RBD, and N than those who recovered from mild/moderate disease and healthy seropositive controls, with the latter two groups not showing any differences ( Fig. 1B ). Despite these differences, using an ex vivo memory B-cell ELISpot assay, we found comparable numbers of memory B cells specific to spike and N in healthy seropositive individuals and those who recovered from mild/moderate and severe disease, while healthy seronegative controls lacked detectable IgG + memory B cells ( Fig. 1C ) in blood. Further, individuals who recovered from mild/moderate disease tended to have elevated levels of antibody (Ab) neutralization of SARS-CoV-2 compared to those who recovered from severe disease; however, the difference was not statistically significant ( P  = 0.063) ( Fig. 1D ).

SARS-CoV-2 infection induces robust antibody responses, which are more pronounced in recovery from severe illness. ( A ) Study cohorts including pre-pandemic controls ( n  = 40; recruited in Bangladesh in 2017—ivory color), healthy seronegative controls ( n  = 35, presumably infection naïve—gray color), healthy seropositive controls ( n  = 28, presumably asymptomatic infection—black color), individuals who recovered from mild/moderate disease ( n  = 31—orange color) and severe disease ( n  = 44—red color) due to PCR-confirmed symptomatic SARS-CoV-2 infection and recruited at least 28 days after onset of symptoms. Participants (except pre-pandemic controls) were recruited from September 2020 to November 2020, during Bangladesh’s first wave of the COVID-19 pandemic and prior to the global introduction of vaccines. Seropositivity status was defined by MSD IgG binding assay. ( B ) IgG responses to SARS-CoV-2 spike (S), receptor binding domain (RBD) and nucleocapsid (N) antigens in pre-pandemic controls ( n  = 40), healthy seronegative controls ( n  = 35), healthy seropositive controls ( n  = 28), individuals recovered from mild/moderate disease ( n  = 31), and severe illness ( n  = 44). IgG responses were measured in plasma samples using multiplexed MSD immunoassays and are expressed in arbitrary units (AU)/ml. Horizontal dotted lines represent the cutoff of each assay based on the pre-pandemic sera from UK individuals. ( C ) Spike and N-specific memory B-cell IgG responses in healthy seronegative controls ( n  = 7), healthy seropositive controls ( n  = 17), and individuals recovered from mild/moderate disease ( n  = 12), and severe illness ( n  = 12). Memory B cells were quantified by B-cell ELISpot assay from cryopreserved peripheral blood mononuclear cells (PBMC), and data are shown in antibody spot-forming units (SFU)/million PBMC. ( D ) Neutralizing antibody titers against SARS-CoV-2 by focus reduction neutralization (FRNT) assay in individuals recovered from mild/moderate disease ( n  = 18) and severe illness ( n  = 26). IC50 is the reciprocal dilution of the concentration of plasma required to produce a 50% reduction in infectious focus-forming units of virus in Vero cells (ATCC, CCL-81). Bars for (B) and (C) represent the medians. Groups were compared with Kruskal–Wallis nonparametric test, with only significant two-tailed P values ( P  < .05) shown above linking lines

SARS-CoV-2 infection elicits robust T-cell responses that correlate with disease severity

We assessed T-cell responses to SARS-CoV-2 in our study cohorts using an ex vivo IFN-γ ELISpot assay, a proliferation assay, and an intracellular cytokine assay (ICS). Both healthy seropositive individuals and recovered cohorts (mild/moderate and severe disease) exhibited detectable IFN-γ ELISpot responses to SARS-CoV-2 peptide pools spanning structural (S1, S2, M, N) and accessory (ORF3 and 6, ORF7 and 8) proteins, whereas the pre-pandemic controls and most healthy seronegative controls had no detectable responses above the defined positivity threshold (37 SFU/10 6 PBMC) ( Fig. 2A , Supplementary Fig. S1A ). Subjects who recovered from severe illness mounted substantially greater IFN-γ responses to spike (summed S1 and S2 responses), and ORFs (summed ORF3 and 6, ORF7 and 8) than those who recovered from mild/moderate disease and healthy seropositive controls ( Fig. 2B ). Furthermore, both recovered cohorts had a greater magnitude of IFN-γ responses to M + N compared to the healthy seropositive controls ( Fig. 2B ). Notably, the breadth of IFN-γ responses to SARS-CoV-2 peptide pools was greater in both groups of recovered individuals as compared to healthy seropositive controls ( Supplementary Figure S1A ). These results highlight the robustness of anti-SARS-CoV-2 IFN-γ ELISpot responses across different disease severities.

SARS-CoV-2 infection elicits robust T-cell responses that correlate with disease severity. ( A ) Heatmap displaying the IFN-γ responses to SARS-CoV-2 peptide pools spanning structural and accessory proteins (S1, S2, M, N, ORF3, and 6, ORF7 and 8) in pre-pandemic controls ( n  = 14), healthy seronegative controls ( n  = 23), healthy seropositive controls ( n  = 25), individuals recovered from mild/moderate disease ( n  = 21) and severe illness ( n  = 28). IFN-γ responses were measured by ex vivo IFN-γ ELISpot assay from cryopreserved PBMC samples, and data are shown in IFN-γ spot-forming units (SFU)/million PBMC. ( B ) Comparison of IFN-γ ELISpot responses to SARS-CoV-2 spike (summed responses to S1 and S2 peptide pools), M + N (summed responses to M and N pools), and ORFs (summed responses to ORF3, 6–8) in pre-pandemic controls ( n  = 14), healthy seronegative controls ( n  = 23), healthy seropositive controls ( n  = 25), individuals recovered from mild/moderate disease ( n  = 21) and severe illness ( n  = 28). ( C ) Heatmap displaying the relative frequency of CD4 + and CD8 + T cells proliferating to individual peptide pools S1, S2, M, N, ORF3&6, and ORF7&8, assessed by flow cytometry (gating strategy shown in Supplementary Fig. S2A ) from cryopreserved PBMC, in pre-pandemic controls ( n  = 15), healthy seronegative controls ( n  = 24), healthy seropositive controls ( n  = 25), individuals recovered from mild/moderate disease ( n  = 20), and severe illness ( n  = 31). ( D ) Comparison of relative frequency of CD4 + (top panels) and CD8 + (bottom panels) T cells proliferating to SARS-CoV-2 individual peptide pools in pre-pandemic controls ( n  = 15), healthy seronegative controls ( n  = 24), healthy seropositive controls ( n  = 25), individuals recovered from mild/moderate disease ( n  = 20), and severe illness ( n  = 31). (E) The spike and M + N-specific IFNγ, IL-2, and TNF expression levels are reported as a percentage of the CD4 + T-cell population (top panels) and CD8 + T-cell population (bottom panels). Cryopreserved PBMCs from healthy seronegative controls ( n  = 5), healthy seropositive controls ( n  = 13), and individuals recovered from mild/moderate disease ( n  = 10) and severe illness ( n  = 8) were analyzed by intracellular cytokine staining and flow cytometry (gating strategy is shown in Supplementary Fig. S2B ). Bars for (B), (D), and (E) represent the medians. Groups were compared with Kruskal–Wallis nonparametric test, with only significant two-tailed P values ( P  < 0.05) shown above linking lines

To obtain a deeper understanding of the induction of T-cell-based immunity in our cohorts, we next performed proliferation assays, a sensitive method to assess the SARS-CoV-2 specific circulating CD4 + and CD8 + T cells [ 27 ]. Recovered individuals with severe disease had greater magnitude and breadth of CD4 + and CD8 + T-cell proliferative responses to SARS-CoV-2 peptide pools than healthy seropositive controls ( Fig. 2C–D , Supplementary Fig. S1B–C ). Notably, CD8 + T-cell proliferation was significantly higher and broader in recovered individuals with severe illness than those who recovered from mild/moderate disease ( Fig. 2D , Supplementary Figure S1C ). Moreover, healthy seropositive controls displayed a higher magnitude and breadth of T-cell proliferative responses in comparison to healthy seronegative controls, with some of the latter group showing detectable CD4 + proliferative responses to SARS-CoV-2 peptide pools ( Fig. 2C and ​ andD, D , Supplementary Fig. S1B and C ). Interestingly, there was no detectable T-cell proliferation in our pre-pandemic controls ( Fig. 2C and ​ andD, D , Supplementary Fig. S1B–C ), in contrast to UK pre-pandemic controls [ 27 ].

In ICS, we observed higher CD4 + T cell IFN-γ, IL-2, and TNF responses to SARS-CoV-2 spike and higher CD4 + T cell IL-2, and TNF responses to M + N in participants who recovered from severe illness compared to the healthy seropositive controls ( Fig. 2E ). Similarly, those who recovered from mild/moderate illness had higher CD4 + T cell IL-2 and TNF responses to spike, and TNF responses to M + N than healthy seropositive controls ( Fig. 2E ). In contrast, we did not detect any differences among the groups in the levels of CD8 + T cell cytokine expressions ( Fig. 2E ).

Individuals with overweight/obesity had lower neutralizing antibody titers, but higher T responses to SARS-CoV-2 following recovery

We next went on to assess immune features associated with obesity in individuals who had recovered from PCR-confirmed SARS-CoV-2 symptomatic infection. We combined the participants from our two recovered cohorts (mild/moderate and severe disease) and grouped them as lean (BMI = 18.5–22.9 kg/m 2 ), and overweight/obesity (Ov/Ob, BMI ≥ 23 kg/m 2 ) following the WHO recommendation for Asian BMI [ 32 ]. Notably, participants with Ov/Ob were considerably younger than those who were lean ( Supplementary Table S1a and b ).

The recovered participants with Ov/Ob had 2-fold lower neutralizing antibody titers compared to the lean subjects, with no differences in the IgG responses to SARS-CoV-2 spike, RBD and N ( Fig. 3A ). In addition, the memory B-cell responses to SARS-CoV-2 N were 2-fold lower in Ov/Ob compared to lean ( Fig. 3A ). In contrast, IFN-γ ELISpot responses to SARS-CoV-2 M + N peptide pools were 2-fold higher in the participants with Ov/Ob ( Fig. 3B ). In ICS, IFN-γ expression by CD4 + T cells was 3-fold higher in response to M + N, and IL-2 expression by CD8 + T cells was 16-fold higher in response to spike in Ov/Ob compared to lean ( Supplementary Fig. S3 ). CD8 + T-cell proliferative responses to S1, S2, and N peptide pools were 2 to 4-fold higher in Ov/Ob, while CD4 + T-cell proliferation was comparable between lean and Ov/Ob ( Fig. 3C ). Separate analysis of mild-moderate and severe disease recovered cases elucidates that the effects attributed to Ov/Ob in our combined analysis ( Fig. 3 ) are primarily influenced by individuals who recovered from severe disease ( Supplementary Fig. S6 ). There was no significant impact of overweight/obesity on antibody and T-cell responses to SARS-CoV-2 in healthy seropositive individuals ( Supplementary Fig. S8 ).

Overweight/obesity is associated with lower neutralizing antibody titers and memory B-cell responses, but higher T responses to SARS-CoV-2 in participants who survived COVID-19 with symptoms. ( A ) Comparison of SARS-CoV-2 spike, RBD, N-specific IgG responses, neutralizing antibody titers, and spike, N-specific memory B cells in lean (BMI = 18.5–22.9 kg/m 2 ) and overweight/obese (BMI ≥ 23 kg/m 2 ) individuals, who recovered from symptomatic SARS-CoV-2 infection. IgG responses, neutralizing antibody titers, and memory B-cell responses are measured by multiplexed MSD immunoassays, Focus reduction neutralization (FRNT) assay, and B-cell ELISpot assay, respectively, and data are shown in arbitrary units (AU)/ml, IC50, and antibody spot-forming units (SFU)/million PBMC respectively. ( B ) Comparison of IFN-γ ELISpot responses to SARS-CoV-2 spike (summed responses to S1 and S2 peptide pools), M + N (summed responses to M and N pools), and ORFs (summed responses to ORF3, 6–8) from cryopreserved PBMCs in lean (BMI = 18.5–22.9 kg/m 2 ) and overweight/obese (BMI ≥ 23 kg/m 2 ) individuals in recovery following symptomatic SARS-CoV-2 infection. Data are shown in IFN-γ spot-forming units (SFU)/million PBMC. ( C ) Comparison of the relative frequency of CD4 + (top panels) and CD8 + (bottom panels) T cells proliferating to individual peptide pools S1, S2, M, N, ORF3&6, ORF7&8, assessed by flow cytometry (gating strategy shown in Supplementary Fig. S2A ) from cryopreserved PBMC, in lean (BMI = 18.5–22.9 kg/m 2 ) and overweight/obese (BMI ≥ 23 kg/m 2 ) SARS-CoV-2-recovered patients. A two-tailed Wilcoxon rank-sum test was used to compare between the groups (without correction for multiple testing), and fold changes in brackets referring to the P value comparisons directly below are shown on the top of the dot plots, in case of significant differences. The number of individuals ( n ) evaluated per assay is displayed at the bottom of the corresponding dot plots. Horizontal bars represent the medians, and the median values are shown in brackets immediately above the number of individuals ( n ) in each column

Individuals with diabetes had higher IgG and CD4 + T-cell proliferative responses to SARS-CoV-2 following recovery

We then compared the immune responses between the recovered individuals with diabetes (DM, HbA1c ≥ 6.5%) and without diabetes (non-DM, HbA1c < 6.5%). It is noteworthy that the individuals with DM were considerably older and had more frequently recovered from severe disease compared to the non-DM individuals ( Supplementary Table S1c and d ).

IgG responses to SARS-CoV-2 spike, RBD, and N were 5–6-fold higher in the individuals with DM compared to non-DM ( Fig. 4A ). We also noticed higher neutralizing antibodies in the individuals with DM, who recovered from severe disease ( Supplementary Fig. S7A ), in contrast to the lower levels seen in obesity ( Fig. 3A , Supplementary Fig. S6A ), but no difference was observed in either memory B-cell responses to SARS-CoV-2 ( Fig. 4A ) or SARS-CoV-2-specific IFN-γ ELISpot responses between individuals with DM and non-DM ( Fig. 4B ). Interestingly, CD4 + T-cell proliferation was around 3-fold higher to S1 and ORF 7 + 8 in individuals with DM, while the proportion of proliferating CD8 + T cells was comparable between the DM and non-DM cohorts ( Fig. 4C ). Similar to Ov/Ob, the impacts associated with DM in our combined analysis ( Fig. 4 ) are chiefly driven by individuals who recovered from severe disease ( Supplementary Fig. S7 ). We did not find any differences in CD4 + and CD8 + cytokine responses by ICS in the DM and non-DM participants. Also, no difference was observed in antibody and T-cell responses to SARS-CoV-2 between healthy seropositive individuals with and without DM ( Supplementary Fig. S9 ).

Antiviral antibody and T-cell responses in participants with diabetes mellitus following recovery from symptomatic SARS-CoV-2 infection. ( A ) Comparison of SARS-CoV-2 spike, RBD, N-specific IgG responses, neutralizing antibody titers, and spike, N-specific memory B cells in non-diabetic (non-DM, HbA1c < 6.5%) and diabetic (DM, HbA1c ≥ 6.5%) individuals, who recovered from symptomatic SARS-CoV-2 infection. IgG responses, neutralizing antibody titers, and memory B-cell responses are measured by multiplexed MSD immunoassays, focus reduction neutralization (FRNT) assay, and B-cell ELISpot assay, respectively, and data are shown in arbitrary units (AU)/ml, IC 50 , and antibody spot-forming units (SFU)/million PBMC, respectively. ( B ) Comparison of IFN-γ ELISpot responses to SARS-CoV-2 spike (summed responses to S1 and S2 peptide pools), M + N (summed responses to M and N pools), and ORFs (summed responses to ORF3, 6–8) from cryopreserved PBMCs in recovered patients with or without diabetes, following symptomatic SARS-CoV-2 infection. Data are shown in IFN-γ spot-forming units (SFU)/million PBMC. ( C ) Comparison of the relative frequency of CD4 + (top panels) and CD8 + (bottom panels) T cells proliferating to individual peptide pools S1, S2, M, N, ORF3&6, ORF7&8, assessed by flow cytometry (gating strategy shown in Supplementary Fig. S2A ) from cryopreserved PBMC, in SARS-CoV-2-recovered patients with or without diabetes. A two-tailed Wilcoxon rank-sum test was used to compare between the groups (without correction for multiple testing), and fold changes in brackets referring to the P value comparisons directly below are shown on the top of the dot plots, in case of significant differences. The number of individuals ( n ) evaluated per assay is displayed at the bottom of the corresponding dot plots. Horizontal bars represent the medians, and the median values are shown in brackets immediately above the number of individuals ( n ) in each column

Clinical factors were associated with antibody and T-cell responses to SARS-CoV-2

We performed a univariable correlation analysis to unravel the impact of a range of variables including age, body mass index (BMI), glycemic status (HbA1c), SpO 2 during infection, and the time since onset of symptoms ( Fig. 5A ) on the antiviral immune response. Our results show that age was positively correlated with IgG and neutralizing antibody titers, IFN-γ ELISpot responses, T-cell proliferation, and CD4 + T-cell cytokines. HbA1c positively correlated with IgG and neutralizing antibody titers, CD4 + and CD8 + T-cell proliferation, while BMI positively correlated with IFN-γ ELISpot responses, CD8 + T-cell proliferation, and CD4 cytokine responses. Notably, we observed inverse relationships between SpO 2 with IgG and neutralizing antibody titers, IFN-γ ELISpot responses, CD8 + T-cell proliferative responses, which indicates that more severe disease induces higher antibody and T-cell responses following recovery. The time since symptom onset was negatively correlated with the antibody responses and CD4 cytokine responses to SARS-CoV-2.

Associations of obesity and diabetes mellitus with antibody, memory B cell, and T-cell responses to SARS-CoV-2. ( A ) Correlations of immunological parameters with age, HbA1c, BMI, SpO 2 (lowest recorded oxygen saturation in blood during infection), and days post-symptoms onset in SARS-CoV-2-recovered patients. Spearman’s correlation coefficient (color coded) and only significant values are shown after adjusting for multiple testing using the Benjamini–Hochberg correction at the significance threshold FDR < 0.05. ( B ) Forest plot illustrating associations of overweight/obesity with antibody, memory B cells, IFN-γ secretion, T-cell proliferation, and intracellular cytokine responses to SARS-CoV-2 in recovered patients. The point estimates represent the standardized unit changes of immunological parameters in overweight/obesity, while adjusted for age, sex, diabetes status, disease severity, and days post-symptoms onset. ( C ) Forest plot illustrating associations of diabetes mellitus with immune responses to SARS-CoV-2 in recovered patients. The point estimates represent the standardized unit changes of immunological parameters in diabetes, while adjusted for age, sex, obesity status, disease severity, and days post-symptoms onset. Error bars represent 95% confidence intervals. The non-significant ( P  ≥ 0.05) results are displayed as hollow points. Detailed results from regression models are shown in Supplementary Table S2 . Error bars for (B) and (C) represent 95% confidence intervals, and the non-significant ( P  ≥ 0.05) results are displayed as hollow points. Detailed results from regression models used for (B) and (C) are shown in Supplementary Table S2a–f .

Obesity is independently associated with immune parameters in SARS-CoV-2-recovered individuals

Next, we conducted multivariable regression analyses to investigate the individual associations of obesity and diabetes mellitus (DM) on immune responses to SARS-CoV-2 ( Fig. 5B–C , Supplementary Tables S2a–f ). Our analysis, which accounted for age, sex, diabetes status, disease severity, and time since symptom onset, revealed that obesity is associated with reduced neutralizing antibody responses to SARS-CoV-2 ( Fig. 5B , Supplementary Table S2a ). In addition, obesity was linked to a higher IFN-γ response to SARS-CoV-2 S1, a higher CD8 + T cells proliferation to S1, S2, ORF 7 and 8, and an increased CD8 IL-2 response to spike ( Fig. 5B , Supplementary Tables S2b, S2d, and S2f ). On the other hand, after adjusting for obesity and other factors, we found no association between DM and antibody or T-cell responses to SARS-CoV-2, except a higher proportion of proliferating CD4 + T cells in DM in responses to ORF 7 and 8 ( Fig. 5C , Supplementary Table S2a–f ). We also performed principal component analysis (PCA) to examine the immunological differences in obesity and DM ( Supplementary Fig. S5 ). Our PCA revealed that immunological parameters, including IgG antibody, IFN-y ELISpot, and T-cell proliferation, account for 42.7% of the variance among SARS-CoV-2-recovered individuals, and separation of individuals was driven by obesity status ( Supplementary Fig. S5A ). The key variables in explaining the variability between individuals were anti-N IgG, anti-RBD IgG, anti-S IgG, CD8 + proliferation to N, S1, and S2, and CD4 + proliferation to S1, N, and M ( Supplementary Fig. S5B–C ). Overall, our results indicate diminished neutralizing capacity of antibodies and higher T-cell responses in obesity, whereas immune responses remain unchanged in DM.

Our study provides insights into the adaptive immunity to SARS-CoV-2 in adults with obesity and diabetes in Bangladesh, where both comorbidities are significant public health concerns. We found that disease severity is associated with higher antibody and T-cell responses to SARS-CoV-2 post-recovery, which is consistent with prior studies on severe COVID-19 [ 33 , 34 ]. Also, recovered individuals with symptomatic PCR-confirmed SARS-CoV-2 infection had stronger immune responses compared to asymptomatic seropositive controls, aligning with our previous research in the UK [ 35 ], and other studies in Bangladesh [ 36 , 37 ], and likely attributable to higher antigen exposure in more severe disease. Notably, using the T-cell proliferation assay, a more sensitive method for detecting memory T-cell responses demonstrated in our prior study [ 27 ], we observed detectable CD4 + T-cell responses to SARS-CoV-2 peptide pools in some of the healthy seronegative individuals, which may represent exposure to the virus without seroconversion in some individuals. It is also possible that some participants had yet to be infected with SARS-CoV-2 in autumn 2020.

Our study has revealed a striking disparity in neutralizing antibody levels among individuals with overweight/obesity (Ov/Ob) compared to those who are lean. Despite comparable anti-SARS-CoV-2 IgG levels, individuals with Ov/Ob exhibited significantly lower neutralizing antibody titers. Notably, this discrepancy persisted even after controlling for age, sex, diabetes status, disease severity, and time since the onset of symptoms. A recent UK cohort study reported rapid waning of neutralizing antibodies following COVID-19 vaccination in individuals with severe obesity (BMI > 40 kg/m 2 ) [ 38 ]. Another USA cohort study showed lower SARS-CoV-2-specific IgG titers and antibody neutralization in obesity [ 39 ]. Our findings highlight the need to investigate the mechanisms underlying the lower neutralizing capacity of antibodies in obesity, which could have critical implications for developing effective interventions and treatments for COVID-19 in diverse populations.

T cells play a crucial role in long-term protection against SARS-CoV-2 infection [ 40 ], yet there is limited research on the impact of obesity on T-cell responses following natural infection. A few studies report that adults with obesity have similar T-cell responses to SARS-CoV-2 compared to lean adults after recovery from COVID-19 [ 41 , 42 ]. Nonetheless, these studies did not consider disease severity, a crucial confounder of adaptive immune responses, as shown in our study. We found increased IFN-γ responses to SARS-CoV-2 in Ov/Ob, which were further supported by proliferation assays and ICS showing higher SARS-CoV-2-specific CD8 + T-cell proliferation and cytokine responses in Ov/Ob. Moreover, our univariable correlation analyses demonstrate a positive association between higher BMI and increased IFN-γ ELISpot responses, CD8 + T-cell proliferation, and CD4 cytokine responses. These findings of higher T-cell responses in Ov/Ob persisted in our multivariable regression analyses, which accounts for confounding factors such as age, sex, diabetes status, disease severity, and time components.

In obesity, we hypothesize that the preexisting immune dysregulation characterized by low-grade chronic inflammation [ 22 , 23 ], neutrophil activation [ 43 ], altered cytokine profile [ 26 ], dysregulated T-cell homeostasis [ 44 ], and impaired cellular immunity [ 27–29 ] may lead to delayed viral clearance in SARS-CoV-2 infection. Animal studies have shown increased viral load in obesity [ 45 ]. Adipose tissue may also serve as a reservoir for the virus [ 19 ]. Such “depot effect” of prolonged exposure to the virus may contribute to the development of the compensatory higher T-cell responses as observed in our individuals with Ov/Ob who survived SARS-CoV-2 infection despite having poor neutralizing antibody titers. Further, longitudinal studies are needed to understand the underlying mechanisms of T-cell immunity in obesity during acute infection and recovery.

The interplay between obesity and diabetes in COVID-19 warrants the need to study the adaptive immunity to SARS-CoV-2 in diabetes. We found that individuals with diabetes had elevated anti-SARS-CoV-2 IgG levels and greater proliferative CD4 + T-cell responses. However, after controlling for disease severity, obesity status, and other confounding variables, the associations between diabetes and immune responses did not persist. Our analyses suggest that those initially observed associations were potentially driven by age and disease severity. Here we emphasize the critical importance of considering these confounding variables in future studies examining the relationship between DM and COVID-19 immune responses.

Our study has several limitations. The participants were mostly healthcare workers (HCWs) with a male majority. HCWs self-reported their symptoms, the time of onset, and SpO 2 readings during infection, which may introduce recall bias. We could not perform all assays on all participants due to the limited sample availability, logistical constraints in shipping cells to the UK, and adhering to high cell viability standards for cellular work, coupled with the inherent cost and time-intensive nature of certain assays. We did not measure neutralizing antibody titers in healthy cohorts. We did not evaluate innate immune responses or mucosal immunity, both of which may be impacted by obesity and DM. Intracellular staining revealed low-level detectable CD8 + cytokine responses to SARS-CoV-2 in the healthy seronegative controls, suggesting background noise in the assay. In addition, the length of the peptide pools (18-mers) was better optimized for the detection of CD4 + responses than CD8 + responses. We could not evaluate the QRISK score among the participants, as obtaining fasting blood samples for lipid profiles was not possible.

Our study suggests that obesity is independently associated with lower neutralizing antibody levels and higher T-cell responses to SARS-CoV-2 following recovery. However, the antiviral adaptive immune responses are preserved in DM. Further analysis using single-cell transcriptomics and flow cytometry will be used to characterize qualitative differences in immune cell subsets.

Supplementary Data

Supplementary data is available at Clinical and Experimental Immunology online.

uxae030_suppl_Supplementary_Materials

Acknowledgements.

We are grateful to all participants in our study. We sincerely thank Advanced Biomedical Research Laboratory, Department of Biochemistry and Molecular Biology, BSMMU for providing laboratory equipment for sample processing and storage in Bangladesh. We thank the PITCH Consortium, funded by the UK Department of Health and Social Care, for supporting the immunological study in Oxford.

Abbreviations:

Contributor Information

Mohammad Ali, Peter Medawar Building for Pathogen Research, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK. Centre for Global Health Research, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK. Mahidol-Oxford Tropical Medicine Research Unit, Mahidol University, Bangkok, Thailand. Directorate General of Health Services, Dhaka, Bangladesh. Department of Biochemistry and Molecular Biology, Bangabandhu Sheikh Mujib Medical University, Dhaka, Bangladesh.

Stephanie Longet, Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK.

Isabel Neale, Peter Medawar Building for Pathogen Research, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK. Centre for Global Health Research, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK. Mahidol-Oxford Tropical Medicine Research Unit, Mahidol University, Bangkok, Thailand.

Patpong Rongkard, Peter Medawar Building for Pathogen Research, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK. Centre for Global Health Research, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK. Mahidol-Oxford Tropical Medicine Research Unit, Mahidol University, Bangkok, Thailand.

Forhad Uddin Hassan Chowdhury, Department of Internal Medicine, Dhaka Medical College, Dhaka, Bangladesh.

Jennifer Hill, Peter Medawar Building for Pathogen Research, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK. Centre for Global Health Research, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK. Mahidol-Oxford Tropical Medicine Research Unit, Mahidol University, Bangkok, Thailand.

Anthony Brown, Peter Medawar Building for Pathogen Research, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK.

Stephen Laidlaw, Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK.

Tom Tipton, Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK.

Ashraful Hoque, Department of Transfusion Medicine, Sheikh Hasina National Burn & Plastics Surgery Institute, Dhaka, Bangladesh.

Nazia Hassan, Department of Internal Medicine, Bangabandhu Sheikh Mujib Medical University, Dhaka, Bangladesh.

Carl-Philipp Hackstein, Peter Medawar Building for Pathogen Research, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK. Translational Gastroenterology Unit, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK.

Sandra Adele, Peter Medawar Building for Pathogen Research, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK. Centre for Global Health Research, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK. Mahidol-Oxford Tropical Medicine Research Unit, Mahidol University, Bangkok, Thailand.

Hossain Delowar Akther, Peter Medawar Building for Pathogen Research, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK. Translational Gastroenterology Unit, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK.

Priyanka Abraham, Peter Medawar Building for Pathogen Research, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK. Centre for Global Health Research, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK. Mahidol-Oxford Tropical Medicine Research Unit, Mahidol University, Bangkok, Thailand.

Shrebash Paul, Department of Internal Medicine, Bangabandhu Sheikh Mujib Medical University, Dhaka, Bangladesh.

Md Matiur Rahman, Department of Biochemistry and Molecular Biology, Bangabandhu Sheikh Mujib Medical University, Dhaka, Bangladesh.

Md Masum Alam, Department of Biochemistry and Molecular Biology, Bangabandhu Sheikh Mujib Medical University, Dhaka, Bangladesh.

Shamima Parvin, Department of Biochemistry and Molecular Biology, Mugda Medical College, Dhaka, Bangladesh.

Forhadul Hoque Mollah, Department of Biochemistry and Molecular Biology, Bangabandhu Sheikh Mujib Medical University, Dhaka, Bangladesh.

Md Mozammel Hoque, Department of Biochemistry and Molecular Biology, Bangabandhu Sheikh Mujib Medical University, Dhaka, Bangladesh.

Shona C Moore, Tropical and Infectious Disease Unit, Liverpool University Hospitals NHS Foundation Trust, Member of Liverpool Health Partners, Liverpool, UK.

Subrata K Biswas, Department of Biochemistry and Molecular Biology, Bangabandhu Sheikh Mujib Medical University, Dhaka, Bangladesh. Department of Molecular and Cell Biology, University of Connecticut, Storrs, Connecticut, USA.

Lance Turtle, Tropical and Infectious Disease Unit, Liverpool University Hospitals NHS Foundation Trust, Member of Liverpool Health Partners, Liverpool, UK.

Thushan I de Silva, Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK.

Ane Ogbe, Peter Medawar Building for Pathogen Research, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK.

John Frater, Peter Medawar Building for Pathogen Research, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK.

Eleanor Barnes, Peter Medawar Building for Pathogen Research, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK. Centre for Global Health Research, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK. NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, UK.

Adriana Tomic, National Emerging Infectious Diseases Laboratories, Boston University, Boston, MA, USA. Department of Microbiology, Boston University School of Medicine, Boston, MA, USA. Department of Biomedical Engineering, Boston University, Boston, MA, USA.

Miles W Carroll, Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK.

Paul Klenerman, Peter Medawar Building for Pathogen Research, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK. Centre for Global Health Research, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK. Translational Gastroenterology Unit, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK. NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, UK.

Barbara Kronsteiner, Peter Medawar Building for Pathogen Research, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK. Centre for Global Health Research, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK. Mahidol-Oxford Tropical Medicine Research Unit, Mahidol University, Bangkok, Thailand.

Fazle Rabbi Chowdhury, Mahidol-Oxford Tropical Medicine Research Unit, Mahidol University, Bangkok, Thailand. Department of Internal Medicine, Bangabandhu Sheikh Mujib Medical University, Dhaka, Bangladesh.

Susanna J Dunachie, Peter Medawar Building for Pathogen Research, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK. Centre for Global Health Research, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK. Mahidol-Oxford Tropical Medicine Research Unit, Mahidol University, Bangkok, Thailand. NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, UK.

Ethical Approval

The study was approved by the Oxford Tropical Research Ethics Committee (OxTREC reference: 56-20, date: 24/11/2020), the DMC Ethical Review Committee (DMC reference: ERC-DMC/ECC/2020/315, date: 10/09/2020), and the BSMMU Institutional Review Board (BSMMU reference: BSMMU/2020/8760, date: 04/10/2020). Cryopreserved samples from healthy pre-pandemic controls were used from a previous unrelated study approved by OxTREC (reference: 51-60, date 12/01/2016) and DMC Ethical Review Committee (DMC reference: MEU-DMC/ECC/2015/97(D), date: 04/06/2017).

Conflict of Interests

The authors declared no conflict of interest.

M.A. is funded by Prime Minister Fellowship, Bangladesh and supported by Bangladesh Medical Research Council. S.J.D. is funded by an NIHR Global Research Professorship (NIHR300791), P.K. and E.B. are NIHR Senior Investigators, and S.J.D., P.K., E.B., and M.C. are part of the NIHR Oxford Biomedical Research Centre. P.K. is funded by WT109965MA. F.R.C. is funded by BSMMU Research grant (BSMMU/2021/5567). A.T. is supported by the EU’s Horizon2020 Marie Sklodowska-Curie Fellowship (FluPRINT, grant number 796636). T.dS is funded by a Wellcome Trust Intermediate Clinical Fellowship (110058/Z/15/Z). The Wellcome Center for Human Genetics is supported by the Wellcome Trust (grant 090532/Z/09/Z). L.T. is supported by the Wellcome Trust (grant number 205228/Z/16/Z), the National Institute for Health Research Health Protection Research Unit (NIHR HPRU) in Emerging and Zoonotic Infections (EZI) (NIHR200907), and the Centre of Excellence in Infectious Diseases Research (CEIDR) and the Alder Hey Charity. M.C. and L.T. are supported by the US Food and Drug Administration Medical Countermeasures Initiative contract 75F40120C00085. M.C. is supported by the Oak Foundation. S.Par is supported by Bangladesh Medical Research Council. Some reagents including peptides for the T-cell assays were supplied by the PITCH Consortium, with funding from UK Department of Health and Social Care, UKRI Medical Research Council (MR/W02067X/1 and MR/T025611/1) and the Huo Family Foundation. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR, the Department of Health and Social Care, Public Health England, or the US Food and Drug Administration.

Patient Consent

Clinical information and blood samples from study participants were obtained following written informed consent.

Data Availability

Author contributions.

M.A., P.K., F.R.C., and S.J.D. conceptualized the study. M.A., A.O., P.K., F.R.C., and S.J.D. developed the methodology. M.A., F.U.H.C., A.H., N.H., and S.P. were involved in recruitment of the study participants. M.A. processed the samples. M.A. carried out the cellular assays. S.L., T.T., and S.La. conducted the antibody binding assays under supervision of M.C. P.R., A.B., S.A., H.D.A., P.A., M.M.R., M.M.A., S.Par, F.H.M., M.M.H., S.C.M., S.K.B., L.T., F.R.C., and A.T. provided resources. M.A. curated the data. M.A. and I.N. conducted the formal analysis. M.A. created the data visualizations. M.A. wrote the original draft. S.J.D., B.K., J.H., C.P.H., P.K., I.N. reviewed and edited the manuscript. B.K., A.O., P.K., F.R.C., and S.J.D. supervised the study. M.A., P.K., and S.J.D. acquired the funding. A.T., L.T., T.dS., J.F., E.B., P.K., and S.J.D. oversaw the data analysis for the study. M.A. and S.J.D. had unrestricted access to all the data. All authors approved the final draft of the manuscript and take responsibility for its content, including the accuracy of the data.