Associations of perfluoroalkyl and polyfluoroalkyl substances with markers of glycaemic control, insulin secretion and sensitivity, and diabetes risk: a systematic review and meta-analyses

Sandra India Aldana; Xin Yu; Meizhen Yao; Nathan Cohen; Eftychia Markopoulou; Maanal Chowdhury; Vishal Midya; Stephanie M Eick; Jessica Trowbridge; Anne P Starling; Dinesh Barupal; Douglas I Walker; Leda Chatzi; Veronica Wendy Setiawan; Ryan W Walker; Elena Colicino; Damaskini Valvi

doi:10.1016/j.eclinm.2025.103747

. 2026 Jan 12;92:103747. doi: 10.1016/j.eclinm.2025.103747

Associations of perfluoroalkyl and polyfluoroalkyl substances with markers of glycaemic control, insulin secretion and sensitivity, and diabetes risk: a systematic review and meta-analyses

Sandra India Aldana ^a,^b,^h,^∗, Xin Yu ^a,^b,^h, Meizhen Yao ^a,^b, Nathan Cohen ^a,^b, Eftychia Markopoulou ^a,^c, Maanal Chowdhury ^a, Vishal Midya ^a,^b, Stephanie M Eick ^d, Jessica Trowbridge ^e, Anne P Starling ^f, Dinesh Barupal ^a,^b, Douglas I Walker ^c, Leda Chatzi ^g, Veronica Wendy Setiawan ^g, Ryan W Walker ^a,^b, Elena Colicino ^a,^b, Damaskini Valvi ^a,^b

PMCID: PMC12947643 PMID: 41768983

Summary

Background

Growing literature examines the impact of per- and polyfluoroalkyl substances (PFAS) on diabetes risk. We aimed to conduct a comprehensive systematic review and meta-analysis of epidemiological studies to characterize the associations of exposures to PFAS with markers of glycemic control, insulin resistance, pancreatic β-cell function, and diabetes risk.

Methods

A systematic search of epidemiological articles published through July 21, 2025 was conducted by two researchers in PubMed/MEDLINE and Ovid/EMBASE following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Experimental studies were excluded from our review. Reported findings were extracted from published articles. Risk of bias was evaluated using the Navigation Guide. Random-effects meta-analyses stratified by study design estimated PFAS associations with gestational diabetes mellitus (GDM), type 2 diabetes (T2D), and continuous measures of Homeostatic Model Assessment for Insulin Resistance (HOMA-IR), HOMA-β, fasting insulin, fasting glucose, and hemoglobin A1c (HbA1c). This study was registered in PROSPERO (CRD42022369711).

Findings

Out of 738 records retrieved, we identified 129 eligible studies. Most studies focused on GDM (n = 25) and/or T2D (n = 36), while three focused on type 1 diabetes (T1D). Participant numbers ranged from n = 40 to n = 1,331,541 in the systematic review and from n = 399 to n = 111,544 in the meta-analyses. We found consistent associations between 8 different PFAS and higher odds of GDM across prospective and other study designs, including PFOS [n = 8, OR (95%CI) per doubling PFOS increase: 1.13 (1.01, 1.26), I² = 0.0%] among other PFAS. We also found positive associations between several legacy PFAS such as PFOS with HOMA-IR [(n = 8), β (95%CI): 0.06 (0.01, 0.12), I² = 0.0%] and fasting insulin [n = 5, β (95% CI) in μU/mL: 0.23 (0.06, 0.40), I² = 0.0%] in prospective studies, and HOMA-β in cross-sectional studies [(n = 6), β (95% CI): 5.93 (1.72, 10.2), I² = 67.0%], among other. Less consistent or null associations were with T2D, fasting glucose, and HbA1c. The evidence was of low-moderate quality and limited strength. Most studies were categorized as low risk of bias for other criteria, except for study design (cross-sectional).

Interpretation

Evidence from observational studies supports PFAS associations with higher odds of GDM and increased markers of insulin resistance and secretion. PFAS associations with established T2D or T1D remain to be elucidated, as evidence is still limited and effect sizes for some continuous diabetes markers were small and should be interpreted with caution. Larger life-course prospective studies with greater representation of well-characterized cases and evaluating emerging PFAS and mixtures are needed to fully capture the potential PFAS impacts on diabetes.

Funding

National Institutes of Health (NIH), National Institute of Environmental Health Sciences (NIEHS).

Keywords: PFAS, Diabetes, Chemical exposures, Systematic review, Meta-analysis

Research in context.

Evidence before this study

Literature examining the impact of exposure to per- and polyfluoroalkyl substances (PFAS) on diabetes risk and diabetes clinical markers has been emerging with inconsistent findings. At the very initial stages of this research, we conducted a preliminary search of epidemiological studies published in PubMed/MEDLINE and Embase/OVID from their inception through January 1, 2025 with no language restrictions and using terms such as “review” AND “PFAS” AND (“diabetes” OR “insulin resistance” OR “beta-cell function” OR “gestational diabetes”) in the title/abstract. Previous reviews on type 2 diabetes (T2D) or gestational diabetes (GDM) had mostly focused on legacy PFAS and diabetes diagnosis, and had not included emerging PFAS or PFAS mixtures, and comprehensively reviewed clinical markers of glycemic control, insulin resistance, and pancreatic β-cell function altogether. Therefore, we conducted a comprehensive systematic review and meta-analysis of epidemiological studies to characterize the associations between exposure to PFAS and markers of glycemic control, insulin resistance, pancreatic β-cell function, and diabetes risk.

Added value of this study

To our knowledge, this is the most comprehensive systematic review and meta-analysis examining the association of PFAS exposures with diabetes risk and markers of glycemic control, insulin resistance, and β-cell function. Our review particularly provides evidence of PFAS associations with higher odds of GDM and increased levels of markers of insulin resistance and secretion. PFAS associations with T2D or T1D remain to be elucidated.

Implications of all the available evidence

The state of evidence suggests PFAS associations with higher odds of GDM and increased insulin resistance and secretion, but a direct link with T2D and T1D remains unclear. Larger life-course prospective studies with greater representation of well-characterized cases and more comprehensive exposure assessments, including also emerging PFAS, are needed to increase the strength and quality of the state of evidence. Given the potential PFAS impacts on GDM and associated long-term risks for both mother and child, clinicians may wish to consider incorporating PFAS exposure history evaluation and environmental exposure counseling into the pre-conception and prenatal care period, advising on strategies for PFAS exposure reduction in individuals considering pregnancy.

Introduction

Diabetes mellitus (DM) is a chronic disease where the pancreas does not produce sufficient insulin to regulate glucose levels or when the body cannot use insulin efficiently. Insulin resistance (or insensitivity) leads to hyperglycemia, which, if untreated, can increase the risk for cardiovascular conditions,¹^,² kidney disease,³^,⁴ neuropathy,⁵ vision loss,⁶ lower limb amputation,⁷ or death.⁸ DM prevalence has increased worldwide since the 1990s, reaching 14% of adults in 2022.⁹ It is estimated to currently affect more than 828 million people globally,⁹^,¹⁰ with an increasing incidence in lower-middle-income (LMIC) countries, where treatment options can be limited.¹¹^,¹²

Over 90% of all global DM cases are type 2 diabetes (T2D),¹⁰^,¹³ which is characterized primarily by insulin resistance and is more often diagnosed in adulthood. Type 1 diabetes (T1D), an autoimmune disease initiating primarily in childhood and characterized by pancreatic islet β-cell damage leading to insulin deficiency, represents ∼5% of diabetes cases.14, 15, 16, 17 Gestational diabetes mellitus (GDM) is another DM condition where glucose intolerance results in hyperglycemia with primary onset occurring during pregnancy.¹⁸ GDM prevalence has significantly increased in the past two decades,¹⁹^,²⁰ currently affecting nearly 10% of all pregnancies.¹⁸^,²¹^,²²

Beyond genetic and lifestyle factors,²³^,²⁴ such as poor diet or lack of exercise,²⁵ that predispose individuals to DM risk, environmental chemical exposures, such as endocrine-disupting chemicals (EDCs), can also contribute to disease etiology but are understudied.²⁶ Per- and polyfluoroalkyl substances (PFAS) are persistent chemicals that interfere with the endocrine system and may increase DM risk.27, 28, 29, 30 PFAS are a large class of >10,000 substances that are ubiquitous in the environment and originate from diverse sources such as paints and dyes,³¹ agricultural products,³² firefighting foam,³³ waterproof gear and sportswear,³³^,³⁴ food packaging,³⁵ non-stick pans,³⁵ or cosmetic and personal care products.³⁶ Exposure routes in the general population typically involve ingestion of contaminated food and water, followed by inhalation and dermal absorption.³⁴^,³⁷^,³⁸ PFAS are also referred to as forever chemicals owing to their ubiquity and long half-lives in the environment and humans.²⁷ Most concerning, PFAS contaminants are detected in nearly all the population in the US,³⁹ and elsewhere.²⁷^,⁴⁰ PFAS have been shown to induce long-term metabolic changes, including insulin resistance, dyslipidemia, and liver steatosis in animals, even at low doses.41, 42, 43 Observational studies in humans have also linked PFAS to metabolic syndrome and/or its components.44, 45, 46 PFAS further exhibit key characteristics of metabolism disruptors,⁴⁷ and may promote insulin resistance, alter the function of the pancreas,48, 49, 50 induce inflammation and cellular stress in metabolic tissues,⁵¹ or alter energy homeostasis.²⁷

Previous reviews on T2D²⁸^,⁵²^,⁵³ or GDM54, 55, 56 mostly focused on legacy PFAS and diagnosed diabetes, and did not include emerging PFAS or PFAS mixtures²⁸^,⁵³ and clinical markers of glycemic control, insulin resistance, and pancreatic β-cell function. Furthermore, to the best of our knowledge, there are only a couple of previous systematic reviews and meta-analyses on the PFAS and T2D⁵² or GDM54, 55, 56 associations that included a limited number of studies published before April 2021 (for T2D) or November 2023 (for GDM), leaving out many recently published studies that are more representative of current exposures. Previous meta-analyses further lacked of harmonization standardizing estimates that can facilitate interpretation.⁵⁷ Therefore, we conducted a comprehensive systematic review and meta-analysis of observational studies to characterize the associations of PFAS exposures with diabetes diagnosis and related measures using state-of-the-art conversion methods for meta-analyses.⁵⁷ This systematic review and meta-analyses offer the most comprehensive update of the state of evidence for a wide list of PFAS examined to July 21, 2025 (including PFAS multi-pollutant studies) in association with T1D, T2D, or GDM diagnosis, along with clinical markers of glycemic control, insulin resistance, and pancreatic β-cell function.

Methods

Search strategy and selection criteria

Our systematic review was registered in PROSPERO (ID: CRD42022369711). We conducted a systematic search of studies in humans published in PubMed/MEDLINE and Ovid/EMBASE through July 21, 2025 in English, Spanish, or Chinese language (search algorithms are provided in Methods S1). Eligible studies that did not appear in the database searches were identified from relevant reference lists and added subsequently. Original research articles were eligible for inclusion if they met our population exposure comparator outcome (PECO) criteria (Methods S2). This included population-based observational research studies (cross-sectional, case–control, nested case–control, retrospective, prospective cohort studies) that focused on a PFAS-diabetes association in humans, with PFAS exposure measured in either a biological matrix or estimated via other means, and with any diagnosis (from electronic health record, clinical-based/lab test, or self-report) of diabetes (T1D, T2D, GDM), or any quantitative measure of glycemic control, insulin resistance, and pancreatic β-cell function from sample collection in any biological matrix available (i.e. serum, plasma, or other). We excluded reviews, qualitative research studies, protocols, questionnaire validation studies, commentaries, conference proceedings, editorials, pre-prints, or opinion letters. The screening of the title, abstract, and full text of all eligible studies and their selection was conducted by at least two independent researchers following the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines.⁵⁸ Discrepancies were resolved by a third reviewer whenever no consensus was reached between the first two reviewers.

Data analysis

The following information was extracted from each article deemed eligible for inclusion. All information was independently extracted by at least two reviewers and was compared with a third if discrepant: first author, publication year, DOI, cohort, sample size, study population, study design, outcome type, definition, and window of assessment, exposure (PFAS types evaluated) and their window of exposure, statistical methods, confounders/covariates, main findings on the direction of association with diagnosis of T1D, T2D, GDM, and continuous markers for homeostatic model assessments (HOMA-IR, HOMA-β), fasting insulin, fasting glucose, HbA1c, as well as prediabetes, oral glucose tolerance test (OGTT) measures, or other DM-related indices. We also abstracted the main conclusions and potential limitations for each study. For the diabetes outcomes most broadly studied previously (i.e. T2D, GDM, and routine screening continuous markers, including HOMA-IR, HOMA-β, fasting insulin, fasting glucose, and HbA1c), we additionally extracted from studies the reported association estimates from Cox regression (HR), logistic regression/conditional logistic regression (OR), Poisson regression (RRs), and/or linear regression (β coefficients or the reported percent change in the outcome) with 95% confidence intervals (CI) or standard errors (SE) from fully-adjusted models, as well as exposure and outcome means/median, units of measure, log-transformation, and scaling for later standardization of estimates. Whenever possible, we consistently extracted results of interest (with PFAS exposures and the outcomes of interest mentioned) from tables showing the most fully adjusted model and with the most PFAS types available. When a case–control study provided stratified estimates for diabetes markers (i.e. HOMA-IR, HOMA-β, fasting insulin, fasting glucose, and HbA1c) both in a control population and in a population with a disease other than diabetes, we selected estimates from the healthy control population to diminish bias, unless cases were diabetes cases (or a related condition i.e. obesity), in which scenario we extracted both case and control estimates. We also contacted at least twice the authors from n = 46 studies to request missing information that was not available in the published article (n = 18 of those responded and provided complete information).

Reported associations between PFAS and diabetes outcomes were defined as statistically significant (p < 0.05) positive, statistically significant negative, and not statistically significant. For each outcome of interest, we summarized the number of studies that reported at least one significant positive and/or negative association(s) with individual PFAS, as well as no significant associations. As an exploratory analysis, we conducted a qualitative synthesis for studies which examined the mixture or multi-pollutant PFAS associations with outcomes of interest. We categorized these studies into four groups: 1) studies that applied PFAS exposure-mixture statistical models, i.e. Bayesian Kernel Machine Regression (BKMR), quantile-based G-computation (QGC), and Weighted Quantile Sum (WQS) regression; 2) studies that reported associations with the sum of PFAS concentrations; 3) studies that applied other variable selection methods (i.e. Least Absolute Shrinkage and Selection Operator (LASSO), elastic net, decision trees) to select key PFAS contributors from a set of PFAS and/or other chemical exposures; and 4) studies using other methods to summarize the combined impact from PFAS exposures, i.e. structural equation models (SEM), unsupervised clustering, and other supervised machine learning models. We also qualitatively summarized the number of studies and directionality of associations in PFAS multi-pollutant studies, as well as summarized the dominant PFAS from the mixture or selected individual PFAS from the variable selection group, when applicable.

We implemented random-effects meta-analyses using the meta package in R (version 4.4.1),⁵⁹ stratified by study design (cross-sectional/case–control, nested case–control, and prospective)⁵² between PFAS measured quantitatively in a biological matrix and each of the following seven most commonly studied diabetes-related outcomes separately: GDM, T2D, and continuous HOMA-IR, HOMA-β, fasting insulin, fasting glucose, and HbA1c. For studies that had used both quantitative and semi-quantitative PFAS measures, we only extracted associations for the quantitative PFAS measures to reduce measurement error (n = 2). We used the Frigerio et al.⁵⁷ conversion method that standardizes the effect estimates across studies (Methods S3). We primarily focused on random-effect estimates whenever we had at least n = 2 studies (from different cohorts) within each study design stratum. We used log₂-transformed PFAS exposures to harmonize association estimates across studies and enhance comparability. Our estimates are represented for each doubling increase in the PFAS exposure after transformation (see also Methods S3 for conversions). Whenever two or more studies focused on the same population (i.e. C8 Health Project in the US, and Isomers of C8 Health Project in China), we chose for inclusion the study implementing a prospective design and having the longest follow-up, or a cross-sectional design with the largest sample size. Similarly, if multiple survey cycles across cross-sectional studies significantly overlapped (i.e. NHANES), we included only the study with the largest sample size and/or the largest number of cycles evaluated. Whenever possible, we aimed to include the most parsimonious number of studies that included the widest age group representation (Methods S4).

We examined risk of bias for each included study using the Navigation Guide.⁶⁰ Two independent researchers used 9 criteria with a few amendments to incorporate as Other criteria classifications for study design, exposure detectability, and missing covariates (Methods S5). In addition, we assessed publication bias for each PFAS and diabetes outcome association using Egger's test when at least 10 studies examined the same PFAS and outcome,⁶¹ as well as generated funnel plots whenever there were fewer than 10 studies examined in a meta-analysis. We assessed differences across study designs and heterogeneity within each study design type using subgroup meta-analysis. To assess the quality and strength of evidence, we rated the body of evidence as a whole according to the Navigation Guide criteria,⁶⁰ which is a method that provides a systematic research synthesis for environmental health studies. For the assessment on the quality and strength of evidence, we implemented a 3-level grading for quality (“low”, “medium”, “high”) and a 4-level grading for the strength of evidence (“sufficient”, “limited”, “inadequate”, “evidence of lack of toxicity”).

We conducted several additional analyses to ensure robustness of findings. First, 1) to diminish potential misclassification of diabetes outcomes, we implemented sensitivity meta-analyses including studies that reported a diagnosis of diabetes based on clinical tests (such as HbA1c/glucose blood tests and/or OGTT) or electronic medical records that provided a clinical test validation. We also conducted 2) sensitivity meta-analyses with only the studies with lower risk of bias (determined using the best judgment of at least two reviewers based on a holistic review of the Navigation Guide risk of bias criteria: excluding those studies with either 3 or more criteria rated as probably high risk of bias, or 2 or more criteria rated as a high risk of bias, or at least 1 of each), and 3) implementing meta-analyses with weighted average z-scores for each association⁶² (and generating corresponding p-values⁶³^,⁶⁴) (Methods S6) with studies that examined associations and reported a β estimate and SE for comparison to main meta-analyses. Our z-score meta-analyses also included those studies for which authors did not respond to provide complete information about exposure and outcome transformations and/or descriptive statistics required to conduct meta-analyses with standardized estimates (Appendix A, Appendix A, Appendix A). Results from weighted z-scores and random-effects meta-analyses were compared with confusion matrices⁶⁵ to ensure reliability and accuracy. We also conducted 4) meta-analyses examining markers for glucose homeostasis and insulin resistance in pregnant populations, separately. Last, 5) whenever we observed indications of a consistent PFAS-outcome association across the different study designs, we also conducted a combined meta-analysis including all study designs (non-stratified meta-analysis) to optimize statistical power.

Role of the funding source

The funder of the study (NIH) had no role in the study design, data collection, data analysis, data interpretation, or writing of the report. The authors had full access to the data in the study and authors X.Y., D.V., and S.I.A. had final responsibility for the decision to submit for publication.

Results

Descriptive statistics

A total of 738 records were retrieved from EMBASE/Ovid (n = 358) and PubMed/MEDLINE (n = 380) search engines focusing on PFAS and DM diagnosis or diabetes-related measures (Fig. 1). After screening, a total of 129 unique records for original epidemiological articles were eligible for inclusion in our systematic review (Table S1). All included studies were written in English language. The studies were conducted across 17 different countries (Fig. 2A). The majority of studies were conducted in the United States (n = 54), followed by China (n = 30), Denmark (n = 8), and Sweden (n = 8). Participant numbers ranged from n = 40 to n = 1,331,541 in the systematic review. A total of 45 different PFAS (including isomers) were measured quantitatively in across studies (Table S2); the most common were PFOA, PFOS, PFHxS, PFNA, PFDA, and PFUnDA (Fig. 2B). PFOS [mean/median min–max: 0.10–42.1 ng/mL] and PFOA [mean/median min–max: 0.10–94.6 ng/mL] presented the largest range of concentrations reported across studies compared to other PFAS. The majority of studies were cross-sectional in design (n = 70), followed by prospective cohorts (n = 30), nested case–control (n = 12), case–control (n = 5), as well as studies including both cross-sectional and prospective designs (n = 9), and retrospective cohort design (n = 3) (Fig. 2C). The majority of studies focused on PFAS exposures in adulthood (n = 75), followed by exposures in the gestational/prenatal period (n = 42), adolescence (n = 22), and childhood (n = 16) (Fig. 2D). Most studies evaluated diabetes outcomes in adulthood (Fig. 2E), with the most common being T2D diagnosis (n = 36), followed by GDM (n = 25), fasting glucose (n = 61) and HOMA-IR (n = 45) (Fig. 2F). Approximately 64%, 19%, and 17% of T2D studies had T2D assessment based on clinical/lab tests, electronic health records (EHR), or self-reports, respectively. GDM studies generally relied on clinical diagnoses, except for one study, which relied on self-reports.⁶⁷ Studies were predominantly conducted in the general population and did not report excluding diabetes: 46% for fasting glucose, 54% fasting insulin, 42% for HbA1c, 63% for HOMA-IR, and 67% for HOMA-β. About 21% of the studies on fasting glucose reported excluding diabetes, as well as 26% of fasting insulin studies, 27% of HbA1c studies, 26% of HOMA-IR studies, and 25% of HOMA-β studies. Up to one-third of the studies examined these diabetes-related markers in pregnant populations, ranging from 8% to 29.5% of the pregnant women, depending on the outcome. Among studies with statistically significant results, most studies focusing on GDM reported increased risk or a positive association with PFAS. Moreover, while many of the fasting glucose results were inconsistent, most studies with OGTT post–challenge glucose results reported predominantly positive associations (n = 14 with solely positive associations and n = 3 reporting at least a positive association out of n = 22). This is consistent with overall positive associations with OGTT postchallenge insulin and the insulinogenic index, as well as overall negative associations with the Matsuda index. Only 3 studies focused on T1D, yielding inconsistent findings: one cross-sectional study found that legacy PFAS were associated with a lower risk of T1D in children and adults,⁶⁸ one prospective cohort study reported null findings in adults,⁶⁹ and one case–control study in children and adolescents found an increased risk of T1D associated with PFOS exposure.⁷⁰

Fig. 1 — PRISMA 2020 flow diagram for PFAS exposures and diabetes in humans summarizing eligible studies through July 21, 2025.

Fig. 2 — **Descriptive Information for Studies Examining the Association between PFAS and Diabetes Mellitus and/or Diabetes-related Markers**. Descriptive statistics for per- and polyfluoroalkyl substances (PFAS) studies examining diabetes mellitus (DM) and diabetes-related markers. A) Number of studies by country and region of study populations; B) Distribution of reported mean or median levels (ng/mL) of 6 commonly studies PFAS in included studies across three regions (Asia & Oceania, Europe, and America); C) Number of studies by study design; D) Number of studies by the combinations of PFAS exposure windows [preconception, gestation (prenatal), childhood (0–10 years of age), adolescence (10–18 years of age), and adulthood (18 years of age and above)]⁶⁶; E) Number of studies by the combinations of outcome assessment windows [gestation (prenatal), childhood (0–10 years of age), adolescence (10–18 years of age), and adulthood (18 years of age and above)]; F) A qualitative summary of reported association directions by outcome [Red represents studies that reported at least one statistically significant positive associations between either PFAS and the outcome; Purple represents studies that reported both statistically significant positive and negative associations between PFASs and the outcome; Blue represents studies that reported at least on statistically significant negative associations between either PFAS and the outcome; Gray represents studies that did not find any statistically significant associations between PFAS and the outcome]. Note Homeostatic Model Assessment for Insulin Resistance (HOMA-IR) represents here the combined HOMA-IR (n = 43) and HOMA2-IR (n = 2) outcomes but in the meta-analyses only HOMA-IR is shown. HOMA-β represents here HOMA-β (n = 11), HOMA%-β (n = 1), HOMA2-β (n = 3) combined but in the meta-analyses only HOMA-β and HOMA%-β combined are shown.

Meta-analyses

A total of 79 studies with 18 different PFAS were evaluated in meta-analyses (Fig. 3; Table S3). In the random-effects meta-analyses were included a total of n = 21 studies for T2D, 20 for GDM, 25 for HOMA-IR, 9 for HOMA- β, 22 for fasting insuling, 39 for fasting glucose, and 14 for HbA1c (Appendix A, Appendix A, Appendix A). Participants ranged from n = 399 to n = 111,544 across meta-analyses.

We observed that several PFAS had a predominantly positive association (Appendix A, Appendix A, Appendix A, Appendix A, Appendix A, Appendix A, Appendix A, Appendix A, Appendix A) with higher odds of GDM (Fig. 3A). Specifically, we found a pattern of higher odds of GDM in prospective studies for each doubling of levels in PFOS [n = 8, OR (95% CI): 1.13 (1.01, 1.26), I² = 0.0%], PFBS [n = 2, OR (95% CI): 1.14 (1.04, 1.24), I² = 0.0%], and 6:2 Cl-PFESA [n = 2, OR (95% CI): 1.30 (1.04, 1.61), I² = 0.0%]. We also observed a similar predominant pattern of positive associations for other PFAS (although not significant) and in nested case–control studies where for every doubling in PFAS levels, higher odds of GDM were observed for PFOA [n = 6, OR (95% CI): 1.23 (1.10, 1.36), I² = 34.7%], PFNA [n = 5, OR (95% CI): 1.21 (1.05, 1.38), I² = 60.7%], PFDA [n = 5, OR (95% CI): 1.12 (1.06, 1.19), I² = 47.9%], PFBS [n = 3, OR (95% CI): 1.12 (1.00, 1.25), I² = 15.5%], PFHpS [n = 3, OR (95% CI): 1.16 (1.04, 1.30), I² = 0.0%], and 6:2 Cl-PFESA [n = 3, OR (95% CI): 1.11 (1.01, 1.21), I² = 30.1%]. Several PFAS were also associated with higher odds of GDM in cross-sectional and case–control studies per doubling in concentrations of PFOA [n = 5, OR (95% CI): 1.10 (1.00, 1.21), I² = 0.0%], and PFDoDA [n = 2, OR (95% CI): 1.24 (1.04, 1.47), I² = 0.0%]. Conversely, we only observed a negative GDM association for PFHpA [n = 2, OR (95% CI): 0.91 (0.88, 0.95), I² = 0.0%] in cross-sectional/case–control studies. Sensitivity analyses grouping all study designs together were consistent with main analyses stratified by study design: for instance, 8 different PFAS were positively associated with GDM across meta-analyses (n = 5 to n = 20 studies) (Table S4).

In meta-analyses for T2D, the associations were non-significant, although an overall positive directionality was observed in prospective studies for PFOA, PFOS, and PFNA (Fig. 3B; Table S3). We found non-significant positive associations with T2D for PFHpS [n = 4, OR (95% CI): 1.48 (0.99, 2.21), I² = 91.9%] in cross-sectional/case–control studies, and in prospective studies, for some legacy PFAS such as PFOA [n = 4, HR (95% CI): 1.04 (0.98, 1.11), I² = 61.0%] or PFNA [n = 2, HR (95% CI): 1.04 (0.94, 1.15), I² = 19.0%]. Sensitivity meta-analyses for T2D (including only cases with clinical validation) indicated overall similar and not statistically significant associations compared to meta-analyses with all eligible studies (Table S5).

In meta-analyses using HOMA, we observed several statistically significant associations with higher levels of insulin resistance and β-cell function for every doubling in PFAS levels. In prospective studies, we found positive HOMA-IR associations for PFOS [n = 8, β (95% CI): 0.06 (0.01, 0.12), I² = 0.0%] (Figure S13) and PFNA [n = 7, β (95% CI): 0.11 (0.01, 0.21), I² = 27.6%] (Figure S14), as well as non-significant positive associations for other legacy PFAS (Fig. 3C; Table S3). However, we observed no other associations or clear pattern of directionality between PFAS and HOMA-IR in cross-sectional studies. For HOMA-β, we observed several legacy PFAS associated with higher levels of HOMA-β (Fig. 3D, Appendix A, Appendix A, Appendix A). These included cross-sectional studies examining PFOA [n = 6, β (95% CI): 5.80 (0.04, 11.6), I² = 54.3%], PFOS [n = 6, β (95% CI): 5.93 (1.72, 10.2), I² = 67.0%], and PFNA [n = 4, β (95% CI): 3.35 (0.12, 6.58), I² = 0.0%], as well as prospective studies examining PFNA [n = 2, β (95% CI): 8.25 (0.10, 16.4), I² = 8.3%]. Sensitivity analyses grouping all study designs together were consistent with main analyses where 2 legacy PFAS were positively associated with HOMA-β across meta-analyses (n = 6 to n = 8) (Table S4). While limited in sample size (n = 1 or n = 2 studies only), sensitivity meta-analyses for HOMA outcomes, including only the pregnant women studies were mostly null (Table S6).

In meta-analyses of fasting insulin measures we also observed legacy PFAS positively associated in prospective studies (Appendix A, Appendix A), while in cross-sectional studies, positive associations did not reach the statistical significance level. Specifically, prospective studies examining fasting insulin showed associations with PFOS [n = 5, β (95% CI) in μU/mL: 0.23 (0.06, 0.40), I² = 0.0%], and PFNA [n = 5, β (95% CI) in μU/mL: 0.34 (0.13, 0.56), I² = 11.9%] (Fig. 3E; Table S3). Similar trends were observed for other legacy PFAS in prospective studies, although these were not statistically significant. Mostly null associations, although limited in sample size (i.e. n = 2 studies), were observed for PFAS in pregnant populations in relation to fasting insulin (Table S6). In addition, we found a few PFAS associations with fasting glucose (Fig. 3F; Table S3; Appendix A, Appendix A) including PFHpA [prospective studies, n = 2, β (95% CI) in mg/dL: 0.30 (0.01, 0.58), I² = 0.0%] and PFHpS [cross-sectional studies, n = 2, β (95% CI): 1.69 (0.47, 2.91), I² = 80.1%]. Sensitivity analyses in pregnant women were mixed in directionality, but indicated mostly negative PFAS associations with fasting glucose in cross-sectional studies for PFOA [n = 5, β (95% CI): −0.52 (−1.03, −0.01), I² = 19.4%], PFNA [n = 5, β (95% CI): −0.68 (−1.27, −0.08), I² = 37.6%], PFDA [n = 5, β (95% CI): −0.68 (−1.26, −0.10), I² = 42.9%], and prospective studies for PFDoDA [n = 3, β (95% CI): −0.60 (−1.04, −0.17), I² = 0.0%] and PFHpA [n = 2, β (95% CI): 0.30 (0.01, 0.58), I² = 0.0%] (Table S6). Last, no significant associations were found for HbA1c (Fig. 3G; Table S3).

Risk of bias

The majority of studies used targeted quantitative assays for PFAS assessment, were conducted in either nationally representative or well- characterized samples, controlled in analyses (by stratification or statistical model adjustment) at least for age, sex, race/ethnicity, and socio-economic status, and had clinical measures of diabetes, and thus were categorized as low risk or probably low risk of bias with respect to exposure assessment (92%, n = 119), study population/baseline differences (82%, n = 106), confounding (71%, n = 92), and/or outcome assessment (95%, n = 122) (Fig. 4; Table S7). However, a total of n = 76 studies (59%) were cross-sectional and were classified in the probably high risk category with respect to study design. Studies with lower risk of bias (n = 98) were also examined in sensitivity meta-analyses whenever results were extractable (n = 67, Table S8): overall, results were consistent in directionality with the main meta-analyses (Table S3; Fig. 3) and all significant random-effects estimates were retained. Moreover, a new positive statistically significant association was found for PFOS exposure with HbA1c [cross-sectional, n = 6, β (95% CI) in %: 0.05 (0.01, 0.09), I² = 86.2%] and fasting glucose [cross-sectional, n = 20, β (95% CI) in mg/dL: 0.46 (0.04, 0.87), I² = 75.9%] when restricting to lower risk of bias studies. Further PFAS associations were found with fasting glucose in the studies with lower risk of bias for PFHpA [prospective studies, n = 2, β (95% CI) in mg/dL: 0.30 (0.01, 0.58), I² = 0.0%] and PFHpS [cross-sectional studies, n = 2, β (95% CI) in mg/dL: 1.69 (0.47, 2.91), I² = 80.1%]. In addition, using Egger's tests, we observed no significant evidence of publication bias in either main meta-analyses (Tables S3, Figure S22, p > 0.05) or sensitivity meta-analyses including studies with lower risk of bias (Table S8; p > 0.05).

Fig. 4 — **Risk of Bias Summary Across Studies (n = 129)**. We used the Navigation Guide with a few modifications (Table S7; Methods S5) to assess risk of bias across individual studies.

Weighted Z-score sensitivity meta-analysis

Meta-analyses with weighted average z-scores with additional studies (n = 89) were overall consistent in directionality with main meta-analyses (n = 79) (Tables S9 and S10; Fig. 1, Appendix A, Appendix A, Appendix A). For instance, in cross-sectional/case–control studies, PFAS had a negative directionality with T2D risk while in prospective studies PFAS had a positive directionality of associations, with a few exceptions that were associated with an increased T2D risk such as PFNA (n = 13; weighted average z-score = 1.11; p = 0.003) and PFHpS (n = 4; weighted average z-score = 4.05; p < 0.001) in cross-sectional/case–control studies. We also observed a predominant positive trend of associations particularly between legacy PFAS such as PFOA, PFOS, PFDA, or PFNA in relation to GDM, HOMA-IR, HOMA-β, fasting insulin, fasting glucose, and/or HbA1c (p < 0.05).

Exploratory Summary for multi-pollutant studies

A total of n = 60 studies on diabetes and/or diabetes-related measures examined multiple PFAS together or jointly with other chemicals (Fig. 5; Appendix A, Appendix A, Appendix A, Appendix A). Throughout the past decade, there has been a growing interest in evaluating multi-pollutant effects on diabetes outcomes, particularly using exposure-mixture analytical methods to study combinations of PFAS with or without other chemicals (Fig. 5A). There were n = 35 studies that included PFAS as an exposure-mixture (i.e. using WQS, BKMR, quantile g-computation methods) with a few of those considering also other environmental chemicals beyond PFAS (n = 6) (Fig. 5B and C; Table S11). In exposure-mixture studies, the dominant PFAS driving associations were legacy PFAS (Fig. 5D). We also observed that the majority of mixture studies examining GDM had a positive association, consistent with our main findings in non-mixture analyses (Fig. 5E). Interestingly, the majority of mixture studies examining T2D (n = 4) and OGTT glucose measures (n = 9) additionally reported positive and/or marginally positive associations. In addition to exposure-mixture studies, there were n = 21 studies that included sums of PFAS concentrations but many of these reported inconsistent or null associations (Table S12; Fig. 5E). There were also n = 7 studies that examined combinations of PFAS that were selected via variable selection methods such as LASSO (n = 2) or elastic net (n = 2), and n = 8 studies used other approaches such as structural equation modeling (SEM, n = 2), supervised (n = 2) or unsupervised (n = 3) machine learning approaches (Fig. 5B and C; Appendix A, Appendix A), all of which were inconsistent in findings across diabetes diagnosis outcomes or diabetes markers.

Quality and strength of evidence

Quality assessment of evidence indicated that we had “moderate” quality of evidence examining long-chain legacy PFAS and GDM, HOMA-IR, HOMA-β, and fasting insulin, whereas there was overall “low” quality of evidence between short-chain and/or emerging PFAS and GDM (Table S15). Similarly, we observed “low” quality of evidence for studies examining PFAS and T2D, fasting glucose, and HbA1c. In addition, the strength of evidence across most outcomes was rated either “inadequate” (short-chain/emerging PFAS with GDM, both short/emerging and long-chain PFAS with T2D, fasting glucose, and HbA1c) or “limited” (long-chain/legacy PFAS with GDM, HOMA-IR, HOMA-β, and fasting insulin) to determine with certainty PFAS associations with these diabetes outcomes in humans.

Discussion

We conducted the most comprehensive systematic review and meta-analysis of epidemiological (observational) studies focused on PFAS exposures and DM-related outcomes to date. The most consistent finding supported by the studies we reviewed was an association between higher PFAS exposure and higher odds of GDM. Evidence on the potential association between PFAS and T2D remains to be elucidated, while the association with T1D remains understudied. Nevertheless, we also observed positive PFAS associations with routine screening diabetes markers in meta-analyses, including higher HOMA-β, and to a lesser extent, associations with higher HOMA-IR, fasting insulin, and fasting glucose, further indicating that PFAS exposures may alter insulin secretion and sensitivity. Furtheremore, PFAS were associated with higher HbA1c levels were only observed among studies with a lower risk of bias. Overall, the state of evidence suggests that exposure to at least some PFAS is associated with higher risk for GDM, as well as alterations in insulin secretion and sensitivity in the general population, with less conclusive and limited evidence on the potential PFAS associations with non-gestational diabetes. Most of the available evidence has so far been limited to legacy PFAS, such as PFOA, PFOS, and PFNA exposures, and focused on adult populations.

One previous systematic review on PFAS and T2D indicated the direction of associations to differ by study design, with positive associations observed in prospective studies and negative/null in cross-sectional studies.⁵² Another systematic review on PFAS and T2D indicated a potential inverse non-linear association.⁷¹ Although we did not observe these patterns as overtly, many of our meta-analyses with prospective studies for T2D, GDM, HOMA-IR, and fasting insulin were positive in directionality in line with findings from this previous review. On the other hand, we observed positive associations, without major differences in directionality across study designs, for GDM, HOMA-β, and fasting insulin. In fact, when we grouped study designs, these associations were consistent, namely mostly positive and statistically significant. It is possible that different heterogeneity levels across study designs influenced stratified meta-analysis estimates. Due to the higher number of studies examined cross-sectionally, higher heterogeneity (i.e. I² >86%; p < 0.05) and wider confidence intervals were observed, in contrast to findings from prospective studies.

Our finding with respect to increased odds of GDM is consistent with the few previous reviews that examined PFAS and GDM.54, 55, 56 Notably, many of the cross-sectional, case–control, and nested case–control GDM studies included in our review were from Asia and other genetic and/or regional factors may have influenced findings and heightened the risk such as higher PFAS exposure levels noted in Asian populations (Fig. 2B). It is possible that the period of pregnancy may also constitute a significant susceptible window to PFAS exposure on GDM occurrence,⁷² unlike for T2D outcome which tends to be diagnosed throughout adulthood and for which results were less consistent. PFAS levels have been associated with placental function changes and adverse maternal outcomes.⁷³^,⁷⁴ There are several potential mechanisms of the impact of PFAS during pregnancy. Dysregulation of maternal thyroid hormones may disrupt glucose homeostasis via the hypothalamic–pituitary–thyroid axis and subsequently affect pancreatic β-cell development and increase insulin.²⁹^,⁷⁵ In our previous systematic review on PFAS and metabolomic signatures,²⁷ we showed metabolic pathways like peroxisome proliferator-activated receptors (PPAR) impairment triggered by PFAS exposure. Given their role in early placental development,⁷⁶^,⁷⁷ PFAS-induced alterations in PPARα and PPARγ in the placenta could influence the pathophysiology of GDM⁷⁶ and related maternal–fetal outcomes, inducing intrauterine growth restriction or preeclampsia.78, 79, 80 During gestation, energy homeostasis and a sufficient supply of glucose are needed for fetal growth. Other mechanisms such as stress hormones and glucagon that may be involved during the hyperglycemic state of pregnancy⁸¹^,⁸² particularly starting during the 2nd and 3rd trimester, could be exacerbated or interact with PFAS exposure and disrupt glucose homeostasis83, 84, 85 but there is a paucity of research examining this hypothesis. Of note, our sensitivity meta-analyses with continuous diabetes markers in pregnant women were mostly null, which could be attributed to the limited number of studies, cross-sectional designs, and the already higher glycemic levels and inflammation processes triggered during the period of pregnancy across all pregnant participants and the lack of variability that could hinder associations.

We conducted a comprehensive systematic review and meta-analysis examining the impact of PFAS on diabetes diagnosis and markers of glycemic control, insulin resistance, and β-cell function. Our analysis provided moderate evidence for PFAS associations with increased insulin resistance and β-cell function that play key role in diabetes development. However, our meta-analyses examining PFAS associations with glucose biomarkers (fasting glucose and glycated hemoglobin) yielded less clear results with a few associations observed that were strengthened in studies with a lower risk of bias. Interestingly, we also observed that the majority of studies with OGTT assessment reported an overall positive association between PFAS and OGTT-derived measures, particularly in studies examining high-risk populations. OGTT is a more sensitive test⁸⁶ for early diabetes diagnosis than fasting glucose, which could explain in part the more consistent associations with OGTT measures.

There are several mechanisms that could explain our findings with diabetes markers. Although not systematic, a previous scoping review on glucose homeostasis and insulin resistance reported a majority of positive associations between PFAS and diabetes-related markers (glucose tolerance and insulin resistance),⁸⁴ a finding that is overall consistent with our review results. PFAS exposures have been implicated in metabolomic pathways related to glucose homeostasis and inflammation, including glucose-dependent insulinotropic polypeptide hormone and PPAR-α pathways,²⁷ which are involved in insulin secretion. PPARs have the ability to influence lipid supply and storage and have the capacity to induce fatty acid oxidation, controlling fat accumulation and insulin sensitivity.⁸⁷ Further, PFAS, as endocrine disruptors, induce transcriptomic changes,⁸⁸ potentiate adipogenesis,⁸⁹ and increase markers of oxidative stress such as reactive oxygen species⁹⁰ and/or can be influenced by redox regulators like Nrf2,⁹¹ in line with potential β-cell damage in the context of diabetes progression.⁹²^,⁹³ In addition to PPARα inducing heightened insulin response,94, 95, 96 G protein-coupled receptor 40 (GPR40) has been shown to stimulate insulin secretion by islet β-cells in experimental studies examining PFAS,⁹⁷ consistent with HOMA-β findings across epidemiological studies. Our findings also pointed towards a positive association between PFAS and β-cell function. Initial progression of diabetes is characterized by pancreatic β-cell stress, which could explain our findings on the PFAS association with increased HOMA-β levels at early pre-disease stages. More specifically, an increase in an immediate β-cell function can lead to an autoimmune response and release of β-cell antigens in T1D,⁹⁸ while in prediabetic T2D stages, an increase of β-cell function and insulin secretion linked to PFAS exposures may be in response to insulin resistance, which can ultimately lead to β-cell failure at later stages of the disease.⁹³^,⁹⁹

We identified several gaps in the state of evidence noteworthy to be addressed in future investigations to elucidate the potential impacts of PFAS exposure on diabetes risk. While there are more than 10,000 of PFAS subclasses,¹⁰⁰ most of these have not been considered in studies, let alone as mixtures. We also observed a very limited number of studies from LMICs, where DM incidence has particularly increased in recent years.¹⁰¹ Our systematic review did not identify any published studies focusing on PFAS and DM conducted in Africa, and Central or South-America up to the date of our literature search. This is an important data gap that needs to be addressed in future investigations, as detectable PFAS concentrations in human biospecimens and environmental media have already been reported in these underrepresented regions¹⁰²^,¹⁰³ in the current literature. Previous studies also focused primarily on PFAS exposures during either adulthood or prenatally. The childhood and adolescent periods can also constitute important windows of susceptibility to PFAS exposure and warrant attention in future investigations. More research is also warranted to examine dose–response relationships between PFAS and diabetes outcomes, particularly during these periods of potential vulnerability. Last, an understudied area of research is the potential impact of PFAS on T1D given the rising incidence of T1D in both children and adults in recent years,¹⁰⁴^,¹⁰⁵ which might be partly due to emerging environmental triggers, such as PFAS, and their interactions with genetic T1D predisposition.

We also observed several limitations in the state of evidence. First, T1D-specific evidence is very scarce, and the majority of studies focusing on T2D or GDM did not explicitly mention T1D exclusions. Even though T1D is significantly less prevalent, it could be that some of the T1D results are masked within the T2D studies, potentially increasing measurement error and outcome misclassification. Another limitation across studies is that the majority of published studies thus far were cross-sectional and therefore, prone to reverse causation,¹⁰⁶ as well as unable to capture potential long-term impacts of PFAS exposure on diabetes development. Moreover, residual confounding by sociodemographic and other exposure-disease determinants is also likely. PFAS and/or diabetes are both related to genetic predisposition¹⁰⁷ and lifestyle factors such as diet,¹⁰⁸ but these factors were infrequently accounted for in the studies we reviewed. Future research on the role of potential effect modifiers such as individual characteristics and behaviors (e.g. genes, age, sex, and diet) can help understand potential sources of heterogeneity in the PFAS-DM association and inform precision medicine interventions for diabetes prevention and management.

A growing area of exposomic research is also evident in this topic where diabetes research could benefit from. Specifically, a more narrowed focus is needed on the use of emerging tools emphasizing exposure-mixture methods over other multi-pollutant methods that may have more limitations and difficulties in interpretation, such as variable selection methods or methods involving PFAS sums.¹⁰⁹ Variable selection methods may not yield meaningful results since they might over-penalize by choosing against PFAS signals,¹¹⁰ which may have small to moderate magnitudes. PFAS sums may also not be a good indicator for exposure-mixtures as these methods ignore the nuances of unequal contributions to the overall mixture association and often do not consider exposure inter-correlations among PFAS signals.¹¹¹

In summary, future prospective studies in larger populations with rigorous or validated diabetes ascertainment and more comprehensive PFAS assessment are required to elucidate the potential impacts of PFAS on diabetes development and progression. In addition, future investigations in this field can benefit from the integration of emerging exposomics and multi-omics approaches that could help obtain a more comprehensive characterization of the impacts of exposure mixtures (PFAS among other chemicals) on biological responses and disease risk.¹¹²^,¹¹³ Such approaches in larger and well- characterized study populations may further accelerate our understanding of potential genome–environment interactions in diabetes.

There were several strengths of this review: 1) the comprehensive inclusion of several different diabetes markers (insulin resistance, β-cell function, HbA1c, fasting glucose and insulin) that have not yet been reviewed systematically or in meta-analyses in relation to 2) multiple legacy and emerging PFAS exposures, 3) the inclusion of novel PFAS-mixture and other multi-pollutant studies emergent in the field that have been understudied in systematic reviews to-date, as well as 4) several sensitivity analyses to evaluate the robustness of findings, 5) rigorous risk of bias assessment, and 6) the use of a method harmonizing beta coefficients and CIs in meta-analyses. Nevertheless, results should be interpreted with caution given the small effect sizes that were observed for some associations, particularly with continuous diabetes markers, and the potential type I error from testing multiple exposure-outcome associations in this review. A total of 50 studies were excluded from the main meta-analyses due to lack of sufficient information to calculate quantitative association estimates, which we aimed to address through sensitivity analyses with z-scores maximizing study inclusion. The most consistent finding in our review was in regard to GDM, where a doubling of PFOA, PFNA, and PFBS exposure increased odds of GDM by 13%–14%. This is a much smaller effect size compared to other well established risk factors for GDM such as age above 30 years and overweight/obesity (ORs > 2).¹¹⁴ Nevertheless, it is worth noting that the continuous and cumulative exposures to multiple PFAS over the life-course (not captured in prior studies) might be linked to even greater risks, which is of potential clinical relevance. Some individuals could also have particularly high exposures to PFAS, increasing vulnerability. Even though clinical guidelines are not yet universally accepted, PFAS screening and interventions to mitigate exposures can be beneficial, especially for pregnant women and other patients at high risk.¹¹⁵

In conclusion, the state of evidence currently supports an association between PFAS exposures and higher odds of GDM, as well as associations with higher β-cell function markers, and mild associations with insulin resistance markers and glucose. A potential association between PFAS and T2D remains to be elucidated, while T1D research has been very scarce. Furthermore, the strength of evidence was limited. Given that PFAS-diabetes studies were predominantly cross-sectional and focused on adults, larger prospective studies with a greater representation of T1D and/or T2D cases across different life stages are needed to strengthen the evidence in this field. Last, emerging studies evaluating emerging PFAS and PFAS mixtures are promising and may advance knowledge about the multiple PFAS joint contributions to diabetes risk.

Contributors

S.I.A.: Conceptualization, Data curation, Methodology, Writing—original draft. X.Y.: Formal analysis, Software, Visualization, Methodology, Writing—review & editing. M.Y.: Formal analysis, Methodology, Writing—review & editing. N.C.: Data curation, Writing—review & editing. E.M.: Data curation, Writing—review & editing. M.C.: Data curation, Writing—review & editing. V.M.: Writing—review & editing. S.M.E.: Writing—review & editing. J.T.: Writing—review & editing. A.P.S.: Writing—review & editing. D.B.: Writing—review & editing. D.I.W.: Writing—review & editing. L.C.: Writing—review & editing. V.W.S.: Writing—review & editing. R.W.W.: Writing—review & editing. E.C.: Methodology, Writing—review & editing. D.V: Conceptualization, Funding acquisition, Methodology, Writing—review & editing. X.Y., M.Y., and S.I.A. have access to the complete data and have verified the underlying study data.

Data sharing statement

Data extracted used for synthesis and meta-analyses would be available upon reasonable request and approval only.

Declaration of interests

L.C. has served as an expert consultant for plaintiffs in litigation related to PFAS-contaminated drinking water. V.M. advises on hair biomarker-related work at Linus Biotechnology Inc., a startup company affiliated with the Mount Sinai Health System. S.I.A. recently obtained the NIEHS grant K99ES037024. X.Y., M.Y., N.C., E.M., M.C., S.M.E., J.T., A.P.S., D.B., D.I.W., V.W.S., R.W.W., E.C., and D.V. declare no competing interests.

Acknowledgements

This research was funded by the National Institute of Environmental Health Sciences (NIEHS) (R01ES033688; PI: D.V.). Additional funding has supported S.I.A. (K99ES037024, NIEHS), D.V. (R21ES035148, NIEHS), L.C (P42ES36506, R01ES033688, P30ES007048, NIEHS), and E.C. (R01ES032242, NIEHS).

We thank medical illustrator Jill Gregory at Mount Sinai for assisting in generating the graphical abstract. We also thank the volunteer Shubani Singh for her assistance in the data extraction at the preliminary stages of this study.

Footnotes

^{Appendix A}

Supplementary data related to this article can be found at https://doi.org/10.1016/j.eclinm.2025.103747.

Appendix A. Supplementary data

Supplementary Methods and Figures

mmc1.pdf^{(4MB, pdf)}

Supplementary Tables

mmc2.xlsx^{(234.3KB, xlsx)}

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PERMALINK

Associations of perfluoroalkyl and polyfluoroalkyl substances with markers of glycaemic control, insulin secretion and sensitivity, and diabetes risk: a systematic review and meta-analyses

Sandra India Aldana

Xin Yu

Meizhen Yao

Nathan Cohen

Eftychia Markopoulou

Maanal Chowdhury

Vishal Midya

Stephanie M Eick

Jessica Trowbridge

Anne P Starling

Dinesh Barupal

Douglas I Walker

Leda Chatzi

Veronica Wendy Setiawan

Ryan W Walker

Elena Colicino

Damaskini Valvi

Summary

Background

Methods

Findings

Interpretation

Funding

Research in context.

Evidence before this study

Added value of this study

Implications of all the available evidence

Introduction

Methods

Search strategy and selection criteria

Data analysis

Role of the funding source

Results

Descriptive statistics

Fig. 1.

Fig. 2.

Meta-analyses

Fig. 3.

Risk of bias

Fig. 4.

Weighted Z-score sensitivity meta-analysis

Exploratory Summary for multi-pollutant studies

Fig. 5.

Quality and strength of evidence

Discussion

Contributors

Data sharing statement

Declaration of interests

Acknowledgements

Footnotes

Appendix A. Supplementary data

References

Associated Data

Supplementary Materials

ACTIONS

PERMALINK

RESOURCES

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