Воздействие размера кузова на предклинический тип 1 Диабетное развитие и прогресс.

Introduction

Type 1 diabetes develops in genetically predisposed individuals exposed to environmental triggers. As the incidence of type 1 diabetes continues to rise by approximately 3% per year, understanding the modifiable determinants of type 1 diabetes risk is increasingly important. Data from various studies, including Type 1 Diabetes TrialNet (TrialNet) Pathway to Prevention (PTP), have shown that higher BMI increases the likelihood of progressing through the preclinical stages of type 1 diabetes ( 1 , 2 ). This relationship informed the development of the Diabetes Prevention Trial–Type 1 Risk Score (DPTRS), which included BMI as one of its predictive variables ( 3 ). Findings from studies such as The Environmental Determinants of Diabetes in the Young (TEDDY) indicate that elevated BMI and other anthropometric indicators not only increase risk of developing clinical type 1 diabetes but also enhance the likelihood of seroconversion to islet autoantibodies ( 4 , 5 ). This review synthesizes epidemiologic, genetic, and mechanistic evidence supporting the role of elevated BMI in the initiation of islet autoimmunity, progression, and clinical type 1 diabetes. We critically evaluate current mechanistic hypotheses and identify research gaps to guide obesity-focused interventions aimed at preventing type 1 diabetes. A targeted literature search was conducted using MEDLINE (via PubMed) to identify relevant publications. Search terms included “type 1 diabetes,” “diabetes mellitus, type 1,” “obesity,” “overweight,” “body weight,” “autoimmunity,” and “islet autoantibodies.” Results were restricted to English-language publications. Reference lists of relevant review articles were also examined for identification of seminal studies and additional key publications not captured by the initial search. Studies with descriptions of subpopulations of individuals with increased susceptibility to the effects of overweight and obesity on progression to islet autoimmunity and stage 3 type 1 diabetes were included, summarized in Tables 1 and 2 .

Table 1 Subpopulations with increased susceptibility to the effect of BMI on islet autoimmunity The table presents participant population, age, sex, associated characteristics, weight measurement, P value and or 95 percent confidence interval, and cohort reference number. All entries describe pediatric participants. Age ranges include 9 months, early childhood, infancy, and 9 years or older. Associated characteristics include H L A genotypes, G A D A first islet autoimmunity, and puberty in females. Weight measurements include body mass index z score, time varying body mass index, weight gain in kilograms per year, and cumulative excess body mass index. Cohorts include T E D D Y, P O I n T, and Trial Net P T P. Participant population Age Sex Associated characteristics Weight measurement HR R (95% CI), P value (when available) Cohort (reference no.) Pediatric >9 months — HLA DR4/4 and HLA DR4/8 BMI z score HLA∗BMI interaction , P < 0.005 TEDDY ( 18 ) Pediatric Early childhood — — Time-varying BMI 1.16 (1.01, 1.32), P = 0.031 POInT ( 20 ) Pediatric Infancy — GADA-first islet autoimmunity Weight gain (kg/year) 1.15 (1.04, 1.28), P = 0.009 TEDDY ( 4 ) Pediatric ≥9 years — Neither HLA DR3-DQ2 nor DR4-DQ8 ceBMI (≥0 kg/m²) 7.32 (1.88, 28.5), P = 0.004 TrialNet PTP ( 2 ) Pediatric — Female Puberty BMI z score 2.33 (1.28, 4.22), P = 0.005 TEDDY ( 17 ) *Studies varied in their evaluation of potential effect modifiers: in some age, sex, and associated characteristics were assessed but with findings of no statistically significant associations, while in others evaluation for these variables was not included. If a characteristic is not listed, evaluation for this factor was not included in the study or the factor was not found to be significant in its association.

Subpopulations with increased susceptibility to the effect of BMI on islet autoimmunity

*Studies varied in their evaluation of potential effect modifiers: in some age, sex, and associated characteristics were assessed but with findings of no statistically significant associations, while in others evaluation for these variables was not included. If a characteristic is not listed, evaluation for this factor was not included in the study or the factor was not found to be significant in its association.

Table 2 Summary, subpopulations with increased susceptibility to the effect of BMI on progression to stage 3 type 1 diabetes The table presents participant population, age, sex, associated characteristics, weight measurement, P value and or 95 percent confidence interval, and cohort reference number. Pediatric and adult participants are included. Pediatric age groups range from early childhood to 13 years, and adult groups include younger than 35 years and older than 35 years. Weight measurements include weight gain in kilograms per year, weight z score, body mass index z score, cumulative excess body mass index, and body mass index percentile. Cohorts include T E D D Y, T R I G R, C S H R R, and Trial Net P T P. Participant population Age Sex Associated characteristics Weight measurement HR * (95% CI), P value Cohort (reference no.) Pediatric <4 years — GADA-first islet autoimmunity Weight gain (kg/year) 2.57 (1.34, 4.91), P = 0.004 TEDDY ( 4 ) Pediatric 2–10 years — — Obesity and overweight 2.39 (1.46, 3.92), P < 0.001 TRIGR ( 24 ) Pediatric 7 and 13 years — — BMI z score 7 years old: OR 1.23 (1.09, 1.37), P < 0.001; 13 years old: OR 1.20 (1.04, 1.40), P = 0.016 CSHRR ( 32 ) Pediatric <12 years Female — ceBMI ceBMI −1.35 or greater: 1.70 (1.09, 2.66), P = 0.02 TrialNet PTP ( 1 ) Pediatric <12 years — Hispanic Obesity or overweight 3.8 (1.6, 9.1), P = 0.0026 TrialNet PTP ( 34 ) Adult ≥35 years Male — ceBMI 1.15, P = 0.013 TrialNet PTP ( 33 ) Adult <35 years Female — Persistent obesity (ceBMI ≥5 kg/m 2 ) 4.7 (1.67, 13.2), P = 0.003 TrialNet PTP ( 33 ) CSHRR, Copenhagen School Health Record Register. Studies varied in their evaluation of potential effect modifiers: in some age, sex, and associated characteristics were assessed but with findings of no statistically significant associations, while others did not include evaluation for these variables. If a characteristic is not listed, evaluation for this factor was not included in the study or the factor was not found to be significant in its association. *Except when otherwise indicated.

Summary, subpopulations with increased susceptibility to the effect of BMI on progression to stage 3 type 1 diabetes

CSHRR, Copenhagen School Health Record Register. Studies varied in their evaluation of potential effect modifiers: in some age, sex, and associated characteristics were assessed but with findings of no statistically significant associations, while others did not include evaluation for these variables. If a characteristic is not listed, evaluation for this factor was not included in the study or the factor was not found to be significant in its association.

*Except when otherwise indicated.

Etiology, Epidemiology, and Progression of Type 1 Diabetes

Type 1 diabetes is one of the most prevalent chronic conditions of childhood, arising from autoimmune-mediated destruction of pancreatic β-cells in genetically predisposed individuals following exposure to environmental triggers. More than 90 genetic loci have been linked to type 1 diabetes, with approximately one-half of these mapping to the human HLA region, particularly HLA DR3-DQ2 and HLA DR4-DQ8 haplotypes ( 6 ). Environmental triggers hypothesized to lead to immune activation are varied but include infections, particularly with enteroviruses, alterations in gut microbiota, and elevated BMI ( 7 ), among others. However, knowledge remains limited on how specific environmental exposures converge with genetic susceptibility to initiate and accelerate the autoimmune process.

Globally, an estimated 9.5 million individuals were living with type 1 diabetes at the start of 2025 ( 8 ). In 2021 alone, 0.5 million new cases were diagnosed ( 9 ), with incidence shown to be increasing in most regions of the world ( 8 ). By 2040, the prevalence of type 1 diabetes is expected to rise to 13.5–17.4 million, with the greatest increase in low-income and lower-middle-income countries ( 9 ). This epidemiologic trajectory underscores the need to identify modifiable influences on autoimmunity and disease progression ( 10 , 11 ), among which obesity has emerged as a particularly compelling contributor.

Understanding of the staged progression of type 1 diabetes provides a useful framework for examining how such factors may shape disease development. The earliest detectable phase, stage 0, begins with the appearance of the first islet autoantibody ( 12 ). Stage 1 involves two or more autoantibodies, without dysglycemia or symptoms, and in stage 2 dysglycemia emerges but glycemic measures remain below the diagnostic threshold for diabetes ( 13 ). Finally, stage 3 is defined by glycemic abnormalities diagnostic of diabetes, typically accompanied by clinical symptoms such as polyuria, polydipsia, and weight loss ( 13 ). The staging model allows investigators to pinpoint specific intervals in which environmental or physiological stressors, such as obesity, may influence initiation of autoimmunity, its progression, or the rate of transition between stages.

Growth, Overweight, and Obesity in the Pediatric Population

Globally, the prevalence of overweight and obesity in children has doubled and obesity alone has tripled, with a significant spike in low- and middle-income countries ( 14 ). Growth in the pediatric population follows age-dependent trajectories, characterized by rapid growth during infancy followed by relatively slower growth during early childhood ( 4 ). In pediatrics, growth and adiposity assessment varies by age. For children age <2 years, the American Academy of Pediatrics recommends use of the World Health Organization weight-for-length charts, with values ≥97th percentile indicating excess adiposity ( 15 ). In children aged ≥2 years, adiposity is typically assessed using age- and sex-specific BMI percentiles or z scores, with overweight defined as BMI ≥85th to <95th percentile, obesity as ≥95th percentile, and severe obesity as ≥120% of the 95th percentile ( 15 ). Although DEXA remains the gold standard for body composition assessment, its use is limited in large studies ( 15 ). Importantly, BMI z scores are less informative at the extremes of adiposity due to score compression, and complementary measures such as waist circumference or waist-to-height ratio may better capture obesity-related metabolic risk for pediatric populations. These developmental considerations are essential in evaluating the role of overweight and obesity as modifiers of islet autoimmunity progression and type 1 diabetes risk in children.

Obesity in the Initiation and Progression of Islet Autoimmunity

Obesity not only increases transition from islet autoantibody positivity to stage 3 type 1 diabetes but also promotes the initiation and progression of islet autoimmunity. In the TEDDY cohort of 7,522 genetically at-risk children, faster weight gain in infancy (12–24 months of age) was associated with increased risk of seroconversion to islet autoantibodies (hazard ratio [HR] 1.09 per 1 kg/year [95% CI 1.02, 1.17]) ( 4 , 16 ). Furthermore, rising BMI z scores during puberty were associated with increased risk of seroconversion, whereas puberty itself did not independently influence risk ( 17 ). A recent study expanded on these findings to reveal a haplotype-dependent relationship between high BMI z score and islet autoimmunity during childhood ( 18 ). For children with HLA DR4/DR4 or HLA DR4/DR8 (where DR8 represents a lower-risk haplotype), a high BMI z score at the age of 9 months predicted an increased risk of developing islet autoimmunity in early childhood, with a significant interaction between HLA genotype and BMI ( 18 ).

Evidence from other cohorts supports these associations. Findings from the Australian Baby Diab Study, which followed children with a first-degree relative affected by type 1 diabetes, indicated that higher weight-for-age and BMI z scores during the first 2 years of life were significant predictors of islet autoantibody seroconversion (HR 1.43 per unit increase in weight z score, HR 1.29 per unit increase in BMI z score, and HR 2.61 for weight z score >0 vs. ≤0) ( 19 ). More recently, Hummel et al. ( 20 ) used data from the Primary Oral Insulin Trial (POInT) cohort under coronavirus disease 2019 conditions and showed that elevated BMI during early childhood, particularly ∼9 months of age, was associated with higher risk of islet autoimmunity even after adjustment for exposure timing and country of residence (HR 1.16 per age-adjusted BMI increase; HR 1.68 for overweight risk at 9 months). Importantly, in this study potential confounding by the coronavirus disease 2019 pandemic was accounted for, with adjustment for both timing of BMI measurements and country of residence.

Beyond growth trajectories, nutritional factors may also influence autoimmunity initiation; however, evidence for independent nutrient-specific effects remains inconclusive. In the TEDDY cohort, dietary intake during early childhood (ages 2–8 years) influenced islet autoimmunity primarily via effects on BMI ( 5 ). Within this cohort, higher protein intake was linked to increased risk of developing GADA ( 5 ). Contrary to these findings, an analysis of the participants in the Australian Baby Diab Study revealed no link between diet and autoimmunity ( 19 ).

Given the variability in dietary findings, early postnatal growth–related factors may provide additional insight. In TEDDY, faster weight gain during infancy conferred a higher risk of GADA-first islet autoimmunity (where GADA is the first detected autoantibody) in comparison with IAA-first autoimmunity ( 4 ). In contrast, when adiposity was assessed using BMI z score in a recent longitudinal analysis of the TEDDY cohort, no interaction was found between BMI and the first detected autoantibody ( 18 ). It is important to note that IAA-first autoimmunity typically appears early in childhood and is associated with fast progression to type 1 diabetes, whereas GADA-first autoimmunity typically occurs later and follows a more moderate progression rate ( 21–23 ). The lack of association between early weight gain and progression among children with IAA-first autoimmunity could reflect this aggressive phenotype in which progression occurs quickly and may be less susceptible to modulation by metabolic factors. In contrast, the slower progression characteristic of GADA-first autoimmunity may permit obesity-related metabolic stress to accelerate progression from islet autoimmunity to clinical disease.

Obesity as a Modifiable Risk Factor for Clinical Type 1 Diabetes

A growing body of observational evidence from longitudinal cohorts and genetic evidence from Mendelian randomization studies supports obesity as a modifiable risk factor for progression to stage 3 type 1 diabetes. In TrialNet PTP, each 1 kg/m 2 increase in cumulative excess BMI (ceBMI), a measure integrating both degree and duration of elevated BMI, was associated with 6% higher risk of progression to stage 3 type 1 diabetes in autoantibody-positive first-degree relatives of individuals with type 1 diabetes (HR 1.06 [95% CI 1.03, 1.10]; P = 0.0006) ( 1 ). Persistent overweight or obesity conferred a 63% greater risk, independent of age, sex, and autoantibody number (HR 1.63 [95% CI 1.22, 2.18]; P = 0.0009) ( 1 ). Similar findings were observed in TEDDY, an international birth cohort with high-risk HLA haplotypes, where greater early-childhood weight gain was associated with increased progression from islet autoimmunity to stage 3 type 1 diabetes, particularly among children whose first autoantibody was GADA (HR 2.57 per 1 kg/year [95% CI 1.34, 4.91]) ( 4 ). In another international study investigators concluded that elevated BMI was associated with earlier age at diabetes onset, suggesting that increased weight gain could be a risk factor for early manifestation of type 1 diabetes ( 24 ).

Meta-analyses further consolidate these observations. In a 2011 review of nine studies investigators found that positive associations between childhood obesity and type 1 diabetes risk were reported in seven, with a pooled odds ratio (OR) of 2.03 (95% CI 1.46, 2.80), including evidence for a dose–response relationship ( 25 ). More recently, a 2023 systematic review of 10 cohort studies including >1.6 million individuals reported pooled risk ratios of 1.35 (95% CI 0.93, 1.97) for overweight and 2.17 (95% CI 1.75, 2.69) for obesity ( 26 ).

Mendelian randomization studies expanded on these findings to support a causal role. Censin et al. ( 27 ) found that childhood adiposity increased type 1 diabetes risk (OR 1.32 per SD BMI [95% CI 1.06, 1.64]), with even stronger effects after sensitivity analyses. Richardson et al. ( 28 ) confirmed these findings in a much larger cohort, observing ORs of 2.05 and 1.94 in discovery and validation data sets, respectively. Similar conclusions were reached in UK Biobank and FinnGen, where genetically predicted BMI was associated with higher risk of several autoimmune diseases, including type 1 diabetes (OR 1.55 [95% CI 1.32, 1.83]; P < 0.001) ( 29 ). Taken together, these data provide compelling evidence that obesity may contribute causally to type 1 diabetes risk. Consistent with this growing body of evidence, BMI remains a key predictor in the widely used DPTRS, which has been validated and applied across multiple cohorts ( 30 ).

Observations from the TEDDY cohort indicate that higher moderate-to-vigorous physical activity in children with islet autoimmunity is associated with reduced progression to clinical type 1 diabetes, with an estimated 8% reduced risk per 10-min increase in daily activity ( 31 ). The mechanisms underlying this association remain unclear and may involve effects on BMI, insulin sensitivity, or distinct immunometabolic pathways. Notably, stronger associations were observed among children with GADA-first autoimmunity, with a 12% reduction in progression risk per 10-min increase in moderate-to-vigorous physical activity. Interpretation of these findings should include consideration that the analysis was limited to children aged 5–15 years, an age range in which GADA-first autoimmunity is more prevalent ( 31 ). In addition, because TEDDY was focused on children with high-risk HLA genotypes, the generalizability of these findings to more diverse populations remains uncertain.

Groups Vulnerable to the Influence of BMI on Type 1 Diabetes Progression

Despite consistent associations between elevated BMI and both the onset of islet autoimmunity and progression, substantial heterogeneity exists, with heightened susceptibility seen for certain subgroups. Age, sex, genetic background, and geographic ancestry modify the strength and direction of obesity’s effects, highlighting the need to account for this variation in designing targeted prevention strategies.

Heterogeneity is evident in the influence of elevated BMI on progression to islet autoimmunity ( Table 1 ). TEDDY data suggest that age-dependent patterns of adiposity influence development of islet autoimmunity, with rapid BMI increases during puberty associated with heightened risk of seroconversion ( 17 ). Genetic background further conditions these effects. In TrialNet PTP, early analyses of single autoantibody–positive participants suggested no association between BMI and progression; however, Ferrara-Cook et al. ( 2 ) in a more detailed evaluation found that persistent overweight or obesity (BMI ≥85th percentile or ceBMI ≥0 kg/m 2 ) nearly doubled the risk of developing multiple autoantibodies among youth ≥9 years old (HR 1.93 [95% CI 1.13, 3.31]; P = 0.017). Strikingly, the effect was even stronger in children without high-risk HLA genotypes (DR3-DQ2 or DR4-DQ8), among whom overweight or obesity increased progression more than sevenfold (HR 7.32 [95% CI 1.88, 28.5]; P = 0.004) ( 2 ). Similarly, in analysis of the TEDDY cohort, elevated risk of autoimmunity was found for children with obesity who lacked HLA DR3-DQ2; however, unlike in the TrialNet PTP study, in TEDDY the presence of HLA DR4-DQ8 was also identified as a risk factor for promotion of autoimmunity ( 18 ). Taken together, these findings indicate that adiposity can influence not only progression but also the autoimmune process itself, with effects modified by age (which could be a proxy for puberty or other developmental factors) and HLA background.

Similarly, age and sex significantly shape how BMI influences progression from established autoimmunity to clinical diabetes ( Table 2 ). In TrialNet PTP, the BMI threshold associated with increased progression risk was lower in children age <12 years and lower in female than in male participants, consistent with the greater contribution of adipose tissue to BMI before puberty and among girls ( 1 ). Findings from population-based cohorts reinforce these developmental effects: in a Danish study of >10,000 children, higher BMI z scores at ages 7 and 13 years predicted type 1 diabetes (OR 1.23 and 1.20, respectively) ( 32 ). Similarly, in Trial to Reduce IDDM in the Genetically at Risk (TRIGR), overweight status between ages 2 and 10 years was associated with elevated risk of progression over a median 11.5 years of follow-up ( 24 ). Among adults, sustained obesity also conferred increased risk, with stronger effects in women aged <35 years and men >35 years ( 33 ). Ethnicity further modified these relationships. Among children younger than 12 years old with multiple autoantibodies, overweight or obesity was associated with 36% increased risk of progression to clinical diabetes for non-Hispanic White children and 400% higher risk for Hispanic children ( 34 ).

Across multiple cohorts, elevated BMI shapes both the initiation and progression of type 1 diabetes in an age-, sex-, ethnicity-, and genotype-dependent manner. Risk is particularly elevated for children <12 years old, selected female subgroups, and Hispanic populations and is further conditioned by HLA haplotypes. Depending on genotype, obesity may amplify existing autoimmune susceptibility or unmask latent vulnerability, supporting a model in which metabolic load interacts with immune and genetic determinants to modulate disease risk. These patterns highlight the need for mechanistic and longitudinal studies where investigators explicitly account for demographic and genetic diversity when evaluating obesity-related influences on type 1 diabetes pathophysiology.

Mechanisms Underlying the Influence of BMI on Type 1 Diabetes Progression

Although the mechanisms linking obesity to type 2 diabetes are well established, the pathways through which elevated BMI influences islet autoimmunity and type 1 diabetes progression remain incompletely understood. Converging evidence implicates a network of interrelated processes, including insulin resistance (IR), β-cell stress, adiposity-induced inflammation, adipokine dysregulation, and microbiome alterations, that can amplify autoimmune risk and accelerate progression from preclinical to clinical disease by modulating immune tolerance, β-cell vulnerability, and metabolic stability ( Fig. 1 ).

Obesity-driven mechanisms linking metabolic dysfunction and islet autoimmunity. Obesity contributes to β-cell stress and autoimmunity through multiple metabolic and inflammatory pathways, including the following. IR and β-cell compensation: increased insulin demand drives β-cell hypersecretion, leading to oxidative and ER stress that enhances autoimmune β-cell injury. Adipose tissue dysfunction: chronic adipose inflammation characterized by macrophage, neutrophil, Th1, and CD8 + T-cell infiltration promotes cytokine release and sustained immune activation. Adipokine dysregulation: altered adipokines such as leptin amplify inflammation, impair immune tolerance, and promote Th1 differentiation. Gut dysbiosis: reduced SCFAs increase intestinal permeability and inflammation, further disrupting immune regulation. Altogether, these processes heighten β-cell autoantigen recognition and CD8 + T-cell–mediated immune attack, linking metabolic dysfunction to increased islet autoimmunity risk. Image created with BioRender.com ( https://biorender.com/1leoey6 ).

IR

IR increases the demand for endogenous insulin; when the compensatory capacity is exceeded, glucose homeostasis cannot be maintained, and diabetes develops. Emerging evidence suggests that a similar mechanism contributes to the progression of type 1 diabetes among autoantibody-positive individuals. In the TrialNet PTP adult cohort, higher IR, measured with HOMA of IR (HOMA1-IR), was associated with greater risk of progression to stage 3 type 1 diabetes (HR 1.17 [95% CI 1.05, 1.31]; P = 0.004) ( 35 ). Similar effects were reported from the Diabetes Prevention Trial–Type 1 (DPT-1), with higher HOMA-IR associated with a nearly threefold increase in progression among moderate-risk participants and almost twofold among high-risk participants ( 36 ). Combined analyses of TrialNet PTP and DPT-1 identified the disposition index (DI), a measure integrating insulin sensitivity and β-cell function, as an independent predictor of clinical onset, even after adjustment for established risk scores ( 37 ). These findings support IR as a key mediator linking obesity to progression from preclinical autoimmunity to dysglycemia (stage 2) and ultimately to clinical type 1 diabetes (stage 3).

The DI offers an important framework for interpreting β-cell function in the context of obesity-associated IR, particularly in trials with assessment of type 1 diabetes progression. Because insulin secretion and insulin sensitivity are inversely related ( 37 ), end points based on insulin secretion alone may misrepresent disease trajectory when insulin sensitivity varies with growth, age, or weight gain ( 38 ). Weight-related IR is typically accompanied by compensatory hypersecretion; however, individuals progressing to type 1 diabetes exhibit concurrent declines in insulin sensitivity and secretion closer to diagnosis, resulting in a reduced DI that reflects β-cell failure rather than appropriate adaptation ( 39 , 40 ). Consequently, similar insulin secretion values may signify adequate β-cell function in insulin-sensitive individuals but relative insufficiency in those with greater IR, leading to differential progression risk ( 36 , 41 , 42 ). In integrating insulin secretion and sensitivity, the DI mitigates this bias and more accurately captures β-cell stress and failure, making it a more robust end point for detecting obesity-related disease acceleration and treatment effects that alter insulin sensitivity.

β-Cell Stress

Compensating for IR imposes increased secretory demand on β-cells. This metabolic pressure triggers endoplasmic reticulum (ER) stress, oxidative stress, altered antigen processing, and heightened innate immune signaling, all of which enhance β-cell immunogenicity ( 43 , 44 ). When unfolded or misfolded proteins accumulate, activation of the unfolded protein response (UPR) transitions from adaptive to proapoptotic, contributing to β-cell dysfunction, T-cell exhaustion, impaired tolerance, and ultimately β-cell apoptosis ( 45–50 ).

Proinsulin–to–C-peptide ratio (PI:C), a marker of β-cell stress, has been shown to outperform the proinsulin-to-insulin ratio in predicting type 2 diabetes development ( 51–58 ). Likewise, in type 1 diabetes, PI:C is increasingly recognized as a biomarker of β-cell stress and accelerated progression ( 59 , 60 ). From a pilot study Sims et al. ( 61 ) reported significantly higher PI:C ratios among progressors to stage 3 type 1 diabetes versus nonprogressors (median 1.81 [interquartile range 0.80–3.09] vs. 1.17 [0.46–1.76], P = 0.03). Similarly, in the TrialNet teplizumab prevention trial (TN10), baseline PI:C predicted both disease progression and treatment responsiveness ( 62 ). In another study in children, investigators found that PI:C progressively increases over the first year post–type 1 diabetes diagnosis, reflecting ongoing β-cell stress ( 63 ). Chronic β-cell stress may also enhance autoantigen release and neoantigen generation, thereby increasing recognition by autoreactive T cells ( 64 ). These data suggest that β-cell stress bridges obesity-related IR with β-cell loss and antigen presentation, driving earlier autoimmune activation and onset of type 1 diabetes.

Adiposity-Induced Inflammation

Beyond intrinsic β-cell stress responses, systemic metabolic disturbances driven by excess adiposity further intensify inflammatory signaling and immune activation that contribute to type 1 diabetes pathogenesis. Excess adiposity promotes adipocyte hypertrophy and dysfunction of white adipose tissue, resulting in release of proinflammatory cytokines, including tumor necrosis factor-α (TNF-α), interleukin (IL)-1β, and IL-6, and recruitment of adipose tissue macrophages ( 65 ) ( Fig. 2 , component 1). As adipose tissue expands, hypoxia and lipotoxic stress drive further immune activation through chemokines such as monocyte chemoattractant protein-1 (MCP-1), also known as C-C motif chemokine ligand 2 (CCL2), which recruit circulating monocytes that differentiate into macrophages within adipose tissue ( 65 ) ( Fig. 2 , component 1). Polarization toward proinflammatory M1 macrophages sustains cytokine and chemokine production, attracting additional effector cells ( 66 ). This chronic low-grade inflammatory state, often referred to as meta-inflammation, disrupts systemic immune homeostasis and promotes IR and creates a proinflammatory milieu conducive to loss of self-tolerance and β-cell–directed autoimmunity ( 67 ) ( Fig. 2 , component 3).

Mechanistic link between adiposity-induced inflammation and islet cell autoimmunity. 1: Adipocyte hypertrophy and expansion induce adipose tissue hypoxia and dysfunction, triggering inflammatory and immune activation. Chemokines, including C-C motif chemokine ligand 2 (CCL2), are released by adipocytes and macrophages in response to adipose tissue dysfunction, promoting the recruitment and infiltration of monocytes into adipose tissue, where these immune cells commonly differentiate into M1 macrophages and subsequently secrete proinflammatory mediators such as cytokines (interleukin [IL]-6, IL-1β, and tumor necrosis factor [TNF]-α), additional chemokines (including CCL2), and adipokines, e.g., leptin. 2: Within the setting of lipotoxicity, these inflammatory mediators promote ER stress in pancreatic β-cells. IL-1β, TNF-α, and γ-interferon (IFN-γ) activate nuclear factor-κB (NF-κB), Janus kinase/signal transducer and activator of transcription (JAK/STAT), and mitogen-activated protein kinase (MAPK) pathways in pancreatic β-cells, upregulating UPR markers, C-X-C motif chemokine ligand 10 (CXCL10), and CCL2, while downregulating sarco/endoplasmic reticulum Ca 2+ -ATPase 2b (SERCA2b). CCL2 sensitizes β-cells to ER stress due to the inflammatory milieu brought by monocyte recruitment. 3: UPR activation during ER stress leads to increased generation and posttranslational modification of autoantigens, enhancing their processing and presentation, which increases β-cell immunogenicity and visibility to the immune system. 4: CXCL10 is released by β-cells in response to proinflammatory stimuli and acts as a chemoattractant for CXCR3-expressing CD8 + T cells, facilitating their recruitment into the islets and promoting insulitis and β-cell–directed autoimmunity. 5: Leptin activates mammalian/mechanistic target of rapamycin (mTOR) in regulatory T cells (Tregs) impairing their function and enhancing Th1/Th17 activity, thereby amplifying proinflammatory responses (including the secretion of INF-γ by Th1) and autoimmunity. 6: IL-1β and TNF-α activate intracellular stress kinases (c-Jun N-terminal kinase [JNK] and inhibitor of nuclear factor κ-B kinase subunit β [IKKβ]), which leads to serine phosphorylation of insulin receptor substrate (IRS)-1 and thus impairs propagation of insulin signaling and contributes to IR. Image created with BioRender.com ( https://biorender.com/ufi7u1a ).

Within pancreatic islets, circulating cytokines such as γ-interferon (IFN-γ), TNF-α, and IL-1β induce β-cell expression of the C-X-C motif chemokine ligand 10 (CXCL10), which binds the C-X-C motif chemokine receptor 3 (CXCR3) on autoreactive CD8 + T cells and directs their migration into islets ( 68 ) ( Fig. 2 , component 4). These proinflammatory cytokines impair β-cell function by triggering ER stress and activating intracellular stress kinases such as c-Jun N-terminal kinase (JNK) and IκB kinase β (IKKβ) ( Fig. 2 , component 2). These pathways lead to serine phosphorylation of insulin receptor substrate (IRS)-1 at Ser307/Ser312 ( 69 , 70 ) ( Fig. 2 , component 6). These modifications decrease insulin signaling and contribute to systemic IR, further exacerbating β-cell stress and antigen presentation. Along similar lines, obesity has been associated with increased risk and worse prognosis in other autoimmune diseases, most notably multiple sclerosis, where adiposity-induced chronic low-grade inflammation and excess production of proinflammatory cytokines are thought to modify immune responses and disease course ( 71 ).

Collectively, these data support a mechanistic model in which adipose tissue dysfunction, cytokine-driven immune activation, and β-cell stress act in concert to accelerate type 1 diabetes pathogenesis through tightly linked immune-metabolic pathways.

Adipokine Dysregulation

Leptin is a robust marker of adiposity in type 1 diabetes, particularly among adolescent boys ( 72 ). Altered leptin and adiponectin signaling provides an endocrine conduit through which obesity may affect immune tolerance and β-cell resilience. Elevated leptin restricts regulatory T-cell proliferation and promotes Th1/Th17 polarization, lowering thresholds for autoimmune activation ( 73 ) ( Fig. 2 , component 5). Adipokine dysregulation, particularly elevated leptin and reduced anti-inflammatory adipokines, has also been implicated in the pathophysiology of multiple sclerosis and rheumatoid arthritis, where leptin promotes proinflammatory immune polarization and disease severity ( 71 , 74 ). Leptin enhances immune activation by upregulating costimulatory machinery and antigen presentation ( 75 ), whereas adiponectin promotes anti-inflammatory signaling (IL-10, IL-1RA) and tolerogenic antigen presentation ( 76 ). β-Cells express receptors for both adipokines; leptin acutely suppresses glucose-stimulated insulin secretion, while adiponectin promotes β-cell survival and reduces cellular stress via ceramidase-dependent and ERK/Akt pathways ( 77 ). In addition, adipose-derived extracellular vesicles act as “adipokine-like” signals, delivering miRNAs that may reprogram β-cells and modulate islet immunobiology ( 78 ). Consistent with these mechanisms, altered leptin and adiponectin profiles have been observed around diagnosis in youth with type 1 diabetes, supportive of adipokine imbalance as a contributor to islet autoimmunity initiation and progression.

Gut Microbiota Dysbiosis

Although evidence linking gut microbiome dysregulation to obesity largely comes from type 2 diabetes, the gut microbiome represents an emerging pathway linking obesity with type 1 diabetes risk as well. Altered microbial diversity and composition (“dysbiosis”) have been observed in children who later develop islet autoimmunity or progress to type 1 diabetes ( 79–81 ). In the TEDDY study, children who seroconverted to islet autoantibody positivity demonstrated reduced microbial diversity and shifts in gut taxa preceding autoimmunity, with enrichment of proinflammatory bacteria and depletion of taxa associated with short-chain fatty acid (SCFA) production ( 79 , 82 ). Parallel research has linked obesity to dysregulated gut microbiota and altered microbial metabolites, which can impair gut barrier function, increase intestinal permeability, and promote systemic inflammation ( 83 , 84 ). Obesity in type 1 diabetes is associated with gut microbiome and metabolome composition ( 85 ); however, it is important to note that the 24-h diet-recall method used in this study provides limited insight into habitual dietary intake. SCFAs can influence insulin secretion and insulin sensitivity ( 83 ), while dysregulation of bile acid metabolism is often linked to gut microbiome alterations ( 84 ). Such dysregulation can contribute to weight gain and IR by affecting energy metabolism, glucose homeostasis, and inflammation. These changes may enhance β-cell stress and neoantigen exposure, thereby amplifying autoreactive immune responses.

Outstanding Gaps in Knowledge and Future Directions

Collectively, IR, β-cell stress, adiposity-related inflammation, adipokine dysregulation, and microbiome alterations operate as interconnected pathways within an integrated immune-metabolic model. Further research is needed to clarify the relative contribution and mechanistic significance of adipokine dysregulation and microbiome dysbiosis in islet autoimmunity and type 1 diabetes progression, as current human studies, including TEDDY, demonstrate consistent associations but do not establish causality. In this model, excess adiposity intensifies β-cell workload, promotes systemic and islet-specific inflammation, and lowers the threshold for autoimmune activation. These processes may contribute to the emergence and progression of islet autoimmunity and acceleration of development of clinical type 1 diabetes. This integrative framework highlights why clarifying obesity’s mechanistic role is central to identifying effective preventive strategies.

Substantial evidence supports a contributory role of elevated BMI in both development of islet autoimmunity and progression to stage 3 type 1 diabetes. However, the biological pathways linking excess adiposity with disease initiation and progression remain incompletely defined, and whether intentional weight reduction can modify risk is not yet known ( 43 ). Additionally, further research is needed to improve understanding of the role of dietary composition and growth-related adiposity in the timing and nature of autoimmunity onset.

Risk prediction tools may also benefit from incorporating anthropometric and metabolic markers. For instance, the M120 metabolic risk score integrates BMI alongside sex, islet autoantibody status, hemoglobin A 1c , and glucose and C-peptide at 120 min during an oral glucose tolerance test to enhance prediction of type 1 diabetes ( 86 ). Similarly, the DPTRS includes BMI as a predictor of disease risk ( 3 ). Whether BMI contributes equivalent predictive value across ages, stages of autoimmunity, and weight categories is unknown and merits systematic evaluation.

As efforts accelerate to develop DMTs that can slow or halt progression across the preclinical stages, understanding how BMI and related metabolic factors shape therapeutic response is increasingly important. Trials of agents such as teplizumab, abatacept, and anti-thymocyte globulin (ATG) indicate that although delayed progression or preserved C-peptide occurs in some participants, responses are heterogeneous ( 87–91 ). Similarly, C-peptide preservation following the onset of type 1 diabetes has been demonstrated with verapamil ( 92 ), and clinical trials are ongoing to evaluate combination therapeutic approaches based on promising preclinical findings ( 93 , 94 ). However, the potential influence of obesity on treatment response has not been systematically assessed. Given that excess weight is an established risk factor for progression, clarifying whether obesity alters the effectiveness of DMTs is a critical research priority and may inform the development of more tailored preventive approaches.

Despite increasing recognition of heterogeneity in type 1 diabetes pathogenesis, key gaps persist in delineating the immune-metabolic mechanisms through which obesity accelerates both seroconversion and progression to clinical disease. β-Cell stress, IR, adiposity-driven inflammation, adipokine dysregulation, and gut microbiota dysbiosis are central contributors, yet clarification is required as to their relative roles to inform effective preventive strategies. Future work should include prioritization of defining BMI thresholds relevant to risk, determining how obesity modulates responses to DMTs, and evaluating lifestyle or pharmacologic interventions that target obesity and its downstream metabolic and inflammatory consequences as a modifiable pathway in type 1 diabetes prevention.