Obesity in Type 1 Diabetes: Rethinking Risks, Complications, and Treatment Beyond the 'Lean' Image

In adults with T1DM, the prevalence of overweight and obesity varies markedly across different regions of the world. The most robust contemporary estimates come from nationally representative surveys. In the pramlintide States (U.S.), a population-based analysis used pooled National Health Interview Survey (NHIS) data from 128,571 nonpregnant adults between 2016 and 2021 [20]. BMI was calculated from self-reported height and weight and categorized as normal weight (< 25 kg/m²), overweight (25 to < 30 kg/m²), or obesity (≥ 30 kg/m²). In this nationally representative sample, 64% of adults without diabetes (DM) and 62% of those with T1DM were living with overweight or obesity, compared with 86% of those with type 2 diabetes (T2DM), indicating that excess body weight is now almost as common in adults with T1DM as in the general U.S. population [20]. By contrast, estimates from registries and clinic-based cohorts are more heterogeneous and may be limited by incomplete BMI capture and selection bias. For example, in Mexico, a national T1DM registry recorded height and weight; among 965 participants (BMI available for 469), 42.4% were living with overweight and 8.1% with obesity [21]. These prevalences were lower than those reported in the Mexican general population, which has among the highest rates of obesity worldwide [22]. In Austria, a small clinic-based study in a small cohort of 186 adults with T1DM reported that the prevalence of overweight or obesity was comparable to that of the general population [23]. However, among participants aged 30–49 years, mean BMI was significantly higher in those with T1DM than in age-matched individuals without DM (26.7 kg/m² vs. 24.8 kg/m², p < 0.001) [23]. In Belgium, a large nationwide cohort (n = 89,834; ages 1–80 years) reported overweight/obesity prevalence similar to the general population and stable over the preceding decade [24].

Data in adolescents with T1DM are equally concerning, although estimates vary by study design and time period. In the T1DM Exchange Clinic Registry in the U.S., baseline data from 5,529 adolescents with T1DM (mean age 15.4) showed a substantial burden of excess adiposity, defined using BMI-based categories [25]. Overall, 22.9% were classified as living with overweight and 13.1% as living with obesity, corresponding to approximately 36% of the cohort. The prevalence of excess weight was particularly high among girls (40.8%) and adolescents of Hispanic/Latino ethnicity (46.1%) [25]. In children and adolescents, the population-based SEARCH for Diabetes in Youth study (2001–2004) in the U.S. compared 3,953 individuals with DM (aged 3–19 years) with 7,666 youth without DM from the National Health and Nutrition Examination Survey (NHANES) [26]. Weight status was assessed using BMI. Among youth with T2DM, 10.4% and 79.4% were living overweight and obesity, respectively; among those with T1DM, 22.1% and 12.6% were living with overweight and obesity (combined 34.7%), respectively. Youth with T1DM had a higher prevalence of overweight, but not of obesity, than youth without DM (22.1% vs. 16.1%; p < 0.05) [26].

In the SWEET registry, an international consortium of pediatric diabetes centers that prospectively collects clinical data from youth with T1DM worldwide, a cross-sectional analysis of 23,026 children and adolescents with T1DM (aged 2–18 years) showed that excess weight was common, with roughly one-third classified as overweight or living with obesity [27]. Using World Health Organization (WHO) BMI standard deviation score (BMI-SDS), the prevalence of overweight and obesity was 22.3% and 7.3% in males, and 27.2% and 6.8% in females, with BMI-SDS significantly higher in girls than in boys (p < 0.0001) [27].

Despite growing evidence that overweight and obesity are now common in people with T1DM across the life course, important limitations remain in the epidemiologic literature. Contemporary, population-level estimates are still sparse for several regions, particularly parts of Asia and Australia, and many available data derive from single-center cohorts or registries with incomplete anthropometric capture, limiting generalizability and cross-country comparisons. In addition, some widely cited pediatric estimates reflect earlier eras and may not fully capture current secular trends in adiposity. Heterogeneity in study design, BMI definitions, and age distributions further complicates interpretation. Although the drivers of geographic variation are incompletely understood and may relate to differences in diabetes care access and quality, these disparities should not foster complacency, as the increasing burden of excess adiposity is likely to adversely impact outcomes in T1DM. Moving forward, updated, standardized surveillance with consistent definitions and stratified reporting (by age, sex, and key subgroups) is needed to better quantify the global burden and inform targeted prevention and management strategies.

Obesity may increase the risk of developing T1DM, supporting a bidirectional relationship between the two conditions [28–30]. According to the “accelerator hypothesis”, genetic predisposition, insulin resistance and autoimmunity drive β-cell loss through apoptosis [28, 29]. In this framework, insulin resistance, which is strongly linked to excess adiposity, leads to β-cell failure and thereby contributes to the development of T1DM [28, 29].

The accelerator hypothesis was originally formulated by Wilking on the basis of a small cohort of 168 children and adolescents (aged 1.1–15.7 years) diagnosed with T1DM between 1980 and 2002 [31]. This study found an inverse correlation between age at T1DM diagnosis and BMI-SDS six months later (r=−0.30; p < 0.001). This suggests that individuals with a higher BMI tend to develop or be diagnosed with T1DM at a younger age compared to those with a lower BMI [31].

Epidemiological evidence has provided broader support for this concept. A meta-analysis of 4 studies that treated childhood obesity as a categorical exposure reported a pooled odds ratio (OR) of 2.03 [95% Confidence Intervals (CI): 1.46–2.80)] for subsequent T1DM [32]. Using a life course Mendelian randomization approach, recent genetic evidence further supports a causal role of higher childhood body size in increasing the risk of T1DM [33]. In univariable analyses, each increment in childhood body size category was associated with approximately a twofold higher odd of T1DM [OR (95% CI): 2.05 (1.20–3.50)], and this effect persisted after adjustment for birth and adult body size in multivariable models [OR (95% CI): 2.32 (1.21–4.42)]. These findings were validated in a large T1DM meta-analysis including 15,573 cases and 158,408 controls [OR (95% CI): 1.21–3.12)], reinforcing the concept that greater childhood adiposity directly increases the risk of subsequent T1DM [33].

Apart from the child’s body weight, familial factors, particularly maternal body weight, may also influence T1DM risk. A meta-analysis of 21 studies reported that, compared with mothers with normal weight, maternal overweight and maternal obesity were associated with a higher risk of childhood-onset T1DM [relative risk (RR) (95% CI): 1.09 (1.03–1.15) and RR (95% CI): 1.25 (1.16–1.34), respectively] [34]. In addition, paternal obesity has also been linked to childhood-onset T1DM. In a pooled analysis of 132,331 children, both maternal pre-pregnancy obesity [adjusted hazard ratio (HR) (95% CI): 1.41 (1.06–1.89) and paternal obesity [adjusted HR (95% CI): 1.51 (1.11–2.04)] were associated with incident childhood-onset T1DM, with broadly similar estimates after mutual adjustment [35].

In summary, available data suggest that greater adiposity, particularly in childhood, may increase T1DM risk and/or accelerate progression to clinical disease, although certainty is limited by heterogeneous study designs and potential confounding, and it remains unclear whether adiposity primarily influences disease initiation versus acceleration. Mechanistically, elevated circulating fatty acids and glucose may impair β-cell function and survival, potentially increasing susceptibility to immune-mediated destruction [36]. Robust prospective cohorts are therefore needed in children and adults living with overweight or obesity who are at increased T1DM risk (e.g., positive islet autoantibodies or family history) to clarify causality and identify susceptible subgroups. These studies should use adiposity measures beyond BMI (e.g., waist circumference, waist-to-hip ratio, and body fat percentage), and interventional work is required to determine whether effective weight control can prevent or delay progression to T1DM and help mitigate the ongoing rise in T1DM incidence.

The contemporary obesogenic environment, characterised by widespread availability of energy-dense foods and increasingly sedentary lifestyles, has been a major driver of the obesity epidemic. Nevertheless, the factors contributing to weight gain in people with T1DM have received comparatively less attention [3].

A key contributor is intensive insulin therapy, the cornerstone of T1DM management, which can paradoxically promote weight gain. Although the mechanisms underlying insulin-associated weight gain are not fully elucidated, several explanations have been proposed [2, 3]. First, improved glycemic control reduces plasma glucose below the renal threshold for excretion, leading to decreased glycosuria and greater retention of calories [2]. Second, exogenous insulin is delivered peripherally rather than through the portal circulation, bypassing first-pass hepatic extraction and potentially resulting in relative peripheral hyperinsulinemia that favors lipogenesis and fat accumulation. Additional mechanisms have also been suggested, including changes in the growth hormone/IGF-1 axis, which plays an important role in regulating body composition through the balance of anabolic and catabolic processes [2, 3].

The extent to which the mode of insulin delivery influences weight trajectory remains debated. Although continuous subcutaneous insulin infusion (CSII) has been suggested to promote greater weight gain than multiple daily injections (MDI), this hypothesis is not supported by high-quality prospective randomized controlled trial (RCT) evidence [2]. In a decade-long retrospective comparison of CSII versus MDI, weight gain did not differ between groups; however, CSII was associated with greater improvements in glycemic control and lower daily insulin requirements [37]. Notably, in the Diabetes Control and Complications Trial (DCCT), weight gain was also observed in the intensively treated arm irrespective of the insulin replacement modality, underscoring that intensification itself, rather than delivery method, may be a key determinant [38].

Furthermore, the prevalent notion that T1DM solely reflects insulin deficiency is inaccurate, as insulin resistance is also an important component of the disease [39]. Obesity promotes insulin resistance and increases insulin requirements, creating a self-reinforcing cycle in which escalating insulin doses may further contribute to weight gain [3]. Insulin resistance can be present in people with T1DM, may be detectable early, and can progress over time in a tissue-specific manner [39]. Euglycemic-hyperinsulinemic clamp studies support distinct defects across key insulin-sensitive tissues [39]. Hepatic insulin resistance may be partly related to nonphysiological portal insulin gradients with subcutaneous insulin and may be compounded by relatively elevated glucagon levels and increased nonesterified fatty acid (NEFA) flux. Skeletal muscle insulin resistance is a consistent finding in T1DM and limits glucose disposal and glycogen synthesis [39]. Adipose tissue insulin resistance impairs suppression of lipolysis, further increasing circulating NEFAs [39]. Vascular dysfunction may additionally restrict insulin delivery through reduced capillary recruitment.

Lifestyle-related factors and nonphysiological peripheral insulin delivery may contribute to insulin resistance in T1DM by promoting glucotoxicity, lipotoxicity, iatrogenic hyperinsulinemia, mitochondrial dysfunction, oxidative stress, and inflammation. However, these mechanisms do not fully explain insulin resistance in T1DM, suggesting that additional disease-specific determinants are involved [39]. Emerging evidence also supports immunometabolic heterogeneity, with a subset of T1DM (“endotype 2”) exhibiting a more pronounced insulin-resistant phenotype [39]. Accordingly, targeting insulin resistance in T1DM may help attenuate cardiometabolic risk and long-term complications, and should be considered an additional therapeutic objective alongside weight management, as discussed in the section “Pharmacological Management of Obesity.”

Beyond behavioral and treatment-related drivers, familial and genetic factors are also likely to contribute to excess weight in people with T1DM. Consistent with this, as mentioned above, children of parents living with obesity appear to have a higher risk of developing childhood-onset T1DM [34]. At the genetic level, susceptibility may partially overlap between obesity and T1DM [40]. Variants in FTO, a gene involved in appetite regulation and eating behavior, have been associated with a higher likelihood of overweight and poorer metabolic control in children with T1DM [41]. In addition, in a large T1DM cohort, TCF7L2 variants, classically linked to T2DM, were associated with a milder immunologic phenotype and greater residual endogenous insulin secretion [42].

Furthermore, hormonal abnormalities in T1DM, including impaired secretion and/or action of insulin, glucagon, amylin, leptin, and other enteroendocrine hormones, can disrupt appetite regulation and energy homeostasis, thereby impairing the normal control of food intake [43].

Overall, weight gain in T1DM is multifactorial, arising from the interaction of an obesogenic environment with treatment-related (intensive insulin therapy, hypoglycemia avoidance and compensatory eating, and behavioral adaptations), metabolic (insulin resistance and hyperinsulinemia), and biological determinants (hormonal dysregulation and genetic susceptibility). The strength of evidence is greatest for insulin intensification and hypoglycemia-related behaviors, whereas the relative contribution of delivery modality and specific genetic/hormonal pathways is less certain and likely varies across subgroups and life stages. Clinically, prevention requires a proactive, individualized approach that integrates optimized insulin strategies (including technology where appropriate), structured hypoglycemia risk mitigation, and targeted lifestyle and behavioral support, while recognizing insulin resistance as an additional therapeutic target alongside weight management.

Sarcopenic obesity, characterized by reduced skeletal muscle mass and function in the presence of excess adiposity, has been extensively studied in people with T2DM. Nevertheless, data in T1DM remain limited [44]. In T1DM, the available literature has focused predominantly on sarcopenia rather than sarcopenic obesity per se [45].

In this context, unfavorable body composition may reflect chronic hyperglycemia, insulin deficiency, and diabetes-related complications, which can accelerate muscle loss and promote fat accumulation. Observational studies suggest that older age, longer diabetes duration, poorer glycemic control, and microvascular complications, particularly diabetic kidney disease, are associated with higher sarcopenia risk in people with T1DM, although findings are not fully consistent across cohorts [45]. Mechanistically, chronic inflammation, insulin resistance, and hormonal perturbations may further contribute to muscle catabolism and adipose gain [44, 45]. Clinically, sarcopenic obesity may increase frailty, functional impairment, and cardiometabolic and renal risk, highlighting the importance of assessing body composition (e.g., DXA or bioimpedance) rather than relying on BMI alone. Well-designed, T1DM-specific longitudinal studies are needed to define the prevalence, determinants, and prognostic impact of sarcopenic obesity and to identify effective prevention and treatment strategies.

Obesity in people with T1DM adds a substantial layer of complexity. In this section, we summarize the evidence on how excess adiposity affects glycemic control in children, adolescents and adults with T1DM.

In a large analysis of pediatric T1DM (median age 12.6 years), data from two large clinical registries in Germany/Austria and the U.S. (n = 32,936) showed a consistent adverse association between adiposity and glycemic control [5]. Across both cohorts, higher BMI z-scores were significantly associated with higher HbA1c levels, indicating poorer glycemic control, and with a greater frequency of severe hypoglycemia episodes [5]. This paradox has been partly attributed to excess body weight, which may contribute to impaired hypoglycemia awareness; however, it has not been replicated in subsequent studies, and the underlying mechanisms remain unclear [4]. Similarly, in adults with T1DM, a multicenter observational study including 719 individuals (mean age 41.5 ± 13.9 years) showed that participants with higher BMI had higher HbA1c levels [6].

On the other hand, a smaller cross-sectional continuous glucose monitor (CGM)-based study in 73 adults with T1DM (mean age 39 years) did not observe similar associations [46]. The proportion achieving optimal glycemic targets was similar across normal-weight, overweight, and individuals living with obesity. BMI was paradoxically positively associated with better glycemic control; however this finding likely reflects the limitations of using BMI alone to assess obesity, rather than evidence of a true “obesity paradox” in T1DM [46]. The analysis was based on CGM-derived metrics recorded over the preceding 14 days, which may be too short to fully capture typical glycemic patterns. Moreover, the small sample size limits the robustness of these findings, and larger, longer CGM-based studies are needed to determine whether this pattern truly holds.

Finally, weight gain can also negatively affect adherence to insulin therapy. Lack of adherence is common in people with T1DM, with estimates ranging from 23% to 77% [47], and may be more pronounced in those who fear gaining weight. In some individuals, this leads to intentional underdosing or omission of insulin to promote weight loss, thereby increasing the risk of diabetic ketoacidosis (DKA) as well as long-term diabetes complications. An earlier U.S. study from 1994 involving 341 girls and women with T1DM (aged 13–60 years) reported that 31% deliberately omitted insulin, with 9% doing so frequently, and approximately half of those who omitted insulin indicating that their primary motivation was weight control [48].

To sum up, the preponderance of evidence, largely from observational cohorts, supports an adverse association between excess adiposity and glycemic control in T1DM, particularly in pediatric populations where higher BMI z-scores correlate with higher HbA1c. However, findings are not entirely consistent, as smaller CGM-based studies have reported null or paradoxical associations, likely reflecting limited sample size, short CGM observation windows, and the constraints of BMI as a sole adiposity metric. Notably, CGM-derived indices provide complementary insight into glycemic quality beyond HbA1c and have been consistently linked to microvascular risk, supporting their use as additional endpoints in this context [49]. Clinically, obesity may also worsen glycemic outcomes indirectly through hypoglycemia-related behaviors and weight concerns that undermine insulin adherence, increasing the risk of DKA. Future studies should leverage longer-duration CGM metrics and more precise measures of adiposity and fat distribution in longitudinal and interventional designs to clarify causality and identify actionable strategies to improve glycemic control in people with T1DM living with overweight or obesity.

In a nationwide analysis from the Swedish National Diabetes Register, 10,184 individuals with adult-onset T1DM and 375,523 individuals with T2DM diagnosed between 2001 and 2020 were compared with 509,172 population controls from the Total Population Register and followed until 2022 [14]. In people with adult-onset T1DM, smoking, BMI, and HbA1c emerged as the principal contributors to adverse outcomes, collectively accounting for approximately 40% of CV events and 20% of deaths, underscoring the central role of modifiable cardiometabolic risk factors in shaping long-term prognosis [14].

Similarly, another nationwide analysis from the Swedish National Diabetes Registry, 26,125 adults with T1DM (mean age 33.3 years) were followed between 1998 and 2012. A BMI > 25 kg/m² was associated with an increase in risk of mortality, major cardiovascular (CV) disease, and heart failure [15]. Notably, these associations were observed in men but were not observed in women.

In the Finnish Diabetic Nephropathy Study, 4,668 adults with T1DM were included in an analysis assessing the impact of general and central obesity on the risk of heart failure [16]. General obesity was defined as BMI ≥ 30 kg/m², whereas central obesity was defined as a waist-to-height ratio ≥ 0.5. Over a median follow-up of 16.4 years, central adiposity and general adiposity, were both associated with heart failure risk. However, the association was stronger for central adiposity [HR (95% CI): 1.51 (1.26–1.81)] than for general adiposity [HR (95% CI): 1.05 (1.01–1.08)] [16].

In a large, contemporary U.S. cohort of adults living with T1DM, CV risk factors were prospectively evaluated in 8,727 participants (median age 33 years) [17]. Over a mean follow-up of 4.6 years, 325 individuals (3.7%) developed CV disease. Higher BMI was associated with an increased risk of CV disease.

In a retrospective analysis of 2,181 adults with T1DM enrolled in a nationwide Japanese registry between 2016 and 2023, participants were categorized by overweight status (overweight vs. not overweight) [18]. Coronary artery disease was more frequent in the overweight group than in the non-overweight group (8.9% vs. 3.4%; p < 0.001), and overweight remained independently associated with coronary artery disease [adjusted OR (95% CI): 2.41 (1.15–5.06)]. Interestingly, no significant associations were observed between overweight and overall CV disease or peripheral arterial disease.

Collectively, the available evidence, predominantly from large, observational cohorts, supports excess adiposity as a clinically relevant, modifiable contributor to CV disease, heart failure, and mortality in T1DM. Associations are generally consistent across settings, with signals that central adiposity may convey greater risk than BMI alone, and that effect estimates may differ by sex and population, underscoring heterogeneity in susceptibility and the importance of subgroup interpretation. While causality cannot be fully inferred from observational designs, the convergence of findings across multiple registries strengthens confidence that weight and adiposity distribution should be routinely addressed alongside traditional risk factors (e.g., HbA1c, smoking, blood pressure, and lipids) to reduce long-term CV burden in people with T1DM.

In a population-based cohort study from the Swedish National Diabetes Registry, 21,575 individuals with T1DM aged 0–39 years were followed between 1998 and 2017 [7]. In models adjusted only for calendar year, overweight/obesity was associated with a higher subsequent incidence of retinopathy; this association was observed both among individuals who had overweight/obesity at any time point, including those whose BMI later returned to the normal range [HR (95% CI): 1.30 (1.12–1.51)], and among those with persistent overweight/obesity [HR (95% CI): 1.31 (1.20–1.43)]. After multivariable adjustment for potential confounders, the association with retinopathy remained statistically significant only among individuals with persistent overweight/obesity [HR (95% CI): 1.14 (1.04–1.24)] [7].

In an Australian hospital-based cohort, 501 adults with T1DM attending a specialist clinic were identified, and clinical and biochemical data were prospectively collected between 1998 and 2011 using a dedicated patient management database [9]. In both men and women, obesity (BMI ≥ 30 kg/m²) was a predominant risk factor for retinopathy, despite comparable HbA1c levels across BMI categories.

In a retrospective study assessing risk factors for diabetic retinopathy in T1DM, medical records were reviewed for all individuals with T1DM from a tertiary center [10]. An analysis was performed comparing patients diagnosed before 18 years of age (early-onset group, n = 140) and those diagnosed at or after 18 years of age (late-onset group, n = 169). Interestingly, higher BMI was significantly associated with proliferative diabetic retinopathy in the early-onset group, whereas no such association was observed in the late-onset group [10].

Consistent with these ocular findings, obesity also appears to adversely affect renal outcomes in T1DM. In a population-based retrospective cohort study from UK electronic health records were used to examine CKD risk in 1,106 adults with T1DM. Over follow-up, 38.5% developed reduced estimated glomerular filtration rate (eGFR), 47.2% had albuminuria, and 23.8% met both criteria for CKD [11]. Higher BMI was independently associated with reduced eGFR, highlighting excess adiposity as an important contributor to renal risk in T1DM [11].

A similar pattern is observed for neuropathic complications. In the European Diabetes (EURODIAB) Prospective Complications Study, risk factors for incident neuropathy (peripheral and cardiac autonomic neuropathy) were evaluated in 1,172 adults with T1DM from 31 centers across Europe [8]. Neuropathy was assessed at baseline (1989–1991) and again at follow-up (1997–1999), over a mean follow-up of 7.3 years. By follow-up, neuropathy had developed in 276 of the 1,172 participants who were free of neuropathy at baseline (23.5%). After adjustment for other risk factors and diabetic complications, higher BMI remained independently associated with the incidence of neuropathy [8].

In a systematic review, higher waist-to-height ratio, a marker of central adiposity, was consistently associated with the presence or development of both peripheral neuropathy and cardiac autonomic neuropathy in children, adolescents, and young adults with T1DM [12]. Building on these observations, a recent DXA-based study that included both T1DM and T2DM reported that visceral adipose tissue mass and markers of central/ectopic adiposity (including trunk-to-leg fat ratio) were more strongly associated with CAN in T1DM than in T2DM [52]. This supports a role for central fat distribution as a contributor to dysautonomia risk in T1DM independently of BMI.

Available evidence, almost exclusively observational, suggests that excess adiposity contributes meaningfully to microvascular risk in T1DM, with generally consistent signals for retinopathy, diabetic kidney disease, and neuropathy even after adjustment for glycemic control and other risk factors. However, effect sizes vary across cohorts and subgroups (e.g., early- vs. late-onset T1DM), and residual confounding and differences in outcome definitions limit causal inference. In addition, adiposity may reflect both lifestyle/socioeconomic factors and mediating pathways (e.g., insulin resistance leading to higher insulin requirements). Nvertheless, the relative contribution of these mechanisms remains uncertain, as they are not uniformly measured or captured across studies.

Importantly, emerging data indicate that central/ectopic adiposity (e.g., waist-to-height ratio, visceral fat, trunk-to-leg ratio) may better capture risk than BMI alone, particularly for neuropathic outcomes such as cardiac autonomic neuropathy. Clinically, these findings support incorporating weight and fat-distribution assessment into routine complication risk stratification and prioritizing integrated interventions that address adiposity alongside established microvascular risk factors in T1DM.

Metabolic dysfunction-associated steatotic liver disease (MASLD) is defined by the coexistence of hepatic steatosis with at least one of the five typical traits of the metabolic syndrome, in the absence of clinically significant alcohol consumption and other secondary causes of steatosis [53]. As overweight and have become increasingly prevalent in people with T1DM, MASLD is being recognized with meaningful frequency in this population.

In a recent systematic review and meta-analysis including 23 studies and 13,006 adults with T1DM, MASLD was found to be relatively common, with a pooled prevalence of 22.2% (95% CI: 15.6–30.7) [54]. Among people with T1DM and MASLD, the pooled prevalence of significant fibrosis (≥ F2) was 13.3% (95% CI: 11.2–15.7) and advanced fibrosis (≥ F3) was 5.1% (95% CI 3.8–6.9). Individuals with T1DM and MASLD were more likely to be older, male, living with overweight, have longer diabetes duration, higher daily insulin requirements, and greater metabolic dysfunction, and they also exhibited a higher risk of microvascular complications compared with those with T1DM without MASLD [54]. Reflecting the emerging relevance of MASLD in T1DM, the American Diabetes Association (ADA) recommends screening for fibrosis in people with T1DM when risk factors for MASLD are present, particularly obesity, elevated aminotransferases, or steatosis as an incidental imaging finding [55].

For context, MASLD appears more prevalent in T2DM, with a meta-analysis of 156 studies (1.8 million individuals) reporting a global prevalence of 65% (95% CI 61.8–68.2) [56], whereas estimates in the general adult population are commonly in the range of 25–35%, depending on the assessment method and population characteristics [53, 57, 58].

Notably, imaging-based studies provide additional insight regarding hepatic fat burden in T1DM. In a population-based Maastricht Study, liver fat content was assessed using liver MRI, the gold-standard non-invasive imaging modality for the quantification of hepatic steatosis and fibrosis [59]. The study included 29 individuals with T1DM, 58 with T2DM, and 58 with normal glucose metabolism, matched for BMI, age, sex, and educational level. Individuals with T1DM had significantly lower liver fat content than those with T2DM and levels comparable to participants without DM. These differences were independent of obesity, lifestyle factors, dietary intake, and CV risk factors, but were attenuated after adjustment for insulin sensitivity and insulin treatment, suggesting that insulin resistance and/or insulin therapy may partly account for between-group differences [59]. Consistently, a cross-sectional study using liver MR and MR elastography evaluated hepatic steatosis and fibrosis-related stiffness in 31 people with Latent Autoimmune Diabetes in Adults (LADA), matched to 31 with T2DM (sex, BMI, and DM duration) and 31 with T1DM (sex, BMI, and age) [60]. Liver fat was quantified using proton density fat fraction (PDFF), while liver stiffness was measured by MR elastography; visceral adipose tissue was additionally assessed by DXA. Steatosis (PDFF > 5.5%) was markedly more prevalent in T2DM (54.8%) than in LADA (3.2%) and was absent in T1DM (0%) (p < 0.001), whereas liver stiffness did not differ across groups. Visceral adipose tissue correlated with PDFF in the overall cohort, while BMI tracked liver fat content and liver stiffness only in T2DM [60]. Collectively, these findings suggest that hepatic fat accumulation and determinants of liver stiffness vary across autoimmune and non-autoimmune diabetes phenotypes, likely reflecting differences in body fat distribution despite similar BMI.

Overall, the available evidence suggests that hepatic steatosis and MASLD are clinically relevant, yet potentially under-recognized, comorbidities in T1DM that are associated with adiposity and broader metabolic risk profiles, whereas advanced fibrosis appears to occur in a smaller subset. At the same time, imaging-based studies indicate that, after careful matching for adiposity and other key confounders, hepatic fat content in people with T1DM may be comparable to that of individuals without DM and lower than that observed in T2DM. Collectively, these data point to heterogeneity in MASLD risk within T1DM and highlight the need for larger, well-designed longitudinal studies with standardized imaging-based phenotyping to better define disease burden, determinants, and long-term hepatic and cardiometabolic outcomes.

T1DM is associated with an increased risk of osteoporosis and fractures [61, 62]. Compared with the general population, fracture risk in people with T1DM is approximately 4.4-fold higher for hip fractures, 1.83-fold higher for upper-limb fractures, and 2-fold higher for ankle fractures [63]. Accordingly, the ADA recommends monitoring bone mineral density using DXA in older adults with DM (≥ 65 years) and in younger individuals with DM who have multiple risk factors, typically every 2–3 years [62].

In the general population, low BMI is associated with a higher risk of osteoporosis, likely reflecting reduced mechanical loading, whereas excess adiposity is often associated with higher areal bone mineral density. However, the relationship between obesity and bone health appears more complex in T1DM [61]. In an 18-month prospective study of 136 youth with T1DM (8–17 years), with DXA assessments at baseline, 12, and 18 months, greater adiposity and more central fat distribution were associated with less favorable bone outcomes [64]. Higher C-reactive protein levels were inversely associated with total-body, pelvic, and leg bone mineral density, while higher percent body fat was inversely associated with total-body and pelvic bone mineral density; percent trunk fat was inversely associated with total-body bone mineral density. Collectively, these findings suggest that adiposity (including central adiposity) and inflammation are linked to lower bone mineral density in youth with T1DM and may contribute to altered osteometabolic pathways [64].

In addition, osteocalcin is a vitamin K-dependent, osteoblast-derived protein and a key biomarker of bone formation. In a cohort of 62 people with T1DM (mean age 51 years), higher osteocalcin levels were inversely associated with an upper-body fat deposition index, defined as the ratio of upper-body fat mass (head, arms, trunk) to lower-body fat mass (hips and legs) (adjusted β −0.484; p = 0.001) [65].

Overall, both overall adiposity and adipose distribution may influence bone health and related osteometabolic pathways in T1DM. However, further studies are needed to clarify the role of adiposity in bone health in people with T1DM.

Management of obesity requires a multidisciplinary strategy that incorporates lifestyle and behavioral interventions, including dietary modification and increased physical activity.

Dietary strategies for weight management and metabolic control have been developed and evaluated predominantly in the context of T2DM. Well-designed RCTs examining dietary approaches, such as low carbohydrate or ketogenic diets, that simultaneously optimize weight management and glycemic control in T1DM are limited, and important safety questions remain incompletely addressed [66]. Low-carbohydrate (including ketogenic) diets are associated with several concerns, particularly an increased risk of hypoglycemia [66]. For example, a small observational study of 11 adults with T1DM showed that a ketogenic diet for a mean ± standard deviation of 2.6 ± 3.3 years, was associated with optimal HbA1c levels (5.3 ± 0.4%) and minimal glycemic variability, but also with dyslipidemia and a high frequency of hypoglycemic episodes [67]. In addition, such diets may deplete glycogen stores and may thereby predispose to DKA in people with T1DM [66].

The evidence available to date is inconsistent and sometimes conflicting. A 3-month pilot RCT in adults aged 19–30 years with T1DM and a BMI 27–40 kg/m² (n = 38) compared the effect of three dietary patterns: a hypocaloric low carbohydrate diet, a hypocaloric Look AHEAD diet (i.e., moderate low fat), and a Mediterranean diet (not calory restricted), on body weight and glycemic indices [68]. The results showed no clear advantage of caloric restriction for combined weight (average 2.7 kg) and glucose outcomes, implying that patient preference and sustained adherence may be the principal drivers of dietary effectiveness. Furthermore, a review of eight studies examining low-carbohydrate diets in T1DM found inconsistent effects on BMI and total daily insulin dose (TDD) [69]. The small sample sizes across these studies prevented a meta-analytic assessment [69].

These findings indicate that, in people with T1DM, weight management is most appropriately pursued through individualized dietary plans that emphasize sustainable caloric reduction while ensuring safety, particularly regarding the risks of both hypoglycemia and hyperglycemia. Well-designed RCTs are required to define the optimal nutrition-based strategies for individuals with T1DM living with obesity.

Similar to dietary strategies, data regarding weight loss through exercise in individuals with T1DM are limited. A comprehensive summary in individuals with T1DM has been thoroughly described [70, 71]. Briefly, a central challenge is that exercise in T1DM is associated with an increased risk of hypoglycemia, which constitutes one of the major barriers to regular physical activity [70, 71]. A multicenter pilot RCT (ISRCTN61403534) is currently assessing the effectiveness and cost-effectiveness of a structured education program (The EXercise in people with Type One Diabetes, EXTOD) for people with T1DM [72]. Furthermore, advances in hybrid closed-loop systems could also enhance safety during physical activity. These systems improve insulin delivery and glucose level alignment and potentially reduce the risk of hypoglycemia during exercise.

To sum up, the evidence base for lifestyle-based weight management in T1DM remains limited and heterogeneous, with few adequately powered RCTs and most data derived from small observational studies or short-duration pilots. The modest weight changes reported in available trials (e.g., 2–3 kg over 3 months) are clinically meaningful for some individuals, but do not yet establish superiority of any single dietary pattern over another; sustained adherence and safety appear to be the dominant determinants of benefit. Applicability also varies across key T1DM subgroups: low-carbohydrate/ketogenic approaches may be higher risk in individuals with prior DKA, those prone to ketosis, those using large insulin dose reductions, or those with eating disorder risk/insulin omission behaviors and therefore require careful supervision. Conversely, people using CGM and hybrid closed-loop systems may be better positioned to adopt structured diet and exercise programs by reducing hypoglycemia burden and enabling safer insulin adjustments around activity. Clinically, the most pragmatic approach is individualized, preference-sensitive dietary counseling and gradual activity increases, coupled with structured insulin adjustment and hypoglycemia/DKA risk mitigation, while emphasizing the need for longer-term, T1DM-specific RCTs that stratify by age, adiposity phenotype, and technology use to define both efficacy and safety.

Bariatric procedures were historically categorized as “restrictive” or “malabsorptive” based on their presumed primary mechanism [110]. However, this classification is now largely outdated because each operation exerts weight-loss and metabolic effects through multiple, overlapping pathways. Roux-en-Y gastric bypass and vertical sleeve gastrectomy are the most commonly performed operations. Notably, most evidence for these techniques comes from studies in T2DM, whereas data in T1DM are largely retrospective, involve small cohorts, and include heterogeneous patient populations [111]. As bariatric procedures and their mechanisms have been described in detail previously [111], we focus here on the most recent data and the outcomes and safety considerations most relevant to T1DM.

The majority of safety and efficacy outcomes of bariatric surgery in T1DM are published with small sample sizes [111]. Overall, bariatric procedures have been associated with substantial and sustained reductions in BMI, TDD, and improvements have been reported in obesity-related comorbidities, including hypertension and dyslipidaemia [111]. In contrast, effects on glycemic control appear modest and are frequently not statistically significant [111]. Importantly, safety concerns have been raised, as peri-operative and post-operative adverse events often relate to metabolic instability, including DKA, severe hypoglycemia, and increased glucose variability. However, the conduct of large-scale prospective trials in this population is challenging, given that bariatric surgery is performed relatively infrequently in people with T1DM [111]. Accordingly, prospective studies are warranted to better delineate the long-term efficacy and safety of bariatric surgery in T1DM, but achieving adequate sample sizes will likely require multicentre, international collaboration [2].