Type 2 diabetes prevalence varies significantly even among individuals with similar body mass index (BMI), highlighting the importance of risk factors beyond overall body weight. While adipose tissue distribution plays a role, with android fat being more detrimental than gynoid fat, it doesn't fully explain the variation in diabetes risk. Skeletal muscle, being the most insulin-sensitive tissue, is crucial for glucose disposal. Therefore, lower skeletal muscle mass relative to adiposity might be a key indicator of type 2 diabetes risk. Previous studies using NHANES data have shown an association between lower appendicular lean mass (ALM)/BMI and increased insulin resistance in older adults, and lower skeletal muscle index with insulin resistance and diabetes in adults of all ages. However, these studies lacked control for sex-specific fat deposition patterns and sex-stratified analyses. Additionally, findings in older adults may not be directly applicable to younger adults due to age-related muscle mass decline and differences in diabetes onset BMI. This study hypothesized that less skeletal muscle mass is associated with greater diabetes prevalence in young men and women, independent of body fat distribution, with a secondary aim to explore potential race-specific differences.

Existing research indicates a correlation between lower muscle mass and increased risk of insulin resistance and type 2 diabetes, particularly in men. Studies utilizing data from the National Health and Nutrition Examination Survey (NHANES) have demonstrated this association, but limitations included insufficient control for body fat distribution patterns and a lack of sex-specific analyses. Furthermore, the existing literature mainly focuses on older adults, potentially neglecting the distinct characteristics of younger adults regarding muscle mass, body composition, and diabetes development. This study addresses these gaps by focusing on young adults (20-49 years) and controlling for sex-specific fat distribution.

This cross-sectional study analyzed data from the 2005-2006 NHANES, focusing on 1764 adults aged 20-49. Participants were excluded if pregnant, nursing, post-bilateral oophorectomy, or using specific medications affecting body composition. Body composition was measured using dual-energy X-ray absorptiometry (DXA), assessing appendicular lean mass (ALM), android and gynoid fat. Diabetes was defined by HbA1c ≥ 48 mmol/mol (6.5%), fasting glucose ≥7 mmol/l, 2-h glucose on OGTT ≥11.1 mmol/l, self-reported diagnosis, or use of diabetes medications. Covariates included age, sex, race/ethnicity, education, smoking status, and physical activity. Multivariate logistic regression models were used to assess the association between ALM/weight and diabetes prevalence, adjusting for covariates and android/gynoid fat ratio. Analyses incorporated NHANES sampling weights and addressed missing data through multiple imputation. Secondary analyses explored race-specific differences and used a self-reported-only definition of diabetes.

After controlling for age, race, height, smoking, and education, a 1% decrease in ALM/weight was associated with a 1.31 times higher odds of diabetes in men (OR 1.31, 95% CI 1.18-1.45, p=0.0001) and a 1.24 times higher odds in women (OR 1.24, 95% CI 1.05-1.46, p=0.01). Even after adjusting for android/gynoid fat ratio, the association remained significant in men (OR 1.20, 95% CI 1.04-1.37, p=0.01), but not in women (OR 1.08, 95% CI 0.90-1.30, p=0.42). Further adjustment for physical inactivity did not materially alter the results for men, but the association remained non-significant in women. A sex-combined model showed a significant association (OR 1.26, 95% CI 1.14-1.39, p=0.0001) before adjusting for android/gynoid fat and physical activity, which was attenuated after these adjustments but remained significant (OR 1.14, 95% CI 1.05-1.24, p=0.003). A secondary analysis using self-reported diabetes data showed similar, though less statistically significant, results, likely due to a smaller number of diabetes cases. Race-specific analyses were underpowered and yielded no significant findings.

This study demonstrates a significant association between lower muscle mass and higher diabetes prevalence in young men, independent of body fat distribution. This extends previous research by showing that this association persists after accounting for android and gynoid adiposity. The lack of a significant association in women might be attributed to several factors, including lower muscle mass and higher gynoid fat in women compared to men, potentially making muscle mass less crucial and gynoid fat more protective in women. The significant finding in men highlights the importance of muscle mass in maintaining metabolic health in young men and suggests that low muscle mass might be a risk factor or marker for type 2 diabetes. The lack of significant race-specific associations might stem from limited statistical power due to small sample sizes in the race-stratified analyses.

This study reveals a strong association between reduced muscle mass and increased diabetes prevalence in young men, independent of body fat distribution. The lack of a similar finding in women warrants further investigation. Future research should explore the causal relationship between low muscle mass and diabetes, potentially through longitudinal studies and intervention trials targeting muscle mass to determine whether it independently affects diabetes risk. Larger, more diverse samples are also needed to fully understand race-specific variations.

As a cross-sectional study, this research cannot establish causality. The correlation between ALM/weight and adiposity measures might lead to residual confounding, even after adjustments. The sample size limited the power of the race-stratified analyses, making it challenging to draw definitive conclusions about race-specific effects. The study's focus on young adults limits the generalizability to older populations who experience age-related muscle loss. Furthermore, the use of self-reported data on physical activity introduces potential biases.