The research team, led by Zhishan Wang and Shujiao Chen at the Third Affiliated People’s Hospital of Fujian University of Traditional Chinese Medicine, enrolled 167 adults with prediabetes and categorized them into four distinct muscle-adipose phenotypes based on their appendicular skeletal muscle index (ASMI) and visceral fat level (VFL). These phenotypes—high-muscle/low-fat, high-muscle/high-fat, low-muscle/high-fat, and low-muscle/low-fat—allowed the investigators to disentangle the independent and combined effects of muscle and fat on insulin resistance, measured using the homeostasis model assessment of insulin resistance (HOMA-IR).

What emerged was a striking metabolic paradox. Participants with the high-muscle/high-fat (HM/HF) phenotype carried the heaviest metabolic burden, displaying the highest levels of fasting blood glucose, triglycerides, and TyG-derived indices. Even more counterintuitively, the appendicular skeletal muscle index was positively correlated with HOMA-IR—defying the expected protective association between greater muscle mass and better insulin sensitivity. This finding suggests that under conditions of energy surplus, skeletal muscle mass may escape net loss and even increase in absolute terms due to heightened mechanical loading, yet myocellular insulin sensitivity and mitochondrial oxidative capacity decline markedly, coinciding with intramuscular fat accumulation.

The study’s hierarchical regression analysis revealed that the HM/HF phenotype was the strongest independent predictor of HOMA-IR, remaining significant after full adjustment for age, sex, and lipid parameters (B = 3.264, P < 0.001). In contrast, the protective effect of the low-muscle/low-fat (LM/LF) phenotype was attenuated to marginal significance after adjusting for blood lipids (B = −0.705, P = 0.082), suggesting that this association largely reflects low visceral fat rather than muscle mass per se. The final model accounted for 36.0% of the variance in HOMA-IR, with body composition phenotypes alone explaining 26.8%.

Perhaps most intriguingly, the researchers found no statistically significant multiplicative interaction between low muscle mass and high visceral fat (P = 0.089), arguing against the hypothesis that these two factors act synergistically to amplify insulin resistance in this population. Instead, the findings point toward visceral fat accumulation as the dominant driver of insulin resistance, with muscle status exerting a secondary or conditional influence within this framework. This aligns with the work of Lingvay and colleagues, who posit that excessive fat accumulation is the fundamental upstream etiology of type 2 diabetes mellitus.

The study also uncovered compelling sex-specific patterns. The HM/HF phenotype showed a particularly strong effect in men (B = 5.662, P < 0.001), whereas the effect was markedly attenuated in women (B = 1.701, P = 0.002). Conversely, the LM/LF phenotype conferred an independent protective effect exclusively in women (B = −0.914, P = 0.025). These discrepancies may stem from the differential programming of body composition distribution and insulin sensitivity by sex hormones. Age also emerged as a potential modifier: among participants younger than 60 years, HM/HF was strongly associated with increased insulin resistance (B = 4.425, P < 0.001), whereas in those aged 60 or older, this association was no longer significant. Instead, triglycerides became the only significant positive predictor in the older group (B = 0.545, P = 0.047), suggesting that in older adults, circulating lipid disturbances may replace body composition patterns as the more dominant factor associated with insulin resistance.

Beyond the muscle-fat phenotyping, the researchers identified two powerful metabolic markers that together form a practical screening tool. The triglyceride-glucose index combined with waist-to-height ratio (TyG-WHtR) emerged as the strongest correlate of HOMA-IR among all TyG-derived indices, and multivariable logistic regression confirmed it as an independent risk factor for insulin resistance with an odds ratio of 8.231 (95% CI: 2.998–22.602, P < 0.001). For each 1-unit increase in TyG-WHtR, the risk of insulin resistance increased approximately 7.2-fold. Meanwhile, high-density lipoprotein cholesterol (HDL-C) served as an independent protective factor (OR = 0.183, 95% CI: 0.044–0.765, P = 0.020), with each 1-unit increase associated with an approximately 81.7% reduction in insulin resistance risk. The Cohen’s d effect sizes for both markers exceeded the conventional large-effect threshold of 0.8, underscoring their clinical significance.

The researchers then constructed a nomogram integrating TyG-WHtR and HDL-C to predict insulin resistance risk in prediabetic individuals. The combined model achieved an area under the receiver operating characteristic curve (AUC) of 0.815 (95% CI: 0.748–0.881), outperforming TyG-WHtR alone (AUC = 0.793) and reverse-coded HDL-C alone (AUC = 0.733). Notably, while TyG-WHtR alone showed high specificity (90.6%) but low sensitivity (49.1%), the combined model achieved a markedly improved sensitivity of 84.2%, though specificity decreased to 47.2%. This profile makes the combined model more suitable for initial screening rather than confirmation: a negative result can help rule out insulin resistance, while a positive result should prompt further diagnostic evaluation.

Model validation was thorough and internally consistent. The Hosmer-Lemeshow test yielded P = 0.650, indicating no evidence of departure from perfect calibration. Calibration curves derived from 1,000 bootstrap resamples closely followed the ideal diagonal line, with the bias-corrected curve nearly coinciding with the ideal line and no clear evidence of overfitting. Decision curve analysis demonstrated that, across threshold probabilities of 0.2 to 0.8, the net benefit of the combined model remained consistently greater than both the treat-all and treat-none strategies. The clinical impact curve showed that the model maintained high event capture with manageable intervention burden up to a cost-benefit ratio of approximately 3:2. These mutually reinforcing results support the combined HDL-C and TyG-WHtR model as a simple and well-calibrated tool for clinical risk stratification in primary care settings.

The study’s conclusions carry important implications for how clinicians and patients think about body composition in the prediabetic state. Rather than relying solely on body mass index or body weight, the findings suggest that integrating visceral fat level and appendicular skeletal muscle index into a combined muscle-adipose phenotype enables a more granular assessment of insulin resistance risk. Maintaining moderate muscle mass in the context of low visceral adiposity is likely to confer the greatest metabolic benefit, while the accumulation of visceral fat—rather than low muscle mass alone—appears to be the primary metabolic threat. The researchers acknowledge several limitations, including the use of bioelectrical impedance analysis rather than gold-standard MRI or CT for body composition assessment, the relatively limited sample size from a single center, and the cross-sectional design that precludes causal inference. Larger multicenter studies with longitudinal follow-up will be needed to validate the phenotype classification and assess the long-term prognostic value of the nomogram model. Nevertheless, this work represents a meaningful step toward personalized metabolic risk assessment, moving the field beyond one-size-fits-all approaches and toward a framework that recognizes the complex interplay between muscle, fat, sex, and age in shaping metabolic health.

Subject of Research: Muscle-adipose phenotypes and insulin resistance in prediabetes

Article Title: Discordant muscle-adipose phenotypes and insulin resistance in prediabetes: sex-specific associations and predictive utility of TyG-WHtR combined with HDL-C

Article References: Discordant muscle-adipose phenotypes and insulin resistance in prediabetes: sex-specific associations and predictive utility of TyG-WHtR combined with HDL-C. (n.d.). https://doi.org/10.3389/fendo.2026.1937778

Image Credits: AI Generated

DOI: 10.3389/fendo.2026.1937778

Keywords: prediabetes, insulin resistance, body composition, visceral fat, skeletal muscle, TyG-WHtR, HDL-C, nomogram, sarcopenic obesity, metabolic syndrome, sex differences, HOMA-IR