Researchers led by Yue Wang and colleagues at the China-Japan Friendship Hospital analyzed data from 706 patients with type 2 diabetes, integrating eleven behavioral and metabolic variables including smoking status, physical activity, body mass index, waist circumference, blood pressure, and lipid profiles. Using latent class analysis, a statistical method that identifies hidden subgroups within a population, the team uncovered three distinct behavioral-metabolic phenotypes.

The first phenotype, characterized by relatively favorable metabolic profiles and healthier behaviors, was labeled the low-risk class. The second, marked by central obesity, elevated blood pressure, and dyslipidemia, was named the metabolic-risk class. The third, distinguished by smoking and relatively preserved metabolic measures, was called the behavioral-risk class.

Crucially, these phenotypes carried different risks of complications. Over a median follow-up of 3.9 years, patients in the metabolic-risk class faced approximately twice the risk of diabetic kidney disease compared with the low-risk class, while those in the behavioral-risk class showed roughly 1.7 times the risk of peripheral neuropathy.

The findings suggest that the metabolic-risk class, with its constellation of central adiposity and hypertension, primarily drives renal hemodynamic abnormalities and fibrosis, whereas the behavioral-risk class, dominated by smoking, promotes oxidative stress, mitochondrial dysfunction, and Schwann cell injury that underlie nerve damage.

Notably, the study also revealed that socioeconomic status shaped phenotype membership. Patients with higher educational attainment were more likely to belong to the low-risk class, while those with lower education were overrepresented in the metabolic-risk and behavioral-risk classes.

The researchers emphasize that their approach moves beyond treating type 2 diabetes as a uniform condition and instead recognizes hidden behavioral-metabolic patterns that can guide precision prevention strategies.

However, the authors caution that the study was conducted at a single center in China, and the phenotypes may differ across populations. They also note that the observational design cannot establish causation, and that longer follow-up is needed to fully capture progressive microvascular complications.

Nevertheless, the findings provide a compelling framework for identifying patients who face the greatest risks of kidney and nerve damage, offering a path toward more targeted interventions for the millions affected by type 2 diabetes worldwide.

The distinction between the two higher-risk phenotypes identified in the study deserves closer attention, because it illustrates why single-factor risk assessment often falls short in clinical practice. The Obesity-Related Metabolic Risk phenotype, which accounted for nearly forty percent of the cohort, combined obesity, physical inactivity, insulin resistance, and suboptimal blood pressure control into a single coherent profile. This clustering matters physiologically: excess adipose tissue, particularly visceral depots, secretes pro-inflammatory adipokines and free fatty acids that worsen insulin resistance, while sustained hypertension raises glomerular capillary pressure and accelerates structural kidney damage. When these abnormalities coexist, their effects on the renal microvasculature are not merely additive but potentially synergistic, which helps explain why this phenotype carried roughly double the risk of incident diabetic kidney disease compared with the low-risk reference group.

The Behavioral-Visceral Adiposity phenotype presents a more counterintuitive picture. Although members of this group had lower overall body mass index, they exhibited higher visceral fat area and engaged in unhealthy lifestyle behaviors such as smoking, excessive alcohol consumption, or poor dietary patterns. This dissociation between general adiposity and visceral fat accumulation is well recognized in metabolic research, sometimes described as the phenomenon of normal-weight individuals with metabolically adverse fat distribution. Visceral adipose tissue is more metabolically active and more lipolytic than subcutaneous fat, releasing fatty acids directly into the portal circulation and promoting hepatic insulin resistance, systemic inflammation, and endothelial dysfunction. For peripheral nerves, which depend on endoneurial microvessels that are vulnerable to oxidative injury, this combination of toxic exposures and hidden visceral fat appears particularly damaging, and indeed this phenotype showed the strongest association with incident diabetic peripheral neuropathy in the study.

The use of latent class analysis rather than conventional regression on individual risk factors represents a methodological choice worth understanding. Traditional approaches typically estimate the independent effect of each variable while statistically adjusting for the others, which can obscure the reality that patients do not experience risk factors in isolation. Latent class analysis instead treats the observed pattern of behaviors and metabolic measurements as expressions of an underlying, unobserved typology, allowing the data themselves to reveal how risk factors naturally group together in real populations. This probabilistic classification also assigns each participant a membership probability across all classes rather than forcing a rigid categorization, which better reflects the gradations seen in clinical practice.

Several technical details of the study design strengthen its internal validity. The investigators established separate outcome-specific risk sets, excluding participants who already had diabetic kidney disease when analyzing kidney outcomes and those with prevalent neuropathy when analyzing nerve outcomes. This approach reduces the possibility of reverse causation, in which subclinical complications influence the behavioral and metabolic characteristics being measured. The use of a C-peptide-based index of insulin resistance, rather than the conventional calculation based on insulin itself, was a pragmatic adaptation for a cohort in which many participants received exogenous insulin therapy, since administered insulin would otherwise distort the measurement of endogenous insulin secretion.

The socioeconomic findings add an important dimension to the interpretation. Socioeconomic status was independently associated with phenotype membership, meaning that social circumstances partly determine which behavioral-metabolic pattern a patient develops. More striking was the effect modification observed in the stratified analyses: the elevated risks associated with the Obesity-Related Metabolic Risk phenotype were concentrated among patients with lower socioeconomic status, while the neuropathy risk linked to the Behavioral-Visceral Adiposity phenotype appeared mainly among those with higher socioeconomic status. This pattern suggests that material deprivation amplifies the metabolic pathway to kidney disease, perhaps through limited access to care, poorer medication adherence, or dietary constraints, whereas the behavioral phenotype may exert its neuropathic effects more strongly in higher-status patients whose metabolic risk is otherwise comparatively contained. Such heterogeneity implies that identical clinical profiles may warrant different intervention priorities depending on a patient’s social context.

From a mechanistic standpoint, the differential organ vulnerability observed across phenotypes is consistent with established pathophysiology. Diabetic kidney disease is driven largely by hemodynamic stress, glomerular hyperfiltration, and progressive fibrosis, processes that are tightly linked to blood pressure control and adiposity-related insulin resistance. Diabetic peripheral neuropathy, by contrast, involves axonal degeneration and Schwann cell injury mediated by oxidative stress, mitochondrial dysfunction, and microvascular rarefaction, processes to which smoking and other toxic behavioral exposures contribute directly. The study’s finding that the metabolic phenotype predicted both complications while the behavioral phenotype selectively predicted neuropathy aligns with this mechanistic divergence and offers a biological rationale for phenotype-specific screening strategies.

For clinicians, the practical implication is that risk stratification in type 2 diabetes could become more informative by considering how risk factors cluster rather than evaluating each in isolation. A patient with modest body mass index but elevated visceral fat and unhealthy habits may be overlooked by conventional screening focused on weight and glycemia, yet the study suggests such patients face substantial neuropathy risk. Conversely, patients with overt obesity and hypertension, particularly those facing socioeconomic disadvantage, warrant intensified surveillance for early kidney involvement, including regular assessment of urinary albumin excretion and estimated glomerular filtration rate.

It should also be acknowledged that the cohort was drawn from a single metabolic management center in Beijing, and the specific phenotype prevalences and hazard ratios may not generalize directly to other populations with different dietary customs, smoking prevalence, or healthcare access. The observational design further means that phenotype membership itself may shift over time, and longitudinal reclassification studies would help clarify whether patients transition between phenotypes and how such transitions modify complication risk. Nonetheless, the demonstration that behavioral and metabolic characteristics combine into recognizable, clinically meaningful patterns, and that socioeconomic context conditions their consequences, provides a foundation for the kind of multidimensional risk assessment that precision prevention in diabetes increasingly demands.

Subject of Research: Behavioral-metabolic phenotypes and latent class analysis in type 2 diabetes

Article Title: Behavioral-metabolic phenotypes and differential risks of diabetic kidney disease and peripheral neuropathy in type 2 diabetes: a latent class analysis

Article References: Wang, Y., Liu, Z., Zhang, Z., Hu, A., Liu, L., Zeng, S., Liu, P., Wang, Y., & Sun, L. (2026). Behavioral-metabolic phenotypes and differential risks of diabetic kidney disease and peripheral neuropathy in type 2 diabetes: a latent class analysis. Frontiers in Endocrinology, 17, Article 1962966. https://doi.org/10.3389/fendo.2026.1962966

Image Credits: AI Generated

DOI: 10.3389/fendo.2026.1962966

Keywords: type 2 diabetes, latent class analysis, diabetic kidney disease, diabetic neuropathy, behavioral-metabolic phenotypes, socioeconomic status, visceral adiposity, Behavioral-metabolic, phenotypes, differential, risks, diabetic

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: Wang, Z., Lin, C., Xie, Y., Huang, L., Xiao, S., Chen, H., Ge, H., Zhou, Y., Chen, J., Wu, W., Wang, X., & Chen, S. (2026). Discordant muscle-adipose phenotypes and insulin resistance in prediabetes: sex-specific associations and predictive utility of TyG-WHtR combined with HDL-C. Frontiers in Endocrinology, 17, Article 1937778. 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

The research, published in Frontiers in Urology by a team led by Manuela Montatore and Giuseppe Guglielmi across the Universities of Foggia and hospitals in Brindisi, Bisceglie, and Barletti, examined 40 men who started ADT at Antonio Perrino Hospital in Brindisi between January 2023 and May 2025. Every patient had a contrast-enhanced CT scan within 30 days before treatment began and a technically comparable follow-up scan roughly a year later. Instead of ordering new imaging, the team extracted quantitative body-composition data from these existing oncologic examinations, an approach known as opportunistic imaging. The results paint a striking picture of simultaneous deterioration across three tissue compartments that conventional clinical measures largely missed.

The technical logic of the study rests on a well-validated principle of quantitative radiology: a single axial CT section at the third lumbar vertebra (L3) serves as a reliable surrogate for whole-body tissue composition. Using semiautomated threshold-based segmentation with attenuation windows of −29 to +150 Hounsfield units for muscle, −190 to −30 HU for subcutaneous fat, and −150 to −50 HU for visceral fat, the researchers measured the skeletal muscle index (SMI), bilateral psoas muscle attenuation, the subcutaneous adipose tissue index (SATI), and the visceral adipose tissue index (VATI). They then extended the analysis into the skeleton itself, placing oval regions of interest within the trabecular bone of the L1 to L3 vertebral bodies to capture vertebral attenuation, an opportunistic marker of bone health that no prior longitudinal ADT study has combined with muscle and fat in the same imaging framework.

After approximately twelve months of androgen suppression, the changes were substantial and consistent. Mean skeletal muscle index fell from 48.4 to 45.5 cm²/m², a relative decline of 6.0 percent. Mean psoas attenuation, which reflects the radiodensity of muscle tissue and indirectly captures fatty infiltration, dropped from 41.0 to 37.8 HU, a 7.8 percent reduction. Subcutaneous fat increased by 11.6 percent and visceral fat by 6.2 percent, while mean vertebral trabecular attenuation declined by 4.4 percent, from 132.1 to 126.3 HU, suggesting concurrent loss of bone quality. Twenty-three of the forty patients, or 57.5 percent, lost at least 5 percent of their skeletal muscle index, a prespecified exploratory threshold. All of these changes reached statistical significance, with p-values ranging from 0.018 to below 0.001.

What makes these findings especially compelling is what did not change: body mass index. BMI rose only modestly, from 26.7 to 27.0 kg/m², a difference that was not statistically significant, and the correlation between BMI change and SMI change was weak and nonsignificant (Spearman ρ = 0.24). The most telling statistic concerns the 23 men with clinically meaningful muscle loss: fifteen of them, or 65.2 percent, had a stable or increased BMI over the same period. Fat gain had effectively masked muscle depletion on the scale. This adiposity-driven camouflage means that a clinician relying on weight or BMI alone could wrongly conclude that a patient is tolerating treatment well, even as his metabolic reserve and physical resilience erode beneath a stable surface.

The study also explored whether any baseline feature could predict who would suffer the steepest muscle decline. In a deliberately parsimonious three-variable logistic regression model restricted to age, baseline psoas attenuation, and metastatic status, lower baseline psoas attenuation emerged as the only factor associated with a 5 percent or greater SMI decline, with an odds ratio of 1.14 per 1-HU decrease. Patients who ultimately lost the most muscle had started with noticeably darker, more fat-infiltrated muscle on their initial scans, averaging 38.9 HU compared with 43.8 HU in those with smaller declines. The authors are careful to frame this as hypothesis-generating: with only 40 patients, the model cannot establish psoas attenuation as an independent predictive biomarker, but it raises the possibility that a single number extracted from a pre-treatment scan could one day flag men at highest risk before therapy begins.

Why should androgen deprivation remodel muscle, fat, and bone simultaneously? Testosterone is an anabolic hormone that supports protein synthesis in skeletal muscle and suppresses adipogenesis. When it is pharmacologically eliminated, muscle catabolism accelerates, fat accumulates preferentially in visceral depots, and the delicate coupling between muscle and skeleton weakens. These compartments are not independent silos; they communicate through mechanical loading, hormonal signaling, inflammatory pathways, and metabolic crosstalk. A growing body of evidence links these tissue changes to hard clinical outcomes. Previous work has associated skeletal muscle loss during ADT with increased non-cancer mortality in high-risk prostate cancer, and more recent longitudinal studies have connected ADT-induced muscle loss and fat gain to subsequent cardiovascular events, suggesting that body composition may be a barometer of whole-patient vulnerability beyond oncologic status alone.

The reproducibility data strengthen the case that these measurements are robust rather than artifacts of measurement technique. When a second blinded radiologist independently re-analyzed ten patients’ paired scans, interobserver agreement was excellent: intraclass correlation coefficients reached 0.97 for SMI, 0.94 for psoas attenuation, 0.98 for SATI, 0.97 for VATI, and 0.93 for vertebral trabecular attenuation. The team also enforced strict technical comparability, requiring all paired scans to use the same portal venous contrast phase, matched tube voltage (120 kVp in 87.5 percent of pairs), and similar reconstruction thickness, because enhancement phase and acquisition parameters can shift attenuation values and contaminate longitudinal comparisons. Vertebral analysis proved feasible in 92.5 percent of potentially assessable vertebral levels, with metastatic involvement, compression deformities, or artifacts accounting for the few exclusions.

Important caveats temper the enthusiasm. The study was retrospective, single-center, and small, and it lacked an untreated comparator group, so the observed remodeling cannot be attributed exclusively to ADT. Aging, cancer progression, chemotherapy, radiotherapy, androgen-receptor pathway inhibitors, comorbidities such as diabetes and cardiovascular disease, and lifestyle factors may all have contributed. The five deaths recorded during follow-up were far too few for any survival inference, and the vertebral attenuation values from contrast-enhanced scans were explicitly not treated as DXA-equivalent bone mineral density or as diagnostic of osteoporosis. Patients who died before the follow-up scan could not contribute to the paired analysis, introducing potential survival bias. The authors emphasize that the study demonstrates feasibility, not proven clinical benefit, and that no treatment decisions should yet be triggered by these measurements.

Even so, the implications are provocative. CT scans are among the most common examinations in oncology, acquired routinely for staging, surveillance, and treatment assessment, and every one of them contains quantitative information about muscle, fat, and bone that currently goes unused. If larger, prospective, controlled studies confirm that integrated CT-derived phenotyping predicts fractures, metabolic complications, cardiovascular risk, or survival during ADT, then the humble staging scan could evolve into a longitudinal body-composition monitoring tool requiring no additional radiation, cost, or patient burden. For the millions of men worldwide who depend on androgen suppression to keep their cancer in check, the study suggests a future in which the price of treatment is measured not just in tumor control, but in the health of every tissue it touches, visible in images doctors already possess.

Subject of Research: CT-derived body composition changes during androgen deprivation therapy for prostate cancer

Article Title: Longitudinal CT-derived muscle–fat–bone remodeling following androgen deprivation therapy initiation in prostate cancer

Article References: Montatore, M., Muscatella, G., Masino, F., Muscogiuri, E., & Guglielmi, G. (2026). Longitudinal CT-derived muscle–fat–bone remodeling following androgen deprivation therapy initiation in prostate cancer. Frontiers in Urology, 6, Article 1985221. https://doi.org/10.3389/fruro.2026.1985221

Image Credits: AI Generated

DOI: 10.3389/fruro.2026.1985221

Keywords: androgen deprivation therapy, prostate cancer, computed tomography, body composition, skeletal muscle index, sarcopenia, visceral adiposity, vertebral trabecular attenuation, opportunistic imaging, bone health, BMI, oncology

The research team, led by investigators at Tehran University of Medical Sciences, turned to the 2021 Iran STEPS survey, a nationally representative assessment conducted under the World Health Organization’s STEPwise approach to noncommunicable disease risk factor surveillance. That framework, used in dozens of countries, gathers standardized information on behavioral and physical risk factors along with biochemical measurements, making it one of the most valuable data sources for studying chronic disease in low- and middle-income settings. From the survey, the researchers analyzed records for 17,945 participants, giving them a sample large enough to test how well different adiposity measures performed across sexes, age groups, and urban versus rural populations.

The measures under scrutiny represent a family of so-called visceral adiposity indices. Unlike BMI, which lumps together muscle, bone, water, and fat into a single number, these indices attempt to capture the metabolically dangerous fat that accumulates around internal organs. The lipid accumulation product, or LAP, combines waist circumference with fasting triglyceride levels, reflecting the idea that a thick waist paired with fat-clogged blood signals overflowing energy stores spilling into ectopic deposits. The visceral adiposity index, or VAI, goes further, weaving together waist circumference, triglycerides, high-density lipoprotein cholesterol, and body mass index itself into a sex-specific formula designed to estimate visceral fat dysfunction. The waist-triglyceride index, or WTI, is a simpler cousin, pairing waist measurement directly with circulating triglycerides. All three can be computed from a tape measure and a standard blood panel — no CT scanner or dual X-ray absorptiometry required.

That accessibility is precisely what makes the results striking. Imaging techniques such as computed tomography and MRI remain the gold standards for quantifying visceral fat directly, and DEXA scans can map regional fat distribution with precision, but none of these are realistic tools for population screening in most of the world. The indices tested in this study require nothing more exotic than equipment already present in a basic clinic. If a tape measure and a routine lipid test can outperform BMI for flagging diabetes risk, the implications for primary care in resource-constrained settings could be substantial, particularly in regions like the Middle East and North Africa where diabetes prevalence has climbed steeply over recent decades.

To assess performance, the researchers employed two complementary statistical approaches. First, they used multivariable logistic regression to examine how strongly each index was associated with prevalent diabetes — that is, diabetes identified at the time of the survey — while accounting for other variables. All of the indices, including BMI itself, showed significant associations with the disease, confirming that every measure of adiposity carries some signal. But association alone does not settle the question of usefulness. A screening tool must also discriminate: it must reliably separate people who have the condition from those who do not. For that, the team turned to receiver operating characteristic analysis, plotting true-positive rates against false-positive rates across the full range of each index’s values and summarizing the trade-off with the area under the curve, or AUC.

The results were revealing. When the researchers added LAP or WTI to a base predictive model, discriminatory ability rose significantly, reaching an AUC of 0.816 for LAP and 0.815 for WTI, with confidence intervals spanning roughly 0.804 to 0.827. BMI, by contrast, failed to deliver the same boost. Indeed, every anthropometric index examined outperformed the base model, but the lipid-waist hybrids stood out as the clear leaders. The team also compared the indices using Somers’ D, a rank-based measure of association, and conducted stratified analyses by sex, age group, and place of residence to check whether the advantage held across different segments of the population. The consistency of the signal across these strata strengthens the case that the finding is not an artifact of one particular subgroup.

Why should waist-and-blood measures beat BMI at detecting diabetes? The answer lies in the biology of fat storage. Subcutaneous fat, the layer beneath the skin, is relatively inert compared with visceral fat packed around the liver, pancreas, and intestines. Visceral adiposity drains directly into the portal circulation, flooding the liver with free fatty acids and promoting insulin resistance — the fundamental defect underlying type 2 diabetes. Triglyceride levels serve as a convenient biochemical echo of this process, rising as fat handling becomes dysregulated. An index that combines a central waist measurement with elevated triglycerides therefore captures two independent markers of the same underlying pathology: fat in the wrong place and fat metabolism gone awry. BMI, meanwhile, can be elevated in muscular individuals with low metabolic risk and deceptively normal in people with thin-outside-fat-inside phenotypes, a pattern increasingly recognized in South Asian and Middle Eastern populations.

This last point carries particular relevance for Iran and its neighbors. Research over the past two decades has shown that people in many Asian and Middle Eastern populations develop metabolic complications at lower BMIs than Europeans, prompting bodies such as the International Diabetes Federation to recommend lower waist circumference thresholds for these groups. A screening strategy built on BMI alone may therefore systematically underestimate risk in exactly the populations where diabetes is expanding fastest. The Iran STEPS analysis, conducted in a country where diabetes is a major contributor to morbidity and mortality, offers a direct test of whether alternative indices could close that gap — and the answer appears to be yes, at least for identifying prevalent disease.

The authors are careful about the limits of their work. Because the study is cross-sectional, it captures a snapshot of who had diabetes at the time of the survey rather than tracking who develops it over time. Discriminating prevalent cases is not the same as predicting incident disease, and the researchers explicitly call for prospective studies to evaluate whether LAP and WTI can forecast future diabetes and not merely flag existing cases. They also note that the indices were tested within a single national survey, and while the STEPS methodology supports comparability, validation in other populations and health systems remains an open task. The study was approved by the research ethics committee of Tehran University of Medical Sciences and conducted with written informed consent from all participants, in line with the Declaration of Helsinki.

Even with those caveats, the study lands at a moment when health systems worldwide are searching for low-cost ways to target prevention. Diabetes screening at scale cannot depend on imaging or specialized testing, and the global burden of the disease continues to grow fastest in developing regions where resources are thinnest. If two numbers — a waist measurement and a triglyceride value — can meaningfully sharpen the identification of people living with diabetes beyond what BMI achieves, clinicians and public health planners gain a practical upgrade to their toolkit at essentially no added cost. The message from this large Iranian dataset is not that BMI should be discarded, but that it should no longer stand alone. The fat that endangers metabolic health hides in the abdomen and in the blood, and the simplest measures of both may finally be ready for prime time.

Subject of Research: Comparison of visceral adiposity indices with body mass index for detecting prevalent diabetes in the Iranian population

Article Title: Comparison of novel visceral adiposity indices with body mass index for identification of prevalent diabetes: findings from the 2021 Iran STEPS survey

Article References: ErfanFazel, A., Semnani, K., Mohseni, S., Khosravi, S., Pejman Sani, M., Tabatabaei-Malazy, O., & Qorbani, M. (2026). Comparison of novel visceral adiposity indices with body mass index for identification of prevalent diabetes: findings from the 2021 Iran STEPS survey. BMC Endocrine Disorders. https://doi.org/10.1186/s12902-026-02533-2

Image Credits: AI Generated

DOI: 10.1186/s12902-026-02533-2

Keywords: diabetes, body mass index, visceral adiposity, lipid accumulation product, waist-triglyceride index, waist circumference, obesity, Iran STEPS survey, insulin resistance, screening, triglycerides, epidemiology

The study, published in BMC Endocrine Disorders, recruited 7,023 adults with type 2 diabetes mellitus from two medical centers in China between June 2020 and August 2025. Its central tool was the triglyceride-glucose-Chinese visceral adiposity index, abbreviated TyG-CVAI, a calculated composite that merges two established markers of metabolic risk. The first component, the triglyceride-glucose index, is a simple arithmetic transformation of fasting triglyceride and glucose concentrations that serves as a widely validated proxy for insulin resistance. The second, the Chinese visceral adiposity index, integrates age, sex, body mass index, waist circumference, triglycerides and high-density lipoprotein cholesterol into a score that estimates the burden of fat packed around internal organs, the metabolically aggressive depot most strongly linked to cardiovascular and endocrine disease.

By multiplying these two risk dimensions into one index, the investigators created what they describe as a joint signature of obesity and insulin resistance. The logic is straightforward: neither insulin resistance nor visceral adiposity alone captures the full metabolic picture, and patients who carry both tend to fare worst across a range of outcomes. What remained unclear before this study was whether that combined burden also extends to the thyroid axis, specifically to what endocrinologists call thyroid hormone sensitivity, the efficiency with which circulating thyroid hormones feed back to the brain and act on peripheral tissues.

Thyroid hormone sensitivity is notoriously difficult to measure directly. Instead, researchers rely on composite laboratory indicators derived from routine thyroid function tests. The study employed four of them. The thyroid feedback quantile index, or TFQI, compares an individual’s free T4 level against the population distribution while factoring in thyroid-stimulating hormone, yielding a population-normalized measure of how well the hypothalamic-pituitary-thyroid feedback loop is calibrated. The thyroid-stimulating hormone index, or TSHI, and the thyrotroph T4 resistance index, or TT4RI, quantify how much the pituitary has to be driven, or how much T4 is circulating, to maintain a given TSH level, with higher values suggesting the brain and body are resisting the hormone’s signal. The ratio of free triiodothyronine to free thyroxine, FT3/FT4, offers a peripheral window into how readily T4 is converted into the more active T3.

The results were consistent across the main sensitivity markers. In fully adjusted multivariable linear regression models that accounted for demographic, lifestyle and clinical confounders, each one-standard-deviation increase in TyG-CVAI was significantly associated with an elevated TFQI, with a beta coefficient of 0.01 and a 95 percent confidence interval of 0.00 to 0.02, reaching a p-value of 0.019. The same pattern held for TSHI, which rose by 0.07 per standard deviation of the composite index, with a confidence interval of 0.02 to 0.12 and a p-value of 0.005. The strongest signal appeared for TT4RI, which climbed by 3.06 points per standard deviation increase, with a confidence interval of 2.07 to 4.05 and a p-value below 0.001. Higher TFQI, TSHI and TT4RI values all point in the same direction: reduced sensitivity to thyroid hormone, meaning the pituitary must work harder, and more hormone circulates, to achieve the same biological effect.

The researchers also stratified participants into tertiles, or thirds, of TyG-CVAI. Those in the highest third showed markedly greater increases in the sensitivity indices compared with those in the lowest third. For TT4RI, the difference between the top and bottom tertiles was a beta of 6.30, with a confidence interval of 3.90 to 8.70 and a p-value below 0.001, a substantial gap for an index derived from routine blood work. The graded relationship across tertiles strengthens the biological plausibility of the association, because a dose-response pattern is harder to explain by chance or confounding than a simple binary difference.

Two subgroup findings deserve particular attention. The associations were more pronounced in women and in patients with a shorter duration of diabetes. The sex difference echoes a broader literature in which thyroid disorders disproportionately affect women and in which adiposity-related endocrine disruption appears to follow sexually dimorphic pathways. The finding that recently diagnosed patients show the strongest link suggests that the metabolic-thyroid coupling may be most visible early in the disease course, before long-standing diabetes, medication effects and age-related changes muddy the physiological waters. If confirmed, that could make the composite index most useful precisely at the stage when clinicians are deciding how aggressively to intervene on weight and glucose control.

The study’s design imposes important limits on interpretation. As a cross-sectional analysis, it captures a single moment in time and cannot establish whether higher TyG-CVAI causes reduced thyroid hormone sensitivity, whether impaired thyroid action promotes visceral fat and insulin resistance, or whether both arise from shared upstream drivers such as inflammation, altered fat metabolism or mitochondrial dysfunction. Reverse causation is a genuine possibility, since thyroid hormone is itself a major regulator of lipolysis, gluconeogenesis and resting energy expenditure, and reduced hormone sensitivity could plausibly feed back into the very metabolic derangements the index measures. The authors note that the associations were robust in sensitivity analyses, but only longitudinal cohorts and mechanistic studies can settle the direction of the arrow.

Even so, the clinical implications are tantalizing. The TyG-CVAI requires nothing beyond a standard metabolic panel and anthropometric measurements, making it cheap and reproducible in almost any clinic. If the index genuinely flags patients whose tissues are becoming resistant to thyroid hormone, it could help identify people with type 2 diabetes who warrant closer thyroid monitoring, or conversely, it could serve as a motivational metric, a single number that improves as patients lose visceral fat and regain insulin sensitivity, with thyroid physiology improving in parallel. The concept of thyroid hormone resistance is also attracting renewed interest in metabolic research, with some investigators proposing that impaired peripheral conversion and action of thyroid hormone may be an adaptive response to overnutrition rather than a primary defect, a hypothesis this study’s correlational data can neither confirm nor refute.

What the study does establish is a clear and quantifiable association in a large, well-characterized population: in adults with type 2 diabetes, the combined burden of insulin resistance and visceral adiposity, as captured by TyG-CVAI, rises in lockstep with laboratory markers of diminished thyroid hormone sensitivity. The work was conducted under the Declaration of Helsinki with ethical approval from both participating hospitals, and all participants provided written informed consent. The research was supported by the Science and Technology Plan Project of Taizhou and the National Natural Science Foundation of China. Whether the relationship proves causal or merely correlational, the message for patients and clinicians is the same: the metabolic and thyroid axes are talking to each other, and the conversation is loudest in those carrying the most visceral fat and the deepest insulin resistance.

Subject of Research: Association between a combined insulin resistance and visceral adiposity index and thyroid hormone sensitivity in type 2 diabetes

Article Title: Association of the triglyceride glucose-Chinese visceral adiposity index with thyroid hormone sensitivity in patients with type 2 diabetes

Article References: Liu, Y., Cai, N., Huang, R., Pan, Y., Chen, M., Feng, P., Song, Y., Zheng, C., & Chen, M. (2026). Association of the triglyceride glucose-Chinese visceral adiposity index with thyroid hormone sensitivity in patients with type 2 diabetes. BMC Endocrine Disorders. https://doi.org/10.1186/s12902-026-02562-x

Image Credits: AI Generated

DOI: 10.1186/s12902-026-02562-x

Keywords: type 2 diabetes, insulin resistance, visceral adiposity, TyG-CVAI, thyroid hormone sensitivity, TFQI, TSHI, TT4RI, cross-sectional study, endocrinology, metabolic syndrome, thyroid function

The study was conducted as a cross-sectional investigation, meaning that researchers measured diet and cardiometabolic markers at a single point in time rather than following participants over years. Participants were recruited from outpatient endocrine clinics in Zanjan, a city in northwestern Iran, and the work was approved by the Ethics Committee of Zanjan University of Medical Sciences under the Declaration of Helsinki, with all participants providing written informed consent before enrollment. The single-author study, led by Mohammed Yousif Merza of Hawler Medical University in Erbil, received no external funding, and the author declared no competing interests.

To capture habitual eating behavior, the researchers used a validated 168-item food frequency questionnaire asking participants to recall their intake over the previous year. From those responses, the team calculated the Lifelines Diet Score, or LLDS, a metric built from 12 food groups. Unlike some diet indices that simply reward or penalize foods based on arbitrary thresholds, the LLDS scores food groups according to their relative intake within the study population, weighting both healthful groups such as vegetables, fruit, whole grains, legumes, nuts, dairy, fish, and unsweetened coffee and tea, and unhealthful groups such as red and processed meat, butter and margarine, sugar-sweetened beverages, and alcohol. Participants were then sorted into tertiles, or thirds, of diet quality, allowing the investigators to compare those with the highest scores against those with the lowest.

The primary outcome was the Atherogenic Index of Plasma, abbreviated AIP, a composite measure derived from blood lipids that reflects the balance between pro-atherogenic and anti-atherogenic fractions of circulating fat. AIP is typically calculated from triglycerides and high-density lipoprotein cholesterol, and higher values are associated with a greater burden of small, dense LDL particles, which are considered particularly damaging to arterial walls. Because it integrates two routine laboratory measurements into a single number, AIP has become an increasingly popular surrogate marker in epidemiological studies of cardiovascular risk, especially in settings where advanced lipoprotein testing is unavailable.

Beyond AIP, the study examined five correlated secondary indices that capture complementary dimensions of metabolic dysfunction. The triglyceride-glucose index, or TyG, is a simple proxy for insulin resistance computed from fasting triglyceride and glucose values. The Lipid Accumulation Product, or LAP, combines waist circumference with triglycerides to estimate lipid overaccumulation in central depots. A Body Shape Index and the Body Roundness Index use waist measurements alongside height and weight to estimate the risk conferred by central adiposity beyond what body mass index alone reveals. Finally, the Visceral Adiposity Index, or VAI, integrates body mass index, waist circumference, triglycerides, and HDL cholesterol into an indirect estimate of visceral fat dysfunction. Together, these indices map glycemic, atherogenic, and adiposity-related pathways of cardiometabolic risk.

The results were consistent across the board. Participants in the highest tertile of diet quality had lower body mass index, smaller waist circumferences, lower fasting blood glucose, lower triglycerides, lower liver enzymes, and lower values on the cardiometabolic risk indices than participants in the lowest tertile. Lower circulating liver enzymes, including alanine aminotransferase, aspartate aminotransferase, and gamma-glutamyl transferase, are often interpreted as indicators of reduced hepatic fat accumulation and better liver health, which is itself tightly connected to insulin sensitivity and lipid metabolism.

In the primary adjusted statistical model, which controlled for age, sex, education level, smoking status, physical activity, and total energy intake, the highest versus lowest LLDS tertile was associated with a lower AIP, with an unstandardized regression coefficient of -0.041 and a 95 percent confidence interval spanning -0.063 to -0.019, a difference that reached statistical significance at P less than 0.001. Inverse associations were also observed for TyG, LAP, ABSI, BRI, and VAI, meaning that better diet quality tracked with lower values on every one of the secondary indices examined. The consistency of direction across markers that tap into different physiological pathways strengthens the plausibility of a genuine relationship between overall dietary pattern and cardiometabolic health in this population.

The investigators also ran a clinical sensitivity analysis, designated Model 3, which added baseline clinical-status variables and medication use to the adjustment set. In this more conservative model, the inverse association between diet quality and AIP persisted, with an unstandardized coefficient of -0.036 and a 95 percent confidence interval of -0.058 to -0.014, and a P value of 0.001. The fact that the association survived additional statistical control for clinical conditions and medications suggests that the relationship is not simply an artifact of sick participants eating differently or taking lipid-lowering drugs, although sensitivity analyses in observational research cannot eliminate every possible source of confounding.

The authors are careful to note the central limitation of the work: because the design is cross-sectional, the associations observed do not establish temporal or causal relationships. It remains possible that people with healthier metabolic profiles find it easier to eat well, or that unmeasured characteristics such as income, food security, or psychological wellbeing simultaneously shape both diet and cardiometabolic health. Reverse causation is a perennial concern in nutritional epidemiology, and only prospective cohort studies or randomized interventions can determine whether improving diet quality actually lowers these risk indices over time. The study’s authors explicitly call for prospective research to establish the longitudinal and clinical significance of the findings.

Even with those caveats, the study adds a meaningful data point to a growing international literature on diet quality scoring. Iran faces a substantial burden of obesity and cardiometabolic disease, and dietary patterns in the region have been shifting toward higher intakes of refined carbohydrates, sugar-sweetened beverages, and processed foods. Demonstrating that a validated European diet quality metric behaves as expected in Iranian adults with excess weight provides a foundation for future longitudinal work in the region and for clinicians who wish to frame dietary counseling in terms of overall pattern rather than single nutrients. As surrogate markers such as AIP and TyG continue to gain traction as accessible windows into cardiovascular and metabolic risk, studies like this one help clarify which populations stand to benefit most from population-level improvements in the food environment and individual-level dietary change.

Subject of Research: Association between diet quality measured by the Lifelines Diet Score and cardiometabolic risk indices in overweight and obese adults

Article Title: Diet quality assessed by the Lifelines Diet Score and cardiometabolic risk indices in overweight and obese Iranian adults: implications for cardiometabolic physiology

Article References: Merza, M. Y. (2026). Diet quality assessed by the Lifelines Diet Score and cardiometabolic risk indices in overweight and obese Iranian adults: implications for cardiometabolic physiology. BMC Endocrine Disorders. https://doi.org/10.1186/s12902-026-02608-0

Image Credits: AI Generated

DOI: 10.1186/s12902-026-02608-0

Keywords: Lifelines Diet Score, diet quality, Atherogenic Index of Plasma, cardiometabolic risk, obesity, insulin resistance, TyG index, visceral adiposity, nutritional epidemiology, Iran, cross-sectional study, triglycerides