The research team, led by Anastasia Trouva of Karolinska Institutet together with colleagues in Sweden and Norway, did not set out to run a new trial. Instead, they mined data from two earlier randomized, double-blind, placebo-controlled studies, the Pilot study and PregMet, in which pregnant women with PMOS had been assigned to metformin or placebo from the first trimester until delivery. Blood samples had been drawn at inclusion, between gestational weeks 5 and 12, and again at weeks 19, 32 and 36. That unusually dense sampling schedule allowed the investigators to reconstruct how thyroid function evolved over time and to test whether that evolution predicted subsequent metabolic markers, rather than simply correlating two snapshots taken at the same moment.

The metabolic readouts in the analysis were the standard currency of insulin resistance research. Fasting and post-load glucose were measured during 75-gram oral glucose tolerance tests at baseline, week 19 and week 32, with an additional fasting glucose at week 36. Fasting insulin was quantified centrally by ELISA, and the two values were combined into the homeostatic model assessment of insulin resistance, HOMA-IR, a proxy index calculated as fasting glucose multiplied by fasting insulin and divided by 22.5. Thyroid function was characterized through serum TSH and free thyroxine, with thyroid peroxidase antibodies assessed to flag autoimmune thyroid disease. None of the participants had overt hypothyroidism, and TSH values stayed within the manufacturer’s reference range throughout.

At entry into the trials, the women carried a mean body mass index of 28.9, at the upper edge of the overweight range, and a mean HOMA-IR of 3.2, a value generally interpreted as insulin resistance in non-pregnant populations. That baseline metabolic burden is not incidental. Insulin resistance is a pathophysiological cornerstone of PMOS, present in most affected women independently of body weight, and it compounds the natural insulin resistance of pregnancy, which intensifies sharply after mid-gestation as placental hormones rise. Women with PMOS face roughly a threefold increased risk of gestational diabetes compared with their peers, making any signal that tracks metabolic decline in this group clinically interesting.

The central result of the new analysis is an inverse one: the smaller the rise in TSH across pregnancy, the higher the glucose levels that followed. A one milliunit-per-liter smaller increase in TSH between baseline and week 19 corresponded to post-load glucose at week 19 that was 0.24 millimoles per liter higher. The same pattern held later in pregnancy, with smaller TSH increases from baseline to week 32 and to week 36 predicting higher fasting glucose at week 36. Free thyroxine told a parallel story. Lower fT4 at baseline predicted higher fasting insulin at weeks 19 and 36, and lower fT4 at weeks 19 and 32 predicted higher insulin and HOMA-IR at week 36. A smaller TSH rise was also linked to higher HOMA-IR at week 32.

Crucially, these associations emerged independently of whether the women were taking metformin or placebo. The statistical models included treatment group as a covariate, and interaction tests found no evidence that the drug modified the thyroid-metabolism links. Metformin did leave its own fingerprint: the treated group showed significant early-pregnancy reductions in insulin and HOMA-IR that the placebo group did not. But by the later trimesters the two arms converged, echoing the consistent finding from prior trials that metformin, despite its insulin-sensitizing credentials, has not reduced the incidence or severity of gestational diabetes in women with PMOS. The thyroid signal, in other words, persisted beneath and beyond the pharmacological noise.

Why would a blunted TSH rise or lower thyroxine foreshadow worsening insulin resistance? The physiology is plausible even if causality remains unproven. Thyroid hormones are master regulators of metabolic rate, stimulating hepatic glycogenolysis and gluconeogenesis, promoting adipose lipolysis, and modulating pancreatic secretion of insulin and glucagon. They also influence hepatic glucose production through a sympathetic neural pathway running from the hypothalamic paraventricular nucleus to the liver. In a healthy pregnancy, TSH drifts slightly upward from the first trimester toward term while free thyroid hormones dip minimally. A failure to mount that normal TSH increase, or a relatively low free thyroxine level, could reflect a thyroid axis that is not keeping pace with the metabolic demands of gestation, leaving glucose regulation to strain against a rising tide of placental insulin resistance.

The authors are careful to frame the work as exploratory and hypothesis-generating rather than practice-changing. Because the analyses are observational in nature, with no randomization to thyroid function, residual confounding cannot be excluded, and body mass index absorbed a substantial share of the associations in adjusted models, a reminder that adiposity shapes both thyroid physiology and insulin action. The researchers also note methodological caveats: glucose was measured locally at participating hospitals while insulin and thyroid markers were assayed centrally, trimester-specific reference ranges for thyroid hormones were unavailable, and immunoassay-based free thyroxine measurements can be influenced by the pregnancy-induced changes in thyroid-binding proteins. Whether thyroid parameters add predictive power beyond established risk factors such as BMI and baseline HOMA-IR remains an open question for future studies.

Even with those caveats, the study adds a distinctive piece to a growing puzzle. Previous work in non-PMOS populations has linked declining or low-normal free thyroxine to less favorable metabolic profiles and to gestational diabetes, and hypothyroxinemia in the second trimester has been associated with the condition. What is new here is the longitudinal, within-pregnancy dimension in a high-risk group, and the observation that the change in TSH, not merely its absolute value, carries information. A prior study in non-pregnant women with PMOS found that TSH at or above 2 milliunits per liter was associated with insulin resistance, yet the women in the current cohort mostly sat below that threshold, suggesting that even subtle shifts within the normal range may matter when metabolic vulnerability is already high.

The practical implications are likely to unfold slowly. If confirmed in prospective cohorts designed to test mechanism and prediction, thyroid trajectories could eventually complement existing metabolic markers in identifying pregnant women with PMOS who are drifting toward glucose intolerance, opening a window for earlier intervention in a condition where prevention has proven stubbornly difficult. For now, the message is one of biological insight rather than clinical prescription: in pregnancy, the thyroid and the pancreas appear to be in constant conversation, and in women with PMOS, a quieter thyroid conversation may foreshadow a louder metabolic one.

Subject of Research: Associations between maternal thyroid function and insulin resistance during pregnancy in women with polyendocrine metabolic ovarian syndrome

Article Title: Maternal thyroid function and subsequent markers of insulin resistance in pregnant women with polyendocrine metabolic ovarian syndrome

Article References: Trouva, A., Vanky, E., Alvarsson, M., Calissendorff, J., Åsvold, B. O., Ujvari, D., & Hirschberg, A. L. (2026). Maternal thyroid function and subsequent markers of insulin resistance in pregnant women with polyendocrine metabolic ovarian syndrome. Frontiers in Endocrinology, 17, Article 1884684. https://doi.org/10.3389/fendo.2026.1884684

Image Credits: AI Generated

DOI: 10.3389/fendo.2026.1884684

Keywords: thyroid function, insulin resistance, pregnancy, PCOS, PMOS, gestational diabetes, TSH, free thyroxine, HOMA-IR, metformin, endocrinology, glucose metabolism

The research team analyzed data from 919 women with a body mass index of at least 25 who initiated care through a specialized women’s health telehealth platform between January and December 2024, with follow-up extending through June 2025. More than 60 percent of the cohort carried a diagnosis of the syndrome, identified through electronic health record codes assigned according to Rotterdam criteria, the standard diagnostic framework for the condition. Participants were required to have at least six months of follow-up data for at least one laboratory measure, yielding a mean observation period of 9.6 months during which women attended an average of 6.6 physician visits and 5.8 registered dietitian visits.

The baseline metabolic picture starkly separated the two groups. Women with the syndrome were younger, on average 33.5 versus 36.3 years, but carried a higher body mass index of 35.4 versus 33.4 and weighed more, averaging 210 pounds compared with 198.6 pounds. More striking were the laboratory differences: half of the women with the syndrome showed elevated homeostasis model assessment of insulin resistance, or HOMA-IR, above 4, compared with about a third of women without the diagnosis. Nearly 30 percent had fasting insulin above 24 micro-international units per milliliter, versus 15 percent of their counterparts, and more than a third had elevated ALT, a liver enzyme reflecting hepatic dysfunction, compared with under 19 percent. Low HDL cholesterol and elevated triglycerides were also significantly more common, a pattern consistent with insulin resistance as the central physiological driver of the disorder.

Against this backdrop, the results were remarkable. Across the full cohort, women achieved mean reductions of 14 percent in HOMA-IR, 15.6 percent in fasting insulin, 13 percent in fasting glucose, 6.2 percent in hemoglobin A1c, 21.2 percent in ALT, 25.5 percent in AST, 8 percent in total cholesterol and 4.9 percent in body weight. Every one of these changes was statistically significant after correction for multiple comparisons, both in the overall cohort and separately within each diagnostic group. Crucially, unadjusted comparisons between women with and without the syndrome showed no significant differences in the magnitude of improvement across any outcome, suggesting that the benefits of the care model extended fully to those with the most complex metabolic profiles.

To understand which components of care drove these changes, the researchers used linear regression models adjusted for baseline values, age, body mass index, race and ethnicity, follow-up time and medication status. GLP-1 receptor agonist therapy emerged as the strongest predictor of weight loss, associated with an additional 10.1 pounds of weight reduction when prescribed alone and 7.1 pounds when combined with metformin, compared with no pharmacotherapy. Metformin alone was not associated with significant weight change, though all medication groups showed greater reductions in hemoglobin A1c than women receiving no drugs. Notably, medication status was not significantly associated with changes in insulin resistance markers, fasting glucose, liver enzymes or cholesterol after adjustment, hinting that the care model itself, rather than pharmacology alone, may be responsible for much of the broader metabolic improvement.

That interpretation gains support from the outcomes among women who received no medications at all. Roughly 12 percent of the cohort fell into this category, and even they achieved significant reductions in weight, fasting glucose, liver enzymes and total cholesterol. Because access to GLP-1 receptor agonists remains constrained by cost, supply and side effects, this finding carries practical weight: structured behavioral and dietary support delivered through a telehealth model appears capable of moving metabolic markers independently of drug therapy. The authors suggest this supports the value of behavior change and dietary optimization alongside, or in place of, pharmacological management.

The study also addressed a question of direct clinical relevance: whether GLP-1 receptor agonists work as well for women with the syndrome as for those without. The absence of a significant interaction between diagnosis and medication group for weight loss indicates that the weight-reducing benefit of these drugs generalizes across both populations, an important consideration given the high prevalence of overweight and obesity among affected women. One exploratory signal did emerge for hemoglobin A1c, where women with the syndrome who received no medication saw their A1c rise while their counterparts without the diagnosis saw it fall. The authors caution that this interaction, initially significant at a p value of 0.011, did not survive Bonferroni correction across the eight interaction tests performed, and should therefore be interpreted cautiously as potentially reflecting chance.

The magnitude of improvement deserves context. Reductions of 14 to 16 percent in insulin resistance markers and more than 20 percent in liver enzymes over roughly nine months compare favorably with outcomes reported in structured in-person intervention programs for these patients, which typically demand far greater resources and travel. The weight loss observed with GLP-1 receptor agonists also aligns with results from large-scale telehealth anti-obesity medication programs, reinforcing that virtual care can reproduce clinical trial-adjacent results outside controlled research settings. What distinguishes the current analysis is its multi-marker approach: rather than focusing solely on weight, it captured insulin dynamics, glycemic control, hepatic function and lipid profile, dimensions particularly relevant for a population in which insulin resistance and liver dysfunction may persist or predominate independently of weight change.

The authors are careful to enumerate limitations. The retrospective design precludes causal inference, medication assignment was not randomized, and prescribing patterns likely reflected differences in clinical severity and patient preference. Diagnosis was inferred from billing codes, introducing possible misclassification in both directions, and prescription records may not perfectly reflect actual medication use, biases that would tend to push results toward the null. The cohort was drawn from a single platform serving a predominantly privately insured population, limiting generalizability, and the requirement for at least six months of follow-up excluded women who disengaged early, who tended to have less severe metabolic profiles. Without a control group, causation cannot be firmly established, though the authors note that longitudinal data show untreated cohorts typically worsen over time, lending indirect support to a genuine treatment effect.

Despite these caveats, the findings arrive at a moment when access to integrated metabolic care remains a persistent barrier for millions of women. Recent clinical guidance emphasizes the importance of multidisciplinary management addressing both metabolic and reproductive dimensions of the syndrome, yet fragmented care pathways and shortages of specialists with relevant expertise continue to leave many patients undertreated. This study suggests that a telehealth model pairing physician oversight with registered dietitian support can deliver metabolic outcomes comparable to resource-intensive in-person programs, even for women at elevated metabolic risk. As virtual care platforms scale and payers weigh coverage decisions, real-world evidence of this kind may prove instrumental in determining whether structured metabolic care becomes a routine standard for a condition long dismissed as merely reproductive.

Subject of Research: Real-world metabolic outcomes of multidisciplinary telehealth care in women with and without polycystic ovary syndrome

Article Title: Real‐World Metabolic Improvements in Women With and Without Polyendocrine Metabolic Syndrome via a Multidisciplinary Telehealth Care Platform

Article References: Huddleston, H. G., Blank, R., Sommers, N., Hellman, N., Ramallo, F., Pham, T., & Lindquist, K. (2026). Real‐World Metabolic Improvements in Women With and Without Polyendocrine Metabolic Syndrome via a Multidisciplinary Telehealth Care Platform. Endocrinology, Diabetes & Metabolism, 9(6), Article e70332. https://doi.org/10.1002/edm2.70332

Image Credits: AI Generated

DOI: 10.1002/edm2.70332

Keywords: PCOS, telehealth, insulin resistance, metabolic health, GLP-1 receptor agonists, metformin, HOMA-IR, liver enzymes, HbA1c, registered dietitian, women's health, obesity

The review, led by researchers from Ukrainian medical universities including Danylo Halytsky Lviv National Medical University and Bogomolets National Medical University, set out to settle a question that has divided the literature for years: does transferring gut microbes from lean, metabolically healthy donors into people with metabolic disease actually improve glucose metabolism? The answer, based on the most rigorous statistical treatment yet applied to this evidence, appears to be a cautious no, at least for now.

The research team searched PubMed and Scopus for English-language randomised controlled trials published between January 2000 and May 2026. The searches identified 887 records, of which 18 studies met the eligibility criteria, covering adults with type 2 diabetes, prediabetes, insulin resistance, metabolic syndrome, obesity and metabolic dysfunction-associated steatotic liver disease. The primary outcome was the change from baseline in HOMA-IR, the homeostatic model assessment of insulin resistance, a widely used mathematical index calculated from fasting glucose and fasting insulin. Secondary outcomes included HbA1c, fasting glucose and clamp-derived peripheral insulin sensitivity, measured as the glucose disposal rate during a hyperinsulinaemic-euglycaemic clamp, the gold-standard physiological test of how well muscle tissue takes up glucose under insulin stimulation.

Only six trials, involving just 120 participants, could be pooled in the primary HOMA-IR analysis. The result was a mean difference of minus 1.29 in HOMA-IR favouring faecal microbiota transplantation, but with a 95 per cent confidence interval stretching from minus 3.04 to plus 0.47, crossing the line of no effect. Statistical heterogeneity was substantial, with an I-squared of 72 per cent, meaning that most of the variability between studies reflected real differences in trial design, populations and interventions rather than chance alone. A sensitivity analysis combining HOMA-IR with HOMA2-IR, a related but mathematically distinct index computed with updated consensus models, also produced a non-significant pooled estimate of minus 1.11 across seven trials and 190 participants.

The certainty of the evidence was rated very low for HOMA-IR and HbA1c, and low for peripheral insulin sensitivity, using the GRADE framework that weighs risk of bias, inconsistency, imprecision and other domains. Four of the 18 included trials were judged to be at low overall risk of bias, thirteen raised some concerns, and one was judged at high risk. The most common concern, affecting 14 of the 18 studies, related to the selection of the reported result, a subtle form of bias that arises when researchers choose which analyses to highlight after seeing the data.

What makes the new analysis methodologically distinctive is its deliberately conservative statistical machinery. Instead of the traditional DerSimonian-Laird random-effects model with Wald confidence intervals, which previous meta-analyses had used, the team employed Paule-Mandel estimation of between-study variance combined with Hartung-Knapp-Sidik-Jonkman adjusted confidence intervals, an approach that produces wider, more honest uncertainty bounds when the number of studies is small. The difference mattered. In the scale-mixed analysis, the conventional DerSimonian-Laird model yielded a statistically significant estimate of minus 1.07, whereas the Paule-Mandel model with Hartung-Knapp-Sidik-Jonkman inference yielded a non-significant minus 1.11 with a wider interval. In other words, whether faecal microbiota transplantation appeared to work depended on which statistical method was chosen, a finding that itself carries a warning for the field.

The influence analyses revealed another fragility. A single trial by Wu and colleagues, comparing faecal microbiota transplantation alone and combined with metformin against metformin alone in adults with newly diagnosed type 2 diabetes, exerted the greatest influence on the pooled estimate. Removing that study reduced the pooled effect from minus 1.29 to minus 0.69 and lowered heterogeneity from 72 per cent to 57 per cent. That trial was also affected by a marked baseline imbalance in HOMA-IR between groups, complicating the interpretation of its apparently strong result. Comparator design mattered throughout: the placebo- or sham-controlled analysis of three trials showed a non-significant estimate, the two autologous-transplant-controlled trials showed essentially no effect, and the single metformin-controlled trial favoured the intervention but was undermined by the baseline imbalance.

For HbA1c, the long-term marker of average blood sugar, the data were too sparse and too heterogeneous to pool across comparator types. One trial found no difference between transplantation and sham, another reported a reduction relative to metformin that was affected by the baseline imbalance, and a third reported a small within-group increase in HbA1c after the intervention. The clamp-derived insulin sensitivity findings were more intriguing: two trials assessing transplantation from lean donors produced a pooled estimate favouring the intervention, but the confidence interval was imprecise, and a separate comparison involving donors with metabolic impairment, digitised from a published figure, suggested a possible donor-phenotype effect that remained inconclusive. The authors described that comparison as hypothesis-generating rather than established evidence.

The biological rationale for the approach remains intact, the researchers emphasise. Gut microbial composition and function are linked to energy harvest, intestinal barrier integrity, immune activation, bile acid signalling and glucose metabolism. Microbial metabolites, including short-chain fatty acids, secondary bile acids, branched-chain amino acid metabolites and indole derivatives, can influence the molecular pathways that regulate glucose homeostasis. Microbiota-related endotoxaemia may promote the low-grade inflammation that underpins insulin resistance. But the review also found that microbial engraftment, the measurable persistence of donor bacteria and bacteriophages in the recipient gut, does not necessarily translate into clinical benefit. Donor-derived microbes may persist without improving glycaemic outcomes, and engraftment itself is shaped by donor composition, recipient microbiota, diet, inflammation and bile-acid profiles.

The clinical implications are stark. Current evidence does not support the routine use of faecal microbiota transplantation for improving glycaemic control, and consensus documents already recommend that the procedure be used for metabolic disorders only within clinical trials, with efficacy and long-term safety remaining uncertain outside its established role in recurrent Clostridioides difficile infection. Safety monitoring remains essential, the authors stress, noting that transmission of drug-resistant organisms has been documented after transplantation, which is why future metabolic trials should use validated stool-bank procedures, donor quarantine, repeat pathogen screening and long-term adverse-event surveillance.

The authors lay out a roadmap for what must happen next. Future trials should use a single predefined comparator, with sham-controlled designs assessing biological efficacy, autologous-transplant designs testing against a procedural and biological control, and active-drug comparisons reported as adjunctive treatment studies. HOMA-IR and HOMA2-IR should be analysed separately, trials should report adjusted between-group effects with complete dispersion measures and a predefined primary time point, and clamp-derived insulin sensitivity should be included in adequately powered mechanistic subsets. Donor metabolic phenotype should be prospectively defined, recipient characteristics, diet, medication and baseline microbiome composition should be recorded, and microbial engraftment should be accompanied by functional measurements such as metabolomics, short-chain fatty acids and bile-acid profiles.

The review acknowledges its own limitations candidly. The primary meta-analysis included only six trials and 120 participants, comparator-specific analyses contained one to three trials and were underpowered, and the protocol was not prospectively registered in PROSPERO, a transparency gap the authors themselves flag. Some effect estimates required conversion from medians or reconstruction of change-score standard deviations, populations differed in metabolic condition, baseline insulin resistance, co-interventions, administration route, dose, donor phenotype and follow-up duration, and publication bias cannot be excluded. The search was restricted to PubMed, Scopus and reference-list screening, and to English-language reports.

For now, the verdict on faecal microbiota transplantation as a diabetes therapy is a sobering one: the estimates are imprecise, heterogeneous and influenced by comparator design, and larger trials with standardised outcomes and predefined donor phenotypes are required before the procedure can be considered for metabolic disorders. The gut microbiome remains one of the most compelling therapeutic frontiers in metabolic medicine, but this analysis shows that turning microbial promise into proven clinical benefit will demand bigger trials, sharper methods and a willingness to let the data, not the hype, decide.

Subject of Research: Faecal microbiota transplantation shows no clear glycaemic benefit in adults with metabolic disorders, according to a systematic review and meta-analysis of randomised controlled trials.

Article Title: Effects of faecal microbiota transplantation on glycaemic outcomes in adults with metabolic disorders: a systematic review and meta-analysis of randomised controlled trials

Article References: Savytska, M., Halich, V.-S., Kuzmenko, L., Halabitska, I., Kamyshna, I., Petakh, P., Kamyshnyi, O., & Kobyliak, N. (2026). Effects of faecal microbiota transplantation on glycaemic outcomes in adults with metabolic disorders: a systematic review and meta-analysis of randomised controlled trials. Frontiers in Medicine, 13, Article 1983162. https://doi.org/10.3389/fmed.2026.1983162

Image Credits: AI Generated

DOI: 10.3389/fmed.2026.1983162

Keywords: faecal microbiota transplantation, HOMA-IR, insulin resistance, meta-analysis, metabolic syndrome, systematic review, type 2 diabetes mellitus, gut microbiome, Effects, faecal, microbiota, transplantation

The cross-sectional observational study enrolled 360 participants from clinical cohorts in the Congolese capital and divided them into three groups. Group I comprised 240 patients with isolated type 2 diabetes, Group II included 100 patients with isolated chronic hepatitis C infection, and Group III consisted of 20 patients in whom both diseases were present simultaneously. For every participant, the team measured fasting plasma glucose, fasting insulin, and a panel of inflammatory biomarkers: high-sensitivity C-reactive protein (hs-CRP), interleukin-6 (IL-6), and tumor necrosis factor-alpha (TNF-α). From the fasting glucose and insulin values, the researchers calculated the homeostatic model assessment of insulin resistance, or HOMA-IR, using the conventional mass-unit formula adjusted for glucose expressed in grams per liter — a technical detail that matters, because inconsistent unit conventions are a well-known source of error in HOMA-IR calculations across studies.

The headline result concerns the corrected median HOMA-IR scores. After strict recalculation of the metabolic parameters, the comorbid Group III recorded a median HOMA-IR of 7.11, significantly higher than the 5.43 observed in the type 2 diabetes group and the 1.98 seen in the chronic hepatitis C group, with a P value below 0.001. To put those numbers in context, a HOMA-IR score of 2.5 is conventionally taken as the threshold above which a person is considered insulin resistant — the state in which the body’s tissues respond poorly to insulin and the pancreas must produce ever-larger amounts of the hormone to keep blood glucose under control. The comorbid patients were not merely above that threshold; they were nearly three times above it.

The frequency of insulin resistance followed the same gradient. Defined as HOMA-IR of at least 2.5, insulin resistance was present in 95.0 percent of the dual-pathology patients, with a 95 percent confidence interval stretching from 75.1 to 99.9 percent — a range so wide precisely because the comorbid group contained only 20 individuals, but a point estimate that leaves little doubt about the direction of the effect. Among patients with type 2 diabetes alone, 84.6 percent were insulin resistant, while only 31.0 percent of those with chronic hepatitis C alone met the criterion. Within the broader hepatitis C clinical sample of 120 patients, type 2 diabetes coexisted in 16.7 percent, or 20 of 120 cases — a proportion that hints at how often the two conditions overlap in routine Congolese clinical practice.

The inflammatory biomarker panel told a parallel story. High-sensitivity C-reactive protein, IL-6, and TNF-α all displayed significant elevation in the presence of dual pathology compared with either condition in isolation. These three molecules are central players in the biology of chronic inflammation. C-reactive protein, produced by the liver in response to inflammatory signals, is one of the most reliable single indicators of low-grade systemic inflammation. IL-6 is a cytokine secreted by immune cells and, notably, by adipose tissue itself, and it acts as a messenger that recruits and activates further inflammatory responses. TNF-α, another pro-inflammatory cytokine, has long been implicated in disrupting insulin signaling pathways, promoting the serine phosphorylation of insulin receptor substrates that impairs the downstream transmission of the insulin signal.

Crucially, the study found that the inflammatory markers demonstrated a strong continuous correlation with HOMA-IR values — meaning that as insulin resistance deepened, the inflammatory burden rose in lockstep across the cohort. This correlation is biologically plausible and, in the view of many metabolic researchers, reflects a genuine mechanistic loop rather than a statistical coincidence. Chronic hepatitis C infection is known to perturb glucose metabolism through direct viral effects on insulin signaling in hepatocytes, while type 2 diabetes is itself characterized by a state of chronic low-grade inflammation driven largely by excess adipose tissue and ectopic fat deposition. When both processes operate in the same patient, the two sources of inflammatory drive can compound, feeding a vicious cycle in which inflammation worsens insulin resistance and insulin resistance, in turn, promotes further inflammation.

The clinical intersection between chronic hepatitis C and type 2 diabetes has been characterized in other regions of the world — studies from Egypt, for instance, have explored insulin receptor genetic variants in patients with both conditions, and research following direct-acting antiviral therapy has shown that clearing the virus can improve glucose metabolism — but Central Africa has remained largely a blank space on this map. The Congolese study is among the first to bring quantitative metabolic and immunological data to bear on the question in a Central African population, where the epidemiology of both diseases differs from that of North America, Europe, or North Africa. Hepatitis C in the region is often acquired through healthcare-associated transmission and remains underdiagnosed, while diabetes is expanding rapidly as urban diets and lifestyles shift.

Statistically, the team handled the comparison with methods appropriate for skewed laboratory data. Because biomarker distributions are rarely normal, inter-group comparisons were executed using Kruskal-Wallis tests — a non-parametric alternative to analysis of variance that compares medians rather than means — followed by Dunn’s post-hoc tests with Bonferroni correction to control for the inflated risk of false positives that arises when multiple pairwise comparisons are performed. The Bonferroni correction, which tightens the significance threshold in proportion to the number of comparisons, is a conservative safeguard, and the fact that the differences survived it strengthens confidence in the reported gradient of insulin resistance across the three groups.

The study’s limitations are worth acknowledging alongside its strengths. The comorbid group contained only 20 participants, which is why the confidence interval around the 95.0 percent estimate of insulin resistance is so broad, and the cross-sectional design means the data can show association but not causation — it cannot determine whether hepatitis C infection accelerates the development of diabetes, whether diabetes worsens the metabolic impact of hepatitis C, or whether both are driven by shared underlying factors such as obesity, diet, or genetic susceptibility. Longitudinal follow-up of dual-pathology patients, ideally with serial measurements of HOMA-IR and inflammatory markers before and after antiviral treatment, would be needed to disentangle the direction of the relationship.

Nevertheless, the practical message for clinicians is clear and urgent. The authors conclude that in this Congolese population, the coexistence of chronic hepatitis C and type 2 diabetes is strongly associated with an aggravated profile of insulin resistance and heightened systemic inflammation compared to either condition alone, and they underscore the need for systematic metabolic surveillance in patients presenting with dual clinical presentations. In concrete terms, that means a patient walking into a Congolese clinic with type 2 diabetes should be screened for hepatitis C, and a patient diagnosed with chronic hepatitis C should have their fasting glucose, fasting insulin, and inflammatory markers checked rather than assumed normal. The research received no specific grant from any funding agency and was approved by the Comité d’Éthique de la Recherche en Sciences de la Santé, the national ethics committee of the Republic of the Congo, with written informed consent obtained from all participants in accordance with the Declaration of Helsinki. As diabetes continues its expansion across sub-Saharan Africa and hepatitis C persists in the shadows of underfunded health systems, studies like this one make the case that the two epidemics can no longer be managed in separate silos — the metabolic consequences of their overlap are simply too severe to ignore.

Subject of Research: coexisting chronic hepatitis C and type 2 diabetes in a Congolese population

Article Title: Association of coexisting chronic hepatitis C and type 2 diabetes with insulin resistance and inflammatory profiles in a Congolese population

Article References: Varret, M. B., Ludmilhia, F. G., Wilson, N. D., Miguel, L., Moukassa, D., & Etienne, N. (2026). Association of coexisting chronic hepatitis C and type 2 diabetes with insulin resistance and inflammatory profiles in a Congolese population. BMC Endocrine Disorders. https://doi.org/10.1186/s12902-026-02643-x

Image Credits: AI Generated

DOI: 10.1186/s12902-026-02643-x

Keywords: hepatitis C, type 2 diabetes, insulin resistance, HOMA-IR, inflammation, Congo, hs-CRP, IL-6, TNF-alpha, Association, coexisting, chronic

The research team, led by Sai Long and Yao Li of the School of Physical Education at Jiangxi Normal University Science and Technology College in China, conducted the review in accordance with the PRISMA 2020 reporting standard and prospectively registered the protocol in PROSPERO. They searched six major databases—PubMed, Embase, Cochrane CENTRAL, Scopus, Web of Science, and SPORTDiscus—from inception through June 2026, screening 4,694 records and ultimately including 25 trials published between 2003 and 2026. The trials spanned Asia, Europe, North America, Australia, and the Middle East, and the exercise programs they tested were remarkably diverse: aerobic training, resistance training, high-intensity interval training, combined regimens, Pilates, yoga, and walking-based programs, lasting anywhere from six weeks to twelve months.

The headline numbers are striking. Across 15 trials involving 504 participants, exercise reduced alanine aminotransferase, or ALT, by a mean of 7.33 units per liter compared with control conditions, with a 95 percent confidence interval of −10.71 to −3.95. Aspartate aminotransferase, or AST, fell by 5.52 units per liter across 13 trials with 439 participants. Body mass index dropped by a pooled 1.54 kilograms per square meter across 17 trials, and peak or maximal oxygen uptake—VO2peak or VO2max, the gold standard measure of cardiorespiratory fitness—rose by 4.08 milliliters per kilogram per minute across seven trials. These are not trivial changes. ALT and AST are the workhorse blood markers of hepatocellular injury, and reductions of this magnitude are comparable to those seen with some pharmacological interventions studied in fatty liver disease.

But the analysis also delivered a sobering counterpoint: the pooled effects on liver fat itself, insulin resistance measured by HOMA-IR, and longer-term glycemic control measured by HbA1c did not reach statistical significance. Seven trials with 233 participants contributed liver fat or steatosis data, yielding a standardized mean difference of −0.36 with a confidence interval spanning −0.93 to 0.20—suggestive of a benefit but far from conclusive. HOMA-IR, pooled from six trials, showed a non-significant reduction of 0.49, and HbA1c, also from six trials, showed a non-significant drop of 0.18 percentage points. The authors are careful to note that improvements in BMI, glycemic markers, or fitness are clinically relevant in the broader cardiometabolic context of MASLD but should not be read as direct evidence that the liver disease itself has resolved.

The methodological machinery behind these conclusions is worth unpacking, because it reveals both the strength and the fragility of the evidence. The researchers used the Cochrane RoB 2 tool to assess risk of bias and found that no trial was judged at high overall risk; roughly half were rated low risk and the remainder had some concerns, most often involving incomplete reporting of randomization procedures or missing outcome data. They then applied the GRADE framework to rate certainty of evidence, and the results were uneven: moderate certainty for the ALT and BMI findings, low certainty for cardiorespiratory fitness, and very low certainty for AST, liver fat, HOMA-IR, and HbA1c. That gradation matters enormously for how clinicians and patients should weigh each result.

Heterogeneity was a persistent theme. Statistical heterogeneity, quantified by the I² statistic, reached 71.3 percent for ALT, 93.8 percent for AST, and 72.3 percent for VO2peak—meaning that the trials varied substantially in the size of the effects they reported. The authors addressed this in several ways. They ran leave-one-out sensitivity analyses, which generally preserved the direction of the pooled estimates. They conducted duration subgroup analyses, splitting trials into those lasting twelve weeks or less versus longer programs. Intriguingly, shorter interventions showed a larger ALT reduction—10.09 units per liter versus 2.46 units per liter for longer programs, a statistically significant difference. But the authors caution against interpreting this as evidence that shorter programs are physiologically superior; the comparison is observational, and duration may simply proxy for differences in baseline enzyme levels, exercise intensity, supervision, and adherence across trials.

One trial in particular shaped the AST picture. A 2025 study by Kim and colleagues reported an unusually large AST reduction, and when the authors excluded it in a dedicated sensitivity analysis, the pooled effect shrank from 5.52 to 3.25 units per liter while heterogeneity collapsed from 93.8 percent to 33.5 percent. The direction of the benefit held, but the magnitude proved sensitive to a single influential study. For BMI, funnel-plot asymmetry detected by Egger regression—significant at p=0.018—raised the possibility of small-study effects, though the authors stopped short of calling it definitive publication bias, noting that asymmetry can arise from multiple sources and that the BMI dataset included small, imprecise trials. No such asymmetry was detected for ALT or AST.

Perhaps the most conceptually important contribution of the review is its insistence on separating domains of response. ALT and AST are markers of hepatocellular injury, not direct measures of hepatic function, so their reduction signals biochemical improvement rather than histological resolution. Liver fat was measured with non-equivalent methods across trials—some used MRI-derived proton density fat fraction or proton magnetic resonance spectroscopy, which directly quantify intrahepatic lipid, while others used the controlled attenuation parameter from transient elastography, an indirect marker. Pooling these on a standardized scale cannot erase that measurement mismatch. Meanwhile, gains in aerobic capacity are biologically consistent with improved skeletal-muscle oxidative capacity, glucose disposal, fatty-acid utilization, and whole-body metabolic flexibility—adaptations that may reduce metabolic substrate delivery to the liver, but which the non-significant HOMA-IR and HbA1c results show were not uniform across metabolic domains.

The findings arrive amid a rapidly evolving evidence landscape. A 2024 network meta-analysis by Huang and colleagues ranked exercise modalities across metabolic and liver-function outcomes; a 2025 synthesis examined Mediterranean diet combined with exercise; and a 2026 meta-analysis by Wang and colleagues focused specifically on high-intensity interval training versus moderate-intensity continuous training, including dose-response analyses. The new review distinguishes itself by treating structured exercise as a single common exposure and applying one consistent post-intervention analytical framework across seven prespecified outcome domains. It also reports prediction intervals—ranges describing the expected effect in a future, comparable setting—which for ALT, BMI, and VO2peak all crossed the null, a statistical way of saying that the average benefit does not guarantee a uniform response in every clinical context.

For the millions of people living with MASLD, the practical message is cautiously encouraging. Structured exercise emerges as a physiologically relevant component of management, particularly when the goals include lowering elevated liver enzymes, reducing body mass, and building cardiorespiratory fitness. But the authors are explicit that the optimal prescription—the right combination of mode, intensity, and duration to produce reproducible hepatic adaptations independent of weight loss—remains unknown. They call for future trials that report exercise dose and adherence in sufficient detail, use standardized and sensitive measures of intrahepatic fat, and prespecify metabolic and cardiorespiratory outcomes. Until then, the liver enzyme improvements documented here stand as the most consistent signal that exercise does something real for the fatty liver, even as the deeper question of whether it reshapes the diseased organ itself awaits stronger evidence.

Subject of Research: Effects of exercise training on hepatic, metabolic, and cardiorespiratory outcomes in adults with metabolic dysfunction-associated steatotic liver disease

Article Title: Exercise training improves hepatic enzymes, body mass index, and cardiorespiratory fitness in adults with metabolic dysfunction-associated steatotic liver disease: a systematic review and meta-analysis

Article References: Long, S., & Li, Y. (2026). Exercise training improves hepatic enzymes, body mass index, and cardiorespiratory fitness in adults with metabolic dysfunction-associated steatotic liver disease: a systematic review and meta-analysis. Frontiers in Physiology, 17, Article 1976073. https://doi.org/10.3389/fphys.2026.1976073

Image Credits: AI Generated

DOI: 10.3389/fphys.2026.1976073

Keywords: MASLD, NAFLD, exercise training, liver enzymes, ALT, AST, body mass index, cardiorespiratory fitness, meta-analysis, liver fat, insulin resistance, HOMA-IR

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 biological logic behind the index is elegant in its simplicity. Insulin resistance refers to the reduced ability of peripheral tissues, primarily skeletal muscle, liver and adipose tissue, to respond adequately to insulin signaling, resulting in impaired cellular glucose uptake. When tissues resist insulin, the pancreatic beta cells compensate by secreting more of the hormone to maintain normal blood sugar. C-peptide, a short peptide cleaved from proinsulin when insulin is produced, is released in a one-to-one ratio with insulin but is not cleared by the liver as rapidly, making it a stable and convenient proxy for insulin secretion. By combining fasting C-peptide with fasting glucose in a single formula, the new index attempts to capture the essence of insulin resistance: how much insulin the body must produce to keep glucose in check.

What makes the commentary timely is the shifting landscape of pediatric metabolic disease. Childhood obesity continues to rise worldwide, and with it the incidence of type 2 diabetes in young people, a condition once considered exclusively adult. At the same time, a new generation of therapies, most notably glucagon-like peptide-1 receptor agonists, has been approved for use in adolescents, giving clinicians genuine pharmacological options for the first time. But identifying which children would benefit from such interventions requires a dependable, accessible measure of insulin resistance, and that is precisely where the current toolkit falls short, according to Kennedy and Hawkes.

The most widely used surrogate in pediatric research is the homeostatic model assessment of insulin resistance, or HOMA-IR, originally described by Matthews and colleagues in 1985. HOMA-IR multiplies fasting insulin by fasting glucose and applies a correction constant, exploiting the same compensatory logic as the new index. It has been validated against the euglycemic-hyperinsulinemic clamp, the gold-standard physiological test in which insulin is infused at a fixed rate and glucose metabolism is measured directly. Yet HOMA-IR depends on fasting insulin assays, which vary considerably between laboratories and lack standardization, a problem that has plagued pediatric studies for decades. Schwartz and colleagues demonstrated in 2008 that surrogate measures can diverge meaningfully from clamp-derived insulin sensitivity in children, and George and colleagues showed that their performance degrades further as glucose tolerance deteriorates from normal to prediabetes to frank diabetes.

An alternative approach sidesteps insulin assays altogether. The triglyceride glucose index, or TyG index, developed by Simental-Mendía, Rodríguez-Morán and Guerrero-Romero in 2008, uses fasting triglycerides and fasting glucose, both cheap and highly standardized laboratory measurements, to estimate insulin resistance. Studies in overweight youth, including work by Hannon and colleagues, have shown that lipid markers can help identify insulin-resistant adolescents, lending the TyG index practical appeal for large-scale screening. Glycosylated hemoglobin, or HbA1c, offers yet another window, reflecting average glycemia over roughly three months, though it captures the downstream consequences of insulin resistance rather than the underlying physiological defect itself.

Against this backdrop, El Amrousy and colleagues set out to determine whether the 20/(fasting C-peptide x fasting plasma glucose) formula could outperform these established surrogates in children with obesity. The index was first proposed by Ohkura and colleagues in 2013 as a simple and effective marker of insulin resistance in adults with type 2 diabetes, where preliminary evidence suggested it might be superior to more commonly used alternatives. Its theoretical advantage over HOMA-IR rests on the properties of C-peptide: because C-peptide is secreted equimolarly with insulin but avoids first-pass hepatic extraction and exhibits less assay variability, the formula may offer a more reproducible window into beta-cell compensation. Whether that advantage translates to children, whose insulin dynamics are shaped by growth hormone surges, puberty and developmental stage, was an open question until now.

The developmental context matters enormously. The EarlyBird cohort study by Jeffery and colleagues demonstrated that insulin resistance begins to rise before the onset of puberty, tracking with age rather than pubertal stage alone, and that the physiological insulin resistance of adolescence compounds any obesity-related resistance. This means a biomarker validated in adults cannot simply be transplanted into pediatric practice without careful testing against age-appropriate reference physiology. Kennedy and Hawkes emphasize that the definition of insulin resistance itself affects reported prevalence rates in children, as a systematic review by van der Aa and colleagues made clear, with different cutoffs and different surrogate measures yielding strikingly different estimates of how many young people are affected.

The implications of a validated, easy-to-calculate pediatric index would extend well beyond the research laboratory. A single fasting blood draw yielding C-peptide and glucose could, in principle, allow pediatricians to stratify children with obesity by metabolic risk, identifying those who might benefit most from lifestyle intervention, closer surveillance or pharmacotherapy with GLP-1 receptor agonists. It could also standardize outcome measures across clinical trials, enabling meaningful comparisons between studies that currently use heterogeneous definitions and assays. Conversely, if the index performs no better than HOMA-IR or the TyG index in children, the field would be spared the cost and complexity of adding C-peptide assays to routine pediatric testing, a result that would be almost as informative as a positive one.

Kennedy and Hawkes frame the moment as one that demands rigor rather than enthusiasm. The commentary’s title, pointing to the need to test the foundations of insulin resistance measurement in children, reflects a broader concern that decades of pediatric research have been built on surrogate measures whose validity in young populations has never been fully established. With new medications now available and childhood metabolic disease accelerating, the stakes of getting measurement right have never been higher. The study by El Amrousy and colleagues represents exactly the kind of foundational validation work the field needs, comparing a promising adult-derived formula head-to-head with the surrogates most commonly used in pediatric studies.

For now, the message for clinicians and researchers alike is one of cautious optimism paired with methodological vigilance. Insulin resistance is a treatable precursor to some of the most burdensome chronic diseases of our time, and children deserve diagnostic tools designed and validated for their unique physiology. As GLP-1 receptor agonists and other metabolic therapies move into pediatric care, the humble formula scrawled on a lab report, 20 divided by the product of two fasting blood values, may prove to be either a genuine advance or a reminder that in medicine, as in science generally, every new tool must earn its place through evidence. The Pediatric Research commentary makes clear that this evidence is finally being gathered, one carefully designed study at a time.

Subject of Research: Validation of a novel fasting C-peptide and glucose index as a surrogate biomarker of insulin resistance in children with obesity

Article Title: Insulin resistance in children: time to test the foundations

Article References: Kennedy, E. C., & Hawkes, C. P. (2026). Insulin resistance in children: time to test the foundations. Pediatric Research. https://doi.org/10.1038/s41390-026-05458-7

Image Credits: AI Generated

DOI: 10.1038/s41390-026-05458-7

Keywords: insulin resistance, children, obesity, C-peptide, HOMA-IR, TyG index, HbA1c, type 2 diabetes, pediatric research, biomarkers, GLP-1 receptor agonists, beta-cell function

The stakes of the research are considerable. According to data cited by the authors, roughly 890 million people worldwide live with obesity and 589 million adults aged 20 to 79 live with diabetes, while nearly 197 million more suffer from impaired glucose tolerance. Modern Western dietary patterns, dominated by calorie-dense, high-fat and processed foods, drive much of this crisis. Crucially, scientists now view obesity not merely as an over-accumulation of fat but as a state of chronic, low-grade tissue inflammation. That inflammation fuels insulin resistance, which in turn progresses toward type 2 diabetes, and it disrupts the regulation of metabolic hormones such as leptin and adiponectin that normally keep appetite and energy balance in check.

To model this pathology, the team turned to the workhorse of metabolic research: the high-fat diet mouse. Forty Swiss albino mice, each weighing about 23 grams and aged six to eight weeks, were randomly assigned to five groups of eight animals. One group received a normal diet, one received a high-fat diet deriving 60 percent of its calories from fat, and three treatment groups ate the high-fat diet while receiving curcumin at 90 milligrams per kilogram of body weight, disulfiram at 200 milligrams per kilogram, or both compounds together by oral gavage over 12 weeks. The researchers then tracked food intake, body weight, the Lee obesity index, fasting glucose, glycated hemoglobin, glucose tolerance, serum insulin and leptin, lipid profiles, liver enzymes, and liver histology.

The results on food intake and body weight were dramatic. From the fifth week onward, high-fat diet mice treated with either compound ate significantly less food; by week 12, curcumin-treated mice had consumed 16.76 grams and disulfiram-treated mice 17.68 grams, compared with 23.54 grams in the untreated high-fat group. The combination group ate even less, roughly 32 percent below the high-fat-only animals and lower than even the normal chow controls. Body weight followed the same pattern. The untreated high-fat mice ballooned to a mean of 36.70 grams, nearly 40 percent heavier than controls, while the curcumin group reached only 28.28 grams, a 22.94 percent reduction in weight gain, the largest numerical drop of any intervention. The combination stabilized around 29.32 grams and disulfiram alone at 29.40 grams.

Metabolic measurements reinforced the picture. The Lee obesity index, a standard morphometric gauge of adiposity, climbed above 310 in the untreated high-fat mice but stayed below that threshold in all three treated groups. Fasting blood glucose in the high-fat group reached 230 milligrams per deciliter, more than double the control value of 112.67, and glycated hemoglobin rose more than two-fold to 6.55 millimoles per mole, confirming long-term hyperglycemia and type 2 diabetes. Here the synergy became unmistakable: curcumin and disulfiram monotherapies each lowered HbA1c, but the combination brought it down to 4.68 millimoles per mole, the value closest to the control baseline of 3.10.

Glucose tolerance testing told a similar story. Untreated high-fat mice began the oral glucose tolerance test at nearly double the control baseline glucose and remained highly elevated even after 120 minutes, with an area under the curve almost 54 percent larger than controls. Both monotherapies restored 120-minute glucose to around 108 milligrams per deciliter, but the combination group cleared the glucose load fastest, peaking at just 165 milligrams per deciliter at 15 minutes and falling to 123 by the two-hour mark while maintaining the most stable overall profile. The hormonal data were equally striking. Untreated high-fat mice showed roughly five-fold elevated insulin and a six-fold increase in the HOMA-IR index of insulin resistance, along with leptin levels of 13.28 nanograms per milliliter versus 4.27 in controls, hallmarks of compensatory insulin hypersecretion and leptin resistance. The combination treatment restored insulin almost entirely to the control baseline of 0.76 nanograms per milliliter and cut leptin to 6.01, outperforming either drug alone.

The liver, a central casualty of diet-induced obesity, also benefited. Untreated high-fat mice developed severely deranged lipid profiles, with triglycerides of 120.54 milligrams per deciliter and cholesterol of 208.23 versus control values of 96.20 and 92.20 respectively, alongside sharply elevated LDL and depressed HDL. The combination treatment brought all four lipid parameters back to levels statistically indistinguishable from controls. Liver injury enzymes told the same tale: serum SGPT rose from 42.96 to 87.28 units per liter and SGOT from 70.18 to 152.40 in the high-fat group, but the combination drove SGPT down to 43.99, essentially the control value, and SGOT to 105.93, better than either monotherapy. Under the microscope, the untreated high-fat livers showed inflammatory cell infiltration, ballooning hepatocytes, and extensive intracellular fat droplets, while the combination-treated livers regained an architecture closely resembling healthy tissue, with only mild sinusoidal dilations persisting.

Mechanistically, the two compounds attack obesity from complementary angles. Disulfiram, historically known as Antabuse, inhibits aldehyde dehydrogenase, and because the ALDH1A1 enzyme participates in retinoic acid biosynthesis and energy homeostasis, blocking it promotes weight loss. The drug has also been shown to suppress the NLRP3 inflammasome, a key driver of chronic inflammation and insulin resistance, and to activate autophagy, improving glucose handling. Curcumin, meanwhile, deploys phenolic hydroxyl groups with potent antioxidant activity that protect the liver and pancreas, and it activates AMP-activated protein kinase, a cellular fuel gauge that promotes glucose uptake in muscle, stimulates fatty acid oxidation, and inhibits gluconeogenesis in the liver. Together, reduced appetite, dampened inflammation, and improved glucose and fat metabolism appear to reinforce one another.

The authors are careful to note that enthusiasm must be tempered by caveats. The 200 milligrams per kilogram disulfiram dose used in mice converts, by standard body surface area methods, to roughly 1,000 milligrams per day in humans, well above the FDA-approved maximum of 500 milligrams per day, and disulfiram has been associated with neurological and psychological adverse events. Curcumin, for its part, suffers from poor bioavailability, limiting how much active compound actually reaches tissues. The team calls for comprehensive toxicological assessment of disulfiram and for advanced delivery methods, such as the nanoparticle formulations already showing promise in vitro, to improve curcumin’s clinical utility. The study was approved by the Institutional Animal Ethical Committee at Banaras Hindu University.

Still, as a proof of concept, the findings are compelling. Mice eating an obesity-inducing diet stayed lean, kept their glucose tolerance, normalized their hormones and blood fats, and protected their livers, all with two inexpensive and widely available compounds, one of them sitting in the spice rack. If future work can resolve the safety and bioavailability questions, the curcumin-disulfiram pairing could offer a cost-effective preventive strategy against the intertwined epidemics of obesity and type 2 diabetes, a goal that conventional single-drug approaches have struggled to achieve.

Subject of Research: Synergistic anti-obesity effects of curcumin and disulfiram in high-fat diet-fed mice

Article Title: Anti-obesogenic potential of curcumin, disulfiram, and their synergistic effects in HFD-fed obese Swiss albino mice

Article References: Patel, J. K., More, R. S., Singh, A. N., & Gupta, A. (2026). Anti-obesogenic potential of curcumin, disulfiram, and their synergistic effects in HFD-fed obese Swiss albino mice. Frontiers in Endocrinology, 17, Article 1954754. https://doi.org/10.3389/fendo.2026.1954754

Image Credits: AI Generated

DOI: 10.3389/fendo.2026.1954754

Keywords: curcumin, disulfiram, obesity, type 2 diabetes, high-fat diet, insulin resistance, leptin, fatty liver, drug repurposing, mouse model, metabolic syndrome, HOMA-IR

The study team, led by researchers from Aerospace Center Hospital and the Chinese PLA General Hospital in Beijing, enrolled 30,343 adults who received routine health examinations between January 2017 and November 2021. For participants who visited more than once, the earliest examination served as the baseline record. This large, unselected health-examination population offers something clinical trials often lack: a snapshot of ordinary people across the full spectrum of metabolic health, from lean individuals with normal insulin to those with obesity, elevated blood pressure, and overt insulin resistance.

The researchers focused on two distinct targets of metabolic injury. The first was arterial stiffness, measured using brachial-ankle pulse wave velocity, or baPWV, a noninvasive technique that tracks how quickly a pressure wave travels along the arteries between the arm and the ankle. Stiffer arteries transmit the pulse faster, so a higher velocity indicates harder vessel walls. The team defined arterial stiffness as a mean baPWV of 1400 centimeters per second or higher. The second target was subclinical myocardial injury, assessed through cardiac troponin I or T, proteins released by heart muscle cells when they are damaged. Detectable troponin at low levels in apparently healthy people can flag early heart muscle stress long before symptoms appear.

Fasting insulin was examined in three complementary ways: as quartile categories comparing the highest insulin group with the lowest, as a log-transformed continuous variable to capture dose-response patterns, and as a binary hyperinsulinemia indicator defined by the cohort-specific 75th percentile. The investigators also calculated HOMA-IR, the homeostatic model assessment of insulin resistance, which combines fasting insulin and glucose into a single index of how hard the body must work to keep blood sugar under control. Logistic regression models quantified the associations, and restricted cubic spline modeling explored whether the relationships were linear or followed more complex curves. All statistical analysis was performed using Python version 3.13.0.

The results painted a consistent picture. People in higher insulin categories carried more adiposity, higher blood pressure, higher glucose, greater insulin resistance as measured by HOMA-IR, higher triglycerides, and, critically, higher baPWV values and more arterial stiffness. In the fully adjusted model, hyperinsulinemia was associated with a 31 percent increase in the odds of arterial stiffness, with an odds ratio of 1.31 and a 95 percent confidence interval of 1.22 to 1.40, a highly statistically significant result. When the researchers compared the highest insulin quartile with the lowest, the contrast was even sharper: the top quartile faced 56 percent greater odds of arterial stiffness, with an odds ratio of 1.56 and a confidence interval of 1.41 to 1.74.

The spline analysis added an important technical nuance. Both fasting insulin and HOMA-IR showed positive but non-linear relationships with mean baPWV, meaning the association was not a simple straight line. This kind of dose-response detail matters because it suggests the vascular consequences of elevated insulin may not accumulate uniformly across the metabolic spectrum; the shape of the curve hints at where along the insulin range risk begins to accelerate, information that could eventually help clinicians decide when an insulin measurement becomes clinically meaningful rather than merely a laboratory value.

Not every finding favored insulin as a cardiovascular culprit. When the researchers turned to the composite myocardial injury endpoint, built from troponin I or T measurements, the association with hyperinsulinemia did not reach statistical significance. In other words, in this cohort, elevated fasting insulin tracked strongly with stiff arteries but only weakly, if at all, with early heart muscle damage detectable by troponin. The authors interpret this asymmetry carefully: fasting insulin may serve as a potential early metabolic indicator of vascular stiffening, while its relationship with myocardial injury remains unresolved and requires further longitudinal investigation to clarify.

The study’s design deserves scrutiny when weighing these conclusions. As a retrospective, cross-sectional analysis of health examination records, it captures associations at a single point in time and cannot prove that hyperinsulinemia causes arteries to stiffen. It is possible that insulin is a marker riding along with other processes, such as visceral fat accumulation, sympathetic nervous system activation, or chronic low-grade inflammation, that independently damage vessel walls. The researchers addressed this concern with subgroup analyses and sensitivity analyses, the latter reported in supplementary tables, to test whether the association held across different population strata and modeling choices. The consistency of the signal across unadjusted and adjusted models strengthens the case, but only longitudinal follow-up can establish temporal sequence.

Why would excess insulin stiffen arteries in the first place? Biologically, insulin is more than a glucose-regulating hormone. At normal levels it promotes vasodilation through nitric oxide pathways in the endothelium, the inner lining of blood vessels. But in states of insulin resistance, this vascular-protective signaling blunts while other insulin-driven pathways, including those promoting smooth muscle cell growth, sodium retention, and sympathetic activation, may persist or even intensify. The result is a milieu that favors vascular remodeling, reduced arterial elasticity, and rising blood pressure. Elevated insulin also correlates with dyslipidemia and adiposity, both of which feed the same pathological loop. The strong correlation between insulin quartiles and nearly every cardiometabolic measure in this dataset illustrates how tightly these mechanisms intertwine.

The clinical implications are potentially significant. Fasting insulin is inexpensive, widely available, and already measured in many health examinations, yet it rarely receives the attention given to glucose, cholesterol, or blood pressure. If future prospective studies confirm that insulin identifies people at risk of vascular stiffening before hypertension or diabetes emerges, a simple blood test could help flag patients who would benefit from earlier lifestyle intervention, whether weight management, exercise, or dietary change, at a stage when arteries may still recover elasticity. For now, the study’s authors stop short of such recommendations, noting that the link between hyperinsulinemia and subclinical myocardial injury was weaker and that longitudinal research is needed. But with more than 30,000 participants and a robust dose-response signal for arterial stiffness, the findings add weight to a growing argument that insulin itself, not merely the blood glucose it governs, deserves a place in the early warning system for cardiovascular disease.

Subject of Research: The association of hyperinsulinemia with arterial stiffness and myocardial injury in a large real-world health examination cohort

Article Title: Hyperinsulinemia and its associations with arterial stiffness and myocardial injury: a real-world cohort study

Article References: Liu, H., Liu, J., Li, N., Hua, Y., Wang, H., Deng, X., Wang, L., Dong, S., Fu, X., & Yan, S. (2026). Hyperinsulinemia and its associations with arterial stiffness and myocardial injury: a real-world cohort study. BMC Endocrine Disorders. https://doi.org/10.1186/s12902-026-02521-6

Image Credits: AI Generated

DOI: 10.1186/s12902-026-02521-6

Keywords: hyperinsulinemia, arterial stiffness, baPWV, insulin resistance, HOMA-IR, myocardial injury, troponin, fasting insulin, cardiovascular risk, cohort study, metabolic syndrome, restricted cubic spline

The research team, led by investigators at the University of Murcia in Spain together with collaborators at Harvard Medical School, Massachusetts General Hospital and the University of Colorado Boulder, recruited 809 healthy adults of European descent from the Spanish ONTIME-MT cohort, a study registered with ClinicalTrials.gov under number NCT03036592. The participants, whose average age was about 37 years and roughly 71 percent of whom were women, wore sensors for seven consecutive days while going about their ordinary lives. Light exposure was recorded continuously, and so was wrist temperature, a surprisingly informative physiological signal that rises during rest and sleep as blood flows back toward the skin and falls during activity and wakefulness. Rather than examining each rhythm metric in isolation, the researchers used principal component analysis, a statistical technique that compresses many correlated variables into a small number of composite factors, to capture the overall architecture of each person’s circadian functioning.

The analysis produced three factors in each of the two domains, light exposure and wrist temperature, and together these factors explained roughly 73 percent of the variance in light rhythms and 80 percent of the variance in temperature rhythms. In both domains, the first factor carried the greatest weight, accounting for about 41 to 42 percent of the variance. This dominant factor reflected what the researchers call rhythm robustness: a combination of high relative amplitude, meaning a large difference between the peaks and troughs of the daily cycle, and greater interdaily stability, meaning the rhythm repeats reliably from one day to the next, along with the person’s average levels of light exposure or wrist temperature. In practical terms, a person scoring high on this factor lived a life of bright days, dark nights and a body temperature rhythm that rose and fell like clockwork, day after day.

What happened when those robustness scores were matched against blood tests and body measurements was the heart of the study. People with stronger, more stable light exposure rhythms had lower serum triglyceride concentrations, a relationship that reached statistical significance at P equals 0.013. Triglycerides are fats circulating in the blood, and elevated levels are a well-established marker of cardiovascular risk. The association between light and blood fats is biologically plausible: light is the primary external synchronizer of the circadian system, and laboratory work has shown that when the circadian clock is misaligned with behavioral cycles, glucose tolerance, insulin sensitivity and lipid metabolism all deteriorate. Animal studies have gone further, showing that light at night can increase body mass partly by shifting the timing of food intake into hours when the metabolism is poorly prepared for it.

The wrist temperature results were, if anything, even more compelling. Higher wrist temperature Factor 1, the robustness composite, was associated with lower body mass index and lower body fat percentage in women, as well as lower fasting insulin, lower HOMA-IR, a standard index of insulin resistance calculated from glucose and insulin levels, lower triglycerides and higher HDL cholesterol, the so-called good cholesterol. All of these associations were statistically significant at P values below 0.05. Wrist temperature is an integrated readout of the circadian system because it reflects the interplay between the autonomic nervous system, vascular tone and the master clock in the hypothalamus. Previous work by some of the same investigators had shown that obese women display flatter, less rhythmic wrist temperature profiles than normal-weight women, and the new study extends that observation to a much larger sample and ties it directly to a panel of metabolic risk markers.

Perhaps the most intriguing finding came from the mediation analyses, which probed whether the two rhythm domains influence each other in a way that shapes metabolic outcomes. The results suggested that the interdaily stability of wrist temperature rhythms may act as a conduit in the relationship between light exposure stability and triglyceride levels, accounting for approximately 11 percent of the total effect, with a P value of 0.021. In other words, part of the reason that well-structured light exposure is associated with healthier blood fats may be that stable light patterns help stabilize the body’s internal temperature rhythm, which in turn supports healthier lipid metabolism. This kind of chained relationship, from environmental signal to physiological rhythm to metabolic outcome, is exactly what circadian theory would predict, but demonstrating it in free-living humans with real-world sensor data is rare.

The study’s design carries important caveats. It was cross-sectional, meaning that light, temperature and metabolic markers were all measured at roughly the same time, so the direction of causality cannot be established with certainty. It is entirely possible that people with healthier metabolisms simply live more regular lives, or that obesity itself flattens circadian rhythms rather than the other way around. The cohort consisted of healthy adults of European descent from a single Spanish population, which limits generalizability to other ethnic groups and to people with existing metabolic disease. The authors are careful on this point: the findings show consistent associations, not proof that stabilizing your rhythms will lower your triglycerides. Still, the consistency of the pattern across two independent physiological domains, and the dose-response-like structure revealed by the principal component analysis, strengthens the case that rhythm robustness is genuinely informative rather than statistical noise.

The broader context makes these results timely. A landmark 2024 analysis of more than 88,000 people wearing light sensors found that brighter nights and darker days predicted higher mortality risk, and a growing literature links circadian disruption, from shift work to evening chronotypes to social jet lag, with cardiometabolic disease. Controlled laboratory experiments by Frank Scheer and colleagues, a co-author on the new paper, have shown that forced circadian misalignment in humans raises glucose and insulin levels and blood pressure within days. What the ONTIME-MT study adds is a demonstration that these relationships are visible in ordinary people, in their own homes, using nothing more than a week of passive wrist-worn monitoring and a well-chosen statistical framework.

The practical implications are twofold. First, the study highlights the potential of circadian-related markers derived from wearable technologies as non-invasive indicators of metabolic risk. Instead of relying solely on blood draws taken at a single moment, clinicians of the future might read a week of light and temperature actigraphy to gauge how robustly a patient’s internal clocks are running, and use that information to identify people who would benefit from lifestyle interventions. Second, it points toward concrete behavioral targets: brighter light during the day, darker environments at night, and daily routines regular enough to keep the body’s temperature rhythm oscillating with a strong, reliable beat. None of this replaces diet and exercise, but it suggests a third pillar of metabolic health that has been hiding in plain sight, glowing on our screens and pulsing quietly at our wrists. As wearable sensors become ubiquitous, the science of chronodisruption is moving from the laboratory bench to the population scale, and the body’s daily rhythms are emerging as a vital sign in their own right.

Subject of Research: Associations between daily light exposure and wrist temperature circadian rhythms and metabolic risk markers in healthy adults

Article Title: Disrupted daily rhythms in light exposure and wrist temperature are associated with metabolic risk markers and obesity

Article References: Dozzani, I., Rodríguez-Martín, M., Dashti, H. S., Vetter, C., Fernández-Breis, J. T., Florez, J. C., Saxena, R., Scheer, F. A. J. L., & Garaulet, M. (2026). Disrupted daily rhythms in light exposure and wrist temperature are associated with metabolic risk markers and obesity. International Journal of Obesity. https://doi.org/10.1038/s41366-026-02225-w

Image Credits: AI Generated

DOI: 10.1038/s41366-026-02225-w

Keywords: circadian rhythms, light exposure, wrist temperature, obesity, metabolic risk, triglycerides, insulin resistance, wearables, chronobiology, HOMA-IR, interdaily stability, ONTIME-MT