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To evaluate the insulin resistance in patients with menopause who were newly diagnosed with endometrial cancer and its association with disease development. The study included 356 patients with menopause who underwent hysteroscopy at Beijing Obstetrics and Gynecology Hospital between September 2013 and July 2018. Data on age, height, weight, menarche and menopausal age, pregnancies, births, and family history of tumors, hypertension, and diabetes were collected. Blood tests provided fasting blood glucose, triglycerides, total cholesterol, high-density lipoprotein, and low-density lipoprotein levels. Pathological testing determined whether patients had endometrial cancer or precancerous lesions. Differences in influencing factors between patients with endometrial cancer or precancerous lesions and those with normal or benign conditions were analyzed using univariate analysis. Quartile grouping of the Metabolic Score for Insulin Resistance (METS-IR) and Triglyceride-Glucose (TyG) index were applied to examine the impact of different insulin resistance on the development of endometrial cancer or precancerous lesions. Univariate analysis revealed that the proportion of patients with hypertension and diabetes was significantly higher among those with endometrial cancer and precancerous lesions. METS-IR and TyG levels were significantly increased in patients with endometrial cancer and precancerous lesions. The quartile grouping results of METS-IR and TyG suggested that age, BMI, FBG, TG, hypertension, and diabetes prevalence rates increased with an increase in METS-IR or TyG, whereas lipid indicators were negatively correlated and unstable Logistic regression suggested that none of the above influencing factors and METS-IR or TyG were related to the pathological results of the patients. Patients with endometrial or precancerous lesions showed evidence of insulin resistance compared to others, though this was not directly associated with disease presence. This study provides relevant information for preventing of endometrial cancer in the future. Larger studies are needed to evaluate the role of METS-IR and TyG in endometrial cancer prevention.

Insulin resistance (IR) is a key driver of cardiovascular disease (CVD), the leading cause of global mortality. This study evaluated the prognostic value of two surrogate IR indices—the McAuley index and the Metabolic Score for Insulin Resistance (METS‐IR)—for predicting all‐cause and CVD mortality. Data from 22,308 NHANES participants with established CVD (1999–2018) was analyzed. Outcomes were all‐cause and CVD mortality. Cox proportional hazards models and restricted cubic splines assessed associations, while machine learning methods (random forest, XGBoost, CoxBoost, DeepHit) evaluated predictive performance. Model interpretability was assessed using SHapley Additive exPlanations (SHAP). Over a median 9.2‐year follow‐up, 3484 deaths occurred, including 1093 from CVD. A higher McAuley Index was inversely associated with risk, with each 1‐unit increase predicting a 9.2% reduction in all‐cause and 11.3% reduction in CVD mortality. Higher METS‐IR values were associated with increased mortality. Restricted cubic spline analysis confirmed significant U‐shaped relationships. Across nine models, the Cox model demonstrated the best performance (C‐index: 0.87 for all‐cause and 0.85 for CVD mortality), with time‐dependent AUCs consistently above 0.8. SHAP analysis highlighted the McAuley Index and METS‐IR as leading predictors. The McAuley Index and METS‐IR are robust, independent predictors of all‐cause and CVD mortality. Their integration with interpretable machine learning enhances risk stratification, underscoring the role of metabolic dysfunction and central adiposity in long‐term outcomes. These indices may help identify high‐risk patients who could benefit from targeted interventions. This study analyzed data from 22,308 participants in NHANES (1999–2018) with a median follow‐up of 9.2 years. Two insulin resistance indices—METS‐IR and McAuley index—were evaluated using nine machine learning models. METS‐IR showed a negative association with all‐cause and cardiovascular mortality and achieved the best predictive performance (C‐index: 0.87 for all‐cause, 0.85 for CVD mortality; time‐dependent AUCs > 0.8 at 1, 3, 5, and 10 years). In contrast, the McAuley index demonstrated a positive association with mortality outcomes.

We aimed to evaluate the METS-IR (metabolic score for insulin resistance) index for the prediction of incident cardiovascular disease (CVD) and its subtypes (coronary artery disease and stroke) in patients with hypertension and obstructive sleep apnea (OSA). A retrospective cohort study was conducted with 2031 adults with hypertension and OSA, participants from the Urumqi Research on Sleep Apnea and Hypertension study (UROSAH). The hazard ratios and 95% CIs (credibility interval) for CVD and its subtypes were estimated using multivariate Cox proportional hazards regression models. After a median follow-up of 6.80 years (interquartile range: 5.90-8.00 years), a total of 317 (15.61%) participants developed new-onset CVD, including 198 (9.75%) incident coronary heart disease (CHD) and 119 (5.86%) incident stroke. After adjusting for as many relevant confounding factors as possible, each SD increase in METS-IR was associated with a 30% increased risk of new onset overall CVD events, a 32% increased risk of new onset CHD, and a 27% increased risk of new onset stroke. When METS-IR was assessed as tertiles, after adjustment for fully confounding factors, the highest tertiles versus the lowest tertiles were associated with a greater hazard of CVD (HR 2.05; 95% CI 1.52,-2.77), CHD (HR 1.96; 95% CI 1.35-2.84), and stroke (HR 2.24; 95% CI 1.35-3.72). The results of various subgroups and sensitivity analyses were similar. When METS-IR was added, CVD predictions were reclassified and identified more accurately than baseline models for the C-index, continuous net reclassification improvement, and integrated discrimination index. CHD and stroke showed similar results. METS-IR is a powerful predictor of CVD and its subtypes in patients with hypertension and OSA, which can facilitate the identification of high-risk individuals and provide individualized CVD prevention.

To compare the whole-pregnancy insulin resistance level between women with gestational diabetes mellitus A1 (GDM A1) and healthy pregnant women by means of the METS-IR and the TyG index, and to assess the impact of such resistance on pregnancy outcomes. 344 parturients were classified as GDM A1 (n=118) or normal by 75-g oral glucose tolerance test (OGTT) conducted at 24-28 weeks of gestation. Body mass index (BMI), fasting plasma glucose, triglycerides, and high-density lipoprotein cholesterol were measured in early, mid-, and late pregnancy to calculate METS-IR and TyG index values for each period. Longitudinal changes and early-pregnancy values of METS-IR and TyG were compared between groups and related to obstetric outcomes. Furthermore, mediation analysis was conducted to assess the mediating effect of BMI gain on the relationship between TyG index and METS-IR during pregnancy. METS-IR indicated that insulin resistance in mid- and late pregnancy was significantly higher in the GDM A1 group than in the control group, while the TyG index showed a similar trend beginning in early pregnancy. However, the magnitude of increase in insulin resistance from early to mid-pregnancy did not differ significantly between the two groups. From mid- to late pregnancy, METS-IR increased more rapidly in the control group than in the GDM A1 group, whereas no significant difference was observed in TyG index changes. Early-pregnancy METS-IR and TyG values were not significantly associated with mode of delivery, neonatal birth weight, or placental weight. Mediation analysis revealed that BMI gain had a significantly greater mediating effect on METS-IR elevation than on TyG increase. Women with GDM A1 exhibited higher insulin resistance throughout pregnancy compared to healthy controls. Early-pregnancy METS-IR and TyG indices were associated with GDM A1 diagnosis but not with delivery characteristics or fetal parameters. Due to the influence of BMI changes during pregnancy, METS-IR may not be a reliable surrogate marker of insulin resistance in this population.

Nocturia, marked by frequent nighttime urination, significantly impacts quality of life. This study explores the association of METS-IR (Metabolic Score for Insulin Resistance) and ABSI (A Body Shape Index) with nocturia, using data from the National Health and Nutrition Examination Survey (NHANES). A cross-sectional analysis of NHANES data from 2005 to 2020 was performed. Multivariable logistic regression assessed the associations between METS-IR, ABSI, and nocturia, adjusting for demographic characteristics, chronic diseases, and lifestyle factors. Generalized additive models and smoothing splines were used to describe relationship dynamics. Among the 16,450 participants, both METS-IR (OR = 1.15, 95% CI: 1.11–1.20, p  < 0.0001) and ABSI (OR = 1.14, 95% CI: 1.10–1.19, p  < 0.0001) were significantly associated with nocturia based on z-scores. An incremental rise in the quartiles of METS-IR and ABSI was associated with a higher risk of nocturia. Specifically, compared to the lowest quartile (Q1), participants in the highest quartile (Q4) had an OR of 1.45 (95% CI: 1.30–1.61, p  < 0.0001) for METS-IR and 1.38 (95% CI: 1.23–1.55, p  < 0.0001) for ABSI. Subgroup analyses showed a stronger association between ABSI and nocturia among individuals living alone and those aged 20–38 years. Nonlinear modeling indicated a threshold effect for ABSI, with nocturia risk significantly increasing when ABSI exceeded 76.2. Higher METS-IR and ABSI indices are closely linked to a greater prevalence of nocturia, indicating that these indices can be valuable in clinical assessments for evaluating nocturia risk and supporting preventive strategies.

Objective This study focused on the association of the Insulin resistance metabolic score (METS‐IR) with the risk of gestational diabetes mellitus (GDM) using data from the National Health and Nutrition Examination Survey (NHANES). Methods Data from 6 cycles of NHANES (2007–2018) were analysed. Weighted logistic regression models were constructed to explore the relationship between METS‐IR and GDM. Stratified and subgroup analyses with adjustment for confounding factors were carried out to explore the association between METS‐IR and GDM. Results A total of 5189 samples were analysed. Based on the weighted logistic regression model, Ln(METS‐IR) was positively associated with GDM with full adjustment (OR = 1.94, 95% CI 1.08–3.46, p < 0.005). After transferring Ln(METS‐IR) into a categorical variable by quartiles, the positive connection between Ln(METS‐IR) and GDM was still observed in the higher Ln(METS‐IR) group compared to the lowest Ln(METS‐IR) interval (OR of 1.86, 1.76 for participants in the Q3(3.73, 3.93) and Q4(3.93, 4.83) quartile, respectively, p < 0.05). The threshold effect model showed that when Ln(METS‐IR) ≤ 4, the positive correlation between Ln(METS‐IR) and GDM was more significant (β = 2.69, 95% CI 1.55–4.67, p = 0.0004). The area under the ROC curves of Ln(METS‐IR) for GDM was 0.603, suggesting Ln(METS‐IR) a more systematic predictor for GDM. Specifically, the OR and 95% CIs of GDM for women above high school in the Q2, Q3, and Q4 quartiles were 2.05 (1.04, 4.02), 3.41 (1.72, 6.78) and 2.78 (1.55, 4.99), respectively. Conclusion METS‐IR in women elevates the likelihood of GDM occurrence. METS‐IR serves as a comprehensive alternative to HOMA‐IR rather than HbA1c and non‐based insulin level to predict GDM. The results showed a strong association between METS‐IR and GDM. Considering that METS‐IR includes biochemical indicator parameters related to adipose tissue metabolism, it is suggested that METS‐IR is a more systematic predictor of GDM.

Background: Periodontitis is one of the most common diseases associated with the oral cavity. Previous studies have suggested that there is an association between periodontitis and metabolic dysfunction. Recently, the triglyceride glucose (TyG) index, high-density lipoprotein cholesterol (TG/HDL-C) ratio, and metabolic score for insulin resistance (METS-IR) index have been identified as useful markers for assessing insulin resistance. Objective: This study aimed to evaluate the relationship between periodontitis and non-insulin-based insulin resistance (IR) indices and compare the predictive values of these indices in the Korean population. Design: This is a cross-sectional study. Methods: A total of 13,584 participants were included in the 2013–2015 Korean National Health and Nutrition Examination Survey data. A community periodontal index score⩾3 was used to define periodontitis. Participants were divided into quartiles according to each index. Odds ratios (ORs) and 95% confidence intervals (CIs) for the prevalence of periodontitis and the TyG index, TG/HDL-C ratio, and METS-IR index quartiles were calculated using multiple logistic regression analysis. We estimated the areas under the receiver operating characteristic curves (AUCs) of the indices to compare the predictive values of the three indices. Results: Compared with quartile 1, the fourth quartile ORs (95% CIs) for periodontitis were 1.23 (1.01–1.49) for the TyG index, 1.23 (1.02–1.48) for the TG/HDL-C ratio, and 1.53 (1.25–1.88) for the METS-IR index after adjustment for confounders. The AUC (95% CIs) was 0.608 (0.598–0.618) for the TyG index, 0.600 (0.590–0.610) for the TG/HDL-C ratio, and 0.617 (0.608–0.627) for the METS-IR index to identify periodontitis. The predictive power of METS-IR was significantly higher than that of the TyG index and TG/HDL-C. Conclusion: Higher TG/HDL-C ratio, TyG, and METS-IR indices are associated with a higher prevalence of periodontitis. The METS-IR index is a more powerful predictor of periodontitis prevalence than the TyG index and TG/HDL-C ratio.

Psoriasis is linked to insulin resistance (IR). Nevertheless, the applicability of the METS-IR index, a new IR evaluation tool, for evaluating changes in insulin sensitivity in psoriasis populations is currently unknown. This study aimed to investigate the relationship between the METS-IR index and psoriasis in a US adult population. This cross-sectional study utilized data from adults aged 20 to 80 years from the U.S. National Health and Nutrition Examination Survey (NHANES) spanning 2003–2006 and 2009–2014. The associations between the METS-IR index and psoriasis were examined using multivariate logistic regression and smoothed curve fitting. Subgroup analyses and interaction tests were conducted to verify the stability of the association within the population. This study included 5,966 participants, of whom 182 had psoriasis. In the fully adjusted model, the METS-IR index was positively associated with psoriasis, showing a 1.7% increase in psoriasis prevalence for each one-unit increase in the METS-IR index (Model 2: OR 1.017, 95% CI 1.006–1.028). Participants in the highest quartile group were 91.9% more likely to develop psoriasis compared to those in the lowest quartile group (OR = 1.919, 95% CI 1.180–3.118). Smooth curve fitting revealed a nonlinear association between the METS-IR index and psoriasis, with an inflection point of 41.675. This positive association was more pronounced in females, non-obese individuals, those with light alcohol consumption, comorbid coronary heart disease and hyperlipidemia, non-hypertensive and non-diabetic individuals. The results of the study suggest that higher METS-IR scores are associated with an increased likelihood of psoriasis among U.S. adults. The METS-IR index is specifically recommended as a clinical indicator for the management and treatment of psoriasis in women, non-obese individuals, light alcohol consumers, individuals with comorbid coronary artery disease andhyperlipidemia, non-hypertensive and non-diabetic individuals. However, Considering the many known and unknown covariates that may be associated with psoriasis and influence theresults of the study, we remain cautious about the results obtained and look forward to the addition of subsequent studies.

Insulin resistance (IR) is strongly associated with obstructive sleep apnea (OSA). Whereas, few studies have focused on the potential association between the Metabolic Score for Insulin Resistance (METS-IR), a novel non-insulin-dependent IR index, and OSA. Subjects from the National Health and Nutrition Examination Survey (NHANES) spanning 2005-2008 and 2015-2018 were recruited. The potential relationship between METS-IR and other IR indices with OSA was explored through three logistic regression analysis models and restricted cubic spline (RCS) curves. Receiver operating characteristic (ROC) curves were used to assess the diagnostic value of these indicators for OSA. On the basis of age, sex, race, body mass index (BMI), hypertension, diabetes， and cardiovascular disease (CVD), subgroup analyses were conducted to test the robustness of the METS-IR and OSA relationship. A total of 8,306 participants were enrolled, with an OSA prevalence of 30.69%. After adjusting for potential confounders, METS-IR, the triglyceride to high-density lipoprotein cholesterol (TG/HDL-C) ratio, the triglyceride glucose Index (TyG), and the homeostatic model assessment of insulin resistance (HOMA-IR) showed positive associations with OSA prevalence. In the highest tertile of METS-IR, TG/HDL-C, TyG index, and HOMA-IR, OSA prevalence was 2.96-fold, 1.42-fold, 1.29-fold, and 1.41-fold higher, respectively, compared to the lowest tertile (METS-IR: OR = 2.96, 95% CI: 2.50, 3.52, P < 0.0001; TG/HDL-C: OR = 1.42, 95% CI: 1.17, 1.73, P < 0.001; TyG index: OR = 1.29, 95% CI: 1.07, 1.55, P = 0.008; HOMA-IR: OR = 1.41, 95% CI: 1.18, 1.69, P < 0.001). ROC analysis revealed that METS-IR had the highest diagnostic accuracy for OSA (AUC = 0.652). The positive associations between these four IR indices and OSA remain stable across most cases (P for interaction > 0.05); however, all of them show significant interactions with diabetes (P for interaction < 0.05). The METS-IR index is positively associated with the prevalence of OSA and shows superior diagnostic accuracy compared to HOMA-IR, TG/HDL-C, and TyG index.

Background Several studies have investigated the association of insulin resistance (IR) surrogates and the risk of hypertension. However, it is unclear whether there exist differences between different IR surrogates and hypertension risk. Therefore, this study aimed to explore the association of four IR surrogates (triglyceride-glucose index (TyG index), triglyceride-glucose index with body mass index (TyG-BMI), triglycerides/high-density lipoprotein cholesterol ratio (TG/HDL-c), and metabolic score for IR (METS-IR)) with the prevalence of hypertension. Methods This is a cross-sectional study with a total of 117,056 participants. Data were extracted from a computerized database established by Rich Healthcare Group in China, which included all medical records of participants who received a health check-up from 2010 to 2016. IR surrogates were grouped into quartiles as continuous variables, and multivariate logistic regression was performed to estimate the association between different IR surrogate levels and the prevalence of hypertension. Results were expressed as odds ratios (ORs) and 95% confidence intervals (CIs). Missing data were accounted by multiple imputation. These analyses were considered as the sensitivity analysis. Meanwhile, the Bayesian network (BN) model was constructed to further evaluate the relationship between baseline characteristics and the four IR surrogates and the prevalence of hypertension, as well as the importance of every single variable for the prevalence of hypertension. Results Multivariate logistic regression analysis revealed that TyG-BMI and METS-IR were independent risk factors for the prevalence of hypertension that increased significantly with increasing TyG-BMI and METS-IR (p for trend < 0.001). The area under the TyG-BMI curve (AUC) was 0.681 [95% CI: 0.677–0.685], and the cut-off value was 199.5, with a sensitivity and specificity of 65.57% and 61.18%, respectively. While the area under the METS-IR curve (AUC) was 0.679 [95% CI: 0.674–0.683], and the cut-off value was 33.61, with a sensitivity and specificity of 69.67% and 56.67%, respectively. The BN model presented that among these four IR surrogates and related variables, TyG-BMI was the most important predictor of hypertension prevalence, with a significance of 34%. The results before and after multiple imputation were similar. Conclusion TyG-BMI and METS-IR were independent risk factors for the prevalence of hypertension. TyG-BMI and METS-IR had good predictive value for the prevalence of hypertension, and TyG-BMI was superior to METS-IR.