Association Between a Novel Metabolic Score for Insulin Resistance and Mortality in People With Diabetes

Biological aging mediates the associations of metabolic score for insulin resistance with all-cause and cardiovascular disease mortality among US adults: A nationwide cohort study

AIM

To investigate the associations of metabolic score for insulin resistance (METS-IR) with all-cause and cardiovascular disease (CVD)-specific mortality and the potential mediating role of biological ageing.

METHODS

A cohort of 19 204 participants from the National Health and Nutrition Examination Survey (NHANES) 1999-2018 was recruited for this study. Cox regression models, restricted cubic splines, and Kaplan-Meier survival curves were used to determine the relationships of METS-IR with all-cause and CVD-specific mortality. Mediation analyses were performed to explore the possible intermediary role of biological ageing markers, including phenotypic age (PhenoAge) and biological age (BioAge).

RESULTS

During a median follow-up of 9.17 years, we observed 2818 deaths, of which 875 were CVD-specific. Multivariable Cox regression showed that the highest METS-IR level (Q4) was associated with increased all-cause (hazard ratio [HR] 1.38, 95% confidence interval [CI] 1.14-1.67) and CVD mortality (HR 1.52, 95% CI 1.10-2.12) compared with the Q1 level. Restricted cubic splines showed a nonlinear relationship between METS-IR and all-cause mortality. Only METS-IR above the threshold (41.02 μg/L) was positively correlated with all-cause death. METS-IR had a linear positive relationship with CVD mortality. In mediation analyses, we found that PhenoAge mediated 51.32% (p < 0.001) and 41.77% (p < 0.001) of the association between METS-IR and all-cause and CVD-specific mortality, respectively. For BioAge, the mediating proportions of PhenoAge were 21.33% (p < 0.001) and 15.88% (p < 0.001), respectively.

CONCLUSIONS

This study highlights the detrimental effects of insulin resistance, as measured by METS-IR, on all-cause and CVD mortality. Moreover, it underscores the role of biological ageing in mediating these associations, emphasizing the need for interventions targeting both insulin resistance and ageing processes to mitigate mortality risks in metabolic disorders.

The association between METS-IR, an indirect index for insulin resistance, and lung cancer risk

BACKGROUND

Insulin resistance has been reported to increase the risk of breast, prostate and colorectal cancer. However, the role of insulin resistance and its interaction with genetic risk in the development of lung cancer remains controversial. Therefore, we aimed to explore the association between a novel metabolic score for insulin resistance (METS-IR) and lung cancer risk.

METHODS

A total of 395 304 participants without previous cancer at baseline were included. The Cox proportional hazards regression model was performed to investigate the association between METS-IR and lung cancer risk. In addition, a Mendelian randomization analysis was also performed to explore the causal relationship. The joint effects and additive interactions between METS-IR and polygenetic risk score (PRS) of lung cancer were also investigated.

RESULTS

During a median follow-up of 11.03 years (Inter-quartile range (IQR): 10.30-11.73), a total of 3161 incident lung cancer cases were diagnosed in 395 304 participants. There was a significant association between METS-IR and lung cancer risk, with an HR of 1.28 (95% CI: 1.17-1.41). Based on the Mendelian randomization analysis, however, no causal associations were observed. We observed a joint effect but no interaction between METS-IR and genetic risk. The lung cancer incidence was estimated to be 100.42 (95% CI: 91.45-109.38) per 100 000 person-year for participants with a high METS-IR and PRS, while only 42.76 (95% CI: 36.94-48.59) with low METS-IR and PRS.

CONCLUSIONS

High METS-IR was significantly associated with an increased risk of lung cancer. Keeping a low level of METS-IR might help reduce the long-term incident risk of lung cancer.

Association between metabolic score for insulin resistance and clinical outcomes: insights from the Tehran lipid and glucose study

BACKGROUND

We aimed to assess the relationship between Metabolic Score for Insulin Resistance (METS-IR) and the incidence of coronary heart disease (CHD), stroke, mortality, diabetes, hypertension, and chronic kidney disease (CKD) in a population from the Middle East and North Africa (MENA) region.

METHOD

Individuals aged ≥ 20 years were enrolled. Cox proportional hazards regression models were applied to assess the association between METS-IR and incident CHD, stroke, all-cause mortality, diabetes, hypertension, and CKD.

RESULTS

Over a median follow-up period of 9-18 years, 1080 (10.6%), 267 (2.6%), 1022 (9.6%), 1382 (16.4%), 2994 (58.5%), and 2002 (23.0%) CHD, stroke, all-cause mortality, diabetes, hypertension, and CKD events occurred, respectively. Compared to the lowest quartile (reference), the hazard ratios (HR) associated with the highest quartile of METS-IR were 1.527 (95% confidence interval [CI]: 1.208-1.930, P for trend 0.001), 1.393 (0.865-2.243, > 0.05), 0.841 (0.682-1.038, > 0.05), 3.277 (2.645-4.060, < 0.001), 1.969 (1.752-2.214, < 0.001), and 1.020 (0.874-1.191, > 0.05) for CHD, stroke, all-cause mortality, diabetes, hypertension, and CKD, respectively. METS-IR, as a continuous variable, was significantly associated with the risk of incident CHD [HR, 95% CI: 1.106, 1.034-1.184], diabetes [1.524, 1.438-1.616], and hypertension [1.321, 1.265-1.380]. These associations were also independent of metabolic syndrome (METS) and remained unchanged in a subgroup of individuals without METS and/or diabetes.

CONCLUSIONS

Increasing levels of METS-IR were significantly associated with a greater risk of incident CHD, diabetes, and hypertension; therefore, this index can be a useful tool for capturing the risk of these clinical outcomes.

METS-IR and all-cause mortality in Korean over 60 years old: Korean genome and epidemiology study-health examinees (KoGES-HEXA) cohorts

Background

The metabolic score for insulin resistance index (METS-IR) is a novel non insulin-based marker that indicates the risk for metabolic syndrome and type 2 diabetes mellitus (T2DM). However, METS-IR has not been investigated in relation to all-cause mortality. We investigated the longitudinal effect of METS-IR on all-cause mortality in a significantly large cohort of Korean adults over 60 years old.

Methods

Data were assessed from 30,164 Korean participants over 60 years of age from the Korean Genome and Epidemiology Study-Health Examinees (KoGES-HEXA) cohort data, linked with the death certificate database of the National Statistical Office. The participants were grouped into three according to METS-IR tertiles. We used multivariate Cox proportional-hazard regression models to prospectively assess hazard ratios (HRs) for all-cause mortality with 95% confidence intervals (CIs) over an 11-year postbaseline period.

Results

During the mean 11.7 years of follow-up, 2,821 individuals expired. The HRs of mortality for METS-IR tertiles were 1.16 (95% CI, 1.01-1.34) in T3 after adjustment for metabolic parameters, but the T2 did not show statistical significance towards increases for incident mortality respectively. In subgroup analysis depending on the cause of mortality, higher METS-IR was associated with cancer mortality (HR, 1.23, 95% CI, 1.01-1.51) but not with cardiovascular mortality (HR, 1.14, 95% CI, 0.83-1.57) after adjustment for the same confounding variables.

Conclusion

The METS-IR may be a useful predictive marker for all-cause mortality and cancer mortality, but not for cardiovascular mortality in subjects over 60 years of age. This implies that early detection and intervention strategies for metabolic syndrome could potentially benefit this identified group.

Association of cardiometabolic factors and insulin resistance surrogates with mortality in participants from the Korean Genome and Epidemiology Study

BACKGROUND

Simple biochemical and anthropometric measurements such as fasting blood glucose (FBG), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), waist circumference (WC), and body mass index (BMI) are used to formulate insulin resistance (IR) indices. Whether these indices provide new predictive information for mortality remains unknown. This study examined the relationships of biochemical, anthropometric, and IR indices with mortality risk, as well as their predictive performance.

METHODS

The data source was the Korean Genome and Epidemiology Study (2004-2020) involving 114,957 participants whose data were linked to death records. The IR indices- triglyceride-glucose index (TyG), TyG-BMI, TyG-WC, visceral adiposity index (VAI), lipid accumulation product (LAP), and metabolic score for insulin resistance (METS-IR) were computed using standard formulae. The associations were examined using restricted cubic splines. The predictive performance was compared using the log-likelihood ratio chi-square test.

RESULTS

Body mass index was U-shaped, HDL-C was reverse J-shaped, and FBG and TG levels were J-shaped associated with all-cause mortality. Results showed U-shaped (TyG), J-shaped (TyG-BMI, VAI, LAP, and METS-IR), and reverse J-shaped (TyG-WC) associations with all-cause mortality. The percentages of new predictive information for all-cause mortality explained by the FBG level, BMI, TyG-BMI, and METIR were 3.34%, 2.33%, 1.47%, and 1.37%, respectively. Other IR indices and biochemical and anthropometric measurements provided < 1.0% of new predictive information. For cardiovascular disease mortality, the FBG, BMI, METIR, TyG-BMI, and HDL-C levels explained 2.57%, 2.12%, 1.59%, 1.30%, and 1.27% of new predictive information respectively. Moreover, the risks of cancer mortality explained by FBG level, VAI, and HDL-C level were 2.05%, 1.49%, and 1.28%, respectively.

CONCLUSIONS

Fasting blood glucose level is a superior predictor of mortality risk and may be used as a simple predictive and preventative factor.

Predictive values of metabolic score for insulin resistance on risk of major adverse cardiovascular events and comparison with other insulin resistance indices among Chinese with and without diabetes mellitus: Results from the 4C cohort study

AIMS/INTRODUCTION

To investigate the relationship between the metabolic score for insulin resistance (METS-IR) index and major adverse cardiac events (MACEs) and to compare its ability to predict MACEs with other IR indices including homeostatic model assessment for IR (HOMA-IR) and triglyceride glucose (TyG) index-related parameters.

MATERIALS AND METHODS

We conducted a cohort study enrolling 7,291 participants aged ≥40 years. Binary logistic regression and restricted cubic splines were performed to determine the association between METS-IR and MACEs, and the receiver operating curve (ROC) was utilized to compare the predictive abilities of IR indices and to determine the optimal cut-off points.

RESULTS

There were 348 (4.8%) cases of MACEs during a median follow-up of 3.8 years. Compared with participants with a METS-IR in the lowest quartile, the multivariate-adjusted RRs and 95% CIs for participants with a METS-IR in the highest quartile were 1.47 (1.05-2.77) in all participants, 1.42 (1.18-2.54) for individuals without diabetes, and 1.75 (1.11-6.46) for individuals with diabetes. Significant interactions were found between the METS-IR and the risk of MACEs by sex in all participants and by age and sex in individuals without diabetes (all P values for interaction < 0.05). In the ROC analysis, the METS-IR had a higher AUC value than other indices for predicting MACEs in individuals with diabetes and had a comparable or higher AUC than other indices for individuals without diabetes.

CONCLUSIONS

The METS-IR can be an effective clinical indicator for identifying MACEs, as it had superior predictive power when compared with other IR indices in individuals with diabetes.

Prediction of Diabetic Kidney Disease in Newly Diagnosed Type 2 Diabetes Mellitus

Background

Diabetic kidney disease (DKD), a common microvascular complication of diabetes mellitus (DM), is always asymptomatic until it develops to the advanced stage. Thus, we aim to develop a nomogram prediction model for progression to DKD in newly diagnosed type 2 diabetes mellitus (T2DM).

Methods

This was a single-center analysis of prospective data collected from 521 newly diagnosed patients with T2DM. All related clinical records were incorporated, including the triglyceride-glucose index (TyG index). The least absolute shrinkage and selection operator (LASSO) was used to build a prediction model. In addition, discrimination, calibration, and clinical practicality of the nomogram were evaluated.

Results

In this study, 156 participants were incorporated as the validation set, while the remaining 365 were incorporated into the training set. The predictive factors included in the individualized nomogram prediction model included 5 variables. The area under the curve (AUC) for the prediction model was 0.826 (95% CI 0.775 to 0.876), indicating excellent discrimination performance. The model performed exceptionally well in terms of predictive accuracy and clinical applicability, according to calibration curves and decision curve analysis.

Conclusion

The predictive nomogram for the risk of DKD in newly diagnosed T2DM patients had outstanding discrimination and calibration, which could help in clinical practice.

Association between MetS-IR and prediabetes risk and sex differences: a cohort study based on the Chinese population

Objective

The metabolic score for insulin resistance (MetS-IR) is an emerging surrogate marker for insulin resistance (IR). This study aimed to investigate the association and sex differences between MetS-IR and prediabetes risk in a Chinese population.

Methods

This cohort study included 100,309 adults with normoglycemia at baseline and had followed longitudinally for 5 years, and with prediabetes, defined according to the 2018 American Diabetes Association (ADA) recommended diagnostic criteria, as the outcome of interest. Multivariate Cox proportional hazards regression and restricted cubic spline (RCS) regression models were used to assess the association between MetS-IR and prediabetes risk.

Results

During an observation period of 312,843 person-years, 7,735 (14.84%) men and 4,617 (9.57%) women with pre-diabetes onset were recorded. After fully adjusting for confounders, we found an independent and positive correlation between MetS-IR and the risk of prediabetes in the Chinese population, and the degree of correlation was stronger in women than in men (HR: 1.24 vs 1.16, P-interaction<0.05). Furthermore, using RCS nested in the Cox regression model, we found that there was a nonlinear correlation between MetS-IR and prediabetes risk in both sexes with an obvious saturation effect point, and when the MetS-IR was greater than the value of the saturation effect point, the risk of prediabetes was gradually leveling off. We further calculated the saturation effect points of MetS-IR used to evaluate the risk of prediabetes which in men was 42.82, and in women was 41.78.

Conclusion

In this large cohort study, our results supported that MetS-IR was independently and positively associated with the risk of prediabetes in the Chinese population, with the association being stronger in women than in men.

Association between METS-IR and Prediabetes or Type 2 Diabetes Mellitus among Elderly Subjects in China: A Large-Scale Population-Based Study

The metabolic score for insulin resistance (METS-IR) was recently proposed as a non-insulin-based, novel index for assessing insulin resistance (IR) in the Western population. However, evidence for the link between METS-IR and prediabetes or type 2 diabetes mellitus (T2DM) among the elderly Chinese population was still limited. We aimed to investigate the associations between METS-IR and prediabetes or T2DM based on large-scale, cross-sectional, routine physical examination data. In a total of 18,112 primary care service users, an increased METS-IR was independently associated with a higher prevalence of prediabetes (adjusted odds ratio [aOR] = 1.457, 95% confidence interval [CI]: 1.343 to 1.581, p < 0.001) and T2DM (aOR = 1.804, 95%CI: 1.720 to 1.891, p < 0.001), respectively. The aOR for prediabetes in subjects with the highest quartile of METS-IR was 3.060-fold higher than that in those with the lowest quartile of METS-IR. The aOR for T2DM in subjects with the highest quartile of METS-IR was 6.226-fold higher than that in those with the lowest quartile of METS-IR. Consistent results were obtained in subgroup analyses. Our results suggested that METS-IR was significantly associated with both prediabetes and T2DM. The monitoring of METS-IR may add value to early identification of individuals at risk for glucose metabolism disorders in primary care.