Assessment of insulin resistance indexes with longitudinal trajectories of cardiometabolic multimorbidity: insights from the UK Biobank.

IF 3.9 2区 医学 Q2 NUTRITION & DIETETICS
Yingdong Han, Juan Wu, Menghui Yao, Zhikai Li, Tiange Xie, Yun Zhang, Xuejun Zeng
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引用次数: 0

Abstract

BACKGROUND: Insulin resistance is a key pathophysiological mechanism potentially linking cardiometabolic diseases, yet the comparative performance of various surrogate indexes in predicting disease trajectories remains unclear. Therefore, this study aimed to evaluate the associations between insulin resistance indexes and cardiometabolic disease trajectories, and to identify the most suitable index for clinical risk assessment. METHODS: This prospective cohort study included 357,759 participants free of cardiometabolic diseases at baseline from the UK Biobank. We assessed seven insulin resistance indexes (estimated glucose disposal rate [eGDR], triglyceride-glucose index [TyG], triglyceride-glucose-waist circumference index [TyG-WC], triglyceride-glucose-body mass index [TyG-BMI], triglyceride-glucose-waist-to-height ratio [TyG-WHtR], triglyceride to high-density lipoprotein cholesterol ratio [TG/HDL-C], and metabolic score for insulin resistance [METS-IR]) and applied multi-state models to analyze disease trajectories from baseline to first cardiometabolic disease (FCMD), cardiometabolic multimorbidity (CMM) and death. RESULTS: During follow-up, 47,125 participants developed FCMD, 5,326 progressed to CMM, and 26,074 died. Among all indexes, eGDR exhibited the highest predictive value for both FCMD (AUC = 0.690) and CMM (AUC = 0.754), followed by TyG-WHtR and TyG-WC. Higher eGDR was associated with lower risks of transitioning from baseline to FCMD (HR = 0.63, 95%CI:0.62–0.64) and from FCMD to CMM (HR = 0.78, 95%CI:0.74–0.81). Conversely, higher TyG-WC and TyG-WHtR increased risks of transitions from baseline to FCMD (HR = 1.52, 95%CI:1.48–1.56; HR = 1.55, 95%CI:1.52–1.59) and from FCMD to CMM (HR = 1.21, 95%CI:1.13–1.30; HR = 1.24, 95%CI:1.16–1.32). Disease-specific analysis revealed insulin resistance indexes exhibited strongest effects on type 2 diabetes-related transitions (eGDR: HR = 0.49, 95%CI:0.48–0.50; TyG-WC: HR = 2.37, 95%CI:2.28–2.45). These associations remained consistent across major demographic subgroups. CONCLUSIONS: Insulin resistance indexes, particularly eGDR, effectively predict cardiometabolic disease trajectories. These findings underscore insulin resistance’s role in disease progression and suggest eGDR as a valuable clinical risk stratification tool.

评估胰岛素抵抗指数与心脏代谢多病的纵向轨迹:来自英国生物银行的见解。
背景:胰岛素抵抗是潜在与心脏代谢疾病相关的关键病理生理机制,但各种替代指标在预测疾病轨迹方面的比较性能尚不清楚。因此,本研究旨在评估胰岛素抵抗指标与心脏代谢疾病轨迹之间的关系,并确定最适合临床风险评估的指标。方法:这项前瞻性队列研究包括357,759名来自英国生物银行的无心脏代谢疾病的参与者。我们评估了7项胰岛素抵抗指标(估计葡萄糖处置率[eGDR]、甘油三酯-葡萄糖指数[TyG]、甘油三酯-葡萄糖-腰围指数[TyG- wc]、甘油三酯-葡萄糖-体重指数[TyG- bmi]、甘油三酯-葡萄糖-腰高比[TyG- whtr]、甘油三酯-高密度脂蛋白胆固醇比[TG/HDL-C]、和胰岛素抵抗代谢评分[METS-IR]),并应用多状态模型分析从基线到首次心脏代谢性疾病(FCMD)、心脏代谢性多病(CMM)和死亡的疾病轨迹。结果:随访期间,47125名参与者发展为FCMD, 5326名进展为CMM, 26074名死亡。在所有指标中,eGDR对手足口病的预测值最高(AUC = 0.690),对CMM的预测值最高(AUC = 0.754),其次是TyG-WHtR和TyG-WC。较高的eGDR与从基线转变为口蹄疫(HR = 0.63, 95%CI: 0.62-0.64)和从口蹄疫转变为CMM (HR = 0.78, 95%CI: 0.74-0.81)的风险较低相关。相反,较高的TyG-WC和TyG-WHtR增加了从基线转变为FCMD (HR = 1.52, 95%CI: 1.48-1.56; HR = 1.55, 95%CI:1.52 - 1.59)和从FCMD转变为CMM (HR = 1.21, 95%CI: 1.13-1.30; HR = 1.24, 95%CI: 1.16-1.32)的风险。疾病特异性分析显示,胰岛素抵抗指数对2型糖尿病相关转变的影响最大(eGDR: HR = 0.49, 95%CI: 0.48-0.50; TyG-WC: HR = 2.37, 95%CI: 2.28-2.45)。这些关联在主要人口亚组中保持一致。结论:胰岛素抵抗指标,尤其是eGDR,可以有效预测心脏代谢疾病的发展轨迹。这些发现强调了胰岛素抵抗在疾病进展中的作用,并提示eGDR是一种有价值的临床风险分层工具。
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来源期刊
Nutrition & Metabolism
Nutrition & Metabolism 医学-营养学
CiteScore
8.40
自引率
0.00%
发文量
78
审稿时长
4-8 weeks
期刊介绍: Nutrition & Metabolism publishes studies with a clear focus on nutrition and metabolism with applications ranging from nutrition needs, exercise physiology, clinical and population studies, as well as the underlying mechanisms in these aspects. The areas of interest for Nutrition & Metabolism encompass studies in molecular nutrition in the context of obesity, diabetes, lipedemias, metabolic syndrome and exercise physiology. Manuscripts related to molecular, cellular and human metabolism, nutrient sensing and nutrient–gene interactions are also in interest, as are submissions that have employed new and innovative strategies like metabolomics/lipidomics or other omic-based biomarkers to predict nutritional status and metabolic diseases. Key areas we wish to encourage submissions from include: -how diet and specific nutrients interact with genes, proteins or metabolites to influence metabolic phenotypes and disease outcomes; -the role of epigenetic factors and the microbiome in the pathogenesis of metabolic diseases and their influence on metabolic responses to diet and food components; -how diet and other environmental factors affect epigenetics and microbiota; the extent to which genetic and nongenetic factors modify personal metabolic responses to diet and food compositions and the mechanisms involved; -how specific biologic networks and nutrient sensing mechanisms attribute to metabolic variability.
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