心脏代谢指数作为非酒精性脂肪性肝病患者新发糖尿病的预测因子:纵向队列分析

IF 2.8 3区 医学 Q3 ENDOCRINOLOGY & METABOLISM
Shanbing Hou, Yuqing Wang, Zixuan Wei, Haijun Li, Haonan Fang, Tingting Zhang, Yihang Zhu, Lixia Cui, Lixiang Zhang, Xia Chen, Min Dou
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引用次数: 0

摘要

目的:本研究的目的是通过利用心脏代谢指数(CMI)来评估非酒精性脂肪性肝病(NAFLD)患者与糖尿病发病相关的临床风险,CMI来源于甘油三酯、高密度脂蛋白、身高和腰围。关注CMI在NAFLD患者糖尿病风险评估中的应用的研究仍然很少,并且尚未进行CMI与该人群中糖尿病发生之间关系的探索。本研究旨在阐明这种联系,从而为NAFLD患者糖尿病进展的预防提供新的见解。方法:数据来自日本政府进行的一项涉及15435名参与者的横断面研究,最终产生了2503名18岁及以上符合评估资格标准的成年人。CMI的计算公式为:TG (mmol/L) / HDL-C (mmol/L) * WHtR。CMI值根据其得分分为四分位数(Q1至Q4),从低到高排列。该研究利用logistic回归模型、受限三次样条分析和亚组分析,并对连续模型进行调整,以阐明慢性心肌梗死与NAFLD患者糖尿病发展之间的关系,以及慢性心肌梗死与该结果之间的非线性关系。结果:调查对象2503人,平均年龄44.79±8.33岁,其中新发糖尿病204人(8.15%)。多因素logistic回归分析显示,在CMI四分位数分组中,最高四分位数(Q4) NAFLD患者与最低四分位数(Q1) NAFLD患者相比,新发糖尿病的可能性最大(优势比,OR = 3.239, 95%可信区间[CI]: 1.993-5.264, P 0.05)。结论:本研究结果表明,CMI评分升高与NAFLD患者新发糖尿病的概率之间存在显著的非线性正相关。临床试验号:不适用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Cardiac metabolic index as a predictor of new-onset diabetes in non-alcoholic fatty liver disease patients: a longitudinal cohort analysis.

Objective: The purpose of this investigation is to assess the clinical risk linked to the onset of diabetes in individuals with non-alcoholic fatty liver disease (NAFLD) through the utilization of the cardiac metabolic index (CMI), which is derived from triglycerides, high-density lipoprotein, height, and waist circumference. Research focusing on the application of CMI for evaluating diabetes risk among NAFLD patients remains scarce, and an exploration of the association between CMI and the emergence of diabetes within this demographic has not been conducted. This investigation aims to illuminate this connection, thereby providing novel insights into the prevention of diabetes progression in individuals with NAFLD.

Methods: Data were procured from a cross-sectional study involving 15,435 participants conducted by the Japanese government, resulting in a final cohort of 2,503 adults aged 18 and above who met the eligibility criteria for evaluation. The CMI is determined using the formula: TG (mmol/L) / HDL-C (mmol/L) * WHtR. CMI values were categorized into quartiles (Q1 to Q4) based on their scores, arranged from low to high. The investigation utilized logistic regression models, restricted cubic spline analysis, and subgroup analyses, with adjustments made for continuous models to elucidate the association between CMI and diabetes development among individuals with NAFLD, as well as the non-linear relationship between CMI and this outcome.

Results: The investigation comprised 2,503 subjects with an average age of 44.79 ± 8.33 years, of whom 204 were diagnosed with new-onset diabetes (8.15%). Multivariate logistic regression analysis indicated that within the CMI quartile groupings, individuals with NAFLD in the uppermost quartile (Q4) exhibited the greatest likelihood of new-onset diabetes relative to those in the lowest quartile (Q1) (odds ratio, OR = 3.239, 95% confidence interval [CI]: 1.993-5.264, P < 0.001). Additionally, escalating CMI values corresponded to an increasing probability of new-onset diabetes in NAFLD subjects (OR = 1.705, 95% CI: 1.362-2.133, P < 0.001). Restricted cubic spline analysis demonstrated a non-linear connection between CMI and new-onset diabetes in NAFLD patients (P overall < 0.001, P non-linear = 0.035), with a risk inflection point identified at a CMI of 0.5554. Subgroup analysis revealed no significant interactions between gender, age, physical activity, alcohol consumption, or smoking status and the likelihood of new-onset diabetes in this patient population (P for interaction > 0.05).

Conclusion: Findings from this study indicate a notable positive non-linear connection between elevated CMI scores and the probability of new-onset diabetes in individuals afflicted with NAFLD.

Clinical trial number: Not applicable.

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来源期刊
BMC Endocrine Disorders
BMC Endocrine Disorders ENDOCRINOLOGY & METABOLISM-
CiteScore
4.40
自引率
0.00%
发文量
280
审稿时长
>12 weeks
期刊介绍: BMC Endocrine Disorders is an open access, peer-reviewed journal that considers articles on all aspects of the prevention, diagnosis and management of endocrine disorders, as well as related molecular genetics, pathophysiology, and epidemiology.
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