Healthy lifestyle scores associate with incidence of type 2 diabetes mediated by uric acid.

IF 3.9 2区医学 Q2 NUTRITION & DIETETICS Nutrition & Metabolism Pub Date : 2023-11-01 DOI:10.1186/s12986-023-00763-y

Xinyue He, Wei Shao, Senhai Yu, Jiazhou Yu, Changzhen Huang, Haiqing Ren, Chengguo Liu, Yuying Xu, Yimin Zhu

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Abstract

Background: Whether and to what extent serum uric acid (SUA) mediates the association between combined lifestyle behaviors and type 2 diabetes mellitus (T2DM) remain unclear. This study aimed to investigate the role of SUA in the relationship between healthy lifestyle scores (HLS) and the incidence of T2DM.

Methods: This prospective study used data from Zhejiang Metabolic Syndrome cohort. A HLS (5-point scale including healthy waist circumference (WC), never smoking, high physical activity, healthy diet and moderate alcohol intake) was estimated in 13,919 participants, who had SUA at baseline examination in 2009-2014, and were followed-up to 2021-2022 to ascertain incident of T2DM. Cox proportional hazards models and mediation analysis were used to examine the associations between HLS, SUA and T2DM.

Results: We included 13,919 participants aged 18 years or older without diabetes at baseline (mean age 54.6 [SD 13.9] years, 58.7% female). During a median follow-up of 9.94 years, 645 cases of T2DM occurred. Compared with participants with a poor HLS, those with 4-5 low-risk lifestyle factors showed a 60% reduction in the risk of developing T2DM (adjusted HR, 0.40; 95% CI: 0.28-0.57). Further, the population-attributable risk percent (95% CI) of T2DM for poor adherence to the overall healthy lifestyle (< 4 low-risk factors) was 43.24% (30.02%, 56.46%). The HLS was inversely associated with SUA level. With per score increased in HLS, the beta (95% CI) of SUA (log transformed) was - 0.03 (- 0.03, - 0.02), and the odds ratio (95% CI) of hyperuricemia was 0.82 (0.77, 0.86). The relationship between the HLS and risk of T2DM was mediated by SUA with a 13.06% mediation effect. There was no significant combined effect of HLS and SUA on risk of T2DM (P = 0.097).

Conclusions: The relationship between overall healthy lifestyle behaviors and T2DM was reconfirmed and the association appeared to be mediated by SUA. The mediation effect of baseline SUA was more pronounced among women who were below 60 years old.

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健康生活方式评分与尿酸介导的2型糖尿病的发病率相关。

背景：血清尿酸（SUA）是否以及在多大程度上介导综合生活方式行为与2型糖尿病（T2DM）之间的关系尚不清楚。本研究旨在探讨SUA在健康生活方式评分（HLS）与T2DM发病率之间的关系中的作用。方法：本前瞻性研究使用了浙江代谢综合征队列的数据。对13919名参与者进行了HLS（5分量表，包括健康腰围（WC）、从不吸烟、高体力活动、健康饮食和适度饮酒）评估，这些参与者在2009-2014年的基线检查中患有SUA，并随访至2021-2022年，以确定T2DM的发生率。Cox比例风险模型和中介分析用于检验HLS、SUA和T2DM之间的相关性。结果：我们纳入了13919名18岁或18岁以上基线时没有糖尿病的参与者（平均年龄54.6[SD13.9]岁，58.7%为女性）。在9.94年的中位随访中，发生了645例T2DM。与HLS较差的参与者相比，那些有4-5种低风险生活方式因素的参与者患T2DM的风险降低了60%（调整后的HR为0.40；95%CI:0.28-0.57）。此外，2型糖尿病患者对整体健康生活方式依从性差的人群归因风险百分比（95%CI）（结论：总体健康生活方式行为与2型糖尿病之间的关系得到了重新确认，这种关系似乎是由SUA介导的。基线SUA的介导作用在60岁以下的女性中更为明显。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Nutrition & Metabolism 医学-营养学

CiteScore

8.40

自引率

0.00%

发文量

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.