正常血糖人群挑战后 2 小时血糖与空腹血糖之间的差异与心血管疾病风险的关系:德黑兰血脂和血糖研究

IF 3.9 2区 医学 Q2 NUTRITION & DIETETICS
Amir Abdi, Karim Kohansal, Davood Khalili, Fereidoun Azizi, Farzad Hadaegh
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引用次数: 0

摘要

众所周知,空腹血浆葡萄糖(FPG)和挑战后 2 小时血糖(2hPG)水平升高是心血管疾病(CVD)的独立风险因素。然而,有关这些指标之间的差异与心血管疾病风险之间关联的数据却很有限。本研究旨在调查血糖正常的伊朗成年人,尤其是血糖正常值较低的伊朗成年人的血糖与心血管疾病风险之间的关系。这项前瞻性队列研究纳入了德黑兰血脂和血糖研究中 4594 名 30-65 岁的参与者。使用多变量考克斯比例危险回归模型,对年龄、性别、体重指数、高血压、高胆固醇血症、吸烟、教育水平和 FPG 进行调整,计算出连续和分类变量 2hPG-FPG 与心血管疾病风险之间的危险比 (HR) 和 95% 置信区间 (95%CI)。为了确定最佳的 2hPG-FPG 临界值,还对接收者操作特征曲线进行了分析。在中位数为 17.9 年的随访期间,共发生了 459 起心血管疾病事件。在血糖正常者(HR 1.10,95% CI (1.01-1.19))和低正常 FPG 者(HR 1.16,95% CI (1.04-1.30))中,2hPG-FPG 每增加一个单位与心血管疾病风险的升高有显著相关性;在血糖正常者中,这种相关性不受胰岛素抵抗稳态模型评估(HOMA-IR)调整的影响。然而,与 2hPG 水平低于或等于 FPG 的人群相比,2hPG 水平高于 FPG 水平的人群发生心血管疾病的风险增加并不显著,正常血糖人群的相应 HR 值为 1.18(95% CI:0.95-1.46),低正常血糖人群的相应 HR 值为 1.32(95% CI:0.98-1.79)。对于心血管疾病的发生,2hPG-FPG 的最佳临界值为 1.06 mmol/L,适用于血糖正常和 FPG 偏低的人群;根据这一标准,相应的心血管疾病发生风险分别为 1.36(95% CI:1.12-1.64)和 1.57(95% CI:1.22-2.03)。血糖正常范围内 2hPG 和 FPG 水平的差异与心血管疾病风险的增加有关,这一问题与 HOMA-IR 无关。2hPG-FPG>1.06毫摩尔/升的临界点可能会对高危人群进行分层。这些发现在低正常 FPG 的人群中尤为明显。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The difference between 2-hour post-challenge and fasting plasma glucose associates with the risk of cardiovascular disease in a normoglycemic population: the Tehran lipid and glucose study
Elevated fasting plasma glucose (FPG) and 2-hour post-challenge glucose (2hPG) levels are known to be independent risk factors for cardiovascular disease (CVD). However, there is limited data on the association of the difference between these measures and the risk of CVD. This study aims to investigate this association in normoglycemic Iranian adults, particularly in those with low-normal FPG levels. This prospective cohort study included 4,594 30-65-year-old participants from the Tehran Lipid and Glucose Study. Using multivariable Cox proportional hazards regression models adjusting for age, sex, body mass index, hypertension, hypercholesterolemia, smoking, education level and FPG, hazard ratios (HRs) and 95% confidence intervals (95% CIs) were calculated for the association between 2hPG-FPG, both as continuous and categorical variables, and the CVD risk. Analyses of receiver operating characteristic curves were undertaken to determine the optimal 2hPG-FPG cut-off value. During a median of 17.9 years of follow-up, 459 CVD events occurred. A one-unit increase in 2hPG-FPG was significantly associated with an elevated risk of cardiovascular disease in both normoglycemic (HR 1.10, 95% CI (1.01–1.19)) and low-normal FPG individuals (HR 1.16, 95% CI (1.04–1.30)); this association resisted adjustment for Homeostatic Model Assessment for Insulin Resistance (HOMA-IR) among normoglycemic individuals. However, those with 2hPG levels greater than FPG levels had a non-significant increased risk of incident CVD compared to those with 2hPG levels of less than or equal to FPG, with corresponding HR values of 1.18 (95% CI: 0.95–1.46) in normoglycemic and 1.32 (95% CI: 0.98–1.79) in low-normal FPG, respectively. For incident CVD, the optimal cut-off value for the 2hPG-FPG was found to be 1.06 mmol/L, which was applicable for both normoglycemic and low FPG populations; using this criterion, the corresponding risks for incident CVD were 1.36 (95% CI: 1.12–1.64) and 1.57 (95% CI: 1.22–2.03), respectively. The difference between 2hPG and FPG levels within the normoglycemic range is related to an increased risk of CVD, an issue that was independent of HOMA-IR. A cut-off point for 2hPG-FPG > 1.06 mmol/L may stratify persons at higher risk. These findings were particularly notable in those with low-normal FPG.
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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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