背景:肥胖的患病率在世界范围内并没有下降,肥胖相关的发病率一直在稳步上升。然而,很少有研究调查导致体重正常个体体重增加的因素。因此,在这项基于社区的队列研究中,我们旨在调查导致体重正常参与者体重增加的因素.
方法:在正常体重(体重指数(BMI)21.0-24.9kg/m2)的年龄在40-64岁的参与者中,对临床变量和10年体重增加10%(10%IBW10Y)进行了回顾性调查。使用机器学习和逻辑回归分析(嵌套病例对照研究)来预测10%IBW10Y。
结果:总计,6.8%的男性和8.9%的女性有10%的IBW10Y(P<0.0001)。10年后肥胖(BMI≥25.0kg/m2)和体重增加的患病率在IBW10Y为10%的参与者中更高(72.3%,8.9千克)(病例组)与无10%IBW10Y(11.5%,-0.4公斤)(对照组),分别。机器学习显示了10%的IBW10Y的积极贡献因素,按降序排列,年龄40年代初,目前吸烟,女性性别,低血清甘油三酯(≤59mg/dL),低糖化血红蛋白(HbA1c)(≤4.9%)。年龄60年代初,禁止吸烟,男性,高甘油三酯(≥200mg/dL),HbA1c6.0%-6.9%是负面因素。Logistic回归分析显示,除了高HbA1c(≥7.5%)为阳性因素外,结果相似。
结论:在体重正常且接受定期健康检查的中年人中,某些有利的特征(女性,低甘油三酯,和低HbA1c),以及未来可能会改变的吸烟习惯,这可能会导致体重增加,可能会被忽视。250<250字。
BACKGROUND: The prevalence of obesity has not decreased worldwide and obesity-related morbidities have been increasing steadily. However, few studies have investigated factors contributing to weight gain in normal-weight individuals. Thus, in this community-based cohort
study, we aimed to investigate factors contributing to weight gain in normal-weight participants.
METHODS: Clinical variables and 10 % increase in weight over 10 years (10 %IBW10Y) were retrospectively investigated in apparently healthy 615,077 normal-weight (body mass index (BMI) 21.0-24.9 kg/m2) participants aged 40-64 years who had regularly undergone health checkup. Machine learning and logistic regression analysis (nested case-control
study) were used to predict 10 %IBW10Y.
RESULTS: In total, 6.8 % of men and 8.9 % of women had 10 %IBW10Y (P < 0.0001). The prevalence of obesity (BMI ≥25.0 kg/m2) after 10 years and weight gain were higher in participants with 10 %IBW10Y (72.3 %, 8.9 kg) (case-group) versus those without 10 %IBW10Y (11.5 %, -0.4 kg) (control-group), respectively. Machine learning showed positive contributing factors to 10 %IBW10Y were, in descending order, age early 40 s, current smoking, female sex, low serum
triglyceride (≤59 mg/dL), and low glycated hemoglobin (HbA1c) (≤4.9 %). Age early 60 s, non-smoking, male sex, high
triglyceride (≥200 mg/dL), and HbA1c 6.0 %-6.9 % were negative contributing factors. Logistic regression analysis showed similar results except for high HbA1c (≥7.5 %) as a positive contributing factor.
CONCLUSIONS: In middle-aged individuals with normal weight who undergo regular health check-ups, certain favorable features (female sex, low
triglyceride, and low HbA1c), as well as smoking habits that are subject to change in the future, which could lead to weight gain, may be overlooked. 250 <250 words.