背景:在全球范围内,超过39%的人肥胖。代谢综合征,通常伴有肥胖,被认为是非传染性疾病的主要贡献者。鉴于这种关系,代谢健康和不健康肥胖的概念,考虑到代谢状态,一直在进化。人们正在关注代谢健康的肥胖人群,他们向非传染性疾病的过渡率相对较低。随着肥胖率持续上升,不健康行为在年轻人中普遍存在,考虑到这些代谢状态的肥胖管理需求日益增加.列线图可用作预测从代谢健康状态转变为代谢不健康肥胖的风险的有效工具。
目的:这项研究旨在确定人口统计学因素,健康行为,和5种代谢状态与20至44岁人群从代谢健康肥胖到不健康肥胖的转变有关,并开发一种筛查工具来预测这种转变。
方法:这项二级分析研究使用了韩国国民健康保险系统的国民健康数据。我们使用SAS(SASInstituteInc)分析了定制数据,并进行了逻辑回归,以确定与从代谢健康到不健康肥胖转变相关的因素。使用确定的因素开发了一个列线图来预测过渡。
结果:在3,351,989人中,从代谢健康肥胖到不健康肥胖的转变与一般特征之间存在显著关联,健康行为,和代谢成分。男性参与者向代谢不健康肥胖过渡的几率比女性参与者高1.30。经济地位最低的人群也面临转型风险(比值比1.08,95%CI1.05-1.1).吸烟状况,消耗>30克酒精,定期锻炼不足与过渡呈负相关。每个相关变量被分配一个点值。当列线图总点数达到295时,从代谢健康肥胖到不健康肥胖的转变具有>50%的预测率。
结论:这项研究确定了年轻人从健康肥胖过渡到不健康肥胖的关键因素,创建一个预测列线图。这个列线图,包括甘油三酯,腰围,高密度脂蛋白胆固醇,血压,和空腹血糖,即使是普通人群,也可以轻松评估肥胖风险。该工具简化了肥胖率上升和干预措施的预测。
BACKGROUND: Globally, over 39% of individuals are obese. Metabolic syndrome, usually accompanied by
obesity, is regarded as a major contributor to noncommunicable diseases. Given this relationship, the concepts of metabolically healthy and unhealthy
obesity, considering metabolic status, have been evolving. Attention is being directed to metabolically healthy people with obesity who have relatively low transition rates to noncommunicable diseases. As
obesity rates continue to rise and unhealthy behaviors prevail among young adults, there is a growing need for
obesity management that considers these metabolic statuses. A nomogram can be used as an effective tool to predict the risk of transitioning to metabolically unhealthy obesity from a metabolically healthy status.
OBJECTIVE: The study aimed to identify demographic factors, health behaviors, and 5 metabolic statuses related to the transition from metabolically healthy obesity to unhealthy obesity among people aged between 20 and 44 years and to develop a screening tool to predict this transition.
METHODS: This secondary analysis study used national health data from the National Health Insurance System in South Korea. We analyzed the customized data using SAS (SAS Institute Inc) and conducted logistic regression to identify factors related to the transition from metabolically healthy to unhealthy obesity. A nomogram was developed to predict the transition using the identified factors.
RESULTS: Among 3,351,989 people, there was a significant association between the transition from metabolically healthy to unhealthy obesity and general characteristics, health behaviors, and metabolic components. Male participants showed a 1.30 higher odds ratio for transitioning to metabolically unhealthy obesity than female participants, and people in the lowest economic status were also at risk for the transition (odds ratio 1.08, 95% CI 1.05-1.1). Smoking status, consuming >30 g of alcohol, and insufficient regular exercise were negatively associated with the transition. Each relevant variable was assigned a point value. When the nomogram total points reached 295, the shift from metabolically healthy to unhealthy obesity had a prediction rate of >50%.
CONCLUSIONS: This study identified key factors for young adults transitioning from healthy to unhealthy obesity, creating a predictive nomogram. This nomogram, including triglycerides, waist circumference, high-density lipoprotein-cholesterol, blood pressure, and fasting glucose, allows easy assessment of obesity risk even for the general population. This tool simplifies predictions amid rising
obesity rates and interventions.