Abstract：
BACKGROUND: Since cardiovascular disease (CVD) stands as the primary cause of death in those with diabetes, and given the substantial influence of obesity as a common risk factor for both diabetes and atherosclerotic conditions, this investigation sought to find the relationship between anthropometric indicators and CVD risk within these populations.
METHODS: Our study examined 36,329 adults, including those with diagnosed diabetes, pre-diabetes, and without diabetes from National Health and Nutrition Examination Survey (NHANES) data spanning 1999 to 2018. Various anthropometric indicators such as body mass index (BMI), waist circumference, weight-adjusted waist index (WWI), waist-to-height ratio (WHtR), weight, and height were assessed. Baseline characteristics were compared among the three groups after weighting. Participants were then grouped based on anthropometric indicators, and logistic regression models were used to analyze the association between these indicators and CVD risk in the total diabetes group (including diabetic and pre-diabetic individuals). Threshold effect analysis was conducted to explore nonlinear relationships, and mediation analyses assessed whether serum parameters influenced these relationships.
RESULTS: This cross-sectional study involved 36,329 participants, weighted to a count of approximately 160.9 million, including over 45.9 million pre-diabetic individuals and around 16.6 million diabetic individuals. Baseline analysis showed significant associations between all six anthropometric indicators and CVD risk across patients with different diabetes statuses. Weighted restricted cubic spline (RCS) curve analysis highlighted increased CVD risk among the total diabetes group for each anthropometric indicator compared to the non-diabetic group. Anthropometric indicators were then divided into quartiles, and after adjusting for confounders, Model 3 revealed that the highest BMI group had a heightened risk of CVD compared to the lowest BMI group. Similar trends were observed in the WWI and WHtR subgroups. Threshold effect analysis of anthropometric indicators unveiled nonlinear associations between waist circumference, height, WWI and CVD risk. Mediation analysis suggested that lipid parameters, especially HDL, significantly mediated these relationships.
CONCLUSIONS: In individuals with diabetes and pre-diabetes, BMI, weight, and WHtR displayed a consistent, linear increase correlation with CVD risk. Conversely, the link between waist circumference, height, and WWI and CVD risk showcased a more complex, nonlinear pattern. Moreover, HDL level emerged as notable mediator in the association between anthropometric indicators and the risk of CVD.
METHODS: Our study examined 36,329 adults, including those with diagnosed diabetes, pre-diabetes, and without diabetes from National Health and Nutrition Examination Survey (NHANES) data spanning 1999 to 2018. Various anthropometric indicators such as body mass index (BMI), waist circumference, weight-adjusted waist index (WWI), waist-to-height ratio (WHtR), weight, and height were assessed. Baseline characteristics were compared among the three groups after weighting. Participants were then grouped based on anthropometric indicators, and logistic regression models were used to analyze the association between these indicators and CVD risk in the total diabetes group (including diabetic and pre-diabetic individuals). Threshold effect analysis was conducted to explore nonlinear relationships, and mediation analyses assessed whether serum parameters influenced these relationships.
RESULTS: This cross-sectional study involved 36,329 participants, weighted to a count of approximately 160.9 million, including over 45.9 million pre-diabetic individuals and around 16.6 million diabetic individuals. Baseline analysis showed significant associations between all six anthropometric indicators and CVD risk across patients with different diabetes statuses. Weighted restricted cubic spline (RCS) curve analysis highlighted increased CVD risk among the total diabetes group for each anthropometric indicator compared to the non-diabetic group. Anthropometric indicators were then divided into quartiles, and after adjusting for confounders, Model 3 revealed that the highest BMI group had a heightened risk of CVD compared to the lowest BMI group. Similar trends were observed in the WWI and WHtR subgroups. Threshold effect analysis of anthropometric indicators unveiled nonlinear associations between waist circumference, height, WWI and CVD risk. Mediation analysis suggested that lipid parameters, especially HDL, significantly mediated these relationships.
CONCLUSIONS: In individuals with diabetes and pre-diabetes, BMI, weight, and WHtR displayed a consistent, linear increase correlation with CVD risk. Conversely, the link between waist circumference, height, and WWI and CVD risk showcased a more complex, nonlinear pattern. Moreover, HDL level emerged as notable mediator in the association between anthropometric indicators and the risk of CVD.
摘要：
背景：由于心血管疾病（CVD）是糖尿病患者的主要死亡原因，考虑到肥胖是糖尿病和动脉粥样硬化疾病的共同危险因素，本研究旨在发现这些人群中人体测量指标与CVD风险之间的关系.
方法：我们的研究检查了36,329名成年人，包括那些被诊断为糖尿病的人，糖尿病前期,从1999年至2018年的国家健康和营养检查调查（NHANES）数据中没有糖尿病。各种人体测量指标，如体重指数（BMI），腰围,体重调整腰围指数(WWI)，腰围与身高比(WHtR)，体重,和身高进行了评估。加权后比较三组的基线特征。然后根据人体测量指标对参与者进行分组，和logistic回归模型用于分析这些指标与全糖尿病组(包括糖尿病和糖尿病前期个体)CVD风险之间的关联.进行了阈值效应分析，以探索非线性关系，和中介分析评估血清参数是否影响这些关系.
结果：这项横断面研究涉及36,329名参与者，加权约1.609亿，包括超过4590万糖尿病前期个体和约1660万糖尿病个体。基线分析显示，在不同糖尿病状态的患者中，所有六个人体测量指标与CVD风险之间存在显著关联。加权限制三次样条（RCS）曲线分析强调了与非糖尿病组相比，每个人体测量指标在总糖尿病组中CVD风险增加。然后将人体测量指标分为四分位数，在调整了混杂因素后，模型3显示，与BMI最低组相比，BMI最高组的CVD风险更高。在WWI和WHtR亚组中观察到类似的趋势。人体测量指标的阈值效应分析揭示了腰部之间的非线性关联，高度,WWI和CVD风险。中介分析表明，血脂参数，尤其是HDL,显著介导了这些关系。
结论：在患有糖尿病和糖尿病前期的个体中，BMI,体重,和WHTR显示一致的，与CVD风险线性增加相关。相反，腰围之间的联系，高度,和一战和心血管疾病的风险展示了一个更复杂的，非线性模式。此外，HDL水平在人体测量指标与CVD风险之间的关系中表现为显著的介质。
方法：我们的研究检查了36,329名成年人，包括那些被诊断为糖尿病的人，糖尿病前期,从1999年至2018年的国家健康和营养检查调查（NHANES）数据中没有糖尿病。各种人体测量指标，如体重指数（BMI），腰围,体重调整腰围指数(WWI)，腰围与身高比(WHtR)，体重,和身高进行了评估。加权后比较三组的基线特征。然后根据人体测量指标对参与者进行分组，和logistic回归模型用于分析这些指标与全糖尿病组(包括糖尿病和糖尿病前期个体)CVD风险之间的关联.进行了阈值效应分析，以探索非线性关系，和中介分析评估血清参数是否影响这些关系.
结果：这项横断面研究涉及36,329名参与者，加权约1.609亿，包括超过4590万糖尿病前期个体和约1660万糖尿病个体。基线分析显示，在不同糖尿病状态的患者中，所有六个人体测量指标与CVD风险之间存在显著关联。加权限制三次样条（RCS）曲线分析强调了与非糖尿病组相比，每个人体测量指标在总糖尿病组中CVD风险增加。然后将人体测量指标分为四分位数，在调整了混杂因素后，模型3显示，与BMI最低组相比，BMI最高组的CVD风险更高。在WWI和WHtR亚组中观察到类似的趋势。人体测量指标的阈值效应分析揭示了腰部之间的非线性关联，高度,WWI和CVD风险。中介分析表明，血脂参数，尤其是HDL,显著介导了这些关系。
结论：在患有糖尿病和糖尿病前期的个体中，BMI,体重,和WHTR显示一致的，与CVD风险线性增加相关。相反，腰围之间的联系，高度,和一战和心血管疾病的风险展示了一个更复杂的，非线性模式。此外，HDL水平在人体测量指标与CVD风险之间的关系中表现为显著的介质。
