Zubair Niha, Kuzawa Chris W, McDade Thomas W, Adair Linda S
Department of Nutrition, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC 27516, USA.
Asia Pac J Clin Nutr. 2012;21(2):271-81.
With modernization, the Philippines has experienced increasing rates of obesity and related cardiometabolic diseases. Studying how risk factors cluster in individuals may offer insight into cardiometabolic disease etiology. We used cluster analysis to group women who share the following cardiometabolic biomarkers: fasting triglycerides, HDL-C and LDL-C, C-reactive protein, systolic and diastolic blood pressure, homeostasis model assessment of insulin resistance, and fasting glucose. Participants included 1,768 women (36-69 years) in the Cebu Longitudinal Health and Nutrition Survey. We identified five distinct clusters characterized by: 1) low levels of all risk factors (except HDL-C and LDL-C) or "healthy"; 2) low HDL-C in the absence of other risk factors; 3) elevated blood pressure; 4) insulin resistance; and 5) high C-reactive protein. We identified predictors of cluster membership using multinomial logistic regression. Clusters differed by age, menopausal status, socioeconomic status, saturated fat intake, and combinations of overweight (BMI >23) and high waist circumference (>80 cm). In comparison to the healthy cluster, overweight women without high waist circumference were more likely to be in the high CRP cluster (OR=2.26, 95% CI=1.24-4.11), while women with high waist circumference and not overweight were more likely to be in the elevated blood pressure (OR=2.56, 95% CI=1.20-5.46) or insulin resistant clusters (OR=4.05, 95% CI=1.39-11.8). In addition, a diet lower in saturated fat uniquely increased the likelihood of membership to the low HDL-C cluster. Cluster analysis identified biologically meaningful groups, predicted by modifiable risk factors; this may have implications for the prevention of cardiometabolic diseases.
随着现代化进程，菲律宾肥胖及相关心脏代谢疾病的发病率不断上升。研究个体中风险因素的聚集情况可能有助于深入了解心脏代谢疾病的病因。我们使用聚类分析对具有以下心脏代谢生物标志物的女性进行分组：空腹甘油三酯、高密度脂蛋白胆固醇（HDL-C）和低密度脂蛋白胆固醇（LDL-C）、C反应蛋白、收缩压和舒张压、胰岛素抵抗的稳态模型评估以及空腹血糖。参与者包括宿务纵向健康与营养调查中的1768名女性（36 - 69岁）。我们确定了五个不同的聚类，其特征分别为：1）所有风险因素（HDL-C和LDL-C除外）水平较低或“健康”；2）在无其他风险因素的情况下HDL-C水平较低；3）血压升高；4）胰岛素抵抗；5）C反应蛋白水平较高。我们使用多项逻辑回归确定聚类成员的预测因素。聚类在年龄、绝经状态、社会经济地位、饱和脂肪摄入量以及超重（体重指数>23）和高腰围（>80厘米）的组合方面存在差异。与健康聚类相比，腰围不高的超重女性更有可能属于高C反应蛋白聚类（比值比=2.26，95%置信区间=1.24 - 4.11），而腰围高但不超重的女性更有可能属于血压升高聚类（比值比=2.56，95%置信区间=1.20 - 5.46）或胰岛素抵抗聚类（比值比=4.05，95%置信区间=1.39 - 11.8）。此外，饱和脂肪含量较低的饮食独特地增加了属于低HDL-C聚类的可能性。聚类分析确定了由可改变的风险因素预测的具有生物学意义的组；这可能对心脏代谢疾病的预防具有启示意义。
