Adult Body Height and Cardiometabolic Disease Risk: The China National Health Survey in Shaanxi

Abstract

Objectives: Based on data from the China National Health Survey, we aimed to examine the association between body height and cardiometabolic disease (CMD) in a large adult population from Shaanxi province, and further to test whether this association was hinged upon other population characteristics.

Methods: This population-based study was conducted in 2014 in Shaanxi Province, China. Utilizing a multi-stage stratified cluster sampling method, total 5,905 adults with complete data were eligible for analysis, and 1,151 (19.5%) of them had CMD. Of 1,151 CMD patients, 895 (15.1%) had one disorder and 256 (4.4%) had ≥2 disorders.

Results: Using the bi-directional stepwise method and all-subsets regression, five factors-age, body mass index, family histories of CMD, exercise, and height-constituted the optimal model when predicting CMD risk. Restricted cubic spline regression showed a reduced tendency towards CMD with the increase of body height, with per 10 cm increment in body height corresponding to 14% reduced risk. Ordinal Logistic regression supported the contribution of body height on both continuous and categorical scales to CMD risk before and after adjustment, yet this contribution was significantly confounded by exercise and education, especially by exercise, which can explain 65.4% of total impact. For example, short stature was associated with an increased risk of CMD after multivariable adjustment not including exercise and education (odds ratio, 95% confidence interval, P: 1.42, 1.21 to 1.66, <0.001), and tall stature was associated with a reduced risk (0.77, 0.64 to 0.92, 0.003).

Conclusions: Our findings indicate short stature was a risk factor, yet tall stature was a protective factor for CMD in Chinese. Notably, the prediction of short and tall stature for CMD may be mediate in part by exercise.

Keywords: adult; body height; cardiometabolic disease; optimal model; risk prediction.

Figure 1

Restricted cubic spline regression analysis on the prediction of body height on a continuous scale for overall CMD risk under the model 0 (A), model 1 (B), and model 2 (C), respectively. Further analyses targeting participants with ≤3 times of exercise per week (D) or with education of secondary school degree or below (E). CMD, cardiometabolic disease. Under the model 0, no confounder was adjusted. Under the model 1, age, sex, marital status, personal income, drinking, and family histories of CMD were adjusted, and exercise and education were additionally adjusted under the model 2.

Figure 2

Decision curve analysis on the net benefits gained by the five factors in the optimal model when predicting CMD risk. CMD, cardiometabolic disease. The orange solid line corresponds to the basic model that includes sex, ethnicity, area, marital status, personal income, physical activity, smoking, and drinking. The green solid line corresponds to the full model that includes all factors in the basic model and the five factors in the optimal model including age, body mass index, family histories of CMD, exercise, and height.

Figure 3

The prediction nomogram model of the five factors in the optimal model for quantifying the risk of CMD (A), as well as the calibration curve of this model (B). CMD, cardiometabolic disease; BMI, body mass index. This nomogram can be used to manually obtain predicted values from a regression model that was fitted with the five factors. In detail, there is a reference line at the top for reading scoring points (range: 0 to 100) from all factors in the regression model, which were summed together to calculate the total points, and then the predicted values can be read at the bottom.