Factors associated with cardio-metabolic diseases and perception of current health status: An analysis of NHANES 2017-2018.

BACKGROUND

We analyzed how exposure to multiple risk factors relates to the risk of cardio-metabolic disease and perceived current health status (CHS).

METHODS

This cross-sectional study was based on 8897 participants of the National Health and Nutritional Examination Survey (NHANES) (2017-2018). Multivariate-adjusted logistic regression models were applied to assess the associations between single and joint exposures with the outcomes. Bayesian Kernel Machine Regression (BKMR) and Weighted Quantile Sum (WQS) models were further implemented to predict individual and cumulative effects of the exposures.

RESULTS

Participants with a sleep disorder (OR = 2.43; 95 %CI: 2.08- 2.85; P < 0.01) and depression (OR = 2.91; 95 %CI: 2.51- 3.39; P < 0.01) had an increased risk of cardiovascular disease (CVD). Similarly, sleep disorder (OR = 0.45; 95 %CI: 0.39- 0.53; P < 0.01), depression (OR = 0.29; 95 %CI: 025- 0.34; P < 0.01), drug use (OR = 0.65; 95 %CI: 0.54- 0.78; P < 0.01), and poor diet behavior (OR = 0.23; 95 %CI: 0.20- 0.27; P < 0.01) were associated with reduced odds of perceived good CHS. High cholesterol levels increased the risk of CVD (OR = 1.73; 95 %CI: 1.51- 1.98; P < 0.01) and diabetes (OR = 2.39; 95 %CI: 2.06- 2.78; P < 0.01). A high multi-exposure risk score was associated with increased risk of CVD (OR = 6.01; 95 %CI: 1.16- 36.33; P = 0.04), and significantly lower odds of reporting good CHS (OR = 0.06; 95 %CI: 0.01- 0.23; P < 0.01). WQS models further indicated that overall exposure increased the risk of CVD (OR = 18.02; 95 %C: 13.14- 24.71; P < 0.01) and diabetes (OR = 1.55, 95 %CI: 1.02- 2.35; P = 0.04), while it was associated with decreased odds of perceived CHS (OR = 0.12; 95 %C: 0.09- 0.16; P < 0.01).

CONCLUSION

A high multi-exposure risk score increased the risk of cardio-metabolic diseases and reduced the odds of perceived good CHS. Interventions targeting multiple risk factors in one may maximize efficiency and benefits for overall health and well-being.