Dyslipidemia and its associated factors among community adults located in Shangcheng district, Zhejiang province

Abstract

Dyslipidemia is highly prevalent and an important modifiable risk factor of cardiovascular disease in China. However, there is little information on the dyslipidemia in Shangcheng district, eastern China. Therefore, this study aims to investigate the prevalence and associated factors of dyslipidemia among community adults in this area. A community based cross-sectional study was conducted from August 1 to November 30, 2020. The study utilized a multi-stage probability sampling method to enroll permanent residents (those who have resided in this region for 6 months or more) who were 18 years old or above. Firstly, five streets were selected randomly, and then two communities were randomly selected from each of the chosen streets, finally, systematic sampling at the household level was conducted. All participants were interviewed by trained investigators and underwent anthropometric and biochemical measurements using standard criteria. LASSO (least absolute shrinkage and selection operator) and multivariate binary logistic regression were employed to identify the factors associated with dyslipidemia. In total, 3153 participants were enrolled into this study, resulting in a response rate of 93.28%. 33 subjects were excluded because of incomplete data. Finally, 3120 participants with a mean age of 55.26 (SD = 17.97) years were included into analysis. The overall prevalence of dyslipidemia was 35.96%. 21 variables were screened to multivariate binary logistic regression through the implementation of LASSO method. The multivariate binary logistic regression analysis revealed that individuals aged 40-49 [adjusted odds ratio (aOR) = 2.197, 95% confidence interval (CI) 1.445-3.341], 50-59 (aOR = 3.213, 95% CI 2.121-4.868), 60-69 (aOR = 4.777, 95% CI 3.169-7.201), and 70 and above (aOR = 5.067, 95% CI 3.301-7.777), with an educational level of junior middle school (aOR = 1.503, 95% CI 1.013-2.229), with an educational level of senior middle school (aOR = 1.731, 95% CI 1.25-2.397), with an educational level of under graduate and above (aOR = 2.125, 95% CI 1.46-3.095), without hypertension (aOR = 0.627, 95% CI 0.517-0.76), without diabetes (aOR = 0.625, 95% CI 0.498-0.785), obesity (aOR = 1.887, 95% CI 1.13-3.154), frequent smoking (aOR = 1.727, 95% CI 1.293-2.308), frequent drinking (aOR = 0.738, 95% CI 0.556-0.981), without family history of CVD (aOR = 0.505, 95% CI 0.342-0.744), and daily seafood intakes between 42.87 and 71.43 g (aOR = 1.31, 95% CI 1.05-1.634) were significantly associated with dyslipidemia. Gender-stratified analyses showed that aged 70 and above (aOR = 2.127, 95% CI 1.195-3.785), without hypertension (aOR = 0.643, 95% CI 0.484-0.854), without diabetes (aOR = 0.603, 95% CI 0.436-0.834), without CVD (aOR = 0.494, 95% CI 0.309-0.791), without stroke (aOR = 1.767, 95% CI 1.036-3.012), frequent smoking (aOR = 1.951, 95% CI 1.415-2.691), former smoking (aOR = 1.703, 95% CI 1.16-2.502) were significantly associated with dyslipidemia in male. Aged 40-49 (aOR = 3.51, 95% CI 1.789-6.887), 50-59 (aOR = 7.03, 95% CI 3.584-13.791), 60-69 (aOR = 15.728, 95% CI 8.005-30.9), and 70 and above (aOR = 12.929, 95% CI 6.449-25.921), with an educational level of senior middle school (aOR = 1.926, 95% CI 1.288-2.881), with an educational level of under graduate and above (aOR = 2.91, 95% CI 1.75-4.837), without hypertension (aOR = 0.592, 95% CI 0.45-0.779), without diabetes (aOR = 0.619, 95% CI 0.443-0.865), without family history of CVD (aOR = 0.429, 95% CI 0.251-0.733), without family history of cancer (aOR = 0.542, 95% CI 0.316-0.929), daily vegetables intakes between 251 and 500 g (aOR = 0.734, 95% CI 0.545-0.99), daily seafood intakes between 42.87 and 71.43 g (aOR = 1.421, 95% CI 1.04-1.942) were significantly associated with dyslipidemia in female. In the age-stratified analyses, it was found that without hypertension (aOR = 0.522, 95% CI 0.375-0.727) or diabetes (aOR = 0.445, 95% CI 0.267-0.744), obesity (aOR = 2.956, 95% CI 1.258-6.942), frequent smoking (aOR = 1.826, 95% CI 1.196-2.787), showed a significant association with dyslipidemia in individuals aged younger than 60 years. Female (aOR = 1.764, 95% CI 1.316-2.366), with an educational level of junior middle school (aOR = 1.793, 95% CI 1.169-2.749), with an educational level of senior middle school (aOR = 2.002, 95% CI 1.406-2.849), with an educational level of under graduate and above (aOR = 2.849, 95% CI 1.791-4.532), without hypertension (aOR = 0.604, 95% CI 0.477-0.764), without diabetes (aOR = 0.63, 95% CI 0.486-0.818), without CVD (aOR = 0.66, 95% CI 0.473-0.921), frequent smoking (aOR = 1.513, 95% CI 1.02-2.245), former smoking (aOR = 1.647, 95% CI 1.089-2.491), without family history of CVD (aOR = 0.406, 95% CI 0.239-0.692), daily seafood intakes between 42.87 and 71.43 g (aOR = 1.376, 95% CI 1.018-1.859) were significantly associated with dyslipidemia among participants aged 60 and above. Dyslipidemia is a prevalent condition observed among adults residing in Shangcheng district. Risk factors such as gender, age, education, hypertension, diabetes, cardiovascular disease, stroke, obesity, smoking, drinking, family history of cardiovascular disease, family history of cancer, daily vegetables intakes, daily seafood intakes were associated with dyslipidemia and varied across population of different gender and age groups. Enhancing education and promoting self-awareness regarding the necessity of behavior modification and regular medication intake would be beneficial in reducing the occurrence of dyslipidemia among adults in the Shangcheng district.

Figure 1

Shows the flow chart of population selection.

Figure 2

Selection of significant parameters in variables associated with dyslipidemia. (A) Ten time cross-validation for tuning parameter selection in the LASSO model. (B) LASSO coefficient profiles. The LASSO was used for regression of high dimensional predictors. The method uses an L1 penalty to shrink some regression coefficients to exactly zero. The binomial deviance curve was plotted versus log (λ), where λ is the tuning parameter (A). LASSO coefficient profiles of variables (B). LASSO least absolute shrinkage and selection operator.