Association of cholesterol, high-density lipoprotein, and glucose (CHG) index with chronic kidney disease in Chinese community adults: findings from the REACTION study.
While a limited-scale study in Turkey identified an association between the cholesterol, high-density lipoprotein, and glucose (CHG) index and diabetic nephropathy, evidence on the relationship between the CHG index and chronic kidney disease (CKD) is lacking in Chinese general population. This study aimed to investigate the association of CHG index with CKD in this specific demographic.
A total of 9,095 Chinese participants aged ≥40 years were recruited from five regional communities of Luzhou city between May 2011 and December 2011. CHG index was calculated, and its possible relationships with CKD were evaluated by multivariate logistic regression analyses. Receiver operating characteristic (ROC) analysis was conducted to identify the predictive performance, and subgroup analysis evaluated its applicability in different populations.
The subjects with higher CHG index quartiles had significantly higher prevalence of CKD compared to those with lower quartiles (P for trend < 0.01). Multivariate logistic regression analysis demonstrated that per Standard deviation (SD) increase in CHG index remains significantly associated with a 57.7% increased risk of CKD [odds ratios (OR) = 1.577; 95% confidence intervals (CI) 1.301-1.911; P < 0.01], and subjects in the highest quartile of CHG index were significantly associated with a 32.8% increased risk of CKD when compared to those in the lowest quartile (OR = 1.328; 95% CI = 1.069-1.648; P < 0.01). Stratified analysis revealed that the associations between CHG index quartiles and CKD risk were observed only in subjects who were men, non-smoker, non-drinker, aged ≥60 years, receiving a less than high school education, having overweight/obesity, type 2 diabetes mellitus, dyslipidemia, normal blood pressure, and no atherosclerotic cardiovascular disease (P for trend < 0.01 or P for trend < 0.05). The optimal cutoff point for CHG index to distinguish patients with CKD from those without was 0.601, with a sensitivity of 73.6% and a specificity of 40.6%.
The CHG index was closely associated with CKD, and might be a potential biomarker for CKD in Chinese community adults.
A total of 9,095 Chinese participants aged ≥40 years were recruited from five regional communities of Luzhou city between May 2011 and December 2011. CHG index was calculated, and its possible relationships with CKD were evaluated by multivariate logistic regression analyses. Receiver operating characteristic (ROC) analysis was conducted to identify the predictive performance, and subgroup analysis evaluated its applicability in different populations.
The subjects with higher CHG index quartiles had significantly higher prevalence of CKD compared to those with lower quartiles (P for trend < 0.01). Multivariate logistic regression analysis demonstrated that per Standard deviation (SD) increase in CHG index remains significantly associated with a 57.7% increased risk of CKD [odds ratios (OR) = 1.577; 95% confidence intervals (CI) 1.301-1.911; P < 0.01], and subjects in the highest quartile of CHG index were significantly associated with a 32.8% increased risk of CKD when compared to those in the lowest quartile (OR = 1.328; 95% CI = 1.069-1.648; P < 0.01). Stratified analysis revealed that the associations between CHG index quartiles and CKD risk were observed only in subjects who were men, non-smoker, non-drinker, aged ≥60 years, receiving a less than high school education, having overweight/obesity, type 2 diabetes mellitus, dyslipidemia, normal blood pressure, and no atherosclerotic cardiovascular disease (P for trend < 0.01 or P for trend < 0.05). The optimal cutoff point for CHG index to distinguish patients with CKD from those without was 0.601, with a sensitivity of 73.6% and a specificity of 40.6%.
The CHG index was closely associated with CKD, and might be a potential biomarker for CKD in Chinese community adults.
Authors
Bai Bai, Wu Wu, Wang Wang, Miao Miao, Zhang Zhang, Liu Liu, Huang Huang, Teng Teng, Xu Xu, Wan Wan, Yan Yan
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