Back

Association of non-HDL-C and risk of incident hypertension

He, X.; Lei, Y.; Zhao, Y.; Yang, B.; Zhao, Y.; Fu, X.; Wu, Y.; Wang, M.; Liu, M.; Su, Y.; ren, Y.; Liu, Y.; Zhang, M.; Hu, D.

2025-01-15 public and global health
10.1101/2025.01.14.25320570 medRxiv
Show abstract

BackgroundThe study aimed to investigate the association between non-high-density lipoprotein cholesterol (non-HDL-C) and risk of incident hypertension, especially concerning the association of different levels of low-density lipoprotein cholesterol (LDL-C) and non-HDL-C with risk of incident hypertension. MethodsA total of 10,623 participants from the Rural Chinese Cohort Study were included in the analyses. Non-HDL-C is the total cholesterol in the blood minus high-density lipoprotein cholesterol (HDL-C). The odds ratios (ORs) and 95% confidence intervals (95% CIs) between non-HDL-C, LDL-C, and risk of incident hypertension were estimated using logistic regression models. ResultsDuring the 10-year follow-up, 3,110 newly diagnosed hypertensive patients were identified. After adjustment for potential confounders, baseline non-HDL-C level was associated with higher risk of hypertension: the OR for the highest vs lowest quartile was 1.79 (95% CI: 1.43-2.24). For each standard deviation (SD) increase in non-HDL-C, risk of incident hypertension increased by 41%. When LDL-C was within the ideal level (<2.6 mmol/L) but non-HDL-C was above the ideal level ([&ge;]3.4 mmol/L), the risk of incident hypertension was elevated by 56% compared to when both of them were at ideal levels. Conversely, no such increased risk was observed when the LDL-C level was non-ideal while non-HDL-C remained within the ideal level. ConclusionsSerum non-HDL-C level was found to be positively associated with the risk of incident hypertension in rural Chinese adults, an association independent of LDL-C, suggesting that further lowering of non-HDL-C by interventions to reduce residual risk of incident hypertension would be beneficial.

Matching journals

The top 7 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.