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

Association of non-HDL-C and risk of incident hypertension | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function 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2 Henan Provincial Key Laboratory of Cardiac Remodeling and Transplantation , Zhengzhou, Henan, People’s Republic of China Find this author on Google Scholar Find this author on PubMed Search for this author on this site Lei Yin 1 Department of Cardiovascular Medicine, The Seventh People’s Hospital of Zhengzhou , Zhengzhou, Henan, People’s Republic of China 2 Henan Provincial Key Laboratory of Cardiac Remodeling and Transplantation , Zhengzhou, Henan, People’s Republic of China Find this author on Google Scholar Find this author on PubMed Search for this author on this site Yujie Zhao 1 Department of Cardiovascular Medicine, The Seventh People’s Hospital of Zhengzhou , Zhengzhou, Henan, People’s Republic of China 2 Henan Provincial Key Laboratory of Cardiac Remodeling and Transplantation , Zhengzhou, Henan, People’s Republic of China Find this author on Google Scholar Find this author on PubMed Search for this author on this site Bin Yang 1 Department of Cardiovascular Medicine, The Seventh People’s Hospital of Zhengzhou , Zhengzhou, Henan, People’s Republic of China 2 Henan Provincial Key Laboratory of Cardiac Remodeling and Transplantation , Zhengzhou, Henan, People’s Republic of China Find this author on Google Scholar Find this author on PubMed Search for this author on this site Yang Zhao 3 Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University , Zhengzhou, China 4 Department of Biostatistics and Epidemiology, School of Public Health, Shenzhen University Medical School, Shenzhen University , Shenzhen, Guangdong, 518055, People’ s Republic of China Find this author on Google Scholar Find this author on PubMed Search for this author on this site Xueru Fu 3 Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University , Zhengzhou, China 4 Department of Biostatistics and Epidemiology, School of Public Health, Shenzhen University Medical School, Shenzhen University , Shenzhen, Guangdong, 518055, People’ s Republic of China Find this author on Google Scholar Find this author on PubMed Search for this author on this site Yuying Wu 3 Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University , Zhengzhou, China 4 Department of Biostatistics and Epidemiology, School of Public Health, Shenzhen University Medical School, Shenzhen University , Shenzhen, Guangdong, 518055, People’ s Republic of China Find this author on Google Scholar Find this author on PubMed Search for this author on this site Mengdi Wang 3 Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University , Zhengzhou, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site Mengna Liu 3 Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University , Zhengzhou, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site Yijia Su 3 Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University , Zhengzhou, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site Yongcheng Ren 5 Institute of Health Data Management, Huanghuai University , Zhumadian 463000, He’nan, People’s Republic of China Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Yongcheng Ren Yu Liu 6 Department of General Practice, The Affiliated Luohu Hospital of Shenzhen University Medical School , Shenzhen, Guangdong, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ming Zhang 4 Department of Biostatistics and Epidemiology, School of Public Health, Shenzhen University Medical School, Shenzhen University , Shenzhen, Guangdong, 518055, People’ s Republic of China Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Ming Zhang Dongsheng Hu 1 Department of Cardiovascular Medicine, The Seventh People’s Hospital of Zhengzhou , Zhengzhou, Henan, People’s Republic of China 2 Henan Provincial Key Laboratory of Cardiac Remodeling and Transplantation , Zhengzhou, Henan, People’s Republic of China Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Dongsheng Hu For correspondence: dongshenghu563{at}126.com Abstract Full Text Info/History Metrics Data/Code Preview PDF Abstract Background The 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. Methods A 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. Results During 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 (≥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. Conclusions Serum 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. Introduction Due to population aging, lifestyle changes, and longer life expectancies, the worldwide prevalence of hypertension has been on a consistent rise over the last 20 to 30 years. The count of individuals aged between 30 and 79 suffering from hypertension globally increased from 648 million to 1.28 billion over the period from 1990 to 2019 1 . About 256.7 million adults in China between the ages of 30 and 79 had hypertension in 2019, with an overall age-standardized prevalence of 27% 2 . Alongside this, a high blood pressure level increases the risk of a number of diseases, collectively causing nearly 8.5 million deaths each year 3 , 4 . Identifying risk factors for risk of incident hypertension, early detection, and managing blood pressure are essential due to the condition’s widespread occurrence and heavy impact on health. A significant contributor to hypertension is dyslipidemia, a condition marked by elevated concentrations of total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), and non-high-density lipoprotein cholesterol (non-HDL-C) 5 , 6 . Although statins are predominantly prescribed to decrease LDL-C concentrations, the possibility of cardiovascular diseases (CVDs) persists at heightened levels even when LDL-C is kept within the prescribed targets 7 . Non-HDL-C might be another indicator within the lipid profile that contributes to the development of CVDs, according to recent studies 8 , 9 . Non-HDL-C is the total cholesterol (TC) in the blood minus high-density lipoprotein cholesterol (HDL-C) 10 , 11 . Launched by the American Heart Association (AHA) in 2022, Life’s Essential 8 modernizes the idea of cardiovascular health, particularly with regard to lipid testing, suggesting non-HDL-C as a crucial indicator 12 . Hypertension is a primary risk factor for nearly all types of CVDs that individuals may develop during their lifetime 13 . Previous studies have shown that higher non-HDL-C levels increase the risk of incident hypertension for individuals compared to those with lower levels 5 , 14 . The aim of this study, therefore, was to investigate the relationship of non-HDL-C with risk of incident hypertension while further assessing the relationship of varying LDL-C and non-HDL-C levels with risk of incident hypertension in an effort to furnish further epidemiological evidence for the proactive prevention of this condition. Materials and Methods Study population The Rural Chinese Cohort Study is a prospective cohort study that utilized cluster random sampling to recruit participants from a rural area within Henan Province, China 15 . Participants were at least 18 years old and free of serious mental illnesses, physical impairments, dementia, Alzheimer’s disease, tuberculosis, HIV/AIDS, or other infectious diseases 15 . The study’s timeline included a baseline survey from July-August 2007 to July-August 2008, a first follow-up survey from July-August 2013 to July-October 2014 with an 85.5% follow-up rate, and a second follow-up survey from July-August 2018 to July-August 2020 with a 92.8% follow-up rate 16 . Details about the study’s participants, design, and data collection have already been provided 15 . 20,194 individuals were enrolled in total at baseline. Participants in this study were not included if they were: (1) people with baseline hypertension (n = 6,299); (2) taking lipid-lowering drugs at baseline (n = 433); (3) absent of baseline TC, HDL-C, and LDL-C data (n = 344); or (4) their hypertension status was unknown during the 10-year follow-up (n = 2,495) (Supplementary Figure 1). Baseline data collection A team of trained investigators used laboratory measurements, anthropometry, physical examinations, and in-person questionnaire interviews to collect pertinent data. The questionnaire contained a range of socio-demographic attributes, including age, gender, occupation, education, marital status, and lifestyles. Additionally, it covered personal medical histories pertaining to blood pressure, blood glucose, and lipid-lowering pharmaceuticals, and the family history of diseases 15 . Smoking was defined as consuming 100 or more cigarettes during one’s lifetime 17 . Drinking was defined as consuming alcohol more than 12 times in the previous 12 months 18 . The diagnostic criteria for dyslipidemia encompass the following: TC ≥5.2 mmol/L, LDL-C ≥3.4 mmol/L, triglycerides (TG)≥1.7 mmol/L, HDL-C<1.00 mmol/L, and non-HDL-C ≥4.1 mmol/L. Meeting one or more of these criteria is sufficient for a diagnosis of dyslipidemia 19 . The International Physical Activity Questionnaire was used to classify physical activity into three levels: low, medium, and high 20 . The heights of the participants were recorded with precision to the nearest 0.1 cm as they stood upright. Their weights were similarly assessed to the closest 0.5 kg using a vertical weight scale 21 . After a minimum fast of 8 hours, blood specimens were collected, and the Hitachi 7060 Automatic Analyzer was used to assess fasting plasma glucose (FPG), TG, TC, HDL-C, and LDL-C. The non-HDL-C level was computed using the formula: Classification criteria for LDL-C and non-HDL-C We categorized LDL-C and non-HDL-C using uniform optimal concentration thresholds, in line with the Chinese Guideline for Lipid Management (Primary Care Version 2024), as well as the AHA’s “Life’s Essential 8” guidelines 12 , 19 . It is ideal to have LDL-C level<2.6 mmol/L and non-HDL-C level<3.4 mmol/L. Accordingly, we categorized the study population into 4 different groups: those with LDL-C<2.6 mmol/L and non-HDL-C<3.4 mmol/L as the control group, those with LDL-C<2.6 mmol/L and non-HDL-C ≥3.4 mmol/L, those with LDL-C ≥2.6 mmol/L and non-HDL-C<3.4 mmol/L, and those with both LDL-C ≥2.6 mmol/L and non-HDL-C ≥3.4 mmol/L. The study aim was to assess the risk of incident hypertension within each group relative to the control, with a particular emphasis on the instances where LDL-C or non-HDL-C were not optimal. Outcomes assessment Thirty minutes before the blood pressure measurement, the participants had to empty their bladders, avoid caffeine, alcohol, and smoking, and sit still for five minutes before the measurement, with their elbows at heart level during the measurement. A trained health worker utilized an electronic blood pressure monitoring device (HEM-770 AFuzzy) to conduct three measurements spaced 30 seconds apart, in line with the American Heart Association’s recommended protocol 22 . If any two of the three measurements differed by more than 5 mmHg, they were re-measured, with the average of the measured values then used for analysis. Hypertension is defined by a systolic blood pressure of ≥140 mmHg, a diastolic blood pressure of ≥90 mmHg, or use of antihypertensive medication 23 . Follow-up measures employed the same strategies as at baseline, using the same definition of hypertension. Statistical analysis The population was divided into four groups (Q1 to Q4) based on lower quartile, median, and upper quartile of non-HDL-C levels at baseline, representing the lowest (Q1), lower middle (Q2), upper middle (Q3), and highest (Q4) levels, and participants in each quartile were compared with participants in the lowest quartile. In this study, all continuous variables exhibited skewness, thus their distributional properties were characterized by the median (interquartile range from the upper to the lower quartile). To assess group differences, the Wilcoxon rank-sum test and the Kruskal-Wallis test were employed. Categorical variables were summarized by frequency counts and percentages, with group comparisons conducted using the χ 2 test. The odds ratios (ORs) and 95% confidence intervals (95% CIs) for non-HDL-C and risk of incident hypertension, and for various levels of LDL-C and non-HDL-C and risk of incident hypertension, were calculated using logistic regression models. Further, 4 models that accounted for baseline confounders were established. Model 1 did not adjust; model 2 adjusted for age and gender; and model 3 adjusted for marital status, education, smoking, drinking, physical activity, family history of hypertension, BMI, and FPG on the basis of model 2. Further, we adjusted LDL-C based on model 3 to create model 4, with the aim of examining the independent association of non-HDL-C with risk of incident hypertension, disassociated from the influence of LDL-C. Restricted cubic splines (RCS) with 4 knots were used to explore the dose-response relationship of non-HDL-C with risk of incident hypertension. Subgroup analyses of different subgroups (age, gender, smoking, drinking, dyslipidemia, physical activity, and family history of hypertension) were performed. In addition, sensitivity analyses were performed to further evaluate the robustness of the findings after excluding patients with baseline dyslipidemia, prehypertension, cancer, end-stage renal disease, and CVDs. We further incorporated an interaction term for LDL-C level (<2.6 mmol/L versus ≥2.6 mmol/L) and non-HDL-C level (<3.4 mmol/L versus ≥3.4 mmol/L) into the model to assess the combined and interactive effects. The measure of multiplicative interaction was expressed as the OR and its 95% CI for the interaction term. Three metrics were employed for the additive interaction: the synergy index (SI), proportion attributable to interaction (AP), and relative excess risk caused by interaction (RERI). Specifically, RERI = 0, AP = 0, and SI = 1 imply no additive interaction between LDL-C and non-HDL-C for risk of incident hypertension. If RERI > 0, AP > 0, and SI > 1, the combined effect of LDL-C and non-HDL-C on risk of incident hypertension exceeds individual effects, suggesting synergy; however, RERI < 0, AP < 0, and SI < 1 indicate the combined effect is less than the sum of individual effects. We calculated 95% CIs for these three measures using the delta method. Analyses were conducted using SAS 9.4 software (SAS Institute, Cary, NC, USA) and R version 4.3.0 (R Foundation). All statistical tests performed were two-tailed, and a P -value of less than 0.05 was deemed indicative of statistical significance. Results Baseline characteristics During the 10-year follow-up, 3,110 newly diagnosed hypertension cases were recorded, comprising 1,247 males and 1,863 females. Age, marital status, educational level, physical activity, family history of hypertension, BMI, waist circumference (WC), SBP, DBP, TC, TG, HDL-C, LDL-C, FPG, and non-HDL-C all differed statistically significantly ( P 0.05) ( Table 1 ). View this table: View inline View popup Table 1 Baseline characteristics of 10,623 participants Association of baseline serum non-HDL-C level and risk of incident hypertension Table 2 shows the ORs and 95% CIs for the relationship between non-HDL-C and risk of incident hypertension. The cumulative incidence of hypertension for participants in groups 1 to 4 of the non-HDL-C levels were 21.42%, 26.85%, 33.01%, and 35.68%, respectively. The OR (95% CI) for risk of incident hypertension in the top vs the bottom quartile was 2.04 (95% CI: 1.80-2.30, P for trend <0.001) in the unadjusted model 1. 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, P for trend <0.001, model 4). Further, the risk of incident hypertension elevated with non-HDL-C with each standard deviation (SD) increase, with an adjusted OR (95% CI) of 1.41 (1.27-1.57) in model 4. Figure 1 shows the dose-response association of non-HDL-C level and risk of incident hypertension, revealing a positive, nonlinear correlation ( P nonlinear =0.003). Compared with the non-HDL-C concentrations of <3.01 mmol/L, as the non-HDL-C increased, the adjusted OR value for risk of incident hypertension also increased radically. View this table: View inline View popup Download powerpoint Table 2 Association of baseline non-HDL-C level and risk of incident hypertension Download figure Open in new tab Subgroup analysis and sensitivity analysis A significant interaction was identified between non-HDL-C concentration and age for the risk of incident hypertension ( P interaction < 0.001), with stronger associations between non-HDL-C and risk of incident hypertension in individuals under 60 years versus those over 60. Additionally, a significant interaction was observed between alcohol and non-HDL-C with risk of incident hypertension ( P interaction < 0.05), suggesting a more obvious association between non-HDL-C and incident hypertension among drinkers versus non-drinkers. Further, a significant interaction was identified between non-HDL-C and a family history of hypertension ( P interaction < 0.001). Despite the absence of significant interactions between non-HDL-C and other potential hypertension hazard factors in our findings, we found substantial positive associations in males, smokers, and individuals with low to moderate level of physical activity ( Figure 2 ). Download figure Open in new tab Patients with prehypertension, malignancy, end-stage renal disease, cardiovascular disease, and dyslipidemia at baseline were excluded to enable sensitivity analysis. Sensitivity analysis showed strong overall results, in alignment with the primary results (Supplementary table 2). Association between varying levels of LDL-C and non-HDL-C and risk of incident hypertension The joint association of LDL-C and non-HDL-C for primary outcomes is shown in Table 3 . With ideal level of LDL-C (<2.6 mmol/L) and non-HDL-C (<3.4 mmol/L) as the reference group, the risk of incident hypertension increased to an OR of 1.56 (95% CI: 1.23-1.98) for individuals with LDL-C <2.6 mmol/L and non-HDL-C ≥3.4 mmol/L. For those with LDL-C ≥2.6 mmol/L and non-HDL-C <3.4 mmol/L, the risk slightly increased, with an OR of 1.05 (95% CI: 0.91-1.22). When both of them exceeded their respective ideal levels, the risk of incident hypertension increased to an OR of 1.34 (95% CI: 1.20-1.49). Further, there were no discernible multiplicative or additive interactions between LDL-C and non-HDL-C on risk of incident hypertension (multiplicative effect: OR (95% CI): 1.05, 0.79-1.40; additive effect: RERI = 0.06, 95% CI: −0.26, 0.41; Supplementary table 1, model 3). View this table: View inline View popup Download powerpoint Table 3 Association of different levels of LDL-C and non-HDL-C with risk of incident hypertension Discussion To our knowledge, this is the first prospective cohort study to investigate the association of non-HDL-C level with risk of incident hypertension, categorized according to ideal levels of non-HDL-C and LDL-C to assess their distinct influences on risk of incident hypertension. Group 4 was related to a 79% increased risk of incident hypertension compared to group 1, and there was a nonlinear dose-response relationship between serum concentrations of non-HDL-C and risk of incident hypertension, with each additional SD of non-HDL-C corresponding to a 41% heightened risk of incident hypertension. Additionally, compared to ideal levels of LDL and non-HDL-C, those with ideal LDL-C but high blood concentration of non-HDL-C had a 56% greater risk of incident hypertension; however, no correlation was observed when non-HDL was ideal and LDL-C was suboptimal. In line with current guidelines, the utilization of lipid-lowering drugs should prioritize LDL-C as the primary therapeutic target, with non-HDL-C concentrations as the secondary goal, given that dyslipidemia is a contributing factor to the risk of incident hypertension 24 , 25 . Increasing evidence suggest however that non-HDL-C is superior to other standard lipid markers, including LDL-C, and has been demonstrated to be a more valid risk indicator in prevention studies 26 – 28 . Non-HDL-C, which can be measured without fasting and has critical values that are simple to calculate, is unaffected by TC level, making it a more reliable indicator than LDL-C when triglycerides are elevated, and it is also easier to explain in the context of health education and risk management 29 – 31 . Our results indicate an active association of non-HDL-C with risk of incident hypertension, aligning with earlier studies. Similarly, a cross-sectional study of 117,056 Chinese adults without type 2 diabetes mellitus found a positive association between non-HDL-C and risk of incident hypertension, with a 6.1% increase per SD of non-HDL-C 14 . Additionally, a prospective study involving 1,019 men showed that males with the highest levels of non-HDL-C had a 36% greater risk of incident hypertension 5 . The possible mechanism of serum non-HDL-C leading to hypertension is as follows. First, non-HDL-C’s lipid components possess the ability to reach blood vessels through the wall, resulting in endothelial dysfunction and decreased nitric oxide generation, which in turn decreases vascular dilation capacity and raises vascular tension 32 . Second, non-HDL-C promotes the release of inflammatory factors through oxidative stress reactions, damaging the vessel wall and causing it to thicken and lose elasticity, thereby raising blood pressure 33 , 34 . Third, abnormal blood lipids can reduce the release of substances that dilate blood vessels while increasing the release of vasoconstrictor substances such as endothelin 35 . Finally, non-HDL-C may cause the renin-angiotensin-aldosterone system to become activated, which would aggravate the development of hypertension by causing water and sodium retention as well as vascular contraction 36 . These mechanisms interact with each other, driving the pathogenesis of hypertension. We are investigating additional lipid management tactics beyond just lowering LDL-C as the main treatment, aiming to provide extra benefit when preventing and managing disease risk. The study indicates that non-HDL-C exhibits greater significance than LDL-C for evaluating risk of incident hypertension, as more-than-ideal non-HDL-C concentrations greatly raise the risk of incident hypertension, even if LDL-C level is optimal, whereas the risk does not rise significantly if the non-HDL-C level is ideal. This result is similar to those of earlier studies on outcomes like diabetes, all-cause mortality, and heart-related incidents 27 , 37 , 38 . A notable finding is that the risk of incident hypertension was lower when both of them were non-ideal than when only non-HDL-C was non-ideal. This may be due to the fact that when non-HDL-C is increased and LDL-C is lowered, residual cholesterol (RC) is also raised, with some previous studies showing that RC level is associated with an increased risk of incident hypertension. Multiple previous studies have found that higher RC concentration is linked to an added risk of incident hypertension 39 , 40 . On the one hand, RC penetrates the inner wall of the artery and accumulates, stimulating monocytes and macrophages to release pro-inflammatory factors, promoting free fatty acid release and foam cell formation, thereby causing endothelial dysfunction and inflammation 41 , 42 . This chronic inflammation may trigger oxidative stress, exacerbating impairment of arterial function 33 . On the other hand, RC stimulates aldosterone secretion, resulting in sodium conservation as well as elevated blood volume, and thereby raising blood pressure and fostering atherosclerosis 43 , 44 . The study showed a strong association between non-HDL-C and risk of incident hypertension among individuals under 60, males, smokers, drinkers, and those with low to moderate physical activity. Changes with age, lower basal metabolic rate, increased incidence of chronic diseases, and changes in diet may mask or alter the relationship between non-HDL-C level and risk of incident hypertension 45 , 46 . In addition, male hormones such as testosterone are affected, which may increase lipid levels and promote the development of atherosclerosis 47 . Smoking and drinking both damage blood vessel walls, increase blood viscosity, and promote inflammatory responses, while these factors work together with raised concentrations of non-HDL-C to increase the risk of incident hypertension 48 , 49 . Simultaneously, levels of low to moderate physical activity may not be sufficient to enhance lipid metabolism or offset the detrimental impacts of such unhealthy lifestyles, resulting in the notable association of non-HDL-C with risk of incident hypertension 50 . This is the first prospective cohort study to thoroughly explore the association between blood non-HDL-C concentrations and risk of incident hypertension in rural Chinese adults while also classifying LDL and non-HDL-C into ideal levels, using a large sample and rigorously controlling variables. Still, the study has some limitations. First, although the method for estimating TC is considered accurate, biases in HDL-C measurement could influence the precision of non-HDL-C concentration calculations, especially in the presence of extremely high triglyceride levels. Such discrepancies may introduce bias into the computation of non-HDL-C. Second, the findings probably will not be applicable to other populations since the study was in a rural Chinese setting. Further studies across different populations are needed. Lastly, although we controlled for confounding variables with 4 models, unseen factors like psychological factors could have affected the outcomes. Conclusion This study indicates that higher baseline a non-HDL-C level is nonlinearly and positively associated with risk of incident hypertension among rural Chinese adults. Notably, this correlation was independent of LDL-C level, suggesting that further reductions in non-HDL-C, based on already controlled LDL-C, were associated with a further decrease in the risk of incident hypertension. To validate our findings, further studies with larger populations of varying ethnic populations are required. Data Availability Contact the corresponding author for all data relating to this study upon reasonable request. Funding This study was supported by the National Natural Science Foundation of China (grant nos. 82073646, 81973152, 82103940, 82273707 and 82304228), the Postdoctoral Research Foundation of China (grant no. 2021M692903), Guangdong Basic and Applied Basic Research Foundation (grant nos. 2021A1515012503, and 2022A1515010503), Shenzhen Science and Technology Program (grant nos. JCYJ20210324093612032 and JCYJ20220818095818040), the Key R & D and promotion projects in Henan Province (grant no.232102311017), and Nanshan District Science and Technology Program Key Project of Shenzhen (grant no. NS2022009). Disclosure statement All authors declare that they have no competing interests. Supplemental Material Tables S1-S2 Figures S1 Acknowledgement We acknowledge all the authors, particularly Prof. Dongsheng Hu for methodological advice and help in modifying the manuscript. Non-standard Abbreviations and Acronym AHA American Heart Association AP proportion attributable to interaction BMI body mass index CVDs cardiovascular diseases FPG fasting plasma glucose LDL-C low-density lipoprotein Non-HDL-C non-high-density lipoprotein cholesterol RCS restricted cubic splines RC residual cholesterol RERI relative excess risk due to interaction SI synergy index TC total cholesterol TG triglycerides Reference 1. ↵ Worldwide trends in hypertension prevalence and progress in treatment and control from 1990 to 2019: a pooled analysis of 1201 population-representative studies with 104 million participants . Lancet (London, England) . Sep 11 2021 ; 398 ( 10304 ): 957 – 980 . doi: 10.1016/s0140-6736(21)01330-1 OpenUrl CrossRef PubMed 2. ↵ World Health Organization . Global report on hypertension: the race against a silent kille . https://www.who.int/publications/i/item/9789240081062 3. ↵ Olsen MH , Angell SY , Asma S , et al. A call to action and a lifecourse strategy to address the global burden of raised blood pressure on current and future generations: the Lancet Commission on hypertension . Lancet (London, England) . Nov 26 2016 ; 388 ( 10060 ): 2665 – 2712 . doi: 10.1016/s0140-6736(16)31134-5 OpenUrl CrossRef 4. ↵ Lawes CM , Vander Hoorn S , Rodgers A. Global burden of blood-pressure-related disease, 2001 . Lancet (London, England) . May 3 2008 ; 371 ( 9623 ): 1513 – 8 . doi: 10.1016/s0140-6736(08)60655-8 OpenUrl CrossRef PubMed 5. ↵ Halperin RO , Sesso HD , Ma J , Buring JE , Stampfer MJ , Gaziano JM . Dyslipidemia and the risk of incident hypertension in men . Hypertension (Dallas, Tex : 1979) . Jan 2006 ; 47 ( 1 ): 45 – 50 . doi: 10.1161/01.HYP.0000196306.42418.0e OpenUrl Abstract / FREE Full Text 6. ↵ Otsuka T , Takada H , Nishiyama Y , et al. Dyslipidemia and the Risk of Developing Hypertension in a Working-Age Male Population . Journal of the American Heart Association . Mar 25 2016 ; 5 ( 3 ): e003053 . doi: 10.1161/jaha.115.003053 OpenUrl CrossRef 7. ↵ Sirimarco G , Labreuche J , Bruckert E , et al. Atherogenic dyslipidemia and residual cardiovascular risk in statin-treated patients . Stroke . May 2014 ; 45 ( 5 ): 1429 – 36 . doi: 10.1161/strokeaha.113.004229 OpenUrl Abstract / FREE Full Text 8. ↵ Frost PH , Davis BR , Burlando AJ , et al. Serum lipids and incidence of coronary heart disease. Findings from the Systolic Hypertension in the Elderly Program (SHEP) . Circulation . Nov 15 1996 ; 94 ( 10 ): 2381 – 8 . doi: 10.1161/01.cir.94.10.2381 OpenUrl Abstract / FREE Full Text 9. ↵ Ridker PM , Rifai N , Cook NR , Bradwin G , Buring JE . Non-HDL cholesterol, apolipoproteins A-I and B100, standard lipid measures, lipid ratios, and CRP as risk factors for cardiovascular disease in women . Jama . Jul 20 2005 ; 294 ( 3 ): 326 – 33 . doi: 10.1001/jama.294.3.326 OpenUrl CrossRef PubMed Web of Science 10. ↵ Executive Summary of The Third Report of The National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, And Treatment of High Blood Cholesterol In Adults (Adult Treatment Panel III) . Jama . May 16 2001 ; 285 ( 19 ): 2486 – 97 . doi: 10.1001/jama.285.19.2486 OpenUrl CrossRef PubMed Web of Science 11. ↵ Kilgore M , Muntner P , Woolley JM , Sharma P , Bittner V , Rosenson RS . Discordance between high non-HDL cholesterol and high LDL-cholesterol among US adults . Journal of clinical lipidology . Jan-Feb 2014 ; 8 ( 1 ): 86 – 93 . doi: 10.1016/j.jacl.2013.11.001 OpenUrl CrossRef PubMed 12. ↵ Lloyd-Jones DM , Allen NB , Anderson CAM , et al. Life’s Essential 8: Updating and Enhancing the American Heart Association’s Construct of Cardiovascular Health: A Presidential Advisory From the American Heart Association . Circulation . Aug 2 2022 ; 146 ( 5 ): e18 – e43 . doi: 10.1161/cir.0000000000001078 OpenUrl CrossRef PubMed 13. ↵ Fuchs FD , Whelton PK. High Blood Pressure and Cardiovascular Disease . Hypertension (Dallas, Tex : 1979) . Feb 2020 ; 75 ( 2 ): 285 – 292 . doi: 10.1161/hypertensionaha.119.14240 OpenUrl CrossRef 14. ↵ Cheng W , Zhuang J , Chen S . Dyslipidemia and the Prevalence of Hypertension: A Cross-Sectional Study Based on Chinese Adults Without Type 2 Diabetes Mellitus . Frontiers in cardiovascular medicine . 2022 ; 9 : 938363 . doi: 10.3389/fcvm.2022.938363 OpenUrl CrossRef PubMed 15. ↵ Zhang M , Zhao Y , Sun L , et al. Cohort Profile: The Rural Chinese Cohort Study . International journal of epidemiology . Jul 9 2021 ; 50 ( 3 ): 723 – 724l . doi: 10.1093/ije/dyaa204 OpenUrl CrossRef PubMed 16. ↵ Zhang D , Huo W , Chen W , et al. Association of traditional and novel obesity indicators with stroke risk: Findings from the Rural Chinese cohort study . Nutrition, metabolism, and cardiovascular diseases : NMCD . Sep 2024 ; 34 ( 9 ): 2065 – 2074 . doi: 10.1016/j.numecd.2024.05.001 OpenUrl CrossRef 17. ↵ Zhao Y , Zhang J , Chen C , et al. Comparison of six surrogate insulin resistance indexes for predicting the risk of incident stroke: The Rural Chinese Cohort Study . Diabetes/metabolism research and reviews . Oct 2022 ; 38 ( 7 ): e3567 . doi: 10.1002/dmrr.3567 OpenUrl CrossRef PubMed 18. ↵ Zhang M , Wang B , Liu Y , et al. Cumulative increased risk of incident type 2 diabetes mellitus with increasing triglyceride glucose index in normal-weight people: The Rural Chinese Cohort Study . Cardiovascular diabetology . Mar 1 2017 ; 16 ( 1 ): 30 . doi: 10.1186/s12933-017-0514-x OpenUrl CrossRef PubMed 19. ↵ ZW W, YL G. Chinese Guideline for Lipid Management (Primary Care Version 2024) . Chinese Circulation Journal . 2024 ; 39 ( 04 ): 313 – 321 . OpenUrl 20. ↵ Craig CL , Marshall AL , Sjöström M , et al. International physical activity Questionnaire: 12-country reliability and validity . Medicine and science in sports and exercise . Aug 2003 ; 35 ( 8 ): 1381 – 95 . doi: 10.1249/01.Mss.0000078924.61453.Fb OpenUrl CrossRef PubMed Web of Science 21. ↵ Han M , Qie R , Li Q , et al. Chinese visceral adiposity index, a novel indicator of visceral obesity for assessing the risk of incident hypertension in a prospective cohort study . The British journal of nutrition . Aug 28 2021 ; 126 ( 4 ): 612 – 620 . doi: 10.1017/s0007114520004298 OpenUrl CrossRef PubMed 22. ↵ Perloff D , Grim C , Flack J , et al. Human blood pressure determination by sphygmomanometry . Circulation . Nov 1993 ; 88 ( 5 Pt 1 ): 2460 – 70 . doi: 10.1161/01.cir.88.5.2460 OpenUrl FREE Full Text 23. ↵ Chobanian AV , Bakris GL , Black HR , et al. Seventh report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure . Hypertension (Dallas, Tex : 1979) . Dec 2003 ; 42 ( 6 ): 1206 – 52 . doi: 10.1161/01.HYP.0000107251.49515.c2 OpenUrl Abstract / FREE Full Text 24. ↵ Stone NJ , Robinson JG , Lichtenstein AH , et al. 2013 ACC/AHA guideline on the treatment of blood cholesterol to reduce atherosclerotic cardiovascular risk in adults: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines . Journal of the American College of Cardiology . Jul 1 2014 ; 63 ( 25 Pt B ): 2889 – 934 . doi: 10.1016/j.jacc.2013.11.002 OpenUrl FREE Full Text 25. ↵ Catapano AL , Graham I , De Backer G , et al. 2016 ESC/EAS Guidelines for the Management of Dyslipidaemias . European heart journal . Oct 14 2016 ; 37 ( 39 ): 2999 – 3058 . doi: 10.1093/eurheartj/ehw272 OpenUrl CrossRef PubMed 26. ↵ Colivicchi F , Di Fusco SA , Arca M , et al. Non-high-density lipoprotein cholesterol versus low-density lipoprotein cholesterol in clinical practice: ANMCO position paper . Journal of cardiovascular medicine (Hagerstown, Md) . Aug 1 2021 ; 22 ( 8 ): 609 – 617 . doi: 10.2459/jcm.0000000000001175 OpenUrl CrossRef PubMed 27. ↵ Boekholdt SM , Arsenault BJ , Mora S , et al. Association of LDL cholesterol, non-HDL cholesterol, and apolipoprotein B levels with risk of cardiovascular events among patients treated with statins: a meta-analysis . Jama . Mar 28 2012 ; 307 ( 12 ): 1302 – 9 . doi: 10.1001/jama.2012.366 OpenUrl CrossRef PubMed Web of Science 28. ↵ Mora S , Martin SS , Virani SS . Cholesterol Insights and Controversies From the UK Biobank Study . Circulation . Aug 13 2019 ; 140 ( 7 ): 553 – 555 . doi: 10.1161/circulationaha.119.042134 OpenUrl CrossRef PubMed 29. ↵ Reiner Ž , Catapano AL , De Backer G , et al. [ESC/EAS Guidelines for the management of dyslipidaemias] . Revista espanola de cardiologia . Dec 2011 ; 64 ( 12 ): 1168.e1 – 1168.e60 . Guía de la ESC/EAS sobre el manejo de las dislipemias. doi: 10.1016/j.recesp.2011.09.014 OpenUrl CrossRef PubMed 30. Bays HE , Jones PH , Brown WV , Jacobson TA . National Lipid Association Annual Summary of Clinical Lipidology 2015 . Journal of clinical lipidology . Nov-Dec 2014 ; 8 ( 6 Suppl ): S1 – 36 . doi: 10.1016/j.jacl.2014.10.002 OpenUrl CrossRef 31. ↵ de Vries M , Klop B , Castro Cabezas M . The use of the non-fasting lipid profile for lipid-lowering therapy in clinical practice - point of view . Atherosclerosis . Jun 2014 ; 234 ( 2 ): 473 – 5 . doi: 10.1016/j.atherosclerosis.2014.03.024 OpenUrl CrossRef PubMed 32. ↵ Wolfort RM , Stokes KY , Granger DN . CD4+ T lymphocytes mediate hypercholesterolemia-induced endothelial dysfunction via a NAD(P)H oxidase-dependent mechanism . American journal of physiology Heart and circulatory physiology . Jun 2008 ; 294 ( 6 ): H2619 – 26 . doi: 10.1152/ajpheart.00989.2007 OpenUrl CrossRef PubMed 33. ↵ Guzik TJ , Touyz RM. Oxidative Stress, Inflammation, and Vascular Aging in Hypertension . Hypertension (Dallas, Tex : 1979) . Oct 2017 ; 70 ( 4 ): 660 – 667 . doi: 10.1161/hypertensionaha.117.07802 OpenUrl CrossRef 34. ↵ Schulz E , Gori T , Münzel T . Oxidative stress and endothelial dysfunction in hypertension . Hypertension research : official journal of the Japanese Society of Hypertension . Jun 2011 ; 34 ( 6 ): 665 – 73 . doi: 10.1038/hr.2011.39 OpenUrl CrossRef PubMed 35. ↵ Dhaun N , Webb DJ . Endothelins in cardiovascular biology and therapeutics . Nature reviews Cardiology . Aug 2019 ; 16 ( 8 ): 491 – 502 . doi: 10.1038/s41569-019-0176-3 OpenUrl CrossRef PubMed 36. ↵ Ma J , Li Y , Yang X , et al. Signaling pathways in vascular function and hypertension: molecular mechanisms and therapeutic interventions . Signal transduction and targeted therapy . Apr 20 2023 ; 8 ( 1 ): 168 . doi: 10.1038/s41392-023-01430-7 OpenUrl CrossRef PubMed 37. ↵ Johannesen CDL , Mortensen MB , Langsted A , Nordestgaard BG . Apolipoprotein B and Non-HDL Cholesterol Better Reflect Residual Risk Than LDL Cholesterol in Statin-Treated Patients . Journal of the American College of Cardiology . Mar 23 2021 ; 77 ( 11 ): 1439 – 1450 . doi: 10.1016/j.jacc.2021.01.027 OpenUrl CrossRef PubMed 38. ↵ Yuan L , Liu J , Huang Z , et al. Elevated remnant cholesterol increase 6-year type 2 diabetes mellitus onset risk . Clinica chimica acta; international journal of clinical chemistry . Feb 15 2023 ; 541 : 117253 . doi: 10.1016/j.cca.2023.117253 OpenUrl CrossRef 39. ↵ Varbo A , Nordestgaard BG . Remnant cholesterol and risk of ischemic stroke in 112,512 individuals from the general population . Annals of neurology . Apr 2019 ; 85 ( 4 ): 550 – 559 . doi: 10.1002/ana.25432 OpenUrl CrossRef PubMed 40. ↵ Wadström BN , Wulff AB , Pedersen KM , Jensen GB , Nordestgaard BG . Elevated remnant cholesterol increases the risk of peripheral artery disease, myocardial infarction, and ischaemic stroke: a cohort-based study . European heart journal . Sep 7 2022 ; 43 ( 34 ): 3258 – 3269 . doi: 10.1093/eurheartj/ehab705 OpenUrl CrossRef PubMed 41. ↵ Nakamura T , Takano H , Umetani K , et al. Remnant lipoproteinemia is a risk factor for endothelial vasomotor dysfunction and coronary artery disease in metabolic syndrome . Atherosclerosis . Aug 2005 ; 181 ( 2 ): 321 – 7 . doi: 10.1016/j.atherosclerosis.2005.01.012 OpenUrl CrossRef PubMed Web of Science 42. ↵ Oparil S , Acelajado MC , Bakris GL , et al. Hypertension . Nature reviews Disease primers . Mar 22 2018 ; 4 : 18014 . doi: 10.1038/nrdp.2018.14 OpenUrl CrossRef PubMed 43. ↵ Krug AW , Ehrhart-Bornstein M . Aldosterone and metabolic syndrome: is increased aldosterone in metabolic syndrome patients an additional risk factor? Hypertension (Dallas, Tex : 1979) . May 2008 ; 51 ( 5 ): 1252 – 8 . doi: 10.1161/hypertensionaha.107.109439 OpenUrl CrossRef 44. ↵ Xing Y , Rainey WE , Apolzan JW , Francone OL , Harris RB , Bollag WB . Adrenal cell aldosterone production is stimulated by very-low-density lipoprotein (VLDL) . Endocrinology . Feb 2012 ; 153 ( 2 ): 721 – 31 . doi: 10.1210/en.2011-1752 OpenUrl CrossRef PubMed Web of Science 45. ↵ Hornburg D , Wu S , Mori M , et al. Dynamic lipidome alterations associated with human health, disease and ageing . Nature metabolism . Sep 2023 ; 5 ( 9 ): 1578 – 1594 . doi: 10.1038/s42255-023-00880-1 OpenUrl CrossRef PubMed 46. ↵ Pontzer H , Yamada Y , Sagayama H , et al. Daily energy expenditure through the human life course . Science (New York, NY) . Aug 13 2021 ; 373 ( 6556 ): 808 – 812 . doi: 10.1126/science.abe5017 OpenUrl Abstract / FREE Full Text 47. ↵ Takov K , Wu J , Denvir MA , Smith LB , Hadoke PWF . The role of androgen receptors in atherosclerosis . Molecular and cellular endocrinology . Apr 15 2018 ; 465 : 82 – 91 . doi: 10.1016/j.mce.2017.10.006 OpenUrl CrossRef PubMed 48. ↵ Vacca A , Bulfone L , Cicco S , et al. Alcohol Intake and Arterial Hypertension: Retelling of a Multifaceted Story . Nutrients . Feb 15 2023 ; 15 ( 4 ) doi: 10.3390/nu15040958 OpenUrl CrossRef 49. ↵ Seiler CL , Song JUM , Kotandeniya D , et al. Inhalation exposure to cigarette smoke and inflammatory agents induces epigenetic changes in the lung . Scientific reports . Jul 9 2020 ; 10 ( 1 ): 11290 . doi: 10.1038/s41598-020-67502-8 OpenUrl CrossRef PubMed 50. ↵ Muscella A , Stefàno E , Marsigliante S . The effects of exercise training on lipid metabolism and coronary heart disease . American journal of physiology Heart and circulatory physiology . Jul 1 2020 ; 319 ( 1 ): H76 – h88 . doi: 10.1152/ajpheart.00708.2019 OpenUrl CrossRef PubMed View the discussion thread. Back to top Previous Next Posted January 15, 2025. Download PDF Data/Code Email Thank you for your interest in spreading the word about medRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. 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Share Association of non-HDL-C and risk of incident hypertension Xinxin He , Lei Yin , Yujie Zhao , Bin Yang , Yang Zhao , Xueru Fu , Yuying Wu , Mengdi Wang , Mengna Liu , Yijia Su , Yongcheng Ren , Yu Liu , Ming Zhang , Dongsheng Hu medRxiv 2025.01.14.25320570; doi: https://doi.org/10.1101/2025.01.14.25320570 Share This Article: Copy Citation Tools Association of non-HDL-C and risk of incident hypertension Xinxin He , Lei Yin , Yujie Zhao , Bin Yang , Yang Zhao , Xueru Fu , Yuying Wu , Mengdi Wang , Mengna Liu , Yijia Su , Yongcheng Ren , Yu Liu , Ming Zhang , Dongsheng Hu medRxiv 2025.01.14.25320570; doi: https://doi.org/10.1101/2025.01.14.25320570 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Public and Global Health Subject Areas All Articles Addiction Medicine (568) Allergy and Immunology (863) Anesthesia (300) Cardiovascular Medicine (4436) Dentistry and Oral Medicine (444) Dermatology (382) Emergency Medicine (608) Endocrinology (including Diabetes Mellitus and Metabolic Disease) (1509) Epidemiology (15229) Forensic Medicine (30) Gastroenterology (1124) Genetic and Genomic Medicine (6600) Geriatric Medicine (668) Health Economics (997) Health Informatics (4538) Health Policy (1368) Health Systems and Quality Improvement (1613) Hematology (542) HIV/AIDS (1264) Infectious Diseases (except HIV/AIDS) (15916) Intensive Care and Critical Care Medicine (1103) Medical Education (623) Medical Ethics (146) Nephrology (667) Neurology (6599) Nursing (346) Nutrition (998) Obstetrics and Gynecology (1144) Occupational and Environmental Health (957) Oncology (3333) Ophthalmology (974) Orthopedics (369) Otolaryngology (420) Pain Medicine (436) Palliative Medicine (130) Pathology (663) Pediatrics (1693) Pharmacology and Therapeutics (691) Primary Care Research (711) Psychiatry and Clinical Psychology (5447) Public and Global Health (9232) Radiology and Imaging (2198) Rehabilitation Medicine and Physical Therapy (1370) Respiratory Medicine (1196) Rheumatology (593) Sexual and Reproductive Health (712) Sports Medicine (530) Surgery (712) Toxicology (99) Transplantation (289) Urology (265) (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'a0102718ac3cf047',t:'MTc3OTY2NjEwMw=='};var a=document.createElement('script');a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();