ABSTRACT Hypertension has become a major public health challenge. However, numerous research results reported in the literature focus primarily on risk factors of hypertension, little is

known about how the whole continuum of blood pressure (BP) is associated with risk factors of hypertension. This study aims to reveal quantile-specific associations of BP with its risk

factors. A cross-sectional survey based on a sample of 23,050 adults aged 18 to 79 years was conducted in Jilin Province in 2012, and some subjects were excluded due to missing values in BP

or having BP control according to the purpose of this study. Quantile regression (QR) was employed to investigate the associations between systolic/diastolic blood pressure (SBP/DBP) and the

cholesterol (HDL-C) were positively associated with BP measures in males. And the coefficient of total cholesterol (TC) in QR increased with BP in females who used to live in town. SIMILAR

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CHANGES INVERSE ASSOCIATIONS OF HDL-CHOLESTEROL WITH BLOOD PRESSURE AND HYPERTENSION TO POSITIVE ASSOCIATIONS Article 11 May 2021 INTRODUCTION Hypertension is believed as a significant risk

factor for cardiovascular disease (CVD), and has brought heavy economic burden to individuals, families, and society1,2,3,4. It is a great challenge for public health worldwide, because of

its high prevalence, which accounts for nearly half of the cardiovascular morbidity and mortality in the world5,6. In 2010, people died from hypertension approximately 2 million in China,

accounting for about 24.60% of total mortality7. Therefore, hypertension is an important public health problem that are supposed to be addressed urgently. At present, hypertension has become

a hot research spot8,9, where most of studies focused primarily on hypertension, including prevalence, influencing factors, and health problems in cross-sectional and cohort

studies10,11,12,13. Sun _et al_.’s findings suggested that waist circumference (WC) was an independent predictor of hypertension incidence10, and Zhang _et al_.’s prospective study suggested

that body mass index (BMI) dynamic gain may be related to incident hypertension for men of all ages and young and middle-aged women13. Generally, hypertension was viewed as a categorical

variable in these studies, but the incidence of hypertension is a chronic and continuous process. In other words, literatures about risk factors of hypertension have been mounting, yet there

is a little of publications on determinants of the normal parts of blood pressure (BP). Fortunately, quantile regression (QR) is not limited to explain the prevalence risk of hypertension.

What’s more, it could also be used to explain the risk of the BP at any point on its distribution. QR comprehensively shows heterogeneous changes in the dispersion of the relationship of

risk factors with BP continuum across its distribution. In addition, there is no distributional assumptions about the error term in QR model, thus, it enjoys high flexibility for modeling

data with heterogeneous conditional distributions14. In this study, we aimed mainly to investigate how the whole continuum of BP was associated with commonly researched influencing factors

of hypertension using QR model. Jilin Province is situated in the northeast of China (latitude 40°~46°, longitude 121°~131°), with a cold climate and different residential dietary

pattern1,15. Therefore, we could develop more effective prevention strategies for different BP populations, so as to achieve the purpose of “precision prevention”. RESULTS DESCRIPTIVE

CHARACTERISTICS OF PARTICIPANTS BY GENDER Table 1 shows the basic characteristics of the participants, where BMI, SBP, DBP, TG and FPG were all significantly higher in males than those in

females (_P_ < 0.05), but age, TC, LDL-C and HDL-C were just the opposite (_P_ < 0.05). And the proportions of demographics (drinking, smoking, hypertension, dyslipidemia and diabetes,

etc.) were significantly different between genders. As shown in Table 2, the distribution of BP and quantiles between males and females were different. Therefore, we separately identified

the factors for males and females by using QR model. QR STATISTICS BETWEEN THE BP MEASURES IN FEMALES Tables 3~4 show QR coefficients and 95% confidence intervals of the influencing factors

for BP in females. Age was positively associated with both SBP and DBP. Similarly, BMI, TG, TC and family history of CVD showed significantly positive associations with SBP/DBP across the

entire conditional BP distribution. Females who used to live in town were positively associated with a higher DBP/SBP for most of the quantiles (≥10). Undergraduate females were negatively

associated with SBP for most of the quantiles (≥20). Middle school females were negatively associated with SBP for part of the quantiles. Female manual labors and mental labors were

positively associated with a higher DBP for part of the quantiles, compared with unemployed and retired females. QR STATISTICS BETWEEN THE BP MEASURES IN MALES Tables 5~6 provide QR

coefficients and 95% confidence intervals of the influencing factors for BP in males. Similar to females, age, BMI, TG and family history of CVD presented positive associations with both BP

measures across the entire conditional BP distribution. Drinking showed positive associations with SBP/DBP. High-salt diet manifested a slightly increasing positive association with higher

SBP (quantiles ≥ 70), but with a higher DBP for middle part of the quantiles (30~75), compared with the bland diet. Undergraduate males were negatively associated with SBP for most of the

quantiles (≥20). Male manual labors and mental labors were positively associated with a higher DBP across the entire conditional DBP distribution compared with unemployed and retired males.

In addition, HDL-C also showed a positive association with SBP/DBP. In general, the quantiles of HDL-C among participants with hypertension were lower than those with normal BP in males

(Table 7). In contrast, the relationship changed in different BMI groups: the relationship remained the same in normal weight group, however, the quantiles of HDL-C among participants with

hypertension were higher than those with normal BP in underweight, overweight and obese groups. Further, the results of generalized additive models (GAM) showed that HDL-C performed

non-linear associations with both SBP (_F_ = 3.513, _P_ = 0.012) and DBP (_F_ = 7.388, _P_ < 0.001) (Fig. 1). DISCUSSION Hypertension has become a major public health problem in China7,

and a number of researchers have investigated the risk factors of hypertension5,16, but little is known about how the whole continuum of DBP and SBP is associated with commonly researched

influencing factors of hypertension. Measures in reducing the prevalence of hypertension in Jilin Province could be put forward after knowing the risk factors associated with BP. This study

explored the heterogeneity of the relationship of environmental and individual determinants with hypertension across the entire conditional distribution of BP measures using a QR model. It

was indicated that unhealthy eating habits was a predictor for higher BP measures in males, with a larger extent of the positive association at a higher quantile. Previous studies have

reported that BP was more prone to be elevated by alcohol drinking in males than in females17. In addition, it has been proven that high-salt diet was associated with BP18. Our results

showed that the high-salt was positively associated with BP only for the males, and the mechanism was probably correlated with estrogen and salt sensitivity. Estrogen affects the sodium

reabsorption and excretion via up regulation of NO production and different expression level of angiotensin receptors and ET receptors19. Therefore, males who maintain healthy lifestyle are

also likely to manage their BP better, and thereby have a lower risk of hypertension. Our study also investigated the effects of BMI and serum lipids on BP among adults. BMI showed a

positive association with BP in our study, which was consistent with the literature that obese people were more likely to be hypertensive compared with nonobese people20. Activation of the

renin–angiotensin system as well as physical compression of the kidney might be important factors in linking body weight and elevated BP21. Further, it has been proven that dyslipidemia was

associated with BP22. Our results showed that TG level was positively correlated with BP, and the mechanism was probably related with the insulin resistance23. Elevated TG level could make a

higher level of free fatty acids in serum, which would lead to insulin resistance, then insulin resistance further contributed to hyperinsulinemia, which might directly contribute to

elevation of BP by increasing renal sodium retention24. However, TC and HDL-C showed different associations with BP between males and females, which might be attributed to differences in sex

hormones, where estrogen was commonly believed to play an important role in lipolysis25. Moreover, HDL-C was positively associated with SBP/DBP in males, which was inconsistent with the

results in literature21,26. One possible reason was the nonlinear relationship between HDL-C and SBP/DBP, which showed a “U” shape that both too lower and too higher levels of HDL-C might

increase the risk of hypertension. Further, there might be an interaction between BMI and HDL-C, which showed that the HDL-C was higher among participants with hypertension than those with

normal BP in almost all BMI groups except those with normal BMI. Besides, other confounding risk factors27,28 that were not under our consideration might also have impacts on this

association. Furthermore, it has been proven that education was associated with hypertension29. Our results showed that education negatively associated with SBP. On one hand, the people with

higher level of education would have greater possibilities to engage in healthy lifestyles. On the other hand, older people were expected to have lower level of education than young people,

which might also lead to higher SBP30. In addition, we also found that working people were more likely to have an elevated DBP, and the possible reason might be related to social

engagement. Finally, the regression coefficient of family history of CVD increased with BP among all the research participates, which was consistent with other studies31. Thus, it was

implied that elderly people with family history of CVD, low-literacy and workers should pay more attention to their BP levels. Some limitations of our study should be noted. Firstly, the

results were conducted from a cross-sectional study in Jilin province, which might limit our ability to generalize the results. Secondly, other confounders that might have implications for

hypertension, such as parameters of glucose metabolism, renal function indices and genes, were not under our consideration this time, which might have some slight effects on our results.

CONCLUSIONS This study revealed interesting clues on how the whole continuum of SBP and DBP were associated with commonly researched factors of hypertension. High-salt diet, drinking and

HDL-C were positively associated with BP measures only in males. And the risk of TC had an increased trend with BP in females who used to live in town. The elderly, the obese, workers and

people with lower level of education and family history of CVD were expected to be positively associated with a higher SBP and/or DBP. METHODS STUDY POPULATION The large-scale

cross-sectional survey was conducted among people who aged 18 to79 years old and were living in Jilin Province for over 6 months in 2012. A total of 23,050 participants were selected through

multistage stratified random cluster sampling32 (see details in Part 1 of the Supplementary Material). For the purpose of the present analysis, the subjects who had anti-hypertensive

treatments were excluded, as well as those who had missing values in SBP or DBP. Finally, a total of 16,524 subjects were included in the present analyses. All participants provided written

informed consent, and the study was approved by the Institutional Review Board of the School of Public Health, Jilin University. And all methods were performed in accordance with the

relevant guidelines and regulations. DATA COLLECTION AND MEASUREMENT The data of this study included demographics (e.g., gender, age, etc.), health-related behaviors (e.g., smoking,

drinking, etc.), anthropometric measurements (e.g., height, weight, hypertension, etc.) and laboratory measurements (such as serum cholesterol and triglycerides). All investigation was

trained and followed the same questionnaire instructions. Height and weight were measured according to a standardized protocol and techniques, with the participants wearing clothing but no

shoes. A calibrated mercury sphygmomanometer was used to determine the blood pressure of subjects on the right arm, after at least 5 min of seated rest. The blood sample was obtained in the

morning from subjects after fasting for at least eight hours, and then conserved in tubes which contained ethylene diamine tetra acetic acid (EDTA). Fasting plasma glucose (FPG) and serum

lipids were measured using a Bai Ankang fingertip blood glucose monitor (Bayer, Leverkusen, Germany) and a MODULE P800 biochemical analysis machine (Roche Co., Ltd., Shanghai, China),

respectively33 (see details in Part 2 of the Supplementary Material). ASSESSMENT CRITERIA According to Seventh Joint National Commission Guidelines (JNC7), hypertension was defined as a

resting systolic blood pressure (SBP)/diastolic blood pressure (DBP) ≥ 140/90 mmHg or current use of antihypertensive medication34. Dyslipidemia was defined as the use of lipid-lowering

drugs in the past two weeks and/or meeting one or more of the following criteria: total cholesterol (TC) ≥ 6.22 mmol/L, triglyceride(TG) ≥ 2.26 mmol/L, low density lipoprotein cholesterol

(LDL-C) ≥ 4.14 mmol/L, high density lipoprotein cholesterol (HDL-C) < 1.04 mmol/L35. We defined diabetes as participants who reported diabetes mellitus previously diagnosed by physician

or those who have fasting plasma glucose (FPG) ≥ 7.0 mmol/L or oral glucose tolerance test (OGTT) 2 h plasma glucose (PG) ≥ 11.1 mmol/L36. BMI was defined as weight (kilogram) divided by

height (meter) squared. Drinker was defined as a person who consumed more than one alcoholic drink per week, including any form of alcohol37. High-salt diet was defined as a person who daily

salt intake more than 6 g38. Education stage was classified into compulsory education and lower education, secondary school education, senior school education, college education,

postgraduate education and higher education. STATISTICAL ANALYSIS All questionnaires were coded and double-entered. EpiData (version 3.1) was used for data entry and validation and R version

3.3.3(University of Auckland, Oakland, New Zealand) for data analysis. The data are presented as mean ± standard deviations (SD) or rate. In addition, quantitative variables and categorical

variables were analyzed using _t_ test and Rao-scott-_χ2_ test, respectively. Finally, QR in the quantreg package was used to estimate the conditional quantile of the distribution of SBP

and DBP under the influence of various risk factors. DATA AVAILABILITY The survey was implemented by School of Public Health, Jilin University and Jilin Center for Disease Control and

Prevention in Jilin Province in 2012. According to relevant regulations, we were sorry that the data can’t be shared. ABOUT THE DATA The survey was implemented by School of Public Health,

Jilin University and Jilin Center for Disease Control and Prevention in Jilin Province in 2012. According to relevant regulations, we were sorry that the data can’t be shared. REFERENCES *

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61 prospective studies. _LANCET_ 360, 1903–1913 (2002). Article  PubMed  Google Scholar  Download references ACKNOWLEDGEMENTS I would like to express my gratitude to all those who have

helped me during the writing of this thesis. This work was supported by the Outstanding Youth Foundation of Science and Technology Department of Jilin Province, China (grant number:

20170520049JH); the National Natural Science Foundation of China (grant number: 11301213, 11571068); and the Scientific Research Foundation of the Health Bureau of Jilin Province, China

(grant number: 2011Z116). AUTHOR INFORMATION AUTHORS AND AFFILIATIONS * Epidemiology and Biostatistics, School of Public Health, NO. 1163 Xinmin Street, Jilin University, Changchun, Jilin,

China, 130021 Junsen Ye, Zhongmin Li, Yaogai Lv, Lan An, Jianxing Yu, Xin Guo, Yan Yao, Yaqin Yu & Lina Jin Authors * Junsen Ye View author publications You can also search for this

author inPubMed Google Scholar * Zhongmin Li View author publications You can also search for this author inPubMed Google Scholar * Yaogai Lv View author publications You can also search for

this author inPubMed Google Scholar * Lan An View author publications You can also search for this author inPubMed Google Scholar * Jianxing Yu View author publications You can also search

for this author inPubMed Google Scholar * Xin Guo View author publications You can also search for this author inPubMed Google Scholar * Yan Yao View author publications You can also search

for this author inPubMed Google Scholar * Yaqin Yu View author publications You can also search for this author inPubMed Google Scholar * Lina Jin View author publications You can also

search for this author inPubMed Google Scholar CONTRIBUTIONS The authors would like to thank Dr Lina Jin and others who kindly provided their R code for analysis and experience for writing

paper. Junsen Ye and Lina Jin had the original idea for the study and carried out the design. Lina Jin and Zhongmin Li provided valuable insight regarding the methodological approach and

organization of the manuscript. Junsen Ye, Jianxing Yu, Yaogai Lv and Lan An carried out the statistical analysis and reviewed the consistency of data included in the paper. Junsen Ye and

Zhongmin Li drafted the manuscript. Xin Guo, Yan Yao and Yaqin Yu revised the manuscript. All authors read and approved the final manuscript. CORRESPONDING AUTHOR Correspondence to Lina Jin.

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Pressure with the Factors among Adults in Jilin Province: A Cross-Sectional Study Using Quantile Regression Analysis. _Sci Rep_ 7, 13613 (2017). https://doi.org/10.1038/s41598-017-14045-0

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