Body mass index time in target range and incident hypertension in middle-aged and older adults: a longitudinal study

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Methods This study used the longitudinal data from the China Health and Retirement Longitudinal Study (CHARLS), which consists of 4,369 residents who met the inclusion and exclusion criteria. Body Mass Index Time in Target Range (BMI-TTR) was defined as the number of times an individual's body mass index (BMI) fell within the target range (18.5 kg/m² ≤ BMI < 23 kg/m²) across three measurement waves (2011, 2013, and 2015). Based on these measurements, participants were systematically classified into four progressive categories: TTR1 (never in range) to TTR4 (always in range). Incident hypertension and time-to-event were identified during follow-up. The associations of BMI-TTR with hypertension were evaluated using multivariable Cox proportional hazards models, which used baseline BMI and other factors as confounding variables. Results In comparison to the TTR1 group, the risk of developing hypertension in the TTR4 group was significantly lower (adjusted hazard ratio [HR] = 0.717, 95% CI: 0.633–0.813, P < 0.001). This inverse association persisted after additional adjustment for baseline BMI (HR = 0.718, 95% CI: 0.631–0.816, P < 0.001). A significant inverse dose-response relationship was observed, with the incidence of hypertension decreasing progressively across higher BMI-TTR groups ( P for trend < 0.001). In women and in those aged 45–60 years, the protective association was more pronounced in the subgroup analyses. Furthermore, individuals whose BMI improved from out-of-range at baseline to within the target range at later visits had a similar risk reduction to those who maintained normal weight throughout. Conclusion Sustaining a BMI within the desired range over the long term is linked to a lower risk of developing hypertension in adults aged 45 and older, regardless of their initial BMI. This protective effect is particularly evident in women and middle-aged individuals. Our results highlight the significance of ongoing weight management in the primary prevention of hypertension. Body mass index Time in target range Hypertension Incidence Middle-aged and elderly CHARLS Figures Figure 1 Figure 2 Introduction Hypertension, the most prevalent chronic cardiovascular disease worldwide, is a leading modifiable risk factor for morbidity and mortality attributable to cardiac, cerebrovascular, and renal damage [ 1 , 2 ]. Epidemiological estimates reveal that over 1.28 billion individuals are affected globally, with an adult prevalence of approximately 25% in China that rises markedly with age, contributing to a substantial and growing disease burden [ 3 , 4 ]. Among modifiable determinants, obesity is a well-established contributor to the incidence and progression of hypertension. Elevated body mass index (BMI), a widely used surrogate measure of adiposity, is consistently associated with an increased risk of hypertension [ 5 , 6 ]. Research investigating the association between BMI and hypertension has traditionally relied on single or infrequent static measurements [ 7 , 8 ]. However, body weight fluctuates over time, and a single assessment cannot adequately capture long-term fluctuation patterns or cumulative exposure. The dynamic metric known as "time in target range" (TTR) has emerged as a significant tool in chronic disease management. Evidence indicates that TTR outperforms single measurements or simple averages in predicting complications and clinical outcomes in conditions such as diabetes and hypertension [ 9 – 11 ]. Currently, there is a lack of research systematically exploring the relationship between Body Mass Index Time in Target Range (BMI-TTR), a long-term dynamic indicator, and the risk of hypertension. To address this knowledge gap, we utilized longitudinal data from the nationally representative China Health and Retirement Longitudinal Study (CHARLS) to analyze the association between BMI-TTR and the incidence of hypertension. The findings are expected to provide new evidence for the long-term benefits of weight management and to offer a scientific foundation for primary prevention strategies for hypertension. Methods Study design and participants This study used data from the China Health and Retirement Longitudinal Study (CHARLS), a nationally representative longitudinal survey of adults aged 45 years and older in China. This survey collects detailed household and individual level information across successive waves (see http://charls.pku.edu.cn/) [12]. To ensure temporal separation between exposure and outcome, we utilized BMI measurements from waves 1 to 3 (2011, 2013, 2015) to construct the BMI-TTR exposure and identified incident hypertension during follow‑up visits in waves 2 to 4 (2013, 2015, 2018). The exclusion criteria included: individuals younger than 45 years, those with hypertension at baseline (2011), or those whose baseline blood pressure measurements met the diagnostic criteria for hypertension (systolic pressure ≥ 140 mmHg or diastolic pressure ≥ 90 mmHg). After applying these criteria, a total of 4,369 participants were included in the final analysis (see Fig. 1 for the flow diagram). The study was conducted in accordance with the STROBE guidelines, approved by the Ethics Review Committee of Peking University, and all participants provided written informed consent prior to enrollment. Evaluation of BMI and definition of BMI-TTR The calculation of BMI involves dividing weight in kilograms by the square of height in meters (kg/m²). The definition for BMI-TTR was established as the range from 18.5 kg/m² to under 23 kg/m², in accordance with the World Health Organization's guidelines for Asian populations. To evaluate the maintenance of weight over an extended period, we developed a BMI Time in Target Range (BMI-TTR) indicator utilizing complete BMI data from 2011 (baseline), 2013, and 2015. BMI data from the 2018 wave were excluded from the TTR calculation to prevent temporal overlap with the ascertainment of hypertension outcome. Participants were categorized into four distinct groups based on the frequency with which their BMI measurements fell within the target range across the three specified years. The classification was as follows: TTR1: BMI was below the standard in all three measurements; TTR2: BMI met the standard in any one measurement; TTR3: BMI met the standard in any two measurements; TTR4: BMI met the standard in all three measurements. This classification reflects the progressive increase in the level of long-term control of BMI within the recommended range, which will be treated as an ordered variable in subsequent analyses. Incident hypertension Incident hypertension was defined as the initial occurrence of any of the following criteria during the follow-up visits in 2013, 2015, or 2018: (1) self-reported doctor-diagnosed hypertension; (2) self-reported use of antihypertensive medication; (3) an average systolic blood pressure of ≥ 140 mmHg or average diastolic blood pressure of ≥ 90 mmHg based on three consecutive blood pressure measurements. The time to event was calculated from the earliest follow-up wave at which any of these criteria were met. All participants were followed for up to 6 years, and incident cases were identified by aggregating data from the three follow-up rounds. Statistical analysis Statistical analysis was conducted using R software (version 4.2.3). Categorical variables are reported as percentages, while continuous variables are presented as the median and interquartile range (IQR). Group comparisons were performed using one-way ANOVA, non-parametric tests, chi-square tests, or Fisher's exact test. Trend tests were conducted to assess the distribution of baseline characteristics across different TTR groups and to evaluate the trend of changes in group variables. The influence of BMI-TTR on the risk of hypertension incidence was analyzed using Cox regression and binary logistic regression models. Effect indicators include hazard ratio (HR) and odds ratio (OR), with a 95% confidence interval (CI). Kaplan-Meier curves were utilized to illustrate the probability of outcome occurrence. The covariates adjusted for include age, sex, hypertension, smoking, and other related factors. The Cox regression model comprises: Model1: Age, gender, Marital status, education, Area, ever smoking, ever drinking, Diabetes mellitus, Dyslipidemia, Cancer or malignant, Chronic lung disease, Liver disease, Kidney disease, Stroke, Heart disease, Memory-related disease, Psychosomatic disease, uric acid (UA), blood urea nitrogen (BUN), glucose, creatinine (Crea), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), C-reactive protein (CRP), glycated hemoglobin (HbA1c) and BMI-TTR； Model 2: On the basis of Model 1, add the 2011 BMI values (baseline); Model 3: Based on Model 2, add the BMI data from 2011, 2013, and 2015. Group analysis was conducted based on gender and age, and the impact of interactions was assessed. Data that were missing were addressed using multiple imputation techniques. (see S-Table 1). P value < 0.05 was considered statistically significant. Results Baseline characteristics of participants This study ultimately included 4,369 participants who did not have hypertension at baseline. As shown in Table 1, based on the frequency with which the BMI met the established criteria across three different measurements, the participants were categorized into four distinct groups: TTR1 to TTR4. With increasing BMI-TTR level, baseline characteristics exhibited systematic changes. Metabolic and physiological profiles improved showed significant improvements, with glucose and LDL-C progressively declining (both P for trend < 0.001). In terms of demographic characteristics, the proportion of women and individuals residing in rural areas increased in higher TTR groups (both P for trend <0.001), while the proportions of smokers and drinkers also gradually increased (both P for trend < 0.001). Association between BMI-TTR and hypertension incidence During the 6-year follow-up, 1,524 cases of incident hypertension occurred, with a cumulative incidence of 34.9%. The occurrence of hypertension reduced progressively in a higher BMI-TTR group. The incidence of hypertension in TTR1 was 40.2% (n=720), in TTR2 was 34.7% (n=191), in TTR3 was 31.2% (n=187) and in TTR4 was 29.9% (n=426) ( P for trend <0.001). Cox proportional hazards regression confirmed an inverse dose‑response relationship (Table 2). Compared with TTR1, the TTR4 group demonstrated a significantly lower risk of hypertension in the crude model (HR = 0.704, 95% CI: 0.625–0.794, P < 0.001). This association remained significant after sequential adjustment for demographic, lifestyle, and clinical confounders (Model 1: HR = 0.717, 95% CI: 0.633–0.813, P < 0.001) and for baseline BMI (Model 2: HR = 0.718, 95% CI: 0.631–0.816, P < 0.001). In the fully adjusted model that included BMI data from all three waves (Model 3), the inverse associations for TTR3 and TTR4 remained significant (HR = 0.768 and 0.733, respectively; both P < 0.05). In Model 3, BMI measured at Wave 2 was independently associated with an increased risk of hypertension (HR = 1.005, 95% CI: 1.001–1.010, P = 0.010). The Kaplan-Meier survival curve (Figure 2) visually demonstrates the significant difference in cumulative incidence rates across groups (Log-rank P < 0.001). The results of the sensitivity analysis using binary logistic regression were consistent with the findings presented above (S-Table 2). To further investigate the impact of dynamic weight changes, participants were reclassified into three trajectory-based groups: Group 1, the consistently normal group (BMI within the target range at all three waves, n=1,427, 32.6%); Group 2, the improvement group (baseline BMI above the target range but attaining the range in at least one subsequent wave, n=460, 10.5%); and Group 3,the other group (n = 2,482, 56.8%). As shown in S-Table 4, compared to Group 3, the risk of newly developed hypertension in Group 2 was significantly lower than in Group 1 (adjusted HR ranges of 0.764-0.777 and 0.748-0.765, respectively, both P 0.05). Subgroup analyses revealed that this beneficial pattern associated with weight improvement was consistent across both genders (S-Table 5). However, a significant age interaction was detected ( P for interaction < 0.05): the protective effect of weight improvement was evident and statistically significant among participants aged 45–60 years, but it was attenuated and not significant in those aged ≥ 60 years. Subgroup analysis To ensure accuracy, subgroup analyses by gender and age were conducted to validate these. As indicated by Table 3, a high BMI-TTR (TTR4 in particular) coincided with a reduced risk of hypertension among both sexes. This protective effect appeared to be more pronounced in women (adjusted for baseline BMI, HR for TTR4 = 0.689, 95% CI: 0.572–0.830, P 0.05). In stratified analyses, the protective association was markedly stronger in participants aged 45 to 60 years (TTR4 HR = 0.660, 95% CI: 0.536–0.813, P < 0.001) than in those aged ≥60 years (TTR4 HR = 0.848, 95% CI: 0.704–1.022, P = 0.083). A significant interaction was observed between age and BMI‑TTR ( P for interaction < 0.001). The results of the sensitivity analysis with binary logistic regression (S-Table 3) are highly consistent with the Cox regression (above), which further corroborates the above findings. Discussions Based on longitudinal data from CHARLS (2011–2018), this study aimed to explore the relationship between BMI-TTR and hypertension risk among middle-aged and elderly people in China. The results indicated that the long -term maintenance of BMI within the recommended range is significantly associated with a reduced risk of hypertension, independent of baseline BMI. This protective association is particularly pronounced among women and those aged 45 to 60 years. Furthermore, dynamic trajectory analysis indicated that participants whose BMI improved from above the target range at baseline to within the range during follow-up achieved a risk reduction comparable to that of individuals who consistently a normal weight. Hypertension significantly compromises both quality of life and life expectancy by increasing the risk of severe complications, including stroke, kidney failure, and retinopathy [ 13 – 15 ]. In the management of chronic disease, TTR as a measure has been shown to be both a better measure of long-term quality of control and a better predictor of long-term risk when compared to a single measured value. Research indicates [ 16 , 17 ] that in adults with hypertension, the proportion of time spent with blood pressure within the target range (BP-TTR) is associated with a lower risk of kidney damage and cardiovascular events. Furthermore, TTR of blood glucose and glycated hemoglobin has been notably linked to adverse outcomes, such as complications and mortality, serving as a critical marker for prognostic evaluation [ 21 , 22 ]. Additionally, TTR has applications for other measures, including the TTR of blood potassium and proteinuria, which are also significantly related to adverse outcomes, including disease-related complications and mortality [ 18 , 19 ]. While BMI-TTR is an effective indicator for assessing long-term weight status achievement [ 20 , 21 ], the association between BMI-TTR and the risk of developing hypertension remains unclear. The baseline profile of this study indicates that long-term maintenance of BMI within the normal range is associated with a healthier overall metabolic phenotype. This finding aligns with prior evidence linking sustained weight management to improved cardiometabolic risk [ 22 , 23 ]. The study also found that among those with long-term BMI control, the proportion of smokers and alcohol drinkers was higher. This may reflect the tendency of some individuals, while successfully managing their weight, to relax restrictions on other health behaviors or to utilize use these behaviors as alternative strategies for managing stress or maintaining weight [ 24 ]. Additionally, rural residents constituted a higher proportion of the group that maintained normal weight long -term, potentially due to their elevated physical activity levels and relatively traditional dietary habits [ 25 ]. In contrast, women were underrepresented in this group, which may be attributable to physiological factors such as hormonal changes after middle age, leading to body fat redistribution and a decrease in basal metabolic rate [ 26 , 27 ]. Despite these complex baseline differences, the subsequent Cox regression model indicated that individuals who maintained a normal BMI over the long term exhibited an independent association with a reduced risk of developing hypertension. Even after controlling for baseline BMI levels, a significant correlation persisted between a higher TTR group and a reduced risk of new-onset hypertension. These findings suggest that weight management should not only aim to maintain BMI within the target range but also focus on sustaining this state in the long term, thereby effectively reducing the risk of hypertension onset and related adverse clinical outcomes. According to pathophysiology, maintaining stable body weight over the long term may reduce the risk of hypertension through multiple pathways. Weight fluctuation itself is considered a risk factor for cardiovascular metabolic diseases [ 23 ]. Research involving animals indicates that fluctuations in body weight can induce inflammation in adipose tissue, macrophage infiltration, and impair systemic insulin sensitivity [ 28 ]. In human studies, weight fluctuations are associated with chronic low-grade inflammation and increasing sympathetic nervous system activity. These changes elevate blood pressure by jointly damaging endothelial function, increasing renal sodium reabsorption, and enhancing vascular constriction responses [ 29 – 31 ]. The primary contribution of this study is to assess not the “variability” of body weight but the “sustainability of health status”. This is done to quantify the association between the percentage of time that people spend maintaining their weight within the accepted healthy range, in the long term, and the incidence of hypertension. This study also found that the protective effect of BMI-TTR on the onset of hypertension was particularly significant in women and individuals aged 45 to 60, indicating a notable age interaction. In women, estrogen may exert a protective effect on the vascular endothelium and regulate the renin-angiotensin system, thereby enhancing vascular benefits during weight improvement [ 32 ]. In addition, greater subcutaneous fat storage might make it easier for women to avoid hypertension, compared to their men counterparts, who produce more visceral fat [ 33 ]. Additionally, hypertension in younger populations is often related to increased volume load and sympathetic nervous excitation, making them more responsive to weight loss, whereas with aging, arterial stiffening gradually becomes dominant, reducing sensitivity to weight changes [ 34 ]. Age-related muscle loss may further attenuate the blood pressure-lowering benefits achievable through weight management aimed at improving overall metabolism [ 35 ]. Consequently, there exists a "golden window period" for the advantages of weight management in preventing hypertension, underscoring the urgency and importance of initiating long-term, stable weight interventions both older age and earlier. This study has several limitations. First, the principal methodology used in diagnosing hypertension is the self-reports and blood pressure measurements at the research site. While the average of three measurements has been accepted, it could still be subject to measurement bias. Second, the study population is restricted to middle-aged and elderly individuals in China from a specific cohort, necessitating caution when generalizing the findings to other ethnicities, regions, or younger populations. Third, the calculation of BMI-TTR was based solely on three measurements taken several years apart. The TTR, which relies on intensive monitoring in blood pressure management, may vary in accuracy and may not adequately capture more subtle weight fluctuations. Future research should incorporate more frequent weight monitoring data to calculate a more precise TTR. Fourth, the inability to differentiate the intentional and unintentional weight changes occurring during the study time could have implications of a very different nature upon blood pressure [ 36 – 39 ]. Moreover, inclusion and analysis of detailed lifestyle information such as dietary structure, sodium-potassium intake, etc. might also have acted as confounders. Lastly, while we primarily identified associations based on epidemiological data and inferred them through relevant mechanistic literature. However, this study did not directly measure intermediary biomarkers such as inflammatory factors, sympathetic nervous activity, or renin-angiotensin system activity, thus the explanation of potential mechanisms remains hypothetical. Conclusions This study is the first to confirm that maintaining the body mass index in the target range for a long period is a protective factor for hypertension for middle-aged and elderly Chinese people. This association is particularly pronounced in women and those aged 45 to 60, with subsequent improvements in weight also offering significant benefits. The findings give robust support that long-term maintenance of a healthy weight should be considered as a core strategy for primary prevention of hypertension. Declarations Acknowledgement We thank all participants for their contribution to this study and China Health and Retirement Longitudinal Study project team for providing free public data. Author Contributions All authors participated in concept and design of this study. Qian Wu: Select a topic, Data analysis, Writing manuscript; Bing Wang: Make overall arrangements, Participate in writing and revising papers; Xiaohong Yang: Paper framework design and revision; Yujia Wang: Conceptualization; Wenjun Cai: Collect data and analyze data. The author(s) read and approved the final manuscript. Funding Sources This work was supported by the Department of Education of Zhejiang Province (Y202043955), the National Scientific and Technological Major Project of China( 2017ZX10105001),and Huzhou University Project (JG202246）. Data Availability All data generated or analyzed during this study are included in this article.Further inquiries can be directed to the corresponding author. Statement of Ethics CHARLS was approved by the Ethical Review Committee (IRB) at Peking University, Beijing, China.tement of Ethics.（IRB00001052-11015） Clinical trial number Not applicable. Consent for publication Not applicable. Conflict of Interest Statement The authors have no relevant financial or non-financial interests to disclose. Informed consent Each participant included in this study signed a written informed consent form before taking part in the survey. References GBD R F C. 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Basic characteristics according to TTR-BMI.（n=4,369） Variable TTR1 （ n=1791 ） TTR2 （ n=551 ） TTR3 （ n=600 ） TTR4 （ n=1427 ） P P for trend BMI, kg/m² 25.24（23.80,27.08） 22.56（21.23,23.59） 21.54（19.39,22.69） 20.69（19.83,21.59） <0.001 / Age,years 56.00（49.00,62.00） 56.00（50.00,63.00） 58.00（52.00,64.00） 58.00（52.00,64.00） <0.001 / Female, n (%) 1082（60.4） 302（54.8） 287（47.8） 612（42.9） <0.001 <0.001 Marital status 0.001 / Married or partnered 1661（92.70） 494（89.70） 530（88.30） 1275（89.30） Other marital status 130（7.30） 57（10.30） 70（11.70） 152（10.70） Education, n (%) 0.106 / Less than lower secondary 1595（89.1） 506（91.80） 541（90.20） 1310（91.8） upper secondary & vocational training 179（10.0） 43（7.80） 53（8.80） 103（7.2） tertiary 17（0.90） 2（0.40） 6（1.0） 14（1.0） Area,% <0.001 <0.001 Urban 636（35.5） 161（29.2） 161（26.8） 350（24.5） Rural 1155（64.5） 390（70.8） 439（73.2） 1077（75.5） Ever smoking, n (%) 563（31.4） 219（39.7） 255（42.5） 740（48.1） <0.001 <0.001 Ever drinking, n (%) 605（33.8） 194（35.2） 230（38.3） 631（44.2） <0.001 <0.001 Diabetes mellitus, n (%) 96（5.4） 12（2.2） 20（3.3） 30（2.1） <0.001 / Dyslipidemia, n (%) 126（7.0） 25（4.5） 22（3.7） 47（3.3） <0.001 / Cancer or malignant, n (%) 16（0.9） 3（0.5） 4（0.7） 11（0.8） 0.915 / Chronic lung disease, n (%) 166（9.3） 49（8.9） 71（11.8） 123（8.6） 0.144 / Liver disease, n (%) 58（3.2） 16（2.9） 26（4.3） 48（3.4） 0.542 / Kidney disease, n (%) 92（5.1） 44（8.0） 42（7.0） 76（5.3） 0.038 / Stroke, n (%) 29（1.6） 2（0.4） 7（1.2） 17（1.2） 0.136 / Heart disease, n (%) 170（9.5） 39（7.1） 43（7.2） 85（6.0） 0.002 / Memory-related disease, n (%) 12（0.7） 5（0.9） 10（1.7） 11（0.8） 0.165 / Psychosomatic disease, n (%) 19（1.1） 8（1.5） 9（1.5） 19（1.3） 0.788 / UA, mg/dl 4.18（3.47,4.99） 4.13（3.43,4.90） 4.17（3.45,5.04） 4.17（3.47,5.02） 0.626 / BUN, mg/dl 14.90（12.41,17.87） 15.83（12.67,18.85） 15.40（12.28,18.30） 15.43（12.88,18.60） <0.001 / Glucose, mg/dl 101.88（93.78，113.40） 100.62（92.51,112.68） 100.35（91.71,109.91） 100.23（91.69,111.24） <0.001 ＜0.001 Creatinine, mg/dl 0.75（0.64,0.86） 0.73（0.63,0.87） 0.76（0.64,0.88） 0.77（0.66,0.88） 0.003 / TC, mg/dl 190.59（168.56,215.34） 187.02（163.53,211.08） 189.05（160.83,211.08） 185.18（163.53,185.18） <0.001 / HDL-C, mg/dl 48.71（39.43,59.54） 52.58（44.07,63.02） 53.35（44.46,64.08） 54.51（44.46,64.56） <0.001 / LDL-C, mg/dl 114.43（94.33,137.63） 111.73（91.62,133.76） 110.72（90.46,134.83） 110.18（90.85,132.60） <0.001 ＜0.001 CRP, mg/L 1.10（0.53,2.56） 0.87（0.44,2.20） 0.75（0.39,2.19） 0.80（0.40,2.16） <0.001 / Hba1c, % 5.20（4.90,5.50） 5.10（4.90,5.40） 5.10（4.80，5.40） 5.10（4.80,5.40） <0.001 / New-onset Hypertension, % 720（40.2） 191（34.7） 187（31.2） 426（29.9） <0.001 / Note： TTR time in target range, DM diabetes mellitus, BMI body mass index, UA uric acid, BUN blood urea nitrogen, TC total cholesterol, HDL high-density cholesterol, LDL low-density cholesterol, CRP C-reactive protein, HbA1c glycated hemoglobin A1c. Table 2. Association between BMI-TTR and incident hypertension: Cox proportional hazards models Variable Crude HR (95% Cl) P value Adjusted HR a (95% Cl) P value Adjusted HR b (95% Cl) P value Adjusted HR c (95% Cl) P value TTR1 TTR2 0.839（0.716,0.985） 0.032 0.882（0.750,1.037） 0.127 0.882（0.749,1.038） 0.130 0.892（0.758,1.051） 0.173 TTR3 0.749（0.638,0.880） 0.000 0.759（0.664,0.894） 0.001 0.759（0.643,0.896） 0.001 0.768（0.650,0.907） 0.002 TTR4 0.704（0.625,0.794） 0.000 0.717（0.633,0.813） 0.000 0.718（0.631,0.816） 0.000 0.733（0.644,0.835） 0.000 Note： TTR time in target range, HR hazard ratio, CI, confidence interval a Adjucted for Age,gender,Marital status,education,Area,Ever smoking,Ever drinking,Diabetes mellitus、Dyslipidemia,Cancer or malignant,Chronic lung disease,Liver disease,Kidney disease,Stroke，Heart disease，Memory-related disease，Psychosomatic disease，uric acid (UA), blood urea nitrogen (BUN), glucose, creatinine (Crea), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), C-reactive protein (CRP), glycated hemoglobin (HbA1c) b Adjucted for Age,gender,Marital status,education,Area,Ever smoking,Ever drinking,Diabetes mellitus、Dyslipidemia,Cancer or malignant,Chronic lung disease,Liver disease,Kidney disease,Stroke，Heart disease，Memory-related disease，Psychosomatic disease，uric acid (UA), blood urea nitrogen (BUN), glucose, creatinine (Crea), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), C-reactive protein (CRP), glycated hemoglobin (HbA1c) and BMI of wave 1. c Adjucted for Age,gender,Marital status,education,Area,Ever smoking,Ever drinking,Diabetes mellitus、Dyslipidemia,Cancer or malignant,Chronic lung disease,Liver disease,Kidney disease,Stroke，Heart disease，Memory-related disease，Psychosomatic disease，uric acid (UA), blood urea nitrogen (BUN), glucose, creatinine (Crea), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), C-reactive protein (CRP), glycated hemoglobin (HbA1c) and BMI of wave 1, 2, and 3. Table 3. Association between BMI-TTR and incident hypertension risk: subgroup analysis based on Cox proportional hazards models. Variable Adjusted HR a (95% Cl) P value Adjusted HR b (95% Cl) P value Adjusted HR a (95% Cl) P value Adjusted HR b (95% Cl) P value Male (n=2,086) Age<60 (n=2,715) TTR1 TTR2 0.895(0.705-1.134) 0.358 0.897 (0.703-1.143) 0.379 0.716 (0.572-0.895) 0.003 0.739 (0.584-0.936) 0.012 TTR3 0.784 (0.623-0.988) 0.039 0.787 (0.619-1.001) 0.051 0.781 (0.627-0.972) 0.027 0.815 (0.641-1.036) 0.094 TTR4 0.745 (0.625-0.888) 0.001 0.748 (0.616-0.907) 0.003 0.629 (0.527-0.750) <0.001 0.660 (0.536-0.813) <0.001 Female (n=2,283) Age ≥ 60 (n=1,654) TTR1 TTR2 0.870 (0.696-1.088) 0.222 0.868 (0.694-1.087) 0.222 1.179 (0.928-1.497) 0.177 1.174 (0.924-1.492) 0.190 TTR3 0.742 (0.585-0.9) 0.014 0.739 (0.582-0.938) 0.014 0.773 (0.603-0.993) 0.044 0.770 (0.599-0.989) 0.041 TTR4 0.689 (0.573-0.829) <0.001 0.689 (0.572-0.830) <0.001 0.883 (0.710-1.025) 0.090 0.848 (0.704-1.022) 0.083 P for interaction 0.971 0.971 <0.001 <0.001 Note: HR hazard ratio, CI, confidence interval a Adjucted for Age,gender,Marital status,education,Area,Ever smoking,Ever drinking,Diabetes mellitus、Dyslipidemia,Cancer or malignant,Chronic lung disease,Liver disease,Kidney disease,Stroke，Heart disease，Memory-related disease，Psychosomatic disease，uric acid (UA), blood urea nitrogen (BUN), glucose, creatinine (Crea), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), C-reactive protein (CRP), glycated hemoglobin (HbA1c) b Adjucted for Age,gender,Marital status,education,Area,Ever smoking,Ever drinking,Diabetes mellitus、Dyslipidemia,Cancer or malignant,Chronic lung disease,Liver disease,Kidney disease,Stroke，Heart disease，Memory-related disease，Psychosomatic disease，uric acid (UA), blood urea nitrogen (BUN), glucose, creatinine (Crea), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), C-reactive protein (CRP), glycated hemoglobin (HbA1c) and BMI of wave 1. Additional Declarations No competing interests reported. Supplementary Files Stable2.docx Stable1.docx Stable4.docx Stable5.docx Stable3.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 23 Jan, 2026 Editor invited by journal 01 Jan, 2026 Editor assigned by journal 29 Dec, 2025 Submission checks completed at journal 29 Dec, 2025 First submitted to journal 27 Dec, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8461092","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":580304310,"identity":"a045bf52-eca4-4501-a71f-528f607eb724","order_by":0,"name":"Qian Wu","email":"","orcid":"","institution":"Department of Emergency, The Second people's Hospital of Hefei, Hefei Hospital Affiliated to Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"Qian","middleName":"","lastName":"Wu","suffix":""},{"id":580304311,"identity":"5f9e64b1-97af-4b15-b9b6-a5e7da878e33","order_by":1,"name":"Bing Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyklEQVRIiWNgGAWjYBACNmbmAwc+VDAwG4B4PMRo4WdvS3w44wwpWiR7zhgb87YxMBCvxeBGjpk077w6dnOJBMYHb9sY5M0Ja0krk5y7jY3ZckYCs+HcNgbDnQ0EtSRvk3i7jYfZ4EYCmzTQhQkGBwhqSTCT4J0jAdLC/psoLZI9R4wNeRsMwLYwE6UFEsjHEpgNzjxslpxzTsJwAyEtkKisqUs2OJ588MObMht5grbAQDIDA2MDkJYgUj0Q2BGvdBSMglEwCkYcAABTZj750kGWRQAAAABJRU5ErkJggg==","orcid":"","institution":"Huzhou Key Laboratory of Precise Prevention and Control of Major Chronic Diseases, Huzhou University","correspondingAuthor":true,"prefix":"","firstName":"Bing","middleName":"","lastName":"Wang","suffix":""},{"id":580304312,"identity":"94efab55-f4f1-49ee-b9c1-50bd10408444","order_by":2,"name":"Xiaohong Yang","email":"","orcid":"","institution":"Department of Medicine, Huzhou University","correspondingAuthor":false,"prefix":"","firstName":"Xiaohong","middleName":"","lastName":"Yang","suffix":""},{"id":580304313,"identity":"82651906-c31d-4368-82d7-8fdece51b4a0","order_by":3,"name":"Yujia Wang","email":"","orcid":"","institution":"Faculty of Business Administration, University of Macau","correspondingAuthor":false,"prefix":"","firstName":"Yujia","middleName":"","lastName":"Wang","suffix":""},{"id":580304314,"identity":"c663259a-901a-4c94-8114-6c6a103efd99","order_by":4,"name":"Wenjun Cai","email":"","orcid":"","institution":"Department of Emergency, The Second people's Hospital of Hefei, Hefei Hospital Affiliated to Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"Wenjun","middleName":"","lastName":"Cai","suffix":""}],"badges":[],"createdAt":"2025-12-27 12:38:32","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8461092/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8461092/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101363252,"identity":"5b3c848e-cb7e-4686-b324-d463269a7f86","added_by":"auto","created_at":"2026-01-29 00:34:55","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":151452,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlow diagram showing the selection of the study population.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8461092/v1/c84a08d91e5e5215cb7fe843.png"},{"id":101363245,"identity":"3f59bf59-e581-4482-8a4e-c708c0f50b6d","added_by":"auto","created_at":"2026-01-29 00:34:55","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":59563,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSurvival analysis. Kaplan–Meier survival curves for TTR from the 6-year incidence of new-onset Hypertension.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8461092/v1/32eb5d643cf52dd42b384195.png"},{"id":101942719,"identity":"8fa3d0a4-1a0f-43b2-aced-b70550cf7800","added_by":"auto","created_at":"2026-02-05 09:35:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1105273,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8461092/v1/5de1e592-cf2d-435b-8fcb-2c3449da444a.pdf"},{"id":101398625,"identity":"3da310c7-a243-4dfc-bf87-1ee68e39800c","added_by":"auto","created_at":"2026-01-29 09:43:05","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":13160,"visible":true,"origin":"","legend":"","description":"","filename":"Stable2.docx","url":"https://assets-eu.researchsquare.com/files/rs-8461092/v1/bc37d7485bef74e4d7f49497.docx"},{"id":101363246,"identity":"d76b184f-8a7a-4c94-9ff3-549eb082747a","added_by":"auto","created_at":"2026-01-29 00:34:55","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":12413,"visible":true,"origin":"","legend":"","description":"","filename":"Stable1.docx","url":"https://assets-eu.researchsquare.com/files/rs-8461092/v1/904e54064e2eb5d677a88cd8.docx"},{"id":101363250,"identity":"79a07ab0-2204-496e-ae9a-3cfb9b95872f","added_by":"auto","created_at":"2026-01-29 00:34:55","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":14205,"visible":true,"origin":"","legend":"","description":"","filename":"Stable4.docx","url":"https://assets-eu.researchsquare.com/files/rs-8461092/v1/44e982e898fabcca948ce343.docx"},{"id":101363248,"identity":"8277fca6-2aa2-4ea3-be25-5c3a86940ea2","added_by":"auto","created_at":"2026-01-29 00:34:55","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":16764,"visible":true,"origin":"","legend":"","description":"","filename":"Stable5.docx","url":"https://assets-eu.researchsquare.com/files/rs-8461092/v1/1e340bbc2e82f4b9f6c5e8ec.docx"},{"id":101363251,"identity":"650c5b26-9ee8-4367-b697-5bb89a1a8399","added_by":"auto","created_at":"2026-01-29 00:34:55","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":14137,"visible":true,"origin":"","legend":"","description":"","filename":"Stable3.docx","url":"https://assets-eu.researchsquare.com/files/rs-8461092/v1/a2b45cb4a90099ccb8e1dfcc.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Body mass index time in target range and incident hypertension in middle-aged and older adults: a longitudinal study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHypertension, the most prevalent chronic cardiovascular disease worldwide, is a leading modifiable risk factor for morbidity and mortality attributable to cardiac, cerebrovascular, and renal damage [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Epidemiological estimates reveal that over 1.28\u0026nbsp;billion individuals are affected globally, with an adult prevalence of approximately 25% in China that rises markedly with age, contributing to a substantial and growing disease burden [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Among modifiable determinants, obesity is a well-established contributor to the incidence and progression of hypertension. Elevated body mass index (BMI), a widely used surrogate measure of adiposity, is consistently associated with an increased risk of hypertension [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eResearch investigating the association between BMI and hypertension has traditionally relied on single or infrequent static measurements [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. However, body weight fluctuates over time, and a single assessment cannot adequately capture long-term fluctuation patterns or cumulative exposure. The dynamic metric known as \"time in target range\" (TTR) has emerged as a significant tool in chronic disease management. Evidence indicates that TTR outperforms single measurements or simple averages in predicting complications and clinical outcomes in conditions such as diabetes and hypertension [\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCurrently, there is a lack of research systematically exploring the relationship between Body Mass Index Time in Target Range (BMI-TTR), a long-term dynamic indicator, and the risk of hypertension. To address this knowledge gap, we utilized longitudinal data from the nationally representative China Health and Retirement Longitudinal Study (CHARLS) to analyze the association between BMI-TTR and the incidence of hypertension. The findings are expected to provide new evidence for the long-term benefits of weight management and to offer a scientific foundation for primary prevention strategies for hypertension.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy design and participants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study used data from the China Health and Retirement Longitudinal Study (CHARLS), a nationally representative longitudinal survey of adults aged 45 years and older in China. This survey collects detailed household and individual level information across successive waves (see http://charls.pku.edu.cn/) [12]. To ensure temporal separation between exposure and outcome, we utilized BMI measurements from waves 1 to 3 (2011, 2013, 2015) to construct the BMI-TTR exposure and identified incident hypertension during follow‑up visits in waves 2 to 4 (2013, 2015, 2018). The exclusion criteria included: individuals younger than 45 years, those with hypertension at baseline (2011), or those whose\u0026nbsp;baseline blood pressure measurements met the diagnostic criteria for hypertension (systolic pressure \u0026ge; 140 mmHg or diastolic pressure \u0026ge; 90 mmHg). After applying these criteria, a total of 4,369 participants were included in the final analysis (see Fig. 1 for the flow diagram). The study was conducted in accordance with the STROBE guidelines, approved by the Ethics Review Committee of Peking University, and all participants provided written informed consent prior to enrollment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEvaluation of BMI\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eand definition of\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eBMI-TTR\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe calculation of BMI involves dividing weight in kilograms by the square of height in meters (kg/m\u0026sup2;). The definition for BMI-TTR was established as the range from 18.5 kg/m\u0026sup2; to under 23 kg/m\u0026sup2;, in accordance with the World Health Organization\u0026apos;s guidelines for Asian populations. To evaluate the maintenance of weight over an extended period, we developed a BMI Time in Target Range (BMI-TTR) indicator utilizing complete BMI data from 2011 (baseline), 2013, and 2015. BMI data from the 2018 wave were excluded from the TTR calculation to prevent temporal overlap with the ascertainment of hypertension outcome. Participants were categorized into four distinct groups based on the frequency with which their BMI measurements fell within the target range across the three specified years. The classification was as follows: TTR1: BMI was below the standard in all three measurements; TTR2: BMI met the standard in any one measurement; TTR3: BMI met the standard in any two measurements; TTR4: BMI met the standard in all three measurements. This classification reflects the progressive increase in the level of long-term control of BMI within the recommended range, which will be treated as an ordered variable in subsequent analyses.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIncident hypertension\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIncident hypertension was defined as the initial occurrence of any of the following criteria during the follow-up visits in 2013, 2015, or 2018: (1) self-reported doctor-diagnosed hypertension; (2) self-reported use of antihypertensive medication; (3) an average systolic blood pressure of \u0026ge; 140 mmHg or average diastolic blood pressure of \u0026ge; 90 mmHg based on three consecutive blood pressure measurements. The time to event was calculated from the earliest follow-up wave at which any of these criteria were met. All participants were followed for up to 6 years, and incident cases were identified by aggregating data from the three follow-up rounds.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStatistical analysis was conducted using R software (version 4.2.3). Categorical variables are reported as percentages, while continuous variables are presented as\u0026nbsp;the median and interquartile range (IQR). Group comparisons were performed using one-way ANOVA, non-parametric tests, chi-square tests, or Fisher\u0026apos;s exact test. Trend tests were conducted to assess the distribution of baseline characteristics across different TTR groups and to evaluate the trend of changes in group variables. The influence of BMI-TTR on the risk of hypertension incidence was analyzed using Cox regression and binary logistic regression models. Effect indicators include hazard ratio (HR) and odds ratio (OR), with a 95% confidence interval (CI). Kaplan-Meier curves were utilized to illustrate the probability of outcome occurrence. The covariates adjusted for include age, sex, hypertension, smoking, and other related factors. The Cox regression model comprises:\u003c/p\u003e\n\u003cp\u003eModel1: Age, gender, Marital status, education, Area, ever smoking, ever drinking,\u003c/p\u003e\n\u003cp\u003eDiabetes mellitus, Dyslipidemia, Cancer or malignant, Chronic lung disease, Liver disease, Kidney disease, Stroke, Heart disease, Memory-related disease, Psychosomatic disease, uric acid (UA), blood urea nitrogen (BUN), glucose, creatinine (Crea), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), C-reactive protein (CRP), glycated hemoglobin (HbA1c) and BMI-TTR；\u003c/p\u003e\n\u003cp\u003eModel 2: On the basis of Model 1, add the 2011 BMI values (baseline);\u003c/p\u003e\n\u003cp\u003eModel 3: Based on Model 2, add the BMI data from 2011, 2013, and 2015. Group analysis was conducted based on gender and age, and the impact of interactions was assessed. Data that were missing were addressed using multiple imputation techniques. (see S-Table 1). \u003cem\u003eP\u003c/em\u003e value \u0026lt; 0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eBaseline characteristics of participants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study ultimately included 4,369 participants who did not have hypertension at baseline. As shown in Table 1, based on the frequency with which the BMI met the established criteria across three different measurements, the participants were categorized into four distinct groups: TTR1 to TTR4. With increasing BMI-TTR level, baseline characteristics exhibited systematic changes. Metabolic and physiological profiles improved showed significant improvements, with glucose and LDL-C progressively declining (both \u003cem\u003eP\u0026nbsp;\u003c/em\u003efor trend \u0026lt; 0.001). In terms of demographic characteristics, the proportion of women and individuals residing in rural areas increased in higher TTR groups (both \u003cem\u003eP\u003c/em\u003e for trend \u0026lt;0.001), while the proportions of smokers and drinkers also gradually increased (both \u003cem\u003eP\u003c/em\u003e for trend \u0026lt; 0.001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssociation between\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eBMI-TTR\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;and hypertension incidence\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDuring the 6-year follow-up, 1,524 cases of incident hypertension occurred, with a cumulative incidence of 34.9%. The occurrence of hypertension reduced progressively in a higher BMI-TTR group. The incidence of hypertension in TTR1 was 40.2% (n=720), in TTR2 was 34.7% (n=191), in TTR3 was 31.2% (n=187) and in TTR4 was 29.9% (n=426) (\u003cem\u003eP\u003c/em\u003e for trend \u0026lt;0.001). Cox proportional hazards regression confirmed an inverse dose‑response relationship (Table 2). Compared with TTR1, the TTR4 group demonstrated a significantly lower risk of hypertension in the crude model (HR = 0.704, 95% CI: 0.625\u0026ndash;0.794, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001). This association remained significant after sequential adjustment for demographic, lifestyle, and clinical confounders (Model 1: HR = 0.717, 95% CI: 0.633\u0026ndash;0.813, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001) and for baseline BMI (Model 2: HR = 0.718, 95% CI: 0.631\u0026ndash;0.816, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001). In the fully adjusted model that included BMI data from all three waves (Model 3), the inverse associations for TTR3 and TTR4 remained significant (HR = 0.768 and 0.733, respectively; both \u003cem\u003eP \u003c/em\u003e\u0026lt; 0.05). In Model 3, BMI measured at Wave 2 was independently associated with an increased risk of hypertension (HR = 1.005, 95% CI: 1.001\u0026ndash;1.010, \u003cem\u003eP \u003c/em\u003e= 0.010). The Kaplan-Meier survival curve (Figure 2) visually demonstrates the significant difference in cumulative incidence rates across groups (Log-rank \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001). The results of the sensitivity analysis using binary logistic regression were consistent with the findings presented above (S-Table 2).\u003c/p\u003e\n\u003cp\u003eTo further investigate the impact of dynamic weight changes, participants were reclassified into three trajectory-based groups: Group 1, the \u003cem\u003econsistently normal group\u0026nbsp;\u003c/em\u003e(BMI within the target range at all three waves, n=1,427, 32.6%); Group 2, the \u003cem\u003eimprovement\u0026nbsp;group\u003c/em\u003e (baseline BMI above the target range but attaining the range in at least one subsequent wave, n=460, 10.5%); and Group 3,the \u003cem\u003eother group\u003c/em\u003e (n = 2,482, 56.8%). As shown in S-Table 4, compared to Group 3, the risk of newly developed hypertension in Group 2 was significantly lower than in Group 1 (adjusted HR ranges of 0.764-0.777 and 0.748-0.765, respectively, both \u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 0.01), while no significant difference in risk was observed between Group 2 and Group 1 (\u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026gt; 0.05). Subgroup analyses revealed that this beneficial pattern associated with weight improvement was consistent across both genders (S-Table 5). However, a significant age interaction was detected (\u003cem\u003eP\u003c/em\u003e for interaction \u0026lt; 0.05): the protective effect of weight improvement was evident and statistically significant among participants aged 45\u0026ndash;60 years, but it was attenuated and not significant in those aged \u0026ge; 60 years.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eSubgroup analysis\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo ensure accuracy, subgroup analyses by gender and age were conducted to validate these. As indicated by Table 3, a high BMI-TTR (TTR4 in particular) coincided with a reduced risk of hypertension among both sexes. This protective effect appeared to be more pronounced in women (adjusted for baseline BMI, HR for TTR4 = 0.689, 95% CI: 0.572\u0026ndash;0.830, \u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 0.001) compared to men (HR = 0.748, 95% CI: 0.616\u0026ndash;0.907, \u003cem\u003eP\u003c/em\u003e = 0.003). However, the interaction between gender and BMI-TTR was not statistically significant (\u003cem\u003eP\u003c/em\u003e for interaction \u0026gt; 0.05). In stratified analyses, the protective association was markedly stronger in participants aged 45 to 60 years (TTR4 HR = 0.660, 95% CI: 0.536\u0026ndash;0.813, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001) than in those aged \u0026ge;60 years (TTR4 HR = 0.848, 95% CI: 0.704\u0026ndash;1.022, \u003cem\u003eP\u003c/em\u003e = 0.083). A significant interaction was observed between age and BMI‑TTR (\u003cem\u003eP\u003c/em\u003e for interaction \u0026lt; 0.001). The results of the sensitivity analysis with binary logistic regression (S-Table 3) are highly consistent with the Cox regression (above), which further corroborates the above findings.\u003c/p\u003e"},{"header":"Discussions","content":"\u003cp\u003eBased on longitudinal data from CHARLS (2011\u0026ndash;2018), this study aimed to explore the relationship between BMI-TTR and hypertension risk among middle-aged and elderly people in China. The results indicated that the long -term maintenance of BMI within the recommended range is significantly associated with a reduced risk of hypertension, independent of baseline BMI. This protective association is particularly pronounced among women and those aged 45 to 60 years. Furthermore, dynamic trajectory analysis indicated that participants whose BMI improved from above the target range at baseline to within the range during follow-up achieved a risk reduction comparable to that of individuals who consistently a normal weight.\u003c/p\u003e \u003cp\u003eHypertension significantly compromises both quality of life and life expectancy by increasing the risk of severe complications, including stroke, kidney failure, and retinopathy [\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. In the management of chronic disease, TTR as a measure has been shown to be both a better measure of long-term quality of control and a better predictor of long-term risk when compared to a single measured value. Research indicates [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] that in adults with hypertension, the proportion of time spent with blood pressure within the target range (BP-TTR) is associated with a lower risk of kidney damage and cardiovascular events. Furthermore, TTR of blood glucose and glycated hemoglobin has been notably linked to adverse outcomes, such as complications and mortality, serving as a critical marker for prognostic evaluation [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Additionally, TTR has applications for other measures, including the TTR of blood potassium and proteinuria, which are also significantly related to adverse outcomes, including disease-related complications and mortality [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. While BMI-TTR is an effective indicator for assessing long-term weight status achievement [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], the association between BMI-TTR and the risk of developing hypertension remains unclear.\u003c/p\u003e \u003cp\u003eThe baseline profile of this study indicates that long-term maintenance of BMI within the normal range is associated with a healthier overall metabolic phenotype. This finding aligns with prior evidence linking sustained weight management to improved cardiometabolic risk [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The study also found that among those with long-term BMI control, the proportion of smokers and alcohol drinkers was higher. This may reflect the tendency of some individuals, while successfully managing their weight, to relax restrictions on other health behaviors or to utilize use these behaviors as alternative strategies for managing stress or maintaining weight [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Additionally, rural residents constituted a higher proportion of the group that maintained normal weight long -term, potentially due to their elevated physical activity levels and relatively traditional dietary habits [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. In contrast, women were underrepresented in this group, which may be attributable to physiological factors such as hormonal changes after middle age, leading to body fat redistribution and a decrease in basal metabolic rate [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Despite these complex baseline differences, the subsequent Cox regression model indicated that individuals who maintained a normal BMI over the long term exhibited an independent association with a reduced risk of developing hypertension. Even after controlling for baseline BMI levels, a significant correlation persisted between a higher TTR group and a reduced risk of new-onset hypertension. These findings suggest that weight management should not only aim to maintain BMI within the target range but also focus on sustaining this state in the long term, thereby effectively reducing the risk of hypertension onset and related adverse clinical outcomes.\u003c/p\u003e \u003cp\u003eAccording to pathophysiology, maintaining stable body weight over the long term may reduce the risk of hypertension through multiple pathways. Weight fluctuation itself is considered a risk factor for cardiovascular metabolic diseases [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Research involving animals indicates that fluctuations in body weight can induce inflammation in adipose tissue, macrophage infiltration, and impair systemic insulin sensitivity [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. In human studies, weight fluctuations are associated with chronic low-grade inflammation and increasing sympathetic nervous system activity. These changes elevate blood pressure by jointly damaging endothelial function, increasing renal sodium reabsorption, and enhancing vascular constriction responses [\u003cspan additionalcitationids=\"CR30\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. The primary contribution of this study is to assess not the \u0026ldquo;variability\u0026rdquo; of body weight but the \u0026ldquo;sustainability of health status\u0026rdquo;. This is done to quantify the association between the percentage of time that people spend maintaining their weight within the accepted healthy range, in the long term, and the incidence of hypertension.\u003c/p\u003e \u003cp\u003eThis study also found that the protective effect of BMI-TTR on the onset of hypertension was particularly significant in women and individuals aged 45 to 60, indicating a notable age interaction. In women, estrogen may exert a protective effect on the vascular endothelium and regulate the renin-angiotensin system, thereby enhancing vascular benefits during weight improvement [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. In addition, greater subcutaneous fat storage might make it easier for women to avoid hypertension, compared to their men counterparts, who produce more visceral fat [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Additionally, hypertension in younger populations is often related to increased volume load and sympathetic nervous excitation, making them more responsive to weight loss, whereas with aging, arterial stiffening gradually becomes dominant, reducing sensitivity to weight changes [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Age-related muscle loss may further attenuate the blood pressure-lowering benefits achievable through weight management aimed at improving overall metabolism [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Consequently, there exists a \"golden window period\" for the advantages of weight management in preventing hypertension, underscoring the urgency and importance of initiating long-term, stable weight interventions both older age and earlier.\u003c/p\u003e \u003cp\u003eThis study has several limitations. First, the principal methodology used in diagnosing hypertension is the self-reports and blood pressure measurements at the research site. While the average of three measurements has been accepted, it could still be subject to measurement bias. Second, the study population is restricted to middle-aged and elderly individuals in China from a specific cohort, necessitating caution when generalizing the findings to other ethnicities, regions, or younger populations. Third, the calculation of BMI-TTR was based solely on three measurements taken several years apart. The TTR, which relies on intensive monitoring in blood pressure management, may vary in accuracy and may not adequately capture more subtle weight fluctuations. Future research should incorporate more frequent weight monitoring data to calculate a more precise TTR. Fourth, the inability to differentiate the intentional and unintentional weight changes occurring during the study time could have implications of a very different nature upon blood pressure [\u003cspan additionalcitationids=\"CR37 CR38\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Moreover, inclusion and analysis of detailed lifestyle information such as dietary structure, sodium-potassium intake, etc. might also have acted as confounders. Lastly, while we primarily identified associations based on epidemiological data and inferred them through relevant mechanistic literature. However, this study did not directly measure intermediary biomarkers such as inflammatory factors, sympathetic nervous activity, or renin-angiotensin system activity, thus the explanation of potential mechanisms remains hypothetical.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study is the first to confirm that maintaining the body mass index in the target range for a long period is a protective factor for hypertension for middle-aged and elderly Chinese people. This association is particularly pronounced in women and those aged 45 to 60, with subsequent improvements in weight also offering significant benefits. The findings give robust support that long-term maintenance of a healthy weight should be considered as a core strategy for primary prevention of hypertension.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e\u003cspan id=\"_Toc472330563\"\u003eAcknowledgement\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank all participants for their contribution to this study and China Health and Retirement Longitudinal Study project team for providing free public data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors participated in concept and design of this study. Qian Wu: Select a topic, Data analysis, Writing manuscript; Bing Wang: Make overall arrangements, Participate in writing and revising papers; Xiaohong Yang: Paper framework design and revision; Yujia Wang: Conceptualization; Wenjun Cai: Collect data and analyze data. The author(s) read and approved the final manuscript.\u003c/p\u003e\n\u003cp id=\"_Toc472330566\"\u003e\u003cstrong\u003eFunding Sources\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Department of Education of Zhejiang Province (Y202043955), the National Scientific and Technological Major Project of China( 2017ZX10105001),and Huzhou University Project (JG202246）.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analyzed during this study are included in this article.Further inquiries can be directed to the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatement of Ethics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCHARLS was approved by the Ethical Review Committee (IRB) at Peking University, Beijing, China.tement of Ethics.（IRB00001052-11015）\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp id=\"_Toc472330565\"\u003e\u003cstrong\u003eConflict of Interest Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed consent\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEach participant included in this study signed a written informed consent form before taking part in the survey.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eGBD R F C. 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Diabetes \u0026amp; endocrinology, 2014,2(12):954-962.\u003c/li\u003e\n\u003cli\u003eVink R G, Roumans N J T, Arkenbosch L A J, et al. The effect of rate of weight loss on long-term weight regain in adults with overweight and obesity[J]. Obesity (Silver Spring, Md.), 2016,24(2):321-327.\u003c/li\u003e\n\u003cli\u003eBoutari C, DeMarsilis A, Mantzoros C S. Obesity and diabetes[J]. Diabetes research and clinical practice, 2023,202:110773.\u003c/li\u003e\n\u003cli\u003ePowell-Wiley T M, Poirier P, Burke L E, et al. Obesity and Cardiovascular Disease: A Scientific Statement From the American Heart Association[J]. Circulation, 2021,143(21):e984-e1010.\u003c/li\u003e\n\u003cli\u003eAudrain-McGovern J, Benowitz N L. Cigarette smoking, nicotine, and body weight[J]. Clinical pharmacology and therapeutics, 2011,90(1):164-168.\u003c/li\u003e\n\u003cli\u003eEkezie J, Anyanwu E G, Danborno B, et al. Impact of urbanization on obesity, anthropometric profile and blood pressure in the Igbos of Nigeria[J]. North American journal of medical sciences, 2011,3(5):242-246.\u003c/li\u003e\n\u003cli\u003eKo S, Kim H. Menopause-Associated Lipid Metabolic Disorders and Foods Beneficial for Postmenopausal Women[J]. Nutrients, 2020,12(1):202.\u003c/li\u003e\n\u003cli\u003eLombardo M, Feraco A, Armani A, et al. Gender differences in body composition, dietary patterns, and physical activity: insights from a cross-sectional study[J]. Frontiers in nutrition, 2024,11:1414217.\u003c/li\u003e\n\u003cli\u003eThillainadesan S, Madsen S R, James D E, et al. The impact of weight cycling on health outcomes in animal models: A systematic review and meta-analysis[J]. Obesity reviews : an official journal of the International Association for the Study of Obesity, 2022,23(5):e13416.\u003c/li\u003e\n\u003cli\u003eEngeli S, Schling P, Gorzelniak K, et al. The adipose-tissue renin-angiotensin-aldosterone system: role in the metabolic syndrome?[J]. The international journal of biochemistry \u0026amp; cell biology, 2003,35(6):807-825.\u003c/li\u003e\n\u003cli\u003eGrassi G. Sympathetic neural activity in hypertension and related diseases[J]. American journal of hypertension, 2010,23(10):1052-1060.\u003c/li\u003e\n\u003cli\u003eRahmouni K, Correia M L G, Haynes W G, et al. Obesity-associated hypertension: new insights into mechanisms[J]. Hypertension (Dallas, Tex. : 1979), 2005,45(1):9-14.\u003c/li\u003e\n\u003cli\u003eVillar I C, Hobbs A J, Ahluwalia A. Sex differences in vascular function: implication of endothelium-derived hyperpolarizing factor[J]. The Journal of endocrinology, 2008,197(3):447-462.\u003c/li\u003e\n\u003cli\u003eYang X, Smith U. Adipose tissue distribution and risk of metabolic disease: does thiazolidinedione-induced adipose tissue redistribution provide a clue to the answer?[J]. Diabetologia, 2007,50(6):1127-1139.\u003c/li\u003e\n\u003cli\u003eMistriotis P, Andreadis S T. Vascular aging: Molecular mechanisms and potential treatments for vascular rejuvenation[J]. Ageing research reviews, 2017,37:94-116.\u003c/li\u003e\n\u003cli\u003eBuch A, Carmeli E, Boker L K, et al. Muscle function and fat content in relation to sarcopenia, obesity and frailty of old age--An overview[J]. Experimental gerontology, 2016,76:25-32.\u003c/li\u003e\n\u003cli\u003eHe J, Whelton P K, Appel L J, et al. Long-term effects of weight loss and dietary sodium reduction on incidence of hypertension[J]. Hypertension (Dallas, Tex. : 1979), 2000,35(2):544-549.\u003c/li\u003e\n\u003cli\u003eNeter J E, Stam B E, Kok F J, et al. Influence of weight reduction on blood pressure: a meta-analysis of randomized controlled trials[J]. Hypertension (Dallas, Tex. : 1979), 2003,42(5):878-884.\u003c/li\u003e\n\u003cli\u003eYao S, Marron M M, Farsijani S, et al. A Metabolite Score of Unintentional Weight Loss Explained a Substantial Proportion of Associated Mortality and Mobility Limitation Risk in a Biracial Older Cohort[J]. Aging cell, 2025,24(10):e70181.\u003c/li\u003e\n\u003cli\u003eHussain S M, Newman A B, Beilin L J, et al. Associations of Change in Body Size With All-Cause and Cause-Specific Mortality Among Healthy Older Adults[J]. JAMA network open, 2023,6(4):e237482.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1. \u0026nbsp;Basic characteristics according to TTR-BMI.（n=4,369）\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"1033\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10.3582%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.1026%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.94%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTTR1 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e（\u003c/strong\u003e\u003cstrong\u003en=1791\u003c/strong\u003e\u003cstrong\u003e）\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1336%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTTR2\u003c/strong\u003e\u003cstrong\u003e（\u003c/strong\u003e\u003cstrong\u003en=551\u003c/strong\u003e\u003cstrong\u003e）\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.2623%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTTR3\u003c/strong\u003e\u003cstrong\u003e（\u003c/strong\u003e\u003cstrong\u003en=600\u003c/strong\u003e\u003cstrong\u003e）\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5528%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTTR4\u003c/strong\u003e\u003cstrong\u003e（\u003c/strong\u003e\u003cstrong\u003en=1427\u003c/strong\u003e\u003cstrong\u003e）\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.42207%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.22846%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP for trend\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10.3582%;\"\u003e\n \u003cp\u003eBMI, kg/m\u0026sup2;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.1026%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.94%;\"\u003e\n \u003cp\u003e25.24（23.80,27.08）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1336%;\"\u003e\n \u003cp\u003e22.56（21.23,23.59）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.2623%;\"\u003e\n \u003cp\u003e21.54（19.39,22.69）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5528%;\"\u003e\n \u003cp\u003e20.69（19.83,21.59）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.42207%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.22846%;\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10.3582%;\"\u003e\n \u003cp\u003eAge,years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.1026%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.94%;\"\u003e\n \u003cp\u003e56.00（49.00,62.00）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1336%;\"\u003e\n \u003cp\u003e56.00（50.00,63.00）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.2623%;\"\u003e\n \u003cp\u003e58.00（52.00,64.00）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5528%;\"\u003e\n \u003cp\u003e58.00（52.00,64.00）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.42207%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.22846%;\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10.3582%;\"\u003e\n \u003cp\u003eFemale, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.1026%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.94%;\"\u003e\n \u003cp\u003e1082（60.4）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1336%;\"\u003e\n \u003cp\u003e302（54.8）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.2623%;\"\u003e\n \u003cp\u003e287（47.8）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5528%;\"\u003e\n \u003cp\u003e612（42.9）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.42207%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.22846%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10.3582%;\"\u003e\n \u003cp\u003eMarital status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.1026%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.94%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1336%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.2623%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5528%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.42207%;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.22846%;\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10.3582%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.1026%;\"\u003e\n \u003cp\u003eMarried or partnered\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.94%;\"\u003e\n \u003cp\u003e1661（92.70）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1336%;\"\u003e\n \u003cp\u003e494（89.70）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.2623%;\"\u003e\n \u003cp\u003e530（88.30）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5528%;\"\u003e\n \u003cp\u003e1275（89.30）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.42207%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.22846%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10.3582%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.1026%;\"\u003e\n \u003cp\u003eOther marital status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.94%;\"\u003e\n \u003cp\u003e130（7.30）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1336%;\"\u003e\n \u003cp\u003e57（10.30）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.2623%;\"\u003e\n \u003cp\u003e70（11.70）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5528%;\"\u003e\n \u003cp\u003e152（10.70）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.42207%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.22846%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10.3582%;\"\u003e\n \u003cp\u003eEducation, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.1026%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.94%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1336%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.2623%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5528%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.42207%;\"\u003e\n \u003cp\u003e0.106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.22846%;\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10.3582%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.1026%;\"\u003e\n \u003cp\u003eLess than lower secondary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.94%;\"\u003e\n \u003cp\u003e1595（89.1）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1336%;\"\u003e\n \u003cp\u003e506（91.80）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.2623%;\"\u003e\n \u003cp\u003e541（90.20）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5528%;\"\u003e\n \u003cp\u003e1310（91.8）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.42207%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.22846%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10.3582%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.1026%;\"\u003e\n \u003cp\u003eupper secondary \u0026amp; vocational training\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.94%;\"\u003e\n \u003cp\u003e179（10.0）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1336%;\"\u003e\n \u003cp\u003e43（7.80）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.2623%;\"\u003e\n \u003cp\u003e53（8.80）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5528%;\"\u003e\n \u003cp\u003e103（7.2）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.42207%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.22846%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10.3582%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.1026%;\"\u003e\n \u003cp\u003etertiary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.94%;\"\u003e\n \u003cp\u003e17（0.90）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1336%;\"\u003e\n \u003cp\u003e2（0.40）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.2623%;\"\u003e\n \u003cp\u003e6（1.0）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5528%;\"\u003e\n \u003cp\u003e14（1.0）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.42207%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.22846%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10.3582%;\"\u003e\n \u003cp\u003eArea,%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.1026%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.94%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1336%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.2623%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5528%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.42207%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.22846%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10.3582%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.1026%;\"\u003e\n \u003cp\u003eUrban\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.94%;\"\u003e\n \u003cp\u003e636（35.5）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1336%;\"\u003e\n \u003cp\u003e161（29.2）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.2623%;\"\u003e\n \u003cp\u003e161（26.8）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5528%;\"\u003e\n \u003cp\u003e350（24.5）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.42207%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.22846%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10.3582%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.1026%;\"\u003e\n \u003cp\u003eRural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.94%;\"\u003e\n \u003cp\u003e1155（64.5）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1336%;\"\u003e\n \u003cp\u003e390（70.8）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.2623%;\"\u003e\n \u003cp\u003e439（73.2）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5528%;\"\u003e\n \u003cp\u003e1077（75.5）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.42207%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.22846%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10.3582%;\"\u003e\n \u003cp\u003eEver smoking, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.1026%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.94%;\"\u003e\n \u003cp\u003e563（31.4）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1336%;\"\u003e\n \u003cp\u003e219（39.7）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.2623%;\"\u003e\n \u003cp\u003e255（42.5）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5528%;\"\u003e\n \u003cp\u003e740（48.1）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.42207%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.22846%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10.3582%;\"\u003e\n \u003cp\u003eEver drinking, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.1026%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.94%;\"\u003e\n \u003cp\u003e605（33.8）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1336%;\"\u003e\n \u003cp\u003e194（35.2）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.2623%;\"\u003e\n \u003cp\u003e230（38.3）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5528%;\"\u003e\n \u003cp\u003e631（44.2）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.42207%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.22846%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10.3582%;\"\u003e\n \u003cp\u003eDiabetes mellitus, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.1026%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.94%;\"\u003e\n \u003cp\u003e96（5.4）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1336%;\"\u003e\n \u003cp\u003e12（2.2）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.2623%;\"\u003e\n \u003cp\u003e20（3.3）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5528%;\"\u003e\n \u003cp\u003e30（2.1）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.42207%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.22846%;\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10.3582%;\"\u003e\n \u003cp\u003eDyslipidemia, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.1026%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.94%;\"\u003e\n \u003cp\u003e126（7.0）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1336%;\"\u003e\n \u003cp\u003e25（4.5）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.2623%;\"\u003e\n \u003cp\u003e22（3.7）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5528%;\"\u003e\n \u003cp\u003e47（3.3）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.42207%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.22846%;\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10.3582%;\"\u003e\n \u003cp\u003eCancer or malignant, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.1026%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.94%;\"\u003e\n \u003cp\u003e16（0.9）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1336%;\"\u003e\n \u003cp\u003e3（0.5）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.2623%;\"\u003e\n \u003cp\u003e4（0.7）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5528%;\"\u003e\n \u003cp\u003e11（0.8）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.42207%;\"\u003e\n \u003cp\u003e0.915\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.22846%;\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10.3582%;\"\u003e\n \u003cp\u003eChronic lung disease, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.1026%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.94%;\"\u003e\n \u003cp\u003e166（9.3）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1336%;\"\u003e\n \u003cp\u003e49（8.9）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.2623%;\"\u003e\n \u003cp\u003e71（11.8）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5528%;\"\u003e\n \u003cp\u003e123（8.6）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.42207%;\"\u003e\n \u003cp\u003e0.144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.22846%;\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10.3582%;\"\u003e\n \u003cp\u003eLiver disease, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.1026%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.94%;\"\u003e\n \u003cp\u003e58（3.2）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1336%;\"\u003e\n \u003cp\u003e16（2.9）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.2623%;\"\u003e\n \u003cp\u003e26（4.3）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5528%;\"\u003e\n \u003cp\u003e48（3.4）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.42207%;\"\u003e\n \u003cp\u003e0.542\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.22846%;\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10.3582%;\"\u003e\n \u003cp\u003eKidney disease, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.1026%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.94%;\"\u003e\n \u003cp\u003e92（5.1）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1336%;\"\u003e\n \u003cp\u003e44（8.0）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.2623%;\"\u003e\n \u003cp\u003e42（7.0）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5528%;\"\u003e\n \u003cp\u003e76（5.3）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.42207%;\"\u003e\n \u003cp\u003e0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.22846%;\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10.3582%;\"\u003e\n \u003cp\u003eStroke, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.1026%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.94%;\"\u003e\n \u003cp\u003e29（1.6）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1336%;\"\u003e\n \u003cp\u003e2（0.4）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.2623%;\"\u003e\n \u003cp\u003e7（1.2）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5528%;\"\u003e\n \u003cp\u003e17（1.2）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.42207%;\"\u003e\n \u003cp\u003e0.136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.22846%;\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10.3582%;\"\u003e\n \u003cp\u003eHeart \u0026nbsp;disease, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.1026%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.94%;\"\u003e\n \u003cp\u003e170（9.5）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1336%;\"\u003e\n \u003cp\u003e39（7.1）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.2623%;\"\u003e\n \u003cp\u003e43（7.2）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5528%;\"\u003e\n \u003cp\u003e85（6.0）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.42207%;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.22846%;\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10.3582%;\"\u003e\n \u003cp\u003eMemory-related disease, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.1026%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.94%;\"\u003e\n \u003cp\u003e12（0.7）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1336%;\"\u003e\n \u003cp\u003e5（0.9）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.2623%;\"\u003e\n \u003cp\u003e10（1.7）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5528%;\"\u003e\n \u003cp\u003e11（0.8）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.42207%;\"\u003e\n \u003cp\u003e0.165\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.22846%;\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10.3582%;\"\u003e\n \u003cp\u003ePsychosomatic disease, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.1026%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.94%;\"\u003e\n \u003cp\u003e19（1.1）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1336%;\"\u003e\n \u003cp\u003e8（1.5）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.2623%;\"\u003e\n \u003cp\u003e9（1.5）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5528%;\"\u003e\n \u003cp\u003e19（1.3）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.42207%;\"\u003e\n \u003cp\u003e0.788\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.22846%;\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10.3582%;\"\u003e\n \u003cp\u003eUA, mg/dl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.1026%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.94%;\"\u003e\n \u003cp\u003e4.18（3.47,4.99）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1336%;\"\u003e\n \u003cp\u003e4.13（3.43,4.90）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.2623%;\"\u003e\n \u003cp\u003e4.17（3.45,5.04）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5528%;\"\u003e\n \u003cp\u003e4.17（3.47,5.02）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.42207%;\"\u003e\n \u003cp\u003e0.626\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.22846%;\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10.3582%;\"\u003e\n \u003cp\u003eBUN, mg/dl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.1026%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.94%;\"\u003e\n \u003cp\u003e14.90（12.41,17.87）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1336%;\"\u003e\n \u003cp\u003e15.83（12.67,18.85）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.2623%;\"\u003e\n \u003cp\u003e15.40（12.28,18.30）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5528%;\"\u003e\n \u003cp\u003e15.43（12.88,18.60）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.42207%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.22846%;\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10.3582%;\"\u003e\n \u003cp\u003eGlucose, mg/dl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.1026%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.94%;\"\u003e\n \u003cp\u003e101.88（93.78，113.40）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1336%;\"\u003e\n \u003cp\u003e100.62（92.51,112.68）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.2623%;\"\u003e\n \u003cp\u003e100.35（91.71,109.91）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5528%;\"\u003e\n \u003cp\u003e100.23（91.69,111.24）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.42207%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.22846%;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10.3582%;\"\u003e\n \u003cp\u003eCreatinine, mg/dl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.1026%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.94%;\"\u003e\n \u003cp\u003e0.75（0.64,0.86）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1336%;\"\u003e\n \u003cp\u003e0.73（0.63,0.87）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.2623%;\"\u003e\n \u003cp\u003e0.76（0.64,0.88）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5528%;\"\u003e\n \u003cp\u003e0.77（0.66,0.88）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.42207%;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.22846%;\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10.3582%;\"\u003e\n \u003cp\u003eTC, mg/dl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.1026%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.94%;\"\u003e\n \u003cp\u003e190.59（168.56,215.34）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1336%;\"\u003e\n \u003cp\u003e187.02（163.53,211.08）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.2623%;\"\u003e\n \u003cp\u003e189.05（160.83,211.08）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5528%;\"\u003e\n \u003cp\u003e185.18（163.53,185.18）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.42207%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.22846%;\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10.3582%;\"\u003e\n \u003cp\u003eHDL-C, mg/dl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.1026%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.94%;\"\u003e\n \u003cp\u003e48.71（39.43,59.54）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1336%;\"\u003e\n \u003cp\u003e52.58（44.07,63.02）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.2623%;\"\u003e\n \u003cp\u003e53.35（44.46,64.08）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5528%;\"\u003e\n \u003cp\u003e54.51（44.46,64.56）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.42207%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.22846%;\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10.3582%;\"\u003e\n \u003cp\u003eLDL-C, mg/dl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.1026%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.94%;\"\u003e\n \u003cp\u003e114.43（94.33,137.63）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1336%;\"\u003e\n \u003cp\u003e111.73（91.62,133.76）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.2623%;\"\u003e\n \u003cp\u003e110.72（90.46,134.83）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5528%;\"\u003e\n \u003cp\u003e110.18（90.85,132.60）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.42207%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.22846%;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10.3582%;\"\u003e\n \u003cp\u003eCRP, mg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.1026%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.94%;\"\u003e\n \u003cp\u003e1.10（0.53,2.56）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1336%;\"\u003e\n \u003cp\u003e0.87（0.44,2.20）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.2623%;\"\u003e\n \u003cp\u003e0.75（0.39,2.19）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5528%;\"\u003e\n \u003cp\u003e0.80（0.40,2.16）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.42207%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.22846%;\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10.3582%;\"\u003e\n \u003cp\u003eHba1c, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.1026%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.94%;\"\u003e\n \u003cp\u003e5.20（4.90,5.50）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1336%;\"\u003e\n \u003cp\u003e5.10（4.90,5.40）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.2623%;\"\u003e\n \u003cp\u003e5.10（4.80，5.40）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5528%;\"\u003e\n \u003cp\u003e5.10（4.80,5.40）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.42207%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.22846%;\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10.3582%;\"\u003e\n \u003cp\u003eNew-onset Hypertension, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.1026%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.94%;\"\u003e\n \u003cp\u003e720（40.2）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1336%;\"\u003e\n \u003cp\u003e191（34.7）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.2623%;\"\u003e\n \u003cp\u003e187（31.2）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5528%;\"\u003e\n \u003cp\u003e426（29.9）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.42207%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.22846%;\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eNote：\u003cem\u003eTTR\u003c/em\u003e time in target range, \u003cem\u003eDM\u003c/em\u003e diabetes mellitus, \u003cem\u003eBMI\u003c/em\u003e body mass index, \u003cem\u003eUA\u003c/em\u003e uric acid, \u003cem\u003eBUN\u003c/em\u003e blood urea nitrogen, \u003cem\u003eTC\u0026nbsp;\u003c/em\u003etotal cholesterol, \u003cem\u003eHDL\u003c/em\u003e high-density cholesterol, \u003cem\u003eLDL\u003c/em\u003e low-density cholesterol, \u003cem\u003eCRP\u003c/em\u003e C-reactive protein, \u003cem\u003eHbA1c\u003c/em\u003e glycated hemoglobin A1c.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 2. Association between BMI-TTR and incident hypertension: Cox proportional hazards models\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 10.7822%;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.945%;\"\u003e\n \u003cp\u003eCrude HR\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(95% Cl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3594%;\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.6279%;\"\u003e\n \u003cp\u003eAdjusted HR\u003csup\u003ea\u003c/sup\u003e (95% Cl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.6765%;\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.6279%;\"\u003e\n \u003cp\u003eAdjusted HR\u003csup\u003eb\u0026nbsp;\u003c/sup\u003e(95% Cl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.6765%;\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.6279%;\"\u003e\n \u003cp\u003eAdjusted HR\u003csup\u003ec\u0026nbsp;\u003c/sup\u003e(95% Cl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.6765%;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 10.7822%;\"\u003e\n \u003cp\u003eTTR1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.945%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3594%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.6279%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.6765%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.6279%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.6765%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.6279%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.6765%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 10.7822%;\"\u003e\n \u003cp\u003eTTR2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.945%;\"\u003e\n \u003cp\u003e0.839（0.716,0.985）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3594%;\"\u003e\n \u003cp\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.6279%;\"\u003e\n \u003cp\u003e0.882（0.750,1.037）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.6765%;\"\u003e\n \u003cp\u003e0.127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.6279%;\"\u003e\n \u003cp\u003e0.882（0.749,1.038）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.6765%;\"\u003e\n \u003cp\u003e0.130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.6279%;\"\u003e\n \u003cp\u003e0.892（0.758,1.051）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.6765%;\"\u003e\n \u003cp\u003e0.173\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 10.7822%;\"\u003e\n \u003cp\u003eTTR3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.945%;\"\u003e\n \u003cp\u003e0.749（0.638,0.880）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3594%;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.6279%;\"\u003e\n \u003cp\u003e0.759（0.664,0.894）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.6765%;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.6279%;\"\u003e\n \u003cp\u003e0.759（0.643,0.896）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.6765%;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.6279%;\"\u003e\n \u003cp\u003e0.768（0.650,0.907）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.6765%;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 10.7822%;\"\u003e\n \u003cp\u003eTTR4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.945%;\"\u003e\n \u003cp\u003e0.704（0.625,0.794）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3594%;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.6279%;\"\u003e\n \u003cp\u003e0.717（0.633,0.813）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.6765%;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.6279%;\"\u003e\n \u003cp\u003e0.718（0.631,0.816）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.6765%;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.6279%;\"\u003e\n \u003cp\u003e0.733（0.644,0.835）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.6765%;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote：\u003cem\u003eTTR\u003c/em\u003e time in target range, \u003cem\u003eHR\u0026nbsp;\u003c/em\u003ehazard ratio,\u0026nbsp;\u003cem\u003eCI,\u003c/em\u003e confidence interval\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ea\u003c/sup\u003e Adjucted for Age,gender,Marital status,education,Area,Ever smoking,Ever drinking,Diabetes mellitus、Dyslipidemia,Cancer or malignant,Chronic lung disease,Liver disease,Kidney disease,Stroke，Heart disease，Memory-related disease，Psychosomatic disease，uric acid (UA), blood urea nitrogen (BUN), glucose, creatinine (Crea), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), C-reactive protein (CRP), glycated hemoglobin (HbA1c)\u003c/p\u003e\n\u003cp\u003e\u003csup\u003eb\u003c/sup\u003e Adjucted for Age,gender,Marital status,education,Area,Ever smoking,Ever drinking,Diabetes mellitus、Dyslipidemia,Cancer or malignant,Chronic lung disease,Liver disease,Kidney disease,Stroke，Heart disease，Memory-related disease，Psychosomatic disease，uric acid (UA), blood urea nitrogen (BUN), glucose, creatinine (Crea), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), C-reactive protein (CRP), glycated hemoglobin (HbA1c) and BMI of wave 1.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ec\u0026nbsp;\u003c/sup\u003eAdjucted for Age,gender,Marital status,education,Area,Ever smoking,Ever drinking,Diabetes mellitus、Dyslipidemia,Cancer or malignant,Chronic lung disease,Liver disease,Kidney disease,Stroke，Heart disease，Memory-related disease，Psychosomatic disease，uric acid (UA), blood urea nitrogen (BUN), glucose, creatinine (Crea), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), C-reactive protein (CRP), glycated hemoglobin (HbA1c) and BMI of wave 1, 2, and 3.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 3. Association between BMI-TTR and incident hypertension risk: subgroup analysis based on Cox proportional hazards models.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdjusted HR\u003csup\u003ea\u003c/sup\u003e (95% Cl)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdjusted HR\u003csup\u003eb\u003c/sup\u003e (95% Cl)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdjusted HR\u003csup\u003ea\u003c/sup\u003e (95% Cl)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdjusted HR\u003csup\u003eb\u003c/sup\u003e (95% Cl)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMale (n=2,086)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u0026lt;60 (n=2,715)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003eTTR1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003eTTR2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 15px;\"\u003e\n \u003cp\u003e0.895(0.705-1.134)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.358\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e0.897 (0.703-1.143)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.379\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e0.716 (0.572-0.895)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e0.739 (0.584-0.936)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003eTTR3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 15px;\"\u003e\n \u003cp\u003e0.784 (0.623-0.988)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e0.787 (0.619-1.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e0.781 (0.627-0.972)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e0.815 (0.641-1.036)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e0.094\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003eTTR4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 15px;\"\u003e\n \u003cp\u003e0.745 (0.625-0.888)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e0.748 (0.616-0.907)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e0.629 (0.527-0.750)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e0.660 (0.536-0.813)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFemale (n=2,283)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003cstrong\u003e\u0026ge;\u003c/strong\u003e\u003cstrong\u003e60 (n=1,654)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003eTTR1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003eTTR2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 15px;\"\u003e\n \u003cp\u003e0.870 (0.696-1.088)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.222\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e0.868 (0.694-1.087)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.222\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e1.179 (0.928-1.497)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e0.177\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e1.174 (0.924-1.492)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e0.190\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003eTTR3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 15px;\"\u003e\n \u003cp\u003e0.742 (0.585-0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e0.739 (0.582-0.938)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e0.773 (0.603-0.993)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e0.044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e0.770 (0.599-0.989)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003eTTR4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 15px;\"\u003e\n \u003cp\u003e0.689 (0.573-0.829)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e0.689 (0.572-0.830)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e0.883 (0.710-1.025)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e0.090\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e0.848 (0.704-1.022)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e0.083\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e for interaction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 17px;\"\u003e\n \u003cp\u003e0.971\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 22px;\"\u003e\n \u003cp\u003e0.971\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: \u003cem\u003eHR\u0026nbsp;\u003c/em\u003ehazard ratio,\u0026nbsp;\u003cem\u003eCI,\u003c/em\u003e confidence interval\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ea\u003c/sup\u003e Adjucted for Age,gender,Marital status,education,Area,Ever smoking,Ever drinking,Diabetes mellitus、Dyslipidemia,Cancer or malignant,Chronic lung disease,Liver disease,Kidney disease,Stroke，Heart disease，Memory-related disease，Psychosomatic disease，uric acid (UA), blood urea nitrogen (BUN), glucose, creatinine (Crea), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), C-reactive protein (CRP), glycated hemoglobin (HbA1c)\u003c/p\u003e\n\u003cp\u003e\u003csup\u003eb\u003c/sup\u003e Adjucted for Age,gender,Marital status,education,Area,Ever smoking,Ever drinking,Diabetes mellitus、Dyslipidemia,Cancer or malignant,Chronic lung disease,Liver disease,Kidney disease,Stroke，Heart disease，Memory-related disease，Psychosomatic disease，uric acid (UA), blood urea nitrogen (BUN), glucose, creatinine (Crea), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), C-reactive protein (CRP), glycated hemoglobin (HbA1c) and BMI of wave 1.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Body mass index, Time in target range, Hypertension, Incidence, Middle-aged and elderly, CHARLS","lastPublishedDoi":"10.21203/rs.3.rs-8461092/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8461092/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThis study aimed to determine the association of the time spent in the target range of body mass index (BMI) with the onset of developing hypertension.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis study used the longitudinal data from the China Health and Retirement Longitudinal Study (CHARLS), which consists of 4,369 residents who met the inclusion and exclusion criteria. Body Mass Index Time in Target Range (BMI-TTR) was defined as the number of times an individual's body mass index (BMI) fell within the target range (18.5 kg/m\u0026sup2; \u0026le; BMI\u0026thinsp;\u0026lt;\u0026thinsp;23 kg/m\u0026sup2;) across three measurement waves (2011, 2013, and 2015). Based on these measurements, participants were systematically classified into four progressive categories: TTR1 (never in range) to TTR4 (always in range). Incident hypertension and time-to-event were identified during follow-up. The associations of BMI-TTR with hypertension were evaluated using multivariable Cox proportional hazards models, which used baseline BMI and other factors as confounding variables.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eIn comparison to the TTR1 group, the risk of developing hypertension in the TTR4 group was significantly lower (adjusted hazard ratio [HR]\u0026thinsp;=\u0026thinsp;0.717, 95% CI: 0.633\u0026ndash;0.813, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This inverse association persisted after additional adjustment for baseline BMI (HR\u0026thinsp;=\u0026thinsp;0.718, 95% CI: 0.631\u0026ndash;0.816, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). A significant inverse dose-response relationship was observed, with the incidence of hypertension decreasing progressively across higher BMI-TTR groups (\u003cem\u003eP\u003c/em\u003e for trend\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In women and in those aged 45\u0026ndash;60 years, the protective association was more pronounced in the subgroup analyses. Furthermore, individuals whose BMI improved from out-of-range at baseline to within the target range at later visits had a similar risk reduction to those who maintained normal weight throughout.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eSustaining a BMI within the desired range over the long term is linked to a lower risk of developing hypertension in adults aged 45 and older, regardless of their initial BMI. This protective effect is particularly evident in women and middle-aged individuals. Our results highlight the significance of ongoing weight management in the primary prevention of hypertension.\u003c/p\u003e","manuscriptTitle":"Body mass index time in target range and incident hypertension in middle-aged and older adults: a longitudinal study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-29 00:34:50","doi":"10.21203/rs.3.rs-8461092/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-01-23T07:45:51+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-01-01T08:47:32+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-30T01:52:47+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-30T01:52:21+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2025-12-27T12:33:53+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"87e0f144-c9b3-4091-a868-01b02b4f3878","owner":[],"postedDate":"January 29th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-01-29T00:34:50+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-29 00:34:50","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8461092","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8461092","identity":"rs-8461092","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}