Obesity and hypertension are two highly prevalent chronic diseases that pose significant health risks[1,2]. Both are known independent risk factors for cardiovascular disease and significantly increase global morbidity and mortality[3, 4]. In China,the coexistence of these two conditions—obesity combined with hypertension—has increased significantly in recent years. A 2013 survey revealed that the prevalence of hypertension among Chinese adults reached 27.8%[5], while the obesity rate gradually rose from 4% in 2002 to 11.3% in 2011[6,7]. Even more concerning is that the prevalence of hypertension among obese individuals is more than double that of non-obese individuals[8]. The combined burden of obesity and hypertension not only increases healthcare costs but also leads to poorer health outcomes, underscoring the urgent need for effective prevention and management strategies.

Physical activity has long been recognized as a crucial factor in mitigating the adverse effects of obesity and hypertension[9,10]. The various dimensions of physical activity including intensity, duration, frequency, and total volume are critical determinants of its effectiveness. Multiple studies indicate that regular physical activity helps reduce the risk of obesity-related hypertension by improving vascular function, reducing fat mass, and enhancing metabolic health[11,12]. However, the associations between specific dimensions of physical activity and these health outcomes remain under-explored, especially regarding their differential effects across subgroups like gender and age[13]. Furthermore, while some evidence supports the beneficial effects of physical activity, findings from other studies are inconsistent due to methodological issues including small sample sizes, lack of standardized assessment tools, and inadequate consideration of confounding variables.

Therefore, this study will build upon previous research by employing appropriate statistical adjustments, a large sample size, and clearer assessment of exposure and outcomes. It aims to address gaps in existing research by examining the relationship between different dimensions of physical activity and the risk of obesity combined with hypertension. Through this investigation, we hope to provide valuable insights for developing more targeted public health strategies to advance the prevention of obesity and hypertension.

Study Design and Participants

This cross-sectional study was done based on the China Health and Retirement Longitudinal Study (CHARLS) in 2015.The China Health and Retirement Longitudinal Study (CHARLS) is a national survey of adults aged 45 years and above, which includes household questionnaires, clinical measurements, and blood-based bioassays. The survey covers 450 urban communities and rural areas in 28 provinces, municipalities, and autonomous regions across China.It was approved by the Biomedical Ethics Review Committee of Peking University (approval number: IRB00001052-11015), and informed consent was required of all participants.

Among the 16,075 participants in 2015, participants (age ≥ 45 years) should have complete data on physical activity record, gender, place of residence, marital status, educational level, sleep status, smoking status, drinking status, obesity, hypertension, DM and dyslipidemia record. Participants younger than 45 years or with missing data for any of the above will be excluded (n = 877), participants would be excluded due to have missing date on baseline characteristics (n = 1963), and participants would be excluded because of missing information of PA (n = 6641). Finally, 6594 participants were included in this cross-sectional study.

Physical Activity

Physical activity was measured using a modified version of the International Physical Activity Questionnaire Short Form (IPAQ-SF) [14]. In the CHARLS survey, each participant was asked: “Do you usually do this type of activity for at least 10 minutes per week? ”specific types of physical activity included: (1) VPA: activities that cause shortness of breath, which may include carrying heavy loads, digging, hoeing, aerobic exercise, fast cycling, and riding a freight bicycle or motorcycle. (2) MPA: activities that make you breathe faster than normal, which may include carrying light loads, cycling at a normal pace, mopping floors, Tai Chi, and brisk walking. (3) LPA: such as walking from place to place at work or home, and walking for leisure, sport, exercise, or recreation. If participants answered "no", they were considered not to have engaged in that type of PA during the week. If participants answered "yes", they were further asked about the frequency and duration for each PA intensity. Based on previous research [15], PA frequency was categorized into 4 levels: 6-7 days/week (d/w), 3-5 d/w, 1-2 d/w, and no activity. We divided PA duration into five levels: ≥240 min/d, 120-239 min/d, 30-119 min/d, 10-29 min/d, and no activity. TPA was calculated using metabolic equivalent (MET) values, taking into account PA intensity. We assigned different MET values for LPA, MPA, and VPA: 3.3 METs, 4.0 METs, and 8.0 METs, respectively [14].

Assessment of Obesity-Related Hypertension

Based on the relevant definitions in the Chinese expert consensus on obesity-related hypertension management [16], we defined obesity-related hypertension as: (1) systolic blood pressure(SBP) ≥140 mmHg and/or diastolic blood pressure ≥90 mmHg, or a self-reported diagnosis; (2) body mass index (BMI) ≥ 28 kg/m² or waist circumference (WC) ≥ 90 cm (male) or 85 cm (female).

Covariates

Covariates in the present study were sex (male and female), age , place of residence (urban, rural), marital status (married and living with spouse, widowed, other), education level (primary school or below, junior high school or vocational school, high school, college or above), smoking status (current smoker, ex-smoker, never smoked), alcohol consumption frequency (≤1/month, >1/month, never drank), sleep duration (<7 h, 7-8 h, ≥8 h). Diabetes was defined as any of the following: (1) self-reported diagnosis of diabetes, (2) fasting blood glucose ≥ 126 mg/dl (7.0 mmol/L), and (3) glycated hemoglobin (HbA1c) ≥ 6.5% [17]. Dyslipidemia was defined as any of the following: (1) a self-reported diagnosis of dyslipidemia, (2) total cholesterol (TC) ≥ 240 mg/dl (6.2 mmol/L), (3) low-density lipoprotein cholesterol (LDL-C) ≥ 160 mg/dl (4.1 mmol/L), (4) high-density lipoprotein cholesterol (HDL-C) < 40 mg/dl (1.0 mmol/L), or (5) triglycerides (TG) ≥ 200 mg/dl (2.3 mmol/L) [18].

Statistical Analysis

Data processing and analysis were using R4.5.1, categorical variables were expressed as counts and percentages and were compared using chi-square test,the continuous data are expressed as mean ± SD and were analyzed with student’s t-test, and this article does not use weighting.Binary logistic regression analysis to explore the relationship between different dimensions of PA and the risk of obesity, hypertension and obesity-related hypertension, adjusting for potential confounding factors.The crude model was unadjusted, Model 1 was adjusted for demographic characteristics (including age, sex, marital status, education, place of residence); Model 2 was further adjusted for health-related behaviors (smoking status, alcohol consumption frequency, sleep duration, history of diabetes, history of dyslipidemia). Restricted cubic splines (RCS) were used to assess the dose-response relationship between the risk of obesity-related hypertension and PA, using the 25th, 50th, and 75th percentiles of the TPA as fixed knots.To test the robustness of the results, we performed sensitivity analyses using the imputed data. All tests were two-tailed, and a p-value < 0.05 was considered statistically significant.

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