Abstract
Aims: Evidence regarding the modification effects of age, sex, ethnicity, socioeconomic status, or weight status on the associations of sedentary behavior (SB) with cardiovascular diseases (CVDs) is limited. Moreover, the mechanisms for the associations also remain unclear. We aimed to investigate the possible influence of these factors on the associations of SB with CVD events and whether the associations are mediated by metabolic phenotypes.
Methods: This study included 42,619 participants aged 20-74 years, recruited from the Shanghai Suburban Adult Cohort and Biobank study. SB was assessed at baseline and integrated with health information systems to predict future CVD events. Cox proportional hazards models, interaction analyses, restricted cubic splines and causal mediation analyses were used for assessments.
Results: Compared to those with ＜3 h/d sedentary time, participants having SB ≥ 5 h/d had significantly higher risks of CVD (HR[95%CI]: 1.27[1.12-1.44]), coronary heart disease (CHD, 1.35[1.14-1.60]), and ischemic stroke (IS, 1.30[1.06-1.60]). The association of CHD was more pronounced in the retired individuals than their counterparts (1.45[1.20-1.76] versus 1.06[0.74-1.52], pinteraction=0.046). When SB was expressed as a continuous variable, a 1 h/d increment in SB was positively associated with risks of CVD (1.03[1.01-1.05]), CHD (1.04[1.01-1.07]), and IS (1.05[1.01-1.08]). High-density lipoprotein cholesterol (HDL-C, proportion mediated: 12.54%, 12.23%, and 11.36%, all p＜0.001), followed by triglyceride (TG, 5.28%, 4.77%, and 4.86%, all p＜0.01) and serum uric acid (SUA, 3.64%, 4.24%, and 2.29%, all p＜0.05) were major mediators through metabolic phenotypes.
Conclusions: Higher SB was associated with elevated risks of CVD events. The detrimental effect of SB on CHD risk was more pronounced among retired individuals. Moreover, HDL-C, TG and SUA partially mediated the relationships between SB and CVD events. Our findings may have implications for preventing and controlling CVD associated with SB.
ORCID iD: Yong Ling, 0000-0002-2628-4203; Maryam Zaid, 0009-0008-5478-2091
Introduction
Cardiovascular disease (CVD) stands as the leading cause of mortality worldwide, accounting for 32% of all deaths in 2019, posing as a major public health concern1). Developing countries bore the majority burden of CVD-related deaths, with over three-quarters occurring in these regions1, 2). In China, CVD prevalence increased by 14.7% between 1990 and 2016 3) and contributed to 41% of the total mortality from 1990 to 2013 4). Nevertheless, most CVDs can be prevented by addressing behavioral risk factors, such as tobacco use, unhealthy diet and obesity, physical inactivity, and harmful use of alcohol1).
In recent decades, there has been a growing interest in sedentary behavior (SB) as a modifiable behavioral risk factor for CVD5). Nearly one-third of adults in China and around the world are physically inactive6). SB is widespread and on the rise7), further amplified by the COVID-2019 pandemic8). Results from several meta-analyses suggested that SB was associated with an elevated risk of CVD incidence9-11). It remains unclear whether the relationships between SB and incident CVD events vary by age, sex, ethnicity, socioeconomic status, or weight status. This issue was highlighted as a major evidence gap by the 2018 U.S. Physical Activity Guidelines Advisory Committee Scientific Report (PAGAC), which will help determine how generalizable the potential benefits of reducing SB are in preventing CVD and whether specific recommendations are necessary for different population groups12). Moreover, most of the studies on SB and CVD were from high-income countries and the results cannot be generalized to China and other developing nations due to significant differences in the epidemiological characteristics of CVD13-15), creating a gap for evidence-based guideline recommendations for Chinese adults and similar populations. Prolonged SB was associated with increased risk of being overweight or obese, as well as metabolic abnormalities, such as hypertension, diabetes, and dyslipidemia16, 17), which may contribute to the development of CVD18). However, no empirical analysis has tested these mediating hypotheses.
Regarding the above-mentioned considerations, we aimed to investigate the modification effects of age, sex, socioeconomic status, and weight status on the associations of SB with incident CVD events in a general population of China and explore the possible mediating role of metabolic phenotypes in the associations.
Methods
Study Design and Population
The present study utilized data from the Shanghai Suburban Adult Cohort and Biobank study (SSACB), an ongoing large-scale natural cohort study aimed at identifying risk factors for chronic noncommunicable diseases among Chinese adults. Detailed methodology of the SSACB can be found in previous publications19). Recruitment was achieved using multistage cluster sampling. Briefly, four communities in Songjiang district and three communities in Jiading district were selected according to their population size and economic status. One-third of the neighborhoods or villages within each community were then randomly chosen as study sites for the SSACB. Eligible participants were residents aged 20 to 74 years who had been living in Shanghai for at least 5 years and expressed willingness to participate in the study. Between April 2016 and December 2017, a total of 46,441 individuals were recruited at baseline. After exclusion of those with incomplete or implausible data on SB, physical activity (PA), or histories of coronary heart disease (CHD) or stroke at baseline, 42,619 participants were included in our analysis (Fig.1). This study was approved by the Medical Research Ethics Committee of the School of Public Health, Fudan University (IRB approval number 2016-04-0586), and all participants provided written informed consent.
All participants completed a standardized questionnaire through face-to-face interviews. The collected data included demographic characteristics (age, sex, educational level, marital status, occupation, family income, etc.), personal and family history of chronic diseases (hypertension, diabetes, CHD, stroke, dyslipidemia, etc.), lifestyle factors (smoking, alcohol consumption, SB, PA, etc.), and dietary patterns. Anthropometric measurements, including height, weight, waist circumference, systolic blood pressure (SBP), diastolic blood pressure (DBP), and heart rate, were conducted with standardized methods by clinicians at the Community Health Service Centers. Standard protocols and techniques of the questionnaire interview and anthropometric measurements have been previously described20).
Laboratory Assays
Participants were instructed to fast for a minimum of 8 hours before blood sample collection in the morning. Serum lipids were tested by an automatic biochemical analyzer (Roche Cobas C501) with colorimetry method for triglyceride (TG) and with enzymatic colorimetry method for low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and total cholesterol (TC). Fasting plasma glucose (FPG) was examined with hexokinase method by a Roche Modular P800 automatic biochemical analyzer. Glycated hemoglobin (HbA1c) was analyzed with high-pressure liquid chromatography method using a Tosoh G8 automatic glycohemoglobin analyzer. Serum uric acid (SUA) was measured with colorimetry method, and serum homocysteine (Hcy) was assessed using performance rate method by a Roche Cobas C702 analyzer.
Assessment of SB
Information on SB was collected during the baseline face-to-face interviews with the question “How much time do you usually spend sitting or leaning each day?”. The response was recorded as hours per day. In our study, SB was divided into three categories:＜3, 3-＜5, and ≥ 5 h/d based on the tertile values, with the lowest tertile as the reference category.
Follow-Up and Ascertainment of Incident CVD Events
Follow-up was performed based on the linkage to health information systems including the Cardiovascular and Cerebrovascular Disease Registration and Reporting System (CCDRR), the Electronic Medical Record System (EMR), and the Cause-of-Death Surveillance System (CDSS). The CCDRR maintained records of CVD cases diagnosed by clinicians in different hospitals throughout Shanghai, including the disease name, subtype, and date of onset. The EMR recorded diagnosis details, such as disease name, International Classification of Diseases 10th Revision (ICD-10) codes, and date of diagnosis. CVD-related deaths were collected through the CDSS. CVD was defined as the first diagnosis of CHD (ICD-10, I20-I25: angina pectoris, acute myocardial infarction, subsequent myocardial infarction, certain current complications following acute myocardial infarction, other acute ischemic heart diseases, and chronic ischemic heart disease), stroke (ICD-10, I60-I64: subarachnoid hemorrhage, intracerebral hemorrhage, other nontraumatic intracranial hemorrhage, cerebral infarction, and stroke, not specified as hemorrhage or infarction), or death with a cardiovascular event. Our analysis investigated ischemic stroke (IS) only as an outcome, rather than the combined total of IS and hemorrhagic stroke, considering the potential differences in the biological mechanisms that associate SB with stroke across these subtypes. Furthermore, the limited number of hemorrhagic strokes was considered insufficient for conducting meaningful analyses as a separate endpoint.
Assessment of Covariates
The study participants provided self-reported information on age, sex, educational level, marital status, occupation, family income, current smoking status, current alcohol drinking status, and family history of CVD. PA was evaluated as the metabolic equivalents (METs) multiplied by the total minutes per week (METs-min/w) based on the International Physical Activity Questionnaire (IPAQ)21), and divided into tertiles (low:＜2,226, moderate: 2,226-＜4,746, and high: ≥ 4,746). Body mass index (BMI) was calculated by dividing the weight in kilograms by the square of height in meters and categorized into three groups: normal weight (＜24.0 kg/m2), overweight (24-＜28.0 kg/m2), and obese (≥ 28 kg/m2)22). Hypertension was defined as SBP ≥ 140 mmHg, DBP ≥ 90 mmHg, or previous diagnosis of hypertension23). Diabetes was defined as FPG ≥ 7.0 mmol/L, HbA1c ≥ 6.5%, or previous diagnosis of diabetes24). Dyslipidemia was defined as TC ≥ 6.20 mmol/L, HDL-C＜1.00 mmol/L, LDL-C ≥ 4.10 mmol/L, TG ≥ 2.30 mmol/L, or previous diagnosis of dyslipidemia25). Hyperuricemia was defined as SUA ≥ 420 µmol/L in males or ≥ 360 µmol/L in females26), and hyperhomocysteinemia was defined as serum Hcy ＞15 µmol/L27).
Statistical Analysis
The baseline characteristics of study participants were presented as median values with interquartile range (IQR) for continuous variables that were not normally distributed, and as frequency (percentage) for categorical variables, stratified by categories of SB. The Kruskal-Wallis rank test was used to compare continuous variables across SB categories, and the Chi-squared test was used for categorical variables.
Cox proportional hazards models were used to assess the hazard ratios (HRs) and 95% confidence intervals (CIs) for CVD events (CVD, CHD, and IS). To account for potential confounding factors, three adjustment models were applied. Model 1 was adjusted for age and sex. Model 2 was additionally adjusted for educational level, marital status, occupation, family history of CVD, current smoking status, and current alcohol drinking status. Model 3 was further adjusted for PA level, which serves as the main analysis model. We additionally adjusted for potential mediators (BMI, hypertension, diabetes, dyslipidemia, hyperuricemia, and hyperhomocysteinemia) in the sensitivity analyses18). Furthermore, we carried out another sensitivity analysis by excluding incident CVD events within the first six months of follow-up.
To confirm whether the variables of interest (age, sex, socioeconomic status (educational level, family income, and retirement), BMI group, PA level, medical history (hypertension, diabetes, dyslipidemia, hyperuricemia, and hyperhomocysteinemia), and family history of CVD) interact with SB category in the associations between SB and CVD events, we performed interaction tests by including a cross-product term into Model 3. Subgroup analyses were then conducted to assess the modification effects of those variables.
In order to evaluate the continuous dose-response associations of SB with CVD events, restricted cubic spline (RCS) analyses were performed to identify possible nonlinear associations. Four knots were used in the RCS curves: the 5th, 35th, 65th, and 95th percentiles of the sedentary time, with the 35th knot served as the reference. Likelihood ratio tests were adopted to examine nonlinearity by comparing models with only the linear term to models that incorporated both linear and cubic spline terms. If no nonlinear associations were identified, linear models were used.
Causal mediation analyses were used to assess the extent to which the associations of SB with CVD events mediated by metabolic phenotypes (e.g. obesity, hypertension, diabetes, dyslipidemia, hyperuricemia and hyperhomocysteinemia)28). The total effect (TE) was divided into two parts measured as HR: the natural direct effect (NDE) and the natural indirect effect (NIE). The NDE represented the direct effect of SB on CVD events, while the NIE represented the effect of SB on CVD events via metabolic phenotypes. The mediation effect was measured by proportion mediated, computed as NIE/TE*100% on a log-transformed HR scale, which is the percentage of the TE that the mediator mediates (Fig.2).
Statistical analyses were performed using SAS version 9.4 and R version 4.2.1. All tests were two-tailed, and statistical significance was defined as p＜0.05.
Results
Baseline Characteristics and the Incidence of CVD Events
The baseline characteristics of 42,619 participants across SB categories are shown in Table 1. Of all the participants, 40.4% were males, and the median (IQR) age was 58 (50-64) years. Participants with higher SB were younger, more likely to be men, officers, and current smokers, having higher educational levels and higher prevalence of hyperuricemia and hyperhomocysteinemia, and they were less likely to have a family history of CVD. During a median (IQR) follow-up of 4.8 (4.6-5.4) years, there were 1,423 incident CVD events (794 coronary heart diseases (CHDs), 569 ischemic strokes (ISs), and 114 other strokes).
Table 1.Baseline characteristics of participants according to categories of sedentary behavior
| Characteristics |
Total |
Sedentary behavior |
p value
|
|
Tertile 1
＜3 h/d
|
Tertile 2
3-＜5 h/d
|
Tertile 3
≥ 5 h/d
|
| N |
42,619 |
13,834 |
13,218 |
15,567 |
|
| Age (year) |
58 (50-64) |
59 (52-65) |
59 (52-64) |
55 (46-63) |
＜0.001 |
| Male (%) |
17,205 (40.4) |
5,103 (36.9) |
5,085 (38.5) |
7,017 (45.1) |
＜0.001 |
| Educational levels (%) |
|
|
|
|
＜0.001 |
| Primary school or below |
17,596 (41.3) |
6,508 (47.0) |
5,830 (44.1) |
5,258 (33.8) |
|
| Junior high school |
16,530 (38.8) |
5,238 (37.9) |
5,285 (40.0) |
6,007 (38.6) |
|
| Senior high school |
4,318 (10.1) |
1,297 (9.4) |
1,255 (9.5) |
1,766 (11.3) |
|
| College or above |
4,175 (9.8) |
791 (5.7) |
848 (6.4) |
2,536 (16.3) |
|
| Marital status (%) |
|
|
|
|
＜0.001 |
| Married |
39,611 (92.9) |
12,873 (93.1) |
12,327 (93.3) |
14,411 (92.6) |
|
| Unmarried |
582 (1.4) |
106 (0.8) |
106 (0.8) |
370 (2.4) |
|
| Divorced or other |
2,426 (5.7) |
855 (6.2) |
785 (5.9) |
786 (5.0) |
|
| Occupation (%) |
|
|
|
|
＜0.001 |
| Worker |
4,682 (11.0) |
1,382 (10.0) |
1,352 (10.2) |
1,948 (12.5) |
|
| Farmer |
1,133 (2.7) |
400 (2.9) |
393 (3.0) |
340 (2.2) |
|
| Officer |
5,836 (13.7) |
1,433 (10.4) |
1,431 (10.8) |
2,972 (19.1) |
|
| Professional |
1,840 (4.3) |
513 (3.7) |
453 (3.4) |
874 (5.6) |
|
| Retired |
24,560 (57.6) |
8,736 (63.1) |
8,354 (63.2) |
7,470 (48.0) |
|
| Other |
4,568 (10.7) |
1,370 (9.9) |
1,235 (9.3) |
1,963 (12.6) |
|
| Family income (%)§
|
|
|
|
|
0.004 |
| ≤ 50k CNY/year |
7,253 (32.6) |
2,483 (33.4) |
2,348 (33.2) |
2,422 (31.3) |
|
| 51k-100k CNY/year |
8,013 (36.0) |
2,714 (36.5) |
2,526 (35.7) |
2,773 (35.9) |
|
| ≥ 101k CNY/year |
6,984 (31.4) |
2,248 (30.2) |
2,196 (31.1) |
2,540 (32.8) |
|
| Current smoking (%) |
8,909 (20.9) |
2,619 (18.9) |
2,698 (20.4) |
3,592 (23.1) |
＜0.001 |
| Current alcohol drinking (%) |
5,297 (12.4) |
1,684 (12.2) |
1,668 (12.6) |
1,945 (12.5) |
0.513 |
| Physical activity level (%) |
|
|
|
|
＜0.001 |
| Low |
14,191 (33.3) |
4,091 (29.6) |
3,528 (26.7) |
6,572 (42.2) |
|
| Moderate |
13,948 (32.7) |
4,377 (31.6) |
4,369 (33.1) |
5,202 (33.4) |
|
| High |
14,480 (34.0) |
5,366 (38.8) |
5,321 (40.3) |
3,793 (24.4) |
|
| BMI (%) |
|
|
|
|
0.007 |
| Normal (＜24 kg/m2)
|
20,411 (49.5) |
6,713 (50.2) |
6,163 (48.2) |
7,535 (49.9) |
|
| Overweight (24-＜28 kg/m2)
|
15,741 (38.1) |
5,052 (37.8) |
4,976 (38.9) |
5,713 (37.8) |
|
| Obese (≥ 28 kg/m2)
|
5,116 (12.4) |
1,606 (12.0) |
1,657 (12.9) |
1,853 (12.3) |
|
| History of chronic diseases (%) |
|
|
|
|
|
| Hypertension |
21,228 (49.8) |
7,089 (51.2) |
7,000 (53.0) |
7,139 (45.9) |
＜0.001 |
| Diabetes |
5,907 (13.9) |
1,915 (13.8) |
2,000 (15.1) |
1,992 (12.8) |
＜0.001 |
| Dyslipidemia |
13,962 (32.8) |
4,192 (30.3) |
4,487 (33.9) |
5,283 (33.9) |
＜0.001 |
| Hyperuricemia |
5,294 (12.4) |
1,610 (11.6) |
1,654 (12.5) |
2,030 (13.0) |
0.001 |
| Hyperhomocysteinemia |
9,620 (22.6) |
2,741 (19.8) |
2,857 (21.6) |
4,022 (25.8) |
＜0.001 |
| Family history of cardiovascular disease (%) |
5,857 (13.7) |
1,986 (14.4) |
1,814 (13.7) |
2,057 (13.2) |
0.018 |
Abbreviation: BMI, body mass index; CNY, Chinese Yuan.
Median (interquartile range) was displayed for continuous variables and frequency (percentage) for categorical variables. Kruskal–Wallis rank test was used for continuous variables and Chi-squared test was used for categorical variables. §Percent was calculated based on participants with available family income data (n = 22,250).
As shown in Table 2, in Model 3, compared to those with SB＜3 h/d, individuals with SB ≥ 5 h/d had significantly higher risks of CVD (HR [95% CI]: 1.27 [1.12-1.44]), CHD (1.35 [1.14-1.60]), and IS (1.30 [1.06-1.60]). Similar results were observed in Model 1 and Model 2. After further adjustment for potential mediators, the associations remained significant, although there were some attenuations in the strengths of the associations for CVD events (Supplemental Table 1). Statistically positive linear associations of SB (per 1 h/d) with CVD events were observed. The results did not appreciably change after excluding incident CVD events within the first six months of follow-up (Supplemental Table 2).
Table 2.Hazard ratios (95% CIs) of cardiovascular disease events by categories of sedentary behavior
| Outcomes |
Sedentary behavior |
| Per 1 h/d |
Tertile 1
＜3 h/d
|
Tertile 2
3-＜5 h/d
|
Tertile 3
≥ 5 h/d
|
| CVD |
| Cases |
1,423 |
443 |
449 |
531 |
| Person-years |
206,675 |
66,830 |
63,936 |
75,910 |
| Model 1 |
1.03 (1.01-1.05)＊
|
1.00 (Reference) |
1.06 (0.93-1.20) |
1.24 (1.09-1.40)＊＊
|
| Model 2 |
1.03 (1.01-1.05)＊＊
|
1.00 (Reference) |
1.06 (0.93-1.21) |
1.26 (1.11-1.43)＊＊＊
|
| Model 3 |
1.03 (1.01-1.05)＊＊
|
1.00 (Reference) |
1.06 (0.93-1.21) |
1.27 (1.12-1.44)＊＊＊
|
| CHD |
| Cases |
794 |
241 |
249 |
304 |
| Person-years |
208,159 |
67,325 |
64,419 |
76,416 |
| Model 1 |
1.03 (1.00-1.06)＊
|
1.00 (Reference) |
1.08 (0.90-1.29) |
1.32 (1.11-1.56)＊＊
|
| Model 2 |
1.03 (1.01-1.06)＊
|
1.00 (Reference) |
1.09 (0.91-1.30) |
1.34 (1.13-1.59)＊＊＊
|
| Model 3 |
1.04 (1.01-1.07)＊
|
1.00 (Reference) |
1.09 (0.91-1.30) |
1.35 (1.14-1.60)＊＊＊
|
| IS |
| Cases |
569 |
170 |
184 |
215 |
| Person-years |
209,957 |
67,816 |
64,927 |
77,215 |
| Model 1 |
1.05 (1.01-1.08)＊＊
|
1.00 (Reference) |
1.12 (0.91-1.39) |
1.28 (1.05-1.57)＊
|
| Model 2 |
1.05 (1.02-1.08)＊＊
|
1.00 (Reference) |
1.13 (0.91-1.39) |
1.31 (1.07-1.60)＊＊
|
| Model 3 |
1.05 (1.01-1.08)＊＊
|
1.00 (Reference) |
1.13 (0.92-1.40) |
1.30 (1.06-1.60)＊
|
Abbreviation: CHD, coronary heart disease; CI, confidence interval; CVD, cardiovascular disease; IS, ischemic stroke.
Model 1 was adjusted for age and sex. Model 2 was further adjusted for educational level, marital status, occupation, family history of CVD, current smoking status and current alcohol drinking status. Model 3 was further adjusted for physical activity level. ＊p＜0.05, ＊＊p＜0.01, ＊＊＊p＜0.001.
Supplemental Table 1.Hazard ratios (95% CIs) of cardiovascular disease events by categories of sedentary behavior after further adjustment for potential mediators
| Outcomes |
Sedentary behavior |
| Per 1 h/d |
Tertile 1
＜3 h/d
|
Tertile 2
3-＜5 h/d
|
Tertile 3
≥ 5 h/d
|
| CVD |
1.02 (1.00-1.05)＊
|
1.00 (Reference) |
1.02 (0.90-1.17) |
1.19 (1.04-1.35)＊＊
|
| CHD |
1.03 (1.00-1.06) |
1.00 (Reference) |
1.04 (0.87-1.24) |
1.25 (1.05-1.49)＊
|
| IS |
1.04 (1.00-1.07)＊
|
1.00 (Reference) |
1.09 (0.88-1.35) |
1.24 (1.01-1.53)＊
|
Abbreviation: BMI, body mass index; CHD, coronary heart disease; CI, confidence interval; CVD, cardiovascular disease; IS, ischemic stroke. Model was adjusted for age, sex, educational level, marital status, occupation, family history of CVD, current smoking status, current alcohol drinking status, physical activity level, BMI and histories of chronic diseases, including hypertension, diabetes, dyslipidemia, hyperuricemia and hyperhomocysteinemia. ＊p＜0.05, ＊＊p＜0.01, ＊＊＊p＜0.001.
Supplemental Table 2.Hazard ratios (95% CIs) of cardiovascular disease events by categories of sedentary behavior after excluding those developing cardiovascular disease within the first six months of follow-up
| Outcomes |
Sedentary behavior |
| Per 1 h/d |
Tertile 1
＜3 h/d
|
Tertile 2
3-＜5 h/d
|
Tertile 3
≥ 5 h/d
|
| CVD |
| Cases |
1,307 |
402 |
417 |
488 |
| Person-years |
206,638 |
66,816 |
63,927 |
75,894 |
| Model 1 |
1.03 (1.01-1.05)＊
|
1.00 (Reference) |
1.08 (0.94-1.24) |
1.25 (1.09-1.43)＊＊
|
| Model 2 |
1.03 (1.01-1.06)＊＊
|
1.00 (Reference) |
1.09 (0.95-1.25) |
1.28 (1.12-1.46)＊＊＊
|
| Model 3 |
1.03 (1.01-1.06)＊＊
|
1.00 (Reference) |
1.09 (0.95-1.25) |
1.29 (1.13-1.47)＊＊＊
|
| CHD |
| Cases |
745 |
226 |
236 |
283 |
| Person-years |
208,138 |
67,318 |
64,414 |
76,407 |
| Model 1 |
1.03 (1.00-1.06) |
1.00 (Reference) |
1.09 (0.91-1.31) |
1.30 (1.09-1.55)＊＊
|
| Model 2 |
1.03 (1.00-1.06)＊
|
1.00 (Reference) |
1.10 (0.92-1.32) |
1.33 (1.12-1.59)＊＊
|
| Model 3 |
1.03 (1.00-1.06)＊
|
1.00 (Reference) |
1.10 (0.92-1.32) |
1.35 (1.13-1.61)＊＊
|
| IS |
| Cases |
517 |
151 |
170 |
196 |
| Person-years |
209,944 |
67,811 |
64,924 |
77,209 |
| Model 1 |
1.05 (1.01-1.08)＊＊
|
1.00 (Reference) |
1.17 (0.94-1.45) |
1.31 (1.06-1.62)＊
|
| Model 2 |
1.05 (1.02-1.09)＊＊
|
1.00 (Reference) |
1.17 (0.94-1.46) |
1.34 (1.08-1.66)＊＊
|
| Model 3 |
1.05 (1.02-1.09)＊＊
|
1.00 (Reference) |
1.18 (0.95-1.47) |
1.34 (1.08-1.66)＊＊
|
Abbreviation: CHD, coronary heart disease; CI, confidence interval; CVD, cardiovascular disease; IS, ischemic stroke.
Model 1 was adjusted for age and sex. Model 2 was further adjusted for educational level, marital status, occupation, family history of CVD, current smoking status and current alcohol drinking status. Model 3 was further adjusted for physical activity level. ＊p＜0.05, ＊＊p＜0.01, ＊＊＊p＜0.001.
In our subgroup analyses (Table 3), we found no effect modifications on CVD and IS resulting from age, sex, BMI, educational level, family income, or retirement (all pinteraction＞0.05); whereas the association between SB and CHD was stronger in the retired group as compared to the counterpart (1.45 [1.20-1.76] versus 1.06 [0.74-1.52], pinteraction=0.046). Within specific subgroups, including those aged ≥ 60 years, males, individuals with normal weight, those having an educational level of junior high school or below, and those with a family income ≤ 100k Chinese Yuan (CNY)/year, as well as retired individuals, SB ≥ 5 h/d was significantly associated with an increased risk of CVD. Similarly, SB ≥ 5 h/d was significantly associated with a higher risk of CHD within the subgroups of those aged ≥ 60 years, both males and females, individuals with normal weight, those having an educational level of primary school or below, those with a family income ranging from ≥ 51k to ≤ 100k CNY/year, and retired individuals. Furthermore, subgroups at increased risk of IS with SB ≥ 5 h/d included those aged ≥ 60 years, males, individuals with normal weight, those with a junior or senior high school education, those with a family income ≤ 50k CNY/year, and non-retired individuals. No significant modifications were observed for CVD, CHD, and IS concerning the PA level, medical history including hypertension, diabetes, dyslipidemia, hyperuricemia, and hyperhomocysteinemia, as well as family history of CVD (Supplemental Table 3).
Table 3.Hazard ratios (95% CIs) of cardiovascular disease events by categories of sedentary behavior across various subgroups
| Outcome |
Subgroups |
Cases/total |
Sedentary behavior (h/d) |
p for interaction
|
| Per 1 h/d |
Tertile 1
＜3 h/d
|
Tertile 2
3-＜5 h/d
|
Tertile 3
≥ 5 h/d
|
| CVD |
Age |
|
|
|
|
|
0.526 |
| Less than 60 |
413/24,598 |
1.02 (0.98-1.06) |
1.00 (Reference) |
1.02 (0.80-1.31) |
1.13 (0.89-1.43) |
|
| 60 or above |
1,010/18,021 |
1.04 (1.01-1.07)＊＊
|
1.00 (Reference) |
1.08 (0.92-1.26) |
1.32 (1.14-1.54)＊＊＊
|
|
| Sex |
|
|
|
|
|
0.104 |
| Male |
742/17,205 |
1.04 (1.01-1.07)＊＊
|
1.00 (Reference) |
1.17 (0.96-1.42) |
1.44 (1.20-1.72)＊＊＊
|
|
| Female |
681/25,414 |
1.02 (0.98-1.05) |
1.00 (Reference) |
0.98 (0.82-1.18) |
1.11 (0.92-1.33) |
|
| BMI |
|
|
|
|
|
0.975 |
| Normal (＜24 kg/m2)
|
513/20,411 |
1.04 (1.00-1.08)＊
|
1.00 (Reference) |
1.03 (0.83-1.27) |
1.29 (1.04-1.59)＊
|
|
| Overweight (24-＜28 kg/m2)
|
637/15,741 |
1.02 (0.99-1.05) |
1.00 (Reference) |
1.05 (0.86-1.28) |
1.18 (0.98-1.43) |
|
| Obese (≥ 28 kg/m2)
|
224/5,116 |
1.04 (0.98-1.09) |
1.00 (Reference) |
1.08 (0.77-1.51) |
1.19 (0.86-1.64) |
|
| Educational level |
|
|
|
|
|
0.396 |
| Primary school or below |
863/17,596 |
1.04 (1.01-1.07)＊＊
|
1.00 (Reference) |
1.07 (0.91-1.26) |
1.28 (1.09-1.50)＊＊
|
|
| Junior high school |
436/16,530 |
1.03 (0.99-1.07) |
1.00 (Reference) |
1.13 (0.88-1.44) |
1.31 (1.04-1.66)＊
|
|
| Senior high school |
98/4,318 |
1.03 (0.95-1.11) |
1.00 (Reference) |
0.85 (0.49-1.47) |
1.38 (0.85-2.21) |
|
| College or above |
26/4,175 |
0.82 (0.69-0.98)＊
|
1.00 (Reference) |
0.80 (0.31-2.09) |
0.47 (0.18-1.20) |
|
| Family income |
|
|
|
|
|
0.545 |
| ≤ 50k CNY/year |
360/7,253 |
1.03 (0.99-1.08) |
1.00 (Reference) |
1.21 (0.93-1.57) |
1.39 (1.08-1.80)＊
|
|
| 51k-100k CNY/year |
265/8,013 |
1.05 (1.00-1.10) |
1.00 (Reference) |
1.06 (0.78-1.45) |
1.36 (1.02-1.82)＊
|
|
| ≥ 101k CNY/year |
172/6,984 |
0.98 (0.91-1.04) |
1.00 (Reference) |
1.11 (0.77-1.59) |
1.02 (0.70-1.49) |
|
| Retirement |
|
|
|
|
|
0.277 |
| Yes |
314/24,560 |
1.03 (1.01-1.06)＊
|
1.00 (Reference) |
1.04 (0.89-1.20) |
1.30 (1.12-1.50)＊＊＊
|
|
| No |
1,109/18,059 |
1.03 (0.99-1.08) |
1.00 (Reference) |
1.17 (0.88-1.56) |
1.17 (0.89-1.55) |
|
| CHD |
Age |
|
|
|
|
|
0.215 |
| Less than 60 |
244/24,598 |
1.01 (0.96-1.06) |
1.00 (Reference) |
0.95 (0.69-1.31) |
1.07 (0.79-1.46) |
|
| 60 or above |
550/18,021 |
1.05 (1.01-1.08)＊
|
1.00 (Reference) |
1.15 (0.93-1.43) |
1.49 (1.21-1.83)＊＊＊
|
|
| Sex |
|
|
|
|
|
0.454 |
| Male |
393/17,205 |
1.03 (0.99-1.07) |
1.00 (Reference) |
1.23 (0.95-1.60) |
1.45 (1.13-1.86)＊＊
|
|
| Female |
401/25,414 |
1.05 (1.00-1.09)＊
|
1.00 (Reference) |
0.98 (0.77-1.25) |
1.28 (1.01-1.63)＊
|
|
| BMI |
|
|
|
|
|
0.983 |
| Normal (＜24 kg/m2)
|
274/20,411 |
1.04 (0.99-1.10) |
1.00 (Reference) |
1.07 (0.79-1.44) |
1.35 (1.01-1.81)＊
|
|
| Overweight (24-＜28 kg/m2)
|
364/15,741 |
1.02 (0.98-1.07) |
1.00 (Reference) |
1.07 (0.83-1.39) |
1.27 (0.99-1.64) |
|
| Obese (≥ 28 kg/m2)
|
127/5,116 |
1.03 (0.96-1.11) |
1.00 (Reference) |
0.98 (0.63-1.53) |
1.19 (0.78-1.82) |
|
| Educational level |
|
|
|
|
|
0.168 |
| Primary school or below |
495/17,596 |
1.05 (1.02-1.09)＊＊
|
1.00 (Reference) |
1.19 (0.95-1.49) |
1.52 (1.22-1.89)＊＊＊
|
|
| Junior high school |
229/16,530 |
1.02 (0.97-1.08) |
1.00 (Reference) |
1.01 (0.72-1.40) |
1.21 (0.88-1.67) |
|
| Senior high school |
54/4,318 |
1.00 (0.90-1.11) |
1.00 (Reference) |
0.70 (0.34-1.45) |
1.05 (0.56-1.95) |
|
| College or above |
16/4,175 |
0.78 (0.61-1.00)＊
|
1.00 (Reference) |
0.84 (0.27-2.64) |
0.34 (0.09-1.24) |
|
| Family income |
|
|
|
|
|
0.149 |
| ≤ 50k CNY/year |
202/7,253 |
1.00 (0.95-1.07) |
1.00 (Reference) |
1.13 (0.80-1.60) |
1.26 (0.90-1.78) |
|
| 51k-100k CNY/year |
158/8,013 |
1.07 (1.01-1.14)＊
|
1.00 (Reference) |
1.20 (0.78-1.83) |
1.85 (1.25-2.73)＊＊
|
|
| ≥ 101k CNY/year |
100/6,984 |
0.97 (0.89-1.06) |
1.00 (Reference) |
0.97 (0.61-1.56) |
0.88 (0.54-1.44) |
|
| Retirement |
|
|
|
|
|
0.046 |
| Yes |
188/24,560 |
1.05 (1.01-1.08)＊＊
|
1.00 (Reference) |
1.05 (0.86-1.29) |
1.45 (1.20-1.76)＊＊＊
|
|
| No |
606/18,059 |
1.01 (0.95-1.06) |
1.00 (Reference) |
1.19 (0.83-1.71) |
1.06 (0.74-1.52) |
|
| IS |
Age |
|
|
|
|
|
0.932 |
| Less than 60 |
147/24,598 |
1.05 (0.99-1.11) |
1.00 (Reference) |
1.20 (0.78-1.83) |
1.40 (0.94-2.10) |
|
| 60 or above |
422/18,021 |
1.05 (1.01-1.09)＊
|
1.00 (Reference) |
1.12 (0.88-1.42) |
1.27 (1.01-1.61)＊
|
|
| Sex |
|
|
|
|
|
0.228 |
| Male |
319/17,205 |
1.07 (1.03-1.12)＊＊＊
|
1.00 (Reference) |
1.24 (0.92-1.67) |
1.52 (1.15-2.00)＊＊
|
|
| Female |
250/25,414 |
1.00 (0.94-1.06) |
1.00 (Reference) |
1.04 (0.77-1.40) |
1.07 (0.78-1.46) |
|
| BMI |
|
|
|
|
|
0.729 |
| Normal (＜24 kg/m2)
|
221/20,411 |
1.06 (1.00-1.11)＊
|
1.00 (Reference) |
1.24 (0.89-1.73) |
1.39 (1.00-1.94)＊
|
|
| Overweight (24-＜28 kg/m2)
|
244/15,741 |
1.02 (0.97-1.08) |
1.00 (Reference) |
0.96 (0.70-1.33) |
1.14 (0.84-1.55) |
|
| Obese (≥ 28 kg/m2)
|
86/5,116 |
1.08 (0.99-1.17) |
1.00 (Reference) |
1.40 (0.80-2.45) |
1.47 (0.85-2.53) |
|
| Educational level |
|
|
|
|
|
0.593 |
| Primary school or below |
336/17,596 |
1.05 (1.01-1.10)＊
|
1.00 (Reference) |
1.05 (0.81-1.37) |
1.18 (0.91-1.54) |
|
| Junior high school |
191/16,530 |
1.05 (1.00-1.11) |
1.00 (Reference) |
1.29 (0.88-1.88) |
1.48 (1.03-2.13)＊
|
|
| Senior high school |
34/4,318 |
1.09 (0.96-1.23) |
1.00 (Reference) |
1.72 (0.62-4.76) |
2.69 (1.05-6.86)＊
|
|
| College or above |
8/4,175 |
0.72 (0.52-1.00) |
1.00 (Reference) |
0.63 (0.10-3.81) |
0.36 (0.07-1.81) |
|
| Family income |
|
|
|
|
|
0.641 |
| ≤ 50k CNY/year |
147/7,253 |
1.08 (1.01-1.15)＊
|
1.00 (Reference) |
1.33 (0.88-2.02) |
1.54 (1.02-2.31)＊
|
|
| 51k-100k CNY/year |
106/8,013 |
1.02 (0.94-1.10) |
1.00 (Reference) |
0.93 (0.58-1.49) |
1.02 (0.64-1.61) |
|
| ≥ 101k CNY/year |
64/6,984 |
1.00 (0.90-1.11) |
1.00 (Reference) |
1.35 (0.73-2.49) |
1.25 (0.67-2.36) |
|
| Retirement |
|
|
|
|
|
0.441 |
| Yes |
108/24,560 |
1.03 (0.99-1.07) |
1.00 (Reference) |
1.09 (0.87-1.37) |
1.23 (0.98-1.54) |
|
| No |
461/18,059 |
1.11 (1.04-1.18)＊＊
|
1.00 (Reference) |
1.39 (0.82-2.36) |
1.71 (1.05-2.80)＊
|
|
Abbreviation: BMI, body mass index; CHD, coronary heart disease; CI, confidence interval; CNY, Chinese Yuan; CVD, cardiovascular disease; IS, ischemic stroke.
Model was adjusted for age, sex, educational level, marital status, occupation, family history of CVD, current smoking status, current alcohol drinking status, and physical activity level. ＊p＜0.05, ＊＊p＜0.01, ＊＊＊p＜0.001.
Supplemental Table 3.Hazard ratios (95% CIs) of cardiovascular disease events by categories of sedentary behavior across various subgroups
| Outcome |
Subgroups |
Cases/total |
Sedentary behavior (h/d) |
p for interaction
|
| Per 1 h/d |
Tertile 1
＜3 h/d
|
Tertile 2
3-＜5 h/d
|
Tertile 3
≥ 5 h/d
|
| CVD |
Physical activity level |
|
|
|
|
|
0.092 |
| Low |
528/14,191 |
1.06 (1.03-1.10)＊＊＊
|
1.00 (Reference) |
1.37 (1.06-1.78)＊
|
1.59 (1.26-2.01)＊＊＊
|
|
| Moderate |
445/13,948 |
1.00 (0.95-1.04) |
1.00 (Reference) |
0.86 (0.68-1.09) |
1.04 (0.83-1.29) |
|
| High |
450/14,480 |
1.02 (0.98-1.06) |
1.00 (Reference) |
1.07 (0.87-1.30) |
1.25 (1.01-1.55)＊
|
|
| Hypertension |
|
|
|
|
|
0.268 |
| Yes |
408/21,228 |
1.02 (1.00-1.05) |
1.00 (Reference) |
1.01 (0.86-1.18) |
1.17 (1.01-1.36)＊
|
|
| No |
1,105/21,391 |
1.05 (1.01-1.09)＊
|
1.00 (Reference) |
1.18 (0.91-1.51) |
1.51 (1.19-1.93)＊＊＊
|
|
| Diabetes |
|
|
|
|
|
0.815 |
| Yes |
1,019/5,907 |
1.02 (0.98-1.07) |
1.00 (Reference) |
1.07 (0.84-1.37) |
1.16 (0.91-1.48) |
|
| No |
404/36,712 |
1.03 (1.01-1.06)＊
|
1.00 (Reference) |
1.04 (0.89-1.21) |
1.29 (1.11-1.50)＊＊＊
|
|
| Dyslipidemia |
|
|
|
|
|
0.990 |
| Yes |
813/13,962 |
1.02 (0.98-1.05) |
1.00 (Reference) |
1.05 (0.85-1.29) |
1.19 (0.98-1.45) |
|
| No |
610/28,657 |
1.04 (1.01-1.07)＊
|
1.00 (Reference) |
1.05 (0.89-1.25) |
1.28 (1.08-1.51)＊＊
|
|
| Hyperuricemia |
|
|
|
|
|
0.626 |
| Yes |
1,176/5,294 |
1.01 (0.96-1.07) |
1.00 (Reference) |
0.93 (0.67-1.28) |
1.15 (0.85-1.55) |
|
| No |
247/37,325 |
1.03 (1.01-1.06)＊＊
|
1.00 (Reference) |
1.09 (0.94-1.26) |
1.29 (1.12-1.48)＊＊＊
|
|
| Hyperhomocysteinemia |
|
|
|
|
|
0.921 |
| Yes |
999/9,620 |
1.04 (1.01-1.08)＊
|
1.00 (Reference) |
1.10 (0.85-1.43) |
1.31 (1.04-1.67)＊
|
|
| No |
424/32,999 |
1.02 (1.00-1.05) |
1.00 (Reference) |
1.05 (0.90-1.22) |
1.23 (1.06-1.44)＊＊
|
|
| Family history of cardiovascular disease |
|
|
|
|
|
0.977 |
| Yes |
1,216/5,857 |
1.00 (0.94-1.05) |
1.00 (Reference) |
1.10 (0.78-1.54) |
1.26 (0.90-1.77) |
|
| No |
207/36,762 |
1.04 (1.01-1.06)＊＊
|
1.00 (Reference) |
1.06 (0.92-1.22) |
1.27 (1.10-1.45)＊＊＊
|
|
| CHD |
Physical activity level |
|
|
|
|
|
0.245 |
| Low |
300/14,191 |
1.06 (1.01-1.11)＊＊
|
1.00 (Reference) |
1.47 (1.03-2.12)＊
|
1.83 (1.32-2.54)＊＊＊
|
|
| Moderate |
236/13,948 |
1.01 (0.96-1.07) |
1.00 (Reference) |
0.88 (0.64-1.20) |
1.11 (0.83-1.49) |
|
| High |
258/14,480 |
1.02 (0.97-1.08) |
1.00 (Reference) |
1.08 (0.83-1.41) |
1.27 (0.96-1.69) |
|
| Hypertension |
|
|
|
|
|
0.318 |
| Yes |
230/21,228 |
1.03 (1.00-1.07) |
1.00 (Reference) |
0.99 (0.81-1.22) |
1.26 (1.03-1.54)＊
|
|
| No |
564/21,391 |
1.05 (0.99-1.10) |
1.00 (Reference) |
1.31 (0.94-1.83) |
1.60 (1.15-2.21)＊＊
|
|
| Diabetes |
|
|
|
|
|
0.662 |
| Yes |
584/5,907 |
1.04 (0.98-1.09) |
1.00 (Reference) |
1.09 (0.78-1.53) |
1.16 (0.83-1.63) |
|
| No |
210/36,712 |
1.03 (1.00-1.07) |
1.00 (Reference) |
1.07 (0.87-1.31) |
1.41 (1.15-1.71)＊＊＊
|
|
| Dyslipidemia |
|
|
|
|
|
0.932 |
| Yes |
455/13,962 |
1.03 (0.99-1.08) |
1.00 (Reference) |
1.05 (0.80-1.39) |
1.31 (1.01-1.71)＊
|
|
| No |
339/28,657 |
1.03 (0.99-1.07) |
1.00 (Reference) |
1.09 (0.87-1.38) |
1.33 (1.06-1.67)＊
|
|
| Hyperuricemia |
|
|
|
|
|
0.643 |
| Yes |
655/5,294 |
1.03 (0.96-1.10) |
1.00 (Reference) |
0.92 (0.60-1.43) |
1.22 (0.82-1.83) |
|
| No |
139/37,325 |
1.03 (1.00-1.07)＊
|
1.00 (Reference) |
1.12 (0.93-1.36) |
1.37 (1.13-1.65)＊＊
|
|
| Hyperhomocysteinemia |
|
|
|
|
|
0.694 |
| Yes |
562/9,620 |
1.07 (1.02-1.19)＊＊
|
1.00 (Reference) |
1.20 (0.84-1.71) |
1.55 (1.12-2.15)＊＊
|
|
| No |
232/32,999 |
1.02 (0.98-1.05) |
1.00 (Reference) |
1.05 (0.86-1.29) |
1.26 (1.03-1.55)＊
|
|
| Family history of cardiovascular disease |
|
|
|
|
|
0.718 |
| Yes |
683/5,857 |
0.94 (0.87-1.03) |
1.00 (Reference) |
0.97 (0.61-1.53) |
1.09 (0.69-1.71) |
|
| No |
111/36,762 |
1.05 (1.02-1.08)＊＊
|
1.00 (Reference) |
1.11 (0.92-1.35) |
1.40 (1.16-1.68)＊＊＊
|
|
| IS |
Physical activity level |
|
|
|
|
|
0.492 |
| Low |
199/14,191 |
1.09 (1.04-1.14)＊＊＊
|
1.00 (Reference) |
1.37 (0.93-2.01) |
1.51 (1.06-2.15)＊
|
|
| Moderate |
195/13,948 |
0.98 (0.91-1.04) |
1.00 (Reference) |
0.92 (0.63-1.33) |
0.99 (0.69-1.42) |
|
| High |
175/14,480 |
1.04 (0.97-1.11) |
1.00 (Reference) |
1.17 (0.84-1.64) |
1.45 (1.02-2.06)＊
|
|
| Hypertension |
|
|
|
|
|
0.675 |
| Yes |
156/21,228 |
1.04 (1.00-1.08) |
1.00 (Reference) |
1.07 (0.84-1.37) |
1.21 (0.96-1.54) |
|
| No |
413/21,391 |
1.07 (1.01-1.14)＊
|
1.00 (Reference) |
1.27 (0.84-1.90) |
1.54 (1.04-2.29)＊
|
|
| Diabetes |
|
|
|
|
|
0.696 |
| Yes |
371/5,907 |
1.04 (0.98-1.10) |
1.00 (Reference) |
1.04 (0.72-1.49) |
1.29 (0.91-1.82) |
|
| No |
198/36,712 |
1.05 (1.01-1.09)＊
|
1.00 (Reference) |
1.16 (0.90-1.49) |
1.28 (0.99-1.65) |
|
| Dyslipidemia |
|
|
|
|
|
0.690 |
| Yes |
309/13,962 |
1.01 (0.96-1.06) |
1.00 (Reference) |
1.14 (0.84-1.56) |
1.14 (0.84-1.55) |
|
| No |
260/28,657 |
1.07 (1.03-1.12)＊＊
|
1.00 (Reference) |
1.09 (0.82-1.44) |
1.38 (1.05-1.81)＊
|
|
| Hyperuricemia |
|
|
|
|
|
0.679 |
| Yes |
467/5,294 |
1.01 (0.93-1.09) |
1.00 (Reference) |
1.04 (0.64-1.70) |
1.09 (0.68-1.76) |
|
| No |
102/37,325 |
1.05 (1.02-1.09)＊＊
|
1.00 (Reference) |
1.15 (0.91-1.45) |
1.35 (1.08-1.69)＊＊
|
|
| Hyperhomocysteinemia |
|
|
|
|
|
0.586 |
| Yes |
395/9,620 |
1.04 (0.98-1.10) |
1.00 (Reference) |
1.22 (0.82-1.80) |
1.22 (0.84-1.77) |
|
| No |
174/32,999 |
1.05 (1.01-1.10)＊
|
1.00 (Reference) |
1.10 (0.86-1.41) |
1.34 (1.05-1.71)＊
|
|
| Family history of cardiovascular disease |
|
|
|
|
|
0.248 |
| Yes |
479/5,857 |
1.04 (0.97-1.13) |
1.00 (Reference) |
1.69 (0.99-2.91) |
1.78 (1.04-3.05)＊
|
|
| No |
90/36,762 |
1.05 (1.01-1.09)＊
|
1.00 (Reference) |
1.05 (0.84-1.32) |
1.23 (0.99-1.53) |
|
Model was adjusted for age, sex, educational level, marital status, occupation, family history of CVD, current smoking status, current alcohol drinking status, and physical activity level. ＊p＜0.05, ＊＊p＜0.01, ＊＊＊p＜0.001. CHD indicates coronary heart disease; CI, confidence interval; CVD, cardiovascular disease; and IS, ischemic stroke.
Fig.3 presents the dose-response associations with risks of CVD, CHD, and IS when SB was expressed continuously. An increase of 1 h/d in SB was significantly associated with higher risks of CVD (1.03 [1.01-1.05], plinearity=0.005, pnon-linearity=0.222), CHD (1.04 [1.01-1.07], plinearity=0.020, pnon-linearity=0.053), and IS (1.05 [1.01-1.08], plinearity=0.006, pnon-linearity=0.941) in linear dose-response manners.
Mediation Analyses
In the mediation analyses (Table 4), dyslipidemia accounted for 13.37%, 12.08%, and 12.65% of the associations of sedentary time with CVD, CHD, and IS, respectively (all p＜0.001). The proportions mediated through hyperuricemia were 2.20%, 2.19%, and 1.65% for the associations of sedentary time with CVD (p=0.039), CHD (p=0.059), and IS (p=0.088), respectively. When further assessing the mediation effects of metabolic markers of dyslipidemia and hyperuricemia on associations of sedentary time with CVD events, we found that HDL-C contributed to the largest mediated effects (Table 5), accounting for 12.54%, 12.23%, and 11.36% for CVD, CHD, and IS, respectively (all p＜0.001). This was followed by TG, which mediated 5.28% (p＜0.001), 4.77% (p=0.002), and 4.86% (p＜0.001), respectively. Then SUA mediated 3.64% (p=0.003), 4.24% (p=0.004), and 2.29% (p=0.031), respectively. LDL-C explained 1.81% and 2.03% of the relationships of SB with CVD (p=0.044) and IS (p=0.046), respectively. HDL-C followed by TG and SUA were identified as possible major mediators between SB and CVD events through metabolic phenotypes.
Table 4.Mediation analyses for metabolic phenotypes as possible mediators of the associations of sedentary time with cardiovascular disease events
| Outcome |
Mediator |
Hazard ratio (95% CI) |
Proportion mediated by mediator, % |
| Natural direct effect |
Natural indirect effect |
Total effect |
| CVD |
Obesity |
1.58 (1.11-2.24)＊
|
1.00 (1.00-1.01) |
1.58 (1.11-2.25)＊
|
0.88 |
| Hypertension |
1.62 (1.15-2.29)＊＊
|
1.00 (0.98-1.01) |
1.61 (1.14-2.28)＊＊
|
-1.29 |
| Diabetes |
1.61 (1.14-2.27)＊＊
|
1.01 (1.00-1.03) |
1.64 (1.16-2.31)＊＊
|
1.34 |
| Dyslipidemia |
1.53 (1.09-2.16)＊
|
1.05 (1.03-1.07)＊＊＊
|
1.62 (1.15-2.28)＊＊
|
13.37 |
| Hyperuricemia |
1.60 (1.14-2.26)＊＊
|
1.01 (1.00-1.02)＊
|
1.62 (1.15-2.28)＊＊
|
2.20 |
| Hyperhomocysteinemia |
1.60 (1.13-2.25)＊＊
|
1.01 (1.00-1.03) |
1.62 (1.15-2.28)＊＊
|
3.79 |
| CHD |
Obesity |
1.66 (1.04-2.67)＊
|
1.00 (1.00-1.01) |
1.67 (1.04-2.68)＊
|
0.84 |
| Hypertension |
1.71 (1.08-2.71)＊
|
1.00 (0.98-1.01) |
1.70 (1.07-2.70)＊
|
-1.14 |
| Diabetes |
1.70 (1.07-2.69)＊
|
1.01 (1.00-1.03) |
1.72 (1.09-2.73)＊
|
2.82 |
| Dyslipidemia |
1.62 (1.02-2.57)＊
|
1.05 (1.03-1.08)＊＊＊
|
1.71 (1.08-2.70)＊
|
12.08 |
| Hyperuricemia |
1.69 (1.07-2.68)＊
|
1.01 (1.00-1.02) |
1.71 (1.08-2.70)＊
|
2.19 |
| Hyperhomocysteinemia |
1.69 (1.06-2.68)＊
|
1.01 (0.99-1.04) |
1.71 (1.08-2.71)＊
|
2.96 |
| IS |
Obesity |
2.03 (1.18-3.48)＊
|
1.00 (1.00-1.01) |
2.03 (1.18-3.49)＊＊
|
0.50 |
| Hypertension |
2.07 (1.22-3.51)＊＊
|
0.99 (0.98-1.01) |
2.06 (1.22-3.50)＊＊
|
-1.02 |
| Diabetes |
2.04 (1.20-3.45)＊＊
|
1.02 (0.99-1.05) |
2.08 (1.23-3.53)＊＊
|
4.33 |
| Dyslipidemia |
1.92 (1.13-3.26)＊
|
1.07 (1.04-1.10)＊＊＊
|
2.05 (1.21-3.48)＊＊
|
12.65 |
| Hyperuricemia |
2.04 (1.20-3.46)＊＊
|
1.01 (1.00-1.02) |
2.06 (1.22-3.49)＊＊
|
1.65 |
| Hyperhomocysteinemia |
2.04 (1.20-3.46)＊＊
|
1.01 (0.99-1.04) |
2.06 (1.22-3.50)＊＊
|
2.47 |
Abbreviation: CHD, coronary heart disease; CI, confidence interval; CVD, cardiovascular disease; IS, ischemic stroke.
Model was adjusted for age, sex, educational level, marital status, occupation, family history of CVD, current smoking status, current alcohol drinking status, and physical activity level. ＊p＜0.05, ＊＊p＜0.01, ＊＊＊p＜0.001.
Table 5.Mediation analyses for metabolic markers as possible mediators of the association of sedentary time with cardiovascular disease events
| Outcome |
Mediator |
Hazard ratio (95% CI) |
Proportion mediated by mediator, % |
| Natural direct effect |
Natural indirect effect |
Total effect |
| CVD |
TC |
1.66 (1.18-2.34)＊＊ |
0.99 (0.99-1.00) |
1.65 (1.17-2.33)＊＊ |
-1.63 |
| HDL-C |
1.54 (1.09-2.18)＊ |
1.05 (1.03-1.07)＊＊＊ |
1.62 (1.14-2.28)＊＊ |
12.54 |
| LDL-C |
1.65 (1.17-2.33)＊＊ |
0.99 (0.99-1.00)＊ |
1.64 (1.16-2.31)＊＊ |
-1.81 |
| TG |
1.60 (1.14-2.27)＊＊ |
1.02 (1.01-1.03)＊＊＊ |
1.64 (1.16-2.31)＊＊ |
5.28 |
| SUA |
1.60 (1.13-2.26)＊＊ |
1.01 (1.00-1.02)＊＊ |
1.62 (1.15-2.29)＊＊ |
3.64 |
| CHD |
TC |
1.75 (1.10-2.77)＊ |
1.00 (0.99-1.00) |
1.74 (1.10-2.76)＊ |
-0.70 |
| HDL-C |
1.62 (1.02-2.57)＊ |
1.05 (1.03-1.08)＊＊＊ |
1.71 (1.07-2.71)＊ |
12.23 |
| LDL-C |
1.73 (1.09-2.75)＊ |
1.00 (0.99-1.00) |
1.73 (1.09-2.74)＊ |
-0.82 |
| TG |
1.69 (1.07-2.69)＊ |
1.02 (1.01-1.03)＊＊ |
1.73 (1.09-2.74)＊ |
4.77 |
| SUA |
1.68 (1.06-2.67)＊ |
1.02 (1.01-1.03)＊＊ |
1.71 (1.08-2.71)＊ |
4.24 |
| IS |
TC |
2.14 (1.27-3.64)＊＊ |
0.99 (0.98-1.00) |
2.12 (1.25-3.60)＊＊ |
-1.90 |
| HDL-C |
1.95 (1.15-3.32)＊ |
1.06 (1.03-1.09)＊＊＊ |
2.08 (1.22-3.53)＊＊ |
11.36 |
| LDL-C |
2.13 (1.25-3.61)＊＊ |
0.99 (0.98-1.00)＊ |
2.11 (1.24-3.57)＊＊ |
-2.03 |
| TG |
2.05 (1.21-3.48)＊＊ |
1.03 (1.01-1.04)＊＊＊ |
2.11 (1.24-3.57)＊＊ |
4.86 |
| SUA |
2.07 (1.22-3.52)＊＊ |
1.01 (1.00-1.02)＊ |
2.10 (1.24-3.56)＊＊ |
2.29 |
Abbreviation: CHD, coronary heart disease; CI, confidence interval; CVD, cardiovascular disease; HDL-C, high-density lipoprotein cholesterol; IS, ischemic stroke; LDL-C, low-density lipoprotein cholesterol; SUA, Serum uric acid; TC, total cholesterol; TG, triglyceride.
Model was adjusted for age, sex, educational level, marital status, occupation, family history of CVD, current smoking status, current alcohol drinking status, and physical activity level. ＊p＜0.05, ＊＊p＜0.01, ＊＊＊p＜0.001.
Discussion
In this large prospective cohort study of 42,619 Chinese adults with a median follow-up of 4.8 years, we found that prolonged SB was associated with higher risks of developing CVD events. The association maintained after adjustment for PA and potential mediators. With each additional hour of SB per day, participants had 3%, 4%, and 5% increased risks of CVD, CHD, and IS, respectively. The deleterious relationship between SB and CHD was more pronounced in retired individuals than their counterparts. Furthermore, the associations between SB and CVD events were partially mediated by dyslipidemia and hyperuricemia, with the mediation effects largely related to HDL-C, TG, and SUA. Our findings provided significant evidence for further understanding of the relationships between SB and CVD events.
SB and CVD Events
Several previous studies have also demonstrated dose-response associations between SB and CVD events. A meta-analysis involving nine prospective cohort studies from western populations, which included 720,425 participants (mean age: 54.4 years) and median follow-up of 11 years, identified a nonlinear association between self-reported SB and incident CVD, with an increased risk observed among those with SB ＞10 h/d (1.08 [1.00-1.14])9). Another pooled analysis of seven prospective cohort studies consisting of 677,614 individuals with a median follow-up of 12.2 years suggested a nonlinear association between SB and risk of stroke, and every additional hour increased stroke risk by 6% (1.06[1.01-1.11]) when SB increased to 6.5 h/d11). In a cohort study of 7,607 US adults aged ≥ 45 years from the Reasons for Geographic and Racial Differences in Stroke (REGARDS) with a mean 7.4-year follow-up, Hooker et al.29) revealed that objectively measured SB was associated with a significantly higher risk of incident stroke in a linear dose-response manner (1.14[1.02-1.28] per 1 h/d increase in SB). Furthermore, results from the UK biobank study that included 360,047 participants during a follow-up time ＞10 years showed a linear dose-response association of self-reported SB with ischemic heart disease, while no significant association was observed for SB and stroke30). In contrast to other studies9, 11, 30), our study revealed linear relationships between SB and the risks of developing CVD and IS. One possible explanation for the different results obtained from the meta-analysis11) and UK biobank30) was the disparity in the mechanisms underlying the development of ischemic and hemorrhagic stroke. Most IS are caused by embolism, either from atherosclerotic plaque in the aortic arch or in the cervical arteries, or from the heart31), while hemorrhagic strokes are mainly due to deep perforating vasculopathy or arteriolosclerosis, which is related to high blood pressure32). The divergent findings across studies may also be explained by variations in SB measurement methods (e.g., self-reported vs. objective measured), heterogeneity in ethnicities (e.g., Chinese vs. Western populations) and populations (e.g., broad age range vs. middle-aged to older adults), and differences in follow-up duration.
Relationships of SB with CVD Events across Subgroups
We found no statistical meaningful differences for CVD, CHD and IS across age, sex, weight status, PA level, medication history (hypertension, diabetes, dyslipidemia, hyperuricemia and hyperhomocysteinemia), or family history of CVD (no evidence of interactions). Recently, some studies have also investigated potential effect modifiers on the associations between SB and CVD events. The Whitehall II accelerometer sub-study, an occupational cohort study which consisted of 3,319 older adults (26.7% women, mean age: 68.9 years) over a mean follow-up of 6.2 years, demonstrated no evidence of effect modifications by age (pinteraction=0.83), sex (pinteraction=0.46), or BMI (pinteraction=0.14) on the relationship between SB and incident CVD33). Similarly, Peter-Marske et al.34) reported that among 16,031 postmenopausal women aged ≥ 62 years with a mean 7.1-year follow-up, the associations of objective-measured SB with incident CVD, myocardial infarction, and IS were not modified by age and obesity. Another cohort study involving 7,607 US adults aged ≥ 45 years from the REGARDS, with a mean follow-up period of 7.4 years, found that the associations of sedentary time tertiles with incident stroke did not vary by age, sex, or BMI category29). However, it is important to note that these studies either have focused on older individuals (aged ＞60 years), occupational group or postmenopausal women, or had relatively small sample sizes.
In the Aerobics Center Longitudinal Study of 7,744 men (20-89 years) with 21 years of follow-up, Warren et al.35) indicated no interaction effects of hypertension (pinteraction=0.62) with riding in a car on CVD mortality. Correspondingly, the result from REGARDS36) including 22,257 participants with a mean follow-up of 7.1 years elucidated that no significant interaction between TV viewing and diabetes on risk of incident stroke. Despite that, the association between TV viewing and stroke incidence was attenuated by socioeconomic factors (employment status, education and income) (≥ 4 h/d vs ＜2 h/d, 1.37 [1.10-1.71] to 1.21 [0.96-1.53]), with a significant interaction observed among those who were unemployed36). It is possible that individuals who are unemployed may be retired older adults who tend to spend more time sitting than their younger counterparts37). Alternatively, they may have engaged in more prolonged bouts of TV viewing without breaks, which has previously been linked with poorer postprandial blood glucose control38). Our study, however, suggested a greater risk of developing CHD, rather than IS, among retired individuals. In our current study, retired participants (mean age: 62.1 years) were found to be older than those who were not retired (47.2 years), and individuals ≥ 60 years old in the highest SB tertile appeared to have a higher risk of CHD (1.49 [1.21-1.83]) than those under 60 years old (1.07 [0.79-1.46]), although our interaction test was not statistically significant (pinteraction=0.215). Nevertheless, the retired participants had higher PA (median: 3,920 METs-min/w vs 2,520 METs-min/w, primarily due to more time spent on housework), a lower SB duration (median: 3 h/d vs 4 h/d, potentially attributable to less occupational SB) and a higher percentage of females (65.7% vs 51.3%). The particular mechanisms underlying the disparities remain unknown. Additionally, these two studies evaluated the relationship of TV viewing or riding in a car, which are less informative measures of overall SB, with stroke or CVD mortality. Meanwhile, Warren et al.’s35) study only included men. Our study examined the modifying effects of these factors on associations of total SB with CVD events (CVD, CHD, and IS) in a large sample size of a general population with a broad age-range (20-74 years), which is more generalizable than other studies. To date, there have been no other studies that have investigated potential modification effects of dyslipidemia, hyperuricemia, hyperhomocysteinemia, or family history of CVD on the relationships between SB and CVD events. Further research is required to better understand possible differences in these subgroups, which will help develop more specific sedentary guidelines for different population groups.
Potential Mechanisms
Our study employed mediation analyses to explore potential mechanisms, and found that dyslipidemia (percent mediation: 12.08%-13.37%) and hyperuricemia (percent mediation: 1.65%-2.20%) mediated the associations between SB and CVD events. Specifically, as biomarkers of dyslipidemia, HDL-C mediated 11.36%-12.54% and TG mediated 4.77%-5.28% of the associations. Dyslipidemia predisposes individuals to the accelerated progression of atherosclerosis and CVD39). While there is limited scientific evidence suggesting an association between SB and TC or LDL-C in healthy populations40), studies have identified positive associations of SB with HDL-C and TG among asymptomatic groups40-42), which are largely unrelated to PA41, 42). Our mediation analyses extended the findings to a general population demonstrating that HDL-C and TG largely mediated the associations of SB with CVD events, while LDL-C mediated the associations for CVD and IS with a small proportion. Moreover, we identified the mediation effects of SUA on CVD events. A previous cross-sectional study by Park et al.43), which included 161,064 healthy participants aged ≥ 18 years, found that individuals with SB ≥ 10 h/d were more likely to have prevalent hyperuricemia (OR [95% CI]: 1.08 [1.03-1.12]) compared to those with SB＜5 h/d. However, evidence regarding the association between SB and hyperuricemia remains limited, and the relationship of SUA with CVD in general populations is unclear44). More studies are warranted to replicate our results and investigate the biological mechanisms linking SB with CVD events.
Strengths and Limitations
The strengths of our study included a large sample size of a general population with a broad age-range (which includes young adults) and a longitudinal study design. We examined the modification effects of age, sex, socioeconomic status, weight status, and other factors on the associations between SB and specific CVD events, which is crucial and essential for more targeted sedentary guidelines that cater to diverse populations. Furthermore, the current study explored, for the first time, the mediation effects of metabolic phenotypes (e.g. obesity, hypertension, diabetes, dyslipidemia, hyperuricemia and hyperhomocysteinemia) on the relationships of SB with CVD events using a causal mediation framework, thereby integrating prior evidence into comprehensive pathways that can be used to guide clinical practice.
Nevertheless, there were some limitations in our study, such as the reliance on self-reported questionnaires to evaluate SB rather than objective measures. The measurement error associated with self-report in assessment of sedentary time could attenuate the strength of the observed risk estimates. Our duration of SB was comparable to other large-scale Chinese studies that utilized questionnaires45-47), while lower than the accelerometer data48). Subjective assessments might underestimate daily SB by a range of 2 to 3.5 hours49); however, there is currently no universally recognized gold standard for measuring SB. Accelerometers potentially misclassify motionless standing as SB50). Therefore, it is recommended to employ both self-report and accelerometer-based measures to comprehensively assess SB51). Additionally, the data on SB was collected at the baseline of the study, therefore any subsequent changes were not accounted for.
Conclusions
Our study demonstrated significant associations between prolonged SB and elevated risks of CVD events among Chinese adults. The deleterious relationship between SB and CHD was more pronounced in retired individuals. Furthermore, dyslipidemia, primarily mediated through HDL-C and TG, as well as hyperuricemia, mediated via SUA, were mediators of the associations between SB and CVD events. These findings may have implications for the prevention and control of CVD resulting from prolonged SB. Further studies are necessary to confirm and expand upon our findings.
Acknowledgements
We would like to thank all the participants in the study, the investigators responsible for the enrollment of study subjects, and all the members participating in the Shanghai Suburban Adult Cohort and Biobank study.
Notice of Grant Support
This study was funded by the Shanghai Key Disciplines of Public Health (2023-2025) for New Three-year Action Plan (Grant No. GWVI-11.1-23), the Fudan School of Public Health-Jiading CDC key disciplines for the high-quality development of public health (Grant No. GWGZLXK-2023-02), and the Local High Level Discipline Construction Project of Shanghai. The funder played no role in the design and conduct of the study, nor at any stage of the manuscript writing.
Conflicts of Interest
The authors declare that they have no conflict of interests.
Author Contributions
Y.L. and M.Z. conceived and designed the study. Y.L., Y.M., and J.P. conducted the data cleaning. Y.L. contributed to the analysis of all data, interpretation of study results, and drafting the manuscript. All authors revised the manuscript, gave final approval, and agreed to be accountable for all aspects of work ensuring integrity and accuracy.
IRB Information
The study protocol was approved by the Ethics Committee of the Fudan University, School of Public Health (IRB#2016-04-0586), and complied with the principles of the Declaration of Helsinki. Written informed consent was obtained from all participants before data collection.
Data Availability
The datasets generated and analyzed during the current study are not publicly available due to the data of the SSACB cohort not being open access, but are available from the corresponding author on reasonable request.
Footnotes
Abbreviations
BMI: Body mass index; CCDRR: Cardiovascular and Cerebrovascular Disease Registration and Reporting System; CHD: Coronary heart disease; CI: Confidence interval; CNY: Chinese Yuan; CVD: Cardiovascular disease; DBP: Diastolic blood pressure; EMR: Electronic Medical Record System; FPG: Fasting plasma glucose; HbA1c: Glycated hemoglobin; Hcy: Serum homocysteine; HDL-C: High density lipoprotein cholesterol; HR: Hazard ratio; ICD-10: International Classification of Diseases 10th Revision; IPAQ: International Physical Activity Questionnaire; IQR: Interquartile range; IS: Ischemic stroke; LDL-C: Low-density lipoproteins cholesterol; MET: Metabolic equivalent; NDE: Natural direct effect; NIE: Natural indirect effect; PA: Physical activity; PAGAC: Physical Activity Guidelines Advisory Committee Scientific Report; RCS: Restricted cubic spline; SB: Sedentary behavior; SBP: Systolic blood pressure; SSACB: Shanghai Suburban Adult Cohort and Biobank; SUA: Serum uric acid; TC: Total cholesterol; TE: total effect; TG: Triglyceride
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