Association of overweight and obesity with cardiovascular disease morbidity and adverse outcomes in older adults: a retrospective cohort study

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The aim of this study was to examine the association between overweight, obesity and CVD morbidity, mortality, and all-cause mortality in Chinese older individuals. Methods: This retrospective cohort study analyzed data from electronic health examination records of 86,049 older individuals aged ≥ 60 years in Xinzheng City, Henan Province, China, from January 2011 to December 2019. Cox proportional risk regression models and competing risk models were utilized to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for CVD morbidity and mortality, as well as all-cause mortality, in overweight and obese individuals. Restricted cubic splines were employed to evaluate dose-response associations. Results: During a median follow-up of 5.96 years, 35,731 older individuals were diagnosed with CVD. The total number of participant deaths was 17,029, with 7,605 deaths from CVD. The morbidity of CVD was higher in the overweight and obese groups compared to the normal BMI group, with HRs of 1.06(95%CI, 1.02-1.10) and 1.23(95%CI, 1.16-1.30), respectively. Competing risk models controlling for fatal events showed an increased morbidity of CVD in the overweight and obese groups, with HRs of 1.15(95%CI, 1.11-1.18) and 1.31(95%CI, 1.26-1.37), respectively. In contrast, the overweight group had a reduced risk of all-cause mortality and CVD mortality compared to the normal BMI group, with HRs of 0.91(95%CI, 0.88-0.94) and 0.89(95%CI, 0.82-0.97), respectively. The study found that the risk of all-cause mortality was lower in the obese group, with HRs of 0.89(95%CI, 0.82-0.97). Participants had the lowest risk of all-cause mortality and CVD mortality when their BMI was between 26 and 28 kg/m². The restricted cubic spline plots showed a J-shaped association between BMI and CVD morbidity and an inverse J-shaped association with CVD mortality and all-cause mortality. Conclusion: Overweight and obesity are positively correlated with the morbidity of CVD and negatively correlated with all-cause mortality in Chinese older individuals. However, it cannot be assumed that there is a negative correlation between obesity and CVD mortality. Therefore, obese individuals should aim to reduce weight appropriately, and overweight individuals should take appropriate measures to prevent obesity. overweight obesity cardiovascular disease morbidity cardiovascular disease mortality all-cause mortality Figures Figure 1 Figure 2 Introduction There has been a significant increase in the prevalence of overweight and obesity (BMI > 25 kg/m 2 ) worldwide in recent years. By 2035, more than 4 billion people, or 50% of the total population, are expected to be affected [ 1 ]. Higher BMI has been associated with a wide range of chronic diseases, including hypertension, diabetes mellitus, and CVD. The burden of CVD continues to increase annually, with approximately 330 million CVD patients worldwide [ 2 – 4 ]. In terms of mortality, two out of five deaths are attributable to CVD [ 5 ]. Several major studies and meta-analyses have found strong associations between participants’ overweight and obesity and CVD morbidity, mortality, or all-cause mortality [ 6 – 8 , 4 , 9 ]. Conflicting evidence exists regarding the association between long-term changes in BMI and the occurrence of CVD and adverse outcomes. Cohort studies and meta-analyses conducted in Western countries have found a J-shaped association between increasing BMI and the risk of all-cause mortality and CVD mortality [ 3 , 10 ]. However, a previous meta-analysis of 97 BMI and mortality found that overweight and obesity were associated with lower mortality [ 11 ]. In addition, more and more studies have shown a U-shaped association between overweight, obesity, and all-cause mortality [ 6 ], giving rise to the concept of the obesity paradox [ 12 , 13 ]. At the same time, some researchers have considered that the inclusion of current or former smokers as study participants may have distorted the results in the direction of a U-shaped association [ 14 ]. According to the ‘Healthy China 2030’ plan, the average life expectancy of the Chinese population is expected to increase from 76.3 years in 2015 to 79 years in 2030. Early detection and prevention of poor health in the elderly population is crucial to extending life expectancy and improving the quality of life in old age. We investigated the association of overweight and obesity with CVD and adverse outcomes in older individuals by designing and conducting a large cohort study. At the same time, we will search for the optimal range of BMI in this population that minimizes the risk of CVD and all-cause mortality in older individuals. Additionally, we excluded smokers and former smokers from the cohort, assuming that smoking is a strong confounder, and repeated the primary outcome analysis after comparing the results with those of previous studies [ 3 , 10 , 9 ]. Results 2.1 Baseline characteristics of study participants Table 1 presents the baseline characteristics of all participants. Of the 85,861 participants, 40,713 (47.4%) were male and 45,148 (52.6%) were female. The study participants had a mean age of 67.87 ± 7.44 years, and the median follow-up time was 5.96 years (IQR: 3.19–9.53). Of the participants, 43,428 (50.6%) had a normal weight at baseline, 1,913 (2.2%) were underweight, 30,719 (35.8%) were overweight, and 9,801 (11.4%) were obese. The higher BMI group showed a higher prevalence of hypertension and type 2 diabetes, as well as higher systolic, diastolic, serum total cholesterol (TC), and triglyceride (TG) levels. 2.2 Association between BMI and CVD morbidity During a median follow-up of 5.96 years, 35,731 older individuals were diagnosed with CVD (prevalence 115.29/1,000 person-years), 32.673 were diagnosed with CHD (prevalence 108.44/1,000 person-years), and 3,939 were diagnosed with stroke (prevalence 10.49/1,000 person-years). Table 2 shows the relationship between BMI and the morbidity of CVD and their subtypes. Model 2, after adjustment for other covariates such as age, sex, marital status, smoking, alcohol consumption, physical activity, hypertension, and diabetes, the HRs for CVD morbidity was 1.06(95%CI, 0.99–1.14) for underweight participants, 1.01(95%CI, 0.99–1.04) for overweight participants, and 1.11(95%CI, 1.07–1.14) for obese participants. Similar effects on the morbidity of CHD and stroke were seen in the different BMI subgroups. The HRs for CHD morbidity in overweight and obese participants was 1.10 (95%CI, 0.98–1.03) and 1.11 (95%CI, 1.07–1.15), respectively. The HRs for stroke morbidity in overweight and obese participants was 1.14 (95%CI, 1.07–1.23) and 1.10 (95% CI, 0.99–1.22), respectively. For continuous BMI, the HRs for morbidity of CVD, CHD, and stroke were 1.03(95%CI, 1.01–1.04), 1.02(95%CI, 1.01–1.04), and 1.06(95%CI, 1.02–1.09), respectively, for each 1 SD increase in BMI. After controlling for fatal events, compared with participants in the normal BMI group, the HRs for CVD morbidity in overweight and obese group was 1.15 (95%CI, 1.11–1.18), 1.31 (95%CI, 1.26–1.37), the HRs for CHD morbidity was 1.02 (95%CI, 0.97–1.97), 1.23 (95%CI, 1.43–1.57), and the HRs for stroke morbidity was 1.26 (95%CI, 1.14–1.39) and 1.22 (95%CI, 1.05–1.41), respectively. 2.3 Association between BMI and CVD Mortality and All-Cause Mortality During the 370,424 person-years of follow-up, a total of 17,029 participants died from all-causes, with 7,605 deaths attributed to CVD. Table 3 displays the multivariate associations between BMI levels and all-cause and CVD mortality. After adjusting for age, sex, marital status, smoking, alcohol consumption, physical activity, hypertension, and diabetes mellitus, each SD increase in participant BMI was linked to a 6% reduction in the risk of all-cause mortality (adjusted HR: 0.94, 95%CI: 0.93–0.96) and a 4% reduction in the risk of CVD mortality (adjusted HR: 0.96, 95%CI: 0.94–0.99). The HRs for all-cause mortality was 1.24 (95%CI, 1.15–1.34) for underweight participants, 0.91(95%CI, 0.88–0.95) for overweight participants, and 0.94 (95%CI, 0.89–0.99) for obese participants, compared to normal weight participants. The HRs for CVD mortality was 1.20 (95%CI, 1.06–1.36) for underweight participants, 0.92 (95%CI, 0.87–0.96) for overweight participants, and 1.00 (95%CI, 0.92–1.08) for obese participants, compared to normal weight participants. Subsequently, the BMI was categorized into eight groups based on a 2 kg/m 2 basis. The participants’ risk of CVD mortality and all-cause mortality was lowest at a BMI of 26–28 kg/m 2 (Table 4 ). Restricted cubic spline plots revealed non-linear associations between BMI and CVD morbidity (P non−linear < 0.001, Fig. 2 A), all-cause mortality (P non−linear < 0.001, Fig. 2 B), and CVD mortality (P non−linear < 0.001, Fig. 2 C). An approximate J-shaped association was found between BMI and CVD morbidity, but an approximate inverse J-shaped association was found with all-cause mortality and CVD mortality. The study found that the lowest risk ratio for all-cause mortality was 0.94 (95% CI, 0.92–0.97) at a BMI of 26.74 kg/m 2 . 2.4 Subgroup analyses and sensitivity analyses Subgroup analyses revealed that overweight and obesity did not significantly decrease the risk of CVD mortality in participants aged 75 years or older, women, and smokers. However, overweight and obesity in smoking participants also did not significantly decrease the risk of all-cause mortality compared to non-smokers. Furthermore, sensitivity analyses produced similar results to the main analysis (Tables S1, S2, S3, S4). Method 5.1 Study population A retrospective cohort study was conducted using electronic health examination records of residents of Xinzheng City, Henan Province, Central China. The study population consisted of individuals aged 60 years and above. Health examination records were created by physicians at each resident’s first hospital visit or health examination since January 1, 2011. At the beginning of the study, 85,861 older individuals were eligible for inclusion. Participants with any of the following conditions were excluded from the study: (1) no follow-up (n = 2004); (2) missing information on height and weight at baseline (n = 501); and (3) missing any one or more of the covariates such as smoking, alcohol consumption, marital status, physical activity, WC, etc. (n = 12225). In addition, 23,423 participants with less than 2 years of follow-up and 11,416 participants who is or has been a smoker were excluded from the sensitivity analyses. The process of screening the data is presented in Fig. 1 . The management of this study was approved by the Ethics Commission of Zhengzhou University (reference number: ZZUIRB2019-019), and the research team was granted a license to use the data by the Zhengzhou Health Commission. 5.2 Data collection We collected information on basic demographic characteristics, lifestyle, and medical history during each participant’s health examination. The basic demographic characteristics collected were age, sex (male/female), and marital status (married/unmarried/widowed/divorced). Lifestyle information included smoking status (never smokers /former smokers /current smokers), drinking status (never/occasionally/more than once a week/day), and regular exercise (never/occasionally/more than once a week/daily). The medical history information includes whether the patient has high blood pressure (yes/no), type 2 diabetes mellitus (yes/no), CHD (yes/no), stroke (yes/no), chronic obstructive pulmonary disease (yes/no), and severe mental illness (yes/no). Current smoking is defined as having smoked more than 100 cigarettes in a lifetime and still smoking. Alcohol consumption is defined as drinking more than 30g of alcohol in a single session [ 15 ]. Regular exercise is defined as 30 minutes of moderate-intensity exercise or 20 minutes of vigorous activity per session[ 16 ]. Trained health workers measured the height, weight, lipids, blood glucose, systolic blood pressure (SBP), and diastolic blood pressure (DBP) of the participants. Blood samples were collected after an 8-hour fast to measure blood glucose and lipids. Blood pressure was measured by taking two readings of SBP and DBP in the right brachial artery, using an electronic sphygmomanometer (Omron HEM-7125, Kyoto, Japan), after a 5-minute rest in a standard supine position. The average of the two readings was recorded as the result of the blood pressure measurements. Hypertension was defined as having a SBP of 140 mmHg or higher, a DBP of 90 mmHg or higher, or taking antihypertensive medication [ 17 ]. BMI was calculated by dividing weight (in kilograms) by the square of height (in meters). Three methods were used to analyze BMI: (1) classification into four groups based on the Chinese BMI scoring standard: underweight (BMI < 18.5 kg/m 2 ), normal (18.5 ≤ BMI < 24 kg/m 2 ), overweight (24.0 ≤ BMI < 28.0 kg/m 2 ), and obese (BMI ≥ 28 kg/m 2 ) [ 18 ]; (2) division into eight groups ( 30 kg/m 2 ); and (3) as a continuous variable. 5.3 Follow-up and outcome definition The study cohort began in January 2011 and was followed until December 2019. An electronic health examination database was created by questionnaire collection. The database included basic demographic information, physical examination, and laboratory tests. For mortality surveillance, we collected participants’ death information from January 2011 to December 2019 from the Center for Disease Control and Prevention (CDC) in Xinzheng City and linked it to the medical examination and death database through each participant’s unique personal identification number. The study focused on morbidity and mortality from CVD and all-cause mortality in participants. The composite outcome of CHD and stroke was used to define all-cause mortality. The causes of morbidity and mortality were recorded by means of International Classification of Diseases (ICD-10) codes. This includes ICD-10 codes I20-I25 for CHD and ICD-10 codes I60-I69 for stroke. Deaths due to CVD are defined as deaths due to either CHD or stroke, while deaths due to all-causes are defined as deaths due to any cause. 5.4 Statistical analysis The basic characteristics of the participants were presented using BMI as a categorical variable. BMI categories were defined as follows: BMI < 18.5 kg/m 2 , 18.5 ≤ BMI < 23.9 kg/m 2 , 24 ≤ BMI < 28 kg/m 2 , and BMI ≥ 28 kg/m 2 . Continuous variables were presented as mean ± standard deviation (SD) or median ± interquartile range (IQR), depending on their distribution. Descriptive variables were expressed as percentages and frequencies. To compare baseline characteristics, categorical variables were analyzed using the Pearson chi-square test, and continuous variables were analyzed using the Kruskal-Wallis H test. Cox proportional risk regression model with time scales was used to fit the results of HRs and 95%CIs of BMI to CVD morbidity, mortality and all-cause mortality. Model 1 was adjusted for age and sex. Model 2 was adjusted for age, sex, marital status, smoking, alcohol consumption, physical activity, SBP, DBP, history of type 2 diabetes, and history of hypertension based on model 1. For the survival analysis of CVD morbidity, we utilized a competing risk survival regression model (model 3) to analyze the risk of CVD, CHD, and stroke morbidity. This is necessary because assuming that CVD will not recur if participants die from causes other than cardiovascular is not accurate if deaths from other causes are treated as censored data [ 19 ]. Restricted cubic spline plots were utilized to characterize the dose-response relationship and explore the potential linear or non-linear association of BMI as a continuous variable with CVD morbidity and mortality and all-cause mortality. The overall association was initially examined for the presence of an association. If the overall association test was significant, the results of the linear and non-linear tests were further examined. An overall association of P < 0.05 indicated a significant overall association, and a non-linear P < 0.05 indicated the presence of a non-linear association. In stratified analysis, we stratified participants by sex (male/female), age (< 75/≥ 75), and smoking status (yes/no) at baseline to examine differences in outcomes between subgroups. In order to test the robustness of this study, we have also carried out a sensitivity analysis. (1) To minimize the possibility of inverse causality, we excluded study participants with < 2 years of follow-up, and we refrained from adjusting for diabetes and excluded diabetic participants at baseline to avoid a possible mediating role of diabetes [ 3 ]. (2) Smoking is associated with lung disease and specific causes of death, and may have had a strong confounding effect on the study [ 20 – 22 ]. Therefore, we excluded participants with COPD [ 23 ] or those who were current or former smokers at baseline from the analysis. Discussion The study found that overweight participants had a higher risk of developing CVD but a lower risk of all-cause mortality and CVD mortality compared to normal weight participants. In contrast, obesity did not significantly reduce the risk of CVD mortality. Even after adjusting for competing risk models for death from other causes, both overweight and obesity were still associated with an increased risk of CVD onset. Based on the 8-group BMI model, the participants’ risk of all-cause mortality and CVD mortality was lowest at a BMI of 26–28 kg/m 2 . Additionally, we observed a J-shaped association between BMI and CVD morbidity, and an inverse J-shaped association with all-cause mortality and CVD mortality. Being overweight or obesity increases the risk of developing CVD, including CHD and stroke. This study supports the findings of the Framingham Heart Study and the Atherosclerosis Risk in Communities study [ 24 – 26 ]. In a prospective cohort study conducted in Korea, a J-shaped association was found between BMI and the CVD morbidity in male participants. The risk of CVD began to increase at a BMI of 26–28 kg/m 2 [ 4 ]. Similarly, large prospective cohort studies conducted in the United States have shown a higher lifetime risk of CVD in middle-aged adults in the overweight and obese group compared with the normal BMI group (18.5 < BMI ≤ 24.9 kg/m 2 ) [ 8 ]. The study conducted by Korean scholars analysed the risk of CVD in two age groups, 40-year-olds and 66-year-olds. The results showed a U-shaped association between BMI and the CVD morbidity in the 40-year-old age group and an inverse J-shaped association in the 66-year-old age group [ 27 ]. It is worth noting that these findings are similar to the results of our study. The difference in body composition between older and younger individuals may explain this variation. Older individuals tend to have less lean tissue and more fat due to physical inactivity, even when their BMI is the same as that of younger individuals [ 28 ]. However, as people age, subcutaneous fat tends to accumulate in the abdomen, which is not fully accounted for by BMI [ 29 ], The impairment of BMI as a measure of obesity in older individuals may explain the attenuated, U-shaped association between BMI and the risk of developing CVD. Therefore, future researchers should consider studying the association between overweight and obesity with waist circumference(WC) when investigating their link to cardiovascular events and adverse outcomes. Although the health risks of obesity have long been recognised, recent studies have sparked debate about the specific relationship between overweight status and mortality. We found that participants with a BMI of 26–28 kg/m 2 had the lowest risk of all-cause and CVD mortality in the BMI group. Similar to our findings, a prospective cohort study conducted in 30 provinces across mainland China found that the BMI range associated with the lowest risk of mortality was 25-26.9 kg/m 2 [ 30 ]. Several studies conducted in developed countries have found that the optimal BMI associated with all-cause mortality and CVD mortality falls within the overweight category (25.0 ≤ BMI < 30 kg/m 2 ) [ 31 , 7 , 32 – 35 ]. In contrast, a large cohort study conducted in Japan found that the mortality rate for CVD was lowest for both men and women when their BMI was between 21.0 and 24.9 kg/m 2 [ 36 ]. Meanwhile, participants in a large cohort study that included 3.6 million adults had a lower BMI at the lowest risk of CVD mortality and all-cause mortality compared to our study [ 3 ]. This difference in weight distribution may be attributed to variations in population demographics across countries and regions, with higher rates of overweight and obesity observed among older individuals in developed nations [ 37 ], This means that older people have more adipose tissue to store energy and nutrients, which provides the body with essential nutrients while also maintaining bone density. Additionally, this adipose tissue acts as a cushion, protecting older people in the event of a fall [ 38 ]. In comparison to our study, the optimal BMI range for older individuals in developed countries was larger and closer to the obesity criteria. It is important to note that most studies examining the association of BMI with all-cause and CVD mortality have reported only linear or non-linear associations. Only a few studies have subdivided BMI by 2kg/m 2 to explore the optimal range of BMI. The determination of the optimal BMI range has been a controversial topic, providing ideas for subsequent studies. In a 32-year cohort study conducted in Sweden, researchers discovered that there was an inverse J-shaped curve between participant BMI and CVD mortality as well as all-cause mortality [ 39 ]. A national cohort study conducted in Malaysia revealed that overweight and obese participants had a lower risk of CVD mortality and all-cause mortality compared to those with normal weight. The study also showed an inverse J-shaped curve after excluding individuals who died within two years of the first follow-up [ 7 ]. The results of the Japan Synergy Cohort (JAAC) study on BMI and CVD mortality similarly showed an inverse J-shaped curve for BMI and CVD mortality [ 36 ]. Several studies have found an inverse J-shaped curve between BMI and all-cause mortality [ 4 , 40 – 42 ]. The reasons for the emergence of this phenomenon are explained in terms of physiological, psychological, and social aspects of the participants. Firstly, CVD leads to catabolism, resulting in additional weight loss. Overweight and obese patients have enough muscle and fat to provide a relatively favorable environment for the organism compared to underweight patients [ 43 , 44 ]. Secondly, adipose tissue produces adipokines, such as lipocalins and reticulin, that have various favorable effects on cardiovascular function [ 45 ]. Obesity is associated with lower stage disease, smaller tumors, and less aggressive biological subtypes [ 46 ]. Finally, excess adipose tissue affects the pharmacokinetics of cancer treatment regimens and provides a supply of nutrients for surgical and anticancer therapies, potentially impacting treatment outcomes [ 47 ]. Psychologically, obese individuals are more likely to monitor their health, be diagnosed and treated in the early stages of illness, and comply with medical advice [ 48 , 49 ]. Socially, some studies have suggested a correlation between obesity and higher socioeconomic status [ 50 ], which could potentially explain this result. However, other studies have found that low neighborhood socioeconomic status is associated with a higher risk of overweight and obesity [ 51 ], making it difficult to draw a clear conclusion based solely on socioeconomic status. Our study supports the notion that being overweight, and to some extent obese, may have a protective effect against all-cause mortality and CVD mortality. However, when obesity exceeds a certain threshold, this protective effect may turn into a harmful one. It is important to note that determining this threshold is challenging due to variations in sociological characteristics among different populations and differences in the exclusion criteria for study participants. Similarly, Yang et al. found an inverse J-shaped curve between BMI and all-cause mortality [ 33 ]. However, the study population was limited to a relatively thin rural population, resulting in a small and unrepresentative sample size of obese individuals. Furthermore, the study did not provide a secondary categorization of overweight and obese participants, which could have provided more detailed insights. In contrast, a prospective cohort study conducted using the UK CPRD found a J-shaped association between BMI and overall mortality as well as CVD mortality [ 3 ]. Differences in the causes of death in different countries may explain the observed variations. Studies have found that a lower BMI is linked to a higher risk of death from respiratory diseases [ 52 ], while a higher BMI is associated with a higher risk of death from atherosclerotic CVD or cancer [ 1 ]. It is worth noting that CVD is the primary cause of death in Western countries, where overweight and obesity are prevalent. As a result, U-shaped or J-shaped associations are more likely to be observed [ 53 ]. Our analyses indicate that overweight and obesity in older female participants cannot be considered a protective factor against CVD mortality. Potential reasons for this are as follows: Women’s estrogen facilitates energy storage in subcutaneous adipose tissue, protecting them from the accumulation of visceral fat [ 54 ]. However, the reduction of estrogen in women after menopause means that excess lipids are not transferred to the subcutaneous tissue but accumulate in the viscera. Excess visceral fat disrupts glucose and lipid metabolism and blood pressure, ultimately increasing the risk of death from CVD [ 21 , 55 , 56 ]. Our study found no significant difference between overweight and obesity and lower risk of CVD mortality and all-cause mortality in smokers, unlike the results for all non-smoking participants. We confirm the emergence of smoking as an independent risk factor for CVD mortality and all-cause mortality. It is noteworthy that previous studies have shown that reverse causality and confounding by smoking result in lower risk ratios for death when follow-up is shorter or when smokers are included [ 3 ]. Our sensitivity analyses, in which we selected participants with more than 2 years of follow-up and who were non-smokers (former smoker were also excluded), yielded similar results to the primary analyses. The study has several strengths. Firstly, the data were obtained from records of annual health examination in Xinzheng City, Henan Province, China, and the study focuses on a large sample size of older individuals aged 60 years and above. Secondly, instruments were used to measure participants’ height and weight rather than relying on self-reporting, reducing information bias. Furthermore, the mortality data was obtained from the Centers for Disease Control and Prevention, and the causes of death were reviewed by at least three clinical experts. Additionally, we utilized BMI cut-offs that are most appropriate for the body mass of the Chinese population. The BMI data were collected prior to the onset of disease, thereby reducing the possibility of reverse causality. However, this study has some limitations. Firstly, the analysis of the latent category trajectory model (LCGM) highlighted the importance of considering dynamic changes in participants’ BMI during follow-up, regardless of their baseline BMI. Additionally, the questionnaire did not include data on diet and cardiorespiratory fitness, which could have helped to further exclude reverse causation and residual confounders. Secondly, we utilized a measure of obesity, BMI, which although not providing an explanation for the degree of visceral obesity and fat distribution but is the most clinically relevant measure. Participants with diabetes, cancer, and other debilitating diseases were not directly excluded at baseline, which increases the likelihood of reverse causation. However, patients with COPD and diabetes were excluded separately for sensitivity analyses. Finally, the study focused solely on older individuals aged 60 years and older, which may limit generalization to the wider population. Conclusion Overall, the study concluded that overweight and obesity heightened the risk of CVD in elderly Chinese individuals, but it also found that the risk of CVD mortality and all-cause mortality decreased. The study also found that the lowest risk of CVD and all-cause mortality in older individuals was associated with a BMI of 26–28 kg/m 2 . This indicates that healthy weight should be assessed in light of age factors. However, further research is required to determine whether weight gain is indeed beneficial for older individuals. Abbreviations BMI, Body mass index; CHD, Coronary heart disease; CI, Confidence interval; CVD, Cardiovascular disease; DBP, Diastolic blood pressure; HR, Hazards ratio; RHR, Resting heart rate; SBP, Systolic blood pressure; SD, Standard deviation; TC, Total cholesterol; TG, Triglyceride; T2DM, Type 2 diabetes mellitus; WC, Waist Circumference. Declarations 8.1 Ethics approval The Ethics Committee of Zhengzhou University approved the study, and written informed consent was obtained from all participants. 8.2 Consent for publication Not applicable. 8.3 Availability of data and materials The datasets generated and analysed during the current study are not publicly available due to confidentiality requirements of third parties, but are available from the corresponding author on request. 8.4 Conflict of interest There is no conflict of interest here. 8.5 Funding This study was supported by National Key Research and Development Program “Research on prevention and control of major chronic non-communicable diseases” of China (Grant No:2017YFC1307705). 8.6 Author Contribution All authors contributed to the study conception and design.DS and LC designed the study. DS, JD, JW conceived this article and drafted the manuscript. DS analyzed the data. LC and JD was responsible for study concept. Songhe Shi took on the role of funding acquisition, project management, supervision, revision of important knowledge content, and final approval of the submitted version. All authors were involved in the collection of data and approve of the final version of the manuscript. 8.7 Acknowledgments The investigators are grateful to the dedicated participants and all research staff of the study. References Ng M, Fleming T, Robinson M, Thomson B, Graetz N, Margono C, et al. Global, regional, and national prevalence of overweight and obesity in children and adults during 1980–2013: a systematic analysis for the Global Burden of Disease Study 2013. The Lancet. 2014;384(9945):766–81. https://doi.org/10.1016/s0140-6736(14)60460-8 . Bai K, Chen X, Shi Z, He K, Hu X, Song R, et al. Hypertension modifies the associations of body mass index and waist circumference with all-cause mortality among older Chinese: a retrospective cohort study. 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Tables Table 1 Baseline Characteristics of Subjects Recruited by BMI Category Variables BMI, kg/m 2 BMI < 18.5 18.5 ≤ BMI < 24 24 ≤ BMI < 28 BMI ≥ 28 P-value n(%) 1913 (2.2) 43428 (50.6) 30719 (35.8) 9801 (11.4) Age ,years 72.20 ± 8.77 68.66 ± 7.85 66.92 ± 6.77 66.47 ± 6.42 <0.001 Gender ,% <0.001 Male 743 (38.8) 21561 (49.6) 14605 (47.5) 3804 (38.8) Female 1170 (61.2) 21867 (50.4) 16114 (52.5) 5997 (61.2) Marital status ,% <0.001 married 1246 (65.1) 32834 (75.6) 24994 (81.4) 8123 (82.9) unmarried 46 (2.4) 954 (2.2) 425 (1.4) 102 (1.0) widowed 589 (30.8) 9089 (20.9) 4960 (16.1) 1488 (15.2) divorced 32 (1.7) 551 (1.3) 340 (1.1) 88 (0.9) Weight ,kg 44.45 ± 5.36 57.19 ± 7.02 66.18 ± 7.13 74.67 ± 8.44 <0.001 WC ,cm 72.69 ± 8.47 78.88 ± 8.06 84.94 ± 8.52 92.43 ± 10.36 <0.001 Hypertension ,% 836 (43.7) 20669 (47.6) 18216 (59.3) 6973 (71.1) <0.001 T2DM ,% 205 (10.7) 6646 (15.3) 6802 (22.1) 2877 (29.4) <0.001 Physical exercise ,% <0.001 Highly active 199 (10.4) 5286 (12.2) 4652 (15.2) 1676 (17.1) Sufficiently active 43 (2.2) 1213 (2.8) 1024 (3.3) 295 (3.0) Insufficiently active 73 (3.8) 2031 (4.7) 1458 (4.8) 447 (4.6) Inactive 1597 (83.5) 34812 (80.3) 23527 (76.7) 7368 (75.4) Smoking ,% <0.001 Never smoker 1684 (88.2) 37431 (86.5) 26512 (86.6) 8563 (87.6) Former smoker 27 (1.4) 777 (1.8) 682 (2.2) 237 (2.4) Current smoker 199 (10.4) 5089 (11.8) 3432 (11.2) 973 (10.0) Drinking ,% <0.001 Never 1824 (95.6) 40735 (94.2) 28278 (92.5) 9001 (92.2) Once in a while 48 (2.5) 1435 (3.3) 1254 (4.1) 429 (4.4) More than once a week 9 (0.5) 266 (0.6) 269 (0.9) 70 (0.7) Every day 27 (1.4) 807 (1.9) 770 (2.5) 259 (2.7) RHR, beats 75.06 ± 9.77 73.98 ± 10.52 73.48 ± 10.70 73.59 ± 9.08 <0.001 SBP, mmHg 128.13 ± 19.80 130.04 ± 18.87 134.04 ± 26.83 139.59 ± 26.66 <0.001 DBP, mmHg 75.86 ± 11.00 77.96 ± 9.75 80.45 ± 10.16 82.84 ± 10.71 <0.001 TC, mmol/L 4.70 (4.19–5.30) * 4.71 (4.14–5.32) * 4.80 (4.20–5.41) * 4.90 (4.28–5.56) * <0.001 TG, mmol/L 1.10 (0.81–1.41) * 1.20 (0.86–1.52) * 1.32 (0.98–1.76) * 1.47 (1.10–2.07) * <0.001 Data are presented as mean ± standard deviation, median (interquartile range), or number (percentage). Abbreviations: BMI, body mass index; DBP, diastolic blood pressure; RHR, resting heart rate; SBP, systolic blood pressure; TC, total cholesterol; TG, triglyceride; T2DM, type 2 diabetes mellitus; WC, Waist circumference. Table 2 Risk of CVD morbidity according to BMI category n Events Person-year Incidence rate a HR (95%CI) Model1 Model2 Model3 CVD BMI < 18.5 1913 791 6861 115.29 1.12 (1.04, 1.20) 1.06 (0.99, 1.14) 0.91 (0.84, 0.99) 18.5 ≤ BMI < 24 43428 17493 168923 103.56 1.00 (ref) 1.00 (ref) 1.00 (ref) 24 ≤ BMI < 28 30719 12922 118667 108.89 1.05 (1.03, 1.08) 1.01 (0.99, 1.04) 1.11 (1.08, 1.14) BMI ≥ 28 9801 4525 35763 126.53 1.22 (1.18, 1.26) 1.11 (1.07, 1.14) 1.25 (1.21, 1.30) P for trend <0.001 <0.001 <0.001 BMI as a continuous variable (per SD increment) 1.06 (1.04, 1.07) 1.03 (1.01, 1.04) 1.09 (1.07, 1.10) CHD BMI < 18.5 1913 744 6861 108.44 1.15 (1.07, 1.23) 1.07 (1.00, 1.16) 0.94 (0.85, 1.02) 18.5 ≤ BMI < 24 43428 16083 168923 95.21 1.00 (ref) 1.00 (ref) 1.00 (ref) 24 ≤ BMI < 28 30719 11681 118667 98.44 1.03 (1.01, 1.06) 1.00 (0.98, 1.03) 1.08 (1.05, 1.11) BMI ≥ 28 9801 4164 35763 116.43 1.23 (1.18, 1.27) 1.11 (1.07, 1.15) 1.24 (1.19, 1.28) P for trend <0.001 <0.001 <0.001 BMI as a continuous variable (per SD increment) 1.05 (1.04, 1.06) 1.02 (1.01, 1.04) 1.08 (1.06, 1.09) Stroke BMI < 18.5 1913 72 6861 10.49 1.00 (0.79, 1.26) 1.04 (0.82, 1.31) 0.83 (0.60, 1.13) 18.5 ≤ BMI < 24 43428 1787 168923 10.58 1.00 (ref) 1.00 (ref) 1.00 (ref) 24 ≤ BMI < 28 30719 1586 118667 13.37 1.26 (1.18, 1.35) 1.14 (1.07, 1.23) 1.36 (1.25, 1.48) BMI ≥ 28 9801 494 35763 13.81 1.30 (1.18, 1.44) 1.10 (0.99, 1.22) 1.32 (1.17, 1.49) P for trend <0.001 <0.001 <0.001 BMI as a continuous variable (per SD increment) 1.13 (1.09, 1.16) 1.06 (1.02, 1.09) 1.17 (1.13, 1.22) Model 1: Adjusted for gender and age. Model 2: Adjusted for variables in Model 1, including marital status, current smoking, alcohol consumption, history of T2DM, systolic and diastolic blood pressure, RHR, TC, and TG. Model 3: Competitive risk survival regression adjusts for the same covariates as in Model 2. Abbreviations: BMI, body mass index; CHD, coronary heart disease; CI, confidence interval; CVD, cardiovascular disease; DBP, diastolic blood pressure; HR, hazards ratio; RHR, resting heart rate; SBP, systolic blood pressure; TC, total cholesterol; TG, triglyceride; T2DM, type 2 diabetes mellitus. a Per 1000 person-years. Table 3 Association between BMI category and risk of all-cause mortality and CVD mortality Outcomes No. of deaths No. of person-years Mortality rate a HR (95% CI) Model 1 Model 2 All-cause mortality BMI < 18.5 680 8316 81.77 1.56 (1.44, 1.68) 1.24 (1.15, 1.34) 18.5 ≤ BMI < 24 9802 191631 51.15 1.00 (ref) 1.00 (ref) 24 ≤ BMI < 28 5048 130951 38.55 0.79 (0.76, 0.81) 0.91 (0.88, 0.95) BMI ≥ 28 1499 39526 37.92 0.79 (0.75, 0.84) 0.94 (0.89, 0.99) P for trend <0.001 <0.001 BMI as a continuous variable (per SD increment) 0.85 (0.84, 0.86) 0.94 (0.93, 0.96) Cardiovascular disease mortality BMI < 18.5 275 8316 33.07 1.31 (1.16, 1.48) 1.20 (1.06, 1.36) 18.5 ≤ BMI < 24 4209 191631 21.96 1.00 (ref) 1.00 (ref) 24 ≤ BMI < 28 2342 130951 17.88 0.85 (0.81, 0.90) 0.92 (0.87, 0.96) BMI ≥ 28 779 39526 19.71 0.93 (0.86, 1.01) 1.00 (0.92, 1.08) P for trend <0.001 <0.001 BMI as a continuous variable (per SD increment) 0.92 (0.90, 0.94) 0.96 (0.94, 0.99) Model 1: Adjusted for gender and age. Model 2: Adjusted for variables in Model 1, including marital status, current smoking, alcohol consumption, history of T2DM, systolic and diastolic blood pressure, RHR, TC, and TG. Abbreviations: BMI, body mass index; CI, confidence interval; DBP, diastolic blood pressure; HR, hazards ratio; RHR, resting heart rate; SBP, systolic blood pressure; TC, total cholesterol; TG, triglyceride; T2DM, type 2 diabetes mellitus. a Per 1000 person-years. Table 4 Association of BMI categories per 2 kg/m2 with risk of all-cause mortality and CVD mortality Outcomes No. of deaths No. of person-years Cumulative mortality rate a HR (95%CI) Model 1 Model 2 All-cause mortality BMI < 18 439 5108 85.94 1.77 (1.60, 1.95) 1.27 (1.15, 1.40) 18 ≤ BMI < 20 1573 22917 68.64 1.34 (1.27, 1.42) 1.13 (1.07, 1.20) 20 ≤ BMI < 22 3477 63106 55.10 1.15 (1.10, 1.20) 1.09 (1.04, 1.14) 22 ≤ BMI < 24 4993 108816 45.88 1.00 (ref) 1.00 (ref) 24 ≤ BMI < 26 3195 80066 39.90 0.88 (0.84, 0.92) 0.97 (0.93, 1.01) 26 ≤ BMI < 28 1853 50885 36.42 0.81 (0.77, 0.86) 0.93 (0.88, 0.98) 28 ≤ BMI < 30 928 24236 38.10 0.86 (0.80, 0.93) 0.98 (0.91, 1.05) BMI ≥ 30 571 15290 37.34 0.86 (0.79, 0.94) 0.98 (0.90, 1.07) Cardiovascular disease mortality BMI < 18 178 5108 34.85 1.41 (1.21, 1.64) 1.25 (1.07, 1.46) 18 ≤ BMI < 20 635 22917 27.71 1.13 (1.03, 1.24) 1.03 (0.94, 1.13) 20 ≤ BMI < 22 1485 63106 23.53 1.09 (1.02, 1.16) 1.07 (1.00, 1.14) 22 ≤ BMI < 24 2186 108816 20.09 1.00 (ref) 1.00 (ref) 24 ≤ BMI < 26 1466 80066 18.31 0.91 (0.85, 0.97) 0.95 (0.89, 1.02) 26 ≤ BMI < 28 876 50885 17.22 0.85 (0.79, 0.92) 0.92 (0.85–0.99) 28 ≤ BMI < 30 484 24236 19.97 1.00 (0.91, 1.11) 1.06 (0.96, 1.17) BMI ≥ 30 295 15290 19.29 0.93 (0.82, 1.05) 0.96 (0.85, 1.08) Model 1: Adjusted for gender and age. Model 2: Adjusted for variables in Model 1, including marital status, current smoking, alcohol consumption, history of T2DM, systolic and diastolic blood pressure, RHR, TC, and TG. Abbreviations: BMI, body mass index; CI, confidence interval; DBP, diastolic blood pressure; HR, hazards ratio; RHR, resting heart rate; SBP, systolic blood pressure; TC, total cholesterol; TG, triglyceride; T2DM, type 2 diabetes mellitus. a Per 1000 person-years. Additional Declarations No competing interests reported. Supplementary Files AppendixA.docx Figure.S2.tif Figure. S1 Figure ABC and Figure DEF show the relationship between BMI and cardiovascular disease morbidity, all-cause mortality, and cardiovascular disease mortality for participants with less than 2 years of follow-up excluded and for participants who excluded smoking, respectively. Circles indicate points where nodes are located (5th, 25th, 50th, 75th, and 95th percentiles). The area between the two dashed lines indicates the 95% CI. The model was adjusted for age, sex, marital status, physical activity, smoking and alcohol consumption. Abbreviations: BMI, body mass index; CI, confidence interval; HR, hazards ratio. Cite Share Download PDF Status: Posted Version 1 posted 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-3844842","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":266105458,"identity":"46a76093-be6d-4a4f-9005-3808edfd8d5d","order_by":0,"name":"Donghai Su","email":"","orcid":"","institution":"Zhengzhou University","correspondingAuthor":false,"prefix":"","firstName":"Donghai","middleName":"","lastName":"Su","suffix":""},{"id":266105459,"identity":"2d5aa532-ad2b-4e96-a61d-3ae2431380fd","order_by":1,"name":"Liyuan Chen","email":"","orcid":"","institution":"Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Liyuan","middleName":"","lastName":"Chen","suffix":""},{"id":266105460,"identity":"5b468b96-dfe3-4a48-8e89-755edd7f58e6","order_by":2,"name":"Jiacheng Ding","email":"","orcid":"","institution":"Zhengzhou University","correspondingAuthor":false,"prefix":"","firstName":"Jiacheng","middleName":"","lastName":"Ding","suffix":""},{"id":266105461,"identity":"4aeecb13-a916-402e-b4c1-9af785d6ea51","order_by":3,"name":"Junjie Wang","email":"","orcid":"","institution":"Zhengzhou University","correspondingAuthor":false,"prefix":"","firstName":"Junjie","middleName":"","lastName":"Wang","suffix":""},{"id":266105462,"identity":"c548ea25-7e1a-4bf2-9164-739813748cb3","order_by":4,"name":"Songhe Shi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAoElEQVRIiWNgGAWjYFACHsYHEEYC8VqYDUjWwiZBmhb5/rPHKn/8OczAz55jwPBzBxFaDG7kpd2Q4DnMINnzxoCx9wwxWiR4zG4YSBwG6s0xYGZsI8phZ8wKEgwOM9gTrYXhQI4Zw4EEoC0SxGoBusdYsuFAOo/EmWcFB3uJdJjhxx9/rOX425M3PvhJlMOggAfsSBI0jIJRMApGwSjABwBSczE+lICz6QAAAABJRU5ErkJggg==","orcid":"","institution":"Zhengzhou University","correspondingAuthor":true,"prefix":"","firstName":"Songhe","middleName":"","lastName":"Shi","suffix":""}],"badges":[],"createdAt":"2024-01-08 07:59:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3844842/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3844842/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":49491081,"identity":"1accf605-0964-4cc1-87fd-030f29ef056d","added_by":"auto","created_at":"2024-01-11 18:20:15","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":43445,"visible":true,"origin":"","legend":"\u003cp\u003eParticipant screening flowchart\u003c/p\u003e","description":"","filename":"Figure.1.png","url":"https://assets-eu.researchsquare.com/files/rs-3844842/v1/a5815accea0222e4516bb848.png"},{"id":49491082,"identity":"2bc82dc7-2c8d-4f10-90f4-01a24a25219c","added_by":"auto","created_at":"2024-01-11 18:20:15","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":59466,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of the dose-response association between participants’ BMI and CVD morbidity, mortality, and all-cause mortality. Circles indicate points where nodes are located (5th, 25th, 50th, 75th, and 95th percentiles). The area between the two dashed lines indicates the 95% CI. The model was adjusted for age, sex, marital status, physical activity, smoking and alcohol consumption. Abbreviations: BMI, body mass index; CI, confidence interval; HR, hazards ratio.\u003c/p\u003e","description":"","filename":"Figure.2.png","url":"https://assets-eu.researchsquare.com/files/rs-3844842/v1/9b7af0d36b569f200c10434f.png"},{"id":54180744,"identity":"edcdb222-2ac0-495d-86d1-2ad0f77312f1","added_by":"auto","created_at":"2024-04-05 16:31:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":672763,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3844842/v1/c70d5ca4-345a-469f-b313-cb6c1d6ac799.pdf"},{"id":49491080,"identity":"f701933f-6b6f-4316-9d6a-052f5d22a242","added_by":"auto","created_at":"2024-01-11 18:20:15","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":28808,"visible":true,"origin":"","legend":"","description":"","filename":"AppendixA.docx","url":"https://assets-eu.researchsquare.com/files/rs-3844842/v1/fa2e5a1f1e5345fce5ba32f9.docx"},{"id":49491083,"identity":"1ef68ea7-40ea-4060-a4db-946edc1ddb01","added_by":"auto","created_at":"2024-01-11 18:20:15","extension":"tif","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":216924,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure. S1\u003c/strong\u003e Figure ABC and Figure DEF show the relationship between BMI and cardiovascular disease morbidity, all-cause mortality, and cardiovascular disease mortality for participants with less than 2 years of follow-up excluded and for participants who excluded smoking, respectively. Circles indicate points where nodes are located (5th, 25th, 50th, 75th, and 95th percentiles). The area between the two dashed lines indicates the 95% CI. The model was adjusted for age, sex, marital status, physical activity, smoking and alcohol consumption. Abbreviations: BMI, body mass index; CI, confidence interval; HR, hazards ratio.\u003c/p\u003e","description":"","filename":"Figure.S2.tif","url":"https://assets-eu.researchsquare.com/files/rs-3844842/v1/6fb7d63d7fd53ed980b03544.tif"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association of overweight and obesity with cardiovascular disease morbidity and adverse outcomes in older adults: a retrospective cohort study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThere has been a significant increase in the prevalence of overweight and obesity (BMI\u0026thinsp;\u0026gt;\u0026thinsp;25 kg/m\u003csup\u003e2\u003c/sup\u003e) worldwide in recent years. By 2035, more than 4\u0026nbsp;billion people, or 50% of the total population, are expected to be affected [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Higher BMI has been associated with a wide range of chronic diseases, including hypertension, diabetes mellitus, and CVD. The burden of CVD continues to increase annually, with approximately 330\u0026nbsp;million CVD patients worldwide [\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. In terms of mortality, two out of five deaths are attributable to CVD [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Several major studies and meta-analyses have found strong associations between participants\u0026rsquo; overweight and obesity and CVD morbidity, mortality, or all-cause mortality [\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Conflicting evidence exists regarding the association between long-term changes in BMI and the occurrence of CVD and adverse outcomes. Cohort studies and meta-analyses conducted in Western countries have found a J-shaped association between increasing BMI and the risk of all-cause mortality and CVD mortality [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. However, a previous meta-analysis of 97 BMI and mortality found that overweight and obesity were associated with lower mortality [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. In addition, more and more studies have shown a U-shaped association between overweight, obesity, and all-cause mortality [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], giving rise to the concept of the obesity paradox [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. At the same time, some researchers have considered that the inclusion of current or former smokers as study participants may have distorted the results in the direction of a U-shaped association [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAccording to the \u0026lsquo;Healthy China 2030\u0026rsquo; plan, the average life expectancy of the Chinese population is expected to increase from 76.3 years in 2015 to 79 years in 2030. Early detection and prevention of poor health in the elderly population is crucial to extending life expectancy and improving the quality of life in old age. We investigated the association of overweight and obesity with CVD and adverse outcomes in older individuals by designing and conducting a large cohort study. At the same time, we will search for the optimal range of BMI in this population that minimizes the risk of CVD and all-cause mortality in older individuals. Additionally, we excluded smokers and former smokers from the cohort, assuming that smoking is a strong confounder, and repeated the primary outcome analysis after comparing the results with those of previous studies [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Baseline characteristics of study participants\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the baseline characteristics of all participants. Of the 85,861 participants, 40,713 (47.4%) were male and 45,148 (52.6%) were female. The study participants had a mean age of 67.87\u0026thinsp;\u0026plusmn;\u0026thinsp;7.44 years, and the median follow-up time was 5.96 years (IQR: 3.19\u0026ndash;9.53). Of the participants, 43,428 (50.6%) had a normal weight at baseline, 1,913 (2.2%) were underweight, 30,719 (35.8%) were overweight, and 9,801 (11.4%) were obese. The higher BMI group showed a higher prevalence of hypertension and type 2 diabetes, as well as higher systolic, diastolic, serum total cholesterol (TC), and triglyceride (TG) levels.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Association between BMI and CVD morbidity\u003c/h2\u003e \u003cp\u003eDuring a median follow-up of 5.96 years, 35,731 older individuals were diagnosed with CVD (prevalence 115.29/1,000 person-years), 32.673 were diagnosed with CHD (prevalence 108.44/1,000 person-years), and 3,939 were diagnosed with stroke (prevalence 10.49/1,000 person-years). Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the relationship between BMI and the morbidity of CVD and their subtypes. Model 2, after adjustment for other covariates such as age, sex, marital status, smoking, alcohol consumption, physical activity, hypertension, and diabetes, the HRs for CVD morbidity was 1.06(95%CI, 0.99\u0026ndash;1.14) for underweight participants, 1.01(95%CI, 0.99\u0026ndash;1.04) for overweight participants, and 1.11(95%CI, 1.07\u0026ndash;1.14) for obese participants. Similar effects on the morbidity of CHD and stroke were seen in the different BMI subgroups. The HRs for CHD morbidity in overweight and obese participants was 1.10 (95%CI, 0.98\u0026ndash;1.03) and 1.11 (95%CI, 1.07\u0026ndash;1.15), respectively. The HRs for stroke morbidity in overweight and obese participants was 1.14 (95%CI, 1.07\u0026ndash;1.23) and 1.10 (95% CI, 0.99\u0026ndash;1.22), respectively. For continuous BMI, the HRs for morbidity of CVD, CHD, and stroke were 1.03(95%CI, 1.01\u0026ndash;1.04), 1.02(95%CI, 1.01\u0026ndash;1.04), and 1.06(95%CI, 1.02\u0026ndash;1.09), respectively, for each 1 SD increase in BMI.\u003c/p\u003e \u003cp\u003eAfter controlling for fatal events, compared with participants in the normal BMI group, the HRs for CVD morbidity in overweight and obese group was 1.15 (95%CI, 1.11\u0026ndash;1.18), 1.31 (95%CI, 1.26\u0026ndash;1.37), the HRs for CHD morbidity was 1.02 (95%CI, 0.97\u0026ndash;1.97), 1.23 (95%CI, 1.43\u0026ndash;1.57), and the HRs for stroke morbidity was 1.26 (95%CI, 1.14\u0026ndash;1.39) and 1.22 (95%CI, 1.05\u0026ndash;1.41), respectively.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Association between BMI and CVD Mortality and All-Cause Mortality\u003c/h2\u003e \u003cp\u003eDuring the 370,424 person-years of follow-up, a total of 17,029 participants died from all-causes, with 7,605 deaths attributed to CVD. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e displays the multivariate associations between BMI levels and all-cause and CVD mortality. After adjusting for age, sex, marital status, smoking, alcohol consumption, physical activity, hypertension, and diabetes mellitus, each SD increase in participant BMI was linked to a 6% reduction in the risk of all-cause mortality (adjusted HR: 0.94, 95%CI: 0.93\u0026ndash;0.96) and a 4% reduction in the risk of CVD mortality (adjusted HR: 0.96, 95%CI: 0.94\u0026ndash;0.99). The HRs for all-cause mortality was 1.24 (95%CI, 1.15\u0026ndash;1.34) for underweight participants, 0.91(95%CI, 0.88\u0026ndash;0.95) for overweight participants, and 0.94 (95%CI, 0.89\u0026ndash;0.99) for obese participants, compared to normal weight participants. The HRs for CVD mortality was 1.20 (95%CI, 1.06\u0026ndash;1.36) for underweight participants, 0.92 (95%CI, 0.87\u0026ndash;0.96) for overweight participants, and 1.00 (95%CI, 0.92\u0026ndash;1.08) for obese participants, compared to normal weight participants. Subsequently, the BMI was categorized into eight groups based on a 2 kg/m\u003csup\u003e2\u003c/sup\u003e basis. The participants\u0026rsquo; risk of CVD mortality and all-cause mortality was lowest at a BMI of 26\u0026ndash;28 kg/m\u003csup\u003e2\u003c/sup\u003e (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRestricted cubic spline plots revealed non-linear associations between BMI and CVD morbidity (P \u003csub\u003enon\u0026minus;linear\u003c/sub\u003e \u0026lt; 0.001, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003eA), all-cause mortality (P \u003csub\u003enon\u0026minus;linear\u003c/sub\u003e \u0026lt; 0.001, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003eB), and CVD mortality (P \u003csub\u003enon\u0026minus;linear\u003c/sub\u003e \u0026lt; 0.001, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). An approximate J-shaped association was found between BMI and CVD morbidity, but an approximate inverse J-shaped association was found with all-cause mortality and CVD mortality. The study found that the lowest risk ratio for all-cause mortality was 0.94 (95% CI, 0.92\u0026ndash;0.97) at a BMI of 26.74 kg/m\u003csup\u003e2\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Subgroup analyses and sensitivity analyses\u003c/h2\u003e \u003cp\u003eSubgroup analyses revealed that overweight and obesity did not significantly decrease the risk of CVD mortality in participants aged 75 years or older, women, and smokers. However, overweight and obesity in smoking participants also did not significantly decrease the risk of all-cause mortality compared to non-smokers. Furthermore, sensitivity analyses produced similar results to the main analysis (Tables S1, S2, S3, S4).\u003c/p\u003e "},{"header":"Method","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e5.1 Study population\u003c/h2\u003e \u003cp\u003eA retrospective cohort study was conducted using electronic health examination records of residents of Xinzheng City, Henan Province, Central China. The study population consisted of individuals aged 60 years and above. Health examination records were created by physicians at each resident\u0026rsquo;s first hospital visit or health examination since January 1, 2011. At the beginning of the study, 85,861 older individuals were eligible for inclusion. Participants with any of the following conditions were excluded from the study: (1) no follow-up (n\u0026thinsp;=\u0026thinsp;2004); (2) missing information on height and weight at baseline (n\u0026thinsp;=\u0026thinsp;501); and (3) missing any one or more of the covariates such as smoking, alcohol consumption, marital status, physical activity, WC, etc. (n\u0026thinsp;=\u0026thinsp;12225). In addition, 23,423 participants with less than 2 years of follow-up and 11,416 participants who is or has been a smoker were excluded from the sensitivity analyses. The process of screening the data is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The management of this study was approved by the Ethics Commission of Zhengzhou University (reference number: ZZUIRB2019-019), and the research team was granted a license to use the data by the Zhengzhou Health Commission.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e5.2 Data collection\u003c/h2\u003e \u003cp\u003eWe collected information on basic demographic characteristics, lifestyle, and medical history during each participant\u0026rsquo;s health examination. The basic demographic characteristics collected were age, sex (male/female), and marital status (married/unmarried/widowed/divorced). Lifestyle information included smoking status (never smokers /former smokers /current smokers), drinking status (never/occasionally/more than once a week/day), and regular exercise (never/occasionally/more than once a week/daily). The medical history information includes whether the patient has high blood pressure (yes/no), type 2 diabetes mellitus (yes/no), CHD (yes/no), stroke (yes/no), chronic obstructive pulmonary disease (yes/no), and severe mental illness (yes/no). Current smoking is defined as having smoked more than 100 cigarettes in a lifetime and still smoking. Alcohol consumption is defined as drinking more than 30g of alcohol in a single session [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Regular exercise is defined as 30 minutes of moderate-intensity exercise or 20 minutes of vigorous activity per session[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Trained health workers measured the height, weight, lipids, blood glucose, systolic blood pressure (SBP), and diastolic blood pressure (DBP) of the participants. Blood samples were collected after an 8-hour fast to measure blood glucose and lipids. Blood pressure was measured by taking two readings of SBP and DBP in the right brachial artery, using an electronic sphygmomanometer (Omron HEM-7125, Kyoto, Japan), after a 5-minute rest in a standard supine position. The average of the two readings was recorded as the result of the blood pressure measurements. Hypertension was defined as having a SBP of 140 mmHg or higher, a DBP of 90 mmHg or higher, or taking antihypertensive medication [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. BMI was calculated by dividing weight (in kilograms) by the square of height (in meters). Three methods were used to analyze BMI: (1) classification into four groups based on the Chinese BMI scoring standard: underweight (BMI\u0026thinsp;\u0026lt;\u0026thinsp;18.5 kg/m\u003csup\u003e2\u003c/sup\u003e), normal (18.5\u0026thinsp;\u0026le;\u0026thinsp;BMI\u0026thinsp;\u0026lt;\u0026thinsp;24 kg/m\u003csup\u003e2\u003c/sup\u003e), overweight (24.0\u0026thinsp;\u0026le;\u0026thinsp;BMI\u0026thinsp;\u0026lt;\u0026thinsp;28.0 kg/m\u003csup\u003e2\u003c/sup\u003e), and obese (BMI\u0026thinsp;\u0026ge;\u0026thinsp;28 kg/m\u003csup\u003e2\u003c/sup\u003e) [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]; (2) division into eight groups (\u0026lt;\u0026thinsp;18, 18\u0026ndash;20, 20\u0026ndash;24, 24\u0026ndash;26, 26\u0026ndash;28, 28\u0026ndash;30, and \u0026gt;\u0026thinsp;30 kg/m\u003csup\u003e2\u003c/sup\u003e); and (3) as a continuous variable.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e5.3 Follow-up and outcome definition\u003c/h2\u003e \u003cp\u003eThe study cohort began in January 2011 and was followed until December 2019. An electronic health examination database was created by questionnaire collection. The database included basic demographic information, physical examination, and laboratory tests. For mortality surveillance, we collected participants\u0026rsquo; death information from January 2011 to December 2019 from the Center for Disease Control and Prevention (CDC) in Xinzheng City and linked it to the medical examination and death database through each participant\u0026rsquo;s unique personal identification number. The study focused on morbidity and mortality from CVD and all-cause mortality in participants. The composite outcome of CHD and stroke was used to define all-cause mortality. The causes of morbidity and mortality were recorded by means of International Classification of Diseases (ICD-10) codes. This includes ICD-10 codes I20-I25 for CHD and ICD-10 codes I60-I69 for stroke. Deaths due to CVD are defined as deaths due to either CHD or stroke, while deaths due to all-causes are defined as deaths due to any cause.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e5.4 Statistical analysis\u003c/h2\u003e \u003cp\u003eThe basic characteristics of the participants were presented using BMI as a categorical variable. BMI categories were defined as follows: BMI\u0026thinsp;\u0026lt;\u0026thinsp;18.5 kg/m\u003csup\u003e2\u003c/sup\u003e, 18.5\u0026thinsp;\u0026le;\u0026thinsp;BMI\u0026thinsp;\u0026lt;\u0026thinsp;23.9 kg/m\u003csup\u003e2\u003c/sup\u003e, 24\u0026thinsp;\u0026le;\u0026thinsp;BMI\u0026thinsp;\u0026lt;\u0026thinsp;28 kg/m\u003csup\u003e2\u003c/sup\u003e, and BMI\u0026thinsp;\u0026ge;\u0026thinsp;28 kg/m\u003csup\u003e2\u003c/sup\u003e. Continuous variables were presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) or median\u0026thinsp;\u0026plusmn;\u0026thinsp;interquartile range (IQR), depending on their distribution. Descriptive variables were expressed as percentages and frequencies. To compare baseline characteristics, categorical variables were analyzed using the Pearson chi-square test, and continuous variables were analyzed using the Kruskal-Wallis H test. Cox proportional risk regression model with time scales was used to fit the results of HRs and 95%CIs of BMI to CVD morbidity, mortality and all-cause mortality. Model 1 was adjusted for age and sex. Model 2 was adjusted for age, sex, marital status, smoking, alcohol consumption, physical activity, SBP, DBP, history of type 2 diabetes, and history of hypertension based on model 1. For the survival analysis of CVD morbidity, we utilized a competing risk survival regression model (model 3) to analyze the risk of CVD, CHD, and stroke morbidity. This is necessary because assuming that CVD will not recur if participants die from causes other than cardiovascular is not accurate if deaths from other causes are treated as censored data [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRestricted cubic spline plots were utilized to characterize the dose-response relationship and explore the potential linear or non-linear association of BMI as a continuous variable with CVD morbidity and mortality and all-cause mortality. The overall association was initially examined for the presence of an association. If the overall association test was significant, the results of the linear and non-linear tests were further examined. An overall association of P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicated a significant overall association, and a non-linear P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicated the presence of a non-linear association.\u003c/p\u003e \u003cp\u003eIn stratified analysis, we stratified participants by sex (male/female), age (\u0026lt;\u0026thinsp;75/\u0026ge; 75), and smoking status (yes/no) at baseline to examine differences in outcomes between subgroups. In order to test the robustness of this study, we have also carried out a sensitivity analysis. (1) To minimize the possibility of inverse causality, we excluded study participants with \u0026lt;\u0026thinsp;2 years of follow-up, and we refrained from adjusting for diabetes and excluded diabetic participants at baseline to avoid a possible mediating role of diabetes [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. (2) Smoking is associated with lung disease and specific causes of death, and may have had a strong confounding effect on the study [\u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Therefore, we excluded participants with COPD [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] or those who were current or former smokers at baseline from the analysis.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe study found that overweight participants had a higher risk of developing CVD but a lower risk of all-cause mortality and CVD mortality compared to normal weight participants. In contrast, obesity did not significantly reduce the risk of CVD mortality. Even after adjusting for competing risk models for death from other causes, both overweight and obesity were still associated with an increased risk of CVD onset. Based on the 8-group BMI model, the participants\u0026rsquo; risk of all-cause mortality and CVD mortality was lowest at a BMI of 26\u0026ndash;28 kg/m\u003csup\u003e2\u003c/sup\u003e. Additionally, we observed a J-shaped association between BMI and CVD morbidity, and an inverse J-shaped association with all-cause mortality and CVD mortality.\u003c/p\u003e \u003cp\u003eBeing overweight or obesity increases the risk of developing CVD, including CHD and stroke. This study supports the findings of the Framingham Heart Study and the Atherosclerosis Risk in Communities study [\u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. In a prospective cohort study conducted in Korea, a J-shaped association was found between BMI and the CVD morbidity in male participants. The risk of CVD began to increase at a BMI of 26\u0026ndash;28 kg/m\u003csup\u003e2\u003c/sup\u003e [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Similarly, large prospective cohort studies conducted in the United States have shown a higher lifetime risk of CVD in middle-aged adults in the overweight and obese group compared with the normal BMI group (18.5\u0026thinsp;\u0026lt;\u0026thinsp;BMI\u0026thinsp;\u0026le;\u0026thinsp;24.9 kg/m\u003csup\u003e2\u003c/sup\u003e) [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The study conducted by Korean scholars analysed the risk of CVD in two age groups, 40-year-olds and 66-year-olds. The results showed a U-shaped association between BMI and the CVD morbidity in the 40-year-old age group and an inverse J-shaped association in the 66-year-old age group [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. It is worth noting that these findings are similar to the results of our study. The difference in body composition between older and younger individuals may explain this variation. Older individuals tend to have less lean tissue and more fat due to physical inactivity, even when their BMI is the same as that of younger individuals [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. However, as people age, subcutaneous fat tends to accumulate in the abdomen, which is not fully accounted for by BMI [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], The impairment of BMI as a measure of obesity in older individuals may explain the attenuated, U-shaped association between BMI and the risk of developing CVD. Therefore, future researchers should consider studying the association between overweight and obesity with waist circumference(WC) when investigating their link to cardiovascular events and adverse outcomes.\u003c/p\u003e \u003cp\u003eAlthough the health risks of obesity have long been recognised, recent studies have sparked debate about the specific relationship between overweight status and mortality. We found that participants with a BMI of 26\u0026ndash;28 kg/m\u003csup\u003e2\u003c/sup\u003e had the lowest risk of all-cause and CVD mortality in the BMI group. Similar to our findings, a prospective cohort study conducted in 30 provinces across mainland China found that the BMI range associated with the lowest risk of mortality was 25-26.9 kg/m\u003csup\u003e2\u003c/sup\u003e [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Several studies conducted in developed countries have found that the optimal BMI associated with all-cause mortality and CVD mortality falls within the overweight category (25.0\u0026thinsp;\u0026le;\u0026thinsp;BMI\u0026thinsp;\u0026lt;\u0026thinsp;30 kg/m\u003csup\u003e2\u003c/sup\u003e) [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan additionalcitationids=\"CR33 CR34\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. In contrast, a large cohort study conducted in Japan found that the mortality rate for CVD was lowest for both men and women when their BMI was between 21.0 and 24.9 kg/m\u003csup\u003e2\u003c/sup\u003e [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Meanwhile, participants in a large cohort study that included 3.6\u0026nbsp;million adults had a lower BMI at the lowest risk of CVD mortality and all-cause mortality compared to our study [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. This difference in weight distribution may be attributed to variations in population demographics across countries and regions, with higher rates of overweight and obesity observed among older individuals in developed nations [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], This means that older people have more adipose tissue to store energy and nutrients, which provides the body with essential nutrients while also maintaining bone density. Additionally, this adipose tissue acts as a cushion, protecting older people in the event of a fall [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. In comparison to our study, the optimal BMI range for older individuals in developed countries was larger and closer to the obesity criteria. It is important to note that most studies examining the association of BMI with all-cause and CVD mortality have reported only linear or non-linear associations. Only a few studies have subdivided BMI by 2kg/m\u003csup\u003e2\u003c/sup\u003e to explore the optimal range of BMI. The determination of the optimal BMI range has been a controversial topic, providing ideas for subsequent studies.\u003c/p\u003e \u003cp\u003eIn a 32-year cohort study conducted in Sweden, researchers discovered that there was an inverse J-shaped curve between participant BMI and CVD mortality as well as all-cause mortality [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. A national cohort study conducted in Malaysia revealed that overweight and obese participants had a lower risk of CVD mortality and all-cause mortality compared to those with normal weight. The study also showed an inverse J-shaped curve after excluding individuals who died within two years of the first follow-up [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. The results of the Japan Synergy Cohort (JAAC) study on BMI and CVD mortality similarly showed an inverse J-shaped curve for BMI and CVD mortality [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Several studies have found an inverse J-shaped curve between BMI and all-cause mortality [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan additionalcitationids=\"CR41\" citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. The reasons for the emergence of this phenomenon are explained in terms of physiological, psychological, and social aspects of the participants. Firstly, CVD leads to catabolism, resulting in additional weight loss. Overweight and obese patients have enough muscle and fat to provide a relatively favorable environment for the organism compared to underweight patients [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Secondly, adipose tissue produces adipokines, such as lipocalins and reticulin, that have various favorable effects on cardiovascular function [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Obesity is associated with lower stage disease, smaller tumors, and less aggressive biological subtypes [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Finally, excess adipose tissue affects the pharmacokinetics of cancer treatment regimens and provides a supply of nutrients for surgical and anticancer therapies, potentially impacting treatment outcomes [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Psychologically, obese individuals are more likely to monitor their health, be diagnosed and treated in the early stages of illness, and comply with medical advice [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Socially, some studies have suggested a correlation between obesity and higher socioeconomic status [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e], which could potentially explain this result. However, other studies have found that low neighborhood socioeconomic status is associated with a higher risk of overweight and obesity [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e], making it difficult to draw a clear conclusion based solely on socioeconomic status. Our study supports the notion that being overweight, and to some extent obese, may have a protective effect against all-cause mortality and CVD mortality. However, when obesity exceeds a certain threshold, this protective effect may turn into a harmful one. It is important to note that determining this threshold is challenging due to variations in sociological characteristics among different populations and differences in the exclusion criteria for study participants.\u003c/p\u003e \u003cp\u003eSimilarly, Yang et al. found an inverse J-shaped curve between BMI and all-cause mortality [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. However, the study population was limited to a relatively thin rural population, resulting in a small and unrepresentative sample size of obese individuals. Furthermore, the study did not provide a secondary categorization of overweight and obese participants, which could have provided more detailed insights. In contrast, a prospective cohort study conducted using the UK CPRD found a J-shaped association between BMI and overall mortality as well as CVD mortality [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Differences in the causes of death in different countries may explain the observed variations. Studies have found that a lower BMI is linked to a higher risk of death from respiratory diseases [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e], while a higher BMI is associated with a higher risk of death from atherosclerotic CVD or cancer [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. It is worth noting that CVD is the primary cause of death in Western countries, where overweight and obesity are prevalent. As a result, U-shaped or J-shaped associations are more likely to be observed [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur analyses indicate that overweight and obesity in older female participants cannot be considered a protective factor against CVD mortality. Potential reasons for this are as follows: Women\u0026rsquo;s estrogen facilitates energy storage in subcutaneous adipose tissue, protecting them from the accumulation of visceral fat [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. However, the reduction of estrogen in women after menopause means that excess lipids are not transferred to the subcutaneous tissue but accumulate in the viscera. Excess visceral fat disrupts glucose and lipid metabolism and blood pressure, ultimately increasing the risk of death from CVD [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. Our study found no significant difference between overweight and obesity and lower risk of CVD mortality and all-cause mortality in smokers, unlike the results for all non-smoking participants. We confirm the emergence of smoking as an independent risk factor for CVD mortality and all-cause mortality. It is noteworthy that previous studies have shown that reverse causality and confounding by smoking result in lower risk ratios for death when follow-up is shorter or when smokers are included [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Our sensitivity analyses, in which we selected participants with more than 2 years of follow-up and who were non-smokers (former smoker were also excluded), yielded similar results to the primary analyses.\u003c/p\u003e \u003cp\u003eThe study has several strengths. Firstly, the data were obtained from records of annual health examination in Xinzheng City, Henan Province, China, and the study focuses on a large sample size of older individuals aged 60 years and above. Secondly, instruments were used to measure participants\u0026rsquo; height and weight rather than relying on self-reporting, reducing information bias. Furthermore, the mortality data was obtained from the Centers for Disease Control and Prevention, and the causes of death were reviewed by at least three clinical experts. Additionally, we utilized BMI cut-offs that are most appropriate for the body mass of the Chinese population. The BMI data were collected prior to the onset of disease, thereby reducing the possibility of reverse causality.\u003c/p\u003e \u003cp\u003eHowever, this study has some limitations. Firstly, the analysis of the latent category trajectory model (LCGM) highlighted the importance of considering dynamic changes in participants\u0026rsquo; BMI during follow-up, regardless of their baseline BMI. Additionally, the questionnaire did not include data on diet and cardiorespiratory fitness, which could have helped to further exclude reverse causation and residual confounders. Secondly, we utilized a measure of obesity, BMI, which although not providing an explanation for the degree of visceral obesity and fat distribution but is the most clinically relevant measure. Participants with diabetes, cancer, and other debilitating diseases were not directly excluded at baseline, which increases the likelihood of reverse causation. However, patients with COPD and diabetes were excluded separately for sensitivity analyses. Finally, the study focused solely on older individuals aged 60 years and older, which may limit generalization to the wider population.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOverall, the study concluded that overweight and obesity heightened the risk of CVD in elderly Chinese individuals, but it also found that the risk of CVD mortality and all-cause mortality decreased. The study also found that the lowest risk of CVD and all-cause mortality in older individuals was associated with a BMI of 26\u0026ndash;28 kg/m\u003csup\u003e2\u003c/sup\u003e. This indicates that healthy weight should be assessed in light of age factors. However, further research is required to determine whether weight gain is indeed beneficial for older individuals.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eBMI, Body mass index; CHD, Coronary heart disease; CI, Confidence interval; CVD, Cardiovascular disease; DBP, Diastolic blood pressure; HR, Hazards ratio; RHR, Resting heart rate; SBP, Systolic blood pressure; SD, Standard deviation; TC, Total cholesterol; TG, Triglyceride; T2DM, Type 2 diabetes mellitus; WC, Waist Circumference.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003e\u0026nbsp;\u003c/h2\u003e\n\u003ch2\u003e\u003cstrong\u003e8.1 Ethics approval\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThe Ethics Committee of Zhengzhou University approved the study, and written informed consent was obtained from all participants.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003e8.2 Consent for publication\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch2\u003e8.3 Availability of data and materials\u003c/h2\u003e\n\u003cp\u003eThe datasets generated and analysed during the current study are not publicly available due to confidentiality requirements of third parties, but are available from the corresponding author on request.\u003c/p\u003e\n\u003ch2\u003e8.4 Conflict of interest\u003c/h2\u003e\n\u003cp\u003eThere is no conflict of interest here.\u003c/p\u003e\n\u003ch2\u003e8.5 Funding\u003c/h2\u003e\n\u003cp\u003eThis study was supported by National Key Research and Development Program \u0026ldquo;Research on prevention and control of major chronic non-communicable diseases\u0026rdquo; of China (Grant No:2017YFC1307705).\u003c/p\u003e\n\u003ch2\u003e8.6 Author Contribution\u003c/h2\u003e\n\u003cp\u003eAll authors contributed to the study conception and design.DS and LC designed the study. DS, JD, JW conceived this article and drafted the manuscript. DS analyzed the data. LC and JD was responsible for study concept. Songhe Shi took on the role of funding acquisition, project management, supervision, revision of important knowledge content, and final approval of the submitted version. All authors were involved in the collection of data and approve of the final version of the manuscript.\u003c/p\u003e\n\u003ch2\u003e8.7 Acknowledgments\u003c/h2\u003e\n\u003cp\u003eThe investigators are grateful to the dedicated participants and all research staff of the study.\u003c/p\u003e\n\u003ch2\u003e\u0026nbsp;\u003c/h2\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eNg M, Fleming T, Robinson M, Thomson B, Graetz N, Margono C, et al. Global, regional, and national prevalence of overweight and obesity in children and adults during 1980\u0026ndash;2013: a systematic analysis for the Global Burden of Disease Study 2013. 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J Am Heart Association. 2021;10(5):e017511. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1161/jaha.120.017511\u003c/span\u003e\u003cspan address=\"10.1161/jaha.120.017511\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMatsuzawa Y, Funahashi T, Nakamura T. The Concept of Metabolic Syndrome: Contribution of Visceral Fat Accumulation and Its Molecular Mechanism. J Atheroscler Thromb. 2011;18(8):629\u0026ndash;39. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5551/jat.7922\u003c/span\u003e\u003cspan address=\"10.5551/jat.7922\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFried SK, Lee M-J, Karastergiou K. Shaping fat distribution: New insights into the molecular determinants of depot- and sex-dependent adipose biology. Obesity. 2015;23(7):1345\u0026ndash;52. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/oby.21133\u003c/span\u003e\u003cspan address=\"10.1002/oby.21133\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eBaseline Characteristics of Subjects Recruited by BMI Category\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"5\" align=\"left\"\u003e\n \u003cp\u003eBMI, kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI\u0026thinsp;\u0026lt;\u0026thinsp;18.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.5\u0026thinsp;\u0026le;\u0026thinsp;BMI\u0026thinsp;\u0026lt;\u0026thinsp;24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24\u0026thinsp;\u0026le;\u0026thinsp;BMI\u0026thinsp;\u0026lt;\u0026thinsp;28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI\u0026thinsp;\u0026ge;\u0026thinsp;28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1913 (2.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43428 (50.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30719 (35.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9801 (11.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge ,years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e72.20\u0026thinsp;\u0026plusmn;\u0026thinsp;8.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e68.66\u0026thinsp;\u0026plusmn;\u0026thinsp;7.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66.92\u0026thinsp;\u0026plusmn;\u0026thinsp;6.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66.47\u0026thinsp;\u0026plusmn;\u0026thinsp;6.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGender ,%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e743 (38.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21561 (49.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14605 (47.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3804 (38.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1170 (61.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21867 (50.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16114 (52.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5997 (61.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMarital status ,%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1246 (65.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32834 (75.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24994 (81.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8123 (82.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eunmarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46 (2.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e954 (2.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e425 (1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e102 (1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ewidowed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e589 (30.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9089 (20.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4960 (16.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1488 (15.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003edivorced\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32 (1.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e551 (1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e340 (1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e88 (0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWeight ,kg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44.45\u0026thinsp;\u0026plusmn;\u0026thinsp;5.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57.19\u0026thinsp;\u0026plusmn;\u0026thinsp;7.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66.18\u0026thinsp;\u0026plusmn;\u0026thinsp;7.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e74.67\u0026thinsp;\u0026plusmn;\u0026thinsp;8.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWC ,cm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e72.69\u0026thinsp;\u0026plusmn;\u0026thinsp;8.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e78.88\u0026thinsp;\u0026plusmn;\u0026thinsp;8.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e84.94\u0026thinsp;\u0026plusmn;\u0026thinsp;8.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e92.43\u0026thinsp;\u0026plusmn;\u0026thinsp;10.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHypertension ,%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e836 (43.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20669 (47.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18216 (59.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6973 (71.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT2DM ,%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e205 (10.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6646 (15.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6802 (22.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2877 (29.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePhysical exercise ,%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHighly active\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e199 (10.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5286 (12.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4652 (15.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1676 (17.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSufficiently active\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43 (2.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1213 (2.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1024 (3.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e295 (3.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInsufficiently active\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e73 (3.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2031 (4.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1458 (4.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e447 (4.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInactive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1597 (83.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34812 (80.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23527 (76.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7368 (75.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSmoking ,%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNever smoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1684 (88.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37431 (86.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26512 (86.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8563 (87.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFormer smoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27 (1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e777 (1.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e682 (2.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e237 (2.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCurrent smoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e199 (10.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5089 (11.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3432 (11.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e973 (10.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDrinking ,%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1824 (95.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40735 (94.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28278 (92.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9001 (92.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOnce in a while\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48 (2.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1435 (3.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1254 (4.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e429 (4.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMore than once a week\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9 (0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e266 (0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e269 (0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70 (0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEvery day\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27 (1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e807 (1.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e770 (2.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e259 (2.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRHR, beats\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75.06\u0026thinsp;\u0026plusmn;\u0026thinsp;9.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e73.98\u0026thinsp;\u0026plusmn;\u0026thinsp;10.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e73.48\u0026thinsp;\u0026plusmn;\u0026thinsp;10.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e73.59\u0026thinsp;\u0026plusmn;\u0026thinsp;9.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSBP, mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e128.13\u0026thinsp;\u0026plusmn;\u0026thinsp;19.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e130.04\u0026thinsp;\u0026plusmn;\u0026thinsp;18.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e134.04\u0026thinsp;\u0026plusmn;\u0026thinsp;26.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e139.59\u0026thinsp;\u0026plusmn;\u0026thinsp;26.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDBP, mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75.86\u0026thinsp;\u0026plusmn;\u0026thinsp;11.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e77.96\u0026thinsp;\u0026plusmn;\u0026thinsp;9.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80.45\u0026thinsp;\u0026plusmn;\u0026thinsp;10.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e82.84\u0026thinsp;\u0026plusmn;\u0026thinsp;10.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTC, mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.70 (4.19\u0026ndash;5.30) *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.71 (4.14\u0026ndash;5.32) *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.80 (4.20\u0026ndash;5.41) *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.90 (4.28\u0026ndash;5.56) *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTG, mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.10 (0.81\u0026ndash;1.41) *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.20 (0.86\u0026ndash;1.52) *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.32 (0.98\u0026ndash;1.76) *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.47 (1.10\u0026ndash;2.07) *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" align=\"left\"\u003e\n \u003cp\u003eData are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation, median (interquartile range), or number (percentage).\u003c/p\u003e\n \u003cp\u003eAbbreviations: BMI, body mass index; DBP, diastolic blood pressure; RHR, resting heart rate; SBP, systolic blood pressure; TC, total cholesterol; TG, triglyceride; T2DM, type 2 diabetes mellitus; WC, Waist circumference.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eRisk of CVD morbidity according to BMI category\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003en\u003c/p\u003e\n \u003c/th\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eEvents\u003c/p\u003e\n \u003c/th\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003ePerson-year\u003c/p\u003e\n \u003c/th\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eIncidence rate \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003eHR (95%CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModel1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModel2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModel3\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCVD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI\u0026thinsp;\u0026lt;\u0026thinsp;18.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1913\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e791\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6861\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e115.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.12 (1.04, 1.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.06 (0.99, 1.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.91 (0.84, 0.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.5\u0026thinsp;\u0026le;\u0026thinsp;BMI\u0026thinsp;\u0026lt;\u0026thinsp;24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43428\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17493\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e168923\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e103.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24\u0026thinsp;\u0026le;\u0026thinsp;BMI\u0026thinsp;\u0026lt;\u0026thinsp;28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30719\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12922\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e118667\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e108.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.05 (1.03, 1.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.01 (0.99, 1.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.11 (1.08, 1.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI\u0026thinsp;\u0026ge;\u0026thinsp;28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9801\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4525\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35763\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e126.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.22 (1.18, 1.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.11 (1.07, 1.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.25 (1.21, 1.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" align=\"left\"\u003e\n \u003cp\u003eP for trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" align=\"left\"\u003e\n \u003cp\u003eBMI as a continuous variable (per SD increment)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.06 (1.04, 1.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.03 (1.01, 1.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.09 (1.07, 1.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCHD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI\u0026thinsp;\u0026lt;\u0026thinsp;18.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1913\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e744\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6861\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e108.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.15 (1.07, 1.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.07 (1.00, 1.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.94 (0.85, 1.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.5\u0026thinsp;\u0026le;\u0026thinsp;BMI\u0026thinsp;\u0026lt;\u0026thinsp;24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43428\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16083\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e168923\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24\u0026thinsp;\u0026le;\u0026thinsp;BMI\u0026thinsp;\u0026lt;\u0026thinsp;28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30719\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11681\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e118667\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e98.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.03 (1.01, 1.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00 (0.98, 1.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.08 (1.05, 1.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI\u0026thinsp;\u0026ge;\u0026thinsp;28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9801\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35763\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e116.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.23 (1.18, 1.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.11 (1.07, 1.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.24 (1.19, 1.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP for trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" align=\"left\"\u003e\n \u003cp\u003eBMI as a continuous variable (per SD increment)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.05 (1.04, 1.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.02 (1.01, 1.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.08 (1.06, 1.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStroke\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI\u0026thinsp;\u0026lt;\u0026thinsp;18.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1913\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6861\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00 (0.79, 1.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.04 (0.82, 1.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.83 (0.60, 1.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.5\u0026thinsp;\u0026le;\u0026thinsp;BMI\u0026thinsp;\u0026lt;\u0026thinsp;24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43428\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1787\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e168923\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24\u0026thinsp;\u0026le;\u0026thinsp;BMI\u0026thinsp;\u0026lt;\u0026thinsp;28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30719\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1586\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e118667\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.26 (1.18, 1.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.14 (1.07, 1.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.36 (1.25, 1.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI\u0026thinsp;\u0026ge;\u0026thinsp;28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9801\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e494\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35763\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.30 (1.18, 1.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.10 (0.99, 1.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.32 (1.17, 1.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP for trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" align=\"left\"\u003e\n \u003cp\u003eBMI as a continuous variable (per SD increment)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.13 (1.09, 1.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.06 (1.02, 1.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.17 (1.13, 1.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\" align=\"left\"\u003e\n \u003cp\u003eModel 1: Adjusted for gender and age.\u003c/p\u003e\n \u003cp\u003eModel 2: Adjusted for variables in Model 1, including marital status, current smoking, alcohol consumption, history of T2DM, systolic and diastolic blood pressure, RHR, TC, and TG.\u003c/p\u003e\n \u003cp\u003eModel 3: Competitive risk survival regression adjusts for the same covariates as in Model 2.\u003c/p\u003e\n \u003cp\u003eAbbreviations: BMI, body mass index; CHD, coronary heart disease; CI, confidence interval; CVD, cardiovascular disease; DBP, diastolic blood pressure; HR, hazards ratio; RHR, resting heart rate; SBP, systolic blood pressure; TC, total cholesterol; TG, triglyceride; T2DM, type 2 diabetes mellitus.\u003c/p\u003e\n \u003cp\u003e\u003csup\u003ea\u003c/sup\u003e Per 1000 person-years.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eAssociation between BMI category and risk of all-cause mortality and CVD mortality\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eOutcomes\u003c/p\u003e\n \u003c/th\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eNo. of\u003c/p\u003e\n \u003cp\u003edeaths\u003c/p\u003e\n \u003c/th\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eNo. of\u003c/p\u003e\n \u003cp\u003eperson-years\u003c/p\u003e\n \u003c/th\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eMortality rate \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eHR (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"6\" align=\"left\"\u003e\n \u003cp\u003eAll-cause\u003c/p\u003e\n \u003cp\u003emortality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI\u0026thinsp;\u0026lt;\u0026thinsp;18.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e680\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8316\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e81.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.56 (1.44, 1.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.24 (1.15, 1.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.5\u0026thinsp;\u0026le;\u0026thinsp;BMI\u0026thinsp;\u0026lt;\u0026thinsp;24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9802\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e191631\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24\u0026thinsp;\u0026le;\u0026thinsp;BMI\u0026thinsp;\u0026lt;\u0026thinsp;28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e130951\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.79 (0.76, 0.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.91 (0.88, 0.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI\u0026thinsp;\u0026ge;\u0026thinsp;28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1499\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39526\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.79 (0.75, 0.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.94 (0.89, 0.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" align=\"left\"\u003e\n \u003cp\u003eP for trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" align=\"left\"\u003e\n \u003cp\u003eBMI as a continuous variable (per SD increment)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.85 (0.84, 0.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.94 (0.93, 0.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"6\" align=\"left\"\u003e\n \u003cp\u003eCardiovascular\u003c/p\u003e\n \u003cp\u003edisease mortality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI\u0026thinsp;\u0026lt;\u0026thinsp;18.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e275\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8316\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.31 (1.16, 1.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.20 (1.06, 1.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.5\u0026thinsp;\u0026le;\u0026thinsp;BMI\u0026thinsp;\u0026lt;\u0026thinsp;24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4209\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e191631\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24\u0026thinsp;\u0026le;\u0026thinsp;BMI\u0026thinsp;\u0026lt;\u0026thinsp;28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2342\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e130951\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.85 (0.81, 0.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.92 (0.87, 0.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI\u0026thinsp;\u0026ge;\u0026thinsp;28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e779\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39526\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.93 (0.86, 1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00 (0.92, 1.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" align=\"left\"\u003e\n \u003cp\u003eP for trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" align=\"left\"\u003e\n \u003cp\u003eBMI as a continuous variable (per SD increment)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.92 (0.90, 0.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.96 (0.94, 0.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" align=\"left\"\u003e\n \u003cp\u003eModel 1: Adjusted for gender and age.\u003c/p\u003e\n \u003cp\u003eModel 2: Adjusted for variables in Model 1, including marital status, current smoking, alcohol consumption, history of T2DM, systolic and diastolic blood pressure, RHR, TC, and TG.\u003c/p\u003e\n \u003cp\u003eAbbreviations: BMI, body mass index; CI, confidence interval; DBP, diastolic blood pressure; HR, hazards ratio; RHR, resting heart rate; SBP, systolic blood pressure; TC, total cholesterol; TG, triglyceride; T2DM, type 2 diabetes mellitus.\u003c/p\u003e\n \u003cp\u003e\u003csup\u003ea\u003c/sup\u003e Per 1000 person-years.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eAssociation of BMI categories per 2 kg/m2 with risk of all-cause mortality and CVD mortality\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eOutcomes\u003c/p\u003e\n \u003c/th\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eNo. of deaths\u003c/p\u003e\n \u003c/th\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eNo. of\u003c/p\u003e\n \u003cp\u003eperson-years\u003c/p\u003e\n \u003c/th\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eCumulative\u003c/p\u003e\n \u003cp\u003emortality rate \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eHR (95%CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"8\" align=\"left\"\u003e\n \u003cp\u003eAll-cause\u003c/p\u003e\n \u003cp\u003emortality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI\u0026thinsp;\u0026lt;\u0026thinsp;18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e439\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e85.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.77 (1.60, 1.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.27 (1.15, 1.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18\u0026thinsp;\u0026le;\u0026thinsp;BMI\u0026thinsp;\u0026lt;\u0026thinsp;20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1573\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22917\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e68.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.34 (1.27, 1.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.13 (1.07, 1.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20\u0026thinsp;\u0026le;\u0026thinsp;BMI\u0026thinsp;\u0026lt;\u0026thinsp;22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3477\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e63106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.15 (1.10, 1.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.09 (1.04, 1.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22\u0026thinsp;\u0026le;\u0026thinsp;BMI\u0026thinsp;\u0026lt;\u0026thinsp;24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4993\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e108816\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24\u0026thinsp;\u0026le;\u0026thinsp;BMI\u0026thinsp;\u0026lt;\u0026thinsp;26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3195\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80066\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.88 (0.84, 0.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.97 (0.93, 1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26\u0026thinsp;\u0026le;\u0026thinsp;BMI\u0026thinsp;\u0026lt;\u0026thinsp;28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1853\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50885\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.81 (0.77, 0.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.93 (0.88, 0.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28\u0026thinsp;\u0026le;\u0026thinsp;BMI\u0026thinsp;\u0026lt;\u0026thinsp;30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e928\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24236\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.86 (0.80, 0.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.98 (0.91, 1.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI\u0026thinsp;\u0026ge;\u0026thinsp;30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e571\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15290\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.86 (0.79, 0.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.98 (0.90, 1.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"8\" align=\"left\"\u003e\n \u003cp\u003eCardiovascular\u003c/p\u003e\n \u003cp\u003edisease mortality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI\u0026thinsp;\u0026lt;\u0026thinsp;18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.41 (1.21, 1.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.25 (1.07, 1.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18\u0026thinsp;\u0026le;\u0026thinsp;BMI\u0026thinsp;\u0026lt;\u0026thinsp;20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e635\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22917\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.13 (1.03, 1.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.03 (0.94, 1.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20\u0026thinsp;\u0026le;\u0026thinsp;BMI\u0026thinsp;\u0026lt;\u0026thinsp;22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1485\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e63106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.09 (1.02, 1.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.07 (1.00, 1.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22\u0026thinsp;\u0026le;\u0026thinsp;BMI\u0026thinsp;\u0026lt;\u0026thinsp;24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2186\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e108816\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24\u0026thinsp;\u0026le;\u0026thinsp;BMI\u0026thinsp;\u0026lt;\u0026thinsp;26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1466\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80066\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.91 (0.85, 0.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.95 (0.89, 1.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26\u0026thinsp;\u0026le;\u0026thinsp;BMI\u0026thinsp;\u0026lt;\u0026thinsp;28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e876\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50885\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.85 (0.79, 0.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.92 (0.85\u0026ndash;0.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28\u0026thinsp;\u0026le;\u0026thinsp;BMI\u0026thinsp;\u0026lt;\u0026thinsp;30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e484\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24236\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00 (0.91, 1.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.06 (0.96, 1.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI\u0026thinsp;\u0026ge;\u0026thinsp;30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e295\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15290\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.93 (0.82, 1.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.96 (0.85, 1.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" align=\"left\"\u003e\n \u003cp\u003eModel 1: Adjusted for gender and age.\u003c/p\u003e\n \u003cp\u003eModel 2: Adjusted for variables in Model 1, including marital status, current smoking, alcohol consumption, history of T2DM, systolic and diastolic blood pressure, RHR, TC, and TG.\u003c/p\u003e\n \u003cp\u003eAbbreviations: BMI, body mass index; CI, confidence interval; DBP, diastolic blood pressure; HR, hazards ratio; RHR, resting heart rate; SBP, systolic blood pressure; TC, total cholesterol; TG, triglyceride; T2DM, type 2 diabetes mellitus.\u003c/p\u003e\n \u003cp\u003e\u003csup\u003ea\u003c/sup\u003e Per 1000 person-years.\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\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"overweight, obesity, cardiovascular disease morbidity, cardiovascular disease mortality, all-cause mortality","lastPublishedDoi":"10.21203/rs.3.rs-3844842/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3844842/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eThe effect of baseline overweight and obesity status on cardiovascular disease(CVD) morbidity and adverse outcomes remains unclear. The aim of this study was to examine the association between overweight, obesity and CVD morbidity, mortality, and all-cause mortality in Chinese older individuals.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003eThis retrospective cohort study analyzed data from electronic health examination records of 86,049 older individuals aged ≥ 60 years in Xinzheng City, Henan Province, China, from January 2011 to December 2019. Cox proportional risk regression models and competing risk models were utilized to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for CVD morbidity and mortality, as well as all-cause mortality, in overweight and obese individuals. Restricted cubic splines were employed to evaluate dose-response associations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003eDuring a median follow-up of 5.96 years, 35,731 older individuals were diagnosed with CVD. The total number of participant deaths was 17,029, with 7,605 deaths from CVD. The morbidity of CVD was higher in the overweight and obese groups compared to the normal BMI group, with HRs of 1.06(95%CI, 1.02-1.10) and 1.23(95%CI, 1.16-1.30), respectively. Competing risk models controlling for fatal events showed an increased morbidity of CVD in the overweight and obese groups, with HRs of 1.15(95%CI, 1.11-1.18) and 1.31(95%CI, 1.26-1.37), respectively. In contrast, the overweight group had a reduced risk of all-cause mortality and CVD mortality compared to the normal BMI group, with HRs of 0.91(95%CI, 0.88-0.94) and 0.89(95%CI, 0.82-0.97), respectively. The study found that the risk of all-cause mortality was lower in the obese group, with HRs of 0.89(95%CI, 0.82-0.97). Participants had the lowest risk of all-cause mortality and CVD mortality when their BMI was between 26 and 28 kg/m². The restricted cubic spline plots showed a J-shaped association between BMI and CVD morbidity and an inverse J-shaped association with CVD mortality and all-cause mortality.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003eOverweight and obesity are positively correlated with the morbidity of CVD and negatively correlated with all-cause mortality in Chinese older individuals. However, it cannot be assumed that there is a negative correlation between obesity and CVD mortality. Therefore, obese individuals should aim to reduce weight appropriately, and overweight individuals should take appropriate measures to prevent obesity.\u003c/p\u003e","manuscriptTitle":"Association of overweight and obesity with cardiovascular disease morbidity and adverse outcomes in older adults: a retrospective cohort study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-11 18:20:10","doi":"10.21203/rs.3.rs-3844842/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4ca544f6-d1dc-425d-9f91-c05a4f48b020","owner":[],"postedDate":"January 11th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-04-13T08:59:26+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-11 18:20:10","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3844842","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3844842","identity":"rs-3844842","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}