The relationship between the trajectory of body mass index changes over a long lifecycle and the risk of all-cause mortality in patients with cardiovascular disease: NHANES longitudinal cohort study

doi:10.21203/rs.3.rs-4880906/v1

Download PDF

Research Article

The relationship between the trajectory of body mass index changes over a long lifecycle and the risk of all-cause mortality in patients with cardiovascular disease: NHANES longitudinal cohort study

https://doi.org/10.21203/rs.3.rs-4880906/v1

This work is licensed under a CC BY 4.0 License

You are reading this latest preprint version

Background

The relationship between changes in body mass index (BMI) over a long lifecycle and the risk of all-cause mortality among patients with cardiovascular disease (CVD) remains understudied. This study aims to investigate the association between BMI changes (from age 25 to after 50) and the risk of all-cause mortality in CVD patients.

Methods

This study leveraged data from the National Health and Nutrition Examination Survey (NHANES) 2001–2018 and the National Death Index (NDI) to construct a longitudinal cohort. Employing weighted multivariable Cox regression and Restricted Cubic Spline (RCS) analyses, we evaluated both the linear and nonlinear associations between BMI (at age 25 and after 50), its changes, and the risk of all-cause mortality among patients with CVD.

Results

A total of 2304 CVD patients were included in this study. During a median follow-up of 68 months, 774 participants died. The lowest risk of mortality was observed when BMI was 19.61 at age 25 and 26.55 after the age of 50. The impact of BMI change between these two time points on all-cause mortality risk exhibited a U-shaped relationship Specifically, when the change in BMI exceeded 8.27, it was positively associated with all-cause mortality risk [HR = 1.16, 95%CI=(1.00, 1.33)].

Conclusion

Among CVD patients, changes in BMI during the long-life span were nonlinearly associated with the risk of all-cause mortality. When formulating weight management strategies in the long life cycle based on BMI, individualized approaches should be taken rather than blindly emphasizing weight loss.

Cardiovascular diseases

Body mass index

Change trajectory

all-cause mortality

NHANES

Cardiovascular disease (CVD) is a major threat to human health. The latest statistical report[1] jointly released by the American Heart Association, indicates that 48.6% of American adults over 20 years old suffer from CVD, including coronary heart disease, angina pectoris, myocardial infarction, stroke, and so on. CVD has surpassed cancer as the leading cause of death annually (596,786 vs. 502,847). Identifying independent risk factors associated with CVD mortality is crucial for reducing the mortality rate among CVD patients and extending the life expectancy of the population.

Overweight and obesity are the second most significant modifiable risk factors for CVD[2]. Body mass index (BMI) remains a commonly used indicator for defining overweight and obesity[3]. The inclusion of BMI in both the Life's Simple 7 and the updated Life's Essential 8 metrics of cardiovascular health underscores its close relationship with CVD, emphasizing its importance in assessing and managing cardiovascular risk[4]. Incorporating BMI measurements at different time points can provide evidence for weight management strategies for CVD patients over their extended lifespan. A nationwide cohort study conducted in the UK found that BMI changes over a decade in adulthood are an independent risk factor for mortality. Specifically, individuals who maintained a very high BMI over 10 years had a 1.72-fold and 2.31-fold increased risk of all-cause and cardiovascular mortality, respectively[5][5]. Another study in Sweden identified the correlation between dynamic BMI changes during male adolescence (aged 8 to 20) and the incidence of acute coronary events[6]. However, there remains a lack of research on the relationship between BMI trajectories from young adulthood to middle and old age and the risk of all-cause mortality among CVD patients. This deficiency undermines the evidence for formulating weight management strategies for CVD patients across their extended lifespan.

To provide evidence for the formulation of long-term weight management strategies for patients with CVD, we conducted a cohort study using the National Health and Nutrition Examination Survey (NHANES) and National Death Index (NDI) data from the National Center for Health Statistics (NCHS) in the United States to explore the relationship between the trajectory of BMI changes from young adulthood to middle/old age and the risk of all-cause mortality in CVD patients. This study aims to have a positive impact on reducing the mortality rate of CVD patients and prolonging the life expectancy of the population.

Study design

The data were derived from the NHANES and NDI. NHANES is a cross-sectional survey conducted biennially among the non-institutionalized US population, collecting data on demographics, laboratory tests, physical examinations, and more. Through a complex sampling survey design, the data from NHANES can reflect the health, nutrition, and disease characteristics of the entire US population[7]. NDI comprises death certificate records for deceased individuals in the United States. By linking NHANES with NDI using probabilistic matching algorithms, mortality information for NHANES participants can be obtained, thereby constructing a longitudinal cohort of NHANES that includes survival data. NHANES has been reviewed and approved by the NCHS Ethics Committee, and all participants have signed written informed consent forms. Detailed information on the NHANES can be accessed at the following website: https://www.cdc.gov/nchs/nhanes/.

This study focused on individuals aged 50 and above with CVD who participated in NHANES 2001–2018. CVD refers to patients with coronary heart disease, angina pectoris, myocardial infarction, and stroke. Disease diagnoses were obtained through questionnaire surveys. During the interviews, participants were asked, "Have you ever been told by a doctor that you have coronary heart disease/angina pectoris/heart attack/stroke?" Those who answered "yes" to any of these questions were classified as having CVD. NHANES questionnaire survey data have been extensively used in clinical research and are considered highly reliable. According to the study objectives, NHANES participants were excluded based on the following criteria: 1) individuals without CVD, 2) individuals with cancer or malignant tumors, 3) participants under the age of 50, and 4) participants without data on changes in BMI.

BMI and changing trajectory

The independent variable was the trajectory of BMI changes over a long lifecycle, specifically the difference between BMI after the age of 50 and BMI during young adulthood at 25 years old. The BMI after 50 years old was calculated based on the standing height and weight measured during the survey. The BMI at 25 years old was estimated using participants' recalled heights and weights. The BMI was calculated using the formula: weight (in kilograms) divided by the square of height (in meters). BMI change was defined as the difference between the BMI at the time of the interview and the BMI at 25 years old. Additionally, cut-off points of 18.5 and 25 for BMI were used to categorize individuals into underweight, normal weight, and overweight categories.

All-cause death determination

The all-cause mortality data were sourced from the NDI. Participants' survival status outcomes were obtained through a series of identifying codes. Follow-up for survival status was terminated on December 31, 2019. If no death information for a participant was matched in the NDI, the participant was considered alive. Conversely, if death information was matched, the date of death was recorded[3].

Covariates

The covariates encompassed demographic data, laboratory tests, and medical history. Demographic data included age, gender, marital status, education, and race. Laboratory tests encompassed blood lipids (total cholesterol, triglycerides, low-density lipoprotein cholesterol LDL-C, high-density lipoprotein cholesterol HDL-C), blood glucose (fasting plasma glucose, glycated hemoglobin), liver function (aspartate aminotransferase AST, alanine aminotransferase ALT), renal function (urea nitrogen, creatinine), uric acid, and blood cell counts (white blood cell count, platelet count, hemoglobin). The blood cell counts among the laboratory test data were measured using the Beckman Coulter DxH 800 instrument, while the remaining laboratory tests were obtained using the Roche Cobas 6000 (c501 module) analyzer. Medical history included tobacco use, physical activity, and blood pressure. Demographic and medical history data were obtained through questionnaire surveys. Tobacco use refers to smoking more than 100 cigarettes in one's lifetime. Blood pressure was measured, and normal blood pressure was defined as a systolic pressure of less than 130 mmHg and a diastolic pressure of less than 80 mmHg; otherwise, it was considered elevated. The criteria for physical activity were based on previous studies, where participants were considered to meet the recommended level if they engaged in moderate-to-vigorous physical exercise for more than 150 minutes per week[8].

Statistical Analysis

Continuous variables were described using means and standard deviations or medians and quartiles, while categorical variables were described using percentages. Statistical differences in population characteristics between the survival and death groups were determined using t-tests, rank-sum tests, and chi-square tests, depending on the applicable conditions.

We examined the relationship between changes in BMI and all-cause mortality risk, while also exploring the associations between BMI during young adulthood, midlife, and all-cause mortality risk. Multivariable Cox regression models were employed to investigate the associations between BMI during young adulthood, midlife, and its changes with all-cause mortality risk, respectively. To assess the robustness of the results, BMI at each life stage was included in the models as both a continuous variable and a categorical variable, while BMI change was included as both a continuous variable and a binary variable (increase or decrease). To control for confounding biases, three regression models were constructed by adjusting for different covariates. Model 1 included only the independent variables. Model 2 adjusted for age and gender. Model 3 further adjusted for tobacco use, physical activity, blood pressure, TC, and fasting blood glucose based on Model 2. The selection criteria for adjustment variables were based on their associations with CVD risk and adverse outcomes, as referenced from Life's Simple 7. To further evaluate the non-linear relationship between the independent variables and all-cause mortality risk, we conducted multivariable restricted cubic spline (RCS) analyses. When non-linear relationships were identified, potential inflection points were sought, and segmented effects were calculated.

Statistical analyses were performed using IBM SPSS Statistics (version 26.0) and R software (version 4.4.0). Given the complex sampling survey design of NHANES, we performed weighted analyses using adjusted minimal subsample weights, which were accomplished through the "Survey" package of R. Additionally, the "mice" package was employed to perform multiple imputation for missing covariates based on the random forest algorithm. A two-sided P-value less than 0.05 was considered statistically significant.

Study population characteristics

A total of 2304 CVD participants were enrolled in this study, among whom 774 died during a median follow-up period of 68 months. The screening process of the study population was illustrated in Fig. 1. Compared to survivors, deceased participants exhibited several differences in demographics, laboratory tests, and medical history. Specifically, deceased participants were older, had a higher proportion of being single and tobacco use, and had a lower level of education. Laboratory data revealed higher levels of fasting blood glucose, glycated hemoglobin, urea nitrogen, creatinine, uric acid, and white blood cell count, as well as lower hemoglobin levels. These results were summarized in Table 1.

Table 1

Characteristics of the study population
Variables	Overall	Surviving participants	Dead participants	P value
Demographic information
Age,years	67.30 (9.14)	65.35 (8.56)	72.21 (8.72)	< 0.001
Gender,%
Male	58.2	59.0	56.3	0.421
Female	41.8	41.0	43.7
Marriage,%
Have a partner	61.2	65.5	50.4	< 0.001
Without partner	38.8	34.5	49.6
Education,%
High school and below	51.6	46.2	65.1	< 0.001
Above high school	48.4	53.8	34.9
Race,%
Mexican American	4.1	4.4	3.4	0.004
Other Hispanic	3.3	3.6	2.3
Non-Hispanic White	74.6	72.6	79.8
Non-Hispanic Black	11.2	11.6	10.4
Other Race	6.8	7.8	4.1
Laboratory tests
Total cholesterol,mmol/L	4.64 (1.16)	4.64 (1.15)	4.63 (1.19)	0.881
Triglycerides,mmol/L	1.52 (1.00)	1.53 (1.08)	1.51 (0.76)	0.818
LDL-C,mmol/L	2.53 (0.97)	2.55 (0.94)	2.49 (1.03)	0.504
HDL-C,mmol/L	1.30 (0.41)	1.31 (0.42)	1.30 (0.40)	0.686
Fasting blood glucose,mmol/L	6.47 (2.57)	6.35 (2.43)	6.79 (2.88)	0.002
Glycated hemoglobin,%	6.18 (1.20)	6.14 (1.17)	6.28 (1.27)	0.029
AST,U/L	23.00 (19.00, 28.00)	23.00 (19.00, 28.00)	22.25 (19.00, 28.00)	0.741
ALT,U/L	20.00 (16.00, 27.00)	21.00 (17.00, 27.00)	18.00 (15.00, 24.55)	< 0.001
Urea nitrogen,mmol/L	6.17 (2.69)	5.81 (2.24)	7.09 (3.44)	< 0.001
Creatinine,umol/L	86.63 (72.49, 104.31)	83.98 (70.72, 97.24)	95.71 (78.68, 116.65)	< 0.001
Uric acid,umol/L	354.88 (90.37)	347.22 (82.76)	374.57 (105.01)	< 0.001
White blood cell count,1000 cells/uL	7.41 (2.11)	7.30 (1.98)	7.71 (2.38)	< 0.001
Platelet count,1000 cells/uL	225.59 (68.32)	224.72 (66.51)	227.80 (72.73)	0.436
Hemoglobin,g/dL	14.07 (1.58)	14.20 (1.50)	13.75 (1.72)	< 0.001
Medical history
Tobacco use,%
Yes	61.5	59.9	65.5	0.038
No	38.5	40.1	34.5
Physical activity,%
No	1.6	1.7	0	0.739
Yes	98.4	98.3	100
Increased blood pressure,%
No	44.2	44.8	42.5	0.477
Yes	55.8	55.2	57.5
BMI at interview,Kg/m^2	30.51 (6.70)	30.76 (6.60)	29.87 (6.93)	0.018
BMI at age 25,Kg/m^2	23.67 (4.47)	23.71 (4.58)	23.57 (4.20)	0.559
BMI change,Kg/m^2	6.16 (2.75, 10.47)	6.44 (3.23, 10.60)	5.56 (2.10, 9.95)	0.013

LDL-C Low density lipoprotein cholesterol, HDL-C High density lipoprotein cholesterol, AST Aspartate aminotransferase, ALT Alanine aminotransferase, BMI Body mass index, BMI change refers to BMI at interview minus BMI at age 25.

The average BMI at the age of 25 was 23.67, while at the interview was 30.51. The changes in BMI showed a skewed distribution, with a median increase of approximately 6.16. A BMI range of 18.5 to 25 is generally considered as a healthy weight. The proportions of participants achieving this healthy BMI range in each survey year were presented in Fig. 2. Across the seven survey cycles, the proportion of participants with a healthy BMI at the age of 25 fluctuated between 50% and 70%, while the proportion decreased significantly to between 17% and 24% after the age of 50.

Association of BMI and its changes with all-cause mortality

We employed multivariable Cox regression to evaluate the relationship between BMI at different time points and its changes with all-cause mortality risk. After adjusting for multiple confounding variables, the relationship between BMI during middle and older adulthood (after 50 years old) and all-cause mortality risk was not statistically significant [HR = 1.01, 95%CI=(0.99, 1.02)]. When BMI after 50 years old was categorized, light-weight CVD patients were found to have a 3.06-fold increased risk of all-cause mortality compared to those with normal weight [HR = 4.06, 95%CI=(2.33, 7.05)]. BMI during young adulthood (at 25 years old) was positively associated with all-cause mortality risk [HR = 1.04, 95%CI=(1.02, 1.05)]. Regarding BMI changes, when included in Model 3 as a continuous variable, the relationship with all-cause mortality risk was not statistically significant [HR = 0.99, 95%CI=(0.97, 1.01)]. Notably, compared to participants who lost weight, those who gained weight exhibited a lower risk of all-cause mortality [HR = 0.70, 95%CI=(0.56, 0.89)]. The results of these multivariable Cox regression analyses were presented in Table 2.

Table 2

Relationship between BMI change and all-cause mortality risk in CVD patients
						Model2				Model3
	HR	95%CI			P value	HR	95%CI	P value		HR	95%CI	P value
BMI at interview,Kg/m^2
Continuous	0.98			0.97, 1.00	0.026	1.01	0.99, 1.03		0.200	1.01	0.99, 1.02		0.500
Classified
< 18.5	2.47			1.19, 5.11	0.015	3.96	2.24, 6.98		< 0.001	4.06	2.33, 7.05		< 0.001
>=18.5&<25	Reference					Reference				Reference
>=25	0.78			0.64, 0.94	0.009	0.93	0.78, 1.11		0.400	0.88	0.74, 1.05		0.200
BMI at age 25,Kg/m^2
Continuous	1.00		0.98, 1.02		> 0.999	1.04	1.02, 1.06		< 0.001	1.04	1.02, 1.05		< 0.001
Classified
< 18.5	0.99		0.73, 1.34		> 0.999	1.00	0.75, 1.34		> 0.999	0.94	0.70, 1.26		0.700
>=18.5&<25	Reference					Reference				Reference
>=25	0.87		0.71, 1.06		0.200	1.17	0.98, 1.39		0.092	1.10	0.91, 1.33		0.300
BMI change,Kg/m^2
Continuous	0.98		0.97, 1.00		0.018	1.00	0.98, 1.01		0.500	0.99	0.97, 1.01		0.200
Classified
< 0	Reference					Reference				Reference
>=0	0.66		0.51, 0.85		0.002	0.71	0.56, 0.91		0.006	0.70	0.56, 0.89		0.003

Model 1 is a univariate Cox regression model. Model 2 is adjusted for age and gender. Model 3 is adjusted for tobacco use, physical activity, blood pressure, total cholesterol, and fasting blood glucose based on Model 2. HR Hazard Ratio, CI Confidence interval. BMI change refers to BMI at interview minus BMI at age 25.

Nonlinear relationship between BMI, its changes, and all-cause mortality

To further explore the potential nonlinear relationship between BMI and its changes with all-cause mortality risk, we conducted the multivariable RCS analysis. As depicted in Fig. 3, the BMI at the age of 25 showed a generally positive correlation with all-cause mortality risk, with the lowest risk observed at a BMI of 19.61. In contrast, the BMI during middle and older adulthood (at the time of the interview) exhibited a roughly U-shaped association with all-cause mortality risk, with the lowest risk observed at a BMI of 26.55.

When examining the relationship between the BMI change and all-cause mortality risk, the RCS analysis revealed a typical U-shaped association. Specifically, the lowest risk of all-cause mortality was observed when the BMI change was 8.27. Further segmented multivariable Cox regression analysis showed that when the change in BMI was less than 8.27, it was negatively associated with all-cause mortality risk [HR = 0.80, 95%CI=(0.74, 0.87)], whereas when the change exceeded 8.27, it was positively associated with all-cause mortality risk [HR = 1.16, 95%CI=(1.00, 1.33)].

In this study, we established the NHANES longitudinal cohort using open-source data from the NCHS. By employing multivariate Cox regression models and RCS, we discovered a nonlinear relationship between BMI changes and the all-cause mortality in CVD patients. Patients with a BMI of 19.61 at age 25 and 26.55 after age 50 exhibited the lowest long-term all-cause mortality risk. The BMI change from young adulthood to middle-old age displayed a U-shaped correlation with all-cause mortality risk. Our research indicates that both excessive weight gain and weight loss can increase the risk of all-cause mortality among middle-aged and elderly CVD patients. Therefore, when formulating weight management strategies for this population, individualization should be achieved rather than blindly emphasizing weight loss.

We observed a U-shaped association between BMI changes over a long lifespan and the risk of all-cause mortality among CVD patients, which appears to contradict the conventional understanding that simply reducing BMI leads to benefits. However, our findings align with numerous past studies. A study[9] conducted in South Korea among adults with cardiac arrest arrived at nearly identical conclusions to ours, revealing a U-shaped correlation between BMI changes in the four years before cardiac arrest, with excessive weight loss increasing the risk of cardiac arrest, particularly prominent among overweight individuals. A decrease in BMI by more than 15% was associated with approximately a 3.1-fold increase in the risk of cardiac arrest. Another study[10] among cancer patients in Japan suggested that maintaining a stable BMI might be a superior strategy for reducing the incidence of coronary heart disease, heart failure, and atrial fibrillation, even among overweight individuals. This is because both excessive reduction and excessive increase in BMI were found to elevate the risk of CVD events, with the CVD risk associated with excessive weight loss far exceeding that of excessive weight gain (16% vs. 10%). Studies conducted in China have also unveiled a nonlinear relationship between BMI changes and blood pressure[11, 12]. While most previous studies were conducted in Asia or Europe, ours is among the first to be undertaken in a nationally representative sample of the American population. This contributes significantly to the mutual validation and generalization of research findings.

We further quantified the dose-response relationship between BMI changes and all-cause mortality risk. Our study revealed that 50–70% of participants had a BMI within the range of 18.5–25 at age 25, whereas this proportion declined to 17%-24% after the age of 50, aligning with the previous understanding that weight gain is a natural process in life[13]. We discovered that an excessive emphasis on weight loss could paradoxically increase the risk of all-cause mortality. This phenomenon is known as the "obesity paradox"[14], and several theories have been proposed to explain it. Overweight and obese individuals tend to have higher levels of cardiorespiratory fitness, which may counteract the adverse effects of overweight and other cardiovascular risk factors[15–17]. However, excessive weight loss can disrupt this balance between cardiorespiratory fitness and overweight. Additionally, another explanation posits that excessive weight loss can lead to alterations in body composition. Moderate weight loss results in a reduction in visceral adipose tissue, whereas excessive weight loss depletes muscle mass, leading to a frail state associated with poor outcomes in various diseases[18–20].

Our conclusions can provide evidence for individual long-term BMI management to reduce the risk of death among CVD patients, which is of great significance for improving the healthy life expectancy of the population. For instance, based on our findings, we recommend maintaining BMI within the normal range during young and middle adulthood by controlling weight, as BMI during this period is roughly positively correlated with all-cause mortality risk, while BMI in middle and old age exhibits a U-shaped correlation with all-cause mortality risk, where excessive weight loss and gain can both increase mortality risk. When assessing the all-cause mortality risk of CVD patients using BMI changes over a long life cycle, it is essential to recognize the nonlinear U-shaped relationship between them.

There are several limitations in this study. Firstly, we have merely considered BMI at two time points: 25 years old and after 50 years old, without examining the intermediate changes between these two points. The BMI at 25 years old and the diagnosis of CVD were obtained through questionnaire surveys. Although NHANES questionnaire survey data have been extensively used in clinical studies, the potential for information bias still exists. Secondly, we have not investigated the causes that led to changes in BMI. Lastly, due to the limitation of sample size, we have not conducted a subgroup analysis to explore the population specificity of the above results.

Among CVD patients, a U-shaped relationship is observed between BMI changes (from young adulthood to middle-aged and elderly stages) and the risk of all-cause mortality, where both excessive increases and decreases in BMI contribute to an elevated risk. Consequently, BMI management strategies throughout the long lifecycle should be tailored to individual BMI trajectories, rather than solely focusing on weight loss.

BMI Body mass index,

NHANES National Health and Nutrition Examination Surveys,

NDI National Death Index,

NCHS National Center for Health Statistics,

RCS Restricted Cubic Splines,

LDL-C Low density lipoprotein cholesterol,

HDL-C High density lipoprotein cholesterol,

AST Aspartate aminotransferase,

ALT Alanine aminotransferase,

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Author Contributions

Conceptualization, W.S.H. and H.X.Z.; Methodology, L.Y.F.; Software, H.X.Z.; Validation, W.Q.; Formal Analysis, H.X.Z.; Resources, W.S.H.; Data Curation, Y.X.J.; Writing – Original Draft Preparation, H.X.Z.; Visualization, Y.Y.T.; Supervision, Y.C.Y.; Project Administration, W.S.H. and H.X.Z.; Funding Acquisition, W.S.H. and W.Q.

Funding

This work was supported by grants from the General Project of National Natural Science Foundation of China (No.82374421), the General Project of Natural Science Foundation of Beijing (No.72372311) and Science and technology innovation project of China Academy of Chinese Medical Sciences (No.C12021A00921). This research received no external funding.

Institutional Review Board Statement

Ethical review and approval were waived for this study, due to this was a study based on publicly available data.

Informed Consent Statement

Patient consent was waived due to this was a study on based publicly available data.

Acknowledgments

We would like to acknowledge the following financial support: the General Project of National Natural Science Foundation of China (No.82374421), the General Project of Natural Science Foundation of Beijing (No.72372311) and Science and technology innovation project of China Academy of Chinese Medical Sciences (No.C12021A00921). We thank the NHANES database for sharing the data.

Data Availability Statement

The data obtained and analysed during the current study are available in https://www.cdc.gov/nchs/nhanes/

Martin SS, Aday AW, Almarzooq ZI, Anderson C, Arora P, Avery CL, Baker-Smith CM, Barone GB, Beaton AZ, Boehme AK et al. 2024 Heart Disease and Stroke Statistics: A Report of US and Global Data From the American Heart Association. CIRCULATION 2024, 149(8):e347-e913.
Dwivedi AK, Dubey P, Cistola DP, Reddy SY. Association Between Obesity and Cardiovascular Outcomes: Updated Evidence from Meta-analysis Studies. CURR CARDIOL REP. 2020;22(4):25.
Hou XZ, Lv YF, Li YS, Wu Q, Lv QY, Yang YT, Li LL, Ye XJ, Yang CY, Wang MS, et al. Association between different insulin resistance surrogates and all-cause mortality in patients with coronary heart disease and hypertension: NHANES longitudinal cohort study. CARDIOVASC DIABETOL. 2024;23(1):86.
Howard G, Cushman M, Blair J, Wilson NR, Yuan Y, Safford MM, Levitan EB, Judd SE, Howard VJ. Comparative Discrimination of Life's Simple 7 and Life's Essential 8 to Stratify Cardiovascular Risk: Is the Added Complexity Worth It? CIRCULATION 2024, 149(12):905–913.
Iyen B, Weng S, Vinogradova Y, Akyea RK, Qureshi N, Kai J. Long-term body mass index changes in overweight and obese adults and the risk of heart failure, cardiovascular disease and mortality: a cohort study of over 260,000 adults in the UK. BMC Public Health. 2021;21(1):576.
Kindblom JM, Bygdell M, Hjelmgren O, Martikainen J, Rosengren A, Bergström G, Ohlsson C. Pubertal Body Mass Index Change Is Associated With Adult Coronary Atherosclerosis and Acute Coronary Events in Men. ARTERIOSCL THROM VAS. 2021;41(8):2318–27.
Hou XZ, Liu EQ, Liu SQ, Lv H, Cui HF, Han J. The negative association between serum albumin levels and coronary heart disease risk in adults over 45 years old: a cross-sectional survey. SCI REP-UK. 2023;13(1):672.
Divney AA, Murillo R, Rodriguez F, Mirzayi CA, Tsui EK, Echeverria SE, Adults US. Diabetes Care. 2019;42(7):1241–7.
Kim YJ, Kim MJ, Kim YJ, Kim WY. Comparison of the impact of longitudinal body mass index changes on cardiac arrest risk between normal and overweight populations. J CACHEXIA SARCOPENI 2024.
Ueno K, Kaneko H, Suzuki Y, Okada A, Fujiu K, Jo T, Takeda N, Kamiya K, Ako J, Morita H, et al. Change in Body Mass Index and Cardiovascular Outcomes in Patients With Cancer. MAYO CLIN PROC. 2024;99(6):891–901.
Xu J, Guo R, Xie Y, Zheng J, Wang Y, Dai Y, Sun Z, Xing L, Zhang X, Sun Y, et al. Effects of long- and short-term body mass index changes on incident hypertension are different. NUTRITION. 2020;74:110755.
Seimon RV, Espinoza D, Ivers L, Gebski V, Finer N, Legler UF, Sharma AM, James WP, Coutinho W, Caterson ID. Changes in body weight and blood pressure: paradoxical outcome events in overweight and obese subjects with cardiovascular disease. INT J Obes. 2014;38(9):1165–71.
Adams KF, Leitzmann MF, Ballard-Barbash R, Albanes D, Harris TB, Hollenbeck A, Kipnis V. Body mass and weight change in adults in relation to mortality risk. AM J EPIDEMIOL. 2014;179(2):135–44.
Kunutsor SK, Laukkanen JA. A U-Shaped Challenge: Addressing Cardiovascular Outcomes in Cancer Patients With Body Mass Index Changes. MAYO CLIN PROC. 2024;99(6):861–3.
Ross R, Blair SN, Arena R, Church TS, Després JP, Franklin BA, Haskell WL, Kaminsky LA, Levine BD, Lavie CJ et al. Importance of Assessing Cardiorespiratory Fitness in Clinical Practice: A Case for Fitness as a Clinical Vital Sign: A Scientific Statement From the American Heart Association. CIRCULATION 2016, 134(24):e653-e699.
Piepoli MF, Corrà U, Veglia F, Bonomi A, Salvioni E, Cattadori G, Metra M, Lombardi C, Sinagra G, Limongelli G, et al. Exercise tolerance can explain the obesity paradox in patients with systolic heart failure: data from the MECKI Score Research Group. EUR J HEART FAIL. 2016;18(5):545–53.
Lavie CJ, Sharma A, Alpert MA, De Schutter A, Lopez-Jimenez F, Milani RV, Ventura HO. Update on Obesity and Obesity Paradox in Heart Failure. PROG CARDIOVASC DIS. 2016;58(4):393–400.
Ponti F, Santoro A, Mercatelli D, Gasperini C, Conte M, Martucci M, Sangiorgi L, Franceschi C, Bazzocchi A. Aging and Imaging Assessment of Body Composition: From Fat to Facts. FRONT ENDOCRINOL. 2019;10:861.
Chaston TB, Dixon JB. Factors associated with percent change in visceral versus subcutaneous abdominal fat during weight loss: findings from a systematic review. INT J Obes. 2008;32(4):619–28.
Afilalo J. Unsuccessful ageing: from frailty to cardiovascular disease. EUR HEART J. 2024;45(12):1069–71.

No competing interests reported.

Download PDF

Editorial decision: Revision requested
23 Oct, 2024
Reviews received at journal
09 Sep, 2024
Reviews received at journal
03 Sep, 2024
Reviews received at journal
30 Aug, 2024
Reviews received at journal
21 Aug, 2024
Reviews received at journal
16 Aug, 2024
Reviews received at journal
15 Aug, 2024
Reviewers agreed at journal
15 Aug, 2024
Reviewers agreed at journal
14 Aug, 2024
Reviewers agreed at journal
14 Aug, 2024
Reviewers agreed at journal
14 Aug, 2024
Reviewers agreed at journal
14 Aug, 2024
Reviewers agreed at journal
14 Aug, 2024
Reviewers agreed at journal
14 Aug, 2024
Reviewers agreed at journal
14 Aug, 2024
Reviewers invited by journal
14 Aug, 2024
Editor invited by journal
12 Aug, 2024
Editor assigned by journal
12 Aug, 2024
Submission checks completed at journal
08 Aug, 2024
First submitted to journal
08 Aug, 2024

You are reading this latest preprint version

The relationship between the trajectory of body mass index changes over a long lifecycle and the risk of all-cause mortality in patients with cardiovascular disease: NHANES longitudinal cohort study

Status:

Version 1

Abstract

Background

Methods

Results

Conclusion

Figures

Introduction

Methods

Study design

BMI and changing trajectory

All-cause death determination

Covariates

Statistical Analysis

Results

Study population characteristics

Association of BMI and its changes with all-cause mortality

Nonlinear relationship between BMI, its changes, and all-cause mortality

Discussion

Conclusion

Abbreviations

Declarations

References

Additional Declarations

Status:

Version 1