Body mass index trajectories during mid to late life and risks of mortality and cardiovascular outcomes: Results from four prospective cohorts

Yun-JiuChengab1 Zhen-GuangChenc1 Su-HuaWuab1 Wei-YiMeiab Feng-JuanYaod MingZhange Dong-LingLuof

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Citation: Cheng, Y. J., Chen, Z. G., Wu, S. H., Mei, W. Y., Yao, F. J., Zhang, M., & Luo, D. L. (2021). Body mass index trajectories during mid to late life and risks of mortality and cardiovascular outcomes: Results from four prospective cohorts. EClinicalMedicine33, 100790.

Note: This article is available under the Creative Commons CC-BY-NC-ND license and permits non-commercial use of the work as published, without adaptation or alteration provided the work is fully attributed.

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Our understanding of the weight-outcome association mainly comes from single-time body mass index (BMI) measurement. However, data on long-term trajectories of within-person changes in BMI on diverse study outcomes are sparse. Therefore, this study is to determine the associations of individual BMI trajectories and cardiovascular outcomes.


The present analysis was based on data from 4 large prospective cohorts and restricted to participants aged ≥45 years with at least two BMI measurements. Hazard ratios (HR) and 95% confidence intervals(95%CI) for each outcome according to different BMI trajectories were calculated in Cox regression models.


The final sample comprised 29,311 individuals (mean age 58.31 years, and 77.31% were white), with a median 4 BMI measurements used in this study. During a median follow-up of 21.16 years, there were a total of 10,192 major adverse cardiovascular events (MACE) and 11,589 deaths. A U-shaped relation was seen with all study outcomes. Compared with maintaining stable weight, the multivariate adjusted HR for MACE were 1.53 (95%CI 1.40–1.66), 1.26 (95%CI 1.16–1.37) and 1.08 (95%CI 1.02–1.15) respectively for rapid, moderate and slow weight loss; 1.01 (95%CI 0.95–1.07), 1.13 (95%CI 1.05–1.21) and 1.29 (95%CI 1.20–1.40) respectively for slow, moderate and rapid weight gain. Identical patterns of association were observed for all other outcomes. The development of BMI differed markedly between the outcome-free individuals and those who went on to experience adverse events, generally beginning to diverge 10 years before the occurrence of the events.


Our findings may signal an underlying high-risk population and inspire future studies on weight management.


National Natural Science Foundation of China, Guangdong Natural Science Foundation.


Trajectories Body mass index Cardiovascular events Mortality Mid-to-late life

Research in context

Evidence before this study

We searched Pubmed for articles published in English assessing risk of cardiovascular disease and all-cause mortality in relation to BMI and BMI trajectories, using the search terms “BMI”, “change in BMI”, “BMI trajectories”, “cardiovascular diseases”, “major adverse cardiovascular events”, “death”, “mortality”, “coronary heart disease”, “stroke”, “heart failure”, “myocardial infarction” and “risk”, from the inception to December 15, 2020. We found numerous studies discussing the associations of single time BMI measurements and cardiovascular risks, but few of them explored the associations of individual change trajectories and adverse outcomes.

Added value of this study

In this large population-based study, a U-shaped relation was observed between BMI trajectories and subsequent risk of different health outcomes. Both weight gain and weight loss conferred increased risks for cardiovascular events and all-cause mortality. In addition, we found for the first time that falling off the BMI trajectory could be a warning sign for future occurrence of adverse events.

Implications of all the available evidence

Our findings may signal an underlying high-risk population and underscore the importance of maintaining body weight over the middle to late adulthood.

1. Introduction

In light of the obesity epidemic [1,2], it is imperative to understand the relationship of weight to the risks of mortality and cardiovascular diseases (CVD). Although this relation is well documented in previous researches, most of them were based on single-time assessment of body weight (or body mass index, BMI) [3][4][5][6][7][8]. As noted, the relation of single-time BMI measurement to adverse outcomes changed during the observation period [9]. Specifically, the magnitude of this association weakens among middle-aged and elderly populations [10,11].

Further, using single-time BMI may fail to recognize the effect of weight change on the associated risks. Weight changes are highly variable over the life course [12][13][14][15]. Both weight loss and gain in middle-aged adults rendered increased risk of all-cause and CVD mortality [4,[16][17][18][19]]. However, the patterns of BMI change may differ among individuals; thus, a life-course perspective is essential. Mapping the longitudinal trajectory of BMI may directly capture the within-person change in BMI, and better characterize the associated risks.

Although increasing number of studies have investigated the relationship of BMI trajectories and cardiovascular outcomes, most were assuming the population lies within a mixture of latent groups, using either growth curve model or group-based latent model [11,[20][21][22][23][24][25][26][27][28][29]]. These models are largely based on subgroup means over a specific period of time and might be imprecise. Till now, there are at least 2 to 6 different BMI trajectory patterns being reported [11,[20][21][22][23][24][25][26][27][28][29][30]], even using the same dataset [20,26].

Therefore, in order to obtain a more precise association between BMI trajectory and cardiovascular outcomes, we here used the original BMI slope from each individual to represent individual BMI change trajectory. As far as we know, less than ten papers have reported the value of BMI slope in cardiovascular system [13,14,[31][32][33][34][35][36][37]]. Most of them investigated the association of BMI slope and change in cardiovascular risk factors [14,31,[33][34][35][36]]. Only two researches illustrated its association with cardiovascular outcomes [13,32]. However, models were not fully adjusted and differences on weight change direction were not taken into consideration.

Therefore, in our current study, we separated weight gain and weight loss by different degrees of change to comprehensively illustrate the relation of individual BMI trajectory to diverse study outcomes. As a second aim, we explored and characterized the developmental paths of BMI prior to individual outcomes.


Model 1, adjusted for age, gender and race; Model 2, adjusted for age, gender, race, smoking status, current alcoholic use, education level, marital status, income, physical activity, consumption of fruits and vegetables, history of hypertension, diabetes, HF, CHD, cancer, COPD and stroke, baseline BMI, serum level of glucose, total cholesterol, LDLCHDLC and triglyceride.

Abbreviation: MACE, major adverse cardiovascular events; MI, myocardial infarction; CHF, chronic heart failure; CVD, cardiovascular disease; Non-CVD, non-cardiovascular disease; CHD, coronary heart disease.

3.2.1. Primary outcomes

Compared with maintaining stable weight, the multivariate adjusted HRs for MACE were 1.53 (95%CI 1.40–1.66), 1.26 (95%CI 1.16–1.37) and 1.08 (95%CI 1.02–1.15) respectively for rapid, moderate and slow weight loss; 1.01 (95%CI 0.95–1.07), 1.13 (95%CI 1.05–1.21) and 1.29 (95%CI 1.20–1.40) respectively for slow, moderate and rapid weight gain. Models examining the associations with outcomes of MI and CHF yielded similar results as MACE. While for stroke, the hazard was significantly increased in participants with moderate-to-rapid weight loss and moderate weight gain, but for slow weight loss or slow weight gain, the association was insignificant (Table 2). Consistently, Fig. 1-A shows a U-shaped relation of the entire range of annual BMI change to individual cardiovascular outcomes in the cubic spline models.

Fig 1

3.2.2. Secondary outcomes

Similarly, the HRs for all-cause mortality were 1.98 (95%CI 1.83–2.13), 1.38 (95%CI 1.28–1.49) and 1.18 (95%CI 1.12–1.25) respectively for rapid, moderate and slow weight loss; 0.99 (95%CI 0.93–1.04), 1.08 (95%CI 1.01–1.15) and 1.29 (95%CI 1.20–1.38) respectively for slow, moderate and rapid weight gain, when compared to maintaining stable weight. Identical patterns of association were observed for CVD, non-CVD and CHD death (Table 2). Likewise, in the restricted cubic spline models, we detected a U-shaped relationship between annual BMI change and mortality risk, with a nadir around 0 kg/m2/year (Fig. 1-B).

3.3. BMI trajectories prior to different outcomes

Fig. 2 is an illustrative drawing to represent the general developmental patterns of BMI prior to different outcomes. We found that the development of BMI differed markedly between the outcome-free individuals and those who went on to experience adverse events. Trajectories appeared similar for the outcomes of MACE, all-cause, CVD and non-CVD death. The outcome-free participants followed a trajectory where the average BMI levels rise initially, remain stable or steadily decreased throughout follow-up. Those who went on to experience events generally showed lower baseline levels of BMI, steeper rise initially and faster fall before the occurrence of the events. With regards to MI and CHD death, the average BMI level was comparable in participants with or without the outcomes, but an accelerated decline was observed in those who died or experienced the events. Interestingly, although the developmental trend was identical among participants with and without CHF, those who experienced CHF had a generally higher BMI level during their life. With respect to the outcome of stroke, the BMI trajectories were less distinctive between groups.

Fig 2

3.4. Additional information and stratified analysis

We repeated the primary analyses in a series of sensitivity analyses. Excluding participants with missing values on baseline covariates (supplementary Table 3 in Appendix 3), with preexisting illnesses at baseline (supplementary Table 4 in Appendix 3), or with highest weight variability during follow-up (supplementary Table 5 in Appendix 3) did not appreciably change the results. In terms of percent change of BMI, the association patterns for cardiovascular outcomes were identical to our primary analysis (supplementary Table 6 in Appendix 3). But for the death outcomes, we only found significant increased risk in weight loss quintiles (quintile 1 and 2). When separating the primary analysis by individual cohort, consistent findings were observed (supplementary Table 7 in Appendix 3).

As depicted in Fig. 3, the associations of BMI trajectories and MACE were generally consistent in stratified analyses by sex, race and smoking status. It should be noted that the BMI-MACE association was significantly modified by age and borderline by baseline BMI. The hazards for MACE were significantly higher in those younger than 60 years, but lower in those who were initially with obesity. For all-cause mortality, the associations with BMI trajectories were generally consistent in white or non-white population and significantly modified by age, sex, and smoking status. It’s revealed that male and individuals younger than 60 years had higher hazards for death. But surprisingly, the hazards were lower in the smoker subgroups. Similar to the MACE outcome, the hazards for death were lower in subgroup with obesity, but the modification effect by baseline BMI was insignificant. The association of BMI trajectories and other outcomes across the predefined subgroups are provided in supplementary Table 8 and Table 9 (Appendix 3).

4. Discussion

In our analyses of the overall cohort of 29,311 participants, a U-shaped relation was observed between BMI trajectories and subsequent risk of cardiovascular events and all-cause mortality. Significant increase of risks for MACE and all-cause death were noted for people assigned in weight loss or weight gain categories. The hazard risks for adverse outcomes were consistently lowest among individuals maintaining their body weight. Although effect modification was observed in several subgroups, our findings were generally robust in a number of sensitivity analysis. Furthermore, our study for the first time delineates the characteristics of BMI trajectories prior to different health outcomes, showing an accelerated decline in BMI almost ten years before the occurrence of the events.

More than 38.9% of US adults have obesity [1]; however, much of our understanding of the BMI-mortality association comes from single-time BMI measurement, without considering within-person variation over the long term. Since weight change is highly variable across adulthood, more studies are now focusing on BMI trajectories and different health outcomes [11,13,[20][21][22][23][24][25][26][27][28][29][30],32]. However, most of these studies were grouping people using growth curve model or group-based latent model [11,[20][21][22][23][24][25][26][27][28][29]]. Using the above models, one can identify individuals with distinct BMI trajectories from the available data [25,48]. However, class membership is not determined with certainty for each individual since it relies on the selected models (linear, curvilinear, cubic and other forms) and probability of belonging [20,49]. Thus, misclassification is possible and the associated risk of adverse outcomes could be invalid. In articles published by Zeng al. and Zajacova et al., the authors used the same data but identified different patterns of BMI trajectory [20,26]. As of now, at least two to six patterns of BMI trajectories have been reported in the general population and majority of them were depicting an ascending trend or paralleling with each other [21,26,28,[50][51][52]]. It is unrealistic that all participants were going the same way over the life course. There must be some groups of individuals experiencing gradual weight loss or even rapid loss in their weight. Furthermore, most of the existing studies differentiate the curves by studying changes in pre-defined BMI categories: defining a change within normal weight as “normal-stable” [28], or a change from overweight category to category with obesity as “overweight obesity” trajectory [26]. This crude categorization of BMI trajectories would probably yield over- or under-deterministic results. It should be noted that a variety of changes could occur within the same categories; even a small change in BMI would pose a significant deleterious effect on health [26]. Furthermore, the rate of change, the direction of change, or the slope of the trajectory was all likely to make a difference in the negative outcomes [13,53].

Thus, from the current study, we derived an overall BMI trajectory (annul change in BMI or BMI slope) for each individual, giving further support to the associations of long term trajectories and diverse health outcomes. In our study, the slope of BMI throughout middle and older age, either positive or negative, rendered increased risks of MACE and mortality: the larger the changes the greater the risk. More specifically, BMI falling faster than 0.1 kg/m2 per year resulted in at least 8% higher risks of MACE and 18% of death. On the other hand, increasing BMI by 0.3 kg/m2 per year was associated with at least 13% higher hazards for MACE and 8% for death. Although several prior studies were conducted with a similar method, findings were mixed and inconsistent. As demonstrated in Framingham Heart Study, BMI slopes were inversely associated with the outcome of total mortality and morbidity due to CHD [13]. On the contrary, in the study of Chicago Western Electric Company, weight loss slope was significantly associated with total and cardiovascular mortality, while the weight gain slope showed nonsignificant increased risk of each endpoint for 25-year follow-up [32]. These inconsistencies may result from inadequate adjustment for potential cofounders and not considering the weight change direction.

Identical pattern of association was noted in subgroups of the population, after stratification for age, sex, race, smoking status and baseline BMI. However, effect modification of these stratification variables varied with respect to different outcomes. Generally, the hazardous effects of weight change and adverse outcomes were more pronounced in male participants and at younger age (<60 years). Two additional results should be noted. First, although previous studies have suggested that smoking status is a crucial modifier on the association of BMI and cardiovascular risks, we reveal that the hazards of cardiovascular outcomes were generally consistent in the three categories of smoking status. While for all-cause and non-CVD death, the association with weight loss were inconclusively modified by smoking status. The inconsistent observed relation could be the result of diverse weight change patterns associated with smoking or accounted for the unmeasured confounders [12,33]. Second, decreased weight in individuals with higher BMI (overweight or with obesity) may result in a better outcome when compared to those with normal weight at baseline, which could partially explain the phenomenon of “obesity paradox” [54]. The risk differences for weight gain among normal, overweight or individuals with obesity were less obvious.

In this study, we not only captured the characteristics of individual BMI change trajectory, but also directly evaluated the average BMI trajectories for those with and without specific outcomes. We found for the first time that patterns of change in BMI prior to different outcomes were different. Overall, the BMI trajectories appeared similar for most of the study outcomes: the outcome-free participants followed a trajectory where the average BMI levels remained relatively stable, while for those who went on to experience adverse events, the trajectories began to fall 10 years before the event. Although it’s unclear whether the observed weight loss was the antecedent cause or the consequence of the outcome, these findings may signal an underlying high-risk population and underscore the importance of maintaining body weight over the middle to late adulthood.

The major strengths of this study include the availability of multiple BMI measurements within identical time interval and using the linear mixed model, which entails a more accurate assessment of individual BMI trajectory. Furthermore, although distinguishing intentional and unintentional weight loss is challenging, we try to separate them by using weight variability, in which the highest variability subgroup represents intentional weight loss subcategory. As a result, in weight loss participants with high weight variability, moderate and rapid weight loss was not significantly correlated with increased risk of cardiovascular events. But likewise, we did not observe a beneficial effect in this group of population. One possible explanation for this is that weight rebound following intentional weight loss may offset the positive effect brought by losing weight [55]. Therefore, for weight loss individuals, it is imperative to first examine the reasons for weight loss: intentional or unintentional. If someone is losing weight intentionally, avoiding weight regain or achieving sustained weight loss may be the cornerstone of the accrued benefits brought by losing weight from a high BMI.

Despite of the strengths provided above, several limitations should be noted. Firstly, our findings relate solely to changes in BMI while the changes of fat mass, muscle mass and the general change of the body composition were unknown. In addition, since majority of the study participants were white US people, the results could not be generalized to more heterogeneous populations. Secondly, although we are trying to distinguish whether weight loss was intentional or unintentional in our sensitivity analysis, data on the causes of weight loss were unavailable in the current study. And thus, we could not confirm the above speculation and further studies are warranted.

In this large population-based study, a U-shaped relation was observed between BMI trajectories and subsequent risk of different health outcomes. Both weight gain and weight loss conferred increased risks for cardiovascular events and all-cause mortality. In addition, we found for the first time that patterns of change in BMI prior to different outcomes were different. Falling off the BMI trajectory could be a warning sign for future occurrence of adverse events; thus, maintaining body weight during the middle to late adulthood may be essential. Despite the observational nature of the current study, the trajectories and risk patterns identified here may inspire future studies on the cause and potentially weight management guidelines.

The ARIC, CHS, MESA and FHS studies are carried out as a collaborative study supported by National Heart, Lung, and Blood Institute contracts. The study was also financially supported by the grants from National Natural Science Foundation of China (81600260), Guangdong Natural Science Foundation (2016A030313210), the Science and Technology Planning Project of Guangdong Province (2017A020215174), the Fundamental Research Funds for the Central Universities in Sun Yat-Sen University (18ykpy08), and the project of Kelin new star of the First Affiliated Hospital of Sun Yat-Sen University (Y50186).

Declaration of Competing Interest

We declare no competing interests.


All authors contributed to the study concept and design. YC, CZ and WS contributed equally to this work. DL and YC are senior and corresponding authors who also contributed equally to this study. DL, YC, CZ and WS have full access to all the data in this study and take full responsibility as guarantors for the integrity of the data and the accuracy of the data analysis. CY, LD, CZ and WS contributed to the study design. CZ and WS contributed to analysis and data interpretation. CY and LD drafted the manuscript and contributed to the final approval of the manuscript. MW, YF and ZM contributed to critical revision of the manuscript for important intellectual content.

Data sharing statement

The cohort data sets were obtained from the NIH Biologic Specimen and Data Repository Information Coordinating Center (BioLINCC) and could be applied to the corresponding author upon reasonable request.

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[1]C.M. Hales, C.D. Fryar, M.D. Carroll, D.S. Freedman, Y. Aoki, C.L. OgdenDifferences in obesity prevalence by demographic characteristics and urbanization level among adults in the United States, 2013-2016JAMA, 319 (23) (2018), pp. 2419-2429View Record in ScopusGoogle Scholar[2]C.L. Ogden, M.D. Carroll, C.D. Fryar, K.M. FlegalPrevalence of obesity among adults and youth: United States, 2011-2014NCHS Data Brief (219) (2015), pp. 1-8View Record in ScopusGoogle Scholar[3]D. Gu, J. He, X. Duan, et al.Body weight and mortality among men and women in ChinaJAMA, 295 (7) (2006), pp. 776-783CrossRefView Record in ScopusGoogle Scholar[4]X.F. Pan, J.M. Yuan, W.P. Koh, A. PanWeight change in relation to mortality in middle-aged and elderly Chinese: the Singapore Chinese health studyInt J Obes (Lond), 43 (8) (2019), pp. 1590-1600CrossRefView Record in ScopusGoogle Scholar[5]W. Xu, M. Shubina, S.I. Goldberg, A. TurchinBody mass index and all-cause mortality in patients with hypertensionObesity, 23 (8) (2015), pp. 1712-1720CrossRefView Record in ScopusGoogle Scholar[6]K.A. Kong, J. Park, S.H. Hong, Y.S. Hong, Y.A. Sung, H. LeeAssociations between body mass index and mortality or cardiovascular events in a general Korean populationPLoS ONE, 12 (9) (2017), Article e0185024CrossRefView Record in ScopusGoogle Scholar[7]V. Arndt, D. Rothenbacher, B. Zschenderlein, S. Schuberth, H. BrennerBody mass index and premature mortality in physically heavily working men–a ten-year follow-up of 20,000 construction workersJ Occup Environ Med, 49 (8) (2007), pp. 913-921View Record in ScopusGoogle Scholar[8]D. Aune, A. Sen, M. Prasad, et al.BMI and all cause mortality: systematic review and non-linear dose-response meta-analysis of 230 cohort studies with 3.74 million deaths among 30.3 million participantsBMJ, 353 (2016), p. i2156CrossRefView Record in ScopusGoogle Scholar[9]D.H. Lee, M.W. Steffes, M. Gross, et al.Differential associations of weight dynamics with coronary artery calcium versus common carotid artery intima-media thickness: the CARDIA StudyAm J Epidemiol, 172 (2) (2010), pp. 180-189CrossRefView Record in ScopusGoogle Scholar[10]K. Dhana, M.A. Ikram, A. Hofman, O.H. Franco, M. KavousiAnthropometric measures in cardiovascular disease prediction: comparison of laboratory-based versus non-laboratory-based modelHeart, 101 (5) (2015), pp. 377-383CrossRefView Record in ScopusGoogle Scholar[11]K. Dhana, J. van Rosmalen, D. Vistisen, et al.Trajectories of body mass index before the diagnosis of cardiovascular disease: a latent class trajectory analysisEur J Epidemiol, 31 (6) (2016), pp. 583-592CrossRefView Record in ScopusGoogle Scholar[12]J.W.G. YarnellComparison of weight in middle age, weight at 18 years, and weight change between, in predicting subsequent 14 year mortality and coronary events: caerphilly Prospective StudyJ Epidemiol Commun Health, 54 (5) (2000), pp. 344-348View Record in ScopusGoogle Scholar[13]L. Lissner, P.M. Odell, R.B. D’Agostino, et al.Variability of body weight and health outcomes in the Framingham populationN Engl J Med, 324 (26) (1991), pp. 1839-1844View Record in ScopusGoogle Scholar[14]J.S. Lee, K. Kawakubo, Y. Kobayashi, K. Mori, H. Kasihara, M. TamuraEffects of ten year body weight variability on cardiovascular risk factors in Japanese middle-aged men and womenInt J Obes Relat Metab Disord, 25 (7) (2001), pp. 1063-1067CrossRefView Record in ScopusGoogle Scholar[15]K.D. Brownell, R.W. J.Improving long-term weight loss: pushing the limits of treatmentBehav Ther, 18 (1987), pp. 353-374ArticleDownload PDFView Record in ScopusGoogle Scholar[16]R. AndresLong-term effects of change in body weight on all-cause mortality: a reviewAnn Intern Med, 119 (7_Part_2) (1993)Google Scholar[17]S.G. Wannamethee, A.G. Shaper, M. WalkerWeight change, weight fluctuation, and mortalityArch Intern Med, 162 (22) (2002)Google Scholar[18]W. Rathmann, B. Haastert, A. Icks, G. Giani, J.M. RosemanTen-year change in serum uric acid and its relation to changes in other metabolic risk factors in young black and white adults: the CARDIA studyEur J Epidemiol, 22 (7) (2007), pp. 439-445CrossRefView Record in ScopusGoogle Scholar[19]A. Karahalios, D.R. English, J.A. S.Change in body size and mortality: a systematic review and meta-analysisInt J Epidemiol, 46 (2017), pp. 526-546View Record in ScopusGoogle Scholar[20]A. Zajacova, J. AilshireBody mass trajectories and mortality among older adults: a joint growth mixture–discrete-time survival analysisGerontologist, 54 (2) (2014), pp. 221-231CrossRefView Record in ScopusGoogle Scholar[21]H. Murayama, J. Liang, J.M. Bennett, et al.Trajectories of body mass index and their associations with mortality among older Japanese: do they differ from those of western populations?Am J Epidemiol, 182 (7) (2015), pp. 597-605CrossRefView Record in ScopusGoogle Scholar[22]R.S. Peter, F. Keller, J. Klenk, H. Concin, G. NagelBody mass trajectories, diabetes mellitus, and mortality in a large cohort of Austrian adultsMedicine (Baltimore), 95 (49) (2016), p. e5608View Record in ScopusGoogle Scholar[23]M. Wang, Y. Yi, B. Roebothan, et al.Trajectories of body mass index among Canadian seniors and associated mortality riskBMC Public Health, 17 (1) (2017), p. 929View Record in ScopusGoogle Scholar[24]A. Botoseneanu, J. LiangLatent heterogeneity in long-term trajectories of body mass index in older adultsJ Aging Health, 25 (2) (2013), pp. 342-363CrossRefView Record in ScopusGoogle Scholar[25]Y. Yang, P.A. Dugue, B.M. Lynch, et al.Trajectories of body mass index in adulthood and all-cause and cause-specific mortality in the Melbourne Collaborative Cohort StudyBMJ Open, 9 (8) (2019), Article e030078CrossRefView Record in ScopusGoogle Scholar[26]H. Zheng, D. Tumin, Z. QianObesity and mortality risk: new findings from body mass index trajectoriesAm J Epidemiol, 178 (11) (2013), pp. 1591-1599CrossRefView Record in ScopusGoogle Scholar[27]S.K. Kahng, R.E. Dunkle, J.S. JacksonThe relationship between the trajectory of body mass index and health trajectory among older adultsRes Aging, 26 (1) (2016), pp. 31-61Google Scholar[28]M. Wang, Y. Yi, B. Roebothan, et al.Body mass index trajectories among middle-aged and elderly canadians and associated health outcomesJ Environ Public Health, 2016 (2016), pp. 1-9CrossRefView Record in ScopusGoogle Scholar[29]A. Hulman, D.B. Ibsen, A.S.D. Laursen, C.C. DahmBody mass index trajectories preceding first report of poor self-rated health: a longitudinal case-control analysis of the English longitudinal study of ageingPLoS ONE, 14 (2) (2019), Article e0212862CrossRefView Record in ScopusGoogle Scholar[30]T.C. Russ, I.M. Lee, H.D. Sesso, G. Muniz-Terrera, G.D. BattyFive-decade trajectories in body mass index in relation to dementia death: follow-up of 33,083 male Harvard University alumniInt J Obes (Lond), 43 (9) (2019), pp. 1822-1829CrossRefView Record in ScopusGoogle Scholar[31]C.B. Taylor, D.E. Jatulis, S.P. Fortmann, H.C. KraemerWeight variability effects: a prospective analysis from the Stanford five-city projectAm J Epidemiol, 141 (5) (1995), pp. 461-465CrossRefView Record in ScopusGoogle Scholar[32]A.R. Dyer, J. Stamler, P. GreenlandAssociations of weight change and weight variability with cardiovascular and all-cause mortality in the Chicago Western Electric Company StudyAm J Epidemiol, 152 (4) (2000), pp. 324-333View Record in ScopusGoogle Scholar[33]C.B. Taylor, D.E. Jatulis, M.A. Winkleby, B.J. Rockhill, H.C. KraemerEffects of life-style on body mass index changeEpidemiology, 5 (6) (1994), pp. 599-603CrossRefView Record in ScopusGoogle Scholar[34]J.S. Lee, K. Kawakubo, H. Kashihara, K. MoriEffect of long-term body weight change on the incidence of hypertension in Japanese men and womenInt J Obes Relat Metab Disord, 28 (3) (2004), pp. 391-395View Record in ScopusGoogle Scholar[35]L. Li, R. Hardy, D. Kuh, C. PowerLife-course body mass index trajectories and blood pressure in mid life in two British birth cohorts: stronger associations in the later-born generationInt J Epidemiol, 44 (3) (2015), pp. 1018-1026CrossRefView Record in ScopusGoogle Scholar[36]N. Nakanishi, K. Nakamura, K. Suzuki, K. TataraEffects of weight variability on cardiovascular risk factors; a study of nonsmoking Japanese male office workersInt J Obes Relat Metab Disord, 24 (9) (2000), pp. 1226-1230CrossRefView Record in ScopusGoogle Scholar[37]L. Lissner, C. Bengtsson, L. Lapidus, et al.Body weight vari- ability and mortality in the Gothenburg prospective studies of men and womenP. Bjorntorp, S. Rossner (Eds.), Obesity in Europe ‘88: proceedings of the first European congress on obesity, John Libbey and Company Ltd, London, United Kingdom (1989), pp. 55-60Google Scholar[38]The Atherosclerosis Risk in communities (ARIC) study: design and objectives. The ARIC investigatorsAm J Epidemiol, 129 (4) (1989), pp. 687-702Google Scholar[39]L.P. Fried, N.O. Borhani, P. Enright, et al.The cardiovascular health study: design and rationaleAnn Epidemiol, 1 (3) (1991), pp. 263-276ArticleDownload PDFView Record in ScopusGoogle Scholar[40]D.E. Bild, D.A. Bluemke, G.L. Burke, et al.Multi-ethnic study of atherosclerosis: objectives and designAm J Epidemiol, 156 (9) (2002), pp. 871-881View Record in ScopusGoogle Scholar[41]T.R. Dawber, G.F. Meadors, F.E. Moore Jr.Epidemiological approaches to heart disease: the Framingham studyAm J Public Health Nations Health, 41 (3) (1951), pp. 279-281View Record in ScopusGoogle Scholar[42]H. Kuivaniemi, C.A. Giffen, E.L. Wagner, et al.Providing researchers with online access to NHLBI biospecimen collections: the results of the first six years of the NHLBI BioLINCC programPLoS ONE, 12 (6) (2017), Article e178141Google Scholar[43]C.A. Giffen, L.E. Carroll, J.T. Adams, S.P. Brennan, S.A. Coady, E.L. WagnerProviding contemporary access to historical biospecimen collections: development of the NHLBI biologic specimen and data repository information coordinating center (BioLINCC)Biopreserv Biobank, 13 (4) (2015), pp. 271-279CrossRefView Record in ScopusGoogle Scholar[44]D.E. Alley, E.J. Metter, M.E. Griswold, et al.Changes in weight at the end of life: characterizing weight loss by time to death in a cohort study of older menAm J Epidemiol, 172 (5) (2010), pp. 558-565CrossRefView Record in ScopusGoogle Scholar[45]J. Delgado, K. Bowman, A. Ble, et al.Blood pressure trajectories in the 20 years before deathJAMA Intern Med, 178 (1) (2018), pp. 93-99CrossRefView Record in ScopusGoogle Scholar[46]J.A. Critchley, I.M. Carey, T. Harris, S. DeWilde, D.G. CookVariability in glycated hemoglobin and risk of poor outcomes among people with type 2 diabetes in a large primary care cohort studyDiabetes Care, 42 (12) (2019), pp. 2237-2246CrossRefView Record in ScopusGoogle Scholar[47]J.B. Li, S. Luo, M.C.S. Wong, et al.Longitudinal associations between BMI change and the risks of colorectal cancer incidence, cancer-relate and all-cause mortality among 81,388 older adults: BMI change and the risks of colorectal cancer incidence and mortalityBMC Cancer, 19 (1) (2019), p. 1082CrossRefView Record in ScopusGoogle Scholar[48]H. Murayama, B.A. ShawHeterogeneity in trajectories of body mass index and their associations with mortality in old age: a literature reviewJ Obesity Metabol Syndrome, 26 (3) (2017), pp. 181-187CrossRefView Record in ScopusGoogle Scholar[49]T. Jung, K.A.S. WickramaAn introduction to latent class growth analysis and growth mixture modelingSoc Personal Psychol Compass, 2 (1) (2008), pp. 302-317CrossRefView Record in ScopusGoogle Scholar[50]A. Botoseneanu, J. LiangSocial stratification of body weight trajectory in middle-age and older americans: results from a 14-year longitudinal studyJ Aging Health, 23 (3) (2011), pp. 454-480CrossRefView Record in ScopusGoogle Scholar[51]S.P. Kelly, H. Lennon, M. Sperrin, et al.Body mass index trajectories across adulthood and smoking in relation to prostate cancer risks: the NIH-AARP Diet and Health StudyInt J Epidemiol, 48 (2) (2019), pp. 464-473CrossRefView Record in ScopusGoogle Scholar[52]J. Salmela, E. Mauramo, T. Lallukka, O. Rahkonen, N. KanervaAssociations between childhood disadvantage and adult body mass index trajectories: a follow-up study among midlife Finnish municipal employeesObes Facts, 12 (5) (2019), pp. 564-574CrossRefView Record in ScopusGoogle Scholar[53]Y. Yuan, C. Chu, W.L. Zheng, et al.Body mass index trajectories in early life is predictive of cardiometabolic riskJ Pediatr, 219 (2020), pp. 31-37e6View Record in ScopusGoogle Scholar[54]A. Elagizi, S. Kachur, C.J. Lavie, et al.An overview and update on obesity and the obesity paradox in cardiovascular diseasesProg Cardiovasc Dis, 61 (2) (2018), pp. 142-150ArticleDownload PDFView Record in ScopusGoogle Scholar[55]S. Bangalore, R. Fayyad, R. Laskey, D.A. DeMicco, F.H. Messerli, D.D. WatersBody-weight fluctuations and outcomes in coronary diseaseN Engl J Med, 376 (14) (2017), pp. 1332-1340View Record in ScopusGoogle Scholar1

These authors contributed equally to the work.View Abstract© 2021 The Authors. Published by Elsevier Ltd.

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