Body Mass Index Trajectories During Young Adulthood and Incident Hypertension: A Longitudinal Cohort in Chinese Population

Fan, Bingbing; Yang, Yachao; Dayimu, Alimu; Zhou, Guangshuai; Liu, Yanxun; Li, Shengxu; Chen, Wei; Zhang, Tao; Xue, Fuzhong

doi:10.1161/jaha.119.011937

Cited by 44 publications

(54 citation statements)

References 32 publications

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“…Of the 59 included studies, 19 studies stratified trajectories by sex and 13 studies only included sex‐specific populations (i.e., males only or females only). Two studies 41,66 reported they stratified trajectories by sex, however results were not reported since the results were similar to crude analyses. For most studies, the names and number of trajectories were consistent in both men and women, however visual inspection showed slight differences.…”

Section: Resultsmentioning

confidence: 96%

“…Of these 22 studies with growth trajectories beginning in childhood, five studies 20,22,38,41,51 had measures until young adulthood (19–23 years old), 12 studies 16,30–32,36,42–50 had measures until midadulthood (38–50 years old), and five studies 5,14,17,18,46 had measures until older adulthood (60–80 years old). The remaining studies ( n = 37) 15,19,21–29,33–35,37,39,40,52–72 did not include any childhood measures, and only included measures >18 years of age. Of these 37 studies that only reported growth measures in adulthood, six studies 26,61–64,70 only included measures during the older adulthood period of life (≥60 years of age).…”

Section: Resultsmentioning

confidence: 99%

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Group‐based trajectory modeling of body mass index and body size over the life course: A scoping review

Rubeis

Andreacchi

Sharpe

et al. 2020

Obesity Science & Practice

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Background Group‐based trajectory modeling has been applied to identify distinct trajectories of growth across the life course. Objectives of this study were to describe the methodological approaches for group‐based modeling of growth across the life course and to summarize outcomes across studies. Methods A scoping review with a systematic search of Medline, EMBASE, CINAL, and Web of Science was conducted. Studies that used a group‐based procedure to identify trajectories on any statistical software were included. Data were extracted on trajectory methodology, measures of growth, and association with outcomes. Results A total of 59 studies were included, and most were published from 2013 to 2020. Body mass index was the most common measure of growth (n = 43). The median number of identified trajectories was 4 (range: 2–9). PROC TRAJ in SAS was used by 33 studies, other procedures used include TRAJ in STATA, lcmm in R, and Mplus. Most studies evaluated associations between growth trajectories and chronic disease outcomes (n = 22). Conclusions Group‐based trajectory modeling of growth in adults is emerging in epidemiologic research, with four distinct trajectories observed somewhat consistently from all studies. Understanding life course growth trajectories may provide further insight for population health interventions.

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Section: Resultsmentioning

confidence: 96%

Section: Resultsmentioning

confidence: 99%

Group‐based trajectory modeling of body mass index and body size over the life course: A scoping review

Rubeis

Andreacchi

Sharpe

et al. 2020

Obesity Science & Practice

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show abstract

“…28 A longitudinal cohort in Chinese population characterized four distinct trajectory groups during adulthood associated with hypertension. 19 To our knowledge, there are no studies that explored the relationship between longitudinal BMI profiles in early to mid-life adulthood and the development of diabetes among Asian population in later life.…”

Section: Discussionmentioning

confidence: 99%

“…Multiple LCGMMs with different trajectory shapes including linear and nonlinear parameters were tested using the strategy we previously described in the online supplementary material. 19 The LCGMM computes estimation of the quadratic curve parameters, including fixed effect parameters (for a group) and random-effect parameters (individual specific). The random effect coefficients represent the difference between the fixed effect parameters and the observed values for each individual.…”

Section: Outcome Measuresmentioning

confidence: 99%

Trajectories of early to mid-life adulthood BMI and incident diabetes: the China Health and Nutrition Survey

Fan

Wei

et al. 2020

BMJ Open Diab Res Care

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IntroductionThis longitudinal study aims to characterize distinct body mass index (BMI) trajectories during early to mid-life adulthood and to explore the association between BMI change from young adulthood to midlife and incident diabetes.Research design and methodsThis study included 7289 adults who had repeatedly measured BMI 3–9 times during 1989–2011 and information on incident diabetes. Latent class growth mixed model (LCGMM) was used to identify different BMI trajectories. Cox proportional hazard models were used to investigate the association between the trajectory group membership and incident hyperglycemia, adjusting for covariates. The hyperglycemia group included individuals with prediabetes or diabetes. The model-estimated BMI levels and slopes were calculated at each age point in 1-year intervals according to the model parameters and their ﬁrst derivatives, respectively. Logistic regression analyses were used to examine the association of model-estimated levels and slopes of BMI at each age point with incident hyperglycemia. The area under the curve (AUC) was computed from longitudinal growth curve models during the follow-up for each individual. Prior to the logistic regression analyses, quartiles of total, baseline, and incremental AUC values were calculated.ResultsThree distinct trajectories were characterized by LCGMM, comprising of low-increasing group (n=5136), medium-increasing group (n=1914), and high-increasing group (n=239). Compared with the low-increasing group, adjusted HRs and 95% CIs were 1.21 (0.99 to 1.48) and 1.56 (1.06 to 2.30) for the medium-increasing and the high-increasing group, respectively. The adjusted standardized ORs of model-estimated BMI levels increased among 20–50 years, ranging from 0.98 (0.87 to 1.10) to 1.19 (1.08 to 1.32). The standardized ORs of level-adjusted linear slopes increased gradually from 1.30 (1.16 to 1.45) to 1.42 (1.21 to 1.67) during 20–29 years, then decreased from 1.41 (1.20 to 1.66) to 1.20 (1.08 to 1.33) during 30–43 years, and finally increased to 1.20 (1.04 to 1.38) until 50 years. The fourth quartile of incremental AUC (OR=1.31, 95% CI 1.03 to 1.66) was significant compared with the first quartile, after adjustment for covariates.ConclusionsThese findings indicate that the BMI trajectories during early adulthood were significantly associated with later-life diabetes. Young adulthood is a crucial period for the development of diabetes, which has implications for early prevention.

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“…This method assumes that individual differences in trajectories can be summarized by a finite set of different polynomial functions of time. In the past years, this method has been successfully used to examine the association between BMI trajectories and the risk of hypertension, cancer and all‐cause mortality. However, to date, just a few studies have explored the association between BMI trajectories identified by GBTM and diabetes risk.…”

Section: Introductionmentioning

confidence: 99%

Distinct developmental trajectories of body mass index and diabetes risk: A 5‐year longitudinal study of Chinese adults

Dai

Bragazzi

et al. 2019

J of Diabetes Invest

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Aims/Introduction This longitudinal study aimed to explore whether distinct developmental trajectories of body mass index (BMI) would be predictive of diabetes risk in general Chinese adults. Materials and Methods A total of 4,519 participants aged >18 years who were free of diabetes in 2011 (baseline of the current analysis) were enrolled in this study. All participants completed a medical examination every year during 2011–2016, and BMI levels were measured two to six (average 5.6) times. Group‐based trajectory modeling was applied to identify BMI trajectories over time. New‐onset diabetes was confirmed in 2016. Results During 2011–2016, four distinct BMI trajectories were identified according to BMI range and changing pattern over time: “low” (19.6%), “moderate” (33.4%), “moderate‐high” (33.4%) and “high” (13.6%). A total of 168 (3.7%) new‐onset diabetes cases were confirmed in 2016. Compared with the “low” BMI trajectory, participants in the “high” BMI trajectory were at significantly higher risk for new‐onset diabetes (adjusted relative risk 3.24, 95% confidence interval 1.27–8.24). Notably, BMI trajectories based on the first four or three annual BMI tests yielded similar results. By contrast, no significant correlation was found between categories of baseline BMI and new‐onset diabetes in 2016 after multivariate adjustment. Conclusions The present results show that distinct BMI trajectories, even identified using just four or three annual BMI tests, are significantly associated with new‐onset diabetes. Monitoring BMI trajectories over time might provide an important approach to identify subpopulations at higher risk for developing diabetes.

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Body Mass Index Trajectories During Young Adulthood and Incident Hypertension: A Longitudinal Cohort in Chinese Population

Cited by 44 publications

References 32 publications

Group‐based trajectory modeling of body mass index and body size over the life course: A scoping review

Group‐based trajectory modeling of body mass index and body size over the life course: A scoping review

Trajectories of early to mid-life adulthood BMI and incident diabetes: the China Health and Nutrition Survey

Distinct developmental trajectories of body mass index and diabetes risk: A 5‐year longitudinal study of Chinese adults

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