Body mass index trajectories from adolescent to young adult for incident high blood pressure and high plasma glucose

Ahanchi, Noushin Sadat; Ramezankhani, Azra; Munthali, Richard; Asgari, Samaneh; Azizi, Fereidoun; Hadaegh, Farzad

doi:10.1371/journal.pone.0213828

Cited by 22 publications

(46 citation statements)

References 43 publications

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“…9 Except for the absolute level of BMI, longitudinal BMI trajectory across the lifecourse also plays an important role in the development of diabetes. [10][11][12][13][14] However, it is unclear whether the dynamic trends in body weight gain contribute to future diabetes risk, especially during young adulthood. We hypothesized that the velocity of body weight change during young adulthood was significantly associated with the development of diabetes in later life, independent of BMI absolute values.…”

Section: Introductionmentioning

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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Section: Introductionmentioning

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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“…Interest in the heterogeneity of childhood BMI trajectory patterns has increased in response to the epidemic of pediatric overweight and obesity 25 . Cohort studies have confirmed that distinct BMI trajectories from childhood to early adulthood are related to various adult cardiometabolic risks 13,15,18,19 . Moreover, the Nurses’ Health Study and Health Professionals Follow‐up Study also found that participants with distinct BMI trajectory groups from childhood and throughout the life span exhibited different levels of type 2 diabetes, cardiovascular disease (CVD), and mortality risk 11,12 …”

Section: Discussionmentioning

confidence: 99%

“…In brief, the maximum likelihood method was used to fit the longitudinal data on BMI in a censored normal model with a polynomial function of age 9 . The optimal model was determined using the Bayesian information criterion (BIC), mean posterior probabilities for each group (>0.8), membership of each group (we required each group to include more than 1% of all participants), and visual inspection of the patterns 10,18 …”

Section: Methodsmentioning

confidence: 99%

“…Statistical techniques have been developed to identify the heterogeneity of BMI trajectories in given populations 9,10 . Longitudinal studies have shown that different childhood BMI trajectory patterns confer significantly different risks of future adverse health consequences 11‐20 . However, few similar studies have been conducted in the Chinese pediatric population.…”

Section: Introductionmentioning

confidence: 99%

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Body mass index trajectory across childhood and subsequent risk of elevated blood pressure

Fan

Zhang

2020

J of Clinical Hypertension

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We investigated the relationship between the body mass index (BMI) trajectory across childhood and the subsequent occurrence of elevated blood pressure (BP) in the Chinese pediatric population. The study cohort from the China Health and Nutrition Survey comprised 1484 children, each of whom underwent three BP and BMI assessments during childhood and had a non-elevated BP during the first childhood assessment. A group-based trajectory model was used to identify four distinct BMI trajectories across childhood: lean-stable increase, medium-marked increase, heavy

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“…In Equation (1), and present evidence of whether adult BMI is associated with FTO variants and birth weight, respectively, is a vector of individual‐level controls including gender, birth year (age), and birth order. I control for gender to account for potential gender differences in the trajectory of BMI (Ahanchi et al, ; Hargrove, ; Liu & Umberson, ). The inclusion of age (birth year) and birth order partially controls for differential timing of births.…”

Section: Methodsmentioning

confidence: 99%

Are genes destiny? Exploring the role of intrauterine environment in moderating genetic influences on body mass

Kim

2019

American J Hum Biol

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Objectives: Genes influence every aspect of human behavior, physiology, and health. Obesity, a major global public health concern, is no exception. However, relatively little is known about whether environmental factors moderate the effects of genetic influences on body mass. By incorporating molecular genetics and the fetal origins of obesity hypothesis into a gene-environment interaction framework, this study investigates how the effect of the obesityassociated gene (FTO) interacts with the effect of intrauterine environment to influence body mass during adulthood. Methods: This study uses data from the Wisconsin Longitudinal Study, and uses sibling comparisons that allow for quasi-experimental variation in both genetic and environmental factors. Results: This study found positive, but noisy, within-family estimates of the FTO-body mass index (BMI) association. The association between birth weight and adult BMI was robust to controlling for unobserved family background factors. More importantly, this study revealed that the association between having a risk allele of the FTO gene and BMI (and obesity status) is largely concentrated among individuals who were heavier at birth. This provides evidence of a gene-environment interaction on BMI (and the development of obesity). Conclusions:Genes are not destiny and environmental factors may offset the effects of obesity-promoting genes. Public health efforts to counteract genetic effects on body mass may begin as early as in utero. Efforts to prevent higher birth weight may thus help reduce the risk of obesity later in life, by directly addressing the programming effects of the in utero environment and also indirectly moderating obesity-promoting genetic effects.

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Body mass index trajectories from adolescent to young adult for incident high blood pressure and high plasma glucose

Cited by 22 publications

References 43 publications

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

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

Body mass index trajectory across childhood and subsequent risk of elevated blood pressure

Are genes destiny? Exploring the role of intrauterine environment in moderating genetic influences on body mass

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