The authors concluded by suggesting that obesity prevention should not be limited to children and should include other age groups at particularly high risk, such as young adults aged 18 to 24 years. ...For decades, the field has relied on repeated, cross-sectional data to infer trends in obesity across the life cycle, as there have been few long-term follow-up studies in very large and ethnically diverse population-based data sets that capture the full range of the life cycle. ...it is critical to address the complex disease of obesity across the full range of the life cycle and particularly across vulnerable subpopulations who are at highest risk.O Funding agencies:
No longitudinal analyses using national data have evaluated the increase in obesity from adolescence into early adulthood. We examined obesity incidence, persistence, and reversal in a nationally ...representative cohort of US teens followed into their early 30s, using measured height and weight data, in individuals enrolled in wave II (1996; 12–21 years), wave III (2001; 17–26 years), and wave IV (2008 early release data; 24–32 years) of the National Longitudinal Study of Adolescent Health (N = 8,675). Obesity was defined as a BMI ≥95th percentile of the 2000 Centers for Disease Control/National Center for Health Statistics growth charts or ≥30 kg/m2 for individuals <20 years and ≥30 kg/m2 in individuals ≥20 years. In 1996, 13.3% of adolescents were obese. By 2008, obesity prevalence increased to 36.1%, and was highest among non‐Hispanic black females (54.8%). Ninety percent of the obese adolescents remained obese in 2008. While annual obesity incidence did not decline in the total sample across the two study intervals (2.3% per year 1996–2001 vs. 2.2% per year 2001–2008), rates among white females declined (2.7 to 1.9% per year) and were highest among non‐Hispanic black and Hispanic females (3.8 and 2.7% per year, 1996–2001 vs. 3.0 and 2.6% per year, 2002–2008, respectively). Obesity prevalence doubled from adolescence to the early 20s, and doubled again from the early to late 20s or early 30s, with strong tracking from adolescence into adulthood. This trend is likely to continue owing to high rates of pediatric obesity. Effective preventive and treatment efforts are critically needed.
Of note, individuals in the disordered eating subgroup (Class 2), who may be particularly sensitive to external cues to eat and/or have disrupted or dysregulated satiety signaling, lost more weight ...post surgery than the other subgroups. ...Field et al. draw attention to heterogeneity in psychological, behavioral, and biological parameters within the patient population with obesity. Yet it is important to note that the LABS study sample, the focus of this work, includes individuals planning to have bariatric surgery within 30 days and these participants were predominantly nonHispanic white and female. ...future studies are needed to address diverse populations as well as heterogeneity in response to a variety of obesity-related treatments. To move forward, we need large studies with sufficient sample size to detect and study heterogeneity as well as deep phenotyping that addresses a range of factors across behavioral, genetic, physiological, metabolic, and biological domains to understand the nature of this heterogeneity.
Missing data are common in longitudinal cohort studies and can lead to bias, particularly in studies with informative missingness. Many common methods for handling informatively missing data in ...survey samples require correctly specifying a model for missingness. Although doubly robust methods exist to provide unbiased regression coefficients in the presence of missing outcome data, these methods do not account for correlation due to clustering inherent in longitudinal or cluster‐sampled studies. In this work, we developed a doubly robust method to estimate the regression of an outcome on a predictor in the presence of missing multilevel data on the outcome, which results in consistent estimation of regression coefficients assuming correct specification of either (1) the probability of missingness or (2) the outcome model. This method involves specification of separate hierarchical models for missingness and for the outcome, conditional on observed auxiliary variables and cluster‐specific random effects, to account for correlation among observations. We showed this proposed estimator is doubly robust and derived its asymptotic distribution, conducted simulation studies to compare the method to an existing doubly robust method developed for independent data, and applied the method to data from the China Health and Nutrition Survey, an ongoing multilevel longitudinal cohort study.
Objective
In adulthood, excess BMI is associated with cardiovascular disease (CVD); it is unknown whether risk differs by BMI trajectories from adolescence to adulthood.
Design and Methods
The ...National Longitudinal Study of Adolescent Health, a nationally representative, longitudinal adolescent cohort (mean age: 16.9 years) followed into adulthood (mean age: 28.8 years) n = 13,984 individuals (41,982 observations) was examined. Separate logistic regression models for diabetes, hypertension, and inflammation were used to examine odds of risk factors at given adult BMI according to varying BMI trajectories from adolescence to adulthood.
Results
CVD risk factor prevalence at follow‐up ranged from 5.5% (diabetes) to 26.4% (hypertension) and 31.3% (inflammation); risk differed across BMI trajectories. For example, relative to men aged 27 years (BMI = 23 kg/m2 maintained over full study period), odds for diabetes were comparatively higher for men of the same age and BMI ≈ 30 kg/m2 with ≈8 BMI unit gain between 15 and 20 years (OR = 2.35; 95% CI, 1.51, 3.66) or in those who maintained BMI ≈ 30 kg/m2 across the study period (OR = 2.33; 1.92, 2.83) relative to the same ≈8 BMI unit gain, but between 20 and 27 years (OR = 1.44; 1.10, 1.87).
Conclusions
Specific periods and patterns of weight gain in the transition from adolescence to adulthood might be critical for CVD preventive efforts.
Objective
This study aimed to understand how an increase in abdominal adiposity relative to overall adiposity is associated with blood pressure (BP) change.
Methods
A sex‐stratified mixed linear ...model was used to examine the association (95% CI) between annual changes in waist circumference (WC) and systolic blood pressure and diastolic blood pressure, estimated from two to eight repeated measures across the 1993‐2015 China Health and Nutrition Survey, among 5,742 men and 5,972 women (18‐66 years) with no history of antihypertension medication use.
Results
The association between annual WC change and BP change remained statistically significant but was attenuated after controlling for annual BMI change, regardless of baseline abdominal obesity or overweight status. Each 10‐cm annual WC gain in men and women was associated with a 0.98‐mm Hg (95% CI: 0.61‐1.35) and a 0.97‐mm Hg (95% CI: 0.62‐1.32) annual increase in systolic blood pressure and a 1.13‐mm Hg (95% CI: 0.87‐1.38) and a 0.74‐mm Hg (95% CI: 0.51‐0.97) annual increase in diastolic blood pressure, respectively, independent of annual BMI change.
Conclusions
WC gain may elevate BP even in the absence of BMI gain. BP management that addresses only BMI gain could overlook individuals at risk of elevated BP who have increased WC but not BMI.
The global obesity epidemic is well established, with increases in obesity prevalence for most countries since the 1980s. Obesity contributes directly to incident cardiovascular risk factors, ...including dyslipidemia, type 2 diabetes, hypertension, and sleep disorders. Obesity also leads to the development of cardiovascular disease and cardiovascular disease mortality independently of other cardiovascular risk factors. More recent data highlight abdominal obesity, as determined by waist circumference, as a cardiovascular disease risk marker that is independent of body mass index. There have also been significant advances in imaging modalities for characterizing body composition, including visceral adiposity. Studies that quantify fat depots, including ectopic fat, support excess visceral adiposity as an independent indicator of poor cardiovascular outcomes. Lifestyle modification and subsequent weight loss improve both metabolic syndrome and associated systemic inflammation and endothelial dysfunction. However, clinical trials of medical weight loss have not demonstrated a reduction in coronary artery disease rates. In contrast, prospective studies comparing patients undergoing bariatric surgery with nonsurgical patients with obesity have shown reduced coronary artery disease risk with surgery. In this statement, we summarize the impact of obesity on the diagnosis, clinical management, and outcomes of atherosclerotic cardiovascular disease, heart failure, and arrhythmias, especially sudden cardiac death and atrial fibrillation. In particular, we examine the influence of obesity on noninvasive and invasive diagnostic procedures for coronary artery disease. Moreover, we review the impact of obesity on cardiac function and outcomes related to heart failure with reduced and preserved ejection fraction. Finally, we describe the effects of lifestyle and surgical weight loss interventions on outcomes related to coronary artery disease, heart failure, and atrial fibrillation.
Background
Clinical studies implicate trimethylamine N‐oxide (TMAO; a gut microbiota‐dependent nutrient metabolite) in cardiovascular disease risk. There is a lack of population‐based data on the ...role of TMAO in advancing early atherosclerotic disease. We tested the prospective associations between TMAO and coronary artery calcium (CAC) and carotid intima‐media thickness (cIMT).
Methods and Results
Data were from the Coronary Artery Risk Development in Young Adults Study (CARDIA), a biracial cohort of US adults recruited in 1985–1986 (n=5115). We randomly sampled 817 participants (aged 33–55 years) who attended examinations in 2000–2001, 2005–2006, and 2010–2011, at which CAC was measured by computed tomography and cIMT (2005–2006) by ultrasound. TMAO was quantified using liquid chromotography mass spectrometry on plasma collected in 2000–2001. Outcomes were incident CAC, defined as Agatston units=0 in 2000–2001 and >0 over 10‐year follow‐up, CAC progression (any increase over 10‐year follow‐up), and continuous cIMT. Over the study period, 25% (n=184) of those free of CAC in 2000–2001 (n=746) developed detectable CAC. In 2000–2001, median (interquartile range) TMAO was 2.6 (1.8–4.2) μmol/L. In multivariable‐adjusted models, TMAO was not associated with 10‐year CAC incidence (rate ratio=1.03; 95% CI: 0.71–1.52) or CAC progression (0.97; 0.68–1.38) in Poisson regression, or cIMT (beta coefficient: −0.009; −0.03 to 0.01) in linear regression, comparing the fourth to the first quartiles of TMAO.
Conclusions
In this population‐based study, TMAO was not associated with measures of atherosclerosis: CAC incidence, CAC progression, or cIMT. These data indicate that TMAO may not contribute significantly to advancing early atherosclerotic disease risk among healthy early‐middle‐aged adults.
In memoriam: Francis E. Johnston (1931–2020) Schell, Lawrence M.; Gordon‐Larsen, Penny; Valleroy, Linda A.
American journal of physical anthropology,
April 2021, 2021-04-00, 20210401, Letnik:
174, Številka:
4
Journal Article
Although there is a literature on the distribution of food stores across geographic and social space, much of this research uses cross-sectional data. Analyses attempting to understand whether the ...availability of stores across neighbourhoods is associated with diet and/or health outcomes are limited by a lack of understanding of factors that shape the emergence of new stores and the closure of others. We used quarterly data on supermarket and convenience store locations spanning seven years (2006–2012) and tract level census data in four US cities: Birmingham, Alabama; Chicago, Illinois; Minneapolis, Minnesota; San Francisco, California. A spatial discrete time survival model was used to identify factors that are associated with an earlier and/or later closure time of a store. Sales volume was typically the strongest indicator of store survival. We identified heterogeneity in the association between tract level poverty and racial composition with respect to store survival. Stores in high poverty, non-white tracts were often at a disadvantage in terms of survival length. The observed patterns of store survival varied by some of the same neighbourhood sociodemographic factors as associated with lifestyle and health outcomes, which could lead to confusion in interpretation in studies of the estimated effects of introduction of food stores into neighbourhoods on health.
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