Weight Gain Trajectories Associated With Elevated C‐Reactive Protein Levels in Chinese Adults

BACKGROUND: Recent longitudinal work suggests that weight change is an important risk factor for inflammation across the full range of BMI. However, few studies have examined whether the risk of inflammation differs by patterns of weight gain over time. Using latent class trajectory analysis, we test whether patterns of weight gain are associated with elevated high-sensitivity C-reactive protein (hs-CRP 2-10 mg/L). METHODS AND RESULTS: Data come from China Health and Nutrition Survey (CHNS) participants (n=5536), aged 18 at baseline to 66 years in 2009, with measured weight over 18 years. Latent class trajectory analysis was used to identify weight-change trajectories in 6 age and sex strata. Multivariable general linear mixed-effects models fit with a logit link were used to assess the risk of elevated hs-CRP across weight trajectory classes. Models were fit within age and sex strata, controlling for baseline weight, adult height, and smoking, and included random intercepts to account for community-level correlation. Steeper weight-gain trajectories were associated with greater risk of elevated hs-CRP compared to more moderate weight-gain trajectories in men and women. Initially high weight gain followed by weight loss was associated with lower risk of elevated hs-CRP in women aged 18 to 40. CONCLUSIONS: Latent class trajectory analysis identified heterogeneity in adult weight change associated with differential risk of inflammation independently of baseline weight and smoking. These results suggest that trajectories of weight gain are an important clinical concern and may identify those at risk for inflammation and the development of cardiometabolic disease

Longitudinal BMI trajectories and adolescent dietary patterns in the RIGHT Track research project

Obesity represents a public health epidemic affecting an increasing number of children as well as adults (Hales, Carroll, Fryar, & Ogden, 2017). Obesity is associated with a multitude of negative health implications, and excessive adiposity can also weaken mental health through increasing risk of depression, anxiety, and low self-esteem (Fruh, 2017). Obese children and adolescents are more likely to become severely obese adults, making prevention and early intervention extremely important for minimizing the negative effects of obesity over time (The, Suchindran, North, Popkin, & Gordon-Larsen, 2010). Thus, identification of obesity-related modifiable behaviors can help target future obesity prevention research efforts and in turn, has the potential to improve the quality of life for many children and adolescents. Emerging adulthood, a developmental period during 18 to 25y (Nelson, Story, Larson, Neumark-Sztainer, & Lytle, 2008), is becoming increasingly important as a time when not only initial changes in markers of chronic disease risk can be seen, but as a unique opportunity for behavior change interventions (Gilmore, 2019). Currently there is little research describing how longitudinal BMI and adolescent dietary patterns relate to obesity-related biomarkers in emerging adulthood. Furthermore, despite the evidence that children’s self-regulation skills may play a role in obesity development, there is inadequate longitudinal research on how self-regulatory behaviors in childhood may affect longitudinal BMI growth. Thus, the specific aims of the proposed research included to i) characterize unique trajectories of BMI from childhood through adolescence (4 to 18 year) and describe the association between BMI trajectory membership and body composition and biomarkers in emerging adulthood; ii) determine the prospective association between pre-school self-regulation and BMI trajectory membership; and iii) describe unique patterns of adolescent dietary consumption and determine the corresponding association between adolescent dietary pattern membership and later anthropometrics and biomarkers including BMI, percent body fat, fasting glucose, fasting insulin, and HOMA-IR collected in emerging adulthood. Data from the RIGHT Track Parent and RIGHT Track Health longitudinal studies were used to address the study aims. The combination of data from the two studies provided the necessary data to address the study aims and included baseline sociodemographic information, childhood behavioral data, longitudinal anthropometrics throughout childhood and adolescence, adolescent dietary intake, and biomarker and body composition data collected in emerging adulthood. Participants in the RIGHT Track studies could be characterized into two unique longitudinal BMI trajectories: i) stable normal weight and ii) normal weight to overweight transition. Compared to the stable normal weight group, membership in the normal weight to overweight transition group was positively associated with fasting glucose, fasting insulin, HOMA-IR, waist circumference, and percent body fat, even after controlling for sex, race, and socioeconomic status. Results were attenuated when each model additionally controlled for adult waist circumference or adult percent body fat. Importantly, higher childhood self-regulatory behavior, as measured by a gift-delay task, decreased the likelihood of a child being in the “higher-risk”, that is “normal weight to overweight transition” group. Higher childhood self-regulation as measured by a food-related task was not associated with BMI trajectory membership. However, moderate food-related self-regulation was suggestive of decreased risk of membership in the BMI transition group compared to those who were considered unregulated (p=0.09). Even though this relation was not statistically significant, this finding supports exploration of “consuming any foods in moderation” as a useful technique when educating children on nutrition. Finally, two unique patterns of adolescent dietary intake were found in our sample: i) balanced (higher consumption of unsweetened beverages, fruits, and non-starchy vegetables) and ii) unbalanced (greater consumption of sugar-sweetened beverages, fried potatoes, and full fat/fried meats). While there were differences in types of foods consumed by those in each of these patterns, adolescents in both patterns had an overall poor diet quality. No significant associations were found between adolescent dietary patterns and any of the adult health measures (i.e., fasting glucose, fasting insulin, HOMA-IR, percent body fat or BMI), which could possibly partially be explained by our limited number of individuals who had both dietary and biomarker data. This study provides insight into longitudinal growth patterns for children and adolescents and corresponding childhood behavioral predictors that could serve as targets for public health interventions to decrease obesity-related health risks. Additional research is needed to examine self-regulatory behaviors at different time points during childhood to determine the best age at which implementation of behavioral interventions would be most effective in minimizing future adiposity-related health risks

Characterizing Long-Term Patterns of Weight Change in China Using Latent Class Trajectory Modeling

BackgroundOver the past three decades, obesity-related diseases have increased tremendously in China, and are now the leading causes of morbidity and mortality. Patterns of weight change can be used to predict risk of obesity-related diseases, increase understanding of etiology of disease risk, identify groups at particularly high risk, and shape prevention strategies.MethodsLatent class trajectory modeling was used to compute weight change trajectories for adults aged 18 to 66 using the China Health and Nutrition Survey (CHNS) data (n = 12,611). Weight change trajectories were computed separately for males and females by age group at baseline due to differential age-related patterns of weight gain in China with urbanization. Generalized linear mixed effects models examined the association between weight change trajectories and baseline characteristics including urbanicity, BMI category, age, and year of study entry.ResultsTrajectory classes were identified for each of six age-sex subgroups corresponding to various degrees of weight loss, maintenance and weight gain. Baseline BMI status was a significant predictor of trajectory membership for all age-sex subgroups. Baseline overweight/obesity increased odds of following ‘initial loss with maintenance’ trajectories. We found no significant association between baseline urbanization and trajectory membership after controlling for other covariates.ConclusionTrajectory analysis identified patterns of weight change for age by gender groups. Lack of association between baseline urbanization status and trajectory membership suggests that living in a rural environment at baseline was not protective. Analyses identified age-specific nuances in weight change patterns, pointing to the importance of subgroup analyses in future research

Aqueous Vascular Endothelial Growth Factor as a Predictor of Macular Thickening Following Cataract Surgery in Patients With Diabetes Mellitus

To study associations between serum and aqueous vascular endothelial growth factor (VEGF) and insulin-like growth factor 1 (IGF-1) and macular edema measured with optical coherence tomography (OCT) following phacoemulsification in diabetic patients

Eighteen year weight trajectories and metabolic markers of diabetes in modernising China: which timescale is most relevant? Reply to Vistisen D and Færch K [letter]

Aims/HypothesisAlthough obesity is a major risk factor for diabetes, little is known about weight gain trajectories across adulthood, and whether they are differentially associated with metabolic markers of diabetes.MethodsWe used fasting blood samples and longitudinal weight data for 5,436 adults (5,734 observations, aged 18–66years) from the China Health and Nutrition Survey (1991–2009). Using latent class trajectory analysis, we identified different weight gain trajectories in six age and sex strata, and used multivariable general linear mixed effects models to assess elevated metabolic markers of diabetes (fasting glucose, HbA1c, HOMA-IR, insulin) across weight trajectory classes. Models were fitted within age and sex strata, and controlled for baseline weight (or baseline weight by weight trajectory interaction terms), height, and smoking habit, with random intercepts to control for community-level correlations.ResultsCompared with weight gain, classes with weight maintenance, weight loss, or a switch from weight gain to loss had lower values for metabolic markers of diabetes. These associations were stronger among younger women (aged 18–29 and 30–39years) and men (18–29years) than in older (40–66years) men and women. An exception was HOMA-IR, which showed class differences across all ages (at least p < 0.004).ConclusionTrajectory analysis identified heterogeneity in adult weight gain associated with diabetes-related metabolic markers, independent of baseline weight. Our findings suggest that variation in metabolic markers of diabetes across patterns of weight gain is masked by a homogeneous classification of weight gain.Electronic supplementary materialThe online version of this article (doi:10.1007/s00125-014-3284-y) contains peer-reviewed but unedited supplementary material, which is available to authorised users

Weight Gain Trajectories Associated With Elevated C‐Reactive Protein Levels in Chinese Adults

BACKGROUND: Recent longitudinal work suggests that weight change is an important risk factor for inflammation across the full range of BMI. However, few studies have examined whether the risk of inflammation differs by patterns of weight gain over time. Using latent class trajectory analysis, we test whether patterns of weight gain are associated with elevated high‐sensitivity C‐reactive protein (hs‐CRP 2–10 mg/L). METHODS AND RESULTS: Data come from China Health and Nutrition Survey (CHNS) participants (n=5536), aged 18 at baseline to 66 years in 2009, with measured weight over 18 years. Latent class trajectory analysis was used to identify weight‐change trajectories in 6 age and sex strata. Multivariable general linear mixed‐effects models fit with a logit link were used to assess the risk of elevated hs‐CRP across weight trajectory classes. Models were fit within age and sex strata, controlling for baseline weight, adult height, and smoking, and included random intercepts to account for community‐level correlation. Steeper weight‐gain trajectories were associated with greater risk of elevated hs‐CRP compared to more moderate weight‐gain trajectories in men and women. Initially high weight gain followed by weight loss was associated with lower risk of elevated hs‐CRP in women aged 18 to 40. CONCLUSIONS: Latent class trajectory analysis identified heterogeneity in adult weight change associated with differential risk of inflammation independently of baseline weight and smoking. These results suggest that trajectories of weight gain are an important clinical concern and may identify those at risk for inflammation and the development of cardiometabolic disease

Odds Ratios and 95% Confidence Intervals for Baseline Covariates Related to Trajectory Membership, China Health and Nutrition Survey.

<p>* p<0.05</p><p>** p<0.001</p><p>***p<0.0001</p><p>† Baseline BMI status as a three category variable (underweight, normal weight [referent], overweight/obese), and a dichotomous baseline urban-rural variable (referent: urban), age is scaled by a factor of 10 such that OR's represent change in odds with each decade increase in age, and year of study entry as a continuous variable.</p><p>††Reference groups for weight change trajectories were chosen on the basis of a pattern indicating “minimal weight change.”</p><p>Odds Ratios and 95% Confidence Intervals for Baseline Covariates Related to Trajectory Membership, China Health and Nutrition Survey.</p

Descriptive Statistics for Baseline Covariates by Gender and Age-Group, China Health and Nutrition Survey.

<p>*Overweight and obesity classified using the Asian cut-point (BMI ≥ 23 kg/m²) and underweight classified as BMI<18.5 kg/m²[<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0116190#pone.0116190.ref020" target="_blank">20</a>]</p><p>**Wave at study entry varies by design of this household-based survey, with study entry due to family formation and childbirth and due to exogenous weather shocks and subsequent replacement enrollment of new villages with identical sampling techniques.</p><p>***Eligibility for inclusion in the analysis sample was based on each individual having data from at least two anthropometric visits, thus the latest year for study entry was 2006</p><p>Descriptive Statistics for Baseline Covariates by Gender and Age-Group, China Health and Nutrition Survey.</p