Genes and the intergenerational transmission of BMI and obesity

This paper compares the strength of intergenerational transmission of body mass index (BMI) and obesity in a sample of adoptees relative to a matched sample of biological children with similar observable characteristics. We find that BMI and obesity are strongly correlated among biological parent-child pairs, but there are no significant intergenerational associations in these health traits among adoptive parent-child pairs. The intergenerational elasticity of BMI for children to their parents is 0.2 in the matched biological sample, but indistinguishable from zero for adopted children with a standard error more than three times as large as the coefficient. Under reasonable assumptions, these findings indicate that the intergenerational transmission of BMI and obesity occurs primarily through genetic mechanisms. Additional analyses of transmission rates by parental gender and among step-parents and step-children support this conclusion. The role of determinants of BMI and obesity in the household environment in relation to our findings is discussed. Given the negative consequences of obesity on earnings and other economic measures, our results suggest that the genetic transmission of weight problems contributes substantially to intergenerational persistence in economic outcomes.     

Geographic determinants of individual obesity risk in Spain: A multilevel approach

This paper seeks to understand the determinants of individual body weight status and obesity risk in Spain by concurrently examining individual and regional characteristics. The data are drawn from the National Health Survey of Spain for the year 2011–2012 (INE-National Statistical Institute of Spain) and contain information for a representative sample of 12,671 adults across 50 provinces in Spain. A multilevel analysis is carried out to examine the determinants of individual weight status and obesity, controlling not only for the individual effects and those of the immediate environment but also for the broader setting to which individuals and their immediate environment belong. Our findings suggest that attributes from all three levels of analysis have an effect on individual weight status and obesity. Lack of green spaces and criminality taken as proxies of the social environment positively affect individual and women's BMI and obesity, respectively.     

Weight gain in adolescents and their peers

Despite the urgent public health implications, relatively little is yet known about the effect of peers on adolescent weight gain. We describe trends and features of adolescent BMI in a nationally representative dataset and document correlations in weight gain among peers. We find strong correlations between own body mass index (BMI) and peers’ BMI's. Though the correlations are especially strong in the upper ends of the BMI distribution, the relationship is smooth and holds over almost the entire range of adolescent BMI. Furthermore, the results are robust to the inclusion of school fixed effects and basic controls for other confounding factors such as race, sex, and age. Some recent research in this area considers whether or not adolescent weight gain is caused by peers. We discuss the econometric issues in plausibly estimating such effects while accounting for growth spurts and difficulties in defining adolescent obesity. While our work identifies correlations between adolescent BMI and peers’ BMI, it is not intended to and cannot fully address the existence of endogenous peer effects.     

Blood Pressure in Adolescence and Early Adulthood Related to Obesity and Birth Size

Objective: Obesity is an established risk factor for higher systolic (SBP) and diastolic (DBP) blood pressure in adolescence and early adulthood, but birth size may also have a role. We analyzed the effects of adolescent and adult obesity and birth size on BP in the young adult. Research Methods and Procedures: In a prospective longitudinal study, anthropometric measurements were obtained at birth on 67 boys and 67 girls bom in Boston. Their body mass indices (BMI) and BP were recorded 17 years and 30 years later. Results: For women, adolescent and early adult obesity appeared to be the stronger determinants of higher BP, although smaller head and chest circumferences at birth may also be related. We found some evidence of birth (ponderal index [PI] and head circumference) anthropometric influences on age 17 BP levels in boys. By age 30, body mass variables were the dominant predictors of male BP levels. Female BMI at age 17 was positively correlated with birth adiposity (PI), but BMI at 30 was related only to age 17 BMI. Similarly, male BMI at 17 years was higher for those who weighed more at birth, but BMI at 30 years was again related only to age 17 BMI. Discussion: We conclude that adult weight and weight gain are the major determinants of adult BP.     

Maternal employment and childhood obesity in Russia

Existing research on childhood obesity shows that rising maternal employment is associated with increases in child weight. This paper aims to estimate the effect of maternal employment on childhood obesity in Russia, where obesity has been spreading quickly over the last 20 years. To address the endogeneity of maternal employment and estimate its effect on the weight outcomes of older siblings, I use plausibly exogenous variation in childcare enrolment for the youngest child in the household as an instrumental variable for maternal employment. Based on the Russian Longitudinal Monitoring Survey (RLMS-HSE), the results show that maternal employment leads to an increase in children’s BMI and in their probabilities of becoming overweight and obese. In exploring the potential underlying mechanisms, I find that maternal employment is related to less physical activity, to a higher probability of either watching TV or playing video games, and to poorer dietary habits among children.     

Body mass index and employment status: A new look

Earlier literature has usually modelled the impact of obesity on employment status as a binary choice (employed, yes/no). I provide new evidence on the impact of obesity on employment status by treating the dependent variable as a as a multinomial choice variable. Using data from a representative English survey, with measured height and weight on parents and children, I define employment status as one of four: working; looking for paid work; permanently not working due to disability; and, looking after home or family. I use a multinomial logit model controlling for a set of covariates. I also run instrumental variable models, instrumenting for Body Mass Index (BMI) based on genetic variation in weight. I find that BMI and obesity significantly increase the probability of “not working due to disability”. The results for the other employment outcomes are less clear. My findings also indicate that BMI affects employment through its effect on health. Factors other than health may be less important in explaining the impact of BMI/obesity on employment.     

Genetic effects on obesity assessed by bivariate genome scan: the Mexican-American coronary artery disease study

Objective: To identify the genetic determinants of obesity using univariate and bivariate models in a genome scan. Research Methods and Procedures: We evaluated the genetic and environmental effects and performed a genome‐wide linkage analysis of obesity‐related traits in 478 subjects from 105 Mexican‐American nuclear families ascertained through a proband with documented coronary artery disease. The available obesity traits include BMI, body surface area (BSA), waist‐to‐hip ratio (WHR), and trunk fat mass as percentage of body weight. Heritability estimates and multipoint linkage analysis were performed using a variance components procedure implemented in SOLAR software. Results: The heritability estimates were 0.62 for BMI, 0.73 for BSA, 0.40 for WHR, and 0.38 for trunk fat mass as percentage of body weight. Using a bivariate genetic model, we observed significant genetic correlations between BMI and other obesity‐related traits (all p < 0.01). Evidence for univariate linkage was observed at 252 to approximately 267 cM on chromosome 2 for three obesity‐related traits (except for WHR) and at 163 to approximately 167 cM on chromosome 5 for BMI and BSA, with the maximum logarithm of the odds ratio score of 3.12 (empirical p value, 0.002) for BSA on chromosome 2. Use of the bivariate linkage model yielded an additional peak (logarithm of the odds ratio = 3.25, empirical p value, 0.002) at 25 cM on chromosome 7 for the pair of BMI and BSA. Discussion: The evidence for linkage on chromosomes 2q36‐37 and 5q36 is supported both by univariate and bivariate analysis, and an additional linkage peak at 7p15 was identified by the bivariate model. This suggests that use of the bivariate model provides additional information to identify linkage of genes responsible for obesity‐related traits.     

Does smoking affect body weight and obesity in China?

An inverse relationship between smoking and body weight has been documented in the medical literature, but the effect of cigarette smoking on obesity remains inconclusive. In addition, the evidence is mixed on whether rising obesity rates are an unintended consequence of successful anti-smoking policies. This study re-examines these relationships using data from China, the largest consumer and manufacturer of tobacco in the world that is also experiencing a steady rise in obesity rates. We focus on the impact of the total number of cigarettes smoked per day on individuals’ body mass index (BMI) and on the likelihood of being overweight and obese. Instrumental variables estimation is used to correct for the endogeneity of cigarette smoking. We find a moderate negative and significant relationship between cigarette smoking and BMI. Smoking is also negatively related to being overweight and obese, but the marginal effects are small and statistically insignificant for being obese. Quantile regression analyses reveal that the association between smoking and BMI is quite weak among subjects whose BMIs are at the high end of the distribution but are considerably stronger among subjects in the healthy weight range. Ordered probit regression analyses also confirm these findings. Our results thus reconcile an inverse average effect of smoking on body weight with the absence of any significant effect on obesity. From a policy perspective these findings suggest that, while smoking cessation may lead to moderate weight gain among subjects of healthy weight, the effects on obese subjects are modest and should not be expected to lead to a large increase in obesity prevalence rates.     

High allelic burden of four obesity SNPs is associated with poorer weight loss outcomes following gastric bypass surgery

Genome‐wide association and linkage studies have identified multiple susceptibility loci for obesity. We hypothesized that such loci may affect weight loss outcomes following dietary or surgical weight loss interventions. A total of 1,001 white individuals with extreme obesity (BMI >35 kg/m²) who underwent a preoperative diet/behavioral weight loss intervention and Roux‐en‐Y gastric bypass surgery were genotyped for single‐nucleotide polymorphisms (SNPs) in or near the fat mass and obesity‐associated (FTO), insulin induced gene 2 (INSIG2), melanocortin 4 receptor (MC4R), and proprotein convertase subtilisin/kexin type 1 (PCSK1) obesity genes. Association analysis was performed using recessive and additive models with pre‐ and postoperative weight loss data. An increasing number of obesity SNP alleles or homozygous SNP genotypes was associated with increased BMI (P < 0.0006) and excess body weight (P < 0.0004). No association between the amounts of weight lost from a short‐term dietary intervention and any individual obesity SNP or cumulative number of obesity SNP alleles or homozygous SNP genotypes was observed. Linear mixed regression analysis revealed significant differences in postoperative weight loss trajectories across groups with low, intermediate, and high numbers of obesity SNP alleles or numbers of homozygous SNP genotypes (P < 0.0001). Initial BMI interacted with genotype to influence weight loss with initial BMI <50 kg/m², with evidence of a dosage effect, which was not present in individuals with initial BMI ≥50 kg/m². Differences in metabolic rate, binge eating behavior, and other clinical parameters were not associated with genotype. These data suggest that response to a surgical weight loss intervention is influenced by genetic susceptibility and BMI.     

Factors associated with tracking of BMI: a meta-regression analysis on BMI tracking

Stable tracking of body composition is a prerequisite for the long-term effect of preventive measures against obesity and its harmful effects. As BMI tracking estimates reported by individual studies vary considerably, we performed a meta-regression analysis to provide a summary estimate and to assess determinants of BMI tracking. Using the Medline and EMBASE databases, a systematic review was conducted to identify publications reporting correlation coefficients as tracking estimates between BMI at baseline and follow-up measurements and the time interval between these measurements. Additional information recorded included age at baseline measurement, gender, and origin of the studied population. Based on the extracted data, a meta-regression analysis was performed using mixed effects models to account for multiple measurements of the same cohorts. Data on 55,072 individuals (797,094 person-years) extracted from 48 publications with follow-up times between 0.5 and 44 years entered the analysis. The overall estimates for the 1-year tracking correlation coefficient were strong (r = 0.78-0.86 depending on age at baseline measurement) and gradually decreasing over time (0.67-0.78 after 10 years, and 0.27-0.47 after 30 years). Study origin classified by continent was another significant predictor of BMI tracking whereas gender was not. In conclusion, this meta-regression analysis showed a high degree of BMI tracking across all age groups investigated and independent of BMI. Successful prevention in weight control is likely to have long term effects at any age, thereby being beneficial with respect to the associated risks of over- and underweight.     

Underreporting of BMI in adults and its effect on obesity prevalence estimations in the period 1998 to 2001

Objective: To identify the determinants of underreporting BMI and to evaluate the possibilities of using self‐reported data for valid obesity prevalence rate estimations. Research Methods and Procedures: A cross‐sectional monitoring health survey was carried out between 1998 and 2002, and a review of published studies was performed. A total of 1809 men and 1882 women ages 20 to 59 years from The Netherlands were included. Body weight and height were reported and measured. Equations were calculated to estimate individuals’ BMI from reported data. These equations and equations from published studies were applied to the present data to evaluate whether using these equations led to valid estimations of the obesity prevalence rate. Also, size of underestimation of obesity prevalence rate was compared between studies. Results: The prevalence of obesity was underestimated by 26.1% and 30.0% among men and women, respectively, when based on reported data. The most important determinant of underreporting BMI was a high BMI. When equations to calculate individuals’ BMI from reported data were used, the obesity prevalence rate was still underestimated by 12.9% and 8.1% of the “true” obesity prevalence rate among men and women, respectively. The degree of underestimating the obesity prevalence was inconsistent across studies. Applying equations from published studies to the present data led to estimations of the obesity prevalence varying from a 7% overestimation to a 74% underestimation. Discussion: Valuable efforts for monitoring and evaluating prevention and treatment studies require direct measurements of body weight and height.     

Decomposing body mass index gaps between Mediterranean countries: A counterfactual quantile regression analysis

Wide cross-country variation in obesity rates has been reported between European Union member states. Although the existing cross-country differences have not been analyzed in depth, they contain important information on health production determinants. In this paper we apply a methodology for conducting standardized cross-country comparisons of body mass index (BMI). We draw on estimations of the marginal density function of BMI for Italy and Spain in 2003, two countries with similar GDP and socio-economic conditions. We produce different counterfactual distribution estimates using covariates (health production inputs) specified in a quantile regression. Our findings suggest that Spain-to-Italy BMI gaps among females are largely explained by cross-country variation in the returns to each covariate, especially for younger women. We find that adverse underlying determinants do not explain the gap observed in particular between younger Spanish females and their Italian counterfactuals; behavioural differences appear to be the key. We tentatively conclude that Spanish policy on obesity should target mainly younger females.     

The Association of Plasma Cysteine and γ‐Glutamyltransferase With BMI and Obesity

We recently reported a strong positive association of plasma total cysteine (tCys) with fat mass in over 5,000 subjects. As γ‐glutamyltransferase (GGT) enzyme increases cysteine availability by catalyzing glutathione breakdown and is positively associated with BMI and adiposity, we hypothesized that GGT might explain the association of tCys with adiposity. To study whether the associations of tCys and serum GGT with BMI and obesity were interrelated we conducted a cross‐sectional study using data from 1,550 subjects recruited from nine European countries in the COMAC project. Multiple linear and logistic regression models and concentration‐response curves were used. In age and sex‐adjusted analyses, tCys showed strong positive associations with BMI (partial r = 0.19, P < 0.001), and obesity (odds ratio (OR) for 4th vs. 1st tCys quartile: 2.8; 95% confidence interval: 1.6–5.0, P < 0.001), both of which remained robust after adjustment for GGT and other metabolic and lifestyle confounders. Serum GGT was also a positive predictor of BMI (partial r = 0.17, P < 0.001) and obesity (OR for 4th vs. 1st GGT quartile: 4.8; 95% confidence interval: 2.5–9.2, P < 0.001), independent of tCys. However, the associations of GGT with BMI and obesity were weakened by adjustment for obesity‐related factors such as serum lipids and blood pressure. These results indicate that tCys is a strong positive predictor of BMI and obesity, independent of GGT and other obesity‐related factors. We also suggest that the association of serum GGT with BMI and obesity is unrelated to the role of GGT in cysteine turnover. The potential link between cysteine and fat metabolism should be further evaluated.     

Direct and indirect effects of body weight on adult wages

Previous estimates of the association between body weight and wages in the literature have been conditional on education and occupation. In addition to the effect of current body weight status (body mass index (BMI) or obesity) on wages, this paper examines the indirect effect of body weight status in the late-teenage years on wages operating through education and occupation choice. Using the National Longitudinal Survey of Youth 1979 data, for women, we find that a one-unit increase in BMI is directly associated with 1.83% lower hourly wages whereas the indirect BMI wage penalty is not statistically significant. Neither a direct nor an indirect BMI wage penalty is found for men. However, results based on clinical weight classification reveal that the indirect wage penalty occurs to a larger extent at the upper tail of the BMI distribution for both men and women via the pathways of education and occupation outcomes. Late-teen obesity is indirectly associated with 3.5% lower hourly wages for both women and men. These results are important because they imply that the total effect of obesity on wages is significantly larger than has been estimated in previous cross-sectional studies.     

Productivity,obesity, and human capital: Panel data evidence

The consequences of obesity for adults and children are well documented in the extant literature. We use panel data of 105 countries from 1990 to 2019 to estimate the effect of obesity on economic performance. We predict obesity using lagged values of child obesity as instruments. Predicted obesity has a negative and significant effect on productivity. This effect is independent of the effect of human capital and other macroeconomic determinants of economic performance. There is only weak evidence that this effect operates through the deterioration of human capital formation caused by childhood obesity.     

A Genome‐Wide Scan for Body Mass Index among Nigerian Families

Objective: Interest in mapping genetic variants that are associated with obesity remains high because of the increasing prevalence of obesity and its complications worldwide. Data on genetic determinants of obesity in African populations are rare. Research Methods and Procedures: We have undertaken a genome‐wide scan for body mass index (BMI) in 182 Nigerian families that included 769 individuals. Results: The prevalence of obesity was only 5%, yet polygenic heritability for BMI was in the expected range (0.46 ± 0.07). Tandem repeat markers (402) were typed across the genome with an average map density of 9 cM. Pedigree‐based analysis using a variance components linkage model demonstrated evidence for linkage on chromosome 7 (near marker D7S817 at 7p14) with a logarithm of odds (LOD) score of 3.8 and on chromosome 11 (marker D11S2000 at 11q22) with an LOD score of 3.3. Weaker evidence for linkage was found on chromosomes 1 (1q21, LOD = 2.2) and 8 (8p22, LOD = 2.3). Several candidate genes, including neuropeptide Y, DRD2, APOA4, lamin A/C, and lipoprotein lipase, lie in or close to the chromosomal regions where strong linkage signals were found. Discussion: The findings of this study suggest that, as in other populations with higher prevalences of obesity, positive linkage signals can be found on genome scans for obesity‐related traits. Follow‐up studies may be warranted to investigate these linkages, especially the one on chromosome 11, which has been reported in a population at the opposite end of the BMI distribution.     

Surgically Induced Interpregnancy Weight Loss and Prevalence of Overweight and Obesity in Offspring

Introduction

According to the fetal overnutrition hypothesis, obesity in pregnancy predisposes the offspring to obesity. Previous studies have suggested that after biliopancreatic surgery for obesity, the offspring is less likely to be obese. This study aims to further compare the BMI development of children born before and after maternal surgical weight loss.

Method

Women with at least one child born before and one child born after bariatric surgery were identified by record-linkage. Information about maternal BMI was extracted from medical records, as was information about the children''s BMI from birth to 10 years of age. We retrieved BMI data at four years of age for 340 children, born to 223 women (164 children born before surgery (BS), 176 children born after surgery (AS)). We evaluated prevalence of overweight/obesity and mean BMI in children born BS and AS at the ages of four, six and ten using GEE regression models. For 71 families, where we had complete data on mother and both children, we used a fixed-effects regression model to explore the association between differences in maternal BMI in w10 of the pre- and post-operative pregnancies with siblings'' BMI differences at age four.

Results

In no age group did we see a significantly reduced prevalence of overweight/obesity AS. For 10-year-old girls, the AS group had significantly higher rates of obesity. There was no association between differences in maternal BMI in early pregnancy and differences in siblings'' BMI at four years of age (β = −0.01, CI 95% = −0.11; 0.09).

Conclusions

We have been unable to demonstrate any effect of bariatric surgery on weight development in offspring. It seems unlikely that restrictive bariatric surgery conveys a protective effect in offspring with regards to obesity.     

Placental weight mediates the effects of prenatal factors on fetal growth: the extent differs by preterm status

Objective:

Elevated pre‐pregnancy BMI, excessive gestational weight gain (GWG), and gestational diabetes mellitus (GDM) are known determinants of fetal growth. The role of placental weight is unclear. We aimed to examine the extent to which placental weight mediates the associations of pre‐pregnancy BMI, GWG, and GDM with birth weight‐for‐gestational age, and whether the relationships differ by preterm status.

Design and Methods:

We examined 1,035 mother‐infant pairs at birth from the Boston Birth Cohort. Data were collected by questionnaire and clinical measures. Placentas were weighed without membranes or umbilical cords. We performed sequential models excluding and including placental weight, stratified by preterm status.

Results:

We found that 21% of mothers were obese, 42% had excessive GWG, and 5% had GDM. Forty‐one percent were preterm. Among term births, after adjustment for sex, gestational age, maternal age, race, parity, education, smoking, and stress during pregnancy, birth weight‐for‐gestational age z‐score was 0.55 (0.30, 0.80) units higher for pre‐pregnancy obesity vs. normal weight. It was 0.34 (0.13, 0.55) higher for excessive vs. adequate GWG, 0.67 (0.24, 1.10) for GDM vs. no DM, with additional adjustment for pre‐pregnancy BMI. Adding placental weight to the models attenuated the estimates for pre‐pregnancy obesity by 20%, excessive GWG by 32%, and GDM by 21%. Among preterm infants, GDM was associated with 0.67 (0.34, 1.00) higher birth weight‐for‐gestational age z‐score, but pre‐pregnancy obesity and excessive GWG were not. Attenuation by placental weight was 36% for GDM.

Conclusions:

These results suggest that placental weight partially mediates the effects of pre‐pregnancy obesity, GDM, and excessive GWG on fetal growth among term infants.     

Gender Differences in Body Satisfaction

PINGITORE, REGINA, BONNIE SPRING, DAVID GARFIELD. Gender differences in body satisfaction. Although men and women show similar rates of obesity, women more frequently engage in weight loss efforts, with potentially adverse health consequences. We surveyed 320 college-aged men and women to examine gender differences in the determinants of body dissatisfaction and the degree of importance assigned to body-weight and shape. Results indicated that, for both genders, satisfaction with bodyweight and shape decreased as body mass index (BMI) increased. Women, however, showed significantly greater body and weight dissatisfaction than men at most weight categories. Only the underweight (BMI<20) women and men were similarly satisfied with their bodyweight and shape. As BMI increased, however, women became disproportionately more dissatisfied: both normal-weight and overweight women expressed greater dissatisfaction than comparable men. College-aged women also attributed progressively more importance to both weight and shape as BMI increased, unlike college-aged men, who considered body weight equally important to (or slightly less important than) self-esteem as BMI increased. We discuss implications for the self-esteem of obese women and men.     

Assessing the Validity of Body Mass Index Standards in Early Postmenopausal Women

Objective: To examine the relationship between percentage of total body fat (%Fat) and body mass index (BMI) in early postmenopausal women and to evaluate the validity of the BMI standards for obesity established by the NIH. Research Methods and Procedures: Three hundred seventeen healthy, sedentary, postmenopausal women (ages, 40 to 66 years; BMI, 18 to 35 kg/m²; 3 to 10 years postmenopausal) participated in the study. Height, weight, BMI, and %Fat, as assessed by DXA, were measured. Receiver operating characteristic analysis was performed to evaluate the ability of BMI to discriminate obesity from non‐obesity using 38%Fat as the criterion value. Results: A moderately high relationship was observed between BMI and %Fat (r = 0.81; y = 1.41x + 2.65) with a SE of estimate of 3.9%. Eighty‐one percent of other studies examined fell within 1 SE of estimate as derived from our study. Receiver operating characteristic analysis showed that BMI is a good diagnostic test for obesity. The cutoff for BMI corresponding to the criterion value of 38%Fat that maximized the sum of the sensitivity and specificity was 24.9 kg/m². The true‐positive (sensitivity) and false‐positive (1 ? specificity) rates were 84.4% and 14.6%, respectively. The area under the curve estimate for BMI was 0.914. Discussion: There is a strong association between %Fat and BMI in postmenopausal women. Current NIH BMI‐based classifications for obesity may be misleading based on currently proposed %Fat standards. BMI >25 kg/m² rather than BMI >30 kg/m² may be superior for diagnosing obesity in postmenopausal women.