Propagation of obesity across generations: the roles of differential realized fertility and assortative mating by body mass index

The risk prediction of intergenerational transmission of overweight and obesity between mothers and infants during pregnancy

BACKGROUND

Overweight and obesity in mothers before pregnancy lead to overweight and obesity in their offspring, which is the main form of intergenerational transmission of overweight and obesity in early life. Many factors, especially non-genetic factors, may influence intergenerational transmission, but little prediction research has been conducted. Therefore, we analyzed the status of intergenerational transmission in maternal and infant overweight and obesity. Second, we explored the factors during the pregnancy that might affect the the intergenerational transmission; According to the two application scenarios of pregnancy screen and self-management, risk prediction models for pregnant women were carried out.

METHODS

Based on a prospective birth cohort, a total of 908 mothers and offspring were followed up during early life. Follow-up visits were performed at the first trimester, second trimester, third trimester, delivery, 42 days after delivery, and 6 months and 12 months of age. The investigation methods included questionnaire survey, physical examination, biological sample collection and clinical data collection. In terms of risk prediction, univariate analysis was used to screen candidate predictors. Second, multivariable Cox proportional hazard regression models were used to determine the final selected predictors. Third, the corresponding histogram models were drawn, and then the 10-fold cross-validation methods were used for internal verification.

RESULTS

Regarding intergenerational transmission of overweight and obesity between mothers and infants during pregnancy, the risk prediction model for pregnancy screen was constructed. The model established: h(t|X) = h0(t)exp.(- 0.95 × (Bachelor Degree or above) + 0.75 × (Fasting blood glucose in the second trimester) + 0.89 × (Blood pressure in the third trimester) + 0.80 × (Cholesterol in third trimester) + 0.55 × (Abdominal circumference in third trimester))., with good discrimination (AUC = 0.82) and calibration (Hosmer-Lemeshow2 = 4.17). The risk prediction model for self-management was constructed. The model established: h(t|X) = h0(t)exp. (0.98 × (Sedentary >18METs) + 0.88 × (Sleep index≥8) + 0.81 × (Unhealthy eating patterns Q3/Q4) + 0.90 × (Unhealthy eating patterns Q4/Q4) + 0.85 × (Depression)), with good discrimination (AUC = 0.75) and calibration (Hosmer-Lemeshow2 = 3.81).

CONCLUSIONS

The risk predictions of intergenerational transmission of overweight and obesity between mothers and infants were performed for two populations and two application scenarios (pregnancy screening and home self-management). Further research needs to focus on infants and long-term risk prediction models.

Do the Low WHRs and BMIs Judged Most Attractive Indicate Higher Fertility?

We examine the widely accepted view that very low waist-hip ratios and low body mass indices (BMIs) in women in well-nourished populations are judged attractive by men because these features reliably indicate superior fertility. In both subsistence and well-nourished populations, relevant studies of fertility do not support this view. Rather studies indicate lower fertility in women with anthropometric values associated with high attractiveness. Moreover, low maternal BMI predisposes to conditions that compromise infant survival. Consistent with these findings from the literature, new data from a large U.S. sample of women past reproductive age show that women with lower BMIs in the late teens had fewer live births, controlling for education, marital history, and race. They also had later menarche and earlier menopause compared with women with higher youth BMIs. In addition, data from the 2013 U.S. natality database show that mothers with lower prepregnancy BMIs have an increased risk of producing both low-birth-weight and preterm infants controlling for other relevant variables-conditions that would have adversely affected fitness over almost all of human evolution. Thus, a review of the relevant literature and three new tests fail to support the view that highly attractive women are more fertile.

A Mathematical Model for Predicting Obesity Transmission with Both Genetic and Nongenetic Heredity

OBJECTIVE

Obesity is transmissible across generations through both genetic and nongenetic routes, but distinguishing between these factors is challenging. This study aimed to quantitatively examine the contribution of these genetic and nongenetic effects to assess their influence on obesity prevalence.

METHODS

A mathematical model was proposed that incorporated both the genetic and nongenetic effects of obesity. Model parameters were estimated by using observational data. Model simulations were used to assess the sensitivity of model parameters. To strengthen the study's approach, parameter estimation and simulation using data from the United Kingdom were also performed.

RESULTS

Individuals homozygous for a "hypothetical obesogenic gene" were suggested to be more susceptible to both socially contagious risk and spontaneous weight gain risk. The model predicted that obesity prevalence would reach 41.03% (39.28, 44.31) and 26.77% (25.62, 28.06) at 2030 in the United States and United Kingdom, respectively. The socially contagious risk factor had a greater overall impact on the distribution of the population with obesity than did spontaneous weight gain risk or mother-to-child obesity transmission risk.

CONCLUSIONS

Although the proposed "first approximation" model captured the complex interactions between the genetic and nongenetic effects on obesity, this framework remains incomplete. Future work should incorporate other key features driving the obesity epidemic.

Beauty and the Body of the Beholder: Raters' BMI Has Only Limited Association with Ratings of Attractiveness of the Opposite Sex

OBJECTIVE

Assortative mating for adiposity increases the genetic burden on offspring, but its causes remain unclear. One hypothesis is that people who have high adiposity find other people with obesity more physically attractive than lean people.

METHODS

The attractiveness of sets of images of males and females who varied in adiposity were rated by opposite sex subjects (559 males and 340 females) across 12 countries.

RESULTS

There was tremendous individual variability in attractiveness ratings. For female attractiveness, most males favored the leanest subjects, but others favored intermediate fatness, some were indifferent to body composition, and others rated the subjects with obesity as most attractive. For male images rated by females, the patterns were more complex. Most females favored subjects with low levels of adiposity (but not the lowest level), whereas others were indifferent to body fatness or rated the images depicting individuals with obesity as the most attractive. These patterns were unrelated to rater BMI. Among Caucasian males who rated the images of the thinnest females as being more attractive, the magnitude of the effect depended on rater BMI, indicating limited "mutual attraction."

CONCLUSIONS

Individual variations in ratings of physical attractiveness were broadly unrelated to rater BMI and suggest that mutual attraction is an unlikely explanation for assortative mating for obesity.

Phenotype diffusion and one health: A proposed framework for investigating the plurality of obesity epidemics across many species

We propose the idea of "phenotype diffusion," which is a rapid convergence of an observed trait in some human and animal populations. The words phenotype and diffusion both imply observations independent of mechanism as phenotypes are observed traits with multiple possible genetic mechanisms and diffusion is an observed state of being widely distributed. Recognizing shared changes in phenotype in multiple species does not by itself reveal a particular mechanism such as a shared exposure, shared adaptive need, particular stochastic process or a transmission pathway. Instead, identifying phenotype diffusion suggests the mechanism should be explored to help illuminate the ways human and animal health are connected and new opportunities for optimizing these links. Using the plurality of obesity epidemics across multiple species as a prototype for shared changes in phenotype, the goal of this review was to explore eco-evolutionary theories that could inform further investigation. First, evolutionary changes described by hologenome evolution, pawnobe evolution, transposable element (TE) thrust and the drifty gene hypothesis will be discussed within the context of the selection asymmetries among human and animal populations. Secondly, the ecology of common source exposures (bovine milk, xenohormesis and "obesogens"), niche evolution and the hygiene hypothesis will be summarized. Finally, we synthesize these considerations. For example, many agricultural breeds have been aggressively selected for weight gain, microbiota (e.g., adenovirus 36, toxoplasmosis) associated with (or infecting) these breeds cause experimental weight gain in other animals, and these same microbes are associated with human obesity. We propose applications of phenotype diffusion could include zoonotic biosurveillance, biocontainment, antibiotic stewardship and environmental priorities. The One Health field is focused on the connections between the health of humans, animals and the environment, and so identification of phenotype diffusion is highly relevant for practitioners (public health officials, physicians and veterinarians) in this field.

On the origin of obesity: identifying the biological, environmental and cultural drivers of genetic risk among human populations

Genetic predisposition to obesity presents a paradox: how do genetic variants with a detrimental impact on human health persist through evolutionary time? Numerous hypotheses, such as the thrifty genotype hypothesis, attempt to explain this phenomenon yet fail to provide a justification for the modern obesity epidemic. In this critical review, we appraise existing theories explaining the evolutionary origins of obesity and explore novel biological and sociocultural agents of evolutionary change to help explain the modern-day distribution of obesity-predisposing variants. Genetic drift, acting as a form of 'blind justice,' may randomly affect allele frequencies across generations while gene pleiotropy and adaptations to diverse environments may explain the rise and subsequent selection of obesity risk alleles. As an adaptive response, epigenetic regulation of gene expression may impact the manifestation of genetic predisposition to obesity. Finally, exposure to malnutrition and disease epidemics in the wake of oppressive social systems, culturally mediated notions of attractiveness and desirability, and diverse mating systems may play a role in shaping the human genome. As an important first step towards the identification of important drivers of obesity gene evolution, this review may inform empirical research focused on testing evolutionary theories by way of population genetics and mathematical modelling.

Inter-individual inequality in BMI: An analysis of Indonesian Family Life Surveys (1993-2007)

Widening inequalities in mean Body Mass Index (BMI) between social and economic groups are well documented. However, whether changes in mean BMI are followed by changes in dispersion (or variance) and whether these inequalities are also occurring within social groups or across individuals remain understudied. In addition, a substantial body of literature exists on the global increase in mean BMI and prevalence of overweight and obesity. However, whether this weight gain is shared proportionately across the whole spectrum of BMI distribution, also remains understudied. We examined changes in the distribution of BMI at the population level over time to understand how changes in the dispersion reflect between-group compared to within-group inequalities in weight gain. Moreover, we investigated the entire distribution of BMI to determine in which percentiles the most weight gain is occurring over time. Utilizing four waves (from 1993 to 2007) of Indonesian Family Life Surveys (IFLS), we estimated changes in the mean and the variance of BMI over time and across various socioeconomic groups based on education and households' expenditure per capita in 53,648 men and women aged 20-50 years. An increase in mean and standard deviation was observed among men (by 4.3% and 25%, respectively) and women (by 7.3% and 20%, respectively) over time. Quantile-Quantile plots showed that higher percentiles had greater increases in BMI compared to the segment of the population at lower percentiles. While between socioeconomic group differences decreased over time, within-group differences increased and were more prominent among individuals with poor education and lower per capita expenditures. Population changes in BMI cannot be fully described by average trends or single parameters such as the mean BMI. Moreover, greater increases in within-group dispersion compared with between-group differences imply that growing inequalities are not merely driven by these socioeconomic factors at the population level.

Trends in group inequalities and interindividual inequalities in BMI in the United States, 1993-2012

BACKGROUND

Marked increases in mean body mass index (BMI) and prevalence of obesity and overweight in the United States are well known. However, whether these average increases were accompanied by changing dispersion (or SD) remains understudied.

OBJECTIVE

We investigated population-level changes in the BMI distribution over time to understand how changes in dispersion reflect between-group compared with within-group inequalities in weight gain in the United States.

DESIGN

Using data from the Behavioral Risk Factor Surveillance System survey (1993-2012), we analyzed associations between mean, SD, and median BMI and BMI at the 5th and 95th percentiles for 3,050,992 non-Hispanic white, non-Hispanic black, and Hispanic men and women aged 25-64 y.

RESULTS

Overall, an increase of 1.0 in mean BMI (in kg/m²) was associated with an increase of 0.70 (95% CI: 0.67, 0.73) in the SD of BMI. A change of 1.0 in median BMI was associated with a change of 0.18 (95% CI: 0.14, 0.21) in the BMI value at the 5th percentile compared with a change of 2.94 (95% CI: 2.81, 3.07) at the 95th percentile. Quantile-quantile plots showed unequal changes in the BMI distribution, with pronounced changes at higher percentiles. Similar patterns were observed in subgroups stratified by sex, race-ethnicity, and education with non-Hispanic black women and women with less than a high school education having highest mean BMI, SD of BMI, and BMI values at the 5th and 95th percentiles.

CONCLUSIONS

Mean BMI and the percentage of overweight and obese individuals do not fully describe population changes in BMI. Increases in within-group inequality in BMI represent an underrecognized characteristic of population-level weight gain. Crucially, similar increases in dispersion within groups suggest that growing inequalities in BMI at the population level are not driven by these socioeconomic and demographic factors. Future research should focus on understanding factors driving inequalities in weight gain between individuals.

Weighing the Evidence of Common Beliefs in Obesity Research

Obesity is a topic on which many views are strongly held in the absence of scientific evidence to support those views, and some views are strongly held despite evidence to contradict those views. We refer to the former as "presumptions" and the latter as "myths." Here, we present nine myths and 10 presumptions surrounding the effects of rapid weight loss; setting realistic goals in weight loss therapy; stage of change or readiness to lose weight; physical education classes; breastfeeding; daily self-weighing; genetic contribution to obesity; the "Freshman 15"; food deserts; regularly eating (versus skipping) breakfast; eating close to bedtime; eating more fruits and vegetables; weight cycling (i.e., yo-yo dieting); snacking; built environment; reducing screen time in childhood obesity; portion size; participation in family mealtime; and drinking water as a means of weight loss. For each of these, we describe the belief and present evidence that the belief is widely held or stated, reasons to support the conjecture that the belief might be true, evidence to directly support or refute the belief, and findings from randomized controlled trials, if available. We conclude with a discussion of the implications of these determinations, conjecture on why so many myths and presumptions exist, and suggestions for limiting the spread of these and other unsubstantiated beliefs about the obesity domain.

Novel mathematical models for investigating topics in obesity

There is limited insight into the mechanisms, progression, and related comorbidities of obesity through simple modeling tools such as linear regression. Keeping in mind the words of the late George E. P. Box that “all models are wrong, some are useful,” this symposium presented 4 useful mathematical models or methodologic refinements. Presenters placed specific emphasis on how these novel models and methodologies can be applied to further our knowledge of the etiology of obesity.

The aetiology of obesity beyond eating more and exercising less

Although recent increases in availability of energy dense, processed foods and reductions in institutionally driven physical activity have created an environment that is permissible for obesity to occur, several other factors may contribute to the development of obesity in this context. We review evidence for eleven such factors: endocrine disruptors, intrauterine effects, epigenetics, maternal age, differential fecundity and assortative mating by body mass index, microorganisms, reduction in variability of ambient temperatures, smoking cessation, sleep debt, and pharmaceutical iatrogenesis. Evidence for the role of endocrine disruptors, microorganisms, ambient temperatures, sleep and reproductive factors is accumulating, but additional research is needed to confirm the causative role of these factors in human obesity. However, the role of certain pharmaceuticals and smoking cessation in development of human obesity is clear. Practice points for consideration and future research needed are highlighted for each factor.