Early exposure to social disadvantages and later life body mass index beyond genetic predisposition in three generations of Finnish birth cohorts

Obesity risk in young adults from the Jerusalem Perinatal Study (JPS): the contribution of polygenic risk and early life exposure

BACKGROUND/OBJECTIVES

The effects of early life exposures on offspring life-course health are well established. This study assessed whether adding early socio-demographic and perinatal variables to a model based on polygenic risk score (PRS) improves prediction of obesity risk.

METHODS

We used the Jerusalem Perinatal study (JPS) with data at birth and body mass index (BMI) and waist circumference (WC) measured at age 32. The PRS was constructed using over 2.1M common SNPs identified in genome-wide association study (GWAS) for BMI. Linear and logistic models were applied in a stepwise approach. We first examined the associations between genetic variables and obesity-related phenotypes (e.g., BMI and WC). Secondly, socio-demographic variables were added and finally perinatal exposures, such as maternal pre-pregnancy BMI (mppBMI) and gestational weight gain (GWG) were added to the model. Improvement in prediction of each step was assessed using measures of model discrimination (area under the curve, AUC), net reclassification improvement (NRI) and integrated discrimination improvement (IDI).

RESULTS

One standard deviation (SD) change in PRS was associated with a significant increase in BMI (β = 1.40) and WC (β = 2.45). These associations were slightly attenuated (13.7-14.2%) with the addition of early life exposures to the model. Also, higher mppBMI was associated with increased offspring BMI (β = 0.39) and WC (β = 0.79) (p < 0.001). For obesity (BMI ≥ 30) prediction, the addition of early socio-demographic and perinatal exposures to the PRS model significantly increased AUC from 0.69 to 0.73. At an obesity risk threshold of 15%, the addition of early socio-demographic and perinatal exposures to the PRS model provided a significant improvement in reclassification of obesity (NRI, 0.147; 95% CI 0.068-0.225).

CONCLUSIONS

Inclusion of early life exposures, such as mppBMI and maternal smoking, to a model based on PRS improves obesity risk prediction in an Israeli population-sample.

Do compassion and self-compassion moderate the relationship between childhood socioeconomic position and adulthood body composition?

The study aims to investigate the associations of compassion and self-compassion with body composition, and whether adulthood compassion and self-compassion moderate the relationship between childhood SEP and adulthood body composition. The participants came from the Northern Finland Birth Cohort 1986 Study (n = 789, 52.1% women), with a mean age of 34.0 years. Compassion and self-compassion were measured with the Dispositional Positive Emotions Scale and Self-Compassion Scale-Short Form, respectively. Body composition was assessed using anthropometric and body fat measurements at a clinic. Childhood SEP included parental occupation, education, and employment. The results showed that high compassion was associated with three out of the five body composition measurements, namely lower waist circumference (B = -0.960, p = 0.039, 95% CI: -1.870; -0.498), body fat percentage (B = -0.693, p = 0.030, 95% CI: -1.317; -0.069), and fat mass index (B = -0.325, p = 0.023, 95% CI: -0.605; -0.044) (adjusted for sex, and childhood and adulthood SEP) but not with body mass index or waist-to-hip ratio. Self-compassion was not associated with body composition. Neither compassion nor self-compassion moderated the association between childhood SEP and adulthood body composition, as the interaction effects were not significant. Therefore, the dispositions did not protect against the negative effects of childhood SEP on adulthood body composition. High other-directed compassion may be, however, associated with healthier body composition.

Childhood circumstances, social mobility and the obesity transition: Evidence from South Africa

The distribution of obesity tends to shift from rich to poor individuals as countries develop, in a process of shifting sociodemographic patterns of obesity that has been called the 'obesity transition'. This change tends to happen with economic development, but little is known about the specific mechanisms that drive the change. We propose that improvements in childhood circumstances with economic development may be one of the drivers of the obesity transition. We explore whether the social gradient in body weight differs by childhood socioeconomic status (SES), proxied by the respondent's mother having Grade 12, using South Africa's nationally representative panel National Income Dynamics Study. In support of our hypothesis, we find that the social gradient in body weight is less positive for adults who had a high childhood SES, and already appears to have reversed among high-SES women who also had a high childhood SES. Upward social mobility over an individual's life course or across a single generation is associated with higher body weight compared to a stable high SES. But a high SES sustained in childhood and adulthood - or across more than one generation - may decrease adult obesity risk, and result in a reversal of the social gradient in body weight. Random effects within-between models show that the social gradient in body weight and its interaction with childhood SES are driven more by differences in income between individuals than by short-run changes in income within individuals, again suggesting that the obesity transition is driven by long-run changes rather than by very short-run changes. Our results are broadly robust to using several alternative measures of body weight, childhood SES and adult SES. Our results are consistent with the hypothesis that widespread improvements in childhood circumstances and nutrition with economic development may contribute to the shift to later stages of the obesity transition.

Obesity Prediction in Young Adults from the Jerusalem Perinatal Study: Contribution of Polygenic Risk and Early Life Exposures

We assessed whether adding early life exposures to a model based on polygenic risk score (PRS) improves prediction of obesity risk. We used a birth cohort with data at birth and BMI and waist circumference (WC) measured at age 32. The PRS was composed of SNPs identified in GWAS for BMI. Linear and logistic models were used to explore associations with obesity-related phenotypes. Improvement in prediction was assessed using measures of model discrimination (AUC), and net reclassification improvement (NRI). One SD change in PRS was associated with a significant increase in BMI and WC. These associations were slightly attenuated (13.7%-14.2%) with the addition of early life exposures to the model. Also, higher maternal pre-pregnancy BMI was associated with increase in offspring BMI and WC (p<0.001). For prediction obesity (BMI ≥ 30), the addition of early life exposures to the PRS model significantly increase the AUC from 0.69 to 0.73. At an obesity risk threshold of 15%, the addition of early life exposures to the PRS model provided a significant improvement in reclassification of obesity (NRI, 0.147; 95% CI 0.068-0.225). We conclude that inclusion of early life exposures to a model based on PRS improves obesity risk prediction in an Israeli population-sample.

Polygenic and socioeconomic risk for high body mass index: 69 years of follow-up across life

Genetic influences on body mass index (BMI) appear to markedly differ across life, yet existing research is equivocal and limited by a paucity of life course data. We thus used a birth cohort study to investigate differences in association and explained variance in polygenic risk for high BMI across infancy to old age (2-69 years). A secondary aim was to investigate how the association between BMI and a key purported environmental determinant (childhood socioeconomic position) differed across life, and whether this operated independently and/or multiplicatively of genetic influences. Data were from up to 2677 participants in the MRC National Survey of Health and Development, with measured BMI at 12 timepoints from 2-69 years. We used multiple polygenic indices from GWAS of adult and childhood BMI, and investigated their associations with BMI at each age. For polygenic liability to higher adult BMI, the trajectories of effect size (β) and explained variance (R2) diverged: explained variance peaked in early adulthood and plateaued thereafter, while absolute effect sizes increased throughout adulthood. For polygenic liability to higher childhood BMI, explained variance was largest in adolescence and early adulthood; effect sizes were marginally smaller in absolute terms from adolescence to adulthood. All polygenic indices were related to higher variation in BMI; quantile regression analyses showed that effect sizes were sizably larger at the upper end of the BMI distribution. Socioeconomic and polygenic risk for higher BMI across life appear to operate additively; we found little evidence of interaction. Our findings highlight the likely independent influences of polygenic and socioeconomic factors on BMI across life. Despite sizable associations, the BMI variance explained by each plateaued or declined across adulthood while BMI variance itself increased. This is suggestive of the increasing importance of chance ('non-shared') environmental influences on BMI across life.

Socioeconomic differences in the association between maternal age and maternal obesity: a register-based study of 707,728 women in Finland

AIMS

To examine the association between maternal age and maternal obesity across socioeconomic groups and to determine whether socioeconomic status modifies the association between maternal age and maternal obesity with a view to informing public health policies.

METHODS

Data for this register-based study were sourced from the Finnish Medical Birth Register and Statistics Finland, using the information of 707,728 women who gave birth in Finland from 2004 to 2015. We used multivariable regression models to assess the association between maternal age and maternal obesity across socioeconomic groups. We further assessed interactions on both multiplicative and additive scales.

RESULTS

Across all socioeconomic groups, the adjusted odds ratio for the association between maternal age and maternal obesity increased, peaking for women 35 years or older. Using women below 20 years of age in the category of upper-level employees as a single reference group, in the category of upper-level employees, the adjusted odds ratio and 95% confidence intervals among women 35 years or older was 1.92 (1.39-2.64) for maternal obesity. Equally, the adjusted odds ratio and 95% confidence intervals in the category of long-term unemployed was 4.35 (3.16-5.98). Synergistic interactions on both multiplicative and additive scales were found across age and socioeconomic groups.

CONCLUSIONS

The association between maternal age and maternal obesity was strongest among women 35 years or older with lower socioeconomic status. Population-level interventions that address maternal risk factors from teenage years are needed alongside individual-level interventions that target high-risk mothers in areas of low socioeconomic status and maternal obesity.

Understanding the cumulative risk of maternal prenatal biopsychosocial factors on birth weight: a DynaHEALTH study on two birth cohorts

Background

There are various maternal prenatal biopsychosocial (BPS) predictors of birth weight, making it difficult to quantify their cumulative relationship.

Methods

We studied two birth cohorts: Northern Finland Birth Cohort 1986 (NFBC1986) born in 1985–1986 and the Generation R Study (from the Netherlands) born in 2002–2006. In NFBC1986, we selected variables depicting BPS exposure in association with birth weight and performed factor analysis to derive latent constructs representing the relationship between these variables. In Generation R, the same factors were generated weighted by loadings of NFBC1986. Factor scores from each factor were then allocated into tertiles and added together to calculate a cumulative BPS score. In all cases, we used regression analyses to explore the relationship with birth weight corrected for sex and gestational age and additionally adjusted for other factors.

Results

Factor analysis supported a four-factor structure, labelled closely to represent their characteristics as ‘Factor1-BMI’ (body mass index), ‘Factor2-DBP’ (diastolic blood pressure), ‘Factor3-Socioeconomic-Obstetric-Profile’ and ‘Factor4-Parental-Lifestyle’. In both cohorts, ‘Factor1-BMI’ was positively associated with birth weight, whereas other factors showed negative association. ‘Factor3-Socioeconomic-Obstetric-Profile’ and ‘Factor4-Parental-Lifestyle’ had the greatest effect size, explaining 30% of the variation in birth weight. Associations of the factors with birth weight were largely driven by ‘Factor1-BMI’. Graded decrease in birth weight was observed with increasing cumulative BPS score, jointly evaluating four factors in both cohorts.

Conclusion

Our study is a proof of concept for maternal prenatal BPS hypothesis, highlighting the components snowball effect on birth weight in two different European birth cohorts.