Perinatal socio-behavioral and obstetric predictors of metabolically healthy and unhealthy obesity in adult offspring

Antenatal socioeconomic status of childhood family and the risk of pain spreading (ROPS) in early and mid-adulthood - a descriptive study from the northern Finland birth cohort 1966

OBJECTIVE

The Risk of Pain Spreading (ROPS) is a six-item tool capturing key data-driven prognostic factors for chronic pain and its spreading. Higher values on the ROPS indicate a higher risk. Early factors potentially associated with the ROPS are unknown. We aimed to examine the associations between antenatal socioeconomic status of childhood family (antenatal SES) and ROPS at ages 31 and 46 years.

METHODS

The study was based on the Northern Finland Birth Cohort 1966 and previously formulated latent clusters of antenatal family SES: Highest status (the reference), Small, Larger, Average wealth, and Rural families. The ROPS ranged from zero (the reference) to two or more points out of six. A multinomial regression model was used to identify antenatal SES clusters associated with ROPS.

RESULTS

At 31 years (n = 8252), only the Larger families cluster was associated with having accumulated points (two or more) (Odds ratio [OR]: 1.46, 95 % Confidence Interval [CI]: 1.14-1.87) on the ROPS compared to the Highest status families cluster. Corresponding finding was observed at 46 years (n = 6245), but the Small families and Average wealth families clusters were also associated with this outcome. The association of Larger families cluster was, however, the strongest (OR 1.48, 95 % CI 1.16-1.89).

CONCLUSIONS

Offspring born into families with ≥5 members are likely to accumulate higher sums of key data-driven prognostic factors for worse pain across the life course until middle age. In future, associations between antenatal SES and pain would be important to be examined in a light of the ROPS.

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.

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.