Weight trajectories from birth to 5 years and child appetitive traits at 7 years of age: a prospective birth cohort study

Rapid prenatal and postnatal weight gain seem to alter appetite regulation and hypothalamic functions through different pathways; however, little is known on how early life growth trajectories may influence appetitive traits in school-age. We aimed to explore the associations between weight trajectories from birth to 5 years and appetitive traits at 7. Participants were from the Generation XXI birth cohort (n 3855). Four weight trajectories were investigated: 'normal weight gain' (closely overlaps the 50th percentile in the weight-for-age curve), 'weight gain during infancy' (low birth weight and weight gain mainly during infancy), 'weight gain during childhood' (continuous weight gain since birth) and 'persistent weight gain' (always showing higher weight than the average). Appetitive traits were assessed through the Children's Eating Behaviour Questionnaire. Associations were tested using generalised linear models, adjusted for maternal and child characteristics. Compared with 'normal weight gain', those in the other growth trajectories showed greater enjoyment of food and eating in response to food stimuli (i.e. Food Responsiveness) but were less able to compensate for prior food intake and ate faster at 7 (i.e. less Satiety Responsiveness and Slowness in Eating). Also, those with 'weight gain during infancy' showed to have greater Emotional Overeating and less Emotional Undereating and were fussier. Associations were stronger if greater weight gain occurred during infancy. Early infancy seems to be a sensitive period in the development of later appetitive traits. The control of rapid growth during infancy, besides strategies focused on the overall environment where children are living, is necessary.

Maternal prenatal cholesterol levels predict offspring weight trajectories during childhood in the Norwegian Mother, Father and Child Cohort Study

BACKGROUND

Numerous intrauterine factors may affect the offspring's growth during childhood. We aimed to explore if maternal and paternal prenatal lipid, apolipoprotein (apo)B and apoA1 levels are associated with offspring weight, length, and body mass index from 6 weeks to eight years of age. This has previously been studied to a limited extent.

METHODS

This parental negative control study is based on the Norwegian Mother, Father and Child Cohort Study and uses data from the Medical Birth Registry of Norway. We included 713 mothers and fathers with or without self-reported hypercholesterolemia and their offspring. Seven parental metabolites were measured by nuclear magnetic resonance spectroscopy, and offspring weight and length were measured at 12 time points. Data were analyzed by linear spline mixed models, and the results are presented as the interaction between parental metabolite levels and offspring spline (age).

RESULTS

Higher maternal total cholesterol (TC) level was associated with a larger increase in offspring body weight up to 8 years of age (0.03 ≤ P_interaction ≤ 0.04). Paternal TC level was not associated with change in offspring body weight (0.17 ≤ P_interaction ≤ 0.25). Higher maternal high-density lipoprotein cholesterol (HDL-C) and apoA1 levels were associated with a lower increase in offspring body weight up to 8 years of age (0.001 ≤ P_interaction ≤ 0.005). Higher paternal HDL-C and apoA1 levels were associated with a lower increase in offspring body weight up to 5 years of age but a larger increase in offspring body weight from 5 to 8 years of age (0.01 ≤ P_interaction ≤ 0.03). Parental metabolites were not associated with change in offspring height or body mass index up to 8 years of age (0.07 ≤ P_interaction ≤ 0.99).

CONCLUSIONS

Maternal compared to paternal TC, HDL-C, and apoA1 levels were more strongly and consistently associated with offspring body weight during childhood, supporting a direct intrauterine effect.

Prognostic relevance of gradual weight changes on long-term mortality in chronic heart failure

BACKGROUND AND AIMS

While obesity has been linked to better ouctomes (the obesity paradox), cachexia is associated with higher mortality in patients with heart failure with reduced ejection fraction (HFrEF). As opposed to overt cachexia, little is known about the prognostic impact of gradual, long-term weight changes in stable HFrEF.

METHODS AND RESULTS

We included ambulatory patients with clinically stable chronic HFrEF on individually optimized treatment. Next to other clinical and functional parameters, changes in body weight over the past one (n = 733, group 1) or two (n = 636, group 2) years were recorded. Four-year mortality was analysed with respect to baseline BMI and changes in body weight or BMI using fractional polynomials. In addition, outcome was stratified by BMI categories (18.5-25 kg/m²: normal weight, >25-30 kg/m²: overweight, >30 kg/m²: obesity). An obesity paradox was present in both groups, with overweight and obese patients having the best prognosis. In both groups, a gradual weight gain of 5% was associated with the lowest mortality, whereas mortality steadily increases with increasing weight loss. Excessive weight gain >10% was also related to higher mortality. Stratification by baseline BMI categories revealed that weight loss was most detrimental in normal weight patients, whereas the prognostic impact of weight change was weaker in obese patients.

CONCLUSION

In patients with chronic HFrEF, gradual weight loss is associated with steadily increasing mortality, whereas a weight gain of 5% is related to the best prognosis. Prevention of any inappropriate weight loss might be a therapeutic goal in HFrEF patient care.

Predicting Attrition Patterns from Pediatric Weight Management Programs

Obesity is a major public health concern. Multidisciplinary pediatric weight management programs are considered standard treatment for children with obesity who are not able to be successfully managed in the primary care setting. Despite their great potential, high dropout rates (referred to as attrition) are a major hurdle in delivering successful interventions. Predicting attrition patterns can help providers reduce the alarmingly high rates of attrition (up to 80%) by engaging in earlier and more personalized interventions. Previous work has mainly focused on finding static predictors of attrition on smaller datasets and has achieved limited success in effective prediction. In this study, we have collected a five-year comprehensive dataset of 4,550 children from diverse backgrounds receiving treatment at four pediatric weight management programs in the US. We then developed a machine learning pipeline to predict (a) the likelihood of attrition, and (b) the change in body-mass index (BMI) percentile of children, at different time points after joining the weight management program. Our pipeline is greatly customized for this problem using advanced machine learning techniques to process longitudinal data, smaller-size data, and interrelated prediction tasks. The proposed method showed strong prediction performance as measured by AUROC scores (average AUROC of 0.77 for predicting attrition, and 0.78 for predicting weight outcomes).