Childhood obesity is a major public health challenge and its prevalence continues to increase in many, but not all, countries worldwide. International data indicate that the prevalence of obesity is greater among boys than girls 5–19 years of age in the majority of high and upper middle-income countries worldwide. Despite this observed sex difference, relatively few studies have investigated sex-based and gender-based differences in childhood obesity. We propose several hypotheses that may shape the research agenda on childhood obesity. Differences in obesity prevalence may be driven by gender-related influences, such as societal ideals about body weight and parental feeding practices, as well as sex-related influences, such as body composition and hormones. There is an urgent need to understand the observed sex differences in the prevalence of childhood obesity; incorporation of sex-based and gender-based analysis in all childhood obesity studies may ultimately contribute to improved prevention and treatment.

Summary: Many obesity risk factors have increased during the COVID‐19 pandemic, including physical inactivity, poor diet, stress, and poverty. The aim of this systematic review was to evaluate the impact of the COVID‐19 pandemic, as well as associated lockdowns or restrictions, on weight change in children and adults. We searched five databases from January 2020 to November 2021. We included only longitudinal studies with measures from before and during the pandemic that evaluated the change in weight, body mass index (BMI) (or BMI z‐scores for children), waist circumference, or the prevalence of obesity. Random effects meta‐analyses were conducted to obtain pooled estimates of the mean difference in outcomes. Subgroups were evaluated for age groups and diabetes or obesity at baseline. The risk of bias was assessed using a modified version of the Newcastle‐Ottawa Scale, and the certainty of the evidence was assessed using the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) approach. A total of 74 studies were included (3,213,776 total participants): 31 studies of children, 41 studies of adults, and 2 studies of children and adults. In children, the pooled mean difference was 1.65 kg (95% confidence interval [CI]: 0.40, 2.90; 9 studies) for weight and 0.13 (95% CI 0.10, 0.17; 20 studies) for BMI z‐scores, and the prevalence of obesity increased by 2% (95% CI 1%, 3%; 12 studies). In adults, the pooled mean difference was 0.93 kg (95% CI 0.54, 1.33; 27 studies) for weight and 0.38 kg/m² (95% CI 0.21, 0.55; 25 studies) for BMI, and the prevalence of obesity increased by 1% (95% CI 0%, 3%; 11 studies). In children and adults, the pooled mean difference for waist circumference was 1.03 cm (95% CI −0.08, 2.15; 4 studies). There was considerable heterogeneity observed for all outcomes in both children and adults, and the certainty of evidence assessed using GRADE was very low for all outcomes. During the first year of the COVID‐19 pandemic, small but potentially clinically significant increases in weight gain, BMI, and increased prevalence of obesity in both children and adults were observed. Increases were greater in children, and targeted prevention interventions may be warranted.

The field of nutritional epidemiology faces challenges posed by measurement error, diet as a complex exposure, and residual confounding. The objective of this perspective article is to highlight how developments in big data and machine learning can help address these challenges. New methods of collecting 24-h dietary recalls and recording diet could enable larger samples and more repeated measures to increase statistical power and measurement precision. In addition, use of machine learning to automatically classify pictures of food could become a useful complimentary method to help improve precision and validity of dietary measurements. Diet is complex due to thousands of different foods that are consumed in varying proportions, fluctuating quantities over time, and differing combinations. Current dietary pattern methods may not integrate sufficient dietary variation, and most traditional modeling approaches have limited incorporation of interactions and nonlinearity. Machine learning could help better model diet as a complex exposure with nonadditive and nonlinear associations. Last, novel big data sources could help avoid unmeasured confounding by offering more covariates, including both omics and features derived from unstructured data with machine learning methods. These opportunities notwithstanding, application of big data and machine learning must be approached cautiously to ensure quality of dietary measurements, avoid overfitting, and confirm accurate interpretations. Greater use of machine learning and big data would also require substantial investments in training, collaborations, and computing infrastructure. Overall, we propose that judicious application of big data and machine learning in nutrition science could offer new means of dietary measurement, more tools to model the complexity of diet and its relations with diseases, and additional potential ways of addressing confounding.

Variation in plasma levels of cortisol, an essential hormone in the stress response, is associated in population-based studies with cardio-metabolic, inflammatory and neuro-cognitive traits and diseases. Heritability of plasma cortisol is estimated at 30–60% but no common genetic contribution has been identified. The CORtisol NETwork (CORNET) consortium undertook genome wide association meta-analysis for plasma cortisol in 12,597 Caucasian participants, replicated in 2,795 participants. The results indicate that <1% of variance in plasma cortisol is accounted for by genetic variation in a single region of chromosome 14. This locus spans SERPINA6, encoding corticosteroid binding globulin (CBG, the major cortisol-binding protein in plasma), and SERPINA1, encoding α1-antitrypsin (which inhibits cleavage of the reactive centre loop that releases cortisol from CBG). Three partially independent signals were identified within the region, represented by common SNPs; detailed biochemical investigation in a nested sub-cohort showed all these SNPs were associated with variation in total cortisol binding activity in plasma, but some variants influenced total CBG concentrations while the top hit (rs12589136) influenced the immunoreactivity of the reactive centre loop of CBG. Exome chip and 1000 Genomes imputation analysis of this locus in the CROATIA-Korcula cohort identified missense mutations in SERPINA6 and SERPINA1 that did not account for the effects of common variants. These findings reveal a novel common genetic source of variation in binding of cortisol by CBG, and reinforce the key role of CBG in determining plasma cortisol levels. In turn this genetic variation may contribute to cortisol-associated degenerative diseases. Cortisol is a steroid hormone from the adrenal glands that is essential in the response to stress. Most cortisol in blood is bound to corticosteroid binding globulin (CBG). Diseases causing cortisol deficiency (Addison's disease) or excess (Cushing's syndrome) are life-threatening. Variations in plasma cortisol have been associated with cardiovascular and psychiatric diseases and their risk factors. To dissect the genetic contribution to variation in plasma cortisol, we formed the CORtisol NETwork (CORNET) consortium and recruited collaborators with suitable samples from more than 15,000 people. The results reveal that the major genetic influence on plasma cortisol is mediated by variations in the binding capacity of CBG. This is determined by differences in the circulating concentrations of CBG and also in the immunoreactivity of its ‘reactive centre loop’, potentially influencing not only binding affinity for cortisol but also the stability of CBG and hence the tissue delivery of cortisol. These findings provide the first evidence for a common genetic effect on levels of this clinically important hormone, suggest that differences in CBG between individuals are biologically important, and pave the way for further research to dissect causality in the associations of plasma cortisol with common diseases.

Background: Vitamin D, from diet and sunlight exposure, may be associated with reduced breast-cancer risk. This study investigated if candidate gene variants in vitamin D pathways are associated with breast cancer risk, or modify the associations between breast cancer and vitamin D exposure.