Background & AimsNarrative reviews of paediatric NAFLD quote prevalences in the general population that range from 9% to 37%; however, no systematic review of the prevalence of NAFLD in children/adolescents has been conducted. We aimed to estimate prevalence of non-alcoholic fatty liver disease (NAFLD) in young people and to determine whether this varies by BMI category, gender, age, diagnostic method, geographical region and study sample size.MethodsWe conducted a systematic review and meta-analysis of all studies reporting a prevalence of NAFLD based on any diagnostic method in participants 1–19 years old, regardless of whether assessing NAFLD prevalence was the main aim of the study.ResultsThe pooled mean prevalence of NAFLD in children from general population studies was 7.6% (95%CI: 5.5% to 10.3%) and 34.2% (95% CI: 27.8% to 41.2%) in studies based on child obesity clinics. In both populations there was marked heterogeneity between studies (I2 = 98%). There was evidence that prevalence was generally higher in males compared with females and increased incrementally with greater BMI. There was evidence for differences between regions in clinical population studies, with estimated prevalence being highest in Asia. There was no evidence that prevalence changed over time. Prevalence estimates in studies of children/adolescents attending obesity clinics and in obese children/adolescents from the general population were substantially lower when elevated alanine aminotransferase (ALT) was used to assess NAFLD compared with biopsies, ultrasound scan (USS) or magnetic resonance imaging (MRI).ConclusionsOur review suggests the prevalence of NAFLD in young people is high, particularly in those who are obese and in males.

Objective To examine whether sleep traits have a causal effect on risk of breast cancer. Design Mendelian randomisation study. Setting UK Biobank prospective cohort study and Breast Cancer Association Consortium (BCAC) case-control genome-wide association study. Participants 156 848 women in the multivariable regression and one sample mendelian randomisation (MR) analysis in UK Biobank (7784 with a breast cancer diagnosis) and 122 977 breast cancer cases and 105 974 controls from BCAC in the two sample MR analysis. Exposures Self reported chronotype (morning or evening preference), insomnia symptoms, and sleep duration in multivariable regression, and genetic variants robustly associated with these sleep traits. Main outcome measure Breast cancer diagnosis. Results In multivariable regression analysis using UK Biobank data on breast cancer incidence, morning preference was inversely associated with breast cancer (hazard ratio 0.95, 95% confidence interval 0.93 to 0.98 per category increase), whereas there was little evidence for an association between sleep duration and insomnia symptoms. Using 341 single nucleotide polymorphisms (SNPs) associated with chronotype, 91 SNPs associated with sleep duration, and 57 SNPs associated with insomnia symptoms, one sample MR analysis in UK Biobank provided some supportive evidence for a protective effect of morning preference on breast cancer risk (0.85, 0.70, 1.03 per category increase) but imprecise estimates for sleep duration and insomnia symptoms. Two sample MR using data from BCAC supported findings for a protective effect of morning preference (inverse variance weighted odds ratio 0.88, 95% confidence interval 0.82 to 0.93 per category increase) and adverse effect of increased sleep duration (1.19, 1.02 to 1.39 per hour increase) on breast cancer risk (both oestrogen receptor positive and oestrogen receptor negative), whereas evidence for insomnia symptoms was inconsistent. Results were largely robust to sensitivity analyses accounting for horizontal pleiotropy. Conclusions Findings showed consistent evidence for a protective effect of morning preference and suggestive evidence for an adverse effect of increased sleep duration on breast cancer risk.

We conducted genome-wide association studies (GWAS) of relative intake from the macronutrients fat, protein, carbohydrates, and sugar in over 235,000 individuals of European ancestries. We identified 21 unique, approximately independent lead SNPs. Fourteen lead SNPs are uniquely associated with one macronutrient at genome-wide significance (P < 5 × 10 −8), while five of the 21 lead SNPs reach suggestive significance (P < 1 × 10 −5) for at least one other macronutrient. While the phenotypes are genetically correlated, each phenotype carries a partially unique genetic architecture. Relative protein intake exhibits the strongest relationships with poor health, including positive genetic associations with obesity, type 2 diabetes, and heart disease (r g ≈ 0.15-0.5). In contrast, relative carbohydrate and sugar intake have negative genetic correlations with waist circumference, waist-hip ratio, and neighborhood deprivation (|r g | ≈ 0.1-0.3) and positive genetic correlations with physical activity (r g ≈ 0.1 and 0.2). Relative fat intake has no consistent pattern of genetic correlations with poor health but has a negative genetic correlation with educational attainment (r g ≈−0.1). Although our analyses do not allow us to draw causal conclusions, we find no evidence of negative health consequences associated with relative carbohydrate, sugar, or fat intake. However, our results are consistent with the hypothesis that relative protein intake plays a role in the etiology of metabolic dysfunction.