Puberty timing associated with diabetes, cardiovascular disease and also diverse health outcomes in men and women: the UK Biobank study.

Early puberty timing is associated with higher risks for type 2 diabetes (T2D) and cardiovascular disease in women and therefore represents a potential target for early preventive interventions. We characterised the range of diseases and other adverse health outcomes associated with early or late puberty timing in men and women in the very large UK Biobank study. Recalled puberty timing and past/current diseases were self-reported by questionnaire. We limited analyses to individuals of White ethnicity (250,037 women; 197,714 men) and to disease outcomes with at least 500 cases (~ 0.2% prevalence) and we applied stringent correction for multiple testing (corrected threshold P < 7.48 × 10(-5)). In models adjusted for socioeconomic position and adiposity/body composition variables, both in women and men separately, earlier puberty timing was associated with higher risks for angina, hypertension and T2D. Furthermore, compared to the median/average group, earlier or later puberty timing in women or men was associated with higher risks for 48 adverse outcomes, across a range of cancers, cardio-metabolic, gynaecological/obstetric, gastrointestinal, musculoskeletal, and neuro-cognitive categories. Notably, both early and late menarche were associated with higher risks for early natural menopause in women. Puberty timing in both men and women appears to have a profound impact on later health.This research has been conducted using the UK Biobank Resource. This work was supported by the Medical Research Council [Unit Programme number MC_UU_12015/2].This is the final version of the article. It first appeared from NPG via http://dx.doi.org/10.1038/srep1120

Progressive influence of body mass index-associated genetic markers in rural Gambians.

BACKGROUND: In populations of European ancestry, the genetic contribution to body mass index (BMI) increases with age during childhood but then declines during adulthood, possibly due to the cumulative effects of environmental factors. How the effects of genetic factors on BMI change with age in other populations is unknown. SUBJECTS AND METHODS: In a rural Gambian population (N=2535), we used a combined allele risk score, comprising genotypes at 28 'Caucasian adult BMI-associated' single nucleotide polymorphisms (SNPs), as a marker of the genetic influence on body composition, and related this to internally-standardised z-scores for birthweight (zBW), weight-for-height (zWT-HT), weight-for-age (zWT), height-for-age (zHT), and zBMI cross-sectionally and longitudinally. RESULTS: Cross-sectionally, the genetic score was positively associated with adult zWT (0.018±0.009 per allele, p=0.034, N=1426) and zWT-HT (0.025±0.009, p=0.006), but not with size at birth or childhood zWT-HT (0.008±0.005, p=0.11, N=2211). The effect of the genetic score on zWT-HT strengthened linearly with age from birth through to late adulthood (age interaction term: 0.0083 z-scores/allele/year; 95% CI 0.0048 to 0.0118, p=0.0000032). CONCLUSIONS: Genetic variants for obesity in populations of European ancestry have direct relevance to bodyweight in nutritionally deprived African settings. In such settings, genetic obesity susceptibility appears to regulate change in weight status throughout the life course, which provides insight into its potential physiological role

Life course variations in the associations between FTO and MC4R gene variants and body size

The timing of associations between common genetic variants for weight or body mass index (BMI) across the life course may provide insights into the aetiology of obesity. We genotyped variants in FTO (rs9939609) and near MC4R (rs17782313) in 1240 men and 1239 women born in 1946 and participating in the MRC National Survey of Health and Development. Birth weight was recorded and height and weight were measured or self-reported repeatedly at 11 time-points between ages 2 and 53 years. Hierarchical mixed models were used to test whether genetic associations with weight or BMI standard deviation scores (SDS) changed with age during childhood and adolescence (2–20 years) or adulthood (20–53 years). The association between FTO rs9939609 and BMI SDS strengthened during childhood and adolescence (rate of change: 0.007 SDS/A-allele/year; 95% CI: 0.003–0.010, P < 0.001), reached a peak strength at age 20 years (0.13 SDS/A-allele, 0.08–0.19), and then weakened during adulthood (−0.003 SDS/A-allele/year, −0.005 to −0.001, P = 0.001). MC4R rs17782313 showed stronger associations with weight than BMI; its association with weight strengthened during childhood and adolescence (0.005 SDS/C-allele/year; 0.001–0.008, P = 0.006), peaked at age 20 years (0.13 SDS/C-allele, 0.07–0.18), and weakened during adulthood (−0.002 SDS/C-allele/year, −0.004 to 0.000, P = 0.05). In conclusion, genetic variants in FTO and MC4R showed similar biphasic changes in their associations with BMI and weight, respectively, strengthening during childhood up to age 20 years and then weakening with increasing adult age. Studies of the aetiology of obesity spanning different age groups may identify age-specific determinants of weight gain

Variability in the Heritability of Body Mass Index: A Systematic Review and Meta-Regression

Evidence for a major role of genetic factors in the determination of body mass index (BMI) comes from studies of related individuals. Despite consistent evidence for a heritable component of BMI, estimates of BMI heritability vary widely between studies and the reasons for this remain unclear. While some variation is natural due to differences between populations and settings, study design factors may also explain some of the heterogeneity. We performed a systematic review that identified 88 independent estimates of BMI heritability from twin studies (total 140,525 twins) and 27 estimates from family studies (42,968 family members). BMI heritability estimates from twin studies ranged from 0.47 to 0.90 (5th/50th/95th centiles: 0.58/0.75/0.87) and were generally higher than those from family studies (range: 0.24–0.81; 5th/50th/95th centiles: 0.25/0.46/0.68). Meta-regression of the results from twin studies showed that BMI heritability estimates were 0.07 (P = 0.001) higher in children than in adults; estimates increased with mean age among childhood studies (+0.012/year, P = 0.002), but decreased with mean age in adult studies (−0.002/year, P = 0.002). Heritability estimates derived from AE twin models (which assume no contribution of shared environment) were 0.12 higher than those from ACE models (P < 0.001), whilst lower estimates were associated with self reported versus DNA-based determination of zygosity (−0.04, P = 0.02), and with self reported versus measured BMI (−0.05, P = 0.03). Although the observed differences in heritability according to aspects of study design are relatively small, together, the above factors explained 47% of the heterogeneity in estimates of BMI heritability from twin studies. In summary, while some variation in BMI heritability is expected due to population-level differences, study design factors explained nearly half the heterogeneity reported in twin studies. The genetic contribution to BMI appears to vary with age and may have a greater influence during childhood than adult life

Mendelian Randomisation Study of Childhood BMI and Early Menarche

To infer the causal association between childhood BMI and age at menarche, we performed a mendelian randomisation analysis using twelve established “BMI-increasing” genetic variants as an instrumental variable (IV) for higher BMI. In 8,156 women of European descent from the EPIC-Norfolk cohort, height was measured at age 39–77 years; age at menarche was self-recalled, as was body weight at age 20 years, and BMI at 20 was calculated as a proxy for childhood BMI. DNA was genotyped for twelve BMI-associated common variants (in/near FTO, MC4R, TMEM18, GNPDA2, KCTD15, NEGR1, BDNF, ETV5, MTCH2, SEC16B, FAIM2 and SH2B1), and for each individual a “BMI-increasing-allele-score” was calculated by summing the number of BMI-increasing alleles across all 12 loci. Using this BMI-increasing-allele-score as an instrumental variable for BMI, each 1 kg/m2 increase in childhood BMI was predicted to result in a 6.5% (95% CI: 4.6–8.5%) higher absolute risk of early menarche (before age 12 years). While mendelian randomisation analysis is dependent on a number of assumptions, our findings support a causal effect of BMI on early menarche and suggests that increasing prevalence of childhood obesity will lead to similar trends in the prevalence of early menarche

Adult obesity susceptibility variants are associated with greater childhood weight gain and a faster tempo of growth: the 1946 British Birth Cohort Study123

Background: Longitudinal growth associations with genetic variants identified for adult BMI may provide insights into the timing of obesity susceptibility

Associations between genetic obesity susceptibility and early postnatal fat and lean mass: an individual participant meta-analysis

IMPORTANCE: Patterns of body size and body composition associated with genetic obesity susceptibility inform the mechanisms that increase obesity risk. OBJECTIVE: To test associations between genetic obesity susceptibility, represented by a combined obesity risk-allele score, and body size or body composition at birth to age 5 years. DESIGN, SETTING, AND PARTICIPANTS: A total of 3031 children from 4 birth cohort studies in England, France, and Spain were included in a meta-analysis. EXPOSURES: A combined obesity risk-allele score was calculated from genotypes at 16 variants identified by genome-wide association studies of adult body mass index (BMI). MAIN OUTCOMES AND MEASURES: Outcomes were age- and sex-adjusted SD scores (SDS) for weight, length/height, BMI, fat mass, lean mass, and percentage of body fat at birth as well as at ages 1, 2 to 3, and 4 to 5 years. RESULTS: The obesity risk-allele score was not associated with infant size at birth; at age 1 year it was positively associated with weight (β [SE], 0.020 [0.008] SDS per allele; P = .009) and length (β [SE], 0.020 [0.008] SDS per allele; P = .01), but not with BMI (β [SE], 0.013 [0.008] SDS per allele; P = .11). At age 2 to 3 years these associations were stronger (weight: β [SE], 0.033 [0.008] SDS per allele; P  .15 at all ages). CONCLUSIONS AND RELEVANCE: Genetic obesity susceptibility appears to promote a normally partitioned increase in early postnatal, but not prenatal, growth. These findings suggest that symmetrical rapid growth may identify infants with high life-long susceptibility for obesity

Mendelian Randomisation Study of Childhood BMI and Early Menarche.

To infer the causal association between childhood BMI and age at menarche, we performed a mendelian randomisation analysis using twelve established "BMI-increasing" genetic variants as an instrumental variable (IV) for higher BMI. In 8,156 women of European descent from the EPIC-Norfolk cohort, height was measured at age 39-77 years; age at menarche was self-recalled, as was body weight at age 20 years, and BMI at 20 was calculated as a proxy for childhood BMI. DNA was genotyped for twelve BMI-associated common variants (in/near FTO, MC4R, TMEM18, GNPDA2, KCTD15, NEGR1, BDNF, ETV5, MTCH2, SEC16B, FAIM2 and SH2B1), and for each individual a "BMI-increasing-allele-score" was calculated by summing the number of BMI-increasing alleles across all 12 loci. Using this BMI-increasing-allele-score as an instrumental variable for BMI, each 1 kg/m(2) increase in childhood BMI was predicted to result in a 6.5% (95% CI: 4.6-8.5%) higher absolute risk of early menarche (before age 12 years). While mendelian randomisation analysis is dependent on a number of assumptions, our findings support a causal effect of BMI on early menarche and suggests that increasing prevalence of childhood obesity will lead to similar trends in the prevalence of early menarche.Peer Reviewe

Parent-of-origin-specific allelic associations among 106 genomic loci for age at menarche.

Age at menarche is a marker of timing of puberty in females. It varies widely between individuals, is a heritable trait and is associated with risks for obesity, type 2 diabetes, cardiovascular disease, breast cancer and all-cause mortality. Studies of rare human disorders of puberty and animal models point to a complex hypothalamic-pituitary-hormonal regulation, but the mechanisms that determine pubertal timing and underlie its links to disease risk remain unclear. Here, using genome-wide and custom-genotyping arrays in up to 182,416 women of European descent from 57 studies, we found robust evidence (P < 5 × 10(-8)) for 123 signals at 106 genomic loci associated with age at menarche. Many loci were associated with other pubertal traits in both sexes, and there was substantial overlap with genes implicated in body mass index and various diseases, including rare disorders of puberty. Menarche signals were enriched in imprinted regions, with three loci (DLK1-WDR25, MKRN3-MAGEL2 and KCNK9) demonstrating parent-of-origin-specific associations concordant with known parental expression patterns. Pathway analyses implicated nuclear hormone receptors, particularly retinoic acid and γ-aminobutyric acid-B2 receptor signalling, among novel mechanisms that regulate pubertal timing in humans. Our findings suggest a genetic architecture involving at least hundreds of common variants in the coordinated timing of the pubertal transition

Gene × Physical Activity Interactions in Obesity: Combined Analysis of 111,421 Individuals of European Ancestry

Numerous obesity loci have been identified using genome-wide association studies. A UK study indicated that physical activity may attenuate the cumulative effect of 12 of these loci, but replication studies are lacking. Therefore, we tested whether the aggregate effect of these loci is diminished in adults of European ancestry reporting high levels of physical activity. Twelve obesity-susceptibility loci were genotyped or imputed in 111,421 participants. A genetic risk score (GRS) was calculated by summing the BMI-associated alleles of each genetic variant. Physical activity was assessed using self-administered questionnaires. Multiplicative interactions between the GRS and physical activity on BMI were tested in linear and logistic regression models in each cohort, with adjustment for age, age2, sex, study center (for multicenter studies), and the marginal terms for physical activity and the GRS. These results were combined using meta-analysis weighted by cohort sample size. The meta-analysis yielded a statistically significant GRS × physical activity interaction effect estimate (Pinteraction = 0.015). However, a statistically significant interaction effect was only apparent in North American cohorts (n = 39,810, Pinteraction = 0.014 vs. n = 71,611, Pinteraction = 0.275 for Europeans). In secondary analyses, both the FTO rs1121980 (Pinteraction = 0.003) and the SEC16B rs10913469 (Pinteraction = 0.025) variants showed evidence of SNP × physical activity interactions. This meta-analysis of 111,421 individuals provides further support for an interaction between physical activity and a GRS in obesity disposition, although these findings hinge on the inclusion of cohorts from North America, indicating that these results are either population-specific or non-causal