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

Assessment of breast cancer risk factors reveals subtype heterogeneity

Subtype heterogeneity for breast cancer risk factors has been suspected, potentially reflecting etiological differences and implicating risk prediction. Reports are conflicting regarding presence of heterogeneity for many exposures. To examine subtype heterogeneity across known breast cancer risk factors, we conducted a case-control analysis of 2,632 breast cancers and 15,945 controls in Sweden. Molecular subtype was predicted from pathology-record derived immunohistochemistry markers by a classifier trained on PAM50 subtyping. Multinomial logistic regression estimated separate odds ratios for each subtype by the exposures parity, age at first birth, breastfeeding, menarche, HRT use, somatotype at age 18, benign breast disease, mammographic density, polygenic risk score, family history of breast cancer and BRCA mutations. We found clear subtype heterogeneity for genetic factors and breastfeeding. The polygenic risk score was associated with risk of all subtypes except for the basal-like (p heterogeneity < 0.0001). Parous women who never breastfed were at higher risk of basal-like subtype (OR 4.17; 95% CI 1.89 to 9.21) compared to both nulliparous (reference) and breastfeeding women. Breastfeeding was not associated with risk of HER2-overexpressing type, but protective for all other subtypes. The observed heterogeneity in risk of distinct breast cancer subtypes for germline variants supports heterogeneity in etiology and has implications for their use in risk prediction. The increased risk of basal-like subtype among women who never breastfed merits more research into potential causal mechanisms and confounders.Swedish Research CouncilSwedish Cancer SocietyAccepte

Effects of a 6-month dual-task, power-based exercise program on cognitive function, neurological and inflammatory markers in older adults: Secondary analysis of a cluster randomised controlled trial

Functional power-based exercise training can improve physical performance in older adults and cognitive training can improve measures of cognition, but their combined effects on cognition and related risk factors (neurological and inflammatory markers) remains uncertain. This 6-month cluster randomised controlled trial evaluated the effectiveness of dual-task functional power training (DT-FPT) on cognition and circulating neurological and inflammatory markers in older adults at increased falls risk, and whether intervention responses varied by apolipoprotein-E (ApoE) and brain derived neurotrophic factor (BDNF) polymorphisms. Three hundred residents aged ≥ 65 years at increased falls risk residing in 22 independent-living retirement communities, were randomised by village, to DT-FPT (n = 156, 11 villages) involving a multi-component power-based training program performed simultaneously with cognitive and/or motor tasks (45–60 min, 2/week), or a usual care control (CON) group (n = 144, 11 villages). Cognition (computerized CogState battery), inflammatory cytokines, BDNF, insulin-like growth factor-1, vascular endothelial growth factor, amyloid β (1–40) and (1–42) were assessed at baseline and 6-months. Overall, 233 (78%) participants completed the intervention and adherence averaged 50.1%. DT-FPT led to a net 0.18–0.20 SD benefit versus CON in psychomotor ability/attention and reaction time/attention (both P \u3c 0.05). There were no significant intervention effects on circulating markers, except for a net 10.5% benefit in amyloid β (1–40) in DT-FPT versus CON (P \u3c 0.05). Responses were not influenced by APOE or BDNF genotype. In conclusion, DT-FPT in older adults at increased falls risk can provide some cognitive benefits, but these were not related to corresponding changes in inflammatory or neurological markers or influenced by genotype. Australian New Zealand Clinical Trials Registry (ACTRN12613001161718). http://www.anzctr.org.au/ This project was funded by a grant from the National Health and Medical Research Council (NHMRC) Project (APP1046267)

Dynamic metabolomic data analysis: a tutorial review

In metabolomics, time-resolved, dynamic or temporal data is more and more collected. The number of methods to analyze such data, however, is very limited and in most cases the dynamic nature of the data is not even taken into account. This paper reviews current methods in use for analyzing dynamic metabolomic data. Moreover, some methods from other fields of science that may be of use to analyze such dynamic metabolomics data are described in some detail. The methods are put in a general framework after providing a formal definition on what constitutes a ‘dynamic’ method. Some of the methods are illustrated with real-life metabolomics examples

Bone marrow adipose tissue is a unique adipose subtype with distinct roles in glucose homeostasis

Bone marrow adipose tissue (BMAT) comprises >10% of total adipose mass, yet unlike white or brown adipose tissues (WAT or BAT) its metabolic functions remain unclear. Herein, we address this critical gap in knowledge. Our transcriptomic analyses revealed that BMAT is distinct from WAT and BAT, with altered glucose metabolism and decreased insulin responsiveness. We therefore tested these functions in mice and humans using positron emission tomography-computed tomography (PET/CT) with 18F-fluorodeoxyglucose. This revealed that BMAT resists insulin- and cold-stimulated glucose uptake, while further in vivo studies showed that, compared to WAT, BMAT resists insulin-stimulated Akt phosphorylation. Thus, BMAT is functionally distinct from WAT and BAT. However, in humans basal glucose uptake in BMAT is greater than in axial bones or subcutaneous WAT and can be greater than that in skeletal muscle, underscoring the potential of BMAT to influence systemic glucose homeostasis. These PET/CT studies characterise BMAT function in vivo, establish new methods for BMAT analysis, and identify BMAT as a distinct, major adipose tissue subtype

A Genome-Wide Metabolic QTL Analysis in Europeans Implicates Two Loci Shaped by Recent Positive Selection

We have performed a metabolite quantitative trait locus (mQTL) study of the 1H nuclear magnetic resonance spectroscopy (1H NMR) metabolome in humans, building on recent targeted knowledge of genetic drivers of metabolic regulation. Urine and plasma samples were collected from two cohorts of individuals of European descent, with one cohort comprised of female twins donating samples longitudinally. Sample metabolite concentrations were quantified by 1H NMR and tested for association with genome-wide single-nucleotide polymorphisms (SNPs). Four metabolites' concentrations exhibited significant, replicable association with SNP variation (8.6×10−11<p<2.8×10−23). Three of these—trimethylamine, 3-amino-isobutyrate, and an N-acetylated compound—were measured in urine. The other—dimethylamine—was measured in plasma. Trimethylamine and dimethylamine mapped to a single genetic region (hence we report a total of three implicated genomic regions). Two of the three hit regions lie within haplotype blocks (at 2p13.1 and 10q24.2) that carry the genetic signature of strong, recent, positive selection in European populations. Genes NAT8 and PYROXD2, both with relatively uncharacterized functional roles, are good candidates for mediating the corresponding mQTL associations. The study's longitudinal twin design allowed detailed variance-components analysis of the sources of population variation in metabolite levels. The mQTLs explained 40%–64% of biological population variation in the corresponding metabolites' concentrations. These effect sizes are stronger than those reported in a recent, targeted mQTL study of metabolites in serum using the targeted-metabolomics Biocrates platform. By re-analysing our plasma samples using the Biocrates platform, we replicated the mQTL findings of the previous study and discovered a previously uncharacterized yet substantial familial component of variation in metabolite levels in addition to the heritability contribution from the corresponding mQTL effects

Serum S100A6 Concentration Predicts Peritoneal Tumor Burden in Mice with Epithelial Ovarian Cancer and Is Associated with Advanced Stage in Patients

BACKGROUND:Ovarian cancer is the 5th leading cause of cancer related deaths in women. Five-year survival rates for early stage disease are greater than 94%, however most women are diagnosed in advanced stage with 5 year survival less than 28%. Improved means for early detection and reliable patient monitoring are needed to increase survival. METHODOLOGY AND PRINCIPAL FINDINGS:Applying mass spectrometry-based proteomics, we sought to elucidate an unanswered biomarker research question regarding ability to determine tumor burden detectable by an ovarian cancer biomarker protein emanating directly from the tumor cells. Since aggressive serous epithelial ovarian cancers account for most mortality, a xenograft model using human SKOV-3 serous ovarian cancer cells was established to model progression to disseminated carcinomatosis. Using a method for low molecular weight protein enrichment, followed by liquid chromatography and mass spectrometry analysis, a human-specific peptide sequence of S100A6 was identified in sera from mice with advanced-stage experimental ovarian carcinoma. S100A6 expression was documented in cancer xenografts as well as from ovarian cancer patient tissues. Longitudinal study revealed that serum S100A6 concentration is directly related to tumor burden predictions from an inverse regression calibration analysis of data obtained from a detergent-supplemented antigen capture immunoassay and whole-animal bioluminescent optical imaging. The result from the animal model was confirmed in human clinical material as S100A6 was found to be significantly elevated in the sera from women with advanced stage ovarian cancer compared to those with early stage disease. CONCLUSIONS:S100A6 is expressed in ovarian and other cancer tissues, but has not been documented previously in ovarian cancer disease sera. S100A6 is found in serum in concentrations that correlate with experimental tumor burden and with clinical disease stage. The data signify that S100A6 may prove useful in detecting and/or monitoring ovarian cancer, when used in concert with other biomarkers