Electrical impedance spectroscopy detects skin barrier dysfunction in childhood atopic dermatitis.

BACKGROUND Skin barrier dysfunction is associated with the development of atopic dermatitis (AD), however methods to assess skin barrier function are limited. We investigated the use of electrical impedance spectroscopy (EIS) to detect skin barrier dysfunction in children with AD of the CARE (Childhood AlleRgy, nutrition, and Environment) cohort. METHODS EIS measurements taken at multiple time points from 4 months to 3-year-old children, who developed AD (n = 66) and those who did not (n = 49) were investigated. Using only the EIS measurement and the AD status, we developed a machine learning algorithm that produces a score (EIS/AD score) which reflects the probability that a given measurement is from a child with active AD. We investigated the diagnostic ability of this score and its association with clinical characteristics and age. RESULTS Based on the EIS/AD score, the EIS algorithm was able to clearly discriminate between healthy skin and clinically unaffected skin of children with active AD (area under the curve 0.92, 95% CI 0.85-0.99). It was also able to detect a difference between healthy skin and AD skin when the child did not have active AD. There was no clear association between the EIS/AD score and the severity of AD or sensitisation to the tested allergens. The performance of the algorithm was not affected by age. CONCLUSIONS This study shows that EIS can detect skin barrier dysfunction and differentiate skin of children with AD from healthy skin and suggests that EIS may have the ability to predict future AD development

Electrical impedance spectroscopy detects skin barrier dysfunction in childhood atopic dermatitis

Background Skin barrier dysfunction is associated with the development of atopic dermatitis (AD), however methods to assess skin barrier function are limited. We investigated the use of electrical impedance spectroscopy (EIS) to detect skin barrier dysfunction in children with AD of the CARE (Childhood AlleRgy, nutrition, and Environment) cohort. Methods EIS measurements taken at multiple time points from 4 months to 3‐year‐old children, who developed AD (n = 66) and those who did not (n = 49) were investigated. Using only the EIS measurement and the AD status, we developed a machine learning algorithm that produces a score (EIS/AD score) which reflects the probability that a given measurement is from a child with active AD. We investigated the diagnostic ability of this score and its association with clinical characteristics and age. Results Based on the EIS/AD score, the EIS algorithm was able to clearly discriminate between healthy skin and clinically unaffected skin of children with active AD (area under the curve 0.92, 95% CI 0.85–0.99). It was also able to detect a difference between healthy skin and AD skin when the child did not have active AD. There was no clear association between the EIS/AD score and the severity of AD or sensitisation to the tested allergens. The performance of the algorithm was not affected by age. Conclusions This study shows that EIS can detect skin barrier dysfunction and differentiate skin of children with AD from healthy skin and suggests that EIS may have the ability to predict future AD development

New genetic loci implicated in fasting glucose homeostasis and their impact on type 2 diabetes risk.

Levels of circulating glucose are tightly regulated. To identify new loci influencing glycemic traits, we performed meta-analyses of 21 genome-wide association studies informative for fasting glucose, fasting insulin and indices of beta-cell function (HOMA-B) and insulin resistance (HOMA-IR) in up to 46,186 nondiabetic participants. Follow-up of 25 loci in up to 76,558 additional subjects identified 16 loci associated with fasting glucose and HOMA-B and two loci associated with fasting insulin and HOMA-IR. These include nine loci newly associated with fasting glucose (in or near ADCY5, MADD, ADRA2A, CRY2, FADS1, GLIS3, SLC2A2, PROX1 and C2CD4B) and one influencing fasting insulin and HOMA-IR (near IGF1). We also demonstrated association of ADCY5, PROX1, GCK, GCKR and DGKB-TMEM195 with type 2 diabetes. Within these loci, likely biological candidate genes influence signal transduction, cell proliferation, development, glucose-sensing and circadian regulation. Our results demonstrate that genetic studies of glycemic traits can identify type 2 diabetes risk loci, as well as loci containing gene variants that are associated with a modest elevation in glucose levels but are not associated with overt diabetes

Beta-amyloid interacts with and activates the longform phosphodiesterase PDE4D5 in neuronal cells to reduce cAMP availability

Inhibition of the cyclic-AMP degrading enzyme phosphodiesterase type 4 (PDE4) in the brains of animal models is protective in Alzheimer's disease (AD). We show for the first time that enzymes from the subfamily PDE4D not only colocalize with beta-amyloid (Aβ) plaques in a mouse model of AD but that Aβ directly associates with the catalytic machinery of the enzyme. Peptide mapping suggests that PDE4D is the preferential PDE4 subfamily for Aβ as it possesses a unique binding site. Intriguingly, exogenous addition of Aβ to cells overexpressing the PDE4D5 longform caused PDE4 activation and a decrease in cAMP. We suggest a novel mechanism where PDE4 longforms can be activated by Aβ, resulting in the attenuation of cAMP signalling to promote loss of cognitive function in AD

Assessment of the genetic and clinical determinants of fracture risk: genome wide association and mendelian randomisation study.

OBJECTIVES: To identify the genetic determinants of fracture risk and assess the role of 15 clinical risk factors on osteoporotic fracture risk. DESIGN: Meta-analysis of genome wide association studies (GWAS) and a two-sample mendelian randomisation approach. SETTING: 25 cohorts from Europe, United States, east Asia, and Australia with genome wide genotyping and fracture data. PARTICIPANTS: A discovery set of 37 857 fracture cases and 227 116 controls; with replication in up to 147 200 fracture cases and 150 085 controls. Fracture cases were defined as individuals (>18 years old) who had fractures at any skeletal site confirmed by medical, radiological, or questionnaire reports. Instrumental variable analyses were performed to estimate effects of 15 selected clinical risk factors for fracture in a two-sample mendelian randomisation framework, using the largest previously published GWAS meta-analysis of each risk factor. RESULTS: Of 15 fracture associated loci identified, all were also associated with bone mineral density and mapped to genes clustering in pathways known to be critical to bone biology (eg, SOST, WNT16, and ESR1) or novel pathways (FAM210A, GRB10, and ETS2). Mendelian randomisation analyses showed a clear effect of bone mineral density on fracture risk. One standard deviation decrease in genetically determined bone mineral density of the femoral neck was associated with a 55% increase in fracture risk (odds ratio 1.55 (95% confidence interval 1.48 to 1.63; P=1.5×10-68). Hand grip strength was inversely associated with fracture risk, but this result was not significant after multiple testing correction. The remaining clinical risk factors (including vitamin D levels) showed no evidence for an effect on fracture. CONCLUSIONS: This large scale GWAS meta-analysis for fracture identified 15 genetic determinants of fracture, all of which also influenced bone mineral density. Among the clinical risk factors for fracture assessed, only bone mineral density showed a major causal effect on fracture. Genetic predisposition to lower levels of vitamin D and estimated calcium intake from dairy sources were not associated with fracture risk

Exome-Derived Adiponectin-Associated Variants Implicate Obesity and Lipid Biology

Circulating levels of adiponectin, an adipocyte-secreted protein associated with cardiovascular and metabolic risk, are highly heritable. To gain insights into the biology that regulates adiponectin levels, we performed an exome array meta-analysis of 265,780 genetic variants in 67,739 individuals of European, Hispanic, African American, and East Asian ancestry. We identified 20 loci associated with adiponectin, including 11 that had been reported previously (p .60) spanning as much as 900 kb. To identify potential genes and mechanisms through which the previously unreported association signals act to affect adiponectin levels, we assessed cross-trait associations, expression quantitative trait loci in subcutaneous adipose, and biological pathways of nearby genes. Eight of the nine loci were also associated (p <1 x 10(-4)) with at least one obesity or lipid trait. Candidate genes include PRKAR2A, PTH1R, and HDAC9, which have been suggested to play roles in adipocyte differentiation or bone marrow adipose tissue. Taken together, these findings provide further insights into the processes that influence circulating adiponectin levels.Peer reviewe

Genetic Studies of Leptin Concentrations Implicate Leptin in the Regulation of Early Adiposity.

Leptin influences food intake by informing the brain about the status of body fat stores. Rare LEP mutations associated with congenital leptin deficiency cause severe early-onset obesity that can be mitigated by administering leptin. However, the role of genetic regulation of leptin in polygenic obesity remains poorly understood. We performed an exome-based analysis in up to 57,232 individuals of diverse ancestries to identify genetic variants that influence adiposity-adjusted leptin concentrations. We identify five novel variants, including four missense variants, in LEP, ZNF800, KLHL31, and ACTL9, and one intergenic variant near KLF14. The missense variant Val94Met (rs17151919) in LEP was common in individuals of African ancestry only, and its association with lower leptin concentrations was specific to this ancestry (P = 2 × 10-16, n = 3,901). Using in vitro analyses, we show that the Met94 allele decreases leptin secretion. We also show that the Met94 allele is associated with higher BMI in young African-ancestry children but not in adults, suggesting that leptin regulates early adiposity

Genomewide meta-analysis identifies loci associated with IGF-I and IGFBP-3 levels with impact on age-related traits

The growth hormone/insulin-like growth factor (IGF) axis can be manipulated in animal models to promote longevity, and IGF-related proteins including IGF-I and IGF-binding protein-3 (IGFBP-3) have also been implicated in risk of human diseases including cardiovascular diseases, diabetes, and cancer. Throug

Multi-ancestry sleep-by-SNP interaction analysis in 126,926 individuals reveals lipid loci stratified by sleep duration.

Both short and long sleep are associated with an adverse lipid profile, likely through different biological pathways. To elucidate the biology of sleep-associated adverse lipid profile, we conduct multi-ancestry genome-wide sleep-SNP interaction analyses on three lipid traits (HDL-c, LDL-c and triglycerides). In the total study sample (discovery + replication) of 126,926 individuals from 5 different ancestry groups, when considering either long or short total sleep time interactions in joint analyses, we identify 49 previously unreported lipid loci, and 10 additional previously unreported lipid loci in a restricted sample of European-ancestry cohorts. In addition, we identify new gene-sleep interactions for known lipid loci such as LPL and PCSK9. The previously unreported lipid loci have a modest explained variance in lipid levels: most notable, gene-short-sleep interactions explain 4.25% of the variance in triglyceride level. Collectively, these findings contribute to our understanding of the biological mechanisms involved in sleep-associated adverse lipid profiles