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IntroductionElectrocardiographic (ECG) markers of ventricular depolarisation and repolarisation are associated with an increased risk of arrhythmia and sudden cardiac death. Our prior work indicated lower serum calcium concentrations are associated with longer QT and JT intervals in the general population. Here, we investigate whether serum calcium is a causal risk factor for changes in ECG measures using Mendelian Randomisation (MR).MethodsWe performed a new genome-wide association study (GWAS) for serum calcium in >300,000 European-ancestry participants from UK-Biobank. Independent lead variants were extracted to be used as instrumental variables (figure 1). Two-sample MR analyses were performed to approximate the causal effect of serum calcium on QT, JT and QRS intervals using an inverse-weighted method in 76,226 UK-Biobank participants with ECG data, not contributing to the serum calcium GWAS. A secondary analysis wase performed using lead variants from a calcium GWAS corrected for serum albumin. Sensitivity analyses were performed using contemporary methods to test for the presence of horizontal pleiotropy.Results205 independent lead calcium-associated variants were used as instrumental variables for Mendelian Randomisation. A decrease of 0.1 mmol/L genetically-determined serum calcium, was associated with longer QT (3.01ms (95% CI 2.03, 3.99)) and JT (2.89ms (1.91, 3.87)) intervals. A weak association was observed for QRS duration in secondary analyses only (0.39ms (0.08, 0.69)). Results were concordant in all sensitivity analyses.Abstract BS8 Figure 1ConclusionThese analyses support a causal effect of serum calcium levels on ventricular repolarisation, in a middle-aged population of European-ancestry where serum calcium concentrations are likely stable and chronic. Modulation of calcium concentration may therefore directly influence cardiovascular disease risk. Further research into the effects of serum calcium concentration on arrhythmogenesis is warranted and calcium variants could be considered for inclusion in genetic risk score models for predictive testing.Conflict of InterestNone

In recent years, medicine has undergone profound changes, strongly entering a new phase defined as the “era of precision medicine”. In this context, patient clinical management involves various scientific approaches that allow for a comprehensive pathology evaluation: from preventive processes (where applicable) to genetic and diagnostic studies. In this scenario, biobanks play an important role and, over the years, have gained increasing prestige, moving from small deposits to large collections of samples of various natures. Disease-oriented biobanks are rapidly developing as they provide useful information for the management of complex diseases, such as melanoma. Indeed, melanoma, given its highly heterogeneous characteristics, is one of the oncologic diseases with the greatest clinical and therapeutic management complexity. So, the possibility of extrapolating tissue, genetic and imaging data from dedicated biobanks could result in more selective study approaches. In this review, we specifically analyze the several biobank types to evaluate their role in technology development, patient monitoring and research of new biomarkers, especially in the melanoma context.

The BioBank Japan (BBJ) Project was launched in 2003 with the aim of providing evidence for the implementation of personalized medicine by constructing a large, patient-based biobank (BBJ). This report describes the study design and profile of BBJ participants who were registered during the first 5-year period of the project. The BBJ is a registry of patients diagnosed with any of 47 target common diseases. Patients were enrolled at 12 cooperative medical institutes all over Japan from June 2003 to March 2008. Clinical information was collected annually via interviews and medical record reviews until 2013. We collected DNA from all participants at baseline and collected annual serum samples until 2013. In addition, we followed patients who reported a history of 32 of the 47 target diseases to collect survival data, including cause of death. During the 5-year period, 200,000 participants were registered in the study. The total number of cases was 291,274 at baseline. Baseline data for 199,982 participants (53.1% male) were available for analysis. The average age at entry was 62.7 years for men and 61.5 years for women. Follow-up surveys were performed for participants with any of 32 diseases, and survival time data for 141,612 participants were available for analysis. The BBJ Project has constructed the infrastructure for genomic research for various common diseases. This clinical information, coupled with genomic data, will provide important clues for the implementation of personalized medicine. •The BioBank Japan Project (BBJ) enrolled 200,000 patients with 47 target diseases.•The BBJ is one of the largest patient-based biobanks in the world.•The BBJ may allow for personalized medicine in the future.

BackgroundExisting evidence concerning the relationship between daytime napping and type 2 diabetes (T2D) is inconsistent, and whether the effects of napping differ by body fat percentage (BFP) and C‐reactive protein (CRP) is unclear. We aimed to investigate the association between daytime napping frequency and T2D risk and whether such an association was modified by BFP and CRP.MethodsWe included 435 342 participants free of diabetes from the UK Biobank. Participants were categorized as nonnappers, occasional nappers, and frequent nappers based on napping frequency, and BFP/CRP was divided into quartiles. Cox proportional hazards models were used.ResultsDuring a median follow‐up of 9.2 years, 17 592 T2D cases occurred. Higher frequency of daytime napping was significantly associated with an increased risk of T2D. Compared with nonnappers, the adjusted hazard ratios (HRs) for occasional nappers and habitual nappers were 1.28 (95% confidence interval [CI]: 1.24–1.32) and 1.49 (95% CI: 1.41–1.57), respectively. There was a significant additive and multiplicative interaction (relative excess risk due to interaction [RERI] = 0.490, 95% CI 0.307–0.673; p for multiplicative interaction <.001) between napping and BFP, whereby a higher hazard of T2D associated with more frequent napping was greatest among participants in the highest BFP quartile (HR = 4.45, 95% CI: 3.92–5.06). The results for CRP were similar (RERI = 0.266, 95% CI: 0.094–0.439; p for multiplicative interaction <.001).ConclusionsHigher daytime napping frequency is associated with an increased T2D risk, and such relationships are modified by BFP and CRP. These findings underscore the importance of adiposity and inflammation control to mitigate diabetes risk.

Previous research on sleep and aging largely has failed to illustrate the optimal dose–response curve of this relationship. We aimed to analyze the associations between sleep duration and measures of predicted age. In total, 241,713 participants from the UK Biobank were included. Habitual sleep duration was collected from the baseline questionnaire. Four indicators, homeostatic dysregulation (HD), phenoAge (PA), Klemera–Doubal method (KDM), and allostatic load (AL), were chosen to assess predicted age. Multivariate linear regression models were utilized. The association of sleep duration and predicted age followed a U‐shape (All p for nonlinear <0.05). Compared with individuals who sleep for 7 h/day, the multivariable‐adjusted beta of ≤5 and ≥9 h/day were 0.05 (95% CI 0.03, 0.07) and 0.03 (95% CI 0.02, 0.05) for HD, 0.08 (95% CI 0.01, 0.14) and 0.36 (95% CI 0.31, 0.41) for PA, and 0.21 (95% CI 0.12, 0.30) and 0.30 (95% CI 0.23, 0.37) for KDM. Significant independent and joint effects of sleep and cystatin C (CysC) and gamma glutamyltransferase (GGT) on predicted age metrics were future found. Similar results were observed when conducting stratification analyses. Short and long sleep duration were associated with accelerated predicted age metrics mediated by CysC and GGT. U‐shaped association between sleep duration and biological aging, especially in populations that are not considered elderly.

Introduction Shift work may disrupt sleep and circadian rhythms, leading to adverse health outcomes. The objective of this study is to examine whether performing shift work is associated with frailty in middle- to older-aged adults. Methods We examined current shift work exposure and frailty risk in 242,126 UK Biobank participants (38-71 years old; 128,595 females). Using the baseline self-reported employment information, participants were categorized as shift workers (with ‘work schedule that falls outside of the normal daytime working hours of 9am-5pm’) or day workers if no shift work schedule. Based on a previously validated approach, frailty phenotype was identified when three or more of the following five criteria were fulfilled: self-reported weight loss, exhaustion, low physical activity, slow walking pace, and measured low grip strength. Multivariate logistic regression models were used to assess the association between shift work and frailty. Results Among 242,126 participants, 201,489 (83.2%) were day workers and 40,637 (16.8%) shift workers. Frailty was identified in 4,308 participants (1.8%), including 3,312 day workers (1.6%) and 996 shift workers (2.5%). Compared with day workers, shift workers were at higher risk for frailty after adjusting age, sex, and ethnicity, i.e., odds ratio (OR) =1.58 [95% CI, 1.47-1.70] (p < 0.0001). The effect of shift work appeared to be more pronounced in 5,659 permanent night shift workers (OR = 1.98 [95% CI, 1.68-2.32], p < 0.0001). The differences between day workers and shift workers persisted even after further adjusting for social economic status, sleep duration, and chronotype (OR = 1.38 [95% CI, 1.28-1.48] for all shift workers; OR = 1.53 [95% CI, 1.30-1.80] for permanent night shift workers, both p values < 0.0001). Conclusion Middle- to older-aged adults who were currently performing shift work, especially those permanent night shift workers, were more likely to be frail. Longitudinal and interventional studies are warranted to elucidate the causal relationship between shift work and the risk of frailty. Support (if any) BrightFocus Foundation (A2020886S), NIH (RF1AG059867, RF1AG064312)

To implement personalized medicine, we established a large-scale patient cohort, BioBank Japan, in 2003. BioBank Japan contains DNA, serum, and clinical information derived from approximately 200,000 patients with 47 diseases. Serum and clinical information were collected annually until 2012. We analyzed clinical information of participants at enrollment, including age, sex, body mass index, hypertension, and smoking and drinking status, across 47 diseases, and compared the results with the Japanese database on Patient Survey and National Health and Nutrition Survey. We conducted multivariate logistic regression analysis, adjusting for sex and age, to assess the association between family history and disease development. Distribution of age at enrollment reflected the typical age of disease onset. Analysis of the clinical information revealed strong associations between smoking and chronic obstructive pulmonary disease, drinking and esophageal cancer, high body mass index and metabolic disease, and hypertension and cardiovascular disease. Logistic regression analysis showed that individuals with a family history of keloid exhibited a higher odds ratio than those without a family history, highlighting the strong impact of host genetic factor(s) on disease onset. Cross-sectional analysis of the clinical information of participants at enrollment revealed characteristics of the present cohort. Analysis of family history revealed the impact of host genetic factors on each disease. BioBank Japan, by publicly distributing DNA, serum, and clinical information, could be a fundamental infrastructure for the implementation of personalized medicine. •The BioBank Japan Project (BBJ) annually collected clinical information.•Analysis of the clinical information at enrollment characterized the BBJ cohort.•Analysis of family history revealed impacts of host genetic factors on the diseases.

Background The aim of the present review is to discuss how the promising field of biobanking can support health care research strategies. As the concept has evolved over time, biobanks have grown from simple biological sample repositories to complex and dynamic units belonging to large infrastructure networks, such as the Pan-European Biobanking and Biomolecular Resources Research Infrastructure (BBMRI). Biobanks were established to support scientific knowledge. Different professional figures with varied expertise collaborate to obtain and collect biological and clinical data from human subjects. At same time biobanks preserve the human and legal rights of each person that offers biomaterial for research. Methods A literature review was conducted in April 2019 from the online database PubMed, accessed through the Bibliosan platform. Four primary topics related to biobanking will be discussed: (i) evolution, (ii) bioethical issues, (iii) organization, and (iv) imaging. Results Most biobanks were founded as local units to support specific research projects, so they evolved in a decentralized manner. The consequence is an urgent needing for procedure harmonization regarding sample collection, processing, and storage. Considering the involvement of biomaterials obtained from human beings, different ethical issues such as the informed consent model, sample ownership, veto rights, and biobank sustainability are debated. In the face of these methodological and ethical challenges, international organizations such as BBMRI play a key role in supporting biobanking activities. Finally, a unique development is the creation of imaging biobanks that support the translation of imaging biomarkers (identified using a radiomic approach) into clinical practice by ensuring standardization of data acquisition and analysis, accredited technical validation, and transparent sharing of biological and clinical data. Conclusion Modern biobanks permit large-scale analysis for individuation of specific diseases biomarkers starting from biological or digital material (i.e., bioimages) with well-annotated clinical and biological data. These features are essential for improving personalized medical approaches, where effective biomarker identification is a critical step for disease diagnosis and prognosis.

Smoking prevalence is higher amongst individuals with schizophrenia and depression compared with the general population. Mendelian randomisation (MR) can examine whether this association is causal using genetic variants identified in genome-wide association studies (GWAS). We conducted two-sample MR to explore the bi-directional effects of smoking on schizophrenia and depression. For smoking behaviour, we used (1) smoking initiation GWAS from the GSCAN consortium and (2) we conducted our own GWAS of lifetime smoking behaviour (which captures smoking duration, heaviness and cessation) in a sample of 462690 individuals from the UK Biobank. We validated this instrument using positive control outcomes (e.g. lung cancer). For schizophrenia and depression we used GWAS from the PGC consortium. There was strong evidence to suggest smoking is a risk factor for both schizophrenia (odds ratio (OR) 2.27, 95% confidence interval (CI) 1.67-3.08, p < 0.001) and depression (OR 1.99, 95% CI 1.71-2.32, p < 0.001). Results were consistent across both lifetime smoking and smoking initiation. We found some evidence that genetic liability to depression increases smoking (β = 0.091, 95% CI 0.027-0.155, p = 0.005) but evidence was mixed for schizophrenia (β = 0.022, 95% CI 0.005-0.038, p = 0.009) with very weak evidence for an effect on smoking initiation. These findings suggest that the association between smoking, schizophrenia and depression is due, at least in part, to a causal effect of smoking, providing further evidence for the detrimental consequences of smoking on mental health.