Disruptions in circadian rhythm, partly controlled by the hormone melatonin, increase the risk of type 2 diabetes (T2D). Accordingly, a variant of the gene encoding the melatonin receptor 1B (MTNR1B) is robustly associated with increased risk of T2D. This single-nucleotide polymorphism (SNP; rs10830963; G-allele) is an expression quantitative trait locus (eQTL) in human pancreatic islets, conferring increased expression of MTNR1B, which is thought to perturb pancreatic β-cell function. To understand this pathogenic mechanism in detail, we utilized human induced pluripotent stem cells (hiPSC), derived from individuals with T2D carrying the MTNR1B G-allele. Patient-derived fibroblasts were reprogrammed to hiPSC and single-base genome editing by CRISPR/Cas9 was employed to create isogenic lines of either the C/C or G/G genotypes (nonrisk and risk, respectively). In addition, the human embryonic stem cell (hESC) line (HUES4) was subjected to genome editing to create isogenic lines of either the C/C or G/G genotypes. hiPSC and hESC were differentiated into β-like cells, using a 50-day 2D protocol. Single-base genome editing generated cells with the desired genotype at a success rate of > 90%. Expression of stage-specific markers confirmed differentiation of both hiPSC and hESC into β-cells. MTNR1B mRNA levels were consistently low in differentiated β-cells, precluding quantitative analysis of gene expression. Western blot analyses indicated slightly higher levels of the MTNR1B protein in differentiated β-cells carrying the risk allele, which is in accord with the notion that rs10830963 (G-allele) functions as an eQTL in β-cells. Insulin secretion in response to the combination of high glucose and IBMX was comparable between genotypes, whereas the addition of melatonin appeared to reduce insulin secretion more efficiently in cells carrying the G-allele. While our data suggest elevated MTNR1B protein levels in stem cell-derived β-like cells carrying the risk allele, these cells do not appear to be sufficiently mature to establish rs10830963 as an eQTL at the mRNA level. The observed nominal increase in melatonin sensitivity in G-allele-carrying cells is suggestive of a functional contribution of rs10830963 to β-cell dysfunction; however, this interpretation remains tentative and will require further validation in more mature β-cell models.

A recent workshop brought together global leaders in diabetes to assess existing approaches to disease heterogeneity and to identify research gaps, with a goal of achieving precision diabetology for all patients globally.

AbstractObjectiveObesity is driven by modifiable lifestyle factors whose effects may be mediated by epigenetics. Therefore, we investigated lifestyle effects on blood DNA methylation in participants of the LIFE‐Adult study, a well‐characterised population‐based cohort from Germany.Research design and methodsLifestyle scores (LS) based on diet, physical activity, smoking and alcohol intake were calculated in 4107 participants of the LIFE‐Adult study. Fifty subjects with an extremely healthy lifestyle and 50 with an extremely unhealthy lifestyle (5th and 95th percentiles LS) were selected for genome‐wide DNA methylation analysis in blood samples employing Illumina Infinium® Methylation EPIC BeadChip system technology.ResultsDifferences in DNA methylation patterns between body mass index groups (<25 vs. >30 kg/m2) were rather marginal compared to inter‐lifestyle differences (0 vs. 145 differentially methylated positions [DMPs]), which identified 4682 differentially methylated regions (DMRs; false discovery rate [FDR <5%) annotated to 4426 unique genes. A DMR annotated to the glutamine‐fructose‐6‐phosphate transaminase 2 (GFPT2) locus showed the strongest hypomethylation (∼6.9%), and one annotated to glutamate rich 1 (ERICH1) showed the strongest hypermethylation (∼5.4%) in healthy compared to unhealthy lifestyle individuals. Intersection analysis showed that diet, physical activity, smoking and alcohol intake equally contributed to the observed differences, which affected, among others, pathways related to glutamatergic synapses (adj. p < .01) and axon guidance (adj. p < .05). We showed that methylation age correlates with chronological age and waist‐to‐hip ratio with lower DNA methylation age (DNAmAge) acceleration distances in participants with healthy lifestyles. Finally, two identified top DMPs for the alanyl aminopeptidase (ANPEP) locus also showed the strongest expression quantitative trait methylation in blood.ConclusionsDNA methylation patterns help discriminate individuals with a healthy versus unhealthy lifestyle, which may mask subtle methylation differences derived from obesity.

The fields of human genetics and genomics have generated considerable knowledge about the mechanistic basis of many diseases. Genomic approaches to diagnosis, prognostication, prevention and treatment - genomic-driven precision medicine (GDPM) - may help optimize medical practice. Here, we provide a comprehensive review of GDPM of complex diseases across major medical specialties. We focus on technological readiness: how rapidly a test can be implemented into health care. Although these areas of medicine are diverse, key similarities exist across almost all areas. Many medical areas have, within their standards of care, at least one GDPM test for a genetic variant of strong effect that aids the identification/diagnosis of a more homogeneous subset within a larger disease group or identifies a subset with different therapeutic requirements. However, for almost all complex diseases, the majority of patients do not carry established single-gene mutations with large effects. Thus, research is underway that seeks to determine the polygenic basis of many complex diseases. Nevertheless, most complex diseases are caused by the interplay of genetic, behavioural and environmental risk factors, which will likely necessitate models for prediction and diagnosis that incorporate genetic and non-genetic data.

Background The response of the Swedish authorities to the COVID-19 pandemic was less restrictive than in most countries during the first year, with infection and death rates substantially higher than in neighbouring Nordic countries. Because access to PCR testing was limited during the first wave (February to June 2020) and regional data were reported with delay, adequate monitoring of community disease spread was hampered. The app-based COVID Symptom Study was launched in Sweden to disseminate real-time estimates of disease spread and to collect prospective data for research. The aim of this study was to describe the research project, develop models for estimation of COVID-19 prevalence and to evaluate it for prediction of hospital admissions for COVID-19. Methods We enrolled 143 531 study participants ([≥]18 years) throughout Sweden, who contributed 10.6 million daily symptom reports between April 29, 2020 and February 10, 2021. Data from 19 161 self-reported PCR tests were used to create a symptom-based algorithm to estimate daily prevalence of symptomatic COVID-19. The prediction model was validated using external datasets. We further utilized the model estimates to forecast subsequent new hospital admissions. Findings A prediction model for symptomatic COVID-19 based on 17 symptoms, age, and sex yielded an area under the ROC curve of 0.78 (95% CI 0.74-0.83) in an external validation dataset of 943 PCR-tested symptomatic individuals. App-based surveillance proved particularly useful for predicting hospital trends in times of insufficient testing capacity and registration delays. During the first wave, our prediction model estimates demonstrated a lower mean error (0.38 average new daily hospitalizations per 100 000 inhabitants per week (95% CI 0.32, 0.45)) for subsequent hospitalizations in the ten most populated counties, than a model based on confirmed case data (0.72 (0.64, 0.81)). The model further correctly identified on average three out of five counties (95% CI 2.3, 3.7) with the highest rates of hospitalizations the following week during the first wave and four out of five (3.0, 4.6) during the second wave. Interpretation The experience of the COVID Symptom Study highlights the important role citizens can play in real-time monitoring of infectious diseases, and how app-based data collection may be used for data-driven rapid responses to public health challenges.

The present study aims to assess the over-time association of ~300 lifestyle exposures with nine cardiometabolic traits with the ultimate aim of identifying exposures/exposure groups that could inform lifestyle interventions aiming at controlling cardiometabolic diseases. The analyses were undertaken in a longitudinal sample comprising >31000 adults living in northern Sweden. Linear mixed models were used to assess the average associations of lifestyle exposures and linear regression models were used to test association with 10-year change of the cardiometabolic traits. "Physical activity" and "General Health" were the exposure categories containing the highest number of "tentative signals" in analyses assessing the average association of lifestyle variables, while "Tobacco use" was the top-category for the 10-year change association analyses. Thirteen modifiable variables showed a consistent average association among the majority of cardiometabolic traits. These variables belonged to four main groups: i) Smoking, ii) Diet (secoisolariciresinol intake and brewed coffee), iii) Leisure time physical activity and iv) a group of variables more specific to the Swedish lifestyle (snuff status, hunting/fishing during leisure time and boiled coffee). Interestingly, sweet drinks, fish intake and salt content, all lifestyle exposures frequently mentioned in public health recommendations were not broadly associated with the analysed cardiometabolic traits.

Background: Lifestyle exposures play a major role in the development of disease, yet people vary in their susceptibility. A critical step towards precision medicine is identifying individuals who are resilient or sensitive to the environment, and, assess whether the allocation to these predicted groups are more or less likely to develop cardiometabolic disease. Methods: We have used repeated data from the VHU study (n=35440) to identify sensitive and resilient individuals using prediction intervals at the 5th and 95th quantile. Three exposure susceptibility groups were derived per cardiometabolic score using quantile regression forests in the training dataset; next, in the validation dataset, we assessed the different risk of the groups using Cox proportional hazard models for CVD and diabetes. Results: The results of our study suggest that, after ~10 y of follow-up, individuals with sensitivity to the environmental exposures associated with systolic and diastolic blood pressure, blood lipids, and glucose were at higher risk of developing cardiometabolic disease. Moreover, when hazards were pooled with the replication cohort, for those individuals sensitive to the exposures associated with blood pressure traits, the hazards remained significant. Conclusions: Identifying individuals who are predicted to be sensitive are at higher risk of developing disease, this population may be a clinical target for prevention or early intervention and public health strategies.

Type 2 diabetes (T2D) and non-alcoholic fatty liver disease (NAFLD) often co-occur. Defining causal pathways underlying this relationship may help optimize the prevention and treatment of both diseases. Thus, we assessed the strength and magnitude of the putative causal pathways linking dysglycemia and fatty liver, using a combination of causal inference methods. Measures of glycemia, insulin dynamics, magnetic resonance imaging (MRI)-derived abdominal and liver fat content, serological biomarkers, lifestyle, and anthropometry were obtained in participants from the IMI DIRECT cohorts (n=795 with new onset T2D and 2234 individuals free from diabetes). UK Biobank (n=3641) was used for modelling and replication purposes. Bayesian networks were employed to infer causal pathways, with causal validation using two-sample Mendelian randomization. Bayesian networks fitted to IMI DIRECT data identified higher basal insulin secretion rate (BasalISR) and MRI-derived excess visceral fat (VAT) accumulation as the features of dysmetabolism most likely to cause liver fat accumulation; the unconditional probability of fatty liver (>5%) increased significantly when conditioning on high levels of BasalISR and VAT (by 23%, 32% respectively; 40% for both). Analyses in UK Biobank yielded comparable results. MR confirmed most causal pathways predicted by the Bayesian networks. Here, BasalISR had the highest causal effect on fatty liver predisposition, providing mechanistic evidence underpinning the established association of NAFLD and T2D. BasalISR may represent a pragmatic biomarker for NAFLD prediction in clinical practice. ### Competing Interest Statement HR is an employee and shareholder of Sanofi. MIM: The views expressed in this article are those of the author(s) and not necessarily those of the NHS, the NIHR, or the Department of Health. MIM has served on advisory panels for Pfizer, NovoNordisk and Zoe Global, has received honoraria from Merck, Pfizer, Novo Nordisk and Eli Lilly, and research funding from Abbvie, Astra Zeneca, Boehringer Ingelheim, Eli Lilly, Janssen, Merck, NovoNordisk, Pfizer, Roche, Sanofi Aventis, Servier, and Takeda. As of June 2019, MIM is an employee of Genentech, and a holder of Roche stock. AM is a consultant for Lilly and has received research grants from several diabetes drug companies. PWF has received research grants from numerous diabetes drug companies and fess as consultant from Novo Nordisk, Lilly, and Zoe Global Ltd. He is currently the Scientific Director in Patient Care at the Novo Nordisk Foundation. Other authors declare non competing interests. ### Funding Statement The work leading to this publication has received support from the Innovative Medicines Initiative Joint Undertaking under grant agreement 115317 (DIRECT) resources of which are composed of financial contribution from the European Union Seventh Framework Programme (FP7/2007-2013) and EFPIA companies in kind contribution. NAP is supported in part by Henning och Johan Throne-Holsts Foundation, Hans Werthen Foundation, an IRC award from the Swedish Foundation for Strategic Research and a European Research Council award ERC-2015-CoG - 681742\_NASCENT. HPM is supported by an IRC award from the Swedish Foundation for Strategic Research and a European Research Council award ERC-2015-CoG - 681742\_NASCENT. AGJ is supported by an NIHR Clinician Scientist award (17/0005624). RK is funded by the Novo Nordisk Foundation (NNF18OC0031650) as part of a postdoctoral fellowship, an IRC award from the Swedish Foundation for Strategic Research and a European Research Council award ERC-2015-CoG - 681742\_NASCENT. AK, PM, HF, JF and GNG are supported by an IRC award from the Swedish Foundation for Strategic Research and a European Research Council award ERC-2015-CoG - 681742\_NASCENT. TJM is funded by an NIHR clinical senior lecturer fellowship. S.Bru acknowledges support from the Novo Nordisk Foundation (grants NNF17OC0027594 and NNF14CC0001). ATH is a Wellcome Trust Senior Investigator and is also supported by the NIHR Exeter Clinical Research Facility. JMS acknowledges support from Science for Life Laboratory (Plasma Profiling Facility), Knut and Alice Wallenberg Foundation (Human Protein Atlas) and Erling-Persson Foundation (KTH Centre for Precision Medicine). MIM is supported by the following grants; Wellcome (090532, 098381, 106130, 203141, 212259); NIH (U01-DK105535). PWF is supported by an IRC award from the Swedish Foundation for Strategic Research and a European Research Council award ERC-2015-CoG - 681742_NASCENT. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Approval for the study protocol was obtained from each of the regional research ethics review boards separately (Lund, Sweden: 20130312105459927, Copenhagen, Denmark: H-1-2012-166 and H-1-2012-100, Amsterdam, Netherlands: NL40099.029.12, Newcastle, Dundee and Exeter, UK: 12/NE/0132), and all participants provided written informed consent at enrolment. The research conformed to the ethical principles for medical research involving human participants outlined in the Declaration of Helsinki. All necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable. Yes Authors agree to make data and materials supporting the results or analyses presented in their paper available upon reasonable request

Previous studies report that dental caries is partially heritable, but there is uncertainty in the magnitude of genetic effects and little understanding of how genetic factors might influence caries progression or caries subtypes. This study aimed to estimate the relative importance of genetic and environmental factors in the etiology of different caries outcomes using a twin-based design. Analysis included up to 41,678 twins in the Swedish Twin Register aged 7 to 97 y, and dental data were obtained from preexisting dental records. The outcome measures were 1) summary indices of caries experience, 2) parameters representing trajectory in caries progression derived from longitudinal modeling, and 3) caries scores in groups of biologically similar tooth surfaces derived from hierarchical clustering of tooth surfaces (termed caries clusters). Additive genetic factors explained between 49.1% and 62.7% of variation in caries scores and between 50.0% and 60.5% of variation in caries trajectories. Seven caries clusters were identified, which had estimates of heritability lying between 41.9% and 54.3%. Shared environmental factors were important for only some of these clusters and explained 16% of variation in fissure caries in molar teeth but little variation in other clusters of caries presentation. The genetic factors influencing these clusters were only partially overlapping, suggesting that different biological processes are important in different groups of tooth surfaces and that innate liability to some patterns of caries presentation may partially explain why groups of tooth surfaces form clusters within the mouth. These results provide 1) improved quantification of genetic factors in the etiology of caries and 2) new data about the role of genetics in terms of longitudinal changes in caries status and specific patterns of disease presentation, and they may help lay the foundations for personalized interventions in the future.

AIM:To examine the hypothesis that, based on their glucose curves during a seven-point oral glucose tolerance test, people at elevated type 2 diabetes risk can be divided into subgroups with different clinical profiles at baseline and different degrees of subsequent glycaemic deterioration.METHODS:We included 2126 participants at elevated type 2 diabetes risk from the Diabetes Research on Patient Stratification (IMI-DIRECT) study. Latent class trajectory analysis was used to identify subgroups from a seven-point oral glucose tolerance test at baseline and follow-up. Linear models quantified the associations between the subgroups with glycaemic traits at baseline and 18 months.RESULTS:At baseline, we identified four glucose curve subgroups, labelled in order of increasing peak levels as 1-4. Participants in Subgroups 2-4, were more likely to have higher insulin resistance (homeostatic model assessment) and a lower Matsuda index, than those in Subgroup 1. Overall, participants in Subgroups 3 and 4, had higher glycaemic trait values, with the exception of the Matsuda and insulinogenic indices. At 18 months, change in homeostatic model assessment of insulin resistance was higher in Subgroup 4 (β = 0.36, 95% CI 0.13-0.58), Subgroup 3 (β = 0.30; 95% CI 0.10-0.50) and Subgroup 2 (β = 0.18; 95% CI 0.04-0.32), compared to Subgroup 1. The same was observed for C-peptide and insulin. Five subgroups were identified at follow-up, and the majority of participants remained in the same subgroup or progressed to higher peak subgroups after 18 months.CONCLUSIONS:Using data from a frequently sampled oral glucose tolerance test, glucose curve patterns associated with different clinical characteristics and different rates of subsequent glycaemic deterioration can be identified.

ABSTRACTObjectiveMaternal hemodynamics in pregnancy is associated with fetal growth and birth weight, which in turn are associated with offspring cardiovascular disease later in life. The aim of this study was to quantify the extent to which birth weight is associated with cardiac structure and function in adolescence.MethodsA subset of offspring (n = 1964; 55% female) of the Avon Longitudinal Study of Parents and Children were examined with echocardiography at a mean age of 17.7 (SD, 0.3) years. The associations of birth‐weight Z‐score for sex and gestational age with cardiac structure (assessed by relative wall thickness, left ventricular mass index (LVMI) and left atrial diameter index), systolic function (assessed by ejection fraction and left ventricular wall velocity) and diastolic function (assessed by early/late mitral inflow velocity (E/A) and early mitral inflow velocity/mitral annular early diastolic velocity (E/e′)) were evaluated. Linear regression models were adjusted for several potential confounders, including maternal prepregnancy body mass index, age, level of education and smoking during pregnancy.ResultsHigher birth‐weight Z‐score was associated with lower E/A (mean difference, −0.024; 95% CI, −0.043 to −0.005) and E/e′ (mean difference, −0.05; 95% CI, −0.10 to −0.001) and higher LVMI (mean difference, 0.38 g/m2.7; 95% CI, 0.09 to 0.67). There was no or inconsistent evidence of associations of birth‐weight Z‐score with relative wall thickness, left atrial diameter and measurements of systolic function. Further analyses suggested that the association between birth‐weight Z‐score and LVMI was driven mainly by an association observed in participants born small‐for‐gestational age and it did not persist when risk factors in adolescence were accounted for.ConclusionsHigher birth weight adjusted for sex and gestational age was associated with differences in measures of diastolic function in adolescence, but the observed associations were small. It remains to be determined the extent to which these associations translate into increased susceptibility to cardiovascular disease later in life. © 2018 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of the International Society of Ultrasound in Obstetrics and Gynecology.