The detailed characterization of human biology and behaviors is now possible at scale owing to innovations in biomarkers, bioimaging, and wearable technologies; "big data" from electronic medical records, health insurance databases, and other platforms becoming increasingly accessible; and rapidly evolving computational power and bioinformatics methods. Collectively, these advances are creating unprecedented opportunities to better understand diabetes and many other complex traits. Identifying hidden structures within these complex data sets and linking these structures to outcome data may yield unique insights into the risk factors and natural history of diabetes, which in turn may help optimize the prevention and management of the disease. This emerging area is broadly termed "precision medicine." In this Perspective, we give an overview of the evidence and barriers to the development and implementation of precision medicine in type 2 diabetes. We also discuss recently presented paradigms through which complex data might enhance our understanding of diabetes and ultimately our ability to tackle the disease more effectively than ever before.

Genome-wide association analysis advanced understanding of blood pressure (BP), a major risk factor for vascular conditions such as coronary heart disease and stroke. Accounting for smoking behavior may help identify BP loci and extend our knowledge of its genetic architecture. We performed genome-wide association meta-analyses of systolic and diastolic BP incorporating gene-smoking interactions in 610,091 individuals. Stage 1 analysis examined similar to 18.8 million SNPs and small insertion/deletion variants in 129,913 individuals from four ancestries (European, African, Asian, and Hispanic) with follow-up analysis of promising variants in 480,178 additional individuals from five ancestries. We identified 15 loci that were genome-wide significant (p < 5 x 10(-8)) in stage 1 and formally replicated in stage 2. A combined stage 1 and 2 meta-analysis identified 66 additional genome-wide significant loci (13, 35, and 18 loci in European, African, and trans-ancestry, respectively). A total of 56 known BP loci were also identified by our results (p < 5 x 10(-8)). Of the newly identified loci, ten showed significant interaction with smoking status, but none of them were replicated in stage 2. Several loci were identified in African ancestry, highlighting the importance of genetic studies in diverse populations. The identified loci show strong evidence for regulatory features and support shared pathophysiology with cardiometabolic and addiction traits. They also highlight a role in BP regulation for biological candidates such as modulators of vascular structure and function (CDKN1B, BCAR1-CFDP1, PXDN, EEA1), ciliopathies (SDCCAG8, RPGRIP1L), telomere maintenance (TNKS, PINX1, AKTIP), and central dopaminergic signaling MSRA, EBF2).

Aims/hypothesis: Individuals with type 2 diabetes have aberrant intestinal microbiota. However, recent studies suggest that metformin alters the composition and functional potential of gut microbiota, thereby interfering with the diabetes-related microbial signatures. We tested whether specific gut microbiota profiles are associated with prediabetes (defined as fasting plasma glucose of 6.1-7.0 mmol/l or HbA_1c of 42-48 mmol/mol [6.0-6.5%]) and a range of clinical biomarkers of poor metabolic health.

Methods: In the present case-control study, we analysed the gut microbiota of 134 Danish adults with prediabetes, overweight, insulin resistance, dyslipidaemia and low-grade inflammation and 134 age-and sex-matched individuals with normal glucose regulation.

Results: We found that five bacterial genera and 36 operational taxonomic units (OTUs) were differentially abundant between individuals with prediabetes and those with normal glucose regulation. At the genus level, the abundance of Clostridium was decreased (mean log₂ fold change -0.64 (SEM 0.23), p _adj = 0.0497), whereas the abundances of Dorea, [Ruminococcus], Sutterella and Streptococcus were increased (mean log₂ fold change 0.51 (SEM 0.12), p _adj = 5 x 10^-4; 0.51 (SEM 0.11), p _adj = 1 x 10^-4; 0.60 (SEM 0.21), p _adj = 0.0497; and 0.92 (SEM0.21), p _adj = 4 x 10^-4, respectively). The two OTUs that differed the most were a member of the order Clostridiales (OTU 146564) and Akkermansia muciniphila, which both displayed lower abundance among individuals with prediabetes (mean log₂ fold change -1.74 (SEM0.41), p _adj = 2 x 10^-3 and -1.65 (SEM0.34), p _adj = 4 x 10^-4, respectively). Faecal transfer from donors with prediabetes or screen-detected, drug-naive type 2 diabetes to germfree Swiss Webster or conventional C57BL/6 J mice did not induce impaired glucose regulation in recipient mice.

Conclusions/interpretation: Collectively, our data show that individuals with prediabetes have aberrant intestinal microbiota characterised by a decreased abundance of the genus Clostridium and the mucin-degrading bacterium A. muciniphila. Our findings are comparable to observations in overt chronic diseases characterised by low-grade inflammation.

BACKGROUND: Previous results from Scandinavian cohorts have shown that obesity accentuates the effects of common genetic susceptibility variants on increased triglycerides (TG). Whether such interactions are present in the US population and further selective for particular TG-rich lipoprotein subfractions is unknown.

METHODS: We examined these questions using body mass index (BMI) and waist circumference (WC) among women of European ancestry from the Women's Genome Health Study (WGHS) (n = 21840 for BMI; n = 19313 for WC). A weighted genetic risk score (TG-wGRS) based on 40 published TG-associated single-nucleotide polymorphisms was calculated using published effect estimates.

RESULTS: Comparing overweight (BMI ≥ 25 kg/m²) and normal weight (BMI < 25 kg/m²) WGHS women, each unit increase of TG-wGRS was associated with TG increases of 1.013% and 1.011%, respectively, and this differential association was significant (P_interaction = 0.014). Metaanalyses combining results for WGHS BMI with the 4 Scandinavian cohorts (INTER99, HEALTH2006, GLACIER, MDC) (total n = 40026) yielded a more significant interaction (P_interaction = 0.001). Similarly, we observed differential association of the TG-wGRS with TG (P_interaction = 0.006) in strata of WC (<80 cm vs ≥80 cm). Metaanalysis with 2 additional cohorts reporting WC (INTER99 and HEALTH2006) (total n = 27834) was significant with consistent effects (P_interaction = 0.006). We also observed highly significant interactions of the TG-wGRS across the strata of BMI with very large, medium, and small TG-rich lipoprotein subfractions measured by nuclear magnetic resonance spectroscopy (all P_interactions < 0.0001). The differential effects were strongest for very large TG-rich lipoprotein.

CONCLUSIONS: Our results support the original findings and suggest that obese individuals may be more susceptible to aggregated genetic risk associated with common TG-raising alleles, with effects accentuated in the large TG-rich lipoprotein subfraction.

BACKGROUND: Previous results from Scandinavian cohorts have shown that obesity accentuates the effects of common genetic susceptibility variants on increased triglycerides (TG). Whether such interactions are present in the US population and further selective for particular TG-rich lipoprotein subfractions is unknown.

METHODS: We examined these questions using body mass index (BMI) and waist circumference (WC) among women of European ancestry from the Women's Genome Health Study (WGHS) (n = 21840 for BMI; n = 19313 for WC). A weighted genetic risk score (TG-wGRS) based on 40 published TG-associated single-nucleotide polymorphisms was calculated using published effect estimates.

RESULTS: Comparing overweight (BMI ≥ 25 kg/m2) and normal weight (BMI < 25 kg/m2) WGHS women, each unit increase of TG-wGRS was associated with TG increases of 1.013% and 1.011%, respectively, and this differential association was significant (Pinteraction = 0.014). Metaanalyses combining results for WGHS BMI with the 4 Scandinavian cohorts (INTER99, HEALTH2006, GLACIER, MDC) (total n = 40026) yielded a more significant interaction (Pinteraction = 0.001). Similarly, we observed differential association of the TG-wGRS with TG (Pinteraction = 0.006) in strata of WC (<80 cm vs ≥80 cm). Metaanalysis with 2 additional cohorts reporting WC (INTER99 and HEALTH2006) (total n = 27834) was significant with consistent effects (Pinteraction = 0.006). We also observed highly significant interactions of the TG-wGRS across the strata of BMI with very large, medium, and small TG-rich lipoprotein subfractions measured by nuclear magnetic resonance spectroscopy (all Pinteractions < 0.0001). The differential effects were strongest for very large TG-rich lipoprotein.

CONCLUSIONS: Our results support the original findings and suggest that obese individuals may be more susceptible to aggregated genetic risk associated with common TG-raising alleles, with effects accentuated in the large TG-rich lipoprotein subfraction.

Fetuin-A, a hepatic-origin protein, is strongly positively associated with risk of type 2 diabetes in human observational studies, but it is unknown whether this association is causal. Weaimed to study the potential causal relation of circulating fetuin-A to risk of type 2 diabetes in a Mendelian randomization study with single nucleotide polymorphisms located in the fetuin-A-encoding AHSG gene. We used data from eight European countries of the European Prospective Investigation into Cancer and Nutrition (EPIC)-InterAct case-cohort study including 10,020 incident cases. Plasma fetuin-A concentration was measured in a subset of 965 subcohort participants and 654 case subjects. A genetic score of the AHSG single nucleotide polymorphisms was strongly associated with fetuin-A (28% explained variation). Using the genetic score as instrumental variable of fetuin-A, we observed no significant association of a 50 mu g/mL higher fetuin-A concentration with diabetes risk (hazard ratio 1.02 [95% CI 0.97, 1.07]). Combining our results with those from the DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) consortium (12,171 case subjects) also did not suggest a clear significant relation of fetuin-A with diabetes risk. In conclusion, although there is mechanistic evidence for an effect of fetuin-A on insulin sensitivity and secretion, this study does not support a strong, relevant relationship between circulating fetuin-A and diabetes risk in the general population.

Prior studies suggest dental caries traits in children and adolescents are partially heritable, but there has been no large-scale consortium genome-wide association study (GWAS) to date. We therefore performed GWAS for caries in participants aged 2.5-18.0 years from nine contributing centres. Phenotype definitions were created for the presence or absence of treated or untreated caries, stratified by primary and permanent dentition. All studies tested for association between caries and genotype dosage and the results were combined using fixed-effects meta-analysis. Analysis included up to 19 003 individuals (7530 affected) for primary teeth and 13 353 individuals (5875 affected) for permanent teeth. Evidence for association with caries status was observed at rs1594318-C for primary teeth [intronic within ALLC, odds ratio (OR) 0.85, effect allele frequency (EAF) 0.60, P 4.13e-8] and rs7738851-A (intronic within NEDD9, OR 1.28, EAF 0.85, P 1.63e-8) for permanent teeth. Consortium-wide estimated heritability of caries was low [h2 of 1% (95% CI: 0%: 7%) and 6% (95% CI 0%: 13%) for primary and permanent dentitions, respectively] compared with corresponding within-study estimates [h2 of 28% (95% CI: 9%: 48%) and 17% (95% CI: 2%: 31%)] or previously published estimates. This study was designed to identify common genetic variants with modest effects which are consistent across different populations. We found few single variants associated with caries status under these assumptions. Phenotypic heterogeneity between cohorts and limited statistical power will have contributed; these findings could also reflect complexity not captured by our study design, such as genetic effects which are conditional on environmental exposure.

BACKGROUND: Associations between dairy intake and body mass index (BMI) have been inconsistently observed in epidemiological studies, and the causal relationship remains ill defined.

METHODS: We performed Mendelian randomization (MR) analysis using an established dairy intake-associated genetic polymorphism located upstream of the lactase gene (LCT-13910 C/T, rs4988235) as an instrumental variable (IV). Linear regression models were fitted to analyze associations between (a) dairy intake and BMI, (b) rs4988235 and dairy intake, and (c) rs4988235 and BMI in each study. The causal effect of dairy intake on BMI was quantified by IV estimators among 184802 participants from 25 studies.

RESULTS: Higher dairy intake was associated with higher BMI (β = 0.03 kg/m2 per serving/day; 95% CI, 0.00–0.06; P = 0.04), whereas the LCT genotype with 1 or 2 T allele was significantly associated with 0.20 (95% CI, 0.14–0.25) serving/day higher dairy intake (P = 3.15 × 10−12) and 0.12 (95% CI, 0.06–0.17) kg/m2 higher BMI (P = 2.11 × 10−5). MR analysis showed that the genetically determined higher dairy intake was significantly associated with higher BMI (β = 0.60 kg/m2 per serving/day; 95% CI, 0.27–0.92; P = 3.0 × 10−4).

CONCLUSIONS: The present study provides strong evidence to support a causal effect of higher dairy intake on increased BMI among adults.

Aims Middle Eastern immigrants to western countries are at high risk of developing type 2 diabetes. However, the heritability and impact of rst-degree family history (FH) of type 2 diabetes on insulin secretion and action have not been adequately described. Methods Citizens of Malmö, Sweden, aged 30–75 years born in Iraq or Sweden were invited to participate in this population- based study. Insulin secretion (corrected insulin response and oral disposition index) and action (insulin sensitivity index) were assessed by oral glucose tolerance tests.

Results In total, 45.7% of Iraqis (616/1348) and 27.4% of native Swedes (201/733) had FH in parent(s), sibling(s) or single parent and sibling, i.e., FH+. Approximately 8% of Iraqis and 0.7% of Swedes had ≥ 3 sibling(s) and parent(s) with diabetes, i.e., FH++. Irrespective of family size, prediabetes and diabetes increased with family burden (FH− 29.4%; FH+ 38.8%; FH++ 61.7%) without signi cant di erences across ethnicities. With increasing level of family burden, insulin secretion rather than insulin action decreased. Individuals with a combination of ≥ 3 siblings and parents with diabetes presented with the lowest levels of insulin secretion.

Conclusions The Iraqi immigrant population often present with a strong familial burden of type 2 diabetes with the worst glycemic control and highest diabetes risk in individuals with ≥ 3 siblings and parents with diabetes. Our data show that in a population still free from diabetes familial burden in uences insulin secretion to a higher degree than insulin action and may be a logical target for intervention. 

Detailed phenotyping is required to deepen our understanding of the biological mechanisms behind genetic associations. In addition, the impact of potentially modifiable risk factors on disease requires analytical frameworks that allow causal inference. Here, we discuss the characteristics of Recall-by-Genotype (RbG) as a study design aimed at addressing both these needs. We describe two broad scenarios for the application of RbG: studies using single variants and those using multiple variants. We consider the efficacy and practicality of the RbG approach, provide a catalogue of UK-based resources for such studies and present an online RbG study planner.