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Epidemiologists have for many decades reported on the patterns and distributions of diabetes within and between populations and have helped to elucidate the aetiology of the disease. This has helped raise awareness of the tremendous burden the disease places on individuals and societies; it has also identified key risk factors that have become the focus of diabetes prevention trials and helped shape public health recommendations. Recent developments in affordable high-throughput genetic and molecular phenotyping technologies have driven the emergence of a new type of epidemiology with a more mechanistic focus than ever before. Studies employing these technologies have identified gene variants or causal loci, and linked these to other omics data that help define the molecular processes mediating the effects of genetic variation in the expression of clinical phenotypes. The scale of these epidemiological studies is rapidly growing; a trend that is set to continue as the public and private sectors invest heavily in omics data generation. Many are banking on this massive volume of diverse molecular data for breakthroughs in drug discovery and predicting sensitivity to risk factors, response to therapies and susceptibility to diabetes complications, as well as the development of disease-monitoring tools and surrogate outcomes. To realise these possibilities, it is essential that omics technologies are applied to well-designed epidemiological studies and that the emerging data are carefully analysed and interpreted. One might view this as next-generation epidemiology, where complex high-dimensionality data analysis approaches will need to be blended with many of the core principles of epidemiological research. In this article, we review the literature on omics in diabetes epidemiology and discuss how this field is evolving.

Respiratory infections and onset of islet autoimmunity are reported to correlate positively in two small prospective studies. The Environmental Determinants of Diabetes in the Young (TEDDY) study is the largest prospective international cohort study on the environmental determinants of type 1 diabetes that regularly monitors both clinical infections and islet autoantibodies. The aim was to confirm the influence of reported respiratory infections and to further characterise the temporal relationship with autoantibody seroconversion. During the years 2004–2009, 8676 newborn babies with HLA genotypes conferring an increased risk of type 1 diabetes were enrolled at 3 months of age to participate in a 15 year follow-up. In the present study, the association between parent-reported respiratory infections and islet autoantibodies at 3 month intervals up to 4 years of age was evaluated in 7869 children. Time-dependent proportional hazard models were used to assess how the timing of respiratory infections related to persistent confirmed islet autoimmunity, defined as autoantibody positivity against insulin, GAD and/or insulinoma antigen-2, concordant at two reference laboratories on two or more consecutive visits. In total, 87,327 parent-reported respiratory infectious episodes were recorded while the children were under study surveillance for islet autoimmunity, and 454 children seroconverted. The number of respiratory infections occurring in a 9 month period was associated with the subsequent risk of autoimmunity (p < 0.001). For each 1/year rate increase in infections, the hazard of islet autoimmunity increased by 5.6% (95% CI 2.5%, 8.8%). The risk association was linked primarily to infections occurring in the winter (HR 1.42 [95% CI 1.16, 1.74]; p < 0.001). The types of respiratory infection independently associated with autoimmunity were common cold, influenza-like illness, sinusitis, and laryngitis/tracheitis, with HRs (95% CI) of 1.38 (1.11, 1.71), 2.37 (1.35, 4.15), 2.63 (1.22, 5.67) and 1.76 (1.04, 2.98), respectively. Recent respiratory infections in young children correlate with an increased risk of islet autoimmunity in the TEDDY study. Further studies to identify the potential causative viruses with pathogen-specific assays should focus especially on the 9 month time window leading to autoantibody seroconversion.

In a community-based study of second-generation Japanese-American men known to have a high prevalence of both Type 2 (non-insulin-dependent) diabetes and impaired glucose tolerance, there was a highly significant association of coronary heart disease with glucose intolerance in a study sample of 219 men. Intra-abdominal cross sectional fat area determined by computed tomography was significantly elevated in men with coronary heart disease even after adjustment for glucose intolerance and body mass index (p=0.026). Other differences that were significantly related to coronary heart disease after adjustment for glucose intolerance were lower high density lipoprotein cholesterol levels (p=0.001), elevated total triglyceride and very low density lipoprotein triglyceride (p<0.001), and elevated fasting insulin and C-peptide levels p=0.001. When these variables were tested in a stepwise multiple logistic regression model, significant independent associations with coronary heart disease were found only for total triglyceride and fasting C-peptide after adjustment for glucose tolerance status. Variables identified to be associated with coronary heart disease were interpreted as representing or manifesting an insulin resistant state. Thus, insulin resistance may be the underlying risk factor aetiologically linking glucose intolerance with coronary heart disease.

Atherogenesis is a disorder of the artery wall that involves: adhesion of monocytes and lymphocytes to the endothelial cell surface; migration of monocytes into the sub-endothelial space and differentiation into macrophages; ingestion of low density lipoproteins and modified or oxidised low density lipoproteins by macrophages by several pathways, including a scavenger pathway, leading to accumulation of cholesterol esters and formation of “foam cells”. These foam cells together with T lymphocytes form the fatty streak. Vascular smooth muscle cells migrate from the media into the intima and proliferate with the formation of atherosclerotic plaques. These processes which involve cell adhesion, migration, differentiation, proliferation and cell interaction with the extracellular matrix are regulated by a complex network/cascade of cytokines and growth regulatory peptides. Thus, atherosclerosis may be the result of a specialised chronic inflammatory fibroproliferative process which has become excessive and in its excess this protective response has become the disease state.

Studies investigating the structure, neurophysiology and functional outcomes of white matter among type 1 diabetes patients have given conflicting results. Our aim was to investigate the relationship between type 1 diabetes and white matter hyperintensities. We assessed white matter integrity (using magnetic resonance imaging), depressive symptoms and neuropsychological function in 114 type 1 diabetes patients and 58 age-matched non-diabetic controls. Only Fazekas grade 1 and 2 white matter hyperintensities were found among 114 long-duration, relatively young diabetes patients; the severity of lesions did not differ substantially from 58 healthy controls. White matter hyperintensities were not associated with depressive history or with clinical characteristics of diabetes, including retinopathy, severe hypoglycaemia or glycaemia control. Our data do not support an association between diabetes characteristics and white matter hyperintensities among relatively young type 1 diabetes participants.

Diabetes mellitus is associated with increased fracture risk in women but few studies are available in men. To evaluate the relationship between diabetes and prospective non-vertebral fractures in elderly men, we used data from the Osteoporotic Fractures in Men (MrOS) study. The MrOS enrolled 5,994 men (aged ≥65 years). Diabetes (ascertained by self-report, the use of medication for diabetes or an elevated fasting glucose level) was reported in 881 individuals, 80 of whom were using insulin. Hip and spine bone mineral density (BMD) was measured using dual x-ray absorptiometry (DXA). After recruitment, the men were followed for incident non-vertebral fractures using a triannual (3 yearly) questionnaire for an average of 9.1 (SD 2.7) years. The Cox proportional hazards model was used to assess the incident risk of fractures. In models adjusted for age, race, clinic site and total hip BMD, the risk of non-vertebral fracture was higher in men with diabetes compared with normoglycaemic men (HR 1.30, 95% CI 1.09, 1.54) and was elevated in men using insulin (HR 2.46, 95% CI 1.69, 3.59). Men with impaired fasting glucose did not have a higher risk of fracture compared with normoglycaemic men (HR 1.04, 95% CI 0.89, 1.21). After multivariable adjustment, the risk of non-vertebral fracture remained higher only among men with diabetes who were using insulin (HR 1.74, 95% CI 1.13, 2.69). Men with diabetes who are using insulin have an increased risk of non-vertebral fracture for a given age and BMD.