OBJECTIVE -To examine factors associated with variation in the risk for type 2 diabetes in women with prior gestational diabetes mellitus (GDM).RESEARCH DESIGN AND METHODS -We conducted a systematic literature review of articles published between January 1965 and August 2001, in which subjects underwent testing for GDM and then testing for type 2 diabetes after delivery. We abstracted diagnostic criteria for GDM and type 2 diabetes, cumulative incidence of type 2 diabetes, and factors that predicted incidence of type 2 diabetes.RESULTS -A total of 28 studies were examined. After the index pregnancy, the cumulative incidence of diabetes ranged from 2.6% to over 70% in studies that examined women 6 weeks postpartum to 28 years postpartum. Differences in rates of progression between ethnic groups was reduced by adjustment for various lengths of follow-up and testing rates, so that women appeared to progress to type 2 diabetes at similar rates after a diagnosis of GDM. Cumulative incidence of type 2 diabetes increased markedly in the first 5 years after delivery and appeared to plateau after 10 years. An elevated fasting glucose level during pregnancy was the risk factor most commonly associated with future risk of type 2 diabetes.CONCLUSIONS -Conversion of GDM to type 2 diabetes varies with the length of follow-up and cohort retention. Adjustment for these differences reveals rapid increases in the cumulative incidence occurring in the first 5 years after delivery for different racial groups. Targeting women with elevated fasting glucose levels during pregnancy may prove to have the greatest effect for the effort required. Diabetes Care 25:1862-1868, 2002G estational diabetes mellitus (GDM), or impaired glucose intolerance first diagnosed during pregnancy (1), affects ϳ14% of pregnancies, or 135,000 women a year in the U.S., and is a risk factor for type 2 diabetes in the mother (2). The magnitude of the reported risk varies widely; it is unclear how much of the variation is explained by variations in ethnicity, length of follow-up, selection criteria, and tests for GDM and type 2 diabetes (3-5). Understanding the basis of differences in risk could affect screening protocols for type 2 diabetes in women with a history of GDM and identify women with GDM who may be candidates for studies of preventive interventions of type 2 diabetes.To examine the relative importance of several sources of variation on the risk of type 2 diabetes in women with GDM, we performed a systematic review of the literature, examining the cumulative incidence of type 2 diabetes in women with GDM. We examined the study design, ethnicity, criteria for diagnosis of GDM and type 2 diabetes, length of follow-up, and other predictive factors. We hypothesized that much of the difference in the risk reported among studies could be explained by different lengths of follow-up, ethnic variation, and the diagnostic criteria used. RESEARCH DESIGN AND METHODS -We searched PubMedfor studies published from 1965 to 2001 using the search strategy "gestational ...

The incidence of foot ulcers in this cohort of patients with diabetes was nearly 2.0% per year. For those who developed ulcers, morbidity, mortality, and excess care costs were substantial compared with those for patients without foot ulcers. The results appear to support the value of foot-ulcer prevention programs for patients with diabetes.

Despite the diverse structure of the five EMRs of the eMERGE sites, we developed, validated, and successfully deployed 13 electronic phenotype algorithms. Validation is a worthwhile process that not only measures phenotype performance but also strengthens phenotype algorithm definitions and enhances their inter-institutional sharing.

We repurposed existing genotypes in DNA biobanks across the Electronic Medical Records and Genomics network to perform a genome-wide association study for primary hypothyroidism, the most common thyroid disease. Electronic selection algorithms incorporating billing codes, laboratory values, text queries, and medication records identified 1317 cases and 5053 controls of European ancestry within five electronic medical records (EMRs); the algorithms' positive predictive values were 92.4% and 98.5% for cases and controls, respectively. Four single-nucleotide polymorphisms (SNPs) in linkage disequilibrium at 9q22 near FOXE1 were associated with hypothyroidism at genome-wide significance, the strongest being rs7850258 (odds ratio [OR] 0.74, p = 3.96 × 10(-9)). This association was replicated in a set of 263 cases and 1616 controls (OR = 0.60, p = 5.7 × 10(-6)). A phenome-wide association study (PheWAS) that was performed on this locus with 13,617 individuals and more than 200,000 patient-years of billing data identified associations with additional phenotypes: thyroiditis (OR = 0.58, p = 1.4 × 10(-5)), nodular (OR = 0.76, p = 3.1 × 10(-5)) and multinodular (OR = 0.69, p = 3.9 × 10(-5)) goiters, and thyrotoxicosis (OR = 0.76, p = 1.5 × 10(-3)), but not Graves disease (OR = 1.03, p = 0.82). Thyroid cancer, previously associated with this locus, was not significantly associated in the PheWAS (OR = 1.29, p = 0.09). The strongest association in the PheWAS was hypothyroidism (OR = 0.76, p = 2.7 × 10(-13)), which had an odds ratio that was nearly identical to that of the curated case-control population in the primary analysis, providing further validation of the PheWAS method. Our findings indicate that EMR-linked genomic data could allow discovery of genes associated with many diseases without additional genotyping cost.

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An algorithm using commonly available data from five different EMR can accurately identify T2D cases and controls for genetic study across multiple institutions.

Clinical data in Electronic Medical Records (EMRs) is a potential source of longitudinal clinical data for research. The Electronic Medical Records and Genomics Network or eMERGE investigates whether data captured through routine clinical care using EMRs can identify disease phenotypes with sufficient positive and negative predictive values for use in genome wide association studies (GWAS). Using data from five different sets of EMRs, we have identified five disease phenotypes with positive predictive values of 73–98% and negative predictive values of 98–100%. A majority of EMRs captured key information (diagnoses, medications, laboratory tests) used to define phenotypes in a structured format. We identified natural language processing as an important tool to improve case identification rates. Efforts and incentives to increase the implementation of interoperable EMRs will markedly improve the availability of clinical data for genomics research.