Incidental Risk of Type 2 Diabetes Mellitus among Patients with Confirmed and Unconfirmed Prediabetes

确诊和未确诊糖尿病前期患者罹患 2 型糖尿病的偶然风险

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Abstract

OBJECTIVE: To determine the risk of type 2 diabetes (T2DM) diagnosis among patients with confirmed and unconfirmed prediabetes (preDM) relative to an at-risk group receiving care from primary care physicians over a 5-year period. STUDY DESIGN: Utilizing data from the Intermountain Healthcare (IH) Enterprise Data Warehouse (EDW) from 2006-2013, we performed a prospective analysis using discrete survival analysis to estimate the time to diagnosis of T2DM among groups. POPULATION STUDIED: Adult patients who had at least one outpatient visit with a primary care physician during 2006-2008 at an IH clinic and subsequent visits through 2013. Patients were included for the study if they were (a) at-risk for diabetes (BMI ≥ 25 kg/m2 and one additional risk factor: high risk ethnicity, first degree relative with diabetes, elevated triglycerides or blood pressure, low HDL, diagnosis of gestational diabetes or polycystic ovarian syndrome, or birth of a baby weighing >9 lbs); or (b) confirmed preDM (HbA1c ≥ 5.7-6.49% or fasting blood glucose 100-125 mg/dL); or (c) unconfirmed preDM (documented fasting lipid panel and glucose 100-125 mg/dL on the same day). PRINCIPAL FINDINGS: Of the 33,838 patients who were eligible for study, 57.0% were considered at-risk, 38.4% had unconfirmed preDM, and 4.6% had confirmed preDM. Those with unconfirmed and confirmed preDM tended to be Caucasian and a greater proportion were obese compared to those at-risk for disease. Patients with unconfirmed and confirmed preDM tended to have more prevalent high blood pressure and depression as compared to the at-risk group. Based on the discrete survival analyses, patients with unconfirmed preDM and confirmed preDM were more likely to develop T2DM when compared to at-risk patients. CONCLUSIONS: Unconfirmed and confirmed preDM are strongly associated with the development of T2DM as compared to patients with only risk factors for disease.

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