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

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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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          Most cited references9

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          Global prevalence of diabetes: estimates for the year 2000 and projections for 2030.

          The goal of this study was to estimate the prevalence of diabetes and the number of people of all ages with diabetes for years 2000 and 2030. Data on diabetes prevalence by age and sex from a limited number of countries were extrapolated to all 191 World Health Organization member states and applied to United Nations' population estimates for 2000 and 2030. Urban and rural populations were considered separately for developing countries. The prevalence of diabetes for all age-groups worldwide was estimated to be 2.8% in 2000 and 4.4% in 2030. The total number of people with diabetes is projected to rise from 171 million in 2000 to 366 million in 2030. The prevalence of diabetes is higher in men than women, but there are more women with diabetes than men. The urban population in developing countries is projected to double between 2000 and 2030. The most important demographic change to diabetes prevalence across the world appears to be the increase in the proportion of people >65 years of age. These findings indicate that the "diabetes epidemic" will continue even if levels of obesity remain constant. Given the increasing prevalence of obesity, it is likely that these figures provide an underestimate of future diabetes prevalence.
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            Effects of diet and exercise in preventing NIDDM in people with impaired glucose tolerance. The Da Qing IGT and Diabetes Study.

            Individuals with impaired glucose tolerance (IGT) have a high risk of developing NIDDM. The purpose of this study was to determine whether diet and exercise interventions in those with IGT may delay the development of NIDDM, i.e., reduce the incidence of NIDDM, and thereby reduce the overall incidence of diabetic complications, such as cardiovascular, renal, and retinal disease, and the excess mortality attributable to these complications. In 1986, 110,660 men and women from 33 health care clinics in the city of Da Qing, China, were screened for IGT and NIDDM. Of these individuals, 577 were classified (using World Health Organization criteria) as having IGT. Subjects were randomized by clinic into a clinical trial, either to a control group or to one of three active treatment groups: diet only, exercise only, or diet plus exercise. Follow-up evaluation examinations were conducted at 2-year intervals over a 6-year period to identify subjects who developed NIDDM. Cox's proportional hazard analysis was used to determine if the incidence of NIDDM varied by treatment assignment. The cumulative incidence of diabetes at 6 years was 67.7% (95% CI, 59.8-75.2) in the control group compared with 43.8% (95% CI, 35.5-52.3) in the diet group, 41.1% (95% CI, 33.4-49.4) in the exercise group, and 46.0% (95% CI, 37.3-54.7) in the diet-plus-exercise group (P or = 25 kg/m2). In a proportional hazards analysis adjusted for differences in baseline BMI and fasting glucose, the diet, exercise, and diet-plus-exercise interventions were associated with 31% (P < 0.03), 46% (P < 0.0005), and 42% (P < 0.005) reductions in risk of developing diabetes, respectively. Diet and/or exercise interventions led to a significant decrease in the incidence of diabetes over a 6-year period among those with IGT.
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              The 10-Year Cost-Effectiveness of Lifestyle Intervention or Metformin for Diabetes Prevention

              (2012)
              OBJECTIVE The Diabetes Prevention Program (DPP) and its Outcomes Study (DPPOS) demonstrated that either intensive lifestyle intervention or metformin could prevent type 2 diabetes in high-risk adults for at least 10 years after randomization. We report the 10-year within-trial cost-effectiveness of the interventions. RESEARCH DESIGN AND METHODS Data on resource utilization, cost, and quality of life were collected prospectively. Economic analyses were performed from health system and societal perspectives. RESULTS Over 10 years, the cumulative, undiscounted per capita direct medical costs of the interventions, as implemented during the DPP, were greater for lifestyle ($4,601) than metformin ($2,300) or placebo ($769). The cumulative direct medical costs of care outside the DPP/DPPOS were least for lifestyle ($24,563 lifestyle vs. $25,616 metformin vs. $27,468 placebo). The cumulative, combined total direct medical costs were greatest for lifestyle and least for metformin ($29,164 lifestyle vs. $27,915 metformin vs. $28,236 placebo). The cumulative quality-adjusted life-years (QALYs) accrued over 10 years were greater for lifestyle (6.81) than metformin (6.69) or placebo (6.67). When costs and outcomes were discounted at 3%, lifestyle cost $10,037 per QALY, and metformin had slightly lower costs and nearly the same QALYs as placebo. CONCLUSIONS Over 10 years, from a payer perspective, lifestyle was cost-effective and metformin was marginally cost-saving compared with placebo. Investment in lifestyle and metformin interventions for diabetes prevention in high-risk adults provides good value for the money spent.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                18 July 2016
                2016
                : 11
                : 7
                : e0157729
                Affiliations
                [1 ]Institute for Healthcare Delivery Research, Intermountain Healthcare, Salt Lake City, Utah, United States of America
                [2 ]Division of Public Health, School of Medicine, University of Utah, Salt Lake City, Utah, United States of America
                [3 ]Primary Care Clinical Program, Intermountain Healthcare, Salt Lake City, Utah, United States of America
                [4 ]Office of Research, Intermountain Healthcare, Salt Lake City, Utah, United States of America
                Weill Cornell Medical College in Qatar, QATAR
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Conceived and designed the experiments: KDB EAJ MH LHG LAS SH WC JK. Performed the experiments: KDB EAJ MH LHG LAS SH WC JK. Analyzed the data: KDB EAJ MH LHG LAS SH WC JK. Contributed reagents/materials/analysis tools: KDB EAJ MH LHG LAS SH WC JK. Wrote the paper: KDB EAJ MH LHG LAS SH WC JK.

                Article
                PONE-D-15-40257
                10.1371/journal.pone.0157729
                4948775
                27427913
                cb91233b-ec5d-4680-9d41-3c8b35feeea1
                © 2016 Brunisholz et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 14 September 2015
                : 4 June 2016
                Page count
                Figures: 0, Tables: 0, Pages: 9
                Funding
                The authors have no support or funding to report.
                Categories
                Research Article
                Medicine and Health Sciences
                Endocrinology
                Endocrine Disorders
                Diabetes Mellitus
                Medicine and Health Sciences
                Metabolic Disorders
                Diabetes Mellitus
                Medicine and Health Sciences
                Endocrinology
                Endocrine Disorders
                Diabetes Mellitus
                Type 2 Diabetes
                Medicine and Health Sciences
                Metabolic Disorders
                Diabetes Mellitus
                Type 2 Diabetes
                Medicine and Health Sciences
                Health Care
                Primary Care
                Medicine and Health Sciences
                Vascular Medicine
                Blood Pressure
                Hypertension
                Medicine and Health Sciences
                Health Care
                Health Risk Analysis
                Medicine and Health Sciences
                Health Care
                Health Care Providers
                Medical Doctors
                Physicians
                People and Places
                Population Groupings
                Professions
                Medical Doctors
                Physicians
                Medicine and Health Sciences
                Health Care
                Patients
                Outpatients
                Medicine and health sciences
                Diagnostic medicine
                Diabetes diagnosis and management
                HbA1c
                Biology and life sciences
                Biochemistry
                Proteins
                Hemoglobin
                HbA1c
                Custom metadata
                All relevant data are within the paper and its Supporting Information files. For additional information, please contact the corresponding author, Kimberly Brunisholz.

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