RESUMO
Objective: There are currently five widely used definition of prediabetes. We compared the ability of these to predict 5-year conversion to diabetes and investigated whether there were other cut-points identifying risk of progression to diabetes that may be more useful. Research design and methods: We conducted an individual participant meta-analysis using longitudinal data included in the Obesity, Diabetes and Cardiovascular Disease Collaboration. Cox regression models were used to obtain study-specific HRs for incident diabetes associated with each prediabetes definition. Harrell's C-statistics were used to estimate how well each prediabetes definition discriminated 5-year risk of diabetes. Spline and receiver operating characteristic curve (ROC) analyses were used to identify alternative cut-points. Results: Sixteen studies, with 76 513 participants and 8208 incident diabetes cases, were available. Compared with normoglycemia, current prediabetes definitions were associated with four to eight times higher diabetes risk (HRs (95% CIs): 3.78 (3.11 to 4.60) to 8.36 (4.88 to 14.33)) and all definitions discriminated 5-year diabetes risk with good accuracy (C-statistics 0.79-0.81). Cut-points identified through spline analysis were fasting plasma glucose (FPG) 5.1 mmol/L and glycated hemoglobin (HbA1c) 5.0% (31 mmol/mol) and cut-points identified through ROC analysis were FPG 5.6 mmol/L, 2-hour postload glucose 7.0 mmol/L and HbA1c 5.6% (38 mmol/mol). Conclusions: In terms of identifying individuals at greatest risk of developing diabetes within 5 years, using prediabetes definitions that have lower values produced non-significant gain. Therefore, deciding which definition to use will ultimately depend on the goal for identifying individuals at risk of diabetes.
Assuntos
Diabetes Mellitus Tipo 1/patologia , Diabetes Mellitus Tipo 2/patologia , Estado Pré-Diabético/fisiopatologia , Biomarcadores/análise , Diabetes Mellitus Tipo 1/epidemiologia , Diabetes Mellitus Tipo 2/epidemiologia , Progressão da Doença , Humanos , Incidência , Prognóstico , Fatores de RiscoRESUMO
BACKGROUND: Familial correlations, twin studies and adoption studies have all indicated that human obesity has a substantial genetic component. To date, obesity genes have only been identified using mouse models. METHODS: In an attempt to identify human obesity genes large numbers of multigenerational families, in whom extreme obesity segregates, are currently being collected. RESULTS AND CONCLUSIONS: Relative risk estimates and models of genetic heterogeneity indicate that at least 500 affected sibling pairs will need to be collected to identify major genes.