Predicting the Development of Type 2 Diabetes
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PROVIDER: S-EPMC1274287 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Type 1 diabetes is associated with a higher risk of major vascular complications and death. A reliable method that predicted these outcomes early in the disease process would help in risk classification. We therefore developed such a prognostic model and quantified its performance in independent cohorts.Data were analysed from 1,973 participants with type 1 diabetes followed for 7 years in the EURODIAB Prospective Complications Study. Strong prognostic factors for major outcomes were combined in a Weibull regression model. The performance of the model was tested in three different prospective cohorts: the Pittsburgh Epidemiology of Diabetes Complications study (EDC, n?=?554), the Finnish Diabetic Nephropathy study (FinnDiane, n?=?2,999) and the Coronary Artery Calcification in Type 1 Diabetes study (CACTI, n?=?580). Major outcomes included major CHD, stroke, end-stage renal failure, amputations, blindness and all-cause death.A total of 95 EURODIAB patients with type 1 diabetes developed major outcomes during follow-up. Prognostic factors were age, HbA1c, WHR, albumin/creatinine ratio and HDL-cholesterol level. The discriminative ability of the model was adequate, with a concordance statistic (C-statistic) of 0.74. Discrimination was similar or even better in the independent cohorts, the C-statistics being: EDC, 0.79; FinnDiane, 0.82; and CACTI, 0.73.Our prognostic model, which uses easily accessible clinical features can discriminate between type 1 diabetes patients who have a good or a poor prognosis. Such a prognostic model may be helpful in clinical practice and for risk stratification in clinical trials.
| S-EPMC4399797 | biostudies-other
Project description:Background and aimsMany countries lack resources to identify patients at risk of developing Type 2 diabetes mellitus (diabetes). We aimed to develop and validate a diabetes risk score based on easily accessible clinical data.MethodsProspective study including 5277 participants (55.0% women, 51.8±10.5 years) free of diabetes at baseline. Comparison with two other published diabetes risk scores (Balkau and Kahn clinical, respectively 5 and 8 variables) and validation on three cohorts (Europe, Iran and Mexico) was performed.ResultsAfter a mean follow-up of 10.9 years, 405 participants (7.7%) developed diabetes. Our score was based on age, gender, waist circumference, diabetes family history, hypertension and physical activity. The area under the curve (AUC) was 0.772 for our score, vs. 0.748 (p<0.001) and 0.774 (p = 0.668) for the other two. Using a 13-point threshold, sensitivity, specificity, positive and negative predictive values (95% CI) of our score were 60.5 (55.5-65.3), 77.1 (75.8-78.2), 18.0 (16.0-20.1) and 95.9 (95.2-96.5) percent, respectively. Our score performed equally well or better than the other two in the Iranian [AUC 0.542 vs. 0.564 (p = 0.476) and 0.513 (p = 0.300)] and Mexican [AUC 0.791 vs. 0.672 (p<0.001) and 0.778 (p = 0.575)] cohorts. In the European cohort, it performed similarly to the Balkau score but worse than the Kahn clinical [AUC 0.788 vs. 0.793 (p = 0.091) and 0.816 (p<0.001)]. Diagnostic capacity of our score was better than the Balkau score and comparable to the Kahn clinical one.ConclusionOur clinically-based score shows encouraging results compared to other scores and can be used in populations with differing diabetes prevalence.
| S-EPMC6785081 | biostudies-literature
Project description:BackgroundPrevious conventional models for the prediction of diabetes could be updated by incorporating the increasing amount of health data available and new risk prediction methodology.ObjectiveWe aimed to develop a substantially improved diabetes risk prediction model using sophisticated machine-learning algorithms based on a large retrospective population cohort of over 230,000 people who were enrolled in the study during 2006-2017.MethodsWe collected demographic, medical, behavioral, and incidence data for type 2 diabetes mellitus (T2DM) in over 236,684 diabetes-free participants recruited from the 45 and Up Study. We predicted and compared the risk of diabetes onset in these participants at 3, 5, 7, and 10 years based on three machine-learning approaches and the conventional regression model.ResultsOverall, 6.05% (14,313/236,684) of the participants developed T2DM during an average 8.8-year follow-up period. The 10-year diabetes incidence in men was 8.30% (8.08%-8.49%), which was significantly higher (odds ratio 1.37, 95% CI 1.32-1.41) than that in women at 6.20% (6.00%-6.40%). The incidence of T2DM was doubled in individuals with obesity (men: 17.78% [17.05%-18.43%]; women: 14.59% [13.99%-15.17%]) compared with that of nonobese individuals. The gradient boosting machine model showed the best performance among the four models (area under the curve of 79% in 3-year prediction and 75% in 10-year prediction). All machine-learning models predicted BMI as the most significant factor contributing to diabetes onset, which explained 12%-50% of the variance in the prediction of diabetes. The model predicted that if BMI in obese and overweight participants could be hypothetically reduced to a healthy range, the 10-year probability of diabetes onset would be significantly reduced from 8.3% to 2.8% (P<.001).ConclusionsA one-time self-reported survey can accurately predict the risk of diabetes using a machine-learning approach. Achieving a healthy BMI can significantly reduce the risk of developing T2DM.
| S-EPMC7420582 | biostudies-literature
Project description:The increasing prevalence of type 2 diabetes mellitus (T2DM) and its complications including cardiovascular disease and chronic kidney disease threaten Korean Americans (KAs). High dietary sodium intake contributes to both conditions. The purpose of the study was to assess dietary sodium consumption and to examine the predictors of sodium intake among KA with T2DM. A total 232 KA who had uncontrolled diabetes participated in this study. The majority of the sample (69%) consumed more sodium than current national guidelines. A high level of energy intake was the strongest predictor for sodium intake with gender and marital status also related. Our findings identified predictive factors to excessive sodium intake and these data support the need for culturally-tailored education about appropriate dietary sodium and energy intake are needed for patients about T2DM.
| S-EPMC5996387 | biostudies-literature
Project description:OBJECTIVE: The objective of this study was to create a tool that predicts the risk of mortality in patients with type 2 diabetes. RESEARCH DESIGN AND METHODS: This study was based on a cohort of 33,067 patients with type 2 diabetes identified in the Cleveland Clinic electronic health record (EHR) who were initially prescribed a single oral hypoglycemic agent between 1998 and 2006. Mortality was determined in the EHR and the Social Security Death Index. A Cox proportional hazards regression model was created using medication class and 20 other predictor variables chosen for their association with mortality. A prediction tool was created using the Cox model coefficients. The tool was internally validated using repeated, random subsets of the cohort, which were not used to create the prediction model. RESULTS: Follow-up in the cohort ranged from 1 day to 8.2 years (median 28.6 months), and 3,661 deaths were observed. The prediction tool had a concordance index (i.e., c statistic) of 0.752. CONCLUSIONS: We successfully created a tool that accurately predicts mortality risk in patients with type 2 diabetes. The incorporation of medications into mortality predictions in patients with type 2 diabetes should improve treatment decisions.
| S-EPMC2584185 | biostudies-literature
Project description:ObjectiveMetabolic syndrome (MetS) is a cluster of abdominal obesity, hyperglycemia, hypertension, and dyslipidemia, which increases the risk for type 2 diabetes and cardiovascular diseases (CVDs). Some argue that MetS is not a single disorder because the traditional MetS features do not represent one entity, and they would like to exclude features from MetS. Others would like to add additional features in order to increase predictive ability of MetS. The aim of this study was to identify a MetS model that optimally predicts type 2 diabetes and CVD while still representing a single entity.Research design and methodsIn a random sample (n = 1,928) of the EPIC-NL cohort and a subset of the EPIC-NL MORGEN study (n = 1,333), we tested the model fit of several one-factor MetS models using confirmatory factor analysis. We compared predictive ability for type 2 diabetes and CVD of these models within the EPIC-NL case-cohort study of 545 incident type 2 diabetic subjects, 1,312 incident CVD case subjects, and the random sample, using survival analyses and reclassification.ResultsThe standard model, representing the current MetS definition (EPIC-NL comparative fit index [CFI] = 0.95; MORGEN CFI = 0.98); the standard model excluding blood pressure (EPIC-NL CFI = 0.95; MORGEN CFI = 1.00); and the standard model extended with hsCRP (EPIC-NL CFI = 0.95) had an acceptable model fit. The model extended with hsCRP predicted type 2 diabetes (integral discrimination index [IDI]: 0.34) and CVD (IDI: 0.07) slightly better than did the standard model.ConclusionsIt seems valid to represent the traditional MetS features by a single entity. Extension of this entity with hsCRP slightly improves predictive ability for type 2 diabetes and CVD.
| S-EPMC3554322 | biostudies-literature
Project description:ObjectiveTwo aims of this study were to develop and validate A) a metric to identify drivers with type 1 diabetes at high risk of future driving mishaps and B) an online intervention to reduce mishaps among high-risk drivers.Research design and methodsTo achieve aim A, in study 1, 371 drivers with type 1 diabetes from three U.S. regions completed a series of established questionnaires about diabetes and driving. They recorded their driving mishaps over the next 12 months. Questionnaire items that uniquely discriminated drivers who did and did not have subsequent driving mishaps were assembled into the Risk Assessment of Diabetic Drivers (RADD) scale. In study 2, 1,737 drivers with type 1 diabetes from all 50 states completed the RADD online. Among these, 118 low-risk (LR) and 372 high-risk (HR) drivers qualified for and consented to participate in a 2-month treatment period followed by 12 monthly recordings of driving mishaps. To address aim B, HR participants were randomized to receive either routine care (RC) or the online intervention "DiabetesDriving.com" (DD.com). Half of the DD.com participants received a motivational interview (MI) at the beginning and end of the treatment period to boost participation and efficacy. All of the LR participants were assigned to RC. In both studies, the primary outcome variable was driving mishaps.ResultsRelated to aim A, in study 1, the RADD demonstrated 61% sensitivity and 75% specificity. Participants in the upper third of the RADD distribution (HR), compared with those in the lower third (LR), reported 3.03 vs. 0.87 mishaps/driver/year, respectively (P < 0.001). In study 2, HR and LR participants receiving RC reported 4.3 and 1.6 mishaps/driver/year, respectively (P < 0.001). Related to aim B, in study 2, MIs did not enhance participation or efficacy, so the DD.com and DD.com + MI groups were combined. DD.com participants reported fewer hypoglycemia-related driving mishaps than HR participants receiving RC (P = 0.01), but more than LR participants receiving RC, reducing the difference between the HR and LR participants receiving RC by 63%. HR drivers differed from LR drivers at baseline across a variety of hypoglycemia and driving parameters.ConclusionsThe RADD identified higher-risk drivers, and identification seemed relatively stable across time, samples, and procedures. This 11-item questionnaire could inform patients at higher risk, and their clinicians, that they should take preventive steps to reduce driving mishaps, which was accomplished in aim B using DD.com.
| S-EPMC5439415 | biostudies-literature
Project description:BACKGROUND: Proteins directly interacting with each other tend to have similar functions and be involved in the same cellular processes. Mutations in genes that code for them often lead to the same family of disease phenotypes. Efforts have been made to prioritize positional candidate genes for complex diseases utilize the protein-protein interaction (PPI) information. But such an approach is often considered too general to be practically useful for specific diseases. RESULTS: In this study we investigate the efficacy of this approach in type 1 diabetes (T1D). 266 known disease genes, and 983 positional candidate genes from the 18 established linkage loci of T1D, are compiled from the T1Dbase (http://t1dbase.org). We found that the PPI network of known T1D genes has distinct topological features from others, with significantly higher number of interactions among themselves even after adjusting for their high network degrees (p<1e-5). We then define those positional candidates that are first degree PPI neighbours of the 266 known disease genes to be new candidate disease genes. This leads to a list of 68 genes for further study. Cross validation using the known disease genes as benchmark reveals that the enrichment is ~17.1 fold over random selection, and ~4 fold better than using the linkage information alone. We find that the citations of the new candidates in T1D-related publications are significantly (p<1e-7) more than random, even after excluding the co-citation with the known disease genes; they are significantly over-represented (p<1e-10) in the top 30 GO terms shared by known disease genes. Furthermore, sequence analysis reveals that they contain significantly (p<0.0004) more protein domains that are known to be relevant to T1D. These findings provide indirect validation of the newly predicted candidates. CONCLUSION: Our study demonstrates the potential of the PPI information in prioritizing positional candidate genes for T1D.
| S-EPMC2818071 | biostudies-literature
Project description:ObjectiveIn this investigation we evaluated nine metabolic indexes from intravenous glucose tolerance tests (IVGTTs) and oral glucose tolerance tests (OGTTs) in an effort to determine their prognostic performance in predicting the development of type 1 diabetes in those with moderate risk, as defined by familial relation to a type 1 diabetic individual, a positive test for islet cell antibodies and insulin autoantibody, but normal glucose tolerance.Research design and methodsSubjects (n = 186) who had a projected risk of 25-50% for developing type 1 diabetes within 5 years were followed until clinical diabetes onset or the end of the study as part of the Diabetes Prevention Trial-Type 1. Prognostic performance of the metabolic indexes was determined using receiver operating characteristic (ROC) curve and survival analyses.ResultsTwo-hour glucose from an OGTT most accurately predicted progression to disease compared with all other metabolic indicators with an area under the ROC curve of 0.67 (95% CI 0.59-0.76), closely followed by the ratio of first-phase insulin response (FPIR) to homeostasis model assessment of insulin resistance (HOMA-IR) with an area under the curve value of 0.66. The optimal cutoff value for 2-h glucose (114 mg/dl) maintained sensitivity and specificity values >0.60. The hazard ratio for those with 2-h glucose ? 114 mg/dl compared with those with 2-h glucose <114 mg/dl was 2.96 (1.67-5.22).ConclusionsThe ratio of FPIR to HOMA-IR from an IVGTT provided accuracy in predicting the development of type 1 diabetes similar to that of 2-h glucose from an OGTT, which, because of its lower cost, is preferred. The optimal cutoff value determined for 2-h glucose provides additional guidance for clinicians to identify subjects for potential prevention treatments before the onset of impaired glucose tolerance.
| S-EPMC2992179 | biostudies-literature
Project description:Type 1 diabetes (T1D) is the third most common autoimmune disease which develops due to genetic and environmental risk factors. Based on the World Health Organization (WHO) report from 2014 the number of people suffering from all types of diabetes ascended to 422 million, compared to 108 million in 1980. It was calculated that this number will double by the end of 2030. In 2015 American Diabetes Association (ADA) announced that 30.3 million Americans (that is 9.4% of the overall population) had diabetes of which only approximately 1.25 million had T1D. Nowadays, T1D represents roughly 10% of adult diabetes cases total. Multiple genetic abnormalities at different loci have been found to contribute to type 1 diabetes development. The analysis of genome-wide association studies (GWAS) of T1D has identified over 50 susceptible regions (and genes within these regions). Many of these regions are defined by single nucleotide polymorphisms (SNPs) but molecular mechanisms through which they increase or lower the risk of diabetes remain unknown. Genetic factors (in existence since birth) can be detected long before the emergence of immunological or clinical markers. Therefore, a comprehensive understanding of the multiple genetic factors underlying T1D is extremely important for further clinical trials and development of personalized medicine for diabetic patients. We present an overview of current studies and information about regions in the human genome associated with T1D. Moreover, we also put forward information about epigenetic modifications, non-coding RNAs and environmental factors involved in T1D development and onset.
| S-EPMC7882399 | biostudies-literature