Predicting the Development of Type 2 Diabetes
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PROVIDER: S-EPMC1274287 | biostudies-literature | 2005 Nov
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:AimTo investigate the prognostic value of time range metrics, as measured by continuous glucose monitoring, with respect to the development of type 2 diabetes (T2D).Research design and methodsA total of 499 persons without diabetes from the general population were followed-up for 5 years. Time range metrics were measured at the start and medical records were checked over the period study.ResultsTwenty-two subjects (8.3 per 1,000 person-years) developed T2D. After adjusting for age, gender, family history of diabetes, body mass index and glycated hemoglobin concentration, multivariate analysis revealed 'time above range' (TAR, i.e., with a plasma glucose concentration of >140 mg/dL) to be significantly associated with a greater risk (OR = 1.06, CI 1.01-1.11) of developing diabetes (AUC = 0.94, Brier = 0.035).ConclusionsTime above range provides additional information to that offered by glycated hemoglobin to identify patients at a higher risk of developing type 2 diabetes in a population-based study.
| S-EPMC9772988 | biostudies-literature
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:AimsIt is now understood that almost half of newly diagnosed cases of type 1 diabetes are adult-onset. However, type 1 and type 2 diabetes are difficult to initially distinguish clinically in adults, potentially leading to ineffective care. In this study a machine learning model was developed to identify type 1 diabetes patients misdiagnosed as type 2 diabetes.MethodsIn this retrospective study, a machine learning model was developed to identify misdiagnosed type 1 diabetes patients from a population of patients with a prior type 2 diabetes diagnosis. Using Ambulatory Electronic Medical Records (AEMR), features capturing relevant information on age, demographics, risk factors, symptoms, treatments, procedures, vitals, or lab results were extracted from patients' medical history.ResultsThe model identified age, BMI/weight, therapy history, and HbA1c/blood glucose values among top predictors of misdiagnosis. Model precision at low levels of recall (10 %) was 17 %, compared to <1 % incidence rate of misdiagnosis at the time of the first type 2 diabetes encounter in AEMR.ConclusionsThis algorithm shows potential for being translated into screening guidelines or a clinical decision support tool embedded directly in an EMR system to reduce misdiagnosis of adult-onset type 1 diabetes and implement effective care at the outset.
| S-EPMC10631495 | biostudies-literature
Project description:BackgroundDiabetes mellitus is a growing global health challenge and affects patients of all ages. Treatment aims to keep blood glucose levels close to normal and to prevent or delay complications. However, adherence to antidiabetic medicines is often unsatisfactory.PurposeHere, we established and internally validated a medication nonadherence risk nomogram for use in Chinese type 2 diabetes mellitus (T2DM) patients.MethodsThis cross-sectional study was carried out from July-December 2020 on randomly selected T2DM patients visiting a diabetes clinic and included 753 participants. Adherence was analyzed based on an eight-item Morisky Medication Adherence Scale (MMAS-8). Other data, including patient demographics, treatment, complications, and comorbidities, were also collected on questionnaires. Optimization of feature selection to develop the medication nonadherence risk model was achieved using the least absolute shrinkage and selection operator regression model (LASSO). A prediction model comprising features selected from LASSO model was designed by applying multivariable logistic regression analysis. The decision curve analysis, calibration plot, and C-index were utilized to assess the performance of the model in terms of discrimination, calibration, and clinical usefulness. Bootstrapping validation was applied for internal validation.ResultsThe prediction nomogram comprised several factors including sex, marital status, education level, employment, distance, self-monitoringofbloodglucose, disease duration, and dosing frequency of daily hypoglycemics (pills, insulin, or glucagon-like peptide-1). The model exhibited good calibration and good discrimination (C-index = 0.79, 95% CI [0.75-0.83]). In the validation samples, a high C-index (0.75) was achieved. Results of the decision curve analysis revealed that the nonadherence nomogram could be applied in clinical practice in cases where the intervention is decided at a nonadherence possibility threshold of 12%.ConclusionThe number of patients who adhere to anti-diabetes therapy was small. Being single male, having no formal education, employed, far from hospital, long disease duration, and taking antidiabetics twice or thrice daily, had significant negative correlation with medication adherence. Thus, strategies for improving adherence are urgently needed.
| S-EPMC8932313 | biostudies-literature
Project description:Left ventricular diastolic dysfunction (LVDD) can be affected by many factors, including epicardial adipose tissue (EAT), obesity and type-2 diabetes mellitus (T2DM). The aim of this study was to establish and validate an easy-to-use nomogram that predicts the severity of LVDD in patients with T2DM. This is a retrospective study of 84 consecutive subjects with T2DM admitted to the Endocrinology Department, the First People's Hospital of Zunyi City between January 2015 and October 2020. Several echocardiographic characteristics were used to diagnose diastolic dysfunction according to the 2016 diastolic dysfunction ASE guidelines. Anthropometric, demographic, and biochemical parameters were collected. Through a least absolute shrinkage and selection operator (LASSO) regression model, we reduced the dimensionality of the data and determined factors for the nomogram. The mean follow-up was 25.97 months. Cases were divided into two groups, those with LVDD (31) and those without (53). LASSO regression identified total cholesterol (Tol.chol), low-density lipoprotein (LDL), right ventricular anterior wall (RVAW) and epicardial adipose tissue (EAT) were identified as predictive factors in the nomogram. The ROC curve analysis demonstrated that the AUC value for most clinical paramerters was higher than 0.6. The nomogram can be used to promote the individualized prediction of LVDD risk in T2DM patients, and help to prioritize patients diagnosed with echocardiography.
| S-EPMC8818641 | biostudies-literature
Project description:BackgroundObstructive sleep apnea (OSA) is highly prevalent among patients with type 2 diabetes mellitus (T2DM) in China, but few patients with clinical symptoms of OSA are referred for diagnostic polysomnography (PSG). Thus, this study aimed to develop and validate an easy-to-use nomogram that predicts the severity of OSA in patients with T2DM.MethodsThis retrospective study included consecutive patients with T2DM admitted to the Endocrinology Department, Third Affiliated Hospital of Soochow University between January 1, 2016 and December 31, 2019. OSA was diagnosed with PSG. Participants were randomly assigned to a training cohort (70%) and a validation cohort (30%). Demographic, anthropometric, and biochemical data were collected. A least absolute shrinkage and selection operator (LASSO) regression model was used to reduce data dimensionality and identify factors for inclusion in the nomogram (training cohort). Nomogram validation was performed in the validation cohort.ResultsThe study included 280 participants in the training group and 118 participants in the validation group. OSA prevalence was 58.5%. LASSO regression identified waist-to-hip ratio (WHR), smoking status, body mass index (BMI), serum uric acid (UA), the homeostasis model assessment insulin resistance index (HOMA-IR), and history of fatty liver disease as predictive factors for inclusion in the nomogram. Discrimination and calibration in the training group (C-index =0.88) and validation group (C-index =0.881) were good. The nomogram identified patients with T2DM at risk for OSA with an area under the curve of 0.851 [95% confidence interval (CI), 0.788-0.900].ConclusionsOur nomogram could be used to facilitate individualized prediction of OSA risk in patients with T2DM and help prioritize patients for diagnostic PSG.
| S-EPMC7812169 | biostudies-literature
Project description:ObjectiveThe objective of this study was to create a tool that predicts the risk of mortality in patients with type 2 diabetes.Research design and methodsThis 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.ResultsFollow-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.ConclusionsWe 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