Dataset Information

Development of a clinical risk score for incident diabetes: A 10-year prospective cohort study.

ABSTRACT:

Aims/introduction

We developed a self-assessable Korean Diabetes Risk score using the data of the Korean Genome and Epidemiology Study.

Materials and methods

A total of 8,740 participants without diabetes at baseline were followed up biannually over a period of 10 years. We included variables that were significantly different between participants who developed diabetes mellitus and those who did not in the development cohort at baseline. We assigned a maximum score of 100 to the selected variable in each gender group. Next, the 10-year probability of incident diabetes was calculated and validated in the validation cohort. Finally, we compared the predictive power of Korean Diabetes Risk score with models including fasting plasma glucose or glycated hemoglobin and other cohort models of Atherosclerosis Risk in Communities and Korea National Health and Nutrition Examination Survey.

Results

During a median follow-up period of 9.7 years, 22.7% of the participants progressed to diabetes. The Korean Diabetes Risk score included age, living location (urban or rural area), waist circumference, hypertension, family history of diabetes and smoking history. The developed risk score yielded acceptable discrimination for incident diabetes (area under the curve 0.657) and the predictive power was improved when the model included fasting plasma glucose (area under the curve 0.690) or glycated hemoglobin (area under the curve 0.746). In addition, our model predicted incident diabetes more accurately than previous Western or Korean models.

Conclusions

This newly developed self-assessable diabetes risk score is easily applicable to predict the future risk of diabetes even without the necessity for laboratory tests. This score is useful for the Korean diabetes prevention program, because high-risk individuals can be easily screened.

SUBMITTER: Oh TJ

PROVIDER: S-EPMC8015827 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundPredicting the risk of tuberculosis (TB) in people living with HIV (PLHIV) using a single test is currently not possible. We aimed to develop and validate a clinical algorithm, using baseline CD4 cell counts, HIV viral load (pVL), and interferon-gamma release assay (IGRA), to identify PLHIV who are at high risk for incident active TB in low-to-moderate TB burden settings where highly active antiretroviral therapy (HAART) is routinely provided.Materials and methodsA prospective, 5-year, cohort study of adult PLHIV was conducted from 2006 to 2012 in two hospitals in Taiwan. HAART was initiated based on contemporary guidelines (CD4 count < = 350/μL). Cox regression was used to identify the predictors of active TB and to construct the algorithm. The validation cohorts included 1455 HIV-infected individuals from previous published studies. Area under the receiver operating characteristic (ROC) curve was calculated.ResultsSeventeen of 772 participants developed active TB during a median follow-up period of 5.21 years. Baseline CD4 < 350/μL or pVL ≥ 100,000/mL was a predictor of active TB (adjusted HR 4.87, 95% CI 1.49-15.90, P = 0.009). A positive baseline IGRA predicted TB in patients with baseline CD4 ≥ 350/μL and pVL < 100,000/mL (adjusted HR 6.09, 95% CI 1.52-24.40, P = 0.01). Compared with an IGRA-alone strategy, the algorithm improved the sensitivity from 37.5% to 76.5%, the negative predictive value from 98.5% to 99.2%. Compared with an untargeted strategy, the algorithm spared 468 (60.6%) from unnecessary TB preventive treatment. Area under the ROC curve was 0.692 (95% CI: 0.587-0.798) for the study cohort and 0.792 (95% CI: 0.776-0.808) and 0.766 in the 2 validation cohorts.ConclusionsA validated algorithm incorporating the baseline CD4 cell count, HIV viral load, and IGRA status can be used to guide targeted TB preventive treatment in PLHIV in low-to-moderate TB burden settings where HAART is routinely provided to all PLHIV. The implementation of this algorithm will avoid unnecessary exposure of low-risk patients to drug toxicity and simultaneously, reduce the burden of universal treatment on the healthcare system.

Project description:BACKGROUND:Older adults with diabetes take fewer steps per day than those without diabetes. The purpose of the present study was to investigate the association of daily step count with incident diabetes in community-dwelling 70-year-olds. METHODS:This prospective cohort study included N?=?3055 community-dwelling 70-year-olds (52% women) who participated in a health examination in Umeå, Sweden during 2012-2017, and who were free from diabetes at baseline. Daily step count was measured for 1 week using Actigraph GT3X+ accelerometers. Cases of diabetes were collected from the Swedish National Patient Register. The dose-response association was evaluated graphically using a flexible parametric model, and hazard ratios (HR) with 95% confidence intervals (CI) were calculated using Cox regressions. RESULTS:During a mean follow-up of 2.6?years, diabetes was diagnosed in 81 participants. There was an inverse nonlinear dose-response association between daily step count and incident diabetes, with a steep decline in risk of diabetes from a higher daily step count until around 6000 steps/day. From there, the risk decreased at a slower rate until it leveled off at around 8000 steps/day. A threshold of 4500 steps/day was found to best distinguish participants with the lowest risk of diabetes, where those taking ??4500 steps/day, had 59% lower risk of diabetes, compared to those taking fewer steps (HR, 0.41, 95% CI, 0.25-0.66). Adjusting for visceral adipose tissue (VAT) attenuated the association (HR, 0.64, 95% CI, 0.38-1.06), which was marginally altered after further adjusting for sedentary time, education and other cardiometabolic risk factors and diseases (HR, 0.58, 95% CI, 0.32-1.05). CONCLUSIONS:A higher daily step count is associated with lower risk of incident diabetes in community-dwelling 70-year-olds. The greatest benefits occur at the lower end of the activity range, and much earlier than 10,000 steps/day. With the limitation of being an observational study, these findings suggest that promoting even a modest increase in daily step count may help to reduce the risk of diabetes in older adults. Because VAT appears to partly mediate the association, lifestyle interventions targeting diabetes should apart from promoting physical activity also aim to prevent and reduce central obesity.

Project description:BackgroundStrong evidence supports that dietary modifications may decrease incident type 2 diabetes mellitus (T2DM). Numerous diabetes risk models/scores have been developed, but most do not rely specifically on dietary variables or do not fully capture the overall dietary pattern. We prospectively assessed the association of a dietary-based diabetes-risk score (DDS), which integrates optimal food patterns, with the risk of developing T2DM in the SUN ("Seguimiento Universidad de Navarra") longitudinal study.MethodsWe assessed 17,292 participants initially free of diabetes, followed-up for a mean of 9.2 years. A validated 136-item FFQ was administered at baseline. Taking into account previous literature, the DDS positively weighted vegetables, fruit, whole cereals, nuts, coffee, low-fat dairy, fiber, PUFA, and alcohol in moderate amounts; while it negatively weighted red meat, processed meats and sugar-sweetened beverages. Energy-adjusted quintiles of each item (with exception of moderate alcohol consumption that received either 0 or 5 points) were used to build the DDS (maximum: 60 points). Incident T2DM was confirmed through additional detailed questionnaires and review of medical records of participants. We used Cox proportional hazards models adjusted for socio-demographic and anthropometric parameters, health-related habits, and clinical variables to estimate hazard ratios (HR) of T2DM.ResultsWe observed 143 T2DM confirmed cases during follow-up. Better baseline conformity with the DDS was associated with lower incidence of T2DM (multivariable-adjusted HR for intermediate (25-39 points) vs. low (11-24) category 0.43 [95% confidence interval (CI) 0.21, 0.89]; and for high (40-60) vs. low category 0.32 [95% CI: 0.14, 0.69]; p for linear trend: 0.019).ConclusionsThe DDS, a simple score exclusively based on dietary components, showed a strong inverse association with incident T2DM. This score may be applicable in clinical practice to improve dietary habits of subjects at high risk of T2DM and also as an educational tool for laypeople to help them in self-assessing their future risk for developing diabetes.