A newly developed and externally validated non-clinical score accurately predicts 10-year cardiovascular disease risk in the general adult population.

Inclusion of clinical parameters limits the application of most cardiovascular disease (CVD) prediction models to clinical settings. We developed and externally validated a non-clinical CVD risk score with a clinical extension and compared the performance to established CVD risk scores. We derived the scores predicting CVD (non-fatal and fatal myocardial infarction and stroke) in the European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam cohort (n = 25,992, cases = 683) using competing risk models and externally validated in EPIC-Heidelberg (n = 23,529, cases = 692). Performance was assessed by C-indices, calibration plots, and expected-to-observed ratios and compared to a non-clinical model, the Pooled Cohort Equation, Framingham CVD Risk Scores (FRS), PROCAM scores, and the Systematic Coronary Risk Evaluation (SCORE). Our non-clinical score included age, gender, waist circumference, smoking, hypertension, type 2 diabetes, CVD family history, and dietary parameters. C-indices consistently indicated good discrimination (EPIC-Potsdam 0.786, EPIC-Heidelberg 0.762) comparable to established clinical scores (thereof highest, FRS: EPIC-Potsdam 0.781, EPIC-Heidelberg 0.764). Additional clinical parameters slightly improved discrimination (EPIC-Potsdam 0.796, EPIC-Heidelberg 0.769). Calibration plots indicated very good calibration with minor overestimation in the highest decile of predicted risk. The developed non-clinical 10-year CVD risk score shows comparable discrimination to established clinical scores, allowing assessment of individual CVD risk in physician-independent settings.

Association of familial history of diabetes or myocardial infarction and stroke with risk of cardiovascular diseases in four German cohorts.

Since family history of diabetes is a very strong risk factor for type 2 diabetes, which is one of the most important risk factors for cardiovascular disease (CVD), it might be also useful to assess the risk for CVD. Therefore, we aimed to investigate the relationship between a familial (parents and siblings) history of diabetes and the risk of incident CVD. Data from four prospective German cohort studies were used: EPIC-Potsdam study (n = 26,054), CARLA study (n = 1,079), SHIP study (n = 3,974), and KORA study (n = 15,777). A multivariable-adjusted Cox regression was performed to estimate associations between familial histories of diabetes, myocardial infarction or stroke and the risk of CVD in each cohort; combined hazard ratios (HRMeta) were derived by conducting a meta-analysis. The history of diabetes in first-degree relatives was not related to the development of CVD (HRMeta 0.99; 95% CI 0.88-1.10). Results were similar for the single outcomes myocardial infarction (MI) (HRMeta 1.07; 95% CI 0.92-1.23) and stroke (HRMeta 1.00; 95% CI 0.86-1.16). In contrast, parental history of MI and stroke were associated with an increased CVD risk. Our study indicates that diabetes in the family might not be a relevant risk factor for the incidence of CVD. However, the study confirmed the relationship between a parental history of MI or stroke and the onset of CVD.

Selecting the optimal risk threshold of diabetes risk scores to identify high-risk individuals for diabetes prevention: a cost-effectiveness analysis.

AIMS: Although risk scores to predict type 2 diabetes exist, cost-effectiveness of risk thresholds to target prevention interventions are unknown. We applied cost-effectiveness analysis to identify optimal thresholds of predicted risk to target a low-cost community-based intervention in the USA. METHODS: We used a validated Markov-based type 2 diabetes simulation model to evaluate the lifetime cost-effectiveness of alternative thresholds of diabetes risk. Population characteristics for the model were obtained from NHANES 2001-2004 and incidence rates and performance of two noninvasive diabetes risk scores (German diabetes risk score, GDRS, and ARIC 2009 score) were determined in the ARIC and Cardiovascular Health Study (CHS). Incremental cost-effectiveness ratios (ICERs) were calculated for increasing risk score thresholds. Two scenarios were assumed: 1-stage (risk score only) and 2-stage (risk score plus fasting plasma glucose (FPG) test (threshold 100 mg/dl) in the high-risk group). RESULTS: In ARIC and CHS combined, the area under the receiver operating characteristic curve for the GDRS and the ARIC 2009 score were 0.691 (0.677-0.704) and 0.720 (0.707-0.732), respectively. The optimal threshold of predicted diabetes risk (ICER < $50,000/QALY gained in case of intervention in those above the threshold) was 7% for the GDRS and 9% for the ARIC 2009 score. In the 2-stage scenario, ICERs for all cutoffs ≥ 5% were below $50,000/QALY gained. CONCLUSIONS: Intervening in those with ≥ 7% diabetes risk based on the GDRS or ≥ 9% on the ARIC 2009 score would be cost-effective. A risk score threshold ≥ 5% together with elevated FPG would also allow targeting interventions cost-effectively.

Perceived diabetes risk and related determinants in individuals with high actual diabetes risk: results from a nationwide population-based survey.

Objective: The purpose of this study was first, to examine perceived diabetes risk compared with actual diabetes risk in the general population and second, to investigate which factors determine whether persons at increased actual risk also perceive themselves at elevated risk. Research design and methods: The study comprised adults (aged 18-97 years) without known diabetes from a nationwide survey on diabetes-related knowledge and information needs in Germany in 2017. Actual diabetes risk was calculated by an established risk score estimating the 5-year probability of developing type 2 diabetes and was compared with perceived risk of getting diabetes over the next 5 years (response options: 'almost no risk', 'slight risk', 'moderate risk', 'high risk'; n = 2327). Among adults with an increased actual diabetes risk (n=639), determinants of perceived risk were investigated using multivariable logistic regression analysis. Results: Across groups with a 'low' (<2%), 'still low' (2% to<5%), 'elevated' (5% to <10%), and 'high' (≥10%) actual diabetes risk, a proportion of 89.0%, 84.5%, 79.3%, and 78.9%, respectively, perceived their diabetes risk as almost absent or slight. Among those with an increased (elevated/high) actual risk, independent determinants of an increased (moderate/high) perceived risk included younger age (OR 0.92 (95% CI 0.88 to 0.96) per year), family history of diabetes (2.10 (1.06-4.16)), and being informed about an increased diabetes risk by a physician (3.27 (1.51-7.07)), but none of further diabetes risk factors, healthcare behaviors or beliefs about diabetes. Conclusions: Across categories of actual diabetes risk, perceived diabetes risk was low, even if actual diabetes risk was high. For effective strategies of primary diabetes prevention, attention should be directed to risk communication at the population level as well as in primary care practice.

Performance of the UKPDS Outcomes Model 2 for Predicting Death and Cardiovascular Events in Patients with Type 2 Diabetes Mellitus from a German Population-Based Cohort.

BACKGROUND AND OBJECTIVE: Accurate prediction of relevant outcomes is important for targeting therapies and to support health economic evaluations of healthcare interventions in patients with diabetes. The United Kingdom Prospective Diabetes Study (UKPDS) risk equations are some of the most frequently used risk equations. This study aims to analyze the calibration and discrimination of the updated UKPDS risk equations as implemented in the UKPDS Outcomes Model 2 (UKPDS-OM2) for predicting cardiovascular (CV) events and death in patients with type 2 diabetes mellitus (T2DM) from population-based German samples. METHODS: Analyses are based on data of 456 individuals diagnosed with T2DM who participated in two population-based studies in southern Germany (KORA (Cooperative Health Research in the Region of Augsburg)-A: 1997/1998, n = 178; KORA-S4: 1999-2001, n = 278). We compared the participants' 10-year observed incidence of mortality, CV mortality, myocardial infarction (MI), and stroke with the predicted event rate of the UKPDS-OM2. The model's calibration was evaluated by Greenwood-Nam-D'Agostino tests and discrimination was evaluated by C-statistics. RESULTS: Of the 456 participants with T2DM (mean age 65 years, mean diabetes duration 8 years, 56% male), over the 10-year follow-up time 129 died (61 due to CV events), 64 experienced an MI, and 46 a stroke. The UKPDS-OM2 significantly over-predicted mortality and CV mortality by 25% and 28%, respectively (Greenwood-Nam-D'Agostino tests: p < 0.01), but there was no significant difference between predicted and observed MI and stroke risk. The model poorly discriminated for death (C-statistic [95% confidence interval] = 0.64 [0.60-0.69]), CV death (0.64 [0.58-0.71]), and MI (0.58 [0.52-0.66]), and failed to discriminate for stroke (0.57 [0.47-0.66]). CONCLUSIONS: The study results demonstrate acceptable calibration and poor discrimination of the UKPDS-OM2 for predicting death and CV events in this population-based German sample. Those limitations should be considered when using the UKPDS-OM2 for economic evaluations of healthcare strategies or using the risk equations for clinical decision-making.

Derivation and external validation of a clinical version of the German Diabetes Risk Score (GDRS) including measures of HbA1c.

OBJECTIVE: The German Diabetes Risk Score (GDRS) is a diabetes prediction model which only includes non-invasively measured risk factors. The aim of this study was to extend the original GDRS by hemoglobin A1c (HbA1c) and validate this clinical GDRS in the nationwide German National Health Interview and Examination Survey 1998 (GNHIES98) cohort. RESEARCH DESIGN AND METHODS: Extension of the GDRS was based on the European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam study with baseline assessment conducted between 1994 and 1998 (N=27 548, main age range 35-65 years). Cox regression was applied with the original GDRS and HbA1c as independent variables. The extended model was evaluated by discrimination (C-index (95% CI)), calibration (calibration plots and expected to observed (E:O) ratios (95% CI)), and reclassification (net reclassification improvement, NRI (95% CI)). For validation, data from the GNHIES98 cohort with baseline assessment conducted between 1997 and 1999 were used (N=3717, age range 18-79 years). Missing data were handled with multiple imputation. RESULTS: After 5 years of follow-up 593 incident cases of type 2 diabetes occurred in EPIC-Potsdam and 86 in the GNHIES98 cohort. In EPIC-Potsdam, the C-index for the clinical GDRS was 0.87 (0.81 to 0.92) and the overall NRI was 0.26 (0.21 to 0.30), with a stronger improvement among cases compared with non-cases (NRIcases: 0.24 (0.19 to 0.28); NRInon-cases: 0.02 (0.01 to 0.02)). Almost perfect calibration was observed with a slight tendency toward overestimation, which was also reflected by an E:O ratio of 1.07 (0.99 to 1.16). In the GNHIES98 cohort, discrimination was excellent with a C-index of 0.91 (0.88 to 0.94). After recalibration, the calibration plot showed underestimation of diabetes risk in the highest risk group, while the E:O ratio indicated overall perfect calibration (1.02 (0.83 to 1.26)). CONCLUSIONS: The clinical GDRS provides the opportunity to apply the original GDRS as a first step in risk assessment, which can then be extended in clinical practice with HbA1c whenever it was measured.

Temporal changes in predicted risk of type 2 diabetes in Germany: findings from the German Health Interview and Examination Surveys 1997-1999 and 2008-2011.

OBJECTIVE: Over time, prevalence changes in individual diabetes risk factors have been observed for Germany and other European countries. We aimed to investigate the temporal change of a summary measure of type 2 diabetes risk in Germany. DESIGN: Comparison of data from two cross-sectional surveys that are about 12 years apart. SETTING: Two nationwide health examination surveys representative for the non-institutionalised population aged 18-79 years in Germany. PARTICIPANTS: The study included participants without diagnosed diabetes from the national health examination surveys in 1997-1999 (n=6457) and 2008-2011 (n=6095). OUTCOME MEASURES: Predicted 5-year type 2 diabetes risk was calculated using the German Diabetes Risk Score (GDRS), which considers information on age, anthropometry, lifestyle factors, hypertension and family history of diabetes. RESULTS: Between the two survey periods, the overall age- and sex-standardised predicted 5-year risk of type 2 diabetes decreased by 27% from 1.5% (95% CI 1.4% to 1.6%) to 1.1% (1.0% to 1.2%). The decrease in red meat intake and waist circumference had the highest impact on the overall decrease in diabetes risk. In stratified analyses, diabetes risk decreased among both sexes and within strata of age and body mass index. Diabetes risk also decreased among highly educated persons, but remained unchanged among persons with a middle or low educational level. CONCLUSIONS: Monitoring type 2 diabetes risk by a summary measure such as the GDRS could essentially contribute to interpret the dynamics in diabetes epidemiology.

Multiple imputation was a valid approach to estimate absolute risk from a prediction model based on case-cohort data.

OBJECTIVE: To compare weighting methods for Cox regression and multiple imputation (MI) in a case-cohort study in the context of risk prediction modeling. STUDY DESIGN AND SETTING: Based on the European Prospective Investigation into Cancer and Nutrition Potsdam study, we estimated risk scores to predict incident type-2 diabetes using full cohort data and case-cohort data assuming missing information on waist circumference outside the case-cohort (∼90%). Varying weighting approaches and MI were compared with regard to the calculation of relative risks, absolute risks, and predictive abilities including C-index, the net reclassification improvement, and calibration. RESULTS: The full cohort comprised 21,845 participants, and the case-cohort comprised 2,703 participants. Relative risks were similar across all methods and compatible with full cohort estimates. Absolute risk estimates showed stronger disagreement mainly for Prentice and Self & Prentice weighting. Barlow and Langholz & Jiao weighting methods and MI were in good agreement with full cohort analysis. Predictive abilities were closest to full cohort estimates for MI or for Barlow and Langholz & Jiao weighting. CONCLUSIONS: MI seems to be a valid method for deriving or extending a risk prediction model from case-cohort data and might be superior for absolute risk calculation when compared to weighted approaches.

Validation of the German Diabetes Risk Score among the general adult population: findings from the German Health Interview and Examination Surveys.

OBJECTIVE: To evaluate the German Diabetes Risk Score (GDRS) among the general adult German population for prediction of incident type 2 diabetes and detection of prevalent undiagnosed diabetes. METHODS: The longitudinal sample for prediction of incident diagnosed type 2 diabetes included 3625 persons who participated both in the examination survey in 1997-1999 and the examination survey in 2008-2011. Incident diagnosed type 2 diabetes was defined as first-time physician diagnosis or antidiabetic medication during 5 years of follow-up excluding potential incident type 1 and gestational diabetes. The cross-sectional sample for detection of prevalent undiagnosed diabetes included 6048 participants without diagnosed diabetes of the examination survey in 2008-2011. Prevalent undiagnosed diabetes was defined as glycated haemoglobin ≥6.5% (48 mmol/mol). We assessed discrimination as area under the receiver operating characteristic curve (ROC-AUC (95% CI)) and calibration through calibration plots. RESULTS: In longitudinal analyses, 82 subjects with incident diagnosed type 2 diabetes were identified after 5 years of follow-up. For prediction of incident diagnosed diabetes, the GDRS yielded an ROC-AUC of 0.87 (0.83 to 0.90). Calibration plots indicated excellent prediction for low diabetes risk and overestimation for intermediate and high diabetes risk. When considering the entire follow-up period of 11.9 years (ROC-AUC: 0.84 (0.82 to 0.86)) and including incident undiagnosed diabetes (ROC-AUC: 0.81 (0.78 to 0.84)), discrimination decreased somewhat. A previously simplified paper version of the GDRS yielded a similar predictive ability (ROC-AUC: 0.86 (0.82 to 0.89)). In cross-sectional analyses, 128 subjects with undiagnosed diabetes were identified. For detection of prevalent undiagnosed diabetes, the ROC-AUC was 0.84 (0.81 to 0.86). Again, the simplified version yielded a similar result (ROC-AUC: 0.83 (0.80 to 0.86)). CONCLUSIONS: The GDRS might be applied for public health monitoring of diabetes risk in the German adult population. Future research needs to evaluate whether the GDRS is useful to improve diabetes risk awareness and prevention among the general population.

Amino acids, lipid metabolites, and ferritin as potential mediators linking red meat consumption to type 2 diabetes.

BACKGROUND: Habitual red meat consumption was consistently related to a higher risk of type 2 diabetes in observational studies. Potentially underlying mechanisms are unclear. OBJECTIVE: This study aimed to identify blood metabolites that possibly relate red meat consumption to the occurrence of type 2 diabetes. DESIGN: Analyses were conducted in the prospective European Prospective Investigation into Cancer and Nutrition-Potsdam cohort (n = 27,548), applying a nested case-cohort design (n = 2681, including 688 incident diabetes cases). Habitual diet was assessed with validated semiquantitative food-frequency questionnaires. Total red meat consumption was defined as energy-standardized summed intake of unprocessed and processed red meats. Concentrations of 14 amino acids, 17 acylcarnitines, 81 glycerophospholipids, 14 sphingomyelins, and ferritin were determined in serum samples from baseline. These biomarkers were considered potential mediators of the relation between total red meat consumption and diabetes risk in Cox models. The proportion of diabetes risk explainable by biomarker adjustment was estimated in a bootstrapping procedure with 1000 replicates. RESULTS: After adjustment for age, sex, lifestyle, diet, and body mass index, total red meat consumption was directly related to diabetes risk [HR for 2 SD (11 g/MJ): 1.26; 95% CI: 1.01, 1.57]. Six biomarkers (ferritin, glycine, diacyl phosphatidylcholines 36:4 and 38:4, lysophosphatidylcholine 17:0, and hydroxy-sphingomyelin 14:1) were associated with red meat consumption and diabetes risk. The red meat-associated diabetes risk was significantly (P < 0.001) attenuated after simultaneous adjustment for these biomarkers [biomarker-adjusted HR for 2 SD (11 g/MJ): 1.09; 95% CI: 0.86, 1.38]. The proportion of diabetes risk explainable by respective biomarkers was 69% (IQR: 49%, 106%). CONCLUSION: In our study, high ferritin, low glycine, and altered hepatic-derived lipid concentrations in the circulation were associated with total red meat consumption and, independent of red meat, with diabetes risk. The red meat-associated diabetes risk was largely attenuated after adjustment for selected biomarkers, which is consistent with the presumed mediation hypothesis.

Characterization of metabolically unhealthy normal-weight individuals: Risk factors and their associations with type 2 diabetes.

OBJECTIVE: A proportion of type 2 diabetes cases arise from normal-weight individuals who can therefore be considered to be "metabolically unhealthy normal-weight" (MUH-NW). It remains unclear which factors account for this access risk. Our aims were to identify risk factors for type 2 diabetes in normal-weight individuals and to compare the strengths of their associations with type 2 diabetes to that observed in overweight and obese participants. METHODS: A case-cohort, including 2027 sub-cohort participants and 706 incident type 2 cases, was designed within the population-based European Prospective Investigation into Cancer and Nutrition Potsdam study. Adjusted means and relative frequencies of anthropometric, lifestyle and biochemical risk factors were calculated in groups stratified by BMI and incident diabetes status. Cox regressions were applied to evaluate associations between these variables and diabetes risk stratified by BMI category. RESULTS: MUH-NW individuals were characterized by known diabetes risk factors, e.g. they were significantly more likely to be male, former smokers, hypertensive, and less physically active compared to normal-weight individuals without incident diabetes. Higher waist circumference (women: 75.5 vs. 73.1cm; men: 88.0 vs. 85.1cm), higher HbA1c (6.1 vs. 5.3%), higher triglycerides (1.47 vs. 1.11 mmol/l), and higher levels of high sensitive C-reactive protein (0.81 vs. 0.51 mg/l) as well as lower levels of HDL-cholesterol (1.28 vs. 1.49 mmol/l) and adiponectin (6.32 vs. 8.25 µg/ml) characterized this phenotype. Stronger associations with diabetes among normal-weight participants compared to overweight and obese (p for interaction<0.05) were observed for height, waist circumference, former smoking, and hypertension. CONCLUSIONS: Normal-weight individuals who develop diabetes have higher levels of diabetes risk factors, however, frequently still among the normal range. Still, hypertension, elevated HbA1c and lifestyle risk factors might be useful indicators of risk.

A confidence ellipse for the Net Reclassification Improvement.

The Net Reclassification Improvement (NRI) has become a popular metric for evaluating improvement in disease prediction models through the past years. The concept is relatively straightforward but usage and interpretation has been different across studies. While no thresholds exist for evaluating the degree of improvement, many studies have relied solely on the significance of the NRI estimate. However, recent studies recommend that statistical testing with the NRI should be avoided. We propose using confidence ellipses around the estimated values of event and non-event NRIs which might provide the best measure of variability around the point estimates. Our developments are illustrated using practical examples from EPIC-Potsdam study.

Update of the German Diabetes Risk Score and external validation in the German MONICA/KORA study.

AIMS: Several published diabetes prediction models include information about family history of diabetes. The aim of this study was to extend the previously developed German Diabetes Risk Score (GDRS) with family history of diabetes and to validate the updated GDRS in the Multinational MONItoring of trends and determinants in CArdiovascular Diseases (MONICA)/German Cooperative Health Research in the Region of Augsburg (KORA) study. METHODS: We used data from the European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam study for extending the GDRS, including 21,846 participants. Within 5 years of follow-up 492 participants developed diabetes. The definition of family history included information about the father, the mother and/or sibling/s. Model extension was evaluated by discrimination and reclassification. We updated the calculation of the score and absolute risks. External validation was performed in the MONICA/KORA study comprising 11,940 participants with 315 incident cases after 5 years of follow-up. RESULTS: The basic ROC-AUC of 0.856 (95%-CI: 0.842-0.870) was improved by 0.007 (0.003-0.011) when parent and sibling history was included in the GDRS. The net reclassification improvement was 0.110 (0.072-0.149), respectively. For the updated score we demonstrated good calibration across all tenths of risk. In MONICA/KORA, the ROC-AUC was 0.837 (0.819-0.855); regarding calibration we saw slight overestimation of absolute risks. CONCLUSIONS: Inclusion of the number of diabetes-affected parents and sibling history improved the prediction of type 2 diabetes. Therefore, we updated the GDRS algorithm accordingly. Validation in another German cohort study showed good discrimination and acceptable calibration for the vast majority of individuals.

Non-invasive risk scores for prediction of type 2 diabetes (EPIC-InterAct): a validation of existing models.

BACKGROUND: The comparative performance of existing models for prediction of type 2 diabetes across populations has not been investigated. We validated existing non-laboratory-based models and assessed variability in predictive performance in European populations. METHODS: We selected non-invasive prediction models for incident diabetes developed in populations of European ancestry and validated them using data from the EPIC-InterAct case-cohort sample (27,779 individuals from eight European countries, of whom 12,403 had incident diabetes). We assessed model discrimination and calibration for the first 10 years of follow-up. The models were first adjusted to the country-specific diabetes incidence. We did the main analyses for each country and for subgroups defined by sex, age (<60 years vs ≥60 years), BMI (<25 kg/m(2)vs ≥25 kg/m(2)), and waist circumference (men <102 cm vs ≥102 cm; women <88 cm vs ≥88 cm). FINDINGS: We validated 12 prediction models. Discrimination was acceptable to good: C statistics ranged from 0·76 (95% CI 0·72-0·80) to 0·81 (0·77-0·84) overall, from 0·73 (0·70-0·76) to 0·79 (0·74-0·83) in men, and from 0·78 (0·74-0·82) to 0·81 (0·80-0·82) in women. We noted significant heterogeneity in discrimination (pheterogeneity<0·0001) in all but one model. Calibration was good for most models, and consistent across countries (pheterogeneity>0·05) except for three models. However, two models overestimated risk, DPoRT by 34% (95% CI 29-39%) and Cambridge by 40% (28-52%). Discrimination was always better in individuals younger than 60 years or with a low waist circumference than in those aged at least 60 years or with a large waist circumference. Patterns were inconsistent for BMI. All models overestimated risks for individuals with a BMI of <25 kg/m(2). Calibration patterns were inconsistent for age and waist-circumference subgroups. INTERPRETATION: Existing diabetes prediction models can be used to identify individuals at high risk of type 2 diabetes in the general population. However, the performance of each model varies with country, age, sex, and adiposity. FUNDING: The European Union.

The value of genetic information for diabetes risk prediction - differences according to sex, age, family history and obesity.

BACKGROUND: Genome-wide association studies have identified numerous single nucleotide polymorphisms associated with type 2 diabetes through the past years. In previous studies, the usefulness of these genetic markers for prediction of diabetes was found to be limited. However, differences may exist between substrata of the population according to the presence of major diabetes risk factors. This study aimed to investigate the added predictive value of genetic information (42 single nucleotide polymorphisms) in subgroups of sex, age, family history of diabetes, and obesity. METHODS: A case-cohort study (random subcohort N = 1,968; incident cases: N = 578) within the European Prospective Investigation into Cancer and Nutrition Potsdam study was used. Prediction models without and with genetic information were evaluated in terms of the area under the receiver operating characteristic curve and the integrated discrimination improvement. Stratified analyses included subgroups of sex, age (<50 or ≥50 years), family history (positive if either father or mother or a sibling has/had diabetes), and obesity (BMI< or ≥30 kg/m(2)). RESULTS: A genetic risk score did not improve prediction above classic and metabolic markers, but - compared to a non-invasive prediction model - genetic information slightly improved the area under the receiver operating characteristic curve (difference [95%-CI]: 0.007 [0.002-0.011]). Stratified analyses showed stronger improvement in the older age group (0.010 [0.002-0.018]), the group with a positive family history (0.012 [0.000-0.023]) and among obese participants (0.015 [-0.005-0.034]) compared to the younger participants (0.005 [-0.004-0.014]), participants with a negative family history (0.003 [-0.001-0.008]) and non-obese (0.007 [0.000-0.014]), respectively. No difference was found between men and women. CONCLUSION: There was no incremental value of genetic information compared to standard non-invasive and metabolic markers. Our study suggests that inclusion of genetic variants in diabetes risk prediction might be useful for subgroups with already manifest risk factors such as older age, a positive family history and obesity.

Identification of serum metabolites associated with risk of type 2 diabetes using a targeted metabolomic approach.

Metabolomic discovery of biomarkers of type 2 diabetes (T2D) risk may reveal etiological pathways and help to identify individuals at risk for disease. We prospectively investigated the association between serum metabolites measured by targeted metabolomics and risk of T2D in the European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam (27,548 adults) among all incident cases of T2D (n = 800, mean follow-up 7 years) and a randomly drawn subcohort (n = 2,282). Flow injection analysis tandem mass spectrometry was used to quantify 163 metabolites, including acylcarnitines, amino acids, hexose, and phospholipids, in baseline serum samples. Serum hexose; phenylalanine; and diacyl-phosphatidylcholines C32:1, C36:1, C38:3, and C40:5 were independently associated with increased risk of T2D and serum glycine; sphingomyelin C16:1; acyl-alkyl-phosphatidylcholines C34:3, C40:6, C42:5, C44:4, and C44:5; and lysophosphatidylcholine C18:2 with decreased risk. Variance of the metabolites was largely explained by two metabolite factors with opposing risk associations (factor 1 relative risk in extreme quintiles 0.31 [95% CI 0.21-0.44], factor 2 3.82 [2.64-5.52]). The metabolites significantly improved T2D prediction compared with established risk factors. They were further linked to insulin sensitivity and secretion in the Tübingen Family study and were partly replicated in the independent KORA (Cooperative Health Research in the Region of Augsburg) cohort. The data indicate that metabolic alterations, including sugar metabolites, amino acids, and choline-containing phospholipids, are associated early on with a higher risk of T2D.

Assessing improvement in disease prediction using net reclassification improvement: impact of risk cut-offs and number of risk categories.

Net reclassification improvement (NRI) has received much attention for comparing risk prediction models, and might be preferable over the area under the receiver operating characteristics (ROC) curve to indicate changes in predictive ability. We investigated the influence of the choice of risk cut-offs and number of risk categories on the NRI. Using data of the European Prospective Investigation into Cancer and Nutrition-Potsdam study, three diabetes prediction models were compared according to ROC area and NRI with varying cut-offs for two and three risk categories and varying numbers of risk categories. When compared with a basic model, including age, anthropometry, and hypertension status, a model extension by waist circumference improved discrimination from 0.720 to 0.831 (0.111 [0.097-0.125]) while increase in ROC-AUC from 0.831 to 0.836 (0.006 [0.002-0.009]) indicated moderate improvement when additionally considering diet and physical activity. However, NRI based on these two model comparisons varied with varying cut-offs for two (range: 5.59-23.20%; -0.79 to 4.09%) and three risk categories (20.37-40.15%; 1.22-4.34%). This variation was more pronounced in the model extension showing a larger difference in ROC-AUC. NRI increased with increasing numbers of categories from minimum NRIs of 18.41 and 0.46% to approximately category-free NRIs of 79.61 and 19.22%, but not monotonically. There was a similar pattern for this increase in both model comparisons. In conclusion, the choice of risk cut-offs and number of categories has a substantial impact on NRI. A limited number of categories should only be used if categories have strong clinical importance.

Predictors of dependency on nursing care after stroke: results from the Dortmund and Münster stroke registry.

BACKGROUND: The long-term effects of stroke have been inadequately studied. We identified social and clinical factors that were associated with application for insurance payments for long-term care within 3.6 years after stroke. METHODS: In a quality-assurance project called "Stroke Northwest Germany," information was obtained from 2286 stroke patients on their socio-demographic background, type of stroke, comorbidities, and degree of physical impairment during their hospital stay, as measured on the Rankin Scale, the Barthel Index, and the Neurological Symptom Scale. We used logistic regression models to identify possible associations between these factors and application for insurance payments for long-term care within 3.6 years after stroke. We developed an appropriate prognostic model by means of backward selection. RESULTS: 734 (32.1%) of the patients participated in follow-up and reported whether they had applied for insurance payments for long-term care. 22.5% had submitted an application. The rate of application was positively correlated with age, female sex, the number of comorbidities and complications during hospitalization, and the degree of physical impairment. CONCLUSION: Stroke has major long-term effects. The probability that a stroke patient will apply for insurance payments for long-term care is a function of the patient's age, sex, previous stroke history, and physical impairment as measured on the Rankin Scale and the Barthel Index.