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Background: Functional connectivity quantifies the statistical dependencies between the activity of brain regions, measured using neuroimaging data such as functional magnetic resonance imaging (fMRI) blood-oxygenation-level dependent time series. The network representation of functional connectivity, called a functional connectome (FC), has been shown to contain an individual fingerprint allowing participants identification across consecutive testing sessions. Recently, researchers have focused on the extraction of these fingerprints, with potential applications in personalized medicine., Materials and Methods: In this study, we show that a mathematical operation denominated degree-normalization can improve the extraction of FC fingerprints. Degree-normalization has the effect of reducing the excessive influence of strongly connected brain areas in the whole-brain network. We adopt the differential identifiability framework and apply it to both original and degree-normalized FCs of 409 individuals from the Human Connectome Project, in resting-state and 7 fMRI tasks., Results: Our results indicate that degree-normalization systematically improves three fingerprinting metrics, namely differential identifiability, identification rate, and matching rate. Moreover, the results related to the matching rate metric suggest that individual fingerprints are embedded in a low-dimensional space., Discussion: The results suggest that low-dimensional functional fingerprints lie in part in weakly connected sub-networks of the brain and that degree-normalization helps uncovering them. This work introduces a simple mathematical operation that could lead to significant improvements in future FC fingerprinting studies.

Recently, prediction models for type 2 diabetes mellitus (T2DM) in older adults (aged a parts per thousand yen55 year) were developed in the KORA S4/F4 study, Augsburg, Germany. We aimed to externally validate the KORA models in a Dutch population. We used data on both older adults (n = 2,050; aged a parts per thousand yen55 year) and total non-diabetic population (n = 6,317; aged 28-75 year) for this validation. We assessed performance of base model (model 1: age, sex, BMI, smoking, parental diabetes and hypertension) and two clinical models: model 1 plus fasting glucose (model 2); and model 2 plus uric acid (model 3). For 7-year risk of T2DM, we calculated C-statistic, Hosmer-Lemeshow chi(2)-statistic, and integrated discrimination improvement (IDI) as measures of discrimination, calibration and reclassification, respectively. After a median follow-up of 7.7 years, 199 (9.7%) and 374 (5.9%) incident cases of T2DM were ascertained in the older and total population, respectively. In the older adults, C-statistic was 0.66 for model 1. This was improved for model 2 and model 3 (C-statistic = 0.81) with significant IDI. In the total population, these respective C-statistics were 0.77, 0.85 and 0.85. All models showed poor calibration (P

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 (2 vs ≥25 kg/m2), and waist circumference (men heterogeneityheterogeneity>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 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.

Objective To identify existing prediction models for the risk of development of type 2 diabetes and to externally validate them in a large independent cohort.Data sources Systematic search of English, German, and Dutch literature in PubMed until February 2011 to identify prediction models for diabetes.Design Performance of the models was assessed in terms of discrimination (C statistic) and calibration (calibration plots and Hosmer-Lemeshow test). The validation study was a prospective cohort study, with a case cohort study in a random subcohort.Setting Models were applied to the Dutch cohort of the European Prospective Investigation into Cancer and Nutrition cohort study (EPIC-NL).Participants 38 379 people aged 20-70 with no diabetes at baseline, 2506 of whom made up the random subcohort.Outcome measure Incident type 2 diabetes.Results The review identified 16 studies containing 25 prediction models. We considered 12 models as basic because they were based on variables that can be assessed non-invasively and 13 models as extended because they additionally included conventional biomarkers such as glucose concentration. During a median follow-up of 10.2 years there were 924 cases in the full EPIC-NL cohort and 79 in the random subcohort. The C statistic for the basic models ranged from 0.74 (95% confidence interval 0.73 to 0.75) to 0.84 (0.82 to 0.85) for risk at 7.5 years. For prediction models including biomarkers the C statistic ranged from 0.81 (0.80 to 0.83) to 0.93 (0.92 to 0.94). Most prediction models overestimated the observed risk of diabetes, particularly at higher observed risks. After adjustment for differences in incidence of diabetes, calibration improved considerably.Conclusions Most basic prediction models can identify people at high risk of developing diabetes in a time frame of five to 10 years. Models including biomarkers classified cases slightly better than basic ones. Most models overestimated the actual risk of diabetes. Existing prediction models therefore perform well to identify those at high risk, but cannot sufficiently quantify actual risk of future diabetes.

Introduction: To develop and test a diabetes risk score to predict incident diabetes in an elderly Spanish Mediterranean population at high cardiovascular risk. Materials and Methods: A diabetes risk score was derived from a subset of 1381 nondiabetic individuals from three centres of the PREDIMED study (derivation sample). Multivariate Cox regression model ß-coefficients were used to weigh each risk factor. PREDIMED-personal Score included body-mass-index, smoking status, family history of type 2 diabetes, alcohol consumption and hypertension as categorical variables; PREDIMED-clinical Score included also high blood glucose. We tested the predictive capability of these scores in the DE-PLAN-CAT cohort (validation sample). The discrimination of Finnish Diabetes Risk Score (FINDRISC), German Diabetes Risk Score (GDRS) and our scores was assessed with the area under curve (AUC). Results: The PREDIMED-clinical Score varied from 0 to 14 points. In the subset of the PREDIMED study, 155 individuals developed diabetes during the 4.75-years follow-up. The PREDIMED-clinical score at a cutoff of $6 had sensitivity of 72.2%, and specificity of 72.5%, whereas AUC was 0.78. The AUC of the PREDIMED-clinical Score was 0.66 in the validation sample (sensitivity = 85.4%; specificity = 26.6%), and was significantly higher than the FINDRISC and the GDRS in both the derivation and validation samples. Discussion: We identified classical risk factors for diabetes and developed the PREDIMED-clinical Score to determine those individuals at high risk of developing diabetes in elderly individuals at high cardiovascular risk. The predictive capability of the PREDIMED-clinical Score was significantly higher than the FINDRISC and GDRS, and also used fewer items in the questionnaire.