Data-driven assessment, contextualisation and implementation of 134 variables in the risk for type 2 diabetes: an analysis of Lifelines, a prospective cohort study in the Netherlands.

AIMS/HYPOTHESIS: We aimed to assess and contextualise 134 potential risk variables for the development of type 2 diabetes and to determine their applicability in risk prediction. METHODS: A total of 96,534 people without baseline diabetes (372,007 person-years) from the Dutch Lifelines cohort were included. We used a risk variable-wide association study (RV-WAS) design to independently screen and replicate risk variables for 5-year incidence of type 2 diabetes. For identified variables, we contextualised HRs, calculated correlations and assessed their robustness and unique contribution in different clinical contexts using bootstrapped and cross-validated lasso regression models. We evaluated the change in risk, or 'HR trajectory', when sequentially assigning variables to a model. RESULTS: We identified 63 risk variables, with novel associations for quality-of-life indicators and non-cardiovascular medications (i.e., proton-pump inhibitors, anti-asthmatics). For continuous variables, the increase of 1 SD of HbA1c, i.e., 3.39 mmol/mol (0.31%), was equivalent in risk to an increase of 0.53 mmol/l of glucose, 19.8 cm of waist circumference, 8.34 kg/m2 of BMI, 0.67 mmol/l of HDL-cholesterol, and 0.14 mmol/l of uric acid. Other variables required an increase of >3 SD, which is not physiologically realistic or a rare occurrence in the population. Though moderately correlated, the inclusion of four variables satiated prediction models. Invasive variables, except for glucose and HbA1c, contributed little compared with non-invasive variables. Glucose, HbA1c and family history of diabetes explained a unique part of disease risk. Adding risk variables to a satiated model can impact the HRs of variables already in the model. CONCLUSIONS: Many variables show weak or inconsistent associations with the development of type 2 diabetes, and only a handful can reliably explain disease risk. Newly discovered risk variables will yield little over established factors, and existing prediction models can be simplified. A systematic, data-driven approach to identify risk variables for the prediction of type 2 diabetes is necessary for the practice of precision medicine.

Assessing psychache as a suicide risk variable: Data with the Portuguese version of the psychache scale.

Several studies have consistently related psychological pain to suicide risk. Psychache, according to Shneidman's perspective and measured by the Psychache Scale has been confirmed as an important variable in risk prediction. In the present study, we evaluated psychache as a construct related to suicide risk using data obtained with the Portuguese version of the Psychache Scale translated from the original English version. A community sample of 628 individuals responded to the Portuguese version of the Psychache Scale, the Suicidal Behavior Questionnaire-Revised, the CES-D Scale, the Beck Hopelessness Scale, and the Suicide Ideation Questionnaire. Results supported the unidimensional scoring of the Psychache scale, its ability to differentiate between individuals at-risk for suicide from individuals not at-risk, its relationship with different, but related, constructs and its ability to predict suicide ideation.

The Neutrophil/Lymphocyte Ratio is an Independent Predictor of All-Cause Mortality in Patients with Idiopathic Hypereosinophilic Syndrome.

Background: Idiopathic hypereosinophilic syndrome (IHES) often causes inflammatory damage to multiple organs. However, whether immune/inflammatory indicators and other factors are associated with mortality in patients with IHES remains unclear. Patients and Methods: The clinical data and follow-up results of 167 patients with IHES were retrospectively analyzed using Cox regression analysis and receiver operating characteristic curve (ROC). Results: Of 167 patients, 120 were men (71.9%) and 47 were women (28.1%). The median age was 52 (36.0, 68.0) years. The median follow-up period was 42.8 (18.5, 75.1) months, during which all-cause mortality occurred in 26 patients (15.6%). Age (HR: 1.041, 95% CI: 1.015-1.068; p = 0.002), lymphocyte counts (109/L, HR: 0.866, 95% CI: 0.816-0.907; p = 0.013), platelet counts (109/L, HR: 0.994, 95% CI: 0.989-0.999; p = 0.012) and NLR (HR: 1.161, 95% CI: 1.054-1.280; p = 0.003) were independent risk factors for all-cause mortality. There was no relationship between PLR, and SII and all-cause mortality (p = 0.181 and 0.202, respectively). ROC analysis showed that the AUCs of age, lymphocyte count (109/L), platelet count (109/L) and NLR were 0.712 (95% CI: 0.601-0.824), 0.584 (95% CI: 0.448-0.719), 0.686 (95% CI: 0.560-0.812), and 0.797 (95% CI: 0.695-0.899), respectively, with sensitivities of 0.5, 0.462, 0.769, and 0.792, respectively, and specificities of 0.765, 0.745, 0.617, and 0.845, respectively. Kaplan-Meier analysis (Log rank test) showed that patients with age ≥73.5 years, lymphocyte count (109/L) <1.45, platelet count (109/L) <225 and NLR ≥2.54 had high mortality. Patients with high NLR (≥2.54) usually have multiorgan involvement, with cardiac involvement and skin involvement being the most common. Patients with NLR ≥2.54 had significantly higher absolute eosinophil counts (p = 0.047) and percentages (p = 0.041). Conclusion: We identified NLR for the first time as an independent predictive factor for all-cause mortality in patients with IHES, necessitating its further application in clinical practice.

A Prediction Model Based on Blood Biomarker for Mortality Risk in Patients with Acute Venous Thromboembolism.

Background: Most studies to date have focused on predicting the risk of venous thromboembolism (VTE), but prediction models about mortality risk in VTE are rarely reported. We sought to develop and validate a multivariable model to predict the all-cause mortality risk in patients with acute VTE in emergency settings. Methods: A total of 700 patients were included from Qilu Hospital of Shandong University and were randomly assigned into training set (n=490) and validation set (n=210) in an 7:3 ratio. Multivariate logistics regression analysis was performed to identify independent variables and develop a prediction model, which was validated internally using bootstrap method. The discrimination, calibration and clinical utility were evaluated by receiver operating characteristic curve (ROC) analysis, Hosmer-Lemeshow (HL) test, Kaplan-meier (KM) analysis and decision curve analysis (DCA). Results: There were 52 patients (10.6%) dying and 437 (89.4%) surviving in training set. Age (odds ratio [OR]: 4.158, 95% confidence interval [CI]: 2.426-7.127), pulmonary embolism (OR: 1.779, 95% CI: 1.124-2.814), platelet count (OR: 0.507, 95% CI: 0.310-0.830), D-dimer (OR: 1.826, 95% CI: 1.133-2.942) and platelet/lymphocyte ratio (OR: 2.166, 95% CI: 1.259-3.727) were independent risk variables associated with all-cause mortality. The model had good predictive capability with an AUC of 0.746 (95% CI: 0.668,0.825), a sensitivity of 0.769 (95% CI: 0.607,0.889), a specificity of 0.672 (95% CI: 0.634,0.707). The validation model had an AUC of 0.739 (95% CI: 0.685,0.793), a sensitivity of 0.690 (95% CI: 0.580,0.787), a specificity of 0.693 (95% CI: 0.655,0.729). The model is well calibrated and the HL test showed a good fit (χ2=5.291, p=0.726, Nagelkerke R2=0.137). KM analysis and DCA showed a good clinical utility of the nomogram. Conclusion: This study identified independent variables affecting all-cause mortality in patients with acute VTE, and developed a prediction model and provided a nomogram with good prediction capability and clinical utility.