Early Risk Prediction of Diabetes Based on GA-Stacking

Early risk prediction of diabetes could help doctors and patients to pay attention to the disease and intervene as soon as possible, which can effectively reduce the risk of complications. In this paper, a GA-stacking ensemble learning model is proposed to improve the accuracy of diabetes risk prediction. Firstly, genetic algorithms (GA) based on Decision Tree (DT) is used to select individuals with high adaptability, that is, a subset of attributes suitable for diabetes risk prediction. Secondly, the optimized convolutional neural network (CNN) and support vector machine (SVM) are used as the primary learners of stacking to learn attribute subsets, respectively. Then, the output of CNN and SVM is used as the input of the mate learner, the fully connected layer, for classification. Qingdao desensitization physical examination data from 1 January 2017 to 31 December 2019 is used, which includes body temperature, BMI, waist circumference, and other indicators that may be related to early diabetes. We compared the performance of GA-stacking with K-nearest neighbor (KNN), SVM, logistic regression (LR), Naive Bayes (NB), and CNN before and after adding GA through the average prediction time, accuracy, precision, sensitivity, specificity, and F1-score. Results show that prediction efficiency can be improved by adding GA. GA-stacking has higher prediction accuracy. Moreover, the strong generalization ability and high prediction efficiency of GA-stacking have also been verified on the early-stage diabetes risk prediction dataset published by UCI.

Monocyte Trajectories Endotypes Are Associated With Worsening in Septic Patients

Sepsis is a life-threatening organ dysfunction caused by a dysregulated host response to infection. The immune system plays a key role in sepsis onset and remains dysregulated over time in a heterogeneous manner. Here, we decipher the heterogeneity of the first week evolution of the monocyte HLA-DR (mHLA-DR) surface protein expression in septic patients, a key molecule for adaptive immunity onset. We found and verified four distinctive trajectories endotypes in a discovery (n = 276) and a verification cohort (n = 102). We highlight that 59% of septic patients exhibit low or decreasing mHLA-DR expression while in others mHLA-DR expression increased. This study depicts the first week behavior of mHLA-DR over time after sepsis onset and shows that initial and third day mHLA-DR expression measurements is sufficient for an early risk stratification of sepsis patients. These patients might benefit from immunomodulatory treatment to improve outcomes. Going further, our study introduces a way of deciphering heterogeneity of immune system after sepsis onset which is a first step to reach a more comprehensive landscape of sepsis.

Combined Analysis of Early CD4+ T Cell Counts and CMV Serostatus May Improve CMV Risk Assessment after Allogeneic Hematopoietic Cell Transplantation

The incidence and severity of viral complications after cellular therapy are highly variable. Recent publications describe relevant interactions between the human Cytomegalovirus (CMV) and host immunity in recipients of allogeneic hematopoietic cell transplantation (HCT). Although immune monitoring is routinely performed in HCT patients, validated cut-off levels correlating with transplant outcomes such as survival or CMV reactivation are mostly limited to day +100, which is later than the median time for CMV reactivation in the absence of medical prophylaxis. To address this gap in early risk assessment, we applied an unsupervised machine learning technique based on clustering of day +30 CD4+ helper T cell count data, and identified relevant cut-off levels within the diverse spectrum of early CD4+ reconstitution. These clusters were stratified for CMV recipient serostatus to identify early risk groups that predict clinical HCT outcome. Indeed, the new risk groups predicted subsequent clinical events such as NRM, OS, and high CMV peak titers better than the most established predictor, i.e., the positive CMV recipient serostatus (R+). More specifically, patients from the R+/low CD4+ subgroup strongly associated with high CMV peak titers and increased 3-year NRM (subdistribution hazard ratio (SHR) 10.1, 95% CI 1.38–73.8, p = 0.023), while patients from the R-/very high CD4+ subgroup showed comparable NRM risks (SHR 9.57, 95% CI 1.12–81.9, p = 0.039) without such an association. In short, our study established novel cut-off levels for early CD4+ T cells via unsupervised learning and supports the integration of host cellular immunity into clinical risk-assessment after HCT in the context of CMV reactivation.

Oxysterols and Retinal Microvascular Dysfunction as Early Risk Markers for Cardiovascular Disease in Normal, Ageing Individuals

The aim of the present paper is to assess the relationship between oxysterol levels and retinal microvascular function in individuals of various age groups, free of clinically evident diseases. Forty-two apparently healthy individuals were included in the present study (group 1: 19–30 years, group 2: 31–50 years, and group 3: 51–70 years). Retinal microvascular function was assessed using the dynamic retinal vessel analyzer (DVA, IMEDOS GmbH, Jena, Germany). Fasting plasma was obtained from all subjects and quantification of monohydroxy and dihydroxy oxysterols assessment was performed using LC-MS/MS following reverse phase chromatography. A Griess assay was used to evaluate the Nitric Oxide (NO) concentration in all individuals. The glutathione redox ratio was also analyzed by means of whole blood glutathione recycling assay. In all participants, the levels of 7-Ketocholesterol, 25-hydroxycholesterol and 7β-hydroxycholesterol correlated significantly and positively with the time to maximum arteriolar dilation. In addition, 25-hydroxycholesterol and 7β-hydroxycholesterol negatively correlated to the percentage of maximum arteriolar dilation. A negative correlation was observed for 27-hydroxycholesterol and 7β-hydroxycholesterol with microvascular arteriolar constriction. These results suggest that, with age, abnormal oxysterol levels correlate with early changes in microvascular bed function. This relationship could signal early risk for cardiovascular diseases (CVDs) in an ageing population.

Prevalence and early risk factors for bulimia nervosa symptoms in inner-city youth: gender and ethnicity perspectives

Background Research on risk factors associated with bulimia nervosa symptoms (BN) in ethnic minorities has been limited. This study adds to the existing literature by providing the ethnicity- and gender-specific prevalence of BN in US inner-city youth and by exploring the longitudinal associations between a clinical level of BN and early risk factors assessed one year previously. Methods The study was conducted on a representative sample of predominantly ethnic minority youth (N = 2794; 54.1% female; age 11–16 years old (M(SD) = 12.77(1.29)); 60.0% African-American, 26.1% Hispanic American, 13.9% White). Self-reported information was obtained on BN and early risk factors (e.g., depressive and anxiety symptoms, posttraumatic stress, somatic complaints). Multivariate analysis of covariance was used to examine the longitudinal associations. Results The 3-month BN prevalence was higher in girls (5.1%) than in boys (2.3%) (ratio 2.22:1). Significant differences in BN rates were found between White and African American students (higher in Whites), whereas Hispanic-Americans did not differ significantly from either group. Individuals with BN had significantly higher levels of early risk factors one year prior. Conclusions Timely recognition of BN and associated early risk factors is important for early prevention and intervention strategies.