Diabetic retinopathy related homeostatic dysregulation and its association with mortality among diabetes patients: A cohort study from NHANES.

AIMS: To develop a metric termed the diabetic retinopathy-related homeostatic dysregulation (DRHD) value, and estimate its association with future risk of mortality in individuals with type 2 diabetes. METHODS: With the data of the NHANES, the biomarkers associated with DR were identified from 40 clinical parameters using LASSO regression. Subsequently, the DRHD value was constructed utilizing the Mahalanobis distance approach. In the retrospective cohortof 6420 type 2 diabetes patients, we estimated the associations between DRHD values and mortality related to all-cause, cardiovascular disease (CVD) and diabetes-specific causes using Cox proportional hazards regression models. RESULTS: A set of 14 biomarkers associated with DR was identified for the construction of DRHD value. During an average of 8 years of follow-up, the multivariable-adjusted HRs and corresponding 95 % CIs for the highest quartiles of DRHD values were 2.04 (1.76, 2.37), 2.32 (1.78, 3.01), and 2.29 (1.72, 3.04) for all-cause, CVD and diabetes-specific mortality, respectively. Furthermore, we developed a web-based calculator for the DRHD value to enhance its accessibility and usability (https://dzwxl-drhd.streamlit.app/). CONCLUSIONS: Our study constructed the DRHD value as a measure to assess homeostatic dysregulation among individuals with type 2 diabetes. The DRHD values exhibited potential as a prognostic indicator for retinopathy and for mortality in patients affected by type 2 diabetes.

Association of glyphosate exposure with multiple adverse outcomes and potential mediators.

Glyphosate (GLY) is a widely used herbicide with potential adverse effects on public health. However, the current epidemiological evidence is limited. This study aimed to investigate the potential associations between exposure to GLY and multiple health outcomes. The data on urine GLY concentration and nine health outcomes, including type 2 diabetes mellitus (T2DM), hypertension, cardiovascular disease (CVD), obesity, chronic kidney disease (CKD), hepatic steatosis, cancers, chronic obstructive pulmonary disease (COPD), and neurodegenerative diseases (NGDs), were extracted from NHANES (2013-2016). The associations between GLY exposure and each health outcome were estimated using reverse-scale Cox regression and logistic regression. Furthermore, mediation analysis was conducted to identify potential mediators in the significant associations. The dose-response relationships between GLY exposure with health outcomes and potential mediators were analyzed using restricted cubic spline (RCS) regression. The findings of the study revealed that individuals with higher urinary concentrations of GLY had a higher likelihood of having T2DM, hypertension, CVD and obesity (p < 0.001, p = 0.005, p < 0.001 and p = 0.005, respectively). In the reverse-scale Cox regression, a notable association was solely discerned between exposure to GLY and the risk of T2DM (adjusted HR = 1.22, 95% CI: 1.10, 1.36). Consistent outcomes were also obtained via logistic regression analysis, wherein the adjusted OR and 95% CI for T2DM were determined to be 1.30 (1.12, 1.52). Moreover, the present investigation identified serum high-density lipoprotein cholesterol (HDL) as a mediator in this association, with a mediating effect of 7.14% (p = 0.040). This mediating effect was further substantiated by RCS regression, wherein significant dose-response associations were observed between GLY exposure and an increased risk of T2DM (p = 0.002) and reduced levels of HDL (p = 0.001). Collectively, these findings imply an association between GLY exposure and an increased risk of T2DM in the general adult population.

Association between co-exposure to phenols, phthalates, and polycyclic aromatic hydrocarbons with the risk of frailty.

The phenomenon of population aging has brought forth the challenge of frailty. Nevertheless, the contribution of environmental exposure to frailty remains ambiguous. Our objective was to investigate the association between phenols, phthalates (PAEs), and polycyclic aromatic hydrocarbons (PAHs) with frailty. We constructed a 48-item frailty index using data from the National Health and Nutrition Examination Survey (NHANES). The exposure levels of 20 organic contaminants were obtained from the survey circle between 2005 and 2016. The association between individual organic contaminants and the frailty index was assessed using negative binomial regression models. The combined effect of organic contaminants was examined using weighted quantile sum (WQS) regression. Dose-response patterns were modeled using generalized additive models (GAMs). Additionally, an interpretable machine learning approach was employed to develop a predictive model for the frailty index. A total of 1566 participants were included in the analysis. Positive associations were observed between exposure to MIB, P02, ECP, MBP, MHH, MOH, MZP, MC1, and P01 with the frailty index. WQS regression analysis revealed a significant increase in the frailty index with higher levels of the mixture of organic contaminants (aOR, 1.12; 95% CI, 1.05-1.20; p < 0.001), with MIB, ECP, COP, MBP, P02, and P01 identified as the major contributors. Dose-response relationships were observed between MIB, ECP, MBP, P02, and P01 exposure with an increased risk of frailty (both with p < 0.05). The developed predictive model based on organic contaminants exposure demonstrated high performance, with an R2 of 0.9634 and 0.9611 in the training and testing sets, respectively. Furthermore, the predictive model suggested potential synergistic effects in the MIB-MBP and P01-P02 pairs. Taken together, these findings suggest a significant association between exposure to phthalates and PAHs with an increased susceptibility to frailty.

Identification of dietary components in association with abdominal aortic calcification.

The precise impact of dietary components on vascular health remains incompletely understood. To identify the dietary components and their associations with abdominal aortic calcification (AAC), the data from NHANES was employed in this cross-sectional study. The LASSO method and logistic regression were utilized to identify dietary components that exhibited the strongest association with AAC. Grouped WQS regression analysis was employed to evaluate the combined effects of dietary components on AAC. Furthermore, principal component analysis was employed to identify the primary dietary patterns in the study population. The present analysis included 1862 participants, from whom information on 35 dietary macro- and micronutrient components was obtained through 24-hour dietary recall interviews. The assessment of AAC was performed utilizing dual-energy X-ray absorptiometry. The LASSO method identified 10 dietary components that were associated with AAC. Total protein, total fiber, vitamin A, and ß-cryptoxanthin exhibited a negative association with AAC. Compared to the first quartile, the adjusted odds ratios (95% CIs) for the highest quartile were 0.59 (0.38, 0.93), 0.63 (0.42, 0.93), 0.59 (0.41, 0.84), and 0.68 (0.48, 0.94), respectively. Grouped WQS regression demonstrated a positive association between the lipid group and AAC (aOR: 1.29; 95% CI: 1.12, 1.50), while the proteins and phytochemical group exhibited a negative association with AAC (aOR: 0.69; 95% CI: 0.58, 0.82). For the dietary pattern analysis, high adherence to the plant-based pattern (aOR: 0.62; 95% CI: 0.44, 0.88) was associated with a lower risk of AAC, whereas the caffeine and theobromine pattern (aOR: 1.73; 95% CI: 1.25, 2.41) was associated with a higher risk of AAC. The findings of this study indicate that adopting a dietary pattern characterized by high levels of protein and plant-based foods, as well as reduced levels of fat, may offers potential advantages for the prevention of AAC.

Effects of heavy metal exposure on hypertension: A machine learning modeling approach.

Heavy metal exposure is a common risk factor for hypertension. To develop an interpretable predictive machine learning (ML) model for hypertension based on levels of heavy metal exposure, data from the NHANES (2003-2016) were employed. Random forest (RF), support vector machine (SVM), decision tree (DT), multilayer perceptron (MLP), ridge regression (RR), AdaBoost (AB), gradient boosting decision tree (GBDT), voting classifier (VC), and K-nearest neighbour (KNN) algorithms were utilized to generate an optimal predictive model for hypertension. Three interpretable methods, the permutation feature importance analysis, partial dependence plot (PDP), and Shapley additive explanations (SHAP) methods, were integrated into a pipeline and embedded in ML for model interpretation. A total of 9005 eligible individuals were randomly allocated into two distinct sets for predictive model training and validation. The results showed that among the predictive models, the RF model demonstrated the highest performance, achieving an accuracy rate of 77.40% in the validation set. The AUC and F1 score for the model were 0.84 and 0.76, respectively. Blood Pb, urinary Cd, urinary Tl, and urinary Co levels were identified as the main influencers of hypertension, and their contribution weights were 0.0504 ± 0.0482, 0.0389 ± 0.0256, 0.0307 ± 0.0179, and 0.0296 ± 0.0162, respectively. Blood Pb (0.55-2.93 µg/dL) and urinary Cd (0.06-0.15 µg/L) levels exhibited the most pronounced upwards trend with the risk of hypertension within a specific value range, while urinary Tl (0.06-0.26 µg/L) and urinary Co (0.02-0.32 µg/L) levels demonstrated a declining trend with hypertension. The findings on the synergistic effects indicated that Pb and Cd were the primary determinants of hypertension. Our findings underscore the predictive value of heavy metals for hypertension. By utilizing interpretable methods, we discerned that Pb, Cd, Tl, and Co emerged as noteworthy contributors within the predictive model.

A Hybrid System for Automatic Identification of Corneal Layers on In Vivo Confocal Microscopy Images.

Purpose: Accurate identification of corneal layers with in vivo confocal microscopy (IVCM) is essential for the correct assessment of corneal lesions. This project aims to obtain a reliable automated identification of corneal layers from IVCM images. Methods: A total of 7957 IVCM images were included for model training and testing. Scanning depth information and pixel information of IVCM images were used to build the classification system. Firstly, two base classifiers based on convolutional neural networks and K-nearest neighbors were constructed. Second, two hybrid strategies, namely weighted voting method and light gradient boosting machine (LightGBM) algorithm were used to fuse the results from the two base classifiers and obtain the final classification. Finally, the confidence of prediction results was stratified to help find out model errors. Results: Both two hybrid systems outperformed the two base classifiers. The weighted area under the curve, weighted precision, weighted recall, and weighted F1 score were 0.9841, 0.9096, 0.9145, and 0.9111 for weighted voting hybrid system, and were 0.9794, 0.9039, 0.9055, and 0.9034 for the light gradient boosting machine stacking hybrid system, respectively. More than one-half of the misclassified samples were found using the confidence stratification method. Conclusions: The proposed hybrid approach could effectively integrate the scanning depth and pixel information of IVCM images, allowing for the accurate identification of corneal layers for grossly normal IVCM images. The confidence stratification approach was useful to find out misclassification of the system. Translational Relevance: The proposed hybrid approach lays important groundwork for the automatic identification of the corneal layer for IVCM images.

An artificial intelligence approach to classify pathogenic fungal genera of fungal keratitis using corneal confocal microscopy images.

PURPOSE: Fungal keratitis is a common cause of blindness worldwide. Timely identification of the causative fungal genera is essential for clinical management. In vivo confocal microscopy (IVCM) provides useful information on pathogenic genera. This study attempted to apply deep learning (DL) to establish an automated method to identify pathogenic fungal genera using IVCM images. METHODS: Deep learning networks were trained, validated, and tested using a data set of 3364 IVCM images that collected from 100 eyes of 100 patients with culture-proven filamentous fungal keratitis. Two transfer learning approaches were investigated: one was a combined framework that extracted features by a DL network and adopted decision tree (DT) as a classifier; another was a complete supervised DL model which used DL-based fully connected layers to implement the classification. RESULTS: The DL classifier model revealed better performance compared with the DT classifier model in an independent testing set. The DL classifier model showed an area under the receiver operating characteristic curves (AUC) of 0.887 with an accuracy of 0.817, sensitivity of 0.791, specificity of 0.831, G-mean of 0.811, and F1 score of 0.749 in identifying Fusarium, and achieved an AUC of 0.827 with an accuracy of 0.757, sensitivity of 0.756, specificity of 0.759, G-mean of 0.757, and F1 score of 0.716 in identifying Aspergillus. CONCLUSION: The DL model can classify Fusarium and Aspergillus by learning effective features in IVCM images automatically. The automated IVCM image analysis suggests a noninvasive identification of Fusarium and Aspergillus with clear potential application in early diagnosis and management of fungal keratitis.