Unravelling the mechanisms of underweight in Parkinson's disease by investigating into the role of gut microbiome.

Approximately half of patients with Parkinson's disease (PD) suffer from unintentional weight loss and are underweight, complicating the clinical course of PD patients. Gut microbiota alteration has been proven to be associated with PD, and recent studies have shown that gut microbiota could lead to muscle wasting, implying a possible role of gut microbiota in underweight PD. In this study, we aimed to (1) investigate the mechanism underlying underweight in PD patients with respect to gut microbiota and (2) estimate the extent to which gut microbiota may mediate PD-related underweight through mediation analysis. The data were adapted from Hill-Burns et al., in which 330 participants (199 PD, 131 controls) were enrolled in the study. Fecal samples were collected from participants for microbiome analysis. 16S rRNA gene sequence data were processed using DADA2. Mediation analysis was performed to quantify the effect of intestinal microbial alteration on the causal effect of PD on underweight and to identify the key bacteria that significantly mediated PD-related underweight. The results showed that the PD group had significantly more underweight patients (body mass index (BMI) < 18.5) after controlling for age and sex. Ten genera and four species were significantly different in relative abundance between the underweight and non-underweight individuals in the PD group. Mediation analysis showed that 42.29% and 37.91% of the effect of PD on underweight was mediated through intestinal microbial alterations at the genus and species levels, respectively. Five genera (Agathobacter, Eisenbergiella, Fusicatenibacter, Roseburia, Ruminococcaceae_UCG_013) showed significant mediation effects. In conclusion, we found that up to 42.29% of underweight PD cases are mediated by gut microbiota, with increased pro-inflammatory bacteria and decreased SCFA-producing bacteria, which indicates that the pro-inflammatory state, disturbance of metabolism, and interference of appetite regulation may be involved in the mechanism of underweight PD.

Exploring the Causal Effect of Constipation on Parkinson's Disease Through Mediation Analysis of Microbial Data.

Background and Aims: Parkinson's disease (PD) is a worldwide neurodegenerative disease with an increasing global burden, while constipation is an important risk factor for PD. The gastrointestinal tract had been proposed as the origin of PD in Braak's gut-brain axis hypothesis, and there is increasing evidence indicating that intestinal microbial alteration has a role in the pathogenesis of PD. In this study, we aim to investigate the role of intestinal microbial alteration in the mechanism of constipation-related PD. Methods: We adapted our data from Hill-Burns et al., in which 324 participants were enrolled in the study. The 16S rRNA gene sequence data were processed, aligned, and categorized using DADA2. Mediation analysis was used to test and quantify the extent by which the intestinal microbial alteration explains the causal effect of constipation on PD incidence. Results: We found 18 bacterial genera and 7 species significantly different between groups of constipated and non-constipated subjects. Among these bacteria, nine genera and four species had a significant mediation effect between constipation and PD. All of them were short-chain fatty acid (SCFA)-producing bacteria that were substantially related to PD. Results from the mediation analysis showed that up to 76.56% of the effect of constipation on PD was mediated through intestinal microbial alteration. Conclusion: Our findings support that gut dysbiosis plays a critical role in the pathogenesis of constipation-related PD, mostly through the decreasing of SCFA-producing bacteria, indicating that probiotics with SCFA-producing bacteria may be promising in the prevention and treatment of constipation-related PD. Limitations: 1) Several potential confounders that should be adjusted were not provided in the original dataset. 2) Our study was conducted based on the assumption of constipation being the etiology of PD; however, constipation and PD may mutually affect each other. 3) Further studies are necessary to explain the remaining 23.44% effect leading to PD by constipation.

Prediction of white-coat hypertension and white-coat uncontrolled hypertension using machine learning algorithm.

Aims: The detection of white-coat hypertension/white-coat uncontrolled hypertension (WCH/WUCH) with out-of-office blood pressure (BP) monitoring is time- and resource-consuming. We aim to develop a machine learning (ML)-derived prediction model based on the characteristics of patients from a single outpatient visit. Methods and results: Data from two cohorts in Taiwan were used. Cohort one (970 patients) was used for development and internal validation, and cohort two (464 patients) was used for external validation. WCH/WUCH was defined as an office BP of ≥140/90 mmHg and daytime ambulatory BP of <135/85 mmHg in treatment-naïve or treated individuals. Logistic regression, random forest (RF), eXtreme Gradient Boosting, and artificial neural network models were trained using 26 patient parameters. We used SHapley Additive exPlanations values to provide explanations for the risk factors. All models achieved great area under the receiver operating characteristic curve (AUROC), specificity, and negative predictive value in both validations (AUROC = 0.754-0.891; specificity = 0.682-0.910; negative predictive value = 0.831-0.968). The RF model was the best performing (AUROC = 0.884; sensitivity = 0.619; specificity = 0.887; negative predictive value = 0.872; accuracy = 0.819). The five most influential features of the RF model were office diastolic BP, office systolic BP, current smoker, estimated glomerular filtration rate, and fasting glucose level. Conclusion: Our prediction models achieved good performance, underlining the feasibility of applying ML models to outpatient populations for the diagnosis of WCH and WUCH. Further validation with other prospective data sets should be considered in the future.

Prediction of Masked Hypertension and Masked Uncontrolled Hypertension Using Machine Learning.

Objective: This study aimed to develop machine learning-based prediction models to predict masked hypertension and masked uncontrolled hypertension using the clinical characteristics of patients at a single outpatient visit. Methods: Data were derived from two cohorts in Taiwan. The first cohort included 970 hypertensive patients recruited from six medical centers between 2004 and 2005, which were split into a training set (n = 679), a validation set (n = 146), and a test set (n = 145) for model development and internal validation. The second cohort included 416 hypertensive patients recruited from a single medical center between 2012 and 2020, which was used for external validation. We used 33 clinical characteristics as candidate variables to develop models based on logistic regression (LR), random forest (RF), eXtreme Gradient Boosting (XGboost), and artificial neural network (ANN). Results: The four models featured high sensitivity and high negative predictive value (NPV) in internal validation (sensitivity = 0.914-1.000; NPV = 0.853-1.000) and external validation (sensitivity = 0.950-1.000; NPV = 0.875-1.000). The RF, XGboost, and ANN models showed much higher area under the receiver operating characteristic curve (AUC) (0.799-0.851 in internal validation, 0.672-0.837 in external validation) than the LR model. Among the models, the RF model, composed of 6 predictor variables, had the best overall performance in both internal and external validation (AUC = 0.851 and 0.837; sensitivity = 1.000 and 1.000; specificity = 0.609 and 0.580; NPV = 1.000 and 1.000; accuracy = 0.766 and 0.721, respectively). Conclusion: An effective machine learning-based predictive model that requires data from a single clinic visit may help to identify masked hypertension and masked uncontrolled hypertension.