Development of a depression in Parkinson's disease prediction model using machine learning

Acupuncture activates IRE1/XBP1 endoplasmic reticulum stress pathway in Parkinson's disease model rats

Acupuncture has demonstrated its efficacy as a treatment for Parkinson's disease (PD). Thus, the objective of this study was to investigate the potential mechanisms underlying acupuncture's effects on PD treatment. Our approach involved several steps. Firstly, we assessed the behavioral changes in PD rats, the modulation of dopamine (DA) and 5-hydroxytryptamine (5-HT) levels in the striatum, as well as the alteration in α-synuclein (α-syn) levels in the midbrain, aiming to evaluate the efficacy of acupuncture in PD treatment. Secondly, we selected endoplasmic reticulum (ER) stress inhibitors and activators to assess the impact of ER stress on PD rats. Lastly, we utilized an IRE1 inhibitor to observe the influence of acupuncture on the IRE1/XBP1 pathway in PD rats. The findings of this study revealed that acupuncture improved the autonomous motor function, balance ability, coordination, and sensory motor integration function in the PD model rats. Additionally, it increased the levels of DA and 5-HT in the striatum while decreasing the levels of α-syn in the midbrain. Acupuncture also activated the expression of ER stress in the midbrain and upregulated the expression of IRE1/XBP1 in the striatum of PD model rats. Based on these results, we concluded that acupuncture may enhance the behavior of PD rats by activating the IRE1/XBP1 ER stress pathway, associated with the reduction of midbrain α-syn expression and the increase in striatal DA and 5-HT levels in unilateral 6-OHDA lesioned rats.

A scoping review of neurodegenerative manifestations in explainable digital phenotyping

Neurologists nowadays no longer view neurodegenerative diseases, like Parkinson's and Alzheimer's disease, as single entities, but rather as a spectrum of multifaceted symptoms with heterogeneous progression courses and treatment responses. The definition of the naturalistic behavioral repertoire of early neurodegenerative manifestations is still elusive, impeding early diagnosis and intervention. Central to this view is the role of artificial intelligence (AI) in reinforcing the depth of phenotypic information, thereby supporting the paradigm shift to precision medicine and personalized healthcare. This suggestion advocates the definition of disease subtypes in a new biomarker-supported nosology framework, yet without empirical consensus on standardization, reliability and interpretability. Although the well-defined neurodegenerative processes, linked to a triad of motor and non-motor preclinical symptoms, are detected by clinical intuition, we undertake an unbiased data-driven approach to identify different patterns of neuropathology distribution based on the naturalistic behavior data inherent to populations in-the-wild. We appraise the role of remote technologies in the definition of digital phenotyping specific to brain-, body- and social-level neurodegenerative subtle symptoms, emphasizing inter- and intra-patient variability powered by deep learning. As such, the present review endeavors to exploit digital technologies and AI to create disease-specific phenotypic explanations, facilitating the understanding of neurodegenerative diseases as "bio-psycho-social" conditions. Not only does this translational effort within explainable digital phenotyping foster the understanding of disease-induced traits, but it also enhances diagnostic and, eventually, treatment personalization.

Acupuncture inhibits autophagy and repairs synapses by activating the mTOR pathway in Parkinson's disease depression model rats

Acupuncture is a good treatment for depression in Parkinson's disease (DPD), so the possible mechanism of acupuncture in the treatment of DPD was explored in this study. Firstly, observing the behavioral changes of the DPD rat model, the regulation of monoamine neurotransmitters dopamine (DA) and 5-hydroxytryptamine (5-HT) in the midbrain, the change of α-synuclein (α-syn) in the striatum, the efficacy of acupuncture in the treatment of DPD was discussed. Secondly, autophagy inhibitors and activators were selected to judge the effect of acupuncture on autophagy in the DPD rat model. Finally, an mTOR inhibitor was used to observe the effect of acupuncture on the mTOR pathway in the DPD rat model. The results showed that acupuncture could improve the motor and depressive symptoms of DPD model rats, increase the content of DA and 5-HT, and decrease the content of ɑ-syn in the striatum. Acupuncture inhibited the expression of autophagy in the striatum of DPD model rats. At the same time, acupuncture upregulates p-mTOR expression, inhibits autophagy, and promotes synaptic protein expression. Therefore, we concluded that acupuncture might improve the behavior of DPD model rats by activating the mTOR pathway, inhibiting autophagy from removing α-syn and repairing synapses.

Physical function, ADL, and depressive symptoms in Chinese elderly: Evidence from the CHARLS

Background

Depressive symptoms are a serious public health problem that affects the mental health of older adults. However, current knowledge of the association between ADL disability and physical dysfunction and depressive symptoms in Chinese adults is insufficient. We intend to analyze the association between physical function, ADL, and depressive symptoms in older Chinese adults.

Methods

The data obtained from the China Health and Retirement Longitudinal Survey (2015 and 2018) (CHARLS). This includes 3,431 in 2015 and 3,258 in 2018 over the age of 60. Comparing 2015 and 2018 data, multivariate logistic regression models were used to explore the relationship between physical function, ADL, and depressive symptoms in urban and rural older adults, adjusting for sociodemographic factors associated with depression in older adults.

Results

The prevalence of depressive symptoms among older adults in China was 33.8 percent in 2015 and 50.6 percent in 2018. In baseline data from 2015 and 2018, residence, gender, marital status, drinking, physical function, ADL, and self-rated health were all found to be significantly associated with depressive symptoms in older adults. The differences in physical function, ADL and depressive symptoms among older adults in 2015 and 2018 were further analyzed based on urban and rural stratification. Both physical dysfunction and ADL disability were significantly associated with depressive symptoms in rural older adults in 2015 and 2018. And in urban areas, ADL was found to be significantly associated with depressive symptoms in urban older adults. Multivariate logistic regression analysis demonstrated that ADL disability was significantly associated with depressive symptoms among older adults in both urban and rural areas. Physical dysfunction was only significant in rural areas with depressive symptoms. The alpha level was instead set to 0.05 for all statistical tests.

Conclusion

Rural, female, 60-70 years of age, primary school or below, married, non-smoking, non-drinking, physical dysfunction, ADL disability and self-rated poor health make-up a higher proportion of depressed older adults. ADL disability and physical dysfunction were more likely to be associated with depressive symptoms in rural Chinese older adults. Therefore, the physical and mental health of rural elderly should be of concern. The rural older adults should receive additional support from the government and society.

Multi-predictor modeling for predicting early Parkinson’s disease and non-motor symptoms progression

Background

Identifying individuals with high-risk Parkinson’s disease (PD) at earlier stages is an urgent priority to delay disease onset and progression. In the present study, we aimed to develop and validate clinical risk models using non-motor predictors to distinguish between early PD and healthy individuals. In addition, we constructed prognostic models for predicting the progression of non-motor symptoms [cognitive impairment, Rapid-eye-movement sleep Behavior Disorder (RBD), and depression] in de novo PD patients at 5 years of follow-up.

Methods

We retrieved the data from the Parkinson’s Progression Markers Initiative (PPMI) database. After a backward variable selection approach to identify predictors, logistic regression analyses were applied for diagnosis model construction, and cox proportional-hazards models were used to predict non-motor symptom progression. The predictive models were internally validated by correcting measures of predictive performance for “optimism” or overfitting with the bootstrap resampling approach.

Results

For constructing diagnostic models, the final model reached a high accuracy with an area under the curve (AUC) of 0.93 (95% CI: 0.91–0.96), which included eight variables (age, gender, family history, University of Pennsylvania Smell Inventory Test score, Montreal Cognitive Assessment score, RBD Screening Questionnaire score, levels of cerebrospinal fluid α-synuclein, and SNCA rs356181 polymorphism). For the construction of prognostic models, our results showed that the AUC of the three prognostic models improved slightly with increasing follow-up time. The overall AUCs fluctuated around 0.70. The model validation established good discrimination and calibration for predicting PD onset and progression of non-motor symptoms.

Conclusion

The findings of our study facilitate predicting the individual risk at an early stage based on the predictors derived from these models. These predictive models provide relatively reliable information to prevent PD onset and progression. However, future validation analysis is still needed to clarify these findings and provide more insight into the predictive models over more extended periods of disease progression in more diverse samples.

An insight into diagnosis of depression using machine learning techniques: a systematic review

BACKGROUND

In this modern era, depression is one of the most prevalent mental disorders from which millions of individuals are affected today. The symptoms of depression are heterogeneous and often coincide with other disorders such as bipolar disorder, Parkinson's, schizophrenia, etc. It is a serious mental illness that may lead to other health problems if left untreated. Currently, identifying individuals with depression is totally based on the expertise of the clinician's experience. In order to assist clinicians in identifying the characteristics and classifying depressed people, different types of data modalities and machine learning techniques have been incorporated by researchers in this field. This study aims to find the answers to some important questions related to the trend of publications, data modality, machine learning models, dataset usage, pre-processing techniques and feature extraction and selection techniques that are prevalent and guide the direction of future research on depression diagnosis.

METHODS

This systematic review was conducted using a broad range of articles from two major databases: IEEE Xplore and PubMed. Studies ranging from the years 2011 to April 2021 were retrieved from the databases resulting in a total of 590 articles (53 articles from the IEEE Xplore database and 537 articles from the PubMed database). Out of those, the articles which satisfied the defined inclusion criteria were investigated for further analysis.

RESULTS

A total of 135 articles were identified and analysed for this review. High growth in the number of publications has been observed in recent years. Furthermore, significant diversity in the use of data modalities and machine learning classifiers has also been noted in this study. fMRI data with an SVM classifier was found to be the most popular choice among researchers. In most of the studies, data scarcity and small sample size, particularly for neuroimaging data are major concerns. The use of identical data pre-processing tools for similar data modalities can be seen. This study also provides statistical analysis of the current framework with respect to the modality, machine learning classifier, sample size and accuracy by applying one-way ANOVA and the Tukey - Kramer test.

CONCLUSION

The results indicate that an effective fusion of machine learning techniques with a potential data modality has a promising future for assisting clinicians in automatic depression diagnosis.

Screening dementia and predicting high dementia risk groups using machine learning

New technologies such as artificial intelligence, the internet of things, big data, and cloud computing have changed the overall society and economy, and the medical field particularly has tried to combine traditional examination methods and new technologies. The most remarkable field in medical research is the technology of predicting high dementia risk group using big data and artificial intelligence. This review introduces: (1) the definition, main concepts, and classification of machine learning and overall distinction of it from traditional statistical analysis models; and (2) the latest studies in mental science to detect dementia and predict high-risk groups in order to help competent researchers who are challenging medical artificial intelligence in the field of psychiatry. As a result of reviewing 4 studies that used machine learning to discriminate high-risk groups of dementia, various machine learning algorithms such as boosting model, artificial neural network, and random forest were used for predicting dementia. The development of machine learning algorithms will change primary care by applying advanced machine learning algorithms to detect high dementia risk groups in the future.

A web-based novel prediction model for predicting depression in elderly patients with coronary heart disease: A multicenter retrospective, propensity-score matched study

BACKGROUND

Depression is associated with an increased risk of death in patients with coronary heart disease (CHD). This study aimed to explore the factors influencing depression in elderly patients with CHD and to construct a prediction model for early identification of depression in this patient population.

MATERIALS AND METHODS

We used propensity-score matching to identify 1,065 CHD patients aged ≥65 years from four hospitals in Chongqing between January 2015 and December 2021. The patients were divided into a training set (n = 880) and an external validation set (n = 185). Univariate logistic regression, multivariate logistic regression, and least absolute shrinkage and selection operator regression were used to determine the factors influencing depression. A nomogram based on the multivariate logistic regression model was constructed using the selected influencing factors. The discrimination, calibration, and clinical utility of the nomogram were assessed by the area under the curve (AUC) of the receiver operating characteristic curve, calibration curve, and decision curve analysis (DCA) and clinical impact curve (CIC), respectively.

RESULTS

The predictive factors in the multivariate model included the lymphocyte percentage and the blood urea nitrogen and low-density lipoprotein cholesterol levels. The AUC values of the nomogram in the training and external validation sets were 0.762 (95% CI = 0.722-0.803) and 0.679 (95% CI = 0.572-0.786), respectively. The calibration curves indicated that the nomogram had strong calibration. DCA and CIC indicated that the nomogram can be used as an effective tool in clinical practice. For the convenience of clinicians, we used the nomogram to develop a web-based calculator tool (https://cytjt007.shinyapps.io/dynnomapp_depression/).

CONCLUSION

Reductions in the lymphocyte percentage and blood urea nitrogen and low-density lipoprotein cholesterol levels were reliable predictors of depression in elderly patients with CHD. The nomogram that we developed can help clinicians assess the risk of depression in elderly patients with CHD.

Developing a nomogram for predicting the depression of senior citizens living alone while focusing on perceived social support

BACKGROUND

Although the number of senior citizens living alone is increasing, only a few studies have identified factors related to the depression characteristics of senior citizens living alone by using epidemiological survey data that can represent a population group.

AIM

To evaluate prediction performance by building models for predicting the depression of senior citizens living alone that included subjective social isolation and perceived social support as well as personal characteristics such as age and drinking.

METHODS

This study analyzed 1558 senior citizens (695 males and 863 females) who were 60 years or older and completed an epidemiological survey representing the South Korean population. Depression, an outcome variable, was measured using the short form of the Korean version CES-D (short form of CES-D).

RESULTS

The prevalence of depression among the senior citizens living alone was 7.7%. The results of multiple logistic regression analysis showed that the experience of suicidal urge over the past year, subjective satisfaction with help from neighbors, subjective loneliness, age, and self-esteem were significantly related to the depression of senior citizens living alone (P < 0.05). The results of 10-fold cross validation showed that the area under the curve of the nomogram was 0.96, and the F1 score of it was 0.97.

CONCLUSION

It is necessary to strengthen the social network of senior citizens living alone with friends and neighbors based on the results of this study to protect them from depression.

Trend of recognizing depression symptoms and antidepressants use in newly diagnosed Parkinson's disease: Population-based study

OBJECTIVES

Although depression symptoms are common among patients with Parkinson's disease (PD), the medical literature still reports underrecognition of depression in patients with PD. Our main objective is to examine the trend of depression recognition during the first year of PD diagnosis using large population data.

METHODS

We conducted a population-based study of residents in Wales, using the Secure Anonymized Information Linkage (SAIL) Databank. We included newly diagnosed patients with PD aged 40 years or older with a first PD diagnosis between 2000 and 2015. Depression and antidepressants related data were extracted from SAIL. A series of multilevel logistic regressions were run to determine the factors affecting depression recognition. The results were presented using odds ratios (ORs) with 95% confidence intervals (CI).

RESULTS

The study included 6596 patients with PD. About 38% of patients had a recorded code of antidepressants, depression diagnosis, or both within the first year of PD diagnosis. There was a significant association of depression diagnosis, antidepressant use, or both with the year of PD diagnosis (OR 0.972, 95% CI 0.962-0.983). We also found that patients who used monoamine oxidase inhibitors (MAO-B inhibitors) were associated with a lower depression diagnosis, use antidepressants, or both, compared to those who did not use MAO-B inhibitors (OR 0.769, 95% CI 0.627-0.943).

CONCLUSION

There is a slight decrease in depression recognition in PD patients between 2000 and 2015, which could be due to an increase in depression recognition during the prodromal phase of PD.

Predicting the Severity of Parkinson's Disease Dementia by Assessing the Neuropsychiatric Symptoms with an SVM Regression Model

In this study, we measured the convergence rate using the mean-squared error (MSE) of the standardized neuropsychological test to determine the severity of Parkinson's disease dementia (PDD), which is based on support vector machine (SVM) regression (SVR) and present baseline data in order to develop a model to predict the severity of PDD. We analyzed 328 individuals with PDD who were 60 years or older. To identify the SVR with the best prediction power, we compared the classification performance (convergence rate) of eight SVR models (Eps-SVR and Nu-SVR with four kernel functions (a radial basis function (RBF), linear algorithm, polynomial algorithm, and sigmoid)). Among the eight models, the MSE of Nu-SVR-RBF was the lowest (0.078), with the highest convergence rate, whereas the MSE of Eps-SVR-sigmoid was 0.110, with the lowest convergence rate. The results of this study imply that this approach could be useful for measuring the severity of dementia by comprehensively examining axial atypical features, the Korean instrumental activities of daily living (K-IADL), changes in rapid eye movement sleep behavior disorder (RBD), etc. for optimal intervention and caring of the elderly living alone or patients with PDD residing in medically vulnerable areas.