Personalized prediction of depression in patients with newly diagnosed Parkinson's disease: A prospective cohort study

Impulse control behaviors and apathy commonly co-occur in de novo Parkinson's disease and predict the incidence of levodopa-induced dyskinesia

OBJECTIVE

Impulse control behaviors (ICBs) and apathy are believed to represent opposite motivational expressions of the same behavioral spectrum involving hypo- and hyperdopaminergic status, but this has been recently debated. Our study aims to estimate the co-occurrence of ICBs and apathy in early Parkinson's disease (PD) and to determine whether this complex neuropsychiatric condition is an important marker of PD prognoses.

METHODS

Neuropsychiatric symptoms, clinical data, neuroimaging results, and demographic data from de novo PD patients were obtained from the Parkinson's Progression Markers Initiative, a prospective, multicenter, observational cohort. The clinical characteristics of ICBs co-occurring with apathy and their prevalence were analyzed. We compared the prognoses of the different groups during the 8-year follow-up. Multivariate Cox regression analysis was conducted to predict the development of levodopa-induced dyskinesia (LID) using baseline neuropsychiatric symptoms.

RESULTS

A total of 422 PD patients and 195 healthy controls (HCs) were included. In brief, 87 (20.6 %) de novo PD patients and 37 (19.0 %) HCs had ICBs at baseline. Among them, 23 (26.4 %) de novo PD patients and 3 (8.1 %) HCs had clinical symptoms of both ICBs and apathy. The ICBs and apathy group had more severe non-motor symptoms than the isolated ICBs group. Cox regression analysis demonstrated that the co-occurrence of ICBs and apathy was a risk factor for LID development (HR 2.229, 95 % CI 1.209 to 4.110, p = 0.010).

CONCLUSIONS

Co-occurrence of ICBs and apathy is common in patients with early PD and may help to identify the risk of LID development.

Construction of a depression risk prediction model for type 2 diabetes mellitus patients based on NHANES 2007-2014

BACKGROUND

Type 2 diabetes mellitus (T2DM) is a prevalent global health issue that has been linked to an increased risk of depression. The objective of this study was to construct a nomogram model for predicting depression in T2DM patients.

METHODS

A total of 4280 patients with T2DM were included in this study from the 2007-2014 NHANES. The entire dataset was split randomly into training set comprising 70 % of the data and a validation set comprising 30 % of the data. LASSO and multivariate logistic regression analyses identified predictors significantly associated with depression, and the nomogram was constructed with these predictors. The model was assessed by C-index, calibration curve, the hosmer-lemeshow test and decision curve analysis (DCA).

RESULTS

The nomogram model comprised of 9 predictors, namely age, gender, PIR, BMI, education attainment, smoking status, LDL-C, sleep duration and sleep disorder. The C-index of the training set was 0.780, while that of the validation set was 0.752, indicating favorable discrimination for the model. The model exhibited excellent clinical applicability and calibration in both the training and validation datasets. Moreover, the cut-off value of the nomogram is 223.

LIMITATIONS

This study has shortcomings in data collection, lack of external validation, and results non-extrapolation.

CONCLUSIONS

Our nomogram exhibits high clinical predictability, enabling clinicians to utilize this tool in identifying high-risk depressed patients with T2DM. It has the potential to decrease the incidence of depression and significantly improve the prognosis of patients with T2DM.

Discovery of depression-associated factors among childhood trauma victims from a large sample size: Using machine learning and network analysis

BACKGROUND

Experiences of childhood trauma (CT) would lead to serious mental problems, especially depression. Therefore, it becomes crucial to identify influential factors related to depression and explore their associations. The objectives were to 1) identify critical depression-related factors using the extreme gradient boosting (XGBoost) method from a large-scale survey data; 2) explore associations between these factors for targeted interventions and treatments.

METHODS

A large-scale epidemiological study covering 63 universities was conducted in Jilin Province, China. The XGBoost model was trained and tested to classify young adults with CT experiences who had or did not have depression (N = 27,671). The essential factors were selected by SHapley Additive exPlanations (SHAP) value. Multiple logistic regression analyses were conducted for validation. The associations between these depression-related factors were further explored using network analysis.

RESULTS

The XGBoost model selected the top 10 features associated with depression with satisfactory performance (AUC = 0.91; sensitivity = 0.88 and specificity = 0.76). These factors significantly differed between depression and non-depression groups (p < 0.001). There are strong positive associations between anxiety and obsessive-compulsive disorder (OCD), anxiety and post-traumatic stress disorder (PTSD), social anxiety disorder (SAD) and appearance anxiety, and negative associations between sleep quality and anxiety, sleep quality and PTSD among CT participants with depression.

LIMITATIONS

The cross-sectional design cannot draw causality, and biases in self-report measurements cannot be ignored.

CONCLUSIONS

XGBoost model and network analysis were useful methods for discovering and understanding depression-related factors in this epidemiological study. Moreover, these essential factors could offer insights into future interventions and treatments for depressed young adults with CT experiences.

Predicting Future Depressive Episodes from Resting-State fMRI with Generative Embedding

After a first episode of major depressive disorder (MDD), there is substantial risk for a long-term remitting-relapsing course. Prevention and early interventions are thus critically important. Various studies have examined the feasibility of detecting at-risk individuals based on out-of-sample predictions about the future occurrence of depression. However, functional magnetic resonance imaging (MRI) has received very little attention for this purpose so far. Here, we explored the utility of generative models (i.e. different dynamic causal models, DCMs) as well as functional connectivity (FC) for predicting future episodes of depression in never-depressed adults, using a large dataset (N=906) of task-free ("resting state") fMRI data from the UK Biobank. Connectivity analyses were conducted using timeseries from pre-computed spatially independent components of different dimensionalities. Over a three year period, 50% of participants showed indications of at least one depressive episode, while the other 50% did not. Using nested cross-validation for training and a held-out test set (80/20 split), we systematically examined the combination of 8 connectivity feature sets and 17 classifiers. We found that a generative embedding procedure based on combining regression DCM (rDCM) with a support vector machine (SVM) enabled the best predictions, both on the training set (0.63 accuracy, 0.66 area under the curve, AUC) and the test set (0.62 accuracy, 0.64 AUC; p<0.001). However, on the test set, rDCM was only slightly superior to predictions based on FC (0.59 accuracy, 0.61 AUC). Interpreting model predictions based on SHAP (SHapley Additive exPlanations) values suggested that the most predictive connections were widely distributed and not confined to specific networks. Overall, our analyses suggest (i) ways of improving future fMRI-based generative embedding approaches for the early detection of individuals at-risk for depression and that (ii) achieving accuracies of clinical utility may require combination of fMRI with other data modalities.

Machine learning within the Parkinson's progression markers initiative: Review of the current state of affairs

The Parkinson's Progression Markers Initiative (PPMI) has collected more than a decade's worth of longitudinal and multi-modal data from patients, healthy controls, and at-risk individuals, including imaging, clinical, cognitive, and 'omics' biospecimens. Such a rich dataset presents unprecedented opportunities for biomarker discovery, patient subtyping, and prognostic prediction, but it also poses challenges that may require the development of novel methodological approaches to solve. In this review, we provide an overview of the application of machine learning methods to analyzing data from the PPMI cohort. We find that there is significant variability in the types of data, models, and validation procedures used across studies, and that much of what makes the PPMI data set unique (multi-modal and longitudinal observations) remains underutilized in most machine learning studies. We review each of these dimensions in detail and provide recommendations for future machine learning work using data from the PPMI cohort.

Methodological and Quality Flaws in the Use of Artificial Intelligence in Mental Health Research: Systematic Review

BACKGROUND

Artificial intelligence (AI) is giving rise to a revolution in medicine and health care. Mental health conditions are highly prevalent in many countries, and the COVID-19 pandemic has increased the risk of further erosion of the mental well-being in the population. Therefore, it is relevant to assess the current status of the application of AI toward mental health research to inform about trends, gaps, opportunities, and challenges.

OBJECTIVE

This study aims to perform a systematic overview of AI applications in mental health in terms of methodologies, data, outcomes, performance, and quality.

METHODS

A systematic search in PubMed, Scopus, IEEE Xplore, and Cochrane databases was conducted to collect records of use cases of AI for mental health disorder studies from January 2016 to November 2021. Records were screened for eligibility if they were a practical implementation of AI in clinical trials involving mental health conditions. Records of AI study cases were evaluated and categorized by the International Classification of Diseases 11th Revision (ICD-11). Data related to trial settings, collection methodology, features, outcomes, and model development and evaluation were extracted following the CHARMS (Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies) guideline. Further, evaluation of risk of bias is provided.

RESULTS

A total of 429 nonduplicated records were retrieved from the databases and 129 were included for a full assessment-18 of which were manually added. The distribution of AI applications in mental health was found unbalanced between ICD-11 mental health categories. Predominant categories were Depressive disorders (n=70) and Schizophrenia or other primary psychotic disorders (n=26). Most interventions were based on randomized controlled trials (n=62), followed by prospective cohorts (n=24) among observational studies. AI was typically applied to evaluate quality of treatments (n=44) or stratify patients into subgroups and clusters (n=31). Models usually applied a combination of questionnaires and scales to assess symptom severity using electronic health records (n=49) as well as medical images (n=33). Quality assessment revealed important flaws in the process of AI application and data preprocessing pipelines. One-third of the studies (n=56) did not report any preprocessing or data preparation. One-fifth of the models were developed by comparing several methods (n=35) without assessing their suitability in advance and a small proportion reported external validation (n=21). Only 1 paper reported a second assessment of a previous AI model. Risk of bias and transparent reporting yielded low scores due to a poor reporting of the strategy for adjusting hyperparameters, coefficients, and the explainability of the models. International collaboration was anecdotal (n=17) and data and developed models mostly remained private (n=126).

CONCLUSIONS

These significant shortcomings, alongside the lack of information to ensure reproducibility and transparency, are indicative of the challenges that AI in mental health needs to face before contributing to a solid base for knowledge generation and for being a support tool in mental health management.

Prediction models for depression risk among older adults: systematic review and critical appraisal

OBJECTIVE

To provide an overview of prediction models for the risk of major depressive disorder (MDD) among older adults.

METHODS

We conducted a systematic review combined with a meta-analysis and critical appraisal of published studies on existing geriatric depression risk models.

RESULTS

The systematic search screened 23,378 titles and abstracts; 14 studies including 20 prediction models were included. A total of 16 predictors were selected in the final model at least twice. Age, physical health, and cognitive function were the most common predictors. Only one model was externally validated, two models were presented with a complete equation, and five models examined the calibration. We found substantial heterogeneity in predictor and outcome definitions across models; important methodological information was often missing. All models were rated at high or unclear risk of bias, primarily due to methodological limitations. The pooled C-statistics of 12 prediction models was 0.83 (95%CI=0.77-0.89).

CONCLUSION

The usefulness of all models remains unclear due to several methodological limitations. Future studies should focus on methodological quality and external validation of depression risk prediction models.

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.

Depression in Patients with Parkinson's Disease: Current Understanding of its Neurobiology and Implications for Treatment

Depression is one of the most frequent and burdensome non-motor symptoms in Parkinson’s disease (PD), across all stages. Even when its severity is mild, PD depression has a great impact on quality of life for these patients and their caregivers. Accordingly, accurate diagnosis, supported by validated scales, identification of risk factors, and recognition of motor and non-motor symptoms comorbid to depression are critical to understanding the neurobiology of depression, which in turn determines the effectiveness of dopaminergic drugs, antidepressants and non-pharmacological interventions. Recent advances using in vivo functional and structural imaging demonstrate that PD depression is underpinned by dysfunction of limbic networks and monoaminergic systems, depending on the stage of PD and its associated symptoms, including apathy, anxiety, rapid eye movement sleep behavior disorder (RBD), cognitive impairment and dementia. In particular, the evolution of serotonergic, noradrenergic, and dopaminergic dysfunction and abnormalities of limbic circuits across time, involving the anterior cingulate and orbitofrontal cortices, amygdala, thalamus and ventral striatum, help to delineate the variable expression of depression in patients with prodromal, early and advanced PD. Evidence is accumulating to support the use of dual serotonin and noradrenaline reuptake inhibitors (desipramine, nortriptyline, venlafaxine) in patients with PD and moderate to severe depression, while selective serotonin reuptake inhibitors, repetitive transcranial magnetic stimulation and cognitive behavioral therapy may also be considered. In all patients, recent findings advocate that optimization of dopamine replacement therapy and evaluation of deep brain stimulation of the subthalamic nucleus to improve motor symptoms represents an important first step, in addition to physical activity. Overall, this review indicates that increasing understanding of neurobiological changes help to implement a roadmap of tailored interventions for patients with PD and depression, depending on the stage and comorbid symptoms underlying PD subtypes and their prognosis.

Diagnostic classification of Parkinson’s disease based on non-motor manifestations and machine learning strategies

Non-motor manifestations of Parkinson’s disease (PD) appear early and have a significant impact on the quality of life of patients, but few studies have evaluated their predictive potential with machine learning algorithms. We evaluated 9 algorithms for discriminating PD patients from controls using a wide collection of non-motor clinical PD features from two databases: Biocruces (96 subjects) and PPMI (687 subjects). In addition, we evaluated whether the combination of both databases could improve the individual results. For each database 2 versions with different granularity were created and a feature selection process was performed. We observed that most of the algorithms were able to detect PD patients with high accuracy (>80%). Support Vector Machine and Multi-Layer Perceptron obtained the best performance, with an accuracy of 86.3% and 84.7%, respectively. Likewise, feature selection led to a significant reduction in the number of variables and to better performance. Besides, the enrichment of Biocruces database with data from PPMI moderately benefited the performance of the classification algorithms, especially the recall and to a lesser extent the accuracy, while the precision worsened slightly. The use of interpretable rules obtained by the RIPPER algorithm showed that simply using two variables (autonomic manifestations and olfactory dysfunction), it was possible to achieve an accuracy of 84.4%. Our study demonstrates that the analysis of non-motor parameters of PD through machine learning techniques can detect PD patients with high accuracy and recall, and allows us to select the most discriminative non-motor variables to create potential tools for PD screening.

Prodromal depression and subsequent risk of developing Parkinson's disease: a systematic review with meta-analysis

Aim: Parkinson's disease (PD) is a progressive neurological disorder that predominately affects dopaminergic neurons. We believe that this pooling of data will help to better understand the prodromal nature of depression in PD. Materials & methods: We conducted this study in accordance with PRISMA guidelines 2020. Fifteen eligible articles were shortlisted for final analysis. Risk of bias assessment was also conducted Results: The random-effect model revealed that the risk of subsequent Parkinson's disease in patients with prodromal depression was twice as likely (OR, 2.04; 95% CI, 1.02-4.08) as compared with a healthy population. Conclusion: Our meta-analysis concluded that the subsequent risk of PD is significantly higher in patients with depression as compared with healthy individuals.

Personalizing decision-making for persons with Parkinson's disease: where do we stand and what to improve?

Background

The large variety in symptoms and treatment effects across different persons with Parkinson’s disease (PD) warrants a personalized approach, ensuring that the best decision is made for each individual. We aimed to further clarify this process of personalized decision-making, from the perspective of medical professionals.

Methods

We audio-taped 52 consultations with PD patients and their neurologist or PD nurse-specialist, in 6 outpatient clinics. We focused coding of the transcripts on which decisions were made and on if and how decisions were personalized. We subsequently interviewed professionals to elaborate on how and why decisions were personalized, and which decisions would benefit most from a more personalized approach.

Results

Most decisions were related to medication, referral or lifestyle. Professionals balanced clinical factors, including individual (disease-) characteristics, and non-clinical factors, including patients’ preference, for each type of decision. These factors were often not explicitly discussed with the patient. Professionals experienced difficulties in personalizing decisions, mostly because evidence on the impact of characteristics of an individual patient on the outcome of the decision is unavailable. Categories of decisions for which professionals emphasized the importance of a more personalized perspective include choices not only for medication and advanced treatments, but also for referrals, lifestyle and diagnosis.

Conclusions

Clinical decision-making is a complex process, influenced by many different factors that differ for each decision and for each individual. In daily practice, it proves difficult to tailor decisions to individual (disease-) characteristics, probably because sufficient evidence on the impact of these individual characteristics on outcomes is lacking.

Clinical and inflammatory features based machine learning model for fatal risk prediction of hospitalized COVID-19 patients: results from a retrospective cohort study

OBJECTIVES

To appraise effective predictors for COVID-19 mortality in a retrospective cohort study.

METHODS

A total of 1270 COVID-19 patients, including 984 admitted in Sino French New City Branch (training and internal validation sets randomly split at 7:3 ratio) and 286 admitted in Optical Valley Branch (external validation set) of Wuhan Tongji hospital, were included in this study. Forty-eight clinical and laboratory features were screened with LASSO method. Further multi-tree extreme gradient boosting (XGBoost) machine learning-based model was used to rank importance of features selected from LASSO and subsequently constructed death risk prediction model with simple-tree XGBoost model. Performances of models were evaluated by AUC, prediction accuracy, precision, and F1 scores.

RESULTS

Six features, including disease severity, age, levels of high-sensitivity C-reactive protein (hs-CRP), lactate dehydrogenase (LDH), ferritin, and interleukin-10 (IL-10), were selected as predictors for COVID-19 mortality. Simple-tree XGBoost model conducted by these features can predict death risk accurately with >90% precision and >85% sensitivity, as well as F1 scores >0.90 in training and validation sets.

CONCLUSION

We proposed the disease severity, age, serum levels of hs-CRP, LDH, ferritin, and IL-10 as significant predictors for death risk of COVID-19, which may help to identify the high-risk COVID-19 cases. KEY MESSAGES A machine learning method is used to build death risk model for COVID-19 patients. Disease severity, age, hs-CRP, LDH, ferritin, and IL-10 are death risk factors. These findings may help to identify the high-risk COVID-19 cases.

Discovery of Depression-Associated Factors From a Nationwide Population-Based Survey: Epidemiological Study Using Machine Learning and Network Analysis

BACKGROUND

In epidemiological studies, finding the best subset of factors is challenging when the number of explanatory variables is large.

OBJECTIVE

Our study had two aims. First, we aimed to identify essential depression-associated factors using the extreme gradient boosting (XGBoost) machine learning algorithm from big survey data (the Korea National Health and Nutrition Examination Survey, 2012-2016). Second, we aimed to achieve a comprehensive understanding of multifactorial features in depression using network analysis.

METHODS

An XGBoost model was trained and tested to classify "current depression" and "no lifetime depression" for a data set of 120 variables for 12,596 cases. The optimal XGBoost hyperparameters were set by an automated machine learning tool (TPOT), and a high-performance sparse model was obtained by feature selection using the feature importance value of XGBoost. We performed statistical tests on the model and nonmodel factors using survey-weighted multiple logistic regression and drew a correlation network among factors. We also adopted statistical tests for the confounder or interaction effect of selected risk factors when it was suspected on the network.

RESULTS

The XGBoost-derived depression model consisted of 18 factors with an area under the weighted receiver operating characteristic curve of 0.86. Two nonmodel factors could be found using the model factors, and the factors were classified into direct (P<.05) and indirect (P≥.05), according to the statistical significance of the association with depression. Perceived stress and asthma were the most remarkable risk factors, and urine specific gravity was a novel protective factor. The depression-factor network showed clusters of socioeconomic status and quality of life factors and suggested that educational level and sex might be predisposing factors. Indirect factors (eg, diabetes, hypercholesterolemia, and smoking) were involved in confounding or interaction effects of direct factors. Triglyceride level was a confounder of hypercholesterolemia and diabetes, smoking had a significant risk in females, and weight gain was associated with depression involving diabetes.

CONCLUSIONS

XGBoost and network analysis were useful to discover depression-related factors and their relationships and can be applied to epidemiological studies using big survey data.

Pingchan granule for depressive symptoms in parkinson's disease: A randomized, double-blind, placebo-controlled trial

BACKGROUND

Depression in Parkinson's disease (dPD) is closely related to quality of life. Current studies have suggested that Pingchan Granule (PCG) might be effective for treating dPD.

OBJECTIVE

This study determines the efficacy of PCG for depressive symptoms in Parkinson's disease (PD).

DESIGN, SETTING, PARTICIPANTS AND INTERVENTIONS

This was a randomized, double-blind, placebo-controlled trial, conducted in Longhua Hospital, Shanghai, China. Patients diagnosed with idiopathic PD and clinically significant depressive symptoms (defined by a 24-item Hamilton Rating Scale for Depression [HAM-D] score ≥ 8) were included in this study, randomly assigned to PCG or placebo group in a 1:1 ratio and followed for 24 weeks.

MAIN OUTCOME MEASURES

The primary outcome was the change from baseline to week 24 in HAM-D score among the set of patients who completed the study following the treatment protocol (per-protocol set). Secondary outcomes included changes in scores on the Unified Parkinson's Disease Rating Scale (UPDRS) part 2 (UPDRS-II), UPDRS part 3 (UPDRS-III), Parkinson's Disease Sleep Scale (PDSS) and Hamilton Rating Scale for Anxiety (HAM-A), between baseline and week 24.

RESULTS

Eighty-six patients were enrolled, and 85 patients were included in the per-protocol set. HAM-D scores decreased by an adjusted mean of 11.77 (standard error [SE] 0.25) in the PCG group and 3.86 (SE 0.25) in the placebo group (between-group difference = 7.91, 95% confidence interval [7.22, 8.80], P < 0.001), in the multivariable linear regression. Improvements in scores on the UPDRS-II, UPDRS-III, PDSS, and HAM-A scales were also observed.

CONCLUSION

Treatment with PCG was well tolerated and improved depressive symptoms and motor and other non-motor symptoms in PD.

TRIAL REGISTRATION

Chinese Clinical Trial Register: ChiCTR-INR-17011949.