Smell tests to distinguish Parkinson's disease from other neurological disorders: a systematic review and meta-analysis

Multimodal imaging and electrophysiological study in the differential diagnosis of rest tremor

Introduction

Distinguishing tremor-dominant Parkinson's disease (tPD) from essential tremor with rest tremor (rET) can be challenging and often requires dopamine imaging. This study aimed to differentiate between these two diseases through a machine learning (ML) approach based on rest tremor (RT) electrophysiological features and structural MRI data.

Methods

We enrolled 72 patients including 40 tPD patients and 32 rET patients, and 45 control subjects (HC). RT electrophysiological features (frequency, amplitude, and phase) were calculated using surface electromyography (sEMG). Several MRI morphometric variables (cortical thickness, surface area, cortical/subcortical volumes, roughness, and mean curvature) were extracted using Freesurfer. ML models based on a tree-based classification algorithm termed XGBoost using MRI and/or electrophysiological data were tested in distinguishing tPD from rET patients.

Results

Both structural MRI and sEMG data showed acceptable performance in distinguishing the two patient groups. Models based on electrophysiological data performed slightly better than those based on MRI data only (mean AUC: 0.92 and 0.87, respectively; p = 0.0071). The top-performing model used a combination of sEMG features (amplitude and phase) and MRI data (cortical volumes, surface area, and mean curvature), reaching AUC: 0.97 ± 0.03 and outperforming models using separately either MRI (p = 0.0001) or EMG data (p = 0.0231). In the best model, the most important feature was the RT phase.

Conclusion

Machine learning models combining electrophysiological and MRI data showed great potential in distinguishing between tPD and rET patients and may serve as biomarkers to support clinicians in the differential diagnosis of rest tremor syndromes in the absence of expensive and invasive diagnostic procedures such as dopamine imaging.

Opportunities and Pitfalls of REM Sleep Behavior Disorder and Olfactory Dysfunction as Early Markers in Parkinson's Disease

During its pre-motor stage, Parkinson's disease (PD) presents itself with a multitude of non-motor symptoms with different degrees of specificity and sensitivity. The most important among them are REM sleep behavior disorder (RBD) and olfactory dysfunction. RBD is a parasomnia characterized by the loss of REM sleep muscle atonia and dream-enacting behaviors. Olfactory dysfunction in individuals with prodromal PD is usually described as hyposmia (reduced sense of smell) or anosmia (complete loss of olfactory function). These symptoms can precede the full expression of motor symptoms by decades. A close comprehension of these symptoms and the underlying mechanisms may enable early screening as well as interventions to improve patients' quality of life. Therefore, these symptoms have unmatched potential for identifying PD patients in prodromal stages, not only allowing early diagnosis but potentially opening a window for early, possibly disease-modifying intervention. However, they come with certain challenges. This review addresses some of the key opportunities and pitfalls of both RBD and olfactory dysfunction as early markers of PD.

The differential diagnosis value of radiomics-based machine learning in Parkinson's disease: a systematic review and meta-analysis

Background

In recent years, radiomics has been increasingly utilized for the differential diagnosis of Parkinson's disease (PD). However, the application of radiomics in PD diagnosis still lacks sufficient evidence-based support. To address this gap, we carried out a systematic review and meta-analysis to evaluate the diagnostic value of radiomics-based machine learning (ML) for PD.

Methods

We systematically searched Embase, Cochrane, PubMed, and Web of Science databases as of November 14, 2022. The radiomics quality assessment scale (RQS) was used to evaluate the quality of the included studies. The outcome measures were the c-index, which reflects the overall accuracy of the model, as well as sensitivity and specificity. During this meta-analysis, we discussed the differential diagnostic value of radiomics-based ML for Parkinson's disease and various atypical parkinsonism syndromes (APS).

Results

Twenty-eight articles with a total of 6,057 participants were included. The mean RQS score for all included articles was 10.64, with a relative score of 29.56%. The pooled c-index, sensitivity, and specificity of radiomics for predicting PD were 0.862 (95% CI: 0.833-0.891), 0.91 (95% CI: 0.86-0.94), and 0.93 (95% CI: 0.87-0.96) in the training set, and 0.871 (95% CI: 0.853-0.890), 0.86 (95% CI: 0.81-0.89), and 0.87 (95% CI: 0.83-0.91) in the validation set, respectively. Additionally, the pooled c-index, sensitivity, and specificity of radiomics for differentiating PD from APS were 0.866 (95% CI: 0.843-0.889), 0.86 (95% CI: 0.84-0.88), and 0.80 (95% CI: 0.75-0.84) in the training set, and 0.879 (95% CI: 0.854-0.903), 0.87 (95% CI: 0.85-0.89), and 0.82 (95% CI: 0.77-0.86) in the validation set, respectively.

Conclusion

Radiomics-based ML can serve as a potential tool for PD diagnosis. Moreover, it has an excellent performance in distinguishing Parkinson's disease from APS. The support vector machine (SVM) model exhibits excellent robustness when the number of samples is relatively abundant. However, due to the diverse implementation process of radiomics, it is expected that more large-scale, multi-class image data can be included to develop radiomics intelligent tools with broader applicability, promoting the application and development of radiomics in the diagnosis and prediction of Parkinson's disease and related fields.

Systematic review registration

https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=383197, identifier ID: CRD42022383197.

Neurons, Nose, and Neurodegenerative Diseases: Olfactory Function and Cognitive Impairment

Olfactory capacity declines with aging, but increasing evidence shows that smell dysfunction is one of the early signs of prodromal neurodegenerative diseases such as Alzheimer's and Parkinson's disease. The study of olfactory ability and its role in neurodegenerative diseases arouses much interest in the scientific community. In neurology, olfactory impairment is a potential early marker for the onset of neurodegenerative diseases, but the underlying mechanism is poorly understood. The loss of smell is considered a clinical sign of early-stage disease and a marker of the disease's progression and cognitive impairment. Highlighting the importance of biological bases of smell and molecular pathways could be fundamental to improve neuroprotective and therapeutic strategies. We focused on the review articles and meta-analyses on olfactory and cognitive impairment. We depicted the neurobiology of olfaction and the most common olfactory tests in neurodegenerative diseases. In addition, we underlined the close relationship between the olfactory and cognitive deficit due to nasal neuroepithelium, which is a direct extension of the CNS in communication with the external environment. Neurons, Nose, and Neurodegenerative diseases highlights the role of olfactory dysfunction as a clinical marker for early stages of neurodegenerative diseases when it is associated with molecular, clinical, and neuropathological correlations.

Anosmia Testing as Early Detection of SARS-CoV-2 Positivity; A Prospective Study under Screening Conditions

Sudden onset of anosmia is a phenomenon often associated with developing COVID-19 disease and has even been described as an initial isolated symptom in individual cases. In this case-control study, we investigated the feasibility of this condition as a suitable screening test in a population at risk. We performed a prospective study with a total of 313 subjects with suspected SARS-CoV-2 infection. In parallel to routine PCR analysis, a modified commercial scent test was performed to objectify the presence of potential anosmia as a predictor of SARS-CoV-2 positivity. Furthermore, a structured interview assessment of the participants was conducted. A total of 12.1% of the study participants had molecular genetic detection of SARS-CoV-2 infection in the nasopharyngeal swab. It could be demonstrated that these subjects had a significantly weaker olfactory identification performance of the scents. Further analysis of the collected data from the scent test and medical history via random forest (Boruta) algorithm showed that no improvement of the prediction power was achieved by this design. The assay investigated in this study may be suitable for screening general olfactory function. For the screening of COVID-19, it seems to be affected by too many external and internal biases and requires too elaborate and selective pre-test screening.

Smell tests can discriminate Parkinson's disease patients from healthy individuals: A meta-analysis

BACKGROUND

Olfactory impairment is common in Parkinson's disease (PD). The authors aimed to identify the clinical tests used to assess olfactory function and examine their ability to distinguish PD with different disease duration from healthy individuals with physiological aging.

METHODS

Cross-sectional studies published until May 2020 that assessed the olfaction of individuals with PD using search terms related to PD, olfactory function, and assessment were searched on PubMed, PsycInfo, Cinahl, and Web of Science databases.

RESULTS

Twelve smell tests were identified from the reviewed studies (n = 125) that assessed 8776 individuals with PD. Data of 6593 individuals with PD and 8731 healthy individuals were included in the meta-analyses. Individuals with PD presented worse performance than healthy individuals, regardless of the smell test used. The University of Pennsylvania Smell Identification Test (UPSIT) was used by most studies (n = 2310 individuals with PD) and presented smaller heterogeneity. When the studies were subclassified according to the years of PD duration, there were no significant differences.

CONCLUSION

All smell tests were able to discriminate the olfactory function of PD from that of healthy individuals, although the UPSIT was widely used. The abnormal olfaction was not related to the disease duration. Systematic review protocol registration (PROSPERO/2020-CRD42020160878).

The Olfactory System as Marker of Neurodegeneration in Aging, Neurological and Neuropsychiatric Disorders

Research studies that focus on understanding the onset of neurodegenerative pathology and therapeutic interventions to inhibit its causative factors, have shown a crucial role of olfactory bulb neurons as they transmit and propagate nerve impulses to higher cortical and limbic structures. In rodent models, removal of the olfactory bulb results in pathology of the frontal cortex that shows striking similarity with frontal cortex features of patients diagnosed with neurodegenerative disorders. Widely different approaches involving behavioral symptom analysis, histopathological and molecular alterations, genetic and environmental influences, along with age-related alterations in cellular pathways, indicate a strong correlation of olfactory dysfunction and neurodegeneration. Indeed, declining olfactory acuity and olfactory deficits emerge either as the very first symptoms or as prodromal symptoms of progressing neurodegeneration of classical conditions. Olfactory dysfunction has been associated with most neurodegenerative, neuropsychiatric, and communication disorders. Evidence revealing the dual molecular function of the olfactory receptor neurons at dendritic and axonal ends indicates the significance of olfactory processing pathways that come under environmental pressure right from the onset. Here, we review findings that olfactory bulb neuronal processing serves as a marker of neuropsychiatric and neurodegenerative disorders.

Olfactory-Trigeminal Interactions in Patients with Parkinson's Disease

Olfactory dysfunction (OD) is a highly frequent early non-motor symptom of Parkinson's disease (PD). An important step to potentially use OD for the development of early diagnostic tools of PD is to differentiate PD-related OD from other forms of non-parkinsonian OD (NPOD: postviral, sinunasal, post-traumatic, and idiopathic OD). Measuring non-olfactory chemosensory modalities, especially the trigeminal system, may allow to characterize a PD-specific olfactory profile. We here review the literature on PD-specific chemosensory alteration patterns compared with NPOD. Specifically, we focused on the impact of PD on the trigeminal system and particularly on the interaction between olfactory and trigeminal systems. As this interaction is seemingly affected in a disease-specific manner, we propose a model of interaction between both chemosensory systems that is distinct for PD-related OD and NPOD. These patterns of chemosensory impairment still need to be confirmed in prodromal PD; nevertheless, appropriate chemosensory tests may eventually help to develop diagnostic tools to identify individuals at risks for PD.