Failing Sense of Smell Linked to Parkinson’s Duration and Progression

smell and Parkinson's

Loss of the sense of smell is associated with longer disease duration in Parkinson’s, suggesting this sense increasingly fails as the disease progresses.

Its decline may be a clinically useful biomarker of Parkinson’s progression and severity, the study’s researchers said.

These findings were in “Association between olfactory impairment and disease severity and duration in Parkinson’s disease,” published in Movement Disorders Clinical Practice.

Loss of smell is a common non-motor symptom of Parkinson’s, and one many patients start experiencing at early disease stages.

Because this symptom often occurs before motor symptoms are evident, testing for the sense of smell has gained interest as a possible way of screening for Parkinson’s early in its course.

But how it progresses as the diseases advances is less known.

Researchers in Japan measured sense of smell in three groups of people: 79 who were recently diagnosed with Parkinson’s (average disease duration, 7.9 months), 71 others with established Parkinson’s (average disease duration, 47.4 months or almost four years), and 128 people without Parkinson’s (controls). The three groups were similar in terms of age (late 70s, on average) and sex (slightly more females than males in all groups).

People with established Parkinson’s had significantly more severe disease, as indicated by higher scores on the Unified Parkinson’s Disease Rating Scale (10.9 vs. 6.3) and lower scores on the Mini-Mental State Exam (26.4 vs. 27.3).

Smell was evaluated using the odor-stick identification test for Japanese (OSIT-J). This test basically involves giving people samples of common smells, like garlic, curry, and menthol, then asking that they identify them. It is scored based on the number of correctly identified smells; the number of indistinguishable smells, meaning the person could smell something, but couldn’t tell what; or samples thought odorless, meaning the person couldn’t smell anything at all.

Compared with controls, those with early and established Parkinson’s had significantly fewer correct answers, as well as significantly more that they couldn’t distinguish among or found odorless.

Recently diagnosed patients had significantly higher average scores for correct smell identification than did those diagnosed years earlier (4.3 vs. 2.5). Newer patients also had significantly fewer smells that were odorless (1.2 vs. 3.8). These differences remained statistically significant after accounting for factors such as disease severity and treatment duration.

Although some previous research has suggested  sex-based differences in smell perceptions, no noteworthy differences in this study were found between men and woman in any group.

“Olfactory dysfunction (fewer correct answers and more ‘odorless’ responses) in both males and females was more severe in PD [Parkinson’s] patients with a previous diagnosis than in patients with recently diagnosed parkinsonism. Thus, olfactory dysfunction [decreased sense of smell] in PD may get worse over time,” the researchers concluded.

Since a longer disease duration was also linked with more severe disease, the researchers noted that a poorer sense of smell was also associated with greater disease severity.

“The assessment of olfactory function is a useful strategy to detect parkinsonism, particularly at the early stage of PD, and it should be more commonly utilized in clinical practice settings as a biomarker of disease progression and severity in PD,” the researchers wrote.

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Predictive Models Could Help Diagnose Early-stage Parkinson’s, Study Finds

predictive models

Predictive models can help diagnose Parkinson’s disease in early stages, and could be used to distinguish Parkinson’s from conditions that present similarly, a new study suggests. The findings indicate that losing the sense of smell is particularly significant for predicting Parkinson’s.

The study, “Non-motor Clinical and Biomarker Predictors Enable High Cross-Validated Accuracy Detection of Early PD but Lesser Cross-Validated Accuracy Detection of Scans Without Evidence of Dopaminergic Deficit,” was published in Frontiers in Neurology.

It is widely agreed that non-motor manifestations of Parkinson’s disease  typically predate the onset of motor symptoms that are more specifically characteristic of the disease. Identifying Parkinson’s in these very early stages,  which is necessary for starting treatment as soon as possible, is a continuous clinical challenge.

A particular problem in this regard is differentiating between Parkinson’s and SWEDD (scans without evidence of dopamine deficit). SWEDD is a disease category in which a person has symptoms that are indicative of Parkinson’s, but they don’t have the changes in dopamine activity in the brain that are characteristic of Parkinson’s. The researchers who authored the new study described SWEDD as “a [Parkinson’s disease] lookalike.”

In the new study, researchers constructed five different predictive models with the dual goals of differentiating between people with early Parkinson’s and people without disease, and between people with early Parkinson’s and people with SWEDD.

Conceptually, these models were constructed by feeding clinical, biological, and demographic data into computers. Then, using specialized algorithms, the computer develops rules for distinguishing between the two relevant groups. The different models used are essentially just different underlying algorithms for this same purpose.

“Every feature used was first proven relevant in the literature. Of those, we allowed each model to pick out which predictors were most important,” study co-author Charles Leger, a PhD candidate at York University in Canada, said in a press release. “No model is guaranteed to provide the best fit. With five models, if you get the same feature that stands out, then you know that particular variable is very important in distinguishing disease. Neurologists could apply one or more of the models to their own data to … distinguish Parkinson’s pathology from pathology masquerading as Parkinson’s.”

The data used for the models was obtained from the Parkinson’s Progression Markers Initiative (PPMI) database, an observational, international clinical study to establish Parkinson’s biomarkers. The specific features analyzed in the models included loss of sense of smell, education, daytime sleepiness, and rapid eye movement (REM) sleep behavior disorder.

In total, data for 295 people with early PD, 43 with SWEDD, and 130 with no evidence of disease were analyzed. In order to be able to test their models, the researchers built them using only a subset of this data. The models were then tested on the remaining data.

The diagnostic accuracy of the models was assessed by calculating the area under the receiver operating characteristic curve (AUC). AUC is a statistical measurement of how well a given model can distinguish between two groups — in this case, early Parkinson’s vs. no disease and early Parkinson’s vs. SWEDD, in the two respective analyses. AUC values can range from 0 to 1; a value closer to 1 indicated better distinguishing accuracy.

All five models performed well at distinguishing early Parkinson’s from no disease; AUC values ranged from 0.86 to 0.928. Distinguishing early Parkinson’s from SWEDD was less definitive, with AUC values between 0.743 and 0.863.

“The discrepancy of model performance between early PD/control and early PD/SWEDD classification is, at least in part, due to the wide range of disorders encompassed by the SWEDD category,” the researchers wrote, noting that some in the field have called for a removal of the term or category SWEDD for this very reason.

Across the models, loss of sense of smell was the most important differentiator of early Parkinson’s in both analyses, with the second-most important being REM sleep behavior disorder. This indicates the importance of these symptoms in identifying early Parkinson’s disease.

Interestingly, daytime sleepiness, age, and education were important for distinguishing early Parkinson’s from SWEDD, but weren’t important for differentiating early Parkinson’s from no disease. Other variables, including levels of Parkinson’s-related biomarkers in the fluid surrounding the brain and spinal cord (cerebrospinal fluid, CSF), namely alpha-synuclein, were more important for distinguishing early Parkinson’s from no disease than from SWEDD. These findings, “warrant further investigation,” the researchers wrote.

Overall, this study supports the use of these models for detecting early Parkinson’s disease.

“These models could be very useful in differentiating patients who may present with Parkinson’s-like symptoms not related to Parkinson’s pathology from patients who actually have the disease,” said study co-author Joseph DeSouza, PhD, a professor at York University.

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