Affordable Rapid Olfaction Measurement Array: A Novel, Essential Oil-Based Test Strongly Correlated with UPSIT and Subjective Outcome Measures

Age-related differences in olfactory training outcomes: A prospective cohort study

INTRODUCTION

While olfactory function decreases with age, it is unknown how age affects olfactory training (OT) efficacy. This study compared OT in two cohorts of subjects: aged ≤50 (younger cohort) and aged 51+ (older cohort) with olfactory dysfunction (OD) primarily from COVID-19 infection.

METHODS

Subjects with OD primarily secondary to COVID-19 infection were prospectively recruited and enrolled into an OT registry. Baseline data were collected and they were provided with a training kit and asked to complete OT at home twice daily for 6 months. Participants were asked to follow-up at 3 and 6 months during training for olfactory testing and quality-of-life surveys (Sino-Nasal Outcomes Test-22 [SNOT-22] and Questionnaire of Olfactory Disorders Negative Statements [QoD-NS]).

RESULTS

Fifty-six participants completed OT (younger cohort: n = 26, older cohort: n = 30). There were no significant differences between cohorts' Affordable Rapid Olfactory Measurement Array (AROMA), QoD-NS, or SNOT-22 scores at any time point. Both cohorts showed significant AROMA score improvement of more than 16 points from baseline to 3 months (younger cohort: p = 0.001; older cohort: p = 0.008). The younger cohort had significant improvements in QoD-NS (p = 0.008) and SNOT-22 (p = 0.042) between baseline and 3 months while the older cohort improved from 3 to 6 months (QoD-NS: p = 0.027, SNOT-22: p = 0.049).

CONCLUSION

Both cohorts demonstrated similar significant improvement in olfactory function after 3 months of OT. The timeline of subjective improvement was different between cohorts, with younger patients experiencing earlier improvement.

Evaluating olfactory function and quality of life in patients with traumatic brain injury

KEY POINTS

Patients with traumatic brain injury (TBI) had worse olfactory quality of life (QoL) and Sino-Nasal Outcome Test-22 scores compared to a normal cohort. A worse olfactory QoL correlated with concussion symptom burden. Olfactory dysfunction among TBI patients should be addressed to improve overall outcomes.

Disparities in Olfactory Dysfunction in African Americans

OBJECTIVES

To examine olfactory performance in African Americans (AA) and Whites by comparing individual scent scores on objective olfactory tests to assess potential racial-ethnic differences of scent detection.

METHODS

Cross-sectional study of healthy participants, age 18+ years, and without sinonasal inflammatory disease from June 2021 to April 2022. Included participants self-identified as AA or White. Patients were recruited from outpatient clinics at University of Kansas Medical Center, and the local community. Two smelling tests were employed: Affordable Rapid Olfactory Measurement Array (AROMA) and Sniffin' Sticks (SST-12). Sino-Nasal Outcome Test (SNOT-22) was used for self-reported olfactory function . Pearson correlation and chi-square tests were used to detect statistical significance.

RESULTS

Our sample included 102 (46 AA and 56 Whites) participants. AROMA and SST-12 scores were significantly correlated in AA (P < .01, Pearson's Rho = .642) and Whites (P < .05, Pearson's Rho = .297). Mean scores on AROMA were significantly lower for AAs: 64.2 and Whites: 75.5 (P < .01). On AROMA, AA less accurately identified the scents of Licorice, Orange, Lavender, Cinnamon, Clove, and Rosemary (P < .05). Similarly, SST-12 mean scores for AAs: 84.2 were also lower than Whites: 89.9 (P < .01). On SST-12, AA less accurately identified the scent of pineapple Based on SST-12 scoring criteria, 60.9% of AA and 30.4% of Whites were classified as hyposmic (P < .05). SNOT-22 Smell scores were equivalent for both groups.

CONCLUSION

On both tests of olfaction, AA performed worse than Whites and a greater proportion of AA were considered hyposmic compared to Whites. This is a discrepancy with self-reported olfaction, which showed no difference between Whites and AA. AA performed significantly worse than their White counterparts on several scents, with possible implications regarding cultural appropriateness of scents used in olfactory testing.

Olfactory, Auditory, and Vestibular Performance: Multisensory Impairment Is Significantly Associated With Incident Cognitive Impairment

Background

Dysfunction in the olfactory, auditory, and vestibular systems are commonly seen in aging and are associated with dementia. The impact of sensory loss(es) on cognition is not well understood. Our aim was to assess the relationships between performance on objective multisensory testing and quantify the impact of dysfunction on cognition.

Methods

Patients presenting with subjective hearing loss presenting to a tertiary care otologic/audiologic clinic were identified and underwent multisensory testing using the Affordable, Rapid Olfactory Measurement Array (AROMA), pure tone audiometric evaluations, and the Timed “Up and Go” test. Cognitive impairment (CI) was assessed via the Montreal Cognitive Assessment (MoCA) was also administered.

Key Results

180 patients were enrolled. Thirty one percentage (n = 57) screened positive for cognitive impairment. When evaluating single sensory impairments, we found that olfactory dysfunction, gait impairment, and sensorineural hearing loss were all statistically significantly (p &lt; 0.05) associated with a higher risk of cognitive impairment (ORs 3.89, 3.49, and 2.78, respectively) for CI. Multisensory impairment was significantly associated with cognitive impairment. Subjects with dysfunction in all domains were at the highest risk for cognitive impairment (OR 15.7, p &lt; 0.001) vs. those with impairment in 2 domains (OR 5.32, p &lt; 0.001).

Conclusion

Dysfunction of the olfactory, auditory, and vestibular systems is associated with a significantly increased risk of CI. The dramatically increased risk of CI with multisensory dysfunction in all three systems indicated that MSD may synergistically contribute to CI.

International consensus statement on allergy and rhinology: Olfaction

BACKGROUND

The literature regarding clinical olfaction, olfactory loss, and olfactory dysfunction has expanded rapidly over the past two decades, with an exponential rise in the past year. There is substantial variability in the quality of this literature and a need to consolidate and critically review the evidence. It is with that aim that we have gathered experts from around the world to produce this International Consensus on Allergy and Rhinology: Olfaction (ICAR:O).

METHODS

Using previously described methodology, specific topics were developed relating to olfaction. Each topic was assigned a literature review, evidence-based review, or evidence-based review with recommendations format as dictated by available evidence and scope within the ICAR:O document. Following iterative reviews of each topic, the ICAR:O document was integrated and reviewed by all authors for final consensus.

RESULTS

The ICAR:O document reviews nearly 100 separate topics within the realm of olfaction, including diagnosis, epidemiology, disease burden, diagnosis, testing, etiology, treatment, and associated pathologies.

CONCLUSION

This critical review of the existing clinical olfaction literature provides much needed insight and clarity into the evaluation, diagnosis, and treatment of patients with olfactory dysfunction, while also clearly delineating gaps in our knowledge and evidence base that we should investigate further.

Identifying Olfactory Phenotypes to Differentiate Between COVID-19 Olfactory Dysfunction and Sinonasal Inflammatory Disease

The objective of this study was to identify specific olfactory phenotypes-patterns of olfactory performance-across distinct cohorts with or without olfactory dysfunction (OD). Adult patients underwent testing via a novel olfactory testing methodology in 1 of 4 groups based on health status: sinonasal inflammatory condition (chronic rhinosinusitis or allergic rhinitis), ≥4 weeks of self-reported OD after resolved COVID-19 infection, Alzheimer's disease, and healthy control. Participants' scores for each scent were normalized on a scale of 0 to 1 relative to their worst and best scores. Agglomerative hierarchal cluster analysis was performed on normalized data for the COVID-19 and sinonasal cohorts. Resulting clusters from the penultimate merger revealed a sensitivity of 81% and specificity of 63% for the detection of patients with COVID-19. These results support that there are olfactory phenotypes that may discriminate COVID-19 OD from sinonasal inflammatory disease. These phenotypes will likely become increasingly leveraged in the workup and treatment of patients with OD.

Olfactory Phenotypes Differentiate Cognitively Unimpaired Seniors from Alzheimer's Disease and Mild Cognitive Impairment: A Combined Machine Learning and Traditional Statistical Approach

BACKGROUND

Olfactory dysfunction (OD) is an early symptom of Alzheimer's disease (AD). However, olfactory testing is not commonly performed to test OD in the setting of AD.

OBJECTIVE

This work investigates objective OD as a non-invasive biomarker for accurately classifying subjects as cognitively unimpaired (CU), mild cognitive impairment (MCI), and AD.

METHODS

Patients with MCI (n = 24) and AD (n = 24), and CU (n = 33) controls completed two objective tests of olfaction (Affordable, Rapid, Olfactory Measurement Array -AROMA; Sniffin' Sticks Screening 12 Test -SST12). Demographic and subjective sinonasal and olfaction symptom information was also obtained. Analyses utilized traditional statistics and machine learning to determine olfactory variables, and combinations of variables, of importance for differentiating normal and disease states.

RESULTS

Inability to correctly identify a scent after detection was a hallmark of MCI/AD. AROMA was superior to SST12 for differentiating MCI from AD. Performance on the clove scent was significantly different between all three groups. AROMA regression modeling yielded six scents with AUC of the ROC of 0.890 (p < 0.001). Random forest model machine learning algorithms considering AROMA olfactory data successfully predicted MCI versus AD disease state. Considering only AROMA data, machine learning algorithms were 87.5%accurate (95%CI 0.4735, 0.9968). Sensitivity and specificity were 100%and 75%, respectively with ROC of 0.875. When considering AROMA and subject demographic and subjective data, the AUC of the ROC increased to 0.9375.

CONCLUSION

OD differentiates CUs from those with MCI and AD and can accurately predict MCI versus AD. Leveraging OD data may meaningfully guide management and research decisions.

Diagnosis of Anosmia and Hyposmia: A Systematic Review

Background

Anosmia and hyposmia have many etiologies, including trauma, chronic sinusitis, neoplasms, and respiratory viral infections such as rhinovirus and SARS-CoV-2. We aimed to systematically review the literature on the diagnostic evaluation of anosmia/hyposmia.

Methods

PubMed, EMBASE, and Cochrane databases were searched for articles published since January 1990 using terms combined with Medical Subject Headings (MeSH). We included articles evaluating diagnostic modalities for anosmia, written in the English language, used original data, and had two or more patients.

Results

A total of 2065 unique titles were returned upon the initial search. Of these, 226 abstracts were examined, yielding 27 full-text articles meeting inclusion criteria (Level of evidence ranging from 1 to 4; most level 2). The studies included a total of 13,577 patients. The most utilized diagnostic tools were orthonasal smell tests (such as the Sniffin’ Sticks and the UPSIT, along with validated abridged smell tests). Though various imaging modalities (including MRI and CT) were frequently mentioned in the workup of olfactory dysfunction, routine imaging was not used to primarily diagnose smell loss.

Conclusion

The literature includes several studies on validity and reliability for various smell tests in diagnosing anosmia. Along with a thorough history and physical, validated orthonasal smell tests should be part of the workup of the patient with suspected olfactory dysfunction. The most widely studied modality was MRI, but criteria for the timing and sequence of imaging modalities was heterogenous.

Essential Oil Olfactory Test: Comparison of Affordable Rapid Olfaction Measurement Array (AROMA) to Sniffin' Sticks 12

Objectives

To further demonstrate the validity of Affordable Rapid Olfaction Measurement Array (AROMA), an essential oil−based smell test, and compare it to the Sniffin’ Sticks 12 Test (SST12).

Study Design

Prospective cross-sectional study.

Setting

Academic medical center.

Methods

Fifty healthy individuals without sinonasal disease were recruited to the study. AROMA has been previously validated against the University of Pennsylvania Smell Identification Test. The current study tests 2 additional higher concentrations to increase the ability to detect olfactory reserve. Healthy participants completed AROMA, SST12, Sino-Nasal Outcome Test (SNOT-22), and Questionnaire of Olfactory Disorders (QoD). Spearman correlations were used to evaluate AROMA, SST, SNOT-22, and QoD.

Results

AROMA demonstrated strong test-retest reliability (r = 0.757, P < .01). AROMA showed a moderate correlation to SST12 (ρ = 0.412, P < .01). Age and SNOT-22 were significantly correlated (P < .05) with AROMA (ρ = −0.547, −0.331, respectively), and age was weakly correlated with SST (ρ = −0.377, P < .01). Median percent correct scores were as follows: SST12 identification, 92%; AROMA detection, 90%; and AROMA identification, 81%. Median correct odor identification of AROMA concentrations at 1×, 2×, 4×, and 8× were 64%, 75%, 92%, and 92%, respectively.

Conclusion

AROMA has a moderate correlation with SST12. AROMA is more strongly correlated than SST12 to age and SNOT-22. AROMA’s stronger correlation with subjective olfactory status, low cost, and adaptability may help remove barriers to routine olfactory testing in the clinic.