Abstract
Tracking non-motor symptoms and neuropsychological function is critical for assessing neurodegenerative progression. We characterized differences among individuals with Gaucher disease, GBA1 variant carriers and GBA1-associated Parkinson disease to identify candidate markers of Parkinson disease-related change in Gaucher disease or GBA1 variant carriers. We analysed data from 161 adults evaluated at the National Institutes of Health between 1983 and 2024, including 79 with Gaucher disease, 52 GBA1 variant carriers and 30 with GBA1-associated Parkinson disease. Assessments included questionnaires for non-motor symptoms, depression, anxiety, fatigue, sleepiness, olfaction and intelligence quotient measures. Eighty-seven participants contributed longitudinal data from at least two visits. Linear mixed-effects models, adjusted for age, sex and education, examined baseline differences and longitudinal trajectories. At baseline, the GBA1-associated Parkinson disease group showed lower olfactory performance (compared with both groups, P < 0.0001), greater daytime sleepiness (compared with GBA1 variant carriers, P = 0.003; compared with Gaucher disease, P = 0.0002) and higher non-motor symptom burden (compared with both groups, P < 0.0001). Only olfaction declined longitudinally, most steeply in GBA1-associated Parkinson disease (P < 0.0001; rate: 0.65 points per year, P = 0.032). In this longitudinal cohort, olfactory decline was the most robust marker differentiating GBA1-associated Parkinson disease from Gaucher disease and GBA1 variant carriers. Intelligence quotient and non-motor symptom measures did not demonstrate a comparable decline, reflecting their limited sensitivity to longitudinal change, and reinforcing the need for objective, biologically based measures. Among participants without Parkinson disease at study onset, test scores remained stable over follow-up and phenoconversion was rare, underscoring the very low observed Parkinson disease penetrance among individuals with biallelic or heterozygous GBA1 variants.
Keywords: GBA1, glucocerebrosidase, neurodegeneration, olfaction, Parkinson disease
Serlin et al. report that in a natural history cohort spanning 1983–2024, smell identification declined most rapidly in participants with GBA1-associated Parkinson disease compared with GBA1 carriers and Gaucher disease. Self-reported non-motor symptoms and neuropsychological trajectories remained stable, supporting olfaction as the most informative longitudinal measure in this cohort.
Graphical Abstract
Graphical Abstract.
Introduction
Gaucher disease (GD), a lysosomal storage disorder caused by biallelic pathogenic variants in GBA1, the gene encoding the lysosomal enzyme glucocerebrosidase, has classically been divided according to nervous system involvement into type 1, type 2 and type 3 disease. However, the boundaries between non-neuronopathic GD1 and chronic neuronopathic GD3 are increasingly recognized as imprecise, with substantial clinical and genotypic overlap across the spectrum.1 A subset of individuals with non-neuronopathic Gaucher disease were noted to develop Lewy body disorders.2 Subsequently, a large multicentre study established an association between GBA1 variants and Parkinson disease (PD), with an odds ratio of 5.43 for any GBA1 mutation, establishing GBA1 as the most common genetic risk factor for Parkinson disease.3 Yet penetrance is incomplete and age dependent, and most GBA1 carriers (GC) and individuals with Gaucher disease, will never develop Parkinson disease, a central issue for counselling and for enrichment of at-risk cohorts in future interventional studies.4,5 In some ancestrally enriched cohorts, Parkinson disease risk appears broadly similar in GC and Gaucher disease, suggesting that zygosity alone may not determine phenoconversion.6 Variant-level heterogeneity further complicates risk estimation and has motivated classification frameworks based on Parkinson disease-associated risk.7
Identifying clinically scalable markers of early neurodegenerative change in GC and Gaucher disease therefore remains a major unmet need. In established GBA1-associated Parkinson disease (GBA-PD), non-motor features are often prominent,8 but evidence for prodromal manifestations before Parkinson disease onset remains mixed. Some longitudinal studies have reported worsening of prodromal motor and non-motor features in GC and Gaucher disease over time,9 whereas other cohorts did not demonstrate increased prodromal Parkinson disease features among GBA1 variant carriers without Parkinson disease relative to GBA1-negative controls.10 One longitudinal work suggested a trajectory of cognitive decline in GC without Parkinson disease.11 Olfaction, often diminished in Parkinson disease, is widely incorporated into prodromal Parkinson disease research frameworks and can be quantified using the validated University of Pennsylvania Smell Identification Test (UPSIT).12
The present study examined whether non-motor, neuropsychological and olfactory measures distinguish GC, Gaucher disease and GBA-PD, and whether these measures show longitudinal changes that could inform candidate markers of Parkinson disease-related change in at-risk Gaucher disease and GC individuals.
Materials and methods
Study participants
A total of 166 participants with GD1 and GC, with and without Parkinson disease, were enrolled at the National Institutes of Health (NIH) between 1983 and 2024 as part of an ongoing natural history study (active protocol: NCT00001215). Participants were recruited through physician referral, self-referral or the NIH Parkinson Disease Clinic. The parent protocol included individuals across the GBA1 spectrum. Participants younger than 18 years or with missing genotype data were excluded. The remaining 161 eligible adult participants comprised the study cohort. Overall, 108 participants had at least one enrolled blood relative (GC 51/52; Gaucher disease 39/79; GBA-PD 18/30). Eighty-seven participants had at least two evaluations and contributed longitudinal data, whereas 74 participants had only a single available evaluation.
Design
At each visit, participants underwent physical and neurological examinations and a battery of neuropsychological tests. To further evaluate non-motor symptoms (NMS), participants completed the following self-report questionnaires: the Non-Motor Symptoms Questionnaire (NMSQ), a 30-item screening tool covering 10 subdomains (e.g. sleep and fatigue, gastrointestinal, urinary and cardiac function) without a specified abnormal cut-off; the Beck Depression Inventory (BDI), with cut-off scores >9 indicating mild depression and >30 indicating severe depression; the Fatigue Severity Scale (FSS), with a cut-off score >36 for fatigue; and the Epworth Sleepiness Scale (ESS), with a cut-off score >10 for daytime sleepiness. Olfactory function was assessed using the UPSIT, a 40-item scratch-and-sniff test, with a cut-off score <35 for hyposmia. Neuropsychological function was evaluated using the Wechsler Adult Intelligence Scale (WAIS), which provided scores for the Full-Scale Intelligence Quotient (FSIQ) and its components, the Performance Intelligence Quotient (PIQ) and Verbal Intelligence Quotient (VIQ). Montreal Cognitive Assessment (MoCA) data were collected only in recent study years, resulting in limited longitudinal follow-up and precluding analysis of MoCA trajectories. For each measure, baseline was defined as the first visit at which a non-missing value for that measure was available for a given participant. Time was defined as years since the first available assessment for that specific measure for each participant, allowing each model to use all available observations even when follow-up intervals differed across measures. Educational attainment was recorded as the highest degree achieved during follow-up and coded as an ordinal variable. Secondary analyses included stratification by GBA1 genotype-associated Parkinson disease risk or by family history of Parkinson disease within each diagnostic group.
Genotyping
Genotyping was performed using long template PCR amplification of the entire glucocerebrosidase gene, and Sanger sequencing was done as previously described.13 GBA1 genotype-associated Parkinson disease risk (mild versus non-mild) within each subgroup was determined according to consensus-based classifications.7,14 The distribution of GBA1 variants across study groups is summarized in Supplementary Table 1.
Data collection
The study was approved by the NIH institutional review boards. Patients provided written informed consent, as required. Data were collected and managed using REDCap electronic data capture tools.15
Statistical analysis
Analyses were performed in SPSS 29.0. Categorical variables were compared using Pearson χ2-tests. Normality of continuous variables was assessed using the Shapiro–Wilk test. Normally distributed variables were compared using one-way ANOVA followed by Tukey’s HSD. Because symptom and cognitive measures were non-normally distributed at baseline, baseline group comparisons were performed using Kruskal–Wallis tests using one baseline observation per participant per measure. Dunn’s post hoc tests with Bonferroni correction were applied as appropriate. Longitudinal trajectory analyses were performed using linear mixed-effects models with the measure-specific outcome as the dependent variable and fixed effects for time (years since the first available assessment for that measure), group, and a time-by-group interaction term. A separate linear mixed-effects model was fit for each outcome measure (UPSIT, ESS, NMS, FSS, BDI, STAI, FSIQ, PIQ and VIQ). Models were estimated using restricted maximum likelihood with Satterthwaite degrees of freedom. Age at baseline for the measure, sex and level of education were included as covariates when appropriate. An intercept term was modelled as a random effect for each participant. An autoregressive (AR[1]) covariance structure was used to model within-participant correlation, reflecting the expectation that observations closer in time are more strongly correlated. Participant-specific random slopes for time were explored in sensitivity models, but failed to converge, and were therefore not retained. The same final modelling framework was retained for secondary analyses within the GC, Gaucher disease and GBA-PD groups, including a family history-by-time or genotype-by-time interaction term with adjustment for the same covariates as in the primary analyses.
Results
Cohort characteristics
Of the 161 participants included, 52 were identified as GC, carrying one pathogenic GBA1 variant, and 79 as Gaucher disease, confirmed by demonstrating homozygous or compound heterozygous GBA1 variants. Thirty participants were classified as GBA-PD, including 11 GC-PD and 19 GD-PD. Age at the initial visit, sex distribution, mean number of study visits and educational attainment did not differ across groups (Table 1). Disease-timing characteristics are summarized in Supplementary Table 2. The age at Gaucher disease diagnosis and Gaucher disease diagnosis duration at baseline did not differ between Gaucher disease and GD-PD, and the age at Parkinson disease symptom onset did not differ between GD-PD and GC-PD.
Table 1.
Baseline characteristics of study participants
| Demographics | GC (n = 52) |
GD (n = 79) |
GBA-PD (n = 30) |
P-value |
|---|---|---|---|---|
| Age, initial visit, years (mean ± SD) | 54.0 ± 15.1 | 50.3 ± 15.6 | 56.2 ± 10.9 | 0.125 |
| Female sex, N (%) | 29 (58.0%) | 47 (58.8%) | 11 (34.4%) | 0.097 |
| N of visits, mean (range) | 2.0 (1–6) | 2.5 (1–8) | 2.6 (1–7) | 0.146 |
| Follow-up duration, years (mean ± SD) | 6.3 ± 3.1 | 7.7 ± 5.8 | 6.7 ± 8.6 | 0.671 |
| Bachelor’s degree or higher, N (%) | 26 (50.0%) | 48 (60.8%) | 17 (56.7%) | 0.204 |
Sample sizes for baseline and longitudinal outcome analyses varied by outcome because not all participants completed all assessments at all visits. Follow-up duration was calculated for participants with ≥2 visits as the interval between first and last study visit. Education reflects the highest degree attained and is summarized as bachelor’s degree or higher versus less than a bachelor’s degree. Percentages for education are calculated using total group N. Education data were missing for GC (N = 15), GD (N = 20) and GBA-PD (N = 11). P-values reflect comparisons between groups using one-way ANOVA followed by Tukey’s HSD or χ².
GC, Gaucher carrier; GD, Gaucher disease; GBA-PD, GBA1-associated Parkinson disease.
Symptom and neuropsychological trajectories
Baseline differences and longitudinal trajectories were evaluated using linear mixed-effects models adjusting for relevant covariates. Full group-wise statistics and comparisons are detailed in Supplementary Table 3A and B. At baseline, the GBA-PD group demonstrated significantly lower UPSIT performance (versus both GC and Gaucher disease, adj. P < 0.0001), higher reported sleepiness (ESS: versus GC, adj. P = 0.003; versus Gaucher disease, adj. P = 0.0002), and non-motor symptoms (NMS: versus both groups, adj. P < 0.0001, Supplementary Table 3A). No other baseline symptom or neuropsychological measures differed across groups in adjusted analyses (all P ≥ 0.05). Longitudinally, UPSIT showed a significant overall decline over time (F(1, 117.5) = 20.07, P < 0.0001) and a significant group-by-time interaction (F(2, 118.8) = 3.55, P = 0.032), indicating that the rate of olfactory decline was greatest in the GBA-PD group. Model estimates showed that the GBA-PD group declined by 0.65 UPSIT points per year, compared with 0.31 in GC and 0.13 in Gaucher disease, confirming the steepest decline in GBA-PD (Fig. 1, Supplementary Table 3B). All other measures remained stable, and no other group-by-time interactions were detected, demonstrating comparable clinical trajectories across groups regardless of Parkinson disease status (Supplementary Table 3B).
Figure 1.
Individual UPSIT trajectories during follow-up across study groups. Panels show longitudinal olfactory scores for participants with GC (A), Gaucher disease (B), and GBA-PD (C). Each coloured line represents an individual participant trajectory plotted as UPSIT score versus time since first visit (years). Points without connecting lines indicate participants with a single measurement. Lower UPSIT scores indicate worse olfactory performance. Longitudinal trajectories were evaluated using linear mixed-effects models. The model showed a significant overall time effect for UPSIT, with decline over time, F(1, 117.5) = 20.07, P < 0.0001, and a significant group-by-time interaction, F(2, 118.8) = 3.55, P = 0.032, indicating that olfactory decline was greatest in the GBA-PD group. Dashed horizontal lines denote olfactory impairment thresholds, with UPSIT <35 indicating mild microsmia and UPSIT <19 indicating total anosmia. GC, GBA1 variant carrier; GD, Gaucher disease; GBA-PD, GBA1-associated Parkinson disease; UPSIT, University of Pennsylvania Smell Identification Test.
Genotype-associated severity risk for Parkinson disease
The distribution of individual variants within GC, Gaucher disease and GBA-PD groups is presented in Supplementary Table 1. Within GC, genotype severity was associated with reduced baseline UPSIT in participants carrying non-mild variants (n = 22; 40 observations) compared with mild variant carriers (n = 26; 43 observations; P = 0.015). Longitudinally, UPSIT declined significantly over time (F(1, 47.7) = 7.76, P = 0.008). The genotype-by-time interaction did not reach statistical significance (F(1, 45.9) = 3.77, P = 0.059), although model estimates suggested a steeper decline among non-mild carriers. This association was not observed in Gaucher disease or GBA-PD. Within GBA-PD, genotype severity was associated with increased sleepiness and depressive symptoms at baseline among participants carrying non-mild variants (ESS: non-mild, n = 10; 18 observations versus mild, n = 12; 27 observations; P = 0.033; BDI: non-mild, n = 9; 16 observations versus mild, n = 14; 31 observations; P = 0.045). No significant genotype-by-time interactions were observed for ESS or BDI, and longitudinal trajectories remained comparable between participants with mild and non-mild Parkinson disease-risk variants. All other symptom and neuropsychological measures across GC, Gaucher disease and GBA-PD showed no significant baseline differences or longitudinal genotype-associated effects.
Family history of Parkinson disease
Clinical or cognitive measures did not differ by family history status of Parkinson disease for either GC or Gaucher disease participants, except for anxiety. Gaucher disease participants with a family history of Parkinson disease (n = 27; 42 observations) showed higher baseline STAI scores (mean difference ≈ 4.8 points, P = 0.011) and a steeper increase over follow-up (F(1, 54.8) = 6.31, P = 0.015), compared with Gaucher disease participants without a family history of Parkinson disease (n = 20; 30 observations). All other measures across both groups were non-significant.
Discussion
This longitudinal cohort study evaluated the capacity of standardized neuropsychological assessments, self-report questionnaires and olfactory testing to distinguish disease status and track progression across the GBA1 spectrum. Olfactory function emerged as the primary differentiator of presumed neurodegenerative progression, consistent with prior smaller-scale work showing that UPSIT performance distinguishes clinically affected individuals and declines over time.16 While daytime sleepiness and overall non-motor burden separated GBA-PD from Gaucher disease and GC at baseline, only the UPSIT demonstrated a significant group-by-time interaction. The accelerated olfactory decline in GBA-PD of 0.65 points annually represents five times the rate observed in Gaucher disease. Thus, beyond reinforcing prior cross-sectional observations, the mixed-effects framework identified UPSIT as the most informative readout in this battery, capturing both baseline separation of GBA-PD from GC and Gaucher disease and group-specific velocity of olfactory change over time.
The absence of detectable longitudinal change in self-reported non-motor and WAIS-derived IQ measures provides a cautionary signal for future trial design. This stability should not be interpreted as excluding subtle or domain-specific cognitive decline, particularly in a cohort with high baseline cognitive performance and educational attainment. While progressive cognitive decline remains a major source of concern for patients within the GBA1 spectrum,17,18 the invariance of objective cognitive performance across groups over time suggests these instruments may lack the dynamic range required to serve as sensitive progression end-points. This stability also contrasts with the higher and worsening burden often reported in GBA-PD across non-motor subdomains.8 While GBA-PD patients reported higher baseline sleepiness and fatigue, these measures remained stable over time. This indicates that self-reported domains may capture overall symptom burden rather than the pace of neurodegeneration under typical follow-up conditions. Primary genotype analyses using standardized Parkinson disease risk were conducted separately within GC, Gaucher disease and GBA-PD to assess longitudinal trajectories within biologically comparable subgroups. Within GC, non-mild variants were associated with lower baseline olfactory performance, whereas rates of longitudinal decline appeared broadly similar between severity groups. No other symptom or neuropsychological measures showed significant baseline or longitudinal genotype-associated effects across GC, Gaucher disease or GBA-PD.
We found that a family history of Parkinson disease may selectively accelerate anxiety trajectories in Gaucher disease. This may suggest a distinct psychological vulnerability in this subgroup rather than a broad neurodegenerative signal. While Gaucher disease patients may often express heightened concern about future Parkinson disease risk,19 clinical evidence suggests the risk of developing Parkinson disease may be similar between homozygous and heterozygous GC.20 Consequently, the observed increase in STAI scores may reflect family-related psychological distress rather than objective marker of accelerated prodromal progression.
This analysis leveraged a longitudinal design and mixed-effects modelling framework that retained all available observations, adjusted for relevant covariates, and accommodated unequal numbers of observations and variable follow-up across participants. Nonetheless, smaller subgroup sizes, particularly for some neuropsychological outcomes, may have limited power to detect modest longitudinal effects, as also reflected by non-convergence of sensitivity models incorporating participant-specific random slopes for time. Another limitation is the lack of systematic adjustment for Parkinson disease therapies, which may influence self-reported measures such as fatigue and mood. In addition, participants with advanced Parkinson disease were less likely to return for in-person NIH evaluations, which may have limited capture of late-stage cognitive decline, including changes in IQ. Nevertheless, among participants who returned for longitudinal evaluation, the study did not detect significant decline on the available neuropsychological measures during follow-up. WAIS-derived IQ measures provide global estimates of cognitive performance rather than Parkinson disease-specific cognitive staging or dementia classification, and more sensitive domain-specific measures may better detect early cognitive change. Moreover, baseline IQ values above population norms may reflect selection for a highly functioning referral cohort. UPSIT performance may also have been influenced by pandemic-related olfactory effects and cross-cultural differences within this international cohort. An important caveat is that, even in this enriched at-risk cohort, low penetrance and near absence of phenoconversion made it difficult to identify participants on a true Parkinson disease trajectory or validate predictors of conversion. As a referral-based natural history cohort, selection and ascertainment bias may have influenced the sample, including enrichment for family history of Parkinson disease, access to referral pathways and the ability to travel to the NIH. Nonetheless, these findings provide a basis for validation in larger multicentre cohorts. Biologically grounded objective markers may offer a scalable framework for prospective trial enrichment and early risk stratification across the GBA1 spectrum.
Supplementary Material
Acknowledgements
We thank Drs Edythe Wiggs, Laura Segalla and Carolyn Sherer for their expert neuropsychological assessments and gratefully acknowledge the study participants whose contributions made this work possible.
Contributor Information
Yonatan Serlin, Neurophysiology of Epilepsy Unit, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20814, USA.
Samantha Nishimura, Medical Genetics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA.
Nishka Patel, Medical Genetics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA.
Emory Ryan, Medical Genetics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA.
Nahid Tayebi, Medical Genetics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA.
Grisel Lopez, Medical Genetics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA.
Ellen Sidransky, Medical Genetics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA.
Supplementary material
Supplementary material is available at Brain Communications online.
Funding
This work was supported by the Intramural Research Programs of the National Human Genome Research Institute (ZIA HG200336-19) and the National Institutes of Health (NIH), U.S. Department of Health and Human Services. The contributions of the NIH authors are considered Works of the United States Government. The findings and conclusions presented in this paper are those of the authors and do not necessarily reflect the views of the NIH or the U.S. Department of Health and Human Services.
Competing interests
The authors report no competing interests.
Data availability
Data supporting the findings of this study are available from the corresponding author upon request, contingent upon a data transfer agreement in accordance with NIH policy. Data are not publicly available due to privacy or ethical restrictions.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
Data supporting the findings of this study are available from the corresponding author upon request, contingent upon a data transfer agreement in accordance with NIH policy. Data are not publicly available due to privacy or ethical restrictions.