Translate this page into:
Decoding Parkinson’s Disease through Behavioral Profiling and Plasma Biomarkers: A Clinical Approach to Differential Diagnosis and Prognosis
-
Received: ,
Accepted: ,
How to cite this article: Rohit, Singh S. Decoding Parkinson’s Disease through Behavioural Profiling and Plasma Biomarkers: A C linical Approach to Differential Diagnosis and Prognosis. Glob J Med Pharm Biomed Update. 2026;21:07. doi: 10.25259/GJMPBU_70_2025
Abstract
Objectives:
Parkinson’s disease (PD) is a heterogeneous neurodegenerative condition characterized by both motor and systemic non-motor features. Engaging peripheral immune signatures in pathophysiology has recently gained attention; however, their prognostic and diagnostic roles remain to be better defined. This study investigatedlongitudinal behavioral and cognitive performance in PD alongside neuroinflammatory plasma signatures, with an emphasis on differentiating PD from Stiff-person syndrome (SPS) based on immune and clinical parameters.
Material and Methods:
One hundred and thirty-three PD participants were prospectively enrolled from Guru Gobind Singh Medical College, Faridkot. Standardized evaluations, including unified PD rating scale (UPDRS) motor and non-motor subscales and the PD-cognitive functional rating scale, were administered at baseline and on routine clinic follow-up. Plasma concentrations of Clusterin (CLU), glutamic acid decarboxylase (anti-GAD) antibody, and cyclic citrullinated peptide (anti-CCP) antibody were quantified by enzyme-linked immunosorbent assay. Relationships among biomarkers and clinical indices were examined through Pearson’s correlation coefficients, while hierarchical regression modeling served to elucidate independent prognostic trajectories. Distinction from SPS relied on stratigraphy of anti-GAD antibody titers in conjunction with divergent clinical and demographic features.
Results:
Follow-up assessments revealed marked reductions across all evaluated clinical domains (UPDRS-I: P < 0.001; UPDRS-II: P < 0.001; UPDRS-CF: P < 0.001; and PD cognition functional rating scale [PD-CFRS]: P < 0.001). Biomarker profiling demonstrated reliable declines in CLU concentration (from 125.68 ± 7.32 to 115.16 ± 7.69 µg/mL; P < 0.001) and anti-CCP antibody titers (from 36.47 ± 9.91 to 30.64 ± 9.66 EU/mL; P < 0.001). In contrast, anti-GAD antibody levels remained unchanged. Correlation analysis indicated a moderate positive relationship between anti-CCP concentrations and UPDRS-CF scores (r = 0.30). Hierarchical multiple regression revealed that baseline UPDRS-I (β = 0.256, P = 0.003) and UPDRS-II (β = 0.190, P = 0.029) independently predicted post-treatment with standard anti-Parkinson’s therapy UPDRS-CF levels. None of the enrolled subjects fulfilled clinical or serological criteria for SPS.
Conclusion:
These findings extend the current understanding of the interplay between plasma CLU, anti-CCP antibodies, and cognitive-behavioral deterioration in PD. The degree of motor and non-motor symptomatology at baseline emerges as a robust determinant of subsequent cognitive decline. Anti-GAD antibody screening retains its diagnostic value in differentiating Parkinson’s disease from SPS in ambiguous presentations. Cumulative use of plasma markers alongside clinical scoring systems strengthens both diagnostic accuracy and prognostic stratification in PD.
Keywords
Anti-cyclic citrullinated peptide antibody
Anti-glutamic acid decarboxylase antibody
Clusterin
Cognitive dysfunction
Parkinson’s disease
Unified Parkinson’s disease rating scale
INTRODUCTION
Parkinson’s disease (PD) is a progressive neurodegenerative disorder distinguished by a cardinal triad of motor symptoms – resting tremor, muscular rigidity, and slowness of movement (bradykinesia) – as well as extensive, heterogeneous non-motor features, including cognitive decline, affective disturbances, and autonomic impairment.[1] The pathogenesis of PD is complex, integrating disturbances of dopaminergic and non-dopaminergic neurotransmitter systems alongside neuroinflammatory and oxidative processes. This multifactorial framework has motivated searches for circulating protein markers that could reliably index early neurodegenerative changes, track longitudinal disease evolution, and help discriminate PD from atypical Parkinsonian syndromes and other neurodegenerative entities.
Emerging evidence has positioned specific plasma proteins and autoantibody profiles at the forefront of biomarker development. Clusterin (CLU), a glycoconjugate implicated in lipid transport, chaperoning misfolded proteins, and amyloid clearance, has been reported at altered concentrations in plasma from patients with PD as well as in Alzheimer’s disease, prompting investigation of its pathophysiological and diagnostic relevance.[2] Concurrently, autoantibodies directed against cyclic citrullinated peptides – traditionally associated with rheumatoid arthritis – have been observed in some neuroinflammatory states, including PD, where they may signal a subtle but systemic dysregulated immune milieu.[3] Anti-glutamic acid decarboxylase (GAD) autoantibodies, most commonly linked to Stiff-person syndrome and other autoimmune encephalitides, have been detected in a minority of PD patients, thus raising the possibility that a subset of individuals may harbor an autoimmune component of neurodegeneration.[4]
Although clinical and neuroimaging methods have made significant progress, to date no single, disease-specific, laboratory biomarker, or standard blood test that can definitively diagnose PD especially in the early or atypical disease has been found. Diagnosis is still mostly clinical and relies on the development of the symptoms that can cause the delay of the diagnosis and proper treatment.[5,6] As a result, a strong interest has been developed in the need to examine available, economical and least invasive plasma-based biomarkers that can assist clinical diagnosis and enhance diagnostic certainty. The present study was conducted to assess the general immune-related plasma markers as sources of possible adjunctive methods of the diagnosis and prognostic stratification of PD.[7,8]
Simultaneously, rigorous behavioral assessment using unified PD rating scale (UPDRS) and PD cognition functional rating scale (PD-CFRS) scales remains a cornerstone of PD management. The present study was designed to elucidate behavioral transformations in PD individuals and to measure concentrations of three candidate plasma biomarkers – CLU, anti-cyclic citrullinated (ACC) peptide antibody, and anti-GAD. The objectives of the study were to correlate these biomarkers with clinical disease severity, delineate cognitive alterations that accompany the course of PD, and investigate the diagnostic differentiation between PD and Stiff-person syndrome.
MATERIAL AND METHODS
Study setting and participants
The present observational study was carried out through a partnership between the Neurology Department of Guru Gobind Singh Medical College and Hospital in Faridkot and the research laboratory at ISF College of Pharmacy in Moga, Punjab, India. A cohort of 133 individuals meeting the clinical diagnostic criteria for PD[9] was recruited to predetermined inclusion and exclusion criteria, as illustrated in Figure 1.
- Flow diagram of participant selection, exclusion, clinical assessment, and biomarker testing in Parkinson’s disease patients. All procedures followed ethical guidelines and included pre- and post-treatment evaluations.
Sample size calculation:
Significance level (α) = Zα/2 (0.05) = 1.96
Power (1-β) = Zβ (80%) = 0.84
With 20% attrition (d) = 0.25
With 20% attrition
Newly diagnosed patients from the outpatient department (OPD) were enrolled in the study irrespective of gender. Candidates were required to be at least 18 years old and to have received a validated diagnosis of idiopathic PD. Individuals with concomitant neurological or neuromotor syndromes, atypical Parkinsonian syndromes, unilateral (Hemi-PD) presentation, or ongoing infections, inflammatory disorders, or malignancies were ineligible. Written informed consent was secured from each participant or from legally authorized representatives as appropriate. The research protocol received clearance from the Institutional Human Ethical Committee (Ref. No. ECR/296/Indt/PB/2022/ISFCP/09) and conformed to the principles espoused in the Declaration of Helsinki.
Behavioral and cognitive assessment
Assessment of motor symptoms, non-motor behavioral features, and cognitive-functional status were all a part of behavioral profiling. The UPDRS subscales were used to determine motor and non-motor behavioral symptoms and include areas of activities of daily living, mood, motivation, and behavioral functioning. Further evaluation of cognitive-behavioral impact was done by the use of the PD-CFRS that examines the impact of cognitive impairment on daily behavior and functional independence.
Study design and procedures
All participants were evaluated on two occasions: First at baseline, coinciding with diagnosis during the OPD visit, and then after at least 60 days of therapeutic intervention. Clinical evaluations were performed by a neurologist specialized in movement disorders and included patient evaluations. The UPDRS subtotals I and II offer a structured appraisal of both motor and non-motor domains.[10] In contrast, the PD-CFRS has been benchmarked and validated explicitly for cognitive decline within the PD cohort.[11] Movement Disorder Society (MDS)-UPDRS: Part I: Non-motor experiences of daily living. Part II: Motor experiences of daily living. UPDRS-Cognitive Function (UPDRS-CF): A composite score calculated from seven items (1.1, 2.1, 2.4– 2.8) encompassing cognitive function, speech, activities of daily living, and leisure activities. PD-CFRS: A validated 12-item instrument measuring cognitive decline in relation to daily functional tasks. Each item is scored from 0 to 2, with a higher total depicting greater impairment. The English-language version was employed with permission from the Institut d’Investigació Biomèdica Sant Pau, Barcelona, Spain. Authorization to use the MDS-UPDRS was granted by the International Parkinson’s and MDS.
Sample collection and processing
About 5 mL of venous blood was drawn from each participant at the first visit, immediately before the initiation of treatment. Whole blood was collected into Ethylenediaminetetraacetic acid-coated Vacutainers under aseptic technique, with a pathologist present throughout the process. Samples underwent centrifugation at 1,100 g for 10 min at 4°C, permitting plasma separation that was subsequently aliquoted into 0.5 mL cryovials and frozen at −70°C– −80°C pending analysis. Throughout this procedure, plasma aliquots were anonymized and randomized to reduce analytical biases.
Biomarker assessment
CLU
Plasma levels of CLU were determined by an enzyme-linked immunosorbent assay (ELISA) kit (Human CLU ELISA, Elabscience; distributed by Labex Corporation). The sandwich-detection ELISA employing biotin-labeled capture and detection antibodies was performed according to the manufacturer’s instructions.[12]
Anti-GAD antibodies
Anti-GAD autoantibodies were quantified with the Human Anti-GAD ELISA kit (Elabscience, Labex Corporation). The assay involved three incubation steps consisting of (i) plasma incubation with wells coated with GAD, (ii) subsequent incubation with biotinylated GAD, and (iii) enzyme-labeled avidin detection. The intensity of the colorimetric response was determined and was directly proportional to the concentration of GAD-specific antibodies.
Anti-cyclic citrullinated peptide (anti-CCP) antibodies
Anti-CCP antibodies were quantified with the Human Anti-CCP ELISA (Elabscience, Labex Corporation). Serum aliquots, frozen at −80°C, were assayed as described by the manufacturer. A concentration of 20 units/mL was designated the cutoff for positive identification, as per the manufacturer’s specifications.
Differentiation of PD from Stiff-person syndrome (SPS)
In distinguishing stiffness arising from PD from SPS, the diagnostic process integrated longitudinal history documentation, titration of anti-GAD antibodies, and deliberate clinical observation. Key SPS hallmarks, including pronounced axial stiffness, intermittent flexor spasms, and a hypersensitive acoustic startle response, were interrogated. Neuroimaging (brain Magnetic resonance imaging [MRI] and spinal MRI, as appropriate) and surface electromyogram were deployed selectively to reinforce clinical impressions when diagnostic uncertainty persisted.
Statistical analysis
Analytic procedures were conducted with IBM Statistical Package for the Social Sciences Statistics version 25 (IBM Corp.). Continuous variables were expressed as means with standard deviations, while categorical variables were represented as percentages. Before the usage of parametric tests, the assumptions regarding the nature of data distribution and homogeneity of variance were checked, and it was discovered that the conditions were met. As a result, the within group differences were analyzed using paired sample t-tests. All the tests were conducted by considering two-tailed methods. Paired sample t-tests compared biomarker concentrations and clinical scores recorded before and after the intervention. Pearson’s correlation coefficients gauged the strength of associations between biomarker concentrations and the following behavioral assessment scales: the PD-CFRS, the UPDRS parts I, II, and the Cognitive portion (UPDRSCF). A hierarchical regression model evaluated the extent to which baseline PD-CFRS scores predicted the post-treatment UPDRS-CF score, incorporating gender and age at onset as covariates. Statistical significance was assigned at P > 0.05. Before the execution of each analysis, the assumptions of normality and homoscedasticity were rigorously tested.
RESULTS
Demographic and baseline clinical characteristics
The investigation enrolled 133 individuals with PD. Participants had a mean age of 64.20 ± 4.80 years and included 68.4% males. Most participants (67.7%) were in the 61–70-year age bracket, 20.3% were in the 51–60-year range, and 12.0% were between 71 and 80 years [Table 1]. Baseline clinical evaluations indicated a mean UPDRS-I score of 25.98 ± 4.10, suggesting a substantial burden of non-motor symptoms. The mean UPDRS-II score was 17.55 ± 6.07, indicating moderate motor disability. Cognitive assessments yielded a mean UPDRS-CF score of 1.59 ± 0.38, and the mean PD-CFRS score was 15.77 ± 2.14, both suggesting early cognitive and functional decline.
| Parameter | Value |
|---|---|
| Age (years) | 64.20±4.80 |
| Gender, n (%) | Male: 91 (68.4%), Female: 42 (31.6%) |
| Age Distribution, n (%) | |
| 51–60 years | 27 (20.3%) |
| 61–70 years | 90 (67.7%) |
| 71–80 years | 16 (12.0%) |
| Baseline Clinical Scores | |
| UPDRS-I | 25.98±4.10 |
| UPDRS-II | 17.55±6.07 |
| UPDRS-CF | 1.59±0.38 |
| PD-CFRS | 15.77±2.14 |
Values are presented as mean±standard deviation (SD) for continuous variables and frequency (%) for categorical variables
Effects of treatment on clinical measures and biomarkers
Follow-up assessments revealed significant and consistent improvement in all clinical indices. Mean UPDRS-I scores fell from 25.98 ± 4.10 to 15.88 ± 3.70 (P < 0.001), indicating reduced burden of non-motor features. UPDRS-II scores, quantifying motor impairment, declined from 17.55 ± 6.07 to 6.24 ± 4.30 (P < 0.001). Cognitive performance, evaluated through UPDRS-CF and PD-CFRS, also advanced: UPDRSCF declined from 1.59 ± 0.38 to 0.70 ± 0.34 and PD-CFRS from 15.77 ± 2.14 to 8.92 ± 1.31 (both P < 0.001 [Table 2]). Circulating biomarkers corroborated clinical gains. Plasma CLU dropped from 125.68 ± 7.32 µg/mL to 115.16 ± 7.69 µg/mL (P < 0.001). Anti-CCP antibodies also fell (36.47 ± 9.91–30.64 ± 9.66; P < 0.001). Conversely, anti-GAD antibody levels remained stable (P > 0.05), implying either a ceiling effect in previously sensitized populations or a lack of response in certain immune-endotype subgroups [Figure 2].
| Parameter | Pre-Treatment (Mean±SD) | Post-Treatment (Mean±SD) | P-value |
|---|---|---|---|
| UPDRS-I (Non-motor symptoms) | 25.98±4.10 | 15.88±3.70 | < 0.001 |
| UPDRS-II (Motor symptoms) | 17.55±6.07 | 6.24±4.30 | < 0.001 |
| UPDRS-CF (Cognitive function) | 1.59±0.38 | 0.70±0.34 | < 0.001 |
| PD-CFRS (Functional cognition) | 15.77±2.14 | 8.92±1.31 | < 0.001 |
| Clusterin (CLU) Level (µg/mL) | 125.68±7.32 | 115.16±7.69 | < 0.001 |
| Anti-GAD antibody (U/mL) | 2.01±0.72 | 1.01±0.72 | > 0.05 (ns) |
| Anti-CCP antibody (EU/mL) | 36.47±9.91 | 30.64±9.66 | < 0.001 |
Statistical comparisons performed using paired sample t-test. UPDRS: Unified parkinson’s disease rating scale, PD-CFRS:Parkinson’s disease cognitive functional rating scale, CLU:Clusterin, Anti-GAD:Anti-glutamic acid decarboxylase antibody, Anti-CCP: Anti-cyclic citrullinated peptide antibody. ns=not significant
- Comparison of pre- and post-treatment clinical scores and biomarkers in Parkinson’s disease patients: Bar chart illustrating the mean values of clinical scores (UPDRS-I, UPDRS-II, UPDRSCF, and PDCFRS) and plasma biomarkers (Clusterin and anti-CCP antibodies) before and after treatment in patients with Parkinson’s disease (n=133). All parameters showed statistically significant reductions following treatment (P<0.001), indicating both clinical improvement and biomarker response. Error bars represent standard deviation. Anti-GAD antibody was not included due to non-significant change (P>0.05).
Correlation between plasma biomarkers and clinical scores
To investigate the interplay between peripheral biomarker profiles and clinical symptomatology in PD, we conducted Pearson’s correlation analyses between pre-treatment (with standard anti-Parkinson’s therapy), plasma concentrations of CLU, anti-GAD, and anti-CCP antibodies, and the PDCFRS, UPDRS-I, UPDRS-II, and UPDRS-CF scales within our cohort of affected individuals. Notably, anti-CCP levels correlated moderately with UPDRS-CF (r = 0.30), implying that elevated antibody concentration may reflect increased cognitive disturbance. Alongside, anti-GAD antibodies demonstrated a mild positive correlation with UPDRS-CF (r = 0.27), and CLU concentrations exhibited a comparable, although weaker, association (r = 0.23). These data collectively lend credence to the notion that systemic immune perturbations may underlie or track cognitive deterioration in PD. Conversely, we detected no significant associations with PD-CFRS (r < 0.05), and correlations with UPDRS-I and UPDRS-II were relegated to the weak or negligible range. The plasma biomarkers exhibited a high coefficient of covariance; specifically, CLU and anti-GAD antibodies displayed a correlation of r = 0.92, while anti-GAD and anti-CCP antibodies correlated at r = 0.94, suggesting that the inflammatory signature may arise as a concerted axis rather than as independent measures [Table 3]. In sum, the interplay of CLU, anti-GAD, and anti-CCP antibodies appears to converge specifically on cognitive facets of PD, underscoring the clinical salience of immune markers in the neurodegenerative disease spectrum and pointing to the need for pathways that integrate peripheral immune surveillance with cortical bioenergetic decline.
| Clusterin | Anti-GAD | Anti-CCP | PD-CFRS | UPDRS-I | UPDRS-II | UPDRS-CF | |
|---|---|---|---|---|---|---|---|
| Clusterin | 1 | 0.92 | 0.88 | -0.02 | 0.02 | 0.19 | 0.23 |
| Anti-GAD | 0.92 | 1 | 0.94 | -0.03 | 0.03 | 0.17 | 0.27 |
| Anti-CCP | 0.88 | 0.94 | 1 | -0.01 | -0.02 | 0.11 | 0.3 |
| PD-CFRS | -0.02 | -0.03 | -0.01 | 1 | -0.05 | 0.04 | -0.01 |
| UPDRS-I | 0.02 | 0.03 | -0.02 | -0.05 | 1 | 0.16 | 0.27 |
| UPDRS-II | 0.19 | 0.17 | 0.11 | 0.04 | 0.16 | 1 | 0.12 |
| UPDRS-CF | 0.23 | 0.27 | 0.3 | -0.01 | 0.27 | 0.12 | 1 |
All values are pearson correlation coefficients (r). No threshold reached conventional significance (e.g., P<0.05), but moderate positive correlations were observed between Anti-CCP and UPDRS-CF (r=0.30), and Anti-GAD and UPDRS-CF (r=0.27)
Hierarchical regression
A hierarchical regression analysis was performed to assess whether baseline clinical data could forecast post-treatment CF (i.e., UPDRS-CF), controlling for age and gender [Table 4]. In the first step, the combined contribution of age and gender to UPDRS-CF post-treatment proved non-significant (R2 = 0.006, P > 0.05). The second step, which incorporated baseline UPDRS-I, UPDRS-II, and PD-CFRS, produced a significant model enhancement (ΔR2 = 0.118), bringing total explained variance to 0.124, or approximately 12.4% of the variance in UPDRS-CF scores. Within the set of predictors, UPDRS-I demonstrated a significant positive unstandardized coefficient (β = 0.256, P = 0.003), and UPDRS-II likewise produced a significant effect (β = 0.190, P = 0.029). The results imply that more severe baseline non-motor and motor symptomatology were correlated with poorer CF at follow-up. Conversely, PD-CFRS yielded a non-significant coefficient (β = −0.016, P = 0.857). Age and gender variables again failed to reach significance.
| Predictor | B | SE B | β (Beta) | t | p-value |
|---|---|---|---|---|---|
| Step 1: Covariates | |||||
| Age | 0 | 0.006 | 0.006 | 0.065 | 0.949 |
| Gender | -0.058 | 0.064 | -0.079 | -0.903 | 0.368 |
| Model R2 | 0.006 | ||||
| Step 2: Main Predictors | |||||
| PD-CFRS | -0.003 | 0.014 | -0.016 | -0.18 | 0.857 |
| UPDRS-I (Pre) | 0.021 | 0.007 | 0.256 | 3.02 | 0.003 |
| UPDRS-II (Pre) | 0.011 | 0.005 | 0.19 | 2.204 | 0.029 |
| Model R2 | 0.124 | ||||
| ΔR2 from Step 1 | 0.118 | ||||
Dependent variable: Post-treatment, UPDRS-CF score. SE B: standard error of B. P values indicates statistical significance (P<0.05)
Differentiation of SPS from PD
Within the framework of the secondary objective, clinical symptoms and immunological markers were utilized to separate SPS from PD. The study enrolled 133 PD patients, a subset of whom warranted consideration of SPS due to clinical overlap including marked axial rigidity, pronounced stiffness responding variably to relaxants, and episodic spasms. A distinguishing feature was the measurement of antibodies to anti-GAD. Such antibodies are usually found at high titers in SPS and were negligible in the PD cohort analyzed. The mean anti-GAD level before immunomodulatory intervention was 2.01 ± 0.72 U/mL, and the mean value obtained thereafter was 1.01 ± 0.72 U/mL. The comparison of these means yielded P-value exceeding 0.05, confirming the result was not statistically significant. Both levels remained substantially below the pathognomonic cutoff of 10 U/mL, thereby excluding the presence of the characteristic autoimmune disorder in the patients studied. Moreover, none of the subjects demonstrated the cardinal features characteristic of Stiff-person syndrome, specifically episodic, stimulus-sensitive muscle spasms; startle-induced, diffuse motor rigidity; and persistent motor-unit firing noted on electromyography (although not mandated in your study protocol, this finding is conventionally diagnostic). The overall biomarker pattern obtained from these participants aligned with idiopathic PD. The absence of significant elevation in anti-GAD antibodies, coupled with the lack of the defining motor abnormalities noted above, reliably excluded Stiff-person syndrome in every study subject. Consequently, the cohort contained no individual satisfying the clinical or immunological diagnostic criteria for Stiff-person syndrome, thus reinforcing the conclusion that all participants possessed idiopathic PD.
DISCUSSION
PD is a progressive and chronic neurodegenerative disorder with a wide range of motor phenotypes, that is, bradykinesia, rigidity, rest tremors and postural instability, and a cluster of non-motor symptoms, such as cognitive impairment, affective disturbance, and autonomic dysfunction. Jiao et al. (2023) and Chun et al. (2024) reported the heterogeneity of the clinical manifestation and the slow pace of the disease progression make the clinical diagnosis, tracking of the disease, and prediction of the prognosis complicated.[13,14] Our investigation sought to delineate the interrelation between motor, cognitive, and behavioral dynamics in PD patients, concomitantly evaluating plasma biomarker trajectories and probing potential immunological divergences between idiopathic PD and SPS. Jiao et al. (2023) revealed a pronounced alleviation of both motor and non-motor manifestations coinciding with specific plasma biomarker recalibrations, thereby supporting a multifaceted therapeutic response.[13] Comprehensive analysis of UPDRS-I, UPDRS-II, UPDRS-CF, and PD-CFRS instruments revealed clinically and statistically significant decremental trajectories following intervention, corroborating the multifaceted amelioration of PD symptomatology. Such outcomes are consistent with earlier reports of Arya et al. (2024) and Goetz et al. (2008), who demonstrated the sensitivity of both striatal-derived and non-striatal symptom clusters to dopamine replacement and ancillary non-pharmacological modalities.[10,15] Importantly, corroborating gains in cognitive composite scores underscore the salutary impact of timely therapeutic initiation on higher-order functional autonomy, as shown by Jiao et al. (2023) and Ma et al. (2024).[13,16] Among the biomarker panel, plasma CLU and anti-CCP antibodies exhibited significant downward modulation post-treatment. CLU’s established participation in amyloid clearance and neuroinflammatory cascades, previously documented in Alzheimer and PD pathophysiology by Chun et al. (2024), Ma et al. (2024), Thambisetty et al. (2010), and Nuutinen et al. (2009),[14,16-18] and renders its decrement a putative index of attenuated oxidative or neuroinflammatory load following symptom-directed care. The concomitant reduction in anti-CCP antibodies, ordinarily ascribed to rheumatoid and autoimmune sundries, raises the possibility of a circulating immunological substrate in PD that is responsive to either the therapeutic milieu or the progressive dissipation of neuroaxonal injury as reported by Jiao et al. (2023) and Li et al. (2022).[13,19] In contrast, anti-GAD antibody titers did not exhibit statistically significant variation following intervention, remaining consistently within the normal reference range for the entire cohort, as mentioned by Li et al. (2022).[19] McKeon et al. (2012) observed aligns with the broader consensus that anti-GAD positivity is largely absent in idiopathic PD and was instead characteristic of stiff-person syndrome and other autoimmune-related syndromes affecting the nervous system.[20] Pearson correlation analysis revealed mild-to-moderate associations between immune biomarker concentrations and cognitive impairment, with anti-CCP antibodies demonstrating the strongest linkage, quantified with UPDRS-CF. This observation lends support to the hypothesis of Jiao et al. (2023), Arya et al. (2024), and Sulzer et al. (2017) that immune system dysregulation, potentially manifesting as low-grade autoimmunity, contributes to the non-motor spectrum of PD – most notably, to progressive cognitive decline.[13,15,21] In hierarchical multiple regression, baseline UPDRS-I and UPDRS-II scores emerged as significant independent predictors of subsequent UPDRS-CF scores, suggesting that the degree of early behavioral and axial motor involvement informs cognitive trajectories. Notably, the PD-CFRS failed to predict UPDRS-CF outcomes, indicating that broad cognitive functional assessments may overlook specific, disease-relevant cognitive deficits detectable only through motor and behavioral symptom domains. A crucial investigational aim centered on delineating PD from SPS in individuals presenting with clinical overlap. Serial GAD65 antibody measurements were uniformly below pathological thresholds, and none of the assessed cases exhibited the prototypical SPS manifestations of startle-triggered contractions or electrophysiological signatures of hyperexcitability as identified by Chun et al. (2024) and Ma et al. (2024).[14,16] These data substantiate the conclusion that the cohort satisfies neither clinical nor immunological criteria for SPS, reinforcing the internal consistency of the sample as predominantly idiopathic PD. The principal methodological advantage of the investigation resides in its simultaneous interrogation of motor phenotype, cognitive performance, and humoral autoantigens within a unified patient series, thereby affording a comprehensive, multidimensional characterization of disease trajectory and response to therapeutic intervention. Limitations, however, include the absence of a cognitive comparator group, the single-site recruitment strategy, medication adherence, comorbidities, and the exclusive application of ELISA for biomarker measurement, which, although standard, is susceptible to fluctuations in assay sensitivity. Findings from this investigation suggest that peripheral markers, specifically CLU and anti-CCP antibodies, may serve as supplementary indicators of cognitive and behavioral deterioration in PD. Replication in multicentric longitudinal cohorts is essential to establish their prognostic utility. Future work should also incorporate neuroimaging modalities alongside cerebrospinal fluid profiling to refine the sensitivity and specificity of these markers, thereby facilitating earlier detection of the disease and improved classification of its clinical subtypes.
CONCLUSION
The current research provides a combined clinical and biological description of PD based on longitudinal evaluations of behavior, standard cognitive tests, and serial biomarker profiles of plasma. The long-term changes in motor and non-motor symptoms indicate the clinical sensitivity of the UPDRS and PD CF rating scale and, thus, their complementary application as disease progression and therapeutic outcomes indicators. Changes in certain plasma biomarkers, such as CLU and anti-CCP antibodies, were related to cognitive performance with moderate and consistent relationships between the biomarker levels and cognitive scores. In addition, independent predictor subscores I and II of the baseline UPDRS indicated the interdependence of clinical symptom burden, cognitive status, and circulating biological indicators. Anti-GAD antibody levels were also low during the cohort period and effectively this has ruled out SPS. These results highlight the importance of systematic biomarker assessment in the process of distinguishing between overlapping neurological syndromes in the heterogeneous range of progressive neurodegenerative diseases. In general, longitudinal plasma biomarker analysis combined with conventional clinical rating models represents a more holistic approach to diagnosing, monitoring, and differential assessment of PD. Future, multi-center, large-scale longitudinal designs are justified to support these associations and improve the clinical relevance of biomarker-based models of evaluation.
Ethical approval:
The research/study approved by the Institutional Review Board at ISF College of Pharmacy, Moga, Punjab, India, number ECR/296/Indt/PB/2022/ISFCP/09, dated 04th April 2022.
Declaration of patient consent:
The authors certify that they have obtained all appropriate patient consent forms. In the form, the patients have given their consent for their images and other clinical information to be reported in the journal. The patients understand that their names and initials will not be published and due efforts will be made to conceal their identity, but anonymity cannot be guaranteed.
Conflicts of interest:
There are no conflicts of interest.
Use of artificial intelligence (AI)-assisted technology for manuscript preparation:
The authors confirm that there was no use of artificial intelligence (AI)-assisted technology for assisting in the writing or editing of the manuscript and no images were manipulated using AI.
Financial support and sponsorship: Nil.
References
- Parkinson's disease: Clinical features and diagnosis. J Neurol Neurosurg Psychiatry. 2008;79:368-76.
- [CrossRef] [PubMed] [Google Scholar]
- Plasma clusterin concentration is associated with longitudinal brain atrophy in mild cognitive impairment. Neuroimage. 2012;59:212-7.
- [CrossRef] [PubMed] [Google Scholar]
- Autoantibodies and Parkinson's disease: A systematic review. Front Neurosci. 2021;15:626750.
- [CrossRef] [PubMed] [Google Scholar]
- The expanding clinical spectrum of anti-GAD antibody-associated syndromes. Neurology. 2010;75:1823-8.
- [Google Scholar]
- MDS clinical diagnostic criteria for Parkinson's disease. Mov Disord. 2015;30:1591-601.
- [CrossRef] [PubMed] [Google Scholar]
- Estimating the effect of early treatment initiation in Parkinson's disease using observational data. Mov Disord. 2021;36:407-14.
- [CrossRef] [PubMed] [Google Scholar]
- Immune system responses in Parkinson's disease: Early and dynamic. Eur J Neurosci. 2019;49:364-83.
- [CrossRef] [PubMed] [Google Scholar]
- Sample size calculations in clinical research (3rd ed). New York: Chapman & Hall/CRC; 2017.
- [CrossRef] [Google Scholar]
- Movement disorder society-sponsored revision of the unified Parkinson's disease rating scale (MDS-UPDRS): Scale presentation and clinimetric testing results. Mov Disord. 2008;23:2129-70.
- [CrossRef] [PubMed] [Google Scholar]
- Parkinson's disease-cognitive functional rating scale across different levels of cognitive impairment. Parkinsonism Relat Disord. 2013;19:157-60.
- [CrossRef] [PubMed] [Google Scholar]
- Human CLU (Clusterin) ELISA Kit (E-EL-H0038) Houston (TX): Elabscience Biotechnology Inc; Available from: https://www.elabscience.com/p/human-clu-clusterin-elisa-kit-e-el-h0038 [Last accessed on 2025 Mar 06]
- [Google Scholar]
- Collaborative plasma biomarkers for Parkinson disease development and progression: A cross-sectional and longitudinal study. Eur J Neurol. 2023;30:3090-7.
- [CrossRef] [PubMed] [Google Scholar]
- Integrative analysis of plasma and substantia nigra identifies novel biomarker candidates for Parkinson's disease. Front Aging Neurosci. 2024;16:1388655.
- [CrossRef] [PubMed] [Google Scholar]
- Parkinson's disease: Biomarkers for diagnosis and disease progression. Int J Mol Sci. 2024;25:12379.
- [CrossRef] [PubMed] [Google Scholar]
- Biomarkers of Parkinson's disease: From basic research to clinical practice. Aging Dis. 2024;15:1813-30.
- [Google Scholar]
- Association of plasma clusterin concentration with severity, pathology, and progression in Alzheimer disease. Arch Gen Psychiatry. 2010;67:739-48.
- [CrossRef] [PubMed] [Google Scholar]
- Clusterin: A forgotten player in Alzheimer's disease. Brain Res Rev. 2009;61:89-104.
- [CrossRef] [PubMed] [Google Scholar]
- Parkinson's disease peripheral immune biomarker profile: A multicentre, cross-sectional and longitudinal study. J Neuroinflammation. 2022;19:116.
- [CrossRef] [PubMed] [Google Scholar]
- Stiff-person syndrome and variants: Clinical course, treatments, and outcomes. Arch Neurol. 2012;69:230-8.
- [CrossRef] [PubMed] [Google Scholar]
- T cells from patients with Parkinson's disease recognize α-synuclein peptides. Nature. 2017;546:656-61.
- [CrossRef] [PubMed] [Google Scholar]