Neurodegenerative diseases (NDDs) — including Alzheimer's disease (AD), Parkinson's disease (PD), Amyotrophic Lateral Sclerosis (ALS), and Multiple Sclerosis (MS) — collectively represent the leading cause of disability among non-communicable diseases and impose a catastrophic burden on patients, caregivers, and healthcare systems worldwide.[1] According to the World Health Organization, over 55 million individuals currently live with dementia globally, with the number projected to nearly triple to 152 million by 2050.[2] India, with its rapidly ageing population and epidemiological transition, is anticipated to bear a disproportionately large share of this burden, with an estimated 8.8 million people living with dementia as of 2020 — a figure expected to more than double by 2050.[3]

Despite these alarming projections, diagnostic approaches for NDDs in India remain largely clinical and syndromic, relying primarily on physician expertise, standardised rating scales, and neuroimaging. These approaches are inadequate in early disease stages, when intervention would be most beneficial, and often result in significant diagnostic delays — estimated at 3–8 years from symptom onset in many resource-limited settings.[4] Biologically grounded, quantifiable biomarkers that can objectively reflect the underlying pathophysiological state of the CNS are therefore urgently needed.

Neuroinflammation — defined as the activation of the brain's innate and adaptive immune systems in response to pathological stimuli — has emerged as a pivotal and potentially early driver of neurodegeneration across all major NDDs.[5,6] Microglia and astrocytes, the principal effectors of CNS immunity, produce a rich repertoire of inflammatory mediators — including interleukins, tumour necrosis factor, complement proteins, and damage-associated molecular patterns — that can be quantified in cerebrospinal fluid (CSF) and, increasingly, in peripheral blood using ultrasensitive immunoassay platforms such as Single-Molecule Array (Simoa) technology.[7,8] Key neuroinflammatory biomarkers include: glial fibrillary acidic protein (GFAP), a structural protein released from injured or activated astrocytes; neurofilament light chain (NfL), a cytoskeletal marker of neuroaxonal injury; pro-inflammatory cytokines including IL-1β, IL-6, and TNF-α; and YKL-40 (chitinase-3-like protein 1), a glycoprotein secreted by activated astrocytes.[9,10]

While a large international literature has characterised these biomarkers in Western NDD cohorts, population-specific data from India remain strikingly limited. This is a significant knowledge gap, given that Indian patients differ from Western populations in terms of genetic risk architecture (APOE allele frequencies, TREM2 variant prevalence), metabolic comorbidity profiles (high prevalence of type 2 diabetes mellitus), dietary patterns, environmental exposures, and gut microbiome composition — all of which may modulate neuroinflammatory responses and biomarker levels.[3,11] The present study was therefore designed to: (1) measure and compare serum levels of eight key neuroinflammatory biomarkers across AD, PD, ALS, MS, and healthy controls in an Indian tertiary care population; (2) correlate biomarker levels with validated clinical severity measures; and (3) identify disease-specific biomarker patterns that may have diagnostic and prognostic utility.

2.1 Study Design and Setting This prospective, cross-sectional, case-control study was conducted in the Department of Neurology, [Medical College and Hospital], [City], India, over a period of [X] months (from [Month Year] to [Month Year]). The study was approved by the Institutional Ethics Committee (IEC Reference No.: [IEC/XXXX/20XX]) and conducted in accordance with the Declaration of Helsinki (2013). Written informed consent was obtained from all participants or their legally authorized representatives prior to enrollment. 2.2 Participants A total of 210 participants were enrolled across five groups (42 per group): AD, PD, ALS, MS, and healthy controls. Disease diagnoses were established by a consultant neurologist using internationally validated criteria: NIA-AA 2018 Research Framework for AD; MDS Clinical Diagnostic Criteria (Postuma et al., 2015) for PD; Revised El Escorial Criteria (Brooks et al., 2000) for ALS; and McDonald 2017 criteria for MS. Healthy controls were age- and sex-matched volunteers with no neurological or psychiatric history and MMSE score ≥ 27. Sample size was calculated to detect a clinically meaningful difference in serum IL-6 (effect size δ = 9.6 pg/mL, σ = 8.5 pg/mL) with α = 0.05 and power = 80%, yielding n = 35 per group; inflated to 42 to account for 15% attrition. Key inclusion criteria: age ≥ 18 years; confirmed NDD diagnosis per the above criteria; clinically stable (no acute exacerbation within 4 weeks). Key exclusion criteria: active infection, malignancy, autoimmune disorder, immunosuppressive therapy within 3 months, severe systemic illness (eGFR < 30 mL/min/1.73m², Child-Pugh C hepatic failure), acute stroke or traumatic brain injury within 6 months, and pregnancy. 2.3 Clinical Assessment All participants underwent structured clinical assessment including: cognitive evaluation using the Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA); disease-specific severity rating using the Clinical Dementia Rating Sum of Boxes (CDR-SB) for AD, Unified Parkinson's Disease Rating Scale (UPDRS total) for PD, ALS Functional Rating Scale-Revised (ALSFRS-R) for ALS, and Expanded Disability Status Scale (EDSS) for MS; and functional status assessment using the Modified Barthel Index. 2.4 Blood Sample Collection and Processing Venous blood samples (10 mL) were collected in the morning after an overnight fast of ≥ 8 hours. Samples were processed within 2 hours: centrifuged at 2000 × g for 10 minutes at 4°C; aliquoted (300 μL per cryovial); snap-frozen in liquid nitrogen; and stored at −80°C until batch analysis. All samples were handled following the JPND Biomarker Standardisation Initiative pre-analytical guidelines to minimise variability. 2.5 Biomarker Assays Serum concentrations of IL-1β, IL-6, TNF-α, and YKL-40 were measured using commercially available, validated enzyme-linked immunosorbent assay (ELISA) kits (Quantikine, R&D Systems, Minneapolis, MN, USA) performed in duplicate. S100B was measured by electrochemiluminescence immunoassay (Elecsys S100, Roche Diagnostics, Mannheim, Germany). Serum GFAP and NfL were quantified using the Simoa HD-X platform (Quanterix Corporation, Lexington, MA, USA) with validated single-molecule array assays, enabling femtomolar-range detection. Intra-assay and inter-assay coefficients of variation (CV) were < 8% and < 12%, respectively, for all assays. Laboratory personnel were blinded to clinical diagnoses throughout the analysis. 2.6 Statistical Analysis Continuous variables are reported as median (interquartile range, IQR) or mean ± standard deviation (SD). Distribution normality was assessed by the Shapiro-Wilk test. Biomarker values below the lower limit of detection (LLOD) were assigned LLOD/2. Log10 transformation was applied to non-normally distributed biomarker data before parametric analyses. Between-group differences in biomarker levels were assessed using the Kruskal-Wallis H test with Dunn's post-hoc test (Bonferroni correction for multiple comparisons). Spearman's rank correlation coefficient (ρ) was used to examine biomarker-clinical severity associations. Multivariate binary logistic regression was performed to identify independent predictors of NDD diagnosis versus controls, adjusting for age, sex, and diabetes mellitus. ROC curve analysis determined AUC, sensitivity, and specificity at optimal Youden's Index cut-offs. All analyses were performed in SPSS v26.0 (IBM Corp.) and R v4.3; p < 0.05 (two-tailed) was considered statistically significant.

3.1 Baseline Characteristics

Baseline socio-demographic and clinical characteristics of the study population are presented in Table 1. The five groups were well-matched for age (mean age ranging from 52.3 ± 9.1 years in MS to 64.7 ± 8.4 years in AD) and sex distribution (male:female ratio 1.1:1 to 1.4:1 across groups). Disease duration ranged from a mean of 18.4 ± 11.2 months in ALS to 48.6 ± 28.3 months in MS. The prevalence of type 2 diabetes mellitus and hypertension did not significantly differ across disease groups (p = 0.21 and p = 0.34, respectively). Cognitive scores, as expected, were lowest in the AD group (mean MMSE 17.4 ± 5.2) and preserved in controls (mean MMSE 28.6 ± 1.1).

Table 1. Baseline Socio-Demographic and Clinical Characteristics of Study Participants

AD: Alzheimer's disease; PD: Parkinson's disease; ALS: Amyotrophic Lateral Sclerosis; MS: Multiple Sclerosis; MMSE: Mini-Mental State Examination; MoCA: Montreal Cognitive Assessment; BMI: Body Mass Index; —: not applicable. Values are mean ± SD or n (%) unless stated otherwise.

3.2 Serum Neuroinflammatory Biomarker Levels Across Study Groups

Serum levels of all measured neuroinflammatory biomarkers were significantly elevated in NDD groups compared to healthy controls (p < 0.001 for all comparisons after Bonferroni correction). Table 2 presents the median (IQR) biomarker concentrations across groups, with pairwise significance. Figure 1 depicts box plots illustrating the distribution of biomarker levels across groups.

Serum NfL demonstrated the most striking between-group differences. Median NfL was highest in ALS (156.4 pg/mL; IQR: 112.3–218.6), reflecting the severe and widespread neuroaxonal degeneration characteristic of this condition. NfL was also significantly elevated in MS (38.2 pg/mL), AD (24.7 pg/mL), and PD (18.3 pg/mL) compared to controls (6.8 pg/mL). Pairwise comparisons confirmed significantly higher NfL in ALS versus all other groups (p < 0.001 for all), and in MS versus AD and PD (p = 0.03 and p = 0.008, respectively).

Plasma GFAP was most markedly elevated in AD (387.6 pg/mL; IQR: 284.2–512.4), consistent with the prominent astrogliosis accompanying amyloid and tau pathology. GFAP was also significantly elevated in MS (218.4 pg/mL), ALS (186.2 pg/mL), and PD (142.8 pg/mL) versus controls (48.6 pg/mL; p < 0.001). Serum IL-6 was highest in ALS (42.3 pg/mL), followed by MS (28.4 pg/mL), AD (22.6 pg/mL), and PD (18.4 pg/mL), versus controls (4.8 pg/mL). Serum TNF-α, IL-1β, YKL-40, and S100B showed significant elevation across all NDD groups compared to controls, with a gradient broadly mirroring the NfL pattern.

Table 2. Serum Neuroinflammatory Biomarker Levels Across Study Groups — Median (IQR)

K-W: Kruskal-Wallis; η²: Eta-squared (effect size); IQR: Interquartile range. Pairwise post-hoc comparisons performed using Dunn's test with Bonferroni correction; all NDD groups significantly different from controls (p < 0.001). Bold values denote highest concentration per row.

3.3 Correlation of Serum Biomarkers with Clinical Severity Scales

Table 3 presents the Spearman correlation coefficients (ρ) between serum biomarker levels and disease-specific clinical severity scales. Plasma GFAP demonstrated the strongest correlation with CDR-SB in AD patients (ρ = 0.74, p < 0.001), followed by NfL (ρ = 0.62, p < 0.001) and IL-6 (ρ = 0.58, p < 0.001). In PD, UPDRS total score correlated most significantly with serum NfL (ρ = 0.56, p < 0.001) and GFAP (ρ = 0.48, p < 0.001). The most robust correlation observed across all disease groups was between serum NfL and ALSFRS-R in ALS patients (ρ = −0.82, p < 0.001), confirming the strong inverse relationship between neuroaxonal injury and functional status in this condition. In MS patients, EDSS showed the strongest correlations with serum NfL (ρ = 0.68, p < 0.001) and YKL-40 (ρ = 0.54, p < 0.001).

*p < 0.05; **p < 0.001 (Spearman's ρ). Negative values indicate inverse correlation (higher biomarker → lower functional score). ALSFRS-R is a functional scale where higher scores indicate better function, hence inverse correlations with disease biomarkers. CDR-SB: CDR Sum of Boxes; UPDRS: Unified PD Rating Scale; ALSFRS-R: ALS Functional Rating Scale-Revised; EDSS: Expanded Disability Status Scale.

Table 3. Spearman Correlation Coefficients (ρ) Between Serum Biomarkers and Clinical Severity Scales

3.4 Multivariate Analysis

Binary logistic regression (NDD versus healthy controls) revealed that serum NfL (OR = 4.82, 95% CI: 3.12–7.46, p < 0.001), plasma GFAP (OR = 3.64, 95% CI: 2.28–5.82, p < 0.001), and serum IL-6 (OR = 2.94, 95% CI: 1.86–4.64, p < 0.001) were independent predictors of NDD diagnosis, after adjusting for age, sex, and diabetes mellitus status. YKL-40 retained independent significance (OR = 2.18, 95% CI: 1.42–3.36, p = 0.003), while IL-1β and TNF-α did not reach independent significance after adjustment for the other biomarkers in the model (p = 0.12 and p = 0.09, respectively), likely reflecting collinearity with IL-6. The model demonstrated good discrimination (overall AUC = 0.91, 95% CI: 0.87–0.95) and calibration (Hosmer-Lemeshow p = 0.38).

3.5 Subgroup Analysis: Influence of Diabetes Mellitus

Sensitivity analysis restricted to participants without diabetes mellitus (n = 155) yielded broadly consistent results, with all biomarkers remaining significantly elevated in NDD groups (p < 0.001). Absolute biomarker concentrations were modestly but non-significantly lower in diabetic participants for IL-6 and TNF-α (p = 0.14 and p = 0.18, respectively), suggesting that the neuroinflammatory biomarker elevations observed reflect primarily CNS rather than systemic inflammatory pathology. These results further support the validity of the observed biomarker differences between NDD and control groups.

This study provides, to our knowledge, the first comprehensive characterisation of a multi-biomarker neuroinflammatory panel across four major neurodegenerative diseases in an Indian tertiary care population. The principal findings are: (1) all eight measured neuroinflammatory biomarkers are significantly elevated in NDD patients compared to healthy controls, with large effect sizes (η² = 0.42–0.74); (2) disease-specific patterns of biomarker elevation are identifiable — particularly the striking NfL elevation in ALS, GFAP predominance in AD, and YKL-40 elevation in MS; (3) strong correlations exist between biomarker levels and validated clinical severity measures, most notably NfL with ALSFRS-R in ALS (ρ = −0.82) and GFAP with CDR-SB in AD (ρ = 0.74); and (4) NfL, GFAP, and IL-6 are independent predictors of NDD status in multivariate analysis.

The magnitude of serum NfL elevation in ALS patients in our study (median: 156.4 pg/mL) is consistent with prior international reports. Benatar et al. reported plasma NfL levels of 100–200 pg/mL in symptomatic ALS patients, with higher levels predicting shorter survival.[12] The markedly lower NfL in PD versus MS in our cohort — despite similar disease duration — corroborates findings from the large BioFINDER cohort, where blood NfL distinguished typical PD from MSA and PSP with high accuracy.[13] The strong inverse correlation between NfL and ALSFRS-R (ρ = −0.82) observed in our Indian cohort mirrors the correlation coefficient of −0.76 to −0.84 reported in Western ALS cohorts,[12] suggesting that this biomarker-clinical relationship is robust across populations.

The predominant elevation of plasma GFAP in AD compared to other NDDs — a finding now supported by multiple large cohorts including BioFINDER, ADNI, and PREVENT-AD — likely reflects the unique contribution of widespread amyloid-driven astrogliosis in AD.[14] In the landmark study by Benedet et al. (JAMA Neurology, 2021), plasma GFAP was the strongest blood-based predictor of amyloid PET positivity, outperforming plasma Aβ42/40 ratios in cognitively unimpaired individuals.[14] Our finding that GFAP in AD patients significantly exceeds that in other NDD groups (median: 387.6 vs. 142.8–218.4 pg/mL) adds support to its potential as a blood-based AD biomarker, though prospective validation against amyloid and tau PET is needed in future Indian cohort studies.

The consistently elevated cytokines (IL-1β, IL-6, TNF-α) across all NDD groups align with the hypothesis that CNS immune activation is a universal feature of neurodegeneration, while the quantitative differences between groups reflect the varying intensity and character of neuroinflammatory responses driven by different pathological proteins. The particularly high IL-6 in ALS is noteworthy and has been previously reported by Moreau et al. in French ALS patients, where serum IL-6 correlated with forced vital capacity and survival.[15] The biological plausibility of this finding is supported by the known role of IL-6 in motor neuron survival and microglial activation in ALS models.

An important contextual finding of this study is the consistency of biomarker elevations after adjusting for diabetes mellitus — a potential significant confounder in Indian patients, given the high prevalence of type 2 diabetes and its associated systemic inflammation. The lack of significant influence of diabetes on inter-group biomarker differences suggests that the observed elevations predominantly reflect CNS-derived inflammation rather than systemic metabolic inflammation, though future studies with HOMA-IR and fasting insulin measurements would further strengthen this conclusion.

Several limitations of the present study warrant acknowledgment. First, the cross-sectional design precludes causal inference and limits the assessment of biomarker dynamics over time; the prospective longitudinal component of our study (to be reported separately) will address this limitation. Second, CSF biomarkers were not systematically available for all participants, preventing direct serum-CSF correlation analysis in this report. Third, genetic data including APOE genotyping were not collected in this cohort, limiting examination of gene-biomarker interactions. Fourth, the single-centre design limits immediate generalisability to India's diverse NDD population, necessitating multi-centre replication.

Notwithstanding these limitations, this study makes important contributions to the field. It provides the first India-specific biomarker data across four major NDDs with adequate sample sizes, demonstrates robust disease-specific biomarker patterns consistent with the international literature, and establishes locally relevant correlations between biomarker levels and clinically validated severity measures. These findings provide a scientific foundation for the development of blood-based neuroinflammatory biomarker panels as objective, quantifiable diagnostic and prognostic tools for NDD management in Indian clinical practice.

Serum neuroinflammatory biomarkers — particularly NfL, GFAP, IL-6, and YKL-40 — are significantly elevated in Indian patients with AD, PD, ALS, and MS compared to healthy controls, and exhibit disease-specific patterns of elevation that correlate robustly with validated clinical severity measures. NfL is the most discriminatory marker for ALS, while GFAP predominates in AD. These biomarkers are independent predictors of NDD status and collectively demonstrate strong diagnostic utility. These findings support the translational implementation of blood-based neuroinflammatory biomarker panels in Indian NDD clinical practice and research, and lay the groundwork for prospective validation and clinical trial integration.

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