Plasma Biomarkers of Neurodegeneration and Neuroinflammation among Middle- Aged Adults in Western India: Implications of Racial and Geographical Variability

Plasma Biomarkers of Neurodegeneration and Neuroinflammation among Middle- Aged Adults in Western India: Implications of Racial and Geographical Variability | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Plasma Biomarkers of Neurodegeneration and Neuroinflammation among Middle- Aged Adults in Western India: Implications of Racial and Geographical Variability Kuldip Upadhyay, Ankit Viramgami, DhirendraPratap Singh, Nikhil Kulkarni, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7788186/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Neurodegenerative disorders (NDs) are progressive conditions associated with neuronal loss, cognitive decline, and high global morbidity and mortality. Blood-based biomarkers such as amyloid-β (Aβ1–42), tau, α-synuclein, brain-derived neurotrophic factor (BDNF), and glial fibrillary acidic protein (GFAP) hold promise for early detection and monitoring. This study evaluated plasma levels of key neurodegenerative biomarkers in an apparently healthy middle-aged Indian cohort and compared them with global datasets to explore potential racial, genetic, and environmental influences. Methods A cross-sectional community-based study recruited 405 participants (40–60 years, both sexes) from Ahmedabad district, western India, following strict inclusion and exclusion criteria. Demographic and clinical parameters were recorded, and venous blood samples were collected under aseptic conditions. Biomarkers (Aβ1–42, total tau, α-synuclein, BDNF, GFAP) were quantified using high-sensitivity sandwich ELISA. Statistical analysis included t-tests, median comparisons, and age- and sex-stratified analyses. Results Median plasma concentrations were: Aβ1–42 (18.95 pg/mL), total tau (84.38 pg/mL), α-synuclein (804.51 pg/mL), BDNF (2221.98 pg/mL), and GFAP (98.33 pg/mL). Relatively older participants (aged 51–60 years) demonstrated elevated biomarker levels compared to younger counterparts. Comparison with international datasets revealed marked inter-regional variability, suggesting potential genetic, racial, and environmental influences. Conclusion The study describes the levels of plasma neurodegenerative biomarkers in a community of Indian population, further emphasizing the variations in the levels of these markers among healthy adults across the globe. These findings underscore the importance of accounting for racial and geographical differences when interpreting biomarker data and call for longitudinal studies to establish population-specific reference ranges. neurodegenerative markers plasma concentrations Indian cohort geographical variability amyloid-β BDNF Figures Figure 1 Introduction Neurodegenerative disorders (NDs) are a group of progressive conditions characterized by the gradual loss of structure and function of neurons, ultimately leading to neuronal death translated to its impaired functioning. (Dnyandev G. Gadhave et al., 2024 ; D. G. Gadhave et al., 2024 ; Lamptey et al., 2022 ; Pant et al., 2022 ; Tanaka et al., 2020 ). These disorders commonly present with cognitive decline, neuropsychiatric symptoms, and, in many cases, dementia. Prominent examples include Alzheimer’s disease (AD), Parkinson’s disease (PD), Huntington’s disease (HD), amyotrophic lateral sclerosis (ALS), multiple sclerosis (MS), frontotemporal dementia, and corticobasal degeneration (Choonara et al., 2009 ; Hui et al., 2023 ). The clinical manifestations of these disorders vary depending on the involvement of the topographical structures and the severity, making diagnosis challenging (D. G. Gadhave et al., 2024 ). Current diagnostic approaches largely rely on clinical evaluation, neuroimaging, and other supportive investigations (Huang & Zhang, 2023 ). NDs are typically irreversible, associated with significant morbidity, and have limited survival rates. Globally, major neurological disorders affected an estimated 349.2 million individuals and accounted for nearly 10 million deaths in 2019 alone (Ding et al., 2022 ; Huang et al., 2023 ). Recent advancements have identified several blood-based biomarkers that offer promise for improving the early detection and monitoring of neurodegenerative diseases. Specific assays detecting the hallmark Alzheimer’s disease pathologies, such as amyloid-β peptides and tau proteins are developed. In addition, a range of nonspecific biomarkers indicative of neuronal injury—including neurofilament light chain (NfL), α-synuclein, β-synuclein, and ubiquitin-C-terminal hydrolase-L1 (UCH-L1)—as well as markers of glial activation such as glial fibrillary acidic protein (GFAP), developed. (Alcolea et al., 2023 ; Koníčková et al., 2022 ). These biomarkers reflect key pathophysiological processes in neurodegeneration and neuroinflammation and hold substantial potential for use in screening, diagnosis, and monitoring disease progression or response to therapy. While their use is currently concentrated in research settings, rapid progress in assay development and standardization indicates their likely translation into clinical practice across a range of healthcare settings (Alcolea et al., 2023 ; Luebke et al., 2023 ). The present study explores the levels of these blood-based biomarkers among urban residents in western India, contributing to the growing body of evidence on their applicability in diverse population settings. Methods Participant selection Current study is part of larger study, that investigates the levels of neurodegeneration and neuro-inflammation markers among large group of community residents (Balachandar et al., 2025 ). Apparently healthy residents with no ongoing medications, aged between 40–60 years of either sex are included in the current study. Participants with history of current / past neurological illness or on medication suggestive of neurological condition or with family history/first degree relative with hereditary neurological conditions are excluded from the study. Additionally, participants on any form of medications including multivitamins for either short term (acute) illness / long term illness are excluded. However, participants on supplements such as multivitamins are included in the study, ensuring the multivitamin(s) are not consumed under prescription for therapeutic purposes. Recruitment of Participants The study, participants residing in Ahmedabad district of western India are recruited Total 405 participants meeting the selection criteria are recruited. Details of inclusion and exclusion is illustrated in the flow chart below. The study is initiated after obtaining necessary institutional ethical clearance, adhering to the national ethical guidelines for biomedical and health research involving human (Mathur, 2017 ). Details of the study was shared with the local community by word of mouth, community leaders / representatives and community health workers. Interested volunteering participants ae invited to participate at nearby specific locations such as primary health facility / community health care centers and make-shift arrangements at nearby residents, that enabled data collection, including aseptic collection of blood. Consenting participants meeting the inclusion and exclusion criteria are recruited. Necessary socio-clinical demographic details of the participants are recorded using pre-validated structured questionnaire. Collection of biological samples Under aseptic condition about 5 ml venous blood is drawn by trained phlebotomist ensuring safety and comfort of the participant. All blood samples tagged with participant ID are transported under cold chain to the laboratory the same day. The blood samples after returning the room temperature (20–240C) are centrifuged to separate the serum. The serum of each sample are aliquot in 2 cryo-vials and stored at -80 o C. Other assessments Blood pressure, body weight, and height of the consenting participants are measured using pre-validated and calibrated tools as described previously (Upadhyay et al., 2022 ). Body mass index was calculated by the conventional method (i.e., a ratio of weight in kilograms to the square of the height in meters - (Weight in Kg)/(height in m2). Hemoglobin and random blood sugar are estimated using Mission (Accon laboratories, California) and Accu-Chek active (Roche diabetes care India Pvt. Ltd.) point of care devices respectively. A single drop of blood drawn from the fingertip is directly collected under aseptic precautions on to the respective probes to obtain the haemoglobin (g/dL) and random blood sugar (mg/dL) as recommended by the manufacturer. All participants are screened for gross cognitive dysfunction by trained personnel, for orientation of day, time, place, purpose of their visit and mode of arrival at the study location. Further, checked for three object immediate and delayed recall, simple arithmetic calculation and recent local – regional updates regarding local community leader or recent local celebrations. Participants failing to responding to all these questions are excluded from the study and referred for further evaluation of gross cognitive dysfunction. Estimation of neurodegenerative markers The markers of neuro inflammation and neurodegenerative disorders (Aβ 1−42 , Total Tau, ⍺-Synuclein, BDNF, and GFAP) are estimated from the samples using high sensitivity sandwich ELISA method Elab Science Inc., USA, as described by the manufacturer (Elab Science Inc., USA). Wherein, the analyte protein available with the standard or sample is combined to form a sandwich complex with the target-specific antibody pre-coated on microplates and a biotinylated detection antibody. The optical density (OD) is measured by spectrophotometrically at a 450 nm wavelength. The intensity of this signal (OD) is directly proportional to the concentration of the target present in the original specimen. The levels of markers in the samples are determined by comparing the OD of the samples to the standard curve. Statistical analysis The normality of the distributions was tested using the Kolmogorov–Smirnov test. Data following a normal distribution are presented as the mean ± standard deviation (SD), while data not following a normal distribution are expressed as the median and interquartile range (IQR). Student’s t-test was applied to compare plasma biomarkers (Aβ1–42, Total Tau, α-Synuclein, BDNF, and GFAP) between age groups and gender groups. All tests were two-tailed, with a significance level set at p < 0.05. Outlier observations that fell above the upper bound limit (75th percentile + 1.5 x IQR) and below the lower bound limit (25th percentile − 1.5 x IQR) were removed from the analysis. Data that did not follow a normal distribution were logarithmically transformed prior to all analyses and transformed data were then converted back to their original scale using an antilog transformation before reporting. Statistical analyses were performed using SPSS version 26.0 (IBM Corporation, Armonk, NY, USA). Results Participants Characteristics: Following the recruitment and screening process outlined in Fig. 1 , a total of 405 participants aged 40–60 years met the inclusion and exclusion criteria. ----- Insert Fig. 1 ----- Baseline demographic and clinical characteristics of the study participants, including the distribution of age, sex, and key health parameters such as body mass index (BMI), systolic and diastolic blood pressure, hemoglobin concentration, and random blood sugar levels, are summarized in Table 1. ----- Insert table 1 ----- The mean BMI of the participants is 24.54 ± 4.87 kg/m², indicating that the average participant is in the overweight category. About 21.7% (n = 88) of the participants are identified with elevated blood pressures (i.e. either SBP ≥ 140 and DBP ≥ 90 mm Hg)(James et al., 2014 ). The average hemoglobin level among participants are suggestive of mild – moderate anemia, further, about 1.48% (n = 6) participants exhibited elevated glucose levels (≥ 200 mg/dL)(Mouri MI, 2023 ; WHO, 2011 ). Participants with elevated blood pressure, blood glucose levels and anemia are referred to the community non-communicable disease clinic for further evaluation and management. Plasma Levels of Biomarkers of Neuroinflammatory and Neurodegenerative Status: Table-2 presents the distribution of plasma biomarkers related to neurodegenerative status among study participants. The biomarkers assessed included amyloid-β (1–42), total tau, α-synuclein, brain-derived neurotrophic factor (BDNF), and glial fibrillary acidic protein (GFAP). Data are summarized as median values with interquartile ranges (IQR) for the overall study group, stratified by gender and age groups. ----- Insert table 2 ----- Sex- and age-stratified analysis of plasma biomarkers revealed distinct patterns. Females exhibited higher median levels of amyloid-β (1–42) , α-synuclein, and BDNF compared to males, whereas males showed relatively higher levels of Total τ and GFAP. With respect to age, participants aged 51–60 years demonstrated higher concentrations of all biomarkers except α-synuclein, which remained comparable across age groups. Although these differences indicate potential sex- and age-related trends, the overall data did not establish consistent generalized patterns across the study group. Discussion A systematic search of the PubMed database was conducted to identify studies published since 2015 that reported circulating biomarkers of neurodegeneration and neuroinflammation in apparently healthy adults. Details of these studies are summarized in supplementary tables S3–S7. Considerable variability was observed across regions and cohorts, likely reflecting differences in demographics, assay methodology, and reporting units. The findings from the present study are discussed below in relation to this broader literature. Amyloid β: Seventy datasets from multiple countries reported circulating serum amyloid-β 1–42 levels in healthy adults (Supplement table 1). North America – U.S. studies often involved older cohorts (> 65 years) and reported mean concentrations in the 40–45 pg/mL range (Lu et al., 2025 ; West et al., 2021 ), though much higher values (320 pg/mL) (Lashkari et al., 2020 ) are also noted in specific subgroups. Younger adults (< 50 years) typically present lower means (e.g. 13.8 pg/mL) (Mehta et al., 2020 ). East Asia – Chinese data show marked variation, from very low means (~ 5–8 pg/mL) (Gu et al., 2020 ; L.-M. Li et al., 2025 ) to markedly elevated levels (> 165 pg/mL) (Tian et al., 2018 ). Similarly among the Taiwanese cohorts the levels ranged widely, from ~ 1.3 pg/mL (Chung et al., 2021 ) to ~ 90 pg/mL (Hsu et al., 2021 ), with older cohorts often exhibiting higher levels. Europe – Italian, Spanish, and Finnish studies tend toward intermediate means (10–35 pg/mL) (Gil-Montoya et al., 2017 ; Martiskainen et al., 2017 ; Pilotto et al., 2024 ), though occasional outliers (e.g., > 320 pg/mL in Chinese cohort) (Ding et al., 2017 ) suggest methodological and demographic influences. Other Regions – Isolated datasets from Australia (Sewell et al., 2024 ), India (R. Singh et al., 2024 ), Mexico (Castillo-Mendieta et al., 2021 ), and Poland (Przybylska-Kuć et al., 2019 ) reveal similarly broad variability, with age appearing as a partial driver but not consistently predictive across settings. Studies provided limited evidence regarding sex-related variations in Amyloid β levels, thereby hard to derive directions or conclusions. Total tau ( τ ) : Across 54 datasets from multiple countries, Tau protein concentrations in apparently healthy adults showed marked heterogeneity between populations and regions (Supplement table 2). The tau concentrations among apparently healthy adults, exhibited marked heterogeneity across countries and assay types, with central tendency values ranging from extremely low (0.007 pg/mL, Sweden) (Gren et al., 2016 ) to markedly high (> 250 pg/mL, China) (Ding et al., 2017 ). East Asia – Multiple cohorts from China and Taiwan dominate the literature, but values differ substantially even within the same country. In China, median/mean tau levels range from 0.78 pg/mL (Jingshan Chen et al., 2023 ) to 259.59 pg/mL (Ding et al., 2017 ), with older cohorts (mean age > 70 years) generally showing higher concentrations (Jiang et al., 2019 ). Taiwanese cohorts report means from ~ 1.8 pg/mL (Hsu et al., 2021 ) to > 22 pg/mL (Wei et al., 2024 ), again with higher levels often observed in older adults. Japan – Studies consistently report low tau concentrations, often < 1 pg/mL, such as 0.47 pg/mL in a younger cohort (Kasai et al., 2017 ) and 0.81 pg/mL in older adults (Kasai et al., 2019 ). Europe – Median levels in healthy European adults are generally in the 1–3 pg/mL range, as seen in Spain and UK cohorts (Fortea et al., 2018 ; Gómez-Tortosa et al., 2025 ) and Rome (Abu-Rumeileh et al., 2020 ), though Belgium (De Vos et al., 2015 ) reported higher medians in arbitrary units (AU). North America – U.S. studies show substantial variability, with lower medians (~ 1–4 pg/mL) (Bogoslovsky et al., 2017 ; Gill et al., 2018 ) in mid-aged samples, but unusually high medians in certain Utah cohorts (40.8 pg/mL) (Galenko et al., 2019 ). Younger U.S. samples exhibited very high values when measured in different units (e.g., 62.59 fg/mL) (Rubenstein et al., 2017 ). Other Regions – Ukraine (Lekomtseva, 2020 ) stands out with a high mean of 71.14 pg/mL in a relatively young cohort (mean age ~ 30 years). Mexican controls (Castillo-Mendieta et al., 2021 ) and Swedish cohorts (Shahim et al., 2022 ) typically fall within 1–3 pg/mL. α - Synuclein: About 22 datasets were available across the globe (Supplement table 3). These studies varied in the methodology, bio-specimen used for estimation and reporting of units. East Asia – Chinese cohorts report markedly divergent values, ranging from very low medians (~ 9.42 pg/mL) (Jiao et al., 2025 ) to high means exceeding 3,200 pg/mL (Xu et al., 2024 ). Studies using nanogram-scale reporting units yield mid-range means such as 3.01 ng/mL (Zou et al., 2020 ) and 21.08 ng/mL (Sun et al., 2019 ). Older participants (> 65 years) often exhibit higher values, though exceptions exist 274.31 pg/mL at mean age 64.8 years (Ding et al., 2017 ) vs. 297.1 pg/mL at mean age 58.3 years (Wang et al., 2019 ). Taiwanese studies reveal extremely low means (0.09 pg/mL) (C. H. Lin et al., 2020 ) alongside higher concentrations (80.9 fg/mL) (Hong et al., 2025 ), again suggesting methodological influences. Southeast Asia – Singaporean data show elevated mean values (13,057 pg/mL); (Ng et al., 2019 ) in a mid-60s cohort, aligning more closely with the upper Chinese range than with other Asian reports. Europe – Danish cohorts report high mean concentrations 127 ng/mL (Brudek et al., 2017 ) − 638 ng/mL (Folke et al., 2019 ) in relatively younger samples (mean ages in early to mid-40s). Greek data show both high (28.3 ng/mL) (Emmanouilidou et al., 2020 ) and low (1.9 pg/mL)(Bougea et al., 2020 ) values depending on analytical context. North America – U.S. reports span extreme ranges, from very high means (> 115,000 pg/mL)(Goldman et al., 2018 ) to moderate nanogram-level concentrations (0.64 ng/mL) (Chan et al., 2022 ), Australia-born cohort measured in USA labs. Other Regions – Russian median values are low (0.85 pg/mL) (Pchelina et al., 2017 ), though with wide interquartile ranges. Australian and Chinese ng/mL-range findings(Chan et al., 2022 ; Fan et al., 2020 ) indicate cross-continental overlaps in mid-range levels. Brain Derived Neurotrophic Factor: Data from 78 studies across six continents reported BDNF concentrations among apparently healthy adults (Supplement table 4). Reported BDNF concentrations in apparently healthy adults demonstrate striking heterogeneity across continents, with mean or median values ranging from 88,000 pg/mL in select Chinese samples (Fang et al., 2018 ). South America: Brazilian studies reported moderate-to-high levels, with means from ~ 1,695 pg/mL (Ribeiro et al., 2018 ) to > 28,000 pg/mL (Marston et al., 2019 ). A large cohort from Brazil (Brunoni et al., 2015 ) reported a mean of 5,947 pg/mL, while specific subgroups (Uint et al., 2019 )reached medians above 21,000 pg/mL. Europe: European cohorts displayed wide variability. Ireland (Pratt et al., 2025 ) showed relatively low means (~ 1,350 pg/mL), whereas Polish participants in (Przybylska et al., 2024 )had the highest European mean (50,663 pg/mL). Mediterranean countries like Italy (Diz et al., 2017 ) and Spain (Silva-Peña et al., 2019 ) generally fell in the mid-range (700–6,300 pg/mL). Notably, Scandinavian cohorts showed extremes — Denmark’s (Jørgensen et al., 2019 ) reported a median of 167,700 pg/mL, whereas Sweden (Jasim et al., 2020 ) reported a mean of 151.8 pg/mL. Asia: Taiwanese cohorts spanned a wide range, from ~ 489 pg/mL (Wen et al., 2024 ) to > 20,000 pg/mL (Wang et al., 2024 ). Chinese data showed the largest global spread — low means (~ 353 pg/mL) (Zhao et al., 2017 ) in younger adults, and extreme highs (88,500 pg/mL) (Fang et al., 2018 ) in mid-aged cohorts. Korean studies reported means between ~ 733 pg/mL (Lee et al., 2021 ) and ~ 9,288 pg/mL (An et al., 2019 ). Middle East & Africa: Kuwaiti cohorts demonstrated moderate variability, with means between 3,060 and 3,880 pg/mL (Al-Temaimi et al., 2017 ; Al-Temaimi et al., 2024 ). Ghanaian data (Agyekum & Yeboah, 2024 ) revealed a high mean of 26,100 pg/mL, while Jordanian adults (Alomari et al., 2018 ) exhibited elevated means of 25,000 pg/mL. North America & Oceania: U.S. and Australian cohorts tended toward the lower-to-mid range, with means often below 2,000 pg/mL (Cabral et al., 2022 ; Weickert et al., 2019 ), except for (Marston et al., 2019 )in Australia, reporting > 28,000 pg/mL. Glial Fibrillary Acid Protein: A total of 57 studies from diverse geographical regions, including North America, Europe, Asia, Africa, and Oceania, reported GFAP concentrations among apparently healthy adult control populations (Supplement table 5). Substantial variation in central tendency values was observed across countries and regions. Reported GFAP concentrations among apparently healthy adults varied widely across 70 + cohorts from diverse regions, with mean or median values ranging from 600 pg/mL in West African populations (Sarfo et al., 2018 ). European cohorts generally exhibited moderate-to-high levels. Northern and Western European studies reported means or medians between ~ 50 and 150 pg/mL (Axelsson et al., 2025 ; Beyer et al., 2023 ; Verberk et al., 2024 ), while Southern European populations (e.g., Italy, Spain) often fell in the upper range (Gómez-Tortosa et al., 2025 ; Pilotto et al., 2024 ). Swedish studies (Kanberg et al., 2020 ; Kanberg et al., 2021 ) consistently reported high medians (> 120 pg/mL), particularly in older cohorts. North American datasets demonstrated substantial heterogeneity. While several U.S. cohorts reported low-to-moderate levels (Gill et al., 2018 ; Miner et al., 2024 ), others—particularly those with older participants—showed markedly elevated means (> 120 pg/mL) (Mandelblatt et al., 2024 ; Vallabh et al., 2024 ). Canadian and multi-country North American–European datasets (Heller et al., 2020 ; Sudre et al., 2019 ) also showed upper-range values (~ 100–126 pg/mL). Asian cohorts were more variable. Several Chinese and Taiwanese datasets reported low means (~ 18–56 pg/mL) in middle-aged participants (K. Li et al., 2025 ; Wei et al., 2025 ; Xu et al., 2024 ), but others showed high outliers, such as > 800 pg/mL in a Chinese subgroup (He et al., 2024 ). Japanese data (Hirata et al., 2024 ) fell within the high European range (~ 105 pg/mL). African data were sparse but extreme: in Ghana and Nigeria, (Sarfo et al., 2018 ) reported a mean of 676 pg/mL with very high variability (SD = 928), likely reflecting methodological or demographic factors. The baseline characteristics provides a comprehensive overview of the study cohort and serves as the foundation for interpreting subsequent analyses of neurodegenerative biomarkers. Evidence from this study, together with data from other regions of the world, suggests potential variations in biomarker levels among apparently healthy adults, possibly attributable to racial, genetic, and/or environmental differences. Notably, the Indian cohort demonstrated substantially different levels compared to other populations, indicating possible regional or genetic influences on protein expression. These findings underscore the importance of accounting for racial and ethnic differences when interpreting biomarker data and highlight the need for further research to elucidate the underlying causes of such variability. Limitations and Future Directions: The cross-sectional design of the current study limits the ability to establish cut-off levels for these neurodegenerative biomarker. Longitudinal studies are needed to assess the trajectory of these markers over time. The study relied on plasma samples, which may not capture the full spectrum of neurodegenerative changes occurring in the brain. Future research should consider using cerebrospinal fluid (CSF) samples for a more comprehensive assessment. Conclusion This study highlights the potential variation in these neurodegenerative biomarkers and underscores the importance of considering race, and geographical factors in neurodegenerative disease research. The observed variability in biomarker levels across different geographical groups further points to the need for personalized approaches in understanding and addressing neurodegenerative diseases. These findings contribute to the growing body of evidence linking personal, genetic and environmental factors with neurological health and provide a foundation for future research. Declarations Ethical approval and consent to participate: The study was conducted following approval from the Human Ethics Committee of the ICMR-National Institute of Occupational Health. All procedures involving human participants adhered to the ethical standards outlined in the national guidelines for biomedical and health research involving human participants (Mathur & Swaminathan, 2018). Written informed consent was obtained from all participants prior to data and sample collection. This included consent for the use of their blood samples, demographic profiles, and clinical data for the analysis of plasma biomarkers indicative of neuroinflammatory and neurodegenerative conditions. Consent to publication: This manuscript does not contain any personally identifiable information. All participants were adults who voluntarily consented to the use of anonymized data for scientific publication purposes. Measures were taken to ensure the confidentiality and privacy of participant information throughout the study. Availability of data and materials: Data is provided within the manuscript or supplementary information files. Competing interest: The authors declare no conflicts of interest, financial or otherwise, that could have influenced the outcomes or interpretation of the research presented in this paper. Funding: KU (lead author) received grants from Department of Health Research, Ministry of Health and Family Welfare, Govt. of India (F.No.R.11013/01/2023-GIA/HR dated 21.02.2023), the data from the study was used for this manuscript Authors’ contribution: KU: concept, design, definition of intellectual content, literature search, data acquisition, manuscript preparation, manuscript editing and review AV: concept, design, definition of intellectual content, literature search, data acquisition, data analysis DPS: design, definition of intellectual content, literature search, data acquisition, data analysis NK: concept, design, literature search, data acquisition, data analysis BC: concept, definition of intellectual content, data acquisition, manuscript preparation PS: design, definition of intellectual content, literature search, data acquisition, data analysis, manuscript preparation RB: concept, design, definition of intellectual content, literature search, data acquisition, manuscript preparation, manuscript editing and review Acknowledgments The investigators would like to express their sincere gratitude to the Department of Health Research (DHR) for funding this study. We also acknowledge the support of the Director-General of ICMR, the Director of ICMR-NIOH, and the administrative staff for their invaluable assistance in facilitating the execution of this study. Our heartfelt thanks go to the technical staff for their dedicated efforts in collecting demographic details, biological samples, and performing the analyses that were crucial for data collection. We are deeply grateful to the participants of the study for their willingness to participate and provide consent for the collection of their demographic information and biological samples, without which this study would not have been possible. Competent state authority for providing permission and facilitating the data collection and the medical officers, their support community staff in executing the mobilization of participations. Their unwavering support and dedication have been instrumental in ensuring the successful execution of this research. References Abu-Rumeileh, S., Baiardi, S., Ladogana, A., Zenesini, C., Bartoletti-Stella, A., Poleggi, A., . . . Parchi, P. (2020). Comparison between plasma and cerebrospinal fluid biomarkers for the early diagnosis and association with survival in prion disease. J Neurol Neurosurg Psychiatry , 91 (11), 1181-1188. https://doi.org/10.1136/jnnp-2020-323826 Agyekum, J. A., & Yeboah, K. (2024). Brain-Derived Neurotrophic Factor is Associated with Self-Reported Quality of Sleep in Type 2 Diabetes Patients in Ghana. Exp Clin Endocrinol Diabetes , 132 (7), 407-413. https://doi.org/10.1055/a-2273-6527 Al-Rawaf, H. A., Alghadir, A. H., & Gabr, S. A. (2021). Molecular Changes in Circulating microRNAs' Expression and Oxidative Stress in Adults with Mild Cognitive Impairment: A Biochemical and Molecular Study. Clin Interv Aging , 16 , 57-70. https://doi.org/10.2147/cia.S285689 Al-Temaimi, R., AbuBaker, J., Al-Khairi, I., & Alroughani, R. (2017). Remyelination modulators in multiple sclerosis patients. Exp Mol Pathol , 103 (3), 237-241. https://doi.org/10.1016/j.yexmp.2017.11.004 Al-Temaimi, R., Alshammari, N., & Alroughani, R. (2024). Analysis of potential microRNA biomarkers for multiple sclerosis. Exp Mol Pathol , 137 , 104903. https://doi.org/10.1016/j.yexmp.2024.104903 Alcolea, D., Beeri, M. S., Rojas, J. C., Gardner, R. C., & Lleó, A. (2023). Blood Biomarkers in Neurodegenerative Diseases: Implications for the Clinical Neurologist. Neurology , 101 (4), 172-180. https://doi.org/10.1212/wnl.0000000000207193 Aldoghachi, A. F., Tor, Y. S., Redzun, S. Z., Lokman, K. A. B., Razaq, N. A. A., Shahbudin, A. F., . . . Ling, K. H. (2019). Screening of brain-derived neurotrophic factor (BDNF) single nucleotide polymorphisms and plasma BDNF levels among Malaysian major depressive disorder patients. PLoS One , 14 (1), e0211241. https://doi.org/10.1371/journal.pone.0211241 Alomari, M. A., Khalil, H., Khabour, O. F., Alzoubi, K. H., & Dersieh, E. H. (2018). Altered cardiovascular function is related to reduced BDNF in Parkinson's disease. Exp Aging Res , 44 (3), 232-245. https://doi.org/10.1080/0361073x.2018.1449589 Amadio, P., Baldassarre, D., Sandrini, L., Weksler, B. B., Tremoli, E., & Barbieri, S. S. (2017). Effect of cigarette smoke on monocyte procoagulant activity: Focus on platelet-derived brain-derived neurotrophic factor (BDNF). Platelets , 28 (1), 60-65. https://doi.org/10.1080/09537104.2016.1203403 Amadio, P., Porro, B., Sandrini, L., Fiorelli, S., Bonomi, A., Cavalca, V., . . . Barbieri, S. S. (2019). Patho- physiological role of BDNF in fibrin clotting. Sci Rep , 9 (1), 389. https://doi.org/10.1038/s41598-018-37117-1 An, J. H., Jang, E. H., Kim, A. Y., Fava, M., Mischoulon, D., Papakostas, G. I., . . . Jeon, H. J. (2019). Ratio of plasma BDNF to leptin levels are associated with treatment response in major depressive disorder but not in panic disorder: A 12-week follow-up study. J Affect Disord , 259 , 349-354. https://doi.org/10.1016/j.jad.2019.08.021 Axelsson, T., Zetterberg, H., Blennow, K., Arslan, B., Ashton, N. J., Axelsson, M., . . . Guron, G. (2025). Plasma concentrations of neurofilament light, p-Tau231 and glial fibrillary acidic protein are elevated in patients with chronic kidney disease and correlate with measured glomerular filtration rate. BMC Nephrol , 26 (1), 231. https://doi.org/10.1186/s12882-025-04130-2 Balachandar, R., Viramgami, A., Singh, D. P., Kulkarni, N., Chudasama, B., Sivaperumal, P., & Upadhyay, K. (2025). Association between chronic PM2.5 exposure and neurodegenerative biomarkers in adults from critically polluted area. BMC Public Health , 25 (1), 1413. https://doi.org/10.1186/s12889-025-22641-3 Barthélemy, N. R., Horie, K., Sato, C., & Bateman, R. J. (2020). Blood plasma phosphorylated-tau isoforms track CNS change in Alzheimer's disease. J Exp Med , 217 (11). https://doi.org/10.1084/jem.20200861 Baumeister, D., Eich, W., Saft, S., Geisel, O., Hellweg, R., Finn, A., . . . Tesarz, J. (2019). No evidence for altered plasma NGF and BDNF levels in fibromyalgia patients. Sci Rep , 9 (1), 13667. https://doi.org/10.1038/s41598-019-49403-7 Baumert, B., Przybycień, K., Paczkowska, E., Kotowski, M., Pius-Sadowska, E., Safranow, K., . . . Machaliński, B. (2019). Novel Evidence of the Increase in Angiogenic Factor Plasma Levels after Lineage-Negative Stem/Progenitor Cell Intracoronary Infusion in Patients with Acute Myocardial Infarction. Int J Mol Sci , 20 (13). https://doi.org/10.3390/ijms20133330 Bentivenga, G. M., Baiardi, S., Mastrangelo, A., Zenesini, C., Mammana, A., Rossi, M., . . . Parchi, P. (2024). Diagnostic and Prognostic Value of Plasma GFAP in Sporadic Creutzfeldt-Jakob Disease in the Clinical Setting of Rapidly Progressive Dementia. Int J Mol Sci , 25 (10). https://doi.org/10.3390/ijms25105106 Beyer, L., Stocker, H., Rujescu, D., Holleczek, B., Stockmann, J., Nabers, A., . . . Gerwert, K. (2023). Amyloid-beta misfolding and GFAP predict risk of clinical Alzheimer's disease diagnosis within 17 years. Alzheimers Dement , 19 (3), 1020-1028. https://doi.org/10.1002/alz.12745 Boerwinkle, A. H., Wisch, J. K., Handen, B. L., Head, E., Mapstone, M., Rafii, M. S., . . . Ances, B. M. (2025). The mediating role of plasma glial fibrillary acidic protein in amyloid and tau pathology in Down's syndrome. Alzheimers Dement , 21 (1), e14359. https://doi.org/10.1002/alz.14359 Bogoslovsky, T., Wilson, D., Chen, Y., Hanlon, D., Gill, J., Jeromin, A., . . . Diaz-Arrastia, R. (2017). Increases of Plasma Levels of Glial Fibrillary Acidic Protein, Tau, and Amyloid β up to 90 Days after Traumatic Brain Injury. J Neurotrauma , 34 (1), 66-73. https://doi.org/10.1089/neu.2015.4333 Bolsewig, K., van Unnik, A., Blujdea, E. R., Gonzalez, M. C., Ashton, N. J., Aarsland, D., . . . Lemstra, A. W. (2024). Association of Plasma Amyloid, P-Tau, GFAP, and NfL With CSF, Clinical, and Cognitive Features in Patients With Dementia With Lewy Bodies. Neurology , 102 (12), e209418. https://doi.org/10.1212/wnl.0000000000209418 Bougea, A., Stefanis, L., Emmanouilidou, E., Vekrelis, K., & Kapaki, E. (2020). High discriminatory ability of peripheral and CFSF biomarkers in Lewy body diseases. J Neural Transm (Vienna) , 127 (3), 311-322. https://doi.org/10.1007/s00702-019-02137-2 Broos, J. Y., Loonstra, F. C., de Ruiter, L. R. J., Gouda, M., Fung, W. H., Schoonheim, M. M., . . . Kooij, G. (2023). Association of Arachidonic Acid-Derived Lipid Mediators With Disease Severity in Patients With Relapsing and Progressive Multiple Sclerosis. Neurology , 101 (5), e533-e545. https://doi.org/10.1212/wnl.0000000000207459 Brudek, T., Winge, K., Folke, J., Christensen, S., Fog, K., Pakkenberg, B., & Pedersen, L. (2017). Autoimmune antibody decline in Parkinson's disease and Multiple System Atrophy; a step towards immunotherapeutic strategies. Mol Neurodegener , 12 (1), 44. https://doi.org/10.1186/s13024-017-0187-7 Brunoni, A. R., Lotufo, P. A., Sabbag, C., Goulart, A. C., Santos, I. S., & Benseñor, I. M. (2015). Decreased brain-derived neurotrophic factor plasma levels in psoriasis patients. Braz J Med Biol Res , 48 (8), 711-714. https://doi.org/10.1590/1414-431x20154574 Bubak, A. N., Beseler, C., Como, C. N., Tyring, S. K., Haley, C., Mescher, T., . . . Nagel, M. A. (2020). Acute zoster plasma contains elevated amyloid, correlating with Aβ42 and amylin levels, and is amyloidogenic. J Neurovirol , 26 (3), 422-428. https://doi.org/10.1007/s13365-020-00830-7 Buselli, R., Veltri, A., Baldanzi, S., Marino, R., Bonotti, A., Chiumiento, M., . . . Cristaudo, A. (2019). Plasma Brain-Derived Neurotrophic Factor (BDNF) and serum cortisol levels in a sample of workers exposed to occupational stress and suffering from Adjustment Disorders. Brain Behav , 9 (7), e01298. https://doi.org/10.1002/brb3.1298 Cabral, D. F., Bigliassi, M., Cattaneo, G., Rundek, T., Pascual-Leone, A., Cahalin, L. P., & Gomes-Osman, J. (2022). Exploring the interplay between mechanisms of neuroplasticity and cardiovascular health in aging adults: A multiple linear regression analysis study. Auton Neurosci , 242 , 103023. https://doi.org/10.1016/j.autneu.2022.103023 Casas, S., Perez, A. F., Mattiazzi, M., Lopez, J., Folgueira, A., Gargiulo-Monachelli, G. M., . . . De Nicola, A. F. (2017). Potential Biomarkers with Plasma Cortisol, Brain-derived Neurotrophic Factor and Nitrites in Patients with Acute Ischemic Stroke. Curr Neurovasc Res , 14 (4), 338-346. https://doi.org/10.2174/1567202614666171005122925 Castillo-Mendieta, T., Arana-Lechuga, Y., Campos-Peña, V., Sosa, A. L., Orozco-Suarez, S., Pinto-Almazán, R., . . . Guerra-Araiza, C. (2021). Plasma Levels of Amyloid-β Peptides and Tau Protein in Mexican Patients with Alzheimer's Disease. J Alzheimers Dis , 82 (s1), S271-s281. https://doi.org/10.3233/jad-200912 Chan, D. K. Y., Braidy, N., Chen, R. F., Xu, Y. H., Bentley, S., Lubomski, M., . . . Mellick, G. D. (2022). Strong Predictive Algorithm of Pathogenesis-Based Biomarkers Improves Parkinson's Disease Diagnosis. Mol Neurobiol , 59 (3), 1476-1485. https://doi.org/10.1007/s12035-021-02604-6 Chang, H. I., Huang, K. L., Huang, C. G., Huang, C. W., Huang, S. H., Lin, K. J., & Chang, C. C. (2024). Clinical Significance of the Plasma Biomarker Panels in Amyloid-Negative and Tau PET-Positive Amnestic Patients: Comparisons with Alzheimer's Disease and Unimpaired Cognitive Controls. Int J Mol Sci , 25 (11). https://doi.org/10.3390/ijms25115607 Chen, C. H., Lee, B. C., & Lin, C. H. (2020). Integrated Plasma and Neuroimaging Biomarkers Associated with Motor and Cognition Severity in Parkinson's Disease. J Parkinsons Dis , 10 (1), 77-88. https://doi.org/10.3233/jpd-191766 Chen, J., Zhao, X., Zhang, W., Zhang, T., Wu, S., Shao, J., & Shi, F.-D. (2023). Reference intervals for plasma amyloid-β, total tau, and phosphorylated tau181 in healthy elderly Chinese individuals without cognitive impairment. Alzheimer's Research & Therapy , 15 (1), 100. https://doi.org/10.1186/s13195-023-01246-1 Chen, J., Zhao, X., Zhang, W., Zhang, T., Wu, S., Shao, J., & Shi, F. D. (2023). Reference intervals for plasma amyloid-β, total tau, and phosphorylated tau181 in healthy elderly Chinese individuals without cognitive impairment. Alzheimers Res Ther , 15 (1), 100. https://doi.org/10.1186/s13195-023-01246-1 Chen, T. B., Lee, Y. J., Lin, S. Y., Chen, J. P., Hu, C. J., Wang, P. N., & Cheng, I. H. (2019). Plasma Aβ42 and Total Tau Predict Cognitive Decline in Amnestic Mild Cognitive Impairment. Sci Rep , 9 (1), 13984. https://doi.org/10.1038/s41598-019-50315-9 Chen, Y., Huang, J., Li, Y., Chen, X., & Ye, Q. (2025). Diagnostic value of six plasma biomarkers in progressive supranuclear palsy, multiple system atrophy, and Parkinson's disease. Clin Chim Acta , 565 , 119975. https://doi.org/10.1016/j.cca.2024.119975 Chen, Y., Wang, Y., Tao, Q., Lu, P., Meng, F., Zhuang, L., . . . Peng, G. (2024). Diagnostic value of isolated plasma biomarkers and its combination in neurodegenerative dementias: A multicenter cohort study. Clin Chim Acta , 558 , 118784. https://doi.org/10.1016/j.cca.2024.118784 Chiu, M. J., Chen, T. F., Hu, C. J., Yan, S. H., Sun, Y., Liu, B. H., . . . Yang, S. Y. (2020). Nanoparticle-based immunomagnetic assay of plasma biomarkers for differentiating dementia and prodromal states of Alzheimer's disease - A cross-validation study. Nanomedicine , 28 , 102182. https://doi.org/10.1016/j.nano.2020.102182 Chiu, M. J., Fan, L. Y., Chen, T. F., Chen, Y. F., Chieh, J. J., & Horng, H. E. (2017). Plasma Tau Levels in Cognitively Normal Middle-Aged and Older Adults. Front Aging Neurosci , 9 , 51. https://doi.org/10.3389/fnagi.2017.00051 Choonara, Y. E., Pillay, V., Du Toit, L. C., Modi, G., Naidoo, D., Ndesendo, V. M. K., & Sibambo, S. R. (2009). Trends in the molecular pathogenesis and clinical therapeutics of common neurodegenerative disorders. Int J Mol Sci , 10 (6), 2510-2557. https://doi.org/10.3390/ijms10062510 Chung, C. C., Chan, L., Chen, J. H., Bamodu, O. A., Chiu, H. W., & Hong, C. T. (2021). Plasma extracellular vesicles tau and β-amyloid as biomarkers of cognitive dysfunction of Parkinson's disease. Faseb j , 35 (10), e21895. https://doi.org/10.1096/fj.202100787R Colombo, E., Doretti, A., Rao, R., Verde, F., Sodano, M., De Gobbi, A., . . . Ticozzi, N. (2025). Plasma levels of glial fibrillary acidic protein and neurofilament light chain in patients with chronic migraine: a multicenter case-control study. Neurol Sci , 46 (5), 2209-2216. https://doi.org/10.1007/s10072-025-08011-2 Conti, E., Tremolizzo, L., Santarone, M. E., Tironi, M., Radice, I., Zoia, C. P., . . . Ferrarese, C. (2016). Donepezil modulates the endogenous immune response: implications for Alzheimer's disease. Hum Psychopharmacol , 31 (4), 296-303. https://doi.org/10.1002/hup.2538 Costa, C. M., Oliveira, G. L., Fonseca, A. C. S., Lana, R. C., Polese, J. C., & Pernambuco, A. P. (2019). Levels of cortisol and neurotrophic factor brain-derived in Parkinson's disease. Neurosci Lett , 708 , 134359. https://doi.org/10.1016/j.neulet.2019.134359 Cousins, K. A. Q., Shaw, L. M., Chen-Plotkin, A., Wolk, D. A., Van Deerlin, V. M., Lee, E. B., . . . Irwin, D. J. (2022). Distinguishing Frontotemporal Lobar Degeneration Tau From TDP-43 Using Plasma Biomarkers. JAMA Neurol , 79 (11), 1155-1164. https://doi.org/10.1001/jamaneurol.2022.3265 da Silveira, F. P., Basso, C., Raupp, W., Dalpiaz, M., Bertoldi, K., Siqueira, I. R., . . . Elsner, V. R. (2017). BDNF levels are increased in peripheral blood of middle-aged amateur runners with no changes on histone H4 acetylation levels. J Physiol Sci , 67 (6), 681-687. https://doi.org/10.1007/s12576-016-0496-6 Daray, F. M., Grendas, L. N., Arena Á, R., Tifner, V., Álvarez Casiani, R. I., Olaviaga, A., . . . Errasti, A. E. (2024). Decoding the inflammatory signature of the major depressive episode: insights from peripheral immunophenotyping in active and remitted condition, a case-control study. Transl Psychiatry , 14 (1), 254. https://doi.org/10.1038/s41398-024-02902-2 De Vos, A., Jacobs, D., Struyfs, H., Fransen, E., Andersson, K., Portelius, E., . . . Vanmechelen, E. (2015). C-terminal neurogranin is increased in cerebrospinal fluid but unchanged in plasma in Alzheimer's disease. Alzheimers Dement , 11 (12), 1461-1469. https://doi.org/10.1016/j.jalz.2015.05.012 Deniz, K., Ho, C. C. G., Malphrus, K. G., Reddy, J. S., Nguyen, T., Carnwath, T. P., . . . Belbin, O. (2021). Plasma Biomarkers of Alzheimer’s Disease in African Americans. Journal of Alzheimer’s Disease , 79 (1), 323-334. https://doi.org/10.3233/jad-200828 Ding, C., Wu, Y., Chen, X., Chen, Y., Wu, Z., Lin, Z., . . . Chen, F. (2022). Global, regional, and national burden and attributable risk factors of neurological disorders: The Global Burden of Disease study 1990-2019. Front Public Health , 10 , 952161. https://doi.org/10.3389/fpubh.2022.952161 Ding, J., Zhang, J., Wang, X., Zhang, L., Jiang, S., Yuan, Y., . . . Zhang, K. (2017). Relationship between the plasma levels of neurodegenerative proteins and motor subtypes of Parkinson's disease. J Neural Transm (Vienna) , 124 (3), 353-360. https://doi.org/10.1007/s00702-016-1650-2 Dirks, M., Pflugrad, H., Tryc, A. B., Schrader, A. K., Ding, X., Lanfermann, H., . . . Weissenborn, K. (2020). Impact of immunosuppressive therapy on brain derived cytokines after liver transplantation. Transpl Immunol , 58 , 101248. https://doi.org/10.1016/j.trim.2019.101248 Diz, J. B. M., de Souza Moreira, B., Felício, D. C., Teixeira, L. F., de Jesus-Moraleida, F. R., de Queiroz, B. Z., . . . Pereira, L. S. M. (2017). Brain-derived neurotrophic factor plasma levels are increased in older women after an acute episode of low back pain. Arch Gerontol Geriatr , 71 , 75-82. https://doi.org/10.1016/j.archger.2017.03.005 Dutt, R., Shankar, N., Srivastava, S., Yadav, A., & Ahmed, R. S. (2020). Cardiac autonomic tone, plasma BDNF levels and paroxetine response in newly diagnosed patients of generalised anxiety disorder. Int J Psychiatry Clin Pract , 24 (2), 135-142. https://doi.org/10.1080/13651501.2020.1723642 Emmanouilidou, E., Papagiannakis, N., Kouloulia, S., Galaziou, A., Antonellou, R., Papadimitriou, D., . . . Stefanis, L. (2020). Peripheral alpha-synuclein levels in patients with genetic and non-genetic forms of Parkinson's disease. Parkinsonism Relat Disord , 73 , 35-40. https://doi.org/10.1016/j.parkreldis.2020.03.014 Eratne, D., Kang, M. J. Y., Lewis, C., Dang, C., Malpas, C., Ooi, S., . . . Velakoulis, D. (2024). Plasma neurofilament light outperforms glial fibrillary acidic protein in differentiating behavioural variant frontotemporal dementia from primary psychiatric disorders. J Neurol Sci , 467 , 123291. https://doi.org/10.1016/j.jns.2024.123291 Eratne, D., Lewis, C., Kelso, W., Loi, S., Chiu, W. M., Blennow, K., . . . Walterfang, M. (2024). Plasma neurofilament light chain is increased in Niemann-Pick Type C but glial fibrillary acidic protein remains normal. Acta Neuropsychiatr , 37 , e20. https://doi.org/10.1017/neu.2024.14 Fan, Z., Pan, Y. T., Zhang, Z. Y., Yang, H., Yu, S. Y., Zheng, Y., . . . Wang, X. M. (2020). Systemic activation of NLRP3 inflammasome and plasma α-synuclein levels are correlated with motor severity and progression in Parkinson's disease. J Neuroinflammation , 17 (1), 11. https://doi.org/10.1186/s12974-019-1670-6 Fang, W.-Q., Hwu, W.-L., Chien, Y.-H., Yang, S.-Y., Chieh, J.-J., Chang, L.-M., . . . Chiu, M.-J. (2020). Composite Scores of Plasma Tau and β-Amyloids Correlate with Dementia in Down Syndrome. ACS Chem Neurosci , 11 (2), 191-196. https://doi.org/10.1021/acschemneuro.9b00585 Fang, W. Q., Hwu, W. L., Chien, Y. H., Yang, S. Y., Chieh, J. J., Chang, L. M., . . . Chiu, M. J. (2020). Composite Scores of Plasma Tau and β-Amyloids Correlate with Dementia in Down Syndrome. ACS Chem Neurosci , 11 (2), 191-196. https://doi.org/10.1021/acschemneuro.9b00585 Fang, X., Chen, Y., Wang, Y., Ren, J., & Zhang, C. (2019). Depressive symptoms in schizophrenia patients: A possible relationship between SIRT1 and BDNF. Prog Neuropsychopharmacol Biol Psychiatry , 95 , 109673. https://doi.org/10.1016/j.pnpbp.2019.109673 Fang, Y., Qiu, Q., Zhang, S., Sun, L., Li, G., Xiao, S., & Li, X. (2018). Changes in miRNA-132 and miR-124 levels in non-treated and citalopram-treated patients with depression. J Affect Disord , 227 , 745-751. https://doi.org/10.1016/j.jad.2017.11.090 Fenoglio, C., Serpente, M., Arcaro, M., Carandini, T., Sacchi, L., Pintus, M., . . . Galimberti, D. (2024). Inflammatory plasma profile in genetic symptomatic and presymptomatic Frontotemporal Dementia - A GENFI study. Brain Behav Immun , 122 , 231-240. https://doi.org/10.1016/j.bbi.2024.08.030 Fišar, Z., Jirák, R., Zvěřová, M., Setnička, V., Habartová, L., Hroudová, J., . . . Raboch, J. (2019). Plasma amyloid beta levels and platelet mitochondrial respiration in patients with Alzheimer's disease. Clin Biochem , 72 , 71-80. https://doi.org/10.1016/j.clinbiochem.2019.04.003 Folke, J., Rydbirk, R., Løkkegaard, A., Salvesen, L., Hejl, A. M., Starhof, C., . . . Brudek, T. (2019). Distinct Autoimmune Anti-α-Synuclein Antibody Patterns in Multiple System Atrophy and Parkinson's Disease. Front Immunol , 10 , 2253. https://doi.org/10.3389/fimmu.2019.02253 Fortea, J., Carmona-Iragui, M., Benejam, B., Fernández, S., Videla, L., Barroeta, I., . . . Lleó, A. (2018). Plasma and CSF biomarkers for the diagnosis of Alzheimer's disease in adults with Down syndrome: a cross-sectional study. Lancet Neurol , 17 (10), 860-869. https://doi.org/10.1016/s1474-4422(18)30285-0 Fortea, J., Vilaplana, E., Carmona-Iragui, M., Benejam, B., Videla, L., Barroeta, I., . . . Lleó, A. (2020). Clinical and biomarker changes of Alzheimer's disease in adults with Down syndrome: a cross-sectional study. Lancet , 395 (10242), 1988-1997. https://doi.org/10.1016/s0140-6736(20)30689-9 Fraussen, J., In't Veld, S., van Laake-Geelen, C. C. M., Depreitere, B., Deckers, J., Peuskens, D., . . . Somers, V. (2025). Longitudinal Plasma Biomarker Profiles Predict Neurological Outcome in Traumatic Spinal Cord Injury. Ann Neurol , 97 (6), 1180-1189. https://doi.org/10.1002/ana.27198 Fu, L., Ren, J., Lei, X., Wang, Y., Chen, X., Zhang, R., . . . Zhang, C. (2024). Association of anhedonia with brain-derived neurotrophic factor and interleukin-10 in major depressive disorder. Prog Neuropsychopharmacol Biol Psychiatry , 133 , 111023. https://doi.org/10.1016/j.pnpbp.2024.111023 Gadhave, D. G., Sugandhi, V. V., Jha, S. K., Nangare, S. N., Gupta, G., Singh, S. K., . . . Paudel, K. R. (2024). Neurodegenerative disorders: Mechanisms of degeneration and therapeutic approaches with their clinical relevance. Ageing Research Reviews , 99 , 102357. https://doi.org/https://doi.org/10.1016/j.arr.2024.102357 Gadhave, D. G., Sugandhi, V. V., & Kokare, C. R. (2024). Potential biomaterials and experimental animal models for inventing new drug delivery approaches in the neurodegenerative disorder: Multiple sclerosis. Brain Res , 1822 , 148674. https://doi.org/10.1016/j.brainres.2023.148674 Galenko, O., Jacobs, V., Knight, S., Bride, D., Cutler, M. J., Muhlestein, J. B., . . . Jared Bunch, T. (2019). Circulating Levels of Biomarkers of Cerebral Injury in Patients with Atrial Fibrillation. Am J Cardiol , 124 (11), 1697-1700. https://doi.org/10.1016/j.amjcard.2019.08.027 Gao, F., Lv, X., Dai, L., Wang, Q., Wang, P., Cheng, Z., . . . Shen, Y. (2023). A combination model of AD biomarkers revealed by machine learning precisely predicts Alzheimer's dementia: China Aging and Neurodegenerative Initiative (CANDI) study. Alzheimers Dement , 19 (3), 749-760. https://doi.org/10.1002/alz.12700 Gao, F., Lv, X., Dai, L., Wang, Q., Wang, P., Cheng, Z., . . . Shen, Y. (2023). A combination model of AD biomarkers revealed by machine learning precisely predicts Alzheimer's dementia: China Aging and Neurodegenerative Initiative (CANDI) study. Alzheimer's & Dementia , 19 (3), 749-760. https://doi.org/https://doi.org/10.1002/alz.12700 García-Marchena, N., Silva-Peña, D., Martín-Velasco, A. I., Villanúa, M., Araos, P., Pedraz, M., . . . Pavón, F. J. (2017). Decreased plasma concentrations of BDNF and IGF-1 in abstinent patients with alcohol use disorders. PLoS One , 12 (11), e0187634. https://doi.org/10.1371/journal.pone.0187634 Gil-Montoya, J. A., Barrios, R., Santana, S., Sanchez-Lara, I., Pardo, C. C., Fornieles-Rubio, F., . . . Burgos, J. S. (2017). Association Between Periodontitis and Amyloid β Peptide in Elderly People With and Without Cognitive Impairment. J Periodontol , 88 (10), 1051-1058. https://doi.org/10.1902/jop.2017.170071 Gill, J., Latour, L., Diaz-Arrastia, R., Motamedi, V., Turtzo, C., Shahim, P., . . . Jeromin, A. (2018). Glial fibrillary acidic protein elevations relate to neuroimaging abnormalities after mild TBI. Neurology , 91 (15), e1385-e1389. https://doi.org/10.1212/wnl.0000000000006321 Goldman, J. G., Andrews, H., Amara, A., Naito, A., Alcalay, R. N., Shaw, L. M., . . . Kang, U. J. (2018). Cerebrospinal fluid, plasma, and saliva in the BioFIND study: Relationships among biomarkers and Parkinson's disease Features. Mov Disord , 33 (2), 282-288. https://doi.org/10.1002/mds.27232 Gómez-Tortosa, E., Agüero-Rabes, P., Ruiz-González, A., Wagner-Reguero, S., Téllez, R., Mahillo, I., . . . Sánchez-Juan, P. (2025). Plasma Biomarkers in the Distinction of Alzheimer's Disease and Frontotemporal Dementia. Int J Mol Sci , 26 (3). https://doi.org/10.3390/ijms26031231 Gredicak, M., Nikolac Perkovic, M., Nedic Erjavec, G., Uzun, S., Kozumplik, O., Svob Strac, D., & Pivac, N. (2024). Association between reduced plasma BDNF concentration and MMSE scores in both chronic schizophrenia and mild cognitive impairment. Prog Neuropsychopharmacol Biol Psychiatry , 134 , 111086. https://doi.org/10.1016/j.pnpbp.2024.111086 Gren, M., Shahim, P., Lautner, R., Wilson, D. H., Andreasson, U., Norgren, N., . . . Zetterberg, H. (2016). Blood biomarkers indicate mild neuroaxonal injury and increased amyloid β production after transient hypoxia during breath-hold diving. Brain Inj , 30 (10), 1226-1230. https://doi.org/10.1080/02699052.2016.1179792 Gu, D., Liu, F., Meng, M., Zhang, L., Gordon, M. L., Wang, Y., . . . Zhang, N. (2020). Elevated matrix metalloproteinase-9 levels in neuronal extracellular vesicles in Alzheimer's disease. Ann Clin Transl Neurol , 7 (9), 1681-1691. https://doi.org/10.1002/acn3.51155 Hajari, J. N., Ilginis, T., Pedersen, T. T., Lønkvist, C. S., Saunte, J. P., Hofsli, M., . . . Slidsborg, C. (2025). Novel Blood-Biomarkers to Detect Retinal Neurodegeneration and Inflammation in Diabetic Retinopathy. Int J Mol Sci , 26 (6). https://doi.org/10.3390/ijms26062625 He, Y., Cheng, S., Yang, L., Ding, L., Chen, Y., Lu, J., & Zheng, R. (2024). Associations between plasma markers and symptoms of anxiety and depression in patients with breast cancer. BMC Psychiatry , 24 (1), 678. https://doi.org/10.1186/s12888-024-06143-x Heller, C., Foiani, M. S., Moore, K., Convery, R., Bocchetta, M., Neason, M., . . . Rohrer, J. D. (2020). Plasma glial fibrillary acidic protein is raised in progranulin-associated frontotemporal dementia. J Neurol Neurosurg Psychiatry , 91 (3), 263-270. https://doi.org/10.1136/jnnp-2019-321954 Hinderberger, P., Rullmann, M., Drabe, M., Luthardt, J., Becker, G. A., Blüher, M., . . . Hesse, S. (2016). The effect of serum BDNF levels on central serotonin transporter availability in obese versus non-obese adults: A [(11)C]DASB positron emission tomography study. Neuropharmacology , 110 (Pt A), 530-536. https://doi.org/10.1016/j.neuropharm.2016.04.030 Hirata, K., Matsuoka, K., Tagai, K., Endo, H., Tatebe, H., Ono, M., . . . Takado, Y. (2024). In Vivo Assessment of Astrocyte Reactivity in Patients with Progressive Supranuclear Palsy. Ann Neurol , 96 (2), 247-261. https://doi.org/10.1002/ana.26962 Hong, C. T., Chung, C. C., Hsieh, Y. C., & Chan, L. (2025). Plasma extracellular vesicle pathognomonic proteins as the biomarkers of the progression of Parkinson's disease. Biosci Trends , 19 (1), 116-124. https://doi.org/10.5582/bst.2024.01369 Hori, H., Yoshimura, R., Katsuki, A., Atake, K., Igata, R., Konishi, Y., & Nakamura, J. (2017). Relationships between serum brain-derived neurotrophic factor, plasma catecholamine metabolites, cytokines, cognitive function and clinical symptoms in Japanese patients with chronic schizophrenia treated with atypical antipsychotic monotherapy. World J Biol Psychiatry , 18 (5), 401-408. https://doi.org/10.1080/15622975.2016.1212172 Hsu, J.-L., Lee, W.-J., Liao, Y.-C., Wang, S.-J., & Fuh, J.-L. (2017). The clinical significance of plasma clusterin and Aβ in the longitudinal follow-up of patients with Alzheimer’s disease. Alzheimer's Research & Therapy , 9 (1), 91. https://doi.org/10.1186/s13195-017-0319-x Hsu, J. L., Lin, C. H., Chen, P. L., Lin, K. J., & Chen, T. F. (2021). Genetic study of young-onset dementia using targeted gene panel sequencing in Taiwan. Am J Med Genet B Neuropsychiatr Genet , 186 (2), 67-76. https://doi.org/10.1002/ajmg.b.32836 Huang, P., & Zhang, M. (2023). Magnetic Resonance Imaging Studies of Neurodegenerative Disease: From Methods to Translational Research. Neurosci Bull , 39 (1), 99-112. https://doi.org/10.1007/s12264-022-00905-x Huang, Y., Li, Y., Pan, H., & Han, L. (2023). Global, regional, and national burden of neurological disorders in 204 countries and territories worldwide. J Glob Health , 13 , 04160. https://doi.org/10.7189/jogh.13.04160 Hui, B. S. M., Zhi, L. R., Retinasamy, T., Arulsamy, A., Law, C. S. W., Shaikh, M. F., & Yeong, K. Y. (2023). The Role of Interferon-α in Neurodegenerative Diseases: A Systematic Review. J Alzheimers Dis , 94 (s1), S45-s66. https://doi.org/10.3233/jad-221081 Illán-Gala, I., Lleo, A., Karydas, A., Staffaroni, A. M., Zetterberg, H., Sivasankaran, R., . . . Rojas, J. C. (2021). Plasma Tau and Neurofilament Light in Frontotemporal Lobar Degeneration and Alzheimer Disease. Neurology , 96 (5), e671-e683. https://doi.org/10.1212/wnl.0000000000011226 Iulita, M. F., Ower, A., Barone, C., Pentz, R., Gubert, P., Romano, C., . . . Cuello, A. C. (2016). An inflammatory and trophic disconnect biomarker profile revealed in Down syndrome plasma: Relation to cognitive decline and longitudinal evaluation. Alzheimers Dement , 12 (11), 1132-1148. https://doi.org/10.1016/j.jalz.2016.05.001 James, P. A., Oparil, S., Carter, B. L., Cushman, W. C., Dennison-Himmelfarb, C., Handler, J., . . . Ortiz, E. (2014). 2014 evidence-based guideline for the management of high blood pressure in adults: report from the panel members appointed to the Eighth Joint National Committee (JNC 8). Jama , 311 (5), 507-520. https://doi.org/10.1001/jama.2013.284427 Janel, N., Alexopoulos, P., Badel, A., Lamari, F., Camproux, A. C., Lagarde, J., . . . Delabar, J. M. (2017). Combined assessment of DYRK1A, BDNF and homocysteine levels as diagnostic marker for Alzheimer's disease. Transl Psychiatry , 7 (6), e1154. https://doi.org/10.1038/tp.2017.123 Janelidze, S., Christian, B. T., Price, J., Laymon, C., Schupf, N., Klunk, W. E., . . . Hansson, O. (2022). Detection of Brain Tau Pathology in Down Syndrome Using Plasma Biomarkers. JAMA Neurol , 79 (8), 797-807. https://doi.org/10.1001/jamaneurol.2022.1740 Jasim, H., Ghafouri, B., Gerdle, B., Hedenberg-Magnusson, B., & Ernberg, M. (2020). Altered levels of salivary and plasma pain related markers in temporomandibular disorders. J Headache Pain , 21 (1), 105. https://doi.org/10.1186/s10194-020-01160-z Jiang, T., Gong, P. Y., Tan, M. S., Xue, X., Huang, S., Zhou, J. S., . . . Zhang, Y. D. (2019). Soluble TREM1 concentrations are increased and positively correlated with total tau levels in the plasma of patients with Alzheimer's disease. Aging Clin Exp Res , 31 (12), 1801-1805. https://doi.org/10.1007/s40520-019-01122-9 Jiao, B., Ouyang, Z., Liu, Y., Zhang, C., Xu, T., Yang, Q., . . . Shen, L. (2025). Evaluating the diagnostic performance of six plasma biomarkers for Alzheimer's disease and other neurodegenerative dementias in a large Chinese cohort. Alzheimers Res Ther , 17 (1), 71. https://doi.org/10.1186/s13195-025-01712-y Jin, E., Bai, Y., Huang, L., Zhao, M., Zhang, C., Zhao, M., & Li, X. (2015). Evidence of a novel gene HERPUD1 in polypoidal choroidal vasculopathy. Int J Clin Exp Pathol , 8 (11), 13928-13944. Jin, H., Chen, Y., Wang, B., Zhu, Y., Chen, L., Han, X., . . . Liu, N. (2018). Association between brain-derived neurotrophic factor and von Willebrand factor levels in patients with stable coronary artery disease. BMC Cardiovasc Disord , 18 (1), 23. https://doi.org/10.1186/s12872-018-0762-z Jin, H., Ji, J. J., Zhu, Y., Wang, X. D., Li, Y. P., Shi, Q. Y., & Chen, Y. F. (2021). Brain-Derived Neurotrophic Factor, a New Predictor of Coronary Artery Calcification. Clin Appl Thromb Hemost , 27 , 1076029621989813. https://doi.org/10.1177/1076029621989813 Jin, W.-S., Bu, X.-L., Liu, Y.-H., Shen, L.-L., Zhuang, Z.-Q., Jiao, S.-S., . . . Wang, Y.-J. (2017). Plasma Amyloid-Beta Levels in Patients with Different Types of Cancer. Neurotoxicity Research , 31 (2), 283-288. https://doi.org/10.1007/s12640-016-9682-9 Jordan, W., Dobrowolny, H., Bahn, S., Bernstein, H. G., Brigadski, T., Frodl, T., . . . Steiner, J. (2018). Oxidative stress in drug-naïve first episode patients with schizophrenia and major depression: effects of disease acuity and potential confounders. Eur Arch Psychiatry Clin Neurosci , 268 (2), 129-143. https://doi.org/10.1007/s00406-016-0749-7 Jørgensen, M. L. K., Kjølhede, T., Dalgas, U., & Hvid, L. G. (2019). Plasma brain-derived neurotrophic factor (BDNF) and sphingosine-1-phosphat (S1P) are NOT the main mediators of neuroprotection induced by resistance training in persons with multiple sclerosis-A randomized controlled trial. Mult Scler Relat Disord , 31 , 106-111. https://doi.org/10.1016/j.msard.2019.03.029 Kammeyer, R., Chapman, K., Furniss, A., Hsieh, E., Fuhlbrigge, R., Ogbu, E. A., . . . Piquet, A. L. (2024). Blood-based biomarkers of neuronal and glial injury in active major neuropsychiatric systemic lupus erythematosus. Lupus , 33 (10), 1116-1129. https://doi.org/10.1177/09612033241272961 Kanberg, N., Ashton, N. J., Andersson, L. M., Yilmaz, A., Lindh, M., Nilsson, S., . . . Gisslén, M. (2020). Neurochemical evidence of astrocytic and neuronal injury commonly found in COVID-19. Neurology , 95 (12), e1754-e1759. https://doi.org/10.1212/wnl.0000000000010111 Kanberg, N., Simrén, J., Edén, A., Andersson, L. M., Nilsson, S., Ashton, N. J., . . . Gisslén, M. (2021). Neurochemical signs of astrocytic and neuronal injury in acute COVID-19 normalizes during long-term follow-up. EBioMedicine , 70 , 103512. https://doi.org/10.1016/j.ebiom.2021.103512 Kasai, T., Kojima, Y., Ohmichi, T., Tatebe, H., Tsuji, Y., Noto, Y. I., . . . Tokuda, T. (2019). Combined use of CSF NfL and CSF TDP-43 improves diagnostic performance in ALS. Ann Clin Transl Neurol , 6 (12), 2489-2502. https://doi.org/10.1002/acn3.50943 Kasai, T., Tatebe, H., Kondo, M., Ishii, R., Ohmichi, T., Yeung, W. T. E., . . . Tokuda, T. (2017). Increased levels of plasma total tau in adult Down syndrome. PLoS One , 12 (11), e0188802. https://doi.org/10.1371/journal.pone.0188802 Katsanos, A. H., Makris, K., Stefani, D., Koniari, K., Gialouri, E., Lelekis, M., . . . Tsivgoulis, G. (2017). Plasma Glial Fibrillary Acidic Protein in the Differential Diagnosis of Intracerebral Hemorrhage. Stroke , 48 (9), 2586-2588. https://doi.org/10.1161/strokeaha.117.018409 Katzdobler, S., Nübling, G., Klietz, M., Fietzek, U. M., Palleis, C., Bernhardt, A. M., . . . Levin, J. (2024). GFAP and NfL as fluid biomarkers for clinical disease severity and disease progression in multiple system atrophy (MSA). J Neurol , 271 (10), 6991-6999. https://doi.org/10.1007/s00415-024-12647-z Kim, K., Jang, E. H., Kim, A. Y., Fava, M., Mischoulon, D., Papakostas, G. I., . . . Jeon, H. J. (2019). Pre-treatment peripheral biomarkers associated with treatment response in panic symptoms in patients with major depressive disorder and panic disorder: A 12-week follow-up study. Compr Psychiatry , 95 , 152140. https://doi.org/10.1016/j.comppsych.2019.152140 Kitaguchi, N., Tatebe, H., Sakai, K., Kawaguchi, K., Matsunaga, S., Kitajima, T., . . . Tokuda, T. (2019). Influx of Tau and Amyloid-β Proteins into the Blood During Hemodialysis as a Therapeutic Extracorporeal Blood Amyloid-β Removal System for Alzheimer's Disease. J Alzheimers Dis , 69 (3), 687-707. https://doi.org/10.3233/jad-190087 Koehler, N. K., Stransky, E., Meyer, M., Gaertner, S., Shing, M., Schnaidt, M., . . . Richartz-Salzburger, E. (2015). Alpha-synuclein levels in blood plasma decline with healthy aging. PLoS One , 10 (4), e0123444. https://doi.org/10.1371/journal.pone.0123444 Koníčková, D., Menšíková, K., Tučková, L., Hényková, E., Strnad, M., Friedecký, D., . . . Kaňovský, P. (2022). Biomarkers of Neurodegenerative Diseases: Biology, Taxonomy, Clinical Relevance, and Current Research Status. Biomedicines , 10 (7). https://doi.org/10.3390/biomedicines10071760 Kowalski, R. G., Ledreux, A., Violette, J. E., Neumann, R. T., Ornelas, D., Yu, X., . . . Graner, M. W. (2023). Rapid Activation of Neuroinflammation in Stroke: Plasma and Extracellular Vesicles Obtained on a Mobile Stroke Unit. Stroke , 54 (3), e52-e57. https://doi.org/10.1161/strokeaha.122.041422 Lamptey, R. N. L., Chaulagain, B., Trivedi, R., Gothwal, A., Layek, B., & Singh, J. (2022). A Review of the Common Neurodegenerative Disorders: Current Therapeutic Approaches and the Potential Role of Nanotherapeutics. Int J Mol Sci , 23 (3). https://doi.org/10.3390/ijms23031851 Lashkari, K., Teague, G. C., Beattie, U., Betts, J., Kumar, S., McLaughlin, M. M., & López, F. J. (2020). Plasma biomarkers of the amyloid pathway are associated with geographic atrophy secondary to age-related macular degeneration. PLoS One , 15 (8), e0236283. https://doi.org/10.1371/journal.pone.0236283 Lee, B. H., Park, Y. M., Hwang, J. A., & Kim, Y. K. (2021). Variable alterations in plasma erythropoietin and brain-derived neurotrophic factor levels in patients with major depressive disorder with and without a history of suicide attempt. Prog Neuropsychopharmacol Biol Psychiatry , 110 , 110324. https://doi.org/10.1016/j.pnpbp.2021.110324 Lekomtseva, Y. (2020). Targeting higher levels of lactate in the post-injury period following traumatic brain injury. Clin Neurol Neurosurg , 196 , 106050. https://doi.org/10.1016/j.clineuro.2020.106050 Li, C. H., Fan, S. P., Shih, M. C., Weng, Y. H., Chen, T. F., Li, H., . . . Lin, C. H. (2025). Subcortical tau burden correlates with regional brain atrophy and plasma markers in four-repeat tauopathy parkinsonism. J Parkinsons Dis , 15 (1), 214-226. https://doi.org/10.1177/1877718x241298192 Li, K., Wang, K., Xu, S. X., Xie, X. H., Tang, Y., Zhang, L., & Liu, Z. (2024). Investigating Neuroplasticity Changes Reflected by BDNF Levels in Astrocyte-Derived Extracellular Vesicles in Patients with Depression. Int J Nanomedicine , 19 , 8971-8985. https://doi.org/10.2147/ijn.S477482 Li, K., Wang, K., Xu, S. X., Xie, X. H., Tang, Y., Zhang, L., & Liu, Z. (2025). In vivo evidence of increased vascular endothelial growth factor in patients with major depressive disorder. J Affect Disord , 368 , 151-159. https://doi.org/10.1016/j.jad.2024.09.073 Li, L.-M., Che, P., Liu, D., Wang, Y., Li, J., He, D., . . . Zhang, N. (2025). Diagnostic and discriminative accuracy of plasma phosphorylated tau 217 for symptomatic Alzheimerʼs disease in a Chinese cohort. The Journal of Prevention of Alzheimer's Disease , 12 (5), 100092. https://doi.org/https://doi.org/10.1016/j.tjpad.2025.100092 Li, L. M., Che, P., Liu, D., Wang, Y., Li, J., He, D., . . . Zhang, N. (2025). Diagnostic and discriminative accuracy of plasma phosphorylated tau 217 for symptomatic Alzheimer's disease in a Chinese cohort. J Prev Alzheimers Dis , 12 (5), 100092. https://doi.org/10.1016/j.tjpad.2025.100092 Lin, C. H., Chiu, S. I., Chen, T. F., Jang, J. R., & Chiu, M. J. (2020). Classifications of Neurodegenerative Disorders Using a Multiplex Blood Biomarkers-Based Machine Learning Model. Int J Mol Sci , 21 (18). https://doi.org/10.3390/ijms21186914 Lin, W. C., Lu, C. H., Chiu, P. Y., & Yang, S. Y. (2020). Plasma Total α-Synuclein and Neurofilament Light Chain: Clinical Validation for Discriminating Parkinson's Disease from Normal Control. Dement Geriatr Cogn Disord , 49 (4), 401-409. https://doi.org/10.1159/000510325 Liu, Y., Liu, X., Che, P., Wang, Y., Piao, Z., Wang, Y., . . . Zhang, N. (2025). A cytometric bead array for the measurement of plasma biomarker levels in patients with Alzheimer's disease. Sci Rep , 15 (1), 9767. https://doi.org/10.1038/s41598-024-83919-x Liu, Y. C., Hsiao, H. T., Wang, J. C., Wen, T. C., & Chen, S. L. (2022). TGF-β1 in plasma and cerebrospinal fluid can be used as a biological indicator of chronic pain in patients with osteoarthritis. PLoS One , 17 (1), e0262074. https://doi.org/10.1371/journal.pone.0262074 Lu, R., Shah, K., Toedebusch, C. D., Hess, A., Richardson, R., Mignot, E., . . . Lucey, B. P. (2025). Associations of Cerebrospinal Fluid Orexin-A, Alzheimer Disease Biomarkers, and Cognitive Performance. Ann Clin Transl Neurol , 12 (4), 780-791. https://doi.org/10.1002/acn3.70009 Lue, L. F., Pai, M. C., Chen, T. F., Hu, C. J., Huang, L. K., Lin, W. C., . . . Chiu, M. J. (2019). Age-Dependent Relationship Between Plasma Aβ40 and Aβ42 and Total Tau Levels in Cognitively Normal Subjects. Front Aging Neurosci , 11 , 222. https://doi.org/10.3389/fnagi.2019.00222 Luebke, M., Parulekar, M., & Thomas, F. P. (2023). Fluid biomarkers for the diagnosis of neurodegenerative diseases. Biomarkers in Neuropsychiatry , 8 , 100062. https://doi.org/https://doi.org/10.1016/j.bionps.2023.100062 Mandelblatt, J., Dage, J. L., Zhou, X., Small, B. J., Ahles, T. A., Ahn, J., . . . Saykin, A. J. (2024). Alzheimer disease-related biomarkers and cancer-related cognitive decline: the Thinking and Living with Cancer study. J Natl Cancer Inst , 116 (9), 1495-1507. https://doi.org/10.1093/jnci/djae113 Mansur, R. B., Santos, C. M., Rizzo, L. B., Cunha, G. R., Asevedo, E., Noto, M. N., . . . Brietzke, E. (2016). Inter-relation between brain-derived neurotrophic factor and antioxidant enzymes in bipolar disorder. Bipolar Disord , 18 (5), 433-439. https://doi.org/10.1111/bdi.12418 Marelli, C., Hourregue, C., Gutierrez, L. A., Paquet, C., Menjot de Champfleur, N., De Verbizier, D., . . . Gabelle, A. (2020). Cerebrospinal Fluid and Plasma Biomarkers do not Differ in the Presenile and Late-Onset Behavioral Variants of Frontotemporal Dementia. J Alzheimers Dis , 74 (3), 903-911. https://doi.org/10.3233/jad-190378 Marston, K. J., Brown, B. M., Rainey-Smith, S. R., Bird, S., Wijaya, L., Teo, S. Y. M., . . . Peiffer, J. J. (2019). Twelve weeks of resistance training does not influence peripheral levels of neurotrophic growth factors or homocysteine in healthy adults: a randomized-controlled trial. Eur J Appl Physiol , 119 (10), 2167-2176. https://doi.org/10.1007/s00421-019-04202-w Martiskainen, H., Herukka, S. K., Stančáková, A., Paananen, J., Soininen, H., Kuusisto, J., . . . Hiltunen, M. (2017). Decreased plasma β-amyloid in the Alzheimer's disease APP A673T variant carriers. Ann Neurol , 82 (1), 128-132. https://doi.org/10.1002/ana.24969 Mathur, R. (2017). National ethical guidelines for biomedical and health research involving human participants. New Delhi: Director-General Indian Council of Medical Research . Mathur, R., & Swaminathan, S. (2018). National ethical guidelines for biomedical & health research involving human participants, 2017: A commentary. The Indian journal of medical research , 148 (3), 279. Mehta, P. D., Patrick, B. A., Miller, D. L., Coyle, P. K., & Wisniewski, T. (2020). A Sensitive and Cost-Effective Chemiluminescence ELISA for Measurement of Amyloid-β 1-42 Peptide in Human Plasma. J Alzheimers Dis , 78 (3), 1237-1244. https://doi.org/10.3233/jad-200861 Mengel, D., Liu, W., Glynn, R. J., Selkoe, D. J., Strydom, A., Lai, F., . . . Walsh, D. M. (2020). Dynamics of plasma biomarkers in Down syndrome: the relative levels of Aβ42 decrease with age, whereas NT1 tau and NfL increase. Alzheimer's Research & Therapy , 12 (1), 27. https://doi.org/10.1186/s13195-020-00593-7 Miner, A. E., Groh, J. R., Tripodis, Y., Adler, C. H., Balcer, L. J., Bernick, C., . . . Alosco, M. L. (2024). Examination of plasma biomarkers of amyloid, tau, neurodegeneration, and neuroinflammation in former elite American football players. Alzheimers Dement , 20 (11), 7529-7546. https://doi.org/10.1002/alz.14231 Mo, M., Fu, X. Y., Zhang, X. L., Zhang, S. C., Zhang, H. Q., Wu, L., . . . Zhou, L. (2021). Association of Plasma Pro-Brain-Derived Neurotrophic Factor (proBDNF)/Mature Brain-Derived Neurotrophic Factor (mBDNF) Levels with BDNF Gene Val66Met Polymorphism in Alcohol Dependence. Med Sci Monit , 27 , e930421. https://doi.org/10.12659/msm.930421 Mohaupt, P., Kindermans, J., Vialaret, J., Anderl-Straub, S., Werner, L., Lehmann, S., . . . Oeckl, P. (2024). Blood-based biomarkers and plasma Aβ assays in the differential diagnosis of Alzheimer's disease and behavioral-variant frontotemporal dementia. Alzheimers Res Ther , 16 (1), 279. https://doi.org/10.1186/s13195-024-01647-w Mothapo, K. M., Stelma, F., Janssen, M., Kessels, R., Miners, S., Verbeek, M. M., . . . van der Ven, A. (2015). Amyloid beta-42 (Aβ-42), neprilysin and cytokine levels. A pilot study in patients with HIV related cognitive impairments. J Neuroimmunol , 282 , 73-79. https://doi.org/10.1016/j.jneuroim.2015.03.017 Mouri MI, B. M. (2023). Hyperglycemia . StatPearls Publishing. https://www.ncbi.nlm.nih.gov/books/NBK430900/ Ng, A. S. L., Tan, Y. J., Lu, Z., Ng, E. Y. L., Ng, S. Y. E., Chia, N. S. Y., . . . Tan, L. C. S. (2019). Plasma alpha-synuclein detected by single molecule array is increased in PD. Ann Clin Transl Neurol , 6 (3), 615-619. https://doi.org/10.1002/acn3.729 Ng, K. S. T., Sia, A., Ng, M. K. W., Tan, C. T. Y., Chan, H. Y., Tan, C. H., . . . Ho, R. C. M. (2018). Effects of Horticultural Therapy on Asian Older Adults: A Randomized Controlled Trial. Int J Environ Res Public Health , 15 (8). https://doi.org/10.3390/ijerph15081705 Ng, T. K. S., Coughlan, C., Heyn, P. C., Tagawa, A., Carollo, J. J., Kua, E. H., & Mahendran, R. (2021). Increased plasma brain-derived neurotrophic factor (BDNF) as a potential biomarker for and compensatory mechanism in mild cognitive impairment: a case-control study. Aging (Albany NY) , 13 (19), 22666-22689. https://doi.org/10.18632/aging.203598 Okonkwo, D. O., Puffer, R. C., Puccio, A. M., Yuh, E. L., Yue, J. K., Diaz-Arrastia, R., . . . Manley, G. T. (2020). Point-of-Care Platform Blood Biomarker Testing of Glial Fibrillary Acidic Protein versus S100 Calcium-Binding Protein B for Prediction of Traumatic Brain Injuries: A Transforming Research and Clinical Knowledge in Traumatic Brain Injury Study. J Neurotrauma , 37 (23), 2460-2467. https://doi.org/10.1089/neu.2020.7140 Olğun, Y., Poyraz, C. A., Bozluolçay, M., Konukoğlu, D., & Poyraz, B. (2024). Plasma Biomarkers in Neurodegenerative Dementias: Unrevealing the Potential of Serum Oxytocin, BDNF, NPTX1, TREM2, TNF-alpha, IL-1 and Prolactin. Curr Alzheimer Res , 21 (2), 109-119. https://doi.org/10.2174/0115672050313419240520051751 Ou, Z. A., Byrne, L. M., Rodrigues, F. B., Tortelli, R., Johnson, E. B., Foiani, M. S., . . . Wild, E. J. (2021). Brain-derived neurotrophic factor in cerebrospinal fluid and plasma is not a biomarker for Huntington's disease. Sci Rep , 11 (1), 3481. https://doi.org/10.1038/s41598-021-83000-x Paczkowska, E., Rogińska, D., Pius-Sadowska, E., Jurewicz, A., Piecyk, K., Safranow, K., . . . Machaliński, B. (2015). Evidence for proangiogenic cellular and humoral systemic response in patients with acute onset of spinal cord injury. J Spinal Cord Med , 38 (6), 729-744. https://doi.org/10.1179/2045772314y.0000000227 Pai, M. C., Wu, C. C., Hou, Y. C., Jeng, J. S., Tang, S. C., Lin, W. C., . . . Yang, S. Y. (2022). Evidence of plasma biomarkers indicating high risk of dementia in cognitively normal subjects. Sci Rep , 12 (1), 1192. https://doi.org/10.1038/s41598-022-05177-z Pant, S., Kapri, A., & Nain, S. (2022). Pyrimidine analogues for the management of neurodegenerative diseases. European Journal of Medicinal Chemistry Reports , 6 , 100095. https://doi.org/https://doi.org/10.1016/j.ejmcr.2022.100095 Passaro, A., Soavi, C., Marusic, U., Rejc, E., Sanz, J. M., Morieri, M. L., . . . Pišot, R. (2017). Computerized cognitive training and brain derived neurotrophic factor during bed rest: mechanisms to protect individual during acute stress. Aging (Albany NY) , 9 (2), 393-407. https://doi.org/10.18632/aging.101166 Pchelina, S., Emelyanov, A., Baydakova, G., Andoskin, P., Senkevich, K., Nikolaev, M., . . . Zakharova, E. (2017). Oligomeric α-synuclein and glucocerebrosidase activity levels in GBA-associated Parkinson's disease. Neurosci Lett , 636 , 70-76. https://doi.org/10.1016/j.neulet.2016.10.039 Peng, G., Qiu, J., Liu, H., Zhou, M., Huang, S., Guo, W., . . . Xu, P. (2020). Analysis of Cerebrospinal Fluid Soluble TREM2 and Polymorphisms in Sporadic Parkinson's Disease in a Chinese Population. J Mol Neurosci , 70 (2), 294-301. https://doi.org/10.1007/s12031-019-01424-7 Pereira, J. B., Janelidze, S., Smith, R., Mattsson-Carlgren, N., Palmqvist, S., Teunissen, C. E., . . . Hansson, O. (2021). Plasma GFAP is an early marker of amyloid-β but not tau pathology in Alzheimer's disease. Brain , 144 (11), 3505-3516. https://doi.org/10.1093/brain/awab223 Petersen, L. E., Baptista, T. S. A., Molina, J. K., Motta, J. G., do Prado, A., Piovesan, D. M., . . . Bauer, M. E. (2018). Cognitive impairment in rheumatoid arthritis: role of lymphocyte subsets, cytokines and neurotrophic factors. Clin Rheumatol , 37 (5), 1171-1181. https://doi.org/10.1007/s10067-018-3990-9 Pilotto, A., Ashton, N. J., Lupini, A., Battaglio, B., Zatti, C., Trasciatti, C., . . . Padovani, A. (2024). Plasma NfL, GFAP, amyloid, and p-tau species as Prognostic biomarkers in Parkinson’s disease. Journal of Neurology , 271 (12), 7537-7546. https://doi.org/10.1007/s00415-024-12669-7 Ploydang, T., Khovidhunkit, W., Tanaka, H., & Suksom, D. (2023). Nordic Walking in Water on Cerebrovascular Reactivity and Cognitive Function in Elderly Patients with Type 2 Diabetes. Med Sci Sports Exerc , 55 (10), 1803-1811. https://doi.org/10.1249/mss.0000000000003216 Posti, J. P., Takala, R. S., Runtti, H., Newcombe, V. F., Outtrim, J., Katila, A. J., . . . Tenovuo, O. (2016). The Levels of Glial Fibrillary Acidic Protein and Ubiquitin C-Terminal Hydrolase-L1 During the First Week After a Traumatic Brain Injury: Correlations With Clinical and Imaging Findings. Neurosurgery , 79 (3), 456-464. https://doi.org/10.1227/neu.0000000000001226 Pratt, J., Motanova, E., Narici, M. V., Boreham, C., & De Vito, G. (2025). Plasma brain-derived neurotrophic factor concentrations are elevated in community-dwelling adults with sarcopenia. Age Ageing , 54 (2). https://doi.org/10.1093/ageing/afaf024 Przybylska-Kuć, S., Zakrzewski, M., Dybała, A., Kiciński, P., Dzida, G., Myśliński, W., . . . Mosiewicz, J. (2019). Obstructive sleep apnea may increase the risk of Alzheimer's disease. PLoS One , 14 (9), e0221255. https://doi.org/10.1371/journal.pone.0221255 Przybylska, I., Marusiak, J., Toczyłowska, B., Stępień, A., Brodacki, B., Langfort, J., & Chalimoniuk, M. (2024). Association between the Val66Met (rs6265) polymorphism of the brain-derived neurotrophic factor (BDNF) gene, BDNF protein level in the blood and the risk of developing early‑onset Parkinson's disease. Acta Neurobiol Exp (Wars) , 84 (3), 296-308. https://doi.org/10.55782/ane-2024-2476 Quaresima, V., Pilotto, A., Trasciatti, C., Tolassi, C., Parigi, M., Bertoli, D., . . . Padovani, A. (2024). Plasma p-tau181 and amyloid markers in Alzheimer's disease: A comparison between Lumipulse and SIMOA. Neurobiol Aging , 143 , 30-40. https://doi.org/10.1016/j.neurobiolaging.2024.08.007 Ren, Q. G., Chang, J. H., Lu, W. J., Gong, W. G., & Zhou, H. (2017). Low plasma BDNF is not a biomarker for cognitive dysfunction in elderly T2DM patients. Neurol Sci , 38 (9), 1691-1696. https://doi.org/10.1007/s10072-017-3048-9 Ribeiro, V. G. C., Mendonça, V. A., Souza, A. L. C., Fonseca, S. F., Camargos, A. C. R., Lage, V. K. S., . . . Lacerda, A. C. R. (2018). Inflammatory biomarkers responses after acute whole body vibration in fibromyalgia. Braz J Med Biol Res , 51 (4), e6775. https://doi.org/10.1590/1414-431x20176775 Riezzo, G., Chimienti, G., Orlando, A., D'Attoma, B., Clemente, C., & Russo, F. (2019). Effects of long-term administration of Lactobacillus reuteri DSM-17938 on circulating levels of 5-HT and BDNF in adults with functional constipation. Benef Microbes , 10 (2), 137-147. https://doi.org/10.3920/bm2018.0050 Rosenblum, Y., Pereira, M., Stange, O., Weber, F. D., Bovy, L., Tzioridou, S., . . . Dresler, M. (2024). Divergent Associations of Slow-Wave Sleep versus Rapid Eye Movement Sleep with Plasma Amyloid-Beta. Ann Neurol , 96 (1), 46-60. https://doi.org/10.1002/ana.26935 Rubenstein, R., Chang, B., Yue, J. K., Chiu, A., Winkler, E. A., Puccio, A. M., . . . Vassar, M. J. (2017). Comparing Plasma Phospho Tau, Total Tau, and Phospho Tau-Total Tau Ratio as Acute and Chronic Traumatic Brain Injury Biomarkers. JAMA Neurol , 74 (9), 1063-1072. https://doi.org/10.1001/jamaneurol.2017.0655 Ryan, K. M., Dunne, R., & McLoughlin, D. M. (2018). BDNF plasma levels and genotype in depression and the response to electroconvulsive therapy. Brain Stimul , 11 (5), 1123-1131. https://doi.org/10.1016/j.brs.2018.05.011 Sagud, M., Nikolac Perkovic, M., Vuksan-Cusa, B., Maravic, A., Svob Strac, D., Mihaljevic Peles, A., . . . Pivac, N. (2016). A prospective, longitudinal study of platelet serotonin and plasma brain-derived neurotrophic factor concentrations in major depression: effects of vortioxetine treatment. Psychopharmacology (Berl) , 233 (17), 3259-3267. https://doi.org/10.1007/s00213-016-4364-0 Saraceno, C., Cervellati, C., Trentini, A., Crescenti, D., Longobardi, A., Geviti, A., . . . Ghidoni, R. (2024). Serum Beta-Secretase 1 Activity Is a Potential Marker for the Differential Diagnosis between Alzheimer's Disease and Frontotemporal Dementia: A Pilot Study. Int J Mol Sci , 25 (15). https://doi.org/10.3390/ijms25158354 Sarfo, F. S., Owusu, D., Adamu, S., Awuah, D., Appiah, L., Amamoo, M., . . . Ovbiagele, B. (2018). Plasma Glial Fibrillary Acidic Protein, Copeptin, and Matrix Metalloproteinase-9 Concentrations among West African Stroke Subjects Compared with Stroke-Free Controls. J Stroke Cerebrovasc Dis , 27 (3), 633-644. https://doi.org/10.1016/j.jstrokecerebrovasdis.2017.09.035 Savarraj, J., Park, E. S., Colpo, G. D., Hinds, S. N., Morales, D., Ahnstedt, H., . . . Choi, H. A. (2021). Brain injury, endothelial injury and inflammatory markers are elevated and express sex-specific alterations after COVID-19. J Neuroinflammation , 18 (1), 277. https://doi.org/10.1186/s12974-021-02323-8 Sewell, K. R., Rainey-Smith, S. R., Pedrini, S., Peiffer, J. J., Sohrabi, H. R., Taddei, K., . . . Brown, B. M. (2024). The impact of exercise on blood-based biomarkers of Alzheimer's disease in cognitively unimpaired older adults. Geroscience , 46 (6), 5911-5923. https://doi.org/10.1007/s11357-024-01130-2 Shahim, P., Zetterberg, H., Simrén, J., Ashton, N. J., Norato, G., Schöll, M., . . . Blennow, K. (2022). Association of Plasma Biomarker Levels With Their CSF Concentration and the Number and Severity of Concussions in Professional Athletes. Neurology , 99 (4), e347-e354. https://doi.org/10.1212/wnl.0000000000200615 Sheth, U., Öijerstedt, L., Heckman, M. G., White, L. J., Heuer, H. W., Lario Lago, A., . . . Gendron, T. F. (2025). Comprehensive cross-sectional and longitudinal comparisons of plasma glial fibrillary acidic protein and neurofilament light across FTD spectrum disorders. Mol Neurodegener , 20 (1), 30. https://doi.org/10.1186/s13024-025-00821-4 Shi, Y., Lu, X., Zhang, L., Shu, H., Gu, L., Wang, Z., . . . Zhang, Z. (2019). Potential Value of Plasma Amyloid-β, Total Tau, and Neurofilament Light for Identification of Early Alzheimer's Disease. ACS Chem Neurosci , 10 (8), 3479-3485. https://doi.org/10.1021/acschemneuro.9b00095 Silva-Peña, D., García-Marchena, N., Alén, F., Araos, P., Rivera, P., Vargas, A., . . . Suárez, J. (2019). Alcohol-induced cognitive deficits are associated with decreased circulating levels of the neurotrophin BDNF in humans and rats. Addict Biol , 24 (5), 1019-1033. https://doi.org/10.1111/adb.12668 Silveira-Rodrigues, J. G., Gardênia de Holanda Marinho Nogueira, N., Faria, L. O., Pereira, D. S., & Soares, D. D. (2023). Combined Training Improves Executive Functions Without Changing Brain-Derived Neurotrophic Factor Levels of Middle-Aged and Older Adults with Type 2 Diabetes. Exp Clin Endocrinol Diabetes , 131 (6), 345-353. https://doi.org/10.1055/a-2069-4050 Singh, N. A., Alnobani, A., Graff-Radford, J., Machulda, M. M., Mielke, M. M., Schwarz, C. G., . . . Whitwell, J. L. (2024). Relationships between PET and blood plasma biomarkers in corticobasal syndrome. Alzheimers Dement , 20 (7), 4765-4774. https://doi.org/10.1002/alz.13914 Singh, R., Rai, S., Bharti, P. S., Zehra, S., Gorai, P. K., Modi, G. P., . . . Kumar, S. (2024). Circulating small extracellular vesicles in Alzheimer's disease: a case-control study of neuro-inflammation and synaptic dysfunction. BMC Med , 22 (1), 254. https://doi.org/10.1186/s12916-024-03475-z Startin, C. M., Ashton, N. J., Hamburg, S., Hithersay, R., Wiseman, F. K., Mok, K. Y., . . . Strydom, A. (2019). Plasma biomarkers for amyloid, tau, and cytokines in Down syndrome and sporadic Alzheimer's disease. Alzheimers Res Ther , 11 (1), 26. https://doi.org/10.1186/s13195-019-0477-0 Stratta, P., Sanità, P., Bonanni, R. L., de Cataldo, S., Angelucci, A., Rossi, R., . . . Rossi, A. (2016). Clinical correlates of plasma brain-derived neurotrophic factor in post-traumatic stress disorder spectrum after a natural disaster. Psychiatry Res , 244 , 165-170. https://doi.org/10.1016/j.psychres.2016.07.019 Sudre, C. H., Bocchetta, M., Heller, C., Convery, R., Neason, M., Moore, K. M., . . . Rohrer, J. D. (2019). White matter hyperintensities in progranulin-associated frontotemporal dementia: A longitudinal GENFI study. Neuroimage Clin , 24 , 102077. https://doi.org/10.1016/j.nicl.2019.102077 Sun, H. L., Sun, B. L., Chen, D. W., Chen, Y., Li, W. W., Xu, M. Y., . . . Bu, X. L. (2019). Plasma α-synuclein levels are increased in patients with obstructive sleep apnea syndrome. Ann Clin Transl Neurol , 6 (4), 788-794. https://doi.org/10.1002/acn3.756 Sungkarat, S., Boripuntakul, S., Kumfu, S., Lord, S. R., & Chattipakorn, N. (2018). Tai Chi Improves Cognition and Plasma BDNF in Older Adults With Mild Cognitive Impairment: A Randomized Controlled Trial. Neurorehabil Neural Repair , 32 (2), 142-149. https://doi.org/10.1177/1545968317753682 Sustar, A., Perkovic, M. N., Erjavec, G. N., Strac, D. S., & Pivac, N. (2019). Association between reduced brain-derived neurotrophic factor concentration & coronary heart disease. Indian J Med Res , 150 (1), 43-49. https://doi.org/10.4103/ijmr.IJMR_1566_17 Tanaka, M., Toldi, J., & Vécsei, L. (2020). Exploring the Etiological Links behind Neurodegenerative Diseases: Inflammatory Cytokines and Bioactive Kynurenines. Int J Mol Sci , 21 (7), 2431. https://www.mdpi.com/1422-0067/21/7/2431 Teunissen, C. E., Chiu, M. J., Yang, C. C., Yang, S. Y., Scheltens, P., Zetterberg, H., & Blennow, K. (2018). Plasma Amyloid-β (Aβ42) Correlates with Cerebrospinal Fluid Aβ42 in Alzheimer's Disease. J Alzheimers Dis , 62 (4), 1857-1863. https://doi.org/10.3233/jad-170784 Tian, S., Huang, R., Han, J., Cai, R., Guo, D., Lin, H., . . . Wang, S. (2018). Increased plasma Interleukin-1β level is associated with memory deficits in type 2 diabetic patients with mild cognitive impairment. Psychoneuroendocrinology , 96 , 148-154. https://doi.org/10.1016/j.psyneuen.2018.06.014 Torres-Galván, S., Flores-López, M., Ochoa, E., Requena-Ocaña, N., Araos, P., Herrera-Imbroda, J., . . . García-Marchena, N. (2024). Dysregulation of Plasma Growth Factors and Chemokines in Cocaine Use Disorder: Implications for Dual Diagnosis with Schizophrenia and Antisocial Personality Disorder in an Exploratory Study. Neuropsychobiology , 83 (2), 73-88. https://doi.org/10.1159/000536265 Uint, L., Bastos, G. M., Thurow, H. S., Borges, J. B., Hirata, T. D. C., França, J. I. D., . . . Sousa, A. (2019). Increased levels of plasma IL-1b and BDNF can predict resistant depression patients. Rev Assoc Med Bras (1992) , 65 (3), 361-369. https://doi.org/10.1590/1806-9282.65.3.361 Upadhyay, K., Viramgami, A., Balachandar, R., Pagdhune, A., Sen, S., & Sarkar, K. (2022). A Comparative Health Assessment of Occupationally Lead Exposed Individuals with Blood Lead Levels Range across Upper Acceptable Limit. Indian J Community Med , 47 (3), 343-346. https://doi.org/10.4103/ijcm.ijcm_756_21 Vallabh, S. M., Mortberg, M. A., Allen, S. W., Kupferschmid, A. C., Kivisakk, P., Hammerschlag, B. L., . . . Arnold, S. E. (2024). Fluid Biomarkers in Individuals at Risk for Genetic Prion Disease up to Disease Conversion. Neurology , 103 (2), e209506. https://doi.org/10.1212/wnl.0000000000209506 van Ballegoij, W. J. C., van de Stadt, S. I. W., Huffnagel, I. C., Kemp, S., Willemse, E. A. J., Teunissen, C. E., & Engelen, M. (2020). Plasma NfL and GFAP as biomarkers of spinal cord degeneration in adrenoleukodystrophy. Ann Clin Transl Neurol , 7 (11), 2127-2136. https://doi.org/10.1002/acn3.51188 van Prooije, T. H., Kapteijns, K. C. J., van Asten, J. J. A., IntHout, J., Verbeek, M. M., Scheenen, T. W. J., & van de Warrenburg, B. P. (2024). Multimodal, Longitudinal Profiling of SCA1 Identifies Predictors of Disease Severity and Progression. Ann Neurol , 96 (4), 774-787. https://doi.org/10.1002/ana.27032 Verberk, I. M. W., Jutte, J., Kingma, M. Y., Vigneswaran, S., Gouda, M., van Engelen, M. P., . . . Teunissen, C. E. (2024). Development of thresholds and a visualization tool for use of a blood test in routine clinical dementia practice. Alzheimers Dement , 20 (9), 6115-6132. https://doi.org/10.1002/alz.14088 Vrillon, A., Bousiges, O., Götze, K., Demuynck, C., Muller, C., Ravier, A., . . . Paquet, C. (2024). Plasma biomarkers of amyloid, tau, axonal, and neuroinflammation pathologies in dementia with Lewy bodies. Alzheimers Res Ther , 16 (1), 146. https://doi.org/10.1186/s13195-024-01502-y Wang, J., Gao, L., Liu, J., Dang, L., Wei, S., Hu, N., . . . Qu, Q. (2022). The Association of Plasma Amyloid-β and Cognitive Decline in Cognitively Unimpaired Population. Clin Interv Aging , 17 , 555-565. https://doi.org/10.2147/cia.s357994 Wang, L., Wang, G., Duan, Y., Wang, F., Lin, S., Zhang, F., . . . Li, H. (2019). A Comparative Study of the Diagnostic Potential of Plasma and Erythrocytic α-Synuclein in Parkinson's Disease. Neurodegener Dis , 19 (5-6), 204-210. https://doi.org/10.1159/000506480 Wang, S. M., Chang, H. H., Chang, Y. H., Tsai, T. Y., Chen, P. S., Lu, R. B., & Wang, T. Y. (2024). Shortening of telomere length may be associated with inflammatory cytokine levels in patients with bipolar disorder. J Affect Disord , 365 , 155-161. https://doi.org/10.1016/j.jad.2024.08.084 Wang, T.-Y., Lee, S.-Y., Hu, M.-C., Chen, S.-L., Chang, Y.-H., Chu, C.-H., . . . Lu, R.-B. (2017). More inflammation but less brain-derived neurotrophic factor in antisocial personality disorder. Psychoneuroendocrinology , 85 , 42-48. https://doi.org/https://doi.org/10.1016/j.psyneuen.2017.08.006 Wang, T. Y., Chang, Y. H., Lee, S. Y., Chang, H. H., Tsai, T. Y., Tseng, H. H., . . . Lu, R. B. (2025). Transdiagnostic features of inflammatory markers and executive function across psychiatric disorders. J Psychiatr Res , 181 , 160-168. https://doi.org/10.1016/j.jpsychires.2024.11.037 Wang, Y., Zhang, H., Li, Y., Wang, Z., Fan, Q., Yu, S., . . . Xiao, Z. (2015). BDNF Val66Met polymorphism and plasma levels in Chinese Han population with obsessive-compulsive disorder and generalized anxiety disorder. J Affect Disord , 186 , 7-12. https://doi.org/10.1016/j.jad.2015.07.023 Wei, C., Sun, Y., Chen, N., Chen, S., Xiu, M., & Zhang, X. (2020). Interaction of oxidative stress and BDNF on executive dysfunction in patients with chronic schizophrenia. Psychoneuroendocrinology , 111 , 104473. https://doi.org/10.1016/j.psyneuen.2019.104473 Wei, M., Yu, X., Hu, S., Hu, W., Shi, R., Wang, M., . . . Han, Y. (2025). Differences of longitudinal plasma biomarkers between single memory domain and multidomain subject cognitive decline: Evidence from SILCODE. J Alzheimers Dis , 103 (4), 1060-1074. https://doi.org/10.1177/13872877241309105 Wei, Y. C., Kung, Y. C., Lin, C., Yeh, C. H., Chen, P. Y., Huang, W. Y., . . . Chen, C. K. (2024). Differential neuropsychiatric associations of plasma biomarkers in older adults with major depression and subjective cognitive decline. Transl Psychiatry , 14 (1), 333. https://doi.org/10.1038/s41398-024-03049-w Weickert, C. S., Lee, C. H., Lenroot, R. K., Bruggemann, J., Galletly, C., Liu, D., . . . Weickert, T. W. (2019). Increased plasma Brain-Derived Neurotrophic Factor (BDNF) levels in females with schizophrenia. Schizophr Res , 209 , 212-217. https://doi.org/10.1016/j.schres.2019.04.015 Wen, C. H., Kang, H. Y., & Chan, J. Y. H. (2024). Brain Amyloid-β Peptide Is Associated with Pain Intensity and Cognitive Dysfunction in Osteoarthritic Patients. Int J Mol Sci , 25 (23). https://doi.org/10.3390/ijms252312575 West, T., Kirmess, K. M., Meyer, M. R., Holubasch, M. S., Knapik, S. S., Hu, Y., . . . Yarasheski, K. E. (2021). A blood-based diagnostic test incorporating plasma Aβ42/40 ratio, ApoE proteotype, and age accurately identifies brain amyloid status: findings from a multi cohort validity analysis. Molecular Neurodegeneration , 16 (1), 30. https://doi.org/10.1186/s13024-021-00451-6 WHO. (2004). Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies. The Lancet , 363 (9403), 157-163. https://doi.org/https://doi.org/10.1016/S0140-6736(03)15268-3 WHO. (2011). Haemoglobin concentrations for the diagnosis of anaemia and assessment of severity (WHO/NMH/NHD/MNM/11.1) https://iris.who.int/bitstream/handle/10665/85839/WHO_NMH_NHD_MNM_11.1_eng.pdf?sequence=22 Wilhelm, C. J., Fuller, B. E., Huckans, M., & Loftis, J. M. (2017). Peripheral immune factors are elevated in women with current or recent alcohol dependence and associated with altered mood and memory. Drug Alcohol Depend , 176 , 71-78. https://doi.org/10.1016/j.drugalcdep.2017.02.023 Xiong, H., Yang, H., Liang, M., Ou, Y., Huang, X., Cai, Y., . . . Zheng, Y. (2016). Plasma brain-derived neurotrophic factor levels are increased in patients with tinnitus and correlated with therapeutic effects. Neurosci Lett , 622 , 15-18. https://doi.org/10.1016/j.neulet.2016.04.032 Xiong, J., Zhang, Z., Ma, Y., Li, Z., Zhou, F., Qiao, N., . . . Liao, W. (2020). The effect of combined scalp acupuncture and cognitive training in patients with stroke on cognitive and motor functions. NeuroRehabilitation , 46 (1), 75-82. https://doi.org/10.3233/nre-192942 Xu, L., Zhang, Y., Zhang, R., Zhang, H., Song, P., Ma, T., . . . Shen, C. (2019). Elevated plasma BDNF levels are correlated with NK cell activation in patients with traumatic spinal cord injury. Int Immunopharmacol , 74 , 105722. https://doi.org/10.1016/j.intimp.2019.105722 Xu, T., Weng, L., Zhang, C., Xiao, X., Yang, Q., Zhu, Y., . . . Shen, L. (2024). Genetic spectrum features and diagnostic accuracy of four plasma biomarkers in 248 Chinese patients with frontotemporal dementia. Alzheimers Dement , 20 (10), 7281-7295. https://doi.org/10.1002/alz.14215 Xu, Y. Y., Ge, J. F., Chen, J., Liang, J., Pang, L. J., Gao, W. F., . . . Xia, Q. R. (2020). Evidence of a Relationship Between Plasma Leptin, Not Nesfatin-1, and Craving in Male Alcohol-Dependent Patients After Abstinence. Front Endocrinol (Lausanne) , 11 , 159. https://doi.org/10.3389/fendo.2020.00159 Yang, B., Deng, Q., Zhang, W., Feng, Y., Dai, X., Feng, W., . . . Wu, T. (2016). Exposure to Polycyclic Aromatic Hydrocarbons, Plasma Cytokines, and Heart Rate Variability. Sci Rep , 6 , 19272. https://doi.org/10.1038/srep19272 Yang, C. C., Chiu, M. J., Chen, T. F., Chang, H. L., Liu, B. H., & Yang, S. Y. (2018). Assay of Plasma Phosphorylated Tau Protein (Threonine 181) and Total Tau Protein in Early-Stage Alzheimer's Disease. J Alzheimers Dis , 61 (4), 1323-1332. https://doi.org/10.3233/jad-170810 Yang, S. Y., Chiu, M. J., Chen, T. F., Lin, C. H., Jeng, J. S., Tang, S. C., . . . Wu, C. C. (2017). Analytical performance of reagent for assaying tau protein in human plasma and feasibility study screening neurodegenerative diseases. Sci Rep , 7 (1), 9304. https://doi.org/10.1038/s41598-017-09009-3 Yang, Y., Liu, Y., Wang, G., Hei, G., Wang, X., Li, R., . . . Zhao, J. (2019). Brain-derived neurotrophic factor is associated with cognitive impairments in first-episode and chronic schizophrenia. Psychiatry Res , 273 , 528-536. https://doi.org/10.1016/j.psychres.2019.01.051 Yu, K., Yang, B., Jiang, H., Li, J., Yan, K., Liu, X., . . . Wu, T. (2019). A multi-stage association study of plasma cytokines identifies osteopontin as a biomarker for acute coronary syndrome risk and severity. Sci Rep , 9 (1), 5121. https://doi.org/10.1038/s41598-019-41577-4 Yue, J. K., Yuh, E. L., Korley, F. K., Winkler, E. A., Sun, X., Puffer, R. C., . . . Manley, G. T. (2019). Association between plasma GFAP concentrations and MRI abnormalities in patients with CT-negative traumatic brain injury in the TRACK-TBI cohort: a prospective multicentre study. Lancet Neurol , 18 (10), 953-961. https://doi.org/10.1016/s1474-4422(19)30282-0 Zamani, M., Hosseini, S. V., Behrouj, H., Erfani, M., Dastghaib, S., Ahmadi, M., . . . Mokarram, P. (2019). BDNF Val66Met genetic variation and its plasma level in patients with morbid obesity: A case-control study. Gene , 705 , 51-54. https://doi.org/10.1016/j.gene.2019.04.045 Zecca, C., Pasculli, G., Tortelli, R., Dell'Abate, M. T., Capozzo, R., Barulli, M. R., . . . Logroscino, G. (2021). The Role of Age on Beta-Amyloid(1-42) Plasma Levels in Healthy Subjects. Front Aging Neurosci , 13 , 698571. https://doi.org/10.3389/fnagi.2021.698571 Zecca, C., Tortelli, R., Panza, F., Arcuti, S., Piccininni, M., Capozzo, R., . . . Logroscino, G. (2018). Plasma β-amyloid1–42 reference values in cognitively normal subjects. J Neurol Sci , 391 , 120-126. https://doi.org/https://doi.org/10.1016/j.jns.2018.06.006 Zhang, Y., Fang, X., Fan, W., Tang, W., Cai, J., Song, L., & Zhang, C. (2018). Brain-derived neurotrophic factor as a biomarker for cognitive recovery in acute schizophrenia: 12-week results from a prospective longitudinal study. Psychopharmacology (Berl) , 235 (4), 1191-1198. https://doi.org/10.1007/s00213-018-4835-6 Zhao, G., Zhang, C., Chen, J., Su, Y., Zhou, R., Wang, F., . . . Fang, Y. (2017). Ratio of mBDNF to proBDNF for Differential Diagnosis of Major Depressive Disorder and Bipolar Depression. Mol Neurobiol , 54 (7), 5573-5582. https://doi.org/10.1007/s12035-016-0098-6 Zou, J., Guo, Y., Wei, L., Yu, F., Yu, B., & Xu, A. (2020). Long Noncoding RNA POU3F3 and α-Synuclein in Plasma L1CAM Exosomes Combined with β-Glucocerebrosidase Activity: Potential Predictors of Parkinson's Disease. Neurotherapeutics , 17 (3), 1104-1119. https://doi.org/10.1007/s13311-020-00842-5 Tables Table-1: Characteristics of Study Participants Parameter Mean ± SD Range Demographic details of the study participants Total Participants (N) 405 Male (%) 197(48.6%) Age (Years) 48.60 ± 6.15 40 - 60 BMI (kg/m 2 ) 24.54 ± 4.87 13.0 – 43.4 Systolic BP (mm Hg) 132.42 ± 18.43 90 - 208 Diastolic BP (mm Hg) 87.13 ± 11.12 58 - 174 Hemoglobin (mg/dL) 9.98 ± 1.53 4.0 – 14.7 Random Blood Sugar (mg/dL) 110.85 ± 31.34 72.0 – 356.0 The table provides mean ± standard deviation and range for each parameter. Table-2: Plasma concentrations of neurodegenerative biomarkers in the study population Parameter Plasma concentration in pg/mL Median (IQR) Overall (N=405) Gender Age Group Male (N=197) Female (N=208) 41 – 50 (n=229) 51 – 60 (n=176) Amyloid b (1-42) 18.95 (11.05 – 44.42) 16.60 (11.05 – 38.29) 21.68 (11.05 – 49.15) 21.29 (11.05 – 45.95) 14.72 (11.05 – 39.89) Total t 84.38 (28.67 – 199.61) 81.52 (33.88 – 177.19) 98.77 (21.41 – 233.18) 85.20 (33.68 – 215.96) 82.62 (26.05 – 179.14) α-Synuclein 804.51 (294.74 – 1741.97) 804.51 (142.28 – 1863.98) 806.19 (360.19 – 1587.06) 804.51 (290.46 – 1777.80) 806.19 (297.47 – 1618.10) BDNF 2221.98 (1057.82 – 6274.14) 1994.96 (1116.10 – 5833.87) 2458.92 (972.94 – 6543.88) 2423.56 (1077.29 – 6568.48) 1823.46 (1017.80 – 5956.58) GFAP 98.33 (55.43 – 129.62) 98.85 (78.42 – 120.65) 96.69 (72.04 – 134.77) 97.97 (74.00 – 128.81) 98.81 (77.25 – 131.66) This table presents the median (IQR) plasma concentrations of (Amyloid β(1-42), Total τ, α-Synuclein, BDNF, and GFAP) for the total study group, and further stratified by gender and age groups (41-50 years and 51-60 years). Data is shown in pg/mL. Additional Declarations No competing interests reported. Supplementary Files SupplementaryTables.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7788186","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":545810258,"identity":"72c67265-3579-4ecf-98ed-4e26939436dd","order_by":0,"name":"Kuldip Upadhyay","email":"","orcid":"","institution":"National Institute of Occupational Health","correspondingAuthor":false,"prefix":"","firstName":"Kuldip","middleName":"","lastName":"Upadhyay","suffix":""},{"id":545810261,"identity":"cc97707e-b892-4e82-9333-c9118c851164","order_by":1,"name":"Ankit Viramgami","email":"","orcid":"","institution":"National Institute of Occupational Health","correspondingAuthor":false,"prefix":"","firstName":"Ankit","middleName":"","lastName":"Viramgami","suffix":""},{"id":545810262,"identity":"20bd4e83-48a5-44c0-a77a-db40d1ce2fea","order_by":2,"name":"DhirendraPratap Singh","email":"","orcid":"","institution":"National Institute of Occupational Health","correspondingAuthor":false,"prefix":"","firstName":"DhirendraPratap","middleName":"","lastName":"Singh","suffix":""},{"id":545810263,"identity":"914b99f0-529a-4f21-86f9-18cc1ee102b0","order_by":3,"name":"Nikhil Kulkarni","email":"","orcid":"","institution":"National Institute of Occupational Health","correspondingAuthor":false,"prefix":"","firstName":"Nikhil","middleName":"","lastName":"Kulkarni","suffix":""},{"id":545810264,"identity":"949847c1-9315-4528-924b-dc6477803e84","order_by":4,"name":"Beena Chudasama","email":"","orcid":"","institution":"National Institute of Occupational Health","correspondingAuthor":false,"prefix":"","firstName":"Beena","middleName":"","lastName":"Chudasama","suffix":""},{"id":545810265,"identity":"bd0adeec-3849-4ce7-add5-6b60ea51ba37","order_by":5,"name":"Sivaperumal P","email":"","orcid":"","institution":"National Institute of Occupational Health","correspondingAuthor":false,"prefix":"","firstName":"Sivaperumal","middleName":"","lastName":"P","suffix":""},{"id":545810266,"identity":"9fbb595e-932c-4a0d-9395-b9995fde2cb9","order_by":6,"name":"Rakesh Balachandar","email":"data:image/png;base64,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","orcid":"","institution":"ICMR Regional Occupational Health Centre","correspondingAuthor":true,"prefix":"","firstName":"Rakesh","middleName":"","lastName":"Balachandar","suffix":""}],"badges":[],"createdAt":"2025-10-06 05:38:39","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7788186/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7788186/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":96492575,"identity":"4b0da4a8-64e9-4bc3-9191-bfcd99513932","added_by":"auto","created_at":"2025-11-21 18:08:39","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":309998,"visible":true,"origin":"","legend":"","description":"","filename":"ManuscriptNDMarkerswithGeographicalvariation01092025.docx","url":"https://assets-eu.researchsquare.com/files/rs-7788186/v1/bb8487d36e74bc1f10a7cb77.docx"},{"id":96492578,"identity":"5993af51-33ca-495c-a77b-ce64b586ff9a","added_by":"auto","created_at":"2025-11-21 18:08:40","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":10166,"visible":true,"origin":"","legend":"","description":"","filename":"f561e125782a490db8f00073689e51da.json","url":"https://assets-eu.researchsquare.com/files/rs-7788186/v1/74224d24aa9ce270fa5eb6f8.json"},{"id":96492577,"identity":"98d2d21c-b77e-45bf-8672-25e43d985ab8","added_by":"auto","created_at":"2025-11-21 18:08:39","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":525345,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTables.docx","url":"https://assets-eu.researchsquare.com/files/rs-7788186/v1/e880f3794b1682e4f25d6c70.docx"},{"id":96603750,"identity":"f6b9ff15-39df-405e-b381-eff797ccd972","added_by":"auto","created_at":"2025-11-24 09:11:26","extension":"xml","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":508353,"visible":true,"origin":"","legend":"","description":"","filename":"f561e125782a490db8f00073689e51da1enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-7788186/v1/9a3b283680db81132b4205e7.xml"},{"id":96604068,"identity":"18126f98-a0be-49f2-b6c1-1c5c8bdf6cba","added_by":"auto","created_at":"2025-11-24 09:12:38","extension":"jpeg","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1074,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7788186/v1/8b46b1777fe434a3770c6fcc.jpeg"},{"id":96603901,"identity":"344beaf5-7099-4a47-af9e-eaa249560b47","added_by":"auto","created_at":"2025-11-24 09:12:01","extension":"jpeg","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":48397,"visible":true,"origin":"","legend":"","description":"","filename":"groupimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7788186/v1/ff204c94f1a87d63d05d7ec0.jpeg"},{"id":96604217,"identity":"ecb61bfa-8c1a-49ef-acab-63afa83c2c8e","added_by":"auto","created_at":"2025-11-24 09:13:13","extension":"png","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":935,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7788186/v1/1b22683a627bffe8120c6a94.png"},{"id":96603904,"identity":"c89a5b52-15f9-4544-98cd-542e7faf7370","added_by":"auto","created_at":"2025-11-24 09:12:01","extension":"png","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":24719,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinegroupimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7788186/v1/b61ec043474f332508a39e2c.png"},{"id":96492583,"identity":"5aa8c520-0df7-4fb5-a593-8ec75033a692","added_by":"auto","created_at":"2025-11-21 18:08:40","extension":"xml","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":488648,"visible":true,"origin":"","legend":"","description":"","filename":"f561e125782a490db8f00073689e51da1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7788186/v1/f2ce8a6266c1bc72bbf035e2.xml"},{"id":96492585,"identity":"c50b7ec0-7a6f-4733-ab7d-4864c6975801","added_by":"auto","created_at":"2025-11-21 18:08:40","extension":"html","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":525582,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7788186/v1/a05967c478c4149f3f350336.html"},{"id":96492574,"identity":"319265b6-c4fd-4ff4-ad50-f99701d273e3","added_by":"auto","created_at":"2025-11-21 18:08:39","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":28232,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of Participant Recruitment and Screening Process\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7788186/v1/8e78841c2d4cc52e79bf22a4.png"},{"id":99314268,"identity":"bf6f31d3-1056-4f37-ba53-8eeab97edd6a","added_by":"auto","created_at":"2025-12-31 16:21:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1395758,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7788186/v1/671d9057-1ab2-4d2c-9f9d-96b60607265a.pdf"},{"id":96492580,"identity":"add411f0-ccff-4c54-9434-89ef70a79e47","added_by":"auto","created_at":"2025-11-21 18:08:40","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":525345,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTables.docx","url":"https://assets-eu.researchsquare.com/files/rs-7788186/v1/d7bd6c9c039a1dda9bd7b6ad.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Plasma Biomarkers of Neurodegeneration and Neuroinflammation among Middle- Aged Adults in Western India: Implications of Racial and Geographical Variability","fulltext":[{"header":"Introduction","content":"\u003cp\u003eNeurodegenerative disorders (NDs) are a group of progressive conditions characterized by the gradual loss of structure and function of neurons, ultimately leading to neuronal death translated to its impaired functioning. (Dnyandev G. Gadhave et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; D. G. Gadhave et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Lamptey et al., \u003cspan citationid=\"CR122\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Pant et al., \u003cspan citationid=\"CR160\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Tanaka et al., \u003cspan citationid=\"CR197\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). These disorders commonly present with cognitive decline, neuropsychiatric symptoms, and, in many cases, dementia. Prominent examples include Alzheimer\u0026rsquo;s disease (AD), Parkinson\u0026rsquo;s disease (PD), Huntington\u0026rsquo;s disease (HD), amyotrophic lateral sclerosis (ALS), multiple sclerosis (MS), frontotemporal dementia, and corticobasal degeneration (Choonara et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Hui et al., \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe clinical manifestations of these disorders vary depending on the involvement of the topographical structures and the severity, making diagnosis challenging (D. G. Gadhave et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Current diagnostic approaches largely rely on clinical evaluation, neuroimaging, and other supportive investigations (Huang \u0026amp; Zhang, \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). NDs are typically irreversible, associated with significant morbidity, and have limited survival rates. Globally, major neurological disorders affected an estimated 349.2\u0026nbsp;million individuals and accounted for nearly 10\u0026nbsp;million deaths in 2019 alone (Ding et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Huang et al., \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eRecent advancements have identified several blood-based biomarkers that offer promise for improving the early detection and monitoring of neurodegenerative diseases. Specific assays detecting the hallmark Alzheimer\u0026rsquo;s disease pathologies, such as amyloid-β peptides and tau proteins are developed. In addition, a range of nonspecific biomarkers indicative of neuronal injury\u0026mdash;including neurofilament light chain (NfL), α-synuclein, β-synuclein, and ubiquitin-C-terminal hydrolase-L1 (UCH-L1)\u0026mdash;as well as markers of glial activation such as glial fibrillary acidic protein (GFAP), developed. (Alcolea et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Kon\u0026iacute;čkov\u0026aacute; et al., \u003cspan citationid=\"CR120\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThese biomarkers reflect key pathophysiological processes in neurodegeneration and neuroinflammation and hold substantial potential for use in screening, diagnosis, and monitoring disease progression or response to therapy. While their use is currently concentrated in research settings, rapid progress in assay development and standardization indicates their likely translation into clinical practice across a range of healthcare settings (Alcolea et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Luebke et al., \u003cspan citationid=\"CR137\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The present study explores the levels of these blood-based biomarkers among urban residents in western India, contributing to the growing body of evidence on their applicability in diverse population settings.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eParticipant selection\u003c/strong\u003e\u003cp\u003eCurrent study is part of larger study, that investigates the levels of neurodegeneration and neuro-inflammation markers among large group of community residents (Balachandar et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Apparently healthy residents with no ongoing medications, aged between 40\u0026ndash;60 years of either sex are included in the current study. Participants with history of current / past neurological illness or on medication suggestive of neurological condition or with family history/first degree relative with hereditary neurological conditions are excluded from the study. Additionally, participants on any form of medications including multivitamins for either short term (acute) illness / long term illness are excluded. However, participants on supplements such as multivitamins are included in the study, ensuring the multivitamin(s) are not consumed under prescription for therapeutic purposes.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eRecruitment of Participants\u003c/strong\u003e\u003cp\u003eThe study, participants residing in Ahmedabad district of western India are recruited Total 405 participants meeting the selection criteria are recruited. Details of inclusion and exclusion is illustrated in the flow chart below. The study is initiated after obtaining necessary institutional ethical clearance, adhering to the national ethical guidelines for biomedical and health research involving human (Mathur, \u003cspan citationid=\"CR143\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\u003c/p\u003e\u003cp\u003eDetails of the study was shared with the local community by word of mouth, community leaders / representatives and community health workers. Interested volunteering participants ae invited to participate at nearby specific locations such as primary health facility / community health care centers and make-shift arrangements at nearby residents, that enabled data collection, including aseptic collection of blood. Consenting participants meeting the inclusion and exclusion criteria are recruited. Necessary socio-clinical demographic details of the participants are recorded using pre-validated structured questionnaire.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eCollection of biological samples\u003c/strong\u003e\u003cp\u003eUnder aseptic condition about 5 ml venous blood is drawn by trained phlebotomist ensuring safety and comfort of the participant. All blood samples tagged with participant ID are transported under cold chain to the laboratory the same day. The blood samples after returning the room temperature (20\u0026ndash;240C) are centrifuged to separate the serum. The serum of each sample are aliquot in 2 cryo-vials and stored at -80\u003csup\u003eo\u003c/sup\u003eC.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eOther assessments\u003c/strong\u003e\u003cp\u003eBlood pressure, body weight, and height of the consenting participants are measured using pre-validated and calibrated tools as described previously (Upadhyay et al., \u003cspan citationid=\"CR202\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Body mass index was calculated by the conventional method (i.e., a ratio of weight in kilograms to the square of the height in meters - (Weight in Kg)/(height in m2). Hemoglobin and random blood sugar are estimated using Mission (Accon laboratories, California) and Accu-Chek active (Roche diabetes care India Pvt. Ltd.) point of care devices respectively. A single drop of blood drawn from the fingertip is directly collected under aseptic precautions on to the respective probes to obtain the haemoglobin (g/dL) and random blood sugar (mg/dL) as recommended by the manufacturer. All participants are screened for gross cognitive dysfunction by trained personnel, for orientation of day, time, place, purpose of their visit and mode of arrival at the study location. Further, checked for three object immediate and delayed recall, simple arithmetic calculation and recent local \u0026ndash; regional updates regarding local community leader or recent local celebrations. Participants failing to responding to all these questions are excluded from the study and referred for further evaluation of gross cognitive dysfunction.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eEstimation of neurodegenerative markers\u003c/strong\u003e\u003cp\u003eThe markers of neuro inflammation and neurodegenerative disorders (Aβ\u003csub\u003e1\u0026minus;42\u003c/sub\u003e, Total Tau, ⍺-Synuclein, BDNF, and GFAP) are estimated from the samples using high sensitivity sandwich ELISA method Elab Science Inc., USA, as described by the manufacturer (Elab Science Inc., USA). Wherein, the analyte protein available with the standard or sample is combined to form a sandwich complex with the target-specific antibody pre-coated on microplates and a biotinylated detection antibody. The optical density (OD) is measured by spectrophotometrically at a 450 nm wavelength. The intensity of this signal (OD) is directly proportional to the concentration of the target present in the original specimen. The levels of markers in the samples are determined by comparing the OD of the samples to the standard curve.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003cp\u003eThe normality of the distributions was tested using the Kolmogorov\u0026ndash;Smirnov test. Data following a normal distribution are presented as the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD), while data not following a normal distribution are expressed as the median and interquartile range (IQR). Student\u0026rsquo;s t-test was applied to compare plasma biomarkers (Aβ1\u0026ndash;42, Total Tau, α-Synuclein, BDNF, and GFAP) between age groups and gender groups. All tests were two-tailed, with a significance level set at \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Outlier observations that fell above the upper bound limit (75th percentile\u0026thinsp;+\u0026thinsp;1.5 x IQR) and below the lower bound limit (25th percentile \u0026minus;\u0026thinsp;1.5 x IQR) were removed from the analysis. Data that did not follow a normal distribution were logarithmically transformed prior to all analyses and transformed data were then converted back to their original scale using an antilog transformation before reporting. Statistical analyses were performed using SPSS version 26.0 (IBM Corporation, Armonk, NY, USA).\u003c/p\u003e\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003eParticipants Characteristics:\u003c/h2\u003e\u003cp\u003eFollowing the recruitment and screening process outlined in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, a total of 405 participants aged 40\u0026ndash;60 years met the inclusion and exclusion criteria.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e----- Insert Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e -----\u003c/p\u003e\u003cp\u003eBaseline demographic and clinical characteristics of the study participants, including the distribution of age, sex, and key health parameters such as body mass index (BMI), systolic and diastolic blood pressure, hemoglobin concentration, and random blood sugar levels, are summarized in Table\u0026nbsp;1.\u003c/p\u003e\u003cp\u003e----- Insert table 1 -----\u003c/p\u003e\u003cp\u003eThe mean BMI of the participants is 24.54\u0026thinsp;\u0026plusmn;\u0026thinsp;4.87 kg/m\u0026sup2;, indicating that the average participant is in the overweight category. About 21.7% (n\u0026thinsp;=\u0026thinsp;88) of the participants are identified with elevated blood pressures (i.e. either SBP\u0026thinsp;\u0026ge;\u0026thinsp;140 and DBP\u0026thinsp;\u0026ge;\u0026thinsp;90 mm Hg)(James et al., \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The average hemoglobin level among participants are suggestive of mild \u0026ndash; moderate anemia, further, about 1.48% (n\u0026thinsp;=\u0026thinsp;6) participants exhibited elevated glucose levels (\u0026ge;\u0026thinsp;200 mg/dL)(Mouri MI, \u003cspan citationid=\"CR151\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; WHO, \u003cspan citationid=\"CR221\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Participants with elevated blood pressure, blood glucose levels and anemia are referred to the community non-communicable disease clinic for further evaluation and management.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003ePlasma Levels of Biomarkers of Neuroinflammatory and Neurodegenerative Status:\u003c/h3\u003e\n\u003cp\u003eTable-2 presents the distribution of plasma biomarkers related to neurodegenerative status among study participants. The biomarkers assessed included amyloid-β (1\u0026ndash;42), total tau, α-synuclein, brain-derived neurotrophic factor (BDNF), and glial fibrillary acidic protein (GFAP). Data are summarized as median values with interquartile ranges (IQR) for the overall study group, stratified by gender and age groups.\u003c/p\u003e\u003cp\u003e----- Insert table 2 -----\u003c/p\u003e\u003cp\u003eSex- and age-stratified analysis of plasma biomarkers revealed distinct patterns. Females exhibited higher median levels of amyloid-β\u003csub\u003e(1\u0026ndash;42)\u003c/sub\u003e, α-synuclein, and BDNF compared to males, whereas males showed relatively higher levels of Total τ and GFAP. With respect to age, participants aged 51\u0026ndash;60 years demonstrated higher concentrations of all biomarkers except α-synuclein, which remained comparable across age groups. Although these differences indicate potential sex- and age-related trends, the overall data did not establish consistent generalized patterns across the study group.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eA systematic search of the PubMed database was conducted to identify studies published since 2015 that reported circulating biomarkers of neurodegeneration and neuroinflammation in apparently healthy adults. Details of these studies are summarized in supplementary tables S3\u0026ndash;S7. Considerable variability was observed across regions and cohorts, likely reflecting differences in demographics, assay methodology, and reporting units. The findings from the present study are discussed below in relation to this broader literature.\u003c/p\u003e\n\u003ch3\u003eAmyloid β:\u003c/h3\u003e\n\u003cp\u003eSeventy datasets from multiple countries reported circulating serum amyloid-β\u003csub\u003e1\u0026ndash;42\u003c/sub\u003e levels in healthy adults (Supplement table 1). North America \u0026ndash; U.S. studies often involved older cohorts (\u0026gt;\u0026thinsp;65 years) and reported mean concentrations in the 40\u0026ndash;45 pg/mL range (Lu et al., \u003cspan citationid=\"CR135\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; West et al., \u003cspan citationid=\"CR219\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), though much higher values (320 pg/mL) (Lashkari et al., \u003cspan citationid=\"CR123\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) are also noted in specific subgroups. Younger adults (\u0026lt;\u0026thinsp;50 years) typically present lower means (e.g. 13.8 pg/mL) (Mehta et al., \u003cspan citationid=\"CR145\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). East Asia \u0026ndash; Chinese data show marked variation, from very low means (~\u0026thinsp;5\u0026ndash;8 pg/mL) (Gu et al., \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; L.-M. Li et al., \u003cspan citationid=\"CR126\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) to markedly elevated levels (\u0026gt;\u0026thinsp;165 pg/mL) (Tian et al., \u003cspan citationid=\"CR199\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Similarly among the Taiwanese cohorts the levels ranged widely, from ~\u0026thinsp;1.3 pg/mL (Chung et al., \u003cspan citationid=\"CR154\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) to ~\u0026thinsp;90 pg/mL (Hsu et al., \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), with older cohorts often exhibiting higher levels. Europe \u0026ndash; Italian, Spanish, and Finnish studies tend toward intermediate means (10\u0026ndash;35 pg/mL) (Gil-Montoya et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Martiskainen et al., \u003cspan citationid=\"CR142\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Pilotto et al., \u003cspan citationid=\"CR166\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), though occasional outliers (e.g., \u0026gt;\u0026thinsp;320 pg/mL in Chinese cohort) (Ding et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) suggest methodological and demographic influences. Other Regions \u0026ndash; Isolated datasets from Australia (Sewell et al., \u003cspan citationid=\"CR183\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), India (R. Singh et al., \u003cspan citationid=\"CR189\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), Mexico (Castillo-Mendieta et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and Poland (Przybylska-Kuć et al., \u003cspan citationid=\"CR170\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) reveal similarly broad variability, with age appearing as a partial driver but not consistently predictive across settings. Studies provided limited evidence regarding sex-related variations in Amyloid β levels, thereby hard to derive directions or conclusions.\u003c/p\u003e\u003cp\u003e\u003cb\u003eTotal tau (\u003c/b\u003e\u003cb\u003eτ\u003c/b\u003e\u003cb\u003e)\u003c/b\u003e:\u003c/p\u003e\u003cp\u003eAcross 54 datasets from multiple countries, Tau protein concentrations in apparently healthy adults showed marked heterogeneity between populations and regions (Supplement table 2). The tau concentrations among apparently healthy adults, exhibited marked heterogeneity across countries and assay types, with central tendency values ranging from extremely low (0.007 pg/mL, Sweden) (Gren et al., \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) to markedly high (\u0026gt;\u0026thinsp;250 pg/mL, China) (Ding et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). East Asia \u0026ndash; Multiple cohorts from China and Taiwan dominate the literature, but values differ substantially even within the same country. In China, median/mean tau levels range from 0.78 pg/mL (Jingshan Chen et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) to 259.59 pg/mL (Ding et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), with older cohorts (mean age\u0026thinsp;\u0026gt;\u0026thinsp;70 years) generally showing higher concentrations (Jiang et al., \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Taiwanese cohorts report means from ~\u0026thinsp;1.8 pg/mL (Hsu et al., \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) to \u0026gt;\u0026thinsp;22 pg/mL (Wei et al., \u003cspan citationid=\"CR216\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), again with higher levels often observed in older adults. Japan \u0026ndash; Studies consistently report low tau concentrations, often\u0026thinsp;\u0026lt;\u0026thinsp;1 pg/mL, such as 0.47 pg/mL in a younger cohort (Kasai et al., \u003cspan citationid=\"CR114\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and 0.81 pg/mL in older adults (Kasai et al., \u003cspan citationid=\"CR113\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Europe \u0026ndash; Median levels in healthy European adults are generally in the 1\u0026ndash;3 pg/mL range, as seen in Spain and UK cohorts (Fortea et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; G\u0026oacute;mez-Tortosa et al., \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) and Rome (Abu-Rumeileh et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), though Belgium (De Vos et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) reported higher medians in arbitrary units (AU). North America \u0026ndash; U.S. studies show substantial variability, with lower medians (~\u0026thinsp;1\u0026ndash;4 pg/mL) (Bogoslovsky et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Gill et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) in mid-aged samples, but unusually high medians in certain Utah cohorts (40.8 pg/mL) (Galenko et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Younger U.S. samples exhibited very high values when measured in different units (e.g., 62.59 fg/mL) (Rubenstein et al., \u003cspan citationid=\"CR177\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Other Regions \u0026ndash; Ukraine (Lekomtseva, \u003cspan citationid=\"CR125\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) stands out with a high mean of 71.14 pg/mL in a relatively young cohort (mean age\u0026thinsp;~\u0026thinsp;30 years). Mexican controls (Castillo-Mendieta et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and Swedish cohorts (Shahim et al., \u003cspan citationid=\"CR184\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) typically fall within 1\u0026ndash;3 pg/mL.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eα - Synuclein:\u003c/h2\u003e\u003cp\u003eAbout 22 datasets were available across the globe (Supplement table 3). These studies varied in the methodology, bio-specimen used for estimation and reporting of units. East Asia \u0026ndash; Chinese cohorts report markedly divergent values, ranging from very low medians (~\u0026thinsp;9.42 pg/mL) (Jiao et al., \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) to high means exceeding 3,200 pg/mL (Xu et al., \u003cspan citationid=\"CR226\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Studies using nanogram-scale reporting units yield mid-range means such as 3.01 ng/mL (Zou et al., \u003cspan citationid=\"CR239\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and 21.08 ng/mL (Sun et al., \u003cspan citationid=\"CR194\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Older participants (\u0026gt;\u0026thinsp;65 years) often exhibit higher values, though exceptions exist 274.31 pg/mL at mean age 64.8 years (Ding et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) vs. 297.1 pg/mL at mean age 58.3 years (Wang et al., \u003cspan citationid=\"CR152\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Taiwanese studies reveal extremely low means (0.09 pg/mL) (C. H. Lin et al., \u003cspan citationid=\"CR131\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) alongside higher concentrations (80.9 fg/mL) (Hong et al., \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), again suggesting methodological influences. Southeast Asia \u0026ndash; Singaporean data show elevated mean values (13,057 pg/mL); (Ng et al., \u003cspan citationid=\"CR152\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) in a mid-60s cohort, aligning more closely with the upper Chinese range than with other Asian reports. Europe \u0026ndash; Danish cohorts report high mean concentrations 127 ng/mL (Brudek et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) \u0026minus;\u0026thinsp;638 ng/mL (Folke et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) in relatively younger samples (mean ages in early to mid-40s). Greek data show both high (28.3 ng/mL) (Emmanouilidou et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and low (1.9 pg/mL)(Bougea et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) values depending on analytical context. North America \u0026ndash; U.S. reports span extreme ranges, from very high means (\u0026gt;\u0026thinsp;115,000 pg/mL)(Goldman et al., \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) to moderate nanogram-level concentrations (0.64 ng/mL) (Chan et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), Australia-born cohort measured in USA labs. Other Regions \u0026ndash; Russian median values are low (0.85 pg/mL) (Pchelina et al., \u003cspan citationid=\"CR162\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), though with wide interquartile ranges. Australian and Chinese ng/mL-range findings(Chan et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Fan et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) indicate cross-continental overlaps in mid-range levels.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eBrain Derived Neurotrophic Factor:\u003c/h3\u003e\n\u003cp\u003eData from 78 studies across six continents reported BDNF concentrations among apparently healthy adults (Supplement table 4). Reported BDNF concentrations in apparently healthy adults demonstrate striking heterogeneity across continents, with mean or median values ranging from \u0026lt;\u0026thinsp;50 pg/mL in Slovenian cohorts(Passaro et al., \u003cspan citationid=\"CR161\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) to \u0026gt;\u0026thinsp;88,000 pg/mL in select Chinese samples (Fang et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). South America: Brazilian studies reported moderate-to-high levels, with means from ~\u0026thinsp;1,695 pg/mL (Ribeiro et al., \u003cspan citationid=\"CR174\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) to \u0026gt;\u0026thinsp;28,000 pg/mL (Marston et al., \u003cspan citationid=\"CR141\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). A large cohort from Brazil (Brunoni et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) reported a mean of 5,947 pg/mL, while specific subgroups (Uint et al., \u003cspan citationid=\"CR201\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)reached medians above 21,000 pg/mL. Europe: European cohorts displayed wide variability. Ireland (Pratt et al., \u003cspan citationid=\"CR169\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) showed relatively low means (~\u0026thinsp;1,350 pg/mL), whereas Polish participants in (Przybylska et al., \u003cspan citationid=\"CR171\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)had the highest European mean (50,663 pg/mL). Mediterranean countries like Italy (Diz et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and Spain (Silva-Pe\u0026ntilde;a et al., \u003cspan citationid=\"CR187\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) generally fell in the mid-range (700\u0026ndash;6,300 pg/mL). Notably, Scandinavian cohorts showed extremes \u0026mdash; Denmark\u0026rsquo;s (J\u0026oslash;rgensen et al., \u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) reported a median of 167,700 pg/mL, whereas Sweden (Jasim et al., \u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) reported a mean of 151.8 pg/mL. Asia: Taiwanese cohorts spanned a wide range, from ~\u0026thinsp;489 pg/mL (Wen et al., \u003cspan citationid=\"CR218\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) to \u0026gt;\u0026thinsp;20,000 pg/mL (Wang et al., \u003cspan citationid=\"CR210\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Chinese data showed the largest global spread \u0026mdash; low means (~\u0026thinsp;353 pg/mL) (Zhao et al., \u003cspan citationid=\"CR238\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) in younger adults, and extreme highs (88,500 pg/mL) (Fang et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) in mid-aged cohorts. Korean studies reported means between ~\u0026thinsp;733 pg/mL (Lee et al., \u003cspan citationid=\"CR124\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and ~\u0026thinsp;9,288 pg/mL (An et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Middle East \u0026amp; Africa: Kuwaiti cohorts demonstrated moderate variability, with means between 3,060 and 3,880 pg/mL (Al-Temaimi et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Al-Temaimi et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Ghanaian data (Agyekum \u0026amp; Yeboah, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) revealed a high mean of 26,100 pg/mL, while Jordanian adults (Alomari et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) exhibited elevated means of 25,000 pg/mL. North America \u0026amp; Oceania: U.S. and Australian cohorts tended toward the lower-to-mid range, with means often below 2,000 pg/mL (Cabral et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Weickert et al., \u003cspan citationid=\"CR217\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), except for (Marston et al., \u003cspan citationid=\"CR141\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)in Australia, reporting\u0026thinsp;\u0026gt;\u0026thinsp;28,000 pg/mL.\u003c/p\u003e\n\u003ch3\u003eGlial Fibrillary Acid Protein:\u003c/h3\u003e\n\u003cp\u003eA total of 57 studies from diverse geographical regions, including North America, Europe, Asia, Africa, and Oceania, reported GFAP concentrations among apparently healthy adult control populations (Supplement table 5). Substantial variation in central tendency values was observed across countries and regions. Reported GFAP concentrations among apparently healthy adults varied widely across 70\u0026thinsp;+\u0026thinsp;cohorts from diverse regions, with mean or median values ranging from \u0026lt;\u0026thinsp;1 pg/mL in some U.S. and Greek datasets (Bogoslovsky et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Katsanos et al., \u003cspan citationid=\"CR115\" class=\"CitationRef\"\u003e2017\u003c/span\u003e)to \u0026gt;\u0026thinsp;600 pg/mL in West African populations (Sarfo et al., \u003cspan citationid=\"CR181\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). European cohorts generally exhibited moderate-to-high levels. Northern and Western European studies reported means or medians between ~\u0026thinsp;50 and 150 pg/mL (Axelsson et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Beyer et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Verberk et al., \u003cspan citationid=\"CR206\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), while Southern European populations (e.g., Italy, Spain) often fell in the upper range (G\u0026oacute;mez-Tortosa et al., \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Pilotto et al., \u003cspan citationid=\"CR166\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Swedish studies (Kanberg et al., \u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Kanberg et al., \u003cspan citationid=\"CR112\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) consistently reported high medians (\u0026gt;\u0026thinsp;120 pg/mL), particularly in older cohorts. North American datasets demonstrated substantial heterogeneity. While several U.S. cohorts reported low-to-moderate levels (Gill et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Miner et al., \u003cspan citationid=\"CR147\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), others\u0026mdash;particularly those with older participants\u0026mdash;showed markedly elevated means (\u0026gt;\u0026thinsp;120 pg/mL) (Mandelblatt et al., \u003cspan citationid=\"CR138\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Vallabh et al., \u003cspan citationid=\"CR203\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Canadian and multi-country North American\u0026ndash;European datasets (Heller et al., \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Sudre et al., \u003cspan citationid=\"CR193\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) also showed upper-range values (~\u0026thinsp;100\u0026ndash;126 pg/mL). Asian cohorts were more variable. Several Chinese and Taiwanese datasets reported low means (~\u0026thinsp;18\u0026ndash;56 pg/mL) in middle-aged participants (K. Li et al., \u003cspan citationid=\"CR126\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Wei et al., \u003cspan citationid=\"CR215\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Xu et al., \u003cspan citationid=\"CR226\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), but others showed high outliers, such as \u0026gt;\u0026thinsp;800 pg/mL in a Chinese subgroup (He et al., \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Japanese data (Hirata et al., \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) fell within the high European range (~\u0026thinsp;105 pg/mL). African data were sparse but extreme: in Ghana and Nigeria, (Sarfo et al., \u003cspan citationid=\"CR181\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) reported a mean of 676 pg/mL with very high variability (SD\u0026thinsp;=\u0026thinsp;928), likely reflecting methodological or demographic factors.\u003c/p\u003e\u003cp\u003eThe baseline characteristics provides a comprehensive overview of the study cohort and serves as the foundation for interpreting subsequent analyses of neurodegenerative biomarkers. Evidence from this study, together with data from other regions of the world, suggests potential variations in biomarker levels among apparently healthy adults, possibly attributable to racial, genetic, and/or environmental differences. Notably, the Indian cohort demonstrated substantially different levels compared to other populations, indicating possible regional or genetic influences on protein expression. These findings underscore the importance of accounting for racial and ethnic differences when interpreting biomarker data and highlight the need for further research to elucidate the underlying causes of such variability.\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eLimitations and Future Directions:\u003c/h2\u003e\u003cp\u003eThe cross-sectional design of the current study limits the ability to establish cut-off levels for these neurodegenerative biomarker. Longitudinal studies are needed to assess the trajectory of these markers over time. The study relied on plasma samples, which may not capture the full spectrum of neurodegenerative changes occurring in the brain. Future research should consider using cerebrospinal fluid (CSF) samples for a more comprehensive assessment.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study highlights the potential variation in these neurodegenerative biomarkers and underscores the importance of considering race, and geographical factors in neurodegenerative disease research. The observed variability in biomarker levels across different geographical groups further points to the need for personalized approaches in understanding and addressing neurodegenerative diseases. These findings contribute to the growing body of evidence linking personal, genetic and environmental factors with neurological health and provide a foundation for future research.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical approval and consent to participate:\u003c/strong\u003e The study was conducted following approval from the Human Ethics Committee of the ICMR-National Institute of Occupational Health. All procedures involving human participants adhered to the ethical standards outlined in the national guidelines for biomedical and health research involving human participants (Mathur \u0026amp; Swaminathan, 2018).\u003c/p\u003e\n\u003cp\u003eWritten informed consent was obtained from all participants prior to data and sample collection. This included consent for the use of their blood samples, demographic profiles, and clinical data for the analysis of plasma biomarkers indicative of neuroinflammatory and neurodegenerative conditions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publication:\u003c/strong\u003e This manuscript does not contain any personally identifiable information. All participants were adults who voluntarily consented to the use of anonymized data for scientific publication purposes. Measures were taken to ensure the confidentiality and privacy of participant information throughout the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u003c/strong\u003e Data is provided within the manuscript or supplementary information files.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interest:\u003c/strong\u003e The authors declare no conflicts of interest, financial or otherwise, that could have influenced the outcomes or interpretation of the research presented in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e KU (lead author) received grants from Department of Health Research, Ministry of Health and Family Welfare, Govt. of India (F.No.R.11013/01/2023-GIA/HR dated 21.02.2023), the data from the study was used for this manuscript\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contribution:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eKU:\u003c/em\u003e\u003c/strong\u003e concept, design, definition of intellectual content, literature search, data acquisition, manuscript preparation, manuscript editing and review\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAV:\u003c/em\u003e\u003c/strong\u003e concept, design, definition of intellectual content, literature search, data acquisition, data analysis\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eDPS:\u003c/em\u003e\u003c/strong\u003e design, definition of intellectual content, literature search, data acquisition, data analysis\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eNK:\u003c/em\u003e\u003c/strong\u003e concept, design, literature search, data acquisition, data analysis\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eBC:\u003c/em\u003e\u003c/strong\u003e concept, definition of intellectual content, data acquisition, manuscript preparation\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003ePS:\u003c/em\u003e\u003c/strong\u003e design, definition of intellectual content, literature search, data acquisition, data analysis, manuscript preparation\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eRB:\u003c/em\u003e\u003c/strong\u003e concept, design, definition of intellectual content, literature search, data acquisition, manuscript preparation, manuscript editing and review\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe investigators would like to express their sincere gratitude to the Department of Health Research (DHR) for funding this study. We also acknowledge the support of the Director-General of ICMR, the Director of ICMR-NIOH, and the administrative staff for their invaluable assistance in facilitating the execution of this study. Our heartfelt thanks go to the technical staff for their dedicated efforts in collecting demographic details, biological samples, and performing the analyses that were crucial for data collection. We are deeply grateful to the participants of the study for their willingness to participate and provide consent for the collection of their demographic information and biological samples, without which this study would not have been possible. Competent state authority for providing permission and facilitating the data collection and the medical officers, their support community staff in executing the mobilization of participations. Their unwavering support and dedication have been instrumental in ensuring the successful execution of this research.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbu-Rumeileh, S., Baiardi, S., Ladogana, A., Zenesini, C., Bartoletti-Stella, A., Poleggi, A., . . . Parchi, P. (2020). Comparison between plasma and cerebrospinal fluid biomarkers for the early diagnosis and association with survival in prion disease. \u003cem\u003eJ Neurol Neurosurg Psychiatry\u003c/em\u003e,\u003cem\u003e 91\u003c/em\u003e(11), 1181-1188. https://doi.org/10.1136/jnnp-2020-323826 \u003c/li\u003e\n\u003cli\u003eAgyekum, J. A., \u0026amp; Yeboah, K. (2024). Brain-Derived Neurotrophic Factor is Associated with Self-Reported Quality of Sleep in Type 2 Diabetes Patients in Ghana. \u003cem\u003eExp Clin Endocrinol Diabetes\u003c/em\u003e,\u003cem\u003e 132\u003c/em\u003e(7), 407-413. https://doi.org/10.1055/a-2273-6527 \u003c/li\u003e\n\u003cli\u003eAl-Rawaf, H. A., Alghadir, A. H., \u0026amp; Gabr, S. A. (2021). Molecular Changes in Circulating microRNAs\u0026apos; Expression and Oxidative Stress in Adults with Mild Cognitive Impairment: A Biochemical and Molecular Study. \u003cem\u003eClin Interv Aging\u003c/em\u003e,\u003cem\u003e 16\u003c/em\u003e, 57-70. https://doi.org/10.2147/cia.S285689 \u003c/li\u003e\n\u003cli\u003eAl-Temaimi, R., AbuBaker, J., Al-Khairi, I., \u0026amp; Alroughani, R. (2017). Remyelination modulators in multiple sclerosis patients. \u003cem\u003eExp Mol Pathol\u003c/em\u003e,\u003cem\u003e 103\u003c/em\u003e(3), 237-241. https://doi.org/10.1016/j.yexmp.2017.11.004 \u003c/li\u003e\n\u003cli\u003eAl-Temaimi, R., Alshammari, N., \u0026amp; Alroughani, R. (2024). Analysis of potential microRNA biomarkers for multiple sclerosis. \u003cem\u003eExp Mol Pathol\u003c/em\u003e,\u003cem\u003e 137\u003c/em\u003e, 104903. https://doi.org/10.1016/j.yexmp.2024.104903 \u003c/li\u003e\n\u003cli\u003eAlcolea, D., Beeri, M. S., Rojas, J. C., Gardner, R. C., \u0026amp; Lle\u0026oacute;, A. (2023). Blood Biomarkers in Neurodegenerative Diseases: Implications for the Clinical Neurologist. \u003cem\u003eNeurology\u003c/em\u003e,\u003cem\u003e 101\u003c/em\u003e(4), 172-180. https://doi.org/10.1212/wnl.0000000000207193 \u003c/li\u003e\n\u003cli\u003eAldoghachi, A. F., Tor, Y. S., Redzun, S. Z., Lokman, K. A. B., Razaq, N. A. A., Shahbudin, A. F., . . . Ling, K. H. (2019). Screening of brain-derived neurotrophic factor (BDNF) single nucleotide polymorphisms and plasma BDNF levels among Malaysian major depressive disorder patients. \u003cem\u003ePLoS One\u003c/em\u003e,\u003cem\u003e 14\u003c/em\u003e(1), e0211241. https://doi.org/10.1371/journal.pone.0211241 \u003c/li\u003e\n\u003cli\u003eAlomari, M. A., Khalil, H., Khabour, O. F., Alzoubi, K. H., \u0026amp; Dersieh, E. H. (2018). Altered cardiovascular function is related to reduced BDNF in Parkinson\u0026apos;s disease. \u003cem\u003eExp Aging Res\u003c/em\u003e,\u003cem\u003e 44\u003c/em\u003e(3), 232-245. https://doi.org/10.1080/0361073x.2018.1449589 \u003c/li\u003e\n\u003cli\u003eAmadio, P., Baldassarre, D., Sandrini, L., Weksler, B. B., Tremoli, E., \u0026amp; Barbieri, S. S. (2017). Effect of cigarette smoke on monocyte procoagulant activity: Focus on platelet-derived brain-derived neurotrophic factor (BDNF). \u003cem\u003ePlatelets\u003c/em\u003e,\u003cem\u003e 28\u003c/em\u003e(1), 60-65. https://doi.org/10.1080/09537104.2016.1203403 \u003c/li\u003e\n\u003cli\u003eAmadio, P., Porro, B., Sandrini, L., Fiorelli, S., Bonomi, A., Cavalca, V., . . . Barbieri, S. S. (2019). Patho- physiological role of BDNF in fibrin clotting. \u003cem\u003eSci Rep\u003c/em\u003e,\u003cem\u003e 9\u003c/em\u003e(1), 389. https://doi.org/10.1038/s41598-018-37117-1 \u003c/li\u003e\n\u003cli\u003eAn, J. H., Jang, E. H., Kim, A. Y., Fava, M., Mischoulon, D., Papakostas, G. I., . . . Jeon, H. J. (2019). Ratio of plasma BDNF to leptin levels are associated with treatment response in major depressive disorder but not in panic disorder: A 12-week follow-up study. \u003cem\u003eJ Affect Disord\u003c/em\u003e,\u003cem\u003e 259\u003c/em\u003e, 349-354. https://doi.org/10.1016/j.jad.2019.08.021 \u003c/li\u003e\n\u003cli\u003eAxelsson, T., Zetterberg, H., Blennow, K., Arslan, B., Ashton, N. J., Axelsson, M., . . . Guron, G. (2025). Plasma concentrations of neurofilament light, p-Tau231 and glial fibrillary acidic protein are elevated in patients with chronic kidney disease and correlate with measured glomerular filtration rate. \u003cem\u003eBMC Nephrol\u003c/em\u003e,\u003cem\u003e 26\u003c/em\u003e(1), 231. https://doi.org/10.1186/s12882-025-04130-2 \u003c/li\u003e\n\u003cli\u003eBalachandar, R., Viramgami, A., Singh, D. P., Kulkarni, N., Chudasama, B., Sivaperumal, P., \u0026amp; Upadhyay, K. (2025). Association between chronic PM2.5 exposure and neurodegenerative biomarkers in adults from critically polluted area. \u003cem\u003eBMC Public Health\u003c/em\u003e,\u003cem\u003e 25\u003c/em\u003e(1), 1413. https://doi.org/10.1186/s12889-025-22641-3 \u003c/li\u003e\n\u003cli\u003eBarth\u0026eacute;lemy, N. R., Horie, K., Sato, C., \u0026amp; Bateman, R. J. (2020). Blood plasma phosphorylated-tau isoforms track CNS change in Alzheimer\u0026apos;s disease. \u003cem\u003eJ Exp Med\u003c/em\u003e,\u003cem\u003e 217\u003c/em\u003e(11). https://doi.org/10.1084/jem.20200861 \u003c/li\u003e\n\u003cli\u003eBaumeister, D., Eich, W., Saft, S., Geisel, O., Hellweg, R., Finn, A., . . . Tesarz, J. (2019). No evidence for altered plasma NGF and BDNF levels in fibromyalgia patients. \u003cem\u003eSci Rep\u003c/em\u003e,\u003cem\u003e 9\u003c/em\u003e(1), 13667. https://doi.org/10.1038/s41598-019-49403-7 \u003c/li\u003e\n\u003cli\u003eBaumert, B., Przybycień, K., Paczkowska, E., Kotowski, M., Pius-Sadowska, E., Safranow, K., . . . Machaliński, B. (2019). Novel Evidence of the Increase in Angiogenic Factor Plasma Levels after Lineage-Negative Stem/Progenitor Cell Intracoronary Infusion in Patients with Acute Myocardial Infarction. \u003cem\u003eInt J Mol Sci\u003c/em\u003e,\u003cem\u003e 20\u003c/em\u003e(13). https://doi.org/10.3390/ijms20133330 \u003c/li\u003e\n\u003cli\u003eBentivenga, G. M., Baiardi, S., Mastrangelo, A., Zenesini, C., Mammana, A., Rossi, M., . . . Parchi, P. (2024). Diagnostic and Prognostic Value of Plasma GFAP in Sporadic Creutzfeldt-Jakob Disease in the Clinical Setting of Rapidly Progressive Dementia. \u003cem\u003eInt J Mol Sci\u003c/em\u003e,\u003cem\u003e 25\u003c/em\u003e(10). https://doi.org/10.3390/ijms25105106 \u003c/li\u003e\n\u003cli\u003eBeyer, L., Stocker, H., Rujescu, D., Holleczek, B., Stockmann, J., Nabers, A., . . . Gerwert, K. (2023). Amyloid-beta misfolding and GFAP predict risk of clinical Alzheimer\u0026apos;s disease diagnosis within 17 years. \u003cem\u003eAlzheimers Dement\u003c/em\u003e,\u003cem\u003e 19\u003c/em\u003e(3), 1020-1028. https://doi.org/10.1002/alz.12745 \u003c/li\u003e\n\u003cli\u003eBoerwinkle, A. H., Wisch, J. K., Handen, B. L., Head, E., Mapstone, M., Rafii, M. S., . . . Ances, B. M. (2025). The mediating role of plasma glial fibrillary acidic protein in amyloid and tau pathology in Down\u0026apos;s syndrome. \u003cem\u003eAlzheimers Dement\u003c/em\u003e,\u003cem\u003e 21\u003c/em\u003e(1), e14359. https://doi.org/10.1002/alz.14359 \u003c/li\u003e\n\u003cli\u003eBogoslovsky, T., Wilson, D., Chen, Y., Hanlon, D., Gill, J., Jeromin, A., . . . Diaz-Arrastia, R. (2017). Increases of Plasma Levels of Glial Fibrillary Acidic Protein, Tau, and Amyloid \u0026beta; up to 90 Days after Traumatic Brain Injury. \u003cem\u003eJ Neurotrauma\u003c/em\u003e,\u003cem\u003e 34\u003c/em\u003e(1), 66-73. https://doi.org/10.1089/neu.2015.4333 \u003c/li\u003e\n\u003cli\u003eBolsewig, K., van Unnik, A., Blujdea, E. R., Gonzalez, M. C., Ashton, N. J., Aarsland, D., . . . Lemstra, A. W. (2024). Association of Plasma Amyloid, P-Tau, GFAP, and NfL With CSF, Clinical, and Cognitive Features in Patients With Dementia With Lewy Bodies. \u003cem\u003eNeurology\u003c/em\u003e,\u003cem\u003e 102\u003c/em\u003e(12), e209418. https://doi.org/10.1212/wnl.0000000000209418 \u003c/li\u003e\n\u003cli\u003eBougea, A., Stefanis, L., Emmanouilidou, E., Vekrelis, K., \u0026amp; Kapaki, E. (2020). High discriminatory ability of peripheral and CFSF biomarkers in Lewy body diseases. \u003cem\u003eJ Neural Transm (Vienna)\u003c/em\u003e,\u003cem\u003e 127\u003c/em\u003e(3), 311-322. https://doi.org/10.1007/s00702-019-02137-2 \u003c/li\u003e\n\u003cli\u003eBroos, J. Y., Loonstra, F. C., de Ruiter, L. R. J., Gouda, M., Fung, W. H., Schoonheim, M. M., . . . Kooij, G. (2023). Association of Arachidonic Acid-Derived Lipid Mediators With Disease Severity in Patients With Relapsing and Progressive Multiple Sclerosis. \u003cem\u003eNeurology\u003c/em\u003e,\u003cem\u003e 101\u003c/em\u003e(5), e533-e545. https://doi.org/10.1212/wnl.0000000000207459 \u003c/li\u003e\n\u003cli\u003eBrudek, T., Winge, K., Folke, J., Christensen, S., Fog, K., Pakkenberg, B., \u0026amp; Pedersen, L. (2017). Autoimmune antibody decline in Parkinson\u0026apos;s disease and Multiple System Atrophy; a step towards immunotherapeutic strategies. \u003cem\u003eMol Neurodegener\u003c/em\u003e,\u003cem\u003e 12\u003c/em\u003e(1), 44. https://doi.org/10.1186/s13024-017-0187-7 \u003c/li\u003e\n\u003cli\u003eBrunoni, A. R., Lotufo, P. A., Sabbag, C., Goulart, A. C., Santos, I. S., \u0026amp; Bense\u0026ntilde;or, I. M. (2015). Decreased brain-derived neurotrophic factor plasma levels in psoriasis patients. \u003cem\u003eBraz J Med Biol Res\u003c/em\u003e,\u003cem\u003e 48\u003c/em\u003e(8), 711-714. https://doi.org/10.1590/1414-431x20154574 \u003c/li\u003e\n\u003cli\u003eBubak, A. N., Beseler, C., Como, C. N., Tyring, S. K., Haley, C., Mescher, T., . . . Nagel, M. A. (2020). Acute zoster plasma contains elevated amyloid, correlating with A\u0026beta;42 and amylin levels, and is amyloidogenic. \u003cem\u003eJ Neurovirol\u003c/em\u003e,\u003cem\u003e 26\u003c/em\u003e(3), 422-428. https://doi.org/10.1007/s13365-020-00830-7 \u003c/li\u003e\n\u003cli\u003eBuselli, R., Veltri, A., Baldanzi, S., Marino, R., Bonotti, A., Chiumiento, M., . . . Cristaudo, A. (2019). Plasma Brain-Derived Neurotrophic Factor (BDNF) and serum cortisol levels in a sample of workers exposed to occupational stress and suffering from Adjustment Disorders. \u003cem\u003eBrain Behav\u003c/em\u003e,\u003cem\u003e 9\u003c/em\u003e(7), e01298. https://doi.org/10.1002/brb3.1298 \u003c/li\u003e\n\u003cli\u003eCabral, D. F., Bigliassi, M., Cattaneo, G., Rundek, T., Pascual-Leone, A., Cahalin, L. P., \u0026amp; Gomes-Osman, J. (2022). Exploring the interplay between mechanisms of neuroplasticity and cardiovascular health in aging adults: A multiple linear regression analysis study. \u003cem\u003eAuton Neurosci\u003c/em\u003e,\u003cem\u003e 242\u003c/em\u003e, 103023. https://doi.org/10.1016/j.autneu.2022.103023 \u003c/li\u003e\n\u003cli\u003eCasas, S., Perez, A. F., Mattiazzi, M., Lopez, J., Folgueira, A., Gargiulo-Monachelli, G. M., . . . De Nicola, A. F. (2017). Potential Biomarkers with Plasma Cortisol, Brain-derived Neurotrophic Factor and Nitrites in Patients with Acute Ischemic Stroke. \u003cem\u003eCurr Neurovasc Res\u003c/em\u003e,\u003cem\u003e 14\u003c/em\u003e(4), 338-346. https://doi.org/10.2174/1567202614666171005122925 \u003c/li\u003e\n\u003cli\u003eCastillo-Mendieta, T., Arana-Lechuga, Y., Campos-Pe\u0026ntilde;a, V., Sosa, A. L., Orozco-Suarez, S., Pinto-Almaz\u0026aacute;n, R., . . . Guerra-Araiza, C. (2021). Plasma Levels of Amyloid-\u0026beta; Peptides and Tau Protein in Mexican Patients with Alzheimer\u0026apos;s Disease. \u003cem\u003eJ Alzheimers Dis\u003c/em\u003e,\u003cem\u003e 82\u003c/em\u003e(s1), S271-s281. https://doi.org/10.3233/jad-200912 \u003c/li\u003e\n\u003cli\u003eChan, D. K. Y., Braidy, N., Chen, R. F., Xu, Y. H., Bentley, S., Lubomski, M., . . . Mellick, G. D. (2022). Strong Predictive Algorithm of Pathogenesis-Based Biomarkers Improves Parkinson\u0026apos;s Disease Diagnosis. \u003cem\u003eMol Neurobiol\u003c/em\u003e,\u003cem\u003e 59\u003c/em\u003e(3), 1476-1485. https://doi.org/10.1007/s12035-021-02604-6 \u003c/li\u003e\n\u003cli\u003eChang, H. I., Huang, K. L., Huang, C. G., Huang, C. W., Huang, S. H., Lin, K. J., \u0026amp; Chang, C. C. (2024). Clinical Significance of the Plasma Biomarker Panels in Amyloid-Negative and Tau PET-Positive Amnestic Patients: Comparisons with Alzheimer\u0026apos;s Disease and Unimpaired Cognitive Controls. \u003cem\u003eInt J Mol Sci\u003c/em\u003e,\u003cem\u003e 25\u003c/em\u003e(11). https://doi.org/10.3390/ijms25115607 \u003c/li\u003e\n\u003cli\u003eChen, C. H., Lee, B. C., \u0026amp; Lin, C. H. (2020). Integrated Plasma and Neuroimaging Biomarkers Associated with Motor and Cognition Severity in Parkinson\u0026apos;s Disease. \u003cem\u003eJ Parkinsons Dis\u003c/em\u003e,\u003cem\u003e 10\u003c/em\u003e(1), 77-88. https://doi.org/10.3233/jpd-191766 \u003c/li\u003e\n\u003cli\u003eChen, J., Zhao, X., Zhang, W., Zhang, T., Wu, S., Shao, J., \u0026amp; Shi, F.-D. (2023). Reference intervals for plasma amyloid-\u0026beta;, total tau, and phosphorylated tau181 in healthy elderly Chinese individuals without cognitive impairment. \u003cem\u003eAlzheimer\u0026apos;s Research \u0026amp; Therapy\u003c/em\u003e,\u003cem\u003e 15\u003c/em\u003e(1), 100. https://doi.org/10.1186/s13195-023-01246-1 \u003c/li\u003e\n\u003cli\u003eChen, J., Zhao, X., Zhang, W., Zhang, T., Wu, S., Shao, J., \u0026amp; Shi, F. D. (2023). Reference intervals for plasma amyloid-\u0026beta;, total tau, and phosphorylated tau181 in healthy elderly Chinese individuals without cognitive impairment. \u003cem\u003eAlzheimers Res Ther\u003c/em\u003e,\u003cem\u003e 15\u003c/em\u003e(1), 100. https://doi.org/10.1186/s13195-023-01246-1 \u003c/li\u003e\n\u003cli\u003eChen, T. B., Lee, Y. J., Lin, S. Y., Chen, J. P., Hu, C. J., Wang, P. N., \u0026amp; Cheng, I. H. (2019). Plasma A\u0026beta;42 and Total Tau Predict Cognitive Decline in Amnestic Mild Cognitive Impairment. \u003cem\u003eSci Rep\u003c/em\u003e,\u003cem\u003e 9\u003c/em\u003e(1), 13984. https://doi.org/10.1038/s41598-019-50315-9 \u003c/li\u003e\n\u003cli\u003eChen, Y., Huang, J., Li, Y., Chen, X., \u0026amp; Ye, Q. (2025). Diagnostic value of six plasma biomarkers in progressive supranuclear palsy, multiple system atrophy, and Parkinson\u0026apos;s disease. \u003cem\u003eClin Chim Acta\u003c/em\u003e,\u003cem\u003e 565\u003c/em\u003e, 119975. https://doi.org/10.1016/j.cca.2024.119975 \u003c/li\u003e\n\u003cli\u003eChen, Y., Wang, Y., Tao, Q., Lu, P., Meng, F., Zhuang, L., . . . Peng, G. (2024). Diagnostic value of isolated plasma biomarkers and its combination in neurodegenerative dementias: A multicenter cohort study. \u003cem\u003eClin Chim Acta\u003c/em\u003e,\u003cem\u003e 558\u003c/em\u003e, 118784. https://doi.org/10.1016/j.cca.2024.118784 \u003c/li\u003e\n\u003cli\u003eChiu, M. J., Chen, T. F., Hu, C. J., Yan, S. H., Sun, Y., Liu, B. H., . . . Yang, S. Y. (2020). Nanoparticle-based immunomagnetic assay of plasma biomarkers for differentiating dementia and prodromal states of Alzheimer\u0026apos;s disease - A cross-validation study. \u003cem\u003eNanomedicine\u003c/em\u003e,\u003cem\u003e 28\u003c/em\u003e, 102182. https://doi.org/10.1016/j.nano.2020.102182 \u003c/li\u003e\n\u003cli\u003eChiu, M. J., Fan, L. Y., Chen, T. F., Chen, Y. F., Chieh, J. J., \u0026amp; Horng, H. E. (2017). Plasma Tau Levels in Cognitively Normal Middle-Aged and Older Adults. \u003cem\u003eFront Aging Neurosci\u003c/em\u003e,\u003cem\u003e 9\u003c/em\u003e, 51. https://doi.org/10.3389/fnagi.2017.00051 \u003c/li\u003e\n\u003cli\u003eChoonara, Y. E., Pillay, V., Du Toit, L. C., Modi, G., Naidoo, D., Ndesendo, V. M. K., \u0026amp; Sibambo, S. R. (2009). Trends in the molecular pathogenesis and clinical therapeutics of common neurodegenerative disorders. \u003cem\u003eInt J Mol Sci\u003c/em\u003e,\u003cem\u003e 10\u003c/em\u003e(6), 2510-2557. https://doi.org/10.3390/ijms10062510 \u003c/li\u003e\n\u003cli\u003eChung, C. C., Chan, L., Chen, J. H., Bamodu, O. A., Chiu, H. W., \u0026amp; Hong, C. T. (2021). Plasma extracellular vesicles tau and \u0026beta;-amyloid as biomarkers of cognitive dysfunction of Parkinson\u0026apos;s disease. \u003cem\u003eFaseb j\u003c/em\u003e,\u003cem\u003e 35\u003c/em\u003e(10), e21895. https://doi.org/10.1096/fj.202100787R \u003c/li\u003e\n\u003cli\u003eColombo, E., Doretti, A., Rao, R., Verde, F., Sodano, M., De Gobbi, A., . . . Ticozzi, N. (2025). Plasma levels of glial fibrillary acidic protein and neurofilament light chain in patients with chronic migraine: a multicenter case-control study. \u003cem\u003eNeurol Sci\u003c/em\u003e,\u003cem\u003e 46\u003c/em\u003e(5), 2209-2216. https://doi.org/10.1007/s10072-025-08011-2 \u003c/li\u003e\n\u003cli\u003eConti, E., Tremolizzo, L., Santarone, M. E., Tironi, M., Radice, I., Zoia, C. P., . . . Ferrarese, C. (2016). Donepezil modulates the endogenous immune response: implications for Alzheimer\u0026apos;s disease. \u003cem\u003eHum Psychopharmacol\u003c/em\u003e,\u003cem\u003e 31\u003c/em\u003e(4), 296-303. https://doi.org/10.1002/hup.2538 \u003c/li\u003e\n\u003cli\u003eCosta, C. M., Oliveira, G. L., Fonseca, A. C. S., Lana, R. C., Polese, J. C., \u0026amp; Pernambuco, A. P. (2019). Levels of cortisol and neurotrophic factor brain-derived in Parkinson\u0026apos;s disease. \u003cem\u003eNeurosci Lett\u003c/em\u003e,\u003cem\u003e 708\u003c/em\u003e, 134359. https://doi.org/10.1016/j.neulet.2019.134359 \u003c/li\u003e\n\u003cli\u003eCousins, K. A. Q., Shaw, L. M., Chen-Plotkin, A., Wolk, D. A., Van Deerlin, V. M., Lee, E. B., . . . Irwin, D. J. (2022). Distinguishing Frontotemporal Lobar Degeneration Tau From TDP-43 Using Plasma Biomarkers. \u003cem\u003eJAMA Neurol\u003c/em\u003e,\u003cem\u003e 79\u003c/em\u003e(11), 1155-1164. https://doi.org/10.1001/jamaneurol.2022.3265 \u003c/li\u003e\n\u003cli\u003eda Silveira, F. P., Basso, C., Raupp, W., Dalpiaz, M., Bertoldi, K., Siqueira, I. R., . . . Elsner, V. R. (2017). BDNF levels are increased in peripheral blood of middle-aged amateur runners with no changes on histone H4 acetylation levels. \u003cem\u003eJ Physiol Sci\u003c/em\u003e,\u003cem\u003e 67\u003c/em\u003e(6), 681-687. https://doi.org/10.1007/s12576-016-0496-6 \u003c/li\u003e\n\u003cli\u003eDaray, F. M., Grendas, L. N., Arena \u0026Aacute;, R., Tifner, V., \u0026Aacute;lvarez Casiani, R. I., Olaviaga, A., . . . Errasti, A. E. (2024). Decoding the inflammatory signature of the major depressive episode: insights from peripheral immunophenotyping in active and remitted condition, a case-control study. \u003cem\u003eTransl Psychiatry\u003c/em\u003e,\u003cem\u003e 14\u003c/em\u003e(1), 254. https://doi.org/10.1038/s41398-024-02902-2 \u003c/li\u003e\n\u003cli\u003eDe Vos, A., Jacobs, D., Struyfs, H., Fransen, E., Andersson, K., Portelius, E., . . . Vanmechelen, E. (2015). C-terminal neurogranin is increased in cerebrospinal fluid but unchanged in plasma in Alzheimer\u0026apos;s disease. \u003cem\u003eAlzheimers Dement\u003c/em\u003e,\u003cem\u003e 11\u003c/em\u003e(12), 1461-1469. https://doi.org/10.1016/j.jalz.2015.05.012 \u003c/li\u003e\n\u003cli\u003eDeniz, K., Ho, C. C. G., Malphrus, K. G., Reddy, J. S., Nguyen, T., Carnwath, T. P., . . . Belbin, O. (2021). Plasma Biomarkers of Alzheimer\u0026rsquo;s Disease in African Americans. \u003cem\u003eJournal of Alzheimer\u0026rsquo;s Disease\u003c/em\u003e,\u003cem\u003e 79\u003c/em\u003e(1), 323-334. https://doi.org/10.3233/jad-200828 \u003c/li\u003e\n\u003cli\u003eDing, C., Wu, Y., Chen, X., Chen, Y., Wu, Z., Lin, Z., . . . Chen, F. (2022). Global, regional, and national burden and attributable risk factors of neurological disorders: The Global Burden of Disease study 1990-2019. \u003cem\u003eFront Public Health\u003c/em\u003e,\u003cem\u003e 10\u003c/em\u003e, 952161. https://doi.org/10.3389/fpubh.2022.952161 \u003c/li\u003e\n\u003cli\u003eDing, J., Zhang, J., Wang, X., Zhang, L., Jiang, S., Yuan, Y., . . . Zhang, K. (2017). Relationship between the plasma levels of neurodegenerative proteins and motor subtypes of Parkinson\u0026apos;s disease. \u003cem\u003eJ Neural Transm (Vienna)\u003c/em\u003e,\u003cem\u003e 124\u003c/em\u003e(3), 353-360. https://doi.org/10.1007/s00702-016-1650-2 \u003c/li\u003e\n\u003cli\u003eDirks, M., Pflugrad, H., Tryc, A. B., Schrader, A. K., Ding, X., Lanfermann, H., . . . Weissenborn, K. (2020). Impact of immunosuppressive therapy on brain derived cytokines after liver transplantation. \u003cem\u003eTranspl Immunol\u003c/em\u003e,\u003cem\u003e 58\u003c/em\u003e, 101248. https://doi.org/10.1016/j.trim.2019.101248 \u003c/li\u003e\n\u003cli\u003eDiz, J. B. M., de Souza Moreira, B., Fel\u0026iacute;cio, D. C., Teixeira, L. F., de Jesus-Moraleida, F. R., de Queiroz, B. Z., . . . Pereira, L. S. M. (2017). Brain-derived neurotrophic factor plasma levels are increased in older women after an acute episode of low back pain. \u003cem\u003eArch Gerontol Geriatr\u003c/em\u003e,\u003cem\u003e 71\u003c/em\u003e, 75-82. https://doi.org/10.1016/j.archger.2017.03.005 \u003c/li\u003e\n\u003cli\u003eDutt, R., Shankar, N., Srivastava, S., Yadav, A., \u0026amp; Ahmed, R. S. (2020). Cardiac autonomic tone, plasma BDNF levels and paroxetine response in newly diagnosed patients of generalised anxiety disorder. \u003cem\u003eInt J Psychiatry Clin Pract\u003c/em\u003e,\u003cem\u003e 24\u003c/em\u003e(2), 135-142. https://doi.org/10.1080/13651501.2020.1723642 \u003c/li\u003e\n\u003cli\u003eEmmanouilidou, E., Papagiannakis, N., Kouloulia, S., Galaziou, A., Antonellou, R., Papadimitriou, D., . . . Stefanis, L. (2020). Peripheral alpha-synuclein levels in patients with genetic and non-genetic forms of Parkinson\u0026apos;s disease. \u003cem\u003eParkinsonism Relat Disord\u003c/em\u003e,\u003cem\u003e 73\u003c/em\u003e, 35-40. https://doi.org/10.1016/j.parkreldis.2020.03.014 \u003c/li\u003e\n\u003cli\u003eEratne, D., Kang, M. J. Y., Lewis, C., Dang, C., Malpas, C., Ooi, S., . . . Velakoulis, D. (2024). Plasma neurofilament light outperforms glial fibrillary acidic protein in differentiating behavioural variant frontotemporal dementia from primary psychiatric disorders. \u003cem\u003eJ Neurol Sci\u003c/em\u003e,\u003cem\u003e 467\u003c/em\u003e, 123291. https://doi.org/10.1016/j.jns.2024.123291 \u003c/li\u003e\n\u003cli\u003eEratne, D., Lewis, C., Kelso, W., Loi, S., Chiu, W. M., Blennow, K., . . . Walterfang, M. (2024). Plasma neurofilament light chain is increased in Niemann-Pick Type C but glial fibrillary acidic protein remains normal. \u003cem\u003eActa Neuropsychiatr\u003c/em\u003e,\u003cem\u003e 37\u003c/em\u003e, e20. https://doi.org/10.1017/neu.2024.14 \u003c/li\u003e\n\u003cli\u003eFan, Z., Pan, Y. T., Zhang, Z. Y., Yang, H., Yu, S. Y., Zheng, Y., . . . Wang, X. M. (2020). Systemic activation of NLRP3 inflammasome and plasma \u0026alpha;-synuclein levels are correlated with motor severity and progression in Parkinson\u0026apos;s disease. \u003cem\u003eJ Neuroinflammation\u003c/em\u003e,\u003cem\u003e 17\u003c/em\u003e(1), 11. https://doi.org/10.1186/s12974-019-1670-6 \u003c/li\u003e\n\u003cli\u003eFang, W.-Q., Hwu, W.-L., Chien, Y.-H., Yang, S.-Y., Chieh, J.-J., Chang, L.-M., . . . Chiu, M.-J. (2020). Composite Scores of Plasma Tau and \u0026beta;-Amyloids Correlate with Dementia in Down Syndrome. \u003cem\u003eACS Chem Neurosci\u003c/em\u003e,\u003cem\u003e 11\u003c/em\u003e(2), 191-196. https://doi.org/10.1021/acschemneuro.9b00585 \u003c/li\u003e\n\u003cli\u003eFang, W. Q., Hwu, W. L., Chien, Y. H., Yang, S. Y., Chieh, J. J., Chang, L. M., . . . Chiu, M. J. (2020). Composite Scores of Plasma Tau and \u0026beta;-Amyloids Correlate with Dementia in Down Syndrome. \u003cem\u003eACS Chem Neurosci\u003c/em\u003e,\u003cem\u003e 11\u003c/em\u003e(2), 191-196. https://doi.org/10.1021/acschemneuro.9b00585 \u003c/li\u003e\n\u003cli\u003eFang, X., Chen, Y., Wang, Y., Ren, J., \u0026amp; Zhang, C. (2019). Depressive symptoms in schizophrenia patients: A possible relationship between SIRT1 and BDNF. \u003cem\u003eProg Neuropsychopharmacol Biol Psychiatry\u003c/em\u003e,\u003cem\u003e 95\u003c/em\u003e, 109673. https://doi.org/10.1016/j.pnpbp.2019.109673 \u003c/li\u003e\n\u003cli\u003eFang, Y., Qiu, Q., Zhang, S., Sun, L., Li, G., Xiao, S., \u0026amp; Li, X. (2018). Changes in miRNA-132 and miR-124 levels in non-treated and citalopram-treated patients with depression. \u003cem\u003eJ Affect Disord\u003c/em\u003e,\u003cem\u003e 227\u003c/em\u003e, 745-751. https://doi.org/10.1016/j.jad.2017.11.090 \u003c/li\u003e\n\u003cli\u003eFenoglio, C., Serpente, M., Arcaro, M., Carandini, T., Sacchi, L., Pintus, M., . . . Galimberti, D. (2024). Inflammatory plasma profile in genetic symptomatic and presymptomatic Frontotemporal Dementia - A GENFI study. \u003cem\u003eBrain Behav Immun\u003c/em\u003e,\u003cem\u003e 122\u003c/em\u003e, 231-240. https://doi.org/10.1016/j.bbi.2024.08.030 \u003c/li\u003e\n\u003cli\u003eFi\u0026scaron;ar, Z., Jir\u0026aacute;k, R., Zvěřov\u0026aacute;, M., Setnička, V., Habartov\u0026aacute;, L., Hroudov\u0026aacute;, J., . . . Raboch, J. (2019). Plasma amyloid beta levels and platelet mitochondrial respiration in patients with Alzheimer\u0026apos;s disease. \u003cem\u003eClin Biochem\u003c/em\u003e,\u003cem\u003e 72\u003c/em\u003e, 71-80. https://doi.org/10.1016/j.clinbiochem.2019.04.003 \u003c/li\u003e\n\u003cli\u003eFolke, J., Rydbirk, R., L\u0026oslash;kkegaard, A., Salvesen, L., Hejl, A. M., Starhof, C., . . . Brudek, T. (2019). Distinct Autoimmune Anti-\u0026alpha;-Synuclein Antibody Patterns in Multiple System Atrophy and Parkinson\u0026apos;s Disease. \u003cem\u003eFront Immunol\u003c/em\u003e,\u003cem\u003e 10\u003c/em\u003e, 2253. https://doi.org/10.3389/fimmu.2019.02253 \u003c/li\u003e\n\u003cli\u003eFortea, J., Carmona-Iragui, M., Benejam, B., Fern\u0026aacute;ndez, S., Videla, L., Barroeta, I., . . . Lle\u0026oacute;, A. (2018). Plasma and CSF biomarkers for the diagnosis of Alzheimer\u0026apos;s disease in adults with Down syndrome: a cross-sectional study. \u003cem\u003eLancet Neurol\u003c/em\u003e,\u003cem\u003e 17\u003c/em\u003e(10), 860-869. https://doi.org/10.1016/s1474-4422(18)30285-0 \u003c/li\u003e\n\u003cli\u003eFortea, J., Vilaplana, E., Carmona-Iragui, M., Benejam, B., Videla, L., Barroeta, I., . . . Lle\u0026oacute;, A. (2020). Clinical and biomarker changes of Alzheimer\u0026apos;s disease in adults with Down syndrome: a cross-sectional study. \u003cem\u003eLancet\u003c/em\u003e,\u003cem\u003e 395\u003c/em\u003e(10242), 1988-1997. https://doi.org/10.1016/s0140-6736(20)30689-9 \u003c/li\u003e\n\u003cli\u003eFraussen, J., In\u0026apos;t Veld, S., van Laake-Geelen, C. C. M., Depreitere, B., Deckers, J., Peuskens, D., . . . Somers, V. (2025). Longitudinal Plasma Biomarker Profiles Predict Neurological Outcome in Traumatic Spinal Cord Injury. \u003cem\u003eAnn Neurol\u003c/em\u003e,\u003cem\u003e 97\u003c/em\u003e(6), 1180-1189. https://doi.org/10.1002/ana.27198 \u003c/li\u003e\n\u003cli\u003eFu, L., Ren, J., Lei, X., Wang, Y., Chen, X., Zhang, R., . . . Zhang, C. (2024). Association of anhedonia with brain-derived neurotrophic factor and interleukin-10 in major depressive disorder. \u003cem\u003eProg Neuropsychopharmacol Biol Psychiatry\u003c/em\u003e,\u003cem\u003e 133\u003c/em\u003e, 111023. https://doi.org/10.1016/j.pnpbp.2024.111023 \u003c/li\u003e\n\u003cli\u003eGadhave, D. G., Sugandhi, V. V., Jha, S. K., Nangare, S. N., Gupta, G., Singh, S. K., . . . Paudel, K. R. (2024). Neurodegenerative disorders: Mechanisms of degeneration and therapeutic approaches with their clinical relevance. \u003cem\u003eAgeing Research Reviews\u003c/em\u003e,\u003cem\u003e 99\u003c/em\u003e, 102357. https://doi.org/https://doi.org/10.1016/j.arr.2024.102357 \u003c/li\u003e\n\u003cli\u003eGadhave, D. G., Sugandhi, V. V., \u0026amp; Kokare, C. R. (2024). Potential biomaterials and experimental animal models for inventing new drug delivery approaches in the neurodegenerative disorder: Multiple sclerosis. \u003cem\u003eBrain Res\u003c/em\u003e,\u003cem\u003e 1822\u003c/em\u003e, 148674. https://doi.org/10.1016/j.brainres.2023.148674 \u003c/li\u003e\n\u003cli\u003eGalenko, O., Jacobs, V., Knight, S., Bride, D., Cutler, M. J., Muhlestein, J. B., . . . Jared Bunch, T. (2019). Circulating Levels of Biomarkers of Cerebral Injury in Patients with Atrial Fibrillation. \u003cem\u003eAm J Cardiol\u003c/em\u003e,\u003cem\u003e 124\u003c/em\u003e(11), 1697-1700. https://doi.org/10.1016/j.amjcard.2019.08.027 \u003c/li\u003e\n\u003cli\u003eGao, F., Lv, X., Dai, L., Wang, Q., Wang, P., Cheng, Z., . . . Shen, Y. (2023). A combination model of AD biomarkers revealed by machine learning precisely predicts Alzheimer\u0026apos;s dementia: China Aging and Neurodegenerative Initiative (CANDI) study. \u003cem\u003eAlzheimers Dement\u003c/em\u003e,\u003cem\u003e 19\u003c/em\u003e(3), 749-760. https://doi.org/10.1002/alz.12700 \u003c/li\u003e\n\u003cli\u003eGao, F., Lv, X., Dai, L., Wang, Q., Wang, P., Cheng, Z., . . . Shen, Y. (2023). A combination model of AD biomarkers revealed by machine learning precisely predicts Alzheimer\u0026apos;s dementia: China Aging and Neurodegenerative Initiative (CANDI) study. \u003cem\u003eAlzheimer\u0026apos;s \u0026amp; Dementia\u003c/em\u003e,\u003cem\u003e 19\u003c/em\u003e(3), 749-760. https://doi.org/https://doi.org/10.1002/alz.12700 \u003c/li\u003e\n\u003cli\u003eGarc\u0026iacute;a-Marchena, N., Silva-Pe\u0026ntilde;a, D., Mart\u0026iacute;n-Velasco, A. I., Villan\u0026uacute;a, M., Araos, P., Pedraz, M., . . . Pav\u0026oacute;n, F. J. (2017). Decreased plasma concentrations of BDNF and IGF-1 in abstinent patients with alcohol use disorders. \u003cem\u003ePLoS One\u003c/em\u003e,\u003cem\u003e 12\u003c/em\u003e(11), e0187634. https://doi.org/10.1371/journal.pone.0187634 \u003c/li\u003e\n\u003cli\u003eGil-Montoya, J. A., Barrios, R., Santana, S., Sanchez-Lara, I., Pardo, C. C., Fornieles-Rubio, F., . . . Burgos, J. S. (2017). Association Between Periodontitis and Amyloid \u0026beta; Peptide in Elderly People With and Without Cognitive Impairment. \u003cem\u003eJ Periodontol\u003c/em\u003e,\u003cem\u003e 88\u003c/em\u003e(10), 1051-1058. https://doi.org/10.1902/jop.2017.170071 \u003c/li\u003e\n\u003cli\u003eGill, J., Latour, L., Diaz-Arrastia, R., Motamedi, V., Turtzo, C., Shahim, P., . . . Jeromin, A. (2018). Glial fibrillary acidic protein elevations relate to neuroimaging abnormalities after mild TBI. \u003cem\u003eNeurology\u003c/em\u003e,\u003cem\u003e 91\u003c/em\u003e(15), e1385-e1389. https://doi.org/10.1212/wnl.0000000000006321 \u003c/li\u003e\n\u003cli\u003eGoldman, J. G., Andrews, H., Amara, A., Naito, A., Alcalay, R. N., Shaw, L. M., . . . Kang, U. J. (2018). Cerebrospinal fluid, plasma, and saliva in the BioFIND study: Relationships among biomarkers and Parkinson\u0026apos;s disease Features. \u003cem\u003eMov Disord\u003c/em\u003e,\u003cem\u003e 33\u003c/em\u003e(2), 282-288. https://doi.org/10.1002/mds.27232 \u003c/li\u003e\n\u003cli\u003eG\u0026oacute;mez-Tortosa, E., Ag\u0026uuml;ero-Rabes, P., Ruiz-Gonz\u0026aacute;lez, A., Wagner-Reguero, S., T\u0026eacute;llez, R., Mahillo, I., . . . S\u0026aacute;nchez-Juan, P. (2025). Plasma Biomarkers in the Distinction of Alzheimer\u0026apos;s Disease and Frontotemporal Dementia. \u003cem\u003eInt J Mol Sci\u003c/em\u003e,\u003cem\u003e 26\u003c/em\u003e(3). https://doi.org/10.3390/ijms26031231 \u003c/li\u003e\n\u003cli\u003eGredicak, M., Nikolac Perkovic, M., Nedic Erjavec, G., Uzun, S., Kozumplik, O., Svob Strac, D., \u0026amp; Pivac, N. (2024). Association between reduced plasma BDNF concentration and MMSE scores in both chronic schizophrenia and mild cognitive impairment. \u003cem\u003eProg Neuropsychopharmacol Biol Psychiatry\u003c/em\u003e,\u003cem\u003e 134\u003c/em\u003e, 111086. https://doi.org/10.1016/j.pnpbp.2024.111086 \u003c/li\u003e\n\u003cli\u003eGren, M., Shahim, P., Lautner, R., Wilson, D. H., Andreasson, U., Norgren, N., . . . Zetterberg, H. (2016). Blood biomarkers indicate mild neuroaxonal injury and increased amyloid \u0026beta; production after transient hypoxia during breath-hold diving. \u003cem\u003eBrain Inj\u003c/em\u003e,\u003cem\u003e 30\u003c/em\u003e(10), 1226-1230. https://doi.org/10.1080/02699052.2016.1179792 \u003c/li\u003e\n\u003cli\u003eGu, D., Liu, F., Meng, M., Zhang, L., Gordon, M. L., Wang, Y., . . . Zhang, N. (2020). Elevated matrix metalloproteinase-9 levels in neuronal extracellular vesicles in Alzheimer\u0026apos;s disease. \u003cem\u003eAnn Clin Transl Neurol\u003c/em\u003e,\u003cem\u003e 7\u003c/em\u003e(9), 1681-1691. https://doi.org/10.1002/acn3.51155 \u003c/li\u003e\n\u003cli\u003eHajari, J. N., Ilginis, T., Pedersen, T. T., L\u0026oslash;nkvist, C. S., Saunte, J. P., Hofsli, M., . . . Slidsborg, C. (2025). Novel Blood-Biomarkers to Detect Retinal Neurodegeneration and Inflammation in Diabetic Retinopathy. \u003cem\u003eInt J Mol Sci\u003c/em\u003e,\u003cem\u003e 26\u003c/em\u003e(6). https://doi.org/10.3390/ijms26062625 \u003c/li\u003e\n\u003cli\u003eHe, Y., Cheng, S., Yang, L., Ding, L., Chen, Y., Lu, J., \u0026amp; Zheng, R. (2024). Associations between plasma markers and symptoms of anxiety and depression in patients with breast cancer. \u003cem\u003eBMC Psychiatry\u003c/em\u003e,\u003cem\u003e 24\u003c/em\u003e(1), 678. https://doi.org/10.1186/s12888-024-06143-x \u003c/li\u003e\n\u003cli\u003eHeller, C., Foiani, M. S., Moore, K., Convery, R., Bocchetta, M., Neason, M., . . . Rohrer, J. D. (2020). Plasma glial fibrillary acidic protein is raised in progranulin-associated frontotemporal dementia. \u003cem\u003eJ Neurol Neurosurg Psychiatry\u003c/em\u003e,\u003cem\u003e 91\u003c/em\u003e(3), 263-270. https://doi.org/10.1136/jnnp-2019-321954 \u003c/li\u003e\n\u003cli\u003eHinderberger, P., Rullmann, M., Drabe, M., Luthardt, J., Becker, G. A., Bl\u0026uuml;her, M., . . . Hesse, S. (2016). The effect of serum BDNF levels on central serotonin transporter availability in obese versus non-obese adults: A [(11)C]DASB positron emission tomography study. \u003cem\u003eNeuropharmacology\u003c/em\u003e,\u003cem\u003e 110\u003c/em\u003e(Pt A), 530-536. https://doi.org/10.1016/j.neuropharm.2016.04.030 \u003c/li\u003e\n\u003cli\u003eHirata, K., Matsuoka, K., Tagai, K., Endo, H., Tatebe, H., Ono, M., . . . Takado, Y. (2024). In Vivo Assessment of Astrocyte Reactivity in Patients with Progressive Supranuclear Palsy. \u003cem\u003eAnn Neurol\u003c/em\u003e,\u003cem\u003e 96\u003c/em\u003e(2), 247-261. https://doi.org/10.1002/ana.26962 \u003c/li\u003e\n\u003cli\u003eHong, C. T., Chung, C. C., Hsieh, Y. C., \u0026amp; Chan, L. (2025). Plasma extracellular vesicle pathognomonic proteins as the biomarkers of the progression of Parkinson\u0026apos;s disease. \u003cem\u003eBiosci Trends\u003c/em\u003e,\u003cem\u003e 19\u003c/em\u003e(1), 116-124. https://doi.org/10.5582/bst.2024.01369 \u003c/li\u003e\n\u003cli\u003eHori, H., Yoshimura, R., Katsuki, A., Atake, K., Igata, R., Konishi, Y., \u0026amp; Nakamura, J. (2017). Relationships between serum brain-derived neurotrophic factor, plasma catecholamine metabolites, cytokines, cognitive function and clinical symptoms in Japanese patients with chronic schizophrenia treated with atypical antipsychotic monotherapy. \u003cem\u003eWorld J Biol Psychiatry\u003c/em\u003e,\u003cem\u003e 18\u003c/em\u003e(5), 401-408. https://doi.org/10.1080/15622975.2016.1212172 \u003c/li\u003e\n\u003cli\u003eHsu, J.-L., Lee, W.-J., Liao, Y.-C., Wang, S.-J., \u0026amp; Fuh, J.-L. (2017). The clinical significance of plasma clusterin and A\u0026beta; in the longitudinal follow-up of patients with Alzheimer\u0026rsquo;s disease. \u003cem\u003eAlzheimer\u0026apos;s Research \u0026amp; Therapy\u003c/em\u003e,\u003cem\u003e 9\u003c/em\u003e(1), 91. https://doi.org/10.1186/s13195-017-0319-x \u003c/li\u003e\n\u003cli\u003eHsu, J. L., Lin, C. H., Chen, P. L., Lin, K. J., \u0026amp; Chen, T. F. (2021). Genetic study of young-onset dementia using targeted gene panel sequencing in Taiwan. \u003cem\u003eAm J Med Genet B Neuropsychiatr Genet\u003c/em\u003e,\u003cem\u003e 186\u003c/em\u003e(2), 67-76. https://doi.org/10.1002/ajmg.b.32836 \u003c/li\u003e\n\u003cli\u003eHuang, P., \u0026amp; Zhang, M. (2023). Magnetic Resonance Imaging Studies of Neurodegenerative Disease: From Methods to Translational Research. \u003cem\u003eNeurosci Bull\u003c/em\u003e,\u003cem\u003e 39\u003c/em\u003e(1), 99-112. https://doi.org/10.1007/s12264-022-00905-x \u003c/li\u003e\n\u003cli\u003eHuang, Y., Li, Y., Pan, H., \u0026amp; Han, L. (2023). Global, regional, and national burden of neurological disorders in 204 countries and territories worldwide. \u003cem\u003eJ Glob Health\u003c/em\u003e,\u003cem\u003e 13\u003c/em\u003e, 04160. https://doi.org/10.7189/jogh.13.04160 \u003c/li\u003e\n\u003cli\u003eHui, B. S. M., Zhi, L. R., Retinasamy, T., Arulsamy, A., Law, C. S. W., Shaikh, M. F., \u0026amp; Yeong, K. Y. (2023). The Role of Interferon-\u0026alpha; in Neurodegenerative Diseases: A Systematic Review. \u003cem\u003eJ Alzheimers Dis\u003c/em\u003e,\u003cem\u003e 94\u003c/em\u003e(s1), S45-s66. https://doi.org/10.3233/jad-221081 \u003c/li\u003e\n\u003cli\u003eIll\u0026aacute;n-Gala, I., Lleo, A., Karydas, A., Staffaroni, A. M., Zetterberg, H., Sivasankaran, R., . . . Rojas, J. C. (2021). Plasma Tau and Neurofilament Light in Frontotemporal Lobar Degeneration and Alzheimer Disease. \u003cem\u003eNeurology\u003c/em\u003e,\u003cem\u003e 96\u003c/em\u003e(5), e671-e683. https://doi.org/10.1212/wnl.0000000000011226 \u003c/li\u003e\n\u003cli\u003eIulita, M. F., Ower, A., Barone, C., Pentz, R., Gubert, P., Romano, C., . . . Cuello, A. C. (2016). An inflammatory and trophic disconnect biomarker profile revealed in Down syndrome plasma: Relation to cognitive decline and longitudinal evaluation. \u003cem\u003eAlzheimers Dement\u003c/em\u003e,\u003cem\u003e 12\u003c/em\u003e(11), 1132-1148. https://doi.org/10.1016/j.jalz.2016.05.001 \u003c/li\u003e\n\u003cli\u003eJames, P. A., Oparil, S., Carter, B. L., Cushman, W. C., Dennison-Himmelfarb, C., Handler, J., . . . Ortiz, E. (2014). 2014 evidence-based guideline for the management of high blood pressure in adults: report from the panel members appointed to the Eighth Joint National Committee (JNC 8). \u003cem\u003eJama\u003c/em\u003e,\u003cem\u003e 311\u003c/em\u003e(5), 507-520. https://doi.org/10.1001/jama.2013.284427 \u003c/li\u003e\n\u003cli\u003eJanel, N., Alexopoulos, P., Badel, A., Lamari, F., Camproux, A. C., Lagarde, J., . . . Delabar, J. M. (2017). Combined assessment of DYRK1A, BDNF and homocysteine levels as diagnostic marker for Alzheimer\u0026apos;s disease. \u003cem\u003eTransl Psychiatry\u003c/em\u003e,\u003cem\u003e 7\u003c/em\u003e(6), e1154. https://doi.org/10.1038/tp.2017.123 \u003c/li\u003e\n\u003cli\u003eJanelidze, S., Christian, B. T., Price, J., Laymon, C., Schupf, N., Klunk, W. E., . . . Hansson, O. (2022). Detection of Brain Tau Pathology in Down Syndrome Using Plasma Biomarkers. \u003cem\u003eJAMA Neurol\u003c/em\u003e,\u003cem\u003e 79\u003c/em\u003e(8), 797-807. https://doi.org/10.1001/jamaneurol.2022.1740 \u003c/li\u003e\n\u003cli\u003eJasim, H., Ghafouri, B., Gerdle, B., Hedenberg-Magnusson, B., \u0026amp; Ernberg, M. (2020). Altered levels of salivary and plasma pain related markers in temporomandibular disorders. \u003cem\u003eJ Headache Pain\u003c/em\u003e,\u003cem\u003e 21\u003c/em\u003e(1), 105. https://doi.org/10.1186/s10194-020-01160-z \u003c/li\u003e\n\u003cli\u003eJiang, T., Gong, P. Y., Tan, M. S., Xue, X., Huang, S., Zhou, J. S., . . . Zhang, Y. D. (2019). Soluble TREM1 concentrations are increased and positively correlated with total tau levels in the plasma of patients with Alzheimer\u0026apos;s disease. \u003cem\u003eAging Clin Exp Res\u003c/em\u003e,\u003cem\u003e 31\u003c/em\u003e(12), 1801-1805. https://doi.org/10.1007/s40520-019-01122-9 \u003c/li\u003e\n\u003cli\u003eJiao, B., Ouyang, Z., Liu, Y., Zhang, C., Xu, T., Yang, Q., . . . Shen, L. (2025). Evaluating the diagnostic performance of six plasma biomarkers for Alzheimer\u0026apos;s disease and other neurodegenerative dementias in a large Chinese cohort. \u003cem\u003eAlzheimers Res Ther\u003c/em\u003e,\u003cem\u003e 17\u003c/em\u003e(1), 71. https://doi.org/10.1186/s13195-025-01712-y \u003c/li\u003e\n\u003cli\u003eJin, E., Bai, Y., Huang, L., Zhao, M., Zhang, C., Zhao, M., \u0026amp; Li, X. (2015). Evidence of a novel gene HERPUD1 in polypoidal choroidal vasculopathy. \u003cem\u003eInt J Clin Exp Pathol\u003c/em\u003e,\u003cem\u003e 8\u003c/em\u003e(11), 13928-13944. \u003c/li\u003e\n\u003cli\u003eJin, H., Chen, Y., Wang, B., Zhu, Y., Chen, L., Han, X., . . . Liu, N. (2018). Association between brain-derived neurotrophic factor and von Willebrand factor levels in patients with stable coronary artery disease. \u003cem\u003eBMC Cardiovasc Disord\u003c/em\u003e,\u003cem\u003e 18\u003c/em\u003e(1), 23. https://doi.org/10.1186/s12872-018-0762-z \u003c/li\u003e\n\u003cli\u003eJin, H., Ji, J. J., Zhu, Y., Wang, X. D., Li, Y. P., Shi, Q. Y., \u0026amp; Chen, Y. F. (2021). Brain-Derived Neurotrophic Factor, a New Predictor of Coronary Artery Calcification. \u003cem\u003eClin Appl Thromb Hemost\u003c/em\u003e,\u003cem\u003e 27\u003c/em\u003e, 1076029621989813. https://doi.org/10.1177/1076029621989813 \u003c/li\u003e\n\u003cli\u003eJin, W.-S., Bu, X.-L., Liu, Y.-H., Shen, L.-L., Zhuang, Z.-Q., Jiao, S.-S., . . . Wang, Y.-J. (2017). Plasma Amyloid-Beta Levels in Patients with Different Types of Cancer. \u003cem\u003eNeurotoxicity Research\u003c/em\u003e,\u003cem\u003e 31\u003c/em\u003e(2), 283-288. https://doi.org/10.1007/s12640-016-9682-9 \u003c/li\u003e\n\u003cli\u003eJordan, W., Dobrowolny, H., Bahn, S., Bernstein, H. G., Brigadski, T., Frodl, T., . . . Steiner, J. (2018). Oxidative stress in drug-na\u0026iuml;ve first episode patients with schizophrenia and major depression: effects of disease acuity and potential confounders. \u003cem\u003eEur Arch Psychiatry Clin Neurosci\u003c/em\u003e,\u003cem\u003e 268\u003c/em\u003e(2), 129-143. https://doi.org/10.1007/s00406-016-0749-7 \u003c/li\u003e\n\u003cli\u003eJ\u0026oslash;rgensen, M. L. K., Kj\u0026oslash;lhede, T., Dalgas, U., \u0026amp; Hvid, L. G. (2019). Plasma brain-derived neurotrophic factor (BDNF) and sphingosine-1-phosphat (S1P) are NOT the main mediators of neuroprotection induced by resistance training in persons with multiple sclerosis-A randomized controlled trial. \u003cem\u003eMult Scler Relat Disord\u003c/em\u003e,\u003cem\u003e 31\u003c/em\u003e, 106-111. https://doi.org/10.1016/j.msard.2019.03.029 \u003c/li\u003e\n\u003cli\u003eKammeyer, R., Chapman, K., Furniss, A., Hsieh, E., Fuhlbrigge, R., Ogbu, E. A., . . . Piquet, A. L. (2024). Blood-based biomarkers of neuronal and glial injury in active major neuropsychiatric systemic lupus erythematosus. \u003cem\u003eLupus\u003c/em\u003e,\u003cem\u003e 33\u003c/em\u003e(10), 1116-1129. https://doi.org/10.1177/09612033241272961 \u003c/li\u003e\n\u003cli\u003eKanberg, N., Ashton, N. J., Andersson, L. M., Yilmaz, A., Lindh, M., Nilsson, S., . . . Gissl\u0026eacute;n, M. (2020). Neurochemical evidence of astrocytic and neuronal injury commonly found in COVID-19. \u003cem\u003eNeurology\u003c/em\u003e,\u003cem\u003e 95\u003c/em\u003e(12), e1754-e1759. https://doi.org/10.1212/wnl.0000000000010111 \u003c/li\u003e\n\u003cli\u003eKanberg, N., Simr\u0026eacute;n, J., Ed\u0026eacute;n, A., Andersson, L. M., Nilsson, S., Ashton, N. J., . . . Gissl\u0026eacute;n, M. (2021). Neurochemical signs of astrocytic and neuronal injury in acute COVID-19 normalizes during long-term follow-up. \u003cem\u003eEBioMedicine\u003c/em\u003e,\u003cem\u003e 70\u003c/em\u003e, 103512. https://doi.org/10.1016/j.ebiom.2021.103512 \u003c/li\u003e\n\u003cli\u003eKasai, T., Kojima, Y., Ohmichi, T., Tatebe, H., Tsuji, Y., Noto, Y. I., . . . Tokuda, T. (2019). Combined use of CSF NfL and CSF TDP-43 improves diagnostic performance in ALS. \u003cem\u003eAnn Clin Transl Neurol\u003c/em\u003e,\u003cem\u003e 6\u003c/em\u003e(12), 2489-2502. https://doi.org/10.1002/acn3.50943 \u003c/li\u003e\n\u003cli\u003eKasai, T., Tatebe, H., Kondo, M., Ishii, R., Ohmichi, T., Yeung, W. T. E., . . . Tokuda, T. (2017). Increased levels of plasma total tau in adult Down syndrome. \u003cem\u003ePLoS One\u003c/em\u003e,\u003cem\u003e 12\u003c/em\u003e(11), e0188802. https://doi.org/10.1371/journal.pone.0188802 \u003c/li\u003e\n\u003cli\u003eKatsanos, A. H., Makris, K., Stefani, D., Koniari, K., Gialouri, E., Lelekis, M., . . . Tsivgoulis, G. (2017). Plasma Glial Fibrillary Acidic Protein in the Differential Diagnosis of Intracerebral Hemorrhage. \u003cem\u003eStroke\u003c/em\u003e,\u003cem\u003e 48\u003c/em\u003e(9), 2586-2588. https://doi.org/10.1161/strokeaha.117.018409 \u003c/li\u003e\n\u003cli\u003eKatzdobler, S., N\u0026uuml;bling, G., Klietz, M., Fietzek, U. M., Palleis, C., Bernhardt, A. M., . . . Levin, J. (2024). GFAP and NfL as fluid biomarkers for clinical disease severity and disease progression in multiple system atrophy (MSA). \u003cem\u003eJ Neurol\u003c/em\u003e,\u003cem\u003e 271\u003c/em\u003e(10), 6991-6999. https://doi.org/10.1007/s00415-024-12647-z \u003c/li\u003e\n\u003cli\u003eKim, K., Jang, E. H., Kim, A. Y., Fava, M., Mischoulon, D., Papakostas, G. I., . . . Jeon, H. J. (2019). Pre-treatment peripheral biomarkers associated with treatment response in panic symptoms in patients with major depressive disorder and panic disorder: A 12-week follow-up study. \u003cem\u003eCompr Psychiatry\u003c/em\u003e,\u003cem\u003e 95\u003c/em\u003e, 152140. https://doi.org/10.1016/j.comppsych.2019.152140 \u003c/li\u003e\n\u003cli\u003eKitaguchi, N., Tatebe, H., Sakai, K., Kawaguchi, K., Matsunaga, S., Kitajima, T., . . . Tokuda, T. (2019). Influx of Tau and Amyloid-\u0026beta; Proteins into the Blood During Hemodialysis as a Therapeutic Extracorporeal Blood Amyloid-\u0026beta; Removal System for Alzheimer\u0026apos;s Disease. \u003cem\u003eJ Alzheimers Dis\u003c/em\u003e,\u003cem\u003e 69\u003c/em\u003e(3), 687-707. https://doi.org/10.3233/jad-190087 \u003c/li\u003e\n\u003cli\u003eKoehler, N. K., Stransky, E., Meyer, M., Gaertner, S., Shing, M., Schnaidt, M., . . . Richartz-Salzburger, E. (2015). Alpha-synuclein levels in blood plasma decline with healthy aging. \u003cem\u003ePLoS One\u003c/em\u003e,\u003cem\u003e 10\u003c/em\u003e(4), e0123444. https://doi.org/10.1371/journal.pone.0123444 \u003c/li\u003e\n\u003cli\u003eKon\u0026iacute;čkov\u0026aacute;, D., Men\u0026scaron;\u0026iacute;kov\u0026aacute;, K., Tučkov\u0026aacute;, L., H\u0026eacute;nykov\u0026aacute;, E., Strnad, M., Friedeck\u0026yacute;, D., . . . Kaňovsk\u0026yacute;, P. (2022). Biomarkers of Neurodegenerative Diseases: Biology, Taxonomy, Clinical Relevance, and Current Research Status. \u003cem\u003eBiomedicines\u003c/em\u003e,\u003cem\u003e 10\u003c/em\u003e(7). https://doi.org/10.3390/biomedicines10071760 \u003c/li\u003e\n\u003cli\u003eKowalski, R. G., Ledreux, A., Violette, J. E., Neumann, R. T., Ornelas, D., Yu, X., . . . Graner, M. W. (2023). Rapid Activation of Neuroinflammation in Stroke: Plasma and Extracellular Vesicles Obtained on a Mobile Stroke Unit. \u003cem\u003eStroke\u003c/em\u003e,\u003cem\u003e 54\u003c/em\u003e(3), e52-e57. https://doi.org/10.1161/strokeaha.122.041422 \u003c/li\u003e\n\u003cli\u003eLamptey, R. N. L., Chaulagain, B., Trivedi, R., Gothwal, A., Layek, B., \u0026amp; Singh, J. (2022). A Review of the Common Neurodegenerative Disorders: Current Therapeutic Approaches and the Potential Role of Nanotherapeutics. \u003cem\u003eInt J Mol Sci\u003c/em\u003e,\u003cem\u003e 23\u003c/em\u003e(3). https://doi.org/10.3390/ijms23031851 \u003c/li\u003e\n\u003cli\u003eLashkari, K., Teague, G. C., Beattie, U., Betts, J., Kumar, S., McLaughlin, M. M., \u0026amp; L\u0026oacute;pez, F. J. (2020). Plasma biomarkers of the amyloid pathway are associated with geographic atrophy secondary to age-related macular degeneration. \u003cem\u003ePLoS One\u003c/em\u003e,\u003cem\u003e 15\u003c/em\u003e(8), e0236283. https://doi.org/10.1371/journal.pone.0236283 \u003c/li\u003e\n\u003cli\u003eLee, B. H., Park, Y. M., Hwang, J. A., \u0026amp; Kim, Y. K. (2021). Variable alterations in plasma erythropoietin and brain-derived neurotrophic factor levels in patients with major depressive disorder with and without a history of suicide attempt. \u003cem\u003eProg Neuropsychopharmacol Biol Psychiatry\u003c/em\u003e,\u003cem\u003e 110\u003c/em\u003e, 110324. https://doi.org/10.1016/j.pnpbp.2021.110324 \u003c/li\u003e\n\u003cli\u003eLekomtseva, Y. (2020). Targeting higher levels of lactate in the post-injury period following traumatic brain injury. \u003cem\u003eClin Neurol Neurosurg\u003c/em\u003e,\u003cem\u003e 196\u003c/em\u003e, 106050. https://doi.org/10.1016/j.clineuro.2020.106050 \u003c/li\u003e\n\u003cli\u003eLi, C. H., Fan, S. P., Shih, M. C., Weng, Y. H., Chen, T. F., Li, H., . . . Lin, C. H. (2025). Subcortical tau burden correlates with regional brain atrophy and plasma markers in four-repeat tauopathy parkinsonism. \u003cem\u003eJ Parkinsons Dis\u003c/em\u003e,\u003cem\u003e 15\u003c/em\u003e(1), 214-226. https://doi.org/10.1177/1877718x241298192 \u003c/li\u003e\n\u003cli\u003eLi, K., Wang, K., Xu, S. X., Xie, X. H., Tang, Y., Zhang, L., \u0026amp; Liu, Z. (2024). Investigating Neuroplasticity Changes Reflected by BDNF Levels in Astrocyte-Derived Extracellular Vesicles in Patients with Depression. \u003cem\u003eInt J Nanomedicine\u003c/em\u003e,\u003cem\u003e 19\u003c/em\u003e, 8971-8985. https://doi.org/10.2147/ijn.S477482 \u003c/li\u003e\n\u003cli\u003eLi, K., Wang, K., Xu, S. X., Xie, X. H., Tang, Y., Zhang, L., \u0026amp; Liu, Z. (2025). In vivo evidence of increased vascular endothelial growth factor in patients with major depressive disorder. \u003cem\u003eJ Affect Disord\u003c/em\u003e,\u003cem\u003e 368\u003c/em\u003e, 151-159. https://doi.org/10.1016/j.jad.2024.09.073 \u003c/li\u003e\n\u003cli\u003eLi, L.-M., Che, P., Liu, D., Wang, Y., Li, J., He, D., . . . Zhang, N. (2025). Diagnostic and discriminative accuracy of plasma phosphorylated tau 217 for symptomatic Alzheimerʼs disease in a Chinese cohort. \u003cem\u003eThe Journal of Prevention of Alzheimer\u0026apos;s Disease\u003c/em\u003e,\u003cem\u003e 12\u003c/em\u003e(5), 100092. https://doi.org/https://doi.org/10.1016/j.tjpad.2025.100092 \u003c/li\u003e\n\u003cli\u003eLi, L. M., Che, P., Liu, D., Wang, Y., Li, J., He, D., . . . Zhang, N. (2025). Diagnostic and discriminative accuracy of plasma phosphorylated tau 217 for symptomatic Alzheimer\u0026apos;s disease in a Chinese cohort. \u003cem\u003eJ Prev Alzheimers Dis\u003c/em\u003e,\u003cem\u003e 12\u003c/em\u003e(5), 100092. https://doi.org/10.1016/j.tjpad.2025.100092 \u003c/li\u003e\n\u003cli\u003eLin, C. H., Chiu, S. I., Chen, T. F., Jang, J. R., \u0026amp; Chiu, M. J. (2020). Classifications of Neurodegenerative Disorders Using a Multiplex Blood Biomarkers-Based Machine Learning Model. \u003cem\u003eInt J Mol Sci\u003c/em\u003e,\u003cem\u003e 21\u003c/em\u003e(18). https://doi.org/10.3390/ijms21186914 \u003c/li\u003e\n\u003cli\u003eLin, W. C., Lu, C. H., Chiu, P. Y., \u0026amp; Yang, S. Y. (2020). Plasma Total \u0026alpha;-Synuclein and Neurofilament Light Chain: Clinical Validation for Discriminating Parkinson\u0026apos;s Disease from Normal Control. \u003cem\u003eDement Geriatr Cogn Disord\u003c/em\u003e,\u003cem\u003e 49\u003c/em\u003e(4), 401-409. https://doi.org/10.1159/000510325 \u003c/li\u003e\n\u003cli\u003eLiu, Y., Liu, X., Che, P., Wang, Y., Piao, Z., Wang, Y., . . . Zhang, N. (2025). A cytometric bead array for the measurement of plasma biomarker levels in patients with Alzheimer\u0026apos;s disease. \u003cem\u003eSci Rep\u003c/em\u003e,\u003cem\u003e 15\u003c/em\u003e(1), 9767. https://doi.org/10.1038/s41598-024-83919-x \u003c/li\u003e\n\u003cli\u003eLiu, Y. C., Hsiao, H. T., Wang, J. C., Wen, T. C., \u0026amp; Chen, S. L. (2022). TGF-\u0026beta;1 in plasma and cerebrospinal fluid can be used as a biological indicator of chronic pain in patients with osteoarthritis. \u003cem\u003ePLoS One\u003c/em\u003e,\u003cem\u003e 17\u003c/em\u003e(1), e0262074. https://doi.org/10.1371/journal.pone.0262074 \u003c/li\u003e\n\u003cli\u003eLu, R., Shah, K., Toedebusch, C. D., Hess, A., Richardson, R., Mignot, E., . . . Lucey, B. P. (2025). Associations of Cerebrospinal Fluid Orexin-A, Alzheimer Disease Biomarkers, and Cognitive Performance. \u003cem\u003eAnn Clin Transl Neurol\u003c/em\u003e,\u003cem\u003e 12\u003c/em\u003e(4), 780-791. https://doi.org/10.1002/acn3.70009 \u003c/li\u003e\n\u003cli\u003eLue, L. F., Pai, M. C., Chen, T. F., Hu, C. J., Huang, L. K., Lin, W. C., . . . Chiu, M. J. (2019). Age-Dependent Relationship Between Plasma A\u0026beta;40 and A\u0026beta;42 and Total Tau Levels in Cognitively Normal Subjects. \u003cem\u003eFront Aging Neurosci\u003c/em\u003e,\u003cem\u003e 11\u003c/em\u003e, 222. https://doi.org/10.3389/fnagi.2019.00222 \u003c/li\u003e\n\u003cli\u003eLuebke, M., Parulekar, M., \u0026amp; Thomas, F. P. (2023). Fluid biomarkers for the diagnosis of neurodegenerative diseases. \u003cem\u003eBiomarkers in Neuropsychiatry\u003c/em\u003e,\u003cem\u003e 8\u003c/em\u003e, 100062. https://doi.org/https://doi.org/10.1016/j.bionps.2023.100062 \u003c/li\u003e\n\u003cli\u003eMandelblatt, J., Dage, J. L., Zhou, X., Small, B. J., Ahles, T. A., Ahn, J., . . . Saykin, A. J. (2024). Alzheimer disease-related biomarkers and cancer-related cognitive decline: the Thinking and Living with Cancer study. \u003cem\u003eJ Natl Cancer Inst\u003c/em\u003e,\u003cem\u003e 116\u003c/em\u003e(9), 1495-1507. https://doi.org/10.1093/jnci/djae113 \u003c/li\u003e\n\u003cli\u003eMansur, R. B., Santos, C. M., Rizzo, L. B., Cunha, G. R., Asevedo, E., Noto, M. N., . . . Brietzke, E. (2016). Inter-relation between brain-derived neurotrophic factor and antioxidant enzymes in bipolar disorder. \u003cem\u003eBipolar Disord\u003c/em\u003e,\u003cem\u003e 18\u003c/em\u003e(5), 433-439. https://doi.org/10.1111/bdi.12418 \u003c/li\u003e\n\u003cli\u003eMarelli, C., Hourregue, C., Gutierrez, L. A., Paquet, C., Menjot de Champfleur, N., De Verbizier, D., . . . Gabelle, A. (2020). Cerebrospinal Fluid and Plasma Biomarkers do not Differ in the Presenile and Late-Onset Behavioral Variants of Frontotemporal Dementia. \u003cem\u003eJ Alzheimers Dis\u003c/em\u003e,\u003cem\u003e 74\u003c/em\u003e(3), 903-911. https://doi.org/10.3233/jad-190378 \u003c/li\u003e\n\u003cli\u003eMarston, K. J., Brown, B. M., Rainey-Smith, S. R., Bird, S., Wijaya, L., Teo, S. Y. M., . . . Peiffer, J. J. (2019). Twelve weeks of resistance training does not influence peripheral levels of neurotrophic growth factors or homocysteine in healthy adults: a randomized-controlled trial. \u003cem\u003eEur J Appl Physiol\u003c/em\u003e,\u003cem\u003e 119\u003c/em\u003e(10), 2167-2176. https://doi.org/10.1007/s00421-019-04202-w \u003c/li\u003e\n\u003cli\u003eMartiskainen, H., Herukka, S. K., Stanč\u0026aacute;kov\u0026aacute;, A., Paananen, J., Soininen, H., Kuusisto, J., . . . Hiltunen, M. (2017). Decreased plasma \u0026beta;-amyloid in the Alzheimer\u0026apos;s disease APP A673T variant carriers. \u003cem\u003eAnn Neurol\u003c/em\u003e,\u003cem\u003e 82\u003c/em\u003e(1), 128-132. https://doi.org/10.1002/ana.24969 \u003c/li\u003e\n\u003cli\u003eMathur, R. (2017). National ethical guidelines for biomedical and health research involving human participants. \u003cem\u003eNew Delhi: Director-General Indian Council of Medical Research\u003c/em\u003e. \u003c/li\u003e\n\u003cli\u003eMathur, R., \u0026amp; Swaminathan, S. (2018). National ethical guidelines for biomedical \u0026amp; health research involving human participants, 2017: A commentary. \u003cem\u003eThe Indian journal of medical research\u003c/em\u003e,\u003cem\u003e 148\u003c/em\u003e(3), 279. \u003c/li\u003e\n\u003cli\u003eMehta, P. D., Patrick, B. A., Miller, D. L., Coyle, P. K., \u0026amp; Wisniewski, T. (2020). A Sensitive and Cost-Effective Chemiluminescence ELISA for Measurement of Amyloid-\u0026beta; 1-42 Peptide in Human Plasma. \u003cem\u003eJ Alzheimers Dis\u003c/em\u003e,\u003cem\u003e 78\u003c/em\u003e(3), 1237-1244. https://doi.org/10.3233/jad-200861 \u003c/li\u003e\n\u003cli\u003eMengel, D., Liu, W., Glynn, R. J., Selkoe, D. J., Strydom, A., Lai, F., . . . Walsh, D. M. (2020). Dynamics of plasma biomarkers in Down syndrome: the relative levels of A\u0026beta;42 decrease with age, whereas NT1 tau and NfL increase. \u003cem\u003eAlzheimer\u0026apos;s Research \u0026amp; Therapy\u003c/em\u003e,\u003cem\u003e 12\u003c/em\u003e(1), 27. https://doi.org/10.1186/s13195-020-00593-7 \u003c/li\u003e\n\u003cli\u003eMiner, A. E., Groh, J. R., Tripodis, Y., Adler, C. H., Balcer, L. J., Bernick, C., . . . Alosco, M. L. (2024). Examination of plasma biomarkers of amyloid, tau, neurodegeneration, and neuroinflammation in former elite American football players. \u003cem\u003eAlzheimers Dement\u003c/em\u003e,\u003cem\u003e 20\u003c/em\u003e(11), 7529-7546. https://doi.org/10.1002/alz.14231 \u003c/li\u003e\n\u003cli\u003eMo, M., Fu, X. Y., Zhang, X. L., Zhang, S. C., Zhang, H. Q., Wu, L., . . . Zhou, L. (2021). Association of Plasma Pro-Brain-Derived Neurotrophic Factor (proBDNF)/Mature Brain-Derived Neurotrophic Factor (mBDNF) Levels with BDNF Gene Val66Met Polymorphism in Alcohol Dependence. \u003cem\u003eMed Sci Monit\u003c/em\u003e,\u003cem\u003e 27\u003c/em\u003e, e930421. https://doi.org/10.12659/msm.930421 \u003c/li\u003e\n\u003cli\u003eMohaupt, P., Kindermans, J., Vialaret, J., Anderl-Straub, S., Werner, L., Lehmann, S., . . . Oeckl, P. (2024). Blood-based biomarkers and plasma A\u0026beta; assays in the differential diagnosis of Alzheimer\u0026apos;s disease and behavioral-variant frontotemporal dementia. \u003cem\u003eAlzheimers Res Ther\u003c/em\u003e,\u003cem\u003e 16\u003c/em\u003e(1), 279. https://doi.org/10.1186/s13195-024-01647-w \u003c/li\u003e\n\u003cli\u003eMothapo, K. M., Stelma, F., Janssen, M., Kessels, R., Miners, S., Verbeek, M. M., . . . van der Ven, A. (2015). Amyloid beta-42 (A\u0026beta;-42), neprilysin and cytokine levels. A pilot study in patients with HIV related cognitive impairments. \u003cem\u003eJ Neuroimmunol\u003c/em\u003e,\u003cem\u003e 282\u003c/em\u003e, 73-79. https://doi.org/10.1016/j.jneuroim.2015.03.017 \u003c/li\u003e\n\u003cli\u003eMouri MI, B. M. (2023). \u003cem\u003eHyperglycemia\u003c/em\u003e. StatPearls Publishing. https://www.ncbi.nlm.nih.gov/books/NBK430900/\u003c/li\u003e\n\u003cli\u003eNg, A. S. L., Tan, Y. J., Lu, Z., Ng, E. Y. L., Ng, S. Y. E., Chia, N. S. Y., . . . Tan, L. C. S. (2019). Plasma alpha-synuclein detected by single molecule array is increased in PD. \u003cem\u003eAnn Clin Transl Neurol\u003c/em\u003e,\u003cem\u003e 6\u003c/em\u003e(3), 615-619. https://doi.org/10.1002/acn3.729 \u003c/li\u003e\n\u003cli\u003eNg, K. S. T., Sia, A., Ng, M. K. W., Tan, C. T. Y., Chan, H. Y., Tan, C. H., . . . Ho, R. C. M. (2018). Effects of Horticultural Therapy on Asian Older Adults: A Randomized Controlled Trial. \u003cem\u003eInt J Environ Res Public Health\u003c/em\u003e,\u003cem\u003e 15\u003c/em\u003e(8). https://doi.org/10.3390/ijerph15081705 \u003c/li\u003e\n\u003cli\u003eNg, T. K. S., Coughlan, C., Heyn, P. C., Tagawa, A., Carollo, J. J., Kua, E. H., \u0026amp; Mahendran, R. (2021). Increased plasma brain-derived neurotrophic factor (BDNF) as a potential biomarker for and compensatory mechanism in mild cognitive impairment: a case-control study. \u003cem\u003eAging (Albany NY)\u003c/em\u003e,\u003cem\u003e 13\u003c/em\u003e(19), 22666-22689. https://doi.org/10.18632/aging.203598 \u003c/li\u003e\n\u003cli\u003eOkonkwo, D. O., Puffer, R. C., Puccio, A. M., Yuh, E. L., Yue, J. K., Diaz-Arrastia, R., . . . Manley, G. T. (2020). Point-of-Care Platform Blood Biomarker Testing of Glial Fibrillary Acidic Protein versus S100 Calcium-Binding Protein B for Prediction of Traumatic Brain Injuries: A Transforming Research and Clinical Knowledge in Traumatic Brain Injury Study. \u003cem\u003eJ Neurotrauma\u003c/em\u003e,\u003cem\u003e 37\u003c/em\u003e(23), 2460-2467. https://doi.org/10.1089/neu.2020.7140 \u003c/li\u003e\n\u003cli\u003eOlğun, Y., Poyraz, C. A., Bozluol\u0026ccedil;ay, M., Konukoğlu, D., \u0026amp; Poyraz, B. (2024). Plasma Biomarkers in Neurodegenerative Dementias: Unrevealing the Potential of Serum Oxytocin, BDNF, NPTX1, TREM2, TNF-alpha, IL-1 and Prolactin. \u003cem\u003eCurr Alzheimer Res\u003c/em\u003e,\u003cem\u003e 21\u003c/em\u003e(2), 109-119. https://doi.org/10.2174/0115672050313419240520051751 \u003c/li\u003e\n\u003cli\u003eOu, Z. A., Byrne, L. M., Rodrigues, F. B., Tortelli, R., Johnson, E. B., Foiani, M. S., . . . Wild, E. J. (2021). Brain-derived neurotrophic factor in cerebrospinal fluid and plasma is not a biomarker for Huntington\u0026apos;s disease. \u003cem\u003eSci Rep\u003c/em\u003e,\u003cem\u003e 11\u003c/em\u003e(1), 3481. https://doi.org/10.1038/s41598-021-83000-x \u003c/li\u003e\n\u003cli\u003ePaczkowska, E., Rogińska, D., Pius-Sadowska, E., Jurewicz, A., Piecyk, K., Safranow, K., . . . Machaliński, B. (2015). Evidence for proangiogenic cellular and humoral systemic response in patients with acute onset of spinal cord injury. \u003cem\u003eJ Spinal Cord Med\u003c/em\u003e,\u003cem\u003e 38\u003c/em\u003e(6), 729-744. https://doi.org/10.1179/2045772314y.0000000227 \u003c/li\u003e\n\u003cli\u003ePai, M. C., Wu, C. C., Hou, Y. C., Jeng, J. S., Tang, S. C., Lin, W. C., . . . Yang, S. Y. (2022). Evidence of plasma biomarkers indicating high risk of dementia in cognitively normal subjects. \u003cem\u003eSci Rep\u003c/em\u003e,\u003cem\u003e 12\u003c/em\u003e(1), 1192. https://doi.org/10.1038/s41598-022-05177-z \u003c/li\u003e\n\u003cli\u003ePant, S., Kapri, A., \u0026amp; Nain, S. (2022). Pyrimidine analogues for the management of neurodegenerative diseases. \u003cem\u003eEuropean Journal of Medicinal Chemistry Reports\u003c/em\u003e,\u003cem\u003e 6\u003c/em\u003e, 100095. https://doi.org/https://doi.org/10.1016/j.ejmcr.2022.100095 \u003c/li\u003e\n\u003cli\u003ePassaro, A., Soavi, C., Marusic, U., Rejc, E., Sanz, J. M., Morieri, M. L., . . . Pi\u0026scaron;ot, R. (2017). Computerized cognitive training and brain derived neurotrophic factor during bed rest: mechanisms to protect individual during acute stress. \u003cem\u003eAging (Albany NY)\u003c/em\u003e,\u003cem\u003e 9\u003c/em\u003e(2), 393-407. https://doi.org/10.18632/aging.101166 \u003c/li\u003e\n\u003cli\u003ePchelina, S., Emelyanov, A., Baydakova, G., Andoskin, P., Senkevich, K., Nikolaev, M., . . . Zakharova, E. (2017). Oligomeric \u0026alpha;-synuclein and glucocerebrosidase activity levels in GBA-associated Parkinson\u0026apos;s disease. \u003cem\u003eNeurosci Lett\u003c/em\u003e,\u003cem\u003e 636\u003c/em\u003e, 70-76. https://doi.org/10.1016/j.neulet.2016.10.039 \u003c/li\u003e\n\u003cli\u003ePeng, G., Qiu, J., Liu, H., Zhou, M., Huang, S., Guo, W., . . . Xu, P. (2020). Analysis of Cerebrospinal Fluid Soluble TREM2 and Polymorphisms in Sporadic Parkinson\u0026apos;s Disease in a Chinese Population. \u003cem\u003eJ Mol Neurosci\u003c/em\u003e,\u003cem\u003e 70\u003c/em\u003e(2), 294-301. https://doi.org/10.1007/s12031-019-01424-7 \u003c/li\u003e\n\u003cli\u003ePereira, J. B., Janelidze, S., Smith, R., Mattsson-Carlgren, N., Palmqvist, S., Teunissen, C. E., . . . Hansson, O. (2021). Plasma GFAP is an early marker of amyloid-\u0026beta; but not tau pathology in Alzheimer\u0026apos;s disease. \u003cem\u003eBrain\u003c/em\u003e,\u003cem\u003e 144\u003c/em\u003e(11), 3505-3516. https://doi.org/10.1093/brain/awab223 \u003c/li\u003e\n\u003cli\u003ePetersen, L. E., Baptista, T. S. A., Molina, J. K., Motta, J. G., do Prado, A., Piovesan, D. M., . . . Bauer, M. E. (2018). Cognitive impairment in rheumatoid arthritis: role of lymphocyte subsets, cytokines and neurotrophic factors. \u003cem\u003eClin Rheumatol\u003c/em\u003e,\u003cem\u003e 37\u003c/em\u003e(5), 1171-1181. https://doi.org/10.1007/s10067-018-3990-9 \u003c/li\u003e\n\u003cli\u003ePilotto, A., Ashton, N. J., Lupini, A., Battaglio, B., Zatti, C., Trasciatti, C., . . . Padovani, A. (2024). Plasma NfL, GFAP, amyloid, and p-tau species as Prognostic biomarkers in Parkinson\u0026rsquo;s disease. \u003cem\u003eJournal of Neurology\u003c/em\u003e,\u003cem\u003e 271\u003c/em\u003e(12), 7537-7546. https://doi.org/10.1007/s00415-024-12669-7 \u003c/li\u003e\n\u003cli\u003ePloydang, T., Khovidhunkit, W., Tanaka, H., \u0026amp; Suksom, D. (2023). Nordic Walking in Water on Cerebrovascular Reactivity and Cognitive Function in Elderly Patients with Type 2 Diabetes. \u003cem\u003eMed Sci Sports Exerc\u003c/em\u003e,\u003cem\u003e 55\u003c/em\u003e(10), 1803-1811. https://doi.org/10.1249/mss.0000000000003216 \u003c/li\u003e\n\u003cli\u003ePosti, J. P., Takala, R. S., Runtti, H., Newcombe, V. F., Outtrim, J., Katila, A. J., . . . Tenovuo, O. (2016). The Levels of Glial Fibrillary Acidic Protein and Ubiquitin C-Terminal Hydrolase-L1 During the First Week After a Traumatic Brain Injury: Correlations With Clinical and Imaging Findings. \u003cem\u003eNeurosurgery\u003c/em\u003e,\u003cem\u003e 79\u003c/em\u003e(3), 456-464. https://doi.org/10.1227/neu.0000000000001226 \u003c/li\u003e\n\u003cli\u003ePratt, J., Motanova, E., Narici, M. V., Boreham, C., \u0026amp; De Vito, G. (2025). Plasma brain-derived neurotrophic factor concentrations are elevated in community-dwelling adults with sarcopenia. \u003cem\u003eAge Ageing\u003c/em\u003e,\u003cem\u003e 54\u003c/em\u003e(2). https://doi.org/10.1093/ageing/afaf024 \u003c/li\u003e\n\u003cli\u003ePrzybylska-Kuć, S., Zakrzewski, M., Dybała, A., Kiciński, P., Dzida, G., Myśliński, W., . . . Mosiewicz, J. (2019). Obstructive sleep apnea may increase the risk of Alzheimer\u0026apos;s disease. \u003cem\u003ePLoS One\u003c/em\u003e,\u003cem\u003e 14\u003c/em\u003e(9), e0221255. https://doi.org/10.1371/journal.pone.0221255 \u003c/li\u003e\n\u003cli\u003ePrzybylska, I., Marusiak, J., Toczyłowska, B., Stępień, A., Brodacki, B., Langfort, J., \u0026amp; Chalimoniuk, M. (2024). Association between the Val66Met (rs6265) polymorphism of the brain-derived neurotrophic factor (BDNF) gene, BDNF protein level in the blood and the risk of developing early‑onset Parkinson\u0026apos;s disease. \u003cem\u003eActa Neurobiol Exp (Wars)\u003c/em\u003e,\u003cem\u003e 84\u003c/em\u003e(3), 296-308. https://doi.org/10.55782/ane-2024-2476 \u003c/li\u003e\n\u003cli\u003eQuaresima, V., Pilotto, A., Trasciatti, C., Tolassi, C., Parigi, M., Bertoli, D., . . . Padovani, A. (2024). Plasma p-tau181 and amyloid markers in Alzheimer\u0026apos;s disease: A comparison between Lumipulse and SIMOA. \u003cem\u003eNeurobiol Aging\u003c/em\u003e,\u003cem\u003e 143\u003c/em\u003e, 30-40. https://doi.org/10.1016/j.neurobiolaging.2024.08.007 \u003c/li\u003e\n\u003cli\u003eRen, Q. G., Chang, J. H., Lu, W. J., Gong, W. G., \u0026amp; Zhou, H. (2017). Low plasma BDNF is not a biomarker for cognitive dysfunction in elderly T2DM patients. \u003cem\u003eNeurol Sci\u003c/em\u003e,\u003cem\u003e 38\u003c/em\u003e(9), 1691-1696. https://doi.org/10.1007/s10072-017-3048-9 \u003c/li\u003e\n\u003cli\u003eRibeiro, V. G. C., Mendon\u0026ccedil;a, V. A., Souza, A. L. C., Fonseca, S. F., Camargos, A. C. R., Lage, V. K. S., . . . Lacerda, A. C. R. (2018). Inflammatory biomarkers responses after acute whole body vibration in fibromyalgia. \u003cem\u003eBraz J Med Biol Res\u003c/em\u003e,\u003cem\u003e 51\u003c/em\u003e(4), e6775. https://doi.org/10.1590/1414-431x20176775 \u003c/li\u003e\n\u003cli\u003eRiezzo, G., Chimienti, G., Orlando, A., D\u0026apos;Attoma, B., Clemente, C., \u0026amp; Russo, F. (2019). Effects of long-term administration of Lactobacillus reuteri DSM-17938 on circulating levels of 5-HT and BDNF in adults with functional constipation. \u003cem\u003eBenef Microbes\u003c/em\u003e,\u003cem\u003e 10\u003c/em\u003e(2), 137-147. https://doi.org/10.3920/bm2018.0050 \u003c/li\u003e\n\u003cli\u003eRosenblum, Y., Pereira, M., Stange, O., Weber, F. D., Bovy, L., Tzioridou, S., . . . Dresler, M. (2024). Divergent Associations of Slow-Wave Sleep versus Rapid Eye Movement Sleep with Plasma Amyloid-Beta. \u003cem\u003eAnn Neurol\u003c/em\u003e,\u003cem\u003e 96\u003c/em\u003e(1), 46-60. https://doi.org/10.1002/ana.26935 \u003c/li\u003e\n\u003cli\u003eRubenstein, R., Chang, B., Yue, J. K., Chiu, A., Winkler, E. A., Puccio, A. M., . . . Vassar, M. J. (2017). Comparing Plasma Phospho Tau, Total Tau, and Phospho Tau-Total Tau Ratio as Acute and Chronic Traumatic Brain Injury Biomarkers. \u003cem\u003eJAMA Neurol\u003c/em\u003e,\u003cem\u003e 74\u003c/em\u003e(9), 1063-1072. https://doi.org/10.1001/jamaneurol.2017.0655 \u003c/li\u003e\n\u003cli\u003eRyan, K. M., Dunne, R., \u0026amp; McLoughlin, D. M. (2018). BDNF plasma levels and genotype in depression and the response to electroconvulsive therapy. \u003cem\u003eBrain Stimul\u003c/em\u003e,\u003cem\u003e 11\u003c/em\u003e(5), 1123-1131. https://doi.org/10.1016/j.brs.2018.05.011 \u003c/li\u003e\n\u003cli\u003eSagud, M., Nikolac Perkovic, M., Vuksan-Cusa, B., Maravic, A., Svob Strac, D., Mihaljevic Peles, A., . . . Pivac, N. (2016). A prospective, longitudinal study of platelet serotonin and plasma brain-derived neurotrophic factor concentrations in major depression: effects of vortioxetine treatment. \u003cem\u003ePsychopharmacology (Berl)\u003c/em\u003e,\u003cem\u003e 233\u003c/em\u003e(17), 3259-3267. https://doi.org/10.1007/s00213-016-4364-0 \u003c/li\u003e\n\u003cli\u003eSaraceno, C., Cervellati, C., Trentini, A., Crescenti, D., Longobardi, A., Geviti, A., . . . Ghidoni, R. (2024). Serum Beta-Secretase 1 Activity Is a Potential Marker for the Differential Diagnosis between Alzheimer\u0026apos;s Disease and Frontotemporal Dementia: A Pilot Study. \u003cem\u003eInt J Mol Sci\u003c/em\u003e,\u003cem\u003e 25\u003c/em\u003e(15). https://doi.org/10.3390/ijms25158354 \u003c/li\u003e\n\u003cli\u003eSarfo, F. S., Owusu, D., Adamu, S., Awuah, D., Appiah, L., Amamoo, M., . . . Ovbiagele, B. (2018). Plasma Glial Fibrillary Acidic Protein, Copeptin, and Matrix Metalloproteinase-9 Concentrations among West African Stroke Subjects Compared with Stroke-Free Controls. \u003cem\u003eJ Stroke Cerebrovasc Dis\u003c/em\u003e,\u003cem\u003e 27\u003c/em\u003e(3), 633-644. https://doi.org/10.1016/j.jstrokecerebrovasdis.2017.09.035 \u003c/li\u003e\n\u003cli\u003eSavarraj, J., Park, E. S., Colpo, G. D., Hinds, S. N., Morales, D., Ahnstedt, H., . . . Choi, H. A. (2021). Brain injury, endothelial injury and inflammatory markers are elevated and express sex-specific alterations after COVID-19. \u003cem\u003eJ Neuroinflammation\u003c/em\u003e,\u003cem\u003e 18\u003c/em\u003e(1), 277. https://doi.org/10.1186/s12974-021-02323-8 \u003c/li\u003e\n\u003cli\u003eSewell, K. R., Rainey-Smith, S. R., Pedrini, S., Peiffer, J. J., Sohrabi, H. R., Taddei, K., . . . Brown, B. M. (2024). The impact of exercise on blood-based biomarkers of Alzheimer\u0026apos;s disease in cognitively unimpaired older adults. \u003cem\u003eGeroscience\u003c/em\u003e,\u003cem\u003e 46\u003c/em\u003e(6), 5911-5923. https://doi.org/10.1007/s11357-024-01130-2 \u003c/li\u003e\n\u003cli\u003eShahim, P., Zetterberg, H., Simr\u0026eacute;n, J., Ashton, N. J., Norato, G., Sch\u0026ouml;ll, M., . . . Blennow, K. (2022). Association of Plasma Biomarker Levels With Their CSF Concentration and the Number and Severity of Concussions in Professional Athletes. \u003cem\u003eNeurology\u003c/em\u003e,\u003cem\u003e 99\u003c/em\u003e(4), e347-e354. https://doi.org/10.1212/wnl.0000000000200615 \u003c/li\u003e\n\u003cli\u003eSheth, U., \u0026Ouml;ijerstedt, L., Heckman, M. G., White, L. J., Heuer, H. W., Lario Lago, A., . . . Gendron, T. F. (2025). Comprehensive cross-sectional and longitudinal comparisons of plasma glial fibrillary acidic protein and neurofilament light across FTD spectrum disorders. \u003cem\u003eMol Neurodegener\u003c/em\u003e,\u003cem\u003e 20\u003c/em\u003e(1), 30. https://doi.org/10.1186/s13024-025-00821-4 \u003c/li\u003e\n\u003cli\u003eShi, Y., Lu, X., Zhang, L., Shu, H., Gu, L., Wang, Z., . . . Zhang, Z. (2019). Potential Value of Plasma Amyloid-\u0026beta;, Total Tau, and Neurofilament Light for Identification of Early Alzheimer\u0026apos;s Disease. \u003cem\u003eACS Chem Neurosci\u003c/em\u003e,\u003cem\u003e 10\u003c/em\u003e(8), 3479-3485. https://doi.org/10.1021/acschemneuro.9b00095 \u003c/li\u003e\n\u003cli\u003eSilva-Pe\u0026ntilde;a, D., Garc\u0026iacute;a-Marchena, N., Al\u0026eacute;n, F., Araos, P., Rivera, P., Vargas, A., . . . Su\u0026aacute;rez, J. (2019). Alcohol-induced cognitive deficits are associated with decreased circulating levels of the neurotrophin BDNF in humans and rats. \u003cem\u003eAddict Biol\u003c/em\u003e,\u003cem\u003e 24\u003c/em\u003e(5), 1019-1033. https://doi.org/10.1111/adb.12668 \u003c/li\u003e\n\u003cli\u003eSilveira-Rodrigues, J. G., Gard\u0026ecirc;nia de Holanda Marinho Nogueira, N., Faria, L. O., Pereira, D. S., \u0026amp; Soares, D. D. (2023). Combined Training Improves Executive Functions Without Changing Brain-Derived Neurotrophic Factor Levels of Middle-Aged and Older Adults with Type 2 Diabetes. \u003cem\u003eExp Clin Endocrinol Diabetes\u003c/em\u003e,\u003cem\u003e 131\u003c/em\u003e(6), 345-353. https://doi.org/10.1055/a-2069-4050 \u003c/li\u003e\n\u003cli\u003eSingh, N. A., Alnobani, A., Graff-Radford, J., Machulda, M. M., Mielke, M. M., Schwarz, C. G., . . . Whitwell, J. L. (2024). Relationships between PET and blood plasma biomarkers in corticobasal syndrome. \u003cem\u003eAlzheimers Dement\u003c/em\u003e,\u003cem\u003e 20\u003c/em\u003e(7), 4765-4774. https://doi.org/10.1002/alz.13914 \u003c/li\u003e\n\u003cli\u003eSingh, R., Rai, S., Bharti, P. S., Zehra, S., Gorai, P. K., Modi, G. P., . . . Kumar, S. (2024). Circulating small extracellular vesicles in Alzheimer\u0026apos;s disease: a case-control study of neuro-inflammation and synaptic dysfunction. \u003cem\u003eBMC Med\u003c/em\u003e,\u003cem\u003e 22\u003c/em\u003e(1), 254. https://doi.org/10.1186/s12916-024-03475-z \u003c/li\u003e\n\u003cli\u003eStartin, C. M., Ashton, N. J., Hamburg, S., Hithersay, R., Wiseman, F. K., Mok, K. Y., . . . Strydom, A. (2019). Plasma biomarkers for amyloid, tau, and cytokines in Down syndrome and sporadic Alzheimer\u0026apos;s disease. \u003cem\u003eAlzheimers Res Ther\u003c/em\u003e,\u003cem\u003e 11\u003c/em\u003e(1), 26. https://doi.org/10.1186/s13195-019-0477-0 \u003c/li\u003e\n\u003cli\u003eStratta, P., Sanit\u0026agrave;, P., Bonanni, R. L., de Cataldo, S., Angelucci, A., Rossi, R., . . . Rossi, A. (2016). Clinical correlates of plasma brain-derived neurotrophic factor in post-traumatic stress disorder spectrum after a natural disaster. \u003cem\u003ePsychiatry Res\u003c/em\u003e,\u003cem\u003e 244\u003c/em\u003e, 165-170. https://doi.org/10.1016/j.psychres.2016.07.019 \u003c/li\u003e\n\u003cli\u003eSudre, C. H., Bocchetta, M., Heller, C., Convery, R., Neason, M., Moore, K. M., . . . Rohrer, J. D. (2019). White matter hyperintensities in progranulin-associated frontotemporal dementia: A longitudinal GENFI study. \u003cem\u003eNeuroimage Clin\u003c/em\u003e,\u003cem\u003e 24\u003c/em\u003e, 102077. https://doi.org/10.1016/j.nicl.2019.102077 \u003c/li\u003e\n\u003cli\u003eSun, H. L., Sun, B. L., Chen, D. W., Chen, Y., Li, W. W., Xu, M. Y., . . . Bu, X. L. (2019). Plasma \u0026alpha;-synuclein levels are increased in patients with obstructive sleep apnea syndrome. \u003cem\u003eAnn Clin Transl Neurol\u003c/em\u003e,\u003cem\u003e 6\u003c/em\u003e(4), 788-794. https://doi.org/10.1002/acn3.756 \u003c/li\u003e\n\u003cli\u003eSungkarat, S., Boripuntakul, S., Kumfu, S., Lord, S. R., \u0026amp; Chattipakorn, N. (2018). Tai Chi Improves Cognition and Plasma BDNF in Older Adults With Mild Cognitive Impairment: A Randomized Controlled Trial. \u003cem\u003eNeurorehabil Neural Repair\u003c/em\u003e,\u003cem\u003e 32\u003c/em\u003e(2), 142-149. https://doi.org/10.1177/1545968317753682 \u003c/li\u003e\n\u003cli\u003eSustar, A., Perkovic, M. N., Erjavec, G. N., Strac, D. S., \u0026amp; Pivac, N. (2019). Association between reduced brain-derived neurotrophic factor concentration \u0026amp; coronary heart disease. \u003cem\u003eIndian J Med Res\u003c/em\u003e,\u003cem\u003e 150\u003c/em\u003e(1), 43-49. https://doi.org/10.4103/ijmr.IJMR_1566_17 \u003c/li\u003e\n\u003cli\u003eTanaka, M., Toldi, J., \u0026amp; V\u0026eacute;csei, L. (2020). Exploring the Etiological Links behind Neurodegenerative Diseases: Inflammatory Cytokines and Bioactive Kynurenines. \u003cem\u003eInt J Mol Sci\u003c/em\u003e,\u003cem\u003e 21\u003c/em\u003e(7), 2431. https://www.mdpi.com/1422-0067/21/7/2431 \u003c/li\u003e\n\u003cli\u003eTeunissen, C. E., Chiu, M. J., Yang, C. C., Yang, S. Y., Scheltens, P., Zetterberg, H., \u0026amp; Blennow, K. (2018). Plasma Amyloid-\u0026beta; (A\u0026beta;42) Correlates with Cerebrospinal Fluid A\u0026beta;42 in Alzheimer\u0026apos;s Disease. \u003cem\u003eJ Alzheimers Dis\u003c/em\u003e,\u003cem\u003e 62\u003c/em\u003e(4), 1857-1863. https://doi.org/10.3233/jad-170784 \u003c/li\u003e\n\u003cli\u003eTian, S., Huang, R., Han, J., Cai, R., Guo, D., Lin, H., . . . Wang, S. (2018). Increased plasma Interleukin-1\u0026beta; level is associated with memory deficits in type 2 diabetic patients with mild cognitive impairment. \u003cem\u003ePsychoneuroendocrinology\u003c/em\u003e,\u003cem\u003e 96\u003c/em\u003e, 148-154. https://doi.org/10.1016/j.psyneuen.2018.06.014 \u003c/li\u003e\n\u003cli\u003eTorres-Galv\u0026aacute;n, S., Flores-L\u0026oacute;pez, M., Ochoa, E., Requena-Oca\u0026ntilde;a, N., Araos, P., Herrera-Imbroda, J., . . . Garc\u0026iacute;a-Marchena, N. (2024). Dysregulation of Plasma Growth Factors and Chemokines in Cocaine Use Disorder: Implications for Dual Diagnosis with Schizophrenia and Antisocial Personality Disorder in an Exploratory Study. \u003cem\u003eNeuropsychobiology\u003c/em\u003e,\u003cem\u003e 83\u003c/em\u003e(2), 73-88. https://doi.org/10.1159/000536265 \u003c/li\u003e\n\u003cli\u003eUint, L., Bastos, G. M., Thurow, H. S., Borges, J. B., Hirata, T. D. C., Fran\u0026ccedil;a, J. I. D., . . . Sousa, A. (2019). Increased levels of plasma IL-1b and BDNF can predict resistant depression patients. \u003cem\u003eRev Assoc Med Bras (1992)\u003c/em\u003e,\u003cem\u003e 65\u003c/em\u003e(3), 361-369. https://doi.org/10.1590/1806-9282.65.3.361 \u003c/li\u003e\n\u003cli\u003eUpadhyay, K., Viramgami, A., Balachandar, R., Pagdhune, A., Sen, S., \u0026amp; Sarkar, K. (2022). A Comparative Health Assessment of Occupationally Lead Exposed Individuals with Blood Lead Levels Range across Upper Acceptable Limit. \u003cem\u003eIndian J Community Med\u003c/em\u003e,\u003cem\u003e 47\u003c/em\u003e(3), 343-346. https://doi.org/10.4103/ijcm.ijcm_756_21 \u003c/li\u003e\n\u003cli\u003eVallabh, S. M., Mortberg, M. A., Allen, S. W., Kupferschmid, A. C., Kivisakk, P., Hammerschlag, B. L., . . . Arnold, S. E. (2024). Fluid Biomarkers in Individuals at Risk for Genetic Prion Disease up to Disease Conversion. \u003cem\u003eNeurology\u003c/em\u003e,\u003cem\u003e 103\u003c/em\u003e(2), e209506. https://doi.org/10.1212/wnl.0000000000209506 \u003c/li\u003e\n\u003cli\u003evan Ballegoij, W. J. C., van de Stadt, S. I. W., Huffnagel, I. C., Kemp, S., Willemse, E. A. J., Teunissen, C. E., \u0026amp; Engelen, M. (2020). Plasma NfL and GFAP as biomarkers of spinal cord degeneration in adrenoleukodystrophy. \u003cem\u003eAnn Clin Transl Neurol\u003c/em\u003e,\u003cem\u003e 7\u003c/em\u003e(11), 2127-2136. https://doi.org/10.1002/acn3.51188 \u003c/li\u003e\n\u003cli\u003evan Prooije, T. H., Kapteijns, K. C. J., van Asten, J. J. A., IntHout, J., Verbeek, M. M., Scheenen, T. W. J., \u0026amp; van de Warrenburg, B. P. (2024). Multimodal, Longitudinal Profiling of SCA1 Identifies Predictors of Disease Severity and Progression. \u003cem\u003eAnn Neurol\u003c/em\u003e,\u003cem\u003e 96\u003c/em\u003e(4), 774-787. https://doi.org/10.1002/ana.27032 \u003c/li\u003e\n\u003cli\u003eVerberk, I. M. W., Jutte, J., Kingma, M. Y., Vigneswaran, S., Gouda, M., van Engelen, M. P., . . . Teunissen, C. E. (2024). Development of thresholds and a visualization tool for use of a blood test in routine clinical dementia practice. \u003cem\u003eAlzheimers Dement\u003c/em\u003e,\u003cem\u003e 20\u003c/em\u003e(9), 6115-6132. https://doi.org/10.1002/alz.14088 \u003c/li\u003e\n\u003cli\u003eVrillon, A., Bousiges, O., G\u0026ouml;tze, K., Demuynck, C., Muller, C., Ravier, A., . . . Paquet, C. (2024). Plasma biomarkers of amyloid, tau, axonal, and neuroinflammation pathologies in dementia with Lewy bodies. \u003cem\u003eAlzheimers Res Ther\u003c/em\u003e,\u003cem\u003e 16\u003c/em\u003e(1), 146. https://doi.org/10.1186/s13195-024-01502-y \u003c/li\u003e\n\u003cli\u003eWang, J., Gao, L., Liu, J., Dang, L., Wei, S., Hu, N., . . . Qu, Q. (2022). The Association of Plasma Amyloid-\u0026beta; and Cognitive Decline in Cognitively Unimpaired Population. \u003cem\u003eClin Interv Aging\u003c/em\u003e,\u003cem\u003e 17\u003c/em\u003e, 555-565. https://doi.org/10.2147/cia.s357994 \u003c/li\u003e\n\u003cli\u003eWang, L., Wang, G., Duan, Y., Wang, F., Lin, S., Zhang, F., . . . Li, H. (2019). A Comparative Study of the Diagnostic Potential of Plasma and Erythrocytic \u0026alpha;-Synuclein in Parkinson\u0026apos;s Disease. \u003cem\u003eNeurodegener Dis\u003c/em\u003e,\u003cem\u003e 19\u003c/em\u003e(5-6), 204-210. https://doi.org/10.1159/000506480 \u003c/li\u003e\n\u003cli\u003eWang, S. M., Chang, H. H., Chang, Y. H., Tsai, T. Y., Chen, P. S., Lu, R. B., \u0026amp; Wang, T. Y. (2024). Shortening of telomere length may be associated with inflammatory cytokine levels in patients with bipolar disorder. \u003cem\u003eJ Affect Disord\u003c/em\u003e,\u003cem\u003e 365\u003c/em\u003e, 155-161. https://doi.org/10.1016/j.jad.2024.08.084 \u003c/li\u003e\n\u003cli\u003eWang, T.-Y., Lee, S.-Y., Hu, M.-C., Chen, S.-L., Chang, Y.-H., Chu, C.-H., . . . Lu, R.-B. (2017). More inflammation but less brain-derived neurotrophic factor in antisocial personality disorder. \u003cem\u003ePsychoneuroendocrinology\u003c/em\u003e,\u003cem\u003e 85\u003c/em\u003e, 42-48. https://doi.org/https://doi.org/10.1016/j.psyneuen.2017.08.006 \u003c/li\u003e\n\u003cli\u003eWang, T. Y., Chang, Y. H., Lee, S. Y., Chang, H. H., Tsai, T. Y., Tseng, H. H., . . . Lu, R. B. (2025). Transdiagnostic features of inflammatory markers and executive function across psychiatric disorders. \u003cem\u003eJ Psychiatr Res\u003c/em\u003e,\u003cem\u003e 181\u003c/em\u003e, 160-168. https://doi.org/10.1016/j.jpsychires.2024.11.037 \u003c/li\u003e\n\u003cli\u003eWang, Y., Zhang, H., Li, Y., Wang, Z., Fan, Q., Yu, S., . . . Xiao, Z. (2015). BDNF Val66Met polymorphism and plasma levels in Chinese Han population with obsessive-compulsive disorder and generalized anxiety disorder. \u003cem\u003eJ Affect Disord\u003c/em\u003e,\u003cem\u003e 186\u003c/em\u003e, 7-12. https://doi.org/10.1016/j.jad.2015.07.023 \u003c/li\u003e\n\u003cli\u003eWei, C., Sun, Y., Chen, N., Chen, S., Xiu, M., \u0026amp; Zhang, X. (2020). Interaction of oxidative stress and BDNF on executive dysfunction in patients with chronic schizophrenia. \u003cem\u003ePsychoneuroendocrinology\u003c/em\u003e,\u003cem\u003e 111\u003c/em\u003e, 104473. https://doi.org/10.1016/j.psyneuen.2019.104473 \u003c/li\u003e\n\u003cli\u003eWei, M., Yu, X., Hu, S., Hu, W., Shi, R., Wang, M., . . . Han, Y. (2025). Differences of longitudinal plasma biomarkers between single memory domain and multidomain subject cognitive decline: Evidence from SILCODE. \u003cem\u003eJ Alzheimers Dis\u003c/em\u003e,\u003cem\u003e 103\u003c/em\u003e(4), 1060-1074. https://doi.org/10.1177/13872877241309105 \u003c/li\u003e\n\u003cli\u003eWei, Y. C., Kung, Y. C., Lin, C., Yeh, C. H., Chen, P. Y., Huang, W. Y., . . . Chen, C. K. (2024). Differential neuropsychiatric associations of plasma biomarkers in older adults with major depression and subjective cognitive decline. \u003cem\u003eTransl Psychiatry\u003c/em\u003e,\u003cem\u003e 14\u003c/em\u003e(1), 333. https://doi.org/10.1038/s41398-024-03049-w \u003c/li\u003e\n\u003cli\u003eWeickert, C. S., Lee, C. H., Lenroot, R. K., Bruggemann, J., Galletly, C., Liu, D., . . . Weickert, T. W. (2019). Increased plasma Brain-Derived Neurotrophic Factor (BDNF) levels in females with schizophrenia. \u003cem\u003eSchizophr Res\u003c/em\u003e,\u003cem\u003e 209\u003c/em\u003e, 212-217. https://doi.org/10.1016/j.schres.2019.04.015 \u003c/li\u003e\n\u003cli\u003eWen, C. H., Kang, H. Y., \u0026amp; Chan, J. Y. H. (2024). Brain Amyloid-\u0026beta; Peptide Is Associated with Pain Intensity and Cognitive Dysfunction in Osteoarthritic Patients. \u003cem\u003eInt J Mol Sci\u003c/em\u003e,\u003cem\u003e 25\u003c/em\u003e(23). https://doi.org/10.3390/ijms252312575 \u003c/li\u003e\n\u003cli\u003eWest, T., Kirmess, K. M., Meyer, M. R., Holubasch, M. S., Knapik, S. S., Hu, Y., . . . Yarasheski, K. E. (2021). A blood-based diagnostic test incorporating plasma A\u0026beta;42/40 ratio, ApoE proteotype, and age accurately identifies brain amyloid status: findings from a multi cohort validity analysis. \u003cem\u003eMolecular Neurodegeneration\u003c/em\u003e,\u003cem\u003e 16\u003c/em\u003e(1), 30. https://doi.org/10.1186/s13024-021-00451-6 \u003c/li\u003e\n\u003cli\u003eWHO. (2004). Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies. \u003cem\u003eThe Lancet\u003c/em\u003e,\u003cem\u003e 363\u003c/em\u003e(9403), 157-163. https://doi.org/https://doi.org/10.1016/S0140-6736(03)15268-3 \u003c/li\u003e\n\u003cli\u003eWHO. (2011). \u003cem\u003eHaemoglobin concentrations for the diagnosis of anaemia and assessment of severity\u003c/em\u003e (WHO/NMH/NHD/MNM/11.1) https://iris.who.int/bitstream/handle/10665/85839/WHO_NMH_NHD_MNM_11.1_eng.pdf?sequence=22 \u003c/li\u003e\n\u003cli\u003eWilhelm, C. J., Fuller, B. E., Huckans, M., \u0026amp; Loftis, J. M. (2017). Peripheral immune factors are elevated in women with current or recent alcohol dependence and associated with altered mood and memory. \u003cem\u003eDrug Alcohol Depend\u003c/em\u003e,\u003cem\u003e 176\u003c/em\u003e, 71-78. https://doi.org/10.1016/j.drugalcdep.2017.02.023 \u003c/li\u003e\n\u003cli\u003eXiong, H., Yang, H., Liang, M., Ou, Y., Huang, X., Cai, Y., . . . Zheng, Y. (2016). Plasma brain-derived neurotrophic factor levels are increased in patients with tinnitus and correlated with therapeutic effects. \u003cem\u003eNeurosci Lett\u003c/em\u003e,\u003cem\u003e 622\u003c/em\u003e, 15-18. https://doi.org/10.1016/j.neulet.2016.04.032 \u003c/li\u003e\n\u003cli\u003eXiong, J., Zhang, Z., Ma, Y., Li, Z., Zhou, F., Qiao, N., . . . Liao, W. (2020). The effect of combined scalp acupuncture and cognitive training in patients with stroke on cognitive and motor functions. \u003cem\u003eNeuroRehabilitation\u003c/em\u003e,\u003cem\u003e 46\u003c/em\u003e(1), 75-82. https://doi.org/10.3233/nre-192942 \u003c/li\u003e\n\u003cli\u003eXu, L., Zhang, Y., Zhang, R., Zhang, H., Song, P., Ma, T., . . . Shen, C. (2019). Elevated plasma BDNF levels are correlated with NK cell activation in patients with traumatic spinal cord injury. \u003cem\u003eInt Immunopharmacol\u003c/em\u003e,\u003cem\u003e 74\u003c/em\u003e, 105722. https://doi.org/10.1016/j.intimp.2019.105722 \u003c/li\u003e\n\u003cli\u003eXu, T., Weng, L., Zhang, C., Xiao, X., Yang, Q., Zhu, Y., . . . Shen, L. (2024). Genetic spectrum features and diagnostic accuracy of four plasma biomarkers in 248 Chinese patients with frontotemporal dementia. \u003cem\u003eAlzheimers Dement\u003c/em\u003e,\u003cem\u003e 20\u003c/em\u003e(10), 7281-7295. https://doi.org/10.1002/alz.14215 \u003c/li\u003e\n\u003cli\u003eXu, Y. Y., Ge, J. F., Chen, J., Liang, J., Pang, L. J., Gao, W. F., . . . Xia, Q. R. (2020). Evidence of a Relationship Between Plasma Leptin, Not Nesfatin-1, and Craving in Male Alcohol-Dependent Patients After Abstinence. \u003cem\u003eFront Endocrinol (Lausanne)\u003c/em\u003e,\u003cem\u003e 11\u003c/em\u003e, 159. https://doi.org/10.3389/fendo.2020.00159 \u003c/li\u003e\n\u003cli\u003eYang, B., Deng, Q., Zhang, W., Feng, Y., Dai, X., Feng, W., . . . Wu, T. (2016). Exposure to Polycyclic Aromatic Hydrocarbons, Plasma Cytokines, and Heart Rate Variability. \u003cem\u003eSci Rep\u003c/em\u003e,\u003cem\u003e 6\u003c/em\u003e, 19272. https://doi.org/10.1038/srep19272 \u003c/li\u003e\n\u003cli\u003eYang, C. C., Chiu, M. J., Chen, T. F., Chang, H. L., Liu, B. H., \u0026amp; Yang, S. Y. (2018). Assay of Plasma Phosphorylated Tau Protein (Threonine 181) and Total Tau Protein in Early-Stage Alzheimer\u0026apos;s Disease. \u003cem\u003eJ Alzheimers Dis\u003c/em\u003e,\u003cem\u003e 61\u003c/em\u003e(4), 1323-1332. https://doi.org/10.3233/jad-170810 \u003c/li\u003e\n\u003cli\u003eYang, S. Y., Chiu, M. J., Chen, T. F., Lin, C. H., Jeng, J. S., Tang, S. C., . . . Wu, C. C. (2017). Analytical performance of reagent for assaying tau protein in human plasma and feasibility study screening neurodegenerative diseases. \u003cem\u003eSci Rep\u003c/em\u003e,\u003cem\u003e 7\u003c/em\u003e(1), 9304. https://doi.org/10.1038/s41598-017-09009-3 \u003c/li\u003e\n\u003cli\u003eYang, Y., Liu, Y., Wang, G., Hei, G., Wang, X., Li, R., . . . Zhao, J. (2019). Brain-derived neurotrophic factor is associated with cognitive impairments in first-episode and chronic schizophrenia. \u003cem\u003ePsychiatry Res\u003c/em\u003e,\u003cem\u003e 273\u003c/em\u003e, 528-536. https://doi.org/10.1016/j.psychres.2019.01.051 \u003c/li\u003e\n\u003cli\u003eYu, K., Yang, B., Jiang, H., Li, J., Yan, K., Liu, X., . . . Wu, T. (2019). A multi-stage association study of plasma cytokines identifies osteopontin as a biomarker for acute coronary syndrome risk and severity. \u003cem\u003eSci Rep\u003c/em\u003e,\u003cem\u003e 9\u003c/em\u003e(1), 5121. https://doi.org/10.1038/s41598-019-41577-4 \u003c/li\u003e\n\u003cli\u003eYue, J. K., Yuh, E. L., Korley, F. K., Winkler, E. A., Sun, X., Puffer, R. C., . . . Manley, G. T. (2019). Association between plasma GFAP concentrations and MRI abnormalities in patients with CT-negative traumatic brain injury in the TRACK-TBI cohort: a prospective multicentre study. \u003cem\u003eLancet Neurol\u003c/em\u003e,\u003cem\u003e 18\u003c/em\u003e(10), 953-961. https://doi.org/10.1016/s1474-4422(19)30282-0 \u003c/li\u003e\n\u003cli\u003eZamani, M., Hosseini, S. V., Behrouj, H., Erfani, M., Dastghaib, S., Ahmadi, M., . . . Mokarram, P. (2019). BDNF Val66Met genetic variation and its plasma level in patients with morbid obesity: A case-control study. \u003cem\u003eGene\u003c/em\u003e,\u003cem\u003e 705\u003c/em\u003e, 51-54. https://doi.org/10.1016/j.gene.2019.04.045 \u003c/li\u003e\n\u003cli\u003eZecca, C., Pasculli, G., Tortelli, R., Dell\u0026apos;Abate, M. T., Capozzo, R., Barulli, M. R., . . . Logroscino, G. (2021). The Role of Age on Beta-Amyloid(1-42) Plasma Levels in Healthy Subjects. \u003cem\u003eFront Aging Neurosci\u003c/em\u003e,\u003cem\u003e 13\u003c/em\u003e, 698571. https://doi.org/10.3389/fnagi.2021.698571 \u003c/li\u003e\n\u003cli\u003eZecca, C., Tortelli, R., Panza, F., Arcuti, S., Piccininni, M., Capozzo, R., . . . Logroscino, G. (2018). Plasma \u0026beta;-amyloid1\u0026ndash;42 reference values in cognitively normal subjects. \u003cem\u003eJ Neurol Sci\u003c/em\u003e,\u003cem\u003e 391\u003c/em\u003e, 120-126. https://doi.org/https://doi.org/10.1016/j.jns.2018.06.006 \u003c/li\u003e\n\u003cli\u003eZhang, Y., Fang, X., Fan, W., Tang, W., Cai, J., Song, L., \u0026amp; Zhang, C. (2018). Brain-derived neurotrophic factor as a biomarker for cognitive recovery in acute schizophrenia: 12-week results from a prospective longitudinal study. \u003cem\u003ePsychopharmacology (Berl)\u003c/em\u003e,\u003cem\u003e 235\u003c/em\u003e(4), 1191-1198. https://doi.org/10.1007/s00213-018-4835-6 \u003c/li\u003e\n\u003cli\u003eZhao, G., Zhang, C., Chen, J., Su, Y., Zhou, R., Wang, F., . . . Fang, Y. (2017). Ratio of mBDNF to proBDNF for Differential Diagnosis of Major Depressive Disorder and Bipolar Depression. \u003cem\u003eMol Neurobiol\u003c/em\u003e,\u003cem\u003e 54\u003c/em\u003e(7), 5573-5582. https://doi.org/10.1007/s12035-016-0098-6 \u003c/li\u003e\n\u003cli\u003eZou, J., Guo, Y., Wei, L., Yu, F., Yu, B., \u0026amp; Xu, A. (2020). Long Noncoding RNA POU3F3 and \u0026alpha;-Synuclein in Plasma L1CAM Exosomes Combined with \u0026beta;-Glucocerebrosidase Activity: Potential Predictors of Parkinson\u0026apos;s Disease. \u003cem\u003eNeurotherapeutics\u003c/em\u003e,\u003cem\u003e 17\u003c/em\u003e(3), 1104-1119. https://doi.org/10.1007/s13311-020-00842-5 \u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable-1: Characteristics of Study Participants\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eParameter\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean \u0026plusmn; SD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRange\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eDemographic details of the study participants\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003eTotal Participants (N)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e405\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003eMale (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e197(48.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003eAge (Years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e48.60 \u0026plusmn; 6.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e40 - 60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e24.54 \u0026plusmn; 4.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e13.0 \u0026ndash; 43.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003eSystolic BP (mm Hg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e132.42 \u0026plusmn; 18.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e90 - 208\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003eDiastolic BP (mm Hg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e87.13 \u0026plusmn; 11.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e58 - 174\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003eHemoglobin (mg/dL)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e9.98 \u0026plusmn; 1.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e4.0 \u0026ndash; 14.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003eRandom Blood Sugar (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e110.85 \u0026plusmn; 31.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e72.0 \u0026ndash; 356.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe table provides mean \u0026plusmn; standard deviation and range for each parameter.\u003cstrong\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable-2: Plasma concentrations of neurodegenerative biomarkers in the study population\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"124%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eParameter\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePlasma concentration in pg/mL\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eMedian (IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverall\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(N=405)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 35px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 36px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge Group\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMale (N=197)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFemale (N=208)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e41 \u0026ndash; 50 (n=229)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e51 \u0026ndash; 60 (n=176)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAmyloid\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eb\u003c/strong\u003e\u003cstrong\u003e\u003csub\u003e(1-42)\u003c/sub\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e18.95\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(11.05 \u0026ndash; 44.42)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e16.60\u003c/p\u003e\n \u003cp\u003e(11.05 \u0026ndash; 38.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e21.68\u003c/p\u003e\n \u003cp\u003e(11.05 \u0026ndash; 49.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e21.29\u003c/p\u003e\n \u003cp\u003e(11.05 \u0026ndash; 45.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e14.72\u003c/p\u003e\n \u003cp\u003e(11.05 \u0026ndash; 39.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003et\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e84.38\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(28.67 \u0026ndash; 199.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e81.52\u003c/p\u003e\n \u003cp\u003e(33.88 \u0026ndash; 177.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e98.77\u003c/p\u003e\n \u003cp\u003e(21.41 \u0026ndash; 233.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e85.20\u003c/p\u003e\n \u003cp\u003e(33.68 \u0026ndash; 215.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e82.62\u003c/p\u003e\n \u003cp\u003e(26.05 \u0026ndash; 179.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026alpha;-Synuclein \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e804.51\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(294.74 \u0026ndash; 1741.97)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e804.51\u003c/p\u003e\n \u003cp\u003e(142.28 \u0026ndash; 1863.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e806.19\u003c/p\u003e\n \u003cp\u003e(360.19 \u0026ndash; 1587.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e804.51\u003c/p\u003e\n \u003cp\u003e(290.46 \u0026ndash; 1777.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e806.19\u003c/p\u003e\n \u003cp\u003e(297.47 \u0026ndash; 1618.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBDNF\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e2221.98\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(1057.82 \u0026ndash; 6274.14)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e1994.96\u003c/p\u003e\n \u003cp\u003e(1116.10 \u0026ndash; 5833.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e2458.92\u003c/p\u003e\n \u003cp\u003e(972.94 \u0026ndash; 6543.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e2423.56\u003c/p\u003e\n \u003cp\u003e(1077.29 \u0026ndash; 6568.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e1823.46\u003c/p\u003e\n \u003cp\u003e(1017.80 \u0026ndash; 5956.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGFAP\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e98.33\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(55.43 \u0026ndash; 129.62)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e98.85\u003c/p\u003e\n \u003cp\u003e(78.42 \u0026ndash; 120.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e96.69\u003c/p\u003e\n \u003cp\u003e(72.04 \u0026ndash; 134.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e97.97\u003c/p\u003e\n \u003cp\u003e(74.00 \u0026ndash; 128.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e98.81\u003c/p\u003e\n \u003cp\u003e(77.25 \u0026ndash; 131.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThis table presents the median (IQR) plasma concentrations of (Amyloid \u0026beta;(1-42), Total \u0026tau;, \u0026alpha;-Synuclein, BDNF, and GFAP) for the total study group, and further stratified by gender and age groups (41-50 years and 51-60 years). Data is shown in pg/mL.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"neurodegenerative markers, plasma concentrations, Indian cohort, geographical variability, amyloid-β, BDNF","lastPublishedDoi":"10.21203/rs.3.rs-7788186/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7788186/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eNeurodegenerative disorders (NDs) are progressive conditions associated with neuronal loss, cognitive decline, and high global morbidity and mortality. Blood-based biomarkers such as amyloid-β (Aβ1\u0026ndash;42), tau, α-synuclein, brain-derived neurotrophic factor (BDNF), and glial fibrillary acidic protein (GFAP) hold promise for early detection and monitoring. This study evaluated plasma levels of key neurodegenerative biomarkers in an apparently healthy middle-aged Indian cohort and compared them with global datasets to explore potential racial, genetic, and environmental influences.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eA cross-sectional community-based study recruited 405 participants (40\u0026ndash;60 years, both sexes) from Ahmedabad district, western India, following strict inclusion and exclusion criteria. Demographic and clinical parameters were recorded, and venous blood samples were collected under aseptic conditions. Biomarkers (Aβ1\u0026ndash;42, total tau, α-synuclein, BDNF, GFAP) were quantified using high-sensitivity sandwich ELISA. Statistical analysis included t-tests, median comparisons, and age- and sex-stratified analyses.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eMedian plasma concentrations were: Aβ1\u0026ndash;42 (18.95 pg/mL), total tau (84.38 pg/mL), α-synuclein (804.51 pg/mL), BDNF (2221.98 pg/mL), and GFAP (98.33 pg/mL). Relatively older participants (aged 51\u0026ndash;60 years) demonstrated elevated biomarker levels compared to younger counterparts. Comparison with international datasets revealed marked inter-regional variability, suggesting potential genetic, racial, and environmental influences.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eThe study describes the levels of plasma neurodegenerative biomarkers in a community of Indian population, further emphasizing the variations in the levels of these markers among healthy adults across the globe. These findings underscore the importance of accounting for racial and geographical differences when interpreting biomarker data and call for longitudinal studies to establish population-specific reference ranges.\u003c/p\u003e","manuscriptTitle":"Plasma Biomarkers of Neurodegeneration and Neuroinflammation among Middle- Aged Adults in Western India: Implications of Racial and Geographical Variability","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-21 18:08:35","doi":"10.21203/rs.3.rs-7788186/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"61144913-a80a-4bab-8b31-543cf285e72f","owner":[],"postedDate":"November 21st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-28T08:53:47+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-21 18:08:35","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7788186","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7788186","identity":"rs-7788186","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}