Serum Neurofilament Light Chain and Inflammatory Cytokines as Biomarkers for Early Detection of Alzheimer's Disease

Serum Neurofilament Light Chain and Inflammatory Cytokines as Biomarkers for Early Detection of Alzheimer's Disease | 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 Article Serum Neurofilament Light Chain and Inflammatory Cytokines as Biomarkers for Early Detection of Alzheimer's Disease Xinyang Jing, Lan Wang, Mei Song, Hao Geng, Wei Li, Yaxin Huo, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3828911/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Objective: To investigate the association between serum neurofilament light chain (NfL) levels, inflammatory cytokines, and cognitive function to assess their utility in the early detection of Alzheimer's disease (AD). Methods: We conducted a cross-sectional study involving 157 community-dwelling individuals aged 55 years and above, categorized into healthy controls, mild cognitive impairment (MCI), and probable AD. Serum levels of NfL, inflammatory cytokines, and AD pathology markers were measured using enzyme-linked immunosorbent assay (ELISA). Correlations between these biomarkers and cognitive function were analyzed, for the potential of serum NfL in recognizing MCI. Results: Serum NfL levels were significantly elevated in MCI and probable AD groups compared to healthy controls. Positive correlations were found between serum NfL and inflammatory cytokines IL-1β, IL-6, and Aβ40. Combining serum NfL with p-tau217 and the Boston Naming Test significantly enhanced the predictive accuracy for MCI. However, combining serum NfL with inflammatory markers did not improve MCI prediction accuracy. Conclusions: Elevated serum NfL is associated with cognitive impairment and inflammatory markers, suggesting its potential as a peripheral blood biomarker for early AD detection. The combination of serum NfL with p-tau217 and cognitive tests could offer a more accurate prediction of MCI, providing new insights for AD treatment strategies. Health sciences/Biomarkers Health sciences/Medical research Neuroflament Light Inflammatory Cytokines Mild Cognitive Impairment Alzheimer's disease Biomarkers Figures Figure 1 1. BACKGROUND Alzheimer's disease (AD) is a neurodegenerative disorder hallmarked by the accumulation of amyloid-beta (Aβ) and tau proteins, brain tissue atrophy, and progressive cognitive decline 1 .The global prevalence of AD is escalating in tandem with the aging population. The International Alzheimer's Disease Association reported approximately 46 million individuals with AD worldwide in 2018, with projections suggesting a rise to 131.5 million by 2050 2 . In China, recent epidemiological data indicates a dementia prevalence of 6.04%, with AD accounting for 3.94% among those over 60, translating to nearly 9.83 million AD patients and an increasing socio-economic impact 3 . The challenge of timely and accurate dementia diagnosis is formidable, with an estimated three-quarters of dementia cases remaining undiagnosed globally, a figure that could be as high as 90% in low- and middle-income countries. Current diagnostic markers, such as cerebrospinal fluid (CSF) Aβ and tau proteins, along with neuroimaging techniques like MRI, tau-PET, β-amyloid-PET, and F-FDG-PET, facilitate early AD detection but are limited by invasiveness, cost, and accessibility 4 . Consequently, there is an urgent need for less invasive and more cost-effective biomarkers. Neurofilaments, specifically the light chain (NfL), have garnered attention as potential peripheral biomarkers for neuronal damage. Neurofilaments are neuron-specific, highly phosphorylated proteins that form a key component of the axonal cytoskeleton, with NfL being a critical subunit involved in axonal growth and stability 5 . Under physiological conditions, axonal cells release minimal amounts of NfL into the CSF, which can then enter the bloodstream, albeit at lower concentrations. However, pathological conditions trigger a marked increase in NfL, making it a sensitive marker for axonal injury 6 . Elevated CSF NfL levels have been associated with cognitive impairment, with studies demonstrating its rise in early AD and its correlation with cognitive decline and brain structural changes 7 . Elevated CSF NfL has also been observed in other dementias, correlating with lower Mini-Mental State Examination (MMSE) scores and reduced survival times 8 . Furthermore, higher NfL levels have been linked to cognitive decline in mild cognitive impairment (MCI) and AD patients, irrespective of vascular burden 9 . Given the invasive nature of CSF collection, the focus has shifted towards blood-based NfL measurements. Significant correlations between plasma NfL and CSF biomarkers have been reported, with increased plasma NfL levels observed in MCI and AD, suggesting its potential as a non-invasive biomarker for cognitive impairment 1,10 . The relationship between NfL and cognitive function in the older adults is further supported by its role as an axonal damage marker, which is closely related to microglial activation in the central immune system 11 . Inflammation is increasingly recognized as a contributor to neurodegeneration, with systemic elevations of pro-inflammatory cytokines like TNF-α implicated in cognitive decline in AD 12,13 . The dysregulation of inflammatory pathways in MCI may also serve as a predictor for the rate of cognitive deterioration 14 . While there is evidence of a strong correlation between plasma NfL and both peripheral and cortical inflammation during AD progression, and suggestions of AD as a chronic autoimmune condition with heightened pro-inflammatory factors 15,16 , research into the relationship between blood NfL and inflammatory markers remains limited. This study, therefore, aims to explore the role of serum NfL and inflammatory cytokines in the early detection of AD and to clarify the relationship between serum NfL and inflammatory markers. 2. MATERIALS AND METHODS 2.1 Study Design and Participants This cross-sectional study was conducted from April 2021 to February 2022 in Hebei Province, China. Participants were recruited from the Yuhua District of Shijiazhuang City and Renze District of Xingtai City, resulting in a cohort of 157 older adults aged 55 years and older, comprising 74 males and 83 females. All participants had signed informed consent forms, and the study was approved by the Ethics Committee of the First Hospital of Hebei Medical University (ethical approval number: 20190416). We confirm that all research were performed in accordance with relevant guidelines and regulations. 2.2 Inclusion and Exclusion Criteria Inclusion criteria for the study were as follows: ( 1 ) age 55 years or older; ( 2 ) willingness and ability to participate in and complete a comprehensive questionnaire survey and cognitive function evaluation; ( 3 ) consent to provide blood samples for analysis. Exclusion criteria were established to omit individuals with potential alternative causes of cognitive decline, including those with a clear history of stroke, brain tumor, Parkinson's disease, epilepsy, severe head trauma, and significant mental disorders. Additionally, individuals with severe physical illnesses or substantial visual or hearing impairments that could interfere with cognitive function testing were excluded. 2.3 Diagnostic Criteria Participants were classified into two groups: Healthy older adults with normal cognition (healthy control, HC) and those with cognitive impairment (MCI and probable AD). HC Group Diagnostic Criteria: ( 1 ) Objective evidence of normal cognitive function, stratified by educational level with respective cut-off scores on the MMSE: ≥19 points for illiterate, ≥ 22 points for elementary school, and ≥ 24 points for junior high school and above 17 . Additionally, Montreal Cognitive Assessment (MoCA) scores of ≥ 13 points, ≥ 19 points, and ≥ 24 points were required for the respective educational levels 18 . ( 2 ) Normal daily living abilities, as indicated by a score of ≤ 26 points on the Activity of Daily Living Scale (ADL) 19 . MCI Diagnostic Criteria: As per the consensus established by the Chinese Expert Group on Cognitive Impairment Prevention and Treatment in 2006 20 , MCI diagnosis included: Self-reported or family-observed significant memory impairment. Preservation of other cognitive functions or only mild impairment, with MMSE scores of ≥ 19 for illiterate, ≥ 22 for elementary school, and ≥ 24 for junior high school or above. Corresponding MoCA scores should be < 13, <19, and < 24, respectively. Maintenance of basic daily living activities, with an ADL score ≤ 26. Exclusion of dementia, ensuring that the criteria for dementia diagnosis are not met. Exclusion of other systemic diseases that could affect cognitive function. Probable AD Diagnostic Criteria 21 : ( 1 ) Documented history of cognitive decline, as evidenced by reduced scores on neurocognitive tests, specifically MMSE scores of < 19 for illiterate, < 22 for elementary school, and 26. 2.4 Data Collection Procedures Trained survey personnel conducted the data collection process, which included administering a custom-designed general information survey form to collect demographic data such as age, gender, education level, smoking and alcohol consumption habits, and medical history. 2.5 Neuropsychological Assessments Cognitive function was assessed using the MMSE 17 and the MoCA 18 . The MMSE is a widely utilized tool that evaluates nine cognitive domains, including temporal and spatial orientation, immediate memory, calculation, delayed recall, naming, repetition, comprehension, and visuospatial abilities, with a maximum score of 30 points. The scoring is adjusted for cultural differences. The MoCA is known for its higher sensitivity in detecting mild cognitive deficits compared to the MMSE. It evaluates several cognitive domains: visuospatial and executive functions, naming, memory, attention, language, abstraction, delayed recall, and orientation. Additionally, the Boston Naming Test (BNT) 22 was used to assess language production, specifically the ability to name pictorial representations, and the Digit Span Test (DST) 23 measured immediate memory, attention, and processing speed. 2.6 Blood Sample Collection and Processing Blood samples were collected from participants following an overnight fast. Standardized venipuncture procedures were performed in the morning by trained nurses, who collected 5 mL of venous blood from each participant. The whole blood samples were allowed to clot at room temperature for 2 hours before centrifugation at 3000 rpm for 10 minutes. The resulting serum was aliquoted and stored at -80°C until analysis. Serum concentrations of NfL, Interleukin-1β (IL-1β), Interleukin-6 (IL-6), Tumor Necrosis Factor-α (TNFα), Amyloid-β40 (Aβ40), Amyloid-β42 (Aβ42), and hyperphosphorylated tau protein 217 (p-tau217) were measured using enzyme-linked immunosorbent assay (ELISA) kits (Product IDs: CSB-E16094h, EK101B − 24, EK106/2–24, EK182–24, CSB-E08299h, CSB-E10684h, respectively). The ELISA procedure was conducted as follows: Reagents were equilibrated to room temperature (18–25°C) for at least 30 minutes. Following the manufacturer's instructions, reagents were prepared and added to the ELISA plate wells, with 100µl of standard or test sample per well. After mixing and sealing, the plates were incubated at 37°C for 2 hours. Post-incubation, the liquid was discarded, and the wells were washed three times with a 2-minute soak of 200µl per well. Subsequently, 100µl of biotin-labeled antibody working solution was added to each well and incubated for another hour at 37°C. Following this, the wells were washed five times, and 100µl of horseradish peroxidase-labeled avidin working solution was added to each well. After a final hour of incubation and washing, 90µl of substrate solution was added to each well, and the plates were incubated in the dark for 15–30 minutes at 37°C. The reaction was stopped by adding 50µl of stop solution to each well. The optical density (OD) of each well was measured at a wavelength of 450nm within 5 minutes of adding the stop solution. A standard curve was generated from the OD values of the standard samples, and the concentrations of the proteins in the test samples were calculated accordingly. 2.7 Statistical Analysis Data analysis was performed using SPSS software, version 26.0. The Shapiro-Wilk test was applied to determine the normality of the continuous variables. Continuous data that followed a normal distribution across the three groups were analyzed using one-way analysis of variance (ANOVA), and the results were reported as means ± standard deviations. For continuous data that did not follow a normal distribution, the Kruskal-Wallis rank sum test was utilized, with results presented as medians with the 25th and 75th percentiles [M (P25, P75)]. Categorical data were compared using the chi-square test. The diagnostic performance of the cognitive assessment scales and serum biomarker concentrations was evaluated using receiver operating characteristic (ROC) curve analysis. This method calculated the area under the curve (AUC) and determined the sensitivity and specificity of each test. GraphPad Prism version 8.0 was used for the generation of graphical representations of the data. To investigate the relationships between serum NfL levels, age, and cognitive assessment scale scores across different groups, Spearman's rank correlation analysis was conducted. A P -value of less than 0.05 was considered statistically significant, indicating that the observed differences were unlikely to have occurred by chance. 3. RESULTS 3.1 Demographic Comparisons The demographic analysis revealed no significant differences in gender, age, or educational level among the HC, MCI, and probable AD groups, ensuring comparability of the demographic data. The prevalence of hypertension and hyperlipidemia was also similar across the groups. However, significant differences emerged in the history of smoking, alcohol consumption, diabetes, and coronary heart disease among the participants (Table 1 ). Table 1 Demographic comparisons among the three groups [(‾Χ ± S)༏M(P25, P75)/n(%)] HC (n = 97) MCI (n = 44) Probable AD (n = 16) F/X2/Z P Sex 0.861 0.638 Male 48.0 (49.5) 20.0 (45.5) 6.0 (37.5) Female 49.0 (50.5) 24.0 (54.5) 10.0 (62.5) Age 68.7 ± 4.0 72.6 ± 8.0 70.6 ± 8.7 5.426 0.066 Education level 4.419 0.356 Illiteracy 9.0 (9.3) 2.0 (4.5) 3.0 (21.4) Primary school 16.0 (16.5) 5.0 (11.4) 1.0 (6.3) Junior high school and above 72.0 (74.2) 37.0 (84.7) 12.0 (75.0) Smoking history 12.924 0.011 * Never smoked 64.0 (66.0) 16.0 (36.4) 13.0 (75.0) Have not quit smoking 8.0 (8.2) 7.0 (15.9) 1.0 (6.3) Quit smoking 25.0 (25.8) 21.0 (47.7) 3.0 (18.8) Drinking history 15.769 0.015 * Often 11.0 (11.3) 6.0 (13.6) 2.0 (12.5) Have stopped drinking 18.0 (18.6) 20.0 (45.5) 2.0 (12.5) occasionally 8.0 (8.2) 4.0 (9.1) 1.0 (6.3) Never 60.0 (61.9) 14.0 (31.8) 11.0 (68.8) Hypertension 1.044 0.601 No 49.0 (50.5) 21.0 (47.7) 10.0 (62.5) Yes 48.0 (49.5) 23.0 (52.3) 6.0 (37.5) Hyperlipemia 2.237 0.341 No 67.0 (69.1) 27.0 61.4) 13.0 (81.3) Yes 30.0 (30.9) 17.0 (38.6) 3.0 (18.8) Diabetes 8.829 0.012 * No 69.0 (71.1) a 20.0 (45.5) b 11.0 (68.8) ab Yes 28.0 (28.9) a 24.0 (54.5) b 5.0 (31.3) ab Coronary heart disease 11.375 0.004 ** No 77.0 (79.4) a 24.0 (54.5) b 14.0 (87.5) ab Yes 20.0 (20.6) a 20.0 (45.5) b 2.0 (12.5) ab Note: Educational attainment: The corner scale abc indicates that there was a difference between the two groups, a and b represent differences between HC and MCI; * denote P < 0.05, ** denote P < 0.01. 3.2 Cognitive Function Comparisons Cognitive function assessments among the HC, MCI, and probable AD groups revealed significant differences in performance on the MoCA, MMSE, BNT, and DST. Both the MCI and probable AD groups scored significantly lower than the HC group on all cognitive tests, indicating a decline in cognitive function associated with disease progression (Table 2 ). Table 2 Comparison of cognitive function among the three groups HC group (n = 97) MCI group (n = 44) Probably AD group (n = 16) F P MoCA 27.0 (25.5, 28.5) 23.0 (20.3, 24.0) 19.0 (14.0, 22.8) 57.46 P < 0.001 ** MMSE 29.0 (27.0, 30.0) 27.0 (25.0, 27.0) 22.0 (17.3, 23.8) 95.38 P < 0.001 ** BNT 26.0 (24.0, 28.0) 26.0 (24.0, 28.0) 22.5 (17.8, 24.8) 10.79 P < 0.001 ** DST 9.0 (7.50, 10.0) 8.5 (7.0, 10.0) 6.00 (5.0, 7.8) 10.80 P < 0.001 ** Note: MoCA: Montreal Cognitive Assessment;MMSE: Mini-mental state examination; BNT: Boston naming test; DST: Digit span test; ** denote P < 0.01. 3.3 Hematological Indicators Comparison Upon comparing hematological indicators among the HC, MCI, and probable AD groups, no significant differences were found in levels of IL-1β, TNF-α, and p-tau217. However, a notable difference was observed in serum IL-6 concentrations (F = 8.752, P = 0.013), with the MCI group exhibiting the highest levels, followed by the probable AD group, and the lowest in the HC group. Serum NfL concentrations also differed significantly across the groups (F = 7.582, P = 0.023), with the MCI group showing higher levels than the HC group. The MCI group's NfL levels were higher than those of the probable AD group, although this difference did not reach statistical significance. Aβ40 concentrations varied significantly among the groups, with the MCI group having the highest levels. Serum Aβ42 concentrations were significantly different across the groups, with the highest levels in the HC group and a significant difference between the HC and MCI groups. The Aβ42/Aβ40 ratio did not show a statistical difference (Table 3 ). Table 3 Comparison of blood indicators among the three groups HC group ① (n = 97) MCI group ② (n = 44) Probable AD group ③ (n = 16) H P Pairwise comparison IL-1β (pg/mL, P25, P75) 0.7 (0.4, 1.3) 1.0 (0.4, 2.2) 0.8 (0.4, 1.2) 3.49 0.18 IL-6 (pg/mL, P25, P75) 1.1 (0.8, 1.6) 1.5 (1.0, 2.3) 1.3 (1.0, 2.0) 8.75 0.013 * ①༜③, ①༜② * , ③༜② TNF-α (pg/mL, P25, P75) 3.3 (2.1, 5.7) 2.6 (1.6, 4.3) 4.2 (1.8, 6.2) 2.39 0.30 NfL (pg/mL, P25, P75) 1591.8 (1145.4, 2007.1) 1932.1 (1352.4, 2778.7) 1632.1 (1082.2, 2245.2) 7.58 0.023 * ①༜③, ①༜② * , ③༜② Aβ40 (pg/mL, P25, P75) 32.2 (7.1,76.8) 157.9 (24.4, 242.7) 23.6 (7.2, 70.6) 17.00 P < 0.001 ** ③༜①, ③༜② * , ①༜② * Aβ42 (ng/mL, P25, P75) 0.7 (0.4, 1.1) 0.2 (0.1, 0.9) 0.6 (0.4, 0.9) 7.31 0.026 * ②༜① * , ②༜③, ③༜① p-tau217 (pg/mL, P25, P75) 685.3 (521.3, 871.9) 795.3 (576.2, 871.9) 624.9 (436.9, 782.6) 5.70 0.06 Aβ42/Aβ40 28.5 (7.2, 81.3) 0.8 (0.6, 39.5) 46.0 (14.0, 90.6) 0.80 0.45 Note: IL-1β: Interleukin 1β; IL-6: Interleukin 6; TNFα: Tumor necrosis factor α; NfL: Neuroflament light chain; Aβ40: β amyloid 40; Aβ42: β amyloid 42; * denote P < 0.05, ** denote P < 0.01. 3.4 Correlation Analysis Between Cognitive and Hematological Indicators Spearman correlation analysis explored the relationships between serum NfL levels, inflammatory factors, and cognitive assessment scale scores. Age showed a positive correlation with IL-1β and Aβ40, but a negative correlation with Aβ42 and MMSE scores. Serum NfL was positively correlated with IL-1β and IL-6, as well as Aβ40, but no significant correlation was found with cognitive assessment scores. TNF-α correlated with Aβ42, Aβ40, and p-tau217. Aβ40 showed a positive correlation with DST scores (correlation coefficient = 0.288). Significant positive correlations were observed among the cognitive measurement scales (Table 4 ). Table 4 Correlation analysis of age, cognitive assessment and blood indicators in all populations Age NfL IL-1β IL-6 TNF-α Aβ42 Aβ40 p-tau217 MoCA MMSE BNT DST Age 1 NfL 0.077 1 IL-1β 0.197 ** 0.471 ** 1 IL-6 0.096 0.269 ** 0.253 ** 1 TNF-α -0.084 -0.047 -0.078 -0.064 1 Aβ42 -0.318 ** -0.018 -0.287 ** -0.063 0.277 ** 1 Aβ40 0.272 ** 0.386 ** 0.629 ** 0.221 ** -0.348 ** -0.419 ** 1 p-tau217 0.093 0.13 0.055 -0.108 -0.182 * -0.11 0.281 ** 1 MoCA -0.146 -0.115 -0.046 -0.064 -0.009 0.096 -0.039 -0.04 1 MMSE -0.225 ** -0.099 -0.079 -0.063 0.002 0.159 * -0.107 -0.073 0.787 ** 1 BNT -0.002 -0.03 -0.028 0.077 -0.064 -0.067 0.013 -0.032 0.578 ** 0.448 ** 1 DST -0.031 0.144 0.242 ** 0.154 -0.084 -0.15 0.288 ** 0.055 0.533 ** 0.494 ** 0.355 ** 1 Note: IL-1β: Interleukin 1β; IL-6:Interleukin 6; TNFα:Tumor necrosis factor α; NfL: Neuroflament light chain; Aβ40: β amyloid 40; Aβ42: β amyloid 42; MoCA: Montreal Cognitive Assessment; MMSE: Mini-mental state examination; BNT: Boston naming test; DST: Digit span test; * denote P < 0.05, ** denote P < 0.01. 3.5 Sensitivity and Specificity Analysis of Indicators Predicting MCI ROC curve analysis assessed the sensitivity and specificity of various blood and cognitive tests for predicting MCI. The BNT showed an AUC of 0.690, with a sensitivity of 0.773 and a cutoff value of 25.5. The DST had limited predictive ability, with an AUC of only 0.546(Fig. 1A). IL-6 demonstrated higher accuracy in predicting MCI compared to IL-1β and TNFα, with AUCs of 0.648, 0.595, and 0.562, respectively (Fig. 1B). Serum NfL, Aβ42, Aβ40, and p-tau217 showed high specificity in predicting MCI, with Aβ40 outperforming the others (AUC = 0.707). The AUC for NfL was 0.646, and although p-tau217 had a lower AUC of 0.599, its specificity reached 0.907(Fig. 1C). Combining NfL with p-tau217 increased the predictive accuracy (AUC = 0.687), but combining NfL with IL-6 did not improve accuracy beyond that of NfL alone. The combination of NfL and BNT increased the accuracy of MCI prediction (AUC = 0.732) (Fig. 1D and 1E). 3.6 Gender Differences in Predicting MCI Analysis of gender differences in the predictive accuracy of blood indicators and cognitive tests for MCI revealed that IL-6 was more accurate in predicting MCI in men than in women. The combination of NfL and BNT improved the accuracy of identifying MCI in men (AUC = 0.777). In contrast, TNF-α was a more accurate predictor for MCI in women than other indicators, while IL-6 was not a significant predictor for MCI in women (Table 5 ). Table 5 Gender differences in MCI sensitivity and specificity with different blood and cognitive measures Female (n = 74) Male (n = 83) AUC Cut off Sensitivity Specificity P AUC Cut off Sensitivity Specificity P IL-1β 0.525 - - - 0.747 0.661 0.92 0.75 0.644 0.026 IL-6 0.719 1.33 0.80 0.667 0.005 0.596 - - 0.185 TNF-α 0.564 - - - 0.408 0.677 3.26 0.792 0.592 0.023 NfL 0.663 2016.7 0.45 0.875 0.035 0.625 2156.7 0.458 0.826 0.085 BNT 0.692 27.5 0.90 0.417 0.013 0.700 23.5 0.667 0.653 0.006 DST A 0.719 - 0.80 0.667 0.005 0.500 - 0.875 0.407 0.040 DST B 0.777 - 0.80 0.708 P < 0.001 0.700 - 0.667 0.653 0.006 Note: IL-1β: Interleukin 1β; IL-6: Interleukin 6; TNFα: Tumor necrosis factor α; NfL: Neuroflament light chain; BNT: Boston naming test; DST: Digit span test 4. DISCUSSION The NfL protein, a neuron-specific element of the neuronal cytoskeleton, has been identified as a biomarker for neuronal damage, with elevated levels in CSF and blood linked to various neurological conditions, including multiple sclerosis, frontotemporal dementia, and Guillain-Barre syndrome 24,25 . Additionally, research has shown that higher CSF NfL concentrations correlate with compromised white matter integrity, as assessed by diffusion tensor imaging, suggesting that white matter damage is a primary contributor to memory decline in MCI 26 . Recent hypotheses propose that AD may be a chronic autoimmune disease, with cognitive decline closely associated with inflammatory processes 16 . Our study sought to explore the relationship between serum NfL, inflammatory factors, and cognitive function. We observed that serum NfL levels were elevated in both the MCI and AD groups compared to the HC group, with the highest concentrations found in the MCI group. This aligns with previous findings 27 and suggests that serum NfL levels begin to rise even before significant cognitive decline or impairment in daily living activities becomes apparent, offering potential implications for drug development and early intervention strategies. Longitudinal studies have indicated that higher baseline NfL levels are predictive of more rapid cognitive deterioration in individuals with MCI 28 . Furthermore, increases in plasma NfL levels have been associated with structural brain changes, including alterations in gray matter, white matter, and the cingulate gyrus, as well as with the volume of the lateral ventricles, hippocampus, and cortical thickness in AD patients 29,30 . The interaction between inflammatory processes and plasma NfL may also influence cognitive integrity through the disruption of core subsystems involved in AD progression and the frontoparietal network 31 . In our study, serum IL-6 levels differed significantly across the groups, and IL-1β levels correlated with DST scores, suggesting that monitoring these cytokines could aid in the early detection of MCI in at-risk populations. The serum Aβ40 levels were higher in the MCI group and lower in the AD group, while serum Aβ42 levels were lowest in the MCI group and higher in the AD group compared to the MCI group. These findings contrast with some previous studies where Aβ40 and Aβ42 levels were found to be lower in MCI than in HC groups 32 . Other research indicates that plasma Aβ levels may decrease significantly during the AD stage, potentially due to changes in the blood-brain barrier permeability, lymphatic function, or activation of vascular components or microglia, leading to reduced clearance of Aβ from the CSF to the periphery 33,34 . Animal studies support this, suggesting that blood Aβ levels drop as Aβ begins to deposit in the brain 35 . The discrepancy in Aβ40 and Aβ42 levels observed in our study may reflect complex and as yet not fully understood mechanisms of Aβ production and clearance. While previous research has indicated an association between age and NfL concentration 36 , our study did not observe this relationship. This discrepancy may be attributed to the different methodologies employed for quantifying blood NfL, such as ELISA, electrochemiluminescence immunoassay, and Single Molecule Array (Simoa), with Simoa being the most sensitive 37 . The ELISA method, used in our study, may have influenced our findings, as another study utilizing ELISA also reported no correlation between age and NfL levels 38 . This suggests that the choice of detection method could significantly impact the observed correlations between NfL and age. Our investigation into the correlation between NfL and inflammatory cytokines revealed a positive association between NfL and IL-1β and IL-6. This aligns with animal studies that have documented an increase in various inflammatory factors, including IL-2, IL-4, IL-6, and TNF-α, in conjunction with elevated CSF NfL in the context of chronic neuroinflammation and immune response 39 . These findings lend credence to the hypothesis that AD may be viewed as an innate autoimmune disease 16 . The association between NfL and Aβ40 suggests that axonal damage may play a significant role in the AD disease process, alongside the well-established roles of Aβ and tau pathology. The relationship between inflammation and cognitive decline is widely acknowledged, yet the precise mechanisms and pathways of brain inflammation remain elusive. In the early stages of AD, the brain's neuroprotective mechanisms, such as amyloid clearance and antioxidant defenses, are believed to be effective. However, as AD progresses, stress-related upregulation of immune system mediators leads to an overproduction of pro-inflammatory molecules, resulting in brain inflammation. The accumulation of Aβ and the formation of neurofibrillary tangles in the AD brain can activate the immune system and trigger inflammatory stress. This sustained inflammatory response can create a positive feedback loop, culminating in irreversible neuronal damage 40 . We hypothesized that combining NfL with inflammatory markers might enhance the accuracy of MCI detection. However, our study did not support this hypothesis, which may be due to the limited sample size. Nonetheless, combining NfL with the BNT or p-tau217 improved diagnostic accuracy, highlighting the multifaceted nature of AD pathology and the limitations of relying on a single biomarker. Autopsy studies have demonstrated a strong correlation between plasma p-tau217 levels and the extent of neurofibrillary tangles in AD patients, with p-tau217 showing high diagnostic performance in differentiating AD from other neurodegenerative diseases 41 . Although NfL is a marker of axonal injury and lacks specificity, as axonal damage can result from various neurological conditions, it still holds value in the early identification of MCI. Our findings suggest that the combination of NfL with p-tau217 can enhance the diagnostic accuracy for MCI, offering a potential avenue for future diagnostic approaches in AD. Our study contributes to the understanding of AD by demonstrating correlations between NfL and other AD markers. Although most correlations were not statistically significant, this is consistent with previous studies 30 , suggesting that AD pathology is driven by diverse pathological conditions, such as Aβ pathology, tau pathology, and axonal degeneration, each eliciting different biomarker responses. Overall, the correlations between these biomarkers were weak, indicating the complexity of AD's pathophysiological mechanisms. Gender-based analysis in our study revealed that the diagnostic accuracy of certain biomarkers for MCI did not significantly differ between males and females, which could be attributed to the small sample size. However, this observation may also reflect inherent gender differences in inflammatory responses. Previous research has indicated that sex may influence the regulation of pro-inflammatory and anti-inflammatory biomarkers 42 , and gender differences in inflammation have been reported 43 . While some studies suggest gender disparities in NfL levels, the evidence is not yet conclusive, and further research with larger sample sizes is necessary to elucidate these potential differences. The strength of this study lies in its integrative approach, combining serum NfL with inflammatory markers and other pathological indicators of AD, thereby shedding light on their interrelationships within the complex AD pathology. The inclusion of an elderly Eastern population provides valuable insights that are pertinent to our regional context and diversifies the demographic representation in AD research. Nevertheless, our study has limitations that warrant consideration. The small sample size and the regional confinement to Hebei Province may limit the generalizability of our findings. Additionally, the cross-sectional design precludes the ability to observe dynamic changes over time in the relationship between serum NfL, inflammatory markers, and cognitive function. The absence of neuroimaging data also means that we could not explore the association between serum NfL levels and brain structural changes. 5. CONCLUSION This study underscores the potential of serum NfL as a biomarker for the early detection of MCI and AD, with elevated levels observed in affected individuals compared to healthy controls. The positive correlations between serum NfL and inflammatory cytokines IL-1β, IL-6, as well as Aβ40, highlight the multifaceted nature of AD pathology. While the combination of serum NfL with p-tau217 and the BNT enhances the predictive accuracy for MCI, the addition of inflammatory markers does not yield further improvement. These findings suggest that serum NfL, particularly when combined with other specific biomarkers, could serve as a valuable tool in the early identification of AD, thereby informing potential therapeutic strategies and interventions. Declarations Acknowledgements This study was supported by the Special Fund Project of the Central Government to Guide the Local Science and Technology Development of Hebei Province [grant numbers 199477138G], Science and Technology Program of Hebei Province [grant numbers SG2021189], The government funded clinical medicine excellent talents training project of Hebei Province [grant numbers ZF2024136]. X. Jing and L. Wang contributed equally to this work. Authors’ contributions Jing X: Investigation, Formal analysis, Data curation, Writing-original draft. Wang L: Investigation, Writing-original draft. Song M: Investigation. Geng H: Investigation. Li W: Investigation. Huo Y: Investigation. Huang A: Investigation. Wang X: Conceptualization. An C: Conceptualization, Funding acquisition, Writing –review & editing. The author(s) read and approved the final manuscript. Declarations of competing interest The funder had no role in study design, data collection, analysis, or writing of the manuscript. The authors have no conflict of interest to report. Data availability The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request. Ethic committees All participants had signed informed consent forms, and the study was approved by the Ethics Committee of the First Hospital of Hebei Medical University (ethical approval number: 20190416). References Lewczuk, P. et al. Plasma neurofilament light as a potential biomarker of neurodegeneration in Alzheimer's disease. Alzheimers Res Ther 10, 71, doi: 10.1186/s13195-018-0404-9 (2018). Patterson, C. World Alzheimer Report 2018The state of the art of dementia research:New frontiers. 48 (2018). Ren, R. et al. The China Alzheimer Report 2022. Gen Psychiatr 35, e100751, doi: 10.1136/gpsych-2022-100751 (2022). Teunissen, C. E. et al. 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J Peripher Nerv Syst 23, 174–177, doi: 10.1111/jns.12279 (2018). Karantali, E., Kazis, D., Chatzikonstantinou, S., Petridis, F. & Mavroudis, I. The role of neurofilament light chain in frontotemporal dementia: a meta-analysis. Aging Clin Exp Res 33, 869–881, doi: 10.1007/s40520-020-01554-8 (2021). Moore, E. E. et al. Neurofilament relates to white matter microstructure in older adults. Neurobiol Aging 70, 233–241, doi: 10.1016/j.neurobiolaging.2018.06.023 (2018). Giacomucci, G. et al. Plasma neurofilament light chain as a biomarker of Alzheimer's disease in Subjective Cognitive Decline and Mild Cognitive Impairment. J Neurol 269, 4270–4280, doi: 10.1007/s00415-022-11055-5 (2022). Mattsson, N., Andreasson, U., Zetterberg, H., Blennow, K. & Alzheimer's Disease Neuroimaging, I. Association of Plasma Neurofilament Light With Neurodegeneration in Patients With Alzheimer Disease. JAMA Neurol 74, 557–566, doi: 10.1001/jamaneurol.2016.6117 (2017). Lin, Y. S., Lee, W. J., Wang, S. J. & Fuh, J. L. Levels of plasma neurofilament light chain and cognitive function in patients with Alzheimer or Parkinson disease. Sci Rep 8, 17368, doi: 10.1038/s41598-018-35766-w (2018). Weston, P. S. J. et al. Longitudinal measurement of serum neurofilament light in presymptomatic familial Alzheimer's disease. Alzheimers Res Ther 11, 19, doi: 10.1186/s13195-019-0472-5 (2019). Mattsson, N., Cullen, N. C., Andreasson, U., Zetterberg, H. & Blennow, K. Association Between Longitudinal Plasma Neurofilament Light and Neurodegeneration in Patients With Alzheimer Disease. JAMA Neurol 76, 791–799, doi: 10.1001/jamaneurol.2019.0765 (2019). Yao, W. et al. Inflammation Disrupts Cognitive Integrity via Plasma Neurofilament Light Chain Coupling Brain Networks in Alzheimer's Disease. J Alzheimers Dis 89, 505–518, doi: 10.3233/JAD-220475 (2022). Shi, Y. et al. Potential Value of Plasma Amyloid-beta, Total Tau, and Neurofilament Light for Identification of Early Alzheimer's Disease. ACS Chem Neurosci 10, 3479–3485, doi: 10.1021/acschemneuro.9b00095 (2019). Janelidze, S. et al. Plasma beta-amyloid in Alzheimer's disease and vascular disease. Sci Rep 6, 26801, doi: 10.1038/srep26801 (2016). Ramanathan, A., Nelson, A. R., Sagare, A. P. & Zlokovic, B. V. Impaired vascular-mediated clearance of brain amyloid beta in Alzheimer's disease: the role, regulation and restoration of LRP1. Front Aging Neurosci 7, 136, doi: 10.3389/fnagi.2015.00136 (2015). Kawarabayashi, T. et al. Age-dependent changes in brain, CSF, and plasma amyloid (beta) protein in the Tg2576 transgenic mouse model of Alzheimer's disease. J Neurosci 21, 372–381, doi: 10.1523/JNEUROSCI.21-02-00372.2001 (2001). Baldacci, F. et al. Aging and sex impact plasma NFL and t-Tau trajectories in individuals at risk for Alzheimer’s disease. Alzheimer's & Dementia 16, doi: 10.1002/alz.041792 (2020). Kuhle, J. et al. Comparison of three analytical platforms for quantification of the neurofilament light chain in blood samples: ELISA, electrochemiluminescence immunoassay and Simoa. Clin Chem Lab Med 54, 1655–1661, doi: 10.1515/cclm-2015-1195 (2016). Guo Chenchen, Z. Q., et al. The role of neurofilament light chain protein in the early diagnosis of Alzheimer's disease.. Journal of Chongqing Medical University 46, 5, doi: 10.13406/j.cnki.cyxb.002914 (2021). Hsiao, T.-C. et al. Serum Neurofilament Light Polypeptide is a Biomarker for Inflammation in Cerebrospinal Fluid Caused by Fine Particulate Matter. Aerosol and Air Quality Research, doi: 10.4209/aaqr.2019.08.0376 (2020). Casoli, T. et al. Peripheral inflammatory biomarkers of Alzheimer's disease: the role of platelets. Biogerontology 11, 627–633, doi: 10.1007/s10522-010-9281-8 (2010). Heneka, M. T., O'Banion, M. K., Terwel, D. & Kummer, M. P. Neuroinflammatory processes in Alzheimer's disease. J Neural Transm (Vienna) 117, 919–947, doi: 10.1007/s00702-010-0438-z (2010). McNaull, B. B., Todd, S., McGuinness, B. & Passmore, A. P. Inflammation and anti-inflammatory strategies for Alzheimer's disease–a mini-review. Gerontology 56, 3–14, doi: 10.1159/000237873 (2010). Pan, M. H., Lai, C. S. & Ho, C. T. Anti-inflammatory activity of natural dietary flavonoids. Food Funct 1, 15–31, doi: 10.1039/c0fo00103a (2010). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 05 Feb, 2024 Reviews received at journal 01 Feb, 2024 Reviewers agreed at journal 23 Jan, 2024 Reviewers invited by journal 22 Jan, 2024 Editor assigned by journal 22 Jan, 2024 Editor invited by journal 06 Jan, 2024 Submission checks completed at journal 06 Jan, 2024 First submitted to journal 02 Jan, 2024 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-3828911","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":265535585,"identity":"c31aade2-5870-497d-a396-ff36627d6929","order_by":0,"name":"Xinyang Jing","email":"","orcid":"","institution":"The First Hospital of Hebei Medical University, Hebei Technical Innovation Center for Mental Health Assessment and Intervention","correspondingAuthor":false,"prefix":"","firstName":"Xinyang","middleName":"","lastName":"Jing","suffix":""},{"id":265535587,"identity":"177a6fa6-455a-48f7-ad4c-4771e81fa769","order_by":1,"name":"Lan Wang","email":"","orcid":"","institution":"The First Hospital of Hebei Medical University, Hebei Technical Innovation Center for Mental Health Assessment and Intervention","correspondingAuthor":false,"prefix":"","firstName":"Lan","middleName":"","lastName":"Wang","suffix":""},{"id":265535589,"identity":"84ca4483-2ab5-4f7b-84bf-e87e3d08c520","order_by":2,"name":"Mei Song","email":"","orcid":"","institution":"The First Hospital of Hebei Medical University, Hebei Technical Innovation Center for Mental Health Assessment and Intervention","correspondingAuthor":false,"prefix":"","firstName":"Mei","middleName":"","lastName":"Song","suffix":""},{"id":265535590,"identity":"36ac4863-4c7e-4d3b-979d-4897772dbc31","order_by":3,"name":"Hao Geng","email":"","orcid":"","institution":"The First Hospital of Hebei Medical University, Hebei Technical Innovation Center for Mental Health Assessment and Intervention","correspondingAuthor":false,"prefix":"","firstName":"Hao","middleName":"","lastName":"Geng","suffix":""},{"id":265535591,"identity":"2ce15ae4-676d-44d0-8e58-d0f4dc2c5e75","order_by":4,"name":"Wei Li","email":"","orcid":"","institution":"The First Hospital of Hebei Medical University, Hebei Technical Innovation Center for Mental Health Assessment and Intervention","correspondingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Li","suffix":""},{"id":265535594,"identity":"1f59fa0a-7a83-4ee1-92b7-53ef1be1e24d","order_by":5,"name":"Yaxin Huo","email":"","orcid":"","institution":"The First Hospital of Hebei Medical University, Hebei Technical Innovation Center for Mental Health Assessment and Intervention","correspondingAuthor":false,"prefix":"","firstName":"Yaxin","middleName":"","lastName":"Huo","suffix":""},{"id":265535595,"identity":"e8cff52a-4f94-43f4-b6c5-b143e08abc05","order_by":6,"name":"Anqi Huang","email":"","orcid":"","institution":"The First Hospital of Hebei Medical University, Hebei Technical Innovation Center for Mental Health Assessment and Intervention","correspondingAuthor":false,"prefix":"","firstName":"Anqi","middleName":"","lastName":"Huang","suffix":""},{"id":265535597,"identity":"09e9132d-76ae-443c-bbdf-b23d778d36c9","order_by":7,"name":"Xueyi Wang","email":"","orcid":"","institution":"The First Hospital of Hebei Medical University, Hebei Technical Innovation Center for Mental Health Assessment and Intervention","correspondingAuthor":false,"prefix":"","firstName":"Xueyi","middleName":"","lastName":"Wang","suffix":""},{"id":265535598,"identity":"da5fe88a-b935-46c4-a35a-a96ef240ccbb","order_by":8,"name":"Cuixia An","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4klEQVRIiWNgGAWjYDADfgkGNgjrALFaJGeQrMXgBrFa+NvPGH/m3XEncfPt5mOPbrYxyPHdSGD8XIBHi8SZtDRp3jPPErfdOZZunNvGYCx5I4FZegY+a24wH2PmbTucuO1Gjpk0UEvihhsJbMw8eHTI32Bs/gzSsnlG/jeQlnqCWgxuMB+QBmnZIJHDBtKSYEBIiyHQL5Jz2w4bz7iRZm6cc07CcOaZh83S+LTIHT9j/OFt22HZ/hnJzx7nlNnI8x1PPvgZnxYYcGyA0BJAzNhAhAYGBnuiVI2CUTAKRsHIBACzq0612UlMTgAAAABJRU5ErkJggg==","orcid":"","institution":"The First Hospital of Hebei Medical University, Hebei Technical Innovation Center for Mental Health Assessment and Intervention","correspondingAuthor":true,"prefix":"","firstName":"Cuixia","middleName":"","lastName":"An","suffix":""}],"badges":[],"createdAt":"2024-01-02 08:14:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3828911/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3828911/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":49327300,"identity":"29dcb63b-3d79-4a88-9ab5-9eda961fa408","added_by":"auto","created_at":"2024-01-08 17:36:39","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":184932,"visible":true,"origin":"","legend":"\u003cp\u003eROC analysis of different cognitive scales and blood indexes\u003c/p\u003e\n\u003cp\u003eNote: MoCA=Montreal Cognitive Assessment; MMSE=Mini-mental state examination; BNT=Boston naming test; DST=Digit span test; IL=Interleukin; TNFα=Tumor necrosis factor α；NfL=Neuroflament light chain; Aβ=β amyloid；p-tau=hyperphosphorylated tau\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-3828911/v1/bbaa1872dcc46703a5808ba3.png"},{"id":49327636,"identity":"5534325e-c165-490e-bba7-a845144bbc9c","added_by":"auto","created_at":"2024-01-08 17:44:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":548736,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3828911/v1/d20107de-a066-4978-a052-2215c14d4685.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Serum Neurofilament Light Chain and Inflammatory Cytokines as Biomarkers for Early Detection of Alzheimer's Disease","fulltext":[{"header":"1. BACKGROUND","content":"\u003cp\u003eAlzheimer's disease (AD) is a neurodegenerative disorder hallmarked by the accumulation of amyloid-beta (Aβ) and tau proteins, brain tissue atrophy, and progressive cognitive decline \u003csup\u003e1\u003c/sup\u003e .The global prevalence of AD is escalating in tandem with the aging population. The International Alzheimer's Disease Association reported approximately 46\u0026nbsp;million individuals with AD worldwide in 2018, with projections suggesting a rise to 131.5\u0026nbsp;million by 2050 \u003csup\u003e2\u003c/sup\u003e. In China, recent epidemiological data indicates a dementia prevalence of 6.04%, with AD accounting for 3.94% among those over 60, translating to nearly 9.83\u0026nbsp;million AD patients and an increasing socio-economic impact \u003csup\u003e3\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe challenge of timely and accurate dementia diagnosis is formidable, with an estimated three-quarters of dementia cases remaining undiagnosed globally, a figure that could be as high as 90% in low- and middle-income countries. Current diagnostic markers, such as cerebrospinal fluid (CSF) Aβ and tau proteins, along with neuroimaging techniques like MRI, tau-PET, β-amyloid-PET, and F-FDG-PET, facilitate early AD detection but are limited by invasiveness, cost, and accessibility \u003csup\u003e4\u003c/sup\u003e. Consequently, there is an urgent need for less invasive and more cost-effective biomarkers.\u003c/p\u003e \u003cp\u003eNeurofilaments, specifically the light chain (NfL), have garnered attention as potential peripheral biomarkers for neuronal damage. Neurofilaments are neuron-specific, highly phosphorylated proteins that form a key component of the axonal cytoskeleton, with NfL being a critical subunit involved in axonal growth and stability\u003csup\u003e5\u003c/sup\u003e. Under physiological conditions, axonal cells release minimal amounts of NfL into the CSF, which can then enter the bloodstream, albeit at lower concentrations. However, pathological conditions trigger a marked increase in NfL, making it a sensitive marker for axonal injury\u003csup\u003e6\u003c/sup\u003e. Elevated CSF NfL levels have been associated with cognitive impairment, with studies demonstrating its rise in early AD and its correlation with cognitive decline and brain structural changes\u003csup\u003e7\u003c/sup\u003e. Elevated CSF NfL has also been observed in other dementias, correlating with lower Mini-Mental State Examination (MMSE) scores and reduced survival times\u003csup\u003e8\u003c/sup\u003e. Furthermore, higher NfL levels have been linked to cognitive decline in mild cognitive impairment (MCI) and AD patients, irrespective of vascular burden\u003csup\u003e9\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eGiven the invasive nature of CSF collection, the focus has shifted towards blood-based NfL measurements. Significant correlations between plasma NfL and CSF biomarkers have been reported, with increased plasma NfL levels observed in MCI and AD, suggesting its potential as a non-invasive biomarker for cognitive impairment\u003csup\u003e1,10\u003c/sup\u003e. The relationship between NfL and cognitive function in the older adults is further supported by its role as an axonal damage marker, which is closely related to microglial activation in the central immune system\u003csup\u003e11\u003c/sup\u003e. Inflammation is increasingly recognized as a contributor to neurodegeneration, with systemic elevations of pro-inflammatory cytokines like TNF-α implicated in cognitive decline in AD\u003csup\u003e12,13\u003c/sup\u003e. The dysregulation of inflammatory pathways in MCI may also serve as a predictor for the rate of cognitive deterioration\u003csup\u003e14\u003c/sup\u003e. While there is evidence of a strong correlation between plasma NfL and both peripheral and cortical inflammation during AD progression, and suggestions of AD as a chronic autoimmune condition with heightened pro-inflammatory factors \u003csup\u003e15,16\u003c/sup\u003e, research into the relationship between blood NfL and inflammatory markers remains limited. This study, therefore, aims to explore the role of serum NfL and inflammatory cytokines in the early detection of AD and to clarify the relationship between serum NfL and inflammatory markers.\u003c/p\u003e"},{"header":"2. MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n\u003ch2\u003e2.1 Study Design and Participants\u003c/h2\u003e\n\u003cp\u003eThis cross-sectional study was conducted from April 2021 to February 2022 in Hebei Province, China. Participants were recruited from the Yuhua District of Shijiazhuang City and Renze District of Xingtai City, resulting in a cohort of 157 older adults aged 55 years and older, comprising 74 males and 83 females. All participants had signed informed consent forms, and the study was approved by the Ethics Committee of the First Hospital of Hebei Medical University (ethical approval number: 20190416). We confirm that all research were performed in accordance with relevant guidelines and regulations.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n\u003ch2\u003e2.2 Inclusion and Exclusion Criteria\u003c/h2\u003e\n\u003cp\u003eInclusion criteria for the study were as follows: (\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e) age 55 years or older; (\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e) willingness and ability to participate in and complete a comprehensive questionnaire survey and cognitive function evaluation; (\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e) consent to provide blood samples for analysis. Exclusion criteria were established to omit individuals with potential alternative causes of cognitive decline, including those with a clear history of stroke, brain tumor, Parkinson's disease, epilepsy, severe head trauma, and significant mental disorders. Additionally, individuals with severe physical illnesses or substantial visual or hearing impairments that could interfere with cognitive function testing were excluded.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n\u003ch2\u003e2.3 Diagnostic Criteria\u003c/h2\u003e\n\u003cp\u003eParticipants were classified into two groups: Healthy older adults with normal cognition (healthy control, HC) and those with cognitive impairment (MCI and probable AD).\u003c/p\u003e\n\u003cp\u003eHC Group Diagnostic Criteria:\u003c/p\u003e\n\u003cp\u003e(\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e) Objective evidence of normal cognitive function, stratified by educational level with respective cut-off scores on the MMSE: \u0026ge;19 points for illiterate, \u0026ge;\u0026thinsp;22 points for elementary school, and \u0026ge;\u0026thinsp;24 points for junior high school and above \u003csup\u003e17\u003c/sup\u003e. Additionally, Montreal Cognitive Assessment (MoCA) scores of \u0026ge;\u0026thinsp;13 points, \u0026ge;\u0026thinsp;19 points, and \u0026ge;\u0026thinsp;24 points were required for the respective educational levels \u003csup\u003e18\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e(\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e) Normal daily living abilities, as indicated by a score of \u0026le;\u0026thinsp;26 points on the Activity of Daily Living Scale (ADL) \u003csup\u003e19\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eMCI Diagnostic Criteria:\u003c/p\u003e\n\u003cp\u003eAs per the consensus established by the Chinese Expert Group on Cognitive Impairment Prevention and Treatment in 2006\u003csup\u003e20\u003c/sup\u003e, MCI diagnosis included:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eSelf-reported or family-observed significant memory impairment.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003ePreservation of other cognitive functions or only mild impairment, with MMSE scores of \u0026ge;\u0026thinsp;19 for illiterate, \u0026ge;\u0026thinsp;22 for elementary school, and \u0026ge;\u0026thinsp;24 for junior high school or above. Corresponding MoCA scores should be \u0026lt;\u0026thinsp;13, \u0026lt;19, and \u0026lt;\u0026thinsp;24, respectively.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eMaintenance of basic daily living activities, with an ADL score\u0026thinsp;\u0026le;\u0026thinsp;26.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eExclusion of dementia, ensuring that the criteria for dementia diagnosis are not met.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eExclusion of other systemic diseases that could affect cognitive function.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eProbable AD Diagnostic Criteria\u003csup\u003e21\u003c/sup\u003e :\u003c/p\u003e\n\u003cp\u003e(\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e) Documented history of cognitive decline, as evidenced by reduced scores on neurocognitive tests, specifically MMSE scores of \u0026lt;\u0026thinsp;19 for illiterate, \u0026lt;\u0026thinsp;22 for elementary school, and \u0026lt;\u0026thinsp;24 for junior high school or higher education.\u003c/p\u003e\n\u003cp\u003e(\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e) Slight impairment in daily living activities, with an ADL score\u0026thinsp;\u0026gt;\u0026thinsp;26.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n\u003ch2\u003e2.4 Data Collection Procedures\u003c/h2\u003e\n\u003cp\u003eTrained survey personnel conducted the data collection process, which included administering a custom-designed general information survey form to collect demographic data such as age, gender, education level, smoking and alcohol consumption habits, and medical history.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n\u003ch2\u003e2.5 Neuropsychological Assessments\u003c/h2\u003e\n\u003cp\u003eCognitive function was assessed using the MMSE\u003csup\u003e17\u003c/sup\u003e and the MoCA\u003csup\u003e18\u003c/sup\u003e. The MMSE is a widely utilized tool that evaluates nine cognitive domains, including temporal and spatial orientation, immediate memory, calculation, delayed recall, naming, repetition, comprehension, and visuospatial abilities, with a maximum score of 30 points. The scoring is adjusted for cultural differences.\u003c/p\u003e\n\u003cp\u003eThe MoCA is known for its higher sensitivity in detecting mild cognitive deficits compared to the MMSE. It evaluates several cognitive domains: visuospatial and executive functions, naming, memory, attention, language, abstraction, delayed recall, and orientation.\u003c/p\u003e\n\u003cp\u003eAdditionally, the Boston Naming Test (BNT)\u003csup\u003e22\u003c/sup\u003e was used to assess language production, specifically the ability to name pictorial representations, and the Digit Span Test (DST)\u003csup\u003e23\u003c/sup\u003e measured immediate memory, attention, and processing speed.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n\u003ch2\u003e2.6 Blood Sample Collection and Processing\u003c/h2\u003e\n\u003cp\u003eBlood samples were collected from participants following an overnight fast. Standardized venipuncture procedures were performed in the morning by trained nurses, who collected 5 mL of venous blood from each participant. The whole blood samples were allowed to clot at room temperature for 2 hours before centrifugation at 3000 rpm for 10 minutes. The resulting serum was aliquoted and stored at -80\u0026deg;C until analysis.\u003c/p\u003e\n\u003cp\u003eSerum concentrations of NfL, Interleukin-1\u0026beta; (IL-1\u0026beta;), Interleukin-6 (IL-6), Tumor Necrosis Factor-\u0026alpha; (TNF\u0026alpha;), Amyloid-\u0026beta;40 (A\u0026beta;40), Amyloid-\u0026beta;42 (A\u0026beta;42), and hyperphosphorylated tau protein 217 (p-tau217) were measured using enzyme-linked immunosorbent assay (ELISA) kits (Product IDs: CSB-E16094h, EK101B \u0026minus;\u0026thinsp;24, EK106/2\u0026ndash;24, EK182\u0026ndash;24, CSB-E08299h, CSB-E10684h, respectively).\u003c/p\u003e\n\u003cp\u003eThe ELISA procedure was conducted as follows: Reagents were equilibrated to room temperature (18\u0026ndash;25\u0026deg;C) for at least 30 minutes. Following the manufacturer's instructions, reagents were prepared and added to the ELISA plate wells, with 100\u0026micro;l of standard or test sample per well. After mixing and sealing, the plates were incubated at 37\u0026deg;C for 2 hours. Post-incubation, the liquid was discarded, and the wells were washed three times with a 2-minute soak of 200\u0026micro;l per well. Subsequently, 100\u0026micro;l of biotin-labeled antibody working solution was added to each well and incubated for another hour at 37\u0026deg;C. Following this, the wells were washed five times, and 100\u0026micro;l of horseradish peroxidase-labeled avidin working solution was added to each well. After a final hour of incubation and washing, 90\u0026micro;l of substrate solution was added to each well, and the plates were incubated in the dark for 15\u0026ndash;30 minutes at 37\u0026deg;C. The reaction was stopped by adding 50\u0026micro;l of stop solution to each well. The optical density (OD) of each well was measured at a wavelength of 450nm within 5 minutes of adding the stop solution. A standard curve was generated from the OD values of the standard samples, and the concentrations of the proteins in the test samples were calculated accordingly.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n\u003ch2\u003e2.7 Statistical Analysis\u003c/h2\u003e\n\u003cp\u003eData analysis was performed using SPSS software, version 26.0. The Shapiro-Wilk test was applied to determine the normality of the continuous variables. Continuous data that followed a normal distribution across the three groups were analyzed using one-way analysis of variance (ANOVA), and the results were reported as means\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviations. For continuous data that did not follow a normal distribution, the Kruskal-Wallis rank sum test was utilized, with results presented as medians with the 25th and 75th percentiles [M (P25, P75)]. Categorical data were compared using the chi-square test.\u003c/p\u003e\n\u003cp\u003eThe diagnostic performance of the cognitive assessment scales and serum biomarker concentrations was evaluated using receiver operating characteristic (ROC) curve analysis. This method calculated the area under the curve (AUC) and determined the sensitivity and specificity of each test. GraphPad Prism version 8.0 was used for the generation of graphical representations of the data.\u003c/p\u003e\n\u003cp\u003eTo investigate the relationships between serum NfL levels, age, and cognitive assessment scale scores across different groups, Spearman's rank correlation analysis was conducted. A \u003cem\u003eP\u003c/em\u003e-value of less than 0.05 was considered statistically significant, indicating that the observed differences were unlikely to have occurred by chance.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3. RESULTS","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Demographic Comparisons\u003c/h2\u003e \u003cp\u003eThe demographic analysis revealed no significant differences in gender, age, or educational level among the HC, MCI, and probable AD groups, ensuring comparability of the demographic data. The prevalence of hypertension and hyperlipidemia was also similar across the groups. However, significant differences emerged in the history of smoking, alcohol consumption, diabetes, and coronary heart disease among the participants (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic comparisons among the three groups [(\u0026oline;Χ\u0026thinsp;\u0026plusmn;\u0026thinsp;S)༏M(P25, P75)/n(%)]\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHC\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;97)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMCI\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;44)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProbable AD\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;16)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eF/X2/Z\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.861\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.638\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48.0 (49.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.0 (45.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.0 (37.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49.0 (50.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.0 (54.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.0 (62.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e68.7\u0026thinsp;\u0026plusmn;\u0026thinsp;4.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72.6\u0026thinsp;\u0026plusmn;\u0026thinsp;8.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e70.6\u0026thinsp;\u0026plusmn;\u0026thinsp;8.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.426\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.066\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducation level\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e4.419\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.356\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIlliteracy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.0 (9.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.0 (4.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.0 (21.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16.0 (16.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.0 (11.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.0 (6.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJunior high school and above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72.0 (74.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37.0 (84.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.0 (75.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSmoking history\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12.924\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.011\u003c/b\u003e\u003csup\u003e\u003cb\u003e*\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever smoked\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64.0 (66.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.0 (36.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.0 (75.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHave not quit smoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.0 (8.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.0 (15.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.0 (6.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuit smoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.0 (25.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.0 (47.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.0 (18.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDrinking history\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e15.769\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e\u003cb\u003e0.015\u003c/b\u003e\u003csup\u003e\u003cb\u003e*\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOften\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.0 (11.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.0 (13.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.0 (12.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHave stopped drinking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.0 (18.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.0 (45.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.0 (12.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eoccasionally\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.0 (8.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.0 (9.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.0 (6.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60.0 (61.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.0 (31.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.0 (68.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHypertension\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.601\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49.0 (50.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.0 (47.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.0 (62.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48.0 (49.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.0 (52.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.0 (37.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHyperlipemia\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.237\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.341\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e67.0 (69.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.0 61.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.0 (81.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30.0 (30.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.0 (38.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.0 (18.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDiabetes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.829\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.012\u003c/b\u003e\u003csup\u003e\u003cb\u003e*\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e69.0 (71.1)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.0 (45.5)\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.0 (68.8)\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28.0 (28.9)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.0 (54.5)\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.0 (31.3)\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCoronary heart disease\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11.375\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003csup\u003e\u003cb\u003e**\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e77.0 (79.4)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.0 (54.5)\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.0 (87.5)\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20.0 (20.6)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.0 (45.5)\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.0 (12.5)\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eNote: Educational attainment: The corner scale \u003csup\u003eabc\u003c/sup\u003e indicates that there was a difference between the two groups, a and b represent differences between HC and MCI; \u003csup\u003e*\u003c/sup\u003edenote \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, \u003csup\u003e**\u003c/sup\u003e denote \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Cognitive Function Comparisons\u003c/h2\u003e \u003cp\u003eCognitive function assessments among the HC, MCI, and probable AD groups revealed significant differences in performance on the MoCA, MMSE, BNT, and DST. Both the MCI and probable AD groups scored significantly lower than the HC group on all cognitive tests, indicating a decline in cognitive function associated with disease progression (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of cognitive function among the three groups\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHC group\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;97)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMCI group\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;44)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProbably AD group\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;16)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eF\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMoCA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e27.0 (25.5, 28.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23.0 (20.3, 24.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19.0 (14.0, 22.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e57.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMMSE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e29.0 (27.0, 30.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27.0 (25.0, 27.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22.0 (17.3, 23.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e95.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBNT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26.0 (24.0, 28.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26.0 (24.0, 28.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22.5 (17.8, 24.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9.0 (7.50, 10.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.5 (7.0, 10.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.00 (5.0, 7.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eNote: MoCA: Montreal Cognitive Assessment;MMSE: Mini-mental state examination; BNT: Boston naming test; DST: Digit span test; \u003csup\u003e**\u003c/sup\u003e denote \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Hematological Indicators Comparison\u003c/h2\u003e \u003cp\u003eUpon comparing hematological indicators among the HC, MCI, and probable AD groups, no significant differences were found in levels of IL-1β, TNF-α, and p-tau217. However, a notable difference was observed in serum IL-6 concentrations (F\u0026thinsp;=\u0026thinsp;8.752, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.013), with the MCI group exhibiting the highest levels, followed by the probable AD group, and the lowest in the HC group. Serum NfL concentrations also differed significantly across the groups (F\u0026thinsp;=\u0026thinsp;7.582, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.023), with the MCI group showing higher levels than the HC group. The MCI group's NfL levels were higher than those of the probable AD group, although this difference did not reach statistical significance. Aβ40 concentrations varied significantly among the groups, with the MCI group having the highest levels. Serum Aβ42 concentrations were significantly different across the groups, with the highest levels in the HC group and a significant difference between the HC and MCI groups. The Aβ42/Aβ40 ratio did not show a statistical difference (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of blood indicators among the three groups\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHC group\u003csup\u003e①\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;97)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMCI group\u003csup\u003e②\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;44)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProbable AD group\u003csup\u003e③\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;16)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eH\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePairwise comparison\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIL-1β (pg/mL, P25, P75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.7 (0.4, 1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.0 (0.4, 2.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.8 (0.4, 1.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIL-6\u003c/p\u003e \u003cp\u003e(pg/mL, P25, P75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.1\u003c/p\u003e \u003cp\u003e(0.8, 1.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.5 (1.0, 2.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.3 (1.0, 2.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.013\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e①༜③, ①༜②\u003csup\u003e*\u003c/sup\u003e, ③༜②\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTNF-α (pg/mL, P25, P75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.3\u003c/p\u003e \u003cp\u003e(2.1, 5.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.6 (1.6, 4.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.2 (1.8, 6.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNfL\u003c/p\u003e \u003cp\u003e(pg/mL, P25, P75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1591.8\u003c/p\u003e \u003cp\u003e(1145.4, 2007.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1932.1 (1352.4, 2778.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1632.1 (1082.2, 2245.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.023\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e①༜③, ①༜②\u003csup\u003e*\u003c/sup\u003e, ③༜②\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAβ40\u003c/p\u003e \u003cp\u003e(pg/mL, P25, P75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e32.2 (7.1,76.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e157.9 (24.4, 242.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23.6 (7.2, 70.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e③༜①, ③༜②\u003csup\u003e*\u003c/sup\u003e, ①༜②\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAβ42\u003c/p\u003e \u003cp\u003e(ng/mL, P25, P75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.7 (0.4, 1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.2 (0.1, 0.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.6 (0.4, 0.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.026\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e②༜①\u003csup\u003e*\u003c/sup\u003e, ②༜③, ③༜①\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep-tau217 (pg/mL, P25, P75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e685.3 (521.3, 871.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e795.3 (576.2, 871.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e624.9 (436.9, 782.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAβ42/Aβ40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28.5 (7.2, 81.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.8 (0.6, 39.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e46.0 (14.0, 90.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eNote: IL-1β: Interleukin 1β; IL-6: Interleukin 6; TNFα: Tumor necrosis factor α; NfL: Neuroflament light chain; Aβ40: β amyloid 40; Aβ42: β amyloid 42; \u003csup\u003e*\u003c/sup\u003e denote \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, \u003csup\u003e**\u003c/sup\u003e denote \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Correlation Analysis Between Cognitive and Hematological Indicators\u003c/h2\u003e \u003cp\u003eSpearman correlation analysis explored the relationships between serum NfL levels, inflammatory factors, and cognitive assessment scale scores. Age showed a positive correlation with IL-1β and Aβ40, but a negative correlation with Aβ42 and MMSE scores. Serum NfL was positively correlated with IL-1β and IL-6, as well as Aβ40, but no significant correlation was found with cognitive assessment scores. TNF-α correlated with Aβ42, Aβ40, and p-tau217. Aβ40 showed a positive correlation with DST scores (correlation coefficient\u0026thinsp;=\u0026thinsp;0.288). Significant positive correlations were observed among the cognitive measurement scales (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCorrelation analysis of age, cognitive assessment and blood indicators in all populations\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"13\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNfL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIL-1β\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIL-6\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTNF-α\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAβ42\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAβ40\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003ep-tau217\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eMoCA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eMMSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eBNT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003eDST\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"8\" morerows=\"2\" nameend=\"c13\" namest=\"c6\" rowspan=\"3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNfL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIL-1β\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.197\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.471\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIL-6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.269\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.253\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTNF-α\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAβ42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.318\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.287\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.277\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAβ40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.272\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.386\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.629\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.221\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.348\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.419\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep-tau217\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.182\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.281\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMoCA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMMSE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.225\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.159\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.787\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBNT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.578\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.448\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.242\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.288\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.533\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.494\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.355\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"13\"\u003eNote: IL-1β: Interleukin 1β; IL-6:Interleukin 6; TNFα:Tumor necrosis factor α; NfL: Neuroflament light chain; Aβ40: β amyloid 40; Aβ42: β amyloid 42; MoCA: Montreal Cognitive Assessment; MMSE: Mini-mental state examination; BNT: Boston naming test; DST: Digit span test; * denote \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, ** denote \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Sensitivity and Specificity Analysis of Indicators Predicting MCI\u003c/h2\u003e \u003cp\u003eROC curve analysis assessed the sensitivity and specificity of various blood and cognitive tests for predicting MCI. The BNT showed an AUC of 0.690, with a sensitivity of 0.773 and a cutoff value of 25.5. The DST had limited predictive ability, with an AUC of only 0.546(Fig.\u0026nbsp;1A). IL-6 demonstrated higher accuracy in predicting MCI compared to IL-1β and TNFα, with AUCs of 0.648, 0.595, and 0.562, respectively (Fig.\u0026nbsp;1B).\u003c/p\u003e \u003cp\u003eSerum NfL, Aβ42, Aβ40, and p-tau217 showed high specificity in predicting MCI, with Aβ40 outperforming the others (AUC\u0026thinsp;=\u0026thinsp;0.707). The AUC for NfL was 0.646, and although p-tau217 had a lower AUC of 0.599, its specificity reached 0.907(Fig.\u0026nbsp;1C). Combining NfL with p-tau217 increased the predictive accuracy (AUC\u0026thinsp;=\u0026thinsp;0.687), but combining NfL with IL-6 did not improve accuracy beyond that of NfL alone. The combination of NfL and BNT increased the accuracy of MCI prediction (AUC\u0026thinsp;=\u0026thinsp;0.732) (Fig.\u0026nbsp;1D and 1E).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Gender Differences in Predicting MCI\u003c/h2\u003e \u003cp\u003eAnalysis of gender differences in the predictive accuracy of blood indicators and cognitive tests for MCI revealed that IL-6 was more accurate in predicting MCI in men than in women. The combination of NfL and BNT improved the accuracy of identifying MCI in men (AUC\u0026thinsp;=\u0026thinsp;0.777). In contrast, TNF-α was a more accurate predictor for MCI in women than other indicators, while IL-6 was not a significant predictor for MCI in women (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGender differences in MCI sensitivity and specificity with different blood and cognitive measures\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003eFemale (n\u0026thinsp;=\u0026thinsp;74)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c11\" namest=\"c7\"\u003e \u003cp\u003eMale (n\u0026thinsp;=\u0026thinsp;83)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCut off\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSensitivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSpecificity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eCut off\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSensitivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eSpecificity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIL-1β\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.525\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.747\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.661\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.644\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIL-6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.719\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.667\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.596\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.185\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTNF-α\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.564\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.408\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.677\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.792\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.592\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNfL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.663\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2016.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.875\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.625\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2156.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.458\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.826\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.085\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBNT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.692\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.417\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e23.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.667\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.653\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDST A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.719\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.667\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.875\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.407\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDST B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.777\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.708\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.667\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.653\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003eNote: IL-1β: Interleukin 1β; IL-6: Interleukin 6; TNFα: Tumor necrosis factor α; NfL: Neuroflament light chain; BNT: Boston naming test; DST: Digit span test\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. DISCUSSION","content":"\u003cp\u003eThe NfL protein, a neuron-specific element of the neuronal cytoskeleton, has been identified as a biomarker for neuronal damage, with elevated levels in CSF and blood linked to various neurological conditions, including multiple sclerosis, frontotemporal dementia, and Guillain-Barre syndrome \u003csup\u003e24,25\u003c/sup\u003e. Additionally, research has shown that higher CSF NfL concentrations correlate with compromised white matter integrity, as assessed by diffusion tensor imaging, suggesting that white matter damage is a primary contributor to memory decline in MCI\u003csup\u003e26\u003c/sup\u003e. Recent hypotheses propose that AD may be a chronic autoimmune disease, with cognitive decline closely associated with inflammatory processes\u003csup\u003e16\u003c/sup\u003e. Our study sought to explore the relationship between serum NfL, inflammatory factors, and cognitive function.\u003c/p\u003e \u003cp\u003eWe observed that serum NfL levels were elevated in both the MCI and AD groups compared to the HC group, with the highest concentrations found in the MCI group. This aligns with previous findings \u003csup\u003e27\u003c/sup\u003e and suggests that serum NfL levels begin to rise even before significant cognitive decline or impairment in daily living activities becomes apparent, offering potential implications for drug development and early intervention strategies. Longitudinal studies have indicated that higher baseline NfL levels are predictive of more rapid cognitive deterioration in individuals with MCI \u003csup\u003e28\u003c/sup\u003e. Furthermore, increases in plasma NfL levels have been associated with structural brain changes, including alterations in gray matter, white matter, and the cingulate gyrus, as well as with the volume of the lateral ventricles, hippocampus, and cortical thickness in AD patients \u003csup\u003e29,30\u003c/sup\u003e. The interaction between inflammatory processes and plasma NfL may also influence cognitive integrity through the disruption of core subsystems involved in AD progression and the frontoparietal network \u003csup\u003e31\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn our study, serum IL-6 levels differed significantly across the groups, and IL-1β levels correlated with DST scores, suggesting that monitoring these cytokines could aid in the early detection of MCI in at-risk populations. The serum Aβ40 levels were higher in the MCI group and lower in the AD group, while serum Aβ42 levels were lowest in the MCI group and higher in the AD group compared to the MCI group. These findings contrast with some previous studies where Aβ40 and Aβ42 levels were found to be lower in MCI than in HC groups\u003csup\u003e32\u003c/sup\u003e. Other research indicates that plasma Aβ levels may decrease significantly during the AD stage, potentially due to changes in the blood-brain barrier permeability, lymphatic function, or activation of vascular components or microglia, leading to reduced clearance of Aβ from the CSF to the periphery \u003csup\u003e33,34\u003c/sup\u003e. Animal studies support this, suggesting that blood Aβ levels drop as Aβ begins to deposit in the brain \u003csup\u003e35\u003c/sup\u003e. The discrepancy in Aβ40 and Aβ42 levels observed in our study may reflect complex and as yet not fully understood mechanisms of Aβ production and clearance.\u003c/p\u003e \u003cp\u003eWhile previous research has indicated an association between age and NfL concentration\u003csup\u003e36\u003c/sup\u003e, our study did not observe this relationship. This discrepancy may be attributed to the different methodologies employed for quantifying blood NfL, such as ELISA, electrochemiluminescence immunoassay, and Single Molecule Array (Simoa), with Simoa being the most sensitive\u003csup\u003e37\u003c/sup\u003e. The ELISA method, used in our study, may have influenced our findings, as another study utilizing ELISA also reported no correlation between age and NfL levels \u003csup\u003e38\u003c/sup\u003e. This suggests that the choice of detection method could significantly impact the observed correlations between NfL and age.\u003c/p\u003e \u003cp\u003eOur investigation into the correlation between NfL and inflammatory cytokines revealed a positive association between NfL and IL-1β and IL-6. This aligns with animal studies that have documented an increase in various inflammatory factors, including IL-2, IL-4, IL-6, and TNF-α, in conjunction with elevated CSF NfL in the context of chronic neuroinflammation and immune response\u003csup\u003e39\u003c/sup\u003e. These findings lend credence to the hypothesis that AD may be viewed as an innate autoimmune disease \u003csup\u003e16\u003c/sup\u003e. The association between NfL and Aβ40 suggests that axonal damage may play a significant role in the AD disease process, alongside the well-established roles of Aβ and tau pathology.\u003c/p\u003e \u003cp\u003eThe relationship between inflammation and cognitive decline is widely acknowledged, yet the precise mechanisms and pathways of brain inflammation remain elusive. In the early stages of AD, the brain's neuroprotective mechanisms, such as amyloid clearance and antioxidant defenses, are believed to be effective. However, as AD progresses, stress-related upregulation of immune system mediators leads to an overproduction of pro-inflammatory molecules, resulting in brain inflammation. The accumulation of Aβ and the formation of neurofibrillary tangles in the AD brain can activate the immune system and trigger inflammatory stress. This sustained inflammatory response can create a positive feedback loop, culminating in irreversible neuronal damage \u003csup\u003e40\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWe hypothesized that combining NfL with inflammatory markers might enhance the accuracy of MCI detection. However, our study did not support this hypothesis, which may be due to the limited sample size. Nonetheless, combining NfL with the BNT or p-tau217 improved diagnostic accuracy, highlighting the multifaceted nature of AD pathology and the limitations of relying on a single biomarker.\u003c/p\u003e \u003cp\u003eAutopsy studies have demonstrated a strong correlation between plasma p-tau217 levels and the extent of neurofibrillary tangles in AD patients, with p-tau217 showing high diagnostic performance in differentiating AD from other neurodegenerative diseases \u003csup\u003e41\u003c/sup\u003e. Although NfL is a marker of axonal injury and lacks specificity, as axonal damage can result from various neurological conditions, it still holds value in the early identification of MCI. Our findings suggest that the combination of NfL with p-tau217 can enhance the diagnostic accuracy for MCI, offering a potential avenue for future diagnostic approaches in AD.\u003c/p\u003e \u003cp\u003eOur study contributes to the understanding of AD by demonstrating correlations between NfL and other AD markers. Although most correlations were not statistically significant, this is consistent with previous studies \u003csup\u003e30\u003c/sup\u003e, suggesting that AD pathology is driven by diverse pathological conditions, such as Aβ pathology, tau pathology, and axonal degeneration, each eliciting different biomarker responses. Overall, the correlations between these biomarkers were weak, indicating the complexity of AD's pathophysiological mechanisms.\u003c/p\u003e \u003cp\u003eGender-based analysis in our study revealed that the diagnostic accuracy of certain biomarkers for MCI did not significantly differ between males and females, which could be attributed to the small sample size. However, this observation may also reflect inherent gender differences in inflammatory responses. Previous research has indicated that sex may influence the regulation of pro-inflammatory and anti-inflammatory biomarkers\u003csup\u003e42\u003c/sup\u003e, and gender differences in inflammation have been reported \u003csup\u003e43\u003c/sup\u003e. While some studies suggest gender disparities in NfL levels, the evidence is not yet conclusive, and further research with larger sample sizes is necessary to elucidate these potential differences.\u003c/p\u003e \u003cp\u003eThe strength of this study lies in its integrative approach, combining serum NfL with inflammatory markers and other pathological indicators of AD, thereby shedding light on their interrelationships within the complex AD pathology. The inclusion of an elderly Eastern population provides valuable insights that are pertinent to our regional context and diversifies the demographic representation in AD research. Nevertheless, our study has limitations that warrant consideration. The small sample size and the regional confinement to Hebei Province may limit the generalizability of our findings. Additionally, the cross-sectional design precludes the ability to observe dynamic changes over time in the relationship between serum NfL, inflammatory markers, and cognitive function. The absence of neuroimaging data also means that we could not explore the association between serum NfL levels and brain structural changes.\u003c/p\u003e"},{"header":"5. CONCLUSION","content":"\u003cp\u003eThis study underscores the potential of serum NfL as a biomarker for the early detection of MCI and AD, with elevated levels observed in affected individuals compared to healthy controls. The positive correlations between serum NfL and inflammatory cytokines IL-1β, IL-6, as well as Aβ40, highlight the multifaceted nature of AD pathology. While the combination of serum NfL with p-tau217 and the BNT enhances the predictive accuracy for MCI, the addition of inflammatory markers does not yield further improvement. These findings suggest that serum NfL, particularly when combined with other specific biomarkers, could serve as a valuable tool in the early identification of AD, thereby informing potential therapeutic strategies and interventions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eThis study was supported by the Special Fund Project of the Central Government to Guide the Local Science and Technology Development of Hebei Province [grant numbers 199477138G], Science and Technology Program of Hebei Province [grant numbers SG2021189], The government funded clinical medicine excellent talents training project of Hebei Province [grant numbers ZF2024136]. X. Jing and L. Wang contributed equally to this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJing X: Investigation, Formal analysis, Data curation, Writing-original draft. Wang L: Investigation, Writing-original draft. Song M: Investigation. Geng H: Investigation. Li W: Investigation. Huo Y: Investigation. Huang A: Investigation. Wang X: Conceptualization. An C: Conceptualization, Funding acquisition, Writing \u0026ndash;review \u0026amp; editing. The author(s) read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclarations of competing interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe funder had no role in study design, data collection, analysis, or writing of the manuscript. The authors have no conflict of interest to report.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthic\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ecommittees\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll participants had signed informed consent forms, and the study was approved by the Ethics Committee of the First Hospital of Hebei Medical University (ethical approval number: 20190416).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLewczuk, P. \u003cem\u003eet al.\u003c/em\u003e Plasma neurofilament light as a potential biomarker of neurodegeneration in Alzheimer's disease. 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Food Funct 1, 15\u0026ndash;31, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1039/c0fo00103a\u003c/span\u003e\u003cspan address=\"10.1039/c0fo00103a\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2010).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Neuroflament Light, Inflammatory Cytokines, Mild Cognitive Impairment, Alzheimer's disease, Biomarkers","lastPublishedDoi":"10.21203/rs.3.rs-3828911/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3828911/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective: \u003c/strong\u003eTo investigate the association between serum neurofilament light chain (NfL) levels, inflammatory cytokines, and cognitive function to assess their utility in the early detection of Alzheimer's disease (AD).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eWe conducted a cross-sectional study involving 157 community-dwelling individuals aged 55 years and above, categorized into healthy controls, mild cognitive impairment (MCI), and probable AD. Serum levels of NfL, inflammatory cytokines, and AD pathology markers were measured using enzyme-linked immunosorbent assay (ELISA). Correlations between these biomarkers and cognitive function were analyzed, for the potential of serum NfL in recognizing MCI.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Serum NfL levels were significantly elevated in MCI and probable AD groups compared to healthy controls. Positive correlations were found between serum NfL and inflammatory cytokines IL-1β, IL-6, and Aβ40. Combining serum NfL with p-tau217 and the Boston Naming Test significantly enhanced the predictive accuracy for MCI. However, combining serum NfL with inflammatory markers did not improve MCI prediction accuracy.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eElevated serum NfL is associated with cognitive impairment and inflammatory markers, suggesting its potential as a peripheral blood biomarker for early AD detection. The combination of serum NfL with p-tau217 and cognitive tests could offer a more accurate prediction of MCI, providing new insights for AD treatment strategies.\u003c/p\u003e","manuscriptTitle":"Serum Neurofilament Light Chain and Inflammatory Cytokines as Biomarkers for Early Detection of Alzheimer's Disease","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-08 17:36:34","doi":"10.21203/rs.3.rs-3828911/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-02-05T09:36:12+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-02-02T02:05:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"5f074bbc-af8e-4c1a-bede-521e4d85c0db","date":"2024-01-23T08:20:24+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-01-22T11:21:05+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-01-22T11:13:33+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-01-06T05:38:53+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-01-06T05:36:58+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-01-02T07:59:44+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c9d9fea2-09be-47bc-a320-78bc8b8a86be","owner":[],"postedDate":"January 8th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":27987315,"name":"Health sciences/Biomarkers"},{"id":27987316,"name":"Health sciences/Medical research"}],"tags":[],"updatedAt":"2024-04-11T10:06:33+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-08 17:36:34","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3828911","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3828911","identity":"rs-3828911","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}