Neurofilament Light Chain as a Key Predictor of Cognitive Function and Mortality in Centenarians: A Study of Plasma Neural Biomarkers in Aging

Neurofilament Light Chain as a Key Predictor of Cognitive Function and Mortality in Centenarians: A Study of Plasma Neural Biomarkers in Aging | 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 Neurofilament Light Chain as a Key Predictor of Cognitive Function and Mortality in Centenarians: A Study of Plasma Neural Biomarkers in Aging Yasumichi Arai, Ryo Shikimoto, Takashi Sasaki, Yukiko Abe, Kenji Tai, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6808383/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Blood-based neural biomarkers linked to systemic aging may provide insights into the biological endpoint of human lifespan. However, the key biomarker for predicting cognitive function and survival at extreme ages remains unclear. In this study, the relationship between neural biomarkers amyloid-β42/amyloid-β40 ratio (Aβ42/40), phosphorylated Tau181 (pTau181), and Neurofilament Light Chain (NfL), and cognitive function was examined in 495 centenarians. Longitudinal analysis was also performed on the same cohort. The results showed that NfL, a marker of non-specific axonal injury, was the strongest predictor of Mini-Mental State Examination (MMSE) scores (B [95% CI] = −2.28 [−3.72 – −0.85]) after adjusting for confounders. Higher NfL levels were also associated with increased mortality (HR [95% CI] = 2.82 [1.71 − 4.65]). These findings suggest NfL reflects neurodegeneration linked to late-life biological aging. Health sciences/Biomarkers/Prognostic markers Health sciences/Medical research/Biomarkers/Predictive markers Neural biomarker Neurofilament Light Chain Centenarian Amyloid-β Phosphorylated Tau Very old adults Octogenarian Nonagenarian Figures Figure 1 Figure 2 Figure 3 Introduction Research on age-related neural changes now emphasizes on their interactions with broader systemic processes, rather than considering them in isolation 1–6 . Significant progress has been made in understanding neuronal aging, largely due to advances in studying neurodegenerative diseases as part of a continuum, including the accumulation of misfolded proteins such as amyloid-β and phosphorylated tau 7 . However, strategies targeting classical protein aggregates like amyloid-β and phosphorylated tau have not been fully effective in preventing cognitive decline, often yielding only a 20-30% reduction, which still results in progressive deterioration 8,9 . Moreover, dementia linked to these pathologies appears to decline in prevalence among the very old age, suggesting alternative neurodegenerative mechanisms may emerge 10 . Notably, among centenarians aged 100 years or older, some individuals preserve cognitive function despite significant amyloid-β and tau deposition, or retain executive function especially in socially relevant contexts despite deficits detected through conventional methods 11 . This underscores the need to identify novel biomarkers and integrate them with established neurobiological markers to better characterize neural aging, particularly in super-aged populations. Blood biomarkers related to the nervous system, such as amyloid-β, tau, and NfL, have been extensively studied as predictors of cognitive function across various diseases and populations 12,13 . Amyloid-β and tau are particularly specific to Alzheimer's disease (AD) and are well-established markers for disease onset, severity, and prognosis. They have also been associated with all-cause dementia 14,15 . In contrast, NfL has been recognized as a predictive marker of cognitive function in non-Alzheimer's dementias and conditions associated with cognitive decline including vascular damage, multiple sclerosis, Parkinson's disease, and delirium 16 . Among older adults without dementia, blood tau and NfL have also been reported to associate with cognitive function 17 , 18 . However, a consistent biomarker for the general population has not yet been established. Furthermore, reports on biomarkers predicting cognitive function in centenarians remain extremely limited. While some findings have linked blood amyloid P and TNF-α with cognitive function 19 , 20 , no studies have demonstrated correlations between recently established high-precision biomarkers, including NfL, and cognitive function. Recently, Nishimoto et al. demonstrated that centenarians exhibit a distinct cognitive function profile 21 . Therefore, research on biomarkers that predicting cognitive function in populations aged 100 years and older is needed. On the other hand, mortality is determined by a variety of pathological and physiological factors. Serum total tau and NfL have been reported as predictors of all-cause mortality in general populations 22–24 . In centenarians, the most reliable indicator of mortality has been a comprehensive assessment of physical and cognitive function 25 . We reported that cardiovascular and inflammatory blood markers such as NT-proBNP, interleukin-6, cystatin C, and cholinesterase 26,27 have also been found to predict mortality in centenarians. Kaeser et al. reported in a study including centenarians (n = 135) that plasma NfL levels in old age were equally or more strongly associated with mortality compared to previously described multi-item scales of cognitive or physical function 28 . These findings highlight the importance of establishing select neurodegenerative markers as the strongest predictors of mortality in the oldest age groups. These findings support the idea that a comprehensive assessment of both physical and neurological functions, along with relevant biomarkers, is critical for predicting mortality risk in aging. However, no study has simultaneously examined the relationship between blood-based neurodegenerative markers, cognition, and survival within the same cohort. We analyzed NfL levels in the blood of centenarians alongside traditional neurobiological markers (amyloid-β and tau) and emphasized the importance of NfL as a simultaneous major marker of cognitive function and life expectancy. Results Participant Characteristics Table 1 shows the characteristics of the participants. Of the 495 participants, 398 (80.4%) were women, and the mean age was 104.1 years (standard deviation [SD]=3.0). Cognitive function assessments were conducted on 419 individuals, with a mean MMSE score of 14.9 (SD=6.9). Among the 436 participants with a Clinical Dementia Rating (CDR) score, 83 (16.8%) had a score of 0, 59 (11.9%) had a score of 0.5, and 129 (26.1%) had a score of 1. During up to 17 years of follow-up, 466 participants (95.5%) died (Table 1). Association of Plasma Biomarkers with Cognitive Function Table 2 presents the association of plasma levels of amyloid-β40, amyloid-β42, the amyloid-β42/amyloid-β40 ratio, pTau181, and NfL with MMSE scores. In Model 1, adjusted for sex and age, both the Amyloid-β42/amyloid-β40 ratio (B [95% CI] = 8.06 [4.15 − 11.97]) and NfL (per log e pg/ml increase) (B [95% CI] = −3.13 [−4.28 – −1.98)]) were significantly associated with MMSE scores. Similar results were observed in Model 2. However, in Model 3, which adjusted for potential confounders identified in the preliminary analysis (factors with p < 0.01), including current alcohol use, residence (facility), past history of HT, and laboratory markers (hemoglobin [Hb], platelet [Plt], albumin [Alb], estimated glomerular filtration rate [eGFR], high-density lipoprotein cholesterol [HDLC], glutamate oxaloacetate transaminase [GOT], lactate dehydrogenase [LDH], C-reactive protein [CRP], and cholinesterase [ChE]), only NfL remained significantly associated with MMSE (B [95% CI] = −2.28 [−3.72 – −0.85]). The variance inflation factor for all covariates was less than 5 (Table 2). Survival Analysis and Hazard Ratios Figure 2 presents the survival analysis, Kaplan-Meier curve, and Cox regression-adjusted hazard ratios. In the Kaplan-Meier analysis, both CDR and NfL were significantly associated with survival. Cox models were adjusted for sex, age, ApoE4 positivity, education (high school or higher), Barthel Index Score, CDR level, Hb, eGFR, and plasma levels of other biomarkers. The model for the amyloid-β42/amyloid-β40 ratio was adjusted for sex, age, ApoE4 positivity, education, Barthel Index Score, eGFR, and plasma levels of pTau181 and NfL only. For the highest CDR (HR [95% CI]) = 1.83 [1.15 − 2.91]), and for the highest NfL (HR [95% CI] = 2.82 [1.71 − 4.65]), mortality risk was higher than in the reference group (Figure 2). Correlations between Biomarkers and Clinical Variables In the supplementary correlation heat map 1, log-transformed NfL was positively correlated with age (r = 0.214) and residence status (Facility) (r = 0.189), and negatively with Hb (r = −0.193), Alb (r = −0.144), eGFR (r = −0.406), HDLC (r = −0.170), and LDL (r = −0.138), all p < 0.005 after Bonferroni correction (Supplementary 1). Supplementary Correlation Heatmap 2 showed associations between blood biomarkers and MMSE scores. Log amyloid-β42/amyloid-β40 was negatively correlated only with the MMSE total and attention subscale scores (r = −0.2 to −0.4, p < 0.0001). In contrast, log-transformed NfL showed negative correlations with a broader range of MMSE subscales, including the total score, time, place, registration, attention, naming repetition, writing, and copying (r = −0.2 to −0.4, p < 0.0001) (Supplementary 2). Discussion Our study highlights the role of plasma NfL as a key biomarker for simultaneously predicting cognitive function and survival in centenarians. NfL was the most significant predictor of MMSE scores, showing a strong negative association after adjusting for potential confounders. Moreover, NfL levels were significantly associated with increased mortality, with individuals in the highest quartile of NfL having a 2.82-fold higher risk of death compared to those in the lowest quartile. These findings position NfL as a guiding marker not only of cognitive decline but also of survival beyond 100 years, suggesting that a broad range of age-related psychophysiological processes in the brain may influence extreme longevity. Predictors of Cognitive Function Our data confirm that elevated NfL levels are associated with lower cognitive performance, even after controlling for confounders, suggesting that NfL may serve as a primary indicator of neurodegenerative processes, independent of other biomarkers. This aligns with previous studies reporting associations between NfL and cognitive function not only in AD 29 but also in all-cause dementia 30 , and in various non-AD diseases 12 , including Parkinson disease, Cardiovascular disease, and frontotemporal dementia 12,16,31 , as well as in predicting future cognitive decline 18 . As our report is the first, the association between NfL and cognitive function in centenarians has not previously been studied, though several findings support this link. Centenarians show substantial variability in cognitive function, with some displaying amyloid and tau accumulation without dementia, suggesting that NfL may reflect this neuropathological heterogeneity 32 . Additionally, neurovascular disorders and non-AD pathologies commonly affect cognition in centenarians, positioning NfL as a stronger correlate by capturing a broader range of underlying brain changes. In terms of exposure over time, centenarians likely experience long-term neurodegeneration, and NfL levels may serve as a useful predictor of cognitive decline during this process 33 . Therefore, our results align with the role of NfL in reflecting non-specific neuronal injury, which may capture a broader spectrum of neurodegeneration, including cerebrovascular pathology and other neurodegenerative conditions prevalent in centenarians 11,33 . In contrast, Aβ42/40 ratios and pTau181 showed only limited or no association with cognitive function in our cohort. Amyloid-β and tau are believed to play important roles in the onset of Alzheimer's disease 34–36 , but there is ongoing debate regarding their relationship and their predictive ability. In fact, a meta-analysis of the association between plasma ATN and cognitive function concluded that Amyloid-β was inconsistent, that pTau181 was strongly associated with cognitive impairment and AD, and that NfL was associated across a wide range of subjects. This finding diverges from the established role of these biomarkers in AD pathology 30, 37-39,40 , suggesting that centenarians may exhibit different patterns of neurodegenerative processes compared to those populations 41,42 . Predictors of Mortality Our longitudinal survival analysis revealed that NfL was the strongest predictor of mortality in centenarians, with higher NfL levels corresponding to an increased risk of death. This finding aligns with previous studies indicating that NfL levels can predict mortality in aging populations, including centenarians 43,24,28,22 . Notably, NfL appeared to be a more reliable predictor of survival than Aβ or pTau181, suggesting that non-specific neuronal injury, as reflected by NfL, may better represent the terminal stages of cognitive aging. Plasma NfL has also been associated with systemic factors such as body mass index (BMI) and cardiovascular disease 44 . Moreover, as suggested in previous studies, our findings supports the association between NfL levels and systemic health markers including eGFR, albumin, and hemoglobin, all of which are known to influence longevity 45 . This reinforces the idea that NfL serves as a broad indicator of overall health in the aging organism. The lack of association between Aβ42/40 ratios, and pTau181 with mortality in our study is particularly noteworthy. Some previous research has reported that serum total tau is associated with mortality 23 . However, amyloid-β has not been examined for its association with mortality in centenarian or near-centenarian groups. While Aβ deposition has long been considered a hallmark of AD, and pTau181 is a related protein to Aβ, they appear to be less predictive of mortality in the oldest old compared to neurodegeneration-associated biomarkers such as NfL. This suggests that non-amyloid-β pathology or systemic pathology affecting neurodegeneration rather than amyloid-β pathology itself may play a more significant role in the morbidity and mortality of centenarians, which is compatible with recent findings showing a distinct pattern of cognitive decline in centenarians compared to patients with AD 21 . The role of physical system decline in driving chronic inflammation, worsening nutritional status, and declining renal function as reflected in NfL levels underscores the complex systemic interactions that likely influence survival in this population. Limitations and Future Directions While our study presents compelling evidence for NfL as a predictor of cognitive decline and mortality in centenarians, certain limitations warrant consideration. First, this study did not account for a formal diagnosis of dementia. Since participants with a CDR of 0-0.5 represented only a quarter of the total, the number of participants without dementia was insufficient for meaningful statistical analysis. Second, reliance on blood-based biomarkers limits direct comparison with brain-specific pathology, such as neurofibrillary tangles and cerebrovascular lesions. However, recent advancements in blood-based biomarker technologies are promising, and in our study, we employed highly sensitive assays that have demonstrated validity through correlation with brain PET imaging 46 offering a non-invasive method to examine neuropathological processes in centenarians 13 . Third, although the large sample size and long follow-up period strengthen our findings, the study cohort was limited to a Japanese population. Replication in other ethnic groups and regions will be essential to evaluate the generalizability of these findings and to examine potential cultural or genetic influences on NfL as a biomarker of aging. Finally, as our study was observational and cross-sectional in nature, causal inferences cannot be definitively established. In conclusion, our study demonstrates that NfL is a promising biomarker for predicting cognitive decline and mortality in centenarians. Unlike Aβ42/40 and pTau181, which are associated with AD, NfL appears to be a more generalizable marker of neurodegeneration that reflects the complex interactions between the nervous system and physiological systems such as immunity and vascular function. These findings suggest that NfL may serve as a valuable biomarker for assessing the health trajectory of extremely old adults and may offer critical insights into the mechanisms underlying cognitive aging at the limits of human life. Further research, including longitudinal analyses, neuroimaging, and autopsy-based studies, is required to elucidate the role of NfL in the aging brain and to explore its potential as a biomarker for extending healthy lifespan. Online Methods Study population Japanese centenarians were recruited from 2000 to 2021 (n=495). Within three observational population-based cohorts in Japan, the analytic sample consisted of 495 centenarians, including 204 younger centenarians (100–104 years), 275 semi-supercentenarians (105–109 years), and 16 supercentenarians (≥110 years) at enrollment (Figure 1). We aggregated data from two prospective cohort studies: the Tokyo Centenarian Study (TCS) (20), and Japanese Semi-supercentenarian Study (JSS) 49,50 . The sampling methodologies have been described previously. We complied with all relevant ethical regulations for research involving human participants, including the tenets of the Declaration of Helsinki. Written informed consent was obtained from either the participants or their proxies if the individuals lacked the capacity to consent. All cohort protocols were approved by the ethics committee of Keio University School of Medicine (ID: 20021020, 20022020, 20070047) and registered as observational studies in the University Hospital Medical Information Network Clinical Trial Registry (ID: UMIN000040446, UMIN000040447, UMIN000001842). Procedures Baseline assessment consisted of an in-home or institutional interview, a self-administered questionnaire supported by a primary caregiver, and a clinic-based examination. Sex assigned at birth and age were confirmed using official insurance cards issued by the Japanese government. Participants were assessed and examined by trained geriatricians (N.H., Y.A., and M.T.) according to protocols described previously 47,48 . Measures Neural Blood Biomarkers Blood samples were collected from 495 participants. Due to factors such as biobanking procedures and limited sample availability, the number of samples analyzed for each biomarker varied: amyloid β 42/40 (Aβ 42/40) (n=474), Phosphorylated Tau 181 (pTau181) (n=464), and Neurofilament Light (NfL) (n=371). Plasma biomarkers were measured as follows: Aβ 42/40 was quantified using the Human/Rat β Amyloid (40) ELISA Kit Wako II and the Human/Rat β Amyloid (42) ELISA Kit Wako. pTau181 and NfL were assayed using Simoa (Single Molecule Array) technology, with the High-Sensitive Simoa TM p-Tau181 Advantage Kit v2.1 (104111) and the Simoa NF-Light Advantage Kit v2 (104073), respectively. Analyses were conducted using the Simoa HD-1 Analyzer TM and the Simoa HD-X Analyzer TM . Additionally, we measured comprehensive blood biomarkers for physical assessment: the genetic variant ApoE4, complete blood count, comprehensive metabolic panel, and inflammatory biomarkers. ApoE4 is a known risk factor for AD and is also recognized as a genetic variant associated with longevity. Cognitive function Neuropsychologists screened mental health and assessed cognition using the Mini-Mental State Examination (MMSE) 51 and the CDR scale 52 . The MMSE and CDR were administered by trained professionals, including psychologists and physicians, with expertise in cognitive assessments. Objective evaluation of the CDR was based on information provided by family members or facility caregivers. Other physical and psychological evaluations at baseline Demographics, including BMI, education, alcohol status, smoking status, living situation (home or facility), and comorbidities (such as hypertension, hyperlipidemia, diabetes, chronic heart disease, respiratory diseases, renal diseases, cerebrovascular diseases, dementia, fractures, and prior surgeries), were evaluated by physicians through participant observation and caregiver-provided information. The comorbidity list was consistent with those used in the TCS and JSS studies and was classified according to ICD-10. Basic activities of daily living were assessed using the Barthel Index. The quality, coherence, and completeness of the data were reviewed by an experienced geriatrician during the examination. Overall mortality Mortality data were obtained from follow-up surveys conducted with participants or their families/caregivers over a period of up to 18 years. Statistics Cross-sectional analysis Descriptive statistics, including frequencies (N, %) for categorical variables and mean ± SD for continuous variables, were used to summarize baseline demographics and health measures. Associations between plasma levels of amyloid-β40, amyloid-β42, the amyloid-β42/amyloid-β40 ratio, pTau181, and neurofilament light (NfL) with MMSE scores were evaluated using multiple linear regression models. Model 1 was adjusted for sex and age. Model 2 included additional adjustments for ApoE4 positivity, education (high school or higher), and Barthel Index Score. In Model 3, further adjustment was made for plasma levels of other biomarkers. For the amyloid-β42/amyloid-β40 ratio, Model 3 was modified to include only pTau181 and NfL. Finally, Model 4 included additional covariates identified through correlation analysis with MMSE or ATN biomarkers (Aβ40, Aβ42, Aβ42/Aβ40 ratio, pTau181, and NfL), using a p-value threshold of < 0.01. These covariates included current alcohol use, living situation (facility), past history of hypertension, and laboratory measures such as hemoglobin, platelet count, albumin, estimated glomerular filtration rate (eGFR), high-density lipoprotein cholesterol, glutamate oxaloacetate transaminase, low-density lipoprotein cholesterol, C-reactive protein, and cholinesterase. For participants with missing MMSE data, missing items were imputed with a score of 0. Additionally, for participants with severe dementia (Barthel Index ≤ 25 and/or CDR 3 or 4), where MMSE evaluation was not feasible, an MMSE score of 0 was assigned. For all other variables, participants with missing data were excluded from the analysis. Longitudinal Analysis To investigate associations between cognitive function, as assessed by the CDR score, biomarkers, and survival, survival analysis was conducted using Kaplan-Meier curves and Cox regression models. The CDR score was categorized into four levels: 0, 0.5, 1, and 2–5 to ensure a more balanced sample size across categories. Neural biomarker, data were divided into quartiles based on their distribution. Cox regression models were adjusted for sex, age, ApoE4 positivity, education level (high school or higher), Barthel Index Score, CDR level, hemoglobin, eGFR, and plasma levels of other neural biomarkers. For the amyloid-β42/amyloid-β40 ratio, the Cox model was adjusted for sex, age, ApoE4 positivity, education level, Barthel Index Score, hemoglobin, and eGFR, with additional adjustment only for plasma levels of pTau181 and NfL. Two-sided p-values were considered statistically significant at < 0.05. All analyses were performed using SPSS v29.0 (IBM Corp., Chicago, IL, USA). Declarations Data availability The analyzed datasets are restricted due to the inclusion of sensitive information and are available upon request with approval from the Keio University School of Medicine Research Ethics Committee, via the corresponding author. Author Contributions R.S. and Y. Arai conceived and designed the study. N. Hirose, Y. Arai, and Y. Abe performed the experiments and collected the data. R.S. and Y. Arai analyzed the data and interpreted the results. R.S. wrote the manuscript under the supervision of Y. Arai. R.S., Y. Arai, T.S. and Y. Abe accessed and verified the underlying data. All authors contributed to the revision of the manuscript and accept responsibility for the submission. Competing of interests Y. Arai has received research support from Ezaki Glico Co., Ltd., Johnson & Johnson K.K., and Tuning Folk Bio Japan. H. Okano is a Co-founder and Chief Science Officer of K PharMa Inc. The other authors report no relevant disclosures. Acknowledgments This study was supported by a grant from the Japan Agency for Medical Research and Development (AMED); grant numbers JP17pc0101006); the Ministry of Health, Welfare, and Labour for the Scientific Research Project for Longevity; a Grant-in-Aid for Scientific Research (No. 24K20224) from the Japan Society for the Promotion of Science; and the Program for Initiative Research Projects from Keio University and the Keio University Global Research Institute (KGRI). References Smith, E. E. et al. Systemic determinants of brain health in ageing. Nat. Rev. Neurol. 20 , 647–659 (2024). Johansen, M. C. et al. 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Physical Independence and Mortality at the Extreme Limit of Life Span: Supercentenarians Study in Japan. J. Gerontol. Ser. A 69 , 486–494 (2014). Arai, Y. et al. Inflammation, But Not Telomere Length, Predicts Successful Ageing at Extreme Old Age: A Longitudinal Study of Semi-supercentenarians. EBioMedicine 2 , 1549–1558 (2015). Folstein, M. F., Folstein, S. E. & McHugh, P. R. ‘Mini-mental state’. A practical method for grading the cognitive state of patients for the clinician. J. Psychiatr. Res. 12 , 189–198 (1975). Morris, J. C. Clinical dementia rating: a reliable and valid diagnostic and staging measure for dementia of the Alzheimer type. Int. Psychogeriatr. 9 Suppl 1 , 173–176; discussion 177-178 (1997). Tables Table 1. Characteristics of participants (N=495) Variable N (%)/mean ± SD Missing Baseline information Women 398 (80.4) 0 Age 104.1 ±3.0 0 Centenarian 100-104 years 204 (41.2) Semi Supercentenarian 105-109 years 275 (55.6) Supercentenarian 110- years 16 (3.2) ApoE4 positive 43 (8.7) 8 Education, high school or higher 197 (39.8) 14 Alcohol current 76 (15.4) 35 Smoking current 6 (1.2) 6 Facility Residents 263 (53.1) 12 Body Mass Index 19.0 ±5.1 159 Barthel Index Score 47.2 ±31.5 13 Hypertension 281 (67.1) 76 Hyperlipidemia 71 (16.9) 74 Diabetes 31 (7.4) 74 Cerebro Vascular Disease 111 (26.6) 78 Chronic Heart Disease 66 (15.8) 78 Stroke 61 (14.6) 78 Cancer 53 (11.1) 18 Surgery 303 (63.3) 16 MMSE 13.6 ±7.3 76 Amyloid-β40 116.2 ±28.4 21 Amyloid-β42 10.9 ±3.0 21 Amyloid-β42/amyloid-β40 0.095 ±0.01 21 pTau181 48.0 ±19.7 31 Neurofilament Light Chain 116.9 ±116.3 124 Follow-up Information Follow-up period, days 849.5 ±730.4 9 15 −6223 Death 466 (95.5) Cencered 22 (4.5) This table summarizes the demographic and baseline characteristics of the study participants. The values are presented as frequencies (N, %) for categorical variables and mean ± standard deviation (SD) for continuous variables. The ranges of age and other continuous variables are provided where applicable. Table 2. Association of plasma levels of amyloid-β40, amyloid-β42, the amyloid-β42/amyloid-β40 ratio, pTau181, and NfL with MMSE score. Model 1 Model 2 Model 3 N B (95%CI) P N B (95%CI) P N B (95%CI) P Amyloid-β40 (per log e pg/ml increase) 474 −2.35 (−4.90 − 0.21) 0.07 337 −2.50 (−7.31 − 2.31) 0.31 251 −1.19 (−6.63 − 4.26) 0.67 Amyloid-β42 (per log e pg/ml increase) 474 0.94 (−1.54 − 3.42) 0.46 337 5.30 (0.88 − 9.71) 0.02 251 4.07 (−0.73 − 8.88) 0.10 Amyloid-β42/amyloid-β40 ratio 474 8.06 (4.15 − 11.97) <.001 337 4.51 (0.15 − 8.86) 0.04 251 3.37 (−1.37 − 8.11) 0.16 pTau181 (per log e pg/ml increase) 464 −2.38 (−3.95 – − 0.81) 0.00 337 −1.02 (−2.73 − 0.69) 0.24 251 −1.61 (−3.68 − 0.45) 0.13 NfL (per log e pg/ml increase) 371 −3.13 (−4.28 – − 1.98) <.001 337 −1.84 (−3.05 – − 0.62) 0.003 251 −2.28 (−3.72 – − 0.85) 0.002 This table presents the associations between plasma biomarkers (Amyloid-β40, Amyloid-β42, amyloid-β42/amyloid-β40 ratio, pTau181, and NfL) and Mini-Mental State Examination (MMSE) scores. The values are presented as regression coefficients (B) with 95% confidence intervals (CI) and p-values. Statistical adjustments were made for various confounders (sex, age, ApoE4 positivity, education, Barthel Index Score, etc.). The ranges of coefficients and p-values are provided for each model. Model 1: Adjusted for sex, age. Model 2: + additional adjustment for ApoE4 positive, education (high school or higher), Barthel Index Score, and plasma levels of other biomarkers. Model 2 for Amyloid-β42/amyloid-β40 ratio: Model 1 + additional adjustment ApoE4 positive, education (high school or higher), Barthel Index Score, and plasma levels of pTau181 and NfL. Model 3: + additional adjustment for factors identified by correlation with MMSE or ATN, with p < 0.01. CDR, Clinical Dementia Rating; NfL, Neurofilament Light Chain *p <0.05 is shown in bold Additional Declarations Yes there is potential Competing Interest. Y. Arai has received research support from Ezaki Glico Co., Ltd., Johnson & Johnson K.K., and Tuning Folk Bio Japan. H. Okano is a Co-founder and Chief Science Officer of K PharMa Inc. The other authors report no relevant disclosures. Supplementary Files supplementary20250531b.docx SUPPLEMENTAL MATERIAL Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6808383","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":469402327,"identity":"da577507-a77d-4351-8c8e-9ca08c97ae48","order_by":0,"name":"Yasumichi Arai","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvElEQVRIiWNgGAWjYNACA4YEfh4GhgMMDGwkaJHsIU0LA0OCwRmizT9+/JnUjQK7POMzhw8e+MDAl0dYy5kcM+kcg+Ris7NtCQdnMLAVE9ZyIIcNqOVA4rbzPAaHeRjYEhsIajn//BlYy+Z+/g9EarmRYAbWsoG3h4E4LZI33hhbA/2SOOPMMYODMwyI8Avf+fSHt3P+2CX29yQ//vCh4hjhEFM4gOrOYwkEtcg3oPJrCGsZBaNgFIyCEQcACqZBfSd1QqoAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0003-1746-965X","institution":"Keio University School of Medicine","correspondingAuthor":true,"prefix":"","firstName":"Yasumichi","middleName":"","lastName":"Arai","suffix":""},{"id":469402328,"identity":"018fede1-e824-4f89-87c0-c7c61c79b59b","order_by":1,"name":"Ryo Shikimoto","email":"","orcid":"","institution":"Center for Supercentenarian Medical Research, Keio University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Ryo","middleName":"","lastName":"Shikimoto","suffix":""},{"id":469402329,"identity":"a991c74d-033c-4ca1-82ff-3c2fbdc0da30","order_by":2,"name":"Takashi Sasaki","email":"","orcid":"https://orcid.org/0000-0002-6275-046X","institution":"Keio University","correspondingAuthor":false,"prefix":"","firstName":"Takashi","middleName":"","lastName":"Sasaki","suffix":""},{"id":469402330,"identity":"6917b4f9-8e2a-4abd-8838-8e25ab7a7596","order_by":3,"name":"Yukiko Abe","email":"","orcid":"","institution":"Center for Supercentenarian Medical Research, Keio University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Yukiko","middleName":"","lastName":"Abe","suffix":""},{"id":469402331,"identity":"3974bd29-2e11-4d19-bd5a-d0b8f2b7b448","order_by":4,"name":"Kenji Tai","email":"","orcid":"","institution":"Eisai-Keio Innovation Laboratory for Dementia, Neurology Discovery, DHBL, Eisai Co., Ltd.","correspondingAuthor":false,"prefix":"","firstName":"Kenji","middleName":"","lastName":"Tai","suffix":""},{"id":469402332,"identity":"c4da3769-ca0b-42dd-9963-d437e85a0b5b","order_by":5,"name":"Nobuyoshi Hirose","email":"","orcid":"","institution":"Keio University","correspondingAuthor":false,"prefix":"","firstName":"Nobuyoshi","middleName":"","lastName":"Hirose","suffix":""},{"id":469402333,"identity":"bd13f812-e88f-4349-aeb0-a24d9b242c65","order_by":6,"name":"Hideyuki Okano","email":"","orcid":"https://orcid.org/0000-0001-7482-5935","institution":"Keio University Regenerative Medicine Research Center","correspondingAuthor":false,"prefix":"","firstName":"Hideyuki","middleName":"","lastName":"Okano","suffix":""}],"badges":[],"createdAt":"2025-06-03 07:45:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6808383/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6808383/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84439880,"identity":"92a87f2b-20e5-4a4a-86db-d2ed4a8850e9","added_by":"auto","created_at":"2025-06-12 03:37:46","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":76045,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart representing the derivation of the analytic sample\u003c/p\u003e\n\u003cp\u003eThis figure provides a flowchart that outlines the process used to derive the analytic sample from the initial cohort of centenarians. It visually presents the inclusion and exclusion criteria, including the number of participants at each stage. The sample was derived from a population of 495 centenarians, with final inclusion based on the availability of key data. Classification into centenarian, semi-supercentenarian (SSC), and supercentenarian (SC) groups was based on age at entry. Specifically, individuals aged 100–104 were classified as centenarians, those aged 105–109 as SSC, and those aged 110 or older as SC. Classification into centenarian, semi-supercentenarian (SSC), and supercentenarian (SC) groups was based on age at entry. Specifically, individuals aged 100–104 were classified as centenarians, those aged 105–109 as SSC, and those aged 110 or older as SC.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6808383/v1/7d6d7fae243bd86b3d64423b.png"},{"id":84439882,"identity":"f07c51a0-a8d3-40a9-a11b-29f4396ef6a7","added_by":"auto","created_at":"2025-06-12 03:37:47","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":630543,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier Survival analysis\u003c/p\u003e\n\u003cp\u003eThis figure shows the Kaplan-Meier survival curves for different biomarkers associated with survival in centenarians. Each panel illustrates the relationship between a specific biomarker and survival outcomes, with corresponding p-values provided for statistical significance. Panels A through F represent different biomarkers: \u003cstrong\u003eA\u003c/strong\u003e represents the Clinical Dementia Rating (CDR) score, \u003cstrong\u003eB\u003c/strong\u003e Amyloid-β40, \u003cstrong\u003eC\u003c/strong\u003e Amyloid-β42, \u003cstrong\u003eD\u003c/strong\u003eamyloid-β42/amyloid-β40 ratio, \u003cstrong\u003eE\u003c/strong\u003e pTau181, and \u003cstrong\u003eF\u003c/strong\u003e Neurofilament Light Chain (NfL).\u003c/p\u003e\n\u003cp\u003e* Follow-up duration ranged from 15 to 6,223 days (mean: 850.95 ± 732.63 days).\u003c/p\u003e\n\u003cp\u003e* Of the 495 centenarians, 486 completed follow-up. There were 9 missing cases: 3 with unknown survival status and follow-up duration, 4 with unknown status, and 2 with unknown duration.\u003c/p\u003e\n\u003cp\u003e* During the follow-up period, 466 participants (95.9%) died, and 20 were censored.\u003c/p\u003e\n\u003cp\u003e* The CDR score was categorized into four levels: 0, 0.5, 1, and 2-5.\u003c/p\u003e\n\u003cp\u003e* CDR, Clinical Dementia Rating; NfL, Neurofilament Light Chain\u003c/p\u003e\n\u003cp\u003e* p \u0026lt;0.05 is shown in bold\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6808383/v1/7f87f68037b90fbd63269d3b.png"},{"id":84439881,"identity":"d580d700-1fbb-49f9-b3b9-116cfe02f370","added_by":"auto","created_at":"2025-06-12 03:37:47","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":81015,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of MMSE Scores Across Different Biomarker Categories\u003c/p\u003e\n\u003cp\u003e* The p-value from the ANOVA test indicates whether there are statistically significant differences in mean MMSE scores across the quartiles of each biomarker.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6808383/v1/aeb2a667ab7d4dd68b8cee3a.png"},{"id":88264286,"identity":"f5c05470-2723-42bb-8a73-bf8a02b80853","added_by":"auto","created_at":"2025-08-04 15:58:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1703763,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6808383/v1/5e02341d-ec08-4ebf-b80c-0154aa58f359.pdf"},{"id":84439883,"identity":"95795de8-8c6c-4562-b684-ac5bb4cca4ce","added_by":"auto","created_at":"2025-06-12 03:37:47","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1607676,"visible":true,"origin":"","legend":"\u003cp\u003eSUPPLEMENTAL MATERIAL\u003c/p\u003e","description":"","filename":"supplementary20250531b.docx","url":"https://assets-eu.researchsquare.com/files/rs-6808383/v1/df6e65d677f26a8de47078de.docx"}],"financialInterests":"\u003cb\u003eYes\u003c/b\u003e there is potential Competing Interest.\nY. Arai has received research support from Ezaki Glico Co., Ltd., Johnson \u0026 Johnson K.K., and Tuning Folk Bio Japan. H. Okano is a Co-founder and Chief Science Officer of K PharMa Inc. The other authors report no relevant disclosures.","formattedTitle":"Neurofilament Light Chain as a Key Predictor of Cognitive Function and Mortality in Centenarians: A Study of Plasma Neural Biomarkers in Aging","fulltext":[{"header":"Introduction","content":"\u003cp\u003eResearch on age-related neural changes now emphasizes on their interactions with broader systemic processes, rather than considering them in isolation\u003csup\u003e1\u0026ndash;6\u003c/sup\u003e. Significant progress has been made in understanding neuronal aging, largely due to advances in studying neurodegenerative diseases as part of a continuum, including the accumulation of misfolded proteins such as amyloid-\u0026beta; and phosphorylated tau\u003csup\u003e7\u003c/sup\u003e. However, strategies targeting classical protein aggregates like amyloid-\u0026beta; and phosphorylated tau have not been fully effective in preventing cognitive decline, often yielding only a 20-30% reduction, which still results in progressive deterioration\u003csup\u003e8,9\u003c/sup\u003e. Moreover, dementia linked to these pathologies appears to decline in prevalence among the very old age, suggesting alternative neurodegenerative mechanisms may emerge\u003csup\u003e10\u003c/sup\u003e. Notably, among centenarians aged 100 years or older, some individuals preserve cognitive function despite significant amyloid-\u0026beta; and tau deposition, or retain executive function especially in socially relevant contexts despite deficits detected through conventional methods\u003csup\u003e11\u003c/sup\u003e. This underscores the need to identify novel biomarkers and integrate them with established neurobiological markers to better characterize neural aging, particularly in super-aged populations.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBlood biomarkers related to the nervous system, such as amyloid-\u0026beta;, tau, and NfL, have been extensively studied as predictors of cognitive function across various diseases and populations\u003csup\u003e12,13\u003c/sup\u003e. Amyloid-\u0026beta; and tau are particularly specific to Alzheimer\u0026apos;s disease (AD) and are well-established markers for disease onset, severity, and prognosis. They have also been associated with all-cause dementia\u003csup\u003e14,15\u003c/sup\u003e. In contrast, NfL has been recognized as a predictive marker of cognitive function in non-Alzheimer\u0026apos;s dementias and conditions associated with cognitive decline including vascular damage, multiple sclerosis, Parkinson\u0026apos;s disease, and delirium\u003csup\u003e16\u003c/sup\u003e. Among older adults without dementia, blood tau and NfL have also been reported to associate with cognitive function\u003csup\u003e17\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e18\u003c/sup\u003e. However, a consistent biomarker for the general population has not yet been established. Furthermore, reports on biomarkers predicting cognitive function in centenarians remain extremely limited. While some findings have linked blood amyloid P and TNF-\u0026alpha; with cognitive function\u003csup\u003e19\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e20\u003c/sup\u003e, no studies have demonstrated correlations between recently established high-precision biomarkers, including NfL, and cognitive function. Recently, Nishimoto et al. demonstrated that centenarians exhibit a distinct cognitive function profile\u003csup\u003e21\u003c/sup\u003e. Therefore, research on biomarkers that predicting cognitive function in populations aged 100 years and older is needed.\u003c/p\u003e\n\u003cp\u003eOn the other hand, mortality is determined by a variety of pathological and physiological factors. Serum total tau and NfL have been reported as predictors of all-cause mortality in general populations\u003csup\u003e22\u0026ndash;24\u003c/sup\u003e. In centenarians, the most reliable indicator of mortality has been a comprehensive assessment of physical and cognitive function\u003csup\u003e25\u003c/sup\u003e. We reported that cardiovascular and inflammatory blood markers such as NT-proBNP, interleukin-6, cystatin C, and cholinesterase\u003csup\u003e26,27\u003c/sup\u003e have also been found to predict mortality in centenarians. Kaeser et al. reported in a study including centenarians (n = 135) that plasma NfL levels in old age were equally or more strongly associated with mortality compared to previously described multi-item scales of cognitive or physical function\u003csup\u003e28\u003c/sup\u003e. These findings highlight the importance of establishing select neurodegenerative markers as the strongest predictors of mortality in the oldest age groups.\u003c/p\u003e\n\u003cp\u003eThese findings support the idea that a comprehensive assessment of both physical and neurological functions, along with relevant biomarkers, is critical for predicting mortality risk in aging. However, no study has simultaneously examined the relationship between blood-based neurodegenerative markers, cognition, and survival within the same cohort. We analyzed NfL levels in the blood of centenarians alongside traditional neurobiological markers (amyloid-\u0026beta; and tau) and emphasized the importance of NfL as a simultaneous major marker of cognitive function and life expectancy.\u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eParticipant Characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 1 shows the characteristics of the participants. Of the 495 participants, 398 (80.4%) were women, and the mean age was 104.1 years (standard deviation [SD]=3.0). Cognitive function assessments were conducted on 419 individuals, with a mean MMSE score of 14.9 (SD=6.9). Among the 436 participants with a Clinical Dementia Rating (CDR) score, 83 (16.8%) had a score of 0, 59 (11.9%) had a score of 0.5, and 129 (26.1%) had a score of 1. During up to 17 years of follow-up, 466 participants (95.5%) died (Table 1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssociation of Plasma Biomarkers with Cognitive Function\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 2 presents the association of plasma levels of amyloid-\u0026beta;40, amyloid-\u0026beta;42, the amyloid-\u0026beta;42/amyloid-\u0026beta;40 ratio, pTau181, and NfL with MMSE scores. In Model 1, adjusted for sex and age, both the Amyloid-\u0026beta;42/amyloid-\u0026beta;40 ratio (B [95% CI] = 8.06 [4.15 \u0026minus; 11.97]) and NfL (per log e pg/ml increase) (B [95% CI] = \u0026minus;3.13 [\u0026minus;4.28 \u0026ndash;\u0026nbsp;\u0026minus;1.98)]) were significantly associated with MMSE scores. Similar results were observed in Model 2. However, in Model 3, which adjusted for potential confounders identified in the preliminary analysis (factors with p \u0026lt; 0.01), including current alcohol use, residence (facility), past history of HT, and laboratory markers (hemoglobin [Hb], platelet [Plt], albumin [Alb], estimated glomerular filtration rate [eGFR], high-density lipoprotein cholesterol [HDLC], glutamate oxaloacetate transaminase [GOT], lactate dehydrogenase [LDH], C-reactive protein [CRP], and cholinesterase [ChE]), only NfL remained significantly associated with MMSE (B [95% CI] = \u0026minus;2.28 [\u0026minus;3.72 \u0026ndash;\u0026nbsp;\u0026minus;0.85]). The variance inflation factor for all covariates was less than 5 (Table 2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSurvival Analysis and Hazard Ratios\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFigure 2 presents the survival analysis, Kaplan-Meier curve, and Cox regression-adjusted hazard ratios. In the Kaplan-Meier analysis, both CDR and NfL were significantly associated with survival. Cox models were adjusted for sex, age, ApoE4 positivity, education (high school or higher), Barthel Index Score, CDR level, Hb, eGFR, and plasma levels of other biomarkers. The model for the amyloid-\u0026beta;42/amyloid-\u0026beta;40 ratio was adjusted for sex, age, ApoE4 positivity, education, Barthel Index Score, eGFR, and plasma levels of pTau181 and NfL only. For the highest CDR (HR [95% CI]) = 1.83 [1.15 \u0026minus; 2.91]), and for the highest NfL (HR [95% CI] = 2.82 [1.71 \u0026minus; 4.65]), mortality risk was higher than in the reference group (Figure 2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorrelations between Biomarkers and Clinical Variables\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the supplementary correlation heat map 1, log-transformed NfL was positively correlated with age (r = 0.214) and residence status (Facility) (r = 0.189), and negatively with Hb (r = \u0026minus;0.193), Alb (r = \u0026minus;0.144), eGFR (r = \u0026minus;0.406), HDLC (r = \u0026minus;0.170), and LDL (r = \u0026minus;0.138), all p \u0026lt; 0.005 after Bonferroni correction (Supplementary 1). Supplementary Correlation Heatmap 2 showed associations between blood biomarkers and MMSE scores. Log amyloid-\u0026beta;42/amyloid-\u0026beta;40 was negatively correlated only with the MMSE total and attention subscale scores (r = \u0026minus;0.2 to \u0026minus;0.4, p \u0026lt; 0.0001). In contrast, log-transformed NfL showed negative correlations with a broader range of MMSE subscales, including the total score, time, place, registration, attention, naming repetition, writing, and copying (r = \u0026minus;0.2 to \u0026minus;0.4, p \u0026lt; 0.0001) (Supplementary 2).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur study highlights the role of plasma NfL as a key biomarker for simultaneously predicting cognitive function and survival in centenarians. NfL was the most significant predictor of MMSE scores, showing a strong negative association after adjusting for potential confounders. Moreover, NfL levels were significantly associated with increased mortality, with individuals in the highest quartile of NfL having a 2.82-fold higher risk of death compared to those in the lowest quartile. These findings position NfL as a guiding marker not only of cognitive decline but also of survival beyond 100 years, suggesting that a broad range of age-related psychophysiological processes in the brain may influence extreme longevity.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePredictors of Cognitive Function\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur data confirm that elevated NfL levels are associated with lower cognitive performance, even after controlling for confounders, suggesting that NfL may serve as a primary indicator of neurodegenerative processes, independent of other biomarkers. This aligns with previous studies reporting associations between NfL and cognitive function not only in AD\u003csup\u003e29\u003c/sup\u003e but also in all-cause dementia\u003csup\u003e30\u003c/sup\u003e, and in various non-AD diseases\u003csup\u003e12\u003c/sup\u003e, including Parkinson disease, Cardiovascular disease, and frontotemporal dementia\u003csup\u003e12,16,31\u003c/sup\u003e, as well as in predicting future cognitive decline\u003csup\u003e18\u003c/sup\u003e. As our report is the first, the association between NfL and cognitive function in centenarians has not previously been studied, though several findings support this link. Centenarians show substantial variability in cognitive function, with some displaying amyloid and tau accumulation without dementia, suggesting that NfL may reflect this neuropathological heterogeneity\u003csup\u003e32\u003c/sup\u003e. Additionally, neurovascular disorders and non-AD pathologies commonly affect cognition in centenarians, positioning NfL as a stronger correlate by capturing a broader range of underlying brain changes. In terms of exposure over time, centenarians likely experience long-term neurodegeneration, and NfL levels may serve as a useful predictor of cognitive decline during this process\u003csup\u003e33\u003c/sup\u003e. Therefore, our results align with the role of NfL in reflecting non-specific neuronal injury, which may capture a broader spectrum of neurodegeneration, including cerebrovascular pathology and other neurodegenerative conditions prevalent in centenarians\u003csup\u003e11,33\u003c/sup\u003e. In contrast, A\u0026beta;42/40 ratios and pTau181 showed only limited or no association with cognitive function in our cohort. Amyloid-\u0026beta; and tau are believed to play important roles in the onset of Alzheimer\u0026apos;s disease\u003csup\u003e34\u0026ndash;36\u003c/sup\u003e, but there is ongoing debate regarding their relationship and their predictive ability. In fact, a meta-analysis of the association between plasma ATN and cognitive function concluded that Amyloid-\u0026beta; was inconsistent, that pTau181 was strongly associated with cognitive impairment and AD, and that NfL was associated across a wide range of subjects. This finding diverges from the established role of these biomarkers in AD pathology\u003csup\u003e30, 37-39,40\u003c/sup\u003e, suggesting that centenarians may exhibit different patterns of neurodegenerative processes compared to those populations\u003csup\u003e41,42\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePredictors of Mortality\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur longitudinal survival analysis revealed that NfL was the strongest predictor of mortality in centenarians, with higher NfL levels corresponding to an increased risk of death. This finding aligns with previous studies indicating that NfL levels can predict mortality in aging populations, including centenarians\u003csup\u003e43,24,28,22\u003c/sup\u003e. Notably, NfL appeared to be a more reliable predictor of survival than A\u0026beta; or pTau181, suggesting that non-specific neuronal injury, as reflected by NfL, may better represent the terminal stages of cognitive aging. Plasma NfL has also been associated with systemic factors such as body mass index (BMI) and cardiovascular disease\u003csup\u003e44\u003c/sup\u003e. Moreover, as suggested in previous studies, our findings supports the association between NfL levels and systemic health markers including eGFR, albumin, and hemoglobin, all of which are known to influence longevity\u003csup\u003e45\u003c/sup\u003e. This reinforces the idea that NfL serves as a broad indicator of overall health in the aging organism. The lack of association between A\u0026beta;42/40 ratios, and pTau181 with mortality in our study is particularly noteworthy. Some previous research has reported that serum total tau is associated with mortality\u003csup\u003e23\u003c/sup\u003e. However, amyloid-\u0026beta; has not been examined for its association with mortality in centenarian or near-centenarian groups. While A\u0026beta; deposition has long been considered a hallmark of AD, and pTau181 is a related protein to A\u0026beta;, they appear to be less predictive of mortality in the oldest old compared to neurodegeneration-associated biomarkers such as NfL. This suggests that non-amyloid-\u0026beta; pathology or systemic pathology affecting neurodegeneration rather than amyloid-\u0026beta; pathology itself may play a more significant role in the morbidity and mortality of centenarians, which is compatible with recent findings showing a distinct pattern of cognitive decline in centenarians compared to patients with AD\u003csup\u003e21\u003c/sup\u003e. The role of physical system decline in driving chronic inflammation, worsening nutritional status, and declining renal function as reflected in NfL levels underscores the complex systemic interactions that likely influence survival in this population.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLimitations and Future Directions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWhile our study presents compelling evidence for NfL as a predictor of cognitive decline and mortality in centenarians, certain limitations warrant consideration. First, this study did not account for a formal diagnosis of dementia. Since participants with a CDR of 0-0.5 represented only a quarter of the total, the number of participants without dementia was insufficient for meaningful statistical analysis. Second, reliance on blood-based biomarkers limits direct comparison with brain-specific pathology, such as neurofibrillary tangles and cerebrovascular lesions. However, recent advancements in blood-based biomarker technologies are promising, and in our study, we employed highly sensitive assays that have demonstrated validity through correlation with brain PET imaging\u003csup\u003e46\u003c/sup\u003e offering a non-invasive method to examine neuropathological processes in centenarians\u003csup\u003e13\u003c/sup\u003e. Third, although the large sample size and long follow-up period strengthen our findings, the study cohort was limited to a Japanese population. Replication in other ethnic groups and regions will be essential to evaluate the generalizability of these findings and to examine potential cultural or genetic influences on NfL as a biomarker of aging. Finally, as our study was observational and cross-sectional in nature, causal inferences cannot be definitively established.\u003c/p\u003e\n\u003cp\u003eIn conclusion, our study demonstrates that NfL is a promising biomarker for predicting cognitive decline and mortality in centenarians. Unlike A\u0026beta;42/40 and pTau181, which are associated with AD, NfL appears to be a more generalizable marker of neurodegeneration that reflects the complex interactions between the nervous system and physiological systems such as immunity and vascular function. These findings suggest that NfL may serve as a valuable biomarker for assessing the health trajectory of extremely old adults and may offer critical insights into the mechanisms underlying cognitive aging at the limits of human life.\u003c/p\u003e\n\u003cp\u003eFurther research, including longitudinal analyses, neuroimaging, and autopsy-based studies, is required to elucidate the role of NfL in the aging brain and to explore its potential as a biomarker for extending healthy lifespan.\u003c/p\u003e"},{"header":"Online Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJapanese centenarians were recruited from 2000 to 2021 (n=495). Within three observational population-based cohorts in Japan, the analytic sample consisted of 495 centenarians, including 204 younger centenarians (100\u0026ndash;104 years), 275 semi-supercentenarians (105\u0026ndash;109 years), and 16 supercentenarians (\u0026ge;110 years) at enrollment (Figure 1). We aggregated data from two prospective cohort studies: the Tokyo Centenarian Study (TCS) (20), and Japanese Semi-supercentenarian Study (JSS) \u003csup\u003e49,50\u003c/sup\u003e. The sampling methodologies have been described previously.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe complied with all relevant ethical regulations for research involving human participants, including the tenets of the Declaration of Helsinki. Written informed consent was obtained from either the participants or their proxies if the individuals lacked the capacity to consent. All cohort protocols were approved by the ethics committee of Keio University School of Medicine (ID: 20021020, 20022020, 20070047) and registered as observational studies in the University Hospital Medical Information Network Clinical Trial Registry (ID: UMIN000040446, UMIN000040447, UMIN000001842).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eProcedures\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBaseline assessment consisted of an in-home or institutional interview, a self-administered questionnaire supported by a primary caregiver, and a clinic-based examination. Sex assigned at birth and age were confirmed using official insurance cards issued by the Japanese government. Participants were assessed and examined by trained geriatricians (N.H., Y.A., and M.T.) according to protocols described previously\u003csup\u003e47,48\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMeasures\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNeural Blood Biomarkers\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBlood samples were collected from 495 participants. Due to factors such as biobanking procedures and limited sample availability, the number of samples analyzed for each biomarker varied: amyloid \u0026beta; 42/40 (A\u0026beta; 42/40) (n=474), Phosphorylated Tau 181 (pTau181) (n=464), and Neurofilament Light (NfL) (n=371). Plasma biomarkers were measured as follows: A\u0026beta; 42/40 was quantified using the Human/Rat \u0026beta; Amyloid (40) ELISA Kit Wako II and the Human/Rat \u0026beta; Amyloid (42) ELISA Kit Wako. pTau181 and NfL were assayed using Simoa (Single Molecule Array) technology, with the High-Sensitive Simoa\u003csup\u003eTM\u003c/sup\u003e p-Tau181 Advantage Kit v2.1 (104111) and the Simoa NF-Light Advantage Kit v2 (104073), respectively. Analyses were conducted using the Simoa HD-1 Analyzer\u003csup\u003eTM\u003c/sup\u003e and the Simoa HD-X Analyzer\u003csup\u003eTM\u003c/sup\u003e. Additionally, we measured comprehensive blood biomarkers for physical assessment: the genetic variant ApoE4, complete blood count, comprehensive metabolic panel, and inflammatory biomarkers. ApoE4 is a known risk factor for AD and is also recognized as a genetic variant associated with longevity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCognitive function\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNeuropsychologists screened mental health and assessed cognition using the Mini-Mental State Examination (MMSE)\u003csup\u003e51\u003c/sup\u003e and the CDR scale\u003csup\u003e52\u003c/sup\u003e. The MMSE and CDR were administered by trained professionals, including psychologists and physicians, with expertise in cognitive assessments. Objective evaluation of the CDR was based on information provided by family members or facility caregivers. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOther physical and psychological evaluations at baseline\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDemographics, including BMI, education, alcohol status, smoking status, living situation (home or facility), and comorbidities (such as hypertension, hyperlipidemia, diabetes, chronic heart disease, respiratory diseases, renal diseases, cerebrovascular diseases, dementia, fractures, and prior surgeries), were evaluated by physicians through participant observation and caregiver-provided information. The comorbidity list was consistent with those used in the TCS and JSS studies and was classified according to ICD-10. Basic activities of daily living were assessed using the Barthel Index. The quality, coherence, and completeness of the data were reviewed by an experienced geriatrician during the examination.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOverall mortality\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMortality data were obtained from follow-up surveys conducted with participants or their families/caregivers over a period of up to 18 years.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCross-sectional analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDescriptive statistics, including frequencies (N, %) for categorical variables and mean \u0026plusmn; SD for continuous variables, were used to summarize baseline demographics and health measures. Associations between plasma levels of amyloid-\u0026beta;40, amyloid-\u0026beta;42, the amyloid-\u0026beta;42/amyloid-\u0026beta;40 ratio, pTau181, and neurofilament light (NfL) with MMSE scores were evaluated using multiple linear regression models. Model 1 was adjusted for sex and age. Model 2 included additional adjustments for ApoE4 positivity, education (high school or higher), and Barthel Index Score. In Model 3, further adjustment was made for plasma levels of other biomarkers. For the amyloid-\u0026beta;42/amyloid-\u0026beta;40 ratio, Model 3 was modified to include only pTau181 and NfL. Finally, Model 4 included additional covariates identified through correlation analysis with MMSE or ATN biomarkers (A\u0026beta;40, A\u0026beta;42, A\u0026beta;42/A\u0026beta;40 ratio, pTau181, and NfL), using a p-value threshold of \u0026lt; 0.01. These covariates included current alcohol use, living situation (facility), past history of hypertension, and laboratory measures such as hemoglobin, platelet count, albumin, estimated glomerular filtration rate (eGFR), high-density lipoprotein cholesterol, glutamate oxaloacetate transaminase, low-density lipoprotein cholesterol, C-reactive protein, and cholinesterase.\u003c/p\u003e\n\u003cp\u003eFor participants with missing MMSE data, missing items were imputed with a score of 0. Additionally, for participants with severe dementia (Barthel Index \u0026le; 25 and/or CDR 3 or 4), where MMSE evaluation was not feasible, an MMSE score of 0 was assigned. For all other variables, participants with missing data were excluded from the analysis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLongitudinal Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo investigate associations between cognitive function, as assessed by the CDR score, biomarkers, and survival, survival analysis was conducted using Kaplan-Meier curves and Cox regression models. The CDR score was categorized into four levels: 0, 0.5, 1, and 2\u0026ndash;5 to ensure a more balanced sample size across categories. Neural biomarker, data were divided into quartiles based on their distribution. Cox regression models were adjusted for sex, age, ApoE4 positivity, education level (high school or higher), Barthel Index Score, CDR level, hemoglobin, eGFR, and plasma levels of other neural biomarkers. For the amyloid-\u0026beta;42/amyloid-\u0026beta;40 ratio, the Cox model was adjusted for sex, age, ApoE4 positivity, education level, Barthel Index Score, hemoglobin, and eGFR, with additional adjustment only for plasma levels of pTau181 and NfL. Two-sided p-values were considered statistically significant at \u0026lt; 0.05. All analyses were performed using SPSS v29.0 (IBM Corp., Chicago, IL, USA).\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe analyzed datasets are restricted due to the inclusion of sensitive information and are available upon request with approval from the Keio University School of Medicine Research Ethics Committee, via the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eR.S. and Y. Arai conceived and designed the study. N. Hirose, Y. Arai, and Y. Abe performed the experiments and collected the data. R.S. and Y. Arai analyzed the data and interpreted the results. R.S. wrote the manuscript under the supervision of Y. Arai. R.S., Y. Arai, T.S. and Y. Abe accessed and verified the underlying data. All authors contributed to the revision of the manuscript and accept responsibility for the submission.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting of interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eY. Arai has received research support from Ezaki Glico Co., Ltd., Johnson \u0026amp; Johnson K.K., and Tuning Folk Bio Japan. H. Okano is a Co-founder and Chief Science Officer of K PharMa Inc. The other authors report no relevant disclosures.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by a grant from the Japan Agency for Medical Research and Development (AMED); grant numbers JP17pc0101006); the Ministry of Health, Welfare, and Labour for the Scientific Research Project for Longevity; a Grant-in-Aid for Scientific Research (No. 24K20224) from the Japan Society for the Promotion of Science; and the Program for Initiative Research Projects from Keio University and the Keio University Global Research Institute (KGRI). \u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSmith, E. E. \u003cem\u003eet al.\u003c/em\u003e Systemic determinants of brain health in ageing. \u003cem\u003eNat. Rev. Neurol.\u003c/em\u003e \u003cstrong\u003e20\u003c/strong\u003e, 647\u0026ndash;659 (2024).\u003c/li\u003e\n\u003cli\u003eJohansen, M. 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Plasma Abeta(1-40) and Abeta(1-42) and the risk of dementia: a prospective case-cohort study. \u003cem\u003eLancet Neurol.\u003c/em\u003e \u003cstrong\u003e5\u003c/strong\u003e, 655\u0026ndash;660 (2006).\u003c/li\u003e\n\u003cli\u003eLim, Y. Y. \u003cem\u003eet al.\u003c/em\u003e Plasma Amyloid-\u0026beta; Biomarker Associated with Cognitive Decline in Preclinical Alzheimer\u0026rsquo;s Disease. \u003cem\u003eJ. Alzheimers Dis. JAD\u003c/em\u003e \u003cstrong\u003e77\u003c/strong\u003e, 1057\u0026ndash;1065 (2020).\u003c/li\u003e\n\u003cli\u003eLi, Z., Fan, Z. \u0026amp; Zhang, Q. The Associations of Phosphorylated Tau 181 and Tau 231 Levels in Plasma and Cerebrospinal Fluid with Cognitive Function in Alzheimer\u0026rsquo;s Disease: A Systematic Review and Meta-Analysis. \u003cem\u003eJ. Alzheimers Dis. 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A\u003c/em\u003e \u003cstrong\u003e69\u003c/strong\u003e, 486\u0026ndash;494 (2014).\u003c/li\u003e\n\u003cli\u003eArai, Y. \u003cem\u003eet al.\u003c/em\u003e Inflammation, But Not Telomere Length, Predicts Successful Ageing at Extreme Old Age: A Longitudinal Study of Semi-supercentenarians. \u003cem\u003eEBioMedicine\u003c/em\u003e \u003cstrong\u003e2\u003c/strong\u003e, 1549\u0026ndash;1558 (2015).\u003c/li\u003e\n\u003cli\u003eFolstein, M. F., Folstein, S. E. \u0026amp; McHugh, P. R. \u0026lsquo;Mini-mental state\u0026rsquo;. A practical method for grading the cognitive state of patients for the clinician. \u003cem\u003eJ. Psychiatr. Res.\u003c/em\u003e \u003cstrong\u003e12\u003c/strong\u003e, 189\u0026ndash;198 (1975).\u003c/li\u003e\n\u003cli\u003eMorris, J. C. Clinical dementia rating: a reliable and valid diagnostic and staging measure for dementia of the Alzheimer type. \u003cem\u003eInt. Psychogeriatr.\u003c/em\u003e \u003cstrong\u003e9 Suppl 1\u003c/strong\u003e, 173\u0026ndash;176; discussion 177-178 (1997).\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1. Characteristics of participants (N=495)\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"450\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 53.1111%;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 34.4444%;\"\u003e\n \u003cp\u003eN (%)/mean \u0026plusmn; SD\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.4444%;\"\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 53.1111%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eBaseline information\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.8889%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.5556%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4444%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 53.1111%;\"\u003e\n \u003cp\u003eWomen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.8889%;\"\u003e\n \u003cp\u003e398\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.5556%;\"\u003e\n \u003cp\u003e(80.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.4444%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 53.1111%;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.8889%;\"\u003e\n \u003cp\u003e104.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.5556%;\"\u003e\n \u003cp\u003e\u0026plusmn;3.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.4444%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 4%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49.1111%;\"\u003e\n \u003cp\u003eCentenarian 100-104 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.8889%;\"\u003e\n \u003cp\u003e204\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.5556%;\"\u003e\n \u003cp\u003e(41.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.4444%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 4%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49.1111%;\"\u003e\n \u003cp\u003eSemi Supercentenarian 105-109 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.8889%;\"\u003e\n \u003cp\u003e275\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.5556%;\"\u003e\n \u003cp\u003e(55.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.4444%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 4%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49.1111%;\"\u003e\n \u003cp\u003eSupercentenarian 110- years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.8889%;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.5556%;\"\u003e\n \u003cp\u003e(3.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.4444%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 53.1111%;\"\u003e\n \u003cp\u003eApoE4 positive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.8889%;\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.5556%;\"\u003e\n \u003cp\u003e(8.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.4444%;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 53.1111%;\"\u003e\n \u003cp\u003eEducation, high school or higher\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.8889%;\"\u003e\n \u003cp\u003e197\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.5556%;\"\u003e\n \u003cp\u003e(39.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.4444%;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 53.1111%;\"\u003e\n \u003cp\u003eAlcohol current\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.8889%;\"\u003e\n \u003cp\u003e76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.5556%;\"\u003e\n \u003cp\u003e(15.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.4444%;\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 53.1111%;\"\u003e\n \u003cp\u003eSmoking current\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.8889%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.5556%;\"\u003e\n \u003cp\u003e(1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.4444%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 53.1111%;\"\u003e\n \u003cp\u003eFacility Residents\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.8889%;\"\u003e\n \u003cp\u003e263\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.5556%;\"\u003e\n \u003cp\u003e(53.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.4444%;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 53.1111%;\"\u003e\n \u003cp\u003eBody Mass Index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.8889%;\"\u003e\n \u003cp\u003e19.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.5556%;\"\u003e\n \u003cp\u003e\u0026plusmn;5.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.4444%;\"\u003e\n \u003cp\u003e159\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 53.1111%;\"\u003e\n \u003cp\u003eBarthel Index Score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.8889%;\"\u003e\n \u003cp\u003e47.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.5556%;\"\u003e\n \u003cp\u003e\u0026plusmn;31.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.4444%;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 53.1111%;\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.8889%;\"\u003e\n \u003cp\u003e281\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.5556%;\"\u003e\n \u003cp\u003e(67.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.4444%;\"\u003e\n \u003cp\u003e76\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 53.1111%;\"\u003e\n \u003cp\u003eHyperlipidemia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.8889%;\"\u003e\n \u003cp\u003e71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.5556%;\"\u003e\n \u003cp\u003e(16.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.4444%;\"\u003e\n \u003cp\u003e74\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 53.1111%;\"\u003e\n \u003cp\u003eDiabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.8889%;\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.5556%;\"\u003e\n \u003cp\u003e(7.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.4444%;\"\u003e\n \u003cp\u003e74\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 53.1111%;\"\u003e\n \u003cp\u003eCerebro Vascular Disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.8889%;\"\u003e\n \u003cp\u003e111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.5556%;\"\u003e\n \u003cp\u003e(26.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.4444%;\"\u003e\n \u003cp\u003e78\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 53.1111%;\"\u003e\n \u003cp\u003eChronic Heart Disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.8889%;\"\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.5556%;\"\u003e\n \u003cp\u003e(15.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.4444%;\"\u003e\n \u003cp\u003e78\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 53.1111%;\"\u003e\n \u003cp\u003eStroke\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.8889%;\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.5556%;\"\u003e\n \u003cp\u003e(14.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.4444%;\"\u003e\n \u003cp\u003e78\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 53.1111%;\"\u003e\n \u003cp\u003eCancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.8889%;\"\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.5556%;\"\u003e\n \u003cp\u003e(11.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.4444%;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 53.1111%;\"\u003e\n \u003cp\u003eSurgery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.8889%;\"\u003e\n \u003cp\u003e303\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.5556%;\"\u003e\n \u003cp\u003e(63.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.4444%;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 53.1111%;\"\u003e\n \u003cp\u003eMMSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.8889%;\"\u003e\n \u003cp\u003e13.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.5556%;\"\u003e\n \u003cp\u003e\u0026plusmn;7.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.4444%;\"\u003e\n \u003cp\u003e76\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 53.1111%;\"\u003e\n \u003cp\u003eAmyloid-\u0026beta;40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.8889%;\"\u003e\n \u003cp\u003e116.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.5556%;\"\u003e\n \u003cp\u003e\u0026plusmn;28.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.4444%;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 53.1111%;\"\u003e\n \u003cp\u003eAmyloid-\u0026beta;42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.8889%;\"\u003e\n \u003cp\u003e10.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.5556%;\"\u003e\n \u003cp\u003e\u0026plusmn;3.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.4444%;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 53.1111%;\"\u003e\n \u003cp\u003eAmyloid-\u0026beta;42/amyloid-\u0026beta;40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.8889%;\"\u003e\n \u003cp\u003e0.095\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.5556%;\"\u003e\n \u003cp\u003e\u0026plusmn;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.4444%;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 53.1111%;\"\u003e\n \u003cp\u003epTau181\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.8889%;\"\u003e\n \u003cp\u003e48.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.5556%;\"\u003e\n \u003cp\u003e\u0026plusmn;19.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.4444%;\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 53.1111%;\"\u003e\n \u003cp\u003eNeurofilament Light Chain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.8889%;\"\u003e\n \u003cp\u003e116.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.5556%;\"\u003e\n \u003cp\u003e\u0026plusmn;116.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.4444%;\"\u003e\n \u003cp\u003e124\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 53.1111%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eFollow-up Information\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.8889%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.5556%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.4444%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 53.1111%;\"\u003e\n \u003cp\u003eFollow-up period, days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.8889%;\"\u003e\n \u003cp\u003e849.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.5556%;\"\u003e\n \u003cp\u003e\u0026plusmn;730.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.4444%;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 4%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49.1111%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.8889%;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.5556%;\"\u003e\n \u003cp\u003e\u0026minus;6223\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.4444%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 53.1111%;\"\u003e\n \u003cp\u003eDeath\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.8889%;\"\u003e\n \u003cp\u003e466\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.5556%;\"\u003e\n \u003cp\u003e(95.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.4444%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 53.1111%;\"\u003e\n \u003cp\u003eCencered\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.8889%;\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.5556%;\"\u003e\n \u003cp\u003e(4.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.4444%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThis table summarizes the demographic and baseline characteristics of the study participants. The values are presented as frequencies (N, %) for categorical variables and mean \u0026plusmn; standard deviation (SD) for continuous variables. The ranges of age and other continuous variables are provided where applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 2. Association of plasma levels of amyloid-\u0026beta;40, amyloid-\u0026beta;42, the amyloid-\u0026beta;42/amyloid-\u0026beta;40 ratio, pTau181, and NfL with MMSE score.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"947\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" style=\"width: 265px;\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" style=\"width: 259px;\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" style=\"width: 282px;\"\u003e\n \u003cp\u003eModel 3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 161px;\"\u003e\n \u003cp\u003eB (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 161px;\"\u003e\n \u003cp\u003eB (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 161px;\"\u003e\n \u003cp\u003eB (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003eAmyloid-\u0026beta;40 (per log e pg/ml increase)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e474\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026minus;2.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e(\u0026minus;4.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026minus; 0.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e337\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026minus;2.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e(\u0026minus;7.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026minus; 2.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e251\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026minus;1.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e(\u0026minus;6.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026minus; 4.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003eAmyloid-\u0026beta;42 (per log e pg/ml increase)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e474\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e(\u0026minus;1.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026minus; 3.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e337\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e5.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e(0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026minus; 9.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e251\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e4.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e(\u0026minus;0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026minus; 8.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 142px;\"\u003e\n \u003cp\u003eAmyloid-\u0026beta;42/amyloid-\u0026beta;40 ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e474\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e8.06\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(4.15\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026minus; 11.97)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e337\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4.51\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(0.15\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026minus; 8.86)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.04\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e251\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e3.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e(\u0026minus;1.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026minus; 8.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 142px;\"\u003e\n \u003cp\u003epTau181 (per log e pg/ml increase)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e464\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026minus;2.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e(\u0026minus;3.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026ndash; \u0026minus; 0.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e337\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026minus;1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e(\u0026minus;2.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026minus; 0.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e251\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026minus;1.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e(\u0026minus;3.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026minus; 0.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 142px;\"\u003e\n \u003cp\u003eNfL (per log e pg/ml increase)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e371\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026minus;3.13\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(\u0026minus;4.28\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026ndash; \u0026minus; 1.98)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e337\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026minus;1.84\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(\u0026minus;3.05\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026ndash; \u0026minus; 0.62)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.003\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e251\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026minus;2.28\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(\u0026minus;3.72\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026ndash; \u0026minus; 0.85)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThis table presents the associations between plasma biomarkers (Amyloid-\u0026beta;40, Amyloid-\u0026beta;42, amyloid-\u0026beta;42/amyloid-\u0026beta;40 ratio, pTau181, and NfL) and Mini-Mental State Examination (MMSE) scores. The values are presented as regression coefficients (B) with 95% confidence intervals (CI) and p-values. Statistical adjustments were made for various confounders (sex, age, ApoE4 positivity, education, Barthel Index Score, etc.). The ranges of coefficients and p-values are provided for each model.\u003c/p\u003e\n\u003cp\u003eModel 1: Adjusted for sex, age.\u003c/p\u003e\n\u003cp\u003eModel 2: + additional adjustment for ApoE4 positive, education (high school or higher), Barthel Index Score, and plasma levels of other biomarkers.\u003c/p\u003e\n\u003cp\u003eModel 2 for Amyloid-\u0026beta;42/amyloid-\u0026beta;40 ratio: Model 1 + additional adjustment ApoE4 positive, education (high school or higher), Barthel Index Score, and plasma levels of pTau181 and NfL.\u003c/p\u003e\n\u003cp\u003eModel 3: + additional adjustment for factors identified by correlation with MMSE or ATN, with p \u0026lt; 0.01.\u003c/p\u003e\n\u003cp\u003eCDR, Clinical Dementia Rating; NfL, Neurofilament Light Chain\u003c/p\u003e\n\u003cp\u003e*p \u0026lt;0.05 is shown in bold\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Neural biomarker, Neurofilament Light Chain, Centenarian, Amyloid-β, Phosphorylated Tau, Very old adults, Octogenarian, Nonagenarian","lastPublishedDoi":"10.21203/rs.3.rs-6808383/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6808383/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Blood-based neural biomarkers linked to systemic aging may provide insights into the biological endpoint of human lifespan. However, the key biomarker for predicting cognitive function and survival at extreme ages remains unclear. In this study, the relationship between neural biomarkers amyloid-β42/amyloid-β40 ratio (Aβ42/40), phosphorylated Tau181 (pTau181), and Neurofilament Light Chain (NfL), and cognitive function was examined in 495 centenarians. Longitudinal analysis was also performed on the same cohort. The results showed that NfL, a marker of non-specific axonal injury, was the strongest predictor of Mini-Mental State Examination (MMSE) scores (B [95% CI] = −2.28 [−3.72 – −0.85]) after adjusting for confounders. Higher NfL levels were also associated with increased mortality (HR [95% CI] = 2.82 [1.71 − 4.65]). These findings suggest NfL reflects neurodegeneration linked to late-life biological aging.","manuscriptTitle":"Neurofilament Light Chain as a Key Predictor of Cognitive Function and Mortality in Centenarians: A Study of Plasma Neural Biomarkers in Aging","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-12 03:37:42","doi":"10.21203/rs.3.rs-6808383/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"37c649d7-e100-4835-9020-2c4750e64d5d","owner":[],"postedDate":"June 12th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":49917010,"name":"Health sciences/Biomarkers/Prognostic markers"},{"id":49917011,"name":"Health sciences/Medical research/Biomarkers/Predictive markers"}],"tags":[],"updatedAt":"2025-08-04T15:50:33+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-12 03:37:42","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6808383","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6808383","identity":"rs-6808383","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}