Impaired lung function is associated with elevated blood biomarkers of AD/ADRD: Unraveling the interplay with risk of dementia

Impaired lung function is associated with elevated blood biomarkers of AD/ADRD: Unraveling the interplay with risk of dementia | 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 Impaired lung function is associated with elevated blood biomarkers of AD/ADRD: Unraveling the interplay with risk of dementia Sithara Vivek, Eileen M Crimmins, Jung Ki Kim, Jessica Faul, David R Jacobs, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8311583/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 14 Apr, 2026 Read the published version in Scientific Reports → Version 1 posted 8 You are reading this latest preprint version Abstract Background and Objectives: Impaired lung function (ILF) has been associated with cognitive decline and dementia risk in multiple cohorts, yet the role of circulating Alzheimer disease (AD) biomarkers in this relationship is not well understood. We aim to assess the associations between ILF and AD biomarkers and to determine whether these biomarkers mediate the relationship between ILF and incident dementia. Methods: Serum p-Tau181 and plasma Aβ42/40, NfL, and GFAP were measured in 4,072 participants (mean age 66 ± 10; 59% women) in the 2016 Health and Retirement Study. Peak Expiratory Flow (PEF) was assessed in 2012/2014, and cognitive function was measured at four time points between 2014 and 2020 (every two years) to determine dementia status. Impaired lung function (ILF) was defined as predicted PEF <80%. Multivariable regression examined associations between lung function and AD biomarkers; causal mediation analysis evaluated their role in linking lung function to incident dementia. Results: In total, 881 (21.6%) participants had ILF and 272 (6.8%) participants developed dementia. After adjusting for demographics, education, BMI, smoking, comorbidities, inflammation, eGFR and APOE e4 , ILF was associated with a higher risk of dementia (HR=1.74; 95% CI (1.34, 225)). Individuals with ILF had 0.10 SD higher NfL (SE= 0.03; p= 0.004) and 0.09 SD higher p-Tau 181 (SE= 0.03; p= 0.002) compared to those without ILF. NfL mediated 7.3% (p=0.01) of the total effect of ILF on dementia, while p-Tau 181 mediated 5% (p=0.05) of this association. Discussion: ILF was associated with elevated levels of neurodegeneration markers NfL and p-Tau 181, which partially mediated its relationship with dementia risk. These findings highlight the importance of monitoring blood protein biomarkers in individuals with impaired lung health to facilitate early interventions. Health sciences/Biomarkers Health sciences/Diseases Health sciences/Medical research Health sciences/Neurology Biological sciences/Neuroscience Lung function Dementia AD biomarkers Older adults Mechanism Figures Figure 1 Figure 2 BACKGROUND Extensive research across diverse populations has consistently shown that reduced lung function is associated with cognitive decline and an increased risk of dementia 1-7 . Maintaining optimal lung health may therefore play a critical role in preserving cognitive abilities. Studies with extended follow-up data reported that higher lung function measures - such as FEV 1 , FVC and FEV 1 /FVC ratio - were associated with a slower rate of cognitive decline across multiple domains, including memory, language, and processing speed/attention 7 , 8 . A study in the Atherosclerosis Risk in Communities (ARIC) cohort showed that lung disease, including both restrictive and obstructive lung diseases, among middle-aged adults was associated with increased risk of developing dementia later in life 9 . Additionally, a recent study in the National Health and Aging Trends Study (NHATS) cohort, a nationally representative sample of older adults in the US, found that higher levels of lung function measured by peak expiratory flow (PEF) was associated with lower risk of developing dementia, exhibiting a dose-dependent relationship 6 . Emerging evidence suggests that impaired lung function (ILF) may contribute to poor cognitive outcomes through both neurodegenerative and vascular pathways. In the Rush Memory and Aging Project (MAP), poor pulmonary function was associated with pathological features of Alzheimer's disease (AD) -such as global AD pathology, amyloid beta (Aβ) load, and neurofibrillary tangles- as well as markers of cerebral vascular disease 10 . Recent meta-analyses 11 further support this hypothesis by demonstrating associations between ILF and brain imaging biomarkers of neurodegeneration, vascular injury and AD pathology. However, it remains unclear whether circulating AD biomarkers—now widely used as early indicators of neurodegeneration and pathological change—contribute to this association. Ultrasensitive protein assays now enable detection of AD-related proteins from small blood samples, and these minimally invasive biomarkers shown promise in early identification of cognitive decline and in staging of AD/ADRD 12-14 . Several key protein biomarkers measured in plasma and serum, including Amyloid beta 42/40 ratio 15 , Neurofilament Light Chain (NfL) 16 , Glial Fibrillary Acidic Protein (GFAP), and phosphorylated tau (p-Tau 181 and 217 12,17 ) were associated with decline in cognitive function and risk of AD/ADRD 16 . Plasma p-Tau proteins have emerged as a promising candidate marker during symptomatic and preclinical AD when it is used with Aβ42/Aβ40 13 . A recent case-control study demonstrated the promise of using all plasma biomarkers and APOE e4 for prediction of AD clinical diagnosis that reached area under receiver operating characteristic curve (AUC) = 0.81 18 . Increasing research in blood-based biomarkers has demonstrated the clinical utility of these AD protein biomarkers for risk stratification and targeted interventions. Improving the validity of AD/ADRD biomarkers is crucial for improving early diagnosis and potentially developing treatments for this condition 13,19 . This requires identifying factors that influence biomarker concentrations. For example, a recent study demonstrated the need for accounting for renal function and obesity in the analysis of NfL and GFAP 20 . In Multiple Sclerosis patients, higher BMI was inversely associated with circulating NfL 21 and GFAP 22 levels. However, the role of lung function in influencing these biomarkers remains unclear. We hypothesized that impaired lung function is associated with higher levels of circulating AD/ADRD protein biomarkers and that these biomarkers mediate the association between impaired lung function to the higher risk of dementia in older adults. To test this, we (1) evaluated the association between PEF and circulating levels of AD biomarkers (Aβ 42/40 ratio, p-Tau 181, NfL, and GFAP) and, (2) investigated the role of AD biomarkers mediating the association between impaired lung function (ILF) and risk of dementia in a nationally representative sample of older adults in the Health and Retirement Study (HRS). METHODS Study population Health and Retirement Study (HRS) is a biennial survey of older adults in the United States that started in 1992 based on a multi-stage area probability design involving geographical stratification and clustering and oversampling of certain demographic groups and collects a wide-range of data on health, biomarkers, genetics, employment, wealth and family 23 . HRS follows participants longitudinally until death and employs a steady-state design to replenish the sample with new participants to maintain population representativeness as the study sample has aged. Additional details of the HRS study design and measurements can be found in previous publications 24-26 . We analyzed data from a subsample of individuals (n=4427) who participated in the 2016 HRS Venous Blood Study (VBS) 27 and had AD biomarker assessments. The final analytic sample for the primary analysis comprised of 4072 individuals after excluding those missing data on exposure, outcomes or covariates are shown in Figure 1. The HRS has been approved by the Health Sciences and Behavioral Sciences Institutional Review Board at the University of Michigan. Informed consent was obtained from all respondents in the HRS. Exposure measurement: Peak Expiratory Flow (PEF) In the HRS, trained interviewers employed a standardized assessment of lung function using a peak flow meter, measuring how much air a person can exhale in one breath and reporting the measure as peak expiratory flow (PEF) in L/minute 28 . The assessment was repeated three times. We used the highest of three PEF readings from the 2012 or 2014 HRS in-person visits, as physical measures were collected from a random half-sample in 2012 and the remaining half in 2014 due to the biennial data collection design. Subsequently, we estimated the percent predicted PEF using Hankinson’s equation 29 , which accounts for individual characteristics including age, sex, race and height. We classified participants as having ‘Impaired lung function (ILF)’ if their percent predicted PEF was less than 80% based on the baseline 2012/2014 measure. To assess lung function decline, we calculated the 4-year change in percent predicted PEF from the 2012/2014 to 2016/2018 measures. Blood-based AD protein biomarkers AD protein biomarkers were measured in a probability sample drawn from HRS participants in the 2016 Venous Blood Study (VBS). This included individuals aged 60 and older eligible for the 2016 Harmonized Cognitive Assessment Protocol (HCAP), as well as a random half-sample of participants under age 65 who are expected to be eligible for a future HCAP 27 . The Simoa Human Neurology 4-Plex E (N4PE) assay (Quanterix Inc., Billerica, MA) was used to measure levels of three biomarkers from plasma samples, amyloid beta 42/ 40 ratio(Aβ42/40), Glial Fibrillary Acidic Protein (GFAP), and Neurofilament light (NfL). Serum was used to assay p-Tau 181. Sample preparation and assays were performed at the University of Minnesota in the Advanced Research Diagnostics Laboratory (ARDL) based on the protocol previously validated 30 . Incident dementia Cognitive function was assessed in the HRS every 2 years from 2014 to 2020. A composite score of overall cognitive performance consisted of scores from four tests: immediate and delayed 10-noun word recall, serial 7-subtraction test, and a backward count from 20. Based on previously published work, we employed the Langa-Weir classification algorithm 24 to define dementia based on the 27-point cognitive function scale. Participants scoring between 0 and 6 on the 27-point scale were classified as having Dementia, those scoring between 7 and 11 as having Cognitive impairment no dementia (CIND), and those scoring between 12 and 27 as Normal. After excluding participants with dementia in the 2014 survey, we estimated incident dementia among those with Normal or CIND status, using cognitive test scores from the 2016, 2018, and 2020 surveys. Follow-up time ranged from 6 to 8 years, depending on whether lung function was measured in 2014 or 2012, respectively. Covariates Demographic characteristics at baseline—including age, sex, race/ethnicity (White, Black, Hispanic, and Other), smoking status (current, former, or never smoker), and years of education—were collected during the 2012/2014 core survey. Body mass index (BMI) was calculated using measured height and weight from 2012/ 2014 surveys or self-reported values if measured height and weight were not available. BMI (kg/m²) was calculated using the equation weight (pounds)/ (height * height (inches)) * 703. We estimated comorbidity index by counting the number of self-reported chronic conditions such as type 2 diabetes, cancer, hypertension, stroke, heart condition, arthritis and psychiatric problems. We calculated estimated glomerular filtration rate (eGFR) using the new CKD Epi race-free equation based on serum levels of creatinine and cystatin C measures in the 2016 VBS 27 . An inflammatory latent variable was estimated using a confirmatory factor analysis to represent systemic inflammation based on C-reactive protein ( hsCRP ), neutrophil to lymphocyte ratio (NLR) and Cytokines ( IL-6, IL-10, IL-1RA, IGF1, and sTNFR-1 ) measured in the 2016 VBS 31 . Additionally, we adjusted for APOE ε4 allele status, a genetic risk factor for Alzheimer's disease (AD), determined by the TaqMan assay, with carriers defined as individuals possessing one or two ε4 alleles 32,33 . Statistical analysis AD biomarkers were log-transformed to address skewed distributions and then standardized to facilitate comparability with other cohorts in statistical analyses. We standardized the % predicted PEF in 2012/2014 and the change in PEF from 2012/2014 to 2016/2018, so that one unit corresponds to one standard deviation. We used ANOVA tests for continuous variables and chi-square tests for categorical variables to determine differences in participant characteristics across ILF and normal PEF groups. Multi-variable linear regression models were used to determine the association of baseline ILF (2012/2014) and decline in lung function (from 2012/2014 to 2016/2018) with each continuous measure of AD biomarkers in the 2016 survey. Models were adjusted for age, sex, race and ethnicity, years of education, body mass index, smoking status, comorbidity index, kidney function (eGFR), systemic inflammation and APOE ε 4 allele status. We used Cox proportional hazards regression model to estimate association between ILF and risk of dementia and reported hazard ratios and 95% CI over six years of follow-up. For participants who did not develop dementia, follow-up time was censored at their last assessment in the 2020 core survey. Causal mediation analysis was performed to assess the mediating role of AD biomarkers in linking the association between ILF and risk of dementia using the causalmed procedure in SAS. All statistical analyses were performed in SAS v9.4 (SAS Institute, Inc., Cary, NC). RESULTS Among 4072 participants in the study, 58.9% were women (n=2397), with mean (±SD) age of 66.2 (±10.3), 21.6% (n=881) had ILF (% predicted PEF < 80%) and 2.5% (n=103) had prevalent dementia in 2014 and 6.9% (n=272) developed dementia over 6 years of follow-up. Black, Hispanic individuals and current smokers had a higher prevalence of ILF at baseline (Table 1). Association between lung function and blood biomarkers of AD We found that lower baseline percent predicted PEF, modeled as a continuous predictor, was significantly associated with higher concentrations of p-Tau181, NfL, and GFAP after adjustment for all covariates (Table 2A). Each 1-SD higher percent predicted PEF was associated with lower levels of p-Tau181 (β = –0.04, p = 0.004), NfL (β = –0.05, p < 0.001), and GFAP (β = –0.04, p = 0.001). No significant association was observed with the Aβ42/40 ratio. In fully adjusted models, participants in the lowest PEF quartile had the highest biomarker levels, showing a graded inverse relationship across quartiles (Table 2A). When using impaired lung function (ILF; PEF <80%) as a binary predictor, multivariable-adjusted linear regression models showed that individuals with ILF had significantly higher levels of p-Tau181 (β = 0.10, p = 0.0040) and NfL (β = 0.09, p = 0.0023) compared to those with normal PEF (Table 2B). Beta coefficients represent the standardized difference (in SD units) in biomarker levels between ILF and normal PEF groups. Additional adjustment for systolic blood pressure and blood glucose measured in 2016 did not alter the observed associations. Decline in lung function and AD biomarkers A greater decline in % predicted PEF from 2012/2014 to 2016/2018 was associated with higher levels of neurodegeneration markers in 2016 (Table 2C). Participants in the quartile with the greatest decline in PEF had significantly higher concentrations of p-Tau181 (β = 0.11, p = 0.009), NfL (β = 0.18, p < 0.001), and GFAP (β = 0.09, p = 0.008) compared with those in the quartile with the smallest decline. When modeled continuously, each 1-SD greater decline in PEF was associated with higher levels of p-Tau181 (β = 0.05, p = 0.002), NfL (β = 0.07, p < 0.001), and GFAP (β = 0.05, p = 0.0002). No significant associations were observed for the Aβ42/40 ratio. Impaired lung function, AD biomarkers and risk of dementia: Individuals with impaired lung function (ILF: % predicted PEF < 80) had higher prevalence of dementia (OR=1.63, 95%CI = [1.04, 2.55]; p=0.0300) and CIND (OR=1.82, 95%CI = [1.47, 2.25]; p <.0001) in 2014 compared to those with normal PEF (% predicted PEF ≥ 80) after adjustment for all covariates. Among those participants without dementia in 2014 (n=3969, combined Normal and CIND), 6.9% (n=272) developed dementia over 6 years of follow-up. Individuals with ILF in 2014 had higher risk of developing dementia (HR=1.74, 95%CI = [1.34, 2.25]; p<.0001) compared to those with normal PEF after adjusting for all covariates. Including the AD biomarkers in the model as covariates modestly attenuated the strength of association between ILF and incident dementia (HR=1.67, 95%CI = [1.29, 2.17]; p=0.0001). AD biomarkers mediated the association between ILF and incident dementia over 6 years of follow up: Causal mediation analysis was conducted to evaluate the role of AD biomarkers in the association between baseline impaired lung function (ILF) and incident dementia over a 6-year follow-up period among 3969 individuals without dementia in 2014 survey. Among the AD biomarkers associated with baseline ILF, plasma NfL and serum p-Tau 181 demonstrated partial mediation of this relationship in independent causal mediation models. Plasma NfL accounted for 7.3% of the total association between ILF and dementia (p = 0.01; Figure 2), while serum p-Tau181 mediated 4.9% of the association (p = 0.05; figure not shown) after accounting for baseline age, sex, race, BMI, smoking status, education, comorbidity index and APOE e4 . In a sensitivity analysis using dementia follow-up from 2016, we evaluated the mediation effect of NfL. The estimated proportion of the effect mediated increased slightly from 7.3% to 8.1%, although its statistical significance was attenuated (p-value increased from 0.01 to 0.07). Despite this, both the natural direct effect (OR = 1.55, p = 0.049) and the natural indirect effect (OR = 1.03, p = 0.044) remained statistically significant. DISCUSSION In this study of older adults from the Health and Retirement Study, impaired lung function was associated with elevated levels of key AD blood biomarkers including NfL and p-Tau 181 and increased risk of developing dementia over a 6-year follow-up period. To our knowledge, this is the first study to establish an association between impaired lung function and circulating AD protein biomarkers. Notably, we found that plasma NfL and serum p-Tau 181 partially mediated the association between baseline impaired lung function and future risk of dementia, suggesting a potential neurodegenerative pathway linking respiratory dysfunction to cognitive decline. Blood biomarkers of AD/ADRD have gained significant attention in recent years due to their potential clinical utility in early identification and risk classification for neurodegeneration, dementia, and ultimately, Alzheimer’s disease 13,19 . Previous studies of AD biomarkers demonstrated that the cardiovascular and metabolic risk factors including BMI, renal function and vascular risk factors such as hypertension and diabetes affect the distribution of levels of AD biomarkers in blood 34 , 20,35 . Our study marks the first attempt to investigate the effect of lung function on blood biomarkers of AD. We demonstrated that lower baseline PEF is associated with higher levels of NfL and p-Tau 181 after two- four years of follow-up. Additionally, a greater decline in PEF over 2 years is associated with elevated levels of NfL, p-Tau 181 and GFAP, indicating that respiratory health may contribute to neurodegenerative pathology. Prior cohort studies have established a link between impaired lung function and neuropathological changes, such as reduced brain volume and increased white matter lesions, suggesting potential mechanisms through which respiratory health may influence future cognitive decline 36-38 . Our findings extend this evidence by demonstrating a link between impaired lung function and elevated levels of blood biomarkers of neuropathology, suggesting that neurodegenerative pathways linking impaired respiratory health and greater risk of dementia. Consistent with our results, a meta-analysis reported that lower FEV 1 and FVC were significantly associated with reduced neuroimaging markers of brain integrity, including total brain, gray matter, and hippocampal volumes, as well as greater white matter hyperintensity burden 11 . A large longitudinal study in the UK Biobank also established association of restrictive and obstructive impairment in lung function with all-cause dementia and brain MRI structural features of dementia 5 . We found that baseline impaired lung function is associated with higher odds of having CIND and dementia. In our study, individuals with impaired lung function (PEF < 80%) had a 74% higher risk of developing dementia over a six-year follow-up period. Our findings are consistent with several reports of an association between better lung function and reduced dementia rate in other cohort studies 1,6,10 . Investigations in a younger cohort in the ARIC study with a longer follow-up also showed that individuals with impaired baseline lung function and restrictive/obstructive lung diseases have higher odds of cognitive impairment and dementia in later life 8,9 . Lutsey et al. 9 reported that restrictive lung diseases, including idiopathic pulmonary fibrosis, were associated with a 58% increased risk of dementia or mild cognitive impairment (MCI), while obstructive lung diseases, such as COPD, were linked to a 33% higher risk. Another recent study in the ARIC cohort by Shrestha et al. with extended follow-up data reported that better lung function—measured by FEV 1 , FVC, and FEV 1 /FVC ratio—was associated with a slower cognitive decline across multiple domains and reduced dementia rate 7 . Additionally, prospective analyses from the CARDIA study, which followed participants from young adulthood to midlife, demonstrated that lower cumulative pulmonary function (FEV₁ and FVC measured repeatedly over 20 years) was associated with higher midlife cognitive performance. Specifically, cumulative FEV₁ and FVC were linked to better executive function (Stroop test) and psychomotor speed/attention (Digit Symbol Substitution Test (DSST)), even after adjusting for age, sex, race, smoking, and comorbidities. Notably, lower cumulative FEV₁ also showed a marginal association with higher verbal memory (RAVLT), suggesting lung health may differentially impact cognitive domains 4 . The Rotterdam Study showed that the FVC but not FEV 1 or ratio (PRISm (FEV 1 /FVC≥70% and FEV 1 < 80% predicted)) to be associated with dementia, independent of COPD 1 . They found that participants with FVC % predicted values in the lowest quartile compared to those in the highest quartile were at increased risk of all cause dementia (adjusted HR = 2.28; 95% CI = 1.31-3.98) and AD (HR = 2.13; 95% CI= 1.13–4.02), but no significant association was observed between FEV 1 and FEV 1 /FVC ratio with incident all cause dementia or AD 1 . These findings highlight that early-life impaired lung health, particularly restrictive lung function, increases susceptibility to cognitive impairment and dementia. We demonstrated, for the first time, that plasma neurofilament light (NfL) and serum phosphorylated tau 181 (p-Tau 181) were identified as partial mediators, accounting for 7.3% and 5% of the association between impaired lung function and dementia risk, respectively, suggesting a potential biological pathway linking ILF to neurodegeneration. Though AD biomarkers measured concurrently when follow-up of dementia started. We performed sensitivity analysis following up participants after AD biomarker measures and observed that impaired lung function associated with incident dementia and NfL moderately mediated the association. These findings add to the growing body of evidence linking respiratory health to cognitive decline. A study examining the correlation between physical activity, serum NfL concentration, and cognitive decline found that participants with high levels of serum NfL who engaged in medium and high physical activity had a slower rate of cognitive decline compared to those with low physical activity 39 . This might suggest the potential influence of physical activity on improved lung function in mitigating the impact of Alzheimer's disease pathology on cognitive function. Also, previous studies indicate that chronic hypoxia from respiratory illnesses such as COPD and sleep apnea can cause cognitive deficits, affecting attention, memory, and executive function 40 . This evidence highlights the need for clinical assessment of patients with lung function decline or COPD who have symptoms of neurodegeneration 41 . Evidence from a recent study on COVID-19 patients showed that higher GFAP levels at follow-up were associated with mild cognitive dysfunction. Since COVID-19 primarily affects respiratory function, its long-term impact on neuroinflammation and neurodegeneration has raised concerns about its potential role in Alzheimer’s disease (AD) development 42,43 . In our study, we observed that AD-related biomarkers—particularly those reflecting general neurodegeneration—partially mediated the relationship between impaired lung function and increased dementia risk in older adults. These findings are consistent with a hypothesized pathway in which lung impairment contributes to neurodegenerative processes, possibly through mechanisms involving hypoxia and systemic inflammation 20,44 . However, the complexity of these interactions suggests that additional factors may be involved, warranting further investigation to fully understand the underlying mechanisms. These findings highlight the importance of early detection of cognitive impairment through blood-based biomarkers in individuals with impaired lung function. Strengths and limitations: A major strength of this study is the availability of repeated measures of lung function, and cognitive function in a nationally representative sample of older adults, along with interim AD biomarker assessments. Additionally, the study enhances the robustness of the findings by effectively controlling for multiple confounding variables associated with both AD protein biomarkers and lung function. However, there are several limitations. First, only PEF was available as a measure of lung function, which may not fully capture respiratory impairment. Future studies incorporating more sensitive measures, such as FEV 1 and FVC, are warranted. Second, AD biomarkers were measured at a single time point, limiting the ability to assess the longitudinal relationship between lung function decline and changes in AD biomarker levels. Third, in this study we investigated only four key AD-related biomarkers (Aβ42/40, p-Tau 181, GFAP, and NfL), which, while informative, do not capture the full spectrum of vascular dysfunction, neuroinflammation, or other potential pathways linking respiratory health to dementia. Future research should incorporate a broader panel of biomarkers, including markers of endothelial dysfunction, systemic inflammation, and cerebrovascular health, to better characterize the biological mechanisms underlying this association. Conclusion In the Health and Retirement Study, impaired lung function was associated with elevated levels of key neuropathology biomarkers in blood, with NfL and p-Tau 181 partially mediating its association with risk of dementia. Our study findings highlight the importance of monitoring AD protein biomarkers in individuals with impaired respiratory health, which may help identify those at higher risk for cognitive decline and support timely interventions to mitigate neurodegenerative processes. These results warrant the need for further research to explore additional molecular biomarkers that mediate the association between impaired respiratory health and future risk of cognitive impairment and dementia. Declarations Data availability statement: We used the HRS publicly available datasets and sensitive biomarker data for this study analysis. This data can be found here: https://hrsdata.isr.umich.edu/data-products/public-survey-data and https://hrsdata.isr.umich.edu/dataproducts/sensitive-health and can be accessed by completing required data use agreement. Ethics statement: The venous blood study involving human samples was approved by University of Minnesota Institutional Review Board. Author contributions: SV: Conceptualization, formal analysis and methodology, writing – original draft, review and editing EC: Data collection, Writing – critical review and editing. JKK: Data development, Writing – critical review and editing. JF: Data collection, Writing – critical review and editing. DJ: Analysis methodology, Writing – critical review and editing. WG: Analysis methodology, Writing – critical review and editing. BT: Data collection, conceptualization, Writing – critical review and editing. Disclosure of Potential Conflicts of Interest: The authors confirm that research was carried out without any affiliations or financial associations that could be perceived as potential conflicts of interest. Funding: The author(s) acknowledge financial backing for the study, writing, and/or publication of this article. This project received support from the department grants and the Health and Retirement Study is sponsored by NIA through grant U01 AG009740. References Xiao, T. et al. Lung Function Impairment and the Risk of Incident Dementia: The Rotterdam Study. J. Alzheimer's disease: JAD . 82 (2), 621–630. 10.3233/jad-210162 (2021). Gilsanz, P. et al. Early Midlife Pulmonary Function and Dementia Risk. Alzheimer disease and associated disorders . Oct-Dec 32 (4), 270–275. 10.1097/wad.0000000000000253 (2018). Xie, W. et al. Reduced Lung Function and Cognitive Decline in Aging: A Longitudinal Cohort Study. Annals Am. Thorac. 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Tables Table 1 Descriptive statistics of participant characteristics in the HRS 2012/2014 survey across lung function groups Estimates Mean ± SD / Frequency (%) Overall n = 4072 Normal (Predicted PEF ≥ 80%) n = 3191 (78.4%) Impaired lung function (Predicted PEF < 80%) n = 881 (21.6%) p value % predicted PEF 2012/2014 98.96 ± 25.74 108.65 ± 19.08 63.85 ± 13.03 <.0001 Age 2014 (years) 66.19 ± 10.29 66.32 ± 10.26 65.70 ± 10.38 0.11 Sex (% females) 2397 (58.9%) 1891 (59.3%) 506 (57.4%) 0.3300 Race/ethnicity Blacks Hispanics Other Whites 667 (16.4%) 601 (14.8%) 129 (3.2%) 2675 (65.7%) 499 (15.6%) 453 (14.2%) 91 (2.9%) 2148 (67.3%) 168 (19.07%) 148 (16.8%) 38 (4.3%) 527 (59.8%) 0.0003 Smoking status 2014 Current Former Never 501 (12.3%) 991 (24.3%) 2580 (63.4%) 284 (8.9%) 790 (24.8%) 2117 (66.3%) 217 (24.6%) 201 (22.8%) 463 (52.6%) < .0001 Education 0–11 y 12 y 13-15y 16 + y 747 (18.3%) 1252 (30.8%) 1023 (25.1%) 1050(25.8%) 503 (15.8%) 944 (29.6%) 843 (26.4%) 901 (28.2%) 244 (27.7%) 308 (35.0%) 180 (20.4%) 149 (16.9%) < .0001 Body Mass Index (kg/m 2 ) 2012/2014 30.56 ± 6.91 30.65 ± 6.74 30.27 ± 7.50 0.1400 Comorbidity Index 2014 1.88 ± 1.29 1.83 ± 1.26 2.06 ± 1.36 < .0001 eGFR (Cys and CR) 2016 68.79 ± 21.08 68.61 ± 21.17 69.44 ± 20.76 0.3100 Inflammatory latent variable 2016 0.08 ± 0.51 0.09 ± 0.51 0.04 ± 0.49 0.0060 APOE e4 allele, %Yes 1122 (27.6%) 898 (28.1%) 224 (25.4%) 0.1100 Cognitive function 2014 (0–27) 15.55 ± 4.22 15.89 ± 4.14 14.32 ± 4.28 < .0001 Cognition category in 2014 Normal CIND Dementia 3388 (83.2%) 581 (14.3%) 103 (2.5%) 2742 (85.9%) 380 (11.9%) 69 (2.2%) 646 (73.3%) 201 (22.8%) 34 (3.9%) < .0001 Incident dementia* n = 3969 272 (6.9%) 172 (5.5%) 99 (11.6%) < .0001 Note :* Dementia was based on a cognitive score of 1–6 in 2016, 2018 or 2020 among participants who had normal cognition or CIND in 2014. Table 2 A: Association between percent predicted PEF in 2012/2014 (baseline) and blood biomarkers of AD in 2016 AD biomarkers Q1 β (SE), p value Q2 β (SE), p value Q3 β (SE), p value Q4 1 SD unit β (SE), p value Aβ 42/40 ratio ~ % pred PEF 0.04 (0.05), 0.42 0.03 (0.05), 0.48 0.008 (0.04), 0.86 Reference -0.01 (0.02), 0.4600 p-Tau 181 ~ % pred PEF 0.15 (0.04), 0.0002 0.09 (0.04), 0.03 0.11 (0.04), 0.007 Reference -0.04 (0.01), 0.0040 NfL ~ % pred PEF 0.11 (0.03), 0.0006 0.09 (0.03), 0.005 0.02 (0.03), 0.63 Reference -0.05 (0.01), < .0001 GFAP ~ % pred PEF 0.10 (0.03), 0.004 0.09 (0.03), 0.008 0.07 (0.03), 0.03 Reference -0.04 (0.01), 0.001 Note: AD biomarkers were log transformed and standardized to approximate the normal distribution. Models were adjusted for age, sex, race, education, BMI, smoking status, comorbidity index 2014, inflammatory latent variable 2016, eGFR 2016, and APOE e4 allele. Table 2 B: Association between impaired lung function in 2012/2014 and blood biomarkers of AD in 2016 β (SE), p value Aβ 42/40 ratio 0.03 (0.04); 0.52 p-Tau 181 0.10 (0.03); 0.004 NfL 0.09 (0.03); 0.002 GFAP 0.04 (0.03); 0.22 Note: AD biomarkers were log transformed and standardized to approximate the normal distribution. Models were adjusted for age, sex, race, education, BMI, smoking status, comorbidity index, inflammatory latent variable, eGFR, and APOE e4 allele. Table 2 C: Association of decline in % predicted PEF from 2014 to 2016 as quartile and continuous variable with blood biomarkers of AD in 2016 AD biomarkers Q1 Q2 β (SE), p value Q3 β (SE), p value Q4 β (SE), p value 1 SD unit β (SE), p value Aβ 42/40 ratio Reference -0.02 (0.05), 0.69 -0.04 (0.05), 0.42 -0.03 (0.05), 0.51 -0.003 (0.02), 0.8500 p-Tau 181 Reference -0.02 (0.04), 0.59 0.08 (0.04), 0.07 0.11 (0.04), 0.009 0.05 (0.02), 0.0020 NfL Reference 0.05 (0.03), 0.12 0.08 (0.03), 0.02 0.18 (0.03), < .0001 0.07 (0.01), < .0001 GFAP Reference -0.06 (0.03), 0.08 0.01 (0.03), 0.86 0.09 (0.03), 0.008 0.05 (0.01), 0.0002 Note: AD biomarkers were log transformed and standardized to approximate the normal distribution. Models were adjusted for % predicted PEF in 2014 (baseline), age, sex, race, education, BMI, smoking status, comorbidity index 2014, inflammatory latent variable 2016, eGFR 2016, and APOE e4 allele. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 14 Apr, 2026 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 17 Mar, 2026 Reviews received at journal 13 Feb, 2026 Reviewers agreed at journal 27 Jan, 2026 Reviewers invited by journal 19 Dec, 2025 Editor invited by journal 18 Dec, 2025 Editor assigned by journal 09 Dec, 2025 Submission checks completed at journal 09 Dec, 2025 First submitted to journal 08 Dec, 2025 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. 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00:18:05","extension":"html","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":142376,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8311583/v1/70df327b2c10e10174553f1c.html"},{"id":99316358,"identity":"f46ec7e1-ce89-47d2-b98e-f3aa2cfb8fbe","added_by":"auto","created_at":"2025-12-31 16:28:20","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":48947,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStudy design and timeline:\u003c/strong\u003e In the Health and Retirement Study (HRS) cohort, we identified participants who have lung function measure available in 2012/2014 biennial surveys and blood AD biomarkers measured in 2016 survey and cognitive function measures available from 2014 – 2020 every two years.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8311583/v1/1dd8ae7a2cc7f0498e3eebf4.jpg"},{"id":99188320,"identity":"8800ee1d-dfb3-458e-815c-8ac4b15a361b","added_by":"auto","created_at":"2025-12-30 00:18:05","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":41933,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCausal mediation analysis of the association between decline in % predicted PEF and incident dementia mediated by plasma NfL. \u003c/strong\u003eThe path diagram of the causal mediation model with a three-variable system. In Path A, Impaired lung function ILF (exposure) has a significantly positive relationship with NfL (mediator) in a multivariable linear regression model. In Path B, ILF (exposure) was shown as an independent predictor for incident dementia (outcome) in the multivariable Cox hazards regression model without NfL. In Path C, both ILF (exposure) and NfL (mediator) remained significant to predict incident dementia using the multivariable causal mediation model with NfL partially mediate the association between ILF and incident dementia. HR: hazard ratio; CI: confidence interval, β: regression coefficient.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8311583/v1/2b9691113aa3c9cbd3647bb7.jpg"},{"id":107352515,"identity":"4b940a88-8aa1-4ba4-9804-e8db1f948515","added_by":"auto","created_at":"2026-04-20 16:14:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":876722,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8311583/v1/2798ead9-d2b8-484b-b6e0-6682c04c08b2.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Impaired lung function is associated with elevated blood biomarkers of AD/ADRD: Unraveling the interplay with risk of dementia","fulltext":[{"header":"BACKGROUND","content":"\u003cp\u003eExtensive research across diverse populations has consistently shown that reduced lung function is associated with cognitive decline and an increased risk of dementia\u003csup\u003e1-7\u003c/sup\u003e. Maintaining optimal lung health may therefore play a critical role in preserving cognitive abilities. Studies with \u0026nbsp;extended follow-up data reported that higher lung function measures - such as FEV\u003csub\u003e1\u003c/sub\u003e, FVC and FEV\u003csub\u003e1\u003c/sub\u003e/FVC ratio - were associated with a slower rate of cognitive decline across multiple domains, including memory, language, and processing speed/attention\u003csup\u003e7\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e8\u003c/sup\u003e. A study in the Atherosclerosis Risk in Communities (ARIC) cohort showed that lung disease, including both restrictive and obstructive lung diseases, among middle-aged adults was associated with increased risk of developing dementia later in life\u003csup\u003e9\u003c/sup\u003e. Additionally, a recent study in the National Health and Aging Trends Study (NHATS) cohort, a nationally representative sample of older adults in the US, found that higher levels of lung function measured by peak expiratory flow (PEF) was associated with lower risk of developing dementia, exhibiting a dose-dependent relationship\u003csup\u003e6\u003c/sup\u003e. Emerging evidence suggests that impaired lung function (ILF) may contribute to poor cognitive outcomes through both neurodegenerative and vascular pathways. In the Rush Memory and Aging Project (MAP), poor pulmonary function was associated with pathological features of Alzheimer's disease (AD) -such as global AD pathology, amyloid beta (Aβ) load, and neurofibrillary tangles- as well as markers of cerebral vascular disease\u003csup\u003e10\u003c/sup\u003e. Recent meta-analyses\u003csup\u003e11\u003c/sup\u003e further support this hypothesis by demonstrating associations between ILF and brain imaging biomarkers of neurodegeneration, vascular injury and AD pathology. However, it remains unclear whether circulating AD biomarkers—now widely used as early indicators of neurodegeneration and pathological change—contribute to this association.\u003c/p\u003e\n\u003cp\u003eUltrasensitive protein assays now enable detection of AD-related proteins from small blood samples, and these minimally invasive biomarkers shown promise in early identification of cognitive decline and in staging of AD/ADRD\u003csup\u003e12-14\u003c/sup\u003e. Several key protein biomarkers measured in plasma and serum, including Amyloid beta 42/40 ratio\u003csup\u003e15\u003c/sup\u003e, Neurofilament Light Chain (NfL)\u003csup\u003e16\u003c/sup\u003e, Glial Fibrillary Acidic Protein (GFAP), and phosphorylated tau (p-Tau 181 and 217\u003csup\u003e12,17\u003c/sup\u003e) were associated with decline in cognitive function and risk of AD/ADRD\u003csup\u003e16\u003c/sup\u003e. Plasma p-Tau proteins have emerged as a promising candidate marker during symptomatic and preclinical AD when it is used with Aβ42/Aβ40\u003csup\u003e13\u003c/sup\u003e. A recent case-control study demonstrated the promise of using all plasma biomarkers and \u003cem\u003eAPOE e4\u003c/em\u003e for prediction of AD clinical diagnosis that reached area under receiver operating characteristic curve (AUC) = 0.81\u003csup\u003e18\u003c/sup\u003e. Increasing research in blood-based biomarkers has demonstrated the clinical utility of these AD protein biomarkers for risk stratification and targeted interventions. Improving the validity of AD/ADRD biomarkers is crucial for improving early diagnosis and potentially developing treatments for this condition\u003csup\u003e13,19\u003c/sup\u003e. This requires identifying factors that influence biomarker concentrations. For example, a recent study demonstrated the need for accounting for renal function and obesity in the analysis of NfL and GFAP\u003csup\u003e20\u003c/sup\u003e. In Multiple Sclerosis patients, higher BMI was inversely associated with circulating NfL\u003csup\u003e21\u003c/sup\u003e and GFAP\u003csup\u003e22\u003c/sup\u003e levels. However, the role of lung function in influencing these biomarkers remains unclear.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe hypothesized that impaired lung function is associated with higher levels of circulating AD/ADRD protein biomarkers and that these biomarkers mediate the association between impaired lung function to the higher risk of dementia in older adults. To test this, we (1) evaluated the association between PEF and circulating levels of AD biomarkers (Aβ 42/40 ratio, p-Tau 181, NfL, and GFAP) and, (2) investigated the role of AD biomarkers mediating the association between impaired lung function (ILF) and risk of dementia in a nationally representative sample of older adults in the Health and Retirement Study (HRS).\u003c/p\u003e"},{"header":"METHODS","content":"\u003cp\u003e\u003cstrong\u003eStudy population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHealth and Retirement Study (HRS) is a biennial survey of older adults in the United States that started in 1992 based on a multi-stage area probability design involving geographical stratification and clustering and oversampling of certain demographic groups and collects a wide-range of data on health, biomarkers, genetics, employment, wealth and family\u003csup\u003e23\u003c/sup\u003e. HRS follows participants longitudinally until death and employs a steady-state design to replenish the sample with new participants to maintain population representativeness as the study sample has aged. Additional details of the HRS study design and measurements can be found in previous publications\u003csup\u003e24-26\u003c/sup\u003e. We analyzed data from a subsample of individuals (n=4427) who participated in the 2016 HRS Venous Blood Study (VBS)\u003csup\u003e27\u003c/sup\u003e and had \u0026nbsp;AD biomarker assessments. The final analytic sample for the primary analysis comprised of 4072 individuals after excluding those missing data on exposure, outcomes or covariates are shown in Figure 1.\u003c/p\u003e\n\u003cp\u003eThe HRS has been approved by the Health Sciences and Behavioral Sciences Institutional Review Board at the University of Michigan. Informed consent was obtained from all respondents in the HRS.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExposure measurement: Peak Expiratory Flow (PEF)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the HRS, trained interviewers employed a standardized assessment of lung function using a peak flow meter, measuring how much air a person can exhale in one breath and reporting the measure as peak expiratory flow (PEF) in L/minute\u003csup\u003e28\u003c/sup\u003e. The assessment was repeated three times. We used the highest of three PEF readings from the 2012 or 2014 HRS in-person visits, as physical measures were collected from a random half-sample in 2012 and the remaining half in 2014 due to the biennial data collection design. Subsequently, we estimated the percent predicted PEF using Hankinson’s equation\u003csup\u003e29\u003c/sup\u003e, which accounts for individual characteristics including age, sex, race and height. We classified participants as having ‘Impaired lung function (ILF)’ if their percent predicted PEF was less than 80% based on the baseline 2012/2014 measure. To assess lung function decline, we calculated the 4-year change in percent predicted PEF from the 2012/2014 to 2016/2018 measures.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBlood-based AD protein biomarkers\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAD protein biomarkers were measured in a probability sample drawn from HRS participants in the 2016 Venous Blood Study (VBS). This included individuals aged 60 and older eligible for the 2016 Harmonized Cognitive Assessment Protocol (HCAP), as well as a random half-sample of participants under age 65 who are expected to be eligible for a future HCAP\u003csup\u003e27\u003c/sup\u003e. The Simoa Human Neurology 4-Plex E (N4PE) assay (Quanterix Inc., Billerica, MA) was used to measure levels of three biomarkers from plasma samples, amyloid beta 42/ 40 ratio(Aβ42/40), Glial Fibrillary Acidic Protein (GFAP), and Neurofilament light (NfL). Serum was used to assay p-Tau 181. Sample preparation and assays were performed at the University of Minnesota in the Advanced Research Diagnostics Laboratory (ARDL) based on the protocol previously validated\u003csup\u003e30\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIncident dementia\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCognitive function was assessed in the HRS every 2 years from 2014 to 2020. A composite score of overall cognitive performance consisted of scores from four tests: immediate and delayed 10-noun word recall, serial 7-subtraction test, and a backward count from 20. Based on previously published work, we employed the Langa-Weir classification algorithm\u003csup\u003e24\u003c/sup\u003e to define dementia based on the 27-point cognitive function scale. Participants scoring between 0 and 6 on the 27-point scale were classified as having Dementia, those scoring between 7 and 11 as having Cognitive impairment no dementia (CIND), and those scoring between 12 and 27 as Normal. After excluding participants with dementia in the 2014 survey, we estimated incident dementia among those with Normal or CIND status, using cognitive test scores from the 2016, 2018, and 2020 surveys. Follow-up time ranged from 6 to 8 years, depending on whether lung function was measured in 2014 or 2012, respectively.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCovariates\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDemographic characteristics at baseline—including age, sex, race/ethnicity (White, Black, Hispanic, and Other), smoking status (current, former, or never smoker), and years of education—were collected during the 2012/2014 core survey. Body mass index (BMI) was calculated using measured height and weight from 2012/ 2014 surveys or self-reported values if measured height and weight were not available. BMI (kg/m²) was calculated using the equation weight (pounds)/ (height * height (inches)) * 703. We estimated comorbidity index by counting the number of self-reported chronic conditions such as type 2 diabetes, cancer, hypertension, stroke, heart condition, arthritis and psychiatric problems. We calculated estimated glomerular filtration rate (eGFR) using the new CKD Epi race-free equation based on serum levels of creatinine and cystatin C measures in the 2016 VBS\u003csup\u003e27\u003c/sup\u003e. An inflammatory latent variable was estimated using a confirmatory factor analysis to represent systemic inflammation based on C-reactive protein (\u003cem\u003ehsCRP\u003c/em\u003e), neutrophil to lymphocyte ratio (NLR) and\u0026nbsp;Cytokines (\u003cem\u003eIL-6, IL-10, IL-1RA, IGF1,\u003c/em\u003e and \u003cem\u003esTNFR-1\u003c/em\u003e) measured in the 2016 VBS\u003csup\u003e31\u003c/sup\u003e. Additionally, we adjusted for \u003cem\u003eAPOE\u003c/em\u003e ε4 allele status, a genetic risk factor for Alzheimer's disease (AD), determined by the TaqMan assay, with carriers defined as individuals possessing one or two ε4 alleles\u0026nbsp;\u003csup\u003e32,33\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAD biomarkers were log-transformed to address skewed distributions and then standardized to facilitate comparability with other cohorts in statistical analyses. We standardized the % predicted PEF in 2012/2014 and the change in PEF from 2012/2014 to 2016/2018, so that one unit corresponds to one standard deviation. We used\u0026nbsp;ANOVA tests for continuous variables and chi-square tests for categorical variables to determine differences in participant characteristics across ILF and normal PEF groups. Multi-variable linear regression models were used to determine the association of baseline ILF (2012/2014) and decline in lung function (from 2012/2014 to 2016/2018) with each continuous measure of AD biomarkers in the 2016 survey. Models were adjusted for age, sex, race and ethnicity, years of education, body mass index, smoking status, comorbidity index, kidney function (eGFR), systemic inflammation and \u003cem\u003eAPOE\u0026nbsp;\u003c/em\u003eε\u003cem\u003e4\u003c/em\u003e allele status.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe used Cox proportional hazards regression model to estimate association between ILF and risk of dementia and reported hazard ratios and 95% CI over six years of follow-up. For participants who did not develop dementia, follow-up time was censored at their last assessment in the 2020 core survey. Causal mediation analysis was performed to assess the mediating role of AD biomarkers in linking the association between ILF and risk of dementia using the \u003cem\u003ecausalmed\u003c/em\u003e procedure in SAS. All statistical analyses were performed in SAS v9.4 (SAS Institute, Inc., Cary, NC).\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003eAmong 4072 participants in the study, 58.9% were women (n=2397), with mean (±SD) age of 66.2 (±10.3), 21.6% (n=881) had ILF (% predicted PEF \u0026lt; 80%) and 2.5% (n=103) had prevalent dementia in 2014 and 6.9% (n=272) developed dementia over 6 years of follow-up. Black, Hispanic individuals and current smokers had a higher prevalence of ILF at baseline (Table 1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssociation between lung function and blood biomarkers of AD\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe found that lower baseline percent predicted PEF, modeled as a continuous predictor, was significantly associated with higher concentrations of p-Tau181, NfL, and GFAP after adjustment for all covariates (Table 2A). Each 1-SD higher percent predicted PEF was associated with lower levels of p-Tau181 (β = –0.04, p = 0.004), NfL (β = –0.05, p \u0026lt; 0.001), and GFAP (β = –0.04, p = 0.001). No significant association was observed with the Aβ42/40 ratio. In fully adjusted models, participants in the lowest PEF quartile had the highest biomarker levels, showing a graded inverse relationship across quartiles (Table 2A). When using impaired lung function (ILF; PEF \u0026lt;80%) as a binary predictor, multivariable-adjusted linear regression models showed that individuals with ILF had significantly higher levels of p-Tau181 (β = 0.10, p = 0.0040) and NfL (β = 0.09, p = 0.0023) compared to those with normal PEF (Table 2B). Beta coefficients represent the standardized difference (in SD units) in biomarker levels between ILF and normal PEF groups. Additional adjustment for systolic blood pressure and blood glucose measured in 2016 did not alter the observed associations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDecline in lung function and AD biomarkers\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA greater decline in % predicted PEF from 2012/2014 to 2016/2018 was associated with higher levels of neurodegeneration markers in 2016 (Table 2C). Participants in the quartile with the greatest decline in PEF had significantly higher concentrations of p-Tau181 (β = 0.11, p = 0.009), NfL (β = 0.18, p \u0026lt; 0.001), and GFAP (β = 0.09, p = 0.008) compared with those in the quartile with the smallest decline. When modeled continuously, each 1-SD greater decline in PEF was associated with higher levels of p-Tau181 (β = 0.05, p = 0.002), NfL (β = 0.07, p \u0026lt; 0.001), and GFAP (β = 0.05, p = 0.0002). No significant associations were observed for the Aβ42/40 ratio.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImpaired lung function, AD biomarkers and risk of dementia:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIndividuals with impaired lung function (ILF: % predicted PEF \u0026lt; 80) had higher prevalence of dementia (OR=1.63, 95%CI = [1.04, 2.55]; p=0.0300) and CIND (OR=1.82, 95%CI = [1.47, 2.25]; p \u0026lt;.0001) in 2014 compared to those with normal PEF (% predicted PEF\u0026nbsp;≥\u0026nbsp;80) after adjustment for all covariates. Among those participants without dementia in 2014 (n=3969, combined Normal and CIND), 6.9% (n=272) developed dementia over 6 years of follow-up. Individuals with ILF in 2014 had higher risk of developing dementia (HR=1.74, 95%CI = [1.34, 2.25]; p\u0026lt;.0001) compared to those with normal PEF after adjusting for all covariates. Including the AD biomarkers in the model as covariates modestly attenuated the strength of association between ILF and incident dementia (HR=1.67, 95%CI = [1.29, 2.17]; p=0.0001).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAD biomarkers mediated the association between ILF and incident dementia over 6 years of follow up:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCausal mediation analysis was conducted to evaluate the role of AD biomarkers in the association between baseline impaired lung function (ILF) and incident dementia over a 6-year follow-up period among 3969 individuals without dementia in 2014 survey. Among the AD biomarkers associated with baseline ILF, plasma NfL and serum p-Tau 181 demonstrated partial mediation of this relationship in independent causal mediation models. Plasma NfL accounted for 7.3% of the total association between ILF and dementia (p = 0.01; Figure 2), while serum p-Tau181 mediated 4.9% of the association (p = 0.05; figure not shown) after accounting for baseline age, sex, race, BMI, smoking status, education, comorbidity index and \u003cem\u003eAPOE e4\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003eIn a sensitivity analysis using dementia follow-up from 2016, we evaluated the mediation effect of NfL. The estimated proportion of the effect mediated increased slightly from 7.3% to 8.1%, although its statistical significance was attenuated (p-value increased from 0.01 to 0.07). Despite this, both the natural direct effect (OR = 1.55, p = 0.049) and the natural indirect effect (OR = 1.03, p = 0.044) remained statistically significant.\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eIn this study of older adults from the Health and Retirement Study, impaired lung function was associated with elevated levels of key AD blood biomarkers including NfL and p-Tau 181 and increased risk of developing dementia over a 6-year follow-up period. To our knowledge, this is the first study to establish an association between impaired lung function and circulating AD protein biomarkers. Notably, we found that plasma NfL and serum p-Tau 181 partially mediated the association between baseline impaired lung function and future risk of dementia, suggesting a potential neurodegenerative pathway linking respiratory dysfunction to cognitive decline.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBlood biomarkers of AD/ADRD have gained significant attention in recent years due to their potential clinical utility in early identification and risk classification for neurodegeneration, dementia, and ultimately, Alzheimer’s disease\u003csup\u003e13,19\u003c/sup\u003e. Previous studies of AD biomarkers demonstrated that the cardiovascular and metabolic risk factors including BMI, renal function and vascular risk factors such as hypertension and diabetes affect the distribution of levels of AD biomarkers in blood\u003csup\u003e34\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e20,35\u003c/sup\u003e. Our study marks the first attempt to investigate the effect of lung function on blood biomarkers of AD. We demonstrated that lower baseline PEF is associated with higher levels of NfL and p-Tau 181 after two- four years of follow-up. Additionally, a greater decline in PEF over 2 years is associated with elevated levels of NfL, p-Tau 181 and GFAP, indicating that respiratory health may contribute to neurodegenerative pathology. Prior cohort studies have established a link between impaired lung function and neuropathological changes, such as reduced brain volume and increased white matter lesions, suggesting potential mechanisms through which respiratory health may influence future cognitive decline\u003csup\u003e36-38\u003c/sup\u003e. Our findings extend this evidence by demonstrating a link between impaired lung function and elevated levels of blood biomarkers of neuropathology, suggesting that neurodegenerative pathways linking impaired respiratory health and greater risk of dementia.\u0026nbsp;Consistent with our results, a meta-analysis reported that lower FEV\u003csub\u003e1\u003c/sub\u003e and FVC were significantly associated with reduced neuroimaging markers of brain integrity, including total brain, gray matter, and hippocampal volumes, as well as greater white matter hyperintensity burden\u003csup\u003e11\u003c/sup\u003e. A large longitudinal study in the UK Biobank also established association of restrictive and obstructive impairment in lung function with all-cause dementia and brain MRI structural features of dementia\u003csup\u003e5\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe found that baseline impaired lung function is associated with higher odds of having CIND and dementia. In our study, individuals with impaired lung function (PEF \u0026lt; 80%) had a 74% higher risk of developing dementia over a six-year follow-up period. Our findings are consistent with several reports of an association between better lung function and reduced dementia rate in other cohort studies\u003csup\u003e1,6,10\u003c/sup\u003e. Investigations in a younger cohort in the ARIC study with a longer follow-up also showed that individuals with impaired baseline lung function and restrictive/obstructive lung diseases have higher odds of cognitive impairment and dementia in later life\u003csup\u003e8,9\u003c/sup\u003e. Lutsey et al.\u003csup\u003e9\u003c/sup\u003e reported that restrictive lung diseases, including idiopathic pulmonary fibrosis, were associated with a 58% increased risk of dementia or mild cognitive impairment (MCI), while obstructive lung diseases, such as COPD, were linked to a 33% higher risk. Another recent study in the ARIC cohort by Shrestha et al. with extended follow-up data reported that better lung function—measured by FEV\u003csub\u003e1\u003c/sub\u003e, FVC, and FEV\u003csub\u003e1\u003c/sub\u003e/FVC ratio—was associated with a slower cognitive decline across multiple domains and reduced dementia rate\u003csup\u003e7\u003c/sup\u003e. \u0026nbsp;Additionally, prospective analyses from the CARDIA study, which followed participants from young adulthood to midlife, demonstrated that lower cumulative pulmonary function (FEV₁\u0026nbsp;and FVC measured repeatedly over 20 years) was associated with higher midlife cognitive performance. Specifically, cumulative FEV₁\u0026nbsp;and FVC were linked to better executive function (Stroop test) and psychomotor speed/attention (Digit Symbol Substitution Test (DSST)), even after adjusting for age, sex, race, smoking, and comorbidities. Notably, lower cumulative FEV₁\u0026nbsp;also showed a marginal association with higher verbal memory (RAVLT), suggesting lung health may differentially impact cognitive domains\u003csup\u003e4\u003c/sup\u003e.\u0026nbsp;The Rotterdam Study showed that the FVC but not FEV\u003csub\u003e1\u003c/sub\u003e or ratio\u0026nbsp;(PRISm (FEV\u003csub\u003e1\u003c/sub\u003e/FVC≥70% and FEV\u003csub\u003e1\u003c/sub\u003e \u0026lt; 80% predicted))\u0026nbsp;to be associated with dementia, independent of COPD\u003csup\u003e1\u003c/sup\u003e. They found that\u0026nbsp;participants with FVC % predicted values in the lowest quartile compared to those in the highest quartile were at increased risk of all cause dementia (adjusted HR = 2.28; 95% CI = 1.31-3.98) and AD (HR = 2.13; 95% CI= 1.13–4.02),\u0026nbsp;but no significant association was observed between FEV\u003csub\u003e1\u003c/sub\u003e and FEV\u003csub\u003e1\u003c/sub\u003e/FVC ratio with incident all cause dementia or AD\u003csup\u003e1\u003c/sup\u003e. These findings highlight that early-life impaired lung health, particularly restrictive lung function, increases susceptibility to cognitive impairment and dementia.\u003c/p\u003e\n\u003cp\u003eWe demonstrated, for the first time, that plasma neurofilament light (NfL) and serum phosphorylated tau 181 (p-Tau 181) were identified as partial mediators, accounting for 7.3% and 5% of the association between impaired lung function and dementia risk, respectively, suggesting a potential biological pathway linking ILF to neurodegeneration. Though AD biomarkers measured concurrently when follow-up of dementia started. We performed sensitivity analysis following up participants after AD biomarker measures and observed that impaired lung function associated with incident dementia and NfL moderately mediated the association.\u0026nbsp;These findings add to the growing body of evidence linking respiratory health to cognitive decline.\u0026nbsp;A study examining the correlation between physical activity, serum NfL concentration, and cognitive decline found that participants with high levels of serum NfL who engaged in medium and high physical activity had a slower rate of cognitive decline compared to those with low physical activity\u003csup\u003e39\u003c/sup\u003e. This might suggest the potential influence of physical activity on improved lung function in mitigating the impact of Alzheimer's disease pathology on cognitive function. Also, previous studies indicate that chronic hypoxia from respiratory illnesses such as COPD and sleep apnea can cause cognitive deficits, affecting attention, memory, and executive function\u003csup\u003e40\u003c/sup\u003e. This evidence highlights the need for clinical assessment of patients with lung function decline or COPD who have symptoms of neurodegeneration\u003csup\u003e41\u003c/sup\u003e. Evidence from a recent study on COVID-19 patients showed that higher GFAP levels at follow-up were associated with mild cognitive dysfunction. Since COVID-19 primarily affects respiratory function, its long-term impact on neuroinflammation and neurodegeneration has raised concerns about its potential role in Alzheimer’s disease (AD) development\u003csup\u003e42,43\u003c/sup\u003e. In our study, we observed that AD-related biomarkers—particularly those reflecting general neurodegeneration—partially mediated the relationship between impaired lung function and increased dementia risk in older adults. These findings are consistent with a hypothesized pathway in which lung impairment contributes to neurodegenerative processes, possibly through mechanisms involving hypoxia and systemic inflammation\u003csup\u003e20,44\u003c/sup\u003e. However, the complexity of these interactions suggests that additional factors may be involved, warranting further investigation to fully understand the underlying mechanisms. These findings highlight the importance of early detection of cognitive impairment through blood-based biomarkers in individuals with impaired lung function.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStrengths and limitations:\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA major strength of this study is the availability of repeated measures of lung function, and cognitive function in a nationally representative sample of older adults, along with interim AD biomarker assessments. Additionally, the study enhances the robustness of the findings by effectively controlling for multiple confounding variables associated with both AD protein biomarkers and lung function. However, there are several limitations. First, only PEF was available as a measure of lung function, which may not fully capture respiratory impairment. Future studies incorporating more sensitive measures, such as FEV\u003csub\u003e1\u003c/sub\u003e and FVC, are warranted. Second, AD biomarkers were measured at a single time point, limiting the ability to assess the longitudinal relationship between lung function decline and changes in AD biomarker levels. Third, in this study we investigated only four key AD-related biomarkers (Aβ42/40, p-Tau 181, GFAP, and NfL), which, while informative, do not capture the full spectrum of vascular dysfunction, neuroinflammation, or other potential pathways linking respiratory health to dementia. Future research should incorporate a broader panel of biomarkers, including markers of endothelial dysfunction, systemic inflammation, and cerebrovascular health, to better characterize the biological mechanisms underlying this association.\u0026nbsp;\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn the Health and Retirement Study, impaired lung function was associated with elevated levels of key neuropathology biomarkers in blood, with NfL and p-Tau 181 partially mediating its association with risk of dementia. Our study findings highlight the importance of monitoring AD protein biomarkers in individuals with impaired respiratory health, which may help identify those at higher risk for cognitive decline and support timely interventions to mitigate neurodegenerative processes. These results warrant the need for further research to explore additional molecular biomarkers that mediate the association between impaired respiratory health and future risk of cognitive impairment and dementia.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability statement:\u0026nbsp;\u003c/strong\u003eWe used the HRS publicly available datasets and sensitive biomarker data for this study analysis. This data can be found here: https://hrsdata.isr.umich.edu/data-products/public-survey-data and https://hrsdata.isr.umich.edu/dataproducts/sensitive-health and can be accessed by completing required data use agreement.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics statement:\u0026nbsp;\u003c/strong\u003eThe venous blood study involving human samples was approved by University of Minnesota Institutional Review Board.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSV: Conceptualization, formal analysis and methodology, writing – original draft, review and editing\u003c/p\u003e\n\u003cp\u003eEC: Data collection, Writing – critical review and editing.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eJKK: Data development, Writing – critical review and editing.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eJF: Data collection, Writing – critical review and editing.\u003c/p\u003e\n\u003cp\u003eDJ: Analysis methodology, Writing – critical review and editing.\u003c/p\u003e\n\u003cp\u003eWG: Analysis methodology, Writing – critical review and editing.\u003c/p\u003e\n\u003cp\u003eBT: Data collection, conceptualization, Writing – critical review and editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclosure of Potential Conflicts of Interest:\u003c/strong\u003e The authors confirm that research was carried out without any affiliations or financial associations that could be perceived as potential conflicts of interest.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThe author(s) acknowledge financial backing for the study, writing, and/or publication of this article. 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Health and retirement study: candidate gene and SNP data description. \u003cem\u003eHealth Retire Study, Univ Mich, Ann Arbor, MI\u003c/em\u003e (2014). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://hrsonline isr umich edu/sitedocs/genetics/candidategene/CandidateGeneSNPDataDescription\u003c/span\u003e\u003cspan address=\"http://hrsonline isr umich edu/sitedocs/genetics/candidategene/CandidateGeneSNPDataDescription\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e \u003cem\u003eGoogle Scholar Article Location\u003c/em\u003e. .\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFaul, J., Smith, J. \u0026amp; Zhao, W. Health and Retirement Study: Candidate genes for cognition/behavior. \u003cem\u003eAnn Arbor MI: Univ. Michigan\u003c/em\u003e ; (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHoost, S. S. et al. Effects of Vascular Risk Factors on the Association of Blood-Based Biomarkers with Alzheimer's Disease. \u003cem\u003eMedical Res. archives Sep.\u003c/em\u003e \u003cb\u003e11\u003c/b\u003e (9). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.18103/mra.v11i9.4468\u003c/span\u003e\u003cspan address=\"10.18103/mra.v11i9.4468\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePichet Binette, A. et al. Confounding factors of Alzheimer's disease plasma biomarkers and their impact on clinical performance. \u003cem\u003eAlzheimer's \u0026amp; dementia: J. Alzheimer's Association Apr\u003c/em\u003e. \u003cb\u003e19\u003c/b\u003e (4), 1403\u0026ndash;1414. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/alz.12787\u003c/span\u003e\u003cspan address=\"10.1002/alz.12787\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuo, X. et al. Midlife respiratory function related to white matter lesions and lacunar infarcts in late life: the Prospective Population Study of Women in Gothenburg, Sweden. \u003cem\u003eStroke Jul\u003c/em\u003e. \u003cb\u003e37\u003c/b\u003e (7), 1658\u0026ndash;1662. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1161/01.STR.0000226403.00963.af\u003c/span\u003e\u003cspan address=\"10.1161/01.STR.0000226403.00963.af\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2006).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMurray, A. D. et al. Brain White Matter Hyperintensities: Relative Importance of Vascular Risk Factors in Nondemented Elderly People. \u003cem\u003eRadiology\u003c/em\u003e 2005/10/01 2005;\u003cb\u003e237\u003c/b\u003e(1):251\u0026ndash;257. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1148/radiol.2371041496\u003c/span\u003e\u003cspan address=\"10.1148/radiol.2371041496\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiao, D. et al. Lower pulmonary function and cerebral subclinical abnormalities detected by MRI: the Atherosclerosis Risk in Communities study. \u003cem\u003eChest Jul\u003c/em\u003e. \u003cb\u003e116\u003c/b\u003e (1), 150\u0026ndash;156. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1378/chest.116.1.150\u003c/span\u003e\u003cspan address=\"10.1378/chest.116.1.150\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (1999).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDesai, P. et al. Examination of Neurofilament Light Chain Serum Concentrations, Physical Activity, and Cognitive Decline in Older Adults. \u003cem\u003eJAMA Netw. open Mar.\u003c/em\u003e \u003cb\u003e1\u003c/b\u003e (3), e223596. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1001/jamanetworkopen.2022.3596\u003c/span\u003e\u003cspan address=\"10.1001/jamanetworkopen.2022.3596\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZheng, G. Q., Wang, Y. \u0026amp; Wang, X. T. Chronic hypoxia-hypercapnia influences cognitive function: a possible new model of cognitive dysfunction in chronic obstructive pulmonary disease. \u003cem\u003eMed. Hypotheses\u003c/em\u003e. \u003cb\u003e71\u003c/b\u003e (1), 111\u0026ndash;113. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.mehy.2008.01.025\u003c/span\u003e\u003cspan address=\"10.1016/j.mehy.2008.01.025\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2008).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAreza-Fegyveres, R., Kairalla, R. A., Carvalho, C. R. R. \u0026amp; Nitrini, R. Cognition and chronic hypoxia in pulmonary diseases. \u003cem\u003eDementia \u0026amp; neuropsychologia\u003c/em\u003e. Jan-Mar. ;4(1):14\u0026ndash;22. (2010). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1590/s1980-57642010dn40100003\u003c/span\u003e\u003cspan address=\"10.1590/s1980-57642010dn40100003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMiners, S., Kehoe, P. G. \u0026amp; Love, S. Cognitive impact of COVID-19: looking beyond the short term. \u003cem\u003eAlzheimer's Res. \u0026amp; Therapy\u003c/em\u003e 2020/12/30 2020;\u003cb\u003e12\u003c/b\u003e(1):170. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s13195-020-00744-w\u003c/span\u003e\u003cspan address=\"10.1186/s13195-020-00744-w\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOrtiz, G. G. et al. Alzheimer's Disease and SARS-CoV-2: Pathophysiological Analysis and Social Context. \u003cem\u003eBrain sciences Oct.\u003c/em\u003e \u003cb\u003e18\u003c/b\u003e (10). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/brainsci12101405\u003c/span\u003e\u003cspan address=\"10.3390/brainsci12101405\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim, K. Y., Shin, K. Y. \u0026amp; Chang, K. A. GFAP as a Potential Biomarker for Alzheimer's Disease: A Systematic Review and Meta-Analysis. \u003cem\u003eCells May\u003c/em\u003e. \u003cb\u003e4\u003c/b\u003e (9). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/cells12091309\u003c/span\u003e\u003cspan address=\"10.3390/cells12091309\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eDescriptive statistics of participant characteristics in the HRS 2012/2014 survey across lung function groups\u003c/div\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eEstimates\u003c/div\u003e\n \u003cdiv class=\"SimplePara\"\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD / Frequency (%)\u003c/div\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eOverall\u003c/div\u003e\n \u003cdiv class=\"SimplePara\"\u003en\u0026thinsp;=\u0026thinsp;4072\u003c/div\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eNormal\u003c/div\u003e\n \u003cdiv class=\"SimplePara\"\u003e(Predicted PEF\u0026thinsp;\u0026ge;\u0026thinsp;80%)\u003c/div\u003e\n \u003cdiv class=\"SimplePara\"\u003en\u0026thinsp;=\u0026thinsp;3191 (78.4%)\u003c/div\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eImpaired lung function\u003c/div\u003e\n \u003cdiv class=\"SimplePara\"\u003e(Predicted PEF\u0026thinsp;\u0026lt;\u0026thinsp;80%)\u003c/div\u003e\n \u003cdiv class=\"SimplePara\"\u003en\u0026thinsp;=\u0026thinsp;881 (21.6%)\u003c/div\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003ep value\u003c/div\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"Bold\"\u003e% predicted PEF 2012/2014\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e98.96\u0026thinsp;\u0026plusmn;\u0026thinsp;25.74\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e108.65\u0026thinsp;\u0026plusmn;\u0026thinsp;19.08\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e63.85\u0026thinsp;\u0026plusmn;\u0026thinsp;13.03\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u0026lt;.0001\u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"Bold\"\u003eAge 2014 (years)\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e66.19\u0026thinsp;\u0026plusmn;\u0026thinsp;10.29\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e66.32\u0026thinsp;\u0026plusmn;\u0026thinsp;10.26\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e65.70\u0026thinsp;\u0026plusmn;\u0026thinsp;10.38\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.11\u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"Bold\"\u003eSex (% females)\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e2397 (58.9%)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e1891 (59.3%)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e506 (57.4%)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.3300\u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"Bold\"\u003eRace/ethnicity\u003c/span\u003e\u003c/div\u003e\n \u003cdiv class=\"SimplePara\"\u003eBlacks\u003c/div\u003e\n \u003cdiv class=\"SimplePara\"\u003eHispanics\u003c/div\u003e\n \u003cdiv class=\"SimplePara\"\u003eOther\u003c/div\u003e\n \u003cdiv class=\"SimplePara\"\u003eWhites\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e667 (16.4%)\u003c/div\u003e\n \u003cdiv class=\"SimplePara\"\u003e601 (14.8%)\u003c/div\u003e\n \u003cdiv class=\"SimplePara\"\u003e129 (3.2%)\u003c/div\u003e\n \u003cdiv class=\"SimplePara\"\u003e2675 (65.7%)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e499 (15.6%)\u003c/div\u003e\n \u003cdiv class=\"SimplePara\"\u003e453 (14.2%)\u003c/div\u003e\n \u003cdiv class=\"SimplePara\"\u003e91 (2.9%)\u003c/div\u003e\n \u003cdiv class=\"SimplePara\"\u003e2148 (67.3%)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e168 (19.07%)\u003c/div\u003e\n \u003cdiv class=\"SimplePara\"\u003e148 (16.8%)\u003c/div\u003e\n \u003cdiv class=\"SimplePara\"\u003e38 (4.3%)\u003c/div\u003e\n \u003cdiv class=\"SimplePara\"\u003e527 (59.8%)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.0003\u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"Bold\"\u003eSmoking status 2014\u003c/span\u003e\u003c/div\u003e\n \u003cdiv class=\"SimplePara\"\u003eCurrent\u003c/div\u003e\n \u003cdiv class=\"SimplePara\"\u003eFormer\u003c/div\u003e\n \u003cdiv class=\"SimplePara\"\u003eNever\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e501 (12.3%)\u003c/div\u003e\n \u003cdiv class=\"SimplePara\"\u003e991 (24.3%)\u003c/div\u003e\n \u003cdiv class=\"SimplePara\"\u003e2580 (63.4%)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e284 (8.9%)\u003c/div\u003e\n \u003cdiv class=\"SimplePara\"\u003e790 (24.8%)\u003c/div\u003e\n \u003cdiv class=\"SimplePara\"\u003e2117 (66.3%)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e217 (24.6%)\u003c/div\u003e\n \u003cdiv class=\"SimplePara\"\u003e201 (22.8%)\u003c/div\u003e\n \u003cdiv class=\"SimplePara\"\u003e463 (52.6%)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;.0001\u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"Bold\"\u003eEducation\u003c/span\u003e\u003c/div\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"Bold\"\u003e0\u0026ndash;11 y\u003c/span\u003e\u003c/div\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"Bold\"\u003e12 y\u003c/span\u003e\u003c/div\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"Bold\"\u003e13-15y\u003c/span\u003e\u003c/div\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"Bold\"\u003e16\u0026thinsp;+\u0026thinsp;y\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e747 (18.3%)\u003c/div\u003e\n \u003cdiv class=\"SimplePara\"\u003e1252 (30.8%)\u003c/div\u003e\n \u003cdiv class=\"SimplePara\"\u003e1023 (25.1%)\u003c/div\u003e\n \u003cdiv class=\"SimplePara\"\u003e1050(25.8%)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e503 (15.8%)\u003c/div\u003e\n \u003cdiv class=\"SimplePara\"\u003e944 (29.6%)\u003c/div\u003e\n \u003cdiv class=\"SimplePara\"\u003e843 (26.4%)\u003c/div\u003e\n \u003cdiv class=\"SimplePara\"\u003e901 (28.2%)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e244 (27.7%)\u003c/div\u003e\n \u003cdiv class=\"SimplePara\"\u003e308 (35.0%)\u003c/div\u003e\n \u003cdiv class=\"SimplePara\"\u003e180 (20.4%)\u003c/div\u003e\n \u003cdiv class=\"SimplePara\"\u003e149 (16.9%)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;.0001\u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"Bold\"\u003eBody Mass Index (kg/m\u003c/span\u003e\u003csup\u003e\u003cspan class=\"Bold\"\u003e2\u003c/span\u003e\u003c/sup\u003e\u003cspan class=\"Bold\"\u003e) 2012/2014\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e30.56\u0026thinsp;\u0026plusmn;\u0026thinsp;6.91\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e30.65\u0026thinsp;\u0026plusmn;\u0026thinsp;6.74\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e30.27\u0026thinsp;\u0026plusmn;\u0026thinsp;7.50\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.1400\u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"Bold\"\u003eComorbidity Index 2014\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e1.88\u0026thinsp;\u0026plusmn;\u0026thinsp;1.29\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e1.83\u0026thinsp;\u0026plusmn;\u0026thinsp;1.26\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e2.06\u0026thinsp;\u0026plusmn;\u0026thinsp;1.36\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;.0001\u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"Bold\"\u003eeGFR (Cys and CR) 2016\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e68.79\u0026thinsp;\u0026plusmn;\u0026thinsp;21.08\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e68.61\u0026thinsp;\u0026plusmn;\u0026thinsp;21.17\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e69.44\u0026thinsp;\u0026plusmn;\u0026thinsp;20.76\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.3100\u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"Bold\"\u003eInflammatory latent variable 2016\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.51\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.51\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.49\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.0060\u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"BoldItalic\"\u003eAPOE e4\u003c/span\u003e \u003cspan class=\"Bold\"\u003eallele, %Yes\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e1122 (27.6%)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e898 (28.1%)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e224 (25.4%)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.1100\u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"Bold\"\u003eCognitive function 2014 (0\u0026ndash;27)\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e15.55\u0026thinsp;\u0026plusmn;\u0026thinsp;4.22\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e15.89\u0026thinsp;\u0026plusmn;\u0026thinsp;4.14\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e14.32\u0026thinsp;\u0026plusmn;\u0026thinsp;4.28\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;.0001\u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"Bold\"\u003eCognition category in 2014\u003c/span\u003e\u003c/div\u003e\n \u003cdiv class=\"SimplePara\"\u003eNormal\u003c/div\u003e\n \u003cdiv class=\"SimplePara\"\u003eCIND\u003c/div\u003e\n \u003cdiv class=\"SimplePara\"\u003eDementia\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e3388 (83.2%)\u003c/div\u003e\n \u003cdiv class=\"SimplePara\"\u003e581 (14.3%)\u003c/div\u003e\n \u003cdiv class=\"SimplePara\"\u003e103 (2.5%)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e2742 (85.9%)\u003c/div\u003e\n \u003cdiv class=\"SimplePara\"\u003e380 (11.9%)\u003c/div\u003e\n \u003cdiv class=\"SimplePara\"\u003e69 (2.2%)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e646 (73.3%)\u003c/div\u003e\n \u003cdiv class=\"SimplePara\"\u003e201 (22.8%)\u003c/div\u003e\n \u003cdiv class=\"SimplePara\"\u003e34 (3.9%)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;.0001\u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"Bold\"\u003eIncident dementia*\u003c/span\u003e n\u0026thinsp;=\u0026thinsp;3969\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e272 (6.9%)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e172 (5.5%)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e99 (11.6%)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;.0001\u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003e\u003cspan class=\"Bold\"\u003eNote\u003c/span\u003e:* Dementia was based on a cognitive score of 1\u0026ndash;6 in 2016, 2018 or 2020 among participants who had normal cognition or CIND in 2014.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"char\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eA: Association between percent predicted PEF in 2012/2014 (baseline) and blood biomarkers of AD in 2016\u003c/div\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eAD biomarkers\u003c/div\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eQ1\u003c/div\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u0026beta; (SE), p value\u003c/div\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eQ2\u003c/div\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u0026beta; (SE), p value\u003c/div\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eQ3\u003c/div\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u0026beta; (SE), p value\u003c/div\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eQ4\u003c/div\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e1 SD unit\u003c/div\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u0026beta; (SE), p value\u003c/div\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eA\u0026beta; 42/40 ratio ~ % pred PEF\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.04 (0.05), 0.42\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.03 (0.05), 0.48\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.008 (0.04), 0.86\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eReference\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e-0.01 (0.02), 0.4600\u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003ep-Tau 181 ~ % pred PEF\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.15 (0.04), 0.0002\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.09 (0.04), 0.03\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.11 (0.04), 0.007\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eReference\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e-0.04 (0.01), 0.0040\u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eNfL ~ % pred PEF\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.11 (0.03), 0.0006\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.09 (0.03), 0.005\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.02 (0.03), 0.63\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eReference\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e-0.05 (0.01), \u0026lt;\u0026thinsp;.0001\u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eGFAP ~ % pred PEF\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.10 (0.03), 0.004\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.09 (0.03), 0.008\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.07 (0.03), 0.03\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eReference\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e-0.04 (0.01), 0.001\u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003eNote: AD biomarkers were log transformed and standardized to approximate the normal distribution. Models were adjusted for age, sex, race, education, BMI, smoking status, comorbidity index 2014, inflammatory latent variable 2016, eGFR 2016, and \u003cspan class=\"Italic\"\u003eAPOE e4\u003c/span\u003e allele.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eB: Association between impaired lung function in 2012/2014 and blood biomarkers of AD in 2016\u003c/div\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u0026beta; (SE), p value\u003c/div\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eA\u0026beta; 42/40 ratio\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.03 (0.04); 0.52\u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003ep-Tau 181\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.10 (0.03); 0.004\u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eNfL\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.09 (0.03); 0.002\u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eGFAP\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.04 (0.03); 0.22\u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\"\u003eNote: AD biomarkers were log transformed and standardized to approximate the normal distribution. Models were adjusted for age, sex, race, education, BMI, smoking status, comorbidity index, inflammatory latent variable, eGFR, and \u003cspan class=\"Italic\"\u003eAPOE e4\u003c/span\u003e allele.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eC: Association of decline in % predicted PEF from 2014 to 2016 as quartile and continuous variable with blood biomarkers of AD in 2016\u003c/div\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eAD biomarkers\u003c/div\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eQ1\u003c/div\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eQ2\u003c/div\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u0026beta; (SE), p value\u003c/div\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eQ3\u003c/div\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u0026beta; (SE), p value\u003c/div\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eQ4\u003c/div\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u0026beta; (SE), p value\u003c/div\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e1 SD unit\u003c/div\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u0026beta; (SE), p value\u003c/div\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eA\u0026beta; 42/40 ratio\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eReference\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e-0.02 (0.05), 0.69\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e-0.04 (0.05), 0.42\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e-0.03 (0.05), 0.51\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e-0.003 (0.02), 0.8500\u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003ep-Tau 181\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eReference\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e-0.02 (0.04), 0.59\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.08 (0.04), 0.07\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.11 (0.04), 0.009\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.05 (0.02), 0.0020\u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eNfL\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eReference\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.05 (0.03), 0.12\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.08 (0.03), 0.02\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.18 (0.03), \u0026lt;\u0026thinsp;.0001\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.07 (0.01), \u0026lt;\u0026thinsp;.0001\u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eGFAP\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eReference\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e-0.06 (0.03), 0.08\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.01 (0.03), 0.86\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.09 (0.03), 0.008\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.05 (0.01), 0.0002\u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003eNote: AD biomarkers were log transformed and standardized to approximate the normal distribution. Models were adjusted for % predicted PEF in 2014 (baseline), age, sex, race, education, BMI, smoking status, comorbidity index 2014, inflammatory latent variable 2016, eGFR 2016, and \u003cspan class=\"Italic\"\u003eAPOE e4\u003c/span\u003e allele.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Lung function, Dementia, AD biomarkers, Older adults, Mechanism","lastPublishedDoi":"10.21203/rs.3.rs-8311583/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8311583/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground and Objectives: \u003c/strong\u003eImpaired lung function (ILF) has been associated with cognitive decline and dementia risk in multiple cohorts, yet the role of circulating Alzheimer disease (AD) biomarkers in this relationship is not well understood. We aim to assess the associations between ILF and AD biomarkers and to determine whether these biomarkers mediate the relationship between ILF and incident dementia.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eSerum p-Tau181 and plasma Aβ42/40, NfL, and GFAP were measured in 4,072 participants (mean age 66 ± 10; 59% women) in the 2016 Health and Retirement Study. Peak Expiratory Flow (PEF) was assessed in 2012/2014, and cognitive function was measured at four time points between 2014 and 2020 (every two years) to determine dementia status. Impaired lung function (ILF) was defined as predicted PEF \u0026lt;80%. Multivariable regression examined associations between lung function and AD biomarkers; causal mediation analysis evaluated their role in linking lung function to incident dementia.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eIn total, 881 (21.6%) participants had ILF and 272 (6.8%) participants developed dementia. After adjusting for demographics, education, BMI, smoking, comorbidities, inflammation, eGFR and \u003cem\u003eAPOE e4\u003c/em\u003e, ILF was associated with a higher risk of dementia (HR=1.74; 95% CI (1.34, 225)). Individuals with ILF had 0.10 SD higher NfL (SE= 0.03; p= 0.004) and 0.09 SD higher p-Tau 181 (SE= 0.03; p= 0.002) compared to those without ILF. NfL mediated 7.3% (p=0.01) of the total effect of ILF on dementia, while p-Tau 181 mediated 5% (p=0.05) of this association.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDiscussion: \u003c/strong\u003eILF was associated with elevated levels of neurodegeneration markers NfL and p-Tau 181, which partially mediated its relationship with dementia risk. These findings highlight the importance of monitoring blood protein biomarkers in individuals with impaired lung health to facilitate early interventions.\u003c/p\u003e","manuscriptTitle":"Impaired lung function is associated with elevated blood biomarkers of AD/ADRD: Unraveling the interplay with risk of dementia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-30 00:18:00","doi":"10.21203/rs.3.rs-8311583/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-17T08:28:42+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-13T20:03:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"2044960629880810531228484838191939627","date":"2026-01-27T16:32:33+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-19T09:14:59+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-12-18T18:26:16+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-09T07:32:48+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-09T07:29:39+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-12-09T00:18:51+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"cffc292b-16a5-45ff-9fb4-4fb5edc23d14","owner":[],"postedDate":"December 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":59938014,"name":"Health sciences/Biomarkers"},{"id":59938015,"name":"Health sciences/Diseases"},{"id":59938016,"name":"Health sciences/Medical research"},{"id":59938017,"name":"Health sciences/Neurology"},{"id":59938018,"name":"Biological sciences/Neuroscience"}],"tags":[],"updatedAt":"2026-04-20T16:13:17+00:00","versionOfRecord":{"articleIdentity":"rs-8311583","link":"https://doi.org/10.1038/s41598-026-48115-z","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2026-04-14 15:59:49","publishedOnDateReadable":"April 14th, 2026"},"versionCreatedAt":"2025-12-30 00:18:00","video":"","vorDoi":"10.1038/s41598-026-48115-z","vorDoiUrl":"https://doi.org/10.1038/s41598-026-48115-z","workflowStages":[]},"version":"v1","identity":"rs-8311583","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8311583","identity":"rs-8311583","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}