Associations of chronic inflammatory diseases with brain health: a prospective cohort study

Associations of chronic inflammatory diseases with brain health: a prospective cohort study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Associations of chronic inflammatory diseases with brain health: a prospective cohort study fei han, Ruixia Zhang, Fan Yi, Yiheng Mao, Zhilong Chen, Zhengxing Huang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7893606/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Chronic inflammatory diseases (CIDs) are common and characterized by persistent systemic inflammation, but their broader impact on brain health remains poorly understood. We aimed to examine the associations of 14 common CIDs with brain health in the UK Biobank participants. Methods We conducted a prospective cohort study using data from 502,411 UK Biobank participants aged 40–69 years at baseline (2006–2010), with follow-up through linked hospital, primary care, and mortality records. Fourteen common CIDs were identified. Neurological outcomes included stroke, dementia, Parkinson’s disease, cognitive performance, and magnetic resonance imaging derived phenotypes. Associations were assessed using Cox proportional hazards models and logistic regressions. Mediation analyses were conducted to evaluate the potential role of systemic inflammatory markers in these associations. Results Among 502,411 participants (56.5 ± 8.1 years; 54.4% women), 44.4% had at least one CID. Over a mean follow-up of 8.9 years, CID presence was associated with increased risks of stroke (hazard ratio [HR] 1.46; 95% confidence interval, 1.43–1.50), dementia (HR 1.43, 1.36–1.50), and Parkinson’s disease (HR 1.34, 1.26–1.43), with stronger associations among individuals younger than 65 years. Type 1 diabetes conferred the highest risk, with approximately threefold increased risks of stroke and dementia. Up to 24% of observed associations were mediated by neutrophil-based inflammatory indices. In contrast, allergic rhinitis was linked to reduced neurological risk and better cognition. Neuroimaging analysis revealed widespread white matter microstructural disruption across CIDs. Conclusions Our findings highlight the broader neurological impact of CIDs and reveal substantial heterogeneity across CID subtypes. Early identification and management of CIDs may help mitigate long-term risks to brain health. Chronic inflammatory disease Dementia Cognition Stroke Brain structure Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Chronic inflammatory diseases (CIDs) encompass a broad spectrum of autoimmune, allergic, and chronic infectious conditions characterized by persistent immune dysregulation and systemic inflammation. Despite varied clinical manifestations, sustained inflammation is a unifying hallmark, contributing to widespread health consequences beyond localized symptoms( 1 ). The rising prevalence and established links to cardiovascular and metabolic diseases have underscored CIDs as a major public health concern( 2 , 3 ). However, their broader impact on brain health remains underexplored. Age-related neurological disorders, such as stroke, dementia, and Parkinson’s disease, are among the leading causes of disability and mortality globally( 4 ). Increasing evidence implicates peripheral inflammation as a critical contributor to their pathogenesis, potentially through endothelial dysfunction, blood brain barrier disruption, microglial activation, and dysregulated immune responses( 5 – 8 ). Yet it remains unclear whether these effects are driven by specific inflammatory diseases or reflect a generalized state of chronic low-grade inflammation. Addressing this gap is critical for identifying both shared and disease-specific pathways, with important implications for prevention and intervention strategies. Most prior studies have focused on individual diseases, often with limited sample sizes and short follow-up durations. Few have comprehensively evaluated the associations between a wide spectrum of CIDs and brain health. To address these limitations, we conducted a large-scale prospective analysis using data from the UK Biobank, integrating clinical diagnoses, cognitive assessments, and neuroimaging measures. We systematically examined associations between 14 common CIDs, both individually and collectively, and major neurological outcomes. This study aims to provide a comprehensive framework and novel insights into the broader impacts of chronic inflammation on brain health across the aging continuum. Methods Study design and participants This study utilized data from the UK Biobank ( https://www.ukbiobank.ac.uk/ ), a prospective population-based cohort of over 500,000 participants aged 40–69 years recruited between 2006 and 2010. Baseline assessments included questionnaires, physical measures, and blood sampling, with longitudinal follow-up via linked electronic health records. The present analysis included 502,411 individuals. Ethical approval for the UK Biobank was obtained from the Northwest Multi-Centre Research Ethics Committee (REC reference 11/NW/0382). Written informed consent was provided by all participants. This study was conducted under UK Biobank application number 89757 and adhered to the principles of the Declaration of Helsinki. The overall study framework is illustrated in supplementary Figure S1 . Exposures: Chronic inflammatory diseases This study investigated 14 common CIDs with over 1,000 recorded cases in the UK Biobank, including allergic rhinitis, ankylosing spondylitis, asthma, celiac disease, Crohn’s disease, dermatitis and eczema, gout, osteoarthritis, oesophagitis, psoriasis, psoriatic arthropathy, rheumatoid arthritis, type 1 diabetes mellitus (T1DM), and ulcerative colitis. Diagnoses were identified through a combination of self-reports, hospital inpatient records, and primary care data, using the International Classification of Diseases, 10th Revision (ICD-10), and earlier ICD-9 coding (supplementary Table S1 ). To capture both disease-specific and cumulative inflammatory effects, we evaluated each CID individually, constructed a composite exposure variable indicating the presence of any CID, and additionally calculated the total number of distinct CIDs per participant as a proxy for inflammatory disease burden. Outcomes Neurological diseases The primary outcomes were incident stroke, dementia, and Parkinson’s disease, identified via linked hospital admissions, primary care records, and death registries using ICD-9 and ICD-10 codes, supplemented by non-cancer-specific self-reported conditions (supplementary Table S1 ). Each disease was analyzed separately to capture potentially distinct associations with CIDs. Time to event was calculated from baseline to the earliest occurrence of diagnosis, death, or end of follow-up. Cognitive performance Cognitive function was assessed using a battery of validated tests administered by the UK Biobank, encompassing working memory, prospective memory, attention, processing speed, and executive function. The following assessments were included: fluid intelligence score (Gf), maximum digits remembered correctly (DSpan), prospective memory result (PMR), number of symbol digit matches made correctly (SDMT-C), mean time to correctly identify matches (MTCM), duration to complete alphanumeric path (APCT), and errors before selecting correct item in alphanumeric path (APE). Full descriptions, including test interpretation and field identifiers, are provided in supplementary Table S2 . Brain image-derived phenotype (IDPs) We utilized data from 33,224 individuals of European ancestry in the UK Biobank who underwent brain magnetic resonance scanning (Field 20252). Imaging data were processed following standardized protocols described by Smith et al.( 9 ) From over 3,000 measures, we curated a subset of 587 biologically relevant IDPs, comprising 203 cortical, 24 subcortical, and 360 white matter tract connectivity metrics. Cortical measures were extracted using FreeSurfer based on the Desikan-Killiany atlas, while subcortical volumes were derived from both the FIRST tool and FreeSurfer’s automated segmentation. White matter connectivity features were obtained from diffusion magnetic resonance imaging through tract-based spatial statistics and probabilistic tractography. IDPs were further categorized into 13 anatomical regions and nine measurement categories, namely, regional and tissue volume, cortical area, cortical thickness, white matter tract fractional anisotropy (FA), mean diffusivity (MD), orientation dispersion (OD), mode of anisotropy (MO), isotropic volume fraction (ISOVF), and intracellular volume fraction (ICVF), ensuring comprehensive coverage of brain structural phenotypes. A complete list of the 587 IDPs, along with their categories and descriptions, is provided in supplementary Table S3. Covariates Baseline covariates were selected to account for potential confounding factors. These included age, sex, smoking status, alcohol consumption, education, income, and body mass index (BMI). Multicollinearity was assessed using variance inflation factors, all of which were below 10, indicating no significant collinearity. Blood samples were analyzed at the UK Biobank central laboratory within 24 hours of collection. Systemic inflammation was assessed using baseline levels of neutrophils, lymphocytes, monocytes, platelets, and C-reactive protein (CRP). Based on these parameters, four composite inflammatory indices were calculated: neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), lymphocyte-to-monocyte ratio (LMR), and systemic immune-inflammation index (SII). Additional details are available in the UK Biobank online showcase and protocol. Statistical analysis Baseline characteristics were summarized as counts and percentages for categorical variables, and as means with standard deviations (SDs) for continuous variables. Associations between each of the 14 individual CIDs, as well as cumulative CID burden (categorized as 0, one, two, or ≥ three CIDs), and neurological outcomes were examined. Cox proportional hazards models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for risk of stroke, dementia, and Parkinson’s disease. Kaplan-Meier curves were generated to visualize cumulative incidence. To explore potential mechanistic pathways, mediation analyses were conducted using systemic inflammatory markers as candidate mediators (supplementary Figure S2 ). Sensitivity analyses included stratification by age group (< 65 vs. ≥65 years) to examine potential effect modification, and repetition of all models after excluding individuals with follow-up time less than one year to reduce potential reverse causality. For cognitive performance and IDPs, logistic regression models were applied. All models were adjusted for age, sex, smoking, alcohol use, education, income, and BMI. Continuous variables were standardized as Z-scores within the cohort to ensure comparability across different scales. Multiple imputations with chained equations were used to impute missing values using the MICE package in R (R Foundation for Statistical Computing). All statistical analyses were performed using R Software, version 4.3.2. Multiple comparisons were addressed using the false discovery rate (FDR) correction, with statistical significance defined as FDR corrected P < 0.05. Results Baseline characteristics of participants A total of 502,411 participants were included (mean age 56.5 ± 8.1 years; 54.4% female) (Table 1 ). Among them, 223,245 (44.4%) had at least one CID, with 30.5% having one, 10.4% two, and 3.6% three or more. Compared with participants without CIDs, those with CIDs were older, more often female, had lower socioeconomic and educational status, higher BMI, and were more likely to smoke and consume alcohol (all P < 0.001). Inflammatory markers, including neutrophils, monocytes, platelets, and CRP, as well as derived indices (NLR, PLR, and SII) were elevated in the CID group. Lymphocytes were similar between groups, whereas LMR was lower in those with CIDs. Over a mean follow-up of 8.9 ± 3.8 years, 5.1% of participants developed stroke, 1.4% developed dementia, and 0.8% developed Parkinson’s disease. Table 1 Baseline characteristics of participants (N = 502,411) Overall (N = 502,411) CID cohort (N = 223,245) Non-CID cohort (N = 279,166) P value Age, mean (SD), years 56.5 (8.1) 57.8 (7.9) 55.5 (8.1) < 0.001 Female (%) 273,325 (54.40) 122,093 (54.69) 151,232 (54.17) < 0.001 Low family income * (%) 97,180 (19.34) 50,278 (22.52) 46,902 (16.80) < 0.001 College or university education (%) 161,128 (32.07) 65,206 (29.21) 95,922 (34.36) < 0.001 Smoking status (%) < 0.001 Never 273,475 (54.43) 115,708 (51.83) 157,767 (56.51) Former 173,024 (34.44) 83,285 (37.30) 89,739 (32.14) Current 52,962 (10.54) 22,864 (10.24) 30,098 (10.78) Alcohol intake frequency (%) < 0.001 Never or special occasions only 217,175 (43.23) 94,455 (42.31) 122,720 (43.96) 1–3 Times a month 129,271 (25.73) 55,695 (24.95) 73,576 (26.36) Once or twice a week 55,840 (11.11) 24,725 (11.08) 31,115 (11.15) 3–4 Times a week 57,996 (11.54) 27,864 (12.48) 30,132 (10.79) Daily or most daily 40,627 (8.09) 19,875 (8.90) 20,752 (7.43) Body mass index, mean (SD), kg/m 2 27.43(4.80) 28.21 (5.12) 26.81 (4.44) < 0.001 Follow up time, mean (SD), years 8.9 (3.8) 9.2 (3.4) 8.6 (4.0) < 0.001 Incident neurological disorders (%) Stroke 25,427 (5.07) 14,878 (6.66) 10,549 (3.78) < 0.001 Dementia 6,986 (1.39) 4,332 (1.94) 2,654 (0.95) < 0.001 Parkinson’s disease 3,786 (0.75) 2,229 (1.00) 1,557 (0.56) < 0.001 Number of CIDs (%) None 279,167 0 279,167 1 152,964 152,964 0 2 52,305 52,305 0 ≥ 3 17,976 17,976 0 Systemic inflammatory marker, mean (SD) Lymphocytes, ×10 9 /L 1.97 (1.17) 1.97 (1.18) 1.96 (1.17) 0.321 Monocytes, ×10 9 /L 0.48 (0.27) 0.49(0.34) 0.47 (0.21) < 0.001 Neutrophils, ×10 9 /L 4.23 (1.42) 4.32 (1.47) 4.15 (1.37) < 0.001 Platelet, ×10 9 /L 252.94 (60.06) 255.20(61.40) 251.13 (58.90) < 0.001 C-reactive protein, mg/L 2.60 (4.36) 3.05 (4.90) 2.24 (3.83) < 0.001 Neutrophil-to-lymphocyte ratio 2.36 (1.28) 2.43(1.40) 2.31 (1.17) < 0.001 Lymphocyte-to-monocyte ratio 4.63 (4.27) 4.57 (4.81) 4.68 (3.78) < 0.001 Platelet-to-lymphocyte ratio 141.97 (69.54) 143.87 (80.78) 140.45(58.98) < 0.001 Systemic immune inflammation index † 599.71(378.00) 623.16 (420.48) 580.94 (339.01) < 0.001 Abbreviations: CID, chronic inflammatory disease. * Low family income level indicates less than £18 000 of average total household income before tax. † Systemic immune-inflammation index = neutrophil×platelets/lymphocytes. Abbreviations: AR, allergic rhinitis; CD, Crohn’s disease; CED, celiac disease; DE, dermatitis and eczema; OES, oesophagitis; OST, osteoarthritis; RA, rheumatoid arthritis; T1DM, type 1 diabetes mellitus. Associations with neurological disorders All three neurological disorders occurred more frequently among individuals with CIDs (Fig. 1 , supplementary Figures S3-S5). Presence of any CID was associated with significantly increased risks of stroke (HR 1.46, 95% CI 1.43 to 1.50), dementia (HR 1.43, 95% CI 1.36 to 1.50), and Parkinson’s disease (HR 1.34, 95% CI 1.26 to 1.43). The risks of stroke and dementia increased progressively with greater CID burden. Compared with individuals without CIDs, those with one, two, and three or more CIDs had incrementally higher risks of stroke (HRs: 1.35, 1.58, and 1.94, respectively) and dementia (HRs: 1.30, 1.55, and 1.89, respectively). Most individual CIDs were associated with increased risks of at least one neurological outcome. Type 1 diabetes mellitus showed the strongest associations, conferring threefold higher risks of stroke (HR 3.03, 95% CI 2.84 to 3.25) and dementia (HR 3.21, 95% CI 2.85 to 3.62), and a moderately elevated risk of Parkinson’s disease (HR 1.50, 95% CI 1.20 to 1.89). Several other CIDs, including dermatitis and eczema, rheumatoid arthritis, oesophagitis, osteoarthritis, and ulcerative colitis, were consistently linked to increased risks across all three disorders. Certain CIDs, such as ankylosing spondylitis, asthma, celiac disease, Crohn’s disease, psoriasis, and psoriatic arthropathy were associated with higher risk for only one or two outcomes. In contrast, allergic rhinitis was consistently associated with reduced risk for stroke (HR 0.75, 95% CI 0.70 to 0.79), dementia (HR, 0.82, 95% CI 0.74 to 0.92), and Parkinson’s disease (HR 0.86, 95% CI 0.74 to 0.99). Gout was also associated with a lower risk of Parkinson’s disease (HR 0.82, 95% CI 0.70 to 0.96). In age-stratified analyses, associations between CIDs and neurological disorders were generally stronger among individuals younger than 65 years (supplementary Table S4). Compared with those aged ≥ 65 years, individuals aged < 65 years had higher HRs for any CID in relation to stroke (1.56 vs. 1.31; 19.1% higher relative risk), dementia (1.62 vs. 1.30; 24.6% higher), and Parkinson’s disease (1.48 vs. 1.20; 23.3% higher). Additionally, results remained consistent when excluding participants with less than one year of follow-up, reducing the likelihood of reverse causality (supplementary Table S5). Mediation analysis Among the 378 pathways involving 14 CIDs, nine inflammatory mediators, and three neurological diseases, neutrophils, NLR, and SII demonstrated the strongest and most consistent mediating effects (Fig. 2 , supplementary Table S6). The neutrophil-based markers mediated a substantial proportion of the associations with neurological outcomes, particularly in stroke, where mediation proportions ranged from 3.29% to 24.25%. In dementia and Parkinson’s disease, the corresponding proportions ranged from 2.14% to 21.41% and 1.10% to 21.90%, respectively. Importantly, stroke showed the highest overall level of mediation by inflammatory markers, indicating that peripheral inflammation may play a particularly crucial role in the development of cerebrovascular events. This was further supported by the prominent roles of CRP, which showed strong mediation effects (ranging from 1.64% to 24.14%) specifically in stroke, but not in dementia and Parkinson’s disease. In contrast, lymphocyte and LMR contributed minimally across outcomes, with mediation proportions generally below 1%. Associations with cognitive performance CIDs demonstrated varied associations with specific cognitive domains (Fig. 3 , supplementary Table S7). Allergic rhinitis was the only condition consistently associated with better cognitive performance, showing protective associations with fluid intelligence (Gf, odds ratio [OR] 1.17, 95% CI 1.14 to 1.20), working memory (DSpan, OR 1.11, 95% CI 1.07 to 1.14), prospective memory (PMR, OR 1.24, 95% CI 1.08 to 1.42), and executive function (APCT, OR 0.92, 95% CI 0.89 to 0.96). In contrast, T1DM was linked to significantly poorer attention and processing speed (MTCM, OR 1.26, 95% CI 1.20 to 1.32), while rheumatoid arthritis was linked to reduced processing speed (SDMT-C, OR 0.79, 95% CI 0.68 to 0.92), indicating increased vulnerability to cognitive impairment. Associations with structural brain alterations Seven CIDs were significantly associated with alterations in brain structure, predominantly affecting white matter microstructure (Fig. 4 , supplementary Table S8). T1DM showed the most widespread associations, characterized by reduced FA and ICVF, along with increased MD and ISOVF, indicative of diffuse microstructural disruption. Reduced FA was observed across multiple anatomical fibers, including commissural fibers (e.g., genu of the corpus callosum, OR 0.58, 95% CI 0.46 to 0.72), projection fibers (e.g., left posterior thalamic radiation, OR 0.62, 95% CI 0.49 to 0.77), as well as association fibers (e.g., left superior fronto-occipital fasciculus, OR 0.60, 95% CI 0.48 to 0.75). Rheumatoid arthritis, osteoarthritis, and gout were linked to more focal changes, particularly in the internal and external capsules. T1DM was also associated with reduced volumes in the bilateral thalamus and cerebellar white matter, as well as enlargement of the third ventricle (volume of the third ventricle, OR 1.47, 95%CI 1.21 to 1.79), reflecting both region-specific and global brain atrophy. Structural alterations in cortical areas and regional brain volumes were otherwise minimal across the remaining CIDs. Although allergic rhinitis was associated with better cognitive performance, it did not exhibit corresponding protective associations with brain structure. Discussion This large-scale population-based study provides a comprehensive evaluation of the associations between 14 chronic inflammatory diseases and brain health. Most CIDs were related to increased risks of stroke, dementia, and Parkinson’s disease, with stroke showing the most robust associations. Notably, type 1 diabetes mellitus, rheumatoid arthritis, osteoarthritis, dermatitis and eczema, oesophagitis, and ulcerative colitis demonstrated consistent associations with elevated risk across all outcomes. These patterns were particularly evident among younger individuals. CIDs were also linked to poorer cognitive function and disruption of white matter microstructure. In contrast, allergic rhinitis emerged as an exception, showing consistently protective associations, including lower risks of all three neurological disorders and better cognitive performance. These findings underscore both shared and heterogeneous pathways through which chronic inflammation influences brain health, shaped by generalized systemic inflammatory burden and distinct disease-specific immune mechanisms. The observed associations between CIDs and brain outcomes likely reflect a convergence of vascular and neurodegenerative mechanisms triggered by persistent immune activation. Among the three neurological diseases, stroke exhibited the strongest and most consistent associations with CIDs, with the greatest proportion mediated by inflammatory markers, suggesting a particular vulnerability of the cerebrovascular system inflammatory injury( 10 ). Chronic inflammation may impair vascular integrity through endothelial dysfunction and oxidative stress and may further promote platelet activation and a prothrombotic state( 5 ). Supporting this, neuroimaging analyses revealed more pronounced disruption of white matter microstructure, which is more susceptible to microvascular damage, compared to changes in cortical area or regional brain volumes. These findings point to inflammation-induced vascular dysfunction as a major pathway linking CIDs to brain pathology( 11 ). Beyond vascular injury, our findings also implicate neurodegenerative mechanisms, as evidenced by associations with dementia and Parkinson’s disease. Chronic peripheral inflammation may disrupt blood-brain barrier integrity, enabling peripheral immune cells and cytokines to infiltrate the brain parenchyma, activate microglia, and impair neuronal homeostasis. These processes contribute to key pathological features of neurodegeneration, including amyloid-β deposition, tau hyperphosphorylation, and dopaminergic neuron loss ( 12 – 15 ). Prior Mendelian randomization studies have yielded inconsistent evidence regarding the causal relationship between certain CIDs and neurological disorders( 8 , 16 – 20 ), suggesting that their neurological consequences may be more likely driven by acquired inflammatory processes rather than genetic predisposition. This interpretation is further supported by our mediation analyses, which showed that circulating inflammatory markers accounted for a meaningful proportion of the associations across multiple CIDs, reinforcing the role of systemic inflammation as a downstream mediator. Notably, the mediating effects were primarily driven by neutrophil-related markers, whereas lymphocyte-based indices contributed minimally. This pattern aligns with previous studies identifying neutrophils and SII as strong predictors of brain disorders ( 21 , 22 ), and suggests that innate immune activation, rather than adaptive immune responses, may be the dominant inflammatory pathway driving brain injury in the context of chronic systemic inflammation. While systemic inflammation broadly contributes to neurological risk, our findings also reveal substantial heterogeneity across different CIDs, suggesting that disease-specific immune mechanisms may play a critical role. This variation may reflect differences in immune polarization, the balance between systemic and localized inflammation, and organ-specific pathways( 23 – 25 ). For instance, the pronounced neurological burden of T1DM may result from a combination of autoimmune inflammation and chronic hyperglycemia( 26 ). Rheumatoid arthritis, a seropositive autoimmune disease marked by widespread immune activation, was associated with increased risks across all neurological outcomes( 27 – 29 ). In contrast, psoriatic arthropathy and ankylosing spondylitis, both classified as seronegative spondyloarthropathies, were linked to higher stroke risk but not to dementia or Parkinson’s disease, possibly indicating a more vascular-restricted inflammatory profile. Other conditions, such as inflammatory bowel diseases, may lead to increased intestinal permeability, and influence brain health via gut-brain axis interactions, underscoring the multifaceted nature of inflammatory neurobiology( 30 – 32 ). Interestingly, allergic rhinitis emerged as an outlier, associated with lower risks of all three neurological diseases, as well as better cognitive performance. Although existing evidence remains conflicting( 33 – 35 ), this protective profile may relate to its Th2-dominant immune response, characterized by upregulation of anti-inflammatory cytokines such as Interleukin-4 and Interleukin-13( 36 ). These mediators may counterbalance the proinflammatory Th1- and Th17-pathways predominant in other CIDs, thereby limiting systemic immune activation, preserving vascular and blood-brain barrier integrity, and mitigating neuroinflammatory damage. Collectively, these observations reinforce the notion that not all chronic inflammatory conditions confer equal neurological risk, and that immune phenotype may play a pivotal role, underscoring the potential of targeted immunomodulation in preserving brain health( 37 , 38 ). Notably, age-stratified analysis showed that the associations between CIDs and neurological outcomes were stronger among younger individuals (< 65 years), with substantially higher HRs compared to older adults. This pattern may reflect greater immune responsiveness earlier in life, during which systemic inflammation may exert a more pronounced impact on vascular and neuronal function. In contrast, these effects may be attenuated in older adults due to immunosenescence or masked by competing age-related comorbidities. These findings suggest that age modifies the neurological impact of chronic inflammation and highlight the importance of early identification and intervention in younger individuals with CIDs. This study benefits from the large sample size and extensive phenotyping of the UK Biobank, allowing for a comprehensive assessment of diverse CIDs and brain health outcomes. The inclusion of autoimmune, allergic, and metabolic conditions enables broad evaluation across distinct inflammatory profiles. The use of advanced neuroimaging offers structural correlates of inflammation-related brain changes, while mediation analyses offer insight into potential systemic inflammatory mechanisms. This study is subject to certain limitations. Firstly, UK Biobank is based on volunteer participants rather than random selection, which may introduce volunteer bias. However, the large sample size of the cohort is likely to mitigate this concern, and prior studies have shown that such bias has minimal impact on the validity of exposure–outcome associations. Secondly, the observational design limits causal inference, and residual confounding from unmeasured factors cannot be excluded. Thirdly, the absence of detailed immune phenotyping constrains mechanistic interpretation, and the lack of longitudinal imaging data precludes assessment of temporal changes. Fourthly, the predominantly European ancestry of the cohort may limit generalizability to other populations. Conclusions In this large, population-based study, chronic inflammatory diseases were associated with increased risks of stroke, dementia, Parkinson’s disease, cognitive decline, and white matter microstructural disruption. Allergic rhinitis was consistently associated with lower neurological risk and better cognitive outcomes, suggesting a potential protective role. These findings underscore the neurological impact of systemic inflammation and highlight the heterogeneity across CID subtypes. Early identification and targeted management of individuals with CIDs may help mitigate long-term risks to brain health. Future research should aim to refine immune phenotyping and elucidate modifiable inflammatory pathways to inform prevention and neuroprotective strategies. Abbreviations CIDs, Chronic inflammatory diseases T1DM, type 1 diabetes mellitus Gf, fluid intelligence score DSpan, maximum digits remembered correctly PMR, prospective memory result SDMT-C, number of symbol digit matches made correctly MTCM, mean time to correctly identify matches APCT, duration to complete alphanumeric path APE, errors before selecting correct item in alphanumeric path IDP, image-derived phenotype FA, fractional anisotropy MD, mean diffusivity OD, orientation dispersion MO, mode of anisotropy ISOVF, isotropic volume fraction ICVF, intracellular volume fraction BMI, body mass index CRP, C-reactive protein NLR, neutrophil-to-lymphocyte ratio PLR, platelet-to-lymphocyte ratio LMR, lymphocyte-to-monocyte ratio SII, systemic immune-inflammation index SD, standard deviations HR, hazard ratio CI, confidence interval FDR, false discovery rate Declarations Ethics approval and consent to participate This research was conducted using the UK Biobank Resource ( http://www.ukbiobank.ac.uk/ ) under application number 89757. This study was performed under generic ethical approval obtained by UK Biobank from the National Health Service National Research Ethics Service (approval letter ref 11/NW/0382). Consent for publication All the authors have consented for publication Competing interests The authors declare no competing interests. Funding This study was supported by the National Natural Science Foundation of China (No. 82271368). Acknowledgements The authors are grateful to all the staff and participants of the UK Biobank. Data availability The data supporting the findings of this study are available from the UK Biobank ( http://www.ukbiobank.ac.uk/ ), but there are restrictions on the availability of these data, which have been used under the license for the current study and are therefore not publicly available. References Furman D, Campisi J, Verdin E, Carrera-Bastos P, Targ S, Franceschi C, et al. Chronic inflammation in the etiology of disease across the life span. Nat Med. 2019;25(12):1822–32. 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CNS Neurosci Ther. 2025;31(1):e70204. Huang YY, Wang HF, Wu BS, Ou YN, Ma LZ, Yang L, et al. Clinical laboratory tests and dementia incidence: A prospective cohort study. J Affect Disord. 2024;351:1–7. Li D, Hong X, Chen T. Association Between Rheumatoid Arthritis and Risk of Parkinson's Disease: A Meta-Analysis and Systematic Review. Front Neurol. 2022;13:885179. Sharma SR, Chen Y. Rheumatoid Arthritis and Cognitive Impairment or Dementia: An Updated Review of Epidemiological Data. J Alzheimers Dis. 2023;95(3):769–83. Al-Ewaidat OA, Naffaa MM. Stroke risk in rheumatoid arthritis patients: exploring connections and implications for patient care. Clin Exp Med. 2024;24(1):30. Ronnow Sand J, Troelsen FS, Horvath-Puho E, Henderson VW, Sorensen HT, Erichsen R. Risk of dementia in patients with inflammatory bowel disease: a Danish population-based study. Aliment Pharmacol Ther. 2022;56(5):831–43. Sun J, Halfvarson J, Appelros P, Bergman D, Ebrahimi F, Roelstraete B, et al. Long-term Risk of Stroke in Patients With Inflammatory Bowel Disease: A Population-Based, Sibling-Controlled Cohort Study, 1969–2019. Neurology. 2023;101(6):e653–64. Riggott C, Ford AC, Gracie DJ. Review article: The role of the gut-brain axis in inflammatory bowel disease and its therapeutic implications. Aliment Pharmacol Ther. 2024;60(9):1200–14. Crans Yoon AM, Chiu V, Rana JS, Sheikh J. Association of allergic rhinitis, coronary heart disease, cerebrovascular disease, and all-cause mortality. Ann Allergy Asthma Immunol. 2016;117(4):359–64. e1. Joh HK, Kwon H, Son KY, Yun JM, Cho SH, Han K, et al. Allergic Diseases and Risk of Incident Dementia and Alzheimer's Disease. Ann Neurol. 2023;93(2):384–97. Su CH, Huang KH, Yang Y, Gau SY, Chung NJ, Wu PT, et al. Cumulative Dose Effects of H1 Antihistamine Use on the Risk of Dementia in Patients With Allergic Rhinitis. J Allergy Clin Immunol Pract. 2024;12(8):2155–65. Wang Y, Song XY, Wei SZ, Wang HR, Zhang WB, Li YM, et al. Brain response in allergic rhinitis: Profile and proposal. J Neurosci Res. 2023;101(4):480–91. Jairath V, Acosta Felquer ML, Cho RJ. IL-23 inhibition for chronic inflammatory disease. Lancet. 2024;404(10463):1679–92. Mannion JM, McLoughlin RM, Lalor SJ. The Airway Microbiome-IL-17 Axis: a Critical Regulator of Chronic Inflammatory Disease. Clin Rev Allergy Immunol. 2023;64(2):161–78. Supplementary Files GraphicAbstract.jpg Supplement.pdf Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7893606","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":539375033,"identity":"aee7987f-37c8-419b-ab94-82dcd06f3065","order_by":0,"name":"fei 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07:08:54","extension":"xml","order_by":19,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":113004,"visible":true,"origin":"","legend":"","description":"","filename":"JTRMD25183130structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7893606/v1/7b8c1e3809f3cf2300756783.xml"},{"id":95845069,"identity":"95555b20-7efe-453e-bdbb-674b861329fc","added_by":"auto","created_at":"2025-11-13 14:48:14","extension":"html","order_by":20,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":121818,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7893606/v1/fd3ae658deb360c75c1de322.html"},{"id":95845055,"identity":"36e43468-b199-4ce8-a03c-325308422bdb","added_by":"auto","created_at":"2025-11-13 14:48:13","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1146972,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHazard ratios for neurological diseases among individuals with chronic inflammatory diseases (CIDs). (A) Stroke; (B) Dementia; (C) Parkinson’s disease (PD). \u003c/strong\u003eHazard ratios and 95% confidence intervals were estimated using Cox proportional hazards model adjusted for age and sex. For \"Any CID\" and \"Number of CIDs\", the reference group consisted of individuals without any CID. For each specific CID, the reference group consisted of individuals without the corresponding condition.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7893606/v1/e27d6ca0b77f943c18a8c64b.jpg"},{"id":96241103,"identity":"87ded518-5db4-48fe-9c9b-cd2588eb16f0","added_by":"auto","created_at":"2025-11-19 07:10:11","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":600888,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMediation effects of chronic inflammatory diseases (CIDs) on stroke, dementia, and Parkinson’s disease via systemic inflammatory markers. \u003c/strong\u003eThis bubble plot illustrates the significant positive indirect effects of CIDs on neurological disorders mediated through peripheral inflammatory markers. Each bubble represents a CID-marker pair with a significant mediation effect (false discovery rate corrected \u003cem\u003eP \u003c/em\u003e\u0026lt; 0.05). Bubble color intensity corresponds to the magnitude of the indirect effect (β), and the bubble size reflects the proportion of the total effect mediated by the corresponding marker. The proportion mediated is expressed as a percentage, calculated as the ratio of the indirect effect to the total effect. Abbreviations: CRP, C-reactive protein; LMR, lymphocyte-to-monocyte ratio; LYM, lymphocytes; MONO, monocytes; NEUT, neutrophils; NLR, neutrophil-to-lymphocyte ratio; PLR, platelet-to-lymphocyte ratio; PLT, platelet; SII, systemic immune-inflammation index.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7893606/v1/d18e21800065a10174246dec.jpg"},{"id":95845056,"identity":"5aeed701-905e-4b57-a610-7547c66b29ac","added_by":"auto","created_at":"2025-11-13 14:48:13","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2757152,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAssociations between chronic inflammatory diseases (CIDs) and cognitive performance across multiple domains.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Heatmap displaying odds ratios and 95% confidence intervals for associations between each CID and performance across seven cognitive tests. Each cell represents an association that remained statistically significant after false discovery rate correction (\u003cem\u003eP\u003c/em\u003e \u0026lt;0.05). Colors indicate the direction of association: red denotes worse cognitive performance (detrimental effect), while blue donates better performance (potentially protective effect). All models were adjusted for age, sex, body mass index, smoking status, alcohol consumption, income level, and educational attainment.\u003cbr\u003e\n(B) Cognitive tests included in the analysis, along with corresponding abbreviations, cognitive domain assessed, and interpretation of effect direction.\u003c/p\u003e\n\u003cp\u003eAbbreviations: AR, allergic rhinitis; CD, Crohn’s disease; CED, celiac disease; DE, dermatitis and eczema; OES, oesophagitis; OST, osteoarthritis; RA, rheumatoid arthritis; T1DM, type 1 diabetes mellitus.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7893606/v1/7fd7c5222040916eb66e4d3e.jpg"},{"id":95845058,"identity":"96866fe3-02b8-431f-aae1-494d21cdcc15","added_by":"auto","created_at":"2025-11-13 14:48:14","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1230829,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAssociations between chronic inflammatory diseases (CIDs) and brain imaging-derived phenotypes (IDPs). \u003c/strong\u003eBubble plots display significant associations (false discovery rate [FDR]-corrected P \u0026lt; 0.05) between CIDs and IDPs, grouped by brain structural categories. Bubble size represents the statistical significance, expressed as -log\u003csub\u003e10\u003c/sub\u003e(FDR \u003cem\u003eP\u003c/em\u003e value), and bubble color indicates effect size, shown as log\u003csub\u003e2\u003c/sub\u003e odds ratio (OR), with blue indicating negative associations (OR\u0026lt;1) and red indicating positive associations (OR\u0026gt;1). The x-axis shows individual IDPs within each structural category, and the y-axis lists specific CIDs. Only associations meeting the FDR threshold are shown. All models were adjusted for age, sex, body mass index, smoking status, alcohol consumption, income level, and educational attainment. Notably, type 1 diabetes mellitus showed the most widespread associations, primarily involving white matter microstructural alterations. Abbreviations: AR, allergic rhinitis; AST, asthma; OST, osteoarthritis; PS, psoriatic arthropathy; RA, rheumatoid arthritis; T1DM, type 1 diabetes mellitus; IDP, imaging-derived phenotype; WM, white matter; FA, fractional anisotropy; MD, mean diffusivity; MO, mode of anisotropy; ICVF, intracellular volume fraction; ISOVF, isotropic volume fraction; OD, orientation dispersion index; L, left; R, right.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7893606/v1/1347bdcfa980eb0ad29564ab.jpg"},{"id":96454436,"identity":"aa72f974-bd3a-4813-b9ba-07c9c1e3c1a2","added_by":"auto","created_at":"2025-11-21 10:02:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6743695,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7893606/v1/00f2bb56-379a-4c22-b189-46c04142f2f2.pdf"},{"id":95845066,"identity":"ff23c91d-fffb-4e63-8b27-746dcf09c1bc","added_by":"auto","created_at":"2025-11-13 14:48:14","extension":"jpg","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":2867573,"visible":true,"origin":"","legend":"","description":"","filename":"GraphicAbstract.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7893606/v1/82a1d10d823aae2d893e1bdd.jpg"},{"id":95845059,"identity":"d771c40b-36fa-4848-91ed-f9160c34ffdd","added_by":"auto","created_at":"2025-11-13 14:48:14","extension":"pdf","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":1699318,"visible":true,"origin":"","legend":"","description":"","filename":"Supplement.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7893606/v1/84df74de326e46c94a746ff0.pdf"}],"financialInterests":"","formattedTitle":"Associations of chronic inflammatory diseases with brain health: a prospective cohort study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eChronic inflammatory diseases (CIDs) encompass a broad spectrum of autoimmune, allergic, and chronic infectious conditions characterized by persistent immune dysregulation and systemic inflammation. Despite varied clinical manifestations, sustained inflammation is a unifying hallmark, contributing to widespread health consequences beyond localized symptoms(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). The rising prevalence and established links to cardiovascular and metabolic diseases have underscored CIDs as a major public health concern(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). However, their broader impact on brain health remains underexplored.\u003c/p\u003e\u003cp\u003eAge-related neurological disorders, such as stroke, dementia, and Parkinson\u0026rsquo;s disease, are among the leading causes of disability and mortality globally(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Increasing evidence implicates peripheral inflammation as a critical contributor to their pathogenesis, potentially through endothelial dysfunction, blood brain barrier disruption, microglial activation, and dysregulated immune responses(\u003cspan additionalcitationids=\"CR6 CR7\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Yet it remains unclear whether these effects are driven by specific inflammatory diseases or reflect a generalized state of chronic low-grade inflammation. Addressing this gap is critical for identifying both shared and disease-specific pathways, with important implications for prevention and intervention strategies. Most prior studies have focused on individual diseases, often with limited sample sizes and short follow-up durations. Few have comprehensively evaluated the associations between a wide spectrum of CIDs and brain health.\u003c/p\u003e\u003cp\u003eTo address these limitations, we conducted a large-scale prospective analysis using data from the UK Biobank, integrating clinical diagnoses, cognitive assessments, and neuroimaging measures. We systematically examined associations between 14 common CIDs, both individually and collectively, and major neurological outcomes. This study aims to provide a comprehensive framework and novel insights into the broader impacts of chronic inflammation on brain health across the aging continuum.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy design and participants\u003c/h2\u003e\u003cp\u003eThis study utilized data from the UK Biobank (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ukbiobank.ac.uk/\u003c/span\u003e\u003cspan address=\"https://www.ukbiobank.ac.uk/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), a prospective population-based cohort of over 500,000 participants aged 40\u0026ndash;69 years recruited between 2006 and 2010. Baseline assessments included questionnaires, physical measures, and blood sampling, with longitudinal follow-up via linked electronic health records. The present analysis included 502,411 individuals. Ethical approval for the UK Biobank was obtained from the Northwest Multi-Centre Research Ethics Committee (REC reference 11/NW/0382). Written informed consent was provided by all participants. This study was conducted under UK Biobank application number 89757 and adhered to the principles of the Declaration of Helsinki. The overall study framework is illustrated in supplementary Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eExposures: Chronic inflammatory diseases\u003c/h3\u003e\n\u003cp\u003eThis study investigated 14 common CIDs with over 1,000 recorded cases in the UK Biobank, including allergic rhinitis, ankylosing spondylitis, asthma, celiac disease, Crohn\u0026rsquo;s disease, dermatitis and eczema, gout, osteoarthritis, oesophagitis, psoriasis, psoriatic arthropathy, rheumatoid arthritis, type 1 diabetes mellitus (T1DM), and ulcerative colitis. Diagnoses were identified through a combination of self-reports, hospital inpatient records, and primary care data, using the International Classification of Diseases, 10th Revision (ICD-10), and earlier ICD-9 coding (supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). To capture both disease-specific and cumulative inflammatory effects, we evaluated each CID individually, constructed a composite exposure variable indicating the presence of any CID, and additionally calculated the total number of distinct CIDs per participant as a proxy for inflammatory disease burden.\u003c/p\u003e\n\u003ch3\u003eOutcomes\u003c/h3\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003eNeurological diseases\u003c/h2\u003e\u003cp\u003eThe primary outcomes were incident stroke, dementia, and Parkinson\u0026rsquo;s disease, identified via linked hospital admissions, primary care records, and death registries using ICD-9 and ICD-10 codes, supplemented by non-cancer-specific self-reported conditions (supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Each disease was analyzed separately to capture potentially distinct associations with CIDs. Time to event was calculated from baseline to the earliest occurrence of diagnosis, death, or end of follow-up.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eCognitive performance\u003c/h3\u003e\n\u003cp\u003eCognitive function was assessed using a battery of validated tests administered by the UK Biobank, encompassing working memory, prospective memory, attention, processing speed, and executive function. The following assessments were included: fluid intelligence score (Gf), maximum digits remembered correctly (DSpan), prospective memory result (PMR), number of symbol digit matches made correctly (SDMT-C), mean time to correctly identify matches (MTCM), duration to complete alphanumeric path (APCT), and errors before selecting correct item in alphanumeric path (APE). Full descriptions, including test interpretation and field identifiers, are provided in supplementary Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eBrain image-derived phenotype (IDPs)\u003c/h2\u003e\u003cp\u003eWe utilized data from 33,224 individuals of European ancestry in the UK Biobank who underwent brain magnetic resonance scanning (Field 20252). Imaging data were processed following standardized protocols described by Smith et al.(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e) From over 3,000 measures, we curated a subset of 587 biologically relevant IDPs, comprising 203 cortical, 24 subcortical, and 360 white matter tract connectivity metrics. Cortical measures were extracted using FreeSurfer based on the Desikan-Killiany atlas, while subcortical volumes were derived from both the FIRST tool and FreeSurfer\u0026rsquo;s automated segmentation. White matter connectivity features were obtained from diffusion magnetic resonance imaging through tract-based spatial statistics and probabilistic tractography. IDPs were further categorized into 13 anatomical regions and nine measurement categories, namely, regional and tissue volume, cortical area, cortical thickness, white matter tract fractional anisotropy (FA), mean diffusivity (MD), orientation dispersion (OD), mode of anisotropy (MO), isotropic volume fraction (ISOVF), and intracellular volume fraction (ICVF), ensuring comprehensive coverage of brain structural phenotypes. A complete list of the 587 IDPs, along with their categories and descriptions, is provided in supplementary Table S3.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eCovariates\u003c/h3\u003e\n\u003cp\u003eBaseline covariates were selected to account for potential confounding factors. These included age, sex, smoking status, alcohol consumption, education, income, and body mass index (BMI). Multicollinearity was assessed using variance inflation factors, all of which were below 10, indicating no significant collinearity. Blood samples were analyzed at the UK Biobank central laboratory within 24 hours of collection. Systemic inflammation was assessed using baseline levels of neutrophils, lymphocytes, monocytes, platelets, and C-reactive protein (CRP). Based on these parameters, four composite inflammatory indices were calculated: neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), lymphocyte-to-monocyte ratio (LMR), and systemic immune-inflammation index (SII). Additional details are available in the UK Biobank online showcase and protocol.\u003c/p\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eBaseline characteristics were summarized as counts and percentages for categorical variables, and as means with standard deviations (SDs) for continuous variables. Associations between each of the 14 individual CIDs, as well as cumulative CID burden (categorized as 0, one, two, or \u0026ge;\u0026thinsp;three CIDs), and neurological outcomes were examined. Cox proportional hazards models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for risk of stroke, dementia, and Parkinson\u0026rsquo;s disease. Kaplan-Meier curves were generated to visualize cumulative incidence. To explore potential mechanistic pathways, mediation analyses were conducted using systemic inflammatory markers as candidate mediators (supplementary Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). Sensitivity analyses included stratification by age group (\u0026lt;\u0026thinsp;65 vs. \u0026ge;65 years) to examine potential effect modification, and repetition of all models after excluding individuals with follow-up time less than one year to reduce potential reverse causality. For cognitive performance and IDPs, logistic regression models were applied. All models were adjusted for age, sex, smoking, alcohol use, education, income, and BMI. Continuous variables were standardized as Z-scores within the cohort to ensure comparability across different scales. Multiple imputations with chained equations were used to impute missing values using the MICE package in R (R Foundation for Statistical Computing).\u003c/p\u003e\u003cp\u003eAll statistical analyses were performed using R Software, version 4.3.2. Multiple comparisons were addressed using the false discovery rate (FDR) correction, with statistical significance defined as FDR corrected \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eBaseline characteristics of participants\u003c/h2\u003e\u003cp\u003eA total of 502,411 participants were included (mean age 56.5\u0026thinsp;\u0026plusmn;\u0026thinsp;8.1 years; 54.4% female) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Among them, 223,245 (44.4%) had at least one CID, with 30.5% having one, 10.4% two, and 3.6% three or more. Compared with participants without CIDs, those with CIDs were older, more often female, had lower socioeconomic and educational status, higher BMI, and were more likely to smoke and consume alcohol (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Inflammatory markers, including neutrophils, monocytes, platelets, and CRP, as well as derived indices (NLR, PLR, and SII) were elevated in the CID group. Lymphocytes were similar between groups, whereas LMR was lower in those with CIDs. Over a mean follow-up of 8.9\u0026thinsp;\u0026plusmn;\u0026thinsp;3.8 years, 5.1% of participants developed stroke, 1.4% developed dementia, and 0.8% developed Parkinson\u0026rsquo;s disease.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBaseline characteristics of participants (N\u0026thinsp;=\u0026thinsp;502,411)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOverall\u003c/p\u003e\u003cp\u003e(N\u0026thinsp;=\u0026thinsp;502,411)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCID cohort\u003c/p\u003e\u003cp\u003e(N\u0026thinsp;=\u0026thinsp;223,245)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNon-CID cohort\u003c/p\u003e\u003cp\u003e(N\u0026thinsp;=\u0026thinsp;279,166)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge, mean (SD), years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e56.5 (8.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e57.8 (7.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e55.5 (8.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e273,325 (54.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e122,093 (54.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e151,232 (54.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow family income\u003csup\u003e*\u003c/sup\u003e (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e97,180 (19.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e50,278 (22.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e46,902 (16.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCollege or university education (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e161,128 (32.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e65,206 (29.21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e95,922 (34.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoking status (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNever\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e273,475 (54.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e115,708 (51.83)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e157,767 (56.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFormer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e173,024 (34.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e83,285 (37.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e89,739 (32.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCurrent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e52,962 (10.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22,864 (10.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e30,098 (10.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlcohol intake frequency (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNever or special occasions only\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e217,175 (43.23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e94,455 (42.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e122,720 (43.96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u0026ndash;3 Times a month\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e129,271 (25.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e55,695 (24.95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e73,576 (26.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOnce or twice a week\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e55,840 (11.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24,725 (11.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e31,115 (11.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u0026ndash;4 Times a week\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e57,996 (11.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27,864 (12.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e30,132 (10.79)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDaily or most daily\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e40,627 (8.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e19,875 (8.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e20,752 (7.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBody mass index, mean (SD), kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e27.43(4.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e28.21 (5.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e26.81 (4.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFollow up time, mean (SD), years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.9 (3.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9.2 (3.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.6 (4.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIncident neurological disorders (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStroke\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e25,427 (5.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14,878 (6.66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10,549 (3.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDementia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6,986 (1.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4,332 (1.94)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2,654 (0.95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParkinson\u0026rsquo;s disease\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3,786 (0.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2,229 (1.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1,557 (0.56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of CIDs (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNone\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e279,167\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e279,167\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e152,964\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e152,964\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e52,305\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e52,305\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17,976\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17,976\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSystemic inflammatory marker, mean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLymphocytes, \u0026times;10\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.97 (1.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.97 (1.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.96 (1.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.321\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMonocytes, \u0026times;10\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.48 (0.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.49(0.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.47 (0.21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNeutrophils, \u0026times;10\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.23 (1.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.32 (1.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.15 (1.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlatelet, \u0026times;10\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e252.94 (60.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e255.20(61.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e251.13 (58.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eC-reactive protein, mg/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.60 (4.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.05 (4.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.24 (3.83)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNeutrophil-to-lymphocyte ratio\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.36 (1.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.43(1.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.31 (1.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLymphocyte-to-monocyte ratio\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.63 (4.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.57 (4.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.68 (3.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlatelet-to-lymphocyte ratio\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e141.97 (69.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e143.87 (80.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e140.45(58.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSystemic immune inflammation index\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e599.71(378.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e623.16 (420.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e580.94 (339.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eAbbreviations: CID, chronic inflammatory disease. \u003csup\u003e*\u003c/sup\u003eLow family income level indicates less than \u0026pound;18 000 of average total household income before tax. \u003csup\u003e\u0026dagger;\u003c/sup\u003eSystemic immune-inflammation index\u0026thinsp;=\u0026thinsp;neutrophil\u0026times;platelets/lymphocytes.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eAbbreviations: AR, allergic rhinitis; CD, Crohn\u0026rsquo;s disease; CED, celiac disease; DE, dermatitis and eczema; OES, oesophagitis; OST, osteoarthritis; RA, rheumatoid arthritis; T1DM, type 1 diabetes mellitus.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eAssociations with neurological disorders\u003c/h2\u003e\u003cp\u003eAll three neurological disorders occurred more frequently among individuals with CIDs (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, supplementary Figures S3-S5). Presence of any CID was associated with significantly increased risks of stroke (HR 1.46, 95% CI 1.43 to 1.50), dementia (HR 1.43, 95% CI 1.36 to 1.50), and Parkinson\u0026rsquo;s disease (HR 1.34, 95% CI 1.26 to 1.43). The risks of stroke and dementia increased progressively with greater CID burden. Compared with individuals without CIDs, those with one, two, and three or more CIDs had incrementally higher risks of stroke (HRs: 1.35, 1.58, and 1.94, respectively) and dementia (HRs: 1.30, 1.55, and 1.89, respectively).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eMost individual CIDs were associated with increased risks of at least one neurological outcome. Type 1 diabetes mellitus showed the strongest associations, conferring threefold higher risks of stroke (HR 3.03, 95% CI 2.84 to 3.25) and dementia (HR 3.21, 95% CI 2.85 to 3.62), and a moderately elevated risk of Parkinson\u0026rsquo;s disease (HR 1.50, 95% CI 1.20 to 1.89). Several other CIDs, including dermatitis and eczema, rheumatoid arthritis, oesophagitis, osteoarthritis, and ulcerative colitis, were consistently linked to increased risks across all three disorders. Certain CIDs, such as ankylosing spondylitis, asthma, celiac disease, Crohn\u0026rsquo;s disease, psoriasis, and psoriatic arthropathy were associated with higher risk for only one or two outcomes. In contrast, allergic rhinitis was consistently associated with reduced risk for stroke (HR 0.75, 95% CI 0.70 to 0.79), dementia (HR, 0.82, 95% CI 0.74 to 0.92), and Parkinson\u0026rsquo;s disease (HR 0.86, 95% CI 0.74 to 0.99). Gout was also associated with a lower risk of Parkinson\u0026rsquo;s disease (HR 0.82, 95% CI 0.70 to 0.96).\u003c/p\u003e\u003cp\u003eIn age-stratified analyses, associations between CIDs and neurological disorders were generally stronger among individuals younger than 65 years (supplementary Table S4). Compared with those aged\u0026thinsp;\u0026ge;\u0026thinsp;65 years, individuals aged\u0026thinsp;\u0026lt;\u0026thinsp;65 years had higher HRs for any CID in relation to stroke (1.56 vs. 1.31; 19.1% higher relative risk), dementia (1.62 vs. 1.30; 24.6% higher), and Parkinson\u0026rsquo;s disease (1.48 vs. 1.20; 23.3% higher). Additionally, results remained consistent when excluding participants with less than one year of follow-up, reducing the likelihood of reverse causality (supplementary Table S5).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eMediation analysis\u003c/h2\u003e\u003cp\u003eAmong the 378 pathways involving 14 CIDs, nine inflammatory mediators, and three neurological diseases, neutrophils, NLR, and SII demonstrated the strongest and most consistent mediating effects (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, supplementary Table S6). The neutrophil-based markers mediated a substantial proportion of the associations with neurological outcomes, particularly in stroke, where mediation proportions ranged from 3.29% to 24.25%. In dementia and Parkinson\u0026rsquo;s disease, the corresponding proportions ranged from 2.14% to 21.41% and 1.10% to 21.90%, respectively. Importantly, stroke showed the highest overall level of mediation by inflammatory markers, indicating that peripheral inflammation may play a particularly crucial role in the development of cerebrovascular events. This was further supported by the prominent roles of CRP, which showed strong mediation effects (ranging from 1.64% to 24.14%) specifically in stroke, but not in dementia and Parkinson\u0026rsquo;s disease. In contrast, lymphocyte and LMR contributed minimally across outcomes, with mediation proportions generally below 1%.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eAssociations with cognitive performance\u003c/h2\u003e\u003cp\u003eCIDs demonstrated varied associations with specific cognitive domains (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, supplementary Table S7). Allergic rhinitis was the only condition consistently associated with better cognitive performance, showing protective associations with fluid intelligence (Gf, odds ratio [OR] 1.17, 95% CI 1.14 to 1.20), working memory (DSpan, OR 1.11, 95% CI 1.07 to 1.14), prospective memory (PMR, OR 1.24, 95% CI 1.08 to 1.42), and executive function (APCT, OR 0.92, 95% CI 0.89 to 0.96). In contrast, T1DM was linked to significantly poorer attention and processing speed (MTCM, OR 1.26, 95% CI 1.20 to 1.32), while rheumatoid arthritis was linked to reduced processing speed (SDMT-C, OR 0.79, 95% CI 0.68 to 0.92), indicating increased vulnerability to cognitive impairment.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eAssociations with structural brain alterations\u003c/h2\u003e\u003cp\u003eSeven CIDs were significantly associated with alterations in brain structure, predominantly affecting white matter microstructure (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, supplementary Table S8). T1DM showed the most widespread associations, characterized by reduced FA and ICVF, along with increased MD and ISOVF, indicative of diffuse microstructural disruption. Reduced FA was observed across multiple anatomical fibers, including commissural fibers (e.g., genu of the corpus callosum, OR 0.58, 95% CI 0.46 to 0.72), projection fibers (e.g., left posterior thalamic radiation, OR 0.62, 95% CI 0.49 to 0.77), as well as association fibers (e.g., left superior fronto-occipital fasciculus, OR 0.60, 95% CI 0.48 to 0.75). Rheumatoid arthritis, osteoarthritis, and gout were linked to more focal changes, particularly in the internal and external capsules. T1DM was also associated with reduced volumes in the bilateral thalamus and cerebellar white matter, as well as enlargement of the third ventricle (volume of the third ventricle, OR 1.47, 95%CI 1.21 to 1.79), reflecting both region-specific and global brain atrophy. Structural alterations in cortical areas and regional brain volumes were otherwise minimal across the remaining CIDs. Although allergic rhinitis was associated with better cognitive performance, it did not exhibit corresponding protective associations with brain structure.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis large-scale population-based study provides a comprehensive evaluation of the associations between 14 chronic inflammatory diseases and brain health. Most CIDs were related to increased risks of stroke, dementia, and Parkinson\u0026rsquo;s disease, with stroke showing the most robust associations. Notably, type 1 diabetes mellitus, rheumatoid arthritis, osteoarthritis, dermatitis and eczema, oesophagitis, and ulcerative colitis demonstrated consistent associations with elevated risk across all outcomes. These patterns were particularly evident among younger individuals. CIDs were also linked to poorer cognitive function and disruption of white matter microstructure. In contrast, allergic rhinitis emerged as an exception, showing consistently protective associations, including lower risks of all three neurological disorders and better cognitive performance. These findings underscore both shared and heterogeneous pathways through which chronic inflammation influences brain health, shaped by generalized systemic inflammatory burden and distinct disease-specific immune mechanisms.\u003c/p\u003e\u003cp\u003eThe observed associations between CIDs and brain outcomes likely reflect a convergence of vascular and neurodegenerative mechanisms triggered by persistent immune activation. Among the three neurological diseases, stroke exhibited the strongest and most consistent associations with CIDs, with the greatest proportion mediated by inflammatory markers, suggesting a particular vulnerability of the cerebrovascular system inflammatory injury(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Chronic inflammation may impair vascular integrity through endothelial dysfunction and oxidative stress and may further promote platelet activation and a prothrombotic state(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Supporting this, neuroimaging analyses revealed more pronounced disruption of white matter microstructure, which is more susceptible to microvascular damage, compared to changes in cortical area or regional brain volumes. These findings point to inflammation-induced vascular dysfunction as a major pathway linking CIDs to brain pathology(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Beyond vascular injury, our findings also implicate neurodegenerative mechanisms, as evidenced by associations with dementia and Parkinson\u0026rsquo;s disease. Chronic peripheral inflammation may disrupt blood-brain barrier integrity, enabling peripheral immune cells and cytokines to infiltrate the brain parenchyma, activate microglia, and impair neuronal homeostasis. These processes contribute to key pathological features of neurodegeneration, including amyloid-β deposition, tau hyperphosphorylation, and dopaminergic neuron loss (\u003cspan additionalcitationids=\"CR13 CR14\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e).\u003c/p\u003e\u003cp\u003ePrior Mendelian randomization studies have yielded inconsistent evidence regarding the causal relationship between certain CIDs and neurological disorders(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan additionalcitationids=\"CR17 CR18 CR19\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e), suggesting that their neurological consequences may be more likely driven by acquired inflammatory processes rather than genetic predisposition. This interpretation is further supported by our mediation analyses, which showed that circulating inflammatory markers accounted for a meaningful proportion of the associations across multiple CIDs, reinforcing the role of systemic inflammation as a downstream mediator. Notably, the mediating effects were primarily driven by neutrophil-related markers, whereas lymphocyte-based indices contributed minimally. This pattern aligns with previous studies identifying neutrophils and SII as strong predictors of brain disorders (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e), and suggests that innate immune activation, rather than adaptive immune responses, may be the dominant inflammatory pathway driving brain injury in the context of chronic systemic inflammation.\u003c/p\u003e\u003cp\u003eWhile systemic inflammation broadly contributes to neurological risk, our findings also reveal substantial heterogeneity across different CIDs, suggesting that disease-specific immune mechanisms may play a critical role. This variation may reflect differences in immune polarization, the balance between systemic and localized inflammation, and organ-specific pathways(\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). For instance, the pronounced neurological burden of T1DM may result from a combination of autoimmune inflammation and chronic hyperglycemia(\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Rheumatoid arthritis, a seropositive autoimmune disease marked by widespread immune activation, was associated with increased risks across all neurological outcomes(\u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). In contrast, psoriatic arthropathy and ankylosing spondylitis, both classified as seronegative spondyloarthropathies, were linked to higher stroke risk but not to dementia or Parkinson\u0026rsquo;s disease, possibly indicating a more vascular-restricted inflammatory profile. Other conditions, such as inflammatory bowel diseases, may lead to increased intestinal permeability, and influence brain health via gut-brain axis interactions, underscoring the multifaceted nature of inflammatory neurobiology(\u003cspan additionalcitationids=\"CR31\" citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eInterestingly, allergic rhinitis emerged as an outlier, associated with lower risks of all three neurological diseases, as well as better cognitive performance. Although existing evidence remains conflicting(\u003cspan additionalcitationids=\"CR34\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e), this protective profile may relate to its Th2-dominant immune response, characterized by upregulation of anti-inflammatory cytokines such as Interleukin-4 and Interleukin-13(\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). These mediators may counterbalance the proinflammatory Th1- and Th17-pathways predominant in other CIDs, thereby limiting systemic immune activation, preserving vascular and blood-brain barrier integrity, and mitigating neuroinflammatory damage. Collectively, these observations reinforce the notion that not all chronic inflammatory conditions confer equal neurological risk, and that immune phenotype may play a pivotal role, underscoring the potential of targeted immunomodulation in preserving brain health(\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eNotably, age-stratified analysis showed that the associations between CIDs and neurological outcomes were stronger among younger individuals (\u0026lt;\u0026thinsp;65 years), with substantially higher HRs compared to older adults. This pattern may reflect greater immune responsiveness earlier in life, during which systemic inflammation may exert a more pronounced impact on vascular and neuronal function. In contrast, these effects may be attenuated in older adults due to immunosenescence or masked by competing age-related comorbidities. These findings suggest that age modifies the neurological impact of chronic inflammation and highlight the importance of early identification and intervention in younger individuals with CIDs.\u003c/p\u003e\u003cp\u003eThis study benefits from the large sample size and extensive phenotyping of the UK Biobank, allowing for a comprehensive assessment of diverse CIDs and brain health outcomes. The inclusion of autoimmune, allergic, and metabolic conditions enables broad evaluation across distinct inflammatory profiles. The use of advanced neuroimaging offers structural correlates of inflammation-related brain changes, while mediation analyses offer insight into potential systemic inflammatory mechanisms. This study is subject to certain limitations. Firstly, UK Biobank is based on volunteer participants rather than random selection, which may introduce volunteer bias. However, the large sample size of the cohort is likely to mitigate this concern, and prior studies have shown that such bias has minimal impact on the validity of exposure\u0026ndash;outcome associations. Secondly, the observational design limits causal inference, and residual confounding from unmeasured factors cannot be excluded. Thirdly, the absence of detailed immune phenotyping constrains mechanistic interpretation, and the lack of longitudinal imaging data precludes assessment of temporal changes. Fourthly, the predominantly European ancestry of the cohort may limit generalizability to other populations.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn this large, population-based study, chronic inflammatory diseases were associated with increased risks of stroke, dementia, Parkinson\u0026rsquo;s disease, cognitive decline, and white matter microstructural disruption. Allergic rhinitis was consistently associated with lower neurological risk and better cognitive outcomes, suggesting a potential protective role. These findings underscore the neurological impact of systemic inflammation and highlight the heterogeneity across CID subtypes. Early identification and targeted management of individuals with CIDs may help mitigate long-term risks to brain health. Future research should aim to refine immune phenotyping and elucidate modifiable inflammatory pathways to inform prevention and neuroprotective strategies.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eCIDs, Chronic inflammatory diseases\u003c/p\u003e\u003cp\u003eT1DM, type 1 diabetes mellitus\u003c/p\u003e\u003cp\u003eGf, fluid intelligence score\u003c/p\u003e\u003cp\u003eDSpan, maximum digits remembered correctly\u003c/p\u003e\u003cp\u003ePMR, prospective memory result\u003c/p\u003e\u003cp\u003eSDMT-C, number of symbol digit matches made correctly\u003c/p\u003e\u003cp\u003eMTCM, mean time to correctly identify matches\u003c/p\u003e\u003cp\u003eAPCT, duration to complete alphanumeric path\u003c/p\u003e\u003cp\u003eAPE, errors before selecting correct item in alphanumeric path\u003c/p\u003e\u003cp\u003eIDP, image-derived phenotype\u003c/p\u003e\u003cp\u003eFA, fractional anisotropy\u003c/p\u003e\u003cp\u003eMD, mean diffusivity\u003c/p\u003e\u003cp\u003eOD, orientation dispersion\u003c/p\u003e\u003cp\u003eMO, mode of anisotropy\u003c/p\u003e\u003cp\u003eISOVF, isotropic volume fraction\u003c/p\u003e\u003cp\u003eICVF, intracellular volume fraction\u003c/p\u003e\u003cp\u003eBMI, body mass index\u003c/p\u003e\u003cp\u003eCRP, C-reactive protein\u003c/p\u003e\u003cp\u003eNLR, neutrophil-to-lymphocyte ratio\u003c/p\u003e\u003cp\u003ePLR, platelet-to-lymphocyte ratio\u003c/p\u003e\u003cp\u003eLMR, lymphocyte-to-monocyte ratio\u003c/p\u003e\u003cp\u003eSII, systemic immune-inflammation index\u003c/p\u003e\u003cp\u003eSD, standard deviations\u003c/p\u003e\u003cp\u003eHR, hazard ratio\u003c/p\u003e\u003cp\u003eCI, confidence interval\u003c/p\u003e\u003cp\u003eFDR, false discovery rate\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e\u003cp\u003eThis research was conducted using the UK Biobank Resource (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.ukbiobank.ac.uk/\u003c/span\u003e\u003cspan address=\"http://www.ukbiobank.ac.uk/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) under application number 89757. This study was performed under generic ethical approval obtained by UK Biobank from the National Health Service National Research Ethics Service (approval letter ref 11/NW/0382).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\u003cp\u003e All the authors have consented for publication\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis study was supported by the National Natural Science Foundation of China (No. 82271368).\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e\u003cp\u003eThe authors are grateful to all the staff and participants of the UK Biobank.\u003c/p\u003e\u003ch2\u003eData availability\u003c/h2\u003e\u003cp\u003eThe data supporting the findings of this study are available from the UK Biobank (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.ukbiobank.ac.uk/\u003c/span\u003e\u003cspan address=\"http://www.ukbiobank.ac.uk/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), but there are restrictions on the availability of these data, which have been used under the license for the current study and are therefore not publicly available.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eFurman D, Campisi J, Verdin E, Carrera-Bastos P, Targ S, Franceschi C, et al. 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Neurology. 2023;101(6):e653\u0026ndash;64.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRiggott C, Ford AC, Gracie DJ. Review article: The role of the gut-brain axis in inflammatory bowel disease and its therapeutic implications. Aliment Pharmacol Ther. 2024;60(9):1200\u0026ndash;14.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCrans Yoon AM, Chiu V, Rana JS, Sheikh J. Association of allergic rhinitis, coronary heart disease, cerebrovascular disease, and all-cause mortality. Ann Allergy Asthma Immunol. 2016;117(4):359\u0026ndash;64. e1.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJoh HK, Kwon H, Son KY, Yun JM, Cho SH, Han K, et al. Allergic Diseases and Risk of Incident Dementia and Alzheimer's Disease. Ann Neurol. 2023;93(2):384\u0026ndash;97.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSu CH, Huang KH, Yang Y, Gau SY, Chung NJ, Wu PT, et al. Cumulative Dose Effects of H1 Antihistamine Use on the Risk of Dementia in Patients With Allergic Rhinitis. J Allergy Clin Immunol Pract. 2024;12(8):2155\u0026ndash;65.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang Y, Song XY, Wei SZ, Wang HR, Zhang WB, Li YM, et al. Brain response in allergic rhinitis: Profile and proposal. J Neurosci Res. 2023;101(4):480\u0026ndash;91.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJairath V, Acosta Felquer ML, Cho RJ. IL-23 inhibition for chronic inflammatory disease. Lancet. 2024;404(10463):1679\u0026ndash;92.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMannion JM, McLoughlin RM, Lalor SJ. The Airway Microbiome-IL-17 Axis: a Critical Regulator of Chronic Inflammatory Disease. Clin Rev Allergy Immunol. 2023;64(2):161\u0026ndash;78.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Chronic inflammatory disease, Dementia, Cognition, Stroke, Brain structure","lastPublishedDoi":"10.21203/rs.3.rs-7893606/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7893606/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eChronic inflammatory diseases (CIDs) are common and characterized by persistent systemic inflammation, but their broader impact on brain health remains poorly understood. We aimed to examine the associations of 14 common CIDs with brain health in the UK Biobank participants.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eWe conducted a prospective cohort study using data from 502,411 UK Biobank participants aged 40\u0026ndash;69 years at baseline (2006\u0026ndash;2010), with follow-up through linked hospital, primary care, and mortality records. Fourteen common CIDs were identified. Neurological outcomes included stroke, dementia, Parkinson\u0026rsquo;s disease, cognitive performance, and magnetic resonance imaging derived phenotypes. Associations were assessed using Cox proportional hazards models and logistic regressions. Mediation analyses were conducted to evaluate the potential role of systemic inflammatory markers in these associations.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eAmong 502,411 participants (56.5\u0026thinsp;\u0026plusmn;\u0026thinsp;8.1 years; 54.4% women), 44.4% had at least one CID. Over a mean follow-up of 8.9 years, CID presence was associated with increased risks of stroke (hazard ratio [HR] 1.46; 95% confidence interval, 1.43\u0026ndash;1.50), dementia (HR 1.43, 1.36\u0026ndash;1.50), and Parkinson\u0026rsquo;s disease (HR 1.34, 1.26\u0026ndash;1.43), with stronger associations among individuals younger than 65 years. Type 1 diabetes conferred the highest risk, with approximately threefold increased risks of stroke and dementia. Up to 24% of observed associations were mediated by neutrophil-based inflammatory indices. In contrast, allergic rhinitis was linked to reduced neurological risk and better cognition. Neuroimaging analysis revealed widespread white matter microstructural disruption across CIDs.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eOur findings highlight the broader neurological impact of CIDs and reveal substantial heterogeneity across CID subtypes. Early identification and management of CIDs may help mitigate long-term risks to brain health.\u003c/p\u003e","manuscriptTitle":"Associations of chronic inflammatory diseases with brain health: a prospective cohort study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-13 14:48:09","doi":"10.21203/rs.3.rs-7893606/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b7838511-da8e-48ff-be33-2d73bf4aab49","owner":[],"postedDate":"November 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-20T19:05:33+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-13 14:48:09","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7893606","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7893606","identity":"rs-7893606","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}