Interaction effects of type 2 diabetes mellitus and air pollutants on white matter structures and cognitive function

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However, the interaction effects between the two factors on WM structures remain unclear. Our study aimed to elucidate the interaction effects of APs and T2DM on WM integrity and to examine how this interaction influences cognitive function. Methods This study included participants from the UK Biobank, categorized into a diabetes group and a control group (sample size: 665 vs. 13382). APs data, WM structure data, and cognitive data were incorporated into the analysis. Linear regression models were used to assess the interaction effect of T2DM and APs on WM structures and cognitive function, and then the mediation and moderation analyses were performed. Finally, propensity score matching (PSM) was performed at a 1:2 ratio to reselect the control group. After strictly controlling for covariates, the study results were validated. Results Significant interaction effects between T2DM and particulate matter ≤ 2.5µm (PM 2.5 ) were detected on the fractional anisotropy (FA) and oriented diffusivity (OD) values of the left inferior cerebellar peduncle (ICP). The FA value of the left ICP mediated the relationships between T2DM and both reaction time and digit symbol substitution test (DSST) scores. As the concentration of PM 2.5 increased, the mediating effect of FA became more pronounced. Additionally, both T2DM and APs had independent effects on multiple WM tracts, with APs primarily affecting the superior cerebellar peduncle (SCP). Conclusions Patients with T2DM have increased sensitivity of the left ICP to PM 2.5 , and this fiber tract plays a mediating role in the relationship between T2DM and cognitive function, with the mediating effect being moderated by PM 2.5 , highlighting the critical role of environmental pollution in brain function and behavioral health. Interaction effect PM2.5 T2DM WM structure Cognition Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction White matter (WM) accounts for approximately half of the total volume of the brain, with its fibers extending throughout the brain, connecting cortical and subcortical gray matter regions to form functional neural networks that underpin cognition. It is particularly vulnerable to a range of factors, including aging, vascular disease, inflammation, metabolic disorders, demyelination, and trauma [ 1 ] . WM degeneration impairs nerve signal transmission and disrupts neuronal connections, leading to cognitive and behavioral disorders, such as vascular dementia and Alzheimer's disease. It is also linked to declines in executive functions and slower information processing speed [ 2 ] . Although the cognitive decline caused by WM alterations is a gradual process, it can be prevented or slowed down to a certain extent through a variety of interventions [ 3 ] . As one of the risk factors for WM health, type 2 diabetes mellitus (T2DM) is a widely prevalent metabolic disease characterized by insulin resistance and hyperglycemia and has become a major global health challenge and a significant socioeconomic burden [ 4 ] . Many interrelated factors adversely affect the WM and cognition of individuals with T2DM, including potential metabolic, inflammation, oxidative stress, and microvascular determinants [ 5 – 7 ] . Consistent abnormal WM changes in T2DM patients have been identified, such as the commissural fiber genu and body of the corpus callosum, the association fiber cingulum and superior frontal-occipital fasciculus, and the projection fibers anterior corona radiata and superior corona radiata [ 8 ] . The reduced integrity of these WM tracts lowers the efficiency of information processing, thereby accelerating cognitive decline [ 9 ] . Research indicated that individuals with T2DM have a significantly greater risk of dementia than the general population and experience an overall decline in cognitive function [ 10 ] . In summary, T2DM poses a substantial risk to WM integrity and cognitive health. Air pollutants (APs), particularly particulate matter ≤ 2.5µm (PM 2.5 ) and nitrogen dioxide (NO 2 ), pose an additional threat to human WM integrity and cognitive health. Studies have shown that long-term exposure to high levels of APs can increase the risk of WM damage and dementia and accelerate cognitive decline [ 11 ] . Owing to its fine particles, PM 2.5 can penetrate the blood-brain barrier, triggering brain inflammation and oxidative stress, which damages WM structures and leads to cognitive decline [ 12 ] . Additionally, increased concentrations of PM 2.5 and NO 2 , are closely associated with the worsening of symptoms related to anxiety, schizophrenia, and depression [ 13 ] . Both T2DM and APs are known to damage WM through mechanisms such as inflammation and oxidative stress. However, the interaction between T2DM and APs on WM remains largely unexplored, and the impact of these WM changes on cognitive function has not been adequately addressed. Previous studies have focused on fractional anisotropy (FA) and mean diffusivity (MD) to assess WM integrity, but these metrics have limitations in capturing the full complexity of white matter microstructure. While FA and MD reflect overall WM integrity, they do not provide detailed insights into the cellular architecture of the tissue. In contrast, neurite orientation dispersion and density imaging (NODDI), which generates three key metrics—isotropic volume fraction (ISOVF), intracellular volume fraction (ICVF), and oriented diffusivity (OD)—offers a more comprehensive assessment of WM microstructure [ 14 ] . Compared with traditional diffusion tensor imaging (DTI) metrics, NODDI can more comprehensively evaluate changes in the microstructural integrity of WM and has been proposed as an alternative and improved diffusion model in neurodegenerative diseases [ 15 – 17 ] , making it a valuable tool for investigating the mechanisms of WM alterations in the context of T2DM and APs exposure. To address this significant gap in the literature, this study investigated the interactive effects of T2DM and APs on WM integrity, using advanced imaging techniques, particularly NODDI. This approach allows for a more comprehensive understanding of the underlying mechanisms of WM alterations in the context of T2DM and APs exposure. Our findings not only shed light on the combined impact of T2DM and APs on WM but also provide valuable evidence to establish targeted preventive and intervention strategies for controlling cognitive decline in T2DM patients. Methods Database and Inclusion Criteria In this study, we utilized data from the UK Biobank, which included over 500,000 participants between 2006 and 2010 aged 40 to 69 years at the time of enrollment. The participants provided their demographic information through a touch-screen questionnaire and a computer-assisted interview. All participants completed a series of assessments, including electronic questionnaires, face-to-face interviews, physical examinations, and laboratory tests, to collect detailed data on biological, socioeconomic, and lifestyle factors [ 18 ] . The study was approved by the local ethics committee, and informed consent was obtained from all participants at enrollment. During the screening process, individuals with central nervous system disorders, psychiatric conditions, or a history of traumatic brain injury were excluded based on the ICD-10 criteria. The participants were then classified into two groups, namely, those with and without diabetes, based on Field 2443. In the diagnosed diabetes group, individuals with an age of diagnosis ≤ 35 years or a history of gestational diabetes were excluded [ 19 ] . In the nondiabetes group, only individuals with normal glycated hemoglobin levels (35-42mmol/L) were included [ 20 ] . Additionally, participants who did not undergo brain imaging were excluded from the study. Air Pollution A pan-European land use regression (LUR) model, developed following the European Study of Cohorts for Air Pollution Effects protocol, was used to estimate annualized concentrations of APs for the 2010 calendar year at the time of enrollment. These APs included nitrogen oxides (NO x ), NO 2 , PM 2.5 , particulate matter 2.5–10µm (PM 2.5−10 ), and particulate matter ≤ 10µm (PM 10 ) [ 21 – 23 ] . Detailed descriptions are available on the UK Biobank website ( https://biobank.ndph.ox.ac.uk/showcase/refer.cgi?id=2010 ). In summary, LUR models estimate APs levels at monitoring sites based on land-use characteristics. The resulting β coefficients are applied to land-use characteristics from other locations to predict APs levels in unmonitored areas. For this study, LUR models were utilized to estimate APs concentrations at participants’ residences, with separate models developed for PM and NO x . PM was monitored in cities such as Manchester and London/Oxford, with the LUR model refined and optimized via local geographic and environmental data. A comparable method was employed for NO 2 and NO x , utilizing the more extensive network of monitoring sites. Brain Imaging and Cognitive Data From 2014 to 2020, the UK Biobank acquired brain imaging data for approximately 50,000 participants via a Siemens Skyra 3T MRI scanner with a standard 32-channel head coil [ 24 ] . FSL (version 5.0.10) and FreeSurfer (version 6.0) software were used for image processing. For the raw imaging data, FA and MD were generated via DTI, while ISOVF, ICVF, and OD were generated via NODDI [ 25 ] . FA and MD values are used to assess the integrity of the WM structure, whereas ISOVF, ICVF, and OD provide insights into the volume fraction of extracellular isotropic free water, neurite density, and intracellular neurite dispersion within the WM, respectively [ 16 , 26 , 27 ] . In addition, UK Biobank participants underwent a series of cognitive tests. For this study, the following cognitive assessments were selected: reaction time, numeric memory, fluid intelligence/reasoning, trail making, symbol digit substitution, tower rearranging, and pair matching. Detailed methodologies are available on the UK Biobank official website ( https://biobank.ndph.ox.ac.uk/showcase/label.cgi?id=100026 ). Statistical Analysis Analyses were performed in R (version 4.2.1). All the data were standardized via z-scores to eliminate dimensional differences and ensure model stability. To exclude outliers, a threshold of six standard deviations was applied, removing abnormal values in the annual average concentrations of APs and WM structural metrics. To investigate the interaction effects of T2DM and APs on WM structural metrics, we first excluded participants with missing brain imaging data or APs information. Linear regression models were subsequently employed, with WM structural metrics as the dependent variables. The independent variables included group status (T2DM vs. control) and annual average concentrations of APs. Covariates, including age, sex, ethnicity, education level, and imaging center, were also adjusted for in the analysis. Next, we further examined the main effects of T2DM and annual average APs concentrations on WM structural metrics. Consistent with the previous analysis, T2DM and APs concentrations were analyzed separately as independent variables to investigate their impact on WM structural metrics. All results were adjusted using Bonferroni correction to control for false positives arising from multiple comparisons. To investigate the mediating role of WM structural metrics in the relationship between T2DM and cognitive function and to assess the moderating effect of APs, we conducted a mediation analysis using Model 7 from the SPSS Process macro. In this framework, group status (T2DM vs. nondiabetes) was treated as the independent variable, WM structural metrics were treated as mediators, cognitive performance was treated as the dependent variable, and annual average APs concentrations were treated as moderators. To ensure the reliability of the results, this study included all available data from the UK Biobank. Although covariates such as age, sex, education level, ethnicity, and imaging center were controlled for during the statistical analysis, the substantial difference in sample size between the two groups may have led to inadequate covariate matching, potentially affecting the results. Therefore, propensity score matching (PSM) was further employed in this study to improve sample comparability. Using age, sex, education level, ethnicity, and imaging center as covariates, a new control group was constructed by selecting individuals from the HCs group whose propensity scores were closest to those of the T2DM group, at a 1:2 patient-to-control ratio. This approach ensured that there were no significant statistical differences between the two groups in terms of age, sex, education level, ethnicity, or imaging center. Statistical analyses were then conducted on the WM metrics that showed significant interaction effects in the previous analysis, to validate the interaction between T2DM and PM 2.5 on these WM metrics. Furthermore, the potential mediating role of these metrics in the relationship between T2DM and cognitive function was examined. Ethics approval and consent to participate This study used data from the UK Biobank (Application Number: 75556). UK Biobank received ethical approval from the National Information Governance Board for Health and Social Care and the NHS North West Centre for Research Ethics Committee (Ref: 11/NW/0382). All procedures complied with the Declaration of Helsinki. Written informed consent was obtained from all participants at recruitment. No additional consent was required for this secondary analysis. Data were fully anonymized and handled in accordance with the UK Data Protection Act and GDPR (2018). Clinical trial number: Not applicable. Results Data Analysis All participants in this study were drawn from the UK Biobank database, including 665 individuals in the T2DM group and 13,382 in the nondiabetes group. The specific steps for data selection and final participant enrollment are detailed in Figure 1. Table 1 shows the demographic information and APs levels of the participants. Imaging metric maps (FA, MD, ISOVF, ICVF, and OD) and cognitive information are presented in Supplement materials. Interaction Effects and Main Effects The analysis revealed a significant interaction effect between T2DM and the PM 2.5 concentration on the WM structure of the left inferior cerebellar peduncle (ICP) (FA value: P = 4.5231E-05, β = -0.1676; OD value: P = 1.5886E-04, β = 0.1462). With increasing annual PM 2.5 concentrations, the FA value of the left ICP in individuals with T2DM decreases, whereas the OD value increases. In contrast, no significant changes in FA or OD values were observed in the control group. These results indicated that individuals with T2DM have greater sensitivity to the effects of PM 2.5 on the WM tracts in the left ICP, leading to reduced WM integrity and increased neurite dispersion in this region (Figure 2). Additionally, we further examined the independent effects of T2DM and APs concentrations on WM structure. The results revealed that T2DM significantly impacted the WM structure of several fiber tracts, particularly in the following regions: the genu of the corpus callosum, fornix, left cerebral peduncle, left sagittal stratum, bilateral superior cerebellar peduncle (SCP), bilateral anterior corona radiata, bilateral external capsule, bilateral cingulum cingulate gyrus, bilateral fornix cres+stria terminalis, and bilateral superior fronto-occipital fasciculus. Notably, individuals with T2DM presented significantly reduced MD and ISOVF values in the pontine crossing tract and right corticospinal tract, as well as significantly decreased OD values in the posterior limb of the internal capsule and the bilateral superior corona radiata (Figure 3). Moreover, the effects of APs were predominantly observed in the bilateral SCP (Figure 4). Mediation Effects After examining the WM structural metrics with significant interaction effects, we further conducted mediation analysis. The results revealed that only FA of the left ICP served as a mediator in the relationship between T2DM and cognitive function. T2DM exhibited significant direct effects on both reaction time and DSST performance (reaction time 95% confidence intervals (CIs): [0.0216, 0.1847]; DSST 95% CIs: [-0.2149, -0.0321]), indicating that T2DM directly influences these two cognitive abilities. Second, the indirect effects of the left ICP FA value, estimated using the bootstrap method, were also significant, with 95% CIs of [0.0020, 0.0105] for reaction time and [-0.0130, -0.0024] for DSST, both excluding zero. These findings indicated that T2DM significantly influences the reaction time and DSST ability indirectly by affecting the FA value of the left ICP. Finally, the moderation analysis demonstrated that the PM 2.5 concentration significantly moderated the mediating effect of the FA value in the left ICP on the relationship between T2DM and cognition. Specifically, as the PM 2.5 concentration increased, the mediating effect of the FA value became more pronounced. In summary, T2DM not only directly impacts cognitive ability but also indirectly influences it through the FA value of the left ICP. Moreover, PM 2.5 acts as a moderating variable, significantly amplifying the mediating effect of the FA value and highlighting the critical role of environmental pollution in brain function and behavioral health (Figure 5, Table 2). Validation Analysis Results After applying PSM, there were no significant statistical differences between the two groups in terms of age, sex, ethnicity, and education level (Table 3). Further statistical analysis showed a significant interaction between T2DM and PM 2.5 on FA (β = -0.1221, P = 0.0143) and OD (β = 0.1017, P = 0.0310) values in the left ICP. In the mediation analysis, the FA value of the left ICP showed a significant mediating effect in the relationship between T2DM and cognitive function (reaction time 95% CIs: [0.1875, 2.9023]; DSST 95% CIs: [-0.1351, -0.0044]). Moreover, the level of PM 2.5 exposure significantly moderated the mediating effect of FA in the left ICP. These findings validated the results obtained from the original dataset, confirming the interaction between T2DM and PM 2.5 on FA and OD values in the left ICP. Moreover, the FA value mediated the relationship between T2DM and cognitive function, and this mediation effect was moderated by the level of PM 2.5 exposure. Discussion This study investigated the interaction effect of T2DM and APs on WM structure and further analyzed how WM structure influences cognitive function. We identified a significant interaction between T2DM and the annual average concentrations of APs, particularly PM 2.5 , in the WM structure of the left ICP. Specifically, T2DM patients exposed to higher PM 2.5 concentrations presented a significant reduction in FA values and an increase in OD values in this region, indicating greater WM sensitivity to PM 2.5 in these individuals. Furthermore, T2DM indirectly influenced cognitive performance, including reaction time and the DSST, through its effect on the FA value of the left ICP. Notably, the PM 2.5 concentration significantly moderated this relationship, suggesting that environmental pollution may play a critical role in the mechanisms of cognitive impairment associated with T2DM. The results of this study support the association between T2DM and WM damage, particularly in the context of environmental pollution. The literature has shown that WM lesions are more severe in individuals with T2DM, and these changes are closely linked to declines in cognitive function [28, 29] . This study revealed that the left ICP in individuals with T2DM is more sensitive to PM 2.5 exposure. The ICP, as a fiber tract connecting the cerebellum to other regions of the nervous system, transmits information from the spinal cord and the olivary nuclei to the cerebellum while also conveying signals from the cerebellum to the vestibular nuclei [30, 31] . These anatomical functions suggest that damage to the ICP can result in a variety of neurological impairments, primarily affecting motor coordination [32, 33] . Previous studies have confirmed that damage to the inferior cerebellar peduncle is associated with impaired learning ability during prism adaptation [34] . Additionally, microstructural changes in the left ICP, such as decreased FA values or increased MD values, are closely related to a decline in motor adaptation abilities [35] . In addition to its role in motor function, the ICP is also closely associated with cognitive disorders. Recent anatomical studies of cerebellar pathways have revealed that cerebellar output fibers project to non-motor areas in the prefrontal and posterior parietal cortex [36] . This anatomical framework provides a physiological basis for understanding how lesions in cerebellar peduncles can affect cognitive function. Studies have shown that increased MD values in the left ICP are strongly associated with cognitive impairment [37] . Additionally, research on schizophrenia has revealed that patients with this condition exhibit reduced FA values in the left ICP [38] . We also found that the decrease in FA values in the left ICP mediates the relationship between T2DM and declines in reaction time and information processing ability. These findings further highlight the critical role of the left ICP in cognitive function, suggesting that it could be a potential target for the treatment of cognitive impairments. The heightened sensitivity of the left ICP may be attributed to inflammatory responses, oxidative stress, and cerebrovascular damage resulting from elevated blood glucose levels in these patients, which are thought to have already caused damage to the left ICP, rendering this structure more vulnerable to the effects of PM 2.5 . These findings provide valuable evidence for public health policy, suggesting that stricter air quality controls and health protection measures should be implemented for vulnerable populations, such as individuals with diabetes, to mitigate the additional detrimental effects of environmental pollution on brain health and cognitive function. In addition to examining the interaction effects, we investigated the independent impacts of T2DM and APs on WM structure. Previous research has demonstrated that APs, particularly PM and NO X , exert harmful effects not only on the respiratory and cardiovascular systems but also on brain health. These adverse effects are observed across the lifespan, from fetal development through middle and older age [39] . During early life, APs can interfere with the growth and development of WM fibers, leading to abnormal information processing and long-term cognitive effects [40] . In contrast, for fully developed WM fibers, APs can induce damage through mechanisms such as inflammation and oxidative stress, resulting in structural changes to WM and subsequent cognitive impairments [41, 42] . We found that the effects of APs, including PM 2.5 , PM 10 , NO x , NO 2 , and PM 2.5-10 , on WM structure are primarily observed in the bilateral SCP, which are composed primarily of efferent fibers. These fibers are responsible for transmitting information from the deep cerebellar nuclei to the contralateral cortex via the thalamus, playing a key role in motor coordination [30, 31] . Structural changes in the SCP have been linked to various cognitive disorders. This microstructural change is not only linked to declines in motor function but also correlate with slower information processing speed, which contributes to poorer performance on tasks such as simple number writing [43] . The APs may contribute to cognitive decline by affecting the SCP. This suggests that, when addressing cognitive health, the potential impact of environmental factors should not be overlooked. This study revealed that T2DM is associated with structural alterations in multiple WM fiber tracts, including the bilateral superior fronto-occipital fasciculus, bilateral fornix cres+stria terminalis, bilateral external capsule, bilateral SCP, and the genu and body of the corpus callosum. Besides the consistent results with previous studies [44] , our study revealed that in the T2DM group, the MD and ISOVF values of the right corticospinal tract and the pontine crossing tract were reduced. Additionally, PM 2.5-10 caused a decrease in the OD value of the pontine crossing tract. These contrary findings suggest that, for evaluating a given fiber tract, a comprehensive approach that incorporates multiple metrics should be highlighted. This study has several limitations. First, the UK Biobank sample is predominantly composed of white participants from the United Kingdom, which may limit the generalizability of the results to other ethnic groups or regions. Second, the majority of participants are volunteers with higher socioeconomic status [45] , which may lead to an underestimation of the potential impact of APs and T2DM on cognitive function in the general population. Additionally, the APs data used in this study were from 2010, while the brain imaging and cognitive data were collected in 2014, resulting in a temporal mismatch. Although annual concentrations tend to be stable, the four-year gap may still affect the accuracy of the results. Future research should consider using more real-time APs data and expanding the sample size to improve the generalizability of the findings. This study highlights the significant impact of T2DM and APs on WM structure and cognitive function. We found that T2DM interacts with APs, particularly PM 2.5 concentrations, in several regions of the WM structure, further influencing cognitive function. Through mediation analysis, we determined that the FA values in the left ICP mediate the effect of T2DM on cognitive decline, with PM 2.5 concentrations significantly moderating this process. These findings suggest that environmental pollution plays an important role in brain health and cognitive function in individuals with diabetes. 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The Air We Breathe: Air Pollution as a Prevalent Proinflammatory Stimulus Contributing to Neurodegeneration [J]. Front Cell Neurosci, 2021, 15: 647643. FRITZ N E, EDWARDS E M, YE C, et al. Cerebellar Contributions to Motor and Cognitive Control in Multiple Sclerosis(✰✰✰) [J]. Arch Phys Med Rehabil, 2022, 103(8): 1592-9. MA T, LI Z Y, YU Y, et al. Gray and white matter abnormality in patients with T2DM-related cognitive dysfunction: a systemic review and meta-analysis [J]. Nutr Diabetes, 2022, 12(1): 39. BRAYNE C, MOFFITT T E. The limitations of large-scale volunteer databases to address inequalities and global challenges in health and aging [J]. Nat Aging, 2022, 2(9): 775-83. Tables T2DM (N = 665) Healthy control (N = 13382) P value Age [mean (SD)] 58.60 (6.5) 56.76 (7.1) ＜0.001 Sex, n (%) Female 210 (31.6) 6,993 (52.3) ＜0.001 Male 455 (68.4) 6,389 (47.7) Ethnic, n (%) White 604 (90.8) 12,905 (96.4) ＜0.001 Other 61 (9.2) 477 (3.6) Education, n (%) College or higher 385 (57.9) 8,286 (61.9) 0.037 Less than college 280 (42.1) 5,096 (38.1) Centre, n (%) Centre1 404 (60.8) 7,820 (58.4) 0.539 Centre2 107 (16.1) 2,146 (16.0) Centre3 153 (23.0) 3,402 (25.4) Centre4 1 (0.1) 14 (0.2) Air pollutants [mean (SD)], μg/m³ NO 2 26.0781 (6.9520) 25.6739 (7.2549) 0.162 NO x 43.2666 (14.1472) 42.4297 (14.7075) 0.154 PM 10 16.1582 (1.9391) 15.9820 (1.8655) 0.021 PM 2.5 9.9557 (1.0115) 9.8928 (1.0419) 0.140 PM 2.5-10 6.4344 (0.9462) 6.3488 (0.8581) 0.027 Table 1 Characteristics of the study population Mean (SD) values and percentages are reported for continuous and categorical variables, respectively. Abbreviations: SD, standard deviation; T2DM, type 2 diabetes mellitus; NO 2 , nitrogen dioxide; NO x , nitrogen oxides; PM 10 , particulate matter ≤ 10 μm; PM 2.5 , particulate matter ≤ 2.5 μm; PM 2.5-10 , particulate matter 2.5-10 μm; μg, micrograms Cognition Conditional indirect effect analysis at different PM 2.5 levels (M±SD) Effect BootSE BootLLCI BootULCI Reaction M - 1SD -0.0006 0.0025 -0.0058 0.0042 M 0.0066 0.0024 0.0024 0.0119 M + 1SD 0.0138 0.0044 0.0061 0.0236 DSST M - 1SD 0.0011 0.0032 -0.0050 0.0077 M -0.0080 0.0030 -0.0146 -0.0029 M + 1SD -0.0171 0.0056 -0.0294 -0.0074 Table 2 Results of the moderated mediation analysis Abbreviations: PM 2.5 , particulate matter ≤ 2.5 μm; M, mean; SD, standard deviation; DSST, digit symbol substitution test; T2DM, type 2 diabetes mellitus; FA, fractional anisotropy; ICP, inferior cerebellar peduncle T2DM ( N = 626 ) Healthy control ( N = 1252 ) P value Age [mean (SD)] 58.60 (6.54) 58.72 (6.77) 0.721 Sex, n (%) Female 195 (0.31) 382 (0.31) 0.784 Male 431 (0.69) 870 (0.69) Ethnic, n (%) White 567 (0.91) 1138 (0.91) 0.869 Other 59 (0.09) 114 (0.09) Education, n (%) College or higher 356 (0.57) 738 (0.59) 0.402 Less than college 270 (0.43) 514 (0.41) Centre, n (%) Centre1 401 (0.64) 812 (0.65) 0.932 Centre2 106 (0.17) 214 (0.17) Centre3 118 (0.19) 225 (0.18) Centre4 1 (0.00) 1 (0.00) Table 3 Group Comparison of Demographic Characteristics After PSM Mean (SD) values and percentages are reported for continuous and categorical variables, respectively. Abbreviations: SD, standard deviation; T2DM, type 2 diabetes mellitus; PSM, propensity score matching Additional Declarations No competing interests reported. Supplementary Files Supply.xlsx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 13 Nov, 2025 Reviewers invited by journal 08 Jul, 2025 Editor assigned by journal 03 Jul, 2025 Submission checks completed at journal 26 Jun, 2025 First submitted to journal 25 Jun, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-6970716","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":482927089,"identity":"67ce7679-6c37-4190-bfe6-f3ddf120b34c","order_by":0,"name":"Jiaxuan Zhao","email":"","orcid":"","institution":"Tianjin Medical University General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jiaxuan","middleName":"","lastName":"Zhao","suffix":""},{"id":482927090,"identity":"b6bf5463-7f1d-4a89-abfb-e0c76792ef52","order_by":1,"name":"Lining Guo","email":"","orcid":"","institution":"Tianjin Medical University General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Lining","middleName":"","lastName":"Guo","suffix":""},{"id":482927091,"identity":"e066a791-7d9b-4341-9be0-bee3d539eab6","order_by":2,"name":"Qiyu Zhao","email":"","orcid":"","institution":"Tianjin Medical University General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Qiyu","middleName":"","lastName":"Zhao","suffix":""},{"id":482927092,"identity":"dac52b75-9c55-476c-a34f-74edb0e46c82","order_by":3,"name":"Ying Zhai","email":"","orcid":"","institution":"Tianjin Medical University General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Ying","middleName":"","lastName":"Zhai","suffix":""},{"id":482927093,"identity":"c878186d-e8ff-4698-8a76-12003bf9569e","order_by":4,"name":"Yayuan Chen","email":"","orcid":"","institution":"Tianjin Medical University General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yayuan","middleName":"","lastName":"Chen","suffix":""},{"id":482927094,"identity":"45ee0c07-5798-4d7e-a1bd-b3de75364210","order_by":5,"name":"Shaoying Wang","email":"","orcid":"","institution":"Tianjin Medical University General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Shaoying","middleName":"","lastName":"Wang","suffix":""},{"id":482927095,"identity":"74d2bbb9-fe86-4adb-85d8-adc5ad4a19be","order_by":6,"name":"Wen Qin","email":"","orcid":"","institution":"Tianjin Medical University General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Wen","middleName":"","lastName":"Qin","suffix":""},{"id":482927096,"identity":"62397424-8e97-4c3e-aa93-1977bd4f4c0f","order_by":7,"name":"Quan Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2klEQVRIiWNgGAWjYLACxgYgwd6AYBOphecwyVokkonUwncj+dnDnzvs8uQj3x/dzMNgI7vhAPOzB/i0SN5IMzeQPJNcbHg7me02D0Oa8YYDbOYG+LQY3EgwkzBsY07cOBus5XDihgM8bBL4taR/k0hsq0/cOPMwSMt/YrTkmEkcbDucOF+CGaTlAGEtkmfelEk2th1P3MCTbHZzjkGyMdA2M7xa+I6nb5P82VadOL/94LMbbyrsZPuONz/Dq4XhAMyFYAYoqJjxqkfSIt9ASOUoGAWjYBSMWAAABrhNsAsdvhgAAAAASUVORK5CYII=","orcid":"","institution":"Tianjin Medical University General Hospital","correspondingAuthor":true,"prefix":"","firstName":"Quan","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2025-06-25 05:23:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6970716/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6970716/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":86658996,"identity":"c217bccb-267a-4a18-90e3-dd7ca8953f18","added_by":"auto","created_at":"2025-07-14 10:28:22","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":176619,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlowchart of the inclusion criteria for analysis\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6970716/v1/2855fcc5489942103e8708a3.png"},{"id":86660739,"identity":"e91b511e-73c9-4218-ab30-4726c383477f","added_by":"auto","created_at":"2025-07-14 10:36:22","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":241126,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eInteraction effect of T2DM and PM\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e2.5\u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003e on the left ICP\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAbbreviations: PM\u003csub\u003e2.5\u003c/sub\u003e, particulate matter ≤ 2.5 μm; T2DM, type 2 diabetes mellitus; ICP, inferior cerebellar peduncle; FA, fractional anisotropy; OD, oriented diffusivity\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6970716/v1/6710fb536ea356a1e94d4ef7.png"},{"id":86659011,"identity":"53c0434b-3146-41d8-976a-1afed108ddf4","added_by":"auto","created_at":"2025-07-14 10:28:22","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":451024,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMain effect of T2DM\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAbbreviations: T2DM, type 2 diabetes mellitus; FA, fractional anisotropy; MD, mean diffusivity; ISOVF, isotropic volume fraction; ICVF, intracellular volume fraction; OD, oriented diffusivity\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6970716/v1/67871517b09d6a9cd7a57af9.png"},{"id":86658998,"identity":"47318451-c1bc-4af1-be2b-75dde94d9388","added_by":"auto","created_at":"2025-07-14 10:28:22","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":209832,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMain effects of air pollutants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAbbreviations: NO\u003csub\u003e2\u003c/sub\u003e, nitrogen dioxide; NO\u003csub\u003ex\u003c/sub\u003e, nitrogen oxides; PM\u003csub\u003e2.5\u003c/sub\u003e, particulate matter ≤ 2.5 μm; PM\u003csub\u003e2.5-10\u003c/sub\u003e, particulate matter 2.5-10 μm; PM\u003csub\u003e10\u003c/sub\u003e, particulate matter ≤ 10 μm; SCP, superior cerebellar peduncle; FA, fractional anisotropy; MD, mean diffusivity; OD, oriented diffusivity\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6970716/v1/6b70ab61a1bcfaeb126cccdb.png"},{"id":86660745,"identity":"b24074f0-f784-4971-ba70-7a2462923b12","added_by":"auto","created_at":"2025-07-14 10:36:23","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":251810,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLeft ICP mediates the relationship between T2DM and reaction time (A), DSST (B)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAbbreviations: T2DM, type 2 diabetes mellitus; WM, white matter; ICP, inferior cerebellar peduncle; FA, fractional anisotropy; DSST, digit symbol substitution test\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-6970716/v1/7a472d51d29d798562c15b33.png"},{"id":87466898,"identity":"f41efb03-1030-4afc-ac36-5bfdbcc0a7b5","added_by":"auto","created_at":"2025-07-24 07:36:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2227451,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6970716/v1/69fc17cc-9f5a-4ea9-a891-da17f4886d5c.pdf"},{"id":86659001,"identity":"3bc86f7d-63e9-4bea-97bf-f3273771f8d8","added_by":"auto","created_at":"2025-07-14 10:28:22","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":127577,"visible":true,"origin":"","legend":"","description":"","filename":"Supply.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6970716/v1/ea663c2454105a836ba0fa4a.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Interaction effects of type 2 diabetes mellitus and air pollutants on white matter structures and cognitive function","fulltext":[{"header":"Introduction","content":"\u003cp\u003eWhite matter (WM) accounts for approximately half of the total volume of the brain, with its fibers extending throughout the brain, connecting cortical and subcortical gray matter regions to form functional neural networks that underpin cognition. It is particularly vulnerable to a range of factors, including aging, vascular disease, inflammation, metabolic disorders, demyelination, and trauma\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. WM degeneration impairs nerve signal transmission and disrupts neuronal connections, leading to cognitive and behavioral disorders, such as vascular dementia and Alzheimer's disease. It is also linked to declines in executive functions and slower information processing speed\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. Although the cognitive decline caused by WM alterations is a gradual process, it can be prevented or slowed down to a certain extent through a variety of interventions\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eAs one of the risk factors for WM health, type 2 diabetes mellitus (T2DM) is a widely prevalent metabolic disease characterized by insulin resistance and hyperglycemia and has become a major global health challenge and a significant socioeconomic burden\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. Many interrelated factors adversely affect the WM and cognition of individuals with T2DM, including potential metabolic, inflammation, oxidative stress, and microvascular determinants\u003csup\u003e[\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. Consistent abnormal WM changes in T2DM patients have been identified, such as the commissural fiber genu and body of the corpus callosum, the association fiber cingulum and superior frontal-occipital fasciculus, and the projection fibers anterior corona radiata and superior corona radiata\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. The reduced integrity of these WM tracts lowers the efficiency of information processing, thereby accelerating cognitive decline\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. Research indicated that individuals with T2DM have a significantly greater risk of dementia than the general population and experience an overall decline in cognitive function\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. In summary, T2DM poses a substantial risk to WM integrity and cognitive health.\u003c/p\u003e\u003cp\u003eAir pollutants (APs), particularly particulate matter\u0026thinsp;\u0026le;\u0026thinsp;2.5\u0026micro;m (PM\u003csub\u003e2.5\u003c/sub\u003e) and nitrogen dioxide (NO\u003csub\u003e2\u003c/sub\u003e), pose an additional threat to human WM integrity and cognitive health. Studies have shown that long-term exposure to high levels of APs can increase the risk of WM damage and dementia and accelerate cognitive decline\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. Owing to its fine particles, PM\u003csub\u003e2.5\u003c/sub\u003e can penetrate the blood-brain barrier, triggering brain inflammation and oxidative stress, which damages WM structures and leads to cognitive decline\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. Additionally, increased concentrations of PM\u003csub\u003e2.5\u003c/sub\u003e and NO\u003csub\u003e2\u003c/sub\u003e, are closely associated with the worsening of symptoms related to anxiety, schizophrenia, and depression\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eBoth T2DM and APs are known to damage WM through mechanisms such as inflammation and oxidative stress. However, the interaction between T2DM and APs on WM remains largely unexplored, and the impact of these WM changes on cognitive function has not been adequately addressed. Previous studies have focused on fractional anisotropy (FA) and mean diffusivity (MD) to assess WM integrity, but these metrics have limitations in capturing the full complexity of white matter microstructure. While FA and MD reflect overall WM integrity, they do not provide detailed insights into the cellular architecture of the tissue. In contrast, neurite orientation dispersion and density imaging (NODDI), which generates three key metrics\u0026mdash;isotropic volume fraction (ISOVF), intracellular volume fraction (ICVF), and oriented diffusivity (OD)\u0026mdash;offers a more comprehensive assessment of WM microstructure\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. Compared with traditional diffusion tensor imaging (DTI) metrics, NODDI can more comprehensively evaluate changes in the microstructural integrity of WM and has been proposed as an alternative and improved diffusion model in neurodegenerative diseases\u003csup\u003e[\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e, making it a valuable tool for investigating the mechanisms of WM alterations in the context of T2DM and APs exposure.\u003c/p\u003e\u003cp\u003eTo address this significant gap in the literature, this study investigated the interactive effects of T2DM and APs on WM integrity, using advanced imaging techniques, particularly NODDI. This approach allows for a more comprehensive understanding of the underlying mechanisms of WM alterations in the context of T2DM and APs exposure. Our findings not only shed light on the combined impact of T2DM and APs on WM but also provide valuable evidence to establish targeted preventive and intervention strategies for controlling cognitive decline in T2DM patients.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eDatabase and Inclusion Criteria\u003c/h2\u003e\u003cp\u003eIn this study, we utilized data from the UK Biobank, which included over 500,000 participants between 2006 and 2010 aged 40 to 69 years at the time of enrollment. The participants provided their demographic information through a touch-screen questionnaire and a computer-assisted interview. All participants completed a series of assessments, including electronic questionnaires, face-to-face interviews, physical examinations, and laboratory tests, to collect detailed data on biological, socioeconomic, and lifestyle factors\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. The study was approved by the local ethics committee, and informed consent was obtained from all participants at enrollment. During the screening process, individuals with central nervous system disorders, psychiatric conditions, or a history of traumatic brain injury were excluded based on the ICD-10 criteria. The participants were then classified into two groups, namely, those with and without diabetes, based on Field 2443. In the diagnosed diabetes group, individuals with an age of diagnosis\u0026thinsp;\u0026le;\u0026thinsp;35 years or a history of gestational diabetes were excluded\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. In the nondiabetes group, only individuals with normal glycated hemoglobin levels (35-42mmol/L) were included\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. Additionally, participants who did not undergo brain imaging were excluded from the study.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eAir Pollution\u003c/h3\u003e\n\u003cp\u003eA pan-European land use regression (LUR) model, developed following the European Study of Cohorts for Air Pollution Effects protocol, was used to estimate annualized concentrations of APs for the 2010 calendar year at the time of enrollment. These APs included nitrogen oxides (NO\u003csub\u003ex\u003c/sub\u003e), NO\u003csub\u003e2\u003c/sub\u003e, PM\u003csub\u003e2.5\u003c/sub\u003e, particulate matter 2.5\u0026ndash;10\u0026micro;m (PM\u003csub\u003e2.5\u0026minus;10\u003c/sub\u003e), and particulate matter\u0026thinsp;\u0026le;\u0026thinsp;10\u0026micro;m (PM\u003csub\u003e10\u003c/sub\u003e)\u003csup\u003e[\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. Detailed descriptions are available on the UK Biobank website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://biobank.ndph.ox.ac.uk/showcase/refer.cgi?id=2010\u003c/span\u003e\u003cspan address=\"https://biobank.ndph.ox.ac.uk/showcase/refer.cgi?id=2010\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). In summary, LUR models estimate APs levels at monitoring sites based on land-use characteristics. The resulting β coefficients are applied to land-use characteristics from other locations to predict APs levels in unmonitored areas. For this study, LUR models were utilized to estimate APs concentrations at participants\u0026rsquo; residences, with separate models developed for PM and NO\u003csub\u003ex\u003c/sub\u003e. PM was monitored in cities such as Manchester and London/Oxford, with the LUR model refined and optimized via local geographic and environmental data. A comparable method was employed for NO\u003csub\u003e2\u003c/sub\u003e and NO\u003csub\u003ex\u003c/sub\u003e, utilizing the more extensive network of monitoring sites.\u003c/p\u003e\n\u003ch3\u003eBrain Imaging and Cognitive Data\u003c/h3\u003e\n\u003cp\u003eFrom 2014 to 2020, the UK Biobank acquired brain imaging data for approximately 50,000 participants via a Siemens Skyra 3T MRI scanner with a standard 32-channel head coil\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. FSL (version 5.0.10) and FreeSurfer (version 6.0) software were used for image processing. For the raw imaging data, FA and MD were generated via DTI, while ISOVF, ICVF, and OD were generated via NODDI\u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. FA and MD values are used to assess the integrity of the WM structure, whereas ISOVF, ICVF, and OD provide insights into the volume fraction of extracellular isotropic free water, neurite density, and intracellular neurite dispersion within the WM, respectively\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e. In addition, UK Biobank participants underwent a series of cognitive tests. For this study, the following cognitive assessments were selected: reaction time, numeric memory, fluid intelligence/reasoning, trail making, symbol digit substitution, tower rearranging, and pair matching. Detailed methodologies are available on the UK Biobank official website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://biobank.ndph.ox.ac.uk/showcase/label.cgi?id=100026\u003c/span\u003e\u003cspan address=\"https://biobank.ndph.ox.ac.uk/showcase/label.cgi?id=100026\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eAnalyses were performed in R (version 4.2.1). All the data were standardized via z-scores to eliminate dimensional differences and ensure model stability. To exclude outliers, a threshold of six standard deviations was applied, removing abnormal values in the annual average concentrations of APs and WM structural metrics. To investigate the interaction effects of T2DM and APs on WM structural metrics, we first excluded participants with missing brain imaging data or APs information. Linear regression models were subsequently employed, with WM structural metrics as the dependent variables. The independent variables included group status (T2DM vs. control) and annual average concentrations of APs. Covariates, including age, sex, ethnicity, education level, and imaging center, were also adjusted for in the analysis. Next, we further examined the main effects of T2DM and annual average APs concentrations on WM structural metrics. Consistent with the previous analysis, T2DM and APs concentrations were analyzed separately as independent variables to investigate their impact on WM structural metrics. All results were adjusted using Bonferroni correction to control for false positives arising from multiple comparisons.\u003c/p\u003e\u003cp\u003eTo investigate the mediating role of WM structural metrics in the relationship between T2DM and cognitive function and to assess the moderating effect of APs, we conducted a mediation analysis using Model 7 from the SPSS Process macro. In this framework, group status (T2DM vs. nondiabetes) was treated as the independent variable, WM structural metrics were treated as mediators, cognitive performance was treated as the dependent variable, and annual average APs concentrations were treated as moderators.\u003c/p\u003e\u003cp\u003eTo ensure the reliability of the results, this study included all available data from the UK Biobank. Although covariates such as age, sex, education level, ethnicity, and imaging center were controlled for during the statistical analysis, the substantial difference in sample size between the two groups may have led to inadequate covariate matching, potentially affecting the results. Therefore, propensity score matching (PSM) was further employed in this study to improve sample comparability. Using age, sex, education level, ethnicity, and imaging center as covariates, a new control group was constructed by selecting individuals from the HCs group whose propensity scores were closest to those of the T2DM group, at a 1:2 patient-to-control ratio. This approach ensured that there were no significant statistical differences between the two groups in terms of age, sex, education level, ethnicity, or imaging center. Statistical analyses were then conducted on the WM metrics that showed significant interaction effects in the previous analysis, to validate the interaction between T2DM and PM\u003csub\u003e2.5\u003c/sub\u003e on these WM metrics. Furthermore, the potential mediating role of these metrics in the relationship between T2DM and cognitive function was examined.\u003c/p\u003e\u003c/div\u003e\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study used data from the UK Biobank (Application Number: 75556). UK Biobank received ethical approval from the National Information Governance Board for Health and Social Care and the NHS North West Centre for Research Ethics Committee (Ref: 11/NW/0382). All procedures complied with the Declaration of Helsinki. Written informed consent was obtained from all participants at recruitment. No additional consent was required for this secondary analysis. Data were fully anonymized and handled in accordance with the UK Data Protection Act and GDPR (2018).\u003c/p\u003e\n\u003cp\u003eClinical trial number: Not applicable.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eData\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eAnalysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll participants in this study were drawn from the UK Biobank database, including 665 individuals in the T2DM group and 13,382 in the nondiabetes group. The specific steps for data selection and final participant enrollment are detailed in Figure 1. Table 1 shows the demographic information and APs levels of the participants. Imaging metric maps (FA, MD, ISOVF, ICVF, and OD) and cognitive information are presented in Supplement materials.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInteraction Effects and Main Effects\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe analysis revealed a significant interaction effect between T2DM and the PM\u003csub\u003e2.5\u003c/sub\u003e concentration on the WM structure of the left inferior cerebellar peduncle (ICP) (FA value: P = 4.5231E-05, \u0026beta; = -0.1676; OD value: P = 1.5886E-04, \u0026beta; = 0.1462). With increasing annual PM\u003csub\u003e2.5\u003c/sub\u003e concentrations, the FA value of the left ICP in individuals with T2DM decreases, whereas the OD value increases. In contrast, no significant changes in FA or OD values were observed in the control group. These results indicated that individuals with T2DM have greater sensitivity to the effects of PM\u003csub\u003e2.5\u003c/sub\u003e on the WM tracts in the left ICP, leading to reduced WM integrity and increased neurite dispersion in this region (Figure 2).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAdditionally, we further examined the independent effects of T2DM and APs concentrations on WM structure. The results revealed that T2DM significantly impacted the WM structure of several fiber tracts, particularly in the following regions: the genu of the corpus callosum, fornix, left cerebral peduncle, left sagittal stratum, bilateral superior cerebellar peduncle (SCP), bilateral anterior corona radiata, bilateral external capsule, bilateral cingulum cingulate gyrus, bilateral fornix cres+stria terminalis, and bilateral superior fronto-occipital fasciculus. Notably, individuals with T2DM presented significantly reduced MD and ISOVF values in the pontine crossing tract and right corticospinal tract, as well as significantly decreased OD values in the posterior limb of the internal capsule and the bilateral superior corona radiata (Figure 3). Moreover, the effects of APs were predominantly observed in the bilateral SCP (Figure 4).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMediation Effects\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfter examining the WM structural metrics with significant interaction effects, we further conducted mediation analysis. The results revealed that only FA of the left ICP served as a mediator in the relationship between T2DM and cognitive function. T2DM exhibited significant direct effects on both reaction time and DSST performance (reaction time 95% confidence intervals (CIs): [0.0216, 0.1847]; DSST 95% CIs: [-0.2149, -0.0321]), indicating that T2DM directly influences these two cognitive abilities. Second, the indirect effects of the left ICP FA value, estimated using the bootstrap method, were also significant, with 95% CIs of [0.0020, 0.0105] for reaction time and [-0.0130, -0.0024] for DSST, both excluding zero. These findings indicated that T2DM significantly influences the reaction time and DSST ability indirectly by affecting the FA value of the left ICP. Finally, the moderation analysis demonstrated that the PM\u003csub\u003e2.5\u003c/sub\u003e concentration significantly moderated the mediating effect of the FA value in the left ICP on the relationship between T2DM and cognition. Specifically, as the PM\u003csub\u003e2.5\u003c/sub\u003e concentration increased, the mediating effect of the FA value became more pronounced. In summary, T2DM not only directly impacts cognitive ability but also indirectly influences it through the FA value of the left ICP. Moreover, PM\u003csub\u003e2.5\u003c/sub\u003e acts as a moderating variable, significantly amplifying the mediating effect of the FA value and highlighting the critical role of environmental pollution in brain function and behavioral health (Figure 5, Table 2).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eValidation Analysis Results\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfter applying PSM, there were no significant statistical differences between the two groups in terms of age, sex, ethnicity, and education level (Table 3). Further statistical analysis showed a significant interaction between T2DM and PM\u003csub\u003e2.5\u003c/sub\u003e on FA (\u0026beta; = -0.1221, P = 0.0143) and OD (\u0026beta; = 0.1017, P = 0.0310) values in the left ICP. In the mediation analysis, the FA value of the left ICP showed a significant mediating effect in the relationship between T2DM and cognitive function (reaction time 95% CIs: [0.1875, 2.9023]; DSST 95% CIs: [-0.1351, -0.0044]). Moreover, the level of PM\u003csub\u003e2.5\u003c/sub\u003e exposure significantly moderated the mediating effect of FA in the left ICP. These findings validated the results obtained from the original dataset, confirming the interaction between T2DM and PM\u003csub\u003e2.5\u003c/sub\u003e on FA and OD values in the left ICP. Moreover, the FA value mediated the relationship between T2DM and cognitive function, and this mediation effect was moderated by the level of PM\u003csub\u003e2.5\u003c/sub\u003e exposure.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study investigated the interaction effect of T2DM and APs on WM structure and further analyzed how WM structure influences cognitive function. We identified a significant interaction between T2DM and the annual average concentrations of APs, particularly PM\u003csub\u003e2.5\u003c/sub\u003e, in the WM structure of the left ICP. Specifically, T2DM patients exposed to higher PM\u003csub\u003e2.5\u003c/sub\u003e concentrations presented a significant reduction in FA values and an increase in OD values in this region, indicating greater WM sensitivity to PM\u003csub\u003e2.5\u003c/sub\u003e in these individuals. Furthermore, T2DM indirectly influenced cognitive performance, including reaction time and the DSST, through its effect on the FA value of the left ICP. Notably, the PM\u003csub\u003e2.5\u003c/sub\u003e concentration significantly moderated this relationship, suggesting that environmental pollution may play a critical role in the mechanisms of cognitive impairment associated with T2DM.\u003c/p\u003e\n\u003cp\u003eThe results of this study support the association between T2DM and WM damage, particularly in the context of environmental pollution. The literature has shown that WM lesions are more severe in individuals with T2DM, and these changes are closely linked to declines in cognitive function\u003csup\u003e[28, 29]\u003c/sup\u003e. This study revealed that the left ICP in individuals with T2DM is more sensitive to PM\u003csub\u003e2.5\u003c/sub\u003e exposure. The ICP, as a fiber tract connecting the cerebellum to other regions of the nervous system, transmits information from the spinal cord and the olivary nuclei to the cerebellum while also conveying signals from the cerebellum to the vestibular nuclei\u003csup\u003e[30, 31]\u003c/sup\u003e. These anatomical functions suggest that damage to the ICP can result in a variety of neurological impairments, primarily affecting motor coordination\u003csup\u003e[32, 33]\u003c/sup\u003e. Previous studies have confirmed that damage to the inferior cerebellar peduncle is associated with impaired learning ability during prism adaptation\u003csup\u003e[34]\u003c/sup\u003e. Additionally, microstructural changes in the left ICP, such as decreased FA values or increased MD values, are closely related to a decline in motor adaptation abilities\u003csup\u003e[35]\u003c/sup\u003e. In addition to its role in motor function, the ICP is also closely associated with cognitive disorders. Recent anatomical studies of cerebellar pathways have revealed that cerebellar output fibers project to non-motor areas in the prefrontal and posterior parietal cortex\u003csup\u003e[36]\u003c/sup\u003e. This anatomical framework provides a physiological basis for understanding how lesions in cerebellar peduncles can affect cognitive function. Studies have shown that increased MD values in the left ICP are strongly associated with cognitive impairment\u003csup\u003e[37]\u003c/sup\u003e. Additionally, research on schizophrenia has revealed that patients with this condition exhibit reduced FA values in the left ICP \u003csup\u003e[38]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eWe also found that the decrease in FA values in the left ICP mediates the relationship between T2DM and declines in reaction time and information processing ability. These findings further highlight the critical role of the left ICP in cognitive function, suggesting that it could be a potential target for the treatment of cognitive impairments. The heightened sensitivity of the left ICP may be attributed to inflammatory responses, oxidative stress, and cerebrovascular damage resulting from elevated blood glucose levels in these patients, which are thought to have already caused damage to the left ICP, rendering this structure more vulnerable to the effects of PM\u003csub\u003e2.5\u003c/sub\u003e. These findings provide valuable evidence for public health policy, suggesting that stricter air quality controls and health protection measures should be implemented for vulnerable populations, such as individuals with diabetes, to mitigate the additional detrimental effects of environmental pollution on brain health and cognitive function.\u003c/p\u003e\n\u003cp\u003eIn addition to examining the interaction effects, we investigated the independent impacts of T2DM and APs on WM structure. Previous research has demonstrated that APs, particularly PM and NO\u003csub\u003eX\u003c/sub\u003e, exert harmful effects not only on the respiratory and cardiovascular systems but also on brain health. These adverse effects are observed across the lifespan, from fetal development through middle and older age\u003csup\u003e[39]\u003c/sup\u003e. During early life, APs can interfere with the growth and development of WM fibers, leading to abnormal information processing and long-term cognitive effects\u003csup\u003e[40]\u003c/sup\u003e. In contrast, for fully developed WM fibers, APs can induce damage through mechanisms such as inflammation and oxidative stress, resulting in structural changes to WM and subsequent cognitive impairments\u003csup\u003e[41, 42]\u003c/sup\u003e. We found that the effects of APs, including PM\u003csub\u003e2.5\u003c/sub\u003e, PM\u003csub\u003e10\u003c/sub\u003e, NO\u003csub\u003ex\u003c/sub\u003e, NO\u003csub\u003e2\u003c/sub\u003e, and PM\u003csub\u003e2.5-10\u003c/sub\u003e, on WM structure are primarily observed in the bilateral SCP, which are composed primarily of efferent fibers. These fibers are responsible for transmitting information from the deep cerebellar nuclei to the contralateral cortex via the thalamus, playing a key role in motor coordination\u003csup\u003e[30, 31]\u003c/sup\u003e. Structural changes in the SCP have been linked to various cognitive disorders. This microstructural change is not only linked to declines in motor function but also correlate with slower information processing speed, which contributes to poorer performance on tasks such as simple number writing\u003csup\u003e[43]\u003c/sup\u003e. The APs may contribute to cognitive decline by affecting the SCP. This suggests that, when addressing cognitive health, the potential impact of environmental factors should not be overlooked.\u003c/p\u003e\n\u003cp\u003eThis study revealed that T2DM is associated with structural alterations in multiple WM fiber tracts, including the bilateral superior fronto-occipital fasciculus, bilateral fornix cres+stria terminalis, bilateral external capsule, bilateral SCP, and the genu and body of the corpus callosum. Besides the consistent results with previous studies\u003csup\u003e[44]\u003c/sup\u003e, our study revealed that in the T2DM group, the MD and ISOVF values of the right corticospinal tract and the pontine crossing tract were reduced. Additionally, PM\u003csub\u003e2.5-10\u003c/sub\u003e caused a decrease in the OD value of the pontine crossing tract. These contrary findings suggest that, for evaluating a given fiber tract, a comprehensive approach that incorporates multiple metrics should be highlighted.\u003c/p\u003e\n\u003cp\u003eThis study has several limitations. First, the UK Biobank sample is predominantly composed of white participants from the United Kingdom, which may limit the generalizability of the results to other ethnic groups or regions. Second, the majority of participants are volunteers with higher socioeconomic status\u003csup\u003e[45]\u003c/sup\u003e, which may lead to an underestimation of the potential impact of APs and T2DM on cognitive function in the general population. Additionally, the APs data used in this study were from 2010, while the brain imaging and cognitive data were collected in 2014, resulting in a temporal mismatch. Although annual concentrations tend to be stable, the four-year gap may still affect the accuracy of the results. Future research should consider using more real-time APs data and expanding the sample size to improve the generalizability of the findings.\u003c/p\u003e\n\u003cp\u003eThis study highlights the significant impact of T2DM and APs on WM structure and cognitive function. We found that T2DM interacts with APs, particularly PM\u003csub\u003e2.5\u003c/sub\u003e concentrations, in several regions of the WM structure, further influencing cognitive function. Through mediation analysis, we determined that the FA values in the left ICP mediate the effect of T2DM on cognitive decline, with PM\u003csub\u003e2.5\u003c/sub\u003e concentrations significantly moderating this process. These findings suggest that environmental pollution plays an important role in brain health and cognitive function in individuals with diabetes. Our research provides a new theoretical foundation for future interventions targeting the cognitive impairments associated with T2DM, particularly in the context of worsening environmental pollution, and underscores the importance of improving environmental quality as a potential intervention strategy.\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding information:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was funded by Tianjin Key Medical Discipline (Specialty) Construction Project (TJYXZDXK-001 A).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eFILLEY C M, FIELDS R D. White matter and cognition: making the connection [J]. J Neurophysiol, 2016, 116(5): 2093-104.\u003c/li\u003e\n\u003cli\u003ePRINS N D, SCHELTENS P. White matter hyperintensities, cognitive impairment and dementia: an update [J]. 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Atmospheric Environment, 2013, 72: 10-23.\u003c/li\u003e\n\u003cli\u003eALFARO-ALMAGRO F, JENKINSON M, BANGERTER N K, et al. Image processing and Quality Control for the first 10,000 brain imaging datasets from UK Biobank [J]. Neuroimage, 2018, 166: 400-24.\u003c/li\u003e\n\u003cli\u003eS M SMITH, ALFARO-ALMAGRO F, MILLER K L. UK Biobank Brain Imaging Documentation. [2024]. https://biobank.ndph.ox.ac.uk/showcase/ukb/docs/brain_mri.pdf\u003c/li\u003e\n\u003cli\u003eZHANG H, HUBBARD P L, PARKER G J, et al. Axon diameter mapping in the presence of orientation dispersion with diffusion MRI [J]. Neuroimage, 2011, 56(3): 1301-15.\u003c/li\u003e\n\u003cli\u003eKAMAGATA K, ANDICA C, KATO A, et al. Diffusion Magnetic Resonance Imaging-Based Biomarkers for Neurodegenerative Diseases [J]. Int J Mol Sci, 2021, 22(10).\u003c/li\u003e\n\u003cli\u003eGAO S, CHEN Y, SANG F, et al. White Matter Microstructural Change Contributes to Worse Cognitive Function in Patients With Type 2 Diabetes [J]. Diabetes, 2019, 68(11): 2085-94.\u003c/li\u003e\n\u003cli\u003eGAO J, PAN P, LI J, et al. Analysis of white matter tract integrity using diffusion kurtosis imaging reveals the correlation of white matter microstructural abnormalities with cognitive impairment in type 2 diabetes mellitus [J]. Front Endocrinol (Lausanne), 2024, 15: 1327339.\u003c/li\u003e\n\u003cli\u003ePERRINI P, TIEZZI G, CASTAGNA M, et al. Three-dimensional microsurgical anatomy of cerebellar peduncles [J]. Neurosurg Rev, 2013, 36(2): 215-24; discussion 24-25.\u003c/li\u003e\n\u003cli\u003eWRIGHT M, SKAGGS W, \u0026Aring;RUP NIELSEN F. The Cerebellum [J]. WikiJournal of Medicine, 2016, 3(1).\u003c/li\u003e\n\u003cli\u003eKIM Y, KIM S H, HONG B Y, et al. Integrity of the Inferior Cerebellar Peduncle Correlates with Ambulatory Function after Hemorrhagic Stroke [J]. J Stroke Cerebrovasc Dis, 2021, 30(12): 106164.\u003c/li\u003e\n\u003cli\u003eSTOODLEY C J, SCHMAHMANN J D. Functional topography in the human cerebellum: a meta-analysis of neuroimaging studies [J]. Neuroimage, 2009, 44(2): 489-501.\u003c/li\u003e\n\u003cli\u003eMARTIN T A, KEATING J G, GOODKIN H P, et al. Throwing while looking through prisms. I. Focal olivocerebellar lesions impair adaptation [J]. Brain, 1996, 119 ( Pt 4): 1183-98.\u003c/li\u003e\n\u003cli\u003eJOSSINGER S, MAWASE F, BEN-SHACHAR M, et al. Locomotor Adaptation Is Associated with Microstructural Properties of the Inferior Cerebellar Peduncle [J]. Cerebellum, 2020, 19(3): 370-82.\u003c/li\u003e\n\u003cli\u003eSTRICK P L, DUM R P, FIEZ J A. Cerebellum and nonmotor function [J]. Annu Rev Neurosci, 2009, 32: 413-34.\u003c/li\u003e\n\u003cli\u003eWEI Y C, HSU C H, HUANG W Y, et al. White Matter Integrity Underlies the Physical-Cognitive Correlations in Subjective Cognitive Decline [J]. Front Aging Neurosci, 2021, 13: 700764.\u003c/li\u003e\n\u003cli\u003eCHANG X, JIA X, WANG Y, et al. Alterations of cerebellar white matter integrity and associations with cognitive impairments in schizophrenia [J]. Front Psychiatry, 2022, 13: 993866.\u003c/li\u003e\n\u003cli\u003eCORY-SLECHTA D A, MERRILL A, SOBOLEWSKI M. Air Pollution-Related Neurotoxicity Across the Life Span [J]. Annu Rev Pharmacol Toxicol, 2023, 63: 143-63.\u003c/li\u003e\n\u003cli\u003eHERTING M M, BOTTENHORN K L, COTTER D L. Outdoor air pollution and brain development in childhood and adolescence [J]. Trends Neurosci, 2024, 47(8): 593-607.\u003c/li\u003e\n\u003cli\u003eDE PRADO BERT P, MERCADER E M H, PUJOL J, et al. The Effects of Air Pollution on the Brain: a Review of Studies Interfacing Environmental Epidemiology and Neuroimaging [J]. Curr Environ Health Rep, 2018, 5(3): 351-64.\u003c/li\u003e\n\u003cli\u003eJANKOWSKA-KIELTYKA M, ROMAN A, NALEPA I. The Air We Breathe: Air Pollution as a Prevalent Proinflammatory Stimulus Contributing to Neurodegeneration [J]. Front Cell Neurosci, 2021, 15: 647643.\u003c/li\u003e\n\u003cli\u003eFRITZ N E, EDWARDS E M, YE C, et al. Cerebellar Contributions to Motor and Cognitive Control in Multiple Sclerosis(✰✰✰) [J]. Arch Phys Med Rehabil, 2022, 103(8): 1592-9.\u003c/li\u003e\n\u003cli\u003eMA T, LI Z Y, YU Y, et al. Gray and white matter abnormality in patients with T2DM-related cognitive dysfunction: a systemic review and meta-analysis [J]. Nutr Diabetes, 2022, 12(1): 39.\u003c/li\u003e\n\u003cli\u003eBRAYNE C, MOFFITT T E. The limitations of large-scale volunteer databases to address inequalities and global challenges in health and aging [J]. Nat Aging, 2022, 2(9): 775-83.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cdiv\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"524\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 215px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eT2DM\u0026nbsp;\u003cbr\u003e\u0026nbsp;(N = 665)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHealthy control\u0026nbsp;\u003cbr\u003e\u0026nbsp;(N = 13382)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAge [mean (SD)]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e58.60 (6.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e56.76 (7.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSex, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e210 (31.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6,993 (52.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e455 (68.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6,389 (47.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eEthnic, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eWhite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e604 (90.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12,905 (96.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e61 (9.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e477 (3.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eEducation, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCollege or higher\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e385 (57.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8,286 (61.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e0.037\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLess than college\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e280 (42.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5,096 (38.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCentre, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCentre1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e404 (60.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7,820 (58.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\"\u003e\n \u003cp\u003e0.539\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCentre2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e107 (16.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2,146 (16.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCentre3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e153 (23.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3,402 (25.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCentre4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1 (0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e14 (0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAir pollutants [mean (SD)], \u0026mu;g/m\u0026sup3;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e26.0781 (6.9520)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e25.6739 (7.2549)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.162\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNO\u003csub\u003ex\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e43.2666 (14.1472)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e42.4297 (14.7075)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.154\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePM\u003csub\u003e10\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e16.1582 (1.9391)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e15.9820 (1.8655)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePM\u003csub\u003e2.5\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9.9557 (1.0115)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9.8928 (1.0419)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.140\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePM\u003csub\u003e2.5-10\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.4344 (0.9462)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.3488 (0.8581)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1 Characteristics of the study population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMean (SD) values and percentages are reported for continuous and categorical variables, respectively.\u003c/p\u003e\n\u003cp\u003eAbbreviations: SD, standard deviation; T2DM, type 2 diabetes mellitus; NO\u003csub\u003e2\u003c/sub\u003e, nitrogen dioxide; NO\u003csub\u003ex\u003c/sub\u003e, nitrogen oxides; PM\u003csub\u003e10\u003c/sub\u003e, particulate matter \u0026le; 10 \u0026mu;m; PM\u003csub\u003e2.5\u003c/sub\u003e, particulate matter \u0026le; 2.5 \u0026mu;m; PM\u003csub\u003e2.5-10\u003c/sub\u003e, particulate matter 2.5-10 \u0026mu;m; \u0026mu;g, micrograms\u0026nbsp;\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"556\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCognition\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eConditional indirect effect analysis at different PM\u003csub\u003e2.5\u003c/sub\u003e levels (M\u0026plusmn;SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEffect\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBootSE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBootLLCI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBootULCI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 89px;\"\u003e\n \u003cp\u003eReaction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003eM - 1SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e-0.0006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e0.0025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e-0.0058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e0.0042\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e0.0066\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e0.0024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e0.0024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e0.0119\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003eM + 1SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e0.0138\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e0.0044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e0.0061\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e0.0236\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 89px;\"\u003e\n \u003cp\u003eDSST\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003eM - 1SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e0.0011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e0.0032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e-0.0050\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e0.0077\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e-0.0080\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e0.0030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e-0.0146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e-0.0029\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003eM + 1SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e-0.0171\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e0.0056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e-0.0294\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e-0.0074\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2 Results of the moderated mediation analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAbbreviations: PM\u003csub\u003e2.5\u003c/sub\u003e, particulate matter \u0026le; 2.5 \u0026mu;m; M, mean; SD, standard deviation; DSST, digit symbol substitution test; T2DM, type 2 diabetes mellitus; FA, fractional anisotropy; ICP, inferior cerebellar peduncle\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"549\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eT2DM\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(\u003c/strong\u003e\u003cstrong\u003eN\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;=\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e626\u003c/strong\u003e\u003cstrong\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHealthy control\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(\u003c/strong\u003e\u003cstrong\u003eN\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;=\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e1252\u003c/strong\u003e\u003cstrong\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003eAge [mean (SD)]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e58.60\u0026nbsp;(6.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e58.72\u0026nbsp;(6.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e0.721\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003eSex, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e195\u0026nbsp;(0.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e382\u0026nbsp;(0.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 77px;\"\u003e\n \u003cp\u003e0.784\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e431\u0026nbsp;(0.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e870\u0026nbsp;(0.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003eEthnic, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003eWhite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e567\u0026nbsp;(0.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e1138\u0026nbsp;(0.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 77px;\"\u003e\n \u003cp\u003e0.869\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e59\u0026nbsp;(0.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e114\u0026nbsp;(0.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003eEducation, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003eCollege or higher\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e356\u0026nbsp;(0.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e738\u0026nbsp;(0.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 77px;\"\u003e\n \u003cp\u003e0.402\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003eLess than college\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e270\u0026nbsp;(0.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e514\u0026nbsp;(0.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003eCentre, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003eCentre1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e401\u0026nbsp;(0.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e812\u0026nbsp;(0.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 77px;\"\u003e\n \u003cp\u003e0.932\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003eCentre2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e106\u0026nbsp;(0.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e214\u0026nbsp;(0.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003eCentre3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e118\u0026nbsp;(0.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e225\u0026nbsp;(0.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003eCentre4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e1\u0026nbsp;(0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e1\u0026nbsp;(0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3 Group Comparison of Demographic Characteristics After PSM\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMean (SD) values and percentages are reported for continuous and categorical variables, respectively.\u003c/p\u003e\n\u003cp\u003eAbbreviations: SD, standard deviation; T2DM, type 2 diabetes mellitus; PSM, propensity score matching\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"brain-imaging-and-behavior","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bior","sideBox":"Learn more about [Brain Imaging and Behavior](https://www.springer.com/journal/11682)","snPcode":"11682","submissionUrl":"https://submission.nature.com/new-submission/11682/3","title":"Brain Imaging and Behavior","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Interaction effect, PM2.5, T2DM, WM structure, Cognition","lastPublishedDoi":"10.21203/rs.3.rs-6970716/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6970716/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground and Objectives:\u003c/h2\u003e\u003cp\u003eAlterations in white matter (WM) integrity, which are linked to cognitive impairments, are influenced by both type 2 diabetes mellitus (T2DM) and air pollutants (APs). However, the interaction effects between the two factors on WM structures remain unclear. Our study aimed to elucidate the interaction effects of APs and T2DM on WM integrity and to examine how this interaction influences cognitive function.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eThis study included participants from the UK Biobank, categorized into a diabetes group and a control group (sample size: 665 vs. 13382). APs data, WM structure data, and cognitive data were incorporated into the analysis. Linear regression models were used to assess the interaction effect of T2DM and APs on WM structures and cognitive function, and then the mediation and moderation analyses were performed. Finally, propensity score matching (PSM) was performed at a 1:2 ratio to reselect the control group. After strictly controlling for covariates, the study results were validated.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eSignificant interaction effects between T2DM and particulate matter\u0026thinsp;\u0026le;\u0026thinsp;2.5\u0026micro;m (PM\u003csub\u003e2.5\u003c/sub\u003e) were detected on the fractional anisotropy (FA) and oriented diffusivity (OD) values of the left inferior cerebellar peduncle (ICP). The FA value of the left ICP mediated the relationships between T2DM and both reaction time and digit symbol substitution test (DSST) scores. As the concentration of PM\u003csub\u003e2.5\u003c/sub\u003e increased, the mediating effect of FA became more pronounced. Additionally, both T2DM and APs had independent effects on multiple WM tracts, with APs primarily affecting the superior cerebellar peduncle (SCP).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003ePatients with T2DM have increased sensitivity of the left ICP to PM\u003csub\u003e2.5\u003c/sub\u003e, and this fiber tract plays a mediating role in the relationship between T2DM and cognitive function, with the mediating effect being moderated by PM\u003csub\u003e2.5\u003c/sub\u003e, highlighting the critical role of environmental pollution in brain function and behavioral health.\u003c/p\u003e","manuscriptTitle":"Interaction effects of type 2 diabetes mellitus and air pollutants on white matter structures and cognitive function","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-14 10:28:17","doi":"10.21203/rs.3.rs-6970716/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"44715000718635280342974551382647392971","date":"2025-11-13T22:24:23+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-08T23:24:46+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-03T06:30:54+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-27T02:09:44+00:00","index":"","fulltext":""},{"type":"submitted","content":"Brain Imaging and Behavior","date":"2025-06-25T05:12:52+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"brain-imaging-and-behavior","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bior","sideBox":"Learn more about [Brain Imaging and Behavior](https://www.springer.com/journal/11682)","snPcode":"11682","submissionUrl":"https://submission.nature.com/new-submission/11682/3","title":"Brain Imaging and Behavior","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"2aaa8f54-0ba8-4eee-aeb4-562381db4513","owner":[],"postedDate":"July 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-07-14T10:28:18+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-14 10:28:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6970716","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6970716","identity":"rs-6970716","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}