Temporal Dynamic of Cognitive Decline in Type 2 Diabetes Mellitus Patients: A Multimodal Biomarker Analysis using Event-Based Modal and Principal Component Analysis

Temporal Dynamic of Cognitive Decline in Type 2 Diabetes Mellitus Patients: A Multimodal Biomarker Analysis using Event-Based Modal and Principal Component Analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Temporal Dynamic of Cognitive Decline in Type 2 Diabetes Mellitus Patients: A Multimodal Biomarker Analysis using Event-Based Modal and Principal Component Analysis Min-Hua Ni, Bo Hu, Xiao-Yan Bai, Yao Tong, Zi-Yang Ma, Hao Xie, and 10 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7380267/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 14 Nov, 2025 Read the published version in Diabetology & Metabolic Syndrome → Version 1 posted 9 You are reading this latest preprint version Abstract Background Type 2 diabetes mellitus (T2DM) is associated with cognitive impairment, affecting life quality. The progression of cognitive decline and its neural basis in T2DM are unclear due to limitations in previous studies. This study integrates Event-Based Model (EBM) and Principal Component Analysis (PCA) to explore these aspects in T2DM patients. Methods This study assessed 119 T2DM patients and 87 healthy controls with neuropsychological tests (CVLT, Stroop, WCST) and MRI for gray matter volume (GMV). PCA simplified cognitive scores into composites for memory and executive function. EBM estimated the sequence of cognitive and neurostructural changes. Partial correlation analyses were used to examine associations with clinical factors with controlling covariance. Results Cognitive decline in T2DM began with attention and working memory, followed by executive function and episodic memory. GMV loss started in the insular gyrus, spreading to other regions. T2DM showed advanced disease progression (0.54 (0.12) vs. 0.49 (0.10), P = 0.001). A negative correlation linked long-delay memory (CVLT-PC4) to random blood glucose ( r = -0.581, P FDR = 0.025). Conclusion This study reveals the sequence of cognitive and neuroanatomical changes in T2DM. Memory decline and insular gyrus atrophy may serve as early biomarkers for T2DM-related cognitive impairment, which may be helpful in the development of personalized interventions to improve life quality. Cognitive decline Event-based model Gray matter volume Principal component analysis Type 2 diabetes mellitus Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Diabetes mellitus is a complicated metabolic disorder characterized by hyperglycemia and insulin resistance, with type 2 diabetes mellitus (T2DM) accounting for 90–95% of cases (Srikanth et al. 2020 ; Sun et al. 2022 ). Cognitive impairment is one of the common complications of T2DM and leads to difficulties in self-management and social functions, significantly decreasing the quality of life (Biessels and Despa 2018 ; Zheng et al. 2018 ). While substantial evidence has characterized the neurocognitive manifestations of T2DM as multidimensional manifestations including verbal and visual memory, attention and concentration, processing speed, executive function, and motor control, the developmental trajectories of these domain-specific deficits demonstrate non-linear progression patterns (Gorniak et al. 2019 ; Antal et al. 2022 ; Srikanth et al. 2020 ). Additionally, neuroimaging advances have delineated morphometric alterations of cognitive impairment (Dong et al. 2019 ). Previous researches have demonstrated that T2DM patients with cognitive impairment exhibit reduced gray matter volume (GMV) in several brain regions, including the superior temporal gyrus, right middle frontal gyrus, right Rolandic operculum, left fusiform gyrus, cerebellum, and frontal opercular cortex (Wu et al. 2017 ; H.Y. Zhang, Shen, et al. 2024; D. Zhang, Lei, et al. 2021). However, extant investigations manifest two critical methodological constraints: (1) the predominant reliance on cross-sectional designs obscures the temporal dynamics of cognitive deterioration and neuroanatomical trajectories of GMV reorganization in T2DM patients, hindering identification of critical windows for intervention; (2) multidimensional clinical assessments demonstrate substantial psychometric redundancy, necessitating dimensionality reduction to retain informative features for further exploration (Callisaya et al. 2019 ; R.Y. Kim, Joo, et al. 2024). Principal component analysis (PCA) is a dimensionality reduction technique that identifies latent variables by decomposing covariance structures through orthogonal transformation of high-dimensional datasets (Hu et al. 2025 ; Pascuzzo et al. 2020 ). The event-based model (EBM) estimates the ordered abnormality sequence of biomarkers by combining severity information across biomarkers and individuals, without reference to a given individual’s clinical status (Firth et al. 2020 ). Previous studies have successfully applied these methods to various neurodegenerative diseases, including Alzheimer’s disease (AD) (Firth et al. 2020 ; Leuzy et al. 2022 ), Parkinson’s disease (Tan et al. 2023 ), and schizophrenia (Y. Jiang et al. 2024 ), and multiple sclerosis (Eshaghi et al. 2021 ), indicating that this method is effective and robust.. However, current methodological innovations remain conspicuously underutilized in T2DM research, particularly regarding the development of longitudinal profiling of cognitive decline and GMV trajectory. In this study, we established an integrative analytical framework combining EBM with PCA to decode temporal sequences of cognitive deterioration and imaging biomarkers in T2DM using cross-sectional datasets. After multimodal integration, we systematically investigate clinical correlation between disease-specific event patterns and pathophysiological biomarkers in T2DM. The findings may offer evidentiary support in clinical decision-making, demonstrating potential clinical utility particularly regarding cognitive trajectory monitoring and identification of critical interventional windows. 2. Materials and methods 2.1. Participants One hundred and nineteen T2DM patients and eighty-seven HC subjects were recruited from the endocrinology department of Tangdu hospital and the local community. The T2DM patients were defined as fasting blood glucose (FBG) ≥ 7.0 mmol/L and/or 2-hour post oral glucose tolerance test (OGTT) glucose ≥ 11.1 mmol/L. The subjects with FBG < 6.1 mmol/L and 2-hour post-OGTT glucose < 7.8 mmol/L were included in HC group. Participants were excluded if they had i) other types of diabetes (type 1 diabetes or gestational diabetes); ii) neurological disorders of the central nervous system or diseases seriously impairing neurological function; iii) any psychiatric or neurological illness; iv) retinopathy or neuropathy; v) a history of substance, alcohol or drug abuse. The clinical characteristics data including age, sex, years of education, body mass index (BMI), disease duration, blood pressure, FBG, Postprandial blood glucose (PBG), Random blood glucose (GLU), hemoglobin A1C (HbA1c), total cholesterol, triglyceride, high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and urinary microalbumin (MAlb). 2.2. Neuropsychological tests All participants completed the MMSE, the MoCA, Self-Assessment Scale for Anxiety (SAS), Self-Rating Scale for Depression (SDS), the California Verbal Learning Test (CVLT), the Wisconsin Card Sorting Test (WCST), the Stroop Color-Word Test (STROOP), and Trail Making Test (TMT). This battery of psychological assessment was used to assess general cognitive capability, anxiety and depressive states, memory function, and executive function, respectively. 2.3. Data acquisition MRIs were acquired using a 3.0 T GE Discovery MR 750 scanner (GE Healthcare, Milwaukee, WI, USA) with an eight-channel prototype quadrature birdcage head coil array. Foam padding was used to minimize head movement. During the whole scanning, all subjects were required to close eyes and do not think about anything. Structural images were acquired by using three-dimensional brain volume (3D-BRAVO) and the routine clinical protocol (T1 weighted images, T2 weighted images, T2 fluid attenuated inversion recovery images, and time-of-flight magnetic resonance angiography) were acquired to detect brain abnormalities. Detailed MRI settings were detailed in previous publications (Ni et al. 2024 ; Hu et al. 2018 ). 2.4. Data preprocessing Three-dimensional T1-weighted images from all participants underwent preprocessing using Statistical Parametric Mapping version 12 (SPM12) and the Computational Anatomy Toolbox (CAT) 12.9 (r2577) in MATLAB R2023a. The preprocessing pipeline encompassed image segmentation, registration, and spatial normalization. For the segmentation process, the East Asian brain-specific tissue probability maps (TPMs) from the CAT12 were utilized. The imaging data underwent intrinsic resampling using a spatially adaptive non-local mean filter to 1.5 × 1.5 × 1.5 mm 3 , followed by bias field correction and affine registration to standard space. Subsequently, the images were processed through the standard SPM pipeline including "unified segmentation" algorithm and skull stripping procedures. This comprehensive preprocessing workflow ensured accurate tissue classification while maintaining anatomical consistency across subjects. Then, tissue classification was refined through local intensity modulation across gray matter, white matter, and cerebrospinal fluid. Adaptive maximum a posteriori segmentation with partial volume estimation quantified fractional tissue composition at the voxel level, accompanied by total intracranial volume (TIV) quantification. Subsequently, spatial normalization to Montreal Neurological Institute (MNI) reference space was achieved through high-dimensional diffeomorphic registration using geodesic shooting algorithms (Chen et al. 2024 ). All images with data quality below level C were excluded and finally spatial smoothing using an isotropic Gaussian kernel (6 mm full width at half maximum) to mitigate inter-subject anatomical variability while preserving mesoscopic structural features. Here, all of the Anatomical Automatic Labeling (AAL) were separated into 17 features (frontal lobe, temporal lobe, parietal lobe, occipital lobe, insula, cingulate, sensorimotor, Broca’s area, cerebellum, hippocampus, parahippocampus, amygdala, caudate, putamen, pallidum, nucleus accumbens and thalamus) which were selected as the region of interests (ROIs), as details described in previous study (Yuchao Jiang et al. 2023 ). 2.5. PCA To reduce the scores of two cognitive domains (memory and executive function) to more meaningful components and meet the requirements of subsequent EBM model input data, PCA was applied to the obtained z-scores of the CVLT, STROOP and WCST for each subject. PCA is an efficient dimensionality reduction technique that simplifies data structures by transforming high-dimensional features into an orthogonal set of low-dimensional PCs. This approach significantly reduces the complexity of the data while preserving key information (Wang et al. 2024 ). PCA was conducted on the cognitive domain scores using the scikit-learn library version 0.24 within a Python 3.9 environment. The selection of PCs was guided by the criteria established by Jolliffe (Banquet-Teran et al. 2016 ), which recommends that the cumulative explained variance should exceed 70%, and that the eigenvalues of the components should be greater than 0.7, ensuring robust representation of data variability (H.E. Kim, Kim, et al. 2024 ). 2.6. EBM EBM is a sophisticated data-driven model that forecasts disease progression by mapping the timeline of biomarker abnormalities using a single cross-sectional dataset (Young et al. 2014 ; Pascuzzo et al. 2020 ). This methodology is particularly effective in cohorts with diverse disease severity, as early biomarkers tend to show abnormal values more frequently than those that manifest later in the disease's course. The sequence of events is probabilistically determined in a data-driven fashion by aggregating the severity of biomarkers, which is indicative of their event probabilities, across multiple individuals. In essence, biomarkers with a higher prevalence are positioned earlier in the sequence. The model has been extensively utilized in research to delineate the progression and sequence of AD cognitive impairment characteristics (Firth et al. 2020 ; Eshaghi et al. 2018 ; Oxtoby et al. 2018 ). Its strengths are manifold: (1) By capitalizing on the fact that included subjects exhibit varying degrees of cognitive impairment, we can deduce the sequence of disease characteristic changes during the dynamic evolution of cognitive impairment from cross-sectional study data. (2) The model employs a probabilistic approach that automatically discerns the normal and abnormal distributions of each input feature from the data, eliminating the need for manual outlier definition and thereby reducing the influence of subjective error. These features make EBM to be a robust tool for understanding the complex trajectories of cognitive impairment diseases (Fonteijn et al. 2012 ). 2.6.1.1. Spatiotemporal cascades of cognitive function scores or GMV abnormalities We used EBM to estimate the spatiotemporal cascade of abnormal biomarker events, which in this study refers to the three-step process of fitting the longitudinal order of different cognitive function dimensions scores and brain structural changes (GMV) in each T2DM patient. First, EBM estimates the degree of abnormality of each biomarker by linearly mapping the cognitive function scores or GMV of each subject to probabilities of regional abnormality (0: 0, 1: 0.33, 2: 0.67,3: 1). Second, EBM estimates the spatiotemporal cascade of events for each subject, by ordering these probabilities. Third, the mean spatiotemporal cascade for cognitive function scores or GMV is estimated as the sequence that minimizes the sum of probabilistic Kendall’s Tau distances to the spatiotemporal cascades of T2DM subjects (Venkatraghavan et al. 2023 ). In addition to determining the average sequence of biomarker abnormalities, the methodology also quantifies the relative temporal intervals separating these events. This analysis yields a collection of "event-centers" (ECs), which are positioned along a disease progression timeline that is normalized to span from 0 to 1. These ECs serve as reference points, illustrating the chronological order and relative timing of biomarker abnormalities within the context of disease development (Venkatraghavan et al. 2019 ). 2.6.1.2. Patients Staging This study employs the Expectation-Maximization (EM) algorithm to optimize the disease staging for individual samples. By constructing a likelihood function that integrates the conditional probabilities of various biomarkers at different time points, the function assesses the likelihood of the dataset given a specific sequence of disease progression. The EM algorithm is utilized to iteratively identify the sequence that maximizes the likelihood function, thereby determining the most accurate stage of disease progression for the individual (Young et al. 2014 ). 2.6.1.3. Validation We conducted cross-validation on our event-based models by re-estimating each complete model, which includes event distributions and maximum likelihood sequences, using 100 bootstrap samples with replacement. 2.6.2. Statistical analysis Statistical analyses were conducted using the Statistical Package for the Social Sciences (SPSS version 26.0). We calculated the mean (standard deviation [SD]) or median (interquartile range [IQR]) of characteristics for the entire study population. Multivariate linear regression analysis was used to compare differences in demographic, clinical characteristics, MoCA scores, MMSE scores, TMT-A, TMT-B, SAS scores, SDS scores, CVLT principal components (PCs), STROOP PCs, and WCST PCs between T2DM and the HC groups. The analysis was adjusted for baseline age, gender, and education level. Differences in disease progression stages estimated by EBM (EBM staging) were compared using a two-sample t-test, as covariates had already been adjusted in the EBM fitting. Partial correlation analysis was used to estimate the correlation between cognitive or GMV indicators that showed significant difference between the two groups and clinical risk factors, adjusting for baseline age, gender, and education level. The false discovery rate (FDR) method in MATLAB R2023b platform was used for the multiple comparison correction of the correlation analysis. Significant levels were set at P < 0.05 after FDR correction. Flow diagrams are shown in Fig. 1 . 3. Results 3.1. Demographic and clinical characteristics of the participants. Demographic and clinical data were evaluated between the T2DM and HC groups (Table 1 ). After covariance analysis between these two groups, the results revealed that there were no significant differences in BMI, postprandial blood glucose, microalbuminuria, total cholesterol, triglycerides, HDL-C, LDL-C and diastolic pressure. Higher age ( P < 0.001), HbA1c (P < 0.001), fast blood glucose (P < 0.001), random blood glucose (P < 0.001), systolic pressure ( P = 0.021) and lower education years ( P = 0.001) were found in the T2DM group. Table 1 Demographic and clinical characteristics of the participants. T2DM (n = 119) HC (n = 87) F /χ 2 P Age (years) 54.87 ± 9.074 49.92 ± 7.621 4.136 < 0.001 ** Education (years) 12.02 ± 3.063 13.66 ± 3.888 -3.257 0.001 * Male/female 87/32 50/37 5.517 0.019 * Diabetes duration (years) 9.632 ± 7.037 - - - BMI (kg/m 2 ) 25.06 ± 3.290 24.16 ± 3.245 0.467 0.497 Biochemical indicator HbA1c (%) 8.57 ± 2.060 5.56 ± 0.297 15.630 < 0.001 ** Fast blood glucose (mmol/L) 7.94 ± 2.910 3.972 ± 2.436 10.628 < 0.001 ** Postprandial blood glucose(mmol/L) 10.977 ± 4.186 7.320 ± 0.910 1.623 0.208 Random blood glucose (mmol/L) 8.503 ± 2.848 5.978 ± 1.025 6.253 < 0.001 ** Microalbuminuria (mmol/L) 79.703 ± 151.511 17.420 ± 11.797 0.134 0.716 Total cholesterol (mmol/L) 4.388 ± 1.129 4.696 ± 1.077 -0.020 0.888 Triglycerides (mmol/L) 1.861 ± 1.275 1.937 ± 1.322 -1.634 0.207 HDL (mmol/L) 1.097 ± 0.240 1.196 ± 0.385 -0.030 0.864 LDL (mmol/L) 2.542 ± 1.294 2.658 ± 0.745 -0.058 0.811 Systolic pressure (mmHg) 130.216 ± 16.060 126.750 ± 18.450 5.631 0.021 * Diastolic pressure (mmHg) 81.897 ± 9.364 82.026 ± 12.666 -0.523 0.473 Data were reported as mean ± SD. T2DM, type 2 diabetes mellitus; HC, healthy controls; BMI: Body Mass Index; HbA1c: hemoglobin A1C; HDL: high-density lipoprotein; LDL: low-density lipoprotein. * P < 0.05; ** P < 0.001. 3.2. Cognitive characteristics of the participants. Detailed information on the CVLT, STROOP and WCST assessment of the two groups is presented in Table S1 . The Kaiser-Meyer-Olkin test indicated a measure of sampling adequacy of 0.708, 0.782, 0.735 respectively and all Bartlett’s test of sphericity were found to be significant ( P < 0.001), suggesting that the CVLT, STROOP and WCST results were suitable for PCA. The rubble diagram of PCA is shown in Fig. 2 . Gravel with eigenvalue greater than 1 is selected as the main component. Four PCs were extracted from the CVLT accounting for 71.56% of the total variable. The first component (PC1) mainly reflected immediate recall explaining 45.48% of the total variables including the trial 1, trial 2, trial 3, trial 4, trial 5, trial 1–5 and trial B. The second component (PC2) mainly reflected long delayed recognition accounting for 13.73% of the total variance including the short-delay free recall, short-delay cued recall, long-delay free recall and long-delay cued recall. The third component (PC3) mainly reflected intrusion and repetition explaining 6.64% of the total variables including the free recall intrusions, cued recall intrusions, total intrusions and total repetitions. The fourth component (PC4) reflected long delayed recognition explaining 5.72% of the total variables including long-delay true positive recognition and long-delay false positive recognition. A synthesized score (CVLT-PC) was calculated based on the four above components, providing a comprehensive assessment of episodic memory. Two PCs were extracted from the STROOP accounting for 80.51% of the total variable. The PC1 mainly reflected correct and reaction time explaining 69.75% of the total variables including the correct number, congruent correct number, congruent reaction time, incongruent number, incongruent reaction time, pronunciation relevant correct number, pronunciation relevant reaction time, irrelevant correct number and irrelevant reaction time. The PC2 mainly reflects error and omission accounting for 10.76% of the total variance including the error number and omission number. A synthesized score (STROOP-PC) was calculated based on the two above components, providing a comprehensive assessment of executive function including attention and inhibitory control. As for WCST, four PCs were extracted from this score accounting for 89.80% of the total variable. The PC1 mainly reflected Overall cognitive function and cognitive flexibility explaining 57.22% of the total variables including the total response, total errors, percentage of total errors, total perseverative responses, percentage of total perseverative responses, total non-perseverative errors, percentage of total non-perseverative errors and time. The PC2 mainly reflects perseverative errors explaining 14.65% of the total variance including the perseverative errors and percentage of perseverative errors. The PC3 mainly reflected abstract thinking explaining 10.42% of the total variables including the total correct responses, the conceptual responses and failure to maintain set. The PC4 mainly reflected learning ability explaining 7.51% of the total variables including the complete first category and learning to learn. A synthesized score (WCST-PC) was calculated based on the four above components, providing a comprehensive assessment of executive function including cognitive flexibility, attention, and abstract thinking. 3.3. Sequence of cognitive decline in the T2DM group. The probability sequence of detectable cognitive changes in the T2DM group shown in Fig. 3 (A-B) , which was estimated by the EBM method using bootstrapping, using a set of neuropsychological tests in our battery. The posterior position variance indicates the degree of confidence (from left to right) of the model with respect to ordering (from top to bottom) and the dark sections of positional variance show high confidence in the ordering. After applying PCA to reduce the dimensionality of the cognitive scale, the results from the EBM demonstrated that the TMT-A (a measure of attention and working memory) tends to show abnormalities earlier in the T2DM group, while the model estimates relatively late deficits in executive function followed by episodic memory, general cognition and general mental status. Along with this process, EBM also estimated the stage of disease progression for each participant. The demographic distribution of the two groups was shown in Fig. 4 (C) . The stage of patients in the T2DM group is higher than that in the HC group (T2DM: 0.54 ± 0.12, HC: 0.49 ± 0.10, P = 0.001). 3.4. Sequence of GMV in the T2DM group. Figure 3 (D-E) shows a visualization of the probabilistic sequence of abnormality in imaging biomarkers of 35 ROIs (17 ROI on the left and right sides and vermis of the cerebellum) as estimated by EBM. In summary, the GMV in the insula gyrus showed abnormalities first, followed by the GMV in the most areas of the deep gray matter nuclei, then the GMV in the temperal gyrus, and finally the GMV in globus pallidus. The stage of patients in the T2DM group is higher than that in the HC group (T2DM: 0.54 ± 0.12, HC: 0.48 ± 0.10, P = 0.001). Details were shown in Fig. 3 (F) . 3.5. Cognitive function and GMV between two groups. After controlling for age, sex and years of education, covariance analysis was performed. For cognitive function, T2DM group showed significantly lower CVLT-PC ( P FDR =0.029), CVLT-PC2 ( P FDR =0.029), CVLT-PC4 ( P FDR =0.029), CVLT-PC ( P FDR =0.029) and higher MoCA ( P FDR =0.049) compared with HC group. No significant changes were found in all GMV in all ROIs between these two groups. The results were shown in Table S2 and Fig. 4 . 3.6. The association between cognition and clinical risk factors in the T2DM group. Partial correlation analysis was performed between the cognitive scores with significant differences between the two groups and the relevant clinical indicators of T2DM (PBG, FBG, GLU, Malb, duration of illness). After adjusted by FDR, the CVLT-PC4 were negatively associated with random blood glucose ( r = -0.581, P FDR = 0.025). The detailed information was presented in Fig. 5 . 4. Discussion This study integrated PCA and EBM to elucidate the temporal progression of cognitive and neuroanatomical alterations in T2DM patients using cross-sectional data. Our findings revealed that cognitive decline in T2DM patients is characterized by an initial deterioration in working memory, followed by progressive impairments in executive function and episodic memory. Neuroimaging analyses further indicated that GMV reductions first manifest in the insula, with subsequent involvement of deep gray matter nuclei and temporal regions. These sequential changes exhibit a strong association with disease progression, offering novel perspectives on the spatiotemporal dynamics of cognitive dysfunction in T2DM. The application of PCA in this study provided a reliable and efficient dimensionality reduction approach for the multidimensional assessment of cognitive function in T2DM patients. By employing PCA, we simplified the complex cognitive scores from CVLT, STROOP, and WCST into a few interpretable PCs, each reflecting key cognitive domains such as memory, executive function, and attention. For instance, the PCs derived from CVLT captured memory-related features, including immediate recall and delayed recognition, while those from STROOP and WCST characterized executive functions such as inhibitory control, cognitive flexibility, and abstract thinking. This dimensionality reduction not only minimized data redundancy but also highlighted the core dimensions of cognitive impairment in T2DM, thereby providing a robust analytical framework for subsequent investigations. Previous studies have demonstrated that PCA effectively extracts key variability from neuropsychological test data, exhibiting robust reliability and validity in studies of neurodegenerative disorders such as Alzheimer’s disease, Obstructive Sleep Apnea and common psychiatric illness (Firth et al. 2020 ; Pase et al. 2023 ; Chopra et al. 2024 ). In the current study, the Kaiser-Meyer-Olkin measure and Bartlett’s test of sphericity confirmed the suitability of the data for dimensionality reduction, further validating the applicability of PCA in T2DM-related cognitive research. Moreover, the PCs extracted by PCA demonstrated clear clinical interpretability, offering valuable insights into the heterogeneity of cognitive impairment in T2DM patients (Hu et al. 2025 ). The EBM is a statistical framework that evaluates the compatibility between observed and target variables by defining an energy function, demonstrating particular advantages in mapping disease progression patterns through gradient-based minimization techniques (Firth et al. 2020 ; Eshaghi et al. 2018 ; Eshaghi et al. 2021 ). In this study, the application of EBM has revealed the spatiotemporal progression of cognitive dysfunction and brain structural abnormalities in T2DM patients. At the cognitive level, working memory is initially affected, followed by impairments in executive functions and episodic memory. This finding is consistent with previous research (Gorniak et al. 2019 ; Sadanand et al. 2016 ; R.Y. Kim, Joo, et al. 2024). Working memory is a central component of short-term memory and its proper functioning depends on the coordinated interaction between the prefrontal cortex and limbic structures such as the hippocampus(Eriksson et al. 2015 ). The maintenance of this memory is the result of the interaction of long-term memory representations with fundamental cognitive processes, including attention (Daume, Kaminski, Schjetnan, et al. 2024; Daume, Kaminski, Salimpour, et al. 2024). This dysfunction in T2DM patients may be affected by chronic hyperglycemia and insulin resistance, as these metabolic disturbances may disrupt the prefrontal-hippocampal pathway thus leading to early deficits in working memory (Ehtewish et al. 2022 ; Srikanth et al. 2020 ). In contrast to the findings of other studies (Antal et al. 2022 ; Palta et al. 2017 ), our research indicates that executive dysfunction showed up later than working memory dysfunction in T2DM patients. This implies that cognitive impairment in T2DM patients may be selective in the early stage, and the executive dysfunction may become more pronounced as the disease progresses. Structurally, GM atrophy initially appeared in the insular cortex and progressively spread to deep gray matter nuclei and the temporal lobes. The insula gyrus, commonly regarded as the integration hub of the brain, is anatomically located between the frontal and temporal lobes, along with the limbic system (Namkung et al. 2017 ). This gyrus plays a crucial role in processes such as visceral perception, emotional regulation, and cognitive control (R. Zhang, Deng, et al. 2024; Gasquoine 2014 ). In the early stages of T2DM patients, atrophy of the insula gyrus may disrupt synergistic interactions between memory-related networks (e.g., the hippocampus-ventral prefrontal cortex-insula circuits in the default mode network). Such structural alterations have an adverse effect on cognitive function through impaired information prioritization and contextual memory consolidation (Llorens et al. 2023 ; Yang et al. 2021 ; He et al. 2023 ). This also provides an important clue to the potential imaging mechanisms underlying the early onset of working memory impairment in T2DM patients. Future studies can further explore the specific relationship between memory alterations and the insular gyrus in T2DM patients to reveal its neurobiological significance in the early stages of the disease. Although GMV was not significantly altered in T2DM patients after adjusting for age, sex, and duration of education, we found specific cognitive deficits. The significant decrease in CVLT-related PCs (PC1, PC2, PC4) indicates that memory dysfunction in T2DM patients has multidimensional characteristics, particularly evident in immediate recall (PC1), delayed retrieval (PC2), and recognition specificity (PC4). These findings are consistent with previous research (Srikanth et al. 2020 ; Palta et al. 2014 ) suggesting that T2DM associated metabolic disturbances may preferentially affect memory encoding and consolidation processes dependent on the hippocampal-prefrontal pathway (Gupta et al. 2023 ; Ertas et al. 2023 ; Huang et al. 2016 ). Notably, the paradoxical elevation of MoCA scores in the T2DM group compared to HCs may be attributed to compensatory cognitive strategies or cohort heterogeneity, potentially reflecting adaptive cognitive mechanisms developed through chronic disease management (Y. Zhang, Zhang, et al. 2021 ; Macpherson et al. 2017 ). Despite significant cognitive impairments, no significant differences in GMV were observed at the ROI level between groups, and these differences may be explained as follows. (1) brain functional abnormalities in the early stages of T2DM, such as reduced neural activity synchronization or changes in white matter microstructure, may precede macrostructural changes (D. Zhang et al. 2023 ; Cheng et al. 2021 ); (2) the existing ROI classification currently might not be able to capture the region-specific atrophy patterns unique to T2DM patients. In the future, research could take surface morphometry or functional connectivity analysis into consideration. Moreover, the significant negative correlation between CVLT-PC4 and GLU levels suggests that acute hyperglycemic excursions may exert domain-specific detrimental effects on memory function. CVLT-PC4 reflects the discriminative capacity between true and false positives in long-term recognition, and its dysfunction may stem from synaptic plasticity defects induced by chronic hyperglycemia and the inhibition of hippocampal neurogenesis (Gupta et al. 2023 ; Goncalves et al. 2022 ). This finding partially supports the hypothesis that acute glucose fluctuations and chronic hyperglycemia synergistically exacerbate cognitive impairment (Yang et al. 2025 ; van Duinkerken and Ryan 2020 ), indicating that an increase in GLU (a marker of acute metabolic disorder) may, either alone or in combination with poor long-term blood glucose control, lead to cognitive decline associated with T2DM by disrupting the neural plasticity involved in memory encoding (Xia et al. 2020 ). This study has some limitations. First, the modest sample size may constrain the generalizability of our findings. Second, the cross-sectional nature of the study design limits our ability to infer causal relationships between T2DM and cognitive decline. Future investigations should adopt longitudinal approaches to validate our observations and further elucidate the mechanistic underpinnings of cognitive dysfunction in T2DM, as well as to assess the effectiveness of potential therapeutic interventions. Moreover, the integration of advanced multimodal neuroimaging techniques with comprehensive neuropsychological evaluations could offer a more nuanced understanding of the pathophysiological processes driving T2DM-associated cognitive impairment. 5. Conclusion In conclusion, this study delineates sequential cognitive decline from attentional and working memory deficits to executive dysfunction and episodic memory impairment in T2DM, accompanied by hierarchical GMV reduction initiating in the insular cortex and progressing to deep gray nuclei. Notably, long-delay recognition memory demonstrated heightened vulnerability with the increase of GLU. These findings have established the loss of GMV in the insular gyrus and memory decline as potential biomarkers for monitoring T2DM-associated cognitive impairment, emphasizing the need for dynamic metabolic-cognitive surveillance in clinical practice. Declarations Compliance with Ethical Standards The experiment was approved by the “Ethics Committee of Tangdu Hospital” (2014-03-03) and registered with ClinicalTrials.gov (NCT02420470, https://www.clinicaltrials.gov/ ). All experiments were in accordance with the principles of the Declaration of Helsinki. Written informed consent was obtained from all participants Authorship statements LFY, BG and GBC, Conceptualization; XWY and XH, Data curation; MHN, Formal analysis; BH and GBC, Funding acquisition; XYB and YT, Investigation; YY and BH Methodology; SNL, LJD and PD, Project administration; BH, Resources; XYC and YYC Software; YY and GBC, Supervision; ZYM, Validation; MHN, Visualization; MHN Roles/Writing - original draft; YY, Writing - review & editing Competing interest Declarations of interest: none Data Sharing and Data Availability Upon reasonable request, the original data set can be obtained from the corresponding author Funding This work was supported by the National Natural Science Foundation of China (No. 82471936 to GBC, 2024), and the National Natural Science Foundation of China (8230214821 to BH, 2023). Author Contribution LFY, BG and GBC, Conceptualization; XWY and XH, Data curation; MHN, Formal analysis; BH and GBC, Funding acquisition; XYB and YT, Investigation; YY and BH Methodology; SNL, LJD and PD, Project administration; BH, Resources; XYC and YYC Software; YY and GBC, Supervision; ZYM, Validation; MHN, Visualization; MHN Roles/Writing - original draft; YY, Writing - review & editing Acknowledgments The authors want to thank the clinical and the nursing team of the Endocrinology Department in Tangdu Hospital for their cooperation with working on patients' recruiting. References Antal B, McMahon LP, Sultan SF et al. 2022. Type 2 diabetes mellitus accelerates brain aging and cognitive decline: Complementary findings from UK Biobank and meta-analyses. Elife 11. https://doi.org/10.7554/eLife.73138 Banquet-Teran J, Johnson-Restrepo B, Hernandez-Morelo A, et al. 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Ann Clin Transl Neurol. 2023;10(12):2305–15. https://doi.org/10.1002/acn3.51918 . Zhang D, Lei Y, Gao J, et al. Right Frontoinsular Cortex: A Potential Imaging Biomarker to Evaluate T2DM-Induced Cognitive Impairment. Front Aging Neurosci. 2021;13:674288. https://doi.org/10.3389/fnagi.2021.674288 . Zhang HY, Shen G, Yang C, et al. The Reduced Gray Matter Volume and Functional Connectivity of the Cerebellum in Type 2 Diabetes Mellitus with High Insulin Resistance. Neuroendocrinology. 2024;114(4):386–99. https://doi.org/10.1159/000535860 . Zhang R, Deng H, Xiao X. The Insular Cortex: An Interface Between Sensation, Emotion and Cognition. Neurosci Bull. 2024;40(11):1763–73. https://doi.org/10.1007/s12264-024-01211-4 . Zhang Y, Zhang X, Ma G, et al. Neurovascular coupling alterations in type 2 diabetes: a 5-year longitudinal MRI study. BMJ Open Diabetes Res Care. 2021;9(1). https://doi.org/10.1136/bmjdrc-2020-001433 . Zheng Y, Ley SH, Hu FB. Global aetiology and epidemiology of type 2 diabetes mellitus and its complications. Nat Rev Endocrinol. 2018;14(2):88–98. https://doi.org/10.1038/nrendo.2017.151 . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 14 Nov, 2025 Read the published version in Diabetology & Metabolic Syndrome → Version 1 posted Editorial decision: Revision requested 08 Sep, 2025 Reviews received at journal 07 Sep, 2025 Reviews received at journal 31 Aug, 2025 Reviewers agreed at journal 28 Aug, 2025 Reviewers agreed at journal 22 Aug, 2025 Reviewers invited by journal 21 Aug, 2025 Editor assigned by journal 20 Aug, 2025 Submission checks completed at journal 20 Aug, 2025 First submitted to journal 15 Aug, 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. 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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-7380267","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":507103371,"identity":"5aa9b739-b493-452c-9ddf-8671fe92fe0a","order_by":0,"name":"Min-Hua Ni","email":"","orcid":"","institution":"Tangdu Hospital, Air Force Medical University","correspondingAuthor":false,"prefix":"","firstName":"Min-Hua","middleName":"","lastName":"Ni","suffix":""},{"id":507103372,"identity":"92e53406-a843-4fff-b206-0cf219926f1e","order_by":1,"name":"Bo Hu","email":"","orcid":"","institution":"Tangdu Hospital, Air Force Medical 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STROOP: Stroop Color Word Test; WCST: Wisconsin Card Sorting Test; TMT: Trail Making Test; SAS: Self-Rating Anxiety Scale; SDS: Self-rating depression scale; FDR: false discovery rate.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7380267/v1/cc9ec5513d70af442b071f9c.png"},{"id":90312987,"identity":"6a66e93a-eef4-4be5-8b6f-1e925a81b84b","added_by":"auto","created_at":"2025-09-01 10:05:17","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":274509,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe\u003c/strong\u003e \u003cstrong\u003eprincipal component of multiple cognitive function estimated by the PCA.\u003c/strong\u003e \u003cstrong\u003e(A)(D)(G)\u003c/strong\u003e Two-dimensional scatter plots of PCA for the CVLT, STROOP and WCST respectively, each plot showing the relationship between the two PC (PC1 and PC2). Each dot represents a sample, and the colors and shapes may represent different categories or groups. The ellipse represents the distribution of the data with a 95% confidence interval.\u003cstrong\u003e (B)(E)(H)\u003c/strong\u003e The lithotripsy map of PCA for the CVLT, STROOP and WCST respectively. Each plot shows the eigenvalues of the PC. The PC whose eigenvalue is greater than 1 is selected as the effective principal component. There were four effective components in CVLT, and the eigenvalues were 8.19, 2.47, 1.95 and 1.03, respectively. There were two effective components in STROOP, and the eigenvalues were 7.67 and 1.84, respectively. There were 4 effective components in WCST, and the eigenvalues were 10.30, 2.64, 1.88 and 1.35, respectively.\u003cstrong\u003e (C)(F)(I)\u003c/strong\u003e The bubble diagram of PCA for the CVLT, STROOP and WCST respectively. Each diagram shows the loadings of different variables on the PC. The load represents the degree to which each variable contributes to the PC. The points in the diagram represent the load value of each variable on different PCs, and the size of the points may represent the absolute value of the load. PCA: principal component analysis; CVLT: California Verbal Learning Test; STROOP: Stroop Color Word Test; WCST: Wisconsin Card Sorting Test; PC: principal component.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7380267/v1/ab2c13c808ffe087304e546c.png"},{"id":90316415,"identity":"448a3e4c-783e-4fe6-84ac-712f25de1207","added_by":"auto","created_at":"2025-09-01 10:21:17","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":248569,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe sequence of multi-dimension cognitive function and brain imaging biomarkers estimated by the EBM.\u003c/strong\u003e \u003cstrong\u003e(A)(D)\u003c/strong\u003e the saturation gradient of each square corresponds to the frequency of parameter localization during bootstrap resampling, with maximal chromatic density identifying the predominant temporal sequence for each biomarker in cognition and GMV in T2DM group respectively. \u003cstrong\u003e(B)(E) \u003c/strong\u003ethe event center and variance diagram illustrates the estimated stage at which the features deviated from normality and the associated variance within the population in cognition and GMV in T2DM group respectively. \u003cstrong\u003e(C)(F)\u003c/strong\u003ethe demographic distribution of all subjects at different EBM stages in cognition and GMV respectively. MoCA: Montreal Cognitive Assessment; MMSE: Mini-Mental State Examination; CVLT: California Verbal Learning Test; PC: principal component; STROOP: Stroop Color Word Test; WCST: Wisconsin Card Sorting Test; TMT: Trail Making Test; SAS: Self-Rating Anxiety Scale; SDS: Self-rating depression scale; L: left; R: right; NAC: Nucleus Accumbens;EBM: event-based model; T2DM: type 2 diabetes mellitus.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7380267/v1/80128875de1f77ea65d0b6c2.png"},{"id":90316417,"identity":"cd7ac2e4-c8b9-4d69-a871-33a915d8390d","added_by":"auto","created_at":"2025-09-01 10:21:17","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":157219,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBetween-group comparison of multi-dimension cognitive function and brain imaging biomarkers. \u003c/strong\u003eMoCA: Montreal Cognitive Assessment; CVLT: California Verbal Learning Test; PC: principal component; T2DM: type 2 diabetes mellitus; HC: healthy controls; FDR: false discovery rate. \u003csup\u003e**\u003c/sup\u003e \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05 after FDR correction.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7380267/v1/841df8261577991b6d3a792e.png"},{"id":90312990,"identity":"bf2a6b8f-7c0d-44d5-9d03-22746ec4cb26","added_by":"auto","created_at":"2025-09-01 10:05:17","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":76031,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation analysis between abnormal cognitive function and clinical indicators in T2DM group. (A)\u003c/strong\u003eThe overall parietal correlation analysis between abnormal cognitive function and clinical indicators in T2DM group. \u003cstrong\u003e(B) \u003c/strong\u003eThe correlations remained significant after FDR correction. CVLT, California Verbal Learning Test; PC: principal component; PBG: postprandial blood glucose; MAlb: Microalbumin; FBG: fasting blood glucose; GLU: Glucose; FDR: false discovery rate; T2DM: type 2 diabetes mellitus.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7380267/v1/ec9ad3b9eb1ab6471974a232.png"},{"id":96105077,"identity":"207fe0fa-ec38-4365-bd93-c088d482287e","added_by":"auto","created_at":"2025-11-17 16:08:17","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2014052,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7380267/v1/7511e2f1-ac91-482f-8d61-faa0a2a5b615.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Temporal Dynamic of Cognitive Decline in Type 2 Diabetes Mellitus Patients: A Multimodal Biomarker Analysis using Event-Based Modal and Principal Component Analysis","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eDiabetes mellitus is a complicated metabolic disorder characterized by hyperglycemia and insulin resistance, with type 2 diabetes mellitus (T2DM) accounting for 90\u0026ndash;95% of cases (Srikanth et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Sun et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Cognitive impairment is one of the common complications of T2DM and leads to difficulties in self-management and social functions, significantly decreasing the quality of life (Biessels and Despa \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Zheng et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWhile substantial evidence has characterized the neurocognitive manifestations of T2DM as multidimensional manifestations including verbal and visual memory, attention and concentration, processing speed, executive function, and motor control, the developmental trajectories of these domain-specific deficits demonstrate non-linear progression patterns (Gorniak et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Antal et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Srikanth et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Additionally, neuroimaging advances have delineated morphometric alterations of cognitive impairment (Dong et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Previous researches have demonstrated that T2DM patients with cognitive impairment exhibit reduced gray matter volume (GMV) in several brain regions, including the superior temporal gyrus, right middle frontal gyrus, right Rolandic operculum, left fusiform gyrus, cerebellum, and frontal opercular cortex (Wu et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; H.Y. Zhang, Shen, et al. 2024; D. Zhang, Lei, et al. 2021). However, extant investigations manifest two critical methodological constraints: (1) the predominant reliance on cross-sectional designs obscures the temporal dynamics of cognitive deterioration and neuroanatomical trajectories of GMV reorganization in T2DM patients, hindering identification of critical windows for intervention; (2) multidimensional clinical assessments demonstrate substantial psychometric redundancy, necessitating dimensionality reduction to retain informative features for further exploration (Callisaya et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; R.Y. Kim, Joo, et al. 2024).\u003c/p\u003e\u003cp\u003ePrincipal component analysis (PCA) is a dimensionality reduction technique that identifies latent variables by decomposing covariance structures through orthogonal transformation of high-dimensional datasets (Hu et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Pascuzzo et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The event-based model (EBM) estimates the ordered abnormality sequence of biomarkers by combining severity information across biomarkers and individuals, without reference to a given individual\u0026rsquo;s clinical status (Firth et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Previous studies have successfully applied these methods to various neurodegenerative diseases, including Alzheimer\u0026rsquo;s disease (AD) (Firth et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Leuzy et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), Parkinson\u0026rsquo;s disease (Tan et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), and schizophrenia (Y. Jiang et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), and multiple sclerosis (Eshaghi et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), indicating that this method is effective and robust.. However, current methodological innovations remain conspicuously underutilized in T2DM research, particularly regarding the development of longitudinal profiling of cognitive decline and GMV trajectory.\u003c/p\u003e\u003cp\u003eIn this study, we established an integrative analytical framework combining EBM with PCA to decode temporal sequences of cognitive deterioration and imaging biomarkers in T2DM using cross-sectional datasets. After multimodal integration, we systematically investigate clinical correlation between disease-specific event patterns and pathophysiological biomarkers in T2DM. The findings may offer evidentiary support in clinical decision-making, demonstrating potential clinical utility particularly regarding cognitive trajectory monitoring and identification of critical interventional windows.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1. Participants\u003c/h2\u003e\u003cp\u003eOne hundred and nineteen T2DM patients and eighty-seven HC subjects were recruited from the endocrinology department of Tangdu hospital and the local community. The T2DM patients were defined as fasting blood glucose (FBG)\u0026thinsp;\u0026ge;\u0026thinsp;7.0 mmol/L and/or 2-hour post oral glucose tolerance test (OGTT) glucose\u0026thinsp;\u0026ge;\u0026thinsp;11.1 mmol/L. The subjects with FBG\u0026thinsp;\u0026lt;\u0026thinsp;6.1 mmol/L and 2-hour post-OGTT glucose\u0026thinsp;\u0026lt;\u0026thinsp;7.8 mmol/L were included in HC group. Participants were excluded if they had i) other types of diabetes (type 1 diabetes or gestational diabetes); ii) neurological disorders of the central nervous system or diseases seriously impairing neurological function; iii) any psychiatric or neurological illness; iv) retinopathy or neuropathy; v) a history of substance, alcohol or drug abuse.\u003c/p\u003e\u003cp\u003eThe clinical characteristics data including age, sex, years of education, body mass index (BMI), disease duration, blood pressure, FBG, Postprandial blood glucose (PBG), Random blood glucose (GLU), hemoglobin A1C (HbA1c), total cholesterol, triglyceride, high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and urinary microalbumin (MAlb).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2. Neuropsychological tests\u003c/h2\u003e\u003cp\u003eAll participants completed the MMSE, the MoCA, Self-Assessment Scale for Anxiety (SAS), Self-Rating Scale for Depression (SDS), the California Verbal Learning Test (CVLT), the Wisconsin Card Sorting Test (WCST), the Stroop Color-Word Test (STROOP), and Trail Making Test (TMT). This battery of psychological assessment was used to assess general cognitive capability, anxiety and depressive states, memory function, and executive function, respectively.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3. Data acquisition\u003c/h2\u003e\u003cp\u003eMRIs were acquired using a 3.0 T GE Discovery MR 750 scanner (GE Healthcare, Milwaukee, WI, USA) with an eight-channel prototype quadrature birdcage head coil array. Foam padding was used to minimize head movement. During the whole scanning, all subjects were required to close eyes and do not think about anything. Structural images were acquired by using three-dimensional brain volume (3D-BRAVO) and the routine clinical protocol (T1 weighted images, T2 weighted images, T2 fluid attenuated inversion recovery images, and time-of-flight magnetic resonance angiography) were acquired to detect brain abnormalities. Detailed MRI settings were detailed in previous publications (Ni et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Hu et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4. Data preprocessing\u003c/h2\u003e\u003cp\u003eThree-dimensional T1-weighted images from all participants underwent preprocessing using Statistical Parametric Mapping version 12 (SPM12) and the Computational Anatomy Toolbox (CAT) 12.9 (r2577) in MATLAB R2023a. The preprocessing pipeline encompassed image segmentation, registration, and spatial normalization. For the segmentation process, the East Asian brain-specific tissue probability maps (TPMs) from the CAT12 were utilized. The imaging data underwent intrinsic resampling using a spatially adaptive non-local mean filter to 1.5 \u0026times; 1.5 \u0026times; 1.5 mm\u003csup\u003e3\u003c/sup\u003e, followed by bias field correction and affine registration to standard space. Subsequently, the images were processed through the standard SPM pipeline including \"unified segmentation\" algorithm and skull stripping procedures. This comprehensive preprocessing workflow ensured accurate tissue classification while maintaining anatomical consistency across subjects.\u003c/p\u003e\u003cp\u003eThen, tissue classification was refined through local intensity modulation across gray matter, white matter, and cerebrospinal fluid. Adaptive maximum a posteriori segmentation with partial volume estimation quantified fractional tissue composition at the voxel level, accompanied by total intracranial volume (TIV) quantification. Subsequently, spatial normalization to Montreal Neurological Institute (MNI) reference space was achieved through high-dimensional diffeomorphic registration using geodesic shooting algorithms (Chen et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). All images with data quality below level C were excluded and finally spatial smoothing using an isotropic Gaussian kernel (6 mm full width at half maximum) to mitigate inter-subject anatomical variability while preserving mesoscopic structural features. Here, all of the Anatomical Automatic Labeling (AAL) were separated into 17 features (frontal lobe, temporal lobe, parietal lobe, occipital lobe, insula, cingulate, sensorimotor, Broca\u0026rsquo;s area, cerebellum, hippocampus, parahippocampus, amygdala, caudate, putamen, pallidum, nucleus accumbens and thalamus) which were selected as the region of interests (ROIs), as details described in previous study (Yuchao Jiang et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5. PCA\u003c/h2\u003e\u003cp\u003eTo reduce the scores of two cognitive domains (memory and executive function) to more meaningful components and meet the requirements of subsequent EBM model input data, PCA was applied to the obtained z-scores of the CVLT, STROOP and WCST for each subject. PCA is an efficient dimensionality reduction technique that simplifies data structures by transforming high-dimensional features into an orthogonal set of low-dimensional PCs. This approach significantly reduces the complexity of the data while preserving key information (Wang et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003ePCA was conducted on the cognitive domain scores using the scikit-learn library version 0.24 within a Python 3.9 environment. The selection of PCs was guided by the criteria established by Jolliffe (Banquet-Teran et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), which recommends that the cumulative explained variance should exceed 70%, and that the eigenvalues of the components should be greater than 0.7, ensuring robust representation of data variability (H.E. Kim, Kim, et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.6. EBM\u003c/h2\u003e\u003cp\u003eEBM is a sophisticated data-driven model that forecasts disease progression by mapping the timeline of biomarker abnormalities using a single cross-sectional dataset (Young et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Pascuzzo et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This methodology is particularly effective in cohorts with diverse disease severity, as early biomarkers tend to show abnormal values more frequently than those that manifest later in the disease's course. The sequence of events is probabilistically determined in a data-driven fashion by aggregating the severity of biomarkers, which is indicative of their event probabilities, across multiple individuals. In essence, biomarkers with a higher prevalence are positioned earlier in the sequence. The model has been extensively utilized in research to delineate the progression and sequence of AD cognitive impairment characteristics (Firth et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Eshaghi et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Oxtoby et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Its strengths are manifold: (1) By capitalizing on the fact that included subjects exhibit varying degrees of cognitive impairment, we can deduce the sequence of disease characteristic changes during the dynamic evolution of cognitive impairment from cross-sectional study data. (2) The model employs a probabilistic approach that automatically discerns the normal and abnormal distributions of each input feature from the data, eliminating the need for manual outlier definition and thereby reducing the influence of subjective error. These features make EBM to be a robust tool for understanding the complex trajectories of cognitive impairment diseases (Fonteijn et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e\u003cdiv id=\"Sec9\" class=\"Section4\"\u003e\u003ch2\u003e2.6.1.1. Spatiotemporal cascades of cognitive function scores or GMV abnormalities\u003c/h2\u003e\u003cp\u003eWe used EBM to estimate the spatiotemporal cascade of abnormal biomarker events, which in this study refers to the three-step process of fitting the longitudinal order of different cognitive function dimensions scores and brain structural changes (GMV) in each T2DM patient. First, EBM estimates the degree of abnormality of each biomarker by linearly mapping the cognitive function scores or GMV of each subject to probabilities of regional abnormality (0: 0, 1: 0.33, 2: 0.67,3: 1). Second, EBM estimates the spatiotemporal cascade of events for each subject, by ordering these probabilities. Third, the mean spatiotemporal cascade for cognitive function scores or GMV is estimated as the sequence that minimizes the sum of probabilistic Kendall\u0026rsquo;s Tau distances to the spatiotemporal cascades of T2DM subjects (Venkatraghavan et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In addition to determining the average sequence of biomarker abnormalities, the methodology also quantifies the relative temporal intervals separating these events. This analysis yields a collection of \"event-centers\" (ECs), which are positioned along a disease progression timeline that is normalized to span from 0 to 1. These ECs serve as reference points, illustrating the chronological order and relative timing of biomarker abnormalities within the context of disease development (Venkatraghavan et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section4\"\u003e\u003ch2\u003e2.6.1.2. Patients Staging\u003c/h2\u003e\u003cp\u003eThis study employs the Expectation-Maximization (EM) algorithm to optimize the disease staging for individual samples. By constructing a likelihood function that integrates the conditional probabilities of various biomarkers at different time points, the function assesses the likelihood of the dataset given a specific sequence of disease progression. The EM algorithm is utilized to iteratively identify the sequence that maximizes the likelihood function, thereby determining the most accurate stage of disease progression for the individual (Young et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section4\"\u003e\u003ch2\u003e2.6.1.3. Validation\u003c/h2\u003e\u003cp\u003eWe conducted cross-validation on our event-based models by re-estimating each complete model, which includes event distributions and maximum likelihood sequences, using 100 bootstrap samples with replacement.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section3\"\u003e\u003ch2\u003e2.6.2. Statistical analysis\u003c/h2\u003e\u003cp\u003eStatistical analyses were conducted using the Statistical Package for the Social Sciences (SPSS version 26.0). We calculated the mean (standard deviation [SD]) or median (interquartile range [IQR]) of characteristics for the entire study population. Multivariate linear regression analysis was used to compare differences in demographic, clinical characteristics, MoCA scores, MMSE scores, TMT-A, TMT-B, SAS scores, SDS scores, CVLT principal components (PCs), STROOP PCs, and WCST PCs between T2DM and the HC groups. The analysis was adjusted for baseline age, gender, and education level. Differences in disease progression stages estimated by EBM (EBM staging) were compared using a two-sample t-test, as covariates had already been adjusted in the EBM fitting. Partial correlation analysis was used to estimate the correlation between cognitive or GMV indicators that showed significant difference between the two groups and clinical risk factors, adjusting for baseline age, gender, and education level. The false discovery rate (FDR) method in MATLAB R2023b platform was used for the multiple comparison correction of the correlation analysis. Significant levels were set at \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 after FDR correction. Flow diagrams are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e3.1. Demographic and clinical characteristics of the participants.\u003c/h2\u003e\u003cp\u003eDemographic and clinical data were evaluated between the T2DM and HC groups (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). After covariance analysis between these two groups, the results revealed that there were no significant differences in BMI, postprandial blood glucose, microalbuminuria, total cholesterol, triglycerides, HDL-C, LDL-C and diastolic pressure. Higher age (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), HbA1c \u003cem\u003e(P\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), fast blood glucose \u003cem\u003e(P\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), random blood glucose \u003cem\u003e(P\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), systolic pressure (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.021) and lower education years (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001) were found in the T2DM group.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDemographic and clinical characteristics of the participants.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT2DM (n\u0026thinsp;=\u0026thinsp;119)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHC (n\u0026thinsp;=\u0026thinsp;87)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eF\u003c/em\u003e/χ\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e54.87\u0026thinsp;\u0026plusmn;\u0026thinsp;9.074\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e49.92\u0026thinsp;\u0026plusmn;\u0026thinsp;7.621\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.136\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducation (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12.02\u0026thinsp;\u0026plusmn;\u0026thinsp;3.063\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13.66\u0026thinsp;\u0026plusmn;\u0026thinsp;3.888\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-3.257\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale/female\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e87/32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e50/37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.517\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.019\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetes duration (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9.632\u0026thinsp;\u0026plusmn;\u0026thinsp;7.037\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e25.06\u0026thinsp;\u0026plusmn;\u0026thinsp;3.290\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24.16\u0026thinsp;\u0026plusmn;\u0026thinsp;3.245\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.467\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.497\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBiochemical indicator\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHbA1c (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.57\u0026thinsp;\u0026plusmn;\u0026thinsp;2.060\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.56\u0026thinsp;\u0026plusmn;\u0026thinsp;0.297\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15.630\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFast blood glucose (mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.94\u0026thinsp;\u0026plusmn;\u0026thinsp;2.910\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.972\u0026thinsp;\u0026plusmn;\u0026thinsp;2.436\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10.628\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePostprandial blood glucose(mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10.977\u0026thinsp;\u0026plusmn;\u0026thinsp;4.186\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.320\u0026thinsp;\u0026plusmn;\u0026thinsp;0.910\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.623\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.208\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRandom blood glucose (mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.503\u0026thinsp;\u0026plusmn;\u0026thinsp;2.848\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.978\u0026thinsp;\u0026plusmn;\u0026thinsp;1.025\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.253\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMicroalbuminuria (mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e79.703\u0026thinsp;\u0026plusmn;\u0026thinsp;151.511\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17.420\u0026thinsp;\u0026plusmn;\u0026thinsp;11.797\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.134\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.716\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal cholesterol (mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.388\u0026thinsp;\u0026plusmn;\u0026thinsp;1.129\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.696\u0026thinsp;\u0026plusmn;\u0026thinsp;1.077\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.888\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTriglycerides (mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.861\u0026thinsp;\u0026plusmn;\u0026thinsp;1.275\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.937\u0026thinsp;\u0026plusmn;\u0026thinsp;1.322\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-1.634\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.207\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHDL (mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.097\u0026thinsp;\u0026plusmn;\u0026thinsp;0.240\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.196\u0026thinsp;\u0026plusmn;\u0026thinsp;0.385\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.030\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.864\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLDL (mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.542\u0026thinsp;\u0026plusmn;\u0026thinsp;1.294\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.658\u0026thinsp;\u0026plusmn;\u0026thinsp;0.745\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.058\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.811\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSystolic pressure (mmHg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e130.216\u0026thinsp;\u0026plusmn;\u0026thinsp;16.060\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e126.750\u0026thinsp;\u0026plusmn;\u0026thinsp;18.450\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.631\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.021\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiastolic pressure (mmHg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e81.897\u0026thinsp;\u0026plusmn;\u0026thinsp;9.364\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e82.026\u0026thinsp;\u0026plusmn;\u0026thinsp;12.666\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.523\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.473\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eData were reported as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD. T2DM, type 2 diabetes mellitus; HC, healthy controls; BMI: Body Mass Index; HbA1c: hemoglobin A1C; HDL: high-density lipoprotein; LDL: low-density lipoprotein. \u003csup\u003e*\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; \u003csup\u003e**\u003c/sup\u003e \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e3.2. Cognitive characteristics of the participants.\u003c/h2\u003e\u003cp\u003eDetailed information on the CVLT, STROOP and WCST assessment of the two groups is presented in \u003cb\u003eTable S1\u003c/b\u003e. The Kaiser-Meyer-Olkin test indicated a measure of sampling adequacy of 0.708, 0.782, 0.735 respectively and all Bartlett\u0026rsquo;s test of sphericity were found to be significant (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), suggesting that the CVLT, STROOP and WCST results were suitable for PCA. The rubble diagram of PCA is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Gravel with eigenvalue greater than 1 is selected as the main component.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFour PCs were extracted from the CVLT accounting for 71.56% of the total variable. The first component (PC1) mainly reflected immediate recall explaining 45.48% of the total variables including the trial 1, trial 2, trial 3, trial 4, trial 5, trial 1\u0026ndash;5 and trial B. The second component (PC2) mainly reflected long delayed recognition accounting for 13.73% of the total variance including the short-delay free recall, short-delay cued recall, long-delay free recall and long-delay cued recall. The third component (PC3) mainly reflected intrusion and repetition explaining 6.64% of the total variables including the free recall intrusions, cued recall intrusions, total intrusions and total repetitions. The fourth component (PC4) reflected long delayed recognition explaining 5.72% of the total variables including long-delay true positive recognition and long-delay false positive recognition. A synthesized score (CVLT-PC) was calculated based on the four above components, providing a comprehensive assessment of episodic memory.\u003c/p\u003e\u003cp\u003eTwo PCs were extracted from the STROOP accounting for 80.51% of the total variable. The PC1 mainly reflected correct and reaction time explaining 69.75% of the total variables including the correct number, congruent correct number, congruent reaction time, incongruent number, incongruent reaction time, pronunciation relevant correct number, pronunciation relevant reaction time, irrelevant correct number and irrelevant reaction time. The PC2 mainly reflects error and omission accounting for 10.76% of the total variance including the error number and omission number. A synthesized score (STROOP-PC) was calculated based on the two above components, providing a comprehensive assessment of executive function including attention and inhibitory control.\u003c/p\u003e\u003cp\u003eAs for WCST, four PCs were extracted from this score accounting for 89.80% of the total variable. The PC1 mainly reflected Overall cognitive function and cognitive flexibility explaining 57.22% of the total variables including the total response, total errors, percentage of total errors, total perseverative responses, percentage of total perseverative responses, total non-perseverative errors, percentage of total non-perseverative errors and time. The PC2 mainly reflects perseverative errors explaining 14.65% of the total variance including the perseverative errors and percentage of perseverative errors. The PC3 mainly reflected abstract thinking explaining 10.42% of the total variables including the total correct responses, the conceptual responses and failure to maintain set. The PC4 mainly reflected learning ability explaining 7.51% of the total variables including the complete first category and learning to learn. A synthesized score (WCST-PC) was calculated based on the four above components, providing a comprehensive assessment of executive function including cognitive flexibility, attention, and abstract thinking.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e3.3. Sequence of cognitive decline in the T2DM group.\u003c/h2\u003e\u003cp\u003eThe probability sequence of detectable cognitive changes in the T2DM group shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e \u003cb\u003e(A-B)\u003c/b\u003e, which was estimated by the EBM method using bootstrapping, using a set of neuropsychological tests in our battery. The posterior position variance indicates the degree of confidence (from left to right) of the model with respect to ordering (from top to bottom) and the dark sections of positional variance show high confidence in the ordering.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAfter applying PCA to reduce the dimensionality of the cognitive scale, the results from the EBM demonstrated that the TMT-A (a measure of attention and working memory) tends to show abnormalities earlier in the T2DM group, while the model estimates relatively late deficits in executive function followed by episodic memory, general cognition and general mental status. Along with this process, EBM also estimated the stage of disease progression for each participant. The demographic distribution of the two groups was shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u003cb\u003e(C)\u003c/b\u003e. The stage of patients in the T2DM group is higher than that in the HC group (T2DM: 0.54\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12, HC: 0.49\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e3.4. Sequence of GMV in the T2DM group.\u003c/h2\u003e\u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cb\u003e(D-E)\u003c/b\u003e shows a visualization of the probabilistic sequence of abnormality in imaging biomarkers of 35 ROIs (17 ROI on the left and right sides and vermis of the cerebellum) as estimated by EBM. In summary, the GMV in the insula gyrus showed abnormalities first, followed by the GMV in the most areas of the deep gray matter nuclei, then the GMV in the temperal gyrus, and finally the GMV in globus pallidus. The stage of patients in the T2DM group is higher than that in the HC group (T2DM: 0.54\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12, HC: 0.48\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001). Details were shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cb\u003e(F)\u003c/b\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e3.5. Cognitive function and GMV between two groups.\u003c/h2\u003e\u003cp\u003eAfter controlling for age, sex and years of education, covariance analysis was performed. For cognitive function, T2DM group showed significantly lower CVLT-PC (\u003cem\u003eP\u003c/em\u003e\u003csub\u003eFDR\u003c/sub\u003e=0.029), CVLT-PC2 (\u003cem\u003eP\u003c/em\u003e\u003csub\u003eFDR\u003c/sub\u003e=0.029), CVLT-PC4 (\u003cem\u003eP\u003c/em\u003e\u003csub\u003eFDR\u003c/sub\u003e=0.029), CVLT-PC (\u003cem\u003eP\u003c/em\u003e\u003csub\u003eFDR\u003c/sub\u003e=0.029) and higher MoCA (\u003cem\u003eP\u003c/em\u003e\u003csub\u003eFDR\u003c/sub\u003e=0.049) compared with HC group. No significant changes were found in all GMV in all ROIs between these two groups. The results were shown in \u003cb\u003eTable S2\u003c/b\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e3.6. The association between cognition and clinical risk factors in the T2DM group.\u003c/h2\u003e\u003cp\u003ePartial correlation analysis was performed between the cognitive scores with significant differences between the two groups and the relevant clinical indicators of T2DM (PBG, FBG, GLU, Malb, duration of illness). After adjusted by FDR, the CVLT-PC4 were negatively associated with random blood glucose (\u003cem\u003er\u003c/em\u003e = -0.581, \u003cem\u003eP\u003c/em\u003e\u003csub\u003eFDR\u003c/sub\u003e = 0.025). The detailed information was presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study integrated PCA and EBM to elucidate the temporal progression of cognitive and neuroanatomical alterations in T2DM patients using cross-sectional data. Our findings revealed that cognitive decline in T2DM patients is characterized by an initial deterioration in working memory, followed by progressive impairments in executive function and episodic memory. Neuroimaging analyses further indicated that GMV reductions first manifest in the insula, with subsequent involvement of deep gray matter nuclei and temporal regions. These sequential changes exhibit a strong association with disease progression, offering novel perspectives on the spatiotemporal dynamics of cognitive dysfunction in T2DM.\u003c/p\u003e\u003cp\u003eThe application of PCA in this study provided a reliable and efficient dimensionality reduction approach for the multidimensional assessment of cognitive function in T2DM patients. By employing PCA, we simplified the complex cognitive scores from CVLT, STROOP, and WCST into a few interpretable PCs, each reflecting key cognitive domains such as memory, executive function, and attention. For instance, the PCs derived from CVLT captured memory-related features, including immediate recall and delayed recognition, while those from STROOP and WCST characterized executive functions such as inhibitory control, cognitive flexibility, and abstract thinking. This dimensionality reduction not only minimized data redundancy but also highlighted the core dimensions of cognitive impairment in T2DM, thereby providing a robust analytical framework for subsequent investigations. Previous studies have demonstrated that PCA effectively extracts key variability from neuropsychological test data, exhibiting robust reliability and validity in studies of neurodegenerative disorders such as Alzheimer\u0026rsquo;s disease, Obstructive Sleep Apnea and common psychiatric illness (Firth et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Pase et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Chopra et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In the current study, the Kaiser-Meyer-Olkin measure and Bartlett\u0026rsquo;s test of sphericity confirmed the suitability of the data for dimensionality reduction, further validating the applicability of PCA in T2DM-related cognitive research. Moreover, the PCs extracted by PCA demonstrated clear clinical interpretability, offering valuable insights into the heterogeneity of cognitive impairment in T2DM patients (Hu et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe EBM is a statistical framework that evaluates the compatibility between observed and target variables by defining an energy function, demonstrating particular advantages in mapping disease progression patterns through gradient-based minimization techniques (Firth et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Eshaghi et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Eshaghi et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In this study, the application of EBM has revealed the spatiotemporal progression of cognitive dysfunction and brain structural abnormalities in T2DM patients. At the cognitive level, working memory is initially affected, followed by impairments in executive functions and episodic memory. This finding is consistent with previous research (Gorniak et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Sadanand et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; R.Y. Kim, Joo, et al. 2024). Working memory is a central component of short-term memory and its proper functioning depends on the coordinated interaction between the prefrontal cortex and limbic structures such as the hippocampus(Eriksson et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The maintenance of this memory is the result of the interaction of long-term memory representations with fundamental cognitive processes, including attention (Daume, Kaminski, Schjetnan, et al. 2024; Daume, Kaminski, Salimpour, et al. 2024). This dysfunction in T2DM patients may be affected by chronic hyperglycemia and insulin resistance, as these metabolic disturbances may disrupt the prefrontal-hippocampal pathway thus leading to early deficits in working memory (Ehtewish et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Srikanth et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In contrast to the findings of other studies (Antal et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Palta et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), our research indicates that executive dysfunction showed up later than working memory dysfunction in T2DM patients. This implies that cognitive impairment in T2DM patients may be selective in the early stage, and the executive dysfunction may become more pronounced as the disease progresses. Structurally, GM atrophy initially appeared in the insular cortex and progressively spread to deep gray matter nuclei and the temporal lobes. The insula gyrus, commonly regarded as the integration hub of the brain, is anatomically located between the frontal and temporal lobes, along with the limbic system (Namkung et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). This gyrus plays a crucial role in processes such as visceral perception, emotional regulation, and cognitive control (R. Zhang, Deng, et al. 2024; Gasquoine \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). In the early stages of T2DM patients, atrophy of the insula gyrus may disrupt synergistic interactions between memory-related networks (e.g., the hippocampus-ventral prefrontal cortex-insula circuits in the default mode network). Such structural alterations have an adverse effect on cognitive function through impaired information prioritization and contextual memory consolidation (Llorens et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Yang et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; He et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This also provides an important clue to the potential imaging mechanisms underlying the early onset of working memory impairment in T2DM patients. Future studies can further explore the specific relationship between memory alterations and the insular gyrus in T2DM patients to reveal its neurobiological significance in the early stages of the disease.\u003c/p\u003e\u003cp\u003eAlthough GMV was not significantly altered in T2DM patients after adjusting for age, sex, and duration of education, we found specific cognitive deficits. The significant decrease in CVLT-related PCs (PC1, PC2, PC4) indicates that memory dysfunction in T2DM patients has multidimensional characteristics, particularly evident in immediate recall (PC1), delayed retrieval (PC2), and recognition specificity (PC4). These findings are consistent with previous research (Srikanth et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Palta et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) suggesting that T2DM associated metabolic disturbances may preferentially affect memory encoding and consolidation processes dependent on the hippocampal-prefrontal pathway (Gupta et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Ertas et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Huang et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Notably, the paradoxical elevation of MoCA scores in the T2DM group compared to HCs may be attributed to compensatory cognitive strategies or cohort heterogeneity, potentially reflecting adaptive cognitive mechanisms developed through chronic disease management (Y. Zhang, Zhang, et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Macpherson et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Despite significant cognitive impairments, no significant differences in GMV were observed at the ROI level between groups, and these differences may be explained as follows. (1) brain functional abnormalities in the early stages of T2DM, such as reduced neural activity synchronization or changes in white matter microstructure, may precede macrostructural changes (D. Zhang et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Cheng et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e); (2) the existing ROI classification currently might not be able to capture the region-specific atrophy patterns unique to T2DM patients. In the future, research could take surface morphometry or functional connectivity analysis into consideration. Moreover, the significant negative correlation between CVLT-PC4 and GLU levels suggests that acute hyperglycemic excursions may exert domain-specific detrimental effects on memory function. CVLT-PC4 reflects the discriminative capacity between true and false positives in long-term recognition, and its dysfunction may stem from synaptic plasticity defects induced by chronic hyperglycemia and the inhibition of hippocampal neurogenesis (Gupta et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Goncalves et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This finding partially supports the hypothesis that acute glucose fluctuations and chronic hyperglycemia synergistically exacerbate cognitive impairment (Yang et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; van Duinkerken and Ryan \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), indicating that an increase in GLU (a marker of acute metabolic disorder) may, either alone or in combination with poor long-term blood glucose control, lead to cognitive decline associated with T2DM by disrupting the neural plasticity involved in memory encoding (Xia et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThis study has some limitations. First, the modest sample size may constrain the generalizability of our findings. Second, the cross-sectional nature of the study design limits our ability to infer causal relationships between T2DM and cognitive decline. Future investigations should adopt longitudinal approaches to validate our observations and further elucidate the mechanistic underpinnings of cognitive dysfunction in T2DM, as well as to assess the effectiveness of potential therapeutic interventions. Moreover, the integration of advanced multimodal neuroimaging techniques with comprehensive neuropsychological evaluations could offer a more nuanced understanding of the pathophysiological processes driving T2DM-associated cognitive impairment.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn conclusion, this study delineates sequential cognitive decline from attentional and working memory deficits to executive dysfunction and episodic memory impairment in T2DM, accompanied by hierarchical GMV reduction initiating in the insular cortex and progressing to deep gray nuclei. Notably, long-delay recognition memory demonstrated heightened vulnerability with the increase of GLU. These findings have established the loss of GMV in the insular gyrus and memory decline as potential biomarkers for monitoring T2DM-associated cognitive impairment, emphasizing the need for dynamic metabolic-cognitive surveillance in clinical practice.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eCompliance with Ethical Standards\u003c/h2\u003e\u003cp\u003eThe experiment was approved by the \u0026ldquo;Ethics Committee of Tangdu Hospital\u0026rdquo; (2014-03-03) and registered with ClinicalTrials.gov (NCT02420470, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.clinicaltrials.gov/\u003c/span\u003e\u003cspan address=\"https://www.clinicaltrials.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). All experiments were in accordance with the principles of the Declaration of Helsinki.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e Written informed consent was obtained from all participants\u003c/p\u003e\u003cp\u003e\u003cb\u003eAuthorship statements\u003c/b\u003e\u003c/p\u003e\u003cp\u003eLFY, BG and GBC, Conceptualization; XWY and XH, Data curation; MHN, Formal analysis; BH and GBC, Funding acquisition; XYB and YT, Investigation; YY and BH Methodology; SNL, LJD and PD, Project administration; BH, Resources; XYC and YYC Software; YY and GBC, Supervision; ZYM, Validation; MHN, Visualization; MHN Roles/Writing - original draft; YY, Writing - review \u0026amp; editing\u003c/p\u003e\u003cp\u003e\u003ch2\u003eCompeting interest\u003c/h2\u003e\u003cp\u003eDeclarations of interest: none\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eData Sharing and Data Availability\u003c/h2\u003e\u003cp\u003eUpon reasonable request, the original data set can be obtained from the corresponding author\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis work was supported by the National Natural Science Foundation of China (No. 82471936 to GBC, 2024), and the National Natural Science Foundation of China (8230214821 to BH, 2023).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eLFY, BG and GBC, Conceptualization; XWY and XH, Data curation; MHN, Formal analysis; BH and GBC, Funding acquisition; XYB and YT, Investigation; YY and BH Methodology; SNL, LJD and PD, Project administration; BH, Resources; XYC and YYC Software; YY and GBC, Supervision; ZYM, Validation; MHN, Visualization; MHN Roles/Writing - original draft; YY, Writing - review \u0026amp; editing\u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e\u003cp\u003eThe authors want to thank the clinical and the nursing team of the Endocrinology Department in Tangdu Hospital for their cooperation with working on patients' recruiting.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAntal B, McMahon LP, Sultan SF et al. 2022. 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Nat Rev Endocrinol. 2018;14(2):88\u0026ndash;98. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/nrendo.2017.151\u003c/span\u003e\u003cspan address=\"10.1038/nrendo.2017.151\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"diabetology-and-metabolic-syndrome","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dims","sideBox":"Learn more about [Diabetology \u0026 Metabolic Syndrome](http://dmsjournal.biomedcentral.com/)","snPcode":"13098","submissionUrl":"https://submission.nature.com/new-submission/13098/3","title":"Diabetology \u0026 Metabolic Syndrome","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Cognitive decline, Event-based model, Gray matter volume, Principal component analysis, Type 2 diabetes mellitus","lastPublishedDoi":"10.21203/rs.3.rs-7380267/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7380267/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground \u003c/strong\u003eType 2 diabetes mellitus (T2DM) is associated with cognitive impairment, affecting life quality. The progression of cognitive decline and its neural basis in T2DM are unclear due to limitations in previous studies. This study integrates Event-Based Model (EBM) and Principal Component Analysis (PCA) to explore these aspects in T2DM patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods \u003c/strong\u003eThis study assessed 119 T2DM patients and 87 healthy controls with neuropsychological tests (CVLT, Stroop, WCST) and MRI for gray matter volume (GMV). PCA simplified cognitive scores into composites for memory and executive function. EBM estimated the sequence of cognitive and neurostructural changes. Partial correlation analyses were used to examine associations with clinical factors with controlling covariance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults \u003c/strong\u003eCognitive decline in T2DM began with attention and working memory, followed by executive function and episodic memory. GMV loss started in the insular gyrus, spreading to other regions. T2DM showed advanced disease progression (0.54 (0.12) vs. 0.49 (0.10), \u003cem\u003eP\u003c/em\u003e = 0.001). A negative correlation linked long-delay memory (CVLT-PC4) to random blood glucose (\u003cem\u003er\u003c/em\u003e = -0.581, \u003cem\u003eP\u003c/em\u003e\u003csub\u003eFDR\u003c/sub\u003e = 0.025).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion \u003c/strong\u003eThis study reveals the sequence of cognitive and neuroanatomical changes in T2DM. Memory decline and insular gyrus atrophy may serve as early biomarkers for T2DM-related cognitive impairment, which may be helpful in the development of personalized interventions to improve life quality.\u003c/p\u003e","manuscriptTitle":"Temporal Dynamic of Cognitive Decline in Type 2 Diabetes Mellitus Patients: A Multimodal Biomarker Analysis using Event-Based Modal and Principal Component Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-01 10:05:12","doi":"10.21203/rs.3.rs-7380267/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-08T17:23:19+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-08T02:19:26+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-01T01:39:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"143391846575393991127251575977166087354","date":"2025-08-28T20:21:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"194045644992986409917579526487231158815","date":"2025-08-22T17:22:15+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-21T10:37:31+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-20T13:34:50+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-20T13:33:31+00:00","index":"","fulltext":""},{"type":"submitted","content":"Diabetology \u0026 Metabolic Syndrome","date":"2025-08-15T09:28:27+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"diabetology-and-metabolic-syndrome","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dims","sideBox":"Learn more about [Diabetology \u0026 Metabolic Syndrome](http://dmsjournal.biomedcentral.com/)","snPcode":"13098","submissionUrl":"https://submission.nature.com/new-submission/13098/3","title":"Diabetology \u0026 Metabolic Syndrome","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4aa88bdc-3153-4f6e-a8e5-251fc13b2713","owner":[],"postedDate":"September 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-11-17T16:02:35+00:00","versionOfRecord":{"articleIdentity":"rs-7380267","link":"https://doi.org/10.1186/s13098-025-02003-0","journal":{"identity":"diabetology-and-metabolic-syndrome","isVorOnly":false,"title":"Diabetology \u0026 Metabolic Syndrome"},"publishedOn":"2025-11-14 15:57:21","publishedOnDateReadable":"November 14th, 2025"},"versionCreatedAt":"2025-09-01 10:05:12","video":"","vorDoi":"10.1186/s13098-025-02003-0","vorDoiUrl":"https://doi.org/10.1186/s13098-025-02003-0","workflowStages":[]},"version":"v1","identity":"rs-7380267","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7380267","identity":"rs-7380267","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}