Advances in Non-Invasive Neuromodulation Techniques for Improving Cognitive Function: A Review.

Non-invasive neuromodulation techniques are widely utilized to study and improve cognitive function, with the aim of modulating different cognitive processes. For workers performing high-intensity mental and physical tasks, extreme fatigue may not only affect their working efficiency but may also lead to cognitive decline or cognitive impairment, which, in turn, poses a serious threat to their physical health. The use of non-invasive neuromodulation techniques has important research value for improving and enhancing cognitive function. In this paper, we review the research status, existing problems, and future prospects of transcranial direct current stimulation (tDCS), transcranial alternating current stimulation (tACS), transcranial magnetic stimulation (TMS), and transcutaneous acupoint stimulation (TAS), which are the most studied physical methods in non-invasive neuromodulation techniques to improve and enhance cognition. The findings presented in this paper will be of great reference value for the in-depth study of non-invasive neuromodulation techniques in the field of cognition.

Efficacy and safety analysis of hypofractionated and conventional fractionated radiotherapy in postoperative breast cancer patients.

OBJECTIVES: In this meta-analysis, we conducted a comparative analysis of the safety and efficacy of hypofractionated and conventional fractionated radiotherapy in individuals who had undergone surgery for breast cancer. METHODS: This study involved a systematic and independent review of relevant research articles published in reputable databases such as PubMed, Embase, Cochrane Library, and Web of Science. Two investigators conducted the review, which included studies published up to January 3, 2023. The quality of the eligible studies was evaluated and data were extracted using Review Manager software 5.4 (RevMan 5.4) to calculate odds ratios (ORs) and 95% confidence intervals (CIs). RESULTS: The analysis comprised 35 studies and encompassed a collective sample of 18,246 individuals diagnosed with breast cancer. We did not find a statistically significant disparity in efficacy between conventional fractionated (CF) radiotherapy and hypofractionated (HF) radiotherapy regarding local recurrence (LR; OR = 0.91, 95% CI: 0.76-1.09, P = 0.30), disease-free survival (DFS; OR = 1.20, 95% CI: 1.01-1.42, P = 0.03), and overall survival (OS; OR = 1.08, 95% CI: 0.93-1.26, P = 0.28). Concerning safety, there was no significant difference between the HF and CF regimens in terms of breast pain, breast atrophy, lymphedema, pneumonia, pulmonary fibrosis, telangiectasia, and cardiotoxicity. However, the HF regimen resulted in lower skin toxicity (OR = 0.43, 95% CI: 0.33-0.55, P < 0.01) and improved patient fatigue outcomes (OR = 0.73, 95% CI: 0.60 - 0.88, P < 0.01). CONCLUSIONS: Although there is no substantial difference in LR, DFS, OS, or many other side effects between the HF and CF regimens, the HF regimen reduces skin toxicity and relieves patient fatigue. If these two issues need to be addressed in clinical situations, the HF regimen may be a superior alternative to conventional radiotherapy in postoperative breast cancer patients.

Research on fatigue identification methods based on low-load wearable ECG monitoring devices.

The identification of fatigue in personal workers in particular environments can be achieved through early warning techniques. In order to prevent excessive fatigue of medical workers staying in infected areas in the early phase of the coronavirus disease pandemic, a system of low-load wearable electrocardiogram (ECG) devices was used as intelligent acquisition terminals to perform a continuous measurement ECG collection. While machine learning (ML) algorithms and heart rate variability (HRV) offer the promise of fatigue detection for many, there is a demand for ever-increasing reliability in this area, especially in real-life activities. This study proposes a random forest-based classification ML model to identify the four categories of fatigue levels in frontline medical workers using HRV. Based on the wavelet transform in ECG signal processing, stationary wavelet transform was applied to eliminate the main perturbation of ECG in the motion state. Feature selection was performed using ReliefF weighting analysis in combination with redundancy analysis to optimize modeling accuracy. The experimental results of the overall fatigue identification achieved an accuracy of 97.9% with an AUC value of 0.99. With the four-category identification model, the accuracy is 85.6%. These results proved that fatigue analysis based on low-load wearable ECG monitoring at low exertion can accurately determine the level of fatigue of caregivers and provide further ideas for researchers working on fatigue identification in special environments.

Simulation of dynamic monitoring for intracerebral hemorrhage based on magnetic induction phase shift technology.

Intracerebral hemorrhage (ICH) is a common and severe brain disease associated with high mortality and morbidity. Accurate measurement of the ICH area is an essential indicator for doctors to determine whether a surgical operation is necessary. However, although currently used clinical detection methods, such as computed tomography (CT) and magnetic resonance imaging (MRI), provide high-quality images, they may have limitations such as high costs, large equipment size, and radiation exposure to the human body in the case of CT. It makes long-term bedside monitoring infeasible. This paper presents a dynamic monitoring method for ICH areas based on magnetic induction. This study investigates the influence of the bleeding area and the position of ICH on the phase difference at the detection point near the area to be measured. The study applies a neural network algorithm to predict the bleeding area using the phase difference data received by the detection coil as the network input and the bleeding area as the network output. The relative error between the predicted and actual values of the neural network is calculated, and the error of each group of data is less than 4%, which confirms the feasibility of this method for detecting and even trend monitoring of the ICH area.

Quantitative identification of daily mental fatigue levels based on multimodal parameters.

Fatigue has become an important health problem in modern life; excessive mental fatigue may induce various cardiovascular diseases. Most current mental fatigue recognition is based only on specific scenarios and tasks. To improve the accuracy of daily mental fatigue recognition, this paper proposes a multimodal fatigue grading method that combines three signals of electrocardiogram (ECG), photoplethysmography (PPG), and blood pressure (BP). We collected ECG, PPG, and BP from 22 subjects during three time periods: morning, afternoon, and evening. Based on these three signals, 56 characteristic parameters were extracted from multiple dimensions, which comprehensively covered the physiological information in different fatigue states. The extracted parameters were compared with the feature optimization ability of recursive feature elimination (RFE), maximal information coefficient, and joint mutual information, and the optimum feature matrix selected was input into random forest (RF) for a three-level classification. The results showed that the accuracy of classification of fatigue using only one physiological feature was 88.88%, 92.72% using a combination of two physiological features, and 94.87% using all three physiological features. This study indicates that the fusion of multiple physiological traits contains more comprehensive information and better identifies the level of mental fatigue, and the RFE-RF model performs best in fatigue identification. The BP variability index is useful for fatigue classification.

The identification of blood pressure variation with hypovolemia based on the volume compensation method.

Objective: The purpose of this study is to identify the blood pressure variation, which is important in continuous blood pressure monitoring, especially in the case of low blood volume, which is critical for survival. Methods: A pilot study was conducted to identify blood pressure variation with hypovolemia using five Landrace pigs. New multi-dimensional morphological features of Photoplethysmography (PPG) were proposed based on experimental study of hemorrhagic shock in pigs, which were strongly correlated with blood pressure changes. Five machine learning methods were compared to develop the blood pressure variation identification model. Results: Compared with the traditional blood pressure variation identification model with single characteristic based on single period area of PPG, the identification accuracy of mean blood pressure variation based on the proposed multi-feature random forest model in this paper was up to 90%, which was 17% higher than that of the traditional blood pressure variation identification model. Conclusion: By the proposed multi-dimensional features and the identification method, it is more accurate to detect the rapid variation in blood pressure and to adopt corresponding measures. Significance: Rapid and accurate identification of blood pressure variation under low blood volume ultimately has the potential to effectively avoid complications caused by abnormal blood pressure in patients with clinical bleeding trauma.

Cardiorespiratory dose comparison among six radiotherapy regimens for patients with left-sided breast cancer.

There is uncertainty regarding the benefits and drawbacks of various radiation protocols for the treatment of left-sided breast cancer. To address this issue, we conducted a Bayesian network analysis. First, we searched several electronic databases for eligible literature. Next, we pooled the data from twelve studies concerning three-dimensional conformal radiation therapy (3D-CRT), intensity modulated radiation therapy (IMRT), and volumetric modulated arc therapy (VMAT), combined with either deep inspiratory breath-holding (DIBH) or free-breathing (FB) modalities. The integrated cardiac and pulmonary dosimetric indexes for all included treatments were compared using Bayesian networks. A direct meta-analysis indicated that for the two methods of 3D-CRT and IMRT, DIBH technology was more effective than FB in reducing the radiation dose to the heart and lungs. Additionally, according to the network results, DIBH was superior to FB in all six treatment options, regardless of whether the plan was 3D-CRT, IMRT, or VMAT. Besides, the combined data indicated that the FB-3D-CRT regimen had the weakest dosimetric advantage of all the treatments. Excluding FB-3D-CRT, each of the other five treatments had its own specific benefits. This is the first Bayesian study of several radiotherapy regimens for breast cancer patients on the left side, and the findings can be used to select appropriate radiotherapy programs for breast cancer patients.

Non-invasive continuous blood pressure prediction based on ECG and PPG fusion map.

To achieve real-time blood pressure monitoring, a novel non-invasive method is proposed in this article. Electrocardiographic (ECG) and pulse wave signals (PPG) are fused from a multi-omics signal-level perspective. A physiological signal fusion matrix and fusion map, which can estimate the blood pressure of blood loss(BPBL), are constructed. The results demonstrate the efficacy of the fusion map model, with correlation values of 0.988 and 0.991 between the estimated BPBL and the true systolic blood pressure (SBP) and diastolic blood pressure (DBP), respectively. The root mean square errors for SBP and DBP were 3.21 mmHg and 3.00 mmHg, respectively. The model validation showed that the fusion map method is capable of simultaneous highlighting of the respective characteristics of ECG and PPG and their correlation, improving the utilization of the information and the accuracy of BPBL. This article validates that physiological signal fusion map can effectively improve the accuracy of BPBL estimation and provides a new perspective for the field of physiological information fusion.

Adaptive window space direction laser speckle contrast imaging to improve vascular visualization.

Vascular visualization is crucial in monitoring, diagnosing, and treating vascular diseases. Laser speckle contrast imaging (LSCI) is widely used for imaging blood flow in shallow or exposed vessels. However, traditional contrast computation using a fixed-sized sliding window introduces noise. In this paper, we propose dividing the laser speckle contrast image into regions and using the variance criterion to extract pixels more suitable for the corresponding regions for calculation, and changing the shape and size of the analysis window at the vascular boundary regions. Our results show that this method has a higher noise reduction and better image quality in deeper vessel imaging, revealing more microvascular structure information.

Urinary protein biomarkers based on LC-MS/MS analysis to discriminate vascular dementia from Alzheimer's disease in Han Chinese population.

Objective: This study aimed to identify the potential urine biomarkers of vascular dementia (VD) and unravel the disease-associated mechanisms by applying Liquid chromatography tandem-mass spectrometry (LC-MS/MS). Methods: LC-MS/MS proteomic analysis was applied to urine samples from 3 groups, including 14 patients with VD, 9 patients with AD, and 21 normal controls (NC). By searching the MS data by Proteome Discoverer software, analyzing the protein abundances qualitatively and quantitatively, comparing between groups, combining bioinformatics analysis using Gene Ontology (GO) and pathway crosstalk analysis using Kyoto Encyclopedia of Genes and Genomes (KEGG), and literature searching, the differentially expressed proteins (DEPs) of VD can be comprehensively determined at last and were further quantified by receiver operating characteristic (ROC) curve methods. Results: The proteomic findings showed quantitative changes in patients with VD compared to patients with NC and AD groups; among 4,699 identified urine proteins, 939 and 1,147 proteins displayed quantitative changes unique to VD vs. NC and AD, respectively, including 484 overlapped common DEPs. Then, 10 unique proteins named in KEGG database (including PLOD3, SDCBP, SRC, GPRC5B, TSG101/STP22/VPS23, THY1/CD90, PLCD, CDH16, NARS/asnS, AGRN) were confirmed by a ROC curve method. Conclusion: Our results suggested that urine proteins enable detection of VD from AD and VC, which may provide an opportunity for intervention.

Effects of probiotic supplementation on related side effects after chemoradiotherapy in cancer patients.

Objectives: Chemotherapy and radiotherapy generally cause serious adverse side effects in cancer patients, thereby affecting subsequent treatment. Numerous studies have shown that taking probiotics is an option for preventing and treating these side effects. In this investigation, a meta-analysis of the effects of oral probiotics on side effects brought on by radiotherapy, chemotherapy, or chemoradiotherapy treatment will be carried out. Methods: Two researchers independently and carefully reviewed all pertinent studies that were published before June 30, 2022 and were accessible on PubMed, Embase, Cochrane Library, and the Web of Science. Moreover, the Cochrane Collaboration's Tool was used to evaluate the risk of bias. Utilizing Review Manager software version 5.4, data were retrieved from eligible studies to evaluate their merits and determine odds ratios (OR) and 95% confidence intervals (CIs) (RevMan 5.4). Results: 2 097 patients from 16 randomized controlled trials were extracted, and standard meta-analysis methods were used to examine the data. Compared with the placebo groups, oral probiotics significantly reduced the side effects caused by radiotherapy and chemotherapy on various types of cancer, such as head and neck cancer, pelvic and abdominal cancer, breast cancer, lung cancer, etc. (OR: 0.31, 95% CI: 0.20 - 0.48; P < 0.005). Further analysis found that the incidence of diarrhea in patients with pelvic and abdominal cancers (OR: 0.32, 95% CI: 0.16 - 0.65; P < 0.005) and the frequency of oral mucositis in patients with head and neck tumors were also significantly lower (OR: 0.28, 95% CI: 0.18 - 0.43; P < 0.005) after the oral administration of probiotics. This suggests that probiotics have a positive influence on the treatment of side effects after chemoradiotherapy. Additionally, a funnel plot revealed that there was no significant publication bias in this study. Conclusions: Probiotics may help to reduce the occurrence of cancer therapy-related side effects, especially oral mucositis in head and neck tumors and diarrhea in patients with pelvic and abdominal tumors. However, given the small number of clinical trials involved, additional randomized, double-blind, multicentric trials in a larger population are required. This paper may assist researchers in improving trial design in the selection of probiotic strains and selecting appropriate patients who may benefit from probiotic treatments.

Comparison of Deep Inspiration Breath Hold Versus Free Breathing in Radiotherapy for Left Sided Breast Cancer.

Objectives: Modern breast cancer techniques, such as the deep inspiration breath-hold (DIBH) technique has been applied for left-sided breast cancer. Whether the DIBH regimen is the optimal solution for left-sided breast cancer remains unclear. This meta-analysis aims to elucidate the differences of DIBH and free-breathing (FB) for patients receiving radiotherapy for left-sided breast cancer and provide a practical reference for clinical practice. Methods: Relevant research available on PubMed, Embase, Cochrane Library, and the Web of Science published before November 30, 2021 was independently and systematically examined by two investigators. Data were extracted from eligible studies for assessing their qualities and calculating the standardized mean difference (SMD) and 95% confidence intervals (CIs) using Review Manager software 5.4 (RevMan 5.4). Results: Forty-one studies with a total of 3599 left-sided breast cancer patients were included in the meta-analysis. Compared with FB, DIBH reduced heart dose (D mean, D max, V30, V10, V5), left anterior descending branch (LAD) dose (D mean, D max), ipsilateral lung dose (D mean, V20, V10, V5), and heart volume significantly. Lung volume increased greatly, and a statistically significant difference. For contralateral breast mean dose, DIBH has no obvious advantage over FB. The funnel plot suggested this study has no significant publication bias. Conclusions: Although DIBH has no obvious advantage over FB in contralateral breast mean dose, it can significantly reduce heart dose, LAD dose, ipsilateral lung dose, and heart volume. Conversely, it can remarkably increase the ipsilateral lung volume. This study suggests that soon DIBH could be more widely utilized in clinical practice because of its excellent dosimetric performance.

Happy or sad? Recognizing emotions with wavelet coefficient energy mean of EEG signals.

BACKGROUND: Emotional intelligence plays a vital role in human-computer interaction, and EEG signals are an objective response to human emotions. OBJECTIVE: We propose a method to extract the energy means of detail coefficients as feature values for emotion recognition helps to improve EEG signal-based emotion recognition accuracy. METHOD: We used movie clips as the eliciting material to stimulate the real emotions of the subjects, preprocessed the collected EEG signals, extracted the feature values, and classified the emotions based on them using Support Vector Machine (SVM) and Stacked Auto-Encoder (SAE). The method was verified based on the SJTU emotion EEG database (SEED) and the self-acquisition experiment. RESULTS: The results show that the accuracy is better using SVM. The results based on the SEED database are 89.06% and 79.90% for positive-negative and positive-neutral-negative, respectively. The results based on the self-acquisition data are 98.05% and 89.83% for the same, with an average recognition rate of 86.57% for the four categories of fear, sad (negative), peace (neutral) and happy (positive). CONCLUSION: The results demonstrate the validity of the feature values and provide a theoretical basis for implementing human-computer interaction.

How to ensure uninterrupted treatment of radiotherapy patients during delta variants of SARS-CoV-2 virus epidemic: the latest experience from Xi'an, China.

This letter is some reflections on the articles by Lee et al. that have been published in MEDICAL ONCOLOGY. In this article, the experience of transferring, screening, and uninterrupted treatment of cancer patients in our radiotherapy center during Delta Variants of SARS-CoV-2 virus epidemic in Xi'an, China is presented in the form of a flowchart. By taking effective control measures, the radiotherapy center has not only effectively prevented the spread of the virus, but also ensured uninterrupted treatment for all patients. Therefore, we quickly share our center's experience so that more radiotherapy patients can benefit.

Comparison of therapeutic efficacy and toxicity of docetaxel, cisplatin, and fluorouracil (TPF)-based induction chemotherapy plus concurrent chemoradiotherapy and chemoradiotherapy alone in locally advanced nasopharyngeal carcinoma.

PURPOSE: In recent years, docetaxel, cisplatin, and fluorouracil (TPF)-based induction chemotherapy plus concurrent chemoradiotherapy (CCRT) has been commonly applied for locally advanced nasopharyngeal carcinoma (LA-NPC). However, whether TPF+CCRT regimen is the best choice for LA-NPC remains unclear. This meta-analysis aims to elucidate and compare the efficacy and toxicity of TPF+CCRT versus CCRT alone for LA-NPC. METHODS: Two investigators independently and systematically searched relevant studies available on PubMed, Embase, Cochrane Library, and Web of Science published before January 7, 2021. Data were extracted from eligible studies for assessing their qualities, and calculating pooled hazard ratios (HR), odds ratio (OR) and 95% confidence intervals (CI) using Review Manager software 5.3 (RevMan 5.3). RESULTS: Five studies involving 759 LA-NPC patients were analyzed in the meta-analysis. Compared to CCRT alone, TPF-based IC plus CCRT significantly improved overall survival (OS) (HR = 0.53, 95% CI: 0.35-0.81, P = .003), progression-free survival (PFS) (HR = 0.63, 95% CI: 0.46-0.86, P = .004), distant metastasis-free survival (DMFS) (HR = 0.58, 95% CI: 0.39-0.86, P = .008), and locoregional failure-free survival (LRFFS) (HR 0.62, 95% CI: 0.43-0.90, P = .01). In addition, TPF-based IC plus CCRT mainly increased risks of grade 3/4 acute hematological toxicity and non-hematological toxicities like leukopenia (OR = 1.84, 95% CI: 0.42-8.03, P = .42), neutropenia (OR = 1.78, 95% CI: 0.23-13.82, P = .58), thrombocytopenia (OR = 1.76, 95% CI: 0.53-5.81, P = .35), febrile neutropenia (OR = 2.76, 95% CI: 0.07-101.89, P = .58), vomiting (OR = 18.94, 95% CI: 0.99-362.02, P = .05) and dry mouth (OR = 2.23, 95% CI: 0.22-22.57, P = .50), which were uncomplicated and manageable. CONCLUSIONS: TPF + CCRT is superb than CCRT alone for the management of LA-NPC. However, TPF+CCRT increases the incidences of grade 3/4 acute hematological toxicity and some non-hematological toxicities.

Genome-Wide DNA Methylation Pattern in Whole Blood Associated With Primary Intracerebral Hemorrhage.

Primary intracerebral hemorrhage (ICH) is a significant cause of morbidity and mortality throughout the world. ICH is a multifactorial disease that emerges from interactions among multiple genetic and environmental factors. DNA methylation plays an important role in the etiology of complex traits and diseases. We used the Illumina Infinium Human Methylation 850k BeadChip to detect changes in DNA methylation in peripheral blood samples from patients with ICH and healthy controls to explore DNA methylation patterns in ICH. Here, we compared genomic DNA methylation patterns in whole blood from ICH patients (n = 30) and controls (n = 34). The ICH and control groups showed significantly different DNA methylation patterns at 1530 sites (p-value < 5.92E-08), with 1377 hypermethylated sites and 153 hypomethylated sites in ICH patients compared to the methylation status in healthy controls. A total of 371 hypermethylated sites and 35 hypomethylated sites were in promoters, while 738 hypermethylated sites and 67 hypomethylated sites were in coding regions. Furthermore, the differentially methylated genes between ICH patients and controls were largely related to inflammatory pathways. Abnormalities in the DNA methylation pattern identified in the peripheral blood of ICH patients may play an important role in the development of ICH and warranted further investigation.

Rapid diagnosis and continuous monitoring of intracerebral hemorrhage with magnetic induction tomography based on stacked autoencoder.

Magnetic induction tomography (MIT) is a promising approach in rapid diagnosis and continuous monitoring of cerebral hemorrhage. A new algorithm for the reconstruction of intracerebral hemorrhage with MIT, including the location and volume of hemorrhage, is proposed in this study. First, 2D magnetic resonance imaging and computed tomography images of patients with cerebral hemorrhage were used for the development of simulation models. The Stacked Autoencoder (SAE) network was then used to predict the location and volume of hemorrhage by conductivity reconstruction. Finally, the one-dimensional quantitative monitoring index is proposed as an auxiliary diagnostic indicator for assessment of real-time intracranial electrical characteristics. The 2D simulation results showed that the SAE was able to quickly image the location and volume of the hemorrhages. Compared with the back-projection algorithm, the prediction speed of each frame was improved 15-fold, and the accuracy improved by 90.53%. The extracted one-dimensional quantitative monitoring indicators can describe the bleeding status. The diagnostic accuracy and the imaging speed of cerebral hemorrhage were both improved by constructing a realistic head section model and using the proposed SAE network. This research provides a new alternative for dynamic monitoring of hemorrhages and shows the potential advantages of MIT in noninvasive detection.

A convolutional neural network algorithm for breast tumor detection with magnetic detection electrical impedance tomography.

Breast cancer is a malignant tumor disease for which early detection, diagnosis, and treatment are of paramount significance in prolonging the life of patients. Magnetic Detection Electrical Impedance Tomography (MDEIT) based on the Convolutional Neural Network (CNN), which aims to realize non-invasive, high resolution detection of breast tumors, is proposed. First, the MDEIT forward problem of the coronal and horizontal planes of the breast was simulated and solved using the Finite Element Method to obtain sample datasets of different lesions. Then, the CNN was built and trained to predict the conductivity distribution in different orientations of the breast model. Finally, noise and phantom experiments were performed in order to assess the anti-noise performance of the CNN algorithm and its feasibility of detecting breast tumors in practical applications. The simulation results showed that the reconstruction relative error with the CNN algorithm can be reduced to 10%, in comparison with the truncated singular value decomposition algorithm and back propagation algorithm. The CNN algorithm had better stability in the anti-noise performance test. When the noise of 60 dB was added, the shape of the breast tumor could still be restored by the CNN algorithm. The phantom experimental results showed that through the CNN based reconstruction algorithm, the reconstruction conductivity distribution image was legible and the position of the breast tumor could be determined. It is reasonable to conclude that the MDEIT reconstruction method proposed in this study has practical importance for the early and non-invasive detection of breast tumors.

LC-MS-Based Untargeted Metabolomics Reveals Early Biomarkers in STZ-Induced Diabetic Rats With Cognitive Impairment.

Diabetes in the elderly increases cognitive impairment, but the underlying mechanisms are still far from fully understood. A non-targeted metabolomics approach based on liquid chromatography-mass spectrometry (LC-MS) was performed to screen out the serum biomarkers of diabetic mild cognitive impairment (DMMCI) in rats. Total 48 SD rats were divided into three groups, Normal control (NC) group, high-fat diet (HFD) fed group and type 2 diabetes mellitus (T2DM) group. The T2DM rat model was induced by intraperitoneal administration of streptozotocin (STZ, 35 mg/kg) after 6 weeks of high-fat diet (HFD) feeding. Then each group was further divided into 4-week and 8-week subgroups, which were calculated from the time point of T2DM rat model establishment. The novel object recognition test (NORT) and the Morris water maze (MWM) method were used to evaluate the cognitive deficits in all groups. Compared to the NC-8w and HFD-8w groups, both NOR and MWM tests indicated significant cognitive dysfunction in the T2DM-8w group, which could be used as an animal model of DMMCI. Serum was ultimately collected from the inferior vena cava after laparotomy. Metabolic profiling analysis was conducted using ultra high performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF-MS) technology. Principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were used to verify the stability of the model. According to variable importance in the project (VIP > 1) and the p-value of t-test (P < 0.05) obtained by the OPLS-DA model, the metabolites with significant differences were screened out as potential biomarkers. In total, we identified 94 differentially expressed (44 up-regulated and 50 down-regulated) endogenous metabolites. The 10 top up-regulated and 10 top down-regulated potential biomarkers were screened according to the FDR significance. These biomarkers by pathway topology analysis were primarily involved in the metabolism of sphingolipid (SP) metabolism, tryptophan (Trp) metabolism, Glycerophospholipid (GP) metabolism, etc. Besides, SP metabolism, Trp metabolism and GP metabolism mainly belonging to the lipid metabolism showed marked perturbations over DMMCI and may contribute to the development of disease. Taken collectively, our results revealed that T2DM could cause cognitive impairment by affecting a variety of metabolic pathways especially lipid metabolism. Besides, serum PE, PC, L-Trp, and S1P may be used as the most critical biomarkers for the early diagnosis of DMMCI.