Association Between Inflammatory Biomarkers and Cognitive Dysfunction Analyzed by MRI in Diabetes Patients.

Introduction: The glymphatic system is regarded as a key factor in the pathogenesis of neurodegenerative diseases. Given the heightened risk of cognitive impairment in patients with type 2 diabetes mellitus (T2DM), the possible alterations in the glymphatic system in T2DM patients remain to be explored. Diffusion tensor imaging (DTI) analysis along the perivascular space (ALPS) index can be utilized to model the glymphatic system in humans. Our aim was to explore the relationship between the ALPS index and cognitive function in patients with T2DM and whether this relationship is modulated by gray matter (GM) integrity anchored by the ALPS index. Methods: All participants underwent evaluation using a comprehensive cognitive assessment scale to determine their neurocognitive status. The ALPS index was calculated based on DTI data, and the disparity in ALPS index values between patients with T2DM and healthy controls (HCs) was examined. Furthermore, multiple linear regression analysis was conducted in the T2DM group to identify the GM regions associated with the ALPS index, and the volumes of the GM partitions were extracted. The volume of GM partitions was used as the regulating variable, the ALPS index was used as the independent variable, and cognitive test scores were used as the dependent variable in our analysis. Results: The ALPS index differed significantly between the two groups, and the ALPS index in patients with T2DM was significantly lower than that in HCs. In addition, our analysis revealed a correlation between the ALPS index and GM volume in the insular region, consistent with the observed GM atrophy in the patient cohort. Moreover, a significant negative correlation was observed between the ALPS index in patients and performance on the Trail-Making Test-A (TMT-A), and this relationship was moderated by GM integrity. In patients with more severe GM atrophy, the ALPS index was more strongly correlated with cognitive function. Conclusions: In this study, a decreased ALPS index was found in T2DM patients, indicating impaired glymphatic function in this population. Furthermore, a significant association was detected between the ALPS index and cognitive performance in T2DM patients, and this correlation was influenced by GM integrity. Therefore, the ALPS index has the potential to be used as a biomarker of cognitive impairment in diabetic patients. Further studies are needed to investigate the diagnostic and therapeutic implications of glymphatic dysfunction in T2DM patients with cognitive impairment.

Diabetic nephropathy (DN) is a major cause of type 2 diabetes mellitus (T2DM) mortality. Innate immunity has been shown to be closely associated with the occurrence and progression of T2DM-associated complications. In this study, we investigated the expression of Toll-like receptor 4 (TLR4) and CD14(+)CD16(+) monocytes in patients with T2DM and DN patients with uremia and TLR4 response to lipopolysaccharide (LPS), and to further explore the potential effects of inflammatory immune response in T2DM and DN uremia. Thirty DN patients with uremia, 28 T2DM patients, and 20 healthy volunteers were enrolled for the determination of CD14(+)CD16(+) fluorescence intensity and TLR4 expression on monocytes by using peripheral blood flow cytometry. Serum C-reactive protein (CRP) level was determined by using the immunoturbidimetry. Peripheral blood mononuclear cells (PBMCs) were isolated and stimulated with LPS for 24 h. monocytes were collected to detect NF-κB p65 and phosphorylated STAT5(p-STAT5) expressions by using Western blotting. Supernatants were sampled for the determination of interleukin-6 (IL-6) concentration by using ELISA. Compared to normal control, T2DM patients and DN uremic patients had a significantly higher CD14(+)CD16(+) fluorescence intensity, TLR4 expression, serum IL-6 and CRP level, whilst these biomarkers were more upregulated in DN uremic patients than in T2DM patients. Following the exposure to LPS, PBMCs showed a significant upregulation in NF-κB-p65 and p-STAT5 expression and a remarked increase in Supernatants IL-6 level, in a positive correlation with disease severity. Our results suggest that the disturbance in proinflammatory CD14(+)CD16(+) monocytes occurs in T2DM and DN uremic patients. Such immunological dysfunction may be related to the activation of TLR4/NF-κB and STAT5 signaling pathways underlying the immune abnormalities of CD14(+)CD16(+) monocytes.

Introduction and Aim:Atherosclerotic cardiovascular disease (ASCVD)and Framingham score (FRS) are two important scores used for the prediction of coronary events in an individual.Both of these scores do not account for certain known predisposing factors such as BMI, duration of diabetes, LDL cholesterol and glycemic index levels among type 2 diabetes mellitus (T2DM) subjects. So, this study aims to assess whether addition of any of these important risk factors might help in improving these scoring systems in T2DM patients.
 Methodology: Our study is a cross-sectional study which included 320 T2DM patients without CAD and 120 T2DM patients with CAD. Duration of T2DM, BMI, glycated hemoglobin and fasting lipid profile values of the T2DM patients were recorded from the Laboratory information system.
 Results: IBM Statistical Package for the Social Sciences (SPSS) 16 version was used for statistical analysis. T2DM patients with CAD had more years of duration of T2DM than the patients without CAD and a significant association was found (p=.045*). A strong significant positive correlation was observed between FRS score and duration of T2DM in diabetic patients with CAD (r=.331, p=<.0001*).
 Conclusion:Our observations imply that inclusion of important parameter such as duration of T2DM might improve in better calculation of these risk scores in T2DM patients. Future studies are needed to assess the performance of existing risk scores by including important parameter such as duration of diabetes which might help in improving these scoring systems in T2DM patients.

Background : Type 2 Diabetes Mellitus (T2DM) represents one of the most important risk factors for atrial fibrillation (AF). Numerous studies have shown that T2DM and poor glycemic control reflected by glycated hemoglobin A1c (HbA1c) levels are independently associated with new onset AF. Recent experimental studies reported that the increased susceptibility to AF in the diabetic patients was presumably due to the slowing of conduction associated with increased interstitial fibrosis. Systemic inflammation cam play role in the development of atrial fibrillation. High-sensitivity C-reactive protein (hsCRP) is an inflammatory biomarker that independently predicts the cardiovascular risk. This study aimed to determine the association between hsCRP level and glycemic control with total interatrial conduction time in T2DM patients. Subjects and Methods: This was an analytic study with cross-sectional design. A total of samples were 41 patients with T2DM. Peripheral venous blood samples to measure hsCRP and HbA1c were drawn in all study population. The total interatrial conduction time was measured by tissue Doppler echocardiography. Multivariate analysis was performed using multiple regression analysis. P < 0,050 was considered to indicate a statistically significant difference. Results : The high-sensitivity C-reactive protein level was higher in the T2DM patients with HbA1c≥7% than in the T2DM patients with HbA1c<7%, but not statistically significant (0,44±0,30 vs 0,32±0,22; p = 0,183). The total atrial conduction time was longer in the T2DM patients with HbA1c≥7% than in T2DM patients with HbA1c<7%, but not statistically significant (100,29±28,53 vs 94,88±16,50; p = 0,449). Multiple regression analysis showed that hsCRP levels and glycemic control had significant positive correlation with total interatrial conduction time in T2DM patients (r = 0,51; p = 0,004). Conclusions: The hsCRP levels and glycemic control were significant positively correlated with total interatrium conduction time in patients with type 2 diabetes. Keywords : hsCRP, glycemic control, HbA1c, total interatrial conduction time. Correspondence: Isyana Miranti K. Biomedic, Family Medicine Program, Sebelas Maret University, Surakarta. Indonesian Journal of Medicine (2016), 1(1): 58-70 https://doi.org/10.26911/theijmed.2016.01.01.08

Aim: To evaluate serum renalase and salivary alpha amylase concentrations and their relationship with plasma catecholamine levels in patients with Metabolic Syndrome (MS), Type 2 Diabetes Mellitus (T2DM), and End-Stage Renal Disease (ESRD). Method: In cross-sectional study 163 patients were enrolled; 43 patients with MS, 93 withT2DM, 13 with ESRD, and 14 healthy subjects (control group). All patients had their ascription to the UMAA/UMF No. 75, in Morelia, Michoacan, Mexico. Glucose, creatinine, cholesterol, triglycerides, HDL, and renalase were measurement in serum, and Dopamine (DA), Epinephrine (E) and Norepinephrine (NE) in plasma. An aliquot of saliva was collected for alpha amylase determination. Results: High plasma concentrations of DA, E and NE (p&lt;0.0001) was founded in T2DM and ESRD patients. Renalase was lower in ESRD compared to MS and T2DM patients (p&lt;0.0001). High concentrations of alpha amylase were found in MS, T2DM, and ESRD patients in comparison with control group (p&lt;0.0001). Catecholamines correlated positively with alpha amylase and diabetes evolution. Conclusion: Sympathetic hyperactivity in MS, T2DM and ESRD patients was founded. Renalase could be proposed as biomarker of renal function and salivary alpha amylase as sympathetic hyperactivity. Additional studies are required to evaluate the pathophysiological mechanisms involved of SNS in CKD development.

Type 2 diabetes mellitus (T2DM) is a long-term metabolic disorder. It is characterized by hyperglycemia, insulin resistance (IR), and relative impairment in insulin secretion. IR plays a major role in the pathogenesis of T2DM. Many previous studies have investigated the relationship between estrogen, androgen, and obesity, but few focused on the relationship between sex hormones, abnormal lipid metabolism, and IR. The goal for the present study was to identify the association of IR with sex hormone, abnormal lipid metabolism in type 2 diabetes, and impaired glucose tolerance (IGT) patients.In total 13,400 participants were analyzed based on the results of the glucose tolerance test. Using a cross-sectional study, we showed the relationship between IR and the level of sex hormones among 3 different glucose tolerance states: normal control people, IGT, and T2DM patients. We also analyzed the relationship between IR and abnormal lipid metabolism.Significantly, luteinizing, progesterone, estradiol, prolactin, and follicle-stimulating hormone levels decreased in T2DM and IGT patients compared with those in normal control people. The association between IR and lipid metabolism disorders in T2DM and IGT patients was also observed.Our clinical findings may offer new insights into understanding the mechanism of metabolic disorders and in new therapeutic methods for the treatment of the prevalence of type 2 diabetes.

BackgroundIndividuals with both type 2 diabetes mellitus (T2DM) and hypertension have an increased risk of cognitive impairment (CI) compared to those with either T2DM or hypertension. Therefore, this study aims to identify the prevalence of CI and its associated factors in T2DM patients with hypertension in Hunan, China.MethodsThis cross-sectional study included T2DM patients with hypertension admitted to the Department of Endocrinology of Yuanjiang People’s Hospital in Hunan Province from July 2022 to February 2023. Data on sociodemographic, lifestyle, disease-related characteristics, and cognitive function were collected through face-to-face interviews. Cognitive function was assessed using the Mini-Mental State Examination. Backward stepwise multivariable logistic regression analysis was used to identify factors associated with CI. The predictive power was examined using the receiver operating characteristic (ROC) curve.ResultsA total of 475 participants were included. The prevalence of CI was 38.9% (95% confidence interval [95% CI]: 34.5%–43.3%). Multivariable logistic regression analysis showed that advanced age (odds ratio [OR]=3.21, 95% CI: 1.74–5.93), elementary school or below (OR=3.51, 95% CI: 1.19–10.31), per capita monthly household income ≤2000 RMB (OR=5.29, 95% CI: 2.66–10.51), not current reading books or newspapers (OR=4.48, 95% CI: 1.26–15.99), not current playing cards or mahjong (OR=3.52, 95% CI: 1.91–6.47), current average time of physical activity ≤30 minutes per day (OR=8.66, 95% CI: 4.65–16.12), diabetic nephropathy (OR=1.95, 95% CI: 1.05–3.61) and stroke (OR=7.41, 95% CI: 3.41–16.11) were associated with a higher risk of CI in T2DM patients with hypertension. The area under the ROC curve of this model was 0.925 (95% CI: 0.902–0.949).ConclusionsThe prevalence of CI was high in T2DM patients with hypertension in Hunan, China. Age, educational level, household income, current reading books or newspapers status, current playing cards or mahjong status, current average time of physical activity per day, diabetic nephropathy, and stroke were associated with CI in T2DM patients with hypertension.

Introduction: Diabetic ketoacidosis (DKA) is traditionally associated with type 1 diabetes mellitus (T1DM) but is increasingly observed in type 2 diabetes mellitus (T2DM). Despite notable pathophysiological differences between these conditions, current DKA management follows standardized protocols regardless of diabetes classification. This “one-size-fits-all” approach raises concerns about potential treatment-related metabolic complications that are specific to distinct diabetes phenotypes.Objectives: To compare the rates of metabolic complications - specifically hypoglycemia and hypokalemia - during the initial management of DKA between T1DM and T2DM patients and identify associated factors. Secondary outcomes included evaluating the length of hospital stay (LOS), complication rates, and in-hospital mortality, and identifying predictors of hypoglycemia and hypokalemia during DKA treatment.Methods: A retrospective cohort study of DKA admissions at Penang General Hospital, Malaysia, was conducted between 2019 and 2023. Data on DKA admissions, metabolic complications, and hospitalization outcomes were extracted and analysed.Results: We reviewed the case records of 215 DKA admissions: 100 (46.5%) T1DM, 115 (53.5%) T2DM. The prevalence of hypoglycemia was significantly higher in patients with T1DM compared to T2DM (33/100, 33.0% vs 18/115, 15.7%, p=0.003). Conversely, the prevalence of hypokalemia did not differ significantly between the groups (55/100, 55.0% vs 62/115, 53.9%, p=0.873). Multivariate analysis identified T1DM and low body weight as independent predictors of hypoglycemia. Independent predictors for hypokalemia included low pH, low initial serum potassium, shorter diabetes duration, and absence of nephropathy. Patients with T1DM had a lower observed mortality rate (0/100, 0.0% vs 4/115, 3.5%) and fewer 30-day readmissions (4/100, 4.0% vs 10/115, 8.7%) compared to T1DM, but not statistically significant. T1DM patients required more intensive care, whereas T2DM patients experienced significantly longer hospital stays and higher rates of nosocomial infection.Conclusion: Significant differences exist in both metabolic safety profiles and clinical trajectories when managing T1DM and T2DM patients with DKA. While T1DM patients are prone to hypoglycemia, T2DM patients carry a higher burden of comorbidities leading to prolonged hospitalization and infection risks. Tailoring DKA management strategies specifically to diabetes type may reduce treatment-related complications and improve overall clinical outcomes.

IDF21-0074 Role of methylglyoxal modified low density lipoprotein in type 2 diabetes mellitus and its role in periodontal disease

BackgroundPatients with type 2 diabetes mellitus (T2DM) exhibit a heightened susceptibility to developing dementia, especially those who already present with mild cognitive impairment (MCI). Nevertheless, the fundamental etiology remains elusive, underscoring the pressing need for an objective and precise diagnostic approach in clinical settings. This study investigates the utilization of machine learning algorithms in conjunction with high-resolution sagittal T1-weighted structural imaging to facilitate automated diagnosis of T2DM patients with MCI, differentiating them from both T2DM patients without MCI and healthy controls (HCs).MethodsThirty patients with T2DM and MCI, thirty T2DM patients without MCI, and thirty matched healthy controls (HCs) were enrolled to identify independent biomarkers and develop a diagnostic model for early cognitive impairment in T2DM. Whole-brain structural features-including cortical surface area, volume, thickness, curvature index, folding index, Gaussian curvature, mean curvature, thickness standard deviation, nuclear volume, hippocampal volume, and white matter volume-were extracted from the images of brains using automated segmentation methods. The minimum redundancy maximum relevance (MRMR) method was employed to filter out irrelevant and redundant features, reducing the dimensionality of the dataset. Subsequently, the least absolute shrinkage and selection operator (LASSO) algorithm was applied for further feature selection, ensuring the retention of only the most diagnostic features. The Random Forest (RF) classifier, a powerful machine learning model within the realm of artificial intelligence, was meticulously trained utilizing a curated feature set that had undergone rigorous refinement. To ensure the robust diagnostic performance and generalizability of the established random forest model, a 5-fold cross-validation was employed, providing a dependable estimation of the model’s effectiveness.ResultsThe FreeSurfer software automatically segmented the cerebral imaging data into up to 70 regions. For model establishment, a comprehensive set of 705 structural features, a series of neuropsychological tests, and standard laboratory tests were utilized. Ultimately, 8 features were selected through two feature selection strategies aimed at refining the optimal features. These included bilateral brainstem volume, left hippocampus volume, left transverse temporal gyrus volume, bilateral posterior corpus callosum volume, left medial orbitofrontal cortex Gaussian curvature, glycosylated hemoglobin, blood sugar levels, and the Digit Span Test (DST) backward score. The Random Forest (RF) model, based on the combined features, exhibited the excellent performance, with mean AUCs of 0.959 (95% CI, 0.940–0.997, mean specificity = 94.2%, mean sensitivity = 88.3%, mean accuracy = 88.3% and mean precision = 88.3%) for the training dataset and mean AUCs of 0.887 (95% CI, 0.746–0.992, mean specificity = 85.0%, mean sensitivity = 70.0%, mean accuracy = 70.0% and mean precision = 69.6%) for the testing dataset, based on 5-fold cross-validation.ConclusionThe RF model, leveraging a combination of features, demonstrates high accuracy in diagnosing T2DM with MCI. The exclusion of T2DM patients with complications may limit generalizability to the broader T2DM population, potentially inflating performance estimates. Among these features, 8 optimal indicators comprising 5 structural features, 1 neuropsychological test feature, and 2 standard laboratory test features emerge as the potential independent biomarkers for detecting early-stage cognitive impairment in T2DM patients. These features hold significant importance in understanding the pathophysiological mechanisms of T2DM-related cognitive impairment. Our fully automated model is capable of swiftly processing MRI data, enabling precise and objective differentiation of T2DM with MCI. This model significantly enhances diagnostic efficiency and holds considerable value in clinical practice, facilitating early diagnosis of T2DM with MCI.

<b>Abstract ID 13801</b> <b>Poster Board 256</b> Neuroinflammatory changes together with the associated alteration in cognitive dysfunction are increasingly recognized as bona fide complications of type 2 diabetes mellitus (T2DM). Moreover, research has uncovered a strong metabolic component in neurodegenerative disorders, including Alzheimer’s Disease, to the extent that the former is often referred to as type 3 diabetes. Thus far, disease-modifying therapeutic interventions in cognitive disorders have not been forthcoming. However, there is mounting interest in the pleiotropic effects of novel anti-diabetic drugs, particularly their neuroprotective action. This study examines the impact of combining metformin with dipeptidyl peptidase-4 inhibitors (DPP-4i) or sodium-glucose cotransporter-2 inhibitors (SGLT2i) on cognitive decline in T2DM patients. We designed a prospective observational cohort study, in which T2DM patients taking metformin in combination with DPP4i or SGLT2i were compared to a control group receiving metformin monotherapy. The study was conducted following approval by the Ethics Committee of the Faculty of Medicine of Alexandria University (serial number 0106970). The study was registered on ClinicalTrials.gov NCT05347459. Cognitive function was assessed using a validated Arabic version of the Montreal Cognitive Assessment (MoCA) Battery. Lab tests were conducted for glucose and inflammatory markers (CRP and IL-6) levels, as well as kidney, liver, and lipid profiles. All parameters were assessed at baseline and after six months of follow-up in the 3 treatment groups, in addition to a group of healthy non-diabetics to serve as a baseline reference. One-way ANOVA was conducted for statistical analysis using GraphPad Prism. A P-value &lt;0.05 was considered significant. The study began in March 2022 with a total of 89 subjects (36 males and 53 females) with a mean age of 58.62 ± 6.01 years recruited after giving their written informed consent to the study. Data collected at baseline showed that patients in either combination therapy group had MoCA scores (19.61±1.12 and 19.88±1.27 for DPP-4i and SGLT2i, respectively) that were comparable to healthy subjects (21.62±0.96) in contrast to those on metformin monotherapy who had lower MoCA scores (18.64±0.63). In regards to glucose levels, although HbA1c was higher in all T2DM patients, random blood glucose level was higher only in patients on metformin monotherapy and patients on metformin in combination with DPP-4i. However, neither parameter seemed to affect MoCA scores in correlation studies. Regarding the remaining laboratory tests, T2DM patients did not show abnormal liver or kidney functions nor did they show an altered lipid profile compared to healthy volunteers. Furthermore, none of these parameters appeared to be correlated with the subjects’ MoCA scores. It is worth noting that similar to previous literature, neither drug class seemed to affect serum inflammatory markers (CRP &amp; IL-6) that were associated with impaired cognition in T2DM patients. SGLT2i and DPP-4i appear to positively impact cognitive function in T2DM patients through pathways that do not involve glycemic control, mitigation of diabetic complications, or systemic inflammation. Follow-up assessment of the patients is ongoing on the different parameters measured. An in-depth proteomic and glycomic study is underway to attempt to elucidate the potential mechanistic impact of these drug classes on neuronal function and injury parameters in diabetic patients.

Type 2 diabetes mellitus (T2DM) is a known cause of cognitive dysfunction, and brain-derived neurotrophic factor (BDNF) is a key protein in promoting memory growth and survival of neurons. However, the relationship between plasma BDNF and diabetic cognitive dysfunction is still elusive. A total of 89 patients over 60years with T2DM and 40 well-matched health controls were enrolled. All participants received a set of multi-dimensional neuropsychological tests for the cognitive assessment. The subjects were divided into amnesic mild cognitive impairment (aMCI) and non-aMCI groups. An enzyme-linked immunosorbent assay (ELISA) was used to measure plasma BDNF concentrations for all subjects. No significant difference was found between T2DM patients and healthy control in MMSE scores. The T2DM patients performed significantly worse in four cognitive domains (including episodic memory, executive function, visuospatial function, and information processing speed) compared with the controls (all p<0.05). The prevalence of aMCI in T2DM population was higher [OR=4.032 (1.536~10.582), 37/89-6/40]. Additionally, the plasma concentration of BDNF in T2DM patients was significantly lower than that in controls (p<0.01). However, no significant correlation was found between plasma BDNF and cognitive function in T2DM. Our results suggested that T2DM have a higher prevalence of cognitive impairment. The plasma BDNF concentration in T2DM patients was significantly lower than that in controls, but low BDNF was not a biomarker for cognitive dysfunction in T2DM patients.

The present study aimed to investigate the effects of a 6-month multicomponent exercise program (MCEP) on functional, cognitive, and hemodynamic parameters of older Type 2 diabetes mellitus (T2DM) patients. Moreover, additional analyses were performed to evaluate if T2DM patients present impaired adaptability in response to physical exercise when compared to nondiabetic volunteers. A total of 72 T2DM patients and 72 age-matched healthy volunteers (CG) were recruited and submitted to functional, cognitive, and hemodynamic evaluations before and after six months of a MCEP. The program of exercise was performed twice a week at moderate intensity. Results indicate T2DM and nondiabetic patients present an increase in mobility (i.e., usual walking speed) after the MCEP. However, improvements in maximal walking speed, transfer capacity, and executive function were only observed in the CG. On the other hand, only T2DM group reveals a marked decline in blood pressure. In conclusion, data of the current study indicate that a 6-month MCEP improves mobility and reduce blood pressure in T2DM patients. However, maximal walking speed, transfer capacity, and executive function were only improved in CG, indicating that T2DM may present impaired adaptability in response to physical stimulus.

Objective: This study investigates the correlation between plasma uric acid levels and renal function in Type 2 Diabetes Mellitus (T2DM) patients.Elevated uric acid levels have been associated with diabetic complications, particularly renal damage.Methods: A total of 160 participants, including 80 T2DM (Group D) patients and 80 normal controls (Group N), were examined.Plasma levels of glucose, creatinine, urea, electrolytes, and uric acid were measured.Renal function was assessed using estimated glomerular filtration rate (eGFR).The study categorized T2DM patients based on sex, age, and eGFR stages.Results: T2DM patients exhibited significantly higher plasma uric acid levels compared to controls (p<0.001).eGFR found to be significantly lesser in T2DM patients compared to controls (P<0.05).Gender-based comparisons revealed lesser eGFR levels in females. Conclusion:Plasma uric acid levels are elevated in T2DM patients.Uric acid may serve as a marker for renal damage in T2DM, highlighting its role in diabetic nephropathy progression.

Globally, type 2 diabetes mellitus (T2DM) is a growing health concern, currently affecting over 537 million adults and projected to rise significantly in the coming decades. In India alone, approximately 101 million individuals are impacted. Beyond the well-documented complications such as retinopathy, kidney disease, and cardiovascular issues, recent studies highlight a concerning association between T2DM and cognitive decline, with prevalence rates of cognitive impairment ranging from 21.8% to 45%. This decline, if unrecognized, can complicate diabetes self-management, leading to poor glycemic control, and increased health risks. This review synthesizes existing literature on the cognitive health of individuals with T2DM and examines the potential of yoga as a therapeutic intervention. Yoga, an ancient mind–body practice, incorporates physical postures, breath control, and meditation, which have been shown to enhance cognitive function by reducing stress, promoting brain plasticity, and improving glucose regulation. The study discusses specific yoga practices that may benefit cognitive health in T2DM patients and suggests their integration into clinical diabetes care. The findings advocate for holistic approaches, such as yoga, to foster cognitive resilience and enhance overall well-being in individuals managing T2DM. Cognitive impairment in T2DM: Cognitive decline among T2DM patients is a growing concern, with studies indicating a wide range of impairment rates. Factors contributing to this decline include chronic hyperglycemia, vascular damage, and neuroinflammation. Recognizing cognitive impairment in this population is vital for timely intervention, yet routine cognitive screening remains rare in diabetes care settings. The role of yoga: Yoga offers a promising, integrative approach to enhancing cognitive health among T2DM patients. This review suggests that yoga can reduce stress and anxiety, which are known to adversely affect cognitive function. Specific practices, such as mindfulness meditation, asanas, and pranayama, have demonstrated benefits in improving attention, memory, and overall cognitive performance. Furthermore, yoga’s influence on physical health such as improved glycemic control also indirectly supports cognitive function.