Effect of hypoglycemic events on cognitive function in individuals with type 2 diabetes mellitus: a dose–response meta-analysis
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SUBMITTER: Ye M
PROVIDER: S-EPMC11347434 | biostudies-literature | 2024 Jan
REPOSITORIES: biostudies-literature
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Project description:IntroductionWith an incidence rate as high as 46%-58%, hypoglycemia is a common complication of glycemic management among those suffering from type 2 diabetes mellitus(T2DM). According to preclinical research, hypoglycemia episodes may impair cognition by harming neurons. However, there is still controversy regarding the clinical evidence for the relationship between hypoglycemic events and the likelihood of cognitive impairment. Furthermore, little research has been done on the dose-response association between hypoglycemia incidents and the possibility of cognitive impairment. To address these knowledge gaps, the present research intends to update the comprehension of the association among hypoglycemic events and the risk of cognitive impairment and to clarify the correlation between dose and response by incorporating the most recent investigations.Method and analysisThis work has developed a protocol for a systematic review and meta-analysis that will examine, via a well-organized assessment of several databases, the relationship between the incidence of hypoglycemia and the probability of cognitive impairment. Observational studies investigating the connection between hypoglycemia episodes and cognitive impairment will be included. The databases that will be searched are PubMed, Web of Science, the Chinese Biomedical Literature Database (CBM), Cochrane Library, Embase, the China National Knowledge (CNKI), Wan Fang, the Chinese Science and Technology Periodical Database (VIP), and Du Xiu. Literature from the establishment of each database to December 2023 will be included in the search. Two researchers will independently screen the studies that satisfy the requirements for both inclusion and exclusion. A third researcher will be asked to mediate any disputes. The methodological caliber of the studies included will be assessed utilizing the Newcastle-Ottawa Scale (NOS) or the Joanna Briggs Institute (JBI) critical appraisal method. With regard to GRADE, which stands for Grading of Recommendations, Assessment, Development, and Evaluation, the quality of the evidence will be evaluated. ROBIS Tool will be used to evaluate the risk of bias in the development of the systematic review. If the data is accessible, meta-analysis and dose-response curve analysis will be employed by Stata software. However, if the data does not allow for such analysis, a descriptive review will be performed.Discussion and conclusionHypoglycemic episodes may raise the likelihood of cognitive impairment, according to earlier investigations. This study will update the relevant evidence and explore the dose-response connection between hypoglycemic episodes and cognitive impairment. The results of this review will have significant effects on decision-making by individuals with diabetes, healthcare providers, and government policy institutions.Trial registrationProspero registration number: CRD42023432352.
| S-EPMC10836671 | biostudies-literature
Project description:IntroductionSevere hypoglycemic events (SHEs) are associated with significant morbidity, mortality and costs. However, the more common non-severe hypoglycemic events (NSHEs) are less well explored. We investigated the association between reported frequency of NSHEs and SHEs among patients with type 1 diabetes mellitus (T1DM) and type 2 diabetes mellitus (T2DM) in the PREDICTIVE study.MethodsPREDICTIVE was a global, prospective, observational study. Patients with T1DM (n = 7,420) or T2DM (n = 12,981), starting treatment with insulin detemir, reported the number of NSHEs and SHEs experienced during the 4 weeks prior to baseline and follow-up visits (mean 14.4 weeks). Logistic regression was used to determine the odds ratio (OR) of experiencing ≥1 SHE, in patients having 1-4 or ≥5 NSHEs, versus those having 0 NSHEs, while controlling for baseline covariates.ResultsHypoglycemia rates were lower at follow-up than baseline. At baseline 59.2% (T1DM) and 18.8% (T2DM) reported any hypoglycemia and at follow-up 39.5% (T1DM) and 8.6% (T2DM). There was a significant (P < 0.0001) increase in the odds of ≥1 SHEs with increasing frequency of NSHEs in T1DM and T2DM, for both crude and adjusted estimates. At baseline, in T1DM, ORs for ≥1 SHE were 1.92 and 2.13 for 1-4 and ≥5 NSHEs, respectively; the corresponding ORs in T2DM were 10.83 and 15.36, respectively. At follow-up, the ORs for ≥1 SHE were 2.01 and 3.20 (T1DM) and 18.99 and 24.29 (T2DM) for 1-4 and ≥5 NSHEs, respectively.ConclusionA statistically significant association between NSHE and SHE frequency was found in T1DM and T2DM. These data provide a clear rationale for the reduction of hypoglycemic events, regardless of severity, while striving for optimal glycemic control.
| S-EPMC4269646 | biostudies-literature
Project description:IntroductionTo improve the utilization of continuous- and flash glucose monitoring (CGM/FGM) data we have tested the hypothesis that a machine learning (ML) model can be trained to identify the most likely root causes for hypoglycemic events.MethodsCGM/FGM data were collected from 449 patients with type 1 diabetes. Of the 42,120 identified hypoglycemic events, 5041 were randomly selected for classification by two clinicians. Three causes of hypoglycemia were deemed possible to interpret and later validate by insulin and carbohydrate recordings: (1) overestimated bolus (27%), (2) overcorrection of hyperglycemia (29%) and (3) excessive basal insulin presure (44%). The dataset was split into a training (n = 4026 events, 304 patients) and an internal validation dataset (n = 1015 events, 145 patients). A number of ML model architectures were applied and evaluated. A separate dataset was generated from 22 patients (13 'known' and 9 'unknown') with insulin and carbohydrate recordings. Hypoglycemic events from this dataset were also interpreted by five clinicians independently.ResultsOf the evaluated ML models, a purpose-built convolutional neural network (HypoCNN) performed best. Masking the time series, adding time features and using class weights improved the performance of this model, resulting in an average area under the curve (AUC) of 0.921 in the original train/test split. In the dataset validated by insulin and carbohydrate recordings (n = 435 events), i.e. 'ground truth,' our HypoCNN model achieved an AUC of 0.917.ConclusionsThe findings support the notion that ML models can be trained to interpret CGM/FGM data. Our HypoCNN model provides a robust and accurate method to identify root causes of hypoglycemic events.
| S-EPMC10203083 | biostudies-literature
Project description:While hemoglobin A1c (HbA1c) is commonly used to monitor therapy response in type 2 diabetes (T2D), GV is emerging as an essential additional metric for optimizing glycemic control. Our goal was to learn more about the impact of hypoglycemic agents on HbA1c levels and GV in patients with T2D. A systematic review and network meta-analysis (NMA) of randomized controlled trials were performed to assess the effects of glucagon-like peptide 1 receptor agonists (GLP-1 RAs), sodium-glucose cotransporter (SGLT)-2 inhibitors, dipeptidyl peptidase (DPP)-4 inhibitors, sulfonylurea and thiazolidinediones on Mean Amplitude of Glycemic Excursions (MAGE) and HbA1c. Searches were performed using PubMed and EMBASE. A random-effect model was used in the NMA, and the surface under the cumulative ranking was used to rank comparisons. All studies were checked for quality according to their design and also for heterogeneity before inclusion in this NMA. The highest reduction in MAGE was achieved by GLP-1 RAs (SUCRA 0.83), followed by DPP-4 inhibitors (SUCRA: 0.72), and thiazolidinediones (SUCRA: 0.69). In terms of HbA1c reduction, GLP-1 RAs were the most effective (SUCRA 0.81), followed by DPP-4 inhibitors (SUCRA 0.72) and sulfonylurea (SUCRA 0.65). Our findings indicated that GLP-1 RAs have relatively high efficacy in terms of HbA1c and MAGE reduction when compared with other hypoglycemic agents and can thus have clinical application. Future studies with a larger sample size and appropriate subgroup analyses are warranted to completely understand the glycemic effects of these agents in various patients with T2D. The protocol for this systematic review was registered with the International Prospective Register of Systematic Reviews (CRD42021256363).
| S-EPMC9189550 | biostudies-literature
Project description:ObjectiveCognitive dysfunction is a common complication of diabetes after central nervous system involvement. The impact of exercise, as an important non-pharmacological intervention strategy, on cognitive function remains controversial. Thus, we conducted a meta-analysis to assess the impact of exercise on cognitive function of elderly patients with type2 diabetes mellitus (T2DM).MethodsWe computer searched PubMed, Web of Science, Embase, CINAHL, Cochrane Library, CNKI, Wanfang date, and VIP, and traced back the references included in the literature from 1974 to July 2024. We used RevMan5.4 software for data analysis, and also conducted sensitivity, subgroup, and publication bias analyses.ResultsEight eligible studies with a combined total of 747 elderly patients with T2DM were included. Meta-analysis showed that the combined effect size of exercise intervention on cognitive improvement in elderly patients with T2DM was significant [SMD = 0.65, 95% CI (0.48, 0.82), P < 0.01]. The following three factors had significant effects on the overall cognitive function of participants: subgroups (MoCA group [MD = 2.22 95% CI (1.26, 3.18), P < 0.01] and MMSE group [MD = 1.81, 95% CI (0.71,2.90), P = 0.001]); intervention times (3-month intervention [MD = 3.14, 95% CI (2.50, 3.78), P < 0.01], 6-month intervention [SMD = 0.32, 95% CI (0.12. 0.52), P = 0.002], and > 6 month intervention [SMD = 0.21, 95% CI (0.45, 0.81), P < 0.01]); intervention forms (single exercise [SMD = 0.21, 95% CI (0.45, 0.81), P < 0.01] and multiple exercise [SMD = 0.86, 95% CI ( 0.39,1.33), P < 0.0001]).ConclusionExercise intervention may improve cognitive function in elderly patients with T2DM.
| S-EPMC11411744 | biostudies-literature
Project description:A major problem in the insulin therapy of patients with diabetes type 2 (T2DM) is the increased occurrence of hypoglycemic events which, if left untreated, may cause confusion or fainting and in severe cases seizures, coma, and even death. To elucidate the potential contribution of the liver to hypoglycemia in T2DM we applied a detailed kinetic model of human hepatic glucose metabolism to simulate changes in glycolysis, gluconeogenesis, and glycogen metabolism induced by deviations of the hormones insulin, glucagon, and epinephrine from their normal plasma profiles. Our simulations reveal in line with experimental and clinical data from a multitude of studies in T2DM, (i) significant changes in the relative contribution of glycolysis, gluconeogenesis, and glycogen metabolism to hepatic glucose production and hepatic glucose utilization; (ii) decreased postprandial glycogen storage as well as increased glycogen depletion in overnight fasting and short term fasting; and (iii) a shift of the set point defining the switch between hepatic glucose production and hepatic glucose utilization to elevated plasma glucose levels, respectively, in T2DM relative to normal, healthy subjects. Intriguingly, our model simulations predict a restricted gluconeogenic response of the liver under impaired hormonal signals observed in T2DM, resulting in an increased risk of hypoglycemia. The inability of hepatic glucose metabolism to effectively counterbalance a decline of the blood glucose level becomes even more pronounced in case of tightly controlled insulin treatment. Given this Janus face mode of action of insulin, our model simulations underline the great potential that normalization of the plasma glucagon profile may have for the treatment of T2DM.
| S-EPMC3481299 | biostudies-literature
Project description:BackgroundIt is controversial whether the level of glycemic control in patients with type 1 diabetes mellitus (T1DM) correlates with reduced cognitive function. This study explored the influence of glycemic management quality on cognitive function in T1DM patients by examining the association between glycemic control level and impaired cognitive function.MethodsThe electronic databases PubMed, Embase, Cochrane Library, China National Knowledge Infrastructure, China Science and Technology Journal database, Wanfang database, and China Biology Medicine disc database were systematically searched to identify eligible studies published before January 2023. Search, selection, and data extraction were performed by two independent reviewers. RevMan 5.4 software was used for meta-analysis, and standardized mean difference (SMD) between groups was calculated.ResultsSix studies involving 351 patients with T1DM were included in this study. Compared with T1DM subjects with good glycemic control, those with poor glycemic control performed worse in full-scale intellectual quotient (P = 0.01, SMD = -0.79, 95%CI = -1.42 to -0.17), but no significant differences were observed in verbal intellectual quotient (P = 0.08, SMD = -1.03, 95%CI = -2.20 to 0.13), memory (P = 0.05, SMD = -0.41, 95%CI = -0.82 to 0.00), and attention (P = 0.23, SMD = -0.26, 95%CI = -0.69 to 0.16).ConclusionsT1DM patients with suboptimal glycemic control may have a worse cognitive function, mainly focusing on the full-scale intellectual quotient. The current study highlights the significance of maintaining satisfactory glycemic control in T1DM patients to improve their health status and quality of life. Standardized tests should be employed in clinical neuropsychological practice to provide early and complete cognitive assessment of individuals with poor glycemic control.Systematic review registrationThe study protocol has been registered in the PROSPERO database (CRD42023390456).
| S-EPMC10763190 | biostudies-literature
Project description:ObjectivePatients with type 2 diabetes mellitus (T2DM) often experience hypoglycaemia and weight gain due to treatment side effects. Sulfonylureas (SU) and the combination of SU and metformin (SU+MET) were the most common monotherapy and combination therapies used in Thailand tertiary care hospitals. This study aimed to assess the glycaemic goal attainment rates, hypoglycaemic episodes, weight gain and treatment compliance among patients with T2DM receiving SU or SU+MET.Research design and methodsA multicentre cross-sectional survey and retrospective review was conducted in five tertiary care hospitals, Thailand. Patients with T2DM aged ≥30 years were included consecutively during a 12-month period. Glycaemic control, experiences of hypoglycaemia, weight gain and compliance were evaluated. Glycaemic goal attainment was defined by HbA1c level less than 7%.ResultsOut of the 659 patients (mean age (±SD)), 65.5 (10.0) years and median duration of T2DM (IQR), 10 (5-15) years), 313 (47.5%) achieved the glycaemic goal. HbA1c levels in the patients with goal attainment was significantly lower compared with those without (6.3%±0.5% vs 8.1%±1.2%, p<0.001). Goal attainment was significantly lower among patients treated with SU+MET than those treated with SU alone (43.5% vs 63.0%; OR 0.45, 95% CI 0.31, 0.66, p<0.001). A third of patients reported experiencing hypoglycaemia (30.7%) and weight gain (35.4%). Weight gain in the SU+MET group was lower than those receiving SU alone (33.1% vs 44.6%, p=0.015), but there was no difference in hypoglycaemic events. Major events in the previous 12 months were experienced by 68 patients, most commonly congestive heart failure and ischaemic heart disease. Approximately half of the patients (52.2%) reported not always taking their medication as prescribed.ConclusionsAmong patients with T2DM receiving SU or SU+MET, only about half of the patients achieved glycaemic goal and compliance with the treatment. Hypoglycaemia and weight gain posed a significant burden with risk of weight gain higher in the SU group.
| S-EPMC7045111 | biostudies-literature
Project description:BackgroundThe impact of obesity on cognitive function in patients with type 2 diabetes mellitus (T2DM) remains controversial. This study aimed to evaluate whether obesity, assessed by body mass index (BMI) was associated with cognitive function among T2DM patients and whether the effect of obesity on cognitive function was through brain structure.MethodsThis was a post-hoc analysis of the Action to Control Cardiovascular Risk in Diabetes-Memory in Diabetes (ACCORD-MIND) study. The cognitive test battery included the Digit Symbol Substitution Test (DSST), Mini-Mental State Exam (MMSE), Rey Auditory Verbal Learning Test (RAVLT), and STROOP test, which were administered at baseline, and at 20, 40, and 80 months. A subgroup (n = 614) of the ACCORD-MIND study underwent MRI scanning at baseline and at 40 and 80 months. The total brain volume (TBV), abnormal white matter volume (AWM), abnormal gray matter volume (AGM), and abnormal basal ganglia volume (ABG) were estimated. The outcomes of this study were cognitive function and brain structure.ResultsIn the adjusted analyses, BMI was positively associated with the MMSE (β:0.08, 95%CI,0.01-0.16, per standard deviation [SD] increase) and RAVLT scores (β:0.09, 95%CI,0.01-0.18). It was also associated with a greater TBV (β:7.48, 95%CI,0.29-14.67). BMI was not associated with the DSST or STROOP scores, and AWM, AGM, ABG. Mediation analysis found that the effect of BMI on MMSE/RAVLT was mediated through TBV.ConclusionObesity may be associated with greater cognitive function and the effect of BMI on cognitive function may be mediated by TBV among patients with T2DM.Clinical trial registrationhttp://www.clinicaltrials.gov, identifier NCT00000620.
| S-EPMC9637978 | biostudies-literature
Project description:Materials and methodsWe screened four databases (PubMed, Embase, Cochran Library, and CNKI) for the observational studies about the OSA and T2DM. Studies were collected from database establishment to October 2020. We performed a traditional subgroup meta-analysis. What is more, linear and spline dose-response models were applied to assess the association between apnea-hypopnea index (AHI), an indicator to evaluate the severity of OSA, and the risk of T2DM. Review Manager, version 5.3, software and Stata 16.0 were used for the analysis.ResultSeven observational studies were included in the research. We excluded a study in the conventional meta-analysis. In the subgroup analysis, mild-dose AHI increased the risk of T2DM (odds ratio = 1.23, 95% confidence interval = 1.06-1.41, P < 0.05). Moderate-dose AHI increased the risk of T2DM with a higher odds ratio (OR = 1.35, 95% CI = 1.13-1.61, P < 0.05). Moderate-to-severe-dose AHI increased the risk of T2DM with a higher odds ratio (OR = 2.14, 95% CI = 1.72-2.67, P < 0.05). Severe-dose AHI increased the risk of T2DM with a higher odds ratio (OR = 2.19 95% CI = 1.30-3.68, P < 0.05). Furthermore, the spline and linear dose-response meta-analysis results revealed that the risk of T2DM increased with increasing AHI values.ConclusionThrough the dose-response meta-analysis, we found a potential dose-response relationship existed between the severity of OSA and the risk of T2DM. This relationship in our passage should be considered in the prevention of T2DM in the future.
| S-EPMC8497107 | biostudies-literature