Oral diabetes medication and risk of dementia in elderly patients with type 2 diabetes

Kim, Ju Young; Ku, Young Sook; Trinh, Nga Thi; Kim, Woorim; Jeong, Bomi; Heo, Tae Young; Lee, Myung Koo; Lee, Jun Young

doi:10.1016/j.diabres.2019.07.004

Cited by 28 publications

(34 citation statements)

References 24 publications

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“…Finally, 133 articles were excluded for lack of relevant information (n = 124) or the absence of a full publication (n = 9). A total of 22 studies, consisting of 18 cohort studies [4,[12][13][14][15][16][24][25][26][27][28][29][30][31][32][33][34][35], three case-control studies [36][37][38], and one cross-sectional study [39] met the inclusion criteria. (Fig.…”

Section: Literature Search Outcomes and Validity Assessmentmentioning

confidence: 99%

“…For the network meta-analysis, nine studies with a total of 530,355 individuals were selected [4,[14][15][16]24,28,29,31,34], of which three were regarding Alzheimer's dementia [4,15,34], and two regarding vascular dementia [15]. The network meta-analysis of dementia and dementia due to Alzheimer's disease involved seven different antidiabetic agents: metformin, sulfonylurea, thiazolidinediones, DPP-4 inhibitors, glucose inhibitors, benzoic acid derivatives (a non-sulfonylurea insulin secretagogue), and insulin.…”

Section: Network Meta-analysis Of the Association Between Antidiabetic Agents And The Risk Of Dementiamentioning

confidence: 99%

“…Of the 17 studies containing 1,258,879 individuals which focused on the impact of using antidiabetic agents on the risk of dementia [4,[13][14][15][16][24][25][26][27][28][29][30][31][32][33][34][35], two studies included acarbose [4,31], two included benzoic acid derivatives [4,31], two included DPP-4 inhibitors [31,34], three included insulin [4,25,33], ten included metformin [4,[13][14][15][24][25][26][29][30][31], five included sulfonylurea [4,14,16,25,31] and seven included thiazolidinediones [4,16,27,28,31,32,35]. Furthermore, six studies with 168,293 people showed the relationship between antidiabetic agent use and the ...…”

Section: Meta-analysis Of the Impact Of Using Antidiabetic Agents On The Risk Of Dementiamentioning

confidence: 99%

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Impact of antidiabetic agents on dementia risk: A Bayesian network meta-analysis

et al. 2020

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Background: Dementia is more prevalent among people with type 2 diabetes, but little is known regarding the influence of antidiabetic agents on this association. Objective: This study assessed the impact of various antidiabetic agents on the risk of dementia among patients with Type 2 diabetes mellitus. Methods: Relevant studies were retrieved from the PubMed, Embase, Cochrane Central Register of Controlled Trials (CENTRAL), and ClinicalTrials.gov databases. Nine antidiabetic agents were included in the search. Data were pooled via network meta-analysis and meta-analysis. Results: Nine studies were selected for the network meta-analysis with 530,355 individuals and 17 studies for the meta-analysis with 1,258,879 individuals. The analysis excluded glucagon-like peptide 1 (GLP-1) analogs and sodium-dependent glucose transporter 2 (SGLT-2) inhibitors due to the absence of relevant data. The use of dipeptidyl peptidase-4 (DPP-4) inhibitors, metformin, thiazolidinedione, and sulfonylurea was associated with a decreased risk of dementia in comparison to no treatment with antidiabetic agents (hazard ratio [HR] for DPP-4 inhibitors, 0.54; 95% confidence interval [CI], 0.38-0.74, HR for metformin, 0.75; 95% CI, 0.63-0.86; HR for sulfonylurea, 0.85; 95%CI, 0.73-0.98 and HR for thiazolidinedione, 0.70; 95% CI, 0.55-0.89, respectively). However, the node-splitting analysis showed the inconsistency of direct and indirect estimates in sulfonylurea (P = 0.042). DPP-4 inhibitors, metformin, thiazolidinedione, and sulfonylurea exhibited a significant impact on the risk of dementia in diabetics compared with insulin (HR, 0.35; 95%CI, 0.20-0.59, HR, 0.48; 95% CI, 0.30-0.77, HR, 0.45; 95% CI, 0.29-0.73 and HR, 0.55; 95% CI, 0.34-0.88, respectively). DPP-4 inhibitors also exhibited a protective effect on the risk of Alzheimer's dementia compared with the no treatment with antidiabetic agents (HR, 0.48; 95% CI, 0.25-0.92). The meta-analysis demonstrated a protective effect of using metformin and DPP-4 inhibitors on the risk of dementia (HR, 0.86; 95% CI, 0.74-1.00 and HR, 0.65; 95% CI, 0.55-0.76, respectively). Further analysis showed insulin was associated with an increased risk of Alzheimer's dementia (HR, 1.60; 95% CI, 1.13-2.26). Only two case-control studies mentioned GLP-1 analogs and SGLT-2 inhibitors, and the pooled ORs showed no evidence of an association with dementia (GLP-1 analogs: 0.71; 95% CI, 0.46-1.10 and SGLT-2 inhibitors: 0.74; 95% CI, 0.47-1.15). Conclusion: This analysis indicated that patients with type 2 diabetes under treatment with DPP-4 inhibitors presented with the lowest risk of dementia, followed by those treated with metformin and thiazolidinedione, while treatment with insulin was associated with the highest risk. For the increasing focus on the protective effect on dementia, further specific clinical studies are needed to evaluate the impact of GLP-1 analogs and SGLT-2 inhibitors on the risk of dementia.

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Section: Literature Search Outcomes and Validity Assessmentmentioning

confidence: 99%

Section: Network Meta-analysis Of the Association Between Antidiabetic Agents And The Risk Of Dementiamentioning

confidence: 99%

Section: Meta-analysis Of the Impact Of Using Antidiabetic Agents On The Risk Of Dementiamentioning

confidence: 99%

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Impact of antidiabetic agents on dementia risk: A Bayesian network meta-analysis

et al. 2020

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“…We found antidiabetics were significantly associated with reduced MCI-to-Dementia risk, and specifically for women with CDRSOB as the outcome. Despite the association of diabetes with lower cognitive function, several studies have reported a reduced dementia risk in those taking oral hypoglycaemic medication (Hsu et al, 2011;Kim et al, 2019;Wu et al, 2020). Further analysis of drug subcategories in the present study revealed reduced MCI-to-Dementia risk with CDRSOB associated with sulfonylureas.…”

Section: Discussionmentioning

confidence: 99%

Impact of different diagnostic measures on drug class association with dementia progression risk: a longitudinal prospective cohort study

Kaur¹,

Bucholc

Finn

et al. 2021

Preprint

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Background: Clinical Dementia Rating Sum of Boxes (CDRSOB) scale is known to be highly indicative of cognitive-functional status, but it is unclear whether it is consistent with clinician diagnosis in evaluating drug class associations with risk of progression to mild cognitive impairment (MCI) and dementia. Methods: We employed multivariate logistic regression on longitudinal NACC data, to identify drug classes associated with disease progression risk, using clinician diagnosis and CDRSOB as the outcome. Results: Non-steroidal anti-inflammatory drugs, anxiolytics, antidiabetics, and Parkinson's medications were significantly associated with decreased progression to mild cognitive impairment (MCI)/dementia, and antihypertensives and Alzheimer's medications significantly associated with increased progression risk. Associations were however dependant on the diagnostic measure used, e.g., antihypertensives were associated with increased Healthy-to-Dementia risk using clinical diagnosis as the outcome (OR:2.05, FDR p<0.001), but not for CDRSOB. Additionally, some associations appear to be gender specific; for instance, antidiabetics had lower MCI-to-Dementia risk for women (OR:0.58, FDR p=0.006) using CDRSOB. Further, in accordance with existing literature, acetylcholinesterase inhibitors were not beneficial in delaying dementia. Conclusions: Overall, we demonstrate that choice of diagnostic measure can influence the magnitude of risk or protection attributed to drug classes. A consensus must be reached within the research community with respect to the most accurate diagnostic outcome to identify risk and improve reproducibility.

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“…Other researchers have developed a model for predicting the prevalence and incidence of obesity and diabetes as well as the direct costs of treating diabetes and its complications [12]. In another study, a population-based analysis of an elderly cohort was carried out to investigate whether oral antidiabetic agent use can decrease the risk of dementia in type 2 diabetes patients, and the correlation of the incidence of dementia to the duration of diabetes was explored [13]. In Taiwan, models for estimating the diabetes-associated risk of hospitalization and the risk of type 2 diabetes inpatient mortality were devised in order to facilitate the identification of at-risk patients [14].…”

Section: Introductionmentioning

confidence: 99%

Forecasting the Prevalence of Diabetes Mellitus Using Econometric Models

Mukasheva

Saparkhojayev

Akanov³

et al. 2019

Diabetes Ther

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IntroductionThe prevalence of diabetes in Kazakhstan has reached epidemic proportions, and this disease is becoming a major financial burden. In this research, regression analysis methods were employed to build models for predicting the number of diabetic patients in Kazakhstan in 2019, as this should aid the costing and policy-making performed by medical institutions and governmental offices regarding diabetes prevention and treatment strategies.MethodsA brief review of mathematical models that are potentially useful for the task of interest was performed, and the most suitable methods for building predictive models were selected. The chosen models were applied to explore the correlation between population growth and the number of patients with diabetes as well as the correlation between the increase in gross regional product and the growth in the number of patients with diabetes. Moreover, the relationship of population growth and gross domestic product with the growth in the number of patients with diabetes in Kazakhstan was determined. Our research made use of the scikit-learn library for the Python programming language and functions for regression analysis built into the Microsoft Excel software.ResultsThe predictive models indicated that the prevalence of diabetes in Kazakhstan will increase in 2019.ConclusionMathematical models were used to find patterns in a comprehensive statistical dataset on registered diabetes patients in Kazakhstan over the last 15 years, and these patterns were then used to build models that can accurately predict the prevalence of diabetes in Kazakhstan.

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Oral diabetes medication and risk of dementia in elderly patients with type 2 diabetes

Cited by 28 publications

References 24 publications

Impact of antidiabetic agents on dementia risk: A Bayesian network meta-analysis

Impact of antidiabetic agents on dementia risk: A Bayesian network meta-analysis

Impact of different diagnostic measures on drug class association with dementia progression risk: a longitudinal prospective cohort study

Forecasting the Prevalence of Diabetes Mellitus Using Econometric Models

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