Cognitive impairment in type 2 diabetes and its impact on daily living and self-care: A case - Control study in Kanpur, North India

Verma, Shraddha; Agarwal, Saurabh; Tashok, Sorang; Verma, Alok; Giri, Richa

doi:10.1016/j.dsx.2021.05.020

Cited by 10 publications

(6 citation statements)

References 18 publications

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“…Women with type 2 diabetes experience a higher risk of depression and need more social support [67][68][69]. High HbA1c levels (≥10%) increase the need for social support [70].…”

Section: Risk Factors For Psychosocial Problems In Type 2 Diabetesmentioning

confidence: 99%

The Main Risk Factors in Type 2 Diabetes for Cognitive Dysfunction, Depression, and Psychosocial Problems: A Systematic Review

Randväli,

Toomsoo,

Šteinmiller

2024

Diabetology

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The aim of this study is to analyze the risk factors that lead to cognitive impairment, depression, and psychosocial problems in type 2 diabetes and discern what aspects they have in common. Type 2 diabetes is associated with a higher risk of cognitive impairment, including dementia, which in turn increases the risk of hospitalization, falls, and premature mortality. In this study, we conducted a systematic review to achieve this goal, including searches on electronic databases such as PubMed, Medline, Web of Science, EBSCO Discovery, EBSCO host, Scopus, and ScienceDirect, from 2016 onwards. Additionally, we carried out manual searches in leading journals in the field. After evaluating and analyzing the articles, 60 remained, focusing on the following four main themes: disorders due to biological, psychological, social, and pharmacological causes that lead to neuropsychological complications. Based on the results, consistently analogous risk factors contributing to the onset of cognitive impairments, depression, and psychosocial predicaments encompass comorbid ailments, dysglycemia, gender, heightened levels of apprehension and anxiety, educational attainment, socio-economic standing, and pharmaceutical interventions. Furthermore, in the realm of type 2 diabetes, factors such as disease duration, adiposity, specifically overweight and obesity, and advancing age were also identified as significant contributors to cognitive impairments and depression. Concomitantly, the absence of a robust support system and social network emerged as a shared risk factor, predisposing individuals to psychosocial challenges and depressive states. These findings emphasize that the risk factors for cognitive impairments, depression, and psychosocial issues for type 2 diabetes are similar, highlighting the importance of psychosocial support, education, and patient-centered treatment to optimize outcomes and quality of life.

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“…Women with type 2 diabetes experience a higher risk of depression and need more social support [67][68][69]. High HbA1c levels (≥10%) increase the need for social support [70].…”

Section: Risk Factors For Psychosocial Problems In Type 2 Diabetesmentioning

confidence: 99%

The Main Risk Factors in Type 2 Diabetes for Cognitive Dysfunction, Depression, and Psychosocial Problems: A Systematic Review

Randväli,

Toomsoo,

Šteinmiller

2024

Diabetology

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“…Accumulated evidence has consistently shown that the presence of MCI in the general population was associated with increased risk of dementia (Davis et al., 2018 ; Mitchell & Shiri‐Feshki, 2009 ; Zhang et al., 2021 ). In addition to that, MCI in T2DM could lead to reduced treatment compliance, medication management, and self‐care ability (Kim & Fritschi, 2021 ; Verma et al., 2021 ), which may be explained by the decrements in working memory, learning, executive function, and processing speed observed in MCI (Christman et al., 2010 ; Santos et al., 2018 ), though the function of daily activities is essentially preserved. Therefore, the management of MCI in T2DM patients is crucial, and early identification of those with MCI is a starting point.…”

Section: Introductionmentioning

confidence: 99%

Predictive model for identifying mild cognitive impairment in patients with type 2 diabetes mellitus: A CHAID decision tree analysis

Maimaitituerxun,

Chen,

Xiang

et al. 2024

Brain and Behavior

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BackgroundAs the population ages, mild cognitive impairment (MCI) and type 2 diabetes mellitus (T2DM) become common conditions that often coexist. Evidence has shown that MCI could lead to reduced treatment compliance, medication management, and self‐care ability in T2DM patients. Therefore, early identification of those with increased risk of MCI is crucial from a preventive perspective. Given the growing utilization of decision trees in prediction of health‐related outcomes, this study aimed to identify MCI in T2DM patients using the decision tree approach.MethodsThis hospital‐based case–control study was performed in the Endocrinology Department of Xiangya Hospital affiliated to Central South University between March 2021 and December 2022. MCI was defined based on the Petersen criteria. Demographic characteristics, lifestyle factors, and T2DM‐related information were collected. The study sample was randomly divided into the training and validation sets in a 7:3 ratio. Univariate and multivariate analyses were performed, and a decision tree model was established using the chi‐square automatic interaction detection (CHAID) algorithm to identify key predictor variables associated with MCI. The area under the curve (AUC) value was used to evaluate the performance of the established decision tree model, and the performance of multivariate regression model was also evaluated for comparison.ResultsA total of 1001 participants (705 in the training set and 296 in the validation set) were included in this study. The mean age of participants in the training and validation sets was 60.2 ± 10.3 and 60.4 ± 9.5 years, respectively. There were no significant differences in the characteristics between the training and validation sets (p > .05). The CHAID decision tree analysis identified six key predictor variables associated with MCI, including age, educational level, household income, regular physical activity, diabetic nephropathy, and diabetic retinopathy. The established decision tree model had 15 nodes composed of 4 layers, and age is the most significant predictor variable. It performed well (AUC = .75 [95% confidence interval (CI): .71–.78] and .67 [95% CI: .61–.74] in the training and validation sets, respectively), was internally validated, and had comparable predictive value compared to the multivariate logistic regression model (AUC = .76 [95% CI: .72–.80] and .69 [95% CI: .62–.75] in the training and validation sets, respectively).ConclusionThe established decision tree model based on age, educational level, household income, regular physical activity, diabetic nephropathy, and diabetic retinopathy performed well with comparable predictive value compared to the multivariate logistic regression model and was internally validated. Due to its superior classification accuracy and simple presentation as well as interpretation of collected data, the decision tree model is more recommended for the prediction of MCI in T2DM patients in clinical practice.

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“… 16 In addition, the presence of MCI in T2DM patients can lead to reduced diabetes self‐management and poor glycemic control, 8 , 17 , 18 , 19 , 20 which may ultimately contribute to poor health‐related outcomes and heavy social and economic burdens. 21 Therefore, early diagnosis of MCI is crucial for T2DM patients.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, the overall unadjusted pooled odd ratio (OR) for the progression of MCI to dementia in those with diabetes was found to be 1.53 (95% CI: 1.20–1.97) 16 . In addition, the presence of MCI in T2DM patients can lead to reduced diabetes self‐management and poor glycemic control, 8,17–20 which may ultimately contribute to poor health‐related outcomes and heavy social and economic burdens 21 . Therefore, early diagnosis of MCI is crucial for T2DM patients.…”

Section: Introductionmentioning

confidence: 99%

The use of nomogram for detecting mild cognitive impairment in patients with type 2 diabetes mellitus

Maimaitituerxun

Chen

Xiang

et al. 2023

Journal of Diabetes

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Background Type 2 diabetes mellitus (T2DM) is highly prevalent worldwide and may lead to a higher rate of cognitive dysfunction. This study aimed to develop and validate a nomogram‐based model to detect mild cognitive impairment (MCI) in T2DM patients. Methods Inpatients with T2DM in the endocrinology department of Xiangya Hospital were consecutively enrolled between March and December 2021. Well‐qualified investigators conducted face‐to‐face interviews with participants to retrospectively collect sociodemographic characteristics, lifestyle factors, T2DM‐related information, and history of depression and anxiety. Cognitive function was assessed using the Mini‐Mental State Examination scale. A nomogram was developed to detect MCI based on the results of the multivariable logistic regression analysis. Calibration, discrimination, and clinical utility of the nomogram were subsequently evaluated by calibration plot, receiver operating characteristic curve, and decision curve analysis, respectively. Results A total of 496 patients were included in this study. The prevalence of MCI in T2DM patients was 34.1% (95% confidence interval [CI]: 29.9%–38.3%). Age, marital status, household income, diabetes duration, diabetic retinopathy, anxiety, and depression were independently associated with MCI. Nomogram based on these factors had an area under the curve of 0.849 (95% CI: 0.815–0.883), and the threshold probability ranged from 35.0% to 85.0%. Conclusions Almost one in three T2DM patients suffered from MCI. The nomogram, based on age, marital status, household income, duration of diabetes, diabetic retinopathy, anxiety, and depression, achieved an optimal diagnosis of MCI. Therefore, it could provide a clinical basis for detecting MCI in T2DM patients.

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Cognitive impairment in type 2 diabetes and its impact on daily living and self-care: A case - Control study in Kanpur, North India

Cited by 10 publications

References 18 publications

The Main Risk Factors in Type 2 Diabetes for Cognitive Dysfunction, Depression, and Psychosocial Problems: A Systematic Review

The Main Risk Factors in Type 2 Diabetes for Cognitive Dysfunction, Depression, and Psychosocial Problems: A Systematic Review

Predictive model for identifying mild cognitive impairment in patients with type 2 diabetes mellitus: A CHAID decision tree analysis

The use of nomogram for detecting mild cognitive impairment in patients with type 2 diabetes mellitus

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