Cognitive assessment tools for mild cognitive impairment screening

Zhuang, Lei; Yang, Yan; Gao, Jianqun

doi:10.1007/s00415-019-09506-7

Cited by 131 publications

(103 citation statements)

References 44 publications

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“…FINS was assayed by the electrochemiluminescence method (Roche Elecsys Insulin kit; Roche Diagnostics, Mannheim, Germany). TG, TC, LDL-C and HDL-C were determined using enzymatic colorimetry by an automatic analyzer (Beckman AU5800, Tokyo, Japan) according to the standard protocol 29 . Insulin resistance was estimated based on the homoeostasis model assessment of insulin resistance (HOMA-IR) formula: FBG (mmol/L) 9 FINS (mU/L) / 22.5.…”

Section: Blood Assaysmentioning

confidence: 99%

Prevalence of mild cognitive impairment in type 2 diabetes mellitus is associated with serum galectin‐3 level

et al. 2020

J of Diabetes Invest

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Aims/Introduction Galectin‐3 (Gal3) contributes to insulin resistance, inflammation and obesity, the three risk factors for mild cognitive impairment (MCI) in type 2 diabetes mellitus patients. Materials and methods A total of 134 hospitalized type 2 diabetes mellitus patients were assessed by the Montreal Cognitive Assessment method, and divided into 65 MCI and 69 controls. Levels of variables, Gal3 and Aβ42, were investigated in relation with cognitive function in both type 2 diabetes mellitus patients with MCI and high‐fat diet/streptozotocin induced type 2 diabetes mellitus rats. Results Significantly higher levels of serum Gal3 and lower levels of plasma Aβ42 (all P < 0.05) were found in the MCI type 2 diabetes mellitus group as compared with the non‐MCI type 2 diabetes mellitus control. Partial correlation analysis showed that Gal3 is negatively correlated with both MMSE score (r = −0.51, P < 0.01) and Montreal Cognitive Assessment score (r = −0.47, P < 0.001) after adjustment for glycated hemoglobin, homoeostasis model assessment of insulin resistance and Aβ42 in all type 2 diabetes mellitus patients, with a stronger effect seen in the MCI type 2 diabetes mellitus group after further analysis with MCI strata. A simple logistic regression model showed that Gal3 and Aβ42 are significantly associated with MCI type 2 diabetes mellitus patients after adjustment with the covariates sex, age, body mass index, glycated hemoglobin, homoeostasis model assessment of insulin resistance and antidiabetic drugs. Serum and brain Gal3 levels were significantly increased in high‐fat diet/streptozotocin diabetic rats, which correlate to the impairment of learning and memory ability. Gal3 inhibitor modified citrus pectin decreased serum and brain Gal3 levels in diabetic rats, accompanied by the amelioration of learning and memory impairment. Conclusions Gal3 might be associated with cognitive impairment in type 2 diabetes mellitus, and serum Gal3 level might be a new risk factor of MCI in type 2 diabetes mellitus patients.

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Section: Blood Assaysmentioning

confidence: 99%

Prevalence of mild cognitive impairment in type 2 diabetes mellitus is associated with serum galectin‐3 level

et al. 2020

J of Diabetes Invest

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“…Recent studies in mainland China suggested that the cutoffs for MCI were 24 in a clinical sample [ 8 ] and ranged from 18 to 25 in normative samples [ 9 – 11 ]. These studies suggested that the cutoffs were contingent upon sampling (i.e., normative versus clinical), age, education, cultural background, and validating criteria [ 12 ]. Such inconsistency and contingency cast doubt on whether seeking a single cutoff score is an accomplishable goal and whether an alternative procedure might shed new light on any cutoffs.…”

Section: Introductionmentioning

confidence: 99%

“…However, a large variety of reference measures could lead to variations in cutoffs. For instance, sensitivity and specificity of the cutoff varied with the validating tools [ 12 ]. As such, there is no gold standard as to a cutoff score (i.e., validation criterion), as one scholar rather disappointingly stated: “There is no gold standard .…”

Section: Introductionmentioning

confidence: 99%

Montreal Cognitive Assessment: Seeking a Single Cutoff Score May Not Be Optimal

Yang

Dong

et al. 2021

Evidence-Based Complementary and Alternative Medicine

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Background. Cutoff scores of the Montreal cognitive assessment (MoCA) for screening mild cognitive impairment in older adults differ across the world and within the Chinese culture. It is argued that to seek a cutoff score is essential to classify test participants. It was unknown how taking a classifying approach might reveal the cutoff score for identifying mildly cognitively impaired older adults. Methods. Participants, selected from 13 communities in Wuhan, China, were tested with the Chinese version of MoCA and rated with the Activities of Daily Living and the Clinical Dementia Rating scales. Mixture modeling was applied to the data with certain covariates and MoCA sum scores as the outcome of the latent class. Models with different numbers of classes were compared in terms of information criteria, likelihood ratio test, entropy, and interpretability. Results. A 3-class model (normal, mildly impaired, and severely impaired) was found to fit the data best. The normal class averaged a MoCA score of 24, while the severely impaired class averaged a score below 18. For those cases with MoCA scores above 18 and below 24, it is not certain if they are in the normal or the severely impaired classes. Conclusion. Latent variable classification modeling provides another option to identify MCI in older adults. Some categorically different cases of MCI cannot be captured with any single MoCA sum score. A range of 18–24 MoCA scores might serve as a better screening criterion of MCI. Older adults who scored within this gray zone should be monitored for potential interventions.

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“…The annual conversion rate from MCI to dementia is reportedly ∼10-15%, which is 10 times that in the normal population (1). Thus, MCI has been regarded as a crucial stage in which the long-term outcome of dementia may be improved if early diagnosis and intervention are achieved (2). MCI is commonly classified into two subtypes: amnestic MCI (aMCI) and non-amnestic MCI (naMCI).…”

Section: Introductionmentioning

confidence: 99%

“…Great efforts have been made to explore diagnostic and screening tools for aMCI, such as psychological batteries (2,4,5), cerebrospinal fluid biomarkers (6), and molecular imaging approaches (7). These methods have their special strengths and limitations.…”

Section: Introductionmentioning

confidence: 99%

Changes in Brain Function Networks in Patients With Amnestic Mild Cognitive Impairment: A Resting-State fMRI Study

Wang

Liu

et al. 2020

Front. Neurol.

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Patients with amnestic mild cognitive impairment (aMCI) are at high risk of developing dementia. This study used resting-state functional magnetic resonance imaging (rs-fMRI) and an independent component analysis (ICA) approach to explore changes in functional connectivity (FC) in the default mode network (DMN), executive control network (ECN), and salience network (SN). Thirty patients with aMCI and 30 healthy controls (HCs) were enrolled. All the participants underwent an rs-fMRI scan. The brain FC in DMN, ECN, and SN was calculated using the ICA approach. We found that the FC of brain regions in DMN decreased significantly and that of brain regions in ECN increased, which was in accordance with the findings of previous studies on Alzheimer's disease (AD) and aMCI. We also found that the FC of brain regions in SN increased, which was different from the findings of previous studies on AD. The increase in FC in brain regions in SN might result from different pathophysiological states in AD and aMCI, indicating that a decrease in FC in SN does not occur in a person with aMCI. These results are consistent with those of previous studies using the voxel-mirrored homotopic connectivity approach and seed-based correlation analysis. We therefore considered that the decrease in FC in DMN and the increase in FC in ECN and SN might be peculiar patterns observed on the rs-fMRI of a person with aMCI. These findings may contribute to the development of imaging biomarkers for the diagnosis of aMCI.

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Cognitive assessment tools for mild cognitive impairment screening

Cited by 131 publications

References 44 publications

Prevalence of mild cognitive impairment in type 2 diabetes mellitus is associated with serum galectin‐3 level

Prevalence of mild cognitive impairment in type 2 diabetes mellitus is associated with serum galectin‐3 level

Montreal Cognitive Assessment: Seeking a Single Cutoff Score May Not Be Optimal

Changes in Brain Function Networks in Patients With Amnestic Mild Cognitive Impairment: A Resting-State fMRI Study

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