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Biomarkers for cognitive decline in patients with diabetes mellitus: Evidence from clinical studies. Zhao X, Han Q, Lv Y, Sun L, Gang X, Wang G. Plasma microRNA biomarker detection for mild cognitive impairment using differential correlation analysis. Kayano M, Higaki S, Satoh J, Matsumoto K, Matsubara E, Takikawa O, et al. Plasma microRNA biomarkers for detection of mild cognitive impairment: Biomarker validation study. Sheinerman KS, Tsivinsky VG, Abdullah L, Crawford F, Umansky SR. A healthy diet, brain training, and social activities were associated with better cognitive function. The age, hypertension, cardiovascular diseases, duration of diabetes, and frequency of hypoglycemic episodes are risk factors for cognitive impairment. Logistic analysis revealed that diabetics aged above 50 was the only significant predicting factor for MCI with an OR 2.9 (95% CI: 3.8–123.3), p < 0.001.ĬONCLUSION: TIIDM is significantly associated with 3-times increasing risk of having MCI compared to non-diabetics. A healthy diet, brain training, and social activities were found to be significantly associated with normal cognition. Among diabetics, the MCI was significantly higher among those with secondary education, having heart diseases, longer duration of DM, or repeated hypoglycemia attack, p < 0.05. Among the two studied groups, the rate of MCI was significantly higher among those aged over 50 years compared to younger age as well as among hypertensive compared to non-hypertensive persons, (p < 0.05). The rate of MCI was significantly higher among TIIDM patients (22%) compared to non-diabetics (9%), p < 0.01 and odds ratio (OR) 2.8 (95% confidence interval 1.2–6.5). The mean of objective MoCA score was significantly lower among diabetics (25.9 ± 2.5) compared to non-diabetics (27.4 ± 2.4), p < 0.001. RESULTS: The subjective complaint of memory impairment among diabetics was significantly higher (34%) compared to non-diabetics (13.0%), p < 0.05. Cognitive function was assessed using Montreal Cognitive Assessment (MoCA) test and risk factors for MCI were assessed. METHODS: Two comparative cross-sectional studies were carried out enrolling 100 diabetics and 100 age, sex, and education matching non-diabetics. TIIDM is commonly associated with obesity, insulin resistance, hypertension, and dyslipidemia, all of which can have negative impact on brain.ĪIM: The aim of the study was to study the risk of mild cognitive impairment (MCI) among both diabetics and non-diabetics and to identify risk factors to MCI among both groups.
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Conclusions MMSE may still present the most applicable tools for quick screen of cognitive functions, especially when environmental conditions may interfere with participants' attention.Community Medicine Research Department, National Research Center, Cairo, Egyptĭepartment of Community, Environmental and Occupational Medicine, Faculty of Medicine, Ain Shams University, Cairo, Egyptĭiabetic, Non-diabetic, Mild cognitive impairment, Montreal cognitive assessment, Healthy, Lifestyle AbstractīACKGROUND: Type II diabetes mellitus (TIIDM) has been associated with structural and functional changes in the brain. MoCA and Isaacs test of Verbal Fluency (VF) score had correlation with most the other scores, while MMSE only significantly associated with VF and MoCA. Analysis of "abnormal" test scores showed that MMSE had the highest percentage of valid data (98.8%).
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Using adjusted MMSE, 39.6% of participants were identified with CI, while the percentage was 87.2% when adjusted Montreal Cognitive Assessment (MoCA) with cutoff of 26 was used. Age and education showed significant association with cognitive test scores. Results 172 participants with relatively low education levels were included. In addition, Pearson's correlation test was used to detect association between different test scores. Other cognitive function scores were compared between participants with and without CI. Mini-Mental State Examination (MMSE) score adjusted according to the correlation coefficients was used to detect CI with a cutoff of 24. Multivariate linear regressions were performed to test correlations between demographic characteristics, including gender, age, education, and marital status, with cognitive test scores. The participants underwent tests with 7 cognitive function screening instruments. Method In a cross-sectional study from October 2017 to September 2018 in 7 communities in Shanghai, China, elder (â§60) residential volunteers with no history of major cardiovascular diseases, cancers and other comorbidities known to affect cognitive functions were recruited. The aim is to assess the consistence and applicability of different CI screening tools. Abstract : Background Screening for cognitive impairment (CI) is often hampered by lack of consensus as to which screening instrument to use.