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Background: The Mini-mental State Examination (MMSE) is the most widely used cognitive test, both in clinical settings and in epidemiological studies. However, correcting its score for education may create ceiling effects when used for poorly educated people and floor effects for those with higher education. Methods: MMSE and a recent cognitive test, the seven minute screen (7MS), were serially administered to a community sample of Mexican elderly. 7MS test scores were equated to MMSE scores. MMSE-equated 7MS differences indicated ceiling or floor effects. An ordinal logistic regression model was fitted to identify predictors of such effects. Results: Poorly educated persons were more prevalent on the side of MMSE ceiling effects. Concentration (serial-sevens), orientation and memory were the three MMSE subscales showing the strongest relationship to MMSE ceiling effects in the multivariate model. Conclusion: Even when MMSE scores are corrected for educational level they still have ceiling and floor effects. These effects should be considered when interpreting data from longitudinal studies of cognitive decline. When an education-adjusted MMSE test is used to screen for cognitive impairment, additional testing may be required to rule out the possibility of mild cognitive impairment.

Background The Mini-Mental State Examination (MMSE) and the Montreal Cognitive Assessment (MoCA) are the most commonly used scales to detect mild cognitive impairment (MCI) in population-based epidemiologic studies. However, their comparison on which is best suited to assess cognition is scarce in samples from multiple regions of China. Methods We conducted a cross-sectional analysis of 4923 adults aged ≥55 years from the Community-based Cohort Study on Nervous System Diseases. Objective cognition was assessed by Chinese versions of MMSE and MoCA, and total score and subscores of cognitive domains were calculated for each. Education-specific cutoffs of total score were used to diagnose MCI. Demographic and health-related characteristics were collected by questionnaires. Correlation and agreement for MCI between MMSE and MoCA were analyzed; group differences in cognition were evaluated; and multiple logistic regression model was used to clarify risk factors for MCI. Results The overall MCI prevalence was 28.6% for MMSE and 36.2% for MoCA. MMSE had good correlation with MoCA (Spearman correlation coefficient = 0.8374, p  < 0.0001) and moderate agreement for detecting MCI with Kappa value of 0.5973 ( p  < 0.0001). Ceiling effect for MCI was less frequent using MoCA versus MMSE according to the distribution of total score. Percentage of relative standard deviation, the measure of inter-individual variance, for MoCA (26.9%) was greater than for MMSE (19.0%) overall ( p  < 0.0001). Increasing age (MMSE: OR = 2.073 for ≥75 years; MoCA: OR = 1.869 for≥75 years), female (OR = 1.280 for MMSE; OR = 1.163 for MoCA), living in county town (OR = 1.386 and 1.862 for MMSE and MoCA, respectively) or village (OR = 2.579 and 2.721 for MMSE and MoCA, respectively), smoking (OR = 1.373 and 1.288 for MMSE and MoCA, respectively), hypertension (MMSE: OR = 1.278; MoCA: OR = 1.208) and depression (MMSE: OR = 1.465; MoCA: OR = 1.350) were independently associated with greater likelihood of MCI compared to corresponding reference group in both scales (all p  < 0.05). Conclusions MoCA is a better measure of cognitive function due to lack of ceiling effect and with good detection of cognitive heterogeneity. MCI prevalence is higher using MoCA compared to MMSE. Both tools identify concordantly modifiable factors for MCI, which provide important evidence for establishing intervention measures.

[This corrects the article DOI: 10.3389/fnins.2024.1417342.].

Diet modification may contribute to the prevention of age-related cognitive decline. The association between dairy product consumption and cognitive function in older people remains unknown. We investigated whether cheese intake is associated with lower cognitive function (LCF) in community-dwelling older adults. This cross-sectional study included 1503 adults aged over 65 years. The analyzed data were obtained through face-to-face interviews and functional ability measurement. Cognitive function was assessed using the mini-mental state examination (MMSE), and a score ≤23 was defined as LCF. The prevalence of LCF was 4.6%, and this group had smaller calf circumference, slower usual walking speed, and a more frequent history of anemia than subjects with MMSE scores >23. After adjusting for confounding factors, logistic regression analysis revealed cheese intake (odds ratio (OR) = 0.404, 95% confidence interval (CI) = 0.198–0.824), age (OR = 1.170, 95% CI = 1.089–1.256), usual walking speed (OR = 0.171, 95% CI = 0.062–0.472) and calf circumference (OR = 0.823, 95% CI = 0.747–0.908) to be significant factors associated with LCF. Although the present study was an analysis of cross-sectional data of Japanese community-dwelling older adults, the results suggest that cheese intake is inversely associated with LCF.

INTRODUCTION The presence and interaction of multiple comorbid neuropathologies are a major contributor to the worldwide dementia burden. METHODS We analyzed 1183 subjects from the National Alzheimer's Coordinating Center dataset with various combinations of isolated and mixed neurodegenerative pathologies and conducted mixed‐effects multiple linear regression modeling to comprehensively compare the neurocognitive and neuropsychological trajectories between groups over time. RESULTS In combination with Alzheimer's disease neuropathologic change, various combinations of limbic‐predominant age‐related transactive response DNA‐binding protein 43 encephalopathy, Lewy body dementia, and cerebrovascular disease further impair global cognition and specific neurocognitive domains; however, they do not appear to extensively affect the rate of decline with time across these domains, suggesting an additive but not synergistic effect. DISCUSSION These findings corroborate the known cumulative effects of mixed pathologies on cognition and add nuance to our understanding of their specific interactions, which is crucial for the development of biomarkers and effective therapeutics. Highlights Mixed neurodegenerative pathologies are common in the elderly population. The most common neurodegenerative pathologies were Alzheimer's disease neuropathologic change (ADNC), cerebrovascular disease (CVD), Lewy body dementia (LBD), and limbic‐predominant age‐related transactive response DNA‐binding protein 43 encephalopathy (LATE). The addition of various combinations of comorbid CVD, LBD, and LATE to ADNC worsened overall performance on cognitive and neuropsychological testing across time. In general, the addition of multiple comorbid neurodegenerative pathologies did not influence the rate of decline across the evaluated time period.

Diabetes belongs to the chronic disease and is a progressive in nature causing an impact on many body organs including brain functions; such as cognition. It has become a common concern that hyperglycemia and metabolic dysregulation may have impactesd memory, attention, as well as executive function among diabetics. This work has as its objective to assess the effects of diabetes on cognitive function, by comparing performance in a domain of cognitive function between diabetics and non-diabetic subjects from the population of Dakhla. Authors used a combined qualitative and quantitative method. The study was carried out in Dakhla for three months with 100 subjects, over the age of 45 years (50 diabetic and 50 non-diabetic). Data were collected using a structured questionnaire with sociodemographic and medical data, and Mini-Mental State Examination (MMSE) for cognitive function. Findings show a marked cognitive deficit in persons with known DM, primarily in the areas of memory, attention and executive function. Diabetic participants had a statistically significantly lower MMSE mean score (24.4) as compared with non diabetic individuals (28.8), thus attesting the link between diabetes and cognitive decrease. ANOVA analysis revealed there were differences between groups. Additionally, negative correlation was found between the diabetes duration and MMSE score (r = −0.472; p < 0.001), as well as HbA1c levels and MMSE score (r = −0.440; p < 0.001), implying that poor glycemic control stimulates cognitive decline.

One of the leading age-related changes affecting most older adults is the worsening of sleep quality. The literature suggests that the reduction of daily physical activity and increased frequency of daytime sleep periods in older adults are some of the main events contributing to the reduction in sleep quality, which may also negatively affect cognitive function. Considering this, it is expected that older people submitted to exercise should improve their sleep quality, physical function, reduce daily fatigue, and improve vigilance and cognitive function. The main objective of this study was to assess the effect of physical exercise on sleep quality and its repercussions on cognitive function in elderly residents in nursing homes. The studied sample was composed of 31 elderly people residents in nursing homes. The Mini Mental State Examination (MMSE) was used to assess cognitive function and exclude elderly people with cognitive deficits. Subsequently, the frailty level test (SPPB) and the handgrip strength test (HG) (Camry EH101) were performed. Finally, questionnaires were applied to measure sleep quality levels (MQS) and functional independence (BI). The sample age was 84.4±8.5 years (65-97 years) and was mainly composed of women (71%). The main results revealed that the exercise program induced an improvement in the physical fitness of the elderly (SPPB: t=-3.105; p=0.004; HGright: t=-3.292; p=0.003; HGleft: t=-4.792; p=0.000;). Sleep quality improved significantly (p=0.000), with no significant changes in cognitive function. Our results demonstrate that the implementation of physical exercise programs can be one of the most effective ways to increase sleep quality levels in these elderly people, retarding an age-related decline in cognitive and physical function.

This study estimated the minimal clinically important difference (MCID) for Mini Mental State Examination, Clinical Dementia Rating Scale sum of boxes, and Functional Activities Questionnaire across the Alzheimer's disease (AD) spectrum. Retrospective analysis of the National Alzheimer's Coordinating Center Uniform Data Set (9/2005-9/2016) and MCID for clinical outcomes were estimated using anchor-based (clinician's assessment of meaningful decline) and distribution-based (1/2 baseline standard deviation) approaches, stratified by severity of cognitive impairment. On average, a 1-3 point decrease in Mini Mental State Examination, 1-2 point increase in Clinical Dementia Scale sum of boxes, and 3-5 point increase in Functional Activities Questionnaire were indicative of a meaningful decline. The MCID values generally increased by disease severity; the effect size and standardized response mean for those with meaningful decline were consistently in the acceptable ranges for MCID. These findings can inform design and interpretation of future clinical trials.

Introduction: Research related to the automatic detection of Alzheimer's disease (AD) is important, given the high prevalence of AD and the high cost of traditional diagnostic methods. Since AD significantly affects the content and acoustics of spontaneous speech, natural language processing, and machine learning provide promising techniques for reliably detecting AD. There has been a recent proliferation of classification models for AD, but these vary in the datasets used, model types and training and testing paradigms. In this study, we compare and contrast the performance of two common approaches for automatic AD detection from speech on the same, well-matched dataset, to determine the advantages of using domain knowledge vs. pre-trained transfer models. Methods: Audio recordings and corresponding manually-transcribed speech transcripts of a picture description task administered to 156 demographically matched older adults, 78 with Alzheimer's Disease (AD) and 78 cognitively intact (healthy) were classified using machine learning and natural language processing as “AD” or “non-AD.” The audio was acoustically-enhanced, and post-processed to improve quality of the speech recording as well control for variation caused by recording conditions. Two approaches were used for classification of these speech samples: (1) using domain knowledge: extracting an extensive set of clinically relevant linguistic and acoustic features derived from speech and transcripts based on prior literature, and (2) using transfer-learning and leveraging large pre-trained machine learning models: using transcript-representations that are automatically derived from state-of-the-art pre-trained language models, by fine-tuning Bidirectional Encoder Representations from Transformer (BERT)-based sequence classification models. Results: We compared the utility of speech transcript representations obtained from recent natural language processing models (i.e., BERT) to more clinically-interpretable language feature-based methods. Both the feature-based approaches and fine-tuned BERT models significantly outperformed the baseline linguistic model using a small set of linguistic features, demonstrating the importance of extensive linguistic information for detecting cognitive impairments relating to AD. We observed that fine-tuned BERT models numerically outperformed feature-based approaches on the AD detection task, but the difference was not statistically significant. Our main contribution is the observation that when tested on the same, demographically balanced dataset and tested on independent, unseen data, both domain knowledge and pretrained linguistic models have good predictive performance for detecting AD based on speech. It is notable that linguistic information alone is capable of achieving comparable, and even numerically better, performance than models including both acoustic and linguistic features here. We also try to shed light on the inner workings of the more black-box natural language processing model by performing an interpretability analysis, and find that attention weights reveal interesting patterns such as higher attribution to more important information content units in the picture description task, as well as pauses and filler words. Conclusion: This approach supports the value of well-performing machine learning and linguistically-focussed processing techniques to detect AD from speech and highlights the need to compare model performance on carefully balanced datasets, using consistent same training parameters and independent test datasets in order to determine the best performing predictive model.

This study was performed to examine the usefulness of subscores on the Mini-Mental State Examination (MMSE) for predicting the progression of Alzheimer's disease (AD) dementia in individuals with mild cognitive impairment (MCI). A total of 306 MCI individuals in the Alzheimer's Disease Neuroimaging Initiative database were included in the study. Standardized clinical and neuropsychological tests were performed at baseline and at 2-year follow-up. Logistic regression analysis was conducted to examine the MMSE total and subscale scores to predict progression to AD dementia in MCI individuals. The MMSE total score and the MMSE memory, orientation, and construction subscores were inversely associated with AD progression after controlling for all potential confounders; MMSE attention and language subscores were not correlated with AD progression. The MMSE delayed recall score among the MMSE memory subscores and the MMSE time score among the orientation subscores, especially week and day, were inversely associated with AD progression; the MMSE immediate recall and place scores were not correlated with progression. Our findings suggest that the MMSE memory, orientation, and construction subscores, which are simple and readily available clinical measures, could provide useful information to predict AD dementia progression in MCI individuals in practical clinical settings.