Development and validity of computerized neuropsychological assessment devices for screening mild cognitive impairment: Ensemble of models with feature space heterogeneity and retrieval practice effect

The association of dietary inflammatory potential with sarcopenia in Chinese community-dwelling older adults

BACKGROUND

Dietary-induced inflammation is potentially associated with sarcopenia. Nevertheless, few studies have investigated the structure of the inflammatory diet and its correlation with muscle function and performance in both the upper and lower limbs. This study was performed to explore the association of the dietary inflammatory index (DII) with sarcopenia and its diagnostic parameters.

METHODS

We conducted a cross-sectional survey on a sample of 515 Chinese community-dwelling older adults selected through multistage cluster sampling from three districts in Shanghai. DII scores were calculated using a validated food frequency questionnaire. Sarcopenia and its diagnostic parameters were determined based on the definition set by the Asian Working Group on Sarcopenia (AWGS).

RESULTS

The mean age of study participants was 71.31 ± 4.71 years. The prevalence of sarcopenia in the cohort was 12.4%. Older adults in the highest DII quartile had a 3.339 times increased risk of sarcopenia compared to those in the lowest quartile (OR _Quartile4vs1:3.339, 95%CI: 1.232, 9.052, p-trend: 0.004) after adjusting for confounding factors. Additionally, a more pro-inflammatory diet was associated with lower appendicular skeletal muscle index (ASMI) (OR _Quartile4vs1: 3.005, 95%CI: 1.275, 7.318, p-trend: 0.005), a higher 5-times sit-stand test time score (OR _Quartile4vs1: 4.942, 95%CI: 1.745, 13.993, p-trend: 0.005), and lower gait speed (OR _Quartile4vs1: 2.392, 95%CI: 1.104, 5.185, p-trend: 0.041) after adjusting for confounding factors. However, there was no significant association between DII, handgrip strength, and Short Physical Performance Battery (SPPB) score in either the unadjusted or adjusted model.

CONCLUSION

This study found that the association between consuming a more pro-inflammatory diet and sarcopenia in Chinese community-dwelling older adults was mainly due to underlying low intakes of dietary energy, protein, and anti-inflammatory foods, and not due to the high intake of pro-inflammatory foods. Meanwhile, DII was more highly correlated with lower limb muscle strength and performance compared to upper limb muscle strength.

Literacy acquisition facilitates inversion effects for faces with full-, low-, and high-spatial frequency: evidence from illiterate and literate adults

Previous studies have found that literacy acquisition modulates configural face processing (i.e., holistic and second-order configural processing). However, it remains unclear how literacy acquisition impacts the configural processing indexed by the inversion effect of normal or filtered faces. We asked Chinese illiterate and literate adults to judge whether two sequentially-presented stimuli, including faces, houses (experiment 1), and high- or low-pass filtered faces (experiment 2) were identical. Literate adults outperformed illiterate controls in the upright face and house conditions (experiment 1) and the upright high- and low-pass filtered conditions (experiment 2) but not in the inverted conditions. Notably, the size of an inversion effect (i.e., subtracting inverted accuracy from upright accuracy) was greater among literate adults than that among illiterate adults in both experiments. These findings support that literacy acquisition promotes configural face processing.

Detecting mild cognitive impairment and dementia in older adults using naturalistic driving data and interaction-based classification from influence score

Several recent studies indicate that atypical changes in driving behaviors appear to be early signs of mild cognitive impairment (MCI) and dementia. These studies, however, are limited by small sample sizes and short follow-up duration. This study aims to develop an interaction-based classification method building on a statistic named Influence Score (i.e., I-score) for prediction of MCI and dementia using naturalistic driving data collected from the Longitudinal Research on Aging Drivers (LongROAD) project. Naturalistic driving trajectories were collected through in-vehicle recording devices for up to 44 months from 2977 participants who were cognitively intact at the time of enrollment. These data were further processed and aggregated to generate 31 time-series driving variables. Because of high dimensional time-series features for driving variables, we used I-score for variable selection. I-score is a measure to evaluate variables' ability to predict and is proven to be effective in differentiating between noisy and predictive variables in big data. It is introduced here to select influential variable modules or groups that account for compound interactions among explanatory variables. It is explainable regarding to what extent variables and their interactions contribute to the predictiveness of a classifier. In addition, I-score boosts the performance of classifiers over imbalanced datasets due to its association with the F1 score. Using predictive variables selected by I-score, interaction-based residual blocks are constructed over top I-score modules to generate predictors and ensemble learning aggregates these predictors to boost the prediction of the overall classifier. Experiments using naturalistic driving data show that our proposed classification method achieves the best accuracy (96%) for predicting MCI and dementia, followed by random forest (93%) and logistic regression (88%). In terms of F1 score and AUC, our proposed classifier achieves 98% and 87%, respectively, followed by random forest (with an F1 score of 96% and an AUC of 79%) and logistic regression (with an F1 score of 92% and an AUC of 77%). The results indicate that incorporating I-score into machine learning algorithms could considerably improve the model performance for predicting MCI and dementia in older drivers. We also performed the feature importance analysis and found that the right to left turn ratio and the number of hard braking events are the most important driving variables to predict MCI and dementia.

Relationship between Social Support, Sarcopenia, and Cognitive Impairment in Chinese Community-Dwelling Older Adults

BACKGROUND

Cognitive impairment and sarcopenia have become important challenges for the growing aging population. Social support has been shown to protect against cognitive impairment, but its impact on sarcopenia remains unknown. The purpose of this study was to explore the correlation between social support, sarcopenia, and cognitive impairment in Chinese older adults.

METHOD

A multi-stage whole group sampling method was used to conduct a cross-sectional survey of 720 community-dwelling older people in Shanghai. The definition of sarcopenia was in accordance with the criteria of the Asian Working Group for Sarcopenia (AWGS) 2019. Cognitive impairment was evaluated using a computerized neuropsychological assessment device that had been previously validated. Social support was assessed using the Social Support Rate Scale. Logistic regression analyses were conducted to explore the relationship between social support cognitive impairment and sarcopenia, fully adjusting for all potential confounding factors.

RESULTS

Our study found that 230 (31.94%) of the participants had cognitive impairment and 97 (13.47%) of the participants had sarcopenia. The mean social support score was 35.10 ± 7.54. Besides, the results showed that cognitive impairment was associated with sarcopenia (OR:1.650, 95% CI: 1.048, 2.596, P=0.030) after adjusting for confounding factors. Older adults with high level social support had the lowest risk of cognitive impairment (OR: 0.297, 95% CI: 0.115, 0.680, P=0.021) and sarcopenia (OR: 0.113, 95% CI: 0.031, 0.407, P=0.001), respectively.

CONCLUSION

Our analysis revealed that high level social support was negatively associated with sarcopenia and cognitive impairment. These findings provide strong support for the health promotion effect of social networks against sarcopenia and cognitive impairment in Chinese community-dwelling older adults, with important implications for healthcare policy makers.

Validation of the Computerized Cognitive Assessment Test: NNCT

Population aging brings with it cognitive impairment. One of the challenges of the coming years is the early and accessible detection of cognitive impairment. Therefore, this study aims to validate a neuropsychological screening test, self-administered and in software format, called NAIHA Neuro Cognitive Test (NNCT), designed for elderly people with and without cognitive impairment. This test aims to digitize cognitive assessments to add greater accessibility than classic tests, as well as to present results in real time and reduce costs. To this end, a comparison is made with tests such as MMSE, Clock Drawing Test (CDT) and CAMCOG. For this purpose, the following statistical analyses were performed: correlations, ROC curves, and three ANOVAs. The NNCT test evaluates seven cognitive areas and shows a significant and positive correlation with other tests, at total and subareas levels. Scores are established for the detection of both mild cognitive impairment and dementia, presenting optimal sensitivity and specificity. It is concluded that the NNCT test is a valid method of detection of cognitive impairment.