Early Identification Of Cognitive Impairment Research Articles

Early Identification of Cognitive Impairment in Community Environments Through Modeling Subtle Inconsistencies in Questionnaire Responses: Machine Learning Model Development and Validation.

The underdiagnosis of cognitive impairment hinders timely intervention of dementia. Health professionals working in the community play a critical role in the early detection of cognitive impairment, yet still face several challenges such as a lack of suitable tools, necessary training, and potential stigmatization. This study explored a novel application integrating psychometric methods with data science techniques to model subtle inconsistencies in questionnaire response data for early identification of cognitive impairment in community environments. This study analyzed questionnaire response data from participants aged 50 years and older in the Health and Retirement Study (waves 8-9, n=12,942). Predictors included low-quality response indices generated using the graded response model from four brief questionnaires (optimism, hopelessness, purpose in life, and life satisfaction) assessing aspects of overall well-being, a focus of health professionals in communities. The primary and supplemental predicted outcomes were current cognitive impairment derived from a validated criterion and dementia or mortality in the next ten years. Seven predictive models were trained, and the performance of these models was evaluated and compared. The multilayer perceptron exhibited the best performance in predicting current cognitive impairment. In the selected four questionnaires, the area under curve values for identifying current cognitive impairment ranged from 0.63 to 0.66 and was improved to 0.71 to 0.74 when combining the low-quality response indices with age and gender for prediction. We set the threshold for assessing cognitive impairment risk in the tool based on the ratio of underdiagnosis costs to overdiagnosis costs, and a ratio of 4 was used as the default choice. Furthermore, the tool outperformed the efficiency of age or health-based screening strategies for identifying individuals at high risk for cognitive impairment, particularly in the 50- to 59-year and 60- to 69-year age groups. The tool is available on a portal website for the public to access freely. We developed a novel prediction tool that integrates psychometric methods with data science to facilitate "passive or backend" cognitive impairment assessments in community settings, aiming to promote early cognitive impairment detection. This tool simplifies the cognitive impairment assessment process, making it more adaptable and reducing burdens. Our approach also presents a new perspective for using questionnaire data: leveraging, rather than dismissing, low-quality data.

Standardizing Identification of Cognitive Impairment in the Acute Hospital Setting: Toward a Common Language.

Although many hospitals universally screen their patients for functional mobility and activity impairment, there is no common method to do this for cognitive impairment. Establishing an interdisciplinary and standardized process to screen for cognitive impairment is essential for early identification of impairments, optimization of patients' function, and safe discharge planning. Previously, the reliability and validity of the Activity Measure for Post-Acute Care Applied Cognitive Inpatient Short Form "6-Clicks" (AM-PAC ACISF) were measured among occupational therapy practitioners and speech-language pathologists; however, its reliability among other team members, specifically nurses, is unknown. This study reports the interprofessional interrater reliability of the AM-PAC ACISF in the acute care hospital setting. To examine the nursing-therapist interrater reliability of the AM-PAC ACISF in the acute care hospital setting. A prospective study using a convenience sample. Acute care hospital. Fifty adult patients, six nurses, five occupational therapy practitioners, and one speech-language pathologist. Interrater reliability for the AM-PAC ACISF among therapists and nurses. Interrater reliability was good among nurses and therapists, with an intraclass correlation coefficient of .88 (95% confidence interval [.79, .93]) for the AM-PAC ACISF. Findings suggest that the AM-PAC ACISF is reliable for use among interdisciplinary teams, helps to establish a common language for early identification of cognitive impairment, and can be used for guiding clinical discussions around patient cognition in the acute care setting. Plain-Language Summary: Having an interdisciplinary and standardized process to screen patients early for cognitive impairment is essential for identifying impairments, optimizing patients' function, and facilitating safe discharge planning in the acute care setting. Early identification of cognitive impairment by the interdisciplinary team can also allow for timely occupational therapy and other rehabilitation service interventions. The findings of this study suggest that the Activity Measure for Post-Acute Care Applied Cognitive Inpatient Short Form "6-Clicks" (AM-PAC ACISF) can reliably be used by interdisciplinary teams, establishes a common language for early identification of cognitive impairment, and can be used for guiding clinical discussions around patient cognition in the acute care setting.

Machine vision-based gait scan method for identifying cognitive impairment in older adults.

Early identification of cognitive impairment in older adults could reduce the burden of age-related disabilities. Gait parameters are associated with and predictive of cognitive decline. Although a variety of sensors and machine learning analysis methods have been used in cognitive studies, a deep optimized machine vision-based method for analyzing gait to identify cognitive decline is needed. This study used a walking footage dataset of 158 adults named West China Hospital Elderly Gait, which was labelled by performance on the Short Portable Mental Status Questionnaire. We proposed a novel recognition network, Deep Optimized GaitPart (DO-GaitPart), based on silhouette and skeleton gait images. Three improvements were applied: short-term temporal template generator (STTG) in the template generation stage to decrease computational cost and minimize loss of temporal information; depth-wise spatial feature extractor (DSFE) to extract both global and local fine-grained spatial features from gait images; and multi-scale temporal aggregation (MTA), a temporal modeling method based on attention mechanism, to improve the distinguishability of gait patterns. An ablation test showed that each component of DO-GaitPart was essential. DO-GaitPart excels in backpack walking scene on CASIA-B dataset, outperforming comparison methods, which were GaitSet, GaitPart, MT3D, 3D Local, TransGait, CSTL, GLN, GaitGL and SMPLGait on Gait3D dataset. The proposed machine vision gait feature identification method achieved a receiver operating characteristic/area under the curve (ROCAUC) of 0.876 (0.852-0.900) on the cognitive state classification task. The proposed method performed well identifying cognitive decline from the gait video datasets, making it a prospective prototype tool in cognitive assessment.

Association between Cardiometabolic Index and Cognitive Function: A Cross-Sectional Study in a Diabetic-Based Population

s Introduction: Diabetes is a significant risk factor for cognitive impairment. Therefore, early identification of cognitive impairment in diabetic patients is particularly important. The aim of this study was to assess the relationship between Cardiometabolic index (CMI) and cognitive function in a diabetic population. Methods: A cross-sectional study was conducted by collecting information from the National Health and Nutrition Examination Survey (NHANES) 2011–2014. Multiple linear regression models were used to investigate the correlation between CMI and low cognitive function in a diabetic population. Threshold effects analysis and fitted smoothing curves were used to describe the nonlinear links. Interaction tests and subgroup analyses were also performed. Results: A total of 1,050 people participated in this study, including 561 men and 489 women. In the fully corrected model, CMI was positively associated with low cognitive performance as assessed by CERAD Word List Learning Test (CERAD W-L), Animal Fluency Test (AFT), and Digit Symbol Substitution Test (DSST) (OR = 1.37 [1.14, 1.72], p = 7.4 × 10−3), (OR = 1.21 [1.04, 1.51], p = 1.26 × 10−2), and (OR = 1.27 [1.08, 1.63], p = 2.53 × 10−2). Our study found that diabetic patients with higher CMI were at greater risk of developing low cognitive function. The effect of the subgroups on the positive association of CMI with cognitive impairment was not significant. A non-linear association between low cognitive performance and CMI was determined by CERAD W-L, AFT, and DSST (log-likelihood ratio <5 × 10−2). In addition, our also study found that CMI was a better predictor of cognitive impairment in diabetes than weight-adjusted waist index (WWI). Conclusion: Increased CMI is associated with an increased risk of cognitive impairment in people with diabetes. CMI can be used as a new anthropometric measure for predicting cognitive impairment in diabetes, with stronger predictive power than WWI.

The thalamic covariance network is associated with cognitive deficits in patients with cerebral small vascular disease.

Abnormalities in the gray matter structure of cerebral small vessel disease (CSVD) have been observed throughout the brain. However, whether cortico-cortical connections exist between regions of gray matter atrophy in patients with CSVD has not been fully elucidated. This question was tested by comparing the gray matter covariance networks in CSVD patients with and without cognitive impairment (CI). We performed multivariate modeling of the gray matter volume measurements of 61 patients with CI (CSVD-CI), 85 patients without CI (CSVD-NC), and 108 healthy controls using source-based morphological analysis (SBM) to obtain gray matter structural covariance networks at the population level. Then, correlations between structural covariance networks and cognitive functions were analyzed in CSVD patients. Finally, a support vector machine (SVM) classifier was used with the gray matter covariance network as a classification feature to identify CI among the CSVD population. The results of the analysis of all the subjects showed that compared with healthy controls, the expression of the thalamic covariance network, cerebellum covariance network, and calcarine cortex covariance network was reduced in patients with CSVD. Moreover, CSVD-CI patients showed a significant reduction in the expression of the thalamic covariance network, encompassing the thalamus and the parahippocampal gyrus, relative to CSVD-NC patients, which persisted after excluding CSVD patients with thalamic lacunes. In patients with CSVD, cognitive functions were positively correlated with measures of the thalamic covariance network. More than 80% of CSVD patients with CI were correctly identified by the SVM classifier. Our findings provide new evidence to explain the distribution state of gray matter reduction in CSVD patients, and the thalamic covariance network is the core region for early gray matter reduction during the development of CSVD disease, which is related to cognitive deficits. Reduced expression of thalamic covariance networks may provide a neuroimaging biomarker for the early identification of cognitive impairment in CSVD patients.

Cognitive Functioning Among Community-dwelling Older Adults in Rural Population of Lucknow and Its Association with Comorbidities.

The transitional state between normal aging and dementia is known as Cognitive impairment (CI) where a person has memory complaints and objective evidence of CI but no evidence of dementia. With the globe undergoing a "demographic transition," the magnitude of neurodegenerative disorders is rising. In India, 27.3% of older persons with comorbidities had CI. Early identification of CI will likely help initiate proper remedial intervention, leading to better overall outcomes. In order to determine the prevalence of CI in older persons and whether it is associated with co-morbid conditions, this study was designed. A descriptive cross-sectional study was conducted among 350 older adults aged ≥60 residing in rural areas of Lucknow, selected using multistage cluster sampling. The Hindi Mental State Examination (HMSE) scale was used to assess cognitive function. A pretested semi-structured questionnaire was used to collect information on sociodemographic characteristics and comorbidity status. Among 350 participants, with mean ± SD age 70.66 ± 9.53 years, the prevalence of CI as per HMSE (<23) was 24.9%. Overall, the mean HMSE score was less in individuals with (25.2) than without (27.19) comorbidities. Those with comorbidities had significantly lower mean scores in all individual domains of HMSE. CI affects one-fourth of the older adult population. Risk increases with the presence of comorbidities. Hence, screening, and early treatment are recommended.

Serum neurofilament light chain and cognition decline in US elderly: A cross-sectional study.

Early identification of cognitive impairment in neurodegenerative diseases like Alzheimer's disease (AD) is crucial. Neurofilament, a potential biomarker for neurological disorders, has gained attention. Our study aims to investigate the relationship between serum neurofilament light (sNfL) levels and cognitive function in elderly individuals in the United States. This cross-sectional study analyzed data from participants aged 60 and above in the National Health and Nutrition Examination Survey (2013-2014). We collected sNfL levels, cognitive function tests, sociodemographic characteristics, comorbidities, and other variables. Weighted multiple linear regression models examined the relationship between ln(sNfL) and cognitive scores. Restricted cubic spline (RCS) visualization explored nonlinear relationships. The stratified analysis examined subgroups' ln(sNfL) and cognitive function association. The study included 446 participants (47.73% male). Participants with ln(sNfL) levels between 2.58 and 2.81 pg/mL (second quintile) performed relatively well in cognitive tests. After adjusting for multiple factors, ln(sNfL) levels were negatively correlated with cognitive function, with adjusted β (95% CI) as follows: immediate recall test (IRT): -0.763 (-1.301 to -0.224), delayed recall test (DRT): -0.308 (-0.576 to -0.04), animal fluency test (AFT): -1.616 (-2.639 to -0.594), and digit symbol substitution test (DSST): -2.790 (-4.369 to -1.21). RCS curves showed nonlinear relationships between ln(sNfL) and DRT, AFT, with inflection points around 2.7 pg/mL. The stratified analysis revealed a negative correlation between ln(sNfL) and cognition in specific subgroups with distinct features, with an interaction between diabetes and ln(sNfL). Higher sNfL levels are associated with poorer cognitive function in the elderly population of the United States. sNfL shows promise as a potential biomarker for early identification of cognitive decline.

Potential association of neutrophil extracellular traps with cognitive impairment in cerebral small vessel disease.

No acceptable biomarker can facilitate the early identification of cognitive impairment associated with cerebral small vessel disease (CSVD) in the older persons. The neutrophil extracellular traps (NETs) in the inflammation response of circulatory and central systems are essential in destroying the blood-brain barrier. The present study aims to explore the potential associations of plasma NETs with cognitive performance in CSVD. We recruited 146 CSVD patients and 66 healthy controls (HCs), and comprehensive neuropsychological assessments and multimodal magnetic resonance imaging were conducted. Three NETs markers, viz., citrullination of histone H3, neutrophil elastase-DNA, and myeloperoxidase (MPO)-DNA, and four oxidative stress-related indexes in plasma samples were measured. The plasma levels of three NETs markers were more significantly elevated in CSVD patients than in HCs. Significant correlations of the three NETs markers were observed with multiple cognitive domain scores. Furthermore, higher plasma MDA and NETs levels were significantly associated with the worse Montreal Cognitive Assessment scores among CSVD patients. Moreover, plasma MPO-DNA levels significantly mediated the effect of the amplitude of low-frequency fluctuation value within the bilateral caudate and the scores of global cognitive function, executive function, and information processing speed. Additionally, a panel of three NETs markers had the highest area under the curve value to distinguish the cognitively impaired CSVD patients from HCs and non-impaired ones. Therefore, plasma NETs may be potential biomarkers for early diagnosis of CSVD-related cognitive impairment. Activated lipid peroxidation in circulation and impaired caudate function support potential associations of plasma NETs in cognitively impaired CSVD patients.

Relationships between quantitative retinal microvascular characteristics and cognitive function based on automated artificial intelligence measurements.

Introduction: The purpose of this study is to assess the relationship between retinal vascular characteristics and cognitive function using artificial intelligence techniques to obtain fully automated quantitative measurements of retinal vascular morphological parameters. Methods: A deep learning-based semantic segmentation network ResNet101-UNet was used to construct a vascular segmentation model for fully automated quantitative measurement of retinal vascular parameters on fundus photographs. Retinal photographs centered on the optic disc of 3107 participants (aged 50-93years) from the Beijing Eye Study 2011, a population-based cross-sectional study, were analyzed. The main parameters included the retinal vascular branching angle, vascular fractal dimension, vascular diameter, vascular tortuosity, and vascular density. Cognitive function was assessed using the Mini-Mental State Examination (MMSE). Results: The results showed that the mean MMSE score was 26.34 ± 3.64 (median: 27; range: 2-30). Among the participants, 414 (13.3%) were classified as having cognitive impairment (MMSE score < 24), 296 (9.5%) were classified as mild cognitive impairment (MMSE: 19-23), 98 (3.2%) were classified as moderate cognitive impairment (MMSE: 10-18), and 20 (0.6%) were classified as severe cognitive impairment (MMSE < 10). Compared with the normal cognitive function group, the retinal venular average diameter was significantly larger (p = 0.013), and the retinal vascular fractal dimension and vascular density were significantly smaller (both p < 0.001) in the mild cognitive impairment group. The retinal arteriole-to-venular ratio (p = 0.003) and vascular fractal dimension (p = 0.033) were significantly decreased in the severe cognitive impairment group compared to the mild cognitive impairment group. In the multivariate analysis, better cognition (i.e., higher MMSE score) was significantly associated with higher retinal vascular fractal dimension (b = 0.134, p = 0.043) and higher retinal vascular density (b = 0.152, p = 0.023) after adjustment for age, best corrected visual acuity (BCVA) (logMAR) and education level. Discussion: In conclusion, our findings derived from an artificial intelligence-based fully automated retinal vascular parameter measurement method showed that several retinal vascular morphological parameters were correlated with cognitive impairment. The decrease in retinal vascular fractal dimension and decreased vascular density may serve as candidate biomarkers for early identification of cognitive impairment. The observed reduction in the retinal arteriole-to-venular ratio occurs in the late stages of cognitive impairment.

Early identification of cognitive impairment among vulnerable older adults living at home

AbstractBackgroundAfrican Americans are twice as likely as Caucasians to develop Alzheimer’s Disease and Related Dementias (AD/ADRD) and less likely to seek a diagnosis before experiencing a crisis. This study evaluated the impact of adding Mini‐Cog© screenings to Area Agency on Aging (AAA) assessments on early detection of cognitive impairment among low‐income older adults.MethodMini‐Cog™ assessments were offered to new and existing clients of a AAA Neighborhood Advisor. Clients with scores indicating impairment were referred to an academic geriatric practice for comprehensive evaluation. Inclusion criteria were age 65 or older, English speaking, living at home in the Advisor’s service area, and not previously diagnosed as AD/ADRD. Data collected over 9 months included Mini‐Cog™ scores, age, race, ethnicity, and geriatric referrals. Quantitative analysis included bivariate descriptive statistics (significance alpha of .10) and simple linear regression (significance alpha of .05).ResultsOf 69 study participants, 84% were female, 54% African American, and 93% non‐Hispanic. Mean age was 74.7 years and mean Mini‐Cog™ score was 3.61 (range: 0‐5). Independent t‐tests show that African American participants had lower Mini‐Cog™ scores than Caucasians (3.35±.23 vs 3.91±.22, p = .092). Caucasian males were younger with higher cognition scores than Caucasian women and African American males were younger with lower scores than African American females. Participants qualifying for referral (18 or 26%) were significantly older than those who did not (74.4 vs 70.0, p&lt;.05). Of 11 participants offered referrals, all Caucasians (3) declined and 5 of 8 African Americans (62%) accepted. Regression analysis determined that Mini‐Cog™ scores were associated with older age (β* = ‐0.255, SE = 0.02, p&lt;.05) and being African American (β* = ‐0.261, SE = .344, p&lt;.05) but not with sex, ethnicity, or living alone.ConclusionResults show that 25% of participants were identified with previously undetected cognitive impairment and that trained social service providers can effectively administer the Mini‐Cog™ instrument to assess cognitive impairment. Large scale studies are needed to further evaluate use of the Mini‐Cog™ instrument in early detection of cognitive impairment in minority and economically vulnerable older adults in non‐clinical settings.

Early Identification of Cognitive Impairment: Utility of the Mini-Cog in Non-Clinical Settings.

While we currently cannot cure Alzheimer's disease or change the course of the disease, there are advantages to early detection. Routine, evidence based, brief cognitive screens offer destigmatized opportunities for diagnosis and improve the possibility of early identification of cognitive impairment. This community-based participatory research project evaluated the use of the Mini-Cog™ instrument to detect cognitive impairment in vulnerable community-dwelling older adults when administered by trained social services providers. Over 9months, a case manager screened 69 clients ages 65 to 94 (mean 74.67) who met inclusion criteria for the pilot; 84.1% were female, 53.6% were Black, 26% were living with undetected cognitive impairment. Although participants agreed to Mini-Cog™ screening, two-thirds with Mini-Cog™ scores indicating cognitive impairment refused referrals for further evaluation. Future interventions should reduce stigma by educating the public about dementia and engaging members of racial and cultural communities in outreach.

Radiomic and clinical nomogram for cognitive impairment prediction in Wilson's disease.

To investigate potential biomarkers for the early detection of cognitive impairment in patients with Wilson's disease (WD), we developed a computer-assisted radiomics model to distinguish between WD and WD cognitive impairment. Overall, 136 T1-weighted MR images were retrieved from the First Affiliated Hospital of Anhui University of Chinese Medicine, including 77 from patients with WD and 59 from patients with WD cognitive impairment. The images were divided into training and test groups at a ratio of 70:30. The radiomic features of each T1-weighted image were extracted using 3D Slicer software. R software was used to establish clinical and radiomic models based on clinical characteristics and radiomic features, respectively. The receiver operating characteristic profiles of the three models were evaluated to assess their diagnostic accuracy and reliability in distinguishing between WD and WD cognitive impairment. We combined relevant neuropsychological test scores of prospective memory to construct an integrated predictive model and visual nomogram to effectively assess the risk of cognitive decline in patients with WD. The area under the curve values for distinguishing WD and WD cognitive impairment for the clinical, radiomic, and integrated models were 0.863, 0.922, and 0.935 respectively, indicative of excellent performance. The nomogram based on the integrated model successfully differentiated between WD and WD cognitive impairment. The nomogram developed in the current study may assist clinicians in the early identification of cognitive impairment in patients with WD. Early intervention following such identification may help improve long-term prognosis and quality of life of these patients.

A risk prediction model based on machine learning for early cognitive impairment in hypertension: Development and validation study.

Clinical practice guidelines recommend early identification of cognitive impairment in individuals with hypertension with the help of risk prediction tools based on risk factors. The aim of this study was to develop a superior machine learning model based on easily collected variables to predict the risk of early cognitive impairment in hypertensive individuals, which could be used to optimize early cognitive impairment risk assessment strategies. For this cross-sectional study, 733 patients with hypertension (aged 30-85, 48.98% male) enrolled in multi-center hospitals in China were divided into a training group (70%) and a validation group (30%). After least absolute shrinkage and selection operator (LASSO) regression analysis with 5-fold cross-validation determined the modeling variables, three machine learning classifiers, logistic regression (LR), XGBoost (XGB), and gaussian naive bayes (GNB), were developed. The area under the ROC curve (AUC), accuracy, sensitivity, specificity, and F1 score were used to evaluate the model performance. Shape Additive explanation (SHAP) analysis was performed to rank feature importance. Further decision curve analysis (DCA) assessed the clinical performance of the established model and visualized it by nomogram. Hip circumference, age, education levels, and physical activity were considered significant predictors of early cognitive impairment in hypertension. The AUC (0.88), F1 score (0.59), accuracy (0.81), sensitivity (0.84), and specificity (0.80) of the XGB model were superior to LR and GNB classifiers. The XGB model based on hip circumference, age, educational level, and physical activity has superior predictive performance and it shows promise in predicting the risk of cognitive impairment in hypertensive clinical settings.

Identifying Cognitive Impairment in the Acute Care Hospital Setting: Finding an Appropriate Screening Tool.

Identifying cognitive impairment in adults in acute care is essential so that providers can address functional deficits and plan for safe discharge. Occupational therapy practitioners play an essential role in screening for, evaluating, and treating cognitive impairment. To test and compare the psychometrics and feasibility of three cognitive screens and select the ideal screen for use in acute care. Prospective mixed methods. Acute care hospital. Fifty adults. We examined the interrater reliability, administration time, and usability of the Brief Cognitive Assessment Tool Short Form (BCAT-SF), the Activity Measure for Post-Acute Care "6-Clicks" Applied Cognitive Inpatient Short Form (AM-PAC ACISF), and the Montreal Cognitive Assessment (MoCA). We compared the construct validity, sensitivity, and specificity of the BCAT-SF and AM-PAC ACISF with those of the MoCA. Interrater reliability was good to excellent; ICCs were .98 for the MoCA, .97 for the BCAT-SF, and .86 for the AM-PAC ACISF. The BCAT-SF and the AM-PAC ACISF both had 100% sensitivity, and specificity was 74% for the BCAT-SF and 98% for the AM-PAC ACISF. The optimal cutoff score for cognitive impairment on the AM-PAC ACISF was <22. Administration time of the AM-PAC ACISF (1.0 min) was significantly less than that of the BCAT-SF (5.0 min) and the MoCA (13.3 min; p < .001). Each screen demonstrated acceptable reliability and construct validity. The AM-PAC ACISF had the optimum mix of performance and feasibility for the fast-paced acute care setting. What This Article Adds: Early identification of cognitive impairment using the AM-PAC ACISF can allow for timely occupational therapy intervention in acute care settings.

Plasma p-tau181, neurofilament light chain and association with cognition in Parkinson’s disease

Early identification of cognitive impairment in Parkinson’s disease (PD) has important clinical and research implications. The aim of our study was to investigate the role of plasma tau phosphorylated at amino acid 181 (p-tau181) and plasma neurofilament light chain (NfL) as biomarkers of cognition in PD. Baseline concentrations of plasma p-tau181 and NfL were measured in a cohort of 136 patients with PD and 63 healthy controls (HC). Forty-seven PD patients were followed up for up to 2 years. Cross-sectional and longitudinal associations between baseline plasma biomarkers and cognitive progression were investigated using linear regression and linear mixed effects models. At baseline, plasma p-tau181 concentration was significantly higher in PD subjects compared with HC (p = 0.026). In PD patients, higher plasma NfL was associated with lower MMSE score at baseline, after adjusting for age, sex and education (p = 0.027). Baseline plasma NfL also predicted MMSE decline over time in the PD group (p = 0.020). No significant association between plasma p-tau181 concentration and baseline or longitudinal cognitive performance was found. While the role of p-tau181 as a diagnostic biomarker for PD and its relationship with cognition need further elucidation, plasma NfL may serve as a feasible, non-invasive biomarker of cognitive progression in PD.

Implementation of Nurse-Led Cognitive Screening During Medicare Annual Wellness Visits

Dementia is a serious and costly illness. Early identification of cognitive impairment provides opportunity for earlier intervention, and there is growing evidence suggesting that early intervention may help delay the onset of dementia. There is limited concensus and standardized recommendations for when and how cognitive screening should occur. This quality improvement project implemented a standardized nurse-led cognitive screening workflow during the Medicare annual wellness visit. Statistically significant differences were found between the baseline and implementation groups for Mini-Cog screening rates and referral for follow-up for further neurocognitive evaluation. A structured nurse-led workflow improved the cognitive screening process, providing opportunity for further evaluation and intervention.

Discrimination of the Cognitive Function of Community Subjects Using the Arterial Pulse Spectrum and Machine-Learning Analysis.

Early identification of cognitive impairment would allow affected patients to receive care at earlier stage. Changes in the arterial stiffness have been identified as a prominent pathological feature of dementia. This study aimed to verify if applying machine-learning analysis to spectral indices of the arterial pulse waveform can be used to discriminate different cognitive conditions of community subjects. 3-min Radial arterial blood pressure waveform (BPW) signals were measured noninvasively in 123 subjects. Eight machine-learning algorithms were used to evaluate the following 4 pulse indices for 10 harmonics (total 40 BPW spectral indices): amplitude proportion and its coefficient of variation; phase angle and its standard deviation. Significant differences were noted in the spectral pulse indices between Alzheimer’s-disease patients and control subjects. Using them as training data (AUC = 70.32% by threefold cross-validation), a significant correlation (R2 = 0.36) was found between the prediction probability of the test data (comprising community subjects at two sites) and the Mini-Mental-State-Examination score. This finding illustrates possible physiological connection between arterial pulse transmission and cognitive function. The present findings from pulse-wave and machine-learning analyses may be useful for discriminating cognitive condition, and hence in the development of a user-friendly, noninvasive, and rapid method for the early screening of dementia.

Help-seeking for cognitive problems in older adults without dementia: A systematic review.

Understanding the help-seeking decision-making process in older adults experiencing cognitive problems is needed to improve early identification of cognitive impairment. The purpose of this systematic review was to determine what factors influence whether community-dwelling older adults without dementia seek help for cognitive problems. We conducted a mixed-methods systematic review in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. A literature search was conducted in PubMed, CINAHL, PsycINFO, Web of Science, and ProQuest Dissertations and Theses Global in June 2020. Quality appraisal was conducted using the Mixed Methods Appraisal Tool. A narrative synthesis was used to integrate findings across studies. Eighteen articles met eligibility criteria. All were cross-sectional studies, including nine quantitative, eight qualitative, and one containing quantitative and qualitative components. Factors that facilitated help-seeking were problem intensity, social support, valuing early detection, having a family history of dementia, and positive views of the healthcare system. Factors that detracted from help-seeking were normalizing cognitive problems, attributing problems to psychosocial causes, and not perceiving a benefit to disclosing cognitive problems. The decision to seek help for cognitive problems is influenced by a variety of factors. Our review suggests these differ in older adults without dementia compared to previous reviews of help-seeking for a dementia diagnosis. Given the importance of early detection of cognitive impairment to improve long-term outcomes, intervention development based on the factors we identified has important implications for older adults' cognitive health.

Olfactory dysfunction is a risk factor for the comorbidity of mild cognitive impairment and Type 2 diabetes mellitus.

Diabetes can accelerate cognitive decline and hence affect the prognosis of patients with Type 2 diabetes mellitus (T2DM). Olfactory assessment can facilitate the early identification of cognitive impairment among T2DM patients. This study aims to evaluate the effects of olfactory function on mild cognitive impairment (MCI) in patients with T2DM. A total of 472 T2DM patients who were hospitalized in a first-class hospital in Changsha City from June 2018 to June 2019 were enrolled for this study. Olfactory function and cognitive function were assessed by the alcohol sniff test and the Montreal Cognitive Assessment (MoCA) scale, respectively. Participants were categorized into a comorbidity of MCI and T2DM group and a T2DM group. General information was collected and some biochemical indices were tested. Difference in the alcohol sniff test score between the 2 groups was assessed by 2-sample t-test. Difference in the presence of olfactory dysfunction between the 2 groups was assessed by χ2 test, and multivariable logistic regression was used to determine the relevant factors contributing to the comorbidity of MCI and T2DM. Of the 472 participants, 162 were identified with MCI, making the comorbidity rate at 34.3%. Values of isopropyl alcohol sniff test were significantly different between the 2 groups [(9.15±3.22) cm vs (21.03±4.36) cm, P<0.05]. The number of patients with olfactory dysfunction also differed significantly between the 2 groups (120 vs 50). After adjustment for age, educational level, T2DM duration, fasting insulin, and glycosylated hemoglobin (HbA1c), multivariate logistic regression analysis showed older age (OR=1.14, 95% CI 1.09 to 1.20), longer course of diabetes (OR=1.21, 95% CI 1.12 to 1.31), and olfactory-impaired (OR=4.61, 95% CI 3.04 to 6.18) were independent risk factors for T2DM combined with MCI, and the high education level (OR=0.26, 95% CI 0.15 to 0.38) was an independent protective factor for T2DM combined with MCI. Olfactory dysfunction is an independent risk factor for the comorbidity of MCI and T2DM. Special attention should be paid to those with olfactory dysfunction when carrying out cognitive interventions in T2DM patients.

Burning Fog: Cognitive Impairment in Burning Mouth Syndrome.

Background: Due to its common association with chronic pain experience, cognitive impairment (CI) has never been evaluated in patients with burning mouth syndrome (BMS). The purpose of this study is to assess the prevalence of CI in patients with BMS and to evaluate its relationship with potential predictors such as pain, mood disorders, blood biomarkers, and white matter changes (WMCs).Methods: A case-control study was conducted by enrolling 40 patients with BMS and an equal number of healthy controls matched for age, gender, and education. Neurocognitive assessment [Mini Mental State Examination (MMSE), Digit Cancellation Test (DCT), the Forward and Backward Digit Span task (FDS and BDS), Corsi Block-Tapping Test (CB-TT), Rey Auditory Verbal Learning Test (RAVLT), Copying Geometric Drawings (CGD), Frontal Assessment Battery (FAB), and Trail Making A and B (TMT-A and TMT-B)], psychological assessment [Hamilton Rating Scale for Depression and Anxiety (HAM-D and HAM-A), Pittsburgh Sleep Quality Index (PSQI), Epworth Sleepiness Scale (ESS), and 36-Item Short Form Health Survey (SF-36)], and pain assessment [Visual Analogic Scale (VAS), Total Pain Rating index (T-PRI), Brief Pain Inventory (BPI), and Pain DETECT Questionnaire (PD-Q)] were performed. In addition, blood biomarkers and MRI of the brain were recorded for the detection of Age-Related WMCs (ARWMCs). Descriptive statistics, the Mann-Whitney U-test, the Pearson Chi-Squared test and Spearman's correlation analysis were used.Results: Patients with BMS had impairments in most cognitive domains compared with controls (p < 0.001**) except in RAVLT and CGD. The HAM-D, HAM-A, PSQI, ESS, SF-36, VAS, T-PRI, BPI and PD-Q scores were statistically different between BMS patients and controls (p < 0.001**) the WMCs frequency and ARWMC scores in the right temporal (RT) and left temporal (LT) lobe were higher in patients with BMS (p = 0.023*).Conclusions: Meanwhile, BMS is associated with a higher decline in cognitive functions, particularly attention, working memory, and executive functions, but other functions such as praxis-constructive skills and verbal memory are preserved. The early identification of CI and associated factors may help clinicians to identify patients at risk of developing time-based neurodegenerative disorders, such as Alzheimer's disease (AD) and vascular dementia (VD), for planning the early, comprehensive, and multidisciplinary assessment and treatment.