Determining optimal cutoff scores of Cognitive Abilities Screening Instrument to identify dementia and mild cognitive impairment in Taiwan.

BACKGROUND: The early detection of dementia depends on efficient methods for the assessment of cognitive capacity. Existing cognitive screening tools are ill-suited to the differentiation of cognitive status, particularly when dealing with early-stage impairment. METHODS: The study included 8,979 individuals (> 50 years) with unimpaired cognitive functions, mild cognitive impairment (MCI), or dementia. This study sought to determine optimal cutoffs values for the Cognitive Abilities Screening Instrument (CASI) aimed at differentiating between individuals with or without dementia as well as between individuals with or without mild cognitive impairment. Cox proportional hazards models were used to evaluate the value of CASI tasks in predicting conversion from MCI to all-cause dementia, dementia of Alzheimer's type (DAT), or to vascular dementia (VaD). RESULTS: Our optimized cutoff scores achieved high accuracy in differentiating between individuals with or without dementia (AUC = 0.87-0.93) and moderate accuracy in differentiating between CU and MCI individuals (AUC = 0.67 - 0.74). Among individuals without cognitive impairment, scores that were at least 1.5 × the standard deviation below the mean scores on CASI memory tasks were predictive of conversion to dementia within roughly 2 years after the first assessment (all-cause dementia: hazard ratio [HR] = 2.81 - 3.53; DAT: 1.28 - 1.49; VaD: 1.58). Note that the cutoff scores derived in this study were lower than those reported in previous studies. CONCLUSION: Our results in this study underline the importance of establishing optimal cutoff scores for individuals with specific demographic characteristics and establishing profiles by which to guide CASI analysis.

Classification Prediction of Alzheimer's Disease and Vascular Dementia Using Physiological Data and ECD SPECT Images.

Alzheimer's disease (AD) and vascular dementia (VaD) are the two most common forms of dementia. However, their neuropsychological and pathological features often overlap, making it difficult to distinguish between AD and VaD. In addition to clinical consultation and laboratory examinations, clinical dementia diagnosis in Taiwan will also include Tc-99m-ECD SPECT imaging examination. Through machine learning and deep learning technology, we explored the feasibility of using the above clinical practice data to distinguish AD and VaD. We used the physiological data (33 features) and Tc-99m-ECD SPECT images of 112 AD patients and 85 VaD patients in the Taiwanese Nuclear Medicine Brain Image Database to train the classification model. The results, after filtering by the number of SVM RFE 5-fold features, show that the average accuracy of physiological data in distinguishing AD/VaD is 81.22% and the AUC is 0.836; the average accuracy of training images using the Inception V3 model is 85% and the AUC is 0.95. Finally, Grad-CAM heatmap was used to visualize the areas of concern of the model and compared with the SPM analysis method to further understand the differences. This research method can quickly use machine learning and deep learning models to automatically extract image features based on a small amount of general clinical data to objectively distinguish AD and VaD.

Development of a Revised Version of Subjective Memory Complaints Questionnaire in Taiwan: A Preliminary Study.

OBJECTIVES: Research has suggested that subjective memory complaints (SMC) are predictive of cognitive decline in cases of Alzheimer's disease; however, multidimensional characteristics of SMC make it difficult to formulate causal links. The Subjective Memory Complaints Questionnaire (SMCQ) has proven effective in capturing the nature of SMC. In this study, we developed a revised version of SMCQ (SMCQ-R) with corresponding normative data for application in Taiwan. METHODS: This study recruited 100 cognitively normal participants (> 45 years) stratified according to demographic characteristics. Assessments were performed to evaluate test-retest reliability, criterion-related validity, and construct validity of SMCQ-R. SMCQ-R scores of 20 matched patients with mild cognitive impairment (MCI) were also compared with those of normal participants to test construct validity. RESULTS: Reliability of SMCQ-R was satisfactory (0.81-0.95). Factor analysis revealed a three-factor structure: everyday memory problems (EMP), recent severe memory problems (RSMP), and long-term memory problems (LTMP). EMP and RSMP scores were negatively associated with objective cognitive function (r = -.20 to .39). Depressive symptoms were positively associated with all factors (r = .23-.33). Age was positively associated with total (b = 0.09, p < .05) and EMP scores (b = 0.06, p < .01). MCI patients obtained higher scores (p < .05) on all subscales. SMCQ-R scores discriminated between normal and MCI individuals (area under the curve = 0.77). This study established a norm based on scores adjusted to control for effects of age. CONCLUSIONS: SMCQ-R has sound psychometric properties and could potentially be used as a tool to assess SMC in clinical settings.

Minimal amount of exercise prevents incident dementia in cognitively normal older adults with osteoarthritis: a retrospective longitudinal follow-up study.

Robust evidence suggests that regular exercise, including walking more than 6000 steps, is effective for preventing dementia; however, such activity is less feasible in older people with osteoarthritis (OA) or other motor disabilities. Therefore, we aimed to test whether the minimal amount of exercise (MAE) could help prevent dementia in older adults with OA. A retrospective longitudinal study was performed and a non-demented cohort (≥ 50-years-old) of 242 people (155 [64.0%] non-converters and 87 [36.0%] converters) from three centers in Taiwan was analyzed with a mean follow-up of 3.1 (range 0.3-5.9) and 2.9 (range 0.5-6.0) years, respectively. MAE was defined as walking for approximately 15-30 min or 1500-3000 steps. Rate of MAE (0, 1-2, or ≥ 3) within one week were defined as MAE-no, MAE-weekly, or MAE-daily, respectively. The incidence rates of dementia were compared between groups. Multivariate logistic regression and Cox proportional hazards analyses were used to study the influence of MAE on dementia occurrence. Age, education, sex, activities of daily living, neuropsychiatric symptoms, cognition, multiple vascular risk factors, and related medications were adjusted. Compared to the MAE-no group, the odds ratios for the incidents of dementia were 0.48 and 0.19 in the MAE-weekly and MAE-daily groups, respectively. In addition, older age, poorer cognition, poorer ADL performance, and congestive heart failure increased the incidence of dementia. Daily and weekly MAE prevented dementia in older adults with OA. As such, an informative public health policy may help promote adequate exercise in at-risk groups.

Cholesterol Levels, Hormone Replacement Therapy, and Incident Dementia among Older Adult Women.

Previous studies revealed that hormone replacement therapy (HRT) probably has a protective effect for preventing dementia in post-menopausal women. However, the results were still controversial. The association between cholesterol levels and incident dementia in older women is not fully understood either. We conducted a retrospective analysis on a cohort of non-demented women aged older than 50 years, which was registered in the History-based Artificial Intelligence Clinical Dementia Diagnostic System database from September 2015 to August 2021. We followed this cohort longitudinally to examine the rates of conversion to dementia. Using a Cox regression model, we investigated the impact of the quartile of total cholesterol (TC) levels on incident dementia, adjusting for age, sex, education, neuropsychiatric symptoms, neuropsychological assessments, HRT, as well as various vascular risk factors and medications. We examined a cohort of 787 participants, comprising 539 (68.5%) individuals who did not develop dementia (non-converters). Among these non-converters, 68 individuals (12.6%) were treated with HRT. By contrast, there were 248 (31.5%) who did develop dementia (converters). Among the converters, 28 individuals (11.3%) were treated with HRT. The average follow-up durations were 2.9 ± 1.5 and 3.3 ± 1.6 years for non-converters and converters, respectively. Compared to the lowest quartile of TC levels (<153), the hazard ratios (HR) for converting to dementia were 0.61, 0.58, and 0.58 for the second (153-176), third (177-201), and highest (>201) quartiles, respectively (all p < 0.05). However, the low-density lipoprotein cholesterol (LDL-C) level and HRT did not alter the rate of conversion to dementia. In conclusion, the lowest quartile of TC increased incident dementia in post-menopausal women without dementia; however, HRT did not contribute to conversion to dementia. Some studies suggest that post-menopausal women who have reduced estrogen levels might have an increased risk of Alzheimer's disease if they also have high cholesterol. Nonetheless, the evidence is inconclusive, as not all studies support this finding. The "Lower LDL-C is better" strategy for preventing cardiac vascular disease should be re-examined for the possible serial adverse effects of new onset dementia due to very low cholesterol levels.

Generative adversarial network-based attenuation correction for 99mTc-TRODAT-1 brain SPECT.

Background: Attenuation correction (AC) is an important correction method to improve the quantification accuracy of dopamine transporter (DAT) single photon emission computed tomography (SPECT). Chang's method was developed for AC (Chang-AC) when CT-based AC was not available, assuming uniform attenuation coefficients inside the body contour. This study aims to evaluate Chang-AC and different deep learning (DL)-based AC approaches on 99mTc-TRODAT-1 brain SPECT using clinical patient data on two different scanners. Methods: Two hundred and sixty patients who underwent 99mTc-TRODAT-1 SPECT/CT scans from two different scanners (scanner A and scanner B) were retrospectively recruited. The ordered-subset expectation-maximization (OS-EM) method reconstructed 120 projections with dual-energy scatter correction, with or without CT-AC. We implemented a 3D conditional generative adversarial network (cGAN) for the indirect deep learning-based attenuation correction (DL-ACµ) and direct deep learning-based attenuation correction (DL-AC) methods, estimating attenuation maps (µ-maps) and attenuation-corrected SPECT images from non-attenuation-corrected (NAC) SPECT, respectively. We further applied cross-scanner training (cross-scanner indirect deep learning-based attenuation correction [cull-ACµ] and cross-scanner direct deep learning-based attenuation correction [call-AC]) and merged the datasets from two scanners for ensemble training (ensemble indirect deep learning-based attenuation correction [eDL-ACµ] and ensemble direct deep learning-based attenuation correction [eDL-AC]). The estimated µ-maps from (c/e)DL-ACµ were then used in reconstruction for AC purposes. Chang's method was also implemented for comparison. Normalized mean square error (NMSE), structural similarity index (SSIM), specific uptake ratio (SUR), and asymmetry index (%ASI) of the striatum were calculated for different AC methods. Results: The NMSE for Chang's method, DL-ACµ, DL-AC, cDL-ACµ, cDL-AC, eDL-ACµ, and eDL-AC is 0.0406 ± 0.0445, 0.0059 ± 0.0035, 0.0099 ± 0.0066, 0.0253 ± 0.0102, 0.0369 ± 0.0124, 0.0098 ± 0.0035, and 0.0162 ± 0.0118 for scanner A and 0.0579 ± 0.0146, 0.0055 ± 0.0034, 0.0063 ± 0.0028, 0.0235 ± 0.0085, 0.0349 ± 0.0086, 0.0115 ± 0.0062, and 0.0117 ± 0.0038 for scanner B, respectively. The SUR and %ASI results for DL-ACµ are closer to CT-AC, Followed by DL-AC, eDL-ACµ, cDL-ACµ, cDL-AC, eDL-AC, Chang's method, and NAC. Conclusion: All DL-based AC methods are superior to Chang-AC. DL-ACµ is superior to DL-AC. Scanner-specific training is superior to cross-scanner and ensemble training. DL-based AC methods are feasible and robust for 99mTc-TRODAT-1 brain SPECT.

Cholesterol Paradox in Older People with Type 2 Diabetes Mellitus Regardless of Lipid-Lowering Drug Use: A Cross-Sectional Cohort Study.

Lipid-lowering drugs (LLDs) have protective effects against coronary artery disease (CAD) and cerebrovascular disease (CVD); however, a paradoxical association with cholesterol has been identified in several diseases, such as diabetes, dementia, and atrial fibrillation. We aimed to analyze the association between LLDs and cholesterol levels in older adults with type 2 diabetes mellitus (T2DM). This cross-sectional study enrolled consecutive patients aged ≥50 years from three centers in Taiwan. A multiple logistic regression model was used, and odds ratios (ORs) for different levels of total cholesterol (TC) or low-density-lipoprotein cholesterol (LDL-C) compared with the highest level were adjusted for age, triglyceride level, sex, comorbidities, and medications. Among the 3688 participants, 572 with and 676 without T2DM used LLDs. After adjusting for age and sex, the non-T2DM group demonstrated better medical conditions, cognition, and daily function than the T2DM group, regardless of LLD use. Compared to the highest TC level (≥240 mg/dL), ORs were significantly increased as TC levels decreased. A similar pattern of T2DM prevalence was observed in LDL-C levels. Older people with T2DM demonstrated low cognitive and daily functions. Significantly reduced TC and LDL levels were associated with a higher T2DM prevalence in older adults regardless of LLD use. T2DM was associated with impaired cognitive and daily functioning. A higher prevalence of T2DM in older people with low cholesterol levels raises doubt surrounding cognition and daily function being jeopardized when the "lower is better" strategy is applied for the secondary prevention of CAD or CVD.

Predictive value of hypercholesterolemia, vegetarian diet, and hypertension for incident dementia among elderly Taiwanese individuals with low educational levels.

Objective: Early management of modifiable dementia-related factors is seen as a novel approach to preventing dementia onset; however, these efforts are often hindered by the complexity of interactions among these factors. In addition, different modifiable dementia-related factors may contribute to different etiologies of dementia. Design: The current study investigated the effects of common modifiable dementia-related factors on prediction of incident dementia, dementia of the Alzheimer's type (DAT), and vascular dementia (VaD). Methods: Vascular- and lifestyle-related factors were used as predictors of incident dementia, DAT, and VaD among 1,285 elderly individuals without obvious signs of dementia or mild cognitive impairment. Cox proportional hazard models were used to evaluate the risks associated with each modifiable factor. Results: After controlling for factors other than stroke-related factors, hypercholesterolemia was correlated with a relatively low risk of all-cause incident dementia and DAT, whereas a vegetarian diet was correlated with an elevated risk of all-cause incident dementia and VaD. Hypertension was correlated with incident VaD. After controlling for stroke-related factors, a vegetarian diet was correlated with an elevated risk of all-cause dementia. A history of myocardiac infarction and the use of anti-platelet medication were, respectively, associated with a reduced risk of DAT and elevated risk of VaD. The use of anti-hypertensives was associated with a reduced risk of all-cause dementia, whereas the use of anti-lipid agents was associated with slow progression DAT (i.e. exceeding the average conversion time). Hypercholesterolemia was associated with an elevated risk for slow progression DAT. Conclusion: These findings could perhaps be used as clinical markers in predicting and preventing incident dementia, DAT, and VaD.

Development of a simple screening tool for determining cognitive status in Alzheimer's disease.

Cognitive screening is often a first step to document cognitive status of patients suspected having Alzheimer's disease (AD). Unfortunately, screening neuropsychological tests are often insensitivity in the detection. The goal of this study was to develop a simple and sensitive screening neuropsychological test to facilitate early detection of AD. This study recruited 761 elderly individuals suspected of having AD and presenting various cognitive statuses (mean age: 77.69 ± 8.45 years; proportion of females: 65%; cognitively unimpaired, CU, n = 133; mild cognitive impairment, MCI, n = 231; dementia of Alzheimer's type, DAT, n = 397). This study developed a novel screening neuropsychological test incorporating assessments of the core memory deficits typical of early AD and an interview on memory function with an informant. The proposed History-based Artificial Intelligence-Show Chwan Assessment of Cognition (HAI-SAC) was assessed in terms of psychometric properties, test time, and discriminative ability. The results were compared with those obtained using other common screening tests, including Cognitive Abilities Screening Instrument (CASI), Montreal Cognitive Assessment (MoCA), and an extracted Mini-Mental State Examination score from CASI. HAI-SAC demonstrated acceptable internal consistency. Factor analysis revealed two factors: memory (semantic and contextual) and cognition-related information from informants. The assessment performance of HAI-SAC was strongly correlated with that of the common screening neuropsychological tests addressed in this study. HAI-SAC outperformed the other tests in differentiating CU individuals from patients with MCI (sensitivity: 0.87; specificity: 0.58; area under the curve [AUC]: 0.78) or DAT (sensitivity: 0.99; specificity: 0.89; AUC: 0.98). Performance of HAI-SAC on differentiating MCI from DAT was on par with performances of other tests (sensitivity: 0.78; specificity: 0.84; AUC: 0.87), while the test time was less than one quarter that of CASI and half that of MoCA. HAI-SAC is psychometrically sound, cost-effective, and sensitive in discriminating the cognitive status of AD.

Sum of boxes of the clinical dementia rating scale highly predicts conversion or reversion in predementia stages.

Background: The clinical dementia rating (CDR) scale is commonly used to diagnose dementia due to Alzheimer's disease (AD). The sum of boxes of the CDR (CDR-SB) has recently been emphasized and applied to interventional trials for tracing the progression of cognitive impairment (CI) in the early stages of AD. We aimed to study the influence of baseline CDR-SB on disease progression to dementia or reversion to normal cognition (NC). Materials and methods: The baseline CDR < 1 cohort registered from September 2015 to August 2020 with longitudinal follow-up in the History-based Artificial Intelligence Clinical Dementia Diagnostic System (HAICDDS) database was retrospectively analyzed for the rates of conversion to CDR ≥ 1. A Cox regression model was applied to study the influence of CDR-SB levels on progression, adjusting for age, education, sex, neuropsychological tests, neuropsychiatric symptoms, parkinsonism, and multiple vascular risk factors. Results: A total of 1,827 participants were analyzed, including 1,258 (68.9%) non-converters, and 569 (31.1%) converters with mean follow-up of 2.1 (range 0.4-5.5) and 1.8 (range 0.3-5.0) years, respectively. Conversion rates increased with increasing CDR-SB scores. Compared to a CDR-SB score of 0, the hazard ratios (HR) for conversion to dementia were 1.51, 1.91, 2.58, 2.13, 3.46, 3.85, 3.19, 5.12, and 5.22 for CDR-SB scores of 0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, and ≥4.5, respectively (all p < 0.05 except for CDR-SB score = 0.5). In addition, older age, lower education, lower cognitive performance, and a history of diabetes also increased conversion rates. Furthermore, reversions to NC were 12.5, 5.6, 0.9, and 0% for CDR-SB scores of 0.5, 1.0-2.0, 2.5-3.5 and ≥4.0, respectively (p < 0.001). Conclusion: CDR-SB in predementia or very mild dementia (VMD) stages highly predicts progression to dementia or reversion to NC. Therefore, CDR-SB could be a good candidate for tracing the effectiveness of pharmacological and non-pharmacological interventions in populations without dementia.

Relevance of plasma biomarkers to pathologies in Alzheimer's disease, Parkinson's disease and frontotemporal dementia.

Amyloid plaques and tau tangles are pathological hallmarks of Alzheimer's disease (AD). Parkinson's disease (PD) results from the accumulation of α-synuclein. TAR DNA-binding protein (TDP-43) and total tau protein (T-Tau) play roles in FTD pathology. All of the pathological evidence was found in the biopsy. However, it is impossible to perform stein examinations in clinical practice. Assays of biomarkers in plasma would be convenient. It would be better to investigate the combinations of various biomarkers in AD, PD and FTD. Ninety-one subjects without neurodegenerative diseases, 76 patients with amnesic mild cognitive impairment (aMCI) or AD dementia, combined as AD family, were enrolled. One hundred and nine PD patients with normal cognition (PD-NC) or dementia (PDD), combined as PD family, were enrolled. Twenty-five FTD patients were enrolled for assays of plasma amyloid ß 1-40 (Aß1-40), Aß1-42, T-Tau, α-synuclein and TDP-43 using immunomagnetic reduction (IMR). The results show that Aßs and T-Tau are major domains in AD family. α-synuclein is highly dominant in PD family. FTD is closely associated with TDP-43 and T-Tau. The dominant plasma biomarkers in AD family, PD family and FTD are consistent with pathology. This implies that plasma biomarkers are promising for precise and differential assessments of AD, PD and FTD in clinical practice.

Real-World Testing of a Machine Learning-Derived Visual Scale for Tc99m TRODAT-1 for Diagnosing Lewy Body Disease: Comparison with a Traditional Approach Using Semiquantification.

Objectives: Abnormal dopamine transporter (DAT) uptake is an important biomarker for diagnosing Lewy body disease (LBD), including Parkinson's disease (PD) and dementia with Lewy bodies (DLB). We evaluated a machine learning-derived visual scale (ML-VS) for Tc99m TRODAT-1 from one center and compared it with the striatal/background ratio (SBR) using semiquantification for diagnosing LBD in two other centers. Patients and Methods: This was a retrospective analysis of data from a history-based computerized dementia diagnostic system. MT-VS and SBR among normal controls (NCs) and patients with PD, PD with dementia (PDD), DLB, or Alzheimer's disease (AD) were compared. Results: We included 715 individuals, including 122 NCs, 286 patients with PD, 40 with AD, 179 with DLB, and 88 with PDD. Compared with NCs, patients with PD exhibited a significantly higher prevalence of abnormal DAT uptake using all methods. Compared with the AD group, PDD and DLB groups exhibited a significantly higher prevalence of abnormal DAT uptake using all methods. The distribution of ML-VS was significantly different between PD and NC, DLB and AD, and PDD and AD groups (all p < 0.001). The correlation coefficient of ML-VS/SBR in all participants was 0.679. Conclusions: The ML-VS designed in one center is useful for differentiating PD from NC, DLB from AD, and PDD from AD in other centers. Its correlation with traditional approaches using different scanning machines is also acceptable. Future studies should develop models using data pools from multiple centers for increasing diagnostic accuracy.

White matter hyperintensities in dementia with Lewy bodies are associated with poorer cognitive function and higher dementia stages.

Purpose: White matter hyperintensities (WMHs) are frequently found in elderly individuals with or without dementia. However, the association between WMHs and clinical presentations of dementia with Lewy bodies (DLB) has rarely been studied. Methods: We conducted a retrospective analysis of patients with DLB registered in a dementia database. WMHs were rated visually using the Fazekas scale, and its associated factors including dementia severity, cognitive functions, neuropsychiatric symptoms, and core clinical features were compared among different Fazekas scores. Domains in the Clinical Dementia Rating (CDR), Cognitive abilities Screening Instruments (CASI), and Neuropsychiatric Inventory (NPI) were compared among different Fazekas groups after adjusting for age, sex, education, and disease duration. Results: Among the 449 patients, 76, 207, 110, and 56 had Fazekas score of 0, 1, 2, and 3, respectively. There was a positive association between dementia severity and WMHs severity, and the mean sums of boxes of the Clinical Dementia Rating (CDR-SB) were 5.9, 7.8, 9.5, and 11.2 (f = 16.84, p < 0.001) for the Fazekas scale scores 0, 1, 2, and 3, respectively. There was a negative association between cognitive performance and WMHs severity, and the mean CASI were 57.7, 45.4, 4.06, and 33.4 (f = 14.22, p < 0.001) for the Fazekas scale scores 0, 1, 2, and 3, respectively. However, WMHs were not associated with the core clinical features of DLB. After adjustment, all cognitive domains in CDR increased as the Fazekas score increased. In addition, performance on all cognitive domains in CASI decreased as the Fazekas score increased (all p < 0.001). Among neuropsychiatric symptoms, delusions, euphoria, apathy, aberrant motor behavior, and sleep disorders were significantly worse in the higher Fazekas groups compared to those in the group with Fazekas score of 0 after adjustment. Conclusion: WMHs in DLB might contribute to deterioration of cognitive function, neuropsychiatric symptoms, and dementia stages. However, core clinical features were not significantly influenced by WMHs in DLB.

Synuclein Motor Dysfunction Composite Scale for the Discrimination of Dementia With Lewy Bodies From Alzheimer's Disease.

Background: An abnormal increase of α-synuclein in the brain is the hallmark of dementia with Lewy bodies (DLB). However, the diagnostic power of plasma α-synuclein in DLB is not yet confirmed. Parkinsonism is highly associated with and is one of the core clinical features of DLB. We studied plasma α-synuclein and developed a novel tool that combined plasma α-synuclein level and Motor Dysfunction Questionnaire (MDQ), namely Synuclein Motor Dysfunction Composite Scale (SMDCS), for the clinical discrimination of DLB from Alzheimer's disease (AD). Methods: This cross-sectional study analyzed participants' demographical data, plasma α-synuclein level, MDQ, structured clinical history questionnaire, neuropsychological and motor function tests, and neuroimaging studies. The power of plasma α-synuclein level, MDQ, and SMDCS for discriminating DLB from non-demented controls (NC) or AD were compared. Results: Overall, 121 participants diagnosed as 58 DLB, 31 AD, and 31 NC were enrolled. Patients with DLB had significantly higher mean plasma α-synuclein level (0.24 ± 0.32 pg/ml) compared to the NC group (0.08 ± 0.05 pg/ml) and the AD group (0.08 ± 0.05 pg/ml). The DLB group demonstrated higher MDQ (2.95 ± 1.60) compared to the NC (0.42 ± 0.98) or AD (0.44 ± 0.99) groups. The sensitivity/specificity of plasma α-synuclein level, MDQ, and SMDCS for differentiating DLB from non-DLB were 0.80/0.64, 0.83/0.89, and 0.88/0.93, respectively. Conclusion: Both plasma α-synuclein and MDQ were significantly higher in patients with DLB compared to the NC or AD groups. The novel SMDCS, significantly improved accuracy for the clinical differentiation of DLB from AD or NC.

Emergency department visits among people with predementia highly predicts conversion to dementia.

Emergency department visits (EDV) are common among older adults with and without dementia. The risk factors and demands of EDVs for people with dementia have been well studied; however, the association between EDVs and conversion to dementia among people with predementia has not been thoroughly explored. To study the predictive value of EDVs in predementia's progression to dementia. The baseline predementia cohort registered from September 2015 to August 2017, with longitudinal follow-up in the History-based Artificial Intelligent Clinical Dementia Diagnostic System database, was retrospectively analyzed. The rates of conversion among the different EDVs were compared. Multivariate logistic regression and Cox proportional hazards analyses were applied to study the influence of EDVs on progression. Age, education, sex, neuropsychological tests, activities of daily living, neuropsychiatric symptoms, parkinsonism, and multiple vascular risk factors were adjusted for. A total of 512 participants were analyzed, including 339 (66.2%) non-converters and 173 (33.8%) converters with a mean follow-up of 3.3 (range 0.4-6.1) and 2.8 (range 0.5-5.9) years, respectively. Compared to people without EDV (EDV 0), the hazard ratios for conversion to dementia were 3.6, 5.9, and 6.9 in those with EDV once (EDV 1), twice (EDV 2), and more than twice (EDV >2), respectively. In addition, older age, lower education, poorer cognition, poorer ADL performance, and longer follow-up periods also increased the conversion rates. EDVs in the predementia stages highly predict progression to dementia. Therefore, a sound public health as well as primary healthcare system that provide strategies for better management of mental and physical condition might help prevention of EDVs among older people in the predementia stages.

Arrhythmia and other modifiable risk factors in incident dementia and MCI among elderly individuals with low educational levels in Taiwan.

Introduction: There is increasing evidence that arrhythmia is a risk factor for dementia; however, it appears that arrhythmia affects the cognitive function of individuals differentially across age groups, races, and educational levels. Demographic differences including educational level have also been found to moderate the effects of modifiable risk factors for cognitive decline. Methods: This study recruited 1,361 individuals including a group of cognitively unimpaired (CU) individuals, a group of patients with mild cognitive impairment (MCI), and a group of patients with dementia with low education levels. The participants were evaluated in terms of modifiable risk factors for dementia, including arrhythmia and neuropsychiatric symptoms. Results: Cox proportional hazard regression models revealed that among older MCI patients (>75 years), those with arrhythmia faced an elevated risk of dementia. Among younger MCI patients, those taking anti-hypertensive drugs faced a relatively low risk of dementia. Among younger MCI patients, male sex and higher educational level were associated with an elevated risk of dementia. Among CU individuals, those with coronary heart disease and taking anti-lipid compounds faced an elevated risk of MCI and those with symptoms of depression faced an elevated risk of dementia. Discussion: The risk and protective factors mentioned above could potentially be used as markers in predicting the onset of dementia in clinical settings, especially for individuals with low educational levels.

Assessment of High Risk for Alzheimer's Disease Using Plasma Biomarkers in Subjects with Normal Cognition in Taiwan: A Preliminary Study.

BACKGROUND: In Alzheimer's disease (AD), cognitive impairment begins 10-15 years later than neurodegeneration in the brain. Plasma biomarkers are promising candidates for assessing neurodegeneration in people with normal cognition. It has been reported that subjects with the concentration of plasma amyloid-ß 1-42×total tau protein higher than 455 pg2/ml2 are assessed as having a high risk of amnesic mild impairment or AD, denoted as high risk of AD (HRAD). OBJECTIVE: The prevalence of high-risk for dementia in cognitively normal controls is explored by assaying plasma biomarkers. METHODS: 422 subjects with normal cognition were enrolled around Taiwan. Plasma Aß1-40, Aß1-42, and T-Tau levels were assayed using immunomagnetic reduction to assess the risk of dementia. RESULTS: The results showed that 4.6% of young adults (age: 20-44 years), 8.5% of middle-aged adults (age: 45-64 years), and 7.3% of elderly adults (age: 65-90 years) had HRAD. The percentage of individuals with HRAD dramatically increased in middle-aged and elderly adults compared to young adults. CONCLUSION: The percentage of HRAD in cognitively normal subjects are approximately 10%, which reveals that the potentially public-health problem of AD in normal population. Although the subject having abnormal levels of Aß or tau is not definitely going on to develop cognitive declines or AD, the risk of suffering cognitive impairment in future is relatively high. Suitable managements are suggested for these high-risk cognitively normal population. Worth noting, attention should be paid to preventing cognitive impairment due to AD, not only in elderly adults but also middle-aged adults.

The Feasibility of Differentiating Lewy Body Dementia and Alzheimer's Disease by Deep Learning Using ECD SPECT Images.

The correct differential diagnosis of dementia has an important impact on patient treatment and follow-up care strategies. Tc-99m-ECD SPECT imaging, which is low cost and accessible in general clinics, is used to identify the two common types of dementia, Alzheimer's disease (AD) and Lewy body dementia (LBD). Two-stage transfer learning technology and reducing model complexity based on the ResNet-50 model were performed using the ImageNet data set and ADNI database. To improve training accuracy, the three-dimensional image was reorganized into three sets of two-dimensional images for data augmentation and ensemble learning, then the performance of various deep learning models for Tc-99m-ECD SPECT images to distinguish AD/normal cognition (NC), LBD/NC, and AD/LBD were investigated. In the AD/NC, LBD/NC, and AD/LBD tasks, the AUC values were around 0.94, 0.95, and 0.74, regardless of training models, with an accuracy of 90%, 87%, and 71%, and F1 scores of 89%, 86%, and 76% in the best cases. The use of transfer learning and a modified model resulted in better prediction results, increasing the accuracy by 32% for AD/NC. The proposed method is practical and could rapidly utilize a deep learning model to automatically extract image features based on a small number of SPECT brain perfusion images in general clinics to objectively distinguish AD and LBD.

Operational Determination of Subjective Cognitive Decline, Mild Cognitive Impairment, and Dementia Using Sum of Boxes of the Clinical Dementia Rating Scale.

Objectives: The Clinical Dementia Rating (CDR) Scale is the gold standard for the staging of dementia due to Alzheimer's disease (AD). However, the application of CDR for the staging of subjective cognitive decline (SCD) and mild cognitive impairment (MCI) in AD remains controversial. This study aimed to use the sum of boxes of the CDR (CDR-SB) plus an SCD single questionnaire to operationally determine the different stages of cognitive impairment (CI) due to AD and non-AD. Methods: This was a two-phase study, and we retrospectively analyzed the Show Chwan Dementia registry database using the data selected from 2015 to 2020. Individuals with normal cognition (NC), SCD, MCI, and mild dementia (MD) due to AD or non-AD with a CDR < 2 were included in the analysis. Results: A total of 6,946 individuals were studied, including 875, 1,009, 1,585, and 3,447 with NC, SCD, MCI, and MD, respectively. The cutoff scores of CDR-SB for NC/SCD, SCD/MCI, and MCI/dementia were 0/0.5, 0.5/1.0, and 2.5/3.0, respectively. The receiver operating characteristic (ROC) analysis showed that the area under the curve (AUC) values of the test groups were 0.85, 0.90, and 0.92 for discriminating NC from SCD, SCD from MCI, and MCI from dementia, respectively. Compared with the Cognitive Abilities Screening Instrument or the Montreal Cognitive Assessment, the use of CDR-SB is less influenced by age and education. Conclusion: Our study showed that the operational determination of SCD, MCI, and dementia using the CDR-SB is practical and can be applied in clinical settings and research on CI or dementia.

Focused Ultrasound Thalamotomy for the Treatment of Essential Tremor: A 2-Year Outcome Study of Chinese People.

Background: Essential tremor (ET) is a common movement disorder among elderly individuals worldwide and is occasionally associated with a high risk for mild cognitive impairment and dementia. This retrospective study aimed to determine the clinical outcome of unilateral magnetic resonance-guided focused ultrasound (MRgFUS) thalamotomy in Chinese patients with ET. Methods: In total, 31 male and 17 female patients with drug-refractory ET were enrolled in this research study from January 2017 to September 2019. The severity of tremor and disability were assessed using the Clinical Rating Scale for Tremor (CRST) within a 2-year follow-up period. Results: The mean age of the participants was 59.14 ± 13.5 years. The mean skull density ratio (SDR) was 0.5 ± 0.1. The mean highest temperature was 57.0 ± 2.4°C. The mean number of sonications was 10.0 ± 2.6. The average maximum energy was 19,710.5 ± 8,624.9 J. The total CRST scores and sub-scores after MRgFUS thalamotomy significantly reduced during each follow-up (p < 0.001). All but four (8.3%) of the patients had reversible adverse events (AEs) after the procedure. Conclusions: MRgFUS had sustained clinical efficacy 2 years after treatment for intractable ET. Only few patients presented with thalamotomy-related AEs including numbness, weakness, and ataxia for an extended period. Most Chinese patients were treated safely and effectively despite their low SDR.