The association between midlife serum high-density lipoprotein and mild cognitive impairment and dementia after 19 years of follow-up

A third of dementia cases could be attributable to modifiable risk-factors. Midlife high-density lipoprotein cholesterol (HDL-C) is a measure which could help identify individuals at reduced risk of developing age-related cognitive decline. The Japan Public Health Centre-based prospective (JPHC) Study is a large population-based cohort which started in 1990. This study included 1299 participants from Saku area in Nagano prefecture. Participants had HDL-C measured in 1995-1996, and underwent a mental health screening in 2014-2015. Of these, 1114 participants were included in MCI analyses, and 781 participants were included in dementia analyses. Logistic regression models were used to determine odds ratios (OR) and 95% confidence intervals (CI) for the association between HDL-C quartiles and MCI and dementia, respectively. For dementia analysis, quartiles 2-4 were collapsed due to low number of cases. Missing data was addressed through multiple imputations. There were 386 cases of MCI and 53 cases of dementia. Compared to the lowest HDL-C quartile, the highest HDL-C quartile was significantly inversely associated with MCI (OR = 0.47, 95% CI, 0.28-0.79) in the multivariable analysis. High HDL-C (quartiles 2-4) was inversely associated with dementia compared to low HDL-C (quartile 1) (OR = 0.37, 95% CI, 0.16-0.88). This study has found that high midlife HDL-C levels are inversely associated with both late-life MCI and dementia in a Japanese population

The estimated cost of dementia in Japan, the most aged society in the world.

OBJECTIVE:Dementia has become a global critical issue. It is estimated that the global cost of dementia was 818 billion USD in 2015. The situation in Japan, which is the most aged country in the world, should be critical. However, the societal cost of dementia in Japan has not yet been estimated. This study was designed to estimate cost of dementia from societal perspective. DESIGN:We estimated the cost from societal perspective with prevalence based approach. SETTING, PARTICIPANTS AND MEASURES:Main data sources for the parameters to estimate the costs are the National Data Base, a nationwide representative individual-level database for healthcare utilization, the Survey of Long-Term Care Benefit Expenditures, a nationwide survey based on individual-level secondary data for formal long-term care utilization, and the results of an informal care time survey for informal care cost. We conducted the analyses with 'probabilistic modeling' using the parameters obtained to estimate the costs of dementia. We also projected future costs. RESULTS:The societal costs of dementia in Japan in 2014 were estimated at JPY 14.5 trillion (se 66.0 billion). Of these, the costs for healthcare, long-term care, and informal care are JPY 1.91 trillion (se 4.91 billion), JPY 6.44 trillion (se 63.2 billion), and JPY 6.16 trillion (se 12.5 billion) respectively. The cost per person with dementia appeared to be JPY5.95 million (se 27 thousand). The total costs would reach JPY 24.3 trillion by 2060, which is 1.6 times higher than that in 2014. CONCLUSIONS:The societal cost of dementia in Japan appeared to be considerable. Interventions to mitigate this impact should be considered

Coping in Mid- to Late Life and Risk of Mild Cognitive Impairment Subtypes and Dementia : A JPHC Saku Mental Health Study

BACKGROUND: The relationship between coping in mid- to late life and cognitive functions remains unclear. OBJECTIVE: To investigate the relationship between habitual coping behaviors of a large Japanese population in their mid- to late-lives and their risk of cognitive decline 15 years later. METHODS: Overall 1,299 participants were assessed for coping behaviors (in 2000) and cognition (2014-2015). We used the Stress and Coping Inventory to assess the frequency of six coping behaviors (i.e., consulting, planning, positive reappraisal, avoidance, fantasizing, and self-blame). Logistic regression analyses were conducted to examine odds ratios (ORs) for the diagnosis of mild cognitive impairment (MCI), MCI subtypes (single- and multiple-domain MCI), and dementia for coping behaviors. RESULTS: Among the eligible 1,015 participants (72.6 [SD = 5.5] years old in 2014-2015), the numbers for cognitively normal, single-domain MCI, multiple-domain MCI, and dementia were 650 (64.0%), 116 (11.4%), 213 (21.0%), and 36 (3.5%), respectively. Among the six coping behaviors, avoidant coping was significantly associated with noticeable cognitive decline (multiple-domain MCI and dementia). This association remained significant after adjusting for sex, age, education, diagnosis of current major depressive disorder, past history of ischemic heart disease, diabetes, regular alcohol consumption, and smoking (OR = 2.52, 95% CI = 1.23 to 5.15). No significant association with other coping behaviors was found. CONCLUSION: Avoidant coping in mid- and late life is associated with cognitive decline among older people

Amyloid-β prediction machine learning model using source-based morphometry across neurocognitive disorders

Abstract Previous studies have developed and explored magnetic resonance imaging (MRI)-based machine learning models for predicting Alzheimer’s disease (AD). However, limited research has focused on models incorporating diverse patient populations. This study aimed to build a clinically useful prediction model for amyloid-beta (Aβ) deposition using source-based morphometry, using a data-driven algorithm based on independent component analyses. Additionally, we assessed how the predictive accuracies varied with the feature combinations. Data from 118 participants clinically diagnosed with various conditions such as AD, mild cognitive impairment, frontotemporal lobar degeneration, corticobasal syndrome, progressive supranuclear palsy, and psychiatric disorders, as well as healthy controls were used for the development of the model. We used structural MR images, cognitive test results, and apolipoprotein E status for feature selection. Three-dimensional T1-weighted images were preprocessed into voxel-based gray matter images and then subjected to source-based morphometry. We used a support vector machine as a classifier. We applied SHapley Additive exPlanations, a game-theoretical approach, to ensure model accountability. The final model that was based on MR-images, cognitive test results, and apolipoprotein E status yielded 89.8% accuracy and a receiver operating characteristic curve of 0.888. The model based on MR-images alone showed 84.7% accuracy. Aβ-positivity was correctly detected in non-AD patients. One of the seven independent components derived from source-based morphometry was considered to represent an AD-related gray matter volume pattern and showed the strongest impact on the model output. Aβ-positivity across neurological and psychiatric disorders was predicted with moderate-to-high accuracy and was associated with a probable AD-related gray matter volume pattern. An MRI-based data-driven machine learning approach can be beneficial as a diagnostic aid

Performance of plasma Aβ42/40, measured using a fully automated immunoassay, across a broad patient population in identifying amyloid status

Abstract Background Plasma biomarkers have emerged as promising screening tools for Alzheimer’s disease (AD) because of their potential to detect amyloid β (Aβ) accumulation in the brain. One such candidate is the plasma Aβ42/40 ratio (Aβ42/40). Unlike previous research that used traditional immunoassay, recent studies that measured plasma Aβ42/40 using fully automated platforms reported promising results. However, its utility should be confirmed using a broader patient population, focusing on the potential for early detection. Methods We recruited 174 participants, including healthy controls (HC) and patients with clinical diagnoses of AD, frontotemporal lobar degeneration, dementia with Lewy bodies/Parkinson’s disease, mild cognitive impairment (MCI), and others, from a university memory clinic. We examined the performance of plasma Aβ42/40, measured using the fully automated high-sensitivity chemiluminescence enzyme (HISCL) immunoassay, in detecting amyloid-positron emission tomography (PET)-derived Aβ pathology. We also compared its performance with that of Simoa-based plasma phosphorylated tau at residue 181 (p-tau181), glial fibrillary acidic protein (GFAP), and neurofilament light (NfL). Results Using the best cut-off derived from the Youden Index, plasma Aβ42/40 yielded an area under the receiver operating characteristic curve (AUC) of 0.949 in distinguishing visually assessed 18F-Florbetaben amyloid PET positivity. The plasma Aβ42/40 had a significantly superior AUC than p-tau181, GFAP, and NfL in the 167 participants with measurements for all four biomarkers. Next, we analyzed 99 participants, including only the HC and those with MCI, and discovered that plasma Aβ42/40 outperformed the other plasma biomarkers, suggesting its ability to detect early amyloid accumulation. Using the Centiloid scale (CL), Spearman’s rank correlation coefficient between plasma Aβ42/40 and CL was -0.767. Among the 15 participants falling within the CL values indicative of potential future amyloid accumulation (CL between 13.5 and 35.7), plasma Aβ42/40 categorized 61.5% (8/13) as Aβ-positive, whereas visual assessment of amyloid PET identified 20% (3/15) as positive. Conclusion Plasma Aβ42/40 measured using the fully automated HISCL platform showed excellent performance in identifying Aβ accumulation in the brain in a well-characterized cohort. This equipment may be useful for screening amyloid pathology because it has the potential to detect early amyloid pathology and is readily applied in clinical settings

CKD, Brain Atrophy, and White Matter Lesion Volume: The Japan Prospective Studies Collaboration for Aging and DementiaPlain-Language summary

Rationale &amp; Objective: Chronic kidney disease, defined by albuminuria and/or reduced estimated glomerular filtration rate (eGFR), has been reported to be associated with brain atrophy and/or higher white matter lesion volume (WMLV), but there are few large-scale population-based studies assessing this issue. This study aimed to examine the associations between the urinary albumin-creatinine ratio (UACR) and eGFR levels and brain atrophy and WMLV in a large-scale community-dwelling older population of Japanese. Study Design: Population-based cross-sectional study. Setting &amp; Participants: A total of 8,630 dementia-free community-dwelling Japanese aged greater than or equal to 65 years underwent brain magnetic resonance imaging scanning and screening examination of health status in 2016-2018. Exposures: UACR and eGFR levels. Outcomes: The total brain volume (TBV)-to-intracranial volume (ICV) ratio (TBV/ICV), the regional brain volume-to-TBV ratio, and the WMLV-to-ICV ratio (WMLV/ICV). Analytical Approach: The associations of UACR and eGFR levels with the TBV/ICV, the regional brain volume-to-TBV ratio, and the WMLV/ICV were assessed by using an analysis of covariance. Results: Higher UACR levels were significantly associated with lower TBV/ICV and higher geometric mean values of the WMLV/ICV (P for trend = 0.009 and <0.001, respectively). Lower eGFR levels were significantly associated with lower TBV/ICV, but not clearly associated with WMLV/ICV. In addition, higher UACR levels, but not lower eGFR, were significantly associated with lower temporal cortex volume-to-TBV ratio and lower hippocampal volume-to-TBV ratio. Limitations: Cross-sectional study, misclassification of UACR or eGFR levels, generalizability to other ethnicities and younger populations, and residual confounding factors. Conclusions: The present study demonstrated that higher UACR was associated with brain atrophy, especially in the temporal cortex and hippocampus, and with increased WMLV. These findings suggest that chronic kidney disease is involved in the progression of morphologic brain changes associated with cognitive impairment