Neuroimaging in the Oldest-Old: A Review of the Literature

Comprehensive assessment of TDP-43 neuropathology data in the National Alzheimer's Coordinating Center database

TDP-43 proteinopathy is a salient neuropathologic feature in a subset of frontotemporal lobar degeneration (FTLD-TDP), in amyotrophic lateral sclerosis (ALS-TDP), and in limbic-predominant age-related TDP-43 encephalopathy neuropathologic change (LATE-NC), and is associated with hippocampal sclerosis of aging (HS-A). We examined TDP-43-related pathology data in the National Alzheimer's Coordinating Center (NACC) in two parts: (I) availability of assessments, and (II) associations with clinical diagnoses and other neuropathologies in those with all TDP-43 measures available. Part I: Of 4326 participants with neuropathology data collected using forms that included TDP-43 assessments, data availability was highest for HS-A (97%) and ALS (94%), followed by FTLD-TDP (83%). Regional TDP-43 pathologic assessment was available for 77% of participants, with hippocampus the most common region. Availability for the TDP-43-related measures increased over time, and was higher in centers with high proportions of participants with clinical FTLD. Part II: In 2142 participants with all TDP-43-related assessments available, 27% of participants had LATE-NC, whereas ALS-TDP or FTLD-TDP (ALS/FTLD-TDP) was present in 9% of participants, and 2% of participants had TDP-43 related to other pathologies ("Other TDP-43"). HS-A was present in 14% of participants, of whom 55% had LATE-NC, 20% ASL/FTLD-TDP, 3% Other TDP-43, and 23% no TDP-43. LATE-NC, ALS/FTLD-TDP, and Other TDP-43, were each associated with higher odds of dementia, HS-A, and hippocampal atrophy, compared to those without TDP-43 pathology. LATE-NC was associated with higher odds for Alzheimer's disease (AD) clinical diagnosis, AD neuropathologic change (ADNC), Lewy bodies, arteriolosclerosis, and cortical atrophy. ALS/FTLD-TDP was associated with higher odds of clinical diagnoses of primary progressive aphasia and behavioral-variant frontotemporal dementia, and cortical/frontotemporal lobar atrophy. When using NACC data for TDP-43-related analyses, researchers should carefully consider the incomplete availability of the different regional TDP-43 assessments, the high frequency of participants with ALS/FTLD-TDP, and the presence of other forms of TDP-43 pathology.

Alcohol Use Disorder and Dementia: A Review

PURPOSE

By 2040, 21.6% of Americans will be over age 65, and the population of those older than age 85 is estimated to reach 14.4 million. Although not causative, older age is a risk factor for dementia: every 5 years beyond age 65, the risk doubles; approximately one-third of those older than age 85 are diagnosed with dementia. As current alcohol consumption among older adults is significantly higher compared to previous generations, a pressing question is whether drinking alcohol increases the risk for Alzheimer's disease or other forms of dementia.

SEARCH METHODS

Databases explored included PubMed, Web of Science, and ScienceDirect. To accomplish this narrative review on the effects of alcohol consumption on dementia risk, the literature covered included clinical diagnoses, epidemiology, neuropsychology, postmortem pathology, neuroimaging and other biomarkers, and translational studies. Searches conducted between January 12 and August 1, 2023, included the following terms and combinations: "aging," "alcoholism," "alcohol use disorder (AUD)," "brain," "CNS," "dementia," "Wernicke," "Korsakoff," "Alzheimer," "vascular," "frontotemporal," "Lewy body," "clinical," "diagnosis," "epidemiology," "pathology," "autopsy," "postmortem," "histology," "cognitive," "motor," "neuropsychological," "magnetic resonance," "imaging," "PET," "ligand," "degeneration," "atrophy," "translational," "rodent," "rat," "mouse," "model," "amyloid," "neurofibrillary tangles," "α-synuclein," or "presenilin." When relevant, "species" (i.e., "humans" or "other animals") was selected as an additional filter. Review articles were avoided when possible.

SEARCH RESULTS

The two terms "alcoholism" and "aging" retrieved about 1,350 papers; adding phrases-for example, "postmortem" or "magnetic resonance"-limited the number to fewer than 100 papers. Using the traditional term, "alcoholism" with "dementia" resulted in 876 citations, but using the currently accepted term "alcohol use disorder (AUD)" with "dementia" produced only 87 papers. Similarly, whereas the terms "Alzheimer's" and "alcoholism" yielded 318 results, "Alzheimer's" and "alcohol use disorder (AUD)" returned only 40 citations. As pertinent postmortem pathology papers were published in the 1950s and recent animal models of Alzheimer's disease were created in the early 2000s, articles referenced span the years 1957 to 2024. In total, more than 5,000 articles were considered; about 400 are herein referenced.

DISCUSSION AND CONCLUSIONS

Chronic alcohol misuse accelerates brain aging and contributes to cognitive impairments, including those in the mnemonic domain. The consensus among studies from multiple disciplines, however, is that alcohol misuse can increase the risk for dementia, but not necessarily Alzheimer's disease. Key issues to consider include the reversibility of brain damage following abstinence from chronic alcohol misuse compared to the degenerative and progressive course of Alzheimer's disease, and the characteristic presence of protein inclusions in the brains of people with Alzheimer's disease, which are absent in the brains of those with AUD.

Factors and brain imaging features associated with cognition in oldest-old patients with Alzheimer-type dementia

BACKGROUND

The underlying pathophysiology of cognitive dysfunction in oldest-old patients with Alzheimer-type dementia (AD) has not been clarified to date. We aimed to determine the factors and brain imaging features associated with cognition in oldest-old patients with AD.

METHODS

We enrolled 456 consecutive outpatients with probable AD (145 men and 311 women, age range: 51-95 years). Demographic factors, such as education level, disease duration at initial visit, body mass index, comorbidities, frailty, and leisure activity, and brain imaging features, including severity of medial temporal lobe (MTL) atrophy, white matter lesions and infarcts, and frequency of posterior cerebral hypoperfusion were compared among pre-old (≤ 74 years), old (75 to 84 years), and oldest-old (≥ 85 years) subgroups.

RESULTS

The oldest-old subgroup showed significantly longer disease duration, lower education level, more severe frailty, less leisure activity, worse cognitive impairment, a tendency of slower progression of cognitive decline, greater MTL atrophy, more severe white matter hyperintensities and infarcts, and lower frequency of posterior hypoperfusion than the younger age subgroups. Regarding the brain imaging subtypes, there were significantly more patients with the limbic-predominant subtype and fewer patients with the hippocampal-sparing subtype in the oldest-old AD group than the pre-old AD group.

CONCLUSIONS

Oldest-old patients with AD show different factors and brain imaging features associated with cognition from pre-old and old patients. Our results are expected to provide useful information towards understanding the pathophysiology of oldest-old patients with AD, and for determining their clinical diagnosis and appropriate management methods.

Longitudinal hippocampal atrophy in hippocampal sclerosis of aging

Hippocampal sclerosis of aging (HS-A) is a common degenerative neuropathology in older individuals and is associated with dementia. HS-A is characterized by disproportionate hippocampal atrophy at autopsy but cannot be diagnosed during life. Therefore, little is known about the onset and progression of hippocampal atrophy in individuals with HS-A. To better understand the onset and progression of hippocampal atrophy in HS-A, we examined longitudinal hippocampal atrophy using serial MRI in participants with HS-A at autopsy (HS-A+, n = 8) compared to participants with limbic-predominant age-related TDP-43 encephalopathy neuropathological change (LATE-NC) without HS-A (n = 13), Alzheimer's disease neuropathologic change (ADNC) without HS-A or LATE-NC (n = 16), and those without these pathologies (n = 7). We found that participants with HS-A had lower hippocampal volumes compared to the other groups, and this atrophy preceded the onset of dementia. There was also some evidence that rates of hippocampal volume loss were slightly slower in those with HS-A. Together, these results suggest that the disproportionate hippocampal atrophy seen in HS-A may begin early prior to dementia.

Modifiable Risk Factors for Brain Health and Dementia and Opportunities for Intervention: A Brief Review

Maintaining brain health and promoting healthy lifestyle strategies to manage modifiable risk factors is vital to ensuring well-being for all - not only for the individuals with memory challenges but also their family caregivers and professional providers. In this brief review paper, we highlight modifiable risk and protective factors and opportunities for dementia risk reduction (e.g., limited alcohol use and reduced exposure to air pollution, secondhand smoke, and excessive noise); provide an overview of the World-Wide FINGERS Network and its goal to adapt the original Finnish Geriatric Intervention Study to Prevent Cognitive Impairment and Disability (FINGER) multidomain lifestyle approach in various settings to determine whether the protocol is effective across different populations in varied geographic, cultural, and economic settings and to optimize the model across a continuum of cognitive decline; and, comment on challenges and opportunities for researchers and clinicians including opportunities for risk reduction and intervention in primary care settings and the need to establish linkages across multiple levels of intervention to sustain behavior change in prevention, treatment, and care.

Diabetes and cognitive function: An evidence-based current perspective

Several epidemiological studies have clearly identified diabetes mellitus (DM) as a major risk factor for cognitive dysfunction, and it is going to be a major public health issue in the coming years because of the alarming rise in diabetes prevalence across the world. Brain and neural tissues predominantly depend on glucose as energy substrate and hence, any alterations in carbohydrate meta-bolism can directly impact on cerebral functional output including cognition, executive capacity, and memory. DM affects neuronal function and mental capacity in several ways, some of which include hypoperfusion of the brain tissues from cerebrovascular disease, diabetes-related alterations of glucose transporters causing abnormalities in neuronal glucose uptake and metabolism, local hyper- and hypometabolism of brain areas from insulin resistance, and recurrent hypoglycemic episodes inherent to pharmacotherapy of diabetes resulting in neuronal damage. Cognitive decline can further worsen diabetes care as DM is a disease largely self-managed by patients. Therefore, it is crucial to understand the pathobiology of cognitive dysfunction in relation to DM and its management for optimal long-term care plan for patients. A thorough appraisal of normal metabolic characteristics of the brain, how alterations in neural metabolism affects cognition, the diagnostic algorithm for patients with diabetes and dementia, and the management and prognosis of patients when they have this dangerous combination of illnesses is imperative in this context. This evidence-based narrative with the back-up of latest clinical trial reviews elaborates the current understanding on diabetes and cognitive function to empower physicians to manage their patients in day-to-day clinical practice.

Poor Oral Health as a Risk Factor for Dementia in a Swedish Population: A Cohort Study with 40 Years of Follow-Up

BACKGROUND

Whether poor oral health is associated with dementia risk remains unclear.

OBJECTIVE

We conducted a cohort study of 14,439 participants who were followed up for up to 40 years in Uppsala County, central Sweden, aiming to explore the association between poor oral health, namely the number of tooth loss, dental plaque status, and oral mucosal lesions, and the risk of dementia.

METHODS

We used Cox proportional hazards regression model to derive cause-specific hazard ratios (HR) and corresponding 95% confidence intervals (CI), while adjusting for baseline potential confounders as well as a time-varying covariate, Charlson's Comorbidity Index score.

RESULTS

Dementia risk was substantially higher among those with a higher number of tooth loss; compared to the group with tooth loss 0-10, the HRs were 1.21 (95% CI: 1.02, 1.42), 1.17 (95% CI: 0.97, 1.40), and 1.30 (95% CI: 1.09, 1.54) respectively for groups with increasing number of tooth loss. There was some evidence of dose-risk association in this study, with a HR of 1.10 (1.04, 1.18) comparing adjacent groups (ptrend = 0.001). In a stratified analysis by attained age, tooth loss was more pronouncedly associated with the risk of dementia onset before age 80 (those with 21-32 versus 0-10 lost teeth, HR = 1.82, (95% CI: 1.32, 2.51); HR = 1.22 (95% CI: 1.10, 1.35) comparing adjacent groups, ptrend < 0.001).

CONCLUSION

In summary, there are some indications that poor oral health, as indicated by more tooth loss, is positively associated with an increased risk of dementia, especially for dementia onset before age 80.

Evaluation of in vivo staging of amyloid deposition in cognitively unimpaired elderly aged 78-94

Amyloid-beta (Aβ) deposition is common in cognitively unimpaired (CU) elderly >85 years. This study investigated amyloid distribution and evaluated three published in vivo amyloid-PET staging schemes from a cognitively unimpaired (CU) cohort aged 84.9 ± 4.3 years (n = 75). SUV-based principal component analysis (PCA) was applied to 18F-flutemetamol PET data to determine an unbiased regional covariance pattern of tracer uptake across grey matter regions. PET staging schemes were applied to the data and compared to the PCA output. Concentration of p-tau181 was measured in blood plasma. The PCA revealed three distinct components accounting for 91.2% of total SUV variance. PC1 driven by the large common variance of uptake in neocortical and striatal regions was significantly positively correlated with global SUVRs, APOE4 status and p-tau181 concentration. PC2 represented mainly non-specific uptake in typical amyloid-PET reference regions, and PC3 the occipital lobe. Application of the staging schemes demonstrated that the majority of the CU cohort (up to 93%) were classified as having pathological amount and distribution of Aβ. Good correspondence existed between binary (+/-) classification and later amyloid stages, however, substantial differences existed between schemes for low stages with 8-17% of individuals being unstageable, i.e., not following the sequential progression of Aβ deposition. In spite of the difference in staging outcomes there was broad spatial overlap between earlier stages and PC1, most prominently in default mode network regions. This study critically evaluated the utility of in vivo amyloid staging from a single PET scan in CU elderly and found that early amyloid stages could not be consistently classified. The majority of the cohort had pathological Aβ, thus, it remains an open topic what constitutes abnormal brain Aβ in the oldest-old and what is the best method to determine that.

White Matter Alterations in Depressive Disorder

Depressive disorder is the most prevalent affective disorder today. Depressive disorder has been linked to changes in the white matter. White matter changes in depressive disorder could be a result of impaired cerebral blood flow (CBF) and CBF self-regulation, impaired blood-brain barrier function, inflammatory factors, genes and environmental factors. Additionally, white matter changes in patients with depression are associated with clinical variables such as differential diagnosis, severity, treatment effect, and efficacy assessment. This review discusses the characteristics, possible mechanisms, clinical relevance, and potential treatment of white matter alterations caused by depressive disorders.

Estimation of Human Cerebral Atrophy Based on Systemic Metabolic Status Using Machine Learning

Background

Based on the assumption that systemic metabolic disorders affect cognitive function, we have developed a deep neural network (DNN) model that can estimate cognitive function based on basic blood test data that do not contain dementia-specific biomarkers. In this study, we used the same DNN model to assess whether basic blood data can be used to estimate cerebral atrophy.

Methods

We used data from 1,310 subjects (58.32 ± 12.91years old) enrolled in the Brain Doc Bank. The average Mini Mental State Examination score was 28.6 ± 1.9. The degree of cerebral atrophy was determined using the MRI-based index (GM-BHQ). First, we evaluated the correlations between the subjects' age, blood data, and GM-BHQ. Next, we developed DNN models to assess the GM-BHQ: one used subjects' age and blood data, while the other used only blood data for input items.

Results

There was a negative correlation between age and GM-BHQ scores (r = -0.71). The subjects' age was positively correlated with blood urea nitrogen (BUN) (r = 0.40), alkaline phosphatase (ALP) (r = 0.22), glucose (GLU) (r = 0.22), and negative correlations with red blood cell counts (RBC) (r = −0.29) and platelet counts (PLT) (r = −0.26). GM-BHQ correlated with BUN (r = −0.30), GLU (r = −0.26), PLT (r = 0.26), and ALP (r = 0.22). The GM-BHQ estimated by the DNN model with subject age exhibited a positive correlation with the ground truth GM-BHQ (r = 0.70). Furthermore, even if the DNN model without subject age was used, the estimated GM-BHQ showed a significant positive correlation with ground truth GM-BHQ (r = 0.58). Age was the most important variable for estimating GM-BHQ.

Discussion

Aging had the greatest effect on cerebral atrophy. Aging also affects various organs, such as the kidney, and causes changes in systemic metabolic status, which may contribute to cerebral atrophy and cognitive impairment. The DNN model may serve as a new screening test for dementia using basic blood tests for health examinations. Finally, the blood data reflect systemic metabolic disorders in each subject—this method may thus contribute to personalized care.