Hamid R. Sohrabi

Background
The coronavirus disease (COVID-19) pandemic and mobility-restricting policies (MRPs) have created substantial barriers to healthcare access globally. This study quantified the causal impact of government-imposed MRPs and perceived COVID-19 risk on household unmet medical needs in Nigeria, examining the differential effects for vulnerable populations.

Methods
We conducted difference-in-differences analyses using nationally representative household panel data from the pre-pandemic period (January-February 2019, n = 1,596 households) and the early pandemic period (April-August 2020). Unmet needs were measured as self-reported forgone medical care at the household level. We employed two approaches to disentangle voluntary from policy-driven restrictions: comparing states with total versus partial lockdown, and comparing states with below- versus above-median mobility reductions (Google Mobility data). We estimated COVID-19 risk effects across four state-level case burden categories during and after the lockdown.

Results
Households in total lockdown states experienced 13.0% points (95%CI: 3.0–23.0) (doubling baseline rate) higher unmet needs compared to households in partial lockdown states. This converges with mobility approach showing 15.0% points (95%CI: 6.0–25.0) higher unmet needs among individuals with below-median mobility. During lockdown, the COVID-19 case burden had no significant differential impact. However, during the post-lockdown period (June-August 2020), households in Lagos (the epicentre, with more than 10,000 cases) experienced 24.0% points (95%CI: 2.0–46.0) higher unmet needs compared to low-risk states. Persons with disabilities faced disproportionate barriers during lockdown, with a 40.0% point (95% CI: 6.0–74.0) higher rate in high-risk states and a 74.0% point (95%CI: 22.0-126.0) higher rate in the epicentre. No differential impacts were observed for poverty or chronic disease status.

Conclusion
Considering the detrimental effects of unmet medical needs, this study suggests that policymakers should evaluate the risks of COVID-19 in relation to the implementation of MRPs to protect households and vulnerable groups during future pandemics in Nigeria.

Data on dementia epidemiology in the Middle East and North Africa (MENA) region is limited. This systematic review and meta-analysis examined dementia prevalence across MENA. Databases were searched up to October 2024. Analyses were stratified by country and sex. Pooled prevalence was estimated using a random-effects model with a 95% confidence interval (CI). Fifty-two studies on the selected countries met inclusion criteria, covering 87,219 individuals with dementia from a total population of 1,045,908. The pooled prevalence was 12.16% (95% CI: 9.61–14.96) for the region and the Israel had the highest prevalence (17.00%), followed by Iran (13.20%), Turkey (11.40%), Saudi Arabia (8.34%), and Egypt (6.86%). Dementia was more common in women than men (13.84% vs. 8.69%). Dementia is prevalent in MENA, with significant variation across countries. The region's aging population highlights the need for ongoing monitoring of dementia trends.

Background
The agreement between plasma Aβ42/40 and Aβ positron emission tomography (PET) is approximately 75 %, with ∼85 % of discrepancies due to positive plasma but negative PET results. It is unclear whether this reflects Aβ changes in plasma before PET-detectable.

Objectives
To assess the influence of Aβ42/40 positivity on risk of progression to Aβ PET positivity, and feasibility of using plasma Aβ42/40 tests to enrich a primary prevention trial.

Design
A prospective longitudinal cohort study.

Setting
Participants of Australian Imaging, Biomarkers and Lifestyle study (AIBL), Alzheimer’s Disease Neuroimaging Initiative (ADNI), and Open Access Series of Imaging Studies 3 (OASIS3).

Participants
507 cognitively unimpaired adults at baseline, with a baseline Aβ PET < 20 Centiloid (CL) and available longitudinal Aβ PET data.

Measurements
Baseline Aβ PET and plasma Aβ42/40 measurement by mass-spectrometry, followed by 1–6 additional Aβ PET scans every 1.5–3 years. Those < 5 CL were classified as PET- and 5–20 CL as PETLow. Plasma -/+ was defined using the Aβ42/40 Youden’s Index threshold (0.119), corresponding to Aβ PET status.

Results
At baseline, 283 were Plasma-/PET-, 97 Plasma+/PET-, 76 Plasma-/PETLow, and 51 Plasma+/PETLow. Among Plasma+/PET- individuals, 19 % progressed to PET+ (>20 CL), indicating a higher risk of progression, compared to Plasma-/PET- (HR: 3.90 [90 % CI: 2.00–7.61], p < 0.001). This elevated risk remained significant after matching the groups’ baseline CL (3.43 [1.43–8.26], p = 0.010), or adjustment for age, sex, APOE ε4 and baseline CL (2.48 [1.22 - 5.07], p = 0.013). Plasma+/PET- individuals accumulated Aβ ∼8 times faster (1.14 CL/year) than Plasma-/PET- (0.15 CL/year, p < 0.001). Plasma+/PET- progressors became PET+ two years earlier than Plasma-/PET- progressors. Among the Plasma+/PETLow individuals, 67 % progressed to PET+. Their progression was faster and earlier than in the Plasma-/PETLow group (HR: 20.82 [11.28 - 38.42], p < 0.001 vs. 6.67 [3.51 - 12.65], p < 0.001; reference: Plasma-/PET-), largely driven by higher baseline CL in the Plasma+ group. In a primary prevention paradigm targeting high-risk PETLow individuals, pre-screening with Aβ42/40 blood test reduced the number of PET scans by 49 %, compared to a PET-only strategy.

Conclusions
Cognitively unimpaired individuals with abnormal Aβ42/40 are at increased risk for future Aβ PET positivity. In the 5–20 CL subgroup, baseline CL is the main driver of this risk. Combining blood-based pre-screening with PET imaging may help efficiently enrich primary prevention trials.

Background
Dementia is currently the second leading cause of death in Australia and the seventh leading cause of death worldwide. Diagnosis of Alzheimer's disease (AD), the major cause of dementia, is difficult and time consuming. Current clinical imaging technologies are costly to use for widespread early screening of AD and have limited availability. In contrast, the retina is unique, where blood vessels and neural tissue can be viewed and imaged non-invasively and relatively inexpensively. As part of the central nervous system, the retina exhibits similarities to the brain and can display indicators of various neurological disorders, including AD. We aimed to image the retina and analyse its spectral features to develop a robust machine learning (ML) classification model that distinguishes between brain amyloid-beta (Aβ) positive and negative individuals.

Method
Sixty-eight consenting volunteers with varying levels of brain Aβ protein underwent non-invasive imaging using a hyperspectral retinal camera and illumination of wavelengths from 450 to 905 nm. Multiple retina features from the central and superior views were selected and analysed to identify their variability among individuals with different brain amyloid loads. Eight ML models were evaluated for their performance in predicting brain Aβ levels using the retina images and systemic factors like age, gender and apolipoprotein E (APOE) genotype.

Result
The retinal reflectance spectra in the 405–585 nm wavelengths exhibited a significant difference in individuals with increasing brain amyloid. The retinal features in the superior view showed higher inter-subject variability. Our comparison of eight different ML algorithms revealed that the Multi-Layer Perceptron (MLP) model exhibited superior classification performance, achieving an accuracy of 0.86, precision of 0.88, recall of 0.82, F1-score of 0.85, and area under curve (AUC) of 0.90.

Conclusion
This study indicates that there are spectral variations of retinal features associated with brain amyloid loads. It also demonstrates the feasibility of the retinal hyperspectral imaging (rHSI) technique as a potential non-invasive, inexpensive screening method to identify individuals in the preclinical phase of AD.

Short-chain fatty acids (SCFAs) produced by gut microbial fermentation influence host metabolism and neuroinflammatory processes implicated in Alzheimer’s disease (AD). However, the relationship between fecal SCFAs, microbial taxa, and cerebral amyloid-β (Aβ) burden in cognitively unimpaired individuals remains unclear. Fecal SCFAs were quantified using GC-MS, and microbial species were profiled by shotgun metagenomics in 87 participants. Associations between SCFAs, demographics, APOE ε4 status, and Aβ burden were tested using nonparametric statistics and multivariable regression. Microbial–SCFA links were evaluated using Spearman correlations and multivariate ordinations, with mediation analysis exploring potential indirect pathways. Acetate was the predominant SCFA and demonstrated the most robust microbial associations. Higher acetate concentrations were positively associated with Bacteroides ovatus and Faecalibacterium prausnitzii, whereas lower acetate levels were linked to species such as Bifidobacterium animalis and Lachnoclostridium scindens. Stratified analyses indicated that individuals with elevated Aβ burden exhibited more pronounced species–SCFA relationships, including a notable association between Bacteroides thetaiotaomicron and butyrate. Multivariate ordination further identified a significant overall coupling between SCFA profiles and microbial community structure. Mediation analysis suggested that an Oscillospiraceae species may represent a potential intermediary linking valerate concentrations with Aβ status. SCFA concentrations were not strongly influenced by demographic or genetic factors, but specific species demonstrated robust associations with acetate levels. Distinct SCFA–microbial interaction patterns in Aβ High individuals suggest subtle early gut microbial alterations linked to amyloid burden. These findings highlight the potential role of SCFA-related microbial pathways in preclinical AD.

Background: CSF and blood soluble TREM2 (sTREM2) levels have been found to increase at early stage of Alzheimer's disease (AD). The relationships between sTREM2, AD-related biomarkers, and other neuroinflammation biomarkers remain unclear. Moreover, the impact of rare variants in TREM2 gene (R47H/R62H), which are associated with increased risk of AD, on plasma sTREM2 has not been elucidated.

Objective: Investigate the association of plasma sTREM2 levels with brain amyloid-beta (A beta) load and AD-related blood biomarkers, i.e., phosphorylated tau (pTau)-181, pTau-231, GFAP, NFL, and other neuroinflammation and peripheral inflammation markers in cognitively normal (CN) older adults at risk of AD (CN A beta+) compared to CN A beta-, including the effect of AD-linked TREM2 rare variants.

Methods: Plasma sTREM2 concentrations were measured by MesoScale Discovery (MSD) assay from the KARVIAH cohort. Participants underwent cognitive tests and PET amyloid imaging. Genetic data and blood biomarkers were included for correlation analysis. Associations with plasma sTREM2 were investigated upon stratification by PET-A beta load SUVR ((CN A beta- (n = 65) and CN A beta+ (n = 35)) as the main analysis. A subgroup analysis based on the TREM2 R47H and R62H genotype was conducted as exploratory analysis.

Results: Plasma sTREM2 positively correlated with plasma pTau181, and pTau231 in CN A beta+ group. Plasma sTREM2 positively correlated with serum microglial kynurenine pathway metabolites. Plasma sTREM2 and brain A beta load were higher in R47H TREM2 carriers compared to non-carriers.

Conclusions: Our findings suggest plasma sTREM2 relates to downstream tau processes in amyloid-positive individuals, providing novel insights into the roles of peripheral TREM2 signaling that reflects microglial activity in early AD neuropathological development.

Background
Type 2 diabetes is associated with increased Alzheimer’s disease risk and brain beta amyloid (Aβ) burden, suggesting an underlying mechanistic relationship between Alzheimer’s disease and type 2 diabetes. Animal studies show exercise reduces levels of brain Aβ and tau, and while human studies are somewhat limited, some studies have reported physical activity is associated with lower brain Aβ and tau levels. Exercise has well established links to reductions in insulin resistance; thus, as physical activity can impact both insulin resistance and Alzheimer’s disease pathology and/or biomarkers, it is reasonable to hypothesise that a mediating relationship may exist. The objective of this review was to identify what evidence exists that examines the association between insulin, physical activity, Aβ and tau in research conducted on animal models and in human cohorts. We specifically aimed to identify whether insulin resistance has a mediating role in the relationship between physical activity and Aβ and tau.

Methods
A systematic search was performed in Cochrane library, PsycINFO, PubMed and World of Science to identify publications. The search identified 343 articles with 20 articles meeting the full inclusion criteria.

Results
Most animal studies showed that exercise could simultaneously reduce insulin resistance and Alzheimer’s disease pathology and/or biomarkers. We found limited evidence from human research that physical activity was associated with both reduced insulin resistance and Aβ or tau levels. We did not find any evidence that insulin resistance mediates the physical activity – Aβ or tau relationship.

Conclusion
Exercise can simultaneously impact insulin resistance and Alzheimer’s disease pathology in animal models. Results from human research are limited, and no robust evaluation of the potential mediating role of insulin resistance in the physical activity – Aβ or tau relationship exists. Future research should focus on identifying the mediating pathways that may link physical activity to biomarkers of Alzheimer’s disease.

Background
This study investigates the crowding-out effect of out-of-pocket (OOP) health expenditures for non-communicable diseases (NCDs) on household consumption in Nigeria, a critical issue in sub-Saharan Africa (SSA) given the escalating prevalence of NCDs and associated high care costs.

Methods
Using data from the 2018/2019 Nigerian Living Standard Survey, we employ a Generalised Method of Moments estimator and conditional Engel curve equations derived from the Quadratic Almost Ideal Demand System to analyse the impact of NCD and non-NCD OOP expenditures on 13 household consumption categories.

Results
Despite representing <2% of household budgets, NCD OOP expenditures significantly crowded out essential spending, particularly on healthier food options (fruits, vegetables, protein) and cooking energy sources, disproportionately affecting lower-income households. Intriguingly, discretionary spending on sugar, alcoholic/sugary beverages, entertainment (including tobacco) and meals outside the home remained unaffected, possibly indicating reliance on these fast meal alternatives due to limited cooking fuel access. Middle-income households also experienced crowding out of staples and education expenditures.

Conclusions
These findings highlight the urgent need for universal health coverage to reduce OOP burdens, alongside targeted interventions addressing NCD burden, dietary quality, the energy crisis and health inequities in Nigeria and, by extension, SSA.

Residents in long-term care settings do not receive adequate support for hearing and vision devices. This study aimed to identify factors influencing support for residents' daily use of hearing and vision devices. Semi-structured interviews with 23 staff were analyzed deductively to categorize data within the Capability, Opportunity, and Motivation model of Behavior (COM-B), and then inductively to identify themes and subthemes within each domain guided by the Theoretical Domains Framework. Capability factors included a lack of skills in managing and maintaining sensory devices, interpersonal communication skills, and forgetting about device use. Opportunity factors included device management not being included on care task lists and inaccessible sensory devices. Staff reported a strong sense of duty and positive feelings about providing device support, reflecting high motivation.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder and the most common cause of dementia. AD diagnosis often involves a thorough assessment, including clinical evaluation, cognitive testing, medical history examination, genetic testing, and biomarker analysis. Currently, the invasive nature and high costs of biomarker testing, such as cerebrospinal fluid analysis and neuroimaging, have limited the early detection and intervention of the disease. Therefore, researchers have explored cheaper and less invasive options, such as ocular imaging. This review discusses the close relationship between the eye and the central nervous system and ocular changes as a potential non-invasive AD biomarker. Ocular changes in AD have frequently been reported in the literature, particularly in the later stages of the disease. However, identifying biomarkers specifically attributable to AD remains a challenge. Additionally, this review provides a comprehensive overview of existing studies and highlights potential pathways for enhancing AD detection through ocular structural and functional evaluation.

Highlights
Widespread screening for early detection of Alzheimer's disease (AD) is limited by cost and time constraints with current methods.
Non-invasive and inexpensive methods are needed to overcome these difficulties.
The eye is readily accessible and has shared developmental origins, neurobiology, and neurochemistry with the brain.
The expanding field of ocular biomarker studies needs larger, well-characterized cohorts and longitudinal studies to understand the ocular changes for preclinical AD screening.
Standards should be established for more methodical investigative approaches to identify ocular biomarkers that are specifically attributable to AD.