Ultrasensitive assays have been developed which enable biomarkers of Alzheimer’s disease pathology and neurodegeneration to be measured in blood. These biomarkers can aid in diagnosis, and have been used to predict risk of cognitive decline and Alzheimer’s disease. The ease and cost-effectiveness of blood collections means that these biomarkers could be applied more broadly in population-based screening, however it is critical to first understand what other factors could affect blood biomarker levels. The aim of this review was to determine the extent that sociodemographic, lifestyle and health factors have been associated with blood biomarkers of Alzheimer’s disease and neuropathology. Of the 32 studies included in this review, all but one measured biomarker levels in plasma, and age and sex were the most commonly investigated factors. The most consistent significant findings were a positive association between age and neurofilament light chain (NfL) and glial fibrillary acidic protein (GFAP), and females had higher GFAP than men. Apolipoprotein ε4 allele carriers had lower Aβ42 and Aβ42/40 ratio. Body mass index was negatively associated with GFAP and NfL, and chronic kidney disease with higher levels of all biomarkers. Too few studies have investigated other chronic health conditions and this requires further investigation. Given the potential for plasma biomarkers to enhance Alzheimer’s disease diagnosis in primary care, it is important to understand how to interpret the biomarkers in light of factors that physiologically impact blood biomarker levels. This information will be critical for the establishment of reference ranges and thus the correct interpretation of these biomarkers in clinical screening.
Up to 40% of dementia cases are theoretically avoidable and population-level interventions (i.e., universal prevention) are a key component in facing the global public health challenge of dementia. However, information on the agenda for the universal prevention of dementia at the national and sub-national levels is still lacking.
We aim to provide a comprehensive description of the universal prevention strategies specific to dementia in Italian regions and autonomous provinces (APs).
We conducted a document analysis of the 21 Italian Regional Prevention Plans (RPPs), with a focus on interventions that target potentially modifiable risk factors for dementia. We analysed the final version of the documents, which were previously downloaded from the dedicated section of the Italian Ministry of Health website in January 2023. We classified the interventions as direct, indirect, or absent. Additionally, we created a quality checklist to outline the essential programmatic elements and applied it to summarise the key findings of the RPPs.
We reported the number of populationlevel interventions specific for dementia with sub-national detail. We reported information on the risk factor targeted by the interventions, the age groups and populations they were designed for. We summarized the presence or absence of 63 programmatic items using a four-domain checklist.
We identified 248 interventions for dementia prevention among the assessed RPPs: 100% of the plans addressed physical inactivity; 30–35% addressed smoking, alcohol, obesity, and social isolation; 25% addressed hypertension, diabetes, and air pollution; only 5–10% addressed education, depression, and hearing loss. Most interventions targeted the general population. Quality checklist scores significantly varied among regions, with demographics and prevention strategies domains scoring higher than disease burden and intervention feasibility ones.
The population-level interventions in the Italian Regional Prevention Programs dedicated to dementia prevention primarily focus on vascular risk factors, with limited coverage of dementia-specific factors such as traumatic brain injury and hearing loss. This data should be considered when planning future interventions for dementia prevention.
This study aimed to investigate the relationship between childhood adversity and cognitive impairment in older adults.
We analysed data from 1568 participants aged 72–79 (M = 75.1, SD = 1.5, % male = 52.6%) from Wave 4 of the Personality and Total Health (PATH) Through Life Project. The outcome variable was the presence of mild cognitive impairment (MCI) or dementia, determined through a clinically validated algorithmic diagnostic criteria. Childhood adversity was assessed using a 17-item scale covering various domestic adversities such as poverty, neglect, physical abuse, and verbal abuse. Adversity was operationalised using cumulative analysis, dichotomisation (<3 adversities; 3+ adversities), and latent class analysis. Multiple logistic regressions were employed to estimate the association between childhood adversity and cognitive impairment, while controlling for covariates including education, gender, ethnicity, and APOE ε4 status.
Our analyses revealed no significant association between childhood adversity and the presence of MCI or dementia across all tested models. Sensitivity analyses, exploring alternative scenarios, consistently failed to yield statistically significant findings.
In contrast to prevailing research findings, this study does not support a link between childhood domestic adversity and late-life cognitive outcomes. These results underscore the mixed results on adversity and cognition, highlighting the need for further research. Future investigations should consider the roles of potential mediating and protective factors within this complex relationship.
The reported inverse association between cancer and subsequent Alzheimer’s disease and related dementias (ADRD) remains uncertain.
To investigate the association between these common conditions of old age and explore possible causal factors.
We conducted a large population-based cohort analysis using data from 3,021,508 individuals aged 60 and over in the UK Clinical Practice Research Datalink (CPRD), over a period up to 30 years (1988–2018). Cox proportional hazards models were fitted to estimate hazard ratios (HR) for risk of dementia associated with previous cancer diagnosis. Competing risk models were employed to account for competing risk of death. Two-sample Mendelian Randomization analysis based on meta-analysis data from large-scale GWAS studies was also conducted.
In the CPRD cohort, 412,903 participants had cancer diagnosis and 230,558 were subsequently diagnosed with dementia over a median follow-up period of 7.9 years. Cancer survivors had a 25% lower risk of developing dementia (HR=0.75, 95% CI:0.74–0.76) after adjustment for potential confounders. Accounting for competing risk of death provided a sub-distribution HR of 0.56 (95% CI:0.55–0.56). Results were consistent for prevalent and incident cancer and different common cancer types. Two-sample Mendelian Randomization analysis, using 357 cancer-related instrumental single-nucleotide polymorphisms (SNPs) revealed evidence of vertical pleiotropy between genetically predicted cancer and reduced risk of Alzheimer’s disease (OR=0.97,95% CI:0.95–0.99).
Our results provide strong epidemiological evidence of the inverse association between cancer and risk of ADRD and support the potential causal nature of this association via genetic instruments. Further investigations into the precise underlying biological mechanisms may reveal valuable information for new therapeutic approaches.
Abnormal cognitive aging is closely related to dementia.
This study aimed to estimate the effect of cardiovascular health (CVH) metrics on abnormal cognitive aging.
A longitudinal cohort study.
Participants were recruited from the Chinese Longitudinal Health Longevity Survey.
A total of 3298 participants aged ≥65 years with normal cognitive performance at baseline were included.
Cognitive performance was measured by the Chinese version of the Mini-Mental State Examination (MMSE). CVH was assessed with six metrics, including hypertension, diabetes, exercise, body mass index (BMI), diet, and smoking. Group-based trajectory model was used to identify the trajectory groups of cognitive aging over 12 years (2002–2014 and 2005–2018). The parametric g-formula was applied to estimate the effect of each single six CVH metrics and their combinations on the 12-year cognitive aging trajectory.
Four trajectory groups of cognitive aging were identified: Stable-high (77.4%), Unstable (4.9%), Slow decline (11.1%), and Rapid decline (6.6%). Unstable, Slow decline, and Rapid decline trajectory groups were considered as abnormal cognitive aging (22.6%). Single interventions on hypertension, exercise, BMI, and diet could reduce the risk of abnormal cognitive aging. Moreover, the risk ratios of joint intervention on exercise, BMI, and diet for Unstable, Slow decline, and Rapid decline trajectory groups were 0.38 (95% CI: 0.30–0.48), 0.45 (95% CI: 0.37–0.54), and 0.3 (95% CI: 0.23–0.41), respectively.
A considerable proportion of the participants experienced abnormal cognitive aging during their aging process. Interventions on these CVH metrics (i.e., exercise, BMI, and diet), which are fairly practical and feasible for older adults, may be effective strategies for preventing abnormal cognitive aging.
Aging is one of the most important risk factors for Alzheimer’s disease (AD). Biological aging is a better indicator of the body’s functional state than age (chronological aging). Leukocyte telomere length (LTL) and epigenetic clocks constructed from DNA methylation patterns have emerged as reliable markers of biological aging. Recent studies have shown that it may be possible to slow down or even reverse biological aging, offering promising prospects for treating AD. Several observational studies have reported an association between biological aging, AD, and cognitive function, but the causality behind this association and the effects of different biological aging markers on AD risk and cognitive function remain unclear. Therefore, we explored the causal relationship between them by Mendelian randomization (MR) study. Inverse-variance weighted (IVW) method is the most dominant analytical method in MR studies, which is a weighted average of estimates from different genotype combinations, and this weighted average provides an overall estimate of the causal effect. The results of the IVW analyses showed that HannumAge acceleration and LTL shortening were able to increase the risk of late-onset AD (LOAD), but not early-onset AD (EOAD). Excellent prospective memory and fluid intelligence are potentially protective against GrimAge acceleration. GrimAge acceleration and HorvathAge acceleration increase the risk of LOAD through effects on LTL. Our findings provide important insights into the role of biological aging in the pathogenesis of AD, while also highlighting the interplay of different biological aging markers and their complexity in different AD subtypes.
Alzheimer’s disease (AD) is a progressive neurodegenerative disorder characterized by amyloid-beta (Aβ) plaque accumulation and neurofibrillary tangles. The recent approval of anti-amyloid therapeutic medications highlights the crucial need for early detection of Aβ pathological abnormalities in individuals without dementia to facilitate timely intervention and treatment.
The primary aim of this study was to identify cerebrospinal fluid (CSF) biomarkers strongly associated with Aβ pathological positivity in a non-demented cohort and evaluate their clinical values.
A comprehensive analysis was conducted on 51 CSF proteins (excluding Aβ42, pTau, and Tau) obtained from 474 non-demented participants sourced from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. By utilizing the Least Absolute Shrinkage and Selection Operator (LASSO) regression, we identified potential proteins indicative of Aβ pathological positivity and evaluated their performance in tracking longitudinal pathological progression.
Our LASSO analysis unveiled three candidates: apolipoprotein E (APOE), chitinase-3-like protein 1 (CHI3L1), and SPARC-related modular calcium-binding protein 1 (SMOC1). While SMOC1 did not correlate with Aβ42-related cognitive alterations, it displayed better abilities in discriminating both CSF-Aβ positivity and Aβ-positron emission tomography (PET) positivity than the other two candidates. It could precisely predict longitudinal Aβ-PET status conversion. Notably, SMOC1 was the only protein showing associations with longitudinal Aβ-PET trajectory and enhancing the diagnostic accuracy of Aβ42. The assessment of combined Aβ42 and SMOC1 yielded valuable clinical insights.
Our findings elucidated SMOC1 as a potential biomarker for detecting Aβ abnormalities. Aβ42 combining SMOC1 offered critical implications in AD pathological diagnosis and management.
This study investigates the synergistic relationship between blood low-density lipoprotein cholesterol (LDL-C) and cerebral beta-amyloid (Aβ) in relation to tau deposition, a key factor in the pathology of Alzheimer’s disease (AD), in older adults across a diverse cognitive spectrum.
To examine whether higher levels of LDL-C in the blood moderate the association of cerebral Aβ with tau deposition in older adults, including those with normal cognition, mild cognitive impairment, and Alzheimer’s disease dementia.
Cross-sectional design. Setting: The study was conducted as a part of a prospective cohort study. All assessments were done at the Seoul National University Hospital, Seoul, South Korea. Participants: A total of 136 older adults (aged 60–85 years) with normal cognition, mild cognitive impairment or Alzheimer’s disease (AD) dementia were included.
Serum lipid measurements, [11C] Pittsburgh Compound B-positron emission tomography (PET), [18F] AV-1451 PET, and magnetic resonance imaging were performed on all participants.
There was a significant Aβ × LDL-C interaction effect on tau deposition indicating a synergistic moderation effect of LDL-C on the relationship between Aβ and tau deposition. Subsequent subgroup analysis showed that the positive association between Aβ and tau deposition was stronger in higher LDL-C group than in lower LDL-C group. In contrast, other lipids, such as total cholesterol, high-density lipoprotein cholesterol, and triglycerides, did not show a similar moderation effect on the relationship between Aβ deposition and tau deposition.
Our findings suggest that blood LDL-C synergistically enhances the influence of Aβ deposition on tau pathology, emphasizing the need for greater attention to the role of LDL-C in AD progression.
Numerous studies have shown that there are socioeconomic disparities in people’s health. Health behavior is considered to be an effective strategy to alleviate socioeconomic differences. However, the independent or joint relationship between socioeconomic status (SES) and lifestyle behaviors (LBs) on the cognition of Chinese elderly are not clear. Therefore, this study aimed to reveal the impact of SES and LBs on cognitive impairment in elder Chinese.
The data from the 2017–2018 wave of Chinese Longitudinal Healthy Longevity Survey was used. SES was created using latent class analysis based on annual percapita household income, education level, and occupation. Six LBs were considered in calculating LB scores. Restricted cubic splines were used to model the association of LB scores and cognitive impairment to investigate the dose-response relationship. LB scores were divided into three groups: unhealthy, intermediate, and healthy lifestyle. Multivariate Logistic regression models were applied to explore both the independent and joint effects of SES and LB scores on cognitive impairment.
Among 10,116 participants, 1,872 (18.51%) were recorded as having cognitive impariment. After adjusting for multivariable confounding factors, compared with participants of high SES, those of low SES had higher risks of cognitive impairment [Odds ratio (OR): 1.385; 95% confidence interval (CI): 1.137–1.689]. In contrast to those with unhealthy lifestyle, participants adhering to a healthy lifestyle were found to be associated with a reduced risk of cognitive impairment (OR: 0.198; 95%CI: 0.150–0.263). A non-linear relationship was observed between LB scores and cognitive impairment (Pnonlinearity =0.001), indicating a protective effect on cognitive impairment when having more than two LBs. Participants with high SES and engaged in healthy lifestyle had the lowest risk of cognitive impairment compared to those with low SES and unhealthy lifestyle (OR: 0.123; 95% CI 0.073–0.207).
Cognitive impairment has socioeconomic disparities among the elderly Chinese population. A healthy lifestyle may attenuate the impact of socioeconomic inequality on cognitive impairment, emphasizing the important role of LBs modification in reducing the disease burden of cognitive impairment, especially in the elderly population with low SES.
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