Blood Pressure Level Is Associated With Changes in Plasma Aβ1 -40 and Aβ1-42 Levels: A Cross-sectional Study Conducted in the Suburbs of Xi'an, China

Influence of Physiological Variables and Comorbidities on Plasma Aβ40, Aβ42, and p-tau181 Levels in Cognitively Unimpaired Individuals

Plasma biomarkers for Alzheimer's disease (AD) are a promising tool that may help in early diagnosis. However, their levels may be influenced by physiological parameters and comorbidities that should be considered before they can be used at the population level. For this purpose, we assessed the influences of different comorbidities on AD plasma markers in 208 cognitively unimpaired subjects. We analyzed both plasma and cerebrospinal fluid levels of Aβ40, Aβ42, and p-tau181 using the fully automated Lumipulse platform. The relationships between the different plasma markers and physiological variables were studied using linear regression models. The mean differences in plasma markers according to comorbidity groups were also studied. The glomerular filtration rate showed an influence on plasma Aβ40 and Aβ42 levels but not on the Aβ42/Aβ40 ratio. The amyloid ratio was significantly lower in diabetic and hypertensive subjects, and the mean p-tau181 levels were higher in hypertensive subjects. The glomerular filtration rate may have an inverse relationship on plasma Aβ40 and Aβ42 levels but not on the amyloid ratio, suggesting that the latter is a more stable marker to use in the general population. Cardiovascular risk factors might have a long-term effect on the amyloid ratio and plasma levels of p-tau181.

Vascular Risk Predicts Plasma Amyloid β 42/40 Through Cerebral Amyloid Burden in Apolipoprotein E ε4 Carriers

BACKGROUND

Understanding the neurobiological underpinnings between established multimodal dementia risk factors and noninvasive blood-based biomarkers may lead to greater precision and earlier identification of older adults at risk of accelerated decline and dementia. We examined whether key vascular and genetic risk impact the association between cerebral amyloid burden and plasma aβ (amyloid β) 42/40 in nondemented older adults.

METHODS

We used nondemented older adults from the UCD-ADRC (University of California, Davis-Alzheimer's Disease Research Center) study (n=96) and Alzheimer's Disease Neuroimaging Initiative (n=104). Alzheimer's Disease Neuroimaging Initiative was examined as confirmatory study cohort. We followed a cross-sectional design and examined linear regression followed by mediation analyses. Vascular risk score was obtained as the sum of hypertension, diabetes, hyperlipidemia, coronary artery disease, and cerebrovascular disease. Apolipoprotein E (APOE) ε4+ risk was genotyped, and plasma aβ42 and aβ40 were assayed. Cerebral amyloid burden was quantified using Florbetapir-PET scans. Baseline age was included as a covariate in all models.

RESULTS

Vascular risk significantly predicted cerebral amyloid burden in Alzheimer's Disease Neuroimaging Initiative but not in the UCD-ADRC cohort. Cerebral amyloid burden was associated with plasma aβ 42/40 in both cohorts. Higher vascular risk increased cerebral amyloid burden was indirectly associated with reduced plasma aβ 42/40 in Alzheimer's Disease Neuroimaging Initiative but not in UCD-ADRC cohort. However, when stratified by APOE ε4+ risk, we consistently observed this indirect relationship only in APOE ε4+ carriers across both cohorts.

CONCLUSIONS

Vascular risk is indirectly associated with the level of plasma aβ 42/40 via cerebral amyloid burden only in APOE ε4+ carriers. Nondemented older adults with genetic vulnerability to dementia and accelerated decline may benefit from careful monitoring of vascular risk factors directly associated with cerebral amyloid burden and indirectly with plasma aβ 42/40.

Medical comorbidities and ethnicity impact plasma Alzheimer's disease biomarkers: Important considerations for clinical trials and practice

INTRODUCTION

Despite the clinical implementation, there remain significant gaps in our knowledge regarding the impact of race/ethnicity or common medical comorbidity on plasma Alzheimer's disease (AD) biomarkers.

METHODS

Plasma biomarkers of amyloid beta (Aβ)_40, Aβ₄₂ , total tau, and neurofilament light chain (NfL) were measured across cognitively normal Mexican Americans (n = 445) and non-Hispanic Whites (n = 520).

RESULTS

Dyslipidemia was associated with elevated Aβ₄₀ (P = .01) and Aβ₄₂ (P = .001) while hypertension was associated with elevated Aβ₄₀ (P = .003), Aβ₄₂ (P < .001), and total tau (P = .002) levels. Diabetes was associated with higher Aβ₄₀ (P < .001), Aβ₄₂ (P < .001), total tau (P < .001), and NfL (P < .001) levels. Chronic kidney disease (CKD) was associated with elevations in Aβ₄₀ (P < .001), Aβ₄₂ (P < .001), total tau (P < .001), and NfL (P < .001) levels. Mexican Americans had significantly lower Aβ₄₀ (P < .001) and higher total tau (P = .005) levels.

DISCUSSION

Plasma AD biomarkers vary significantly in association with common medical comorbidities as well as ethnicity. These findings are important for those using these biomarkers in clinical practice and clinical trials.

Associations of Midlife Lifestyle and Health Factors with Long-Term Changes in Blood-Based Biomarkers of Alzheimer's Disease and Neurodegeneration

BACKGROUND

Pathological biomarkers of Alzheimer's disease (AD) and other dementias can change decades before clinical symptoms. Lifestyle and health factors might be relevant modifiable risk factors for dementia. Many previous studies have been focusing on associations of lifestyle and health-related factors with clinical outcomes later in life.

OBJECTIVE

We aimed to determine to what extent midlife factors of lifestyle, inflammation, vascular, and metabolic health were associated with long-term changes in blood-based biomarkers of AD (amyloid beta (Aβ)) and neurodegeneration (neurofilament light chain (NfL); total tau(TTau)).

METHODS

In 1,529 Beaver Dam Offspring Study (BOSS) participants (mean age 49 years, standard deviation (SD) = 9; 54% were women), we applied mixed-effects models with baseline risk factors as determinants and 10-year serum biomarker change as outcomes.

RESULTS

We found that education and inflammatory markers were associated with levels and/or change over time across all three markers of AD and neurodegeneration in the blood. There were baseline associations of measures of cardiovascular health with lower Aβ42/Aβ40. TTau changed little over time and was higher in individuals with diabetes. Individuals with lower risk in a number of cardiovascular and metabolic risk factors, including diabetes, hypertension, and atherosclerosis had slower accumulation of neurodegeneration over time, as determined by NfL levels.

CONCLUSION

Various lifestyle and health factors, including education and inflammation, were associated with longitudinal changes of neurodegenerative and AD biomarker levels in midlife. If confirmed, these findings could have important implications for developing early lifestyle and health interventions that could potentially slow processes of neurodegeneration and AD.

The Relationships Between Lipid Accumulation Product Levels and Cognitive Decline Over 4 Years in a Rural Area of Xi'an, China

Background and Aims: The relationships between blood lipid levels and obesity and cognitive impairment have not been fully determined. Considering that the lipid accumulation product (LAP) is a composite index of blood lipid levels and obesity, we investigated the relationships between LAP levels at baseline and cognitive decline over 4 years.

Methods: A total of 983 subjects (≥40 years) from a longitudinal cohort in a village of Xi’an, China, who completed the baseline survey were followed-up for 4 years. All participants underwent face-to-face interviews and cognitive assessments at baseline and at the 4-year follow-up. The Mini-Mental State Examination (MMSE) was used to assess cognitive function, and an MMSE score dropping ≥ 2 points from baseline was defined as cognitive decline. The relationships between LAP and cognitive decline were analyzed by linear regression models.

Results: During the 4-year follow-up, 172 patients exhibited cognitive decline (17.5%). Univariate analysis showed that the rate of change in MMSE score was significantly different between the low-LAP group and the high-LAP group (t = −2.26, p = 0.024). Multiple linear regression indicated that a high LAP was positively associated with cognitive decline (β = 0.564, p = 0.012). Stratified multivariate analysis showed that LAP was positively associated with cognitive decline in the normal blood pressure female subgroup (β = 1.29, p = 0.002) but not in the high blood pressure group or the male group.

Conclusions: High LAP is associated with cognitive decline in females with normal blood pressure but not in those with high blood pressure or males. This indicates that the relationships between blood lipid levels and obesity and cognitive impairment may be affected by blood pressure and sex.