Background: DNA methylation at CpG sites is a vital epigenetic modification of the human genome affecting gene expression, and potentially, health outcomes. However, evidence is just budding on the effects of aerobic exercise-induced adaptation on DNA methylation in older mild cognitively impaired (MCI) elderly African American (AAs). Therefore, we examined the effects of a 6-month aerobic exercise-intervention on genome-wide DNA methylation in elderly AA MCI volunteers.

Design: Elderly AA volunteers confirmed MCI assigned into a 6-month program of aerobic exercise (eleven participants) underwent a 40-min supervised-training 3-times/week and controls (eight participants) performed stretch training. Participants had maximal oxygen consumption (VO₂max) test and Genome-wide methylation levels at CpG sites using the Infinium HumanMethylation450 BeadChip assay at baseline and after a 6-month exercise program. We computed false discovery rates (FDR) using Sidak to account for multiplicity of tests and performed quantitative real-time polymerase chain-reaction (qRT-PCR) to confirm the effects of DNA methylations on expression levels of the top 5 genes among the aerobic participants. CpG sites identified from aerobic-exercise participants were similarly analyzed by the stretch group to quantify the effects of exercise-induced methylation changes among the group of stretch participants.

Results: Eleven MCI participants (aerobic: 73% females; mean age 72.3 ± 6.6 years) and eight MCI participants (stretch: 75% female; mean age 70.6 ± 6.7 years) completed the training. Aerobic exercise-training was associated with increases in VO₂max and with global hypo- and hypermethylation changes. The most notable finding was CpG hypomethylation within the body of the VPS52 gene (P = 5.4 × 10^–26), a Golgi-associated protein, involved in intracellular protein trafficking including amyloid precursor protein. qRT-PCR confirmed a nearly twofold increased expression of VPS52. Other top findings with FDR q-value < 10^–5, include hypomethylations of SCARB1 (8.8 × 10^–25), ARTN (6.1 × 10^–25), NR1H2 (2.1 × 10^–18) and PPP2R5D (9.8 × 10^–18).

Conclusion: We conclude that genome-wide DNA methylation patterns is associated with exercise training-induced methylation changes. Identification of methylation changes around genes previously shown to interact with amyloid biology, intracellular protein trafficking, and lipoprotein regulations provide further support to the likely protective effect of exercise in MCI. Future studies in larger samples are needed to confirm our findings.

Background: Alzheimer's disease (AD) is the most common type of dementia and has no effective treatment to date. It is essential to develop a minimally invasive blood-based biomarker as a tool for screening the general population, but the efficacy remains controversial. This cross-sectional study aimed to evaluate the ability of plasma biomarkers, including amyloid β (Aβ), total tau (t-tau), and neurofilament light chain (NfL), to detect probable AD in the South Chinese population.

Methods: A total of 277 patients with a clinical diagnosis of probable AD and 153 healthy controls with normal cognitive function (CN) were enrolled in this study. The levels of plasma Aβ42, Aβ40, t-tau, and NfL were detected using ultra-sensitive immune-based assays (SIMOA). Lumbar puncture was conducted in 89 patients with AD to detect Aβ42, Aβ40, t-tau, and phosphorylated (p)-tau levels in the cerebrospinal fluid (CSF) and to evaluate the consistency between plasma and CSF biomarkers through correlation analysis. Finally, the diagnostic value of plasma biomarkers was further assessed by constructing a receiver operating characteristic (ROC) curve.

Results: After adjusting for age, sex, and the apolipoprotein E (APOE) alleles, compared to the CN group, the plasma t-tau, and NfL were significantly increased in the AD group (p < 0.01, Bonferroni correction). Correlation analysis showed that only the plasma t-tau level was positively correlated with the CSF t-tau levels (r = 0.319, p = 0.003). The diagnostic model combining plasma t-tau and NfL levels, and age, sex, and APOE alleles, showed the best performance for the identification of probable AD [area under the curve (AUC) = 0.89, sensitivity = 82.31%, specificity = 83.66%].

Conclusion: Blood biomarkers can effectively distinguish patients with probable AD from controls and may be a non-invasive and efficient method for AD pre-screening.