Metabolically healthy obesity and lipids may be protective factors for pathological changes of alzheimer's disease in cognitively normal adults.

The associations between obesity and Alzheimer's disease (AD) at different ages have been debated. Recent evidence implied the protective effects of metabolically healthy obesity on AD. We hypothesized that obesity and lipids could mitigate the detrimental impacts of AD pathological changes among metabolically healthy individuals in late life. In this study, a total of 604 metabolically healthy participants with normal cognition were included from the Chinese Alzheimer's Biomarker and LifestylE (CABLE) database. Multiple linear regression models were used to test the associations of body mass index (BMI) or lipids with cerebrospinal fluid (CSF) biomarkers after adjustment for age, gender, education, and Apolipoprotein E-ɛ4 (APOE-ɛ4). The results showed the lower CSF levels of total tau protein (t-tau: p = .0048) and phosphorylated tau protein (p-tau: p = .0035) in obese participants than in non-obese participants, even after correcting for covariates. Moreover in late life, higher BMI was associated with decreased CSF t-tau (ß: -0.15, p = .0145) and p-tau (ß: -0.17, p = .0052). As for lipids, higher levels of total cholesterol (TC) and low-density lipoprotein cholesterol (LDL-C) were associated with decreased CSF t-tau (TC: ß: -0.16, p = .0115; LDL-C: ß: -0.16, p = .0082) and p-tau (TC: ß: -0.15, p = .0177; LDL-C: ß: -0.14, p = .0225) in obese participants. Furthermore, these associations were only significant in participants with late-life obesity and APOE-ɛ4 non-carriers. Overall, in a cognitively normal population, we found metabolically healthy obesity and lipids in late life might be protective factors for neurodegenerative changes.

Predicting the reversion from mild cognitive impairment to normal cognition based on magnetic resonance imaging, clinical, and neuropsychological examinations.

BACKGROUND: Reversion from mild cognitive impairment (MCI) to normal cognition (NC) is not uncommon and indicates a better cognitive trajectory. This study aims to identify predictors of MCI reversion and develop a predicting model. METHOD: A total of 391 MCI subjects (mean age = 74.3 years, female = 61 %) who had baseline data of magnetic resonance imaging, clinical, and neuropsychological measurements were followed for two years. Multivariate logistic analyses were used to identify the predictors of MCI reversion after adjusting for age and sex. A stepwise backward logistic regression model was used to construct a predictive nomogram for MCI reversion. The nomogram was validated by internal bootstrapping and in an independent cohort. RESULT: In the training cohort, the 2-year reversion rate was 19.95 %. Predictors associated with reversion to NC were higher education level (p = 0.004), absence of APOE4 allele (p = 0.001), larger brain volume (p < 0.005), better neuropsychological measurements performance (p < 0.001), higher glomerular filtration rate (p = 0.035), and lower mean arterial pressure (p = 0.060). The nomogram incorporating five predictors (education, hippocampus volume, the Alzheimer's Disease Assessment Scale-Cognitive score, the Rey Auditory Verbal Learning Test-immediate score, and mean arterial pressure) achieved good C-indexes of 0.892 (95 % confidence interval [CI], 0.859-0.926) and 0.806 (95 % CI, 0.709-0.902) for the training and validation cohort. LIMITATION: Observational duration is relatively short; The predicting model warrant further validation in larger samples. CONCLUSION: This prediction model could facilitate risk stratification and early management for the MCI population.