Faculty Bibliography

Background Vascular function is compromised in Alzheimer disease (AD) years before amyloid and tau pathology are detected and a substantial body of work shows abnormal platelet activation states in patients with AD. The aim of our study was to investigate whether platelet function in middle age is independently associated with future risk of AD. Methods and Results We examined associations of baseline platelet function with incident dementia risk in the community-based FHS (Framingham Heart Study) longitudinal cohorts. The association between platelet function and risk of dementia was evaluated using the cumulative incidence function and inverse probability weighted Cox proportional cause-specific hazards regression models, with adjustment for demographic and clinical covariates. Platelet aggregation response was measured by light transmission aggregometry. The final study sample included 1847 FHS participants (average age, 53.0 years; 57.5% women). During follow-up (median, 20.5 years), we observed 154 cases of incident dementia, of which 121 were AD cases. Results from weighted models indicated that platelet aggregation response to adenosine diphosphate 1.0 µmol/L was independently and positively associated with dementia risk, and it was preceded in importance only by age and hypertension. Sensitivity analyses showed associations with the same directionality for participants defined as adenosine diphosphate hyper-responders, as well as the platelet response to 0.1 µmol/L epinephrine. Conclusions Our study shows individuals free of antiplatelet therapy with a higher platelet response are at higher risk of dementia in late life during a 20-year follow-up, reinforcing the role of platelet function in AD risk. This suggests that platelet phenotypes may be associated with the rate of dementia and potentially have prognostic value.

BACKGROUND:Evaluating the risk of Alzheimer's disease (AD) in cognitively normal (CN) and patients with mild cognitive impairment (MCI) is extremely important. While MCI-to-AD progression risk has been studied extensively, few studies estimate CN-to-MCI conversion risk. The Cox proportional hazards (PH), a widely used survival analysis model, assumes a linear predictor-risk relationship. Generalizing the PH model to more complex predictor-risk relationships may increase risk estimation accuracy. OBJECTIVE:The aim of this study was to develop a PH model using an Xgboost regressor, based on demographic, genetic, neuropsychiatric, and neuroimaging predictors to estimate risk of AD in patients with MCI, and the risk of MCI in CN subjects. METHODS:We replaced the Cox PH linear model with an Xgboost regressor to capture complex interactions between predictors, and non-linear predictor-risk associations. We endeavored to limit model inputs to noninvasive and more widely available predictors in order to facilitate future applicability in a wider setting. RESULTS:In MCI-to-AD (n = 882), the Xgboost model achieved a concordance index (C-index) of 84.5%. When the model was used for MCI risk prediction in CN (n = 100) individuals, the C-index was 73.3%. In both applications, the C-index was statistically significantly higher in the Xgboost in comparison to the Cox PH model. CONCLUSION/CONCLUSIONS:Using non-linear regressors such as Xgboost improves AD dementia risk assessment in CN and MCI. It is possible to achieve reasonable risk stratification using predictors that are relatively low-cost in terms of time, invasiveness, and availability. Future strategies for improving AD dementia risk estimation are discussed.

INTRODUCTION/BACKGROUND:Tumor necrosis factor (TNF) inhibitors are widely used to treat rheumatoid arthritis (RA) and their potential to retard Alzheimer's disease (AD) progression has been reported. However, their long-term effects on the dementia/AD risk remain unknown. METHODS:A propensity scored matched retrospective cohort study was conducted among 40,207 patients with RA within the US Veterans Affairs health-care system from 2000 to 2020. RESULTS:A total of 2510 patients with RA prescribed TNF inhibitors were 1:2 matched to control patients. TNF inhibitor use was associated with reduced dementia risk (hazard ratio [HR]: 0.64, 95% confidence interval [CI]: 0.52-0.80), which was consistent as the study period increased from 5 to 20 years after RA diagnosis. TNF inhibitor use also showed a long-term effect in reducing the risk of AD (HR: 0.57, 95% CI: 0.39-0.83) during the 20 years of follow-up. CONCLUSION/CONCLUSIONS:TNF inhibitor use is associated with lower long-term risk of dementia/AD among US veterans with RA.

BACKGROUND:Inflammatory mechanisms are believed to contribute to the manifestation of major depressive disorder (MDD). Central cholinergic activity may moderate this effect. Here, we tested if volume of the cholinergic basal forebrain is associated with cerebrospinal fluid (CSF) levels of sTREM2 as a marker of microglial activation in people with late life MDD. METHODS:Basal forebrain volume was determined from structural MRI scans and levels of CSF sTREM2 with immunoassay in 29 people with late-life MDD and 20 healthy older controls at baseline and 3 years follow-up. Associations were determined using Bayesian analysis of covariance. RESULTS:and total tau. Evidence was in favor of absence of an effect for baseline levels of CSF sTREM2 in MDD cases and for baseline and follow up data in controls. LIMITATIONS/CONCLUSIONS:The sample size of repeated CSF examinations was relatively small. Therefore, we used Bayesian sequential analysis to assess if effects were affected by sample size. Still, the number of cases was too small to stratify effects for different antidepressive treatments. CONCLUSIONS:Our data agree with the assumption that central cholinergic system integrity may contribute to regulation of microglia activity in late-life MDD.

BACKGROUND:Decreased cholinergic tone associated with increased proinflammatory cytokines has been observed in several human diseases associated with low-grade inflammation. We examined if this attenuated cholinergic anti-inflammatory pathway (CAP) mechanism contributed to increased neuroinflammation observed in depression. METHODS:We measured cerebrospinal fluid (CSF) cholinergic markers (AChE and BChE activities) in 28 individuals with longstanding late-life major depression (LLMD) and 19 controls and their relationship to central and peripheral levels of pro-inflammatory cytokines (IL-6 and IL-8). Additionally, we examined if these cholinergic indices were related to CSF markers of microglial activation and neuroinflammation (sTREM2 and complement C3). RESULTS:Compared with controls, LLMD patients had a significant reduction in CSF BChE levels. Lower CSF BChE and AChE activities were associated with lower CSF markers of microglial and neuroinflammation (sTREM2 and C3). In addition, in LLMD patients we found an inverse relationship between peripheral marker of inflammation (plasma IL-6) and CSF BChE and AChE levels. CONCLUSIONS:Our results suggest an upregulation of the CAP mechanism in LLMD with an elevation in peripheral markers of inflammation and concomitant reduction in markers of glial activation associated with a higher cholinergic tone. Future studies should confirm these findings in a larger sample including individuals with acute and more severe depressive episodes and across all ages.

High-resolution whole slide image scans of histopathology slides have been widely used in recent years for prediction in cancer. However, in some cases, clinical informatics practitioners may only have access to low-resolution snapshots of histopathology slides, not high-resolution scans. We evaluated strategies for training neural network prognostic models in non-small cell lung cancer (NSCLC) based on low-resolution snapshots, using data from the Veterans Affairs Precision Oncology Data Repository. We compared strategies without transfer learning, with transfer learning from general domain images, and with transfer learning from publicly available high-resolution histopathology scans. We found transfer learning from high-resolution scans achieved significantly better performance than other strategies. Our contribution provides a foundation for future development of prognostic models in NSCLC that incorporate data from low-resolution pathology slide snapshots alongside known clinical predictors.

Depressed individuals are twice as likely to develop Alzheimer's disease (AD) as compared to controls. Brain amyloid-β (Aβ) deposition is believed to have a major role in AD pathogenesis but studies also suggest associations of Aβ dynamics and depression. The aim of this study was to test if plasma Aβ levels are longitudinally associated to late-life depression. We measured plasma levels of amyloid-β_1-40 (Aβ40) and amyloid-β_1-42 (Aβ42) peptides longitudinally for three consecutive years in 48 cognitively intact elderly subjects with late-life major depressive disorder (LLMD) and 45 age-matched cognitively healthy controls. We found that the Aβ42/Aβ40 plasma ratio was significantly and steadily lower in depressed subjects compared to controls (p < 0.001). At screening, Aβ42/Aβ40 plasma did not correlate with depression severity (as measured with Hamilton Depression Scale) or cognitive performance (as measured with Mini-Mental State Examination) but was associated to depression severity at 3 years after adjustment for age, education, cognitive performance, and antidepressants use. This study showed that reduced plasma Aβ42/Aβ40 ratio is consistently associated with LLMD diagnosis and that increased severity of depression at baseline predicted low Aβ42/Aβ40 ratio at 3 years. Future studies are needed to confirm these findings and examine if the consistently lower plasma Aβ42/Aβ40 ratio in LLMD reflects increased brain amyloid deposition, as observed in AD subjects, and an increased risk for progressive cognitive decline and AD.