Basic Science and Pathogenesis.

Background: Alzheimer's Disease (AD) is characterized by cognitive decline due to synaptic loss and neuron death, with amyloid-β plaques and neurofibrillary tau tangles as key pathological hallmarks. Although genetics account for about 70% of AD risk, modifiable factors also significantly contribute to AD and dementia onset and AD-resilience. Utilizing the All of Us (AoU) cohort, this study explores the relationship between clinical conditions, quantitative phenotypes (including lab tests and anthropometric measurements), and AD risk, shedding light on potential preventative measures.

Risk Factors for Alzheimer's Disease and Related Dementia Diagnoses in American Indians.

The burden of Alzheimer's disease and related dementias (ADRD) has increased substantially in the United States, particularly in health disparity populations. Little is known about the epidemiology of ADRD in American Indian (AI) adults, although they have a high prevalence of ADRD risk factors including hypertension, diabetes, obesity, and smoking. Using electronic health records from a large health care organization during 2016-18, we describe characteristics of AI patients aged ≥55 years with and without an ADRD diagnosis, assess ADRD risk factors and contrast findings with results from age- and sex-matched non-Hispanic White (NHW) patients.

Adoption of a clinical pharmacogenomics implementation program during outpatient care--initial results of the University of Chicago "1,200 Patients Project".

Pharmacogenomic testing is viewed as an integral part of precision medicine. To achieve this, we originated The 1,200 Patients Project which offers broad, preemptive pharmacogenomic testing to patients at our institution. We analyzed enrollment, genotype, and encounter-level data from the first year of implementation to assess utility of providing pharmacogenomic results.

COPD Hospitalization Risk Increased with Distinct Patterns of Multiple Systems Comorbidities Unveiled by Network Modeling.

Earlier studies on hospitalization risk are largely based on regression models. To our knowledge, network modeling of multiple comorbidities is novel and inherently enables multidimensional scoring and unbiased feature reduction. Network modeling was conducted using an independent validation design starting from 38,695 patients, 1,446,581 visits, and 430 distinct clinical facilities/hospitals.