Transcriptomic Analysis of Alzheimer's Disease Pathways in a Pakistani Population.

Background: Alzheimer's disease (AD) is a multifactorial neurodegenerative disorder that is most prevalent in elderly individuals, especially in developed countries, and its prevalence is now increasing in developing countries like Pakistan.

Objective: Our goal was to characterize key genes and their levels of expression and related molecular transcriptome networks associated with AD pathogenesis in a pilot case-control study in a Pakistani population.

Methods: To obtain the spectrum of molecular networks associated with pathogenesis in AD patients in Pakistan (comparing cases and controls), we used high-throughput qRT-PCR (TaqMan Low-Density Array;  = 33 subjects) coupled with Affymetrix Arrays ( = 8) and Ingenuity Pathway Analysis (IPA) to identify signature genes associated with Amyloid processing and disease pathways.

Transcriptomics of MASLD Pathobiology in African American Patients in the Washington DC Area .

Metabolic-dysfunction-associated steatotic liver disease (MASLD) is becoming the most common chronic liver disease worldwide and is of concern among African Americans (AA) in the United States. This pilot study evaluated the differential gene expressions and identified the signature genes in the disease pathways of AA individuals with MASLD. Blood samples were obtained from MASLD patients ( = 23) and non-MASLD controls ( = 24) along with their sociodemographic and medical details.

Insights on the pathogenesis of type 2 diabetes as revealed by signature genomic classifiers in an African American population in the Washington, DC area.

Aims: African Americans (AA) in the United States have a high risk of type 2 diabetes mellitus (T2DM) and suffer from disparities in the prevalence, mortality, and comorbidities of the disease compared to other Americans. The present study aimed to shed light on the molecular mechanisms of disease pathogenesis of T2DM among AA in the Washington, DC region.

Methods: We performed TaqMan Low Density Arrays (TLDA) on 24 genes of interest that belong to three categories: metabolic disease and disorders, cancer-related genes, and neurobehavioural disorders genes.