Predictive Algorithm for Hepatic Steatosis Detection Using Elastography Data in the Veterans Affairs Electronic Health Records.

BACKGROUND AND AIMS: Nonalcoholic fatty liver disease (NAFLD) has reached pandemic proportions. Early detection can identify at-risk patients who can be linked to hepatology care. The vibration-controlled transient elastography (VCTE) controlled attenuation parameter (CAP) is biopsy validated to diagnose hepatic steatosis (HS). We aimed to develop a novel clinical predictive algorithm for HS using the CAP score at a Veterans' Affairs hospital. METHODS: We identified 403 patients in the Greater Los Angeles VA Healthcare System with valid VCTEs during 1/2018-6/2020. Patients with alcohol-associated liver disease, genotype 3 hepatitis C, any malignancies, or liver transplantation were excluded. Linear regression was used to identify predictors of NAFLD. To identify a CAP threshold for HS detection, receiver operating characteristic analysis was applied using liver biopsy, MRI, and ultrasound as the gold standards. RESULTS: The cohort was racially/ethnically diverse (26% Black/African American; 20% Hispanic). Significant positive predictors of elevated CAP score included diabetes, cholesterol, triglycerides, BMI, and self-identifying as Hispanic. Our predictions of CAP scores using this model strongly correlated (r = 0.61, p < 0.001) with actual CAP scores. The NAFLD model was validated in an independent Veteran cohort and yielded a sensitivity of 82% and specificity 83% (p < 0.001, 95% CI 0.46-0.81%). The estimated optimal CAP for our population cut-off was 273.5 dB/m, resulting in AUC = 75.5% (95% CI 70.7-80.3%). CONCLUSION: Our HS predictive algorithm can identify at-risk Veterans for NAFLD to further risk stratify them by non-invasive tests and link them to sub-specialty care. Given the biased referral pattern for VCTEs, future work will need to address its applicability in non-specialty clinics. Proposed clinical algorithm to identify patients at-risk for NAFLD prior to fibrosis staging in Veteran.

Expression of the pituitary stem/progenitor marker GFRα2 in human pituitary adenomas and normal pituitary.

PURPOSE: Recent studies suggest that adult pituitary stem cells may play a role in pituitary tumorigenesis. We sought to explore whether the Glial cell-line derived neurotrophic factor receptor alpha 2 (GFRα2), a recently described pituitary stem/progenitor marker, might be differentially expressed in pituitary adenomas versus normal pituitary. METHODS: The expression of GFRα2 and other members of the GFR receptor family (GFRα1, α3, α4) were analyzed using RT-PCR, western blot, and immunohistochemistry in 39 pituitary adenomas, 14 normal pituitary glands obtained at autopsy, and cDNA from 3 normal pituitaries obtained commercially. RESULTS: GFRα2 mRNA was ~2.6 fold under-expressed in functioning adenomas (p < 0.01) and ~3.5 fold over-expressed in non-functioning adenomas (NFAs) (p < 0.05) compared to normal pituitary. Among NFAs, GFRα2 was significantly over-expressed (~5-fold) in the gonadotropinoma subtype only (p < 0.05). GFRα2 protein expression appeared to be higher in most NFAs, although there was heterogeneity in protein expression in this group. GFRα2 protein expression appeared consistently lower in functioning adenomas by IHC and western blot. In normal pituitary, GFRα2 was localized in Rathke's remnant, the putative pituitary stem cell niche, and in corticotropes. CONCLUSION: Our results suggest that the pituitary stem cell marker GFRα2 is under-expressed in functioning adenomas and over-expressed in NFAs, specifically gonadotropinomas. Further studies are required to elucidate whether over-expression of GFRα2 in gonadotropinomas might play a role in pituitary tumorigenesis.