Most patients in care who may have NAFLD are not being recognized and evaluated for this condition. Our data suggest that providers may be using an incorrect heuristic in delivering NAFLD care by concentrating on those with high ALT levels.

Background Despite tremendous interest in modulating the microbiome to improve health, the association between diet and the colonic mucosa–associated gut microbiome in healthy individuals has not been examined. Objective To investigate the associations between Healthy Eating Index (HEI)–2005 and the colonic mucosa–associated microbiota. Methods In this cross-sectional observational study, we analyzed bacterial community composition and structure using 16S rRNA gene (V4 region) sequencing of 97 colonic mucosal biopsies obtained endoscopically from different colon segments of 34 polyp-free participants. Dietary consumption was ascertained using an FFQ. Differences in α- and β-diversity and taxonomic relative abundances between the higher and lower score of total HEI and its components were compared, followed by multivariable analyses. Results The structure of the microbiota significantly differed by the scores for total HEI, total and whole fruits (HEI 1 and HEI 2), whole grains (HEI 6), milk products and soy beverages (HEI 7), and solid fat, alcohol, and added sugar (HEI 12). A lower score for total HEI and HEIs 2, 7, and 12 was associated with significantly lower richness. A lower score for total HEI was associated with significantly reduced relative abundance of Parabacteroides, Roseburia, and Subdoligranulum but higher Fusobacterium. A lower score for HEI 2 was associated with lower Roseburia but higher Bacteroides. A lower score for HEI 7 was associated with lower Faecalibacterium and Fusobacterium but higher Bacteroides. A lower score for HEI 12 was associated with lower Subdoligranulum but higher Escherichia and Fusobacterium (false discovery rate–adjusted P values <0.05). The findings were confirmed by multivariate analysis. Less abundant bacteria such as Alistipes, Odoribacter, Bilophila, and Tyzzerella were also associated with dietary quality. Conclusions A lower score for total HEI–2005 was significantly associated with reduced relative abundance of potentially beneficial bacteria but increased potentially harmful bacteria in the colonic mucosa of endoscopically normal individuals.

Background In practice, non-alcoholic fatty liver (NAFLD) is diagnosed based on elevated liver enzymes and confirmatory liver biopsy or abdominal imaging. Neither method is feasible in identifying individuals with NAFLD in a large-scale healthcare system. Aim To develop and validate an algorithm to identify patients with NAFLD using automated data. Methods Using the Veterans Administration Corporate Data Warehouse, we identified patients who had persistent ALT elevation (≥2 values ≥40IU/ml ≥6 months apart) and did not have evidence of hepatitis B, hepatitis C, or excessive alcohol use. We conducted a structured chart review of 450 patients classified as NAFLD and 150 patients who were classified as non-NAFLD by the database algorithm, and subsequently refined the database algorithm. Results The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) for the initial database definition of NAFLD were 78.4% (95%CI=70.0-86.8%), 74.5% (95%CI=68.1-80.9%), 64.1% (95%CI: 56.4-71.7%), and 85.6% (95%Ci: 79.4-91.8%), respectively. Reclassifying patients as having NAFLD if they had 2 elevated ALTs that were at-least 6 months apart but within 2 years of each other, increased the specificity and PPV of the algorithm to 92.4% (95%CI=88.8 - 96.0%) and 80.8% (95%CI=72.5 - 89.0%), respectively. However, the sensitivity and NPV decreased to 55.0% (95%CI=46.1 - 63.9%) and 78.0% (95%CI=72.1 - 83.8%), respectively. Conclusions Predictive algorithms using automated data can be used to identify patients with NAFLD, determine prevalence of NAFLD at the system-wide level, and may help select a target population for future clinical studies in veterans with NAFLD.