Predicting and elucidating the etiology of fatty liver disease: A machine learning modeling and validation study in the IMI DIRECT cohorts.

BACKGROUND: Non-alcoholic fatty liver disease (NAFLD) is highly prevalent and causes serious health complications in individuals with and without type 2 diabetes (T2D). Early diagnosis of NAFLD is important, as this can help prevent irreversible damage to the liver and, ultimately, hepatocellular carcinomas. We sought to expand etiological understanding and develop a diagnostic tool for NAFLD using machine learning. METHODS AND FINDINGS: We utilized the baseline data from IMI DIRECT, a multicenter prospective cohort study of 3,029 European-ancestry adults recently diagnosed with T2D (n = 795) or at high risk of developing the disease (n = 2,234). Multi-omics (genetic, transcriptomic, proteomic, and metabolomic) and clinical (liver enzymes and other serological biomarkers, anthropometry, measures of beta-cell function, insulin sensitivity, and lifestyle) data comprised the key input variables. The models were trained on MRI-image-derived liver fat content (<5% or ≥5%) available for 1,514 participants. We applied LASSO (least absolute shrinkage and selection operator) to select features from the different layers of omics data and random forest analysis to develop the models. The prediction models included clinical and omics variables separately or in combination. A model including all omics and clinical variables yielded a cross-validated receiver operating characteristic area under the curve (ROCAUC) of 0.84 (95% CI 0.82, 0.86; p < 0.001), which compared with a ROCAUC of 0.82 (95% CI 0.81, 0.83; p < 0.001) for a model including 9 clinically accessible variables. The IMI DIRECT prediction models outperformed existing noninvasive NAFLD prediction tools. One limitation is that these analyses were performed in adults of European ancestry residing in northern Europe, and it is unknown how well these findings will translate to people of other ancestries and exposed to environmental risk factors that differ from those of the present cohort. Another key limitation of this study is that the prediction was done on a binary outcome of liver fat quantity (<5% or ≥5%) rather than a continuous one. CONCLUSIONS: In this study, we developed several models with different combinations of clinical and omics data and identified biological features that appear to be associated with liver fat accumulation. In general, the clinical variables showed better prediction ability than the complex omics variables. However, the combination of omics and clinical variables yielded the highest accuracy. We have incorporated the developed clinical models into a web interface (see: https://www.predictliverfat.org/) and made it available to the community. TRIAL REGISTRATION: ClinicalTrials.gov NCT03814915.

TET2 and CSMD1 genes affect SBP response to hydrochlorothiazide in never-treated essential hypertensives.

BACKGROUND: Thiazide diuretics have been recommended as a first-line antihypertensive treatment, although the choice of 'the right drug in the individual essential hypertensive patient' remains still empirical. Essential hypertension is a complex, polygenic disease derived from the interaction of patient's genetic background with the environment. Pharmacogenomics could be a useful tool to pinpoint gene variants involved in antihypertensive drug response, thus optimizing therapeutic advantages and minimizing side effects. METHODS AND RESULTS: We looked for variants associated with blood pressure response to hydrochlorothiazide over an 8-week follow-up by means of a genome-wide association analysis in two Italian cohorts of never-treated essential hypertensive patients: 343 samples from Sardinia and 142 from Milan. TET2 and CSMD1 as plausible candidate genes to affect SBP response to hydrochlorothiazide were identified. The specificity of our findings for hydrochlorothiazide was confirmed in an independent cohort of essential hypertensive patients treated with losartan. Our best findings were also tested for replication in four independent hypertensive samples of European Ancestry, such as GENetics of drug RESponsiveness in essential hypertension, Genetic Epidemiology of Responses to Antihypertensives, NORdic DILtiazem intervention, Pharmacogenomics Evaluation of Antihypertensive Responses, and Campania Salute Network-StayOnDiur. We validated a polymorphism in CSMD1 and UGGT2. CONCLUSION: This exploratory study reports two plausible loci associated with SBP response to hydrochlorothiazide: TET2, an aldosterone-responsive mediator of αENaC gene transcription; and CSMD1, previously described as associated with hypertension in a case-control study.