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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 (, In a modelling study, Naeimeh Atabaki-Pasdar and colleagues apply machine learning techniques to develop models to predict non-alcoholic fatty liver disease diagnosis using multi-omic and clinical data from individuals with and without type 2 diabetes in the IMI DIRECT cohorts., Author summary Why was this study done? Globally, about 1 in 4 adults have non-alcoholic fatty liver disease (NAFLD), which adversely affects energy homeostasis (in particular blood glucose concentrations), blood detoxification, drug metabolism, and food digestion. Although numerous noninvasive tests to detect NAFLD exist, these typically include inaccurate blood-marker tests or expensive imaging methods. The purpose of this work was to develop accurate noninvasive methods to aid in the clinical prediction of NAFLD. What did the researchers do and find? The analyses applied machine learning methods to data from the deep-phenotyped IMI DIRECT cohorts (n = 1,514) to identify sets of highly informative variables for the prediction of NAFLD. The criterion measure was liver fat quantified from MRI. We developed a total of 18 prediction models that ranged from very inexpensive models of modest accuracy to more expensive biochemistry- and/or omics-based models with high accuracy. We found that models using measures commonly collected in either clinical settings or research studies proved adequate for the prediction of NAFLD. The addition of detailed omics data significantly improved the predictive utility of these models. We also found that of all omics markers, proteomic markers yielded the highest predictive accuracy when appropriately combined. What do these findings mean? We envisage that these new approaches to predicting fatty liver may be of clinical value when screening at-risk populations for NAFLD. The identification of specific molecular features that underlie the development of NAFLD provides novel insights into the disease’s etiology, which may lead to the development of new treatments.

Dysfunction of the endoplasmic reticulum (ER) in insulin‐producing beta cells results in cell loss and diabetes mellitus. Here we report on five individuals from three different consanguineous families with infancy‐onset diabetes mellitus and severe neurodevelopmental delay caused by a homozygous p.(Arg371Ser) mutation in FICD. The FICD gene encodes a bifunctional Fic domain‐containing enzyme that regulates the ER Hsp70 chaperone, BiP, via catalysis of two antagonistic reactions: inhibitory AMPylation and stimulatory deAMPylation of BiP. Arg371 is a conserved residue in the Fic domain active site. The FICDR371S mutation partially compromises BiP AMPylation in vitro but eliminates all detectable deAMPylation activity. Overexpression of FICDR371S or knock‐in of the mutation at the FICD locus of stressed CHO cells results in inappropriately elevated levels of AMPylated BiP and compromised secretion. These findings, guided by human genetics, highlight the destructive consequences of de‐regulated BiP AMPylation and raise the prospect of tuning FICD's antagonistic activities towards therapeutic ends.

Dysfunction of the endoplasmic reticulum (ER) in insulin-producing beta cells results in cell loss and diabetes mellitus. Here we report on five individuals from three different consanguineous families with infancy-onset diabetes mellitus and severe neurodevelopmental delay caused by a homozygous p.(Arg371Ser) mutation in FICD. The FICD gene encodes a bifunctional Fic domain-containing enzyme that regulates the ER Hsp70 chaperone, BiP, via catalysis of two antagonistic reactions: inhibitory AMPylation and stimulatory deAMPylation of BiP. Arg371 is a conserved residue in the Fic domain active site. The FICDR371S mutation partially compromises BiP AMPylation in vitro but eliminates all detectable deAMPylation activity. Overexpression of FICDR371S or knock-in of the mutation at the FICD locus of stressed CHO cells results in inappropriately elevated levels of AMPylated BiP and compromised secretion. These findings, guided by human genetics, highlight the destructive consequences of de-regulated BiP AMPylation and raise the prospect of tuning FICD's antagonistic activities towards therapeutic ends., Supported by a Wellcome Trust Principal Research Fellowship to D.R. (Wellcome 200848/Z/16/Z). A Diabetes UK RD Lawrence fellowship to E.D.F. (Grant number 19_0005971), and a Wellcome Trust Senior Research Fellowship to S.E.F. (105636/Z/14/Z). M.N.W. is in receipt of an Independent Fellowship from the Exeter Diabetes Centre of Excellence funded by Research England’s Expanding Excellence in England (E3) fund