Abstract
Metabolic syndrome (MetS), prediabetes (PreDM) and Fatty Liver Disease (FLD) share pathophysiological pathways concerning type 2 diabetes mellitus (T2DM) onset. The non-invasive assessment of fatty liver combined with PreDM and MetS features screening might provide further accuracy in predicting hyperglycemic status in the clinical setting with the putative description of singular phenotypes. The objective of the study is to evaluate and describe the links of a widely available FLD surrogate -the non-invasive serological biomarker Hepatic Steatosis Index (HSI)- with previously described T2DM risk predictors, such as preDM and MetS in forecasting T2DM onset. A retrospective ancillary cohort study was performed on 2799 patients recruited in the Vascular-Metabolic CUN cohort. The main outcome was the incidence of T2DM according to ADA criteria. MetS and PreDM were defined according to ATP III and ADA criteria, respectively. Hepatic steatosis index (HSI) with standardized thresholds was used to discriminate patients with FLD, which was referred as estimated FLD (eFLD). MetS and PreDM were more common in patients with eFLD as compared to those with an HSI < 36 points (35% vs 8% and 34% vs. 18%, respectively). Interestingly, eFLD showed clinical effect modification with MetS and PreDM in the prediction of T2DM [eFLD-MetS interaction HR = 4.48 (3.37-5.97) and eFLD-PreDM interaction HR = 6.34 (4.67-8.62)]. These findings supported the description of 5 different liver status-linked phenotypes with increasing risk of T2DM: Control group (1,5% of T2DM incidence), eFLD patients (4,4% of T2DM incidence), eFLD and MetS patients (10,6% of T2DM incidence), PreDM patients (11,1% of T2DM incidence) and eFLD and PreDM patients (28,2% of T2DM incidence). These phenotypes provided independent capacity of prediction of T2DM incidence after adjustment for age, sex, tobacco and alcohol consumption, obesity and number of SMet features with a c-Harrell=0.84. Estimated Fatty Liver Disease using HSI criteria (eFLD) interplay with MetS features and PreDM might help to discriminate patient risk of T2DM in the clinical setting through the description of independent metabolic risk phenotypes. [Correction added on 15 June 2023, after first online publication: The abstract section was updated in this current version.].
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