Altered lipid metabolism and the development of metabolic-associated fatty liver disease.

PURPOSE OF REVIEW: An increasing amount of research has underscored the significant role of lipoproteins in the pathogenesis of metabolic-associated fatty liver disease (MAFLD). This comprehensive review examines the intricate relationship between lipoprotein abnormalities and the development of MAFLD. RECENT FINDINGS: Atherogenic dyslipidemia seen in insulin resistance states play a significant role in initiating and exacerbating hepatic lipid accumulation. There are also specific genetic factors ( PNPLA3 , TM6SF2 , MBOAT7 , HSD17B13 , GCKR- P446L) and transcription factors (SREBP-2, FXR, and LXR9) that increase susceptibility to both lipoprotein disorders and MAFLD. Most monogenic primary lipid disorders do not cause hepatic steatosis unless accompanied by metabolic stress. Hepatic steatosis occurs in the presence of secondary systemic metabolic stress in conjunction with predisposing environmental factors that lead to insulin resistance. Identifying specific aberrant lipoprotein metabolic factors promoting hepatic fat accumulation and subsequently exacerbating steatohepatitis will shed light on potential targets for therapeutic interventions. SUMMARY: The clinical implications of interconnection between genetic factors and an insulin resistant environment that predisposes MAFLD is many fold. Potential therapeutic strategies in preventing or mitigating MAFLD progression include lifestyle modifications, pharmacological interventions, and emerging therapies targeting aberrant lipoprotein metabolism.

Children and Adolescents With Hybrid Diabetes: A Management Conundrum.

OBJECTIVE: Hybrid diabetes (HD); ie, insulin resistance with positive diabetes-associated autoantibodies (DAAs) is increasing in children. We aimed to compare the characteristics of children with HD with those with type 1 diabetes (T1DM) and type 2 diabetes (T2DM) at diagnosis and after 2 years. METHODS: A retrospective review of patients aged 0 to 19 years, with C-peptide and 4 DAA measurements available, who were diangosed with new-onset diabetes from 2016 to 2020 were included in the analysis. RESULTS: Overall, 102 subjects were included, 32 with T1DM, 21 with HD, and 49 with T2DM. Amongst the groups (T1DM vs HD vs T2DM), there were differences in the proportion of non-Hispanic Whites (81.3% vs 47.6% vs 16.4%, P < .001), frequency of family history of T2DM (37.5% vs 100% vs 85.4%, P < .001), acanthosis nigricans (0% vs 42.9% vs 93.9%, P <.001), median body mass index z-score (-0.55 vs 1.8 vs 2.4, P <.001), and median C-peptide (0.4 ng/mL vs 0.9 ng/mL vs 2.4 ng/mL, P <.001). At 2 years, differences were seen in median body mass index z-scores (0.3 vs 1.9 vs 2.3, P <.001), mean HDL-cholesterol (58.0 mg/dL vs 48.2 mg/dL vs 39.5 mg/dL, P <.001), and the use of basal insulin (100% vs 100% vs 74.4%, P <.001). CONCLUSION: Phenotypic and metabolic differences were seen in youth with T1DM, HD, and T2DM at diagnosis and follow-up. At 2 years, all subjects with HD remained insulin dependent whereas some with T2DM were not, indicating the need for targeted interventions to address the etiopathogenesis.

Adrenal Insufficiency in Peroxisomal Disorders: A Single Institution Case Series.

INTRODUCTION: There are two major categories of peroxisomal disorders (PDs): peroxisomal biogenesis disorders (PBDs) due to defects in peroxisomal (PEX) genes and deficiency of other peroxisomal enzymes (such as D-bifunctional enzyme deficiency due to HSD17B4). PDs are characterized by abnormal elevations of very-long-chain fatty acids (VLCFA). We aimed to evaluate the clinical phenotype of adrenal insufficiency in patients with PD and to assess any genotype-phenotype correlations with adrenal insufficiency. CASE PRESENTATION: We performed a retrospective electronic medical record review at a single university medical center, of data over 12 years and identified 7 patients with PD. Of the 7 patients identified, 6 patients had a diagnosis of PBD and one had a single peroxisomal enzyme deficiency, HSD17B4. The average age of the patients at diagnosis were 0.61 ± 0.66 years. Four patients (66.7%) had primary adrenal insufficiency: 3, out of the 4, patients had elevated baseline ACTH. Three patients failed to have increased response after the Cortrosyn™ stimulation test. Three patients were on daily hydrocortisone replacement, and 1 patient was on stress-dose hydrocortisone only as needed. Specific genetic variant analysis revealed that all the 3 patients with PBD and adrenal insufficiency who were on steroid supplementation had the compound heterozygous pathogenic variant in exon 13 of PEX1 c.2097dupT (p.Ile700Tyrfs*42) and c.2528G>A (p.Gly843Asp), while the 1 patient with peroxisomal enzyme deficiency and adrenal insufficiency had compound heterozygous pathogenic variants in HSD17B4 c.1369A>T (p.Asn457Tyr) and c.1210 - 1G>A (splice acceptor). Two of these patients with PEX1 variants also required mineralocorticoid supplementation. The 3 PBD patients without adrenal insufficiency did not have a PEX1 variant. DISCUSSION/CONCLUSION: Primary adrenal insufficiency is common in patients with PD. Based on our data, patients with the compound heterozygous PEX1 pathogenic variants of exon 13 (c.2097dupT and c.2528G>A) tend to have adrenal insufficiency. Aldosterone deficiency, though rare, can occur in PD.

GenomeDiver: a platform for phenotype-guided medical genomic diagnosis.

PURPOSE: Making a diagnosis from clinical genomic sequencing requires well-structured phenotypic data to guide genotype interpretation. A patient's phenotypic features can be documented using the Human Phenotype Ontology (HPO), generating terms used to prioritize genes potentially causing the patient's disease. We have developed GenomeDiver to provide a user interface for clinicians that allows more effective collaboration with the clinical diagnostic laboratory, with the goal of improving the success of the diagnostic process. METHODS: GenomeDiver uses genomic data to prompt reverse phenotyping of patients undergoing genetic testing, enriching the amount and quality of structured phenotype data for the diagnostic laboratory, and helping clinicians to explore and flag diseases potentially causing their patient's presentation. RESULTS: We show how GenomeDiver communicates the clinician's informed insights to the diagnostic lab in the form of HPO terms for interpretation of genomic sequencing data. We describe our user-driven design process, the engineering of the software for efficiency, security and portability, and examples of the performance of GenomeDiver using genomic testing data. CONCLUSION: GenomeDiver is a first step in a new approach to genomic diagnostics that enhances laboratory-clinician interactions, with the goal of directly engaging clinicians to improve the outcome of genomic diagnostic testing.