American Association of Clinical Endocrinology Clinical Practice Guideline for the Diagnosis and Management of Nonalcoholic Fatty Liver Disease in Primary Care and Endocrinology Clinical Settings

Cusi, Kenneth; Isaacs, Scott; Barb, Diana; Basu, Rita; Caprio, Sonia; Garvey, W. Timothy; Kashyap, Sangeeta R.; Mechanick, Jeffrey I.; Mouzaki, Marialena; Nadolsky, Karl; Rinella, Mary E.; Vos, Miriam B.; Younossi, Zobair M.

doi:10.1016/j.eprac.2022.03.010

Cited by 386 publications

(497 citation statements)

References 387 publications

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“…Prospective evaluations of potential biomarkers in humans require further evaluation. However, screening guidelines for NAFLD recently released by the American Association of Clinical Endocrinology recommend calculation of a fibrosis-4 (FIB-4) index for high-risk patients [ 45 ] in primary care or endocrinology outpatient settings. This is noteworthy as platelet levels are a crucial component of FIB-4 index calculations, and our omic data-driven mechanistic hypotheses predict changes to risk of thrombosis as shown by DE of 15 thrombosis-related traits across all datasets ( Table S3 ).…”

Section: Discussionmentioning

confidence: 99%

Mechanisms Underlying Antipsychotic-Induced NAFLD and Iron Dysregulation: A Multi-Omic Approach

et al. 2022

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Atypical antipsychotic (AA) medications are widely prescribed for the treatment of psychiatric disorders, including schizophrenia, bipolar disorder and treatment-resistant depression. AA are associated with myriad metabolic and endocrine side effects, including systemic inflammation, weight gain, dyslipidemia and insulin resistance, all of which are associated with increased incidence of non-alcoholic fatty liver disease (NAFLD). NAFLD is highly prevalent in patients with mental illness, and AA have been shown to increase incidence of NAFLD pre-clinically and clinically. However, the underlying mechanisms have not been described. We mined multi-omic datasets from preclinical murine models of sub-chronic risperidone or olanzapine treatment, in vitro exposure of human cells to risperidone and psychiatric patients following onset of aripiprazole therapy focused on pathways associated with the pathophysiology of NAFLD, including iron accumulation, systemic inflammation and dyslipidemia. We identified numerous differentially expressed traits affecting these pathways conserved across study systems and AA medications. We used these findings to propose mechanisms for AA-associated development of NAFLD and dysregulated iron homeostasis.

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Section: Discussionmentioning

confidence: 99%

Mechanisms Underlying Antipsychotic-Induced NAFLD and Iron Dysregulation: A Multi-Omic Approach

et al. 2022

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“…Despite its usefulness, an abdominal ultrasound cannot be available for every primary care setting or for population-based screening. In the 2022 clinical practice guideline by the American Association of Clinical Endocrinology and American Association for the Study of Liver Diseases [23], The area under the curve of the fatty liver index was 0.648 (0.624 to 0.671), and that of the xgBoost model was 0.723 (0.701 to 0.744).…”

Section: Discussionmentioning

confidence: 99%

“…Despite its usefulness, an abdominal ultrasound cannot be available for every primary care setting or for population-based screening. In the 2022 clinical practice guideline by the American Association of Clinical Endocrinology and American Association for the Study of Liver Diseases [ 23 ], screening high-risk patients (prediabetes, type 2 diabetes, obesity, and/or metabolic syndromes) with noninvasive biomarkers such as the fatty liver index is recommended, followed by referral ultrasound examination. Such two-step screening is feasible, and may reduce screening time and costs [ 24 ].…”

Section: Discussionmentioning

confidence: 99%

Machine-Learning Algorithm for Predicting Fatty Liver Disease in a Taiwanese Population

Chen

Lin

Yen

et al. 2022

JPM

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The rising incidence of fatty liver disease (FLD) poses a health challenge, and is expected to be the leading global cause of liver-related morbidity and mortality in the near future. Early case identification is crucial for disease intervention. A retrospective cross-sectional study was performed on 31,930 Taiwanese subjects (25,544 training and 6386 testing sets) who had received health check-ups and abdominal ultrasounds in Changhua Christian Hospital from January 2009 to January 2019. Clinical and laboratory factors were included for analysis by different machine-learning algorithms. In addition, the performance of the machine-learning algorithms was compared with that of the fatty liver index (FLI). Totally, 6658/25,544 (26.1%) and 1647/6386 (25.8%) subjects had moderate-to-severe liver disease in the training and testing sets, respectively. Five machine-learning models were examined and demonstrated exemplary performance in predicting FLD. Among these models, the xgBoost model revealed the highest area under the receiver operating characteristic (AUROC) (0.882), accuracy (0.833), F1 score (0.829), sensitivity (0.833), and specificity (0.683) compared with those of neural network, logistic regression, random forest, and support vector machine-learning models. The xgBoost, neural network, and logistic regression models had a significantly higher AUROC than that of FLI. Body mass index was the most important feature to predict FLD according to the feature ranking scores. The xgBoost model had the best overall prediction ability for diagnosing FLD in our study. Machine-learning algorithms provide considerable benefits for screening candidates with FLD.

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“…To evaluate the probability of advanced liver fibrosis in patients with NAFLD, the NAFLD fibrosis score (NFS) and Fibrosis-4 (FIB-4) were applied [ 27 ]. NFS and FIB-4 were calculated using the following formulas: NFS = −1.675 + 0.037 × age (years) + 0.094 × BMI (kg/m 2 ) + 1.13 × impaired fasting glucose (IFG) or diabetes (yes = 1, no = 0) + 0.99 × aspartate aminotransferase (AST) (U/L)/alanine aminotransferase (ALT) (U/L) − 0.013 × platelet (10 9 /L) − 0.66 × albumin (g/dL); FIB-4 = age (years) × AST (U/L)/[platelet (10 9 /L) × ALT ½ (U/L)].…”

Section: Methodsmentioning

confidence: 99%

Increased Risk of NAFLD in Adults with Glomerular Hyperfiltration: An 8-Year Cohort Study Based on 147,162 Koreans

Koo¹,

Lee

Jung

et al. 2022

JPM

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This study evaluated whether glomerular hyperfiltration (GHF) could predict nonalcoholic fatty liver disease (NAFLD) and fibrosis. A longitudinal cohort study including 147,479 participants aged 20–65 years without NAFLD and kidney disease at baseline was performed. GHF cutoff values were defined as age- and sex-specific estimated glomerular filtration rate (eGFRs) above the 95th percentile, and eGFR values between the 50th and 65th percentiles were used as reference groups. NAFLD was diagnosed via abdominal ultrasonography, and the fibrosis status was evaluated using the NAFLD fibrosis score and Fibrosis-4. During 598,745 person years of follow-up (median, 4.6 years), subjects with GHF at baseline had the highest hazard ratio (HR) for the development of NAFLD (HR 1.21; 95% CI 1.14–1.29) and fibrosis progression (HR 1.42; 95% CI 1.11–1.82) after adjusting for confounding factors. A higher baseline eGFR percentile maintained a higher risk of NAFLD and fibrosis probability. The persistent GHF group during follow-up had the highest HR for NAFLD compared to the persistent non-GHF group (HR 1.31; 95% CI 1.14–1.51). These results were consistent in all subgroups and statistically more prominent in participants without diabetes. GHF was positively associated with increased risk of NAFLD and probability of liver fibrosis in healthy adults.

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American Association of Clinical Endocrinology Clinical Practice Guideline for the Diagnosis and Management of Nonalcoholic Fatty Liver Disease in Primary Care and Endocrinology Clinical Settings

Cited by 386 publications

References 387 publications

Mechanisms Underlying Antipsychotic-Induced NAFLD and Iron Dysregulation: A Multi-Omic Approach

Mechanisms Underlying Antipsychotic-Induced NAFLD and Iron Dysregulation: A Multi-Omic Approach

Machine-Learning Algorithm for Predicting Fatty Liver Disease in a Taiwanese Population

Increased Risk of NAFLD in Adults with Glomerular Hyperfiltration: An 8-Year Cohort Study Based on 147,162 Koreans

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