Angiopoietin-like 8 (ANGPTL8) as a potential predictor of NAFLD in paediatric patients with Prader-Willi Syndrome

Nonalcoholic Fatty Liver Disease, Liver Fibrosis, and Utility of Noninvasive Scores in Patients With Acromegaly

CONTEXT

Nonalcoholic fatty liver disease (NAFLD) is a metabolical disorder and can lead to liver fibrosis. Because it is commonly seen, several noninvasive scores (NS) have been validated to identify high-risk patients. Patients with NAFLD have been shown to have higher serum angiopoietin-like protein-8 (ANGPTL-8) levels.

OBJECTIVE

The risk of NAFLD is known insufficiently in acromegaly. Moreover, the utility of the NS and the link between NAFLD and ANGPTL-8 in acromegaly is unknown.

METHODS

Thirty-two patients with acromegaly (n = 15, active [AA] and n = 17, controlled acromegaly [CA]) and 19 healthy controls were included. Magnetic resonance imaging (MRI)-proton density fat fraction (PDFF) was used to evaluate hepatic steatosis, and magnetic resonance elastography to evaluate liver stiffness measurement. ANGPTL-8 levels were measured with ELISA.

RESULTS

Median liver MRI-PDFF and NAFLD prevalence in AA were lower than in CA (P = .026 and P < .001, respectively). Median magnetic resonance elastography-liver stiffness measurement were similar across groups. Of the NS, visceral adiposity index, fatty liver index, hepatic steatosis index, and triglyceride-glucose index (TyG) all showed positive correlation with the liver MRI-PDFF in the control group. However, only TyG significantly correlated with liver fat in the AA and CA groups. There was no correlation between traditional NAFLD risk factors (body mass index, waist circumference, C-reactive protein, homeostasis model assessment for insulin resistance, visceral adipose tissue) and liver MRI-PDFF in the AA and CA. Patients with acromegaly with NAFLD had lower GH, IGF-1, and ANGPTL-8 levels than in those without NAFLD (P = .025, P = .011, and P = .036, respectively).

CONCLUSION

Active acromegaly may protect from NAFLD because of high GH. In patients with acromegaly, NAFLD risk cannot be explained with classical risk factors; hence, additional risk factors must be identified. TyG is the best score to evaluate NAFLD risk. Lower ANGPTL-8 in patients with acromegaly and NAFLD implies this hormone may be raised because of insulin resistance rather than being a cause for NAFLD.

Correlation of the Pediatric Metabolic Index with NAFLD or MAFLD diagnosis, and serum adipokine levels in children

INTRODUCTION

Non-alcoholic fatty liver disease (NAFLD) is characterized by ectopic fat deposition in the liver. However, a recent classification of this condition, which also integrates the presence of coexisting metabolic disorders, termed Metabolic dysfunction Associated Fatty Liver Disease (MAFLD), has been proposed. NAFLD is increasingly common in early childhood, partly due to the increase in metabolic disease in this age. Thus, studying hepatic steatosis in the metabolic context has become important in this population as well. However, NAFLD, and thus MAFLD, diagnosis in children is challenging by the lack of non-invasive diagnostic tools comparable to the gold standard of hepatic biopsy. Recent studies have reported that the Pediatric Metabolic Index (PMI) could be a marker of insulin resistance and abnormal liver enzymes, but its association with NAFLD, MAFLD, or altered adipokines in these conditions has not been reported. The aim of this study is to evaluate the correlation between PMI with the diagnosis of NAFLD or MAFLD, together with serum levels of leptin and adiponectin, in school-age children.

METHODS

A cross sectional study was carried out in two hundred and twenty-three children without medical history of hypothyroidism, genetic, or chronic diseases. Anthropometry, liver ultrasound, and serum levels of lipids, leptin, and adiponectin were evaluated. The children were classified as having NAFLD or non-NAFLD, and a subgroup of MAFLD in the NAFLD group was analyzed. The PMI was calculated by the established formulas for age and gender.

RESULTS

PMI correlated positively with the presence and severity of NAFLD (r=0.62, p<0.001 and r= 0.79, p<0.001 respectively) and with the presence of MAFLD (r=0.62; p<0.001). Also, this index correlated positively with serum leptin levels (r=0.66; p<0.001) and negatively with serum adiponectin levels (r= -0.65; p<0.001). PMI showed to be a good predictor for diagnosing NAFLD in school-age children when performing a ROC curve analysis (AUROC=0.986, p< 0.0001).

CONCLUSION

PMI could be a useful tool for the early diagnosis of NAFLD or MAFLD in children. However, future studies are necessary to establish validated cut-off points for each population.

Circulating Irisin in Children and Adolescents With Prader-Willi Syndrome: Relation With Glucose Metabolism

Irisin is a myokine involved in the browning of white adipose tissue and regulation of energy expenditure, glucose homeostasis and insulin sensitivity. Debated evidence exists on the metabolic role played by irisin in children with overweight or obesity, while few information exist in children with Prader Willi Syndrome (PWS), a condition genetically prone to obesity. Here we assessed serum irisin in relation to the metabolic profile and body composition in children and adolescents with and without PWS. In 25 PWS subjects [age 6.6-17.8y; body mass index standard deviation score (BMI SDS) 2.5 ± 0.3] and 25 age, and BMI-matched controls (age 6.8-18.0y; BMI SDS, 2.8 ± 0.1) we assessed irisin levels and metabolic profile inclusive of oral glucose tolerance test (OGTT), and body composition by dual-energy X-ray absorptiometry (DXA). In PWS, we recorded lower levels of fat-free mass (FFM) (p <0.05), fasting (p<0.0001) and 2h post-OGTT insulin (p<0.05) and lower insulin resistance as expressed by homeostatic model of insulin resistance (HOMA-IR) (p<0.0001). Irisin levels were significantly lower in PWS group than in controls with common obesity (p<0.05). In univariate correlation analysis, positive associations linked irisin to insulin OGTT₀ (p<0.05), insulin OGTT₁₂₀ (p<0.005), HOMA-IR (p<0.05) and fasting C-peptide (p<0.05). In stepwise multivariable regression analysis, irisin levels were independently predicted by insulin OGTT₁₂₀. These results suggest a link between irisin levels and insulin sensitivity in two divergent models of obesity.

A predictive model for the diagnosis of non-alcoholic fatty liver disease based on an integrated machine learning method

Diagnostic markers for non-alcoholic fatty liver disease (NAFLD) are still needed for screening individuals at risk. In recent years, the machine learning method was used to search for the diagnostic markers of multiple diseases. In this study, we developed and validated a machine learning model to diagnose NAFLD using laboratory indicators. NAFLD patients and non-NAFLD controls were recruited in the training and validation cohorts. The laboratory indicators of the participants in the training cohort were collected, and six indicators including alanine aminotransferase/aspartate aminotransferase (ALT/AST), white blood cells (WBC), alpha-L-fucosidase (AFU), hemoglobin (Hb), triglycerides (TG) and gamma-glutamyl transpeptidase (GGT) were screened out with higher weights by an integrate machine learning method. The areas under the receiver operating characteristic curves (AUROCs) for the selected indicators using logistic regression (LR), random forest (RF) and support vector machine (SVM) were 0.814, 0.837 and 0.810, respectively. Then the binary logistic regression was used to construct the predictive model. What's more, the AUROC of the predicted model was 0.732 in the validation cohort of patients with NAFLD. And the combined AUROC of the six parameters was 0.716 in the mouse model fed with high-fat diet (HFD). In summary, we created a predictive model with six laboratory indicators for the diagnosis of NAFLD based on the machine learning method, which has the potential value for the diagnosis of the NAFLD.

Association of ANGPTL8 and Resistin With Diabetic Nephropathy in Type 2 Diabetes Mellitus

Background

Previous studies showed altered angiopoietin-like protein-8 (ANGPTL-8) and resistin circulating levels in type 2 diabetes mellitus (T2DM). Whether or not the alteration in ANGPTL-8 and resistin level can be a predictive maker for increased diabetic nephropathy risk remains unclear.

Aim

To Investigate the possible association of ANGPTL-8 and resistin with DN, and whether this association is affected by NAFLD status.

Methods

A total of 278 T2DM patients were enrolled. Serum levels of ANGPTL8, resistin, BMI, blood pressure, duration of diabetes, glycosylated hemoglobin (HbA1c), fasting blood glucose (FPG), hypersensitive C-reactive protein (hs-CRP), lipid profile, liver, and kidney function tests were assessed. The relationship between DN with ANGPTL8 and resistin was analyzed in the unadjusted and multiple-adjusted regression models.

Results

Serum levels of ANGPTL8 and resistin were significantly higher in DN compared with T2DM subjects without DN (respectively; P <0.001), especially in non-NAFLD populations. ANGPTL8 and resistin showed positive correlation with hs-CRP (respectively; P<0.01), and negative correlation with estimated GFR (eGFR) (respectively; P=<0.001) but no significant correlation to HOMA-IR(respectively; P>0.05). Analysis showed ANGPTL8 levels were positively associated with resistin but only in T2DM patients with DN(r=0.1867; P<0.05), and this significant correlation disappeared in T2DM patients without DN. After adjusting for confounding factors, both ANGPTL8(OR=2.095, 95%CI 1.253-3.502 P=0.005) and resistin (OR=2.499, 95%CI 1.484-4.208 P=0.001) were risk factors for DN. Data in non-NAFLD population increased the relationship between ANGPTL8 (OR=2.713, 95% CI 1.494-4.926 P=0.001), resistin (OR=4.248, 95% CI 2.260-7.987 P<0.001)and DN. The area under the curve (AUC) on receiver operating characteristic (ROC) analysis of the combination of ANGPTL8 and resistin was 0.703, and the specificity was 70.4%. These data were also increased in non-NAFLD population, as the AUC (95%CI) was 0.756, and the specificity was 91.2%.

Conclusion

This study highlights a close association between ANGPTL8, resistin and DN, especially in non-NAFLD populations. These results suggest that ANGPTL-8 and resistin may be risk predictors of DN.