Prevalence of ultrasound-diagnosed non-alcoholic fatty liver disease in a hospital cohort and its association with anthropometric, biochemical and sonographic characteristics

Association between obesity phenotypes and non-alcoholic fatty liver: a large population- based study

BACKGROUND

The aim of this study was to examine the association between different metabolic obesity phenotypes and the non-alcoholic fatty liver disease (NAFLD).

METHODS

This cross-sectional analysis utilized data from the baseline phase of the Ravansar non-communicable diseases (RaNCD) cohort study, which involved 8,360 adults. Participants with a Fatty Liver Index (FLI) score of ≥ 60 was classified as having NAFLD. The FLI score was calculated using liver non-invasive markers and anthropometric measurements. Participants were categorized into four phenotypes based on the presence or absence of metabolic syndrome and obesity. Logistic regression analysis was used to evaluate the association of NAFLD and obesity phenotypes.

RESULTS

According to the FLI index, the prevalence of NAFLD was 39.56%. Participants with FLI scores of ≥ 60 had higher energy intake compared to those in the FLI < 60 group (P = 0.033). In subjects with metabolically unhealthy phenotypes, the level of physical activity was lower compared to those with metabolically healthy phenotypes. The risk of NAFLD in males with the metabolically healthy-obese phenotype increased by 8.92 times (95% CI: 2.20, 15.30), those with the metabolically unhealthy-non-obese phenotype increased by 7.23 times (95% CI: 5.82, 8.99), and those with the metabolically unhealthy-obese phenotype increased by 32.97 times (95% CI: 15.70, 69.22) compared to the metabolically healthy-non-obese phenotype. Similarly, these results were observed in females.

CONCLUSION

This study demonstrated that the risk of NAFLD is higher in individuals with metabolically healthy/obese, metabolically unhealthy/non-obese, and metabolically unhealthy/obese phenotypes compared to those with non-obese/metabolically healthy phenotypes.

Undernutrition, risk of malnutrition and obesity in gastroenterological patients: A multicenter study

AIM: To investigate the prevalence of undernutrition, risk of malnutrition and obesity in the Italian gastroenterological population.

METHODS: The Italian Hospital Gastroenterology Association conducted an observational, cross-sectional multicenter study. Weight, weight loss, and body mass index were evaluated. Undernutrition was defined as unintentional weight loss > 10% in the last three-six months. Values of Malnutrition Universal Screening Tool (MUST) > 2, NRS-2002 > 3, and Mini Nutritional Assessment (MNA) from 17 to 25 identified risk of malnutrition in outpatients, inpatients and elderly patients, respectively. A body mass index ≥ 30 indicated obesity. Gastrointestinal pathologies were categorized into acute, chronic and neoplastic diseases.

RESULTS: A total of 513 patients participated in the study. The prevalence of undernutrition was 4.6% in outpatients and 19.6% in inpatients. Moreover, undernutrition was present in 4.3% of the gastrointestinal patients with chronic disease, 11.0% of those with acute disease, and 17.6% of those with cancer. The risk of malnutrition increased progressively and significantly in chronic, acute and neoplastic gastrointestinal diseases in inpatients and the elderly population. Logistical regression analysis confirmed that cancer was a risk factor for undernutrition (OR = 2.7; 95%CI: 1.2-6.44, P = 0.02). Obesity and overweight were more frequent in outpatients.

CONCLUSION: More than 63% of outpatients and 80% of inpatients in gastroenterological centers suffered from significant changes in body composition and required specific nutritional competence and treatment.

Diagnosing fatty liver disease: a comparative evaluation of metabolic markers, phenotypes, genotypes and established biomarkers

BACKGROUND

To date, liver biopsy is the only means of reliable diagnosis for fatty liver disease (FLD). Owing to the inevitable biopsy-associated health risks, however, the development of valid noninvasive diagnostic tools for FLD is well warranted.

AIM

We evaluated a particular metabolic profile with regard to its ability to diagnose FLD and compared its performance to that of established phenotypes, conventional biomarkers and disease-associated genotypes.

METHODS

The study population comprised 115 patients with ultrasound-diagnosed FLD and 115 sex- and age-matched controls for whom the serum concentration was measured of 138 different metabolites, including acylcarnitines, amino acids, biogenic amines, hexose, phosphatidylcholines (PCs), lyso-PCs and sphingomyelins. Established phenotypes, biomarkers, disease-associated genotypes and metabolite data were included in diagnostic models for FLD using logistic regression and partial least-squares discriminant analysis. The discriminative power of the ensuing models was compared with respect to area under curve (AUC), integrated discrimination improvement (IDI) and by way of cross-validation (CV).

RESULTS

Use of metabolic markers for predicting FLD showed the best performance among all considered types of markers, yielding an AUC of 0.8993. Additional information on phenotypes, conventional biomarkers or genotypes did not significantly improve this performance. Phospholipids and branched-chain amino acids were most informative for predicting FLD.

CONCLUSION

We show that the inclusion of metabolite data may substantially increase the power to diagnose FLD over that of models based solely upon phenotypes and conventional biomarkers.