1
|
Maduri VD, Eresha J, Dulani S, Pujitha W. Association of fatty liver with serum gamma-glutamyltransferase and uric acid in obese children in a tertiary care centre. BMC Pediatr 2025; 25:144. [PMID: 40011867 DOI: 10.1186/s12887-025-05484-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/23/2024] [Accepted: 02/04/2025] [Indexed: 02/28/2025] Open
Abstract
BACKGROUND Obesity among the young is an emerging health problem with many metabolic changes including liver damage. Our objective was to investigate the association of fatty liver with serum uric acid (UA) and gamma-glutamyltransferase (GGT) in a cohort of obese children in Sri Lanka. METHODS A cross-sectional analytical study was conducted among 5-15-year-old obese children (based on WHO 2007 standards). After a 12-hour overnight fast, blood was drawn for glucose, lipid profile, alanine aminotransferase (ALT), aspartate aminotransferase (AST), insulin, UA and GGT. Height, weight, waist circumference, blood pressure and fat mass were measured. Ultrasound scan of abdomen was performed to determine fatty liver. RESULTS We studied 146 obese children with a mean age (SD) 9.86 (2.1) years. The fatty liver group showed significantly elevated levels (p < 0.05) of UA, oral glucose tolerance test (OGTT), triglycerides (TG), AST, ALT, GGT, insulin resistance (HOMA-IR) and a reduced AST/ALT ratio, compared to the non-fatty liver group. Chi square test showed statistically significant associations between fatty liver and AST, ALT, AST/ALT ratio, HOMA-IR, UA and GGT. With existing cut offs, GGT (> 30 U/L) and UA (> 330 µmol/L) the sensitivity and specificity of GGT in predicting fatty liver was 26.9% and 94.1% respectively while for UA it was 38.5% and 83.8% respectively. A cut-off value of 18.5 U/L (sensitivity 76.9% and specificity 52.9%) for GGT, 277µmol/L (sensitivity 70.5% and specificity 57.4%) for UA, 27.5 U/L (sensitivity 70.5%, specificity 51.5%) for AST, 21.5 U/L (sensitivity 80.8% and specificity 61.8%) for ALT, a ratio of 0.99 (sensitivity 77.9% and specificity 55.1%) for AST/ALT and 2.02 (sensitivity 73.2%, specificity 58.5%) for HOMA-IR predicted fatty liver. CONCLUSION GGT and UA are associated with fatty liver and these biomarkers can be used to predict fatty liver disease.
Collapse
Affiliation(s)
- Vidanapathirana Dinesha Maduri
- Department of Chemical Pathology, Lady Ridgeway Hospital, Colombo, Sri Lanka.
- Department of Pathology, Faculty of Medical Sciences, University of Sri Jayewardenepura, Nugegoda, Sri Lanka.
| | - Jasinge Eresha
- Department of Chemical Pathology, Lady Ridgeway Hospital, Colombo, Sri Lanka
| | - Samaranayake Dulani
- Department of Community Medicine, Faculty of Medicine, University of Colombo, Colombo, Sri Lanka
| | - Wickramasinghe Pujitha
- Department of Paediatrics, Faculty of Medicine, University of Colombo, Colombo, Sri Lanka
| |
Collapse
|
2
|
Hernández-Pérez M, Riado D, Pena E, Méndez C, Pinedo F, Ramos P, Castillo P, Romero M, Fernández-Rodríguez C, Olveira A. The overlap with metabolic dysfunction-associated steatotic liver disease negatively affects outcomes of primary biliary cholangitis. Aliment Pharmacol Ther 2024; 60:613-619. [PMID: 38924185 DOI: 10.1111/apt.18134] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/10/2024] [Revised: 04/02/2024] [Accepted: 06/17/2024] [Indexed: 06/28/2024]
Abstract
BACKGROUND AND AIMS The relationship between primary biliary cholangitis (PBC) and metabolic dysfunction-associated steatotic liver disease, and its impact on treatment response and prognosis, remains underexplored. METHODS Patient cohort from two centres comprising long-term follow-up data. All patients had histologically confirmed PBC. Biopsies were classified according to Non-Alcoholic Steatohepatitis Clinical Research Network. Diagnosis of metabolic dysfunction-associated steatotic liver disease was established when steatosis exceeded 5%, along with at least one metabolic risk factor. Patients with specific aetiologies of steatosis, other liver diseases, incomplete results and inadequate treatment with ursodeoxycholic acid were excluded. Data from patients initiating second-line treatment were censored. Treatment response was assessed using the Toronto, Paris II and AST-to-platelet at 12-month criteria. The UK PBC and Globe scores, and liver events were utilized as outcome measures. RESULTS The study included 129 patients, 36 showing histologically confirmed overlap between PBC and steatosis. Patients with overlap showed worse prognosis according to Paris II (61.1% vs. 33.3%, p = 0.004), Toronto (52.5% vs. 24.7%, p = 0.002), AST-to-platelet 12-month >0.54 (36.1% vs. 17.2%, p = 0.021), Globe >0.30 (49.2% vs. 29.2%, p = 0.033) and UK PBC at 5, 10 and 15 years (p ≤ 0.001). Liver-related mortality and liver transplant were more prevalent in the overlap group (p = 0.001). In the multivariate analysis, steatosis, dyslipidaemia and advanced fibrosis were independently associated to worse outcomes. CONCLUSIONS Our findings suggest that metabolic dysfunction-associated steatotic liver disease worsens the prognosis of PBC.
Collapse
Affiliation(s)
- María Hernández-Pérez
- Gastroenterology and Hepatology Department, La Paz University Hospital, Madrid, Spain
| | - Daniel Riado
- Gastroenterology and Hepatology Department, Alcorcón Foundation University Hospital, Alcorcón, Spain
| | - Eva Pena
- Pathology Department, La Paz University Hospital, Madrid, Spain
| | - Carmen Méndez
- Pathology Department, La Paz University Hospital, Madrid, Spain
| | - Fernando Pinedo
- Pathology Department, Alcorcón Foundation University Hospital, Alcorcón, Spain
| | - Paloma Ramos
- Pathology Department, Alcorcón Foundation University Hospital, Alcorcón, Spain
| | - Pilar Castillo
- Gastroenterology and Hepatology Department, La Paz University Hospital, Madrid, Spain
| | - Miriam Romero
- Gastroenterology and Hepatology Department, La Paz University Hospital, Madrid, Spain
| | - Conrado Fernández-Rodríguez
- Gastroenterology and Hepatology Department, Alcorcón Foundation University Hospital, Alcorcón, Spain
- Universidad Rey Juan Carlos, Alcorcón, Madrid, Spain
| | - Antonio Olveira
- Gastroenterology and Hepatology Department, La Paz University Hospital, Madrid, Spain
| |
Collapse
|
3
|
Cai Y, Chen J, Deng X, Wang B, Huang J, Lian N, Lian N. Triglyceride-glucose index and combined indicators: effective indicators for screening NAFLD in snoring patients. BMC Pulm Med 2024; 24:359. [PMID: 39049008 PMCID: PMC11270774 DOI: 10.1186/s12890-024-03166-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Accepted: 07/12/2024] [Indexed: 07/27/2024] Open
Abstract
AIMS Nonalcoholic fatty liver disease (NAFLD) is a common complication in snoring patients, especially in patients with obstructive sleep apnea syndrome (OSA). Triglyceride-glucose (TyG) index was a simple indicator of metabolic status and a surrogate marker of insulin resistance. This study aimed to explore the relationship between NAFLD and TyG index in snoring patients. METHODS A retrospective study was conducted. The successive snoring patients enrolled in the Sleep Center of the First Affiliated Hospital of Fujian Medical University and had abdominal ultrasonography were included. The clinical characteristics of patients in different quartile TyG groups were compared. The relationship of the TyG index and NAFLD were valued via logistic regression models and restricted cubic spline analysis. The value of TyG index in predicting NAFLD was determined by receiver operating characteristic curve (ROC curve). RESULTS A total of 463 NAFLD cases were found among the 654 snoring patients. TyG index was a risk factor of NAFLD in snoring patients (OR = 2.38, 95% CI = 1.71-3.36). The risk of NAFLD was much higher in patients with the highest quartile of TyG index (OR = 5.12, 95% CI = 2.85-9.22), compared with the lowest quartile group. Restricted cubic spline (RCS) analysis showed a significant dose-response relationship between TyG index and risk of NAFLD (p for non-linearity < 0.001). A combination of TyG, neck circumference and ESS score presented the acceptable AUC for the detection of NAFLD in snoring patients (0.746, 95% CI 0.701-0.790, p < 0.001). CONCLUSION The TyG index was a risk factor of NAFLD in snoring patients. A combination of TyG, neck circumferences and ESS score could act as a convenient and effective indicator for screening NAFLD in snoring patients.
Collapse
Affiliation(s)
- Yuqing Cai
- Department of Respiratory and Critical Care Medicine, Respiratory Disease Research Institute, The First Affiliated Hospital, Fujian Medical University, Fuzhou, 350005, China
- Department of Respiratory and Critical Care Medicine, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, 350212, China
| | - Jia Chen
- Department of Respiratory and Critical Care Medicine, Respiratory Disease Research Institute, The First Affiliated Hospital, Fujian Medical University, Fuzhou, 350005, China
- Department of Respiratory and Critical Care Medicine, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, 350212, China
| | - Xiaoyu Deng
- Department of Respiratory and Critical Care Medicine, Respiratory Disease Research Institute, The First Affiliated Hospital, Fujian Medical University, Fuzhou, 350005, China
- Department of Respiratory and Critical Care Medicine, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, 350212, China
| | - Biying Wang
- Department of Respiratory and Critical Care Medicine, Respiratory Disease Research Institute, The First Affiliated Hospital, Fujian Medical University, Fuzhou, 350005, China
- Department of Respiratory and Critical Care Medicine, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, 350212, China
| | - Jiefeng Huang
- Department of Respiratory and Critical Care Medicine, Respiratory Disease Research Institute, The First Affiliated Hospital, Fujian Medical University, Fuzhou, 350005, China
- Department of Respiratory and Critical Care Medicine, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, 350212, China
| | - Ningfang Lian
- Department of Respiratory and Critical Care Medicine, Respiratory Disease Research Institute, The First Affiliated Hospital, Fujian Medical University, Fuzhou, 350005, China
- Department of Respiratory and Critical Care Medicine, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, 350212, China
| | - Ningfang Lian
- Department of Respiratory and Critical Care Medicine, Respiratory Disease Research Institute, The First Affiliated Hospital, Fujian Medical University, Fuzhou, 350005, China.
- Department of Respiratory and Critical Care Medicine, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, 350212, China.
| |
Collapse
|
4
|
Qiu J, Kuang M, He S, Yu C, Wang C, Huang X, Sheng G, Zou Y. Gender perspective on the association between liver enzyme markers and non-alcoholic fatty liver disease: insights from the general population. Front Endocrinol (Lausanne) 2023; 14:1302322. [PMID: 38125795 PMCID: PMC10731038 DOI: 10.3389/fendo.2023.1302322] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Accepted: 11/21/2023] [Indexed: 12/23/2023] Open
Abstract
Objective Every distinct liver enzyme biomarker exhibits a strong correlation with non-alcoholic fatty liver disease (NAFLD). This study aims to comprehensively analyze and compare the associations of alanine aminotransferase (ALT), aspartate aminotransferase (AST), and gamma-glutamyl transferase (GGT) with NAFLD from a gender perspective. Methods This study was conducted on 6,840 females and 7,411 males from the NAGALA cohort. Multivariable logistic regression analysis was used to compare the associations between liver enzyme markers and NAFLD in both genders, recording the corresponding adjusted odds ratios (ORs) and 95% confidence intervals (CIs). Receiver operating characteristic (ROC) curves were used to evaluate the accuracy of individual liver enzyme markers and different combinations of them in identifying NAFLD. Results Liver enzyme markers ALT, AST, and GGT were all independently associated with NAFLD and exhibited significant gender differences (All P-interaction<0.05). In both genders, ALT exhibited the most significant association with NAFLD, with adjusted standardized ORs of 2.19 (95% CI: 2.01-2.39) in males and 1.60 (95% CI: 1.35-1.89) in females. Additionally, ROC analysis showed that ALT had significantly higher accuracy in identifying NAFLD than AST and GGT in both genders (Delong P-value < 0.05), and the accuracy of ALT in identifying NAFLD in males was higher than that in females [Area under the ROC curve (AUC): male 0.79, female 0.77]. Furthermore, out of the various combinations of liver enzymes, ALT+GGT showed the highest accuracy in identifying NAFLD in both genders, with AUCs of 0.77 (95% CI: 0.75-0.79) in females and 0.79 (95% CI: 0.78-0.81) in males. Conclusion Our study revealed significant gender differences in the associations of the three commonly used liver enzyme markers with NAFLD. In both genders, the use of ALT alone may be the simplest and most effective tool for screening NAFLD, especially in males.
Collapse
Affiliation(s)
- Jiajun Qiu
- Department of Internal Medicine, Medical College of Nanchang University, Jiangxi Provincial People’s Hospital, Nanchang, Jiangxi, China
- Jiangxi Cardiovascular Research Institute, Jiangxi Provincial People’s Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, Jiangxi, China
- Jiangxi Provincial Geriatric Hospital, Jiangxi Provincial People’s Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, Jiangxi, China
| | - Maobin Kuang
- Department of Internal Medicine, Medical College of Nanchang University, Jiangxi Provincial People’s Hospital, Nanchang, Jiangxi, China
- Jiangxi Cardiovascular Research Institute, Jiangxi Provincial People’s Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, Jiangxi, China
- Jiangxi Provincial Geriatric Hospital, Jiangxi Provincial People’s Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, Jiangxi, China
| | - Shiming He
- Department of Internal Medicine, Medical College of Nanchang University, Jiangxi Provincial People’s Hospital, Nanchang, Jiangxi, China
- Jiangxi Cardiovascular Research Institute, Jiangxi Provincial People’s Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, Jiangxi, China
- Jiangxi Provincial Geriatric Hospital, Jiangxi Provincial People’s Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, Jiangxi, China
| | - Changhui Yu
- Department of Internal Medicine, Medical College of Nanchang University, Jiangxi Provincial People’s Hospital, Nanchang, Jiangxi, China
- Jiangxi Cardiovascular Research Institute, Jiangxi Provincial People’s Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, Jiangxi, China
- Jiangxi Provincial Geriatric Hospital, Jiangxi Provincial People’s Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, Jiangxi, China
| | - Chao Wang
- Department of Internal Medicine, Medical College of Nanchang University, Jiangxi Provincial People’s Hospital, Nanchang, Jiangxi, China
- Jiangxi Cardiovascular Research Institute, Jiangxi Provincial People’s Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, Jiangxi, China
- Jiangxi Provincial Geriatric Hospital, Jiangxi Provincial People’s Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, Jiangxi, China
| | - Xin Huang
- Department of Internal Medicine, Medical College of Nanchang University, Jiangxi Provincial People’s Hospital, Nanchang, Jiangxi, China
- Jiangxi Cardiovascular Research Institute, Jiangxi Provincial People’s Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, Jiangxi, China
- Jiangxi Provincial Geriatric Hospital, Jiangxi Provincial People’s Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, Jiangxi, China
| | - Guotai Sheng
- Jiangxi Provincial Geriatric Hospital, Jiangxi Provincial People’s Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, Jiangxi, China
| | - Yang Zou
- Jiangxi Cardiovascular Research Institute, Jiangxi Provincial People’s Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, Jiangxi, China
| |
Collapse
|
5
|
Guimarães TCM, Taranto DODL, Couto CA, Nardelli MJ, Cândido AL, Hott CDA, Anastácio LR, Reis FM, Rocha ALL, Faria LC. Dietary pattern in women with polycystic ovary syndrome with and without associated non-alcoholic fatty liver disease: A cross-sectional study. Clinics (Sao Paulo) 2023; 78:100288. [PMID: 38052105 PMCID: PMC10746390 DOI: 10.1016/j.clinsp.2023.100288] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/20/2023] [Revised: 08/14/2023] [Accepted: 09/26/2023] [Indexed: 12/07/2023] Open
Abstract
INTRODUCTION Women with Polycystic Ovary Syndrome (PCOS) have a higher prevalence of Nonalcoholic Fatty Liver Disease (NAFLD) than the general population. PCOS and NAFLD have common metabolic risk factors, however, the role of diet in NAFLD development is still uncertain in PCOS women. OBJECTIVE To evaluate and compare the dietary patterns and nutritional intake in patients with PCOS with and without NAFLD. METHOD Cross-sectional study that included patients with PCOS diagnosed according to Rotterdam criteria. All participants were submitted to abdominal ultrasound to investigate liver steatosis. Dietary profile was assessed by 24-hour food recall (24hR), and Food Frequency Questionnaire (FFQ). Diet quality was assessed by the Healthy Eating Index (HEI) adapted for the Brazilian population. Physical activity practice was also assessed. RESULTS 87 participants were included (average age 35.2 ± 5.7 years), among whom, 67 (77%) had NAFLD. The group with PCOS and NAFLD presented higher body mass index (BMI) (34.9 ± 4.5 vs. 30.4 ± 4.9 kg/m2; p = 0.001), Waist Circumference (WC) (103 [97‒113] vs. 95 [87.5‒100] cm; p < 0.001) and were considered physically active less frequently than those without NAFLD (34.3% vs. 60%; p = 0.04). Food intake and dietary patterns assessed by 24hR, FFQ and HEI presented no difference between the groups. CONCLUSIONS PCOS women with coexistent NAFLD had higher BMI, WC and were less physically active than those without NAFLD. Dietary evaluation showed that PCOS women with NAFLD had no significant difference in macro and micronutrients or food group intake and diet quality in comparison to those without NAFLD.
Collapse
Affiliation(s)
| | - Daniela Oliveira de Lima Taranto
- Pós-Graduação em Ciências Aplicadas à Saúde do Adulto, Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil; Serviço de Diagnóstico por Imagem do Hospital das Clínicas da Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil
| | - Claudia Alves Couto
- Pós-Graduação em Ciências Aplicadas à Saúde do Adulto, Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil; Departamento de Clínica Médica, Faculdade de Medicina da Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil; Ambulatório de Doença Hepática Gordurosa Não Alcoólica, Instituto Alfa de Gastroenterologia, Hospital das Clínicas da Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil
| | - Mateus Jorge Nardelli
- Pós-Graduação em Ciências Aplicadas à Saúde do Adulto, Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil
| | - Ana Lucia Cândido
- Departamento de Clínica Médica, Faculdade de Medicina da Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil; Ambulatório de Doença Hepática Gordurosa Não Alcoólica, Instituto Alfa de Gastroenterologia, Hospital das Clínicas da Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil
| | - Cristina de Almeida Hott
- Pós-Graduação em Ciências Aplicadas à Saúde do Adulto, Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil
| | - Lucilene Rezende Anastácio
- Departamento de Alimentos, Faculdade de Farmácia da Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil
| | - Fernando M Reis
- Ambulatório de Hiperandrogenismo, Serviço de Endocrinologia, Hospital das Clínicas da Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil; Departamento de Ginecologia e Obstetrícia, Faculdade de Medicina da Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil
| | - Ana Luiza Lunardi Rocha
- Ambulatório de Hiperandrogenismo, Serviço de Endocrinologia, Hospital das Clínicas da Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil; Departamento de Ginecologia e Obstetrícia, Faculdade de Medicina da Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil
| | - Luciana Costa Faria
- Pós-Graduação em Ciências Aplicadas à Saúde do Adulto, Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil; Departamento de Clínica Médica, Faculdade de Medicina da Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil; Ambulatório de Doença Hepática Gordurosa Não Alcoólica, Instituto Alfa de Gastroenterologia, Hospital das Clínicas da Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil.
| |
Collapse
|
6
|
Kuang M, Qiu J, Li D, Hu C, Zhang S, Sheng G, Zou Y. The newly proposed Metabolic Score for Visceral Fat is a reliable tool for identifying non-alcoholic fatty liver disease, requiring attention to age-specific effects in both sexes. Front Endocrinol (Lausanne) 2023; 14:1281524. [PMID: 38089634 PMCID: PMC10711077 DOI: 10.3389/fendo.2023.1281524] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Accepted: 11/10/2023] [Indexed: 12/18/2023] Open
Abstract
Objective The newly proposed Metabolic Visceral Fat Score (METS-VF) is considered a more effective measure for visceral adipose tissue (VAT) than other obesity indicators. This study aimed to reveal the association between METS-VF and non-alcoholic fatty liver disease (NAFLD), and its variations across age groups within both sexes. Methods Data from 14,251 medical examiners in the NAGALA project were employed in this study. 3D fitted surface plots were constructed based on multivariate logistic regression models to visualize the isolated and combined effects of aging and METS-VF on NAFLD. Receiver operating characteristic curve (ROC) analysis was conducted to compare the diagnostic performance of METS-VF with other VAT surrogate markers in predicting NAFLD. Results The results of multivariate logistic regression analysis showed that each unit increase in METS-VF was independently associated with a 333% and 312% increase in the odds of NAFLD in males and females, respectively. Additionally, the 3D fitted surface plot showed that age significantly influenced the association between METS-VF and the odds of NAFLD in both sexes, as follows: (i) In males, when METS-VF was less than 6.2, the METS-VF-related odds of NAFLD increased gradually with age in the 20-45 age group, reached a plateau in the 45-65 age group, and then decreased in the group above 65 years old; however, when male METS-VF exceeded 6.2, aging and METS-VF combined to further increase the odds of NAFLD in all age groups, particularly in the 45-65 age group. (ii) In females, aging seemed to reduce METS-VF-related odds of NAFLD in the 18-40 age group, but significantly increased it in the 40-60 age group, particularly for those with higher METS-VF levels. Further ROC analysis revealed that compared to other VAT surrogate markers, METS-VF showed the highest diagnostic accuracy for NAFLD in females, especially in those under 45 years of age [area under the curve (AUC) = 0.9256]. Conclusions This study firstly revealed a significant positive correlation between METS-VF and the odds of NAFLD, with METS-VF surpassing other VAT surrogate markers in NAFLD diagnosis. Moreover, age significantly influenced the METS-VF-related odds of NAFLD and METS-VF's diagnostic efficacy for NAFLD in both sexes.
Collapse
Affiliation(s)
- Maobin Kuang
- Department of Internal Medicine, Medical College of Nanchang University, Jiangxi Provincial People’s Hospital, Nanchang, Jiangxi, China
- Jiangxi Cardiovascular Research Institute, Jiangxi Provincial People’s Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, Jiangxi, China
- Jiangxi Provincial Geriatric Hospital, Jiangxi Provincial People’s Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, Jiangxi, China
| | - Jiajun Qiu
- Department of Internal Medicine, Medical College of Nanchang University, Jiangxi Provincial People’s Hospital, Nanchang, Jiangxi, China
- Jiangxi Cardiovascular Research Institute, Jiangxi Provincial People’s Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, Jiangxi, China
- Jiangxi Provincial Geriatric Hospital, Jiangxi Provincial People’s Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, Jiangxi, China
| | - Dongdong Li
- Department of Internal Medicine, Medical College of Nanchang University, Jiangxi Provincial People’s Hospital, Nanchang, Jiangxi, China
- Jiangxi Cardiovascular Research Institute, Jiangxi Provincial People’s Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, Jiangxi, China
- Jiangxi Provincial Geriatric Hospital, Jiangxi Provincial People’s Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, Jiangxi, China
- Department of Pulmonary and Critical Care Medicine, Jiangxi Provincial People’s Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, Jiangxi, China
| | - Chong Hu
- Department of Gastroenterology, Jiangxi Provincial People’s Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, Jiangxi, China
| | - Shuhua Zhang
- Jiangxi Cardiovascular Research Institute, Jiangxi Provincial People’s Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, Jiangxi, China
| | - Guotai Sheng
- Jiangxi Provincial Geriatric Hospital, Jiangxi Provincial People’s Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, Jiangxi, China
| | - Yang Zou
- Jiangxi Cardiovascular Research Institute, Jiangxi Provincial People’s Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, Jiangxi, China
| |
Collapse
|
7
|
De Robertis R, Spoto F, Autelitano D, Guagenti D, Olivieri A, Zanutto P, Incarbone G, D'Onofrio M. Ultrasound-derived fat fraction for detection of hepatic steatosis and quantification of liver fat content. LA RADIOLOGIA MEDICA 2023; 128:1174-1180. [PMID: 37568072 PMCID: PMC10547617 DOI: 10.1007/s11547-023-01693-8] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Accepted: 07/27/2023] [Indexed: 08/13/2023]
Abstract
PURPOSE To compare ultrasound (US) and US-derived fat fraction (UDFF) with magnetic resonance proton density fat fraction (MRI-PDFF) for the detection of hepatic steatosis and quantification of liver fat content. MATERIALS AND METHODS Between October and December 2022, 149 patients scheduled for an abdominal MRI agreed to participate in this study and underwent MRI-PDFF, US and UDFF. Inclusion criteria were: (a) no chronic liver disease or jaundice; (b) no MRI motion artifacts; (c) adequate liver examination at US. Exclusion criteria were: (a) alcohol abuse, chronic hepatitis, cirrhosis, or jaundice; (b) MRI artifacts or insufficient US examination. The median of 10 MRI-PDFF and UDFF measurements in the right hepatic lobe was analyzed. UDFF and MRI-PDFF were compared by Bland-Altman difference plot and Pearson's test. Sensitivity, specificity, positive and negative predictive values, accuracy, and area under the receiver-operator curve (AUC-ROC) of US and UDFF were calculated using an MRI-PDFF cut-off value of 5%. p values ≤ 0.05 were statistically significant. RESULTS 122 patients were included (61 men, mean age 60 years, standard deviation 15 years). The median MRI-PDFF value was 4.1% (interquartile range 2.9-6); 37.7% patients had a median MRI-PDFF value ≥ 5%. UDFF and MRI-PDFF had high agreement (p = 0.11) and positive correlation (⍴ = 0.81, p < 0.001). UDFF had a higher diagnostic value than US for the detection of steatosis, with AUC-ROCs of 0.75 (95% CI 0.65, 0.84) and 0.53 (95% CI 0.42, 0.64), respectively. CONCLUSIONS UDFF reliably quantifies liver fat content and improves the diagnostic value of US for the detection of hepatic steatosis.
Collapse
Affiliation(s)
- Riccardo De Robertis
- Department of Radiology, Ospedale G.B. Rossi AOUI Verona, 37134, Verona, Italy.
- Department of Diagnostics and Public Health, University of Verona, Piazzale L.A. Scuro 10, 37134, Verona, Italy.
| | - Flavio Spoto
- Department of Radiology, Ospedale G.B. Rossi AOUI Verona, 37134, Verona, Italy
- Department of Diagnostics and Public Health, University of Verona, Piazzale L.A. Scuro 10, 37134, Verona, Italy
| | - Daniele Autelitano
- Department of Radiology, Ospedale G.B. Rossi AOUI Verona, 37134, Verona, Italy
- Department of Diagnostics and Public Health, University of Verona, Piazzale L.A. Scuro 10, 37134, Verona, Italy
| | - Daniela Guagenti
- Department of Radiology, Ospedale G.B. Rossi AOUI Verona, 37134, Verona, Italy
- Department of Diagnostics and Public Health, University of Verona, Piazzale L.A. Scuro 10, 37134, Verona, Italy
| | - Antonia Olivieri
- Department of Radiology, Ospedale G.B. Rossi AOUI Verona, 37134, Verona, Italy
- Department of Diagnostics and Public Health, University of Verona, Piazzale L.A. Scuro 10, 37134, Verona, Italy
| | - Piero Zanutto
- Department of Radiology, Ospedale G.B. Rossi AOUI Verona, 37134, Verona, Italy
- Department of Diagnostics and Public Health, University of Verona, Piazzale L.A. Scuro 10, 37134, Verona, Italy
| | - Greta Incarbone
- Department of Radiology, Ospedale G.B. Rossi AOUI Verona, 37134, Verona, Italy
- Department of Diagnostics and Public Health, University of Verona, Piazzale L.A. Scuro 10, 37134, Verona, Italy
| | - Mirko D'Onofrio
- Department of Radiology, Ospedale G.B. Rossi AOUI Verona, 37134, Verona, Italy
- Department of Diagnostics and Public Health, University of Verona, Piazzale L.A. Scuro 10, 37134, Verona, Italy
| |
Collapse
|
8
|
Vianna P, Calce SI, Boustros P, Larocque-Rigney C, Patry-Beaudoin L, Luo YH, Aslan E, Marinos J, Alamri TM, Vu KN, Murphy-Lavallée J, Billiard JS, Montagnon E, Li H, Kadoury S, Nguyen BN, Gauthier S, Therien B, Rish I, Belilovsky E, Wolf G, Chassé M, Cloutier G, Tang A. Comparison of Radiologists and Deep Learning for US Grading of Hepatic Steatosis. Radiology 2023; 309:e230659. [PMID: 37787678 DOI: 10.1148/radiol.230659] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
Background Screening for nonalcoholic fatty liver disease (NAFLD) is suboptimal due to the subjective interpretation of US images. Purpose To evaluate the agreement and diagnostic performance of radiologists and a deep learning model in grading hepatic steatosis in NAFLD at US, with biopsy as the reference standard. Materials and Methods This retrospective study included patients with NAFLD and control patients without hepatic steatosis who underwent abdominal US and contemporaneous liver biopsy from September 2010 to October 2019. Six readers visually graded steatosis on US images twice, 2 weeks apart. Reader agreement was assessed with use of κ statistics. Three deep learning techniques applied to B-mode US images were used to classify dichotomized steatosis grades. Classification performance of human radiologists and the deep learning model for dichotomized steatosis grades (S0, S1, S2, and S3) was assessed with area under the receiver operating characteristic curve (AUC) on a separate test set. Results The study included 199 patients (mean age, 53 years ± 13 [SD]; 101 men). On the test set (n = 52), radiologists had fair interreader agreement (0.34 [95% CI: 0.31, 0.37]) for classifying steatosis grades S0 versus S1 or higher, while AUCs were between 0.49 and 0.84 for radiologists and 0.85 (95% CI: 0.83, 0.87) for the deep learning model. For S0 or S1 versus S2 or S3, radiologists had fair interreader agreement (0.30 [95% CI: 0.27, 0.33]), while AUCs were between 0.57 and 0.76 for radiologists and 0.73 (95% CI: 0.71, 0.75) for the deep learning model. For S2 or lower versus S3, radiologists had fair interreader agreement (0.37 [95% CI: 0.33, 0.40]), while AUCs were between 0.52 and 0.81 for radiologists and 0.67 (95% CI: 0.64, 0.69) for the deep learning model. Conclusion Deep learning approaches applied to B-mode US images provided comparable performance with human readers for detection and grading of hepatic steatosis. Published under a CC BY 4.0 license. Supplemental material is available for this article. See also the editorial by Tuthill in this issue.
Collapse
Affiliation(s)
- Pedro Vianna
- From the Department of Imaging and Engineering (P.V., S.I.C., C.L.R., L.P.B., E.M., H.L., S.K., M.C., G.C., A.T.), Laboratory of Biorheology and Medical Ultrasonics (P.V., G.C.), and Clinical Laboratory of Image Processing (E.M., A.T.), Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montréal, Canada; Institute of Biomedical Engineering (P.V., G.C.) and Department of Computer Science and Operations Research (S.G., I.R., G.W.), Université de Montréal, Montréal, Canada; Departments of Radiology (S.I.C., P.B., C.L.R., L.P.B., Y.H.L., E.A., J.M., T.M.A., K.N.V., J.M.L., J.S.B., A.T.) and Pathology (B.N.N.), Centre Hospitalier de l'Université de Montréal (CHUM), 1058 rue Saint-Denis, Montréal, QC, Canada H2X 3J4; Department of Computer Engineering, École Polytechnique de Montréal, Montréal, Canada (S.K.); Mila-Quebec Artificial Intelligence Institute, Montréal, Canada (S.G., B.T., I.R., E.B., G.W.); and Department of Computer Science and Software Engineering, Concordia University, Montréal, Canada (B.T., E.B.)
| | - Sara-Ivana Calce
- From the Department of Imaging and Engineering (P.V., S.I.C., C.L.R., L.P.B., E.M., H.L., S.K., M.C., G.C., A.T.), Laboratory of Biorheology and Medical Ultrasonics (P.V., G.C.), and Clinical Laboratory of Image Processing (E.M., A.T.), Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montréal, Canada; Institute of Biomedical Engineering (P.V., G.C.) and Department of Computer Science and Operations Research (S.G., I.R., G.W.), Université de Montréal, Montréal, Canada; Departments of Radiology (S.I.C., P.B., C.L.R., L.P.B., Y.H.L., E.A., J.M., T.M.A., K.N.V., J.M.L., J.S.B., A.T.) and Pathology (B.N.N.), Centre Hospitalier de l'Université de Montréal (CHUM), 1058 rue Saint-Denis, Montréal, QC, Canada H2X 3J4; Department of Computer Engineering, École Polytechnique de Montréal, Montréal, Canada (S.K.); Mila-Quebec Artificial Intelligence Institute, Montréal, Canada (S.G., B.T., I.R., E.B., G.W.); and Department of Computer Science and Software Engineering, Concordia University, Montréal, Canada (B.T., E.B.)
| | - Pamela Boustros
- From the Department of Imaging and Engineering (P.V., S.I.C., C.L.R., L.P.B., E.M., H.L., S.K., M.C., G.C., A.T.), Laboratory of Biorheology and Medical Ultrasonics (P.V., G.C.), and Clinical Laboratory of Image Processing (E.M., A.T.), Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montréal, Canada; Institute of Biomedical Engineering (P.V., G.C.) and Department of Computer Science and Operations Research (S.G., I.R., G.W.), Université de Montréal, Montréal, Canada; Departments of Radiology (S.I.C., P.B., C.L.R., L.P.B., Y.H.L., E.A., J.M., T.M.A., K.N.V., J.M.L., J.S.B., A.T.) and Pathology (B.N.N.), Centre Hospitalier de l'Université de Montréal (CHUM), 1058 rue Saint-Denis, Montréal, QC, Canada H2X 3J4; Department of Computer Engineering, École Polytechnique de Montréal, Montréal, Canada (S.K.); Mila-Quebec Artificial Intelligence Institute, Montréal, Canada (S.G., B.T., I.R., E.B., G.W.); and Department of Computer Science and Software Engineering, Concordia University, Montréal, Canada (B.T., E.B.)
| | - Cassandra Larocque-Rigney
- From the Department of Imaging and Engineering (P.V., S.I.C., C.L.R., L.P.B., E.M., H.L., S.K., M.C., G.C., A.T.), Laboratory of Biorheology and Medical Ultrasonics (P.V., G.C.), and Clinical Laboratory of Image Processing (E.M., A.T.), Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montréal, Canada; Institute of Biomedical Engineering (P.V., G.C.) and Department of Computer Science and Operations Research (S.G., I.R., G.W.), Université de Montréal, Montréal, Canada; Departments of Radiology (S.I.C., P.B., C.L.R., L.P.B., Y.H.L., E.A., J.M., T.M.A., K.N.V., J.M.L., J.S.B., A.T.) and Pathology (B.N.N.), Centre Hospitalier de l'Université de Montréal (CHUM), 1058 rue Saint-Denis, Montréal, QC, Canada H2X 3J4; Department of Computer Engineering, École Polytechnique de Montréal, Montréal, Canada (S.K.); Mila-Quebec Artificial Intelligence Institute, Montréal, Canada (S.G., B.T., I.R., E.B., G.W.); and Department of Computer Science and Software Engineering, Concordia University, Montréal, Canada (B.T., E.B.)
| | - Laurent Patry-Beaudoin
- From the Department of Imaging and Engineering (P.V., S.I.C., C.L.R., L.P.B., E.M., H.L., S.K., M.C., G.C., A.T.), Laboratory of Biorheology and Medical Ultrasonics (P.V., G.C.), and Clinical Laboratory of Image Processing (E.M., A.T.), Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montréal, Canada; Institute of Biomedical Engineering (P.V., G.C.) and Department of Computer Science and Operations Research (S.G., I.R., G.W.), Université de Montréal, Montréal, Canada; Departments of Radiology (S.I.C., P.B., C.L.R., L.P.B., Y.H.L., E.A., J.M., T.M.A., K.N.V., J.M.L., J.S.B., A.T.) and Pathology (B.N.N.), Centre Hospitalier de l'Université de Montréal (CHUM), 1058 rue Saint-Denis, Montréal, QC, Canada H2X 3J4; Department of Computer Engineering, École Polytechnique de Montréal, Montréal, Canada (S.K.); Mila-Quebec Artificial Intelligence Institute, Montréal, Canada (S.G., B.T., I.R., E.B., G.W.); and Department of Computer Science and Software Engineering, Concordia University, Montréal, Canada (B.T., E.B.)
| | - Yi Hui Luo
- From the Department of Imaging and Engineering (P.V., S.I.C., C.L.R., L.P.B., E.M., H.L., S.K., M.C., G.C., A.T.), Laboratory of Biorheology and Medical Ultrasonics (P.V., G.C.), and Clinical Laboratory of Image Processing (E.M., A.T.), Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montréal, Canada; Institute of Biomedical Engineering (P.V., G.C.) and Department of Computer Science and Operations Research (S.G., I.R., G.W.), Université de Montréal, Montréal, Canada; Departments of Radiology (S.I.C., P.B., C.L.R., L.P.B., Y.H.L., E.A., J.M., T.M.A., K.N.V., J.M.L., J.S.B., A.T.) and Pathology (B.N.N.), Centre Hospitalier de l'Université de Montréal (CHUM), 1058 rue Saint-Denis, Montréal, QC, Canada H2X 3J4; Department of Computer Engineering, École Polytechnique de Montréal, Montréal, Canada (S.K.); Mila-Quebec Artificial Intelligence Institute, Montréal, Canada (S.G., B.T., I.R., E.B., G.W.); and Department of Computer Science and Software Engineering, Concordia University, Montréal, Canada (B.T., E.B.)
| | - Emre Aslan
- From the Department of Imaging and Engineering (P.V., S.I.C., C.L.R., L.P.B., E.M., H.L., S.K., M.C., G.C., A.T.), Laboratory of Biorheology and Medical Ultrasonics (P.V., G.C.), and Clinical Laboratory of Image Processing (E.M., A.T.), Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montréal, Canada; Institute of Biomedical Engineering (P.V., G.C.) and Department of Computer Science and Operations Research (S.G., I.R., G.W.), Université de Montréal, Montréal, Canada; Departments of Radiology (S.I.C., P.B., C.L.R., L.P.B., Y.H.L., E.A., J.M., T.M.A., K.N.V., J.M.L., J.S.B., A.T.) and Pathology (B.N.N.), Centre Hospitalier de l'Université de Montréal (CHUM), 1058 rue Saint-Denis, Montréal, QC, Canada H2X 3J4; Department of Computer Engineering, École Polytechnique de Montréal, Montréal, Canada (S.K.); Mila-Quebec Artificial Intelligence Institute, Montréal, Canada (S.G., B.T., I.R., E.B., G.W.); and Department of Computer Science and Software Engineering, Concordia University, Montréal, Canada (B.T., E.B.)
| | - John Marinos
- From the Department of Imaging and Engineering (P.V., S.I.C., C.L.R., L.P.B., E.M., H.L., S.K., M.C., G.C., A.T.), Laboratory of Biorheology and Medical Ultrasonics (P.V., G.C.), and Clinical Laboratory of Image Processing (E.M., A.T.), Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montréal, Canada; Institute of Biomedical Engineering (P.V., G.C.) and Department of Computer Science and Operations Research (S.G., I.R., G.W.), Université de Montréal, Montréal, Canada; Departments of Radiology (S.I.C., P.B., C.L.R., L.P.B., Y.H.L., E.A., J.M., T.M.A., K.N.V., J.M.L., J.S.B., A.T.) and Pathology (B.N.N.), Centre Hospitalier de l'Université de Montréal (CHUM), 1058 rue Saint-Denis, Montréal, QC, Canada H2X 3J4; Department of Computer Engineering, École Polytechnique de Montréal, Montréal, Canada (S.K.); Mila-Quebec Artificial Intelligence Institute, Montréal, Canada (S.G., B.T., I.R., E.B., G.W.); and Department of Computer Science and Software Engineering, Concordia University, Montréal, Canada (B.T., E.B.)
| | - Talal M Alamri
- From the Department of Imaging and Engineering (P.V., S.I.C., C.L.R., L.P.B., E.M., H.L., S.K., M.C., G.C., A.T.), Laboratory of Biorheology and Medical Ultrasonics (P.V., G.C.), and Clinical Laboratory of Image Processing (E.M., A.T.), Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montréal, Canada; Institute of Biomedical Engineering (P.V., G.C.) and Department of Computer Science and Operations Research (S.G., I.R., G.W.), Université de Montréal, Montréal, Canada; Departments of Radiology (S.I.C., P.B., C.L.R., L.P.B., Y.H.L., E.A., J.M., T.M.A., K.N.V., J.M.L., J.S.B., A.T.) and Pathology (B.N.N.), Centre Hospitalier de l'Université de Montréal (CHUM), 1058 rue Saint-Denis, Montréal, QC, Canada H2X 3J4; Department of Computer Engineering, École Polytechnique de Montréal, Montréal, Canada (S.K.); Mila-Quebec Artificial Intelligence Institute, Montréal, Canada (S.G., B.T., I.R., E.B., G.W.); and Department of Computer Science and Software Engineering, Concordia University, Montréal, Canada (B.T., E.B.)
| | - Kim-Nhien Vu
- From the Department of Imaging and Engineering (P.V., S.I.C., C.L.R., L.P.B., E.M., H.L., S.K., M.C., G.C., A.T.), Laboratory of Biorheology and Medical Ultrasonics (P.V., G.C.), and Clinical Laboratory of Image Processing (E.M., A.T.), Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montréal, Canada; Institute of Biomedical Engineering (P.V., G.C.) and Department of Computer Science and Operations Research (S.G., I.R., G.W.), Université de Montréal, Montréal, Canada; Departments of Radiology (S.I.C., P.B., C.L.R., L.P.B., Y.H.L., E.A., J.M., T.M.A., K.N.V., J.M.L., J.S.B., A.T.) and Pathology (B.N.N.), Centre Hospitalier de l'Université de Montréal (CHUM), 1058 rue Saint-Denis, Montréal, QC, Canada H2X 3J4; Department of Computer Engineering, École Polytechnique de Montréal, Montréal, Canada (S.K.); Mila-Quebec Artificial Intelligence Institute, Montréal, Canada (S.G., B.T., I.R., E.B., G.W.); and Department of Computer Science and Software Engineering, Concordia University, Montréal, Canada (B.T., E.B.)
| | - Jessica Murphy-Lavallée
- From the Department of Imaging and Engineering (P.V., S.I.C., C.L.R., L.P.B., E.M., H.L., S.K., M.C., G.C., A.T.), Laboratory of Biorheology and Medical Ultrasonics (P.V., G.C.), and Clinical Laboratory of Image Processing (E.M., A.T.), Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montréal, Canada; Institute of Biomedical Engineering (P.V., G.C.) and Department of Computer Science and Operations Research (S.G., I.R., G.W.), Université de Montréal, Montréal, Canada; Departments of Radiology (S.I.C., P.B., C.L.R., L.P.B., Y.H.L., E.A., J.M., T.M.A., K.N.V., J.M.L., J.S.B., A.T.) and Pathology (B.N.N.), Centre Hospitalier de l'Université de Montréal (CHUM), 1058 rue Saint-Denis, Montréal, QC, Canada H2X 3J4; Department of Computer Engineering, École Polytechnique de Montréal, Montréal, Canada (S.K.); Mila-Quebec Artificial Intelligence Institute, Montréal, Canada (S.G., B.T., I.R., E.B., G.W.); and Department of Computer Science and Software Engineering, Concordia University, Montréal, Canada (B.T., E.B.)
| | - Jean-Sébastien Billiard
- From the Department of Imaging and Engineering (P.V., S.I.C., C.L.R., L.P.B., E.M., H.L., S.K., M.C., G.C., A.T.), Laboratory of Biorheology and Medical Ultrasonics (P.V., G.C.), and Clinical Laboratory of Image Processing (E.M., A.T.), Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montréal, Canada; Institute of Biomedical Engineering (P.V., G.C.) and Department of Computer Science and Operations Research (S.G., I.R., G.W.), Université de Montréal, Montréal, Canada; Departments of Radiology (S.I.C., P.B., C.L.R., L.P.B., Y.H.L., E.A., J.M., T.M.A., K.N.V., J.M.L., J.S.B., A.T.) and Pathology (B.N.N.), Centre Hospitalier de l'Université de Montréal (CHUM), 1058 rue Saint-Denis, Montréal, QC, Canada H2X 3J4; Department of Computer Engineering, École Polytechnique de Montréal, Montréal, Canada (S.K.); Mila-Quebec Artificial Intelligence Institute, Montréal, Canada (S.G., B.T., I.R., E.B., G.W.); and Department of Computer Science and Software Engineering, Concordia University, Montréal, Canada (B.T., E.B.)
| | - Emmanuel Montagnon
- From the Department of Imaging and Engineering (P.V., S.I.C., C.L.R., L.P.B., E.M., H.L., S.K., M.C., G.C., A.T.), Laboratory of Biorheology and Medical Ultrasonics (P.V., G.C.), and Clinical Laboratory of Image Processing (E.M., A.T.), Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montréal, Canada; Institute of Biomedical Engineering (P.V., G.C.) and Department of Computer Science and Operations Research (S.G., I.R., G.W.), Université de Montréal, Montréal, Canada; Departments of Radiology (S.I.C., P.B., C.L.R., L.P.B., Y.H.L., E.A., J.M., T.M.A., K.N.V., J.M.L., J.S.B., A.T.) and Pathology (B.N.N.), Centre Hospitalier de l'Université de Montréal (CHUM), 1058 rue Saint-Denis, Montréal, QC, Canada H2X 3J4; Department of Computer Engineering, École Polytechnique de Montréal, Montréal, Canada (S.K.); Mila-Quebec Artificial Intelligence Institute, Montréal, Canada (S.G., B.T., I.R., E.B., G.W.); and Department of Computer Science and Software Engineering, Concordia University, Montréal, Canada (B.T., E.B.)
| | - Hongliang Li
- From the Department of Imaging and Engineering (P.V., S.I.C., C.L.R., L.P.B., E.M., H.L., S.K., M.C., G.C., A.T.), Laboratory of Biorheology and Medical Ultrasonics (P.V., G.C.), and Clinical Laboratory of Image Processing (E.M., A.T.), Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montréal, Canada; Institute of Biomedical Engineering (P.V., G.C.) and Department of Computer Science and Operations Research (S.G., I.R., G.W.), Université de Montréal, Montréal, Canada; Departments of Radiology (S.I.C., P.B., C.L.R., L.P.B., Y.H.L., E.A., J.M., T.M.A., K.N.V., J.M.L., J.S.B., A.T.) and Pathology (B.N.N.), Centre Hospitalier de l'Université de Montréal (CHUM), 1058 rue Saint-Denis, Montréal, QC, Canada H2X 3J4; Department of Computer Engineering, École Polytechnique de Montréal, Montréal, Canada (S.K.); Mila-Quebec Artificial Intelligence Institute, Montréal, Canada (S.G., B.T., I.R., E.B., G.W.); and Department of Computer Science and Software Engineering, Concordia University, Montréal, Canada (B.T., E.B.)
| | - Samuel Kadoury
- From the Department of Imaging and Engineering (P.V., S.I.C., C.L.R., L.P.B., E.M., H.L., S.K., M.C., G.C., A.T.), Laboratory of Biorheology and Medical Ultrasonics (P.V., G.C.), and Clinical Laboratory of Image Processing (E.M., A.T.), Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montréal, Canada; Institute of Biomedical Engineering (P.V., G.C.) and Department of Computer Science and Operations Research (S.G., I.R., G.W.), Université de Montréal, Montréal, Canada; Departments of Radiology (S.I.C., P.B., C.L.R., L.P.B., Y.H.L., E.A., J.M., T.M.A., K.N.V., J.M.L., J.S.B., A.T.) and Pathology (B.N.N.), Centre Hospitalier de l'Université de Montréal (CHUM), 1058 rue Saint-Denis, Montréal, QC, Canada H2X 3J4; Department of Computer Engineering, École Polytechnique de Montréal, Montréal, Canada (S.K.); Mila-Quebec Artificial Intelligence Institute, Montréal, Canada (S.G., B.T., I.R., E.B., G.W.); and Department of Computer Science and Software Engineering, Concordia University, Montréal, Canada (B.T., E.B.)
| | - Bich N Nguyen
- From the Department of Imaging and Engineering (P.V., S.I.C., C.L.R., L.P.B., E.M., H.L., S.K., M.C., G.C., A.T.), Laboratory of Biorheology and Medical Ultrasonics (P.V., G.C.), and Clinical Laboratory of Image Processing (E.M., A.T.), Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montréal, Canada; Institute of Biomedical Engineering (P.V., G.C.) and Department of Computer Science and Operations Research (S.G., I.R., G.W.), Université de Montréal, Montréal, Canada; Departments of Radiology (S.I.C., P.B., C.L.R., L.P.B., Y.H.L., E.A., J.M., T.M.A., K.N.V., J.M.L., J.S.B., A.T.) and Pathology (B.N.N.), Centre Hospitalier de l'Université de Montréal (CHUM), 1058 rue Saint-Denis, Montréal, QC, Canada H2X 3J4; Department of Computer Engineering, École Polytechnique de Montréal, Montréal, Canada (S.K.); Mila-Quebec Artificial Intelligence Institute, Montréal, Canada (S.G., B.T., I.R., E.B., G.W.); and Department of Computer Science and Software Engineering, Concordia University, Montréal, Canada (B.T., E.B.)
| | - Shanel Gauthier
- From the Department of Imaging and Engineering (P.V., S.I.C., C.L.R., L.P.B., E.M., H.L., S.K., M.C., G.C., A.T.), Laboratory of Biorheology and Medical Ultrasonics (P.V., G.C.), and Clinical Laboratory of Image Processing (E.M., A.T.), Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montréal, Canada; Institute of Biomedical Engineering (P.V., G.C.) and Department of Computer Science and Operations Research (S.G., I.R., G.W.), Université de Montréal, Montréal, Canada; Departments of Radiology (S.I.C., P.B., C.L.R., L.P.B., Y.H.L., E.A., J.M., T.M.A., K.N.V., J.M.L., J.S.B., A.T.) and Pathology (B.N.N.), Centre Hospitalier de l'Université de Montréal (CHUM), 1058 rue Saint-Denis, Montréal, QC, Canada H2X 3J4; Department of Computer Engineering, École Polytechnique de Montréal, Montréal, Canada (S.K.); Mila-Quebec Artificial Intelligence Institute, Montréal, Canada (S.G., B.T., I.R., E.B., G.W.); and Department of Computer Science and Software Engineering, Concordia University, Montréal, Canada (B.T., E.B.)
| | - Benjamin Therien
- From the Department of Imaging and Engineering (P.V., S.I.C., C.L.R., L.P.B., E.M., H.L., S.K., M.C., G.C., A.T.), Laboratory of Biorheology and Medical Ultrasonics (P.V., G.C.), and Clinical Laboratory of Image Processing (E.M., A.T.), Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montréal, Canada; Institute of Biomedical Engineering (P.V., G.C.) and Department of Computer Science and Operations Research (S.G., I.R., G.W.), Université de Montréal, Montréal, Canada; Departments of Radiology (S.I.C., P.B., C.L.R., L.P.B., Y.H.L., E.A., J.M., T.M.A., K.N.V., J.M.L., J.S.B., A.T.) and Pathology (B.N.N.), Centre Hospitalier de l'Université de Montréal (CHUM), 1058 rue Saint-Denis, Montréal, QC, Canada H2X 3J4; Department of Computer Engineering, École Polytechnique de Montréal, Montréal, Canada (S.K.); Mila-Quebec Artificial Intelligence Institute, Montréal, Canada (S.G., B.T., I.R., E.B., G.W.); and Department of Computer Science and Software Engineering, Concordia University, Montréal, Canada (B.T., E.B.)
| | - Irina Rish
- From the Department of Imaging and Engineering (P.V., S.I.C., C.L.R., L.P.B., E.M., H.L., S.K., M.C., G.C., A.T.), Laboratory of Biorheology and Medical Ultrasonics (P.V., G.C.), and Clinical Laboratory of Image Processing (E.M., A.T.), Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montréal, Canada; Institute of Biomedical Engineering (P.V., G.C.) and Department of Computer Science and Operations Research (S.G., I.R., G.W.), Université de Montréal, Montréal, Canada; Departments of Radiology (S.I.C., P.B., C.L.R., L.P.B., Y.H.L., E.A., J.M., T.M.A., K.N.V., J.M.L., J.S.B., A.T.) and Pathology (B.N.N.), Centre Hospitalier de l'Université de Montréal (CHUM), 1058 rue Saint-Denis, Montréal, QC, Canada H2X 3J4; Department of Computer Engineering, École Polytechnique de Montréal, Montréal, Canada (S.K.); Mila-Quebec Artificial Intelligence Institute, Montréal, Canada (S.G., B.T., I.R., E.B., G.W.); and Department of Computer Science and Software Engineering, Concordia University, Montréal, Canada (B.T., E.B.)
| | - Eugene Belilovsky
- From the Department of Imaging and Engineering (P.V., S.I.C., C.L.R., L.P.B., E.M., H.L., S.K., M.C., G.C., A.T.), Laboratory of Biorheology and Medical Ultrasonics (P.V., G.C.), and Clinical Laboratory of Image Processing (E.M., A.T.), Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montréal, Canada; Institute of Biomedical Engineering (P.V., G.C.) and Department of Computer Science and Operations Research (S.G., I.R., G.W.), Université de Montréal, Montréal, Canada; Departments of Radiology (S.I.C., P.B., C.L.R., L.P.B., Y.H.L., E.A., J.M., T.M.A., K.N.V., J.M.L., J.S.B., A.T.) and Pathology (B.N.N.), Centre Hospitalier de l'Université de Montréal (CHUM), 1058 rue Saint-Denis, Montréal, QC, Canada H2X 3J4; Department of Computer Engineering, École Polytechnique de Montréal, Montréal, Canada (S.K.); Mila-Quebec Artificial Intelligence Institute, Montréal, Canada (S.G., B.T., I.R., E.B., G.W.); and Department of Computer Science and Software Engineering, Concordia University, Montréal, Canada (B.T., E.B.)
| | - Guy Wolf
- From the Department of Imaging and Engineering (P.V., S.I.C., C.L.R., L.P.B., E.M., H.L., S.K., M.C., G.C., A.T.), Laboratory of Biorheology and Medical Ultrasonics (P.V., G.C.), and Clinical Laboratory of Image Processing (E.M., A.T.), Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montréal, Canada; Institute of Biomedical Engineering (P.V., G.C.) and Department of Computer Science and Operations Research (S.G., I.R., G.W.), Université de Montréal, Montréal, Canada; Departments of Radiology (S.I.C., P.B., C.L.R., L.P.B., Y.H.L., E.A., J.M., T.M.A., K.N.V., J.M.L., J.S.B., A.T.) and Pathology (B.N.N.), Centre Hospitalier de l'Université de Montréal (CHUM), 1058 rue Saint-Denis, Montréal, QC, Canada H2X 3J4; Department of Computer Engineering, École Polytechnique de Montréal, Montréal, Canada (S.K.); Mila-Quebec Artificial Intelligence Institute, Montréal, Canada (S.G., B.T., I.R., E.B., G.W.); and Department of Computer Science and Software Engineering, Concordia University, Montréal, Canada (B.T., E.B.)
| | - Michaël Chassé
- From the Department of Imaging and Engineering (P.V., S.I.C., C.L.R., L.P.B., E.M., H.L., S.K., M.C., G.C., A.T.), Laboratory of Biorheology and Medical Ultrasonics (P.V., G.C.), and Clinical Laboratory of Image Processing (E.M., A.T.), Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montréal, Canada; Institute of Biomedical Engineering (P.V., G.C.) and Department of Computer Science and Operations Research (S.G., I.R., G.W.), Université de Montréal, Montréal, Canada; Departments of Radiology (S.I.C., P.B., C.L.R., L.P.B., Y.H.L., E.A., J.M., T.M.A., K.N.V., J.M.L., J.S.B., A.T.) and Pathology (B.N.N.), Centre Hospitalier de l'Université de Montréal (CHUM), 1058 rue Saint-Denis, Montréal, QC, Canada H2X 3J4; Department of Computer Engineering, École Polytechnique de Montréal, Montréal, Canada (S.K.); Mila-Quebec Artificial Intelligence Institute, Montréal, Canada (S.G., B.T., I.R., E.B., G.W.); and Department of Computer Science and Software Engineering, Concordia University, Montréal, Canada (B.T., E.B.)
| | - Guy Cloutier
- From the Department of Imaging and Engineering (P.V., S.I.C., C.L.R., L.P.B., E.M., H.L., S.K., M.C., G.C., A.T.), Laboratory of Biorheology and Medical Ultrasonics (P.V., G.C.), and Clinical Laboratory of Image Processing (E.M., A.T.), Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montréal, Canada; Institute of Biomedical Engineering (P.V., G.C.) and Department of Computer Science and Operations Research (S.G., I.R., G.W.), Université de Montréal, Montréal, Canada; Departments of Radiology (S.I.C., P.B., C.L.R., L.P.B., Y.H.L., E.A., J.M., T.M.A., K.N.V., J.M.L., J.S.B., A.T.) and Pathology (B.N.N.), Centre Hospitalier de l'Université de Montréal (CHUM), 1058 rue Saint-Denis, Montréal, QC, Canada H2X 3J4; Department of Computer Engineering, École Polytechnique de Montréal, Montréal, Canada (S.K.); Mila-Quebec Artificial Intelligence Institute, Montréal, Canada (S.G., B.T., I.R., E.B., G.W.); and Department of Computer Science and Software Engineering, Concordia University, Montréal, Canada (B.T., E.B.)
| | - An Tang
- From the Department of Imaging and Engineering (P.V., S.I.C., C.L.R., L.P.B., E.M., H.L., S.K., M.C., G.C., A.T.), Laboratory of Biorheology and Medical Ultrasonics (P.V., G.C.), and Clinical Laboratory of Image Processing (E.M., A.T.), Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montréal, Canada; Institute of Biomedical Engineering (P.V., G.C.) and Department of Computer Science and Operations Research (S.G., I.R., G.W.), Université de Montréal, Montréal, Canada; Departments of Radiology (S.I.C., P.B., C.L.R., L.P.B., Y.H.L., E.A., J.M., T.M.A., K.N.V., J.M.L., J.S.B., A.T.) and Pathology (B.N.N.), Centre Hospitalier de l'Université de Montréal (CHUM), 1058 rue Saint-Denis, Montréal, QC, Canada H2X 3J4; Department of Computer Engineering, École Polytechnique de Montréal, Montréal, Canada (S.K.); Mila-Quebec Artificial Intelligence Institute, Montréal, Canada (S.G., B.T., I.R., E.B., G.W.); and Department of Computer Science and Software Engineering, Concordia University, Montréal, Canada (B.T., E.B.)
| |
Collapse
|
9
|
Henry L, Eberly KE, Shah D, Kumar A, Younossi ZM. Noninvasive Tests Used in Risk Stratification of Patients with Nonalcoholic Fatty Liver Disease. Clin Liver Dis 2023; 27:373-395. [PMID: 37024214 DOI: 10.1016/j.cld.2023.01.022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/08/2023]
Abstract
As the prevalence of obesity and type 2 diabetes increases around the world, the prevalence of nonalcoholic fatty liver disease (NAFLD) has grown proportionately. Although most patients with NAFLD do not experience progressive liver disease, about 15% to 20% of those with nonalcoholic steatohepatitis can and do progress. Because liver biopsy's role in NAFLD has become increasingly limited, efforts have been undertaken to develop non-invasive tests (NITs) to help identify patients at high risk of progression. The following article discusses the NITs that are available to determine the presence of NAFLD as well as high-risk NAFLD.
Collapse
Affiliation(s)
- Linda Henry
- Inova Medicine, Inova Health System, 3300 Gallows Road, Falls Church, VA 22042, USA; Liver and Obesity Research Program, Inova Health System, 3300 Gallows Road, Falls Church, VA 22042, USA; Department of Medicine, Center for Liver Diseases, Inova Fairfax Medical Campus, 3300 Gallows Road, Falls Church, VA 22042, USA; Center for Outcomes Research in Liver Diseases, 2411 I Street, Northwest Washington, DC 20037, USA
| | - Katherine Elizabeth Eberly
- Inova Medicine, Inova Health System, 3300 Gallows Road, Falls Church, VA 22042, USA; Department of Medicine, Center for Liver Diseases, Inova Fairfax Medical Campus, 3300 Gallows Road, Falls Church, VA 22042, USA
| | - Dipam Shah
- Inova Medicine, Inova Health System, 3300 Gallows Road, Falls Church, VA 22042, USA; Department of Medicine, Center for Liver Diseases, Inova Fairfax Medical Campus, 3300 Gallows Road, Falls Church, VA 22042, USA
| | - Ameeta Kumar
- Inova Medicine, Inova Health System, 3300 Gallows Road, Falls Church, VA 22042, USA; Department of Medicine, Center for Liver Diseases, Inova Fairfax Medical Campus, 3300 Gallows Road, Falls Church, VA 22042, USA
| | - Zobair M Younossi
- Inova Medicine, Inova Health System, 3300 Gallows Road, Falls Church, VA 22042, USA; Liver and Obesity Research Program, Inova Health System, 3300 Gallows Road, Falls Church, VA 22042, USA; Department of Medicine, Center for Liver Diseases, Inova Fairfax Medical Campus, 3300 Gallows Road, Falls Church, VA 22042, USA.
| |
Collapse
|
10
|
Tahmasebi A, Wang S, Wessner CE, Vu T, Liu JB, Forsberg F, Civan J, Guglielmo FF, Eisenbrey JR. Ultrasound-Based Machine Learning Approach for Detection of Nonalcoholic Fatty Liver Disease. JOURNAL OF ULTRASOUND IN MEDICINE : OFFICIAL JOURNAL OF THE AMERICAN INSTITUTE OF ULTRASOUND IN MEDICINE 2023. [PMID: 36807314 DOI: 10.1002/jum.16194] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Revised: 12/05/2022] [Accepted: 01/25/2023] [Indexed: 06/18/2023]
Abstract
OBJECTIVES Current diagnosis of nonalcoholic fatty liver disease (NAFLD) relies on biopsy or MR-based fat quantification. This prospective study explored the use of ultrasound with artificial intelligence for the detection of NAFLD. METHODS One hundred and twenty subjects with clinical suspicion of NAFLD and 10 healthy volunteers consented to participate in this institutional review board-approved study. Subjects were categorized as NAFLD and non-NAFLD according to MR proton density fat fraction (PDFF) findings. Ultrasound images from 10 different locations in the right and left hepatic lobes were collected following a standard protocol. MRI-based liver fat quantification was used as the reference standard with >6.4% indicative of NAFLD. A supervised machine learning model was developed for assessment of NAFLD. To validate model performance, a balanced testing dataset of 24 subjects was used. Sensitivity, specificity, positive predictive value, negative predictive value, and overall accuracy with 95% confidence interval were calculated. RESULTS A total of 1119 images from 106 participants was used for model development. The internal evaluation achieved an average precision of 0.941, recall of 88.2%, and precision of 89.0%. In the testing set AutoML achieved a sensitivity of 72.2% (63.1%-80.1%), specificity of 94.6% (88.7%-98.0%), positive predictive value (PPV) of 93.1% (86.0%-96.7%), negative predictive value of 77.3% (71.6%-82.1%), and accuracy of 83.4% (77.9%-88.0%). The average agreement for an individual subject was 92%. CONCLUSIONS An ultrasound-based machine learning model for identification of NAFLD showed high specificity and PPV in this prospective trial. This approach may in the future be used as an inexpensive and noninvasive screening tool for identifying NAFLD in high-risk patients.
Collapse
Affiliation(s)
- Aylin Tahmasebi
- Department of Radiology, Thomas Jefferson University, Philadelphia, Pennsylvania, USA
| | - Shuo Wang
- Department of Radiology, Thomas Jefferson University, Philadelphia, Pennsylvania, USA
| | - Corinne E Wessner
- Department of Radiology, Thomas Jefferson University, Philadelphia, Pennsylvania, USA
| | - Trang Vu
- Department of Radiology, Thomas Jefferson University, Philadelphia, Pennsylvania, USA
| | - Ji-Bin Liu
- Department of Radiology, Thomas Jefferson University, Philadelphia, Pennsylvania, USA
| | - Flemming Forsberg
- Department of Radiology, Thomas Jefferson University, Philadelphia, Pennsylvania, USA
| | - Jesse Civan
- Department of Medicine, Division of Gastroenterology and Hepatology, Thomas Jefferson University, Philadelphia, Pennsylvania, USA
| | - Flavius F Guglielmo
- Department of Radiology, Thomas Jefferson University, Philadelphia, Pennsylvania, USA
| | - John R Eisenbrey
- Department of Radiology, Thomas Jefferson University, Philadelphia, Pennsylvania, USA
| |
Collapse
|
11
|
Kjaergaard M, Lindvig KP, Hansen CD, Detlefsen S, Krag A, Thiele M. Hepatorenal Index by B-Mode Ratio Versus Imaging and Fatty Liver Index to Diagnose Steatosis in Alcohol-Related and Nonalcoholic Fatty Liver Disease. JOURNAL OF ULTRASOUND IN MEDICINE : OFFICIAL JOURNAL OF THE AMERICAN INSTITUTE OF ULTRASOUND IN MEDICINE 2023; 42:487-496. [PMID: 35475550 PMCID: PMC10084348 DOI: 10.1002/jum.15991] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/05/2022] [Revised: 03/26/2022] [Accepted: 03/28/2022] [Indexed: 05/12/2023]
Abstract
OBJECTIVES We aimed to evaluate the accuracy of the hepatorenal index by B-mode ratio to diagnose hepatic steatosis, compared to ultrasound steatosis score, controlled attenuation parameter, and the fatty liver index using histology as the gold standard. METHODS We prospectively included participants with alcohol-related or nonalcoholic fatty liver disease for same-day noninvasive investigations and liver biopsy. RESULTS We included 137 participants, 72% male, median age 60 years (53-65) and body mass index 32 kg/m2 (28-38). Eighty percent had steatosis (S0/S1/S2/S3 = 20/37/24/19%). B-mode ratio had moderate diagnostic accuracy for any steatosis (≥S1, area under the receiver operating characteristics curve [AUROC] = 0.79; 95% confidence interval 0.70-0.88), significant steatosis (≥S2, AUROC = 0.76; 0.66-0.85), and severe steatosis (=S3, AUROC = 0.74; 0.62-0.86), independent of disease etiology. The cutoff values to rule-out and rule-in any steatosis were 1.09 and 1.45. While B-mode ratio and controlled attenuation parameter correlated poorly, their diagnostic accuracies were comparable to each other and to ultrasound steatosis scoring. Fatty liver index did not differ from B-mode ratio in detecting any steatosis but had poor accuracy to detect higher steatosis grades. B-mode ratio measurements failed in 12% of patients, compared to 1% for ultrasound steatosis scoring and 2% for controlled attenuation parameter. CONCLUSION The hepatorenal index by B-mode ratio diagnose steatosis with moderate accuracy in patients with alcohol-related or nonalcoholic fatty liver disease, comparable to B-mode ultrasound steatosis scoring and controlled attenuation parameter. However, its clinical use is limited by a high failure rate.
Collapse
Affiliation(s)
- Maria Kjaergaard
- Department of Gastroenterology and HepatologyOdense University HospitalOdenseDenmark
- Institute of Clinical Research, University of Southern DenmarkOdenseDenmark
| | - Katrine Prier Lindvig
- Department of Gastroenterology and HepatologyOdense University HospitalOdenseDenmark
- Institute of Clinical Research, University of Southern DenmarkOdenseDenmark
| | - Camilla Dalby Hansen
- Department of Gastroenterology and HepatologyOdense University HospitalOdenseDenmark
- Institute of Clinical Research, University of Southern DenmarkOdenseDenmark
| | - Sönke Detlefsen
- Institute of Clinical Research, University of Southern DenmarkOdenseDenmark
- Department of PathologyOdense University HospitalOdenseDenmark
| | - Aleksander Krag
- Department of Gastroenterology and HepatologyOdense University HospitalOdenseDenmark
- Institute of Clinical Research, University of Southern DenmarkOdenseDenmark
| | - Maja Thiele
- Department of Gastroenterology and HepatologyOdense University HospitalOdenseDenmark
- Institute of Clinical Research, University of Southern DenmarkOdenseDenmark
| |
Collapse
|
12
|
Hojreh A, Lischka J, Tamandl D, Ramazanova D, Mulabdic A, Greber-Platzer S, Ba-Ssalamah A. Relative Enhancement in Gadoxetate Disodium-Enhanced Liver MRI as an Imaging Biomarker in the Diagnosis of Non-Alcoholic Fatty Liver Disease in Pediatric Obesity. Nutrients 2023; 15:nu15030558. [PMID: 36771265 PMCID: PMC9921256 DOI: 10.3390/nu15030558] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Revised: 01/16/2023] [Accepted: 01/17/2023] [Indexed: 01/24/2023] Open
Abstract
Relative enhancement (RE) in gadoxetate disodium (Gd-EOB-DTPA)-enhanced MRI is a reliable, non-invasive method for the evaluation and differentiation between simple steatosis and non-alcoholic steatohepatitis in adults. This study evaluated the diagnostic accuracy of RE in Gd-EOB-DTPA-enhanced liver MRI and hepatic fat fraction (HFF) in unenhanced liver MRI and ultrasound (US) for non-alcoholic fatty liver disease (NAFLD) screening in pediatric obesity. Seventy-four liver US and MRIs from 68 pediatric patients (13.07 ± 2.95 years) with obesity (BMI > BMI-for-age + 2SD) were reviewed with regard to imaging biomarkers (liver size, volume, echogenicity, HFF, and RE in Gd-EOB-DTPA-enhanced MRIs, and spleen size), blood biomarkers, and BMI. The agreement between the steatosis grade, according to HFF in MRI and the echogenicity in US, was moderate. Alanine aminotransferase correlated better with the imaging biomarkers in MRI than with those in US. BMI correlated better with liver size and volume on MRI than in US. In patients with RE < 1, blood biomarkers correlated better with RE than those in the whole sample, with a significant association between gamma-glutamyltransferase and RE (p = 0.033). In conclusion, the relative enhancement and hepatic fat fraction can be considered as non-invasive tools for the screening and follow-up of NAFLD in pediatric obesity, superior to echogenicity on ultrasound.
Collapse
Affiliation(s)
- Azadeh Hojreh
- Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, 1090 Vienna, Austria
- Correspondence: ; Tel.: +43-1-40400-48180
| | - Julia Lischka
- Clinical Division of Pediatric Pulmonology, Allergology and Endocrinology, Department of Pediatrics and Adolescent Medicine, Medical University of Vienna, Waehringer Guertel 18-20, 1090 Vienna, Austria
| | - Dietmar Tamandl
- Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, 1090 Vienna, Austria
| | - Dariga Ramazanova
- Section for Medical Statistics, Center for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna, Spitalgasse 23, 1090 Vienna, Austria
| | - Amra Mulabdic
- Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, 1090 Vienna, Austria
| | - Susanne Greber-Platzer
- Clinical Division of Pediatric Pulmonology, Allergology and Endocrinology, Department of Pediatrics and Adolescent Medicine, Medical University of Vienna, Waehringer Guertel 18-20, 1090 Vienna, Austria
| | - Ahmed Ba-Ssalamah
- Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, 1090 Vienna, Austria
| |
Collapse
|
13
|
Tang S, Wu J, Xu S, Li Q, He J. Clinical-radiomic analysis for non-invasive prediction of liver steatosis on non-contrast CT: A pilot study. Front Genet 2023; 14:1071085. [PMID: 37021007 PMCID: PMC10069650 DOI: 10.3389/fgene.2023.1071085] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2022] [Accepted: 03/09/2023] [Indexed: 04/07/2023] Open
Abstract
Purpose: Our aim is to build and validate a clinical-radiomic model for non-invasive liver steatosis prediction based on non-contrast computed tomography (CT). Methods: We retrospectively reviewed 342 patients with suspected NAFLD diagnoses between January 2019 and July 2020 who underwent non-contrast CT and liver biopsy. Radiomics features from hepatic and splenic regions-of-interests (ROIs) were extracted based on abdominal non-contrast CT imaging. The radiomics signature was constructed based on reproducible features by adopting the least absolute shrinkage and selection operator (LASSO) regression. Then, multivariate logistic regression analysis was applied to develop a combined clinical-radiomic nomogram integrating radiomics signature with several independent clinical predictors in a training cohort of 124 patients between January 2019 and December 2019. The performance of models was determined by the area under the receiver operating characteristic curves and calibration curves. We conducted an internal validation during 103 consecutive patients between January 2020 and July 2020. Results: The radiomics signature was composed of four steatosis-related features and positively correlated with pathologic liver steatosis grade (p < 0.01). In both subgroups (Group One, none vs. steatosis; Group Two, none/mild vs. moderate/severe steatosis), the clinical-radiomic model performed best within the validation cohort with an AUC of 0.734 and 0.930, respectively. The calibration curve confirmed the concordance of excellent models. Conclusion: We developed a robust clinical-radiomic model for accurate liver steatosis stage prediction in a non-invasive way, which may improve the clinical decision-making ability.
Collapse
Affiliation(s)
- Shengnan Tang
- Department of Nuclear Medicine, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, China
| | - Jin Wu
- Department of Nuclear Medicine, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, China
| | - Shanshan Xu
- Department of Nuclear Medicine, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, China
| | - Qi Li
- Department of Pathology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, China
| | - Jian He
- Department of Nuclear Medicine, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, China
- *Correspondence: Jian He,
| |
Collapse
|
14
|
Li YW, Jiao Y, Chen N, Gao Q, Chen YK, Zhang YF, Wen QP, Zhang ZM. How to select the quantitative magnetic resonance technique for subjects with fatty liver: A systematic review. World J Clin Cases 2022; 10:8906-8921. [PMID: 36157636 PMCID: PMC9477046 DOI: 10.12998/wjcc.v10.i25.8906] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/25/2022] [Revised: 05/25/2022] [Accepted: 07/22/2022] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND Early quantitative assessment of liver fat content is essential for patients with fatty liver disease. Mounting evidence has shown that magnetic resonance (MR) technique has high accuracy in the quantitative analysis of fatty liver, and is suitable for monitoring the therapeutic effect on fatty liver. However, many packaging methods and postprocessing functions have puzzled radiologists in clinical applications. Therefore, selecting a quantitative MR imaging technique for patients with fatty liver disease remains challenging. AIM To provide information for the proper selection of commonly used quantitative MR techniques to quantify fatty liver. METHODS We completed a systematic literature review of quantitative MR techniques for detecting fatty liver, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses protocol. Studies were retrieved from PubMed, Embase, and Cochrane Library databases, and their quality was assessed using the Quality Assessment of Diagnostic Studies criteria. The Reference Citation Analysis database (https:// www.referencecitationanalysis.com) was used to analyze citation of articles which were included in this review. RESULTS Forty studies were included for spectroscopy, two-point Dixon imaging, and multiple-point Dixon imaging comparing liver biopsy to other imaging methods. The advantages and disadvantages of each of the three techniques and their clinical diagnostic performances were analyzed. CONCLUSION The proton density fat fraction derived from multiple-point Dixon imaging is a noninvasive method for accurate quantitative measurement of hepatic fat content in the diagnosis and monitoring of fatty liver progression.
Collapse
Affiliation(s)
- You-Wei Li
- Department of Radiology, Beijing Rehabilitation Hospital, Capital Medical University, Beijing 100144, China
| | - Yang Jiao
- Department of Rehabilitation Psychology, Beijing Rehabilitation Hospital, Capital Medical University, Beijing 100144, China
| | - Na Chen
- Department of Otorhinolaryngology, Beijing Rehabilitation Hospital, Capital Medical University, Beijing 100144, China
| | - Qiang Gao
- Department of Gastroenterology and Hepatology, Beijing Rehabilitation Hospital, Capital Medical University, Beijing 100144, China
| | - Yu-Kun Chen
- Department of Radiology, Beijing Rehabilitation Hospital, Capital Medical University, Beijing 100144, China
| | - Yuan-Fang Zhang
- Department of Radiology, Beijing Rehabilitation Hospital, Capital Medical University, Beijing 100144, China
| | - Qi-Ping Wen
- Department of Radiology, Beijing Rehabilitation Hospital, Capital Medical University, Beijing 100144, China
| | - Zong-Ming Zhang
- Department of General Surgery, Beijing Electric Power Hospital, State Grid Corporation of China, Capital Medical University, Beijing 100073, China
| |
Collapse
|
15
|
Benefits of Physical Exercise as Approach to Prevention and Reversion of Non-Alcoholic Fatty Liver Disease in Children and Adolescents with Obesity. CHILDREN 2022; 9:children9081174. [PMID: 36010064 PMCID: PMC9406958 DOI: 10.3390/children9081174] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Revised: 07/28/2022] [Accepted: 08/03/2022] [Indexed: 12/15/2022]
Abstract
Non-alcoholic fatty liver disease (NAFLD) is an important health concern during childhood; indeed, it is the most frequent cause of chronic liver diseases in obese children. No valid pharmacological therapies for children affected by this condition are available, and the recommended treatment is lifestyle modification, usually including nutrition and exercise interventions. In this narrative review, we summarized up-to-date information on the benefits of physical exercise on NAFLD in children and adolescents with obesity. The role of exercise as non-pharmacological treatment was emphasized in order to provide recent advances on this topic for clinicians not deeply involved in the field. Several studies on obese children and adults confirm the positive role of physical activity (PA) in the treatment of NAFLD, but to date, there are no pediatric randomized clinical trials on exercise versus usual care. Among the pathogenic mechanisms involved in the PA effects on NAFLD, the main players seem to be insulin resistance and related inflammation, oxidative stress, and gut dysbiosis, but further evaluations are necessary to deeply understand whether these factors are correlated and how they synergistically act. Thus, a deeper research on this theme is needed, and it would be extremely interesting.
Collapse
|
16
|
Zhang L, Zhang M, Wang M, Wang M, Zhang R, Wang H, Zhang W, Ding Y, Wang J. External validation and comparison of simple tools to screen for nonalcoholic fatty liver disease in Chinese community population. Eur J Gastroenterol Hepatol 2022; 34:865-872. [PMID: 35802528 PMCID: PMC9273302 DOI: 10.1097/meg.0000000000002399] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Accepted: 04/12/2022] [Indexed: 12/21/2022]
Abstract
BACKGROUND Various noninvasive tools based on anthropometric indicators, blood lipids, and liver enzymes, etc. have been developed to screen for nonalcoholic fatty liver disease (NAFLD), with different diagnostic performance and cutoff values among studies. We aimed to validate and compare eight NAFLD-related models developed by simple indicators and to define their cutoff values in Chinese community population. METHODS A cross-sectional study was conducted in a health examination cohort of 3259 people. NAFLD was diagnosed by ultrasonography. General, anthropometric and biochemical data were collected. Fatty liver index (FLI), fatty liver disease index (FLD), Zhejiang University index (ZJU), lipid accumulation product (LAP), regression formula of controlled attenuation parameter (CAP), waist-to-height ratio (WHtR), triglyceride and glucose index (TyG), and visceral adiposity index (VAI) were calculated. The accuracy and cutoff points to detect NAFLD were evaluated by area under the receiver operator characteristic curve and the maximum Youden index analysis, respectively. A head-to-head comparison between these models and Decision Curve Analysis (DCA) was conducted. RESULTS In eight noninvasive diagnostic models of NAFLD, AUCs of FLI and FLD for NAFLD were higher than those of other models in the whole (0.852 and 0.852), male (0.826 and 0.824), and female (0.897 and 0.888) population, respectively. DCA showed that FLI, FLD, and ZJU have higher net benefit to screen for NAFLD compared to other models. CONCLUSIONS FLI and FLD could be the most accurate and applicable of eight models for the noninvasive diagnosis of NAFLD in both male and female groups.
Collapse
Affiliation(s)
- Liuxin Zhang
- Department of Neurosurgery, The Affiliated Brain Hospital of Nanjing Medical University
| | - Mengting Zhang
- Department of Fundamental and Community Nursing, School of Nursing, Nanjing Medical University
| | - Min Wang
- Department of Fundamental and Community Nursing, School of Nursing, Nanjing Medical University
| | - Minxian Wang
- Department of Fundamental and Community Nursing, School of Nursing, Nanjing Medical University
| | - Ru Zhang
- Department of Fundamental and Community Nursing, School of Nursing, Nanjing Medical University
| | - Hongliang Wang
- Department of General Practice, Ninghai Road Community Health Service Center of Nanjing, Nanjing
| | - Wei Zhang
- Department of Epidemiology, Shanghai Cancer Institute, Shanghai, China
| | - Yajie Ding
- Department of Fundamental and Community Nursing, School of Nursing, Nanjing Medical University
| | - Jie Wang
- Department of Fundamental and Community Nursing, School of Nursing, Nanjing Medical University
| |
Collapse
|
17
|
Liang Y, Chen H, Liu Y, Hou X, Wei L, Bao Y, Yang C, Zong G, Wu J, Jia W. Association of MAFLD With Diabetes, Chronic Kidney Disease, and Cardiovascular Disease: A 4.6-Year Cohort Study in China. J Clin Endocrinol Metab 2022; 107:88-97. [PMID: 34508601 PMCID: PMC8684479 DOI: 10.1210/clinem/dgab641] [Citation(s) in RCA: 122] [Impact Index Per Article: 40.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Indexed: 12/24/2022]
Abstract
CONTEXT In 2020, the terminology of metabolic dysfunction-associated fatty liver disease (MAFLD) was proposed to replace nonalcoholic fatty liver disease (NAFLD). OBJECTIVES This work aimed to investigate the prevalence and incidence of MAFLD and evaluate its effects on incident extrahepatic diseases. METHODS A total of 6873 individuals, with a 4.6-year follow-up, were included in this study. Associations of MAFLD and NAFLD with diabetes, chronic kidney disease (CKD), and cardiovascular disease (CVD) were examined using logistic regression and Cox proportional hazards models. RESULTS The prevalence of NAFLD and MAFLD was 40.3% (95% CI, 39.2%-41.5%) and 46.7% (95% CI, 45.6%-47.9%), respectively. Additionally, 321 (4.7%) and 156 (2.3%) participants had MAFLD with excessive alcohol consumption and hepatitis B virus (HBV) infection. During the follow-up period, the incidence of NAFLD and MAFLD was 22.7% (95% CI, 21.3%-24.0%) and 27.0% (95% CI, 25.5%-28.4%). MAFLD was associated with higher risks of incident diabetes (risk ratio [RR] 2.08; 95% CI, 1.72-2.52), CKD (RR 1.64; 95% CI, 1.39-1.94), and CVD (hazard ratio 1.44; 95% CI, 1.15-1.81). Similar associations for NAFLD were observed. Furthermore, the MAFLD subgroups with excessive alcohol consumption (RR 2.49; 95% CI, 1.64-3.78) and HBV infection (RR 1.98; 95% CI, 1.11-3.52) were associated with higher risks of incident diabetes. CONCLUSION The change from NAFLD to MAFLD did not greatly affect the associations with diabetes, CKD, and CVD. MAFLD further identified those patients of metabolically fatty liver combined with excessive alcohol consumption and HBV infection, who had increased risks of incident diabetes compared with those of non-fatty liver.
Collapse
Affiliation(s)
- Yebei Liang
- Shanghai Diabetes Institute, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai Key Laboratory of Diabetes Mellitus, Department of Endocrinology and Metabolism, Shanghai Clinical Center for Diabetes, Shanghai Key Clinical Center for Metabolic Disease, Shanghai 200233, China
| | - Hongli Chen
- Shanghai Diabetes Institute, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai Key Laboratory of Diabetes Mellitus, Department of Endocrinology and Metabolism, Shanghai Clinical Center for Diabetes, Shanghai Key Clinical Center for Metabolic Disease, Shanghai 200233, China
| | - Yuexing Liu
- Shanghai Diabetes Institute, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai Key Laboratory of Diabetes Mellitus, Department of Endocrinology and Metabolism, Shanghai Clinical Center for Diabetes, Shanghai Key Clinical Center for Metabolic Disease, Shanghai 200233, China
| | - Xuhong Hou
- Shanghai Diabetes Institute, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai Key Laboratory of Diabetes Mellitus, Department of Endocrinology and Metabolism, Shanghai Clinical Center for Diabetes, Shanghai Key Clinical Center for Metabolic Disease, Shanghai 200233, China
| | - Li Wei
- Shanghai Diabetes Institute, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai Key Laboratory of Diabetes Mellitus, Department of Endocrinology and Metabolism, Shanghai Clinical Center for Diabetes, Shanghai Key Clinical Center for Metabolic Disease, Shanghai 200233, China
| | - Yuqian Bao
- Shanghai Diabetes Institute, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai Key Laboratory of Diabetes Mellitus, Department of Endocrinology and Metabolism, Shanghai Clinical Center for Diabetes, Shanghai Key Clinical Center for Metabolic Disease, Shanghai 200233, China
| | - Chunguang Yang
- Shanghai Diabetes Institute, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai Key Laboratory of Diabetes Mellitus, Department of Endocrinology and Metabolism, Shanghai Clinical Center for Diabetes, Shanghai Key Clinical Center for Metabolic Disease, Shanghai 200233, China
| | - Geng Zong
- Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China
- Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai, 200233, China
| | - Jiarui Wu
- CAS Key Laboratory of Systems Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai 200031, China
| | - Weiping Jia
- Shanghai Diabetes Institute, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai Key Laboratory of Diabetes Mellitus, Department of Endocrinology and Metabolism, Shanghai Clinical Center for Diabetes, Shanghai Key Clinical Center for Metabolic Disease, Shanghai 200233, China
| |
Collapse
|
18
|
Zarei F, Moini M, Abedi M, Ravanfar Haghighi R, Zeinali-Rafsanjani B. Liver Fibrosis Assessment Using Transient Elastography by FibroScan and Shear Wave Elastography by Sonography: A Comparative Cross-sectional Study in an Outpatient Liver Clinic. IRANIAN JOURNAL OF RADIOLOGY 2021; 18. [DOI: 10.5812/iranjradiol.112589] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/04/2025]
Abstract
Background: Non-alcoholic fatty liver disease (NAFLD) is the second most common cause of liver transplantation in the United States, with a continuously growing prevalence. There are several non-invasive methods to detect liver fibrosis, which is defined as the accumulation of extracellular matrix proteins, particularly collagens. It is most commonly associated with chronic liver diseases, such as NAFLD. Objectives: This study aimed to investigate the concordance between transient elastography (TE) and shear wave elastography (SWE) for liver fibrosis staging and also to examine the congruence between the controlled attenuation parameter (CAP) and the B-mode hepatorenal ratio for hepatic steatosis grading in patients with NAFLD. Patients and Methods: In this cross-sectional study conducted during March 2018 - 2019, NAFLD patients, referred to the liver clinic of our center for the non-invasive assessment of hepatic fibrosis, were enrolled. However, patients with sonographic features of cirrhosis, multiple hepatic masses, or moderate to large ascites were excluded; also, patients who were uncooperative during the tests were excluded. Measurements obtained by different tools were recorded. Kolmogorov-Smirnov test, Chi-square test, independent t-test, or Mann-Whitney tests, as well as Pearson’s correlation coefficient test, were used to analyze the data. Results: Sixty-five patients (male-to-female ratio, 1:13), with a median age of 47 years, were included in the study. The tools for assessing fibrosis (r = 0.9538, 95% CI: 0.9252 - 0.9717, P < 0.0001) and steatosis (r = 0.429, 95% CI: 0.2048 - 0.6104, P < 0.0001) were perfectly and moderately correlated, respectively. Sex, age, and body mass index (BMI) did not affect the results. Conclusion: The two elastography modalities showed a strong correlation for fibrosis staging in our study population. Also, the CAP and B-mode hepatorenal ratio were moderately correlated for grading hepatosteatosis. Overall, selection of the best assessment method among the studied modalities depends on factors other than internal validity.
Collapse
|
19
|
Kang SH, Lee HW, Yoo JJ, Cho Y, Kim SU, Lee TH, Jang BK, Kim SG, Ahn SB, Kim H, Jun DW, Choi JI, Song DS, Kim W, Jeong SW, Kim MY, Koh H, Jeong S, Lee JW, Cho YK. KASL clinical practice guidelines: Management of nonalcoholic fatty liver disease. Clin Mol Hepatol 2021; 27:363-401. [PMID: 34154309 PMCID: PMC8273632 DOI: 10.3350/cmh.2021.0178] [Citation(s) in RCA: 166] [Impact Index Per Article: 41.5] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Accepted: 06/22/2021] [Indexed: 02/06/2023] Open
Affiliation(s)
- Seong Hee Kang
- Department of Internal Medicine, Yonsei University Wonju College of Medicine, Wonju, Korea
| | - Hye Won Lee
- Department of Internal Medicine, Yonsei University College of Medicine, Seoul Korea
| | - Jeong-Ju Yoo
- Department of Internal Medicine, SoonChunHyang University Bucheon Hospital, Bucheon, Korea
| | - Yuri Cho
- Center for Liver and Pancreatobiliary Cancer, National Cancer Center, Goyang, Korea
| | - Seung Up Kim
- Department of Internal Medicine, Yonsei University College of Medicine, Seoul Korea
| | - Tae Hee Lee
- Department of Internal Medicine, Konyang University College of Medicine, Daejeon, Korea
| | - Byoung Kuk Jang
- Department of Internal Medicine, Keimyung University School of Medicine, Daegu, Korea
| | - Sang Gyune Kim
- Department of Internal Medicine, SoonChunHyang University Bucheon Hospital, Bucheon, Korea
| | - Sang Bong Ahn
- Department of Internal Medicine, Nowon Eulji Medical Center, Eulji University School of Medicine, Seoul, Korea
| | - Haeryoung Kim
- Department of Pathology, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Korea
| | - Dae Won Jun
- Department of Internal Medicine, Hanyang University College of Medicine, Seoul, Korea
| | - Joon-Il Choi
- Department of Radiology, Seoul St.Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Do Seon Song
- Department of Internal Medicine, St. Vincent’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Won Kim
- Department of Internal Medicine, Seoul Metropolitan Government Boramae Medical Center, Seoul National University College of Medicine, Seoul, Korea
| | - Soung Won Jeong
- Department of Internal Medicine, Soonchunhyang University Seoul Hospital, Soonchunhyang University College of Medicine, Seoul, Korea
| | - Moon Young Kim
- Department of Internal Medicine, Yonsei University Wonju College of Medicine, Wonju, Korea
| | - Hong Koh
- Division of Gastroenterology, Hepatology and Nutrition, Department of Pediatrics, Yonsei University College of Medicine, Severance Children's Hospital, Seoul, Korea
| | - Sujin Jeong
- Division of Pediatric Gastroenterology Hepatology and Nutrition, CHA Bundang Medical Center, CHA University, Seongnam, Korea
| | - Jin-Woo Lee
- Department of Internal Medicine, Inha University Hospital, Inha University School of Medicine, Incheon, Korea
| | - Yong Kyun Cho
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Korea
| |
Collapse
|
20
|
Šamadan L, Jeličić M, Vince A, Papić N. Nonalcoholic Fatty Liver Disease-A Novel Risk Factor for Recurrent Clostridioides difficile Infection. Antibiotics (Basel) 2021; 10:antibiotics10070780. [PMID: 34198964 PMCID: PMC8300633 DOI: 10.3390/antibiotics10070780] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2021] [Revised: 06/23/2021] [Accepted: 06/25/2021] [Indexed: 12/17/2022] Open
Abstract
Recurrent Clostridioides difficile infections (rCDI) have a substantial impact on healthcare systems, with limited and often expensive therapeutic options. Nonalcoholic fatty liver disease (NAFLD) affects about 25% of the adult population and is associated with metabolic syndrome, changes in gut microbiome and bile acids biosynthesis, all possibly related with rCDI. The aim of this study was to determine whether NAFLD is a risk factor associated with rCDI. A retrospective cohort study included patients ≥ 60 years hospitalized with CDI. The cohort was divided into two groups: those who were and were not readmitted with CDI within 3 months of discharge. Of the 329 patients included, 107 patients (32.5%) experienced rCDI. Patients with rCDI were older, had higher Charlson Age-Comorbidity Index (CACI) and were more frequently hospitalized within 3 months. Except for chronic kidney disease and NAFLD, which were more frequent in the rCDI group, there were no differences in other comorbidities, antibiotic classes used and duration of antimicrobial therapy. Multivariable Cox regression analysis showed that age >75 years, NAFLD, CACI >6, chronic kidney disease, statins and immobility were associated with rCDI. In conclusion, our study identified NAFLD as a possible new host-related risk factor associated with rCDI.
Collapse
Affiliation(s)
- Lara Šamadan
- School of Medicine, University of Zagreb, 10000 Zagreb, Croatia; (L.Š.); (A.V.)
| | - Mia Jeličić
- University Hospital for Infectious Diseases, 10000 Zagreb, Croatia;
| | - Adriana Vince
- School of Medicine, University of Zagreb, 10000 Zagreb, Croatia; (L.Š.); (A.V.)
- University Hospital for Infectious Diseases, 10000 Zagreb, Croatia;
| | - Neven Papić
- School of Medicine, University of Zagreb, 10000 Zagreb, Croatia; (L.Š.); (A.V.)
- University Hospital for Infectious Diseases, 10000 Zagreb, Croatia;
- Correspondence:
| |
Collapse
|
21
|
Xavier SA, Monteiro SO, Arieira CM, Castro FD, Magalhães JT, Leite SM, Marinho CM, Cotter JB. US-FLI score - Is it possible to predict the steatosis grade with an ultrasonographic score? Mol Genet Metab 2021; 132:204-209. [PMID: 33558081 DOI: 10.1016/j.ymgme.2021.01.007] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/28/2020] [Revised: 01/14/2021] [Accepted: 01/15/2021] [Indexed: 12/19/2022]
Abstract
OBJECTIVES A recent ultrasonographic score (Ultrasonographic fatty liver indicator (US-FLI)) allows to grade steatosis severity on ultrasound (US).We aimed to evaluate the agreement of US-FLI with the controlled attenuation parameter (CAP) in patients with non-alcoholic fatty liver disease (NAFLD). METHODS Initially, inter-observer agreement for the score was assessed between 3 physicians using a sample of 31 patients.Later, 96 patients with NAFLD were included and several anthropometric/clinical/analytical parameters were assessed and US and transient elastography was performed. RESULTS Physicians showed an excellent absolute agreement regarding the total score, with an average Interclass Correlation Coefficient of 0.972(95% CI 0.949-0.986). Comparing US-FLI with CAP, considering the previously defined cut-off for steatosis >S1(268dB/m) and > S2(280dB/m), US-FLI had a good discriminative capacity for both grades, with areas under the curve (AUC) of 0.88(p < 0.001) and 0.90(p < 0.001), respectively.Also, US-FLI ≤ 3 points had a negative predictive value of 100% for steatosis >S2 and US-FLI ≥6 points had a positive predictive value (PPV) of 94.0% for steatosis >S2. When comparing the clinical score Fatty Liver Index (FLI) for the same CAP cut-offs, it showed a weak discriminative capacity for both grades, with AUC of 0.65(p = 0.030) and 0.66(p = 0.017). AUC for US-FLI and FLI were significantly different for both cut-offs (p < 0.001). CONCLUSION US-FLI has an excellent reproducibility and a good discriminative capacity for the different steatosis grades.Scores ≤3points exclude significant steatosis and scores ≥6 points have a PPV of 94,0% for steatosis >S2.US-FLI was significantly superior to the clinical score FLI in the discrimination between steatosis grades.
Collapse
Affiliation(s)
- Sofia A Xavier
- Hospital Senhora da Oliveira, Gastroenterology Department, Guimarães, Portugal; School of Medicine, University of Minho, Braga, Portugal; ICVS/3B's Associate Laboratory, University of Minho, Braga, Portugal.
| | - Sara O Monteiro
- Hospital Senhora da Oliveira, Gastroenterology Department, Guimarães, Portugal; School of Medicine, University of Minho, Braga, Portugal; ICVS/3B's Associate Laboratory, University of Minho, Braga, Portugal
| | - Cátia M Arieira
- Hospital Senhora da Oliveira, Gastroenterology Department, Guimarães, Portugal; School of Medicine, University of Minho, Braga, Portugal; ICVS/3B's Associate Laboratory, University of Minho, Braga, Portugal
| | - Francisca D Castro
- Hospital Senhora da Oliveira, Gastroenterology Department, Guimarães, Portugal; School of Medicine, University of Minho, Braga, Portugal; ICVS/3B's Associate Laboratory, University of Minho, Braga, Portugal
| | - Joana T Magalhães
- Hospital Senhora da Oliveira, Gastroenterology Department, Guimarães, Portugal; School of Medicine, University of Minho, Braga, Portugal; ICVS/3B's Associate Laboratory, University of Minho, Braga, Portugal
| | - Sílvia M Leite
- Hospital Senhora da Oliveira, Gastroenterology Department, Guimarães, Portugal; School of Medicine, University of Minho, Braga, Portugal; ICVS/3B's Associate Laboratory, University of Minho, Braga, Portugal
| | - Carla M Marinho
- Hospital Senhora da Oliveira, Gastroenterology Department, Guimarães, Portugal; School of Medicine, University of Minho, Braga, Portugal; ICVS/3B's Associate Laboratory, University of Minho, Braga, Portugal
| | - José B Cotter
- Hospital Senhora da Oliveira, Gastroenterology Department, Guimarães, Portugal; School of Medicine, University of Minho, Braga, Portugal; ICVS/3B's Associate Laboratory, University of Minho, Braga, Portugal
| |
Collapse
|
22
|
Bashyam A, Frangieh CJ, Raigani S, Sogo J, Bronson RT, Uygun K, Yeh H, Ausiello DA, Cima MJ. A portable single-sided magnetic-resonance sensor for the grading of liver steatosis and fibrosis. Nat Biomed Eng 2020; 5:240-251. [PMID: 33257853 DOI: 10.1038/s41551-020-00638-0] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2019] [Accepted: 09/28/2020] [Indexed: 12/19/2022]
Abstract
Low-cost non-invasive diagnostic tools for staging the progression of non-alcoholic chronic liver failure from fatty liver disease to steatohepatitis are unavailable. Here, we describe the development and performance of a portable single-sided magnetic-resonance sensor for grading liver steatosis and fibrosis using diffusion-weighted multicomponent T2 relaxometry. In a diet-induced mouse model of non-alcoholic fatty liver disease, the sensor achieved overall accuracies of 92% (Cohen's kappa, κ = 0.89) and 86% (κ = 0.78) in the ex vivo grading of steatosis and fibrosis, respectively. Localization of the measurements in living mice through frequency-dependent spatial encoding led to an overall accuracy of 87% (κ = 0.81) for the grading of steatosis. In human liver samples, the sensor graded steatosis with an overall accuracy of 93% (κ = 0.88). The use of T2 relaxometry as a sensitive measure in fully automated low-cost magnetic-resonance devices at the point of care would alleviate the accessibility and cost limits of magnetic-resonance imaging for diagnosing liver disease and assessing liver health before liver transplantation.
Collapse
Affiliation(s)
- Ashvin Bashyam
- David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA.,Electrical Engineering & Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Chris J Frangieh
- David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA.,Electrical Engineering & Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Siavash Raigani
- Division of Transplantation, Department of Surgery, Massachusetts General Hospital, Boston, MA, USA
| | - Jeremy Sogo
- David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA.,Electrical Engineering & Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Roderick T Bronson
- Department of Microbiology and Immunobiology, Division of Immunology, Harvard Medical School, Boston, MA, USA
| | - Korkut Uygun
- Center for Engineering in Medicine and Surgery, Massachusetts General Hospital, Boston, MA, USA
| | - Heidi Yeh
- Division of Transplantation, Department of Surgery, Massachusetts General Hospital, Boston, MA, USA
| | - Dennis A Ausiello
- Division of Nephrology, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA.,Center for Assessment Technology and Continuous Health, Massachusetts General Hospital, Boston, MA, USA
| | - Michael J Cima
- David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA. .,Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.
| |
Collapse
|
23
|
Liang Y, Ye M, Hou X, Chen P, Wei L, Jiang F, Feng L, Zhong L, Liu H, Bao Y, Jia W. Development and validation of screening scores of non-alcoholic fatty liver disease in middle-aged and elderly Chinese. Diabetes Res Clin Pract 2020; 169:108385. [PMID: 32853691 DOI: 10.1016/j.diabres.2020.108385] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/23/2020] [Revised: 07/06/2020] [Accepted: 08/19/2020] [Indexed: 02/07/2023]
Abstract
AIM Non-alcoholic fatty liver disease (NAFLD) is one of the most common causes of chronic liver disease and also closely related to cardiometabolic disease. Its prevalence was estimated at over one-fourth in the general population in China. We aimed to develop effective score tools for detecting NAFLD. METHODS A total of 17,212 participants aged 45-70 years old were surveyed in Shanghai between 2013 and 2014, and 13,293 participants were included in this analysis. All participants were randomly classified into the exploratory group or the validation group. Candidate categorical variables were selected using a logistic regression model. The score points were generated according to the β-coefficients. RESULTS We developed the Shanghai Nicheng NAFLD Score I (SHNC NAFLD Score I), which included body mass index and waist circumference with an area under the receiver-operating characteristic curve (AUC) of 0.802 (95% CI 0.792-0.811) in the exploratory group and 0.802 (95% CI 0.793-0.812) in the validation group. We further developed the SHNC NAFLD Score II by adding fasting plasma glucose, triglyceride, and alanine aminotransferase/aspartate aminotransferase ratio to the SHNC NAFLD Score I, achieving an AUC of 0.852 (95% CI 0.843-0.861) in the exploratory group and 0.843 (95% CI 0.834-0.852) in the validation group. The two score tools also performed well in subjects with normal alanine aminotransferase (ALT) levels. CONCLUSIONS Based on anthropometric and clinical categorical variables, our two scores are effective tools for detecting NAFLD in both this southern Chinese population and their subpopulation with normal ALT levels.
Collapse
Affiliation(s)
- Yebei Liang
- Department of Endocrinology and Metabolism, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, 600 Yishan Road, Shanghai 200233, China; Shanghai Diabetes Institute, 600 Yishan Road, Shanghai 200233, China; Shanghai Clinical Center for Diabetes, 600 Yishan Road, Shanghai 200233, China; Shanghai Key Laboratory of Diabetes Mellitus, 600 Yishan Road, Shanghai 200233, China
| | - Mao Ye
- Shanghai Jiao Tong University Affiliated Sixth People's Hospital East, 222 Huanhu Xisan Road, Shanghai 201306, China
| | - Xuhong Hou
- Department of Endocrinology and Metabolism, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, 600 Yishan Road, Shanghai 200233, China; Shanghai Diabetes Institute, 600 Yishan Road, Shanghai 200233, China; Shanghai Clinical Center for Diabetes, 600 Yishan Road, Shanghai 200233, China; Shanghai Key Laboratory of Diabetes Mellitus, 600 Yishan Road, Shanghai 200233, China.
| | - Peizhu Chen
- Department of Endocrinology and Metabolism, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, 600 Yishan Road, Shanghai 200233, China; Shanghai Diabetes Institute, 600 Yishan Road, Shanghai 200233, China; Shanghai Clinical Center for Diabetes, 600 Yishan Road, Shanghai 200233, China; Shanghai Key Laboratory of Diabetes Mellitus, 600 Yishan Road, Shanghai 200233, China
| | - Li Wei
- Department of Endocrinology and Metabolism, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, 600 Yishan Road, Shanghai 200233, China; Shanghai Diabetes Institute, 600 Yishan Road, Shanghai 200233, China; Shanghai Clinical Center for Diabetes, 600 Yishan Road, Shanghai 200233, China; Shanghai Key Laboratory of Diabetes Mellitus, 600 Yishan Road, Shanghai 200233, China
| | - Fusong Jiang
- Department of Endocrinology and Metabolism, Shanghai Jiao Tong University Affiliated Sixth People's Hospital East, 222 Huanhu Xisan Road, Shanghai 201306, China
| | - Liang Feng
- Department of Ultrasound in Medicine, Shanghai Jiao Tong University Affiliated Sixth People's Hospital East, 222 Huanhu Xisan Road, Shanghai 201306, China
| | - Lichang Zhong
- Department of Ultrasound in Medicine, Shanghai Jiao Tong University Affiliated Sixth People's Hospital East, 222 Huanhu Xisan Road, Shanghai 201306, China
| | - Huaiyu Liu
- Department of Prevention and Health Care, Shanghai Jiao Tong University Affiliated Sixth People's Hospital East, 222 Huanhu Xisan Road, Shanghai 201306, China
| | - Yuqian Bao
- Department of Endocrinology and Metabolism, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, 600 Yishan Road, Shanghai 200233, China; Shanghai Diabetes Institute, 600 Yishan Road, Shanghai 200233, China; Shanghai Clinical Center for Diabetes, 600 Yishan Road, Shanghai 200233, China; Shanghai Key Laboratory of Diabetes Mellitus, 600 Yishan Road, Shanghai 200233, China
| | - Weiping Jia
- Department of Endocrinology and Metabolism, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, 600 Yishan Road, Shanghai 200233, China; Shanghai Diabetes Institute, 600 Yishan Road, Shanghai 200233, China; Shanghai Clinical Center for Diabetes, 600 Yishan Road, Shanghai 200233, China; Shanghai Key Laboratory of Diabetes Mellitus, 600 Yishan Road, Shanghai 200233, China.
| |
Collapse
|
24
|
Liaqat M, Fatima M, Malik SS, Gillani SA, Manzoor I. Ultrasonographic Features Associated with Diffuse Hepatosteatosis among Diabetic Obese and Normal Body Mass Index Patients. J Med Ultrasound 2020; 28:235-238. [PMID: 33659163 PMCID: PMC7869742 DOI: 10.4103/jmu.jmu_94_19] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2019] [Revised: 01/09/2020] [Accepted: 03/06/2020] [Indexed: 11/04/2022] Open
Abstract
BACKGROUND The purpose of the study is to evaluate and compare the changes associated with hepatosteatosis in diabetic obese versus diabetic normal-weight patients through ultrasonography. It is estimated that with the prevalence of about 30%-75% of obese individuals accordingto the body mass index (BMI) criteria are at increase risk of developing simple fatty live. Besides obesity, diabetes mellitus is also considered to be one of the important causes of hepatosteatosis. METHODS This prospective study was conducted in February 2015-December 2015 on a group of 181 diabetic patients, including 65 males and 116 females with an age range of 40-80 years. The patients were divided into two diabetic groups: those having a BMI ≥30 kg/m2 were included in the obese group (n = 116) and those with a BMI of 18.5-25 kg/m2 were included in the normal BMI group (n = 65). Ultrasound machine Esaote MyLab 50 equipped with a 3.5-5 MHz curvilinear multifrequency transducer was used to scan the liver. Independent samples t-test was performed to compare the liver span in the two groups. Chi-square tests were applied to compare the frequencies of fatty changes, border, and surface characteristics. RESULTS The presence of fatty changes among obese groups was statistically significant in the diabetic obese group compared to the normal-weight individuals with P < 0.0001. Similarly, hepatic spans were found to be significantly greater in the diabetic obese group than the diabetic normal BMI group on independent samples t-test with P < 0.0001. Females were seen to develop hepatosteatosis more frequently compared to males in all diabetic individuals with P = 0.02. CONCLUSION It is concluded that diabetic obese patients are more prone to develop hepatosteatosis as compared to normal BMI diabetic individuals.
Collapse
Affiliation(s)
- Mahjabeen Liaqat
- University Institute of Radiological Sciences and Medical Imaging Technology, Faculty of Allied Health Sciences, University of Lahore, Lahore, Pakistan
| | - Mehreen Fatima
- University Institute of Radiological Sciences and Medical Imaging Technology, Faculty of Allied Health Sciences, University of Lahore, Lahore, Pakistan
| | - Sajid Shaheen Malik
- University Institute of Radiological Sciences and Medical Imaging Technology, Faculty of Allied Health Sciences, University of Lahore, Lahore, Pakistan
| | - Syed Amir Gillani
- University Institute of Radiological Sciences and Medical Imaging Technology, Faculty of Allied Health Sciences, University of Lahore, Lahore, Pakistan
| | - Iqra Manzoor
- University Institute of Radiological Sciences and Medical Imaging Technology, Faculty of Allied Health Sciences, University of Lahore, Lahore, Pakistan
| |
Collapse
|
25
|
Ma X, Xue X, Zhang J, Liang S, Xu C, Wang Y, Zhu J. Liver Expressed Antimicrobial Peptide 2 is Associated with Steatosis in Mice and Humans. Exp Clin Endocrinol Diabetes 2020; 129:601-610. [PMID: 32932529 DOI: 10.1055/a-1210-2357] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
BACKGROUND AND AIMS Liver expressed antimicrobial peptide 2 (LEAP2) is recently identified as a regulator in energy metabolism. This study aims to 1) investigate the role of leap2 in hepatic steatosis in C57BL/6 mice; 2) evaluate the association between circulating LEAP2 levels and liver fat contents in a hospital based case-control study. METHODS The rodent experiment: western blotting and qPCR were performed to evaluate leap2 levels, lipid metabolism pathways and insulin signaling. shRNA was used to knockdown leap2. The clinical study: commercial ELISA kits were used to measure circulating LEAP2 levels (validated by western blotting). Liver fat content was estimated using MRI-derived proton density fat fraction and FibroScan-derived controlled attenuation parameter. RESULTS The rodent experiment found the hepatic expression and secreted levels of leap2 were increased in mice with diet-induced steatosis. Leap2 knockdown ameliorated steatosis via lipolytic/lipogenic pathway and improved insulin sensitivity via IRS/AKT signaling. The clinical study reported increased circulating levels of LEAP2 in the subjects with steatosis. Moreover, LEAP2 correlated positively with age, body mass index, waist-to-hip ratio, liver fat content, fasting insulin and HOMA-IR, whereas inversely with acyl-ghrelin. Furthermore, the circulating levels of LEAP2 are dependent on liver fat content, acyl-ghrelin and fasting glucose. Lastly, circulating LEAP2 is an independent predictor of NAFLD. CONCLUSIONS The study suggests LEAP2 is associated with hepatic steatosis, which may involve lipolytic/lipogenic pathway and insulin signaling.
Collapse
Affiliation(s)
- Xiaoming Ma
- Department of General Surgery, The Affiliated Suqian Hospital of Xuzhou Medical University, Suqian, China
| | - Xing Xue
- Department of Radiology, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
| | - Jingxin Zhang
- Department of General Surgery, Affiliated People's Hospital of Jiangsu University, Zhenjiang, China
| | - Shuang Liang
- Medical School of Nantong University, Nantong 226001, Jiangsu, China
| | - Chunfang Xu
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Yue Wang
- Department of Hepatology, The Fifth People's Hospital of Suzhou, Suzhou, China
| | - Jinzhou Zhu
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou, China
| |
Collapse
|
26
|
Jermendy G, Kolossváry M, Dudás I, Jermendy ÁL, Panajotu A, Suhai IF, Drobni ZD, Karády J, Tárnoki ÁD, Tárnoki DL, Voros S, Merkely B, Maurovich-Horvat P. Effect of genetic and environmental influences on hepatic steatosis: A classical twin study based on computed tomography. IMAGING 2020. [DOI: 10.1556/1647.2020.00006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Abstract
AbstractBackground and aimsNon-alcoholic fatty liver disease (NAFLD) increases cardiovascular morbidity and mortality, and carries poor long-term hepatic prognosis. Data about the role of genetic and environmental factors in the hepatic lipid accumulation are limited. The aim of the study was to evaluate the genetic and environmental impact on the hepatic lipid accumulation within a cohort of adult twin pairs.Patients and methodsWe investigated 182 twin subjects [monozygotic (MZ, n = 114) and dizygotic (DZ, n = 68) same-gender twins (age 56.0 ± 9.6 years; BMI 27.5 ± 5.0 kg/m2; females 65.9%)] who underwent computed tomography (CT) with a 256-slice scanner. Using non-enhanced CT-images, we calculated the average value of hepatic attenuation [expressed in Hounsfield unit (HU)] suggesting hepatic lipid content. Crude data were adjusted to age, sex, BMI and HbA1c values. Intra-pair correlations were established, and structural equation models were used for quantifying the contribution of additive genetic (A), common environmental (C) and unique environmental (E) components to the investigated phenotype.ResultsThe study cohort represented a moderately overweight, middle-aged Caucasian population. There was no significant difference between MZ and DZ twin subjects regarding hepatic CT-attenuation (57.9 ± 12.6 HU and 59.3 ± 11.7 HU, respectively; p = 0.747). Age, sex, BMI and HbA1c adjusted co-twin correlations between the siblings showed that MZ twins have stronger correlations of HU values than DZ twins (rMZ = 0.592, p < 0.001; rDZ = 0.047, p = 0.690, respectively). Using the structural equation model, a moderate additive genetic dependence (A: 38%, 95% CI 15–58%) and a greater unique environmental influence (E: 62%, 95% CI 42–85%) was found. Common environmental influence was not identified (C: 0%).ConclusionThe results of our classical CT-based twin study revealed moderate genetic and greater environmental influences on the phenotypic appearance of hepatic steatosis, commonly referred to as NAFLD. Favorable changes of modifiable environmental factors are of great importance in preventing or treating NAFLD.
Collapse
Affiliation(s)
- György Jermendy
- 1Bajcsy-Zsilinszky Hospital and Outpatient Department, Budapest, Hungary
| | - Márton Kolossváry
- 2MTA-SE Cardiovascular Imaging Research Group, Heart and Vascular Center, Semmelweis University, Budapest, Hungary
| | - Ibolya Dudás
- 2MTA-SE Cardiovascular Imaging Research Group, Heart and Vascular Center, Semmelweis University, Budapest, Hungary
| | - Ádám L. Jermendy
- 2MTA-SE Cardiovascular Imaging Research Group, Heart and Vascular Center, Semmelweis University, Budapest, Hungary
| | - Alexisz Panajotu
- 2MTA-SE Cardiovascular Imaging Research Group, Heart and Vascular Center, Semmelweis University, Budapest, Hungary
| | - Imre F. Suhai
- 3Department of Radiology, Medical Imaging Centre, Semmelweis University, Budapest, Hungary
| | - Zsófia D. Drobni
- 2MTA-SE Cardiovascular Imaging Research Group, Heart and Vascular Center, Semmelweis University, Budapest, Hungary
| | - Júlia Karády
- 2MTA-SE Cardiovascular Imaging Research Group, Heart and Vascular Center, Semmelweis University, Budapest, Hungary
- 4Cardiovascular Imaging Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Ádám D. Tárnoki
- 3Department of Radiology, Medical Imaging Centre, Semmelweis University, Budapest, Hungary
| | - Dávid L. Tárnoki
- 3Department of Radiology, Medical Imaging Centre, Semmelweis University, Budapest, Hungary
| | | | - Béla Merkely
- 2MTA-SE Cardiovascular Imaging Research Group, Heart and Vascular Center, Semmelweis University, Budapest, Hungary
| | - Pál Maurovich-Horvat
- 2MTA-SE Cardiovascular Imaging Research Group, Heart and Vascular Center, Semmelweis University, Budapest, Hungary
- 3Department of Radiology, Medical Imaging Centre, Semmelweis University, Budapest, Hungary
| |
Collapse
|
27
|
Ponti F, De Cinque A, Fazio N, Napoli A, Guglielmi G, Bazzocchi A. Ultrasound imaging, a stethoscope for body composition assessment. Quant Imaging Med Surg 2020; 10:1699-1722. [PMID: 32742962 DOI: 10.21037/qims-19-1048] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Dysregulation of the human's energy balance, mediated by non-performing endocrine organs (liver, skeletal muscle and adipose tissue, above all), can be related to human metabolic disorders characterized by an impaired body composition (BC), such as obesity and sarcopenia. While it is possible to monitor the BC and its variations at different levels, the tissue-organ composition studies have been proven to provide the most clinically applicable information. Ultrasonography (US), a fast, non-invasive, low-cost and widely available technique, holds great potential in the study of BC, as it can directly measure muscles, organs, visceral and subcutaneous fat tissue in different sections of the abdomen and body, overcoming some limits of anthropometric evaluation and other imaging techniques. Purpose of this review article is to explore the technical aspects and the applied methods of US examination to assess the potential clinical role of this technique in the context of BC characterization, investigating four pivotal topics [abdominal fat compartments, subcutaneous adipose tissue (SAT), skeletal muscle, liver].
Collapse
Affiliation(s)
- Federico Ponti
- Diagnostic and Interventional Radiology, IRCCS Istituto Ortopedico Rizzoli, Bologna, Italy
| | - Antonio De Cinque
- Diagnostic and Interventional Radiology, IRCCS Istituto Ortopedico Rizzoli, Bologna, Italy.,Department of Specialized, Diagnostic, and Experimental Medicine, University of Bologna, Sant'Orsola-Malpighi Hospital, Bologna, Italy
| | - Nicola Fazio
- Technology Transfer Office, IRCCS Istituto Ortopedico Rizzoli, Bologna, Italy
| | - Alessandro Napoli
- Department of Radiologic, Oncologic and Pathologic Science, La Sapienza University of Rome, Rome, Italy
| | - Giuseppe Guglielmi
- Department of Radiology, University of Foggia, Foggia, Italy.,Department of Radiology, Scientific Institute "Casa Sollievo della Sofferenza" Hospital, Foggia, Italy
| | - Alberto Bazzocchi
- Diagnostic and Interventional Radiology, IRCCS Istituto Ortopedico Rizzoli, Bologna, Italy
| |
Collapse
|
28
|
de Araújo IM, Parreiras-E-Silva LT, Carvalho AL, Elias J, Salmon CEG, de Paula FJA. Insulin resistance negatively affects bone quality not quantity: the relationship between bone and adipose tissue. Osteoporos Int 2020; 31:1125-1133. [PMID: 32108240 DOI: 10.1007/s00198-020-05365-5] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/28/2019] [Accepted: 02/21/2020] [Indexed: 12/18/2022]
Abstract
UNLABELLED The present study suggests that insulin resistance has no association with bone quantity, but quality. INTRODUCTION The literature has contradictory results concerning the influence of insulin resistance on bone. The present study sought to evaluate the association of insulin resistance and adipose tissue with either bone mineral density or the trabecular bone score. METHODS The study included 56 individuals (36 women and 20 men): age = 46.6 ± 14.2 years, weight = 67.8 ± 10.9 kg, height = 1.65 ± 0.10 m and BMI = 24.8 ± 3.9 kg/m2. The investigational protocol included biochemical determinations and bone assessment by dual X-ray absorptiometry for evaluation of bone mineral density and trabecular bone score. Magnetic resonance was employed to estimate visceral, subcutaneous and bone marrow adipose tissues, as well as intrahepatic lipids. RESULTS The bone mineral density of the lumbar spine, femoral neck and total hip were not associated with insulin resistance-related parameters [visceral adipose tissue, intrahepatic lipids and homeostatic model assessment of insulin resistance (HOMA-IR)]. In contrast, there was a negative relationship between the trabecular bone score and all these components. The association between the trabecular bone score and HOMA-IR was reinforced after adjustment for age and BMI. Marrow adipose tissue was negatively associated with both bone mineral density and trabecular bone score. CONCLUSIONS The present study shows that the trabecular bone score is negatively associated with marrow adipose tissue, insulin resistance, visceral adipose tissue and intrahepatic lipid measurements. Additionally, there was a negative relationship between saturated lipids in marrow adipose tissue and the trabecular bone score. These results encourage further studies to investigate the role of the trabecular bone score exam in the clinical evaluation of osteoporosis in conditions of insulin resistance.
Collapse
Affiliation(s)
- I M de Araújo
- Department of Internal Medicine, Ribeirão Preto Medical School, USP, 3900 Bandeirantes Avenue, Ribeirao Preto, SP, 14049-900, Brazil
| | - L T Parreiras-E-Silva
- Department of Internal Medicine, Ribeirão Preto Medical School, USP, 3900 Bandeirantes Avenue, Ribeirao Preto, SP, 14049-900, Brazil
| | - A L Carvalho
- Department of Internal Medicine, Ribeirão Preto Medical School, USP, 3900 Bandeirantes Avenue, Ribeirao Preto, SP, 14049-900, Brazil
| | - J Elias
- Department of Internal Medicine, Ribeirão Preto Medical School, USP, 3900 Bandeirantes Avenue, Ribeirao Preto, SP, 14049-900, Brazil
| | - C E G Salmon
- Department of Physics, Faculty of Philosophy, Sciences, and Letters of Ribeirão Preto, USP, 3900 Bandeirantes Avenue, Ribeirao Preto, SP, 14040-901, Brazil
| | - F J A de Paula
- Department of Internal Medicine, Ribeirão Preto Medical School, USP, 3900 Bandeirantes Avenue, Ribeirao Preto, SP, 14049-900, Brazil.
| |
Collapse
|
29
|
Fanin A, Miele L, Bertolini E, Giorgini A, Pontiroli AE, Benetti A. Liver alterations in anorexia nervosa are not caused by insulin resistance. Intern Emerg Med 2020; 15:337-339. [PMID: 31734856 DOI: 10.1007/s11739-019-02227-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/07/2019] [Accepted: 10/25/2019] [Indexed: 12/30/2022]
Abstract
BACKGROUND Liver dysfunction has been widely reported in connection with anorexia nervosa (AN) but the pathogenesis of these alterations has never been fully understood despite reported theories about the presence of insulin resistance (IR) and non-alcoholic fatty liver disease (NAFLD). The aim of this study is to investigate if hypertransaminasemia in AN is linked to IR and NAFLD. METHODS Anthropometric data and laboratory exams of 34 patients and 34 controls were analyzed, including alanine-aminotransferase, aspartate-aminotransferase and homeostatic model assessment of insulin resistance (HOMA-IR) index. All subjects also underwent magnetic resonance imaging (MRI), ultrasonography (US), and transient elastography (TE). RESULTS Evidence of increased alanine aminotransferase in AN patients was confirmed in our sample together with a lower HOMA-IR index compared to controls. Positive results in US appeared in 16 patients vs none in controls (p = 0.0007); patients with liver parenchyma abnormalities in US were not different than normal-US patients in any of the studied variables. Only one patient showed non-alcoholic fatty liver disease in MRI while abnormal TE was found in four patients and never in controls. CONCLUSIONS Liver damage suggested by increased serum liver enzymes cannot be due to liver steatosis but potentially to a different liver disease (not identified by MRI) or to an early liver fibrosis not associated with an insulin-resistant status.
Collapse
Affiliation(s)
- Alice Fanin
- Dipartimento di Scienze della Salute, Ospedale San Paolo, Università degli Studi di Milano, via A. Di Rudinì 8, Milan, Italy.
| | - Lucia Miele
- Dipartimento di Scienze della Salute, Ospedale San Paolo, Università degli Studi di Milano, via A. Di Rudinì 8, Milan, Italy
| | - Emanuela Bertolini
- Dipartimento di Scienze della Salute, Ospedale San Paolo, Università degli Studi di Milano, via A. Di Rudinì 8, Milan, Italy
| | - Alessia Giorgini
- Dipartimento di Scienze della Salute, Ospedale San Paolo, Università degli Studi di Milano, via A. Di Rudinì 8, Milan, Italy
| | - Antonio Ettore Pontiroli
- Dipartimento di Scienze della Salute, Ospedale San Paolo, Università degli Studi di Milano, via A. Di Rudinì 8, Milan, Italy
| | - Alberto Benetti
- Dipartimento di Scienze della Salute, Ospedale San Paolo, Università degli Studi di Milano, via A. Di Rudinì 8, Milan, Italy
| |
Collapse
|
30
|
Parreiras-E-Silva LT, de Araújo IM, Elias J, Nogueira-Barbosa MH, Suen VMM, Marchini JS, Salmon CEG, de Paula FJA. Osteoporosis and Hepatic Steatosis: 2 Closely Related Complications in Short-Bowel Syndrome. JPEN J Parenter Enteral Nutr 2020; 44:1271-1279. [PMID: 32048748 DOI: 10.1002/jpen.1802] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2019] [Revised: 12/20/2019] [Accepted: 01/14/2020] [Indexed: 12/17/2022]
Abstract
BACKGROUND Osteoporosis has scarcely been prospectively investigated in short-bowel syndrome (SBS). This prospective study was designed to evaluate incretins, adipokines, bone mass, and lipid deposits from marrow adipose tissue (MAT), visceral adipose tissue (VAT), subcutaneous adipose tissue (SAT), and liver (IHLs). METHODS The study comprised 2 groups matched by gender, height, and age: the control group (CG) (9 males, 9 females) and the SBS group (SBSG) (6 males, 5 females). The SBSG was evaluated twice in an interval of 1 year (SBSG0 and SBSG1 ). The biochemical evaluation included incretins, leptin, and adiponectin. Dual-energy x-ray absorptiometry and magnetic resonance were, respectively, used to measure BMD and lipid deposits. RESULTS Bone mineral density (BMD) was lower in the SBSG than in the CG, but there was no difference between SBSG0 and SBSG1 . There was no difference in MAT, SAT, and VAT, but IHL was lower in CG than in SBSG0 and SBSG1 . A negative correlation between MAT and third lumbar vertebrae BMD was found in the CG but not in SBSG0 or SBSG1 . There was a negative association between IHL and bone mass considering all participants (CG and SBSG0 ) (R2 = 0.38; P < .05). CONCLUSION Appropriate nutrition assistance recovers body composition, reverts the relationship of bone mass and MAT, and mitigates bone loss in SBS. In spite of this, osteoporosis seems to be an early and persistent complication in SBS. Curiously, SBS seems to be a highly vulnerable condition for the development of hepatic steatosis and shows an association between bone mass and IHL.
Collapse
Affiliation(s)
- Luciana T Parreiras-E-Silva
- Department of Internal Medicine, Ribeirão Preto Medical School, University of São Paulo (USP), São Paulo, Brazil
| | - Iana M de Araújo
- Department of Internal Medicine, Ribeirão Preto Medical School, University of São Paulo (USP), São Paulo, Brazil
| | - Jorge Elias
- Department of Internal Medicine, Ribeirão Preto Medical School, University of São Paulo (USP), São Paulo, Brazil
| | - Marcello H Nogueira-Barbosa
- Department of Internal Medicine, Ribeirão Preto Medical School, University of São Paulo (USP), São Paulo, Brazil
| | - Vivian M M Suen
- Department of Internal Medicine, Ribeirão Preto Medical School, University of São Paulo (USP), São Paulo, Brazil
| | - Julio S Marchini
- Department of Internal Medicine, Ribeirão Preto Medical School, University of São Paulo (USP), São Paulo, Brazil
| | - Carlos E G Salmon
- Department of Physics, Faculty of Philosophy, Sciences and Arts of Ribeirão Preto, University of São Paulo (USP), São Paulo, Brazil
| | | |
Collapse
|
31
|
Marjot T, Moolla A, Cobbold JF, Hodson L, Tomlinson JW. Nonalcoholic Fatty Liver Disease in Adults: Current Concepts in Etiology, Outcomes, and Management. Endocr Rev 2020; 41:5601173. [PMID: 31629366 DOI: 10.1210/endrev/bnz009] [Citation(s) in RCA: 141] [Impact Index Per Article: 28.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/21/2019] [Accepted: 10/14/2019] [Indexed: 02/06/2023]
Abstract
Nonalcoholic fatty liver disease (NAFLD) is a spectrum of disease, extending from simple steatosis to inflammation and fibrosis with a significant risk for the development of cirrhosis. It is highly prevalent and is associated with significant adverse outcomes both through liver-specific morbidity and mortality but, perhaps more important, through adverse cardiovascular and metabolic outcomes. It is closely associated with type 2 diabetes and obesity, and both of these conditions drive progressive disease toward the more advanced stages. The mechanisms that govern hepatic lipid accumulation and the predisposition to inflammation and fibrosis are still not fully understood but reflect a complex interplay between metabolic target tissues including adipose and skeletal muscle, and immune and inflammatory cells. The ability to make an accurate assessment of disease stage (that relates to clinical outcome) can also be challenging. While liver biopsy is still regarded as the gold-standard investigative tool, there is an extensive literature on the search for novel noninvasive biomarkers and imaging modalities that aim to accurately reflect the stage of underlying disease. Finally, although no therapies are currently licensed for the treatment of NAFLD, there are interventions that appear to have proven efficacy in randomized controlled trials as well as an extensive emerging therapeutic landscape of new agents that target many of the fundamental pathophysiological processes that drive NAFLD. It is highly likely that over the next few years, new treatments with a specific license for the treatment of NAFLD will become available.
Collapse
Affiliation(s)
- Thomas Marjot
- Translational Gastroenterology Unit, NIHR Oxford Biomedical Research Centre, University of Oxford, John Radcliffe Hospital, Oxford, UK.,Oxford Centre for Diabetes, Endocrinology and Metabolism, NIHR Oxford Biomedical Research Centre, University of Oxford, Churchill Hospital, Oxford, UK
| | - Ahmad Moolla
- Oxford Centre for Diabetes, Endocrinology and Metabolism, NIHR Oxford Biomedical Research Centre, University of Oxford, Churchill Hospital, Oxford, UK
| | - Jeremy F Cobbold
- Translational Gastroenterology Unit, NIHR Oxford Biomedical Research Centre, University of Oxford, John Radcliffe Hospital, Oxford, UK
| | - Leanne Hodson
- Oxford Centre for Diabetes, Endocrinology and Metabolism, NIHR Oxford Biomedical Research Centre, University of Oxford, Churchill Hospital, Oxford, UK
| | - Jeremy W Tomlinson
- Oxford Centre for Diabetes, Endocrinology and Metabolism, NIHR Oxford Biomedical Research Centre, University of Oxford, Churchill Hospital, Oxford, UK
| |
Collapse
|
32
|
Dioguardi Burgio M, Ronot M, Reizine E, Rautou PE, Castera L, Paradis V, Garteiser P, Van Beers B, Vilgrain V. Quantification of hepatic steatosis with ultrasound: promising role of attenuation imaging coefficient in a biopsy-proven cohort. Eur Radiol 2019; 30:2293-2301. [PMID: 31822978 DOI: 10.1007/s00330-019-06480-6] [Citation(s) in RCA: 68] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2019] [Revised: 08/30/2019] [Accepted: 09/27/2019] [Indexed: 12/11/2022]
Abstract
OBJECTIVES To prospectively assess the role of the US attenuation imaging coefficient (AC) for the diagnosis and quantification of hepatic steatosis. METHODS One hundred and one patients underwent liver biopsy and US-AC measurement on the same day. Liver steatosis was graded according to biopsy as absent (S0 < 5%), mild (S1 5-33%), moderate (S2 33-66%), or severe (S3 > 66%); liver fibrosis was graded from F0 to F4. The correlation between AC and steatosis on pathology (%) was calculated using the Pearson correlation coefficient. The Student t or Mann-Whitney U test was used to compare continuous variables and ROC curve analysis was used to assess diagnostic performance of AC in diagnosing steatosis. RESULTS Overall, 43 (42%), 35 (35%), 12 (12%), and 11 (11%) patients were classified as S0, S1, S2, and S3, respectively. The AC was positively correlated with steatosis as a continuous variable (%) on pathology (r = 0.58, p < 0.01). Patients with steatosis of any grade had a higher AC than those without steatosis (mean 0.77 ± 0.13 vs. 0.63 ± 0.09 dB/cm/MHz, respectively; p < 0.01, AUROC = 0.805). Patients with S2-S3 had a higher AC than patients with S0-1 (0.85 ± 0.11 vs. 0.67 ± 0.11 dB/cm/MHz, respectively; p < 0.01, AUROC = 0.892). AC > 0.69 dB/cm/MHz had a sensitivity and specificity of 76% and 86%, respectively, for diagnosing any grade of steatosis (S1-S3), and AC > 0.72 dB/cm/MHz had a sensitivity and specificity of 96% and 74%, respectively, for diagnosing S2-S3. The presence of advanced fibrosis (F3-F4) did not affect the calculated AC. CONCLUSIONS The attenuation imaging coefficient is a promising quantitative technique for the non-invasive diagnosis and quantification of hepatic steatosis. KEY POINTS • Measurement of the attenuation coefficient is achieved with a very high rate of technical success. • We found a significant positive correlation between the attenuation coefficient and the grade of steatosis on pathology. • The attenuation imaging coefficient is a promising quantitative technique for the noninvasive diagnosis and quantification of hepatic steatosis.
Collapse
Affiliation(s)
- Marco Dioguardi Burgio
- Department of Radiology, University Hospitals Paris Nord Val de Seine, Beaujon, Clichy, Hauts-de-Seine, France.
- INSERM U1149, centre de recherche biomédicale Bichat-Beaujon, CRB3, Paris, France.
| | - Maxime Ronot
- Department of Radiology, University Hospitals Paris Nord Val de Seine, Beaujon, Clichy, Hauts-de-Seine, France
- INSERM U1149, centre de recherche biomédicale Bichat-Beaujon, CRB3, Paris, France
- University Paris Diderot, Sorbonne Paris Cité, Paris, France
| | - Edouard Reizine
- Department of Radiology, University Hospitals Paris Nord Val de Seine, Beaujon, Clichy, Hauts-de-Seine, France
| | - Pierre-Emmanuel Rautou
- Department of Hepatology, University Hospitals Paris Nord Val de Seine, Beaujon, Clichy, Hauts-de-Seine, France
| | - Laurent Castera
- Department of Hepatology, University Hospitals Paris Nord Val de Seine, Beaujon, Clichy, Hauts-de-Seine, France
| | - Valérie Paradis
- Department of Pathology, University Hospitals Paris Nord Val de Seine, Beaujon, Clichy, Hauts-de-Seine, France
| | - Philippe Garteiser
- INSERM U1149, centre de recherche biomédicale Bichat-Beaujon, CRB3, Paris, France
| | - Bernard Van Beers
- Department of Radiology, University Hospitals Paris Nord Val de Seine, Beaujon, Clichy, Hauts-de-Seine, France
- INSERM U1149, centre de recherche biomédicale Bichat-Beaujon, CRB3, Paris, France
- University Paris Diderot, Sorbonne Paris Cité, Paris, France
| | - Valérie Vilgrain
- Department of Radiology, University Hospitals Paris Nord Val de Seine, Beaujon, Clichy, Hauts-de-Seine, France
- INSERM U1149, centre de recherche biomédicale Bichat-Beaujon, CRB3, Paris, France
- University Paris Diderot, Sorbonne Paris Cité, Paris, France
| |
Collapse
|
33
|
Igarashi H, Shigiyama F, Wakui N, Nagai H, Shibuya K, Shiraga N, Hirose T, Kumashiro N. Whole hepatic lipid volume quantification and color mapping by multi-slice and multi-point magnetic resonance imaging. Hepatol Res 2019; 49:1374-1385. [PMID: 31313870 DOI: 10.1111/hepr.13408] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/28/2019] [Revised: 07/04/2019] [Accepted: 07/11/2019] [Indexed: 01/08/2023]
Abstract
AIM Current approaches for hepatic steatosis assess only a small point within the liver and might cause inaccuracy for longitudinal observation. We aimed to establish a reliable non-invasive method for whole hepatic lipid content evaluation. METHODS A total of 52 patients with hepatic steatosis underwent liver biopsy. Hepatic lipid content was assessed by Dixon in-phase/out-of-phase magnetic resonance imaging and proton magnetic resonance spectroscopy. Using multi-slice and multi-point magnetic resonance imaging, we calculated the lipid intensity of every voxel throughout the liver and showed the color-mapped lipid distributions. This new analysis could also quantify the whole hepatic lipid and whole liver volumes absolutely. The diagnostic performance of hepatic lipid content between the new analysis and proton magnetic resonance spectroscopy methods was compared by receiver operating characteristic curve analysis referring to the steatosis scores of the liver biopsy. RESULTS Areas under the receiver operating characteristic for the diagnosis of steatosis scores ≥1, ≥2, and ≥3 using magnetic resonance imaging and proton magnetic resonance spectroscopy were 0.86 (95% confidence interval [CI] 0.70-1.00) and 0.98 (95% CI 0.93-1.00), 0.94 (95% CI 0.87-1.00) and 0.93 (95% CI 0.86-1.00), and 0.95 (95% CI 0.89-1.00) and 0.97 (95% CI 0.93-1.00), respectively, showing comparable diagnostic accuracies. However, color mapping showed some inconsistencies between the methods. CONCLUSIONS We described a non-invasive and repeatable evaluation method of whole hepatic lipid accumulation with absolute quantification and color mapping. Hepatic steatosis was accurately evaluated regardless of heterogeneous lipid accumulation. The whole hepatic lean volume, reflecting the hepatic parenchymal condition, can also be determined by this method.
Collapse
Affiliation(s)
- Hiroyuki Igarashi
- Division of Diabetes, Metabolism, and Endocrinology, Department of Medicine, Toho University Graduate School of Medicine, Tokyo, Japan
| | - Fumika Shigiyama
- Division of Diabetes, Metabolism, and Endocrinology, Department of Medicine, Toho University Graduate School of Medicine, Tokyo, Japan
| | - Noritaka Wakui
- Division of Gastroenterology and Hepatology, Department of Medicine, Toho University Graduate School of Medicine, Tokyo, Japan
| | - Hidenari Nagai
- Division of Gastroenterology and Hepatology, Department of Medicine, Toho University Graduate School of Medicine, Tokyo, Japan
| | - Kazutoshi Shibuya
- Department of Surgical Pathology, Toho University Graduate School of Medicine, Tokyo, Japan
| | - Nobuyuki Shiraga
- Department of Radiology, Toho University Graduate School of Medicine, Tokyo, Japan
| | - Takahisa Hirose
- Division of Diabetes, Metabolism, and Endocrinology, Department of Medicine, Toho University Graduate School of Medicine, Tokyo, Japan
| | - Naoki Kumashiro
- Division of Diabetes, Metabolism, and Endocrinology, Department of Medicine, Toho University Graduate School of Medicine, Tokyo, Japan
| |
Collapse
|
34
|
Abdominal Obesity as a Predictive Factor of Nonalcoholic Fatty Liver Disease Assessed by Ultrasonography and Transient Elastography in Polycystic Ovary Syndrome and Healthy Women. BIOMED RESEARCH INTERNATIONAL 2019; 2019:9047324. [PMID: 31467918 PMCID: PMC6699391 DOI: 10.1155/2019/9047324] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/04/2019] [Revised: 06/11/2019] [Accepted: 07/07/2019] [Indexed: 01/15/2023]
Abstract
Polycystic ovary syndrome (PCOS) and nonalcoholic fatty liver (NAFLD) share similar clinical presentations including obesity, insulin resistance (IR), and metabolic abnormality. The predictive factors of NAFLD in women with PCOS and specifically in Asian women are not well established. Associated factors for NAFLD assessed by ultrasound (US) among a group of PCOS and healthy women were determined and diagnostic accuracy between US and transient elastography (TE) for NAFLD was compared and correlated. Sixty-three women with ages ranging from 20 to 40 years participated in the present cross-sectional study. Forty-two women with PCOS as diagnosed by the Rotterdam criteria and 21 healthy women were recruited into the study. Women with underlying hepatic diseases and history of alcohol consumption >20 g/day were excluded. Biochemical and hormonal testing, anthropometrics, liver US, and TE were assessed. Waist circumference (WC) greater than 80 cm was the only predictive factor for NAFLD as assessed by US in the whole group (adjusted odds ratio [aOR] 5.49, 95% confidence interval [CI]: 1.85–16.26, p <0.001). The value of the TE-based controlled attenuation parameter (CAP) was significantly correlated with stage of steatosis as assessed by US (correlation coefficient = 0.696, p <0.001). The diagnostic accuracies of dichotomized CAP ≥236 dB/m assessed for NAFLD using US as the gold standard were 84% and 78% sensitivity and specificity, respectively, with the area under the curve at 0.81 (p <0.001). Abdominal obesity, rather than the presence of PCOS, was shown to be the independently associated factor for NAFLD. WC could be used as the primary screening tool before performing complicated intervention for detection of steatosis. TE is an alternative noninvasive detection tool in women with PCOS for NAFLD and hepatic fibrosis identification.
Collapse
|
35
|
Shin J, Kim MJ, Shin HJ, Yoon H, Kim S, Koh H, Lee MJ. Quick assessment with controlled attenuation parameter for hepatic steatosis in children based on MRI-PDFF as the gold standard. BMC Pediatr 2019; 19:112. [PMID: 30987634 PMCID: PMC6463656 DOI: 10.1186/s12887-019-1485-8] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/31/2018] [Accepted: 04/03/2019] [Indexed: 02/06/2023] Open
Abstract
Background Controlled attenuation parameter (CAP) is a recently introduced, non-invasive and quantitative method to evaluate hepatic steatosis demonstrated in adults, but limited in obesity and not well evaluated in children. The aim of this study was to investigate the diagnostic performance for assessing hepatic steatosis grades using CAP in children based on MR proton density fat fraction (PDFF). Methods Children evaluated for non-alcoholic fatty liver disease (NAFLD) who were assessed for PDFF and CAP were enrolled retrospectively. Hepatic steatosis grades 0–3 were classified according to PDFF using cutoff values of 6, 17.5, and 23.3%. Subgroup analyses were performed in non-obese and obese groups using the 95th percentile body mass index (BMI) as a cutoff and BMI30 group when BMI > 30 kg/m2. Pearson’s correlations between variables were also analyzed. Results In a total of 86 children, there were 53 in the obese group including 17 of the BMI30 group. CAP demonstrated 98.7% sensitivity and 80% specificity for diagnosing grades 1–3 vs. grade 0 using a cutoff value of 241 dB/m (area under the curve = 0.941, p < 0.001). The diagnostic performance for higher steatosis grades was suboptimal. CAP correlated with abdominal wall thickness in both obese (r = 0.549, p = 0.001) and non-obese (r = 0.386, p = 0.004) groups and did not correlate with PDFF in BMI30 group. Conclusion In children with NAFLD, CAP showed excellent diagnostic performance for differentiating presence and absence of hepatic steatosis using a cutoff value of 241 dB/m. However, CAP was limited in evaluating grades of steatosis, especially in children with BMI > 30 kg/m2.
Collapse
Affiliation(s)
- Jaeseung Shin
- Department of Radiology and Research Institute of Radiological Science, Severance Children's Hospital, Yonsei University College of Medicine, 50-1Yonsei-ro, Seodaemun-gu, Seoul, 03722, South Korea
| | - Myung-Joon Kim
- Department of Radiology and Research Institute of Radiological Science, Severance Children's Hospital, Yonsei University College of Medicine, 50-1Yonsei-ro, Seodaemun-gu, Seoul, 03722, South Korea.,Severance Pediatric Liver Disease Research Group, Severance Children's Hospital, Yonsei University College of Medicine, Seoul, South Korea
| | - Hyun Joo Shin
- Department of Radiology and Research Institute of Radiological Science, Severance Children's Hospital, Yonsei University College of Medicine, 50-1Yonsei-ro, Seodaemun-gu, Seoul, 03722, South Korea.,Severance Pediatric Liver Disease Research Group, Severance Children's Hospital, Yonsei University College of Medicine, Seoul, South Korea
| | - Haesung Yoon
- Department of Radiology and Research Institute of Radiological Science, Severance Children's Hospital, Yonsei University College of Medicine, 50-1Yonsei-ro, Seodaemun-gu, Seoul, 03722, South Korea.,Severance Pediatric Liver Disease Research Group, Severance Children's Hospital, Yonsei University College of Medicine, Seoul, South Korea
| | - Seung Kim
- Severance Pediatric Liver Disease Research Group, Severance Children's Hospital, Yonsei University College of Medicine, Seoul, South Korea.,Department of Pediatrics, Severance Children's Hospital, Yonsei University College of Medicine, Seoul, South Korea
| | - Hong Koh
- Severance Pediatric Liver Disease Research Group, Severance Children's Hospital, Yonsei University College of Medicine, Seoul, South Korea.,Department of Pediatrics, Severance Children's Hospital, Yonsei University College of Medicine, Seoul, South Korea
| | - Mi-Jung Lee
- Department of Radiology and Research Institute of Radiological Science, Severance Children's Hospital, Yonsei University College of Medicine, 50-1Yonsei-ro, Seodaemun-gu, Seoul, 03722, South Korea. .,Severance Pediatric Liver Disease Research Group, Severance Children's Hospital, Yonsei University College of Medicine, Seoul, South Korea.
| |
Collapse
|
36
|
Kromrey ML, Ittermann T, Berning M, Kolb C, Hoffmann RT, Lerch MM, Völzke H, Kühn JP. Accuracy of ultrasonography in the assessment of liver fat compared with MRI. Clin Radiol 2019; 74:539-546. [PMID: 30955836 DOI: 10.1016/j.crad.2019.02.014] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2018] [Accepted: 02/26/2019] [Indexed: 12/18/2022]
Abstract
AIM To investigate the accuracy of ultrasonography in the assessment of hepatic steatosis using magnetic resonance imaging (MRI) as standard of reference and to explore the influence of additional hepatic iron overload. MATERIAL AND METHODS A total of 2,783 volunteers (1,442 women, 1,341 men; mean age, 52.3±13.8 years) underwent confounder-corrected chemical-shift-encoded MRI of the liver at 1.5 T. Proton-density fat fraction (PDFF) and transverse relaxation rate (R2*) were calculated to estimate hepatic steatosis and liver iron overload, respectively. In addition, the presence of hepatic steatosis was assessed by B-mode ultrasonography. The sensitivity, specificity, and accuracy of hepatic ultrasonography were determined for different degrees of hepatic steatosis and different amounts of liver iron. RESULTS MRI revealed hepatic steatosis in 40% of participants (n=1,112), which was mild in 68.9% (n=766), moderate in 26.7% (n=297), and severe in 4.4% (n=49) of patients. Ultrasonography detected hepatic steatosis in 37.8% (n=1,052), corresponding to 74.5% sensitivity and 86.6% specificity. The sensitivity of ultrasound increased with the amount of hepatic fat present and was 65.1%, 95%, and 96% for low, moderate, and high fat content; whereas the specificity was constantly high at 86.6%. The diagnostic accuracy of ultrasound for detection of hepatic steatosis did not vary significantly with the amount of liver iron present. CONCLUSION Ultrasonography is an excellent tool to assess hepatic steatosis in the clinical setting with some limitations in patients with a low liver fat content. The detection of hepatic steatosis by ultrasonography is not influenced by liver iron.
Collapse
Affiliation(s)
- M L Kromrey
- Institute of Diagnostic Radiology and Neuroradiology, University Medicine Greifswald, Greifswald, Germany
| | - T Ittermann
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
| | - M Berning
- Department of Medicine I, Carl Gustav Carus University Hospital, TU Dresden, Dresden, Germany
| | - C Kolb
- Institute and Policlinic of Diagnostic and Interventional Radiology, Carl-Gustav-Carus University, TU Dresden, Dresden, Germany
| | - R T Hoffmann
- Institute and Policlinic of Diagnostic and Interventional Radiology, Carl-Gustav-Carus University, TU Dresden, Dresden, Germany
| | - M M Lerch
- Department of Medicine A, University Medicine Greifswald, Greifswald, Germany
| | - H Völzke
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
| | - J-P Kühn
- Institute of Diagnostic Radiology and Neuroradiology, University Medicine Greifswald, Greifswald, Germany; Institute and Policlinic of Diagnostic and Interventional Radiology, Carl-Gustav-Carus University, TU Dresden, Dresden, Germany.
| |
Collapse
|
37
|
Utility of ALT Concentration in Men and Women with Nonalcoholic Fatty Liver Disease: Cohort Study. J Clin Med 2019; 8:jcm8040445. [PMID: 30987010 PMCID: PMC6517922 DOI: 10.3390/jcm8040445] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2019] [Revised: 03/16/2019] [Accepted: 03/29/2019] [Indexed: 01/11/2023] Open
Abstract
Nonalcoholic fatty liver disease (NAFLD) is the most common cause of elevated alanine aminotransferase (ALT), but the clinical utility of ALT in detecting and following individuals with NAFLD remains unclear. We conducted a retrospective analysis of 30,988 men and 5204 women with NAFLD diagnosed by ultrasound and stratified them according to sex-specific ALT quartiles. We compared metabolic variables at baseline and repeated ultrasound after at least 6 months among ALT quartiles (Q) in men (Q1 5–24, Q2 25–33, Q3 34–48, Q4 ≥ 49 IU/L) and women (Q1 5–14, Q2 15–20, Q3 21–28, Q4 ≥ 29 IU/L). Prevalence of obesity (BMI ≥ 25 kg/m2) and metabolic abnormalities (glucose intolerance, hypertension) significantly (p < 0.001) increased from ALT Q1 to Q4 in both men and women at baseline. After a mean follow-up of 4.93 years, 17.6% of men and 31.1% of women resolved their NAFLD. The odds ratio (OR) of resolving significantly (p < 0.001) decreased by quartiles even after multiple adjustments. The adjusted OR for resolution in Q4 was 0.20 (0.18–0.23) in men and 0.35 (0.26–0.47) in women compared with Q1. Individuals with NAFLD span the full range of ALT concentrations, but those with the highest ALT have the worst metabolic profile and persistent NAFLD.
Collapse
|
38
|
Yang KC, Liao YY, Tsui PH, Yeh CK. Ultrasound imaging in nonalcoholic liver disease: current applications and future developments. Quant Imaging Med Surg 2019; 9:546-551. [PMID: 31143646 PMCID: PMC6511724 DOI: 10.21037/qims.2019.03.14] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2019] [Accepted: 03/19/2019] [Indexed: 12/18/2022]
Affiliation(s)
- Kuen Cheh Yang
- Department of Family Medicine, National Taiwan University Hospital, Beihu Branch, Taipei 10800, Taiwan
- Health Science & Wellness Center, National Taiwan University, Taipei 10617, Taiwan
| | - Yin-Yin Liao
- Department of Biomedical Engineering, Hungkuang University, Taichung 43302, Taiwan
| | - Po-Hsiang Tsui
- Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, Taoyuan 33302, Taiwan
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, Taoyuan 33305, Taiwan
- Medical Imaging Research Center, Institute for Radiological Research, Chang Gung University and Chang Gung Memorial Hospital at Linkou, Taoyuan 33302, Taiwan
| | - Chih-Kuang Yeh
- Department of Biomedical Engineering and Environmental Sciences, National Tsing Hua University, Hsinchu 30013, Taiwan
| |
Collapse
|
39
|
Evaluation of nonalcoholic fatty liver disease using magnetic resonance in obese children and adolescents. J Pediatr (Rio J) 2019; 95:34-40. [PMID: 29438686 DOI: 10.1016/j.jped.2017.12.010] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/08/2017] [Revised: 11/10/2017] [Accepted: 11/13/2017] [Indexed: 01/08/2023] Open
Abstract
OBJECTIVE To determine the frequency of nonalcoholic fatty liver disease using nuclear magnetic resonance as a noninvasive method. METHODOLOGY This was a cross-sectional study conducted on 50 children and adolescents followed up at an outpatient obesity clinic. The subjects were submitted to physical examination, laboratory tests (transaminases, liver function tests, lipid profile, glycemia, and basal insulin) and abdominal nuclear magnetic resonance (calculation of hepatic, visceral, and subcutaneous fat). RESULTS Nonalcoholic fatty liver disease was diagnosed in 14 (28%) participants, as a severe condition in eight (percent fat >18%), and as non-severe in four (percent fat from 9% to 18%). Fatty liver was associated with male gender, triglycerides, AST, ALT, AST/ALT ratio, and acanthosis nigricans. Homeostasis model assessment of insulin resistance and metabolic syndrome did not show an association with fatty liver. CONCLUSION The frequency of nonalcoholic fatty liver disease in the present population of children and adolescents was lower than that reported in the international literature. It is suggested that nuclear magnetic resonance is an imaging exam that can be applied to children and adolescents, thus representing an effective noninvasive tool for the diagnosis of nonalcoholic fatty liver disease in this age range. However, further national multicenter studies with longitudinal design are needed for a better analysis of the correlation between nonalcoholic fatty liver disease and its risk factors, as well as its consequences.
Collapse
|
40
|
Abstract
PURPOSE The purpose of the study is to assess the reader agreement and accuracy of eight ultrasound imaging features for classifying hepatic steatosis in adults with known or suspected hepatic steatosis. METHODS This was an IRB-approved, HIPAA-compliant prospective study of adult patients with known or suspected hepatic steatosis. All patients signed written informed consent. Ultrasound images (Siemens S3000, 6C1HD, and 4C1 transducers) were acquired by experienced sonographers following a standard protocol. Eight readers independently graded eight features and their overall impression of hepatic steatosis on ordinal scales using an electronic case report form. Duplicated images from the 6C1HD transducer were read twice to assess intra-reader agreement. Intra-reader, inter-transducer, and inter-reader agreement were assessed using intraclass correlation coefficients (ICC). Features with the highest intra-reader agreement were selected as predictors for dichotomized histological steatosis using Classification and Regression Tree (CART) analysis, and the accuracy of the decision rule was compared to the accuracy of the radiologists' overall impression. RESULTS 45 patients (18 males, 27 females; mean age 56 ± 12 years) scanned from September 2015 to July 2016 were included. Mean intra-reader ICCs ranged from 0.430 to 0.777, inter-transducer ICCs ranged from 0.228 to 0.640, and inter-reader ICCs ranged from 0.014 to 0.561. The CART decision rule selected only large hepatic vein blurring and achieved similar accuracy to the overall impression (74% to 75% and 68% to 72%, respectively). CONCLUSIONS Large hepatic vein blurring, liver-kidney contrast, and overall impression provided the highest reader agreement. Large hepatic vein blurring may provide the highest classification accuracy for dichotomized grading of hepatic steatosis.
Collapse
|
41
|
Benetolo PO, Fernandes MI, Del Ciampo IR, Elias‐Junior J, Sawamura R. Evaluation of nonalcoholic fatty liver disease using magnetic resonance in obese children and adolescents. JORNAL DE PEDIATRIA (VERSÃO EM PORTUGUÊS) 2019. [DOI: 10.1016/j.jpedp.2018.03.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
|
42
|
Predicting Hepatic Steatosis in Living Liver Donors Via Controlled Attenuation Parameter. Transplant Proc 2018; 50:3533-3538. [DOI: 10.1016/j.transproceed.2018.06.039] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2018] [Accepted: 06/27/2018] [Indexed: 12/14/2022]
|
43
|
Imbault M, Dioguardi Burgio M, Faccinetto A, Ronot M, Bendjador H, Deffieux T, Triquet EO, Rautou PE, Castera L, Gennisson JL, Vilgrain V, Tanter M. Ultrasonic fat fraction quantification using in vivo adaptive sound speed estimation. ACTA ACUST UNITED AC 2018; 63:215013. [DOI: 10.1088/1361-6560/aae661] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Affiliation(s)
- Marion Imbault
- Institut Langevin, ESPCI Paris, PSL Research University, CNRS UMR 7587, INSERM U979, Paris, France. Author to whom any correspondence should be addressed
| | | | | | | | | | | | | | | | | | | | | | | |
Collapse
|
44
|
Buckley AJ, Thomas EL, Lessan N, Trovato FM, Trovato GM, Taylor-Robinson SD. Non-alcoholic fatty liver disease: Relationship with cardiovascular risk markers and clinical endpoints. Diabetes Res Clin Pract 2018; 144:144-152. [PMID: 30170074 DOI: 10.1016/j.diabres.2018.08.011] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/18/2018] [Revised: 08/08/2018] [Accepted: 08/14/2018] [Indexed: 02/08/2023]
Abstract
Non-alcoholic fatty liver disease (NAFLD) is a common diagnosis and is increasing in prevalence worldwide. NAFLD is usually asymptomatic at presentation; progression of the disease is unpredictable, leading to the development of a variety of techniques for screening, diagnosis and risk stratification. Clinical methods in current use include serum biomarker panels, hepatic ultrasound, magnetic resonance imaging, and liver biopsy. NAFLD is strongly associated with the metabolic syndrome, and the most common cause of death for people with the condition is cardiovascular disease. Whether NAFLD is an independent cardiovascular risk factor needs exploration. NAFLD has been associated with surrogate markers of cardiovascular disease such as carotid intima-media thickness, the presence of carotid plaque, brachial artery vasodilatory responsiveness and CT coronary artery calcification score. There is no effective medical treatment for NAFLD and evidence is lacking regarding the efficacy of interventions in mitigating cardiovascular risk. Health care professionals managing patients with NAFLD should tackle the issue with early identification of risk factors and aggressive modification. Current management strategies therefore comprise lifestyle change, with close attention to known cardiovascular risk factors.
Collapse
Affiliation(s)
- Adam J Buckley
- Imperial College London, Division of Diabetes, Endocrinology and Metabolism, Department of Medicine London, United Kingdom; Imperial College London Diabetes Centre, Research Department, Abu Dhabi, United Arab Emirates.
| | - E Louise Thomas
- University of Westminster, Department of Life Sciences, London, United Kingdom.
| | - Nader Lessan
- Imperial College London, Division of Diabetes, Endocrinology and Metabolism, Department of Medicine London, United Kingdom; Imperial College London Diabetes Centre, Research Department, Abu Dhabi, United Arab Emirates.
| | - Francesca M Trovato
- University of Catania, Department of Clinical and Experimental Medicine, Catania, Italy
| | - Guglielmo M Trovato
- University of Catania, Department of Clinical and Experimental Medicine, Catania, Italy
| | - Simon D Taylor-Robinson
- Imperial College London, Division of Integrative Systems Medicine and Digestive Health, Department of Surgery and Cancer, London, United Kingdom.
| |
Collapse
|
45
|
Fernandes DM, Pantangi V, Azam M, Salomao M, Iuga AC, Lefkowitch JH, Gill J, Morotti R, Lavine JE, Mencin AA. Pediatric Nonalcoholic Fatty Liver Disease in New York City: An Autopsy Study. J Pediatr 2018; 200:174-180. [PMID: 29903531 DOI: 10.1016/j.jpeds.2018.04.047] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/12/2018] [Revised: 03/26/2018] [Accepted: 04/20/2018] [Indexed: 02/07/2023]
Abstract
OBJECTIVE To assess the prevalence and severity of nonalcoholic liver disease (NAFLD) in children in a diverse population sample in New York City. STUDY DESIGN Liver specimens were examined from children 2-19 years old who died of unexpected causes within 48 hours of medical presentation and underwent autopsy in New York City from 2005 to 2010. Records were reviewed for age, sex, weight, height, and race. Two hepatopathologists evaluated each liver specimen to determine pathologic diagnosis. RESULTS The final study cohort (n = 582) was 50% black, 33% Hispanic, 12% white, 3% Asian, and 2% other; 36% had a body mass index >85%. There were 26 cases of NAFLD (4.5%) of which 10 had nonalcoholic steatohepatitis (1.7%). There were no cases with severe fibrosis or cirrhosis. One percent (3/290) of black children had NAFLD and none had nonalcoholic steatohepatitis. White and Hispanic children had the highest percentages of NAFLD at 8.3% and 7.9%, respectively. In multiple logistic regression models, we observed that body mass index z-score (P < .001) was associated with NAFLD, and that white (P = .003) and Hispanic (P = .005) children had higher odds of having NAFLD compared with black children. CONCLUSIONS This review of liver tissue demonstrates a lower prevalence and severity of NAFLD in black children compared with the general obese pediatric population. Hispanic children did not have a significantly increased rate of NAFLD compared with white children, most likely related to the large proportion of Caribbean Hispanic children in New York City.
Collapse
Affiliation(s)
| | | | - Muhammad Azam
- St. George's University Hospital, London, United Kingdom
| | | | - Alina C Iuga
- Columbia University Medical Center, New York, NY
| | | | - James Gill
- Office of the Chief Medical Examiner, Farmington, CT
| | | | | | - Ali A Mencin
- Columbia University Medical Center, New York, NY.
| |
Collapse
|
46
|
Green CJ, Parry SA, Gunn PJ, Ceresa CDL, Rosqvist F, Piché ME, Hodson L. Studying non-alcoholic fatty liver disease: the ins and outs of in vivo, ex vivo and in vitro human models. Horm Mol Biol Clin Investig 2018; 41:/j/hmbci.ahead-of-print/hmbci-2018-0038/hmbci-2018-0038.xml. [PMID: 30098284 DOI: 10.1515/hmbci-2018-0038] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2018] [Accepted: 06/22/2018] [Indexed: 02/07/2023]
Abstract
The prevalence of non-alcoholic fatty liver disease (NAFLD) is increasing. Determining the pathogenesis and pathophysiology of human NAFLD will allow for evidence-based prevention strategies, and more targeted mechanistic investigations. Various in vivo, ex situ and in vitro models may be utilised to study NAFLD; but all come with their own specific caveats. Here, we review the human-based models and discuss their advantages and limitations in regards to studying the development and progression of NAFLD. Overall, in vivo whole-body human studies are advantageous in that they allow for investigation within the physiological setting, however, limited accessibility to the liver makes direct investigations challenging. Non-invasive imaging techniques are able to somewhat overcome this challenge, whilst the use of stable-isotope tracers enables mechanistic insight to be obtained. Recent technological advances (i.e. normothermic machine perfusion) have opened new opportunities to investigate whole-organ metabolism, thus ex situ livers can be investigated directly. Therefore, investigations that cannot be performed in vivo in humans have the potential to be undertaken. In vitro models offer the ability to perform investigations at a cellular level, aiding in elucidating the molecular mechanisms of NAFLD. However, a number of current models do not closely resemble the human condition and work is ongoing to optimise culturing parameters in order to recapitulate this. In summary, no single model currently provides insight into the development, pathophysiology and progression across the NAFLD spectrum, each experimental model has limitations, which need to be taken into consideration to ensure appropriate conclusions and extrapolation of findings are made.
Collapse
Affiliation(s)
- Charlotte J Green
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Churchill Hospital, Oxford, UK
| | - Siôn A Parry
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Churchill Hospital, Oxford, UK
| | - Pippa J Gunn
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Churchill Hospital, Oxford, UK
| | - Carlo D L Ceresa
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Churchill Hospital, Oxford, UK
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK
| | - Fredrik Rosqvist
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Churchill Hospital, Oxford, UK
- Department of Public Health and Caring Sciences, Clinical Nutrition and Metabolism, Uppsala University, Uppsala, Sweden
| | - Marie-Eve Piché
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Churchill Hospital, Oxford, UK
- Quebec Heart and Lung Institute, Laval University, Quebec, Canada
| | - Leanne Hodson
- University of Oxford, Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, Churchill Hospital,Old Road Headington, Oxford OX3 7LE, United Kingdom of Great Britain and Northern Ireland
| |
Collapse
|
47
|
Wang J, Ma L, Chen S, Xu L, Miao M, Yu C, Li Y, Xu C. Risk for the development of non-alcoholic fatty liver disease: A prospective study. J Gastroenterol Hepatol 2018; 33:1518-1523. [PMID: 29381226 DOI: 10.1111/jgh.14105] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/29/2017] [Revised: 12/29/2017] [Accepted: 01/18/2018] [Indexed: 12/12/2022]
Abstract
BACKGROUND AND AIM Non-invasive assessment was widely used to identify the risk of non-alcoholic fatty liver disease (NAFLD) among individuals with increased metabolic risks. This study aimed to investigate the prospective relationship between ZJU index and the development of NAFLD in a Chinese population. METHODS A cohort of 6310 initially NAFLD-free participants was enrolled in this prospective study. Abdominal ultrasound was used to diagnosis NAFLD. NAFLD incidence was calculated among participants with different baseline ZJU index quintiles. Cox proportional hazards regression analyses were conducted to calculate the risks for incident NAFLD. RESULTS During 37 705 person-year follow-ups, 1071 incident NAFLD cases were identified. The baseline ZJU index was linear and positively correlated with NAFLD incidence. The incidence was 5.53, 11.75, 23.77, 43.28, and 85.60 cases per 1000 person-year follow-up for participants with baseline ZJU index in quintiles 1-5, respectively. Compared with participants with baseline ZJU index in quintile 1, the hazard ratios (95% confidence interval) for incident NAFLD were 2.092 (1.458-3.002), 4.094 (2.942-5.698), 7.095 (5.167-9.742), and 13.191 (9.684-17.968) for participants with baseline ZJU index in quintiles 2-5, respectively. Further analysis found that the changes of ZJU index during follow-up was also independently associated with risk for incident NAFLD. CONCLUSIONS Baseline ZJU index and absolute ZJU index changes independently predicts the risk for incident NAFLD in Chinese population.
Collapse
Affiliation(s)
- Jinghua Wang
- Department of Gastroenterology, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
- Key Laboratory of Precision Diagnosis and Treatment for Hepatobiliary and Pancreatic Tumor of Zhejiang Province, Hangzhou, China
| | - Liang Ma
- Department of Gastroenterology, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
- Key Laboratory of Precision Diagnosis and Treatment for Hepatobiliary and Pancreatic Tumor of Zhejiang Province, Hangzhou, China
| | - Shenghui Chen
- Department of Gastroenterology, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
- Key Laboratory of Precision Diagnosis and Treatment for Hepatobiliary and Pancreatic Tumor of Zhejiang Province, Hangzhou, China
| | - Lei Xu
- Department of Gastroenterology, Ningbo Hospital, Zhejiang University, Ningbo, China
| | - Min Miao
- Department of Internal Medicine, Zhenhai Lianhua Hospital, Ningbo, China
| | - Chaohui Yu
- Department of Gastroenterology, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
- Key Laboratory of Precision Diagnosis and Treatment for Hepatobiliary and Pancreatic Tumor of Zhejiang Province, Hangzhou, China
| | - Youming Li
- Department of Gastroenterology, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
- Key Laboratory of Precision Diagnosis and Treatment for Hepatobiliary and Pancreatic Tumor of Zhejiang Province, Hangzhou, China
| | - Chengfu Xu
- Department of Gastroenterology, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
- Key Laboratory of Precision Diagnosis and Treatment for Hepatobiliary and Pancreatic Tumor of Zhejiang Province, Hangzhou, China
| |
Collapse
|
48
|
Wang G, Corwin MT, Olson KA, Badawi RD, Sarkar S. Dynamic PET of human liver inflammation: impact of kinetic modeling with optimization-derived dual-blood input function. Phys Med Biol 2018; 63:155004. [PMID: 29847315 PMCID: PMC6105275 DOI: 10.1088/1361-6560/aac8cb] [Citation(s) in RCA: 38] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
The hallmark of nonalcoholic steatohepatitis is hepatocellular inflammation and injury in the setting of hepatic steatosis. Recent work has indicated that dynamic 18F-FDG PET with kinetic modeling has the potential to assess hepatic inflammation noninvasively, while static FDG-PET is less promising. Because the liver has dual blood supplies, kinetic modeling of dynamic liver PET data is challenging in human studies. This paper aims to identify the optimal dual-input kinetic modeling approach for dynamic FDG-PET of human liver inflammation. Fourteen patients with nonalcoholic fatty liver disease were included. Each patient underwent 1 h dynamic FDG-PET/CT scan and had liver biopsy within six weeks. Three models were tested for kinetic analysis: the traditional two-tissue compartmental model with an image-derived single-blood input function (SBIF), a model with population-based dual-blood input function (DBIF), and a new model with optimization-derived DBIF through a joint estimation framework. The three models were compared using Akaike information criterion (AIC), F test and histopathologic inflammation score. Results showed that the optimization-derived DBIF model improved liver time activity curve fitting and achieved lower AIC values and higher F values than the SBIF and population-based DBIF models in all patients. The optimization-derived model significantly increased FDG K1 estimates by 101% and 27% as compared with traditional SBIF and population-based DBIF. K1 by the optimization-derived model was significantly associated with histopathologic grades of liver inflammation while the other two models did not provide a statistical significance. In conclusion, modeling of DBIF is critical for dynamic liver FDG-PET kinetic analysis in human studies. The optimization-derived DBIF model is more appropriate than SBIF and population-based DBIF for dynamic FDG-PET of liver inflammation.
Collapse
Affiliation(s)
- Guobao Wang
- Department of Radiology, University of California at Davis, Sacramento CA 95817, USA
| | - Michael T. Corwin
- Department of Radiology, University of California at Davis, Sacramento CA 95817, USA
| | - Kristin A. Olson
- Department of Pathology and Laboratory Medicine, University of California at Davis, Sacramento CA 95817, USA
| | - Ramsey D. Badawi
- Department of Radiology, University of California at Davis, Sacramento CA 95817, USA
| | - Souvik Sarkar
- Department of Internal Medicine, University of California at Davis, Sacramento CA 95817, USA
| |
Collapse
|
49
|
Abstract
Fatty liver disease is characterized histologically by hepatic steatosis, the abnormal accumulation of lipid in hepatocytes. It is classified into alcoholic fatty liver disease and nonalcoholic fatty liver disease, and is an increasingly important cause of chronic liver disease and cirrhosis. Assessing the severity of hepatic steatosis in these conditions is important for diagnostic and prognostic purposes, as hepatic steatosis is potentially reversible if diagnosed early. The criterion standard for assessing hepatic steatosis is liver biopsy, which is limited by sampling error, its invasive nature, and associated morbidity. As such, noninvasive imaging-based methods of assessing hepatic steatosis are needed. Ultrasound and computed tomography are able to suggest the presence of hepatic steatosis based on imaging features, but are unable to accurately quantify hepatic fat content. Since Dixon's seminal work in 1984, magnetic resonance imaging has been used to compute the signal fat fraction from chemical shift-encoded imaging, commonly implemented as out-of-phase and in-phase imaging. However, signal fat fraction is confounded by several factors that limit its accuracy and reproducibility. Recently, advanced chemical shift-encoded magnetic resonance imaging methods have been developed that address these confounders and are able to measure the proton density fat fraction, a standardized, accurate, and reproducible biomarker of fat content. The use of these methods in the liver, as well as in other abdominal organs such as the pancreas, adrenal glands, and adipose tissue will be discussed in this review.
Collapse
|
50
|
Nascimbeni F, Ballestri S, Machado MV, Mantovani A, Cortez-Pinto H, Targher G, Lonardo A. Clinical relevance of liver histopathology and different histological classifications of NASH in adults. Expert Rev Gastroenterol Hepatol 2018; 12:351-367. [PMID: 29224471 DOI: 10.1080/17474124.2018.1415756] [Citation(s) in RCA: 45] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Nonalcoholic fatty liver disease (NAFLD) encompasses simple steatosis and steatohepatitis (NASH) with or without fibrosis/cirrhosis and hepatocellular carcinoma. NAFLD occurs epidemically in most areas of the world, contributes to cardiovascular events and liver-related mortality and therefore exacts a major economic toll. Areas covered: Here we summarize what clinicians should know about NAFLD histopathology in adults. We report on the individual histological features and scoring systems of NAFLD: the NAFLD activity score (NAS) introduced by the NASH-Clinical Research Network, the 'Fatty Liver Inhibition of Progression' algorithm and Steatosis, Activity, and Fibrosis (SAF) score. Pros and cons of histological classifications in NASH are discussed. Special emphasis is given to liver histopathology in some high-risk patient groups, such as those with severe obesity and type 2 diabetes. Moreover, we also examine the relationship between liver histopathology and clinical features, and the impact of liver histopathology on the long-term prognosis of NAFLD. Finally, we propose an integrated diagnostic approach which utilizes both non-invasive tools and liver biopsy in those individual patients with suspected NAFLD. Expert commentary: Based on expert opinions, we conclude with a research agenda on NAFLD which focuses on the most burning topics to be addressed over the next five years.
Collapse
Affiliation(s)
- Fabio Nascimbeni
- a Ospedale Civile di Baggiovara , Azienda Ospedaliero-Universitaria , Modena , Italy.,b Department of Biomedical, Metabolic and Neural Sciences , University of Modena and Reggio Emilia , Modena , Italy
| | | | - Mariana Verdelho Machado
- d Departamento de Gastrenterologia e Hepatologia , Centro Hospitalar Lisboa Norte, Laboratório de Nutrição, Faculdade de Medicina de Lisboa , Lisboa , Portugal
| | - Alessandro Mantovani
- e Division of Endocrinology, Diabetes and Metabolism, Department of Medicine , University and Azienda Ospedaliera Universitaria Integrata of Verona , Verona , Italy
| | - Helena Cortez-Pinto
- d Departamento de Gastrenterologia e Hepatologia , Centro Hospitalar Lisboa Norte, Laboratório de Nutrição, Faculdade de Medicina de Lisboa , Lisboa , Portugal
| | - Giovanni Targher
- e Division of Endocrinology, Diabetes and Metabolism, Department of Medicine , University and Azienda Ospedaliera Universitaria Integrata of Verona , Verona , Italy
| | - Amedeo Lonardo
- a Ospedale Civile di Baggiovara , Azienda Ospedaliero-Universitaria , Modena , Italy
| |
Collapse
|