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Hu N, Yan G, Tang M, Wu Y, Song F, Xia X, Chan LWC, Lei P. CT-based methods for assessment of metabolic dysfunction associated with fatty liver disease. Eur Radiol Exp 2023; 7:72. [PMID: 37985560 PMCID: PMC10661153 DOI: 10.1186/s41747-023-00387-0] [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: 07/24/2023] [Accepted: 09/12/2023] [Indexed: 11/22/2023] Open
Abstract
Metabolic dysfunction-associated fatty liver disease (MAFLD), previously called metabolic nonalcoholic fatty liver disease, is the most prevalent chronic liver disease worldwide. The multi-factorial nature of MAFLD severity is delineated through an intricate composite analysis of the grade of activity in concert with the stage of fibrosis. Despite the preeminence of liver biopsy as the diagnostic and staging reference standard, its invasive nature, pronounced interobserver variability, and potential for deleterious effects (encompassing pain, infection, and even fatality) underscore the need for viable alternatives. We reviewed computed tomography (CT)-based methods for hepatic steatosis quantification (liver-to-spleen ratio; single-energy "quantitative" CT; dual-energy CT; deep learning-based methods; photon-counting CT) and hepatic fibrosis staging (morphology-based CT methods; contrast-enhanced CT biomarkers; dedicated postprocessing methods including liver surface nodularity, liver segmental volume ratio, texture analysis, deep learning methods, and radiomics). For dual-energy and photon-counting CT, the role of virtual non-contrast images and material decomposition is illustrated. For contrast-enhanced CT, normalized iodine concentration and extracellular volume fraction are explained. The applicability and salience of these approaches for clinical diagnosis and quantification of MAFLD are discussed.Relevance statementCT offers a variety of methods for the assessment of metabolic dysfunction-associated fatty liver disease by quantifying steatosis and staging fibrosis.Key points• MAFLD is the most prevalent chronic liver disease worldwide and is rapidly increasing.• Both hardware and software CT advances with high potential for MAFLD assessment have been observed in the last two decades.• Effective estimate of liver steatosis and staging of liver fibrosis can be possible through CT.
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Affiliation(s)
- Na Hu
- Department of Radiology, The Affiliated Hospital of Guizhou Medical University, Guiyang, China
| | - Gang Yan
- Department of Nuclear Medicine, The Affiliated Hospital of Guizhou Medical University, Guiyang, China
| | - Maowen Tang
- Department of Radiology, The Affiliated Hospital of Guizhou Medical University, Guiyang, China
| | - Yuhui Wu
- Department of Radiology, The Affiliated Hospital of Guizhou Medical University, Guiyang, China
| | - Fasong Song
- Department of Radiology, The Affiliated Hospital of Guizhou Medical University, Guiyang, China
| | - Xing Xia
- Department of Radiology, The Affiliated Hospital of Guizhou Medical University, Guiyang, China
| | - Lawrence Wing-Chi Chan
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China.
| | - Pinggui Lei
- Department of Radiology, The Affiliated Hospital of Guizhou Medical University, Guiyang, China.
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China.
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Bai R, Ying X, Shen J, Wu T, Lai X, Wang L, Yu M, Qi X, Mei Y. The visceral and liver fat are significantly associated with the prevalence of hyperuricemia among middle age and elderly people: A cross-sectional study in Chongqing, China. Front Nutr 2022; 9:961792. [PMID: 36313108 PMCID: PMC9613109 DOI: 10.3389/fnut.2022.961792] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2022] [Accepted: 09/21/2022] [Indexed: 11/22/2022] Open
Abstract
Background The prevalence of hyperuricemia (HUA) has been increasing in recent years. HUA is a crucial risk factor for gout and an independent risk factor for cardiovascular diseases (CVDs). Identifying potentially modifiable factors of HUA is vital for preventing gout and even CVDs. This study aimed to explore the associations of fat distribution with HUA among middle-aged and elderly people in Chongqing, China. Materials and methods A cross-sectional study was conducted between July 2020 and September 2021. People who underwent quantitative computed tomography (QCT) scans were invited to participate in the study. A total of 3,683 individuals whose clinical characteristics and QCT-based fat distribution measurements included visceral fat area (VFA), subcutaneous fat area (SFA), and liver fat content (LFC) were well-recorded were included. HUA was defined as having a serum uric acid level greater than 420.0 μmol/L. Multivariate logistic regression models were used to evaluate the association between these adipose variables and HUA prevalence. Results The HUA prevalence was 25.6% (943/3,683), which was 39.6% (817/2,063) in men and 7.8% (126/1,620) in women. In the fully adjusted model (model 4), the comparison of the highest one with the lowest quartiles of adipose variables showed that the multivariable OR (95% confidence intervals) of HUA were 2.08 (1.36-3.16; P for trend = 0.001) for VFA, 0.89 (0.63-1.25; P for trend = 0.651) for SFA, and 1.83 (1.42-2.34; P for trend < 0.0001) for LFC. For VFA, the association was more evident in men than in women. Conclusion Higher VFA and LFC were significantly associated with the increased prevalence of HUA in middle-aged and elderly Chinese individuals. VFA and LFC may have a predictive effect on HUA. Controlling visceral and liver fat accumulation may be beneficial for middle-aged and older people. HUA can be prevented with specific effective healthy physical activity and balanced diet guidelines.
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Affiliation(s)
- Ruixue Bai
- Health Management Center, The Second Affiliated Hospital, Chongqing Medical University, Chongqing, China
| | - Xiuquan Ying
- Health Management Center, The Second Affiliated Hospital, Chongqing Medical University, Chongqing, China
| | - Jieqiang Shen
- Health Management Center, The Second Affiliated Hospital, Chongqing Medical University, Chongqing, China
| | - Tingting Wu
- Department of Food and Nutrition, College of Medical and Life Sciences, Silla University, Busan, South Korea
| | - Xingyu Lai
- Health Management Center, The Second Affiliated Hospital, Chongqing Medical University, Chongqing, China
| | - Lingyun Wang
- Health Management Center, The Second Affiliated Hospital, Chongqing Medical University, Chongqing, China
| | - Meng Yu
- Health Management Center, The Second Affiliated Hospital, Chongqing Medical University, Chongqing, China
| | - Xiaoya Qi
- Health Management Center, The Second Affiliated Hospital, Chongqing Medical University, Chongqing, China
| | - Ying Mei
- Health Management Center, The Second Affiliated Hospital, Chongqing Medical University, Chongqing, China
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Fataftah JZ, Tayyem R, Qandeel H, Baydoun HA, Al Manasra ARA, Tahboub A, Al-Dwairy SY, Al-Mousa A. The Effect of COVID 19 on Liver Parenchyma Detected and measured by CT scan Hounsfield Units. JOURNAL OF RESEARCH IN MEDICAL SCIENCES : THE OFFICIAL JOURNAL OF ISFAHAN UNIVERSITY OF MEDICAL SCIENCES 2022; 27:26. [PMID: 35419067 PMCID: PMC8995305 DOI: 10.4103/jrms.jrms_1228_20] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 11/18/2020] [Revised: 03/28/2021] [Accepted: 06/23/2021] [Indexed: 06/14/2023]
Abstract
BACKGROUND COVID 19 may affect organs other than lungs, including liver, leading to parenchymal changes. These changes are best assessed by unenhanced computed tomography (CT). We aim to investigate the effect of COVID 19 on liver parenchyma by measuring the attenuation in CT scan Hounsfield unit (HU). MATERIALS AND METHODS A cohort of patients, who tested COVID 19 polymerase chain reaction positive, were enrolled and divided into two groups: fatty liver (FL) group (HU ≤ 40) and nonfatty liver (NFL) group (HU > 40) according to liver parenchyma attenuation measurements by high resolution noncontrast CT scan. The CT scan was performed on admission and on follow up (10-14 days later). Liver enzyme tests were submitted on admission and follow up. RESULTS Three hundred and two patients were enrolled. Liver HU increased significantly from 48.9 on admission to 53.4 on follow up CT scan (P<0.001) in all patients. This increase was more significant in the FL group (increased from 31.9 to 42.9 [P =0.018]) Liver enzymes were abnormal in 22.6% of the full cohort. However, there was no significant change in liver enzymes between the admission and follow up in both groups. CONCLUSION The use of unenhanced CT scan for assessment of liver parenchymal represents an objective and noninvasive method. The significant changes in parenchymal HU are not always accompanied by significant changes in liver enzymes. Increased HU values caused by COVID 19 may be due to either a decrease in the fat or an increase in the fibrosis in the liver.
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Affiliation(s)
| | - Raed Tayyem
- Department of Surgery, Hashemite University, Zarqa, Jordan
| | | | | | | | - Ahmad Tahboub
- Department of Radiology, Hashemite University, Zarqa, Jordan
| | | | - Alaa Al-Mousa
- Department of Neurosurgery, Hashemite University, Zarqa, Jordan
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Hepatic Steatosis: CT-based Prevalence in Adults in China and the United States and Associations with Age, Sex, and Body Mass Index. AJR Am J Roentgenol 2021; 218:846-857. [PMID: 34817193 DOI: 10.2214/ajr.21.26728] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Background: Calibrated CT fat fraction (FFCT) measurements derived from non-enhanced abdominal CT reliably reflect liver fat content, allowing largescale population-level investigations of steatosis prevalence and associations. Objective: To compare prevalence of hepatic steatosis, assessed by calibrated CT measurements, between population-based Chinese and U.S. cohorts, and to investigate in these populations the relationship of steatosis with age, sex, and body mass index (BMI). Methods: This retrospective study included 3176 adults (1985 women, 1191 men) from seven Chinese provinces and 8748 adults (4834 women, 3914 men) from a single U.S. medical center, drawn from earlier studies. All participants were at least 40 years old and underwent unenhanced abdominal CT for the earlier studies. Liver fat content measurements on CT were cross-calibrated to MRI proton density fat fraction measurements using phantoms and expressed as adjusted FFCT. Mild, moderate, and severe steatosis were defined as adjusted FFCT of 5.0%-14.9%, 15.0%-24.9%, and ≥25.0%, respectively. The two cohorts were compared. Results: Median adjusted FFCT was for women 4.7% and 4.8%, and for men 5.8% and 6.2%, in the Chinese and U.S. cohorts, respectively. Steatosis prevalence was for women 46.3% and 48.7%, and for men 58.9% and 61.9%, in the Chinese and U.S. cohorts, respectively. Severe steatosis prevalence was for women 0.9% and 1.8%, and for men, 0.2% and 2.6%, in the Chinese and U.S. cohorts, respectively. Adjusted FFCT did not vary across age decades in women or men in the Chinese cohort, though increased across age decades in women and men in the U.S. cohort. Adjusted FFCT and BMI exhibited weak correlation (r=0.312-0.431). Among participants with normal BMI, 36.8% and 38.5% of those in the Chinese and U.S. cohorts had mild steatosis, and 3.0% and 1.5% had moderate or severe steatosis, respectively. Among U.S. participants with BMI ≥40.0, 17.7% had normal liver content. Conclusion: Steatosis and severe steatosis had higher prevalence in the U.S. than Chinese cohort in both women and men. BMI did not reliably predict steatosis. Clinical Impact: The findings provide new information on the dependence of hepatic steatosis on age, sex, and BMI.
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Trabue SL, Kerr BJ, Scoggin KD, Andersen D, van Weelden M. Swine diets impact manure characteristics and gas emissions: Part II protein source. THE SCIENCE OF THE TOTAL ENVIRONMENT 2021; 763:144207. [PMID: 33383511 DOI: 10.1016/j.scitotenv.2020.144207] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Revised: 11/06/2020] [Accepted: 11/23/2020] [Indexed: 06/12/2023]
Abstract
Soybean meal is the dominate protein source for swine diets in the world driven largely by economics, nutritive value, and availability; but conditions can change requiring growers to consider more economical and available protein alternatives. A feeding trial was conducted to determine the impact dietary protein source material on manure slurry chemical properties and manure gas emissions. A total of 32 gilts averaging 130 kg BW were fed either a control diet formulated with soybean meal (SB) or an alternative protein source that included corn gluten meal (CG); canola meal (CM); or poultry meal (PM), with all diets containing 176 g protein kg-1. Diets were fed for 45 d with an average daily feed intake of 2.68 kg/d. Feces and urine were collected twice daily after each feeding and added to animal-specific manure storage containers. At the end of the study, manure slurries were monitored for gas emissions and chemical properties. Dietary protein source had a significant effect (P < 0.05) on manure pH, total solids, total C, protein N, and total S. Pigs fed the diets containing CM had significantly higher levels of sulfide, butanoic acid, and branch chain fatty acids compared to pigs fed SB diets (P < 0.05). Pigs fed CM diets had significantly lower emissions of NH3 compared to pigs fed SB diets (P < 0.05). There were no significant differences in C or S emissions or in odorant emission as affected by source of dietary protein. Hydrogen sulfide was the most dominate odorants for all dietary treatments.
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Affiliation(s)
- S L Trabue
- USDA-ARS National Laboratory for Agriculture and the Environment, Ames, IA 50011, United States of America.
| | - B J Kerr
- USDA-ARS National Laboratory for Agriculture and the Environment, Ames, IA 50011, United States of America
| | - K D Scoggin
- USDA-ARS National Laboratory for Agriculture and the Environment, Ames, IA 50011, United States of America
| | - D Andersen
- Iowa State University, Department of Agriculture and Biological Engineering, Ames, IA 50011, United States of America
| | - M van Weelden
- Iowa State University, Department of Agriculture and Biological Engineering, Ames, IA 50011, United States of America
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Trabue SL, Kerr BJ, Scoggin KD, Andersen D, van Weelden M. Swine diets impact manure characteristics and gas emissions: Part I protein level. THE SCIENCE OF THE TOTAL ENVIRONMENT 2021; 755:142528. [PMID: 33045605 DOI: 10.1016/j.scitotenv.2020.142528] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Revised: 09/03/2020] [Accepted: 09/17/2020] [Indexed: 06/11/2023]
Abstract
Crude protein (CP) is a key nutrient in swine diets supplying essential amino acids, N, and S to animals for growth are fed in excess to maximize growth. Swine diets reduced in CP and supplemented with crystalline amino acids have been shown effective at maintaining animal growth while increasing overall CP use efficiency. A feeding trial study was conducted to determine the effects of reduced dietary CP levels on manure slurry chemical properties and gas emissions. A total of 24 gilts averaging 111 kg BW were fed corn and soybean meal diets formulated with 8.7, 14.8, and 17.6% CP using crystalline amino acid supplementation in the 8.7 and 14.8% CP diets, but only intact protein, soybean meal, in the diet containing 17.6% CP. Diets were fed for 45 d with an average daily feed intake (ADFI) of 2.70 kg across all diets. Animals were fed twice daily with both feces and urine collected during each feeding and added to animal-specific manure storage containers. At the end of the study, manure slurries were monitored for gas emissions and chemical properties. Increasing dietary CP levels increased manure pH, total solids, total N, and total S, including increased levels of ammonia (NH3), volatile fatty acids, and phenolic compounds. Pigs fed lower CP diets had lower emissions of NH3, branched chain fatty acids (BCFA), and phenol compounds which translated into lower emissions in total odor. Emissions of NH3 and odor were reduced by 8.9% and 4.2%, respectively, for each unit percent decline in dietary CP. Hydrogen sulfide was the dominate odorant associated with manure odor emissions. Based on nutrient mass balance, animal retention of dietary N and S increased by 7.0% and 2.4%, respectively, for each unit percent drop in crude protein fed animals, while C retention in the animal declined by 2.1%.
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Affiliation(s)
- S L Trabue
- USDA-ARS National Laboratory for Agriculture and the Environment, Ames, IA 50011, United States of America.
| | - B J Kerr
- USDA-ARS National Laboratory for Agriculture and the Environment, Ames, IA 50011, United States of America
| | - K D Scoggin
- USDA-ARS National Laboratory for Agriculture and the Environment, Ames, IA 50011, United States of America
| | - D Andersen
- Iowa State University, Department of Agriculture and Biological Engineering, Ames, IA 50011, United States of America
| | - M van Weelden
- Iowa State University, Department of Agriculture and Biological Engineering, Ames, IA 50011, United States of America
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Liver fat quantification: where do we stand? Abdom Radiol (NY) 2020; 45:3386-3399. [PMID: 33025153 DOI: 10.1007/s00261-020-02783-1] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2020] [Revised: 09/09/2020] [Accepted: 09/21/2020] [Indexed: 12/14/2022]
Abstract
Excessive intracellular accumulation of triglycerides in the liver, or hepatic steatosis, is a highly prevalent condition affecting approximately one billion people worldwide. In the absence of secondary cause, the term nonalcoholic fatty liver disease (NAFLD) is used. Hepatic steatosis may progress into nonalcoholic steatohepatitis, the more aggressive form of NAFLD, associated with hepatic complications such as fibrosis, liver failure and hepatocellular carcinoma. Hepatic steatosis is associated with metabolic syndrome, cardiovascular disease and represents an independent risk factor for type 2 diabetes, cardiovascular disease and malignancy. Percutaneous liver biopsy is the current reference standard for NAFLD assessment; however, it is an invasive procedure associated with complications and suffers from high sampling variability, impractical for clinical routine and drug efficiency studies. Therefore, noninvasive imaging methods are increasingly used for the diagnosis and monitoring of NAFLD. Among the methods quantifying liver fat, chemical-shift-encoded MRI (CSE-MRI)-based proton density fat-fraction (PDFF) has shown the most promise. MRI-PDFF is increasingly accepted as quantitative imaging biomarker of liver fat that is transforming daily clinical practice and influencing the development of new treatments for NAFLD. Furthermore, CT is an important imaging method for detection of incidental steatosis, and the practical advantages of quantitative ultrasound hold great promise for the future. Understanding the disease burden of NAFLD and the role of imaging may initiate important interventions aimed at avoiding the hepatic and extrahepatic complications of NAFLD. This article reviews clinical burden of NAFLD, and the role of noninvasive imaging techniques for quantification of liver fat.
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Yao WJ, Guo Z, Wang L, Li K, Saba L, Guglielmi G, Cheng XG, Brown JK, Blake GM, Liu B. Pancreas fat quantification with quantitative CT: an MRI correlation analysis. Clin Radiol 2020; 75:397.e1-397.e6. [PMID: 31992455 DOI: 10.1016/j.crad.2019.12.017] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2019] [Accepted: 12/23/2019] [Indexed: 12/13/2022]
Abstract
AIM To assess the fat content of the pancreas using quantitative computed tomography (QCT) and to correlate the results with chemical-shift-encoded magnetic resonance imaging (CSE-MRI) measurements of proton density fat fraction (PDFF). MATERIAL AND METHODS Institutional review board approval for this research was obtained and 52 participants (25 men, 27 women; mean age 35.1 years; age range 22-50 years), who were enrolled in the Prospective Urban Rural Epidemiology (PURE) Study, underwent QCT and CSE-MRI for quantification of fat content in the pancreas. Two observers placed regions of interest (area of 100-130 mm2) in the head, body, and tail of the pancreas as closely matched as possible on the two scans. Pearson correlation and Bland-Altman analysis were performed to evaluate the correlation between the QCT and CSE-MRI measurements and the systematic difference between the two techniques. RESULTS The QCT and CSE-MRI measurements of pancreatic fat content were well correlated (r=0.805, p<0.0001), although Bland-Altman analysis showed that the QCT measurements were systematically lower by 6.3% compared to CSE-MRI PDFF. CONCLUSION In conclusion, the results of this study suggest good correlation between QCT and CSE-MRI measurements of pancreatic fat content. Further studies are required to improve the numerical agreement of QCT measurements with PDFF.
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Affiliation(s)
- W J Yao
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China; Department of Radiology, The Second Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China
| | - Z Guo
- Department of Radiology, Beijing Jishuitan Hospital, Beijing, 100035, China
| | - L Wang
- Department of Radiology, Beijing Jishuitan Hospital, Beijing, 100035, China
| | - K Li
- Department of Radiology, Beijing Jishuitan Hospital, Beijing, 100035, China
| | - L Saba
- Department of Radiology, Policlinico Universitario, Cagliari, Italy
| | - G Guglielmi
- Department of Clinical and Experimental Medicine, Foggia University School of Medicine, Foggia, Italy
| | - X G Cheng
- Department of Radiology, Beijing Jishuitan Hospital, Beijing, 100035, China
| | - J K Brown
- Mindways Software Inc., Austin, TX, USA
| | - G M Blake
- Biomedical Engineering Department, King's College London, Strand, London, WC2R 2LS, UK
| | - B Liu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China.
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Guo Z, Blake GM, Li K, Liang W, Zhang W, Zhang Y, Xu L, Wang L, Brown JK, Cheng X, Pickhardt PJ. Liver Fat Content Measurement with Quantitative CT Validated against MRI Proton Density Fat Fraction: A Prospective Study of 400 Healthy Volunteers. Radiology 2020; 294:89-97. [PMID: 31687918 DOI: 10.1148/radiol.2019190467] [Citation(s) in RCA: 58] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
Background Although chemical shift-encoded (CSE) MRI proton density fat fraction (PDFF) is the current noninvasive reference standard for liver fat quantification, the liver is more frequently imaged with CT. Purpose To validate quantitative CT measurements of liver fat against the MRI PDFF reference standard. Materials and Methods In this prospective study, 400 healthy participants were recruited between August 2015 and July 2016. Each participant underwent same-day abdominal unenhanced quantitative CT with a calibration phantom and CSE 3.0-T MRI. CSE MRI liver fat measurements were used to calibrate an equation to adjust CT fat measurements and put them on the PDFF measurement scale. CT and PDFF liver fat measurements were plotted as histograms, medians, and interquartile ranges compared; scatterplots and Bland-Altman plots obtained; and Pearson correlation coefficients calculated. Receiver operating characteristic curves including areas under the curve were evaluated for mild (PDFF, 5%) and moderate (PDFF, 14%) steatosis thresholds for both raw and adjusted CT measurements. Sensitivity, specificity, positive predictive value, and negative predictive value were calculated. Results Four hundred volunteers (mean age, 52.6 years ± 15.2; 227 women) were evaluated. MRI PDFF measurements of liver fat ranged between 0% and 28%, with 41.5% (166 of 400) of participants with PDFF greater than 5%. Both raw and adjusted quantitative CT values correlated well with MRI PDFF (r2 = 0.79; P < .001). Bland-Altman analysis of adjusted CT values showed no slope or bias. Both raw and adjusted CT had areas under the receiver operating characteristic curve of 0.87 and 0.99, respectively, to identify participants with mild (PDFF, >5%) and moderate (PDFF, >14%) steatosis, respectively. The sensitivity, specificity, positive predictive value, and negative predictive value for unadjusted CT was 75.9% (126 of 166), 85.0% (199 of 234), 78.3% (126 of 161), and 83.3% (199 of 239), respectively, for PDFF greater than 5%; and 84.8% (28 of 33), 98.4% (361 of 367), 82.4% (28 of 34), and 98.6% (361 of 366), respectively, for PDFF greater than 14%. Results for adjusted CT were mostly identical. Conclusion Quantitative CT liver fat exhibited good correlation and accuracy with proton density fat fraction measured with chemical shift-encoded MRI. © RSNA, 2019 Online supplemental material is available for this article.
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Affiliation(s)
- Zhe Guo
- From the Department of Radiology, Beijing Jishuitan Hospital, 31 Xinjiekou East Street, Beijing 100035, China (Z.G., K.L., W.L., W.Z., Y.Z., L.X., L.W., X.C.); School of Biomedical Engineering & Imaging Sciences, King's College London, St Thomas' Hospital, London, England (G.M.B.); Mindways Software Inc, Austin, Tex (J.K.B.); and Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, Wis (P.J.P.)
| | - Glen M Blake
- From the Department of Radiology, Beijing Jishuitan Hospital, 31 Xinjiekou East Street, Beijing 100035, China (Z.G., K.L., W.L., W.Z., Y.Z., L.X., L.W., X.C.); School of Biomedical Engineering & Imaging Sciences, King's College London, St Thomas' Hospital, London, England (G.M.B.); Mindways Software Inc, Austin, Tex (J.K.B.); and Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, Wis (P.J.P.)
| | - Kai Li
- From the Department of Radiology, Beijing Jishuitan Hospital, 31 Xinjiekou East Street, Beijing 100035, China (Z.G., K.L., W.L., W.Z., Y.Z., L.X., L.W., X.C.); School of Biomedical Engineering & Imaging Sciences, King's College London, St Thomas' Hospital, London, England (G.M.B.); Mindways Software Inc, Austin, Tex (J.K.B.); and Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, Wis (P.J.P.)
| | - Wei Liang
- From the Department of Radiology, Beijing Jishuitan Hospital, 31 Xinjiekou East Street, Beijing 100035, China (Z.G., K.L., W.L., W.Z., Y.Z., L.X., L.W., X.C.); School of Biomedical Engineering & Imaging Sciences, King's College London, St Thomas' Hospital, London, England (G.M.B.); Mindways Software Inc, Austin, Tex (J.K.B.); and Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, Wis (P.J.P.)
| | - Wei Zhang
- From the Department of Radiology, Beijing Jishuitan Hospital, 31 Xinjiekou East Street, Beijing 100035, China (Z.G., K.L., W.L., W.Z., Y.Z., L.X., L.W., X.C.); School of Biomedical Engineering & Imaging Sciences, King's College London, St Thomas' Hospital, London, England (G.M.B.); Mindways Software Inc, Austin, Tex (J.K.B.); and Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, Wis (P.J.P.)
| | - Yong Zhang
- From the Department of Radiology, Beijing Jishuitan Hospital, 31 Xinjiekou East Street, Beijing 100035, China (Z.G., K.L., W.L., W.Z., Y.Z., L.X., L.W., X.C.); School of Biomedical Engineering & Imaging Sciences, King's College London, St Thomas' Hospital, London, England (G.M.B.); Mindways Software Inc, Austin, Tex (J.K.B.); and Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, Wis (P.J.P.)
| | - Li Xu
- From the Department of Radiology, Beijing Jishuitan Hospital, 31 Xinjiekou East Street, Beijing 100035, China (Z.G., K.L., W.L., W.Z., Y.Z., L.X., L.W., X.C.); School of Biomedical Engineering & Imaging Sciences, King's College London, St Thomas' Hospital, London, England (G.M.B.); Mindways Software Inc, Austin, Tex (J.K.B.); and Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, Wis (P.J.P.)
| | - Ling Wang
- From the Department of Radiology, Beijing Jishuitan Hospital, 31 Xinjiekou East Street, Beijing 100035, China (Z.G., K.L., W.L., W.Z., Y.Z., L.X., L.W., X.C.); School of Biomedical Engineering & Imaging Sciences, King's College London, St Thomas' Hospital, London, England (G.M.B.); Mindways Software Inc, Austin, Tex (J.K.B.); and Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, Wis (P.J.P.)
| | - J Keenan Brown
- From the Department of Radiology, Beijing Jishuitan Hospital, 31 Xinjiekou East Street, Beijing 100035, China (Z.G., K.L., W.L., W.Z., Y.Z., L.X., L.W., X.C.); School of Biomedical Engineering & Imaging Sciences, King's College London, St Thomas' Hospital, London, England (G.M.B.); Mindways Software Inc, Austin, Tex (J.K.B.); and Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, Wis (P.J.P.)
| | - Xiaoguang Cheng
- From the Department of Radiology, Beijing Jishuitan Hospital, 31 Xinjiekou East Street, Beijing 100035, China (Z.G., K.L., W.L., W.Z., Y.Z., L.X., L.W., X.C.); School of Biomedical Engineering & Imaging Sciences, King's College London, St Thomas' Hospital, London, England (G.M.B.); Mindways Software Inc, Austin, Tex (J.K.B.); and Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, Wis (P.J.P.)
| | - Perry J Pickhardt
- From the Department of Radiology, Beijing Jishuitan Hospital, 31 Xinjiekou East Street, Beijing 100035, China (Z.G., K.L., W.L., W.Z., Y.Z., L.X., L.W., X.C.); School of Biomedical Engineering & Imaging Sciences, King's College London, St Thomas' Hospital, London, England (G.M.B.); Mindways Software Inc, Austin, Tex (J.K.B.); and Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, Wis (P.J.P.)
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10
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Li YX, Sang YQ, Sun Y, Liu XK, Geng HF, Zha M, Wang B, Teng F, Sun HJ, Wang Y, Qiu QQ, Zang X, Wang Y, Wu TT, Jones PM, Liang J, Xu W. Pancreatic Fat is not significantly correlated with β-cell Dysfunction in Patients with new-onset Type 2 Diabetes Mellitus using quantitative Computed Tomography. Int J Med Sci 2020; 17:1673-1682. [PMID: 32714070 PMCID: PMC7378671 DOI: 10.7150/ijms.46395] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/27/2020] [Accepted: 06/19/2020] [Indexed: 12/14/2022] Open
Abstract
Objective: Type 2 diabetes mellitus (T2DM) is a chronic condition resulting from insulin resistance and insufficient β-cell secretion, leading to improper glycaemic regulation. Previous studies have found that excessive fat deposits in organs such as the liver and muscle can cause insulin resistance through lipotoxicity that affects β-cell function. The relationships between fat deposits in pancreatic tissue, the function of β-cells, the method of visceral fat evaluation and T2DM have been sought by researchers. This study aims to elucidate the role of pancreatic fat deposits in the development of T2DM using quantitative computed tomography (QCT), especially their effects on islet β-cell function. Methods: We examined 106 subjects at the onset of T2DM who had undergone abdominal QCT. Estimated pancreatic fat and liver fat were quantified using QCT and calculated. We analysed the correlations with Homeostatic Model Assessment of Insulin Resistance (HOMA-IR) scores and other oral glucose tolerance test-derived parameters that reflect islet function. Furthermore, correlations of estimated pancreatic fat and liver fat with the area under the curve for insulin (AUCINS) and HOMA-IR were assessed with partial correlation analysis and demonstrated by scatter plots. Results: Associations were found between estimated liver fat and HOMA-IR, AUCINS, the modified β-cell function index (MBCI) and Homeostatic Model Assessment β (HOMA-β). However, no significant differences existed between estimated pancreas fat and those parameters. Similarly, after adjustment for sex, age and body mass index, only estimated liver fat was correlated with HOMA-IR and AUCINS. Conclusions: This study suggests no significant correlation between pancreatic fat deposition and β-cell dysfunction in the early stages of T2DM using QCT as a screening tool. The deposits of fat in the pancreas and the resulting lipotoxicity may play an important role in the late stage of islet cell function dysfunction as the course of T2DM progresses.
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Affiliation(s)
- Y X Li
- Graduate School of Bengbu Medical College, Bengbu, Anhui, China.,Department of Endocrinology, Xuzhou Central Hospital, Xuzhou Institute of Medical Sciences, Xuzhou Clinical School of Nanjing Medical University, Affiliated Hospital of Medical School of Southeast University, Jiangsu, China
| | - Y Q Sang
- Department of Endocrinology, Xuzhou Central Hospital, Xuzhou Institute of Medical Sciences, Xuzhou Clinical School of Nanjing Medical University, Affiliated Hospital of Medical School of Southeast University, Jiangsu, China
| | - Yan Sun
- Department of Endocrinology, Xuzhou Central Hospital, Xuzhou Institute of Medical Sciences, Xuzhou Clinical School of Nanjing Medical University, Affiliated Hospital of Medical School of Southeast University, Jiangsu, China
| | - X K Liu
- Department of Endocrinology, Xuzhou Central Hospital, Xuzhou Institute of Medical Sciences, Xuzhou Clinical School of Nanjing Medical University, Affiliated Hospital of Medical School of Southeast University, Jiangsu, China
| | - H F Geng
- Department of Endocrinology, Xuzhou Central Hospital, Xuzhou Institute of Medical Sciences, Xuzhou Clinical School of Nanjing Medical University, Affiliated Hospital of Medical School of Southeast University, Jiangsu, China
| | - Min Zha
- Department of Endocrinology, Affiliated Hospital of Nanjing University of Chinese Medicine, Jiangsu Province Hospital of Chinese Medicine, Jiangsu, China
| | - Ben Wang
- Department of Endocrinology, Xuzhou Central Hospital, Xuzhou Institute of Medical Sciences, Xuzhou Clinical School of Nanjing Medical University, Affiliated Hospital of Medical School of Southeast University, Jiangsu, China
| | - Fei Teng
- Department of Endocrinology, Xuzhou Central Hospital, Xuzhou Institute of Medical Sciences, Xuzhou Clinical School of Nanjing Medical University, Affiliated Hospital of Medical School of Southeast University, Jiangsu, China
| | - H J Sun
- Department of Endocrinology, Xuzhou Central Hospital, Xuzhou Institute of Medical Sciences, Xuzhou Clinical School of Nanjing Medical University, Affiliated Hospital of Medical School of Southeast University, Jiangsu, China
| | - Yu Wang
- Department of Endocrinology, Xuzhou Central Hospital, Xuzhou Institute of Medical Sciences, Xuzhou Clinical School of Nanjing Medical University, Affiliated Hospital of Medical School of Southeast University, Jiangsu, China
| | - Q Q Qiu
- Department of Endocrinology, Xuzhou Central Hospital, Xuzhou Institute of Medical Sciences, Xuzhou Clinical School of Nanjing Medical University, Affiliated Hospital of Medical School of Southeast University, Jiangsu, China
| | - Xiu Zang
- Department of Endocrinology, Xuzhou Central Hospital, Xuzhou Institute of Medical Sciences, Xuzhou Clinical School of Nanjing Medical University, Affiliated Hospital of Medical School of Southeast University, Jiangsu, China
| | - Yun Wang
- Department of Endocrinology, Xuzhou Central Hospital, Xuzhou Institute of Medical Sciences, Xuzhou Clinical School of Nanjing Medical University, Affiliated Hospital of Medical School of Southeast University, Jiangsu, China
| | - T T Wu
- Department of Endocrinology, Xuzhou Central Hospital, Xuzhou Institute of Medical Sciences, Xuzhou Clinical School of Nanjing Medical University, Affiliated Hospital of Medical School of Southeast University, Jiangsu, China
| | - Peter M Jones
- Diabetes Research Group, Division of Diabetes & Nutritional Sciences, School of Medicine, King's College London, London, UK
| | - Jun Liang
- Department of Endocrinology, Xuzhou Central Hospital, Xuzhou Institute of Medical Sciences, Xuzhou Clinical School of Nanjing Medical University, Affiliated Hospital of Medical School of Southeast University, Jiangsu, China
| | - Wei Xu
- Graduate School of Bengbu Medical College, Bengbu, Anhui, China.,Department of Endocrinology, Xuzhou Central Hospital, Xuzhou Institute of Medical Sciences, Xuzhou Clinical School of Nanjing Medical University, Affiliated Hospital of Medical School of Southeast University, Jiangsu, China.,Diabetes Research Group, Division of Diabetes & Nutritional Sciences, School of Medicine, King's College London, London, UK
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11
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Wu Y, Guo Z, Fu X, Wu J, Gao J, Zeng Q, Fu H, Cheng X. The study protocol for the China Health Big Data (China Biobank) project. Quant Imaging Med Surg 2019; 9:1095-1102. [PMID: 31367563 DOI: 10.21037/qims.2019.06.16] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Background Osteoporosis, obesity, and fatty liver are increasingly common chronic diseases that seriously threaten people's health. Low-dose chest computed tomography (LDCT) scan is frequently used for lung cancer screening in health screenings and checkups. Quantitative computed tomography (QCT) enables the accurate measurement of volumetric bone mineral density (vBMD), liver fat content, and abdominal fat area using the existing LDCT data without extra radiation. We initiated a new project, the China Health Big Data (China Biobank), which combines the LDCT scan images from lung cancer screening of participants in health checkup with QCT to investigate the added value of QCT to LDCT, in order to establish the normative reference database and diagnosis criteria for the three aforementioned conditions. Methods The China Biobank project is a prospective nationwide multicenter cohort study that will combine QCT technology with LDCT scans to measure bone mineral density (BMD), intra-abdominal fat distribution, and liver fat content of the generally healthy checkup participants. Mindways QCT calibration phantom (Mindways Software Inc., Austin, TX, USA) and analysis software QCT PRO v6.0 will be used for all centers. Before data collection begins, the European Spine Phantom (ESP) will be used for quality control analysis at each collaborating center. The inclusion criteria are a healthy checkup participant aged 30-90 years, with LDCT as a part of his/her health checkup protocol. Exclusion criteria are pregnant women or participants with a metal implant in the CT scan area. The LDCT images will be transferred to the Mindways workstation for analysis, and vBMD in the L1 and L2 vertebrae, visceral adipose tissue (VAT), subcutaneous adipose tissue (SAT), and liver fat content will be measured. As part of the health checkup, the demographic, anthropometric parameters, blood pressure, and a routine blood laboratory test will be collected. The estimated sample size will be about 30,000. Results The combination of QCT with LDCT of the chest was validated in this project. The vBMD of spine, visceral fat and liver fat can be measured with a LDCT chest scan. Conclusions The China Biobank project will assess the added value of QCT to LDCT, and enable accurate evaluation of the prevalence of osteoporosis, obesity, and fatty liver disease in a very large Chinese cohort.
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Affiliation(s)
- Yan Wu
- Department of Radiology, the First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China
| | - Zhe Guo
- Department of Radiology, Beijing Jishuitan Hospital, Beijing 100035, China
| | - Xiaoxia Fu
- Editorial Office of the Chinese Health Management Journal, Beijing 100710, China
| | - Jing Wu
- National Center for Chronic and Noncommunicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 100050, China
| | - Jianbo Gao
- Department of Radiology, the First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China
| | - Qiang Zeng
- Health Management Institute, Chinese People's Liberation Army General Hospital, Beijing 100853, China
| | - Haihong Fu
- Department of Radiology, Beijing PUMC Hospital, Beijing 100730, China
| | - Xiaoguang Cheng
- Department of Radiology, Beijing Jishuitan Hospital, Beijing 100035, China
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12
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Cheng X, Blake GM, Guo Z, Keenan Brown J, Wang L, Li K, Xu L. Correction of QCT vBMD using MRI measurements of marrow adipose tissue. Bone 2019; 120:504-511. [PMID: 30583123 DOI: 10.1016/j.bone.2018.12.015] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/29/2018] [Revised: 12/14/2018] [Accepted: 12/18/2018] [Indexed: 01/19/2023]
Abstract
OBJECTIVE Quantitative computed tomography (QCT) measurements of volumetric bone mineral density (vBMD) are subject to errors due to variations in the amount of bone marrow adipose tissue (BMAT). The purpose of our study was to describe and validate a novel method to correct lumbar spine trabecular vBMD measurements for BMAT using chemical shift-encoded magnetic resonance imaging (CSE-MRI). METHODS CSE-MRI measurements of proton density fat fraction (PDFF) were used to correct QCT spine vBMD measurements for BMAT based on the H2O and K2HPO4 basis set equivalent densities of bone, red and yellow bone marrow. BMAT corrected and uncorrected vBMD measurements of the L1 vertebra were compared with dual-energy QCT (DEQCT) measurements in 18 subjects (mean age: 68 y, range 60 to 93 y). A further 400 subjects (mean age: 53 y, range 21 to 82 y) had 120 kVp single-energy QCT and CES-MRI scans of L2-L4 and the data used to simplify the adipose tissue correction by deriving a linear equation between the CSE-MRI vBMD correction and fractional BMAT content. RESULTS Application of the CSE-MRI derived vBMD correction changed the bias (95% limits of agreement) compared with DEQCT from 26.7 (11.0 to 42.4) mg/cm3 to 2.2 (-9.5 to 13.9) mg/cm3 at 80 kVp, and from 22.4 (3.3 to 41.6) mg/cm3 to 2.9 (-12.6 to 18.4) mg/cm3 at 120 kVp. Data for the 400 subjects gave the following relationship valid at 120 kVp: vBMD correction (mg/cm3) = -12.96 + 75.76 × BMAT. CONCLUSION CSE-MRI measurements of PDFF can be used to correct for BMAT content and improve the accuracy of lumbar spine QCT vBMD measurements calibrated using a K2HPO4 phantom.
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Affiliation(s)
- Xiaoguang Cheng
- Department of Radiology, Beijing Jishuitan Hospital, Beijing 100035, China
| | - Glen M Blake
- School of Biomedical Engineering & Imaging Sciences, King's College London, St Thomas' Hospital, London SE1 7EH, United Kingdom.
| | - Zhe Guo
- Department of Radiology, Beijing Jishuitan Hospital, Beijing 100035, China
| | - J Keenan Brown
- Mindways Software Inc., Austin, TX, United States of America
| | - Ling Wang
- Department of Radiology, Beijing Jishuitan Hospital, Beijing 100035, China
| | - Kai Li
- Department of Radiology, Beijing Jishuitan Hospital, Beijing 100035, China
| | - Li Xu
- Department of Radiology, Beijing Jishuitan Hospital, Beijing 100035, China
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Abstract
PURPOSE OF REVIEW Abdominal obesity, especially the increase of visceral adipose tissue (VAT), is closely associated with increased mortality related to cardiovascular disease, diabetes, and fatty liver disease. This review provides an overview of the recent advances for abdominal obesity measurement. RECENT FINDINGS Compared to simple waist circumference, emerging three-dimensional (3D) body-scanning techniques also measure abdominal volume and shape. Abdominal dimension measures have been implemented in bioelectrical impedance analysis to improve accuracy when estimating VAT. Geometrical models have been applied in ultrasound to convert depth measurement into VAT area. Only computed tomography (CT) and MRI can provide direct measures of VAT. Recent advances in imaging allow for evaluating functional aspects of abdominal fat such as brown adipose tissue and fatty acid composition. SUMMARY Waist circumference is a simple, inexpensive method to measure abdominal obesity. CT and MRI are reference methods for measuring VAT. Further studies are needed to establish the accuracy for dual-energy X-ray absorptiometry in estimating longitudinal changes of VAT. Further studies are needed to establish whether bioelectrical impedance analysis, ultrasound, or 3D body scanning is consistently superior to waist circumference in estimating VAT in different populations.
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Affiliation(s)
- Hongjuan Fang
- Department of Endocrinology, Capital Medical University, Beijing Tiantan Hospital, Beijing, China
- Division of Pediatric Gastroenterology, Hepatology and Nutrition, Department of Pediatrics, Columbia University, New York, New York, USA
| | - Elizabeth Berg
- Division of Pediatric Gastroenterology, Hepatology and Nutrition, Department of Pediatrics, Columbia University, New York, New York, USA
| | - Xiaoguang Cheng
- Department of Radiology, Beijing Jishuitan Hospital, Beijing, China
| | - Wei Shen
- Division of Pediatric Gastroenterology, Hepatology and Nutrition, Department of Pediatrics, Columbia University, New York, New York, USA
- Institute of Human Nutrition, Columbia University, New York, New York, USA
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14
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Validation of goose liver fat measurement by QCT and CSE-MRI with biochemical extraction and pathology as reference. Eur Radiol 2017; 28:2003-2012. [PMID: 29238866 DOI: 10.1007/s00330-017-5189-x] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2017] [Revised: 11/05/2017] [Accepted: 11/09/2017] [Indexed: 12/19/2022]
Abstract
OBJECTIVES This study aimed to validate the accuracy and reliability of quantitative computed tomography (QCT) and chemical shift encoded magnetic resonance imaging (CSE-MRI) to assess hepatic steatosis. METHODS Twenty-two geese with a wide range of hepatic steatosis were collected. After QCT and CSE-MRI examinations, the liver of each goose was removed and samples were taken from the left lobe, upper and lower half of the right lobe for biochemical measurement and histology. Fat percentages by QCT and proton density fat fraction by MRI (MRI-PDFF) were measured within the sample regions of biochemical measurement and histology. The accuracy of QCT and MR measurements were assessed through Spearman correlation coefficients (r) and Passing and Bablok regression equations using biochemical measurement as the "gold standard". RESULTS Both QCT and MRI correlated highly with chemical extraction [r = 0.922 (p < 0.001) and r = 0.949 (p < 0.001) respectively]. Chemically extracted triglyceride was accurately predicted by both QCT liver fat percentages (Y = 0.6 + 0.866 × X) and by MRI-PDFF (Y = -1.8 + 0.773 × X). CONCLUSIONS QCT and CSE-MRI measurements of goose liver fat were accurate and reliable compared with biochemical measurement. KEY POINTS • QCT and CSE-MRI can measure liver fat content accurately and reliably • Histological grading of hepatic steatosis has larger sampling variability • QCT and CSE-MRI have potential in the clinical setting.
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