Wang K. et al. introduced the concept of Microbial-Host isozymes (MHIs) and highlighted their role in mediating microbiota-host interactions. They identified bacterial-derived DPP4 as an isoenzyme ...affecting glucose tolerance and showed that host DPP4 inhibitors may not effectively target bacterial DPP4. They developed an MHI screen system, identifying 71 MHIs in healthy gut microbiota. Among them, DPP4 isozymes degrade GLP-1, explaining variable responses to sitagliptin. This breakthrough opens new avenues for metabolic disorder treatment. However, the complex nature of gut symbiotic bacteria requires further research to understand MHI mechanisms, regulatory roles, and interactions with the host. Precise interventions in gut microbiota offer personalized approaches to metabolic diseases.
Metabolic dysfunction‐associated fatty liver disease (MAFLD) has reached epidemic proportions worldwide and is the most frequent cause of chronic liver disease in developed countries. Within the ...spectrum of liver disease in MAFLD, steatohepatitis is a progressive form of liver disease and hepatocyte ballooning (HB) is a cardinal pathological feature of steatohepatitis. The accurate and reproducible diagnosis of HB is therefore critical for the early detection and treatment of steatohepatitis. Currently, a diagnosis of HB relies on pathological examination by expert pathologists, which may be a time‐consuming and subjective process. Hence, there has been interest in developing automated methods for diagnosing HB. This narrative review briefly discusses the development of artificial intelligence (AI) technology for diagnosing fatty liver disease pathology over the last 30 years and provides an overview of the current research status of AI algorithms for the identification of HB, including published articles on traditional machine learning algorithms and deep learning algorithms. This narrative review also provides a summary of object detection algorithms, including the principles, historical developments, and applications in the medical image analysis. The potential benefits of object detection algorithms for HB diagnosis (specifically those combined with a transformer architecture) are discussed, along with the future directions of object detection algorithms in HB diagnosis and the potential applications of generative AI on transformer architecture in this field. In conclusion, object detection algorithms have huge potential for the identification of HB and could make the diagnosis of MAFLD more accurate and efficient in the near future.
Early screening may prevent fibrosis progression in metabolic-associated fatty liver disease (MAFLD).
We developed and validated MAFLD fibrosis score (MFS) for identifying advanced fibrosis (≥F3) ...among MAFLD patients.
This cross-sectional, multicentre study consecutively recruited MAFLD patients receiving tertiary care (Malaysia as training cohort n = 276 and Hong Kong and Wenzhou as validation cohort n = 431). Patients completed liver biopsy, vibration-controlled transient elastography (VCTE), and clinical and laboratory assessment within 1 week. We used machine learning to select 'highly important' predictors of advanced fibrosis, followed by backward stepwise regression to construct MFS formula.
MFS was composed of seven variables: age, body mass index, international normalised ratio, aspartate aminotransferase, gamma-glutamyl transpeptidase, platelet count, and history of type 2 diabetes. MFS demonstrated an area under the receiver-operating characteristic curve of 0.848 95% CI 0.800-898 and 0.823 0.760-0.886 in training and validation cohorts, significantly higher than aminotransferase-to-platelet ratio index (0.684 0.603-0.765, 0.663 0.588-0.738), Fibrosis-4 index (0.793 0.735-0.854, 0.737 0.660-0.814), and non-alcoholic fatty liver disease fibrosis score (0.785 0.731-0.844, 0.750 0.674-0.827) (DeLong's test p < 0.05). MFS could include 92.3% of patients using dual cut-offs of 14 and 15, with a correct prediction rate of 90.4%, resulting in a larger number of patients with correct diagnosis compared to other scores. A two-step MFS-VCTE screening algorithm demonstrated positive and negative predictive values and overall diagnostic accuracy of 93.4%, 89.5%, and 93.2%, respectively, with only 4.0% of patients classified into grey zone.
MFS outperforms conventional non-invasive scores in predicting advanced fibrosis, contributing to screening in MAFLD patients.
Background
Dysregulated bile acid (BA) metabolism has been linked to steatosis, inflammation, and fibrosis in nonalcoholic fatty liver disease (NAFLD).
Aim
To determine whether circulating BA levels ...accurately stage liver fibrosis in NAFLD.
Methods
We recruited 550 Chinese adults with biopsy‐proven NAFLD and varying levels of fibrosis. Ultra‐performance liquid chromatography coupled with tandem mass spectrometry was performed to quantify 38 serum BAs.
Results
Compared to those without fibrosis, patients with mild fibrosis (stage F1) had significantly higher levels of secondary BAs, and increased diastolic blood pressure (DBP), alanine aminotransferase (ALT), body mass index, and waist circumstance (WC). The combination of serum BAs with WC, DBP, ALT, or Homeostatic Model Assessment for Insulin Resistance performed well in identifying mild fibrosis, in men and women, and in those with/without obesity, with AUROCs 0.80, 0.88, 0.75 and 0.78 in the training set (n = 385), and 0.69, 0.80, 0.61 and 0.69 in the testing set (n = 165), respectively. In comparison, the combination of BAs and clinical/biochemical biomarkers performed less well in identifying significant fibrosis (F2‐4). In women and in non‐obese subjects, AUROCs were 0.75 and 0.71 in the training set, 0.65 and 0.66 in the validation set, respectively. However, these AUROCs were higher than those observed for the fibrosis‐4 index, NAFLD fibrosis score, and Hepamet fibrosis score.
Conclusions
Secondary BA levels were significantly increased in NAFLD, especially in those with mild fibrosis. The combination of serum BAs and clinical/biochemical biomarkers for identifying mild fibrosis merits further assessment.
Secondary bile acids were significantly increased in NAFLD, especially in those with mild fibrosis. The combination of serum bile acids and clinical/biochemical biomarkers for identifying mild fibrosis is worthy of further assessment.
The precise estimation of cases with significant fibrosis (SF) is an unmet goal in non-alcoholic fatty liver disease (NAFLD/MASLD).
We evaluated the performance of machine learning (ML) and ...non-patented scores for ruling out SF among NAFLD/MASLD patients.
Twenty-one ML models were trained (N = 1153), tested (N = 283), and validated (N = 220) on clinical and biochemical parameters of histologically-proven NAFLD/MASLD patients (N = 1656) collected across 14 centres in 8 Asian countries. Their performance for detecting histological-SF (≥F2fibrosis) were evaluated with APRI, FIB4, NFS, BARD, and SAFE (NPV/F1-score as model-selection criteria).
Patients aged 47 years (median), 54.6% males, 73.7% with metabolic syndrome, and 32.9% with histological-SF were included in the study. Patients with SFvs.no-SF had higher age, aminotransferases, fasting plasma glucose, metabolic syndrome, uncontrolled diabetes, and NAFLD activity score (p < 0.001, each). ML models showed 7%-12% better discrimination than FIB-4 to detect SF. Optimised random forest (RF) yielded best NPV/F1 in overall set (0.947/0.754), test set (0.798/0.588) and validation set (0.852/0.559), as compared to FIB4 in overall set (0.744/0.499), test set (0.722/0.456), and validation set (0.806/0.507). Compared to FIB-4, RF could pick 10 times more patients with SF, reduce unnecessary referrals by 28%, and prevent missed referrals by 78%. Age, AST, ALT fasting plasma glucose, and platelet count were top features determining the SF. Sequential use of SAFE < 140 and FIB4 < 1.2 (when SAFE > 140) was next best in ruling out SF (NPV of 0.757, 0.724 and 0.827 in overall, test and validation set).
ML with clinical, anthropometric data and simple blood investigations perform better than FIB-4 for ruling out SF in biopsy-proven Asian NAFLD/MASLD patients.
Background & Aims
There is an unmet clinical need for non‐invasive tests to diagnose non‐alcoholic fatty liver disease (NAFLD) and individual fibrosis stages. We aimed to test whether urine protein ...panels could be used to identify NAFLD, NAFLD with fibrosis (stage F ≥ 1) and NAFLD with significant fibrosis (stage F ≥ 2).
Methods
We collected urine samples from 100 patients with biopsy‐confirmed NAFLD and 40 healthy volunteers, and proteomics and bioinformatics analyses were performed in this derivation cohort. Diagnostic models were developed for detecting NAFLD (UPNAFLD model), NAFLD with fibrosis (UPfibrosis model), or NAFLD with significant fibrosis (UPsignificant fibrosis model). Subsequently, the derivation cohort was divided into training and testing sets to evaluate the efficacy of these diagnostic models. Finally, in a separate independent validation cohort of 100 patients with biopsy‐confirmed NAFLD and 45 healthy controls, urinary enzyme‐linked immunosorbent assay analyses were undertaken to validate the accuracy of these new diagnostic models.
Results
The UPfibrosis model and the UPsignificant fibrosis model showed an AUROC of .863 (95% CI: .725–1.000) and 0.858 (95% CI: .712–1.000) in the training set; and .837 (95% CI: .711–.963) and .916 (95% CI: .825–1.000) in the testing set respectively. The UPNAFLD model showed an excellent diagnostic performance and the area under the receiver operator characteristic curve (AUROC) exceeded .90 in the derivation cohort. In the independent validation cohort, the AUROC for all three of the above diagnostic models exceeded .80.
Conclusions
Our newly developed models constructed from urine protein biomarkers have good accuracy for non‐invasively diagnosing liver fibrosis in NAFLD.
Background and Aim
There is an immediate need for non‐invasive accurate tests for diagnosing liver fibrosis in patients with non‐alcoholic steatohepatitis (NASH). Previously, it has been suggested ...that MACK‐3 (a formula that combines homeostasis model assessment‐insulin resistance with serum serum aspartate aminotransferase and cytokeratin CK18‐M30 levels) accurately identifies patients with fibrotic NASH. Our aim was to assess the performance of MACK‐3 and develop a novel, non‐invasive algorithm for diagnosing fibrotic NASH.
Methods
Six hundred and thirty‐six adults with biopsy‐proven non‐alcoholic fatty liver disease (NAFLD) from two independent Asian cohorts were enrolled in our study. Liver stiffness measurement (LSM) was assessed by vibration‐controlled transient elastography (Fibroscan). Fibrotic NASH was defined as NASH with a NAFLD activity score (NAS) ≥ 4 and F ≥ 2 fibrosis.
Results
Metabolic syndrome (MetS), platelet count and MACK‐3 were independent predictors of fibrotic NASH. On the basis of their regression coefficients, we developed a novel nomogram showing a good discriminatory ability (area under receiver operating characteristic curve AUROC: 0.79, 95% confidence interval CI 0.75–0.83) and a high negative predictive value (NPV: 94.7%) to rule out fibrotic NASH. In the validation set, this nomogram had a higher AUROC (0.81, 95%CI 0.74–0.87) than that of MACK‐3 (AUROC: 0.75, 95%CI 0.68–0.82; P < 0.05) with a NPV of 93.2%. The sequential combination of this nomogram with LSM data avoided the need for liver biopsy in 56.9% of patients.
Conclusions
Our novel nomogram (combining MACK‐3, platelet count and MetS) shows promising utility for diagnosing fibrotic NASH. The sequential combination of this nomogram and vibration‐controlled transient elastography limits indeterminate results and reduces the number of unnecessary liver biopsies.
To estimate the prevalence of established diabetes and its association with the clinical severity and in-hospital mortality associated with COVID-19.
We systematically searched PubMed, Scopus and Web ...of Science, from 1st January 2020 to 15th May 2020, for observational studies of patients admitted to hospital with COVID-19. Meta-analysis was performed using random-effects modeling. A total of 83 eligible studies with 78,874 hospitalized patients with laboratory-confirmed COVID-19 were included. The pooled prevalence of established diabetes was 14.34% (95% CI 12.62–16.06%). However, the prevalence of diabetes was higher in non-Asian vs. Asian countries (23.34% 95% CI 16.40–30.28 vs. 11.06% 95% CI 9.73–12.39), and in patients aged ≥60 years vs. those aged <60 years (23.30% 95% CI 19.65–26.94 vs. 8.79% 95% CI 7.56–10.02). Pre-existing diabetes was associated with an approximate twofold higher risk of having severe/critical COVID-19 illness (n = 22 studies; random-effects odds ratio 2.10, 95% CI 1.71–2.57; I2 = 41.5%) and ~threefold increased risk of in-hospital mortality (n = 15 studies; random-effects odds ratio 2.68, 95% CI 2.09–3.44; I2 = 46.7%). Funnel plots and Egger's tests did not reveal any significant publication bias.
Pre-existing diabetes is significantly associated with greater risk of severe/critical illness and in-hospital mortality in patients admitted to hospital with COVID-19.
•Little is known about the association of diabetes with the clinical severity and in-hospital mortality associated with COVID-19.•We meta-analyzed 83 observational studies for a total of 78,874 in-patients with COVID-19.•Diabetes was associated with a greater risk of severe/critical illness and in-hospital mortality associated with COVID-19.
Sarcopenic obesity is regarded as a risk factor for the progression and development of non-alcoholic fatty liver disease (NAFLD). Since male sex is a risk factor for NAFLD and skeletal muscle mass ...markedly varies between the sexes, we examined whether sex influences the association between appendicular skeletal muscle mass to visceral fat area ratio (SVR), that is, an index of skeletal muscle mass combined with abdominal obesity, and the histological severity of NAFLD. The SVR was measured by bioelectrical impedance in a cohort of 613 (M/F = 443/170) Chinese middle-aged individuals with biopsy-proven NAFLD. Multivariable logistic regression and subgroup analyses were used to test the association between SVR and the severity of NAFLD (i.e. non-alcoholic steatohepatitis (NASH) or NASH with the presence of any stage of liver fibrosis). NASH was identified by a NAFLD activity score ≥5, with a minimum score of 1 for each of its categories. The presence of fibrosis was classified as having a histological stage ≥1. The SVR was inversely associated with NASH in men (adjusted OR 0·62; 95 % CI 0·42, 0·92,
= 0·017 for NASH, adjusted OR 0·65; 95 % CI 0·43, 0·99,
= 0·043 for NASH with the presence of fibrosis), but not in women (1·47 (95 % CI 0·76, 2·83),
= 0·25 for NASH, and 1·45 (95 % CI 0·74, 2·83),
= 0·28 for NASH with the presence of fibrosis). There was a significant interaction for sex and SVR (
= 0·017 for NASH and
= 0·033 for NASH with the presence of fibrosis). Our findings show that lower skeletal muscle mass combined with abdominal obesity is strongly associated with the presence of NASH only in men.
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