A multistakeholder approach to innovations in NAFLD care

Clinical utility of the Fibrosis-4 index for predicting mortality in patients with heart failure with or without metabolic dysfunction-associated steatotic liver disease: a prospective cohort study

Background

The liver-heart axis potentially influences the risk of mortality in patients with heart failure. We aimed to identify the clinical utility of the fibrosis-4 (FIB-4) index in patients with heart failure for predicting mortality in the context of metabolic dysfunction-associated steatotic liver disease (MASLD).

Methods

Patients with heart failure and a subsample of healthy participants were enrolled in the MyoVasc study (NCT04064450) and followed for nine years. Participants with excessive alcohol consumption were excluded. The Fatty Liver Index (FLI) and FIB-4 index were used to classify MASLD and hepatic fibrosis, respectively. Data were adjusted for potential confounders. The primary endpoint was all-cause mortality.

Findings

2726 participants, including 172 healthy individuals, were included in the study. The participants had a mean age of 64.4 ± 11.2 years and a median FIB-4 index of 1.59 (interquartile range [1.17; 2.17]). There were 532 deaths. The FIB-4 index was predictive for all-cause mortality (hazard ratio (HR) 1.341, 95% confidence interval (CI) [1.273; 1.412], p < 0.0001). The HRs and 95% CIs for the FIB-4 index in FLI categories were 1.597 [1.256; 2.031] (p = 0.00013, FLI <30), 1.802 [1.519; 2.138] (p < 0.0001, FLI 30-60), and 1.292 [1.215; 1.374] (p < 0.0001, FLI ≥60). The interaction term for the FIB-4 index with FLI ≥60 (reference FLI <30) was HR 0.774 [0.617; 0.972] (p = 0.027), indicating a smaller impact of the FIB-4 index in FLI ≥60 than in FLI <30 (HR 1.664 [1.333; 2.077], p < 0.0001). Multivariable linear regressions revealed relevant independent relationships between the FIB-4 index and N-terminal pro-B-type natriuretic peptide, systolic dysfunction, diastolic dysfunction and left ventricular hypertrophy in participants with a FLI below 60.

Interpretation

In patients with heart failure, the FIB-4 index predicts all-cause mortality and relates to cardiac functional and structural changes, especially in those without MASLD.

Funding

Johannes Gutenberg-University Mainz.

PGxQA: A Resource for Evaluating LLM Performance for Pharmacogenomic QA Tasks

Pharmacogenetics represents one of the most promising areas of precision medicine, with several guidelines for genetics-guided treatment ready for clinical use. Despite this, implementation has been slow, with few health systems incorporating the technology into their standard of care. One major barrier to uptake is the lack of education and awareness of pharmacogenetics among clinicians and patients. The introduction of large language models (LLMs) like GPT-4 has raised the possibility of medical chatbots that deliver timely information to clinicians, patients, and researchers with a simple interface. Although state-of-the-art LLMs have shown impressive performance at advanced tasks like medical licensing exams, in practice they still often provide false information, which is particularly hazardous in a clinical context. To quantify the extent of this issue, we developed a series of automated and expert-scored tests to evaluate the performance of chatbots in answering pharmacogenetics questions from the perspective of clinicians, patients, and researchers. We applied this benchmark to state-of-the-art LLMs and found that newer models like GPT-4o greatly outperform their predecessors, but still fall short of the standards required for clinical use. Our benchmark will be a valuable public resource for subsequent developments in this space as we work towards better clinical AI for pharmacogenetics.

Rapid and automated interpretation of CRISPR-Cas13-based lateral flow assay test results using machine learning

CRISPR-Cas-based lateral flow assays (LFAs) have emerged as a promising diagnostic tool for ultrasensitive detection of nucleic acids, offering improved speed, simplicity and cost-effectiveness compared to polymerase chain reaction (PCR)-based assays. However, visual interpretation of CRISPR-Cas-based LFA test results is prone to human error, potentially leading to false-positive or false-negative outcomes when analyzing test/control lines. To address this limitation, we have developed two neural network models: one based on a fully convolutional neural network and the other on a lightweight mobile-optimized neural network for automated interpretation of CRISPR-Cas-based LFA test results. To demonstrate proof of concept, these models were applied to interpret results from a CRISPR-Cas13-based LFA for the detection of the SARS-CoV-2 N gene, a key marker for COVID-19 infection. The models were trained, evaluated, and validated using smartphone-captured images of LFA devices in various orientations with different backgrounds, lighting conditions, and image qualities. A total of 3146 images (1569 negative, 1577 positive) captured using an iPhone 13 or Samsung Galaxy A52 Android smartphone were analyzed using the trained models, which classified the LFA results within 0.2 s with 96.5% accuracy compared to the ground truth. These results demonstrate the potential of machine learning to accurately interpret test results of CRISPR-Cas-based LFAs using smartphone-captured images in real-world settings, enabling the practical use of CRISPR-Cas-based diagnostic tools for self- and at-home testing.

Comparing Patient's Confidence in Clinical Capabilities in Urology: Large Language Models Versus Urologists

Background and objective

Data on interaction of patients with artificial intelligence (AI) are limited, primarily derived from small-scale studies, cross-sectional surveys, and qualitative reviews. Most patients have not yet encountered AI in their clinical experience. This study explored patients' confidence in AI, specifically large language models, after a direct interaction with a chatbot in a clinical setting. Through hands-on experience, the study sought to reduce potential biases due to an anticipated lack of AI experience in a real-world urological patient sample.

Methods

A total of 300 patients scheduled for counseling were enrolled from February to July 2024. Participants voluntarily conversed about their medical questions with a GPT-4 powered chatbot, followed by a survey assessing their confidence in clinical capabilities of AI compared with their counseling urologists. Clinical capabilities included history taking, diagnostics, treatment recommendation, anxiety reduction, and time allocation.

Key findings and limitations

Of the 292 patients who completed the study, AI was significantly preferred to physicians for consultation time allocation (p < 0.001). However, urologists were overwhelmingly favored for all other capabilities, especially treatment recommendations and anxiety reduction. Notably, age did not influence patients' confidence in AI. Limitations include a potential social desirability bias.

Conclusions and clinical implications

Our study demonstrates that urological patients prefer AI as a powerful complement to-rather than a replacement for-human expertise in clinical care. Patients appreciated the additional consultation time provided by AI. Interestingly, age was not associated with confidence in AI, suggesting that large language models are user-friendly tools for patients of all age groups.

Patient summary

In this report, we explored how patients feel about using an artificial intelligence (AI)-powered chatbot in a medical setting. Patients interacted with the AI for medical questions and compared its skills with those of doctors through a survey. They appreciated the AI for providing more time during consultations but preferred doctors for other tasks, for example, diagnostics, recommendation of treatments, and reduction of anxieties.

A Survey Assessing Nonalcoholic Fatty Liver Disease Knowledge Among Hepatologists and Non-Hepatologists in China

Background and Aim

A global increase in nonalcoholic fatty liver disease (NAFLD) prevalence has been observed in the last decade. This study assesses knowledge, awareness, and clinical practice gaps of hepatologists and non-hepatologists in NAFLD management across hospitals in China.

Methods

A web-based quantitative survey was conducted, and participants included hepatologists (gastroenterologists and infectious disease specialists) and non-hepatologists (internal medicine specialists, cardiologists, and pharmacists) from various hospitals across China.

Results

In total, 1627 healthcare practitioners (HCPs) responded to the survey. This included 658 hepatologists and 969 non-hepatologists. In comparison to 92.6% hepatologists, only 58.0% of non-hepatologists were aware of NAFLD. A higher proportion of hepatologists (82.8%) performed screening for NAFLD compared to non-hepatologists (56.9%). Majority of the hepatologists (70%) and non-hepatologists (67%) were aware of the four primary recommendations for managing NAFLD. Only 11% of hepatologists did not manage NAFLD patients, mainly because they felt they did not have enough time (66.7%). Of the 36% non-hepatologists who did not manage NAFLD, 78.4% stated that NAFLD is not their specialty, and 38.6% were not familiar with the treatment options.

Conclusion

Most hepatologists were aware of and agreed to performing screening for NAFLD compared to non-hepatologists. Both hepatologists and non-hepatologists exhibited similar level of understanding on NAFLD management. However, a small percentage of both hepatologists and non-hepatologists admitted that they did not manage NAFLD patients because they were not familiar with available treatment options. This underscores the importance of further educating HCPs involved in managing NAFLD.

Predictors of liver fibrosis progression in cohort of type 2 diabetes mellitus patients with MASLD

AIM

To investigate predictors of liver fibrosis progression in patients with type 2 diabetes mellitus (T2DM) over a minimum follow-up duration of three years.

METHODS

Two hundred and thirty-three patients completed the follow-up period and their clinical, laboratory and liver FibroScan data are reported. Patients were categorized into progressors 42 (18.0 %) and non-progressors 191 (82.0 %) based on liver fibrosis progression. Factors influencing fibrosis progression were identified by comparing these groups.

RESULTS

Progressors showed significantly increased aspartate aminotransferase (AST) (p = 0.010), increased alkaline phosphatase (ALP) (p = 0.001) and decreased platelet count (p = 0.002). Non-progressors exhibited significant decreases in diastolic blood pressure (DBP) (p = 0.050), body mass index (BMI) (p < 0.001), waist circumference (p < 0.001), gamma-glutamyl transferase (GGT) (p < 0.001), albumin (p < 0.001), alanine aminotransferase (ALT) (p = 0.022), glycosylated haemoglobin (HbA1c) (p = 0.002) and fasting blood sugar (FBS) (p = 0.030) with increase in HDL-cholesterol (p < 0.001), creatinine (p < 0.001), bilirubin (p < 0.001), and ALP (p = 0.007). Baseline parameters predictive of liver fibrosis progression included elevated AST and reduced platelet count. Delta changes from baseline to follow-up revealed that increases in ALP, BMI, waist circumference, and reduction in platelet count were correlated with fibrosis progression. Use of GLP-1 receptor agonist was associated with reduced progression.

CONCLUSION

In conclusion, increase in ALP and waist circumference and reduction in platelet count are predictive of liver fibrosis progression in patients with T2DM. GLP-1 receptor agonists use seems to have a promising protective effect against liver fibrosis progression.

CLINICALTRIALS

govID:NCT05697991.

Patient-Reported Outcomes in Metabolic Dysfunction-Associated Steatotic Liver Disease

Metabolic dysfunction-associated steatotic liver disease (MASLD) is the most common chronic liver disease worldwide and can progress to serious complications, including metabolic dysfunction-associated steatohepatitis (MASH), cirrhosis, end-stage liver disease, and hepatocellular carcinoma. Predisposing risk factors for MASH include obesity, type 2 diabetes, dyslipidemia, and metabolic syndrome. Patients with MASH often experience significant impairments in their health-related quality of life and other patient-reported outcomes (PROs), particularly in physical functioning domains, fatigue, and vitality. Incorporating PROs offers valuable insights into patients' perspectives on their symptoms, treatment efficacy, and overall well-being, thereby guiding more holistic and patient-centered care strategies. This review aims to investigate the utilization of patient-reported outcome measures (PROMs) in the context of MASLD and MASH care, identify which PROMs are employed, and summarize the outcomes reported.

Steatotic liver disease

Steatotic liver disease is the overarching term for conditions characterised by abnormal lipid accumulation in the liver (liver or hepatic steatosis). Steatotic liver disease encompasses what was previously termed non-alcoholic fatty liver disease (NAFLD), which is now called metabolic dysfunction-associated steatotic liver disease (MASLD). Additionally, steatotic liver disease includes alcohol-related liver disease (ALD) and MetALD, the new classification for the overlap between MASLD and ALD, and rare causes of liver steatosis. Cirrhosis is globally the 11th leading cause of death, and steatotic liver disease has become the leading cause of cirrhosis in the EU and USA. Steatotic liver disease affects around 30% of the global population and is mainly driven by obesity, type 2 diabetes, and alcohol intake, but only a minor proportion with steatotic liver disease progress to cirrhosis. The presence and progression of liver fibrosis led by hepatic inflammation is the main predictor of liver-related death across the entire spectrum of steatotic liver diseases. A combination of recent advancements of widely available biomarkers for early detection of liver fibrosis together with considerable advancements in therapeutic interventions offer the possibility to reduce morbidity and mortality in patients with steatotic liver disease. This Seminar covers the recent reclassification of steatotic liver disease and how it reflects clinical practice and prognosis. For early detection of liver fibrosis, we propose a collaborative diagnostic framework between primary care and liver specialists. Lastly, we discuss current best practices for managing steatotic liver disease, we explore therapeutic targets across the spectrum of steatotic liver diseases, and we review the pipeline of drugs in development for MASLD.

MASLD and MASH at the crossroads of hepatology trials and cardiorenal metabolic trials

Steatotic liver disease (SLD) is a worldwide public health problem, causing considerable morbidity and mortality. Patients with SLD are at increased risk for major adverse cardiovascular (CV) events, type 2 diabetes mellitus and chronic kidney disease. Conversely, patients with cardiometabolic conditions have a high prevalence of SLD. In addition to epidemiological evidence linking many of these conditions, there is evidence of shared pathophysiological processes. In December 2022, a unique multi-stakeholder, multi-specialty meeting, called MOSAIC (Metabolic multi Organ Science Accelerating Innovation in Clinical Trials) was convened to foster collaboration across metabolic, hepatology, nephrology and CV disorders. One of the goals of the meeting was to consider approaches to drug development that would speed regulatory approval of treatments for multiple disorders by combining liver and cardiorenal endpoints within a single study. Non-invasive tests, including biomarkers and imaging, are needed in hepatic and cardiorenal trials. They can be used as trial endpoints, to enrich trial populations, to diagnose and risk stratify patients and to assess treatment efficacy and safety. Although they are used in proof of concept and phase 2 trials, they are often not acceptable for regulatory approval of therapies. The challenge is defining the optimal combination of biomarkers, imaging and morbidity/mortality outcomes and ensuring that they are included in future trials while minimizing the burden on patients, trialists and trial sponsors. This paper provides an overview of some of the wide array of CV, liver and kidney measurements that were discussed at the MOSAIC meeting.

Towards a Standardization of Learning Curve Assessment in Minimally Invasive Liver Surgery

OBJECTIVE

The aim was to analyze the learning curves of minimal invasive liver surgery(MILS) and propose a standardized reporting.

SUMMARY BACKGROUND DATA

MILS offers benefits compared to open resections. For a safe introduction along the learning curve, formal training is recommended. However, definitions of learning curves and methods to assess it lack standardization.

METHODS

A systematic review of PubMed, Web of Science, and CENTRAL databases identified studies on learning curves in MILS. The primary outcome was the number needed to overcome the learning curve. Secondary outcomes included endpoints defining learning curves, and characterization of different learning phases(competency, proficiency and mastery).

RESULTS

60 articles with 12'241 patients and 102 learning curve analyses were included. The laparoscopic and robotic approach was evaluated in 71 and 18 analyses and both approaches combined in 13 analyses. Sixty-one analyses (60%) based the learning curve on statistical calculations. The most often used parameters to define learning curves were operative time (n=64), blood loss (n=54), conversion (n=42) and postoperative complications (n=38). Overall competency, proficiency and mastery were reached after 34 (IQR 19-56), 50 (IQR 24-74), 58 (IQR 24-100) procedures respectively. Intraoperative parameters improved earlier (operative time: competency to proficiency to mastery: -13%, 2%; blood loss: competency to proficiency to mastery: -33%, 0%; conversion rate (competency to proficiency to mastery; -21%, -29%), whereas postoperative complications improved later (competency to proficiency to mastery: -25%, -41%).

CONCLUSIONS

This review summarizes the highest evidence on learning curves in MILS taking into account different definitions and confounding factors. A standardized three-phase reporting of learning phases (competency, proficiency, mastery) is proposed and should be followed.

Natural history of metabolic dysfunction-associated steatotic liver disease

Metabolic dysfunction-associated steatotic liver disease (MASLD), which has been the term for non-alcoholic fatty liver disease (NAFLD) since June 2023, represents the most common liver disease worldwide and is a leading cause of liver-related morbidity and mortality. A thorough knowledge of the disease's natural history is required to promptly stratify patients' risks, since MASLD is a multifaceted disorder with a broad range of clinical phenotypes. The histological disease spectrum ranges from isolated hepatic steatosis, currently named as metabolic dysfunction-associated steatotic liver (MASL), to metabolic dysfunction-associated steatohepatitis (MASH) and eventually may accumulate hepatic fibrosis and develop cirrhosis and/or hepatocellular carcinoma (HCC). Several risk factors for fibrosis progression have been identified, while the disease's progression displays notable dynamism and bidirectionality. When compared to the general population, all MASLD histological stages are substantially related with greater overall mortality, and this association exhibits a disease severity-dependent pattern. Interestingly, the fibrosis stage is the most accurate predictor of mortality among MASLD patients. The mortality attributed to MASLD predominantly stems from issues linked with the liver and cardiovascular system, as well as HCC and extrahepatic cancers. In light of the disease natural course, it is crucial to prioritize the identification of at-risk patients for disease progression in order to effectively address and change modifiable risk factors, hence mitigating disease complications. Further investigation is required to define the phenotype of rapid progressors more precisely as well as to improve risk stratification for HCC in non-cirrhotic individuals.

Practical Lifestyle Management of Nonalcoholic Fatty Liver Disease for Busy Clinicians

Weight loss achieved through a combination of healthy eating patterns that encompass the principles of the Mediterranean diet and regular physical activity is the most evidence-based treatment for nonalcoholic fatty liver disease. Although other types of diets have demonstrated efficacy in liver fat reduction, the Mediterranean diet confers additional cardiometabolic benefits. Macronutrient composition, food choices, and timing of eating can be tailored to individual preferences, culture, and financial circumstances; however, recommended healthy eating patterns are characterized by minimally processed or unprocessed foods (vegetables, legumes, nuts and seeds, fruits, whole grains, and unprocessed meats and fish) that are low in sugar, refined carbohydrates, and saturated fat and high in fiber, polyphenols, vitamins, minerals, and healthy fats. Physical activity can independently improve steatosis, prevent fibrosis and cirrhosis, and reduce mortality.

Liver stiffness as a cornerstone in heart disease risk assessment

Metabolic dysfunction-associated steatotic liver disease (MASLD) typically presents with hepatic fibrosis in advanced disease, resulting in increased liver stiffness. A subset of patients further develops liver cirrhosis and hepatocellular carcinoma. Cardiovascular disease is a common comorbidity in patients with MASLD and its prevalence is increasing in parallel. Recent evidence suggests that especially liver stiffness, whether or not existing against a background of MASLD, is associated with heart diseases. We conducted a narrative review on the role of liver stiffness in the prediction of highly prevalent heart diseases including heart failure, cardiac arrhythmias (in particular atrial fibrillation), coronary heart disease, and aortic valve sclerosis. Research papers were retrieved from major scientific databases (PubMed, Web of Science) until September 2023 using 'liver stiffness' and 'liver fibrosis' as keywords along with the latter cardiac conditions. Increased liver stiffness, determined by vibration-controlled transient elastography or hepatic fibrosis as predicted by biomarker panels, are associated with a variety of cardiovascular diseases, including heart failure, atrial fibrillation, and coronary heart disease. Elevated liver stiffness in patients with metabolic liver disease should lead to considerations of cardiac workup including N-terminal pro-B-type natriuretic peptide/B-type natriuretic peptide determination, electrocardiography, and coronary computed tomography angiography. In addition, patients with MASLD would benefit from heart disease case-finding strategies in which liver stiffness measurements can play a key role. In conclusion, increased liver stiffness should be a trigger to consider a cardiac workup in metabolically compromised patients.

Tailored Model of Care for Patients with Metabolic Dysfunction-Associated Steatotic Liver Disease

Metabolic dysfunction-associated steatotic liver disease (MASLD), formerly known as nonalcoholic fatty liver disease (NAFLD), is increasing globally, creating a growing public health concern. However, this disease is often not diagnosed, and accurate data on its epidemiology are limited in many geographical regions, making it challenging to provide proper care and implement effective national plans. To combat the increasing disease burden, screening and diagnosis must reach a significant number of high-risk subjects. Addressing MASLD as a health care challenge requires a multidisciplinary approach involving prevention, diagnosis, treatment, and care, with collaboration between multiple stakeholders in the health care system. This approach must be guided by national and global strategies, to be combined with efficient models of care developed through a bottom-up process. This review article highlights the pillars of the MASLD model of care (MoC), including screening, risk stratification, and establishing a clinical care pathway for management, in addition to discussing the impact of nomenclature change on the proposed MoC.

Predictive Algorithm for Hepatic Steatosis Detection Using Elastography Data in the Veterans Affairs Electronic Health Records

BACKGROUND AND AIMS

Nonalcoholic fatty liver disease (NAFLD) has reached pandemic proportions. Early detection can identify at-risk patients who can be linked to hepatology care. The vibration-controlled transient elastography (VCTE) controlled attenuation parameter (CAP) is biopsy validated to diagnose hepatic steatosis (HS). We aimed to develop a novel clinical predictive algorithm for HS using the CAP score at a Veterans' Affairs hospital.

METHODS

We identified 403 patients in the Greater Los Angeles VA Healthcare System with valid VCTEs during 1/2018-6/2020. Patients with alcohol-associated liver disease, genotype 3 hepatitis C, any malignancies, or liver transplantation were excluded. Linear regression was used to identify predictors of NAFLD. To identify a CAP threshold for HS detection, receiver operating characteristic analysis was applied using liver biopsy, MRI, and ultrasound as the gold standards.

RESULTS

The cohort was racially/ethnically diverse (26% Black/African American; 20% Hispanic). Significant positive predictors of elevated CAP score included diabetes, cholesterol, triglycerides, BMI, and self-identifying as Hispanic. Our predictions of CAP scores using this model strongly correlated (r = 0.61, p < 0.001) with actual CAP scores. The NAFLD model was validated in an independent Veteran cohort and yielded a sensitivity of 82% and specificity 83% (p < 0.001, 95% CI 0.46-0.81%). The estimated optimal CAP for our population cut-off was 273.5 dB/m, resulting in AUC = 75.5% (95% CI 70.7-80.3%).

CONCLUSION

Our HS predictive algorithm can identify at-risk Veterans for NAFLD to further risk stratify them by non-invasive tests and link them to sub-specialty care. Given the biased referral pattern for VCTEs, future work will need to address its applicability in non-specialty clinics. Proposed clinical algorithm to identify patients at-risk for NAFLD prior to fibrosis staging in Veteran.

A Machine Learning Method to Identify the Risk Factors for Liver Fibrosis Progression in Nonalcoholic Steatohepatitis

AIM

Nonalcoholic fatty liver disease (NAFLD) is a silent epidemy that has become the most common chronic liver disease worldwide. Nonalcoholic steatohepatitis (NASH) is an advanced stage of NAFLD, which is linked to a high risk of cirrhosis and hepatocellular carcinoma. The aim of this study is to develop a predictive model to identify the main risk factors associated with the progression of hepatic fibrosis in patients with NASH.

METHODS

A database from a multicenter retrospective cross-sectional study was analyzed. A total of 215 patients with NASH biopsy-proven diagnosed were collected. NAFLD Activity Score and Kleiner scoring system were used to diagnose and staging these patients. Noninvasive tests (NITs) scores were added to identify which one were more reliable for follow-up and to avoid biopsy. For analysis, different Machine Learning methods were implemented, being the eXtreme Gradient Booster (XGB) system the proposed algorithm to develop the predictive model.

RESULTS

The most important variable in this predictive model was High-density lipoprotein (HDL) cholesterol, followed by systemic arterial hypertension and triglycerides (TG). NAFLD Fibrosis Score (NFS) was the most reliable NIT. As for the proposed method, XGB obtained higher results than the second method, K-Nearest Neighbors, in terms of accuracy (95.05 vs. 90.42) and Area Under the Curve (0.95 vs. 0.91).

CONCLUSIONS

HDL cholesterol, systemic arterial hypertension, and TG were the most important risk factors for liver fibrosis progression in NASH patients. NFS is recommended for monitoring and decision making.

Impact of Chronotype and Mediterranean Diet on the Risk of Liver Fibrosis in Patients with Non-Alcoholic Fatty Liver Disease

Late chronotype, the individual's aptitude to perform daily activities late in the day, has been associated with low adherence to the Mediterranean diet (MedDiet) and metabolic syndrome. The aim of this work was to investigate the potential association of chronotype and adherence to the MedDiet with the liver fibrosis risk in patients with non-alcoholic fatty liver disease (NAFLD). Liver stiffness was assessed in 126 patients by FibroScan^®530. Significant (F ≥ 2) and advanced (F ≥ 3) hepatic fibrosis were defined according to liver stiffness values ≥7.1 kPa and ≥8.8 kPa, respectively. Chronotype (MSFsc) was defined by the Munich Chronotype Questionnaire, and adherence to the MedDiet was defined by the Mediterranean diet score (MDS). Overall, the median age was 55 (46-63) years, and 57.9% of participants were male. The principal comorbidities were type-2 diabetes mellitus (T2DM) (26.1%), arterial hypertension (53.1%), dyslipidaemia (63.4%), obstructive sleep apnoea (5.5%) and depression (5.5%). Most subjects (65.0%) had intermediate + late chronotype and showed higher mid-sleep on workdays (p < 0.001) and on work-free days (p < 0.001) compared to those with early chronotype. In the logistic regression model, intermediate + late chronotype (p = 0.024), MDS (p = 0.019) and T2DM (p = 0.004) were found to be significantly and independently associated with the risk of both F ≥ 2 And F ≥ 3. We observed that the intermediate + late chronotype and low adherence to the MedDiet were associated with both significant and advanced liver fibrosis in patients with NAFLD.

Adherence to Mediterranean Diet: Any Association with NAFLD?

Oxidative stress is considered one of the main determinants in the pathophysiology of non-alcoholic fatty liver disease (NAFLD) and obesity. The alterations of oxidant/antioxidant balance are related to chronic impairment of metabolism leading to mitochondrial dysfunction. Increased oxidative stress also triggers hepatocytes stress pathways, leading to inflammation and contributing to the progression of non-alcoholic steatohepatitis (NASH). Currently, the first-line therapeutic treatment of NAFLD is based on lifestyle interventions, suggesting the Mediterranean Diet (MD) as a preferable nutritional approach due to its antioxidant properties. However, it is still debated if adherence to MD could have a role in determining the risk of developing NAFLD directly or indirectly through its effect on weight. We enrolled 336 subjects (aged 35.87 ± 10.37 years; BMI 31.18 ± 9.66 kg/m²) assessing anthropometric parameters, lifestyle habits, metabolic parameters (fasting plasma glucose, fasting plasma insulin, triglycerides (TG), total cholesterol, low-density (LDL) and high-density lipoprotein (HDL) cholesterol, alanine transaminase (ALT), aspartate aminotransferase (AST), and γ-glutamyltransferase (γGT), cardio-metabolic indices [Homeostatic Model Assessment Insulin Resistance (HoMA-IR), visceral adipose index (VAI) and fatty liver index (FLI)] and adherence to MD [with the PREvención con DIetaMEDiterránea (PREDIMED) questionnaire]. Subjects with NAFLD had significantly higher anthropometric parameters, cardio-metabolic indices and lower adherence to MD than subjects without NAFLD. In a multiple regression analysis, PREDIMED score was the main predictor of FLI (p < 0.001) and came in first, followed by HoMA-IR, while VAI was not a predictor. A PREDIMED score value of <6 could serve as a threshold to identify patients who are more likely to have NAFLD (p < 0.001). In conclusion, high adherence to MD resulted in a lower risk of having NAFLD. Adherence to MD could have a direct role on the risk of developing NAFLD, regardless of visceral adipose tissue.

Research Priorities for Precision Medicine in NAFLD

NAFLD is a multisystem condition and the leading cause of chronic liver disease globally. There are no approved NAFLD-specific dugs. To advance in the prevention and treatment of NAFLD, there is a clear need to better understand the pathophysiology and genetic and environmental risk factors, identify subphenotypes, and develop personalized and precision medicine. In this review, we discuss the main NAFLD research priorities, with a particular focus on socioeconomic factors, interindividual variations, limitations of current NAFLD clinical trials, multidisciplinary models of care, and novel approaches in the management of patients with NAFLD.

Human visual explanations mitigate bias in AI-based assessment of surgeon skills

Artificial intelligence (AI) systems can now reliably assess surgeon skills through videos of intraoperative surgical activity. With such systems informing future high-stakes decisions such as whether to credential surgeons and grant them the privilege to operate on patients, it is critical that they treat all surgeons fairly. However, it remains an open question whether surgical AI systems exhibit bias against surgeon sub-cohorts, and, if so, whether such bias can be mitigated. Here, we examine and mitigate the bias exhibited by a family of surgical AI systems-SAIS-deployed on videos of robotic surgeries from three geographically-diverse hospitals (USA and EU). We show that SAIS exhibits an underskilling bias, erroneously downgrading surgical performance, and an overskilling bias, erroneously upgrading surgical performance, at different rates across surgeon sub-cohorts. To mitigate such bias, we leverage a strategy -TWIX-which teaches an AI system to provide a visual explanation for its skill assessment that otherwise would have been provided by human experts. We show that whereas baseline strategies inconsistently mitigate algorithmic bias, TWIX can effectively mitigate the underskilling and overskilling bias while simultaneously improving the performance of these AI systems across hospitals. We discovered that these findings carry over to the training environment where we assess medical students' skills today. Our study is a critical prerequisite to the eventual implementation of AI-augmented global surgeon credentialing programs, ensuring that all surgeons are treated fairly.