Non-alcoholic to metabolic associated fatty liver disease: Cardiovascular implications of a change in terminology in patients living with HIV

Background and Aims: It has recently been suggested that the definition of non-alcoholic fatty liver disease (NAFLD) be changed to Metabolic Associated FLD (MAFLD) to better reflect the complex metabolic aspects of this syndrome. We compared the ability of MAFLD and NAFLD to correctly identify high CV risk patients, sub-clinical atherosclerosis or a history of prior CV events (CVEs) in patients living with HIV (PWH). Methods: Single center, cross-sectional study of PWH on stable anti-retrovirals. NAFLD was diagnosed by transient liver elastography; published criteria were used to diagnose MAFLD (JHepatol.2020;73(1):202-209). Four mutually exclusive groups were considered: low (<7.5%) vs high (>7.5%) ASCVD risk, subclinical CVD (carotid IMT ≥1 mm and/or coronary calcium score >100), and prior CVEs. The association of NAFLD and MAFLD with the CVD risk groups was explored via a multinominal model adjusted for age, sex, liver fibrosis, HIV duration, nadir CD4 and current CD4 cell count. Results: We included 1249 PWH (mean age 55 years, 74% men, median HIV duration 24 years). Prevalence of overweight/obesity and diabetes was 40% and 18%. Prevalence of NAFLD and MAFLD and overlapping groups are shown in Fig 1A. Fig 1B shows distribution of NAFLD/MAFLD in the 4 patient categories (p-for-trend <0.001). Both MAFLD and NAFLD were significantly associated with an increased risk of CVD compared to the reference level (ASCVD<7.5%) (all p-values <0.004; Fig 2). Conclusions: NAFLD and MAFLD perform equally in detecting CVD or its risk. The proposed change in terminology may not help to identify PWH requiring enhanced surveillance and preventative interventions for cardiovascular disease

Detailed characterization of SARS-CoV-2-specific T and B cells after infection or heterologous vaccination

: The formation of a robust long-term antigen (Ag)-specific memory, both humoral and cell-mediated, is created following severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection or vaccination. Here, by using polychromatic flow cytometry and complex data analyses, we deeply investigated the magnitude, phenotype, and functionality of SARS-CoV-2-specific immune memory in two groups of healthy subjects after heterologous vaccination compared to a group of subjects who recovered from SARS-CoV-2 infection. We find that coronavirus disease 2019 (COVID-19) recovered patients show different long-term immunological profiles compared to those of donors who had been vaccinated with three doses. Vaccinated individuals display a skewed T helper (Th)1 Ag-specific T cell polarization and a higher percentage of Ag-specific and activated memory B cells expressing immunoglobulin (Ig)G compared to those of patients who recovered from severe COVID-19. Different polyfunctional properties characterize the two groups: recovered individuals show higher percentages of CD4+ T cells producing one or two cytokines simultaneously, while the vaccinated are distinguished by highly polyfunctional populations able to release four molecules, namely, CD107a, interferon (IFN)-γ, tumor necrosis factor (TNF), and interleukin (IL)-2. These data suggest that functional and phenotypic properties of SARS-CoV-2 adaptive immunity differ in recovered COVID-19 individuals and vaccinated ones

Quality of life and intrinsic capacity in patients with post-acute COVID-19 syndrome is in relation to frailty and resilience phenotypes.

Background- The objective of this study was to characterize frailty and resilience in people evaluated for Post-Acute COVID-19 Syndrome (PACS), in relation to quality of life (QoL) and Intrinsic Capacity (IC). Methods- This cross-sectional, observational, study included consecutive people previously hospitalized for severe COVID-19 pneumonia attending Modena (Italy) PACS Clinic from July 2020 to April 2021. Four frailty-resilience phenotypes were built: “fit/resilient”, “fit/non-resilient”, “frail/resilient” and “frail/non-resilient”. Frailty and resilience were defined according to frailty phenotype and Connor Davidson resilience scale (CD-RISC-25) respectively. Study outcomes were: QoL assessed by means of Symptoms Short form health survey (SF-36) and health-related quality of life (EQ-5D-5L) and IC by means of a dedicated questionnaire. Their predictors including frailty-resilience phenotypes were explored in logistic regressions. Results- 232 patients were evaluated, median age was 58.0 years. PACS was diagnosed in 173 (74.6%) patients. Scarce resilience was documented in 114 (49.1%) and frailty in 72 (31.0%) individuals. Predictors for SF-36 score <61.60 were the phenotypes “frail/non-resilient” (OR=4.69, CI:2.08-10.55), “fit/non-resilient” (OR=2.79, CI:1.00-7.73). Predictors for EQ-5D-5L <89.7% were the phenotypes “frail/non-resilient” (OR=5.93, CI: 2.64-13.33) and “frail/resilient” (OR=5.66, CI:1.93-16.54). Predictors of impaired IC (below the mean score value) were “frail/non-resilient” (OR=7.39, CI:3.20-17.07), and “fit/non-resilient” (OR=4.34, CI:2.16-8.71) phenotypes. Conclusions- Resilience is complementary to frailty in the identification of clinical phenotypes with different impact on wellness and QoL. Frailty and resilience should be evaluated in hospitalized COVID-19 patients to identify vulnerable individuals to prioritize urgent health interventions in people with PACS

Do all critically ill patients with COVID-19 disease benefit from adding tocilizumab to glucocorticoids? A retrospective cohort study.

Background: Treatment guidelines recommend the tocilizumab use in patients with a CRP of >7.5 mg/dL. We aimed to estimate the causal effect of glucocorticoids + tocilizumab on mortality overall and after stratification for PaO2/FiO2 ratio and CRP levels. Methods: This was an observational cohort study of patients with severe COVID-19 pneumonia. The primary endpoint was day 28 mortality. Survival analysis was conducted to estimate the conditional and average causal effect of glucocorticoids + tocilizumab vs. glucocorticoids alone using Kaplan–Meier curves and Cox regression models with a time-varying variable for the intervention. The hypothesis of the existence of effect measure modification by CRP and PaO2/FiO2 ratio was tested by including an interaction term in the model. Results: In total, 992 patients, median age 69 years, 72.9% males, 597 (60.2%) treated with monotherapy, and 395 (31.8%), adding tocilizumab upon respiratory deterioration, were included. At BL, the two groups differed for median values of CRP (6 vs. 7 mg/dL; p 7.5 mg/dL prior to treatment initiation and the largest effect for a CRP > 15 mg/dL. Large randomized studies are needed to establish an exact cut-off for clinical use

A Machine Learning Approach to Predict Weight Change in ART-Experienced People Living with HIV

Introduction:The objective of the study was to develop machine learning (ML) models that predict the percentage weight change in each interval of time in antiretroviral therapy-experienced people living with HIV.Methods:This was an observational study that comprised consecutive people living with HIV attending Modena HIV Metabolic Clinic with at least 2 visits. Data were partitioned in an 80/20 training/test set to generate 10 progressively parsimonious predictive ML models. Weight gain was defined as any weight change &gt;5%, at the next visit. SHapley Additive exPlanations values were used to quantify the positive or negative impact of any single variable included in each model on the predicted weight changes.Results:A total of 3,321 patients generated 18,322 observations. At the last observation, the median age was 50 years and 69% patients were male. Model 1 (the only 1 including body composition assessed with dual-energy x-ray absorptiometry) had an accuracy greater than 90%. This model could predict weight at the next visit with an error of &lt;5%.Conclusions:ML models with the inclusion of body composition and metabolic and endocrinological variables had an excellent performance. The parsimonious models available in standard clinical evaluation are insufficient to obtain reliable prediction, but are good enough to predict who will not experience weight gain.</p

A Machine Learning Approach to Predict Weight Change in ART-Experienced People Living with HIV

Introduction:The objective of the study was to develop machine learning (ML) models that predict the percentage weight change in each interval of time in antiretroviral therapy-experienced people living with HIV.Methods:This was an observational study that comprised consecutive people living with HIV attending Modena HIV Metabolic Clinic with at least 2 visits. Data were partitioned in an 80/20 training/test set to generate 10 progressively parsimonious predictive ML models. Weight gain was defined as any weight change &gt;5%, at the next visit. SHapley Additive exPlanations values were used to quantify the positive or negative impact of any single variable included in each model on the predicted weight changes.Results:A total of 3,321 patients generated 18,322 observations. At the last observation, the median age was 50 years and 69% patients were male. Model 1 (the only 1 including body composition assessed with dual-energy x-ray absorptiometry) had an accuracy greater than 90%. This model could predict weight at the next visit with an error of &lt;5%.Conclusions:ML models with the inclusion of body composition and metabolic and endocrinological variables had an excellent performance. The parsimonious models available in standard clinical evaluation are insufficient to obtain reliable prediction, but are good enough to predict who will not experience weight gain.</p