HIV experts on the decision to use early ART for prevention in France: are we there yet?

International audienc

The Relationship between Visceral Adiposity and Nonalcoholic Fatty Liver Disease Diagnosed by Controlled Attenuation Parameter in People with HIV: A Pilot Study

Background: Fat alterations are frequent in people with HIV (PWH) and predict worse cardiometabolic outcomes. Visceral adipose tissue (VAT) is associated with ectopic fat accumulation in the liver. We aimed to investigate nonalcoholic fatty liver disease (NAFLD) diagnosed by controlled attenuation parameter (CAP) as a potential marker of visceral adiposity in PWH. Methods: We conducted a prospective pilot study of HIV mono-infected patients undergoing metabolic characterization and paired CAP measured by transient elastography with dual-energy X-ray absorptiometry (DEXA) scan. NAFLD was defined as CAP >= 285 dB/m, in absence of alcohol abuse. Excess visceral adiposity was defined as VAT > 1.32 Kg. Pairwise correlation, area under the curve (AUC) and logistic regression analysis were employed to study the association between VAT and CAP. Results: Thirty patients were included, of whom 50% had NAFLD. CAP was correlated with VAT (r = 0.650, p < 0.001) measured by DEXA scan. After adjusting for duration of HIV infection, body mass index and waist circumference, CAP remained the only independent predictor of excess VAT (adjusted odds ratio 1.05, 95% confidence interval [CI] 1.01-1.10). The AUC analysis determined CAP had excellent performance to diagnose excess VAT (AUC 0.92, 95% CI 0.81-1.00), higher than BMI and waist circumference. The optimized CAP cut-off to diagnose excess VAT was 266 dB/m, with a sensitivity of 88.3% and a specificity of 84.6%. Conclusions: NAFLD diagnosed by CAP is associated with VAT in PWH independently of anthropometric measures of obesity. CAP may be a potential diagnostic marker of visceral adiposity in the practice of HIV medicine

Repurposing Metformin in Nondiabetic People With HIV:Influence on Weight and Gut Microbiota

Background. People with HIV (PWH) taking antiretroviral therapy (ART) may experience weight gain, dyslipidemia, increased risk of non-AIDS comorbidities, and long-term alteration of the gut microbiota. Both low CD4/CD8 ratio and chronic inflammation have been associated with changes in the gut microbiota of PWH. The antidiabetic drug metformin has been shown to improve gut microbiota composition while decreasing weight and inflammation in diabetes and polycystic ovary syndrome. Nevertheless, it remains unknown whether metformin may benefit PWH receiving ART, especially those with a low CD4/CD8 ratio. Methods. In the Lilac pilot trial, we recruited 23 nondiabetic PWI I receiving ART for more than 2 years with a low CD4/CD8 ratio ( Results. Metformin decreased weight in PWH, and weight loss was inversely correlated with plasma levels of the satiety factor GDF-15. Furthermore, metformin changed the gut microbiota composition by increasing the abundance of anti-inflammatory bacteria such as butyrate-producing species and the protective Akkermansia muciniphila. Conclusions. Our study provides the first evidence that a 12-week metformin treatment decreased weight and favored anti-inflammatory bacteria abundance in the microbiota of nondiabetic ART-treated PWH. Larger randomized placebo-controlled clinical trials with longer metformin treatment will be needed to further investigate the role of metformin in reducing inflammation and the risk of non-AIDS comorbidities in ART-treated PWH

CXCL13 as a Biomarker of Immune Activation During Early and Chronic HIV Infection

Background: CXCL13 is preferentially secreted by Follicular Helper T cells (TFH) to attract B cells to germinal centers. Plasma levels of CXCL13 have been reported to be elevated during chronic HIV-infection, however there is limited data on such elevation during early phases of infection and on the effect of ART. Moreover, the contribution of CXCL13 to disease progression and systemic immune activation have been partially defined. Herein, we assessed the relationship between plasma levels of CXCL13 and systemic immune activation.Methods: Study samples were collected in 114 people living with HIV (PLWH) who were in early (EHI) or chronic (CHI) HIV infection and 35 elite controllers (EC) compared to 17 uninfected controls (UC). A subgroup of 11 EHI who initiated ART and 14 who did not were followed prospectively. Plasma levels of CXCL13 were correlated with CD4 T cell count, CD4/CD8 ratio, plasma viral load (VL), markers of microbial translocation [LPS, sCD14, and (1→3)-β-D-Glucan], markers of B cell activation (total IgG, IgM, IgA, and IgG1-4), and inflammatory/activation markers like IL-6, IL-8, IL-1β, TNF-α, IDO-1 activity, and frequency of CD38+HLA-DR+ T cells on CD4+ and CD8+ T cells.Results: Plasma levels of CXCL13 were elevated in EHI (127.9 ± 64.9 pg/mL) and CHI (229.4 ± 28.5 pg/mL) compared to EC (71.3 ± 20.11 pg/mL), and UC (33.4 ± 14.9 pg/mL). Longitudinal analysis demonstrated that CXCL13 remains significantly elevated after 14 months without ART (p < 0.001) and was reduced without normalization after 24 months on ART (p = 0.002). Correlations were observed with VL, CD4 T cell count, CD4/CD8 ratio, LPS, sCD14, (1→3)-β-D-Glucan, total IgG, TNF-α, Kynurenine/Tryptophan ratio, and frequency of CD38+HLA-DR+ CD4 and CD8 T cells. In addition, CMV+ PLWH presented with higher levels of plasma CXCL13 than CMV- PLWH (p = 0.005).Conclusion: Plasma CXCL13 levels increased with HIV disease progression. Early initiation of ART reduces plasma CXCL13 and B cell activation without normalization. CXCL13 represents a novel marker of systemic immune activation during early and chronic HIV infection and may be used to predict the development of non-AIDS events

Association Between Gut Microbiota and CD4 Recovery in HIV-1 Infected Patients

Composition of the gut microbiota has been linked with human immunedeficiency virus (HIV)-infected patients on antiretroviral therapy (ART). Evidence suggests that ART-treated patients with poor CD4+ T-cell recovery have higher levels of microbial translocation and immune activation. However, the association of the gut microbiota and immune recovery remains unclear. We performed a cross-sectional study on 30 healthy controls (HC) and 61 HIV-infected individuals, including 15 immunological ART responders (IRs), 20 immunological ART non-responders (INRs) and 26 untreated individuals (VU). IR and INR groups were classified by CD4+ T-cell counts of ≥350 cells/mm3 and <350 cells/mm3 after 2 years of ART, respectively. Each subject’s gut microbiota composition was analyzed by metagenomics sequencing. Levels of CD4+ T cells, CD8+HLA-DR+ T cells and CD8+CD38+ T cells were measured by flow cytometry. We identified more Prevotella and fewer Bacteroides in HIV-infected individuals than in HC. Patients in INR group were enriched with Faecalibacterium prausnitzii, unclassified Subdoligranulum sp. and Coprococcus comes when compared with those in IR group. F. prausnitzii and unclassified Subdoligranulum sp. were overrepresented in individuals in VU group with CD4+ T-cell counts <350 cells/mm3. Moreover, we found that the relative abundance of unclassified Subdoligranulum sp. and C. comes were positively correlated with CD8+HLA-DR+ T-cell count and CD8+HLA-DR+/CD8+ percentage. Our study has shown that gut microbiota changes were associated with CD4+ T-cell counts and immune activation in HIV-infected subjects. Interventions to reverse gut dysbiosis and inhibit immune activation could be a new strategy for improving immune reconstitution of HIV-1-infected individuals

Generalization optimizing machine learning to improve CT scan radiomics and assess immune checkpoint inhibitors’ response in non-small cell lung cancer: a multicenter cohort study

BackgroundRecent developments in artificial intelligence suggest that radiomics may represent a promising non-invasive biomarker to predict response to immune checkpoint inhibitors (ICIs). Nevertheless, validation of radiomics algorithms in independent cohorts remains a challenge due to variations in image acquisition and reconstruction. Using radiomics, we investigated the importance of scan normalization as part of a broader machine learning framework to enable model external generalizability to predict ICI response in non-small cell lung cancer (NSCLC) patients across different centers.MethodsRadiomics features were extracted and compared from 642 advanced NSCLC patients on pre-ICI scans using established open-source PyRadiomics and a proprietary DeepRadiomics deep learning technology. The population was separated into two groups: a discovery cohort of 512 NSCLC patients from three academic centers and a validation cohort that included 130 NSCLC patients from a fourth center. We harmonized images to account for variations in reconstruction kernel, slice thicknesses, and device manufacturers. Multivariable models, evaluated using cross-validation, were used to estimate the predictive value of clinical variables, PD-L1 expression, and PyRadiomics or DeepRadiomics for progression-free survival at 6 months (PFS-6).ResultsThe best prognostic factor for PFS-6, excluding radiomics features, was obtained with the combination of Clinical + PD-L1 expression (AUC = 0.66 in the discovery and 0.62 in the validation cohort). Without image harmonization, combining Clinical + PyRadiomics or DeepRadiomics delivered an AUC = 0.69 and 0.69, respectively, in the discovery cohort, but dropped to 0.57 and 0.52, in the validation cohort. This lack of generalizability was consistent with observations in principal component analysis clustered by CT scan parameters. Subsequently, image harmonization eliminated these clusters. The combination of Clinical + DeepRadiomics reached an AUC = 0.67 and 0.63 in the discovery and validation cohort, respectively. Conversely, the combination of Clinical + PyRadiomics failed generalizability validations, with AUC = 0.66 and 0.59.ConclusionWe demonstrated that a risk prediction model combining Clinical + DeepRadiomics was generalizable following CT scan harmonization and machine learning generalization methods. These results had similar performances to routine oncology practice using Clinical + PD-L1. This study supports the strong potential of radiomics as a future non-invasive strategy to predict ICI response in advanced NSCLC

Intestinal microbiota influences clinical outcome and side effects of early breast cancer treatment.

The prognosis of early breast cancer (BC) relies on cell autonomous and immune parameters. The impact of the intestinal microbiome on clinical outcome has not yet been evaluated. Shotgun metagenomics was used to determine the composition of the fecal microbiota in 121 specimens from 76 early BC patients, 45 of whom were paired before and after chemotherapy. These patients were enrolled in the CANTO prospective study designed to record the side effects associated with the clinical management of BC. We analyzed associations between baseline or post-chemotherapy fecal microbiota and plasma metabolomics with BC prognosis, as well as with therapy-induced side effects. We examined the clinical relevance of these findings in immunocompetent mice colonized with BC patient microbiota that were subsequently challenged with histo-compatible mouse BC and chemotherapy. We conclude that specific gut commensals that are overabundant in BC patients compared with healthy individuals negatively impact BC prognosis, are modulated by chemotherapy, and may influence weight gain and neurological side effects of BC therapies. These findings obtained in adjuvant and neoadjuvant settings warrant prospective validation

Drug-microbiota interactions and treatment response: Relevance to rheumatoid arthritis

Knowledge about associations between changes in the structure and/or function of intestinal microbes (the microbiota) and the pathogenesis of various diseases is expanding. However, interactions between the intestinal microbiota and different pharmaceuticals and the impact of these on responses to treatment are less well studied. Several mechanisms are known by which drug-microbiota interactions can influence drug bioavailability, efficacy, and/or toxicity. This includes direct activation or inactivation of drugs by microbial enzymes which can enhance or reduce drug effectiveness. The extensive metabolic capabilities of the intestinal microbiota make it a hotspot for drug modification. However, drugs can also influence the microbiota profoundly and change the outcome of interactions with the host. Additionally, individual microbiota signatures are unique, leading to substantial variation in host responses to particular drugs. In this review, we describe several known and emerging examples of how drug-microbiota interactions influence the responses of patients to treatment for various diseases, including inflammatory bowel disease, type 2 diabetes and cancer. Focussing on rheumatoid arthritis (RA), a chronic inflammatory disease of the joints which has been linked with microbial dysbiosis, we propose mechanisms by which the intestinal microbiota may affect responses to treatment with methotrexate which are highly variable. Furthering our knowledge of this subject will eventually lead to the adoption of new treatment strategies incorporating microbiota signatures to predict or improve treatment outcomes

Nanotechnology intervention of the microbiome for cancer therapy

The microbiome is emerging as a key player and driver of cancer. Traditional modalities to manipulate the microbiome (for example, antibiotics, probiotics and microbiota transplants) have been shown to improve efficacy of cancer therapies in some cases, but issues such as collateral damage to the commensal microbiota and consistency of these approaches motivates efforts towards developing new technologies specifically designed for the microbiome–cancer interface. Considering the success of nanotechnology in transforming cancer diagnostics and treatment, nanotechnologies capable of manipulating interactions that occur across microscopic and molecular length scales in the microbiome and the tumour microenvironment have the potential to provide innovative strategies for cancer treatment. As such, opportunities at the intersection of nanotechnology, the microbiome and cancer are massive. In this Review, we highlight key opportunistic areas for applying nanotechnologies towards manipulating the microbiome for the treatment of cancer, give an overview of seminal work and discuss future challenges and our perspective on this emerging area