Influence of Postprandial Hyperglycemic Conditions on Arterial Stiffness in Patients With Type 2 Diabetes

Context: Patients with type 2 diabetes (T2D) are at an increased risk of cardiovascular disease. Objective: The objective of the study was to determine whether postprandial hyperglycemia affects arterial function in T2D. Design: Asingle-center, open-label study of three groups of men were studied: 1) T2D patients with albuminuria (n = 22), 2) T2D patients without albuminuria (n = 24), and 3) nondiabetic controls (n = 25). Patients were randomized to a two-period crossover study schedule, ingesting breakfast, with or without insulin lispro (to induce low or high postprandial glycemia). Main Outcome Measures: Arterial stiffness was assessed by calculating pulse wave velocity (PWV) and augmentation index using applanation tonometry, and endothelial dysfunction was assessed using peripheral arterial tonometry, 30 minutes before breakfast and up to 240 minutes after breakfast. Results: At baseline, arterial stiffness was increased in patients. When adjusted for age and body mass index, in a combined group of patients with and without albuminuria, brachial PWV was higher during low (P = .032) and high (P = .038) postprandial glycemia vs controls. These differences were driven by the albuminuria group vs controls during low (P = .014) and high (P = .018) postprandial glycemia. No differences were observed in aortic PWV, augmentation index, or peripheral arterial tonometry ratio between patients and controls. Endothelin-1 and IL-6 were higher, and superoxide dismutase was lower, during postprandial hyperglycemia in T2D patients vs controls. Conclusions: In patients with T2D and albuminuria, brachial PWV was higher under postprandial hyperglycemic conditions, relative to controls. These data suggest that hyperglycemia induces an increase in stiffness of intermediate-sized arteries. We found no changes in other parts of the arterial bed.Peer reviewe

Evolution of comorbidities in people living with HIV between 2004 and 2014: cross-sectional analyses from ANRS CO3 Aquitaine cohort

International audienceBACKGROUND: The objective of the study was to describe the evolution of chronic non-AIDS related diseases and their risk factors, in patients living with HIV (PLHIV) in the French ANRS CO3 Aquitaine prospective cohort, observed both in 2004 and in 2014 in order to improve long-term healthcare management.METHODS: The ANRS CO3 Aquitaine cohort prospectively collects epidemiological, clinical, biological and therapeutic data on PLHIV in the French Aquitaine region. Two cross sectional analyses were performed in 2004 and 2014, to investigate the patient characteristics, HIV RNA, CD4 counts and prevalence of some common comorbidities and treatment.RESULTS: 2138 PLHIV (71% male, median age 52.2 years in 2014) were identified for inclusion in the study, including participants who were registered in the cohort with at least one hospital visit recorded in both 2004 and 2014. Significant increases in the prevalence of diagnosed chronic kidney disease (CKD), bone fractures, cardiovascular events (CVE), hypertension, diabetes and dyslipidaemia, as well as an increase in treatment or prevention for these conditions (statins, clopidogrel, aspirin) were observed. It was also reflected in the increase in the proportion of patients in the "high" or "very high" risk groups of the disease risk scores for CKD, CVE and bone fracture score.CONCLUSIONS: Between 2004 and 2014, the aging PLHIV population identified in the French ANRS CO3 Aquitaine prospective cohort experienced an overall higher prevalence of non-HIV related comorbidities, including CKD and CVD. Long-term healthcare management and long-term health outcomes could be improved for PLHIV by: careful HIV management according to current recommendations with optimal selection of antiretrovirals, and early management of comorbidities through recommended lifestyle improvements and preventative measures

Influence of fluoxetine on olanzapine pharmacokinetics

Conventional antidepressant treatment fails for up to 30% of patients with major depression. When there are concomitant psychotic symptoms, response rates are even worse. Thus, subsequent treatment often includes combinations of antidepressants or augmentation with antipsychotic agents. Atypical antipsychotic agents such as olanzapine cause fewer extrapyramidal adverse effects than conventional antipsychotics; for that reason, they are an advantageous augmentation strategy for treatment-resistant and psychotic depression. The purpose of this study was to assess the potential for pharmacokinetic interaction between olanzapine and fluoxetine, a popular antidepressant that is a selective serotonin reuptake inhibitor. The pharmacokinetics of 3 identical single therapeutic doses of olanzapine (5 mg) were determined in 15 healthy nonsmoking volunteers. The first dose of olanzapine was taken alone, the second given after a single oral dose of fluoxetine (60 mg), and the third given after 8 days of treatment with fluoxetine 60 mg, qd. Olanzapine mean Cmax was slightly higher (by about 18%) and mean CL/F was slightly lower (by about 15%) when olanzapine was coadministered with fluoxetine in single or multiple doses. Olanzapine mean t1/2 and median tmax did not change. Although the pharmacokinetic effects of fluoxetine on olanzapine were statistically significant, the effects were small and are unlikely to modify olanzapines safety profile. The mechanism of influence is consistent with an inhibition of CYP2D6, which is known to control a minor pathway of olanzapine metabolism

Effect of immediate initiation of antiretroviral treatment in HIV-positive individuals aged 50 years or older

Submitted by Fábio Marques ([email protected]) on 2018-03-12T19:03:26Z No. of bitstreams: 1 Effect of immediate initiation of antiretroviral treatment in HIV-positive individuals aged 50 years or older.pdf: 2183723 bytes, checksum: ccb422bcccab25adadd98d7f47e99144 (MD5)Approved for entry into archive by Raquel Dinelis ([email protected]) on 2018-03-20T15:09:24Z (GMT) No. of bitstreams: 1 Effect of immediate initiation of antiretroviral treatment in HIV-positive individuals aged 50 years or older.pdf: 2183723 bytes, checksum: ccb422bcccab25adadd98d7f47e99144 (MD5)Made available in DSpace on 2018-03-20T15:09:24Z (GMT). No. of bitstreams: 1 Effect of immediate initiation of antiretroviral treatment in HIV-positive individuals aged 50 years or older.pdf: 2183723 bytes, checksum: ccb422bcccab25adadd98d7f47e99144 (MD5) Previous issue date: 2017Harvard T.H. Chan School of Public Health. Department of Epidemiology. Boston.Institut Pierre Louis d’épidémiologie et de Santé Publique. Sorbonne Universités, INSERM, UPMC Univ Paris 06. France.Institute of Global Health.University College London. London, United Kingdom.Instituto de Salud Carlos III. Centro Nacional de Epidemiologia. Madrid, Spain / Instituto de Salud Carlos III. CIBERESP. Madrid, Spain.Harvard T.H. Chan School of Public Health. Department of Epidemiology. Boston.Institut Pierre Louis d’épidémiologie et de Santé Publique. Sorbonne Universités, INSERM, UPMC Univ. Paris. Paris, France / Hôpital Antoine Béclère, Service de Médecine Interne. AP-HP, Clamart, France.Stichting HIV Monitoring. Amsterdam, the Netherlands / Academic Medical Centre, Department of Global Health and Division of Infectious Diseases, University of Amsterdam, Amsterdam, the Netherlands / Amsterdam Institute for Global Health and Development, Amsterdam, the Netherlands.Stichting HIV Monitoring. Amsterdam, the Netherlands.Institute of Global Health.University College London. London, United Kingdom.Hospital San Pedro—CIBIR, Logroño, Spain.Instituto de Salud Carlos III. Centro Nacional de Epidemiologia. Madrid, Spain / Instituto de Salud Carlos III. CIBERESP. Madrid, Spain.University of Basel. University Hospital Basel. Basel Institute for Clinical Epidemiology and Biostatistics, Basel, Switzerland.University of Zurich. University Hospital Zurich. Division of Infectious Diseases and Hospital Epidemiology. Zurich, Switzerland.Hospital Parc Tauli. Infectious Disease Department. Sabadell, Spain.Hospital Clinic-IDIBAPS. Barcelona, Spain.Patras University Hospital. Division of Infectious Diseases. Patras, Greece.National and Kapodistrian University of Athens. Faculty of Medicine. Department of Hygiene, Epidemiology and Medical Statistics. Athens, Greece.Université de Bordeaux, ISPED, Centre INSERM U1219-Epidemiologie-Biostatistique, Bordeaux, France / Université de Bordeaux. Centre INSERM U1219- Centre Inserm Epidémiologie et Biostatistique, Bordeaux, France.Université de Bordeaux, ISPED, Centre INSERM U1219-Epidemiologie-Biostatistique, Bordeaux, France. / Université de Bordeaux. Centre INSERM U1219- Centre Inserm Epidémiologie et Biostatistique, Bordeaux, France / Bordeaux University Hospital. Department of Internal Medicine, Bordeaux, France.Université Paris Sud, UMR 1018, le Kremlin Bicêtre, Paris, France / Inserm, UMR 1018, le Kremlin Bicêtre, Paris, France / AP-HP, Hôpital de Bicêtre, Service de Santé Publique, le Kremlin Bicêtre, Paris, France.Inserm, UMR 1018, le Kremlin Bicêtre, Paris, France / AP-HP, Hôpital de Bicêtre, Service de Santé Publique, le Kremlin Bicêtre, Paris, France.Southern Alberta Clinic, Calgary, AB, Canada / Department of Medicine, University of Calgary, Calgary, AB, Canada.Southern Alberta Clinic, Calgary, AB, Canada / Department of Medicine, University of Calgary, Calgary, AB, Canada.Instituto de Salud Carlos III. Centro Nacional de Epidemiologia. Madrid, Spain.Instituto de Salud Carlos III. Centro Nacional de Epidemiologia. Madrid, Spain.Fundacao Oswaldo Cruz. Instituto Nacional de Infectologia Evandro Chagas, Rio de Janeiro, Brasil.Fundacao Oswaldo Cruz. Programa de Computação Científica, Rio de Janeiro, Brasil.Hospital Universitari Germans Trias i Pujol, Badalona, Spain.Yale University School of Medicine. Department of Internal Medicine, New Haven.Yale University School of Medicine. Department of Internal Medicine, New Haven / VA Connecticut Healthcare System, West Haven, CT.Harvard T.H. Chan School of Public Health. Department of Epidemiology. Boston / Harvard T.H. Chan School of Public Health. Department of Biostatistics, Boston / Harvard-MIT Division of Health Sciences and Technology, Boston, MA.Clinical guidelines recommend immediate initiation of combined antiretroviral therapy for all HIV-positive individuals. However, those guidelines are based on trials of relatively young participants

Using observational data to emulate a randomized trial of dynamic treatment-switching strategies: an application to antiretroviral therapy

Background: When a clinical treatment fails or shows suboptimal results, the question of when to switch to another treatment arises. Treatment switching strategies are often dynamic because the time of switching depends on the evolution of an individual’s time-varying covariates. Dynamic strategies can be directly compared in randomized trials. For example, HIV-infected individuals receiving antiretroviral therapy could be randomized to switching therapy within 90 days of HIV-1 RNA crossing above a threshold of either 400 copies/ml (tight-control strategy) or 1000 copies/ml (loose-control strategy). Methods: We review an approach to emulate a randomized trial of dynamic switching strategies using observational data from the Antiretroviral Therapy Cohort Collaboration, the Centers for AIDS Research Network of Integrated Clinical Systems and the HIV-CAUSAL Collaboration. We estimated the comparative effect of tight-control vs. loose-control strategies on death and AIDS or death via inverse-probability weighting. Results: Of 43 803 individuals who initiated an eligible antiretroviral therapy regimen in 2002 or later, 2001 met the baseline inclusion criteria for the mortality analysis and 1641 for the AIDS or death analysis. There were 21 deaths and 33 AIDS or death events in the tight-control group, and 28 deaths and 41 AIDS or death events in the loose-control group. Compared with tight control, the adjusted hazard ratios (95% confidence interval) for loose control were 1.10 (0.73, 1.66) for death, and 1.04 (0.86, 1.27) for AIDS or death. Conclusions: Although our effective sample sizes were small and our estimates imprecise, the described methodological approach can serve as an example for future analyses

Tobacco, alcohol, cannabis, and illicit drug use and their association with CD4/CD8 cell count ratio in people with controlled HIV: a cross-sectional study (ANRS CO3 AQUIVIH-NA-QuAliV)

International audienceBackground: To evaluate drug use (alcohol, tobacco, cannabis and other drugs) and its association with mean CD4/CD8 T cell count ratio, a marker of chronic inflammation, in virally suppressed people living with HIV-1 (PLWH) in Nouvelle Aquitaine, France. Methods: A multi-centric, cross-sectional analysis was conducted in 2018–19 in the QuAliV study—ANRS CO3 AQUIVIH-NA cohort. Tobacco, alcohol, cannabis, and other drug use (poppers, cocaine, amphetamines, synthetic cathinones, GHB/GBL) were self-reported. CD4 and CD8 T cell counts and viral load measures, ± 2 years of self-report, and other characteristics were abstracted from medical records. Univariable and multivariable linear regression models, adjusted for age, sex, HIV risk group, time since HIV diagnosis, and other drug use were fit for each drug and most recent CD4/CD8 ratio. Results: 660 PLWH, aged 54.7 ± 11.2, were included. 47.7% [315/660] had a CD4/CD8 ratio of < 1. Their mean CD4/CD8 ratio was 1.1 ± 0.6. 35% smoked; 40% were considered to be hazardous drinkers or have alcohol use disorder; 19.9% used cannabis and 11.9% other drugs. Chemsex-associated drug users’ CD4/CD8 ratio was on average 0.226 (95% confidence interval [95% CI] − 0.383, − 0.070) lower than that of non-users in univariable analysis (p = 0.005) and 0.165 lower [95% CI − 0.343, 0.012] in multivariable analysis (p = 0.068). Conclusions: Mean differences in CD4/CD8 ratio were not significantly different in tobacco, alcohol and cannabis users compared to non-users. However, Chemsex-associated drug users may represent a population at risk of chronic inflammation, the specific determinants of which merit further investigation. Trial registration number: NCT03296202. © 2023, The Author(s)

A highly virulent variant of HIV-1 circulating in the Netherlands.

We discovered a highly virulent variant of subtype-B HIV-1 in the Netherlands. One hundred nine individuals with this variant had a 0.54 to 0.74 log &lt;sub&gt;10&lt;/sub&gt; increase (i.e., a ~3.5-fold to 5.5-fold increase) in viral load compared with, and exhibited CD4 cell decline twice as fast as, 6604 individuals with other subtype-B strains. Without treatment, advanced HIV-CD4 cell counts below 350 cells per cubic millimeter, with long-term clinical consequences-is expected to be reached, on average, 9 months after diagnosis for individuals in their thirties with this variant. Age, sex, suspected mode of transmission, and place of birth for the aforementioned 109 individuals were typical for HIV-positive people in the Netherlands, which suggests that the increased virulence is attributable to the viral strain. Genetic sequence analysis suggests that this variant arose in the 1990s from de novo mutation, not recombination, with increased transmissibility and an unfamiliar molecular mechanism of virulence