Adrian Streinu-Cercel

Computational models can predict response to HIV therapy without a genotype and may reduce treatment failure in different resource-limited settings

OBJECTIVES Genotypic HIV drug-resistance testing is typically 60%-65% predictive of response to combination antiretroviral therapy (ART) and is valuable for guiding treatment changes. Genotyping is unavailable in many resource-limited settings (RLSs). We aimed to develop models that can predict response to ART without a genotype and evaluated their potential as a treatment support tool in RLSs. METHODS Random forest models were trained to predict the probability of response to ART (≤400 copies HIV RNA/mL) using the following data from 14 891 treatment change episodes (TCEs) after virological failure, from well-resourced countries: viral load and CD4 count prior to treatment change, treatment history, drugs in the new regimen, time to follow-up and follow-up viral load. Models were assessed by cross-validation during development, with an independent set of 800 cases from well-resourced countries, plus 231 cases from Southern Africa, 206 from India and 375 from Romania. The area under the receiver operating characteristic curve (AUC) was the main outcome measure. RESULTS The models achieved an AUC of 0.74-0.81 during cross-validation and 0.76-0.77 with the 800 test TCEs. They achieved AUCs of 0.58-0.65 (Southern Africa), 0.63 (India) and 0.70 (Romania). Models were more accurate for data from the well-resourced countries than for cases from Southern Africa and India (P < 0.001), but not Romania. The models identified alternative, available drug regimens predicted to result in virological response for 94% of virological failures in Southern Africa, 99% of those in India and 93% of those in Romania. CONCLUSIONS We developed computational models that predict virological response to ART without a genotype with comparable accuracy to genotyping with rule-based interpretation. These models have the potential to help optimize antiretroviral therapy for patients in RLSs where genotyping is not generally available.

Insulin resistance and adipokines serum levels in a caucasian cohort of hiv-positive patients undergoing antiretroviral therapy: a cross sectional study

BackgroundInsulin resistance is frequent in human immunodeficiency virus (HIV) infection and may be related to antiretroviral therapy. Cytokines secreted by adipose tissue (adipokines) are linked to insulin sensitivity. The present study is aimed to assess the prevalence of insulin resistance (IR) and its association with several adipokines, in a non-diabetic Romanian cohort of men and women with HIV-1 infection, undergoing combination antiretroviral therapy (cART).MethodsA cross-sectional study was conducted in an unselected sample of 89 HIV-1-positive, non-diabetic patients undergoing stable cART for at least 6 months. Metabolic parameters were measured, including fasting plasma insulin, and circulating adiponectin, leptin, resistin, tumor necrosis factor alpha (TNF-alpha) and interleukin-6 (IL-6) levels. Insulin resistance was estimated by measuring the Quantitative Insulin Sensitivity Check Index (QUICKI), using a cut-off value of 0.33. A linear regression model was fitted to QUICKI to test the association of IR and adipokines levels.ResultsA total of 89 patients (aged 18–65, median: 28 years) including 51 men (57.3%) and 38 women (42.7%) were included in the study. Fifty nine patients (66.3%) were diagnosed with IR based on QUICKI values lower than the cut-off point. IR prevalence was 72.5% in men and 57.6% in women. The presence of the IR was not influenced by either the time of the HIV diagnosis or by the duration of cART. Decreased adiponectin and increased serum triglycerides were associated with increased IR in men (R=0.43, p=0.007). Hyperleptinemia in women was demonstrated to be associated with the presence of IR (R=0.33, p=0.03).ConclusionsGiven the significant prevalence of the IR in our young non-diabetic cohort with HIV infection undergoing antiretroviral therapy reported in our study and the consecutive risk of diabetes and cardiovascular events, we suggest that the IR management should be a central component of HIV-infection therapeutic strategy. As adipokines play major roles in regulating glucose homeostasis with levels varying according to the sex, we suggest that further studies investigating adipokines should base their analyses on gender differences.

Clinical relevance of the plasma load of cytomegalovirus in patients infected with HIV—A survival analysis

To investigate whether asymptomatic cytomegalovirus (CMV) viraemia impact the course of human immunodeficiency virus (HIV) infection, this study evaluated the effect of CMV replication on progression of newly‐diagnosed HIV infected individuals towards AIDS events and death. In a 3‐year prospective study on co‐infected patients, clinical, immunological, and virological tests were performed in a national reference hospital quarterly. CMV viraemia was quantified by RoboGene® HCMV DNA Quantification Kit (Analytik Jena, Germany), on ABI Prism® 7000 Sequence Detection System (Applied Biosystems, USA). One hundred and five patients were enrolled with a balanced sex distribution and a median age of 30.7 years. Median CD4+ cell count at enrollment was 164/mm3 and median HIV RNA 4.6 log10copies/ml. Detectable CMV viraemia was found in 25.7% of the patients. Kaplan–Meier analysis showed progression of HIV infection to be significantly increased in those with active CMV replication and/or low CD4+ cell count. Cox regression indicated the risk of developing new AIDS events was 2.6 times greater in patients with detectable CMV viraemia versus those without (CI95% 1–6.6; P = 0.04). Also in multivariate analysis, the overall risk of progression to AIDS events or death was 3‐fold higher in those with detectable CMV viraemia (CI95% 1.3–6.7; P = 0.008) and 2.3‐fold higher if CD4+ cell count was below 100/mm3 (CI95% 1–5.1; P = 0.04). In these young Romanian HIV‐seropositives, active CMV replication increased morbidity, even when treated with combination antiretroviral therapy. Further studies are needed to evaluate if serial quantitative CMV‐DNA levels might correlate with non‐infectious inflammation‐related risks in patients with HIV and active CMV infection. J. Med. Virol. 86:1821–1827, 2014.

Leptin expression in HIV-infected patients during antiretroviral therapy.

BACKGROUND Leptin is an adipokine with complex metabolic, neuroendocrine and immune functions. Our objective was to evaluate leptin serum levels in a cohort of Romanian HIV-infected patients undergoing antiretroviral therapy in relation to their immune-virological status, lipid and glucose metabolic abnormalities and the presence of metabolic syndrome (MS). METHODS We enrolled consecutive non-diabetic HIV-infected patients aged 18 and over on stable cART for at least 6 months. Blood samples were tested for: leptin, CD4 T cells count, HIV viral load and lipid panel. RESULTS A total of 90 HIV-infected patients were included in the study: 50 males (55.6%) with a mean age of 33.3 years and 40 females with a mean age of 30.4 years. Most patients (74.4%) had HIV viral load below the limit of detection and the median CD4 count for the cohort was 476 (410) cells/cmm. More than one third of the patients (41.1%) had hypoleptinemia. The prevalence of MS was 13.3%. Hypoleptinemia was significantly more frequent in men. In a subset of patients with undetectable HIV viral load, the median leptin value was 0.6 (6.07) ng/mL in patients with poor immune recovery (CD4 count ≤ 200/cmm) compared to 2 (3.07) ng/mL for those with better immune response (CD4 count > 200/cmm), without statistical significance. The median values of leptin were similar for persons with and without MS criteria. HDL-cholesterol values were positively correlated to leptin values in a linear regression model. CONCLUSION A significant proportion of patients in our study presented low levels of leptin; this finding was not associated with immune and virological parameters or the presence of MS. Hypoleptinemia was significantly correlated with lower levels of HDL-cholesterol, a key cardiovascular risk factor.

2018 update to the HIV-TRePS system: the development of new computational models to predict HIV treatment outcomes, with or without a genotype, with enhanced usability for low-income settings

Objectives Optimizing antiretroviral drug combination on an individual basis can be challenging, particularly in settings with limited access to drugs and genotypic resistance testing. Here we describe our latest computational models to predict treatment responses, with or without a genotype, and compare their predictive accuracy with that of genotyping. Methods Random forest models were trained to predict the probability of virological response to a new therapy introduced following virological failure using up to 50 000 treatment change episodes (TCEs) without a genotype and 18 000 TCEs including genotypes. Independent data sets were used to evaluate the models. This study tested the effects on model accuracy of relaxing the baseline data timing windows, the use of a new filter to exclude probable non-adherent cases and the addition of maraviroc, tipranavir and elvitegravir to the system. Results The no-genotype models achieved area under the receiver operator characteristic curve (AUC) values of 0.82 and 0.81 using the standard and relaxed baseline data windows, respectively. The genotype models achieved AUC values of 0.86 with the new non-adherence filter and 0.84 without. Both sets of models were significantly more accurate than genotyping with rules-based interpretation, which achieved AUC values of only 0.55-0.63, and were marginally more accurate than previous models. The models were able to identify alternative regimens that were predicted to be effective for the vast majority of cases in which the new regimen prescribed in the clinic failed. Conclusions These latest global models predict treatment responses accurately even without a genotype and have the potential to help optimize therapy, particularly in resource-limited settings.