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IntroductionElevated IL‐6 levels have been linked to increased risk of cardiovascular disease (CVD), cancer and death. Compared to the general population, treated HIV+ persons have 50–100% higher IL‐6 levels, but few data on the determinants of IL‐6 levels during HIV infection currently exist.Material and MethodsParticipants in three international HIV trials (SMART, ESPRIT and SILCAAT) with IL‐6 plasma levels measured at baseline were included (N=9864). Factors independently associated with log2‐transformed IL‐6 level were identified by multivariate linear regression; exponentiated estimates corresponding to fold differences (FDs) in IL‐6 were calculated. Demographics (age, gender, race, BMI) and HIV‐specific variables (nadir and entry CD4 counts, HIV‐RNA, use of different ART regimens) were investigated in all three trials. In SMART (N=4498), smoking, comorbidities (CVD, diabetes, hepatitis B/C [HBV/HCV]), HDL‐cholesterol, renal function (eGFR) and educational level were also assessed.ResultsDemographics associated with higher IL‐6 were older age (FD [95% CI]: 1.09 [1.08–1.11] per 10 yr) and higher BMI (1.02 [1.01–1.04] per 5 kg/m2), whereas black race was associated with reduced IL‐6 (0.96 [0.93–0.99]). As for HIV variables, patients not receiving ART (1.36 [1.29–1.43]) and with higher HIV‐RNA (1.24 [1.01–1.52] for >100,000 vs. ≤500 copies/mL) had increased IL‐6. Participants taking protease inhibitors (PI) had higher IL‐6 (1.14[1.09–1.19]). Higher nadir CD4 count (0.98 [0.97–0.99]/100 cells/µL) was related to lower IL‐6. All evaluated comorbidities were related to higher IL‐6; FDs in IL‐6 were 1.08 [1.04–1.12] for smoking, 1.12 [1.02–1.24] for CVD, 1.07 [1.00–1.16] for diabetes and 1.12 [1.02–1.24] for HBV (1.15 [1.02–1.30]) and 1.53 [1.45–1.62] for HCV. IL‐6 increased with decreasing eGFR (0.98 [0.97–1.00]/10 mL/min) and HDL‐cholesterol (0.98 [0.96–0.99]/10 mg/mL). Lower education was related to higher IL‐6 (1.09 [1.03–1.15] for high school vs. bachelor's degree).ConclusionsHigher IL‐6 levels were associated with older age and non‐black race, higher BMI and lower HDL‐cholesterol, ongoing HIV replication, low nadir CD4 counts, comorbidities and decreased renal function. This suggests that there are multiple causes of inflammation in treated HIV infection. A possible contribution from PI use was also observed. Contribution from inflammation to explain variation in clinical outcomes for these factors should be investigated.

IntroductionSeveral resistance mutations have been shown to affect viral fitness, and the presence of certain mutations might result in clinical benefit for patients kept on a virologically failing regimen due to an exhaustion of drug options. We sought to quantify the effect of resistance mutations on CD4 slopes in patients undergoing episodes of viral failure.Materials and MethodsPatients from the EuroSIDA and UK CHIC cohorts undergoing at least one episode of virological failure (>3 consecutive RNA measurements >500 on ART) with at least three CD4 measurements and a resistance test during the episode were included. Mutations were identified using the IAS‐US (2013) list, and were presumed to be present from detection until the end of an episode. Multivariable linear mixed models with a random intercept and slope adjusted for age, baseline CD4 count, hepatitis C, drug type, RNA (log‐scale), risk group and subtype were used to estimate CD4 slopes. Individual mutations with a population prevalence of >10% were tested for their effect on the CD4 slope.ResultsA total of 2731 patients experiencing a median of 1 (range 1–4) episodes were included in this analysis. The prevalence of any resistance per episode was 88.4%; NNRTI resistance was most common (78.5%). Overall, CD4 counts declined by 17.1 (−19.7; −14.5) cells per year; this decline was less marked with partial viral suppression (current HIV RNA more than 1.5 log below the setpoint; p=0.01). In multivariable models adjusting for viral load, CD4 decline was slower during episodes with detected resistance compared to episodes without detected resistance (21.0 cells/year less, 95% CI 11.75–30.31, p<0.001). Among those with more than one resistance mutation, there was only weak evidence that class‐specific mutations had any effect on the CD4 slope (Table 1). The effects of individual mutations (incl. M184V) were explored, but none were significantly associated with the CD4 slope; for these comparisons, a Bonferroni‐corrected p‐value level was 0.003.ConclusionsIn our study population, detected resistance was associated with slightly less steep CD4 declines. This may be due to a biological effect of resistance on CD4 slopes, or other unmeasured factors such as poor adherence among individuals without resistance. Among individuals with detected drug resistance, we found no evidence suggesting that the presence of individual mutations was associated with beneficial CD4 slope changes.

IntroductionIt is uncertain if plasma HIV‐1 tropism is an independent predictor of short‐term risk of clinical progression / death, in addition to the CD4 count and HIV RNA level. We conducted a nested case‐control study within EuroSIDA to assess this question amongst people with current HIV RNA level >1000 copies/mL, including both people on ART and those ART naïve.MethodsPeople with an AIDS diagnosis or who died from any causes for whom there was a stored plasma sample with HIV‐1 RNA (VL)≥1,000 copies/mL available in the time window of 3–12 months prior to the event were identified. At least one control was selected for each case matched for age, VL and HCV status at the time of sampling. Controls were event‐free after a matched duration of time from the date of sampling. Plasma HIV tropism was estimated using 454 and population sequencing (PS). Non‐R5 HIV was defined as: (a) ≥2% of sequences with a Geno2Pheno (G2P) FPR≤3.75% by 454, and (b) a G2P FPR≤10% by PS. We also compared CD4 slopes over the 12 months following the date of sampling using a linear mixed model with random intercept according to HIV tropism and ART status.ResultsThe study included 266 subjects, 100 cases and 166 controls, with sample taken on average in 2006; 23% and 24% had non‐R5 HIV by 454 and PS respectively. There were 19% women, 25% MSM, 92% Caucasians, 22% HCV+. At the time of sampling, 26% were ART‐naïve, 25% had started but were off ART and 49% were receiving ART. The median age, CD4 and viral load was 41 years, 350 cells/mm3 and 4.81 log c/mL, respectively. Baseline characteristics were well balanced by tropism. Factors independently associated with clinical progression or death were female gender (OR=2.12; 95% CI=1.04, 4.36; p=0.038), CD4+ count (OR=0.90 per 100 cells/mm3 higher; 95% CI 0.80, 1.00; p=0.058), being on ART (OR=2.72; 95% CI 1.15, 6.41; p=0.022) and calendar year of sample (OR=0.84 per more recent year; 95% CI=0.77, 0.91; p<0.001). Baseline plasma tropism was not an independent risk factor for clinical progression or death by either 454 or PS. No significant interaction was observed between tropism and ART status. There were no significant differences in the CD4+ slope within or between tropism groups.ConclusionsPlasma HIV‐1 tropism does not appear to add to the ability of CD4 count and viral load to predict the short term risk of AIDS and death outcomes, even with 454 sequencing.

IntroductionHIV‐positive patients receiving combination antiretroviral therapy (cART) frequently experience metabolic complications such as dyslipidemia and insulin resistance, as well as lipodystrophy, increasing the risk of cardiovascular disease (CVD) and diabetes mellitus (DM). Rates of DM and other glucose‐associated disorders among HIV‐positive patients have been reported to range between 2 and 14%, and in an ageing HIV‐positive population, the prevalence of DM is expected to continue to increase. This study aims to develop a model to predict the short‐term (six‐month) risk of DM in HIV‐positive populations and to compare the existing models developed in the general population.MethodsAll patients recruited to the Data Collection on Adverse events of Anti‐HIV Drugs (D:A:D) study with follow‐up data, without prior DM, myocardial infarction or other CVD events and with a complete DM risk factor profile were included. Conventional risk factors identified in the general population as well as key HIV‐related factors were assessed using Poisson‐regression methods. Expected probabilities of DM events were also determined based on the Framingham Offspring Study DM equation. The D:A:D and Framingham equations were then assessed using an internal‐external validation process; area under the receiver operating characteristic (AUROC) curve and predicted DM events were determined.ResultsOf 33,308 patients, 16,632 (50%) patients were included, with 376 cases of new onset DM during 89,469 person‐years (PY). Factors predictive of DM included higher glucose, body mass index (BMI) and triglyceride levels, and older age. Among HIV‐related factors, recent CD4 counts of<200 cells/µL and lipodystrophy were predictive of new onset DM. The mean performance of the D:A:D and Framingham equations yielded AUROC of 0.894 (95% CI: 0.849, 0.940) and 0.877 (95% CI: 0.823, 0.932), respectively. The Framingham equation over‐predicted DM events compared to D:A:D for lower glucose and lower triglycerides, and for BMI levels below 25 kg/m2.ConclusionsThe D:A:D equation performed well in predicting the short‐term onset of DM in the validation dataset and for specific subgroups provided better estimates of DM risk than the Framingham.

AbstractIntroductionSuboptimal ART adherence, despite HIV viral suppression, has been associated with chronic residual inflammation. Whether this association extends to individuals who initiate ART during early HIV infection remains unknown, which was the objective of this study.MethodsPlasma levels of interleukin‐6 (IL‐6), high‐sensitivity C‐reactive protein, serum amyloid A protein (SAA), IL‐27, soluble intercellular adhesion molecule‐1, soluble vascular adhesion molecule‐1, D‐dimer and the CD4+/CD8+ T‐cell ratio, were analysed at baseline and eight months after ART initiation in treatment‐naïve participants with HIV and CD4+ T‐cells >500 cells/mm3 enrolled in the immediate arm of START. Adherence was assessed by seven‐day self‐report. Multivariable linear regression was utilized to analyse the association between ART adherence and each biomarker at the eight‐month visit in participants who achieved virologic suppression (<50 copies/mL).ResultsWe evaluated 1627 participants (422 female) who achieved virologic suppression at the eight‐month visit in the period between 2009 and 2013. Median (IQR) CD4+ T‐cell count before ART was 651 (585, 769) cells/mm3. Incomplete adherence was reported in 109 (7%) participants at the eight month visit. After adjusting for covariates, plasma IL‐6 was 1.12 (95% CI, 1.00 to 1.26; p = 0.047) fold higher in participants reporting incomplete versus 100% adherence. A similar association for SAA was observed in an exploratory analysis (1.29 (95% CI 1.04 to 1.60); p = 0.02). No significant differences in other biomarkers were observed.ConclusionsIncomplete ART adherence was associated with higher IL‐6 levels in individuals who achieved virologic suppression early after ART initiation in START. A potential similar association for SAA requires confirmation. These findings suggest a role for identifying strategies to maximize ART adherence even during virologic suppression. ClinicalTrials.gov number: NCT00867048.

AbstractIntroductionThere is paucity of data related to potential gender differences in the use of interventions to prevent and treat cardiovascular disease (CVD) among HIV‐positive individuals. We investigated whether such differences exist in the observational D:A:D cohort study.MethodsParticipants were followed from study enrolment until the earliest of death, six months after last visit or February 1, 2015. Initiation of CVD interventions [lipid‐lowering drugs (LLDs), angiotensin‐converting enzyme inhibitors (ACEIs), anti‐hypertensives, invasive cardiovascular procedures (ICPs) were investigated and Poisson regression models calculated whether rates were lower among women than men, adjusting for potential confounders.ResultsWomen (n = 12,955) were generally at lower CVD risk than men (n = 36,094). Overall, initiation rates of CVD interventions were lower in women than men; LLDs: incidence rate 1.28 [1.21, 1.35] vs. 2.40 [2.34, 2.46]; ACEIs: 0.88 [0.82, 0.93] vs. 1.43 [1.39, 1.48]; anti‐hypertensives: 1.40 [1.33, 1.47] vs. 1.72 [1.68, 1.77] and ICPs: 0.08 [0.06, 0.10] vs. 0.30 [0.28, 0.32], and this was also true for most CVD interventions when exclusively considering periods of follow‐up for which individuals were at high CVD risk. In fully adjusted models, women were less likely to receive CVD interventions than men (LLDs: relative rate 0.83 [0.78, 0.88]; ACEIs: 0.93 [0.86, 1.01]; ICPs: 0.54 [0.43, 0.68]), except for the receipt of anti‐hypertensives (1.17 [1.10, 1.25]).ConclusionThe use of most CVD interventions was lower among women than men. Interventions are needed to ensure that all HIV‐positive persons, particularly women, are appropriately monitored for CVD and, if required, receive appropriate CVD interventions.