Search

Background Accelerated epigenetic ageing can occur in untreated HIV infection and is partially reversible with effective antiretroviral therapy (ART). We aimed to make a long-term comparison of epigenetic ageing dynamics in people with HIV during untreated HIV infection and during suppressive ART., Methods In this longitudinal study, conducted over 17 years in HIV outpatient clinics in Switzerland, we applied 5 established epigenetic age estimators (epigenetic clocks) in peripheral blood mononuclear cells (PBMCs) in Swiss HIV Cohort Study participants before or during suppressive ART. All participants had a longitudinal set of PBMC samples available at four timepoints (T1-T4). T1 and T2 had to be 3 years or longer apart, as did T3 and T4. We assessed epigenetic age acceleration (EAA) and a novel rate of epigenetic ageing., Findings Between March 13, 1990, and Jan 18, 2018, we recruited 81 people with HIV from the Swiss HIV Cohort Study. We excluded one participant because a sample did not meet quality checks (transmission error). 52 (65%) of 80 patients were men, 76 (95%) were white, and the median patient age was 43 (IQR 37 center dot 5-47) years. Per year of untreated HIV infection (median observation 8 center dot 08 years, IQR 4 center dot 83-11 center dot 09), mean EAA was 0 center dot 47 years (95% CI 0 center dot 37 to 0 center dot 57) for Horvath's clock, 0 center dot 43 years (0 center dot 3 to 0 center dot 57) for Hannum's clock, 0 center dot 36 years (0 center dot 27 to 0 center dot 44) for SkinBlood clock, and 0 center dot 69 years (0 center dot 51 to 0 center dot 86) for PhenoAge. Per year of suppressive ART (median observation 9 center dot 8 years, IQR 7 center dot 2-11), mean EAA was -0 center dot 35 years (95% CI -0 center dot 44 to -0 center dot 27) for Horvath's clock, -0 center dot 39 years (-0 center dot 50 to -0 center dot 27) for Hannum's clock, -0 center dot 26 years (-0 center dot 33 to -0 center dot 18) for SkinBlood clock, and -0 center dot 49 years (-0 center dot 64 to -0 center dot 35) for PhenoAge. Our findings indicate that people with HIV epigenetically aged by a mean of 1 center dot 47 years for Horvath's clock, 1 center dot 43 years for Hannum's clock, 1 center dot 36 years for SkinBlood clock, and 1 center dot 69 years for PhenoAge per year of untreated HIV infection; and 0 center dot 65 years for Horvath's clock, 0 center dot 61 years for Hannum's clock, 0 center dot 74 years for SkinBlood clock, and 0 center dot 51 years for PhenoAge, per year of suppressive ART. GrimAge showed some change in the mean EAA during untreated HIV infection (0 center dot 10 years, 0 center dot 02 to 0 center dot 19) and suppressive ART (-0 center dot 05 years, -0 center dot 12 to 0 center dot 02). We obtained very similar results using the rate of epigenetic ageing. Contribution of multiple HIV-related, antiretroviral, and immunological variables, and of a DNA methylation-associated polygenic risk score to EAA was small., Interpretation In a longitudinal study over more than 17 years, epigenetic ageing accelerated during untreated HIV infection and decelerated during suppressive ART, highlighting the importance of limiting the duration of untreated HIV infection.

BACKGROUND People with HIV (PWH) have increased cardiovascular risk. Higher leukocyte count has been associated with coronary artery disease (CAD) events in the general population. It is unknown whether the leukocyte-CAD association also applies to PWH. METHODS In a case-control study nested within the Swiss HIV Cohort Study, we obtained uni- and multivariable odds ratios (OR) for CAD events, based on traditional and HIV-related CAD risk factors, leukocyte count, and confounders previously associated with leukocyte count. RESULTS We included 536 cases with a first CAD event (2000-2021; median age 56 years, 87% male, 84% with suppressed HIV-RNA) and 1464 event-free controls. Cases had higher latest leukocyte count prior to CAD event than controls (median [interquartile range], 6495 [5300-7995] vs. 5900 [4910-7200]; p 11000/uL) was uncommon (4.3% vs. 2.1%; p = 0.01). In the highest vs. lowest leukocyte quintile at latest time point prior to CAD event, participants had univariable CAD-OR = 2.27 (95% confidence interval, 1.63-3.15) and multivariable adjusted CAD-OR = 1.59 (1.09-2.30). For comparison, univariable CAD-OR for dyslipidemia, diabetes, and recent abacavir exposure were 1.58 (1.29-1.93), 2.19 (1.59-3.03), and 1.73 (1.37-2.17), respectively. Smoking and, to a lesser degree, alcohol and ethnicity attenuated the leukocyte-CAD association. Leukocytes measured up to 8 years pre-event were significantly associated with CAD events. CONCLUSIONS PWH in Switzerland with higher leukocyte counts have an independently increased risk of CAD events, to a degree similar to traditional and HIV-related risk factors.

Midostaurin is often prescribed with azole antifungals in patients with leukemia, either for aspergillosis prophylaxis or treatment. Midostaurin is extensively metabolized by cytochrome (CYP) 3A4. In addition it inhibits and induces various CYPs at therapeutic concentrations. Thus midostaurin is associated with a high potential for drug-drug interactions (DDIs), both as a substrate (victim) and as a perpetrator. However, data on midostaurin as a perpetrator of DDIs are scarce, as most pharmacokinetic studies have focused on midostaurin as a victim drug. We report a clinically relevant bidirectional DDI between midostaurin and voriconazole during induction treatment. A 49-year-old woman with acute myeloid leukemia developed invasive pulmonary aspergillosis after induction chemotherapy. She was treated with voriconazole at standard dosage. Six days after starting midostaurin, she developed visual hallucinations with a concurrent sharp increase in voriconazole blood concentration (Ctrough 10.3 mg L-1 , target Ctrough 1-5 mg L-1 ). Neurotoxicity was considered to be related to voriconazole overexposure. The concentration of midostaurin was concomitantly six-fold above the average expected level, however without safety issues. Midostaurin was stopped and the dosage of voriconazole was adjusted with therapeutic drug monitoring. The evolution was favorable with quick resolution and no recurrence of visual hallucinations. To our knowledge, this is the first case suggesting that midostaurin and voriconazole reciprocally inhibit each other's metabolism leading to increased exposure of both. This case highlights the knowledge gap regarding drug-drug interactions between midostaurin and azole antifungals. Close clinical and therapeutic drug monitoring is advised in such cases.

Additional file 1: Table S1. Co-medication.

AIM We previously observed that some individuals on HIV boosted protease inhibitor containing regimen do not achieve their lipid targets despite elevated statin concentrations. This study evaluated whether the common single polymorphism c.521T>C in SLCO1B1, associated with reduced statin uptake in the liver, could explain this observation. METHODS People living with HIV (PLWH) in the Swiss HIV Cohort Study were eligible if they were on a boosted protease inhibitor concomitantly with a statin for at least 6 months and if their SLCO1B1 genotype was available. Furthermore, their lipids had to be documented before and after the introduction of the statin. The statin efficacy was defined as % change in total-, LDL-, HDL-cholesterol and triglycerides levels after statin initiation compared to pre-treatment levels. Lipid response was adjusted for differences in potency and dose between statins. RESULTS 88 PWH were included, of whom 58, 28 and 2 carried the SLCO1B1 TT, TC and CC genotypes, respectively. The change in lipid levels after statin initiation tended to be lower in carriers of the polymorphism although the difference was not statistically significant (TT vs TC/CC: total-: -11.7% vs -4.8%; LDL-: -20.6% vs -7.4%; HDL-cholesterol: 1.6% vs 0; triglycerides: -11.5% vs -7.9%). In the multiple linear regression, change in total cholesterol was inversely correlated with the total cholesterol level pre-statin treatment (coefficient -6.60, 95%CI: -9.63 to -3.56, p

Background We previously showed that anticholinergic (ACH) medications contribute to self-reported neurocognitive impairment (NCI) in elderly people with human immunodeficiency virus (PWH). The current cross-sectional study further evaluated the effect of ACH and sedative drugs on neurocognitive function in PWH who underwent comprehensive neuropsychological evaluation. Methods A medication review was performed in PWH enrolled in the prospective Neurocognitive Assessment in Metabolic and Aging Cohort within the Swiss HIV Cohort Study. Neurocognitive functions were analyzed in 5 domains (motor skills, speed of information, attention/working memory, executive functions, and verbal learning memory). The effect of ACH and sedative medications on neurocognitive functioning was evaluated using linear regression models for the continuous (mean z-score) outcome and multivariable logistic regression models for the binary (presence/absence) outcome. Results A total of 963 PWH (80% male, 92% Caucasian, 96% virologically suppressed, median age 52) were included. Fourteen percent of participants were prescribed ≥1 ACH medication and 9% were prescribed ≥1 sedative medication. Overall, 40% of participants had NCI. Sedative medication use was associated with impaired attention/verbal learning and ACH medication use with motor skills deficits both in the continuous (mean z-score difference -0.26 to -0.14, P

SHCS#879 is an ongoing Switzerland-wide multicenter observational study conducted within the Swiss HIV Cohort Study (SHCS) for the prospective follow-up of people living with HIV (PLWH) receiving long-acting injectable cabotegravir-rilpivirine (LAI-CAB/RPV). All adults under LAI-CAB/RPV and part of SHCS are enrolled in the project. The study addresses an integrated strategy of treatment monitoring outside the stringent frame of controlled clinical trials, based on relevant patient characteristics, clinical factors, potential drug-drug interactions, and measurement of circulating blood concentrations. So far, 91 blood samples from 46 PLWH have been collected. Most individuals are less than 50 years old, with relatively few comorbidities and comedications. The observed concentrations are globally in accordance with the available values reported in the randomized clinical trials. Yet, low RPV concentrations not exceeding twice the reported protein-adjusted 90% inhibitory concentration have been observed. Data available at present confirm a considerable between-patient variability overall. Based on the growing amount of PK data accumulated during this ongoing study, population pharmacokinetic analysis will characterize individual concentration-time profiles of LAI-CAB/RPV along with their variability in a real-life setting and their association with treatment response and tolerability, thus bringing key data for therapeutic monitoring and precision dosage adjustment of this novel long-acting therapy.

BACKGROUND It is unclear whether data-driven machine learning models, which are trained on large epidemiological cohorts, may improve prediction of co-morbidities in people living with HIV. METHODS In this proof-of-concept study, we included people living with HIV of the prospective Swiss HIV Cohort Study with a first estimated glomerular filtration rate (eGFR) >60 ml/min/1.73 m2 after January 1, 2002. Our primary outcome was chronic kidney disease (CKD) ─ defined as confirmed decrease in eGFR ≤60 ml/min/1.73 m2 over three months apart. We split the cohort data into a training set (80%), validation set (10%), and test set (10%) ─ stratified for CKD status and follow-up length. RESULTS Of 12,761 eligible individuals (median baseline eGFR, 103 ml/min/1.73 m2), 1,192 (9%) developed a CKD after a median of eight years. We used 64 static and 502 time-changing variables: Across prediction horizons and algorithms and in contrast to expert-based standard models, most machine learning models achieved state-of-the-art predictive performances with areas under the receiver operating characteristic curve and precision recall curve ranging from 0.926 to 0.996 and from 0.631 to 0.956, respectively. CONCLUSIONS In people living with HIV, we observed state-of-the-art performances in forecasting individual CKD onsets with different machine learning algorithms.