Telmisartan to reduce insulin resistance in HIV-positive individuals on combination antiretroviral therapy: the TAILoR dose-ranging Phase II RCT

Rationale for the study

There is already strong evidence for the beneficial effects of telmisartan on insulin resistance and other markers of glycaemic control and cardiovascular health in non-HIV populations. This study, and others, has shown that antiretroviral drugs inhibit adipocyte differentiation,23,24 reduce adiponectin secretion,23,49 increase the secretion of detrimental cytokines, IL-6 and TNF-α,23,24 and impair GLUT-4 expression,50 all of which are suggested to contribute to the development of insulin resistance. This study has also shown that telmisartan partially reverses the antiadipogenic effects of antiretrovirals in vitro. 51 Telmisartan partially reversed the antiretroviral drug-induced inhibition of adipocyte lipid accumulation and downregulation of adiponectin and lipin-1. The beneficial effect of telmisartan on adipocyte function in the presence of antiretrovirals has also been shown by Boccara et al. 52 However, the clinical efficacy of telmisartan to reduce insulin resistance in cART-treated HIV-infected patients has not been assessed; this study was designed to address this.

This in vitro study observed a non-monotone relationship of telmisartan on adiponectin and lipin 1 secretion. A study in non-alcoholic steatohepatitis patients also observed that telmisartan improves insulin sensitivity at doses lower than those used for hypertension. 36 This indicates the need to carefully assess the dose–response relationship of telmisartan in vivo.

Magnetic resonance imaging: substudy 1

A subset of patients from Royal Liverpool and Broadgreen University Hospitals NHS Trust (RLBUHT) and the Manchester Centre for Sexual Health were asked to participate in the magnetic resonance imaging (MRI) substudy. It was necessary that these patients were also participating in the main study. The duration of the substudy was 24 weeks, and visits took place at both baseline and 24 weeks.

Interim analysis

An interim analysis was performed when half of the planned maximum number of patients had been followed up for at least 24 weeks (Figure 2). As per the adaptive design, if one active dose group was substantially more effective than control then the study would have been stopped and the corresponding dose would be taken directly into Phase III. Any active dose groups that showed insufficient promise at the interim analysis would be dropped and the study would continue with the remaining doses and control. If no dose showed sufficient promise at the interim analysis, the study would have been stopped. If some improvement over control was detected for at least one of the doses at interim analysis, those dose(s) would be followed up along with the control for a further 24 weeks (total duration of 48 weeks).

FIGURE 2.

Schematic of stage 2 of the adaptive design following interim analysis.

Inclusion criteria

• Adult (aged ≥ 18 years) HIV-positive individuals receiving antiretroviral therapy containing:

  • a boosted protease inhibitor (lopinavir/ritonavir, atazanavir/ritonavir, darunavir/ritonavir, fosamprenavir/ritonavir, saquinavir/ritonavir)

  • and/or efavirenz, rilpivirine, or etravirine for at least 6 months.

• Ability to give informed consent.

• Willingness to comply with all study requirements.

In relation to the antiretroviral therapy, the backbone was based on nucleotide reverse transcriptase inhibitors, raltegravir or maraviroc. Patients on protease inhibitor monotherapy were also included if they met other criteria. However, a decision was made not to include (1) patients on nevirapine or dolutegravir regimens, without concomitant boosted protease inhibitors, (2) patients on elvitegravir which is usually administered in combination with cobicistat [as Stribild^® (Gilead Sciences Inc., CA, USA)] and (3) patients on unboosted atazanavir.

Exclusion criteria

The exclusion criteria were as follows:

• Pre-existing diagnosis of type 1 or 2 diabetes mellitus [i.e. fasting glucose level of > 7.2mmol/l or glycated haemoglobin (HbA_1c) level of ≥ 6.5% (48 mmol/mol) or an abnormal oral glucose tolerance test (OGTT) or random plasma glucose concentration of ≥ 11mmol/l].

• Patients known to have consistently low blood pressure (pre-existing hypotension: a reading below a threshold of 100/60 mmHg on three separate occasions).

• Patients with renal disease [i.e. an estimated glomerular filtration rate (eGFR) of < 60 ml/minute/1.73 m² in the 6 months preceding randomisation].

• Patients with known untreated renal artery stenosis.

• Patients with cholestasis, biliary obstructive disorders or severe hepatic impairment.

• Patients with evidence of an active chronic hepatitis C infection (a previously cleared infection is not an exclusion).

• Patients who were on/have been on hormone therapy (e.g. growth hormone), anabolics (e.g. testosterone) or insulin sensitisers (e.g. metformin) within 6 months preceding randomisation. Patients who were on hormonal contraception were eligible.

• Patients who were already on/had been on other ARBs, angiotensin-converting enzyme (ACE) inhibitors or direct renin inhibitors (e.g. aliskiren) within 4 weeks preceding randomisation.

• Those with suspected poor compliance.

• Pregnant or lactating women.

• Women of childbearing age, unless using reliable contraception (e.g. coil, barrier method, hormonal contraceptive that does not interact with their antiretroviral therapy).

• Co-enrolment in other drug trials.

• Patients who had participated in a trial of an investigational medicinal product (IMP) likely to influence insulin sensitivity, plasma insulin, glucose levels or plasma lipid levels within 6 months preceding randomisation.

• For the subcohort of patients undergoing MRI/magnetic resonance scanning (MRS), normal MRI exclusion criteria applied.

Centre/clinician inclusion criteria

Each participating centre and principal investigator (PI) was identified on the basis of being a specialist HIV treatment centre; having at least one lead clinician with a specific interest in, and responsibility for supervision and management of, patients with HIV; enthusiasm to participate in the study; sufficient time, staff and adequate facilities available for the trial; identifying that they would be able to recruit the required number of patients; and acknowledging and agreeing to conform to the administrative, ethical and study specific requirements.

Any centre not meeting these criteria was deemed ineligible to participate in the trial.

Investigational medicinal product

The IMP for the TAILoR trial was telmisartan. Telmisartan is an antihypertensive agent used to lower blood pressure and to reduce cardiovascular events in at-risk patients. For this trial, it was used outside the licensed indications of its different manufacturers. Telmisartan is available in doses of 20, 40 and 80 mg. It is produced by a range of manufacturers; the generic formulations were considered to be bioequivalent to the brand leader Micardis^® (Boehringer Ingelheim Ltd, Ingelheim am Rhein, Germany). Appendix 2 details the brands of telmisartan approved for use in the trial.

The IMP was sourced through standard NHS procurement processes at each site and was dispensed to trial participants on receipt of a valid trial specific prescription. Study drug accountability was documented by the pharmacy teams at each site and was monitored by the trial co-ordinator/Clinical Trials Research Centre (CTRC).

Primary outcome

Reduction in insulin resistance (as measured by HOMA-IR) in telmisartan-treated arm(s) after 24 weeks of treatment in comparison with control. This was a pure efficacy outcome.

Secondary outcomes

1. Change in lipid profile at 12, 24 and 48 weeks [increase in high-density lipoprotein cholesterol (HDL-C), reduction in total cholesterol, triglycerides and low-density lipoprotein cholesterol (LDL-C)] between telmisartan-treated arm(s) and the control arm.

2. Change in body fat redistribution as measured by MRI/MRS at 24 weeks between telmisartan-treated arm(s) and control arm (reduction in visceral fat, change in intrahepatic fat, change in lower leg muscle fat).

3. Change in plasma concentrations of biomarkers [adiponectin, leptin, interleukin 8 (IL-8), TNF-α, resistin and hs-CRP] at 12, 24 and 48 weeks between telmisartan-treated arm(s) and the control arm.

4. Change in insulin resistance, measured longitudinally, in telmisartan-treated arm(s) in comparison with the control arm.

5. Difference in expected and unexpected serious adverse events between different telmisartan-treated dose arm(s) and the control arm.

Two additional secondary outcome measures were added on the advice of the Independent Data Safety and Monitoring Committee (IDSMC) and the PIs, which were added after recruitment began:

1. Reduction in insulin resistance (as measured by QUICKI and revised QUICKI) in telmisartan-treated arm(s) after 24 weeks of treatment in comparison with control.

2. Change in urinary biomarker levels (ACR; NGAL) at 12, 24 and 48 weeks between telmisartan-treated arm(s) and the control arm.

Paper case report form

A paper case report form (CRF) was used to collect patient data at each study visit. Paper CRFs were designed especially for the study in line with the trial protocol.

Database

On receipt of paper CRFs at the CTRC, data were entered to a good clinical practice- (GCP) compliant database (MACRO 3, Elsevier, Amsterdam, the Netherlands) by trial staff at the CTRC. The configuration of the database was specific for the TAILoR trial, and it had built-in validations on certain aspects of the trial data. A full audit trail was maintained.

Timescale of evaluations

The first visit occurred on the same day as randomisation; subsequent visits took place at 12, 24 and 48 weeks. Patients randomised to higher doses of telmisartan (40 and 80 mg) also attended for dose titration visits (see Figure 1 and Table 1).

Schedule of investigations

A summary of tests and investigations undertaken in each patient is provided in Table 1.

Sample collection, processing and storage at participating sites

Biological samples (blood and urine) were collected at four time points during the trial at individual clinical sites participating in the study. For each patient, samples were collected at baseline, 12, 24 and 48 weeks/end of trial visits. At each time point, three fasting blood samples and one urine sample were collected from each participant. The blood samples were collected in:

1. 9-ml tripotassium ethylenediaminetetraacetic acid (K3EDTA) VACUETTE® blood tubes (Grenier Bio-One International Ltd, Gloucestershire, UK) (for DNA extraction)

2. 9-ml Z Serum Clot Activator VACUETTE tubes (for serum separation)

3. 4-ml FX Sodium Fluoride/Potassium Oxalate VACUETTE tubes (for glucose estimation).

The K3EDTA tubes were stored at –20 °C; plasma and serum were extracted locally at each site by the research nurses and aliquoted into two 1.8-ml cryovial tubes (STARLAB, Milton Keynes, UK) and were immediately stored at a minimum temperature of –20 °C. The sample collection, sample processing and storage were all performed in accordance with standardised standard operating procedures (SOPs) supplied by the main study team at Liverpool (all laboratory SOPs can be made available on request).

Sample shipment

Packaging and shipping of all samples collected from the TAILoR study followed the Packaging Provisions for Biological Substances, Category B, UN 3373 and UN1845 (dry ice) guidelines. 53 A SOP was followed by all sites for packaging of samples and shipment of TAILoR samples. The samples were shipped on dry ice in sealed styrofoam™ (The Dow Chemical Company, Midland, MI, USA) boxes by a clinical trial specialist courier with a delivery time of within 24 hours.

Sample collection, processing and storage at the co-ordinating centre (University of Liverpool)

All samples were stored in the Bioanalytical Facility (BAF), Royal Liverpool Hospital, which is a category 2 laboratory equipped to handle and store infectious samples. All samples were stored at a minimum temperature of –20 °C until further processing. Sample transport log sheets were completed, dated and signed by the Wolfson Centre for Personalised Medicine, University of Liverpool, (WCPM) staff and countersigned by the BAF analyst.

The whole-blood samples collected in K3EDTA vacuettes were initially subjected to viral inactivation by incubating the samples at 58 °C in a water bath for 40 minutes. The inactivated samples were transferred to the WCPM for DNA extraction using a magnetic bead-based method on a chemagen chemagic MSM I platform (Perkin Elmer, Waltham, MA, USA). Briefly, a 4.5-ml whole-blood sample was added into 50-ml tubes containing lysis buffer and protease enzyme for cell lysis. Once lysis was completed, binding buffer and magnetic beads were added to elute the extracted DNA. The DNA was dissolved in Tris buffer and its quality and quantity were ascertained using a NanoDrop™ spectrophotometer (ThermoFisher Scientific Ltd, Paisley, UK). The DNA samples were stored at –20 °C for future use. The urine samples were aliquoted into cryovials and stored at –20 °C.

Assessment of efficacy

Other assessments

Magnetic resonance imaging substudy

Magnetic resonance scanning was undertaken at the Magnetic Resonance Imaging and Analysis Research Centre, University of Liverpool, on the Siemens 1.5 T Symphony scanner (Siemens, Erlangen, Germany), using well-established methods. 55

Liver ¹H-MRS spectra were acquired with the Siemens body coil (Siemens, Erlangen, Germany), using a point-resolved spectroscopy (PRESS) sequence [repetition time (TR) 1500 milliseconds/echo time (TE) 135 milliseconds] without water saturation, 64 signal averages. 55 Transverse magnetic resonance (MR) images were used to ensure accurate positioning of three 20 × 20 × 20 mm voxels, avoiding blood vessels, the gall bladder and fatty tissue.

Skeletal muscle ¹H-MRS spectra were acquired using the Siemens CP extremity coil (Siemens, Erlangen, Germany) using PRESS (TR 1500 milliseconds /TE 135 milliseconds) without water saturation, 64 signal averages. 56 Transverse MR images were used to ensure accurate positioning of a 20- × 20- × 20-mm voxel in each of the soleus and tibialis anterior. Spectra were analysed in the time domain using the AMARES (advanced method for accurate, robust, and efficient spectra) algorithm included in the jMRUI 3.0 software package. Intramyocellular lipid is expressed as methylene relative to creatine signal. 56 Intrahepatic lipid is expressed as methylene relative to unsuppressed water.

Magnetic resonance imaging of total body adipose content was carried out by a method adapted from Thomas et al. 55 using T1-weighted MR images (TR 705 milliseconds, TE 12 milliseconds) in 10 overlapping blocks of 1-cm slices with a 1-cm gap, in upper and lower halves of the body separately. A validated semiautomatic program was used to segment and analyse the images into total body subcutaneous, total internal, subcutaneous abdominal and intra-abdominal adipose tissue volumes. This work was outsourced to a commercial analysis service, Vardis Group (London, UK).

The scanner was supported by the manufacturers’ top-level service contract, which incorporates specific elements of quality control for MRI.

Causality

Unrelated: there is no evidence of any causal relationship, where an alternative cause for the adverse event (AE) is provided.

Unlikely: there is little evidence to suggest there is a causal relationship (e.g. the event did not occur within a reasonable time after administration of the trial medication). There is another reasonable explanation for the event (e.g. the participant’s clinical condition, other concomitant treatment).

Possibly: there is some evidence to suggest a causal relationship (e.g. because the event occurs within a reasonable time after administration of the trial medication). However, the influence of other factors may have contributed to the event (e.g. the participant’s clinical condition, other concomitant treatments).

Probably: there is evidence to suggest a causal relationship and the influence of other factors is unlikely.

Almost certainly: there is clear evidence to suggest a causal relationship and other possible contributing factors can be ruled out.

Period of observation

Adverse events/reactions for the TAILoR trial were monitored from the time of consent until 7 days after the patient had taken the final dose of telmisartan (wash-out period of telmisartan) after the patient’s participation in the trial was concluded.

Reporting procedures

Adverse reactions and all SAEs (regardless of causality) were reported. The reporting procedures detailed in Figure 3 and below were followed.

FIGURE 3.

Schematic of pharmacovigilance reporting procedures.

Sample size

Sample size for the substudy 1 (magnetic resonance imaging/proton magnetic resonance spectroscopy)

To explore the secondary objective of using MRI/¹H-MRS in a subset of patients to identify the effect of telmisartan on visceral fat distribution and hepatic and muscle fat, we intended to recruit 48 patients locally (12 each in telmisartan dose arms and 12 in the control arm) to undergo whole-body MRI scans and liver and calf MRS at baseline and at 24 weeks. There is limited information44,45 on the effect of telmisartan on visceral fat distribution for the dose groups considered and no reliable estimates of the within-group variance were available to carry out a formal power calculation. A sample size of 10 patients per group was expected to provide enough data for a reliable estimate of the within-group variance (sample size was increased to 12 to account for 10% dropout). To put this into context, the proposed sample size would allow the detection of a linear reduction in visceral fat of at least 10 cm²/20 mg with an 80% power at the 5% significance level, assuming a within-group standard deviation of visceral fat reduction, σ = 27 cm². Even if deviations from the sample standard deviation (σ = 27 cm²) occurred (e.g. σ increases to 40 cm²), the sample size proposed would still have been sufficient to detect a linear reduction in visceral fat distribution ≥ 15 cm²/20 mg (nQuery calculator, version 6; Statistical Solutions Ltd, Cork, Ireland). The final sample size achieved was affected by a lower recruitment rate than initially expected and by the decision to stop recruitment for the main study for treatment arms B and C.

Sample size for substudy 2 (renal biomarkers)

To explore the secondary objective of whether or not there was a change in urinary biomarker levels at 12, 24 and 48 weeks between telmisartan-treated arm(s) and the control arm, we used the same sample size as for the main study.

Interim analysis and stopping guidelines

The interim analysis was scheduled to take place once the 24-week change in HOMA-IR score was available for at least 42 patients in each arm (n = 168, which was half of the planned maximum of 336 patients). The sample standard deviation pooled across all four arms was used to construct test statistics expressing the advantage of each of the three active treatments over the control arm. The prespecified analysis was as follows:

1. If the largest of these statistics is below a critical value (equal to –2.782), this would mean that one active dose group shows a substantially higher mean reduction of 24-week HOMA-IR score than the control group and, therefore, the study will be stopped and the corresponding dose will be recommended for further testing.

2. If any active dose shows no improvement over control (i.e. has an increase in HOMA-IR), that active dose will be dropped from the second stage.

3. If all three active doses satisfy this criterion, then the study will be stopped and no significant improvement over control will be claimed for any of the active doses.

4. If some improvement over control is detected for at least one of the doses (i.e. if at least one test statistic is between 0 and –2.782), then the study will progress to the second stage.

Consequently, if arms are dropped after the interim analysis, randomisation will continue to be in an equal ratio (i.e. 1 : 1 or 1 : 1 : 1).

Blinding

The TAILoR trial was an open trial, with the investigators and patients not blinded to the allocated treatment. However, allocation concealment was possible as participants were randomised using a secure (24-hour) web-based randomisation program controlled centrally by the CTRC.

Method of assignment to treatment

In the first stage of the study, patients were randomised in a 1 : 1 : 1 : 1 ratio to receive 20, 40 or 80 mg of telmisartan or no intervention (control) using a secure (24 hour) web-based randomisation program controlled centrally by the CTRC CTU.

Randomisation lists were generated in a 1 : 1 : 1 : 1 ratio using simple block randomisation with random variable block length. For each recruiting centre, randomisation was stratified by ethnicity (black or non-black). Following the interim analysis, as the trial continued, those eligible that consented to take part in the study were randomised in an equal ratio to receive one of the remaining doses or no intervention (control).

During both stages of the trial, patients were enrolled to the study by clinicians and/or research nurses at each individual site who were delegated to do so by the PI.

Sequence and duration of all study periods

A schematic of the study design can be found in Data collection, including descriptions and timings of all assessments and procedures that were needed throughout. In summary, follow-up assessments occurred at 12, 24 and 48 weeks after randomisation.

Statistical analysis plan

Statistical analysis plans were developed for the interim and final analyses of the trial by the trial investigators and trial statistician, and were reviewed and agreed by the TSC and Data Monitoring and Ethics Committee prior to the end of the recruitment period at each stage of the trial. The complete detail of all statistical analysis plans are given in Appendix 4.

Statistical methods

Additional analysis

All analyses were undertaken adjusting for weight change. Joint models included data from the dropped arms at the interim analysis and were fitted with longitudinal measurements from all four arms to adjust for informative dropout further and to account for changes in weight over time. Bivariate joint models were fitted using the joineRML package in R (The R Foundation for Statistical Computing, Vienna, Austria). 66,67 Two additional ad hoc exploratory compliance-adjusted analysis were undertaken for HOMA-IR at 24 weeks to address some selection bias.

Trial management

The trial was managed by the CTRC at the University of Liverpool, which is a UK Clinical Research Collaboration (UKCRC) – Registered Clinical Trial Unit (CTU), a part of the Liverpool Trials Collaborative. The CTRC was responsible for trial management, quality assurance, data management and trial statistics. A dedicated trial manager, data manager and statistician were appointed to the CTRC.

Trial sponsor

The TAILoR trial was co-sponsored by the University of Liverpool and the Royal Liverpool and Broadgreen University Hospitals NHS Trust.

Ethical considerations, regulatory requirements, and research governance framework

The TAILoR trial was conducted in accordance with the European Clinical Trials Directive, ICH GCP Guidelines, the Declaration of Helsinki, NHS Research governance framework, and the Medicines for Human Use (Clinical Trials) Regulations 2004.

The trial was authorised to proceed by the MHRA on 29 June 2012. Its EudraCT number is 2012-000935-18. The trial also received REC (National Research Ethics Service committee – North West – Liverpool Central) approval prior to the start of the study (original approval dated 2 April 2012).

Prior to beginning research at each participating site, REC approval was sought as were local permissions from the R&D departments at each site.

National Institute for Health Research portfolio

The TAILoR trial was adopted onto the National Institute for Health Research (NIHR) portfolio and fulfilled the criteria for UK Clinical Research Network support.

Trial Management Group

A TMG was set up to be responsible for the day-to-day management of the TAILoR trial. The TMG met on a monthly basis to discuss the conduct and progress of the trial. The TMG membership was as follows:

• Professor Sir Munir Pirmohamed (chief investigator)

• Claire Taylor (trial co-ordinator)

• Catherine Spowart (until June 2016) (supervisory trial manager)

• Dr Ruwanthi Kolamunnage-Dona (study statistician)

• Professor Thomas Jaki (senior study statistician)

• Professor Paula Williamson (senior statistician and co-applicant)

• Dr Sudeep Pushpakom (co-applicant and scientist)

• Professor Saye Khoo (co-applicant and PI at RLBUHT)

• Professor John Whitehead (until retirement in 2014; co-applicant and statistician)

• Helen Reynolds (research nurse at RLBUHT)

• Jenny Harrison (until October 2015; research nurse at RLBUHT)

• Dr Duncan Churchill (PI at the Elton John Centre)

• Dr Gabriel Schembri (PI at the Manchester Centre for Sexual Health)

• Steve Earle (patient representative).

Trial Steering Committee

The TSC was established to provide overall supervision of the trial and to ensure that the trial was conducted to ICH GCP guidelines. The TSC met annually throughout the course of the trial via both teleconference and e-mail. Copies of the minutes were provided to the study funder (NIHR). The TSC were also consulted about any amendments to the trial protocol. The TSC membership was as follows:

• Professor Stephane de Wit (chairperson)

• Professor Lucinda Billingham (independent member)

• Professor Mahesh Parmar (independent member)

• Simon Collins (independent member, patient representative)

• Professor Sir Munir Pirmohamed (chief investigator)

• Professor Paula Williamson (co-applicant).

Meetings were also attended by members of the TMG and sponsor representatives:

• Claire Taylor (trial co-ordinator)

• Catherine Spowart (until June 2016; supervisory trial manager)

• Dr Ruwanthi Kolamunnage-Dona (study statistician)

• Professor Thomas Jaki (senior study statistician)

• Dr Sudeep Pushpakom (co-applicant and scientist)

• Professor Saye Khoo (co-applicant and PI at RLBUHT)

• Professor John Whitehead (until retirement in 2014; co-applicant and statistician)

• Heather Rogers (RLBUHT sponsor representative)

• Lindsay Carter (April 2012–March 2013; University of Liverpool sponsor representative)

• Karen Wilding (University of Liverpool sponsor representative).

Independent Data and Safety Monitoring Committee

An Independent Data and Safety Monitoring Committee (IDSMC) was established at the start of the trial. Its purpose was to review trial data, assess safety issues and/or address any ethics concerns. The IDSMC met annually throughout the course of the trial by both teleconference and e-mail. The IDSMC also received safety reports on a quarterly basis. The IDSMC assessed the interim analysis to decide on the ongoing format of the trial. The IDSMC members were:

• Professor Sir Ian Weller (chairperson)

• Dr Adrian Mander (independent statistician)

• Professor Jacqueline Capeau (independent member).

Risk assessment

A risk assessment was performed by the CTRC team in collaboration with the chief investigator and sponsor at the beginning of the trial. The risk assessment indicated that the TAILoR study was a low-risk study and that monitoring would take place centrally, with site visits taking place if required to resolve issues.

Monitoring and data management

Monitoring was performed centrally. The MACRO database (version 3) contained predefined ranges that flagged queries to the study data manager if data outside the ranges were entered. In addition, the trial statistician performed regular checks on the data to produce IDSMC and trial monitoring reports to look for errors and inconsistencies and to highlight any protocol deviations.

Sites were requested to fax consent forms to the trial co-ordinator within 7 days of completion and these were then checked by the trial co-ordinator to ensure that they were valid.

Site training/initiation visits took place at all participating sites. On site monitoring visits took place if central monitoring indicated a need (e.g. CRFs not being returned/returned late, inconsistencies in CRFs).

Screening and participant flow

In total, 1950 patients were screened at the participating centres over the duration of the trial. Of the 1118 patients meeting the eligibility criteria, 698 declined to participate. Tables 14–16 in Appendix 6 summarise the numbers screened and eligible patients recruited to the trial. Further details of screening are given in Appendix 6, with Table 16 summarising the reasons for non-recruitment. The flow of patients is summarised in Figure 4, including the numbers of patients screened, randomised, followed up and analysed.

FIGURE 4.

Consolidated Standards of Reporting Trials flow diagram. a, Screening data are missing for three patients. b, A patient was randomised in arm B, even before they attended the clinic, because of a communication error. c, Patient concerned about leg swelling (not an AE) and decided to stop study drug. d, Patient has left the country; patient preference. e, Medication ran out 2 days before final visit; appointment was missed, as patient misunderstood the follow-up schedule; patient lost study medication and failed to inform research team until 48-week follow-up visit. f, Patient moved to London with no forwarding address; did not wish to continue the study because of organisational reasons; because of advice from study team following MRI incidental finding; to enter another clinical trial; eGFR < 60 ml/minute/1.73 m² at screening; on nevirapine. g, Patient developed a cough and wanted to discontinue study medication; taking Rampiril (general practitioner’s orders); switch in ARV; not able to commit to study; eGFR < 60 ml/minute/1.73 m² at baseline; did not meet eligibility criteria; did not return for appointment and did not respond to contacts; decided to become pregnant. h, Patient ran out of study medication; eGFR < 60 ml/minute/1.73 m²; patient ran out of study medication but did not collect extra medication; forgot to take study medication when on holiday abroad; ran out of study medication; ran out of study medication but was unable to come to collect extra medication; did not take study medication as required. i, All three patients had run out of study drug.

Randomisation checking

Randomisation numbers were sequential by date randomised, and there were no missing randomisation numbers. The last day of randomisation was 20 July 2015. Treatments were balanced across strata (ethnicity) (Table 3).

Recruitment and retention

Recruitment took place from March 2013 until July 2015, taking a total of 28 months. This was longer than the originally allocated recruitment period of 1 year. There were a number of delays in starting the trial and in the opening of sites to recruitment. The major issues encountered were:

• lack of funding for excess treatment costs (ETCs)

• slower recruitment

• increased participant dropout and subsequent need to increase from the original sample size

• high staff turnover at some participating sites, which further contributed to delay/interruption in recruitment.

The TAILoR team took a number of measures to counter the above cited impediments:

1. A waiver to underwrite the ETCs was negotiated with the trial sponsor (University of Liverpool) to get the trial started. Over the course of the trial, all participating sites were contacted to cover ETCs and this was eventually agreed by all sites by the time the last patient was recruited.

2. Setting up of additional sites to reach the recruitment target in the same time frame – the number of recruitment sites was increased from the originally planned eight sites to 19 sites.

3. Encouraging patient participation and increasing publicity for the trial among the patient population – the trial team held discussions with the patient and public involvement representatives in the TMG and the TSC on ways to increase recruitment rates. As a result of this, the participant information sheet was altered to make them more reader friendly; we also developed posters for use in participating clinics, and developed a recommended recruitment pitch for researchers. A study website (www.tailortrial.org) was also created to increase visibility and to update the participants of the various developments related to the trial.

4. Ensuring recruiter engagement – to increase recruitment, we continued to engage with research teams at sites holding regular PI and research nurse meetings, and having regular newsletter updates.

Recruitment rate

The target recruitment rate for the study was two to five patients per month per site, based on the original eight sites recruiting and a target recruitment figure of 370 patients. Monthly and cumulative monthly accrual of patients was slower than anticipated and is shown in Figure 5.

FIGURE 5.

Monthly and cumulative monthly accrual of patients. Post interim analysis: interim analysis has shown that there was a higher than anticipated rate of withdrawals and/or missing primary outcome data. In order to have the required number of patients for final analysis, a total of 377 patients was required.

Numbers analysed

In total, 307 (81%) of the patients attended all study assessment visits, and 72% of patients had complete records of longitudinal outcome as planned. Table 21 (see Appendix 6) shows the numbers for missed visits and missing data because of sample issues, and reasons for missingness are given in Table 22 (see Appendix 6).

Interim analysis

There were 48, 49, 47 and 45 patients who were randomised to arms A, B, C and D, respectively, with a total of 189 patients available for interim analysis. However, only 154 patients had a complete set of baseline and 24-week HOMA-IR data and were therefore included in the interim analysis. A total of 32 patients did not have a HOMA-IR value at the 24-week time point, and three patients did not have baseline HOMA-IR data. In 31 out of 32 cases, the 24-week HOMA-IR data were unavailable because of withdrawal from the study, visit not attended and loss to follow-up. There were seven patients (14.6%) in arm A, four patients (8.2%) in arm B, 11 patients (23.4%) in arm C and nine patients (20.0%) in arm D who were unavailable for interim analysis. Figure 6 shows the box plots for HOMA-IR at baseline and 24 weeks by treatment arm and the summary statistics are given in Table 23 (see Appendix 6).

FIGURE 6.

Box plots for HOMA-IR at baseline and 24 weeks by treatment arm at the interim analysis. A refers to the control arm; B, C and D refer to arms treated with 20, 40 and 80 mg of telmisartan, respectively. 0, baseline; 24, 24 weeks.

The HOMA-IR data were not normally distributed; hence the analysis was performed in log-scale. The model estimates are presented in Table 6; the t-statistic for arms B and C showed a positive value (i.e. higher than 0), which implied that there was no reduction in the HOMA-IR over control (arm A) for arms B and C and, therefore, these active dose arms were dropped from the second stage. As some improvement over control was detected for arm D (i.e. the t-statistic was between 0 and –2.782), this arm was selected to progress into the second stage of the study and the patients were thereafter randomised between arm D and the control arm (arm A).

Final analysis

At the end of the study, there were 105 patients randomised in arm A and 106 patients in arm D (total n = 211). One patient from arm D had an extreme HOMA-IR value at 24 weeks (62.0). This was because of a high insulin value of 1242 (glucose value was 7.8). The summary statistics for HOMA-IR at baseline and 24 weeks by treatment arm are given in Table 7, and the model estimates are presented in Table 8. As the HOMA-IR was not normally distributed, all analyses were performed in log scale. Since the test statistic (t-value 0.065) was not smaller than the critical value of –2.086, it was concluded that there was no significant difference in HOMA-IR between arms D and A (t-value 0.007, SE 0.106).

We have investigated the effect of inclusion of this extreme value further by plotting the change in HOMA-IR at 24 weeks from baseline against HOMA-IR at baseline; this is presented in Figure 10 (see Appendix 6). This patient was considered to be a statistical outlier and was therefore excluded from the analyses shown below.

A total of 85 patients from arm A and 78 patients from arm D with both baseline and 24-week HOMA-IR measurements were included in the final analysis. Figure 7 shows the box plots for HOMA-IR at baseline and 24 weeks by treatment arm, and the summary statistics are given in Table 24 (see Appendix 6). The model estimates are presented in Table 25 (see Appendix 6). Since the test statistic (–0.347) was not smaller than the critical value of –2.086, it was concluded that there was no significant difference in HOMA-IR between arms D and A (–0.034, SE 0.099).

FIGURE 7.

Box plots for HOMA-IR at baseline and 24 weeks by treatment arm at the final analysis for continued treatment arms. A refers to the control arm; D refers to arm treated with 80 mg of telmisartan. 0, baseline; 24, 24 weeks.

The model was further adjusted for change in weight between 24 weeks and baseline in a post hoc analysis. A total of 84 patients from arm A and 77 patients from arm D (one patient was excluded because of unavailability of weight at baseline data) were included in this analysis. The test statistic, –0.399, was still not smaller than the critical value (–2.086), and thus there was no significant difference in HOMA-IR between the two arms (–0.039, SE 0.097; see Appendix 6, Table 26).

Sensitivity analyses were also carried out to adjust for (1) potential missing HOMA-IR values and (2) treatment compliance. The parameter estimates in each of these sensitivity analyses [imputation for missing HOMA-IR, test statistic = –0.393, effect size –0.038, SE 0.096; treatment compliance, –0.010 (95% CI –0.028 to 0.008; p = 0.3] showed that there was no significant difference between arms A and D. Details of these analyses are presented in Appendix 6, Tables 27–30.

Alternative indices of insulin resistance (Quantitative Insulin Sensitivity Check and revised Quantitative Insulin Sensitivity Check)

We used two alternative measures of insulin resistance, QUICKI and revised QUICKI, to further investigate the effect of telmisartan. Figure 8 shows the box plots for QUICKI and revised QUICKI at baseline and 24 weeks by treatment arm, and the summary statistics are given in Tables 31 and 32 (see Appendix 6).

FIGURE 8.

Box plots for QUICKI and revised QUICKI at baseline and 24 weeks by treatment arm. A refers to the control arm; D refers to arm treated with 80 mg of telmisartan. 0, baseline; 24, 24 weeks.

For QUICKI, the test statistic (0.4471) was not smaller than the critical value (–2.086), suggesting no difference between arms A and D (0.0006, SE 0.0013), a result similar to that observed with HOMA-IR (see Table 33 in Appendix 6). Similarly, for revised QUICKI, the test statistic (0.6882) was not smaller than the critical value (–2.086), again showing no difference between arms A and D (0.0017, SE 0.0025; see Table 34 in Appendix 6).

The model diagnostics related to the above models are shown in Appendix 6.

Longitudinal outcomes

Serious adverse events

From a total of 377 patients, 21 SAEs were reported from 19 (5.0%) patients: five patients (4.8% of the allocated 105 patients; six events) in arm A (control), three patients (3.6%; three events) in arm B (20 mg), four patients (4.9%; four events) in arm C (40 mg) and seven patients (6.6%; eight events) in arm D (80 mg). No SUSARs were reported (see Table 59 in Appendix 5 for full details). We compared the percentage of patients who had one or more SAEs between telmisartan-treated arms and the control arm using the Fisher’s exact test. There was no evidence of difference in percentage of SAEs between telmisartan-treated arms and the control arms (p = 0.8).

Compliance with study drug schedule

Any unused drug was collected during the follow-up and study medication compliance was assessed by counting the number of pills returned and entries made in the treatment diary. Table 57 (see Appendix 6) presents a descriptive summary of compliance of the total dose consumed according to the treatment diary and according to the number of pills returned at each visit (at weeks 2, 4, 12 and 24) split by treatment arm. The baseline characteristics of those who provided compliance information were compared with the characteristics of patients who did not provide compliance information (see Table 61 in Appendix 6).

There were no clinically important differences observed in the systolic or diastolic blood pressure, CD4 cell count and eGFR between those with compliance data and those without. However, on average, HIV viral load was higher in those who provided compliance data than in those who did not (see Table 61 in Appendix 6). Analysis according to compliance (dose received) was carried out as a sensitivity analysis using IV regression (with randomisation as the instrument). We also carried out two additional exploratory compliance-adjusted analyses for HOMA-IR at 24 weeks to address the selection bias, given the above observation on HIV viral load. The results remained the same as the original sensitivity analysis (see Appendix 6, Tables 28–30).

Primary outcome

The primary objective of the trial was to investigate whether or not telmisartan, at any of the doses tested, resulted in a reduction in HOMA-IR, a validated marker of insulin resistance, in individuals infected with HIV over a period of 24 weeks. Using a novel adaptive design, the trial was divided into two stages; the first was to identify the best dose(s) of telmisartan (20, 40 or 80 mg) when compared with current standard of care, and the second stage was to compare one or more doses of telmisartan for a further 24 weeks. The first stage, conducted in 154 patients, identified one dose (80 mg of telmisartan) that met the prespecified end point and was subsequently tested in the second stage. The final analysis, conducted in 211 individuals, demonstrated that telmisartan (80 mg) did not result in a statistically significant reduction in HOMA-IR when compared with the control arm over 24 weeks.

Secondary outcomes

We used QUICKI and revised QUICKI, two alternative indices of insulin resistance, to confirm the effect of telmisartan on HOMA-IR. Telmisartan did not show a statistically significant increase (QUICKI and revised QUICKI are log measures and, therefore, are expected to increase if there is any reduction in insulin resistance) in these indices at 24 weeks in comparison with the control arm.

A longitudinal analysis of the effect of telmisartan on HOMA-IR, QUICKI and revised QUICKI over a period of 48 weeks, when compared with the control arm, showed that while there was no change in HOMA-IR and QUICKI, there was a significant, but marginal, improvement in revised QUICKI (p = 0.05). Telmisartan (80 mg) did not have any effect on serum lipids (cholesterol, triglycerides, HDL-C and LDL-C) and adipokines (markers of homeostasis and inflammation: adiponectin, leptin, resistin, IL-8, TNF-α), but there was a significant decrease in hs-CRP over 48 weeks.

Two different substudies were conducted as part of this trial:

• Substudy 1 was an exploratory evaluation of the effect of telmisartan on total body, liver (intrahepatic) and limb (intramyocellular) fat distribution using MRI and ¹H-MRS. Telmisartan did not show any significant reduction in total body fat or limb fat, but significantly reduced liver fat over a period of 24 weeks.

• Substudy 2 investigated the effect of telmisartan on renal parameters. Telmisartan (80 mg) showed a significant decrease in ACR (p = 0.04) over a period of 48 weeks in patients with microalbuminuria (ACR > 3 mg/mmol), but did not affect urinary NGAL, a marker of kidney disease.

There were no safety concerns with any of the doses of telmisartan and, although 21 SAEs were reported throughout the study, these were similar between the treatment arms and the non-intervention arm, and there was no dose-dependent increase in AEs. Of course, telmisartan has been used in a significant number of patients with hypertension, and it was not expected that this study would find any new, previously unreported, AEs. However, the finding of no decrease in blood pressure, even in patients without hypertension, was reassuring.

Telmisartan does not result in the reduction of Homeostatic Model Assessment of Insulin Resistance

Most of the evidence for telmisartan’s beneficial effect on insulin resistance and other glycaemic markers has so far come from non-HIV clinical studies. 35,38 Since the inception of this trial, three studies have reported in patients infected with HIV:

• two observational studies showed a reduction in insulin resistance with telmisartan48,72 with smaller sample sizes (18 and 13 participants)

• a single-arm open-label trial conducted in 35 individuals infected with HIV (the MATH trial73) that failed to find a significant change in HOMA-IR over 24 weeks.

Telmisartan has two main modes of action: it acts as an angiotensin II receptor antagonist and is a partial agonist at the adipocyte nuclear receptor PPAR-γ. Its effects have been attributed to the activation of PPAR-γ,74 but its anti-angiotensin effects may also play a role. 52

Human immunodeficiency virus causes disruption in glucose metabolism; in particular, the HIV viral protein inhibits PPAR-γ. 75 Adipose tissue, a major mediator of insulin sensitivity in the body, acts as a reservoir for HIV. 76 HIV infection causes inflammation that can lead to dysregulation of insulin signalling. HIV replication also causes upregulation of fatty acid synthase, an enzyme responsible for fatty acid synthesis;77 this increases the production of fatty acids, which could result in lipotoxicity and insulin resistance. In addition to the effect of HIV, cART is known to be toxic to adipose tissue, with cART-induced inhibition of PPAR-γ well documented. 52,78,79 Therefore, both HIV and cART, independently, have a direct effect on PPAR-γ. Therefore, our decision to trial telmisartan to reduce insulin resistance had a biological basis. The reason that we did not any reduction in HOMA-IR find may have been either because (1) telmisartan was not potent enough to compete with the adverse effects of HIV and cART on adipocytes and/or (2) the cause of the rise in insulin resistance in HIV is dependent on many other pathways, and blocking one is not adequate enough to lead to an improvement.

In the non-HIV setting, there have only been a handful of RCTs to assess the effect of telmisartan on insulin resistance as a primary outcome measure. Two of the largest RCTs compared telmisartan with other ARBs but in the presence of either rosiglitazone35 or rosuvastatin,38 two drugs with independent effects on glucose and lipid homeostasis. Derosa et al. ’s study35 in 188 T2DM patients with MS who were on rosiglitazone demonstrated that telmisartan (40 mg) significantly reduced HOMA-IR by 17% at 24 weeks and by 29% at 48 weeks. Rizos et al. ’s comparison38 of telmisartan (80 mg) with olmesartan and irbesartan (n = 151) in hypertensive individuals with impaired glucose function demonstrated that telmisartan reduced HOMA-IR by 29% (whereas the other ARBs resulted in an increase in HOMA-IR). It is important to note that the patients studied in these trials were highly insulin resistant at baseline (median baseline HOMA-IR was 7.2 in the telmisartan arm in Derosa et al. ;35 and 2.6 in the telmisartan arm in Rizos et al. 38). By contrast, in the present trial, the median baseline HOMA-IR was 1.6 in arm D. Therefore, detecting a reduction in HOMA-IR even further with telmisartan would be improbable without a much larger trial. It is also important to note that patients infected with HIV are treated with different cART combinations, each with a differing propensity to cause insulin resistance; this heterogeneity may have masked any positive effect, given the small numbers of patients in each drug combination group. Recruitment restricted to patients with higher HOMA-IRs may have been beneficial, but the feasibility of recruiting to such a trial would have been difficult.

Finally, it is important to highlight that 80 mg of telmisartan did have a positive impact on HOMA-IR. Even though patients in both the treatment and the control arms showed an increase in HOMA-IR between baseline and 24 weeks, the increase was of lesser magnitude in the treatment arm (+0.18; 7% increase) when compared with the control arm (+0.5; 20% increase). This was also observed at 48 weeks: HOMA-IR increased less with telmisartan (+0.74; 29% increase) than in the control arm (+1.2; 49% increase). It is not clear from the literature whether or not the effects of telmisartan increase with duration of treatment; we chose a 24-week time period based on observations in the non-HIV literature. A longer duration of treatment may have been more ideal but previous studies have shown that HOMA-IR changes within 4 weeks of starting antiretrovirals. 22

Telmisartan showed a marginal beneficial effect on revised QUICKI (p = 0.05) in a longitudinal analysis over 48 weeks. Although HOMA-IR has been most commonly used as an index of insulin resistance under fasting conditions, a meta-analysis of surrogate measures of insulin resistance/sensitivity under fasting conditions identified revised QUICKI as the index that has the strongest correlation (r = 0.68) with the gold standard, the hyperinsulinaemic–euglycaemic clamp. 80 Revised QUICKI takes into account fasting serum NEFA levels, in addition to plasma glucose and serum insulin, which has been suggested to improve its correlation with the clamp-based index of insulin sensitivity and its discriminatory power, particularly in non-obese individuals who present with mild insulin resistance. 81 Given that the TAILoR cohort had a median baseline BMI of 26.7 kg/m² (marginally overweight as per NICE Clinical Guidelines82), revised QUICKI may be a more a sensitive indicator of the effect of telmisartan than HOMA-IR.

Telmisartan significantly reduced liver fat, but not total body or limb fat

A limited meta-analysis of three RCTs83 with telmisartan treatment duration of 16–24 weeks (combined sample size of 62 participants) showed that telmisartan caused a significant reduction in visceral, but not in subcutaneous, fat suggesting a beneficial effect on body fat. However, the current study did not observe any significant reduction in internal visceral fat with any of the telmisartan doses over a period of 24 weeks. This is in line with the findings from the only other study that assessed the effect of telmisartan in patients infected with HIV on cART. 73 It is possible a longer timeframe may be required to reduce visceral fat in patients infected with HIV.

Our trial did find a significant reduction in liver, but not limb, fat in the 80 mg of telmisartan arm over a period of 24 weeks. This is the first time a positive effect on liver fat has been reported with telmisartan in patients infected with HIV, but given the small numbers (n = 10 in each of the three treatment arms; n = 13 in the non-intervention arm), the findings have to be interpreted with caution. Indeed, intrahepatic fat has been suggested to be a better marker of metabolic disease than visceral fat,84 and may provide a better estimate of ectopic fatty acid deposition, which is one of the main reasons for the development of insulin resistance. However, our finding contrasts with a previous randomised trial85 in obese adult individuals using 160 mg of telmisartan that did not find any changes in total, liver or limb fat.

Telmisartan did not change any of the plasma markers except high-sensitivity C-reactive protein

Telmisartan treatment did not improve lipids (total cholesterol, triglycerides, HDL and LDL-C) or plasma adipokines (adiponectin, leptin, resistin, IL-8 and TNFα) over a period of 48 weeks. This is in line with results from the MATH trial,73 which also did not find any change in lipids or adipokines with the exception of a marginal increase in TNFα. However, meta-analyses of non-HIV clinical studies have shown that telmisartan results in a reduction in lipids42 and adiponectin. 41 The effect of telmisartan on leptin, resistin, IL-8 and TNFα in patients not infected with HIV, however, has been contradictory and may be primarily because of small scale studies with limited generalisability. 35,86,87 The lack of effect of telmisartan on lipids in our trial may be because of the more complex clinical picture in patients infected with HIV (interaction with HIV and cART, duration of treatment) and because 58 (15.4%) of our patients were already on statins.

Many non-HIV clinical studies,38,88 and a meta-analysis43 (n = 2632 patients), have shown that telmisartan reduces hs-CRP levels. We observed a similar effect of telmisartan on hs-CRP over a period of 48 weeks in this trial. C-reactive protein promotes the proatherogenic activity of angiotensin, directly and indirectly stimulates structural and functional modification of arterial walls, heart and vascular remodelling and is considered to be an inflammatory marker. 89 In patients infected with HIV, hs-CRP may be an independent predictor of CVD;90 the anti-inflammatory effect of telmisartan, through its direct blockade of angiotensin action, may be responsible for the reduction in hs-CRP in patients infected with HIV, and may thus have the potential to reduce the associated CVD risk.

Telmisartan and effect on microalbuminuria

There is already some preliminary evidence of the renoprotective effect of telmisartan in patients infected with HIV; Ucciferri et al. 48 showed that telmisartan reduced microalbuminuria in patients infected with HIV. A meta-analysis of 20 RCTs, including > 12,000 patients not infected with HIV on telmisartan, by Takagi et al. 47 also showed that telmisartan results in a significant reduction in urinary ACR. Our study was not designed to investigate renal outcomes, but telmisartan (80 mg) did result in a significant reduction (p = 0.04) in ACR over 48 weeks in patients with microalbuminuria (defined by an ACR of > 3 mg/mmol) when compared with the current standard of care. However, it should be noted that this effect was observed in a small sample size of only 22 participants who had microalbuminuria and, therefore, the results should be interpreted with caution. Telmisartan did not have an effect on NGAL, another marker of renal health. It may also be important to note that NGAL did not show any correlation with urinary ACR. ACR is a marker of glomerular injury, whereas NGAL is a marker of tubular damage.

Design of the study

The study used a novel adaptive design in order to investigate the optimal dose of telmisartan, with the potential to cause a reduction in HOMA-IR. Adaptive designs are allowed to incorporate a prospectively planned opportunity for modification of one or more specified aspects of the study design and hypotheses based on analysis of the interim data. 91 Adaptive trial designs are innovative designs recommended by both the US Food and Drug Administration92 and the European Medicines Agency93 for clinical R&D because they offer flexibility in identifying the optimal clinical benefit of the test treatment under study without undermining the validity and integrity of the study. In the current study, the adaptive design allowed for testing three different doses of telmisartan simultaneously and dropping two different ‘loser doses’ so that a potential ‘winner dose’ could be taken into the second stage of the study. Such a design also took into account the fact that the dose–response profile of telmisartan, when used, as in our study, to reduce insulin resistance, may differ from its dose–response profile when used for its licensed indication, which is to reduce hypertension. This design also provided an opportunity to stop the study at the interim analysis stage if the required benefit was not identified with any of the doses.

Given that no further data were collected on dose arms dropped at interim analysis, a potential weakness is our incomplete understanding of the effect of these dose arms.

Standard operating procedures and log sheets relevant to shipping and storage:

• SOP1 _ TAILoR Sample Processing and Storage. Version 1.0; dated 12 February 2013.

• SOP2 _ TAILoR Sample Processing and Storage for Samples Processed Offsite. Version 1.0; dated 11 July 2013.

• SOP3 _ TAILoR SOP _ Packaging and Shipment of Samples. Version 4.0; dated 24 March 2014.

• TAILoR SOP _ Sample Reception. Version 3.0; dated 25 March 2014.

• TAILoR Sample Transfer Log Sheet (BAF to Aintree Labs). Version 3.0; dated 10 April 2014.

• TAILoR Sample Transfer Log Sheet (BAF to LCL and back). Version 1.0; dated 15 January 2015.

• TAILoR Sample Transfer Log Sheet (BAF to WCPM). Version 1.0; dated 10 April 2014.

Standard operating procedures relevant to biomarker analyses

• SOP _ Preparation of TAILoR Samples Master Plate. Version 2.0; dated 25 March 2016.

• SOP _ Adiponectin Assay – TAILoR samples. Version 1.0; dated 25 March 2016.

• SOP _ Resisitn Assay – TAILoR Samples. Version 2.0; dated 27 April 2016.

• SOP _ Leptin Assay – TAILoR Samples. Version 2.0; dated 27 April 2016.

• SOP _ Proinflammatory markers Assay – TAILoR Samples. Version 3.0; dated 25 April 2016.

3. Urinary biomarkers:

• Neutrophil gelatinase-associated lipocalin: this will be performed in the WCPM utilising electrochemiluminescence immunoassays as per the manufacturer’s protocol and analysed on a Meso Scale Discovery Sector Imager 2400A.

  • All samples will be analysed in duplicate.

  • Appropriate validation of the assay will be conducted and parameters such as accuracy, precision, linearity, reproducibility, sample recovery after spiking, inter and intra-assay variability will be measured before undertaking full sample analysis.

• Albumin-to-creatinine ratio: this will be carried out in the LCL, Royal Liverpool Hospital.

Patient groups for analysis

The principle of intention to treat (ITT), as far as is practically possible, will be the main strategy of the analysis adopted for the primary outcome. The analysis will be conducted on all patients randomised to the treatment arms.

Patients who withdrew consent for trial continuation will contribute outcome data up until the point of withdrawal, unless the patient specifically requests that the data not to be used.

The membership of the analysis set will be determined and documented, and reasons for participant exclusion will be given prior to the randomisation lists being requested. Reasons may include missing data, loss to follow-up and treatment withdrawal (not excluded for ITT analysis set).

Analysis of primary outcome

The primary analysis will use the principle of ITT based on all the randomised participants, as far as is practically possible.

Homeostatic model assessment of insulin resistance was calculated by:

The conversion factor for fasting insulin is 0.144 to convert from pmol/l to µU/ml.

In order to satisfy the primary objective, we will evaluate three different doses against control in the first stage of the study. We will conduct an interim analysis that will allow ineffective doses to be eliminated quickly, while a dose showing a positive effect can be taken forward.

At the interim analysis, an analysis of covariance (ANCOVA) model is used by fitting the regression model:

Analysis of covariance is a more efficient approach when dealing with small group numbers and when there is imbalance in baseline HOMA-IR.

The test statistics are given by the t-values for each active dose treatment.

The largest of these test statistics will be compared with the interim critical value (2.782). The above ANCOVA model means that the coefficient related to treatment is positive for an increase in HOMA-IR. Therefore, we look at the negative of the test statistic and use in the decision below. Exceeding this value would correspond to a significant improvement in HOMA-IR score for the corresponding dose over control and would lead to this dose being immediately taken forward for further study, and to the trial being stopped.

The analysis will be proceeding as follows:

• If the largest of these statistics exceeds a critical value (equal to 2.782), this would mean that one active dose group shows a substantially higher mean reduction of 24-week HOMA-IR score than the control group and, therefore, the study will be stopped and the corresponding dose will be recommended for further testing.

• If any active dose show no improvement over control (i.e. has a negative measure of advantage), then that active dose will be dropped from the second stage*.

• If all three active doses satisfy this criterion, then the study will be stopped and no significant improvement over control will be claimed for any of the active doses.

• If some improvement over control is detected for at least one of the doses (i.e. if at least one test statistic is between 0 and 2.782), the study will progress to the second stage and the patients will be randomised between these dose(s) and control.

The design has been constructed under the assumption that for all patients the response (HOMA-IR score) is normally distributed with a common standard deviation, σ. These assumptions will be checked at the interim analysis stage (Levene’s test for checking equal group variances and histogram for checking normality).

If any arm is dropped and the study progresses to the second stage, then the patients in the dropped arm(s) will stop the medication and will not be involved in second stage of the study.

Analysis of secondary efficacy outcomes

This analysis will not be presented in the interim analysis report.

Analyses of missing data/withdrawals

A summary table of the missing data will be presented, detailing the proportion of missing assessments overall and for each visit split by each treatment arm.

No sensitivity analyses will be carried out at this interim analysis stage.

The decision to drop treatments at the interim analysis stage will be based only on observed data at 24 weeks. The non-fasting samples data will be excluded from the analysis as they could interfere with the primary outcome (PI clinical judgement).

Data sets analysed

The principle of ITT, as far as is practically possible, will be the main strategy of the analysis adopted for the primary outcome. The analysis will be conducted on all patients randomised to the treatment arms, and for whom the outcome(s) of interest have been observed/measured. No imputations will be made.

The membership of the analysis set will be determined and documented, and reasons for participant exclusion will be given prior to the randomisation lists being requested. Reasons may include missing data, loss to follow-up and treatment withdrawal (not excluded for ITT analysis set).

Per-protocol analysis will not be considered.

Demographic and other baseline characteristics

Patients in each arm will be described separately with respect to sex, ethnicity, age, BMI, waist circumference (cm), systolic blood pressure (mmHg), diastolic blood pressure (mmHg), CD4 cell count (cells/mm³), HIV viral load (copies/ml), urea (mmol/l), potassium (mmol/l), creatinine (mmol/l), eGFR (ml/minute/1.73 m²), ALT (IU/l), albumin (g/l) and sodium (mmol/l).

Categorical data will be presented using counts and percentages, and continuous data will be presented using number of patients, mean, mode, median, SD, minimum, maximum and interquartile range (IQR). Tests of statistical significance will not be undertaken for baseline characteristics; rather, the clinical importance of any imbalance will be noted.

Compliance with treatment

A descriptive summary of compliance will be presented. Total dose consumed according to the treatment diary is summarised by mean, mode, median, SD, minimum, maximum and IQR and split by site and by treatment.

Total dose will also be calculated according to the number of pills returned at each visit (week 2, 4, 12 and 24).

Discrepancies between these two estimates of total dose will be summarised by mean, mode, median, SD, minimum, maximum and IQR and split by site and by treatment.

Analysis of the primary outcome

Secondary outcome analyses

Sensitivity analyses

A summary table of the missing primary outcome data will be presented detailing the proportion of missing assessments overall and for each visit split by each treatment arm.

Safety evaluations

Deviations from the final statistical analysis plan

Investigation of the extreme Homeostatic Model Assessment of Insulin Resistance value

FIGURE 10.

Change in HOMA-IR at 24 weeks from baseline against HOMA-IR at baseline. (a) all values; (b) values excluding patient 01552152.

Sensitivity analysis 1

We fitted the same ANCOVA model by imputing values for missing HOMA-IR values at baseline and 24 weeks using the MICE algorithm. The MICE algorithm imputed missing HOMA-IR values conditional on available HOMA-IR values at baseline, 12 weeks and 24 weeks, treatment allocation (arm D/arm A) and stratification factor (black/non-black).

The test statistic is –0.393, and as this value is not smaller than the critical value (–2.086), we failed to reject the null hypothesis (i.e. no difference between arm D and arm A).

Sensitivity analysis 2

A compliance-adjusted primary outcome analysis was undertaken using IV regression, in order to estimate the effect of actual dose on outcome. The model included patients from arm A (assumed to have received a telmisartan dose of 0 mg) and patients from arm D who provided compliance data from both the treatment diary and pill count. Dose is based on the average between two measures of compliance (treatment diary and pill count).

The p-value (of 0.2885 > 0.05) implies that there is no effect of telmisartan after adjusting for dose. The test of endogeneity indicated that there was insufficient evidence to reject the null hypothesis of exogeneity (Durbin score, chi-squared(1) = 0.0234; p = 0.8783; Wu–Hausman F(1,138) = 0.0226; p = 0.8807), implying that dose is independent of the error and, thus, standard regression analysis is appropriate. The randomisation was an informative (strong) instrument in this analysis, as demonstrated by a high correlation between compliance and randomised treatment arm (0.9928) and a highly significant p-value (i.e. < 0.0001), rejecting the null hypothesis that randomised treatment is a weak instrument.

Same IV regression was carried out but additionally accounting for baseline HIV viral load. We assume that baseline HIV viral load was missing at random, and there were no other factors (i.e. other than HIV viral load) that influence whether or not patients provided compliance data.

In the next analysis, multiple imputation was used to impute missing compliance information prior to carrying out IV regression, with HIV viral load at baseline as the key predictor in the imputation model (i.e. imputed compliance information used for all participants who had missing compliance information but who had baseline HIV viral load data). Twenty imputations were generated per patient using predictive mean matching with baseline HIV viral load as predictor variable rather than including baseline HIV viral load in model.

Histograms to check normality for Homeostatic Model Assessment of Insulin Resistance at baseline and 24 weeks

FIGURE 11.

Normality of HOMA-IR (a) at baseline; (b) at 24 weeks; (c) log-transformed at baseline; and (d) log-transformed at 24 weeks.

Histograms to check normality for Quantitative Insulin Sensitivity Check Index at baseline and 24 weeks

FIGURE 12.

Normality of QUICKI at (a) baseline; and (b) 24 weeks.

Histograms to check normality for revised Quantitative Insulin Sensitivity Check Index at baseline and 24 weeks

FIGURE 13.

Normality of revised QUICKI at (a) baseline; and (b) 24 weeks.

Homeostatic Model Assessment of Insulin Resistance

Histograms to check normality for Homeostatic Model Assessment of Insulin Resistance

FIGURE 14.

Normality of HOMA-IR. (a) original scale; and (b) log-transformed.

Three outlining HOMA-IR values were excluded from the longitudinal analysis [two from arm A (55.82720 at 12 weeks and 64.14336 at 48 weeks) and one from arm D (62.00064 at 24 weeks; this is the same value excluded at the primary analysis of HOMA-IR)].

FIGURE 15.

HOMA-IR mean profiles by treatment arm (original scale). (a) HOMA-IR profile plots; (b) HOMA-IR profile plots by dropout; and (c) HOMA-IR reverse-time profile plots.

TABLE 38 - Joint model estimates: log-HOMA-IR

Component	Parameter	Estimate	95% CI	p-value
Longitudinal marker	Intercept	0.420	0.234 to 0.606	< 0.0001
	Time	0.003	0.001 to 0.006	0.0159
	Baseline marker	0.669	0.589 to 0.748	< 0.0001
	Treatment B vs. A	–0.084	–0.245 to 0.077	0.3043
	Treatment C vs. A	0.010	–0.172 to 0.193	0.9106
	Treatment D vs. A	–0.083	–0.247 to 0.082	0.3243
	Ethnicity	–0.107	–0.247 to 0.033	0.1343
Longitudinal weight	Intercept	1.047	–1.131 to 3.226	0.3459
	Time	0.022	–0.002 to 0.045	0.0686
	Baseline weight	0.987	0.965 to 1.008	< 0.0001
	Treatment B vs. A	0.458	–0.551 to 1.467	0.3741
	Treatment C vs. A	0.690	–0.367 to 1.746	0.2008
	Treatment D vs. A	0.117	–0.842 to 1.076	0.8111
	Ethnicity	–0.246	–1.055 to 0.562	0.5504
Dropout	Treatment B vs. A	–0.355	–1.270 to 0.561	0.4477
	Treatment C vs. A	–0.076	–0.976 to 0.824	0.8690
	Treatment D vs. A	0.081	–0.716 to 0.877	0.8427
	Ethnicity	–0.036	–0.847 to 0.775	0.9300
Association parameters	Marker	–0.262	–1.433 to 0.909	0.6611
	Weight	0.044	–0.208 to 0.296	0.7337

The bivariate joint model included 321 patients and 812 records. The treatment effect [arm D: telmisartan (80 mg daily) compared with arm A: non-intervention (control)] on the longitudinal log-HOMA-IR is –0.083 (95% CI –0.247 to 0.082; p = 0.3243), implying that there is no significant difference between the treatments on HOMA-IR.

Quantitative Insulin Sensitivity Check Index

Histograms to check normality for Quantitative Insulin Sensitivity Check Index

FIGURE 16.

Normality of QUICKI (original scale).

FIGURE 17.

QUICKI mean profiles by treatment arm (original scale). (a) QUICKI profile plots; (b) QUICKI profile plots by dropout; and (c) QUICKI reverse-time profile plots.

TABLE 39 - Joint model estimates: QUICKI

Component	Parameter	Estimate	95% CI	p-value
Longitudinal marker	Intercept	0.039	0.029 to 0.048	< 0.0001
	Time	0.000	0.000 to 0.000	0.0126
	Baseline marker	0.649	0.572 to 0.726	< 0.0001
	Treatment B vs. A	0.001	–0.001 to 0.003	0.2596
	Treatment C vs. A	0.000	–0.003 to 0.002	0.9125
	Treatment D vs. A	0.001	–0.001 to 0.003	0.3426
	Ethnicity	0.001	–0.001 to 0.003	0.2102
Longitudinal weight	Intercept	1.091	–1.066 to 3.248	0.3217
	Time	0.021	–0.002 to 0.045	0.0755
	Baseline weight	0.986	0.965 to 1.007	< 0.0001
	Treatment B vs. A	0.479	–0.533 to 1.490	0.3537
	Treatment C vs. A	0.704	–0.348 to 1.756	0.1898
	Treatment D vs. A	0.135	–0.823 to 1.093	0.7823
	Ethnicity	–0.253	–1.053 to 0.546	0.5344
Dropout	Treatment B vs. A	–0.354	–1.269 to 0.561	0.4485
	Treatment C vs. A	–0.075	–0.982 to 0.832	0.8714
	Treatment D vs. A	0.082	–0.716 to 0.879	0.8408
	Ethnicity	–0.037	–0.835 to 0.762	0.9279
Association parameters	Marker	18.535	–75.599 to 112.668	0.6996
	Weight	0.042	–0.210 to 0.295	0.7428

The bivariate joint model included 321 patients and 812 records. The treatment effect [arm D: telmisartan (80 mg daily) compared with arm A: non-intervention (control)] on the longitudinal QUICKI is 0.001 (95% CI –0.001 to 0.003; p = 0.3426), implying that there is no significant difference between the treatments on QUICKI.

Revised Quantitative Insulin Sensitivity Check Index

Histograms to check normality for revised Quantitative Insulin Sensitivity Check Index

FIGURE 18.

Normality of revised QUICKI (original scale).

FIGURE 19.

Revised QUICKI mean profiles by treatment arm (original scale). (a) revised QUICKI profile plots; (b) revised QUICKI profile plots by dropout; and (c) revised QUICKI reverse-time profile plots.

The bivariate joint model included 320 patients and 808 records. Revised QUICKI scores were fitted in their original scale.

The treatment effect [arm D (80 mg) compared with arm A (control)] on the longitudinal revised QUICKI was 0.004 (95% CI 0.000 to 0.008; p = 0.0510). As the p-value was just above 0.05, this implies a marginally significant difference between the treatments on revised QUICKI.

TABLE 40 - Joint model estimates: revised QUICKI

Component	Parameter	Estimate	95% CI	p-value
Longitudinal marker	Intercept	0.058	0.046 to 0.070	< 0.0001
	Time	0.000	0.000 to 0.000	0.0402
	Baseline marker	0.550	0.473 to 0.627	< 0.0001
	Treatment B vs. A	0.003	–0.001 to 0.007	0.1023
	Treatment C vs. A	0.001	–0.003 to 0.005	0.5993
	Treatment D vs. A	0.004	0.000 to 0.008	0.0510
	Ethnicity	0.001	–0.002 to 0.004	0.5071
Longitudinal weight	Intercept	1.039	–1.087 to 3.164	0.3382
	Time	0.020	–0.003 to 0.044	0.0899
	Baseline weight	0.987	0.966 to 1.009	< 0.0001
	Treatment B vs. A	0.473	–0.493 to 1.438	0.3374
	Treatment C vs. A	0.690	–0.310 to 1.690	0.1765
	Treatment D vs. A	0.187	–0.748 to 1.123	0.6945
	Ethnicity	–0.250	–1.039 to 0.539	0.5340
Dropout	Treatment B vs. A	–0.364	–1.272 to 0.544	0.4319
	Treatment C vs. A	0.183	–0.633 to 0.999	0.6608
	Treatment D vs. A	0.078	–0.726 to 0.881	0.8496
	Ethnicity	–0.087	–0.845 to 0.671	0.8218
Association parameters	Marker	10.682	–47.031 to 68.395	0.7168
	Weight	0.028	–0.206 to 0.262	0.8153

Lipid profiles

High-density lipoprotein cholesterol

Histograms to check normality for high-density lipoprotein cholesterol

FIGURE 20.

Normality of HDL-C. (a) original scale; and (b) log-transformed.

FIGURE 21.

The HDL-C mean profiles by treatment arm (original scale). (a) HDL-C profile plots; (b) HDL-C profile plots by dropout; and (c) HDL-C reverse-time profile plots.

The bivariate joint model included 329 patients and 849 records. HDL-C scores were fitted in log-transformed scale for normality.

TABLE 41 - Joint model estimates: log-HDL-C

Component	Parameter	Estimate	95% CI	p-value
Longitudinal marker	Intercept	0.021	–0.034 to 0.076	0.4583
	Time	0.000	–0.001 to 0.001	0.6088
	Baseline marker	0.878	0.833 to 0.924	< 0.0001
	Treatment B vs. A	–0.012	–0.060 to 0.035	0.6089
	Treatment C vs. A	–0.038	–0.085 to 0.008	0.1086
	Treatment D vs. A	0.001	–0.046 to 0.047	0.9816
	Ethnicity	–0.018	–0.063 to 0.028	0.4455
Longitudinal weight	Intercept	0.285	–2.072 to 2.641	0.8129
	Time	0.019	–0.007 to 0.045	0.1537
	Baseline weight	0.994	0.971 to 1.017	< 0.0001
	Treatment B vs. A	0.477	–0.627 to 1.580	0.3972
	Treatment C vs. A	0.742	–0.415 to 1.899	0.2088
	Treatment D vs. A	0.075	–0.975 to 1.125	0.8881
	Ethnicity	–0.067	–0.945 to 0.811	0.8817
Dropout	Treatment B vs. A	0.089	–0.856 to 1.033	0.8540
	Treatment C vs. A	0.036	–0.986 to 1.058	0.9452
	Treatment D vs. A	0.329	–0.545 to 1.204	0.4608
	Ethnicity	–0.216	–1.078 to 0.646	0.6232
Association parameters	Marker	–0.638	–5.534 to 4.259	0.7986
	Weight	–0.046	–0.244 to 0.151	0.6465

The treatment effect [arm D (80 mg) compared with arm A (control)] on the longitudinal log-HDL-C is 0.001 (95% CI –0.046 to 0.047), implying that there is no significant difference between the treatments on HDL-C.