Evaluation of bone mineral density and body composition among patients on second-line ART.
Transcript of Evaluation of bone mineral density and body composition among patients on second-line ART.
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Evaluation of bone mineral density and body composition among patients on
second-line ART
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EARNEST: Europe Africa Research Network for Evaluation of Second-line Therapy
‣Definitive RCT to evaluate options for 2nd-line therapy in patients failing first-line ART regimens
‣Successor to DART trial‣1200 patients in 3 arms‣To commence 04/10 in
Uganda, Malawi, and Zimbabwe
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Trial design
1200 ELIGIBLE PATIENTS
RANDOMIZE
bPI + 2 NRTI (NRTIs according to
local standard of care)
bPI + RAL
bPI + RAL (12wk induction)
bPI(Monotherapy)
FOLLOW-UP FOR 144 WEEKS
Primary Outcome – Good clinical HIV disease control – defined as: No new WHO Stage 4 events during clinical trial CD4 cell count > 250 cells/mm3 at wk 96 AND VL < 10,000 copies/ml OR VL >10,000 copies/ml with no PI resistance mutations (wk 96)
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African Sites
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Background: BMD
• TNF and d4T ART regimens associated with initial BMD loss
• Continuous ART assoc with > BMD loss than intermittent ART
• Little data from Sub-Saharan Africa– Suggests high level of unrecognised
osteopenia (57% had low BMD at ≥1 site in 217 HIV negative Botswanan men)
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Background: Body Composition
• Exposure to NRTIs (esp d4T) assoc with peripheral fat loss– Increases with duration of therapy– Less with TNF and abacavir, increased with PI
• ? impact of virological failure
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Sub-Study aims and hypotheses
• To compare mean decline in BMD between randomised arms in patients who have failed first-line NRTI and NNRTI-containing ART
• To compare mean changes in body composition (recovery of peripheral fat) between randomised arms
• To compare mean changes in body composition (increase in central fat ) between randomised arms
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Sample size calculation
• 180 patients (60 in each treatment arm)
• EARNEST patients at IDI, Kampala, Uganda
• DEXA scans (whole body and bone) at baseline, (week 48, 96), 144
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Baseline and follow-up assessmentsat weeks 48, 96 and 144
• Clinical and medication history– demographic information (main protocol)– previous and incident fractures– risk factors for and secondary causes of osteoporosis
• Alcohol intake • Patient self-assessment of changes in body fat• Physician examination of body fat distribution • Questionnaire assessment of physical activity
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Baseline and follow-up assessmentsat weeks 48, 96 and 144
• Bone mineral density (lumbar spine and proximal femur) and body composition (total and regional (limb) body fat) studies using Hologic bone densitometer at Makerere University-John Hopkins University (MU-JHU) facility.
• Body fat composition with Bioelectrical Impedance Analysis (BIA) using RJL101 machine. Impedance values converted to estimates of body cell mass (BMC) and extracellular water using validated equations.
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Serum measures of Bone metabolism (Stored samples)
• Serum vitamin D [25(OH) and 1,25 (OH)], Calcium• Intact PTH• Fibroblast growth factor-23 (FGF-23)• -Serum bone formation markers
– osteocalcin, procollagen type 1 amino-terminal propeptide (P1NP), osteoprotegerin, tartrate-resistant acid phosphatase 5 (TRAP-5)
• - Serum bone resorption markers – C-terminal telopeptide crosslinks (CTX),
• - Regulators of bone metabolism: – (OPG (osteoprotegrin) / RANKL (receptor activator of nuclear factor κB ligand)
• - Urinary bone resorption markers– (hydroxyproline pyridinoline (Pyr), urinary DPD (deoxypyridinoline crosslinks),
urine peptide-bound N-telopeptide crosslinks (NTX))
• - Inflammatory markers – (TNF-alpha, IL1, IL6)
• -Markers of adipose tissue metabolism – (leptin, adiponectin)
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Statistical plan and Interim analysis
• Initial data analysis will be performed after last patient enrolled in sub-study to examine bone mineral density and body composition at baseline.
• Analyses of longitudinal changes according to baseline (switch to second-line) but not by randomised arm.
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Primary outcome measures
Bone Mineral Density:• Percentage change in proximal femur
BMD from baseline to week 48
Body Composition:• Percentage change in limb fat from
baseline to week 48.
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Timelines• April 2010: Revised protocol and piloting og
Dexa, BIA and questionnaires• May 2010: Submission and approval by
JCRC ethics; final piloting of questionnaires• June 2010: Study initiation
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Genetic determinants of HIV progression
• Individuals vary considerably in their control of viral replication and rate of immunological progression.
• Some of variation can be attributed to:-– immunogenetic diversity (MHC homozygosity, specific
HLA types)– polymorphism in chemokine, chemokine receptor and
cytokine genes (CCR5, CCR2, CX3CR1, SDF1, MIP1aCCL3L1, RANTES, IL-10, IL-4)
– variation in genes involved in the HIV-1 life cycle and cellular defense (APOBEC3G, PML, PPIA, TSG101).
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Genetic determinants of HIV-1 long-term non-progression
• About 2% of HIV individuals maintain impressive viral control (viral load <500 or even <50 copies/ml) and stability in CD4 count (>500 cells) over ≥ 15-20 years of infection– described as long-term non-progressors (LTNP) or
viral controllers
• Knowledge of relevant host genetic factors remains limited and in Caucasian cohorts only.– existing cohorts are of insufficient size to meet
requirements of power calculations and control for population stratification.
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1. A whole-genome-association study identifies major determinants of the host control of HIV-1Science 2007(n=486 seroconverters)
2. Common genetic variants and the control of HIV-1 in humans. PLOS Genetics 2010 (n=2554 seroconverters and seroprevalent)
Three single nucleotide polymorphisms located in MHC region explained 14% of variation in viral setpoint and 10% of variation in disease progression
HCP5 T>G variant (HLA B5701) and T>C SNP in HLA-C
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Figure 3: Kaplan-Meier survival estimates for the 3 most associated SNPs indentified in the genome-wide progression scan and for CCR5-32.
Kaplan-Meier survival estimates for the 3 most associated SNPs identified in the genome-wide progression scan and for CCR5-32
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Figure 2: Correlation between HIV-1 set point and the genotypes of the top associated SNPs.
Correlation between HIV setpoint and the genotype of the top associated SNPs
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Figure 5: A simple additive genetic score helps predict HIV-1 disease progression.
A simple additive genetic score helps predict HIV disease progression
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Selection of cases and controls identified from IeDEA database
• “Elite controllers”– High and stable CD4 (>500 cells on two occasions over one
year)– Plasma for viral load +/- whole blood for DNA– Low viral load <2000 copies/ml
• Controls: “Poor viral load controllers”– Patients initiating ART– Pre-ART viral load and DNA
• Costs– VL measurements, and ? Repeat CD4– Research nurse at selected sites to arrange visit and take
blood samples– Storage and transport of specimens– GWAS (Wellcome Trust Sanger or CHAVI)