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Confounding factors and choice of controls in studies of immune activation and inflammation
Caroline Sabin
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Potential conflicts of interest
Over the past year, I have received funding for membership of Data Safety and Monitoring Boards, Advisory Boards and for the preparation of educational materials from the following companies: Gilead Sciences ViiV Healthcare Janssen-Cilag
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Age of UK CHIC participants
UK CHIC dataset (S Jose, C Sabin)
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Age-associated co-morbidities
Literature abundant with studies reporting that HIV causes ‘premature ageing’ or that co- morbidities occur at an earlier age in PLWH
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Age-associated co-morbidities
Literature abundant with studies reporting that HIV causes ‘premature ageing’ or that co- morbidities occur at an earlier age in PLWH
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Age-associated co-morbidities
Literature abundant with studies reporting that HIV causes ‘premature ageing’ or that co- morbidities occur at an earlier age in PLWH
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Age-associated co-morbidities
Literature abundant with studies reporting that HIV causes ‘premature ageing’ or that co- morbidities occur at an earlier age in PLWH Search continues for biological mechanisms that drive this apparent increased risk ‘Inflammageing’? Altered gut microbiota? Mitochondrial dysfunction? Immunosenescence? But in our rush to establish mechanisms, have we forgotten the basic rules of epidemiology?
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Bias due to confounding
Occurs when a spurious association arises (or is hidden) due to a failure to fully adjust for factors related to both the risk factor and outcome ? Factor of interest Outcome
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Bias due to confounding
Occurs when a spurious association arises (or is hidden) due to a failure to fully adjust for factors related to both the risk factor and outcome Confounding factor ? Factor of interest Outcome
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Bias due to confounding
Occurs when a spurious association arises (or is hidden) due to a failure to fully adjust for factors related to both the risk factor and outcome Peanuts, crisps, cheese ? Drinking wine Weight gain
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Confounding PLWH have very different characteristics to the general population, including increased risk of: sexually transmitted infections viral coinfections smoking recreational drug use, etc. Could these other factors confound associations with co-morbidities and/or bio-markers?
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Co-morbidities are often multi-factorial
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Confounding PLWH have very different characteristics to the general population, including increased risk of: sexually transmitted infections viral coinfections smoking recreational drug use, etc. Could these other factors confound associations with co-morbidities and/or bio-markers? Is it true that HIV causes premature ageing – or is this finding simply a result of unmeasured confounding due to the selection of inappropriate control groups?
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Typical ‘control’ groups
General population ‘Healthy controls’ (including those from lab, research team, etc.) Blood bank donors
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Age at onset of co-morbidity
Median age at diagnosis = 67.5 years 20 30 40 50 60 70 80 Age Sabin CA, Reiss P. AIDS 2017 ; 31(Suppl 2): S121-S128.
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Age at onset of co-morbidity
Median age at diagnosis = 57.5 years 20 30 40 50 60 70 80 Age Sabin CA, Reiss P. AIDS 2017 ; 31(Suppl 2): S121-S128.
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Age at onset of co-morbidity
VACS VC – Adjusted difference in mean at diagnosis Event No. events Mean age diagnosis Crude diff. Adjusted diff. 95% CI MI HIV-ve 308 56.0 0.2 -0.11 -0.59, +0.37 HIV+ve 291 56.2 End-stage renal disease 688 59.4 -3.4 -0.46 -0.86, -0.07 447 NADC 2708 58.9 -1.1 -0.10 -0.30, 0.10 1471 57.8 HIV-associated cancers 826 58.6 -2.0 -0.22 -0.52, 0.08 732 56.6 Althoff KN et al. Clin Infect Dis 2015;60:
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The Co-morBidity in Relation to Aids (COBRA) Collaboration
Formed from sub-studies of POPPY and AGEhIV: To investigate association between HIV infection and AANCC To elucidate possible causative links between HIV and AANCC To clarify potential pathogenic mechanisms underlying any links identified To identify biomarkers that may be used for better prevention, treatment and management of AANCC
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Inclusion criteria
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CD4 and CD8 T cell senescence
HIV-positive Blood-bank donors N 40 35 Age (yrs), median (IQR) 58 (53-63) 58 (52-65) Male sex, % 90 51.4 African origin, % 12.5 n/a MSM, % 80.0 CMV+ve, % 95.0 22.9 Anti-CMV IgG 50.9 ( ) 11.3 ( ) High avidity anti-CMV IgG 30.7 ( ) 10.7 ( ) Booiman T et al. (Submitted)
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CD4 and CD8 T cell senescence
Booiman T et al. (Submitted)
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CD4 and CD8 T cell senescence
HIV-positive HIV-negative Blood-bank donors N 40 35 Age (yrs), median (IQR) 58 (53-63) 59 (53-64) 58 (52-65) Male sex, % 90 92.5 51.4 African origin, % 12.5 2.5 n/a MSM, % 80.0 75.0 CMV+ve, % 95.0 77.5 22.9 Anti-CMV IgG 50.9 ( ) 23.9 ( ) 11.3 ( ) High avidity anti-CMV IgG 30.7 ( ) 13.3 ( ) 10.7 ( ) Booiman T et al. (Submitted)
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CD4 and CD8 T cell senescence
Booiman T et al. (Submitted) Booiman T et al. (Submitted)
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CD4 and CD8 T cell senescence
HIV-positive HIV-negative Blood-bank donors N 40 35 Age (yrs), median (IQR) 58 (53-63) 59 (53-64) 58 (52-65) Male sex, % 90 92.5 51.4 African origin, % 12.5 2.5 n/a MSM, % 80.0 75.0 CMV+ve, % 95.0 77.5 22.9 Anti-CMV IgG 50.9 ( ) 23.9 ( ) 11.3 ( ) High avidity anti-CMV IgG 30.7 ( ) 13.3 ( ) 10.7 ( ) Booiman T et al. (Submitted)
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CD4 and CD8 T cell senescence
Booiman T et al. (Submitted)
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Bias due to confounding
Occurs when a spurious association arises (or is hidden) due to a failure to fully adjust for factors related to both the risk factor and outcome CMV infection ? Activation markers HIV
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Age advancement Biological age derived using set of 10 biomarkers identified through EU FP7 MARK-AGE project ( Age advancement: biological - chronological age De Francesco, et al. Poster TULBPEB19, IAS 2017
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Age advancement HIV-positive HIV-negative Blood-bank donors N 134 79
35 Age (yrs), median (IQR) 56 (51-62) 57 (52-65) 59 (52-65) Male sex, % 93 92 51 African origin, % 12 3 n/a MSM, % 78 75 Recreational drug use, % 33 23 Chronic HBV, % Chronic HCV, % 2 CMV infection, % 98 80 De Francesco, et al. Poster TULBPEB19, IAS 2017
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Age advancement De Francesco, et al. Poster TULBPEB19, IAS 2017
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Are we comparing apples to oranges?
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Summary Choice of control group can affect our interpretation of studies of the impact of HIV on co-morbidities and/or bio-markers PLWH often exhibit different lifestyles, and it be a consequence of these, rather than HIV itself, that drives apparent ageing Whilst the association with HIV may be weaker than previously reported, this may actually simplify our search for mechanisms
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The CoBRA Collaboration
Particular thanks to Peter Reiss, Neeltje Kootra and Davide de Francesco for provision of slides
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