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Abstract
We thank Llibre et al. for the opportunity to open a pertinent discussion (1). Given
the high virologic efficacy of current ART regimens, other differentiating factors
such as the effects on immune activation markers in randomized trials and cohort studies
with some ART switches (2–5) are attracting attention. However, inflammation is slow
to change in virally suppressed individuals (6), and our data suggests that the period
typically evaluated in clinical trials risks missing potentially relevant differences.
No observational study is free of residual confounding. Would it be better to have
clinical trials assessing the consequences of reducing the number of antiretrovirals
after many years of follow-up? Yes. Will we see this data? Unlikely. Our study, however,
offers the possibility of exploring the impact of switching to two-drug regimens (2DR)
on long-term inflammation in a real-life scenario in which ART suboptimal adherence,
a driver of inflammation (7) and mortality (8), is expected to be higher than in randomized
trials and over a longer follow-up.
Given the data that ART adherence affects inflammation and prognosis, even in virally
suppressed individuals (7, 8), the differences in inflammatory markers might be easier
to detect in real-life settings than in clinical trials. Noteworthy, because we excluded
NNRTI-based 3DR, most of the 3DR regimens analyzed were not available as single-table
regimens, so it is unlikely that subjects in the 3DR arm received less complex regimens
favoring a better adherence. The rates of low-level viremia and virological failure
were higher with 3DR, suggesting that adherence was lower in participants with 3DR,
as expected in an observational study. Clinicians would be less likely to reduce the
number of drugs in patients with less adherence.
The authors criticize that the fraction of patients who initiated ART in the early
years of the study (2005–2009) and switched to 2DR would represent a highly selected
population, not comparable to that remaining on 3DR (1). However, this was not the
case in our study (9): all participants switched to 2DR after 2010. Indeed, only a
minor fraction of participants in the whole cohort started ART before 2010 (
Figure 1
).
Figure 1
The histograms depict the percentage of participants initiating ART in the whole cohort,
the 2-drug regimen group (2DR), and the year of ART switch in the 2DR group.
The authors raise a concern that our results may not be extrapolated to 2DR regimens
based on dolutegravir (1). While our study is not powered to subanalyze the different
2DR combinations, 74.2% of patients in the 2DR group were receiving dolutegravir-based
2DR (either with lamivudine or rilpivirine), and the biomarker trajectories were similar
in subjects with 2DR based on protease inhibitors or dolutegravir (Figure 3 of 9).
Hence, most of the effect observed in the 2DR group was driven by observations from
individuals who switched to dolutegravir plus lamivudine or rilpivirine, reflecting
the current clinical recommendations (10, 11).
A significant contribution of our study was the possibility of assessing differences
in inflammatory biomarkers during a more extended period than in previous studies,
in which the power to detect differences might be limited by the short period evaluated.
As we learned from the experiences using dolutegravir in monotherapy (12, 13), even
under this inferior regimen, a readily measurable virological event such as a viral
rebound can be slow to occur. Llibre et al. (1) noted that we have recently reported
a similar rate of CD4/CD8 ratio recovery after 48 weeks of dolutegravir plus lamivudine
versus dolutegravir or bictegravir-based 3DR in naïve PLWH (14). These results are
reassuring, but we could only evaluate differences after 48 weeks of ART, and as we
previously showed, the CD4/CD8 ratio correlates poorly with inflammatory biomarkers
(15). We agree that the SWORD, TANGO, and SALSA inflammatory marker subanalyses did
not yield worrisome data, as we mentioned in the discussion. However, our study could
evaluate a longer period, with a median of 5.3 years in the 2DR group, allowing us
to detect differences that might go unnoticed during shorter evaluations. Significantly,
our study appreciates differences only after a median of 3 years after ART switch
during a period of 2DR vs. 3DR that SWORD, TANGO, and SALSA could not evaluate (4,
16–18).
In our view, SWORD, TANGO, and SALSA inflammatory subanalyses have encouraged further
research (4, 16–18). In these studies, the batch effects inherent to have pooled the
temporal observations to be reported separately might have introduced a significant
risk of observation bias, challenging the opportunity to detect consistent patterns
of changes during the follow-up. In our case, the samples were carefully grouped to
pool the temporal observations of each subject and achieve a similar group representation
in each batch (9). We observed a consistent pattern of change for IL-6, hs-CRP, and
D-dimer. Llibre et al. claim that these biomarkers strongly correlate between them
in previous studies (1). To the best of our knowledge, this is not the case. Even
in the studies they reference (19–22), the correlations between the inflammatory biomarkers
are, at most, modest, in keeping with those reported in our manuscript (9).
We agree and advocate that we should not only rely on statistical significance to
guide the interpretations of the effects of ART strategies on inflammation, especially
when generated as post hoc analyses in large-scale studies powered to detect differences
of uncertain clinical relevance. Also, we should not confer the same prognostic relevance
to every inflammatory biomarker measured in these studies. For example, while the
evidence linking sVCAM-1 with clinical events in the general population or during
treated HIV is scarce, IL-6 is arguably the most robust inflammatory biomarker linked
with all-cause mortality in PLWH (20, 22–27) and also predicts mortality risk in the
general population (28). Importantly, each biomarker—IL-6, D-dimers, and CD4/CD8 ratio—
seem to independently contribute to the risk prediction (29), arguing that each biomarker
reflects unique pathogenic pathways. When interpreting these patterns, we should also
consider the effect sizes. For example, the magnitude of sCD14 decreases observed
in TANGO at week 48 was minimal (3%, treatment ratio 0.97) at week 48 (18), compared
to a more considerable and also significant increase in IL-6 levels (16%, treatment
ratio 1.16) that persisted at week 144 after switching to DTG/3TC vs. staying on TAF-based
triple therapy [statistical significance reported in a conference (30), but not in
the published manuscript (4)]. We agree that we must be extremely cautious with these
observations. Accordingly, we must demand a transparent reporting of the methods and
statistical analyses performed and a fair interpretation of the results. Assuming
that a smaller sCD14 decrease in the 2DR arm in TANGO attenuates the concerns raised
by the larger IL-6 increases is, at best, a simplistic interpretation.
Whether the differences reported in inflammation between ART choices are clinically
relevant or not remains an open question. We appreciate the effort by the RESPOND
European-Australian consortium to assess the rates of clinical outcomes with 3DR compared
to 2DR (31). However, there was high heterogeneity in the ART combinations in this
cohort, including regimens not currently recommended in clinical guidelines (10, 11)
and no inflammatory markers were measured. Thus, no associations between the risk
of outcomes and the differential effects of inflammation could be established. Furthermore,
the study was likely underpowered to detect the differences predicted by the model.
The incidence ratio of clinical events on 2DR compared to 3DR was 1.28 (95%CI 0.88-1.87),
indicating that there was a 28% higher incidence risk of adverse outcomes that was
not statistically significant. The wideness of the confidence interval does not allow
concluding that the risk is similar. We have recently shown in a Markov model study
that to detect differences on clinical outcomes between 2DR and 3DR based on the effects
of IL-6 and D-dimers on severe non-AIDS events previously reported (27) and the IL-6
and D-dimer changes appreciated in TANGO (30) and our study (9), a larger sample size
or a longer follow-up will be needed (32).
Our work was intended and presented as hypothesis generating rather than hypothesis
testing, and we hope that this commentary will help to avoid misinterpretations of
our findings. We believe that whether the number and type of antiretrovirals or the
method of delivery differentially affect inflammation is far from being settled, especially
in the scenario of long-term treatment. It is still unclear what are the mechanisms
driving differences in long-term immune activation between ART choices. Distinct effects
on weight change, tolerability impacting ART adherence, or particular drug distribution
to lymphoid tissues resulting in low-level production of viral proteins eliciting
immune activation could play a role. While translational studies will help understand
the mechanisms, large cohort studies and randomized clinical trials designed to address
these knowledge gaps (33, 34) are ongoing and will enable move the field forward.
Author Contributions
SS-V and SM conceptualized the work, SS-V wrote the first draft, SM reviewed and approved
the final manuscript.
Conflict of Interest
Outside the submitted work, S.S-V. reports personal fees from ViiV Healthcare, Janssen
Cilag, Gilead Sciences, and MSD as well as non-financial support from ViiV Healthcare
and Gilead Sciences and research grants from MSD and Gilead Sciences. SM reports grants,
personal fees and non-financial support from ViiV Healthcare, personal fees and non-financial
support from Janssen, grants, personal fees and non-financial support from MSD, grants,
personal fees and non-financial support from Gilead, outside the submitted work.
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Introduction The Strategies for Management of Anti-Retroviral Therapy (SMART) trial compared episodic use of antiretroviral treatment (ART) guided by CD4+ count with the current practice of continuous ART. Risk of opportunistic disease (OD) or death was more than twice as great for those in the episodic compared to the continuous ART group (hazard ratio = 2.6; p 400 copies/ml), CD4+ cell count, smoking status, body mass index (BMI), prior CVD, diabetes, use of blood pressure (BP) medication, use of lipid-lowering medication, total/HDL cholesterol, co-infection with hepatitis B or C, and treatment group. For some biomarkers very large ORs for the associations with mortality were observed. To investigate the possibility that these large ORs were due to sparse data [27], the number of covariates considered [28], or outliers, four sensitivity analyses were performed: (1) conditional logistic models that included only baseline covariates that were significant at the 0.10 level in a multivariable analysis with mortality (age, prior CVD, co-infection with hepatitis B or C, smoking, and baseline CD4+ cell count); (2) unconditional logistic models; (3) conditional logistic models that excluded outliers, i.e., levels below the lower fence, defined as the lower quartile – 1.5 × IQR or above the upper fence (upper quartile + 1.5 × IQR) [29]; and (4) conditional logistic models that used four controls per case with controls only matched on date of randomization (± 3 mo). The associations between baseline biomarker levels and mortality were considered for DC and VS participants separately. To assess whether associations between biomarker levels and mortality varied by treatment group, an interaction term (product of log10 transformed biomarker and treatment group) was included in the logistic models. Similar methods were used for studying subgroups defined by prior disease history, co-infection with hepatitis, and baseline CD4+ cell count. For analyses of latest biomarker levels and mortality, log10 transformed levels were used, and the study entry level of the biomarker that was the focus of the analysis was included as a covariate with other previously cited covariates. To assess the effects of biomarker differences between the DC and VS groups on the DC/VS OR for mortality, conditional logistic models that included the latest level of the biomarker as well as the treatment indicator were considered. For these analyses, deaths and the four controls matched on date of randomization were used. This expanded case–control study (four instead of two controls) was used because there was a chance difference in the number of DC and VS controls with latest biomarker levels in the case–control study with 1:2 matching. This resulted in an unadjusted DC versus VS difference in mortality that was not as great as previously reported [1]. Because the focus was on the risk of death in the DC versus VS group, a comparison protected by randomization, matching was limited to date of randomization to ensure latest biomarker levels were measured at approximately the same time for each death and the four matched controls. The association between each biomarker and other covariates at study entry was studied using linear regression analysis. Biomarker changes after 1 mo were compared for DC and VS participants using analysis of covariance. The association of biomarker change (after log10 transformation) and HIV-RNA change at 1 mo for DC participants with an HIV-RNA level ≤400 copies/ml at study entry was studied using linear regression analysis. Statistical analyses were performed using SAS (Version 9.1) [30]. All reported p-values are two-sided. Results Case–Control Sample: Baseline Biomarkers and All-Cause Mortality Most of the deaths (79 of 85) occurred in the US. Sites in the US began enrollment 2 to 3 y before most sites in other countries and accounted for 55% of the randomized participants. In univariate analyses, cases and controls differed with respect to age (p = 0.007), baseline CD4+ count (p = 0.03), co-infection with hepatitis B or C (p = 0.0008), smoking status (p = 0.0001), diabetes (p = 0.03), use of BP-lowering treatment (p = 0.02), and prior CVD (p = 0.04) (Table 1). In multivariate analyses that included the baseline covariates described in Methods for adjusted analyses, age (p = 0.02), smoking (p = 0.01), prior CVD (p = 0.04), co-infection with hepatitis B or C (p = 0.03), and baseline CD4+ cell count (p = 0.10) were associated (p = 0.10 or lower) with mortality. Table 1 Characteristics of Deaths and Matched Controls at Study Entry With the exception of amyloid P, levels of the inflammatory markers were higher in deaths than matched controls (Table 2, “study entry”). Differences between deaths and controls for IL-6 and D-dimer were highly significant (p 75th percentile) was striking. For D-dimer, there were 23 controls with levels in the lowest quartile (<0.18 μg/ml) that were matched with deaths that had levels in the upper quartile. In contrast, there were only two controls with D-dimer levels in the upper quartile matched with deaths in the lowest quartile. For IL-6, there were 25 controls with levels in the lowest quartile (<1.6 pg/ml) matched with deaths that had levels in the upper quartile, and three controls with IL-6 in the upper quartile matched with deaths in the lowest quartile. Strong risk gradients with mortality were evident for both IL-6 and D-dimer (Table 3). For IL-6, unadjusted ORs (for each of the three upper quartiles versus lowest) were 8.3 (95% CI, 3.3–20.8), 3.2 (95% CI, 1.3–7.9), and 1.3 (95% CI, 0.5–3.6). For D-dimer corresponding ORs were 12.4 (95% CI, 4.2–37.0), 4.0 (95% CI, 1.3–12.3), and 3.2 (95% CI, 1.1–9.0). In models that considered these biomarkers as continuous variables after log10 transformation, a difference corresponding to the IQR was associated with an OR of 3.4 (95% CI, 2.2–5.4) for IL-6 and 3.9 (95% CI, 2.3–6.6) for D-dimer. Covariate adjustment tended to strengthen these associations (Table 3), and sensitivity analyses yielded consistent findings (Table S2). Similar analyses with the 1:4 matching yielded results with reduced, but still very large, ORs for IL-6 and D-dimer (Table S3). For example, unadjusted ORs (upper quartile versus lowest) were 6.1 (95% CI, 2.7–13.6) and 6.6 (95% CI, 2.9–14.9) for IL-6 and D-dimer, respectively. Table 3 Risk of Death Associated with Biomarker Levels at Study Entry Significant associations between study entry levels of hsCRP and amyloid P and mortality were also evident. Risk of death increased with increasing levels of hsCRP but not as strongly as for IL-6 and D-dimer. The unadjusted OR (upper versus lower quartile of hsCRP) was 2.0 (95% CI, 1.0–4.1). Higher levels of amyloid P were associated with a lower risk of death in analyses based on the continuous biomarker level, but no apparent trend by quartile was evident. This association was only of borderline significance after covariate adjustment and may be due in part to outliers. After excluding outliers (ten deaths and seven controls), the OR associated with a one IQR higher level of amyloid P was 1.1 (95% CI, 0.7–1.7) (Table S2). When IL-6 and D-dimer were considered together in the same model they each remained significantly associated with all-cause mortality. Unadjusted ORs for the highest versus lowest quartile were 4.7 (95% CI, 1.7–12.7; p = 0.002) for IL-6 and 6.1 (95% CI, 2.0–18.6; p = 0.001) for D-dimer. ORs for a difference corresponding to the IQR were 2.7 (95% CI, 1.6–4.4; p < 0.0001) for IL-6 and 2.6 (95% CI, 1.5–4.6; p = 0.001) for D-dimer. In the analyses described above, the DC and VS groups were combined. In separate analyses for each group (Table 4), associations between biomarker levels at baseline and mortality were similar (Table 3, note last two columns for comparison to models that considered both treatment groups combined). For DC participants, median levels (expressed as deaths, controls) were 4.49, 1.78 μg/ml for hsCRP; 3.85, 2.24 pg/ml for IL-6; and 0.63, 0.22 μg/ml for D-dimer. For VS participants, these median levels were 3.60, 3.07 μg/ml for hsCRP; 3.78, 2.43 pg/ml for IL-6; and 0.37, 0.29 μg/ml for D-dimer. Table 4 Risk of Death Associated with Biomarker Levels at Study Entry for the Drug Conservation (DC) and Viral Suppression (VS) Treatment Groups Likewise, associations were similar for those with prior cancer, CVD, renal or liver disease (31% of deaths and 16% of controls), and those with no history of these conditions (p = 0.33 for hsCRP, p = 0.88 for IL-6, and p = 0.74 for D-dimer interactions); for those co-infected with hepatitis or not (p = 0.37 for hsCRP, p = 0.41 for IL-6, and p = 0.76 for D-dimer interactions); and for those with a baseline CD4+ cell less than 600 cells/mm3 (approximate median) and 600 cells/mm3 or more(p = 0.85 for hsCRP, p = 0.21 for IL-6, and p = 0.41 for D-dimer interactions). Associations with hsCRP, Il-6, and D-dimer were also similar for deaths in the first year (38 deaths) and after the first year (47 deaths). During the first year, the OR corresponding to the IQR for hsCRP, IL-6, and D-dimer were 1.5 (95% CI, 0.9–2.4; p = 0.16), 2.5 (95% CI, 1.4–4.3; p = 0.002), and 3.9 (95% CI, 1.9–8.0; p = 0.0003), respectively. For deaths occurring after the first year, ORs for hsCRP, IL-6, and D-dimer were 1.9 (95% CI, 1.1–3.2; p = 0.01), 5.0 (95% CI, 2.4–10.4; p < 0.0001), and 3.9 (95% CI, 1.8–8.4; p = 0.0005). Deaths due to substance abuse (eight deaths) or to accidents, violence, or suicide (seven deaths) could attenuate the associations between the biomarkers and mortality. Thus, analyses were carried out excluding these 15 deaths. Unadjusted ORs corresponding to a difference equal to the IQR were 1.9 (95% CI, 1.2–2.8) for hsCRP, 3.7 (95% CI, 2.2–6.3) for IL-6, and 4.0 (95% CI, 2.1–7.4) for D-dimer. Each of these ORs is larger by a small amount compared to the corresponding estimates in Table 3. Twenty-one deaths were classified as CVD or were unwitnessed. Unadjusted ORs of CVD or unwitnessed death corresponding to a difference equal to the IQR were 2.3 (95% CI, 1.0–5.0; p = 0.04) for hsCRP, 3.2 (95% CI, 1.2–8.4; p = 0.02) for IL-6, and 3.2 (95% CI, 1.1–9.3; p = 0.04) for D-dimer. Case–Control Sample: Change in Biomarkers and All-Cause Mortality Table 2 (“latest level”) compares latest levels of each biomarker for deaths and matched controls (Table S4 gives similar results for the 1:4 matching). In these univariate analyses, significant differences were observed for all of the biomarkers except F1.2. Deaths had higher latest levels than controls, except amyloid P for which latest levels were lower for deaths than controls. Findings were strongest for hsCRP, IL-6, and D-dimer. Average differences between deaths and controls were greater for latest levels than for study entry levels (Table 2 “study entry” and Table S4). Table 5 gives adjusted ORs for latest levels of each biomarker. Adjusted ORs corresponding to a difference equal to the IQR were 2.4 (95% CI, 1.4–4.2) for hsCRP, 2.0 (95% CI, 1.2–3.1) for IL-6, and 2.2 (95% CI, 1.1–4.1) for D-dimer. These models also included study entry levels of each biomarker. Study entry levels of IL-6 and D-dimer remained significantly associated with all-cause mortality after consideration of latest levels (p = 0.0008 for IL-6 and p = 0.003 for D-dimer). After considering latest level of hsCRP, the study entry level was not significant (p = 0.07). In models that also included latest levels of HIV-RNA and CD4+ cell count, neither of which were significantly associated with all-cause mortality, these associations were diminished slightly but remained significant. For latest levels of hsCRP, IL-6, and D-dimer, the ORs were 2.2 (p = 0.009), 1.7 (p = 0.04), and 2.0 (p = 0.04). Table 5 Risk of Death Associated with Latest Level of Each Biomarker Random Sample: Associations at Baseline As previously reported, the majority of patients in SMART were using ART at entry [1]. In the random sample, 74% were using ART; among those using ART, 71% had an HIV-RNA level 400 copies/ml or lower. Approximately 6% of patients had not previously used ART; the remainder of those not using ART had discontinued it prior to enrolling in SMART. In the random sample, treatment groups were well balanced (Table 6). Table 6 Characteristics of Random Sample of DC and VS Participants at Study Entry Multiple regression analyses of each biomarker (after log10 transformation) on baseline covariates were performed. The covariates used in the regression analyses were the same as those used in the adjusted case–control analysis. An exception was history of CVD since no one in the random sample had a history of CVD (Figure 2). D-dimer was significantly higher for those not on ART than for those on ART with an HIV-RNA level 400 copies/ml or lower (0.15 on log10 scale; p = 0.0007) and for those on ART with an HIV-RNA over 400 copies/ml (0.12; p = 0.02). Other biomarkers did not vary significantly according to use of ART and HIV-RNA level at study entry. Smoking and co-infection with hepatitis B or C, which were both significantly related to mortality, were not significantly associated with either IL-6 (p = 0.19 for smoking and p = 0.16 for co-infection) or D-dimer (p = 0.64 for smoking and p = 0.39 for co-infection) at baseline. Smoking was not significantly associated with any of the biomarkers. hsCRP was lower by 0.217 μg/ml after log10 transformation (p = 0.0001) and amyloid P was lower by 0.068 (p = 0.0005) for those who were co-infected with hepatitis; co-infection was not associated with the other biomarkers. Significant predictors of log10 IL-6 were age (0.069 higher with each 10 y in age; p < 0.0001) and BMI (0.009 higher with each kg/m2 greater BMI; p = 0.0004). For log10 D-dimer, in addition to ART and HIV-RNA level, levels were greater among older participants (0.062 higher with each 10 y; p = 0.002), for black participants (0.123; p = 0.0009), for participants with diabetes (0.161; p = 0.01), and for those with greater BMI (0.008 per unit higher; p = 0.02). D-dimer levels were lower for men (−0.151; p = 0.0003) and for those with higher CD4+ cell counts (−0.023 per 100 cells/mm3 higher; p = 0.002). To put these changes in perspective, the IQRs on the log10 scale for IL-6 and D-dimer were 0.39 pg/ml and 0.58 μg/ml, respectively. Differences as large as the IQR for each marker were associated with 3- to 4-fold greater risks of all-cause mortality. Random Sample: Treatment Differences at 1 mo Table 7 shows average changes in log10 transformed biomarker levels 1 mo after randomization. IL-6 and D-dimer increased significantly (p = 0.0005 and p < 0.0001, respectively) from study entry to 1 mo in the DC group compared to the VS group (p < 0.0001). Considering the non-transformed levels, the median increase in D-dimer for DC patients was 0.05 μg/ml (a 16% increase); IL-6 increased by 0.60 pg/ml in the DC group (a 30% increase). For VS patients, the median increases were 0.0 μg/ml and 0.12 pg/ml (a 5% increase) for D-dimer and IL-6, respectively. Changes in hsCRP and amyloid A were in the same direction—greater increases for DC compared to VS patients—but did not differ significantly between treatment groups. Table 7 Biomarker, CD4+ Cell Count Change, and HIV-RNA Level Change 1 mo after Randomization For both IL-6 and D-dimer, treatment differences were greater for patients who were on ART at entry and had HIV-RNA levels 400 copies/ml or below. For this subgroup (52% of patients), D-dimer increased in the DC group by 0.07 μg/ml (a 27% increase) and declined in the VS group by −0.02 μg/ml (p < 0.0001 for treatment difference). Similarly, for IL-6, the median increases for DC and VS patients were 0.98 (a 43% increase) and 0.08 pg/ml, respectively (p < 0.0001 for difference). The changes in IL-6 and D-dimer for the subgroup of DC patients with HIV-RNA levels 400 copies/ml or below were further examined according to HIV-RNA levels at 1 mo. Following ART interruption, biomarker increases were greater for those with higher HIV-RNA levels at 1 mo (Figures 3 and 4). Figure 3 Change in Log10 IL-6 (pg/ml) from Baseline to 1 mo According to HIV-RNA Level at 1 mo for Participants in the Drug Conservation (DC) Group (CD4+ Guided Intermittent ART) with an HIV-RNA level 400 Copies/ml or Less at Baseline Figure 4 Change in Log10 D-dimer (μg/ml) from Baseline to 1 mo According to HIV-RNA Level at 1 mo for Participants in the Drug Conservation (DC) Group (CD4+ Guided Intermittent ART) with an HIV-RNA level 400 Copies/ml or less at Baseline Results in Table 5 were used to estimate the potential impact of treatment differences on mortality. For IL-6, the DC/VS difference after 1 mo on the log10 scale was 0.08 pg/ml. Based on the regression analysis cited in Table 5, a difference of this magnitude is associated with a 16% increased risk of death (95% CI, 10%–25%). Similarly, the 0.11 log10 higher level of D-dimer for DC compared to VS participants is associated with a 24% (95% CI, 13%–46%) increased risk of death. Impact of Adjustment for Latest Levels of IL-6 and D-dimer on OR for DC Versus VS for All-Cause Mortality Matched logistic models were used to assess the effect of adjusting for latest levels of IL-6 and D-dimer on the DC/VS OR for death. In the model with two controls per case, the unadjusted OR for the DC versus the VS group was 1.3 (95% CI, 0.8–2.2). Because this OR was considerably lower than the hazard ratio previously reported for all-cause mortality [1], we explored reasons for it and created an expanded case–control data set. A chance imbalance in the number of DC and VS participants selected as controls is the reason the OR was lower. Among the 170 controls, 99 were in the DC group and 71 were in the VS group. The expected number was 85 in each group. With the expanded case–control study (four controls for each death), the unadjusted DC/VS OR for all-cause mortality was 1.8 (95% CI, 1.1–3.1). This estimate is identical to that previously reported [1]. With adjustment for latest level of IL-6, the OR was 1.5 (95% CI, 0.8–2.8); with adjustment for latest level of D-dimer the OR was 1.4 (95% CI, 0.8–2.5). We also considered the effect of adjusting for both study entry and latest levels of IL-6 and D-dimer and of adjusting for HIV- RNA and CD4+ cell count on the DC/VS OR (Table S5). Similar to an earlier report, adjustment for CD4+ cell count had a greater effect on the OR (DC/VS) for mortality (OR = 1.2; 95% CI, 0.7–2.2) than adjustment for latest HIV-RNA levels (OR = 1.6; 95% CI, 0.9–2.9) [1]. With adjustment for latest levels of IL-6, D-dimer, CD4+ cell count, and HIV-RNA, the OR (DC/VS) was 1.3 (95% CI, 0.6–2.6). Discussion Elevated levels of either IL-6 or D-dimer at study entry were strongly related to all-cause mortality in the case–control study. In the random sample, both D-dimer and IL-6 increased in the DC group compared to the VS group, particularly in the large subgroup on ART at entry with a suppressed HIV-RNA level. Increases in both markers in the DC group were related to the level of HIV-RNA after 1 mo. Finally, increases in these markers following randomization were associated with mortality. Taken together, these findings suggest that HIV-induced activation of inflammatory and coagulation pathways has an adverse effect on all-cause mortality among patients with relatively preserved CD4+ counts, and that interrupting ART may further increase this risk by raising IL-6 and D-dimer levels. Further research on the relationship of these biomarkers with mortality and morbidity in treated and untreated HIV-infected individuals is warranted. The associations between IL-6 and D-dimer levels at study entry with all-cause mortality were much stronger than in previous studies of non-HIV-infected populations that usually focused on CVD morbidity and mortality [13,14,16–18,20–22,31]. While these strong associations persisted in a number of different analyses, including subgroups defined by hepatitis co-infection, baseline CD4+ cell count, and prior disease history, the relatively small number of deaths considered here compared to studies in the general population suggest that the results should be interpreted with caution and require confirmation. Findings for deaths attributed to CVD or unwitnessed deaths were consistent with those for all-cause mortality, although the number of these deaths was small. The relationship of these biomarkers with CVD morbidity and mortality will be the subject of a separate report. Patients in SMART were relatively healthy and did not have advanced HIV disease. Deaths were attributed to a variety of causes and were largely not due to AIDS, as would be expected in a cohort of patients with CD4+ counts that averaged about 600 cells/mm3 at entry (Table S1) [32]. Associations of D-dimer and IL-6 with all-cause mortality have been reported in other studies that included non-HIV-infected participants [15,23,33]. Relationships between elevated IL-6 and D-dimer levels and all-cause mortality were strong in both DC and VS patients. However, our findings for patients on continuous ART (VS group) require validation, as mortality rates were low. However, if substantiated, and because most patients in the VS group had HIV-RNA levels of 400 copies/ml or less at study entry, ongoing activation of these pathways may exist even in the presence of effective ART, consistent with data demonstrating ongoing viral replication, even in patients with HIV-RNA of less than 50 copies/ml [34]. Considering our finding that ART is associated with lower levels of these biomarkers, more aggressive ART that might lower IL-6 and D-dimer may warrant further investigation. Increases in hsCRP, IL-6, and D-dimer from study entry to the visit preceding the death were associated with an increased risk of death. Whether the activation of tissue factors secondary to inflammation is the key event is likely but unproven in this study. Several alternative hypotheses are possible. One study showed that tissue factor, an initiator of coagulation, is generated by HIV-related proteins and could have pathologic effects [35]. Another study found that HIV-infected leukocytes transmigrate across the endothelium [36], and this dissemination of virus could result in damage to multiple organs. Alternatively, circulating lipopolysaccharide, which has been shown to be higher in HIV-infected compared to uninfected individuals [37], induces tissue factor transcription, which in turn decreases F1.2 and soluble fibrin, resulting in fibrin split products such as D-dimer [38]. Lipopolysaccharide also activates monocytes to produce inflammatory cytokines, including IL-6 [39]. It is also possible that elevations of inflammatory markers, such as hsCRP and IL-6, and of D-dimer are independent events. In analyses that considered the joint influence of IL-6 and D-dimer, each remained strongly associated with all-cause mortality. Specific therapies that reduce the inflammatory response to HIV and decrease hsCRP, IL-6, and D-dimer levels may warrant investigation as an approach for reducing risk of death among HIV-infected individuals [40–42]. ART interruption resulted in increases in IL-6 and D-dimer, therefore the effect of these treatment differences on the DC/VS OR for mortality was explored. Adjustment for follow-up (latest) levels of IL-6 and D-dimer resulted in a modest reduction of the DC/VS OR for mortality, supporting the hypothesis that the excess risk of death in the ART interruption group may be explained by biological mechanisms for which IL-6 and D-dimer are markers. There are some limitations in our work. Confidence intervals for ORs were wide due to the small number of deaths. While we selected six biomarkers from a large number of possible markers based on previous work in non-HIV-infected populations, some associations may have resulted from chance. This is less likely for the associations of IL-6 and D-dimer levels at study entry with all-cause mortality, which were highly significant. Cost considerations limited the number of controls for each death for which we could assess biomarkers. This reduced the statistical power to detect associations with mortality. The limited matching carried out, particularly for the expanded case–control study with four controls, may have resulted in incomplete control of confounding factors. However, regression adjustment for a number of covariates did not alter our overall conclusions. We measured latest levels at the visit immediately prior to the event of interest. For biomarker levels proximal to death, it is possible that reverse causality (i.e., an already present disease process caused the increase in the biomarker instead of vice versa) may explain these findings. In addition, follow-up specimens were not available for 11 deaths and 29 controls, and this limited our ability to assess the prognostic importance of biomarker changes on mortality. Finally, while we did not find evidence for different associations with mortality for DC compared to VS patients, the number of events experienced by VS patients was small, limiting the power for those comparisons. Similarly, power for other subgroup analyses considered was also low. In summary, elevated levels of D-dimer and IL-6 identify HIV-infected patients at high risk of death. The magnitude of the association is clinically relevant and reasons for it require further study. Supporting Information Figure S1 SMART Study Design and Flow Diagram for Case–Control Study with 1:4 Matching for Deaths and Controls Matching was on date of randomization to ensure latest levels for deaths, and controls were determined at similar time point following randomizations. Click here for additional data file. Figure S2 Baseline Level of hsCRP (μg/ml) for Each of 85 Death–Control Triads Ordered by hsCRP Level of Deaths Plotted on Logarithmic Scale (Log10) Solid lines give median levels at baseline for deaths (4.3 μg/ml) and controls (2.1 μg/ml). Red circles and lines are for deaths, and blue circles and lines are for controls. Click here for additional data file. Figure S3 Baseline Level of Amyloid A (mg/l) for Each of 85 Death–Control Triads Ordered by Amyloid A Level of Deaths Plotted on Logarithmic Scale (Log10) Solid lines give median levels at baseline for deaths (4.75 mg/l) and controls (3.65 mg/l). Red circles and lines are for deaths, and blue circles and lines are for controls. Click here for additional data file. Figure S4 Baseline Level of Amyloid P (μg/l) for Each of 85 Death–Control Triads Ordered by Amyloid P Level of Deaths Plotted on Logarithmic Scale (Log10) Solid lines give median levels at baseline for deaths (58.8 μg/l) and controls (67.8 μg/l). Red circles and lines are for deaths, and blue circles and lines are for controls. Click here for additional data file. Figure S5 Baseline Level of IL-6 (pg/ml) for Each of 84 Death–Control Triads Ordered by IL-6 Level of Deaths Plotted on Logarithmic Scale (log10) IL-6 level was missing for one death. Solid lines give median levels at baseline for deaths (3.8 pg/ml) and controls (2.3 pg/ml). Red circles and lines are for deaths, and blue circles and lines are for controls. Click here for additional data file. Figure S6 Baseline Level of D-dimer (μg/ml) for Each of 85 Death–Control Triads Ordered by D-dimer Level of Deaths Plotted on Logarithmic Scale (log10) Solid lines give median levels at baseline for deaths (0.49 μg/ml) and controls (0.26 μg/ml). Red circles and lines are for deaths, and blue circles and lines are for controls. Click here for additional data file. Figure S7 Baseline Level of F1.2 (pmol/l) for Each of 84 Death–Control Triads Ordered by F1.2 Level of Deaths Plotted on Logarithmic Scale (Log10) F1.2 level was missing for one death. Solid lines give median levels at baseline for deaths (344.0 pmol/l) and controls (351.4 pmol/l). Red circles and lines are for deaths, and blue circles and lines are for controls. Click here for additional data file. Table S1 Underlying Cause of Death for Participants in SMART Click here for additional data file. Table S2 Odds Ratio Associated with a One IQR Higher Level of Biomarker at Study Entry: Three Models with Log10-Transformed Biomarker Click here for additional data file. Table S3 Risk of Death Associated with Biomarker Levels at Baseline—Four Controls Per Death Click here for additional data file. Table S4 Study Entry and Latest Levels of Six Biomarkers for Deaths and Matched Controls (Four Matched Controls for Each Death) Click here for additional data file. Table S5 Estimates of the Odds Ratio for DC Versus VS Participants for All-Cause Mortality After Adjustment for Study Entry and Latest Levels of IL-6, D-dimer, CD4+ Cell Count, and HIV RNA Level Click here for additional data file. Text S1 CONSORT Checklist Click here for additional data file. Text S2 Protocol Click here for additional data file.
Background The SMART study was a trial of intermittent use of antiretroviral therapy (ART) (drug conservation [DC]) versus continuous use of ART (viral suppression [VS]) as a strategy to reduce toxicities, including cardiovascular disease (CVD) risk. We studied the predictive value of high sensitivity C-reactive protein (hsCRP), interleukin-6 (IL-6) and D-dimer with CVD morbidity and mortality in HIV-infected patients who were enrolled in SMART beyond other measured CVD risk factors. Methods A blood sample was available in 5098 participants who were enrolled in the SMART study for the measurement of IL-6, hsCRP and D-dimer. Hazard ratios (HR) with 95% CI for CVD events were estimated for each quartile (Q) for each biomarker vs the 1st quartile and for 1 SD higher levels. For both treatment groups combined, unadjusted and adjusted HRs were determined using Cox regression models. Results There were 252 participants who had a CVD event over a median follow-up of 29 months. Adjusted HRs (95% CI) for CVD for Q4 vs Q1 were 4.65 (2.61, 8.29), 2.10 (1.40, 3.16), and 2.14 (1.38, 3.33) for IL-6, hsCRP and D-dimer, respectively. Associations were similar for the DC and VS treatment groups (interaction p-values were >0.30). The addition of the three biomarkers to a model that included baseline covariates significantly improved model fit (p<0.001). Area under the curve (AUC) estimates improved with inclusion of the three biomarkers in a model that included baseline covariates corresponding to other CVD risk factors and HIV factors (0.741 to 0.771; p<0.001 for difference). Conclusions In HIV-infected individuals, IL-6, hsCRP and D-dimer are associated with an increased risk of CVD independent of other CVD risk factors. Further research is needed to determine whether these biomarkers can be used to improve CVD risk prediction among HIV positive individuals.
Chronic human immunodeficiency virus (HIV) infection is associated with intestinal permeability and microbial translocation that contributes to systemic immune activation, which is an independent predictor of HIV disease progression. The association of microbial translocation with clinical outcome remains unknown. This nested case-control study included 74 subjects who died, 120 of whom developed cardiovascular disease and 81 of whom developed AIDS during the Strategies for Management of Anti-Retroviral Therapy (SMART) study with matched control subjects. Intestinal fatty acid binding protein (I-FABP), lipopolysaccharide (LPS), soluble CD14 (sCD14), endotoxin core antibody (EndoCAb), and 16S ribosomal DNA (rDNA) were measured in baseline plasma samples. Subjects with the highest quartile of sCD14 levels had a 6-fold higher risk of death than did those in the lowest quartile (95% confidence interval, 2.2-16.1; P<.001), with minimal change after adjustment for inflammatory markers, CD4(+) T cell count, and HIV RNA level. No other marker was significantly associated with clinical outcomes. I-FABP, LPS, and sCD14 were increased and EndoCAb was decreased in study subjects, compared with healthy volunteers. sCD14 level correlated with levels of IL-6, C-reactive protein, serum amyloid A and D-dimer. sCD14, a marker of monocyte response to LPS, is an independent predictor of mortality in HIV infection. Therapeutic attenuation of innate immune activation may improve survival in patients with HIV infection.
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History
Date
received
: 19
April
2022
Date
accepted
: 04
May
2022
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