Two of the co-authors, Robin Dewar and Adam Rupert, are employees of SAIC who perform bench laboratory research in support of NIAID studies but have no role in funding decisions made by their parent employer (SAIC). The authors declare that they have no other competing interests in regard to this manuscript. This does not alter the authors' adherence to all the PLOS ONE policies on sharing data and materials. One of the co-authors, Sarah Pett, is a PLOS ONE Editorial Board member. This does not alter the authors′ adherence to all the PLOS ONE policies on sharing data and materials.
Edited the manuscript: HCL SLP TMU JMB BA NC JL. Conceived and designed the experiments: RD. Performed the experiments: AR JAM. Analyzed the data: DW JDN AC-L. Wrote the paper: RTD RL DED MHL.
¶ The Writing Group assumes responsibility for the manuscript. Membership of the INSIGHT FLU 002 & 003 Study Groups is included in the Acknowledgements.
Prospective studies establishing the temporal relationship between the degree of inflammation and human influenza disease progression are scarce. To assess predictors of disease progression among patients with influenza A(H1N1)pdm09 infection, 25 inflammatory biomarkers measured at enrollment were analyzed in two international observational cohort studies.
Among patients with RT-PCR-confirmed influenza A(H1N1)pdm09 virus infection, odds ratios (ORs) estimated by logistic regression were used to summarize the associations of biomarkers measured at enrollment with worsened disease outcome or death after 14 days of follow-up for those seeking outpatient care (FLU 002) or after 60 days for those hospitalized with influenza complications (FLU 003). Biomarkers that were significantly associated with progression in both studies (p<0.05) or only in one (p<0.002 after Bonferroni correction) were identified.
In FLU 002 28/528 (5.3%) outpatients had influenza A(H1N1)pdm09 virus infection that progressed to a study endpoint of complications, hospitalization or death, whereas in FLU 003 28/170 (16.5%) inpatients enrolled from the general ward and 21/39 (53.8%) inpatients enrolled directly from the ICU experienced disease progression. Higher levels of 12 of the 25 markers were significantly associated with subsequent disease progression. Of these, 7 markers (IL-6, CD163, IL-10, LBP, IL-2, MCP-1, and IP-10), all with ORs for the 3rd versus 1st tertile of 2.5 or greater, were significant (p<0.05) in both outpatients and inpatients. In contrast, five markers (sICAM-1, IL-8, TNF-α, D-dimer, and sVCAM-1), all with ORs for the 3rd versus 1st tertile greater than 3.2, were significantly (p≤.002) associated with disease progression among hospitalized patients only.
In patients presenting with varying severities of influenza A(H1N1)pdm09 virus infection, a baseline elevation in several biomarkers associated with inflammation, coagulation, or immune function strongly predicted a higher risk of disease progression. It is conceivable that interventions designed to abrogate these baseline elevations might affect disease outcome.
The sudden and unexpected emergence in 2009 and subsequent rapid global spread of a novel influenza virus, A(H1N1)pdm09, was yet another reminder of the ongoing challenges posed by this rapidly evolving class of respiratory viruses to world health
There has been a long-standing effort to identify and better characterize possible predictors of the severity of influenza virus infection in the human host
Both with A(H1N1)pdm09 virus infection and with disease due to other seasonal influenza subtypes, considerable interest has been generated in trying to characterize the potential role that cytokine dysregulation (so-called “cytokine storm”) triggered in the host may play in the pathogenesis and outcome of the viral infection
The INSIGHT H1N1v Outpatient study (FLU 002) and the INSIGHT H1N1v Hospitalization study (FLU 003) are two international observational cohort studies of influenza launched in 2009 whose purpose is to describe adult participants in geographically diverse locations who present for medical treatment due to influenza-like illness (ILI) and are documented to have laboratory-confirmed influenza and their outcomes over 14 days (FLU 002) and 60 days (FLU 003) of follow-up
In this report we present the results of a comprehensive panel of serum biomarker determinations performed on blood specimens obtained at study entry in patients from these two cohorts with confirmed A(H1N1)pdm09 virus infection. Our goal was to study the association of the biomarkers with the risk of developing worsened disease outcomes as defined
The FLU 002 and FLU 003 protocols were approved by the institutional review boards (IRB) or institutional ethics committees (IEC) at the University of Minnesota and at each of the other 63 participating clinical sites worldwide. Formal written documentation of IRB/IEC approval was required of each site Principal Investigator during the registration process that preceded site activation as a study center. Copies of these approval letters are filed with the central coordinating center at the University of Minnesota. All patients gave signed informed consent prior to enrollment.
The two international studies, FLU 002 and FLU 003, were initiated by the National Institutes of Health in August 2009 under the auspices of the INSIGHT (International Network for Strategic Initiatives in Global HIV Trials) clinical trials network. The INSIGHT network conducts these ongoing studies through a central coordinating center at the University of Minnesota and four international coordinating centers located in the United States (Washington DC), Europe (London and Copenhagen), and Australasia (Sydney). The study design and statistical considerations underlying each study were described previously
At the time of enrollment the following information was collected: patient demographics, height, weight and vital signs; date of ILI onset, earliest contact with the health system for current illness; use of neuraminidase inhibitors to prevent or treat influenza in the preceding 14 days; medical history and underlying medical conditions, pregnancy status, smoking history, and current medications; influenza vaccination history since 2008 and pneumococcal vaccination history; and antiviral, antibacterial and other treatments prescribed at enrollment. Also recorded were local laboratory test results for influenza A; chest radiograph findings; and other local laboratory tests performed as part of standard of care.
In both studies respiratory (nasal and oropharyngeal) swabs were collected at enrollment and sent to one of two central laboratories to confirm by RT-PCR the local influenza diagnosis and to determine subtype. Identical methods were used by each laboratory. Serum samples were also collected from each participant at enrollment.
Patients enrolled in the outpatient study (FLU 002) with ILI were followed for 14 days for progression to hospitalization, the development of complications, or death. For the inpatient study (FLU 003) patients could be enrolled while in the general ward or in the intensive care unit (ICU). In either case patients were followed for 60 days. For those enrolled in the general ward, outcomes assessed included death, requirement for the ICU and/or mechanical ventilation, or prolonged hospitalization; the latter was defined as an inpatient stay exceeding 28 days of the 60-day follow-up period, not necessarily consecutively. For participants enrolled after already having been admitted to the ICU, death or prolonged hospitalization for >28 days were the primary outcomes.
Patients were included in this study if they had influenza A(H1N1)pdm09 virus infection confirmed by RT-PCR at a central laboratory and had completed ascertainment of outcome status at day 14 and day 60 in the two studies, respectively. Serum samples obtained at enrollment were analyzed for all such patients in the inpatient study. For efficiency a nested case-control sampling scheme was used in the outpatient study, where samples were analyzed for each patient who experienced an event (28 patients) within the 14 day follow-up period and for three controls (patients who survived the 14 day follow-up period without complications or hospitalization and who were declared to be symptoms-free at day 14) matched on country of enrollment, age (± 5 years) and duration of symptoms. For two cases only 1 suitable control and for one case 2 controls were found (thus, a total of 79 controls instead of 84).
The blood samples were centrifuged and stored at -80 degrees Celsius until analysis. Sera concentrations of multiple cytokines and chemokines were obtained using a Pro-inflammatory 9-plex (IL-1 beta, IL-2, IL-6, IL-8, IL10, IL-12p70, GM-CSF, IFN-gamma, and TNF-alpha), a Chemokine 9-plex (Eotaxin, MIP-1beta, Eotaxin-3, TARC, IP-10, IL-8, MCP-1, MDC, and MCP-4), a Vascular Injury II Panel (CRP, VCAM-1, ICAM-1, and SAA), and LBP (Meso Scale Diagnostics, Gaithersburg, MD). Additional ELISA results were assayed for CRP, sCD14, sCD163, IL-6 (R&D Systems Inc., MN, USA). D-Dimer measurements were made using the VIDAS assay system (BioMerieux, Durham, NC). For biomarkers determined by both multiplex and ELISA, ELISA test results are cited. IL-8 was measured using both a chemokine and cytokine platform, only the latter was cited. Altogether, we report the results for 25 biomarkers. The sera samples for these studies were analyzed blinded to event status. Samples from outpatients and hospitalized patients were analyzed at the same time by the central laboratory at SAIC-Frederick.
While acknowledging that there is considerable overlap in cellular origin, cascading immunological relationships, and downstream effects among many of these biomarkers, for ease of comparison the 25 markers are displayed by broadly grouping them into four functional categories: 1) macrophage pro-inflammatory activation response; 2) acute phase response; 3) T cell activation response; and 4) macrophage chemokine response.
The association of each of the 25 biomarkers with disease severity/progression was assessed in three separate analyses: 1) for disease severity, a cross-sectional comparison of biomarker levels for participants in FLU 002 versus FLU 003, and for those in FLU 003 enrolled in the general ward versus the ICU; 2) a comparison of biomarker levels for participants with disease progression after 14 days of follow-up versus those without progression in FLU 002 (case-control analysis); and 3) a comparison of biomarker levels for participants with disease progression after 60 days of follow-up versus those without progression in FLU 003 (cohort analysis). While power is greater for the cross-sectional comparisons than the follow-up comparisons within each study, a disadvantage of these comparisons is that the temporal association between the biomarker level and disease severity is not known (i.e., whether the biomarker is elevated as a consequence of disease severity or predicts disease progression is uncertain). This problem is overcome, at least in part, with the follow-up comparisons since in both FLU 002 and FLU 003 biomarker levels were determined prior to the disease progression outcome.
Simple summary statistics were use to describe the characteristics of patients in the two biomarker studies. To reduce the impact of outlying levels and to account for the positively skewed distribution of the biomarkers, results are categorized in tertiles and log10 transformed. The tertiles are defined separately for FLU 002 and FLU 003 [
For the cross-sectional comparison, analysis of covariance with covariates corresponding to age and geographic location was used to compare the biomarker levels for patients in the two studies at the time of enrollment. Unlike the follow-up analyses within each study that are described below, we did not adjust for duration of symptoms as we considered it to be on the causal pathway for this cross-sectional comparison. For these comparisons, back-transformed levels (geometric means) are cited for each study. In FLU 003, further comparisons are made for the subgroups defined by location of enrollment (general ward versus ICU). For FLU 002, the log10 transformed biomarker levels for the controls and events were weighted to account for the larger FLU 002 group of patients from which the controls were selected for biomarker analyses.
For the follow-up comparisons in FLU 002 and FLU 003, logistic regression was used to summarize the association of each biomarker with the disease progression outcomes. For FLU 002 conditional logistic regression analyses for matched case–control studies were used. For FLU 003, unconditional logistic regression analyses stratified by location of enrollment (general ward versus ICU) and with covariates corresponding to the matching factors used in FLU 002 (age, duration of symptoms and geographic location) were carried out. Odds ratios (ORs) for the upper tertile versus the lowest tertile are cited along with 95% confidence intervals (CIs) and
Two approaches were taken to minimize the risk of identifying false-positive associations between biomarkers and disease progression by highlighting subsets of individual biomarkers: 1) biomarkers that are significantly (p<0.05) related to disease progression based on the linear trend p-value in both studies; and 2) biomarkers which are significant (p≤ 0.002) in one study but not the other. The latter level of significance corresponds to a Bonferroni adjusted p-value (0.05/25).
To determine the relationship of multiple biomarkers with disease progression, we took advantage of the functional groupings that were identified. A global test procedure proposed by O’Brien for multiple endpoints is used
Statistical analyses were performed using SAS (Version 9.2).
At the time of this biomarker investigation, 737 patients with influenza A(H1N1)pdm09 virus infection centrally confirmed by RT-PCR had completed ascertainment of outcome status in FLU 002 (528 patients) or FLU 003 (209 patients). Patients were enrolled from 63 sites in 14 countries. The risk of serious outcomes and the baseline characteristics varied considerably for participants in FLU 002 and FLU 003. Of the 528 patients with ILI seen at one of the outpatient clinics, 28 (5.3%) had disease progression requiring hospitalization (26 patients), one developed a severe complication (chronic obstructive pulmonary disease exacerbation), and one died within 14 days of enrollment (
The respective study designs and distribution of patients with and without severe disease outcomes in the outpatient case-control study FLU 002 (A) and in the hospitalization cohort study FLU 003 (B).
Of the 209 hospitalized patients enrolled in FLU 003, 170 had been enrolled while in the general ward and 39 from the ICU. These 209 patients were also enrolled during the same two influenza seasons in the Northern (n = 203) and Southern Hemispheres (n = 6). Overall, during the 60-day follow-up period, 49 (23.4%) of the 209 patients died, entered the ICU (if enrolled in the general ward), or had a hospital stay > 28 days.
FLU 002 | FLU 003 | ||||
Entire Cohort | Case-Control | Entire Cohort | General Ward | ICU | |
No. Patients | 528 | 107 | 209 | 170 | 39 |
|
|||||
Age – median (IQR) | 30 (24, 40) | 30 (25, 40) | 49 (37, 61) | 48 (37, 61) | 51 (38, 59) |
Female - % | 51.5 | 56.1 | 47.8 | 52.9 | 25.6 |
Black race - % | 6.1 | 8.4 | 4.3 | 4.1 | 5.1 |
BMI > 30 - % | 6.4 | 12.9 | 23.0 | 23.8 | 19.4 |
Smoker - % | 21.2 | 24.3 | 31.2 | 33.7 | 19.4 |
Pregnant |
1.8 | 5.0 | 15.7 | 17.0 | 0 |
Duration of symptoms – median days (IQR) | 2 (1, 3) | 2 (1, 3) | 5 (3, 8) | 5 (3, 7) | 8 (5, 10) |
2+ qualifying complications - % | N/A | N/A | 34.0 | 29.4 | 53.8 |
Asthma or COPD - % | 6.6 | 7.5 | 25.8 | 29.4 | 10.3 |
CVD, diabetes, liver or renal disease - % | 1.9 | 0.9 | 26.8 | 24.7 | 35.9 |
HIV - % | 7.8 | 11.2 | 3.3 | 2.9 | 5.1 |
Other immunosuppressive disease - % | 0.8 | 0.9 | 9.6 | 9.4 | 10.3 |
Corticosteroids used to treat ILI - % | N/A | N/A | 23.9 | 20.6 | 38.5 |
Taking statin - % | 3.9 | 2.9 | 11.4 | 12.1 | 8.1 |
Took antiviral med w/in 14 days before enrollment - % | 2.6 | 3.7 | 66.8 | 63.5 | 81.6 |
Current or within past 2 weeks, among women aged ≤ 45 years.
Geometric means for each of the 25 biomarkers were compared for outpatients in FLU 002 and inpatients in FLU 003 (overall and according to location of enrollment) [
FLU 003 | ||||||
FLU 002 |
All | General Care | ICU | P-value |
P-value |
|
Macrophage Proinflammatory Activation Response | ||||||
IL-6 (pg/ml) | 8.1 | 9.7 | 8.2 | 19.4 | .09 | <.001 |
sICAM-1 (ng/ml) | 143 | 229 | 204 | 380 | .003 | .03 |
CD 163 (ng/ml) | 468 | 825 | 774 | 1089 | <.001 | .004 |
IL-8 (pg/ml) | 19.4 | 32.3 | 29.1 | 50.7 | <.001 | .005 |
IL-10 (pg/ml) | 13.1 | 16.2 | 14.6 | 24.9 | .08 | .009 |
TNF-α (pg/ml) | 11.2 | 13.4 | 12.4 | 18.7 | .002 | <.001 |
sCD14 (ng/ml) | 2037 | 2395 | 2329 | 2706 | .002 | .05 |
IL-12 p70 (pg/ml) | 2.83 | 3.44 | 3.49 | 3.24 | .33 | .79 |
IL-1β (pg/ml) | 0.23 | 0.18 | 0.19 | 0.15 | .57 | .72 |
Acute Phase Response | ||||||
CRP (µg/ml) | 16.2 | 51.7 | 46.1 | 85.0 | <.001 | .008 |
D-dimer (µg/ml) | 0.88 | 1.30 | 1.08 | 2.84 | <.001 | <.001 |
SAA (µg/ml) | 55 | 141 | 131 | 197 | <.001 | .13 |
LBP (µg/ml) | 13.1 | 19.9 | 17.5 | 34.9 | <.001 | <.001 |
sVCAM-1 (ng/ml) | 263 | 410 | 373 | 619 | <.001 | .002 |
T Cell Activation Response | ||||||
GM-CSF (pg/ml) | 0.6 | 1.5 | 1.2 | 3.1 | .02 | .10 |
IL-2 (pg/ml) | 1.4 | 2.3 | 2.1 | 3.7 | .007 | .003 |
IFN-γ (pg/ml) | 6.4 | 1.9 | 2.0 | 1.5 | <.001 | .59 |
Macrophage Chemokine Response | ||||||
MCP-1 (pg/ml) | 738 | 680 | 596 | 1201 | .33 | <.001 |
MCP-4 (pg/ml) | 621 | 592 | 559 | 760 | .51 | .008 |
IP-10 (pg/ml) | 1846 | 1267 | 1155 | 1900 | .001 | .01 |
MIP-1β (pg/ml) | 144 | 119 | 117 | 131 | .009 | .36 |
Eotaxin (pg/ml) | 986 | 970 | 963 | 1005 | .81 | .71 |
Eotaxin-3 (pg/ml) | 14.1 | 17.6 | 16.7 | 21.7 | .11 | .11 |
MDC (pg/ml) | 3704 | 2405 | 2470 | 2140 | <.001 | .16 |
TARC (pg/ml) | 371 | 238 | 253 | 182 | <.001 | .05 |
The levels in FLU 002 were weighted to take into account sampling controls from a cohort of 528 patients.
ANOVA for differences in log10 biomarker, FLU 002 vs FLU 003.
ANOVA for difference in log10 biomarker, general ward vs ICU enrollment in FLU 003.
FLU 002 | FLU 003 |
|||||
Cases | Controls | P-value |
Events | No Events | P-value |
|
No. Patients | 28 | 103 | 49 | 160 | ||
|
||||||
Age – median (IQR) | 31 (26, 45) | 30 (25, 39) | 0.14 | 50 (40, 61) | 49 (35, 60) | .31 |
Female - % | 64.3 | 52.4 | 0.33 | 36.7 | 51.3 | .43 |
Black race - % | 17.9 | 4.9 | 0.06 | 4.1 | 4.4 | .85 |
BMI > 30 - % | 19.2 | 10.1 | 0.15 | 15.8 | 24.7 | .30 |
Smoker - % | 21.4 | 30.1 | 0.73 | 26.7 | 32.5 | .81 |
Pregnant |
11.1 | 1.9 | 0.53 | 0 | 17.8 | .96 |
Duration of symptoms – median days (IQR) | 2 (1, 3.5) | 2 (1, 3) | N/A | 8 (5, 10) | 5 (3, 7) | .02 |
2+ qualifying complications - % | N/A | N/A | N/A | 46.9 | 30.0 | .20 |
Asthma or COPD - % | 14.3 | 3.9 | 0.19 | 16.3 | 28.8 | .32 |
CVD, diabetes, liver or renal disease - % | 3.6 | 0.0 | 0.99 | 36.7 | 23.8 | .17 |
HIV - % | 17.9 | 8.9 | 0.33 | 6.1 | 2.5 | .31 |
Other immunosuppressive disease - % | 3.6 | 0.0 | 0.99 | 16.3 | 7.5 | .06 |
Corticosteroids used to treat ILI - % | N/A | N/A | N/A | 30.6 | 21.9 | .64 |
Taking statin - % | 7.1 | 1.0 | 0.18 | 2.2 | 14.0 | .06 |
Took antiviral med w/in 14 days before enrollment - % | 3.6 | 2.9 | 0.93 | 83.3 | 61.9 | .03 |
Enrolled from the ICU - % | N/A | N/A | N/A | 42.9 | 11.3 | <.001 |
Entire FLU003 biomarker cohort independent of site of enrollment (general ward versus ICU).
From fitting a conditional logistic regression model.
From fitting a logistic regression model stratified by type of unit at enrollment (general ward versus ICU).
Current or within past 2 weeks, among women aged ≤ 45 years.
Cases | Controls | Odds Ratio |
||||||
Biomarker | Median | IQR | Median | IQR | OR | 95% CI | P-value | P-value |
Macrophage Proinflammatory Activation Response | ||||||||
IL-6 (pg/ml) | 17.1 | 9.8, 26.1 | 8.1 | 5.1, 14.7 | 4.44 | 1.35, 14.6 | .01 | .002 |
sICAM-1 (ng/ml) | 143 | 93, 274 | 159 | 85, 282 | 1.01 | 0.30, 3.38 | .98 | .70 |
CD 163 (ng/ml) | 586 | 494, 893 | 497 | 387, 650 | 2.74 | 0.88, 8.53 | .08 | .01 |
IL-8 (pg/ml) | 20.0 | 11.4, 44.6 | 13.9 | 10.0, 22.9 | 2.68 | 0.87, 8.30 | .09 | .08 |
IL-10 (pg/ml) | 15.3 | 11.2, 29.7 | 10.9 | 7.6, 16.8 | 3.92 | 1.18, 13.0 | .03 | .02 |
TNF-α (pg/ml) | 11.0 | 9.7, 13.8 | 10.3 | 8.3, 13.2 | 2.94 | 0.87, 9.95 | .08 | .29 |
sCD14 (ng/ml) | 2520 | 1878, 3207 | 2010 | 1660, 2516 | 3.05 | 0.99, 9.35 | .05 | .08 |
IL-12 p70 (pg/ml) | 3.49 | 1.57, 4.41 | 1.86 | 1.22, 4.72 | 2.74 | 0.86, 8.74 | .09 | .44 |
IL-1β (pg/ml) | 0.74 | 0.43, 1.43 | 0.58 | 0.23, 1.03 | 2.83 | 0.84, 9.57 | .09 | .77 |
Acute Phase Response | ||||||||
CRP (µg/ml) | 25.2 | 9.5, 73.7 | 18.9 | 6.2, 34.8 | 2.10 | 0.68, 6.44 | .20 | .12 |
D-dimer (µg/ml) | 1.22 | 0.70, 1.72 | 0.71 | 0.42, 1.31 | 4.82 | 1.44, 16.2 | .01 | .06 |
SAA (µg/ml) | 76 | 27, 202 | 62 | 25, 136 | 1.33 | 0.42, 4.23 | .63 | .40 |
LBP (µg/ml) | 18.8 | 12.8, 32.0 | 12.9 | 8.8, 20.8 | 3.80 | 1.17, 12.4 | .03 | .03 |
sVCAM-1 (ng/ml) | 258 | 175, 477 | 303 | 165, 525 | 1.03 | 0.32, 3.31 | .96 | .90 |
T Cell Activation Response | ||||||||
GM-CSF (pg/ml) | 1.1 | 0.7, 3.1 | 0.9 | 0.3, 2.5 | 2.26 | 0.65, 7.90 | .20 | .30 |
IL-2 (pg/ml) | 2.8 | 1.4, 4.8 | 1.6 | 1.1, 2.8 | 7.71 | 1.63, 36.5 | .01 | .03 |
IFN-γ (pg/ml) | 9.0 | 3.6, 18.9 | 7.7 | 4.3, 11.6 | 1.48 | 0.47, 4.63 | .50 | .14 |
Macrophage Chemokine Response | ||||||||
MCP-1 (pg/ml) | 897 | 613, 1335 | 696 | 501, 1181 | 2.48 | 0.80, 7.62 | .11 | .03 |
MCP-4 (pg/ml) | 701 | 429, 912 | 695 | 436, 980 | 0.66 | 0.20, 2.21 | .51 | .90 |
IP-10 (pg/ml) | 3454 | 1981, 5496 | 1853 | 1058, 2807 | 4.53 | 1.44, 14.2 | .010 | .003 |
MIP-1β (pg/ml) | 142 | 107, 202 | 132 | 101, 237 | 1.17 | 0.40, 3.43 | .77 | .36 |
Eotaxin (pg/ml) | 922 | 652, 1538 | 1001 | 786, 1400 | 0.89 | 0.32, 2.47 | .83 | .98 |
Eotaxin-3 (pg/ml) | 18.5 | 13.2, 26.8 | 15.3 | 11.4, 21.0 | 2.99 | 0.91, 9.84 | .07 | .06 |
MDC (pg/ml) | 3754 | 2687, 4980 | 3389 | 2758, 4979 | 0.94 | 0.29, 3.03 | .92 | .52 |
TARC (pg/ml) | 324 | 208, 460 | 342 | 221, 561 | 0.53 | 0.15, 1.80 | .31 | .19 |
No. patients | 28 | 79 |
Odds ratio & p-value for highest vs lowest tertile, from conditional logistic model with matching on age, duration of symptoms and country of enrollment.
As above, p-value for log10-transformed biomarker.
Events | Non-events | Odds Ratio |
||||||
Biomarker | Median | IQR | Median | IQR | OR | 95% CI | P-value | P-value |
Macrophage Proinflammatory Activation Response | ||||||||
IL-6 (pg/ml) | 22.1 | 13.5, 27.2 | 9.5 | 3.9, 17.8 | 6.14 | 1.86, 20.3 | .003 | .004 |
sICAM-1 (ng/ml) | 513 | 277, 729 | 241 | 101, 375 | 6.00 | 2.02, 17.8 | .001 | <.001 |
CD 163 (ng/ml) | 1251 | 824, 1916 | 650 | 512, 961 | 5.55 | 2.08, 14.8 | <.001 | <.001 |
IL-8 (pg/ml) | 52.0 | 24.1, 85.3 | 22.4 | 13.9, 46.6 | 3.97 | 1.44, 10.9 | .008 | .001 |
IL-10 (pg/ml) | 25.0 | 13.0, 69.9 | 10.5 | 6.7, 21.1 | 7.23 | 2.61, 20.0 | <.001 | <.001 |
TNF-α (pg/ml) | 15.5 | 13.0, 24.6 | 12.1 | 9.2, 15.7 | 3.92 | 1.29, 11.9 | .02 | .002 |
sCD14 (ng/ml) | 2990 | 2397, 3698 | 2249 | 1761, 2995 | 3.36 | 1.30, 8.68 | .01 | .03 |
IL-12 p70 (pg/ml) | 2.56 | 1.14, 9.09 | 2.77 | 1.58, 6.69 | 1.09 | 0.45, 2.59 | .85 | .65 |
IL-1β (pg/ml) | 0.52 | 0.25, 1.36 | 0.42 | 0.17, 0.95 | 1.83 | 0.73, 4.55 | .19 | .98 |
Acute Phase Response | ||||||||
CRP (µg/ml) | 124 | 52.2, 203 | 46.4 | 22.7, 101 | 3.98 | 1.47, 10.8 | .007 | .008 |
D-dimer (µg/ml) | 3.07 | 1.54, 4.78 | 1.09 | 0.55, 1.86 | 3.21 | 1.21, 8.48 | .02 | <.001 |
SAA (µg/ml) | 285 | 95, 494 | 187 | 65, 390 | 1.59 | 0.66, 3.88 | .30 | .13 |
LBP (µg/ml) | 39.9 | 22.6, 73.9 | 16.9 | 10.5, 30.3 | 4.46 | 1.68, 11.9 | .003 | .006 |
sVCAM-1 (ng/ml) | 800 | 536, 1152 | 394 | 181, 650 | 6.69 | 2.26, 19.8 | <.001 | <.001 |
T Cell Activation Response | ||||||||
GM-CSF (pg/ml) | 3.2 | 2.1, 6.3 | 2.4 | 0.9, 4.2 | 2.09 | 0.81, 5.42 | .13 | .06 |
IL-2 (pg/ml) | 3.8 | 2.0, 7.6 | 1.9 | 1.2, 3.6 | 2.81 | 1.12, 7.08 | .03 | .005 |
IFN-γ (pg/ml) | 3.2 | 1.4, 8.7 | 2.6 | 1.1, 6.3 | 3.81 | 1.39, 10.4 | .009 | .35 |
Macrophage Chemokine Response | ||||||||
MCP-1 (pg/ml) | 1105 | 579, 2498 | 546 | 363, 804 | 4.04 | 1.55, 10.5 | .004 | <.001 |
MCP-4 (pg/ml) | 550 | 385, 754 | 577 | 389, 886 | 0.48 | 0.18, 1.25 | .13 | .31 |
IP-10 (pg/ml) | 2486 | 954, 7175 | 990 | 532, 1935 | 3.73 | 1.48, 9.44 | .005 | <.001 |
MIP-1β (pg/ml) | 107 | 81, 165 | 118 | 82, 163 | 0.94 | 0.38, 2.33 | .90 | .90 |
Eotaxin (pg/ml) | 980 | 567, 1667 | 981 | 651, 1384 | 0.92 | 0.39, 2.16 | .85 | .95 |
Eotaxin-3 (pg/ml) | 17.0 | 13.6, 29.7 | 16.1 | 11.1, 27.2 | 1.34 | 0.50, 3.55 | .56 | .13 |
MDC (pg/ml) | 1821 | 1346, 2524 | 2525 | 1879, 3430 | 0.26 | 0.10, 0.71 | .009 | .02 |
TARC (pg/ml) | 149 | 93, 245 | 251 | 139, 439 | 0.25 | 0.09, 0.72 | .010 | .01 |
No. patients | 49 | 160 |
Odds ratio & p-value for highest vs lowest tertile, adjusted for enrollment unit (ICU vs general ward), age, symptom duration, and continent of enrollment.
P-value for log10 biomarker, adjustment as above.
Higher levels of 12 markers were significantly associated with disease progression. Seven markers were significant (p< 0.05) in both studies: IL-6, CD163, IL-10, LBP, IL-2, MCP-1, and IP-10. For ease of comparison,
The biomarkers found to be significantly associated with disease progression in both studies (A) and in FLU 003 only (B) are shown. Odds ratios (3rd/1st tertile) and 95% confidence intervals are depicted.
For inpatients (FLU 003), we also examined the relationship of IL-6 with mortality (
A Kaplan-Meier graph of cumulative mortality (%) for FLU 003 participants according to baseline levels of IL-6 as grouped into tertiles.
The odds ratios and 95% confidence intervals for the risk of disease progression in FLU 002 (in blue) and FLU 003 (in red) are depicted according to four functional categorizations of the 25 biomarkers analyzed in these two studies.
In a number of generally cross-sectional studies focusing either partially or exclusively upon confirmed cases of A(H1N1)pdm09 virus infection in specific geographic areas, potential correlations between various cytokine levels and disease severity have been reported. Investigators from Mexico reported that A(H1N1)pdm09 virus infection resulted in stronger
Biomarker analyses from our two large ongoing international studies of influenza described here have strengthened and extended these observations in important ways. A major virtue of the present studies is that these data were collected prospectively according to a common data-set and with defined periods of follow-up to assess disease progression, samples have been garnered from a relatively large number of patients living in geographically disparate regions of the world and analyzed through common central laboratory systems using standardized methodologies, and both studies included enrollments of patients with different severities of A(H1N1)pdm09 virus infection spanning over more than a single influenza season in those areas. Further, the samples were collected, shipped, and processed for analysis using identical procedures to minimize any potential artifactual effects on serum levels of the biomarkers measured.
Even after adjustments for multiple potentially confounding variables within each dataset according to different stringencies, there are several biomarkers whose baseline levels appear to be highly correlated with a worsened disease course according to the definitions of disease progression predefined for each study. Several could have been singled out in this context. However, the seven markers that were significantly correlated with disease progression in both studies were IL-6, CD163, IL-10, LBP, IL-2, MCP-1, and IP-10, a somewhat disparate set spanning all four functional groupings. While cross-sectional comparisons between studies must always be interpreted with caution, for six of these seven markers, the exception being MCP-1, the absolute levels of each at study entry also appeared to correlate with disease severity at time of enrollment, as reflected principally by their higher geometric mean values in hospitalized patients versus outpatients.
Two notable inconsistences between the cross-sectional and follow-up findings were the associations with IFN-γand IP-10. Both markers were lower in the cross-sectional comparison of FLU 003 participants compared to FLU 002 participants, but within the two studies higher, not lower, levels were associated with an increased risk of disease progression during follow-up. The reason for this discrepancy remains unclear at present; however, the difference may reflect a limitation of cross-sectional comparisons in which temporal relationships are uncertain.
As a biomarker with known involvement in the pro-inflammatory cascade associated with many different types of infections, as well as one that has featured prominently in earlier other published analyses of the potential role of biomarkers in predicting influenza disease outcomes, we also chose to validate the strong predictive potential of IL-6 in these two studies. For both outpatients and those requiring hospitalization, serum IL-6 was a strong predictor of disease progression. For the hospitalized patients in FLU 003, those with an IL-6 level in the upper two tertiles were also at an increased of mortality. This is similar to prior observations a decade earlier in a small number of fatal cases of H5N1 infection
Although the strong statistical associations found in these two studies between select individual biomarkers and a worsened disease outcome are compelling, nonetheless these results present an obvious difficulty with extrapolation to the clinical arena at the present time. Most of the biomarkers described here are part of a multiplex testing array generally performed in a research setting and are not a routine part of the diagnostic work-up performed for a typical patient presenting with signs and symptoms of acute influenza. Hence, at present they may be of more value in providing insight into potential mechanisms of viral pathogenesis and host defense rather than in offering direct clinical benefit. There are some potential exceptions to this. D-dimer and CRP assays, for example, are generally available today in most acute care facilities as indicators of recent thrombotic events and abnormal systemic inflammation, respectively, and the test results are generally available in real time.
It is fair to say that, at present, there does not appear to be a single discrete biomarker readily available to the physician at the time of presentation that one can conclude adds unequivocably to the ability of the standard diagnostic assessment to predict the likelihood of disease progression in all patients. Nonetheless, as these multiplex assays become cheaper and more readily available beyond the research setting, this situation may improve. For example, assuming an exaggerated or dysfunctional cytokine response to acute influenza may actually contribute to disease severity in some cases, their additional value may be in pinpointing areas of this response that may be amenable to dampening or other forms of therapeutic intervention. Thus, at the same time that one is treating the virus in at-risk individuals, it is conceivable that adjunctive therapy directed at abrogating or redirecting an overly exuberant host response could also be introduced to further improve prognosis.
Although this leap from the research setting to the bedside still remains a challenge for the reasons cited, it is still reasonable to ask whether in the future even stronger prognostic utility or power might be found in analyzing select combinations or subsets of these markers, whether identified through statistical modeling or
The focus of this present analysis has been on the prognostic value of baseline biomarkers in predicting the subsequent course of disease specifically in A(H1N1)pdm09 virus-infected patients. Since both studies are ongoing on a multi-year basis in both the Northern and Southern Hemispheres, have been broadened to include patients presenting with all major subtypes of seasonal virus in circulation at the time, and now collect serial blood specimens for up to 60 days following enrollment, the intention is that these analyses can be expanded in at least two ways. One goal will be to map the kinetics in serum and plasma of each of the major biomarkers, singly and in groupings, from baseline through the course of acute infection and until the time of resolution and recovery. Surprisingly little is known about how these markers change differentially over time according to such factors as disease severity or the host’s baseline immune status, or how quickly they return to the pre-infection state. A second goal will be to compare the prognostic value of biomarkers in infection due to A(H1N1)pdm09 virus to that found in infection with other influenza viruses. It is unclear at present whether these correlations observed with A(H1N1)pdm09 virus infection reflect broad-based host response pathways that can be applied, for example, universally to influenza A virus infections as a whole, or whether more discrete differences in various cytokine response patterns will emerge as different subtypes are examined further. These and other types of analyses should be readily possible under the bounds of these ongoing studies.
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The INSIGHT FLU 002 and FLU 003 Study Groups wish to acknowledge and thank the many patients who have participated in these two observational trials.
Community Representatives: David Munroe, Claire Rappoport, Siegfried Schwarze.
Coordinating Centers:
Copenhagen: Bitten Aagaard, Dejan Adzic, Jesper Grarup, Patricia Herrero, Per Jansson, Marie Louise Jakobsen, Birgitte Jensen, Karoline B. Jensen, Heidi Juncher, Jesper Kjær, Jens Lundgren, Paco Lopez, Amanda Mocroft, Mary Pearson, Begoña Portas, Caroline Sabin, Klaus Tillmann
London: Abdel Babiker, Nafisah Braimah, Yolanda Collaco-Moraes, Fleur Hudson, Ischa Kummeling, Filippo Pacciarini, Nick Paton
Statistical and Data Management Center – Minneapolis – Alain DuChene, Michelle George, Merrie Harrison, Kathy Herman, Eric Krum, Gregg Larson, Ray Nelson, Kien Quan, Siu-Fun Quan, Cavan Reilly, Terri Schultz, Greg Thompson, Nicole Wyman
Sydney: Anchalee Avihingsanon, Lara Cassar, Kanlaya Charoentonpuban, Sean Emery, Kobkeaw Laohajinda, Thidarat Jupimai, Isabel Lanusse, Alejandra Moricz, Ines Otegui, Kiat Ruxrungtham, Rose Robson
Washington: Elizabeth Finley, Fred Gordin, Adriana Sanchez, Michael Vjecha
Specimen Repositories and Laboratories:
John Baxter, Shawn Brown (SAIC Frederick, Inc.), Elodie Ghedin (JCVI), Rebecca Halpin (JCVI), Marie Hoover (ABML)
National Institute of Allergy and Infectious Disease: Beth Baseler (SAIC Frederick Inc.), Julia A. Metcalf
Other Experts:
Centers for Disease Control: Nancy Cox, Larisa Gubareva, Kathy Hancock, Jackie Katz, Alexander Klimov, Michael Shaw
Department of Health and Human Services: Lewis Rubinson
Argentina: Laura Barcan, Jorge Alberto Corral, Daniel Omar David, Hector Enrique Laplume, Maria Beatriz Lasala, Gustavo Daniel Lopardo, Marcelo H. Losso, Sergio Lupo, Eduardo Warley
Australia: Mark Bloch, Dominic E. Dwyer, Richard Moore, Sarah L. Pett, Norman Roth, Tuck MengSoo, Emanuel Vlahakis
Austria: Heinz Burgmann
Belgium: Nathan Clumeck, Stephan De Wit, Eric Florence, KabambaKabeya, JozefWeckx
Chile: Carlos Perez, Marcelo J. Wolff
Denmark: Jan Gerstoft, Jens D. Lundgren, Lars ∅stergaard
Estonia: Kai Zilmer
Germany: Johannes R. Bogner, Norbert H. Brockmeyer, Gerd Faetkenheuer, Hartwig Klinker, Andreas Plettenberg, Juergen Rockstroh, Christoph Stephan
Greece: Anastasia Antoniadou, Georgios Koratzanis, Nikolaos Koulouris, Vlassis Polixronopoulos, Helen Sambatakou, Nikolaos Vasilopoulos
Lithuania: SauliusCaplinskas
Peru: Alberto La Rosa, Fernando Mendo, Raul Salazar, Jorge Valencia
Poland: ElzbietaBakowska, AndrzejHorban, BrygidaKnysz
Portugal: Francisco Antunes, Manuela Doroana
South Africa: NesriPadayatchi
Spain: David Dalmau, Eduardo Fernandez-Cruz, Jose Maria Gatell, Jesus SanzSanz, Vincent Soriano
Thailand: Ploenchan Chetchotisakd, Kiat Ruxrungtham, Gompol Suwanpimolkul
United Kingdom: Clifford L.S. Leen
United States: Calvin Cohen, David L. Cohn, Jack A. DeHovitz, Wafaa El-Sadr, Marshall Glesby, Fred M. Gordin, Sally Hodder, Norman Markowitz, Richard M. Novak, Robert Schooley, Gary L. Simon, Ellen Marie Tedaldi, ZelalemTemesgen, Joseph Timpone, Daniel Z. Uslan, Barbara Heeter Wade
Argentina: Laura Barcan, Jorge Alberto Corral, Daniel Omar David, Hector Enrique Laplume, Maria Beatriz Lasala, Gustavo Daniel Lopardo, Marcelo H. Losso, Eduardo Warley
Australia: Dominic E. Dwyer, Julian Elliott, Pam Konecny, John McBride, Sarah L. Pett
Austria: Heinz Burgmann
Belgium: Nathan Clumeck, Stephan De Wit, Philippe Jorens, KabambaKabeya
Chile: Marcelo J. Wolff
China: Tak Chiu Wu
Denmark: Jan Gerstoft, Lars Mathiesen, Henrik Nielsen, Lars Østergaard, Svend Stenvang Pedersen
Germany: Frank Bergmann, Johannes R. Bogner, Norbert H. Brockmeyer, Gerd Faetkenheuer, Hartwig Klinker, Juergen Rockstroh, Christoph Stephan
Greece: Anastasia Antoniadou, Georgios Koratzanis, Nikolaos Koulouris, Vlassis Polixronopoulos, Helen Sambatakou, Nikolaos Vasilopoulos
Norway: Anne Maagaard
Peru: Fernando Mendo, Raul Salazar
Poland: ElzbietaBakowska, AndrzejHorban
South Africa: NesriPadayatchi
Spain: David Dalmau, Vicente Estrada, Eduardo Fernandez-Cruz, Hernando Knobel Freud, Rosa M BlazquezGarrido, Jose Maria Gatell, Jose Sanz Moreno, Jose Ramon Pano-Pardo, Jesus SanzSanz, Vincent Soriano
Thailand: PloenchanChetchotisakd, KiatRuxrungtham, GompolSuwanpimolkul
United Kingdom: Brian J. Angus, David R. Chadwick, David Dockrell, Clifford L.S. Leen, Melanie Newport, Ed Wilkins
United States: Harry Anderson III, Jason V. Baker, David L. Cohn, Jack A. DeHovitz, Wafaa El-Sadr, Matthew S. Freiberg, Fred M. Gordin, Roy Gulick, David Gurka, Sally Hodder, Norman Markowitz, Richard M. Novak, Armando Paez, NamrataPatil, Annette Reboli, Michael Sands, Robert Schooley, Gary L. Simon, ZelalemTemesgen, Joseph Timpone, Daniel Z. Uslan, Barbara Heeter Wade