Pre-cART&nbsp;Inflammation, Microbial Translocation and Long-Term Probability of Clinical Progression in People Living With HIV&nbsp;

doi:10.21203/rs.3.rs-153102/v1

Download PDF

Research Article

Pre-cART Inflammation, Microbial Translocation and Long-Term Probability of Clinical Progression in People Living With HIV

https://doi.org/10.21203/rs.3.rs-153102/v1

This work is licensed under a CC BY 4.0 License

You are reading this latest preprint version

Background. We aimed to investigate the role of pre-cART inflammation and microbial translocation (MT) as predictors of clinical progression in HIV+ patients enrolled in the Icona Foundation Study Cohort.

Methods. We included Icona patients with plasma stored within 6 months before cART initiation and at least one CD4 count after therapy available. Circulating biomarker: LPS, sCD14, EndoCab, hs-CRP. Kaplan-Meier curves and Cox regression models were used. We defined the endpoint of clinical progression as the occurrence of a new AIDS-defining condition, severe non-AIDS condition (SNAEs) or death whichever occurred first. Follow-up accrued from the data of starting cART and was censored at the time of last available clinical visit. Biomarkers were evaluated as both binary (above/below median) and continuous variables (log_escale).

Results. We studied 486 patients with 125 clinical events:10.8 (0.77-14.8)/1000 PYFU new AIDS, 18.3 (14.2-23.3)/1000 PYFU SNAEs, and 5.56 (3.40-8.57)/1000 PYFU deaths. Among morbidity events, the incidence of infectious-related events was 19.4 (15.2-24.5)/1000 PYFU. Hs-CRP seemed to be the only biomarker retaining some association with the endpoint of clinical progression (i.e. AIDS/SNAEs/death p=.056).

Conclusions. Circulating pre-cART hs-CRP but not MT seems associated with the risk of disease progression after cART initiation, suggesting that pre-therapy HIV-driven pro-inflammatory milieu might overweight MT and its downstream immune-activation. This result should help guiding the design of interventional studies.

Infectious Diseases

HIV-infection

inflammation

microbial translocation

clinical progression

Despite the unquestioned success of cART in recovering CD4 T-cell count and in preventing disease progression (1–3), a few-year survival gap has been described in large cohorts of successfully cART-treated people living with HIV (PLWHIV) when compared to HIV-negative individuals (4–6), mainly ascribable to an excess risk of serious non-AIDS events (SNAEs) (as reviewed in (7)).

This is particularly true among patients who initiate cART at advanced disease stages and fail to achieve a significant CD4 recovery upon virologically-suppressive cART (8–12). Gloomily, to date, the proportion of individuals who receive an HIV diagnosis during advanced infection is still relevant globally. Indeed, a recent large study recruiting patients from 55 countries, showed that up to 20–30% of patients starting therapy in 2015 in high-income countries are diagnosed with CD4 count < 350 cell/mmc (13). Accordingly, within the Icona cohort, patients presenting with CD4 < 350/cmm at HIV diagnosis account for 43% (data unpublished).

Persistently heightened immune activation and inflammation on cART have been associated with both inefficient CD4 recovery (14–17) and the onset of several non-AIDS defining conditions upon cART initiation (18–22).

In untreated HIV infection, our group and others have shown that the markers of immune activation derived from the systemic translocation of bacterial byproducts through a damaged gut mucosal barrier, mainly lipopolysaccharide (LPS), are independent predictors of disease progression (23, 24). Interestingly, persistently elevated levels of circulating LPS have been described in patients with inefficient CD4 gain on virologically-suppressive cART (25), in turn correlating to a persisting immune activation (26–28), leading to speculate that microbial translocation (MT) might affect immune reconstitution on cART.

Among PLWHIV on suppressive cART in North America, markers of innate immune activation and inflammation were much more strongly predictive of mortality than T-cell activation (29, 30), possibly due to the non-infectious nature of the co-morbidities (e.g., cardiovascular, non-AIDS cancer, etc.). In contrast, in resource-constrained settings, where infectious complications remain much more important causes of death (31, 32), T-cell activation and defects in adaptive immunity are much stronger predictors of mortality in course of virally-suppressive cART (33–35). Recently, biomarkers of systemic inflammation and immune activation have been demonstrated to be predictive of mortality in the first 12 months of cART in South Africa and Uganda, possibly serving as efficient prognostic indicators for patients initiating therapy (36).

Nevertheless, whether or not markers of microbial translocation and systemic inflammation pre-cART predict morbidity/mortality during suppressive cART, independently of other known risk factors, is still under debate.

Given these premises, we explored the possible associations between circulating markers of microbial translocation (LPS, sCD14, EndoCAb) and systemic inflammation (hs-CRP) measured before cART introduction and the risk of long-term disease progression upon cART start in PLWHIV enrolled in the Icona Foundation Study Cohort.

Study Design.

We included patients enrolled in the Icona Foundation Study Cohort with ≥ 1 plasma sample stored within 6 months before cART initiation and with at least one CD4 T-cell count measurement after cART available. Details of the cohort study and data collection have been previously reported (37). The Icona Foundation study was approved by the Ethics Committee (Institutional Review Board) of each participating institution.

Microbial translocation and systemic inflammation markers.

On the extracted stored samples we retrospectively measured plasma levels of microbial translocation markers, such as LPS (LAL test, Lonza Bioscience, Basel, Switzerland), sCD14 (ELISA assay, R&D Systems, a Biotechne brand, Minneapolis, MN, USA), EndoCAb (ELISA, Hycult Biotech, Uden, Netherlands) and the pro-inflammatory marker hs-C reactive protein, CRP (ELISA assay, R&D Systems, a Biotechne brand, Minneapolis, MN, USA), according to manufacturers’ instruction. Only one sample per participants stored prior to ART initiation was used.

Endpoint.

We defined a primary composite endpoint of clinical progression as the occurrence of AIDS-defining condition, non-AIDS severe events (SNAEs) or death whichever occurred first. SNAEs included: cardiac decompensation, chronic renal insufficiency (defined as clinical diagnosis), end-stage liver diseases, myocardial infarction, malignancies, pneumonia, end-stage renal diseases (defined as 2 consecutive measurements of eGFR < 60ml/min at least 3 months apart) and septic infections.

We next defined two alternative composite endpoints after distinguishing non-infectious related events from infectious-related events. The latter group included i) AIDS-defining infections; ii) end-stage liver diseases caused by viral agents; iii) infectious-related malignancies; iii) pneumonia; and iv) sepsis. All other morbidity events were classified as non-infectious and death was counted as an event for both these alternative endpoints.

Statistical analyses.

Standard survival analysis (Kaplan-Meier curves and Cox regression models with time fixed covariate measured before cART initiation) was used to evaluate the association between MT and systemic inflammation markers and each of the defined endpoints. Participants follow-up accrued from the date of starting cART up to one of the endpoint defining events (only the first event was counted in the analysis). For those who did not develop any of the endpoint-defining events, was censored at time of last available clinical. The analysis was conducted according to an intention-to-treat principle, ignoring therapy switches over follow-up. Separate Cox regression models were fitted for each of the biomarkers both univariable and after controlling for key confounding factors (age, CD4, VL, HCV/HBV, years of cART, duration of HIV infection at starting cART and type of cART started). A final model was also constructed under the assumption that the association with each of the markers could have been further confounded by the remaining markers.

MT and pro-inflammatory markers were evaluated using both a binary variable (above and below median) and as a continuous variable (in the natural logarithmic scale). Dichotomisation was based on the median value of the study population because there are no standardised clinical cut-off values for these markers.

We also performed a sensitivity analysis by repeating the main analysis on the subset of patients who had CD4 count less than 350 cell/mmc before cART starting.

Patients’ characteristics.

We analyzed the data of 486 patients fulfilling the inclusion criteria and overall contributing to 1,605 biomarker measures: 278 with LPS, 375 with hs-CRP, 477 with sCD14 and 475 with EndoCAb. Table 1 shows the main characteristics of the study population according to hs-CRP plasma levels above or below the median value of the whole study cohort (i.e. 1.51 mg/L). The distribution of the demographics in the hs-CRP strata is similar to that of the cohort overall. Also, the group of people, for whom measuring hs-CRP was not successful, appeared to be balanced with the other groups with respect to measured characteristics at baseline. Some differences in key confounders such as age and CD4 count were detected comparing people with low and high hs-CRP values. With regards to the other biomarkers, the median plasma LPS levels were 250.8 pg/ml (IQR: 151.8, 392.8), sCD14 2.8 µg/ml (IQR: 2.2, 3.9), EndoCAb 36.5 pg/ml (IQR: 21.3, 66.0) and hs-CRP 1.5 mg/L (IQR: 0.6, 4.1).

Table 1
Main Characteristics of the study population, according to high-sensitive C reactive protein group
Characteristics	Total	hs-CRP ≤ 1.51 mg/L	hs-CRP > 1.51 mg/L	Not measured	p values
	N = 486	N = 188	N = 187	N = 111
Gender, n (%)					0.308
Female	139 (28.6%)	55 (29.3%)	47 (25.1%)	37 (33.3%)
Age, years, median (range)	37 (18, 74)	36 (18, 67)	38 (20, 74)	38 (19, 71)	0.160
Mode of HIV transmission, n (%)					0.428
Homosexual contacts	128 (26.3%)	51 (27.1%))	51 (27.3%)	26 (23.4%)
Heterosexual contacts	232 (47.5%)	80 (42.6%)	91 (48.1%)	61 (55.0%)
IDU	103 (21.2%)	50 (26.6)	36 (19.3%)	17 (15.3%)
Other/unknown	23 (4.7%)	7 (3.7%)	9 (4.8%)	7 (6.3%)
CD4 count, cells/mmc, median (range)	256 (2,1180)	265 (2, 1180)	244 (3, 1049)	256 (3, 868)	0.490
CD4 nadir, cell/mmc, median (IQR)	242 (121–336)	246 (146, 332)	235 (90, 329)	245 (122, 348)	0.497
Viral load, log10 copies/mL, median (IQR)	4.8 (4.1, 5.2)	4.7 (4.0, 5.2)	4.8 (4.3, 5.3)	4.8 (4.0, 5.2)	0.217
Hepatitis co-infection, n (%)					0.076
Negative	176 (36.1%)	64 (34.0%)	73 (38.6%)	39 (35.1%)
Positive	97 (19.9%)	49 (26.1%)	32 (16.9%)	16 (14.4%)
Not tested	215 (44.1%)	75 (39.9%)	84 (44.4%)	56 (50.5%)
Time from HIV diagnosis to cART, months Median (IQR)	44 (38, 50)	44 (41, 47)	44 (38,50)	42 (38, 50)	0.021
Calendar year of starting cART, median (range)	2003 (1998, 2010)	2004 (1998, 2010)	2003 (1998, 2010)	2002 (1998, 2010)	0.029
*Clinical Follow-up time, months Median (IQR)**	79.0 (30.0, 134.0)	87.5 (25.0, 137.5)	73.0 (26.0, 131.0)	78.0 (44.0, 134.0)	0.539
Note: IQR: interquartile range; IDU: injection drug users; cART: combination of antiretroviral therapy. *Clinical follow-up time: time between cART initiation and the clinical event or for those who did not develop any of the endpoint-defining events, time between cART initiation and last CD4 count or available visit.

Effect of microbial translocation and inflammation on clinical progression.

Using the primary composite endpoint of clinical progression, 125 progression events were recorded: 39 (31%) AIDS, 66 (53%) SNAEs and 20 (16%) deaths. Among the 66 SNAEs events we identified: 1 cardiac decompensation, 3 chronic renal insufficiency, 4 liver diseases, 5 myocardial infarctions, 16 malignancies, 1 myelitis, 1 pancreatitis, 11 pneumonia, 22 renal diseases, 2 septic infections. We found no major differences in the Kaplan Meier estimated cumulative probability of disease progression according to plasma LPS (log-rank p = 0.524), sCD14 (p = 0.821), EndoCAb (p = 0.083) and hs-CRP (p = 0.119) strata. Similar results were observed when we restricted the analysis to the 70 infectious-related events (data not shown).

Identification of predictors of clinical progression.

We next sought to investigate the association between pro-inflammatory and MT parameters and risk of outcomes after controlling for potentially confounding factors. Table 2 shows the results of the Cox regression analysis of time to developing the composite endpoint of clinical progression (i.e. new AIDS, SNAEs and/or death), in which biomarkers were included in the models using a binary variable (above or below the median). Interestingly, hs-CRP was the only biomarker associated with disease progression (relative hazard = 1.52 comparing hs-CRP > 1.51 vs. below 1.51, 95% CI 1.01–2.29, p = .044; Table 2). The association was a little attenuated after controlling for key confounding factors (RH = 1.47, 95% CI 0.96–2.26, p = 0.08) and remained virtually unchanged after further adjustment for the other biomarkers (relative hazard = 1.54 comparing > hs-CRP 1.51 vs. below 1.51, 95% CI 0.99–2.39, p = .056; Table 2).

Table 2
RH of clinical progression from fitting a Cox regression model
	Crude and adjusted relative hazards of new AIDS or non-AIDS Severe Events (SNAEs) or death in total population
	Crude RH (95% CI)	p-value	Adjusted^RH (95% CI)*	p-value	Adjusted^RH (95% CI)**	p-value
(a) Biomarkers fitted as categorical variables
LPS, pg/ml
<=250.8	1.0		1.0		1.0
> 250.8	0.85 (0.53, 1.36)	0.495	0.76 (0.46, 1.26)	0.292	0.88 (0.52, 1.49)	0.643
not measured	1.09 (0.72, 1.65)	0.681	1.11 (0.72, 1.73)	0.631	1.22 (0.77, 1.92)	0.400
sCD14, ug/ml
<=2.83	1.0		1.0		1.0
> 2.83	1.10 (0.77, 1.58)	0.586	0.95 (0.65, 1.39)	0.805	0.88 (0.59, 1.29)	0.504
not measured	1.26 (0.40, 4.03)	0.695	0.87 (0.26, 2.86)	0.819	1.44 (0.41, 5.02)	0.570
EndoCAb, MMU/ml
<=36.5	1.0		1.0		1.0
> 36.5	0.75 (0.52, 1.06)	0.107	0.82 (0.56, 1.19)	0.296	0.84 (0.57, 1.24)	0.378
not measured	0.22 (0.03, 1.56)	0.129	0.19 (0.03, 1.38)	0.100	0.18 (0.02, 1.40)	0.100
hs-CRP, mg/L
<=1.51	1.0		1.0		1.0
> 1.51	1.52 (1.01, 2.29)	0.044	1.47 (0.96, 2.26)	0.077	1.54 (0.99, 2.39)	0.056
not measured	1.19 (0.74, 1.93)	0.478	1.26 (0.76, 2.07)	0.369	1.28 (0.77, 2.13)	0.345
(b) Biomarkers fitted as continuous variables in the log_escale
LPS, pg/ml
per log_ehigher	0.99 (0.73, 1.35)	0.971	0.94 (0.68, 1.31)	0.729	0.72 (0.46, 1.12)	0.147
sCD14, ug/ml
per log_ehigher	1.17 (0.84, 1.63)	0.342	0.95 (0.64, 1.40)	0.798	0.79 (0.41, 1.51)	0.471
EndoCAb, MMU/ml
per log_ehigher	0.87 (0.70, 1.07)	0.195	0.94 (0.75, 1.17)	0.567	1.03 (0.70, 1.52)	0.874
hs-CRP, mg/L
per log_ehigher	1.12 (0.99, 1.27)	0.065	1.07 (0.94, 1.21)	0.334	1.06 (0.89, 1.25)	0.539
Note: SNAEs: cardiac decompensation, chronic renal insufficiency, liver diseases, myocardial infarction, malignancies, pneumonia, renal diseases and septic infections * All models (a separate one for each biomarker) adjusted for age, CD4, VL, HCV/HBV, years of cART, duration of HIV infection at starting cART, type of cART started **Further mutually adjusted for all biomarkers.

We next asked whether the involvement of the pre-cART pro-inflammatory milieu might differently affect the pathogenesis of infectious and non-infectious related diseases in course of long-term suppressive cART. In this analysis the breakdown of the 70 infectious-related events were as follows: 39 AIDS-defining infections, 5 infectious-related malignancies, 4 viral hepatitis, 2 septic infections, 11 pneumonia and 9 infectious-driven death. Although the association was weaker, possibly because of the reduced statistical power, again, hs-CRP was the biomarkers which was most strongly associated with clinical progression after controlling for confounders and for the other biomarkers (relative hazard = 1.71 comparing hs-CRP > 1.51 vs. below 1,51, 95% CI 0.94–3.13, p = .081; Table 3.part A). On the opposite, EndoCAb was associated with non-infectious related events, when the biomarker was fitted as categorical variable (relative hazard = 0.52 comparing EndoCAb > 36.5 vs. below 36.5, 95% CI 0.29, 0.92, p = .026), as well as when it was fitted as continuous variable (relative hazard = 0.68, 95% CI 0.48, 0.97, p = .031). However, the association was lost after controlling for confounders and for the other biomarkers (Table 3.part B).

Table 3
RH of clinical progression, defined as the occurrence of an infectious-related or non-infectious related event, from fitting a Cox regression model
PART A. infectious related events	Crude and adjusted relative hazards of infectious-related events in total population
PART A. infectious related events	Crude RH (95% CI)	p-value	Adjusted^RH (95% CI)*	p-value	Adjusted^RH (95% CI)**	p-value
(a) Biomarkers fitted as categorical variables
LPS, pg/ml
<=250.8	1.0		1.0		1.0
> 250.8	0.79 (0.40, 1.56)	0.506	0.67 (0.33, 1.35)	0.262	0.75 (0.36, 1.54)	0.430
not measured	1.34 (0.77, 2.33)	0.305	1.14 (0.63, 2.06)	0.658	1.19 (0.65, 2.19)	0.567
sCD14, ug/ml
<=2.83	1.0		1.0		1.0
> 2.83	1.45 (0.89, 2.35)	0.134	1.19 (0.71, 1.99)	0.511	1.08 (0.64, 1.82)	0.787
not measured	1.83 (0.43, 7.67)	0.411	1.08 (0.25, 4.73)	0.918	1.71 (0.35, 8.37)	0.508
EndoCAb, MMU/ml
<=36.5	1.0		1.0		1.0
> 36.5	0.94 (0.59, 1.51)	0.811	0.95 (0.58, 1.55)	0.826	0.98 (0.59, 1.63)	0.942
not measured	0.48 (0.07, 3.50)	0.469	0.34 (0.05, 2.52)	0.290	0.35 (0.04, 3.13)	0.351
hs-CRP, mg/L
<=1.51	1.0		1.0		1.0
> 1.51	1.58 (0.91, 2.74)	0.103	1.64 (0.92, 2.95)	0.096	1.71 (0.94, 3.13)	0.081
not measured	1.31 (0.69, 2.49)	0.407	1.47 (0.75, 2.87)	0.260	1.49 (0.75, 2.96)	0.250
(b) Biomarkers fitted as continuous variables in the log_escale
LPS, pg/ml
per log_ehigher	0.96 (0.62, 1.49)	0.857	0.85 (0.54, 1.34)	0.486	0.58 (0.31, 1.09)	0.089
sCD14, ug/ml
per log_ehigher	1.21 (0.82, 1.80)	0.344	0.92 (0.57, 1.50)	0.747	1.01 (0.54, 1.90)	0.968
EndoCAb, MMU/ml
per log_ehigher	1.00 (0.76, 1.32)	0.982	1.01 (0.76, 1.35)	0.927	0.95 (0.56, 1.61)	0.857
hs-CRP, mg/L
per log_ehigher	1.09 (0.92, 1.28)	0.316	1.05 (0.89, 1.24)	0.556	1.13 (0.89, 1.43)	0.303
PART B. Non-infectious related events	Crude and adjusted relative hazards of non-infectious related events in total population
PART B. Non-infectious related events	Crude RH (95% CI)	p-value	Adjusted^RH (95% CI)*	p-value	Adjusted^RH (95% CI)**	p-value
(a) Biomarkers fitted as categorical variables
LPS, pg/ml
<=250.8	1.0		1.0		1.0
> 250.8	0.84 (0.42, 1.67)	0.625	0.88 (0.42, 1.85)	0.728	1.08 (0.49, 2.37)	0.856
not measured	0.72 (0.38, 1.39)	0.327	0.95 (0.47, 1.92)	0.892	1.12 (0.54, 2.34)	0.758
sCD14, ug/ml
<=2.83	1.0		1.0		1.0
> 2.83	0.71 (0.41, 1.25)	0.241	0.69 (0.38, 1.27)	0.232	0.63 (0.34, 1.17)	0.147
not measured	0.78 (0.11, 5.72)	0.805	0.68 (0.09, 5.21)	0.712	1.16 (0.14, 9.60)	0.889
EndoCAb, MMU/ml
<=36.5	1.0		1.0		1.0
> 36.5	0.52 (0.29, 0.92)	0.026	0.64 (0.35, 1.19)	0.163	0.64 (0.33, 1.22)	0.172
not measured	0.00 (0.00, .)	0.986	0.00 (0.00, .)	0.987	0.00 (0.00, .)	0.986
hs-CRP, mg/L
<=1.51	1.0		1.0		1.0
> 1.51	1.41 (0.75, 2.65)	0.281	1.25 (0.65, 2.43)	0.505	1.30 (0.65, 2.58)	0.454
not measured	1.04 (0.49, 2.22)	0.921	0.99 (0.46, 2.16)	0.985	0.96 (0.42, 2.16)	0.917
(b) Biomarkers fitted as continuous variables in the log_escale
LPS, pg/ml
per log_ehigher	0.87 (0.56, 1.36)	0.542	0.87 (0.52, 1.44)	0.592	0.89 (0.46, 1.71)	0.730
sCD14, ug/ml
per log_ehigher	1.02 (0.56, 1.86)	0.943	0.95 (0.49, 1.87)	0.887	0.52 (0.18, 1.50)	0.228
EndoCAb, MMU/ml
per log_ehigher	0.68 (0.48, 0.97)	0.031	0.81 (0.56, 1.16)	0.243	1.07 (0.59, 1.91)	0.830
hs-CRP, mg/L
per log_ehigher	1.14 (0.94, 1.38)	0.196	1.05 (0.85, 1.30)	0.643	0.96 (0.73, 1.26)	0.750
Note: * All models (a separate one for each biomarker) adjusted for age, CD4, VL, HCV/HBV, years of cART, duration of HIV infection at starting cART, type of cART started **Further mutually adjusted for all biomarkers

The sensitive analysis restricted to PLWHIV in Icona who started cART with CD4 count < 350/mmc showed no association between hs-CRP and the two clinical composite endpoints (data not shown).

Over years of successful cART, serious non-AIDS events (SNAEs) have become the major causes of morbidity and mortality in PLWHIV (38). Despite the complexity of SNAEs pathogenesis, persistent immune activation and inflammation appear important contributors in PLWHIV on effective treatment (7, 39). Besides, patients with incomplete CD4 recovery have been shown higher residual immune activation/inflammation, also possibly linked to microbial translocation (14, 15, 27, 28).

Here we have evaluated the association between pre-therapy systemic inflammation and microbial translocation and clinical outcome measured after cART.

Our data show some evidence for an association between pre-therapy circulating hs-CRP, but not microbial translocation, and higher risk of clinical progression after cART initiation, regardless of patients' key confounding factors such as age and nadir CD4 count, in all suggesting that pre-therapy HIV-driven pro-inflammatory milieu might be a more important mediator of long-term HIV-disease progression.

While both IL-6 and D-dimer have been shown to be independently associated with serious non-AIDS conditions or death among HIV-positive adults with suppressed virus (40), posing them as ideal biomarkers for interventional strategies, hs-CRP involvement as correlate of disease progression is less clear. Despite its lack of specificity, hs-CRP (an acute phase protein produced by the liver following general inflammation) has significant predictive value in different medical conditions (41–43). Our finding that elevated pre-therapy hs-CRP seems associated with greater risk of clinical progression supports its possible clinical role value in the management of PLWHIV.

Interestingly, by performing the same analysis according to infectious vs non-infectious clinical conditions, a modest and yet non-significant association was retained uniquely between hs-CRP and infectious-related events, whereas a feeble association was shown between EndoCAb and non-infectious diseases. Given that inflammation, as well as endotoxin immunity, has been shown to have a pathogenetic role in both infectious and non-infectious diseases, further studies should specifically address this issue in well-defined cohorts of HIV + patients on cART.

MT and activation/inflammation markers have been associated with mortality and disease progression among cART initiators in some, but not all studies (23, 30, 44, 45). This discrepancy might be due to the temporality and the setting in which biomarkers were measured (some in untreated patients and others in PLWHIV receiving cART).

According to a recent hypothesis (39), the root drivers of inflammation reflected by systemic biomarker measurements and the disease manifestations induced by inflammation are likely to differ according to nadir CD4. As a result, MT products may contribute to several end-organ diseases particularly in individuals with lower CD4 nadir, while viral reservoirs, which are established within the first days of infection, may contribute to disease progression in patients with higher CD4 count.

In our analysis, circulating MT markers failed to predict disease progression even after restricting the analyses to PLWHIV with severe pre-cART CD4 below 350 cell/mmc, suggesting that other factors probably encompass the effect of MT alone in contributing to disease progression. Whilst seemingly divergent from data by Hunt et al. (29) that demonstrate an association between on-therapy markers of gut damage/MT and mortality, our findings support the hypothesis that the clinical predictive role of gut-damage measured before cART is outweighed by the massive pre-therapy pro-inflammatory milieu, and that it might assume greater relevance later on in the setting of effective therapy.

Several limitations to our study need to be acknowledged. First, CD4 count nadir of our study population was low (243 cells/mm3 on average) and this could have influenced the results of the analysis. Further data are needed to confirm the role of both pre-therapy inflammation and MT as clinical predictors also in individuals who initiate ART earlier in the infection. Secondly, our results rely on the usual assumption of a correctly specified model (e.g. all measured confounders have been correctly accounted for) and no unmeasured confounding. We cannot rule out that the lack of association is masked by residual or unmeasured confounding factors. Third, selection bias could be an issue due to the non-negligible proportion of participants with missing data for one or more of the biomarkers (namely LPS was only available for 278 and hsCRP for 375 participants). However, main characteristics of participants with missing values and risk of outcomes were similar to those of the other analyzed groups suggesting that non-successful testing had occurred at random. Importantly, although all of the tested hypotheses have been pre-specified, many comparisons were performed without controlling for possible inflation of the type I error.

Our analysis suggests that the inflammatory marker hs-CRP measured before cART initiation is a predictor of long-term clinical disease progression. As a consequence, more frequent clinical monitoring and disease-specific screening after cART initiation should be considered for PLWHIV showing high levels of hs-CRP while still untreated.

To date, a number of studies have been conducted to evaluate strategies aimed to reduce persistent immune activation on ART, such as cART intensification, treatment of co-infections, the use of anti-inflammatory agents and improvement of immune recovery through cytokine administration (as reviewed in 7 (7)), but there is little conclusive evidence on which strategy will result in clinical benefits. Further research efforts are needed to improve our current understanding of the pathways that connect HIV virus, immune activation/inflammation and the cascade of events leading to the SNAEs before firmly indicating hs-CRP as the main candidate to guide our clinical decisions.

cART

combined antiretroviral treatment; HIV:human immunodeficiency virus; PLWHIV:people living with HIV; MT:microbial translocation; LPS:lipopolysaccharide; sCD14:soluble CD14, EndoCAb:endotoxin core antibodies; hs-CRP:high sensitive C reactive protein; SNAEs:serious non-AIDS events; PYFU:per year follow-up; eGFR:Estimated Glomerular Filtration Rate; VL:viral load; HCV:hepatitis C virus, HBV:hepatitis B virus; IQR:interquartile range.

Ethics approval and consent to participate. The Icona Foundation study was approved by the Ethics Committee (Institutional Review Board) of each participating institution. In particular, the Ethical approval was obtained by the following IRB:

Comitato di Bioetica della ASL Sassari, Sassari, Italia;
Comitato di Bioetica dell’Azienda Ospedali riuniti di Bergamo, Bergamo, Italia;
Comitato Etico dell’Azienda Ospedaliero Universitaria di Cagliari, Cagliari, Italia;
Comitato Etico locale dell’Azienda USL 10 di Firenze, Ospedale Palagi, Firenze, Italia;
Comitato Etico dell’Azienda Ospedaliera Universitaria “San Martino” di Genova, Italia;
Comitato Etico Palermo 1- AOUP Policlinico Giaccone, Palermo, Italia;
Comitato Etico dell’Azienda Ospedaliera – Polo Universitario Ospedale San Paolo, Milano, Italia;
Comitato Etico dell’Azienda Ospedaliera San Gerardo di Monza, Italia;
Comitato Etico Locale per la Sperimentazione Clinica dei Medicinali dell’Azienda Ospedaliera Universitaria Senese di Siena – Le Scotte, Italia;
Comitato Etico Indipendente Azienda Ospedaliero Universitaria di Cagliari, Italia;
Comitato Etico Regionale per la Sperimentazione Clinica della regione Toscana Sezione: AREA VASTA CENTRO;
Comitato Etico Milano Area 2, Italia;
Comitato Etico della Fondazione Centro San Raffaele del Monte Tabor, Istituto Scientifico Ospedale San Raffaele, Milano, Italia;
Comitato Etico dell’Azienda Ospedaliera Ospedale Niguarda Cà Granda, Milano, Italia;
Comitato Etico dell’Area Vasta Romagna ed IRST, Cesena, Italia;
Comitato Etico per la Sperimentazione Clinica della Provincia di Vicenza, Italia;
Comitato Etico dell’Ente Ospedaliero Ospedale Galliera di Genova, Italia;
Comitato Etico per la Sperimentazione Clinica dei Medicinali della Provincia di Venezia, Italia;
Comitato Etico dell’Azienda USL di Latina, Italia;
Comitato Etico Indipendente dell’IRCCS CRO di Aviano, Italia;
Comitato Etico Unico per la Provincia di Parma, Italia;
Comitato Etico Provinciale per la Sperimentazione Clinica, Rovigo, Italia;
Comitato Etico dell’Azienda Ospedaliero Universitaria Santa Maria della Misericordia di Udine, Italia;
Comitato Etico dell’Azienda Sanitaria dell’Alto Adige, Bolzano, Italia;
Comitato Etico dell’Azienda Ospedaliera Spedali Civili di Brescia, Italia;
Comitato Etico dell’Azienda Ospedaliera “Istituti Ospitalieri” di Cremona, Italia;
Comitato di Bioetica della Fondazione IRCCS Policlinico “San Matteo” Pavia, Italia;
Comitato Etico Interaziendale delle AA.SS.LL di Torino, Italia;
Comitato Etico dell’Azienda Ospedaliera “Ospedale di Lecco”, Italia;
Comitato Etico dell’Azienda Ospedaliera Ospedale di Circolo di Busto Arsizio, Italia;
Comitato Etico Indipendente dell’Azienda Ospedaliera-Universitaria di Bologna, Policlinico S Orsola Malpighi, Bologna, Italia;
Comitato Etico Provinciale di Reggio Emilia, Italia;
Comitato Etico per la sperimentazione Clinica della Provincia di Padova, Italia;
Comitato Etico provinciale di Modena, Italia;
Comitato Etico Provinciale di Ferrara, Italia;
Comitato Etico dell’Azienda Ospedaliero-Universitaria Ospedali Riuniti Umberto I-GM Lancisi- G Salesi di Ancona, Italia;
Comitato Etico Interzonale della ASUR zona Territoriale 8 di Civitanova Marche e Zona Territoriale 9 di Macerata, Italia;
Comitato Etico per la Sperimentazione Clinica dei Farmaci della AUSL di Pescara, Italia;
Comitato Etico dell’Università Cattolica del Sacro Cuore, Policlinico Universitario A Gemelli, Roma, Italia;
Comitato Etico dell’IRCCS Istituto Nazionale per le Malattie Infettive “Lazzaro Spallanzani”, Roma, Italia;
Comitato Etico per la Sperimentazione dei Medicinali dell’azienda ospedaliera-Universitaria Careggi di Firenze, Italia;
Comitato Etico delle Aziende Sanitarie dell’Umbria di Perugia, Italia;
Comitato Etico per le Attività Biomediche dell’Università degli Studi “Federico II” di Napoli, Italia;
Comitato Etico dell'azienda Policlinico Umberto I di Roma, Italia;
Comitato Etico Indipendente dell'Azienda Ospedaliera Universitaria Policlinico Tor Vergata di Roma Italia;
Comitato Etico Centrale IRCCS Lazio-Sezione IRCCS IFO-Fondazione GB Bietti, Roma Italia;
Comitato Etico Lazio 1, Roma Italia;
Comitato Etico dell’Università “Sapienza”, Roma Italia;
Comitato Etico delle Aziende Sanitarie della Regione dell'Umbria - Ellera di Corciano (PG), Roma Italia;
Comitato Etico Indipendente Locale dell'Azienda Ospedaliera Ospedale Policlinico Consorziale di Bari, Italia;
Comitato Etico dell'Azienda Ospedaliera Domenico Cotugno di Napoli, Italia;
Comitato di Etica per la Ricerca Biomedica dell’Università degli Studi “G. D’Annunzio” e della ASL di Chieti, Italia;
Comitato Etico Interprovinciale Area 1 dell' Azienda Ospedaliero Universitaria "Ospedali Riuniti" di Foggia Italia;
Comitato Etico Catania 2 - Italia;
Comitato Etico di Messina, Italia;
Comitato Etico Locale per la Sperimentazione Clinica dell’Azienda Ospedaliera “L Sacco”, Milano, Italia

All of the individuals enrolled provided a written informed consent at the time of the enrolment or, if subjects are under 18, from a parent and/or legal guardian. Likewise, in case of dead, informed consent for study participation was obtained by parent and/or legal guardian. All procedures of the study were performed in accordance with the 1964 The Declaration of Helsinki and its later amendments.

Consent for publication. Not applicable

Availability of data and materials. The datasets used and/or analyzed during this study are available from the corresponding author on reasonable request.

Competing interests. EM, ACL, ACa, ACo, SLC, SC, EQR and MAU have nothing to disclose. AA outside the submitted work reports grants, personal fees and non‐financial support from Gilead Sciences, ViiV Healthcare and Bristol Myers Squibb, grants and personal fees from Janssen‐Cilag, personal fees from Merck Sharp and Dohme and personal fees and non‐financial support from Abbvie. ADM outside the submitted work reports grants and personal fees from Gilead Sciences, ViiV Healthcare and Merck Sharp and Dohme and personal fees from Janssen and Bristol Myers Squibb. GM outside the submitted work reports personal fees from Gilead Sciences and ViiV Healthcare.

Funding. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Icona Foundation is sponsored by unrestricted educational grants of Abbvie, BMS, Gilead, Janssen-Cilag, MSD, ViiV Italy

Authors’ contributions. EM performed the laboratory assessments, wrote the manuscript and referred to appropriate literature. ACL contributed to the design of the study, was the main responsible person for data analysis and contributed to the article drafting. ACa, ACo, SLC, SC, EQR and MAU contributed to data interpretation and article drafting. AA and ADM coordinated the Icona Foundation, contributed to data interpretation and reviewed the manuscript. GM conceived and supervised the study and finalized the draft of the manuscript. All authors agreed with final article submission.

Acknowledgments. Presented in part at European AIDS Clinical Society Conference - EACS, October 21-24, 2015 Barcelona, Spain; oral poster # BPD3/1

Icona Foundation Study Group

BOARD OF DIRECTORS: A d’Arminio Monforte (President), A Antinori (Vice-President), M Andreoni, A Castagna, F Castelli, R Cauda, G Di Perri, M Galli, R Iardino, G Ippolito, A Lazzarin, GC Marchetti, G Rezza, F von Schloesser, P Viale.

SCIENTIFIC SECRETARY: A d’Arminio Monforte, A Antinori, A Castagna, F Ceccherini-Silberstein, A Cozzi-Lepri, E Girardi, S Lo Caputo, C Mussini, M Puoti, CF Perno.

STEERING COMMITTEE: A Antinori, F Bai, C Balotta, A Bandera, S Bonora, M Borderi, A Calcagno, A Capetti, MR Capobianchi, A Castagna, F Ceccherini-Silberstein, S Cicalini, A Cingolani, P Cinque, A Cozzi-Lepri, A d’Arminio Monforte, A De Luca, A Di Biagio, E Girardi, N Gianotti, A Gori, G Guaraldi, G Lapadula, M Lichtner, S Lo Caputo, G Madeddu, F Maggiolo, G Marchetti, L Monno, C Mussini, S Nozza, CF Perno, C Pinnetti, M Puoti, E Quiros Roldan, R Rossotti, S Rusconi, MM Santoro, A Saracino, L Sarmati.

STATISTICAL AND MONITORING TEAM: A Cozzi-Lepri, I Fanti, L Galli, P Lorenzini, A Rodano’, M Macchia, A Tavelli.

BIOLOGICAL BANK INMI: F Carletti, S Carrara, A Di Caro, S Graziano, F Petroni, G Prota, S Truffa.

PARTICIPATING PHYSICIANS AND CENTERS: Italy A Giacometti, A Costantini, V Barocci (Ancona); G Angarano, L Monno, E Milano (Bari); F Maggiolo, C Suardi (Bergamo); P Viale, V Donati, G Verucchi (Bologna); F Castelnuovo, C Minardi, E Quiros Roldan (Brescia); B Menzaghi, C Abeli (Busto Arsizio); B Cacopardo, B Celesia (Catania); J Vecchiet, K Falasca (Chieti); A Pan, S Lorenzotti (Cremona); L Sighinolfi, D Segala (Ferrara); P Blanc, F Vichi (Firenze); G Cassola, C Viscoli, A Alessandrini, N Bobbio, G Mazzarello (Genova); M Lichtner, S Vita, (Latina); P Bonfanti, C Molteni (Lecco); A Chiodera, P Milini (Macerata); G Nunnari, G Pellicanò (Messina); A d’Arminio Monforte, M Galli, A Lazzarin, G Rizzardini, M Puoti, A Castagna, ES Cannizzo, MC Moioli, R Piolini, D Bernacchia, S Salpietro, C Tincati, (Milano); C Mussini, C Puzzolante (Modena); C Migliorino, G Lapadula (Monza); V Sangiovanni, G Borgia, V Esposito, F Di Martino, I Gentile, V Rizzo (Napoli); AM Cattelan, S Marinello (Padova); A Cascio, M Trizzino (Palermo); F Baldelli, E Schiaroli (Perugia); G Parruti, F Sozio (Pescara); G Magnani, MA Ursitti (Reggio Emilia); M Andreoni, A Antinori, R Cauda, A Cristaudo, V Vullo, R Acinapura, G Baldin, M Capozzi, A Mondi, A Cingolani, M Rivano Capparuccia, G Iaiani, A Latini, R Gagliardini, MM Plazzi, S Savinelli, A Vergori (Roma); M Cecchetto, F Viviani (Rovigo); G Madeddu, A De Vito(Sassari); A De Luca, B Rossetti (Siena); A Franco, R Fontana Del Vecchio (Siracusa); D Francisci, C Di Giuli (Terni); P Caramello, G Di Perri, S Bonora, GC Orofino, M Sciandra (Torino); M Bassetti, A Londero (Udine); G Pellizzer, V Manfrin (Vicenza); G Starnini, A Ialungo (Viterbo).

Connick E, Lederman MM, Kotzin BL, Spritzler J, Kuritzkes DR, St Clair M, et al. Immune reconstitution in the first year of potent antiretroviral therapy and its relationship to virologic response. J Infect Dis. 2000;181(1):358-63.
Guihot A, Bourgarit A, Carcelain G, Autran B. Immune reconstitution after a decade of combined antiretroviral therapies for human immunodeficiency virus. Trends Immunol. 2011;32(3):131-7.
Li TS, Tubiana R, Katlama C, Calvez V, Ait Mohand H, Autran B. Long-lasting recovery in CD4 T-cell function and viral-load reduction after highly active antiretroviral therapy in advanced HIV-1 disease. Lancet. 1998;351(9117):1682-6.
Marcus JL, Chao CR, Leyden WA, Xu L, Quesenberry CP, Jr., Klein DB, et al. Narrowing the Gap in Life Expectancy Between HIV-Infected and HIV-Uninfected Individuals With Access to Care. J Acquir Immune Defic Syndr. 2016;73(1):39-46.
Nakagawa F, Lodwick RK, Smith CJ, Smith R, Cambiano V, Lundgren JD, et al. Projected life expectancy of people with HIV according to timing of diagnosis. Aids. 2012;26(3):335-43.
Survival of HIV-positive patients starting antiretroviral therapy between 1996 and 2013: a collaborative analysis of cohort studies. Lancet HIV. 2017;4(8):e349-e56.
Hsu DC, Sereti I. Serious Non-AIDS Events: Therapeutic Targets of Immune Activation and Chronic Inflammation in HIV Infection. Drugs. 2016;76(5):533-49.
Mutoh Y, Nishijima T, Inaba Y, Tanaka N, Kikuchi Y, Gatanaga H, et al. Incomplete Recovery of CD4 Cell Count, CD4 Percentage, and CD4/CD8 Ratio in Patients With Human Immunodeficiency Virus Infection and Suppressed Viremia During Long-term Antiretroviral Therapy. Clin Infect Dis. 2018;67(6):927-33.
Zoufaly A, Cozzi-Lepri A, Reekie J, Kirk O, Lundgren J, Reiss P, et al. Immuno-virological discordance and the risk of non-AIDS and AIDS events in a large observational cohort of HIV-patients in Europe. PLoS One. 2014;9(1):e87160.
Tan R, Westfall AO, Willig JH, Mugavero MJ, Saag MS, Kaslow RA, et al. Clinical outcome of HIV-infected antiretroviral-naive patients with discordant immunologic and virologic responses to highly active antiretroviral therapy. J Acquir Immune Defic Syndr. 2008;47(5):553-8.
Lapadula G, Cozzi-Lepri A, Marchetti G, Antinori A, Chiodera A, Nicastri E, et al. Risk of clinical progression among patients with immunological nonresponse despite virological suppression after combination antiretroviral treatment. AIDS. 2013;27(5):769-79.
Loutfy MR, Genebat M, Moore D, Raboud J, Chan K, Antoniou T, et al. A CD4+ cell count <200 cells per cubic millimeter at 2 years after initiation of combination antiretroviral therapy is associated with increased mortality in HIV-infected individuals with viral suppression. J Acquir Immune Defic Syndr. 2010;55(4):451-9.
Global Trends in CD4 Cell Count at the Start of Antiretroviral Therapy: Collaborative Study of Treatment Programs. Clin Infect Dis. 2018;66(6):893-903.
Saison J, Ferry T, Demaret J, Maucort-Boulch D, Venet F, Perpoint T, et al. Relationship between discordant response to HAART, Tregs, immune activation and low-level viraemia. J Int AIDS Soc. 2014;17(4 Suppl 3):19672.
Anthony KB, Yoder C, Metcalf JA, DerSimonian R, Orenstein JM, Stevens RA, et al. Incomplete CD4 T cell recovery in HIV-1 infection after 12 months of highly active antiretroviral therapy is associated with ongoing increased CD4 T cell activation and turnover. J Acquir Immune Defic Syndr. 2003;33(2):125-33.
Goicoechea M, Smith DM, Liu L, May S, Tenorio AR, Ignacio CC, et al. Determinants of CD4+ T cell recovery during suppressive antiretroviral therapy: association of immune activation, T cell maturation markers, and cellular HIV-1 DNA. J Infect Dis. 2006;194(1):29-37.
Massanella M, Negredo E, Perez-Alvarez N, Puig J, Ruiz-Hernandez R, Bofill M, et al. CD4 T-cell hyperactivation and susceptibility to cell death determine poor CD4 T-cell recovery during suppressive HAART. AIDS. 2010.
Borges AH, Silverberg MJ, Wentworth D, Grulich AE, Fatkenheuer G, Mitsuyasu R, et al. Predicting risk of cancer during HIV infection: the role of inflammatory and coagulation biomarkers. Aids. 2013;27(9):1433-41.
Ford ES, Greenwald JH, Richterman AG, Rupert A, Dutcher L, Badralmaa Y, et al. Traditional risk factors and D-dimer predict incident cardiovascular disease events in chronic HIV infection. Aids. 2010;24(10):1509-17.
Tien PC, Choi AI, Zolopa AR, Benson C, Tracy R, Scherzer R, et al. Inflammation and mortality in HIV-infected adults: analysis of the FRAM study cohort. J Acquir Immune Defic Syndr. 2010;55(3):316-22.
Kuller LH, Tracy R, Belloso W, De Wit S, Drummond F, Lane HC, et al. Inflammatory and coagulation biomarkers and mortality in patients with HIV infection. PLoS Med. 2008;5(10):e203.
Balagopal A, Asmuth DM, Yang WT, Campbell TB, Gupte N, Smeaton L, et al. Pre-cART Elevation of CRP and CD4+ T-Cell Immune Activation Associated With HIV Clinical Progression in a Multinational Case-Cohort Study. J Acquir Immune Defic Syndr. 2015;70(2):163-71.
Marchetti G, Cozzi-Lepri A, Merlini E, Bellistrì GM, Castagna A, Galli M, et al. Microbial translocation predicts disease progression of HIV-infected antiretroviral-naive patients with high CD4+ cell count. AIDS. 2011;25(11):1385-94.
Leon A, Leal L, Torres B, Lucero C, Inciarte A, Arnedo M, et al. Association of microbial translocation biomarkers with clinical outcome in controllers HIV-infected patients. Aids. 2015;29(6):675-81.
Vassallo M, Mercie P, Cottalorda J, Ticchioni M, Dellamonica P. The role of lipopolysaccharide as a marker of immune activation in HIV-1 infected patients: a systematic literature review. Virol J. 2012;9:174.
Brenchley J, Price D, Schacker T, Asher T, Silvestri G, Rao S, et al. Microbial translocation is a cause of systemic immune activation in chronic HIV infection. Nat Med. 2006;12(12):1365-71.
Marchetti G, Bellistri GM, Borghi E, Tincati C, Ferramosca S, La Francesca M, et al. Microbial translocation is associated with sustained failure in CD4+ T-cell reconstitution in HIV-infected patients on long-term highly active antiretroviral therapy. AIDS. 2008;22(15):2035-8.
Jiang W, Lederman MM, Hunt P, Sieg SF, Haley K, Rodriguez B, et al. Plasma levels of bacterial DNA correlate with immune activation and the magnitude of immune restoration in persons with antiretroviral-treated HIV infection. J Infect Dis. 2009;199(8):1177-85.
Hunt PW, Sinclair E, Rodriguez B, Shive C, Clagett B, Funderburg N, et al. Gut epithelial barrier dysfunction and innate immune activation predict mortality in treated HIV infection. J Infect Dis. 2014;210(8):1228-38.
Tenorio AR, Zheng Y, Bosch RJ, Krishnan S, Rodriguez B, Hunt PW, et al. Soluble markers of inflammation and coagulation but not T-cell activation predict non-AIDS-defining morbid events during suppressive antiretroviral treatment. J Infect Dis. 2014;210(8):1248-59.
Danel C, Moh R, Gabillard D, Badje A, Le Carrou J, Ouassa T, et al. A Trial of Early Antiretrovirals and Isoniazid Preventive Therapy in Africa. N Engl J Med. 2015;373(9):808-22.
Lundgren JD, Babiker AG, Gordin F, Emery S, Grund B, Sharma S, et al. Initiation of Antiretroviral Therapy in Early Asymptomatic HIV Infection. N Engl J Med. 2015;373(9):795-807.
Hunt PW, Cao HL, Muzoora C, Ssewanyana I, Bennett J, Emenyonu N, et al. Impact of CD8+ T-cell activation on CD4+ T-cell recovery and mortality in HIV-infected Ugandans initiating antiretroviral therapy. Aids. 2011;25(17):2123-31.
Lee SA, Mefford JA, Huang Y, Witte JS, Martin JN, Haas DW, et al. Host genetic predictors of the kynurenine pathway of tryptophan catabolism among treated HIV-infected Ugandans. Aids. 2016;30(11):1807-15.
Byakwaga H, Boum Y, 2nd, Huang Y, Muzoora C, Kembabazi A, Weiser SD, et al. The kynurenine pathway of tryptophan catabolism, CD4+ T-cell recovery, and mortality among HIV-infected Ugandans initiating antiretroviral therapy. J Infect Dis. 2014;210(3):383-91.
Siedner MJ, Bwana MB, Asiimwe S, Musinguzi N, Castillo-Mancilla J, Amanyire G, et al. Inflammatory biomarkers prior to antiretroviral therapy as prognostic markers of 12-month mortality in South Africa and Uganda. Aids. 2019;33(13):2043-8.
d'Arminio Monforte A, Lepri AC, Rezza G, Pezzotti P, Antinori A, Phillips AN, et al. Insights into the reasons for discontinuation of the first highly active antiretroviral therapy (HAART) regimen in a cohort of antiretroviral naive patients. I.CO.N.A. Study Group. Italian Cohort of Antiretroviral-Naive Patients. Aids. 2000;14(5):499-507.
Causes of death in HIV-1-infected patients treated with antiretroviral therapy, 1996-2006: collaborative analysis of 13 HIV cohort studies. Clin Infect Dis. 2010;50(10):1387-96.
Hunt PW, Lee SA, Siedner MJ. Immunologic Biomarkers, Morbidity, and Mortality in Treated HIV Infection. J Infect Dis. 2016;214 Suppl 2:S44-50.
Grund B, Baker JV, Deeks SG, Wolfson J, Wentworth D, Cozzi-Lepri A, et al. Relevance of Interleukin-6 and D-Dimer for Serious Non-AIDS Morbidity and Death among HIV-Positive Adults on Suppressive Antiretroviral Therapy. PLoS One. 2016;11(5):e0155100.
Westhorpe CL, Schneider HG, Dunne M, Middleton T, Sundararajan V, Spelman T, et al. C-reactive protein as a predictor of cardiovascular risk in HIV-infected individuals. Sex Health. 2014;11(6):580-2.
Roine I, Arguedas A, Faingezicht I, Rodriguez F. Early detection of sequela-prone osteomyelitis in children with use of simple clinical and laboratory criteria. Clin Infect Dis. 1997;24(5):849-53.
Mora S, Ridker PM. Justification for the Use of Statins in Primary Prevention: an Intervention Trial Evaluating Rosuvastatin (JUPITER)--can C-reactive protein be used to target statin therapy in primary prevention? Am J Cardiol. 2006;97(2a):33a-41a.
Sandler NG, Wand H, Roque A, Law M, Nason MC, Nixon DE, et al. Plasma levels of soluble CD14 independently predict mortality in HIV infection. J Infect Dis. 2011;203(6):780-90.
Boulware DR, Hullsiek KH, Puronen CE, Rupert A, Baker JV, French MA, et al. Higher levels of CRP, D-dimer, IL-6, and hyaluronic acid before initiation of antiretroviral therapy (ART) are associated with increased risk of AIDS or death. J Infect Dis. 2011;203(11):1637-46.

Competing interest reported. EM, ACL, ACa, ACo, SLC, SC, EQR and MAU have nothing to disclose. AA outside the submitted work reports grants, personal fees and non‐financial support from Gilead Sciences, ViiV Healthcare and Bristol Myers Squibb, grants and personal fees from Janssen‐Cilag, personal fees from Merck Sharp and Dohme and personal fees and non‐financial support from Abbvie. ADM outside the submitted work reports grants and personal fees from Gilead Sciences, ViiV Healthcare and Merck Sharp and Dohme and personal fees from Janssen and Bristol Myers Squibb. GM outside the submitted work reports personal fees from Gilead Sciences and ViiV Healthcare.

Download PDF

Editorial decision: Major revision
31 Mar, 2021
Reviews received at journal
26 Mar, 2021
Reviewers agreed at journal
03 Mar, 2021
Reviews received at journal
21 Feb, 2021
Reviewers agreed at journal
11 Feb, 2021
Reviewers invited by journal
11 Feb, 2021
Editor assigned by journal
03 Feb, 2021
Editor invited by journal
02 Feb, 2021
Submission checks completed at journal
02 Feb, 2021
First submitted to journal
22 Jan, 2021

You are reading this latest preprint version

Pre-cART Inflammation, Microbial Translocation and Long-Term Probability of Clinical Progression in People Living With HIV

Status:

Version 1

Abstract

Introduction

Methods

Results

Discussion

Conclusions

List Of Abbreviations

Declarations

References

Additional Declarations

Status:

Version 1