Combination of inflammatory and vascular markers in the febrile phase of dengue is associated with more severe outcomes.

Background: Early identification of severe dengue patients is important regarding patient management and resource allocation. We investigated the association of 10 biomarkers (VCAM-1, SDC-1, Ang-2, IL-8, IP-10, IL-1RA, sCD163, sTREM-1, ferritin, CRP) with the development of severe/moderate dengue (S/MD).
Methods: We performed a nested case-control study from a multi-country study. A total of 281 S/MD and 556 uncomplicated dengue cases were included.
Results: On days 1-3 from symptom onset, higher levels of any biomarker increased the risk of developing S/MD. When assessing together, SDC-1 and IL-1RA were stable, while IP-10 changed the association from positive to negative; others showed weaker associations. The best combinations associated with S/MD comprised IL-1RA, Ang-2, IL-8, ferritin, IP-10, and SDC-1 for children, and SDC-1, IL-8, ferritin, sTREM-1, IL-1RA, IP-10, and sCD163 for adults. Conclusions: Our findings assist the development of biomarker panels for clinical use and could improve triage and risk prediction in dengue patients. Funding: This study was supported by the EU's Seventh Framework Programme (FP7-281803 IDAMS), the WHO, and the Bill and Melinda Gates Foundation.

Introduction

Dengue is the most common arboviral disease to affect humans globally. In 2019, the World Health Organization (WHO) identified dengue as one of the top 10 threats to global health (World Health Organization, 2019). Transmission occurs in 129 countries, with an estimated 3.9 billion people being at risk (World Health Organization, 2020). Over the last two decades, the number of reported cases per year has increased more than eight-fold (World Health Organization, 2020), and in 2020 the annual number of dengue virus (DENV) infections was estimated to be 105 million, with 51 million cases being clinically apparent (Cattarino et al., 2020). With climate change, increased travel and urbanization, this rise is forecasted to continue over the coming decades (Whitehorn and Yacoub, 2019; Yacoub et al., 2011). Despite the large disease burden, there is still no specific treatment for dengue, and the only licensed vaccine is recommended only in individuals with earlier dengue infection (Redoni et al., 2020).

In many dengue-endemic settings, seasonal epidemics can rapidly overwhelm fragile health systems. Although most symptomatic dengue infections are self-limiting, a small proportion of patients develop complications, most of which manifest at around 4–6 days from symptom onset. Thus, large numbers of patients require regular assessments to identify complications should they arise. The accurate and early identification of such patients, particularly within the first 3 days of illness in the febrile phase, should allow for appropriate care to be provided and potentially increase health system effectiveness. Although the 2009 WHO dengue guidelines set out specific warning signs for use in patient triage, utility of these guidelines at identifying those at risk for complications remains limited (Morra et al., 2018).

The pathogenesis of dengue involves a complex interplay between viral factors and the host response. It is hypothesized that an excessive immune response acting through inflammatory mediators can lead to the observed manifestations of bleeding, shock, and organ dysfunction. Studies have shown that in secondary infections, adaptive immune activation can result in high circulating levels of plasma cytokines and chemokines (Katzelnick et al., 2017; Midgley et al., 2011; Screaton et al., 2015). Binding of viral NS1 protein onto endothelial cells can act in concert with vasoactive substances, cytokines, and chemokines, to result in endothelial activation and glycocalyx disruption, and these processes likely underlie the increased vascular permeability and coagulopathy (McBride and Khanh Lam, 2020; Modhiran et al., 2015; St John et al., 2013).

The role of blood biomarkers in predicting severe outcomes has been investigated in many studies, but mostly at later time-points or at hospital admission and many of these biomarkers either peak too late in the disease course or have too short a half-life to be clinically useful (Ab-Rahman et al., 2016; John et al., 2015; Oliveira et al., 2017; Puerta-Guardo et al., 2019; Rathore et al., 2020; Robinson and Einav, 2020; S S et al., 2017; Soo et al., 2017; Vasey et al., 2020; Yacoub et al., 2017; Yacoub et al., 2016b; Yong et al., 2017). Acknowledging these characteristics, we selected 10 candidate biomarkers from the vascular, immunological, and inflammatory pathways with good evidence supporting their involvement in the pathogenesis of dengue infection – focusing on those likely to be increased early in the disease course. We included vascular cell adhesion molecule-1 (VCAM-1), syndecan-1 (SDC-1), and angiopoietin-2 (Ang-2) because they represent endothelial activation and glycocalyx integrity (Han et al., 2019; Mapalagamage et al., 2020; Suwarto et al., 2017; Yacoub et al., 2016a). For markers of immune activation, we measured interleukin-8 (IL-8) and interferon gamma-induced protein-10 (IP-10) as these are associated with disease severity (Oliveira et al., 2017; Pandey et al., 2015; Rathakrishnan et al., 2012), and IL-1 receptor antagonist (IL-1RA), soluble cluster of differentiation 163 (sCD163), and soluble triggering receptor expressed on myeloid cells-1 (sTREM-1) as these are activation markers of monocytes and macrophages, the major targets for dengue replication (Ab-Rahman et al., 2016; John et al., 2015; S S et al., 2017). For markers of general inflammation, we included ferritin and C-reactive protein (CRP) (Ab-Rahman et al., 2016; Finkelstein et al., 2020; Mukherjee and Tripathi, 2020; Soundravally et al., 2015; Vuong et al., 2020).

The aims of this study were: (1) to investigate the association of these ten biomarkers with development of more severe dengue outcomes, (2) to find the best combination of biomarkers associated with more severe dengue outcomes. The results of the second aim could help in developing multiplex panels for use in outpatient settings to rapidly identify patients who require hospitalization.

Materials and methods

Study design

We conducted a nested case-control study using the samples and clinical information from a large multi-country observational study named ‘Clinical evaluation of dengue and identification of risk factors for severe disease’ (IDAMS study, NCT01550016) (Jaenisch et al., 2016). The IDAMS study and the blood sample analysis were approved by the Scientific and Ethics Committees of all study sites (Hospital for Tropical Diseases [Ho Chi Minh City, Vietnam] Ref No 03/HDDD-05/01/2018; Angkor Hospital for Children [Siem Reap, Cambodia] Ref No 0146/18-AHC; University of Malaya Medical Centre [Kuala Lumpur, Malaysia] Ref No 201865–6361) and by the Oxford Tropical Research Ethics Committee (OxTREC Ref No 502–18). There were 7428 participants in eight countries across Asia and Latin America enrolled in the IDAMS study. Patients were eligible for inclusion if they were aged 5 years or older, had fever or history of fever for less than 72 hr, and had symptoms consistent with dengue, with no features strongly suggestive of another disease. Participants were followed daily with a standard schedule of clinical examination and blood samples. Individual management (including hospitalization) was in accordance with routine practice at each study site. All diagnostic samples were processed and stored following specific protocols, and later transferred to designated sites for diagnostic testing in order to ensure consistency. Laboratory-confirmed dengue was defined by a positive reverse transcriptase polymerase chain reaction (RT-PCR) or a positive NS1 enzyme-linked immunosorbent assay (ELISA) result. Immune status was classified based on capture IgG results on paired samples. A probable primary infection was defined by two negative IgG results on two consecutive specimens taken at least 2 days apart, with at least one specimen obtained during the convalescent phase (after illness day 5). A probable secondary infection was defined by a positive IgG result identified during either or both the febrile and convalescent phases. In all other cases with the absence of suitable specimens at the appropriate time points immune status was classified as inconclusive. Each participant was given an overall severity grade (severe, moderate, or uncomplicated dengue), using all available information and a grading system in line with current guidelines and recommendations to classify clinical endpoints in dengue clinical trials (Tomashek et al., 2018).

Study population

Of the 2694 laboratory-confirmed dengue cases in the IDAMS study, 38 and 266 cases were classified as severe and moderate dengue respectively. For this study, we selected all severe and moderate cases from five study sites in four countries (Vietnam, Cambodia, Malaysia, and El Salvador), as residual plasma from these countries’ sample sets was available at the Oxford University Clinical Research Unit (OUCRU) in Ho Chi Minh City, Vietnam. For the control group, we selected patients with uncomplicated dengue with similar geographic and demographic characteristics at a 2:1 ratio. In total 281 cases and 556 controls were included in the analysis (Figure 1).

Figure 1.

Study flowchart.

*The IDAMS study was performed in eight countries across Asia and Latin America. For this study, we selected cases in four countries (Vietnam, Cambodia, Malaysia, and El Salvador) as the blood samples were stored at the laboratory of the Oxford University Clinical Research Unit in Ho Chi Minh City, Vietnam.

Laboratory evaluation (details in Appendix 1)

The biomarkers were measured at two time points: at enrollment (illness day 1–3) and after recovery (day 10–31 post-symptom onset), if available. Eight biomarkers (CRP and ferritin excepted) were combined in a premixed magnetic bead panel (Cat No. LXSAHM; R and D). CRP was measured using a separate commercial magnetic bead panel (Cat. No. HCVD3MAG-67K; EMD Millipore Corporation). These panels were analyzed using the Luminex200 analyzer with the Luminex calibration (Cat. No. LX200-CAL-K25) and verification kits (Cat. No. LX200-CON-K25). Ferritin was measured using the Human Ferritin ELISA kit (Cat. No. ARG80501, Arigo). All tests were done according to the manufacturer’s specifications.

Study endpoints (details in Appendix 2)

The primary endpoint was combined severe and moderate dengue (S/MD), defined by the development of severe or moderate grades of any of the following – plasma leakage, haemorrhage, or organ impairment (including neurologic, hepatic, or cardiac involvement) (Appendix 2—table 1). We combined severe and moderate dengue to form the primary endpoint (S/MD) as severe dengue events were rare; this combined endpoint is relevant to clinical practice since the moderate group is likely to develop complications and therefore may also require medical intervention and hospitalization. We studied three secondary endpoints: severe dengue alone, severe dengue or dengue with warning signs according to the 2009 WHO classification, and hospitalization. These endpoints were selected as they also reflect the disease burden and severity and are generalizable across different settings. The decision to hospitalize was based only on clinical judgement and local guidelines particular to each study site, without use of any biomarker information.

Appendix 2—table 1.

Definition of severe and moderate dengue components.

Endpoint	Definition
Severe plasma leakage	Dengue shock syndrome or respiratory distress due to plasma leakage
Moderate plasma leakage	Did not fulfill criteria for severe plasma leakage and had at least one of the following criteria: (1) maximum haematocrit change was 20% or more, and (2) having evidence of fluid accumulation
Severe bleeding	Any bleeding into a critical organ or required any blood transfusion of packed red cells or whole blood without pre-anaemia, or bleeding with complication
Moderate bleeding	Did not fulfill criteria for severe bleeding and had at least one of the following criteria: (1) severe bleeding by clinical judgement, (2) bleeding required any blood transfusion other than packed red cells or whole blood, (3) bleeding required other intervention (e.g. nasal packing, cross-match, etc.), and (4) receiving packed red cells or whole blood with pre-existing anaemia and with a consistent haemoglobin value
Severe neurologic involvement	Abnormal neurologic examination and neurologic involvement that resulted in death or ongoing sequelae that impaired daily function, or required intubation, shunting or intensive care
Moderate neurology involvement	Single convulsion without hospitalization or other complication
Severe hepatic involvement	Jaundice or coagulopathy or encephalopathy
Moderate hepatic involvement	Any alanine aminotransferase (ALT) or aspartate aminotransferase (AST) result of 400 IU/L or more
Severe other major organ failure	Creatine kinase or other enzymes (e.g. troponin) abnormalities and functional abnormalities (e.g. reduced cardiac ejection fraction less than 50% or new electrocardiogram [ECG] abnormalities) or required specific intervention (e.g. inotropic support)
Moderate other major organ failure	Troponin abnormalities alone or creatine kinase abnormalities without cardiac ejection fraction less than 50%

Statistical analysis (details in Appendix 3)

Plasma levels of all biomarkers were transformed to the base-2 logarithm (log-2) before analysis as a right skewed distribution was apparent. We used a logistic regression model for all endpoints. We investigated the non-linear effects of all biomarkers and age on the endpoints, using restricted cubic splines with three knots at the 10th, 50th, and 90th percentiles.

For the first aim, that is to investigate the association of all biomarkers with the primary and secondary endpoints, we performed two different analyses: (1) fitting models for each biomarker separately (‘single models’) and (2) fitting models including all biomarkers together (‘global models’). In the ‘single models’ for a particular biomarker, only that biomarker along with age and their interaction were included, whereas in the ‘global models’ all the biomarkers along with their interactions with age were included. We performed the ‘global model’ in order to investigate the influence of the biomarkers when considering them together and this was also the initial step to develop models for the second aim. Results are reported as odds ratio (OR) and presented graphically.

For the second aim to find the best combination of biomarkers associated with the primary endpoint, we built upon the results from the first aim to fit separate models for children and adults (<15 versus ≥15 years of age), as differences were apparent by age. We used variable selection based on the ‘best subset’ approach (Hastie et al., 2017; Hocking and Leslie, 1967). Briefly, this approach screened all possible combinations of biomarkers and selected the best based on the Akaike information criterion (AIC). We chose AIC as a ranking measurement because it quantifies goodness of fit, while guarding against over-fitting. The marker combination with the lowest AIC was taken as the best. From an ‘initial model’ including all biomarkers, we determined the best general combination and the best combinations of 2, 3, 4, and 5 biomarkers. We then performed a bootstrap procedure to check the robustness (stability) of the selected models. For this we resampled 1000 times with replacement from the original dataset. For each of these 1000 bootstrap samples, we performed the ‘best subset’ procedure similar to above to determine the best combination. We calculated the selection frequency of each marker combination over the 1000 samples. The frequency of the combination that was selected when using the original dataset in relation to the other combinations characterizes robustness of the selection.

We carried out several sensitivity analyses. First, we fitted the single and global models taking into account potential differences between serotypes by including serotype variable along with its interaction with the biomarkers. Second, we included viremia (viral RNA measured by RT-PCR) levels as an additional biomarker and performed the single model, global model and best subset procedure. Higher viremia levels have been associated with worse disease outcomes; however, viral load was not considered in the main analysis as the focus was on host markers with the potential for combining in a biomarker rapid test.

All analyses were done using the statistical software R version 3.6.3 (R Core Development Team, 2020) and the packages ‘rms’ (Harrell Jr, 2019), ‘MuMIn’ (Bartoń, 2020) and ‘ggplot2’ (Wickham, 2016). The code is available on GitHub (Vuong, 2021b; copy archived at swh:1:rev:847d8e0f564eeb3f075b443205fb3384598bc2b4).

Results

Patient characteristics

The majority of the patients were from Vietnam (640 cases, 76%). Median (1st, 3rd quartiles) age of the case and control groups were 12 (9, 22) and 16 (10, 24) years. Among the S/MD group, 127 cases (45%) were children and 154 cases (55%) were adults. Male gender was predominant (60% and 54% in the case and control groups respectively). Serotype distribution was similar between the S/MD and control groups, with DENV-1 predominating (42%), particularly in children (48%). Host immune status however differed: there was a higher proportion of secondary infections in the S/MD group compared with controls (78% versus 64%, respectively) and this was consistent in both children and adults. The S/MD had a slightly lower percentage of obese patients than the control group (10% versus 14%). As expected, hospitalization was more common in the S/MD group (57% versus 31%) (Table 1). Overall, 38 patients developed severe dengue, most were severe plasma leakage (33/38 cases, 87%) and 29/38 (76%) were children. Most of the moderate dengue cases were plasma leakage and/or hepatic involvement (Appendix 4—table 1).

Table 1.

Summary of clinical data by primary outcome.

	All patients		Children		Adults
	Uncomplicated dengue (N = 556)	Severe/moderate dengue (N = 281)	Uncomplicated dengue (N = 337)	Severe/moderate dengue (N = 127)	Uncomplicated dengue (N = 219)	Severe/moderate dengue (N = 154)
Country, n (%)
- Cambodia	39 (7)	30 (11)	37 (11)	29 (23)	2 (1)	1 (1)
- El Salvador	23 (4)	18 (6)	23 (7)	18 (14)	0 (0)	0 (0)
- Malaysia	58 (10)	29 (10)	3 (1)	1 (1)	55 (25)	28 (18)
- Vietnam	436 (78)	204 (73)	274 (81)	79 (62)	162 (74)	125 (81)
Age (years), median (1st, 3rd quartiles)	12 (9, 22)	16 (10, 24)	10 (8, 12)	10 (7, 12)	26 (20, 34)	22 (18, 30)
Gender male, n (%)	299 (54)	170 (60)	173 (51)	80 (63)	126 (58)	90 (58)
Illness day at enrolment, n (%)
- 1	91 (16)	49 (17)	57 (17)	25 (20)	34 (16)	24 (16)
- 2	260 (47)	130 (46)	156 (46)	52 (41)	104 (47)	78 (51)
- 3	205 (37)	102 (36)	124 (37)	50 (39)	81 (37)	52 (34)
Serotype, n (%)
- DENV-1	228 (41)	121 (43)	161 (48)	61 (48)	67 (31)	60 (39)
- DENV-2	74 (13)	47 (17)	22 (7)	16 (13)	52 (24)	31 (20)
- DENV-3	59 (11)	29 (10)	43 (13)	18 (14)	16 (7)	11 (7)
- DENV-4	161 (29)	70 (25)	91 (27)	26 (20)	70 (32)	44 (29)
- Unknown	34 (6)	14 (5)	20 (6)	6 (5)	14 (6)	8 (5)
Immune status, n (%)
- Probable primary	124 (22)	41 (15)	86 (26)	15 (12)	38 (17)	26 (17)
- Probable secondary	355 (64)	218 (78)	202 (60)	100 (79)	153 (70)	118 (77)
- Inconclusive	77 (14)	22 (8)	49 (15)	12 (9)	28 (13)	10 (6)
Obesity*, n (%)	78 (14)	29 (10)	62 (18)	19 (15)	16 (7)	10 (6)
Diabetes, n (%)	4 (1)	1 (0)	0 (0)	0 (0)	4 (2)	1 (1)
WHO 2009 classification, n (%)
- Mild dengue	266 (48)	49 (17)	168 (50)	17 (13)	98 (45)	32 (21)
- Dengue with warning signs	288 (52)	186 (66)	169 (50)	81 (64)	119 (54)	105 (68)
- Severe dengue	0 (0)	43 (15)	0 (0)	27 (21)	0 (0)	16 (10)
- Unknown	2 (0)	3 (1)	0 (0)	2 (2)	2 (1)	1 (1)
Hospitalization, n (%)	175 (31)	161 (57)	127 (38)	83 (65)	48 (22)	78 (51)

*Obesity is defined as body mass index of higher than 30 kg/m2 (for patients of older than 18 years) or two standard deviations of the median of body mass index for age (for patients of 18 years or below). WHO: World Health Organization.

Appendix 4—table 1.

Summary of clinical phenotype of the primary endpoint.

	All patients (N = 281)	Children (N = 127)	Adults (N = 154)
Severe dengue, n (%)	38 (14)	29 (23)	9 (6)
- Severe plasma leakage	33 (12)	24 (19)	9 (6)
+Dengue shock syndrome	25 (9)	18 (14)	7 (5)
+Respiratory distress	12 (4)	9 (7)	3 (2)
- Severe neurologic involvement	3 (1)	3 (2)	0 (0)
- Severe bleeding	2 (1)	2 (2)	0 (0)
- Severe other major organ failure	1 (0)	0 (0)	1 (1)
- Severe hepatic involvement	0 (0)	0 (0)	0 (0)
Moderate dengue, n (%)	243 (86)	98 (77)	145 (94)
- Moderate plasma leakage	159 (57)	73 (57)	86 (56)
- Moderate hepatic involvement	102 (36)	35 (28)	67 (44)
- Moderate bleeding	9 (3)	3 (2)	6 (4)
- Moderate other major organ involvement	1 (0)	0 (0)	1 (1)
- Moderate neurologic involvement	0 (0)	0 (0)	0 (0)

Biomarker levels

On average, the patients who progressed to S/MD had higher levels of the biomarkers in both children and adult patients, both at enrollment and at follow-up (Figure 2, Appendix 4—table 2). For most individuals, the levels of five biomarkers (VCAM-1, IL-8, IP-10, IL-1RA, and CRP) decreased between enrollment and follow-up, whereas SDC-1 increased slightly and the other markers showed no clear trends (Appendix 4—figure 1). In some of the cases the biomarkers did not return to normal at convalescence. Moderate-to-strong positive correlations were evident for some markers, in particular IP-10 and IL-1RA, and IP-10 and VCAM-1, both with Spearman’s rank correlation coefficients above 0.6 (Appendix 4—figure 2).

Figure 2.

Biomarker levels by groups.

VCAM-1: vascular cell adhesion molecule-1; SDC-1: syndecan-1; Ang-2: angiopoietin-2; IL-8: interleukin-8; IP-10: interferon gamma-induced protein-10; IL-1RA: interleukin-1 receptor antagonist; sCD163: soluble cluster of differentiation 163; sTREM-1: soluble triggering receptor expressed on myeloid cells-1; CRP: C-reactive protein. Y-axes are transformed using the fourth root transformation.

Appendix 4—table 2.

Summary of biomarkers’ data.

	All patients			Children			Adults
	Uncomplicated dengue	Severe/moderate dengue		Uncomplicated dengue	Severe/moderate dengue		Uncomplicated dengue	Severe/moderate dengue
At enrollment	(N = 556)	(N = 281)		(N = 337)	(N = 127)		(N = 219)	(N = 154)
VCAM-1 (ng/ml)	1404 (540, 2548)	2027 (1122, 3577)		1442 (447, 2546)	2020 (1232, 3384)		1356 (568, 2560)	2092 (1060, 4202)
SDC-1 (pg/ml)	2334 (1864, 3131)	2997 (2230, 4201)		2369 (1861, 3423)	2846 (2164, 4173)		2260 (1879, 2898)	3122 (2278, 4211)
Ang-2 (pg/ml)	1064 (550, 1584)	1521 (899, 2318)		1102 (584, 1563)	1547 (967, 2318)		944 (516, 1585)	1516 (885, 2321)
IL-8 (pg/ml)	12 (8, 22)	17 (11, 28)		15 (9, 26)	16 (10, 27)		10 (7, 15)	19 (12, 29)
IP-10 (pg/ml)	2502 (732, 4509)	4092 (2436, 6441)		2245 (458, 4531)	3942 (2046, 6287)		2793 (1370, 4495)	4242 (2524, 6469)
IL-1RA (pg/ml)	5237 (2603, 9082)	9105 (5933, 14977)		4491 (2318, 8977)	9688 (6109, 16786)		5721 (3479, 9703)	8993 (5953, 12935)
sCD163 (ng/ml)	278 (185, 447)	322 (228, 503)		326 (212, 481)	386 (256, 603)		226 (157, 374)	291 (207, 410)
sTREM-1 (pg/ml)	81 (59, 114)	96 (69, 132)		80 (58, 115)	93 (67, 128)		84 (60, 114)	98 (73, 134)
Ferritin (ng/ml)	233 (116, 406)	261 (133, 433)		177 (99, 324)	224 (110, 402)		303 (161, 510)	278 (160, 448)
CRP (mg/l)	25 (10, 54)	34 (17, 72)		18 (7, 41)	24 (13, 58)		38 (17, 65)	45 (25, 80)
Viremia (106 copies/ml)	15.8 (0.7, 148.5)	79.2 (5.3, 582.7)		21.8 (1.8, 167.0)	105.4 (8.4, 646.0)		9.8 (0.3, 115.5)	56.2 (3.6, 496.0)
At follow-up	(N = 437)	(N = 231)		(N = 292)	(N = 112)		(N = 145)	(N = 119)
VCAM-1 (ng/ml)	402 (102, 730)	686 (344, 961)		579 (182, 858)	782 (402, 1078)		173 (26, 388)	622 (343, 835)
SDC-1 (pg/ml)	2769 (2298, 3514)	3417 (2815, 5495)		2957 (2319, 4115)	3122 (2748, 5507)		2666 (2196, 3058)	3745 (2971, 5495)
Ang-2 (pg/ml)	953 (478, 1479)	1155 (675, 1567)		1163 (738, 1646)	1352 (710, 1856)		565 (302, 923)	1044 (626, 1345)
IL-8 (pg/ml)	4.9 (2.3, 12.4)	5.7 (2.7, 10.4)		6.8 (3.0, 15.1)	5.9 (2.4, 10.5)		2.7 (1.6, 4.8)	5.5 (3.1, 10.4)
IP-10 (pg/ml)	57 (24, 91)	76 (47, 133)		67 (33, 98)	86 (38, 143)		39 (22, 70)	75 (48, 108)
IL-1RA (pg/ml)	412 (279, 635)	455 (328, 626)		441 (323, 687)	501 (352, 664)		336 (210, 480)	407 (308, 615)
sCD163 (ng/ml)	337 (216, 553)	412 (257, 661)		340 (226, 562)	456 (279, 680)		328 (199, 523)	386 (241, 589)
sTREM-1 (pg/ml)	99 (73, 132)	90 (68, 116)		98 (72, 132)	91 (67, 115)		101 (73, 134)	90 (70, 116)
Ferritin (ng/ml)	202 (120, 309)	273 (181, 382)		177 (112, 263)	209 (154, 311)		267 (160, 404)	322 (247, 436)
CRP* (mg/l)	0.7 (0.3, 1.8)	0.8 (0.4, 2.0)		0.6 (0.3, 1.3)	0.6 (0.3, 1.1)		1.1 (0.5, 2.7)	1.1 (0.5, 3.4)

*The number of cases with available data for CRP at follow-up in the uncomplicated and severe/moderate dengue groups are 436 and 228 (all patients); 292 and 111 (children); and 218 and 152 (adults) respectively.

VCAM-1: vascular cell adhesion molecule-1; SDC-1: syndecan-1; Ang-2: angiopoietin-2; IL-8: interleukin-8; IP-10: interferon gamma-induced protein-10; IL-1RA: interleukin-1 receptor antagonist; sCD163: soluble cluster of differentiation 163; sTREM-1: soluble triggering receptor expressed on myeloid cells-1; CRP: C-reactive protein.

Summary statistics are median (1st and 3rd quartiles).

Appendix 4—figure 1.

Biomarker levels by individual.

Y-axes are transformed using the fourth root transformation.

Appendix 4—figure 2.

Pairwise correlation of biomarker levels at enrollment and age.

Viremia levels are transformed using log-10. All other biomarker levels are transformed using log-2. The number inside each scatter plot represents the Spearman’s rank correlation coefficient of the two variables at the corresponding column and row. When the column and row refer to the same variable, the corresponding scatter plot is replaced by a density plot to reflect the distribution of that biomarker. VCAM-1: vascular cell adhesion molecule-1; SDC-1: syndecan-1; Ang-2: angiopoietin-2; IL-8: interleukin-8; IP-10: interferon gamma-induced protein-10; IL-1RA: interleukin-1 receptor antagonist; sCD163: soluble cluster of differentiation 163; sTREM-1: soluble triggering receptor expressed on myeloid cells-1; CRP: C-reactive protein.

Associations between biomarker levels and the endpoints

In the single models, higher levels of each biomarker on illness days 1, 2, or 3 increased the risk of developing S/MD, with the exception of ferritin in adults where there was a downward trend at higher values (Figure 3, Table 2). We observed differences between children and adults for several biomarkers, the most pronounced being SDC-1, IL-8, ferritin, and IL-1RA. Associations between SDC-1 and IL-8 and the S/MD endpoint were stronger in adults than children, while the effects of IL-1RA and ferritin were stronger in children than adults.

Figure 3.

Results from models for the primary endpoint (severe or moderate dengue).

The odds ratio of severe/moderate dengue (the red and blue lines) and 95% confidence interval (the red and blue regions) are estimated from multivariable logistic regression models allowing for a non-linear relation of log-2 of the biomarker level with severe/moderate dengue using restricted cubic splines. Each single model contains the corresponding biomarker, age and their interaction, while the global model contains all biomarkers and their interaction with age. The reference values for the odds ratios (where the odds ratio is equal to 1) are represented by the vertical gray dashed lines. They are chosen as the median of the biomarker levels of the whole study population (VCAM-1: 1636 ng/ml; SDC-1: 2519 pg/ml; Ang-2: 1204 pg/ml; IL-8: 14 pg/ml; IP-10: 3093 pg/ml; IL-1RA: 6434 pg/ml; sCD163: 295 ng/ml; sTREM-1: 85 ng/ml; ferritin: 243 ng/ml; and CRP: 28 mg/l). The x-axis represents biomarker levels; it is transformed using log-2 and its range truncated by the 5th and 95th percentiles of the biomarker levels of the whole study population. The rug plot on the x-axis represents the distribution of individual cases; the bottom rug plot represents the uncomplicated dengue cases and the top rug plot represents the severe/moderate dengue cases (children [<15 years of age] are in red and adults [≥15 years of age] are in blue). The red line and region represent children; results are shown for children at age of 10 years. The blue line and region represents adults; results are shown for adults at age of 25 years. VCAM-1: vascular cell adhesion molecule-1; SDC-1: syndecan-1; Ang-2: angiopoietin-2; IL-8: interleukin-8; IP-10: interferon gamma-induced protein-10; IL-1RA: interleukin-1 receptor antagonist; sCD163: soluble cluster of differentiation 163; sTREM-1: soluble triggering receptor expressed on myeloid cells-1; CRP: C-reactive protein.

Table 2.

Results from models for the primary endpoint (severe or moderate dengue).

	Single models					Global model
	Children OR (95% CI)	Adults OR (95% CI)	Poverall	Pinteraction		Children OR (95% CI)	Adults OR (95% CI)	Poverall	Pinteraction
VCAM-1 (ng/ml)			<0.001	0.715				0.441	0.213
- 1636 vs 818	1.20 (1.04–1.38)	1.35 (1.15–1.58)				0.90 (0.73–1.10)	1.22 (0.96–1.57)
- 3272 vs 1636	1.25 (1.02–1.53)	1.48 (1.19–1.85)				0.87 (0.66–1.15)	1.30 (0.93–1.80)
SDC-1 (pg/ml)			<0.001	0.088				0.002	0.588
- 2519 vs 1260	2.67 (1.31–5.43)	3.33 (1.32–8.42)				2.03 (0.77–5.34)	5.11 (1.56–16.78)
- 5039 vs 2519	1.71 (1.18–2.47)	3.71 (2.09–6.58)				1.76 (0.98–3.14)	2.52 (1.17–5.42)
Ang-2 (pg/ml)			<0.001	0.524				0.039	0.068
- 1204 vs 602	1.64 (1.39–1.94)	1.51 (1.26–1.82)				1.67 (1.23–2.25)	1.01 (0.74–1.38)
- 2409 vs 1204	2.21 (1.58–3.10)	2.00 (1.40–2.85)				1.95 (1.25–3.05)	1.01 (0.65–1.57)
IL-8 (pg/ml)			<0.001	<0.001				<0.001	<0.001
- 14 vs 7	1.42 (1.05–1.91)	2.18 (1.47–3.24)				0.91 (0.63–1.34)	1.69 (1.05–2.71)
- 28 vs 14	0.99 (0.78–1.25)	2.33 (1.63–3.33)				0.53 (0.36–0.77)	2.05 (1.34–3.13)
IP-10 (pg/ml)			<0.001	0.984				0.206	0.630
- 3093 vs 1546	1.46 (1.26–1.68)	1.45 (1.21–1.73)				0.94 (0.73–1.19)	0.80 (0.57–1.12)
- 6186 vs 3093	1.68 (1.35–2.09)	1.69 (1.29–2.22)				1.08 (0.77–1.51)	0.82 (0.52–1.29)
IL-1RA (pg/ml)			<0.001	0.082				<0.001	0.032
- 6434 vs 3217	1.69 (1.42–2.03)	1.48 (1.21–1.81)				2.07 (1.52–2.84)	1.45 (0.98–2.15)
- 12868 vs 6434	1.82 (1.46–2.27)	1.70 (1.29–2.24)				2.16 (1.53–3.05)	1.47 (0.94–2.30)
sCD163 (ng/ml)			<0.001	0.551				0.217	0.341
- 295 vs 147	1.57 (1.14–2.15)	1.49 (1.13–1.98)				1.40 (0.89–2.22)	1.27 (0.84–1.91)
- 589 vs 295	1.46 (1.10–1.93)	1.61 (1.09–2.37)				1.21 (0.87–1.69)	1.39 (0.89–2.18)
sTREM-1 (pg/ml)			0.059	0.997				0.555	0.393
- 85 vs 42	1.87 (1.23–2.84)	1.79 (1.10–2.93)				1.13 (0.70–1.81)	1.21 (0.65–2.26)
- 169 vs 85	1.12 (0.91–1.38)	1.12 (0.82–1.53)				0.89 (0.65–1.21)	0.61 (0.38–0.99)
Ferritin (ng/ml)			0.042	0.054				0.008	0.002
- 243 vs 122	1.18 (1.01–1.38)	1.06 (0.89–1.27)				1.30 (1.04–1.64)	0.78 (0.61–0.99)
- 487 vs 243	1.26 (1.00–1.58)	0.90 (0.66–1.23)				1.22 (0.89–1.67)	0.66 (0.44–1.00)
CRP (mg/l)			<0.001	0.031				0.184	0.138
- 28 vs 14	1.26 (1.12–1.41)	1.25 (1.03–1.52)				1.08 (0.93–1.25)	1.10 (0.85–1.44)
- 56 vs 28	1.13 (0.95–1.34)	1.38 (1.11–1.71)				0.93 (0.75–1.15)	1.36 (1.02–1.81)

Poverall is derived from Wald test for the overall association of the biomarker with the endpoint; Pinteraction is from the test for the interaction between the biomarker and age. The odds ratios are estimated at age of 10 and 25 years, represented as children and adults respectively.

In the global model there were some differences compared to the single models (Figure 3, Table 2). The biomarkers SDC-1 and IL-1RA were the most stable relative to the single models for both children and adults. However, for IP-10 the trend of the association with S/MD changed from positive to negative in both children and adults. In children, VCAM-1 changed the trend from positive to weakly negative and IL-8 changed the trend from weakly positive to negative. Other biomarkers showed weaker associations with the endpoint in the global model based on the ORs. In addition, the differences of the associations between children and adults were more marked, particularly for Ang-2, IL-8, and ferritin.

The sensitivity analysis showed that the association between the biomarkers and S/MD did not differ between DENV-1 and other serotypes (Appendix 5—figure 1; Appendix 5—figure 2; Appendix 5—table 1; Appendix 5—table 2). Similar patterns were observed in the various analyses related to the secondary endpoints, as described in detail in the Appendix 6 (Appendix 6—figure 1; Appendix 6—figure 2, Appendix 6—table 1; Appendix 6—table 2, Appendix 6—table 3).

Appendix 5—figure 1.

Results from single models for severe/moderate dengue with the interaction with serotype.

Appendix 5—figure 2.

Results from global model for severe/moderate dengue with the interaction with serotype.

Appendix 5—table 1.

Results from single models for severe/moderate dengue with the interaction with serotype.

	Children			Adults
	DENV-1 OR (95% CI)	Other serotypes OR (95% CI)		DENV-1 OR (95% CI)	Other serotypes OR (95% CI)	Poverall	P1	P2	P3
VCAM-1 (ng/ml)						0.008	0.822	0.729	0.565
- 1636 vs 818	1.22 (1.02–1.46)	1.14 (0.95–1.37)		1.42 (1.13–1.77)	1.32 (1.10–1.58)
- 3272 vs 1636	1.28 (0.99–1.66)	1.18 (0.91–1.54)		1.57 (1.15–2.14)	1.45 (1.13–1.86)
SDC-1 (pg/ml)						<0.001	0.087	0.326	0.352
- 2519 vs 1260	2.35 (0.89–6.25)	2.56 (0.97–6.74)		2.85 (0.80–10.12)	3.10 (1.11–8.64)
- 5039 vs 2519	2.41 (1.47–3.96)	1.22 (0.69–2.13)		6.39 (2.81–14.53)	3.23 (1.73–6.01)
Ang-2 (pg/ml)						<0.001	0.935	0.923	0.702
- 1204 vs 602	1.67 (1.35–2.05)	1.51 (1.21–1.88)		1.64 (1.28–2.10)	1.48 (1.20–1.83)
- 2409 vs 1204	2.46 (1.60–3.79)	1.97 (1.25–3.11)		2.40 (1.44–4.02)	1.92 (1.28–2.89)
IL-8 (pg/ml)						<0.001	<0.001	<0.001	0.104
- 14 vs 7	1.52 (1.00–2.31)	1.12 (0.76–1.65)		2.83 (1.57–5.12)	2.09 (1.34–3.26)
- 28 vs 14	1.22 (0.90–1.65)	0.84 (0.60–1.17)		3.04 (1.88–4.94)	2.10 (1.45–3.04)
IP-10 (pg/ml)						<0.001	0.975	0.950	0.681
- 3093 vs 1546	1.54 (1.28–1.84)	1.39 (1.13–1.70)		1.64 (1.28–2.10)	1.48 (1.20–1.83)
- 6186 vs 3093	1.84 (1.39–2.43)	1.60 (1.17–2.20)		2.00 (1.37–2.92)	1.75 (1.27–2.40)
IL-1RA (pg/ml)						<0.001	0.577	0.280	0.805
- 6434 vs 3217	1.68 (1.29–2.18)	1.51 (1.20–1.90)		1.96 (1.34–2.86)	1.76 (1.29–2.38)
- 12868 vs 6434	1.87 (1.43–2.44)	1.77 (1.28–2.46)		1.73 (1.23–2.44)	1.64 (1.18–2.29)
sCD163 (ng/ml)						0.002	0.983	0.831	0.932
- 295 vs 147	1.50 (1.03–2.20)	1.60 (1.03–2.48)		1.59 (1.02–2.47)	1.69 (1.18–2.42)
- 589 vs 295	1.35 (0.94–1.94)	1.48 (0.99–2.21)		1.50 (0.92–2.43)	1.64 (1.04–2.59)
sTREM-1 (pg/ml)						0.146	0.979	0.998	0.597
- 85 vs 42	1.55 (0.94–2.57)	2.03 (1.22–3.39)		1.68 (0.92–3.07)	2.20 (1.21–4.02)
- 169 vs 85	1.08 (0.80–1.45)	1.16 (0.87–1.54)		1.08 (0.68–1.71)	1.16 (0.83–1.60)
Ferritin (ng/ml)						0.112	0.139	0.177	0.711
- 243 vs 122	1.18 (0.97–1.44)	1.12 (0.91–1.38)		1.14 (0.88–1.49)	1.08 (0.88–1.32)
- 487 vs 243	1.28 (0.96–1.71)	1.32 (0.91–1.92)		0.87 (0.55–1.38)	0.90 (0.64–1.27)
CRP (mg/l)						<0.001	0.080	0.029	0.755
- 28 vs 14	1.23 (1.06–1.43)	1.36 (1.12–1.66)		1.21 (0.94–1.57)	1.34 (1.07–1.69)
- 56 vs 28	1.06 (0.85–1.33)	1.24 (0.97–1.58)		1.25 (0.93–1.68)	1.46 (1.13–1.87)

Odds ratios are estimated at age of 10 and 25 years, represented as children and adults respectively; Poverall is derived from Wald test for the overall association of the biomarker with the endpoint; P1 is from the test for the overall interaction of the biomarker; P2 is from the test for the interaction between the biomarker and age; P3 is from the test for the interaction between the biomarker and serotype.

Appendix 5—table 2.

Results from global model for severe/moderate dengue with the interaction with serotype.

	Children			Adults
	DENV-1 OR (95% CI)	Other serotypes OR (95% CI)		DENV-1 OR (95% CI)	Other serotypes OR (95% CI)	Poverall	P1	P2	P3
VCAM-1 (ng/ml)						0.449	0.258	0.248	0.327
- 1636 vs 818	0.84 (0.62–1.13)	0.88 (0.66–1.17)		1.18 (0.81–1.72)	1.24 (0.93–1.65)
- 3272 vs 1636	0.75 (0.50–1.12)	0.85 (0.58–1.25)		1.14 (0.70–1.85)	1.29 (0.88–1.90)
SDC-1 (pg/ml)						0.027	0.788	0.821	0.316
- 2519 vs 1260	3.21 (0.79–12.94)	1.38 (0.38–4.94)		5.98 (1.00–35.72)	2.57 (0.66–10.01)
- 5039 vs 2519	2.82 (1.21–6.57)	1.45 (0.62–3.40)		3.70 (1.08–12.72)	1.90 (0.80–4.52)
Ang-2 (pg/ml)						0.067	0.102	0.043	0.472
- 1204 vs 602	1.65 (1.10–2.47)	1.79 (1.18–2.72)		0.89 (0.55–1.44)	0.97 (0.67–1.40)
- 2409 vs 1204	2.22 (1.21–4.05)	1.73 (0.94–3.17)		1.25 (0.63–2.48)	0.97 (0.58–1.62)
IL-8 (pg/ml)						<0.001	<0.001	<0.001	0.591
- 14 vs 7	0.86 (0.48–1.51)	0.73 (0.44–1.24)		2.14 (1.01–4.53)	1.84 (1.05–3.20)
- 28 vs 14	0.68 (0.41–1.13)	0.49 (0.29–0.82)		2.60 (1.27–5.32)	1.88 (1.20–2.96)
IP-10 (pg/ml)						0.068	0.875	0.715	0.888
- 3093 vs 1546	0.98 (0.69–1.40)	0.91 (0.65–1.26)		0.86 (0.53–1.40)	0.80 (0.53–1.19)
- 6186 vs 3093	1.27 (0.77–2.10)	1.10 (0.70–1.74)		1.04 (0.54–2.00)	0.90 (0.53–1.53)
IL-1RA (pg/ml)						<0.001	0.333	0.230	0.711
- 6434 vs 3217	2.63 (1.65–4.20)	2.11 (1.32–3.39)		2.50 (1.28–4.88)	2.01 (1.15–3.51)
- 12868 vs 6434	2.38 (1.45–3.91)	1.90 (1.19–3.04)		1.65 (0.88–3.09)	1.32 (0.78–2.23)
sCD163 (ng/ml)						0.340	0.661	0.455	0.769
- 295 vs 147	1.20 (0.65–2.20)	1.54 (0.83–2.87)		1.24 (0.68–2.27)	1.61 (0.92–2.80)
- 589 vs 295	1.20 (0.77–1.87)	1.20 (0.75–1.94)		1.49 (0.84–2.67)	1.50 (0.85–2.61)
sTREM-1 (pg/ml)						0.441	0.289	0.306	0.071
- 85 vs 42	0.90 (0.48–1.67)	1.23 (0.66–2.28)		0.84 (0.36–1.94)	1.15 (0.54–2.42)
- 169 vs 85	0.56 (0.33–0.96)	1.18 (0.79–1.77)		0.34 (0.15–0.76)	0.71 (0.43–1.19)
Ferritin (ng/ml)						0.067	0.033	0.013	0.331
- 243 vs 122	1.36 (1.00–1.85)	1.25 (0.92–1.69)		0.92 (0.62–1.36)	0.84 (0.63–1.13)
- 487 vs 243	1.08 (0.72–1.63)	1.59 (0.97–2.61)		0.55 (0.29–1.05)	0.81 (0.51–1.29)
CRP (mg/l)						0.156	0.103	0.241	0.136
- 28 vs 14	0.95 (0.78–1.16)	1.25 (0.98–1.60)		0.94 (0.67–1.32)	1.24 (0.90–1.70)
- 56 vs 28	0.80 (0.60–1.07)	1.07 (0.80–1.44)		1.13 (0.75–1.70)	1.51 (1.09–2.09)

Odds ratios are estimated at age of 10 and 25 years, represented as children and adults respectively; Poverall is derived from Wald test for the overall association of the biomarker with the endpoint; P1 is from the test for the overall interaction of the biomarker; P2 is from the test for the interaction between the biomarker and age; P3 is from the test for the interaction between the biomarker and serotype.

Appendix 6—figure 1.

Results from models for severe dengue endpoint.

Appendix 6—figure 2.

Results from models for severe dengue or dengue with warning signs endpoint.

Appendix 6—table 1.

Results from models for severe dengue endpoint.

	Single models					Global model
	Children OR (95% CI)	Adults OR (95% CI)	Poverall	Pinteraction		Children OR (95% CI)	Adults OR (95% CI)	Poverall	Pinteraction
VCAM-1 (ng/ml)	1.28 (0.77–2.12)	2.13 (0.88–5.15)	0.236	0.202		1.20 (0.35–4.12)	3.13 (0.19–50.77)	0.723	0.491
SDC-1 (pg/ml)	1.55 (0.41–5.96)	3.26 (0.69–15.52)	0.307	0.438		1.16 (0.12–11.07)	0.87 (0.03–27.93)	0.987	0.884
Ang-2 (pg/ml)	1.42 (0.70–2.88)	1.79 (0.97–3.31)	0.148	0.580		1.20 (0.33–4.31)	1.02 (0.33–3.19)	0.961	0.830
IL-8 (pg/ml)	1.04 (0.47–2.33)	2.15 (0.67–6.88)	0.420	0.338		0.81 (0.28–2.31)	2.51 (0.16–38.43)	0.741	0.446
IP-10 (pg/ml)	1.32 (0.75–2.33)	1.64 (0.58–4.62)	0.443	0.708		0.76 (0.17–3.30)	0.22 (0.01–7.12)	0.693	0.473
IL-1RA (pg/ml)	1.84 (0.81–4.16)	1.78 (0.80–3.94)	0.098	0.956		2.22 (0.51–9.64)	3.35 (0.45–24.87)	0.386	0.696
sCD163 (ng/ml)	1.43 (0.52–3.94)	2.43 (0.27–21.70)	0.617	0.645		0.98 (0.27–3.59)	2.06 (0.15–27.47)	0.855	0.590
sTREM-1 (pg/ml)	1.20 (0.44–3.28)	1.29 (0.55–3.05)	0.793	0.914		0.93 (0.30–2.88)	0.38 (0.02–8.76)	0.827	0.541
Ferritin (ng/ml)	1.13 (0.59–2.16)	1.58 (0.59–4.23)	0.659	0.501		1.12 (0.55–2.29)	1.34 (0.25–7.02)	0.926	0.819
CRP (mg/l)	1.16 (0.71–1.88)	1.77 (0.56–5.62)	0.620	0.418		0.97 (0.58–1.63)	1.55 (0.33–7.32)	0.773	0.500

Odds ratios (95% confidence intervals) are calculated for each 2-fold increase of the biomarkers and are estimated at age of 10 and 25 years, represented as children and adults respectively; Poverall is derived from Wald test for the overall association of the biomarker with the endpoint; Pinteraction is from the test for the interaction between the biomarker and age.

Appendix 6—table 2.

Results from models for severe dengue or dengue with warning signs endpoint.

	Single models					Global model
	Children OR (95% CI)	Adults OR (95% CI)	Poverall	Pinteraction		Children OR (95% CI)	Adults OR (95% CI)	Poverall	Pinteraction
VCAM-1 (ng/ml)			0.025	0.374				0.469	0.763
- 1636 vs 818	1.06 (0.93–1.22)	1.11 (0.92–1.33)				0.82 (0.66–1.02)	0.92 (0.66–1.30)
- 3272 vs 1636	1.06 (0.87–1.29)	1.13 (0.88–1.46)				0.74 (0.55–1.01)	0.88 (0.56–1.40)
SDC-1 (pg/ml)			0.032	0.363				0.116	0.773
- 2519 vs 1260	1.34 (0.86–2.09)	1.15 (0.56–2.33)				1.57 (0.89–2.75)	1.89 (0.71–5.03)
- 5039 vs 2519	1.40 (0.90–2.17)	2.00 (0.79–5.08)				1.78 (1.00–3.18)	2.88 (0.71–11.69)
Ang-2 (pg/ml)			0.008	0.637				0.009	0.011
- 1204 vs 602	1.23 (1.05–1.44)	1.12 (0.93–1.36)				1.37 (1.07–1.75)	0.95 (0.69–1.30)
- 2409 vs 1204	1.34 (0.95–1.88)	1.22 (0.82–1.80)				1.42 (0.91–2.21)	0.85 (0.48–1.51)
IL-8 (pg/ml)			0.040	0.020				0.053	0.030
- 14 vs 7	1.05 (0.87–1.27)	0.97 (0.67–1.41)				0.96 (0.77–1.21)	0.94 (0.62–1.42)
- 28 vs 14	1.01 (0.85–1.19)	1.45 (0.94–2.22)				0.78 (0.62–0.99)	1.48 (0.89–2.44)
IP-10 (pg/ml)			<0.001	0.176				0.059	0.537
- 3093 vs 1546	1.26 (1.09–1.44)	1.44 (1.16–1.80)				1.16 (0.87–1.55)	1.30 (0.81–2.09)
- 6186 vs 3093	1.39 (1.13–1.71)	1.75 (1.26–2.44)				1.33 (0.89–1.99)	1.49 (0.78–2.87)
IL-1RA (pg/ml)			0.005	0.381				0.425	0.955
- 6434 vs 3217	1.17 (1.06–1.30)	1.15 (0.97–1.36)				1.24 (1.01–1.52)	1.14 (0.80–1.63)
- 12868 vs 6434	1.37 (1.06–1.77)	1.70 (1.13–2.55)				1.38 (0.93–2.05)	1.45 (0.80–2.64)
sCD163 (ng/ml)			0.854	0.719				0.193	0.419
- 295 vs 147	0.96 (0.84–1.08)	1.03 (0.85–1.25)				0.85 (0.71–1.03)	1.12 (0.81–1.55)
- 589 vs 295	0.91 (0.69–1.22)	1.03 (0.61–1.73)				0.71 (0.50–1.00)	0.80 (0.44–1.47)
sTREM-1 (pg/ml)			0.221	0.472				0.002	0.132
- 85 vs 42	0.75 (0.56–1.00)	0.69 (0.41–1.16)				0.48 (0.32–0.73)	0.64 (0.36–1.16)
- 169 vs 85	1.04 (0.84–1.28)	0.78 (0.57–1.07)				0.87 (0.69–1.10)	0.62 (0.39–1.00)
Ferritin (ng/ml)			0.034	0.258				0.024	0.075
- 243 vs 122	1.13 (1.02–1.26)	1.01 (0.85–1.19)				1.17 (1.01–1.35)	0.96 (0.76–1.23)
- 487 vs 243	0.96 (0.77–1.20)	0.76 (0.53–1.08)				0.94 (0.71–1.25)	0.67 (0.42–1.07)
CRP (mg/l)			0.747	0.622				0.662	0.448
- 28 vs 14	1.03 (0.96–1.12)	0.97 (0.79–1.20)				0.99 (0.89–1.10)	0.84 (0.59–1.20)
- 56 vs 28	1.02 (0.87–1.19)	1.19 (0.92–1.54)				0.96 (0.79–1.17)	1.06 (0.75–1.49)

Odds ratios are estimated at age of 10 and 25 years, represented as children and adults respectively; Poverall is derived from Wald test for the overall association of the biomarker with the endpoint; Pinteraction is from the test for the interaction between the biomarker and age.

Appendix 6—table 3.

Results from models for hospitalization endpoint.

	Single models					Global model
	Children OR (95% CI)	Adults OR (95% CI)	Poverall	Pinteraction		Children OR (95% CI)	Adults OR (95% CI)	Poverall	Pinteraction
VCAM-1 (ng/ml)			<0.001	0.009				0.092	0.137
- 1636 vs 818	1.28 (1.11–1.49)	1.28 (1.03–1.60)				1.16 (0.90–1.48)	1.28 (0.87–1.90)
- 3272 vs 1636	1.42 (1.14–1.76)	1.46 (1.08–1.97)				1.16 (0.81–1.64)	1.41 (0.84–2.37)
SDC-1 (pg/ml)			<0.001	0.187				0.006	0.406
- 2519 vs 1260	1.82 (1.01–3.28)	0.79 (0.31–2.05)				1.70 (0.84–3.41)	0.62 (0.16–2.49)
- 5039 vs 2519	2.22 (1.36–3.63)	1.81 (0.65–5.07)				3.70 (1.90–7.22)	1.66 (0.39–7.07)
Ang-2 (pg/ml)			0.012	0.337				0.497	0.789
- 1204 vs 602	1.27 (1.07–1.52)	1.21 (0.91–1.62)				1.27 (0.92–1.77)	1.16 (0.76–1.77)
- 2409 vs 1204	1.58 (1.08–2.32)	1.61 (0.86–3.04)				1.63 (0.90–2.93)	1.45 (0.70–3.01)
IL-8 (pg/ml)			0.007	0.002				0.024	0.021
- 14 vs 7	1.14 (0.90–1.44)	0.73 (0.47–1.15)				0.94 (0.66–1.33)	0.63 (0.36–1.13)
- 28 vs 14	0.98 (0.81–1.18)	1.32 (0.85–2.05)				0.70 (0.50–0.97)	1.59 (0.81–3.12)
IP-10 (pg/ml)			0.002	0.242				0.005	0.212
- 3093 vs 1546	1.32 (1.13–1.54)	1.10 (0.86–1.40)				0.80 (0.56–1.14)	0.77 (0.45–1.30)
- 6186 vs 3093	1.50 (1.18–1.90)	1.17 (0.81–1.70)				0.84 (0.51–1.40)	0.66 (0.32–1.35)
IL-1RA (pg/ml)			<0.001	0.685				<0.001	0.389
- 6434 vs 3217	1.31 (1.17–1.46)	1.13 (0.94–1.36)				2.05 (1.57–2.66)	1.18 (0.73–1.89)
- 12868 vs 6434	1.41 (1.11–1.80)	1.17 (0.79–1.72)				2.25 (1.39–3.64)	1.19 (0.62–2.28)
sCD163 (ng/ml)			0.208	0.722				0.007	0.419
- 295 vs 147	0.90 (0.79–1.04)	0.98 (0.76–1.27)				0.69 (0.53–0.89)	0.96 (0.62–1.50)
- 589 vs 295	0.69 (0.47–1.00)	0.91 (0.46–1.82)				0.49 (0.30–0.80)	0.83 (0.36–1.91)
sTREM-1 (pg/ml)			0.635	0.371				0.011	0.053
- 85 vs 42	0.93 (0.67–1.29)	1.35 (0.71–2.58)				0.53 (0.34–0.81)	1.74 (0.67–4.52)
- 169 vs 85	0.89 (0.71–1.13)	0.83 (0.54–1.28)				0.55 (0.36–0.83)	0.57 (0.26–1.29)
Ferritin (ng/ml)			<0.001	0.011				0.129	0.117
- 243 vs 122	1.22 (1.08–1.38)	0.92 (0.72–1.18)				1.23 (1.02–1.49)	0.91 (0.68–1.21)
- 487 vs 243	1.40 (1.10–1.79)	0.99 (0.68–1.46)				1.25 (0.89–1.75)	0.93 (0.54–1.59)
CRP (mg/l)			0.379	0.190				0.139	0.053
- 28 vs 14	0.97 (0.89–1.06)	1.01 (0.83–1.24)				0.97 (0.86–1.11)	0.95 (0.71–1.27)
- 56 vs 28	0.95 (0.78–1.15)	1.35 (1.01–1.81)				0.89 (0.69–1.15)	1.37 (0.88–2.13)

Odds ratios are estimated at age of 10 and 25 years, represented as children and adults respectively; Poverall is derived from Wald test for the overall association of the biomarker with the endpoint; Pinteraction is from the test for the interaction between the biomarker and age.

Best combinations of biomarkers associated with the primary endpoint

For children, the best subset that showed the clearest association with S/MD was the combination of the six markers IL-1RA, Ang-2, IL-8, ferritin, IP-10, and SDC-1 with an AIC of 465.9. This model was selected most often in the bootstrap procedure, but was not highly robust (it was selected in 134 of the 1000 samples) (Table 3, Appendix 7—table 1). Over the 1000 samples, the six variables had an inclusion frequency ranging from 73.5% for SDC-1 to 100% for IL-1RA. The most important biomarkers in order were IL-1RA, Ang-2, IL-8, and ferritin (Appendix 7—table 2). The best combination of two biomarkers was IL-1RA and ferritin, the best of three added Ang-2, the best of four added IP-10, and the best of five added IL-8. The best combinations of two and five variables were most robust with a selection percentage of 43.7% and 44%. The best of five had almost the same AIC as the best subset of six markers (467.6 versus 465.9) (Table 3). The coefficients of the selected biomarkers were similar to the initial model estimates (Appendix 7—table 2).

Table 3.

Best combinations of biomarkers associated with severe or moderate dengue for children.

	Best of all combinations	Best combination of 2 variables	Best combination of 3 variables	Best combination of 4 variables	Best combination of 5 variables
Variables
- VCAM-1
- SDC-1	+
- Ang-2	+		+	+	+
- IL-8	+				+
- IP-10	+			+	+
- IL-1RA	+	+	+	+	+
- sCD163
- sTREM-1
- Ferritin	+	+	+	+	+
- CRP
AIC of the selected model	465.9	484.7	480.0	473.7	467.6
Bootstrap results
- Model selection frequency, n (%)	134 (13.4)	437 (43.7)	239 (23.9)	317 (31.7)	440 (44.0)
- Rank by selection frequency of the selected model	1	1	2	1	1

VCAM-1: vascular cell adhesion molecule-1; SDC-1: syndecan-1; Ang-2: angiopoietin-2; IL-8: interleukin-8; IP-10: interferon gamma-induced protein-10; IL-1RA: interleukin-1 receptor antagonist; sCD163: soluble cluster of differentiation 163; sTREM-1: soluble triggering receptor expressed on myeloid cells-1; CRP: C-reactive protein; AIC: Akaike information criterion.

Appendix 7—table 1.

Model selection frequencies for children.

Model	Included variables										Count	Percent
	VCAM-1	SDC-1	Ang-2	IL-8	IP-10	IL-1RA	sCD163	sTREM-1	Ferritin	CRP
1		+	+	+	+	+			+		134	13.4
2		+	+	+	+	+	+		+		100	10.0
3			+	+	+	+	+		+		55	5.5
4			+	+	+	+			+		54	5.4
5	+	+	+	+		+	+		+		48	4.8
6	+	+	+	+	+	+			+		47	4.7
7	+	+	+	+	+	+	+		+		46	4.6
8	+	+	+	+		+			+		40	4.0
9		+	+	+	+	+			+	+	39	3.9
10		+	+	+	+	+	+	+	+		36	3.6
11		+	+	+	+	+	+		+	+	28	2.8
12		+	+	+	+	+		+	+		23	2.3
13	+		+	+	+	+			+		23	2.3
14	+		+	+		+			+		17	1.7
15		+	+	+	+	+		+	+	+	15	1.5
16	+	+	+	+		+			+	+	14	1.4
17			+	+	+	+			+	+	13	1.3
18	+		+	+	+	+	+		+		12	1.2
19	+	+	+	+	+	+		+	+		12	1.2
20			+	+	+	+	+		+	+	11	1.1

Selected model is ranked first (bold face).

Appendix 7—table 2.

Model stability for children.

	Initial model		Selected model
Predictors	Estimate	Standard error	Estimate	Standard error	Bootstrap inclusion frequency (%)	RMSD ratio	Relative conditional bias (%)	Bootstrap median	Bootstrap 2.5th percentile	Bootstrap 97.5th percentile
(Intercept)	−20.6801	3.0499	−19.1331	2.8649	100.0	1.1330	−1.4929	−20.3233	−26.6990	−14.0696
IL-1RA	0.7604	0.1443	0.7885	0.1386	100.0	1.2352	1.6338	0.7651	0.4408	1.1236
Ang-2	0.5203	0.1542	0.5371	0.1457	98.2	1.1710	7.6528	0.5416	0.2268	0.8804
IL-8	−0.4250	0.1338	−0.4290	0.1307	97.2	1.0597	4.5484	−0.4341	−0.6995	0
Ferritin	0.2951	0.1044	0.2923	0.1003	93.5	1.2233	11.1579	0.3118	0	0.5358
IP-10	−0.2145	0.1079	−0.2598	0.0925	75.9	1.3272	32.1767	−0.2398	−0.4676	0
SDC-1	0.5079	0.2487	0.4398	0.2302	73.5	1.2692	18.5555	0.4923	0	1.0035
sCD163	0.1778	0.1433			45.8	1.1935	69.8797	0	0	0.4926
VCAM-1	−0.0500	0.0541			35.2	1.1525	125.6348	0	−0.1763	0
sTREM-1	−0.0689	0.1375			20.0	0.9176	159.0696	0	−0.3584	0.2162
CRP	0.0438	0.0712			19.2	0.8804	173.2343	0	0	0.1754

RMSD: root mean squared difference.

For adults, the best subset included the seven markers SDC-1, IL-8, ferritin, sTREM-1, IL-1RA, IP-10, and sCD163. This model was selected 79 times among 1000 bootstrap samples, but still was selected more often than the other models (Table 4, Appendix 7—table 3). Over the 1000 samples, the seven variables had a bootstrap inclusion frequency ranging from 59.1% for sCD163 to 99.2% for SDC-1. The three most important biomarkers in order were SDC-1, IL-8, and ferritin (Appendix 7—table 4). The best combination of two biomarkers included SDC-1 and IL-8, the best of three added ferritin, the best of four added IL-1RA, and the best of five added sTREM-1. The best combination of two was the most robust with a selection percentage of 56.7%, followed by the best of three variables (43.2%) (Table 4). The coefficients of the selected markers were also similar to the initial model estimates (Appendix 7—table 4).

Table 4.

Best combinations of biomarkers associated with severe or moderate dengue for adults.

	Best of all combinations	Best combination of 2 variables	Best combination of 3 variables	Best combination of 4 variables	Best combination of 5 variables
Variables
- VCAM-1
- SDC-1	+	+	+	+	+
- Ang-2
- IL-8	+	+	+	+	+
- IP-10*	+
- IL-1RA	+			+	+
- sCD163	+
- sTREM-1	+				+
- Ferritin	+		+	+	+
- CRP
AIC of the selected model	430.5	441.1	434.2	431.6	430.7
Bootstrap results
- Model selection frequency, n (%)	79 (7.9)	567 (56.7)	432 (43.2)	202 (20.2)	161 (16.1)
- Rank by selection frequency of the selected model	1	1	1	1	1

VCAM-1: vascular cell adhesion molecule-1; SDC-1: syndecan-1; Ang-2: angiopoietin-2; IL-8: interleukin-8; IP-10: interferon gamma-induced protein-10; IL-1RA: interleukin-1 receptor antagonist; sCD163: soluble cluster of differentiation 163; sTREM-1: soluble triggering receptor expressed on myeloid cells-1; CRP: C-reactive protein; AIC: Akaike information criterion.

*Variable is kept as non-linear effect using natural cubic splines with three knots.

Appendix 7—table 3.

Model selection frequencies for adults.

Model	Included variables										Count	Percent
	VCAM-1	SDC-1	Ang-2	IL-8	IP-10*	IL-1RA	sCD163	sTREM-1	Ferritin	CRP
1		+		+	+	+	+	+	+		79	7.9
2	+	+		+	+	+	+	+	+		55	5.5
3	+	+		+	+	+		+	+		36	3.6
4		+		+		+		+	+		33	3.3
5		+		+				+	+	+	30	3.0
6		+	+	+	+	+	+	+	+		29	2.9
7		+		+			+	+	+	+	26	2.6
8		+		+		+			+		25	2.5
9		+		+		+		+	+	+	24	2.4
10		+		+	+		+	+	+	+	20	2.0
11	+	+		+		+	+	+	+		20	2.0
12	+	+		+	+	+	+	+	+	+	20	2.0
13	+	+		+	+	+	+		+		19	1.9
14		+		+	+		+	+	+		17	1.7
15		+		+					+	+	16	1.6
16		+		+			+		+		16	1.6
17		+		+			+	+	+		16	1.6
18		+		+	+		+		+		16	1.6
19		+		+	+	+		+	+		16	1.6
20		+		+		+	+	+	+		15	1.5

*Variable is kept as non-linear effect using natural cubic splines with three knots.

Selected model is ranked first (bold face).

Appendix 7—table 4.

Model stability for adults.

	Initial model		Selected model
Predictors	Estimate	Standard error	Estimate	Standard error	Bootstrap inclusion frequency (%)	RMSD ratio	Relative conditional bias (%)	Bootstrap median	Bootstrap 2.5th percentile	Bootstrap 97.5th percentile
(Intercept)	−16.6085	3.2632	−16.6780	3.2475	100.0	1.3263	2.0586	−16.7262	−26.4755	−9.8095
SDC-1	1.1524	0.2708	1.1740	0.2607	99.2	1.2745	3.0018	1.1767	0.5450	1.8615
IL-8	0.5473	0.1427	0.5544	0.1411	98.9	1.2973	5.8880	0.5739	0.2490	0.9520
Ferritin	−0.2785	0.0916	−0.2682	0.0883	94.6	1.2479	7.9330	−0.2845	−0.5012	0
sTREM-1	−0.2961	0.1499	−0.2864	0.1492	66.5	1.4191	28.1687	−0.2807	−0.6448	0
IL-1RA	0.2582	0.1583	0.2557	0.1427	62.3	1.4788	54.9829	0.2494	0	0.7255
IP-10 (ns1)*	−1.4427	1.0592	−0.8269	0.6118	59.8	1.6064	36.8995	−0.2108	−5.2628	0.7003
IP-10 (ns2)*	−0.1027	0.5763	0.1139	0.4976	59.8	1.2060	43.1473	0	−1.7021	1.2589
sCD163	0.2056	0.1287	0.2351	0.1253	59.1	1.3333	51.3932	0.2148	0	0.4989
CRP	0.0863	0.0990			36.3	1.1203	129.8186	0	0	0.3048
VCAM-1	0.0660	0.0685			35.4	1.3808	98.3111	0	−0.0923	0.2714
Ang-2	−0.0246	0.1193			21.8	1.0047	62.8642	0	−0.2792	0.3008

*As IP-10 is kept as non-linear effect using natural cubic splines with three knots, there are two terms of this variable in the model.

RMSD: root mean squared difference.

In the sensitivity analysis, viremia was not selected in any of the best combinations for children, and the marker combinations remained the same as the main analysis. For adults, the best subset included five markers SDC-1, IL-8, ferritin, viremia and sCD163. The best of two and three were the same as the main analysis; viremia was selected in the best of four and five (Appendix 8—figure 1; Appendix 8—table 1; Appendix 8—table 2; Appendix 8—table 3).

Appendix 8—figure 1.

Results from models for severe/moderate dengue including viremia as a potential biomarker.

Appendix 8—table 1.

Results from models for severe/moderate dengue including viremia as a potential biomarker.

	Single models					Global model
	Children OR (95% CI)	Adults OR (95% CI)	Poverall	Pinteraction		Children OR (95% CI)	Adults OR (95% CI)	Poverall	Pinteraction
VCAM-1 (ng/ml)			<0.001	0.715				0.286	0.136
- 1636 vs 818	1.20 (1.04–1.38)	1.35 (1.15–1.58)				0.94 (0.76–1.17)	1.34 (1.03–1.74)
- 3272 vs 1636	1.25 (1.02–1.53)	1.48 (1.19–1.85)				0.93 (0.69–1.24)	1.45 (1.02–2.04)
SDC-1 (pg/ml)			<0.001	0.088				0.005	0.645
- 2519 vs 1260	2.67 (1.31–5.43)	3.33 (1.32–8.42)				2.07 (0.78–5.47)	4.28 (1.27–14.43)
- 5039 vs 2519	1.71 (1.18–2.47)	3.71 (2.09–6.58)				1.71 (0.95–3.09)	2.55 (1.17–5.57)
Ang-2 (pg/ml)			<0.001	0.524				0.060	0.070
- 1204 vs 602	1.64 (1.39–1.94)	1.51 (1.26–1.82)				1.62 (1.19–2.20)	0.97 (0.71–1.34)
- 2409 vs 1204	2.21 (1.58–3.10)	2.00 (1.40–2.85)				1.92 (1.22–3.01)	0.96 (0.61–1.49)
IL-8 (pg/ml)			<0.001	<0.001				<0.001	<0.001
- 14 vs 7	1.42 (1.05–1.91)	2.18 (1.47–3.24)				0.89 (0.61–1.31)	1.60 (0.99–2.59)
- 28 vs 14	0.99 (0.78–1.25)	2.33 (1.63–3.33)				0.52 (0.36–0.77)	1.96 (1.28–3.02)
IP-10 (pg/ml)			<0.001	0.984				0.150	0.500
- 3093 vs 1546	1.46 (1.26–1.68)	1.45 (1.21–1.73)				0.93 (0.73–1.19)	0.75 (0.53–1.06)
- 6186 vs 3093	1.68 (1.35–2.09)	1.69 (1.29–2.22)				1.07 (0.76–1.50)	0.75 (0.47–1.20)
IL-1RA (pg/ml)			<0.001	0.082				<0.001	0.062
- 6434 vs 3217	1.69 (1.42–2.03)	1.48 (1.21–1.81)				1.97 (1.42–2.73)	1.40 (0.93–2.09)
- 12868 vs 6434	1.82 (1.46–2.27)	1.70 (1.29–2.24)				2.03 (1.41–2.92)	1.38 (0.87–2.19)
sCD163 (ng/ml)			<0.001	0.551				0.124	0.289
- 295 vs 147	1.57 (1.14–2.15)	1.49 (1.13–1.98)				1.51 (0.94–2.42)	1.30 (0.86–1.98)
- 589 vs 295	1.46 (1.10–1.93)	1.61 (1.09–2.37)				1.24 (0.88–1.73)	1.44 (0.91–2.28)
sTREM-1 (pg/ml)			0.059	0.997				0.745	0.594
- 85 vs 42	1.87 (1.23–2.84)	1.79 (1.10–2.93)				1.16 (0.71–1.91)	1.24 (0.65–2.36)
- 169 vs 85	1.12 (0.91–1.38)	1.12 (0.82–1.53)				0.92 (0.67–1.27)	0.67 (0.41–1.10)
Ferritin (ng/ml)			0.042	0.054				0.007	0.002
- 243 vs 122	1.18 (1.01–1.38)	1.06 (0.89–1.27)				1.32 (1.04–1.67)	0.77 (0.60–0.98)
- 487 vs 243	1.26 (1.00–1.58)	0.90 (0.66–1.23)				1.22 (0.89–1.68)	0.64 (0.42–0.98)
CRP (mg/l)			<0.001	0.031				0.185	0.113
- 28 vs 14	1.26 (1.12–1.41)	1.25 (1.03–1.52)				1.06 (0.91–1.23)	1.09 (0.84–1.43)
- 56 vs 28	1.13 (0.95–1.34)	1.38 (1.11–1.71)				0.89 (0.72–1.11)	1.35 (1.01–1.81)
Log-10 viremia (copies/ml)			<0.001	0.747				0.040	0.886
- 7.5 vs 6.5	1.34 (1.18–1.53)	1.33 (1.16–1.53)				1.21 (1.03–1.42)	1.25 (1.04–1.51)
- 8.5 vs 7.5	1.53 (1.25–1.87)	1.48 (1.16–1.88)				1.35 (1.05–1.74)	1.43 (1.05–1.95)

Odds ratios are estimated at age of 10 and 25 years, represented as children and adults respectively; Poverall is derived from Wald test for the overall association of the biomarker with the endpoint; Pinteraction is from the test for the interaction between the biomarker and age.

Appendix 8—table 2.

Best combinations of biomarkers associated with severe or moderate dengue for children.

	Best of all combinations	Best combination of 2 variables	Best combination of 3 variables	Best combination of 4 variables	Best combination of 5 variables
Variables
- VCAM-1
- SDC-1	+
- Ang-2	+		+	+	+
- IL-8	+				+
- IP-10	+			+	+
- IL-1RA	+	+	+	+	+
- sCD163
- sTREM-1
- Ferritin	+	+	+	+	+
- CRP
- Viremia
AIC of the selected model	465.9	484.7	480.0	473.7	467.6

VCAM-1: vascular cell adhesion molecule-1; SDC-1: syndecan-1; Ang-2: angiopoietin-2; IL-8: interleukin-8; IP-10: interferon gamma-induced protein-10; IL-1RA: interleukin-1 receptor antagonist; sCD163: soluble cluster of differentiation 163; sTREM-1: soluble triggering receptor expressed on myeloid cells-1; CRP: C-reactive protein; AIC: Akaike information criterion.

Appendix 8—table 3.

Best combinations of biomarkers associated with severe or moderate dengue for adults.

	Best of all combinations	Best combination of 2 variables	Best combination of 3 variables	Best combination of 4 variables	Best combination of 5 variables
Variables
- VCAM-1
- SDC-1	+	+	+	+	+
- Ang-2
- IL-8	+	+	+	+	+
- IP-10*
- IL-1RA
- sCD163	+				+
- sTREM-1
- Ferritin	+		+	+	+
- CRP
- Viremia	+			+	+
AIC of the selected model	426.4	441.1	434.2	428.5	426.4

VCAM-1: vascular cell adhesion molecule-1; SDC-1: syndecan-1; Ang-2: angiopoietin-2; IL-8: interleukin-8; IP-10: interferon gamma-induced protein-10; IL-1RA: interleukin-1 receptor antagonist; sCD163: soluble cluster of differentiation 163; sTREM-1: soluble triggering receptor expressed on myeloid cells-1; CRP: C-reactive protein; AIC: Akaike information criterion.

*Variable is kept as non-linear effect using natural cubic splines with three knots.

Discussion

This nested case-control study has shown that a range of endothelial, immune activation and inflammatory biomarkers measured during the early febrile phase of dengue are associated with progression to worse clinical outcomes in both children and adults. In children we found IL-1RA to have the most robust association with S/MD, whereas in adults we found SDC-1 and IL-8 to have the most robust association. For children, the best combination (ordered by robustness) included six biomarkers IL-1RA, Ang-2, IL-8, ferritin, IP-10, and SDC-1; for adults the best combination identified comprised seven biomarkers SDC-1, IL-8, ferritin, sTREM-1, IL-1RA, IP-10, and sCD163.

Our results add to the current literature on biomarkers in severe/moderate dengue compared with uncomplicated dengue, by including early time-points prior to the development of the severe manifestations, as well as providing data on the use of biomarker combinations, which takes into consideration the complex inflammatory-vascular pathogenesis of severe dengue. We observed that there were marked changes in the associations between individual biomarkers and outcomes when considering them together, while other biomarkers showed consistent associations. Particularly, the association of IP-10 with S/MD changed significantly from the single to global model, which may be because another biomarker in our model is a mediator or confounder of IP-10 in the pathway to the outcome. This could be IL-1RA as its association with S/MD was similar between the single and global model, and the correlation between IP-10 and IL-1RA was strong (Spearman’s rank correlation coefficient was 0.75). Nonetheless, changing the direction of the association from the single to global model does not diminish the possibility of that biomarker being selected in the best combinations.

Our study also demonstrates some key differences between pediatric and adult dengue. Clinical phenotypes of dengue in children and adults differ, with children experiencing more shock and adults more organ impairment and bleeding, with distinct clinical management guidelines published by the WHO. Our results imply dengue pathogenesis may differ by age, with distinct combinations of immune-activation and vascular markers demonstrated between children and adults. Specifically, the association of IL-8 and ferritin differed between children and adults, which is likely to be due to the composite endpoint of severe and moderate dengue. As shown in the analysis of severe dengue alone (Appendix 4—figure 1, Appendix 4—table 1), the effects of IL-8 and ferritin were similar in children and adults, which suggests these biomarkers are still associated with severe disease in all age groups and that the difference is driven by the moderate dengue group. In addition, uncomplicated dengue in adults have higher ferritin levels compared to in children, with increasing age and chronic conditions in adults likely contributing to this observation. Hence patients’ age should be considered when developing biomarker panels for dengue risk prediction.

The use of biomarker panels for the prediction of severe outcomes in dengue has been investigated in previous studies, using several statistical approaches (Brasier et al., 2012; Conroy et al., 2015; Ju and Brasier, 2013; Lee et al., 2016; Pang et al., 2016). However, because of small sample size and differences in the biomarkers assessed, the associations found vary between studies and as yet there are no validated prognostic panels for dengue. Dengue cases are forecasted to increase over the next few decades and, given the limited healthcare resources available in many endemic settings, particularly during epidemics, there is an urgent need to develop innovative methods to rapidly identify patients likely to develop complications and require hospital care (Rodriguez-Manzano et al., 2018). Previously, we showed that CRP as a single biomarker was useful for early dengue diagnosis and risk identification, which is currently easy to use in all settings (Vuong et al., 2020). Recently, we also showed that higher plasma viremia was associated with increased dengue severity regardless of age, serotype and immune status of patients (Vuong et al., 2021a). However, future point-of-care testing could be improved by using a combination of biomarkers outlined in this study. Our results are applicable to the development of point-of-care panels capable of multiplex analysis and suited for use in outpatient settings for dengue prognosis, with scope for incorporation with innovative point-of-care technologies. To be more applicable by balancing model fit, robustness, and parsimony, we suggest the combination of five biomarkers IL-1RA, Ang-2, IL-8, ferritin, and IP-10 for children, and the combination of three biomarkers SDC-1, IL-8, and ferritin for adults to be used in practice. These combinations had a similar AIC with the best combinations (the difference of AIC was less than 5), but they required fewer number of biomarkers in a test panel. With the advent of novel technologies including microarray platforms and multiplex lateral flow assays, the cost is likely to come down in the future, allowing for wide-spread use in low-to-middle-income countries.

Methods of variable selection have been discussed previously but there remains no clear consensus regarding the best approach (Heinze et al., 2018; Sauerbrei et al., 2020). We adopted a data-driven ‘best subset’ approach which we think offers advantages over other methods, given the complexity of the biomarkers involved and their interactions. We also explored other approaches for variable selection (Heinze et al., 2018; Piironen and Vehtari, 2017; Sauerbrei et al., 2020) and the results were very similar to the best subset procedure (Appendix 9—table 1; Appendix 9—table 2).

Appendix 9—table 1.

Results of variable selection for children.

	VCAM-1	SDC-1	Ang-2	IL-8	IP-10	IL-1RA	sCD163	sTREM-1	Ferritin	CRP
Best subset		+	+	+	+	+			+
Backward elimination		+	+	+	+	+			+
Forward selection		+	+	+	+	+			+
Stepwise forward		+	+	+	+	+			+
Stepwise backward		+	+	+	+	+			+
Augmented backward elimination	+	+	+	+	+	+			+
Bayesian projection			+	+	+	+			+

Appendix 9—table 2.

Results of variable selection for adults.

	VCAM-1	SDC-1	Ang-2	IL-8	IP-10*	IL-1RA	sCD163	sTREM-1	Ferritin	CRP
Best subset		+		+	+	+	+	+	+
Backward elimination		+		+	+	+	+	+	+
Forward selection		+		+		+		+	+
Stepwise forward		+		+		+		+	+
Stepwise backward		+		+	+	+	+	+	+
Augmented backward elimination	+	+		+	+	+	+	+	+
Bayesian projection		+		+

*Variable is kept as non-linear effect using natural cubic splines with three knots.

Strengths of our study include the large sample size and use of a nested case-control dataset from a prospective multi-country cohort study with consistent data collection and standardized outcome definitions and laboratory methodologies. The biomarker panel we selected was guided by pathogenesis studies, focusing on pathways activated early in the disease course, thus ensuring clinical relevance.

There are some limitations in our study. One being we analysed the biomarkers at only one time-point in the early phase; limited financial resources did not allow us to evaluate the full range of biomarkers across the whole IDAMS population and at more time-points. Secondly, this study was not designed to build prediction models so we did not use a measure of predictive value as a criterion, which was motivated by the nested case-control design. Our findings need to be validated in new studies.

In conclusion, higher levels of the ten biomarkers (VCAM-1, SDC-1, Ang-2, IL-8, IP-10, IL-1RA, sCD163, sTREM-1, ferritin, and CRP), when considered individually, are associated with increased risk of adverse clinical outcomes in both children and adults with dengue. The best biomarker combination for children includes IL-1RA, Ang-2, IL-8, ferritin, IP-10, and SDC-1; for adults, SDC-1, IL-8, ferritin, sTREM-1, IL-1RA, IP-10, and sCD163 were selected. These findings serve to assist the development of biomarker panels to improve future triage and early assessment of dengue patients. This would aid not only individual patient management and facilitate healthcare allocation which would be of major public health benefit especially in outbreak settings, but could also serve as potential biological endpoints for dengue clinical trials.

Our editorial process produces two outputs: i) public reviews designed to be posted alongside the preprint for the benefit of readers; ii) feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.

Acceptance summary:

The paper is relevant as dengue diagnosis and prognosis remains a major problem in several parts of the world. This paper should help the development of biomarker panels for clinical use and could improve triage and risk prediction in dengue patients.

Decision letter after peer review:

Thank you for submitting your article "Combination of inflammatory and vascular markers in the febrile phase of dengue is associated with more severe outcomes" for consideration by eLife. Your article has been reviewed by 3 peer reviewers, and the evaluation has been overseen by Balram Bhargava as the Senior and Reviewing Editor. The reviewers have opted to remain anonymous.

The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.

Essential revisions:

Please address the weaknesses of the study which have been highlighted in the reviewers comments as below.

Reviewer #2 (Recommendations for the authors):

Page 8, line 5. Could the authors provide a rationale on why a 1 case:2 control ratio was chosen for this study?

Table 1. The inclusion of results from statistical tests would be useful to accompany the description provided in page 12, lines 1-6.

Table 1. Is the biomarker validation affected by the preponderance of DENV-1 in the study population? Although the number of dengue cases caused by the other serotypes is relatively lower, is it possible to show that at least in terms of trends, there is no difference in the expression of the biomarkers by serotype? This could be a very useful supplementary information to address any concern on whether the findings reported here could be applied to dengue cases caused by non-DENV-1 infection.

Table 1. Most of the severe dengue cases were in children whereas there were proportionately more moderately severe cases in adults. Could the different sets of biomarkers for paediatric and adult dengue cases be predictive for severe vs moderately severe dengue?

Tables 2 and 3 can be shown as supplementary tables since the same datasets are shown as Figures 2 and 3. It may be busy but it would also be useful to indicate in Figure 2 where the statistically significant differences are.

Page 17, line 8. I do not understand how the global model was developed and how it was useful for biomarker development. Indeed, the example highlighted in this paragraph was IP-10, where the direction of correlation changed in the global compared to single model. Despite this finding, Tables 4 and 5 went on to consider IP-10 as amongst the possible biomarkers to predict severe/moderately severe dengue. Please provide a clearer explanation for readers without deep expertise in statistics to appreciate the findings reported here.

The lack of viraemia and NS1 antigenaemia measurements is a striking omission. I think it would be of great advantage to include these parameters into the analysis. If not possible, the authors should provide a discussion on why these parameters were excluded and whether they should be considered for future studies.

Reviewer #3 (Recommendations for the authors):

1. The model can be confusing to understand. it appears to this non statistician reviewer that global model did not improve sensitivity of the test? If so, how does that impact the interpretation of the data?

2. Certain sections of the discussion are repetitive and can be more succinctly summarised.

Reviewer #2 (Recommendations for the authors):

Page 8, line 5. Could the authors provide a rationale on why a 1 case:2 control ratio was chosen for this study?

As there were a limited number of cases with the primary endpoint (severe/moderate dengue) in the clinical study, our aim was to increase the power of the study by doubling the number of controls (uncomplicated dengue).

Table 1. The inclusion of results from statistical tests would be useful to accompany the description provided in page 12, lines 1-6.

We did not perform statistical tests for table 1 because of the following:

– This was for descriptive analysis only and there was no hypothesis to test

– Performing statistical analysis for table 1 would mean that the independent variable is severity (severe/moderate versus uncomplicated dengue) and the outcomes are the clinical characteristics, which reverses the causal pathway.

Table 1. Is the biomarker validation affected by the preponderance of DENV-1 in the study population? Although the number of dengue cases caused by the other serotypes is relatively lower, is it possible to show that at least in terms of trends, there is no difference in the expression of the biomarkers by serotype? This could be a very useful supplementary information to address any concern on whether the findings reported here could be applied to dengue cases caused by non-DENV-1 infection.

Thank you – please see our response to the public review for reviewer 2, the results of the single and global models are similar between DENV-1 and non-DENV-1 infections.

Table 1. Most of the severe dengue cases were in children whereas there were proportionately more moderately severe cases in adults. Could the different sets of biomarkers for paediatric and adult dengue cases be predictive for severe vs moderately severe dengue?

Because the number of severe dengue cases is limited (38 cases), it is not possible to analyse severe versus moderate dengue. In our analysis for severe dengue alone (severe versus moderate/uncomplicated dengue) as shown in appendix 6 (figure S5 and table S6) in the supplementary file, we could see a trend in some of the biomarkers, but confidence intervals were very wide.

Tables 2 and 3 can be shown as supplementary tables since the same datasets are shown as Figures 2 and 3. It may be busy but it would also be useful to indicate in Figure 2 where the statistically significant differences are.

We agree that table 2 is similar to figure 2 and we have moved it to the supplementary file (table S3). However, we would like to keep table 3 in the main file (which is changed to table 2 of the revised manuscript) as it contains important and distinct results which are not represented in figure 3, such as numerical results, p-values for the overall effect and the interaction between biomarkers and age.

Similar to our second response to reviewer 2 above, we did not perform statistical tests for figure 2 as this is for descriptive analysis only; we do not want to test for differences in trend over time. Also, performing tests would result in reversal of the causal pathway, with the biomarker as dependent variable and dengue outcome as predictor. Note that the relationships with p-values and confidence intervals can be found in table 2 and figure 3 of the revised manuscript (P values).

Page 17, line 8. I do not understand how the global model was developed and how it was useful for biomarker development. Indeed, the example highlighted in this paragraph was IP-10, where the direction of correlation changed in the global compared to single model. Despite this finding, Tables 4 and 5 went on to consider IP-10 as amongst the possible biomarkers to predict severe/moderately severe dengue. Please provide a clearer explanation for readers without deep expertise in statistics to appreciate the findings reported here.

When performing variable selection, a backward approach is preferable over a forward approach. Therefore, we started variable selection with a global model that contained all the biomarkers along with the interaction with age. The global model allows investigation of the association between biomarkers and the clinical endpoint when considering them together (aim #1). It was the initial step to develop a set of biomarkers that is most strongly associated with the primary endpoint (aim #2). We have added this point to the Statistical analysis section of the revised manuscript (page 9, line 25 and page 10, lines 1-2).

We fitted the single models because it may provide further information on disease mechanisms when compared with results from the global model (although not our primary study aim). The observation that the direction of the association between IP-10 and the endpoint changed in the global model compared to single model suggests that there might be confounders or intermediate variables of IP-10 in the global model. It could be because of IL-1RA since both markers show a fairly strong correlation (Spearman’s correlation coefficient was 0.75). However, changing the direction of the association from the single to global model does not diminish the possibility of that biomarker being selected in the best combinations. See also the reply to comment #3 of reviewer #1. This explanation was added to the Discussion section of the revised manuscript (page 14, lines 10-17).

The lack of viraemia and NS1 antigenaemia measurements is a striking omission. I think it would be of great advantage to include these parameters into the analysis. If not possible, the authors should provide a discussion on why these parameters were excluded and whether they should be considered for future studies.

Thank you – please see our response to the public review for reviewer 2. We have added viremia data, but NS1 antigenaemia data was not available.

Reviewer #3 (Recommendations for the authors):

1. The model can be confusing to understand. it appears to this non statistician reviewer that global model did not improve sensitivity of the test? If so, how does that impact the interpretation of the data?

Thank you, please also see our response to comment #10 of reviewer #2. The global model is the model to start with and contains all the biomarkers combined, taking into account the interaction with age. In this study, building the global model is important because: (1) it allows investigation into the association between biomarkers and clinical endpoint when considering them together (aim #1), and (2) it was the first step to develop a set of biomarkers that is most strongly associated with the primary endpoint (aim #2). We did not analyse the predictive performance of the models. Choosing a cutoff value for the score to compute the sensitivity is better done in a separate study (see also response to comment #4 of reviewer #1).

2. Certain sections of the discussion are repetitive and can be more succinctly summarised.

Thank you for this suggestion, we have revised and cut down the discussion to make it more concise.

Biomarker	Children					Adults
	model1	model2	model3	model4		model1	model2	model3	model4
VCAM-1	530.8	531.3	530.6	532.6		499.8	496.7	501.3	496.4
SDC-1	537.9	538.0	-	-		459.5	461.0	-	-
Ang-2	511.0	509.6	512.5	509.7		493.5	490.8	493.3	492.8
IL-8	548.5	549.0	548.1	546.8		457.7	457.8	456.5	457.8
IP-10	521.2	517.2	523.0	518.3		500.7	492.6	502.5	494.5
IL-1RA	492.9	494.9	-	-		493.5	494.2	-	-
sCD163	531.9	533.2	531.9	533.2		497.6	499.3	499.5	501.1
sTREM-1	545.7	544.2	545.6	545.4		507.8	509.5	507.0	505.0
Ferritin	542.6	544.4	-	-		509.3	505.2	-	-
CRP	536.7	536.4	-	-		505.1	506.8	-	-

The selected models are in bold face.