Fabienne Venet

Fanny Davin

Caroline Guignant

Audrey Larue

Marie-Ange ´lique Cazalis

Romain Darbon

Caroline Allombert

Bruno Mougin

Christophe Malcus

Franc ¸oise Poitevin-Later

Alain Lepape

PharmD Guillaume Monneret
email: guillaume.monneret@chu-lyon.fr.

EARLY ASSESSMENT OF LEUKOCYTE ALTERATIONS AT DIAGNOSIS OF SEPTIC SHOCK

Keywords: Septic shock, lymphocyte, FOXP3, T-bet, GATA-3, ROR+

A dramatic decrease in circulating lymphocyte number is regularly described after septic shock. However, it is unknown how early this alteration develops after diagnosis of shock and if it remains stable over time. Twenty-one septic shock patients with no comorbidities were included within 2 h after the beginning of vasopressive treatment. Flow cytometry phenotyping of circulating leukocyte subpopulations and quantitative real-time polymerase chain reaction of T-bet, GATA-3, FOXP3, and ROR+ mRNA were performed in patients from the diagnosis of shock and every 6 h during the subsequent 48 h. From their admission in the intensive care unit, patients present with major alterations of circulating leukocyte count (leukocytosis, neutrophilia, and major lymphopenia). The numbers of every lymphocyte subpopulations (T, B, and natural killer cells) were diminished. Gene expression analysis of transcription factors specific for T H 1, T H 2, CD4 + CD25 + regulatory, and T H 17 lymphocytes showed a severe decrease in comparison with healthy individuals' values. These alterations remain stable during the first 48 h after inclusion in the protocol despite early and aggressive resuscitation and antibiotherapy administered in patients. At the time of diagnosis of shock and admission in the intensive care unit, septic patients already present with severe lymphopenia involving every lymphocyte subsets including CD4 + T-cell subpopulations. No significant variation could be detected within the first 48 h. This should be taken into account in the forthcoming clinical trials testing immunomodulating therapies in septic shock patients.

INTRODUCTION

Despite marked improvement in critical care medicine and almost 20 years of anti-inflammatory clinical trials, septic syndromes (association of a systemic inflammatory reaction to an infection) are the leading cause of death in noncoronary intensive care units (ICUs) and a major health care problem worldwide (1Y3). During the last 20 years, septic syndrome mortality has remained constant (from 30% to 50% for septic shock), and incidence has been continuously rising because of the constant aging of the population and the better care of comorbidities (diabetes, cancer, etc.). Severe sepsis, for example, is projected to represent one million cases per year before the year 2020 in the United States alone [START_REF] Angus | Epidemiology of severe sepsis in the United States: analysis of incidence, outcome, and associated costs of care[END_REF][START_REF] Dombrovskiy | Rapid increase in hospitalization and mortality rates for severe sepsis in the United States: a trend analysis from 1993 to 2003[END_REF].

Sepsis initiates a complex immunologic response that varies over time, with the concomitant occurrence of both proinflammatory and anti-inflammatory mechanisms alternatively predominating [START_REF] Hotchkiss | The pathophysiology and treatment of sepsis[END_REF][START_REF] Monneret | Monitoring immune dysfunctions in the septic patient: a new skin for the old ceremony[END_REF]. After a short proinflammatory phase, septic patients enter a stage of protracted immunosuppression illustrated by reactivation of dormant viruses (cytomegalovirus or herpes simplex virus) or infections due to germs normally pathogenic solely in immunocompromised host (2, 5Y7). Importantly, the intensity and duration of this phase of immunosuppression seem to be closely correlated with mortality and the development of nosocomial infections [START_REF] Hotchkiss | The pathophysiology and treatment of sepsis[END_REF][START_REF] Munford | Normal responses to injury prevent systemic inflammation and can be immunosuppressive[END_REF][START_REF] Pugin | Immunostimulation is a rational therapeutic strategy in sepsis[END_REF].

So far, studies have principally investigated dysfunctions of the innate side of the immune response after severe sepsis. However, dramatic alterations of the lymphocytic part of the immune response are also regularly described in patients [START_REF] Hotchkiss | The pathophysiology and treatment of sepsis[END_REF][START_REF] Monneret | Monitoring immune dysfunctions in the septic patient: a new skin for the old ceremony[END_REF]. These dysfunctions have been known for long and are mainly characterized by a decrease in total lymphocyte number [START_REF] Nishijima | Serial changes in cellular immunity of septic patients with multiple organ-system failure[END_REF][START_REF] Mccredie | Predictive value for survival of lymphocytes and polymorphonuclear leukocytes in patients with sepsis[END_REF] and by a state of anergy illustrated both by a decreased proliferation after stimulation ex vivo [START_REF] Roth | Susceptibility to programmed cell death in T-lymphocytes from septic patients: a mechanism for lymphopenia and T H 2 predominance[END_REF] and by decreased response to standard skin tests (10, 13Y16). The major importance of this lymphopenia in sepsis pathophysiology is illustrated by animal studies showing that prevention of lymphocyte apoptosis improves the likelihood of survival after sepsis (17Y20).

Whereas lymphopenia has been shown to persist during the first week after admission of septic patients to the ICU [START_REF] Monserrat | Clinical relevance of the severe abnormalities of the T cell compartment in septic shock patients[END_REF], it is unknown when (i.e., how early after diagnosis of shock/ admission in the ICU) these alterations develop, if they vary over time during the so-called golden hours after diagnosis of shock, and if the different lymphocyte subpopulations are equally diminished. This study was therefore designed to perform a systematic monitoring of the different circulating leukocyte subpopulations measured in septic shock patients from the time of diagnosis of shock and every 6 h during the first 48 h.

PATIENTS AND METHODS

Patients

The study group consisted in 21 patients diagnosed with septic shock according to the diagnostic criteria of the American College of Chest Physician/Society of Critical Care Medicine [START_REF] Bone | The ACCP-SCCM consensus conference on sepsis and organ failure[END_REF]. To minimize heterogeneity, patients were included only if they present with no comorbidity. Septic shock was defined, according to classic definition, by an identifiable site of infection, hypotension persisting despite fluid resuscitation and requiring vasopressor therapy, and evidence of a systemic inflammatory response manifested by at least two of the following criteria: (a) temperature greater than 38-C or less than 36-C, (b) heart rate greater than 90 beats/min, (c) respiratory rate greater than 20 breaths/min, and (d) white blood cell count of more than 12,000/2L or less than 4,000/2L. Severity was assessed by the Simplified Acute Physiology Scale II [START_REF] Gall | A new Simplified Acute Physiology Score (SAPS II) based on a European/North American multicenter study[END_REF]. Mortality was defined as death occurring within 28 days after the onset of shock. Cell phenotyping was performed within 2 h after the onset of vasopressor therapy, arbitrary chosen as the inclusion time, and every 6 h during the following 48 h. This prospective study was reviewed by the institutional ethics committee. Informed consent was obtained for each patient or relatives before the first blood drawing.

Flow cytometry

Apart from white blood cell and differential leukocyte count (obtained from the routine hematology laboratory), the following lymphocyte subsets were analyzed: total T lymphocytes (CD45 + CD3 + ), CD4 + T lymphocytes (CD45 + CD4 + CD3 + ), CD8 + T lymphocytes (CD45 + CD8 + CD3 + ), total B cells (CD45 + CD19 + ), and natural killer cells (NK cells, CD45 + CD3 j CD56 + ). The flow cytometry gating strategy is shown in Figure 1.

Flow cytometric (FC500; Beckman-Coulter, Hialeah, Fla) expression of cell-surface markers was assessed on peripheral whole blood collected on EDTA anticoagulant tubes. Monoclonal antibodies were used according to the manufacturer's recommendations: fluorescein isothiocyanate (FITC)Ylabeled anti-CD45, PC5-labeled anti-CD3, PE-labeled anti-CD4, ECD-labeled anti-CD8, ECD-labeled anti-CD19, and PE-labeled anti-CD56 (Beckman-Coulter). The red cells were lysed on the automated TQ Prep system (Beckman-Coulter). Absolute count was performed using Flow-Count Fluorospheres according to manufacturer's recommendations (Beckman-Coulter). Results are expressed as numbers of cells per microliter of blood. Age-matched reference values were provided by the routine flow cytometry laboratory of our institution.

Quantitative real-time polymerase chain reaction analysis of FOXP3, T-bet, GATA-3, and ROR+

Peripheral blood mRNA expression was measured using samples collected directly in PAXGene Blood RNA tubes (PreAnalytix, Hombrechtikon, Switzerland). Total RNA was extracted using the PAXGene Blood RNA kit (PreAnalytix). The residual genomic DNA was digested using the RNase-free DNase set (Qiagen, Germantown, Md). The integrity and quality of the total RNA were assessed on an Agilent 2100 Bioanalyzer (Agilent, Santa Clara, Calif ) by means of the RNA 6000 Nano assay (Agilent). Total RNAs were reverse transcribed into cDNA using SuperScript real-time polymerase chain reaction (RT-PCR) system (Invitrogen, Carlsbad, Calif ) according to manufacturer's instructions. mRNA expression was quantified using quantitative RT-PCR (qRT-PCR) as previously described [START_REF] Pachot | Messenger RNA expression of major histocompatibility complex class II genes in whole blood from septic shock patients[END_REF]. Briefly, PCR reactions were performed in a LightCycler 2.0 instrument using the Fast-Start DNA Master SYBR Green I RT-PCR kit according to the manufacturer's instructions (Roche Molecular Biochemicals, Indianapolis, Ind). Thermocycling was performed in a final volume of 20 2L containing 3 mM MgCl 2 and 0.5 2M each of the required primers. Polymerase chain reaction was performed with an initial denaturation step of 10 min at 95-C, followed by 40 cycles of a touch-down PCR protocol (10 s at 95-C, 10-s annealing at 68-C to 58-C, and 16-s extension at 72-C). mRNA expression of the housekeeping gene peptidylpropyl isomerase B (PPIB), encoding for cyclophilin B, and the four transcription factors (FOXP3, T-bet, GATA-3, and ROR+) were investigated using specific cDNA standards and ready-to-use primer mixes obtained from Search-LC (Heidelberg, Germany). The efficiency of PPIB mRNA levels has been already demonstrated as reference for target mRNA quantification in human peripheral blood [START_REF] Venet | Increased percentage of CD4 + CD25 + regulatory T cells during septic shock is due to the decrease of CD4 + CD25 j lymphocytes[END_REF]. Serial dilutions of the cDNA standard were prepared in quadruplicate to generate standard curves. Relative standard curves, describing the PCR efficiency of each target genes and PPIB, were created and used to perform efficiency-corrected quantification with the LightCycler software version 4.05. An internal calibrator (pooled mRNA extracted from 10 healthy volunteers' whole blood sampled using PAXGene tubes) was used to compare each reverse transcription. The results were expressed as normalized ratio of FOXP3, T-bet, GATA-3, or ROR+ mRNA/ PPIB mRNA and compared with results from a cohort of age-matched healthy volunteers.

Statistics

Comparisons between groups were made using the nonparametric Mann-Whitney U test (analysis of transcription factor mRNA expressions), whereas comparisons versus normal/test values (medians) were performed using the signed rank sum test with P G 0.05 considered as statistically significant with correction by the number of tests performed. The nonparametric Wilcoxon paired test was used to assess variations between hour 0 (H0) and hour 48 (H48) for each lymphocyte subpopulation. Finally, variability over time for each marker was evaluated using linear mixed-effects models. In these models, the null hypothesis was that, for each marker, the slope calculated over 48 h was equal to zero. The analyses were performed using Sigma Stat software (version 3.5; Systat Software, Chicago, Ill).

RESULTS

Twenty-one septic shock patients with no comorbidities, as shown by a low Charlson score, were prospectively included in the study. Clinical data are presented in Table 1. Four patients died (28-day mortality: 19%), among whom 2 died during the first 48 h. There was no delay between diagnosis of shock and admission in the ICU (Table 1).

Serial blood samples were obtained from patients at the time of diagnosis of shock (i.e., within 2 h after the beginning of vasoactive treatment) and every 6 h during the first 48 h afterward. Flow cytometry phenotyping of circulating cells was performed, and results were compared with normal, agematched values from our routine immunology laboratory.

We first observed that septic shock patients present with a marked and significant leukocytosis that was present from the inclusion time and remained constant during the whole monitoring (linear mixed-effects model, Table 2). This increase in white blood cell count was due to an increase in the number of circulating neutrophils, whereas the number of circulating monocytes was overall not modified in patients in comparison with normal values (Table 2). In marked contrast, total lymphocyte absolute number was markedly and significantly diminished from the inclusion time (Table 2) and remained extremely low during the whole monitoring (i.e., during the first 48 h after inclusion, linear mixed-effects model).

Regarding circulating lymphocyte subpopulations, a marked decrease in T, B, and NK cells was observed (Table 2; Fig. 2). This diminution was present from the time of admission in the ICU and stable during the first 48 h (linear mixed-effects model; Table 2; Fig. 2A). No significant variation could be observed between values measured at H0 and H48 (Fig. 2A).

Within T lymphocytes, CD4 + and CD8 + T-cell numbers were equally decreased during the whole monitoring (Table 2; Fig. 2,A and No significant variations (neither increase nor decrease, linear mixed-effects model) were observed during the first 48 h (Fig. 2, A andB), although for T cells and CD4 + T lymphocytes, the nadir was measured at inclusion in the protocol. As an example, box plots as well as individual values for CD4 + T cells are presented in Figure 2B.

Finally, to evaluate the potential variations of CD4 + Tcell subpopulations, transcription factors specific for T H 2 (GATA-3), T H 1 (T-bet), regulatory T cell (Treg; FOXP3), and newly described T H 17 (ROR+) cells were measured by qRT-PCR in patients and compared with a cohort of age-matched healthy individuals (n = 11, 4 women and 7 men; mean age, 51 [SD, 6] years). Not surprisingly, a significantly reduced expression of each transcription factors was observed. This decrease was measured at inclusion and at each time point during the whole monitoring (Fig. 3). Interestingly, when normalized based on the number of circulating CD4 + T lymphocytes for each patient at each time point, every transcription factors returned within the range of normal values (data not shown).

Of note, significantly lower numbers of circulating neutrophils and CD4 + T lymphocytes were measured at H0 in patients with pulmonary infections in comparison with patients with abdominal infections. No significant differences were observed between patients with gram-negative bacilli (n = 9) and gram-positive cocci (n = 3) infections (we did not consider mixed-germ infections), or between survivors (n = 17) and nonsurvivors (n = 4, data not shown).

DISCUSSION

As a result of their ability to interact with cells of the innate immune system (i.e., macrophages, dendritic cells, neutrophils, NK cells) and with other cells of the adaptive immune system, lymphocytes play a central role in the anti-infectious immune response not only as effectors but also as regulators of this response. This has been illustrated by the observation of a dysregulated, proinflammatory immune response after polymicrobial septic challenge in mice lacking T and B cells [START_REF] Hotchkiss | Caspase inhibitors improve survival in sepsis: a critical role of the lymphocyte[END_REF][START_REF] Shelley | Interaction between the innate and adaptive immune systems is required to survive sepsis and control inflammation after injury[END_REF]. Alterations of the lymphocytic response after septic shock have been known for long. They comprise (a) phenotypic alterations including an increase in inhibitory coreceptor expression associated with a decreased expression of activating coreceptors and CD3, (b) functional alterations mainly characterized by a decreased ex vivo lymphocyte proliferation associated with decreased T H 1 and increased T H 2 immune responses, (c) induction of regulatory T-cell populations, and (d) an increased apoptosis leading to a marked lymphopenia [START_REF] Hotchkiss | The pathophysiology and treatment of sepsis[END_REF][START_REF] Monneret | Monitoring immune dysfunctions in the septic patient: a new skin for the old ceremony[END_REF].

This marked decrease in circulating lymphocyte number has been regularly reported in patients after sepsis and severe trauma (12, 25, 27Y31). Most importantly, the extent of this lymphopenia has been associated with the development of nosocomial infections in ICU patients. For example, Hotchkiss et al. ( 28) observed a marked lymphoid depletion in adults dying of sepsis. These patients had reduced CD4 + T-and B-cell numbers and the degree of reduction in cell numbers related FIG. 2. Septic shock patients present with severe lymphopenia from the time of admission and during the first 48 h. Blood samples were obtained from 21 septic shock patients within 2 h after the beginning of vasopressive treatment and every 6 h during 48 h afterward. The following lymphocyte subsets were analyzed by flow cytometry: total T lymphocytes (CD45 + CD3 + ), CD4 + T lymphocytes (CD45 + CD4 + CD3 + ), CD8 + T lymphocytes (CD45 + CD8 + CD3 + ), total B cells (CD45 + CD19 + ), and NK cells (CD45 + CD3 j CD56 + ). A, Box plots as well as individual values are presented for measurements at H0 (light gray boxes) and H48 (dark gray boxes) for each lymphocyte subpopulations. Box plots (median T 2 SDs, open boxes) for age-matched normal values from our laboratory are shown for each lymphocyte subpopulations. B, Total phenotyping for circulating CD4 + T lymphocytes is presented (light gray boxes) in comparison with normal values from our laboratory. In addition, values for circulating CD4 + T cells previously published by our group (34) at days 3 to 5 in a similar cohort of septic shock patients are shown (dark gray box). Comparisons versus normal/test values (medians) were performed using the signed rank sum test with correction for the number of tests performed. *P G 0.0056, **P G 0.0001 versus normal values.

to the duration of sepsis before death. Similarly, Felmet et al. [START_REF] Felmet | Prolonged lymphopenia, lymphoid depletion, and hypoprolactinemia in children with nosocomial sepsis and multiple organ failure[END_REF] observed that an absolute lymphocyte count of less than 1,000 cell/2L not only identified critically ill children at high risk for nosocomial infection, but also that an absolute lymphocyte count of less than 1,000/2L persisting for 7 days identified those at risk for death from uneradicated nosocomial infection.

However, in the literature, circulating lymphocyte numbers have been mainly monitored in patients starting from day 1 after the onset of shock and usually only once within the first days. What happens before day 1 (i.e., between the time of diagnosis of shock and the end of the first day after shock) is unknown. Based on the biphasic model usually described for the immune response after severe sepsis [START_REF] Hotchkiss | The pathophysiology and treatment of sepsis[END_REF][START_REF] Monneret | Monitoring immune dysfunctions in the septic patient: a new skin for the old ceremony[END_REF][START_REF] Hotchkiss | Caspase inhibitors improve survival in sepsis: a critical role of the lymphocyte[END_REF] and on the fact that the adaptive/lymphocytic immune response is thought to be involved in a second time/later on after an infection, one may postulate that lymphocyte number may be within the normal range at diagnosis of shock but decreases later on. In this context, this study is the first to perform a systematic monitoring of every circulating leukocyte subset in septic shock patients, starting from the time of admission in the ICU and every 6 h during the first 48 h. This systematic monitoring has allowed us to obtain a precise view of the development of these alterations and of their variability over time during the first 48 h. Importantly, we observed that leukocyte alterations described in the literature after septic shock are already present at the time of inclusion in the study: leukocytosis associated with marked neutrophilia and profound lymphopenia. Monocyte number, in contrary, was not modified, although it is well known that many of their functions are altered [START_REF] Hotchkiss | The pathophysiology and treatment of sepsis[END_REF][START_REF] Monneret | Monitoring immune dysfunctions in the septic patient: a new skin for the old ceremony[END_REF]. Among lymphocytes, the numbers of every subpopulation were decreased as well. Interestingly, when measuring CD4 + T-cellY specific transcription factors (T-bet for T H 1, GATA-3 for T H 2, FOXP3 for Treg, and ROR+ for T H 17), they all appeared equally diminished. Because qRT-PCR was performed on mRNA extracted from whole blood, these decreased expressions could be related with the marked diminution in CD4 + T-cell number. Despite the apparent normalization of these mRNA levels when corrected by CD4 + T-cell absolute count, we cannot definitively rule out a specific downregulation of one of these transcription factors in some CD4 + cell subsets. This thus deserves to be confirmed in isolated cells.

These results are in line with previous studies by our group measuring either T-bet and GATA-3 or FOXP3 mRNA at days 1 and 2 after the onset of shock [START_REF] Venet | Increased percentage of CD4 + CD25 + regulatory T cells during septic shock is due to the decrease of CD4 + CD25 j lymphocytes[END_REF][START_REF] Pachot | Longitudinal study of cytokine and immune transcription factor mRNA expression in septic shock[END_REF]. Regarding ROR+, very few studies have been published so far investigating the role of T H 17 cells in sepsis pathophysiology. In a murine model of sepsis, Flierl et al. [START_REF] Flierl | Adverse functions of IL-17A in experimental sepsis[END_REF] observed that IL-17a circulating levels were increased after sepsis and that specific blockade of this cytokine was associated with decreased mortality. However, the present results tend to show that ROR+ behaves after septic shock as other T-cellYspecific transcription factors.

The second important point of this study is that these alterations remain stable during the first 48 h after diagnosis of shock despite early and aggressive resuscitation therapy and antibiotherapy administered in patients. When put into perspective with our previously published data, it seems that they persist even longer, as illustrated in Figure 2B. Indeed, values for total T cells and for CD4 + T cells measured at H48 in this study are very similar to values measured at days 3 to 5 in previous studies by our group [START_REF] Venet | Increased percentage of CD4 + CD25 + regulatory T cells during septic shock is due to the decrease of CD4 + CD25 j lymphocytes[END_REF][START_REF] Venet | Both percentage of gammadelta T lymphocytes and CD3 expression are reduced during septic shock[END_REF][START_REF] Venet | Increased circulating regulatory T cells (CD4(+)CD25 (+)CD127 (j)) contribute to lymphocyte anergy in septic shock patients[END_REF]. This suggests that early and aggressive therapy in patients has no effect on lymphopenia and that it may be interesting to install a more specific treatment to restore normal lymphocyte numbers because lymphopenia has been shown to be related with increased mortality after sepsis.

Overall, our data suggest that the causes of this prolonged lymphopenia and lymphoid depletion, likely multifactorial, most probably occur before admission of patients to the ICU. This highlights the major difference between murine models FIG. 3. Gene expressions for CD4 + T-cellYspecific transcription factors are dramatically decreased after septic shock. Blood samples were obtained from 21 septic shock patients within 2 h after the beginning of vasopressive treatment and every 6 h during 48 h afterwards. mRNA expressions for transcription factors specific for T H 2 (GATA-3), T H 1 (T-bet), CD4 + CD25 + Treg cells (FOXP3), and T H 17 (ROR+) cells were measured by qRT-PCR. As a control group, gene expressions were measured in a cohort of healthy individuals (n = 11, 4 women and 7 men; mean age, 51 [SD, 6] years). Results for each transcription factor measured at each time point in septic shock patients (gray boxes) and healthy individuals (open boxes) are presented as box plots and individual values. Results are expressed as normalized ratio of FOXP3, T-bet, GATA-3, or ROR+ mRNA versus PPIB mRNA. The decrease was statistically significant (P G 0.0056) at all time points for every transcription factor. Comparisons between groups were made using the nonparametric Mann Whitney U test with correction for the number of tests performed.

of sepsis, in which this onset of shock can be precisely timed, and clinical situations, in which when patients are admitted to the ICU several hours may have elapsed after the onset of shock. This is most likely the major limitation of our study: although patients were included immediately after the diagnosis of shock (illustrated by the requirement for catecholamines), the Breal[ start of the infection could never be determined precisely. It is probable that the infectious process has started well before and most likely is culminating with the septic shock clinical picture. Another limitation would be that our limited cohort has a low mortality probably in relation with the absence of comorbidities and also because of a very early management of septic shock in our ICUs. With that said, despite this putative lower severity, lymphopenia measured in these patients is still extremely marked.

Finally, our data, although apparently incremental to the already described lymphopenia after septic shock, show for the first time that these major lymphocytic alterations described are present at the time of diagnosis, remain stable during the first 48 h afterward, and concern every lymphocyte subset. We thus believe this study provides several important and complementary information to previous literature in the field and increases our understanding of the lymphocytic alterations of the immune response occurring in septic shock patients. The sepsis-induced loss of lymphocytes may be particularly important because of the extensive and very early depletion of critical immune effector cells. This should be taken into account in the forthcoming clinical trials testing immunomodulating therapies in septic shock patients.

FIG. 1 .

 1 FIG. 1. Flow cytometry gating strategy for lymphocyte subpopulations immunomonitoring. Immunophenotyping of circulating lymphocyte subpopulations was performed on each sample by flow cytometry. Two different stainings were performed: Tube 1 = FITC-labeled anti-CD45 + PECy5-labeled anti-CD3 + ECD-labeled anti-CD8 + PE-labeled anti-CD4. Tube 2 = FITC-labeled anti-CD45 + PECy5-labeled anti-CD3 + ECDlabeled anti-CD19 + PE-labeled anti-CD56. One example in a septic patient is shown. A, Thanks to a leukogate (SSC vs CD45 dot plot), lymphocytes (Ly) are first selected, and monocytes (Mo), neutrophils (Neu), and debris are excluded. B, Tube 1: Among these selected circulating lymphocytes, the numbers of CD4 + T cells (CD4 + CD3 + ) and of CD8 + T lymphocytes (CD3 + CD8 + ) are then obtained. C, Tube 2: The numbers of NK cells (CD3 j CD56 + ) and B cells (CD19 + CD3 j ) are calculated based on biparametric dot plots gated on total circulating lymphocytes. Absolute counts are calculated (Flow-Count Fluorospheres) according to manufacturer's instructions (Beckman-Coulter, data not shown).

  were obtained from 21 septic shock patients within 2 h after the beginning of vasopressive treatment and every 6 h during 48 h afterward. The following leukocyte subsets were analyzed: total WBC count, number of neutrophils, monocytes, lymphocytes, T lymphocytes (T cells) CD4 + T lymphocytes (CD4 + T), CD8 + T lymphocytes (CD8 + T), total B cells (B cells), and NK cells. Results are expressed as number of cells/2L [median (95% confidence intervals for the median)]. Normal values from our laboratory for age-matched individuals are shown. Comparisons versus normal/test values (medians) were performed using the signed rank sum test with correction for the number of tests performed. *P G 0.0056. † P G 0.0001.

TABLE 1 .

 1 Demographic, clinical, and biological data for septic shock patients

	Parameters	Patients (n = 21), n (%)
	Age at admission,* y	58 (58Y79)
	Sex
	Male	12 (57)
	Simplified Acute Physiology Scale II	45 (34Y57)
	at diagnosis of shock*
	McCabe score
	0	15 (71)
	1	5 (25)
	2	1 (5)
	Charlson score*	2 (1Y3)
	Delay shock-ICU,* d	0 (0Y1)
	Main diagnosis category
	Medical	12 (57)
	Surgery	9 (45)
	Infection
	Diagnosis
	Only radiologically diagnosed	2 (10)
	Only surgically diagnosed	2 (10)
	Microbiologically documented	17 (81)
	Bacilli gram-negative	13 (62)
	Cocci gram-positive	7 (33)
	Fungi	2 (10)
	Others	0 (0)
	Type of infection
	Community acquired	13 (65)
	Hospital acquired	7 (33)
	ICU acquired	1 (5)
	Site of infection
	Pulmonary	4 (20)
	Abdominal	10 (48)
	Urinary	3 (14)
	Catheter	2 (9)
	Others	2 (9)
	Mortality
	Survivors	17 (81)
	For categorical data, results are presented as number of cases and percentages
	respective to the total patient's population between brackets. For continuous data,
	results are presented as medians and interquartile ranges (Q1YQ3).

*Continuous data.

TABLE 2 .

 2 Circulating white blood cell (WBC) phenotyping in septic shock patients

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ACKNOWLEDGMENTS

The authors thank Dr A. Pachot for his great help in the realization of this project.

This work was supported by the Hospices Civils de Lyon and was conducted through logistical support (H. Thizy, M. Provent, and F. Gueyffier) from the Centre d'Investigation Clinique (Clinical Research Center) /SHK.0b013e3181dc0977


Sy-Hoa Ho

Jamel Saadaoui
email: saadaoui@unistra.fr

Symmetric and asymmetric effects of exchange rates on money demand: Empirical evidence from Vietnam

Keywords: Money Demand, Exchange Rate, ARDL models, NARDL models, Dollarization JEL: C22, E41, F31, F33, F41

This empirical investigation aims at exploring the determinants of money demand in Vietnam by using both linear and nonlinear autoregressive distributed lags models over the period spanning from the third quarter of 2000 to the first quarter of 2018. Our findings can be summarized as follows: firstly, when the shock is symmetric (i.e. a permanent nominal appreciation of one percent), the money demand increases by 3.7 percent in the long term. Secondly, when the shock is asymmetric, for a permanent nominal appreciation of one percent, we observe an increase of 15.6 percent in the money demand. Whereas, for a permanent nominal depreciation of one percent, we observe a decrease of 7.4 percent in the money demand. These results are consistent with symmetry tests and lead us to think that asymmetries occur mainly in the short run and are transmitted to the long run.

Introduction

Vietnam has been one of the most buoyant economies in the world with an average economic growth of 6.45% over the period spanning from 2000 to 2018. In this context of high economic growth, the rate of inflation has known several swings during this period. Indeed, it surged to 2-digit inflation in the crisis period between 2008 and 2011. Facing these upward inflation pressures, the government has taken a series of measures1 in 2011 and 2014, to rein in inflation pressures and ensure macroeconomic stability. In this perspective, the State Bank of Vietnam (SBV, hereafter) used several monetary instruments.

On the one hand, the SBV regulates the monetary aggregates to contain price inflation. The money supply growth is regulated by setting the money growth target at the beginning of each year. As a result, the inflation rate has been well controlled, and was stable with an average rate of 3.5% over the period spanning from 2014 to 2018.

On the another hand, the SBV has regulated the exchange rate regime with more flexibility to reflect market developments. The exchange rate regime of the U.S. dollar/Vietnamese dong exchange rate corresponds to a crawling peg. The SBV sets the reference rate to follow foreign exchange market developments, and the exchange rate will move in a band setting by central bank to pursue its policy objectives. Before 2015, the reference rate was an average of the latest traded market price. At the end of 2015, the reference rate was calculated based on a basket of eight currencies in order to be more market oriented. The band was small during the inflation pressure period (-/+1%, then -/+2%), and in the recent period, the band is -/+3%.

In this general context, our study aims to improving the understanding of monetary developments in Vietnam. Indeed, as briefly seen above, monetary aggregates play a crucial role in the conduct of the monetary policy in this country. Thus, our empirical investigation tries to improve the literature on the determinants of money demand for the Vietnamese economy. As noted by [START_REF] Mundell | Capital mobility and stabilization policy under fixed and flexible exchange rates[END_REF], the money demand could depend upon the exchange rate in addition to the level of income and the rate of interest 2 . We try to assess the impact of nominal exchange rate on the money demand thanks to linear and nonlinear dynamic models (i.e. ARDL and NARDL models), as there are no a priori reasons to believe that exchange rate depreciation and appreciation will have the same effect on money demand. After a nominal depreciation against the U.S. dollar, domestic residents can hold more foreign currency to protect themselves from this adverse exchange rate variation. This currency substitution effect may explain the phenomenon of dollarization in Vietnam. As there is no reason indicating that waves of dollarization (after a nominal depreciation) or waves of de-dollarization (after a nominal appreciation) are symmetric, we use two specifications, a symmetric specification and an asymmetric specification that opens the possibility to explore of nonlinear effects. Since the pioneering contribution of [START_REF] Mundell | The monetary dynamics of international adjustment under fixed and flexible exchange rates[END_REF], it is well known that this phenomenon of dollarization may hinder the conduct of an autonomous monetary policy in absence of capital controls3 . As underlined by [START_REF] Aizenman | Living with the trilemma constraint: Relative trilemma policy divergence, crises, and output losses for developing countries[END_REF], the trilemma constraint can raise a number of issues for developing countries. Thus, these empirical results could be especially interesting to improve the interaction between the monetary policy and the exchange rate policy in Vietnam.

In the empirical literature, nonlinear models are explored and used more frequently as rigidity in linear models is prone to biased or distorted interpretation. The literature reviewing such asymmetric and non-linear models begins with [START_REF] Balke | Threshold cointegration[END_REF] who introduce the threshold co-integration with a regime-switching type model. Then, [START_REF] Granger | Hidden cointegration[END_REF] highlight the 'hidden co-integration' explaining that if the positive and negative components are co-integrated, the series bear the 'hidden co-integration'. [START_REF] Schorderet | Asymmetric cointegration[END_REF] develops the paper of [START_REF] Granger | Hidden cointegration[END_REF] to estimate the asymmetric effect of hidden cointegration. Based on the work of [START_REF] Pesaran | Bounds testing approaches to the analysis of level relationships[END_REF], some studies test the co-integration for small samples [START_REF] Narayan | The saving and investment nexus for China: evidence from cointegration tests[END_REF]. Finally, [START_REF] Shin | Modelling asymmetric cointegration and dynamic multipliers in a nonlinear ARDL framework[END_REF] introduce a nonlinear version of the framework of [START_REF] Pesaran | Bounds testing approaches to the analysis of level relationships[END_REF] to explore the asymmetries in the short term and / or in the long term.

The remainder of this paper is organized as follows: we resume the literature review in the second section, then we present the econometric methodology used in this study. We show the results in the fourth section, and we conclude in the last section.

Literature

Industrialized countries

Bahmani-Oskooee and Chomsisengphet (2002) investigate the determinants of money demand and check the stability for industrial countries including Australia, Austria, Canada, France, Italy, Japan, The Netherlands, Norway, Sweden, Switzerland, UK and the USA by using an ECM model over various periods for each country. They found that real income, interest rate on three-month T-Bills and nominal effective exchange rate that are the determinants of money demand. In addition, they demonstrate the stability for all countries except Switzerland and the UK. [START_REF] Dreger | Investigating M3 money demand in the euro area[END_REF] also point out the strong stability in the long term of money demand in the EMU with quarterly data over the period spanning from the first quarter of 1983 to the last quarter of 2004 by including the inflation rate and allowing short-run homogeneity in the co-integration model. Moreover, they use a threshold auto-regressive model to demonstrate the existence of a monetary target (as a threshold for the inflation rate) that impacts money demand. [START_REF] Bahmani-Oskooee | The impact of economic and monetary uncertainty on the demand for money in emerging economies[END_REF] study the same research question, but focus on emerging markets such as six Central and Eastern European emerging economies (Armenia, Bulgaria, the Czech Republic, Hungary, Poland and Russia) and four other emerging economies (Bolivia, South Africa, Colombia and Malaysia). By replacing the interest rate variable by the inflation rate, and including two variables such as economic uncertainty and monetary uncertainty, they point out that money demand is stable in all countries over the studied period. In addition, Bahmani-Oskooee and Rehman (2005) focus on Asian developing countries such as India, Indonesia, Malaysia, Pakistan, the Philippines, Singapore and Thailand in the quarterly periods of 1973-2000. Their results demonstrate that the M1 monetary aggregate is stable over time for India, Indonesia and Singapore, whereas the M2 aggregate is stable for the remaining countries. Moreover, in a recent article [START_REF] Bahmani-Oskooee | More evidence on the asymmetric effects of exchange rate changes on the demand for money: evidence from Asian countries[END_REF] examine the determinants of money demand for India, Indonesia, Japan, Korea, Malaysia, the Philippines, Singapore and Thailand by using both ARDL and NARDL models. They find that exchange rate appreciation and depreciation significantly influence the money demand in all countries. In more detail, the appreciation of the currency has an adverse effect on the money demand for India, Indonesia, but a positive effect for the Philippines and Singapore, and no effect for Korea.

Emerging countries

Bahmani

Evidence for the Vietnamese economy

Very few empirical studies have explored money determinants for Vietnam. [START_REF] Nguyen | The determinants and stability of real money demand in Vietnam[END_REF] employ quarterly data from the first quarter of 1999 to the second quarter of 2009 and error-correction models. They find that the long-run determinants of money demand are national income, foreign interest rate (FED rate) and real stock price. Inflation and nominal exchange rate are included, but are excluded in the final estimation. They also confirm the stability of money demand during the sample period.

The same research question is explored in [START_REF] Lai | Stability of money demand in Vietnam: Application of the bounds testing approach on 1999-2011[END_REF] who extends the sample period by two years thanks to an ARDL model. She uses a variable list including domestic income, expected inflation 4 , nominal exchange rate and gold price to explain the money demand dynamics. Her findings can be summarized as follows: the exchange rate is the most important variable that determines the money demand dynamics. A depreciation of one percent for the Vietnamese dong should induce a decrease between 3.1 and 6 percent for the money demand. [START_REF] Pham | Determinants and stability of demand for money in Vietnam[END_REF] also study this research question by using a vector error correction model in the monthly period from 2003 to 2014. They indicate that gold price and real effective exchange rate are important variables to determine real money demand, whereas the deposit interest rate is not significant.

Even though the relationship between exchange rate and money demand has been investigated in previous research, there are still shortcomings in this area of the literature. Firstly, gold stock exchange was not fully fledged in Vietnam until recently. Hence, the gold price is collected from the unofficial market thus, the empirical result could be impacted by this uncertainty. Secondly, the State Bank of Vietnam does not officially set an inflation target as used by [START_REF] Lai | Stability of money demand in Vietnam: Application of the bounds testing approach on 1999-2011[END_REF]. Therefore, we do not include these variables in our model. Thirdly, previous studies impose the same value for the elasticity in case of appreciation and depreciation, in both the short and long term.

Methodology

Symmetric specification

We follow the recent contribution of [START_REF] Bahmani-Oskooee | Do exchange rate changes have symmetric or asymmetric effects on the demand for money in Turkey?[END_REF] to explore the determinants of money demand in Vietnam that can be explained by the nominal exchange rate, the gross domestic product, and the interest rate.

M 2D t = β 0 + β 1 N E R t + β 2 GDP t + β 3 I R3 t + t (1)
Where M 2D denotes the money demand for the local currency; N E R stands for the nominal exchange rate (VND per USD) 5 , GDP stands for the Gross Domestic Product and, I R3 is the 3-month deposit interest rate. The data used in this investigation and their sources are fully described in Table A.1 and A.2 in Appendix.

To capture the short-run dynamics, we can write the linear ARDL(p, q) model associated with equation (1) as explained in [START_REF] Pesaran | Bounds testing approaches to the analysis of level relationships[END_REF]:

M 2D t = β 0 + p j =1 φ j M 2D t -j + q j =0 (β 2 j N E R t -j + β 3 j GDP t -j + β 4 j I R3 t -j ) + υ t (2)
Following [START_REF] Shin | Modelling asymmetric cointegration and dynamic multipliers in a nonlinear ARDL framework[END_REF], it is straightforward to write the error correction form for the linear ARDL model6 :

∆M 2D t = β 0 + β 1 M 2D t -1 + β 2 N E R t -1 + β 3 GDP t -1 + β 4 I R3 t -1 + p-1 i =1 ϕ j ∆M 2D t -j + q-1 j =0 γ 1 j ∆N E R t -j + γ 2 j ∆GDP t -j + γ 3 j ∆I R3 t -j + υ t = β 1 ξ t -1 + p-1 i =1 ϕ j ∆M 2D t -j + q-1 j =0 γ 1 j ∆N E R t -j + γ 2 j ∆GDP t -j + γ 3 j ∆I R3 t -j + υ t (3)
Where

β 1 = p j =1 φ j -1, ϕ j = - p i = j +1 φ i for j = 1, ..., p -1, β 2 = q j =0 β 2i , β 3 = q j =0 β 3i , β 4 = q j =0 β 4i , γ 2i = β 2i for i = 0, γ 3i = β 3i for i = 0, γ 4i = β 4i for i = 0, γ 1 j = - q i = j +1 β 2 j for j = 1, ..., q -1, γ 2 j = - q i = j +1 β 3 j for j = 1, ..., q -1, γ 3 j = - q i = j +1 β 4 j for j = 1, .
.., q -1, and

ξ t = M 2D t -1 -L 2 N E R t -1 -L 3 GDP t -1 -L 4 I R3 t -1 is the error-correction term where L ner = -β 2 β 1 , L g d p = -β 3 β 1 and L i r 3 = -β 4
β 1 are the associated long run parameters.

Asymmetric specification

Bahmani-Oskooee et al. ( 2017) have demonstrated that the effect of exchange rate on money demand may be asymmetric. By employing the approach of [START_REF] Shin | Modelling asymmetric cointegration and dynamic multipliers in a nonlinear ARDL framework[END_REF], we decompose the exchange rate into asymmetric effects: the partial sums of positive and negative changes of the exchange rate indicating a depreciation for the Vietnamese dong (with a positive superscript) and appreciation for the Vietnamese dong (with a negative superscript).

We defined these partial sums for nominal exchange rate changes as follows:

N E R + t = t j =1 ∆N E R + j = t j =1 max(∆N E R j , 0) N E R - t = t j =1 ∆N E R - j = t j =1 mi n(∆N E R j , 0) (4)
To explore the asymmetric linkages between the exchange rate and money demand7 , we employ the approaches of [START_REF] Schorderet | Asymmetric cointegration[END_REF] and of [START_REF] Shin | Modelling asymmetric cointegration and dynamic multipliers in a nonlinear ARDL framework[END_REF] to formulate 4 different cases: (i) the long-run and short-run asymmetry; (ii) only long-run asymmetry; (iii) only short-run asymmetry; and (iv) the symmetry that corresponds to the linear ARDL as specified in equation 3.

Case (i): long-run and short-run asymmetry

The long-run and short-run asymmetry of exchange rate on money demand can be expressed as follows:

∆M 2D t = β 0 + β 1 M 2D t -1 + β + 2 N E R + t -1 + β - 2 N E R - t -1 + β 3 GDP t -1 + β 4 I R3 t -1 + p i =1 ϕ i ∆M 2D t -i + q i =0 γ + 1i ∆N E R + t -i + γ - 2i ∆N E R - t -i + γ 3i ∆GDP t -i + γ 4i ∆I R3 t -i + υ t (5)
Where To test the existence of co-integration, [START_REF] Pesaran | Bounds testing approaches to the analysis of level relationships[END_REF] propose the bounds test that is a joint test on all the lagged values of explanatory variables in levels. There are two tests: tstatistic of [START_REF] Banerjee | Error-correction mechanism tests for cointegration in a singleequation framework[END_REF] and F-statistic of [START_REF] Pesaran | Bounds testing approaches to the analysis of level relationships[END_REF]. Firstly, the t-statistic tests the null hypothesis of β 1 = 0 against the alternative hypothesis β 1 < 0. Secondly, the Fstatistic tests the null hypothesis of

L ner + = -β + 2 β 1 and L ner -= -β -
β 1 = β + 2 = β - 2 = β 3 = β 4 = 0 for the case of long-run asymme- try, β 1 = β 2 = β 3 = β 4 = 0 in
q i =0 γ + 1i = q i =0 γ - 1i .
The rejection of the null hypothesis of symmetry indicates that we should use a specification allowing asymmetric effect of the nominal exchange rate

(N E R) on the money demand (M 2D) in the short and / or the long run.8

Case (ii): long-run asymmetry and short-run symmetry

When the long-run symmetry test is rejected, but not the short-run symmetry, then the long-run asymmetry of exchange rate on money demand is expressed as:

∆M 2D t = β 0 + β 1 M 2D t -1 + β + 2 N E R + t -1 + β - 2 N E R - t -1 + β 3 GDP t -1 + β 4 I R3 t -1 + p i =1 ϕ i ∆M 2D t -i + q i =0 γ 1i ∆N E R t -i + γ 2i ∆GDP t -i + γ 3i ∆I R3 t -i + υ t (6)

Case (iii): long-run symmetry and short-run asymmetry

When the short-run symmetry test is rejected, but not the long-run symmetry, then the short-run asymmetry of exchange rate on money demand is expressed as:

∆M 2D t = β 0 + β 1 M 2D t -1 + β 2 N E R t -1 + β 3 GDP t -1 + β 4 I R3 t -1 + p i =1 ϕ i ∆M 2D t -i + q i =0 γ + 1i ∆N E R + t -i + γ - 2i ∆N E R - t -i + γ 3i ∆GDP t -i + γ 4i ∆I R3 t -i + υ t (7)

Dynamic multipliers

When the null hypothesis of symmetry is rejected, we can obtain the asymmetric dynamic multipliers of the change of positive and negative variations of the explanatory variables. Here, these asymmetric dynamic multipliers capture the impact of nominal appreciations and nominal depreciations on the money demand. As explained before, there is no reason to expect that nominal appreciation and nominal depreciation will have the same effect on money demand.

We can use the following formulas to compute the asymmetric dynamic multipliers:

m + h = h j =0 ∂M 2D t + j ∂N E R + t m - h = h j =0 ∂M 2D t + j ∂N E R - t (8)
By construction, when h → ∞, m + h → L ner + and m - h → L ner -. These dynamic multipliers could capture the cumulative effects of nominal depreciation and nominal appreciation on the money demand from an initial equilibrium to the new equilibrium9 as shown by [START_REF] Shin | Modelling asymmetric cointegration and dynamic multipliers in a nonlinear ARDL framework[END_REF].

Results

Unit root tests

Even though the methodology used in this paper allows for a mixture of stationary I(0) series and non-stationary I(1) series in the estimation of the error correction model, according to [START_REF] Shin | Modelling asymmetric cointegration and dynamic multipliers in a nonlinear ARDL framework[END_REF], we must verify all variables are really a mixture of I(0) series and I(1) series. Indeed, we need at least two non-stationary I(1) series in the model, and we have to exclude the nonstationary I(2) series (i.e. a series that needs to be differenced twice to be stationary). To this end, we test the levels and the first differences with the Dickey-Fuller GLS and the Phillips-Perron tests as recommended by [START_REF] Philips | Have your cake and eat it too? Cointegration and dynamic inference from autoregressive distributed lag models[END_REF] and [START_REF] Jordan | Cointegration testing and dynamic simulations of autoregressive distributed lag models[END_REF]. The results are shown in Table 1. As the unit root tests indicate that the variables are non-stationary I(1) series, we can move to the next step and estimate an ARDL model as specified in equation ( 3) and an NARDL model as described in equation (5).

Short run dynamics

As we can observe in of continuous depreciation against the U.S. dollar (up to 13 quarters). This opens the possibility for testing whether past exchange rates variations (above 4 quarters) have had an effect on the dynamics of money demand. We implement a general-to-specific approach to choose the optimal lag order for the ARDL model and the NARDL model. First, we choose the largest model with the best combination of the following statistics: the lowest AIC, the lowest RMSE, and the highest error-correction term. This gives an ARDL(7,7) for the symmetric specification and an ARDL(2,12) for the asymmetric model 10 . Second, we use a backward stepwise selection procedure based on the AIC to obtain our final models presented in Table 2 and3.

On the one hand, in the ARDL model, the short-run coefficients of the nominal exchange rate are positive and statistically significant at the one percent level except for the first dynamic lag as we can see in Panel A of Table 2. These positive signs could indicate that the wealth effect dominates the substitution effect in the short run. We will come back to these two effects in the next section. Besides, three out of five short-run coefficients for the gross domestic product are positive and statistically significant. These positive signs are expected and reflect that domestic residents need more money to achieve a higher level of transaction (i.e. the transaction motive).

Finally, the dynamic lags for the interest rate are not significant.

On the other hand, in the NARDL model, nine out of twelve short-term coefficients for positive partial sums of the nominal exchange rate are positive and significant at the five percent level 11 as we can note in Panel A of Table 3. Again, this could imply that wealth effects dominate substitution effects in case of nominal depreciation (see the Panel A of Figure B.2 in Appendix), but only in the short run. Thus, a nominal depreciation leads to an increase in money demand.

Since the domestic residents observe an increase of the value of foreign currency, the consumption increases. As we will see in the following section, the transition to the new equilibrium will 10 We do not include dynamic lags for the other variables for two reasons: first, our variables of interest are the positive and negative partial sums of the nominal exchange rate and, second, to preserve degrees of freedom.

Nevertheless, when we introduce dynamic lags for the gross domestic product and the interest rate, these last variables are not statistically significant and do not improve the AIC or the RMSE.

11 Except for the first dynamic lag that has a negative sign.

change the relative importance of these two effects. For the negative partial sums of the nominal exchange rate, five out of twelve are positive and significant at the five percent level. This could indicate, again, that wealth effects dominate substitutions effects in the short run. Indeed, a nominal appreciation will lead to a reduction in the value of foreign currency. Finally, this reduction will lead to a lower level of consumption.

Long run dynamics

In Table 2 and3, we estimate the symmetric and asymmetric effects of the exchange rate on the money demand for the Vietnamese economy. At first glance, we can see that the longrun coefficients are correctly signed and are statistically significant at the one percent level.

In the Panel B of Table 2, the nominal exchange rate elasticity to money demand is equal to 3.74 percent. Consequently, a nominal appreciation of one percent increases the money demand by 3.74 percent in the long run. This empirical result indicates that the substitution effect dominates the wealth. Indeed, when we observe a nominal appreciation, the value of foreign currency expressed in local currency drops. Facing this negative wealth effect, domestic residents will reduce their consumption, consequently, they need less money for their transactions. However, this negative wealth effect can be more than offset by a substitution effect as the domestic residents will be less prone to acquire assets in dollars to protect themselves from adverse exchange rate variation. In addition, we can note that these results fall within the range found in [START_REF] Lai | Stability of money demand in Vietnam: Application of the bounds testing approach on 1999-2011[END_REF], as she found an elasticity between 3.1 and 6 percent for various monetary aggregates.

As the long-run coefficients are negative, we can conclude that after an appreciation of the Vietnamese dong (i.e. a depreciation of the U.S. dollar), the domestic money demand will grow, as the substitution effect is stronger than the wealth effect. This is confirmed by the results of the NARDL model (Panel B of Table 3) where episodes of negative changes in the nominal exchange rate (i.e. a nominal appreciation) have a positive effect on the money demand in the long term. Here, the long run elasticity is equal to -15.69. At the same time, episodes of positive changes in the nominal exchange rate (i.e. a nominal depreciation) have a negative effect on the money demand in the long term with an elasticity of -7.39. These results suggest that the substitution effect dominates the wealth effect in the long term. Interestingly, a nominal appreciation has a higher elasticity than a nominal depreciation. We will come back to this point in the next section.

For the gross domestic product variable, in Panel B of Table 2, the long-run elasticities are positive and significant at the one percent level in the ARDL specification and in the NARDL specification. These results suggest that higher income increases the money demand for transaction purposes. Besides, the value of the long-run elasticities are 2.21 and 3.22 for the ARDL model and for the NARDL model, respectively. These results are close to those of previous studies about the symmetric specification. For the income elasticity, [START_REF] Lai | Stability of money demand in Vietnam: Application of the bounds testing approach on 1999-2011[END_REF] found values around 1.5 and Nguyen and Pfau ( 2010) found values slightly above 2.5.

For the interest rate variable, in Panel B of Table 2, the long run coefficients are negative and significant at the one percent level in the ARDL specifications. We found a long-run coefficient of -0.03 for the ARDL and NARDL model. These results suggest the existence of an inter-temporal substitution effect. Indeed, an increase in the 3-month deposit rate decreases the money demand because domestic residents will substitute present consumption by future consumption. Furthermore, our results are close to those of [START_REF] Nguyen | The determinants and stability of real money demand in Vietnam[END_REF] who found a long-run coefficient of -0.02 for the interest rate variable.

Coefficient diagnostics

To validate our error-correction model, we need to test whether a long-term relationship exists or not. To this end, we implement the t-test of [START_REF] Banerjee | Error-correction mechanism tests for cointegration in a singleequation framework[END_REF] and F-statistic of [START_REF] Pesaran | Bounds testing approaches to the analysis of level relationships[END_REF]. In Table 4, we can clearly see that the t-statistic is below the critical value at the one percent level and that the F-statistic is above the critical value at the one percent level for the ARDL and the NARDL models. Based on these results, we have relatively strong evidence of co-integration. Notes: standard errors in parentheses. The symbols ***, ** and * correspond to statistical significance at 1, 5 and 10 percent, respectively. We use quarterly data over the period spanning from the third quarter of 2000 to the first quarter of 2018 (T = 70). The value of the dynamic multipliers is obtained thanks to bootstrapping techniques described in [START_REF] Philips | Have your cake and eat it too? Cointegration and dynamic inference from autoregressive distributed lag models[END_REF] that perform well in small samples.

Source: author's calculations. Notes: standard errors in parentheses. The symbols ***, ** and * correspond to statistical significance at 1, 5 and 10 percent, respectively. We use quarterly data over the period spanning from the third quarter of 2000 to the first quarter of 2018 (T = 70). The value of the confidence intervals for the difference between asymmetric cumulative dynamic multipliers is obtained thanks to bootstrapping techniques described in [START_REF] Shin | Modelling asymmetric cointegration and dynamic multipliers in a nonlinear ARDL framework[END_REF] that perform well in small samples.

Constant l n(N E R + t -1 ) l n(N E R - t -1 ) l n(GDP t -1 ) I R3
Source: author's calculations. The p-values are in brackets. The upper bound critical values for the F-test in case of unrestricted intercept, no trend for k=4, n=67 is 5.57 (5.285 when k=5) at the one percent level [START_REF] Narayan | The saving and investment nexus for China: evidence from cointegration tests[END_REF]. Moreover, the upper bound critical values for the t-test in case of unrestricted intercept, no trend for k=4, n=67 is -4.60 (-4.790 when k=5) at the one percent level, but, here, asymptotic critical values are used.

Source: author's calculations.

Then, we test the possible existence of asymmetry in the short term and in the long term in Table 4 as specified in 5. We find that the null hypothesis of short run symmetry is rejected at the one percent level, but the null hypothesis of long run symmetry is rejected only at the ten percent level. In addition, auto-correlation 12 In the two specifications, the error correction terms are close to unity. For the ARDL model, in Table 2, the confidence interval spans from -0.72 to -1.16 at 95 percent level. Besides, in the NARDL model (see Table 3), the error correction term is above unity indicating oscillatory convergence as explained by [START_REF] Loayza | Financial Development, Financial Fragility, and Growth[END_REF] in a panel setting. In Figure 1, we can see that the model tends to oscillate after a nominal appreciation of one percent. Ultimately, the model reaches a new equilibrium when the oscillations become indistinguishable to the naked eye. The mean value before the shock is equal to 4.76. The confidence intervals have been obtained through bootstrapping procedures using 100 replications. Source: author's calculations.

Indeed, the dynamic multipliers up to sixty quarters are presented in Figure 1. They are based on the ARDL and the NARDL specification described in equations ( 3) and ( 5). We can see that the new long-run equilibrium for the money demand after a symmetric or asymmetric shock is not reached monotonically. Instead of converging monotonically, the error-correction process oscillates around the new long-run equilibrium in a dampening manner as underlined by [START_REF] Narayan | What determines migration flows from low-income to high-income countries? An empirical investigation of Fiji-U.S. migration[END_REF]. Once the process is complete, the convergence to the equilib-rium is fast 13 . When the shock is symmetric (i.e. a permanent nominal appreciation of one percent), the money demand increases by 3.7 percent in the long term. On the one hand, when the shock is asymmetric, for a permanent nominal appreciation of one percent, we observe an increase of 15.6 percent for the money demand. On the other hand, for a permanent nominal depreciation of one percent, we observe a decrease of 7.4 percent for the money demand.

These results are consistent with symmetry tests and lead us to think that asymmetries occur mainly in the short run and are transmitted to the long run.

Lastly, we test the stability of the money demand in Figure 2. Indeed, we use two different tests for the ARDL and the NARDL specifications. The first one is based on the cumulative sum of OLS residuals and the second one is constructed with the cumulative sum of recursive residuals. As we can see, the coefficients for the two specifications describing the dynamics of money demand seem to be stable over the sample period.

Conclusion

In the recent modified circular No. 42/2018/TT-NHNN of October 1, 2019, the SBV announces a series of new measures to prohibit foreign currency loans for credit institutions and foreign bank branches in both the short and the long term. These measures aim at limiting the dollarization of the Vietnamese economy. Indeed, the dollarization of this economy could increase the vulnerability to international shocks as shown by the Asian financial crisis of 1997.

Consequently, the SBV pursues its de-dollarization policy to limit the role of the U.S. dollar and foreign currencies in the domestic economy. This may induce an increase of domestic prices relative to foreign prices (as borrowing costs for firms will be higher), but this negative effect could be more than offset by the benefits reaped from a higher economic stability and economic resilience to external shocks. 13 As suggested by Johansen (1995), with a simple example of a bivariate co-integrated VAR(1), a stability condition for an error-correction model is that the error-correction term must be strictly inferior to zero and strictly superior to -2. Indeed, the error-correction model can be written as an AR(1) process for the disequilibrium error. In this context, the objective of this empirical investigation is to examine the effect of exchange rate variations on the demand for money in Vietnam. In our empirical approach, we use linear and nonlinear error-correction models (i.e. ARDL and NARDL models) that may capture symmetric and asymmetric effects of nominal exchange rate variations on the money demand. Indeed, there are no a priori reasons that nominal depreciation and nominal appreciation will have the same impact on the money demand.

In the linear and the nonlinear specifications, our results show that the substitution effect always dominates the wealth effect in both the short and the long term. Facing a nominal depreciation, domestic residents will acquire and hold more foreign currency to protect themselves from adverse exchange rate variations. Thus, the domestic money demand decreases.

Consequently, this empirical result could help to understand the dollarization of the economy.

Whereas, during a nominal appreciation, domestic residents will acquire and hold less foreign currency as the value expressed in foreign currency of the Vietnamese dong increases. Thus, the domestic money demand grows, leading to de-dollarization.

As stated before, our study opens the possibility to explore asymmetric effects. Indeed, when the shock is symmetric (i.e. a permanent nominal appreciation of one percent), the money demand increases by 3.7 percent in the long term. On the one hand, when the shock is asymmetric, for a permanent nominal appreciation of one percent, we observe an increase of 15.6 percent for the money demand. On the other hand, for a permanent nominal depreciation of one percent, we observe a decrease of 7.4 percent for the money demand.

These empirical results could help to improve the understanding of the linkages between monetary policy and exchange policy in a context of dollarization for the Vietnamese economy. Its seems to indicate that de-dollarization policies could be implemented as the long-run elasticity is two times higher in case of nominal appreciation than in case of nominal depreciation. Finally, these asymmetries could be explored with econometric models that allow regime switching for the error-correction term. We leave this for future research.

2 β 1

 1 are the long run coefficients of positive and negative changes of the exchange rate to the money demand. The positive and negative superscripts stand for the partial sums of positive and negative changes of the nominal exchange rate. Besides, L g d p = -β 3 β 1 , L i r 3 = -β 4 β 1 are the long-run coefficients of the GDP and of the interest rate.

  Figure B.1 and B.2 in Appendix B, during the period spanning from the last quarter of 2000 to the first quarter of 2018, the Vietnamese dong underwent large episodes

  , heteroscedasticity and normality tests indicate that the residuals of the ARDL and of the NARDL specification are not affected by severe problems of auto-correlation, heteroscedasticity or non-normality. Except for the residuals of the ARDL model that do not seem to be normally distributed as shown by the p-value of the Shapiro-Wilk W test, but this may be due to a unique outlier as we can see in Panel C of Figure C.1 in Appendix.

Figure

  Figure 1: Dynamic multipliers

Figure 2 :

 2 Figure 2: Stability tests for the ARDL and NARDL specifications

  upper panels A and B display stability tests for the ARDL model and the lower panels C and D display stability tests for the NARDL model. Source: author's calculations.

  case of linear ARDL. The lower bound critical values are computed under the hypothesis that all the variables are stationary, whereas the upper critical value are

calculated under the hypothesis that all the variables are non-stationary. If F-statistic is higher than the upper bound critical value, we reject the null hypothesis of no co-integration, thus, we accept the existence of a long-run relationship.

The long-run symmetry hypothesis can be tested by the Wald test of the null hypothesis of L ner + = L ner -; to test the existence of short-run symmetry, we use the Wald test to test the null hypothesis of

Table 1 :

 1 Unit root test

	Levels

*

Note: * p < 0.05. All the variables are augmented with three lags in the PP test. In these tests, statistical significance amounts to stationarity.

Source: author's calculations.

Table 2 :

 2 Symmetric specification for the money demand equation (ARDL)

			Panel A: Short-run coefficient estimates
				Lag Order
		0	1	2	3	4	5	6
	∆l n(M 2D)			-0.24**	-0.37*** -0.16
				(0.12)	(0.13)	(0.12)
	∆l n(N E R)		1.47	4.79***	5.56***	3.84*** 3.35*** 6.11***
			(0.99)	(0.97)	(1.20)	(1.17)	(1.19)	(1.15)
	∆l n(GDP )	10.75***		4.42	8.01***		4.22	8.44***
		(2.52)		(2.71)	(2.45)		(2.68)	(3.09)
	∆I R3	-0.02	0.02
		(0.01)	(0.01)
			Panel B: Co-integration vector (ECM form)
	Constant	l n(N E R t -1 ) l n(GDP t -1 ) I R3 t -1
	11.93***	-3.52***	2.08***	-0.03***
	(2.03)	(0.51)	(0.28)	(0.01)
			Panel C: Long-run coefficient estimates
	L ner	L g d p	L i r 3
	-3.74***	2.21***	-0.03***
			Panel D: Diagnostic statistics
	ECM	Adj. R2	RMSE	AIC	Obs.
	-0.94***	0.62	0.0843	-116.38	63
	(0.11)

Table 3 :

 3 Asymmetric specification for the money demand equation (NARDL)

			Panel A: Short-run coefficient estimates
				Lag Order
		0	1	2	3	4	5
	∆l n(N E R + )	-3.14**	4.53**	7.52***	7.85***	7.66***	5.55***
		(1.43)	(1.83)	(1.61)	(1.57)	(1.84)	(1.84)
	∆l n(N E R -)	0.97	13.85*	15.23	12.46	17.46**	21.02***
		(5.45)	(7.91)	(9.12)	(8.37)	(7.16)	(7.43)
		6	7	8	9	10	11
	∆l n(N E R + )	4.41**	1.79	6.66***	3.64**	4.99***	3.65*
		(1.60)	(1.54)	(1.60)	(1.64)	(1.69)	(1.89)
	∆l n(N E R -)	35.39***	22.27***	10.16	8.17	13.59*	6.03
		(6.48)	(7.14)	(6.69)	(6.20)	(7.48)	(5.67)

Panel B: Co-integration vector (ECM form)

Table 4 :

 4 Co-integration tests, Symmetry tests and Diagnostic tests : t B M D denotes the t-statistic of[START_REF] Banerjee | Error-correction mechanism tests for cointegration in a singleequation framework[END_REF] and F P SS is the F-statistic of[START_REF] Pesaran | Bounds testing approaches to the analysis of level relationships[END_REF].

	Tests

Notes

These measures are described in two directives issued by the SBV: the directive 11/NQ-CP of February

24, 2011, and the directive 01/CT-NHNN of January 15, 2014.2 We suppose that, at equilibrium, the money demand is equal to money supply, since the demand in excess will not be satisfied.

The well know Impossible Trinity problem (i.e. an impossible combination of an autonomous monetary policy, a perfect capital mobility and a floating exchange rate) is also referred to as the Trilemma, a more neutral term.

For expected inflation, her calculations were based on[START_REF] Gerlach | Money and inflation in the euro area: A case for monetary indicators[END_REF].

An increase in N E R indicates a depreciation of the Vietnamese dong.

The error correction form for the nonlinear model can be found in equation (2.7) of[START_REF] Shin | Modelling asymmetric cointegration and dynamic multipliers in a nonlinear ARDL framework[END_REF]. The explanatory variables are split into positive partial sums and negative partial sums in order to capture asymmetric effects.

This approach has been employed in[START_REF] Delatte | Asymmetric exchange rate pass-through: Evidence from major countries[END_REF] to investigate the asymmetric effects of exchange rate on prices in four major industrial countries. In addition,[START_REF] Konopczak | Can inaction account for the incomplete exchange rate pass-through? Evidence from threshold ARDL model[END_REF] argues that the incompleteness of exchange rate pass-through is a statistical artifact thanks to a threshold NARDL model.

[START_REF] Pal | Asymmetric oil product pricing in India: Evidence from a multiple threshold nonlinear ARDL model[END_REF] implement an augmented version of the NARDL model with both symmetric and asymmetric regressors in an empirical investigation about the transmission from crude to oil product prices in India. Besides, they found that the use of a multiple threshold NARDL model (MTNARDL) improves precision in estimating asymmetric effects.

Even though asymmetric error correction is not explicitly modeled by allowing regime dependency for β 1 , we may still observe asymmetric adjustment trajectories thanks to the asymmetric dynamic multipliers. Alternatively, we could use a time-varying regression model based on the Kalman filter as in[START_REF] Jelassi | Does the J-curve hypothesis hold for a small open economy? Evidence from time-varying coefficients of a distributed-lag model for Tunisia[END_REF] Sidiropoulos et al. (2005).

To test auto-correlation, we use the more versatile framework for the tests of[START_REF] Cumby | Testing the autocorrelation structure of disturbances in ordinary least squares and instrumental variables regressions[END_REF] implemented by[START_REF] Baum | Enhanced routines for instrumental variables/generalized method of moments estimation and testing[END_REF].

pp.281-314. Springer. Sidiropoulos, M., J. Trabelsi, and C. Karfakis (2005). Has the 'franc fort' exchange rate policy affected the inflationary dynamics? Theory and new evidence. International Economic Journal 19(3), 379-395.

Acknowledgments

The authors are grateful to Bertrand Koebel for very valuable comments and remarks and to Katharina Priedl for proofreading. All errors or omissions are ours.

I R3 3-month deposit rate in percent as the average of 3 month deposit rates at the end of period from four large state-owned commercial banks.


MD Jo Ão S Érgio Neves

MD Milton Packer

Jo Ão

MD Pedro Ferreira

MD João Sérgio Neves

Milton Up Pt

MD Packer

MD, PhD João Pedro Ferreira
email: jpferreira@med.up.pt

Prof Pedro João

Ferreira

Increased risk of heart failure hospitalization with GLP-1 receptor agonists in patients with reduced ejection fraction: a meta-analysis of the EXSCEL and FIGHT trials

GLP-1 receptor agonists (GLP-1 RA) are usually described as having beneficial effects on atherosclerotic cardiovascular disease (ASCVD) and neutral effects on heart failure (HF) events. However, recent data suggests that the effect of GLP-1 RA may differ according to baseline HF status, with decreased risk of HF events in those without established HF and increased risk of HF events among those with HF with reduced ejection fraction (HFrEF) [START_REF] Ferreira | Glucagon-like peptide 1 receptor agonists in patients with type 2 diabetes with and without chronic heart failure: A meta-analysis of randomized placebo-controlled outcome trials[END_REF][START_REF] Margulies | Effects of Liraglutide on Clinical Stability Among Patients With Advanced Heart Failure and Reduced Ejection Fraction: A Randomized Clinical Trial[END_REF][START_REF] Jorsal | Effect of liraglutide, a glucagon-like peptide-1 analogue, on left ventricular function in stable chronic heart failure patients with and without diabetes (LIVE)a multicentre, double-blind, randomised, placebo-controlled trial[END_REF][START_REF] Ferreira | Glucagon-like peptide 1 receptor agonists in heart failure: the need for a rewind[END_REF]. Treatment with GLP1-RA liraglutide did not improve clinical stability in the FIGHT (Functional Impact of GLP-1 for Heart Failure Treatment) trial and did not improve left ventricle ejection fraction (LVEF) in the LIVE (Effect of Liraglutide on Left Ventricular Function in Stable Chronic Heart Failure Patients with and without Diabetes) trial [START_REF] Margulies | Effects of Liraglutide on Clinical Stability Among Patients With Advanced Heart Failure and Reduced Ejection Fraction: A Randomized Clinical Trial[END_REF][START_REF] Jorsal | Effect of liraglutide, a glucagon-like peptide-1 analogue, on left ventricular function in stable chronic heart failure patients with and without diabetes (LIVE)a multicentre, double-blind, randomised, placebo-controlled trial[END_REF]. In both randomized clinical trials (RCTs), all patients had HFrEF and there was a trend to increased risk of adverse outcomes. Whether this risk is limited to those with severe HFrEF or is also observed in stable ambulatory type 2 diabetes patients with reduced LVEF is unknown. To this moment, the only RCT with GLP-1 RA in type 2 diabetes that presented data on LVEF was the EXSCEL (Exenatide Study of Cardiovascular Event Lowering) trial. However, in the EXSCEL trial, the influence of baseline LVEF on the effects of GLP-1 RA on HFH has not been previously evaluated.

In this report, we aimed to evaluate the interaction of GLP-1 RA effect on the risk of HFH according to baseline LVEF in the EXSCEL trial and to perform a meta-analysis of the effect of GLP-1 RA on HFH among patients with LVEF <40%.

Data for HFH in the EXSCEL trial according to LVEF was extracted from the tables 2 and S4 of the secondary analysis assessing the effects of exenatide by baseline HF status (of note, in this publication there was an error in table S4 where the headers of groups were switched) [START_REF] Fudim | Effect of Once-Weekly Exenatide in Patients With Type 2 Diabetes Mellitus With and Without Heart Failure and Heart Failure-Related Outcomes: Insights From the EXSCEL Trial[END_REF]. Data for HFH in the FIGHT trial was extracted from the primary analysis of the trial [START_REF] Margulies | Effects of Liraglutide on Clinical Stability Among Patients With Advanced Heart Failure and Reduced Ejection Fraction: A Randomized Clinical Trial[END_REF]. The odds ratio (OR) of HFH was calculated using logistic regression models. Estimates from the FIGHT trial and the subgroup with LVEF <40% in the EXSCEL trial were combined by the use of inverse variance-weighted averages of logarithmic OR in random-effects analysis. In the EXSCEL trial, the effect modification of baseline LVEF (<40%, 40-55% or ≥55%) on the effect of exenatide on HFH was evaluated using a treatment-by-LVEF interaction term. A two-sided P value of <0.05 was considered statistically significant. All analyses were performed with Stata 17.

In the EXSCEL trial, there were 249 first HFH during a median follow-up of 3.2 years among the 4892 participants (with or without HF) with baseline LVEF available (33.2% of EXSCEL population). There was a significant interaction of LVEF with the effect of exenatide on HFH: LVEF >55% OR 0.73, 95%CI 0.48-1.13; LVEF 40-55% OR 0.77, 95% CI 0.50-1.20; LVEF <40% OR 1.70, 95%CI 1.02-2.83 (p-interaction=0.027). Figure 1A. When restricting to participants diagnosed with HF, a similar pattern was observed: LVEF ≥40% OR 0.71, 95%CI 0.44-1.15, LVEF <40% OR 1.49, 95%CI 0.82-2.72 (p-interaction=0.059).

In the FIGHT trial, there were 113 first HFH events during a follow-up of 180 days. The OR for HFH with liraglutide vs. placebo was 1.33, 95% CI 0.83-2.12.

The meta-analyzed GLP-1 RA effect on the risk of HFH in patients with LVEF <40% gave an OR of 1.49, 95%CI 1.05-2.10 (p-value=0.02, I 2 =0%). Figure 1B.

The results of the LIVE, also support an increased risk of adverse events with GLP-1 RA in HFrEF [START_REF] Jorsal | Effect of liraglutide, a glucagon-like peptide-1 analogue, on left ventricular function in stable chronic heart failure patients with and without diabetes (LIVE)a multicentre, double-blind, randomised, placebo-controlled trial[END_REF]. In the LIVE, there was a significant increase in the risk of serious cardiac events with liraglutide, including one death caused by ventricular tachycardia (VT) non-fatal VT, atrial fibrillation, aggravation of ischemic heart disease and one case of worsening of HF over a period of 24 weeks. LIVE was not included in our meta-analysis because only 1 case of worsening of HF was reported.

From a biological perspective, GLP-1 RA are known to increase heart rate and intracellular cyclic AMP (cAMP), and both are known to adversely affect outcomes in HFrEF [START_REF] Ferreira | Glucagon-like peptide 1 receptor agonists in heart failure: the need for a rewind[END_REF]. While drugs that decrease cAMP improve the prognosis in HFrEF (e.g., beta-blockers), drugs that increase cAMP (e.g., milrinone) increase the risk of HFH, ventricular arrhythmias and death in HFrEF.

In conclusion, GLP-1 RA may increase the risk of HFH in patients with a LVEF <40%. These results must be interpreted as exploratory and hypothesis generating because they are based on a subgroup of the EXSCEL trial with LVEF available (n=4892, 33.2% of the study population; which may not be missing at random) and on a small trial with a short follow-up (the FIGHT trial).

  Centre -UnIC@RISE, Department of Surgery and Physiology, Faculty of Medicine of the University of Porto, Alameda Hernâni Monteiro, Porto, 4200-319, Portugal, Tel.: +351 225 513 600, Fax: +351 225 513 601

  More dedicated RCTs evaluating the effects of GLP-1 RA among patients with HF, particularly HFrEF, are needed. Until then, the use of GLP-1 RA should be avoided in patients with reduced LVEF.Declaration of Competing InterestJSN has received consulting or speaker fees from AstraZeneca, BIAL, Boehringer Ingelheim, Lilly, Merck, and Novo Nordisk. MP has received personal fees from Abbvie, Actavis, Amarin, Amgen, AstraZeneca, Boehringer Ingelheim, Caladrius, Casana, CSL Behring, Cytokinetics, Imara, Lilly, Moderna, Novartis, Reata, Relypsa, and Salamandras. JPF is a consultant for Boehringer Ingelheim and AstraZeneca. He has received research support from Novartis, Boehringer Ingelheim, AstraZeneca and Bayer through his institution.

Figure 1 .

 1 Figure 1. Panel A: Heart failure hospitalization in the EXSCEL trial according to HF

Acknowledgments

This work was financed by national funds through FCT (Fundação para a Ciência e Tecnologia), I.P., within the scope of the Cardiovascular R&D Center (UIDB/00051/2020 and UIDP/00051/2020) and RISE (LA/P/0053/2020).