Anne-Laure Faucon

Martin Flamant

Marie Metzger

Jean-Jacques Boffa

Jean-Philippe Haymann

Pascal Houillier

Eric Thervet

François Vrtovsnik

Bénédicte Stengel

Guillaume Geri

Dr Emmanuelle Vidal-Petiot
email: emmanuelle.vidal-petiot@aphp.fr

Extracellular fluid volume is associated with incident end-stage kidney disease and mortality in patients with chronic kidney disease

Keywords: 57, Tables: 3, Figures: 3 BP: blood pressure BSA: body surface area CKD: chronic kidney disease ECF: extracellular fluid ESKD: end-stage kidney disease GFR: glomerular filtration rate eGFR: estimated glomerular filtration rate mGFR: measured glomerular filtration rate chronic kidney disease, extracellular fluid volume, volume overload, sodium

niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

INTRODUCTION

Impaired renal salt and water excretion, in combination with other factors such as hypoalbuminemia, often result in chronic extracellular fluid (ECF) overload during chronic kidney disease (CKD). [START_REF] Ellison | Treatment of Disorders of Sodium Balance in Chronic Kidney Disease[END_REF] In hemodialysis patients, several large-scale studies have shown that fluid overload is a strong and independent risk factor for mortality. [START_REF] Zoccali | Chronic Fluid Overload and Mortality in ESRD[END_REF][START_REF] Kalantar-Zadeh | Fluid retention is associated with cardiovascular mortality in patients undergoing long-term hemodialysis[END_REF] In contrast, studies evaluating the role of fluid overload on renal function and mortality in patients with nondialysis CKD yielded conflicting results. [START_REF] Bansal | Bioelectrical Impedance Analysis Measures and Clinical Outcomes in CKD[END_REF][START_REF] Tsai | Association of fluid overload with cardiovascular morbidity and all-cause mortality in stages 4 and 5 CKD[END_REF][START_REF] Tsai | Association of fluid overload with kidney disease progression in advanced CKD: a prospective cohort study[END_REF] In addition, ECF was estimated using single or multifrequency bioelectrical impedance analysis, and not isotope dilution, which is the most direct and accurate method, although not routinely available. [START_REF] Kyle | Bioelectrical impedance analysis--part I: review of principles and methods[END_REF][START_REF] Kyle | Bioelectrical impedance analysis-part II: utilization in clinical practice[END_REF][START_REF] Bird | Extracellular distribution volumes of hydrophilic solutes used to measure the glomerular filtration rate: comparison between chromium-51-EDTA and iohexol[END_REF][START_REF] Ellis | Human body composition: in vivo methods[END_REF] Similarly, glomerular filtration rate (GFR) was estimated and not measured by a reference method. Therefore, the relationship between ECF and renal outcome and mortality during CKD remains uncertain.

The aim of this prospective observational study was to evaluate the association of ECF with progression of CKD, with end-stage kidney disease (ESKD), and with mortality occurring before ESKD, in non-dialysis CKD patients, using gold standard measurements of ECF and GFR.

RESULTS

Baseline characteristics

1593 patients with available ECF and CKD stage 1 to 4 from the prospective observational Nephrotest cohort were included in the study. Characteristics of the patients are reported in Table 1. Mean age was 58.8±15.1 years, 66.7% were men, 87.8% had a history of hypertension, and 27.0% had diabetes. Mean mGFR was 43.6±18.6mL/min/1.73m². Mean ECF was 15.1±2.2 L/1.73m². Distribution of ECF, crude or scaled to body surface area (BSA) according to gender is shown in Supplementary Figure S1. ECF, anthropometric parameters, and diuretic prescription, in function of GFR subgroups, are reported in Supplementary Figure S2. BSA and crude ECF decreased as GFR decreased, so that the relationship between ECF and mGFR was attenuated when ECF was scaled to BSA: ECF slightly decreased from 15.7 to 14.5 L/1.73m² when GFR decreased from > 60ml/min to 15-30 ml/min. Loop diuretics increased from 16% in patients with GFR>60ml/min to 43% in patients with GFR from 15 to 30 mL/min.

Determinants of ECF

Compared with patients in the first tertile of ECF, patients in the third tertile were older, more often men, more likely to have a history of hypertension and diabetes, had a higher systolic and diastolic blood pressure (BP), body mass index (BMI), mGFR and urinary protein to creatinine ratio (uPCR), and a lower plasma albumin concentration. 24-hour urine sodium excretion did not differ between the three tertiles of ECF (Table 1). Associations were similar in multivariable analysis (Supplementary Table S1).

A higher ECF is associated with ESKD and mortality

After a median follow-up of 5.3 [IQR: 3.0;7.4] years, 324 (20.3%) patients reached ESKD and 185 (11.6%) of those who did not reach ESKD died (67 [4.2%] from cardiovascular cause).

Cumulative incidence of death at 5 years was significantly higher in the third tertile of ECF (3.8; 95%CI [2.3;5.9], 5.4 [3.5;7.8] and 13.2 [10.2;16.6] %, for the 1 st , 2 nd and 3 rd tertile, respectively, P<0.001), with a similar pattern for cardiovascular mortality (Supplementary Figure S3) while no difference was observed across tertiles of ECF on ESKD occurrence (Figure 1). Penalized splines representing the relationship between ECF and adjusted Hazard Ratios (HR) of death and ESKD are shown in Figure 2. The relationship between ECF and ESKD was linear (Figure 2, left panel). In contrast, the relationship between ECF and mortality before ESKD showed an inflection point, with an increasing risk observed for ECF values above 15 L/1.73m² (Figure 2, right panel). Cause-specific Cox regression models showed that ECF was significantly associated with ESKD after adjustment for confounders (adjusted HR per 1 L/1.73m² increase of ECF: 1.14, 95%CI[1.07;1.21], P<0.001), and with mortality before ESKD above a threshold of 15L/1.73m² (adjusted HR per 1L/1.73m² increase of ECF below 15L/1.73m², 0.92 [0.73 ; 1.16], P=0.49, and above 15L/1.73m², 1.28 [1.14;1.45], P<0.001) (Table2). When the model was adjusted for systolic blood pressure as a time-varying covariate, the association between ECF and ESKD was slightly weaker (HR per 1 L/1.73m² increase of ECF: 1.12, 95%CI [1.05;1.19], P<0.001 (Table 2). E-values were 1.42 for the association between ECF and ESKD, and 1.88 for the association between ECF and mortality before ESKD. Associations between ECF and the risks of ESKD or mortality did not depend on uPCR, blood pressure, diabetes, age, gender or mGFR (p-values for interaction tests nonsignificant). Similar results were observed using ECF categorized in tertiles (Supplementary Figure S4), stratification for baseline eGFR instead of baseline mGFR (Supplementary Table S2), and using multiple imputations for missing data (Supplementary Table S2). Similar associations were also observed when ECF was expressed as percentage of body weight (Supplementary Table S3).

A higher ECF is associated with a faster mGFR decline

Analyses of longitudinal data (median number of visits: 2 [1 -4] per patient, median duration between two consecutives visits 1.1 [1.0 ; 1.5] years) showed that the mean mGFR slope was -1.64, 95%CI [-1.82 ; -1.45] mL/min/year in the total population and -1.31[-1.60 ; -1.01], -1.49 [-1.81 ; -1.17] and -2.28 [-2.63 ; -1.92]mL/min/year for the first, second and third tertiles of ECF, respectively. In the fully adjusted linear mixed-effect model, ECF was significantly associated with a faster mGFR decline (mean difference in mGFR slope per 1L/1.73m² increase in ECF: -0.14, 95%CI [-0.23; -0.05] mL/min/year, P=0.002) (Table 3, Figure 3). Similar results were observed when ECF was analyzed in tertiles (Table 3) and in the subgroup of patients with at least 2 visits (Supplementary Table S4). Analyses yielded similar trends when GFR was estimated using the deindexed CKD-EPI formula (Supplementary Table S5).

DISCUSSION

In this multicenter study conducted in 1593 patients with CKD stage 1 to 4, with a median follow-up of 5.3 years, a higher ECF was independently associated with ESKD (linear association) and death (non-linear association, with an increasing risk as ECF increased above a threshold of 15 L/1.73m²). These findings are robust because they rely on goldstandard methods for ECF and GFR measurement [START_REF] Bird | Extracellular distribution volumes of hydrophilic solutes used to measure the glomerular filtration rate: comparison between chromium-51-EDTA and iohexol[END_REF][START_REF] Ellis | Human body composition: in vivo methods[END_REF][START_REF] Zdolsek | Measuring the size of the extracellular fluid space using bromide, iohexol, and sodium dilution[END_REF][START_REF] Peters | Simultaneous measurement of extracellular fluid distribution and renal function with a single injection of 99mTc DTPA[END_REF][START_REF] Visser | Feasibility and impact of the measurement of extracellular fluid volume simultaneous with GFR by 125Iiothalamate[END_REF][START_REF] Brochner-Mortensen | A simple single injection method for determination of the extracellular fluid volume[END_REF][START_REF] Henriksen | The clearance concept with special reference to determination of glomerular filtration rate in patients with fluid retention[END_REF] in a large cohort of nearly 1600 carefully phenotyped patients, and analyses were adjusted for multiple potential confounders. In addition, we showed an association of a higher ECF with mGFR decline, strengthening our results.

Studies evaluating the role of fluid overload on renal function and mortality in patients with non-dialysis CKD have yielded conflicting results. In the largest study so far, recently published, Bansal et al showed in 3751 patients from the CRIC cohort that a shorter vector length -a bioelectrical impedance analysis-derived marker of overhydration -was significantly associated with the risk for heart failure, with adjusted HR for first (highest hydration state) versus third and fourth quartiles of 1.28, 95% CI [1.01 ; 1.61], but not with all-cause mortality or CKD progression. [START_REF] Bansal | Bioelectrical Impedance Analysis Measures and Clinical Outcomes in CKD[END_REF] In contrast, all previous pioneer works evaluating the prognostic role of ECF in patients with non-dialysis CKD had shown that fluid overload was associated with mortality and/or CKD progression, but they suffered a number of limitations as they were conducted in smaller cohorts, with composite endpoints, and bioelectrical impedance analysis (and not a gold standard such as isotope dilution) was used to assess fluid volume. [START_REF] Kyle | Bioelectrical impedance analysis--part I: review of principles and methods[END_REF][START_REF] Kyle | Bioelectrical impedance analysis-part II: utilization in clinical practice[END_REF][START_REF] Ellis | Human body composition: in vivo methods[END_REF] Thus, in a single-center cohort of 472 non-dialysis patients with CKD stage 4-5, Tsai et al. reported that fluid overload was associated with an increased risk of renal replacement therapy (adjusted HR for third versus first tertile = 3.16, 95%CI [1.33;7.50]) and with a faster GFR decline (-1,10 [-2.06;-0.13] ml/min/1.73m² per year in the third versus first tertile), after a median follow-up of 17.3-months. [START_REF] Tsai | Association of fluid overload with kidney disease progression in advanced CKD: a prospective cohort study[END_REF] In the same cohort of patients, fluid overload was later reported to be associated with the combined endpoint of all-cause mortality and cardiovascular morbidity. [START_REF] Tsai | Association of fluid overload with cardiovascular morbidity and all-cause mortality in stages 4 and 5 CKD[END_REF] In a single-center prospective cohort of 338 patients with CKD stage 3 to 5 followed for a median of 2.1 years, Hung et al. showed that patients with volume overload were at a higher risk for the composite endpoint of ESKD or decline in estimated GFR ≥ 50%, and for cardiovascular morbidity and mortality. [START_REF] Hung | Volume overload and adverse outcomes in chronic kidney disease: clinical observational and animal studies[END_REF] Finally, in a retrospective cohort study of 149 patients with CKD followed for nearly 5 years, Tai et al.

showed that the ratio of ECF over expected total body water was independently associated with the combined endpoint of ESKD or GFR decline ≥ 50%. [START_REF] Tai | Association between ratio of measured extracellular volume to expected body fluid volume and renal outcomes in patients with chronic kidney disease: a retrospective single-center cohort study[END_REF] Our study, based on a large cohort and gold-standard GFR and ECF measurements, provides evidence in favor of a deleterious effect of fluid overload on renal function and on mortality in patients with CKD.

The decrease in mGFR attributable to 1L/1.73m² increase in ECF was 0.14mL/min/year. A 10% ECF increase in a patient with an ECF value of 15.1 L/1.73m² (mean value in the cohort) would thus correspond to a yearly decrease in mGFR of 0.21 mL/min/1.73m². Mean mGFR slope in our population was -1.64mL/min/year. The yearly mGFR loss solely attributable to age is estimated to be approximately 0.35 to 0.75 ml/min/1.73m² in healthy subjects [START_REF] Poggio | Demographic and clinical characteristics associated with glomerular filtration rates in living kidney donors[END_REF] and in our cohort, GFR decrease due to uncontrolled hypertension was 0.61 mL/min/1.73m² and that due to a higher (one log-unit) uPCR was 0.43 mL/min/1.73m². Therefore, the independent effect of ECF on GFR decrease is quantitatively small. However, ECF may be increased by several liters in some patients with marked fluid overload, leading to a potentially clinically relevant contribution to GFR decline, independent of its effects on blood pressure and proteinuria. In addition, ECF is also an independent determinant of uncontrolled hypertension [START_REF] Vidal-Petiot | Extracellular Fluid Volume Is an Independent Determinant of Uncontrolled and Resistant Hypertension in Chronic Kidney Disease: A NephroTest Cohort Study[END_REF] and is associated with increased proteinuria, both of which also contributing to its deleterious effect on renal function.

A number of underlying mechanisms may explain the association between volume overload and adverse events. Volume overload is a prominent feature in conditions associated with a grim prognosis such as cirrhosis, nephrotic syndrome and heart failure, in which sodium and water retention are combined with arterial underfilling. [START_REF] Schrier | Pathogenesis and management of sodium and water retention in cardiac failure and cirrhosis[END_REF] In addition, uPCR and low albumin concentration, both well-described prognostic factors for adverse outcome in patients with CKD, [START_REF] Schrier | A critique of the overfill hypothesis of sodium and water retention in the nephrotic syndrome[END_REF][START_REF] Schork | Association of Plasminuria with Overhydration in Patients with CKD[END_REF] are associated with increased ECF, as confirmed in the present study, possibly through ENaC-mediated sodium retention. [START_REF] Schork | Association of Plasminuria with Overhydration in Patients with CKD[END_REF] Although the associations we observed were similar after adjustment for pre-existing cardiovascular disease (including heart failure), plasma albumin concentration and uPCR, suggesting that these are not the only factors explaining the observed associations, we cannot exclude the possibility that the results might reflect the increased retention of salt and water in such comorbid conditions and be explained by residual confounding. However, 24-hour urinary sodium excretion increased as ECF increased in this cohort, whereas in patients with heart failure and liver cirrhosis, urinary sodium excretion is expected to decrease when fluid overload worsens. Furthermore, even though volume-dependent increase in blood pressure may in part explain the link between fluid overload and adverse outcomes, the associations of ECF with ESKD and mortality were independent of blood pressure. In the same vein, the steeper decline of GFR observed for a higher ECF persisted after adjustment for blood pressure and uPCR, even though both factors were themselves associated with GFR decline.

Increased salt intake tends to increase ECF in patients with CKD, who have a higher saltsensitivity. [START_REF] Campbell | A randomized trial of sodium-restriction on kidney function, fluid volume and adipokines in CKD patients[END_REF][START_REF] Koomans | Salt sensitivity of blood pressure in chronic renal failure. Evidence for renal control of body fluid distribution in man[END_REF][START_REF] Koomans | Sodium balance in renal failure. A comparison of patients with normal subjects under extremes of sodium intake[END_REF][START_REF] Mcmahon | A randomized trial of dietary sodium restriction in CKD[END_REF] Therefore, salt intake may play a crucial role in the link between fluid overload and adverse outcomes. However, in our study, 24-hour urine sodium excretion, a surrogate for salt intake, was not associated with outcomes, and our results persisted after adjustment for this covariate.

Finally, there is a pathophysiological plausibility for the observed link between ECF and mortality and ESKD to be, at least in part, causal and independent of all above-mentioned factors. Indeed, fluid overload-induced increased venous pressure increases interstitial pressure, and alters renal microcirculation, leading to impaired renal function. [START_REF] Firth | Raised venous pressure: a direct cause of renal sodium retention in oedema?[END_REF] In addition, increased mean circulatory filling pressure associated with fluid overload may induce pressure-independent alterations of structure and function of large arteries. [START_REF] Tsai | Association of fluid overload with kidney disease progression in advanced CKD: a prospective cohort study[END_REF][START_REF] Safar | Arterial stiffness and kidney function[END_REF][START_REF] Hung | Volume overload correlates with cardiovascular risk factors in patients with chronic kidney disease[END_REF] Finally, recent studies have suggested that fluid overload may also induce inflammatory processes by translocation of endotoxin fragments through congested bowel wall, and/or by inducing splanchnic ischemia, thereby increasing mortality, both in non-dialysis and in hemodialysis patients. [START_REF] Hung | Volume overload correlates with cardiovascular risk factors in patients with chronic kidney disease[END_REF][START_REF] Dekker | Impact of fluid status and inflammation and their interaction on survival: a study in an international hemodialysis patient cohort[END_REF][START_REF] Dekker | Fluid Overload and Inflammation Axis[END_REF] However, our study was observational and by definition prone to confounding, as illustrated by the fairly low calculated E-values (see Methods) [START_REF] Vanderweele | Sensitivity Analysis in Observational Research: Introducing the E-Value[END_REF] : only randomized interventional studies targeting different levels of ECF control using diuretics or low sodium intake can provide evidence for the prognostic value of fluid overload during CKD. Of note, our results suggest that close attention to fluid status should be paid in future blood pressure target randomized trials. Indeed, in the Systolic Blood Pressure Intervention Trial (SPRINT), [START_REF] Wright | A Randomized Trial of Intensive versus Standard Blood-Pressure Control[END_REF] one cannot rule out that part of the beneficial effect observed in the intensive treatment arm may be related to the higher rate of diuretic prescription and therefore potentially to ECF reduction, as suggested by the markedly lower rate of heart failure but not of stroke.

Several large observational studies conducted in patients with CKD showed that a higher salt intake is associated with mortality and cardiovascular events. [START_REF] He | Urinary Sodium and Potassium Excretion and CKD Progression[END_REF][START_REF] Mills | Sodium Excretion and the Risk of Cardiovascular Disease in Patients With Chronic Kidney Disease[END_REF] The association between salt intake and CKD progression is more controversial. [START_REF] He | Urinary Sodium and Potassium Excretion and CKD Progression[END_REF][START_REF] Fan | Urinary sodium excretion and kidney failure in nondiabetic chronic kidney disease[END_REF] Interventional studies of low sodium intake [START_REF] Campbell | A randomized trial of sodium-restriction on kidney function, fluid volume and adipokines in CKD patients[END_REF][START_REF] Mcmahon | A randomized trial of dietary sodium restriction in CKD[END_REF][START_REF] Garofalo | Dietary Salt Restriction in Chronic Kidney Disease: A Meta-Analysis of Randomized Clinical Trials[END_REF][START_REF] Mcmahon | Altered dietary salt intake for people with chronic kidney disease[END_REF][START_REF] Meuleman | Sodium Restriction in Patients With CKD: A Randomized Controlled Trial of Self-management Support[END_REF][START_REF] Vogt | Effects of dietary sodium and hydrochlorothiazide on the antiproteinuric efficacy of losartan[END_REF] and/or diuretics [START_REF] Vogt | Effects of dietary sodium and hydrochlorothiazide on the antiproteinuric efficacy of losartan[END_REF][START_REF] Vasavada | Role of excess volume in the pathophysiology of hypertension in chronic kidney disease[END_REF] have shown a reduction of proteinuria and blood pressure. However randomized controlled trials did not demonstrate that reduction of sodium intake, or diuretics -independently of blood pressure reduction -reduces cardiovascular disease and mortality or slows CKD progression. [START_REF] Fan | Urinary sodium excretion and kidney failure in nondiabetic chronic kidney disease[END_REF][START_REF] Garofalo | Dietary Salt Restriction in Chronic Kidney Disease: A Meta-Analysis of Randomized Clinical Trials[END_REF][START_REF] Mcmahon | Altered dietary salt intake for people with chronic kidney disease[END_REF][START_REF] Nomura | Renal function in relation to sodium intake: a quantitative review of the literature[END_REF][START_REF] Palmer | Dietary interventions for adults with chronic kidney disease[END_REF][START_REF] Taylor | Reduced dietary salt for the prevention of cardiovascular disease: a meta-analysis of randomized controlled trials (Cochrane review)[END_REF] Surprisingly, measured ECF did not increase, and even slightly decreased, as mGFR decreased. This was largely explained by the fact that patients with a lower mGFR had a lower body weight, BMI, BSA, and muscle mass as assessed by 24-hour urinary creatinine excretion, as previously reported, including in the NephroTest cohort. [START_REF] Fouque | Nutrition and chronic kidney disease[END_REF][START_REF] Tynkevich | Decrease in urinary creatinine excretion in early stage chronic kidney disease[END_REF] However, the positive association between ECF and GFR, although markedly attenuated, remained after adjustment for BSA, at least in CKD stage 2 to 4. The cross-sectional analysis of ECF in this hospital-based observational cohort therefore does not reflect the natural history of ECF during CKD. [START_REF] Tsai | Association of fluid overload with kidney disease progression in advanced CKD: a prospective cohort study[END_REF][START_REF] Hung | Volume overload and adverse outcomes in chronic kidney disease: clinical observational and animal studies[END_REF][START_REF] Tai | Association between ratio of measured extracellular volume to expected body fluid volume and renal outcomes in patients with chronic kidney disease: a retrospective single-center cohort study[END_REF][START_REF] Hung | Volume overload correlates with cardiovascular risk factors in patients with chronic kidney disease[END_REF][START_REF] Vasavada | Role of excess volume in the pathophysiology of hypertension in chronic kidney disease[END_REF][START_REF] Essig | Cardiovascular remodelling and extracellular fluid excess in early stages of chronic kidney disease[END_REF] Interpretation of ECF values in this population receiving optimized care is complex as all patients are carefully followed-up by nephrologists and largely prescribed diuretics, with a marked increase in diuretic prescription when GFR decreased (Supplementary Figure S2).

Our study underlines that ECF is an important parameter in the management of patients with CKD. Nevertheless, physical examination and search for peripheral edema, blood pressure measurement, diagnosis of orthostatic hypotension, or weight variations are not sufficient for detection of small variations in ECF, such as those expected during non-dialysis CKD. [START_REF] Essig | Cardiovascular remodelling and extracellular fluid excess in early stages of chronic kidney disease[END_REF] Even if isotopic measurement provides information on both ECF and GFR and is recommended in difficult situations, this gold standard exploration is not routinely available.

The use of different complementary tools (such as bioelectrical impedance spectroscopy, echocardiography, and biomarkers) is thus necessary to monitor ECF as accurately as possible and to adjust treatment in these patients. ECF can be controlled with dietary advice [START_REF] Mcmahon | A randomized trial of dietary sodium restriction in CKD[END_REF] and diuretic use [START_REF] Vasavada | Role of excess volume in the pathophysiology of hypertension in chronic kidney disease[END_REF] which are the cornerstone of the treatment of fluid retention in CKD patients. Even if diuretic doses are not available in the NephroTest database, results of our study suggest that therapeutic inertia (both for pharmacological treatment and dietary sodium restriction) partly explains the increase in ECF. Only 56% of the patients in the third tertile of ECF, and 55% of the patients with mGFR below 30mL/min, were prescribed diuretics. Likewise, sodium intake, estimated from 24-hour urinary sodium excretion, was on average 155mmol (3.6g) per day, which is above the recommended intake, especially in patients with CKD. [START_REF] Mancia | The technical report on sodium intake and cardiovascular disease in low-and middle-income countries by the joint working group of the World Heart Federation, the European Society of Hypertension and the European Public Health Association[END_REF] In addition, 24-hour urinary sodium excretion increased with ECF, from 145 mmol/day in the first tertile to 168 mmol/day in the third tertile. Tight control and monitoring of sodium balance should be considered early in the course of CKD, in combination with other reno-protective treatments such as renin-angiotensin-system blockers.

To our knowledge, this is the largest study evaluating the prognostic value of ECF on renal function and mortality in non-dialysis CKD patients, using gold standard methods of ECF and GFR measurements. Our study has some limitations. First, it was an observational study, by definition prone to confounding despite multiple adjustments. In addition, GFR and ECF were derived from the same [START_REF] Dubois | A formula to estimate the approximate surface area if height and weight be known[END_REF] Cr-EDTA clearance samples, which may generate a spurious relationship between the two variables. However, measurement errors (mainly due to urine loss) would be expected to generate an opposite association (underestimation of GFR when ECF is overestimated), and similar results were observed when eGFR was used instead of mGFR, making this hypothesis unlikely. Another limitation is that GFR slope analyses were based on a median of only two measurements. Moreover, although ECF was measured using a reference method, independent measurement of ECF using bioelectrical impedance spectroscopy, or biological markers of plasma volume (such as vasopressin or natriuretic peptides), would have strengthened our results. Finally, patients from the NephroTest study are closely followed up and receive optimized care, so that this cohort is not an appropriate setting to analyze the independent effect of GFR on ECF; in addition, due the tight control of ECF in this population, this observational study may underestimated the strength of the association between ECF and adverse outcomes.

In conclusion, our results show that ECF is an independent risk factor for ESKD and mortality in patients with non-dialysis CKD, suggesting that it is important to carefully monitor and maintain ECF in these patients, in addition to other hydro-electrolytic disturbance and cardiovascular risk factors. Nevertheless, future prospective interventional studies are needed to confirm the benefit of ECF control in CKD.

PATIENTS AND METHODS

Study design and participants

The NephroTest study is a prospective hospital-based tricentric cohort (Physiology departments of Tenon, Bichat and Georges Pompidou Hospitals, Paris, France), which enrolled 2084 adult patients with CKD of all stages and of various aetiologies, from January 2000 to December 2012. [START_REF] Moranne | Timing of onset of CKD-related metabolic complications[END_REF] Pregnancy, a past history of renal transplantation, and dialysis were exclusion criteria. Patients with no available ECF measurement at baseline (n=288), with CKD stage 5 at inclusion (n=103), or lost to follow-up (n=100) were excluded from the study, leaving 1593 patients for this analysis.

Data collection

Patients were referred by their nephrologist to one of the three renal physiology units for extensive work-up during a 5-hour in-person visit, including GFR and ECF measurements.

Patients were asked to collect 24-hour urine the day before admission, with indications given by a trained nurse and detailed in a written information document. Past medical history including underlying renal disease (as reported by the nephrologist), treatment, anthropometric data and a large set of clinical and laboratory variables were collected.

GFR and ECF measurements

Measured GFR (mGFR) was determined by renal clearance of [START_REF] Dubois | A formula to estimate the approximate surface area if height and weight be known[END_REF] Cr-EDTA (GE Healthcare, Vélizy, France), as previously described. [START_REF] Froissart | Predictive performance of the modification of diet in renal disease and Cockcroft-Gault equations for estimating renal function[END_REF] Briefly, a single dose of 1.8-3.5 MBq of [START_REF] Dubois | A formula to estimate the approximate surface area if height and weight be known[END_REF] Cr-EDTA was injected intravenously. After allowing 1.5 hours for equilibration of the tracer in the ECF, urine was collected and discarded. Average renal [START_REF] Dubois | A formula to estimate the approximate surface area if height and weight be known[END_REF] Cr-EDTA clearance was then determined from the average of six consecutive 30-minute clearance periods. Blood was drawn at the midpoint of each clearance period. ECF was determined as the average of the six consecutive measurements, each of them being calculated as the remaining quantity of the tracer (injected quantity of [START_REF] Dubois | A formula to estimate the approximate surface area if height and weight be known[END_REF] Cr-EDTA, "Qinjected", minus excreted quantity of the tracer, "Q excreted"), divided by the extrapolated serum concentration of the tracer at the corresponding time point. To take into account morphological differences between patients, ECF was scaled to body surface area (BSA); the latter being calculated using Dubois' formula. 51 For each death in the Nephrotest cohort, the main cause of death adjudicated was by three independent physicians. Survival data were censored on December 31, 2013.

Statistical analysis

Categorical variables are reported as frequencies and percentages and continuous variables are reported as mean±SD or median (interquartile range, IQR), as appropriate. Patients' characteristics were compared across tertiles of ECF using Chi-2 test or one-way analysis of variance for qualitative and quantitative variables, respectively.

Determinants of ECF treated quantitatively were analyzed using a multivariable linear regression model (see methodological details in the Supplementary Material). Cumulative incidence curves were estimated with Aalen-Johansen method ("cuminc" function of the R package "cmprsk") to take competing risks into account. [START_REF] Noordzij | When do we need competing risks methods for survival analysis in nephrology?[END_REF] Crude and adjusted hazard ratios (HRs) of ESKD and of mortality before ESKD associated with ECF were estimated using causespecific Cox regression models. Penalized splines were used in the fully adjusted Cox model to represent the functional relationship between ECF treated quantitatively and the risk of ESKD (or death before ESKD). In order to test linear relationships between ECF and outcomes (ESKD and mortality), we compared fully adjusted Cox models including ECF expressed as continuous variable versus pspline function of ECF, using the likelihood ratio test.

Adjustment covariates, selected a priori as potential confounders, included age, gender, ethnicity (African origin versus others), site of recruitment, diabetic status (no diabetes, diabetes without diabetic nephropathy, diabetes with diabetic nephropathy), body mass index (BMI), elevated blood pressure (in two classes: < or ≥ 140/90 mmHg), urinary proteinto-creatinine ratio (uPCR, log-transformed), 24-hour urinary sodium excretion (corrected by potential collection bias using the ratio of creatinine clearance in the collection over fractionated creatinine clearance, as previously described [START_REF] Vidal-Petiot | Estimation of populational 24-h urinary sodium and potassium excretion from spot urine samples: evaluation of four formulas in a large national representative population[END_REF] ), diuretics and renin-angiotensin system inhibitors. For mortality, previous cardiovascular event (myocardial infarction or angioplasty or stroke or heart failure) and plasma albumin concentration were also entered in the model. Proportional hazard assumption was checked for each covariate using log(log(S)) and Schoenfeld residuals against time. Because this assumption was not verified for mGFR, stratification for baseline mGFR level was performed using six classes of mGFR: >60, 50-60, 40-50, 30-40, 20-30, 15-20 mL/min/1.73m². Stratification for baseline eGFR level was also performed in sensitivity analyses. In order to adjust for GFR as a continuous variable, while taking into account its time-dependence, an additional model including a mGFR timedependent coefficient was performed. Finally, sensitivity analyses incorporating systolic blood pressure as a continuous time-varying covariate were also performed. Interactions between ECF and age, gender, uPCR, blood pressure, and mGFR were tested.

Missing data were less than 5% (Supplementary Table S5) and assumed to be missing at random. Single and multiple imputations were performed using the chained equation method(R package "mice", 100 imputed datasets, 20 iterations). [START_REF] White | Multiple imputation using chained equations: Issues and guidance for practice[END_REF] Primary analyses were performed using single imputations for missing data. Sensitivity analyses, using multiple imputations and complete cases were also performed. For each model, the E-valuereflecting the minimal strength of association that potential unmeasured confounders would need to have with both ECF and outcomes to explain away the observed association-was calculated. [START_REF] Vanderweele | Sensitivity Analysis in Observational Research: Introducing the E-Value[END_REF] Finally, the association between baseline ECF and mGFR decline (expressed in mL/min/year) was analyzed using a multivariable linear mixed-effect regression model with random intercept and slope ("lmer" function of the R package "lme4"). [START_REF] Laird | Random-effects models for longitudinal data[END_REF][START_REF] Leffondre | Analysis of risk factors associated with renal function trajectory over time: a comparison of different statistical approaches[END_REF][START_REF] Janmaat | Pitfalls of linear regression for estimating slopes over time and how to avoid them by using linear mixed-effects models[END_REF] The main analysis was

CONCLUSION:

Extracellular fluid volume is associated with incident end-stage kidney disease and mortality in patients with chronic kidney disease.

ECF is independently associated with CKD progression and mortality. Close monitoring and treatment of fluid overload are important for the clinical management of patients with non-dialysis CKD.

  ECF was analyzed both as continuous variable and in tertiles (the first tertile being used as the reference category). Sensibility analyses were conducted with ECF expressed as a ratio of body weight, expressed in percentage, instead of scaled to BSA.OutcomesProgression to ESKD was defined by initiation of chronic dialysis or pre-emptive renal transplantation. ESKD and vital status were obtained by record linkage with the French REIN Registry (Réseau Epidémiologique et Information en Néphrologie, Paris, France) and the national death registry (Répertoire National d'Identification des Personnes Physiques, RNIPP, INSEE, France), respectively. Causes of death were obtained by the national registry CepiDC (Centre d'épidémiologie sur les causes médicales de Décès) using the CIM-10 classification.

Figure 1 .

 1 Figure 1. Cumulative incidence rates of mortality and end-stage kidney disease according

Figure 2 .

 2 Figure 2. Estimated adjusted hazard ratios and 95% confidence intervals for the association

Figure 3 .

 3 Figure 3. Three-dimensional modeling of the decline of glomerular filtration rate according

Faucon-

  For 1L/1.73m² increase in ECF: HR = 1.28 [1.14 ; 1.45] above 15L/1.73m² HR = 1.14 [1.07 ; 1.21] Mean difference in mGFR slope -0.14 [-0.23 ; -0.05] mL/min/ECF in L/1.73m². Association of ECF with: -ESKD and mortality: Cox regression models -mGFR decline: linear mixed-effect regression models -Median follow-up: 5.3 [IQR: 3.0 ; 7.4] years

Table 1 . Baseline clinical and biological characteristics in total population and according to tertiles of extracellular fluid volume.

 1

		Total		ECF (L/1.73m²)
			1 st tertile	2 nd tertile	3 rd tertile
		n = 1593	n = 531	n = 531	n = 531	p
			[8.88 -14.04[	[14.04 -15.83[	[15.83-23.29]
	Demographics and clinical characteristics
	Age (years)	58.8 ± 15.1	53.2 ± 15.2	58.8 ± 14.3	64.2 ± 13.6	<0.001
	Gender (men)	1063 (66.7)	249 (46.9)	365 (68.7)	449 (84.6)	<0.001
	Ethnicity (Sub-Saharian African origin)	217 (14.3)	80 (15.8)	80 (15.8)	57 (11.2)	0.053
	Height (cm)	167.1 (9.4)	164.6 (9.6)	167.7 (9.4)	169.1 (8.6)	<0.001
	Weight (kg)	74.6 (16.3)	67.1 (13.7)	74.4 (13.9)	82.2 (17.3)	<0.001
	Body surface area (m²)	1.83 (0.22)	1.73 (0.20)	1.83 (0.20)	1.92 (0.21)	<0.001
	BMI (kg/m²)	26.6± 5.1	24.7± 4.3	26.4 ± 4.4	28.7 ± 5.8	<0.001
	Tobacco consumption					<0.001
	Non smoker	873 (54.8)	345 (65.0)	284 (53.5)	244 (46.0)
	Former smoker	506 (31.8)	113 (21.3)	168 (31.6)	225 (42.4)
	Current smoker	214 (13.4)	73 (13.7)	79 (14.9)	62 (11.7)
	Systolic blood pressure (mmHg)	135± 20.2	130± 18.6	135 ± 19.7	141± 20.8	<0.001
	Diastolic blood pressure (mmHg)	75± 11.5	74± 11.8	75 ± 11.9	76± 10.8	0.024
	Elevated blood pressure (≥140 and/or 90 mmHg)	573 (37.3)	144 (28.3)	187 (36.7)	242 (46.8)	<0.001
	Medical history
	Hypertension	1399 (87.8)	441 (83.1)	459 (86.4)	499 (94.0)	<0.001
	Diabetes mellitus	430 (27.0)	77 (14.5)	120 (22.6)	233 (43.9)	<0.001
	Dyslipidemia	277 (18.0)	110 (21.4)	84 (16.3)	83 (16.3)	0.047
	Previous cardiovascular event	288 (18.4)	61 (11.7)	81 (15.5)	146 (28.1)	<0.001
	Underlying renal disease					<0.001
	Diabetic nephropathy	154 (9.7)	18 (3.4)	34 (6.4)	102 (19.2)
	Glomerular	224 (14.1)	102 (19.2)	69 (13.0)	53 (10.0)
	Vascular	410 (25.7)	110 (20.7)	146 (27.5)	154 (29.0)
	Polycystic kidney disease	100 (6.3)	35 (6.6)	39 (7.3)	26 (4.9)
	Interstitial	150 (9.4)	69 (13.0)	51 (9.6)	30 (5.6)
	Other or unknown conditions	555 (34.8)	197 (37.1)	192 (36.2)	166 (31.3)
	Treatment
	Number of antihypertensivedrugs	2.3± 1.6	2.0± 1.4	2.3 ± 1.6	2.7 ± 1.6	<0.001
	ACEi and/or ARB (%)	1186 (74.5)	378 (71.2)	386 (72.7)	422 (79.5)	0.004
	Diuretics	759 (47.7)	209 (39.4)	253 (47.6)	297 (56.0)	<0.001
	Loop diuretic	454 (28.5)	114 (21.5)	137 (25.8)	203 (38.3)	<0.001
	Thiazide diuretic	326 (20.5)	96 (18.1)	121 (22.8)	109 (20.6)	0.169
	Amiloride	17 (1.1)	5 (0.9)	6 (1.1)	6 (1.1)	0.943
	Aldosterone antagonist	43 (2.7)	13 (2.5)	19 (3.6)	11 (2.1)	0.291
	Statin	693 (43.6)	196 (37.0)	217 (40.9)	280 (52.8)	<0.001
	Biological parameters
	eGFR CKD-Epi (mL/min/1.73m²)	45.9 ± 21.8	45.2 ± 23.2	46.1 ± 21.7	46.3 ± 20.4	0.668
	mGFR (mL/min/1.73m²)	43.6 ± 18.6	40.6± 17.9	44.5 ± 18.5	45.6 ± 19.0	<0.001
	mGFR (mL/min/1.73m²)					<0.001
	≥ 60	299 (18.8)	77 (14.5)	105 (19.8)	117 (22.0)
	[45-60[	366 (23.0)	108 (20.3)	129 (24.3)	129 (24.3)
	[30-45[	503 (31.6)	168 (31.6)	171 (32.2)	164 (30.9)
	[15-30[	425 (26.7)	178 (33.5)	126 (23.7)	121 (22.8)
	Measured ECF (L)	16.1 ± 3.6	12.9 ± 2.0	15.8 ± 1.8	19.6 ± 3.1	<0.001
	Measured ECF (L/1.73m²)	15.1 ± 2.2	12.8 ± 1.0	14.9 ± 0.5	17.6 ± 1.6	<0.001
	Hemoglobin (g/dL)	12.74 (1.60)	12.68 (1.63)	12.83 (1.59)	12.70 (1.60)	0.267
	Protein (g/L)	70.2 ± 6.0)	70.5± 6.3	70.5± 5.7	69.6 ± 6.0	0.026
	Albumin (g/L)	39.5 ± 4.4	39.9 ± 4.1	40.0 ± 4.1	38.8 ± 4.8	<0.001

inhibitors; ARB: angiotensin II receptor blockers; ECF: extracellular fluid volume; mGFR: measured glomerular filtration rate; eGFR: estimated glomerular filtration rate, calculated using the CKD-EPI formula.

Acknowledgments

The NephroTest CKD cohort study is supported by the following grants: INSERM GIS-IReSP AO 8113LS TGIR, French Ministry of Health AOM 09114, INSERM AO 8022LS, Agence de la Biomédecine R0 8156LL, AURA and Roche 2009-152-447G. * When the model was fully adjusted, but neither stratified nor adjusted for mGFR, HR for ESKD was 0.98 [0.92 ; 1.04], p=0.44, showing that GFR is the covariate explaining that the crude analysis reveals no significant association between ECF and ESKD.

conducted in the total population (n=1593), as mixed-effect regression model allows accounting for patients with only one visit at baseline to reduce selection bias under the missing at random assumption. A sensitivity analysis was conducted in patients with at least twomGFR measurements (n=1009). Detailed methods and covariates are provided in the Supplementary Material. Single imputations were performed for missing data. A 2-tailed pvalue <0.05 was considered statistically significant. All statistical analyses were conducted using R 3.4 software.

Ethics statements

All patients signed informed consent before inclusion in the cohort. The NephroTest study was approved by an ethic committee (Direction Générale pour la Recherche et l'Innovation

Disclosures

The authors declared no relevant competing of interests.

Authorship

Supplementary material

Supplementary methods. Determinants of ECF and Linear mixed-effect regression models. Table S1. Multivariable linear regression model to identify independent determinants of extracellular fluid volume, crude (L), or scaled to body surface area (L/1.73m²).

Table S2. Cause-specific Cox regression models for the association of extracellular fluid volume (L/1.73m²) with end-stage kidney disease and with mortality, using single or multiple imputations for missing data, or complete cases.

Table S3. Cause-specific Cox regression models for the association of extracellular fluid volume (% body weight) with end-stage kidney disease and for mortality. Crude and adjusted hazard ratios are indicated for 1L/1.73m² increase in extracellular fluid volume. Analyses were adjusted for the following covariates: age, gender, site of inclusion, ethnicity, body mass index, diabetic status (no diabetes, diabetes without diabetic nephropathy, diabetes with diabetic nephropathy), elevated blood pressure, urinary proteinto-creatinine ratio (log-transformed), 24-hour urinary sodium excretion, diuretics and reninangiotensin system inhibitors. For mortality, models were also adjusted for previous cardiovascular events (myocardial infarction or angioplasty or stroke or heart failure) and plasma albumin concentration. Single imputations were used for missing data. ESKD: endstage kidney disease.

Supplementary information is available at Kidney International's website

(1) Model stratified for baseline measured glomerular filtration rate (2) Model adjusted for mGFR (expressed as a continuous variable, as a time-dependent coefficient).

(3) Model adjusted for systolic blood pressure as a continuous time-varying covariate

Table 3. Decline of measured glomerular filtration rate according to extracellular fluid volume (n=1593).

ECF was analyzed as a continuous variable (results are indicated per 1L/73m² increase in ECF), or in tertiles. Model 0: time, baseline values of ECF and mGFR levels (>60, 45-60, 30-44, 15-29 mL/min/1.73m²), and interaction terms between time and GFR and time and ECF.

Model 1: Model 0 + all baseline covariates (age, gender, ethnicity, recruitment site, body mass index, diabetic status (no diabetes, diabetes without diabetic nephropathy, diabetes with diabetic nephropathy), elevated blood pressure (< or ≥ 140/90 mmHg), urinary proteinto-creatinine ratio (log-transformed, per 1-log unit increase*), 24-h urinary sodium excretion, diuretics, and renin-angiotensin system inhibitors).

Model 2: Model1 + interaction term between time and elevated blood pressure. Model 3: Model 2 + interaction terms between time and urinary protein-to-creatinine ratio and between time and site of inclusion.

Mean


Kamilia Kemel

Arlette Baillet-Guffroy

Vincent Faivre

Cécile Laugel

ATR-FTIR Characterization of Janus Nanoparticles-Part II: Follow-Up Skin Application

Keywords: Janus nanoparticles, Labrafil, skin, cutaneous application, spectroscopic descriptors, ATR-FTIR

de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Janus nanoparticles (JNP) are the dispersion of objects characterized by the presence of a lipid compartment associated with an aqueous compartment delimited by a phospholipid bilayer. The size of JNP varies between 150 and 300 nm. [START_REF] Tran | Janus nanoparticles: materials, preparation and recent advances in drug delivery[END_REF][START_REF] Truong-Cong | A scalable process to produce lipid-based compartmented Janus nanoparticles with pharmaceutically approved excipients[END_REF] JNP are very promising tools, the encapsulation of two active pharmaceutical ingredients of opposite chemical polarity for the same nanoformulation can be consider. So, they are interesting in the drug delivery and in cosmetic domains. Then, JNP seems to be good candidates for cutaneous application.

The technique of dynamic light scattering (DLS) measures the size distribution of JNP. The cryogenic transmission electron microscopy (cryo-TEM) enables to visualize the coexistence of the two opposite compartments of JNP. [START_REF] Song | Janus Nanoparticles: Preparation, Characterization, and Applications[END_REF] These two methods cannot be used to study vehicle-skin interactions, if we want to understand the behavior of JNP after cutaneous application. Therefore, a technique that is rapid and non-invasive for the system studied is required. Thus, ATR-FTIR method is a very informative analytical tool facilitates to produce the direct analysis of the surface of a sample contacting the ATR crystal. It has been shown that it is suitable for the study of cutaneous tissue. [START_REF] Corbe | Role of ceramide structure and its microenvironment on the conformational order of model stratum corneum lipids mixtures: an approach by FTIR spectroscopy[END_REF] Therefore, ATR-FTIR was used to implement reliable infrared descriptors over time of JNP before and after cutaneous application.

In the last study, the experiments were conducted in an air-conditioned chamber at 22°C. Kinetics were performed in triplicate for JNP. One drop of 25 µL was deposited on the CaF2 window. They were held over 4 hours with spectral measurements every 10 min. A comparative study with a nanoemulsion (NE) and a physical mixture (PM) formulation was conducted. The same JNP formulation method was used for the formulation of NE but without incorporation of phospholipids. PM is unstable dispersion of the same qualitative and quantitative composition as the dispersion of JNP. ATR-FTIR spectroscopic analysis allowed to identify the evolution of intensity ratio of ν(C=O) at 1739 cm -1 and δ(H-O-H) at 1639 cm -1 as a spectroscopic descriptor for JNP. This descriptor can be used to follow the physical stability (presence) of nanoparticles over time. The results of infrared spectroscopy were confirmed by measurements of size distribution of JNP and validated by chemometric tools. [START_REF] Kemel | ATR-FTIR Characterization of Janus Nanoparticles. Part I: Implementation of Spectroscopic Descriptors[END_REF] In the present study, ATR-FTIR was used to follow the behavior of JNP after cutaneous application.

Using Franz cells and human skin biopsies, three kinetics were carried out for untreated skin, biopsies with JNP and biopsies with Labrafil 2125CS ® . The Labrafil represents the lipophilic phase of JNP, and it is the major compound after water. The kinetics of Labrafil was conducted to evaluate the difference of behavior between Labrafil alone and Labrafil contained in JNP. The suitability of the ATR-FTIR descriptor of JNP was evaluated and a search for other descriptors was performed.

Materials and Methods

Materials

Gelucire 50/13 ® (a mixture of mono-, di-and triacylglycerols of palmitic and stearic acid (20%), monoand di-esters of PEG (Polyethylene glycol) 1500 (~ 70% with a majority of di-esters) and free PEG 1500 (~ 8%)) and Labrafil M2125 CS ® (a mixture of mono-, di-and triglycerides of linoleic acid and mono-and diesters of PEG 300 (55% glycerides, 45% PEG ester)) were obtained from Gattefossé (Saint-Priest, France). Phospholipon 90G ® (purified phospholipid mixture obtained from soybean lecithin consisting primarily of phosphatidylcholine (94-102%), lysophosphatidylcholine (≤ 4%) and tocopherol) was synthesized by Lipoid (Köln, Germany). Water was purified with the Milli-Q water system (Millipore, France). Bovine serum albumin was purchased at Sigma-Aldrich (Saint-Quentin Fallavier, France). Sodium chloride salts was purchased at Carlo Erba Réactifs-Sds (Val de Reuil, France).

Methods

Preparation of Janus nanoparticles

The aqueous phase consisted of 2% (w/w) Gelucire 50/13 ® , 1% (w/w) Phospholipon 90G ® and 77% (w/w) Milli-Q water and the lipophilic phase was 20% (w/w) Labrafil M2125CS ® . The mixture of Gelucire 50/13 ® /Phospholipon 90G ® was dispersed in Milli-Q water and heated in a 70°C water bath for 5 min with mechanic stirring. The oil phase, consisting of Labrafil M2125CS ® , was also heated at the same temperature. The oil phase was added dropwise in the aqueous phase and the mixture was stirred with an Ultra-Turrax T18 (Janke & Kunkel GMBH & Co.KG, IKA®-Labotechnik, Germany) at 7,000 rpm (rotations per minute). Rotor speed was then increased to 11,000 rpm for 5 min. In order to reduce and standardize the size of the particles, the dispersion was passed through a high-pressure homogenizer (APV-2000) at pressures of 600 and 200 bar for the first and second stages, for 5 min at 70°C. The final product was a whitish dispersion of nanoparticles in water.

DLS: Measuring the size distribution of Janus nanoparticles

The hydrodynamic diameter and the polydispersity index were measured with a Zetasizer Nano ZS90 (Malvern Instruments, Orsay, France), piloted by Zetasizer Software. The Zetasizer Nano ZS90

detector was positioned at 90° to the sample and the attenuation was determined automatically by the Nanosizer during the measurement sequence. The measurement cell contained 1.5 mL of water to which was added 5 µL of the dispersion of the nanoparticles (a 1/300 dilution) and the cell contents were homogenized. Each sample was analyzed in triplicate at controlled temperature (25°C). The mean hydrodynamic diameter (DH) of the produced batch was 222 ± 66 nm and the polydispersity index (Pdi) was 0.069 ± 0.025.

Ex vivo skin penetration study

Ex vivo skin penetration of JNP was studied using Franz cells (Lara Spirals, Couternon, France) with a diffusion area of 1.54 cm 2 and a volume 4 mL of receptor chamber. Human skin was obtained from abdominal plastic surgery of healthy women, stored at -25°C. Left in the open air before experiments the skin was defrosted and the subcutaneous fatty tissue was removed from the skin using a scalpel.

Cleaned with Millipore Q water and dried with paper, the skin was cut into circle shapes to fit the glass diffusion cells. The human skin was mounted between donor and receptor compartment of Franz cell, avoiding bubbles formation with a receptor liquid containing 0.85% of saline serum, 0.01% of bovine serum albumin and Millipore Q water. In order to mimic the physiological conditions, the Franz cells were kept in a thermostated water bath at 37°C, to maintain the skin surface temperature at 32°C, with a permanent magnetic stirring of the receptor liquid. After 30 min of stabilization, 25 μL of the dispersion of JNP or 25 μL of Labrafil 2125 CS ® were deposited on the surface of the skin. An untreated skin (without application of any formulation) was performed.

Three kinetics were carried out for the untreated skin, biopsies with JNP, assigned skin+JNP and biopsies with Labrafil 2125CS ® , assigned skin+L. Kinetics were held over 24 hours with several measurement times. Experiments were performed in triplicate.

At each time, the donor compartment was removed, and the skin biopsy was examined by ATR-FTIR.

Infrared vibrational spectroscopy: ATR-FTIR spectra recording

The infrared spectrophotometer used for the study was ATR-FTIR Spectrum two from PerkinElmer (Waltham, Massachusetts) piloted by PerkinElmer Spectrum software (version 10.4.2.279). The spectral range used was 450-4000 cm -1 with resolution of 4 cm -1 and 16 scans were acquired by run.

The background of the ATR-FTIR spectrophotometer was air.

Each skin biopsy was deposited on the crystal ATR and a spectrum was recorded on the surface of the sample. The only pre-treatment which was performed for the spectra was baseline correction.

Results and discussion

Band assignments of skin, JNP and L

The spectral signature of skin (SC), JNP and L are shown in figure 1.

The main vibration bands of the SC are: (I) 3400-3200 cm -1 assigned to ν(O-H) of water and ν(N-H) of ceramides; (II) 2920 cm -1 and 2850 cm -1 assigned to the ν (CH2) and ν (CH2) of aliphatic CH2 group of lipids; (III) 1742 cm -1 assigned to the ν(C=O) of the fatty acids; (IV) 1645 cm -1 assigned to the ν(C=O) of the keratin (amide I); (V) 1545 cm -1 assigned to the δ(N-H) and ν(C-N) of ceramide (amide II). [START_REF] Machado | Assessment of the variation of skin barrier function with anatomic site, age, gender and ethnicity[END_REF][START_REF] Olsztyńska-Janus | ATR-IR study of skin components: Lipids, proteins and water. Part I: Temperature effect[END_REF] (Figure 1a)

The main vibration bands of JNP are: (I) 3500-3150 cm -1 assigned to the ν(O-H) of water; (II) 2926 cm - 1 and 2853 cm -1 assigned to the ν (CH2) and ν (CH2) of aliphatic CH2 group of lipids; (III) 1739 cm -1 assigned to the ν(C=O) of the ester carbonyl functional groups of the glycerides and PEG 300 esters of the Labrafil; and (IV) 1639 cm -1 assigned to the δ(H-O-H) of water. [START_REF] Kemel | ATR-FTIR Characterization of Janus Nanoparticles. Part I: Implementation of Spectroscopic Descriptors[END_REF][START_REF] Mojet | Light at the interface: the potential of attenuated total reflection infrared spectroscopy for understanding heterogeneous catalysis in water[END_REF][START_REF] Gomez | Chemical and spectroscopic characterization of a vegetable oil used as dielectric coolant in distribution transformers[END_REF][START_REF] Alexa | The use of FT-IR spectroscopy in the identification of vegetable oils adulteration[END_REF][START_REF] Poiana | Use of ATR-FTIR spectroscopy to detect the changes in extra virgin olive oil by adulteration with soybean oil and high temperature heat treatment[END_REF] (Figure 1b)

The main vibration bands of L are: (I) 3474 cm -1 assigned to the ν(O-H) of mono and diglycerides; (II) the 2923 cm -1 and 2853 cm -1 assigned to the ν (CH2) and ν (CH2) of aliphatic CH2 group of lipids; (III) 1739 cm -1 assigned to the ν(C=O) of the ester carbonyl functional groups of the glycerides and PEG 300 esters, (IV) 1655 cm -1 assigned to the ν(C=C) of glycerides; (V) 1180-1110 cm -1 assigned to the ν(C-O) and ν(C-C) of the glycerides and esters of PEG 300. [START_REF] Gomez | Chemical and spectroscopic characterization of a vegetable oil used as dielectric coolant in distribution transformers[END_REF][START_REF] Alexa | The use of FT-IR spectroscopy in the identification of vegetable oils adulteration[END_REF][START_REF] Poiana | Use of ATR-FTIR spectroscopy to detect the changes in extra virgin olive oil by adulteration with soybean oil and high temperature heat treatment[END_REF][START_REF] Guillén | Characterization of edible oils and lard by fourier transform infrared spectroscopy. Relationships between composition and frequency of concrete bands in the fingerprint region[END_REF] (Figure 1c)

The vibration bands presented above represent interest and susceptible bands to be modified during the present study, for the skin it is the principal bands most often studied.

Ex vivo skin penetration study: untreated skin, Skin+JNP and Skin+L

The kinetics performed on untreated skin showed that the spectral signature of skin did not vary over time (Data not shown).

The kinetics of skin+JNP are shown in figure 2a. At t0h, after cutaneous application of JNP, the spectral signature of the skin disappeared, consequently the infrared spectrum of JNP was observed.

As function of time, the spectra exhibited variations of the intensity of vibration bands. The ν(O-H) from 3150 to 3500 cm -1 and δ(H-O-H) at 1639 cm -1 of water decreased, giving a direct reflection of water evaporation. The ν (CH2) at 2926 cm -1 , ν (CH2) at 2853 cm -1 , ν(C=O) at 1739 cm -1 and ν(C-O) from 1110 to 1180 cm -1 of Labrafil (major compound after water) increased as result of the increasing concentration of the oil. JNP on skin exhibited the same evolution of spectra as observed of JNP deposited on the CaF2 window.

The kinetics of skin+L is shown in figure 2b. At t0h, after cutaneous application of L, the spectral signature of the skin disappeared, and the infrared spectrum of L was observed. The spectra did not exhibit any variations over time.

The evolution of intensity ratio of ν(C=O) at 1739 cm -1 and δ(H-O-H) at 1639 cm -1 , was identified as a spectroscopic descriptor for JNP. It allowed to follow the behavior of JNP in vitro (on the CaF2 window) [START_REF] Kemel | ATR-FTIR Characterization of Janus Nanoparticles. Part I: Implementation of Spectroscopic Descriptors[END_REF] . To follow the behavior of JNP on the surface of skin, the suitability of the ATR-FTIR descriptor of JNP was assessed. The spectrum of skin has vibration bands in the same spectral region 6 as the descriptor of JNP, the ν(C=O) at 1742 cm -1 of lipids and another ν(C=O) of the keratin at 1645 cm -1 . The intensity ratio of 1742 cm -1 and 1645 cm -1 was calculated for untreated skin, its value was very low (around 0.15) and did not change over time (Figure 3). So, the contribution of the signal from skin was not significant on the variation of the descriptor after the application of nanoparticles. The ATR-FTIR descriptor identified in vitro for the nanoparticles was therefore also validated for this study.

Then, the ratio

( ) ( )
was calculated at each time for skin+JNP, the evolution of this ratio with time is shown in figure 3.

At first, an increase of the ratio was observed from t0 h to t1 h for JNP and skin+JNP. In this range time range, JNP were mostly intact with an appearance of whitish liquid formulation (macroscopic observations). Skin+JNP varied with a slightly higher slope than JNP in vitro. Because JNP were on the surface of the skin, they were found in a micro-environment different from that of the CaF2 window.

Contrary to the CaF2 window, which is an inert support, the nanoparticles on the surface of the skin were found in a biological environment. Firstly, the skin biopsies were maintained in the hydrated state thanks to the receptor liquid. Secondly, the surface temperature of the skin was at 32°C and the surface of the CaF2 window at 22°C. Indeed, the physical degradation of JNP are dependent on this second parameter (temperature). It has been observed that the nanoparticles are less fragile at 22°C compared to 32°C (Figure 4). So, the physical degradation of JNP is slower on the CaF2 window compared to JNP on the surface of skin.

Then, a change marked by a slope was detected from t1 h to t3 h for JNP and skin+JNP, with the progressive physical degradation of the JNP. This increase was less important for skin+JNP compared to JNP in vitro for which an important slope appears. This showed a loss of quantity of JNP after the cutaneous application and therefore a penetration of the formulation.

Finally, for JNP on the CaF2 window, a plateau was reached to the value 45. In this case, JNP were mostly degraded with an appearance of a transparent viscous formulation (macroscopic observations).

For JNP on the surface of skin, a second change of slope was detected with decrease of the ratio from t3 h to t6 h. This showed a significant loss of quantity of JNP and therefore a penetration more important of the formulation. Afterwards a plateau was reached to the value 5. The different plateau values of the descriptor confirmed the cutaneous penetration of JNP.

In addition to the descriptor, we examined the variations of ν(C=O) of lipids and δ(O-H) of water before and after cutaneous application of JNP. We also compared them to the results obtained with untreated skin and skin+L.

The elongation of C=O of the lipids was around 1745 cm -1 for skin, at 1739 cm -1 for JNP and L. Over time it did not change for untreated skin. For JNP, skin+JNP and skin+L, the elongation of C=O was around 1739 cm -1 , and did not shift over time. The 1739 cm -1 is characteristic to L alone or in JNP.

This indicated, after 24 h, the Labrafil was still present on the surface of the skin after cutaneous application of JNP and L. (Figure 5)

For JNP (in vitro), over time the deformation of O-H of water at 1639 cm -1 shifted to the elongation of C=C of glycerides ν(C=C) at 1654 cm -1 . This is due to the increasing of the oil concentration, as a result of water evaporation.

For untreated skin, the elongation of C=O of the keratin at 1645 cm -1 did not vary. Also, for skin+L, the elongation of C=C of glycerides at 1652 cm -1 did not shift.

For skin+JNP, from t0 h to t1.5 h, the deformation of O-H of water δ(O-H) at 1639 cm -1 shifted to the elongation of C=C of glycerides ν(C=C) at 1650 cm -1 , as JNP (in vitro). After t1.5 h, the value stabilized around 1650 cm -1 . This indicated again the presence of Labrafil on the surface of skin. (Figure 6)

Conclusion

The cutaneous application of JNP showed the same evolution of spectra as observed of JNP deposited on the CaF2 window. Over time, the ν(O-H) from 3150 to 3500 cm -1 and δ(O-H) at 1639 cm -1 of water decreased. The ν (CH2) at 2926 cm -1 , ν (CH2) at 2853 cm -1 , ν(C=O) at 1739 cm -1 and ν(C-O)

from 1110 to 1180 cm -1 of Labrafil M2125CS ® increased, as a result the progressive physical degradation of JNP.

Over time, the evolution of intensity ratio of ν(C=O) at 1739 cm -1 and δ(O-H) at 1639 cm -1 was validated as descriptor of JNP before cutaneous application (on the CaF2 window). To follow their behavior over time, the suitability of the ATR-FTIR descriptor of JNP on the surface of the skin was assessed. So, the contribution of the signal from skin was not significant. Therefore, the IR descriptor was also appropriate to follow the behavior of JNP on the surface of human skin.

Thus, the ratio

( ) ( )
was calculated to study the evolution of JNP over time. From t0 h to t1 h, JNP were mostly intact. Compared to JNP on the CaF2 window the physical degradation was slightly higher of JNP on the surface of skin, as JNP were sensible to the temperature of the skin. Unlike JNP on CaF2 window, from t1 h to t3 h, the ratio of JNP on the surface of skin increased more slowly. This showed the progressive physical degradation of the JNP and a loss of quantity of JNP. Therefore, the penetration of the formulation had begun. From t3 h to t6, the ratio decreased and then showed a significant loss of quantity. So, the penetration of the formulation was more important than the last time interval. Afterwards a plateau was reached to a value much lower than the value of the plateau obtained with JNP on CaF2 window. This observation confirmed the cutaneous penetration of JNP.

The inspection of the variations of ν(C=O) of lipids and δ(O-H) of water after cutaneous application of JNP, revealed that the Labrafil was still present on the surface of the skin after 24 h.

The kinetics of Labrafil (lipophilic phase of JNP) was conducted to evaluate the difference of behavior between Labrafil alone and Labrafil contained in JNP. The recorded spectra after cutaneous application of Labrafil, did not showed any variations over time. Then, the Labrafil alone did not penetrate in the skin. In contrast, when Labrafil was contained in JNP, a disappearance of the formulation was observed. Therefore, the skin penetration of JNP did occur. i: the intensity of (O-H) from 3150 to 3500 cm -1 decreased over time.

Legends

ii: the intensity of (C=O) at 1739 cm -1 increased and the intensity (O-H) at 1639 cm -1 decreased over time.

Figure 3 : Evolution of intensity ratios of (C=O) (1739 cm -1 ) and (O-H) (1639 cm -1 ) vibration bands versus time, from 0 to 24 h of JNP in vitro (blue), untreated skin (red) and skin+JNP (green).

Figure 1 :Figure 2 :

 12 Figure 1 : ATR-FTIR spectra of untreated skin (a), JNP (b) and L (c)

Figure 4 :

 4 Figure 4 : Evolution of intensity ratios of (C=O) (1739 cm -1 ) and (O-H) (1639 cm -1 ) vibration bands versus time of JNP at 22 °C and 32°C.

Figure 5 :

 5 Figure 5 : Evolution of wavenumber of (C=O) of lipids of skin, JNP and after cutaneous application of JNP and L.

Figure 6 :

 6 Figure 6 : Evolution of wavenumber of (O-H) of water of JNP before and after cutaneous application, (C=O) of the keratin of skin and (C=C) of Labrafil of skin+L.

Abbreviations