Haluzik

V Krejci

D Springer

Z Kleibl

Prof Martin Haluzik

Expression of adipokines and estrogen receptors in adipose tissue and placenta of patients with gestational diabetes mellitus

Keywords: gestational diabetes mellitus, subcutaneous adipose tissue, visceral adipose tissue, placenta, leptin, estrogen receptors

Expression of adipokines and estrogen receptors in adipose tissue and placenta of patients with gestational diabetes mellitus

A c c e p t e d M a n u s c r i p t 2

SUMMARY

The purpose of this study was to assess the expression profile of genes with potential role in the development of insulin resistance (adipokines, cytokines/chemokines, estrogen receptors) in subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT) and placenta of pregnant women with gestational diabetes mellitus (GDM) and age-matched women with physiological pregnancy at the time of Caesarean section. qRT-PCR was used for expression analysis of the studied genes. Leptin gene expression in VAT of GDM group was significantly higher relative to control group. Gene expressions of interleukin-6 and interleukin-8 were significantly increased, whereas the expressions of genes for estrogen receptors α and β were significantly reduced in SAT of GDM group relative to controls, respectively. We found no significant differences in the expression of any genes of interest (LEP, RETN, ADIPOR1, ADIPOR2, CD68,ERα,ERβ) in placentas of women with GDM relative to controls. We conclude that increased expression of leptin in visceral adipose depot together with increased expressions of proinflammatory cytokines and reduced expressions of estrogen receptors in subcutaneous fat may play a role in the etiopathogenesis of GDM.

A c c e p t e d M a n u s c r i p t

INTRODUCTION

Gestational diabetes mellitus (GDM) is the most frequent metabolic disorder in pregnancy, affecting 1-10% of all pregnancies [START_REF] Beischer | Effect of follow-up of women with gestational diabetes on the ratio of IDDM to NIDDM in pregnancy[END_REF]Gabbe, 1986).

Although most of the women with GDM return to normal glucose tolerance after delivery, they still have significantly increased risk of developing diabetes later in life [START_REF] Kim | Gestational diabetes and the incidence of type 2 diabetes: a systematic review[END_REF]. GDM is considered a prediabetic state, therefore it may display many abnormalities that possibly appear in the very early stages of type 2 diabetes mellitus (T2DM) (Pendegrass et al., 1995). In addition to mechanisms similar to patients with T2DM (Friedman et al., 1999 and[START_REF] Kirwan | TNF-alpha is a predictor of insulin resistance in human pregnancy[END_REF], the etiopathogenesis of GDM includes the combination of the disturbed endocrine function of adipose tissue and placenta (Coughlan et al., 2001;Cseh et al., 2002;[START_REF] Kirwan | TNF-alpha is a predictor of insulin resistance in human pregnancy[END_REF][START_REF] Radaelli | Gestational diabetes induces placental genes for chronic stress and inflammatory pathways[END_REF][START_REF] Ranheim | Adiponectin is reduced in gestational diabetes mellitus in normal weight women[END_REF]Meller et al., 2006), and the systemic hormonal changes affecting insulin sensitivity in peripheral tissues [START_REF] Barros | Insulin resistance of pregnancy involves estrogen-induced repression of muscle GLUT4[END_REF].

Adipokines [leptin (LEP; OMIM 164160), resistin (RETN; OMIM 605565), and adiponectin (ADIPOQ; OMIM 605441)], inflammatory cytokines [tumor necrosis factor-α (TNF-α; OMIM 191160), and interleukin-6 (IL-6; OMIM 147620)], and chemokines [interleukin-8 (IL-8;OMIM 146930)] have been implicated in the etiopathogenesis of insulin resistance and in the early defects of T2DM (Bastard et al., 2000;Moller, 2000;[START_REF] Wauters | Associations of leptin with body fat distribution and metabolic parameters in non-insulin-dependent diabetic patients: no effect of apolipoprotein E polymorphism[END_REF]Coppack, 2001;[START_REF] Kern | Adipose tissue tumor necrosis factor and interleukin-6 expression in human obesity and insulin resistance[END_REF][START_REF] Steppan | The hormone resistin links obesity to diabetes[END_REF][START_REF] Weyer | Hypoadiponectinemia in obesity and type 2 diabetes: close association with insulin resistance and hyperinsulinemia[END_REF]Greenberg and McDaniel, 2002;Havel, 2002;[START_REF] Housa | Adipocytokines and cancer[END_REF]Kleiblova et al., 2006 and[START_REF] Lamounier-Zepter | Adrenocortical changes and arterial hypertension in lipoatrophic A-ZIP/F-1 mice[END_REF]. Leptin, adiponectin and TNF-α have been suggested as much stronger predictors of pregnancy-associated insulin resistance than gestational hormones, including human placental lactogen and steroids [START_REF] Lepercq | Overexpression of placental leptin in diabetic pregnancy: a critical role for insulin[END_REF][START_REF] Kirwan | TNF-alpha is a predictor of insulin resistance in human pregnancy[END_REF][START_REF] Radaelli | Gestational diabetes induces placental genes for chronic stress and inflammatory pathways[END_REF]. In previously published studies, circulating levels of leptin, adiponectin A c c e p t e d M a n u s c r i p t 4 and TNF-α in the early pregnancy closely predicted the development of GDM [START_REF] Qiu | Increased maternal plasma leptin in early pregnancy and risk of gestational diabetes mellitus[END_REF]Hernandez Valencia et al., 2005;[START_REF] Maghbooli | Relationship between leptin concentration and insulin resistance[END_REF]Gao et al., 2008).

To our best knowledge, the contribution of different fat depots (subcutaneous and visceral) to altered levels of adipokines and cytokines in GDM is unknown. Studies of patients with obesity and/or T2DM have shown that metabolic and endocrine characteristics of adipose tissue significantly vary between different locations [START_REF] Wajchenberg | Subcutaneous and visceral adipose tissue: their relation to the metabolic syndrome[END_REF]Coppack, 2001;[START_REF] Wagenknecht | Insulin sensitivity, insulin secretion, and abdominal fat: the Insulin Resistance Atherosclerosis Study (IRAS) Family Study[END_REF]Dolinkova et al., 2008). Placenta of patients with GDM typically displays structural and functional abnormalities [START_REF] Alonso | Effects of gestational diabetes mellitus on proteins implicated in insulin signaling in human placenta[END_REF] suggesting possible alterations in its endocrine function including disturbed production of adipokines and/or proinflammatory factors [START_REF] Masuzaki | Nonadipose tissue production of leptin: leptin as a novel placenta-derived hormone in humans[END_REF][START_REF] Radaelli | Gestational diabetes induces placental genes for chronic stress and inflammatory pathways[END_REF][START_REF] Yura | Resistin is expressed in the human placenta[END_REF]. Previous reports confirmed that GDM elicits major changes in the expression profile of placental genes with a prominent increase in markers and mediators of inflammation [START_REF] Radaelli | Gestational diabetes induces placental genes for chronic stress and inflammatory pathways[END_REF].

We hypothesized that dysregulated endocrine function of adipose tissue and placenta

PATIENTS AND METHODS

Study subjects

Ten women with GDM (age 34.6 ± 2.50 years) and thirteen healthy age-matched women with physiological pregnancy (age 33.1 ± 1.25 years) were included in the study. Only women with single pregnancy who delivered by planned Cesarean section in the term (40 ± 2 weeks of pregnancy) were enrolled into the study. All GDM were treated by intensive insulin therapy. All women enrolled into the study underwent the 75-gram 2-hour oral glucose tolerance test (oGTT) between 24th and 28th week of their pregnancy. Physiological ranges of glycemia were set according to Recommendations of the Czech Diabetes Association as follows: fasting glycemia < 5.5 mmol/l, glycemia 1 hour after glucose ingestion < 8.8 mmol/l and glycemia 2 hours after glucose ingestion < 7.6 mmol/l. The positive oGTT and a need for insulin treatment were the inclusion criteria for GDM group. None of GDM patients had history of diabetes and impaired glucose tolerance before pregnancy. All women had normal fasting glucose levels at the beginning of the pregnancy. The study, performed in accordance with the quidelines proposed in the Declaration of Helsinki, was approved by the Ethical Commitee, First Faculty of Medicine, and General University Hospital, Prague, Czech Republic, and all participants signed written informed consent before being enrolled in the study.

Anthropometric examination and blood sampling

A c c e p t e d M a n u s c r i p t 6 All subjects were measured and weighted and their body mass index (BMI) was calculated twice for the study need; at the beginning of pregnancy (these data as well as serum glucose concentrations were obtained retrospectively from maternity card) and 3rd day after delivery at the Department of Obstetrics and Gynecology.

Body fat content was estimated by bioimpedance measurement (Body Fat Monitor, BF 306, Omron) 3rd day after delivery at the same time as body weight and BMI were estimated.

All maternal and fetal blood and maternal tissue samples were collected at the time of delivery. At the beginning of Caesarean section blood drawings (13 ml of blood) were performed. Serum samples for insulin, adipokine and cytokine measurements as well as for all other examinant biochemical parameters were obtained by centrifugation and whole blood samples for glycated hemoglobin measurements were withdrawn. After manual removal of placenta, 5 ml of fetal umbilical blood was also obtained for serum separation. All serum samples were stored in aliquots at -80°C until further analysis.

Hormonal and biochemical assays

Serum adiponectin concentrations were measured by Human Adiponectin ELISA kit (Linco Research, USA) with intra-and inter-assay variability of (1.0 -7.4%) and (2.4 -8.4%), respectively. Serum leptin and resistin concentrations were measured by commercial ELISA kits (BioVendor, Brno, Czech Republic). Sensitivity was 0.2 ng/ml for resistin and 0.12 ng/ml for leptin, and the intra-and interassay variability were < 5% and < 8%, respectively. Serum insulin concentractions were measured by commercial RIA kit (Cis Bio International, Gif-sur-Yvette Cedex, France). Sensitivity was 2.0 µIU/ml and the intra-and interassay variability were 4.2% and 8.8%, respectively.

Tissue samples

Subcutaneous adipose tissue (SAT) from the region of transverse Pfannenstiel incision and visceral (omental) adipose tissue (VAT) samples were obtained during surgery and placental tissue was sampled after manual removal of placenta from approximately 5 mm deep incision from fetal side of placenta in the central part of cotyledon. All samples were immediately immersed into RNAlater (Qiagen) and were stored according to manufacturer instructions until total RNA isolation.

Total RNA isolation and cDNA sample preparation

Total RNA from SAT and VAT were isolated using RNeasy Lipid Tissue Mini Kit (Qiagen) and placental total RNA was isolated by RNeasy Protect Mini Kit (Qiagen) according to manufacturer protocols. Quality of isolated RNA was controlled A c c e p t e d M a n u s c r i p t 8 spectrophotometrically (only the samples with 260/280 nm ratio >1.8 were proceed for further steps of analysis), the RNA integrity was controlled by agarose gel electrophoresis. RNA samples were stored at -80°C until cDNA preparation.

All RNA samples were treated by DNase (Fermentas) prior cDNA synthesis. Reverse transcription was performed in 40 µl reaction volume with 4 µl (placenta) and 2 µl (SAT and VAT) template total RNA, respectively, using SuperScript III reverse transcriptase (Invitrogen) and random hexanucleotides (Roche) according to manufacturer Synthetized cDNA was stored at -20°C until qPCR.

Real-time PCR (qPCR) and gene expression quantification

Gene expression was analyzed by quantitative real-time RT-PCR (qPCR) and quantified by REST (Relative Expression Software Tool) 2008 ver. 2.0.7 (Corbett Research Pty. Ltd) [START_REF] Pfaffl | A new mathematical model for relative quantification in real-time RT-PCR[END_REF][START_REF] Pfaffl | Relative expression software tool (REST) for group-wise comparison and statistical analysis of relative expression results in real-time PCR[END_REF] and/or by qGENE softwares.

Commercially designed primers were used for CD68, ESR1, ESR2, IL-6, IL-8, RETN and TNF-α genes (SuperArray, USA), whereas primers for amplification of other genes were designed by us and synthetized by Generi Biotech, Czech Republic: B2M forward (5'-ATGTCTCGCTCCGTGGCCTTA-3'), reverse (5'-TCGGATGGATGAAACCCAGACACA-3'); GAPDH forward (5'-GGTGAAGGTCGGAGTCAACGG-3'), reverse (5'- The size of PCR amplicons of all tested genes ranged between 100 and 230bp. PCR product specificity for primers designed in our laboratory was confirmed by DNA sequencing

CGCTCCTGGAAGATGGTGATGG-3'); PBGD forward (5'- ATGTCTGGTAACGGCAATGCGG-3'), reverse (5'-TGTCCCCTGTGGTGGACATAGC- 3'); ADIPOQ forward (5'-GTGATGGCAGAGATGGCACCC-3'), reverse (5'- AGGCACCTTCTCCAGGTTCTCC-3'); ADIPOR1 forward (5'- TTGTGTACAAGGTCTGGGAGGGA-3'), reverse (5'

RESULTS

Anthropometric characteristic of healthy women with physiological pregnancy and pregnant women with gestational diabetes mellitus (GDM) and their newborns

The study groups were age-matched. Pregnant women with GDM had significantly higher BMI before pregnancy, higher BMI third day after delivery, and higher percentage of body fat wigth/length ratio of the newborns did not differ beween the groups (Table 1).

Maternal and fetal serum biochemical and hormonal parameters of GDM and control group

Maternal serum levels of adiponectin, resistin, C-peptide, triglycerides, and estradiol did not significantly differ in GDM group as compared with control group. Maternal serum leptin levels tended to be higher in GDM group, but the difference did not reach the statistical significance (p = 0.1). Serum glucose, insulin, and glycated hemoglobin levels were significantly higher in GDM relative to control group. Fetal serum adiponectin, resistin, and insulin levels did not significantly differ between GDM group and control group, whereas fetal serum leptin levels were markedly increased in GDM group as compared to control group. In both groups, fetal adiponectin concentrations were higher compared to maternal adiponectin concentrations (71.3 ± 8.9 vs. 22.7 ± 1.9 mg.l -1 ; p < 0.00001). The opposite was true for leptin concentrations that were higher in maternal relative to fetal serum samples (15.0 ± 3.4 vs. 29.1 ± 4.1 µg l -1 ; p = 0.01). Serum concentrations of resistin and insulin did not significantly differ between mothers and their newborns (Table 2).

Gene expression in subcutaneous adipose tissue, visceral adipose tissue, and placenta: comparison of women with GDM and healthy control pregnant women

Gene expression of resistin, adiponectin, ADIPOR1, ADIPOR2, PPAR-α, TNF-α, and CD68 did not significatly differ between GDM and control group in any tissue studied. Leptin gene expression was 3.5-times higher in VAT of GDM group relative to control group.

mRNA levels of IL-6 and IL-8 were 6.3-times and 4.8-times higher in SAT of GDM group as 3). Gene expression of adiponectin was undetectable in placenta (Table 3).

The comparison of the relative expression of the studied genes between subcutaneous adipose tissue, visceral adipose tissue and placenta

The proportion of relative gene expression (normalized to B2M) of all analyzed genes in all three tested tissues is summarized in Figure 2.

The expressions of genes for leptin, adiponectin, and ERβ were significantly higher in SAT than in VAT in both GDM and control groups. Gene expressions of resistin, ADIPOR1

and ADIPOR2 were significantly higher in SAT than in VAT in the control, but not in GDM group. Gene expression of IL-8 was significantly higher in SAT than in VAT in GDM group only. Gene expression of IL-6 was higher in VAT than in SAT in control group, whereas higher expression of IL-6 in SAT relative to VAT was found in GDM. Gene expression of ERα was significantly higher in SAT than VAT in control group, whereas it did not significantly differ between the SAT and VAT depots in GDM (Figure 2, Table 4).

The expressions of genes for leptin, ADIPOR2 and CD68 were significantly higher in SAT than in placenta in control group, but not in GDM group. The expressions of IL-8, ERα

and ERß were markedly higher in SAT than in placenta in both control and GDM group. The expression of IL-6 was significantly higher in SAT than in placenta in GDM group only.

Inspite of undetectable adiponectin expression in placenta, the expression of ADIPOR1 was 3times higher in placenta than in SAT in both groups studied (Figure 2, Table 4).

DISCUSSION

A c c e p t e d M a n u s c r i p t 13

The most important finding of this study is that visceral fat leptin gene expression and subcutaneous fat IL-6 and IL-8 gene expressions were significantly increased, whereas subcutaneous fat ERα and ERβ gene expressions were significantly decreased in women with GDM relative to healthy age-matched women with physiological pregnancy.

Inspite of previously suggested predictive role for leptin in the development of GDM [START_REF] Qiu | Increased maternal plasma leptin in early pregnancy and risk of gestational diabetes mellitus[END_REF][START_REF] Maghbooli | Relationship between leptin concentration and insulin resistance[END_REF]Gao et al., 2008), there is still a controversy as far as the circulating levels of leptin in GDM are concerned (Festa et al., 1999;[START_REF] Kautzky-Willer | Increased plasma leptin in gestational diabetes[END_REF][START_REF] Simmons | Fetal overnutrition in polynesian pregnancies and in gestational diabetes may lead to dysregulation of the adipoinsular axis in offspring[END_REF]. Such a discrepancy in published results could be attributed to the different gestational age at time of the maternal blood collection and the differences in severity of GDM and its compensation (Festa et al., 1999). Here we observed a strong, but non-significant, trend towards increased circulating leptin levels in the GDM group at the time of delivery. The lack of statistically significant difference between the groups could be at least partially attributable to a satisfactory diabetes compensation of GDM group at the time of blood collection as well as to relatively low number of subjects together with high interindividual circulating leptin concentrations variability.

Visceral fat has been shown to contribute to the development of impaired glucose metabolism in women with history of GDM [START_REF] Lim | Visceral fatness and insulin sensitivity in women with a previous history of gestational diabetes mellitus[END_REF]. Markedly elevated expression of leptin in visceral adipose depot of women with GDM may thus play a role in the development of endocrine dysfunction of visceral fat and later on also in the development of GDM. The mechanism of leptin contribution to the development of insulin resistance may lie in its inhibitory effect on the insulin-signaling cascade; leptin promotes phosphorylation of Ser-318 in Irs1 by a janus kinase 2, Irs2, and PKC-dependent pathway [START_REF] Hennige | Leptin down-regulates insulin action through phosphorylation of serine-318 in insulin receptor substrate 1[END_REF].

Although the findings of majority [START_REF] Lepercq | Overexpression of placental leptin in diabetic pregnancy: a critical role for insulin[END_REF][START_REF] Lea | Placental leptin in normal, diabetic and fetal growth-retarded pregnancies[END_REF][START_REF] Radaelli | Gestational diabetes induces placental genes for chronic stress and inflammatory pathways[END_REF]Meller et al., 2006), but not all [START_REF] Lappas | Release and regulation of leptin, resistin and adiponectin from human placenta, fetal membranes, and maternal adipose tissue and skeletal muscle from normal and gestational diabetes mellitus-complicated pregnancies[END_REF], previous studies showed elevated leptin production in placenta from women with GDM, our results suggest that, at the time of A c c e p t e d M a n u s c r i p t 14 delivery visceral adipose tissue, rather than a placental tissue, appears to be a major contributor to increased leptin levels in GDM. We also suggest that high maternal visceral fat leptin expression and significantly increased fetal serum leptin levels at the time of delivery could play a role in later susceptibility to the potential post-pregnancy development of insulin resistance and T2DM in both mother and fetus (McNeely et al., 1999;[START_REF] Jansson | Endocrine abnormalities in healthy first-degree relatives of type 2 diabetes patients--potential role of steroid hormones and leptin in the development of insulin resistance[END_REF][START_REF] Shahid | Male offspring of both diabetic parents have higher insulin resistance and serum leptin levels compared to those with one diabetic parent[END_REF].

It still remains to be determined what factors trigger increased expression of leptin in visceral fat of women with GDM and whether these factors precede the development of insulin resistance and diabetes or are rather secondary to these metabolic disturbances. One possible contributor to increased leptin expression in visceral fat in GDM might be altered estrogen receptor subtypes expression ratio in adipose depots of GDM in response to rising estrogen level with advancing gestation [START_REF] Sivan | Leptin in human pregnancy: the relationship with gestational hormones[END_REF][START_REF] Brann | Regulation of leptin gene expression and secretion by steroid hormones[END_REF][START_REF] Tanaka | Effects of estrogen on serum leptin levels and leptin mRNA expression in adipose tissue in rats[END_REF][START_REF] Yi | Role of estrogen receptor-alpha and -beta in regulating leptin expression in 3T3-L1 adipocytes[END_REF]. Here we found similarly to Yi and colleagues [START_REF] Yi | Role of estrogen receptor-alpha and -beta in regulating leptin expression in 3T3-L1 adipocytes[END_REF] The second group of candidates possibly affecting leptin expression in visceral fat in pregnancy includes insulin and glucose [START_REF] Boucher | Adipokine expression profile in adipocytes of different mouse models of obesity[END_REF][START_REF] Salvatores | Leptin as a possible marker of augmented metabolic risk during pregnancy[END_REF]. It has been previously documented that insulin increases leptin mRNA expression in human subcutaneous fat [START_REF] Pratley | Insulin increases leptin mRNA expression in abdominal subcutaneous adipose tissue in humans[END_REF]. Mouse models of obesity, diabetes and insulin resistance have markedly increased adipose tissue leptin mRNA expression [START_REF] Boucher | Adipokine expression profile in adipocytes of different mouse models of obesity[END_REF]. Here we showed that visceral fat leptin expression positively correlated with blood glucose (r = 0.52, p = 0.02).

Thirdly, previous reports postulated that the release of leptin from maternal adipose tissue positively correlates with maternal BMI [START_REF] Lappas | Release and regulation of leptin, resistin and adiponectin from human placenta, fetal membranes, and maternal adipose tissue and skeletal muscle from normal and gestational diabetes mellitus-complicated pregnancies[END_REF]. Here we found that visceral fat leptin gene expression positively correlated with maternal BMI before pregnancy (r = 0.46, p = 0.04) and with maternal body weight 3 rd day after delivery (r = 0.48, p = 0.03).

Increasing evidence suggests that GDM is a proinflammatory state similarly as T2DM [START_REF] Radaelli | Gestational diabetes induces placental genes for chronic stress and inflammatory pathways[END_REF]. Increased expression of IL-6 and IL-8 in subcutaneous adipose depot of women with GDM is in agreement with such idea and with previously described increased circulating levels of IL-6 and IL-8 in GDM [START_REF] Kuzmicki | Circulating pro-and anti-inflammatory cytokines in Polish women with gestational diabetes[END_REF]. Interestingly, although visceral adipose tissue is traditionally considered more proinflammatory fat depot [START_REF] Wajchenberg | Subcutaneous and visceral adipose tissue: their relation to the metabolic syndrome[END_REF][START_REF] Wagenknecht | Insulin sensitivity, insulin secretion, and abdominal fat: the Insulin Resistance Atherosclerosis Study (IRAS) Family Study[END_REF] here we found that in GDM patients mRNA expression of IL-6 was higher in subcutaneous than in visceral fat. Even more strikingly, the opposite was true for healthy control group. We also observed significantly lower mRNA expression of IL-6 in placenta than in subcutaneous fat of GDM patients, but not in controls, which further indicates altered inflammatory status regulation in patients with GDM. We have also showed that maternal adipose tissue IL-6 expression positively correlates with fetal serum leptin (r = 0.52, p = 0.01) and that placental IL-8 mRNA inversely correlates with fetal serum resistin (r = -0.50, p = 0.03), suggesting that proinflammatory state of the A c c e p t e d M a n u s c r i p t 16 mother may influence the endocrine regulations of the fetus [START_REF] Henson | Leptin: roles and regulation in primate pregnancy[END_REF][START_REF] Henson | Leptin: roles and regulation in primate pregnancy[END_REF]Nathanielsz et al., 2007).

The results of our study underline a possible importance of estrogen receptors in the regulation of insulin resistance. In experimental studies, ERα activation appears to protect against insulin resistance while the opposite is generally true for ERβ activation (Naaz et al., 2002 andForyst-Ludwig et al., 2008). The selective knock-out of ERα led to adipocyte hyperplasia and hypertrophy, insulin resistance, and glucose intolerance in mice [START_REF] Heine | Increased adipose tissue in male and female estrogen receptor-alpha knockout mice[END_REF]. In mature adipocytes ERα mRNA expression was higher as compared to ERß mRNA levels, but ERß is quantitavely more expressed isoform in women than in men (Deroo and Korach, 2006). Here we showed that at the time of delivery the ERß gene expression is in all three analyzed tissues higher than ERα (ERα/ERß = 0.043 in SAT, 0.078 in VAT and 0.322 in placenta; data not shown). We also showed that GDM is associated with decreased expression of both types of estrogen receptors in subcutaneous fat depot suggesting another possible mechanism of modulation of insulin sensitivity in these patients. Since ERα appears to play a major role in adipose tissue (Naaz et al., 2002) its decreased expression in subcutaneous fat of patients with GDM may contibute to the development of insulin resistance within this adipose tissue depot.

We are aware, that our study has several limitations. The first one is the relatively low number of subjects included and the presence of significantly higher BMI of GDM group relative to control group before the pregnancy. Second, rather paradoxical limitation from the patients' point of view is that all tissue samples were collected at the time of delivery when diabetes of GDM patients was satisfactorily-compensated which may have partially blunted the differences between the groups.

In conclusion, our study has shown that GDM is accompanied by increased expression of leptin in visceral adipose depot together with increased expression of proinflammatory

  during pregnancy may contribute to the development of insulin resistance and GDM. The aim of the present study was to characterize the changes in gene expression of adipokines [leptin, resistin, adiponectin and its receptors -adiponectin receptor 1 (ADIPOR1; OMIM 607945), and adiponectin receptor 2 (ADIPOR2; OMIM 607946)], proinflammatory cytokines (IL-6, TNF-α), chemokines (IL-8) and other receptors with potential role in the regulation of glucose and lipid homeostasis [genes for peroxisome proliferator-activated receptor-α (PPAR-α; OMIM 170998), estrogen receptor α (ESR1, ERα; OMIM 133430), and estrogen receptor β (ESR2, ERβ; OMIM 601663)] in subcutaneous and visceral adipose tissue and placenta of patients with gestational diabetes mellitus relative to those in healthy women with physiological pregnancy.

  measured in the Department of Clinical Biochemistry and Laboratory Medicine, General University Hospital and First Faculty of Medicine, Charles University in Prague, by standard laboratory methods. Serum glucose concentrations were measured by photometric enzyme assay GOD/POD (Pliva -Lachema), serum triglyceride concentrations were measured by photometric enzyme assay GPO/PAP (Human Gesellschaft für Biochemica und Diagnostica mbH) and serum C-peptide concentrations were measured by electrochemiluminiscence -all of them on the MODULAR System (ROCHE Diagnostics). Serum estradiol concentrations were measured by two-step immunoassays using Chemiluminiscent Microparticle Immunoassay (CMIA) technology with flexible assay protocols, referred to as ChemiflexTM. System: Architect i2000 (ABBOTT Laboratories).

  delivery relative to control group. Birth weight, birth length and birth

  group. On the contrary, the expressions of ERα and ERß were significantly lower in SAT of GDM group as compared with control group (Figure1, Table

  a significant positive relationship of visceral adipose tissue leptin gene expression with visceral fat ERα gene expression (r = 0.68, p <0.00001), as well as with visceral fat ERα/ERß mRNA expression ratio in a combined population of both study groups (r = 0.46, p = 0.04). Although we found no significant relationship between LEP and ERß gene expressions in visceral fat depot, we found a significant positive relationship between LEP and ERß gene expressions (r = 0.56, p = 0.009) in subcutaneous adipose depot. Leptin mRNA level in visceral fat was also inversely related to maternal estradiol serum concentrations (r = -0.52, p = 0.02). Furthermore, we found significantly different ERα/ERß ratio in subcutaneous over visceral fat depot between GDM and controls (SAT/VAT expression ratio of ERα/ERß: controls 1.34 vs. GDM 0.46, p < 0.05).Shin et al. (2007) previously showed that the subcutaneous ERα/ERß over visceral ERα/ERß ratio is significantly positively associated with leptin expression in visceral fat depot.

  expression of estrogen receptors in subcutaneous adipose depot. We suggest that these alterations may in concert contribute to some of metabolic disturbances seen in GDM patients not only during pregnancy but also after delivery. .M., Nizielski, S.E., Shao, J., Preston, L., Qiao, L., Friedman, J.E., 2002.

Figure 1 .

 1 Figure Legends:

Figure 2 .

 2 Figure 2. The log scale scatter plot of relative gene expression (compared to B2M) of all

Figure 2

 2 Figure 2

Table 2 .

 2 Downregulated IRS-1 and PPARgamma in obese women with gestational diabetes: relationship to FFA during pregnancy. Am. J. Physiol. Endocrinol. Metab. 282, E522-533. Shnawa, N., Krugluger, W., Hopmeier, P., Schernthaner, G., Haffner, S.M., 1999.Relative hypoleptinaemia in women with mild gestational diabetes mellitus. Diabet. Med. 16, Association between baseline plasma leptin levels and subsequent development of diabetes in Japanese Americans. Diabetes. Care. 22, 65-70. Maternal and fetal serum parameters in healthy pregnant women and patients with gestational diabetes mellitus (GDM) measured at the time of delivery.

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25

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Weigle, D.S., Shofer, J.B., Chessler, S.D., Leonnetti, D.L., Fujimoto, W.Y., 1999.

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Metab. Res. 34, 758-763. Nathanielsz, P.W., Poston, L., Taylor, P.D., 2007.

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Table 3 .

 3 Fold changes of relative gene expression levels in subcutaneous (SAT) and visceral (VAT) adipose tissue and in placenta from women with gestational diabetes mellitus (GDM) (N = 10) relative to healthy pregnant women (N = 13). Relative gene expression of the respective gene of the healthy pregnant group = 1.

	GDM to control group expression		SAT		VAT	PLACENTA
	Gene	ExpR P-value ExpR P-value ExpR P-value
	Leptin (LEP)	1.18	0.4	3.49	0.02	1.56	0.6
	Resistin (RETN)	0.79	0.6	1.13	0.7	1.09	0.8
	Adiponectin (ADIPOQ)	0.74	0.07	0.81	0.6	ND
	Adiponectin receptor 1 (ADIPOR1)	1.11	0.5	1.01	0.96	1.07	0.7
	Adiponectin receptor 2 (ADIPOR2)	1.04	0.8	1.18	0.4	1.21	0.2
	PPAR-alpha (PPAR-α)	0.76	0.3	0.76	0.4	NE
	TNF-alpha (TNF-α)	1.51	0.4	0.97	0.9	1.32	0.4
	CD68	1.18	0.6	0.89	0.3	1.10	0.6
	Interleukin-6 (IL-6)	6.32	0.01	2.66	0.1	0.98	0.9
	Interleukin-8 (IL-8)	4.75	0.03	1.26	0.6	0.95	0.9
	Estrogen receptor alpha (ERα)	0.52	0.045	1.46	0.1	1.75	0.1
	Estrogen receptor beta (ERß)	0.68	0.033	1.21	0.4	1.07	0.8
	ExpR expression ratio; ND not detected; NE not estimated; p<0.05 denoted statistical
	significant changes.

Table 4 .

 4 The comparison of the relative gene expression (normalized to 3 housekeeping genes) of target genes in visceral adipose tissue (VAT) and placenta relative to subcutaneous adipose tissue (SAT) with (CONTROL and GDM subgroups) or without (ALL subjects together) the dependency of GDM status. Relative expression of respective gene in SAT = 1.

	VAT		ALL	CONTROLS		GDM
	Gene	ExpR P-value ExpR P-value ExpR P-value
	Leptin (LEP)	0.08	0.001	0.06	0.001	0.16	0.001
	Resistin (RETN)	0.27	0.001	0.25	0.001	0.35	0.1
	Adiponectin (ADIPOQ)	0.40	0.001	0.38	0.003	0.42	0.004
	Adiponectin receptor 1 (ADIPOR1)	0.73	0.01	0.77	0.03	0.71	0.2
	Adiponectin receptor 2 (ADIPOR2)	0.77	0.03	0.73	0.04	0.83	0.3
	PPAR-alpha (PPAR-α)	1.07	0.7	1.02	0.9	1.02	0.97
	TNF-alpha (TNF-α)	0.62	0.1	0.73	0.3	0.47	0.1
	CD68	0.88	0.4	0.97	0.9	0.74	0.2
	Interleukin-6 (IL-6)	1.24	0.7	1.85	0.3	0.78	0.7
	Interleukin-8 (IL-8)	0.31	0.03	0.55	0.4	0.15	0.002
	Estrogen receptor alpha (ERα)	0.59	0.01	0.38	0.001	1.06	0.8
	Estrogen receptor beta (ERß)	0.40	0.001	0.31	0.001	0.56	0.001
	PLACENTA		ALL	CONTROLS		GDM
	Gene	ExpR P-value ExpR P-value ExpR P-value
	Leptin (LEP)	0.28	0.002	0.24	0.006	0.32	0.1
	Resistin (RETN)	1.12	0.7	1.01	0.98	1.40	0.5
	Adiponectin (ADIPOQ)	ND		ND		ND
	Adiponectin receptor 1 (ADIPOR1)	3.18	0.001	3.30	0.001	3.18	0.001
	Adiponectin receptor 2 (ADIPOR2)	0.79	0.01	0.73	0.002	0.86	0.4
	PPAR-alpha (PPAR-α)	NE		NE		NE
	TNF-alpha (TNF-α)	1.10	0.7	1.15	0.7	1.01	0.98
	CD68	0.60	0.004	0.61	0.02	0.57	0.1
	Interleukin-6 (IL-6)	0.38	0.02	0.82	0.7	0.13	0.001
	Interleukin-8 (IL-8)	0.09	0.001	0.18	0.01	0.04	0.001
	Estrogen receptor alpha (ERα)	0.12	0.001	0.07	0.001	0.24	0.005
	Estrogen receptor beta (ERß)	0.02	0.001	0.01	0.001	0.02	0.001

Acknowledgements

Supported by IGA MHCR no. 8302-5 to MH and PK. We thank to obstetricians and the patients for their collaboration. The special thank belongs to Mrs. Hana Vágnerová for help with the majority of tissue sampling.


Antoine Simon

Régis Delaunay

Erwan Donal

Christophe Leclercq

Mireille Garreau

Cardiac Function Estimation Using Multislice Computed Tomography: A Comparison to Speckle Tracking Imaging

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

Introduction

Multislice Computed Tomography (MSCT), combining ultra-fast rotating gantries, multi-rows detectors and retrospective ECG-gated reconstruction, provides datasets representing the whole cardiac cycle with a high spatial resolution. It enables to have in prospect an anatomical description of the cardiac system (ventricles and coronary vessels) but also a functional description (cardiac motion in three dimensions), both obtained in one single examination.

The estimation of the cardiac function has to help for the diagnosis and therapy planning of pathological situations such as atherosclerosis (related to hypokinesia) or asynchronism. Indeed, such a description would be a great mean to improve Cardiac Resynchronization Therapy (CRT). Actually, if CRT is accepted as a therapeutic option in heart failure patients who remain highly symp-tomatic despite optimized medical treatment [START_REF] Leclercq | Re-timing the failing heart: principles and current clinical status of cardiac resynchronization[END_REF], one third of the patients do not respond to the therapy [START_REF] Mehra | Cardiac resynchronization therapy: caveat medicus![END_REF]. The major ways to improve CRT remain both the identification of the most effective pacing sites and the left ventricular lead positioning.

This work is part of the IMOP project (IMaging for Optimisation of biventricular Pacing) which purpose is to define a CRT optimization method based on the fusion of mechanical, electrical and anatomical data. Our objective is to better plan the placement of CRT leads using the Multislice-CT (MSCT) capabilities in imaging the heart. With this modality, transvenous path finding methods [START_REF] Coatrieux | Transvenous path finding in cardiac resynchronization therapy[END_REF] and motion estimation approaches have been previously proposed [START_REF] Garreau | Surface and motion extraction in cardiac MSCT imaging for the assessment of left ventricular function[END_REF][START_REF] Simon | Cardiac Motion Extraction Using 3D Surface Matching in Multislice Computed Tomography[END_REF]. The goal of this work is to compare the displacements extracted from MSCT data to those obtained with US Speckle Tracking Imaging in order to discuss the capabilities of MSCT to assess local cardiac function.

This paper is organized as follows: first, the cardiac motion estimation method that we have proposed and applied to MSCT imaging is resumed. Speckle Tracking Imaging is then introduced. The method of comparison is then described. Results obtained on two patients databases are presented and discussed in a qualitative way, and followed by some conclusions.

Methods

Cardiac motion estimation in multislice computed tomography

The method we have previously proposed [START_REF] Garreau | Surface and motion extraction in cardiac MSCT imaging for the assessment of left ventricular function[END_REF][START_REF] Simon | Cardiac Motion Extraction Using 3D Surface Matching in Multislice Computed Tomography[END_REF] for cardiac function estimation relies on three steps: (i) segmentation of the left endocardium along the whole temporal sequence; (ii) reconstruction of the surface meshes corresponding to the segmented surfaces; (iii) temporal surface matching to estimate the displacements of the mesh nodes.

The segmentation process is based on a fuzzy connectedness algorithm. The comparison of computed left ventricular volume to clinical measurements (with MSCT and MRI), such as the comparison of the segmentation with an expert segmentation of reference, have provided satisfying results [START_REF] Fleureau | Assessment of global cardiac function in MSCT imaging using fuzzy connectedness segmentation[END_REF]. The segmented surfaces are then reconstructed using the Marching Cubes algorithm.

The motion estimation relies on the multi-resolution matching of each pair of surfaces (S 1 , S 2 ) corresponding to two following moments. It is based on the definition of a Markov Random Field F , whose sites are the nodes of S 1 and labels are the nodes of S 2 . The most probable realization of F , according to a global energy U , leads to the estimated motion field. The matching is guided by mean and Gaussian curvatures. In order to take advantage of the spatiotemporal regularity of the motion field, the energy U includes terms privileging low amplitude and spatially regular displacements. A multi-resolution scheme is used in order to optimize the minimization process. At the lowest resolution, the energy minimization is performed using a simulated annealing algorithm while, at higher resolutions, an iterated conditional mode (ICM) algorithm is used.

The application of this method to each pair of successive surfaces S 1 and S 2 results to one motion field associated to each instant of the cardiac cycle and defined on the set of nodes of the corresponding endocardial mesh.

From the obtained motion fields, motion descriptors can be extracted. For instance, maximal displacement (according to the first time-instant of the sequence), time-instant corresponding to this maximal displacement and total displacement can be computed and displayed using different representation modes (e.g. 3D endocardial surface, bulleye representation based on the 17 cardiac anatomical segments (cf. Fig. 1)).

Cardiac motion estimation using 2D speckle tracking

Speckle tracking is an image processing method that enables to assess cardiac motion from 2D dynamic echocardiographic images [START_REF] Langeland | Comparison of time-domain displacement estimators for two-dimensional RF tracking[END_REF]. Routine B-mode grayscale images are analyzed for frame-by-frame movement of stable patterns of natural acoustic markers, or speckles, present in ultrasound tissue images over the cardiac cycle. The tracking relies on the manual selection of the endocardium contour. It is then extended to consider the whole myocardium and decomposed in segments (about 60 in long-axis view, 40 in short-axis view). Speckle tracking enables to track these segments and therefore to estimate cardiac motion.

The extracted motion is then decomposed, for each segment, in different components used in clinical routine, i.e. transverse and longitudinal components in long-axis view and radial component in short-axis view. Finally, a baseline correction is applied to each motion component in order to correct global displacement caused by patient breathing or probe displacement.

Comparison of the extracted motions

Because MSCT and STI provide motion descriptors with different characteristics (3D vs. 2D, different spatial and temporal resolutions), a multimodal registration stage is required. It is based on four steps:

• Selection of the echographic plane in the CT data and extraction of the associated motion;

• Temporal interpolation of the extracted CT motion;

• Spatial registration of the CT and US contours;

• Association, to each US data point, of the corresponding CT point. These four steps are described in the following paragraphs.

Selection of the echographic plane in the CT data. The selection of the plane associated to the echographic acquisition in the CT data is realized interactively. It relies on the selection of three points on the first reconstructed endocardial surface. Once the plane has been selected, it is used to cut through the reconstructed surface to generate a contour corresponding to the echographic plane (cf. fig. 2). For each point of this contour, the neighboring nodes (with a distance lower than 1.5mm) in the original surface mesh are selected and their displacements are averaged. Finally, displacements are decomposed according to the echographic plane.

Temporal interpolation of the extracted CT motion. In order to obtain the same temporal resolution in CT and US data, a cubic spline interpolation is applied to each motion component and for each point of the CT contour. Similarly to US data, a baseline correction is applied to the interpolated data.

Spatial registration of CT and US contours. A rigid registration process is used to align CT and US extracted contours. Firstly, an ICP (Iterative Closest Point) algorithm associating rotation and translation is applied to the CT contour. Because the spatial resolution of CT is higher than US resolution, a scaling factor is applied (initialized at 1 and iteratively decreased until the sum of the squared Euclidean distances between closest points stops decreasing). Finally, a second ICP is applied to refine the alignment. Association between CT and US data points. Because the CT spatial resolution is higher than the US one, each US data point is associated to several CT data points. Therefore, for each US data point, the nearest CT data points are selected and their motion components averaged.

After these four steps, one set of points is obtained (corresponding to the US data points) and, for each point, motion components estimated from CT and US data.

In order to display these motion components in a common way, we used a classic US representation (Fig. 3). It is based on the path followed from the base of the septal wall to the base of the lateral wall. For each point, the amplitude of the motion component is displayed according to time. It results to spatiotemporal maps used by physicians to detect intraventricular delays.

Results

For two patients, CT data acquisition has been realised with a 64-slices General Electric Lightspeed VCT 64 (GE, Milwaukee, WI, USA), providing 20 3D data volumes (resolution: 0.3x0.3x0.5 mm) representing a whole cardiac cycle. Transthoracic echocardiography has been performed using a GE Vivid 7 system. Two acquisitions have been realised in apical (long axis) view (4 and 2-chambers views) and three in parasternal short-axis views (basal left ventricle (LV), mid-LV and apical-LV). The frame rate is Transverse displacements from apical 4chambers view (patients 1 and 2). greater than 65 fr/s. Measurements of myocardial displacements were performed offline in the longitudinal, transverse and radial axes, using a dedicated software package (GE Echopac).

Motion estimated from long axis data is illustrated by figures 4, 5 and 6. Figures 4 and5 represent, respectively, longitudinal and transverse displacements estimated from 4-chambers acquisitions of the two patients. Figure 6 shows longitudinal and transverse displacements from 2-chambers acquisition of the first patient.

The figure 4 shows that longitudinal components significantly differ between CT and US, especially in the septal segments. This is explained by the well-known aperture phenomenon, highlighting a more difficult estimation of motion components parallel to the considered surface, which is the case of the longitudinal motion in the basal and medial segments of the left ventricle.

For transverse displacements (Fig. 5), results are better, especially in the septal and lateral segments. However, transverse motion estimated in the apical segment show important differences. This can be explained by another well-known problem: the difficulty to visualize the apical area in echography. In the segments with good displacements coherence, a temporal delay appears: CT data seems to have an advance of about 100 ms. It is caused by the dif- ference in the mode of ECG synchronization: US acquisition starts at the beginning of the QRS complex, while CT acquisition is synchronized on the QRS peak. This results to a temporal difference of about 50 to 100 ms that can be noticed on the spatiotemporal maps.

Results obtained on 2-chambers acquisition (Fig. 6) confirm those obtained on 4-chambers data: longitudinal motion shows very important differences in the anterior segments while transverse displacements highlight an important coherence, excepted in the apical segment. The figure 7 represents radial component in short axis view. The coherence of the estimation of this component is good, even if the delay previously exposed remains.

Discussion and conclusions

Assessing local cardiac function with Multislice Computed Tomography would be a great advance towards the anatomical and functional description of the cardiac system within one single examination. In this paper, we propose to compare local motion descriptors estimated with MSCT imaging to those obtained with US Speckle Tracking Imaging. In order to follow clinical routine, displacements are decomposed in different components before being compared.

Results vary greatly according to the represented motion component: transverse and radial displacements show an important coherence while longitudinal displacements differ significantly.

The coherence shown in transverse and especially radial components is very satisfying because it has been proved that radial component study is the most useful motion component for dyssynchrony characterization [START_REF] Suffoletto | Novel speckle-tracking radial strain from routine black-and-white echocardiographic images to quantify dyssynchrony and predict response to cardiac resynchronization therapy[END_REF].

Therefore, this study shows very promising results for the characterization of local cardiac function from MSCT imaging. Moreover, new MSCT generation will provide data with better temporal resolution allowing more precise motion estimation.

Future works will deal with the search for 3D dyssynchrony descriptors from MSCT and on the fusion of these descriptors with venous and myocardial anatomical information.

Figure 1 .

 1 Figure 1. Two representation modes of the cardiac function: maximal displacement (mm) represented in color with a 3D surface (left oblique anterior view) (left) and with a classic bull-eye representation (right).

Figure 2 .

 2 Figure 2. Selection of the echographic plane in the CT data (apical 4-chambers long axis plane (left) and short axis plane (right)).

Figure 3 .

 3 Figure 3. Representation mode used to display motion components.

Figure 4 .

 4 Figure 4.Longitudinal displacements from apical 4chambers view (patients 1 and 2).

Figure 5 .

 5 Figure 5.Transverse displacements from apical 4chambers view (patients 1 and 2).

Figure 6 .

 6 Figure 6. Longitudinal and transverse displacements from apical 2-chambers view, from the anterior to the inferior walls (patient 1).

Figure 7 .

 7 Figure 7. Radial motion from short-axis view (patient 1).

Acknowledgements

This work is supported by the French Research Minister (n 04 T 187 -188 -189 -190 CITH).


G Carrault

A Beuchee

P Pladys

L Senhadji

A Hernandez

Time-Frequency Relationships between Heart Rate and Respiration: A Diagnosis Tool for Late Onset Sepsis in Sick Premature Infants

The diagnosis of late onset sepsis in premature infants remains difficult because clinical signs are subtle and non-specific and none of the laboratory tests, including CRP and blood culture, have high predictive accuracy. Heart rate variability (HRV) analysis emerges as a promising diagnostic tool. Entropy and long-range fractal correlation are decreased in premature infants with proven sepsis. Besides this, respiration and its relations to HRV appear to be less. The objective of this study was to determine if analysis of time-frequency correlations between the heart rate and respiration amplitude may help for the diagnosis of infection in premature infants. An estimator of the linear relationship between nonstationary signals, recently introduced, is explored. The tests were performed on a cohort study of 60 premature infants. The results show that the correlation in the low frequency band tended to be higher in the sepsis group.

Introduction

Late-onset sepsis, defined as a systemic infection in neonates older than 3 days, occurs in approximately 10% of all neonates and in more than 25% of very low birth weight infants who are hospitalized in neonatal intensive care units. In view of the high morbidity and mortality associated with infection, reliable markers are needed.

Recurrent and severe spontaneous apneas and bradycardias is one of the major clinical early indicators of systemic infection in the premature infant. It requires prompt laboratory investigation so that treatment can start without delay. Various hematological and biochemical markers have been evaluated in this indication but they are invasive procedures that cannot be repeated several times.

Heart rate variability measurements such as a decreased approximate entropy 1, 2 or sample entropy 3 are significantly associated with sepsis or sepsis-like illness in premature infants. These diagnostic tools are non invasive, easily available and fast. This work can be viewed as a supplementary work in this direction. The following question is addressed: Could the association between heart rate variability and respiration in a selected population of sick premature infants be used as a diagnosis tool of late onset sepsis?

In this paper, a novel estimator for characterizing the evolution of linear relationship between signals, in the time-frequency domain, is used 4. This estimator is based on the computation of the correlation between Heart Rate Variability (HRV) and respiration signals filtered, in narrow and overlapping frequency bands, using a continuous filter bank 4. The description of the method is proposed in the first section. Then, the protocol -including data collection, statistical tools, inclusion and exclusion criteria-, is described. Results are reported in the third section while interests and perspectives are drawn in the conclusion section.

Methods

For the past decades, numerous signal processing approaches have been proposed to measure the degree of association (in a general sense, i.e. interdependency). These methods may be divided into two categories depending on whether or not the nonlinear nature of the relationship is taken into account. Linear methods were developed first. Many estimators based on linear crosscorrelation or coherence function were proposed. For the second group, mutual information [START_REF] Mars | Propagation of seizure activity in kindled dogs[END_REF] and on nonlinear regression [START_REF] Pijn | Propagation of electrical activity: nonlinear associations and time delays between EEG signals in Basic Mechanisms of the EEG[END_REF][START_REF] Wendling | Interpretation of interdependencies in epileptic signals using a macroscopic physiological model of the EEG[END_REF]) were introduced in the EEG field. More recently nonlinear dynamical systems and chaos [START_REF] Iasemidis | Epileptic seizure prediction and control[END_REF][START_REF] Lehnertz | Non-linear time series analysis of intracranial EEG recordings in patients with epilepsy--an overview[END_REF]) were designed.

In a recent previous work, we studied the interrelationship between HRV and respiration and demonstrated a decrease in the non linear regression coefficient for the sepsis population 10. Unfortunately, if nonlinear methods have the capability to account for the nonlinearity of relationship, they are generally independent from frequency, a key parameter in the heart rate variability and its interrelationship with the respiration. On the opposite, linear methods do not analyze the nonlinear property of relationship but they can characterize its dependence on frequency. However, It is known [START_REF] Zaveri | Measuring the coherence of intracranial electroencephalograms[END_REF] that the coherence function provided estimators generally with strong bias and variance. To alleviate these difficulties, a new estimator was recently proposed and can be of interest for our purpose 4. It uses a local linear correlation coefficient, computed at the outputs of narrow band-pass filter, as the function of frequency and time, which is maximized for time delay. To our knowledge, the application of this latter method in the cardio-respiratory field has never been reported. Let us briefly recall the method:

We consider two observations, x(t) the HRV signal and y(t) the respiration signal. The problem is to characterize the statistical relationship, simultaneously in the time and frequency domains, between the nonstationary signals x(t) and y(t). The estimation of the local linear correlation coefficient is given by :

R x,y 2 (t, f )   m   m max x f (k)y f (k   ) k H 2 H 2            2 x f 2 (k) k H 2 H 2  y f 2 (k   ) k N 2 N 2                     
where x f (t) and y f (t) are zero-mean and narrow band filtered signals over a sliding window of duration H (Figure 1a) of an appropriate filter bank (Figure 1b). R 2

x,y (t,f) is computed with different delays  between the two windows. This last one is derived from the Short-Time Fourier Transform (STFT).

Results

Data were obtained from a cohort of 60 premature infants (post-menstrual age < 33 weeks and chronological age > 72 hours) hospitalized in the neonatal intensive care unit at the university Hospital of Rennes. The infants were placed in bassinets, positioned on their side, wrapped by a single blanket roll, and loosely covered by another.

The monitoring (Powerlab system®, ADInstruments) consisted of two ECG, EOG and EEG leads, one pulse oxymetry saturation (SaO2), nasal flow and abdominal respiration recordings (sampling rate 400 Hz). Finally, babies were paired in age and weight. While Exclusion criteria were :  ongoing inflammatory response (CRP >5mg/l prior to the recording),  medication with antibiotics, morphine, cathecolamin, sedative drugs or Doxapram  intra-tracheal respiratory support, intra-cerebral lesion and malformation.  The study was approved by the local ethics committee. Parents were informed and consent was obtained.

A sequence of 4096 successive cardiac cycles, i.e. about half an hour each, were extracted from ECG recordings, and respiration signal and were subjected to analysis of heart rate variability parameters.

Both Sepsis and No-sepsis groups were compared using Mann and Withney or Kruskall-Wallis tests. The level of significance was set at p= 0.05.

Representative examples (HRV, respiration signal and Time-frequency linear correlation coefficient) in the sepsis and non sepsis group are displayed figure 3 and4. Both signals are nonstationary. The analysis reveal two distinct signatures: i) a higher correlation coefficient for the sepsis group in the lower band around 0.2 Hz, ii) a strong relationship in the higher frequency band for the non sepsis group. These qualitative findings were statistically verified. Figure 4 depicts the sub-band distribution of the timefrequency correlation coefficient R 2 (t,f), over a threshold set to 0.9.

We confirm statistically (p<0.05 whatever the statistical tests) that the higher correlation is retrieved in the low frequency band for the sepsis group (0.02<f<0.2 Hz) while an important distribution of high correlation (p<0.04 whatever the statistical tests) is observed in the non sepsis group for the high frequency (1<f<1.4 Hz) (figure 5).

Several threshold (0.6, 07, 0.8, 0.9) were tested and the threshold 0.9 appears the most discriminative between the two groups.

This conclusion is also in agreement with estimation of the respiration frequency of each baby. A significant difference is observed between the sepsis and non sepsis groups (Figure 6).

Sepsis No Sepsis

Discussion and conclusions

In this study, the question about the functional coupling among respiration and heart rate variability is addressed. There are two main results in this study. Differently from our initial work [START_REF] Loforte | Heart rate and respiration relationships as a diagnostic tool for late onset sepsis in sick preterm infants[END_REF], the linear relationship between HRV and respiration signals is computed as a function of time and frequency. Results show that the relationships are circumscribed within a particular region of the time-frequency plane for the sepsis group and a different one for the non-sepsis group (it is worthwhile to be aware that frequency-independent methods can be blind to relationships set up on a narrow frequency band).

In premature infants less than 33 weeks, with unusual and frequent apneas and bradycardias, we observed in the past that approximate entropy can reliably confirm or refute the diagnosis of systemic infection at an early stage. This measure has a good repeatability and good accuracy to test for the diagnosis of infection in this selected population. These findings corroborate the earlier works of Griffin and Pincus et al [START_REF] Griffin | Heart rate characteristics and laboratory tests in neonatal sepsis[END_REF]. Nevertheless, in the sick premature infant, the mechanism is probably not just a change in regularity. The clinical findings here clearly demonstrate that HRV and respiration and their relationships could be efficient diagnosis tools and may help identifying culture-positive sepsis in a population of infants with recurrent bradycardias.

Figure 1a :

 1a Figure 1a : Selection of the temporal support H for the computation of R 2 (t,f) with different delays 

Figure 2 :

 2 Figure 2: Typical square modulus of Time-frequency linear correlation coefficient for a sepsis baby. The upper traces represent the HRV and respiration signals.

Figure 3 :Figure 4 :

 34 Figure 3: Typical square modulus of Time-frequency representation linear correlation coefficient for a nonsepsis baby. The upper traces represent the HRV and respiration signals

Figure 5 :

 5 Figure 5 : Distribution of the linear time-frequency correlation coefficient square modulus values greater than 0.9 in different sub-bands. The grey lines indicate significant values.

Figure 6 :

 6 Figure 6: Frequency Peak estimation of the respiration spectrum: 1) sepsis babies and 2) non sepsis babies

Acknowledgements

The authors would like to express their thanks to Stephanie Sergant for her fruitful help during the data collection process.