Marion Lecorguillé
email: marion.lecorguille@inserm.fr

Juliane Léger

Anne Forhan

Marie Cheminat

Marie-Noëlle Dufourg

Barbara Heude

Marie-Aline Charles

Pregnancy outcomes in women with pre-existing thyroid diseases: a French cohort study

Keywords: medical history, thyroid diseases, hypothyroidism, pregnancy complications

Women with thyroid diseases at the beginning of pregnancy may have suboptimal thyroid hormone levels because of potential difficulties in compensating for the physiological thyroid hormone changes occurring in pregnancy. Our objective was to study the association between pre-existing thyroid diseases, pregnancy complications and neonatal anthropometry.

In total, 16,395 women from the ELFE French longitudinal birth cohort were included, and 273 declared pre-pregnancy thyroid diseases. Associations were investigated with multivariable regression models, with adjustment for relevant potential confounders. Body mass index (BMI) was additionally adjusted for in a second stage.

As compared with other women, women with pre-pregnancy thyroid diseases were more frequently obese (19.6% vs 9.8%) and had greater odds of gestational diabetes development (OR = 1.58 [95% CI 1.08,2.30]) or had undergone treatment for infertility (OR = 1.57 [95% CI 1.07,2.31]). After adjustment for BMI, the association with gestational diabetes was no longer significant (OR = 1.27 [95% CI 0.86,1.88]). After excluding women with another medical history, those with pre-pregnancy thyroid diseases had increased odds of premature rupture of membranes (OR =1.51 [95% CI 1.01,2.25]). Children born from mothers with hypothyroidism before conception due to a disease or as a potential side effect of treatment had a smaller head circumference at birth than other children (β= -0.23 [95% CI -0.44,-0.01] cm).

In conclusion, pre-pregnancy thyroid diseases were associated with risk of infertility treatment, gestational diabetes, and premature rupture of membranes. The association between history of hypothyroidism and moderate adverse effects on fetal head circumference growth needs replication.

Introduction

Several physiological changes occur during pregnancy that could affect thyroid function.

Adequate maternal thyroid function is especially important in early pregnancy and is crucial for the development of many organs, including the fetal brain [START_REF] Springer | Thyroid in pregnancy: From physiology to screening[END_REF] . During the first trimester of gestation, thyroid hormones from the mother are the only source for the developing embryo because fetal thyroid production begins at 12 to 14 amenorrhea weeks [START_REF] Morreale De Escobar | Role of thyroid hormone during early brain development[END_REF] . Maternal thyroid hormones circulate in fetal blood until birth [START_REF] Springer | Thyroid in pregnancy: From physiology to screening[END_REF][START_REF] Morreale De Escobar | Role of thyroid hormone during early brain development[END_REF][START_REF] Korevaar | Maternal and Birth Characteristics Are Determinants of Offspring Thyroid Function[END_REF] . Maternal thyroid hormone production increases by 20% to 50% to maintain a euthyroid state [START_REF] Alexander | Guidelines of the American Thyroid Association for the Diagnosis and Management of Thyroid Disease During Pregnancy and the Postpartum[END_REF] . For women with pre-pregnancy hypothyroidism, levothyroxine treatment frequently needs to be increased during pregnancy and there is potential for periods of non-optimal treatment [START_REF] Alexander | Guidelines of the American Thyroid Association for the Diagnosis and Management of Thyroid Disease During Pregnancy and the Postpartum[END_REF][START_REF] Léger | Pregnancy outcomes and relationship to treatment adequacy in women treated early for congenital hypothyroidism: a longitudinal population-based study[END_REF] .

The prevalence of thyroid disorders in pregnancy was evaluated in a recent metaanalysis according to diagnostic criteria and screening time. Among studies using the 2.5-97.5 percentile as a normal range for thyroid-stimulating hormone, prevalence rates were 0.5% for overt hypothyroidism, 3.5% for subclinical hypothyroidism and, in the first trimester, 0.9% for overt hyperthyroidism and 2.2% for subclinical hyperthyroidism [START_REF] Dong | Differences in Diagnostic Criteria Mask the True Prevalence of Thyroid Disease in Pregnancy -A Systematic Review and Meta-Analysis[END_REF] . These pathologies must be recognized because they may be associated with pregnancy complications and fetal development [START_REF] Springer | Thyroid in pregnancy: From physiology to screening[END_REF][START_REF] Alexander | Guidelines of the American Thyroid Association for the Diagnosis and Management of Thyroid Disease During Pregnancy and the Postpartum[END_REF][START_REF] Korevaar | Thyroid disease in pregnancy: new insights in diagnosis and clinical management[END_REF] .

In several studies, overt and subclinical hypothyroidism have been associated with adverse outcomes, including miscarriage, haemorrhage, premature rupture of membranes, gestational hypertension, diabetes, prematurity, caesarean section, induced labor and reduced fertility [START_REF] Alexander | Guidelines of the American Thyroid Association for the Diagnosis and Management of Thyroid Disease During Pregnancy and the Postpartum[END_REF][START_REF] Léger | Pregnancy outcomes and relationship to treatment adequacy in women treated early for congenital hypothyroidism: a longitudinal population-based study[END_REF][8][9][START_REF] Teng | Hypothyroidism in pregnancy[END_REF][START_REF] Maraka | Subclinical Hypothyroidism in Pregnancy: A Systematic Review and Meta-Analysis[END_REF][START_REF] Krassas | Thyroid disease and female reproduction[END_REF][START_REF] Cleary-Goldman | Maternal Thyroid Hypofunction and Pregnancy Outcome[END_REF][START_REF] Männistö | Thyroid Diseases and Adverse Pregnancy Outcomes in a Contemporary US Cohort[END_REF] . The neonatal complications are risk of intrauterine growth retardation and low or large birthweight [START_REF] Alexander | Guidelines of the American Thyroid Association for the Diagnosis and Management of Thyroid Disease During Pregnancy and the Postpartum[END_REF]9,[START_REF] Hou | The impact of maternal hypothyroidism during pregnancy on neonatal outcomes: a systematic review and meta-analysis[END_REF] . The adverse effects of subclinical hypothyroidism on pregnancy outcomes are still not clear, and most of these complications are related to overt hypothyroidism and chronic autoimmune thyroiditis [START_REF] Leo | Autoimmune thyroid disease during pregnancy[END_REF] . Maternal hyperthyroidism has been associated with increased risk of infertility, premature birth, gestational hypertension and preeclampsia, placenta abruption and induced labor, intrauterine growth retardation and fetal hyperthyroidism [START_REF] Alexander | Guidelines of the American Thyroid Association for the Diagnosis and Management of Thyroid Disease During Pregnancy and the Postpartum[END_REF]9,[START_REF] Männistö | Thyroid Diseases and Adverse Pregnancy Outcomes in a Contemporary US Cohort[END_REF][START_REF] Karakosta | Thyroid Dysfunction and Autoantibodies in Early Pregnancy Are Associated with Increased Risk of Gestational Diabetes and Adverse Birth Outcomes[END_REF][START_REF] Cooper | Hyperthyroidism in pregnancy[END_REF][START_REF] Laurberg | Management of Graves' hyperthyroidism in pregnancy: focus on both maternal and foetal thyroid function, and caution against surgical thyroidectomy in pregnancy[END_REF][START_REF] Phoojaroenchanachai | Effect of maternal hyperthyroidism during late pregnancy on the risk of neonatal low birth weight[END_REF] . The associations between thyroid autoantibodies and miscarriage and preterm birth are also well established [START_REF] Alexander | Guidelines of the American Thyroid Association for the Diagnosis and Management of Thyroid Disease During Pregnancy and the Postpartum[END_REF][START_REF] Van Den Boogaard | Significance of (sub)clinical thyroid dysfunction and thyroid autoimmunity before conception and in early pregnancy: a systematic review[END_REF][START_REF] Thangaratinam | Association between thyroid autoantibodies and miscarriage and preterm birth: meta-analysis of evidence[END_REF] .

However, few studies have evaluated the risk of complications during pregnancy related specifically to pre-pregnancy thyroid diseases [START_REF] Hirsch | Pregnancy outcomes in women with severe hypothyroidism[END_REF][START_REF] Reid | Interventions for clinical and subclinical hypothyroidism pre-pregnancy and during pregnancy[END_REF][START_REF] Earl | Interventions for hyperthyroidism pre-pregnancy and during pregnancy[END_REF] . In women with pre-pregnancy thyroid diseases, pregnancy may lead to suboptimal thyroid hormone concentration because of the inability of the thyroid gland to adapt to physiological changes at the beginning of pregnancy due to the disease itself or to its treatment 9 . Women with a history of thyroidectomy, radioactive iodine treatment, goiter or hypothyroidism, or Graves' disease need appropriate medical management of thyroid function during pregnancy [START_REF] Alexander | Guidelines of the American Thyroid Association for the Diagnosis and Management of Thyroid Disease During Pregnancy and the Postpartum[END_REF] . Thyroid diseases are heterogenous. However, whatever the disorder, these diseases were known before pregnancy and should have been treated before or adequately managed during pregnancy. Thus, our aim was to explore whether or not, as a group, women with pre-pregnancy thyroid disease had similar pregnancy outcomes as women without such a history. Taking advantage of the data for the French national birth cohort ELFE (Etude Longitudinale Française depuis l'Enfance), we described pregnancy and fetal outcomes in women with and without known pre-pregnancy thyroid diseases.

Methods

Study design

The present analysis relies on data from the ELFE study, the first French national longitudinal birth cohort. The rationale and design of the ELFE cohort were previously described in detail [START_REF] Charles | Cohort Profile: The French National cohort of children ELFE: birth to 5 years[END_REF] . Briefly, participation in the cohort was proposed to women giving birth in 349 maternity hospitals randomly selected among the 544 public and private maternity hospitals in metropolitan France. Recruitment took place on 25 selected days during 2011. The ELFE cohort inclusion criteria were birth at ≥ 33 amenorrhea weeks, single or twin infants, mother ≥ 18 years old, giving informed consent and not having plans to leave metropolitan France within 3 years. Among eligible mothers, 51% agreed to participate.

The ELFE study was approved by the ethics committee of Créteil (CPP), the national committee on information concerning health research (CCTIRS) and the Data Protection

Authority (Commission Nationale de l'Informatique et des Libertés [CNIL]).

Data collection

Research assistants collected information after birth from maternal medical records and during a face-to-face interview while the mother was in the maternity ward.

Pre-pregnancy thyroid diseases

Research assistants collected the diagnosis of severe maternal pre-pregnancy diseases or disability (excluding history of chronic or gestational diabetes or hypertension for which dedicated questions were asked) from medical files where such information is recorded as part of the routine medical history assessment.

From these data, we extracted all diseases potentially associated with thyroid dysfunction. All diseases were coded according to International Classification of Diseases, 10 th Revision. In total, 273 women presented thyroid diseases before pregnancy; we classified these women into three groups: women with 1) hypothyroidism due to a disease or as a potential side effect of treatment (Hashimoto's disease, hypothyroidism unspecified, no thyroid, thyroidectomy, thyroid cancer and women with levothyroxine treatment; n=196), 2) hyperthyroidism (Grave's disease, hyperthyroidism unspecified; n=49) and 3) other thyroid diseases (goiter, nodules, thyroid dysfunction or thyroid diseases unspecified, n=28). We combined women with thyroidectomy (n=16) and thyroid cancer (n=4) in the first group because of the small number of cases.

Pregnancy and fetal outcomes

Among all the data related to pregnancy complications, we selected for analyses the following outcomes with a minimum prevalence of 7% in the population, which ensured a power of 90% to detect a minimal relative risk of 1.75 for these diseases with alpha 5% (power increases for more prevalent outcomes): premature rupture of membranes (12 hr before labor onset), gestational diabetes, induction of labor (spontaneous labor, induction of labor, elective caesarean section), maternal hospitalization and mode of delivery (spontaneous vaginal delivery, assisted vaginal delivery [forceps, vacuum, spatula], caesarean section). Gestational hypertension and premature birth (< 37 amenorrhea weeks) were not studied because of too few cases in the exposed group. We also studied treatment for infertility before pregnancy.

Birth weight, birth length, head circumference and gestational age at birth were treated as continuous variables. Sex and gestational age-specific z scores customized for maternal weight, height and parity were computed using a method adapted for the French 2010 national perinatal survey, from that proposed by Gardosi [START_REF] Ego | Customized and noncustomized French intrauterine growth curves. II -Comparison with existing curves and benefits of customization[END_REF] . Percentiles were then derived and used to define fetal growth categories: small for gestational age (SGA, <10 th percentile), appropriate for gestational age (AGA, 10 th to 90 th percentile) and large for gestational age (LGA, >90 th percentile).

Other maternal variables

Sociodemographic data included maternal age (continuous), parity, education level (tertiary education as reference vs primary and secondary), professional status (employed or student, housewife or parental leave, and other, including unemployment), living with a partner (yes vs no), place of birth (born in France vs other country), and maternity unit level.

Health-related variables included state health insurance coverage (regular or specific to precarious situations), smoking during pregnancy (yes vs no), number of prenatal consultations (< 7, 7-9, ≥ 9), ultrasounds (≤ 5, > 5) and maternal pre-pregnancy weight and height. Body mass index (BMI) was calculated as weight (kg) divided by the height² (m²) and divided into 4 categories according to World Health Organization thresholds: underweight, <18.5 kg/m²; normal weight, 18.5 to <25 kg/m²; overweight, 25.0 to <30 kg/m²; and obesity, ≥ 30 kg/m².

Population selected for analysis

In total, 55 included mothers withdrew from the ELFE cohort and asked for data deletion.

Mothers of twins (n=287) or with missing medical records (n=175) or face-to-face maternal questionnaires (n=59) were excluded. For women with available medical records, we excluded 1070 with missing data on medical history. Figure 1 displays the flow chart for the population selection process. We grouped the selected women into two categories: 16,122 women with no medical history of thyroid diseases and 273 with pre-pregnancy thyroid diseases as described above.

Statistical analysis

Descriptive analysis

We compared sociodemographic characteristics and pregnancy outcomes between patients with and without pre-pregnancy thyroid diseases by chi-square test for categorical variables and ANOVA for continuous variables.

Multivariable analysis

Multivariable logistic, linear and polytomic regression analyses were used to investigate the association between thyroid diseases and pregnancy outcomes, estimating odds ratios (ORs) and 95% confidence intervals (CIs). We first adjusted our analyses for potential confounders, namely parity, smoking during pregnancy, maternal age, and maternal education. For birth head circumference, weight and length, we additionally adjusted for gestational age and sex.

Second, we adjusted for pre-pregnancy BMI as a continuous variable and investigated its potential role as an intermediate factor in the observed associations. We also introduced an interaction term between pre-pregnancy BMI and pre-pregnancy thyroid diseases to test whether the effect of a history of thyroid disease differed by maternal pre-pregnancy BMI.

Sensitivity analysis

To examine whether the observed relations were due to the thyroid diseases themselves and not other pre-pregnancy diseases, which could be more frequent in our affected group, we repeated our main analysis after excluding women with other medical histories such as autoimmune diseases, history of diabetes, hypertension, and other diseases known to affect pregnancy outcomes.

Because different types of thyroid disorders may be related to specific risks, we separately analysed data for women with pre-pregnancy thyroid diseases associated with hypothyroidism (as defined in the Data collection section) as compared with women without pre-pregnancy thyroid diseases. Women with other thyroid diseases (hyperthyroidism or unspecified) were too few to be considered in a separate analysis and were excluded from this analysis.

Results

Population characteristics

The characteristics of the women are summarized in Table 1. Women with pre-pregnancy thyroid diseases were older, had a higher level of education, were twice as frequently obese, and less often smoked during pregnancy than women without pre-pregnancy thyroid diseases.

The two groups were similar in employment status, country of birth or personal health insurance and number of prenatal consultations, but women with pre-pregnancy thyroid diseases had more ultrasounds during pregnancy. Women with pre-pregnancy thyroid diseases more frequently underwent treatment for infertility and had gestational diabetes than women without thyroid diseases (12% vs 7%, p<0.01, for both) (Table S1).

Multivariable analysis

After adjustment for the first set of confounders, the occurrence of pre-pregnancy thyroid diseases was associated with a 1.6-fold increased risk of infertility treatment and gestational diabetes (95% CI 1.07,2.31) and (95% CI 1.08,2.30) (Table 2) but was not associated with mode of delivery, induction of labour, birth weight or length or head circumference. After additional adjustment for BMI, the risk of gestational diabetes was no longer significant (OR =1.27 [95% CI 0.86,1.88]). The interaction between BMI and pre-pregnancy thyroid diseases was not significant for any of the pregnancy outcomes considered (p > 0.05, data not shown).

Exclusion of other medical history

We excluded women with a history of diseases other than thyroid diseases from the thyroid diseases group (n=62) and control group (n=1995), which left 211 and 14,127 women in each group (Table 3). Risk of premature rupture of membranes was increased for women with pre-pregnancy thyroid diseases (OR = 1.51 [95% CI 1.01,2.25]) (p =0.04). The occurrence of prepregnancy thyroid diseases was no longer associated with infertility or gestational diabetes.

Restriction to cases of hypothyroidism

We restricted the analysis to the comparison of women with hypothyroidism before pregnancy (n=196) to women without thyroid diseases before pregnancy. On univariate analysis, infertility treatment, gestational diabetes and premature rupture of membranes were significantly more frequent for women in the hypothyroidism group (p < 0.05; Table 4). In total, 20.4% of the women in this group were overweight and 20.9% were obese at conception as compared with 17.2% and 9.8% of women without thyroid diseases before pregnancy.

After adjustment on educational level, parity, maternal age and smoking, the risks remained significantly increased for the same pregnancy complications (data not shown). After an additional adjustment for BMI, associations with premature rupture of membranes and infertility treatment (both p=0.05) remained, but the association with gestational diabetes was decreased (p=0.4) (Table 4). Mother's hypothyroidism before pregnancy was associated with reduced infant head circumference at birth (β= -0.20 [95% CI -0.39,-0.01] cm, p=0.04). After excluding women with a history of diseases other than thyroid diseases, 149 women were in the pre-pregnancy hypothyroidism group and 14,127 the comparison group. Mother's hypothyroidism was associated with increased risk of premature rupture of membranes and slightly reduced infant head circumference at birth (β= -0.23 [95% CI -0.44,-0.01], p = 0.04) cm (Table 5).

Discussion

Our study showed an increased risk of infertility treatment and gestational diabetes and greater frequency of obesity for women with than without pre-pregnancy thyroid diseases.

After excluding women with other medical conditions known to interfere with pregnancy, the risk of premature rupture of membranes was increased. Finally, when we specifically investigated women with hypothyroidism before pregnancy, due to a disease or as a potential side effect of treatment, their offspring had a smaller head circumference at birth, on average.

Our findings are consistent with the consequences of thyroid dysfunction.

Hypothyroidism, either primary or secondary to treatment of thyroid or pituitary diseases, is often associated with excess weight [START_REF] Léger | Subtle health impairment and socioeducational attainment in young adult patients with congenital hypothyroidism diagnosed by neonatal screening: a longitudinal population-based cohort study[END_REF] . Indeed 19.6% versus 9.8% of our women with and without pre-pregnancy thyroid diseases were obese. In addition, thyroid function may be more often screened in obese patients. Obesity is associated with insulin resistance, hyperinsulinemia and risk of developing gestational diabetes [START_REF] Catalano | Obesity and pregnancy: mechanisms of short term and long term adverse consequences for mother and child[END_REF] . It can explain the increased risk of gestational diabetes in our women with pre-pregnancy thyroid diseases because the OR decreased from 1.58 (adjusted model 1) to 1.27 after adjustment for BMI at the beginning of pregnancy. Thyroid hormones are also essential for energy homeostasis, and a dysfunction could affect metabolism: blood pressure, and high-density lipoprotein cholesterol, triglycerides and glucose levels. Thyroid diseases have been found associated with increased blood glucose levels, insulin resistance, and reduced insulin clearance [START_REF] Iwen | Thyroid Hormones and the Metabolic Syndrome[END_REF] .

Reduced fertility is also a well-known consequence of obesity [START_REF] Catalano | Obesity and pregnancy: mechanisms of short term and long term adverse consequences for mother and child[END_REF] , but adjustment for BMI did not modify the risk of infertility treatment associated with pre-pregnancy thyroid diseases. Conversely, excluding women with other diseases reduced the risk in our study, which indicates that some of these diseases, such as autoimmune, hypothalamic or pituitary anomalies, contribute to the risk of infertility. Care for infertility includes screening for thyroid dysfunction and increases the probability of a diagnosis of thyroid disease before pregnancy. Thus, reverse causality is another potential explanation for our observed association between pre-pregnancy thyroid disease and infertility. In some studies, autoimmune thyroiditis with the presence of anti-thyroid peroxidase antibodies have been found associated with decreased fertility and miscarriage [START_REF] Leo | Autoimmune thyroid disease during pregnancy[END_REF] . Thyroid dysfunction by itself has also been found to affect the physiology of reproduction, miscarriage and ovulation disorders [START_REF] Krassas | Thyroid disease and female reproduction[END_REF] .

An association with premature rupture of membranes was described in several studies of women with subclinical hypothyroidism [START_REF] Maraka | Subclinical Hypothyroidism in Pregnancy: A Systematic Review and Meta-Analysis[END_REF][START_REF] Chen | Effects of Subclinical Hypothyroidism on Maternal and Perinatal Outcomes during Pregnancy: A Single-Center Cohort Study of a Chinese Population[END_REF] or positive antibodies [START_REF] Cleary-Goldman | Maternal Thyroid Hypofunction and Pregnancy Outcome[END_REF] . The pathophysiological mechanisms underlying theses associations are unclear but may include a direct effect of thyroid antibodies by an antibody-mediated cytotoxic effect [START_REF] Thangaratinam | Association between thyroid autoantibodies and miscarriage and preterm birth: meta-analysis of evidence[END_REF] , a subtle thyroid dysfunction and/or a more generalized autoimmune dysfunction [START_REF] Leo | Autoimmune thyroid disease during pregnancy[END_REF][START_REF] Thangaratinam | Association between thyroid autoantibodies and miscarriage and preterm birth: meta-analysis of evidence[END_REF] . However, in our study, the association between pre-pregnancy thyroid diseases and premature rupture of membranes remained after excluding women with other medical conditions including autoimmune diseases.

We found no association of pre-pregnancy thyroid diseases with gestational age of birth, induction of labour or caesarean delivery, in contrast to other studies comparing women with manifest thyroid dysfunction and other women [START_REF] Léger | Pregnancy outcomes and relationship to treatment adequacy in women treated early for congenital hypothyroidism: a longitudinal population-based study[END_REF]8,9,[START_REF] Cleary-Goldman | Maternal Thyroid Hypofunction and Pregnancy Outcome[END_REF][START_REF] Männistö | Thyroid Diseases and Adverse Pregnancy Outcomes in a Contemporary US Cohort[END_REF] . In our study, women with prepregnancy thyroid diseases should have been monitored for thyroid hormone concentrations and we expected more subtle abnormalities than for diseases discovered during pregnancy.

Contrary to previous studies [START_REF] Teng | Hypothyroidism in pregnancy[END_REF][START_REF] Hou | The impact of maternal hypothyroidism during pregnancy on neonatal outcomes: a systematic review and meta-analysis[END_REF] , we did not find any association of pre-pregnancy thyroid diseases with infant birthweight. Women with the more severe fetal outcomes may have been less likely to participate in the study. However, we did not expect such severe cases in relation with pre-pregnancy thyroid disease. Nevertheless, offspring of mothers with prepregnancy hypothyroidism had slightly smaller head circumference at birth than those of women without pre-pregnancy thyroid diseases after adjustment for maternal BMI (Table 5).

A few studies have published results on birth head circumference. Overt and subclinical hypothyroidism disease have been associated with abnormal fetal growth and a decrease in head circumference [START_REF] Blazer | Maternal hypothyroidism may affect fetal growth and neonatal thyroid function[END_REF][START_REF] Su | Maternal thyroid function in the first twenty weeks of pregnancy and subsequent fetal and infant development: a prospective population-based cohort study in China[END_REF] . However, other results reported null associations with head circumference [START_REF] Männistö | Perinatal outcome of children born to mothers with thyroid dysfunction or antibodies: a prospective population-based cohort study[END_REF] . The relation between maternal thyroid dysfunction and neuroimaging outcomes for offspring has been investigated. One study showed an inverted U-shaped association between free thyroxine maternal concentration and total gray-matter volume and cortex volume in children [START_REF] Korevaar | Association of maternal thyroid function during early pregnancy with offspring IQ and brain morphology in childhood: a population-based prospective cohort study[END_REF] . Small head circumference is known to increase the risk of subnormal neurodevelopment, [START_REF] Lundgren | Intellectual and psychological performance in males born small for gestational age with and without catch-up growth[END_REF] and long-term follow-up of the neurodevelopment of these children is warranted. Isolated hypothyroxinaemia (decreased free thyroxine and normal thyroid-stimulating hormone levels) is predominantly associated with adverse neurobehavioral development in children [START_REF] Korevaar | Thyroid disease in pregnancy: new insights in diagnosis and clinical management[END_REF] , risk of poor verbal and non-verbal cognitive development in children at 18 and 30 months [START_REF] Henrichs | Maternal Thyroid Function during Early Pregnancy and Cognitive Functioning in Early Childhood: The Generation R Study[END_REF] and delayed psychomotor development at 1 and 2 years [START_REF] Pop | Maternal hypothyroxinaemia during early pregnancy and subsequent child development: a 3-year follow-up study[END_REF] . Another recent study highlighted the importance of adequate maternal iodine status in the early stages of pregnancy for optimal development of verbal IQ [START_REF] Levie | Association of Maternal Iodine Status With Child IQ: A Meta-Analysis of Individual Participant Data[END_REF] .

Strengths and limitations

The strengths of our study are its nationwide scope and the availability of data from medical records. However, the medical history information still depends on the accuracy of the physician interview and the memory of the women, and its presence in the maternity medical record depends on the women's access to health care. A history of thyroid disease was indeed more frequently reported by women with increased level of education, and missing information in the medical history was more frequent for women in disadvantaged situations.

The lack of information on thyroid function and treatment is another limitation. There was no attempt to search for laboratory or imaging exams or related treatments that could confirm the diagnosis and provide information about thyroid function because the amount of data collected at inclusion in the ELFE cohort was already large. More specific studies on the topic with thyroid hormone measurements are warranted. Moreover, we did not have information on the time between diagnosis and pregnancy that may induce different severity of thyroid diseases. Some of the asymptomatic women may present undiagnosed antibody positivity without thyroid dysfunction, and some women declaring pre-pregnancy hyperthyroidism may have fully recovered from the disease. In our study, although our cohort was large, we could not separately evaluate the effects of prepregnancy hyperthyroidism or thyroiditis on pregnancy outcome because of the limited number of affected women. Another limitation was that we were unable to study some outcomes (hypertension, prematurity) because of too few cases in the exposed group. We did not have information on treatment of thyroid disease during pregnancy and whether fetal outcomes differ between women with adequate and non-adequate treated disease. However, our results represent the overall effect and indicate that the average situation in France may not be optimal. Finally, only live births (≥ 33 amenorrhea weeks) were included in the ELFE cohort. If pre-pregnancy thyroid diseases affect the probability of miscarriage or extreme prematurity, our results could be biased, probably by underestimating the effect of thyroid diseases.

Few epidemiologic studies have evaluated the risk of complications during pregnancy related specifically to thyroid diseases before conception. Our observations reflect the complexity of thyroid function and dysfunction on fertility and pregnancy outcome in women with pre-existing thyroid diseases. Particular care is required in the management of these women. Knowledge about the consequences of thyroid dysfunction should be optimized by educational strategies to improve medical care and compliance with treatment. Normal thyroid function during pre-conceptional phase and throughout pregnancy should be a key objective for the prevention of pregnancy complications and prevention of morbidity in fetuses and neonates. Moreover, in line with the developmental origins of health and disease theory, pre-pregnancy thyroid pathologies may be among the early conditions that could have long-term consequences on cognitive development [START_REF] Levie | Association of Maternal Iodine Status With Child IQ: A Meta-Analysis of Individual Participant Data[END_REF] . Optimal maternal thyroid function is necessary for offspring development, and further studies are needed to assess neurodevelopment outcomes of children of women with pre-pregnancy thyroid diseases and to understand the underlying mechanisms [START_REF] Haddow | Maternal Thyroxine and Fetal Brain Development: The Latest Chapter, a Look Back, and Considerations for the Future[END_REF][START_REF] Korevaar | Clinical associations of maternal thyroid function with fetal brain development:Epidemiological interpretation and overview of available evidence[END_REF] .

Conclusion

In our study, pre-pregnancy thyroid diseases were associated with risk of infertility treatment, gestational diabetes, and premature rupture of membranes. The association between history of hypothyroidism due to a disease or as a potential side effect of surgical or cancer treatment and moderate adverse effects on fetal head circumference growth needs replication with a larger number of participants.

Head circumference (cm)

-0.12 [-0.36,0.11] -0.17[-0.39,0.04] -0.23 [-0.44,-0.01] b § pre-pregnancy hypothyroidism due to a disease or as a potential side effect of treatment (Hashimoto's disease, hypothyroidism unspecified, no thyroid, thyroidectomy, thyroid cancer) ¹N: number of cases in pre-pregnancy hypothyroidism diseases group before adjustment Model 1: adjusted on maternal education level, maternal age, parity, smoking during pregnancy and infant sex and gestational age for head circumference, birth weight and birth length. Model 2: model 1 and additional adjustment for BMI as a continuous variable * Maximal N for outcome in unadjusted model (N=14,197) and minimal N in adjusted model2 (N=12,646). When the sample was restricted to subjects included in the 3 models, results were similar as above for all 3 models. **Forceps, spatulas, vacuum

Figure 1 :

 1 Figure 1: Flow of the women in the study. Selection of women with pre-pregnancy thyroid diseases.

Table 1 :

 1 Characteristics of ELFE women with and without pre-pregnancy thyroid diseases.

			No thyroid diseases	Pre-pregnancy
	Demographic data	N	(N=16,122)	thyroid diseases	P-value
				(N=273)
	Maternal age (years)	16,387	30.6 ± 5.1	32.0 ± 5.0	<.001
	Maternity unit level	16,395
	Level 2 and 3 centers		76.9 (12393)	80.6 (220)	0.15
	Place of birth (France)	16,297	87.2 (13970)	89.3 (242)	0.30
	Maternal education	16,392
	Tertiary education		59.9 (9648)	66.7 (182)	0.023
	Living with a partner	16,321	94.5 (15169)	96.7 (263)	0.12
	(yes)
	Social health insurance	16,326
	coverage
	Related to precarious		8.5 (1362)	7.0 (19)	0.39
	situations
	Employment status	16,027			0.14
	Employed, student		80.2 (12635)	83.4 (226)
	Housewife, parental leave		11.7 (1847)	11.8 (32)
	Other, unemployment		8.1 (1274)	4.8 (13)
	Smoking during	16,300	20.5 (3286)	15.1 (41)	0.028
	pregnancy
	Pre-pregnancy BMI*	16,201			<.001
	Underweight		7.9 (1263)	5.5 (15)
	Normal weight		65.0 (10355)	54.6 (148)
	Overweight		17.2 (2746)	20.3 (55)
	Obese		9.8 (1566)	19.6 (53)
	Primiparous	16,317	45.8 (7350)	43.4 (118)	0.43
	Number of ultrasounds	15,926			0.01
	≤ 5		76.7 (12018)	70.2(186)
	>5		23.3 (3643)	29.8 (79)
	Prenatal consultations	16,150			0.41
	< 7		10.4 (1645)	9.7 (26)
	7-9		68.4 (10859)	65.7 (176)
	> 9		21.3 (3378)	24.6 (66)
	Data are % (n) or mean ± SD.
	*Body mass index (BMI) was classified according to the World Health Organisation as
	underweight, <18.5 kg/m²; normal weight, 18.5 to <25 kg/m²; overweight, 25.0 to <30 kg/m²;
	obesity, ≥30 kg/m².

Table 2 :

 2 Multivariable analysis of pregnancy and birth outcomes for women with and without pre-pregnancy thyroid diseases before and after adjustment for confounding factors and prepregnancy BMI.

	Outcomes*	Unadjusted model	Adjusted model 1	Adjusted model 2
	Categorical variables	OR [95% CI]	OR [95% CI]	OR [95% CI]
	Infertility treatment	1.74 [1.19,2.55]ª	1.57 [1.07,2.31] b	1.55 [1.05,2.28] b
	Gestational diabetes	1.68 [1.15,2.44]ª	1.58 [1.08,2.30] b	1.27 [0.86,1.88]
	Hospitalization during pregnancy	1.28 [0.94,1.74]	1.34 [0.99,1.83]	1.31 [0.96,1.79]
	Induction of labor
	Spontaneous labor	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)
	Induction	1.10 [0.81,1.49]	1.09 [0.80,1.48]	1.01 [0.74,1.37]
	Prelabor caesarean section	1.34 [0.90,1.97]	1.23 [0.83,1.82]	1.10 [0.74,1.64]
	Mode of birth
	Vaginal delivery	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)
	Assisted delivery**	1.05 [0.73,1.51]	1.06 [0.73,1.56]	1.07 [0.73,1.57]
	Caesarean section	1.22 [0.90,1.65]	1.14 [0.84,1.56]	1.02 [0.74,1.40]
	Premature rupture of membranes	1.28 [0.89,1.85]	1.27 [0.87,1.85]	1.27 [0.87,1.85]
	Birth weight categories***
	AGA	1.00 (Reference)	1.00 (Reference)	NA
	SGA	0.82 [0.52,1.29]	0.86 [0.54,1.35]
	LGA	1.03 [0.69,1.54]	1.03 [0.69,1.53]
	Quantitative variables	β [95% CI]	β [95% CI]	β [95% CI]
	Gestational age (amenorrhea weeks)	0.03 [-0.14,0.20]	0.02 [-0.15,0.19]	0.01 [-0.16,0.18]
	Birth weight (g)	41.25 [-15.95,98.45]	29.06 [-18.86,76.98] 8.29 [-39.25,55.82]
	Birth length (cm)	0.27 [0.002,0.54] b	0.19 [-0.04,0.42]	0.15 [-0.08,0.38]
	Head circumference (cm)	-0.005 [-0.18,0.17]	-0.04 [-0.20,0.13]	-0.08 [-0.24,0.08]
	Data are odds ratios (ORs) or beta (β) values and 95% confidence intervals (CIs).
	Model 1: adjusted for maternal education level, maternal age, parity, smoking during
	pregnancy and infant sex and gestational age for head circumference, birth weight and birth
	length. Model 2: model 1 and adjustment for BMI as a continuous variable.
	* Maximal N for outcome in unadjusted model (N=16,302) and minimal N in adjusted model
	2 (N=14,507). When the sample was restricted to women included in the 3 models, results
	were similar as above for all 3 models.
	** Assisted delivery: forceps, spatulas, vacuum
	*** A customized standard to assess fetal growth: SGA, small for gestational age (< 10 th
	percentile); AGA, appropriate for gestational age (10 th to 90 th percentile); LGA, large for
	gestational age (>90 th percentile). (see methods)
	ª p<.01 b p<.05. NA: not applicable (model already adjusted for BMI)

Table 3 :

 3 Multivariable analysis of pregnancy and birth outcomes for women with prepregnancy thyroid diseases and without pre-pregnancy thyroid diseases after excluding women with a history of other diseases from the two groups. ** Customized standard to assess fetal growth: SGA, small for gestational age (< 10 th percentile); AGA, appropriate for gestational age (10 th to 90 th percentile); LGA, large for gestational age (>90 th percentile). (see methods). ªp<.05. NA: not applicable because the model is already adjusted for BMI

	Outcomes*	No. cases¹	Unadjusted model	Adjusted model 1	Adjusted model 2
		(%)
	Categorical		OR [95% CI]	OR [95% CI]	OR [95% CI]
	variables
	Infertility treatment 19 (9.3)	1.39 [0.86,2.24]	1.28 [0.79,2.07]	1.26 [0.77,2.04]
	Gestational diabetes 16 (7.8)	1.60 [0.96,2.68]	1.56 [0.93,2.62]	1.35 [0.80,2.29]
	Hospitalization	37 (17.6)	1.31 [0.91,1.87]	1.39 [0.97,1.99]	1.37 [0.96,1.97]
	during pregnancy
	Induction of labor
	Spontaneous labor		1.00 (Reference)	1.00 (Reference)	1.00 (Reference)
	Induction	43 (20.5)	1.16 [0.82,1.63]	1.17 [0.83,1.65]	1.10 [0.78,1.56]
	Prelabor caesarean	18 (8.6)	1.15 [0.70,1.88]	1.08 [0.66,1.78]	1.00 [0.61,1.65]
	section
	Mode of birth
	Vaginal delivery		1.00 (Reference)	1.00 (Reference)	1.00 (Reference)
	Assisted delivery**	30 (14.3)	1.10 [0.74,1.64]	1.14 [0.75,1.73]	1.14 [0.75,1.74]
	Caesarean section	36 (17.1)	1.07 [0.74,1.55]	1.03 [0.70,1.49]	0.95 [0.65,1.39]
	Premature rupture	30 (14.5)	1.52 [1.03,2.25]ª	1.51 [1.01,2.26]ª	1.51 [1.01,2.25]ª
	of membranes
	Birth weight
	categories***
	AGA		1.00 (Reference)	1.00 (Reference)	NA
	SGA	15 (7.1)	0.78 [0.46,1.33]	0.82 [0.48,1.40]
	LGA	21 (10.0)	0.99 [0.63,1.58]	0.99 [0.63,1.56]
	Quantitative		β [95% CI]	β [95% CI]	β [95% CI]
	variables
	Gestational age		0.07 [-0.11,0.26]	0.07 [-0.12,0.26]	0.05 [-0.14,0.24]
	(amenorrhea weeks)
	Birth weight (g)		45.17 [-19.19,109.5] 21.88[-31.95,75.71] 7.02 [-46.33,60.37]
	Birth length (cm)		0.39 [0.09,0.70]ª	0.28 [0.02,0.54]ª	0.23 [,0.03,0.49]
	Head circumference		-0.02 [-0.22,0.19]	-0.06 [-0.25,0.12]	-0.10 [-0.28,0.08]
	(cm)
	¹N: number of cases in pre-pregnancy thyroid group after excluding other pathologies before
	adjustment.
	Model 1: adjusted on maternal education level, maternal age, parity, smoking during pregnancy
	and infant sex and gestational age for head circumference, birth weight and birth length.
	Model 2: model 1 and additional adjustment for BMI as a continuous variable.
	* Maximal N for outcome in unadjusted model (N=14,258) and minimal N in adjusted model 2
	(N=12,701). When the sample was restricted to women included in the 3 models, results were
	similar as above for all 3 models.

** Assisted delivery: forceps, spatulas, vacuum *

Table 4 :

 4 Comparison of outcomes for women without pre-pregnancy thyroid diseases and with pre-pregnancy hypothyroidism § (hyperthyroidism or unspecified thyroid diseases excluded) pregnancy hypothyroidism due to a disease or as a potential side effect of treatment (Hashimoto's disease, hypothyroidism unspecified, no thyroid, thyroidectomy, thyroid cancer) Model 2: adjusted on maternal education level, maternal age, parity, smoking during pregnancy, BMI and infant sex and gestational age for head circumference, birth weight and birth length. * Assisted delivery: forceps, spatulas, vacuum ** A customized standard to assess fetal growth: SGA, small for gestational age (< 10 th percentile); AGA, appropriate for gestational age (10 th to 90 th percentile); LGA, large for gestational age (>90 th percentile) (see methods)

	Fertility and	N	No pre-	Pre-pregnancy	P-	Adjusted model 2
	pregnancy		pregnancy	hypothyroidism	value
	complications		thyroid diseases	(N=196)
			(N=16,122)
	Infertility treatment	16,155 7.0 (1124)	12.0 (23)	0.007 1.55 [0.99,2.43]
	Gestational diabetes	15,752 7.5 (1165)	12.4 (24)	0.010 1.24 [0.78,1.95]
	Gestational age at	16,225 39.6 ± 1.4	39.6 ± 1.3	0.87	-0.04 [-0.24,0.16]
	birth (amenorrhea
	weeks)
	Hospitalization during	16,225 15.3 (2455)	16.3 (32)	0.70	1.10 [0.75,1.61]
	pregnancy
	Induction of labor	16,201			0.31
	Spontaneous labor		72.1 (11535)	69.4 (136)		1.00 (Reference)
	Induction		19.3 (3084)	18.9 (37)		0.90 [0.62,1.30]
	Prelabor caesarean		8.7 (1386)	11.7 (23)		1.11 [0.70,1.75]
	section
	Mode of delivery	16,078			0.21
	Vaginal delivery		69.7 (11073)	64.6 (126)		1.00 (Reference)
	Assisted delivery*		12.9 (2041)	13.3 (26)		1.11 [0.71,1.72]
	Caesarean section		17.4 (2769)	22.1 (43)		1.12 [0.78,1.60]
	Premature rupture of	15,991 9.8 (1553)	14.5 (28)	0.030 1.49 [0.99,2.27]
	membranes
	Birth weight (g)	16,121 3334.8 ± 478.4	3366.8 ± 467.8	0.35	-0.20 [-55.9,55.5]
	Birth length (cm)	14,897 49.6 ± 2.1	49.8 ± 2.1	0.34	0.02 [-0.25,0.29]
	Head circumference	14,806 34.4 ± 1.4	34.3 ± 1.4	0.34	-0.20 [-0.39,-0.01]
	(cm)
	Birth weight	15,851			0.86
	categories**
	SGA		9.3 (1459)	8.2 (16)		NA
	AGA		80.8 (12646)	81.5 (159)
	LGA		9.9 (1551)	10.3 (20)
	§ pre-

Table 5 :

 5 Multivariable analysis of pregnancy and birth outcomes for women with prepregnancy hypothyroidism § but no other diseases compared to women without pre-pregnancy thyroid nor other diseases .1) 1.86 [1.20,2.86]ª 1.82 [1.16,2.84]ª 1.79 [1.14,2.80] b

	Outcomes*	No.	No adjustment	Adjusted model	Adjusted model 2
		cases¹		1
		(%)
	Qualitative variables		OR [95% CI]	OR [95% CI]	OR [95% CI]
	Infertility treatment 16 (11.0) 1.68 [1.00,2.84]	1.52 [0.89,2.58]	1.49 [0.87,2.53]
	Gestational diabetes 11 (7.5) 1.54 [0.83,2.86]	1.51 [0.81,2.81]	1.24 [0.66,2.34]
	Hospitalization	22 (14.8) 1.06 [0.67,1.67]	1.13 [0.72,1.79]	1.11 [0.70,1.75]
	during pregnancy
	Induction of labor
	Spontaneous labor		1.00 (Reference)	1.00 (Reference) 1.00 (Reference)
	Induction	28 (18.8) 1.04 [0.69,1.58]	1.05 [0.69,1.60]	0.96 [0.63,1.47]
	Prelabor caesarean	13 (8.7) 1.14 [0.64,2.04]	1.09 [0.61,1.95]	0.96 [0.53,1.73]
	section
	Mode of birth
	Vaginal delivery		1.00 (Reference)	1.00 (Reference) 1.00 (Reference)
	Assisted delivery**	24 (16.1) 1.28 [0.82,2.01]	1.33 [0.83,2.14]	1.31 [0.82,2.11]
	Caesarean section	26 (17.4) 1.13 [0.73,1.74]	1.11 [0.72,1.73]	0.99 [0.64,1.55]
	Premature rupture of membranes 25 (17Birth weight
	categories***
	AGA		1.00 (Reference)	1.00 (Reference) NA
	SGA	11 (7.4) 0.8 [0.43,1.49]	0.83 [0.45,1.55]
	LGA	14 (9.4) 0.93 [0.53,1.62]	0.93 [0.53,1.62]

Quantitative variables β [95% CI] β [95% CI] β [95% CI] Gestational age (amenorrhea weeks)

		0.03 [-0.19,0.25]	0.03 [-0.20,0.25] 0.004 [-0.22,0.23]
	Birth weight (g)	29.54	14.42[-49.37,78.22] -8.53 [-71.62,54.57]
		[-46.89,105.97]
	Birth length (cm)	0.31 [-0.04,0.67]	0.23 [-0.08,0.53] 0.16 [-0.14,0.47]

Acknowledgments

The ELFE survey is a joint project between the French Institute for Demographic Studies (INED) and the National Institute of Health and Medical Research (INSERM), in partnership with the French blood transfusion service (Etablissement français du sang, EFS), Santé publique France, the National Institute for Statistics and Economic Studies (INSEE), the Direction générale de la santé (DGS, part of the Ministry of Health and Social Affairs), the Direction générale de la prévention des risques (DGPR, Ministry for the Environment), the Direction de la recherche, des études, de l'évaluation et des statistiques (DREES, Ministry of Health and Social Affairs), the Département des études, de la prospective et des statistiques (DEPS, Ministry of Culture), and the Caisse nationale des allocations familiales (CNAF), with the support of the Ministry of Higher Education and Research and the Institut national de la jeunesse et de l'éducation populaire (INJEP).

We thank the members of the ELFE Ined-Inserm-EFS joint unit and above all the families involved in the ELFE cohort without whom this study would not have been possible.

Financial support

Marion Lecorguillé received a scholarship from the Open Health Institute for this work.

The ELFE study receives a government grant managed by the National Research Agency under the "Investissements d'avenir" program (ANR 11 EQPX 0038).

Conflicts of Interest

The authors have nothing to disclose

Ethical Standards

The authors assert that all procedures contributing to this work have been approved by the ethics committee of Créteil (CPP), the national committee on information concerning health research (CCTIRS) and the Data Protection Authority (Commission Nationale de l'Informatique et des Libertés, CNIL). Supplementary Table S1: Outcomes for women with and without pre-pregnancy thyroid diseases.

Fertility and pregnancy complications


Tsegay Tesfay Mezgebe
email: tsegay-tespay.mezgebe@univ-lorraine.fr

Hind Bril

El Haouzi
email: hind.el-haouzi@univ-lorraine.fr

Morocco "New advances and challenges for sustainable and smart industries" RECURSIVE HYBRID CONTROL ARCHITECTURE TO DEAL WITH REACTIVITY IN THE CONTEXT OF INDUSTRY 4.0

Keywords: Hybrid, Cyber physical System, Control architecture, Blue-collar, White-collar

As a response to unexpected events sourced either from external environment or internal environment, manufacturing companies have to decide and adjust their system autonomously. Indeed, this has brought significant reactivity, adaptability, and stability to disturbances and production changes. However, an ever myopic behavior of locally autonomous entities has led such decision-making process to various drawbacks such as difficulty to get immediate information for performing global decision. Consequently, the interest of hybrid control system has evolved using holonic or multi-agent system paradigms so as to provide an overall and efficient production performance. This paper is aimed to provide state-of-the-art on holonic reference architectures and multi-agent system paradigms and to propose recursive hybrid control architecture by stipulating two decision-making layers namely white-collar layer and blue-collar layer. The decision-making is mainly managed by locally autonomous decision entities incorporated in the blue-collar layer with interfacing and updating role of white-collar entities in the white-collar layer. This layer based control architecture is proposed to fit with the emergence of Cyber Physical System (CPS) and the need of dynamic switching in hybrid control architecture.

INTRODUCTION

In today's competitive world, globally volatile market is one of the biggest troubles for manufacturing industries. Factors such as requirement reactive and adaptive system, shorter life cycle, customized products etc. made this trouble vital. The framework of Industry 4.0 is also urging them to deal with technological advancement in information and communication for effective management of knowledge required for the need to regularly update and reconfigure their system. While struggling to satisfy these factors, the manufacturing industries may impose with new challenges and varieties which in turn these challenges are forcing them to look for new solutions. At this time, it is critical to search for control systems that provide efficient planning of tasks to resources and quickly adapt and react to unexpected events occurred. Accordingly, different control systems in different views, behavioral views [START_REF] El Haouzi | Contribution à La Conception et à l'évaluation Des Architectures de Pilotage Des Systèmes de Production Adaptables: Vers Une MOSIM'20 -November 12-14, 2020 -Agadir -Morocco Scheduling in Disturbed Industrial Context[END_REF] and structural views [START_REF] Dilts | The Evolution of Control Architectures for Automated Manufacturing Systems[END_REF][START_REF] Rey | The Control of Myopic Behavior in Semi-Heterarchical Production Systems: A Holonic Framework[END_REF] for instance, have been introduced and implemented so far.

Structurally, the control system can be catagorized as central, hierarchical, heterarchical, and hybrid. Each of them has shown merits with capability to react and adapt to unexpected event(s) though the impact varies. Through time, the interest on hybrid control system is increasing as it couples the high and predictable performance promised by centralized control approach with the robustness of distributed reactive control against disturbances based on the reactivity of Cyber Physical System (Jimenez et al., 2017;Valckenaers and Handrik Van Brussel, 2016). It is studied and proposed by different researchers since the last decade. [START_REF] Zambrano | An Approach for Temporal Myopia Reduction in Heterarchical Control Architectures[END_REF] and [START_REF] Rey | Reducing Myopic Behavior in FMS Control: A Semi-Heterarchical Simulation-Optimization Approach[END_REF] for example have presented the need of coupling hierarchical and heterarchical control architectures to manage myopia. This paper is aimed to provide a state-of-the-art on holonic and multi-agent system (MAS) reference architectures and to propose a Recursive hybriD Control Architecture (REDCA). The novelty is to present the use of two interlinked decision-making layers namely whitecollar layer and blue-collar layer in order to minimize impact of unexpected events(s) occurred and to perform a dynamic role switching manufacturing system's autonomous entities. The rest of the paper is organized as follow: Section 2 explicitly surveys related materials. Section 3 follows with proposing the recursive hybrid control architecture and its executability explanations. Finally, a concluding remark and future perspectives are presented in section 4.

SURVEY ON RELATED WORKS

The autonomously reactive and centrally predictive behaviors of decision entities in distributed reactive control mode and central predictive control approach MOSIM '20 -November 12-14, 2020 -Agadir -Morocco respectively are encouraging for control systems change. As a response to this changing call, coupling these two control approaches is believed to minimize their over shadowing drawbacks. According to [START_REF] Cardin | Coupling Predictive Scheduling and Reactive Control in Manufacturing Hybrid Control Architectures: State of the Art and Future Challenges[END_REF](Jimenez et al., 2017), for instance, minimizing these drawbacks is highly dependent on the way how these two control approaches could be combined. Holonic manufacturing system and multiagent system paradigms have been handed-over the coupling procedure in order to stabilize manufacturing execution system at glance and performance of whole system then after. Even though both paradigms have a lot in common, the multi-agent system provides multidomain functions through its cooperative and competitive agents while the holonic manufacturing system is used mainly in manufacturing domain through its inherently cooperative holons. This indicates that the search for an effective implementation of holonic reference architectures and multi-agent system paradigms remains a subject of great attractiveness in the community of researchers working on agile-adaptable system.

Fundamentals on Holonic Manufacturing System (HMS) reference architectures

HMS provides reactivity to disturbances, flexibility to changes, and efficient usage of resources. It has got much attention due to its fitness to create synchronized control system during unexpected disturbances (Jarvis et al., 2008) and its fitness to satisfy the requirements of industry 4.0. Three main holonic control architectures have been studied and elaborated by different researchers in the previous decades: Product-Resource-Order-Staff (PROSA) reference Architecture, ADAptive holonic COntrol aRchitecture (ADACOR), and Holonic component-based architecture (HCBA).

PROSA is considered as reference architecture for other HMS architectures. It identifies types of holons necessary for any manufacturing system, its responsibilities, and interaction structure in which they cooperate. According to [START_REF] Van Brussel | Reference Architecture for Holonic Manufacturing Systems: PROSA[END_REF] and (Valckenaers and Hendrik Van Brussel, 2016), PROSA is built from three holons: product, resource, and order holons. Staff holon is added to assist these basic holons with expert knowledge. Product holon stores process and product knowledge needed to insure the correct execution of the product with sufficient quality.

Resource holon is an abstraction of the production means, such as machines, conveyors, pallets, raw materials, tool holders, material storage, personnel, floor space etc. [START_REF] Valckenaers | Schedule Execution in Autonomic Manufacturing Execution Systems[END_REF]. Besides, a resource holon offers production capacity and functionality to the other holons. An order Holon is responsible for doing the work assigned on time and in the right way (Giret and Botti, 2006). It interprets a task within a system and hence prepares a work assigned optimally. Meanwhile, it manages the physical product being processed, the product-state model, and all logistic information related to a job [START_REF] Valckenaers | Schedule Execution in Autonomic Manufacturing Execution Systems[END_REF]. ADACOR, by (Leitão and Restivo, 2006), uses a pheromone-like propagation mechanism to deal with myopic phenomenon by introducing a supervisory holon. It defines a holonic approach for dynamic adaptation and agility in the face of frequent perturbations within flexible manufacturing systems. It is based on a group of autonomous and cooperative holons in order to represent factory components. These holons have an autonomy factor that adapts the holon's autonomy according to its environment [START_REF] Rey | The Control of Myopic Behavior in Semi-Heterarchical Production Systems: A Holonic Framework[END_REF]. A low-autonomy factor implies following supervisory advice and a highautonomy factor allows self-organization and local problem-solving in case of perturbations. Once perturbation is occurred, the locally autonomous holons switch to heterarchical mode and manage their problem (perturbation) without the involvement of supervisory holon. ADACOR groups the holons of a manufacturing system into product holons, task holons, operational holons, and supervisor holons. The supervisory holon introduced in this architecture has specifity over that of the staff holon in PROSA. For instance, it has capability to form group of holarchy and it is in charge of triggering schedules periodically. Each product is represented by one product holon that has all of the product-related knowledge and is responsible for the process planning. To this end, product holon stores information about the product structure and the process planning to produce it. Every manufacturing order is represented by a task holon, which is responsible for controlling and supervising a production execution. The operational holons represent the physical resources of the factory, such as human workers, robots and machines. They manage the behavior of these resources based on their goals, constraints and capabilities, and try to optimize their agenda.

HCBA, much like PROSA, serves as a reference model for numerous implementations and applications of holonic systems. [START_REF] Chirn | A Holonic Component-Based Approach to Reconfigurable Manufacturing Control Architecture[END_REF] and (Farid, 2004) have testimonied that HCBA is derived from the concepts of component-based development (CBD) that provides a guideline for HCBA. As it is presented in [START_REF] Vicent | ANEMONA: A Multi-Agent Methodology for Holonic Manufacturing Systems[END_REF] and [START_REF] Chirn | A Holonic Component-Based Approach to Reconfigurable Manufacturing Control Architecture[END_REF], HCBA defines two major holons: product and resource holons. The resource holon is an embedded system component that can execute operations such as production, assembly, transportation, and checking. The product holon may contain a physical part such as raw materials, product parts and a controlling part may represent the path controlling a production line, process control, decision-making, and product information.

To sum-up, holonic reference architectures have been studied by the holonic community and clearly showed that they are capable to minimize impact of perturbations on a holonic system. However, the setup for holonic structure requires high effort and hence development of MOSIM'20 -November 12-14, 2020 -Agadir -Morocco holonic control applications is sophisticated. This is because modeling of complex dynamic systems is costly and incurs significant processing overhead due to the demand for powerful modeling platforms [START_REF] Dorri | Multi-Agent Systems: A Survey[END_REF]. Hence, the flexibility, autonomy and scalability afforded by agents made agent-based modeling a lowcost and low resource solution for modeling complex systems [START_REF] Dorri | Multi-Agent Systems: A Survey[END_REF]. Subsequently, the developments in multi-agent communication have evolved significant interest and research activity in the area of cooperative MAS.

Fundamentals on Multi-agent System (MAS) paradigm

MAS consists of society of agents that could potentially cooperate with each other and their outer environment in order to perceive, reason, and converge to a suitable solution [START_REF] Vicent | ANEMONA: A Multi-Agent Methodology for Holonic Manufacturing Systems[END_REF]Isern et al., 2011;[START_REF] Wooldridge | An Introduction to Multiagent Systems[END_REF]. In the intelligent manufacturing system domain, it is well accepted that MAS based control system is a good way to deal with disturbances and production changes. It provides software packages for the implementation of HMS through cooperative algorithm such that each agent's beliefs are brought to negotiation until all neighboring agents reaches at their final offer (Farid, 2004). Agents in such control system are characterized by their autonomy in which decisions at the time of necessity are made by these agents. This helps to create robust system with respect to timeevolving communication. In (Holvoet and Valckenaers, 2006;Isern et al., 2011;Jennings et al., 2001;Leitão, 2009;Maturana Shen and Norrie, 1999;[START_REF] Ryu | Agent-Based Fractal Architecture and Modelling for Developing Distributed Manufacturing Systems[END_REF][START_REF] Wong | Dynamic Shop Floor Scheduling in Multi-Agent Manufacturing Systems[END_REF][START_REF] Xiong | A New Immune Multi-Agent System for the Flexible Job Shop Scheduling Problem[END_REF], it is clearly presented that the applicability of MAS is characterized by their large scale in terms of number of agents, dynamic nature, and complex functionality. In [START_REF] Ryu | Agent-Based Fractal Architecture and Modelling for Developing Distributed Manufacturing Systems[END_REF], for instance, the concept of fractals as autonomous cooperating agents to discuss fractal manufacturing systems is introduced. Maturana et al.

(1999) in turn introduced adaptive multi-agent manufacturing system architecture with capability to facilitate multi-agent coordination by minimizing communication and processing overheads.

Synthesis and discussion

Plenty of researchers have given owed concentration to holonic and multi-agent manufacturing reference architectures and paradigms to provide efficient production performance and ensure reactivity face to unpredicted events. However, several issues left unsolved such as autonomy of decisional entities and legal aspects to represent sociability challenges, development of interaction mechanisms and protocols, design of decision models and control architectures, issue of introduction and implementation of technological solutions to explain some of them. To emphasize these challenges, [START_REF] Cardin | Coupling Predictive Scheduling and Reactive Control in Manufacturing Hybrid Control Architectures: State of the Art and Future Challenges[END_REF] in their state of the artwork, for example have pointed out three This indicates that further effort and exploration on control architectures with a target to provide all components of manufacturing system decide immediately while they face unexpected events is still a demanding research area. Besides, even though, both the holonic and multi-agent systems have a lot in common to model and implement these control architectures, the applicability domain of MAS is very wider than that of the holonic paradigm. For example, many researchers have given due attention to the applicability of sociability-based multi-agent systems for controlling communication problems in the field of networking sciences, sociology, anthropology, philosophy, economics etc. However, its applicability for manufacturing control problems was highly comprehended with challenges such as difficulty to guarantee minimal level of information and operational performance, difficulty of cooperation, lack of commercial platforms (Jimenez et al., 2017;[START_REF] Rey | Reducing Myopic Behavior in FMS Control: A Semi-Heterarchical Simulation-Optimization Approach[END_REF] etc. Meanwhile, several challenges within the manufacturing systems such as design of decision model, design of interaction protocols, integration of technological solutions, dynamic switching role of entities remains yet unsolved. In the framework of solving these challenges in general and the last point in particular, agent-based recursive hybrid control architecture is proposed and explained in this paper; section 3.

PROPOSITION: A RECURSIVE HYBRID CONTROL ARCHITECTURE (REDCA)

In This indicates that the model was not able to clearly show a layer-based contribution of each aggregation level. Moreover, it is a product agent who plays all the roles in optimizing the system while the other agents (for example machine agents) have contributing role. Consequently, REDCA is proposed to remedy these limitations by developing a layer-based control and execution system; Figure 1. It follows similar control functions as Viable System Model with self-adaptability in perturbed environment and indefinite repeatability (called recursion in this paper) in order to satisfy control requirements at very local and operational decision levels. [START_REF] Wroblewski | What Is a Blue-Collar Worker and a White-Collar Worker[END_REF]. Furthermore, the work in whitecollar is unstructured and non-routine whereas the work in blue-collar (also named as working class) is manual and/or operational with dirty hands.

The decision entities

All the execution components within a manufacturing system are installed to perform the required actions proposed by decisional entities (Jimenez et al., 2017). In REDCA, two groups of decision entities namely bluecollar decisional entities (BE) and white-collar decisional entities (WE) are introduced to perform cooperative participations for their common goal and/or objective. In order to make the required decision, all the decision entities consider different parameters, variables, communication protocols (such as broadcasting) etc.

White-collar entities (WE)

They are engaged to prepare production and resource plan through Human-machine interfacing, Enterprise resource planning etc. Once demand management and resource plan are developed at ERP level, a manufacturing order (MO), through MPS, is dispatched using these entities to resources for executing the required operations. These entities are also aimed to declare external changes such as rush order such that if this external change is requested after an onlineexecution, the locally impacted entities shift their behavior to this white-collar entity in order to declare this change. This proves that white-collar entities decided both in offline and online sub-systems. On the other hand, the layer-based organization of REDCA differs from that of Pollux on the role of these whitecollar entities. The decision entities in coordinating layer of Pollux, which are parallel to the white-collar entities in REDCA, are responsible for global production optimization by hosting a predictive decision-making technique to guide the achievements of goals while the white-collar entities in REDCA are used for updating and interfacing role only. That is, once perturbation is occurred, the global decision entity in Pollux has equal decision level with the local entities where as in REDCA, the white-collar entities becomes out of the decision-loop after the perturbation is occurred even though there is dynamic role assignment of decision entities within the blue-collar layer.

Blue-collar entities (BE)

These entities are dedicated to perform the required execution ordered by white-collar entities and to rule a decision-making process at the time of local perturbation occurrence. Each blue-collar entity has its own recursive control kernel and this recursiveness continues until the last operation of a product is completed in a given time horizon [START_REF] Bae | Recursion. In JavaScript Data Structures and Algorithms[END_REF]Herrera et al., 2011). After perturbation occurred, the decision-making process MOSIM'20 -November 12-14, 2020 -Agadir -Morocco begins by sensing the perturbed area through sensors (RFID technology for example) and it uses knowledge, information, and data of the affected decision entities. Then after, they cooperate and diagnose the unexpected event occurred, either through changing their behavior to white-collar entity (the first recursion within the distributed reactive control mode) or acting as bluecollar entities. This behavioral change of locally impacted decisional entities in to white-collar entity makes REDCA dynamic. This continues until the unexpected event is no longer a limiting factor. Likewise, the affected blue-collar entities do not look to be helped either from entities in the white-collar layer or blue-collar entities which are not part of the unexpected event. This confirms the non-universal decision-making process at the time of perturbation. Product entities and resource entities constitute this blue-collar entity with strong horizontal cooperation between them.

Product entities, constructed with different product types for different manufacturing orders, are the leading constituent elements within the entire decision loop shown in Figure 1. The aggregated decision levels in Figure 1(B) are instantiated using knowledge, information, and data provided by these entities. Depending on the required communication with, they organize a production process for their different operations by considering their current state and broadcasting their intention to neighbor product entities or available resource entities. They have informational part to assure they are intelligent enough; the first cyberphysical system. This informational part, which has memory on which data and operational requirements are stored, is in charge of transmission of manufacturing orders to resources, reasoning capabilities, and validation of key performance indicators. Moreover, it is a required function to create and manage other neighbor entities. It also accepts/refuses a configuration change using its environemental knowledge and asks/accepts/refuses to swap its configuration with neighbor product entities.

Resource entities, configured with hardware parts (machines, conveyer, routers, sensors, production controllers etc.) and control parts in order to provide recursive services for product entities. The hardware part executes operations requested by product entities. The control part monitors the hardware operations by hosting attributes such as adaptive capacity and manages tasks assigned to its hardware part. It is shown in the physical transformation-loop of Figure 1 that the sensors and actuators follow commands received from product entities (at this instance, the behavior of the product entity is acting as white-collar entity) to perform the required process. Once the white-collar entities pass a central schedule to these entities for executing, they return inform their availability to execute the required operation. This confirms their role to coordinate the entire system.

Structural organization of the control architecture

The structural arrangement of REDCA is organized into two layers namely white-collar and blue-collar layers. White-collar layer is an offline sub-system to coordinate a system towards global performance by preparing production and resource plan through mixed integer programing for example. Once demand management and resource planning are developed at ERP level, a manufacturing order (MO) is dispatched to a shop floor by white-collar entities (WE) in this layer at periodical time. The manufacturing orders in Figure 1(B) have set of attributes such as order number, due date, quantity etc. and each manufacturing order has its own product classifications defined by its product's current state. Meanwhile, this layer informationally interfaces with the blue-collar layer while the blue-collar entities in the blue-collar layer generate their executed output; see the production performance declaration in Figure 1(B). Blue-collar layer is an online sub-system for operational execution of the manufacturing orders dispatched by white-collar layer and for controlling the available executing resources. It is represented by blue-collar entities (BE) responsible for production performance declaration, traceability information, and agile reactivity to unexpected events occurred in addition to the execution control. Decision entities in this layer have dynamic behavior to assign a role among each other (for example blue-collar entities may change their behavior in to white-collar entities within this layer until the required perturbation is addressed) and this continues until the last operation of a product is completed in a given time horizon. However, after the perturbed subsystem is adjusted, the entities in this layer generate the improved system to the white-collar layer for their informational purpose only.

CONCLUSION AND FUTURE REMARKS

This paper has presented state-of-the-art on holonic reference architectures and MAS paradigms and their role in reducing the impact of unexpected events (or perturbations in this paper) on manufacturing systems. It then proposed recursive hybrid control architecture (REDCA) by stipulating two decision-making layers namely white-collar layer and blue-collar layer. The decision-making in the proposed architecture was mainly managed by locally autonomous decision entities incorporated in the blue-collar layer with interfacing and updating role of white-collar entities in the white-collar layer. REDCA was designed to satisfy the requirements (over other control architectures such as Pollux) of scalability, dynamic role assignment of decision entities, better closeness to ERP, and better level of acceptability by workers of industrial enterprises. Two algorithms, through the work of (Mezgebe et al., 2019a), Mezgebe et al., 2019b), have developed and implemented before this work by considering the role of the blue-collar entities but with capability of one of these blue-collar entities to MOSIM'20 -November 12-14, 2020 -Agadir -Morocco dynamically act as white-collar entity at the time of unexpected event(s). This dynamism is one of the novelty and applicability roles of these entities.

Likewise, these two works confirms the applicability of REDCA in any industrial systems.

However, REDCA is closely positioned to distributed reactive control system. This indicates that even though the white-collar entities are involved in the decisionmaking process, the basic decisions are made at bluecollar level. This clearly presents the limitation of the proposed control architecture. In order to bring significant impact of all the decision entities, they must equally oscillate towards the distributed reactive control mode and centrally predictive control approach depending on the type of perturbation(s) a manufacturing system faces. The slack interaction of the white-collar entities must be changed to direct interaction and a full study of decision parameters needs to be showed. This will be the significant work expected after this work.

  confusing choices by decision entities to reschedule a perturbed discrete manufacturing system: (a) do nothing (b) decide autonomously and (c) decide by shrinking towards central scheduler.[START_REF] Cardin | Coupling Predictive Scheduling and Reactive Control in Manufacturing Hybrid Control Architectures: State of the Art and Future Challenges[END_REF] have also presented that three main challenges still need to be investigated in the next few research years: (I) Estimation of future performances (II) Design of efficient synchronization (III) Design of efficient switching strategies integrated into a hybrid control architecture which this work has given attention.

Figure 1 :

 1 Figure 1: The REcursive hybriD Control Architecture Meanwhile, REDCA is designed considering the concept of dynamic hybrid control system introduced in Pollux by Jimenez et al. (2017). Pollux used a layer-based control system organized into coordination layer, operation layer, and physical layer. Subsequently, REDCA has considered a wider operational specialty of blue-collar layer and an updating and interfacing 2 role of white-collar layer; see the structural architecture in Figure 1. The operation layer and physical layer of Pollux fall into the blue-collar layer in REDCA. Hence, REDCA is designed to satisfy the requirements (over Pollux and other control architectures) of scalability, dynamic role assignment of decision entities, better closeness to Enterprise Resource Planning, and better level of acceptability by workers of industrial enterprises. The concepts of white-collar and blue-collar, in this paper, are used to represent the role of global and local decision levels in the previously proposed control architectures respectively. Both terms came into common use in the 20th century to mean these who perform office/administrative level and clerical work

Recursion inside an operating unit, same configuration exists with local regulation and local management and all decisional entities decide at their own level; Stafford Beer's Viable System Model

The role of decision entities in the white-collar layer is not as significant as that of decision entities in blue-collar layer

ACKNOWLEDGMENTS

The authors gratefully acknowledge the financial support of the CPER 20152020 Projet Cyber-Entreprises du programme Sciences du numérique, through regional (Région Lorraine, Grand EST), national (DRRT, CNRS, INRIA) and European (FEDER) funds used to extend The TRACILOGIS Platform. Approche Anthropocentrée Pour La Simulation et Le Pilotage. Habilitation à Diriger Des Recherches, Université de Lorraine, France. Farid, A., 2004. A Review of Holonic Manufacturing Systems Literature, University of Cambridge Institute for Manufacturing, Cambridge UK, Tech. Rep. Giret, Adriana, and Vicente, Botti, 2006. From System Requirements to Holonic Manufacturing System Analysis. International Journal of Production Research, 44(18-19), p. 3917-28. Herrera, C., André Thomas, Sana Belmokhtar, and Rémi Pannequin, 2011. A Viable System Model for Product-Driven Systems. In International Conference on Industrial Engineering and Systems Management, IESM 2011. Holvoet, Tom, and Paul, Valckenaers, 2006. Exploiting the Environment for Coordinating Agent Intentions. In International Workshop on Environments for Multi-Agent Systems, Springer, p. 51-66. Isern, David, David Sánchez, and Antonio Moreno, 2011. Organizational Structures Supported by Agent-Oriented Methodologies. Journal of Systems and Software, 84(2), p. 169-84. Jarvis, Jacqueline, Dennis Jarvis, Ralph Rönnquist, and Lakhmi C. Jain, 2008. Holonic Manufacturing Systems. Holonic Execution: A BDI Approach, p. 7-16. Jennings, Nicholas R., Peyman Faratin, Alessio R. Lomuscio, Simon Parsons, Michael J. Wooldridge, and Carles Sierra, 2001. Automated Negotiation: Prospects, Methods and Challenges. Group Decision and Negotiation, 10(2), p. 199-215. Jimenez, Jose-Fernando, Abdelghani Bekrar, Gabriel Zambrano-Rey, Damien Trentesaux, and Paulo Leitão, 2017. Pollux: A Dynamic Hybrid Control Architecture for Flexible Job Shop Systems. International Journal of Production Research, 55(15), p. 4229-47. Leitão P., 2009. Agent-Based Distributed Manufacturing Control: A State-of-the-Art Survey. Engineering Applications of Artificial Intelligence, 22(7), p. 979-91. Leitão, P., and Francisco R., 2006. ADACOR: A Holonic Architecture for Agile and Adaptive Manufacturing Control. Computers in Industry, 57(2), p. 121-30. Maturana, Francisco, Weiming Shen, and Douglas H. Norrie, 1999. MetaMorph: An Adaptive Agent-Based Architecture for Intelligent Manufacturing. International Journal of Production Research, 37(10), p. 2159-73. Mezgebe, T. T., Demesure, G., El Haouzi, H.B., Pannequin, R., Thomas, A., 2019a. CoMM: A Consensus Algorithm for Multi-Agent-Based Manufacturing System to Deal with Perturbation. The International Journal of Advanced Manufacturing Technology, 105(9), p. 3911-26. Mezgebe, T.T., El Haouzi, H.B., Demesure, G., Pannequin, R., Thomas, A., 2019b. Multi-Agent Systems Negotiation to Deal with Dynamic