Metabolic Syndrome In Pregnancy Research Articles

Effectiveness of Online Learning to Improve Knowledge About Metabolic Syndrome in Pregnancy

Background: Metabolic syndrome is a persistent global health problem and a risk factor for diabetes and heart disease. A metabolic syndrome that occurs during pregnancy will pose a threat to maternal and fetal health. The incidence of metabolic syndrome during pregnancy, especially in developing countries, will become a serious public health problem in the future. This study aims to assess the effectiveness of online learning in improving the knowledge of online learning participants on metabolic syndrome cases in pregnancy. Subjects and Method: This was a cross-sectional study conducted in July through Zoom online learning on Metabolic Syndrome in Pregnancy attended by 125 participants. The dependent variable was knowledge about metabolic syndrome in pregnancy. The independent variable was online learning. The data obtained from this study were in the form of pretest and post-test scores. The data were analyzed by t-test. Results: The mean score of knowledge about metabolic syndrome in pregnancy after online learning was higher (Mean= 90.8; SD= 14.05) than before (Mean= 60.08; SD= 6.94), and this was statistically significant (p= <0.001). Conclusion: Online learning is effective to improve knowledge about metabolic syndrome in pregnancy among Indonesian Obstetrics and Gynecology Association (POGI) members and young POGI members. Keywords: metabolic syndrome, pregnancy, online learning. Correspondence: Nutria Widya Purna Anggraini. Department of Obstetrics and Gynecology, Faculty of Medicine, Universitas Sebelas Maret/Dr. Moewardi General Hospital, Surakarta, Central Java, Indonesia. Mobile: +62 812-2651-819. E-mail: nutria_dr@staff.uns.ac.id

Maternal metabolic syndrome in pregnancy and child development at age 5: exploring mediating mechanisms using cord blood markers

BackgroundThere is limited evidence on how the classification of maternal metabolic syndrome during pregnancy affects children’s developmental outcomes and the possible mediators of this association. This study uses a cohort sample of 12,644 to 13,832 mother–child pairs from the UK Born in Bradford Study to examine the associations between maternal metabolic syndrome classification (MetS) and child development outcomes at age 5, using cord blood markers as candidate mediators.MethodsMaternal cardiometabolic markers included diabetes, obesity, triglycerides, high-density lipoprotein cholesterol, blood pressure, hypertension, and fasting glucose during pregnancy. Cord blood markers of high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, triglycerides, leptin, and adiponectin were used as child mediators. Child outcomes included two starting school variables: British Picture Vocabulary Scale (BPVS) and the Letter Identification Assessment (LID), and five developmental milestone domains from a national UK framework: (1) communication and language (COM); (2) personal, social, and emotional (PSE); (3) physical development (PHY); (4) literacy (LIT); and (5) mathematics (MAT). Mediation models were used to examine the associations between the classification of maternal metabolic syndrome and child developmental milestones. Models were adjusted for potential maternal, socioeconomic, and child confounders such as maternal education, deprivation, and gestational age.ResultsIn mediation models, significant total effects were found for MetS associations with children’s development in the LIT domain at age 5. MetS predicted individual cord blood mediators of lower HDL and increased leptin levels in both adjusted and unadjusted models. Total indirect effects (effects of all mediators combined) for MetS on a child’s COM and PSE domain were significant, through all child cord blood mediators of LDL, HDL, triglycerides, adiponectin, and leptin for adjusted models.ConclusionsThe results support the hypothesis that maternal metabolic syndrome classification during pregnancy is associated with some child developmental outcomes at age 5. After adjusting for maternal, child, and environmental covariates, maternal metabolic syndrome classification during pregnancy was associated with children’s LIT domain through direct effects of maternal metabolic health and indirect effects of cord blood markers (total effects), and COM and PSE domains via changes only in a child’s cord blood markers (total indirect effects).

Prediction of adverse pregnancy outcomes by first-trimester components of metabolic syndrome: a prospective longitudinal study.

This study aimed to identify the optimal cutoff values of each component of metabolic syndrome (MetS) in the first trimester of pregnancy for predicting adverse pregnancy outcomes. A total of 1076 pregnant women in the first trimester of gestation were recruited in this prospective longitudinal cohort study. Specifically, 993 pregnant women at 11-13weeks of gestation who were followed up until the end of pregnancy were included in the final analysis. The cutoff values of each component of MetS in the occurrence of adverse pregnancy outcomes including gestational diabetes (GDM), gestational hypertensive disorders, and preterm birth were obtained via receiver operating characteristic (ROC) curve analysis using the Youden's index. Among the 993 pregnant women studied, the significant associations between the first trimester MetS components and adverse pregnancy outcomes were as follows: triglyceride (TG) and body mass index (BMI) with preterm birth; mean arterial pressure (MAP), TG, and high-density lipoprotein cholesterol (HDL-C) with gestational hypertensive disorders; BMI, fasting plasma glucose (FPG), and TG with GDM (all p values < 0.05). The cutoff point values for the above-mentioned MetS components were: TG > 138mg/dl and BMI < 21kg/m2 for the occurrence of preterm birth; TG > 148mg/dL, MAP > 84, and HDL-C < 84mg/dl for gestational hypertensive disorders; BMI > 25kg/m2, FPG > 84mg/dl, and TG > 161mg/dl for GDM. The study findings imply the importance of early management of metabolic syndrome in pregnancy to improve maternal-fetal outcomes.

Pre-BMI and Lipid Profiles in Association with the Metabolic Syndrome in Pregnancy with Advanced Maternal Age.

We aimed to explore the association of BMI in pre-pregnant women with metabolic syndrome in pregnancy in advanced maternal age. A total of 229 maternal women and 536 maternal women participated in this study. Pregnancy women underwent a 75 g-oral glucose tolerance test and maternal lipid profile test between 24 and 28 weeks. Data about biological and sociodemographic characteristics were recorded for each case. The metabolic equivalent (Met) was 9.6% in the maternal age ≥35 group, 5.4% in the age 20–34 group (P = 0.027), and 6.7% in all pregnant women. Results also demonstrated that gestational diabetes mellitus (GDM) and MetS were more likely to appear in the maternal age ≥35 years group than the maternal age 20–34 years group (41.5% vs. 30.6%; P = 0.001, 9.6% vs. 5.4%, P = 0.027). Risk for preterm delivery and eclampsia were increased with raised MetS (RR 3.434 and RR 1.800); MetS in women aged ≥35 years had the largest area under the curve (AUC) (AUC 0.925, 95% CI 0.885–0.965), and its optimal cutoff point was ≥24.998 kg/m2, and the optimal cutoff point for total cholesterol (TC) (AUC 0.686, 95% CI 0.571–.802) predicting MetS was ≥4.955 mmol/l. MetS in pregnancy are associated with the occurrence of preterm delivery and eclampsia, and pre-BMI and TC can predict MetS in the maternal age ≥35 group.

The metabolic syndrome in pregnancy and its association with child telomere length.

The aim of this study was to determine whether presence of the metabolic syndrome in pregnancy associates with child telomere length or child anthropometry (weight, BMI) and BP, measured at 10years of age. The Screening for Pregnancy Endpoints study (SCOPE) was a multicentre, international prospective cohort of nulliparous pregnant women recruited from Australia, New Zealand, Ireland and the UK (N = 5628). The current analysis is a 10year follow-up of SCOPE pregnant women and their children, from the Australian cohort. Clinical data collected at 14-16weeks' gestation during the SCOPE study were used to diagnose the metabolic syndrome using IDF criteria. Telomere length, a biomarker of ageing, was assessed by quantitative PCR from children's saliva collected at 10years of age. In women who completed follow-up (n = 255), 20% had the metabolic syndrome in pregnancy. After adjusting for a range of confounders, children of mothers who had the metabolic syndrome in pregnancy had 14% shorter telomeres than children of mothers without the metabolic syndrome in pregnancy (mean difference -0.36 [95% CI -0.74, 0.01]). Height- and weight-for-age, and BMI z scores were similar in children of mothers who did and did not have the metabolic syndrome during pregnancy. Children of mothers who had the metabolic syndrome in pregnancy have shorter telomeres, a biomarker of accelerated ageing. These findings warrant further studies in larger cohorts of children, as well as investigations into whether telomere length measured in cord blood associates with telomere length in childhood.

Helicobacter pylori infection increases the risk of metabolic syndrome in pregnancy: a cohort study.

The effects of Helicobacter pylori (H. pylori) infection on metabolic syndrome (MetS) in pregnant women are unclear to date. This study was designed to explore the relationship between H. pylori infection and MetS during pregnancy. Pregnant women were enrolled in the prospective cohort study, and their demographic data and metabolic parameters were collected. H. pylori infection was measured using the C13 urea breath test. All enrolled patients were followed up until the last baby was born. Metabolic disorders, including elevated levels of serum triglycerides (TG), high-density lipoprotein (HDL) cholesterol and blood glucose (BG), and adverse pregnancy outcomes, including gestational diabetes mellitus (GDM), preeclampsia, spontaneous preterm birth (SPB), fetal growth restriction (FGR), and uncomplicated pregnancy, were recorded during follow up. There were 320 pregnant women enrolled in this study. They were divided into two groups according to H. pylori infection, and each group was then divided into two subgroups on whether their BMI was more than 24 or not. The results showed that H. pylori infection significantly increased the incidence of MetS as well as other metabolic disorders, especially in pregnant women with high BMI. Multivariable logistic regression analysis showed that risk factors of MetS were high BMI and H. pylori infection. Besides, H. pylori infection increased the incidence of GDM and preeclampsia and potentially reduced the incidence of uncomplicated pregnancy. H. pylori infection in pregnant women acts as a crucial risk factor of Mets and affects the incidence of several adverse pregnancy outcomes.

Metabolic syndrome in pregnancy and postpartum: prevalence and associated factors

Evaluate the prevalence of metabolic syndrome (MS) and the main associated maternal factors in women without pre-gestational conditions, in early pregnancy and in the immediate postpartum. Two hundred pregnant women were evaluated at the 16th week of pregnancy, and 187 were reassessed postpartum. MS was diagnosed according to the criteria by the National Cholesterol Education Program Adult Treatment Panel III. In addition to the diagnostic criteria, anthropometric measures, blood pressure, metabolic profile, and visceral and subcutaneous fat thickness (by ultrasonography) were collected from the pregnant woman. The student's t-test was used to compare the prevalence of MS and its components in the 16th week and in the postpartum. Multiple logistic regression was performed to identify the principal factors associated with the syndrome. The prevalence of the MS was 3.0% in early pregnancy and 9.7% postpartum (p=0.01). Following multiple logistic regression, the prepregnancy body mass index (BMI) (p=0.04) and high-density lipoprotein cholesterol (HDL-c) (p=0.02) remained associated with MS at 16 weeks, and triglyceride levels evaluated in postpartum (p<0.001) with MS in postpartum. The frequency of the MS was high in the immediate postpartum. The factors associated were prepregnancy BMI and HDL-c at the 16th week, as well as triglyceride levels postpartum.

Skeletal muscle as a protagonist in the pregnancy metabolic syndrome

The pregnant woman normally shows clinical manifestations similar to a metabolic syndrome (MS), due to her metabolic and hemodynamic adaptations in order to share nutrients with the child. If those adjustments are surpassed, a kind of pregnancy MS (PregMS) could appear, characterized by excessive insulin resistance and vascular maladaptation. Skeletal muscle (SKM) must be a protagonist in the PregMS: SKM strength and mass have been associated inversely with MS incidence in non-pregnant patients, and in pregnant women muscular activity modulates metabolic and vascular adaptations that favor better outcomes. Of note, a sedentary lifestyle affects exactly in the other way. Those effects may be explained not only by the old paradigm of SKM being a great energy consumer and store, but because it is an endocrine organ whose chronic activity or deconditioning correspondingly releases myokines modulating insulin sensitivity and cardiovascular adaptation, by direct or indirect mechanisms not well understood.In this document, we present evidence to support the concept of a PregMS and hypothesize on the role of the SKM mass, fiber types composition and myokines in its pathophysiology. Also, we discuss some exercise interventions in pregnancy as a way to test our hypotheses.

Metabolic syndrome in pregnancy and risk for adverse pregnancy outcomes: A prospective cohort of nulliparous women.

BackgroundObesity increases the risk for developing gestational diabetes mellitus (GDM) and preeclampsia (PE), which both associate with increased risk for type 2 diabetes mellitus (T2DM) and cardiovascular disease (CVD) in women in later life. In the general population, metabolic syndrome (MetS) associates with T2DM and CVD. The impact of maternal MetS on pregnancy outcomes, in nulliparous pregnant women, has not been investigated.Methods and findingsLow-risk, nulliparous women were recruited to the multi-centre, international prospective Screening for Pregnancy Endpoints (SCOPE) cohort between 11 November 2004 and 28 February 2011. Women were assessed for a range of demographic, lifestyle, and metabolic health variables at 15 ± 1 weeks’ gestation. MetS was defined according to International Diabetes Federation (IDF) criteria for adults: waist circumference ≥80 cm, along with any 2 of the following: raised trigycerides (≥1.70 mmol/l [≥150 mg/dl]), reduced high-density lipoprotein cholesterol (<1.29 mmol/l [<50 mg/dl]), raised blood pressure (BP) (i.e., systolic BP ≥130 mm Hg or diastolic BP ≥85 mm Hg), or raised plasma glucose (≥5.6 mmol/l). Log-binomial regression analyses were used to examine the risk for each pregnancy outcome (GDM, PE, large for gestational age [LGA], small for gestational age [SGA], and spontaneous preterm birth [sPTB]) with each of the 5 individual components for MetS and as a composite measure (i.e., MetS, as defined by the IDF). The relative risks, adjusted for maternal BMI, age, study centre, ethnicity, socioeconomic index, physical activity, smoking status, depression status, and fetal sex, are reported. A total of 5,530 women were included, and 12.3% (n = 684) had MetS. Women with MetS were at an increased risk for PE by a factor of 1.63 (95% CI 1.23 to 2.15) and for GDM by 3.71 (95% CI 2.42 to 5.67). In absolute terms, for PE, women with MetS had an adjusted excess risk of 2.52% (95% CI 1.51% to 4.11%) and, for GDM, had an adjusted excess risk of 8.66% (95% CI 5.38% to 13.94%). Diagnosis of MetS was not associated with increased risk for LGA, SGA, or sPTB. Increasing BMI in combination with MetS increased the estimated probability for GDM and decreased the probability of an uncomplicated pregnancy. Limitations of this study are that there are several different definitions for MetS in the adult population, and as there are none for pregnancy, we cannot be sure that the IDF criteria are the most appropriate definition for pregnancy. Furthermore, MetS was assessed in the first trimester and may not reflect pre-pregnancy metabolic health status.ConclusionsWe did not compare the impact of individual metabolic components with that of MetS as a composite, and therefore cannot conclude that MetS is better at identifying women at risk. However, more than half of the women who had MetS in early pregnancy developed a pregnancy complication compared with just over a third of women who did not have MetS. Furthermore, while increasing BMI increases the probability of GDM, the addition of MetS exacerbates this probability. Further studies are required to determine if individual MetS components act synergistically or independently.

IRS-1 gene polymorphism and DNA damage in pregnant women with diabetes or mild gestational hyperglycemia.

BackgroundPregnant women with mild gestational hyperglycemia present a high risk for hypertension and obesity, and appear to reproduce the model of metabolic syndrome in pregnancy, including hyperinsulinemia and insulin resistance. Diabetic patients have a higher frequency of the IRS-1 Gly972Arg variant and this polymorphism is directly related to insulin resistance and subsequent hyperglycemia. In diabetes, hyperglycemia and other associated factors generate reactive oxygen species that increase DNA damage. The aims of this study were to evaluate the presence of the IRS-1 Arg972 polymorphism in pregnant women with diabetes or mild gestational hyperglycemia, and in their newborns. Additionally, we evaluated the level of primary DNA damage in lymphocytes of Brazilian pregnant women and the relationship between the amount of genetic damage and presence of the polymorphism.MethodsA based on the oral glucose tolerance test (OGTT) results and on glycemic profiles (GP), as follows: non-diabetic group, mild gestational hyperglycemia (MGH) and diabetic group. Eighty-five newborns were included in the study. Maternal peripheral blood samples and umbilical cord blood samples (5–10 mL) were collected for genotyping by PCR-RFLP and for comet assays.ResultsThe prevalence of genotype Gly/Arg in pregnant women groups was not statistically significant. In newborns, the frequency of Gly/Arg was significantly higher in the MGH and diabetic groups than in the non-diabetic group. Taken together, groups IIA and IIB (IIA + IIB; diabetes) presented lower amounts of DNA damage than the non-diabetic group (p = 0.064). No significant association was detected between genetic damage and the presence of the Arg972 genotype in pregnant women.ConclusionThe polymorphism was more prevalent in newborns of diabetic and MGH women. We believe that it is necessary to increase the number of subjects to be examined in order to better determine the biological role of the Arg972 polymorphism in these patients. Despite being classified as low-risk, pregnant women with mild gestational hyperglycemia characterize a population of maternal and perinatal adverse outcomes, and that, together with their newborns, require better monitoring by professionals and health services.

Oxidative DNA damage in diabetic and mild gestational hyperglycemic pregnant women.

BackgroundPregnant women with mild gestational hyperglycemia present high risk for hypertension, obesity and hyperglycemia, and appeared to reproduce the model of metabolic syndrome in pregnancy, with hyperinsulinemia and insulin resistance. Our clinical studies showed that mild gestational hyperglycemia or gestational diabetes are related to similar adverse maternal and perinatal outcomes. Hyperglycemia and other factors associated with diabetes generate reactive oxygen species that increase DNA damage levels. The aim of this study was to evaluate oxidative DNA damage in lymphocytes of pregnant women with diabetes or mild gestational hyperglycemia.MethodsThe study included 111 pregnant women distributed into three groups based on oral glucose tolerance test (OGTT) and glycemic profiles (GP), as follows: Normal OGTT and GP (control group); Normal OGTT and abnormal GP (mild gestational hyperglycemia group); Abnormal OGTT and GP (diabetic group). Maternal blood samples (5–10 mL) were collected and processed for determination of oxidative DNA damage by the comet assay, using Fpg and Endo III enzymes. Urine samples were also collected for determination of 8-OHdG concentrations by ELISA.ResultsSubjects in the diabetes group presented increased amount of oxidized purines, while mild gestational hyperglycemia women presented with increased oxidized pyrimidines, compared to the control group.ConclusionGestational, overt diabetes and mild gestational hyperglycemia, were all related to increased oxidative DNA damage. Diabetic pregnant women showed increased level of oxidative DNA damage, perhaps mainly due to hyperglycemia. On the other hand, oxidative DNA damage detected in women with mild gestational hyperglycemia might be associated with repercussions from obesity, hypertension and/or insulin resistance. Interestingly, the type of DNA base affected seemed to be dependent on the glycemic profile or oxidative stress.Electronic supplementary materialThe online version of this article (doi:10.1186/1758-5996-7-1) contains supplementary material, which is available to authorized users.

Metabolic syndrome and pregnancy

Metabolic syndrome (MS) is a major problem of public health and health care system, with rising prevalence in the world. There is evidence that obesity, as the main component of MS, is strongly associated with the presence of gestational complications: fetal growth retardation, fetal macrosomia, gestational diabetes, preeclampsia, preterm delivery, stillbirth and perinatal death. The underlying mechanisms of this association are actively investigated nowadays. The importance of MS in pregnancy is also determined by the increase of the risk of venous trombosis.

Association between different levels of dysglycemia and metabolic syndrome in pregnancy

BackgroundIn this study, we sought to evaluate the prevalence of metabolic syndrome (MS) in a cohort of pregnant women with a wide range of glucose tolerance, prepregnancy risk factors for MS during pregnancy, and the effects of MS in the outcomes in the mother and in the newborn.MethodsOne hundred and thirty six women with positive screening for gestational diabetes mellitus (GDM) were classified by two diagnostic methods: glycemic profile and 100 g OGTT as normoglycemic, mild gestational hyperglycemic, GDM, and overt GDM. Markers of MS were measured between 24-28th during the screening.ResultsThe prevalence of MS was: 0%; 20.0%; 23.5% and 36.4% in normoglycemic, mild hyperglycemic, GDM, and overt GDM groups, respectively. Previous history of GDM with or without insulin use, BMI ≥ 25, hypertension, family history of diabetes in first degree relatives, non-Caucasian ethnicity, history of prematurity and polihydramnios were statistically significant prepregnancy predictors for MS in the index pregnancy, that by its turn increased the adverse outcomes in the mother and in the newborn.ConclusionThe prevalence of MS increases with the worsening of glucose tolerance; impaired glycemic profile identifies pregnancies with important metabolic abnormalities even in the presence of a normal OGTT, in patients that are not classified as having GDM.

Adipose Tissue Lymphocytes and Macrophages in Obesity and Insulin Resistance

HomeArteriosclerosis, Thrombosis, and Vascular BiologyVol. 28, No. 7Adipose Tissue Lymphocytes and Macrophages in Obesity and Insulin Resistance Free AccessEditorialPDF/EPUBAboutView PDFView EPUBSections ToolsAdd to favoritesDownload citationsTrack citationsPermissions ShareShare onFacebookTwitterLinked InMendeleyReddit Jump toFree AccessEditorialPDF/EPUBAdipose Tissue Lymphocytes and Macrophages in Obesity and Insulin ResistanceMakers or Markers, and Which Comes First? A. Bouloumié, L. Casteilla and M. Lafontan A. BouloumiéA. Bouloumié From the Institut National de la Santé et de la Recherche Médicale (INSERM) (A.B., M.L.), Unité 858 & Université Toulouse III Paul Sabatier, Institut de Médecine Moléculaire de Rangueil, Equipe No. 1 AVENIR, IFR31, BP84225, 31432 Toulouse, France; and UMR 5241 CNRS UPS IFR31 (L.C.), IFR109 Institut Louis Bugnard, BP 84225, 31432 Toulouse Cedex 4, France. Search for more papers by this author , L. CasteillaL. Casteilla From the Institut National de la Santé et de la Recherche Médicale (INSERM) (A.B., M.L.), Unité 858 & Université Toulouse III Paul Sabatier, Institut de Médecine Moléculaire de Rangueil, Equipe No. 1 AVENIR, IFR31, BP84225, 31432 Toulouse, France; and UMR 5241 CNRS UPS IFR31 (L.C.), IFR109 Institut Louis Bugnard, BP 84225, 31432 Toulouse Cedex 4, France. Search for more papers by this author and M. LafontanM. Lafontan From the Institut National de la Santé et de la Recherche Médicale (INSERM) (A.B., M.L.), Unité 858 & Université Toulouse III Paul Sabatier, Institut de Médecine Moléculaire de Rangueil, Equipe No. 1 AVENIR, IFR31, BP84225, 31432 Toulouse, France; and UMR 5241 CNRS UPS IFR31 (L.C.), IFR109 Institut Louis Bugnard, BP 84225, 31432 Toulouse Cedex 4, France. Search for more papers by this author Originally published1 Jul 2008https://doi.org/10.1161/ATVBAHA.108.168229Arteriosclerosis, Thrombosis, and Vascular Biology. 2008;28:1211–1213Obesity, and more specifically accumulation of adipose tissue in the visceral and subcutaneous abdominal locations, is a major risk factor for the development of cardiovascular pathologies including hypertension and atherosclerosis, as well as metabolic disorders such as type 2 diabetes. During recent years, “metaflammation” or metabolically-triggered inflammation1 has emerged as a key process involved in the clustering of those conditions. Although several metabolically active organs such as the liver, muscle, and, recently, the intestine2 certainly play major roles, the white adipose tissue appears as a central and primary player as both a source and site of inflammation. Accumulation of adipose tissue macrophages (ATMs) has been well-described in obese conditions in mice and humans.3–5 Moreover, the ATM proinflammatory phenotype has been linked to the development of insulin resistance in mice,4 although the exact nature of the proinflammatory myeloid cells, ie, macrophages or dentritic cells, remains to be determined.6 Nevertheless, the causal link between inflammation and insulin resistance was further strengthened by the specific knock-out of the inflammation coordinator IkappaB kinase beta of myeloid cells, which gave protection against insulin resistance.7 The study of Kintscher and al in this issue8 extends those original observations to cells of adaptative immunity. The authors suggest that the accumulation of T-lymphocytes, assessed mainly through gene expression analyses and immunohistochemistry, occurs in the perigonadal adipose tissue of mice on a high-fat diet before the accumulation of macrophages. Moreover, the increased expression of T-lymphocyte markers was concomitant with the initiation of insulin resistance characterized by a reduction in systemic glucose tolerance and insulin sensitivity, at least compared with counterpart animals that were 5 weeks younger. Given these new findings in rodents, the authors suggest that early lymphocyte infiltration of the adipose tissue might be considered as a primary event that orchestrates the adipose tissue inflammation (Figure). This provocative and attractive idea poses a number of questions and requires further clinical investigations to validate its relevance in humans. Download figureDownload PowerPointFigure. Putative processes involved the accumulation of immune cells within adipose tissue and their pathogenic roles in obesity and associated pathologies. Blood circulating lymphocytes might be attracted through the production of the SDF-1 produced by endothelial cells. Extravasation of T-lymphocytes and their accumulation within the fat mass might be the primary event involved in the further accumulation of macrophages through the INFγ-stimulated production of MCP-1 by the stromal preadipocytes. In italics are the open questions that remain to be answered.See accompanying article on page 1304One of the most important tasks is to define the process underlying the accumulation of immune cells and the nature of the ATLs within the adipose tissue. Cells of adaptive immunity are able to home to healthy extralymphoid peripheral tissues, including adipose tissue, where they are thought to carry out peripheral immune monitoring and to reside as long-lived resting memory T cells. Alternatively, effector T cells efficiently home into sites of inflammation and are destined to die. Chemokines and their receptors represent a critical element in immune cell trafficking. Adipose tissue produces a wide range of chemokines, the production of which is modulated by the degree of adiposity.9 The potential contribution of MCP1/CCL2 in ATM accumulation, through its interaction with the monocyte receptor CCR2, has given conflicting results.10 In the current work, the authors suggest that resident adipose tissue cells, particularly stromal and endothelial cells (in agreement with a previous study11), could release the chemoattractant SDF1/CXCL12 and so behave as lymphocyte recruiter cells. In turn, lymphocyte-derived interferon gamma (IFNγ) would stimulate preadipocyte-derived MCP1 release, which would attract circulating monocytes. This simplistic view has to be reconciled with the fact that the main receptor of CXCL12, CXCR4, is widely expressed on all leukocytes including monocytes,12 making it unlikely to participate alone in the selective lymphocyte trafficking into adipose tissue. In-depth analysis of the adhesion molecules and chemokine receptors expressed on ATLs might reveal the specific repertoire determinant for adipose tissue homing. Moreover, as the authors point out, the expression of CD3, as well as CD4 and INFγ, is not restricted to Th1 CD4+ effectors but is found in other T cell subsets such as memory and regulatory T cells as well as NKT cells that are present in adipose tissue, at least in mice.13,14Another interesting point will be to determine clearly whether changes in the degree of adiposity are associated with modulation in the number and phenotype of the ATLs. Indeed, dynamic changes in ATM numbers and phenotype have been demonstrated during the development of obesity on high-fat diets in mice. The ATM shifted from a reparative resident phenotype in lean animals to inflammatory newly recruited ATM in obese mice.15 In humans, ATMs appear less polarized16 and possess a proliferative capacity associated with a remodeling proangiogenic phenotype,17 the hallmark of cells involved in chronic inflammation. By correlative analyses and using gene expression analyses only, the present study suggests a recruitment of Th1 CD4+ cells with obesity. Therefore, the detailed characterization of ATL subsets in both human and mice adipose tissue is necessary to determine, whereas the growth of adipose tissue impacts on the phenotype, function and numbers of resident or infiltrated T cells.Finally, the pathogenic role of ATLs in obesity remains to be established. Although correlative data analyses show a clear association between lymphocyte gene markers and waist circumference in patients with type 2 diabetes, similar approaches with nondiabetic patients will determine whether ATLs are markers or players in adipose tissue growth and obesity-associated pathologies. A recent study has suggested that perivascular fat may serves as reservoir for activated effector T cells which in turn promote vascular dysfunction and hypertension.18 Moreover, local intercellular cross-talk is probably more complex than suspected. Although one might expect local interactions between ATLs and ATMs, adipocyte-derived products, including the main adipokines, leptin and adiponectin, and free fatty acids, which have been shown to modulate T-lymphocyte function and proinflammatory responses, are probably major variables to be taken into account. In this field and for long time, the work of C.M. Pond has stressed the close interactions between lymphoid cells in lymph nodes and the surrounding adipose tissue.19 In addition to this local cross-talk, interactions with adjacent tissues might contribute to the pathogenicity of ATLs. Moreover, although increasing lines of evidence point to the links between the innate and adaptative immune systems and metabolism in rodents, we must keep in mind that the jump to patient-related problems is not easy and requires a number of additional clinical studies. Indeed, there are major differences between young mice submitted to an acute nutritional stress promoted by a high-fat diet and patients who could be at different ages and stages of obesity. ATLs and ATMs, makers or markers? The question is undoubtedly there, but its definite answer and its relevance to humans remains open and clearly further complementary investigations are needed.DisclosuresNone.FootnotesCorrespondence to A. Bouloumié, Institut National de la Santé et de la Recherche Médicale (INSERM), Unité 858 & Université Toulouse III Paul Sabatier, Institut de Médecine Moléculaire de Rangueil, Equipe No. 1 AVENIR, IFR31, BP84225, 31432 Toulouse, France. E-mail anne. [email protected] References 1 Hotamisligil GS. Inflammation and metabolic disorders. Nature. 2006; 444: 860–867.CrossrefMedlineGoogle Scholar2 Cani PD, Amar J, Iglesias MA, Poggi M, Knauf C, Bastelica D, Neyrinck AM, Fava F, Tuohy KM, Chabo C, Waget A, Delmee E, Cousin B, Sulpice T, Chamontin B, Ferrieres J, Tanti JF, Gibson GR, Casteilla L, Delzenne NM, Alessi MC, Burcelin R. Metabolic endotoxemia initiates obesity and insulin resistance. Diabetes. 2007; 56: 1761–1772.CrossrefMedlineGoogle Scholar3 Weisberg SP, McCann D, Desai M, Rosenbaum M, Leibel RL, Ferrante AW Jr. Obesity is associated with macrophage accumulation in adipose tissue. J Clin Invest. 2003; 112: 1796–1808.CrossrefMedlineGoogle Scholar4 Xu H, Barnes GT, Yang Q, Tan G, Yang D, Chou CJ, Sole J, Nichols A, Ross JS, Tartaglia LA, Chen H. Chronic inflammation in fat plays a crucial role in the development of obesity-related insulin resistance. J Clin Invest. 2003; 112: 1821–1830.CrossrefMedlineGoogle Scholar5 Curat CA, Miranville A, Sengenes C, Diehl M, Tonus C, Busse R, Bouloumie A. From blood monocytes to adipose tissue-resident macrophages: induction of diapedesis by human mature adipocytes. Diabetes. 2004; 53: 1285–1292.CrossrefMedlineGoogle Scholar6 Nguyen MT, Favelyukis S, Nguyen AK, Reichart D, Scott PA, Jenn A, Liu-Bryan R, Glass CK, Neels JG, Olefsky JM. A subpopulation of macrophages infiltrates hypertrophic adipose tissue and is activated by free fatty acids via Toll-like receptors 2 and 4 and JNK-dependent pathways. J Biol Chem. 2007; 282: 35279–35292.CrossrefMedlineGoogle Scholar7 Arkan MC, Hevener AL, Greten FR, Maeda S, Li ZW, Long JM, Wynshaw-Boris A, Poli G, Olefsky J, Karin M. IKK-beta links inflammation to obesity-induced insulin resistance. Nat Med. 2005; 11: 191–198.CrossrefMedlineGoogle Scholar8 Kintscher U, Hartge M, Hess K, Foryst-Ludwig A, Clemenz M, Wabitsch M, Fischer-Posovszky P, Barth TF, Dragun D, Skurk T, Hauner H, Bluher M, Unger T, Wolf AM, Knippschild U, Hombach V, Marx N. T-lymphocyte infiltration in visceral adipose tissue. a primary event in adipose tissue inflammation and the development of obesity-mediated insulin resistance. Arterioscler Thromb Vasc Biol. 2008; 28: 1304–1310.LinkGoogle Scholar9 Bouloumie A, Curat CA, Sengenes C, Lolmede K, Miranville A, Busse R. Role of macrophage tissue infiltration in metabolic diseases. Curr Opin Clin Nutr Metab Care. 2005; 8: 347–354.CrossrefMedlineGoogle Scholar10 Inouye KE, Shi H, Howard JK, Daly CH, Lord GM, Rollins BJ, Flier JS. Absence of CC chemokine ligand 2 does not limit obesity-associated infiltration of macrophages into adipose tissue. Diabetes. 2007; 56: 2242–2250.CrossrefMedlineGoogle Scholar11 Sengenes C, Miranville A, Maumus M, de Barros S, Busse R, Bouloumie A. Chemotaxis and differentiation of human adipose tissue CD34+/CD31− progenitor cells: role of stromal derived factor-1 released by adipose tissue capillary endothelial cells. Stem Cells. 2007; 25: 2269–2276.CrossrefMedlineGoogle Scholar12 Cho CH, Koh YJ, Han J, Sung HK, Jong Lee H, Morisada T, Schwendener RA, Brekken RA, Kang G, Oike Y, Choi TS, Suda T, Yoo OJ, Koh GY. Angiogenic role of LYVE-1-positive macrophages in adipose tissue. Circ Res. 2007; 100: e47–e57.LinkGoogle Scholar13 Caspar-Bauguil S, Cousin B, Galinier A, Segafredo C, Nibbelink M, Andre M, Casteilla L, Penicaud L. Adipose tissues as an ancestral immune organ: site-specific change in obesity. FEBS Lett. 2005; 579: 3487–3492.CrossrefMedlineGoogle Scholar14 Caspar-Bauguil S, Cousin B, Andre M, Nibbelink M, Galinier A, Periquet B, Casteilla L, Penicaud L. Weight-dependent changes of immune system in adipose tissue: importance of leptin. Exp Cell Res. 2006; 312: 2195–2202.CrossrefMedlineGoogle Scholar15 Lumeng CN, Bodzin JL, Saltiel AR. Obesity induces a phenotypic switch in adipose tissue macrophage polarization. J Clin Invest. 2007; 117: 175–184.CrossrefMedlineGoogle Scholar16 Zeyda M, Farmer D, Todoric J, Aszmann O, Speiser M, Gyori G, Zlabinger GJ, Stulnig TM. Human adipose tissue macrophages are of an anti-inflammatory phenotype but capable of excessive pro-inflammatory mediator production. Int J Obes (Lond). 2007; 31: 1420–1428.CrossrefMedlineGoogle Scholar17 Bourlier V, Zakaroff-Girard A, Miranville A, De Barros S, Maumus M, Sengenes C, Galitzky J, Lafontan M, Karpe F, Frayn KN, Bouloumie A. Remodeling phenotype of human subcutaneous adipose tissue macrophages. Circulation. 2008; 117: 806–815.LinkGoogle Scholar18 Guzik TJ, Hoch NE, Brown KA, McCann LA, Rahman A, Dikalov S, Goronzy J, Weyand C, Harrison DG. Role of the T cell in the genesis of angiotensin II induced hypertension and vascular dysfunction. J Exp Med. 2007; 204: 2449–2460.CrossrefMedlineGoogle Scholar19 Pond CM. Adipose tissue and the immune system. Prostaglandins Leukot Essent Fatty Acids. 2005; 73: 17–30.CrossrefMedlineGoogle Scholar Previous Back to top Next FiguresReferencesRelatedDetailsCited By Quinn G, Dhillon P and Susztak K (2020) It Takes Two to Tango: The Role of Dysregulated Metabolism and Inflammation in Kidney Disease Development, Seminars in Nephrology, 10.1016/j.semnephrol.2020.01.010, 40:2, (199-205), Online publication date: 1-Mar-2020. Cubillos-Zapata C, Almendros I, Díaz-García E, Toledano V, Casitas R, Galera R, López-Collazo E, Farre R, Gozal D and García-Rio F (2019) Differential effect of intermittent hypoxia and sleep fragmentation on PD-1/PD-L1 upregulation, Sleep, 10.1093/sleep/zsz285, 43:5, Online publication date: 12-May-2020. Duran‐Ortiz S, Young J, Jara A, Jensen E, Basu R, List E, Qian Y, Kopchick J and Berryman D (2020) Differential gene signature in adipose tissue depots of growth hormone transgenic mice, Journal of Neuroendocrinology, 10.1111/jne.12893, 32:11, Online publication date: 1-Nov-2020. Sreng N, Champion S, Martin J, Khelaifia S, Christensen J, Padmanabhan R, Azalbert V, Blasco-Baque V, Loubieres P, Pechere L, Landrier J, Burcelin R and Sérée E (2019) Resveratrol-mediated glycemic regulation is blunted by curcumin and is associated to modulation of gut microbiota, The Journal of Nutritional Biochemistry, 10.1016/j.jnutbio.2019.108218, 72, (108218), Online publication date: 1-Oct-2019. Balato A and Megna M (2017) Adipose Tissue and Cutaneous Inflammation Clinical and Basic Immunodermatology, 10.1007/978-3-319-29785-9_14, (219-238), . Burcelin R (2017) When gut fermentation controls satiety: A PYY story, Molecular Metabolism, 10.1016/j.molmet.2016.11.005, 6:1, (10-11), Online publication date: 1-Jan-2017. Echeverri Tirado L and Yassin L (2017) B cells interactions in lipid immune responses: implications in atherosclerotic disease, Lipids in Health and Disease, 10.1186/s12944-016-0390-5, 16:1, Online publication date: 1-Dec-2017. Cinkajzlová A, Mráz M and Haluzík M (2017) Lymphocytes and macrophages in adipose tissue in obesity: markers or makers of subclinical inflammation?, Protoplasma, 10.1007/s00709-017-1082-3, 254:3, (1219-1232), Online publication date: 1-May-2017. Hanefeld M, Pistrosch F, Bornstein S and Birkenfeld A (2016) The metabolic vascular syndrome - guide to an individualized treatment, Reviews in Endocrine and Metabolic Disorders, 10.1007/s11154-016-9345-4, 17:1, (5-17), Online publication date: 1-Mar-2016. Lee S, Norheim F, Langleite T, Noreng H, Storås T, Afman L, Frost G, Bell J, Thomas E, Kolnes K, Tangen D, Stadheim H, Gilfillan G, Gulseth H, Birkeland K, Jensen J, Drevon C and Holen T (2016) Effect of energy restriction and physical exercise intervention on phenotypic flexibility as examined by transcriptomics analyses of mRNA from adipose tissue and whole body magnetic resonance imaging, Physiological Reports, 10.14814/phy2.13019, 4:21, (e13019), Online publication date: 1-Nov-2016. Bandyopadhyay G, Lu M, Avolio E, Siddiqui J, Gayen J, Wollam J, Vu C, Chi N, O’Connor D and Mahata S (2014) Pancreastatin-Dependent Inflammatory Signaling Mediates Obesity-Induced Insulin Resistance, Diabetes, 10.2337/db13-1747, 64:1, (104-116), Online publication date: 1-Jan-2015. Cancello R (2015) Obesity and Inflammation in Pregnancy Metabolic Syndrome and Complications of Pregnancy, 10.1007/978-3-319-16853-1_5, (65-75), . Teixeira T, Alves R, Moreira A and Peluzio M (2015) Main characteristics of metabolically obese normal weight and metabolically healthy obese phenotypes, Nutrition Reviews, 10.1093/nutrit/nuu007, 73:3, (175-190), Online publication date: 1-Mar-2015. Zhang S, Khalyfa A, Wang Y, Carreras A, Hakim F, Neel B, Brady M, Qiao Z, Hirotsu C and Gozal D (2013) Sleep fragmentation promotes NADPH oxidase 2-mediated adipose tissue inflammation leading to insulin resistance in mice, International Journal of Obesity, 10.1038/ijo.2013.139, 38:4, (619-624), Online publication date: 1-Apr-2014. Inzaugarat M, Billordo L, Vodánovich F, Cervini G, Casavalle P, Vedire C and Cherñavsky A (2013) Alterations in innate and adaptive immune leukocytes are involved in paediatric obesity, Pediatric Obesity, 10.1111/j.2047-6310.2013.00179.x, 9:5, (381-390), Online publication date: 1-Oct-2014. Wang Y, Carreras A, Lee S, Hakim F, Zhang S, Nair D, Ye H and Gozal D (2013) Chronic sleep fragmentation promotes obesity in young adult mice, Obesity, 10.1002/oby.20616, 22:3, (758-762), Online publication date: 1-Mar-2014. Nilius B, Szallasi A and Sibley D (2014) Transient Receptor Potential Channels as Drug Targets: From the Science of Basic Research to the Art of Medicine, Pharmacological Reviews, 10.1124/pr.113.008268, 66:3, (676-814), Online publication date: 1-Jul-2014. Blasco-Baque V, Serino M and Burcelin R (2013) La thérapie métabolique à l’interface entre l’homme et le microbiote intestinal, Annales Pharmaceutiques Françaises, 10.1016/j.pharma.2012.08.003, 71:1, (34-41), Online publication date: 1-Jan-2013. Burcelin R, Serino M, Chabo C, Garidou L, Pomié C, Courtney M, Amar J and Bouloumié A (2013) Metagenome and metabolism: the tissue microbiota hypothesis, Diabetes, Obesity and Metabolism, 10.1111/dom.12157, 15:s3, (61-70), Online publication date: 1-Sep-2013. Pistrosch F, Schaper F and Hanefeld M (2013) The Metabolic Syndrome and Cardiovascular Disease The Metabolic Syndrome, 10.1007/978-3-7091-1331-8_4, (43-54), . Burcelin R, Chabo C, Blasco-Baque V, Sérino M and Amar J (2013) Le microbiote intestinal à l’origine de nouvelles perspectives thérapeutiques pour les maladies métaboliques ?, médecine/sciences, 10.1051/medsci/2013298021, 29:8-9, (800-806), Online publication date: 1-Aug-2013. Luche E, Cousin B, Garidou L, Serino M, Waget A, Barreau C, André M, Valet P, Courtney M, Casteilla L and Burcelin R (2013) Metabolic endotoxemia directly increases the proliferation of adipocyte precursors at the onset of metabolic diseases through a CD14-dependent mechanism, Molecular Metabolism, 10.1016/j.molmet.2013.06.005, 2:3, (281-291), Online publication date: 1-Aug-2013. Burcelin R, Garidou L and Pomié C (2012) Immuno-microbiota cross and talk: The new paradigm of metabolic diseases, Seminars in Immunology, 10.1016/j.smim.2011.11.011, 24:1, (67-74), Online publication date: 1-Feb-2012. Pamir N, McMillen T, Edgel K, Kim F and LeBoeuf R (2012) Deficiency of lymphotoxin-α does not exacerbate high-fat diet-induced obesity but does enhance inflammation in mice, American Journal of Physiology-Endocrinology and Metabolism, 10.1152/ajpendo.00447.2011, 302:8, (E961-E971), Online publication date: 15-Apr-2012. Mitterberger M, Lechner S, Mattesich M, Kaiser A, Probst D, Wenger N, Pierer G and Zwerschke W (2012) DLK1(PREF1) is a negative regulator of adipogenesis in CD105+/CD90+/CD34+/CD31−/FABP4− adipose-derived stromal cells from subcutaneous abdominal fat pats of adult women, Stem Cell Research, 10.1016/j.scr.2012.04.001, 9:1, (35-48), Online publication date: 1-Jul-2012. Vantyghem M, Balavoine A, Douillard C, Defrance F, Dieudonne L, Mouton F, Lemaire C, Bertrand-Escouflaire N, Bourdelle-Hego M, Devemy F, Evrard A, Gheerbrand D, Girardot C, Gumuche S, Hober C, Topolinski H, Lamblin B, Mycinski B, Ryndak A, Karrouz W, Duvivier E, Merlen E, Cortet C, Weill J, Lacroix D and Wémeau J (2012) How to diagnose a lipodystrophy syndrome, Annales d'Endocrinologie, 10.1016/j.ando.2012.04.010, 73:3, (170-189), Online publication date: 1-Jun-2012. Burcelin R (2012) Regulation of Metabolism: A Cross Talk Between Gut Microbiota and Its Human Host, Physiology, 10.1152/physiol.00023.2012, 27:5, (300-307), Online publication date: 1-Oct-2012. Dobrian A, Ma Q, Lindsay J, Leone K, Ma K, Coben J, Galkina E and Nadler J (2011) Dipeptidyl peptidase IV inhibitor sitagliptin reduces local inflammation in adipose tissue and in pancreatic islets of obese mice, American Journal of Physiology-Endocrinology and Metabolism, 10.1152/ajpendo.00463.2010, 300:2, (E410-E421), Online publication date: 1-Feb-2011. Sethi J and Vidal-Puig A (2011) Adipose Tissue Development, Structure and Function Metabolic Basis of Obesity, 10.1007/978-1-4419-1607-5_3, (53-68), . Komorowski J, Jankiewicz-Wika J, Kolomecki K, Cywinski J, Piestrzeniewicz K, Swiętoslawski J and Stepien H (2011) Systemic blood osteopontin, endostatin, and E-selectin concentrations after vertical banding surgery in severely obese adults, Cytokine, 10.1016/j.cyto.2011.03.020, 55:1, (56-61), Online publication date: 1-Jul-2011. Zhang H, Potter B, Cao J and Zhang C (2011) Interferon-gamma induced adipose tissue inflammation is linked to endothelial dysfunction in type 2 diabetic mice, Basic Research in Cardiology, 10.1007/s00395-011-0212-x, 106:6, (1135-1145), Online publication date: 1-Nov-2011. Graziani F, Cialdella P, Liuzzo G, Basile E, Brugaletta S, Pedicino D, Leccesi L, Guidone C, Iaconelli A, Mingrone G, Biasucci L and Crea F (2011) Cardiovascular risk in obesity: Different activation of inflammation and immune system between obese and morbidly obese subjects, European Journal of Internal Medicine, 10.1016/j.ejim.2011.04.010, 22:4, (418-423), Online publication date: 1-Aug-2011. Esposito L, Simpson R, Strohacker K, Carpenter K and McFarlin B (2010) Defining a longitudinal survival model to examine forced treadmill running as a countermeasure for diet-induced weight gain, Laboratory Animals, 10.1258/la.2010.010030, 44:4, (305-311), Online publication date: 1-Oct-2010. Poglio S, De Toni-Costes F, Arnaud E, Laharrague P, Espinosa E, Casteilla L and Cousin B (2010) Adipose Tissue as a Dedicated Reservoir of Functional Mast Cell Progenitors, Stem Cells, 10.1002/stem.523, 28:11, (2065-2072), Online publication date: 1-Nov-2010. Burcelin R, Chabo C, Luche É, Serino M and Corthier G (2010) Les lipopolysaccharides bactériens et les maladies métaboliques, Cahiers de Nutrition et de Diététique, 10.1016/j.cnd.2010.03.001, 45:3, (114-121), Online publication date: 1-Jun-2010. Hu Y, Tong G, Xu W, Pan J, Ryan K, Yang R, Shuldiner A, Gong D and Zhu D (2009) Anti-inflammatory effects of simvastatin on adipokines in type 2 diabetic patients with carotid atherosclerosis, Diabetes and Vascular Disease Research, 10.1177/1479164109339966, 6:4, (262-268), Online publication date: 1-Oct-2009. Caspar-Bauguil S, Cousin B, Bour S, Castiella L, Penicaud L and Carpéné C (2009) Adipose tissue lymphocytes: types and roles, Journal of Physiology and Biochemistry, 10.1007/BF03185938, 65:4, (423-436), Online publication date: 1-Dec-2009. Marteau J, Samara A, Dedoussis G, Pfister M and Visvikis-Siest S (2009) Candidate gene microarray analysis in peripheral blood cells for studying hypertension/obesity, Personalized Medicine, 10.2217/pme.09.6, 6:3, (269-291), Online publication date: 1-May-2009. Quaedackers M, Baan C, Weimar W and Hoogduijn M (2009) Cell contact interaction between adipose-derived stromal cells and allo-activated T lymphocytes, European Journal of Immunology, 10.1002/eji.200939584, 39:12, (3436-3446), Online publication date: 1-Dec-2009. Duffaut C, Galitzky J, Lafontan M and Bouloumié A (2009) Unexpected trafficking of immune cells within the adipose tissue during the onset of obesity, Biochemical and Biophysical Research Communications, 10.1016/j.bbrc.2009.05.002, 384:4, (482-485), Online publication date: 1-Jul-2009. Burcelin R and Amar J (2009) Flore intestinale et maladies métaboliques, Médecine des Maladies Métaboliques, 10.1016/S1957-2557(09)71629-3, 3:2, (159-164), Online publication date: 1-Mar-2009. Poitou C, Divoux A, Faty A, Tordjman J, Hugol D, Aissat A, Keophiphath M, Henegar C, Commans S and Clément K (2009) Role of Serum Amyloid A in Adipocyte-Macrophage Cross Talk and Adipocyte Cholesterol Efflux, The Journal of Clinical Endocrinology & Metabolism, 10.1210/jc.2008-2040, 94:5, (1810-1817), Online publication date: 1-May-2009. Chaldakov G, Fiore M, Ghenev P, Beltowski J, Ranćić G, Tunçel N and Aloe L (2014) Triactome: Neuro–Immune–Adipose Interactions. Implication in Vascular Biology, Frontiers in Immunology, 10.3389/fimmu.2014.00130, 5 Kalathookunnel Antony A, Lian Z and Wu H (2018) T Cells in Adipose Tissue in Aging, Frontiers in Immunology, 10.3389/fimmu.2018.02945, 9 July 2008Vol 28, Issue 7 Advertisement Article InformationMetrics https://doi.org/10.1161/ATVBAHA.108.168229PMID: 18565843 Originally publishedJuly 1, 2008 PDF download Advertisement

Mild gestational hyperglycaemia as a risk factor for metabolic syndrome in pregnancy and adverse perinatal outcomes

The aims of this study were to evaluate the prevalence of metabolic syndrome (MS) in a cohort of pregnant women with a wide range of glucose tolerance, pre-pregnancy risk factors for MS during pregnancy and the effects of MS in the occurrence of adverse perinatal outcomes. One hundred and thirty six women with positive screening for gestational diabetes (GDM) were classified by two diagnostic methods: glycaemic profile and 100 g oral glucose tolerance test (OGTT) as normoglycaemic, mild gestational hyperglycaemic, GDM, and overt GDM. Markers of insulin resistance were measured between 24-28 and 36th week of gestation, and 6 weeks after delivery. The prevalence of MS was 0; 20.0; 23.5 and 36.4% in normoglycaemic, mild hyperglycaemic, GDM and overt GDM groups, respectively. Previous history of GDM with or without insulin use, body mass index (BMI) > or = 25, hypertension, family history of diabetes in first-degree relatives, non-Caucasian ethnicity, history of prematurity and polyhydramnios were statistically significant pre-pregnancy predictors for MS in the index pregnancy, that by its turn increased the occurrence of adverse perinatal outcomes (p = 0.01). The prevalence of MS increases with the worsening of glucose tolerance and is an independent predictor of adverse perinatal outcomes; impaired glycaemic profile identifies pregnancies with important metabolic abnormalities that are linked to the occurrence of adverse perinatal outcomes even in the presence of a normal OGTT, in patients that are not currently classified as having GDM.

Valor predictivo del síndrome metabólico en la gestación para el desarrollo de diabetes mellitus y factores de riesgo vascular en la madre y el hijo, a corto plazo tras el parto (gestaMET)

To evaluate the presence of diabetes mellitus (DM) or short-term alterations in glucose metabolism, obesity and vascular risk factors after birth in women with pregnancy metabolic syndrome (PMS). To evaluate the incidence of obesity, lipaemia, glucaemia disorder, blood pressure (BP), or lipid figures in the period after birth in children of women with PMS. DESIGN. Cohort study. SETTING. Forty two primary care centres.Study cohort (SC): women with PMS and their children. Control cohort (CC): women without primary criteria of PMS and their children.SC, 980 women and CC, also 980. Consecutive sampling.Mother: basic data, 75 g oral overload, lipid profile, insulinaemia, toxic habits, nutrition survey, and physical activity. Child: weight, height, BP, nutrition survey, glucaemia, insulinaemia, and lipid profile. Father: basic data, BP, glucaemia, lipid profile, insulinaemia, toxic habits, nutrition survey, and physical activity. We will study genes related to insulin resistance in all subjects.Comparison of proportions with *2 test; ANOVA to measure means. Evaluation of effect of intra-uteral exposure through logistical regression and COX regression, whilst controlling potentially confusing and interactive variables.This study will contribute to locating the moment when diabetes and vascular risk start and to finding the optimum moment for starting prevention strategies.