Mid-gestation Angiogenic Biomarker Levels are Increased in Women at High Risk for Preeclampsia

Background: Pre-pregnancy hypertension and diabetes mellitus, multiple gestations, prior preeclampsia, are significant risk factors for preeclampsia. Whether altered maternal levels of angiogenic factors contribute to increased preeclampsia risk in these conditions is unknown. Our objective was to compare maternal serum angiogenic biomarker levels in women with major risk factors for preeclampsia and healthy controls. Methods: Women presenting for prenatal care were enrolled if they had one of the following preeclampsia risk factors: pre-pregnancy hypertension and/or diabetes mellitus, nulliparity with pre-pregnancy BMI\u3e30, multiple gestations, or prior preeclampsia. Healthy control pregnancies without these risk factors were enrolled for comparison. Maternal serum samples were collected at 3 pre-specified gestational windows between 23 and 36 weeks gestation. sFlt1, sEng, and PlGF were measured by ELISA. The (sFlt1+sEng):PlGF ratio was calculated and compared for each risk group at each gestational window. Results: Gestational patterns of angiogenic biomarkers differed in high-risk groups vs. healthy control subjects. The angiogenic ratio (sFlt1+sEng):PlGF was higher for all high risk groups except obesity/nulliparity as compared with healthy control subjects after 28 weeks gestation. Biomarker ratio levels were highest in subjects with MG and prior PE, and differences from the health control group became more pronounced as gestation progressed. Women with hypertension/diabetes had more subtle differences as compared with healthy control subjects. Conclusion: Women with preeclampsia risk factors had higher angiogenic ratios compared with healthy control women. This study illuminates the interplay between risk factors and placental angiogenic biomarkers in the pathogenesis of preeclampsia

Associations of Adipose Tissue Architecture, Adipokines and Inflammatory Markers with Body Mass Index and Gestational Weight Gain in Non-diabetic Pregnancies

Background: Some pregnancy weight gain is stored as adipose tissue (AT). Human AT depots vary in their capacity for expansion. Data suggests that subcutaneous (SQ) is adapted for healthy lipid storage. Conversely visceral (V) accumulation is associated with inflammation, obesity-related co-morbidities and Type 2 diabetes (T2DM) risk. We investigated SQ and VAT histologic architecture along with insulin, adipokines and inflammatory markers in relationship to prepregnancy BMI and gestational weight gain (GWG). Methods: Subset of non-diabetic singleton gravidas from the Pregnancy & Postpartum Observational Dietary Study (PPODS), undergoing Cesareans and consenting to SQ & VAT biopsies were included. Average adipocyte size assessed in10 sections/depot/subject. Maternal and cord blood insulin, adiponectin, leptin, PAI-1, CRP, TNFα, IL1b, IL6 and IL8 evaluated using Luminex MAGPIX, laser based fluorescent analytical test instrumentation with MILLIPLEX® multi-analyte panels. GWG determined by difference in pre-pregnancy and last prenatal visit weight. Results: Of 110 subjects enrolled, 19 (17.3%) delivered by Cesarean with 14 consenting to AT sampling, and 7 (50%) having both SQ and VAT available for analysis. These 7 had mean pre-pregnancy BMI 27.8±5.6 kg/m2 and GWG 50.0±25.7 lb (range 19-83) with delivery age 39.2±0.7 wks. Mean SQ and VAT adipocyte sizes were 2892±716 pixels2 (range 1866-3775) and 2427±641 pixels2 (range 1416-3397) respectively (p=0.310); neither were statistically correlated with BMI or GWG. Pre-pregnancy BMI statistically correlated with maternal serum insulin (0.786, p=0.036) at delivery and cord blood leptin (0.886, p=0.019); GWG statistically correlated only with cord blood adiponectin (-0.900, p=0.037). Conclusions: In a small sample of normoglycemic pregnancies undergoing Cesareans and AT sampling, adipocyte size was no different in SQ versus visceral depots, and did not correlate with BMI or GWG. Surprisingly, pre-pregnancy BMI but not GWG correlated with maternal serum insulin at delivery, suggesting that pre-pregnancy weight status may be associated with glycemic control at pregnancy end

Common non-synonymous SNPs associated with breast cancer susceptibility: findings from the Breast Cancer Association Consortium.

Candidate variant association studies have been largely unsuccessful in identifying common breast cancer susceptibility variants, although most studies have been underpowered to detect associations of a realistic magnitude. We assessed 41 common non-synonymous single-nucleotide polymorphisms (nsSNPs) for which evidence of association with breast cancer risk had been previously reported. Case-control data were combined from 38 studies of white European women (46 450 cases and 42 600 controls) and analyzed using unconditional logistic regression. Strong evidence of association was observed for three nsSNPs: ATXN7-K264R at 3p21 [rs1053338, per allele OR = 1.07, 95% confidence interval (CI) = 1.04-1.10, P = 2.9 × 10(-6)], AKAP9-M463I at 7q21 (rs6964587, OR = 1.05, 95% CI = 1.03-1.07, P = 1.7 × 10(-6)) and NEK10-L513S at 3p24 (rs10510592, OR = 1.10, 95% CI = 1.07-1.12, P = 5.1 × 10(-17)). The first two associations reached genome-wide statistical significance in a combined analysis of available data, including independent data from nine genome-wide association studies (GWASs): for ATXN7-K264R, OR = 1.07 (95% CI = 1.05-1.10, P = 1.0 × 10(-8)); for AKAP9-M463I, OR = 1.05 (95% CI = 1.04-1.07, P = 2.0 × 10(-10)). Further analysis of other common variants in these two regions suggested that intronic SNPs nearby are more strongly associated with disease risk. We have thus identified a novel susceptibility locus at 3p21, and confirmed previous suggestive evidence that rs6964587 at 7q21 is associated with risk. The third locus, rs10510592, is located in an established breast cancer susceptibility region; the association was substantially attenuated after adjustment for the known GWAS hit. Thus, each of the associated nsSNPs is likely to be a marker for another, non-coding, variant causally related to breast cancer risk. Further fine-mapping and functional studies are required to identify the underlying risk-modifying variants and the genes through which they act.BCAC is funded by Cancer Research UK (C1287/A10118, C1287/A12014) and by the European Community’s Seventh Framework Programme under grant agreement n8 223175 (HEALTH-F2–2009-223175) (COGS). Meetings of the BCAC have been funded by the European Union COST programme (BM0606). Genotyping of the iCOGS array was funded by the European Union (HEALTH-F2-2009-223175), Cancer Research UK (C1287/A10710), the Canadian Institutes of Health Research for the ‘CIHR Team in Familial Risks of Breast Cancer’ program and the Ministry of Economic Development, Innovation and Export Trade of Quebec (PSR-SIIRI-701). Additional support for the iCOGS infrastructure was provided by the National Institutes of Health (CA128978) and Post-Cancer GWAS initiative (1U19 CA148537, 1U19 CA148065 and 1U19 CA148112—the GAME-ON initiative), the Department of Defence (W81XWH-10-1-0341), Komen Foundation for the Cure, the Breast Cancer Research Foundation, and the Ovarian Cancer Research Fund. The ABCFS and OFBCR work was supported by grant UM1 CA164920 from the National Cancer Institute (USA). The content of this manuscript does not necessarily reflect the views or policies of the National Cancer Institute or any of the collaborating centers in the Breast Cancer Family Registry (BCFR), nor does mention of trade names, commercial products or organizations imply endorsement t by the US Government or the BCFR. The ABCFS was also supported by the National Health and Medical Research Council of Australia, the New South Wales Cancer Council, the Victorian Health Promotion Foundation (Australia) and the Victorian Breast Cancer Research Consortium. J.L.H. is a National Health and Medical Research Council (NHMRC) Senior Principal Research Fellow and M.C.S. is a NHMRC Senior Research Fellow. The OFBCR work was also supported by the Canadian Institutes of Health Research ‘CIHR Team in Familial Risks of Breast Cancer’ program. The ABCS was funded by the Dutch Cancer Society Grant no. NKI2007-3839 and NKI2009-4363. The ACP study is funded by the Breast Cancer Research Trust, UK. The work of the BBCC was partly funded by ELAN-Programme of the University Hospital of Erlangen. The BBCS is funded by Cancer Research UK and Breakthrough Breast Cancer and acknowledges NHS funding to the NIHR Biomedical Research Centre, and the National Cancer Research Network (NCRN). E.S. is supported by NIHR Comprehensive Biomedical Research Centre, Guy’s & St. Thomas’ NHS Foundation Trust in partnership with King’s College London, UK. Core funding to the Wellcome Trust Centre for Human Genetics was provided by the Wellcome Trust (090532/Z/09/Z). I.T. is supported by the Oxford Biomedical Research Centre. The BSUCH study was supported by the Dietmar-Hopp Foundation, the Helmholtz Society and the German Cancer Research Center (DKFZ). The CECILE study was funded by the Fondation de France, the French National Institute of Cancer (INCa), The National League against Cancer, the National Agency for Environmental l and Occupational Health and Food Safety (ANSES), the National Agency for Research (ANR), and the Association for Research against Cancer (ARC). The CGPS was supported by the Chief Physician Johan Boserup and Lise Boserup Fund, the Danish Medical Research Council and Herlev Hospital.The CNIO-BCS was supported by the Genome Spain Foundation the Red Temática de Investigación Cooperativa en Cáncer and grants from the Asociación Española Contra el Cáncer and the Fondo de Investigación Sanitario PI11/00923 and PI081120). The Human Genotyping-CEGEN Unit, CNIO is supported by the Instituto de Salud Carlos III. D.A. was supported by a Fellowship from the Michael Manzella Foundation (MMF) and was a participant in the CNIO Summer Training Program. The CTS was initially supported by the California Breast Cancer Act of 1993 and the California Breast Cancer Research Fund (contract 97-10500) and is currently funded through the National Institutes of Health (R01 CA77398). Collection of cancer incidence e data was supported by the California Department of Public Health as part of the statewide cancer reporting program mandated by California Health and Safety Code Section 103885. HAC receives support from the Lon V Smith Foundation (LVS39420). The ESTHER study was supported by a grant from the Baden Württemberg Ministry of Science, Research and Arts. Additional cases were recruited in the context of the VERDI study, which was supported by a grant from the German Cancer Aid (Deutsche Krebshilfe). The GENICA was funded by the Federal Ministry of Education and Research (BMBF) Germany grants 01KW9975/5, 01KW9976/8, 01KW9977/0 and 01KW0114, the Robert Bosch Foundation, Stuttgart, Deutsches Krebsforschungszentrum (DKFZ), Heidelberg Institute for Prevention and Occupational Medicine of the German Social Accident Insurance, Institute of the Ruhr University Bochum (IPA), as well as the Department of Internal Medicine , Evangelische Kliniken Bonn gGmbH, Johanniter Krankenhaus Bonn, Germany. The HEBCS was supported by the Helsinki University Central Hospital Research Fund, Academy of Finland (132473), the Finnish Cancer Society, The Nordic Cancer Union and the Sigrid Juselius Foundation. The HERPACC was supported by a Grant-in-Aid for Scientific Research on Priority Areas from the Ministry of Education, Science, Sports, Culture and Technology of Japan, by a Grant-in-Aid for the Third Term Comprehensive 10-Year strategy for Cancer Control from Ministry Health, Labour and Welfare of Japan, by a research grant from Takeda Science Foundation , by Health and Labour Sciences Research Grants for Research on Applying Health Technology from Ministry Health, Labour and Welfare of Japan and by National Cancer Center Research and Development Fund. The HMBCS was supported by short-term fellowships from the German Academic Exchange Program (to N.B), and the Friends of Hannover Medical School (to N.B.). Financial support for KARBAC was provided through the regional agreement on medical training and clinical research (ALF) between Stockholm County Council and Karolinska Institutet, the Stockholm Cancer Foundation and the Swedish Cancer Society. The KBCP was financially supported by the special Government Funding (EVO) of Kuopio University Hospital grants, Cancer Fund of North Savo, the Finnish Cancer Organizations, the Academy of Finland and by the strategic funding of the University of Eastern Finland. kConFab is supported by grants from the National Breast Cancer Foundation , the NHMRC, the Queensland Cancer Fund, the Cancer Councils of New South Wales, Victoria, Tasmania and South Australia and the Cancer Foundation of Western Australia. The kConFab Clinical Follow Up Study was funded by the NHMRC (145684, 288704, 454508). Financial support for the AOCS was provided by the United States Army Medical Research and Materiel Command (DAMD17-01-1-0729), the Cancer Council of Tasmania and Cancer Foundation of Western Australia and the NHMRC (199600). G.C.T. and P.W. are supported by the NHMRC. LAABC is supported by grants (1RB-0287, 3PB-0102, 5PB-0018 and 10PB-0098) from the California Breast Cancer Research Program. Incident breast cancer cases were collected by the USC Cancer Surveillance Program (CSP) which is supported under subcontract by the California Department of Health. The CSP is also part of the National Cancer Institute’s Division of Cancer Prevention and Control Surveillance, Epidemiology, and End Results Program, under contract number N01CN25403. LMBC is supported by the ‘Stichting tegen Kanker’ (232-2008 and 196-2010). The MARIE study was supported by the Deutsche Krebshilfe e.V. (70-2892-BR I), the Federal Ministry of Education Research (BMBF) Germany (01KH0402), the Hamburg Cancer Society and the German Cancer Research Center (DKFZ). MBCSG is supported by grants from the Italian Association ciation for Cancer Research (AIRC) and by funds from the Italian citizens who allocated a 5/1000 share of their tax payment in support of the Fondazione IRCCS Istituto Nazionale Tumori, according to Italian laws (INT-Institutional strategic projects ‘5 × 1000’). The MCBCS was supported by the NIH grants (CA122340, CA128978) and a Specialized Program of Research Excellence (SPORE) in Breast Cancer (CA116201), the Breast Cancer Research Foundation and a generous gift from the David F. and Margaret T. Grohne Family Foundation and the Ting Tsung and Wei Fong Chao Foundation. MCCS cohort recruitment was funded by VicHealth and Cancer Council Victoria. The MCCS was further supported by Australian NHMRC grants 209057, 251553 and 504711 and by infrastructure provided by Cancer Council Victoria. The MEC was supported by NIH grants CA63464, CA54281, CA098758 and CA132839. The work of MTLGEBCS was supported by the Quebec Breast Cancer Foundation, the Canadian Institutes of Health Research (grant CRN-87521) and the Ministry of Economic Development, Innovation and Export Trade (grant PSR-SIIRI-701). MYBRCA is funded by research grants from the Malaysian Ministry of Science, Technology and Innovation (MOSTI), Malaysian Ministry of Higher Education (UM.C/HlR/MOHE/06) and Cancer Research Initiatives Foundation (CARIF). Additional controls were recruited by the Singapore Eye Research Institute, which was supported by a grant from the Biomedical Research Council (BMRC08/1/35/19,tel:08/1/35/19./550), Singapore and the National medical Research Council, Singapore (NMRC/CG/SERI/2010). The NBCS was supported by grants from the Norwegian Research council (155218/V40, 175240/S10 to A.L.B.D., FUGE-NFR 181600/ V11 to V.N.K. and a Swizz Bridge Award to A.L.B.D.). The NBHS was supported by NIH grant R01CA100374. Biological sample preparation was conducted the Survey and Biospecimen Shared Resource, which is supported by P30 CA68485. The OBCS was supported by research grants from the Finnish Cancer Foundation, the Sigrid Juselius Foundation, the Academy of Finland, the University of Oulu, and the Oulu University Hospital. The ORIGO study was supported by the Dutch Cancer Society (RUL 1997-1505) and the Biobanking and Biomolecular Resources Research Infrastructure (BBMRI-NLCP16). The PBCS was funded by Intramural Research Funds of the National Cancer Institute, Department of Health and Human Services, USA. pKARMA is a combination of the KARMA and LIBRO-1 studies. KARMA was supported by Ma¨rit and Hans Rausings Initiative Against Breast Cancer. KARMA and LIBRO-1 were supported the Cancer Risk Prediction Center (CRisP; www.crispcenter.org), a Linnaeus Centre (Contract ID 70867902) financed by the Swedish Research Council. The RBCS was funded by the Dutch Cancer Society (DDHK 2004-3124, DDHK 2009-4318). SASBAC was supported by funding from the Agency for Science, Technology and Research of Singapore (A∗STAR), the US National Institute of Health (NIH) and the Susan G. Komen Breast Cancer Foundation KC was financed by the Swedish Cancer Society (5128-B07-01PAF). The SBCGS was supported primarily by NIH grants R01CA64277, R01CA148667, and R37CA70867. Biological sample preparation was conducted the Survey and Biospecimen Shared Resource, which is supported by P30 CA68485. The SBCS was supported by Yorkshire Cancer Research S305PA, S299 and S295. Funding for the SCCS was provided by NIH grant R01 CA092447. The Arkansas Central Cancer Registry is fully funded by a grant from National Program of Cancer Registries, Centers for Disease Control and Prevention (CDC). Data on SCCS cancer cases from Mississippi were collected by the Mississippi Cancer Registry which participates in the National Program of Cancer Registries (NPCR) of the Centers for Disease Control and Prevention (CDC). The contents of this publication are solely the responsibility of the authors and do not necessarily represent the official views of the CDC or the Mississippi Cancer Registry. SEARCH is funded by a programme grant from Cancer Research UK (C490/A10124) and supported by the UK National Institute for Health Research Biomedical Research Centre at the University of Cambridge. The SEBCS was supported by the BRL (Basic Research Laboratory) program through the National Research Foundation of Korea funded by the Ministry of Education, Science and Technology (2012-0000347). SGBCC is funded by the National Medical Research Council Start-up Grant and Centre Grant (NMRC/CG/NCIS /2010). The recruitment of controls by the Singapore Consortium of Cohort Studies-Multi-ethnic cohort (SCCS-MEC) was funded by the Biomedical Research Council (grant number: 05/1/21/19/425). SKKDKFZS is supported by the DKFZ. The SZBCS was supported by Grant PBZ_KBN_122/P05/2004. K. J. is a fellow of International PhD program, Postgraduate School of Molecular Medicine, Warsaw Medical University, supported by the Polish Foundation of Science. The TNBCC was supported by the NIH grant (CA128978), the Breast Cancer Research Foundation , Komen Foundation for the Cure, the Ohio State University Comprehensive Cancer Center, the Stefanie Spielman Fund for Breast Cancer Research and a generous gift from the David F. and Margaret T. Grohne Family Foundation and the Ting Tsung and Wei Fong Chao Foundation. Part of the TNBCC (DEMOKRITOS) has been co-financed by the European Union (European Social Fund – ESF) and Greek National Funds through the Operational Program ‘Education and Life-long Learning’ of the National Strategic Reference Framework (NSRF)—Research Funding Program of the General Secretariat for Research & Technology: ARISTEIA. The TWBCS is supported by the Institute of Biomedical Sciences, Academia Sinica and the National Science Council, Taiwan. The UKBGS is funded by Breakthrough Breast Cancer and the Institute of Cancer Research (ICR). ICR acknowledges NHS funding to the NIHR Biomedical Research Centre. Funding to pay the Open Access publication charges for this article was provided by the Wellcome Trust.This is the advanced access published version distributed under a Creative Commons Attribution License 2.0, which can also be viewed on the publisher's webstie at: http://hmg.oxfordjournals.org/content/early/2014/07/04/hmg.ddu311.full.pdf+htm

Gestational angiogenic biomarker patterns in high risk preeclampsia groups

OBJECTIVE: Several conditions are associated with increased preeclampsia (PE) risk. Whether altered maternal angiogenic factor levels contribute to risk in these conditions is unknown. Our objective was to compare angiogenic biomarker patterns in high-risk pregnancies and low-risk controls. STUDY DESIGN: We conducted a planned secondary analysis of a 2-center observational study of angiogenic biomarkers in high-risk women. A total of 156 pregnant women with a PE risk factor and 59 low-risk controls were studied. Serial maternal serum samples were collected during 3 gestational windows: 23-27 weeks, 28-31 weeks, and 32-35 weeks. Soluble fms-like tyrosine kinase 1 (sFlt1), soluble endoglin (sEng), and placental growth factor (PlGF) were measured by enzyme-linked immunosorbent assay. Geometric mean angiogenic biomarker levels and angiogenic ratio (sFlt1 + sEng):PlGF were compared with low-risk controls for each risk group, at each gestational window. RESULTS: Gestational biomarker patterns differed in PE risk groups as compared with low-risk controls. Women with multiple gestations had markedly higher sFlt1 and sEng at all gestational windows. Women with prior PE had higher sFlt1 and angiogenic ratio, and lower PlGF, from 28 weeks onward. Women with chronic hypertension had significantly higher angiogenic ratio for all 3 gestational windows, but differences disappeared when women with PE were excluded. Obese and nulliparous women had significantly lower PlGF, but no differences in the angiogenic ratio. CONCLUSION: High-risk groups have altered angiogenic biomarker patterns compared with controls, suggesting that altered production or metabolism of these factors may contribute to PE risk, particularly in women with multiple gestations and prior PE

Angiogenic biomarkers for prediction of early preeclampsia onset in high-risk women

Objective: Chronic hypertension, pregestational diabetes mellitus, history of prior preeclampsia and obese nulliparity are maternal conditions associated with increased preeclampsia risk. Whether altered maternal angiogenic factor levels allow for prediction of pending disease is unclear. Our objective was to evaluate angiogenic factors for early preeclampsia prediction in high-risk women. Methods: Serial serum specimens were collected from 157 women at high preeclampsia risk and 50 low-risk controls between 23 and 36 weeks gestation in 3 windows (23-27.6, 28-31.6, and 32-35.6 weeks) in a two-center observational cohort. Soluble fms-like tyrosine kinase-1 (sFlt1), placental growth factor (PlGF) and soluble endoglin (sEng) were measured by ELISA. Results: Multivariate parsimonious logistic regression analyses using backward elimination for prediction of early-preeclampsia (diagnosed\u3c34 weeks) found the best-fitting model included the predictors (1) sFlt1 measured in the second window (28-31.6 weeks) with AUC 0.85, sensitivity 67% and specificity 96% and (2) sFlt1 measured in the first window (23-27.6 weeks) and sEng change between first and second window with AUC 0.91, sensitivity 86% and specificity 96%. Conclusions: Two-stage sampling screening protocol utilizing sFlt1 and sEng is promising for prediction of preeclampsia diagnosed before 34 weeks. Larger studies are needed to confirm these findings. © 2014 Informa UK Ltd. All rights reserved: reproduction in whole or part not permitted

Angiogenic biomarkers for prediction of early preeclampsia onset in high-risk women.

OBJECTIVE: Chronic hypertension, pregestational diabetes mellitus, history of prior preeclampsia and obese nulliparity are maternal conditions associated with increased preeclampsia risk. Whether altered maternal angiogenic factor levels allow for prediction of pending disease is unclear. Our objective was to evaluate angiogenic factors for early preeclampsia prediction in high-risk women. METHODS: Serial serum specimens were collected from 157 women at high preeclampsia risk and 50 low-risk controls between 23 and 36 weeks gestation in 3 windows (23-27.6, 28-31.6, and 32-35.6 weeks) in a two-center observational cohort. Soluble fms-like tyrosine kinase-1 (sFlt1), placental growth factor (PlGF) and soluble endoglin (sEng) were measured by ELISA. RESULTS: Multivariate parsimonious logistic regression analyses using backward elimination for prediction of early-preeclampsia (diagnosed \u3c 34 weeks) found the best-fitting model included the predictors (1) sFlt1 measured in the second window (28-31.6 weeks) with AUC 0.85, sensitivity 67% and specificity 96% and (2) sFlt1 measured in the first window (23-27.6 weeks) and sEng change between first and second window with AUC 0.91, sensitivity 86% and specificity 96%. CONCLUSIONS: Two-stage sampling screening protocol utilizing sFlt1 and sEng is promising for prediction of preeclampsia diagnosed before 34 weeks. Larger studies are needed to confirm these findings

Gestational Angiogenic Biomarker Patterns in High Risk Preeclampsia Groups

OBJECTIVE: Several conditions are associated with increased preeclampsia (PE) risk. Whether altered maternal angiogenic factor levels contribute to risk in these conditions is unknown. Our objective was to compare angiogenic biomarker patterns in high-risk pregnancies and low-risk controls. STUDY DESIGN: We conducted a planned secondary analysis of a 2-center observational study of angiogenic biomarkers in high-risk women. A total of 156 pregnant women with a PE risk factor and 59 low-risk controls were studied. Serial maternal serum samples were collected during 3 gestational windows: 23-27 weeks, 28-31 weeks, and 32-35 weeks. Soluble fms-like tyrosine kinase 1 (sFlt1), soluble endoglin (sEng), and placental growth factor (PlGF) were measured by enzyme-linked immunosorbent assay. Geometric mean angiogenic biomarker levels and angiogenic ratio (sFlt1 + sEng):PlGF were compared with low-risk controls for each risk group, at each gestational window. RESULTS: Gestational biomarker patterns differed in PE risk groups as compared with low-risk controls. Women with multiple gestations had markedly higher sFlt1 and sEng at all gestational windows. Women with prior PE had higher sFlt1 and angiogenic ratio, and lower PlGF, from 28 weeks onward. Women with chronic hypertension had significantly higher angiogenic ratio for all 3 gestational windows, but differences disappeared when women with PE were excluded. Obese and nulliparous women had significantly lower PlGF, but no differences in the angiogenic ratio. CONCLUSION: High-risk groups have altered angiogenic biomarker patterns compared with controls, suggesting that altered production or metabolism of these factors may contribute to PE risk, particularly in women with multiple gestations and prior PE

Phenotype and function of HBV-specific T cells is determined by the targeted epitope in addition to the stage of infection

OBJECTIVE: Chronic HBV infection affects more than 250 million people worldwide and remains a global healthcare problem in part because we lack curative treatment. Sustained viral control requires HBV-specific T cells, but these become functionally impaired in chronic infection. Clinical evidence indicates that functional cure of HBV infection by the host immune response is feasible. Developing T cell-based therapies able to achieve functional cure will require identification of the requirements for a successful T cell response against HBV and the relative contribution of individual T cell specificities to HBV control. DESIGN: The phenotype and function of HBV-specific T cells were studied directly ex vivo using fluorochrome-labelled multimers. We studied multiple HBV-specific T cell specificities targeting different HBV proteins in individuals with either an acute self-limiting or chronic HBV infection. RESULTS: We detected strong T cell responses targeting multiple HBV viral proteins in acute self-limiting and low-frequency core and polymerase-specific T cells in chronic infection. Expression of the T cell inhibitory receptor PD-1, as well as T cell differentiation, T cell function and T cell regulation differed by stages and outcomes of infection. In addition, these features differed significantly between T cells targeting different HBV specificities. CONCLUSION: HBV-specific T cells with different target specificities are characterised by distinct phenotypical and functional profiles. These results have direct implications for the design of immunological studies in HBV infection, and are potentially relevant for informing immunotherapeutic approaches to induce functional cure