Academic Journal
Προβολή σε EDS

Early Antenatal Prediction of Gestational Diabetes in Obese Women: Development of Prediction Tools for Targeted Intervention.

Λεπτομέρειες βιβλιογραφικής εγγραφής
Τίτλος: Early Antenatal Prediction of Gestational Diabetes in Obese Women: Development of Prediction Tools for Targeted Intervention.
Συγγραφείς: White, SL, Lawlor, DA, Briley, AL, Godfrey, KM, Nelson, SM, Oteng-Ntim, E, Robson, SC, Sattar, N, Seed, PT, Vieira, MC, Welsh, P, Whitworth, M, Poston, L, Pasupathy, D, UPBEAT Consortium
Στοιχεία εκδότη: Public Library of Science (PLoS)
Έτος έκδοσης: 2022
Συλλογή: University of Technology Sydney: OPUS - Open Publications of UTS Scholars
Θεματικοί όροι: General Science & Technology, Adult, Diabetes, Gestational, Female, Humans, Obesity, Pregnancy, Prospective Studies
Περιγραφή: All obese women are categorised as being of equally high risk of gestational diabetes (GDM) whereas the majority do not develop the disorder. Lifestyle and pharmacological interventions in unselected obese pregnant women have been unsuccessful in preventing GDM. Our aim was to develop a prediction tool for early identification of obese women at high risk of GDM to facilitate targeted interventions in those most likely to benefit. Clinical and anthropometric data and non-fasting blood samples were obtained at 15+0-18+6 weeks' gestation in 1303 obese pregnant women from UPBEAT, a randomised controlled trial of a behavioural intervention. Twenty one candidate biomarkers associated with insulin resistance, and a targeted nuclear magnetic resonance (NMR) metabolome were measured. Prediction models were constructed using stepwise logistic regression. Twenty six percent of women (n = 337) developed GDM (International Association of Diabetes and Pregnancy Study Groups criteria). A model based on clinical and anthropometric variables (age, previous GDM, family history of type 2 diabetes, systolic blood pressure, sum of skinfold thicknesses, waist:height and neck:thigh ratios) provided an area under the curve of 0.71 (95%CI 0.68-0.74). This increased to 0.77 (95%CI 0.73-0.80) with addition of candidate biomarkers (random glucose, haemoglobin A1c (HbA1c), fructosamine, adiponectin, sex hormone binding globulin, triglycerides), but was not improved by addition of NMR metabolites (0.77; 95%CI 0.74-0.81). Clinically translatable models for GDM prediction including readily measurable variables e.g. mid-arm circumference, age, systolic blood pressure, HbA1c and adiponectin are described. Using a ≥35% risk threshold, all models identified a group of high risk obese women of whom approximately 50% (positive predictive value) later developed GDM, with a negative predictive value of 80%. Tools for early pregnancy identification of obese women at risk of GDM are described which could enable targeted interventions for GDM prevention ...
Τύπος εγγράφου: article in journal/newspaper
Περιγραφή αρχείου: Electronic-eCollection; application/pdf
Γλώσσα: English
ISSN: 1932-6203
Relation: PLoS One; PLoS One, 2016, 11, (12), pp. e0167846; http://hdl.handle.net/10453/153857
Διαθεσιμότητα: http://hdl.handle.net/10453/153857
Rights: info:eu-repo/semantics/openAccess
Αριθμός Καταχώρησης: edsbas.B78C036A
Βάση Δεδομένων: BASE
FullText Text:
  Availability: 0
CustomLinks:
  – Url: http://hdl.handle.net/10453/153857#
    Name: EDS - BASE (ns324271)
    Category: fullText
    Text: View record from BASE
  – Url: https://resolver.ebsco.com/c/fiv2js/result?sid=EBSCO:edsbas&genre=article&issn=19326203&ISBN=&volume=&issue=&date=20220101&spage=&pages=&title=Early Antenatal Prediction of Gestational Diabetes in Obese Women: Development of Prediction Tools for Targeted Intervention.&atitle=Early%20Antenatal%20Prediction%20of%20Gestational%20Diabetes%20in%20Obese%20Women%3A%20Development%20of%20Prediction%20Tools%20for%20Targeted%20Intervention.&aulast=White%2C%20SL&id=DOI:
    Name: Full Text Finder (for New FTF UI) (ns324271)
    Category: fullText
    Text: Full Text Finder
    MouseOverText: Full Text Finder
Header DbId: edsbas
DbLabel: BASE
An: edsbas.B78C036A
RelevancyScore: 920
AccessLevel: 3
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 920.2763671875
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Early Antenatal Prediction of Gestational Diabetes in Obese Women: Development of Prediction Tools for Targeted Intervention.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22White%2C+SL%22">White, SL</searchLink><br /><searchLink fieldCode="AR" term="%22Lawlor%2C+DA%22">Lawlor, DA</searchLink><br /><searchLink fieldCode="AR" term="%22Briley%2C+AL%22">Briley, AL</searchLink><br /><searchLink fieldCode="AR" term="%22Godfrey%2C+KM%22">Godfrey, KM</searchLink><br /><searchLink fieldCode="AR" term="%22Nelson%2C+SM%22">Nelson, SM</searchLink><br /><searchLink fieldCode="AR" term="%22Oteng-Ntim%2C+E%22">Oteng-Ntim, E</searchLink><br /><searchLink fieldCode="AR" term="%22Robson%2C+SC%22">Robson, SC</searchLink><br /><searchLink fieldCode="AR" term="%22Sattar%2C+N%22">Sattar, N</searchLink><br /><searchLink fieldCode="AR" term="%22Seed%2C+PT%22">Seed, PT</searchLink><br /><searchLink fieldCode="AR" term="%22Vieira%2C+MC%22">Vieira, MC</searchLink><br /><searchLink fieldCode="AR" term="%22Welsh%2C+P%22">Welsh, P</searchLink><br /><searchLink fieldCode="AR" term="%22Whitworth%2C+M%22">Whitworth, M</searchLink><br /><searchLink fieldCode="AR" term="%22Poston%2C+L%22">Poston, L</searchLink><br /><searchLink fieldCode="AR" term="%22Pasupathy%2C+D%22">Pasupathy, D</searchLink><br /><searchLink fieldCode="AR" term="%22UPBEAT+Consortium%22">UPBEAT Consortium</searchLink>
– Name: Publisher
  Label: Publisher Information
  Group: PubInfo
  Data: Public Library of Science (PLoS)
– Name: DatePubCY
  Label: Publication Year
  Group: Date
  Data: 2022
– Name: Subset
  Label: Collection
  Group: HoldingsInfo
  Data: University of Technology Sydney: OPUS - Open Publications of UTS Scholars
– Name: Subject
  Label: Subject Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22General+Science+%26+Technology%22">General Science & Technology</searchLink><br /><searchLink fieldCode="DE" term="%22Adult%22">Adult</searchLink><br /><searchLink fieldCode="DE" term="%22Diabetes%22">Diabetes</searchLink><br /><searchLink fieldCode="DE" term="%22Gestational%22">Gestational</searchLink><br /><searchLink fieldCode="DE" term="%22Female%22">Female</searchLink><br /><searchLink fieldCode="DE" term="%22Humans%22">Humans</searchLink><br /><searchLink fieldCode="DE" term="%22Obesity%22">Obesity</searchLink><br /><searchLink fieldCode="DE" term="%22Pregnancy%22">Pregnancy</searchLink><br /><searchLink fieldCode="DE" term="%22Prospective+Studies%22">Prospective Studies</searchLink>
– Name: Abstract
  Label: Description
  Group: Ab
  Data: All obese women are categorised as being of equally high risk of gestational diabetes (GDM) whereas the majority do not develop the disorder. Lifestyle and pharmacological interventions in unselected obese pregnant women have been unsuccessful in preventing GDM. Our aim was to develop a prediction tool for early identification of obese women at high risk of GDM to facilitate targeted interventions in those most likely to benefit. Clinical and anthropometric data and non-fasting blood samples were obtained at 15+0-18+6 weeks' gestation in 1303 obese pregnant women from UPBEAT, a randomised controlled trial of a behavioural intervention. Twenty one candidate biomarkers associated with insulin resistance, and a targeted nuclear magnetic resonance (NMR) metabolome were measured. Prediction models were constructed using stepwise logistic regression. Twenty six percent of women (n = 337) developed GDM (International Association of Diabetes and Pregnancy Study Groups criteria). A model based on clinical and anthropometric variables (age, previous GDM, family history of type 2 diabetes, systolic blood pressure, sum of skinfold thicknesses, waist:height and neck:thigh ratios) provided an area under the curve of 0.71 (95%CI 0.68-0.74). This increased to 0.77 (95%CI 0.73-0.80) with addition of candidate biomarkers (random glucose, haemoglobin A1c (HbA1c), fructosamine, adiponectin, sex hormone binding globulin, triglycerides), but was not improved by addition of NMR metabolites (0.77; 95%CI 0.74-0.81). Clinically translatable models for GDM prediction including readily measurable variables e.g. mid-arm circumference, age, systolic blood pressure, HbA1c and adiponectin are described. Using a ≥35% risk threshold, all models identified a group of high risk obese women of whom approximately 50% (positive predictive value) later developed GDM, with a negative predictive value of 80%. Tools for early pregnancy identification of obese women at risk of GDM are described which could enable targeted interventions for GDM prevention ...
– Name: TypeDocument
  Label: Document Type
  Group: TypDoc
  Data: article in journal/newspaper
– Name: Format
  Label: File Description
  Group: SrcInfo
  Data: Electronic-eCollection; application/pdf
– Name: Language
  Label: Language
  Group: Lang
  Data: English
– Name: ISSN
  Label: ISSN
  Group: ISSN
  Data: 1932-6203
– Name: NoteTitleSource
  Label: Relation
  Group: SrcInfo
  Data: PLoS One; PLoS One, 2016, 11, (12), pp. e0167846; http://hdl.handle.net/10453/153857
– Name: URL
  Label: Availability
  Group: URL
  Data: http://hdl.handle.net/10453/153857
– Name: Copyright
  Label: Rights
  Group: Cpyrght
  Data: info:eu-repo/semantics/openAccess
– Name: AN
  Label: Accession Number
  Group: ID
  Data: edsbas.B78C036A
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.B78C036A
RecordInfo BibRecord:
  BibEntity:
    Languages:
      – Text: English
    Subjects:
      – SubjectFull: General Science & Technology
        Type: general
      – SubjectFull: Adult
        Type: general
      – SubjectFull: Diabetes
        Type: general
      – SubjectFull: Gestational
        Type: general
      – SubjectFull: Female
        Type: general
      – SubjectFull: Humans
        Type: general
      – SubjectFull: Obesity
        Type: general
      – SubjectFull: Pregnancy
        Type: general
      – SubjectFull: Prospective Studies
        Type: general
    Titles:
      – TitleFull: Early Antenatal Prediction of Gestational Diabetes in Obese Women: Development of Prediction Tools for Targeted Intervention.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: White, SL
      – PersonEntity:
          Name:
            NameFull: Lawlor, DA
      – PersonEntity:
          Name:
            NameFull: Briley, AL
      – PersonEntity:
          Name:
            NameFull: Godfrey, KM
      – PersonEntity:
          Name:
            NameFull: Nelson, SM
      – PersonEntity:
          Name:
            NameFull: Oteng-Ntim, E
      – PersonEntity:
          Name:
            NameFull: Robson, SC
      – PersonEntity:
          Name:
            NameFull: Sattar, N
      – PersonEntity:
          Name:
            NameFull: Seed, PT
      – PersonEntity:
          Name:
            NameFull: Vieira, MC
      – PersonEntity:
          Name:
            NameFull: Welsh, P
      – PersonEntity:
          Name:
            NameFull: Whitworth, M
      – PersonEntity:
          Name:
            NameFull: Poston, L
      – PersonEntity:
          Name:
            NameFull: Pasupathy, D
      – PersonEntity:
          Name:
            NameFull: UPBEAT Consortium
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 01
              Type: published
              Y: 2022
          Identifiers:
            – Type: issn-print
              Value: 19326203
            – Type: issn-locals
              Value: edsbas
            – Type: issn-locals
              Value: edsbas.oa
ResultId 1