PreprintCited by 62Gestational Diabetes Research and ManagementGrowth Hormone and Insulin-like Growth FactorsPregnancy and preeclampsia studies

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

Placental RNA sequencing implicates IGFBP1 in insulin sensitivity during pregnancy and in gestational diabetes

Open access
Hivert, Marie-France, White, Frederique, Allard, Catherine, James, Kaitlyn, Majid, Sana, Aguet, François, Ardlie, Kristin, Edlow, Andrea, Florez, Jose, Bouchard, Luigi, Jacques, Pierre-Etienne, Karumanchi, S., Powe, Camille·2023·Nature Medicine

Preprint — published as 10.1038/s41591-024-02936-5 · Paper page

Abstract

Reduced insulin sensitivity (or greater insulin resistance) is a hallmark of normal physiology in late pregnancy and also underlies gestational diabetes mellitus (GDM) pathophysiology. We conducted transcriptomic profiling of 434 human placentas and identified a strong positive association between insulin-like growth factor binding protein 1 gene (IGFBP1) expression in the placenta and insulin sensitivity at ~ 26 weeks’ gestation. Circulating IGFBP1 protein levels rose over the course of pregnancy and declined postpartum, which together with high placental gene expression levels, suggests a placental source. Higher circulating IGFBP1 levels were strongly associated with greater insulin sensitivity (lesser insulin resistance) at ~ 26 weeks’ gestation in the same cohort and two additional pregnancy cohorts. In addition, low circulating IGFBP1 levels in early pregnancy predicted subsequent GDM diagnosis in two cohorts. These results implicate IGFBP1 in the glycemic physiology of pregnancy and suggest a role for placental IGFBP1 deficiency in GDM pathogenesis.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.21203/rs.3.rs-3464151/v1

Cites

1 of 1 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Flagged first, then by citations

Research works printed but not linked: 58

  1. Magliano, D., Boyko, E. & IDF Diabetes Atlas 10th edition scientific committee. IDF DIABETES ATLAS [Internet] Chap. 3, Global picture (Brussels: International Diabetes Federation, 2021).
  2. Catalano, P. M., Kirwan, J. P., Haugel-de Mouzon, S. & King, J. Gestational diabetes and insulin resistance: role in short- and long-term implications for mother and fetus. J. Nutr. 133, 1674S-1683S (2003). · DOI
  3. Barbour, L. A. et al. Cellular mechanisms for insulin resistance in normal pregnancy and gestational diabetes. Diabetes Care 30 Suppl 2, S112-119 (2007).
  4. Sun, Y. et al. Increasing insulin resistance predicts adverse pregnancy outcomes in women with gestational diabetes mellitus. J. Diabetes 12, 438–446 (2020). · DOI
  5. Li, J. et al. Roles of insulin resistance and beta cell dysfunction in macrosomia among Chinese women with gestational diabetes mellitus. Prim. Care Diabetes 12, 565–573 (2018). · DOI
  6. Wang, N. et al. Contribution of gestational diabetes mellitus heterogeneity and prepregnancy body mass index to large-for‐gestational‐age infants—A retrospective case‐control study. J. Diabetes 13, 307–317 (2021). · DOI
  7. Madsen, L. R. et al. Do variations in insulin sensitivity and insulin secretion in pregnancy predict differences in obstetric and neonatal outcomes? Diabetologia 64, 304–312 (2021). · DOI
  8. Immanuel, J. et al. Metabolic phenotypes of early gestational diabetes mellitus and their association with adverse pregnancy outcomes. Diabet. Med. 38, e14413 (2021). · DOI
  9. Powe, C. E. et al. Heterogeneous Contribution of Insulin Sensitivity and Secretion Defects to Gestational Diabetes Mellitus. Diabetes Care 39, 1052–1055 (2016). · DOI
  10. Beck, P. & Daughaday, W. H. Human placental lactogen: studies of its acute metabolic effects and disposition in normal man. J. Clin. Invest. 46, 103–110 (1967). · DOI
  11. Kirwan, J. P. et al. TNF-alpha is a predictor of insulin resistance in human pregnancy. Diabetes 51, 2207–2213 (2002). · DOI · Paper page
  12. Powe, C. E., Huston Presley, L. P., Locascio, J. J. & Catalano, P. M. Augmented insulin secretory response in early pregnancy. Diabetologia 62, 1445–1452 (2019). · DOI
  13. García-Patterson, A. et al. Insulin requirements throughout pregnancy in women with type 1 diabetes mellitus: three changes of direction. Diabetologia 53, 446–451 (2010). · DOI · Paper page
  14. Guillemette, L. et al. Genetics of Glucose regulation in Gestation and Growth (Gen3G): a prospective prebirth cohort of mother-child pairs in Sherbrooke, Canada. BMJ Open 6, e010031 (2016). · DOI
  15. Kirwan, J. P., Huston-Presley, L., Kalhan, S. C. & Catalano, P. M. Clinically useful estimates of insulin sensitivity during pregnancy: validation studies in women with normal glucose tolerance and gestational diabetes mellitus. Diabetes Care 24, 1602–1607 (2001). · DOI
  16. Uhlén, M. et al. Proteomics. Tissue-based map of the human proteome. Science 347, 1260419 (2015). · DOI · Paper page
  17. Duan, C. & Xu, Q. Roles of insulin-like growth factor (IGF) binding proteins in regulating IGF actions. Gen. Comp. Endocrinol. 142, 44–52 (2005). · DOI
  18. Martín-Estal, I. & Castorena-Torres, F. Gestational Diabetes Mellitus and Energy-Dense Diet: What Is the Role of the Insulin/IGF Axis? Front. Endocrinol. 13, 916042 (2022). · DOI
  19. Suryawanshi, H. et al. A single-cell survey of the human first-trimester placenta and decidua. Sci. Adv. 4, eaau4788 (2018). · DOI
  20. Bae, J.-H., Song, D.-K. & Im, S.-S. Regulation of IGFBP-1 in Metabolic Diseases. J. Lifestyle Med. 3, 73–79 (2013).
  21. Gao, J., Mazella, J., Suwanichkul, A., Powell, D. R. & Tseng, L. Activation of the insulin-like growth factor binding protein-1 promoter by progesterone receptor in decidualized human endometrial stromal cells. Mol. Cell. Endocrinol. 153, 11–17 (1999). · DOI
  22. Goldsmith, L. T. & Weiss, G. Relaxin in human pregnancy. Ann. N. Y. Acad. Sci. 1160, 130–135 (2009). · DOI
  23. Powell, D. R. et al. Insulin inhibits transcription of the human gene for insulin-like growth factor-binding protein-1. J. Biol. Chem. 266, 18868–18876 (1991). · DOI · Paper page
  24. Qiao, L. et al. Knockout maternal adiponectin increases fetal growth in mice: potential role for trophoblast IGFBP-1. Diabetologia 59, 2417–2425 (2016). · DOI
  25. Laager, R., Ninnis, R. & Keller, U. Comparison of the effects of recombinant human insulin-like growth factor-I and insulin on glucose and leucine kinetics in humans. J. Clin. Invest. 92, 1903–1909 (1993). · DOI
  26. Jacob, R., Barrett, E., Plewe, G., Fagin, K. D. & Sherwin, R. S. Acute effects of insulin-like growth factor I on glucose and amino acid metabolism in the awake fasted rat. Comparison with insulin. J. Clin. Invest. 83, 1717–1723 (1989). · DOI
  27. Clemmons, D. R., Sleevi, M., Allan, G. & Sommer, A. Effects of Combined Recombinant Insulin-Like Growth Factor (IGF)-I and IGF Binding Protein-3 in Type 2 Diabetic Patients on Glycemic Control and Distribution of IGF-I and IGF-II among Serum Binding Protein Complexes. J. Clin. Endocrinol. Metab. 92, 2652–2658 (2007). · DOI
  28. Scavo, L. M., Karas, M., Murray, M. & Leroith, D. Insulin-Like Growth Factor-I Stimulates Both Cell Growth and Lipogenesis during Differentiation of Human Mesenchymal Stem Cells into Adipocytes. J. Clin. Endocrinol. Metab. 89, 3543–3553 (2004). · DOI
  29. Sferruzzi-Perri, A. N. Regulating needs: Exploring the role of insulin-like growth factor-2 signalling in materno-fetal resource allocation. Placenta 64 Suppl 1, S16–S22 (2018). · DOI
  30. Lopez-Tello, J. et al. Fetal manipulation of maternal metabolism is a critical function of the imprinted Igf2 gene. Cell Metab. 35, 1195–1208.e6 (2023). · DOI
  31. Wheatcroft, S. B. & Kearney, M. T. IGF-dependent and IGF-independent actions of IGF-binding protein-1 and – 2: implications for metabolic homeostasis. Trends Endocrinol. Metab. 20, 153–162 (2009). · DOI
  32. Haywood, N. J. et al. Insulin-Like Growth Factor Binding Protein 1 Could Improve Glucose Regulation and Insulin Sensitivity Through Its RGD Domain. Diabetes 66, 287–299 (2017). · DOI
  33. Crossey, P. A., Jones, J. S. & Miell, J. P. Dysregulation of the insulin/IGF binding protein-1 axis in transgenic mice is associated with hyperinsulinemia and glucose intolerance. Diabetes 49, 457–465 (2000). · DOI · Paper page
  34. Rajwani, A. et al. Increasing circulating IGFBP1 levels improves insulin sensitivity, promotes nitric oxide production, lowers blood pressure, and protects against atherosclerosis. Diabetes 61, 915–924 (2012). · DOI
  35. Haywood, N. J. et al. IGFBP-1 in Cardiometabolic Pathophysiology-Insights From Loss-of-Function and Gain-of-Function Studies in Male Mice. J. Endocr. Soc. 4, bvz006 (2020). · DOI
  36. Powe, C. E., Hivert, M.-F. & Udler, M. S. Defining Heterogeneity Among Women With Gestational Diabetes Mellitus. Diabetes 69, 2064–2074 (2020). · DOI
  37. Tobias, D. K. et al. Second international consensus report on gaps and opportunities for the clinical translation of precision diabetes medicine. Nat. Med. (2023).
  38. Ellen C. Francis et al. Refining the diagnosis of Gestational Diabetes Mellitus: A systematic review to inform efforts in precision medicine. Preprint at https://www.medrxiv.org/content/10.1101/2023.04.19.23288627v1 (2023). · DOI
  39. Alekseenkova, E. N. et al. Maternal insulin-like growth factors and insulin-like growth factor-binding proteins for macrosomia prediction in diabetic and nondiabetic pregnancy: A prospective study. Int. J. Gynaecol. Obstet. Off. Organ Int. Fed. Gynaecol. Obstet. 162, 605–613 (2023). · DOI
  40. Thong, E. P. et al. Optimising Cardiometabolic Risk Factors in Pregnancy: A Review of Risk Prediction Models Targeting Gestational Diabetes and Hypertensive Disorders. J. Cardiovasc. Dev. Dis. 9, 55 (2022).
  41. Kotzaeridi, G. et al. Performance of early risk assessment tools to predict the later development of gestational diabetes. Eur. J. Clin. Invest. 51, e13630 (2021). · DOI · Paper page
  42. Wang, X.-R. et al. Insulin-Like Growth Factor Axis Biomarkers and Gestational Diabetes Mellitus: A Systematic Review and Meta-Analysis. Front. Endocrinol. 10, 444 (2019). · DOI
  43. Matsuda, M. & DeFronzo, R. A. Insulin sensitivity indices obtained from oral glucose tolerance testing: comparison with the euglycemic insulin clamp. Diabetes Care 22, 1462–1470 (1999). · DOI · Paper page
  44. The GTEx Consortium et al. The GTEx Consortium atlas of genetic regulatory effects across human tissues. Science 369, 1318–1330 (2020). · DOI · Paper page
  45. Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013). · DOI · Paper page
  46. Graubert, A., Aguet, F., Ravi, A., Ardlie, K. G. & Getz, G. RNA-SeQC 2: efficient RNA-seq quality control and quantification for large cohorts. Bioinformatics 37, 3048–3050 (2021). · DOI
  47. Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010). · DOI · Paper page
  48. Law, C. W., Chen, Y., Shi, W. & Smyth, G. K. voom: precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biol. 15, R29 (2014). · DOI · Paper page
  49. Wolf, M. et al. Obesity and preeclampsia: the potential role of inflammation. Obstet. Gynecol. 98, 757–762 (2001).
  50. Stuebe, A. M., McElrath, T. F., Thadhani, R. & Ecker, J. L. Second trimester insulin resistance, early pregnancy body mass index and gestational weight gain. Matern. Child Health J. 14, 254–260 (2010). · DOI
  51. Levy, J. C., Matthews, D. R. & Hermans, M. P. Correct homeostasis model assessment (HOMA) evaluation uses the computer program. Diabetes Care 21, 2191–2192 (1998). · DOI
  52. Teschendorff, A. E., Zhuang, J. & Widschwendter, M. Independent surrogate variable analysis to deconvolve confounding factors in large-scale microarray profiling studies. Bioinformatics 27, 1496–1505 (2011). · DOI
  53. Chen, J. et al. Fast and robust adjustment of cell mixtures in epigenome-wide association studies with SmartSVA. BMC Genomics 18, 413 (2017). · DOI
  54. Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015). · DOI · Paper page
  55. Venables, W. N. & Ripley, B. D. Modern applied statistics with S. (Springer, 2002). · DOI
  56. International Association of Diabetes and Pregnancy Study Groups Consensus Panel et al. International association of diabetes and pregnancy study groups recommendations on the diagnosis and classification of hyperglycemia in pregnancy. Diabetes Care 33, 676–682 (2010). · DOI · Paper page
  57. ACOG Practice Bulletin No. 190: Gestational Diabetes Mellitus. Obstet. Gynecol. 131, e49–e64 (2018). · DOI · Paper page
  58. Robin, X. et al. pROC: an open-source package for R and S + to analyze and compare ROC curves. BMC Bioinformatics 12, 77 (2011). · DOI · Paper page

Metadata

Authors
Hivert, M.-F., White, F., Allard, C., James, K., Majid, S., Aguet, F., Ardlie, K., Edlow, A., Florez, J., Bouchard, L., Jacques, P.-E., Karumanchi, S., & Powe, C.
Affiliations
  • Harvard University (US)
  • Universite de Sherbrooke
  • Centre de recherche du CHUS (CA)
  • Broad Institute (US)
  • Massachusetts General Hospital
  • Université de Sherbrooke (CA)
  • Cedars-Sinai Medical Center (US)
Corresponding author
Hivert, Marie-France
Venue
Nature Medicine
Published
2023-10-27
Volume
30
Issue
6
Pages
1689-1695
ISSN
1078-8956 (ISSN-L)
OpenAlex
W4394832188
Cited by
62
References
1
Citations by year
2026: 26 · 2025: 27 · 2024: 9
FWCI
34.15
Type
journal article
Language
en
Open access
Locations
  • Nature Medicine (publishedVersion, cc-by)
  • Research Square (acceptedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • PubMed Central (submittedVersion, cc-by)
Funders
  • U.S. Department of Health and Human Services (K23DK113218, R01HD094150, NHLBI K24 HL157960)
  • American Diabetes Association (1-15-ACE-26)
  • Harvard Catalyst (UL1TR000170, UL1-TR-001102)
  • Diabète Québec (20697, MOP 115071)
  • Government of Canada (#MOP 115071, #PJT-152989)
  • National Institutes of Health (UL1TR000170, UL1TR001102)
  • Canadian Institutes of Health Research (PJT152989, MOP – 115071, 115071, 152989)
  • Fonds de Recherche du Québec - Santé (#20697)
  • National Heart, Lung, and Blood Institute (K24HL157960)
  • National Institute of Diabetes and Digestive and Kidney Diseases (K23DK113218)
  • National Center for Advancing Translational Sciences
  • Eunice Kennedy Shriver National Institute of Child Health and Human Development (R01HD094150)
Topics
  • Health Sciences › Medicine › Obstetrics and Gynecology › Gestational Diabetes Research and Management
  • Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Growth Hormone and Insulin-like Growth Factors
  • Health Sciences › Medicine › Obstetrics and Gynecology › Pregnancy and preeclampsia studies
Keywords
IGFBP-1, placenta, insulin resistance, gestational diabetes mellitus, early pregnancy, insulin sensitivity, placental transcriptome, GDM risk prediction

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review