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Predictive and diagnostic biomarkers for gestational diabetes and its associated metabolic and cardiovascular diseases
Open accessAbstract
Gestational diabetes mellitus (GDM) is defined as the presence of high blood glucose levels with the onset, or detected for the first time during pregnancy, as a result of increased insulin resistance. GDM may be induced by dysregulation of pancreatic β-cell function and/or by alteration of secreted gestational hormones and peptides related with glucose homeostasis. It may affect one out of five pregnancies, leading to perinatal morbidity and adverse neonatal outcomes, and high risk of chronic metabolic and cardiovascular injuries in both mother and offspring. Currently, GDM diagnosis is based on evaluation of glucose homeostasis at late stages of pregnancy, but increased age and body-weight, and familiar or previous occurrence of GDM, may conditionate this criteria. In addition, an earlier and more specific detection of GDM with associated metabolic and cardiovascular risk could improve GDM development and outcomes. In this sense, 1st–2nd trimester-released biomarkers found in maternal plasma including adipose tissue-derived factors such as adiponectin, visfatin, omentin-1, fatty acid-binding protein-4 and retinol binding-protein-4 have shown correlations with GDM development. Moreover, placenta-related factors such as sex hormone-binding globulin, afamin, fetuin-A, fibroblast growth factors-21/23, ficolin-3 and follistatin, or specific micro-RNAs may participate in GDM progression and be useful for its recognition. Finally, urine-excreted metabolites such as those related with serotonin system, non-polar amino-acids and ketone bodies, may complete a predictive or early-diagnostic panel of biomarkers for GDM.
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![Fig. 1 Predictive and diagnostic biomarkers for GDM pregnancies. GDM usually develops from the 2nd trimester of pregnancy in correlation with increased inflammation, insulin resistance, placental dysregulation and/or β-cell disruption, and can be detected at the 24th–28th week by evaluation of glucose homeostasis. However, some specific protein (blue lines), miRs (black lines) and metabolites (red lines) are released into the blood and/or urine from early stages of (complicated) pregnancies and could serve as biomarkers for GDM. In particular, RBP4, SHBG, afamin, FABP4, hs-PCR, adiponectin and several miRs (miR-16-5p, -17-5p, -20a-5p) could be tested at the beginning of pregnancies, mainly in women with risk factors (obesity, advanced aged, previous GDM). In addition, visfatin, fetuin-A, omentin, leptin, ficolin-3 and specific metabolites (i.e., AHBA, L-Tryp) may be useful for the mid-stage of gestation, and FGF-21, PAI-1, fetuin-B and follistatin, and other metabolites [Ceramide (d18:0/23:0), aspartame] could help GDM screening at the 3rd trimester. Then, early interventions on metabolic and cardiovascular abnormalities could attenuate associated post-parturition (perinatal, neonatal and chronic) disorders in women and offspring](/paper-figures/a6b6d6780a544fd6baba842112b8101d/predictive-and-diagnostic-biomarkers-for-gestational-diabetes-and-its-associated-figure-1.jpg)
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“The greatest hopes are connected with afamine, adiponectin and 1,5-anhydroglucitol [34,35].”
From Gestational Diabetes Mellitus—Recent Literature Review (2022) · cites this paper as [34]
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Citations
Cites
Diagnosis and Classification of Diabetes Mellitus
American Diabetes Association (2010) · Diabetes Care · doi:10.2337/dc11-s062 · Cited by 14,094
Standards of Medical Care in Diabetes
Power, D. V. (2006) · Diabetes Care · doi:10.2337/diacare.29.02.06.dc05-1593 · Cited by 8,676
International Association of Diabetes and Pregnancy Study Groups Consensus Panel (2010) · Diabetes Care · doi:10.2337/dc09-1848 · Cited by 5,390
Type 2 diabetes mellitus after gestational diabetes: a systematic review and meta-analysis
Bellamy, L., Casas, J.-P., Hingorani, A. D., & Williams, D. (2009) · The Lancet · doi:10.1016/s0140-6736(09)60731-5 · Cited by 3,253
Gestational Diabetes and the Incidence of Type 2 Diabetes
Kim, C., Newton, K. M., & Knopp, R. H. (2002) · Diabetes Care · doi:10.2337/diacare.25.10.1862 · Cited by 2,276
ACOG Practice Bulletin No. 190: Gestational Diabetes Mellitus
(2018) · Obstetrics & Gynecology · doi:10.1097/aog.0000000000002501 · Cited by 1,820
Physiological changes in pregnancy
Soma-Pillay, P., Nelson-Piercy, C., Tolppanen, H., & Mebazaa, A. (2016) · Cardiovascular Journal of Africa · doi:10.5830/cvja-2016-021 · Cited by 1,483
Ding, E. L., Song, Y., Malik, V. S., & Liu, S. (2006) · JAMA · doi:10.1001/jama.295.11.1288 · Cited by 1,354
Increasing Prevalence of Gestational Diabetes Mellitus
Ferrara, A. (2007) · Diabetes Care · doi:10.2337/dc07-s206 · Cited by 1,171
Standards of Medical Care in Diabetes—2017 Abridged for Primary Care Providers
American Diabetes Association (2017) · Clinical Diabetes · doi:10.2337/cd16-0067 · Cited by 999
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Gestational Diabetes Mellitus—Recent Literature Review
Modzelewski, R., Stefanowicz-Rutkowska, M. M., Matuszewski, W., & Bandurska-Stankiewicz, E. M. (2022) · Journal of Clinical Medicine · doi:10.3390/jcm11195736 · Cited by 223
Gestational Diabetes Mellitus and Maternal Immune Dysregulation: What We Know So Far
McElwain, C. J., McCarthy, F. P., & McCarthy, C. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms22084261 · Cited by 134
Placental Regulation of Energy Homeostasis During Human Pregnancy
Armistead, B., Johnson, E., Vanderkamp, R., Kula-Eversole, E., Kadam, L., Drewlo, S., & Kohan‐Ghadr, H. (2020) · Endocrinology · doi:10.1210/endocr/bqaa076 · Cited by 99
Pathirana, M. M., Lassi, Z. S., Alì, A., Arstall, M. A., Roberts, C. T., & Andraweera, P. H. (2020) · Endocrine · doi:10.1007/s12020-020-02492-1 · Cited by 74
McIntyre, D. H., Kapur, A., Divakar, H., & Hod, M. (2020) · Frontiers in Endocrinology · doi:10.3389/fendo.2020.614533 · Cited by 61
Metadata
- Authors
- Lorenzo-Almorós, A., Hang, T., Peiró, C., Soriano-Guillén, L., Egido, J., Tuñón, J., & Lorenzo, Ó.
- Affiliations
- Instituto de Investigación Sanitaria Fundación Jiménez Díaz (ES)
- Universidad Autónoma de Madrid (ES)
- Centro de Investigación Biomédica en Red Diabetes y Enfermedades Metabólicas Asociadas (ES)
- Hospital Universitario Fundación Jiménez Díaz (ES)
- Corresponding author
- Lorenzo, Ó.
- Venue
- Cardiovascular Diabetology
- Published
- 2019-10-30
- Volume
- 18
- Issue
- 1
- Pages
- 140-140
- ISSN
- 1475-2840 (ISSN-L)
- PMC
- PMC6820966
- OpenAlex
- W2982430392
- Cited by
- 202
- References
- 179
- Citations by year
- 2026: 19 · 2025: 33 · 2024: 35 · 2023: 31 · 2022: 24 · 2021: 29 · 2020: 27 · 2019: 2
- FWCI
- 3.52
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Cardiovascular Diabetology (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Hispana (submittedVersion)
- Biblos-e Archivo (Universidad Autónoma de Madrid) (submittedVersion, public-domain)
- Funders
- Fondo de Investigación Sanitaria (ISC-III) and Fondo Europeo de Desarrollo Regional (FEDER). (PI17/01495)
- Topics
- Health Sciences › Medicine › Obstetrics and Gynecology › Gestational Diabetes Research and Management
- Health Sciences › Medicine › Pediatrics, Perinatology and Child Health › Birth, Development, and Health
- Health Sciences › Medicine › Obstetrics and Gynecology › Pregnancy and preeclampsia studies
- Keywords
- gestational diabetes mellitus, biomarkers, early diagnosis, insulin resistance, beta-cell dysfunction, adiponectin, visfatin, omentin-1, FABP4, retinol-binding protein 4, placental hormones, sex hormone-binding globulin, afamin, fetuin-A, fibroblast growth factor, ficolin-3, follistatin, microRNA, urinary metabolites, ketone bodies, serotonin, metabolic risk, cardiovascular risk, offspring outcomes
- MeSH terms
- Blood Glucose, Cardiovascular Diseases, Energy Metabolism, Female, Humans, Predictive Value of Tests, Pregnancy, Prognosis, Risk Factors, Biomarkers, Comorbidity, Diabetes, Gestational, Risk Assessment, MicroRNAs
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