Skip to main content

Advertisement

SpringerLink
Log in

Integration of suboptimal health status evaluation as a criterion for prediction of preeclampsia is strongly recommended for healthcare management in pregnancy: a prospective cohort study in a Ghanaian population

  • Research
  • Published:
EPMA Journal Aims and scope Submit manuscript

Abstract

Background

Normotensive pregnancy may develop into preeclampsia (PE) and other adverse pregnancy complications (APCs), for which the causes are still unknown. Suboptimal health status (SHS), a physical state between health and disease, might contribute to the development and progression of PE. By integration of a routine health measure in this Ghanaian Suboptimal Health Cohort Study, we explored the usefulness of a 25-question item SHS questionnaire (SHSQ-25) for early screening and prediction of normotensive pregnant women (NTN-PW) likely to develop PE.

Methods

We assessed the overall health status among a cohort of 593 NTN-PW at baseline (10–20 weeks gestation) and followed them at 21–31 weeks until 32–42 weeks. After an average of 20 weeks follow-up, 498 participants returned and were included in the final analysis. Hematobiochemical, clinical and sociodemographic data were obtained.

Results

Of the 498 participants, 49.8% (248/498) had ‘high SHS’ at baseline (61.7% (153/248) later developed PE) and 38.3% (95/248) were NTN-PW, whereas 50.2% (250/498) had ‘optimal health’ (17.6% (44/250) later developed PE) and 82.4% (206/250) were NTN-PW. At baseline, high SHS score yielded a significantly (p < 0.05) increased adjusted odds ratio, a wider area under the curve (AUC) and a higher sensitivity and specificity for the prediction of PE (3.67; 0.898; 91.9% and 87.8%), PE coexisting with intrauterine growth restriction (2.86, 0.838; 91.5% and 75.9%), stillbirth (2.52; 0.783; 96.6% and 60.0%), hemolysis elevated liver enzymes and low platelet count (HELLP) syndrome (2.08; 0.800; 97.2% and 63.8%), acute kidney injury (2.20; 0.825; 95.3% and 70.0%) and dyslipidaemia (2.80; 0.8205; 95.7% and 68.4%) at 32–42 weeks gestation.

Conclusions

High SHS score is associated with increased incidence of PE; hence, SHSQ-25 can be used independently as a risk stratification tool for adverse pregnancy outcomes thereby creating an opportunity for predictive, preventive and personalized medicine.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Abbreviations

SHS:

suboptimal health status

OHS:

optimal health status

SHSQ-25:

25-question-based suboptimal health status questionnaire

GHOACS:

Ghanaian Suboptimal Health Cohort Study

PE:

preeclampsia

APCs:

adverse pregnancy complications

PPPM:

preventive, predictive and personalized medicine

IUGR:

intrauterine growth restriction

HELLP:

hemolysis elevated liver enzymes and low platelet count

SBP:

systolic blood pressure

DBP:

diastolic blood pressure

Mg:

magnesium

Ca:

calcium

Na:

sodium

K:

potassium

Cl:

chloride

LDH:

lactate dehydrogenase

UA:

uric acid

RDW:

red cell distribution width

FBG:

fasting blood glucose

TG:

triglyceride

TC:

total cholesterol

HDL-c:

high-density lipoprotein cholesterol

LDL-c:

low-density lipoprotein cholesterol

ALT:

alanine aminotransferase

AST:

aspartate aminotransferase

GGT:

gamma glutamyl transferase

TP:

total protein

ALB:

albumin

ALP:

alkaline phosphatase

aOR:

adjusted odds ratio

CI:

confidence interval

ROC:

receiver’s operating characteristics

AUC:

area under the ROC curve

References

  1. Alkema L, Chou D, Hogan D, Zhang S, Moller AB, Gemmill A, et al. Global, regional, and national levels and trends in maternal mortality between 1990 and 2015, with scenario-based projections to 2030: a systematic analysis by the UN Maternal Mortality Estimation Inter-Agency Group. Lancet (London, England). 2016;387(10017):462–74. https://doi.org/10.1016/s0140-6736(15)00838-7.

    Article  Google Scholar 

  2. Phipps E, Prasanna D, Brima W, Jim B. Preeclampsia: updates in pathogenesis, definitions, and guidelines. Clin J Am Soc Nephrol. 2016;11(6):1102–13. https://doi.org/10.2215/cjn.12081115.

  3. Turpin CA, Sakyi SA, Owiredu WK, Ephraim RK, Anto EO. Association between adverse pregnancy outcome and imbalance in angiogenic regulators and oxidative stress biomarkers in gestational hypertension and preeclampsia. BMC Pregnancy Childbirth. 2015;15:189. https://doi.org/10.1186/s12884-015-0624-y.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Jim B, Karumanchi SA. Preeclampsia: pathogenesis, prevention, and long-term complications. Semin Nephrol. 2017;37(4):386–97. https://doi.org/10.1016/j.semnephrol.2017.05.011.

    Article  CAS  PubMed  Google Scholar 

  5. Sumankuuro J, Crockett J, Wang S. Maternal health care initiatives: causes of morbidities and mortalities in two rural districts of Upper West Region, Ghana. PLoS One. 2017;12(8):e0183644. https://doi.org/10.1371/journal.pone.0183644.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Adua E, Frimpong K, Li X, Wang W. Emerging issues in public health: a perspective on Ghana’s healthcare expenditure, policies and outcomes. EPMA J. 2017;8(3):197–206. https://doi.org/10.1007/s13167-017-0109-3.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Sandesh P, Bruce H, Yadav B, Sharma P. Psychosocial stress during pregnancy and its relation to fetal outcome: a study from Patan Hospital, Lalitpur, Nepal. J Instit Med. 2018;41(2):73–9.

    Google Scholar 

  8. Golubnitschaja O, Baban B, Boniolo G, Wang W, Bubnov R, Kapalla M, et al. Medicine in the early twenty-first century: paradigm and anticipation—EPMA position paper 2016. EPMA J. 2016;7:23. https://doi.org/10.1186/s13167-016-0072-4.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Golubnitschaja O, Kinkorova J, Costigliola V. Predictive, preventive and personalised medicine as the hardcore of ‘Horizon 2020’: EPMA position paper. EPMA J. 2014;5(1):6. https://doi.org/10.1186/1878-5085-5-6.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Lemke HU, Golubnitschaja O. Towards personal health care with model-guided medicine: long-term PPPM-related strategies and realisation opportunities within ‘Horizon 2020’. EPMA J. 2014;5(1):8. https://doi.org/10.1186/1878-5085-5-8.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Golubnitschaja O. Time for new guidelines in advanced diabetes care: paradigm change from delayed interventional approach to predictive, preventive & personalized medicine. EPMA J. 2010;1(1):3–12. https://doi.org/10.1007/s13167-010-0014-5.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Yan Y-X, Liu Y-Q, Li M, Hu P-F, Guo A-M, Yang X-H, et al. Development and evaluation of a questionnaire for measuring suboptimal health status in urban Chinese. J Epidemiol. 2009;19(6):333–41.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Wang W, Russell A, Yan Y. Traditional Chinese medicine and new concepts of predictive, preventive and personalized medicine in diagnosis and treatment of suboptimal health. EPMA J. 2014;5(1):4. https://doi.org/10.1186/1878-5085-5-4.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Wang Y, Ge S, Yan Y, Wang A, Zhao Z, Yu X, et al. China suboptimal health cohort study: rationale, design and baseline characteristics. J Transl Med. 2016;14(1):291. https://doi.org/10.1186/s12967-016-1046-y.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Wang W, Yan Y. Suboptimal health: a new health dimension for translational medicine. Clin Transl Med. 2012;1(1):28. https://doi.org/10.1186/2001-1326-1-28.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Ge S, Xu X, Zhang J, Hou H, Wang H, Liu D, et al. Suboptimal health status as an independent risk factor for type 2 diabetes mellitus in a community-based cohort: the China suboptimal health cohort study. EPMA J. 2019;10(1):65–72. https://doi.org/10.1007/s13167-019-0159-9.

    Article  PubMed  Google Scholar 

  17. Yan YX, Dong J, Liu YQ, Yang XH, Li M, Shia G, et al. Association of suboptimal health status and cardiovascular risk factors in urban Chinese workers. J Urban Health. 2012;89(2):329–38. https://doi.org/10.1007/s11524-011-9636-8.

  18. Wang Y, Liu X, Qiu J, Wang H, Liu D, Zhao Z, et al. Association between ideal cardiovascular health metrics and suboptimal health status in Chinese population. Sci Rep. 2017;7(1):14975. https://doi.org/10.1038/s41598-017-15101-5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Adua E, Roberts P, Wang W. Incorporation of suboptimal health status as a potential risk assessment for type II diabetes mellitus: a case-control study in a Ghanaian population. EPMA J. 2017;8(4):345–55. https://doi.org/10.1007/s13167-017-0119-1.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Kupaev V, Borisov O, Marutina E, Yan YX, Wang W. Integration of suboptimal health status and endothelial dysfunction as a new aspect for risk evaluation of cardiovascular disease. EPMA J. 2016;7(1):19. https://doi.org/10.1186/s13167-016-0068-0.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Alzain MA, Asweto CO, Zhang J, Fang H, Zhao Z, Guo X, et al. Telomere length and accelerated biological aging in the China suboptimal health cohort: a case-control study. Omics. 2017;21(6):333–9. https://doi.org/10.1089/omi.2017.0050.

  22. Yan YX, Dong J, Liu YQ, Zhang J, Song MS, He Y, et al. Association of suboptimal health status with psychosocial stress, plasma cortisol and mRNA expression of glucocorticoid receptor alpha/beta in lymphocyte. Stress (Amsterdam, Netherlands). 2015;18(1):29–34. https://doi.org/10.3109/10253890.2014.999233.

    Article  CAS  Google Scholar 

  23. American College of Obstetricians and Gynecologists, Task Force on Hypertension in Pregnancy. Hypertension in pregnancy. Report of the American College of Obstetricians and Gynecologists’ Task Force on Hypertension in Pregnancy. Obstet Gynecol. 2013;122(5):1122–31. https://doi.org/10.1097/01.aog.0000437382.03963.88.

    Article  Google Scholar 

  24. Malacova E, Regan A, Nassar N, Raynes-Greenow C, Leonard H, Srinivasjois R, et al. Risk of stillbirth, preterm delivery, and fetal growth restriction following exposure in a previous birth: systematic review and meta-analysis. BJOG : Int J Obstet Gynaecol. 2018;125(2):183–92. https://doi.org/10.1111/1471-0528.14906.

    Article  CAS  Google Scholar 

  25. Alese MO, Moodley J, Naicker T. Preeclampsia and HELLP syndrome, the role of the liver. J Matern Fetal Neonatal Med. 2019:1–7. https://doi.org/10.1080/14767058.2019.1572737.

  26. Rao S, Jim B. Acute kidney injury in pregnancy: the changing landscape for the 21st century. Kidney Int Rep. 2018;3(2):247–57. https://doi.org/10.1016/j.ekir.2018.01.011.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Nasioudis D, Doulaveris G, Kanninen TT. Dyslipidemia in pregnancy and maternal-fetal outcome. Minerva Ginecol. 2019;71(2):155–62. https://doi.org/10.23736/s0026-4784.18.04330-7.

    Article  PubMed  Google Scholar 

  28. Laresgoiti-Servitje E. A leading role for the immune system in the pathophysiology of preeclampsia. J Leukoc Biol. 2013;94(2):247–57. https://doi.org/10.1189/jlb.1112603.

    Article  CAS  PubMed  Google Scholar 

  29. LaMarca B, Cornelius DC, Harmon AC, Amaral LM, Cunningham MW, Faulkner JL, et al. Identifying immune mechanisms mediating the hypertension during preeclampsia. Am J Physiol Regul Integr Comp Physiol. 2016;311(1):R1–9. https://doi.org/10.1152/ajpregu.00052.2016.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Kalafat E, Thilaganathan B. Cardiovascular origins of preeclampsia. Curr Opin Obstet Gynecol. 2017;29(6):383–9. https://doi.org/10.1097/gco.0000000000000419.

    Article  PubMed  Google Scholar 

  31. Perry H, Khalil A, Thilaganathan B. Preeclampsia and the cardiovascular system: an update. Trends Cardiovasc Med. 2018;28(8):505–13. https://doi.org/10.1016/j.tcm.2018.04.009.

    Article  PubMed  Google Scholar 

  32. Carter W, Bick D, Mackintosh N, Sandall J. A narrative synthesis of factors that affect women speaking up about early warning signs and symptoms of pre-eclampsia and responses of healthcare staff. BMC Pregnancy Childbirth. 2017;17(1):63. https://doi.org/10.1186/s12884-017-1245-4.

  33. Delahaije D, Dirksen C, Peeters L, Smits L. PP105. Mental health problems following preeclampsia or HELLP syndrome: do we have a case? A systematic review. Pregnancy Hypertens. 2012;2(3):296. https://doi.org/10.1016/j.preghy.2012.04.216.

    Article  CAS  PubMed  Google Scholar 

  34. Ephraim RK, Osakunor DN, Denkyira SW, Eshun H, Amoah S, Anto EO. Serum calcium and magnesium levels in women presenting with pre-eclampsia and pregnancy-induced hypertension: a case-control study in the Cape Coast metropolis, Ghana. BMC Pregnancy Childbirth. 2014;14:390. https://doi.org/10.1186/s12884-014-0390-2.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Guo X, Xu L, Huang J, Zhao M. Case-control study on serum calcium and magnesium levels in women presenting with preeclampsia. BMC Pregnancy Childbirth. 2017;20(14):390.

    Google Scholar 

  36. Djagbletey R, Darkwa EO, de Graft-Johnson PK, DAY S, Essuman R, Aryee G, et al. serum calcium and magnesium levels in normal Ghanaian pregnant women: a comparative cross-sectional study. Open Access Macedonian J Med Sci. 2018;6(11):2006–11. https://doi.org/10.3889/oamjms.2018.352.

    Article  Google Scholar 

  37. Anto EO, Owiredu WKBA, Sakyi SA, Turpin CA, Ephraim RKD, Fondjo LA, et al. Adverse pregnancy outcomes and imbalance in angiogenic growth mediators and oxidative stress biomarkers is associated with advanced maternal age births: a prospective cohort study in Ghana. PLoS One. 2018;13(7):e0200581. https://doi.org/10.1371/journal.pone.0200581.

    Article  Google Scholar 

  38. Ephraim R, Doe P, Amoah S, Antoh E. Lipid profile and high maternal body mass index is associated with preeclampsia: a case-control study of the Cape Coast Metropolis. Ann Med Health Sci Res. 2014;4(5):746–50. https://doi.org/10.4103/2141-9248.141542.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Demirci O, Tugrul AS, Dolgun N, Sozen H, Eren S. Serum lipids level assessed in early pregnancy and risk of pre-eclampsia. J Obstet Gynaecol Res. 2011;37(10):1427–32. https://doi.org/10.1111/j.1447-0756.2011.01562.x.

    Article  PubMed  Google Scholar 

  40. Tangren JS, Wan M, Adnan WAH, Powe CE, Ecker J, Bramham K, et al. Risk of Preeclampsia and Pregnancy Complications in Women With a History of Acute Kidney Injury. Hypertension. 2018;72(2):451–9. https://doi.org/10.1161/hypertensionaha.118.11161.

  41. O’Brien M, Baczyk D, Kingdom JC. Endothelial dysfunction in severe preeclampsia is mediated by soluble factors, rather than extracellular vesicles. Sci Rep. 2017;7(1):5887. https://doi.org/10.1038/s41598-017-06178-z.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Anto EO, Roberts P, Turpin CA, Wang W. Oxidative stress as a key signaling pathway in placental angiogenesis changes in preeclampsia: updates in pathogenesis, novel biomarkers and therapeutics. Curr Pharmacogenomics Pers Med (Formerly Current Pharmacogenomics). 2018;16(3):167–81.

    Article  CAS  Google Scholar 

  43. Rondo PH, Ferreira RF, Nogueira F, Ribeiro MC, Lobert H, Artes R. Maternal psychological stress and distress as predictors of low birth weight, prematurity and intrauterine growth retardation. Eur J Clin Nutr. 2003;57(2):266–72. https://doi.org/10.1038/sj.ejcn.1601526.

    Article  CAS  PubMed  Google Scholar 

  44. Valsamakis G, Kanaka-Gantenbein C, Malamitsi-Puchner A, Mastorakos G. Causes of intrauterine growth restriction and the postnatal development of the metabolic syndrome. Ann N Y Acad Sci. 2006;1092:138–47. https://doi.org/10.1196/annals.1365.012.

    Article  CAS  PubMed  Google Scholar 

  45. Gibbins KJ, Silver RM, Pinar H, Reddy UM, Parker CB, Thorsten V, et al. Stillbirth, hypertensive disorders of pregnancy, and placental pathology. Placenta. 2016;43:61–8. https://doi.org/10.1016/j.placenta.2016.04.020.

    Article  PubMed  PubMed Central  Google Scholar 

  46. Prakash J, Ganiger VC, Prakash S, Iqbal M, Kar DP, Singh U, et al. Acute kidney injury in pregnancy with special reference to pregnancy-specific disorders: a hospital based study (2014-2016). J Nephrol. 2018;31(1):79–85. https://doi.org/10.1007/s40620-017-0466-y.

    Article  CAS  PubMed  Google Scholar 

  47. Gallos ID, Sivakumar K, Kilby MD, Coomarasamy A, Thangaratinam S, Vatish M. Pre-eclampsia is associated with, and preceded by, hypertriglyceridaemia: a meta-analysis. BJOG. 2013;120(11):1321–32. https://doi.org/10.1111/1471-0528.12375.

  48. Liu D, Chu X, Wang H, Dong J, Ge SQ, Zhao ZY, et al. The changes of immunoglobulin G N-glycosylation in blood lipids and dyslipidaemia. J Transl Med. 2018;16(1):235. https://doi.org/10.1186/s12967-018-1616-2.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We wish to thank the biomedical staff of the Department of Biochemistry and Serology, and midwives of the Department of Obstetrics and Gynaecology of the Komfo Anokye Teaching Hospital, Ghana, for their support during the participant’s recruitment and biological sample processing. We also thank the research assistants of the Department of Molecular Medicine, Kwame Nkrumah University of Science and Technology for their support during the biological sample analysis. We finally thank the American Association of Clinical Chemistry (AACC) Academy Research Fellows for selecting our abstract coined from the present study entitled, ‘Algorithm of Suboptimal Health Status, Serum Magnesium and Calcium Levels as a Novel Approach for Prediction and Identification of Pregnant Women Likely to Develop Preeclampsia and Adverse Perinatal Complications in a Ghanaian Population’ for Scientific Excellence in Maternal and Foetal Medicine and AACC Academy’s Distinguished Abstract Award at the 71st AACC Scientific Annual Meeting, Anaheim, CA.

Funding

This work was supported by the Australia-China International Collaborative Grant (NHMRC-APP1112767-NSFC81561120) and Edith Cowan University (ECU)-Collaborative Enhancement Scheme Round 1 (G1003363). Enoch Odame Anto was supported by ECU-International Postgraduate Research Scholarship.

Author information

Authors and Affiliations

  1. School of Medical and Health Sciences, Edith Cowan University, Perth, WA, Australia

    Enoch Odame Anto, Peter Roberts, David Coall, Eric Adua & Wei Wang

  2. Department of Molecular Medicine, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana

    Enoch Odame Anto

  3. Department of Obstetrics and Gynaecology, Komfo Anokye Teaching Hospital, Kumasi, Ghana

    Cornelius Archer Turpin

  4. Beijing Key Laboratory of Clinical Epidemiology, School of Public Health, Capital Medical University, Beijing, China

    Youxin Wang & Wei Wang

  5. School of Public Health, Taishan Medical University, Taian, China

    Wei Wang

Authors
  1. Enoch Odame Anto
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Peter Roberts
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. David Coall
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Cornelius Archer Turpin
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Eric Adua
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Youxin Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Wei Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

EOA, PR, DC and WW conceived the study. EOA and CAT performed the investigation and collected the data. EOA performed the statistical analysis. EOA, PR, DC, EA, YW and WW wrote the paper. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Wei Wang.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Consent for publication

Not applicable.

Ethical approval and consent to participate

Approval for this study was obtained from the Committee on Human Research Publication and Ethics (CHRPE) of the School of Medical Science (SMS) /KNUST and Komfo Anokye Teaching Hospital (KATH) (CHRPE/AP/146/17) and the Human Research Ethics Committee of Edith Cowan University (ECU) (17509). This study was conducted in accordance with the guidelines of the Helsinki Declaration. Written informed consent in the form of a signature and fingerprint was obtained from all participants and legally authorized representatives after the protocol of the study was explained to them in plain English language and native Ghanaian language where appropriate.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Anto, E.O., Roberts, P., Coall, D. et al. Integration of suboptimal health status evaluation as a criterion for prediction of preeclampsia is strongly recommended for healthcare management in pregnancy: a prospective cohort study in a Ghanaian population. EPMA Journal 10, 211–226 (2019). https://doi.org/10.1007/s13167-019-00183-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13167-019-00183-0

Keywords

Search

Navigation