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EPMA Journal

, Volume 8, Issue 4, pp 345–355 | Cite as

Incorporation of suboptimal health status as a potential risk assessment for type II diabetes mellitus: a case-control study in a Ghanaian population

  • Eric Adua
  • Peter Roberts
  • Wei WangEmail author
Research

Abstract

Due to a paradigm shift in lifestyles, there is growing concern that type 2 diabetes mellitus (T2DM) will reach epidemic proportions in Ghana. However, specific characteristics of the disease are under explored in this region. More challenging are those yet to be diagnosed or who complain of poor health in the absence of a diagnosed disease—suboptimal health status (SHS). We conducted a study to examine various factors that characterise SHS and T2DM. Using a cross-sectional design, we recruited 264 people as controls and 241 T2DM patients from January to June 2016. The controls were categorised into high and low SHS based on how they rated on an SHS questionnaire-25 (SHSQ-25). Anthropometric and biochemical parameters: body mass index (BMI); blood pressure (BP); fasting plasma glucose (FPG); glycated haemoglobin (HbA1c); serum lipids [(total cholesterol, triglycerides (TG), high- and low-density lipoprotein-cholesterol (HDL-c and LDL-c)] were measured. The male to female ratio for T2DM and controls were 99:142 and 98:166, respectively, whilst the mean ages were 55.89 and 51.52 years. Compared to controls, T2DM patients had higher FPG (8.96 ± 4.18 vs. 6.08 ± 1.79; p < 0.0001) and HbA1c (8.23 ± 2.09 vs. 5.45 ± 1.00; p < 0.0001). Primarily sedentary [adjusted odds ratio (aOR) = 2.97 (1.38–6.39); p = 0.034)], systolic blood pressure (SBP) (p = 0.001) and diastolic blood pressure (DBP) (p = 0.001) significantly correlated with high SHS. After adjusting for age and gender, central adiposity [aOR = 1.74 (1.06–2.83); p = 0.027)], underweight [aOR = 5.82 (1.23–27.52); p = 0.018)], high SBP [aOR = 1.86 (1.14–3.05); p = 0.012)], high DBP [aOR = 2.39 (1.40–4.07); p = 0.001)] and high TG [aOR = 2.17 (1.09–4.33); p = 0.029)] were found to be independent risk factors associated with high SHS. The management of T2DM in Ghana is suboptimal and undiagnosed risk factors remain prevalent. The SHSQ-25 can be translated and applied as a practical tool to screen at-risk individuals and hence prove useful for the purpose of predictive, preventive and personalised medicine.

Keywords

Chronic diseases Biomarkers Predictive Preventive and personalised medicine 

Notes

Acknowledgements

The authors wish to thank the laboratory personnel at the Department of Biochemistry at Komfo Anokye Teaching Hospital (KATH) for allowing the use of their automated chemistry analyser. Additionally, we thank the staff and research assistants at the Diabetes Centre, KATH. We also appreciate the support of staff from the Department of Molecular Medicine, School of Medical Sciences, Kwame Nkrumah University of Science and Technology.

Funding

This study is partly supported by a grant from Australian National Health and Medical Research Council and the National Natural Science Foundation of China (NHMRC APP1112767-NSFC 81561128020). EA is supported by Edith Cowan University under an International Postgraduate Research Scholarship.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no competing interests.

Supplementary material

13167_2017_119_MOESM1_ESM.docx (13 kb)
ESM 1 (DOCX 13 kb)

References

  1. 1.
    WHO. Global report on diabetes. World Health Organisation, http://apps.who.int/iris/bitstream/10665/204871/1/9789241565257eng.pdf. Accessed 11 Oct 2016.
  2. 2.
    International Diabets Federation. IDF diabetes atlas. http://www.diabetesatlas.org/resources/2015-atlas.html, Accessed 10 April 2016.
  3. 3.
    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.CrossRefPubMedPubMedCentralGoogle Scholar
  4. 4.
    Chew EY. Screening for diabetic retinopathy in youth-onset diabetes. Ophthalmology. 2017;124(4):422–3.CrossRefPubMedGoogle Scholar
  5. 5.
    Nadeau KJ, Anderson BJ, Berg EG, Chiang JL, Chou H, Copeland KC, et al. Youth-onset type 2 diabetes consensus report: current status, challenges, and priorities. Diabetes Care. 2016;39(9):1635–42.CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    Action to Control Cardiovascular Risk in Diabetes Study Group. Effects of intensive glucose lowering in type 2 diabetes. N Engl J Med. 2008;2008(358):2545–59.Google Scholar
  7. 7.
    Chen L, Magliano DJ, Zimmet PZ. The worldwide epidemiology of type 2 diabetes mellitus—present and future perspectives. Nat Rev Endocrinol. 2012;8(4):228–36.CrossRefGoogle Scholar
  8. 8.
    Gerstein H, Pogue J, Mann J, Lonn E, Dagenais G, McQueen M, et al. The relationship between dysglycaemia and cardiovascular and renal risk in diabetic and non-diabetic participants in the HOPE study: a prospective epidemiological analysis. Diabetologia. 2005;48(9):1749–55.CrossRefPubMedGoogle Scholar
  9. 9.
    Luchsinger JA. Type 2 diabetes and cognitive impairment: linking mechanisms. J Alzheimers Dis. 2012;30(s2):S185–S98.PubMedPubMedCentralGoogle Scholar
  10. 10.
    Rich PA, Shaefer CF, Parkin CG, Edelman SV. Using a quantitative measure of diabetes risk in clinical practice to target and maximize diabetes prevention interventions. Clin Diabetes. 2013;31(2):82–9.CrossRefGoogle Scholar
  11. 11.
    Stratton IM, Adler AI, Neil HAW, Matthews DR, Manley SE, Cull CA, et al. Association of glycaemia with macrovascular and microvascular complications of type 2 diabetes (UKPDS 35): prospective observational study. BMJ. 2000;321(7258):405–12.CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    DeFronzo RA, Abdul-Ghani M. Type 2 diabetes can be prevented with early pharmacological intervention. Diabetes Care. 2011;34(Supplement 2):S202–S9.CrossRefPubMedPubMedCentralGoogle Scholar
  13. 13.
    Frank LK, Kröger J, Schulze MB, Bedu-Addo G, Mockenhaupt FP, Danquah I. Dietary patterns in urban Ghana and risk of type 2 diabetes. Br J Nutr. 2014;112(01):89–98.CrossRefPubMedGoogle Scholar
  14. 14.
    Adua E, Roberts P, Sakyi SA, Yeboah FA, Dompreh A, Frimpong K, et al. Profiling of cardio-metabolic risk factors and medication utilisation among type II diabetes patients in Ghana: a prospective cohort study. Clin Transl Med. 2017;6(1):32.CrossRefPubMedPubMedCentralGoogle Scholar
  15. 15.
    Suckling RJ, Swift PA. The health impacts of dietary sodium and a low-salt diet. Clin Med (Northfield Il). 2015;15(6):585–8.CrossRefGoogle Scholar
  16. 16.
    Guariguata L, Whiting D, Hambleton I, Beagley J, Linnenkamp U, Shaw J. Global estimates of diabetes prevalence for 2013 and projections for 2035. Diabetes Res Clin Pract. 2014;103(2):137–49.CrossRefPubMedGoogle Scholar
  17. 17.
    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.CrossRefPubMedPubMedCentralGoogle Scholar
  18. 18.
    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(1):23.CrossRefPubMedPubMedCentralGoogle Scholar
  19. 19.
    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.CrossRefPubMedPubMedCentralGoogle Scholar
  20. 20.
    Lindström J, Tuomilehto J. The diabetes risk score. Diabetes Care. 2003;26(3):725–31.CrossRefPubMedGoogle Scholar
  21. 21.
    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):1–12.CrossRefGoogle Scholar
  22. 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 α/β in lymphocyte. Stress. 2014;18(1):29–34.CrossRefGoogle Scholar
  23. 23.
    Yan YX, Liu YQ, Li M, Hu PF, Guo AM, Yang XH, et al. Development and evaluation of a questionnaire for measuring suboptimal health status in urban Chinese. J Epidemiol. 2009;19(6):333–41.CrossRefPubMedPubMedCentralGoogle Scholar
  24. 24.
    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(19):1–7.Google Scholar
  25. 25.
    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):1–9.CrossRefGoogle Scholar
  26. 26.
    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.CrossRefPubMedGoogle Scholar
  27. 27.
    Alzain MA, Asweto CO, Zhang J, Fang H, Zhao Z, Guo X, Song M, Zhou Y, Chang N, Wang Y, Wang W. Telomere length and accelerated biological aging in the China suboptimal health cohort: a case–control study. OMICS. 2017;21(6):333–9.Google Scholar
  28. 28.
    Bi Y, Wang T, Xu M, Xu Y, Li M, Lu J, et al. Advanced research on risk factors of type 2 diabetes. Diabetes Metab Res Rev. 2012;28(s2):32–9.CrossRefPubMedGoogle Scholar
  29. 29.
    Stumvoll M, Goldstein BJ, van Haeften TW. Type 2 diabetes: principles of pathogenesis and therapy. Lancet. 2005;365(9467):1333–46.CrossRefPubMedGoogle Scholar
  30. 30.
    Deepa M, Anjana R, Mohan V. Role of lifestyle factors in the epidemic of diabetes: lessons learnt from India. Eur J Clin Nutr. 2017;71(7):825–31.CrossRefPubMedGoogle Scholar
  31. 31.
    Hulsegge G, Spijkerman A, van der Schouw Y, Bakker SJ, Gansevoort R, Smit H, et al. Trajectories of metabolic risk factors and biochemical markers prior to the onset of type 2 diabetes: the population-based longitudinal Doetinchem study. Nutr Diabetes. 2017;7(5):e270.Google Scholar
  32. 32.
    Zou X, Zhou X, Ji L, Yang W, Lu J, Weng J, et al. The characteristics of newly diagnosed adult early-onset diabetes: a population-based cross-sectional study. Sci Rep. 2017;7:46534.CrossRefPubMedPubMedCentralGoogle Scholar
  33. 33.
    Echouffo-Tcheugui JB, Kengne AP, Erqou S, Cooper RS. High blood pressure in sub-Saharan Africa: the urgent imperative for prevention and control. J Clin Hypertens. 2015;17(10):751–5.CrossRefGoogle Scholar
  34. 34.
    Cappuccio FP, Miller MA. Cardiovascular disease and hypertension in sub-Saharan Africa: burden, risk and interventions. Intern Emerg Med. 2016;11(3):299–305.CrossRefPubMedPubMedCentralGoogle Scholar
  35. 35.
    Ofori-Asenso R, Garcia D. Cardiovascular diseases in Ghana within the context of globalization. Cardiovasc Diagn Ther. 2016;6(1):67–77.PubMedPubMedCentralGoogle Scholar
  36. 36.
    Mendis S, Puska P, Norrving B. Global atlas on cardiovascular disease prevention and control. Geneva: World Health Organization; 2011. p. 1–55.Google Scholar
  37. 37.
    Mensah G. Ischaemic heart disease in Africa. Heart. 2008;94(7):836–43.CrossRefPubMedGoogle Scholar
  38. 38.
    Khatibzadeh S, Farzadfar F, Oliver J, Ezzati M, Moran A. Worldwide risk factors for heart failure: a systematic review and pooled analysis. Int J Cardiol. 2013;168(2):1186–94.CrossRefPubMedGoogle Scholar
  39. 39.
    Yu X, Wang Y, Kristic J, Dong J, Chu X, Ge S, et al. Profiling IgG N-glycans as potential biomarker of chronological and biological ages: a community-based study in a Han Chinese population. Medicine (Baltimore). 2016;95(28):e4112.Google Scholar
  40. 40.
    Franco OH, Karnik K, Osborne G, Ordovas JM, Catt M, van der Ouderaa F. Changing course in ageing research: the healthy ageing phenotype. Maturitas. 2009;63(1):13–9.CrossRefPubMedGoogle Scholar
  41. 41.
    Wang W, Yan Y. Suboptimal health: a new health dimension for translational medicine. Clin Transl Med. 2012;1(1):28.CrossRefPubMedPubMedCentralGoogle Scholar
  42. 42.
    Bonora E, Tuomilehto J. The pros and cons of diagnosing diabetes with A1C. Diabetes Care. 2011;34(Supplement 2):S184–S90.CrossRefPubMedPubMedCentralGoogle Scholar
  43. 43.
    Danquah I, Bedu-Addo G, Terpe KJ, Micah F, Amoako YA, Awuku YA, et al. Diabetes mellitus type 2 in urban Ghana: characteristics and associated factors. BMC Public Health. 2012;12(210):1–8.Google Scholar
  44. 44.
    Kahn HS, Cheng YJ, Thompson TJ, Imperatore G, Gregg EW. Two risk-scoring systems for predicting incident diabetes mellitus in US adults age 45 to 64 years. Ann Intern Med. 2009;150:741–51.CrossRefPubMedGoogle Scholar
  45. 45.
    Kolberg JA, Jorgensen T, Gerwien RW, Hamren S, McKenna MP, Moler E, et al. Development of a type 2 diabetes risk model from a panel of serum biomarkers from the Inter99 cohort. Diabetes Care. 2009;32:1207–12.CrossRefPubMedPubMedCentralGoogle Scholar
  46. 46.
    Liu M, Pan C, Jin M. A Chinese diabetes risk score for screening of undiagnosed diabetes and abnormal glucose tolerance. Diabetes Technol Ther. 2011;13:501–7.CrossRefPubMedGoogle Scholar
  47. 47.
    Mehrabi Y, Sarbakhsh P, Hadaegh F, Khadem-Maboudi A. Prediction of diabetes using logic regression. Iran J Endocrinol Metab. 2010;12:16–24.Google Scholar
  48. 48.
    Rathmann W, Kowall B, Heier M, Herder C, Holle R, Thorand B, et al. Prediction models for incident type 2 diabetes mellitus in the older population: KORA S4/F4 cohort study. Diabet Med. 2010;27:1116–23.CrossRefPubMedGoogle Scholar
  49. 49.
    Adua E, Russell A, Roberts P, Wang Y, Song M, Wang W. Innovation analysis on Postgenomic biomarkers: glycomics for chronic diseases. OMICS. 2017;21(4):183–96.Google Scholar
  50. 50.
    Wang YX, Adua E, Russell A, Roberts P, Ge S, Zeng Q, Wang W. Glycomics and its application potential in precision medicine. Science supplement: precision medicine in China. 2016;354(6319):36–9.Google Scholar
  51. 51.
    Wang Y, Klarić L, Yu X, Thaqi K, Dong J, Novokmet M, Wilson J, Polasek O, Liu Y, Krištić J, Ge S. The association between glycosylation of immunoglobulin G and hypertension: a multiple ethnic cross-sectional study. Medicine. 2016;95(17):e3379.Google Scholar
  52. 52.
    Lu JP, Knezevic A, Wang YX, Rudan I, Campbell H, Zou ZK, et al. Screening novel biomarkers for metabolic syndrome by profiling human plasma N-glycans in Chinese Han and Croatian populations. J Proteome Res. 2011;10(11):4959–69.CrossRefPubMedGoogle Scholar

Copyright information

© European Association for Predictive, Preventive and Personalised Medicine (EPMA) 2017

Authors and Affiliations

  1. 1.School of Medical and Health SciencesEdith Cowan UniversityPerthAustralia
  2. 2.Beijing Key Laboratory of Clinical Epidemiology, School of Public HealthCapital Medical UniversityBeijingChina
  3. 3.School of Public HealthTaishan Medical UniversityTaianChina

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