Skip to main content

Targeted proteomics identifies potential biomarkers of dysglycaemia, beta cell function and insulin sensitivity in Black African men and women



Using a targeted proteomics approach, we aimed to identify and validate circulating proteins associated with impaired glucose metabolism (IGM) and type 2 diabetes in a Black South African cohort. In addition, we assessed sex-specific associations between the validated proteins and pathophysiological pathways of type 2 diabetes.


This cross-sectional study included Black South African men (n=380) and women (n=375) who were part of the Middle-Aged Soweto Cohort (MASC). Dual-energy x-ray absorptiometry was used to determine fat mass and visceral adipose tissue, and fasting venous blood samples were collected for analysis of glucose, insulin and C-peptide and for targeted proteomics, measuring a total of 184 pre-selected protein biomarkers. An OGTT was performed on participants without diabetes, and peripheral insulin sensitivity (Matsuda index), HOMA-IR, basal insulin clearance, insulin secretion (C-peptide index) and beta cell function (disposition index) were estimated. Participants were classified as having normal glucose tolerance (NGT; n=546), IGM (n=116) or type 2 diabetes (n=93). Proteins associated with dysglycaemia (IGM or type 2 diabetes) in the MASC were validated in the Swedish EpiHealth cohort (NGT, n=1706; impaired fasting glucose, n=550; type 2 diabetes, n=210).


We identified 73 proteins associated with dysglycaemia in the MASC, of which 34 were validated in the EpiHealth cohort. Among these validated proteins, 11 were associated with various measures of insulin dynamics, with the largest number of proteins being associated with HOMA-IR. In sex-specific analyses, IGF-binding protein 2 (IGFBP2) was associated with lower HOMA-IR in women (coefficient –0.35; 95% CI –0.44, –0.25) and men (coefficient –0.09; 95% CI –0.15, –0.03). Metalloproteinase inhibitor 4 (TIMP4) was associated with higher insulin secretion (coefficient 0.05; 95% CI 0.001, 0.11; p for interaction=0.025) and beta cell function (coefficient 0.06; 95% CI 0.02, 0.09; p for interaction=0.013) in women only. In contrast, a stronger positive association between IGFBP2 and insulin sensitivity determined using an OGTT (coefficient 0.38; 95% CI 0.27, 0.49) was observed in men (p for interaction=0.004). A posteriori analysis showed that the associations between TIMP4 and insulin dynamics were not mediated by adiposity. In contrast, most of the associations between IGFBP2 and insulin dynamics, except for insulin secretion, were mediated by either fat mass index or visceral adipose tissue in men and women. Fat mass index was the strongest mediator between IGFBP2 and insulin sensitivity (total effect mediated 40.7%; 95% CI 37.0, 43.6) and IGFBP2 and HOMA-IR (total effect mediated 39.1%; 95% CI 31.1, 43.5) in men.


We validated 34 proteins that were associated with type 2 diabetes, of which 11 were associated with measures of type 2 diabetes pathophysiology such as peripheral insulin sensitivity and beta cell function. This study highlights biomarkers that are similar between cohorts of different ancestry, with different lifestyles and sociodemographic profiles. The African-specific biomarkers identified require validation in African cohorts to identify risk markers and increase our understanding of the pathophysiology of type 2 diabetes in African populations.

Graphical abstract

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3

Data availability

The datasets generated and/or analysed during the current study are available from the corresponding author on reasonable request.



Disposition index


Dual-energy x-ray absorptiometry


False discovery rate


Fat mass index


Impaired fasting glucose


IGF-binding protein 2


Impaired glucose metabolism


Impaired glucose tolerance


Kidney injury molecule


Middle-Aged Soweto Cohort


Normal glucose tolerance


Subcutaneous adipose tissue


Stem cell factor


Metalloproteinase inhibitor 4


TNF receptor superfamily member 11A


Tissue-type plasminogen activator


Visceral adipose tissue


V-set and immunoglobulin domain-containing protein 2


  1. International Diabetes Federation (2021) IDF Atlas, 10th edition. Available from Accessed on 10 November 2021, DOI:

  2. Statistics South Africa (2021) Mortality and causes of death in South Africa: Findings from death notification 2018. Available from Accessed 10 November 2021, DOI:

  3. Beijer K, Nowak C, Sundström J, Ärnlöv J, Fall T, Lind L (2019) In search of causal pathways in diabetes: a study using proteomics and genotyping data from a cross-sectional study. Diabetologia 62(11):1998–2006.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  4. Molvin J, Pareek M, Jujic A et al (2019) Using a targeted proteomics chip to explore pathophysiological pathways for incident diabetes – The Malmö Preventive Project. Sci Rep 9(1):1–7.

    Article  CAS  Google Scholar 

  5. Abbasi A, Sahlqvist AS, Lotta L et al (2016) A systematic review of biomarkers and risk of incident type 2 diabetes: an overview of epidemiological, prediction and aetiological research literature. PLoS One 11(10):e0163721.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  6. Nowak C, Sundström J, Gustafsson S et al (2016) Protein biomarkers for insulin resistance and type 2 diabetes risk in two large community cohorts. Diabetes 65(1):276–284.

    Article  PubMed  CAS  Google Scholar 

  7. Thorand B, Zierer A, Büyüközkan M et al (2021) A panel of 6 biomarkers significantly improves the prediction of type 2 diabetes in the MONICA/KORA study population. J Clin Endocrinol Metab 106(4):e1647–e1659.

    Article  PubMed  Google Scholar 

  8. Goedecke JH, Olsson T (2020) Pathogenesis of type 2 diabetes risk in black Africans: a South African perspective. J Intern Med 288:284–294.

    Article  PubMed  CAS  Google Scholar 

  9. Hakim O, Bello O, Ladwa M et al (2019) Ethnic differences in hepatic, pancreatic, muscular and visceral fat deposition in healthy men of white European and black west African ethnicity. Diabetes Res Clin Pract 156:107866.

    Article  PubMed  CAS  Google Scholar 

  10. Osei K, Schuster DP, Owusu SK, Amoah AGB (1997) Race and ethnicity determine serum insulin and C-peptide concentrations and hepatic insulin extraction and insulin clearance: comparative studies of three populations of West African ancestry and white Americans. Metabolism 46(1):53–58.

    Article  PubMed  CAS  Google Scholar 

  11. Mohandas C, Bonadonna R, Shojee-Moradie F et al (2018) Ethnic differences in insulin secretory function between black African and white European men with early type 2 diabetes. Diabetes Obes Metab 20(7):1678–1687.

    Article  PubMed  CAS  Google Scholar 

  12. Goedecke JH, Keswell D, Weinreich C et al (2015) Ethnic differences in hepatic and systemic insulin sensitivity and their associated determinants in obese black and white South African women. Diabetologia 58(11):2647–2652.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Osei K, Gaillard T (2017) Ethnic differences in glucose effectiveness and disposition index in overweight/obese African American and white women with prediabetes: a study of compensatory mechanisms. Diabetes Res Clin Pract 130:278–285.

    Article  PubMed  Google Scholar 

  14. Kufe CN, Micklesfield LK, Masemola M et al (2022) Increased risk for type 2 diabetes in relation to adiposity in middle-aged black South African men compared to women. Eur J Endocrinol 186(5):523–533.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  15. Geer EB, Shen W (2009) Gender differences in insulin resistance, body composition, and energy balance. Gend Med 6:60–75.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Goedecke JH, George C, Veras K et al (2016) Sex differences in insulin sensitivity and insulin response with increasing age in black South African men and women. Diabetes Res Clin Pract 122:207–214.

    Article  PubMed  CAS  Google Scholar 

  17. Nordström A, Hadrévi J, Olsson T, Franks PW, Nordström P (2016) Higher prevalence of type 2 diabetes in men than in women is associated with differences in visceral fat mass. J Clin Endocrinol Metab 101(10):3740–3746.

    Article  PubMed  CAS  Google Scholar 

  18. Goedecke JH, Nguyen KA, Kufe C et al (2022) Waist circumference thresholds predicting incident dysglycemia and type 2 diabetes in Black African men and women. Diabetes Obes Metab 24(5):918–927.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Harris PA, Taylor R, Thielke R, Payne J, Gonzalez N, Conde JG (2009) Research electronic data capture (REDCap) – a metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inform 42(2):377–381.

    Article  PubMed  Google Scholar 

  20. World Health Organization (2000) Obesity: preventing and managing the global epidemic. Report of a WHO consultation. World Health Organ Tech Rep Ser 894:i-xii:1–253

    Google Scholar 

  21. World Health Organization (2011) Waist circumference and waist-hip ratio: report of a WHO expert consultation, Geneva, 8-11 December 2008. World Health Organization, Geneva, Switzerland

  22. Micklesfield LK, Goedecke JH, Punyanitya M, Wilson KE, Kelly TL (2012) Dual-energy X-ray performs as well as clinical computed tomography for the measurement of visceral fat. Obesity 20(5):1109–1114.

    Article  PubMed  Google Scholar 

  23. American Diabetes Association (2019) Standards of medical care in diabetes 2019. Diabetes Care 42(Suppl 1):S124–S138.

    Article  Google Scholar 

  24. World Health Organization (2006) Definition and diagnosis of diabetes mellitus and intermediate hyperglycaemia: report of a WHO/IDF consultation. World Health Organization, Geneva, Switzerland.

  25. Matthews DR, Hosker JP, Rudenski AS, Naylor BA, Treacher DF, Turner RC (1985) Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia 28(7):412–419.

    Article  PubMed  CAS  Google Scholar 

  26. Wallace TM, Levy JC, Matthews DR (2004) Use and abuse of HOMA modeling. Diabetes Care 27(6):1487–1495.

    Article  PubMed  Google Scholar 

  27. Matsuda M, DeFronzo RA (1999) Insulin sensitivity indices obtained from oral glucose tolerance testing: comparison with the euglycemic insulin clamp. Diabetes Care 22(9):1462–1470.

    Article  PubMed  CAS  Google Scholar 

  28. Tura A, Kautzky-Willer A, Pacini G (2006) Insulinogenic indices from insulin and C-peptide: comparison of beta-cell function from OGTT and IVGTT. Diabetes Res Clin Pract 72(3):298–301.

    Article  PubMed  CAS  Google Scholar 

  29. Kahn SE, Prigeon RL, McCulloch DK et al (1993) Quantification of the relationship between insulin sensitivity and β-cell function in human subjects: evidence for a hyperbolic function. Diabetes 42(11):1663–1672.

    Article  PubMed  CAS  Google Scholar 

  30. Ahrén B, Pacini G (2004) Importance of quantifying insulin secretion in relation to insulin sensitivity to accurately assess beta cell function in clinical studies. Eur J Endocrinol 150(2):97–104.

    Article  PubMed  Google Scholar 

  31. Lind L, Elmståhl S, Bergman E et al (2013) EpiHealth: a large population-based cohort study for investigation of gene–lifestyle interactions in the pathogenesis of common diseases. Eur J Epidemiol 28(2):189–197.

    Article  PubMed  CAS  Google Scholar 

  32. Warde-Farley D, Donaldson SL, Comes O et al (2010) The GeneMANIA prediction server: biological network integration for gene prioritization and predicting gene function. Nucleic Acids Res 38(Suppl 2):W214–W220.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  33. Zhang X, Gu HF, Frystyk J, Efendic S, Brismar K, Thorell A (2019) Analyses of IGFBP2 DNA methylation and mRNA expression in visceral and subcutaneous adipose tissues of obese subjects. Growth Hormon IGF Res 45:31–36.

    Article  CAS  Google Scholar 

  34. Mejia-Cristobal LM, Reus E, Lizarraga F et al (2015) Tissue inhibitor of metalloproteases-4 (TIMP-4) modulates adipocyte differentiation in vitro. Exp Cell Res 335(2):207–215.

    Article  PubMed  CAS  Google Scholar 

  35. Hayes AF (2017) Introduction to mediation, moderation, and conditional process analysis. Guilford Press, New York, USA

    Google Scholar 

  36. Hudish LI, Reusch JEB, Sussel L (2019) β cell dysfunction during progression of metabolic syndrome to type 2 diabetes. J Clin Invest 129(10):4001–4008.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Ferrannini E, Gastaldelli A, Miyazaki Y, Matsuda M, Mari A, DeFronzo RA (2005) β-cell function in subjects spanning the range from normal glucose tolerance to overt diabetes: a new analysis. J Clin Endocrinol Metab 90(1):493–500.

    Article  PubMed  CAS  Google Scholar 

  38. Mari A, Tura A, Natali A et al (2010) Impaired beta cell glucose sensitivity rather than inadequate compensation for insulin resistance is the dominant defect in glucose intolerance. Diabetologia 53(4):749–756.

    Article  PubMed  CAS  Google Scholar 

  39. Folkersen L, Gustafsson S, Wang Q et al (2020) Genomic and drug target evaluation of 90 cardiovascular proteins in 30,931 individuals. Nat Metab 2(10):1135–1148.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  40. Kolb H, Mandrup-Poulsen T (2005) An immune origin of type 2 diabetes? Diabetologia 48(6):1038–1050.

    Article  PubMed  CAS  Google Scholar 

  41. Herder C, Baumert J, Thorand B et al (2006) Chemokines as risk factors for type 2 diabetes: results from the MONICA/KORA Augsburg study, 1984–2002. Diabetologia 49(5):921–929.

    Article  PubMed  CAS  Google Scholar 

  42. Herder C, Brunner EJ, Rathmann W et al (2009) Elevated levels of the anti-inflammatory interleukin-1 receptor antagonist precede the onset of type 2 diabetes: The Whitehall II Study. Diabetes Care 32(3):421–423.

    Article  PubMed  PubMed Central  Google Scholar 

  43. Larsen CM, Faulenbach M, Vaag A et al (2007) Interleukin-1-receptor antagonist in type 2 diabetes mellitus. N Engl J Med 356(15):1517–1526.

    Article  PubMed  CAS  Google Scholar 

  44. Cavelti-Weder C, Babians-Brunner A, Keller C et al (2012) Effects of gevokizumab on glycemia and inflammatory markers in type 2 diabetes. Diabetes Care 35(8):1654–1662.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  45. Rissanen A, Howard CP, Botha J, Thuren T (2012) Effect of anti-IL-1β antibody (canakinumab) on insulin secretion rates in impaired glucose tolerance or type 2 diabetes: results of a randomized, placebo-controlled trial. Diabetes Obes Metab 14(12):1088–1096.

    Article  PubMed  CAS  Google Scholar 

  46. Eguchi K, Nagai R (2017) Islet inflammation in type 2 diabetes and physiology. J Clin Invest 127(1):14–23.

    Article  PubMed  PubMed Central  Google Scholar 

  47. Donath MY, Schumann DM, Faulenbach M, Ellingsgaard H, Perren A, Ehses JA (2008) Islet inflammation in type 2 diabetes: from metabolic stress to therapy. Diabetes Care 31(Suppl 2):S161–S164.

    Article  PubMed  CAS  Google Scholar 

  48. Dinarello CA (2000) The role of the interleukin-1-receptor antagonist in blocking inflammation mediated by interleukin-1. N Engl J Med 343(10):732–734.

    Article  PubMed  CAS  Google Scholar 

  49. Abdul-Ghani MA, Matsuda M, Balas B, DeFronzo RA (2007) Muscle and liver insulin resistance indexes derived from the oral glucose tolerance test. Diabetes Care 30(1):89–94.

    Article  PubMed  CAS  Google Scholar 

  50. Robinson MW, Harmon C, O’Farrelly C (2016) Liver immunology and its role in inflammation and homeostasis. Cell Mol Immunol 13(3):267–276.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  51. Wittenbecher C, Ouni M, Kuxhaus O et al (2019) Insulin-like growth factor binding protein 2 (IGFBP-2) and the risk of developing type 2 diabetes. Diabetes 68(1):188–197.

    Article  PubMed  CAS  Google Scholar 

  52. Gudmundsdottir V, Zaghlool SB, Emilsson V et al (2020) Circulating protein signatures and causal candidates for type 2 diabetes. Diabetes 69(8):1843–1853.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  53. Elhadad MA, Wilson R, Zaghlool SB et al (2021) Metabolic syndrome and the plasma proteome: from association to causation. Cardiovasc Diabetol 20(1):1–13.

    Article  CAS  Google Scholar 

  54. Hedbacker K, Birsoy K, Wysocki RW et al (2010) Antidiabetic effects of IGFBP2, a leptin-regulated gene. Cell Metab 11(1):11–22.

    Article  PubMed  CAS  Google Scholar 

  55. Belongie KJ, Ferrannini E, Johnson K, Andrade-Gordon P, Hansen MK, Petrie JR (2017) Identification of novel biomarkers to monitor β-cell function and enable early detection of type 2 diabetes risk. PLoS One 12(8):e0182932.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  56. Noordam R, van Heemst D, Suhre K, Krumsiek J, Mook-Kanamori DO (2020) Proteome-wide assessment of diabetes mellitus in Qatari identifies IGFBP-2 as a risk factor already with early glycaemic disturbances. Arch Biochem Biophys 689:108476.

    Article  PubMed  CAS  Google Scholar 

  57. Carter S, Li Z, Lemieux I et al (2014) Circulating IGFBP-2 levels are incrementally linked to correlates of the metabolic syndrome and independently associated with VLDL triglycerides. Atherosclerosis 237(2):645–651.

    Article  PubMed  CAS  Google Scholar 

  58. Fahlbusch P, Knebel B, Hörbelt T et al (2020) Physiological disturbance in fatty liver energy metabolism converges on IGFBP2 abundance and regulation in mice and men. Int J Mol Sci 21(11):4144.

    Article  PubMed Central  CAS  Google Scholar 

  59. Lau ES, Paniagua SM, Guseh JS et al (2019) Sex differences in circulating biomarkers of cardiovascular disease. J Am Coll Cardiol 74(12):1543–1553.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  60. Sakamuri SSVP, Watts R, Takawale A et al (2017) Absence of tissue inhibitor of metalloproteinase-4 (TIMP4) ameliorates high fat diet-induced obesity in mice due to defective lipid absorption. Sci Rep 7(1):1–13.

    Article  Google Scholar 

  61. Maquoi E, Munaut C, Colige A, Collen D, Lijnen HR (2002) Modulation of adipose tissue expression of murine matrix metalloproteinases and their tissue inhibitors with obesity. Diabetes 51(4):1093–1101.

    Article  PubMed  CAS  Google Scholar 

  62. National Department of Health, Statistics South Africa, South African Medical Research Council, ICF (2017) South Africa – Demographic and Health Survey 2016: key findings. National Department of Health, Statistics South Africa, South African Medical Research Council and ICF, Pretoria, South Africa

  63. Peer N, Steyn K, Lombard C, Lambert EV, Vythilingum B, Levitt NS (2012) Rising diabetes prevalence among urban-dwelling black South Africans. PLoS One 7(9):e43336.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

Download references


We are grateful to the participants in these cohorts, as well as the field staff of the Developmental Pathways for Health Research Unit. The graphical abstract was created with

Authors’ relationships and activities

TO and TF are members of the Editorial Board of Diabetologia. The authors declare that there are no other relationships that might bias, or be perceived to bias, their work.

Contribution statement

All authors have participated sufficiently in the work represented by the article and take public responsibility for the content. Participation included (i) conception or design of the work presented by the article, (ii) drafting the article or revising it for critically important content, and (iii) final approval of the version to be published. AEM is responsible for the integrity of the work as a whole.


The study was jointly funded by the South African Medical Research Council via the South African National Department of Health, the UK Medical Research Council (via the Newton Fund) and the GSK Africa Non-Communicable Disease Open Lab (via supporting grant project number ES/N013891/1) and the South African National Research Foundation (grant number UID:99108) and Västerbotten County Council, Sweden. TC is an International Training Fellow supported by Wellcome Trust grant number 214205/Z/18/Z. The study sponsors/funders were not involved in the design of the study, the collection, analysis and interpretation of data or writing the report, and did not impose any restrictions regarding publication of the report.

Author information

Authors and Affiliations


Corresponding author

Correspondence to Amy E. Mendham.

Additional information

Publisher’s note

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

Supplementary information


(PDF 5702 kb)

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Mendham, A.E., Micklesfield, L.K., Karpe, F. et al. Targeted proteomics identifies potential biomarkers of dysglycaemia, beta cell function and insulin sensitivity in Black African men and women. Diabetologia (2022).

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI:


  • Adiposity
  • Beta cell function
  • Ethnicity
  • IGFBP2
  • Impaired glucose metabolism
  • Insulin clearance
  • Insulin secretion
  • Insulin sensitivity
  • Obesity
  • TIMP4
  • Type 2 diabetes