Top

Published in:

Open Access 01-12-2015 | Research article

A comparison of indices of glucose metabolism in five black populations: data from modeling the epidemiologic transition study (METS)

Authors: Yacoba Atiase, Kathryn Farni, Jacob Plange-Rhule, Amy Luke, Pascal Bovet, Terrence G. Forrester, Vicki Lambert, Naomi S. Levitt, Stephanie Kliethermes, Guichan Cao, Ramon A. Durazo-Arvizu, Richard S. Cooper, Lara R. Dugas

Published in: BMC Public Health | Issue 1/2015

Abstract

Background

Globally, Africans and African Americans experience a disproportionate burden of type 2 diabetes, compared to other race and ethnic groups. The aim of the study was to examine the association of plasma glucose with indices of glucose metabolism in young adults of African origin from 5 different countries.

Methods

We identified participants from the Modeling the Epidemiologic Transition Study, an international study of weight change and cardiovascular disease (CVD) risk in five populations of African origin: USA (US), Jamaica, Ghana, South Africa, and Seychelles. For the current study, we included 667 participants (34.8 ± 6.3 years), with measures of plasma glucose, insulin, leptin, and adiponectin, as well as moderate and vigorous physical activity (MVPA, minutes/day [min/day]), daily sedentary time (min/day), anthropometrics, and body composition.

Results

Among the 282 men, body mass index (BMI) ranged from 22.1 to 29.6 kg/m² in men and from 25.8 to 34.8 kg/m² in 385 women. MVPA ranged from 26.2 to 47.1 min/day in men, and from 14.3 to 27.3 min/day in women and correlated with adiposity (BMI, waist size, and % body fat) only among US males after controlling for age. Plasma glucose ranged from 4.6 ± 0.8 mmol/L in the South African men to 5.8 mmol/L US men, while the overall prevalence for diabetes was very low, except in the US men and women (6.7 and 12 %, respectively). Using multivariate linear regression, glucose was associated with BMI, age, sex, smoking hypertension, daily sedentary time but not daily MVPA.

Conclusion

Obesity, metabolic risk, and other potential determinants vary significantly between populations at differing stages of the epidemiologic transition, requiring tailored public health policies to address local population characteristics.

Lozano R, Naghavi M, Foreman K, Lim S, Shibuya K, Aboyans V, et al. Global and regional mortality from 235 causes of death for 20 age groups in 1990 and 2010: a systematic analysis for the Global Burden of Disease Study 2010. Lancet. 2012;380(9859):2095–128. doi:10.1016/S0140-6736(12)61728-0.CrossRefPubMed

Peer N, Steyn K, Lombard C, Lambert EV, Vythilingum B, Levitt NS. Rising diabetes prevalence among urban-dwelling black South Africans. PLoS One. 2012;7(9), e43336. doi:10.1371/journal.pone.0043336.CrossRefPubMedPubMedCentral

McLarty DG, Swai AB, Kitange HM, Masuki G, Mtinangi BL, Kilima PM, et al. Prevalence of diabetes and impaired glucose tolerance in rural Tanzania. Lancet. 1989;1(8643):871–5.CrossRefPubMed

Aspray TJ, Mugusi F, Rashid S, Whiting D, Edwards R, Alberti KG, et al. Rural and urban differences in diabetes prevalence in Tanzania: the role of obesity, physical inactivity and urban living. Trans R Soc Trop Med Hyg. 2000;94(6):637–44.CrossRefPubMed

Owoaje EE, Rotimi CN, Kaufman JS, Tracy J, Cooper RS. Prevalence of adult diabetes in Ibadan. Nigeria East Afr Med J. 1997;74(5):299–302.PubMed

Nyenwe EA, Odia OJ, Ihekwaba AE, Ojule A, Babatunde S. Type 2 diabetes in adult Nigerians: a study of its prevalence and risk factors in Port Harcourt, Nigeria. Diabetes Res Clin Pract. 2003;62(3):177–85.CrossRefPubMed

Amoah AG, Owusu SK, Adjei S. Diabetes in Ghana: a community based prevalence study in Greater Accra. Diabetes Res Clin Pract. 2002;56(3):197–205.CrossRefPubMed

Bovet P, Romain S, Shamlaye C, Mendis S, Darioli R, Riesen W, et al. Divergent fifteen-year trends in traditional and cardiometabolic risk factors of cardiovascular diseases in the Seychelles. Cardiovasc Diabetol. 2009;8:34. doi:10.1186/1475-2840-8-34.CrossRefPubMedPubMedCentral

Faeh D, William J, Tappy L, Ravussin E, Bovet P. Prevalence, awareness and control of diabetes in the Seychelles and relationship with excess body weight. BMC Public Health. 2007;7:163. doi:10.1186/1471-2458-7-163.CrossRefPubMedPubMedCentral

10.

Stringhini S, Viswanathan B, Gedeon J, Paccaud F, Bovet P. The social transition of risk factors for cardiovascular disease in the African region: evidence from three cross-sectional surveys in the Seychelles. Int J Cardiol. 2013;168(2):1201–6. doi:10.1016/j.ijcard.2012.11.064.CrossRefPubMed

11.

Shaw JE, Sicree RA, Zimmet PZ. Global estimates of the prevalence of diabetes for 2010 and 2030. Diabetes Res Clin Pract. 2010;87(1):4–14. doi:10.1016/j.diabres.2009.10.007.CrossRefPubMed

12.

Kodama K, Tojjar D, Yamada S, Toda K, Patel CJ, Butte AJ. Diabetes Care. 2013;36(6):1789–96. doi:10.2337/dc12-1235.CrossRefPubMedPubMedCentral

13.

Lovejoy JC, de la Bretonne JA, Klemperer M, Tulley R. Abdominal fat distribution and metabolic risk factors: effects of race. Metabolism. 1996;45(9):1119–24.CrossRefPubMed

14.

Goedecke JH, Levitt NS, Lambert EV, Utzschneider KM, Faulenbach MV, Dave JA, et al. Differential effects of abdominal adipose tissue distribution on insulin sensitivity in black and white South African women. Obesity. 2009;17(8):1506–12. doi:10.1038/oby.2009.73.CrossRefPubMed

15.

Goedecke JH, Weinreich C, Fan J, Hauksson J, Victor H, Utzschneider K, et al. Ethnic differences in ectopic fat and associations with insulin sensitivity in black and white South African women. JEMSDA. 2012;17:1.

16.

Sumner AE, Cowie CC. Ethnic differences in the ability of triglyceride levels to identify insulin resistance. Atherosclerosis. 2008;196(2):696–703. doi:10.1016/j.atherosclerosis.2006.12.018.CrossRefPubMed

17.

Lawrence JC, Newcomer BR, Buchthal SD, Sirikul B, Oster RA, Hunter GR, et al. Relationship of intramyocellular lipid to insulin sensitivity may differ with ethnicity in healthy girls and women. Obesity. 2011;19(1):43–8. doi:10.1038/oby.2010.148.CrossRefPubMed

18.

Evans J, Goedecke JH, Soderstrom I, Buren J, Alvehus M, Blomquist C, et al. Depot- and ethnic-specific differences in the relationship between adipose tissue inflammation and insulin sensitivity. Clin Endocrinol (Oxf). 2011;74(1):51–9. doi:10.1111/j.1365-2265.2010.03883.x.CrossRef

19.

Bovet P, Faeh D, Gabriel A, Tappy L. The prediction of insulin resistance with serum triglyceride and high-density lipoprotein cholesterol levels in an East African population. Arch Intern Med. 2006;166(11):1236–7. doi:10.1001/archinte.166.11.1236-b.CrossRefPubMed

20.

Rossi IA, Bochud M, Viswanathan B, Riesen W, Bovet P. Relation between high-sensitivity C-reactive protein and cardiovascular and renal markers in a middle-income country in the African region. Int J Cardiol. 2012;156(2):203–8. doi:10.1016/j.ijcard.2011.09.084.CrossRefPubMed

21.

Corona E, Chen R, Sikora M, Morgan AA, Patel CJ, Ramesh A, et al. Analysis of the genetic basis of disease in the context of worldwide human relationships and migration. PLoS Genet. 2013;9(5), e1003447.CrossRefPubMedPubMedCentral

22.

Luke A, Bovet P, Forrester TE, Lambert EV, Plange-Rhule J, Schoeller DA, et al. Protocol for the modeling the epidemiologic transition study: a longitudinal observational study of energy balance and change in body weight, diabetes and cardiovascular disease risk. BMC Public Health. 2012;11:927. doi:10.1186/1471-2458-11-927.CrossRef

23.

Luke A, Bovet P, Plange-Rhule J, Forrester TE, Lambert EV, Schoeller DA, et al. A mixed ecologic-cohort comparison of physical activity & weight among young adults from five populations of African origin. BMC Public Health. 2014;14:397. doi:10.1186/1471-2458-14-397.CrossRefPubMedPubMedCentral

24.

Luke A, Bovet P, Forrester TE, Lambert EV, Plange-Rhule J, Dugas LR, et al. Prediction of fat-free mass using bioelectrical impedance analysis in young adults from five populations of African origin. Eur J Clin Nutr. 2013;67(9):956–60. doi:10.1038/ejcn.2013.123.CrossRefPubMedPubMedCentral

25.

Great Britain Office for National Statistics. The national statistics socio-economic classification: user manual, Report No.: vi,104. New York: Great Britain Office for National Statistics; 2005.

26.

American Diabetes Association. Diagnosis and classification of diabetes mellitus. Diabetes Care. 2013;36(1):S67–74. doi:10.2337/dc13-S067.CrossRef

27.

Colley RC, Tremblay MS. Moderate and vigorous physical activity intensity cut-points for the Actical accelerometer. J Sports Sci. 2011;29(8):783–9. doi:10.1080/02640414.2011.557744.CrossRefPubMed

28.

Wong SL, Colley R, Connor Gorber S, Tremblay M. Actical accelerometer sedentary activity thresholds for adults. J Phys Act Health. 2011;8(4):587–91.CrossRefPubMed

29.

Durazo-Arvizu RA, Camacho P, Bovet P, Forrester T, Lambert EV, Plange-Rhule J, et al. 25-Hydroxyvitamin D in African-origin populations at varying latitudes challenges the construct of a physiologic norm. Am J Clin Nutr. 2014;100(3):908–14. doi:10.3945/ajcn.113.066605.CrossRefPubMedPubMedCentral

30.

Dugas LR, Bovet P, Forrester TE, Lambert EV, Plange-Rhule J, Durazo-Arvizu RA, et al. Comparisons of intensity-duration patterns of physical activity in the US, Jamaica and 3 African countries. BMC Public Health. 2014;14(1):882. doi:10.1186/1471-2458-14-882.CrossRefPubMedPubMedCentral

31.

Assari S. Cross-country variation in additive effects of socio-economics, health behaviors, and comorbidities on subjective health of patients with diabetes. J Diabetes Metab Disord. 2014;13(1):36. doi:10.1186/2251-6581-13-36.CrossRefPubMedPubMedCentral

32.

Assari S, Lankarani RM, Lankarani MM. Cross-country differences in the association between diabetes and disability. J Diabetes Metab Disord. 2014;13(1):3. doi:10.1186/2251-6581-13-3.CrossRefPubMedPubMedCentral

33.

Braveman PA, Cubbin C, Egerter S, Chideya S, Marchi KS, Metzler M, et al. Socioeconomic status in health research: one size does not fit all. JAMA. 2005;294(22):2879–88. doi:10.1001/jama.294.22.2879.CrossRefPubMed

34.

Monteiro CA, Moura EC, Conde WL, Popkin BM. Socioeconomic status and obesity in adult populations of developing countries: a review. Bull World Health Organ. 2004;82(12):940–6.PubMed

35.

Peer N, Kengne AP, Motala AA, Mbanya JC. Diabetes in the Africa Region: an update. Diabetes Res Clin Pract. 2014;103(2):197–205. doi:10.1016/j.diabres.2013.11.006.CrossRefPubMed

36.

Farni K, Shoham DA, Cao G, Luke AH, Layden J, Cooper RS, et al. Physical activity and pre-diabetes-an unacknowledged mid-life crisis: findings from NHANES 2003-2006. PeerJ. 2014;2, e499. doi:10.7717/peerj.499.CrossRefPubMedPubMedCentral

37.

Glenn KR, Slaughter JC, Fowke JH, Buchowski MS, Matthews CE, Signorello LB, et al. Physical activity, sedentary behavior and all-cause mortality among blacks and whites with diabetes. Ann Epidemiol. 2015. doi:10.1016/j.annepidem.2015.04.006.PubMedPubMedCentral

38.

Gopalan A, Lorincz IS, Wirtalla C, Marcus SC, Long JA. Awareness of prediabetes and engagement in diabetes risk-reducing behaviors. Am J Prev Med. 2015. doi:10.1016/j.amepre.2015.03.007.PubMed

39.

Shrivastava U, Misra A, Gupta R, Viswanathan V. Socio-economic factors relating to diabetes and its management in India. J Diabetes. 2015. doi:10.1111/1753-0407.12316.PubMed

40.

Bovet P, Chiolero A, Shamlaye C, Paccaud F. Prevalence of overweight in the Seychelles: 15 year trends and association with socio-economic status. Obes Rev: Off J Intl Assoc Study Obes. 2008;9(6):511–7. doi:10.1111/j.1467-789X.2008.00513.x.CrossRef

41.

Rossi IA, Rousson V, Viswanathan B, Bovet P. Gender and socioeconomic disparities in BMI trajectories in the Seychelles: a cohort analysis based on serial population-based surveys. BMC Public Health. 2011;11:912. doi:10.1186/1471-2458-11-912.CrossRefPubMedPubMedCentral

42.

Cunningham-Myrie C, Younger-Coleman N, Tulloch-Reid M, McFarlane S, Francis D, Ferguson T, et al. Diabetes mellitus in Jamaica: sex differences in burden, risk factors, awareness, treatment and control in a developing country. Trop Med Int Health. 2013;18(11):1365–78. doi:10.1111/tmi.12190.CrossRefPubMed

43.

Meilleur KG, Doumatey A, Huang H, Charles B, Chen G, Zhou J, et al. Circulating adiponectin is associated with obesity and serum lipids in West Africans. J Clin Endocrinol Metab. 2010;95(7):3517–21. doi:10.1210/jc.2009-2765.CrossRefPubMedPubMedCentral

44.

Rossi IA, Bochud M, Bovet P, Paccaud F, Waeber G, Vollenweider P, et al. Sex difference and the role of leptin in the association between high-sensitivity C-reactive protein and adiposity in two different populations. Eur J Epidemiol. 2012;27(5):379–84. doi:10.1007/s10654-012-9671-0.CrossRefPubMed

45.

Boyne MS, Bennett NR, Cooper RS, Royal-Thomas TY, Bennett FI, Luke A, et al. Sex-differences in adiponectin levels and body fat distribution: longitudinal observations in Afro-Jamaicans. Diabetes Res Clin Pract. 2010;90(2):e33–6. doi:10.1016/j.diabres.2010.08.008.CrossRefPubMedPubMedCentral

Title: A comparison of indices of glucose metabolism in five black populations: data from modeling the epidemiologic transition study (METS)
Authors: Yacoba Atiase
Kathryn Farni
Jacob Plange-Rhule
Amy Luke
Pascal Bovet
Terrence G. Forrester
Vicki Lambert
Naomi S. Levitt
Stephanie Kliethermes
Guichan Cao
Ramon A. Durazo-Arvizu
Richard S. Cooper
Lara R. Dugas
Publication date: 01-12-2015
Publisher: BioMed Central
Published in: BMC Public Health / Issue 1/2015
Electronic ISSN: 1471-2458
DOI: https://doi.org/10.1186/s12889-015-2233-0

Keynote webinar | Spotlight on medication adherence

Springer Medicine

A comparison of indices of glucose metabolism in five black populations: data from modeling the epidemiologic transition study (METS)

Abstract

Background

Methods

Results

Conclusion

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Background

Methods

Results

Conclusion

Please log in to get access to this content

Other articles of this Issue 1/2015

Risk evaluations and condom use decisions of homeless youth: a multi-level qualitative investigation

Willingness to participate in prevention programs for cardiometabolic diseases

Influences on influenza transmission within terminal based on hierarchical structure of personal contact network

The prevalence, risk factors, and screening measure for prediabetes and diabetes among Emirati overweight/obese children and adolescents

Depressive symptoms and early retirement intentions among Danish eldercare workers: Cross-sectional and longitudinal analyses

The epidemiology of fatal cyclist crashes over a 14-year period in Alberta, Canada