Skip to main content
Key Specialties
Cardiology Diabetology Endocrinology Gastroenterology Geriatrics and Gerontology Gynecology and Obstetrics Hematology Infectious Disease Internal Medicine Nephrology Neurology Oncology Pediatrics Respiratory Medicine Rheumatology Surgery
Congress Coverage
2024 ACC Congress 2023 ESMO Congress 2023 EASD Congress 2023 ERS Congress 2023 ESC Congress 2023 EHA Congress 2023 ASCO Annual Meeting
Clinical Collections
Advances in Alzheimer’s

ACC 2024 Congress

Catch up on all the top stories and latest evidence in cardiology research with our ACC 2024 Congress hub page.
ADVANCED SEARCH
Log in
Top
BMC Public Health
Published in: BMC Public Health 1/2019

Open Access 01-12-2019 | Obesity | Research article

The association between anthropometric measures and glycated haemoglobin (HbA1c) is different in Russian, Somali and Kurdish origin migrants compared with the general population in Finland: a cross-sectional population-based study

Authors: Natalia Skogberg, Tiina Laatikainen, Eero Lilja, Annamari Lundqvist, Tommi Härkänen, Päivikki Koponen

Published in: BMC Public Health | Issue 1/2019

Login to get access

Abstract

Background

Persons of African and Middle-Eastern origin living in European countries have a high prevalence of type 2 diabetes, accompanied by high prevalence of obesity among women but not always among men. The aim of this study was to examine whether there are differences in the association between anthropometric measures and glucose levels measured with glycated haemoglobin and fasting blood glucose among persons of migrant origin in Finland.

Methods

Cross-sectional population-based data of the 30–64 year-old participants in the health examination of the Migrant Health and Wellbeing Study was used, selecting persons without diabetes (Russian origin n = 293, Somali origin n = 184, Kurdish origin n = 275). The reference group were non-diabetic participants in the Health 2011 Survey (n = 653), representative of the general Finnish population. Anthropometric measures included body mass index (BMI), waist circumference (WC), waist-to-height ratio (WHtR) and waist-to-hip ratio (WHR, available for Maamu Study participants only).

Results

Depending on whether continuous or categorical anthropometric measures were used, age, sex and anthropometrics explained 13–18% of variation in HbA1c among persons of Russian origin, 5–10% among persons of Somali origin, 1–3% among persons of Kurdish origin and 11–13% among the general population. Also depending on whether continuous or categorical anthropometric measures were used, age, sex and anthropometrics explained 13–19% of variation in fasting blood glucose among persons of Russian origin, 15–20% among persons of Somali origin, 13–17% among persons of Kurdish origin and 16–17% among the general population. With exception for BMI, strength of the association between continuous anthropometric measures and HbA1c was significantly lower among persons of Kurdish origin compared with the general Finnish population (p = 0.044 for WC and p = 0.040 for WHtR).

Conclusions

A low degree of association between anthropometric measures and HbA1c was observed among persons of Kurdish origin. Findings of this study suggest caution is warranted when using HbA1c as a screening tool for glucose impairment among persons without diabetes in populations of diverse origin.
Appendix
Available only for authorised users
Literature
1.
Chatterjee S, Khunti K, Davies MJ. Type 2 diabetes. Lancet. 2017;389(10085):2239–51.CrossRef
2.
Tabak AG, Herder C, Rathmann W, Brunner EJ, Kivimaki M. Prediabetes: a high-risk state for diabetes development. Lancet. 2012;379(9833):2279–90.CrossRef
3.
4.
World Health Organisation. Obesity: preventing and managing the global epidemic. WHO technical report series no. 894. Geneva: WHO; 2000.
5.
World Health Organisation. Use of Glycated Haemoglobin (HbA1c) in the diagnosis of diabetes mellitus. Abbreviated report of the WHO consultation 2011(WHO/NMH/CHP/CPM/11.1).
6.
American Diabetes Association. 2. Classification and Diagnosis of Diabetes. Standards of medical Care in Diabetes-2018. Diabetes Care. 2018;41(Suppl 1):S13–27.CrossRef
7.
Herman WH, Cohen RM. Racial and ethnic differences in the relationship between HbA1c and blood glucose: implications for the diagnosis of diabetes. J Clin Endocrinol Metab. 2012 Apr;97(4):1067–72.CrossRef
8.
Hare MJ, Magliano DJ, Zimmet PZ, Soderberg S, Joonas N, Pauvaday V, et al. Glucose-independent ethnic differences in HbA1c in people without known diabetes. Diabetes Care. 2013 Jun;36(6):1534–40.CrossRef
9.
Ghazanfari Z, Haghdoost AA, Alizadeh SM, Atapour J, Zolala F. A comparison of HbA1c and fasting blood sugar tests in general population. Int J Prev Med. 2010 Summer;1(3):187–94.PubMedPubMedCentral
10.
Seidell JC. Time trends in obesity: an epidemiological perspective. Horm Metab Res. 1997;29(4):155–8.CrossRef
11.
Lindstrom J, Tuomilehto J. The diabetes risk score: a practical tool to predict type 2 diabetes risk. Diabetes Care. 2003 Mar;26(3):725–31.CrossRef
12.
Ashwell M, Gibson S. Waist to height ratio is a simple and effective obesity screening tool for cardiovascular risk factors: analysis of data from the British National Diet and Nutrition Survey of adults aged 19-64 years. Obes Facts. 2009;2(2):97–103.CrossRef
13.
Ashwell M, Gunn P, Gibson S. Waist-to-height ratio is a better screening tool than waist circumference and BMI for adult cardiometabolic risk factors: systematic review and meta-analysis. Obes Rev. 2012;13(3):275–86.CrossRef
14.
Ashwell M, Gibson S. Waist-to-height ratio as an indicator of ‘early health risk’: simpler and more predictive than using a ‘matrix’ based on BMI and waist circumference. BMJ Open. 2016;6(3):e010159–2015–010159.CrossRef
15.
Nguyen NT, Nguyen XM, Lane J, Wang P. Relationship between obesity and diabetes in a US adult population: findings from the National Health and nutrition examination survey, 1999-2006. Obes Surg. 2011;21(3):351–5.CrossRef
16.
Skogberg N, Laatikainen T, Lundqvist A, Lilja E, Harkanen T, Koponen P. Which anthropometric measures best indicate type 2 diabetes among Russian, Somali and Kurdish origin migrants in Finland? A cross-sectional study. BMJ Open. 2018;8(5):e019166–2017–019166.CrossRef
17.
Bennet L, Nilsson PM. Country of birth modifies the associations of body mass and hemoglobin A1c with office blood pressure in middle eastern immigrants and native swedes. J Hypertens. 2014;32(12):2362–70 discussion 2370.CrossRef
18.
Meeks KA, Stronks K, Beune EJ, Adeyemo A, Henneman P, Mannens MM, et al. Prevalence of type 2 diabetes and its association with measures of body composition among African residents in the Netherlands--the HELIUS study. Diabetes Res Clin Pract. 2015;110(2):137–46.CrossRef
19.
Tillin T, Sattar N, Godsland IF, Hughes AD, Chaturvedi N, Forouhi NG. Ethnicity-specific obesity cut-points in the development of type 2 diabetes - a prospective study including three ethnic groups in the United Kingdom. Diabet Med. 2015;32(2):226–34.CrossRef
20.
Glans F, Elgzyri T, Shaat N, Lindholm E, Apelqvist J, Groop L. Immigrants from the middle-east have a different form of type 2 diabetes compared with Swedish patients. Diabet Med. 2008;25(3):303–7.CrossRef
21.
Bennet L, Groop L, Lindblad U, Agardh CD, Franks PW. Ethnicity is an independent risk indicator when estimating diabetes risk with FINDRISC scores: a cross sectional study comparing immigrants from the Middle East and native swedes. Prim Care Diabetes. 2014;8(3):231–8.CrossRef
22.
Bennet L, Agardh CD, Lindblad U. Cardiovascular disease in relation to diabetes status in immigrants from the Middle East compared to native swedes: a cross-sectional study. BMC Public Health. 2013;5(13):1133–2458-13-1133.CrossRef
23.
Meeks KAC, Henneman P, Venema A, Burr T, Galbete C, Danquah I, et al. An epigenome-wide association study in whole blood of measures of adiposity among Ghanaians: the RODAM study. Clin Epigenetics. 2017;9:103.CrossRef
24.
Bennet L, Stenkula K, Cushman SW, Brismar K. BMI and waist circumference cut-offs for corresponding levels of insulin sensitivity in a middle eastern immigrant versus a native Swedish population - the MEDIM population based study. BMC Public Health. 2016;16(1):1242.CrossRef
25.
Sumner AE. The relationship of body fat to metabolic disease: influence of sex and ethnicity. Gend Med. 2008 Dec;5(4):361–71.CrossRef
26.
Skogberg N, Laatikainen T, Jula A, Harkanen T, Vartiainen E, Koponen P. Contribution of sociodemographic and lifestyle-related factors to the differences in metabolic syndrome among Russian, Somali and Kurdish migrants compared with Finns. Int J Cardiol. 2017;232:63–9.CrossRef
27.
Spallek J, Zeeb H, Razum O. What do we have to know from migrants’ past exposures to understand their health status? A life course approach. Emerg Themes Epidemiol. 2011;8(1):6.CrossRef
28.
Agyemang C, de-Graft Aikins A, Bhopal R. Ethnicity and cardiovascular health research: pushing the boundaries by including comparison populations in the countries of origin. Ethn Health. 2012;17(6):579–96.CrossRef
29.
Bennet L, Groop L, Franks PW. Ethnic differences in the contribution of insulin action and secretion to type 2 diabetes in immigrants from the Middle East compared to native swedes. Diabetes Res Clin Pract. 2014;105(1):79–87.CrossRef
30.
Vandenheede H, Deboosere P, Stirbu I, Agyemang CO, Harding S, Juel K, et al. Migrant mortality from diabetes mellitus across Europe: the importance of socio-economic change. Eur J Epidemiol. 2012 Feb;27(2):109–17.CrossRef
31.
32.
Tillin T, Hughes AD, Wang Q, Wurtz P, Ala-Korpela M, Sattar N, et al. Diabetes risk and amino acid profiles: cross-sectional and prospective analyses of ethnicity, amino acids and diabetes in a south Asian and European cohort from the SABRE (Southall and Brent REvisited) study. Diabetologia. 2015;58(5):968–79.CrossRef
33.
Skogberg N, Laatikainen T, Koskinen S, Vartiainen E, Jula A, Leiviska J, et al. Cardiovascular risk factors among Russian, Somali and Kurdish migrants in comparison with the general Finnish population. Eur J Pub Health. 2016;26(4):667–73.CrossRef
34.
35.
Lundqvist A, Mäki-Opas T. Health 2011 Survey – methods, National Institute for Health and Welfare Report, vol. 8; 2016. p. 31–2.
36.
Gissler M, Haukka J. Finnish health and social welfare registers in epidemiological research. Norsk Epidemiologi. 2004;14(1):113–20.
37.
Tolonen H, Koponen P, Mindell J, Mannisto S, Kuulasmaa K. European Health examination survey--towards a sustainable monitoring system. Eur J Pub Health. 2014;24(2):338–44.CrossRef
38.
World Health Organisation. Waist circumference and waist-to-hip ratio: report of a WHO expert consultation, Geneva, 8-11 December 2008. Geneva: WHO Library Cataloguing-in-Publication Data; 2011. (ISBN 9789241501491)
39.
Lehtonen R, Pahkinen E. Practical methods for design and analysis of complex surveys. Chichester: Wiley; 2004.
40.
Research Triangle Institute. SUDAAN Language Manual, Volumes 1 and 2, Release 11. Triangle Park: Research Triangle Institute; 2012.
41.
Robins J, Rotnitzky A, Zhao L. Estimation of regression coefficients when some regressors are not always observed. J Am Statist Assoc. 1994;89:845–66.
42.
Manley SE, Round RA, Smith JM. Calibration of HbA1c and its measurement in the presence of variant hemoglobins: report on questionnaire to manufacturers. Ann Clin Biochem. 2006;43:135–45.CrossRef
43.
Derebail VK, Nachman PH, Key NS, Ansede H, Falk RJ, Kshirsagar AV. High prevalence of sickle cell trait in African Americans with ESRD. J Am Soc Nephrol. 2010;21(3):413–7.CrossRef
44.
Hamamy HA, Al-Allawi NAS. Epidemiological profile of common haemoglobinopathies in Arab countries. J Community Genet. 2013;4(2):147–67.CrossRef
45.
Alberti KG, Eckel RH, Grundy SM, Zimmet PZ, Cleeman JI, Donato KA, et al. Harmonizing the metabolic syndrome: a joint interim statement of the international diabetes federation task force on epidemiology and prevention; National Heart, Lung, and Blood Institute; American Heart Association; world heart federation; international atherosclerosis society; and International Association for the Study of obesity. Circulation. 2009;120(16):1640–5.CrossRef
46.
Chiu M, Austin PC, Manuel DG, Shah BR, Tu JV. Deriving ethnic-specific BMI cutoff points for assessing diabetes risk. Diabetes Care. 2011;34(8):1741–8.CrossRef
47.
Stirbu I, Kunst AE, Bopp M, Leinsalu M, Regidor E, Esnaola S, et al. Educational inequalities in avoidable mortality in Europe. J Epidemiol Community Health. 2010;64(10):913–20.CrossRef
48.
Mackenbach JP, Stirbu I, Roskam AJ, Schaap MM, Menvielle G, Leinsalu M, et al. Socioeconomic inequalities in health in 22 European countries. N Engl J Med. 2008;358(23):2468–81.CrossRef
49.
Agyemang C, van Valkengoed I, Hosper K, Nicolaou M, van den Born BJ, Stronks K. Educational inequalities in metabolic syndrome vary by ethnic group: evidence from the SUNSET study. Int J Cardiol. 2010;141(3):266–74.CrossRef
50.
Uchida K, Matuse R, Toyoda E, Okuda S, Tomita S. A new method of inhibiting glycolysis in blood samples. Clin Chim Acta. 1988 Feb 29;172(1):101–8.CrossRef
Metadata
Title
The association between anthropometric measures and glycated haemoglobin (HbA1c) is different in Russian, Somali and Kurdish origin migrants compared with the general population in Finland: a cross-sectional population-based study
Authors
Natalia Skogberg
Tiina Laatikainen
Eero Lilja
Annamari Lundqvist
Tommi Härkänen
Päivikki Koponen
Publication date
01-12-2019
Publisher
BioMed Central
Published in
BMC Public Health / Issue 1/2019
Electronic ISSN: 1471-2458
DOI
https://doi.org/10.1186/s12889-019-6698-0

Other articles of this Issue 1/2019

BMC Public Health 1/2019 Go to the issue

Research article

A longitudinal study assessing differences in causes of death among housed and homeless people diagnosed with HIV in San Francisco

Research article

Increasing socioeconomic disparities in sedentary behaviors in Chinese children

Research article

Alcohol consumption’s attributable disease burden and cost-effectiveness of targeted public health interventions: a systematic review of mathematical models

Research article

Economic burden of migraine in Latvia and Lithuania: direct and indirect costs

Research article

Alcohol use among Nigerian university students: prevalence, correlates and frequency of use

Study protocol

A preschool-based intervention for Early Childhood Education and Care (ECEC) teachers in promoting healthy eating and physical activity in toddlers: study protocol of the cluster randomized controlled trial PreSchool@HealthyWeight