Abstract
Background: The prevalence of type 2 diabetes (T2D) in Qatar and the Middle East is one of the highest in the world. It is estimated that about one quarter of the individuals with tbl2D are undiagnosed. Elevated HbA1c levels are an indicator of tbl2D or a pre-diabetic state. In this study we set out to examine which factors, such as anthropometric and socio-demographic risk factors, are associated with elevated HbA1c levels in a population without tbl2D. Methods: We examined 191 subjects with no record of tbl2D. Anthropometrics and HbA1c were measured. Socio-demographic (age, gender, ethnicity and educational level) and health information were assessed through questionnaires. Elevated HbA1c levels were defined as >6.0% (>42 mmol/mol). Individual risk factors were examined in relationship to having elevated HbA1c levels using logistic regression. Results: Thirty-eight (20%) study participants had elevated HbA1c levels. Participants from South Asian and Filipino descent were more likely to present with elevated HbA1c levels than Arab participants (adjusted odds ratios (OR): 13.30 (95% confidence interval (CI): 4.24, 41.79), p < 0.001 for South Asian and 4.54 (95% CI: 1.04, 19.83), p = 0.04 for Filipinos). A body mass index of above 30 kg/m2 was associated with elevated HbA1c levels (adjusted OR: 2.90 (95% CI: 1.29, 6.51), p = 0.01). Neither gender nor educational level was associated with elevated HbA1c levels. Conclusions: Elevated HbA1c levels in individuals not diagnosed with diabetes were most frequently found in the South Asian and Filipino immigrant population. Special attention should therefore be given to the early identification of tbl2D in these subjects.
Keywords: HbA1c, undiagnosed type 2 diabetes, public health, pre-diabetes, ethnic differences
Introduction
The prevalence of type 2 diabetes (T2D) in Qatar is estimated to be 23% and is one of the highest in the world.1 In 1990, the prevalence was estimated at 6%, thus showing an exceptionally high rate of increase over the past decades. Furthermore, it is estimated that about a quarter of individuals with tbl2D in Qatar are still undiagnosed.1 With this rate of increase, tbl2D has become an increasing burden on the Qatari society.
It has become a growing public health challenge to identify those individuals that are at risk for developing tbl2D. Primary preventive strategies are focused on recognizing risk factors and when possible intervening, while screening strategies are aimed at identifying individuals with undiagnosed tbl2D. For example, elevated HbA1c levels are known to be predictive for the later development of tbl2D. Furthermore, an abundance of studies have shown that characteristics such as a high waist-hip ratio, lower socio-economic status, dark skin-color, fewer years of education and higher BMI are associated with elevated HbA1c levels.2–4
Qatar is a rapidly growing economy with a free nationalized healthcare program, which is accessible to all residents regardless of ethnic or social background. Many of the preventive strategies are based on research that was performed outside of Qatar, often in Western societies.5 Also Qatar, like many other countries around the Gulf region, has a unique and dynamic socio-demographic structure with a stable native population and a rapidly growing expatriate population. Therefore, preventive strategies for tbl2D should be based on up-to-date studies from the region, and they also need to be stratified by population type. Thus, in this pilot study we set out to examine what anthropometric and socio-demographic risk factors are associated with elevated HbA1c levels in a population without tbl2D.
Methods
Study design and enrolment
This study was embedded in the Qatar Metabolomics Study on Diabetes (QMDiab),6 a larger collaborative effort between the Dermatology Department of Hamad Medical Corporation (HMC) in Doha, Qatar and Weill Cornell Medical College – Qatar (WCMC-Q). Subjects were recruited through the Dermatology Department at Rumailah Hospital. All patients, visiting the dermatologist, were asked to participate in the study. Subjects were enrolled between February and July 2012. All subjects needed to be above 18 years of age and have no record of tbl2D. All participants gave written informed consent. Ethical approval was obtained from the Institutional Review Board from both HMC and WCMC-Q.
Population for analysis
In total, 191 subjects without tbl2D were enrolled, based on absence of self-reported tbl2D. Furthermore, we confirmed that the subjects were not taking oral glucose lowering drugs by questionnaire. Elevated HbA1c levels was based on the standard clinical cut-offs of >6.0% (>42 mmol/mol).7 This cut-off has also been approved by the American Diabetes Association (ADA) as a diagnostic cut-off for pre-diabetes.7 Also, in several other populations, HbA1c has been shown to be a reliable alternative to fasting plasma glucose to detect diabetes.8,9 In total, 38 participants (19.9%) had an elevated HbA1c level. Subject characteristics are shown in Table 1.
Table 1.
Subject characteristics.
| HbA1c ≤ 6.0% ( ≤ 42 mmol/mol) | HbA1c >6.0 (>42 mmol/mol) | |
|
| ||
| N = 153 | N = 38 | |
|
| ||
| Mean (standard deviation) or number of participants (%) | ||
|
| ||
| Ethnicity | ||
|
| ||
| Arab* | 102 (67%) | 17 (45%) |
|
| ||
| South Asian** | 27 (18%) | 17 (45%) |
|
| ||
| Filipino | 18 (12%) | 4 (10%) |
|
| ||
| Other or mixed | 6 (4%) | 0 (0%) |
|
| ||
| Age (Years) | 39.0 (12.2) | 49.5 (9.0) |
|
| ||
| Gender | ||
|
| ||
| Male | 67 (44%) | 18 (47%) |
|
| ||
| Female | 86 (56%) | 20 (53%) |
|
| ||
| Highest educational level | ||
|
| ||
| No schooling or primary school | 15 (10%) | 3 (8%) |
|
| ||
| Intermediate or high school | 51 (34%) | 13 (35%) |
|
| ||
| College or university | 83 (56%) | 21 (57%) |
|
| ||
| Weight (kg) | 76.9 (15.7) | 82.1 (16.5) |
|
| ||
| Height (cm) | 164.5 (8.8) | 161.6 (9.3) |
|
| ||
| Body mass index (kg/m2) | 28.4 (5.1) | 31.7 (7.2) |
|
| ||
| Waist circumference (cm) | 94.4 (12.5) | 104.9 (14.6) |
|
| ||
| HbA1c (%) | 5.4 (0.4) | 6.4 (0.5) |
|
| ||
| HbA1c (mmol/mol) | 35.8 (3.8) | 46.4 (5.6) |
|
| ||
* Arab: Bahrain, Egypt, Iraq, Jordan, Kuwait, Lebanon, Morocco, Oman, Palestine, Qatar, Saudi Arabia, Somalia, Sudan, Syria, Tunisia, United Arab Emirates and Yemen.
** South Asian: Bangladesh, India, Nepal, Pakistan and Sri Lanka.
Data collection
Data regarding sex, age, ethnicity and highest educational qualification were obtained through questionnaire. Ethnicity was determined based on the birthplace of the participant, both parents and four grandparents. The Arab population consisted of subjects from Bahrain, Egypt, Iraq, Jordan, Kuwait, Lebanon, Morocco, Oman, Palestine, Qatar, Saudi Arabia, Somalia, Sudan, Syria, Tunisia, United Arab Emirates and Yemen. The South Asian subjects were from Bangladesh, India, Nepal, Pakistan and Sri Lanka.
Using standardized protocols trained researchers performed all measurements. Weight was measured lightly clothed to the nearest decimal with an electronic scale (SECA Scale 813, Hamburg, Germany). Height was measured without shoes and to the nearest decimal using a stadiometer (SECA Mobile Stadiometer 217, Hamburg, Germany). Waist circumference was measured with SECA 201 Ergonomic Circumference Measuring Tape (SECA, Hamburg, Germany) and a high waist circumference was based on ethnic specific cut-offs.10 For Arabs, a high waist circumference was defined as ≥ 94 cm for males and ≥ 80 cm for females. For South Asians and Filipinos, these cut-offs were ≥ 90 cm and ≥ 80 cm, respectively.10 Using standardized protocols, venous blood samples were collected by venipuncture in 4.0 ml EDTA tubes and stored at 7°C prior to processing. Blood samples were taken under non-fasting conditions. Automated analyses of HbA1c levels were performed within four hours of blood collection at the Department of Laboratory Medicine and Pathology of HMC with the cobas® 6000 (Roche Diagnostics, Basel, Switzerland).
Statistical analyses
Individual risk factors were examined in relationship to having elevated HbA1c levels using logistic regression. Since the age distribution was different between the two groups, we used an unadjusted model (model 1), an age- and gender-adjusted model (model 2), and an age-, gender- and body mass index-adjusted model (model 3). Body mass index was calculated as weight divided by height squared (kg/m2). Effect estimates for the risk of increased HbA1c levels for each individual risk factor are presented using odds ratios and their 95% confidence interval (95% CI). A p-value lower than 0.05 was considered statistically significant. All statistical analyses were performed using the IBM Statistical Package for the Social Sciences Version 20.0 (SPSS Inc., Chicago, IL, USA).
Results
Table 2 shows the odds ratios for the risk of having elevated HbA1c levels according to various risk factors. Of the participants from South Asian descent, 39% had HbA1c levels higher than 6.0%. The unadjusted odds ratio for elevated HbA1c among South Asians was 3.78 (95% CI: 1.71, 8.37, p = 0.001). After adjusting for age and gender, the risk for South Asians to have elevated HbA1c levels was over five times higher than for Arabs (odds ratio: 5.40 (95% CI: 2.14, 13.63), p < 0.001). When we additionally adjusted the model for BMI the odds ratios increased (13.30 (95% CI: 4.24, 41.79), p < 0.001 for South Asians and 4.54 (95% CI: 1.04, 19.83), p = 0.04 for Filipinos). Obese (BMI>30 kg/m2) subjects were more likely to have elevated HbA1c levels (odds ratio: 2.90 (95% CI: 1.29, 6.51), p = 0.01). Subjects with an increased waist circumference had higher HbA1c levels, but this association was not significant (odds ratio: 2.36 (95% CI: 0.79, 7.08, p = 0.13). However, when examining the waist circumference as a continuous variable, the odds ratios (per cm increase) for an increased HbA1c were 1.06 (95% CI: 1.03, 1.09), p < 0.001 (for both models: unadjusted and adjusted for age and gender). There was no significant difference between males and females, nor was there an association between educational level and HbA1c levels.
Table 2.
Associated factors for elevated HbA1c levels among participants without tbl2D.
| Ethnicity | HbA1c ≤ 6.0% ( ≤ 42 mmol/mol) | HbA1c>6.0% (>42 mmol/mol) | Odds Ratios | ||
|
| |||||
| Model 1 | Model 2 | Model 3 | |||
|
| |||||
| Arab | 102 (66.7%) | 17 (44.7%) | Reference -0- | Reference -0- | Reference 0- |
|
| |||||
| South Asian | 27 (17.6%) | 17 (44.7%) | 3.78 (1.71, 8.37)p = 0.001 | 5.40 (2.14, 13.63)p < 0.001 | 13.30 (4.24, 41.79)p < 0.001 |
|
| |||||
| Filipino | 18 (11.8%) | 4 (10.5%) | 1.33 (0.40, 4.42)p = 0.64 | 2.16 (0.59, 7.94)p = 0.25 | 4.54 (1.04, 19.83)p = 0.04 |
|
| |||||
| Age | HbA1c ≤ 6.0%( ≤ 42 mmol/mol) | HbA1c>6.0% (>42 mmol/mol) | Odds Ratios | ||
|
| |||||
| Model 1 | Model 2* | Model 3* | |||
|
| |||||
| < 40 years | 91 (59.5%) | 8 (21.1%) | Reference-0- | Reference-0- | Reference-0- |
|
| |||||
| 40–49 years | 33 (21.6%) | 9 (23.7%) | 3.10 (1.11, 8.71)p = 0.03 | 3.14 (1.12, 8.84)p = 0.03 | 3.52 (1.18, 10.46)p = 0.02 |
|
| |||||
| 50–59 years | 20 (13.1%) | 18 (47.4%) | 10.24 (3.91, 26.83)p < 0.001 | 10.61 (3.98, 28.26)p < 0.001 | 12.58 (4.44, 35.68)p < 0.001 |
|
| |||||
| >60 years | 9 (5.9%) | 3 (7.9%) | 3.79 (0.85, 16.88)p = 0.08 | 3.87 (0.87, 17.30)p = 0.08 | 3.59 (0.74, 17.34)p = 0.11 |
|
| |||||
| Gender | HbA1c ≤ 6.0%( ≤ 42 mmol/mol) | HbA1c>6.0%(>42 mmol/mol) | Odds Ratios | ||
|
| |||||
| Model 1 | Model 2** | Model 3** | |||
|
| |||||
| Female | 86 (56.2%) | 20 (52.6%) | Reference -0- | Reference -0- | Reference -0- |
|
| |||||
| Male | 67 (43.8%) | 18 (47.4%) | 1.16 (0.57, 2.36)p = 0.69 | 0.82 (0.38, 1.78)p = 0.82 | 1.26 (0.54, 2.96)p = 0.60 |
|
| |||||
| Highest educational level | HbA1c ≤ 6.0%( ≤ 42 mmol/mol) | HbA1c>6.0%(>42 mmol/mol) | Odds Ratios | ||
|
| |||||
| Model 1 | Model 2 | Model 3 | |||
|
| |||||
| No schooling or primary school | 15 (10.1%) | 3 (8.1%) | Reference -0- | Reference -0- | Reference -0- |
|
| |||||
| Intermediate or high school | 51 (34.2%) | 13 (35.1%) | 1.28 (0.32, 5.07)p = 0.73 | 2.01 (0.46, 8.89)p = 0.36 | 1.91 (0.41, 9.02)p = 0.41 |
|
| |||||
| College or university | 83 (55.7%) | 21 (56.8%) | 1.27 (0.34, 4.78)p = 0.73 | 2.35 (0.55, 10.03)p = 0.25 | 2.14 (0.47, 9.81)p = 0.33 |
|
| |||||
| Body Mass Index | HbA1c ≤ 6.0%( ≤ 42 mmol/mol) | HbA1c > 6.0%(>42 mmol/mol) | Odds Ratios | ||
|
| |||||
| Model 1 | Model 2 | Model 3 | |||
|
| |||||
| Non-obese( < 30 kg/m2) | 103 (67.3%) | 16 (42.1%) | Reference -0- | Reference -0- | N/A |
|
| |||||
| Obese(>30 kg/m2) | 50 (32.7%) | 22 (57.9%) | 2.83 (1.37, 5.86)p = 0.005 | 2.90 (1.29, 6.51)p = 0.01 | N/A |
|
| |||||
| Waist circumference | HbA1c ≤ 6.0%( ≤ 42 mmol/mol) | HbA1c>6.0%(>42 mmol/mol) | Odds Ratios | ||
|
| |||||
| Model 1 | Model 2 | Model 3 | |||
|
| |||||
| Normal | 42 (28.0%) | 5 (13.2%) | Reference-0- | Reference-0- | Reference-0- |
|
| |||||
| High | 108 (72.0%) | 33 (86.8%) | 2.57 (0.94, 7.02)p = 0.07 | 2.36 (0.79, 7.08)p = 0.13 | 1.39 (0.42, 4.62)p = 0.59 |
|
| |||||
Cut-off for normal/high waist circumference were based on cut-off of the International Diabetes Federation (10).
Arab: Bahrain, Egypt, Iraq, Jordan, Kuwait, Lebanon, Morocco, Oman, Palestine, Qatar, Saudi Arabia, Somalia, Sudan, Syria, Tunisia, United Arab Emirates and Yemen.
South Asian: Bangladesh, India, Nepal, Pakistan and Sri Lanka.
Model 1: Unadjusted
Model 2: Adjusted for age and gender
Model 3: Adjusted for age, gender and body mass index
* Model not adjusted for age
** Model not adjusted for gender
Discussion
In this study we set out to examine which factors are related to having elevated HbA1c levels in a population without tbl2D in Qatar. We found more than a 13-fold increased chance of having high HbA1c levels among South Asians in comparison to Arabs and a 4.5-fold increased chance among Filipinos compared to Arabs. Moreover, of all participants from South Asian descent who did not have tbl2D, 38% had HbA1c levels above 6.0% which is indicative of undiagnosed impaired glucose metabolism or tbl2D.
Some methodological issues need to be considered. Most importantly, we consider this study to be a pilot due to the relatively low number of participants. Previous studies performed on undiagnosed diabetes in other populations are generally larger.9,11–13 For a number of the risk factors such as gender and education levels that we investigated, the absence of association may be due to lack of statistical power. Secondly, subjects were recruited through the Dermatology Department, which potentially may have led to selection bias. Some of the individuals may use topical steroids, which increases the risk of developing insulin resistance. Nonetheless, this risk would be increased in all ethnicities and therefore this is unlikely to have influenced our effect estimates. Finally, we used the known clinical cut-off of HbA1c levels above 6.0% (42 mmol/mol). This cut-off is generally accepted by the American Diabetes Association.7 For impaired glucose metabolism and pre-diabetes, however, there has been some recent discussion whether the cut-off should be lowered to 5.7% (39 mmol/mol) instead of 6.0%.14 Using a 5.7% cut-off also gave an increased risk for having high HbA1c levels amongst South Asians and Filipinos (odds ratios: 3.90 (95% CI: 1.73, 8.77), p = 0.001, and 4.43 (95% CI: 1.60, 12.26), p = 0.004, respectively). These results indicate that our findings are robust and not dependent of the clinical cut-offs.
The concentration of HbA1c in plasma reflects glucose metabolism over the past two-three months. Positive associations have been found between the risk of tbl2D or hyperglycemia and total fat intake.15–18 Dietary fat intake may have an effect on glycaemia, and subsequently high HbA1c. High fat intake may also be related to obesity, which is in turn associated with insulin resistance.19–21 The racial and ethnic differences in HbA1c have been recognized for many years but have generally been attributed to differences in access to medical care or quality of care.22 However, various studies have now shown that despite adjustment for socio-demographic characteristics, access to care, quality of care, and self-management behaviors, racial and ethnic differences in HbA1c remain.23–25 In this study, the BMI correction for HbA1c in the various ethnicities made the differences in HbA1c in Arabs compared to South Asian larger. The Arabs in our study had a markedly higher BMI and waist circumference than the South Asians and Filipinos. Therefore, one would expect a higher HbA1c level in the Arabs compared to the South Asians. However, the opposite is true, which emphasizes the important role of ethnicity in relation to HbA1c in Arabs and South Asians.
Qatar has one of the fastest growing economies worldwide, with an equally rapidly increasing population. In our study almost 20% had an elevated HbA1c, which is similar to a screening pilot study that was recently performed in Saudi Arabia.26 Like many other Gulf countries, the size of the local population has been relatively stable over the past decade, while the population growth has come from a huge surge of immigrants. Most of these immigrants are native to South Asia (India, Pakistan, Nepal, Sri Lanka and Bangladesh) and the Philippines. Our data suggests that these immigrant populations have a large increased risk of having undiagnosed tbl2D.
Conclusion
In this study we found large ethnic differences in the HbA1c levels amongst subjects without tbl2D in Qatar. Larger epidemiological studies are necessary to examine these differences and other risk factors in Qatar and the entire Gulf region. The ADA already recommends screening for tbl2D in all asymptomatic adults above 45 years old.27 However, regarding this study, the screening and focus on the early detection of tbl2D should particularly be performed among the South Asian and Filipino populations in Qatar.
Author Disclosure Statement
All authors have no conflict of interest to disclose.
Acknowledgements
We would like to thank all the participants of this study. This study was supported by ‘Biomedical Research Program’ funds at Weill Cornell Medical College in Qatar, a program funded by Qatar Foundation. The statements made herein are solely the responsibility of the authors. We express our appreciation to Amina Al-Obaidli, Jillian Rowe, Wafaa Sekkal Gherbi, Omar Chidiac, Zeinab El-Din, Walaa El Maraghy for their assistance in data collection and management. Futhermore, we thank all the support staff at the Dermatology Department of Hamad Medical Corporation.
References
- 1.Scully T. Diabetes in numbers. Nature. 2012;485(7398):S2–S3. doi: 10.1038/485s2a. Epub 2012/05/23. [DOI] [PubMed] [Google Scholar]
- 2.Degeling C, Rock M, Rogers WA. Testing relationships: Ethical arguments for screening for type 2 diabetes mellitus with HbA1C. Journal of Medical Ethics. 2012;38(3):180–183. doi: 10.1136/medethics-2011-100086. Epub 2011/10/06. [DOI] [PubMed] [Google Scholar]
- 3.Feldman PJ, Steptoe A. Psychosocial and socioeconomic factors associated with glycated hemoglobin in nondiabetic middle-aged men and women. Health Psychology: Official Journal of the Division of Health Psychology, American Psychological Association. 2003;22(4):398–405. doi: 10.1037/0278-6133.22.4.398. Epub 2003/08/28. [DOI] [PubMed] [Google Scholar]
- 4.Selvin E, Steffes MW, Zhu H, Matsushita K, Wagenknecht L, Pankow J, Coresh J, Brancati FL. Glycated hemoglobin, diabetes, and cardiovascular risk in nondiabetic adults. The New England Journal of Medicine. 2010;362(9):800–811. doi: 10.1056/NEJMoa0908359. Epub 2010/03/05. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Knowler WC, Fowler SE, Hamman RF, Christophi CA, Hoffman HJ, Brenneman AT, Brown-Friday JO, Goldberg R, Venditti E, Nathan DM. 10-year follow-up of diabetes incidence and weight loss in the Diabetes Prevention Program Outcomes Study. Lancet. 2009;374(9702):1677–1686. doi: 10.1016/S0140-6736(09)61457-4. Epub 2009/11/03. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Mook-Kanamori DO, El-Din Selim MM, Takiddin AH, Al-Homsi H, Al-Mahmoud KA, Al-Obaidli A, Zirie MA, Rowe J, Yousri NA, Karoly ED, Kocher T, Sekkal Gherbi W, Chidiac OM, Mook-Kanamori MJ, Abdul Kader S, Al Muftah WA, McKeon C, Suhre K. 1,5-anhydroglucitol in saliva is a non-invasive marker of short-term glycemic control. The Journal of Clinical Endocrinology and Metabolism. 2014;99(3):E479–E483. doi: 10.1210/jc.2013-3596. http://www.ncbi.nlm.nih.gov/pubmed/24423354 [DOI] [PubMed] [Google Scholar]
- 7.American Diabetes Association. Diagnosis and classification of diabetes mellitus. Diabetes Care. 2013;36(Suppl 1):S67–S74. doi: 10.2337/dc13-S067. http://www.ncbi.nlm.nih.gov/pubmed/23264425 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Exebio JC, Zarini GG, Vaccaro JA, Exebio C, Huffman FG. Use of hemoglobin A1C to detect Haitian-Americans with undiagnosed Type 2 diabetes. Arquivos brasileiros de endocrinologia e metabologia. 2012;56(7):449–455. doi: 10.1590/s0004-27302012000700007. Epub 2012/10/31. [DOI] [PubMed] [Google Scholar]
- 9.Menchine MD, Arora S, Camargo CA, Ginde AA. Prevalence of undiagnosed and suboptimally controlled diabetes by point-of-care HbA1C in unselected emergency department patients. Academic Emergency Medicine: Official Journal of the Society for Academic Emergency Medicine. 2011;18(3):326–329. doi: 10.1111/j.1553-2712.2011.01014.x. Epub 2011/03/03. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.International Diabetes Federation. The IDF consensus worldwide definition of the metabolic syndrome. 2005:1–7. http://www.idf.org/webdata/docs/MetSyndrome_FINAL.pdf [Google Scholar]
- 11.Hauner H, Hanisch J, Bramlage P, Steinhagen-Thiessen E, Schunkert H, Jockel KH, Wasem J, Moebus S. Prevalence of undiagnosed Type-2-diabetes mellitus and impaired fasting glucose in German primary care: Data from the German Metabolic and Cardiovascular Risk Project (GEMCAS) Experimental and Clinical Endocrinology & Diabetes: Official Journal, German Society of Endocrinology [and] German Diabetes Association. 2008;116(1):18–25. doi: 10.1055/s-2007-985359. Epub 2007/10/11. [DOI] [PubMed] [Google Scholar]
- 12.Heianza Y, Arase Y, Saito K, Hsieh SD, Tsuji H, Kodama S, Tanaka S, Ohashi Y, Shimano H, Yamada N, Hara S, Sone H. Development of a screening score for undiagnosed diabetes and its application in estimating absolute risk of future type 2 diabetes in Japan: Toranomon Hospital Health Management Center Study 10 (TOPICS 10) The Journal of Clinical Endocrinology and Metabolism. 2013;98(3):1051–1060. doi: 10.1210/jc.2012-3092. Epub 2013/02/09. [DOI] [PubMed] [Google Scholar]
- 13.Creatore MI, Booth GL, Manuel DG, Moineddin R, Glazier RH. Diabetes screening among immigrants: A population-based urban cohort study. Diabetes Care. 2012;35(4):754–761. doi: 10.2337/dc11-1393. Epub 2012/02/24. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Zhuo X, Zhang P, Selvin E, Hoerger TJ, Ackermann RT, Li R, Bullard KM, Gregg EW. Alternative HbA1c cutoffs to identify high-risk adults for diabetes prevention: A cost-effectiveness perspective. American Journal of Preventive Medicine. 2012;42(4):374–381. doi: 10.1016/j.amepre.2012.01.003. Epub 2012/03/20. [DOI] [PubMed] [Google Scholar]
- 15.Feskens EJ, Virtanen SM, Rasanen L, Tuomilehto J, Stengard J, Pekkanen J, Nissinen A, Kromhout D. Dietary factors determining diabetes and impaired glucose tolerance. A 20-year follow-up of the Finnish and Dutch cohorts of the Seven Countries Study. Diabetes Care. 1995;18(8):1104–1112. doi: 10.2337/diacare.18.8.1104. Epub 1995/08/01. [DOI] [PubMed] [Google Scholar]
- 16.Marshall JA, Hamman RF, Baxter J. High-fat, low-carbohydrate diet and the etiology of non-insulin-dependent diabetes mellitus: The San Luis Valley Diabetes Study. American Journal of Epidemiology. 1991;134(6):590–603. doi: 10.1093/oxfordjournals.aje.a116132. Epub 1991/09/15. [DOI] [PubMed] [Google Scholar]
- 17.Marshall JA, Hoag S, Shetterly S, Hamman RF. Dietary fat predicts conversion from impaired glucose tolerance to NIDDM. The San Luis Valley Diabetes Study. Diabetes Care. 1994;17(1):50–56. doi: 10.2337/diacare.17.1.50. Epub 1994/01/01. [DOI] [PubMed] [Google Scholar]
- 18.Stern MP, Gonzalez C, Mitchell BD, Villalpando E, Haffner SM, Hazuda HP. Genetic and environmental determinants of type II diabetes in Mexico City and San Antonio. Diabetes. 1992;41(4):484–492. doi: 10.2337/diab.41.4.484. Epub 1992/04/01. [DOI] [PubMed] [Google Scholar]
- 19.Storlien LH, Baur LA, Kriketos AD, Pan DA, Cooney GJ, Jenkins AB, Calvert GD, Campbell LV. Dietary fats and insulin action. Diabetologia. 1996;39(6):621–631. doi: 10.1007/BF00418533. Epub 1996/06/01. [DOI] [PubMed] [Google Scholar]
- 20.Lovejoy J, DiGirolamo M. Habitual dietary intake and insulin sensitivity in lean and obese adults. The American Journal of Clinical Nutrition. 1992;55(6):1174–1179. doi: 10.1093/ajcn/55.6.1174. Epub 1992/06/01. [DOI] [PubMed] [Google Scholar]
- 21.Miller WC, Lindeman AK, Wallace J, Niederpruem M. Diet composition, energy intake, and exercise in relation to body fat in men and women. The American Journal of Clinical Nutrition. 1990;52(3):426–430. doi: 10.1093/ajcn/52.3.426. Epub 1990/09/01. [DOI] [PubMed] [Google Scholar]
- 22.Kirk JK, Bell RA, Bertoni AG, Arcury TA, Quandt SA, Goff DC, Jr, Narayan KM. Ethnic disparities: Control of glycemia, blood pressure, and LDL cholesterol among US adults with type 2 diabetes. The Annals of Pharmacotherapy. 2005;39(9):1489–1501. doi: 10.1345/aph.1E685. Epub 2005/08/04. [DOI] [PubMed] [Google Scholar]
- 23.Adams AS, Trinacty CM, Zhang F, Kleinman K, Grant RW, Meigs JB, Soumerai SB, Ross-Degnan D. Medication adherence and racial differences in A1C control. Diabetes Care. 2008;31(5):916–921. doi: 10.2337/dc07-1924. Epub 2008/02/01. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Boltri JM, Okosun IS, Davis-Smith M, Vogel RL. Hemoglobin A1c levels in diagnosed and undiagnosed black, Hispanic, and white persons with diabetes: Results from NHANES 1999-2000. Ethnicity & Disease. 2005;15(4):562–567. Epub 2005/11/02. [PubMed] [Google Scholar]
- 25.Saydah S, Cowie C, Eberhardt MS, De Rekeneire N, Narayan KM. Race and ethnic differences in glycemic control among adults with diagnosed diabetes in the United States. Ethnicity & Disease. 2007;17(3):529–535. Epub 2007/11/08. [PubMed] [Google Scholar]
- 26.AlGhamdi AS, Merdad K, Sonbul H, Bukhari SM, Elias WY. Dental clinics as potent sources for screening undiagnosed diabetes and prediabetes. The American Journal of the Medical Sciences. 2013;345(4):331–334. doi: 10.1097/MAJ.0b013e318287c96c. Epub 2013/03/28. [DOI] [PubMed] [Google Scholar]
- 27.American Diabetes Association. Standards of medical care in diabetes–2014. Diabetes care. 2014;37(Suppl 1):S14–S80. doi: 10.2337/dc14-S014. http://www.ncbi.nlm.nih.gov/pubmed/24357209 [DOI] [PubMed] [Google Scholar]