Ethnic dependent differences in diagnostic accuracy of glycated hemoglobin (HbA1c) in Canadian adults

Accepted Manuscript Ethnic Dependent Differences in Diagnostic Accuracy of Glycated Hemoglobin (HbA1c) in Canadian adults Ronald A Booth, Ying Jiang, Howard Morrison, Heather Orpana, Susan Rogers Van Katwyk, Chantal Lemieux PII: DOI: Reference:

S0168-8227(17)30893-8 https://doi.org/10.1016/j.diabres.2017.11.035 DIAB 7155

To appear in:

Diabetes Research and Clinical Practice

Received Date: Revised Date: Accepted Date:

30 May 2017 3 November 2017 28 November 2017

Please cite this article as: R.A. Booth, Y. Jiang, H. Morrison, H. Orpana, S. Rogers Van Katwyk, C. Lemieux, Ethnic Dependent Differences in Diagnostic Accuracy of Glycated Hemoglobin (HbA1c) in Canadian adults, Diabetes Research and Clinical Practice (2017), doi: https://doi.org/10.1016/j.diabres.2017.11.035

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Ethnic Dependent Differences in Diagnostic Accuracy of Glycated Hemoglobin (HbA1c) in Canadian adults. Ronald A Booth1,2, Ying Jiang3, Howard Morrison3, Heather Orpana3,4, Susan Rogers Van Katwyk3,5, and Chantal Lemieux4 1

Division of Biochemistry, The Ottawa Hospital.

2

Department of Pathology and Laboratory Medicine, University of Ottawa

3

Social Determinants and Science Integration Directorate, Public Health Agency of Canada

4

School of Psychology, University of Ottawa

5

School of Epidemiology, Public Health and Preventive Medicine, University of Ottawa

* Corresponding Author:

Dr. Ronald A Booth Division of Biochemistry The Ottawa Hospital 501 Smyth Rd. Ottawa, CANADA K1H 8L6 Telephone: (613) 737-8899 x79095 E-mail: [email protected]

Concise Title: Diagnostic accuracy of Glycated Hemoglobin

Word Count: Abstract: 250 Text: 2567 Tables and figures: 4

Conflict of Interest. The authors certify that they have NO affiliations with or involvement in any organization or entity with any financial interest (such as honoraria; educational grants; participation in speakers’ bureaus; membership, employment, consultancies, stock ownership, or other equity interest; and expert testimony or patent-licensing arrangements), or non-financial interest (such as personal or professional relationships, affiliations, knowledge or beliefs) in the subject matter or materials discussed in this manuscript.

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Abstract Aim: Previous studies have shown varying sensitivity and specificity of hemoglobin A1c (HbA1c) to identify diabetes and prediabetes, compared to 2-hour oral glucose tolerance testing (OGTT) and fasting plasma glucose (FPG), in different ethnic groups. Within the Canadian population, the ability of HbA1c to identify prediabetes and diabetes in First Nations, Métis and Inuit, East and South Asian ethnic groups has yet to be determined. Methods: We collected demographic, lifestyle information, biochemical results of glycemic status (FPG, OGTT, and HbA1c) from an ethnically diverse Canadian population sample, which included a purposeful sampling of First Nations, Métis, Inuit, South Asian and East Asian participants. Results: Sensitivity and specificity using Canadian Diabetes Association (CDA) recommended cut-points varied between ethnic groups, with greater variability for identification of prediabetes than diabetes. Dysglycemia (prediabetes and diabetes) was identified with a sensitivity and specificity ranging from 47.1% and 87.5%, respectively in Caucasians to 24.1% and 88.8% in Inuit. Optimal HbA1c ethnic-specific cut-points for dysglycemia and diabetes were determined by receiver operating characteristic (ROC) curve analysis. Conclusions: Our sample showed broad differences in the ability of HbA1c to identify dysglycemia or diabetes in different ethnic groups. Optimal cut-points for dysglycemia or diabetes in all ethnic groups were substantially lower than CDA recommendations. Utilization of HbA1c as the sole biochemical diagnostic marker may produce varying degrees of false negative results depending on the ethnicity of screened individuals. Further research is necessary to identify and validate optimal ethnic specific cutpoints used for diabetic screening in the Canadian population. Key words: Diabetes, Diagnosis, HbA1c, Cut-points, Canadian, Firs Nations, Inuit, Ethnic Differences.

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1. Introduction The prevalence of type 2 diabetes is increasing world-wide, from a prevalence of 4.7% in 1980 to 8.5% in 2014 [1], with the largest increase in Africa, the Middle East and South-East Asia. The Canadian situation is no different; Canada has the 4th highest age-adjusted prevalence of diabetes of the OEDC (Organisation for Economic Co-operation and Development) countries, at 9.2% as of 2009 [2]. In 2011/2012, the prevalence of diagnosed diabetes based on the Canadian Chronic Disease Surveillance System was 9.8%. The diagnosis of diabetes and prediabetes was traditionally based on elevated blood glucose concentrations in a fasting state or post glucose challenge. Inclusion of glycated hemoglobin (HbA1c) by the World Health Organization (WHO), American Diabetes Aassociation, International Expert Committee (report on the role of the A1C assay in the diagnosis of diabetes) and the Canadian Diabetes Association (CDA) [3-5] in the diagnostic algorithm for diabetes adds to the available tools for both diagnosis and identification of patients at risk for developing diabetes. HbA1c has the advantages of convenient testing (fasting is not necessary), low day-to-day variability, and no need to undergo time-consuming and unpleasant glucose tolerance testing. Additionally, HbA1c predicts long-term glucose homeostasis [6]. Furthermonre, HbA1c is a key predictor of microvascular complications including nephropathy, cardiovascular disease and retinopathy [5,7,8]. In 2009 the WHO identified an HbA1c cut-off of 6.5% (47.5 mmol/mol) for diagnosing diabetes [9]. The CDA adopted 6.5% as the cut-point for diagnosis of diabetes and recommends a range of 6.0% (42.1 mmol/mol) to 6.4% (46.4 mmol/mol) for identification of prediabetes [3]. Recent data has shown ethnic differences in mean HbA1c concentration as well as varying ability to identify prediabetics and diabetics [1012]. Optimal diagnostic cut-offs for prediabetes and diabetes appear to vary between ethnic

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groups, with lower values seen in Asian and African ethnic groups relative to Caucasians when compared to 2-hour oral glucose tolerance (OGTT) or fasting plasma glucose (FPG) [13-17]. An early comparison of six studies from Denmark, the U.K., Greenland, Australia, Kenya and India demonstrated substantial variation in diagnostic accuracy of HbA1c, with sensitivities ranging from 17% to 78% using ≥6.5% HbA1c in patients identified by OGTT[18]. An analysis of 96 population studies comparing diabetes prevalence based on HbA1c, fasting glucose and OGTT [19] showed pooled sensitivities of HbA1c compared to FPG was 52.8% and was lower at 30.5% when compared to FPG or OGTT. Within Canada, Indigenous peoples (First Nations, Métis and Inuit) are identified as being at high risk for the development of diabetes[20,21]; however, the ability of HbA1c to identify pre-diabetes and diabetes in First Nations, Métis and Intuit, as well as other ethnic groups has yet to be determined. We examined the diagnostic ability of HbA1c in an ethnically diverse sample of the Canadian population sample to determine optimal ethnic-specific cutpoints for First Nations and Métis, Inuit, and South and East Asian groups using FPG/OGTT as the gold standard.

2. Methods 2.1 Study sample A dataset of 3564 Canadians from seven provinces and two territories was obtained from a combination of two multi-ethnic convenience samples, collectively referred to as the CANRISK sample. Although the sampling method was multi-ethnic, it was not designed to provide a statistically accurate representation of the Canadian population. Briefly, between 2007 and 2011 (CANRISK Phase 1) the Public Health Agency of Canada recruited 6475 adults from British

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Columbia, Saskatchewan, Manitoba, Ontario, New Brunswick, Nova Scotia, Prince Edward Island, primarily aged 40 years and older, into the study to develop and test a questionnairebased diabetes and prediabetes risk tool, described elsewhere [22]. Demographic and lifestyle information as well as glycemic status, as measured by FPG and OGTT were collected. A subset of 855 participants also had HbA1c measured and these were the participants used in the current study from CANRISK Phase 1. A second phase (CANRISK Phase 2) collected data from 2862 participants between 2013 and 2015 in British Columbia, Saskatchewan, Yukon and Nunavut, and targeted East Asian, South Asian, First Nations/Métis, and Inuit individuals primarily between the ages of 20 and 39. Again, demographic and lifestyle information along with biochemical results for FPG, OGTT, and HbA1c were collected. Study eligibility (both CANRISK Phase 1 and 2) included adults (>18 years) without a previous diagnosis of diabetes, not currently pregnant, able to complete the CANRISK questionnaire, and having all blood testing results for FPG, OGTT, and HbA1c. This study included participants from Caucasian, First Nations and Métis, Inuit, South Asian, and East Asian ethnic groups. However, participants from Black, Latin American, and other ethnic groups were removed from data analysis due to small sample sizes.

In CANRISK phase 1, most individuals were recruited through a face-to-face invitation during their visits at community health centres and through mail outs from local health authorities[22]. Phase 2 added radio announcements (Yukon and Nunavut data collection sites), announcements on social media such as Facebook, as well as posters, brochures and pamphlets to advertise the project. A $50 food voucher or cash compensation was given to participants who completed all the required information (questionnaire and blood tests). Local public health nurses were

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available to answer questions at all data collection sites. Individuals who provided informed consent were invited to complete a one-page CANRISK questionnaire, have anthropometric measurements of weight, height, and waist circumference taken, and to have two blood samples for FPG, OGTT and HbA1c drawn.

2.2 Biochemical measurements and diagnostic cut-offs Biochemical testing for OGTT and HbA1c was performed by clinical laboratories at each recruiting centre following local standard procedures. OGTT testing was performed following CDA guidelines [3]. The diagnostic cut-points used to determine participant diabetes status followed CDA guidelines: FPG ≥7.0 mmol/L, 2-hour OGTT glucose ≥11.1 mmol/L or HbA1c ≥6.5%. Prediabetes was defined as impaired fasting glucose, FPG between 6.1 mmol/L and 6.9 mmol/L; or impaired glucose tolerance, 2-hour OGTT glucose ranging from 7.8 mmol/L to 11.0 mmol/L; or HbA1c values between 6.0% and 6.4%.

2.3 Statistical analysis Ethnic groups were defined based on mother’s ethnicity as reported in the CANRISK survey. In CANRISK Phase 1, all Indigenous heritages including First Nations, Métis and Inuit were identified as a single category (Aboriginal). Based on where data were collected, participants identifying Aboriginal heritage were considered to be primarily First Nations and Métis. In CANRISK Phase 2, data collection sites in Nunavut and the Yukon were added, and Inuit heritage was collected separately; for analysis, First Nations and Métis heritage were grouped together. Age, body mass index category (BMI (kg/m2)) and waist circumference were analysed as categorical variables consistent with the categories on the CANRISK questionnaire [22]. The

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categorization for age in years was: 18-29, 30-39, 40-44, 45-54, 55-64 and 65+, BMI as:<25.0, 25.0-29.9, 30.0-34.9 and ≥35.0, waist circumference as: small (male <94 cm and female <80 cm), medium (male 94-102 cm and female 80-88 cm) and large (male >102cm and female >88cm) as per the CANRISK questionnaire. Data analysis was performed using SAS 9.3. Chi-square tests were used to compare demographic characteristics of different ethnic groups. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated with reference to FPG/OGTT to identify the diagnostic accuracy at various HbA1c thresholds. For dysglycemia or diabetes, only Receiver Operating Characteristic (ROC) curve analysis was used to assess HbA1c diagnostic performance. Youden's index for a ROC-analysis was calculated to identify the optimal HbA1c cut-points for detecting dysglycemia or diabetes (diagnosed by FPG/OGTT) for different ethnic groups by maximizing both sensitivity and specificity. An ANOVA was used to investigate the differences between mean HbA1c by ethnicity, adjusted for age group, sex BMI and waist circumference.

3. Results 3.1 Study sample The study sample characteristics are presented in Table 1a. Ages ranged from 18 to 65+, with the 18-29 years group representing the largest proportion (32%). The male to female ratio was 1:2. The proportion of dysglycemia as identified by FPG, OGTT or HbA1c varied across ethnic groups, with HbA1c identifying a higher proportion of individuals than FPG or OGTT alone (Table 1b). As expected, dysglycemia increased with age for each diagnostic test.

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Summary characteristics and chi-square analyses for each ethnic group included in the study are presented in Table 2. Significant differences were found across ethnic groups for sex, age, BMI, waist circumference, FPG/OGTT, HbA1c, and identification of prediabetes but not diabetes. Age distribution varied by ethnicity, with the First Nations and Métis sample having the fewest participants 40 and older. East Asian (74.6%) and South Asian (54.8%) groups in this sample had the highest proportion of participants with BMI <25, while Inuit (42.6 %) and First Nations and Métis (41.1%) groups had the highest proportion of participants with BMI ≥30. Caucasian and Inuit groups had the highest proportion of prediabetes at 26.6% and 26.7% respectively.

3.2 Diagnosis of dysglycemia Sensitivity and specificity using the CDA recommended cut-points varied between ethnic groups. Dysglycemia was identified with a sensitivity and specificity ranging from 47.1% and 87.5% (respectively) in Caucasians to 24.1% and 88.8% in Inuit, while diabetes was identified with a sensitivity and specificity ranging from 50.0% and 99.2% (respectively) in East Asians to 22.2% and 98.4% in Inuit (Table 3). The optimal HbA1c cut-points by ethnic group were identified by maximizing both sensitivity and specificity on the ROC plots. All ethnic groups had optimal cut points below the CDA recommended cut points. However, the AUC value for the Inuit group of 0.6, (95% confidence interval 0.3-0.8) was not significantly different from 0.5, indicating that HbA1c did not discriminate between cases and non-cases of dysglycemia.

3.4 Comparison of HbA1c by Ethnicity Age, sex, BMI and waist circumference adjusted differences in mean HbA1c for the major ethnic groups were compared by ANOVA (Table 4). Mean adjusted HbA1c among all ethnicities,

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except for the First Nations and Métis group, were significantly different than the Caucasian group (p<0.0001). Compared to Caucasians, mean HbA1c was significantly lower in the Inuit population, and significantly higher in the East Asian and South Asian populations after adjusting for age and sex.

4. Discussion The Canadian population is comprised of diverse groups from various ethnic backgrounds including Northern European, East and South Asian, Middle Eastern, African as well as Indigenous people, all with distinct genetic and cultural backgrounds. This is the first study with a relatively large sample size to investigate the accuracy of HbA1c as a diagnostic tool to identify unknown prediabetes and diabetes in multiple ethnic groups in Canada. Each record has the standard sampling frame and multiple diabetes diagnostic tests: HbA1c, FPG, and 2hr OGTT. This study focused on the diagnostic accuracy of HbA1c to identify previously unknown prediabetes and diabetic cases as compared to FPG or/and OGTT and noted significant differences between ethnic groups. Identification of dysglycemia in non-Caucasian ethnic groups in our sample using the CDA recommended cut-points for HbA1c was less sensitive compared to Caucasians. Diabetes detection by HbA1c however, showed better sensitivity in South or East Asian groups but lower sensitivity in First Nations and Métis and Inuit groups as compared to Caucasians. The observed higher sensitivity for the detection of diabetes should be interpreted with caution, as we had few occult diabetic participants.

Previous studies have identified ethnic differences in HbA1c and alternate optimal dysglycemia and diabetes cut-points [18,19]. A systematic review of HbA1c diagnostic accuracy in Chinese

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adults showed a low sensitivity and high specificity for detection of diabetes if a cut-point of 6.5% was used resulting in 49% of diabetics cases missed [23]. An Arab vs European populations meta-analysis demonstrated reduced sensitivity of HbA1c to detect dysglycemia and diabetes [24]. Araneta et al. [25] showed that among Filipino Americans, Japanese Americans, and Native Hawaiians an HbA1c value of ≥6.5% had low sensitivity for detection of diabetes and may limit or delay diagnosis and treatment. Furthermore, mean HbA1c in individuals of South Asian descent was shown to be significantly higher than Caucasians [26]. Ethnic variations in HbA1c may be attributable to genetic differences so group-specific optimal cut-points for detecting diabetes may be necessary [13] .The relative higher mean HbA1c we identified in South Asians may be due to a skewed sample, as our study population was selected by convenience. Higher HbA1c in South Asians should be confirmed in a representative sample of the Canadian population. Only among the First Nations and Métis population mean was age and sex adjusted HbA1c not significantly different from the Caucasian population (p=0.06). Little evidence about the diagnostic accuracy of HbA1c is available for Canadian First Nations, Métis or Inuit groups. The CDA recommended cut-point of ≥6.0% for identifying dysglycemia has low sensitivity in both First Nations and Métis and Inuit in this study sample, with 32.8% and 24.1%, respectively. HbA1c in Greenlandic Inuit compared to Danish adults is significantly higher, and the Inuit were shown to have higher levels of both pre-diabetes and diabetes identified by OGTT [27]. We have shown a decreased ability of HbA1c to identify prediabetes using a cut-point of ≥6.0% most notably in our Inuit group. A recent study identified a common nonsense mutation of the TCB1D4 in Canadian and Alaskan Inuit which strongly associated with higher glucose and insulin 2-hours post glucose load [28]. This common mutation may account for the decreased sensitivity of HbA1c compared to 2-hour OGTT, since individuals with this mutation

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may be inappropriately identified by 2-hour OGTT due to increased 2 hour post load blood glucose. Whether individuals with this mutation are indeed at increased risk for diabetic complications needs to be determined.

Use of HbA1c has become an important tool to identify individuals at risk of developing diabetes and diagnosing occult diabetes. HbA1c has the advantage of testing simplicity, with no requirement for fasting or extended time requirement for those being tested. However, HbA1c does not identify the identical population when compared to screening with FPG/OGTT: it identifies a partially overlapping group of individuals with prediabetes and diabetes. Numerous studies have identified ethnic-specific optimal cut-points[13,14,17,29], while others have seen similar diagnostic accuracy [30]. Our CANRISK sample showed broad differences in the ability of HbA1c to identify dysglycemia or diabetes in different ethnic groups. Optimal cut-points to identify dysglycemia and diabetes in our ethnic groups were substantially lower than CDA recommendations, with cut-points ranging from 5.5% to 5.7% for dysglycemia and 5.7% to 6.1% for diabetes. Use of HbA1c as the sole biochemical diagnostic marker may have varying degrees of false negative results depending on the ethnicity of screened individuals. Further research is necessary to identify and validate optimal ethnic specific cut-points used for diabetic screening in the Canadian population.

4.1 Limitations Our study population was a convenience sample of the Canadian population. Although our population broadly represents the ethnic diversity seen in Canada, we made no attempt to determine if participants were recent immigrants or have acclimatized to Canadian food habits

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that could affect HbA1c values. A significant number of our population was identified as having prediabetes; however, few were identified with occult diabetes. Due to the small sample size, we cannot determine whether the poor predictive functioning of HbA1c among the Inuit sample is simply due to power issues from the small sample size. Finally, the CANRISK studies excluded participants with a previous diagnosis of diabetes or currently using metformin or other glucosemodifying prescription drugs, but did not include collection of participant co-morbidities or use of non-diabetes therapies that may affect glucose homeostasis (e.g. diuretics, corticosteroids, statins or estrogens/progestins) and hence conclusions drawn from HbA1c diagnosed diabetes should be made with caution.

4.2 Conclusions: This study finds that HbA1c test has a low sensitivity to identify pre-diabetes and diabetes particularly in Indigenous and Asian populations when compared to Caucasians. Our study suggest that ethnic specific optimal HbA1c cut-points could help to better identify these individuals within these populations in order to start intervention early, thereby reducing stress on the Canadian healthcare system. These ethnic specific cut-points should include all ethnic groups, including Caucasians as well as Black, Latin American and other ethnic groups not represented in our sample.

Acknowledgments We are grateful to Justin Lang for his help with checking results and reviewing drafts. We thank the multiple study sites who contributed to data collection for both phases of the CANRISK study.

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Table 1. Study Sample Characteristics 1a. Characteristics of the full sample. 1b. Percent of dysglycemia measured by FPG, OGTT, and HbA1c, across ethnic groups and age groups Table 2. Characteristics and Chi-Square Analyses by Ethnic Group Table 3. Diagnostic Accuracy of HbA1c and Optimal Cut-points by Ethnic Group, Using a Gold Standard Test for Diagnosis of Diabetes--FPG and/or 2hour OGTT Table 4. Descriptive Analysis of mean HbA1c across ethnicity, Adjusted for Age and Sex

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Table 1a Ethnicity

Total N=3564

Caucasian First Nations and Métis Inuit South Asian East Asian

N 1040 620 251 580 1073

% 27.98 16.68 6.75 15.6 28.87

Female Male

2362 1202

66.3 33.7

18-29 30-39 40-44 45-54 55-64 65+

1205 1234 138 399 462 279

32.42 33.2 3.71 10.73 12.43 7.51

<25 25-29.9 30-34.9 >35

1813 1005 476 270

50.9 28.2 13.4 7.6

1373 742 1401

39.1 21.1 39.9

3055 418 91

85.7 11.7 2.6

3077 420 67

86.3 11.8 1.9

Sex

Age Group

BMI

Waist Circumference Male<94, Female<80 Male 94-102, Female 80-88 Male >102, Female >88 Diabetes status by OGTT/FPG Normal Pre-diabetes Diabetes Diabetes status by HbA1c Normal Pre-diabetes Diabetes

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Table 1b Ethnicity

FPG (%) 5.58 5.48 7.17 3.79 4.94

Total N=3564 OGTT (%) HbA1c (%) 15.29 18.75 7.9 5.65 14.74 13.94 10.34 13.28 10.62 13.51

Caucasian First Nations and Métis Inuit South Asian East Asian Age Group 18-29 1.59 4.35 30-39 4.02 7.86 40-44 6.09 7.83 45-54 9.12 18.51 55-64 11.78 24.44 65+ 9.59 21.24 Note: pre-diabetes and diabetes are combined for each measure

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3.18 8.46 11.3 22.93 34.44 36.53

Table 2

n(%) Caucasian

First Nations and Métis

Inuit

South Asian

East Asian

Male

321 (30.9)

233 (37.6)

78 (31.1)

225 (38.8)

345 (32.2)

Female

719 (69.1)

387 (62.4)

173 (68.9)

355 (61.2)

728 (67.9)

18-29

250 (24.0)

328 (52.9)

6 (2.4)

223 (38.5)

389 (36.3)

30-39

179 (17.2)

277 (44.7)

80 (31.9)

293 (50.5)

341 (31.8)

40-44

41 (3.9)

sup

36 (14.3)

7 (1.21)

28 (2.6)

45-54

156 (15.0)

0

55 (21.9)

21 (3.6)

130 (12.1)

55-64

249 (23.9)

sup

39 (15.5)

25 (4.3)

132 (12.3)

65+

165 (15.9)

7 (1.1)

35 (13.9)

11 (1.9)

53 (4.9)

<25

419 (40.3)

188 (30.3)

88 (35.1)

318 (54.8)

800 (74.6)

25-29.9

361 (34.7)

177 (28.6)

56 (22.3)

186 (32.1)

225 (21.0)

30-34.9

172 (16.5)

145 (23.4)

54 (21.5)

62 (10.7)

43 (4.0)

88 (8.5)

110 (17.7)

53 (21.1)

14 (2.4)

5(0.5)

Ethnicity

Chi Square P

Sex

0.0023

Age Group

<.0001

BMI

≥35 Waist Circumference (cm)

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<.0001

Male<94, Female<80

328 (32.0)

109 (17.6)

59 (24.0)

246 (42.6)

631 (60.3)

Male 94-102, Female 80-88

225 (21.9)

75 (12.1)

47 (19.1)

134 (23.2)

261 (24.9)

Male >102, Female >88

473 (46.1)

436 (70.3)

140 (56.9)

197 (34.1)

155 (14.8)

No Diabetes

853 (82.0)

553 (89.2)

197( 78.5)

512 (88.3)

940 (87.6)

Pre-diabetes

156 (15.0)

49 (7.9)

45 (17.9)

55 (9.5)

113 (10.53)

Diabetes

31 (2.98)

18 (2.9)

9 (3.6)

13 (2.2)

20( 1.9)

No Diabetes

845 (81.3)

585 (94.4)

216 (86.1)

503 (86.7)

928 (86.5)

Pre-diabetes

169 (16.3)

30 (4.8)

29 (11.6)

66 (11.4)

126 (11.7)

26 (2.5)

5 (0.8)

6 (2.4)

11 (1.9)

19 (1.8)

<.0001

277 (26.6)

69 (11.1)

67 (26.7)

109 (18.8)

199 (18.6)

<.0001

<.0001

OGTT/FPG

<.0001

HbA1c

Diabetes Pre-diabetes Any Positive Diabetes

Any Positive 48 (4.6) 19 (3.1) 13 (5.2) 18 (3.1) 29 (2.7) 0.0787 Note: sup = data suppressed due to cell size less than 5. There will be some overlap between 'Any Positive' for Pre-diabetes, and 'Any Positive' for Diabetes, as some people had a positive test in each category. N=3564

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Table 3. Caucasian

Recomme nded Cutpoints Sensitivity

Specificity

PPV

NPV Optimal Cut-points ensitivity Specificity

PPV

NPV

ROC AUC

First Nations and Métis

Inuit

South Asian

East Asian

Dysglyce mia

Diabet es

Dysglycemia

Diabet es

Dysglyce mia

Diabet es

Dysglyce mia

Diabet es

Dysglyce mia

Diabet es

6.00%

6.50%

6.00%

6.50%

6.00%

6.50%

6.00%

6.50%

6.00%

6.50%

47.1 (39.754.5) 87.5 (85.089.6) 45.1 (39.451.0) 88.3 (86.889.6)

29.0 (14.248.0) 98.3 (97.399.0) 34.6 (20.452.2) 97.8 (97.398.3)

22.2 (6.447.6) 99.8 (99.1100) 80.0 (32.097.1) 97.7 (97.198.2)

24.1 (13.537.6) 88.8 (83.692.9) 37.1 (24.252.3) 81.0 (78.583.3)

22.2 (2.860.0) 98.4 (95.899.6) 33.3 (9.570.4) 97.1 (96.098.0)

44.1 (32.156.7) 90.8 (88.093.2) 39.0 (30.448.3) 92.0 (90.893.8)

46.2 (19.274.9) 99.1 (98.099.7) 54.6 (29.577.0) 98.8 (98.099.3)

46.6 (37.955.5) 91.2 (89.292.9) 42.8 (36.249.6) 92.4 (91.193.4)

50.0 (27.272.8) 99.2 (98.499.6) 52.6 (33.770.9) 99.1 (98.599.4)

5.70%

6.00%

5.70%

5.50%

5.70%

5.60%

6.10%

5.70%

6.10%

79.1 S (72.684.7) 64.5 (61.267.7) 32.8 (30.335.4) 93.4 (91.494.9) 0.78

77.4 (58.990.4) 83.1 (80.685.3) 12.3 (10.015.1) 99.2 (98.499.6) 0.83

72.2 (46.590.3) 89.0 (86.391.4) 16.5 (12.022.1) 99.1 (98.199.6) 0.85

75.9 (62.486.5) 57.4 (50.164.4) 32.8 (28.137.8) 89.7 (84.293.4) 0.7

11.1 (28.048.3) 99.6 (97.7100) 50.0 (6.493.7) 96.8 (96.097.4) 0.55

79.4 (67.988.3) 64.8 (60.569.0) 23.1 (20.226.2) 96.0 (93.797.4) 0.8

92.3 (64.099.8) 94.2 (91.996.0) 26.7 (20.134.4) 99.8 (98.8100) 0.96

72.2 (63.879.6) 69.2 (66.172.1) 24.9 (22.327.6) 94.6 (93.095.9) 0.76

70.0 (45.788.1) 93.2 (91.594.6) 16.3 (11.921.9) 99.4 (98.899.7) 0.85

[.75 .81]

[.74 .93]

[.73 .97]

[.63 .78]

[.32 .78]

[.74 .85]

[.92 – 1.0]

[.71 .81]

[.73 .97]

32.8 (21.945.4) 97.7 (96.098.7) 62.9 (47.276.2) 92.3 (91.093.4) 5.60% 61.2 (48.572.9) 87.7 (84.790.3) 37.6 (31.044.7) 94.9 (93.296.2) 0.78 [.73 - .86]

PPV, Positive predictive value; NPV, negative predictive value; ROC AUC, receiver operator curve area under the curve.

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Table 4. A1c Contrasts between Caucasian and other Ethnicities Ethnicity n Mean SE Contrast Statistics, P 5.638 0.014 Caucasian 1026 5.849 0.019 South Asian 577 <.0001 5.827 0.016 East Asian 1047 <.0001 5.497 0.024 Inuit 246 <.0001 620 5.606 0.020 0.125 First Nations and Métis Note: Means are adjusted for age group, gender, BMI group, WC group according to an ANOVA which also included ethnic category “other”. N=3516

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Study Highlights We utilized data from a 2-phase epidemiological study conducted by the Public Health Agency of Canada which collected demographic, lifestyle information, and biochemical results of glycemic status (FPG, OGTT, and HbA1c) from an ethnically diverse sample of the Canadian population. Our study included a purposeful sampling of First Nations, Métis, Inuit, South Asian and East Asian participants. We analyzed the data to identify the diagnostic accuracy of HbA1c compared to fasting glucose and OGTT in the various ethnic sub-groups of the Canadian population. The highlights of our study are indicated below:     

This is the first study with a large sample size to investigate the accuracy of HbA1c as a diagnostic tool to identify unknown prediabetes and diabetes in multiple ethnic groups in Canada. Identification of dysglycemia in non-Caucasian ethnic groups in our sample using the CDA recommended cut-points for HbA1c was less sensitive compared to Caucasians. Diabetes detection by HbA1c however, showed better sensitivity in South or East Asian groups but lower sensitivity in First Nations and Métis and Inuit groups as compared to Caucasians. Optimal cut-points to identify dysglycemia and diabetes in our ethnic groups were substantially lower than CDA recommendations, with cut-points ranging from 5.5% to 5.7% for dysglycemia and 5.7% to 6.1% for diabetes. Use of HbA1c as the sole biochemical diagnostic marker may have varying degrees of false negative results depending on the ethnicity of screened individuals.

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