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Research article
Identifying persons at risk for developing type 2 diabetes in a concentrated population of high risk ethnicities in Canada using a risk assessment questionnaire and point-of-care capillary blood HbA1c measurement
Chip P Rowan*, Lisa A Miadovnik, Michael C Riddell, Michael A Rotondi, Norman Gledhill and Veronica K Jamnik
Corresponding author:
Chip P Rowan
358 Norman Bethune College, York University, 4700 Keele St., Toronto, Ontario M3J 1P3, Canada
347 Norman Bethune College, York University, 4700 Keele St., Toronto, Ontario M3J 1P3, Canada
364 Norman Bethune College, York University, 4700 Keele St., Toronto, Ontario M3J 1P3, Canada
356 Norman Bethune College, York University, 4700 Keele St., Toronto, Ontario M3J 1P3, Canada
355 Norman Bethune College, York University, 4700 Keele St., Toronto, Ontario M3J 1P3, Canada
For all author emails, please .
BMC Public Health 2014, 14:929&
doi:10.58-14-929
The electronic version of this article is the complete one and can be found online at:
Received:29 January 2014
Accepted:2 September 2014
Published:8 September 2014
& 2014 Rowan et al.; licensee BioMed Central Ltd.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Background
Amidst the growing health care burden created by diabetes, this study aimed to assess
the utility of a prediabetes/type 2 diabetes risk questionnaire in high risk ethnic
communities in Toronto Canada.
Participants (n = 691) provided questionnaire responses and capillary blood tests collected via fingerstick
and results were analysed for HbA1c using the Bio-Rad in2it point-of-care device. The Bland-Altman method was used to
compare point-of-care HbA1c analysis (Bio-Rad, boronate affinity chromatography) to that using high performance
liquid chromatography. ANOVA and linear regression were performed to investigate the
relationship between questionnaire and blood data.
Mean (±SD) HbA1c was 5.99% ± 0.84 and the Bland-Altman analysis revealed no significant biases HbA1c (bias = 0.039, 95% limits of agreement = -1.14 to 1.22). ANOVA showed that with increasing
risk classification based on questionnaire answers (with the exception of "moderate"-to-"high"),
there was a significant increase in mean HbA1c (Welch Statistic 30.449, p & 0.001). Linear regression revealed that the number of
high risk parents, age category, BMI, physical activity participation and previous
diagnosis of high blood sugar were significant contributors (p & 0.05) to the variance
Conclusions
Though not a substitute for established diagnostic protocols, the use of a risk questionnaire
can be an accurate, low cost, educational and time efficient method for assessment
of type 2 diabetes risk. The early detection of prediabetes and type 2 diabetes is
vital to increased awareness and opportunity for intervention with the goal of preventing
or delaying the progression of type 2 diabetes and the known associated complications.
Keywords: (Up to 10) S P B Point of careBackground
Type 2 diabetes mellitus in Canada is rapidly progressing into a dire situation with
enormous public health and economic implications. As of 2009, approximately 2.4 million
Canadians were living with a diagnosis of type 2 diabetes, a number that is expected
to grow to approximately 3.7 million by 2019 []. Perhaps of greater concern is that approximately 20% of type 2 diabetes cases remain
undiagnosed in addition to more than 5 million Canadian adults with prediabetes []. The economic burden of diabetes and its antecedent condition, prediabetes, is unsustainable
moving into the future. The Canadian Diabetes Association (CDA) reports that the 2009
cost of type 2 diabetes and its complications was $12.2 billion and forecasts an additional
$4.7 billion in costs by 2020 []. This projected cost underscores the urgent need to identify those who are undiagnosed
or who have prediabetes so that progression toward a type 2 diabetes diagnosis can
be avoided or, at the very least, delayed.
Type 2 diabetes is acknowledged to be a preventable condition, a premise that is substantiated
by seminal randomized clinical trials [-]. The Diabetes Prevention Program is widely recognized as a landmark research study
which showed a 58% reduction in diabetes incidence over a 4 year time frame among
individuals with prediabetes who participated in a lifestyle intervention involving
physical activity and nutritional counselling []. From a public health perspective, the first step in the prevention process, should
be the identification of frequently occurring risk factors for type 2 diabetes, including:
age ≥40 years, family history of type 2 diabetes, history of gestational diabetes,
poor blood lipid profile, hypertension, abdominal obesity, physical inactivity and
being a member of a high-risk population such as persons of Aboriginal, South Asian,
Chinese, or African descent []. Of particular interest are those risk factors that are directly modifiable through
lifestyle interventions such as abdominal obesity, hypertension, blood lipid profile
and physical activity level. Identification of these risk factors not only provides
an assessment of diabetes risk, but also acts as an important first step providing
awareness and education with the goal of eliciting healthy lifestyle changes. As it
pertains to disease management [,], the type and volume of physical activity has been widely studied among those with
type 2 diabetes but little is known about the result of physical activity interventions
for those with prediabetes. Also, programs that are designed to be culturally specific
and community-based may provide a unique opportunity to offer screening and intervention
opportunities to individuals at highest risk [], although the effectiveness of such programs as interventions has yet to be studied.
An effective exercise prescription showing an appreciation for the various physiological
adaptations to regular aerobic and resistance training as they pertain to type 2 diabetes
prevention is essential []. For persons with prediabetes and type 2 diabetes, the CDA [] and American Diabetes Association (ADA) [] recommends participation in a minimum of 150 minutes of moderate intensity (50-70%
age-predicted maximum heart rate) aerobic physical activity per week such as brisk
walking, cycling or water aerobics in addition to resistance training exercises 2–3
times per week using weight machines, free weights or body-weight exercises.
There have been several attempts to create a front-line risk assessment tool that
can readily identify those at highest risk for developing type 2 diabetes. The Finnish
Diabetes Risk Score (FINDRISC) questionnaire was generated in Finland as a product
of the Finnish diabetes prevention study and it has been modified for use in several
different countries, such as the Canadian Diabetes Risk Questionnaire (CANRISK) questionnaire
by the Public Health Agency of Canada. The FINDRISC questionnaire was selected as
a template based on its ability to effectively detect impaired glucose metabolism
among Scandinavian populations [,]. CANRISK was modified for the Canadian population with the goal of accounting for
the greater ethnic diversity compared to that of Finland []. CANRISK also includes questions about level of education and, for women, if they
had given birth to a large baby (over 9 lb) both of which are known to be associated
with type 2 diabetes risk []. Neither the FINDRISC, nor the CANRISK questionnaires used HbA1c as the primary assessment tool for glycemic control, although CANRISK did include
HbA1c measures in a sub-population [,,].
Regardless of the questionnaire being used, a fast, simple and low-cost option for
detecting type 2 diabetes risk that is validated against standardized diagnostic blood
test scores is an essential tool for programs that aim to reduce the incidence of
type 2 diabetes. The purpose of this investigation was to test the hypothesis that
a pen and paper risk questionnaire could accurately capture type 2 diabetes risk factor
profiles and stratify a person’s overall risk for developing type 2 diabetes that
is comparable to results of a capillary blood test for HbA1c collected via fingerstick.
Study design
The Prediabetes Detection and Physical Activity Intervention Delivery (PRE-PAID) project
focuses on the detection of individuals at high risk for developing type 2 diabetes
using a community-based public health approach. The mandate of the PRE-PAID program
was to focus efforts on ethnicities known to be at elevated risk for developing type
2 diabetes, which include persons of South Asian, African-Caribbean, Chinese and Aboriginal
Selected communities had an elevated prevalence of type 2 diabetes and a concentrated
population of high risk ethnicities. Demographic information was taken from the Institute
for Clinical Evaluative Sciences diabetes atlas for the city of Toronto which provided
information about diabetes incidence and prevalence by neighbourhood as well as a
breakdown of the population by ethnicity []. Study participants were recruited through an established network of community partnerships
with various organizations that provide public health-related programs to their constituents.
Participants were recruited through printed materials, e-mail distribution lists and
public diabetes screening events held in high-traffic areas such as shopping malls
and community health centres. All participants provided written, informed consent
prior to collection of data and all protocols utilized by the PRE-PAID project were
approved by the York University Human Participants Review Committee.
Questionnaire design
The FINDRISC and CANRISK questionnaires provided a detailed and well-established framework
upon which the PRE-PAID risk questionnaire was modeled. Slight alterations from the
CANRISK questionnaire were made to minimize participant burden by removing questions
about fruit and vegetable consumption, level of education and giving birth to a large
baby. The PRE-PAID investigators opted to streamline the time taken to complete the
questionnaire due to the fact that the capillary blood testing immediately followed
its completion and some participants may have been lost due to the additional 15 minute
commitment for the blood testing component. The questionnaire was also modified in
order to include more detailed information (frequency and intensity) regarding the
physical activity habits of those completing the questionnaire. These changes were
also adopted as a result of the published validation of the CANRISK questionnaire
which showed that the question regarding fruit and vegetable consumption, physical
activity and macrosomia (birth to a large baby) were not significant contributors
to their logistic regression model []. The PRE-PAID questionnaire is included as Additional file . Upon completion of the seven questions, an overall risk score was tabulated, based
on a scoring paradigm similar to that of CANRISK, placing individuals into one of
five diffe "Small" (score 0–6), "Moderate" (score 7–11), "High"
(score 12–14), "Very High" (score 15–20) and "Extreme" (score over 20). Trained members
of the research team assisted study participants with questionnaire completion, and
all questionnaire responses were based on self-reported information. BMI charts were
provided to simplify the estimation of BMI from body mass and height (kg/m2). Participants were only required to complete the questions that contributed to the
calculated risk score. The PRE-PAID questionnaire included space to self-report specific
values for height, body mass, age and waist circumference. The inclusion of these
values was encouraged to allow future analysis of participant demographics, but not
required to attain a complete risk score.
Additional file 1. Pre-diabetes/Type 2 diabetes screening tool for PRE-PAID.
Format: PDF
Size: 114KB This file can be viewed with:
Study participants
Persons were considered eligible for inclusion if they were over 18 years of age and
if they did not possess any condition that would preclude them from having a capillary
blood test to assess their glycemic control. English language proficiency was encouraged
but not essential as the questionnaire was translated into Chinese (simplified and
traditional), Punjabi, and Hindi. A total of 691 individuals were recruited in this
Blood testing
Point-of-care fingerstick capillary blood testing was performed to validate the risk
questionnaire outcomes. HbA1c was selected as the primary blood biomarker because it is a simple, minimally invasive
measure that does not require the person to be in a fasted state, thus allowing for
flexible testing capabilities. HbA1c is an indicator of three-month glycemic control and is less variable than fasted
blood glucose sampling on a day-to-day basis. HbA1c has also been adopted as part of the prediabetes and type 2 diagnostic criteria by
CDA as well as the American Diabetes Association (ADA) [,]. For these reasons, HbA1c is a highly appropriate biomarker for the evaluating the validity of the risk questionnaire.
HbA1c was analyzed using the Bio-Rad in2it (Bio-Rad Laboratories, Hercules, CA) point-of-care
device and boronate affinity chromatography. All capillary blood samples were collected
by a trained phlebotomist and sterile techniques were utilized in accordance with
York University biosafety and ethics requirements. In a sub-set of individuals, a
second HbA1c sample (from the same fingerstick) was collected using Bio-Rad capillary tubes for
analysis using high-performance liquid chromatography (HPLC), a standardized HbA1c analysis criterion method that is in accordance with National Glycohemoglobin Standardization
Program regulations. The HPLC analyses described above were performed by Clearstone
Central Laboratories (Mississauga, ON) using the Bio-Rad Variant II Hemoglobin testing
Results of the HbA1c tests were interpreted based on the 2013 Canadian Diabetes Association clinical practice
guidelines diagnostic criteria [] which define prediabetes using an HbA1c range of 6.0-6.4% and type 2 diabetes using an HbA1c range of ≥6.5% []. It should be noted that the ADA use an HbA1c range of 5.7-6.4% for prediabetes and ≥6.5% for diabetes []. Participants were informed that the results from the blood tests taken for the PRE-PAID
project were not designed to provide medical diagnosis of prediabetes or type 2 diabetes.
Individuals who had HbA1c scores ≥6.5% were provided with a letter describing their results and encouraged
to see their primary care physician for further confirmatory testing.
Statistical analyses
Descriptive statistics as well as frequencies of questionnaire responses were analyzed
for all participants who completed the risk questionnaire. Various exclusions within
the dataset took place for further analyses based on missing data that was attributable
to participant error, data entry error, or the participant’s unwillingness to provide
a blood sample. Figure& shows the participant flow diagram for the PRE-PAID risk questionnaire administration.
Participant recruitment and inclusion in the data analyses.
A comparison of the two methods for determining HbA1c was performed using the Bland-Altman method [] to detect any potential biases between the two methods of analysis. All analyses
described in this investigation were performed using a two-sided 5% level for significance.
ANOVA with post-hoc pairwise comparisons was performed using Tamhane’s T2 approach, which allows for unequal variances to compare risk classification based
on the questionnaire score to mean HbA1c measured using the Bio-Rad device. Prior to analysis, the "Very High" and "Extreme"
groups were merged because of a very small number of participants falling within the
"Extreme" classification. From a clinical perspective, individuals within both of
the highest risk groups would be strongly encouraged to visit a physician for further
assessment regardless. In addition to the ANOVA, additional analyses including the
area under the receiver-operator curve and examination of sensitivity and specificity
were performed to examine reliability. These analyses used a cut-point of 6.5 which
corresponds to the "moderate" risk category to better describe the ability of the
risk questionnaire to predict dysglycemia defined by HbA1c ≥ 6.0%.
Finally, step-wise, backward elimination linear regression was performed to quantify
the amount of variance in HbA1c values that was attributable to each of the variables included on the risk questionnaire.
The Bland Altman plots were performed using GraphPad Prism 6 and all other analyses
were performed using SPSS version 20.
Study participants
A total of 691 participants completed the risk questionnaire. The participants were
primarily female (71%) and 83% of participants reported having two parents from an
ethnicity known to be at high-risk for developing type 2 diabetes.
Questionnaire results
The mean overall risk score for all participants was 9.7 ± 5.3 (mean ± SD) which corresponds
to the "Moderate" risk classification. Notable findings include 44.1% of the respondents
reported to be physically active 3 or more times per week compared to 33.1% who reported
once or twice per week and 22.8% reported being physically active rarely or never.
In terms of body composition, self-reported BMI results show that 43.6% fall into
the normal range (BMI &25) while 33.3% were overweight (BMI 25–29) and 23.1% were
obese (BMI ≥30) based on World Health Organization BMI cut points for adults []. The adjusted cut points for Asian populations [] were not used because of the heterogeneity of the participant population. Also of
note, 28.9% of participants reported having been told that they have high blood pressure
by a physician and 14.8% of participants responded "yes" to having been told by a
physician that they have high blood sugar. Finally, 65.5% of participants noted that
they had a family history of diabetes and among these participants, 68.4% noted that
this was an immediate relative (mother, father, brother, sister or own child). Based
on the overall risk score, 30.2% of participants fell into the "Small" risk category,
33.1% into the "Moderate" risk category, 16.5% into the "High" risk category, 15.4%
into the "Very High" risk category, and 4.8% into the "Extreme" risk category. The
frequency data from the questionnaire responses are summarized in Table& along with descriptive data for questionnaire and blood test outcomes.
Summary of questionnaire and capillary blood testing outcomes
Blood results
A total of 670 people went on to provide a capillary blood sample using the Bio-Rad
point-of-care device after completing the risk questionnaire. From this group, a subset
of 311 provided a sample for analysis using HPLC. The mean Bio-Rad HbA1c (n = 670) was 5.99 ± 0.84% while the mean HPLC value was 5.81 ± 0.97%.
Analysis comparing the HbA1c scores collected using the two different methods (Bio-Rad and HPLC) took place for
303 persons and Figure& provides a Bland-Altman plot that describes the relationship between the two test
measures. A non-significant bias of 0.039 (95% limits of agreement = -1.14 to 1.22)
was observed when comparing absolute HbA1c scores using both devices (n = 303).
Bland-Altman plot comparing Bio-Rad and HPLC HbA1C analyses.
Comparison of risk questionnaire and blood outcomes
For this portion of the analysis, participants were excluded if they were missing
data for any component of the risk score on the questionnaire or if they did not have
a Bio-Rad HbA1c value. A total of 589 participants were included in the analysis. A one-way ANOVA
was performed to describe the relationship between HbA1c values and overall risk score classification. The results of the ANOVA revealed that
the assumption of homogeneity of variance was violated (Levene’s statistic 20.6, p & 0.001).
Welch tests were performed which showed that there were significant differences between
groups (Welch Statistic 30.449, p & 0.001). Post-hoc comparisons, using Tamhane’s
T2 approach, which allows for unequal variances, revealed only the "Moderate" and "High"
risk groups were not significantly different (p = 0.72) from each other in terms of
mean HbA1c. The results of the ANOVA are presented in Figure&.
Risk classification based on questionnaire score compared to mean HbA1c [(%) ± 95% Confidence Interval] measured using the Bio-Rad in2it device.
The results of the step-wise, backward elimination linear regression analysis (n = 589)
revealed that the number of high risk parents (standardized β = 0.15, p & 0.001),
age category (standardized β = 0.12, p & 0.001), BMI (standardized β = 0.11, p & 0.001),
physical activity participation (standardized β = 0.12, p & 0.001) and previous diagnosis
of high blood sugar (standardized β = 0.28, p & 0.001) were all significant contributors
to the variance in Bio-Rad HbA1c. The R2 for this model was 0.235. Results from the linear regression are shown in Table&. The area under the receiver-operator curve (AUC) was 0.716 using the definition
of dysglycemia as HbA1c ≥6.0%. The sensitivity and specificity using a score of 6.5 as a cut-point were 0.853
and 0.435, respectively. This shows that, if a person scored 7 or higher (there are
no half points allocated) which corresponds to "moderate" risk or higher, then the
likelihood of detecting true dysglycemia is promising. These results resemble the
values for moderate risk and mirror the incremental reduction in sensitivity with
increased cut-point score selected for the sensitivity/specificity analysis observed
using the CANRISK questionnaire [].
Results from the full step-wise, backward elimination linear regression model
Although participants were made aware that this project was not intended to diagnose
prediabetes or diabetes, it was still possible to ascertain valuable information regarding
the detection of participants previously unaware (undiagnosed) of their high blood
sugar through comparison of their HbA1c value to their response to the question, "have you ever been told by a doctor or
nurse that you have high blood sugar?". This process showed that 79.7% of participants
with an HbA1c ≥ 5.7% (ADA prediabetes cut point), 75% with an HbA1c ≥ 6.0% (CDA prediabetes cut point) and 61.7% with an HbA1c ≥ 6.5% had never been told that they had high blood sugar.
Discussion
When comparing the classifications of diabetes risk based on questionnaire overall
risk score to HbA1c values, significant and expected increases in HbA1c were observed as participants progressed from a risk classification of "Small" toward
"Very High" or "Extreme". After collapsing the "Very High" and "Extreme" groups, the
only groups that did not significantly differ were the "Moderate" and "High" risk
groups. Of particular interest, those in the "Small" risk category based on the questionnaire
responses had average HbA1c values corresponding to the healthy glycemic control while those in the "Moderate"
risk group had average HbA1c values that were approaching a state of prediabetes based on the CDA diagnostic criteria
[]. Furthermore, these "Moderate" risk individuals would be in the prediabetes range
based on ADA standards which define prediabetes using an HbA1c of 5.7-6.4% []. Those in the "High" risk group, based on their questionnaire responses, had corresponding
blood test scores with an average HbA1c value at the cusp of the prediabetes classification according to the CDA range (mean
HbA1c of "High" risk group = 5.99%, CDA Range = 6.0-6.4%) and in the middle of the ADA
prediabetes range (HbA1c of 5.7-6.4%) . Finally, those in the "Very High" risk group
had average HbA1c values (Mean HbA1c = 6.6%) in the diabetes range (≥6.5%) based on both the CDA and ADA guidelines. Another
related finding, with substantial clinical significance, was the extent to which the
screening process identified individuals who were previously unaware of their poor
glycemic control. With 75% of persons in the prediabetes range and ~62% of persons
in the diabetes range based on their HbA1c report having never been told by a physician or nurse that they had high blood sugar,
serious implications regarding the need for diabetes and prediabetes screening are
magnified.
Further investigation into the relationship between questionnaire outcomes and blood
values using multivariate linear regression revealed that, in descending order of
standardized beta values, previous diagnosis of high blood sugar (standardized β = 0.28),
number of high risk parents (standardized β = 0.0.15), physical activity participation
(standardized β = 0.12), age category (standardized β = 0.12), and BMI (standardized
β = 0.11) were all independent significant contributors to the variability in HbA1c. While the R2 statistic suggests that the model only explains 23.5% of the variance in HbA1c, a receiver operator characteristic (ROC) analysis was performed and the area under
the curve (AUC) was 0.716 using dysglycemia (HbA1c ≥6.0%) as the primary outcome with the intention of drawing comparisons to existing
diabetes risk questionnaires. The observed AUC for the ROC analysis is consistent
with findings from the CANRISK (AUROC = 0.75) and FINDRISC (AUROC = 0.648 for men,
0.659 for women) questionnaires for the prediction of dysglycemia (prediabetes + type
2 diabetes) [,]. The relatively low R2 of this model identifies a legitimate area of further investigation to decipher what
may be contributing to the remainder of the variance in HbA1c within high risk populations. Interestingly, an analysis of the CANRISK questionnaire
outcomes found that the response to their physical activity participation question
was not a significant contributor to their model []. This disparity between the CANRISK questionnaire and the PRE-PAID questionnaire,
with respect to the significance of physical activity in the model is likely due to
the fact that the PRE-PAID questionnaire had an altered version of the question which
was more descriptive in its assessment of physical activity and ascertained information
about physical activity frequency. These findings and the corresponding standardized
beta values will be used in the future to establish weighted responses on the questionnaire
with the goal of enhancing its predictive value.
The utilization of HbA1c as the primary blood biomarker for confirmation of risk provided the investigators
with a great deal of freedom in scheduling recruitment and screening events. Through
the use of minimally-invasive point-of-care capillary blood testing, a broad pool
of potential participants was reached. The ability to test blood in a non-fasted state
and provide rapid results made this test more accessible and appealing to potential
participants, thus enhancing the efficacy of recruitment efforts. The comparison between
the Bio-Rad device and HPLC revealed no significant bias between the two measures
which led to the decision to use the Bio-Rad samples (n = 589 with Bio-Rad values
versus 304 with HPLC) for the data analysis comparing blood results to questionnaire
outcomes via ANOVA and linear regression. Further, the accordance between the two
HbA1c supports the use of minimally-invasive point-of-care capillary blood testing for
future type 2 diabetes and prediabetes detection initiatives that are focused on screening,
awareness and education. These tests may be accessible to a larger population because
they can be performed at lower costs and less intrusive to persons at risk while providing
relatively accurate information, especially when used in conjunction with a risk questionnaire.
One of the primary limitations of this investigation is the demographics of the sample.
In an ideal setting, a more diverse sample would provide an opportunity to enhance
the validation of the PRE-PAID questionnaire for use on a broader population. In spite
of this, the mandate of the PRE-PAID investigators and the funding agencies was to
reach those at highest risk for developing type 2 diabetes, thus leading to more targeted
recruitment efforts. The concentrated efforts aimed at reaching high risk ethnicities
supports the notion that the PRE-PAID questionnaire provides a unique and appropriate
tool for use in public health screening initiatives that target these populations.
Another limitation of this investigation is the fact that all responses to the risk
questionnaire were self-reported and several studies have shown that individuals tend
to under-report their weight and waist circumference [,] while over-reporting their physical activity habits [,]. Although this may be a limitation, it is important to realize that during many public
health initiatives, questionnaires are distributed in a similar manner and self-reported
data is easier and less expensive to obtain when compared to actual measurement of
the various risk factors assessed on the PRE-PAID questionnaire which may require
equipment and trained personnel. Another limitation of the investigation pertains
to the wording of questions assessing previous diagnoses of high blood pressure, blood
sugar and family history of diabetes. Those who "didn’t know" were given a score of
zero. Moving forward, a more conservative approach should be taken so that those who
do not know how to respond, are assumed to possess that risk factor and therefore
receive a score for that question, thus contributing to their overall risk score.
Finally, there have been some studies that have documented the presence of hemoglobinopathies
or other conditions such as iron deficiency which would make the use of HbA1c inappropriate for the assessment of diabetes status [,]. The prevalence of hemoglobinopathies varies greatly depending on country and race
but has been reported as high as 10% in some African populations []. During the HPLC assessment of HbA1c, no participants were identified as having hemoglobinopathies that would warrant
their removal from the comparative analysis. It is possible, however, that some of
the study participants who only provided Bio-Rad HbA1c samples possessed some form of hemoglobinopathy. Additionally, there may be other
factors such as prescription medication which may contribute to altered HbA1c values [] and it should be noted that this data was not captured by the risk questionnaire
during this study. Adding questions regarding medication use would increase the complexity
and duration of completing the questionnaire which would increase subject burden.
While the strength of the CANRISK questionnaire lies in its validation using a large,
and representative Canadian sample population, the PRE-PAID risk questionnaire has
shown to be an effective alternative tool for use among high risk ethnicities in Canada.
As a result of the PRE-PAID investigation, the CANRISK questionnaire may enhance its
own predictive value if more detailed questions were included with respect to physical
activity p active transport, sedentary time, physical nature
of their occupation, structured exercise, leisure time physical activity plus intensity
and frequency of daily activities of living. The analysis of the number of high risk
parents is also unique to the PRE-PAID questionnaire which provides important information
to enhance the identification of risk based on ethnicity. The ultimate goal of this
investigation was to develop an inexpensive front-line questionnaire that could accurately
assess a person’s risk for developing diabetes.
Conclusions
Using a simple screening approach involving risk factor identification and HbA1c point-of-care testing, large and diverse population groups become more accessible
and the identification of prediabetes can occur earlier. This early detection provides
increased awareness and opportunity to individuals allowing them to make important
lifestyle changes as quickly as possible with the goal of preventing, or delaying,
the progression towards type 2 diabetes and the known associated complications. The
potential reduction in type 2 diabetes incidence and prevalence would likely translate
into substantial positive implications regarding health care resource utilization
and the current socio-economic burden attributed to diabetes.
Competing interests
The authors declare that they have no competing interests.
Authors’ contributions
CPR, NG, MCR and VKJ initiated and designed the study while MAR was involved in consultation
regarding data analyses. CPR, LM and VKJ were all involved in data collection. CPR,
LM and MAR analysed and interpreted the data. CPR and LM drafted the first draft of
the manuscript while CPR, LM, MAR, MCR, NG and VKJ all contributed revisions. All
authors approved the final version of the manuscript prior to submission.
Acknowledgements
The authors would like to acknowledge the funding from the Ontario Ministry of Health
Promotion for the research portion of the PRE-PAID project as well as the funding
from the Ontario Trillium Foundation for the community outreach portion of the project.
The authors would also like to acknowledge Dr. Jennifer Kuk, Dr. Chris Ardern and
Dr. Paul Ritvo for their contributions to project design and recruitment. Finally,
the authors would also like to acknowledge Clearstone Central Laboratories for their
contributions to this work.
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