ORIGINAL
ARTICLE
Random Blood Glucose: A Robust Risk Factor For Type
2 Diabetes
Michael E. Bowen, MD, MPH,1,2, Lei Xuan, PhD2,
Ildiko Lingvay, MD, MPH, MSCS,3,4, Ethan A. Halm, MD, MPH1,2
1
Division of General Internal Medicine, Department of Medicine, University of Texas Southwestern
Medical Center. Dallas, TX. 2Division of Outcomes and Health Services Research, Department of Clinical
Sciences, University of Texas Southwestern Medical Center. Dallas, TX. 3Division of Endocrinology,
Department of Medicine, University of Texas Southwestern Medical Center. Dallas, TX. 4Department of
Clinical Sciences, University of Texas Southwestern Medical Center. Dallas, TX.
Context: Although random blood glucose (RBG) values are common in clinical practice, the role of
elevated RBG values as a risk factor for type 2 diabetes is not well described.
Objective: To examine non-diagnostic, RBG values as a risk factor for type 2 diabetes
Design: Cross-sectional study of NHANES participants (2005–2010)
Participants: 13,792 non-fasting NHANES participants without diagnosed diabetes.
Primary Outcome: Glycemic status (normal glycemia, undiagnosed prediabetes, or undiagnosed
diabetes) using hemoglobin HbA1C as the criterion standard.
Analysis: Multinomial logistic regression examined associations between diabetes risk factors and
RBG values according to glycemic status. Associations between current US screening strategies and
a hypothetical RBG screening strategy with undiagnosed diabetes were examined.
Results: In unadjusted analyses, a single RBGⱖ100 mg/dL (5.6 mmol/L) was more strongly associated
with undiagnosed diabetes than any single risk factor [Odds Ratio (95% CI) 31.2 (21.3 – 45.5)] and
remained strongly associated with undiagnosed diabetes [20.4 (14.0 – 29.6)] after adjustment for
traditional diabetes risk factors. Using RBG⬍100 mg/dL as a reference, the adjusted odds of undiagnosed diabetes increased significantly as RBG increased: RBG 100 –119 mg/dL [7.1 (4.4 –11.4)],
RBG 120 –139 mg/dL [30.3 (20.0 – 46.0)], RBGⱖ140 mg/dL [256 (150.0 – 436.9)]. As a hypothetical
screening strategy, an elevated RBG was more strongly associated with undiagnosed diabetes than
current USPSTF guidelines (hypertension alone; p⬍0.0001) and similar to ADA guidelines (p⫽0.12).
Conclusions: A single RBG ⱖ 100 mg/dL is more strongly associated with undiagnosed diabetes than
traditional risk factors. Abnormal RBG values are a risk factor for diabetes and should be considered
in screening guidelines.
T
ype 2 diabetes is a significant and costly public health
epidemic that is projected to affect 1 in 3 US adults by
2050 (1). This projected increase is driven largely by the 86
million Americans with prediabetes, which if unrecognized and untreated will likely progress to frank diabetes
(2, 3). In spite of well-established diabetes screening guidelines (4, 5), over 8 million people with diabetes and 80
million people with prediabetes remain undiagnosed or
unaware of their condition (2, 6).
Current US diabetes screening guidelines recommend
targeted screening of high risk individuals; however,
guidelines define diabetes risk differently. The US Preventative Services Task Force (USPSTF) guideline recommends screening only when an individual has a sustained
ISSN Print 0021-972X ISSN Online 1945-7197
Printed in U.S.A.
Copyright © 2015 by the Endocrine Society
Received November 14, 2014. Accepted February 2, 2015.
Abbreviations:
doi: 10.1210/jc.2014-4116
J Clin Endocrinol Metab
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1
2
Random blood glucose as a diabetes risk factor
elevation in blood pressure (BP) (treated or untreated)
⬎135/80 (5). In contrast, the multifactorial American Diabetes Association (ADA) guideline (4) recommends
screening for all individuals age 45 and older and individuals at any age if their BMI is ⱖ 25 kg/m2 and they have
one additional risk factor including: nonwhite race, family
history of diabetes, hypertension, dyslipidemia, history of
cardiovascular disease, physical inactivity, polycystic
ovarian syndrome, history of gestational diabetes, delivery of an infant weighing ⬎ 9 pounds, or other clinical
conditions associated with insulin resistance. Screening is
also recommended for individuals with prediabetes when
HbA1C values, fasting glucose values, and 2-hour glucose
values obtained via an oral glucose tolerance test (OGTT)
are abnormal but fail to reach diagnostic thresholds (4).
The International Diabetes Federation guidelines extend
these recommendations even further to recommend
screening for individuals with random, nondiagnostic glucose values between 100 and 199 mg/dL (5.6 – 11.0 mmol/
L), although the evidence supporting this recommendation is limited (7). Random blood glucose values are not
included as a risk factor in US screening
recommendations.
In order to improve screening strategies and identification of individuals with undiagnosed diabetes and prediabetes, greater understanding of the risk factors for undiagnosed diabetes and prediabetes are needed. This study
seeks to: 1) describe the prevalence diabetes risk factors in
individuals with undiagnosed diabetes and prediabetes; 2)
examine the association between random glucose values
and undiagnosed diabetes and prediabetes; 3) explore the
associations between ADA screening guidelines, USPSTF
screening guidelines, and a hypothetical random glucose
screening strategy with undiagnosed disease.
Research Design and Methods
We analyzed merged data from the 2005–2010 National
Health and Nutrition Examination Surveys (NHANES).
NHANES is a repeated, cross-sectional, stratified survey designed to be representative of the noninstitutionalized US population using a multistage probability sample. Participants complete an in-home interview for basic demographic and health
information along with a scheduled visit to a mobile examination
center for physical examination and laboratory testing (8). All
participants gave written informed consent and the research ethics boards of the National Center for Health Statistics approved
all protocols.
Study Population
The study population consisted of nonpregnant adults age 18
and older who completed both the NHANES interview and
MEC examination components between 2005 and 2010. We
excluded participants with diagnosed diabetes or prediabetes.
Participants responding “yes” when asked “Other than during
pregnancy, have you ever been told by a doctor or health pro-
J Clin Endocrinol Metab
fessional that you have diabetes or sugar diabetes?” were considered to have diabetes and excluded from the analytic sample.
Participants responding “yes” to the question: “Have you ever
been told by a doctor or other health professional that you have
any of the following: prediabetes, impaired fasting glucose, impaired glucose tolerance, borderline diabetes, or that your blood
glucose is higher than normal but not high enough to be called
diabetes or sugar diabetes?” were also excluded. Participants
examined in a nonfasting state who had both hemoglobin
HbA1C and random serum blood glucose (RBG) test results were
included in analysis.
Measures
Patient characteristics and diabetes risk factors including age,
race, family history of diabetes, and family history of cardiovascular disease were obtained from questionnaire data. BMI was
calculated from measured height and weight and classified as
normal weight (BMI ⬍ 25 kg/m2); overweight (BMI 25–30 kg/
m2); or obese (BMI ⱖ 30 kg/m2). Participants self-reporting hypertension or high cholesterol diagnosed by a physician or other
health professional were considered to have hypertension and
high cholesterol. Polycystic ovarian syndrome, acanthosis nigricans, and other clinical conditions associated with insulin resistance were not included as a risk factor for diabetes because we
were unable to identify these participants within NHANES. Gestational diabetes and delivery of an infant weighing ⬎ 9 pounds
were not included as risk factors because data were not available
in 2005–2006 NHANES. Participants meeting age, BMI, family
history, race, hypertension, high cholesterol, or cardiovascular
disease criteria were considered to satisfy the ADA screening
guideline. We subsequently classified patients using the ADA (4)
and USPSTF (5) screening guidelines by whether screening was
indicated (yes/no). All questionnaire and examination components utilized in analysis were identical across the 3 survey cycles
used.
Serum RBG measurements were determined using the Beckman Oxygen electrode, glucose oxidase method. Between 2007
and 2012, one instrument change occurred [Beckman Synchron
LX20 (2007) to the Beckman Unicel CxC800 Synchron (2008 –
2012)]. All measurements were performed by Collaborative
Laboratory Services, LLC (9).
HbA1C assays were conducted using high-performance liquid
chromatography (HPLC) methods on instruments certified by
the National Glycohemoglobin Standardization Program (9). All
HbA1C results were subsequently standardized to the Diabetes
Control and Complications Trial reference standard (10). Between 2007 and 2010, all HbA1C assays were performed at a
single laboratory site (Minneapolis, MN). The instrument used
to analyze HbA1C changed from the Tosoh HbA1C 2.2 Plus
(2005–2007) to the Tosoh HbA1C G7 (2007–2010). In spite of
this, HbA1C values were analyzed without corrections as recommended by the National Center for Health Statistics (11).
We defined diabetes, prediabetes and dysglycemia using
HbA1C as the criterion standard. Diabetes was defined as having
an HbA1C ⱖ 6.5% (48 mmol/mol) and prediabetes as having an
HbA1C 5.7– 6.4% (39 – 46 mmol/mol). Dysglycemia was defined
as having an HbA1C ⱖ 5.7% (39 mmol/mol). Fasting glucose was
not included in our diabetes definition because individuals examined in a fasting state did not have a RBG value available.
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Statistical Analysis
We calculated means and percentages of participant characteristics by glycemic status and compared values across groups
using Rao-Scott ␹2 tests for categorical variables and F tests for
continuous variables. We used multinomial logistic regression to
examine associations between risk factors and undiagnosed diabetes and prediabetes. We further aggregated risk factors according to ADA (4) and USPSTF (5) diabetes screening guidelines
to examine associations between these screening strategies and
undiagnosed diabetes and prediabetes. We also examined unadjusted and covariate adjusted associations between a single
RBG ⱖ 100 mg/dL (5.6 mmol/L) with undiagnosed diabetes and
prediabetes. Models were adjusted for traditional diabetes risk
factors including age, sex, race, BMI, hypertension, hyperlipidemia, cardiovascular disease, and family history of diabetes. The
odds ratios of detecting undiagnosed diabetes and prediabetes
using ADA, USPSTF, and RBG screening strategies were compared using Wald tests. Analyses were conducted in SAS version
9.3 (SAS Institute Inc., Cary, NC) and Stata/SE version 13.1
(StataCorp LP, College Station, TX) using survey procedures to
account for sampling weights. Appropriate sampling weights
were applied to account for unequal selection probabilities and
nonresponse such that estimates are representative of the US
noninstitutionalized civilian population (8). P values ⬍ 0.05
were considered statistically significant. This study was approved by the institutional review board (IRB) at the University
of Texas Southwestern Medical Center at Dallas.
Results
A total of 13 792 participants met eligibility criteria.
Among all participants aged ⱖ 18 years without diagnosed diabetes, 1.9% had undiagnosed diabetes and
20.2% had undiagnosed prediabetes by HbA1C criteria.
Participant characteristics by glycemic status are shown in
Table 1.
Table 1. Overall, the prevalence of traditional diabetes
risk factors is increased in individuals with undiagnosed
prediabetes and diabetes. The prevalence of older age,
nonwhite race, positive family history of diabetes, increasing BMI, and a diagnosis of hypertension, hyperlipidemia,
and cardiovascular disease increases across the spectrum
of dysglycemia from normal to prediabetes, to diabetes,
with the highest prevalence of risk factors present in individuals with undiagnosed diabetes (Table 1). Men were
more likely than women to have undiagnosed diabetes.
While those with undiagnosed diabetes and prediabetes
were more likely to be screened in the past 3 years compared to those with normoglycemia, only slightly more
than half of those with undiagnosed diabetes (56.1%) and
half (49.6%) of those with undiagnosed prediabetes reported having been screened for diabetes in the past 3
years. However, this does not appear to be for lack of
physician visits because only 21.0% and 13.8% of patients with undiagnosed diabetes and prediabetes respectively did not have a doctor’s visit in the past 12 months.
The mean HbA1C increased across the glycemic spectrum: no diabetes HbA1C⫽5.2% (33 mmol/mol); undiagnosed prediabetes HbA1C⫽5.9% (41 mmol/mol); and undiagnosed diabetes HbA1C⫽7.6% (60 mmol/mol) (Table
1). Translating these HbA1C values into estimated average
glucose values over the preceding 3 months (12), participants with no diabetes had an estimated average glucose
of 103 mg/dL (5.7 mmol/L) compared with 123 mg/dL
(6.8 mmol/L) and 171 mg/dL (9.5 mmol/L) in participants
with undiagnosed prediabetes and diabetes respectively.
The mean value of a single RBG measure across groups
Participant Demographics and Risk Factors by Glycemic Status
Characteristic
Mean age, years
Female
Race
Non-Hispanic white
Non-Hispanic black
Mexican American
Other Hispanic
Other race
Education⬍12 yr
Married
Uninsured
Family history diabetes
Mean BMI, kg/m2
Hypertension
Hyperlipidemia
Cardiovascular disease
No doctors visit past 12 months
Screened for diabetes past 3 yr
Mean random blood glucose, mg/dL
Mean HbA1C, %
Mean HbA1C, mmol/mol
3
No
Diabetes
Undiagnosed
Pre-diabetes
Undiagnosed
Diabetes
p- value
41.7 (0.3)
50.8 (0.5)
55.4 (0.4)
51.7 (1.1)
58.6 (0.9)
41.3 (3.0)
⬍0.001
0.009
72.9 (1.8)
8.8 (0.8)
8.3 (0.9)
4.3 (0.6)
5.7 (0.5)
15.3 (0.8)
53.7 (1.0)
21.4 (0.9)
30.9 (0.6)
27.3 (0.1)
20.6 (0.7)
22.1 (0.7)
3.8 (0.3)
19.1 (0.5)
35.9 (0.5)
89.9 (0.2)
5.2 (0.01)
33 (0.1)
65.0 (0.9)
15.6 (0.6)
8.4 (0.4)
4.5 (0.3)
6.5 (0.6)
24.2 (0.9)
58.1 (1.1)
18.5 (0.8)
40.0 (1.1)
30.2 (0.1)
40.5 (1.1)
39.0 (1.1)
9.6 (0.6)
13.8 (0.7)
49.6 (1.1)
99.1 (0.4)
5.9 (0.01)
41 (0.1)
57.0 (5.2)
17.0 (2.4)
14.0 (2.5)
4.8 (1.0)
7.3 (2.7)
29.6 (2.7)
56.0 (3.2)
22.8 (3.3)
46.1 (2.7)
34.2 (0.5)
49.6 (2.8)
37.1 (3.2)
14.8 (1.6)
21.0 (2.8)
56.1 (2.8)
156.0 (5.3)
7.6 (0.1)
60 (1.1)
⬍0.001
⬍0.001
0.005
0.83
0.48
⬍0.001
⬍0.001
0.012
⬍0.001
⬍0.001
⬍0.001
⬍0.001
⬍0.001
⬍0.001
⬍0.001
⬍0.001
⬍0.001
⬍0.001
Data presented as percent (SE.) unless otherwise noted. All estimates are weighted to US population estimates. Random glucose (mg/dL) ⫻
0.5551 ⫽ mmol/liter
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4
Random blood glucose as a diabetes risk factor
increased from 89.9 mg/dL (5.0 mmol/L) in patients with
no diabetes, to 99.1 mg/dL (5.5 mmol/L) in patients with
undiagnosed prediabetes, to 156.0 mg/dL (8.7 mmol/L) in
patients with undiagnosed diabetes.
In univariate analyses, the risk factors most strongly
associated [Odds Ratio (95% CI)] with undiagnosed diabetes were a single RBG ⱖ 100 mg/dL (5.6 mmol/L) [31.2
(21.3 – 45.5)], BMI ⱖ 25 kg/m2 [10.9 (6.8 – 17.5)], and
age ⱖ 45 years [7.9 (5.7 – 10.9)]. Comorbid disease risk
factors such as a history of cardiovascular disease, hypertension, or hyperlipidemia were more modestly associated
with dysglycemia (Table 2). As a single risk factor, race
and family history demonstrated the weakest associations
with undiagnosed diabetes.
Age ⱖ 45 years was the risk factor most strongly associated with undiagnosed prediabetes [OR (95% CI); 4.8
(4.2 – 5.4)] and undiagnosed dysglycemia [4.9 (4.4 – 5.6)].
A single RBG ⱖ 100 mg/dL (5.6 mmol/L) was more
strongly associated with undiagnosed prediabetes [3.3
(3.0 – 3.8)] and undiagnosed dysglycemia [3.9 (3.5 – 4.4)]
than all other risk factors examined except for age (Table
2).
Table 3 shows a strong ‘dose response’ relationship
between increasing RBG values and higher risk-adjusted
odds of undiagnosed prediabetes, undiagnosed diabetes,
and overall dysglycemia. For individuals with a single
RBG 100 –119 mg/dL (5.6 – 6.6 mmol/L), the odds of undiagnosed diabetes were 7.1 (4.4 – 11.4). For glucose values between 120 –139 mg/dL (6.7–7.7 mmol/L) and ⱖ 140
mg/dL (7.8 mmol/L), the odds of undiagnosed diabetes
were 30.3 (20.0 – 46.0) and 256 (150.0 – 436.9)
respectively.
In multivariate models, a single RBG ⱖ 100 mg/dL (5.6
mmol/L) was associated with a twenty-fold increased risk
of undiagnosed diabetes [OR (95% CI); 20.4 (14.0 –
29.6)] and over double the odds of undiagnosed prediabetes [2.4 (2.1 – 2.7)] even after adjusting for age, sex, race,
BMI, hypertension, hyperlipidemia, cardiovascular disease, and family history of diabetes. Overall, a single
J Clin Endocrinol Metab
RBG ⱖ 100 mg/dL (5.6 mmol/L) nearly tripled the riskadjusted odds of undiagnosed dysglycemia [2.8 (2.5 –
3.2)] (Table 4).
As shown in Table 4, patients meeting USPSTF and
ADA screening guidelines had increased odds of having
undiagnosed prediabetes and undiagnosed diabetes.
Meeting the single-factor USPSTF guideline criteria (presence of hypertension alone) nearly tripled the odds of prediabetes [OR (95% CI) 2.6 (2.4 –2.9)] and dysglycemia
[2.7 (2.5–3.0)] and increased the odds of diabetes nearly
4-fold [3.8 (2.9 –5.0)]. Individuals meeting the multirisk
factor ADA guidelines had approximately a ten-fold increase in the odds of prediabetes [9.8 (8.1–11.8)] and dysglycemia [10.6 (8.8 –12.7)]. Meeting ADA guidelines increased the odds of diabetes by over 50-fold [50.6
(17.7–144.5)].
A screening strategy based on a single RBG ⱖ 100
mg/dL (5.6 mmol/L) [OR (95% CI) 20.4 (14.0 – 29.6)]
was much more strongly associated with undiagnosed diabetes than USPSTF guidelines (P ⬍ .0001) and not statistically different from the ADA screening guideline (P ⫽
.12). For detecting undiagnosed prediabetes, the single
RBG strategy was similar to the USPSTF guideline (P ⫽
.32) but not as predictive as the ADA screening strategy
(P ⬍ .0001).
Conclusions
In a nationally representative sample of the US population
without diagnosed diabetes, a single RBG ⱖ 100 mg/dL
(5.6 mmol/L) is strongly associated with undiagnosed diabetes and demonstrates a robust dose response. In fact, a
RBG ⱖ 100 mg/dL (5.6 mmol/L) was the single strongest
predictor of undiagnosed diabetes outperforming all other
traditional risk factors. As a simple strategy to detect undiagnosed diabetes, the association with undiagnosed disease for a single RBG ⱖ 100 mg/dL (5.6 mmol/L) is stronger than the USPSTF diabetes screening guidelines, which
Table 2. Univariate Associations Between Risk Factors and Undiagnosed Prediabetes, Diabetes, and Dysglycemia
(Prediabetes ⫹ Diabetes)
Odds Ratio (95% CI)
Characteristic
Undiagnosed
Prediabetes
Undiagnosed
Diabetes
Age ⱖ 45 yr
BMI ⱖ 25 kg/m2
Non-white race
Family history diabetes
Hypertension
Hyperlipidemia
Cardiovascular disease
Random Glucose ⱖ 100 mg/dL
4.8 (4.2 – 5.4)
2.3 (2.0 – 2.6)
1.4 (1.3 – 1.7)
1.5 (1.4 – 1.6)
2.6 (2.4 – 2.9)
2.3 (2.0 – 2.6)
2.7 (2.2 – 3.3)
3.3 (3.0 – 3.8)
7.9 (5.7 – 10.9)
10.9 (6.8 – 17.5)
2.0 (1.4 – 2.9)
1.9 (1.5 – 2.4)
3.8 (2.9 – 5.0)
2.1 (1.6 – 2.8)
4.5 (3.4 – 5.9)
31.2 (21.3 – 45.5)
Dysglycemia
4.9 (4.4 – 5.6)
2.5 (2.2 – 2.8)
1.5 (1.3 – 1.7)
1.5 (1.4 – 1.7)
2.7 (2.5 – 3.0)
2.3 (2.0 – 2.6)
2.9 (2.4 – 3.4)
3.9 (3.5 – 4.4)
Results presented as odds ratios (95% CI). Weighted standard errors used to compute CIs. Random glucose (mg/dL) ⫻ 0.5551 ⫽ mmol/liter
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5
Table 3. Dose Response Relationship Between Random Glucose and Undiagnosed Diabetes, Prediabetes, and
Dysglycemia (Prediabetes ⫹ Diabetes)
Adjusted Odds Ratio (95% CI)
Glucose Range
Undiagnosed
Prediabetes
Undiagnosed Diabetes
Random glucose ⬍ 100 mg/dL
Random glucose 100 –119 mg/dL
Random glucose 120 –139 mg/dL
Random glucose ⱖ 140 mg/dL
Reference
2.2 (1.9 – 2.5)
3.3 (2.6 – 4.2)
3.5 (2.2 – 5.5)
Reference
7.1 (4.4 – 11.4)
30.3 (20.0 – 46.0)
256.0 (150.0 – 436.9)
Dysglycemia
Reference
2.3 (2.0 – 2.7)
3.8 (3.0 – 4.9)
8.4 (5.7 – 12.3)
All values adjusted for age, sex, race, BMI, hypertension, hyperlipidemia, cardiovascular disease, and family history of diabetes. Random glucose
(mg/dL) ⫻ 0.5551 ⫽ mmol/liter
Table 4. Odds of Having Undiagnosed Diabetes, Prediabetes, or Dysglycemia (Prediabetes ⫹ Diabetes) According
to Different Screening Strategies
Odds Ratio (95% CI)
Screening Strategy
Undiagnosed
Prediabetes
Undiagnosed
Diabetes
Dysglycemia
USPSTF Guidelines
ADA Guidelines
Random Glucose ⱖ 100 mg/dL*
2.6 (2.4 – 2.9)
9.8 (8.1 – 11.8)
2.4 (2.1 – 2.7)
3.8 (2.9 – 5.0)
50.6 (17.7 – 144.5)
20.4 (14.0 – 29.6)
2.7 (2.5 – 3.0)
10.6 (8.8 – 12.7)
2.8 (2.5 – 3.2)
All results presented as weighted odds ratios.
USPSTF Guideline: diagnosed hypertension
ADA Guideline: All individuals ageⱖ45 or those with BMIⱖ25 kg/m2 and family history of diabetes, non-white race, hypertension, high cholesterol,
cardiovascular disease.
*Adjusted for age, sex, race, BMI, hypertension, hyperlipidemia, cardiovascular disease, and family history of diabetes.
Random glucose (mg/dL) ⫻ 0.5551 ⫽ mmol/liter
recommend screening based on the presence of hypertension alone (5), and similar to the multirisk factor, more
complex ADA screening guidelines (4). Nondiagnostic,
random glucose values should be considered in diabetes
risk assessments and may improve detection of undiagnosed cases of diabetes and prediabetes.
Among individuals with undiagnosed prediabetes and
diabetes, traditional diabetes risk factors are common. In
general, the prevalence of individual risk factors increases
across the glycemic spectrum from no diabetes, to undiagnosed prediabetes and undiagnosed diabetes. Similar to
other studies (13–17), we observed significant associations between age, BMI, hypertension, family history and
undiagnosed disease. Although we found race and family
history to be significant risk factors, their association with
undiagnosed disease was more modest than other risk factors. Similar to a recent study finding that men have a
higher prevalence of diagnosed diabetes than women (18),
we found that men were also more likely to have undiagnosed diabetes.
Although the vast majority of individuals with undiagnosed diabetes and prediabetes are insured and have visited a healthcare provider in the past 12 months, similar to
other studies (19), we found that only half of eligible individuals reported being screened diabetes in the past 3
years. Given the burden of undiagnosed disease and the
prevalence of diabetes risk factors, missed opportunities
for diabetes screening are common (15). Although the barriers to diabetes screening in clinical practice are poorly
understood, the assessment of diabetes risk factors and
ordering of diabetes screening is largely at the discretion of
individual clinicians and may be an afterthought in timeconstrained clinic visits. Automated risk-assessment and
clinical decision support can facilitate provider workflow
and be effective tools to increase screening rates for cancer
(20). However, automation and implementation of multifactorial diabetes screening guidelines in clinical practice
is challenging and parsimonious risk assessment models
are needed (21).
The development of automated risk models capable of
informing clinical decision support requires accurate data
that are readily available. Identification and abstraction of
traditional diabetes risk factors from the EMR is largely
dependent on the data entry and coding of end users. However, laboratory data are readily available and electronically integrated into most EMRs. As such, both historic
and prospective RBG values collected in routine clinical
practice could provide a platform to develop automated
diabetes risk assessment. Although prediabetes is a strong
risk factor for diabetes (14, 22), it cannot inform the initial
decision to screen because its diagnosis requires prior completion of a gold-standard diabetes screening test. As such,
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6
Random blood glucose as a diabetes risk factor
prediabetes is very useful in targeting repeat screening to
individuals at high risk for diabetes, but its usefulness as a
risk factor is limited because most individuals with prediabetes remain undiagnosed and overall screening rates
are suboptimal (19).
Although the ADA does not recommend random glucose as a screening test for diabetes and does not provide
guidance to interpret RBG values (4), random glucose is
commonly used as an ‘opportunistic screen’ for diabetes in
clinical practice (23, 24). However, abnormal RBGs are
frequently overlooked and failure to pursue gold-standard
diabetes testing in response to elevated RBG values is common (25). Although the International Diabetes Federation
recommends that individuals with a random glucose value
100 –199 mg/dL (5.6 –11.0 mmol/L) undergo formal diabetes testing, this recommendation is absent from US
guidelines. Our finding that a single RBG ⱖ 100 mg/dL
(5.6 mmol/L) is more strongly associated with undiagnosed diabetes than any single traditional diabetes risk
factor supports the consideration of random glucose as a
diabetes risk factor. Our demonstration of a strong doseresponse relationship between the magnitude of the random glucose elevation and an increased risk for diabetes
further supports this recommendation. Importantly, modest elevations in RBG values (100 –119 mg/dL; 5.6 – 6.6
mmol/L) that are commonly ignored in clinical practice
(23, 25) were strongly associated with undiagnosed disease. Together, these findings indicate that nondiagnostic,
random blood glucose values are an important indicator
of dysglycemia that could play a critical role in screening
and case identification strategies.
The usefulness of random glucose values in the diagnosis of diabetes and prediabetes has a strong physiologic
basis. Glucose homeostasis is a tightly regulated function
of ␤ cell insulin production and insulin sensitivity (26)
such that glucose concentrations are maintained in a narrow range (27). Disruptions in glucose homeostasis initially manifest as impaired glucose tolerance and increased
glucose variability (26). In clinical practice, this is reflected
as nondiagnostic RBG elevations and increased glucose
variability. Given that random glucose testing accounts
for over 95% of glucose testing in clinical practice, (23)
opportunities to recognize random glucose abnormalities
and incorporate them into risk assessment exist in routine
clinical practice.
Although random glucose is not currently included in
US diabetes screening guidelines, our findings demonstrate the potential importance of random glucose values
in screening strategies. Existing studies demonstrate that a
single RBG ⱖ 125 mg/dL (6.9 mmol/L) is a good predictor
of diabetes (28) and performs as well as more complicated
models based on age, BMI, and race (29). We demonstrate
J Clin Endocrinol Metab
that RBG values as low as 100 mg/dL (5.6 mmol/L) confer
a significant risk for diabetes and are associated with undiagnosed diabetes. Our findings suggest that a hypothetical screening strategy in which individuals with a single
RBG ⱖ 100 mg/dL (5.6 mmol/L) undergo gold-standard
diabetes screening and the ADA diabetes screening guidelines, which incorporate multiple risk factors, have similar
associations with undiagnosed disease. Additionally, the
random glucose strategy – even after adjustment for other
risk factors – is more strongly associated with undiagnosed diabetes than the USPSTF guidelines, which likewise recommend screening based on a single risk factor.
A major strength of this study is that the data are nationally representative of the noninstitutionalized, civilian, US population without diagnosed diabetes and prediabetes. However, there are several limitations. First, our
analyses are limited to cross-sectional associations between a single RBG value and glycemic status. Because of
this, we are unable to examine the development of diabetes
over time. Second, individuals with diagnosed diabetes
and prediabetes were excluded based on self-report; however, self-reported diabetes is both highly sensitive and
specific for diagnosed diabetes (30). Third, because we
sought to detect cases of undiagnosed diabetes and prediabetes, we were unable to examine prediabetes as a risk
factor for undiagnosed diabetes. However, most individuals with prediabetes remain undiagnosed (2). Additionally, our estimates of the association between the ADA
guideline and undiagnosed disease likely underestimate
the true association given our inability to include all ADA
screening criteria in our definitions. Fourth, our analyses
utilize serum glucose values rather than plasma glucose
values. However, analytic differences between serum and
plasma glucose values are small and unlikely to exceed the
day-to-day biologic variation of glucose (31). Thus, our
findings reflect real-world practice where serum glucose
values are routinely obtained as part of chemistry panels.
Fifth, our analyses focus on community-dwelling
NHANES participants and may not directly translate to
medical practice if patients are acutely ill. Sixth, our definition of prediabetes and diabetes was based on HbA1C
criteria alone because individuals examined in a fasting
state did not have random glucose data available. As a
result, our findings likely underestimate the burden of undiagnosed prediabetes and diabetes (32).
In conclusion, our findings indicate that a single random glucose value ⱖ 100 mg/dL (5.6 mmol/L) is a robust
risk factor for type 2 diabetes and should prompt clinicians to obtain gold standard diabetes screening tests to
categorize glycemic status. Considering the large number
of individuals in the US with undiagnosed diabetes and
prediabetes (2), our findings have important implications
The Endocrine Society. Downloaded from press.endocrine.org by [${individualUser.displayName}] on 16 February 2015. at 08:48 For personal use only. No other uses without permission. . All rights reserved.
doi: 10.1210/jc.2014-4116
for screening and case finding strategies. First, our findings
highlight the importance of RBG values ⱖ 100 mg/dL (5.6
mmol/L) as a risk factor for diabetes and provide evidence
to support the inclusion of RBG values ⱖ 100 mg/dL as a
risk factor in US diabetes screening guidelines. Second, our
findings suggest that increased awareness of abnormal
random glucose values and targeted diabetes testing may
improve diabetes detection among individuals engaged in
clinical care. Given the high frequency of glucose testing in
clinical practice and the growing repository of computerized laboratory data within electronic medical records, the
development of automated, glucose-driven risk assessment strategies may improve the screening and diagnosis
of type 2 diabetes in clinical practice.
Acknowledgments
This study was conducted using resources supported by the UT
Southwestern Center for Patient-Centered Outcomes Research
(AHRQ R24 HS022418), UT Southwestern National Center for
Advancing Translational Sciences (UL1TR001105) and the
Dedman Family Scholars in Clinical Care.
Dr. Bowen was supported by the National Center for Advancing Translational Sciences of the NIH (KL2TR001103) and
the NIH/NIDDK K23 DK104065. Drs. Halm and Xuan were
supported in part by AHRQ R24 HS022418.
Preliminary data from this study were presented at the 2014
Society of General Internal Medicine national meeting (San Diego, CA).
M.B participated in the design, analysis, data interpretation,
and drafting of the manuscript. L.X. participated in analysis,
data interpretation, and manuscript revision. I.L. participated in
the data interpretation and manuscript revision. E.H participated in the design, analysis, data interpretation, and manuscript
revision.
No potential conflicts of interest relevant to this article are
reported.
Address all correspondence and requests for reprints to: Corresponding Author and Reprint Requests: Michael E. Bowen
MD, MPH, 5323 Harry Hines Blvd, E1.410C, Dallas, TX
75390 –9169, Telephone: 214 – 648-3233, Fax: 214 – 648-3232,
Email: michael.bowen@utsouthwestern.edu.
Disclosure Summary: The authors have nothing to disclose.
Dr. Bowen was supported by the National Center for Advancing Translational Sciences of the NIH (KL2TR001103) and
the NIH/NIDDK K23 DK104065. Drs. Halm and Xuan were
supported in part by AHRQ R24 HS022418.
This work was supported by Grant Support: This study was
conducted using resources supported by the UT Southwestern
Center for Patient-Centered Outcomes Research (AHRQ R24
HS022418), UT Southwestern National Center for Advancing
Translational Sciences (UL1TR001105) and the Dedman Family
Scholars in Clinical Care.
jcem.endojournals.org
7
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