A study to
evaluate HbA1C as an independent diagnostic criterion comparing to fasting
plasma glucose and postprandial glucose levels as a standard test for diagnosis
of diabetes
Lalitha R.1, Shetty
S.B.2, Anil Kumar R.3
1Dr
Lalitha R, Assistant Professor,2Dr Surekha B. Shetty, Assistant
Professor,3Dr Anil Kumar R., Assistant Professor, all are affiliated
to Karnataka Institute of Endocrinology and Research, Bannerghatta Road,
Jayanagar, Bangalore, Karnataka, India.
Corresponding Author: Dr.
Lalitha R, Assistant Professor, Karnataka Institute of Endocrinology and
Research, Bannerghatta Road, Jayanagar, Bangalore, Karnataka.
E-mail: drlalithashivaprakash@gmail.com
Abstract
Aims and objectives: 1)
To compare and correlate glycosylated haemoglobin (HbA1C) as an independent
criteria in diagnosis. 2) To define the sensitivity and specificity of HbA1C
estimates at the ADA recommended cut off of ≥ 6.5%. Study design and methods: Subjects were first tested for Fasting
plasma glucose and two-hours post 75 grams glucose challenge, HbA1c was
estimated for the all the subjects.
Results: The sensitivity and specificity of HbA1C at the ADA recommended ≥
6.5% cut off value in newly detected diabetic patients was 96.70% and 82.92%respectively
with a positive predicted value of 56.05% and a negative predictive value of99.11
% .75.00 % at a p<0.001.We find that we miss 42% of people with diabetes if
fasting plasma glucose levels are considered. Given the risks associated with
PPG levels in our population it is important that these criteria be used in
screening programmes. Conclusion:
Our study shows that HbA1C is comparable to FPG levels estimation but is not
superior enough to replace blood glucose estimation. Use of post prandial
glucose levels are better in detecting diabetes than fasting plasma glucose
levels. A combination of post prandial glucose with HbA1C may be a superior
single test that can overcome the cumbersome oral glucose tolerance test.
Keywords: HbA1C,
Blood Glucose, diagnosis of diabetes
Author Corrected: 25th December 2018 Accepted for Publication: 31st December 2018
Introduction
Type 2 Diabetes mellitus is a chronic
metabolic disorder characterized by hyperglycaemia due to either defects in
insulin secretion or insulin resistance. It is associated with various long
term micro vascular complications like retinopathy, nephropathy and neuropathy.
Diabetes mellitus is a metabolic disorder characterized by chronic
hyperglycaemia resulting from defects in insulin secretion or action or both.
Diabetes is a chronic illness associated with significant micro vascular and
macro vascular complications.
India
leads the world with highest number of diabetic subjects second only to China
with the dubious distinction of being termed the “diabetes capital of the
world”. According to the Diabetes Atlas 2017 published by the International
Diabetes Federation, the number of people with diabetes in India is currently
around 82 million and is expected to rise to 151 million by 2045 unless urgent
preventive steps are taken [1]. A certain specific clinical and biochemical
abnormalities in Indians which consists of an increased insulin resistance,
greater abdominal adiposity with a higher waist hip ratio in spite of a lower
body mass index, lower adiponectin and higher high sensitive C-reactive protein
levels is called the Asian Indian phenotype. This phenotype makes these
individuals more prone to diabetes and premature coronary artery disease [2]. The
early detection of subjects with a high risk of developing type 2 DM is vital to
use preventive ways and scale back the chronic
complications related to it and too improve cardiovascular morbidity
and mortality.
Conventionally,
diabetes was diagnosed based on plasma glucose criteria, either the fasting
plasma glucose (FPG) or the 2-h value post glucose challenge plasma in the 75-g
oral glucose tolerance test (OGTT) [3]. An International Expert Committee in
2009 that included representatives of the ADA, the International Diabetes
Federation (IDF), and the European Association for the Study of Diabetes (EASD)
recommended the use of the A1C test to diagnose diabetes, with a threshold of ≥
6.5%, (5) and the ADA adopted this criterion in 2010 [4]. The diagnostic test
should be performed using a method that is certified by the NGSP and
standardized or traceable to the Diabetes Control and Complications Trial
(DCCT) reference assay. The use of point-of-care (POC) A1C assay for diagnostic
purposes could be problematic because proficiency testing is not mandated for
performing the test even though they may be NGSP certified. A test result
diagnostic of diabetes should be repeated to rule out laboratory error, unless
the diagnosis is clear on clinical grounds. It is preferable that the same test
be repeated for confirmation, since there will be a greater likelihood of
concurrence in this case [2]. For example, if the HbA1C is 7.0% and a repeat
result is 6.8%, the diagnosis of diabetes is confirmed. However, if two
different tests (such as HbA1C and FPG) are both above the diagnostic threshold
values, the diagnosis of diabetes is also confirmed. On the other hand, if two
different tests are available in an individual and the results are discordant,
the test whose result is above the diagnostic cut point should be repeated, and
the diagnosis is made based on the confirmed test.
ADA 2014 Guidelines
for diagnosis of Diabetes
ADA Diagnostic Criteria for type 2 diabetes [5].
The American
Diabetes Association (ADA)
criteria for the diagnosis of diabetes are any of the following:
·
A
hemoglobin A1c (HbA1c) level of 6.5% or higher; the test should be performed in
a laboratory using a method that is certified by the National Glycohaemoglobin
Standardization Program (NGSP) and standardized or traceable to the Diabetes
Control and Complications Trial (DCCT) reference assay,
Or
·
A
fasting plasma glucose (FPG) level of 126 mg/dL (7 mmol/L) or higher; fasting
is defined as no caloric intake for at least 8 hours,
Or
·
A
2-hour plasma glucose level of 200 mg/dL (11.1 mmol/L) or higher during a 75-g
oral glucose tolerance test (OGTT),
Or
·
A
random plasma glucose of 200 mg/dL (11.1 mmol/L) or higher in a patient with
classic symptoms of hyperglycemia (i.e., polyuria, polydipsia, polyphagia,
weight loss) or hyperglycemic crisis.
World Health Organization (WHO) Criteria for diagnosis
of diabetes [6]
The
World Health Organization (WHO) the expert committee met March 2009 and
concluded that HbA1c can be used as a diagnostic test for diabetes, provided
that stringent quality assurance tests are in place and assays are standardised
to criteria aligned to the international reference values, and there are no
conditions present which preclude its accurate measurement.
An
HbA1c of 6.5% is recommended as the cut point for diagnosing diabetes. A value
less than 6.5% does not exclude diabetes diagnosed using glucose tests. The
expert group concluded that there is currently insufficient evidence to make
any formal recommendation on the interpretation of HbA1c levels below 6.5%
Methods and Materials
Study Design: This
was a cross sectional study done at Karnataka Institute of Endocrinology,
Bangalore between the time period November 2012 to July 2013 in individuals with
no history of diabetes and who attended the outpatient department for screening
of diabetes. A sample size of the 500 subjects who underwent an oral glucose
tolerance test during the time period were taken into consideration. The
individuals were first tested for fasting blood glucose (FPG) and then
two-hours Post glucose–plasma glucose (PPG) levels after a 75g glucose load.
HbA1c was estimated at the same time.
Inclusion Criteria
· Individuals
with no history of diabetes previously.
· Individuals
with symptoms of diabetes presenting for the first time.
· Subjects
attending OPD for screening of diabetes.
· Age
more than 18 years.
Exclusion Criteria
· Gestational
diabetes
· Pregnant
women
· Age
below 18 years.
· Renal
dysfunction.
· History
of anaemia
Investigations
· Fasting
plasma glucose levels (FPG)
· Post
75 grams plasma glucose levels (PPG)
· Glycosylated
Haemoglobin (HbAIC).
· Serum
creatinine.
Statistical Methods: Descriptive
and inferential statistical analysis has been used in this study. Microsoft
excel, SPSS and med calculator software have been employed to derive at various
study parameters. Results on continuous measurements are presented on Mean SD (Min-Max)
and results on categorical measurements are presented in number (%).
Significance is assessed at 5% level of significance. 2 x2 table and cross
tabulations has been used for analysis for different parameters using SPSS.
Coding for the values has been carried out to analyse the data accurately. Any
missing data had been discarded to maintain accuracy. The Chi-square/ Fisher
Exact test has been used to find the significance of study parameters on
categorical scale between two or more groups. Diagnostic statistics viz.
Sensitivity, Specificity, PPV, NPV and accuracy have been computed to find the
correlation of FPG, PPG, with different levels of HbA1c. Statistical software:
The Statistical software namely SAS 9.2, SPSS 15.0, Med Calc18.0.1and R environment
ver.3.12.1 were used for the analysis of the data and Microsoft word and Excel
have been used to generate graphs, tables etc.
Results
Out
of the 500 subjects, 7 of them did not have complete data and were excluded
from the analysis. The characteristics of the study subjects and the
investigations are shown in table 1. Equal distribution of subjects based on
gender as seen in table 2. Age distribution of subjects as shown in table 3,
the peak age of onset is between 40 to 60 years with higher incidence seen in
51 to 60 years range of age group.
Of
all the 493 subjects, 355 did not have diabetes according to the post glucose
challenge criteria. Among these 306 had no diabetes according to both HbA1c and
FPG criteria (Table7). The remaining 49 subjects were not diagnosed to have
diabetes according to the ADA or WHO criteria.
Hence
a sensitivity and specificity was done in order to check the accuracy of each
test using as an independent standard. Considering FPG as a standard for diagnosing
type 2 diabetes as compared to PPG we find that the sensitivity and specificity
are 87.91%, 85.86 % respectively (Table10). Comparing FPG as a standard with
HbA1C the sensitivity and specificity is 96.70%, 82.92 %. FPG doesn’t compare
well with PPG compared to HbA1C.Vice versa when PPG is taken as a standard the
sensitivity and specificity is 68.79%, 91.34 % respectively.15 to 20 % of
subjects may go undetected due to FPG criteria as the disease prevalence is
18.42% compared to 31.91% with PPG. This may be due to the fact that Asian
Indian Phenotype may have higher levels of post prandial blood glucoses than
fasting blood glucoses.
Comparing
PPG as a standard with HbA1C the sensitivity and specificity is 78.99%, 86.27 %
respectively. Vice versa using HbA1C as a standard the sensitivity and
specificity is 68.79%, 91.34 % respectively.
|
FPG = Diabetes |
PPG= Diabetes. |
HbA1c
sensitivity |
96.70% |
78.99% |
HbA1c
specificity |
82.92% |
86.27% |
We
have three different criteria for diagnosing diabetes with a tedious test of
glucose challenge and yet the three different criteria have no correlation with
each other. The higher sensitivity of HbA1c shows that it is good at
identifying subjects with diabetes and not identifying those without diabetes. We
find that HbAIC is less sensitive in identifying subjects who are negative for
fasting but positive for post glucose criteria but carries a higher specificity
for no diabetes.
Discussion
Glycosylated
Haemoglobin (HbA1C): Haemoglobin is made up of two globin dimers, each with an
associated haem moiety. Adult Haemoglobin comprises of 97%HbA (α2, β2) and 1.5–
3.5%, A2 (α2, δ2) whereas the foetal haemoglobin (HbF; α2, γ2) forms <2%. These
percentages might modify with bound haemoglobinopathies [7]. As an example, HbF
levels are enhanced in the presence of hereditary persistence of HbF,
β-thalassemia, sickle cell disease, pregnancy, anaemia, and certain
leukaemia’s. Levels may additionally be increased in hospitalized patients. The
components of HbA were known by charge separation on cation exchange resin and
named in keeping with their order of elution as follows: A0, A1a, A1b, and A1C.
A1C is that the haemoglobin element that is composed primarily of
glycohaemoglobin. Glycohaemoglobin is made by the non-enzymatic glycation of
the N-terminal essential amino acid on the β chain of Haemoglobin. HbA1c levels
could vary with patients’ race/ethnicity [8,9].
Advantages and
disadvantages of HbA1c: Plasma glucose levels are easily
and quickly measured. They are cost effective. It additionally reflects the
pathophysiology of diabetes better. Assays used for estimation of blood glucose
levels are time tested and well standardized [10]. Plasma glucose levels are
not affected by erythrocyte turnover and might be employed in patients with
dyslipidaemias, hepatic, renal or thyroid dysfunction. It is widely obtainable
within the primary health care centre and may be used to effectively diagnose
diabetes within the giant rural Indian population. Blood glucose estimates need
rigorous eight hours fast. This is often typically not achieved as most of our
population is unaware and don't adhere to the fasting requirements. Additionally
evening or early morning exercise prior to drawing blood sample could result in
spuriously lower estimates [9]. A1C reflects the typical plasma glucose over
the past eight to twelve weeks and captures chronic hyperglycaemia. It may be
done at any time of the day and doesn't need fasting. It reflects the glycation
of proteins and thus correlates with micro and macro vascular complications
that are because of glycation of proteins. It can even pick up diabetes
patients who are additionally prone to protein glycation and therefore
complications. In additionA1C isn't affected by simultaneous stress, diet,
exercise or smoking. Baseline A1C are often used for additional monitoring of
diabetes treatment and glycaemic management. Assays for A1C are standardized
better today. A1C measurements are high-priced and not widely obtainable
particularly within the Indian context. Haemoglobinopathies although having a
low prevalence of three to four-dimensional in India, interfere with A1C
measurement. A1C is additionally affected by different conditions with
accelerated red cell turnover like protozoal infection, anaemia.]Chronic liver
disease affects erythropoiesis and ends up in reduced A1C whereas chronic renal
disorder will increase glycation and thus A1C. Hypertriglyceridemia will interfere
with the assay with reduced A1C. Hypothyroidism on the other hand offers
elevated A1C levels [10].
Comparison with other studies: NHANES
study in USA showed that a HbA1C cut point of ≥6.5% identifies one-third fewer
cases of diabetes than a fasting glucose [11]. The Strong Heart Study in USA
concluded that using HbA1c alone in initial diabetes screening identifies fewer
cases of diabetes than FPG while using both criteria may identify more people
at risk [12]. A Korean Study concluded that the agreement between the fasting
plasma glucose and HbA1c for the diagnosis of diabetes was moderate for Korean
adults with a kappa index of 0.50 [14]. The New Hoorn Study in Netherland also
showed that the correlations between glucose and HbA1C was moderate in the
general population [15].
HbA1C
level of ≥5.8%, representing 12% of the population, had the highest combination
of sensitivity (72%) and specificity (91%) for identifying newly diagnosed
diabetes[16]. An Indian study by Kavya et al showed the sensitivity and
specificity of the HbA1C is similar to 2 hrs plasma glucose estimates unlike
our study [17].
Table-1: Characteristics
of the subjects recruited in the study and the distribution of study variables
HBA1C |
FBS |
2hrs |
|
N |
493 |
493 |
493 |
Minimum |
4.200 |
63.000 |
59.000 |
Maximum |
16.300 |
362.000 |
521.000 |
Mean |
6.466 |
113.704 |
177.557 |
Geometric
mean |
6.328 |
109.928 |
161.773 |
Harmonic
mean |
6.219 |
107.179 |
149.329 |
Median |
6.000 |
103.000 |
150.500 |
95%
CI |
6.000
to 6.100 |
101.000
to 105.000 |
144.000
to 159.271 |
Variance |
2.3574 |
1253.2170 |
7055.2453 |
SD |
1.5354 |
35.4008 |
83.9955 |
SEM |
0.06915 |
1.5944 |
3.7791 |
25
- 75 P |
5.600
to 6.800 |
94.000
to 119.000 |
116.000
to 215.000 |
Normal
Distr. |
<0.0001 |
<0.0001 |
<0.0001 |
Table-2: Gender distribution
Female |
252 |
50.7 |
Male |
245 |
49.3 |
Total |
497 |
100.0 |
Table-3: Age
distribution in study group
|
Frequency |
Percent |
Valid Percent |
Cumulative Percent |
|
Valid |
21-30 |
21 |
4.2 |
4.2 |
4.2 |
|
31-40 |
95 |
19.1 |
19.1 |
23.3 |
|
41-50 |
115 |
23.1 |
23.1 |
46.5 |
|
51-60 |
159 |
32.0 |
32.0 |
78.5 |
|
61-70 |
85 |
11 |
17.1 |
95.6 |
|
71-80 |
17 |
3.4 |
3.4 |
99.0 |
|
81-90 |
4 |
.8 |
.8 |
99.8 |
|
91-100 |
1 |
.2 |
.2 |
100.0 |
|
Total |
497 |
100.0 |
100.0 |
|
Subjects tested for fasting glucose
levels.≥ 126mg/dl compared with HbA1c criteria.≥6.5% and post glucose challenge
≥200mg/d
Table-5
|
Fasting glucose |
Total |
||
No diabetes |
Diabetes |
|||
HBA1C |
No
diabetes |
25 |
3 |
28=20.28% |
Diabetes |
33 |
77 |
110=79.71% |
|
Total |
58=42% |
80=58% |
138 |
a
Post Glucose = Diabetes
Table-5
|
Value |
df |
Asymp. Sig. (2-sided) |
Exact Sig. (2-sided) |
Exact Sig. (1-sided) |
Pearson
Chi-Square |
32.197 (b) |
1 |
.000 |
|
|
Continuity
Correction(a) |
29.809 |
1 |
.000 |
|
|
Likelihood
Ratio |
34.328 |
1 |
.000 |
|
|
Fisher's
Exact Test |
|
|
|
.000 |
.000 |
Linear-by-Linear
Association |
31.963 |
1 |
.000 |
|
|
N
of Valid Cases |
138 |
|
|
|
|
We
have considered the post glucose challenge plasma glucose levels as positive
for diabetes and find 138 subjects fitting the criteria. Among them 42% people tested
negative for fasting plasma glucose levels at a diabetes range. So we may miss
42% who have diabetes with FPG and 20.28 % with only HbA1C criteria.
Table-6: Post
glucose = no diabetes
|
Cases |
|||||
Valid |
Missing |
Total |
||||
N |
Percent |
N |
Percent |
N |
Percent |
|
HBA1C
* Fasting Glucose |
355 |
100.0% |
0 |
.0% |
355 |
100.0% |
a=
Post Glucose = No Diabetes
355
subjects are diagnosed to not have diabetes with post glucose challenge glucose
levels (PPG)
Table-7:
Hba1c Vs Fasting Plasma Glucose (a)
|
Fasting
Glucose |
Total |
||
No
Diabetes |
Diabetes |
|||
HBA1C |
No diabetes |
306=86.19% |
0 |
306=86.19% |
Diabetes |
38=10.70% |
11=3.09% |
49=13.80% |
|
Total |
344=96.90% |
11=3.09% |
355 |
a = Post Glucose = No Diabetes
Table-8
|
Value |
df |
Asymp. Sig. (2-sided) |
Exact Sig. (2-sided) |
Exact Sig. (1-sided) |
Pearson Chi-Square |
70.890(b) |
1 |
.000 |
|
|
Continuity
Correction(a) |
63.611 |
1 |
.000 |
|
|
Likelihood Ratio |
45.900 |
1 |
.000 |
|
|
Fisher's Exact Test |
|
|
|
.000 |
.000 |
Linear-by-Linear
Association |
70.691 |
1 |
.000 |
|
|
N
of Valid Cases |
355 |
|
|
|
|
a
Computed only for a 2x2 table, b 1 cells (25.0%) have expected count less than
5.
The
minimum expected count is 1.52.c post glucose = no diabetes.
Here
we find that with PPG criteria≤199mg/dl as not having diabetes, 96.9% and
86.19% tested negative for diabetes with FPG and HbA1C criteria respectively.
Table-9: Fasting
Plasma Glucose ≥ 126 mg/dl as a standard for diagnosis of diabetes, compared to
Post 75 g glucose at 2 hours ≥200mg/dl.
Test |
Present |
n |
Absent |
n |
Total |
Positive |
True
Positive |
a=80 |
False
Positive |
c=57 |
a + c
= 137 |
Negative |
False
Negative |
b=11 |
True
Negative |
d=346 |
b + d
= 357 |
Total |
|
a + b = 91 |
|
c + d = 403 |
|
Statistic |
Value |
95% CI |
Sensitivity |
87.91% |
79.40%
to 93.81% |
Specificity |
85.86
% |
82.07%
to 89.11% |
Positive
Likelihood Ratio |
6.22 |
4.83
to 8.00 |
Negative
Likelihood Ratio |
0.14 |
0.08
to 0.25 |
Disease
prevalence |
18.42%
(*) |
15.10%
to 22.13% |
Positive
Predictive Value |
58.39%
(*) |
52.16%
to 64.37% |
Negative
Predictive Value |
96.92
% (*) |
94.75%
to 98.21% |
Accuracy |
86.23%
(*) |
82.88%
to 89.15% |
Table-10: Post 75 g glucose (PPG) =200
mg/dl DM as a standard for diagnosis compared to FPG
Test |
Present |
n |
Absent |
n |
Total |
Positive |
True Positive |
a=81 |
False Positive |
c=11 |
a + c = 92 |
Negative |
False Negative |
b=57 |
True Negative |
d=347 |
b + d = 404 |
Total |
|
a + b
= 138 |
|
c + d
= 358 |
|
Statistic |
Value |
95% CI |
Sensitivity |
58.70% |
50.01%
to 67.00% |
Specificity |
96.93
% |
94.57%
to 98.46% |
Positive
Likelihood Ratio |
19.10 |
10.50
to 34.75 |
Negative
Likelihood Ratio |
0.43 |
0.35
to 0.52 |
Disease
prevalence |
27.82%
(*) |
23.92%
to 31.99% |
Positive
Predictive Value |
88.04%
(*) |
80.19%
to 93.05% |
Negative
Predictive Value |
85.89
% (*) |
83.29%
to 88.14% |
Accuracy |
86.29%
(*) |
82.95%
to 89.19% |
Table-11: Hba1c≥6.5% as
standard compared to PPG
Test |
Present |
n |
Absent |
n |
Total |
Positive |
True
Positive |
a=108 |
False
Positive |
c=29 |
a + c
= 137 |
Negative |
False
Negative |
b=49 |
True
Negative |
d=306 |
b + d
= 355 |
Total |
|
a + b = 157 |
|
c + d = 335 |
|
Table-12
Statistic |
Value |
95% CI |
Sensitivity |
68.79% |
60.92%
to 75.94% |
Specificity |
91.34
% |
87.80%
to 94.13% |
Positive
Likelihood Ratio |
7.95 |
5.52
to 11.43 |
Negative
Likelihood Ratio |
0.34 |
0.27
to 0.43 |
Disease
prevalence |
31.91%
(*) |
27.81%
to 36.23% |
Positive
Predictive Value |
78.83%
(*) |
72.14%
to 84.27% |
Negative
Predictive Value |
86.20
% (*) |
83.16%
to 88.76% |
Accuracy |
84.15%
(*) |
80.61%
to 87.26% |
Table-13: HbA1C≥6.5% as
standard vs FPG
Test |
Present |
n |
Absent |
n |
Total |
Positive |
True
Positive |
a=88 |
False
Positive |
c=3 |
a + c
=91 |
Negative |
False
Negative |
b=69 |
True
Negative |
d=335 |
b + d
=404 |
Total |
|
a + b = 157 |
|
c + d = 338 |
|
Table-14
Statistic |
Value |
95% CI |
Sensitivity |
56.05% |
47.92%
to 63.95% |
Specificity |
99.11
% |
97.43%
to 99.82% |
Positive
Likelihood Ratio |
63.15 |
20.30
to 196.48 |
Negative
Likelihood Ratio |
0.44 |
0.37
to 0.53 |
Disease
prevalence |
31.72%
(*) |
27.64%
to 36.02% |
Positive
Predictive Value |
96.70%
(*) |
90.41%
to 98.92% |
Negative
Predictive Value |
82.92
% (*) |
80.27%
to 85.28% |
Accuracy |
85.45%
(*) |
82.04%
to 88.44% |
Table-15: PPG≥200mg/dl as
a standard vs HBA1C
Disease |
|||||
Test |
Present |
n |
Absent |
n |
Total |
Positive |
True Positive |
a=109 |
False Positive |
c=49 |
a + c = 158 |
Negative |
False Negative |
b=29 |
True Negative |
d=308 |
b + d = 337 |
Total |
a + b
= 138 |
c + d
= 357 |
Table-16
Statistic |
Value |
95%
CI |
Sensitivity |
78.99% |
71.23% to 85.45% |
Specificity |
86.27 % |
82.26% to 89.67% |
Positive Likelihood Ratio |
5.75 |
4.38 to 7.57 |
Negative Likelihood Ratio |
0.24 |
0.18 to 0.34 |
Disease prevalence |
27.88% (*) |
23.97% to 32.05% |
Positive Predictive Value |
68.99% (*) |
62.85% to 74.53% |
Negative Predictive Value |
91.39 % (*) |
88.46% to 93.64% |
Accuracy |
84.24% (*) |
80.73%
to 87.34% |
Table-17: FPG≥126 mg/dl as
a standard vs Hba1c
Disease |
|
||||
Test |
Present |
n |
Absent |
n |
Total |
Positive |
True Positive |
a=88 |
False Positive |
c=69 |
a + c = 157 |
Negative |
False Negative |
b=3 |
True Negative |
d=335 |
b + d = 338 |
Total |
a + b
= 91 |
c + d
= 404 |
Table-18
Statistic |
Value |
95%
CI |
Sensitivity |
96.70% |
90.67% to 99.31% |
Specificity |
82.92 % |
78.89% to 86.46% |
Positive Likelihood Ratio |
5.66 |
4.55 to 7.04 |
Negative Likelihood Ratio |
0.04 |
0.01 to 0.12 |
Disease prevalence |
18.38% (*) |
15.07% to 22.08% |
Positive Predictive Value |
56.05% (*) |
50.63% to 61.34% |
Negative Predictive Value |
99.11 % (*) |
97.35% to 99.71% |
Accuracy |
85.45% (*) |
82.04%
to 88.44% |
Conclusion
Our
study shows that HbA1C correlates well with FPG unlike PPG. The sensitivity of
the test is comparable at and can be used along with plasma glucose level
estimation but is cannot be replaced for plasma glucose estimation. Cost and
standardisation of HbA1C assays is a big hurdle in the Indian context.
Hba1c
and PPG if negative almost rules out diabetes, as the specificity is very high.
While screening a population these two tests can be combined in one test to
rule out diabetes and additional testing can be scheduled at a longer interval.
If
a single test needs to be used a post glucose or a post prandial glucose levels
are more relevant for our population as we may miss 42% of them with only a FPG
level.
Also
the sensitivity and specificity of the HbA1C is similar to Fasting plasma
glucose estimates. Hence whether PPG and HbA1c can be combined in a single test
to diagnose diabetes. Blood glucose estimation are easily available even in
primary health centres, but subjecting people to a fasting test and glucose
challenge can be cumbersome. If a single blood test at any time of the day
could diagnose diabetes it may be worthwhile as we may prevent long term
complications and its morbidity.AnHbA1C test candiagnose diabetes at any time
of the day and be convenient. It is not affected by erythrocyte turnover and
can be used in patients with dyslipidaemias, hepatic, renal or thyroid
dysfunction. My co-authors Dr Surekha Shetty contributed to the study data and
manuscript preparation. Dr Anil Kumar contributed to data and statistical
analyses.
Study inferences for our
population: From our study we find that a higher
number of subjects fit the post glucose criteria than fasting glucose criteria
hence a post prandial or post glucose challenge is compulsory to identify at
risk individuals.
Our
population have a specific Asian phenotype which puts them at a risk for
developing cardiovascular risks compared to other races. In our study we find
that 42% of the people meeting the PPG criteria are missed with the FPG
criteria.
As
the sensitivity of HbAIC is high in people with higher Fasting glucose levels,
a Fasting glucose is not absolutely necessary.
If
a combination of tests in a single sample can diagnose diabetes accurately in
all populations, an ideal diagnostic test would be available. Further studies
may be required to look into the feasibility of developing a single test with
standardised criteria and develop newer assays apart from HbA1C.
References