Repeatability characteristics of insulin response patterns and measures of insulin resistance

Abstract

Background: Hyperinsulinaemia is emerging as an independent risk factor for metabolic disease, but diagnostic measures are limited. It is plausible that insulin resistance measures, such as homeostatic model assessment (HOMA) type 2 variants, may model hyperinsulinaemia, but repeatability data are limited. Kraft and Hayashi insulin response patterns may not only add value in diagnosing hyperinsulinaemia, but also lack suitable repeatability data.

Aim: The aim of this study was to investigate the repeatability of insulin response patterns, and fasting and dynamic measures of insulin resistance, and to determine whether these latter measures can predict the insulin response pattern.

Setting: This study was conducted at Auckland University of Technology Millennium Institute’s sports performance laboratories.

Methods: Oral glucose (100 g) tolerance tests were conducted weekly on eight people. Six people completed four tests, while two completed at least two tests. Each test assessed insulin resistance and response patterns. Insulin resistance measures included fasting tests (HOMA2, McAuley Index) and a dynamic test (oral glucose insulin sensitivity [OGIS]). The insulin response patterns were assessed with both Kraft and Hayashi methodologies. Repeatability characteristics of ordinal variables were assessed by Bland and Altman methods, while Fleiss’ κ was applied to categorical variables.

Results: Fasting measures of insulin resistance recorded poor repeatability (HOMA2) or poor sensitivity (McAuley Index) compared to the dynamic measure (OGIS). Kraft insulin response patterns were more repeatable compared to Hayashi patterns, based on a combination of Fleiss’ κ (0.290 vs. 0.186,) p-value (0.15 vs. 0.798) and 95% confidence intervals.

Conclusions: Both hyperinsulinaemia and insulin resistance should be dynamically assessed with a multi-sampled oral glucose tolerance test. Further investigations are required to confirm a preferred methodology.

Full text

hp://www.insulinresistance.org Open Access
Journal of Insulin Resistance
ISSN: (Online) 2519-7533, (Print) 2412-2785
Page 1 of 9 Original Research
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Authors:
Catherine A.P. Cros1,2
Mark C. Wheldon3
Caryn Zinn2
Fabrice Merien4
Grant Schoeld2
Aliaons:
1School of Interprofessional
Health Studies, Auckland
University of Technology,
Auckland, New Zealand
2Human Potenal Centre,
Auckland University of
Technology, Auckland,
New Zealand
3Department of Biostascs
and Epidemiology, Auckland
University of Technology,
Auckland, New Zealand
4School of Science, Auckland
University of Technology,
Auckland, New Zealand
Corresponding author:
Catherine Cros,
ccros@aut.ac.nz
Dates:
Received: 25 Oct. 2018
Accepted: 08 Jan. 2019
Published: 28 Mar. 2019
How to cite this arcle:
Cros CAP, Wheldon MC, Zinn
C, Merien F, Schoeld G.
Repeatability characteriscs of
insulin response paerns and
measures of insulin resistance.
J. insul. resist. 2019;4(1), a44.
hps://doi.org/ 10.4102/jir.
v4i1.44
Copyright:
© 2019. The Authors.
Licensee: AOSIS. This work
is licensed under the
Creave Commons
Aribuon License.
Introducon
Insulin resistance is recognised as being a significant risk factor for type 2 diabetes and other
metabolic diseases. Yet, insulin resistance measures do not add value to disease risk calculations.1,2
People with insulin resistance generally have chronic hyperinsulinaemia to compensate for poor
glucose uptake rates. This compensatory hyperinsulinaemia is an independent risk factor for
metabolic disease,3 and may be one of the earliest indicators of incipient disease that precedes
changes to blood glucose levels,4,5 and potentially also obesity4 and hypertension.6 This suggests
that there is a need to accurately quantify hyperinsulinaemia in people with normal glucose
tolerance to include the measure as a public health screening tool.
Because hyperinsulinaemia coexists with insulin resistance, it is plausible that insulin resistance
measures may also predict hyperinsulinaemia. The gold-standard method for assessing insulin
resistance is the hyperinsulinaemic-euglycaemic clamp (HIEG). However, this method is often
impractical, especially in clinical settings or with large cohorts, so alternative methods are often
used that model the HIEG. These alternative methods include fasting tests such as homeostatic
model assessment (HOMA) and the McAuley Index. ‘Dynamic’ methods are derived from a
combination of fasting and post-prandial testing during an oral glucose tolerance test (OGTT) and
include oral glucose insulin sensitivity (OGIS).
Despite being widely used, there is limited information regarding population normative values of
either hyperinsulinaemia or insulin resistance, with many studies defining insulin resistance as a
Background: Hyperinsulinaemia is emerging as an independent risk factor for metabolic
disease, but diagnostic measures are limited. It is plausible that insulin resistance measures,
such as homeostatic model assessment (HOMA) type 2 variants, may model hyperinsulinaemia,
but repeatability data are limited. Kraft and Hayashi insulin response patterns may not only
add value in diagnosing hyperinsulinaemia, but also lack suitable repeatability data.
Aim: The aim of this study was to investigate the repeatability of insulin response patterns,
and fasting and dynamic measures of insulin resistance, and to determine whether these latter
measures can predict the insulin response pattern.
Setting: This study was conducted at Auckland University of Technology Millennium
Institute’s sports performance laboratories.
Methods: Oral glucose (100 g) tolerance tests were conducted weekly on eight people. Six
people completed four tests, while two completed at least two tests. Each test assessed insulin
resistance and response patterns. Insulin resistance measures included fasting tests (HOMA2,
McAuley Index) and a dynamic test (oral glucose insulin sensitivity [OGIS]). The insulin
response patterns were assessed with both Kraft and Hayashi methodologies. Repeatability
characteristics of ordinal variables were assessed by Bland and Altman methods, while Fleiss’
κ was applied to categorical variables.
Results: Fasting measures of insulin resistance recorded poor repeatability (HOMA2) or poor
sensitivity (McAuley Index) compared to the dynamic measure (OGIS). Kraft insulin response
patterns were more repeatable compared to Hayashi patterns, based on a combination of
Fleiss’ κ (0.290 vs. 0.186,) p-value (0.15 vs. 0.798) and 95% confidence intervals.
Conclusions: Both hyperinsulinaemia and insulin resistance should be dynamically assessed
with a multi-sampled oral glucose tolerance test. Further investigations are required to confirm
a preferred methodology.
Keywords: insulin resistance; hyperinsulinaemia; Kraft Patterns; Hayashi Patterns; HOMA;
OGIS; McAuley Index; insulin response pattern.
Repeatability characteriscs of insulin response
paerns and measures of insulin resistance
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Page 2 of 9 Original Research
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quantile of the population under investigation, especially
quartiles as recommended by the World Health Organization.7
This method limits the generalisability of results from
different studies and may confound results depending on the
population under investigation.
One explanation for insulin resistance measures reporting a
poor risk predictive value is that many of these measures,
including HOMA, are based on a single sample of fasting
insulin. Unlike many other biomarkers, insulin is a hormone
that is secreted in response to potentially rapidly changing
needs as well as the body’s natural oscillations, stress, food
and exercise to maintain glycaemic control.8 This means that
blood insulin levels, especially fasting insulin, are highly
variable.
It is theorised that using a dynamic method for assessing
insulin resistance may yield better disease predictive values,
but there is limited information to support their use.
Previous research has also suggested using insulin response
patterns following a multiple-sampled OGTT to predict
disease risk. Kraft described five distinct insulin patterns
formed during a 3–5-h OGTT on the basis of magnitude and
timing of the peak plasma insulin level and rate of decay.9 A
normal insulin response (Kraft I pattern) was considered to
be a fasting insulin ≤ 30 µU/mL, with a moderate peak 30–
60 min after the glucose load and a rapid rate of decay.
Independently, in their sample of 400 Japanese American
men, Hayashi and colleagues determined that an insulin
peak at 120 min during a 2-h OGTT (Hayashi pattern 4 and
5) significantly increased the risk of developing type 2
diabetes over the following 10 years.5
Assessing insulin response patterns is expensive as they
require four to five blood samples over a 2–3-h time period. It
is plausible that insulin resistance methods may be able to
predict hyperinsulinaemia given the two conditions are
intertwined. However, to have clinical utility, tests need to
have low variability. There are concerns about the variability
of insulin resistance measures, and the repeatability of insulin
response patterns is unknown.
There are a number of statistical methods used to assess the
variability of a measure. One of the most common is the
coefficient of variation (CV), which is the ratio of the standard
deviation to the mean (Equation 1). Coefficient of variations
indicate the extent of variability in relation to the mean of the
population or sample; the bigger the number, the more
variable the sample.
Coefficient of variation:
CV sd
100
µ
=×
[Eqn 1]
Test–retest reliability, also known as repeatability, is the
closeness of the agreement between the results of successive
measurements of the same variable taken under the same
conditions.10 For two tests to be in agreement, then the two
resulting measures should lie within the repeatability
coefficient (RepCoef) (Equation 2).
Test–retest reliability:
Test 1 ≈ Test 2 ± repeability coefcient [Eqn 2]
Assuming the data are normally distributed, it is expected
that 95% of the results will lie within 2 standard deviations
from the mean. The 95% RepCoef can then be calculated
(Equation 3) according to the methods of Bland and Altman,
where sw is the within-subject variance.10
Repeatability coefficient:
sRepCoef = 1.96 2 w
2 [Eqn 3]
This means that, for clinical tests, if a subsequent test differs
from the former by an amount smaller than the RepCoef, it
suggests biological variation. However, if greater, it suggests
that there is a change to the clinical condition. The RepCoef
may be expressed either as a discreet figure (e.g. 0.5 mmol/L)
or as a percentage relative to the grand mean of the sample
(or mean of the sample population). The latter may be more
useful where population norms are less well known, or when
a co-existent clinical condition defines sample mean; for
example, there may be different RepCoef of HOMA
depending on the underlying glucose tolerance status.11
For example, HOMA and HOMA2 variants have both a high
CV (25% – 50%) and a large RepCoef relative to the population
mean (89% – 135%).11 This may reflect the known variability
of fasting insulin.12 There is limited repeatability data for
other measures. The McAuley Index has one study, showing
a CV of 15% taken over two visits.13 The dynamic insulin
resistance measure, OGIS, has a lower degree of variability as
indicated by CV (7% – 8%) and small RepCoef proportional
to the population mean (22%).11,14 No studies have assessed
the repeatability of either the Kraft or Hayashi patterns.
The aims of this study are twofold: firstly, to assess the test–
retest repeatability of fasting and dynamic insulin resistance
measures, and that of dynamic insulin response patterns,
and, secondly, to determine whether measures of insulin
resistance can predict hyperinsulinaemia.
Methods
Subjects and study design
We recruited 10 healthy participants aged 20–55 years (six
male, four female) for four repeated multiple-sampled OGTTs
with insulin assays. ‘Healthy’ was defined as no acute or
chronic injury or illness requiring medical attention in the
previous 3 months and a current HbA1c < 40 mmol/mol
(5.8%). These tests were conducted according to the protocols
outlined by Kraft9 and standard oral glucose tolerance testing
procedures, including an overnight fast and no vigorous
exercise on the morning of the test. Participants were also
required to maintain the consumption of at least 150 g
carbohydrate per day for at least 14 days prior to the first test

Page 3 of 9 Original Research
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and throughout the testing period.9 Participants maintained
their normal habits, especially physical activity patterns,
throughout the 2-week lead-in and 4-week study period.
A deliberate decision was made to not formally assess diet or
physical activity to make the results more representative of
those that would be seen in clinical practice. The overall design
including the clinical criteria and short study time meant it
would be unlikely that an underlying clinical condition would
influence insulin responses and confound the results.
On each test occasion, after an overnight fast, each subject
had a cannula inserted into their antecubital fossa and
provided fasting venous blood samples before consuming
100 g glucose (400 mL Carbotest™ solution). The glucose was
consumed within 10 min of test commencement (0 min). With
the exception of water, no further food or drink was permitted
until the end of the test. Further venous samples were drawn
at 30, 60, 120 and 180 min. Vein patency was maintained by
flushing with saline before and after each collection, with the
first 2 mL of blood collected being discarded. Blood samples
were collected into plasma separator tubes (PST) vacutainers
(Becton, Dickinson and Company, Franklin Lakes, NJ) for
glucose and insulin analysis. Plasma was extracted from the
PST tubes after centrifugation (1500 × g at 4°C for 10 min),
and then frozen at -20°C within 2 h of collection. This protocol
was repeated weekly for a total of four tests. On the first test
occasion, height, weight and waist girth (smallest girth
between iliac crest and the lowest rib) were measured. On the
initial testing occasion, an additional fasting venous blood
sample was collected into an Ethylenediaminetetraacetic acid
(EDTA) vacutainer (Becton, Dickinson and Company) for
HbA1c analysis.
Analysis
Sample analysis
Prior to analysis, plasma samples were allowed to warm to
room temperature and centrifuged (10 000 × g at 20°C for 30 s)
to remove any protein precipitants. Samples were
batch-analysed by participant to reduce intermediate precision.
All plasma samples were quantitated on the Roche Diagnostics
cobas Modular Analytics E170. Insulin was quantitated on the
E module via electrochemiluminescence (intermediate
precision 2.5% – 4.9%). All other analytes were quantitated on
the P module: Glucose was quantitated via the hexokinase
enzymatic method (intermediate precision 1.7% – 1.9%) and
triglycerides via an enzymatic colorimetric method
(intermediate precision 1.8% – 2.4%). Where possible automated
haemolysis index measured quantified haemolysed samples.
Samples were excluded from further analysis if significant
haemolysis was present. The whole blood EDTA samples were
analysed for HbA1c (Roche Cobas C111, tubidimetric inhibition
immunoassay with interbatch CV of 1.32% – 2.36%).
Calculaons and stascal analysis
Statistical analysis and calculations were performed with
either SPSS 22.0 (Armonk, NY) or Microsoft Excel 2013
(Redmond, WA).
HOMA2 variants (HOMA2 %B, HOMA2 %S, HOMA2 IR)
and OGIS were calculated using their respective
downloadable calculators.15,16 The McAuley Index, which
assesses glucose disposal rate, corrected for fat-free mass
(Mffm/I), was calculated as per the formula in Equation 4.17
Glucose tolerance testing followed World Health
Organization (WHO) protocols.18 Hayashi patterns were
derived according to their protocol,5 and Kraft patterns were
derived according to the 2014 protocol.4
McAuley Index:
Mffm/ I = exp[2.63 − 0.28 ln(fasting insulin) − 0.31 ln
(fasting triglycerides)]. [Eqn 4]
Two group comparisons were made with two-tailed
independent t tests. Missing data were imputed as according
to the most likely clinical scenario for pattern reconstruction
and for Fleiss’s κ calculations only. Insulin and glucose
response curves collected over repeat visits were summarised
by plotting point-wise arithmetic mean concentrations for
each participant.
Test–retest repeatability measures
Fleiss’s κ was calculated as a means of assessing pattern
repeatability for both Kraft and Hayashi patterns (1971). As
there is no standard interpretation of κ, significant agreement
for the pattern was considered to be a combination of Landis
and Koch’s recommendations,19 significance of κ and whether
the 95% confidence intervals (CIs) crossed zero.
For insulin resistance measures, repeatability was quantified
by estimating RepCoef.10 As this method assumes a non-
significant mean-variance relationship, within-subject means
were plotted against within-subject standard deviations to
determine if there was a mean–variance relationship.
Scatterplots and ordinary least squares regression was used
to assess the strength of such relationships. If the slope
coefficient (SC) was significant at the 0.05 significance level,
the process was repeated for the mean and standard deviation
of the natural log of the variable.
If a significant mean–variance relationship was determined,
participants were divided into sub-groups according to test
results. The intent of these sub-groups was to reduce the
mean–variance relationship and therefore the risk of bias in
the RepCoef at each end of the range while maintaining a
clinically meaningful result.
The 95% RepCoef were derived by taking the square root of
the residual mean square errors (sw) from one-way analyses
of variance with subjects as factors fitted to the raw or logged
responses for each outcome variable (Equation 3).10,20
Ranges within which two repeat measurements could be
expected to fall were defined as Equation 2 for non-log-
transformed data or as Equation 5 for log-transformed data.

Page 4 of 9 Original Research
hp://www.insulinresistance.org Open Access
Test–retest reliability for log-transformed data:
Test Test e 1 2 RepCoef
≈×
÷
[Eqn 5]
CV was derived from the RepCoef using Equation 6, where

µ
represents the grand mean of the sample.
Coefficient of variation derived from RepCoef:

CV
RepCoef
1.96 2 RepCoef
2.77
µµ
=⋅




≈⋅ [Eqn 6]
Ethical consideraon
This study was granted ethical approval by Auckland
University of Technology Ethics Committee (AUTEC) on 16
December 2014 (reference no. 14/363).
Results
Ten participants consented to the study, but only eight
participants completed at least two tests; results are included
for the latter eight participants. The baseline characteristics of
these eight participants are displayed in Table 13. Six
participants completed all four tests, one participant (K10)
could not attend on one occasion and one participant (K5) was
unable to adhere to fasting requirements on two occasions.
Figure 13 displays the mean insulin and glucose response
curves for each participant. A higher peak and/or delayed
rate of decay can be observed for participants K2, K5 and K6.
Repeatability coecient for insulin
resistance measures
Mean–variance relationships could only be detected for
fasting glucose, fasting insulin and glucose at 180 min. After
the removal of participant K4 from the data set, a mean–
variance relationship could no longer be detected for either
fasting glucose or glucose at 180 min. Log-transformation of
fasting insulin did not remove the mean–variance
relationship. No mean–variance relationship could be
detected for fasting insulin for the subset of hyperinsulinaemic
participants (K2, K5 and K6).
Table 23 displays the RepCoef for all time points for insulin
and glucose, the McAuley Index, all HOMA measures
and OGIS. With the exception of glucose 0 min, there was no
practical difference in the RepCoef for glucose when
participant K4 was excluded. Among the fasting models of
insulin resistance, the McAuley Index had the lowest RepCoef
compared to the grand mean of the sample (17.4%).
Repeatability of insulin response paerns
Table 33 presents the distribution of each test per participant
for both Kraft and Hayashi insulin response patterns. The
most common Kraft pattern was pattern I, recorded by five of
the eight participants on at least one occasion, while the most
common Hayashi pattern was pattern 3, which was recorded
by every participant on at least one occasion. No participant
recorded a Kraft IV or V pattern, or a Hayashi pattern 5.
Three participants (K5, K6 and K10) were initially excluded
from κ calculations as they did not have four eligible tests for
both pattern responses. However, small participant numbers
meant that missing data decreased the power of the study.
Therefore, we replicated the repeatability calculations after
imputing the clinically most likely, or most frequent, outcome
for participants with missing data (K5, K6 and K10), as shown
in Table 43 and Table 53.
The inclusion of the imputed data did not cause a qualitative
change in the overall results, as shown in Table 63. Estimated
κ for the Kraft patterns was higher than for Hayashi patterns
(0.290 vs. 0.186), but only the κ for the Kraft patterns was
significantly different from zero (95% CIs, 0.515–0.798 and
−1.238 to 1.610 for Kraft and Hayashi, respectively).
Characteriscs of insulin resistance measures
compared to insulin response paerns
Table 73 displays the participants’ insulin resistance measures
when dichotomised into normal (Kraft I) and
hyperinsulinaemic (Kraft IIA, IIB, III) insulin response
patterns.9 Following a two-sample t test (defined by Kraft
pattern), statistically significant differences can be noted for
HOMA2 measures and for OGIS, but not for the McAuley
Index.
Discussion
Numerous tests are available for assessing insulin resistance
and may be either based on fasting measures or dynamically
modelled from multiple-sampled OGTTs. Tests based on
TABLE 1: Parcipant characteriscs.
Code Sex Age (years) Height (m) Weight (kg) Body mass index (kg/m2) Waist (m) Waist:height HbA1c (mmol/mol)
K1 M 47 1.744 81.8 26.9 0.872 0.50 32.4
K2 M53 1.737 81.8 27.1 0.956 0.55 35.4
K3 M 44 1.726 74.0 24.8 0.810 0.47 35.8
K4 F 29 1.721 71.0 24.0 0.792 0.46 37.5
K5 F39 1.515 60.0 26.1 0.755 0.50 36.5
K6 M30 1.634 65.9 24.7 0.832 0.51 34.2
K9 M31 1.852 91.6 26.7 0.823 0.44 32.8
K10 M27 1.774 76.7 24.4 0.804 0.45 35.8
Source: Cros C. Understanding and diagnosing hyperinsulinaemia. PhD thesis. Auckland: Auckland University of Technology; 2015. [cited n.d.]. Available from: hp://aut.researchgateway.ac.nz/
handle/10292/9906

Page 5 of 9 Original Research
hp://www.insulinresistance.org Open Access
0306090 150120 180
0
400
200
600
800
Insulin (pmol/L)
0
2
4
6
8
10
12
Glucose (mmol/L)
Time (minutes)
0306090 120 150 180
Time (minutes)
0
400
200
600
800
Insulin (pmol/L)
0
2
4
6
8
10
12
Glucose (mmol/L)
Insulin Glucose Insulin Glucose
Time (minutes)
0306090 120 150 180
0
200
400
600
800
Insulin (pmol/L)
0
Glucose (mmol/L)
2
4
6
8
10
12
Time (minutes)
0306090 120 150 180
0
200
400
600
800
Insulin (pmol/L)
0
Glucose (mmol/L)
2
4
6
8
10
12
Insulin Glucose Insulin Glucose
Time (minutes)
0306090 120 50 180
0
200
400
600
800
Insulin (pmol/L)
0
Glucose (mmol/L)
2
4
6
8
10
12
Time (minutes)
03060 120 15090 180
0
200
400
600
800
Insulin (pmol/L)
Glucose (mmol/L)
0
2
4
6
8
10
12
Insulin Glucose Insulin Glucose
0306090 150120 180
0
400
200
600
800
Insulin (pmol/L)
0
2
4
6
8
10
12
Glucose (mmol/L)
Time (minutes)
030 60 90 120 150 180
0
400
200
600
800
Insulin (pmol/L)
0
2
4
6
8
10
12
Glucose (mmol/L)
Time (minutes)
Insulin Glucose Insulin Glucose
a
c
b
d
e
g
f
h
Source: Cros C. Understanding and diagnosing hyperinsulinaemia. PhD thesis. Auckland: Auckland University of Technology; 2015. [cited n.d.]. Available from: hp://aut.researchgateway.ac.nz/
handle/10292/9906
Note: Parcipants K4 and K6 both had week 1 results excluded. K4, aberrant 60-min glucose result from unknown origin (asymptomac for hypoglycaemia); K6, extensive haemolysis of the 60-min
sample.
FIGURE 1: Point-wise arithmec mean insulin (pmol/L) and glucose (mmol/L) concentraons for each parcipant: (a) K1; (b) K2; (c) K3; (d) K4: weeks 2-4; (e) K5: two test;
(f) K6: weeks 2-4; (g) K9; (h) K10: three tests.

Page 6 of 9 Original Research
hp://www.insulinresistance.org Open Access
fasting insulin such as HOMA and HOMA2 variants are
popular, as they require fewer resources compared to those
based on dynamic testing (e.g. OGIS). As both insulin resistance
and hyperinsulinaemia are becoming increasingly recognised
as independent disease risk predictors, there is a need for an
effective diagnostic test. However, a lack of repeatability
testing for both insulin resistance and hyperinsulinaemia
measures precludes their clinical use. This study assessed the
repeatability characteristics of the fasting measures (HOMA2
variants and McAuley Index) and the dynamic measure
(OGIS) by comparing each RepCoef to the cohort grand mean.
We also assessed the repeatability of the two insulin response
patterns, Kraft and Hayashi patterns using Fleiss’s κ.
Repeatability of insulin resistance measures
Of the insulin resistance measures (HOMA2, McAuley and
OGIS), only the McAuley Index (fasting measure) and OGIS
(dynamic measure) demonstrated a low RepCoef relative to
the grand mean of the sample population with a change of
17.4% and 14.8%, respectively. By contrast HOMA2 variants
had a greater degree of change (HOMA2 %B = 41.3%,
HOMA2 %S = 55.9% and HOMA2 IR = 75.4%). These
HOMA2 findings are comparable to our previous research in
a population of people with normal glucose tolerance.11
Most studies assess repeatability using CV. Although it may
not be possible to directly compare the repeatability of the
original HOMA model with the HOMA2 model, our findings
(HOMA2 %B = 14.8%, HOMA2 %S = 20.1% and HOMA2 IR
= 27.1%) align with CVs reported from the original model
including that of Mather and colleagues, who reported
HOMA IR having a CV of 24%.20 Coefficient of variation data
for the McAuley Index is limited with one study reporting a
CV of 15.1%.13 This is higher than our finding of 6.3%.
Repeatability of insulin response paerns
There is limited data on the repeatability of the OGTT, yet it
is a very common clinical test.21 Few studies have investigated
TABLE 2: Repeatability coecients for all parcipants.
Variable sw±RepCoef

µ
RepCoef

µ
%CV %
Glucose, 0 min (mmol/L) 0.27 0.74 4.81 15.4 5.5
Glucose, 0 min† (mmol/L) 0.20 0.56 4.86 11.5 4.2
Glucose, 30 min (mmol/L) 1.02 2.81 7.43 37.8 13.7
Glucose, 60 min (mmol/L) 1.83 5.08 6.00 84.7 30.5
Glucose, 120 min (mmol/L) 1.33 3.68 4.94 74.5 26.9
Glucose, 180 min (mmol/L) 0.80 2.23 3.94 56.6 20.4
Insulin, 0 min‡ (pmol/L) 11.00 31.00 44.42 68.9 24.8
Insulin, 30 min (pmol/L) 101.00 279.00 348.94 80.0 28.9
Insulin, 60 min§ (pmol/L) 178.00 494.00 415.16 119.0 42.9
Insulin, 120 min (pmol/L) 102.00 282.00 294.38 95.8 34.6
Insulin, 180 min (pmol/L) 71.00 197.00 152.83 129.0 46.5
McAuley Index (Mm/I) 0.35 0.98 5.62 17.4 6.3
HOMA2 %B 14.20 39.50 95.66 41.3 14.8
HOMA2 %S 26.10 72.40 129.56 55.9 20.1
HOMA2 IR 0.24 0.67 0.89 75.4 27.1
OGIS (mL/min/m2) 27.50 76.10 514.19 14.8 5.3
Source: Cros C. Understanding and diagnosing hyperinsulinaemia. PhD thesis. Auckland: Auckland University of Technology; 2015. [cited n.d.]. Available from: hp://aut.researchgateway.ac.nz/
handle/10292/9906
sw, residual mean square error; RepCoef, repeatability coecient;

µ
, grand mean; CV, coecient of variaon; OGIS, oral glucose insulin sensivity.
†, Excluding K4, week 1 because of an aberrant result.
‡, Signicant mean–variance relaonship.
§, Excluding K6, week 1 because of haemolysis.
TABLE 4: Kra and Hayashi paern frequencies on eight parcipants over four
visits per person aer imputaon.
Parcipant Kra paern Hayashi paern
I IIA IIB III IV V 12345
K1 4-------4- -
K2 - - 2 2 - - - - 2 2 -
K3 4 -----3- 1 - -
K4 3 - - 1 - - 2 - 1 1 -
K5 - - 2 2 - - - - 2 2 -
K6 - 1 3-----4- -
K9 4- - - - - - 2 2 - -
K10 31 - - - - 2 - 2 - -
Source: Cros C. Understanding and diagnosing hyperinsulinaemia. PhD thesis. Auckland:
Auckland University of Technology; 2015. [cited n.d.]. Available from: hp://aut.
researchgateway.ac.nz/handle/10292/9906
TABLE 3: Observed Kra and Hayashi paern frequencies on eight parcipants
over four visits per parcipant.
Parcipant Kra paern Hayashi paern
I IIA IIB III IV V 12345
K1 4-------4- -
K2 - - 2 2 - - - 2 2 -
K3 4 -----3- 1 - -
K4 3 - 1 - - 2 - 1 1 -
K5 - - 1 1 - - - - 1 1 -
K6†- 1 3-----3- -
K9 4------22--
K10 2 1 - - - - 1 - 2 - -
Source: Cros C. Understanding and diagnosing hyperinsulinaemia. PhD thesis. Auckland:
Auckland University of Technology; 2015. [cited n.d.]. Available from: hp://aut.
researchgateway.ac.nz/handle/10292/9906
†, K6: The week 1, 60-min result was extensively aected by haemolysis. Although this did
not aect Kra paerning, the Hayashi paern could not be determined.

Page 7 of 9 Original Research
hp://www.insulinresistance.org Open Access
the repeatability or reproducibility of insulin response
curves; of those that have, no significant differences in
AUCinsulin have been noted.21,22 There are no published studies
that have assessed the repeatability of insulin response
patterns, namely the Kraft and Hayashi patterns. Our study
demonstrated that the Kraft pattern methodology had a
higher reproducibility and was more likely to be consistent
following repeated OGTT when compared to the Hayashi
patterning method. Kraft patterns account for both the
magnitude of the insulin response and rate of decay as well
as the timing of the insulin peaks. By contrast, the Hayashi
pattern algorithm is based solely on the timing of the insulin
peaks. As there is little long-term data associating insulin
response patterns to health outcomes, we suggest that insulin
response patterns should be categorised using a combination
of factors, including the magnitude and timing of the insulin
peak and rate of decay.
Consistency among insulin response patterns was more
common for participants who were predominantly Kraft I
pattern (n = 5). Two participants deviated from Kraft I pattern
for one of the four tested occasions: K4 (week 1) and K10
(week 4). It is unknown why these deviations occurred.
Changes to insulin responses can occur for a variety of
reasons, including sub-acute illnesses, menstrual cycle, stress
or even poor sleep patterns.8,23,24 This suggests that insulin
response patterning should only be conducted during times
of stable clinical condition or with an understanding of these
caveats. Concurrent assessment of inflammatory markers
such as c-reactive protein or cortisol could also be considered.
For those participants who never exhibited a Kraft I pattern
(n = 3), consistency among patterns was lower. Two
participants exhibited a 50:50 split between patterns IIB and
III, while the third was predominately pattern IIB, with one
occasion of pattern IIA. Unlike the participants who deviated
from a predominant Kraft I pattern, there was no clear
plausible clinical indication for these variations. These
fluctuations may indicate that hyperinsulinaemic states are
more transitory than a normal insulin response (Kraft I) and
they support previous findings that demonstrate that people
with hyperinsulinaemia have lower repeatability rates with
OGTT.21 Assessing insulin pattern repeatability in a large
cohort of people with known hyperinsulinaemia is required
to understand these variations. This study suggests that Kraft
patterns are sufficiently reproducible to dichotomise patients
into a Kraft I pattern or ‘normal’ insulin status, or a
hyperinsulinaemic status (Kraft patterns II–III); however, a
larger study is required to confirm these results.
Variation was higher within the Hayashi patterns. Every
participant exhibited a Hayashi 3 pattern at least once. Most
(75%) also exhibited either a Hayashi 1 or 2 pattern, or a
Hayashi 4 pattern. With one exception, no participant had
both a Hayashi 1 or 2 pattern and a Hayashi 4 pattern.
Although this increased variation within the Hayashi
patterns suggests that Kraft patterns should be preferred to
Hayashi patterns in future research, it must also be noted
that Kraft patterns, to date, do not have any longitudinal
outcome data.
Using insulin resistance measures to assess
insulin response paerns
Using the definition of normal insulin tolerance as Kraft I
pattern,9 the McAuley Index was unable to distinguish
between normal and hyperinsulinaemic sub-groups. This
contrasts to the HOMA2 variables and OGIS, which all had
clear delineations between the normal and hyperinsulinaemic
sub-groups. Returning a similar value across a range of Kraft
patterns, HOMA2 and OGIS values suggests the McAuley
Index is less sensitive to changes of physical state than the
other measures.
Although HOMA2 variants clearly delineated between
normal and hyperinsulinaemic states, high variability
decreases the sensitivity of the test. Only OGIS had both
TABLE 7: Insulin resistance measures compared to insulin response paerns.
Variable Kra I (n = 5) Kra IIA, IIB, III (n = 3) p
Mean SD Mean SD
McAuley Index (Mm/I) 4.99 0.82 4.51 0.46 0.095
HOMA2 %B 73.87 19.70 121.11 16.09 < 0.001
HOMA2 %S 183.93 52.96 82.43 20.34 < 0.001
HOMA2 IR 0.58 0.21 1.28 0.35 < 0.001
OGIS (mL/min/m2) 547.49 52.86 450.92 28.18 < 0.001
Source: Cros C. Understanding and diagnosing hyperinsulinaemia. PhD thesis. Auckland:
Auckland University of Technology; 2015. [cited n.d.]. Available from: hp://aut.research
gateway.ac.nz/handle/10292/9906
SD, standard deviaon.
TABLE 6: Fleiss’ κ calculaons before and aer imputaon.
Variable Kra paerns Hayashi paerns
Before Aer Before Aer
p0.015 < 0.001 0.798 0.347
κ0.290 0.417 0.186 0.451
95% CI 0.267–0.622 0.230–0.532 -1.23 to 1.61 -0.49 to 1.39
Source: Cros C. Understanding and diagnosing hyperinsulinaemia. PhD thesis. Auckland:
Auckland University of Technology; 2015. [cited n.d.]. Available from: hp://aut.researc
hgateway.ac.nz/handle/10292/9906
CI, condence interval.
TABLE 5: Kra and Hayashi paern frequencies on eight parcipants over four visits per person aer imputaon.
Parcipant Kra paern explanaon Parcipant Hayashi paern explanaon
K5 One test each added to paern IIB and III as there
was previously a 50:50 split.
K5 One test each added to paerns 3 and 4 as there was previously a 50:50 split.
- - K6 The week 1, 60-min result was extensively aected by haemolysis. Extrapolaon of the
raw data suggested a 60-min peak was the most likely scenario, therefore paern 3.
K10 One test added to paern I as this was (1) the
most common paern, and (2) the paern IIA was
associated with a sub-acute change to normal
clinical state (mild cold).
K10 Unable to extrapolate from raw data whether a paern 1 or 3 was most likely. Both
scenarios run, with negligible dierence to κ.
Source: Cros C. Understanding and diagnosing hyperinsulinaemia. PhD thesis. Auckland: Auckland University of Technology; 2015. [cited n.d.]. Available from: hp://aut.researchgateway.ac.nz/
handle/10292/9906

Page 8 of 9 Original Research
hp://www.insulinresistance.org Open Access
sensitivity and repeatability. This further questions the value
of fasting tests, especially for assessing compensatory
hyperinsulinaemia. Our previous research found a poor
association between a fasting insulin < 30 µU/mL and a
delayed insulin peak.11
Limitaons
We recognise that our study had a number of limitations,
especially with respect to participant dropout rates and small
sample size. However, sample sizes of 10 participants are
common in repeatability studies for insulin resistance.21,25
Nevertheless, this study may be the first to assess the test–
retest repeatability of insulin response patterns. Future
research for diagnosing insulin resistance should focus on a
dynamic test based on an OGTT. There are concerns about
using methodologies based on the oral glucose tests because
of previous reports of poor repeatability or variable glucose
absorption rates. However, our study has shown that dynamic
tests have a higher degree of repeatability compared to those
based on fasting models. The lower rate of repeatability from
models based on fasting tests may be because of the natural
lability of insulin, which, our study shows, has a CV of 25%
– a figure consistent with previous reports.26
Although previous research has focused on diagnosing
insulin resistance for the early diagnosis of many metabolic
diseases, hyperinsulinaemia is an emerging field.3,27 Although
there is an accepted association between insulin resistance
and hyperinsulinaemia, the direction of causality is unknown
and there are multiple plausible aetiologies that could start
with either condition.3,27 It is also becoming accepted that
hyperinsulinaemia may be corrected while insulin resistance
is maintained. Given the high degree of overlap between the
conditions, it is also plausible that diagnostic tests for
hyperinsulinaemia and insulin resistance may overlap. Given
the variability of fasting insulin, this study suggests that
dynamic insulin or glucose modelling or insulin response
patterning may be more effective in diagnosing
hyperinsulinaemia and/or insulin resistance, and this is
where future research should be focused.
Conclusion
Hyperinsulinaemia may indicate metabolic disease earlier
than conventional measures, but a lack of a consistent testing
process hampers ongoing research. As hyperinsulinaemia is
closely associated with insulin resistance, assessing the latter
may also diagnose hyperinsulinaemia. Fasting insulin
resistance measures are not suitable either because of a lack
of repeatability (HOMA2 variants) or sensitivity (McAuley
Index). Dynamic testing, either using OGIS or insulin
response patterns, should be further investigated for
assessing hyperinsulinaemia, but the latter should consider
both the magnitude and timing of the insulin peaks.
Acknowledgements
Dr C.A.P. Crofts was supported by a National Heart
Foundation (NZ) study award (ref. 1522). This article is based
on a chapter (entitled ‘Assessing the repeatability
characteristics of insulin response patterns and measures of
insulin resistance’) of a PhD thesis titled, ‘Understanding and
diagnosing hyperinsulinaemia’, by Dr C.A.P. Crofts,
submitted to Auckland University of Technology, available
at: http://hdl.handle.net/10292/9906.
Compeng interests
The authors declare that they have no financial or personal
relationships that may have inappropriately influenced them
in writing this article.
Authors’ contribuons
C.A.P.C. was the project lead and writer and was responsible
for concept and design, sample collection and analysis, and
data analysis and interpretation. M.C.W. was the reviewer,
was responsible for concept and design, and provided
statistical expertise. C.Z. was the reviewer and performed data
analysis and interpretation. F.M. was the reviewer and
provided expertise in sample analysis and results
interpretation. G.S. was the reviewer and performed data
analysis and interpretation.
Funding
This research received no specific grant from any funding
agency in the public, commercial or not-for-profit sectors.
Disclaimer
The views and opinions expressed in this article are those of
the authors and do not necessarily reflect the official policy or
position of any affiliated agency of the authors.
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