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.
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.
This study was conducted at Auckland University of Technology Millennium Institute’s sports performance laboratories.
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.
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,)
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.
insulin resistance; hyperinsulinaemia; Kraft Patterns; Hayashi Patterns; HOMA; OGIS; McAuley Index; insulin response pattern.
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.
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 quantile of the population under investigation, especially quartiles as recommended by the World Health Organization.
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.
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.
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 (
Coefficient of variation:
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.
Test–retest reliability:
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 (
Repeatability coefficient:
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.
For example, HOMA and HOMA2 variants have both a high CV (25% – 50%) and a large RepCoef relative to the population mean (89% – 135%).
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.
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 Kraft
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 ×
Prior to analysis, plasma samples were allowed to warm to room temperature and centrifuged (10 000 ×
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.
McAuley Index:
Two group comparisons were made with two-tailed independent
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,
For insulin resistance measures, repeatability was quantified by estimating RepCoef.
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 (
Ranges within which two repeat measurements could be expected to fall were defined as
Test–retest reliability for log-transformed data:
CV was derived from the RepCoef using
Coefficient of variation derived from RepCoef:
This study was granted ethical approval by Auckland University of Technology Ethics Committee (AUTEC) on 16 December 2014 (reference no. 14/363).
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
Participant characteristics.
Code | Sex | Age (years) | Height (m) | Weight (kg) | Body mass index (kg/m |
Waist (m) | Waist:height | HbA1c (mmol/mol) |
---|---|---|---|---|---|---|---|---|
K1 | M | 47 | 1.744 | 81.8 | 26.9 | 0.872 | 0.50 | 32.4 |
K2 | M | 53 | 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 | F | 39 | 1.515 | 60.0 | 26.1 | 0.755 | 0.50 | 36.5 |
K6 | M | 30 | 1.634 | 65.9 | 24.7 | 0.832 | 0.51 | 34.2 |
K9 | M | 31 | 1.852 | 91.6 | 26.7 | 0.823 | 0.44 | 32.8 |
K10 | M | 27 | 1.774 | 76.7 | 24.4 | 0.804 | 0.45 | 35.8 |
Point-wise arithmetic mean insulin (pmol/L) and glucose (mmol/L) concentrations for each participant: (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.
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).
Repeatability coefficients for all participants.
Variable | ±RepCoef | CV % | |||
---|---|---|---|---|---|
Glucose, 0 min (mmol/L) | 0.27 | 0.74 | 4.81 | 15.4 | 5.5 |
Glucose, 0 min |
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 |
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 |
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 (Mffm/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/m |
27.50 | 76.10 | 514.19 | 14.8 | 5.3 |
, Excluding K4, week 1 because of an aberrant result.
, Significant mean–variance relationship.
, Excluding K6, week 1 because of haemolysis.
Observed Kraft and Hayashi pattern frequencies on eight participants over four visits per participant.
Participant | Kraft pattern |
Hayashi pattern |
|||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
I | IIA | IIB | III | IV | V | 1 | 2 | 3 | 4 | 5 | |
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 | - | - | - | - | - | - | 2 | 2 | - | - |
K10 | 2 | 1 | - | - | - | - | 1 | - | 2 | - | - |
, K6: The week 1, 60-min result was extensively affected by haemolysis. Although this did not affect Kraft patterning, the Hayashi pattern could not be determined.
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
Kraft and Hayashi pattern frequencies on eight participants over four visits per person after imputation.
Participant | Kraft pattern |
Hayashi pattern |
|||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
I | IIA | IIB | III | IV | V | 1 | 2 | 3 | 4 | 5 | |
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 | 3 | 1 | - | - | - | - | 2 | - | 2 | - | - |
Kraft and Hayashi pattern frequencies on eight participants over four visits per person after imputation.
Participant | Kraft pattern explanation | Participant | Hayashi pattern explanation |
---|---|---|---|
K5 | One test each added to pattern IIB and III as there was previously a 50:50 split. | K5 | One test each added to patterns 3 and 4 as there was previously a 50:50 split. |
- | - | K6 | The week 1, 60-min result was extensively affected by haemolysis. Extrapolation of the raw data suggested a 60-min peak was the most likely scenario, therefore pattern 3. |
K10 | One test added to pattern I as this was (1) the most common pattern, and (2) the pattern IIA was associated with a sub-acute change to normal clinical state (mild cold). | K10 | Unable to extrapolate from raw data whether a pattern 1 or 3 was most likely. Both scenarios run, with negligible difference to |
The inclusion of the imputed data did not cause a qualitative change in the overall results, as shown in
Fleiss’ κ calculations before and after imputation.
Variable | Kraft patterns |
Hayashi patterns |
||
---|---|---|---|---|
Before | After | Before | After | |
0.015 | < 0.001 | 0.798 | 0.347 | |
0.290 | 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 |
CI, confidence interval.
Insulin resistance measures compared to insulin response patterns.
Variable | Kraft I ( |
Kraft IIA, IIB, III ( |
|||
---|---|---|---|---|---|
Mean | SD | Mean | SD | ||
McAuley Index (Mffm/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/m |
547.49 | 52.86 | 450.92 | 28.18 | < 0.001 |
SD, standard deviation.
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 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.
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.
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%.
There is limited data on the repeatability of the OGTT, yet it is a very common clinical test.
Consistency among insulin response patterns was more common for participants who were predominantly Kraft I pattern (
For those participants who never exhibited a Kraft I pattern (
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 the definition of normal insulin tolerance as Kraft I pattern,
Although HOMA2 variants clearly delineated between normal and hyperinsulinaemic states, high variability decreases the sensitivity of the test. Only OGIS had both 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.
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.
Although previous research has focused on diagnosing insulin resistance for the early diagnosis of many metabolic diseases, hyperinsulinaemia is an emerging field.
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.
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:
The authors declare that they have no financial or personal relationships that may have inappropriately influenced them in writing this article.
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.
This research received no specific grant from any funding agency in the public, commercial or not-for-profit sectors.
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.