Why the McAuley index is valuable in assessing risk of insulin resistance


In the first post I published an online calculator for insulin resistance probability based on an index published by McAuley at al[1].

Why is this index valuable to us in insulin resistance prediction?

We know that rates of diabetes and insulin resistance are increasing very rapidly and not only in the western world, with large fraction of people being not diagnosed and therefore on their way to worsening. Most surrogate measures of insulin resistance (other than direct measurement by the clamp method, which doctors will not prescribe just for screening – probably rightly so – it’s not a fun procedure) require either multiple blood draws after glucose challenge with additional insulin measurements (not a standard procedure), which depending on where you live will most likely neither be prescribed nor reimbursed by an insurance policy provider (in some countries you cannot even subject yourself to this test without doctors prescription).

The McAuley index on the other hand uses just two blood parameters obtained from 1 fasting blood drawtriglycerides (done by default almost everywhere) and fasting insulin (you will typically have to ask your physician for that or add it yourself to test panel). McAuley offers greater predictive power than other indexes based just on fasting insulin and fasting glucose, frankly almost the same predictive power as glucose challenge. Comparison of those methods is in the table below, based on paper[2] directly comparing them to clamp measurement.

IndexCut-off pointTrue PositiveTrue NegativeAUCTest type
Matsuda<3.510.630.950.93Glucose challenge + insulin
Stumvoll<0.070.720.930.92Glucose challenge + insulin
McAuley<6.050.640.940.91Fasting TG + insulin
InsulinAUC>1970.690.930.91Glucose challenge + insulin
HOMA-IR>2.610.530.950.89Fasting glucose + insulin
Ins120>650.840.930.89Glucose challenge + insulin
FPI>130.580.920.88Fasting insulin
QUICKI<0.320.590.910.88Fasting glucose + insulin
ISI120<1.330.610.940.88Glucose challenge + insulin
IGR>0.130.510.930.85Fasting glucose + insulin
GIR<7.930.520.930.85Fasting glucose + insulin

How to interpret this table?

In short, the closer the AUC (Area Under Curve) to 1, the better. Value of 1 would signify ability to distinguish 100% – that means 0% false positives and 0% false negatives. As you can see value of 0.91 for McAuley is really high especially when we compare with alternatives.

So with McAuley index we get the best of both worlds – easiness of the test (just adding insulin to your standard blood test) and high predictive power of more cumbersome methods. All you have to do now is to type in your results here to get not only your McAuley index but also likelihood of having insulin resistance.

For an interesting discussion on various predictors of insulin resistance please listen to the BreakNutrition podcast Episode 34.

More about AUC

So AUC means Area Under Curve, but more specifically under ROC – receiver operating characteristic curve (statistician do love their acronyms). The name might sound difficult but the concept is really simple – ROC is a graph of True Positive Rate versus False Positive Rate. We want the former to be high and latter to be low (few mistakes either way). The graph[3] below demonstrates the concept. Good classification methods result in a very steep ROC curve and therefore the area under it (AUC) will be close to 1, and simply guessing will get you AUC of 0.5.

ROC curve; insulin resistance
ROC curve – receiver operating characteristic curve

[1] McAuley KA, Williams SM, Mann JI, et al. Diagnosing insulin resistance in the general population. Diabetes care 2001; 24(3): 460-4.

[2] Abbasi F, Silvers A,  Viren J, Reaven GM, Relationship between Several Surrogate Estimates of Insulin Resistance and a Direct Measure of Insulin-Mediated Glucose Disposal: Comparison of Fasting versus Post-Glucose Load Measurements, Diabetes Research and Clinical Practice, 136, 108 – 115

[3] By ROC_space.png: Indonderivative work: Kai walz (talk) – ROC_space.png, CC BY-SA 3.0, https://commons.wikimedia.org/w/index.php?curid=8326140


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