This insulin sensitivity QUICKI calculator determines the insulin answer of the body based on fasting insulin and fasting glucose levels in blood. There is more information on the method and also on insulin resistance below the form.

Fasting Insulin:
Fasting Glucose:

How does this insulin sensitivity QUICKI calculator work?

This health tool determines insulin sensitivity based on fasting insulin and fasting glucose levels obtained from blood sample. QUICKI stands for Quantitative Insulin Sensitivity Check Index.

The index is defined as the inverse of the sum of the logarithms of fasting insulin and fasting glucose:

QUICKI = 1 / (log(Fasting Insulin) + log(Fasting Glucose))

The calculation is used for measuring insulin sensitivity. The latter is the inverse of insulin resistance.

There are two established methods for determining insulin sensitivity: the glucose clamp (“gold standard”) and the minimal model analysis. However, these are more complicated to implement during clinical research, thus QUICKI is preferred.

By using a fasting blood sample, fasting insulin in µU/mL and fasting glucose in mg/dL are obtained. The insulin sensitivity QUICKI calculator allows users to input glucose in mmol/L as well and the converts it.

Obtainable values for QUICKI range between 0.45 in healthy individuals (noted as unusually healthy in the original study) and 0.30 in diabetics. Lower values reflect greater resistance with values below 0.339 indicating insulin resistance which is associated with obesity and cardiovascular risk factors.

The original study performed hyperinsulinemic isoglycemic glucose clamp and insulin-modified frequently sampled IV glucose tolerance tests on a cohort of 56 patients (28 nonobese, 13 obese, and 15 type 2 diabetic).

The main discovery was that both fasting insulin and glucose values carry critical information about sensitivity.

Also, the linear correlation coefficient of QUICKI with SI(Clamp) is 0.78, better than that between SI(Clamp) and SI(MM) which was in previous reports of 0.57.

A subsequent validation study on 148 subjects tested log homeostasis model assessment (HOMA), the quantitative insulin sensitivity check index (QUICKI), the revised QUICKI, and a new revised QUICKI using fasting plasma glycerol. Comparable correlations were found for all indexes.

Also, QUICKI and HOMA-IR compensate for fasting hyperglycemia.

While none of the indexes is superior in the population with a wide range of insulin sensitivities, QUICKI is confirmed as a trusted tool to estimate insulin sensitivity, usually in the setting of epidemiological studies.

Insulin resistance guidelines

Insulin resistance (IR) is viewed as the pathological condition in which the body does not respond to the actions of insulin, thus preventing the regulation of glucose. It is also part of the metabolic syndrome, which is linked to cardiovascular risks.

Under IR, blood sugars increase, subsequently pancreatic cells increase production of insulin, leading to high blood insulin.

Risk factors include age (over 45), obesity and abdominal fat disposition, sedentary lifestyle and hypertension, amongst others.

There are several symptoms that have been in time associated with insulin resistance such as:

■ High blood sugar;

■ Intestinal bloating;

■ Weight gain;

■ Increased triglycerides;

■ Hypertension;

■ Sleepiness after meals;

■ Inability to concentrate.

Left unaddressed, IR can lead to type 2 diabetes and prediabetes which is a condition in which A1C levels are higher than normal, yet not high enough to warrant diabetes diagnosis.

There is a protective mechanism to insulin resistance as well, that of helping to conserve glucose supply for brain function by preventing its use in the muscles. This can be of benefit during acute illness.


1) Katz A, Nambi SS, Mather K, Baron AD, Follmann DA, Sullivan G, Quon MJ. (2000) . J Clin Endocrinol Metab; 85(7):2402-10.

2) Rabasa-Lhoret R, Bastard JP, Jan V, et. al. (2003) . J Clin Endocrinol Metab; 88(10):4917-23.

3) Chen H, Sullivan G, Quon MJ. (2005) . Diabetes; 54(7):1914-25.

4) Hrebícek J, Janout V, Malincíková J, Horáková D, Cízek L. (2002) . J Clin Endocrinol Metab; 87(1):144-7.

14 Aug, 2016 | 0 comments

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