Quantitative Insulin Sensitivity Check Index: A Simple, Accurate Method for Assessing Insulin Sensitivity In Humans

Minimal model analysis of a frequently sampled iv glucose tolerance test is a well accepted alternative for estimating insulin sensitivity.

“A well accepted alternative for estimating insulin sensitivity involves minimal model analysis of a frequently sampled iv glucose tolerance test (FSIVGTT) (9 -11).”

• cites [9] Bergman, 1987, Equivalence of the insulin sensitivity index in man derived by the minimal model method and the euglycemic glucose clamp., J Clin Invest sourceSupported
• cites [10] 1989, Lilly lecture 1989. Toward physiological understanding of glucose tolerance. Minimal-model approach., Diabetes sourceNot checked — no full text of the source
• cites [11] Saad, 1997, Differences between the tolbutamide-boosted and the insulin-modified minimal model protocols., Diabetes sourceNot checked — no full text of the source

QUICKI and SI Clamp have a comparable overall correlation in an independent group of 21 obese and 14 nonobese subjects from another institution.

“Moreover, we observed a comparable overall correlation between QUICKI and SI Clamp in a totally independent group of 21 obese and 14 nonobese subjects from another institution.”

Obese and diabetic subjects were significantly more insulin resistant than nonobese subjects as measured by glucose clamp.

“Thus, as expected, the obese and diabetic subjects were significantly more insulin resistant than the nonobese subjects (P Ͻ 2 ϫ 10 Ϫ5 ).”

• Also stated in: § Glucose clamp and minimal model estimates of insulin sensitivity · plain

  “As expected, when subjects were evaluated with the gold standard glucose clamp method, obese subjects were more insulin resistant, on the average, than nonobese subjects, and diabetics were the most insulin-resistant group.”

Physiological steady state values, fasting insulin and glucose, contain critical information about insulin sensitivity.

“We performed a sensitivity analysis on our data and discovered that physiological steady state values [i.e.fasting insulin (I 0 ) and glucose (G 0 )] contain critical information about insulin sensitivity.”

The linear regression analysis of the subgroups corresponded more closely to the overall regression line when comparing QUICKI and SI Clamp.

“In addition, the linear regression analysis of the subgroups corresponded more closely to the overall regression line when comparing QUICKI and SI Clamp .”

QUICKI derived from the average results of two fasting blood samples over 10 min was similar to QUICKI calculated from a single sample.

“In addition, QUICKI derived from the average results of two fasting blood samples (over 10 min) was similar to QUICKI calculated from a single sample.”

The product of fasting insulin and glucose yielded an index of insulin sensitivity that was applicable to both diabetic and nondiabetic subjects.

“Interestingly, we found that the product of fasting insulin and glucose yielded an index of insulin sensitivity that was applicable to both dia-betic and nondiabetic subjects.”

Basal glucose levels in the diabetic group were significantly elevated compared with those in the nonobese and obese groups.

“The basal glucose levels in the diabetic group were significantly elevated compared with those in the other groups (166 Ϯ 15 mg/dL; P Ͻ 4 ϫ 10 Ϫ5 ).”

Meaningful values of QUICKI could be calculated for all diabetic subjects studied.

“Note that in contrast to the minimal model-derived SI MM , we were able to calculate meaningful values of QUICKI for all diabetic subjects studied.”

QUICKI mean values were 0.382 ± 0.007, 0.331 ± 0.010, and 0.304 ± 0.007 for nonobese, obese, and diabetic subjects, respectively.

“QUICKI was calculated for all study subjects (mean, 0.382 Ϯ 0.007, 0.331 Ϯ 0.010, and 0.304 Ϯ 0.007 for nonobese, obese, and diabetic subjects, respectively).”

Minimal model predictions of glucose disappearance fit well with the actual glucose disappearance data.

“When glucose and insulin data from the FSIVGTT were analyzed using the MINMOD program, minimal model predictions of glucose disappearance fit well with the actual glucose disappearance data (Fig. 2).”

Basal insulin levels in both the obese and diabetic groups were significantly higher than those in the normal group.

“The basal insulin levels in both the obese and diabetic groups were significantly higher than those in the normal group (P Ͻ 0.02).”

Fasting steady-state insulin and glucose values contain sufficient information to accurately assess insulin sensitivity in vivo over a wide range in a diverse population.

“Sensitivity analyses of an initial subset of data (40%) from glucose clamp studies and the first 20 min of the FSIVGTT revealed that fasting steady state values of insulin and glucose contain sufficient information to accurately assess insulin sensitivity.”

• Also stated in: § Relative merits of QUICKI · plain

  “We conclude that fasting glucose and insulin levels contain sufficient information to accurately assess insulin sensitivity in vivo over a wide range in a diverse population.”

The correlation between QUICKI and SI Clamp is significantly better than that between SI MM and SI Clamp.

“More importantly, the overall correlation between QUICKI and SI Clamp was significantly better than that between SI MM and SI Clamp (P Ͻ 0.028).”

Endogenous insulin secretion (0-20 min) in response to the iv glucose bolus in obese subjects was greater than that in nonobese subjects.

“In addition, endogenous insulin secretion (0 -20 min) in response to the iv glucose bolus in obese subjects was greater than that in nonobese subjects (mean insulin peak, 157 Ϯ 34 vs. 100 Ϯ 14 U/mL; P Ͻ 0.04), whereas the insulin response was markedly diminished in the diabetic subjects.”

HOMA was highly correlated with QUICKI.

“Interestingly, although HOMA was highly correlated with QUICKI, the overall correlation between the gold standard SI Clamp and QUICKI was still significantly better than the overall correlation between SI Clamp and HOMA (P Ͻ 0.004).”

QUICKI and SI Clamp are highly correlated for all subjects (r = 0.78).

“We defined a quantitative insulin sensitivity check index (QUICKI ϭ 1/[log(I 0 ) ϩ log(G 0 )]) that has substantially better correlation with SI Clamp (r ϭ 0.78) than the correlation we observed between SI MM and SI Clamp .”

• Also stated in: § Correlations between indexes of insulin sensitivity · plain

  “we found that QUICKI and SI Clamp were highly correlated for all subjects (r ϭ 0.78; P Ͻ 2 ϫ 10 Ϫ12 ).”

Excluding one nonobese individual with BMI 19.3 kg/m2 and fasting insulin 1.05 U/mL, the overall correlation between QUICKI and SI Clamp would be r = 0.77 (n = 34).

“If the data from this individual were excluded from the analysis, the overall correlation between QUICKI and SI Clamp would be r ϭ 0.77 (n ϭ 34).”

For 7 of the 15 diabetic subjects, minimal model analysis generated large negative values for SI MM.

“Note that for 7 of the 15 diabetic subjects, minimal model analysis generated large negative values for SI MM (implying that rises in insulin somehow cause glucose levels to increase in these”

The 7 excluded diabetic subjects had higher levels of insulin resistance than the other diabetic subjects as assessed by glucose clamp.

“Indeed, these 7 excluded subjects had higher levels of insulin resistance than the other diabetic subjects (as assessed by glucose clamp).”

The insulin response was markedly diminished in the diabetic subjects.

“whereas the insulin response was markedly diminished in the diabetic subjects.”

Equally strong correlations with SI Clamp were obtained when fasting data from either the glucose clamp or FSIVGTT studies were used to calculate QUICKI.

“Furthermore, QUICKI provides a reproducible and robust estimate of insulin sensitivity, because equally strong correlations with SI Clamp were obtained when fasting data from either the glucose clamp or FSIVGTT studies were used to calculate QUICKI.”

For nine nonobese subjects, glucose clamps with high and low insulin infusion rates correlated well (r = 0.69).

“The correlation between glucose clamps with high and low insulin infusion rates for these nine subjects was very good (r ϭ 0.69; P Ͻ 0.04).”

QUICKI could be identified for all study subjects.

“Importantly, QUICKI could be identified for all study subjects, and the correlation of QUICKI with glucose clamp measures of insulin sensitivity was significantly better than the correlation between SI MM and the glucose clamp.”

The overall correlation between QUICKI and SI Clamp was significantly better than that obtained between SI MM and SI Clamp.

“More importantly, the overall correlation between QUICKI and SI Clamp was significantly better than that obtained between SI MM and SI Clamp .”

In subgroup comparisons between QUICKI and SI Clamp, the correlations for obese and diabetic subjects (r = 0.89 and 0.7, respectively) were similar to the overall correlation.

“Interestingly, when we performed subgroup comparisons between QUICKI and SI Clamp , the correlations for the obese and diabetic subjects (r ϭ 0.89 and 0.7, respectively) were similar to the overall correlation.”

For nonobese and obese subjects, fasting insulin levels correlated well with SI Clamp.

“For nonobese and obese subjects, we discovered that fasting insulin levels correlated well with SI Clamp .”

For nine nonobese subjects studied at the lower insulin infusion rate, QUICKI and SI Clamp correlated r = 0.30.

“When QUICKI and SI Clamp were compared for the nine nonobese subjects who were studied at the lower insulin infusion rate, r ϭ 0.30.”

The overall correlation between SI Clamp and QUICKI was significantly better than the overall correlation between SI Clamp and HOMA (P < 0.004).

“the overall correlation between the gold standard SI Clamp and QUICKI was still significantly better than the overall correlation between SI Clamp and HOMA (P Ͻ 0.004).”

The overall correlation between QUICKI and SI MM (r = 0.52) was comparable to the correlation between SI Clamp and SI MM.

“When QUICKI was compared with SI MM , we found that the overall correlation between these two indexes (r ϭ 0.52; P Ͻ 2 ϫ10 Ϫ5 ) was comparable to the correlation obtained between SI Clamp and SI MM (Fig. 5).”

For the 49 subjects with complete information on all three methods, r = 0.74 for the comparison between QUICKI and SI Clamp.

“In this case, r ϭ 0.74 for the comparison between QUICKI and SI Clamp .”

When the same subjects underwent FSIVGTT and minimal model analysis, the obese group seemed to have the greatest level of insulin resistance.

“In contrast, when the same subjects underwent FSIVGTT and minimal model analysis, the obese group seemed to have the greatest level of insulin resistance.”

QUICKI derived from a single blood sample correlated with SI Clamp (r = 0.71).

“Similar results were obtained when QUICKI was derived from a single blood sample (r ϭ 0.71; P Ͻ 6 ϫ 10 Ϫ10 ).”

Log transformation of fasting insulin data was even more highly correlated with SI Clamp.

“Moreover, because fasting insulin levels had a skewed distribution, log transformation of these data was even more highly correlated with SI Clamp .”

The correlation between log [HOMA] and QUICKI was very high (r = 0.98).

“Indeed, the correlation between log [HOMA] and QUICKI was very high (r ϭ 0.98).”

The correlation between QUICKI and SI Clamp is significantly better than the correlation between HOMA and SI Clamp.

“Nevertheless, the correlation between QUICKI and SI Clamp is significantly better than the correlation between HOMA and SI Clamp .”

In the study population, QUICKI was more accurate than either SI MM or HOMA.

“Furthermore, in our study population, QUICKI was more accurate than either SI MM or HOMA and displayed excellent reproducibility.”

HOMA is not linear over wide ranges of insulin sensitivity.

“Furthermore, it is clear that HOMA is not linear over wide ranges of insulin sensitivity, because the slopes of the linear regression lines for each subgroup change and generally correlate with the insulin sensitivity of each subgroup.”

The correlation between HOMA and SI Clamp for all subjects was r = 0.60.

“The dashed line represents the linear regression between SI Clamp and HOMA for all subjects (r ϭ 0.60; P Ͻ 2 ϫ10 Ϫ4 ).”

QUICKI was able to estimate insulin sensitivity for all diabetic subjects, unlike the minimal model approach.

“In addition, the ability to calculate QUICKI does not depend on a robust insulin secretory capacity, and we were able to use this method to estimate insulin sensitivity for all of our diabetic subjects (as opposed to the minimal model approach).”

Of the three alternatives to the glucose clamp method for estimating insulin sensitivity in vivo examined in this study, QUICKI had the best overall linear correlation with the gold standard clamp measurement.

“Of the three alternatives to the glucose clamp method for estimating insulin sensitivity in vivo that we examined in this study, QUICKI had the best overall linear correlation with the gold standard clamp measurement.”

• AssumptionRests on an assumption: The glucose clamp is the gold standard method for directly measuring insulin sensitivity in vivo.

QUICKI can be obtained from a fasting blood sample.

“In contrast to the multiple frequent blood samples and the lengthy time course required for both the glucose clamp and the minimal model approach, QUICKI can be obtained from a fasting blood sample.”

log[HOMA] correlates very highly with QUICKI.

“Indeed, log [HOMA] correlates very highly with QUICKI.”

Insulin resistance is associated with obesity, hypertension, dyslipidemias, and coronary artery disease.

“In addition, insulin resistance is associated with a number of other diseases, including obesity, hypertension, dyslipidemias, and coronary artery disease (3)(4)(5)(6).”

• cites (3)(4)(5)(6)Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

HOMA does not vary linearly across wide ranges of insulin sensitivity and patient groups.

“These results suggest that HOMA does not vary linearly across wide ranges of insulin sensitivity and patient groups.”

Variability in insulin determinations due to limitations in assay sensitivity causes a larger percentage of errors in QUICKI when insulin levels are lowest, typical of the most insulin-sensitive subjects.

“The most likely explanation for this finding is that variability in insulin determinations due to limitations in assay sensitivity causes larger percentage of errors in QUICKI when insulin levels are lowest (typical of the most insulin-sensitive subjects).”

In fasted nondiabetic subjects, normal glucose levels are maintained by appropriately adjusting insulin levels to match the degree of insulin sensitivity.

“This result is consistent with the reasoning that fasted nondiabetic subjects are in a steady state in which normal glucose levels are maintained by appropriately adjusting insulin levels to match the degree of insulin sensitivity.”

The hyperinsulinemic euglycemic glucose clamp technique is the gold standard for quantifying insulin sensitivity in vivo.

“The hyperinsulinemic euglycemic glucose clamp technique is the “gold standard” for quantifying insulin sensitivity in vivo because it directly measures the effects of insulin to promote glucose utilization under steady state conditions (7,8).”

• cites [7] DeFronzo, 1979, Glucose clamp technique: a method for quantifying insulin secretion and resistance, Am J Physiol. sourceNot checked — no full text of the source
• cites [8] Andres, 1966, Manual feedback technique for the control of blood glucose concentration. In: Skeggs Jr LT, edNot matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

The relationship between fasting insulin and SI Clamp is not maintained for diabetic subjects who have fasting hyperglycemia and are unable to appropriately secrete insulin to fully compensate for their insulin resistance.

“However, this relationship between fasting insulin and SI Clamp is not maintained for diabetic subjects who have fasting hyperglycemia and are unable to appropriately secrete insulin to fully compensate for their insulin resistance.”

The minimal model index of insulin sensitivity (SI MM) generally correlates with glucose clamp measurements.

“Furthermore, although the minimal model index of insulin sensitivity (SI MM ) generally correlates with glucose clamp measurements (Table 1), identification of SI MM in subjects with impaired insulin secretion (e.g.patients with diabetes) is often problematic (12).”

• cites Table 1Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

The FSIVGTT is not ideal for large studies because it requires obtaining approximately 30 blood samples over 3 h.

“Al-though this approach is less labor intensive than the glucose clamp, the FSIVGTT is still not ideal for large studies because it requires obtaining approximately 30 blood samples over 3 h.”

The good correlation between studies performed at low and high insulin infusion rates suggests that the higher insulin infusion rate was also appropriate for the nonobese subjects.

“The good correlation we obtained between studies performed at low and high insulin infusion rates suggests that the higher insulin infusion rate was also appropriate for the nonobese subjects.”

The choice of a high insulin infusion rate did not introduce significant error into SI Clamp estimates in nonobese subjects.

“Nevertheless, as discussed above, the good correlation between SI Clamp derived from high and low insulin infusion rates suggests that our choice of high insulin infusion rate did not introduce significant error into SI Clamp estimates in nonobese subjects.”

The apparent greatest level of insulin resistance in the obese group by minimal model analysis is most likely due to the fact that 7 of 15 diabetic subjects had to be excluded from analysis.

“This is most likely due to the fact that 7 of 15 diabetic subjects had to be excluded from analysis because the minimal model was unable to identify meaningful estimates of insulin sensitivity in these cases.”

The large negative SI MM values are a well documented artifact of the minimal model that occurs when data from subjects with poor insulin secretion are analyzed.

“This is a well documented artifact of the minimal model that occurs when data from subjects with poor insulin secretion are analyzed (12).”

• cites [12] Saad, 1994, A comparison between the minimal model and the glucose clamp in the assessment of insulin sensitivity across the spectrum of glucose tolerance. Insulin Resistance Atherosclerosis Study., Diabetes sourceNot checked — no full text of the source

The glucose clamp is not easily applied in large scale investigations because it requires iv insulin infusion, frequent blood samples over 3 h, and continuous adjustment of a glucose infusion.

“However, the glucose clamp is not easily applied in large scale investigations because iv infusion of insulin, frequent blood samples over a 3-h period, and continuous adjustment of a glucose infusion are required for each subject studied.”

Recent studies have demonstrated systematic errors in minimal model estimates of glucose effectiveness and insulin sensitivity that may be due to oversimplified model representations of physiology.

“Moreover, recent studies have demonstrated systematic errors in minimal model estimates of glucose effectiveness and insulin sensitivity that may be due to oversimplified model representations of physiology (13)(14)(15).”

• cites (13)(14)(15)Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

Identification of SI MM in subjects with impaired insulin secretion, such as patients with diabetes, is often problematic.

“identification of SI MM in subjects with impaired insulin secretion (e.g.patients with diabetes) is often problematic (12).”

• cites [12] Saad, 1994, A comparison between the minimal model and the glucose clamp in the assessment of insulin sensitivity across the spectrum of glucose tolerance. Insulin Resistance Atherosclerosis Study., Diabetes sourceNot checked — no full text of the source

QUICKI contains additional independent information about insulin sensitivity that is not captured by the minimal model approach.

“Taken together with the fact that the overall correlation between QUICKI and SI Clamp was also better than the correlation between QUICKI and SI MM , our results suggest that QUICKI contains additional independent information about insulin sensitivity that is not captured by the minimal model approach.”

QUICKI is a novel method for determining insulin sensitivity in humans.

“QUICKI is a novel, simple, accurate, and reproducible method for determining insulin sensitivity in humans that may be a useful tool in large epidemiological investigations that study the role of insulin resistance in the pathophysiology of important public health problems such as obesity, cardiovascular diseases, and diabetes.”

The ability to calculate QUICKI does not depend on a robust insulin secretory capacity.

“In addition, the ability to calculate QUICKI does not depend on a robust insulin secretory capacity, and we were able to use this method to estimate insulin sensitivity for all of our diabetic subjects (as opposed to the minimal model approach).”

QUICKI may be a more accurate index of insulin sensitivity than HOMA across a broad range of insulin sensitivities.

“This suggests that transformation of the data is beneficial for estimating insulin sensitivity and that QUICKI may be a more accurate index of insulin sensitivity than HOMA across a broad range of insulin sensitivities.”

Transformation of the data is beneficial for estimating insulin sensitivity.

“This suggests that transformation of the data is beneficial for estimating insulin sensitivity and that QUICKI may be a more accurate index of insulin sensitivity than HOMA across a broad range of insulin sensitivities.”

• AssumptionRests on an assumption: The distribution of fasting insulin values is skewed.

QUICKI may be a useful tool in large epidemiological investigations that study the role of insulin resistance in the pathophysiology of important public health problems such as obesity, cardiovascular diseases, and diabetes.

“QUICKI is a novel, simple, accurate, and reproducible method for determining insulin sensitivity in humans that may be a useful tool in large epidemiological investigations that study the role of insulin resistance in the pathophysiology of important public health problems such as obesity, cardiovascular diseases, and diabetes.”

The study was unable to determine whether QUICKI is applicable to subjects with severe diabetes who could not be safely taken off their antidiabetic medications.

“In addition, we were unable to determine whether QUICKI is applicable to subjects with severe diabetes who could not be safely taken off of their antidiabetic medications.”