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Diabetes and Metabolism Research Unit, Ottawa Hospital and the University of Ottawa, Ottawa, Canada K1Y 4E9
Address correspondence and requests for reprints to: Dr. Jerry Radziuk, Ottawa Hospital (Civic Campus), 1053 Carling Avenue, Ottawa, Ontario, Canada K1Y 4E9. E-mail: jradziuk{at}ottawahospital.on.ca
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Abstract |
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Introduction |
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TopAbstractIntroductionInsulin and its effect...The hyperinsulinemic euglycemic...Methods based on the...HOMA (12)QUICKIAdditional consideration:...The hyperinsulinemic euglycemic...Additional consideration:...DiscussionReferences |
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Glucose tolerance is an expression of the efficiency with which homeostatic mechanisms restore glycemia to basal levels after a perturbation. Clinically, the most common assessment is following an oral glucose load, a surrogate for a more physiological meal. The homeostatic response includes an increase in the insulin levels and, therefore, also the insulin-dependent processes that lower glycemia. Theoretically, the oral glucose tolerance test should yield an estimate of insulin sensitivity, if insulin concentrations are measured. Indeed, a number of formulae have been developed both in the past (e.g. Ref. 2) as well as more recently (e.g. Refs. 3, 4, 5). After oral glucose or meals, the increments in insulin do not depend entirely on glucose, but also on such factors as gut hormones and neural stimulation, the insulin response deviates from the purely glucose-dependent pattern. Glucose concentrations also change in a manner that is partly dependent on insulin, but also partly on gastric emptying and absorption. In general, therefore, attempts have been made to isolate the glucose-insulin relationship, as much as possible, from other factors.
In the broadest sense, there seems to be two approaches to the measurement of insulin sensitivity: the dynamic intervention (glucose, insulin, and tolbutamide injection or infusion) and the steady-state (usually fasting) assessment. Needless to say, the steady-state situation (when it truly exists) is the culmination of the evolution of processes that bring the glucose system back to a set point, more or less quickly, after a perturbation. The two situations are, therefore, related. One can also characterize the approaches by whether they are "open loop" or "closed loop," that is, whether they evaluate the action of insulin (often exogenous) on a specific parameter, or invoke a more self-contained metabolic model that incorporates a description of the feedback relationships between insulin and glucose. In the first category, we include: the hyperinsulinemic glucose clamp (6), the iv glucose tolerance test (IVGTT; Refs. 7 and 8) approaches, and the insulin tolerance (9) or suppression tests (10). In the second category we have continuous infusion of glucose with model assessment (11), homeostasis model assessment (HOMA; Ref. 12), and now QUICKI (1). It may be of note that the first category also includes dynamic interventions, and the second, perhaps because the closed loop formulation is better equipped to describe the evolution of processes to a steady-state, is often based on fasting measurements.
The correlations between measures of insulin sensitivity (SI) by apparently disparate methods have often been shown to be quite good. As already stated, a reasonable hypothesis might be that this arises, at least partly, from the fact that the methods are built on a common description of the glucose-insulin system. Without elaborating the complete mathematical solutions, we shall attempt to show the basis for this conclusion. Although it likely applies to most of the methods currently in use, we shall focus primarily on the methods discussed in (1).
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Insulin and its effect compartment |
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describes simple one-compartment kinetics for
glucose. It states that the changes in glucose concentration are
determined by the balance between the influx of glucose into the system
(liver, kidney, exogenous administration) and its efflux from the
system. k (which is equivalent to the metabolic clearance of
glucose divided by V) is the parameter that determines how
efficiently glucose is removed from the circulation. It depends on all
the factors that may alter glucose disposal, particularly insulin, but
also glucose. Eq II is an expression of the effect compartment for
insulin: insulin exerts its effect on k. k, however, is not
proportional to i but rather to insulin in a compartment,
remote from circulating insulin. Effect compartments such as this one
have frequently been used in the pharmacodynamic description of drug
actions (e.g. Ref. 13), mediated, for example,
by active metabolites or at sites distal to the circulation. Initially,
they were applied to insulin in the context of euglycemia (14, 15), which is important in the interpretation of clamp data. It
has been suggested, moreover, that for insulin the remote or effect
compartment might be insulin that has penetrated the endothelial
barrier and is present in the interstituum and, therefore, available
for binding to cell-surface receptors (16). |
The hyperinsulinemic euglycemic clamp (6) |
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and II reduce to the simple expression
given by Eq III. During a clamp, insulin is administered as a constant infusion and, therefore, does not reflect the variations inherent in endogenous secretion. Moreover, also unlike the physiological case, insulin is given peripherally, which reverses the normal gradient between portal and peripheral insulin. Finally, the peripheral and the hepatic responses to insulin are assumed to occur in parallel, which, based on the known dose responses, is not likely to occur (17). Nevertheless, because glycemia is kept constant, Ginf and, therefore, k depend only on i, and the ratio is considered as the most reliable measure of SI. It has been widely applied and provides good discrimination between normal subjects and those with insulin resistance (18).
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Methods based on the IVGTT |
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and II. This
model also included an insulin-independent component and was termed the
"minimal model," because it was the mathematical model with the
fewest parameters that was found to provide a good fit to the data—and
the fits to data are remarkably good (e.g. Ref.
7). It should be noted that the fits are obtained using,
for example, nonlinear least squares techniques: parameters are varied
according to a defined strategy and are assigned a final value that
minimizes the sum of squares of differences between the data and the
glucose and insulin values predicted by the model (which is nonlinear)
for any parameter set. The goodness of fit and, therefore, the
reliability of the parameters can then be evaluated statistically
(7, 16). The SI is calculated from the ratio of a2 and a1 (Eq III), parameters that are determined from the model fit. It can be seen that the expression for SI is identical to that which is derived from the clamp technique. It is not surprising, therefore, that good correlations between the methods have been found.
The dynamic and physiological nature of this test and the relative simplicity of its performance, count among its attractive features. Differences and potential problems arise from the same source: the rapid dynamics may confound transients based on the distribution of glucose throughout the system and those due to glucose removal. This and the wide range over which rapid changes in glucose concentration occur, may induce nonlinearities in the system that are not accounted for by the model, such as renal glucose removal. These may, in turn, obscure changes in slope, from which k is obtained, particularly in the context of highly resistant states such as advanced type 2 diabetes, where both the signal (insulin) and the response may be small. To counter this problem, the signal was enhanced using iv administration either of tolbutamide (19) or of insulin (20), 20 min after the glucose injection. This allowed identification of the insulin-independent part of the process from data obtained before tolbutamide or insulin administration, and provided a stronger signal for the insulin-dependent processes afterward. The expression for SI, however, remains the same with the change of protocol. As indicated (1), as well as in other work (21), some discrimination may be lost within the diabetic population because a proportion of the SI becomes less than or equal to zero. Two-pool or higher order descriptions of glucose dynamics and the use of tracers were suggested (22, 23) as possible solutions to such difficulties.
It can be seen that potential changes made to accommodate the widest range of sensitivities possible may render the protocol and the analysis somewhat more complex. This was partially alleviated by reducing the number of samples necessary (24), at least in the context of population studies. It should be pointed out that the methodology remains consistent since the undetectable SI do correspond to very low responses to the insulin signal and, therefore, severe insulin resistance. However, in going from the situation where glycemia is maintained constant with a glucose clamp to that where it is variable, additional assumptions must be made: that the glucose concentrations themselves do not contribute to the dynamics in a nonlinear fashion and that the insulin acts in a uniform manner on all relevant tissues at all concentrations. It is possible that one of these assumptions may not apply at the limits of the range of sensitivities considered. It may, therefore, be difficult to describe this system in an identifiable way, over the entire range of SI, given a limited data set. The sources of these problems, however, also embody the potential of this model: much more development can be done, using this model, in exploring the detailed dynamics of glucose and insulin and the causes of insulin resistance.
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HOMA (12) |
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It was demonstrated in a number of publications (11, 12, 25, 26) that the correlations between derivatives of this formula and clamp-derived SI are surprisingly good considering the simplicity of the formula. Let us examine the possible reasons why.
The starting point of this method was the development of a comprehensive mathematical model of glucose-insulin homeostasis (11, 12). This was based either on a series of functional forms (27) or equations (11) that depicted the nonlinearities inherent in the system. If values based on literature data were assumed for most of the parameters, then glucose and insulin data during a constant glucose infusion over 60 min could be fitted in individual studies by adjusting "insulin resistance" and "ß-cell function" parameters as a fraction of the preset ideal normal case. Because of its comprehensive and closed-loop nature the model could not only predict the evolution of glucose and insulin levels in response to the glucose infusion, but could predict their final steady-state, fasting concentrations (12). Simulations were then used to generate an array of fasting glucose and insulin levels that would be expected for different degrees of ß-cell deficiency and insulin resistance. Conversely, given fasting glucose and insulin concentrations, unique values of relative ß-cell function and insulin resistance can be read from the grid. The approximate formula (Eq IV) is also derived from this graphic representation (12). This approximation has been widely used, although the authors do recommend using the full equations (28).
Interestingly, there is another perspective from which Eq IV may be derived, based only on the assumptions made in the development of the homeostatic approach and Eqs I–III. The basic rationale for the model is stated (12) as: "The basal hyperglycemia of diabetes may be considered as a compensatory response with a major role in maintaining sufficient insulin secretion, from a reduced ß-cell capacity, to control hepatic glucose efflux." Interestingly, precisely the same principle was used (29) to explain the well known increase in insulin concentrations following pancreas transplantation with peripheral venous drainage or the diversion of pancreatic venous drainage from the portal vein to the systemic circulation either by surgical intervention or possibly due to porto-systemic shunting in cirrhosis (30, 31): peripheral insulin concentrations needed to be maintained at levels sufficiently high to generate portal concentrations which can maintain normal basal glucose production. It has also been stated that hyperglycemia and hyperinsulinemia are necessary in the insulin resistant state, to maintain near-normal peripheral glucose uptake when metabolic glucose clearance at a specific inuslin concentration is decreased because of insulin resistance (32). This was supported by muscle biopsies in insulin-resistant humans, showing normalization of glycogen synthesis and synthase activity in the presence of hyperinsulinemia and hyperglycemia (23). Under steady-state conditions then, the feedback loop will both compensate for insulin resistance with higher insulin levels and ensure high enough glycemia to stimulate the higher insulin. Let us see how this can be expressed more quantitatively using Eqs I–III.
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The homeostatic principle quoted asserts that the goal of the
system is to maintain the same rates of basal glucose production (and
utilization) in a test subject; for example, one with diabetes (no
subscript) as in a defined normal (n).
This implies:
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It has sometimes been concluded that the HOMA index does not correlate
well with other measures of insulin sensitivity as can be seen in Fig.
6 of Ref. 1 [the fact that it is (-HOMA) does not change matters].
It is critical to note, however, that HOMA is an index of insulin
resistance (identical to RHOMA), and, as
demonstrated above, an index of resistance will be the inverse of the
corresponding index of sensitivity. It is not surprising, therefore,
that when the HOMA index is plotted against, for example,
SIclamp, the curve is hyperbolic
(26, 27). On the other hand, when ln(HOMA) is plotted
against ln(SIclamp) (or glucose disposal
at euglycemia), the correlation improves dramatically
(26). This is because of the following set of
relationships:
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where A is a constant factor between two SI indices, usually based on the different units used. Clearly, the correlation coefficient improves because the nonlinear hyperbolic relationship is transformed into a linear one. Because ln(SIclamp) is related to SIclamp, even the correlation between SIclamp and ln(RHOMA) will improve, although the correlation coefficient is likely to be intermediate.
The common background of all three models used for comparison in Ref. 1 is the most likely explanation for the good correlations between these methods frequently seen, when the comparison is performed appropriately. Divergence likely arises because of the different additional assumptions made when moving away from the clamp technique. This has already been discussed for the IVGTT/minimal model approaches. For HOMA, the differences lie in the basal nature of the assessment, which is consequently focused somewhat more on the liver than the other methods. It is also dependent on a homeostatic principle that asserts that the maintenance of a fixed basal glucose turnover rate is the primary goal of the system. Because only a basal measurement is used, it is critical and, as indicated by the authors (12), should entail an average of sufficient samples to take into account noise and the pulsatile nature of insulin secretion and concentrations. Although not always found, the reported parallelism between the estimates, at least under steady-state conditions, is nevertheless striking.
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QUICKI |
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The authors point out that the correlation coefficient between the log(RHOMA) and QUICKI is 0.98. Based on the discussion above, the correlation might more likely be between 1/QUICKI and log(RHOMA) or log (HOMA), where HOMA is identical to RHOMA. It is nevertheless interesting and important that QUICKI, which although more empirically derived, correlates well with SIclamp since it, indeed, corresponds to a measure of sensitivity.
Because the HOMA index may not always have been optimally compared with
other indices, as discussed, and because the same may be true in Ref.
1 , it remains to be seen whether QUICKI offers real
advantages compared with HOMA. It is suggested by Katz et
al. (1) that the logarithmic transformations are used
to normalize a skewed distribution of insulin values. Again, this might
be because insulin levels, in themselves, are indicators of insulin
resistance (e.g. Ref. 34) rather than
sensitivity and, therefore, should be inversely related. The fact that
HOMA and QUICKI might well be nearly equivalent is shown in Fig. 1
, based on individual data from Hosker
et al. (11), where both resistance and
sensitivity measures (HOMA) and QUICKI are compared with
SIclamp. Although the statistical analysis
is not done since this is for discussion only, it is clear that when
sensitivity is compared with sensitivity, the correlations will not
likely vary to a great degree between the two measures compared. The
work of Katz et al. (1) is, however, important
because it clearly demonstrates that the comparisons were not always
done appropriately and emphasizes that fasting measures are likely
useful in examining insulin sensitivity among populations. Certainly,
investigators will have ample opportunity to compare the two indices
because the same data are used for both.
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Additional consideration: insulin-independent component of glucose removal |
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The steady-state methods. Of the methods discussed here, these
include clamp-based techniques, HOMA, and QUICKI. In Eq I, let us first
define
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From Eq III, the appropriate relationship between
k and i becomes
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Under basal conditions, the same equality holds:
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The hyperinsulinemic euglycemic clamp |
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k) as well as
insulin-induced decreases in glucose production, which are, therefore,
included in this estimate. Although an expression of insulin
sensitivity, unless Vk >> suppression of
Ra, it does not represent a pure
peripheral insulin sensitivity.
HOMA. The expression of relative insulin sensitivity is
derived under steady-state conditions (Eq VII). To account for
kg, we use Eqs III, XII, and XIV to
write:
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Although the derivation of QUICKI is more empirical, similar considerations apply as for HOMA: this measure is also likely to be more accurate when glycemia is near normal and ß-cell function has not deteriorated greatly.
Nonsteady-state methods. In this review, this has been represented by the minimal model analysis of the IVGTT. Under these conditions, it is more difficult to decouple the insulin-dependent and -independent terms. These are estimated as SI and SG, respectively (38). SG is estimated as the effectiveness of glucose at a basal insulin concentration (38, 39), which means that it includes a component of insulin sensitivity (39). This may contribute to the explanation of why SG was found to be a function of insulin release (40). Thus, SG is, in general, overestimated (22), with the result that there is a compensatory underestimation of the effects of incremental insulin, or SI. (41), also perhaps helping to explain why estimates of SI are lower than expected in insulin-resistant subjects (21). This was addressed by calculating glucose effectiveness at zero insulin (GEZI = SG - SI i, ref 39). This helps to resolve the problem but does not alter any changes that may have occurred in SI. Although, it has been suggested that the effect of insulin on the periphery and the liver may occur at least partially in parallel (16); any deviation from such behavior could also confound the estimates, a problem that is largely avoided in steady state because all fluxes are then constant.
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Additional consideration: assessment of peripheral and hepatic insulin resistance |
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This approach has been routinely applied in clamp studies (18), in the context of more intensive investigations. It has also been demonstrated to provide improved estimates of SI when the minimal model approach to the IVGTT is used (23). In the former case, a (usually primed) infusion of glucose tracer is started before the clamp and, a basal measurement of the metabolic clearance rate (MCRg) of glucose is obtained when concentrations of tracer are constant. This is calculated as the rate of tracer infusion divided by its plasma concentration. A primed infusion of insulin is then initiated, glucose infused at rates appropriate to clamp the levels, and, once steady state of the tracer concentration and glucose infusion rate are again reached, a second measurement of MCRg is made. The rate of endogenous glucose production (Rae) is obtained by first calculating total Ra (MCRg g, where g is again the glucose concentration). Under basal conditions this is the Rae. During the clamp Rae is obtained by subtracting the (steady-state) rate of glucose infusion from the total Ra. Suppression of basal Rae by insulin is obtained by comparing Rae under clamp and basal conditions. Tracer is frequently added to the variable glucose infusion used for clamping to maintain near-constant plasma ratios of tracer to glucose (44, 45). This should not be necessary if true tracer steady-state is reached during the clamp, but may help in calculating changing Rae more accurately (44, 45), during the transient period, particularly if model order is not optimal. The importance of reaching tracer and glucose steady-state both under basal conditions and during clamping must also be emphasized (46, 47).
Although the same tracer infusion protocol could be used with the IVGTT, adding the tracer to the injected glucose has been demonstrated to yield reasonable estimates of Rae following iv glucose injection (48).
Clearly, the use of tracers enables the separate assessment of the effect of insulin on the liver and on the periphery. With more care (and samples), time courses of these changes can be separately determined. Using clamp techniques, dose-response curves to insulin were developed for both the glucose production and clearance, demonstrating the increased sensitivity of the liver to insulin relative to the periphery (17).
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Discussion |
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It is also worth reemphasizing that, in general, insulin sensitivity and resistance measures are related in an inverse fashion and that correlations should be examined among sensitivities or among resistance measurements. Thus, although logarithmic transformations provide reasonable comparisons, the most straightforward comparison between a different index of sensitivity and the HOMA index, which is a measure of insulin resistance, is obtained by first inverting it so that it is also expressed as a sensitivity.
To enhance the information obtained using a given method, tracers can be added, arterio-venous differences measured across organs, various tissue biopsies performed, and different metabolites determined. The choice of approach that is most appropriate in a particular experimental situation is therefore not made on the basis of the relative validity of the basic methods discussed, since all are valid, within the framework of their assumptions. Rather it should be made, based on the goals of a particular study, the size and kind of the population, the interventions which are feasible and precisely what metabolic relationships are to be examined. A balance must be drawn between the interpretative restrictions imposed by the assumptions inherent in a particular method and the experimental or clinical situation.
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Acknowledgments |
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Footnotes |
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Received June 14, 2000.
Revised August 15, 2000.
Accepted August 25, 2000.
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M. Ciampelli, F. Leoni, F. Cucinelli, S. Mancuso, S. Panunzi, A. De Gaetano, and A. Lanzone Assessment of Insulin Sensitivity from Measurements in the Fasting State and during an Oral Glucose Tolerance Test in Polycystic Ovary Syndrome and Menopausal Patients J. Clin. Endocrinol. Metab., March 1, 2005; 90(3): 1398 - 1406. [Abstract] [Full Text] [PDF]
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A. Ceriello, D. Johns, M. Widel, D. J. Eckland, K. J. Gilmore, and M. H. Tan Comparison of Effect of Pioglitazone With Metformin or Sulfonylurea (Monotherapy and Combination Therapy) on Postload Glycemia and Composite Insulin Sensitivity Index During an Oral Glucose Tolerance Test in Patients With Type 2 Diabetes Diabetes Care, February 1, 2005; 28(2): 266 - 272. [Abstract] [Full Text] [PDF]
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H. Ho, A. Pinto, S. D. Hall, D. A. Flockhart, L. Li, T. C. Skaar, P. Cadman, D. T. O'Connor, U. Wagner, N. S. Fineberg, et al. Association Between the CYP3A5 Genotype and Blood Pressure Hypertension, February 1, 2005; 45(2): 294 - 298. [Abstract] [Full Text] [PDF]
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J. He and D. E. Kelley Muscle glycogen content in type 2 diabetes mellitus Am J Physiol Endocrinol Metab, November 1, 2004; 287(5): E1002 - E1007. [Abstract] [Full Text] [PDF]
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 E. D. Werner, J. Lee, L. Hansen, M. Yuan, and S. E. Shoelson Insulin Resistance Due to Phosphorylation of Insulin Receptor Substrate-1 at Serine 302 J. Biol. Chem., August 20, 2004; 279(34): 35298 - 35305. [Abstract] [Full Text] [PDF]
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M. Misra, K. K. Miller, C. Almazan, K. Ramaswamy, A. Aggarwal, D. B. Herzog, G. Neubauer, J. Breu, and A. Klibanski Hormonal and Body Composition Predictors of Soluble Leptin Receptor, Leptin, and Free Leptin Index in Adolescent Girls with Anorexia Nervosa and Controls and Relation to Insulin Sensitivity J. Clin. Endocrinol. Metab., July 1, 2004; 89(7): 3486 - 3495. [Abstract] [Full Text] [PDF]
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B. S. Fuehrlein, M. S. Rutenberg, J. N. Silver, M. W. Warren, D. W. Theriaque, G. E. Duncan, P. W. Stacpoole, and M. L. Brantly Differential Metabolic Effects of Saturated Versus Polyunsaturated Fats in Ketogenic Diets J. Clin. Endocrinol. Metab., April 1, 2004; 89(4): 1641 - 1645. [Abstract] [Full Text] [PDF]
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S. G. Albert and A. D. Mooradian Low-Dose Recombinant Human Growth Hormone as Adjuvant Therapy to Lifestyle Modifications in the Management of Obesity J. Clin. Endocrinol. Metab., February 1, 2004; 89(2): 695 - 701. [Abstract] [Full Text] [PDF]
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R. Z. Lewanczuk, B. W. Paty, and E. L. Toth Comparison of the [13C]Glucose Breath Test to the Hyperinsulinemic-Euglycemic Clamp When Determining Insulin Resistance Diabetes Care, February 1, 2004; 27(2): 441 - 447. [Abstract] [Full Text] [PDF]
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C. L. Laffer, M. Laniado-Schwartzman, A. Nasjletti, and F. Elijovich 20-HETE and Circulating Insulin in Essential Hypertension With Obesity Hypertension, February 1, 2004; 43(2): 388 - 392. [Abstract] [Full Text] [PDF]
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K. Gertow, M. Bellanda, P. Eriksson, S. Boquist, A. Hamsten, M. Sunnerhagen, and R. M. Fisher Genetic and Structural Evaluation of Fatty Acid Transport Protein-4 in Relation to Markers of the Insulin Resistance Syndrome J. Clin. Endocrinol. Metab., January 1, 2004; 89(1): 392 - 399. [Abstract] [Full Text] [PDF]
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 L. Chen and B. L. G. Nyomba Whole body insulin resistance in rat offspring of mothers consuming alcohol during pregnancy or lactation: comparing prenatal and postnatal exposure J Appl Physiol, January 1, 2004; 96(1): 167 - 172. [Abstract] [Full Text] [PDF]
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D. Tripathy, E. Lindholm, B. Isomaa, C. Saloranta, T. Tuomi, and L. Groop Familiality of metabolic abnormalities is dependent on age at onset and phenotype of the type 2 diabetic proband Am J Physiol Endocrinol Metab, December 1, 2003; 285(6): E1297 - E1303. [Abstract] [Full Text] [PDF]
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 J. Sheeder, S. H. Travers, and C. Stevens-Simon Is This Patient Insulin Resistant? How Much Does It Matter? Clinical Pediatrics, November 1, 2003; 42(9): 835 - 839. [PDF]
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 V. De Leo, A. la Marca, and F. Petraglia Insulin-Lowering Agents in the Management of Polycystic Ovary Syndrome Endocr. Rev., October 1, 2003; 24(5): 633 - 667. [Abstract] [Full Text] [PDF]
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A. Gavrila, J. L. Chan, N. Yiannakouris, M. Kontogianni, L. C. Miller, C. Orlova, and C. S. Mantzoros Serum Adiponectin Levels Are Inversely Associated with Overall and Central Fat Distribution but Are Not Directly Regulated by Acute Fasting or Leptin Administration in Humans: Cross-Sectional and Interventional Studies J. Clin. Endocrinol. Metab., October 1, 2003; 88(10): 4823 - 4831. [Abstract] [Full Text] [PDF]
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D. Panidis, A. Kourtis, D. Farmakiotis, T. Mouslech, D. Rousso, and G. Koliakos Serum adiponectin levels in women with polycystic ovary syndrome Hum. Reprod., September 1, 2003; 18(9): 1790 - 1796. [Abstract] [Full Text] [PDF]
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J. C. Bunt, A. D. Salbe, I. T. Harper, R. L. Hanson, and P. A. Tataranni Weight, Adiposity, and Physical Activity as Determinants of an Insulin Sensitivity Index in Pima Indian Children Diabetes Care, September 1, 2003; 26(9): 2524 - 2530. [Abstract] [Full Text] [PDF]
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S. Cianfarani, A. Maiorana, C. Geremia, G. Scire, G. L. Spadoni, and D. Germani Blood Glucose Concentrations are Reduced in Children Born Small for Gestational Age (SGA), and Thyroid-Stimulating Hormone Levels are Increased in SGA with Blunted Postnatal Catch-up Growth J. Clin. Endocrinol. Metab., June 1, 2003; 88(6): 2699 - 2705. [Abstract] [Full Text] [PDF]
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B. L. Herrmann, B. Saller, O. E. Janssen, P. Gocke, A. Bockisch, H. Sperling, K. Mann, and M. Broecker Impact of Estrogen Replacement Therapy in a Male with Congenital Aromatase Deficiency Caused by a Novel Mutation in the CYP19 Gene J. Clin. Endocrinol. Metab., December 1, 2002; 87(12): 5476 - 5484. [Abstract] [Full Text] [PDF]
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L. A. Stadtmauer, B. C. Wong, and S. Oehninger Should patients with polycystic ovary syndrome be treated with metformin?: Benefits of insulin sensitizing drugs in polycystic ovary syndrome--beyond ovulation induction Hum. Reprod., December 1, 2002; 17(12): 3016 - 3026. [Abstract] [Full Text] [PDF]
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G. Perseghin, A. Caumo, L. P. Sereni, A. Battezzati, and L. Luzi Fasting Blood Sample-Based Assessment of Insulin Sensitivity in Kidney-Pancreas-Transplanted Patients Diabetes Care, December 1, 2002; 25(12): 2207 - 2211. [Abstract] [Full Text] [PDF]
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A. Katsuki, Y. Sumida, H. Urakawa, E. C. Gabazza, S. Murashima, K. Morioka, N. Kitagawa, T. Tanaka, R. Araki-Sasaki, Y. Hori, et al. Neither Homeostasis Model Assessment nor Quantitative Insulin Sensitivity Check Index Can Predict Insulin Resistance in Elderly Patients with Poorly Controlled Type 2 Diabetes Mellitus J. Clin. Endocrinol. Metab., November 1, 2002; 87(11): 5332 - 5335. [Abstract] [Full Text] [PDF]
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 K. M. I. Caron, L. R. James, H.-S. Kim, S. G. Morham, M. L. S. S. Lopez, R. A. Gomez, T. L. Reudelhuber, and O. Smithies A genetically clamped renin transgene for the induction of hypertension PNAS, June 11, 2002; 99(12): 8248 - 8252. [Abstract] [Full Text] [PDF]
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R. S. Surwit, R. B. Williams, I. C. Siegler, J. D. Lane, M. Helms, K. L. Applegate, N. Zucker, M. N. Feinglos, C. M. McCaskill, and J. C. Barefoot Hostility, Race, and Glucose Metabolism in Nondiabetic Individuals Diabetes Care, May 1, 2002; 25(5): 835 - 839. [Abstract] [Full Text] [PDF]
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M. R. Palmert, C. M. Gordon, A. I. Kartashov, R. S. Legro, S. J. Emans, and A. Dunaif Screening for Abnormal Glucose Tolerance in Adolescents with Polycystic Ovary Syndrome J. Clin. Endocrinol. Metab., March 1, 2002; 87(3): 1017 - 1023. [Abstract] [Full Text] [PDF]
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M. J. Quon Limitations of the Fasting Glucose to Insulin Ratio as an Index of Insulin Sensitivity J. Clin. Endocrinol. Metab., October 1, 2001; 86(10): 4615 - 4617. [Full Text] [PDF]
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, subjects with diabetes. Data from Ref.