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urine calcium lowering with potassium alkali can lead to “skeletal benefit.” Anthony Sebastian, Lynda Frassetto, and R. Curtis Morris, Jr. Department of Medicine (L.F., R.C.M., A.S.) and General Clinical Research Center (L.F., A.S.), University of California, San Francisco, San Francisco, California 94143

References 1. Heaney RP, Rafferty K, Davies KM 2005 Letter re: Long-term persistence of the urine calcium-lowering effect of potassium bicarbonate in postmenopausal women. J Clin Endocrinol Metab 90:4417 2. Frassetto L, Morris Jr RC, Sebastian A 2005 Long-term persistence of the urine calcium-lowering effect of potassium bicarbonate in postmenopausal women. J Clin Endocrinol Metab 90:831– 834 3. Rafferty K, Davies KM, Heaney RP 2005 Potassium intake and the calcium economy. J Am Coll Nutr 24:99 –106 4. New SA, Bolton-Smith C, Grubb DA, Reid DM 1997 Nutritional influences on bone mineral density: a cross-sectional study in premenopausal women. Am J Clin Nutr 65:1831–1839 5. New SA, Robins SP, Campbell MK, Martin JC, Garton MJ, Bolton-Smith C, Grubb DA, Lee SJ, Reid DM 2000 Dietary influences on bone mass and bone metabolism: further evidence of a positive link between fruit and vegetable consumption and bone health? Am J Clin Nutr 71:142–151 6. Tucker KL, Hannan MT, Chen H, Cupples LA, Wilson PWF, Kiel DP 1999 Potassium, magnesium, and fruit and vegetable intakes are associated with greater bone mineral density in elderly men and women. Am J Clin Nutr 69:727–736 7. Sebastian A, Harris ST, Ottaway JH, Todd KM, Morris Jr RC 1994 Improved mineral balance and skeletal metabolism in postmenopausal women treated with potassium bicarbonate. N Engl J Med 330:1776 –1781 8. Lemann Jr J, Gray RW, Pleuss JA 1989 Potassium bicarbonate, but not sodium bicarbonate, reduces urinary calcium excretion and improves calcium balance in healthy men. Kidney Int 35:688 – 695 9. Marangella M, Di Stefano M, Casalis S, Berutti S, D’Amelio P, Isaia GC 2004 Effects of potassium citrate supplementation on bone metabolism. Calcif Tissue Int 74:330 –335 10. Sellmeyer DE, Schloetter M, Sebastian A 2002 Potassium citrate prevents increased urine calcium excretion and bone resorption induced by a high sodium chloride diet. J Clin Endocrinol Metab 87:2008 –2012 11. Pak CY, Peterson RD, Poindexter J 2002 Prevention of spinal bone loss by potassium citrate in cases of calcium urolithiasis. J Urol 168:31–34 doi: 10.1210/jc.2005-0795

Letter re: Limited Accuracy of Surrogates of Insulin Resistance during Puberty in Obese and Lean Children at Risk for Altered Glucoregulation To the editor: Brandou et al. (1) report comparisons between the insulin sensitivity index (SIMM) obtained from minimal model analysis of an iv glucose tolerance test and other surrogate indexes in a cross-sectional study of peripubertal children. The authors conclude that surrogates, including quantitative insulin sensitivity check index (QUICKI), have limited accuracy and are not useful for predicting insulin resistance during puberty in children. These conclusions are based on the finding that SIMM does not correlate well with other simple surrogates. Conclusions drawn by Brandou et al. (1) are not supported by their data and are misleading. The biggest flaw in the study is the absence of a reference standard directly measuring insulin sensitivity (e.g. euglycemic hyperinsulinemic glucose clamp). SIMM is an indirect estimate obtained by fitting iv glucose tolerance test data to a mathematical model. Another problem is that Brandou et al. use a reduced data set (15 blood samples) rather than the full protocol (30 samples). Moreover, SIMM has well-documented errors in estimating insulin sensitivity (2–5). When compared with glucose clamp (SIClamp), QUICKI has substantially better correlation with SIClamp than SIMM (4, 5). Test characteristics of QUICKI, including coefficient of variation and discriminant ratio, are significantly better than Received March 10, 2005. Address correspondence to: Michael J. Quon, M.D., Ph.D., Chief, Diabetes Unit, National Center for Complementary and Alternative Medicine, National Institutes of Health, 10 Center Drive, Building 10, Room 6C-205, Bethesda, Maryland 208921632. E-mail: [email protected].

Letters to the Editor

other surrogates and are comparable to those of clamp (6). Finally, changes in QUICKI after therapeutic interventions are significantly correlated with changes in SIClamp (5, 7, 8), whereas changes in SIMM are unrelated (5). A metaanalysis of insulin-resistant subjects demonstrates that QUICKI is the best fasting surrogate index for predicting onset of diabetes (9). Thus, discordance between QUICKI and SIMM likely reflects problems with the minimal model rather than QUICKI. Others find excellent correlations between QUICKI and glucose clamp in normal, obese, and diabetic populations (6 – 8, 10, 11). Previous studies of peripubertal children have validated QUICKI against the glucose clamp in populations similar to that studied by Brandou et al. (12, 13). Brandou et al. (1) inaccurately use the term “accuracy.” An “accurate” surrogate reflects the true value of the variable being measured. Brandou et al. examine only correlations. When we evaluated the accuracy of QUICKI to predict insulin sensitivity determined by glucose clamp (14), we found that it is much more accurate than SIMM. In summary, finding that QUICKI and SIMM do not correlate well has been documented previously. However, the conclusion that QUICKI has limited accuracy in peripubertal children is incorrect. If anything, Brandou et al. (1) provide confirmation of the limited utility of the minimal model for assessing insulin sensitivity. Rajaram J. Karne, Hui Chen, Gail Sullivan, and Michael J. Quon Diabetes Unit National Center for Complementary and Alternative Medicine National Institutes of Health Bethesda, Maryland 20892

References 1. Brandou F, Brun JF, Mercier J 2005 Limited accuracy of surrogates of insulin resistance during puberty in obese and lean children at risk for altered glucoregulation. J Clin Endocrinol Metab 90:761–767 2. Quon MJ, Cochran C, Taylor SI, Eastman RC 1994 Non-insulin-mediated glucose disappearance in subjects with IDDM: discordance between experimental results and minimal model analysis. Diabetes 43:890 – 896 3. Cobelli C, Bettini F, Caumo A, Quon MJ 1998 Overestimation of minimal model glucose effectiveness in presence of insulin response is due to undermodeling. Am J Physiol 275:E1031–E1036 4. Katz A, Nambi SS, Mather K, Baron AD, Follmann DA, Sullivan G, Quon MJ 2000 Quantitative insulin sensitivity check index: a simple, accurate method for assessing insulin sensitivity in humans. J Clin Endocrinol Metab 85:2402–2410 5. Chen H, Sullivan G, Yue LQ, Katz A, Quon MJ 2003 QUICKI is a useful index of insulin sensitivity in subjects with hypertension. Am J Physiol Endocrinol Metab 284:E804 –E812 6. Mather KJ, Hunt AE, Steinberg HO, Paradisi G, Hook G, Katz A, Quon MJ, Baron AD 2001 Repeatability characteristics of simple indices of insulin resistance: implications for research applications. J Clin Endocrinol Metab 86: 5457–5464 7. Perseghin G, Caumo A, Mazzaferro V, Pulvirenti A, Piceni Sereni L, Romito R, Lattuada G, Coppa J, Costantino F, Regalia E, Luzi L 2003 Assessment of insulin sensitivity based on a fasting blood sample in men with liver cirrhosis before and after liver transplantation. Transplantation 76:697–702 8. Katsuki A, Sumida Y, Gabazza EC, Murashima S, Urakawa H, Morioka K, Kitagawa N, Tanaka T, Araki-Sasaki R, Hori Y, Nakatani K, Yano Y, Adachi Y 2002 QUICKI is useful for following improvements in insulin sensitivity after therapy in patients with type 2 diabetes mellitus. J Clin Endocrinol Metab 87:2906 –2908 9. Hanley AJ, Williams K, Gonzalez C, D’Agostino Jr RB, Wagenknecht LE, Stern MP, Haffner SM 2003 Prediction of type 2 diabetes using simple measures of insulin resistance: combined results from the San Antonio Heart Study, the Mexico City Diabetes Study, and the Insulin Resistance Atherosclerosis Study. Diabetes 52:463– 469 10. Rabasa-Lhoret R, Bastard JP, Jan V, Ducluzeau PH, Andreelli F, Guebre F, Bruzeau J, Louche-Pellissier C, MaItrepierre C, Peyrat J, Chagne J, Vidal H, Laville M 2003 Modified quantitative insulin sensitivity check index is better correlated to hyperinsulinemic glucose clamp than other fasting-based index of insulin sensitivity in different insulin-resistant states. J Clin Endocrinol Metab 88:4917– 4923 11. Yokoyama H, Emoto M, Fujiwara S, Motoyama K, Morioka T, Komatsu M, Tahara H, Shoji T, Okuno Y, Nishizawa Y 2003 Quantitative insulin sensitivity check index and the reciprocal index of homeostasis model assessment in normal range weight and moderately obese type 2 diabetic patients. Diabetes Care 26:2426 –2432 12. Uwaifo GI, Fallon EM, Chin J, Elberg J, Parikh SJ, Yanovski JA 2002 Indices of insulin action, disposal, and secretion derived from fasting samples and

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Letters to the Editor

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clamps in normal glucose-tolerant black and white children. Diabetes Care 25:2081–2087 13. Gungor N, Saad R, Janosky J, Arslanian S 2004 Validation of surrogate estimates of insulin sensitivity and insulin secretion in children and adolescents. J Pediatr 144:47–55 14. Chen H, Sullivan G, Quon MJ, Assessing the predictive accuracy of QUICKI as a surrogate index for insulin sensitivity using a calibration model. Diabetes, in press doi: 10.1210/jc.2005-0528

Authors’ Response: Limited Accuracy of Surrogates of Insulin Resistance during Puberty in Obese and Lean Children at Risk for Altered Glucoregulation To the editor: In response to the comments of Karne et al. (1), the purpose of our study (2) was not to demonstrate that quantitative insulin sensitivity check index (QUICKI) and other surrogates of insulin sensitivity (SI) are meaningless. On the contrary, we have reported their accuracy in adults (3) and proposed a simplified version (SI ⫽ 40/Ib) (4). However, no biological method is above criticism— even the glucose clamp has some methodological limits (5, 6). Actually, none of those methods cited by Karne et al. (1) for the minimal model (MM) has been recognized as a major flaw, and a huge body of literature demonstrates the robustness of this approach (7, 8). The concerns about glucose effectiveness have no influence on SI calculations (9). Our reduced sampling procedure has been validated (10). No serious scientist would easily believe that the MM (which has been extensively investigated and used in studies published in leading journals over the past 25 yr) provides a less accurate measurement than simple indexes based on baseline values. Despite the statement of Karne et al. (1), SI-MM usually correlates as closely as SI-clamp with all surrogates in adults (11, 12). However, this correlation disappears in certain populations. We do not understand why Karne et al. so angrily dispute the fact, which is evidenced by many investigators (13, 14), that surrogates (including QUICKI) have limits to their validity, as is the case for any physiological model. It is clear that during puberty insulinemia mirrors SI less closely. It is not appropriate to conclude that our findings are false only because Uwaifo et al. (15) have found a correlation in prepubertal children between QUICKI and SI, as others found in healthy pubertal children (16). Those reports do not mean that similar correlations are to be found in pubertal children at risk of disturbed glucoregulation, in whom the feedback loop between SI and insulinemia is even more disturbed. QUICKI and homeostasis model assessment can safely be used as predictors of SI in lean and obese sedentary individuals, but in other populations (e.g. diabetics, athletes, individuals with high SI, puberty, etc.), serious concerns have been raised about their use that argue for caution. Our hope is that further study will extend the range of populations in which surrogates can be employed. However, to deny the relevance of studies that point out their limits of validity is probably not the best way to reach this goal. The potential consequences of such a “rigid” position may unfortunately be that surrogates will lose much of their credibility in the near future. Our purpose in this study was just the opposite. F. Brandou, J. F. Brun, E. Raynaud, and J. Mercier Equipe d’Acceuil EA 701 Physiologie des Interactions Service Central de Physiologie Clinique Centre d’Exploration et de Re´adaptation des Anomalies du Me´tabolisme Musculaire (CERAMM) Centre Hospitalier Universitaire Lapeyronie, 34295 Montpellier Cedex 5, France

References 1. Karne RJ, Chen H, Sullivan G, Quon MJ 2005 Letter re: Limited accuracy of surrogates of insulin resistance during puberty in obese and lean children at risk for altered glucoregulation. J Clin Endocrinol Metab 90:4418 – 4419

Received March 23, 2005. Address correspondence to: J. F. Brun, Centre d’Exploration et de Re´adaptation des Anomalies du Me´tabolisme Musculaire (CERAMM), Centre Hospitalier Universitaire Lapeyronie, 34295 Montpellier Cedex 5, France. E-mail: [email protected].

2. Brandou F, Brun JF, Mercier J 2005 Limited accuracy of surrogates of insulin resistance during puberty in obese and lean children at risk for altered glucoregulation. J Clin Endocrinol Metab 90:761–767 3. Brun JF, Raynaud E, Mercier J 2000 Homeostasis model assessment and related simplified evaluations of insulin sensitivity from fasting insulin and glucose. Diabetes Care 23:1037–1038 4. Raynaud E, Perez-Martin A, Brun JF, Benhaddad AA, Mercier J 1999 Revised concept for the estimation of insulin sensitivity from a single sample. Diabetes Care 22:1003–1004 5. Morris AD, Ueda S, Petrie JR, Connell JM, Elliott HL, Donnelly R 1997 The euglycaemic hyperinsulinaemic clamp: an evaluation of current methodology. Clin Exp Pharmacol Physiol 24:513–518 6. Davis SN, Monti L, Piatti PM, Moller N, Ng L, Coppack S, May M, Brown MD, Orskov H, Alberti KG 1993 Estimates of insulin action in normal, obese, and NIDDM man: comparison of insulin and glucose infusion test, CIGMA, minimal model, and glucose clamp techniques. Diabetes Res 23:1–18 7. Goran MI, Gower BA 2001 Longitudinal study on pubertal insulin resistance. Diabetes 50:2444 –2450 8. Hoffman RP, Vicini P, Sivitz WI, Cobelli C 2000 Pubertal adolescent malefemale differences in insulin sensitivity and glucose effectiveness determined by the one compartment minimal model. Pediatr Res 48:384 –388 9. McDonald C, Dunaif A, Finegood DT 2000 Minimal-model estimates of insulin sensitivity are insensitive to errors in glucose effectiveness. J Clin Endocrinol Metab 85:2504 –2508 10. Brun JF, Guintrand-Hugret R, Boegner C, Bouix O, Orsetti A 1995 Influence of short-term submaximal exercise on parameters of glucose assimilation analyzed with the minimal model. Metabolism 44:833– 840 11. Kishimoto H, Taniguchi A, Sakai M, Fukushima M, Nagasaka S, Fukunaga A, Nagata I, Nakaishi S, Tokuyama K, Nakai Y 2001 Assessments of insulin sensitivity in non-obese Japanese type 2 diabetic patients: comparison of quantitative insulin sensitivity check index with minimal model approach. Diabet Med 18:772–773 12. Brady LM, Gower BA, Lovegrove SS, Williams CM, Lovegrove JA 2004 Revised QUICKI provides a strong surrogate estimate of insulin sensitivity when compared with the minimal model. Int J Obes Relat Metab Disord 28:222–227 13. Vaccaro O, Masulli M, Cuomo V, Rivellese AA, Uusitupa M, Vessby B, Hermansen K, Tapsell L, Riccardi G 2004 Comparative evaluation of simple indices of insulin resistance. Metabolism 53:1522–1526 14. Ferrara CM, Goldberg AP 2001 Limited value of the homeostasis model assessment to predict insulin resistance in older men with impaired glucose tolerance. Diabetes Care 24:245–249 15. Uwaifo GI, Fallon EM, Chin J, Elberg J, Parikh SJ, Yanovski JA 2002 Indices of insulin action, disposal, and secretion derived from fasting samples and clamps in normal glucose-tolerant black and white children. Diabetes Care 25:2081–2087 16. Huang TT, Johnson MS, Goran MI 2002 Development of a prediction equation for insulin sensitivity from anthropometry and fasting insulin in prepubertal and early pubertal children. Diabetes Care 25:1203–1210 doi: 10.1210/jc.2005-0649

Letter re: The Biological Variation of Testosterone and Sex Hormone-Binding Globulin (SHBG) in Polycystic Ovarian Syndrome: Implications for SHBG as a Surrogate Marker of Insulin Resistance To the editor: We read with great interest the paper from Jayagopal et al. (1) published in 2003 in the Journal for Clinical Endocrinology and Metabolism. In this paper, the authors assess the biological variability of two markers of insulin resistance, the homeostasis model assessment method (HOMA-IR) and the serum concentrations of SHBG. Thereby, fasting blood samples were collected at 4-d intervals on 10 consecutive occasions from 12 overweight patients with the polycystic ovary syndrome (PCOS) and 11 age- and weight-matched healthy controls. The authors found that, in contrast to the HOMA-IR, the intraindividual variation in SHBG was lower in patients with PCOS compared with controls. However, in healthy regularly menstruating controls, we and others Received April 12, 2005. Address correspondence to: Jardena J. Puder, Division of Endocrinology, Diabetes and Clinical Nutrition, University Hospital Basel, Petersgraben 4, 4031 Basel, Switzerland. E-mail: [email protected]. A response to this letter was invited, but the authors of the original article chose not to provide one.

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