Clinical Chemistry AACC Online Job Center
HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS
QUICK SEARCH:   [advanced]


     

Clinical Chemistry 52: 1777-1784, 2006. First published June 22, 2006; 10.1373/clinchem.2005.063354
This Article
Right arrow Abstract Freely available
Right arrow Full Text (PDF)
Right arrow All Versions of this Article:
clinchem.2005.063354v1
52/9/1777    most recent
Right arrow Submit an electronic Letter to
the Editor about this paper
Right arrow Alert me when this article is cited
Right arrow Alert me when eLetters are posted
Right arrow Alert me if a correction is posted
Right arrow Citation Map
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in ISI Web of Science
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Download to citation manager
Right arrow reprints & permissions
Citing Articles
Right arrow Citing Articles via HighWire
Right arrow Citing Articles via ISI Web of Science (2)
Right arrow Citing Articles via Google Scholar
Google Scholar
Right arrow Articles by de Ronde, W.
Right arrow Articles by de Jong, F. H.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by de Ronde, W.
Right arrow Articles by de Jong, F. H.
Related Collections
Right arrow Endocrinology and Metabolism
(Clinical Chemistry. 2006;52:1777-1784.)
© 2006 American Association for Clinical Chemistry, Inc.

Endocrinology and Metabolism

Calculation of Bioavailable and Free Testosterone in Men: A Comparison of 5 Published Algorithms

Willem de Ronde1,3,a, Yvonne T. van der Schouw2, Huibert A.P. Pols3,4, Louis J.G. Gooren1, Majon Muller2, Diederick E. Grobbee2 and Frank H. de Jong3

1 Department of Endocrinology, Vrije Universiteit Medical Center, Amsterdam, The Netherlands.
2 Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, The Netherlands.
3 Departments of Internal Medicine; and 4 Epidemiology and Biostatistics, Erasmus Medical Center, Rotterdam, The Netherlands.

aAddress correspondence to this author at: Department of Endocrinology, VU University Medical Center, de Boelelaan 1117, P.O. Box 7057, 1007 MB Amsterdam, The Netherlands. E-mail p.deronde{at}vumc.nl.


   Abstract
Top
 Abstract
Introduction
 Materials and Methods
 Results
 Discussion
 References
 
Background: Estimation of serum concentrations of free testosterone (FT) and bioavailable testosterone (bioT) by calculation is an inexpensive and uncomplicated method. We compared results obtained with 5 different algorithms.

Methods: We used 5 different published algorithms [described by Sodergard et al. (bioTS and FTS), Vermeulen et al. (bioTV and FTV), Emadi-Konjin et al. (bioTE), Morris et al. (bioTM), and Ly et al. (FTL)] to estimate bioT and FT concentrations in samples obtained from 399 independently living men (ages 40–80 years) participating in a cross-sectional, single-center study.

Results: Mean bioT was highest for bioTS (10.4 nmol/L) and lowest for bioTE (3.87 nmol/L). Mean FT was highest for FTS (0.41 nmol/L), followed by FTV (0.35 nmol/L), and FTL (0.29 nmol/L). For bioT concentrations, the Pearson correlation coefficient was highest for the association between bioTS and bioTV (r = 0.98) and lowest between bioTM and bioTE (r = 0.66). FTL was significantly associated with both FTS (r = 0.96) and FTV (r = 0.88). The Pearson correlation coefficient for the association between FTL and bioTM almost reached 1.0. Bland-Altman analysis showed large differences between the results of different algorithms. BioTM, bioTE, bioTV, and FTL were all significantly associated with sex hormone binding globulin (SHBG) concentrations.

Conclusion: Algorithms to calculate FT and bioT must be revalidated in the local setting, otherwise over- or underestimation of FT and bioT concentrations can occur. Additionally, confounding of the results by SHBG concentrations may be introduced.


   Introduction
Top
 Abstract
 Introduction
Materials and Methods
 Results
 Discussion
 References
 
In healthy adult men, ~44% of the circulating testosterone is specifically bound to sex hormone–binding globulin (SHBG),1 50% is nonspecifically bound to albumin, and 3.5% is bound to cortisol-binding globulin, indicating that only 2%–3% is unbound or free(1). SHBG binding decreases the metabolic clearance rate of testosterone(2)(3) and withholds bound hormone from diffusion into the cell, although SHBG may be a necessary cofactor for cellular uptake of testosterone(4). Whether albumin-bound testosterone can dissociate sufficiently fast to enter tissues is controversial(5)(6). However, concentrations of non-SHBG-bound testosterone [bioavailable testosterone (bioT) = free + albumin-bound testosterone] and non-SHBG–nonalbumin–bound testosterone [free testosterone (FT)] are extremely well correlated(7) and interchangeable in most cases.

Because concentrations of SHBG vary widely in healthy men and are related to variables such as diet(8), body mass index (BMI), insulin concentrations, and age(8)(9)(10), measurements of FT and bioT are valuable for correct assessment of the bioactive fraction of testosterone. In aging men, total testosterone concentrations tend to decrease and SHBG concentrations increase(11); therefore, measurements of FT and bioT are advocated to support the diagnosis of hypogonadism, which has nonspecific signs and symptoms in elderly men(12)(13).

BioT can be measured ex vivo with the ammoniumsulfate precipitation technique described by Tremblay and Dube(14). The gold standard for FT measurement is the dialysis method(15), although a mass spectrometry–based assessment of FT in ultrafiltrates was recently proposed as a candidate comparison method(16). However, both ammoniumsulfate precipitation and the dialysis technique are nonautomated, time-consuming, and require expensive techniques and, therefore, are not routinely used in most laboratories. Alternatively, FT concentrations can be measured with a direct RIA, but this assay has been criticized because of lack of accuracy(7)(15)(16)(17). The concentrations of FT and bioT can also be calculated by use of one of several published algorithms. The 2 most widely used equations for calculating FT and bioT are those described by Vermeulen et al.(7) and Sodergard et al.(18). These algorithms assume that when the concentrations of total testosterone, SHBG, and albumin, and the constants for the binding of testosterone to SHBG and albumin are known, FT and bioT can be calculated. These calculations depend on a proper estimation of the association constant for binding of testosterone to SHBG (kt) and albumin (kat). However, values for kt measured with various methods have been reported, including 5.97 x 10E8(18), 10 x 10E8(7), 16 x 10E8(1), and 19 x 10E8(19).

A more pragmatic approach has been to create an algorithm based on measurement of FT or bioT and use this algorithm to predict FT or bioT in future samples on the basis of total testosterone and SHBG concentrations. Three new algorithms based on concentrations measured with a gold standard technique in large numbers of samples were recently proposed for estimating the concentrations of FT or bioT(20)(21)(22). At least 2 algorithms for calculating bioT are available on the internet (www.issam.ch and www.him-link.com ).

Calculated FT and bioT concentrations, most often obtained with the Vermeulen(15)(23)(24)(25)(26)(27)(28) and Sodergard(24)(29)(30)(31)(32)(33) methods, are used widely in the endocrinology literature. In most publications no arguments are given for the choice of a particular method for calculating FT or bioT although, as described above, choosing a particular set of constants will obviously influence results of calculated free and bioavailable hormone concentrations and thus might influence results of analyses. Moreover, it is doubtful whether algorithms composed and validated in one laboratory can be applied to samples from an unrelated laboratory that uses different assay techniques. The aim of this study was to compare the results of 5 published algorithms to calculate FT and bioT.


   Materials and Methods
Top
 Abstract
 Introduction
 Materials and Methods
Results
 Discussion
 References
 
participants
The study was a cross-sectional, single-center study of 399 independently living men ages 40 to 80 years. Participant recruitment methods and details of participant baseline characteristics, lifestyle, and health, have been described extensively(34). All participants gave written informed consent before enrollment, and the institutional review board of the Utrecht University Medical Centre approved the study.

laboratory measurements and calculations of FT and bioT
We obtained fasting blood samples by venipuncture. Cell-free serum was immediately stored at –20°C. We measured testosterone after performing diethylether extraction with an in-house RIA using a polyclonal antitestosterone–antibody (AZG 3290; a gift from Dr. J.J. Pratt, Groningen, the Netherlands). The lower limit of detection of the assay was 0.24 nmol/L, and interassay variation rates were 6.0%, 5.4%, and 8.6% at 2.1, 5.6, and 23 nmol/L, respectively. We measured SHBG with an immunometric technique on an Immulite 2000 Analyzer (Diagnostic Products Corporation). The lower limit of detection was 5 nmol/L, and interassay variation rates were 6.1%, 4.9%, and 6.9% at 11.6, 36, and 93 nmol/L, respectively.

Using the equations summarized in Table 1 , we calculated bioT at a fixed plasma albumin concentration of 43 g/L. The affinity constants for the binding of testosterone to SHBG or albumin were 5.97 x 10E8 L/mol and 4.06 x 10E4 L/mol, as proposed by Sodergard et al.(18) (BioTS); 10 x 10E8 L/mol and 3.6 x 10E4 L/mol, as described by Vermeulen et al.(7) (BioTV); or 1.4 x 10E9 L/mol and 1.3 x 10E4 L/mol, as described by Emadi-Konjin et al.(20) (BioTE) (Table 1A ). We also calculated bioT according to the algorithm presented by Morris et al.(21) (BioTM; Table 1B ) and calculated FT with the equations of Vermeulen (FTV), Sodergard (FTS; Table 1A ), and Ly et al.(22) (FTL; Table 1C ).


View this table:
[in this window]
[in a new window]
  		Table 1. Equations for the calculation of FT and bioT.

To evaluate the impact of varying albumin concentrations on bioT, we used the Vermeulen, Sodergard, and Emadi-Konjin algorithms to recalculate bioT with a fixed plasma albumin concentration of 33 g/L instead of 43 g/L and used the Student t-test for paired comparisons to test differences between estimates.

data analysis
The associations between the calculation results for all possible pairs of algorithms were assessed and expressed as Pearson correlation coefficients. The differences between the results of the calculations were quantified as described by Bland and Altman(35). We used SPSS 11.0 software to perform analyses.


   Results
Top
 Abstract
 Introduction
 Materials and Methods
 Results
Discussion
 References
 
Mean bioT and FT concentrations calculated by the different algorithms are presented in Table 2 , A and B. As anticipated from the different association constants for the binding of testosterone to SHBG, mean bioTS was highest, followed by mean bioTV and mean bioTE. The result for mean bioTM was between those for bioTV and bioTE. The range (%) for bioTM covers only 15% points, indicating that the impact of varying SHBG concentrations on the concentration of bioTM is limited, as confirmed by the strong correlation between concentrations of total testosterone and bioTM (Table 3 ). The ranges for bioT(%), calculated using the other algorithms, varied between 41% and 47%. For FT, FTS was highest, followed by FTV and FTL.


View this table:
[in this window]
[in a new window]
  		Table 2. Mean absolute and fractional concentrations as calculated by various algorithms.


View this table:
[in this window]
[in a new window]
  		Table 3. Pearson coefficients of correlation for the relationship between the results of various calculations.

Pearson correlation coefficients for the relationships between the results of the different algorithms are summarized in Table 3Up . For the calculations of FTV and FTS, we used an albumin concentration of 43 g/L, which led to a fixed relation between FT and bioT. Thus, associations between bioTS and bioTV and other variables were similar for FTS and FTV, respectively. BioTV and bioTS appeared to be strongly correlated. The relationship between bioTM and bioTE was relatively weak. Whereas the relationship between bioTM and total testosterone was very strong, it was weak for bioTE. There was also a striking difference between these 2 estimates and their association with SHBG concentrations. BioTM was positively associated and bioTE was negatively associated, whereas bioTV and bioTS were not or were only weakly related to the concentrations of SHBG. FTL was strongly related to bioTM, was also strongly associated with total testosterone, and was positively associated with SHBG concentrations.

For the Vermeulen, Sodergard, and Emadi-Konjin algorithms, results were recalculated with a standard albumin concentration of 33 g/L (instead of 43 g/L) to evaluate the impact of varying albumin concentrations on the results. For all algorithms, mean bioT concentrations calculated at the lower albumin concentration were slightly but significantly lower (P <0.001 for the differences between results obtained using 33 or 43 g/L for all algorithms). The impact of varying albumin concentrations appeared to be stronger when the fractional bioT concentration was lower. Because the highest results for mean bioT fraction were calculated with the Sodergard algorithm, this algorithm was least sensitive to variations in albumin concentration (percentage difference = 7.2% of mean bioT, range 3.2%–10.9%) followed by Vermeulen (9.1%, range 3.6%–13.2%), and Emadi-Konjin (11.8%, range 4.2%–14.9%; P <0.001 for the difference between algorithms).

In Figs. 1 and 2 , Bland-Altman plots are presented in which the results of the calculations using all pairs of algorithms are compared. Although differences between the results of the calculations were large, they were smallest when bioTS and bioTV were compared. BioTS estimations were systematically higher than bioTV, with a mean difference of +27% of the mean bioT concentration. Both the Vermeulen and Sodergard algorithms provided systematically higher estimates compared with the Emadi and Morris algorithms (mean difference 94% for bioTS vs bioTE, 63% for bioTS vs BioTM, 72% for bioTV vs bioTE, and 38% for bioTV vs bioTM). The mean difference between bioTM and bioTE was limited to 36%; however, the difference between the methods for individual samples ranged between –35% and +92%.


Figure 1
View larger version (25K):
[in this window]
[in a new window]
  		Figure 1. Plots of the percentage differences in calculated bioT concentrations against the average of the 2 applied algorithms.

The solid line represents 0%, the dotted lines the 2 SD of the mean percentage difference.


Figure 2
View larger version (15K):
[in this window]
[in a new window]
  		Figure 2. Plots of the percentage differences in calculated FT concentrations against the average of the 2 applied algorithms.

The solid line represents 0%, the dotted lines the 2 SD of the mean percentage difference.

Both FTS and FTV gave higher estimates than did FTL (mean difference 21% for FTV vs FTL and 35% for FTS vs FTL), but the ranges were much smaller for FTS vs FTL. BioTM and FTL were extremely well correlated, and the plots of their differences with bioTV and bioTS or FTV and FTS, respectively, look similar, although not identical (Figs. 1Up and 2Up ).


   Discussion
Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
References
 
The Vermeulen and Sodergard algorithms on the one hand and the Emadi-Konjin, Ly, and Morris calculations on the other hand were developed from fundamentally different approaches. The Vermeulen and Sodergard equations are based on the law of mass action, in which estimates of total testosterone, SHBG, and the values for the association constants for the binding of testosterone to albumin and SHBG are used to calculate bioT and FT. The association constants for these algorithms were obtained experimentally and were validated by comparing the results of the calculations with a gold standard technique(7)(16)(36). The consistency between the association constants and the results obtained using these constants adds to the credibility of these algorithms, as indicated by the results of van Uytfanghe et al.(16).

Emadi-Konjin et al.(20) and Morris et al.(21) first measured bioT by ammonium-sulfate precipitation and used these results to create an algorithm to predict bioT concentrations in other samples by use of the concentration of total testosterone and SHBG.

To obtain optimal correlation with their results, Emadi-Konjin et al.(20) modified the equation used by Vermeulen and Sodergard by altering the binding constants for testosterone binding to albumin and SHBG. Morris et al.(21) created a regression equation based on their measurements. Ly et al.(22) first used an ultrafiltration assay to estimate FT in ~4000 samples. Then they composed 2 regression models to predict FT in these samples: 1 equation for total testosterone concentrations <5 nmol/L and 1 for concentrations above this concentration. Because all of our study participants had total testosterone concentrations >5 nmol/L, we used only the latter equation. In contrast to the Vermeulen and Sodergard methods, the Morris, Ly, and Emadi-Konjin algorithms rely solely on the accuracy of their measurements.

Calculation of FT and bioT with the Vermeulen and Sodergard equations is accurate only when competition for the binding sites by other steroid hormones is limited. For example, in the presence of supraphysiologic concentrations of estradiol or dihydrotestosterone, calculation with either method will underestimate the concentration of bioT. In clinical practice, however, this problem is rarely encountered. In addition, these methods assume that the association constants for kt and kat are known, but the fact that the Sodergard and Vermeulen equations use different constants shows that this is not the case.

With either algorithm, the results of the calculation of FT and bioT depend on a proper determination of the concentration of total testosterone (also necessary for measurement of FT and bioT concentrations with the dialysis, ultrafiltration, or ammoniumsulfate precipitation techniques) and SHBG(37). Results differ considerably for commercially available assays for testosterone(37)(38)(39) and SHBG(15)(40). Because most commercial automated testosterone assays perform inaccurately in women and children(38)(39), calculation of FT and bioT with these results will also be inaccurate. Differences in the estimation of the SHBG concentration will influence the result of the calculated but not the measured FT or bioT concentration. In this respect, it should be noted that commercial SHBG assays were traditionally standardized against a measurement of binding capacity, whereas more recently several assays were calibrated on the basis of SHBG mass, which may lead to important differences related to the number of steroid binding sites in the SHBG homodimers(41)(42).

The Vermeulen and Sodergard methods appear to be used rather indiscriminately, and proper validation of the calculated results is rarely described. In some studies the calculation as proposed by Sodergard is used and validated by referencing to validation experiments in which the Vermeulen calculation is used(24)(31)(43).

Our study shows that results differ considerably for calculations based on different algorithms applied to testosterone and SHBG concentrations measured in an unrelated laboratory. BioTE and bioTM concentrations actually measured with ammonium-sulfate precipitation were much lower than calculated bioTS and bioTV concentration estimates, possibly indicating that these widely used algorithms systematically overestimate bioT concentrations. This possibility of overestimation is supported by validation experiments in which measured FT concentrations were lower than estimates calculated with the Vermeulen equation(16)(22)(36)(37). With their ultrafiltration technique, Ly et al.(22) obtained FT concentration values that were lower than those calculated with the Vermeulen or Sodergard algorithm.

Although both bioTM and bioTE estimates are based on actual measurements of bioT, the correlation between the results of the adapted algorithms is only 0.66 (Pearson’s r), and the difference between the estimates ranges between –35% and +92% of the mean bioT concentration. As stated above, algorithms depend on the methods used to measure total testosterone and SHBG and thus are not directly transferable to other users. However, as shown in Table 4 , the reference ranges of the assays used in the Morris and Emadi-Konjin studies were not very different from the results in our study, which makes it unlikely that differences between assays can explain all of the variability presented here. The lack of uniformity between the results of the Morris and Emadi-Konjin algorithms suggests flaws in one or both methods. This finding is not entirely surprising because ammonium sulfate precipitation measurement of bioT is a demanding procedure that relies on selective and reproducible precipitation of all SHBG without precipitating albumin and requires a critical concentration of ammonium sulfate(44). The high correlation of bioTM with FTL, the results of which are based on a different gold standard technique, argues in favor of the Morris algorithm. A positive finding of our study is that the 2 most widely used algorithms, those described by Vermeulen and by Sodergard, are fairly concordant, although the Sodergard algorithm provides systematically higher estimates.


View this table:
[in this window]
[in a new window]
  		Table 4. Assay manufacturers and reference ranges of analyzed studies.

BioTE, bioTM, and FTL were significantly associated with SHBG concentrations (Table 3Up ). In healthy men neither a positive nor a negative relationship between SHBG and bioT is expected, because the pituitary is assumed to increase luteinizing hormone stimulation of the testes to compensate for higher SHBG concentrations, and bioT and FT are assumed to truly represent the bioactive fraction of total testosterone. Therefore, a strong association between FT or bioT concentrations and SHBG not only raises questions regarding the reliability of the estimates but also introduces fundamental problems in interpreting these estimates. Confounding of these results by SHBG cannot be solved by establishing method– specific reference ranges. Aging men, for instance, who generally have higher concentrations of SHBG than do young men, will be more prone to have low calculated estimates of bioT concentrations when the Emani-Konjin equation is used and higher concentrations when the Morris or Ly equation is used. The dependency of these estimates on SHBG concentrations is particularly undesirable in older men, in whom accurate estimations of FT and bioT are needed to support a clinical diagnosis of hypogonadism. Neither Morris, Ly, nor Emadi-Konjin described a coefficient of correlation for the relation between bioT or FT concentrations and SHBG in their studies, whereas the absence of such a relation would favor the validity of their results. In our study, bioTS and bioTV were not or were only weakly related to SHBG concentrations, possibly indicating that these estimates are closer to actual bioT concentrations than are estimates from other algorithms.

Application of the Morris or Ly equations yields a narrow range of (fractional) bioT or FT concentration estimates, suggesting that, over a wide range of SHBG concentrations, SHBG has little effect on absolute and fractional bioTM or FTL concentrations. This conclusion is also reflected by the very high association between bioTM or FTL and total testosterone concentrations, an association not found in the original study by Morris et al.(21) and not described by Ly et al.(22). With such a high association, it is questionable whether the calculation of bioT and FT concentrations is any more valuable than the measurement of total testosterone.

As shown in this study, variable albumin concentrations entered in the Sodergard, Vermeulen, and Emadi-Konjin algorithms gain importance when fractional bioT concentrations are lower. Therefore, the actual albumin concentration should be added to the algorithm when gross abnormalities of the albumin concentration are expected (as in nephrotic syndrome or severe malnutrition) or when SHBG concentrations are high (as in hyperthyroidism, women, or aging men).

Although the Morris, Ly, and Emadi-Konjin algorithms were based primarily on measurements in men (percentage males: Morris 100%, Ly 87%, and Emadi 92%), we repeated the analyses in 415 premenopausal women patients at a tertiary fertility outpatient clinic and obtained Bland-Altman plots similar to those for men (data not shown). In women, the differences between the results of the algorithms were larger for all comparisons except for Morris/Vermeulen and Morris/Sodergard. These results are in line with extrapolation in the very low bioFT or FT range of the results for men (Figs. 1Up and 2Up ). Differences between the results of the Sodergard, Vermeulen, and Emadi-Konjin algorithms were larger because of the higher mean concentrations of SHBG in women; the difference between the results of these algorithms was strongly associated with SHBG concentrations.

Although we observed appreciable differences between the results of several published algorithms for calculating bioT and FT, we are not able to recommend one algorithm over another because we did not compare our results with an accepted comparison method for measuring bioT or FT. Even if we had done so, however, we would have been reluctant to disqualify any of the evaluated algorithms. Also, our measurements depended on SHBG and total testosterone assay characteristics and may have been distorted by technical flaws.

We conclude that algorithms to calculate bioT and FT are not simply transferable to samples from other laboratories unless careful revalidation in the local setting has been performed. Otherwise, over- or underestimation of bioT or FT and confounding of results by SHBG concentrations can occur. The latter can be evaluated by quantifying the relationship between SHBG and calculated bioT or FT concentrations or by adjusting the relationship between any variable and calculated bioT or FT for SHBG.

Although these conclusions seem obvious, calculated concentrations of bioT or FT are widely described in scientific journals and probably used in clinical practice, mostly without prior in-house validation of the calculated results. Without in-house validation, the use of these algorithms is highly questionable.


   Footnotes
 
1 Nonstandard abbreviations: SHBG, sex hormone binding globulin; bioT, bioavailable testosterone; FT, free testosterone; BMI, body mass index; kt, association constant for binding of testosterone to SHBG; kat, association constant for binding of testosterone to albumin.


   References
Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 References
 

  1. Dunn JF, Nisula BC, Rodbard D. Transport of steroid hormones: binding of 21 endogenous steroids to both testosterone-binding globulin and corticosteroid-binding globulin in human plasma. J Clin Endocrinol Metab 1981;53:58-68.[Abstract]
  2. Vermeulen A, Ando S. Metabolic clearance rate and interconversion of androgens and the influence of the free androgen fraction. J Clin Endocrinol Metab 1979;48:320-326.[Abstract]
  3. Saez JM, Forest MG, Morera AM, Bertrand J. Metabolic clearance rate and blood production rate of testosterone and dihydrotestosterone in normal subjects, during pregnancy, and in hyperthyroidism. J Clin Invest 1972;51:1226-1234.[ISI][Medline] [Order article via Infotrieve]
  4. Hammes A, Andreassen TK, Spoelgen R, Raila J, Hubner N, Schulz H, et al. Role of endocytosis in cellular uptake of sex steroids. Cell 2005;122:751-762.[CrossRef][ISI][Medline] [Order article via Infotrieve]
  5. Mendel CM. The free hormone hypothesis: a physiologically based mathematical model. Endocr Rev 1989;10:232-274.[Abstract]
  6. Manni A, Pardridge WM, Cefalu W, Nisula BC, Bardin CW, Santner SJ, et al. Bioavailability of albumin-bound testosterone. J Clin Endocrinol Metab 1985;61:705-710.[Abstract]
  7. Vermeulen A, Verdonck L, Kaufman JM. A critical evaluation of simple methods for the estimation of free testosterone in serum. J Clin Endocrinol Metab 1999;84:3666-3672.[Abstract/Free Full Text]
  8. Longcope C, Feldman HA, McKinlay JB, Araujo AB. Diet and sex hormone-binding globulin. J Clin Endocrinol Metab 2000;85:293-296.[Abstract/Free Full Text]
  9. Muller M, den TI, Thijssen JH, Grobbee DE, van der Schouw YT. Endogenous sex hormones in men aged 40–80 years. Eur J Endocrinol 2003;149:583-589.[Abstract]
  10. Vermeulen A, Kaufman JM, Giagulli VA. Influence of some biological indexes on sex hormone-binding globulin and androgen levels in aging or obese males. J Clin Endocrinol Metab 1996;81:1821-1826.[Abstract]
  11. de Ronde W, van der Schouw YT, Pierik FH, Pols HA, Muller M, Grobbee DE, et al. Serum levels of sex hormone-binding globulin (SHBG) are not associated with lower levels of non-SHBG-bound testosterone in male newborns and healthy adult men. Clin Endocrinol (Oxf) 2005;62:498-503.
  12. Nieschlag E, Swerdloff R, Behre HM, Gooren LJ, Kaufman JM, Legros JJ, et al. Investigation, treatment and monitoring of late-onset hypogonadism in males: ISA, ISSAM, and EAU recommendations. Int J Androl 2005;28:125-127.[CrossRef][ISI][Medline] [Order article via Infotrieve]
  13. Kaufman JM, Vermeulen A. The decline of androgen levels in elderly men and its clinical and therapeutic implications. Endocr Rev 2005;26:833-876.[Abstract/Free Full Text]
  14. Tremblay RR, Dube JY. Plasma concentrations of free and non-TeBG bound testosterone in women on oral contraceptives. Contraception 1974;10:599-605.[CrossRef][ISI][Medline] [Order article via Infotrieve]
  15. Miller KK, Rosner W, Lee H, Hier J, Sesmilo G, Schoenfeld D, et al. Measurement of free testosterone in normal women and women with androgen deficiency: comparison of methods. J Clin Endocrinol Metab 2004;89:525-533.[Abstract/Free Full Text]
  16. Van Uytfanghe K, Stockl D, Kaufman JM, Fiers T, De Leenheer A, Thienpont LM. Validation of 5 routine assays for serum free testosterone with a candidate reference measurement procedure based on ultrafiltration and isotope dilution-gas chromatography-mass spectrometry. Clin Biochem 2005;38:253-261.[CrossRef][ISI][Medline] [Order article via Infotrieve]
  17. Rosner W. An extraordinarily inaccurate assay for free testosterone is still with us. J Clin Endocrinol Metab 2001;86:2903.[Free Full Text]
  18. Sodergard R, Backstrom T, Shanbhag V, Carstensen H. Calculation of free and bound fractions of testosterone and estradiol-17 ß to human plasma proteins at body temperature. J Steroid Biochem 1982;16:801-810.[CrossRef][ISI][Medline] [Order article via Infotrieve]
  19. Burke CW, Anderson DC. Sex-hormone-binding globulin is an oestrogen amplifier. Nature 1972;240:38-40.[CrossRef][Medline] [Order article via Infotrieve]
  20. Emadi-Konjin P, Bain J, Bromberg IL. Evaluation of an algorithm for calculation of serum "bioavailable" testosterone (BAT). Clin Biochem 2003;36:591-596.[CrossRef][ISI][Medline] [Order article via Infotrieve]
  21. Morris PD, Malkin CJ, Channer KS, Jones TH. A mathematical comparison of techniques to predict biologically available testosterone in a cohort of 1072 men. Eur J Endocrinol 2004;151:241-249.[Abstract]
  22. Ly LP, Handelsman DJ. Empirical estimation of free testosterone from testosterone and sex hormone-binding globulin immunoassays. Eur J Endocrinol 2005;152:471-478.[Abstract/Free Full Text]
  23. Dhindsa S, Prabhakar S, Sethi M, Bandyopadhyay A, Chaudhuri A, Dandona P. Frequent occurrence of hypogonadotropic hypogonadism in type 2 diabetes. J Clin Endocrinol Metab 2004;89:5462-5468.[Abstract/Free Full Text]
  24. Yildiz BO, Woods KS, Stanczyk F, Bartolucci A, Azziz R. Stability of adrenocortical steroidogenesis over time in healthy women and women with polycystic ovary syndrome. J Clin Endocrinol Metab 2004;89:5558-5562.[Abstract/Free Full Text]
  25. Van Pottelbergh I, Goemaere S, Zmierczak H, Kaufman JM. Perturbed sex steroid status in men with idiopathic osteoporosis and their sons. J Clin Endocrinol Metab 2004;89:4949-4953.[Abstract/Free Full Text]
  26. Bouillon R, Bex M, Vanderschueren D, Boonen S. Estrogens are essential for male pubertal periosteal bone expansion. J Clin Endocrinol Metab 2004;89:6025-6029.[Abstract/Free Full Text]
  27. San Millan JL, Corton M, Villuendas G, Sancho J, Peral B, Escobar-Morreale HF. Association of the polycystic ovary syndrome with genomic variants related to insulin resistance, type 2 diabetes mellitus, and obesity. J Clin Endocrinol Metab 2004;89:2640-2646.[Abstract/Free Full Text]
  28. Gu YQ, Tong JS, Ma DZ, Wang XH, Yuan D, Tang WH, et al. Male hormonal contraception: effects of injections of testosterone undecanoate and depot medroxyprogesterone acetate at eight-week intervals in Chinese men. J Clin Endocrinol Metab 2004;89:2254-2262.[Abstract/Free Full Text]
  29. Goderie-Plomp HW, van der Klift M, de Ronde W, Hofman A, de Jong FH, Pols HA. Endogenous sex hormones, sex hormone-binding globulin, and the risk of incident vertebral fractures in elderly men and women: the Rotterdam Study. J Clin Endocrinol Metab 2004;89:3261-3269.[Abstract/Free Full Text]
  30. Jakubowicz DJ, Essah PA, Seppala M, Jakubowicz S, Baillargeon JP, Koistinen R, et al. Reduced serum glycodelin and insulin-like growth factor-binding protein-1 in women with polycystic ovary syndrome during first trimester of pregnancy. J Clin Endocrinol Metab 2004;89:833-839.[Abstract/Free Full Text]
  31. Araujo AB, O’Donnell AB, Brambilla DJ, Simpson WB, Longcope C, Matsumoto AM, et al. Prevalence and incidence of androgen deficiency in middle-aged and older men: estimates from the Massachusetts Male Aging Study. J Clin Endocrinol Metab 2004;89:5920-5926.[Abstract/Free Full Text]
  32. Rickenlund A, Carlstrom K, Ekblom B, Brismar TB, Von Schoultz B, Hirschberg AL. Effects of oral contraceptives on body composition and physical performance in female athletes. J Clin Endocrinol Metab 2004;89:4364-4370.[Abstract/Free Full Text]
  33. Rickenlund A, Eriksson MJ, Schenck-Gustafsson K, Hirschberg AL. Amenorrhea in female athletes is associated with endothelial dysfunction and unfavorable lipid profile. J Clin Endocrinol Metab 2005;90:1354-1359.[Abstract/Free Full Text]
  34. de Ronde W, van der Schouw YT, Muller M, Grobbee DE, Gooren LJ, Pols HA, et al. Associations of sex-hormone-binding globulin (SHBG) with non-SHBG-bound levels of testosterone and estradiol in independently living men. J Clin Endocrinol Metab 2005;90:157-162.[Abstract/Free Full Text]
  35. Bland JM, Altman DG. Comparing methods of measurement: why plotting difference against standard method is misleading. Lancet 1995;346:1085-1087.[CrossRef][ISI][Medline] [Order article via Infotrieve]
  36. Ross HA, Meuleman EJ, Sweep FC. A simple method for estimating equilibrium constants for serum testosterone binding resulting in an optimal free testosterone index for use in elderly men. Clin Chem Lab Med 2005;43:613-616.[CrossRef][ISI][Medline] [Order article via Infotrieve]
  37. Rinaldi S, Geay A, Dechaud H, Biessy C, Zeleniuch-Jacquotte A, Akhmedkhanov A, et al. Validity of free testosterone and free estradiol determinations in serum samples from postmenopausal women by theoretical calculations. Cancer Epidemiol Biomarkers Prev 2002;11:1065-1071.[Abstract/Free Full Text]
  38. Wang C, Catlin DH, Demers LM, Starcevic B, Swerdloff RS. Measurement of total serum testosterone in adult men: comparison of current laboratory methods versus liquid chromatography-tandem mass spectrometry. J Clin Endocrinol Metab 2004;89:534-543.[Abstract/Free Full Text]
  39. Taieb J, Mathian B, Millot F, Patricot MC, Mathieu E, Queyrel N, et al. Testosterone measured by 10 immunoassays and by isotope-dilution gas chromatography-mass spectrometry in sera from 116 men, women, and children. Clin Chem 2003;49:1381-1395.[Abstract/Free Full Text]
  40. Bukowski C, Grigg MA, Longcope C. Sex hormone-binding globulin concentration: differences among commercially available methods. Clin Chem 2000;46:1415-1416.[Free Full Text]
  41. Petra PH. The plasma sex steroid binding protein (SBP or SHBG). A critical review of recent developments on the structure, molecular biology, and function. J Steroid Biochem Mol Biol 1991;40:735-753.[CrossRef][ISI][Medline] [Order article via Infotrieve]
  42. Hammond GL, Avvakumov GV, Muller YA. Structure/function analyses of human sex hormone-binding globulin: effects of zinc on steroid-binding specificity. J Steroid Biochem Mol Biol 2003;85:195-200.[CrossRef][ISI][Medline] [Order article via Infotrieve]
  43. Mohr BA, Guay AT, O’Donnell AB, McKinlay JB. Normal, bound, and nonbound testosterone levels in normally ageing men: results from the Massachusetts Male Ageing Study. Clin Endocrinol (Oxf) 2005;62:64-73.[CrossRef][Medline] [Order article via Infotrieve]
  44. Davies R, Collier C, Raymond M, Heaton J, Clark A. Indirect measurement of bioavailable testosterone with the Bayer Immuno 1 system. Clin Chem 2002;48:388-390.[Free Full Text]



The following articles in journals at HighWire Press have cited this article:


Home page
 NeurologyHome page
J. A. Talbot, R. Sheldrick, H. Caswell, and S. Duncan
Sexual function in men with epilepsy: How important is testosterone?
Neurology, April 15, 2008; 70(16): 1346 - 1352.
[Abstract] [Full Text] [PDF]

Home page
 J. Clin. Endocrinol. Metab.Home page
M. E. Kevenaar, J. S. E. Laven, S. L. Fong, A. G. Uitterlinden, F. H. de Jong, A. P. N. Themmen, and J. A. Visser
A Functional Anti-Mullerian Hormone Gene Polymorphism Is Associated with Follicle Number and Androgen Levels in Polycystic Ovary Syndrome Patients
J. Clin. Endocrinol. Metab., April 1, 2008; 93(4): 1310 - 1316.
[Abstract] [Full Text] [PDF]

Home page
 Clin. Chem.Home page
H. Dechaud, A. Denuziere, S. Rinaldi, J. Bocquet, H. Lejeune, and M. Pugeat
Age-Associated Discrepancy between Measured and Calculated Bioavailable Testosterone in Men
Clin. Chem., April 1, 2007; 53(4): 723 - 728.
[Abstract] [Full Text] [PDF]

This Article
Right arrow Abstract Freely available
Right arrow Full Text (PDF)
Right arrow All Versions of this Article:
clinchem.2005.063354v1
52/9/1777    most recent
Right arrow Submit an electronic Letter to
the Editor about this paper
Right arrow Alert me when this article is cited
Right arrow Alert me when eLetters are posted
Right arrow Alert me if a correction is posted
Right arrow Citation Map
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in ISI Web of Science
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Download to citation manager
Right arrow reprints & permissions
Citing Articles
Right arrow Citing Articles via HighWire
Right arrow Citing Articles via ISI Web of Science (2)
Right arrow Citing Articles via Google Scholar
Google Scholar
Right arrow Articles by de Ronde, W.
Right arrow Articles by de Jong, F. H.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by de Ronde, W.
Right arrow Articles by de Jong, F. H.
Related Collections
Right arrow Endocrinology and Metabolism

HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS