In physiological conditions intracellular radical formation is mostly due to mitochondrial activity. This is in contrast to clinical and pathophysiological situations, where the oxidant formation is additionally driven by xenobiotics and inflammation.

Oxidative damage accumulation in macromolecules especially in proteins has been considered as a cause of cellular damage and pathology impairing the clinical outcome of patients. However, up to now strategies to measure oxidative stress in clinical settings are limited. A lot of parameters and techniques are available for the determination of oxidized proteins in biological systems. Unfortunately, most of them are no reliable markers in clinical settings due to their unknown clinical relevance or the lack in clinical feasibility. Major problems are the sample availability, sample stability and cost-, time-and man-power intensive methods. The present review focuses on the measurement of protein oxidation products from a chemical, biological, and medical point of view.

A C C E P T E D M A N U S C R I P T A C C E P T E D M A N U S C R I P T

Formation of free radicals and oxidants: pros and cons

An inevitable byproduct of aerobic cellular metabolism is the continuous formation of free radicals and other highly reactive oxidants. Intracellular radical formation is mostly due to mitochondrial activity, releasing large amounts of superoxide anion, resulting from electron leakage of the respiratory chain (for review see [START_REF] Grune | Oxidants and antioxidative defense[END_REF]. But there are several other cellular systems able to release reactive species.

Among them are the peroxisomes, generating hydrogen peroxide as a byproduct, the xanthine oxidase, releasing superoxide anions, and the oxidative burst of macrophages while responding to infections. Additionally, the three enzymes monoamine oxidase, tyrosine hydrolase, and L-amino acid oxidase release hydrogen peroxide during their normal activity. Further, the four nitric oxide synthetases (NOS) are able to produce the nitric oxide radical, which might react with superoxide forming the highly reactive peroxynitrite.

Free radicals and oxidants are often highly reactive and short-lived. Extensive damage is caused by the formation of secondary reactive radicals. For example hydroxyl radicals are generated in the Haber Weiss reaction by the reaction of hydrogen peroxide and superoxide. This reaction can be accelerated by the presence of metals, whereas the metals serve as catalysts. This reaction is known as the Fenton reaction. In subsequent oxidative reactions the target molecules like lipids, proteins, nucleic acids and carbohydrates will be modified, probably leading to a loss or impairment of their biological function. An overproduction of ROS has the potential to disturb the redox balance of a cell leading to the activation of redox sensitive signal pathways and inducing changes in gene expression [START_REF] Suzuki | Oxidants as stimulators of signal transduction[END_REF].

Besides their "damaging" potential ROS play an important role in living organisms. A continuous "natural" production of ROS by some systems within the body is

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considered to be physiological and has beneficial effects [START_REF] Valko | Free radicals and antioxidants in normal physiological functions and human disease[END_REF]. As an example, ROS are generated by cells of the immune system to be involved in the defense of penetrating microorganisms. On the other hand it is known that superoxide anion, hydrogen peroxide and nitric oxide formation are essential for normal cellular function since they constitute a second messenger system involved in the modulation of cellular redox poise, gene expression and cell differentiation [START_REF] Linnane | The essential requirement for superoxide radical and nitric oxide formation for normal physiological function and healthy aging[END_REF].

Under physiological conditions the antioxidant defense system within the body can easily handle the amount of free radicals produced. There are three cellular antioxidative defense lines. The first one contains low molecular antioxidants like glutathione, vitamin C or uric acid. These molecules are able to scavenge free radicals/reactive species and thus less reactive compounds are formed. Enzymatic antioxidants, building the second line, are proteins that convert highly reactive particles into less reactive ones. Superoxide dismutase, catalase, transferrin, and glutathione peroxide belong to the second defense line. Iron and iron-binding proteins play a special role due to the potential of iron (like all transition metals) to be involved in the Fenton-reaction.

Enzymes involved in DNA repair, in membrane repair, and in degradation of lipid peroxidation products belong to the third line of defense. These enzymes are able to repair oxidative damage inflicted to cellular structures. During times of increased oxygen flux or in individual exogenous conditions (diet, environment, smoking and others) free radical production may exceed that of removal ultimately resulting in the oxidation of cellular structures. This situation, where the amount of reactive species in a particular system overwhelms the antioxidative repair capacities, is called oxidative stress [START_REF] Sies | Biochemistry of oxidative stress[END_REF]. During periods of oxidative stress oxidative damage may be potentiated increasing the amount of irreducible oxidized molecules.

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Oxidative stress has been implicated as a major causative agent of cellular dysfunction and cell death. On the other hand, repeated or permanent exposure to sublethal doses of oxidative stress leads to cellular adaption [START_REF] Davies | Transient adaptation to oxidative stress in yeast[END_REF][START_REF] Siems | Uric acid and glutathione levels during short-term whole body cold exposure[END_REF][START_REF] Vile | Heme oxygenase 1 mediates an adaptive response to oxidative stress in human skin fibroblasts[END_REF]. In this context, transient adaption depends upon transcription and translation shown by an altered expression of genes during the adaptive response. Interestingly, the development of a resistance against the used oxidant is limited if either transcription or translation is inhibited during the adaptive response [START_REF] Davies | The broad spectrum of responses to oxidative stress in proliferating cells[END_REF]. It seems possible that an `oversupply` with antioxidants can block the adaptive response of the organism abrogating healthpromoting effects. [START_REF] Ristow | Antioxidants prevent health-promoting effects of physical exercise in humans[END_REF] have shown that ROS are required for the insulin-sensitizing capabilities of physical exercise, whereas this effect was blocked by a supplementation with vitamin C and E.

Measurement of oxidative stress and resulting problems

In theory, the detection of free radicals seems to be a reliable method for the quantification of oxidative stress. A direct determination of free radicals can be achieved by using the Electron Spin Resonance (ESR) method, allowing a sensitive detection of unpaired electrons and paramagnetic substances. Oxidative stress can also be quantified by the degradation or the consumption of substrates, which are potentially involved in radical reactions. For example a declining concentration of non-oxidized membrane lipids might be used as a measure for an ongoing lipid The determination of oxidized products in biological systems exhibits by far the most commonly used method for the measurement of oxidative stress. Depending on the site of generation of oxidative stress, a number of endogenous formed substances are considered to be markers or indicators of oxidative stress. A multitude of potential biomarkers including lipid peroxidation products, protein oxidation products, and DNA oxidation products have been pronounced in the literature [START_REF] Irshad | Oxidant-antioxidant system: role and significance in human body[END_REF]. The present review article is aimed to focus on the measurement of protein oxidation products from a chemical, biological, and medical point of view.

Protein oxidation products from a chemical point of view

Proteins can be oxidatively modified by a variety of free radicals and oxidants. As a result, many different amino acid modifications can occur [START_REF] Stadtman | Oxidation of free amino acids and amino acid residues in proteins by radiolysis and by metal-catalyzed reactions[END_REF][START_REF] Naskalski | Oxidative modifications of protein structures[END_REF] leading to damaged proteins with changed, reduced, or vanished catalytic activity. It can be distinguished between changes that concern single amino acid residues and those leading to fragmentation of polypeptide chains or covalent cross-linking of two amino acids either of the same or of two different proteins [START_REF] Voss | Degradation and accumulation of oxidized proteins in agerelated diseases[END_REF]. Oxidative protein modifications might be differentiated into three stages [START_REF] Jung | The proteasomal system[END_REF] The sulfur-containing amino acids methionine und cysteine are most susceptible to oxidative modifications [START_REF] Levine | Methionine residues may protect proteins from critical oxidative damage[END_REF]. Inter-and intramolecular disulfide bonds as well as the formation of sulfenic acid, sulfinic acid and sulfonic acid are possible implications of an interaction between ROS and cysteine [START_REF] Voss | Degradation and accumulation of oxidized proteins in agerelated diseases[END_REF].

The ROS action with methionine results in methionine sulfoxide, which can further be oxidized to methionine sulfone. Additionally, primary oxidation products of aliphatic amino acids are for example 2-oxo-histidine (from histidine), glyoxylic acid (from aspartate), glutamic semialdehyde (from arginine or proline) and 3-hydroxyleucin, 4hydroxyleucin and 5-hydroxyleucin (from leucine). Protein-bound carbonyls are the remaining structures if aliphatic side chains of amino acid residues are removed via β-scission. Therefore, protein carbonyls are suited to be a reliable biomarker of an ongoing protein oxidation.

Aromatic side chains are further important targets of oxidative damage, resulting in primary products like 2-oxo-histidine (from histidine), 3,4-dihydroxyphenylalanine (from tyrosine) or further modifications like dityrosine and kynurenine [START_REF] Voss | Degradation and accumulation of oxidized proteins in agerelated diseases[END_REF]. Besides these ROS reactions on the amino acid residues, peptide backbone might be another target of ROS resulting either in fragmentation of the protein or aggregation due to polymerization. It has to be mentioned that the described modifications are mainly due to the action of ROS and that a large number of further amino acid derivates might also be formed by the action of reactive species derived from nitrogen monoxide or hypochloric acid (for details see [START_REF] Voss | Degradation and accumulation of oxidized proteins in agerelated diseases[END_REF].

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Considering the multiple adducts of possible protein damage it is a major challenge to establish techniques, especially for the quantification of small amounts of oxidized proteins. The literature provides us with a large number of examples of which methods are useful for the detection of oxidation-induced protein modifications.

Among them, fluorescence detection of peroxynitrite-induced modifications of glyceraldehydes-3-phosphate [START_REF] Buchczyk | Modifications of glyceraldehyde-3-phosphate dehydrogenase induced by increasing concentrations of peroxynitrite: early recognition by 20S proteasome[END_REF], electrophoresis of irradiated GAPDH [START_REF] Voss | Irradiation of GAPDH: a model for environmentally induced protein damage[END_REF] and the quantification of reduced SH-groups by DTNB [START_REF] Voss | Protective effect of antioxidants against sarcoplasmic reticulum (SR) oxidation by Fenton reaction, however without prevention of Ca-pump activity[END_REF]) may serve as selected examples. More specific methods to detect several amino acid modifications are chromatographic and mass spectrometric methods (for review see [START_REF] Tsikas | Recent methodological advances in the mass spectrometric analysis of free and protein-associated 3-nitrotyrosine in human plasma[END_REF][START_REF] Requena | Recent advances in the analysis of oxidized proteins[END_REF]. Most of these methods have the additional potential to determine protein glycation and nitration adduct residues [START_REF] Thornalley | Quantitative screening of protein glycation, oxidation, and nitration adducts by LC-MS/MS: protein damage in diabetes, uremia, cirrhosis, and Alzheimer's disease[END_REF]. Proteomics is another technical approach to investigate the extent of oxidative damage occurring at the protein level offering a rapid means of identifying oxidatively modified proteins without the limitations of immunochemical detection methods. Recent advances in proteomics research made possible the identification of novel biomarkers and therapeutic targets in several human diseases (for review on proteomics see [START_REF] Dalle-Donne | Proteins as sensitive biomarkers of human conditions associated with oxidative stress[END_REF].

Protein oxidation products from a biological point of view

The Although a certain endogenous degradation or repair of oxidative protein modifications is inevitable, it is pivotal for the presence/existence of high circulating levels that the degradation as well as the repair is much lower than the formation of the oxidative protein modification. Since most compounds suitable as biomarkers are not metabolically stable, a successful determination is dependent on the quality of the sampling process. A time-dependent decline in the protein carbonyl content was demonstrated by Jung et al. (2006) after inducing protein oxidation by a H 2 O 2treatment. This emphasizes the relevance of choosing a meaningful sampling time. It

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was recently shown that an overexpression of the repair enzyme methionine sulfoxide reductase 2 noticeably prevents the formation of irreversibly oxidized proteins that accumulate upon oxidative stress by maintaining a low level of intracellular ROS [START_REF] Cabreiro | Overexpression of mitochondrial methionine sulfoxide reductase B2 protects leukemia cells from oxidative stress-induced cell death and protein damage[END_REF].

Methods for the determination of oxidative proteins should be specific and sensitive.

A useful marker of protein oxidation should also be characterized by a high reproducibility. Biological and chemical sample stability is one important factor in this context. Ideally the concentration of the marker is stable over the time from sample drawing until the measurement and freezing and thawing are not significantly changing the readout. Unfortunately, protein oxidation can also take place as an experimental artifact during the analytical process as well as by oxygen in the air, so that it may be difficult to distinguish between the actual and the artificial level of oxidation. Besides methodological influence, the formation of artefacts during sample preparation, sample analysis, and sample storage is discussed to be the main reason for the existence of a low reproducibility.

The formation of protein carbonyls is orders of magnitude greater than other oxidative modifications making them to be the most widely used marker of protein oxidation [START_REF] Stadtman | Oxidatively modified proteins and proteomic technologies[END_REF]. Furthermore, a wide spectrum of methods for the determination of protein carbonyls like colorimetry, ELISA, immunoblotting and immunohistochemistry are available, making the determination of this marker widespread and easy to establish in laboratories. However, since these methods are not very specific and protein carbonyls are partially degraded, its use as a marker of protein oxidation is derogated from a biological point of view. 3-nitrotyrosin is another specific marker of oxidative protein damage which is caused by peroxynitrite or other reactive nitrogen species interacting with protein structures [START_REF] Jung | Nitrotyrosine and protein carbonyls are equally distributed in HT22 cells after nitrosative stress[END_REF]). In contrast to protein carbonyls, only a few methods are available for its determination.

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Protein oxidation products from a medical point of view

In physiological conditions mitochondria produce a large part of the cellular oxidants. This is in contrast to clinical and pathophysiological situations, where also other pathways like a xenobiotic and inflammation-driven oxidant production may significantly contribute [START_REF] Grune | Markers of oxidative stress in ICU clinical settings: present and future[END_REF]. Since it is clearly shown that oxidative stress is deleterious in critically ill patients and associated with poor outcome [START_REF] Motoyama | Possible role of increased oxidant stress in multiple organ failure after systemic inflammatory response syndrome[END_REF], the measurement of oxidative stress is of high clinical relevance. Although numerous parameters have been proposed and used for the determination of oxidative stress in clinical situations, their use is still limited, mostly due to a lack of clinical feasibility. As a consequence, studies on oxidative stress in acute and chronic diseases are still performed in tight collaboration with research-oriented laboratories.

It is discussed that every third protein in a cell of older animals is dysfunctional as enzyme or structural protein due to oxidative damage [START_REF] Poon | Free radicals and brain aging[END_REF]. Therefore, for the prediction of the aging process and age-related diseases the measurement of the protein oxidation is clinically an important tool. To study the contribution of oxidative stress to the clinical outcome, strategies have to be found to measure markers of oxidative stress, especially protein oxidation in clinical routine analysis. although it has limited relevance so far.

Besides the problems resulting from the methods itself (specificity, standardization, accuracy, reproducibility), another problem is the lack of available reference values for protein oxidation parameters. In contrast, reference values for plasma concentrations of HNE in healthy humans are available [START_REF] Gil | Age-associated analysis of oxidative stress parameters in human plasma and erythrocytes[END_REF]. Sample availability is a further important factor which may limit the classification of a biomarker as reliable. Whereas the collection of blood is mostly unproblematic in adults, even small blood volumes of newborns and children may limit the sample availability. Therefore non-invasive invasive methods are appreciated and deliver an advantage over more invasive methods. Examples for non-invasive methods for the measurement of protein oxidation are the determination of modified amino acids in urine and the determination of the skin fluorescence. The collection of buccal mucosa cells is another non-invasive method eligible to be used for the assessment of protein oxidation in a tissue with a relatively short turnover time.

A lot of parameters eligible from a biological point of view, are only limited applicable in clinical settings. A few parameters and techniques are only suitable in experimental models due to highly complex techniques (spin traps, mass spectrometry based methods), to a very short half-life or to their impact by the cellular metabolism. For instance, the blood glutathione status is strongly determined by metabolic influences of the red blood cell and by transport processes of reduced and oxidized glutathione within different tissues [START_REF] Pantke | Oxidized proteins as a marker of oxidative stress during coronary heart surgery[END_REF].

It has been demonstrated in a few clinical settings that protein carbonyls reflect the degree of oxidative stress in a sufficient way. Furthermore, protein carbonyl concentrations in healthy humans related to the individual age have been reported [START_REF] Gil | Age-associated analysis of oxidative stress parameters in human plasma and erythrocytes[END_REF]. From further studies it was concluded that the extent of protein oxidation, assessed by protein carbonyls, correlates well with other markers for oxidative stress. [START_REF] Pantke | Oxidized proteins as a marker of oxidative stress during coronary heart surgery[END_REF] have shown that a 2-to 3-fold increase of It can be summarized that there are some good parameters of protein oxidation available from a biological point of view. Biological significance of a biomarker results from its chemical stability, its formation in high yields, its biological stability and its specificity for protein oxidation (Fig. 1). Biological significance is one of the main

  peroxidation. A decrease in the concentrations of antioxidants as well as antioxidative enzymes in cells or tissues might be a further consequence (or cause) of oxidative stress. Therefore, oxidative stress can be assessed by the analysis of the activity and the function of antioxidative defense systems (f.e. antioxidants, antioxidant enzymes, and essential mineral nutrients). Although a large battery of A C C E P T E D M A N U S C R I P T antioxidants are available, a few methodological disadvantages exist. The primary problem is the stability of antioxidants from the time point of the sample collection over sample storage to the time point of the analysis. The stabilization of samples, which are aimed for vitamin C analysis with metaphosphoric acid, is a possible prevention.

  : proteins in stage I are characterized by a slight oxidation rate, whereas proteins in stage II are additionally defolded. The third stage is the result of long term accumulation of oxidatively modified protein and further chronic A C C E P T E D M A N U S C R I P T oxidation. At this stage proteins might be covalently cross-linked to other proteins in similar stages of modification. Further, by the presence of lipid peroxidation products like malondialdehyde or hydroxynonenal the formation of these aggregates, termed lipofuscin, can be driven.

  nature of the oxidant in the used stress model or individual pathophysiological situation determines the types (the diversity) of oxidative protein products and modifications arising. An accumulation of oxidized proteins and resulting secondary products like protein aggregates and protein cross-links is reported in a multitude of diseases, like atherosclerosis, diabetes, Alzheimer´s disease, Parkinson´s disease, hepatitis, and rheumatic arthritis. Oxidative damage to a protein can result in a loss of function. But this effect does not automatically have a deleterious consequence for A C C E P T E D M A N U S C R I P T the whole cell. It has been shown that key enzymes of the glycolytic pathway are inactivated under oxidative stress, whereas antioxidant responses are driven by the preferential supply with NADPH from the pentose phosphate pathway (Cabiscol and Ros, 2006). Two factors are crucial for the qualification of oxidized proteins or oxidative stressinduced protein modifications as reliable biomarkers of oxidative protein oxidation: biological significance and technical or methodological feasibility. Regarding the biological significance it is important that the biomarker is specific, which means that the formation of the biomarker is mainly due to ongoing oxidative processes and not to other endogenous processes or to an oral uptake. Therefore, the use of a biomarker in biological systems should require the exact knowledge of the routes of formation. Further the biomarker should be formed in the organism in high yields to allow a successful analytical detection and should show a strong correlation/dependence to the extent of (induced) oxidative stress. Despite the usefulness of ortho-tyrosine and dityrosine as indicators of oxidative damage to model proteins and the evidence for age-dependent oxidative changes, it was pointed out that both substances are not useful as indicators for assessing oxidative damage to lens proteins with age (Wells-Knecht et al., 1993).

  There are several factors necessary to consider a parameter of protein oxidation as a useful biomarker in clinical settings. First, the biomarker should be able to estimate the individual profile of protein oxidation and should have clinical relevance. The latter is difficult to realize since the functional relevance of protein oxidation in most pathophysiological situations but also in aging processes is only marginally or not known. A C C E P T E D M A N U S C R I P T Further, it has to be recognized that the determination of a biomarker is mostly a snap-shot. Even so, by means of this singular event the biomarker should fulfil the above mentioned criteria. A reliable and clinically useful biomarker should further be distinguished by a high specificity, accuracy and reproducibility. These factors are mostly influenced by the quality of the sample preparation and stability. The assessment of protein oxidation products is especially hampered by factors like the sample stability since compounds of protein oxidation are very sensitive against autoxidation. Avoiding autoxidation in routine analysis is difficult since a quick stabilization of the samples does mostly not fit with the clinical 'reality' and feasibility. Another problem is that the determination of a lot of biological relevant biomarkers may only be managed by cost-, time-and man power intensive methods. Especially methods based on high-performance liquid chromatography (HPLC) separation and gas-chromatography based methods are time consuming and expensive and therefore hardly applicable in clinical routine analysis. Though, these methods are characterized by a high specificity and sensitivity, especially in the measurement of protein oxidation, making them to the method of choice in research-based laboratories. The measurement of protein carbonyls by antibody-based and spectroscopic methods seems to be an acceptable compromise in clinical settings,

  By the use of validated reference values it would be possible to characterize the individual risk as well as to make inter-study comparisons. The lack of available reference values results mainly from the multitude of available methods for the determination of one A C C E P T E D M A N U S C R I P T parameter (for example protein carbonyls) facing huge differences in data reproduction. Further, the neglected used of those methods in clinical studies so far slows the establishment of reference values additionally.

  the reperfusion period during cardiovasculary surgery correlates strongly with other markers of oxidative stress.

Fig. 1

 1 Fig. 1 Demands for biomarkers of protein oxidation in clinical settings


Judith S Brand

Ingeborg Van Der Tweel

Diederick E Grobbee

Mariellle H Emmelot-Vonk

Yvonne T Van Der Schouw
email: y.t.vanderschouw@umcutrecht.nl

Prof T Yvonne

Van Der

Julius Schouw

Testosterone, SHBG and the metabolic syndrome: a systematic review and meta-analysis of 1 observational studies. 2 3 4

Keywords: Testosterone, SHBG, metabolic syndrome, systematic review, meta-analysis, observational studies Testosterone, SHBG, metabolic syndrome, systematic review, meta-analysis, 21 observational studies

Background: Accumulating evidence suggests a sex-dependent role of circulating testosterone in the 2 metabolic syndrome (MetS). 3 Methods: We conducted a meta-analysis of observational studies (PubMed and EMBASE -May 1, 4 2010) relating MetS to determinants of testosterone status [total testosterone (TT), free testosterone 5 (FT) and sex hormone-binding globulin (SHBG)].

6 Results: Fifty-two studies were identified, comprising 22 043 men and 7839 women and presenting 7 relative risk (RR) estimates or hormone levels for subjects with and without MetS. Endogenous TT 8 and FT levels were lower in men with MetS (TT mean difference = -2.64 nmol/L; 95% CI, -2.95, -9 2.32, FT standardized mean difference = -0.26 pmol/L; 95% CI, -0.39, -0.13) and higher in women 10 with MetS (TT mean difference = 0.14 nmol/L; 95% CI, 0.07, 0.20, FT standardized mean difference 11 = 0.52 pmol/L; 95% CI, 0.33, 0.71) compared with those without. Similarly, men with higher TT 12 levels had a lower MetS risk (RR estimate = 0.38; 95% CI, 0.28, 0.50) while TT increased the risk of 13 MetS in women (RR estimate = 1.68; 95% CI, 1.15, 2.45). In both sexes, higher SHBG levels were 14 associated with a reduced risk (men: RR estimate = 0.29; 95% CI, 0.21, 0.41, women: RR estimate = 15 0.30; 95% CI, 0.21, 0.42).

16

Conclusion: This meta-analysis supports the presence of a sex-dependent association between 17 testosterone and MetS: TT and FT levels are lower in men with MetS, whilst they are higher in 18 women with MetS. There are no indications for a sex-specific association between SHBG and MetS.

19

In both men and women, MetS is associated with lower SHBG levels. 20

The metabolic syndrome (MetS) is a constellation of metabolic risk factors (including hypertension, 2 dyslipidemia, abdominal obesity and impaired glucose metabolism), that is associated with a two-fold 3 increased risk of cardiovascular disease, and an even higher risk of type 2 diabetes 1,2 . Over the past 7

The prevalence of MetS increases with age and is higher in men than in women 6 . MetS associated 8 risks seem to vary according to sex, with MetS being a stronger risk factor for cardiovascular disease 9 in women than men 7,8 . 10 Besides sex differences in prevalence and prognosis, factors associated with the occurrence of MetS 11 may also vary by gender. Previous studies have suggested a role for sex hormones in the development 12 of MetS. Androgen-deprivation therapy in prostate cancer patients 9 , and low total testosterone levels 13 (TT) in hypogonadal men 10,[START_REF] Ford | Risks for all-cause mortality, cardiovascular disease, and diabetes associated with 2 the metabolic syndrome: a summary of the evidence[END_REF] have been associated with the metabolic syndrome. On the other hand, 14

MetS and its individual components are common in hyperandrogenic conditions in women, such as 15 the polycystic ovary syndrome (PCOS) 12,[START_REF] Glueck | Incidence and treatment of 32 metabolic syndrome in newly referred women with confirmed polycystic ovarian syndrome[END_REF] . Sex hormone-binding globulin (SHBG), a testosterone 16 transport protein that affects the circulating levels of free testosterone (FT), has also been linked to 17

MetS. Low SHBG levels have been observed in both men and women with MetS 14,15 . However, little 18 is known about possible sex differences in this association. Furthermore, several studies have 19 examined the relationship between FT and MetS, although their findings have been inconsistent in 20 men [16][17][18] and women 15,19,20 .

21

To systematically asses the associations of MetS with TT, SHBG and FT, and to investigate possible 22 sex differences in these associations, we conducted a meta-analysis of observational studies relating 23 endogenous TT, SHBG and/or FT levels to the metabolic syndrome in men and women separately. 4

The primary measure of association was the mean difference in TT, SHBG and FT levels between 5 subjects with and without MetS. For the calculation of mean differences, medians and geometric 6 means were assumed to equal means. If studies provided ranges or interquartile ranges instead of SDs, 7

approximate SDs were derived using data extraction methods of Higgins 22 and Hozo 23 et al.

8

For studies relating TT, SHBG and FT to MetS risk, RR estimates were included as a secondary 9 measure of association. ORs, RRs, HRs and PRs adjusted for the largest number of confounders were 10 extracted. Adjustments for other hormones and components part of the MetS definition were omitted, 11 as these might obscure true associations. Since individual studies reported RR estimates based on 12 various cut-off levels (tertiles, quartiles or specific thresholds) or as a 1 SD increase in testosterone 13 and SHBG, RR estimates were transformed to a uniform scale (comparing the highest versus lowest 14 tertile of TT, SHBG and FT) using the method of Danesh et al. 24

The quality of each study was assessed against the following criteria: 1. population-based sample. 2.

21

exclusion of subjects on hormonal therapy. 3. use of fasting blood samples for assessment of MetS Attemps were made to contact authors when further information was needed for meta-analytic 1 calculations. We contacted 13 authors for missing data of whom 9 provided additional data 15,26-33 2 3

Data Synthesis and Analysis

4

Measures of association were analyzed for men and women separately, unless results showed no clear 5 indications for an interaction by sex. To compare TT and SHBG levels between subjects with and 6

without MetS, pooled analyses were performed using unstandardized mean differences of TT and 7 SHBG. For the comparison of FT levels, standardized mean differences (mean differences divided by 8 the pooled standard deviation) were used, because individual studies used various methods for FT 9 assessment.

10

Between-study heterogeneity was quantified by the I 2 statistic 22 . Random-effects models of 11

DerSimonian and Laird 34 were applied in obtaining pooled estimates of association measures.

12 Univariable metaregression analyses including sex as covariate were conducted to assess sex were not considered when there were fewer than 10 studies available.

2

To investigate the impact of each quality parameter separately, sensitivity analyses were conducted in 3 which studies not meeting the individual criteria were excluded. Since direct radioimmunoassay 4 (RIA) is a less reliable method for measuring free testosterone levels 36 , the impact of this assay was 5 also investigated in sensitivity analyses. To asses the presence of possible publication bias, funnel 6 plots were drawn and correlations between standardized association measures and their corresponding 7

SEs were analyzed using Egger's test 37 . In case of publication bias, the "trim and fill" method of 8

Duval and Tweedie 38 was used to correct for this bias. All analyses were conducted using STATA 1 and2. In men, 26 studies were cross-sectional, 5

12

were longitudinal and 1 study used a case-control design. In women, 19 studies were cross-sectional 13 and 1 study used a case-control design. Nine studies included PCOS patients. Of these, 5 studies used 14 the NICHD criteria to define PCOS 73 , 3 studies used the Rotterdam criteria [START_REF] Merriam | Revised 2003 19 consensus on diagnostic criteria and long-term health risks related to polycystic ovary 20 syndrome[END_REF] and in 1 study PCOS 2A). In multivariable metaregression 5 analyses this sex-dependent association remained significant (P < 0.001) after adjusting for study level 6 differences in age, BMI, diabetes status and control for age.

7

In men, there was evidence of substantial between-study heterogeneity (I 2 = 89.1%), which was not 8 explained by BMI, diabetes status, control for age or study design. However, in stratified and 9 metaregression analyses TT mean differences were smaller in studies applying NCEP ATP III criteria 10 (P = 0.03) (Table 3). Furthermore, metaregression analyses including age as continuous term showed 11 a trend (P = 0.08) towards a stronger association in younger men. In women, no significant 12 heterogeneity was observed (I 2 = 28.5%), though the association between TT and MetS appeared to be 13 stronger in women without PCOS (P = 0.02) (Table 3). In sensitivity analyses, differences in study 14 quality did not influence associations between TT and MetS in both men and women. (mean difference = 0.52; 95% CI, 0.33, 0.71), while men with the metabolic syndrome had lower 14 levels of FT than those without (mean difference = -0.26; 95% CI, -0.39, -0.13) (Figure 2C). This sex-15 dependent association remained significant in multivariable analyses.

16

Substantial between-study heterogeneity was observed in both men (I 2 = 79.9%) and women (I 2 = 17 61.1%). In men, heterogeneity was partly explained by the different MetS criteria used across studies.

18

As for TT, the inverse association with FT tended to be weaker among studies using NCEP ATP III 19 criteria (P = 0.08) (Table 3). Furthermore, the association between MetS and FT differed according to 20 the mean age of the study population (P = 0.01), with a stronger association being observed in 21 younger men (Table 3). In women, no sources of heterogeneity were identified. In sensitivity analyses, 22 exclusion of studies using RIA did not change the observed associations materially. Associations were 23 also not affected by differences in study quality. Results of this meta-analysis support the presence of a sex-dependent association between endogenous 11 testosterone and MetS. TT levels were lower in men MetS, whilst they were higher in women with 12

MetS. There was also some evidence for a sex-specific association between free testosterone and 13

MetS with FT levels being lower in men with MetS, while being higher in women with MetS.

14 Interestingly no sex specific association was observed for SHBG. In both sexes, MetS was associated 15 with a decrease in SHBG levels. Although the mean difference in SHBG levels between those with 16 and without MetS was larger in women, this sex difference was lost after taking potential confounders 17 into account in pooled analyses of RR estimates.

18 Some limitations of our meta-analysis need to be considered while interpreting the findings. First of 19 all, we could only partly explain between-study heterogeneity. In metaregression analyses we 20 observed that at least some of the heterogeneity in men was explained by differences in age, MetS 21 criteria and study design. In older men the associations of TT and FT with MetS tended to be less 22 pronounced. This effect of age has been reported previously 43 and may be attributed to the age-related showed that the association between SHBG and MetS was more pronounced in leaner women, 9

suggesting that in obesity SHBG is only one of the contributing factors. Another potential source of 10 between-heterogeneity in both men and women is the variety of methods used for measuring free 11 testosterone levels 81,82 . FT values vary between different algorithms and FT measurements by RIA 12 have been criticized due to a lack of accuracy 36 . However, sensitivity analyses showed that the use of

13

RIA did not have a major impact on the association between FT and MetS. In spite of material 14 heterogeneity, we decided to pool the data from all studies. While pooling of heterogeneous studies 15 may affect the validity of the pooled estimates, the results of individual studies were largely 16 compatible with the pooled estimates and pointed in the same direction as the overall estimate. 17

Another concern is the presence of potential publication bias among studies reporting SHBG mean 18 differences in men and FT mean differences in women. However, evaluation of this publication bias 19 by the "trim and fill" method showed that imputation of missing studies did not significantly alter the 20 observed associations of SHBG and FT with MetS. It is important to recognize that asymmetry is not 21 necessarily the result of publication bias, but can also be caused by between-study heterogeneity.

22

A final limitation is the major contribution of cross-sectional studies to our meta-analysis, which 23 precludes us from drawing firm conclusions about temporal associations. In men, findings from four in the opposite direction have been reported as well. In obese men, weight loss and maintenance cause 1 an increase in testosterone and SHBG levels 91,92 and experimental data show suppressive effects of 2 adiposity and insulin on testosterone production in men [93][START_REF] Alberti | Definition, diagnosis and classification of diabetes mellitus and its 10 complications. Part 1: diagnosis and classification of diabetes mellitus provisional report of a 11 WHO consultation[END_REF][95] . Furthermore, MetS has been associated

3 with an increased risk of hypogonadism in middle aged men 96 . Hence, complex, bidirectional 4 relationships between testosterone and MetS seem to be plausible. In women, evidence for a causal 5 role of testosterone in MetS is limited. This is reflected by the lack of longitudinal studies in this 6 meta-analysis. Nevertheless, some recent findings suggest that testosterone may be a risk factor in 7 women as well. In a prospective study 97 , low SHBG and high testosterone levels at baseline were 8

found to be associated with an increased MetS risk. Furthermore, high testosterone levels have been 9

associated with increased risk of diabetes in postmenopausal women 98 and a decrease in insulin 10 sensitivity in female rats 99 . On the other hand, metformin therapy and weight loss reduce androgen 11 excess in women 100,101 , while insulin stimulates the ovarian production of testosterone 102 .

12 Since TT and SHBG are correlated, it is also unclear whether the observed associations between 13 SHBG and MetS reflect an independent effect of SHBG. However, increasing evidence from 14 epidemiological studies support the involvement of SHBG in MetS

F o r R e v i e w

O n l y 30

  Testosterone, SHBG and the metabolic syndrome: a systematic review and meta-analysis of observational studies.

4

  years, various definitions of MetS have been introduced, of which those proposed by the National 5 Cholesterol Education Program -Adult Treatment Panel III (NCEP ATP III) 3 , the World Health 6 Organization (WHO) 4 and the International Diabetes Federation (IDF) 5 are the most widely used.

  22 components. 4. adjusted analysis for potential confounders. An extra criterion was added for studies 23 including men: 5. blood sample collection for hormonal assessment in the morning. Studies with a 24 population based sample were defined as those including subjects from the community, who were not 25 institutionalized, clinic based or known to have MetS. Each criterion was graded as 'yes', 'no' or

9 11 .

 11 1 (StataCorp., College Station, Tex., U.S.A.). process is described in Figure 1. Our initial search yielded 596 articles. Of these, 3 428 articles were excluded based on abstract review. After full text review, an extra 116 studies were 4 excluded because of lack of measure of interest (n = 91), lack of standard MetS definition (n = 7), 5 inappropriateness of reported association measure for inclusion (n = 8), multiple publication (n = 7), 6 unavailability of full text (n = 2) and no correct stratification of MetS (n = 1), leaving 52 studies 7 eligible for inclusion, 32 including men, 19 including women and 1 study including both men and

13

 13

  23decline in testosterone, resulting in a lower contrast in TT and FT with increasing age. Associations of 24 TT and FT with MetS were also weaker when NCEP ATP II criteria were used. These criteria differ 25 from other criteria in degree of emphasis of the individual MetS components. While the NCEP ATP 26 III criteria put equal emphasis on the five MetS components, other criteria assign greater value to a 27 particular component: impaired glucose metabolism (WHO and EGIR) and presence of abdominal 28 ). Therefore, this differential effect of MetS criteria suggests that abdominal obesity and 1 impaired glucose metabolism are important mediators of the observed associations between 2 testosterone and MetS in men. Furthermore, analyses stratified for study design showed stronger 3 associations in cross-sectional studies. This may indicate that the 'rare disease assumption' does not 4 apply to the metabolic syndrome, with odds ratios from cross-sectional studies overestimating the 5 actual association. In women, the association between TT and MetS was weaker in PCOS patients 6 High baseline levels of testosterone in this specific patient population may result in lower 7 interindividual variation and low power to detect an association. Metaregression analyses further 8

  24longitudinal studies18,25,43,48 support a causal role for testosterone in the MetS etiology. Experimental 25 studies have demonstrated that testosterone has beneficial effect on glucose and fat metabolism in 26 male rats[83][84][85][86] . Moreover, intervention studies in hypogonadal have shown improvements in individual 27 components87,88 and even reversal of MetS following testosterone therapy89,90 . However, associations 28

F o r R e v i e w O n l y 31

 31

Figure 3B

 3B Figure 3BRandom effects pooled relative risk estimate for MetS comparing highest versus lowest tertile of SHBG, men and women. CI, confidence interval; MetS, metabolic syndrome; SHBG, sex hormone-binding globulin. Sizes of squares represent the weight of each study.

  sex, mean age and BMI, PCOS status in women, number of subjects with and 2 without MetS, mean and SD (derived if SE or 95% CI reported) of TT, SHBG and FT in subjects with 3 and without MetS and relative risk (RR) estimates).

	1	criterion regarding type 2 diabetes and variables incorporated in multivariable analyses); 2. study
		sample characteristics (
		F o r F o r
		R R
		e v e v
		i e i e
		w w
		O n l O n l
		y y
	24
	25
	26
	27

Table 1A

 1A The lack of a sex specific association between SHBG and MetS is not fully understood. Nevertheless, 1 recent findings from genetic studies 112,113 provide some explanation. In these studies, one particular 2 SHBG SNP, rs1799941, was found to have no effect on total testosterone levels in women while Fukai S, Hashimoto M, et al. Association of low testosterone with metabolic 1 syndrome and its components in middle-aged Japanese men. Hypertens Res 2010; 33: 587-2 91. 3 28 de Oya I, Schoppen S, Lasuncion MA, et al. Sex hormone-binding globulin levels and 4 metabolic syndrome and its features in adolescents. Pediatr Diabetes 2010; 11: 188-94. 5 29 Chen RY, Wittert GA, Andrews GR. Relative androgen deficiency in relation to obesity 6 and metabolic status in older men. Diabetes Obes Metab 2006; 8: 429-35. 7 30 Tong PC, Ho CS, Yeung VT, et al. Association of testosterone, insulin-like growth factor-I, 8 and C-reactive protein with metabolic syndrome in Chinese middle-aged men with a family 9 history of type 2 diabetes. J Clin Endocrinol Metab 2005; 90: 6418-23. 10 31 Laughlin GA, Barrett-Connor E, Bergstrom J. Low serum testosterone and mortality in 11 older men. J Clin Endocrinol Metab 2008; 93: 68-75. 12 32 Weinberg ME, Manson JE, Buring JE, et al. Low sex hormone-binding globulin is 13 associated with the metabolic syndrome in postmenopausal women. Akgul K, Akar Z, et al. Association between severity of lower urinary tract 1 symptoms, erectile dysfunction and metabolic syndrome. Aging Male 2009; 12: 29-34. 2 43 Haring R, Volzke H, Felix SB, et al. Prediction of metabolic syndrome by low serum 3 testosterone levels in men: results from the study of health in Pomerania. Kawai K, Hinotsu S, et al. Negative impact of metabolic syndrome on the 12 responsiveness to sildenafil in Japanese men. J Sex Med 2008; 5: 1443-50. 13 47 Yeh HC, Wang CJ, Lee YC, et al. Association among metabolic syndrome, testosterone 14 level and severity of erectile dysfunction. Kaohsiung J Med Sci 2008; 24: 240-7. 15 48 Rodriguez A, Muller DC, Metter EJ, et al. Aging, androgens, and the metabolic syndrome 16 in a longitudinal study of aging. J Clin Endocrinol Metab 2007; 92: 3568-72. 17 49 Tang YJ, Lee WJ, Chen YT, Liu PH, Lee MC, Sheu WH. Serum testosterone level and 18 related metabolic factors in men over 70 years old. J Endocrinol Invest 2007; 30: 451-8. 19 Characteristics of studies reporting on TT, SHBG and/or FT levels in men with and without MetS

	F o r R e v i e w raising testosterone levels in men. Based on these data, it has been hypothesized that women with genetically lower SHBG levels are exposed to proportionally more of the adverse effects of the biologically active unbound testosterone, such as increasing risk of MetS and diabetes in women. On the other hand, in men there is recent evidence that bound testosterone may be biologically active. If this is the case, then men with lower total testosterone due to lower SHBG will be exposed to less of the protective metabolic effects of androgens, despite similar levels of unbound or free testosterone F o r R e v i e w Key messages 1. Associations between endogenous testosterone and the metabolic syndrome (MetS) are sex-specific with total and free testosterone levels being lower in men with MetS, while being higher in women with MetS. 2. There are no indications for a sex-specific association between SHBG and MetS. In both men and women MetS is associated with lower SHBG levels. 3. The large contribution of cross-sectional studies (particularly in women), stresses the need for more longitudinal studies, Mendelian randomization studies and intervention studies to establish the causal nature of the observed association between testosterone, SHBG and MetS. F o r R e v i e w F o r R e v i e w F o r R e v i e w 14 Muller M, Grobbee DE, den Tonkelaar I, Lamberts SW, van der Schouw YT. Endogenous sex hormones and metabolic syndrome in aging men. J Clin Endocrinol Metab 2005; 90: 2618-23. 15 Maggio M, Lauretani F, Ceda GP, et al. Association of hormonal dysregulation with metabolic syndrome in older women: data from the InCHIANTI study. Am J Physiol Endocrinol Metab 2007; 292: E353-8. 16 Laaksonen DE, Niskanen L, Punnonen K, et al. Sex hormones, inflammation and the metabolic syndrome: a population-based study. Eur J Endocrinol 2003; 149: 601-8. 17 Corona G, Mannucci E, Petrone L, et al. A comparison of NCEP-ATPIII and IDF metabolic syndrome definitions with relation to metabolic syndrome-associated sexual dysfunction. J Sex Med 2007; 4: 789-96. 18 Kupelian V, Page ST, Araujo AB, Travison TG, Bremner WJ, McKinlay JB. Low sex hormone-binding globulin, total testosterone, and symptomatic androgen deficiency are associated with development of the metabolic syndrome in nonobese men. J Clin Endocrinol Metab 2006; 91: 843-50. 19 Leibel NI, Baumann EE, Kocherginsky M, Rosenfield RL. Relationship of adolescent polycystic ovary syndrome to parental metabolic syndrome. J Clin Endocrinol Metab 2006; 91: 1275-83. 20 Korhonen S, Hippelainen M, Vanhala M, Heinonen S, Niskanen L. The androgenic sex hormone profile is an essential feature of metabolic syndrome in premenopausal women: a controlled community-based study. Fertil Steril 2003; 79: 1327-34. 21 Stroup DF, Berlin JA, Morton SC, et al. Meta-analysis of observational studies in epidemiology: a proposal for reporting. Meta-analysis Of Observational Studies in F o r R e v i e w 27 Akishita M, Metabolism 2006; 55: 1473-80. 33 Guay AT, Jacobson J. The relationship between testosterone levels, the metabolic syndrome (by two criteria), and insulin resistance in a population of men with organic erectile dysfunction. J Sex Med 2007; 4: 1046-55. 34 DerSimonian R, Laird N. Meta-analysis in clinical trials. Control Clin Trials 1986; 7: 177-88. 35 Norman RJ, Dewailly D, Legro RS, Hickey TE. Polycystic ovary syndrome. Lancet 2007; 370: 685-97. F o r R e v i e w 56 Nuver J, Smit AJ, Wolffenbuttel BH, et al. The metabolic syndrome and disturbances in hormone levels in long-term survivors of disseminated testicular cancer. J Clin Oncol 2005; 82 Ho CK, Stoddart M, Walton M, Anderson RA, Beckett GJ. Calculated free testosterone in men: comparison of four equations and with free androgen index. Ann Clin Biochem 2006; Nr. Source 3 4 5 6 7 8 9 1 2 3 4 5 6 7 8 9 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 14 15 16 17 18 19 20 21 22 1 2 1 2 Country Study Participants Mean Mean Adjusted for MetS No. Table 1A Continued 42 Demir O, Diabetes 2009; 58: 4 2027-31. 5 44 Emmelot-Vonk MH, Verhaar HJ, Nakhai Pour HR, et al. Effect of testosterone 6 supplementation on functional mobility, cognition, and other parameters in older men: a 7 randomized controlled trial. JAMA 2008; 299: 39-52. 8 45 Goncharov NP, Katsya GV, Chagina NA, Gooren LJ. Three definitions of metabolic 9 syndrome applied to a sample of young obese men and their relation with plasma 10 testosterone. Aging Male 2008; 1-5. 11 46 Suetomi T, 50 Gannage-Yared MH, Khalife S, Semaan M, Fares F, Jambart S, Halaby G. Serum 20 adiponectin and leptin levels in relation to the metabolic syndrome, androgenic profile and 21 somatotropic axis in healthy non-diabetic elderly men. Eur J Endocrinol 2006; 155: 167-76. 22 F o r R e v i e w 23: 3718-25. 3 57 Alemzadeh R, Kichler J, Calhoun M. Spectrum of metabolic dysfunction in relationship 4 with hyperandrogenemia in obese adolescent girls with polycystic ovary syndrome. Eur J 5 Endocrinol 2010; 162: 1093-9. 6 58 Healy LA, Ryan AM, Carroll P, et al. Metabolic syndrome, central obesity and insulin 7 resistance are associated with adverse pathological features in postmenopausal breast cancer. 8 Clin Oncol (R Coll Radiol) 2010; 22: 281-8. 9 59 Ni RM, Mo Y, Chen X, Zhong J, Liu W, Yang D. Low prevalence of the metabolic 10 syndrome but high occurrence of various metabolic disorders in Chinese women with 11 polycystic ovary syndrome. Eur J Endocrinol 2009; 161: 411-8. 12 60 de Sousa G, Brodoswki C, Kleber M, Wunsch R, Reinehr T. Association between 13 androgens, intima-media thickness and the metabolic syndrome in obese adolescent girls. 14 Clin Endocrinol (Oxf) 2010; 72: 770-4. 15 61 Janssen I, Powell LH, Crawford S, Lasley B, Sutton-Tyrrell K. Menopause and the 16 metabolic syndrome: the Study of Women's Health Across the Nation. Arch Intern Med 2008; 17 168: 1568-75. 18 62 Park HR, Choi Y, Lee HJ, Oh JY, Hong YS, Sung YA. The metabolic syndrome in young 19 Korean women with polycystic ovary syndrome. Diabetes Res Clin Pract 2007; 77 Suppl 1: 20 S243-6. 21 63 Coviello AD, Legro RS, Dunaif A. Adolescent girls with polycystic ovary syndrome have 22 an increased risk of the metabolic syndrome associated with increasing androgen levels 23 F o r R e v i e w F o r R e v i e w 43: 389-97. 3 83 Grillo ML, Jacobus AP, Scalco R, et al. Testosterone rapidly stimulates insulin release 4 from isolated pancreatic islets through a non-genomic dependent mechanism. Horm Metab 5 Res 2005; 37: 662-5. 6 84 Xu XF, De Pergola G, Bjorntorp P. Testosterone increases lipolysis and the number of 7 beta-adrenoceptors in male rat adipocytes. Endocrinology 1991; 128: 379-82. 8 85 Singh R, Artaza JN, Taylor WE, et al. Testosterone inhibits adipogenic differentiation in 9 3T3-L1 cells: nuclear translocation of androgen receptor complex with beta-catenin and T-10 cell factor 4 may bypass canonical Wnt signaling to down-regulate adipogenic transcription 11 factors. Endocrinology 2006; 147: 141-54. 12 86 Marin P, Oden B, Bjorntorp P. Assimilation and mobilization of triglycerides in 13 subcutaneous abdominal and femoral adipose tissue in vivo in men: effects of androgens. J 14 Clin Endocrinol Metab 1995; 80: 239-43. 15 87 Kapoor D, Goodwin E, Channer KS, Jones TH. Testosterone replacement therapy improves 16 insulin resistance, glycaemic control, visceral adiposity and hypercholesterolaemia in 17 hypogonadal men with type 2 diabetes. Eur J Endocrinol 2006; 154: 899-906. 18 88 Boyanov MA, Boneva Z, Christov VG. Testosterone supplementation in men with type 2 19 diabetes, visceral obesity and partial androgen deficiency. Aging Male 2003; 6: 1-7. 20 89 Heufelder AE, Saad F, Bunck MC, Gooren L. Fifty-two-week treatment with diet and 21 exercise plus transdermal testosterone reverses the metabolic syndrome and improves 22 glycemic control in men with newly diagnosed type 2 diabetes and subnormal plasma 23 F o r R e v i e w F o r R e w 19 Kaplan et al, 2006 51 U.S.A. CS Men with dyslipidemia 52 27.4 No 265 597 14.0 (4.7) Total Testosterone Mean (SD), nmol/L Mean (SD), nmol/L 16.1 (4.9) -20 Kupelian et al, 2006 18 U.S.A. CS e Men from the Massachusetts Male Aging Study 53 a 27.1 a No 146 950 15.6 (6.4) 18.4 (5.9) 26.1 (11.8) SHBG 33.6 (16.1) -21 Maggio et al, 2006 52 Italy CS Men from the InCHIANTI study 75 26.6 a No 73 389 13.8 (4.8) 15.0 (4.5) 83.6 (30.8) 104.0 (46.1) e 2006 50 i 17 Chen et al, 2006 29 Australia CS Nondiabetic men from the Australian Longitudinal Study of Aging 76 26.0 No 20 140 12.1 (3.6) 14.2 (4.7) --18 Gannagé-Yared et al, Lebanon CS Nondiabetic men 59 27.3 No 94 59 12.5 (3.8) 14.3 (4.0) 34.0 (13.7) 41.0 (15.5) v F o r R e v i e w O n l y design age BMI age Yes No MetS + MetS -MetS + MetS -Nr. Source Country Study Participants Mean Mean Adjusted for No. Total Testosterone SHBG 1 Katabami et al, 2010 39 Japan CS Nondiabetic men 46 23.7 No 70 204 ----design age BMI age 2 MetS + MetS -MetS + MetS -MetS + MetS -de Oya et al, 2010 28 d Spain CS Adolescent boys 14 21.9 No 13 377 --28.8 (15.2) 51.0 (34.6) 3 22 Mousavinasab et al, Finland LO Military service men on a high-caloric high-fat 17-28 24.3 a No 11 169 --15.1 (6.6) 19.1 (10.2) Atlantis et al, 2009 40 Australia CS Men from the Florey Adelaide Male Ageing 53 -No 445 737 12.2 (4.9) 15.3 (5.6) 30.8 (14.0) 37.1 (17.1) 2006 53 diet (1) b Study 23 Robeva et al, 2006 54 Bulgaria CC Nondiabetic, hyperinsulinaemic men with MetS 30 30.6 Yes 10 10 12.1 (3.7) 21.5 (7.5) --Atlantis et al, 2009 40 Australia Cs Men from the Florey Adelaide Male Ageing 53 -No 498 691 12.1 (4.8) 15.5 (5.6) 31.2 (15.5) 37.2 (16.5) and healthy-age matched controls (2) b Study 4 Coviello et al, 2009 41 (1) d U.S.A. CS Fathers of women with PCOS 57 30.2 No 89 122 12.8 (5.2) 15.6 (4.3) 72 (40) 80 (37) Coviello et al, 2009 41 (2) d U.S.A. CS Brothers of women with PCOS 29 28.7 No 13 45 15.0 (4.6) 18.8 (7.0) 46 (22) 55 (26) 5 Demir et al, 2009 42 Turkey CS Men with lower urinary tract symptoms 60 a 27.4 a No 60 130 14.0 (5.2) 16.0 (6.1) --6 Haring et al, 2009 43 Germany LO Men from the Study of Health in Pomerania 49 26.4 No 480 524 15.5 (4.8) 17.7 (5.3) --7 Chubb et al, 2008 10 Australia CS Nondiabetic men from the Health in Men Study 76 a 26.2 a No 602 1900 14.0 (4.9) 16.7 (5.7) 36.8 (14.0) 45.5 (17.0) 8 Emmelot-Vonk et al, 2008 44 Netherlands CS Nondiabetic men with low normal testosterone levels 67 27.3 No 62 160 12.7 (2.3) 13.5 (2.4) 28.9 (8.9) 34.4 (10.5) 9 Goncharov et al, 2008 45 (1) b Russia CS Nondiabetic obese men 31 32.6 No 34 26 11.2 (4.0) 16.3 (6.8) 29.7 (21.5) 45.2 (32.0) Goncharov et al, 2008 45 (2) b Russia CS Nondiabetic obese men 31 32.6 No 23 37 10.9 (4.4) 15.0 (6.3) 33.9 (27.2) 37.4 (28.0) Goncharov et al, 2008 45 (3) b Russia CS Nondiabetic obese men 31 32.6 No 27 33 11.2 (4.6) 15.3 (6.3) 31.4 (22.3) 40.4 (30.7) 10 Laughlin et al, 2008 31 U.S.A. CS Men from the Rancho Bernardo Study 71 25.7 Yes 143 651 8.5 (2.8) 10.8 (3.4) --11 Suetomi et al, 2008 46 Japan CS Men with erectile dysfunction 60 23.9 a No 25 108 15.3 (5.5) 16.0 (5.9) --12 Yeh et al, 2008 47 Taiwan CS Men with erectile dysfunction 58 24.9 a No 38 65 12.4 (5.8) 16.2 (5.9) --13 Corona et al, 2007 17 (1) b Italy CS Male patients with sexual dysfunction 52 -No 348 738 13.6 (6.0) 17.4 (7.2) --Corona et al, 2007 17 (2) b Italy CS Male patients with sexual dysfunction 52 -No 485 601 14.7 (7.4) 18.2 (6.0) --14 Guay et al, 2007 33 (1) b U.S.A. CS Men with erectile dysfunction 54 29.4 No 88 66 ----Guay et al, 2007 33 (2) b U.S.A. CS Men with erectile dysfunction 54 29.4 No 54 100 ----15 Rodriguez et al, 2007 48 U.S.A. CS e Caucasian men from the Baltimore Longitudinal Study of Aging 63 26.0 Yes 113 505 12.8 (0.2) 14.9 (0.1) 62.9 (2.8) 82.1 (1.6) 16 Tang et al, 2007 49 Taiwan CS Men residing in a veterans' nursing home 79 23.8 No 101 280 13.3 (0.6) 16.2 (0.4) 39.9 (1.6) 53.9 (1.2) F o r R e v i e w O n l y 24 Kalme et al, 2005 55 Finland CS Men from the Finish part of the Seven Countries Study 70-89 -Yes 94 241 16.4 (9.4) 23.2 (9.9) 54.4 (27.1) 74.4 (31.0) 25 Muller et al, 2005 14 Netherlands CS Independently living men 60 26.3 No 96 304 15.7 (4.5) 19.4 (5.3) 34.7 (12.4) 42.4 (14.6) 26 Nuver et al, 2005 56 Netherlands CS Testicular cancer patients treated with chemotherapy 38 25.4 No 22 62 18.3 (5.0) 20.0 (8.0) 20.0 (6.0) 26.0 (9.0) 27 Tong et al, 2005 30 (1) d China CS Men from the Hong Kong Diabetes Family Study without a family history of diabetes 44 24.7 Yes 30 98 15.8 (4.0) 18.4 (6.1) 27.1 (9.3) 30.8 (13.2) Tong et al, 2005 30 (2) d China CS Men from the Hong Kong Diabetes Family Study with a family history of diabetes 39 25.9 Yes 70 109 16.0 (3.7) 18.3 (5.6) 21.2 (8.6) 27.4 (14.4) 28 Laaksonen et al, 2003 16 Finland CS Nondiabetic men from the Kuopio Ischaemic Heart Disease Risk Factor Study 53 b 26.8 b No 345 1551 17.6 (6.8) 21.6 (7.4) 31.2 (13.0) 38.1 (15.6) F o r R e v i e w O n l y	Mean (SD), pmol/L Free Testosterone Mean (SD), pmol/L --430.0 (190.0) 470.0 (180.0) 145.7 (48.8) 131.9 (56.6) ----MetS + Free Testosterone MetS -40.6 (13.9) c 51.01 (16.0) c MetS + MetS -----------------274.6 (88.2) 291.2 (90.8) 376.5 (104.0) 345.4 (128.5) 249.0 (94.0) 294.0 (129.0) 230.0 (95.0) 295.0 (120.0) 236.0 (85.0) 296.0 (125.0) --33.7 (12.8) 36.1 (11.8) --34.8 (14.0) c 40.8 (13.7) c 36.2 (14.1) c 42.5 (13.5) c 42.7 (18.4) c 49.3 (22.9) c 39.6 (10.4) c 51.4 (22.9) c --194.5 (76.9) 205.4 (74.7) --321.1 (90.7) 364.7 (98.2) 442.0 (115.0) 495.0 (153.0) ----273.0 (79.0) 307.0 (75.0)
	22 22 29 30 29 30 30 28 27 28 29 29 21 24 25 26 27 28 23 24 25 26 23 24 25 26 27 24 25 26 27 28 24 25 26 27 28	in preceding women in particular. However, longitudinal studies are not perfect as early disease processes before the actual O n l y albumin, or leukocyte count with coronary heart disease: meta-analyses of prospective adjusting for publication bias in meta-analysis. Biometrics 2000; 56: 455-63. 53 Mousavinasab F, Tahtinen T, Jokelainen J, et al. The Pro12Ala polymorphism of the PPAR 66 Dokras A, Bochner M, Hollinrake E, Markham S, Vanvoorhis B, Jagasia DH. Screening 92 Niskanen L, Laaksonen DE, Punnonen K, Mustajoki P, Kaukua J, Rissanen A. Changes in 24 Danesh J, Collins R, Appleby P, Peto R. Association of fibrinogen, C-reactive protein, 38 Duval S, Tweedie R. Trim and fill: A simple funnel-plot-based method of testing and syndrome in older Italian men. J Am Geriatr Soc 2006; 54: 1832-8. 2005; 90: 1929-35. men losing weight. Obes Res 2003; 11: 689-94. O n l y O n l y O n l y O n l y Epidemiology (MOOSE) group. JAMA 2000; 283: 2008-12. 22 Higgins JP, Thompson SG, Deeks JJ, Altman DG. Measuring inconsistency in meta-analyses. BMJ 2003; 327: 557-60. 23 Hozo SP, Djulbegovic B, Hozo I. Estimating the mean and variance from the median, range, and the size of a sample. BMC Med Res Methodol 2005; 5: 13. O n l y 36 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: 3666-72. 37 Egger M, Davey Smith G, Schneider M, Minder C. Bias in meta-analysis detected by a simple, graphical test. BMJ 1997; 315: 629-34. O n l y 51 Kaplan SA, Meehan AG, Shah A. The age related decrease in testosterone is significantly exacerbated in obese men with the metabolic syndrome. What are the implications for the relatively high incidence of erectile dysfunction observed in these men? J Urol 2006; 176: 1524,7; discussion 1527-8. 52 Maggio M, Lauretani F, Ceda GP, et al. Association between hormones and metabolic O n l y independent of obesity and insulin resistance. J Clin Endocrinol Metab 2006; 91: 492-7. 64 Pasanisi P, Berrino F, De Petris M, Venturelli E, Mastroianni A, Panico S. Metabolic syndrome as a prognostic factor for breast cancer recurrences. Int J Cancer 2006; 119: 236-8. 65 Apridonidze T, Essah PA, Iuorno MJ, Nestler JE. Prevalence and characteristics of the metabolic syndrome in women with polycystic ovary syndrome. J Clin Endocrinol Metab O n l y O n l y testosterone. J Androl 2009; 30: 726-33. 90 Haider A, Gooren LJ, Padungtod P, Saad F. Improvement of the metabolic syndrome and of non-alcoholic liver steatosis upon treatment of hypogonadal elderly men with parenteral testosterone undecanoate. Exp Clin Endocrinol Diabetes 2010; 118: 167-71. 91 Kaukua J, Pekkarinen T, Sane T, Mustajoki P. Sex hormones and sexual function in obese O n l y O n l y	27 28 29
	23 23 31 29 30 31 31	conditions such as MetS, and may even be found in earlier stages of disease. Although the exact diagnosis of MetS may influence the level of testosterone and SHBG as well. Therefore, additional studies. JAMA 1998; 279: 1477-82. 39 Katabami T, Kato H, Asahina T, et al. Serum free testosterone and metabolic syndrome in gamma 2 gene influences sex hormone-binding globulin level and its relationship to the women with polycystic ovary syndrome for metabolic syndrome. Obstet Gynecol 2005; 106: sex hormone-binding globulin and testosterone during weight loss and weight maintenance in
	24 24 32 30 31 32 32	mechanisms underlying the sex specific associations between testosterone and MetS are not tools, such as Mendelian randomization studies and intervention studies, are needed to establish 25 Laaksonen DE, Niskanen L, Punnonen K, et al. Testosterone and sex hormone-binding Japanese men. Endocr J 2010; 57: 533-9. development of the metabolic syndrome in young Finnish men. Endocrine 2006; 30: 185-90. 131-7. abdominally obese men with the metabolic syndrome. Diabetes Obes Metab 2004; 6: 208-15.
	25 33 31 33 33 32 25 34 32 33 34 34	completely understood, similar sex-specific effects of testosterone have been observed in animal causation. globulin predict the metabolic syndrome and diabetes in middle-aged men. Diabetes Care 40 Atlantis E, Martin SA, Haren MT, Taylor AW, Wittert GA, Members of the Florey 54 Robeva R, Kirilov G, Tomova A, Kumanov P. Low testosterone levels and unimpaired 67 Golden SH, Ding J, Szklo M, Schmidt MI, Duncan BB, Dobs A. Glucose and insulin 93 Pitteloud N, Hardin M, Dwyer AA, et al. Increasing insulin resistance is associated with a 2004; 27: 1036-41. Adelaide Male Ageing Study. Inverse associations between muscle mass, strength, and the melatonin secretion in young males with metabolic syndrome. Andrologia 2006; 38: 216-20. components of the metabolic syndrome are associated with hyperandrogenism in decrease in Leydig cell testosterone secretion in men. J Clin Endocrinol Metab 2005; 90:
	36 26 33 35 35	160: 540-8. metabolic syndrome. Metabolism 2009; 58: 1013-22. postmenopausal women: the atherosclerosis risk in communities study. Am J Epidemiol 2004; 2636-41.
	27 37 36 37
	28 28 36
		20 21 22

10,25,97

and diabetes 15 etiology

98,103,104

. Moreover, polymorphisms in the SHBG gene have recently been shown to affect not 16 only SHBG levels but also type 2 diabetes risks in men as well as in women

98,105

, suggesting a 17 potential causal role for SHBG in pathophysiological mechanisms.

18

Pooled estimates of our meta-analysis are comparable (regarding strength and direction) with those 19 previously reported for type 2 diabetes by

Ding et al. 106

. This once more suggests a predominant role 20 for glucose metabolism in the associations of testosterone with MetS and further indicates that the 21 sex-dependent role of testosterone is not restricted to type 2 diabetes, but also exist models. Low testosterone levels following castration in male rats, for instance, have been linked to 26 obesity, insulin resistance and dyslipidemia

84,107,108

. Whereas prenatal and postnatal administration of 27 testosterone has adverse effects on various MetS components in female rats

[109][110][111]

. and also experience higher risk of MetS and diabetes 105 . Thus similar 'genetic' levels of SHBG may 10 affect MetS risk in men and women differently, by altering the levels of testosterone in a sex-specific 11 manner. Further research is necessary to elucidate the role of SHBG in the pathophysiology of MetS 12 and diabetes.

13

In conclusion, findings of this meta-analysis support the presence of a sex-dependent association 14 between TT and MetS, with high endogenous TT lowering MetS risk in men, while increasing MetS 15 risk in women. There are also indications for a sex difference in the association between FT and MetS.

16

Higher SHBG levels are associated with a lower MetS risk in both men and women. Differences in 17 age, BMI, MetS criteria, PCOS status and study design account for some of the variability observed.

18

The comparability of our pooled estimates with those available for type 2 diabetes suggests a major 19 contribution of impaired glucose metabolism to the observed associations. To further clarify the causal 20 nature of the observed associations, more large-scale longitudinal studies are required, in

26

Schneider HJ, Sievers C, Klotsche J, et al. Prevalence of low male testosterone levels in 35 primary care in Germany: cross-sectional results from the DETECT study. Clin Endocrinol 36 (Oxf) 2009; 70: 446-54. 41 Coviello AD, Sam S, Legro RS, Dunaif A. High prevalence of metabolic syndrome in first-34 degree male relatives of women with polycystic ovary syndrome is related to high rates of 35 obesity. J Clin Endocrinol Metab 2009; 94: 4361-6. 55 Kalme T, Seppala M, Qiao Q, et al. Sex hormone-binding globulin and insulin-like growth 34 factor-binding protein-1 as indicators of metabolic syndrome, cardiovascular risk, and 35 mortality in elderly men. J Clin Endocrinol Metab 2005; 90: 1550-6. 94 Isidori AM, Caprio M, Strollo F, et al. Leptin and androgens in male obesity: evidence for 36 leptin contribution to reduced androgen levels. J Clin Endocrinol Metab 1999; 84: 3673-80. BMI, body mass index; CC, case-control; CS, cross-sectional; LO, longitudinal; MetS, metabolic syndrome; MetS + , subjects with the metabolic syndrome; MetS -, subjects without the metabolic syndrome; No., number; SHBG, sex hormone-binding globulin; -, not applicable. a mean age/BMI of study sample based on weighted means of age/BMI of subjects with and without MetS. b studies using multiple criteria to define the metabolic syndrome (Atlantis et al, 2009: (1) NCEP ATP III, (2) IDF; Goncharov et al, 2008: (1) NCEP ATP III, (2) WHO, (3) IDF; Corona et al, 2007: (1) NCEP ATP III , (2) IDF; Guay et al, 2007: (1) NCEP ATP III modified, (2) WHO). c free testosterone measured by radioimmunoassay. d mean differences reported for two separate populations (1) and (2). e longitudinal study providing data on hormonal levels in subjects with and without MetS at baseline. SI conversion factors: to convert testosterone (total testosterone/free testosterone) to ng/dL divide by 0.0347. To convert SHBG to µg/mL divide by 8.896.

Table 1B

 1B Characteristics of studies reporting on TT, SHBG and/or FT levels in women with and without MetS

												Mean (SD), nmol/L		Mean (SD), pmol/L
	Nr. Source	Country	Study	Participants	Mean	Mean	Adjusted	No.		Total Testosterone	SHBG	Free Testosterone
				design		age	BMI	for age
									MetS + MetS -	MetS +	MetS -	MetS +	MetS -	MetS +	MetS -
	1	Alemzadeh et al,	U.S.A.	CS	Obese adolescent girls with PCOS	16	36,2	No	35	68	-	-	-	-	48.6 (17.7)	38.5 (15.9)
		2010 57
	2	Healy et al, 2010 58	Ireland	CS	Postmenopausal women with newly diagnosed	68	28.3	No	42	63	1.14 (0.51)	1.07 (0.6)	49.4 (24.6)	57.0 (26.2)	-	-
					breast cancer
	3	de Oya et al, 2010 28	Spain	CS	Adolescent girls	14	21.8	No	4	424	-	-	24.6 (11.2)	64.6 (34.9)	-	-
		c
	4	de Sousa et al, 2010	Germany	CS	Obese postmenarcheal adolescent girls	15	32.6	No	48	112	1.8 (0.7)	1.5 (0.7)	19.1 (7.9)	37.9 (8.5)	49.0 (7.0)	40.0 (15.0)
		60
	5	Ni et al, 2009 59	China	CS	Women with PCOS	27	21.9	No	97	481	2.1 (0.8)	2.2 (0.9)	27.8 (25.6)	55.4 (38.7)	152.7 (97.7)	111.1 (72.0)
	6	Janssen et al, 2008	U.S.A.	CS	Women from the SWAN study at time of their	51	26.9	No	130	819	1.5 (0.6)	1.3 (0.6)	34.1 (19.4)	45.0 (24.2)	-	-
		61			final menstruation period
	7	Maggio et al, 2007	Italy	CS	Women from the InCHIANTI Study 65 years	76	27.6 a	No	145	367	2.3 (1.1)	2.1 (0.9)	97.5 (51.6)	131.2 (66.9)	-	-
		15			and older
	8	Park et al, 2007 62	Korea	CS	Women with PCOS	26	23.6	No	16	97	2.3 (0.9)	2.4 (1.1)	18.8 (8.9)	49.6 (40.6)	9.0 (2.8) b	5.9 (3.1) b
	9	Coviello et al, 2006	U.S.A.	CS	Postmenarcheal adolescent girls with PCOS	17	32.0	No	18	31	2.8 (0.8)	2.5 (0.9)	33.0 (13.0)	77.0 (53.0)	-	-
		63
	10	Ehrmann et al, 2006	U.S.A.	CS	Nondiabetic PCOS women who participated in a	28 a	36.0 a	No	123	245	2.2 (1.2)	2.2 (1.1)	32.8 (15.5)	43.8 (21.9)	41.8 (17.7)	37.8 (20.4)
		12			large multicenter national trial
	11	Leibel et al, 2006 19	U.S.A.	CS	Postmenarcheal adolescent girls with PCOS	16	32.4 a	No	10	26	-	-	8.4 (6.3)	15.4 (9.6)	90.2 (35.7)	67.0 (23.9)
	12	Pasanisi et al, 2006	Italy	CS	Postmenopausal women operated for breast	57	-	No	16	94	1.7 (0.5)	1.4 (0.5)	46.3 (28.1)	67.8 (29.8)	-	-
		64			cancer
	13	Weinberg et al,	U.S.A.	CS	Postmenopausal women from the Women's	65 a	26.2 a	Yes	108	104	0.8 (0.6)	0.6 (0.4)	32.6 (29.2)	55.8 (17.3)	-	-
		2006 32			Health Study (WHS)
	14	Apridonidze et al,	U.S.A.	CS	Women with PCOS	30 a	36.1 a	No	46	60	2.5 (1.0)	2.1 (1.0)	26.2 (31.5)	36.5 (19.8)	55.9 (26.3) b	37.1 (28.1) b
		2005 65
	15	Dokras et al, 2005 66	U.S.A.	CS	Women with PCOS	28	-	No	45	84	1.9 (1.0)	1.9 (1.2)	20.0 (11.1)	32.0 (31.5)	27.4 (15.2)	27.1 (26.0)
	16	Golden et al, 2004	U.S.A.	CS	Postmenopausal women from the ARIC study	62 a	27.4 a	No	60	121	0.9 (0.8)	0.7 (0.6)	-	-	-	-
		67 (1) c			with minimal carotid atherosclerosis
		Golden et al, 2004	U.S.A.	CS	Postmenopausal women from the ARIC study	62 a	27.9 a	No	94	87	0.8 (0.6)	0.7 (0.5)	-	-	-	-
		67 (2) c			with significant atherosclerosis
	17	Korhonen et al,	Finland	CC	Premenopausal women from a community-based	43 a	28.3 a	Yes	63	88	1.4 (0.5)	1.3 (0.6)	37.4 (22.2)	52.9 (25.3)	21.5 (9.5)	16.8 (6.6)
		2003 20			study

ARIC, Atherosclerosis Risk in Communities Study; BMI, body mass index; CS, cross-sectional; LO, longitudinal; MetS, metabolic syndrome; MetS + , subjects with the metabolic syndrome; MetS -, subjects without the metabolic syndrome; No., number; PCOS, polycystic ovary syndrome; SHBG, sex hormone-binding globulin; SWAN, Study of Women's Health Across the Nation; -, not applicable. a mean age/BMI of study sample based on weighted means of age/BMI of subjects with and without MetS; b free testosterone measured by radioimmunoassay. c mean differences reported for two separate populations (1) and (2).

Table 2A

 2A Characteristics of studies presenting relative risk estimates for MetS according to TT, SHBG and/or FT levels, men Characteristics of studies presenting relative risk estimates for MetS according to TT and/or SHBG levels, women

	Nr. Source	Country	Study	Mean	Participants		N	Variables adjusted for	RR estimate TT a	RR estimate SHBG a	RR estimate FT a
				design	follow-up					(95% CI)	(95% CI)	(95% CI)
	1	Akishita et al, 2010 27	Japan	CS	-	Nondiabetic men		194	Age	OR 0.26 (0.11 -0.59)	-	-
	2	Li et al, 2010 68	U.S.A	CS	-	Men from the Third National Health and		1226 Age, smoking, alcohol consumption, physical	PR 0.52 (0.38 -0.69)	PR 0.51 (0.34 -0.79)	PR 0.87 (0.63 -1.20)
						Nutrition Examination Survey (NHANES-III)		activity, race, CRP, LDL cholesterol, HOMA-IR
	3	Haring et al, 2009 43 b	Germany	LO	5.0 yr	Men from the Study of Health in Pomerania	1004 -	RR 0.70 (0.59 -0.83)	-	-
						(SHIP) study
	4	Schneider et al, 2009 26	Germany	CS	-	Men from the Diabetes Cardiovascular Risk-	2719 -	OR 0.26 (0.21 -0.32)	-	-
						Evaluation: Targets and Essential DATA for
						Commitment of Treatment (DETECT)
	5	Chubb et al, 2008 10 b	Australia	CS	-	Nondiabetic men from the Health in Men study	2052 -	OR 0.28 (0.22 -0.36)	OR 0.21 (0.16 -0.28)	-
	6	Emmelot-Vonk et al,	Netherlands	CS	-	Nondiabetic men with low normal testosterone	222	Age, smoking, alcohol consumption	OR 0.45 (0.21 -0.95)	OR 0.25 (0.11 -0.56)	OR 2.15 (1.00 -4.57)
		2008 44 b				levels
	7	Kupelian et al, 2008 69	U.S.A.	CS	-	Men from the Boston Area Community Health	1885 Age, smoking, alcohol consumption, physical	OR 0.16 (0.10 -0.27)	OR 0.13 (0.08 -0.23)	OR 0.22 (0.13 -0.37)
						(BACH) survey			activity, ethnicity
	8	Rodriguez et al, 2007 48	U.S.A.	LO	5.8 yr	Men from the Baltimore Longitudinal Study of	417	Age, BMI	HR 0.46 (0.25 -0.84)	HR 0.30 (0.17 -0.58)	-
		b				Aging
	9	Kupelian et al, 2006 18 b	U.S.A.	LO	14.4 yr	Men from the Massachusetts Male Aging Study	950	-	RR 0.75 (0.55 -0.97)	RR 0.50 (0.37 -0.68)	RR 1.06 (0,81 -1,41)
	10 Muller et al, 2005 14 b	Netherlands	CS	-	Independently living men		400	Age, smoking, alcohol consumption, physical	OR 0.20 (0.10 -0.38)	OR 0.17 (0.08 -0.34)	OR 0.31 (0.15 -0.63)
									activity
	11 Tong et al, 2005 30 b	China	CS	-	Men from the Hong Kong Diabetes Family Study	307	Age, smoking, family history of diabetes, CRP	OR 0.25 (0.12 -0.52)	OR 0.17 (0.08 -0.38)	-
									(TT and SHBG), IGF-1 (SHBG only)
	12 Laaksonen et al, 2004 25	Finland	LO	11.0 yr	Nondiabetic men from the Kuopio Ischaemic	702	Age, BMI, smoking, alcohol consumption,	OR 0.43 (0.25 -0.76)	OR 0.37 (0.21 -0.64)	OR 0.56 (0.31 -0.99)
						Heart Disease Risk Factor Study			presence of CVD, socioeconomic status
	13 Laaksonen et al, 2003 16	Finland	CS	-	Nondiabetic men from the Kuopio Ischaemic	1896 Age, BMI, smoking, alcohol consumption,	OR 0.52 (0.36 -0.75)	OR 0.54 (0.37 -0.77)	OR 0.58 (0.41 -0.83)
		b				Heart Disease Risk Factor Study			presence of CVD, socioeconomic status
	Table 2B Nr. Source	Country	Study	Participants		N	Variables adjusted for	RR estimate TT a	RR estimate SHBG a	RR estimate FT a
				design						(95% CI)	(95% CI)	(95% CI)
	1	Patel et al, 2009 70	U.S.A.	CS	Nondiabetic women 65 years and older from the	301	Age, race, estrogen use and number of ovaries removed	OR 2.49 (1.30 -4.76)	-	OR 1.24 (0..67 -2.32)
					Cardiovascular Health Study (CHS)
	2	Maggio et al, 2007 15 b	Italy	CS	Women from the InCHIANTI Study 65 years and	589	-		OR 1.40 (0.91 -2.16)	OR 0.31 (0.19 -0.49)	-
					older
	3	Chen et al, 2006 71	Taiwan	CS	Women with PCOS not undergoing treatment	106	Age		-	OR 0.10 (0.01 -0.89)	-
	4	Weinberg et al, 2006 32	U.S.A.	CS	Postmenopausal women from the Women's Health	212	Age, BMI, smoking, alcohol consumption, physical	OR 3.20 (1.40 -7.30)	OR 0.14 (0.05 -0.37)	-
		b			Study			activity, and the presence of CVD at follow-up
	5	Santoro et al, 2005 72	U.S.A.	CS	Nondiabetic women from the Study of Women's	2961	Age, smoking, ethnicity, site	OR 1.25 (1.12 -1.40)	OR 0.36 (0.29 -0.43)	-
					Health Across the Nation (SWAN)
	BMI, body mass index; CI, confidence interval; CS, cross-sectional; CVD, cardiovascular disease; FT, free testosterone; MetS, metabolic syndrome; OR, odds ratio; PCOS, polycystic ovary syndrome; SHBG, sex hormone-binding globulin; SWAN, Study of
	Women's Health Across the Nation; TT, total testosterone; -, not applicable.

BMI, body mass index; CI, confidence interval; CS, cross-sectional; CVD, cardiovascular disease; CRP, C-reactive protein; FT, free testosterone; HR, hazard ratio; HOMA-IR, homeostasis model assessment of insulin resistance; IGF-1, insulin-like growth factor 1; LO, longitudinal; MetS, metabolic syndrome; OR, odds ratio; PR, prevalence ratio; RR, relative risk; SHBG, sex hormone-binding globulin; TT, total testosterone; yr, year; -, not applicable. a relative risk estimates of MetS comparing highest versus lowest tertiles of TT, SHBG and FT. b studies reporting both measures of association (relative risk estimates and mean differences). a relative risk estimates of MetS comparing highest versus lowest tertiles of TT, SHBG and FT. b studies reporting both measures of association (relative risk estimates and mean differences).

Table 3

 3 Mean differences of TT, SHBG and FT between subjects with and without the MetS, men and women.

	Men

Table 3 Continued

 3 Figure 2A Random effects pooled mean difference of TT levels between subjects with and without MetS, men and women. CI, confidence interval; MetS, metabolic syndrome; TT, total testosterone. Negative values indicate lower TT levels in subjects with MetS; positive values indicate higher TT levels in subjects with MetS. Sizes of squares represent the weight of each study.Figure 2B Random effects pooled mean difference of SHBG levels between subjects with and without MetS, men and women. CI, confidence interval; MetS, metabolic syndrome; SHBG, sex hormone-binding globulin. Negative values indicate lower SHBG levels in subjects with MetS; positive values indicate higher SHBG levels in subjects with MetS. Sizes of squares represent the weight of each study.

		Studies No.	FT standardized mean difference (95% CI)	I 2	Studies No.	FT standardized mean difference (95% CI)	I 2
			(pmol/L)			(pmol/L)
	Overall random effects	13	-0.26 (-0.39; -0.13)	79.9% (p<0.001)	9	0.52 (0.33, 0.71)	61.1% (p=0.01)
	Age
	-Age < 55 years	7	-0.41 (-0.51, -0.31)	32.9% (p = 0.18)	9	0.52 (0.33, 0.71)	61.1% (p = 0.01)
	-Age ≥ 55 years	6	-0.09 (-0.29, 0.11)	79.3% (p<0.001)	0	-
	BMI
	-BMI < 25	3	-0.35 (-0.71, 0,02)	77.2% (p = 0.01)	2	0.71 (0.27, 1.15)	59.8% (p = 0.12)
	-BMI ≥ 25 kg/m 2	9	-0.20 (-0.36, -0.04)	81.5% (p<0.001)	6	0.54 (0.33, 0.76)	51.2% (p = 0.07)
	PCOS status (women)
	-PCOS (women)	-	-		7	0.49 (0.26, 0.73)	66.7% (p = 0.01)
	-no PCOS (women)	-	-		2	0.64 (0.40, 0.88)	0% (p = 0.71)
	MetS criteria †
	-NCEP ATP III	10	-0,19 (-0,34, -0,05)		6	0.50 (0.21, 0.79)
	-Other (WHO, IDF)	5	-0,47 (-0,55, -0,39)		0	-
	Control for age
	-adjusted for age	0	-	-	1	0.59 (0.26, 0.92)	-
	-not adjusted for age	13	-0.26 (-0.39, -0.13)	79.9% (p< 0.001)	8	0.52 (0.31, 0.73)	64.9% (p = 0.01)
	Type 2 diabetes excluded
	-yes	5	-0.29 (-0.53, 0.05)	87.8% (p<0.001)	3	0.50 (0.08, 0.91)
	-no	8	-0.23 (-0.40, -0.07)	74.1% (p<0.001)	2	0.31 (-0.26, 0.87)
	Study design
	-cross-sectional	13	-0.26 (-0.39; -0.13)	79.9% (p<0.001)	8	0.52 (0.31, 0.73)	64.9% (p = 0.01)
	-case-control	0	-	-	1	0.59 (0.26, 0.92)	-
	-longitudinal	0	-	-	0	-	-
	Method of FT assessment
	-Direct measurement	3	-0.47 (-0.64, -0.30)	38.0% (p < 0.20)	6	0.57 (0.34, 0.80)	59.9% (p = 0.03)
	-Algorithms	9	-0.18 (-0.34, -0.03)	81.7% (p< 0.001)	3	0.44 (-0.04, 0.84)	75.1% (p = 0.02)

CI, confidence interval; BMI, body mass index; FT, free testosterone; MetS, metabolic syndrome; No., number; PCOS, polycystic ovary syndrome; SHBG, sex hormone-binding globulin; TT, total testosterone; -, not applicable.

2 = 85.5%, p < 0.001)

	WMD (95% CI)
	-22.24 (-31.21, -13.27) -22.24 (-31.21, -13.27)
	-6.30 (-8.09, -4.51) -6.30 (-8.09, -4.51)
	-8.00 (-18.59, 2.59) -8.00 (-18.59, 2.59)
	-9.00 (-23.17, 5.17) -9.00 (-23.17, 5.17)
	-8.70 (-10.05, -7.35) -8.70 (-10.05, -7.35)
	-5.55 (-8.29, -2.81) -5.55 (-8.29, -2.81)
	-15.50 (-29.77, -1.23) -15.50 (-29.77, -1.23)
	-19.20 (-19.73, -18.67) -19.20 (-19.73, -18.67)
	-14.00 (-14.34, -13.66) -14.00 (-14.34, -13.66)
	-7.00 (-11.83, -2.17) -7.00 (-11.83, -2.17)
	-7.50 (-9.67, -5.33) -7.50 (-9.67, -5.33)
	-20.40 (-28.82, -11.98) -20.40 (-28.82, -11.98)
	-4.04 (-8.23, 0.15) -4.04 (-8.23, 0.15)
	-20.00 (-26.74, -13.26) -20.00 (-26.74, -13.26)
	-7.72 (-10.68, -4.75) -7.72 (-10.68, -4.75)
	-6.00 (-9.36, -2.64) -6.00 (-9.36, -2.64)
	-3.70 (-7.92, 0.52) -3.70 (-7.92, 0.52)
	-6.20 (-9.57, -2.83) -6.20 (-9.57, -2.83)
	-6.90 (-8.48, -5.32) -6.90 (-8.48, -5.32)
	-9.77 (-12.26, -7.29) -9.77 (-12.26, -7.29)
	-7.60 (-17.47, 2.27) -7.60 (-17.47, 2.27)
	-27.60 (-33.76, -21.44) -27.60 (-33.76, -21.44)
	-40.00 (-51.47, -28.53) -40.00 (-51.47, -28.53)
	-18.80 (-21.53, -16.07) -18.80 (-21.53, -16.07)
	-10.94 (-14.66, -7.22) -10.94 (-14.66, -7.22)
	-33.70 (-44.54, -22.86) -33.70 (-44.54, -22.86)
	-30.80 (-39.98, -21.62) -30.80 (-39.98, -21.62)
	-44.00 (-63.60, -24.40) -44.00 (-63.60, -24.40)
	-11.00 (-14.88, -7.12) -11.00 (-14.88, -7.12)
	-7.00 (-12.37, -1.63) -7.00 (-12.37, -1.63)
	-21.50 (-36.54, -6.46) -21.50 (-36.54, -6.46)
	-23.20 (-29.64, -16.76) -23.20 (-29.64, -16.76)
	-10.26 (-20.64, 0.12) -10.26 (-20.64, 0.12)
	-12.00 (-19.47, -4.53) -12.00 (-19.47, -4.53)
	-15.50 (-23.12, -7.88) -15.50 (-23.12, -7.88)
	-19.39 (-23.81, -14.98) -

19.39 (-23.81, -14.98)

  Figure 2CRandom effects pooled mean difference of FT levels between subjects with and without MetS, men and women. CI, confidence interval; MetS, metabolic syndrome; FT, free testosterone. Negative values indicate lower FT levels in subjects with MetS; positive values indicate higher FT levels in subjects with MetS. Sizes of squares represent the weight of each study.Figure 3ARandom effects pooled relative risk estimate for MetS comparing highest versus lowest TT tertile, men and women. CI, confidence interval; MetS, metabolic syndrome; TT, total testosterone. Sizes of squares represent the weight of each study.

	Source Source							SMD (95% CI) SMD (95% CI) FT standardized mean difference (95% CI) (pmol/L) RR estimate (95% CI) ES (95% CI) (	ES (95% CI)
	Source Men Men Kabatami et al, 2010 Chubb et al, 2008 Akishita et al, 2010 Haring et al, 2010							WMD (95% CI) SHBG mean difference (95% CI) (nmol/L) -0.67 (-0.95, -0.40) -0.18 (-0.28, -0.09) 0.26 (0.11, 0.59) 0.26 (0.11, 0.59) -0.67 (-0.95, -0.40) -0.18 (-0.28, -0.09) 0.70 (0.59, 0.83) 0.70 (0.59, 0.83)
	Emmelot-Vonk et al, 2008 Li et al, 2010							0.25 (-0.04, 0.55) 0.25 (-0.04, 0.55) 0.52 (0.38, 0.69) 0.52 (0.38, 0.69)
	Goncharov et al, 2008 Schneider et al, 2009							-0.41 (-0.92, 0.11) -0.41 (-0.92, 0.11) 0.26 (0.21, 0.32) 0.26 (0.21, 0.32)
	Suetomi et al, 2008 Chubb et al, 2008							-0.20 (-0.64, 0.23) -0.20 (-0.64, 0.23) 0.28 (0.22, 0.36) 0.28 (0.22, 0.36)
	Corona et al, 2007 Emmelot-Vonk et al, 2008							-0.43 (-0.56, -0.31) -0.43 (-0.56, -0.31) 0.45 (0.21, 0.95) 0.45 (0.21, 0.95)
	F o r R e v i e Kupelian et al, 2006 Guay et al, 2007 Tang et al, 2007 Maggio et al, 2006 Muller et al, 2005 Nuver et al, 2005 Laaksonen et al, 2003 Overall (I 2 = 79.9%, p < 0.001) Women Alemzadeh et al, 2010 Ni et al, 2009 de Sousa et al, 2009 Park et al, 2007 Ehrmann et al, 2006 Leibel et al, 2006 Apridonidze et al, 2005 Dokras et al, 2005 Korhonen et al, 2003 Overall (I 2 = 61.1%, p = 0.01) Kupelian et al, 2008 Rodriguez et al, 2007 Kupelian et al, 2006 Muller et al, 2005 Tong et al, 2005 (2) Laaksonen et al, 2004 F Laaksonen et al, 2003 Overall (I 2 = 88.5%, p < 0.001) o r Women Patel et al, 2009 Maggio et al, 2007 R Weinberg et al, 2006 Santoro et al, 2005 e Overall (I 2 = 66.6%, p = 0.03) v i e .05 .1 .5	1 1	5	-0.31 (-0.63, 0.01) -0.14 (-0.37, 0.08) -0.22 (-0.40, -0.05) 0.25 (-0.00, 0.50) -0.45 (-0.68, -0.22) -0.37 (-0.86, 0.12) -0.45 (-0.57, -0.33) -0.26 (-0.39, -0.13) 0.61 (0.19, 1.03) 0.54 (0.32, 0.76) 0.68 (0.34, 1.03) 1.01 (0.47, 1.56) 0.20 (-0.01, 0.42) 0.84 (0.09, 1.60) 0.69 (0.29, 1.08) 0.02 (-0.35, 0.38) 0.59 (0.26, 0.92) 0.52 (0.33, 0.71) -0.31 (-0.63, 0.01) 0.16 (0.10, 0.27) 0.16 (0.10, 0.27) -0.14 (-0.37, 0.08) 0.46 (0.25, 0.83) 0.46 (0.25, 0.83) -0.22 (-0.40, -0.05) 0.74 (0.55, 0.97) 0.74 (0.55, 0.97) 0.25 (-0.00, 0.50) 0.20 (0.10, 0.38) 0.20 (0.10, 0.38) -0.45 (-0.68, -0.22) -0.37 (-0.86, 0.12) -0.45 (-0.57, -0.33) -0.26 (-0.39, -0.13) 0.25 (0.12, 0.52) 0.43 (0.25, 0.76) 0.25 (0.12, 0.52) 0.43 (0.25, 0.76) 0.52 (0.36, 0.75) 0.38 (0.28, 0.50) 0.52 (0.36, 0.75) 0.38 (0.28, 0.50) 0.61 (0.19, 1.03) 0.54 (0.32, 0.76) 0.68 (0.34, 1.03) 1.01 (0.47, 1.56) 0.20 (-0.01, 0.42) 0.84 (0.09, 1.60) 0.69 (0.29, 1.08) 0.02 (-0.35, 0.38) 2.49 (1.30, 4.76) 1.40 (0.91, 2.16) 2.49 (1.30, 4.76) 1.40 (0.91, 2.16) 3.20 (1.40, 7.30) 1.25 (1.13, 1.40) 3.20 (1.40, 7.30) 1.25 (1.13, 1.40) 1.68 (1.15, 2.45) 1.68 (1.15, 2.45) 0.59 (0.26, 0.92) 0.52 (0.33, 0.71) 10
	-1		-.5	0 0	w .5 w	1	1.5	2
	-70	-60	-50	-40	-30	0 0 l -10 O -20 n O n l	10
								y y

highest vs lowest TT tertile)

  Figure 3BRandom effects pooled relative risk estimate for FT comparing highest versus lowest tertile of FT, men and women. CI, confidence interval; MetS, metabolic syndrome; FT, free testosterone. Sizes of squares represent the weight of each study.

	Source							RR estimate (95% CI)	ES (95% CI)
						ES (95% CI) ES (95% CI) (highest vs lowest SHBG tertile)	ES (95% CI)
	Source					ES (95% CI) RR estimate (95% CI) ES (95% CI) (highest vs lowest FT tertile)
									0.51 (0.34, 0.79) 0.51 (0.34, 0.79)
	Men								0.21 (0.16, 0.28) 0.21 (0.16, 0.28)
	Li et al, 2010								0.25 (0.11, 0.56) 0.25 (0.11, 0.56) 0.87 (0.63, 1.20) 0.87 (0.63, 1.20)
	Emmelot-Vonk et al, 2008								0.13 (0.08, 0.23) 0.13 (0.08, 0.23) 2.15 (1.01, 4.57) 2.15 (1.01, 4.57)
	Kupelian et al, 2008								0.30 (0.17, 0.58) 0.50 (0.37, 0.68) 0.30 (0.17, 0.58) 0.50 (0.37, 0.68) 0.22 (0.13, 0.37) 0.22 (0.13, 0.37)
	Kupelian et al, 2006								0.17 (0.08, 0.34) 0.17 (0.08, 0.34) 1.06 (0.81, 1.41) 1.06 (0.81, 1.41)
	Muller et al, 2005								0.17 (0.08, 0.38) 0.17 (0.08, 0.38) 0.31 (0.15, 0.63) 0.31 (0.15, 0.63)
	Laaksonen et al, 2004								0.37 (0.21, 0.64) 0.37 (0.21, 0.64) 0.56 (0.31, 0.99) 0.56 (0.31, 0.99)
	Laaksonen et al, 2003 F Women r o Overall (I 2 = 86.4%, p < 0.001)						0.54 (0.37, 0.77) 0.29 (0.21, 0.41) 0.31 (0.19, 0.49) 0.54 (0.37, 0.77) 0.29 (0.21, 0.41) 0.31 (0.19, 0.49) 0.58 (0.41, 0.83) 0.58 (0.41, 0.83) 0.64 (0.41, 1.01) 0.64 (0.41, 1.01)
	Patel et al, 2009 Overall	R						0.10 (0.01, 0.88) 0.14 (0.05, 0.37) 0.36 (0.29, 0.43) 0.10 (0.01, 0.88) 0.14 (0.05, 0.37) 0.36 (0.29, 0.43) 1.24 (0.67, 2.32) 1.24 (0.67, 2.32) 1.24 (0.67, 2.31) 1.24 (0.67, 2.31)
		.01 .05	.05 .1 e v e i .1 .5	.5 1 1	1 1	5	5	10 10	0.30 (0.21, 0.42) 0.30 (0.21, 0.42)
			w
			O n l
						y

'unclear'.

Coviello et al, 2009 (2) Demir et al, 2009 Chubb et al, 2008 Emmelot-Vonk et al, 2008 Goncharov et al, 2008 Laughlin et al, 2008 Suetomi et al, 2008 Yeh et al, 2008 Corona et al, 2007 Rodriguez et al, 2007 Tang et al, 2007 Chen et al, 2006 Gannage-Yared et al, 2006 Kaplan et al, 2006 Kupelian et al, 2006 Maggio et al, 2006 Robeva et al, 2006 Kalme et al, 2005 Muller et al, 2005 Nuver et al, 2005 Tong et al, 2005 (1) Tong et al, 2005 (2) Laaksonen et al, 2003 Overall (I 2 = 89.1%, p < 0.001) Women Healy et al, 2010 Ni et al, 2009 de Sousa et al, 2009 Janssen et al, 2008 Maggio et al, 2007 Park et al, 2007 Coviello et al, 2006 Ehrmann et al, 2006 Pasanisi et al, 2006 Weinberg et al, 2006 Apridonidze et al, 2005 Dokras et al, 2005 Golden et al, 2004 (1) Golden et al, 2004 (2) Korhonen et al, 2003 Overall (I 2 = 28.5%, p = 0.14)

Acknowledgements

1

We thank all investigators who kindly provided additional information from their studies. 2

This study did not receive any funding or support.