Novel protein markers of androgen activity in humans: proteomic study of plasma from young chemically castrated men.

BACKGROUND: Reliable biomarkers of androgen activity in humans are lacking. The aim of this study was, therefore, to identify new protein markers of biological androgen activity and test their predictive value in relation to low vs normal testosterone values and some androgen deficiency linked pathologies.
METHODS: Blood samples from 30 healthy GnRH antagonist treated males were collected at three time points: (1) before GnRH antagonist administration; (2) 3 weeks later, just before testosterone undecanoate injection, and (3) after additional 2 weeks. Subsequently, they were analyzed by mass spectrometry to identify potential protein biomarkers of testosterone activity. Levels of proteins most significantly associated with testosterone fluctuations were further tested in a cohort of 75 hypo- and eugonadal males suffering from infertility. Associations between levels of those markers and cardiometabolic parameters, bone mineral density as well as androgen receptor (AR) CAG repeat lengths, were explored.
RESULTS: Using receiver operating characteristic analysis, 4-hydroxyphenylpyruvate dioxygenase (4HPPD), insulin-like growth factor-binding protein 6 (IGFBP6), and fructose-bisphosphate aldolase (ALDOB), as well as a Multi Marker Algorithm, based on levels of 4HPPD and IGFBP6, were shown to be best predictors of low (<8 nmol/l) vs normal (>12 nmol/l) testosterone. They were also more strongly associated with metabolic syndrome and diabetes than testosterone levels. Levels of ALDOB and 4HPPD also showed association with AR CAG repeat lengths.
CONCLUSIONS: We identified potential new protein biomarkers of testosterone action. Further investigations to elucidate their clinical potential are warranted. FUNDING: The work was supported by ReproUnion2.0 (grant no. 20201846), which is funded by the Interreg V EU program.

Introduction

The male sex hormone, testosterone (T), plays an important physiological role in regulating function of both reproductive and nonreproductive organs in males as well as in females. In males, the diagnosis of T deficiency (i.e., male hypogonadism) is based on the presence of low serum T levels combined with clinical symptoms, which, however, are not pathognomonic for this condition (Traish et al., 2011).

The most common way of assessing T activity is by measuring the total concentration of this hormone in a fasting morning blood sample. However, total T does not accurately reflect biological androgenic activity (BAA), which might be considered a more useful biological and clinical marker.

The association between T levels and BAA is affected by several biological mechanisms such as the concentration of binding proteins, body mass index, certain diseases (e.g., diabetes), androgen receptor (AR) sensitivity (Zitzmann, 2008), and different cofactors (Furuya et al., 2013). So far, no reliable algorithms for translating T levels into BAA are available, but could be useful for example in the diagnosis of male hypogonadism.

A correct hypogonadism diagnosis is important for proper identification of men for whom androgen replacement therapy is warranted. However, the treatment of men with hypogonadism represents a clinical challenge, because the symptoms associated with the condition are highly nonspecific (Miner et al., 2014). Furthermore, there are limitations in using the level of T in defining testosterone deficiency. Generally, in many clinical guidelines, total T concentration below 8 nmol/l indicates an insufficient hormone concentration, whereas levels above 12 nmol/l are considered normal (Arver and Lehtihet, 2009). Apart from the fact that men with testosterone levels between 8 and 12 nmol/l cannot be assigned to any of these distinct categories, those presenting with a lower or higher hormone concentration may also be misclassified due to an abnormal concentration of sex hormone-binding protein (SHBG). Low SHBG levels, as often seen in obese men, may imply low total T despite unaffected BAA. On the other hand, some degree of reduced androgen sensitivity may be associated with decreased BAA despite normal or high testosterone levels (Diaz-Arjonilla et al., 2009).

Hypogonadism has been identified as a predictor of several noncommunicable chronic diseases as well as premature mortality (Muraleedharan and Jones, 2014). Understanding the biology of androgen action may therefore contribute to clarifying the pathogenetic mechanisms linking androgen deficiency to comorbid conditions. Thus, the approach based on measuring the protein levels downstream of androgen action is a feasible and logical concept for identifying clinical and biological useful markers of BAA.

Proteomics is a technique aimed to study biological systems based on qualitative and quantitative measuring of proteins and, thereby, integrate the cellular output related to transcription as well as translation. Mapping the quantitative protein response downstream of androgen action may provide new clinically valuable markers of BAA. In order to identify such markers, we compared the protein profile of healthy individuals before and after T deprivation. Subsequently, we assessed the predictive value of the newly identified protein markers in relation to hypogonadism and risk of pathologies related to T deficiency.

Materials and methods

Study outline

The study was set up to (1) identify new protein markers of BAA in healthy subjects; (2) test the markers’ predictive values in relation to biochemically diagnosed hypogonadism, metabolic syndrome (MetS), cardiovascular risk lipid profile (CVRLP), diabetes mellitus II (DM), and low bone density (LBD) in infertile men; (3) analyze androgen dependence of the identified proteinsby assessing how their levels associate with AR gene CAG repeat length.

Subjects

All subjects were enrolled with informed written consent. The two studies from which they were recruited were approved by the Swedish Ethical Review Authority (approval number: DNR 2014/311, date of approval May 8, 2014; DNR 2011/1, date of approval January 11, 2011).

The first part of the study includes plasma samples obtained from 30 healthy men (biological replicates) aged 19–32 years, BMI 19.1–26.9 kg/m2. They underwent chemical castration by subcutaneous administration of 240 mg GnRH antagonist (Degeralix, Ferring Pharmaceuticals, Saint-Prex, Switzerland) followed by remediation of testosterone levels by intramuscular injection of 1000 mg testosterone undecanoate (Nebido, Bayer AG, Leverkusen, Germany) after the duration of 3 weeks (Sahlin et al., 2020; Pla et al., 2020). Blood samples were collected at baseline (A), 3 weeks later (B), and, at the end of the study, after two additional weeks (C) (Figure 1).

Figure 1.

Study design.

First, a model of 30 young healthy males was evaluated by proteomics at three time points (A–C), where testosterone changes were induced: (A) baseline; (B) week 3; (C) week 5. Identified proteins proposed as candidate biomarkers were then evaluated in a cohort of infertile males. In both steps of the study, the quality of the blood samples was ensured by following an automated workflow for sample aliquoting and storage (−80°C).

To test the clinical predictive value of the proteins identified in the castrated men, we used a cohort of 75 serum samples from 75 men (biological replication, subject age 32–43 years) previously recruited for a study on hypogonadism among men from infertile couples (Bobjer et al., 2016). Eighty-five patients were randomly selected from 213 infertile men and 223 age-matched controls. The selected patients for the present study had the span of subnormal to upper normal range of T. Out of the 85 patients, 10 patients were excluded; 7 due to Klinefelter syndrome, 1 due to missing value of T, and the last 2 were statistical outliers, which were removed after considering the possible causes. One patient had a high level of T (41.6 nmol/l) without androgen replacement therapy and the other because he was the only one diagnosed with obstructive azoospermia. Background characteristics of these patients can be found in Table 1a, b.

Table 1.

Background characteristics of the infertile patients.

a. Background characteristics of infertile patients.
	Mean (SD)	N
Age at inclusion (years)	37.8 (5.5)	75
BMI	27.2 (4.3)	72
Total testosterone (nmol/l)	12.8 (6.8)	75
FSH (IU/l)	15.8 (14.3)	75
LH (IU/l)	7.5 (5.7)	75
SHBG (nmol/l)	24.0 (4.5–84.5)*	75
Estradiol (pmol/L)	96 (36–321)*	75
Calculated free testosterone (pmol/l)	260 (50–1360)*	75
ApoB/ApoA1	0.7 (0.2)	68
HOMA-IR	1.6 (0.4–13.9)*	75
DEXA score (lumbar z-score)	−0.5 (1.3)	74
CAG (repeated length)	22 (14–31)*	74
b. Characteristics of the cohort of infertile patients
	n (%)
Smoker	9 (12.0)
Current diseases	36 (48.0)
Insulin medication	1 (1.3)
Current ART	8 (10.7)
CVRLP	20 (27.0)
Insulin resistance	20 (27.0)
Diabetes mellitus 2	4 (5.3)
Metabolic syndrome (MetS)	14 (20.9)
Low bone density	23 (30.6)

Characteristics values are expressed as mean (SD), except for those that did not follow a normal distribution (non-Gaussian) and which are shown as median (min–max).

The following comorbidities in the cohort of infertile patients were defined MetS, IR, CVRLP, DM, and LBD. MetS was determined according to the criteria defined at the National Cholesterol Education Program Adult Treatment Panel III 2002 (in Table S1 available here). Homeostatic Model Assessment of Insulin Resistance (HOMA-IR) was calculated as (glucose × insulin)/22.5 and IR was defined as HOMA-IR >2.5 (Wickramasinghe et al., 2017). CVRLP was defined as the ratio apolipoprotein B/apolipoprotein A1 >0.9 (Walldius and Jungner I, 2004). DM was set at fasting blood glucose >7 mmol/l (American Diabetes Association, 2010). LBD was determined based on the DEXA lumbar z-score with the cutoff at <−1 (Isaksson et al., 2017). The methods for laboratory tests (Bobjer et al., 2016), CAG repeat length (Lundin et al., 2003), and proteomics (Smith et al., 1985; MacLean et al., 2010) are described in the supplementary Appendix 1.

Statistical analysis

We briefly describe the statistical analyses performed. A full description of the statistical analysis is available in Appendix 1 – Supplementary Statistical analyses. Proteomics data preprocessing was done using Perseus v1.6.7.0 (Tyanova et al., 2016) software and unless other software is specified, the statistical analyses were performed using R software (RStudio Team, 2016; R Development Core Team, 2016).

Healthy human model

Protein intensities were Log2 transformed and standardized by Subtract Median normalization. Differentially expressed proteins were determined by one-way repeated measures ANOVA followed by a pairwise t-test (two tails and paired). Adjusted p values <0.05 were considered sf were considered candidate biomarker. These candidates were include significant. The power of the candidate biomarkers to discriminate between normal and low T was evaluated by doing receiver operating characteristic (ROC) analysis. Significant proteins (between A and B with significant recovery in B and C) with (1) area under the curve (AUC) >0.80 or (2) AUC between 0.75 and 0.80 (Marshall et al., 2010; Bowers and Zhou, 2019; Simundić, 2009) and highly enriched in liver tissues Human Proteome Map (Kim et al., 2014) and (Kampf et al., 2014; Kholodenko and Yarygin, 2017; Schmucker and Sanchez, 2011) were considered candidate biomarker. These candidates were included as predictors in a stepwise regression (method: backward) to select the best combination of markers that predict the odds of being low T. Bootstrap resampling with replacement method was applied to assess consistency. A new variable called Multi Marker Algorithm (MMA) was derived from the predicted log-odds (of being low T) obtained from a binomial logistic regression analysis (see Appendix 1 – Supplementary Statistical analyses) and it was evaluated together with marker candidates proteins.

Infertile cohort of patients

The normal distribution of the variables that describe background characteristics of the infertile cohort of patients (Table 1) was evaluated by Kolmogorov–Smirnov test. The intensities of the candidate biomarkers were Log2 transformed to achieve normal distributions. In this cohort, MMA variable was created to predict the odds of suffering low T or other medical conditions associated with low T levels.

Changes between two groups were evaluated by two-tailed Student’s t-test (p values <0.05 were considered significant). Overall changes between more than two groups were evaluated by one-way ANOVA followed by a pairwise FDR correction (Benjamini et al., 2006) and adjusted p values <0.05 were considered significant. In order to know if the changes in the candidate markers occur with the change in T as observed in the healthy human model, three groups of patients were created based on total T concentration (Arver and Lehtihet, 2009) (group 1: low T [LT] ≤8 nmol/l [n = 22]; group 2: borderline low T [BL_T] between 8 and 12 nmol/l [n = 17]; group 3: normal T [NT] >12 nmol/l [n = 36]). Calculated free testosterone (cFT) was determined according to the method described by Vermeulen et al., 1999. The cutoff level of 220 pmol/l was used to categorize the subjects as having low cFT (L_cFT; n = 21) or normal cFT (N_cFT; n = 54) (Antonio et al., 2016).

The power of the candidate biomarkers to discriminate between LT and NT (including or not the BL_T) (Chan et al., 2014; Lunenfeld et al., 2012; Zitzmann et al., 2006), or to distinguish patients with medical conditions associated with low T levels (MetS, IR, CVRLP, DM, or LBD) was evaluated by an ROC analysis. The same was done to discriminate between L_cFT and N_cFT. The DeLong’s test (paired) was used to compare the AUCs.

In order to strengthen the evidence of androgenic dependence of the candidate biomarkers, we looked for associations between their expression and the AR CAG repeat length, which was previously reported to have an impact on the activity of the receptor (Casella et al., 2001; Stanworth et al., 2008; Kim et al., 2018; Ferlin et al., 2004). Three categories were defined: reference group 1: patients with CAG repeat length 21 and 22 (n = 18); group 2: patients with CAG repeat length <21 (n = 26), and group 3: patients with CAG repeat length >22. This categorization was undertaken in order to have three groups of sufficient size and the category including the mean CAG length value of 22 was chosen as reference since this CAG number was previously seen, in vitro and in vivo to be associated with highest receptor activity (Nenonen et al., 2010; Nenonen et al., 2011).

Results

Proteins differentially expressed in chemically castrated men

In total, in the healthy men, the expression level of 31 out of 676 proteins was statistically significantly associated with T concentration (in Table S2 available here). The levels of 23 proteins changed in the same direction as T, whereas, the remaining eight markers changed in an opposite way. LH and FSH changed significantly in A and B but not in B and C. The protein changes visualized as boxplots can be found in Figure 2—figure supplement 1 available at https://doi.org/10.6084/m9.figshare.14876562.

Proteins capable to distinguish between low and normal testosterone

Based on p values for AUC in the ROC analysis, among healthy young men, 90% of the 31 proteins distinguished the low T time point (B) from the normal ones (A and C) with statistical significance(in Table S3 available here, Figure 2a). ROC–AUC values greater than 0.80 were obtained for the proteins 4-hydroxyphenylpyruvate dioxygenase (4HPPD) and insulin-like growth factor-binding protein 6 (IGFBP6). Additionally, fructose-bisphosphate aldolase (ALDOB) was the only protein enriched in liver tissue with ROC–AUC between 0.75 and 0.80.

Figure 2.

Proteins influenced by testosterone in the model of young healthy males.

(a) Top 25 significant proteins selected in the healthy human model (receiver operating characteristic [ROC] p < 0.01, Table S3). The arrows indicate the direction of change in protein expression in the different conditions. The tissue with highest expression of each protein is indicated in colors. Also, results from the ROC analysis are shown as bar chart (area under the curve, AUC) and heat-map (p values). (b) Boxplot (mean (min; max)) of the top three significant proteins proposed as biomarker candidates, able to discriminate between low and normal testosterone (in Table S4 available here). The adjusted p values are specified on top of the comparative horizontal lines. (c) ROC of the analytes proposed as biomarker candidates, including Multi Marker Algorithm (MMA).

The stepwise regression method selected 4HPPD and IGFBP6 as the best markers to be combined to predict the odds of being low T, and thus, they were the basis for the new variable MMA (see Material and methods). MMA together with 4HPPD, ALDOB, and IGFBP6 was selected as potential candidate markers for the diagnosis of BAA (Figure 2b, c). The expression of the 4HPPD and ALDOB proteins was significantly increasedat low T (p < 0.001; p < 0.001) and remediated in response to the T treatment, whereas IGFBP6 expression was significantly decreased (p < 0.001) by castration.

Testing of the candidate biomarkers in infertile men

The three proteins and MMA showed statistically significant differences (4HPPD: p < 0.001, ALDOB: p = 0.003, IGFBP6: p = 0.016, MMA: p < 0.001, Figure 3a) between the three groups defined according to the total T levels (in Table S2 available here). 4HPPD, ALDOB, and MMA showed a negative association with T changes, while IGFBP6 displayed a positive association. The three proteins and MMA significantly distinguished the patients with LT from BL_T/NT (4HPPD: AUC = 0.75, p = 0.001; ALDOB: AUC = 0.70, p = 0.008; IGFBP6: AUC = 0.69, p = 0.01; MMA: AUC = 0.79, p < 0.001) (Figure 3b). Additionally, the power to discern low T values improved for all the biomarkers tested when the patients with BL_T were excluded (Table 2). Similar results were obtained for discrimination between low and normal FT (Figure 3d, e; Table 2).

Figure 3.

New markers to discern states of different testosterone levels in men investigated for infertility (n = 75).

(a) Patients grouped by three levels of total testosterone: low testosterone (LT) ≤8 nmol/l (n = 22), borderline testosterone (BL_T) between 8 and 12 nmol/l (n = 17) and normal testosterone (HT) >12 nmol/l (n = 36). Each group is represented by the mean and its 95% CI. Horizontal lines indicate significant differences between groups and the adjusted p values are specified on top of these lines (in Table S5 available here). (b) Receiver operating characteristic (ROC) analysis to discriminate patients with LT in the entire cohort and (c) in a cohort that excluded patients with borderline testosterone levels (LT_B). Multi Marker Algorithm (MMA) is based on is the combination of levels of the proteins 4-hydroxyphenylpyruvate dioxygenase (4HPPD) and insulin-like growth factor-binding protein 6 (IGFBP6). (d) As (a), but grouped according to the levels of calculated free testosterone (cFT): low (L_cFT) (n = 21) − < 220 pmol/l and normal (N_cFT) (n = 54) add symbol 220 pmol/l. (e) As (b) and (c) but for discrimination of L_cFT and N_cFT.

Table 2.

Comparison of receiver operating characteristic (ROC)–areas under the curve for testosterone and the candidate biomarkers in relation to the prediction of hypogonadism and its sequelae in patients.

Analyte	Low T		Low T*		Low cFT		IR		DM		LBD		CVRLP		MetS
	(T ≤ 8 nmol/l)		(T ≤ 8 nmol/l)		(cFT <220 pmol/l)		(HOMA-IR >2.5)		-		(z-score <−1)		(ApoB/ApoA1 ≥0.9)
	AUC (Sp,Se)	p	AUC (Sp,Se)	p	AUC (Sp,Se)	p	AUC (Sp,Se)	p	AUC (Sp,Se)	p	AUC (Sp,Se)	p	AUC (Sp,Se)	p	AUC (Sp,Se)	p
4HPPD	0.75(85,59)	8.38E−04	0.77(86,59)	2.66E−04	0.69(83,57)	5.14E−03	0.79(84,70)	1.20E−04	0.89(93,75)	9.00E−03	0.64(76,61)	2.24E−02	0.74(46,90)	5.79E−03	0.74(95,50)	5.31E−03
ALDOB	0.69(68,73)	8.25E−03	0.70(67,73)	5.39E−03	0.66(67,71)	1.56E−02	0.73(71,75)	2.85E−03	0.85(63,100)	1.80E−02	0.57(98,27)	1.93E−01	0.71(83,55)	4.64E−02	0.74(82,64)	6.02E−03
IGFBP6	0.69(77,59)	1.05E−02	0.70(81,59)	4.89E−03	0.63(44,81)	7.24E−02	0.57(42,80)	3.50E−01	0.59(39,100)	5.40E−01	0.63(43,78)	2.75E−01	0.59(48,80)	1.87E−01	0.65(45,93)	8.45E−02
Testosterone	-	-	-	-	-	-	0.71(76,70)	4.96E−03	0.55(32,100)	7.24E−01	0.75(74,74)	5.12E−04	0.66(65,85)	6.55E−03	0.56(72,57)	5.08E−01
MMA	0.79(74,82)	9.23E−05	0.80(72,86)	3.86E −05	0.70(69,71)	3.90E−03	0.79(82,70)	1.46E−04	0.92(84,100)	5.00E−03	0.78(82,65)	1.40E−02	0.75(73,75)	3.65E−03	0.78(63,86)	1.57E−03

Significant p values are highlighted in bold and underlined. *Excluding patients with testosterone values from the borderline low testosterone (8 < BL_T ≤ 12).

cFT: calculated free testosterone; IR: insulin resistance; DM: diabetes mellitus type 2; LBD: low bone density; CVRLP: cardiovascular risk lipid profile ;MetS: metabolic syndrome; AUC:area under the curve; Spe: specificity in %; Se: sensitivity in %.

Ability to distinguish men with abnormal metabolic comorbidities or reduced bone mineral density

The AUCs for 4HPPD, ALDOB, and MMA in relation to risk of DM and MetS were statistically significant whereas for T the p value for AUC was 0.72. The AUCs for 4HPPD, ALDOB, and MMA were also statistically significantly larger than this for T (DM: 4HPPD [p = 0.005], ALDOB [p = 0.009], MMA [p = 0.002]; MetS: 4HPPD [p = 0.032], ALDOB [p = 0.030], MMA [p = 0.002]; Table 2). Additionally, the AUC values in relation toCVRLP and IR were numerically higher for 4HPPD, ALDOB, and MMA than for T, however, the differences between the AUC values were not statistically significant (CVRLP marker vs T: 4HPPD [p = 0.97], ALDOB [p = 0.61], MMA [p = 0.87]; IR marker vs T: 4HPPD vs T [p = 0.30], ALDOB vs T [p = 0.88], MMA [p = 0.31]). 4HPPD and MMA statistically significantly distinguished between patients with normal bone density and LBD. The same was true for T, the differences between the AUC for T and those for 4HPPD and MMA not being statistically significant (LBD vs T: 4HPPD [p = 0.28], MMA [p = 0.30]). No statistical significance, in relation to prediction of LBD was found for IGFBP6 (Figure 4).

Figure 4.

Results from receiver operating characteristic (ROC) analysis to determine whether the analytes discriminate between the presence of comorbidities or not.

Analytes included in the analysis are 4-hydroxyphenylpyruvate dioxygenase (4HPPD), insulin-like growth factor-binding protein 6 (IGFBP6), fructose-bisphosphate aldolase (ALDOB), and Multi Marker Algorithm (MMA; combination of 4HPPD and IGFBP6). Area under the curve (AUC), p values can be found in Table 2. IR: insulin resistance; DM: type 2 diabetes mellitus; LBD: low bone density; CVRLP: cardiovascular risk lipid profile; MetS: metabolic syndrome.

Association of the candidate biomarkers with AR CAG repeat length

Statistically significant inter-CAG-group overall differences were observed for 4HPPD (p = 0.012) and ALDOB (p = 0.008) (Figure 5). Additionally, the protein expressions were significantly higher in the groups with <21 and >22 CAG repeat length as compared with the reference (Figure 5, Table 3). However, we did not observe any statistically significant association between CAG number and expression of IGFBP6.

Figure 5.

Association between androgen receptor CAG <21 (n = 26) and CAG <22 (n = 30) and 4-hydroxyphenylpyruvate dioxygenase (4HPPD) and fructose-bisphosphate aldolase (ALDOB), respectively, with CAG = 21 and 22 (n = 18) set as reference.

Table 3.

Ratio between mean concentrations of 4-hydroxyphenylpyruvate dioxygenase (4HPPD) and fructose-bisphosphate aldolase (ALDOB) in men with CAG repeat length <21 or > 22 as compared to the reference group.

Proteins	Overall p value	<21 vs reference		>22 vs reference
		Ratio (95% CI)	p value*	Ratio (95% CI)	p value*
4HPPD	0.012	1.34 (1.02–1.76)	0.032	1.62 (1.23–2.13)	0.001
ALDOB	0.008	1.35 (1.03–1.78)	0.029	1.72 (1.29–2.32)	<0.001

p value of the post hoc constrain between groups.

Ratio: ratio between mean concentration in <21 or >22 groups divided by the reference group (21 and 22).

Discussion

We have identified three plasma proteins, which are potential markers of BAA. In young healthy men, the three markers ALDOB, 4HPPD, and IGFB6 were strongly associated with T levels. In a slightly older cohort of infertile men, these markers were indicative of T deficiency that is both total and free serum T was low (Arver and Lehtihet, 2009). Furthermore, levels of two of the markers, ALDOB and 4HPPD, were more strongly associated with risk of metabolic disturbances than total T. The association seemed to become stronger by creating a combined marker MMA, based on both 4HPPD and IGFBP6 levels. Finally, the androgen dependence of ALDOB and 4HPPD was confirmed by the association between the concentration of those proteins and the length of AR CAG repeats.

The ALDOB is a glycolytic enzyme, predominantly expressed in liver and kidney, that catalyzes the reversible cleavage of fructose-1,6-bisphosphate into glyceraldehyde 3-phosphate and dihydroxyacetone phosphate. The B isoform of aldolase, for example ALDOB, in the liver is under dietary control (Munnich et al., 1985). Ingestion of fructose induces ALDOB mRNA expression in the liver, which is otherwise low in fasting conditions. In humans, the absence of functional ALDOB enzyme due to mutations in the ALDOB gene cause hereditary fructose intolerance, characterized by metabolic disturbances that include hypoglycemia, lactic acidosis, and hypophosphatemia (Hannou et al., 2018). An upregulation of ALDOB in human pancreatic β cells occurs upon the development of hyperglycemia and may contribute to the impairment of insulin secretion in humans (Gerst et al., 2018). In a study on goats, the ALDOB gene was found to be downregulated at the time of postnatal initiation of spermatogenesis (Bo et al., 2020). This finding is in accordance with our data showing that rising testosterone is inhibiting ALDOB.

Similar to ALDOB, we found that 4HPPD was negatively associated with T levels. This enzyme is involved in the catabolic pathway of tyrosine and catalyzes the conversion of 4-hydroxyphenylpyruvate to homogentisic acid in the tyrosine catabolism pathway (Hager et al., 1957). The expression of the gene is regulated by hepatocyte-specific and liver-enriched transcription factors, as well as by hormones (Kim et al., 2014). Tyrosine has previously been reported to be upregulated in hypogonadal men and both tyrosine and phenylalanine levels were suggested as predictors of the risk of developing diabetes many years before manifest disease (Wang et al., 2011; Fanelli et al., 2018; Guasch-Ferré et al., 2016). In male tyrosine hydroxylase knockout mice normal body weight, puberty onset, and basal gonadotropin levels in adulthood were evident, although T was significantly elevated in adult mice (Stephens et al., 2017).

The last marker, IGFBP6 is expressed in most tissues (Kim et al., 2014) and is one of the binding proteins for insulin-like growth factor (IGF). The principal function of IGFBP6 is inhibiting IGF-II actions, whereby IGF-II-induced cell proliferation, differentiation, migration, and survival is reduced. Serum levels of IGFBP6 increase gradually with age and are higher in men than in women, but there are conflicting studies of the direct effects of sex steroids on IGFBP6 expression in different tissues (Bach et al., 2013). A positive association between the levels of T and IGFBP6 have previously been found (Rooman et al., 2005; Huang et al., 2019). The latter study has a somewhat similar set up as the present study, based on chemical castration with a GnRH agonist, and has also focused on identifying novel markers of BAA, but with candidate markers previously identified as being associated with changes in fat-free mass. The study showed that early increases in IGFBP6 levels in men receiving testosterone were associated with increases in fat-free mass and muscle strength.

Altogether, our findings may not only be clinically valuable in developing new methods of assessing BAA but also add to our understanding of the biological role of T in human metabolism, regulation of testicular function (ALDOB), as well as muscle strength and body composition (IGFBP6). However, our findings cannot be used as a direct proof of hypogonadism being cause of cardiometabolic disease but the combined parameter MMA may in this context be an important tool in the detection of long-term morbidity, such as bone mineral density and cardiometabolic risk, even before clinical diagnosis.

Our study has some strengths and limitations. Using a chemical castration model in young healthy men, we were able to identify proteins influenced by androgens and select those that were most strongly associated with T levels. By utilizing proteomics, we had an explorative approach to identify new markers of BAA without being restricted by previously published findings.

Another strength is the depth of the analysis due to the depletion abundant proteins from plasma. We were able to identify more than 450 proteins, which were identified in the same concentration range as 87% of FDA-approved biomarkers (Anderson, 2010). If depletion is not performed the detection level is dampened by the components from the digested abundant proteins as the proteins removed are of highest abundance in plasma and plasma proteome and exceed some lower abundance proteins by 10 orders of magnitude (Tu et al., 2010).

Although more clinical testing is needed, we have provided preliminary results showing that these protein markers may also be clinically useful. We have previously shown that median length CAG number is associated with most active AR (Nenonen et al., 2010). Thus, the fact that we find the lowest ALDOB and 4HPPD levels in those subjects having AR CAG repeat length close to median, confirms thatthe candidate markers identified in the present study are androgen dependent.

A limitation of our study is that the lack of reliable criteria for clinical hypogonadism, which made it impossible to test the power of the new markers in men in whom androgen replacement is needed. Furthermore, the clinical part of the study was limited, because we do not have sufficient information about potential factors influencing the inter- and intraindividual variation in the levels of these proteins and, thereby, their suitability as clinical markers. Furthermore, the number of subjects included in the BL-T group was not sufficient to clarify whether, in this testosterone concentration interval, the new markers can be useful in discriminating between truly hypogonadal and men being eugonadal.

Furthermore, we are not reporting absolute values of quantifications for the potential markers, but the relative quantifications for comparing the protein expression between groups. This kind of comparative proteomics is favorable in research studies, in which preliminary results of protein changes between groups are obtained. Also, the sample processing is complicated putting high demands on the laboratory. In proteomics, there can be a high variation between laboratories in reporting absolute concentration proteins in plasma, especially when small sample sizes are reported (Nanjappa et al., 2014). In order to obtain trustworthy absolute concentration ranges or determine the activity level of the enzymes, it is necessary to analyze the potential markers in large cohorts including both healthy subjects and patients.

In this study, we have applied an immunoassay for measuring T levels, although some consider liquid chromatography–tandem mass spectrometry (LC–MS/MS) as gold standard in assessment of sex hormone levels. However, worldwide the former is most commonly used for T measurements. Additionally, in the concentration range seen in males, there seems to be high correlation between concentration values obtained by immunoassay and by LC–MS/MS (Huhtaniemi et al., 2012). Also in identifying men in hypogonadal T range and prediction of cardiometabolic risk, assessment of T by LC–MS/MS was not shown to be superior to that performed by standard methodology (Huhtaniemi et al., 2012; Haring et al., 2013).

For this study, the z-score was employed because of the relatively young age of the subjects. z-Scores, a comparison of an individual’s bone density with that of a healthy reference population (NHANES III) of the same age, sex, and ethnicity and expressed as standard deviations (SDs), were obtained from the DXA machine. In this study, we defined low BMD as z-score below −1.0. The rationale is based primarily on meta-analysis of 12 cohort studies demonstrating significantly increased risk of osteoporotic fractures for men at z-scores ≤ −1 SD (Johnell et al., 2005) and in addition because it has also been shown that the majority of fragility fractures occur in patients with BMD in the osteopenic range, that is T-score between −1 and −2.5. (Unnanuntana et al., 2010). Based on this information, z-score below −1 can be assumed to imply an increased fracture risk.

In conclusion, we have identified three new potential biomarkers of BAA. Those proteins – alone or in combination – are promising as useful parameters in the clinical diagnosis of male hypogonadism and in the prediction of its long-term sequelae, as well as in studying the biology of androgen action. More extensive testing is vital to elucidate their BAA potential, not only in men but also in women and in prepubertal boys.

Data availability

The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE (Perez-Riverol et al., 2019) partner repository with the dataset identifier PXD024448. Supplementary tables (datasets) https://doi.org/10.6084/m9.figshare.14875431 Source data of the Figures can be found on: https://doi.org/10/6084/m9.figshare.14875431 Supplementary Figure S1 (Figure2-figure supplement 1): https://doi.org/10.6084/m9.figshare.14876562. R code: https://github.com/indirapla/TP1_proteins_marker_of_androgen_activity, (copy archived at swh:1:rev:2613c2709a14dec63c727d01edefb5a5f1f1fdc5; Parada, 2022).

This work by Giwercman, et al., interrogates the identity of potential protein biomarkers of androgen activity in humans by carrying out a proteomic analysis in blood from 30 healthy males treated at baseline, after medical castration and at a third time point after testosterone replacement. Proteins most significantly associated with testosterone changes were tested further in a separate cohort. The major findings include the observation that 4 specific proteins are potential protein biomarkers that follow testosterone levels and presumably androgen receptor activity, thus providing new insights into androgen physiology and pathophysiology.

Our editorial process produces two outputs: (i) public reviews designed to be posted alongside the preprint for the benefit of readers; (ii) feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.

Decision letter after peer review:

Thank you for submitting your article "Novel protein markers of androgen activity in humans: proteomic study of plasma from young chemically castrated men" for consideration by eLife. Your article has been reviewed by 2 peer reviewers, one of whom is a member of our Board of Reviewing Editors, and the evaluation has been overseen by a Reviewing Editor and Nancy Carrasco as the Senior Editor. The reviewers have opted to remain anonymous.

The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.

Essential revisions:

1) Testosterone measurements were performed using an immunoassay, and not using the gold standard technique for these measurements which is by mass spectrometry. If one wants to define other/superior markers for androgen activity than testosterone itself, this should be done by comparing this using the gold standard technique of the measurement.

2) As the authors describe in the introduction, SHBG levels are of major importance while interpreting testosterone results in hypogonadal men. It is believed that the free or bioavailable fraction represents the true amount of active hormone. In searching for other markers of androgen activity, these should at least be compared to the free and/or bioavailable levels of the hormone (either measured or calculated using a validated formula).

3) The authors divide the group of infertile men into 3 categories: LT (T {less than or equal to} 8 nmol/L), BL_T (T 8 -12 nmol/L) and NT ({greater than or equal to} 12 nmol/L). This is indeed interesting, because patients in the LT group are highly likely to be androgen deficient, while patients in the NT group are highly likely to have sufficient androgen activity. The issue, as correctly addressed by the authors, situates itself in the BL_T group, where the interpretation of the T levels is difficult, and symptoms described by the patient, which are often rather general or possibly multifactorial (fatigue, erectile dysfunction, decrease libido) are uncertain to be linked to the borderline low T values. It would be therefore of interest, if other biomarkers would be able to differentiate between 'low' and 'normal' especially in this borderline low range, where a lot of uncertainty exist. However, when looking at figure 3a, the selected biomarkers are indeed good in differentiating between LT and NT, and LT and BL_T, but they do not seem to be able to discriminate between BL_T and NT levels (not statistically significantly different). So, when evaluating the patients in the BL_T range by determining these new biomarkers (4HPPD, IGFBP6, ALDOB and MMA), one should conclude that they are not androgen deficient. These markers hence do not provide an added value on top of solely measuring T levels, and this does not resolve the issue as described above, as they are not able to discriminate between BL_T and NT.

4) The use of androgen deficiency linked pathologies to validate new biomarkers of androgen activity has important limitations. The pathologies suggested by the authors have a clear association with androgen deficiency, but these associations have certainly not yet been proven to be causal and probably aren't. If the association between these biomarkers and the pathologies are stronger than the association with testosterone levels, this thus not mean that these markers are better biomarkers of androgen activity than testosterone itself, but may just be better markers of the suggested pathology itself, apart from any possible link with androgen action. It would be therefore much more useful to assess symptoms of hypogonadism (such as low libido and erectile dysfunction), and link these new biomarkers with these symptoms which are known to be caused by androgen deficiency. This is a major limitation for the interpretation of the results.

5) Table 3, which is the ratio between mean concentrations of 4HPPD and ALDOB in men with various CAG repeat lengths, is not very well described in the text. It seems cursory and really an afterthought of the story. Why use these specific numbers and thresholds (CAG repeat length of 21, 22), as opposed to other thresholds? Is this justified by something in the literature? This should be clarified and justified.

6) Compared with the other data on associations between T and protein biomarkers, the CAG repeat data seem to be relatively weak and I worry it might distract from the other strengths in this paper. Other data on CAG and prostate cancer, for example, use thresholds of 18 and 26 CAG repeats (e.g., Giovannucci, et al., PNAS 1997).

7) Similarly, the hypothesis for the data in Figure 5 and Table 3 is not clear. This should really be clarified.

Reviewer #2 (Recommendations for the authors):

This work reveals associations of some protein levels in circulation with androgen status and a first validation in a hypoandrogenism cohort.

Reviewer #3 (Recommendations for the authors):

Major comments:

1) Table 3, which is the ratio between mean concentrations of 4HPPD and ALDOB in men with various CAG repeat lengths, is not very well described in the text. It seems cursory and really an afterthought of the story. Why use these specific numbers and thresholds (CAG repeat length of 21, 22), as opposed to other thresholds? Is this justified by something in the literature? This should be clarified and justified.

2) Compared with the other data on associations between T and protein biomarkers, the CAG repeat data seem to be relatively weak and I worry it might distract from the other strengths in this paper. Other data on CAG and prostate cancer, for example, use thresholds of 18 and 26 CAG repeats (e.g., Giovannucci, et al., PNAS 1997).

3) Similarly, the hypothesis for the data in Figure 5 and Table 3 is not clear. This should really be clarified.

Essential revisions:

1) Testosterone measurements were performed using an immunoassay, and not using the gold standard technique for these measurements which is by mass spectrometry. If one wants to define other/superior markers for androgen activity than testosterone itself, this should be done by comparing this using the gold standard technique of the measurement.

We are aware that use of LC-MS/MS has been suggested as a new gold standard in measuring testosterone levels. However, the access to this method is still very limited and the vast majority of clinical analyses are still performed using immunoassays. A number of studies, including (Huhtaniemi et al., 2012), have demonstrated that in the testosterone concentration range found in males, there is a high correlation between the concentration measured with immunoassays and by LC-MS/MS. Immunoassays are also reliable in identifying men with low testosterone levels. Thus, the superiority of LC-MS/MS is mostly limited to measurements done in prepubertal boys and women, who typically have very low levels of testosterone.

This issue was already addressed in the Discussion part of the first version of our manuscript, but has now been extended by following (Line 362-365): “Additionally, in the concentration range seen in males, there seems to be high correlation between the measurements of serum concentrations obtained by immunoassay and by LC-MS/MS (Huhtaniemi et al., 2012). Also in identifying men in hypogonadal testosterone range and in prediction of cardiometabolic risk, assessment of testosterone by LC-MS/MS was not superior to that performed by standard methodology (Huhtaniemi et al., 2012; Haring et al., 2013).

2) As the authors describe in the introduction, SHBG levels are of major importance while interpreting testosterone results in hypogonadal men. It is believed that the free or bioavailable fraction represents the true amount of active hormone. In searching for other markers of androgen activity, these should at least be compared to the free and/or bioavailable levels of the hormone (either measured or calculated using a validated formula).

The reason why we have focused on total testosterone only is because this type of measurement is most commonly used in clinical practice, whereas the use of measured or calculated free testosterone is still very limited and more of an academic matter. However, following the suggestion of the reviewers and editors, we have added data on calculated free testosterone using the formula by Vermeulen (Vermeulen et al., 1999). These results, now added to Table 2 and Figure 3, show that the protein markers reported by us, are almost as reliable in defining low free testosterone as they are for total testosterone.

3) The authors divide the group of infertile men into 3 categories: LT (T {less than or equal to} 8 nmol/L), BL_T (T 8 -12 nmol/L) and NT ({greater than or equal to} 12 nmol/L). This is indeed interesting, because patients in the LT group are highly likely to be androgen deficient, while patients in the NT group are highly likely to have sufficient androgen activity. The issue, as correctly addressed by the authors, situates itself in the BL_T group, where the interpretation of the T levels is difficult, and symptoms described by the patient, which are often rather general or possibly multifactorial (fatigue, erectile dysfunction, decrease libido) are uncertain to be linked to the borderline low T values. It would be therefore of interest, if other biomarkers would be able to differentiate between ‘low’ and ‘normal’ especially in this borderline low range, where a lot of uncertainty exist. However, when looking at figure 3a, the selected biomarkers are indeed good in differentiating between LT and NT, and LT and BL_T, but they do not seem to be able to discriminate between BL_T and NT levels (not statistically significantly different). So, when evaluating the patients in the BL_T range by determining these new biomarkers (4HPPD, IGFBP6, ALDOB and MMA), one should conclude that they are not androgen deficient. These markers hence do not provide an added value on top of solely measuring T levels, and this does not resolve the issue as described above, as they are not able to discriminate between BL_T and NT.

Thank you very much for this interesting comment. We agree that our markers cannot discriminate between the BL-T and NT groups. However, we do not think that present data exclude that the new markers may be superior to testosterone measurements in diagnosing men with testosterone deficiency. It is obvious that the BL_T group contains a mixture of men who are hypogonadal and those being eugonadal. Therefore, more data are needed to clarify if any of the presented protein markers can be used to discriminate, within the BL_T group, between those being truly hypogonadal and those being eugonadal. However, this question cannot be answered within the framework of current study. We have, therefore, added the following sentence to the Discussion (Line 346-349): “Furthermore, the number of subjects included in the BL-T group was not sufficient to clarify whether, in this testosterone concentration interval, the new markers can be useful in discrimination between truly hypogonadal men and men being eugonadal.”

4) The use of androgen deficiency linked pathologies to validate new biomarkers of androgen activity has important limitations. The pathologies suggested by the authors have a clear association with androgen deficiency, but these associations have certainly not yet been proven to be causal and probably aren’t. If the association between these biomarkers and the pathologies are stronger than the association with testosterone levels, this thus not mean that these markers are better biomarkers of androgen activity than testosterone itself, but may just be better markers of the suggested pathology itself, apart from any possible link with androgen action. It would be therefore much more useful to assess symptoms of hypogonadism (such as low libido and erectile dysfunction), and link these new biomarkers with these symptoms which are known to be caused by androgen deficiency. This is a major limitation for the interpretation of the results.

We completely agree that the pathogenetic mechanism and the direction of association between cardiometabolic disease and hypogonadism is not fully clarified and can be bi-directional. As for the link between hypogonadism and decreased bone density, the causality, which mostly is related to estrogen action, seems clearer.

It was not our intention to claim that low testosterone levels are cause of cardiometabolic disease, but we find that including data on association between the levels of the new protein markers and cardiometabolic parameters adds to understanding the potential clinical relevance of our findings. We have, added the following clarifying sentence to the Discussion part (Line 322-325):

“However, our findings cannot be used as a direct proof of hypogonadism as the cause of cardio-metabolic disease, but the combined parameter MMA may in this context be an important tool in the detection of long-term morbidity, such as bone mineral density and cardio-metabolic risk, even before clinical diagnosis”.

As considers decrease in libido and erectile dysfunction, it is true that some studies, including European Male Aging Study, have found these parameters to be the best clinical markers of hypogonadism. Unfortunately, we do not have sufficient data to include these parameters in our analysis. Furthermore, even those conditions are multi-factorial why an association with the protein markers cannot be considered as a proof of an androgen-dependent effect.

5) Table 3, which is the ratio between mean concentrations of 4HPPD and ALDOB in men with various CAG repeat lengths, is not very well described in the text. It seems cursory and really an afterthought of the story. Why use these specific numbers and thresholds (CAG repeat length of 21, 22), as opposed to other thresholds? Is this justified by something in the literature? This should be clarified and justified.

The following text describes the findings reported in Table 3 and Figure 5 (Line 262-266):

“Statistically significant inter-CAG-group overall differences were observed for 4HPPD (p = 0.012) and ALDOB (p = 0.008) (Figure 5). Additionally, the protein expressions were significantly higher in the groups with <21 and >22 CAG repeat length as compared with the reference (Table 3 and Figure 5). However, we did not observe any statistically significant association between CAG number and expression of IGFBP6.”

We recognise that the background for categorization of CAG lengths was not completely clarified in the first version of the manuscript. The selection of CAG lengths of 21 and 22 as reference was not random, but based on our previous in vitro and in vivo data showing that the CAG number of 22, corresponding to the mean length in our cohort, is associated with highest receptor activity. We have now added the following sentence in the Statistical analysis (Line 193-196):

“This categorization was undertaken in order to have three groups of sufficient size and the category including the mean CAG length value of 22 was chosen as reference since this CAG number was previously seen, in vitro and in vivo to be associated with highest receptor activity (27,28).”

6) Compared with the other data on associations between T and protein biomarkers, the CAG repeat data seem to be relatively weak and I worry it might distract from the other strengths in this paper. Other data on CAG and prostate cancer, for example, use thresholds of 18 and 26 CAG repeats (e.g., Giovannucci, et al., PNAS 1997).

7) Similarly, the hypothesis for the data in Figure 5 and Table 3 is not clear. This should really be clarified.

In order to declare the rational for including data on androgen receptor CAG repeat lengths, we have added the following sentence to the statistical analysis section (Line 188-190):

“In order to strengthen the evidence of androgenic dependence of the candidate biomarkers, we looked for associations between their expression and the androgen receptor (AR) CAG repeat length, which was previously reported to have an impact on the activity of the receptor”.

We think that this part represents an important aspect of this manuscript. In the human model of GnRH-antagonist treated men, candidate markers were identified under conditions of significant changes in testosterone levels but also in the levels of other hormones, e.g. gonadotrophins. By showing that the levels of these protein markers are also dependent on more discrete and genetically determined variation in androgenic activity, as those associated with CAG number, we demonstrate their potential clinical value. We would appreciate to have these data kept in the manuscript but are willing to omit them in case the Reviewers and Editors consider it as crucial for accepting our paper.

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