A more accurate relationship between serum androgen and metabolism among healthy, nonobese, reproductive-age women based on liquid chromatography-tandem mass spectrometry

Abstract

This study explored a more precise association between androgens and glycolipid metabolism in healthy women of different ages. Body mass index (BMI), waist circumference (WC), and waist-to-hip ratio were used as body fat indicators. High-density lipoprotein (HDL), low-density lipoprotein, triglycerides, and total cholesterol were used as lipid markers. Fasting blood glucose (FBG), fasting insulin, and the homeostatic model assessment of insulin resistance were used to assess insulin resistance and glucose metabolism. Liquid chromatography-tandem mass spectrometry was used to measure androgen indicators, including testosterone, sex hormone-binding globulin (SHBG), free testosterone (FT), dihydrotestosterone (DHT), androstenedione (A4), dehydroepiandrosterone (DHEA), and dehydroepiandrosterone sulfate (DHEAS). DHEAS levels varied across age groups. Correlation analyses with Spearman’s coefficient showed that the free androgen index correlated positively with WC (p = 0.040), FT correlated positively with BMI (p = 0.033) and WC (p = 0.049), SHBG correlated positively with HDL (p = 0.013), and A4 correlated positively with FBG (p = 0.017). Multiple linear regression analysis showed that among healthful women aged 36–40 years, A4 increased with FBG, and SHBG increased with HDL. Even within healthy, nonobese women, lipid and glucose metabolism were robustly correlated with androgens. Yearly metabolic assessments are necessary, particularly for FBG and HDL, since these markers can predict the likelihood of hyperandrogenemia, enabling timely interventions.

ANDROGENS are essential for women’s sexual health and well-being. Besides acting as precursors for estrogen synthesis, they also support normal ovarian function, bone metabolism, cognition, and sexual function. However, this does not mean that more androgens are better. Hyperandrogenism (HA) is a common hormone-related disorder with diverse symptoms and includes biochemical hyperandrogenemia and clinical HA. Elevated androgen levels may cause endocrine and metabolic issues and characteristic clinical signs such as acne and hirsutism [1-3]. Obesity, hyperinsulinism, insulin resistance (IR), and acanthosis nigricans are common in hyperandrogenic women [4, 5]. Polycystic ovary syndrome (PCOS) is one of the leading causes of HA in women of childbearing age, characterized by HA, ovulation disorders, and polycystic ovarian changes [6]. The high androgen levels in these patients are associated with various physiological dysfunctions, including cytokine hypersecretion, adipocyte proliferation, and signaling pathway dysregulation [7]. Furthermore, HA disrupts the balance of immune cells, stimulating some while inhibiting others, resulting in chronic inflammation [8].

The effect of androgens on metabolism in women with PCOS has been studied extensively [6, 9, 10]. Androgenic components each have clinical significance, including testosterone (TT), androstenedione (A4), dehydroepiandrosterone sulfate (DHEAS), dehydroepiandrosterone (DHEA), and dihydrotestosterone (DHT). However, it is currently unclear whether androgens affect glucose and lipid metabolism, regardless of obesity, PCOS, geographic region, or ethnicity.

Compared to other ethnic groups, Chinese women generally have lower androgen levels, and clinical manifestations of high androgens are rare. Therefore, diagnosing high androgens in Chinese women relies more on laboratory androgen measurements. However, the position statement from the Endocrine Society indicates significant limitations in the accuracy, precision, sensitivity, and specificity of immunological assays at lower hormone levels [11]. At low concentrations, immunoassay-based methods show poor accuracy because of cross-reactivity with endogenous compounds, such as 4-androsten-3β-ol-17-one [12-18]. Moreover, the agreement between different immunoassays is often poor [19]. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) has been developed to overcome these problems. It has increasingly become the first choice for steroid hormone measurements due to small sample volumes, high sensitivity, high stability, and high comparability [20, 21], making it the current gold standard [22].

In Asia, particularly in China, LC-MS/MS is not yet widely performed, indicating the advancement of our study. In addition, we still do not know the levels of androgens measured by LC-MS/MS or the exact relationships between androgens and metabolism in healthy women. Therefore, this study measured androgen levels in healthy nonobese, reproductive-age women of different ages using LC-MS/MS and explored the relationships between androgens and metabolism to define them more precisely.

Materials and Methods

Participants

This study included an initial sample of 123 healthy women recruited between December 2021 and June 2022 at the Checkup Center of Affiliated Hangzhou First People’s Hospital, Zhejiang University School of Medicine. Each participant underwent clinical examinations, blood tests, and individual interviews.

Inclusion criteria were women of Han ethnicity aged 20–40 years with regular menstrual cycles, a body mass index (BMI) of 18–24 kg/m², and a waist circumference (WC) of <80 cm. The exclusion criteria were as follows: (1) taking estrogen, progestins, corticosteroids, spironolactone, anti-androgens, or other agents known to alter reproductive androgens and glucose homeostasis for at least three months before the study; (2) history of ovarian or adrenal tumors or surgery; (3) combined chronic conditions, such as severe liver and kidney abnormalities, systemic infections, malignancies, hypertension, endocrine metabolic diseases (e.g., diabetes and abnormal adrenocortical function), and a history of adrenal androgen-secreting tumors; (4) smoking or alcohol abuse; (5) mental disorders or other conditions that prevent patients from completing surveys; (6) pregnancy or lactation; (7) PCOS diagnosis according to the 2003 Rotterdam criteria. All subjects received written as well as verbal instructions and provided informed consent. Ethical approval was obtained from the local ethics committee (approval number: 2021-20210412-01).

Anthropometric and biochemical parameters

Four experienced gynecologists conducted medical history interviews, including age, education level, lifestyle habits, reproductive history, family history, surgical history, menstrual status, medication use, and other basic information. A qualified tester measured their height, weight, WC, and hip circumference. Their BMI was computed by dividing their weight (kg) by their height (m) squared (m²). Their waist-to-hip ratio (WHR) was calculated by dividing their WC (cm) by their hip circumference (cm).

Fasting serum samples were collected between 8 and 10 a.m. Triglyceride (TG) and total cholesterol (TC) were measured using the glycerophosphate oxidase-peroxidase and peroxidase method and the cholesterol oxidase-peroxidase antiperoxidase method, respectively. High-density (HDL-C) and low-density (LDL-C) lipoprotein-cholesterol were measured using the direct catalase scavenging method. Fasting glucose was measured using the hexokinase method. Fasting insulin (FINS) was measured by LC-MS/MS. All measurements, except FINS, were performed by the Medical Examination Center at the First People’s Hospital of Hangzhou. FINS was measured at the Dean Examination Center.

Determination of the serum hormone analyses

LC-MS/MS was used to measure the following biomarkers in each participant on days 2–4 of their menstrual cycle: TT, sex hormone-binding globulin (SHBG), free testosterone (FT), DHT, DHEA, DHEAS, and A4. The free androgen index (FAI) was calculated using the following formula: (TT [nmol/L] × 100)/SHBG (nmol/L). The homeostatic model assessment of IR (HOMA-IR) index was calculated using the following formula: (FBG [mmol/L] × FINS [μIU/mL])/22.5.

Androgen profiles were measured by LC-MS/MS using a SCIEX Triple Quad^TM 6500+ LC-MS/MS with electrospray ionization and Analyst software. Briefly, 2 mL of venous blood was collected, then extracted and purified using solid-phase extraction, liquid-liquid extraction, or protein precipitation methods. Next, the extracted sample was reconstituted in a suitable solvent for further analysis. Then, the extracted and purified sample was introduced into a chromatographic column and separated based on its physicochemical properties. The separated analytes are ionized and detected using tandem mass spectrometry.

The specific sample preparations were as follows. To extract DHEAS, a mixture of endogenous sex hormone (ESH) precipitant and diluent (20:180 by volume) was prepared. Next, the protein precipitate was added to a centrifuge tube containing the sample to be tested and thoroughly vortexed to mix. Then, the samples were cooled in a –20°C freezer for 20 minutes. Finally, the supernatant was removed from the tube and centrifuged at 15,000 rpm and 4°C for 10 minutes.

To extract steroid hormones other than DHEAS, 450 μL of the sample to be measured was first added to deionized water and thoroughly mixed. Next, the mixture was transferred to a supported liquid extraction plate and left to stand for 20 minutes after the sample had flowed into the packing. Then, ESH eluate was added and eluted until no liquid emerged; the elution step was repeated after 1 and 2 minutes. Next, the eluates were combined and then dried under a nitrogen stream. Then, ESH diluent and purified water were mixed in a 2:8 ratio by volume to produce a complex solution. The complex solution was added to the sample and vortexed to mix.

Statistical analyses

Data analyses were conducted using SPSS (version 25.0). Data normality was assessed using the Shapiro-Wilk test. Normally distributed continuous variables are presented as the mean ± standard deviation, while non-normally distributed continuous variables are presented as the median with interquartile range (IQR). The participants were placed into three age groups: 26–30 years old (Group A), 31–35 years old (Group B), and 36–40 years old (Group C). Data were compared between groups using an analysis of variance F-test (normally distributed) or a Kruskal-Wallis test (non-normally distributed). Pairwise comparisons were performed using the least significant difference test. Spearman’s rank correlation coefficient (r_s) was used to assess univariate correlations between androgens and different clinical characteristics, with the results presented as heatmaps.

Based on the correlation analysis results, we examined five correlations: FAI and WC, FT and BMI, FT and WC, SHBG and HDL, and A4 and FBG. Multiple linear regression was conducted separately in each age category. In the multiple regression analyses, WC, BMI, HDL, and FBG were considered categorical variables (categorized into quartiles), and we controlled for confounding variables, such as BMI and abdominal circumference. The regression coefficient (β) and 95% confidence interval (CI) were determined for each higher quartile compared to the lowest quartile. The results are presented as forest plots. We also computed the p-value for the trend from the lowest (Q1) to the highest (Q4) quartiles.

Results

Participants’ characteristics

Fig. 1 shows the flow of participants through the study. Of the initial sample, two women were excluded because they were being actively treated with oral contraceptives, 20 because they had a BMI outside the reference ranges or a WC >80 cm, 16 because of insufficient serum to be tested, and three because they had undergone ovarian surgery. Therefore, this study included 82 participants with a median age of 33.50 years (IQR: 31.00–36.25) and a mean BMI of 20.77 ± 1.50 kg/m². The participants’ clinical and metabolic characteristics are shown in Table 1.

Fig. 1

Flow Chart Depicting the Patient Selection

Table 1

Characteristics and clinical characteristics of the study population

Items	M ± SD
Age, years	33.50 (31.00–36.25)
BMI, kg/m2	20.77 ± 1.50
WC, cm	71.00 (66.75–74.00)
WHR	0.80 (0.76–0.83)
HDL-C, mmol/L	1.65 ± 0.30
LDL-C, mmol/L	2.32 ± 0.51
TG, mmol/L	0.75 (0.59–1.00)
TC, mmol/L	4.52 (4.06–4.91)
FBG, mmol/L	4.88 ± 0.31
FINS, μU/mL	6.18 (4.69–7.56)
HOMA-IR	1.30 (1.01–1.61)
FAI, %	12.25 (7.78–18.89)
SHBG, nmol/L	56.95 (43.28–82.70)
FT, pg/mL	1.80 (1.10–2.30)
DHT, pg/mL	84.00 (56.00–104.25)
TT, pg/mL	243.15 (176.73–342.23)
A4, pg/mL	1,281.92 ± 441.47
DHEA, pg/mL	5,654.45 (4,399.48–7,793.45)
DHEAS, μg/mL	2.24 (1.76–2.90)

Continuous variables are reported as the mean ± standard deviation (SD) for normalized distribution or median with interquartile range (IQR) for nonnormal variables.

BMI, body mass index; WC, waist circumference; WHR, waist-to-hip ratio; HDL, high density lipoprotein; LDL, low density lipoprotein; TG, Triglyceride; TC, total cholesterol; FBG, fasting blood glucose; FINS, fasting insulin; HOMA-IR, homeostasis model assessment for insulin resistance; FAI, Free Androgen Index; SHBG, sex hormone binding globulin; FT, free testosterone; DHT, dihydrotestosterone; A4, androstenedione; TT, total testosterone; DHEA, dehydroepiandrosterone; DHEAS, dehydroepiandrosterone sulphate.

Comparison of androgenic and metabolic indices among age groups

The women were divided into three groups: 26–30 years old (Group A), 31–35 years old (Group B), and 36–40 years old (Group C). The glycolipid metabolism indicators did not differ significantly among the age groups. In addition, there were no age-related variations in androgen levels except for DHEAS (Table 2). The overall distribution of serum DHEAS concentrations differed significantly among the three groups (H = 8.797, p = 0.012). Specifically, serum DHEAS concentrations differed significantly between Groups A and B (2.79 [2.12–3.53] vs. 1.99 [1.51–2.78] μg/mL; p = 0.007) but not between Groups A and C (p > 0.05) or Groups B and C (p > 0.05). In addition, serum DHEAS levels were significantly higher in women aged 31–35 years than in women aged 26–30 or 36–40 years.

Table 2

Comparison of androgenic and metabolic indices among different age groups

	A Group (n = 18)	B Group (n = 35)	C Group (n = 29)	p
BMI, kg/m2	20.66 ± 1.613	20.72 ± 1.533	20.91 ± 1.42	0.832
WC, cm	69.28 ± 6.42	70.24 ± 5.22	71.66 ± 4.32	0.293
WHR	0.78 ± 0.04	0.80 ± 0.06	0.80 ± 0.04	0.423
HDL-C, mmol/L	1.59 ± 0.29	1.62 ± 0.29	1.72 ± 0.33	0.327
LDL-C, mmol/L	2.26 ± 0.39	2.30 ± 0.46	2.39 ± 0.62	0.695
TG, mmol/L	0.67 (0.53, 0.97)	0.83 (0.61, 1.33)	0.83 (0.62, 0.96)	0.400
TC, mmol/L	4.44 (3.82, 4.71)	4.55 (4.06–4.84)	4.58 (4.23–5.05)	0.250
FBG, mmol/L	4.88 ± 0.27	4.85 ± 0.31	4.92 ± 0.35	0.671
FINS, μU/mL	6.25 (5.17, 7.44)	6.09 (5.07–7.53)	6.13 (4.40, 7.84)	0.691
HOMA-IR	1.39 (1.12, 1.62)	1.29 (1.04–1.60)	1.30 (0.93, 1.72)	0.715
FAI, %	15.15 (9.83–21.78)	10.80 (5.57–16.61)	13.10 (7.50–18.94)	0.123
SHBG, nmol/L	49.20 (35.73–71.25)	63.40 (44.70–89.90)	58.10 (43.20–74.05)	0.230
FT, pg/mL	1.80 (1.30–2.93)	1.60 (1.00–2.20)	1.80 (1.15–2.25)	0.583
DHT, pg/mL	75.50 (66.50–96.75)	88.00 (54.00–102.00)	83.00 (55.50–110.50)	0.996
TT, pg/mL	270.10 (219.53–305.88)	211.20 (167.70–350.70)	248.30 (158.45–363.40)	0.500
A4, pg/mL	1,330.35 (1,074.30–1,662.85)	1,258.50 (927.70–1,433.00)	1,269.10 (1,013.15–1,631.30)	0.369
DHEA, pg/mL	5,652.65 (4,375.90–7,862.90)	5,413.00 (4,234.70–7,786.40)	5,884.10 (4,367.55–8,541.05)	0.525
DHEAS, μg/mL	2.79 (2.12–3.53)	1.99 (1.51–2.78)*	2.27 (1.80–3.00)	0.012

A Group: 26–30 years old; B Group: 31–35 years old; C Group: 36–40 years old.

* p < 0.01 vs. 25–30 years old group

FAI, Free Androgen Index; SHBG, sex hormone binding globulin; FT, free testosterone; DHT, dihydrotestosterone; TT, total testosterone; A4, androstenedione; DHEA, dehydroepiandrosterone; DHEAS, dehydroepiandrosterone sulpha

Correlation between androgen profiles and glucose and lipid metabolism

In nonobese, healthy women, FAI correlated positively with WC (r_s = 0.228, p = 0.040), FT correlated positively with BMI (r_s = 0.236, p = 0.033) and WC (r_s = 0.218, p = 0.049), and SHBG correlated positively with HDL (r_s = 0.273, p = 0.013). In glucose metabolism, only A4 correlated positively with FBG (r_s = 0.263, p = 0.017; Fig. 2).

Fig. 2

Correlation between androgen profiles with lipid and glucose metabolism

Subgroup analysis and trend tests

Table 3 shows the relationship between FBG and A4 for different age groups in the multiple linear regression. In the unadjusted model, a significant linear trend existed between FBG and A4 in Group C (p = 0.019). After adjusting for confounding factors, in women aged 36–40 years (Group C), the positive correlation between FBG and A4 concentrations was significantly higher in the highest quartile than in the lowest quartile of FBG (β = 690.95, 95% CI = 140.683–1241.209, p = 0.016; Fig. 3C). In contrast, FBG and A4 concentrations were not correlated in women aged 26–30 years (Group A) or 31–35 years (Group B; Fig. 3A, B). The trend test showed a significant increasing trend for A4 with increasing FBG quartile (p < 0.010).

Table 3

The multiple linear regression analysis for the association between FBG and A4 in each age groups

FBG	Q1	Q2 (β 95%CI)	p	Q3 (β 95%CI)	p	Q4 (β 95%CI)	p	p for trend
Unadjusted
A Group	1.000	141.510 (–702.887, 985.907)	0.725	202.260 (–388.695, 793.215)	0.475	256.060 (–420.964, 933.084)	0.431	0.366
B Group	1.000	–112.656 (–491.686, 266.374)	0.549	–57.186 (–463.717, 349.345)	0.776	77.667 (–301.363, 456.697)	0.679	0.628
C Group	1.000	–59.117 (–569.314, 451.079)	0.813	3.009 (–496.167, 502.185)	0.990	599.149 (75.123, 1,123.174)	0.027*	0.019*
Adjusteda
A Group	1.000	11.050 (–1,105.863, 1,127.962)	0.983	206.934 (–493.951, 907.820)	0.529	76.353 (–809.772, 962.478)	0.853	0.594
B Group	1.000	–102.652 (–500.705, 295.401)	0.602	–79.929 (–527.398, 367.539)	0.718	77.727 (–360.081, 515.535)	0.719	0.771
C Group	1.000	53.839 (–2,030.177, 4,979.679)	0.843	112.356 (–459.687, 684.399)	0.688	690.946 (140.683, 1,241.209)	0.016*	0.010*

^a Adjusted model: Adjusted for BMI and WC. * p < 0.05

A Group: 26–30 years old; B Group: 31–35 years old; C Group: 36–40 years old.

FBG, fasting blood glucose; A4, androsteznedione

Fig. 3

Multiple linear regression analysis for the association between FBG and A4 (A, B, C), and between HDL and SHBG (D, E, F) in each age groups

The relationship between HDL and SHBG concentrations across age groups is shown in Table 4. In the unadjusted model, HDL and SHBG concentrations did not differ significantly between the age groups. However, a significant linear trend emerged between HDL and SHBG in Groups A (p = 0.024) and C (p = 0.013). After adjusting for BMI and WC, in women aged 36–40 years (Group C), HDL and SHBG concentrations were positively correlated. A significant positive correlation existed between the highest and lowest quartiles of HDL and SHBG concentrations (β = 27.94, 95% CI = 4.847–51.026, p = 0.020; Fig. 3F). In contrast, HDL and SHBG concentrations were not correlated in women aged 26–30 years (Group A) or 31–35 years (Group B; Fig. 3D, E). The trend test showed a significant linear trend between HDL and SHBG concentrations (p < 0.05; Table 4).

Table 4

The multiple linear regression analysis for the association between HDL and SHBG in each age groups

HDL	Q1	Q2 (β 95%CI)	p	Q3 (β 95%CI)	p	Q4 (β 95%CI)	p	p for trend
Unadjusted
A Group	1.000	–5.885 (–34.599, 22.829)	0.667	24.000 (–3.072, 51.072)	0.078	28.265 (–0.449, 56.979)	0.053	0.024*
B Group	1.000	0.108 (–30.978, 31.194)	0.994	7.735 (–21.495, 36.966)	0.593	4.129 (–29.824, 38.082)	0.806	0.676
C Group	1.000	6.133 (–17.601, 29.867)	0.599	–0.150 (–24.780, 24.480)	0.990	27.643 (5.614, 49.673)	0.06	0.013*
Adjusteda
A Group	1.000	–5.957 (–37.085, 25.171)	0.684	24.293 (–6.674, 55.260)	0.113	29.092 (–3.161, 61.345)	0.073	0.051
B Group	1.000	–5.366 (–35.574, 24.842)	0.719	0.217 (–29.324, 29.758)	0.988	–10.287 (–45.176, 24.603)	0.551	0.641
C Group	1.000	5.895 (–21.012, 32.802)	0.655	–0.303 (–26.407, 25.801)	0.981	27.937 (4.847, 51.026)	0.020*	0.015*

^a Adjusted model: Adjusted for BMI and WC. * p < 0.05

A Group: 26–30 years old; B Group: 31–35 years old; C Group: 36–40 years old.

HDL, high density lipoprotein; SHBG, sex hormone binding globulin.

Discussion

To further elucidate the relationship between female hormones and metabolism, we used a highly sensitive and specific LC-MS/MS analysis to analyze androgen levels in healthy, nonobese women of reproductive age. Our study found that DHEAS levels were associated with age, which were lower in women aged 31–35 years. Additionally, after adjusting for confounding factors, in healthy nonobese women aged 36–40 years, higher FBG levels were associated with higher A4, HDL, and SHBG levels, suggesting that other potential underlying mechanisms besides age and obesity may link metabolic abnormalities to serum androgen levels in women.

DHEAS changes with age. We found differences in serum DHEAS levels among healthy women in three age groups. DHEAS levels were significantly lower in women aged 31–35 years. No other androgen showed age-related changes. DHEAS is mainly secreted by the adrenal glands and is the most abundant steroid in human serum. While methodological differences may result in particular variations in the DHEAS values obtained, different studies have reported highly similar age-related changes. Some studies have reported that the DHEAS level in women gradually decreases with age [23, 24]. One proposed explanation is that the release of gonadotropin-releasing hormone from the hypothalamus gradually decreases with age, reducing the release of androgens from the gonads and adrenal glands. Through longitudinal studies of women across their entire lifespan, Ostrich found that while DHEAS levels are high in newborns, they decline rapidly in the first year of life and remain at their lowest levels until age seven years. Then, there is an increase during adrenal development, reaching a peak in females aged 15–19 years, followed by a gradual fluctuating decline [24]. By the seventh decade, DHEAS levels decrease to approximately one-fifth of their highest values [25] and are unaffected by menopause [23]. Our study only included women aged 26–40, and we found significantly lower DHEAS levels in women aged 31–35 years compared to women aged 26–30 years, consistent with previous studies. While there was an increase in women aged 36–40 years, the difference from those aged 31–35 years was nonsignificant.

Our study used BMI, WC, and WHR as indicators of body fat and HDL-C, LDL-C, TG, and TC as indicators of blood lipids to assess the relationship between different androgen indicators and lipid metabolism. Our results showed that in women aged 36–40 years, HDL and SHBG levels were positively and linearly correlated. This finding suggests that low SHBG levels may be associated with lipid metabolism.

TT is present in the bloodstream in three primary fractions, namely FT (2%–3%), albumin-bound (20%–40%), and SHBG-bound (60%–80%) [26]. Only FT exerts the physiological effects of androgens [27]. As an indirect indicator of androgens, SHBG is a necessary measurement when assessing androgen levels in healthy women. Lower SHBG levels often lead to higher circulating free and biologically active androgen levels. Recently, increasing evidence has indicated that SHBG is important in metabolic health. Winters suggested that SHBG is a key protein regulating blood lipids [28]. Previous evidence has shown that low SHBG levels are predictive of metabolic syndrome. Interestingly, some studies believe that the relationship between SHBG and cardiovascular disease may be mediated indirectly by BMI [29] since SHBG decreases with increasing BMI [30]. However, other studies have shown that SHBG is an independent predictor of cardiovascular disease [31]. Our study indicates that even after controlling for BMI and age, HDL-C remains significantly positively correlated with SHBG. The increase in SHBG with increasing HDL-C increases the ratio of bound androgens in the serum and decreases the activity of androgens in the body. This relationship suggests that SHBG better reflects lipid metabolism abnormalities in healthy women than other androgen indicators. Here, we propose that SHBG may be involved in metabolic pathways through androgen regulation.

FBG, FINS, and HOMA-IR were used to reflect IR and glucose metabolism in vivo. Our study showed a significant positive correlation between A4 and FBG in healthy women aged 36–40 years. A4 is a direct precursor to TT. Approximately 60% of TT in women is converted from its direct precursor A4 by 17-β-hydroxysteroid dehydrogenases [32]. Previous studies have found that androgen can suppress protein kinase C phosphorylation, upregulate uncoupling protein 1 (UCP1) expression, and promote lipolysis in visceral adipocytes, which affects glucose metabolism and leads to IR [33]. Therefore, androgen and its metabolites may be involved in abnormal glucose metabolism development. Furthermore, our results show that A4 may have a more significant effect on glucose metabolism than other androgens, indicating its close association with early endocrine and metabolic disorders. For women with a family history of diabetes or high-risk factors, A4 measurement should be considered in the early stage of clinical practice.

Our study had several advantages. Firstly, sex, age, puberty, oral contraceptive use, and ethnicity are confounding factors in androgen levels. Our study carefully selected members of a healthy population without excessive androgen levels and controlled for these factors. In addition, we used LC-MS/MS technology instead of the chemiluminescence immunoassay commonly used in laboratories to accurately measure low levels of androgens in healthy women, enhancing the persuasiveness of our data. Moreover, we provided uniform and professional training to clinical doctors to collect common clinical data using standardized methods. Blood samples were stored using standardized procedures and measured in the core laboratory. Our study also had limitations. For example, our study only collected real-time metabolic and endocrine data from healthy women of reproductive age, and the early manifestation of endocrine characteristics is believed to persist. In addition, while our heatmap initially indicated correlations between specific androgen and glycolipid metabolism markers, subsequent multiple testing revealed that these correlations did not reach statistical significance within our cohort of women aged 25–40 years, likely due to our modest sample size. Therefore, to further investigate the relationship between androgens and metabolism in women of childbearing age, we plan to collect additional samples for a more comprehensive analysis.

Our findings suggest that androgens are significantly associated with blood lipids and fasting glucose in healthy, nonobese women aged 36–40 years. Conducting annual metabolic screenings, especially for FBG and HDL-C levels, in women aged 36–40 years can help predict their HA risk and provide timely interventions. Our findings have important implications in real clinical settings and epidemiological studies.

Acknowledgments

The authors thank Dean for technical support, the staff of the central laboratory and physical examination center for assistance with the experiments.

Authors’ Contributions

Conceived and designed the study: Sha Ye.

Performed the experiments and acquisition of data: Yepei Huang, Xiaoyan Li, Meiling Ye, Hong Cai and Sha Ye.

Analyzed and interpreted the data: Yi Lu, Hongyu Lu and Junhua Shi.

Drafting the manuscript: Sha Ye.

Revising the manuscript critically for important intellectual content: Jian Huang and Hong Cai.

Funding

This work was supported by the Zhejiang Provincial Administration of Traditional Chinese Medicine Co-Construction of Science and Technology Program [No. GZY-ZJ-KJ-23089]; The Construction Fund of Key Medical Disciplines of Hangzhou [No. OO20200450].

Data Availability

The data and material are not shared to protect the patients’ individual privacy and for further study.

Disclosure

Ethics approval and consent to participate

All subjects received written as well as verbal instructions and gave their informed consent. Ethical approval was obtained from the local ethics committee (No. IRB#2021-20210412-01).

Conflicts of interest

All authors declare that there is no conflict of interest that could be perceived as prejudicing the impartiality of the research reported.

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