Dickkopf1 (DKK1) as a Potential Biomarker in Polycystic Ovary Syndrome and Insulin Resistance: A Cross-Sectional Study

doi:10.21203/rs.3.rs-2988034/v1

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Dickkopf1 (DKK1) as a Potential Biomarker in Polycystic Ovary Syndrome and Insulin Resistance: A Cross-Sectional Study

https://doi.org/10.21203/rs.3.rs-2988034/v1

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Background: Dickkopf1 (DKK1) isa protein with established links to metabolic diseases. However, its association with polycystic ovary syndrome (PCOS) and insulin resistance (IR) remains ambiguous.

Methods: We conducted a cross-sectional study involving 300 participants, including 100 healthy women, 100 women with PCOS, and 100 individuals with IR. We used the STRING database to identify proteins that interact with DKK1 and performed KEGG and GO analyses to determine the biological processes and signaling pathways that are enriched in DKK1-related proteins. Serum DKK1 levels and Adipoq were measured by ELISA kits. The expression of DKK1 in liver tissue wasdetected by western blotting.

Results: Relative to the control group, both the IR and PCOS cohorts exhibited markedly elevated serum DKK1 levels and noticeably reduced Adipoq levels. Correlation analyses revealeda positive relationship between serum DKK1 levels and body mass index(BMI), waist-hip ratio (WHR), body fat percentage (FAT%), systolic blood pressure (SBP), triglycerides (TG), fasting plasma glucose (FPG), fasting insulin (FIns), glycosylated hemoglobin (HbA1c), and homeostasis model assessment of insulin resistance (HOMA-IR) and a negative relationship between Adipoq levels and M-value. Multiple regression analysis indicated that BMI, FAT%, TG, and Adipoq were independent factors affecting DKK1. An analysis of multiple stepwise regressions revealed that DKK1 was a risk factor for IR and PCOS. In the euglycemic-hyperinsulinemic clamp (EHC) test, serum DKK1 levels exhibited a significant increase in the PCOS patients and a pronounced decrease in the IR patients at 30 minutes and returned to baseline at 60 minutes.

Conclusions: Our research revealedthat an increase in DKK1 levels in the blood was significantly associated with PCOS and IR, thereby highlighting the potential involvement of DKK1 in the pathogenesis of PCOS and IR. This insight paves the way for further investigations into the role of DKK1 in PCOS and IR.

Dickkopf1

cytokine

polycystic ovary syndrome

insulin resistance

Polycystic ovary syndrome (PCOS) detrimentally impacts the health and fertility of women. Affecting 6–10% of those of reproductive age, it is characterized by hyperandrogenism, persistent anovulation, and polycystic ovarian morphological changes [1]. The etiology of PCOS involves complex interactions of genetic and environmental factors. Obesity and insulin resistance (IR) are considered primary causes of reproductive dysfunction and metabolic abnormalities in PCOS patients, with approximately 50% of patients having variable degrees of IR. Therefore, improving obesity and IR is beneficial in enhancing reproductive function and reducing complications in women with PCOS. Recent investigations have also identified correlations between circulating cytokines and peptide hormones, such as adiponectin (Adipoq) and betatrophin, and PCOS, making it clinically pertinent to investigate the function of cytokines/peptide hormones in the genesis and progression of PCOS [2].

Dickkopf1 (DKK1) belongs to the DKK gene family, encompassing DKK1-4. As a low molecular weight secretory glycoprote, DKK1 is widely expressed across various organs, including osteoblasts, skin, prostate, placenta, and endothelial cells [3]. DKK1 functions as an inhibitor of the Wnt/β-catenin signaling pathway, participating in the pathogenesis of diverse diseases, such as cancer, hypertension, neurodegenerative disorders, diabetic nephropathy, and arthritis [4, 5]. Recent findings have reported elevated DKK1 levels in obese individuals and those with type 2 diabetes mellitus (T2DM), displaying a positive correlation with body mass index (BMI) [6].

DKK1 expression increases during fat analysis but is lower in mature adipocytes, indicating its potential relationship with obesity [7]. However, the connection between DKK1, PCOS, and IR remains ambiguous.

In the current investigation, we examined the involvement of DKK1 in the pathogenesis of PCOS and IR using a combination of bioinformatics analysis, a population-based cross-sectional study, and multiple intervention studies. Our results offer empirical support to assess the potential of DKK1 as an early diagnostic biomarker for PCOS.

Bioinformatics analysis

To establish a protein‒protein interaction (PPI) network centered on the DKK1 gene, we utilized the STRING database (version 11.5). We applied a threshold interaction score of 0.4 as the cutoff criterion and subsequently visualized the PPI network. To further decipher functional annotations and pathways, we conducted Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses using the clusterProfiler package [8].

Study population

This study encompassed 300 young women (aged 16–35 years) from the Second Affiliated Hospital of Chongqing Medical University, consisting of 100 patients with PCOS, 100 patients with IR, and 100 healthy controls. All participants, who were outpatients, underwent physical examinations. IR was diagnosed employing a homeostasis model assessment index (HOMA-IR) > 3.0 [9], with no presence of PCOS or other metabolic diseases. The Rotterdam criteria, updated in 2003 [10], served as the diagnostic basis for PCOS patients. Exclusion criteria comprised individuals with cardiovascular, hepatic, or renal diseases, thyroid disorders, malignancies, or those who had taken medications such as oral contraceptives and insulin sensitizers within the past 6 months. We obtained written informed consent from all participants, and the study received approval from the Ethics Committee of Chongqing Medical University, adhering to the principles of the Helsinki Declaration.

Anthropometric measurements

An experienced physician conducted standardized anthropometric measurements on all participants, including body weight, height, waist circumference (WC), hip circumference, blood pressure, and fat percentage (Fat%), performed by a single, experienced physician. Body mass index (BMI) was calculated as weight (kg) divided by height squared (m²). The waist-hip ratio (WHR) was calculated as WC (cm) divided by HC (cm). After a 12-hour overnight fast, professional nurses collected venous blood samples to measure blood glucose, insulin, HbA1c, blood lipids, sex hormones, and other indicators. HOMA-IR was computed using the following formula: fasting insulin (FIns, µU/L) × fasting blood glucose (FBG, mmol/L) divided by 22.5 [11].

Determination of biochemical indexes and sex hormones

Fasting blood glucose (FBG), HbA1c, serum insulin, triglycerides (TG), cholesterol (TC), free fatty acids (FFAs), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C) were measured as previously described [12]. Sex hormone assays, encompassing luteinizing hormone (LH), follicle-stimulating hormone (FSH), progesterone (Prog), testosterone (TEST), sex hormone binding globulin (SHBG), estradiol (E2), and dehydroepiandrosterone sulfate (DHEA-S), were performed in accordance with previously published methodologies [12].

Oral glucose tolerance test (OGTT)

Following a 12-hour nocturnal fasting period, participants were subjected to an OGTT. Participants were administered 75 g of glucose orally, and their blood glucose, insulin, and DKK1 levels, as well as other biomarkers, were assessed at 0, 30, 60, and 120-minute time points using venous blood samples, in accordance with the methodology outlined in [13].

Euglycemic-hyperinsulinemic clamp (EHC)

All participants underwent EHC as previously reported [14]. After fasting for 12 hours, insulin and glucose were infused through catheters in the anterior cubital vein and the opposite dorsal hand vein, respectively. An insulin infusion rate of 1 mU/kg/min was maintained for 2 hours, accompanied by a simultaneous infusion of 20% glucose. Blood glucose levels were stabilized at basal concentrations by adjusting the glucose infusion rate. Insulin and DKK1 levels were evaluated using blood samples collected at 0, 80, 100, and 120 minutes.

Serum Cytokine Determination

Serum levels of DKK1 and Adipoq were measured using highly sensitive and specific ELISA kits, exhibiting negligible cross-reactivity. The intra- and interassay coefficients of variation (CVs) for DKK1 were < 10% and < 12%, respectively, with a detection range of 100 − 30,000 pg/mL. Intra- and interassay CVs for Adipoq were as documented in a prior study [14].

Experimental animals

Male C57BL/6J (8-week-old) and ob/ob mice were acquired from the Experimental Animal Centers of the Model Animal Research Center at Nanjing University. Following a 7-day acclimatization period at room temperature (25°C), mice were fed either a normal chow diet (NCD) or a high-fat diet (HFD; carbohydrate, 20%; fat, 60%; protein, 20%; D12492, Research Diets, New Brunswick, NJ) for 12 weeks. Liver tissues were collected and preserved at -180°C until further analysis. The animal experiments received approval from the Animal Studies Committee of Chongqing Medical University and were conducted in accordance with the National Institutes of Health Guide for the Care and Use of Laboratory Animals (NIH Publications No. 8023, revised 1978).

Western blot analysis of proteins

Liver tissue-derived total proteins underwent sodium dodecyl sulfate‒polyacrylamide gel electrophoresis (SDS‒PAGE) and subsequent transfer onto polyvinylidene difluoride (PVDF) membranes. These membranes were incubated with anti-DKK1 antibody (1:1000, #48367, Cell Signaling Technology, USA) and GAPDH (1:1000, #5174, Cell Signaling Technology, USA) as internal controls. Horseradish peroxidase-conjugated secondary antibodies (1:2000, #A0208, Beyotime, China) were subsequently applied. Densitometric analyses were conducted using ImageJ software in accordance with previously published methods [15].

Statistical analysis

Analysis employed SPSS software (v20.0, Chicago, IL, USA), with graphs generated through GraphPad software. Data are presented as the mean ± standard deviation (mean ± SD). For data with a normal distribution, independent samples t-tests or ANOVA were employed for group comparisons, while the Mann‒Whitney U test or Kruskal‒Wallis H test was employed for nonnormally distributed data. Correlations between DKK1 and other variables were analyzed using simple and multiple correlation coefficients. Binary logistic regression analyses, the Row Mean Score Test, and the Cochran-Armitage Trend Test were implemented to investigate the association between circulating DKK1 and PCOS and IR. Receiver operating characteristic (ROC) curves generated by SPSS 20.0 were used to assess the sensitivity and specificity of DKK1 for predicting PCOS and IR. A p-value < 0.05 indicated statistical significance.

Bioinformatics analysis

To investigate the relationship between DKK1 and metabolism, we conducted a preliminary bioinformatics analysis using online data. We constructed a PPI network for DKK1 utilizing the STRING database. Our findings reveal that DKK1 interacts with a multitude of proteins, such as LRP6, LRP5, KREMEN1, WNT1, WNT3A, WNT8B, WNT8A, KREMEN2, FZD1, and RSPO1, among others (Fig. 1A). We performed an enrichment analysis of GO and KEGG using the DAVID online tool, which showed that DKK1-related signaling pathways included the Wnt signaling pathway, Wnt-protein binding, cytokine activity, multicellular organism development, negative regulation of protein phosphorylation, mTOR signaling pathway, cancer pathway, and others (Figure. 1B and C). We also found that WNT-like proteins were associated with system metabolism, suggesting that DKK1 is related to overall energy metabolism.

Elevated hepatic DKK1 expression in obese and diabetic murine models and NAFLD patients.

To elucidate the link between DKK1 and metabolic perturbations, we scrutinized DKK1 expression profiles in murine models of obesity and diabetes, as well as in human subjects diagnosed with NAFLD. Utilizing western blot analyses, we observed a marked upregulation of hepatic DKK1 protein expression in HFD-fed WT mice and ob/ob mice relative to their ND-fed WT counterparts (Fig. 2A and B). Similarly, DKK1 protein expression in the livers of NAFLD patients was substantially elevated compared to that observed in normal control subjects (Figure. 2C). These findings further support the notion that DKK1 is associated with metabolic diseases.

Clinical features of the study population

Table 1 displays a significant elevation in BMI, WHR, FAT%, SBP, TGs, TC, LDL-C, FBG, OGTT blood glucose, FIns, OGTT insulin, HbA1c, and HOMA-IR at each respective time point within the IR and PCOS patients compared to the control group. Concurrently, the insulin sensitivity index (M-values) exhibited a notable decline (Fig. 3A). Additionally, analysis of sex hormones revealed that DHEA-S levels in both the PCOS and IR groups were considerably elevated compared to the control group, while SHBG levels were markedly reduced. Moreover, the PCOS group exhibited significantly increased TEST and LH levels relative to the control group (Table 1). These findings indicate the presence of hormonal imbalances in both the PCOS and IR groups, with PCOS women exhibiting a distinct hyperandrogenemic profile.

Table 1

Main clinical features, laboratory parameters and circulating DKK1 concentrations in the study population
Variable	NC (n = 100)	IR (n = 100)	PCOS (n = 100)
Age (years)	26.97 ± 2.83	27.39 ± 3.40	26.71 ± 3.88
BMI (kg/m²)	20.71 ± 2.53	25.34 ± 2.61**	26.11 ± 3.26 **
WHR	0.78 ± 0.04	0.85 ± 0.05 **	0.86 ± 0.06 **
FAT (%)	26.37 ± 4.43	35.52 ± 5.12 **	34.93 ± 5.92 **
SBP (mmHg)	107.77 ± 7.01	119.17 ± 14.72 **	117.48 ± 12.01 **
DBP (mmHg)	75.11 ± 7.49	77.52 ± 10.93	74.63 ± 9.02
TG (mmol/L)	0.98 ± 0.59	0.91 ± 0.52	1.64 ± 0.97 **
TC (mmol/L)	3.49 ± 1.31	3.75 ± 1.21	4.48 ± 0.98 **
HDL-C (mmol/L)	1.23 ± 0.41	1.26 ± 0.28	1.31 ± 0.37
LDL-C (mmol/L)	2.05 ± 0.89	2.62 ± 0.68 **	2.57 ± 0.66 **
FFA (µmol/L)	0.54 ± 0.28	0.56 ± 0.16	0.54 ± 0.21
FBG (mmol/L)	4.51 ± 0.49	5.45 ± 0.62 **	5.32 ± 0.80 **
0.5 h-BG (mmol/L)	7.25 ± 1.46	9.60 ± 1.72 **	9.00 ± 1.61 **
1 h-BG (mmol/L)	5.82 ± 1.47	9.49 ± 2.37 **	9.20 ± 2.50 **
2 h-BG (mmol/L)	5.10 ± 0.94	7.96 ± 1.83 **	7.81 ± 2.26 **
FIns (mU/L)	7.71 ± 2.65	19.89 ± 9.25 **	19.18 ± 13.73 **
0.5 h-Ins (mU/L)	91.82 ± 58.77	149.25 ± 110.39 **	130.08 ± 81.84 **
1 h-Ins (mU/L)	72.10 ± 70.87	178.71 ± 165.10 **	154.09 ± 115.00 **
2 h-Ins (mU/L)	42.90 ± 36.95	151.05 ± 104.16 **	161.58 ± 142.30 **
HOMA-_IR	1.56 ± 0.59	4.88 ± 2.63 **	4.69 ± 4.04 **
HbA1c (%)	5.10 ± 0.25	5.31 ± 0.44 **	5.45 ± 0.48 **
Adiponectin (mg/L)	12.04 ± 2.69	5.76 ± 1.74 **	3.96 ± 1.14 **
M value (mg/kg/min)	10.29 ± 1.42	5.15 ± 1.33 **	4.56 ± 0.85 **
DHEA-S (µg/dL)	196.69 ± 66.87	241.95 ± 95.67 **	263.91 ± 136.89 **
SHBG (nmol/L)	57.99 ± 23.81	43.00 ± 27.35 **	37.80 ± 24.42 **
E2 (ng/L)	50.58 ± 28.49	47.88 ± 25.82	49.70 ± 32.01
TEST (nmol/L)	1.71 ± 0.64	1.60 ± 0.59	2.00 ± 0.81 **
FSH (IU/L)	7.30 ± 1.58	6.83 ± 2.33	6.78 ± 1.71 *
LH (IU/L)	5.27 ± 2.77	4.88 ± 2.92	7.59 ± 2.51 **
Prog (IU/L)	2.25 ± 1.25	2.05 ± 1.04	2.13 ± 1.33
PRL (mIU/L)	318.52 ± 114.28	318.74 ± 141.99	302.10 ± 137.94
DKK1 (µg/L)	1.34 ± 0.41	1.64 ± 0.32 **	2.08 ± 0.39**
BMI, body mass index; DBP, diastolic blood pressure; DHEA-S, dehydroepiandrosterone sulfate; E2: Estradiol; FFA, free fatty acid; FBG, fasting blood glucose; 2 h-BG, 2 h blood glucose overload; FIns, fasting plasma insulin; 2 h-Ins, 2 h plasma insulin after glucose overload; FPG, fasting plasma glucose; FSH, follicular stimulating hormone; HbA1c: glycosylated hemoglobin; HDL-C, high-density lipoprotein cholesterol; HOMA-_IR, homeostasis model assessment of insulin resistance; IR: insulin resistance; LDL-C, low-density lipoprotein cholesterol; LH, luteinizing hormone; NC: normal control; PCOS, polycystic ovary syndrome; PRL, prolactin; Prog, progesterone; SBP, systolic blood pressure; SHBG, sex hormone-binding globulin; TC, total cholesterol; TEST, testosterone; TG, triglyceride; WHR, waist-hip ratio. Values are given as the means ± SDs. * p < 0.05; * p < 0.01, compared with the NC group.

Circulating cytokine levels and their relationship with other indexes

To elucidate the interplay between DKK1, metabolic disorders, and IR in PCOS patients, we concurrently measured the levels of circulating DKK1 and Adipoq, an insulin-sensitive molecule. Our findings indicated a significant elevation in circulating DKK1 levels accompanied by a substantial decrease in Adipoq levels in women with both PCOS and IR compared to healthy control subjects (Table 1 and Fig. 3B and C). Notably, DKK1 levels were significantly higher in PCOS patients than in IR individuals (Fig. 3B). We also employed linear correlation analysis to assess the association between serum DKK1 and Adipoq, as well as other indicators. Our analysis demonstrated a significant positive correlation between serum DKK1 and BMI, WHR, Fat%, SBP, TGs, blood glucose, FIns, and HbA1c but a negative correlation with Adipoq (Table 2). Moreover, multiple regression analysis identified BMI, fat%, TGs, and Adipoq as independent risk factors influencing serum DKK1 levels (Table 2). The resulting regression equation was Y_DKK1 = 0.134 + 0.022BMI + 0.017FAT + 0.227TG − 0.024Adipoq.

Table 2

**Linear and multiple regression analyses of variables associated with serum DKK1 levels in the study population**
Variable	Simple		Multiple
Variable	r	p	b	p
Age (years)	-0.022	0.701	-	-
BMI (kg/m²)	0.529	< 0.001	0.169	0.05
WHR	0.404	< 0.001	-	-
FAT (%)	0.502	< 0.001	0.231	0.004
SBP (mmHg)	0.212	< 0.001	-	-
DBP (mmHg)	-0.011	0.847	-	-
TG (mmol/L)	0.604	< 0.001	0.373	< 0.001
TC (mmol/L)	0.041	0.477	-	-
HDL-C (mmol/L)	-0.027	0.642	-	-
LDL-C (mmol/L)	0.038	0.512	-	-
FFA (µmol/L)	0.018	0.761	-	-
FPG (mmol/L)	0.252	< 0.001	-	-
FIns (mU/L)	0.304	< 0.001	-	-
HbA1c (%)	0.303	< 0.001	-	-
Adiponectin (mg/L)	-0.179	0.002	-0.201	< 0.001
DHEA-S (µg/dL)	0.045	0.433	-	-
SHBG (nmol/L)	-0.059	0.312	-	-
E2 (ng/L)	-0.008	0.897	-	-
TEST (nmol/L)	0.107	0.064	-	-
LH (IU/L)	0.089	0.123	-	-
Prog (IU/L)	0.079	0.171	-	-
PRL (mIU/L)	-0.087	0.132	-	-

r: correlation coefficient; b: regression coefficient.

Relationship of circulating DKK1 with PCOS and IR

To further explore the link between DKK1 and PCOS and IR, we separated DKK1 levels into three groups within the study population. For IR, the groups were as follows: group 1, < 1.284 µg/L; group 2, 1.285 to 1.71 µg/L; and group 3, > 1.71 µg/L. For PCOS, the groups were defined as follows: group 1, < 1.46 µg/L; group 2, 1.47 to 1.98 µg/L; and group 3, > 1.98 µg/L. Our analysis revealed that when serum DKK1 concentrations were within tertiles 2 and 3, the odds ratios (ORs) for the presence of PCOS and IR were 7.36 [95% confidence interval (CI), 2.90; 18.69] and 18.01 (95% CI, 7.08; 45.77) for PCOS and 2.95 [95% confidence interval (CI), 1.44; 6.04] and 4.95 (95% CI, 2.36; 10.36) for IR, respectively (Fig. 3D and E). Furthermore, a multivariable logistic regression analysis demonstrated a significant association between DKK1 levels and PCOS and IR after accounting for anthropometric variables, age, sex, fat%, BP, and other factors (Table 3). The row mean score difference and the Cochran–Armitage trend test also showed a significant linear trend of increasing DKK1 levels with PCOS and IR (Table 4). Finally, we conducted an ROC analysis, which indicated an area under the curve (AUC) of 0.897 with 91% sensitivity and 71% specificity for PCOS (Fig. 3F) and an AUC of 0.71 with 74% sensitivity and 62% specificity for IR (Fig. 3G).

Table 3

Association of serum DKK1 with IR and PCOS in the fully adjusted model
Model adjust	IR			PCOS
Model adjust	OR	95% CI	p	OR	95% CI	p
Age, BMI	1.001	1.000-1.002	0.192	1.003	1.002–1.005	< 0.001
Age, BMI, WHR	1.001	1.000-1.002	0.221	1.003	1.002–1.005	< 0.001
Age, BMI, WHR, FAT%	1.000	0.999–1.001	0.714	1.003	1.002–1.005	< 0.001
Age, BMI, WHR, FAT%, SBP, DBP	1.000	0.999–1.001	0.719	1.003	1.002–1.005	< 0.001
Age, BMI, WHR, FAT%, SBP, DBP, lipid profile	1.004	1.002–1.007	0.001	1.004	1.002–1.006	< 0.001

Table 4

Row Mean Scores and Cochran–Armitage Trend Test of the impact of serum DKK1 level on IR and PCOS
Model adjusted	IR		PCOS
Model adjusted		p		p
Row Mean Scores Test	17.9630	< 0.001	82.4834	< 0.001
Cochran-Armitage Trend Test	-4.2489	< 0.001	-9.1048	< 0.001

Effect of blood glucose and insulin levels on DKK1

To evaluate the potential impact of blood glucose and insulin levels on circulating DKK1 concentrations, we conducted an OGTT (Fig. 4A). During the OGTT, we observed that serum DKK1 levels in PCOS patients steadily increased (from 2.13 ± 0.36 to 2.39 ± 0.39 µg/L) alongside rising blood glucose and insulin levels. On the other hand, in IR individuals, DKK1 levels significantly declined (from 1.64 ± 0.34 to 1.40 ± 0.21 µg/L) at 30 minutes and then incrementally rose (from 1.76 ± 0.18 to 1.88 ± 0.25 µg/L). In the normal control group, DKK1 levels remained constant throughout (Fig. 4B). These results imply that circulating DKK1 might be influenced by blood glucose and/or insulin in women with PCOS and IR. To further explore whether insulin regulates DKK1, we performed an euglycemic-hyperinsulinemic clamp (EHC) to maintain blood glucose at basal levels while increasing insulin levels (Fig. 4C). Throughout the EHC, blood glucose was kept within the range of 5–6 mmol/L, as insulin levels increased from 7.71 ± 2.65 to 80.5 ± 10.5 µU/L. Our results demonstrated that the DKK1 levels did not change in the normal control group compared to the baseline level under the conditions of normal blood glucose and hyperinsulinemia. In contrast, DKK1 levels in the PCOS group exhibited a significant increase (from 2.10 ± 0.40 to 2.29 ± 0.34 µg/L) compared to baseline levels. In contrast, DKK1 levels in the IR group decreased significantly at 30 min (from 1.62 ± 0.32 to 1.467 ± 0.24 µg/L) and returned to baseline levels at 60 min (Fig. 4D). These results suggest that DKK1 levels may be influenced by both blood glucose and insulin in PCOS and IR individuals.

Previous research has indicated a possible connection between DKK1 and obesity. Notably, PCOS is a recognized metabolic and hormonal disorder [16], and the relationship between DKK1, PCOS, and IR remains unclear. To shed light on this relationship, we conducted a cross-sectional population study using HOMA-IR and EHC studies to compare IR in women with PCOS and control subjects. We found that women with PCOS exhibited more severe IR and significantly increased levels of DKK1, while Adipoq levels were significantly reduced. Additionally, we discovered that serum DKK1 levels demonstrated a positive correlation with clinical markers of obesity, biochemical indicators of glucose and lipid metabolism, and IR-related parameters. Conversely, they were negatively correlated with M-values and circulating Adipoq levels. After controlling for anthropometric variables, binary logistic regression analysis revealed that serum DKK1 was significantly correlated with PCOS and IR. Our intervention study showed that circulating DKK1 was regulated by blood glucose and insulin levels, suggesting that it could be a useful biomarker for PCOS-related metabolic morbidities.

Since more than 70% of PCOS patients have IR and compensatory hyperinsulinemia, identifying biomarkers for PCOS-related metabolic disorders and IR is crucial for diagnosis and prognosis [17]. Our study found that circulating DKK1 levels may be a better indicator of glucose and lipid metabolism disorders in PCOS women. We also used the EHC test to evaluate the relationship between DKK1 and IR and found that DKK1 levels could reflect the IR status in PCOS individuals. We observed that in women with PCOS and IR, circulating DKK1 levels were regulated by blood glucose and insulin levels, while in normal women, OGTT and EHC tests did not result in alterations to DKK1 levels. Interestingly, in the state of high glucose and insulin, serum DKK1 in PCOS patients slightly increased, while in IR individuals, it significantly decreased. We speculate that this discrepancy may be due to several factors, including small sample deviation, the influence of sex hormones, or depletion of DKK1-secreting cells caused by long-term hyperinsulinemia.

Our study found that in PCOS and IR individuals, circulating DKK1 and Adipoq levels showed an opposite change, with a negative correlation between the two. We therefore speculate that DKK1 may act as a negative regulator of metabolic disorders and insulin sensitivity. High levels of DKK1 may indicate severe IR and metabolic disorders in PCOS women. However, this hypothesis requires further validation through large-scale clinical and basic research.

Adipoq is an adipocytokine related to insulin sensitivity, and its levels decrease in the presence of IR [18]. Studies have shown that circulating Adipoq levels decrease at IR and increase again after insulin sensitivity recovery [19]. Our study found that in PCOS and IR individuals, circulating DKK1 and Adipoq levels showed an opposite change, with a negative correlation between the two. We therefore speculate that DKK1 may act as a negative regulator of metabolic disorders and insulin sensitivity. High levels of DKK1 may indicate severe IR and metabolic disorders in PCOS women. However, this hypothesis requires further validation through large-scale clinical and basic research.

While our study has made a new discovery regarding the significance of circulating DKK1 levels in PCOS and metabolic disorders, it is subject to some limitations. First, our research population was Han people, and the results may not apply to other races. Moreover, our study is cross-sectional, and long-term changes in circulating DKK1 levels cannot be observed. We also cannot determine the causal relationship between circulating DKK1 and the occurrence and development of PCOS and IR. Finally, our study findings are limited to obese PCOS patients, necessitating further research to explore the variations in circulating DKK1 levels among lean PCOS patients. In conclusion, our study underscores the substantial elevation in DKK1 levels in PCOS patients and the potential relationship between DKK1 and PCOS and metabolic disorders. Our results propose that DKK1 may function as a useful biomarker for PCOS-related metabolic morbidities, although the physiological and pathological significance of DKK1 changes.

In conclusion, our study highlights the significant increase in DKK1 levels in PCOS patients and reveals the potential relationship between DKK1 and PCOS and metabolic disorders. We also evaluated the regulatory factors of circulating DKK1 by blood glucose and insulin levels. We propose that DKK1 could be a biomarker associated with PCOS and metabolic disorders. Nevertheless, the physiological and pathological implications of DKK1 fluctuations warrant further investigation.

IR, insulin resistance; ELISA, enzyme-linked immunosorbent assay; Adipoq, adiponectin; BMI, body mass index; WHR, waist hip ratio; FAT%, body fat ilustrated; SBP, systolic blood pressure; TG, triglyceride; FPG, fasting plasma glucose; HOMA-IR, homeostasis model assessment of insulin resistance; OGTT, oral glucose tolerance tests; EHC, euglycemic-hyperinsulinemic clamp.

Ethics approval and consent to participate: All participants signed a written informed consent form. This research scheme was approved by the Ethics Committee of Chongqing Medical University and implemented by the Helsinki Declaration. Animal experiments were approved by the Animal Studies Committee of Chongqing Medical University and were consistent with the National Institutes of Health Guide for the Care and Use of Laboratory Animals (NIH Publications No. 8023, revised 1978).

Consent for publication: Not applicable.

Availability of data and materials: The data that support the findings of this study are available on request from the corresponding author upon reasonable request.

Competing interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Funding: This work was supported by research grants from the National Natural Science Foundation of China (82000792 and 82100852), Joint Project of Chongqing Health Commission and Science and Technology Bureau (2021MSXM283), Science and Technology Program of Health Bureau of Chongqing (2016MSXM083 and 2017MSXM20), Postgraduate research and innovation projects of Chongqing Municipal Education Commission (CYS21221), Natural Science Foundation Project of Chongqing (CSTB2022NSCQ-MSX1522), China Postdoctoral Science Foundation (2022MD713711) and Chongqing Nature Science Foundation Project-Postdoctoral Science Foundation Project (2022NSCQ-BHX0541).

Authors’ Contributions: S. L., K. L., H.W., Q. S.and R.L., collected and analyzed these data. X.L. and Y.L. engaged in bioinformatics analysis and analyzed these data. M.Y. critically reviewed and edited the manuscript. J.H. and Z.L. were the guarantors of this work and, as such, had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Acknowledgments: The authors thank all patients and healthy volunteers.

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No competing interests reported.

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Dickkopf1 (DKK1) as a Potential Biomarker in Polycystic Ovary Syndrome and Insulin Resistance: A Cross-Sectional Study

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