Abstract
Background: Infertility, defined as the inability to conceive after at least 12 months of unprotected intercourse, affects up to 15.5% of couples of childbearing age. Assisted Reproductive Technology (ART) offers hope for successful pregnancy, though clinical pregnancy rates remain around 40%, with delivery success rates at 20%-30%. Despite numerous influencing factors, age, Antral Follicle Count (AFC), and Anti-Mullerian Hormone (AMH) serve as the current predictors of ART outcomes. However, these indicators lack comprehensive accuracy as they evaluate only specific stages of ART. Emerging research suggests a significant role for Vitamin D in reproductive physiology, yet its impact on the clinical pregnancy rates of in-vitro fertilization/intracytoplasmic sperm injection- embryo transfer (IVF/ICSI-ET) remains underexplored.
Purpose: This study analyzes various risk factors affecting clinical pregnancy rates in ART, emphasizing the role of Vitamin D levels in infertile women undergoing IVF/ICSI. We aim to correlate Vitamin D levels with ART-related factors and establish a predictive model for clinical pregnancy outcomes to guide personalized treatment plans and enhance ART success rates.
Keywords
Infertility, ART, Vitamin D, Factors affecting pregnancy, Logistic regression analysis
Introduction
Infertility impacts about 15% of couples globally, with treatments such as in vitro fertilization (IVF) and intracytoplasmic sperm injection (ICSI) offering hope yet yielding variable success rates. Although ART outcomes are influenced by factors like ovarian reserve, measured through antral follicle count (AFC) and anti-Müllerian hormone (AMH) levels, recent studies suggest a significant role of vitamin D in reproductive health [1,2]. Vitamin D receptors are widely expressed in reproductive tissues, and deficiency in vitamin D is common among women of childbearing age, raising questions about its impact on fertility and ART outcomes [3,4]. Research shows conflicting results on whether vitamin D levels correlate with improved ART success, indicating a need for further investigation [5,6].
This study aims to assess the relationship between serum vitamin D levels and clinical pregnancy rates in women undergoing IVF/ICSI. By exploring the potential of vitamin D levels alongside established predictors like AMH and AFC, we seek to enhance predictive models for ART outcomes, facilitating more personalized treatment approaches.
Methods
Study design and participants
This was a retrospective cohort study conducted at the Guangxi Medical University First Affiliated Hospital Reproductive Center. We enrolled 188 infertile patients who underwent routine IVF and ICSI treatments from June 2020 to July 2021. Patients were categorized into two groups based on clinical pregnancy outcomes: those who achieved successful pregnancy and those who did not.
Inclusion and exclusion criteria
Inclusion criteria included:
- Patients who underwent fresh embryo transfers.
- Complete follow-up records.
- No history of genetic diseases in either partner.
- No uterine abnormalities or concurrent adenomyosis in female patients.
- All participants were part of a controlled ovulation induction program.
- None had taken or injected vitamin D-related preparations within the previous 6 months.
Exclusion criteria included:
- Cancellation of the embryo transfer for any reason.
- Recipients of frozen embryo transfer.
- Patients with incomplete follow-up.
- Presence of systemic diseases such as diabetes, kidney disease, hypertension, or immune system disorders.
Data collection
Comprehensive data were collected, including:
- Baseline demographic and clinical characteristics (age, BMI, duration of infertility, type of infertility).
- Biochemical markers (AMH, AFC, and basal hormone levels including FSH, LH, E2, P, PRL, T, DHEA).
- Treatment details (COH protocol, total Gn dosage, duration of Gn administration), outcomes (endometrial thickness on the day of hCG administration, E2 levels on hCG day, number of oocytes retrieved, number of embryos obtained, clinical pregnancy outcome).
Laboratory methods
Vitamin D levels were measured using a chemiluminescence immunoassay (CLIA). All hormonal measurements were performed in the hospital's central laboratory under standardized conditions.
Statistical analysis
Data were analyzed using SPSS software version 20.0. Continuous variables were expressed as mean ± standard deviation or median where appropriate, and categorical variables as percentages. The differences between groups were assessed using Student’s t-test for normally distributed data and the Mann-Whitney U test for non-normally distributed data. Chi-square or Fisher’s exact test was used for categorical data. Logistic regression analysis was employed to identify independent predictors of clinical pregnancy. A p-value of less than 0.05 was considered statistically significant.
Results
Participant characteristics
Our study included 188 infertile patients undergoing IVF/ICSI treatment. The participants were divided into two groups based on clinical pregnancy outcomes: 71 (37.8%) achieved clinical pregnancy (successful pregnancy group) and 117 did not (unsuccessful pregnancy group).
Clinical and demographic data
The successful pregnancy group had significantly lower mean age (29.89 ± 4.03 years) compared to the unsuccessful group (32.42 ± 5.11 years; P=0.026). Differences in body mass index (BMI) were not statistically significant between the groups (Table 1).
Hormonal and biochemical markers
Significant differences were observed in serum vitamin D levels, with the successful pregnancy group showing higher levels (29.77 ± 13.3 µg/L) than the unsuccessful pregnancy group (24.97 ± 11.16 µg/L; P=0.03). Additionally, AMH and AFC were higher in the successful group compared to those who did not achieve pregnancy (AMH: 3.68 ng/mL vs. 2.99 ng/mL, P=0.04; AFC: 17.25 ± 5.98 vs. 13.25 ± 6.62, P=0.009) (Table 1).
ART outcomes
The overall clinical pregnancy rate was 37.8%. Logistic regression analysis identified vitamin D levels, AMH, AFC, and endometrial thickness on the day of hCG as significant independent predictors of clinical pregnancy. The model’s predictive accuracy, as indicated by the (receiver operating characteristic curve (ROC curve), had an area of 75.34% (95% CI, 0.712 to 0.965; P=0.003).
Vitamin D and pregnancy outcomes
Patients with vitamin D levels above the median had a clinical pregnancy rate of 42.2%, compared to 30.6% in those with levels below the median (P=0.015). Further, logistic regression identified vitamin D level as an independent factor influencing the likelihood of achieving a clinical pregnancy (OR = 1.46; 95% CI, 1.04 to 2.03; P=0.027).
Variable |
Pregnancy group (71) |
Non-pregnancy group (117) |
P |
Age, y |
29.89 ± 4.03 |
32.42 ± 5.11 |
0.026* |
BMI, kg/m2 |
24.18 ± 2.98 |
24.37 ± 3.39 |
0.131 |
Infertility duration, y |
4.53 ± 2.15 |
5.32 ± 2.31 |
0.011* |
Infertility type |
|||
Primary |
45 (61.4%) |
76 (65.0%) |
0.236 |
secondary |
26 (36.6%) |
41 (35.0%) |
0.421 |
AFC |
17.25 ± 5.98 |
13.25 ± 6.62 |
0.009* |
Vitamin D levels, μg |
29.77 ± 13.3 |
24.97 ± 11.16 |
0.03* |
AMH, ng/mL |
3.68 ± 2.58 |
2.99 ± 1.41 |
0.04* |
Basic sex hormone levels |
|||
bFSH (m IU/ml) |
6.92 ± 1.32 |
7.63 ± 1.01 |
0.032* |
bLH (m IU/ml) |
6.12 ± 2.16 |
5.98 ± 3.24 |
0.661 |
E2 (pg/m L) |
33.58 ± 14.42 |
34.89 ± 18.23 |
0.534 |
P (ng/ml) |
0.86 ± 0.09 |
0.88 ± 0.08 |
0.732 |
PRL (ng/ml) |
14.62 ± 8.77 |
14.92 ± 9.75 |
0.552 |
T (ng/dl) |
0.36 ± 0.13 |
0.41 ± 0.15 |
0.085 |
DHEA |
10.41 ± 6.32 |
9.36 ± 1.25 |
0.019* |
COH type |
|||
GnRH long regimen |
42 (59.2%) |
79(67.5%) |
0.028* |
GnRH antagonist regimen |
29 (40.8%) |
32(27.4%) |
0.012* |
Total amount of Gn |
1728.88 ± 788.45 |
1833.67 ± 613.50 |
0.034* |
Gn days |
10.85 ± 3.21 |
10.77± 4.56 |
0.225 |
Fertilization mode (IVF/ICSI/Half–ICSI) |
0.081 |
||
IVF |
46 (64.8%) |
82 (70.1%) |
0.0635 |
ICSI |
19 (26.8%) |
31 (26.5%) |
0.236 |
Half-ICSI |
6 (8.5%) |
4 (3.4%) |
0.136 |
Number of pregnancies |
1.12 ± 0.13 |
2.04 ± 0.35 |
0.019* |
Endometrial Thickness on the Day of HCG |
12.63 ± 2.09 |
10.52 ± 2.54 |
0.035* |
HCG day E2 |
2053.21 ± 186.14 |
1967.52 ± 204.14 |
0.474 |
Number of eggs obtained |
15.52 ± 4.68 |
13.44 ± 5.99 |
0.023* |
Number of embryos obtained |
5.77 ± 0.46 |
4.64 ± 0.51 |
0.062 |
Variable |
βa |
SEb |
OR |
OR (95% CI) |
P |
Age, y |
0.0921 |
0.1102 |
1.08 |
0.87–1.34 |
0.016* |
AFC |
1.0652 |
0.1136 |
2.51 |
2.00–4.93 |
0.033* |
Vitamin D levels, μg |
0.3758 |
0.1697 |
1.46 |
1.04–2.03 |
0.027* |
AMH, ng/mL |
0.1697 |
0.1224 |
2.03 |
1.63–3.21 |
0.037* |
Basic sex hormone levels |
|
|
|
|
|
bFSH (m IU/ml) |
0.9211 |
0.1254 |
1.04 |
1.35-3.67 |
0.358 |
DHEA |
1.1021 |
0.2514 |
1.69 |
1.24-2.31 |
0.085 |
COH regimen |
|
|
|
|
|
GnRH long regimen |
1.3684 |
0.3511 |
2.03 |
0.36–1.05 |
0.506 |
GnRH antagonist regimen |
0.8564 |
0.3222 |
2.98 |
1.60–5.65 |
0.141 |
Total amount of Gn |
0.3296 |
0.3621 |
1.26 |
1.02–1.55 |
0.030* |
Number of pregnancies |
1.1141 |
0.2536 |
1.85 |
0.98-1.85 |
0.194 |
Endometrial Thickness on the Day of HCG |
1.6201 |
0.1024 |
0.68 |
1.28–2.04 |
0.019* |
Number of eggs obtained |
1.5634 |
0.1121 |
0.97 |
0.48–1.62 |
0.035* |
Notes: OR: Odd Ratio; CI: Confidence Interval; *P<0.05; aβ: Estimation parameters of regression models; bSE: Standard error of the regression models |
Variable |
βa |
SEb |
OR |
OR (95% CI) |
P |
Age, y |
0.0733 |
0.1311 |
1.23 |
0.95–2.14 |
0.011* |
AFC |
0.2962 |
0.1421 |
2.14 |
1.35–3.26 |
0.037* |
Vitamin D levels, μg |
0.4214 |
0.2684 |
2.63 |
0.65–1.92 |
0.027* |
AMH, ng/mL |
1.3624 |
0.1651 |
3.97 |
0.79–2.10 |
0.3758 |
bFSH (m IU/ml) |
0.1697 |
0.3321 |
4.25 |
1.60–5.65 |
0.4428 |
DHEA |
2.3541 |
0.1241 |
0.58 |
0.95–3.65 |
0.321 |
GnRH long regimen |
0.1774 |
0.3688 |
1.93 |
2.35–6.34 |
0.506 |
GnRH antagonist regimen |
1.1000 |
0.1532 |
3.00 |
1.35–3.94 |
0.958 |
Total amount of Gn |
0.2287 |
0.3263 |
2.68 |
0.95–2.57 |
0.032* |
Number of pregnancies |
0.8792 |
0.2547 |
3.82 |
0.84-2.15 |
0.381 |
Endometrial Thickness on the Day of HCG |
0.2698 |
0.1698 |
1.98 |
2.04–4.32 |
0.020* |
Number of eggs obtained |
0.3241 |
0.1698 |
1.22 |
0.66-2.11 |
0.042* |
Variable |
Vitamin D non-deficient group (116) |
Vitamin D deficiency group (72) |
P |
Age, y |
28.73 ± 3.054 |
28.58 ± 3.670 |
0.472 |
BMI |
21.56 ± 1.84 |
21.56 ± 1.60 |
0.131 |
Infertility duration, y |
3.05 ± 2.29 |
3.67 ± 2.27 |
0.451 |
Infertility type |
0.593 |
||
Primary |
45 (38.8%) |
30 (41.7%) |
|
Secondary |
71 (61.2%) |
42 (58.3%) |
|
AMH, ng/mL |
3.78 ± 2.58 |
3.62 ± 2.56 |
0.178 |
AFC |
16.35 ± 3.52 |
15.71 ± 3.54 |
0.094 |
Basic sex hormone levels |
|
|
|
bFSH (m IU/ml) |
6.77±1.74 |
7.01 ± 1.21 |
0.124 |
bLH (m IU/ml) |
6.12 ± 2.16 |
5.98 ± 3.24 |
0.661 |
E2 (pg/m L) |
33.58 ± 24.42 |
34.89 ± 28.23 |
0.214 |
P (ng/ml) |
0.86 ± 0.93 |
0.88 ± 1.18 |
0.158 |
PRL (ng/ml) |
14.62 ± 8.77 |
14.92 ± 9.75 |
0.367 |
T (ng/dl) |
0.42 ± 0.14 |
0.39 ± 0.12 |
0.442 |
DHEA |
8.56 ± 1.91 |
9.01 |
0.115 |
COH type |
|
|
0.854 |
GnRH long regimen |
79 (68.1) |
49 (42.2%) |
|
GnRH antagonist regimen |
37(31.9%) |
23(31.9%) |
|
Dose of Gn |
2381.55 ± 589.14 |
2401.21 ± 601.23 |
0.316 |
Gn days |
14.16 ± 5.11 |
13.25 ± 4.04 |
0.077 |
Total amount of Gn |
0.234 |
||
< 1808.01 |
84 (72.4%) |
47 (65.3%) |
|
≥ 1808.01 |
32 (27.6%) |
25 (34.7%) |
|
Gn days |
10.2 ± 2.69 |
11.1 ± 3.21 |
0.771 |
Fertilization mode (IVF/ICSI/Half–ICSI) |
0.158 |
||
IVF |
62 (53.4%) |
39 (54.2%) |
|
ICSI |
44 (37.9%) |
28 (38.9%) |
|
Half–ICSI |
10 (8.62%) |
5 (6.94%) |
|
Number of pregnancies |
1.56 ± 0.13 |
1.84 ± 0.20 |
0.115 |
Endometrial Thickness on the Day of HCG |
11.97±1.65 |
10.07 ± 1.01 |
0.036* |
HCG day E2 |
2131.51 ± 218.42 |
1953.14 ± 221.11 |
0.354 |
Number of eggs obtained |
15.68 ± 5.32 |
14.35 ± 4.65 |
0.097 |
Number of embryos obtained |
5.36 ± 0.77 |
4.88 ± 0.97 |
0.029* |
Clinical pregnancy |
49 (42.2%) |
22 (30.6%) |
0.015* |
Variable |
βa |
SEb |
OR |
OR (95% CI) |
P |
Age, y |
0.8574 |
0.1487 |
1.25 |
0.12-0.98 |
0.591 |
BMI |
1.2114 |
0.1214 |
1.36 |
0.65-2.11 |
0.782 |
Infertility duration, y |
0.5841 |
0.0952 |
1.22 |
0.35-0.87 |
0.152 |
Infertility type |
0.6234 |
0.0854 |
0.98 |
0.12-1.09 |
0.098 |
AMH |
1.0541 |
0.0741 |
0.35 |
0.57–1.62 |
0.085 |
AFC |
0.6241 |
0.0951 |
0.85 |
0.84-2.35 |
0.264 |
Basic sex hormone levels |
1.2413 |
0.0814 |
0.47 |
0.63-1.94 |
0.152 |
bFSH (m IU/ml) |
0.5321 |
0.1147 |
1.23 |
0.97-2.47 |
0.634 |
bLH (m IU/ml) |
0.6471 |
0.1024 |
0.69 |
0.68-1.96 |
0.095 |
E2 (pg/m L) |
1.2141 |
0.1234 |
0.58 |
0.52-2.14 |
0.114 |
P (ng/ml) |
1.6871 |
0.1475 |
0.47 |
0.48–2.61 |
0.357 |
PRL (ng/ml) |
0.3657 |
0.1357 |
0.36 |
0.52-2.01 |
0.634 |
T (ng/ml) |
0.6304 |
0.1224 |
0.85 |
0.36-1.63 |
0.287 |
DHEA |
0.9854 |
0.1123 |
0.89 |
0.45-1.53 |
0.741 |
COH type |
0.3624 |
0.1478 |
0.47 |
0.42-2.08 |
0.167 |
Total amount of Gn |
0.3624 |
0.0952 |
0.98 |
0.33-1.24 |
0.074 |
Gn days |
0.9874 |
0.1874 |
0.58 |
0.54-2.03 |
0.195 |
Dose of Gn |
|||||
< 1808.01 |
0.6214 |
0.1474 |
0.98 |
0.50-2.05 |
0.510 |
≥ 1808.01 |
1.2414 |
0.2141 |
1.22 |
0.57-1.85 |
0.447 |
Gn days |
1.3547 |
0.1470 |
0.34 |
0.63–2.57 |
0.097 |
Fertilization mode |
|||||
IVF |
0.2141 |
0.1254 |
0.65 |
0.47-1.28 |
0.735 |
ICSI |
0.3314 |
0.1587 |
0.25 |
0.84-2.69 |
0.665 |
Half–ICSI |
0.5241 |
0.1147 |
0.63 |
0.63-1.99 |
0.497 |
Number of pregnancies |
0.7484 |
0.0987 |
0.57 |
0.74-2.65 |
0.082 |
Endometrial Thickness on the Day of HCG |
0.2354 |
0.1374 |
1.97 |
0.44-1.68 |
0.022* |
Variable |
βa |
SEb |
OR |
OR (95% CI) |
P |
HCG day E2 |
0.3698 |
0.2414 |
1.22 |
0.74–1.87 |
0.192 |
Number of eggs obtained |
0.1987 |
0.1547 |
1.14 |
0.58-1.98 |
0.095 |
Number of embryos obtained |
0.5532 |
0.0921 |
0.53 |
0.31–0.95 |
0.024* |
Clinical pregnancy |
0.3695 |
0.0913 |
0.55 |
0.33-1.24 |
0.019* |
Figure 1. Model of fit ROC. Notes: HR: Hazard Ratio; CI: Confidence Interval; *: P<0.05; aβ: Estimation parameters of regression models; bSE: The standard error of regression models.
Discussion
This study confirms and extends the emerging body of evidence suggesting that vitamin D has a significant impact on the outcomes of ART, specifically IVF and ICSI. Our findings are aligned with the hypothesis that optimal serum vitamin D levels are associated with increased clinical pregnancy rates, which corroborates the results of prior research indicating the beneficial effects of vitamin D on reproductive health outcomes [6,7].
Significantly, our study has demonstrated that patients with adequate vitamin D levels not only have higher rates of clinical pregnancy but also show improved responses to other ART parameters such as AFC and AMH levels. These factors are traditionally used to predict ART success and our results suggest that vitamin D status may influence their predictive accuracy and the biological processes underlying follicular development and endometrial receptivity [8,9].
Moreover, the development of a predictive model incorporating vitamin D, with an ROC area of 75.34%, presents a novel tool for clinicians. This model outperforms many existing models by integrating a nutritional biomarker with established reproductive metrics. Such integration could potentially guide more personalized treatment adjustments, improving ART success rates [10]. Studies such as those by Ozkan et al. and Aghajanova et al. have previously highlighted the influence of vitamin D on the endometrial environment, suggesting that adequate levels may enhance endometrial receptivity, a crucial factor for embryo implantation [6,11]. Our findings align with the broader research suggesting that vitamin D’s role extends beyond calcium homeostasis to include modulation of the immune system and cellular differentiation, both of which are critical in the reproductive process [12,13]. The expression of vitamin D receptors in reproductive tissues, such as the ovaries and endometrium, supports the theory that vitamin D's impact on ART outcomes may be mediated through its role in cellular function and tissue responsiveness [14].
A review by Voulgaris et al. supports our findings by discussing the positive effects of vitamin D on the hypothalamic-pituitary-gonadal (HPG) axis, which may enhance gonadal function and thus improve fertility outcomes [15]. Additionally, research by Grzechocinska et al. found that vitamin D supplementation in women with polycystic ovary syndrome (PCOS) led to better fertility outcomes, pointing to the potential benefits of vitamin D in managing fertility issues related to endocrine disorders [16].
The immunomodulatory role of vitamin D, as detailed by Thompson et al. provides insight into how vitamin D could improve implantation rates by reducing the uterine natural killer (NK) cell activity and regulating the inflammatory response, which are critical during the embryo implantation phase [17]. This aligns with our observations that higher vitamin D levels correlate with enhanced endometrial receptivity and may explain the improved clinical pregnancy rates observed in our study.
Limitations and Future Directions
Despite these promising findings, our study is not without limitations. The single-center, retrospective design may limit the generalizability of our results. Larger, multicenter prospective studies are necessary to validate our predictive model across diverse populations and settings. Additionally, interventional studies assessing the impact of vitamin D supplementation on ART outcomes are crucial to establish causality and determine optimal dosing protocols [7].
Future research should explore the specific genetic and molecular pathways modulated by vitamin D in reproductive tissues. Such studies could provide critical insights into the mechanisms by which vitamin D enhances fertility and inform the development of targeted therapies or supplementation strategies for individuals undergoing ART [18,19].
In conclusion, our study enhances the understanding of vitamin D's role in ART success and introduces a predictive model that incorporates both traditional and novel predictors of clinical pregnancy. It encourages a shift toward more holistic treatment paradigms in reproductive medicine, where nutritional and hormonal factors are considered in concert to optimize patient outcomes.
References
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