HOMA-IR partially mediates the association between percent appendicular skeletal muscle mass and IVF/ICSI outcomes

Abstract

Background

Skeletal muscle mass contributes to metabolic health, particularly in relation to insulin sensitivity, and both skeletal muscle mass and metabolic health may influence outcomes after assisted reproductive technology (ART). However, whether insulin sensitivity partially explains the association between skeletal muscle mass and ART outcomes remains unclear. This study aimed to investigate the association between the percentage of appendicular skeletal muscle mass (PASM) and outcomes from in vitro fertilization (IVF) and intracytoplasmic sperm injection (ICSI) and to explore whether insulin resistance, assessed by HOMA-IR, partially mediates this association.

Methods

This retrospective study included 990 patients who underwent IVF/ICSI at Nanjing Medical University from April 2022 to April 2023. PASM was measured by bioelectrical impedance analysis (BIA) and analysed both as quartiles and as a continuous variable. Insulin sensitivity was assessed using the homeostatic model assessment of insulin resistance (HOMA‑IR). We employed Spearman’s correlation analysis, multivariate regression, restricted cubic spline (RCS) models, and mediation analysis with adjustment for relevant confounders.

Results

Unadjusted comparisons showed only non-significant increasing trends across PASM quartiles, whereas adjusted regression analyses indicated more favourable laboratory outcomes in higher PASM quartiles. Each 1-unit increase in PASM was associated with the number of oocytes retrieved (Beta = 0.29), available embryos (Beta = 0.25) and blastocysts (Beta = 0.21), as well as higher available embryo rate (Beta = 1.49) and blastocyst formation rate (Beta = 1.52). PASM was positively linked to cumulative clinical pregnancy rate (CCPR) (OR = 1.30). Higher PASM was inversely associated with HOMA‑IR and insulin resistance (IR) prevalence; elevated HOMA‑IR was independently associated with poorer embryology laboratory indicators (P < 0.05). Mediation analysis suggested that HOMA-IR modestly mediated 5.5%, 5.9%, and 7.3% of the associations between PASM and the number of oocytes retrieved, available embryos and blastocysts, respectively.

Conclusions

Higher PASM was associated with more favourable embryology laboratory indicators and higher pregnancy success after IVF/ICSI, with HOMA-IR showing partial and modest mediation. Interventions targeting skeletal muscle mass may represent a potential strategy for optimizing assisted reproduction outcomes, pending confirmation in prospective studies.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13048-026-02154-2.

Introduction

Currently, about 18% of couples of reproductive age are affected by infertility [1], and obesity is a recognized and modifiable contributor to this burden [2]. Although previous studies have extensively examined the impact of body mass index (BMI) and fat content on reproductive function, in-depth analyses of the role of muscle mass have been limited. The concept of “sarcopenic obesity” (SO), introduced by the European Society for Clinical Nutrition and Metabolism (ESPEN) and the European Association for the Study of Obesity (EASO), highlights a complex condition in which reduced muscle mass coexists with obesity [3]. Compared with simple obesity, the decline in muscle mass and function associated with SO not only exacerbates the metabolic burden but also significantly elevates the risk of various metabolic diseases [4]. This finding underscores the critical importance of muscle mass in managing obesity and its related health issues.

Obesity and its associated metabolic disorders, particularly insulin resistance (IR), are closely linked to female infertility, diminished ovarian function, and lower success rates in assisted reproductive technology (ART) [5–7]. IR reflects reduced insulin sensitivity and impaired insulin-mediated glucose metabolism. As the body’s largest reservoir for glucose, skeletal muscle plays a crucial role in glucose metabolism [8]. Its sensitivity to insulin directly affects glucose uptake and utilization. Moreover, recent research has recognized skeletal muscle as an important endocrine organ. During exercise or muscle contraction, skeletal muscles secrete various myokines—such as interleukin-6 (IL-6) and myostatin—that regulate not only local muscle growth and repair but also systemic metabolic functions, immune responses, and inflammation [9, 10]. Thus, skeletal muscle is essential for maintaining insulin sensitivity and overall metabolic health. Studies indicate that a decrease in skeletal muscle mass leads to diminished insulin sensitivity [6], which in turn reduces glucose uptake and exacerbates IR [11, 12]. These findings suggest that skeletal muscle mass may have potential clinical value for reproductive health.

Despite these biological links, data linking skeletal muscle mass to in vitro fertilization (IVF) and intracytoplasmic sperm injection (ICSI) success remain sparse. We therefore undertook a retrospective cohort study of 990 patients undergoing IVF/ICSI, quantifying appendicular skeletal muscle mass as a percentage of body weight (PASM) using bioelectrical impedance analysis. We tested the hypothesis that higher PASM is associated with more favourable embryology laboratory indicators and pregnancy outcomes and examined whether insulin sensitivity, assessed by the homeostatic model assessment for insulin resistance (HOMA‑IR), mediates this association. Clarifying the impact of muscle mass on ART efficacy could inform targeted lifestyle or pharmacological interventions aimed at simultaneously improving metabolic and reproductive health.

Materials and methods

Study design and population

This study was a single‑center, retrospective cohort study at the Reproductive Medicine Centre, Women’s Hospital of Nanjing Medical University, between April 2022 and April 2023. Participants included patients undergoing IVF/ICSI with a standard gonadotrophin‑releasing hormone antagonist (GnRH‑ant) protocol. Data were collected through a review of medical records and laboratory results. Clinical and laboratory data were extracted from electronic medical records. This study was approved by the institutional review board of the Institutional Ethics Committee of Nanjing Women and Children’s Healthcare Hospital (No. 2022KY-046) and complied with the Declaration of Helsinki; the requirement for individual informed consent was waived owing to the retrospective design.

Exclusion criteria were: (1) patients with neuromuscular, severe hepatic, cardiac, or kidney dysfunction or other conditions affecting body composition (n = 9); (2) patients taking medications that could alter body composition, such as glucocorticoids or thyroid hormones (n = 6); (3) couples with chromosomal abnormalities (n = 23); and (4) patients whose oocyte retrieval cycles were canceled for any reason (n = 12). A total of 990 eligible patients met the inclusion criteria (Fig. 1).

Fig. 1.

Flow chart of the participants included in this study. Abbreviations: PASM, percent appendicular skeletal muscle mass; CCPR, cumulative clinical pregnancy rate; CLBR, cumulative live birth rate

Bioimpedance analysis measurement

On the first day of gonadotrophin (Gn) administration, trained staff measured body composition by bioelectrical impedance analysis (InBody 720, Biospace, Korea). Participants grasped hand electrodes and stood barefoot on foot electrodes according to the manufacturer’s instructions. Output variables included body weight and appendicular skeletal muscle mass (ASM).

Definition and assessment of exposures

PASM was calculated as the ratio of ASM to body weight, multiplied by 100% [13]. Based on PASM values obtained on the day of Gn initiation, participants were categorized into four quartiles: Q1 (< 25.92%), Q2 (25.92–27.46%), Q3 (27.47–29.27%) and Q4 (≥ 29.28%).

Sample assessment and ART procedure

Fasting blood was obtained before ovarian stimulation. Fasting insulin, fasting blood glucose (FBG), total serum cholesterol (TC), and triglyceride (TG) levels were measured using an automated analyzer (AU 5800; Beckman Coulter). The homeostatic model assessment for IR (HOMA-IR) was calculated using the formula: FBG (mmol/L) × fasting insulin (µIU/mL) / 22.5 [14]. The triglyceride glucose (TyG) index was calculated using the formula ln [TG (mg/dL) × FBG (mg/dL) / 2] [15].

Controlled ovarian stimulation was performed using daily recombinant follicle-stimulating hormone (FSH) (150–225 IU; Gonal‑F, Merck‑Serono) from cycle day 2–3. When the lead follicle reached 12–14 mm, a GnRH‑ant (cetrorelix 0.25 mg, Merck‑Serono) was started. Follicular development was monitored by transvaginal ultrasound and serum FSH, luteinizing hormone (LH), estradiol (E2) and progesterone (P). Final oocyte maturation was triggered with 10 000 IU hCG (Lizhu) when ≥ 2 follicles measured ≥ 18 mm; oocytes were retrieved 36 h later. On the day of ovulation trigger, serum LH levels and endometrial thickness were also assessed.

Conventional IVF or ICSI was selected according to sperm quality. Embryos were cultured to day 3 (D3) or day 5/6 (D5) and graded morphologically. Fresh embryo transfer (ET) or cryopreservation was performed according to standard practice, with a maximum of two embryos transferred [16]. Progesterone supplementation continued until 8–10 weeks’ gestation if pregnancy was confirmed.

Definition and assessment of outcomes

Laboratory outcomes comprised the numbers of oocytes retrieved, two‑pronuclear (2PN) embryos, available embryos (cleavage‑stage score ≥ 2) [17], blastocysts and high‑quality blastocysts (Gardner grade ≥ 3BB) [18], together with available embryo, blastocyst‑formation and high‑quality blastocyst rates. Serum β‑hCG was assayed 14 days after ET. Clinical pregnancy was defined as an intra‑uterine gestational sac on ultrasound at 6 weeks. Live birth referred to delivery of a viable infant at ≥ 28 weeks. Cumulative clinical pregnancy rate (CCPR) and cumulative live‑birth rate (CLBR) were calculated per IVF/ICSI cycle, defined as one oocyte-retrieval cycle including fresh embryo transfer and subsequent frozen-thawed embryo transfer (FET) cycles. Follow-up ended when the first clinical pregnancy or live birth occurred, or when all embryos derived from that stimulation cycle had been used.

Statistical analysis

Continuous variables are presented as mean ± SD and categorical variables as number (percentage). Inter‑quartile differences were assessed using the Kruskal-Wallis test for continuous variables and the χ² test for categorical variables. A two-sided P value < 0.05 was considered statistically significant. Except for restricted cubic spline (RCS) analyses and mediation analyses, all statistical analyses were performed using SPSS v26.0.

Associations between PASM and IVF/ICSI outcomes were evaluated using linear or binary logistic regression. Three models were fitted: Model 1, unadjusted; Model 2, adjusted for the percentage of body fat (PBF) category (< 30% vs. ≥30%), age, infertility type, infertility factor, basal FSH, basal LH and anti‑Müllerian hormone (AMH); and Model 3, additionally adjusted for Gn starting dose, total Gn dose and insemination method. Spearman correlation analysis was used to examine the relationships among PASM, HOMA‑IR and reproductive outcomes.

A directed acyclic graph (DAG) informed covariate selection based on clinical relevance and potential causal relationships (Figures S1 and S2). Mediation analysis was conducted using the mediation package in R v4.2.1 to estimate the proportion of the association between PASM and IVF/ICSI outcomes that was mediated by HOMA-IR. Mediation analysis was performed when the following criteria were met: (i) PASM was associated with IVF/ICSI outcomes, (ii) PASM was associated with HOMA‑IR and (iii) HOMA‑IR was associated with IVF/ICSI outcomes. The mediation models were adjusted for PBF, age, type of infertility, factor of infertility, basal FSH, basal LH, and basal AMH, starting dose of Gn, total Gn dose, and insemination method.

RCS models were constructed using the rms package in R v4.2.1 to depict the relationships between PASM and HOMA‑IR or IR risk, with age as a covariate. Linear or binary logistic regression was further used to examine the association between PASM and IR and between HOMA-IR and IVF/ICSI outcomes.

In addition, stratified analyses were conducted to assess whether the associations between PASM and IVF/ICSI outcomes varied across subgroups. Patients were stratified by age (< 35 vs. ≥35 years), BMI category, PBF (< 30% vs. ≥30%), polycystic ovary syndrome (PCOS) status according to the Rotterdam criteria, IR status defined by HOMA-IR ≥ 2.69 based on a previous epidemiological survey in a Chinese population [19], and the insemination method. Within each stratum, multivariable regression models were adjusted for the same covariates as Model 3, except for the stratification variable itself.

Results

Baseline characteristics of study participants

A total of 990 patients (mean ± SD age 32.6 ± 4.6 years) were included; 46.7% had primary infertility, and 89.7% were undergoing their first IVF/ICSI cycle. Compared with the Q1 group, patients in the higher quartiles (Q2-Q4) were slightly older and had higher basal FSH but lower BMI, PBF, LH on trigger day, and gonadotrophin requirements (starting dose, total dose and duration). The prevalence of PCOS, IR and adverse metabolic indices (triglyceride, TC, FBG, fasting insulin, TyG index, and HOMA-IR) all declined across rising PASM quartiles (Table S1).

Associations of PASM with IVF/ICSI outcomes

Across quartiles Q1–Q4, there was a graded, though initially non‑significant, increase in the number of oocytes retrieved and all embryology laboratory indicators (Table S2).

In unadjusted linear models (Model 1), patients in Q4 exhibited markedly higher available embryo rate and blastocyst formation rate than those in Q1 (5.85‑fold and 5.97‑fold, respectively; both P < 0.05). Treating PASM as a continuous variable confirmed positive associations with the available embryo rate (Beta = 0.88, 95% CI: 0.28, 1.48, P = 0.004) and blastocyst formation rate (Beta = 1.05, 95% CI: 0.33, 1.76, P = 0.004) (Fig. 2).

Fig. 2.

Association between PASM and embryology laboratory indicators (linear regression). Notes: Model 1, no covariates were adjusted; Model 2, adjusted for PBF, age, type of infertility, factor of infertility, basal FSH, basal LH, and basal AMH; Model 3, adjusted for PBF, age, type of infertility, factor of infertility, basal FSH, basal LH, and basal AMH, starting dose of Gn, total Gn dose, and insemination method. Abbreviations: AMH, anti-Müllerian hormone; Gn, gonadotropin; FSH, follicle-stimulating hormone; LH, luteinizing hormone; PASM, percent appendicular skeletal muscle mass; PBF, percent body fat; PASM, percent appendicular skeletal muscle mass. * P < 0.05, ** P < 0.01, and *** P < 0.001

After multivariable adjustment (Models 2 and 3), PASM displayed consistent, independent relationships with the number of oocytes retrieved (Beta ≈ 0.25–0.29), available embryos (Beta ≈ 0.24–0.25) and blastocysts (Beta ≈ 0.19–0.21), as well as with available embryo and blastocyst formation rates (all P < 0.05; Fig. 2).

Further analysis using a binary logistic regression model revealed no association between PASM and either CCPR or CLBR (Model 1; Fig. 3). Quartile analyses remained non‑significant after multivariable adjustment (Models 2 and 3). When PASM was analysed as a continuous variable, each 1-unit increment was associated with higher CCPR in Model 2 (OR = 1.23, 95% CI: 1.00, 1.52, P = 0.046) and Model 3 (OR = 1.30, 95% CI: 1.05, 1.60, P = 0.018); the association with CLBR, however, did not reach statistical significance.

Fig. 3.

Association between PASM and pregnancy outcomes (logistic regression). Notes: Model 1, no covariates were adjusted; Model 2, adjusted for PBF, age, type of infertility, factor of infertility, basal FSH, basal LH, and basal AMH; Model 3, adjusted for PBF, age, type of infertility, factor of infertility, basal FSH, basal LH, and basal AMH, starting dose of Gn, total Gn dose, and insemination method. Abbreviations: AMH, anti-Müllerian hormone; CCPR, cumulative clinical pregnancy rate; CLBR, cumulative live birth rate; Gn, gonadotropin; FSH, follicle-stimulating hormone; LH, luteinizing hormone; PASM, percent appendicular skeletal muscle mass; PBF, percent body fat. * P < 0.05

Associations between PASM, HOMA-IR, and IVF/ICSI outcomes

Spearman analysis showed a moderate inverse correlation between PASM and HOMA‑IR (r = -0.42, P < 0.001), and significant inverse correlations between HOMA‑IR and the number of oocytes retrieved and embryology laboratory indicators (Figure S3).

In age‑adjusted models (Fig. 4A), each 1-unit rise in PASM corresponded to a 0.20‑point fall in HOMA‑IR (Beta = -0.20, 95% CI: -0.24, -0.15, P < 0.001) and the risk of IR decreased by 26% (OR = 0.74, 95% CI: 0.69, 0.80, P < 0.001). RCS models were used to visually represent the relationships between PASM and HOMA-IR (Fig. 4B) and between PASM and IR risk (Fig. 4C). Results indicated that HOMA-IR decreased linearly with increasing PASM (P < 0.001, P-nonlinear = 0.081), and IR risk diminished gradually as PASM increased (P < 0.001, P-nonlinear = 0.008).

Fig. 4.

Associations of PASM with HOMA-IR and IR risk. A Multivariable regression model showing the relationships between PASM and HOMA-IR and IR risk. B RCS depicting the relationship between PASM and HOMA‑IR. C RCS depicting the relationship between PASM and IR risk. The models were adjusted for age. Abbreviations: HOMA-IR, homeostatic model assessment for insulin resistance; IR, insulin resistance; PASM, percent appendicular skeletal muscle mass. *** P < 0.001

Higher HOMA‑IR independently predicted poorer embryology laboratory indicators. In fully adjusted models, each unit increase in HOMA-IR reduced the number of oocytes retrieved, available embryos, and blastocysts by 0.16–0.19 and lowered available embryo rate and blastocyst formation rate by 1.76% and 1.97% (P < 0.05). Compared with patients without IR, those with IR had lower numbers of oocytes retrieved, available embryos, and blastocysts decreased by 0.91, 1.15, and 1.12, respectively, alongside reductions of 9.10% and 10.08% in the available embryo rate and blastocyst formation rate (P < 0.05; Figure S4). Unadjusted logistic regression linked higher HOMA‑IR and IR status to lower CCPR and CLBR; however, these associations lost significance after multivariable adjustment (P > 0.05; Figure S5).

Mediation effects of HOMA-IR in the relationship between PASM and IVF/ICSI outcomes

Mediation analysis suggested that HOMA-IR partially and modestly mediated the associations between PASM and selected embryology laboratory indicators. The mediated proportions of HOMA-IR for the associations of PASM with the numbers of oocytes retrieved, available embryos, and blastocysts were 5.5% (95% CI: 0.1%, 27.7%), 5.9% (95% CI: 0.4%, 18.8%), and 7.3% (95% CI: 1.0%, 26.9%), respectively (Fig. 5).

Fig. 5.

Mediation effects of HOMA-IR in the associations between PASM and IVF/ICSI outcomes. Notes: The mediation analyses were adjusted for PBF, age, type of infertility, factor of infertility, basal FSH, basal LH, and basal AMH, starting dose of Gn, total Gn dose, and insemination method. Abbreviations: ACME, average causal mediation effects (indirect effect); ADE, average direct effects; AMH, anti-Müllerian hormone; FSH, follicle-stimulating hormone; Gn, gonadotropin; HOMA-IR, homeostatic model assessment for insulin resistance; ICSI, intracytoplasmic sperm injection; IVF, in vitro fertilization; LH, luteinizing hormone; PASM, percent appendicular skeletal muscle mass; TyG, triglyceride glucose. * P < 0.05, ** P < 0.01, and *** P < 0.001

Stratified analysis

Stratified analysis showed that the associations between PASM and embryology laboratory indicators varied across patient subgroups (Figure S6). In women aged < 35 years, higher PASM was significantly associated with a greater number of oocytes retrieved, available embryos, and blastocysts, as well as a higher available embryo rate, whereas these associations were attenuated or not statistically significant among women aged ≥ 35 years.

When stratified by BMI, the associations were most consistent among women with normal BMI, in whom PASM was positively associated with the number of oocytes retrieved, available embryos, and blastocysts. Some positive associations were also observed among overweight women, particularly for oocytes retrieved, whereas the results were less stable in underweight and obese women. For available embryo rate, a significant positive association was observed in obese women, but not consistently across other BMI categories.

Stratification by PBF showed that PASM was more consistently associated with the number of oocytes retrieved, available embryos, and blastocysts among women with PBF < 30%, whereas these associations were attenuated among women with PBF ≥ 30%. However, the association with available embryo rate was observed mainly among women with PBF ≥ 30%.

In patients without PCOS, PASM was positively associated with several embryology laboratory indicators, particularly available embryos and blastocysts. Among women with PCOS, PASM showed positive associations with some early embryology outcomes, including oocytes retrieved, available embryos, and available embryo rate, but not consistently with blastocyst-related outcomes.

When stratified by IR status, PASM was positively associated with the number of oocytes retrieved and available embryos among women without IR, whereas among women with IR, positive associations were observed for available embryos, blastocysts, and available embryo rate. Regarding insemination method, the associations were most consistently observed in ICSI cycles, including available embryos, blastocysts, available embryo rate, and blastocyst formation rate. Associations in conventional IVF cycles were weaker or not statistically significant, whereas the rescue ICSI subgroup showed a positive association mainly with blastocyst formation rate.

Discussion

In this retrospective cohort study, higher PASM was associated with several IVF/ICSI outcomes, including the numbers of oocytes retrieved, available embryos, and blastocysts, available embryo rate, blastocyst formation rate, and CCPR. Mediation analysis suggested that HOMA-IR partially and modestly mediated the associations between PASM and selected laboratory outcomes, accounting for 5.5%–7.3% of the associations with oocytes retrieved, available embryos, and blastocysts. To our knowledge, this is the first study to quantify the associations among PASM, insulin sensitivity, and IVF/ICSI outcomes, suggesting that PASM may be a valuable body-composition marker for reproductive health and that HOMA-IR partially and modestly mediates its association with laboratory outcomes.

While no previous studies have directly linked skeletal muscle mass to IVF/ICSI outcomes, a large-scale health screening involving 372,399 Koreans found that lower skeletal muscle mass was significantly associated with diabetes and IR [11]. This aligns with our findings; we observed that an increase in PASM correlated with a significant decrease in HOMA-IR, reducing the risk of IR by 26%. Skeletal muscle is the principal site for post‑prandial glucose disposal and secretes myokines (e.g. interleukin‑6) that augment insulin signalling and glucose oxidation [20–22]. Therefore, a higher PASM may be linked to IVF/ICSI outcomes through several possible mechanisms, including improved glucose metabolism, altered myokine secretion, reduced inflammatory burden, and more favourable systemic metabolic homeostasis [23–25]. However, because HOMA-IR explained only a small proportion of the observed associations, the relationship between PASM and embryology outcomes is unlikely to be mediated predominantly by insulin resistance alone. Other unmeasured pathways, such as direct myokine effects, chronic inflammation, ovarian metabolic microenvironment, adipose tissue distribution, physical activity, nutritional status, and ovarian blood flow, may also contribute to the remaining unexplained associations.

Given the established role of skeletal muscle mass in glucose regulation, it is biologically plausible that insulin resistance may partly contribute to the association between PASM and ART outcomes. In PCOS, IR has been linked to fewer mature oocytes, smaller embryo cohorts, and reduced clinical pregnancy and live‑birth rates during IVF/ICSI [26, 27]. Data in non‑PCOS women are mixed: modest IR may coincide with larger follicle counts [28], yet studies in lean cohorts still report fewer mature oocytes and lower blastocyst yields with higher HOMA‑IR [29]. Our results extend these observations, showing inverse associations between HOMA‑IR and oocyte, embryo and blastocyst metrics across all women, with markedly poorer embryology laboratory outcomes in those meeting IR criteria. Although crude correlations suggested lower CCPR and CLBR in patients with IR, the relationship attenuated after multivariable adjustment, implying residual confounding and the need for larger, prospective datasets.

Subgroup analyses suggested that the associations between PASM and embryology outcomes varied across patient characteristics. The associations were more apparent among women aged < 35 years and were attenuated among those aged ≥ 35 years, consistent with the dominant influence of age-related declines in ovarian reserve and oocyte quality [30, 31]. Associations were evident in women with normal or overweight BMI but weakened in obesity, where excess adiposity may blunt the metabolic advantages conferred by larger muscle mass; a similar pattern emerged when stratifying by PBF, with stronger associations at PBF < 30% than at PBF ≥ 30% [32–34]. In patients without PCOS, PASM remained positively associated with embryology laboratory indicators. Among women with PCOS, PASM showed positive associations with some early embryology outcomes, but not consistently with blastocyst-related outcomes. This mixed pattern may be related to the smaller subgroup size, the metabolic heterogeneity of PCOS, and residual confounding by androgen-related endocrine status, as androgen levels were not consistently measured in all participants [35, 36]. The positive association between PASM and embryology outcomes was also observed in patients with IR, suggesting the potential relevance of both skeletal muscle composition and insulin-related metabolic status [37, 38]. Notably, PASM correlated with multiple embryology indicators in ICSI cycles, but not in conventional IVF, perhaps because ICSI minimises male‑factor confounding and better reveals maternal metabolic influences [39]. Thus, PASM may be particularly informative in younger women, women with normal BMI or lower PBF, and those undergoing ICSI, whereas its value in women with obesity, PCOS, or IR requires further validation.

Overall, our findings suggest that higher PASM is associated with more favourable IVF/ICSI laboratory outcomes, and that insulin resistance, as reflected by HOMA-IR, partially and modestly mediates these associations. Although insulin sensitivity appears to explain only a small proportion of the association, our mediation analysis provides preliminary evidence that insulin resistance is one measurable and potentially modifiable metabolic pathway linking PASM to embryology outcomes. Interventions aimed at increasing skeletal muscle mass, such as resistance training or targeted nutritional supplementation, may therefore improve metabolic profiles and represent a potential strategy for optimizing assisted reproduction outcomes, pending confirmation in prospective interventional studies.

Bioelectrical‑impedance analysis, selected for its speed, non‑invasiveness and low cost, facilitated large‑scale muscle assessment and could be incorporated into routine fertility work‑ups. The study still has some limitations. First, this was a retrospective cohort study including all eligible patients during the study period, and no a priori sample-size calculation was performed. The relatively wide confidence intervals for the mediated proportions suggest limited precision of the mediation estimates; therefore, these findings should be interpreted as exploratory and confirmed in larger prospective studies with formal sample-size planning. Second, although we adjusted for major clinical and metabolic covariates, residual confounding from physical activity, dietary intake, nutritional status, androgen levels, inflammatory markers, adipose tissue distribution, and ovarian blood flow could not be excluded. Third, causal interpretation of the mediation analysis relies on the sequential ignorability assumption, including no unmeasured confounding of the mediator–outcome relationship. Although we selected covariates based on clinical knowledge and DAGs, this assumption cannot be directly verified in a retrospective observational study. Finally, follow-up was confined to the index IVF/ICSI cycle, limiting comprehensive assessment of cumulative live-birth rate and precluding evaluation of obstetric outcomes; longer longitudinal studies are required to elucidate the enduring impact of skeletal muscle mass on reproductive and perinatal endpoints.

Conclusions

Higher PASM was associated with more favourable IVF/ICSI laboratory outcomes and higher CCPR in adjusted continuous-variable analyses, whereas no significant association was observed for CLBR. HOMA-IR partially and modestly mediated the associations between PASM and selected laboratory outcomes, suggesting that insulin resistance may be one measurable metabolic pathway linking PASM to embryology outcomes. Given the contribution of skeletal muscle to glucose regulation and the growing prevalence of insulin resistance, interventions that enhance muscle mass and optimise insulin action may represent potential avenues for optimizing ART success. Confirmation of these findings in large, multicentre, longitudinal cohorts extending follow‑up to cumulative live‑birth rate and obstetric outcomes is now required.

Authors’ contributions

Danyu Ni participated in the study design, drafted the article, performed the analysis and wrote the manuscript. Xinyu Wang, Zichen Zheng and Kaidi Yu participated in the acquisition and analysis of data. Qijun Xie, Ye Yang and Xiufeng Ling reviewed the final article and made appropriate corrections and suggestions to improve it. Ye Yang, Chun Zhao, and Xiufeng Ling are corresponding authors and they participated in the study design, performed the final proofreading and confirmed the final version. All authors approved the version to be published.

Funding

This research was supported by the National Key Research and Development Program of China (2022YFC2702201), and the National Natural Science Foundation of China (82171645, 82371670 and 82301874).

Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Declarations

Ethics approval and consent to participate

This study was approved by the Institutional Ethics Committee of Nanjing Women and Children’s Healthcare Hospital (No. 2022KY‑046) and complied with the Declaration of Helsinki; the requirement for individual informed consent was waived owing to the retrospective design.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.