Association of IGF1R Polymorphisms with Idiopathic Short Stature in Children: A Meta-Analysis

Association of IGF1R Polymorphisms with Idiopathic Short Stature in Children: A Meta-Analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Association of IGF1R Polymorphisms with Idiopathic Short Stature in Children: A Meta-Analysis Ahmad Hemmatyar, Maryam Vafapour, Hanieh Talebi, Seyed Alireza Dastgheib, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5865273/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Idiopathic short stature (ISS) poses substantial challenges in pediatric growth assessment due to its multifactorial origins. This meta-analysis explores the relationship between IGF1R polymorphisms and the risk of ISS in children. Methods A comprehensive literature review was performed utilizing PubMed, Web of Knowledge, and CNKI, culminating on January 1, 2025, focusing on studies published before this date. The search employed relevant keywords and MeSH terms related to ISS and genetics factors. The inclusion criteria focused on original case-control, longitudinal, or cohort studies, with no restrictions on language or publication year. Correlations were quantified as odds ratios (ORs) with 95% confidence intervals (CIs) using Comprehensive Meta-Analysis software. Results Eight case-control studies comprising 3,794 children with ISS and 3,018 controls were included. Four studies examined the variant rs1976667 (2,255 cases and 1,642 controls), while the other four focused on rs2684788 (1,539 cases and 1,376 controls). All studies, conducted in China from 2011 to 2018, found no significant associations between IGF1R polymorphisms rs1976667 and rs2684788 and ISS across all five genetic models. Conclusions This meta-analysis reveals no significant association between IGF1R rs1976667 and rs2684788 polymorphisms with ISS. However, the predominance of studies conducted in Asian populations, particularly China, may limit the generalizability of the findings to other ethnic groups. IGF1R polymorphisms idiopathic short stature children meta-analysis genetic association Figures Figure 1 Figure 2 Figure 3 Introduction Idiopathic short stature (ISS) presents a significant challenge in pediatric growth assessment, characterized by a height that is more than two standard deviations below the mean for a particular age and sex, without an identifiable underlying pathology [1–3]. Representing about 1–3% of children in the general population, ISS accounts for a considerable fraction of pediatric referrals to specialists, marking it as a prevalent concern in clinical practice [4]. The multifactorial etiology of ISS encompasses a complex interplay of genetic, epigenetic, and environmental components, making the understanding of its underpinnings crucial for effective diagnosis and management [5, 6]. Recent genetic research has highlighted the connection between specific mutations and ISS, indicating that around 25–40% of ISS cases show identifiable genetic abnormalities through methods such as copy number variant (CNV) analysis, single-gene approaches, and whole-exome sequencing (WES) [5, 7]. Prominent among these are mutations in the Short Stature Homeobox (SHOX) gene and those affecting the growth hormone (GH) and insulin-like growth factor-I (IGF-I) signaling pathways [8, 9]. Notably, defects in the type 1 insulin-like growth factor receptor (IGF1R) gene have emerged as key contributors to growth impairments associated with ISS, manifesting not only in stunted growth but also in developmental delays and metabolic abnormalities [10, 11]. Disruptions in the GH-IGF-I axis, particularly through functional mutations in IGF1R, can lead to conditions characterized by IGF-1 resistance and inadequate growth despite normal or elevated serum IGF-1 levels. Such disruptions complicate the clinical presentation and management of affected children [11, 12]. Several studies have documented the presence of IGF1R polymorphisms in various cohorts, linking specific variants to short stature outcomes [13–15]. The genetic landscape of IGF1R mutations is diverse, with evidence suggesting that these mutations often lead to haploinsufficiency or compound heterozygosity, significantly impacting growth and development [16–18]. Furthermore, the clinical implications of IGF1R mutations extend beyond height, influencing metabolic profiles and broader health outcomes. Given that new variants correlated with severe stunting have been identified, particularly within certain population subsets, there is an urgent need to synthesize this knowledge to enhance our understanding of how IGF1R polymorphisms contribute to ISS [19, 20]. This meta-analysis endeavors to quantitatively evaluate the association between IGF1R polymorphisms and ISS in children. By systematically reviewing existing literature, we aim to elucidate the extent to which specific IGF1R genetic variations influence growth deficiencies and contribute to the broader clinical profile of ISS. Clarifying these associations will not only augment our understanding of the genetic factors implicated in short stature but also inform future research directions and therapeutic interventions tailored to the needs of affected populations. In doing so, this analysis aspires to lay the groundwork for improved diagnostic frameworks and personalized treatment strategies for children grappling with the challenges of ISS. Materials and Methods Literature Search Strategy The meta-analysis did not require ethical approval, as it did not involve direct interactions with human participants, operating under the assumption that all included studies had obtained the necessary ethical clearances from their respective institutional review boards. A comprehensive literature review was conducted across various Chinese and English databases, including MEDLINE, PubMed, PubMed Central (PMC), Europe PubMed Central (Europe PMC), Scopus, Cochrane Library, Google Scholar, Web of Science, Elsevier, CINAHL, ResearchGate, ClinicalTrials.gov, SciELO, MedNexus, MedRxiv, Chinese Biomedical Database (CBD), Chinese National Knowledge Infrastructure (CNKI), Wanfang Data Company, Chaoxing, Circumpolar Health Bibliographic Database (CHBD), China/Asia On Demand (CAOD), Indian Citation Index (ICI), Chinese Medical Citation Index (CMCI), Semantic Scholar, Egyptian Knowledge Bank (EKB), VIP Information Consultancy Company (VIP), Chinese Medical Current Contents (CMCC), and Weipu Periodical Database, up until January 1, 2025. The objective was to systematically evaluate the connection between variations in the IGF1R gene and susceptibility to ISS, with relevant citations from included studies examined manually. The search strategy employed a combination of keywords and phrases such as "IGF1R variations," "genetic predisposition," "idiopathic short stature," "polymorphism," "growth disorders," "height genetics," "genetic association studies," "meta-analysis," "single nucleotide polymorphisms (SNPs)," "association analysis," and "children's growth," aiming to encompass a wide array of research on the links between IGF1R polymorphisms and height-related outcomes in human populations. Studies published in non-English languages were considered, and appropriate translations were conducted to ensure clarity and consistency in interpreting the findings. Inclusion and Exclusion Criteria The study selection criteria were strictly defined to ensure high-quality inclusions. Inclusion criteria encompassed: 1) studies examining the link between IGF1R polymorphisms and ISS; 2) epidemiological case-control or cohort studies with clear definitions and diagnostic criteria for ISS; and 3) publications providing sufficient data for odds ratio (OR) calculations along with 95% confidence intervals (CIs). Exclusion criteria included: 1) literature types such as reviews, editorials, and isolated case reports, due to their lack of original data; 2) studies involving syndromic short stature or known genetic disorders, as these could confound the results; 3) publications lacking comprehensive genetic data or clear population definitions, hindering meaningful synthesis; 4) studies involving animal subjects or in vitro experiments; 5) studies with incomplete genotype frequency data; 6) studies relying on linkage or family-based analyses, such as siblings, twins, and parent-trios; 7) abstracts, case reports, commentaries, conference papers, and meta-analyses; and 8) duplicates or studies that repeat others. Data Extraction Data extraction was performed by two independent reviewers using a standardized form to collect detailed information, including authors' names, publication year, study design (case-control or cohort), population characteristics (sample size and demographics), specific IGF1R polymorphisms, genotype frequencies, and statistical methods. Discrepancies were resolved through discussion or consultation with a third reviewer. Reviewers independently assessed bibliographies, cross-referenced data, and addressed disagreements collaboratively. Literature screening began with title and abstract assessment to exclude irrelevant studies, followed by a full-text review for inclusion confirmation. Key extracted information included the first author's name, publication date, country, ethnic background, genotyping methods, total numbers of cases and controls, genotype frequencies for IGF1R polymorphisms, Hardy-Weinberg equilibrium (HWE) test results, and minor allele frequencies (MAFs) in controls. For studies by the same authors with overlapping data, only the most recent or largest sample size publication was retained for analysis. Quality Score Assessment The Newcastle-Ottawa Score (NOS) was used to assess study quality in the meta-analysis by evaluating methodological aspects of observational research, including case selection, group comparability, and exposure determination through eight specific items. Studies with strong selection and exposure received one star, while comparability could earn up to two stars. Quality was rated on a nine-star scale, with zero indicating poor quality and nine indicating high quality. Studies scoring seven or more were considered high quality, while those scoring at least five were suitable for meta-analysis. Disagreements were resolved through discussion and consensus. Statistical Analysis The investigation into the link between IGF1R genetic variations and ISS involved calculating ORs with 95% CIs. Statistical significance of the pooled ORs was determined using the Z-test, with p < 0.05 considered significant. Five genetic models were analyzed: allelic (M vs. W), homozygote (MM vs. WW), heterozygote (MW vs. WW), dominant (MM + MW vs. WW), and recessive (MM vs. MW + WW), where 'M' indicates the mutant allele and 'W' the wild type. The HWE in the control group was assessed with Fisher's exact test, where p < 0.05 suggested a deviation from HWE. Heterogeneity in the meta-analysis was evaluated using various statistics, including the Q-value, degrees of freedom (df), I-squared (I²), and Tau-squared (τ²). The Q-value tests the null hypothesis of a common effect size across studies, with higher values indicating greater heterogeneity. Degrees of freedom, calculated as the total number of studies minus one, are essential for interpreting the Q-value. The I-squared statistic indicates the percentage of total variation due to heterogeneity, with thresholds of low (0–25%), moderate (26–50%), and high (> 50%) heterogeneity. Tau-squared estimates the variance among studies, reflecting variability in effect sizes. The chi-square test was primarily used to assess heterogeneity, with significance set at p < 0.05. Following Cochrane guidelines, heterogeneity was quantified on a scale of 0 to 100%, with the I² index measuring the proportion of variation due to study differences. Random-effect models (DerSimonian-Laird method) were applied when I² exceeded 50%, while fixed-effect models (Mantel-Haenszel method) were used otherwise. Subgroup analyses based on ethnicity, country, control source, and genotyping methods were conducted to identify potential sources of heterogeneity. A one-way sensitivity analysis tested result stability by excluding one study at a time, and an additional sensitivity analysis removed studies violating HWE [21]. Publication bias was assessed using Begg's funnel plots, where asymmetry indicated potential bias, and Egger's linear regression tested plot symmetry. In cases of detected bias, the trim-and-fill method was used to adjust results. Statistical analyses were performed using Comprehensive Meta-Analysis (CMA) Software version 2.0, with a significance threshold of p < 0.05 for two-sided tests. Study Characteristics The selection process for the studies is depicted in Fig. 1 , which captures the initial identification of 219 records were identified through database searches conducted up to January 1, 2025. After removing duplicates, 137 records were screened, and 59 were excluded due to irrelevance based on title and abstract reviews. Additionally, 71 full-text articles were excluded for various reasons, including being reviews, case reports, letters to editors, focusing on conditions unrelated to ISS, or lacking relevance to the IGF1R gene. Ultimately, eight case-control studies from five publications [22–26] were identified that met the inclusion criteria, encompassing a total of 3,794 children with ISS and 3,018 controls. These studies are summarized in Table 1 , revealing that four studies investigated the variant rs1976667, which included 2,255 cases and 1,642 controls, while another four studies focused on rs2684788, comprising 1,539 cases and 1,376 controls. All studies were conducted in China and published in English and Chinese between 2011 and 2018, utilizing two genotyping methods: SNaPshot and MALDI-TOF MS. Table 1 also provides genotype and MAF information for both polymorphisms, indicating that genotype distributions in healthy subjects generally adhere to HWE, with the exception of two studies involving the rs2684788 polymorphism. Table 1 Main characteristics of studies included in this meta-analysis. First Author Country (Ethnicity) Genotyping Method Case/Control Cases Controls MAFs HWE NOS Genotype Allele Genotype Allele rs1976667 AA AG GG A G AA AG GG A G Hui 2011 China(Asian) SNaPshot 804/575 407 330 67 1144 464 299 228 48 826 324 0.280 0.626 8 Yu 2013 China(Asian) SNaPshot 784/572 394 320 70 1108 460 298 226 48 822 322 0.281 0.579 8 Yang 2013 China(Asian) SNaPshot 486/289 246 202 38 694 278 121 146 22 388 190 0.328 0.013 7 Zhang 2018 China(Asian) MALDI-TOF MS 181/206 83 83 15 249 113 115 77 14 307 105 0.254 0.820 6 rs2684788 GG GA AA G A GG GA AA G A Yu 2013 China(Asian) SNaPshot 447/288 386 165 65 937 295 241 224 103 706 430 0.378 0.001 8 Yang 2013 China(Asian) SNaPshot 616/568 237 106 104 580 314 132 132 24 396 180 0.312 0.257 8 Yu 2015 China(Asian) SNaPshot 295/314 184 33 78 401 189 239 31 44 509 119 0.189 ≤ 0.001 7 Zhang 2018 China(Asian) MALDI-TOF MS 181/206 49 88 44 186 176 55 96 55 206 206 0.500 0.329 6 Abbreviations : MALDI-TOF MS: Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry; MAFs: minor allele frequencies; HWE: Hardy-Weinberg equilibrium; NOS: Newcastle-Ottawa Score. Quality of the Included Studies The quality of studies in the meta-analysis was evaluated based on indicators such as sample size, genotyping methods, NOS scores, and adherence to HWE. Studies by Hui (2011), Yu (2013), and Yang (2013) had robust case and control groups, enhancing their reliability. However, significant HWE deviations were identified in Yang (2013) for rs1976667 (HWE p-value = 0.013) and Yu (2015) for rs2684788 (p ≤ 0.001), raising concerns about biases and population stratification. The varying MAFs suggested diverse ethnic backgrounds and sample characteristics, complicating data interpretation. While the studies offer valuable insights, the HWE and MAF discrepancies necessitate cautious interpretation of meta-analytic conclusions. NOS scores ranged from 6 to 8, with many studies achieving higher scores, indicating generally good methodological quality in selection, comparability, and outcome assessment. Nonetheless, the HWE deviations and MAF reporting discrepancies require further scrutiny to bolster the findings. Quantitative Synthesis Key findings on the correlation between IGF1R polymorphisms and ISS are presented in Table 2 . Table 2 Summary of pooled risk estimates for the association between IGF1R polymorphism and ISS. Subgroup Genetic Model Type of Model Heterogeneity Tau-Squared OR Publication Bias Q-Value df I 2 (%) P H τ² SD Variance Tau OR 95% CI Z OR P OR P Beggs P Eggers rs1976667 Overall A vs. G Fixed 6.702 3 55.23 0.082 0.014 0.020 0.00 0.116 1.025 0.878–1.198 0.752 0.725 0.734 0.757 AA vs. GG Fixed 1.360 3 0.00 0.715 0.00 0.053 0.003 0.00 1.055 0.830–1.341 0.434 0.664 1.000 0.665 AG vs. GG Fixed 10.258 3 70.75 0.016 0.048 0.058 0.003 0.220 1.016 0.786–1.321 0.140 0.889 0.734 0.876 AA + AG vs. GG Fixed 9.982 3 69.94 0.019 0.042 0.051 0.003 0.206 1.026 0.804–1.311 0.209 0.834 0.865 0.734 AA vs. AG + GG Fixed 0.264 3 0.00 0.967 0.00 0.049 0.002 0.00 1.051 0.833–1.326 0.418 0.676 0.734 0.276 rs2684788 Overall A vs. G Random 280.150 3 98.92 ≤ 0.001 1.423 1.197 1.432 1.193 0.872 0.269–2.826 -0.228 0.820 0.734 0.950 AA vs. GG Random 55.064 3 94.55 ≤ 0.001 0.883 0.788 0.622 0.940 1.178 0.456–3.043 0.339 0.735 1.000 0.439 AG vs. GG Random 21.356 3 85.95 ≤ 0.001 0.223 0.228 0.052 0.473 0.706 0.426–1.171 -1.347 0.178 0.089 0.062 AA + AG vs. GG Random 49.273 3 93.91 ≤ 0.001 0.404 0.373 0.139 0.636 0.879 0.461–1.678 -0.390 0.696 0.308 0.250 AA vs. AG + GG Random 50.866 3 94.10 ≤ 0.001 0.709 0.628 0.394 0.842 1.353 0.578–3.172 0.696 0.486 0.308 0.254 Abbreviations: OR - Odds Ratio, CI - Confidence Interval, df - degrees of freedom, I² - I-squared statistic, PH - p-value for heterogeneity, τ² - Tau-squared, SD - Standard Deviation, Z OR - Z-Score for Odds Ratio, POR - p-value for Odds Ratio, PBeggs - p-value for Begg's test, PEggers - p-value for Egger's test. rs1976667 : the analysis revealed no significant association with ISS across multiple genetic models. The A versus G allele comparison yielded an OR of 1.025 (95% CI: 0.878–1.198), indicating a minimal association (Z statistic = 0.752, p = 0.725). The AA versus GG comparison resulted in an OR of 1.055 (95% CI: 0.830–1.341; Z = 0.434, p = 0.664), while the AG versus GG model showed an OR of 1.016 (95% CI: 0.786–1.321; Z = 0.140, p = 0.889). In the AA + AG versus GG comparison, the OR was 1.026 (95% CI: 0.804–1.311; Z = 0.209, p = 0.834). Lastly, the AA versus AG + GG comparison yielded an OR of 1.051 (95% CI: 0.833–1.326; Z = 0.418, p = 0.676). Overall, these results suggest that the IGF1R rs1976667 polymorphism is not significantly associated with ISS. rs2684788 : the analysis produced mixed findings across genetic models. The A versus G comparison showed an OR of 0.872 (95% CI: 0.269–2.826), indicating no significant association (ZOR = -0.228, POR = 0.820). The AA versus GG comparison yielded an OR of 1.178 (95% CI: 0.456–3.043), reflecting a slight, non-significant positive association (ZOR = 0.339, POR = 0.735). The AG versus GG analysis resulted in an OR of 0.706 (95% CI: 0.426–1.171), suggesting a potential negative association without significance (ZOR = -1.347, POR = 0.178). The AA + AG versus GG comparison gave an OR of 0.879 (95% CI: 0.461–1.678), further indicating a lack of significance. Finally, the AA versus AG + GG model produced an OR of 1.353 (95% CI: 0.578–3.172), suggesting a trend toward a positive association, but still not significant (ZOR = 0.696, POR = 0.486). Overall, these findings indicate no strong evidence for a significant association between the IGF1R rs2684788 polymorphism and ISS. Heterogeneity Test The analysis of heterogeneity in the relationship between IGF1R polymorphism and ISS reveals varying degrees of inconsistency across different genetic models and polymorphisms. For the rs1976667 variant, significant heterogeneity was observed in the AG vs. GG model (I² = 70.75%), along with moderate heterogeneity in the AA + AG vs. GG model (I² = 69.94%). In contrast, the AA vs. GG model exhibited no heterogeneity (I² = 0.00%). The overall assessment for rs1976667 showed a moderate level of heterogeneity (I² = 55.23%), with a statistically significant Q-value (Q = 6.702, p = 0.082), suggesting that while there is some variability among studies, it is not overly pronounced. For the rs2684788 variant, a high degree of heterogeneity was evident across all genetic models, with I² values ranging from 85.95% in the AG vs. GG model to 98.92% in the overall assessment. The overall Q-value for rs2684788 was substantial (Q = 280.150, p ≤ 0.001), indicating pronounced variability among the studies pursued. These differential levels of heterogeneity underscore the complexity of the association between IGF1R polymorphisms and ISS across different genetic models. Sensitivity Analysis Multiple meta-analyses were performed, each excluding a distinct study to assess the stability of the results. The findings indicated that both fixed-effects and random-effects estimates remained consistent across various gene models, demonstrating robust integrity in the pooled ORs. Furthermore, a sensitivity analysis that excluded studies not conforming to HWE showed no heterogeneity both before and after these exclusions. This highlights the significant influence of non-HWE data on the overall pooled results. Publication Bias The analysis of publication bias concerning IGF1R polymorphism and ISS yielded mixed results across genetic models. For the rs1976667 polymorphism, Begg's test showed no evidence of bias in most comparisons, with P-values of 0.734 for the A vs. G comparison and 0.734 and 0.865 for the AA vs. AG + GG and AG vs. GG comparisons, respectively. However, the AA vs. GG comparison had a P-value of 1.000, indicating no significant bias. Conversely, Egger's test suggested potential bias for the AG vs. GG comparison with a P-value of 0.876. Regarding rs2684788, no publication bias was detected for the A vs. G and AA vs. GG comparisons, with P-values of 0.734 and 1.000, respectively. However, the AG vs. GG comparison indicated bias in both tests with P-values of 0.089 and 0.062. The AA + AG vs. GG and AA vs. AG + GG comparisons showed no significant bias. Overall, findings suggest a low risk of publication bias across most genetic models, though caution is warranted for specific comparisons, particularly with rs2684788. MAF MAF is a crucial metric in genetic research, reflecting the prevalence of rare variants in a population. This meta-analysis found that the MAF for rs1976667 in healthy Chinese children ranged from 0.254 to 0.328, indicating a moderate frequency among the studied Asian populations. In contrast, the MAFs for rs2684788 varied more widely, from 0.189 to 0.500, highlighting significant differences among participants. This variation in MAF among Chinese pediatric populations regarding IGF1R polymorphisms emphasizes the underlying genetic diversity within this demographic. Such diversity can have implications for understanding disease susceptibility, as certain alleles may be linked to specific health outcomes or responses to treatment. The differences in MAF could also reflect historical population dynamics, selective pressures, or environmental influences impacting genetic variation. Discussion The human IGF-1R gene, located at 15q26.3, spans 315 kbp and consists of 21 exons and 20 introns. It encodes an mRNA of approximately 11,242 bp, including both 5' and 3' untranslated regions (UTRs), with a coding sequence of 4,104 nucleotides that produces a protein of 1,367 amino acids [27, 28]. IGF-1R is a transmembrane multi-subunit protein tyrosine kinase receptor in the insulin receptor family, crucial for the growth hormone (GH)-IGF axis. It mediates various physiological effects, including promoting somatic cell mitosis, anti-apoptotic activities, and enhancing cell survival [29]. The growth-promoting effects of IGF-1, whether secreted, paracrine, or endocrine, depend on IGF-1R. Mutations in the IGF-1R gene can disrupt the IGF signaling pathway, leading to growth retardation and related disorders [30]. The role of IGF-1R in ISS is increasingly recognized, emphasizing its significance in the interplay between genetics and growth disorders. As a key component of the GH-IGF axis, IGF-1R significantly influences height [28]. Notably, the rs1976667 locus is found within the first intron of the IGF-1R gene, while rs2684788 is located in the 3' UTR, suggesting their potential roles in regulating gene expression [24]. Genetic analyses indicate that variations in the IGF1R gene significantly contribute to growth deficiencies, especially in children born small for gestational age (SGA). Specific single nucleotide polymorphisms (SNPs) in IGF1R have been linked to reduced birth length and weight, underscoring the genetic basis of observed phenotypic traits in these populations [31]. Moreover, defects in IGF1R have been observed in patients with idiopathic growth hormone deficiency who resist growth hormone treatments despite normal serum IGF-1 levels. A dose-dependent effect of IGF-1 signaling has been noted in two patient groups with different mutations; both groups have homozygous recessive IGF1 mutations that reduce IGF-1 binding affinity for IGF1R [31–33]. One variant shows an affinity 90 times lower than normal, resulting in a severe phenotype, while the other variant has an affinity only 3.9 times lower, leading to a milder phenotype akin to that of a patient with a heterozygous IGF-1 deletion [31, 32]. However, research on the protein structures encoded at these loci is limited, highlighting the need for further studies to clarify their connection to genetic susceptibility to ISS. This underscores the importance of comprehensive genetic screening in children with short stature and growth concerns, as it may uncover underlying genetic anomalies overlooked by conventional diagnostic methods. A deeper understanding of IGF1R's mechanisms and its impact on growth could lead to more targeted interventions for children with ISS and related disorders. Our analysis of eight case-control studies, involving 3,794 children with ISS and 3,018 controls, followed our established criteria. Four studies focused on the variant rs1976667, encompassing 2,255 cases and 1,642 controls, while the other four examined rs2684788, with 1,539 cases and 1,376 controls. Conducted in China between 2011 and 2018, all studies consistently reported no significant associations between the IGF1R polymorphisms rs1976667 and rs2684788 and ISS. Although several studies explored these associations, results were mixed. Huang et al. (2011) found no significant difference in the distribution of the rs1976667 genotype between 804 children with ISS and 575 controls, although allele A was more prevalent in the ISS group (P < 0.01), suggesting a potential risk [22]. Conversely, Yu et al. (2013) reported significant associations for both SNPs in a cohort of 712 Chinese children, with p-values of 0.03636 for rs1976667 and 0.01352 for rs2684788. The G allele at rs1976667 was significantly linked to ISS susceptibility in both males (P = 0.018) and females (P = 0.011), indicating a G dominant inheritance pattern. A similar trend was noted for rs2684788, further correlating rs1976667 genotypes with IGF-1 standard deviation scores in females (P = 0.006) [23]. In contrast, Yu et al. (2015) found no association between rs1976667 and ISS in 295 children with ISS and 314 controls, although significant results for rs2684788 were noted (P < 0.001), following a G dominant inheritance pattern. This study also revealed a correlation between IGF-1 SDS and the rs2684788 genotype (GG + GA) (P = 0.004) [26]. Lastly, Zhang et al. (2018) investigated GH-IGF-1 axis polymorphisms, including both SNPs, concluding that these variants did not significantly increase short stature susceptibility, as HWE analyses showed no notable discrepancies in the populations studied [25]. Overall, the inconsistent findings across multiple studies highlight the need for further research with larger sample sizes to clarify these associations and their functional implications in growth regulation. Clinical Implications The findings from the meta-analysis on IGF1R polymorphisms and their association with ISS in Chinese children have significant clinical implications. Understanding the moderate MAFs of variants like rs1976667 and rs2684788 can help clinicians identify genetic predispositions to growth disorders, particularly in pediatric populations. While the current evidence shows mixed associations between these polymorphisms and ISS, the potential link between IGF1R variations and growth deficiencies underscores the importance of genetic screening in children presenting with short stature. Identifying specific genetic anomalies could lead to more personalized treatment strategies, improving management outcomes for affected individuals. Moreover, recognizing the role of IGF1R in the growth hormone-IGF axis may facilitate early interventions and tailored therapies, ultimately enhancing the quality of care for children with growth-related concerns. As research continues to evolve, integrating genetic insights into clinical practice could pave the way for advancements in diagnosing and treating growth disorders effectively. Limitations To the best of our knowledge, this is the first meta-analysis on the association between IGF1R polymorphisms and ISS; however, it has several limitations that should be taken into account. 1) The geographical concentration of studies, predominantly conducted in China, may limit the generalizability of findings to other populations and ethnic groups, as variations in genetic backgrounds could influence the observed associations. 2) Language bias is a significant concern in our study, as we found our sources limited to articles published in English and Chinese. This restriction may exclude important research in other languages and critical international data that could enrich our understanding of the subject. 3) The analysis may be affected by publication bias, where studies with non-significant results are less likely to be published, skewing the overall findings. 4) Variability in study quality, sample sizes, and methodologies among the included studies can impact the robustness of the results, and the presence of heterogeneity may undermine the reliability of the conclusions drawn. 5) The focus on only two specific IGF1R polymorphisms (rs1976667 and rs2684788) may overlook other relevant polymorphisms that could also contribute to ISS. 6) Potential confounding factors, such as environmental influences, nutritional status, and socioeconomic conditions, may not have been adequately controlled in the included studies, affecting the risk of ISS. 7) The reliance on case-control study designs could introduce biases related to participant selection and recall bias, potentially compromising the validity of the reported associations. 8) Lastly, the lack of longitudinal data limits the ability to draw causal inferences regarding the relationship between IGF1R polymorphisms and ISS over time. These limitations suggest caution in interpreting the findings and underscore the need for further research across diverse populations and with consideration of additional genetic factors. Conclusion This meta-analysis finds no significant association between the IGF1R polymorphisms rs1976667 and rs2684788 and ISS in children. The results illustrate the complexity of genetic factors influencing ISS, reflecting mixed findings from prior studies. These inconsistencies highlight the need for research involving larger and more diverse populations to clarify the role of IGF-1R variants in growth disorders. Furthermore, the study's limitations, including its focus on only two polymorphisms in a Chinese population, caution against overinterpretation of these results. Future studies should address these limitations, explore a wider array of genetic factors, and include varied populations to deepen our understanding of ISS and improve personalized treatments for pediatric growth deficiencies. Declarations Funding: There is no funding source. Conflicts of interest/Competing interests: The authors declare that they have no conflict of interest. Ethics approval: This article does not contain any studies with human participants or animals performed by any of the authors. Clinical trial number: Not applicable. Consent to participate: Not applicable for this manuscript. Consent to Publish declaration: Not applicable. Availability of data and material: The datasets generated during and/or analyzed during this study are available from the corresponding author on reasonable request. Acknowledgments: The authors wish to extend their heartfelt appreciation to all the contributors of the articles incorporated in this meta-analysis. Their invaluable insights and efforts were crucial to the successful completion of this manuscript. Authors' contributions: A.H., M.V., H.T., and S.A.D.: Methodology, conceptualization, investigation. R.B. and M.P.: Methodology, investigation, writing, original draft preparation. H.R. and A.Sh.: Formal analysis, investigation. A.R. and K.A.: Investigation, writing. H.N.: Investigation. M.A.: Methodology, software. S.N.: Investigation, writing. S.A.D.: Project administration. S.A.D. and H.R.: Writing, reviewing, editing. References Molecular diagnosis is an important indicator for response to growth hormone therapy in children with short stature. Clin Chim Acta. 2024;554:117779. Polidori N, Castorani V, Mohn A, Chiarelli F. Deciphering short stature in children. Ann Pediatr Endocrinol Metab. 2020;25:69–79. Paltoglou G, Ziakas N, Chrousos GP, Yapijakis C. Cephalometric Evaluation of Children with Short Stature of Genetic Etiology: A Review. Child 2024, Vol 11, Page 792. 2024;11:792. He D, Zhang M, Li Y, Liu F, Ban B. Insights into the ANKRD11 variants and short-stature phenotype through literature review and ClinVar database search. Orphanet J Rare Dis 2024 191. 2024;19:1–14. Yuan J, Du Z, Wu Z, Yang Y, Cheng X, Liu X, et al. A Novel Diagnostic Predictive Model for Idiopathic Short Stature in Children. Front Endocrinol (Lausanne). 2021;12:721812. Shao X, Le Stunff C, Cheung W, Kwan T, Lathrop M, Pastinen T, et al. Differentially methylated CpGs in response to growth hormone administration in children with idiopathic short stature. Clin Epigenetics. 2022;14:65. Murray PG, Clayton PE, Chernausek SD. A genetic approach to evaluation of short stature of undetermined cause. lancet Diabetes Endocrinol. 2018;6:564–74. Liberatoscioli N, Andrade M, Cellin LP, Rezende RC, Vasques GA, Lima Jorge AA. Idiopathic Short Stature: What to Expect from Genomic Investigations. Endocrines 2023, Vol 4, Pages 1-17. 2023;4:1–17. Plachy L, Dusatkova P, Amaratunga SA, Neuman V, Sumnik Z, Lebl J, et al. Monogenic causes of familial short stature. Front Endocrinol (Lausanne). 2024;15:1506323. Mastromauro C, Giannini C, Chiarelli F. Short stature related to Growth Hormone Insensitivity (GHI) in childhood. Front Endocrinol (Lausanne). 2023;14:1141039. Mastromauro C, Chiarelli F. Novel Insights Into the Genetic Causes of Short Stature in Children. touchREVIEWS Endocrinol. 2022;18:49. Forbes BE, Blyth AJ, Wit JM. Disorders of IGFs and IGF-1R signaling pathways. Mol Cell Endocrinol. 2020;518:111035. Caliebe J, Broekman S, Boogaard M, Bosch CAJ, Ruivenkamp CAL, Oostdijk W, et al. IGF1, IGF1R and SHOX mutation analysis in short children born small for gestational age and short children with normal birth size (idiopathic short stature). Horm Res Paediatr. 2012;77:250–60. Yoon JS, Hwang IT. Microdeletion in the IGF-1 receptor gene of a patient with short stature and obesity: a case report. J Pediatr Endocrinol Metab. 2021;34:255–9. Hattori A, Katoh-Fukui Y, Nakamura A, Matsubara K, Kamimaki T, Tanaka H, et al. Next generation sequencing-based mutation screening of 86 patients with idiopathic short stature. Endocr J. 2017;64:947–54. Kant SG, Wit JM, Breuning MH. Genetic analysis of short stature. Horm Res. 2003;60:157–65. Ester WA, Van Duyvenvoorde HA, De Wit CC, Broekman AJ, Ruivenkamp CAL, Govaerts LCP, et al. Two short children born small for gestational age with insulin-like growth factor 1 receptor haploinsufficiency illustrate the heterogeneity of its phenotype. J Clin Endocrinol Metab. 2009;94:4717–27. Li X, Yao R, Chang G, Li Q, Song C, Li N, et al. Clinical Profiles and Genetic Spectra of 814 Chinese Children With Short Stature. J Clin Endocrinol Metab. 2022;107:972–85. Batey L, Moon JE, Yu Y, Wu B, Hirschhorn JN, Shen Y, et al. A Novel Deletion of IGF1 in a Patient With Idiopathic Short Stature Provides Insight Into IGF1 Haploinsufficiency. J Clin Endocrinol Metab. 2013;99:E153. Kawashima-Sonoyama Y, Hotsubo T, Hamajima T, Hamajima N, Fujimoto M, Namba N, et al. Various phenotypes of short stature with heterozygous IGF-1 receptor (IGF1R) mutations. Clin Pediatr Endocrinol. 2022;31:59. Neamatzadeh H, Dastgheib SA, Mazaheri M, Masoudi A, Shiri A, Omidi A, et al. Hardy-Weinberg Equilibrium in Meta-Analysis Studies and Large-Scale Genomic Sequencing Era. Asian Pac J Cancer Prev. 2024;25:2229–35. Hui H, Yu Y, Wei W, Li Y, Li-Ling X, Yining W, et al. Association between single nucleotide polymorphism of insulin-like growth factor receptor gene and idiopathic short stature (in Chinese). CJCP. 2011;13:955–8. Yu Y, Hui H, Wei W, Li Y, Liling X, Lan H, et al. Correlation between insulin-like growth factor-1 receptor gene polymorphism and genetic susceptibility in children with idiopathic short stature of different sexes. Chinese J Pract Pediatr. 2013;28:606–9. Yang Y, Huang H, Wang W, Yang L, Xie LL, Huang W. Association of insulin growth factor-1 receptor gene polymorphisms with genetic susceptibility to idiopathic short stature. Genet Mol Res. 2013;12:4768–79. Zhang Y, Zhang M, Chu Y, Ji B, Shao Q, Ban B. Association between Growth Hormone-Insulin-Like Growth Factor-1 Axis Gene Polymorphisms and Short Stature in Chinese Children. Biomed Res Int. 2018;2018:7431050. Yu Y, Hui H, Zhen Y, Wei W, Li Y, Wei H, et al. Association between polymorphisms of IGF-1R gene and idiopathic short stature in Jiangxi province. Chinese J Child Heal Care. 2015;23:710–2. Cannarella R, Mattina T, Condorelli RA, Mongioì LM, Pandini G, La Vignera S, et al. Chromosome 15 structural abnormalities: effect on IGF1R gene expression and function. Endocr Connect. 2017;6:528. Werner H. The IGF1 Signaling Pathway: From Basic Concepts to Therapeutic Opportunities. Int J Mol Sci. 2023;24:14882. LeRoith D, Holly JMP, Forbes BE. Insulin-like growth factors: Ligands, binding proteins, and receptors. Mol Metab. 2021;52:101245. Al-Samerria S, Radovick S. The Role of Insulin-like Growth Factor-1 (IGF-1) in the Control of Neuroendocrine Regulation of Growth. Cells. 2021;10. Janchevska A, Krstevska-Konstantinova M, Pfäffle H, Schlicke M, Laban N, Tasic V, et al. IGF1R Gene Alterations in Children Born Small for Gestitional Age (SGA). Open access Maced J Med Sci. 2018;6:2040–4. Johnston LB, Dahlgren J, Leger J, Gelander L, Savage MO, Czernichow P, et al. Association between Insulin-Like Growth Factor I (IGF-I) Polymorphisms, Circulating IGF-I, and Pre- and Postnatal Growth in Two European Small for Gestational Age Populations. J Clin Endocrinol Metab. 2003;88:4805–10. Savage MO, Hwa V, David A, Rosenfeld RG, Metherell LA. Genetic Defects in the Growth Hormone–IGF-I Axis Causing Growth Hormone Insensitivity and Impaired Linear Growth. Front Endocrinol (Lausanne). 2011;2 DEC:95. Additional Declarations No competing interests reported. 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11:08:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5865273/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5865273/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":74691412,"identity":"3bd2bc57-bfaf-4157-bdbc-94b97a1c5de4","added_by":"auto","created_at":"2025-01-24 18:38:42","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":151077,"visible":true,"origin":"","legend":"\u003cp\u003eStudy selection and inclusion process.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5865273/v1/aa33143d4314bd945c52f5b4.jpg"},{"id":74691387,"identity":"9422928b-1331-4000-b56f-28f9b15ae02f","added_by":"auto","created_at":"2025-01-24 18:38:40","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":482985,"visible":true,"origin":"","legend":"\u003cp\u003eForest plots illustrating the relationship between IGF1R polymorphisms and the risk of ISS in children. rs1976667: A: allelic, B: homozygote; rs268478: C: dominant, D: recessive.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5865273/v1/fe20e66d512cadfa14679f9c.jpg"},{"id":74691417,"identity":"389192a9-6134-458f-b01a-cb0ce840831b","added_by":"auto","created_at":"2025-01-24 18:38:42","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1122093,"visible":true,"origin":"","legend":"\u003cp\u003eBegg’s funnel plots assessing publication bias for IGF1R polymorphisms associated with ISS risk in children. rs1976667: A: allelic, B: homozygote; rs268478: C: dominant, D: recessive.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5865273/v1/84c23e1ae15ee378dfa0c06b.jpg"},{"id":77554839,"identity":"bb31d529-552a-4b24-8380-0066453282ed","added_by":"auto","created_at":"2025-03-03 05:32:38","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2332511,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5865273/v1/fe2dc5a2-15c2-4c71-ae21-06f932289311.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association of IGF1R Polymorphisms with Idiopathic Short Stature in Children: A Meta-Analysis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIdiopathic short stature (ISS) presents a significant challenge in pediatric growth assessment, characterized by a height that is more than two standard deviations below the mean for a particular age and sex, without an identifiable underlying pathology [1\u0026ndash;3]. Representing about 1\u0026ndash;3% of children in the general population, ISS accounts for a considerable fraction of pediatric referrals to specialists, marking it as a prevalent concern in clinical practice [4]. The multifactorial etiology of ISS encompasses a complex interplay of genetic, epigenetic, and environmental components, making the understanding of its underpinnings crucial for effective diagnosis and management [5, 6].\u003c/p\u003e \u003cp\u003eRecent genetic research has highlighted the connection between specific mutations and ISS, indicating that around 25\u0026ndash;40% of ISS cases show identifiable genetic abnormalities through methods such as copy number variant (CNV) analysis, single-gene approaches, and whole-exome sequencing (WES) [5, 7]. Prominent among these are mutations in the Short Stature Homeobox (SHOX) gene and those affecting the growth hormone (GH) and insulin-like growth factor-I (IGF-I) signaling pathways [8, 9]. Notably, defects in the type 1 insulin-like growth factor receptor (IGF1R) gene have emerged as key contributors to growth impairments associated with ISS, manifesting not only in stunted growth but also in developmental delays and metabolic abnormalities [10, 11]. Disruptions in the GH-IGF-I axis, particularly through functional mutations in IGF1R, can lead to conditions characterized by IGF-1 resistance and inadequate growth despite normal or elevated serum IGF-1 levels. Such disruptions complicate the clinical presentation and management of affected children [11, 12].\u003c/p\u003e \u003cp\u003eSeveral studies have documented the presence of IGF1R polymorphisms in various cohorts, linking specific variants to short stature outcomes [13\u0026ndash;15]. The genetic landscape of IGF1R mutations is diverse, with evidence suggesting that these mutations often lead to haploinsufficiency or compound heterozygosity, significantly impacting growth and development [16\u0026ndash;18]. Furthermore, the clinical implications of IGF1R mutations extend beyond height, influencing metabolic profiles and broader health outcomes. Given that new variants correlated with severe stunting have been identified, particularly within certain population subsets, there is an urgent need to synthesize this knowledge to enhance our understanding of how IGF1R polymorphisms contribute to ISS [19, 20].\u003c/p\u003e \u003cp\u003eThis meta-analysis endeavors to quantitatively evaluate the association between IGF1R polymorphisms and ISS in children. By systematically reviewing existing literature, we aim to elucidate the extent to which specific IGF1R genetic variations influence growth deficiencies and contribute to the broader clinical profile of ISS. Clarifying these associations will not only augment our understanding of the genetic factors implicated in short stature but also inform future research directions and therapeutic interventions tailored to the needs of affected populations. In doing so, this analysis aspires to lay the groundwork for improved diagnostic frameworks and personalized treatment strategies for children grappling with the challenges of ISS.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eLiterature Search Strategy\u003c/h2\u003e \u003cp\u003eThe meta-analysis did not require ethical approval, as it did not involve direct interactions with human participants, operating under the assumption that all included studies had obtained the necessary ethical clearances from their respective institutional review boards. A comprehensive literature review was conducted across various Chinese and English databases, including MEDLINE, PubMed, PubMed Central (PMC), Europe PubMed Central (Europe PMC), Scopus, Cochrane Library, Google Scholar, Web of Science, Elsevier, CINAHL, ResearchGate, ClinicalTrials.gov, SciELO, MedNexus, MedRxiv, Chinese Biomedical Database (CBD), Chinese National Knowledge Infrastructure (CNKI), Wanfang Data Company, Chaoxing, Circumpolar Health Bibliographic Database (CHBD), China/Asia On Demand (CAOD), Indian Citation Index (ICI), Chinese Medical Citation Index (CMCI), Semantic Scholar, Egyptian Knowledge Bank (EKB), VIP Information Consultancy Company (VIP), Chinese Medical Current Contents (CMCC), and Weipu Periodical Database, up until January 1, 2025. The objective was to systematically evaluate the connection between variations in the IGF1R gene and susceptibility to ISS, with relevant citations from included studies examined manually. The search strategy employed a combination of keywords and phrases such as \"IGF1R variations,\" \"genetic predisposition,\" \"idiopathic short stature,\" \"polymorphism,\" \"growth disorders,\" \"height genetics,\" \"genetic association studies,\" \"meta-analysis,\" \"single nucleotide polymorphisms (SNPs),\" \"association analysis,\" and \"children's growth,\" aiming to encompass a wide array of research on the links between IGF1R polymorphisms and height-related outcomes in human populations. Studies published in non-English languages were considered, and appropriate translations were conducted to ensure clarity and consistency in interpreting the findings.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eInclusion and Exclusion Criteria\u003c/h3\u003e\n\u003cp\u003eThe study selection criteria were strictly defined to ensure high-quality inclusions. Inclusion criteria encompassed: 1) studies examining the link between IGF1R polymorphisms and ISS; 2) epidemiological case-control or cohort studies with clear definitions and diagnostic criteria for ISS; and 3) publications providing sufficient data for odds ratio (OR) calculations along with 95% confidence intervals (CIs). Exclusion criteria included: 1) literature types such as reviews, editorials, and isolated case reports, due to their lack of original data; 2) studies involving syndromic short stature or known genetic disorders, as these could confound the results; 3) publications lacking comprehensive genetic data or clear population definitions, hindering meaningful synthesis; 4) studies involving animal subjects or in vitro experiments; 5) studies with incomplete genotype frequency data; 6) studies relying on linkage or family-based analyses, such as siblings, twins, and parent-trios; 7) abstracts, case reports, commentaries, conference papers, and meta-analyses; and 8) duplicates or studies that repeat others.\u003c/p\u003e\n\u003ch3\u003eData Extraction\u003c/h3\u003e\n\u003cp\u003eData extraction was performed by two independent reviewers using a standardized form to collect detailed information, including authors' names, publication year, study design (case-control or cohort), population characteristics (sample size and demographics), specific IGF1R polymorphisms, genotype frequencies, and statistical methods. Discrepancies were resolved through discussion or consultation with a third reviewer. Reviewers independently assessed bibliographies, cross-referenced data, and addressed disagreements collaboratively. Literature screening began with title and abstract assessment to exclude irrelevant studies, followed by a full-text review for inclusion confirmation. Key extracted information included the first author's name, publication date, country, ethnic background, genotyping methods, total numbers of cases and controls, genotype frequencies for IGF1R polymorphisms, Hardy-Weinberg equilibrium (HWE) test results, and minor allele frequencies (MAFs) in controls. For studies by the same authors with overlapping data, only the most recent or largest sample size publication was retained for analysis.\u003c/p\u003e\n\u003ch3\u003eQuality Score Assessment\u003c/h3\u003e\n\u003cp\u003eThe Newcastle-Ottawa Score (NOS) was used to assess study quality in the meta-analysis by evaluating methodological aspects of observational research, including case selection, group comparability, and exposure determination through eight specific items. Studies with strong selection and exposure received one star, while comparability could earn up to two stars. Quality was rated on a nine-star scale, with zero indicating poor quality and nine indicating high quality. Studies scoring seven or more were considered high quality, while those scoring at least five were suitable for meta-analysis. Disagreements were resolved through discussion and consensus.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eThe investigation into the link between IGF1R genetic variations and ISS involved calculating ORs with 95% CIs. Statistical significance of the pooled ORs was determined using the Z-test, with p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 considered significant. Five genetic models were analyzed: allelic (M vs. W), homozygote (MM vs. WW), heterozygote (MW vs. WW), dominant (MM\u0026thinsp;+\u0026thinsp;MW vs. WW), and recessive (MM vs. MW\u0026thinsp;+\u0026thinsp;WW), where 'M' indicates the mutant allele and 'W' the wild type. The HWE in the control group was assessed with Fisher's exact test, where p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 suggested a deviation from HWE. Heterogeneity in the meta-analysis was evaluated using various statistics, including the Q-value, degrees of freedom (df), I-squared (I\u0026sup2;), and Tau-squared (τ\u0026sup2;). The Q-value tests the null hypothesis of a common effect size across studies, with higher values indicating greater heterogeneity. Degrees of freedom, calculated as the total number of studies minus one, are essential for interpreting the Q-value. The I-squared statistic indicates the percentage of total variation due to heterogeneity, with thresholds of low (0\u0026ndash;25%), moderate (26\u0026ndash;50%), and high (\u0026gt;\u0026thinsp;50%) heterogeneity. Tau-squared estimates the variance among studies, reflecting variability in effect sizes. The chi-square test was primarily used to assess heterogeneity, with significance set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Following Cochrane guidelines, heterogeneity was quantified on a scale of 0 to 100%, with the I\u0026sup2; index measuring the proportion of variation due to study differences. Random-effect models (DerSimonian-Laird method) were applied when I\u0026sup2; exceeded 50%, while fixed-effect models (Mantel-Haenszel method) were used otherwise. Subgroup analyses based on ethnicity, country, control source, and genotyping methods were conducted to identify potential sources of heterogeneity. A one-way sensitivity analysis tested result stability by excluding one study at a time, and an additional sensitivity analysis removed studies violating HWE [21]. Publication bias was assessed using Begg's funnel plots, where asymmetry indicated potential bias, and Egger's linear regression tested plot symmetry. In cases of detected bias, the trim-and-fill method was used to adjust results. Statistical analyses were performed using Comprehensive Meta-Analysis (CMA) Software version 2.0, with a significance threshold of p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for two-sided tests.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStudy Characteristics\u003c/h2\u003e \u003cp\u003eThe selection process for the studies is depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, which captures the initial identification of 219 records were identified through database searches conducted up to January 1, 2025. After removing duplicates, 137 records were screened, and 59 were excluded due to irrelevance based on title and abstract reviews. Additionally, 71 full-text articles were excluded for various reasons, including being reviews, case reports, letters to editors, focusing on conditions unrelated to ISS, or lacking relevance to the IGF1R gene. Ultimately, eight case-control studies from five publications [22\u0026ndash;26] were identified that met the inclusion criteria, encompassing a total of 3,794 children with ISS and 3,018 controls. These studies are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, revealing that four studies investigated the variant rs1976667, which included 2,255 cases and 1,642 controls, while another four studies focused on rs2684788, comprising 1,539 cases and 1,376 controls. All studies were conducted in China and published in English and Chinese between 2011 and 2018, utilizing two genotyping methods: SNaPshot and MALDI-TOF MS. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e also provides genotype and MAF information for both polymorphisms, indicating that genotype distributions in healthy subjects generally adhere to HWE, with the exception of two studies involving the rs2684788 polymorphism.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMain characteristics of studies included in this meta-analysis.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"17\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c16\" colnum=\"16\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c17\" colnum=\"17\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFirst Author\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCountry\u003c/p\u003e \u003cp\u003e(Ethnicity)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGenotyping\u003c/p\u003e \u003cp\u003eMethod\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCase/Control\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c8\" namest=\"c5\"\u003e \u003cp\u003eCases\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c14\" namest=\"c10\"\u003e \u003cp\u003eControls\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c15\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMAFs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c16\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eHWE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c17\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNOS\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e\u003cb\u003eGenotype\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e\u003cb\u003eAllele\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e \u003cp\u003e\u003cb\u003eGenotype\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003e\u003cb\u003eAllele\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers1976667\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eAA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eAG\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eGG\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003eA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003eG\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003eAA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003eAG\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003eGG\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003eA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u003cb\u003eG\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHui 2011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChina(Asian)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSNaPshot\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e804/575\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e407\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e330\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e464\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e299\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e228\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e826\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e0.280\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e0.626\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYu 2013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChina(Asian)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSNaPshot\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e784/572\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e394\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e320\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e460\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e298\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e226\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e822\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e322\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e0.281\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e0.579\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYang 2013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChina(Asian)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSNaPshot\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e486/289\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e202\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e694\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e388\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e190\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e0.328\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZhang 2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChina(Asian)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMALDI-TOF MS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e181/206\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e307\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e0.254\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e0.820\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers2684788\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eGG\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eGA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eAA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003eG\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003eA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003eGG\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003eGA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003eAA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003eG\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u003cb\u003eA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYu 2013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChina(Asian)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSNaPshot\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e447/288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e386\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e937\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e295\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e241\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e224\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e706\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e430\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e0.378\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYang 2013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChina(Asian)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSNaPshot\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e616/568\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e237\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e580\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e314\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e396\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e0.312\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e0.257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYu 2015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChina(Asian)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSNaPshot\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e295/314\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e401\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e239\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e509\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e0.189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZhang 2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChina(Asian)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMALDI-TOF MS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e181/206\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e206\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e206\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e0.500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e0.329\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"17\"\u003e\u003cb\u003eAbbreviations\u003c/b\u003e: MALDI-TOF MS: Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry; MAFs: minor allele frequencies; HWE: Hardy-Weinberg equilibrium; NOS: Newcastle-Ottawa Score.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eQuality of the Included Studies\u003c/h3\u003e\n\u003cp\u003eThe quality of studies in the meta-analysis was evaluated based on indicators such as sample size, genotyping methods, NOS scores, and adherence to HWE. Studies by Hui (2011), Yu (2013), and Yang (2013) had robust case and control groups, enhancing their reliability. However, significant HWE deviations were identified in Yang (2013) for rs1976667 (HWE p-value\u0026thinsp;=\u0026thinsp;0.013) and Yu (2015) for rs2684788 (p\u0026thinsp;\u0026le;\u0026thinsp;0.001), raising concerns about biases and population stratification. The varying MAFs suggested diverse ethnic backgrounds and sample characteristics, complicating data interpretation. While the studies offer valuable insights, the HWE and MAF discrepancies necessitate cautious interpretation of meta-analytic conclusions. NOS scores ranged from 6 to 8, with many studies achieving higher scores, indicating generally good methodological quality in selection, comparability, and outcome assessment. Nonetheless, the HWE deviations and MAF reporting discrepancies require further scrutiny to bolster the findings.\u003c/p\u003e\n\u003ch3\u003eQuantitative Synthesis\u003c/h3\u003e\n\u003cp\u003eKey findings on the correlation between IGF1R polymorphisms and ISS are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of pooled risk estimates for the association between IGF1R polymorphism and ISS.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"17\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c16\" colnum=\"16\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c17\" colnum=\"17\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSubgroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGenetic Model\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eType of Model\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c7\" namest=\"c4\"\u003e \u003cp\u003eHeterogeneity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c11\" namest=\"c8\"\u003e \u003cp\u003eTau-Squared\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c15\" namest=\"c12\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e \u003cp\u003ePublication Bias\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eQ-Value\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003edf\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eI\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e \u003cb\u003e(%)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eP\u003c/b\u003e\u003csub\u003e\u003cb\u003eH\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eτ\u0026sup2;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003eSD\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003eVariance\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003eTau\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003eOR\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e95% CI\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u003cb\u003eZ\u003c/b\u003e\u003csub\u003e\u003cb\u003eOR\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c15\"\u003e \u003cp\u003e\u003cb\u003eP\u003c/b\u003e\u003csub\u003e\u003cb\u003eOR\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c16\"\u003e \u003cp\u003e\u003cb\u003eP\u003c/b\u003e\u003csub\u003e\u003cb\u003eBeggs\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c17\"\u003e \u003cp\u003e\u003cb\u003eP\u003c/b\u003e\u003csub\u003e\u003cb\u003eEggers\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers1976667\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA vs. G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFixed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.702\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e55.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e1.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.878\u0026ndash;1.198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e0.752\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e0.725\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e0.734\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e0.757\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAA vs. GG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFixed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.360\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.715\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e1.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.830\u0026ndash;1.341\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e0.434\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e0.664\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e0.665\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAG vs. GG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFixed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.258\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e70.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e1.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.786\u0026ndash;1.321\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e0.140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e0.889\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e0.734\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e0.876\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAA\u0026thinsp;+\u0026thinsp;AG vs. GG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFixed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.982\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e69.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.206\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e1.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.804\u0026ndash;1.311\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e0.209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e0.834\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e0.865\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e0.734\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAA vs. AG\u0026thinsp;+\u0026thinsp;GG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFixed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.264\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.967\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e1.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.833\u0026ndash;1.326\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e0.418\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e0.676\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e0.734\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e0.276\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers2684788\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA vs. G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRandom\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e280.150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e98.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.423\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.432\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1.193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.872\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.269\u0026ndash;2.826\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e-0.228\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e0.820\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e0.734\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e0.950\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAA vs. GG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRandom\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e55.064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e94.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.883\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.788\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.622\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.940\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e1.178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.456\u0026ndash;3.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e0.339\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e0.735\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e0.439\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAG vs. GG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRandom\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21.356\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e85.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.228\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.473\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.706\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.426\u0026ndash;1.171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e-1.347\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e0.178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e0.089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e0.062\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAA\u0026thinsp;+\u0026thinsp;AG vs. GG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRandom\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e49.273\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e93.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.404\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.373\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.636\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.879\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.461\u0026ndash;1.678\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e-0.390\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e0.696\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e0.308\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e0.250\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAA vs. AG\u0026thinsp;+\u0026thinsp;GG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRandom\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e50.866\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e94.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.709\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.628\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.394\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.842\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e1.353\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.578\u0026ndash;3.172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e0.696\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e0.486\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e0.308\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e0.254\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eAbbreviations:\u003c/strong\u003e OR - Odds Ratio, CI - Confidence Interval, df - degrees of freedom, I\u0026sup2; - I-squared statistic, PH - p-value for heterogeneity, \u0026tau;\u0026sup2; - Tau-squared, SD - Standard Deviation, Z\u003csup\u003eOR\u003c/sup\u003e - Z-Score for Odds Ratio, POR - p-value for Odds Ratio, PBeggs - p-value for Begg\u0026apos;s test, PEggers - p-value for Egger\u0026apos;s test.\u0026nbsp;\u003c/p\u003e \u003cp\u003e \u003cb\u003ers1976667\u003c/b\u003e: the analysis revealed no significant association with ISS across multiple genetic models. The A versus G allele comparison yielded an OR of 1.025 (95% CI: 0.878\u0026ndash;1.198), indicating a minimal association (Z statistic\u0026thinsp;=\u0026thinsp;0.752, p\u0026thinsp;=\u0026thinsp;0.725). The AA versus GG comparison resulted in an OR of 1.055 (95% CI: 0.830\u0026ndash;1.341; Z\u0026thinsp;=\u0026thinsp;0.434, p\u0026thinsp;=\u0026thinsp;0.664), while the AG versus GG model showed an OR of 1.016 (95% CI: 0.786\u0026ndash;1.321; Z\u0026thinsp;=\u0026thinsp;0.140, p\u0026thinsp;=\u0026thinsp;0.889). In the AA\u0026thinsp;+\u0026thinsp;AG versus GG comparison, the OR was 1.026 (95% CI: 0.804\u0026ndash;1.311; Z\u0026thinsp;=\u0026thinsp;0.209, p\u0026thinsp;=\u0026thinsp;0.834). Lastly, the AA versus AG\u0026thinsp;+\u0026thinsp;GG comparison yielded an OR of 1.051 (95% CI: 0.833\u0026ndash;1.326; Z\u0026thinsp;=\u0026thinsp;0.418, p\u0026thinsp;=\u0026thinsp;0.676). Overall, these results suggest that the IGF1R rs1976667 polymorphism is not significantly associated with ISS.\u003c/p\u003e \u003cp\u003e \u003cb\u003ers2684788\u003c/b\u003e: the analysis produced mixed findings across genetic models. The A versus G comparison showed an OR of 0.872 (95% CI: 0.269\u0026ndash;2.826), indicating no significant association (ZOR = -0.228, POR\u0026thinsp;=\u0026thinsp;0.820). The AA versus GG comparison yielded an OR of 1.178 (95% CI: 0.456\u0026ndash;3.043), reflecting a slight, non-significant positive association (ZOR\u0026thinsp;=\u0026thinsp;0.339, POR\u0026thinsp;=\u0026thinsp;0.735). The AG versus GG analysis resulted in an OR of 0.706 (95% CI: 0.426\u0026ndash;1.171), suggesting a potential negative association without significance (ZOR = -1.347, POR\u0026thinsp;=\u0026thinsp;0.178). The AA\u0026thinsp;+\u0026thinsp;AG versus GG comparison gave an OR of 0.879 (95% CI: 0.461\u0026ndash;1.678), further indicating a lack of significance. Finally, the AA versus AG\u0026thinsp;+\u0026thinsp;GG model produced an OR of 1.353 (95% CI: 0.578\u0026ndash;3.172), suggesting a trend toward a positive association, but still not significant (ZOR\u0026thinsp;=\u0026thinsp;0.696, POR\u0026thinsp;=\u0026thinsp;0.486). Overall, these findings indicate no strong evidence for a significant association between the IGF1R rs2684788 polymorphism and ISS.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eHeterogeneity Test\u003c/h2\u003e \u003cp\u003eThe analysis of heterogeneity in the relationship between IGF1R polymorphism and ISS reveals varying degrees of inconsistency across different genetic models and polymorphisms. For the rs1976667 variant, significant heterogeneity was observed in the AG vs. GG model (I\u0026sup2; = 70.75%), along with moderate heterogeneity in the AA\u0026thinsp;+\u0026thinsp;AG vs. GG model (I\u0026sup2; = 69.94%). In contrast, the AA vs. GG model exhibited no heterogeneity (I\u0026sup2; = 0.00%). The overall assessment for rs1976667 showed a moderate level of heterogeneity (I\u0026sup2; = 55.23%), with a statistically significant Q-value (Q\u0026thinsp;=\u0026thinsp;6.702, p\u0026thinsp;=\u0026thinsp;0.082), suggesting that while there is some variability among studies, it is not overly pronounced. For the rs2684788 variant, a high degree of heterogeneity was evident across all genetic models, with I\u0026sup2; values ranging from 85.95% in the AG vs. GG model to 98.92% in the overall assessment. The overall Q-value for rs2684788 was substantial (Q\u0026thinsp;=\u0026thinsp;280.150, p\u0026thinsp;\u0026le;\u0026thinsp;0.001), indicating pronounced variability among the studies pursued. These differential levels of heterogeneity underscore the complexity of the association between IGF1R polymorphisms and ISS across different genetic models.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eSensitivity Analysis\u003c/h2\u003e \u003cp\u003eMultiple meta-analyses were performed, each excluding a distinct study to assess the stability of the results. The findings indicated that both fixed-effects and random-effects estimates remained consistent across various gene models, demonstrating robust integrity in the pooled ORs. Furthermore, a sensitivity analysis that excluded studies not conforming to HWE showed no heterogeneity both before and after these exclusions. This highlights the significant influence of non-HWE data on the overall pooled results.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003ePublication Bias\u003c/h2\u003e \u003cp\u003eThe analysis of publication bias concerning IGF1R polymorphism and ISS yielded mixed results across genetic models. For the rs1976667 polymorphism, Begg's test showed no evidence of bias in most comparisons, with P-values of 0.734 for the A vs. G comparison and 0.734 and 0.865 for the AA vs. AG\u0026thinsp;+\u0026thinsp;GG and AG vs. GG comparisons, respectively. However, the AA vs. GG comparison had a P-value of 1.000, indicating no significant bias. Conversely, Egger's test suggested potential bias for the AG vs. GG comparison with a P-value of 0.876. Regarding rs2684788, no publication bias was detected for the A vs. G and AA vs. GG comparisons, with P-values of 0.734 and 1.000, respectively. However, the AG vs. GG comparison indicated bias in both tests with P-values of 0.089 and 0.062. The AA\u0026thinsp;+\u0026thinsp;AG vs. GG and AA vs. AG\u0026thinsp;+\u0026thinsp;GG comparisons showed no significant bias. Overall, findings suggest a low risk of publication bias across most genetic models, though caution is warranted for specific comparisons, particularly with rs2684788.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eMAF\u003c/h2\u003e \u003cp\u003eMAF is a crucial metric in genetic research, reflecting the prevalence of rare variants in a population. This meta-analysis found that the MAF for rs1976667 in healthy Chinese children ranged from 0.254 to 0.328, indicating a moderate frequency among the studied Asian populations. In contrast, the MAFs for rs2684788 varied more widely, from 0.189 to 0.500, highlighting significant differences among participants. This variation in MAF among Chinese pediatric populations regarding IGF1R polymorphisms emphasizes the underlying genetic diversity within this demographic. Such diversity can have implications for understanding disease susceptibility, as certain alleles may be linked to specific health outcomes or responses to treatment. The differences in MAF could also reflect historical population dynamics, selective pressures, or environmental influences impacting genetic variation.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe human IGF-1R gene, located at 15q26.3, spans 315 kbp and consists of 21 exons and 20 introns. It encodes an mRNA of approximately 11,242 bp, including both 5' and 3' untranslated regions (UTRs), with a coding sequence of 4,104 nucleotides that produces a protein of 1,367 amino acids [27, 28]. IGF-1R is a transmembrane multi-subunit protein tyrosine kinase receptor in the insulin receptor family, crucial for the growth hormone (GH)-IGF axis. It mediates various physiological effects, including promoting somatic cell mitosis, anti-apoptotic activities, and enhancing cell survival [29]. The growth-promoting effects of IGF-1, whether secreted, paracrine, or endocrine, depend on IGF-1R. Mutations in the IGF-1R gene can disrupt the IGF signaling pathway, leading to growth retardation and related disorders [30]. The role of IGF-1R in ISS is increasingly recognized, emphasizing its significance in the interplay between genetics and growth disorders. As a key component of the GH-IGF axis, IGF-1R significantly influences height [28].\u003c/p\u003e \u003cp\u003eNotably, the rs1976667 locus is found within the first intron of the IGF-1R gene, while rs2684788 is located in the 3' UTR, suggesting their potential roles in regulating gene expression [24]. Genetic analyses indicate that variations in the IGF1R gene significantly contribute to growth deficiencies, especially in children born small for gestational age (SGA). Specific single nucleotide polymorphisms (SNPs) in IGF1R have been linked to reduced birth length and weight, underscoring the genetic basis of observed phenotypic traits in these populations [31]. Moreover, defects in IGF1R have been observed in patients with idiopathic growth hormone deficiency who resist growth hormone treatments despite normal serum IGF-1 levels. A dose-dependent effect of IGF-1 signaling has been noted in two patient groups with different mutations; both groups have homozygous recessive IGF1 mutations that reduce IGF-1 binding affinity for IGF1R [31\u0026ndash;33]. One variant shows an affinity 90 times lower than normal, resulting in a severe phenotype, while the other variant has an affinity only 3.9 times lower, leading to a milder phenotype akin to that of a patient with a heterozygous IGF-1 deletion [31, 32]. However, research on the protein structures encoded at these loci is limited, highlighting the need for further studies to clarify their connection to genetic susceptibility to ISS. This underscores the importance of comprehensive genetic screening in children with short stature and growth concerns, as it may uncover underlying genetic anomalies overlooked by conventional diagnostic methods. A deeper understanding of IGF1R's mechanisms and its impact on growth could lead to more targeted interventions for children with ISS and related disorders.\u003c/p\u003e \u003cp\u003eOur analysis of eight case-control studies, involving 3,794 children with ISS and 3,018 controls, followed our established criteria. Four studies focused on the variant rs1976667, encompassing 2,255 cases and 1,642 controls, while the other four examined rs2684788, with 1,539 cases and 1,376 controls. Conducted in China between 2011 and 2018, all studies consistently reported no significant associations between the IGF1R polymorphisms rs1976667 and rs2684788 and ISS. Although several studies explored these associations, results were mixed. Huang et al. (2011) found no significant difference in the distribution of the rs1976667 genotype between 804 children with ISS and 575 controls, although allele A was more prevalent in the ISS group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01), suggesting a potential risk [22]. Conversely, Yu et al. (2013) reported significant associations for both SNPs in a cohort of 712 Chinese children, with p-values of 0.03636 for rs1976667 and 0.01352 for rs2684788. The G allele at rs1976667 was significantly linked to ISS susceptibility in both males (P\u0026thinsp;=\u0026thinsp;0.018) and females (P\u0026thinsp;=\u0026thinsp;0.011), indicating a G dominant inheritance pattern. A similar trend was noted for rs2684788, further correlating rs1976667 genotypes with IGF-1 standard deviation scores in females (P\u0026thinsp;=\u0026thinsp;0.006) [23]. In contrast, Yu et al. (2015) found no association between rs1976667 and ISS in 295 children with ISS and 314 controls, although significant results for rs2684788 were noted (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), following a G dominant inheritance pattern. This study also revealed a correlation between IGF-1 SDS and the rs2684788 genotype (GG\u0026thinsp;+\u0026thinsp;GA) (P\u0026thinsp;=\u0026thinsp;0.004) [26]. Lastly, Zhang et al. (2018) investigated GH-IGF-1 axis polymorphisms, including both SNPs, concluding that these variants did not significantly increase short stature susceptibility, as HWE analyses showed no notable discrepancies in the populations studied [25]. Overall, the inconsistent findings across multiple studies highlight the need for further research with larger sample sizes to clarify these associations and their functional implications in growth regulation.\u003c/p\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eClinical Implications\u003c/h2\u003e \u003cp\u003eThe findings from the meta-analysis on IGF1R polymorphisms and their association with ISS in Chinese children have significant clinical implications. Understanding the moderate MAFs of variants like rs1976667 and rs2684788 can help clinicians identify genetic predispositions to growth disorders, particularly in pediatric populations. While the current evidence shows mixed associations between these polymorphisms and ISS, the potential link between IGF1R variations and growth deficiencies underscores the importance of genetic screening in children presenting with short stature. Identifying specific genetic anomalies could lead to more personalized treatment strategies, improving management outcomes for affected individuals. Moreover, recognizing the role of IGF1R in the growth hormone-IGF axis may facilitate early interventions and tailored therapies, ultimately enhancing the quality of care for children with growth-related concerns. As research continues to evolve, integrating genetic insights into clinical practice could pave the way for advancements in diagnosing and treating growth disorders effectively.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eTo the best of our knowledge, this is the first meta-analysis on the association between IGF1R polymorphisms and ISS; however, it has several limitations that should be taken into account. 1) The geographical concentration of studies, predominantly conducted in China, may limit the generalizability of findings to other populations and ethnic groups, as variations in genetic backgrounds could influence the observed associations. 2) Language bias is a significant concern in our study, as we found our sources limited to articles published in English and Chinese. This restriction may exclude important research in other languages and critical international data that could enrich our understanding of the subject. 3) The analysis may be affected by publication bias, where studies with non-significant results are less likely to be published, skewing the overall findings. 4) Variability in study quality, sample sizes, and methodologies among the included studies can impact the robustness of the results, and the presence of heterogeneity may undermine the reliability of the conclusions drawn. 5) The focus on only two specific IGF1R polymorphisms (rs1976667 and rs2684788) may overlook other relevant polymorphisms that could also contribute to ISS. 6) Potential confounding factors, such as environmental influences, nutritional status, and socioeconomic conditions, may not have been adequately controlled in the included studies, affecting the risk of ISS. 7) The reliance on case-control study designs could introduce biases related to participant selection and recall bias, potentially compromising the validity of the reported associations. 8) Lastly, the lack of longitudinal data limits the ability to draw causal inferences regarding the relationship between IGF1R polymorphisms and ISS over time. These limitations suggest caution in interpreting the findings and underscore the need for further research across diverse populations and with consideration of additional genetic factors.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis meta-analysis finds no significant association between the IGF1R polymorphisms rs1976667 and rs2684788 and ISS in children. The results illustrate the complexity of genetic factors influencing ISS, reflecting mixed findings from prior studies. These inconsistencies highlight the need for research involving larger and more diverse populations to clarify the role of IGF-1R variants in growth disorders. Furthermore, the study's limitations, including its focus on only two polymorphisms in a Chinese population, caution against overinterpretation of these results. Future studies should address these limitations, explore a wider array of genetic factors, and include varied populations to deepen our understanding of ISS and improve personalized treatments for pediatric growth deficiencies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e There is no funding source.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest/Competing interests:\u003c/strong\u003e The authors declare that they have no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval:\u003c/strong\u003e\u0026nbsp; This article does not contain any studies with human participants or animals performed by any of the authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number:\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate:\u003c/strong\u003e Not applicable for this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Publish declaration:\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material:\u003c/strong\u003e The datasets generated during and/or analyzed during this study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u003c/strong\u003e The authors wish to extend their heartfelt appreciation to all the contributors of the articles incorporated in this meta-analysis. Their invaluable insights and efforts were crucial to the successful completion of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions:\u0026nbsp;\u003c/strong\u003eA.H., M.V., H.T., and S.A.D.: Methodology, conceptualization, investigation. R.B. and M.P.: Methodology, investigation, writing, original draft preparation. H.R. and A.Sh.: Formal analysis, investigation. A.R. and K.A.: Investigation, writing. H.N.: Investigation. M.A.: Methodology, software. S.N.: Investigation, writing. S.A.D.: Project administration. S.A.D. and H.R.: Writing, reviewing, editing.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMolecular diagnosis is an important indicator for response to growth hormone therapy in children with short stature. Clin Chim Acta. 2024;554:117779.\u003c/li\u003e\n\u003cli\u003ePolidori N, Castorani V, Mohn A, Chiarelli F. Deciphering short stature in children. 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Open access Maced J Med Sci. 2018;6:2040\u0026ndash;4.\u003c/li\u003e\n\u003cli\u003eJohnston LB, Dahlgren J, Leger J, Gelander L, Savage MO, Czernichow P, et al. Association between Insulin-Like Growth Factor I (IGF-I) Polymorphisms, Circulating IGF-I, and Pre- and Postnatal Growth in Two European Small for Gestational Age Populations. J Clin Endocrinol Metab. 2003;88:4805\u0026ndash;10.\u003c/li\u003e\n\u003cli\u003eSavage MO, Hwa V, David A, Rosenfeld RG, Metherell LA. Genetic Defects in the Growth Hormone\u0026ndash;IGF-I Axis Causing Growth Hormone Insensitivity and Impaired Linear Growth. Front Endocrinol (Lausanne). 2011;2 DEC:95.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"IGF1R, polymorphisms, idiopathic short stature, children, meta-analysis, genetic association","lastPublishedDoi":"10.21203/rs.3.rs-5865273/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5865273/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eIdiopathic short stature (ISS) poses substantial challenges in pediatric growth assessment due to its multifactorial origins. This meta-analysis explores the relationship between IGF1R polymorphisms and the risk of ISS in children.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA comprehensive literature review was performed utilizing PubMed, Web of Knowledge, and CNKI, culminating on January 1, 2025, focusing on studies published before this date. The search employed relevant keywords and MeSH terms related to ISS and genetics factors. The inclusion criteria focused on original case-control, longitudinal, or cohort studies, with no restrictions on language or publication year. Correlations were quantified as odds ratios (ORs) with 95% confidence intervals (CIs) using Comprehensive Meta-Analysis software.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eEight case-control studies comprising 3,794 children with ISS and 3,018 controls were included. Four studies examined the variant rs1976667 (2,255 cases and 1,642 controls), while the other four focused on rs2684788 (1,539 cases and 1,376 controls). All studies, conducted in China from 2011 to 2018, found no significant associations between IGF1R polymorphisms rs1976667 and rs2684788 and ISS across all five genetic models.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThis meta-analysis reveals no significant association between IGF1R rs1976667 and rs2684788 polymorphisms with ISS. However, the predominance of studies conducted in Asian populations, particularly China, may limit the generalizability of the findings to other ethnic groups.\u003c/p\u003e","manuscriptTitle":"Association of IGF1R Polymorphisms with Idiopathic Short Stature in Children: A Meta-Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-24 18:38:28","doi":"10.21203/rs.3.rs-5865273/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6d827ede-3ddd-4b0a-abc9-2d4270b24668","owner":[],"postedDate":"January 24th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-03-03T05:23:16+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-24 18:38:28","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5865273","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5865273","identity":"rs-5865273","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}