IGF2 confers genetic susceptibility to the risk of ischemic stroke with differential mRNA expression in leukocytes

doi:10.21203/rs.3.rs-4984643/v1

Background

Insulin-like growth factor 2 (IGF2), has been proven to be involved in vascular health, neuroprotection, and inflammation modulation which are relevant to the mechanism of ischemic stroke (IS). This study aimed to investigate the effect of IGF2 variants and mRNA expression on IS.

Method Three tag SNPs rs3741211, rs10770125, rs2585 were genotyped in a case-control study and two cohorts to evaluate the genetic effect on the incidence and prognosis of IS. IGF2 mRNA expression in leukocytes was measured.

Results In the case-control study, the variations of rs3741211 and rs2585 were significantly associated with the increased risk of IS and odds ratios (ORs) of dominant model were 1.130 and 1.151. The association of rs3741211 and IS remains significant in hypertension group (adjusted OR: 1.268; P = 0.003) with additive interactions of hypertension (P < 0.05). IGF2 mRNA expression of IS cases were significantly lower than controls (fold change = 0.740, P = 0.002) and there was a nonlinear relationship between IGF2 mRNA expression of IS (P < 0.05). Furthermore, the variations of rs3741211, rs10770125, and rs2585 are significantly associated with increased expression levels of IGF2 mRNA (P < 0.05). Additionally, a negative correlation was observed between IGF2 mRNA level and both National Institutes of Health Stroke Scale (NIHSS) and Modified Rankin Scale (mRS) scores in IS patients after discharge (P < 0.05).

Conclusions The study reveals that IGF2 gene is associated with the susceptibility of IS and rs3741211, rs10770125, and rs2585 may affect the expression levels of IGF2 mRNA. Furthermore, IGF2 mRNA level was downregulated in IS patients and negatively correlate with poor prognosis.

insulin-like growth factor 2

ischemic stroke

single nucleotide polymorphism

mRNA expression

Ischemic stroke (IS) is a cerebrovascular disease characterized by the interruption of cerebral blood supply due to vascular obstruction, such as blood clots or emboli, leading to severe cellular damage or death (1). Representing the majority of stroke occurrences worldwide, IS accounts for about 70–80% of all stroke cases (1, 2). Between 1990 and 2019, there was a 102.0% escalation in the prevalence of ischemic stroke, accompanied by a 143.0% surge in the associated disability-adjusted life years (DALYs) (3). Projections indicate that by 2030, the age-standardized incidence rate of IS is anticipated to climb to 89.32 per 100,000 individuals (4). Consequently, IS is considered a significant public health issue, posing a substantial challenge to global health.

The occurrence of ischemic stroke (IS) results from a complex interplay between genetics and environmental factors(5). Traditional risk factors, including atherosclerosis, hypertension, smoking, and a history of diabetes mellitus, account for a significant portion of stroke events (6). Nevertheless, previous studies have estimated that genetic factors contribute to approximately 37.9% of the risk associated with strokes (7). Therefore, it is crucial to investigate the correlation between genetic factors and IS to inform preventive and therapeutic strategies.

As a growth factor of the insulin family, insulin-like growth factor 2 (IGF2) is crucial for the maintenance of brain health by regulating the growth, survival, and differentiation of neurons (8). Despite a reduction in its systemic expression after birth, its concentration remains relatively high in the brain, emphasizing its vital role in postnatal neural functions (9). Although current evidence is insufficient to establish a direct correlation between IGF2 and the risk of IS, IGF2 may facilitate vascular remodeling and neovascularization by promoting the recruitment and integration of endothelial progenitor cells into ischemic areas (10, 11). Additionally, the anti-apoptotic properties of IGF2 play a crucial role in mitigating neuronal damage during ischemic and hypoxic events, which are commonly associated with strokes (12). In neuroinflammatory responses, the upregulation of IGF2 expression in microglia induced by lipopolysaccharides effectively protects neurons from cell death caused by cytokines such as IL-1 and IFNγ (13). Intriguingly, some studies have suggested that IGF2 may be involved in the process of atherosclerosis, a key risk factor for stroke, through the formation of foam cells (14, 15). These functions of IGF2 indicate a complex but significant relevance with the pathophysiology of stroke.

Rodríguez et al. (16) conducted a study on the haplotypes of the IGF2-INS-TH gene cluster, finding significant associations between these haplotypes and cardiovascular risk factors such as body weight, blood pressure, and plasma triglycerides. Concurrently, GU et al. (17) reported correlations between IGF2 gene polymorphisms and BMI in middle-aged white men. Additionally, evidence from another study suggests a relationship between IGF2 gene variations and increased systolic blood pressure in obese children(18). In animal models, the IGF2 pathway has been implicated in neurogenesis post-hypoxic-ischemic brain injury (12). Furthermore, it has been observed that the gradual increase in endogenous IGF2 levels contributes to the mitigation of brain damage following hemorrhagic stroke. (19).

Although current research links IGF2 with cardiovascular and neurological conditions, its specific association with IS remains unconfirmed. Therefore, our study is undertaken to investigate the genetic effects of IGF2 gene polymorphisms and mRNA expression level on IS.

2.1 Study population

In the case-control study of genetic association, 2,497 individuals with IS were recruited from multiple medical institutions, including Yixing City People's Hospital, Xuzhou Medical University Affiliated Hospital, Jurong People's Hospital, and Jiangning Hospital in Nanjing. Simultaneously, 3,135 control participants without a history of stroke were chosen from the same region, matched for age (±5 years) and sex. The inclusion and exclusion criteria are consistent with the previous study (20). The TOAST classification system delineates five distinct subcategories of ischemic stroke: Small artery atherosclerosis (SAA), Large-artery atherosclerosis (LAA), Cardioembolism (CE), Stroke with other known causes (SOE), Stroke with indeterminate etiology (SUE). Additionally, leukocyte IGF2 mRNA expression was detected in 318 IS cases and 315 controls, selected from the same or neighboring areas and closely matched in terms of sex and age (±2 years). National Institutes of Health Stroke Scale (NIHSS) and Modified Rankin Scale (mRS) scores were recorded for IS cases at discharge, as well as at 1, 3, and 6 months after discharge.

A total of 4,128 participants were recruited from May to October 2009 in Yixing City, Jiangsu, China. Among them, 4098 subjects free of stroke at baseline were included in the cohort study for stroke, and were followed up to May 25, 2022. Detailed information about this cohort has been previously published (20).

In a hospital-based cohort study, 2,298 patients with IS aged 35 to 80 years were recruited from Yixing City People's Hospital and followed up for an average of 6.49 years to track their long-term mortality outcomes. The follow-up period concluded on May 25, 2022, and the mortality outcomes included all-cause death (n=390), stroke death (n=197), IS death (n=128), and hemorrhagic stroke (HS) death (n=50).

2.2 Selection of tagSNPs

A comprehensive search was conducted for single nucleotide polymorphisms (SNPs) within a 2kb region both upstream and downstream of the IGF2 gene. TagSNPs were selected from the Han Chinese in Beijing, China (CHB) database and the International HapMAP Project for China. Criteria for tagSNP selection included a minimum allele frequency (MAF) of ≥ 0.05 and a linkage disequilibrium (LD) r² of ≥ 0.8. The Biobank Japan database was also explored for IGF2-associated positive SNPs related to IS. The Bioinformatics Effect Prediction Website (SNPinfo) was used to identify SNPs with potential functional relevance. Ultimately, three tagSNPs within the IGF2 gene were chosen: rs3741211(NC_000011.9: g.2169110A>G), rs10770125(NC_000011.9: g.2169014A>G), and rs2585(NC_000011.9: g.2150444T>C).

2.3 Blood collection and SNP genotyping

Blood samples collected from participants were stored in EDTA anticoagulation tubes for preservation. Genomic DNA extraction was performed using protein precipitation method (Eaglink, EGEN2024, China). TaqMan assay primers and probes were designed using Primer Express Oligo Design Software v2.0 (ABI PRISM). Genotyping was carried out using the 7900HT real-time PCR system (Applied Biosystems, Foster City, CA).

2.4 IGF2 mRNA measurement

The methodologies for leukocyte separation and preservation, total RNA extraction, assessment of RNA concentration and quality, and RNA reverse transcription were in line with previous research ^[20]. The internal reference gene GAPDH was selected for this study. Primer design was carried out using Primer-BLAST, and the primer sequences for the IGF2 and GAPDH genes (both forward and reverse) can be found in Supplementary Table S1. The expression of the IGF2 gene in peripheral blood leukocytes was measured using SYBR Green real-time PCR, and the relative expression level of IGF2 was calculated as 2^–ΔΔCT.

2.5 Statistical analysis

Non-normally distributed continuous variables were represented using the median [interquartile range (IQR)], and group comparisons were performed using Kruskal-Wallis or Mann-Whitney tests. Categorical variables were presented with frequencies (n) and percentages (%), and group comparisons were conducted using the chi-square test (χ²). Genotype frequencies were assessed for Hardy-Weinberg equilibrium (HWE) compliance using Fisher's exact test.

The association between IGF2 gene polymorphisms and IS was examined using odds ratios (ORs) with 95% confidence intervals (CIs) by logistic regression. Stratified analyses were conducted within case-control studies based on sex, age groups, hypertension, and diabetes. Heterogeneity was assessed using the Q test. Additive interactions were evaluated through the computation of relative excess risk due to interactions (RERI), attributable proportions (AP), and synergy index (SI) with their respective 95% CIs. Haplotype association analysis was performed to explore the relationship between haplotypes and IS. In the cohort study, hazard ratios (HRs) and their corresponding 95% CIs were calculated using Cox regression.

Spearman's rank correlation analysis was utilized to examine the relationships between IGF2 mRNA expression, mRS scores, and NIHSS scores. Furthermore, restricted cubic spline (RCS) regression analysis was employed to investigate the dose-response relationship between IGF2 mRNA levels and the likelihood of IS occurrence.

The statistical analyses were conducted using R version 4.1.3 and SPSS version 26.0 (SPSS, Inc, Chicago, IL), with statistical significance set at a two-tailed P-value < 0.05.

3.1 Characteristics of the participants

The demographic and clinical characteristics of the genetic case-control study are summarized in Table 1. Compared to the control group, the IS group had an older age (median age 67 vs. 64, P < 0.001), a higher proportion of men (59.7% vs. 38.9%, P < 0.001), and a lower proportion of drinkers (14.3% vs. 22.3%, P < 0.001). Furthermore, in the IS group, the prevalence of hypertension (68.4%), diabetes (26.0%), and dyslipidemia (7.6%) were higher compared to the control group (41.0%, 10.6%, and 4.3%, respectively; P < 0.001). Significant differences were also found in SBP, DBP, TC, HDL-C, and GLU between the IS and control groups (P < 0.01).

In the transcriptome analysis of the case-control study, there were no significant age or sex differences between the IS and control groups. However, the IS group had lower proportions of drinkers (9.7% vs. 30.5%, P < 0.001) and smokers (15.1% vs. 33.7%, P < 0.001) compared to the control group. In the IS group, the prevalence of hypertension (62.3%) and diabetes (25.2%) was higher compared to the control group (46.0% and 15.6%, respectively; P < 0.05). Compared to the control group, the IS group had higher SBP and DBP, and lower GLU, TC, and HDL-C (P < 0.05). Table 1 also provides an overview of the cohort study participants' characteristics, while Supplementary Table 2 contains details regarding the characteristics of the prospective study related to long-term mortality after an IS.

3.2 Association analysis of tagSNPs and IS in the case‐control study

The genotype distributions of the three IGF2 SNPs were consistent with Hardy-Weinberg Equilibrium (HWE) in both the IS and control groups (all P > 0.1) (Supplementary Table 3). Individuals carrying the AG/GG genotypes of IGF2 rs3741211 showed a significantly higher risk of IS (Supplementary Table 3). The OR (95%CI) for the dominant model was 1.130 (1.016-1.256, P = 0.024). The rs2585 (T > C) variant was associated with an increased risk of IS, even after covariate adjustments (Fig.1). The adjusted OR (95% CI) for the dominant model was 1.151 (1.014-1.305, P = 0.047). Additionally, the rs3741211 (A>G) and rs2585 (T>C) variants were significantly associated with an increased risk of LAA, even after covariate adjustments. The adjusted ORs (95% CIs) for the dominant models were 1.196 (1.022-1.399, P = 0.025) and 1.195 (1.007-1.418, P = 0.025), respectively (F ig.1). No significant associations were observed between the rs3741211, rs10770125, and rs2585 variants and the risk of SAA (Supplementary Table 4).

In addition, stratified analysis revealed a significant association between rs10770125 (A > G) variant and an increased risk of IS in the hypertension group, and the adjusted OR (95% CI) of additive model was 1.125 (1.003-1.262) (Supplementary Table 5, 6). Moreover, heterogeneity was observed for the association of rs3741211 with IS in subgroups of men and hypertension (I²: 64.8% and 77.2%, respectively; P values: 0.013 and 0.020, respectively), and for the association of rs10770125 with IS between age groups (I²: 71.6%; P value: 0.060).

3.3 Interaction analysis

A negative interaction between IGF2 rs3741211 and sex was detected with RERI and AP values (95% CIs) of -0.248 (-0.471, -0.225) and -0.519 (-0.977, -0.127) respectively. Additionally, a positive interaction was observed between IGF2 rs3741211 and hypertension was detected, with RERI, AP, and SI values (95% CIs) of 0.824 (0.340, 1.326), 0.234 (0.100, 0.345), and 1.485 (1.161, 1.899) respectively (Table 2).

3.4 Haplotype analyses of rs10770125–rs2585 and IS

Compared to the A-T haplotype of rs10770125 and rs2585, the haplotypes of A-C and G-T showed a significantly higher risk of IS (Table 3). The adjusted ORs (95% CIs) were 1.211 (1.064-1.378) and 1.547 (1.170-2.045) respectively. Additionally, in comparison to the A-T haplotype, the haplotypes of A-C and G-T were significantly associated with the increased risk of LAA [Adjusted ORs (95% CIs): 1.593 (1.103-2.301) and 1.259 (1.060-1.495)] while the haplotype G-T was significantly associated with the increased risk of SAA [Adjusted OR (95% CI): 1.566 (1.139-2.154)] (Table 3).

3.5 Association analysis of tagSNPs and IS incidence risk in cohort study

Over an average follow-up of 11.54 years, 314 of the 4098 (7.78%) participants had an incident stroke. The rs3741211, rs10770125, and rs2585 variants were not significantly associated with the incidence risk of IS, even after adjusting for covariates [Adjusted HRs (95%CIs) for the dominant model were 0.837(0.668-1.049), 0.868(0.697-1.082), and 0.876(0.695-1.104)] (Supplementary Tables 7 and 8). Further stratification analysis did not reveal any significant associations (Supplementary Tables 9 and 10).

3.6 Association analyses of tagSNPs and the long-term death risk after IS

Carriers of the GG genotype for IGF2 rs10770125 exhibited a significantly higher risk of HS, with an adjusted HR (95% CI) of 2.035 (1.038-3.990) in the recessive model (Supplementary Tables 11 and 12). Conversely, rs3741211 AG/GG carriers had a lower risk of stroke death compared to AA carriers, with an adjusted HR (95% CI) of 0.744 (0.559-0.990) in the dominant model (Supplementary Table 12).

3.7 Comparison of IGF2 mRNA expression level between IS cases and controls

The mRNA expression level of IGF2 was found to be significantly lower in IS patients compared to healthy controls (0.665 vs. 0.898, FC = 0.740, P = 0.002). Additionally, within the IS subtypes, LAA (0.668 vs. 0.898, FC = 0.744, P = 0.022) and SAA (0.673 vs. 0.898, FC = 0.750, P = 0.007) showed lower IGF2 mRNA expression levels when compared to the control group (Fig.2).

Significant differences in IGF2 mRNA expression levels were observed among the rs3741211 AA, AG, and GG genotypes in the IS group (P = 0.004) (Fig.3). Additionally, significant differences in IGF2 mRNA expression levels were observed among rs10770125 and rs2585 genotypes in both the control and IS groups (all P values < 0.05). Furthermore, IGF2 mRNA expression tended to increase with the rs3741211 (A > G), rs10770125 (A > G), and rs2585 (T > C) variants in the IS group (P trends were 0.040, 0.001, and < 0.001, respectively) (Supplementary Table 13).

3.8 RCS analysis of IGF2 mRNA expression level with IS

We used RCS to flexibly model and visualize the correlation between IGF2 mRNA expression level and the risk of IS. The risk of IS exhibited a gradual decrease in correlation with the increase in IGF2 mRNA expression levels, until it reached the median value of 0.769, beyond which the risk appeared to stabilize (P_nonlinear = 0.015) (Fig.4).

3.9 Correlation analysis of IGF2 mRNA expression level with NIHSS score and mRS score

Spearman correlation analysis revealed a consistent negative correlation between IGF2 mRNA expression and NIHSS scores in IS cases at 1 month (r = -0.197, P = 0.017), 3 months (r = -0.199, P = 0.016), and 6 months (r = -0.191, P = 0.022) after discharge (Fig.5). Furthermore, a significant negative correlation was observed between IGF2 mRNA expression and mRS scores in IS cases at both 3 months (r = -0.172, P = 0.038) and 6 months (r = -0.163, P = 0.049) after discharge (Fig.5).

This study observed a significant association of IGF2 variants and mRNA expression with the risk of IS. IGF2 mRNA expression level was lower in IS patients than controls, and there is a non-linear relationship between IGF2 mRNA expression level and the decreased risk of IS. Additionally, the variants rs3741211, rs10770125, and rs2585 at IGF2 influenced the expression of IGF2 mRNA. Notably, a negative correlation was observed between IGF2 mRNA expression levels and both NIHSS and mRS scores in IS patients. Thus, this study provided novel population-based evidence for the association of IGF2 variants and mRNA expression with IS odds and prognosis.

IGF2 plays a role in its anti-inflammatory function by preprogramming oxidative phosphorylation (OXPHOS) in mature macrophages, resulting in increased production of regulatory T cells (Tregs) (21). Notably, levels of circulating Tregs 48 hours post-stroke are inversely related to infarct volume (22). Under hypoxic and ischemic conditions, IGF2 promotes the migration of endothelial progenitor cells to the injury site by regulating HIF-α and vascular endothelial growth factor levels and this process ultimately leads to vascular remodeling and neovascularization, potentially aiding in the recovery after a stroke (23, 24). Zhu et al (12) found that the RBM3-IMP2-IGF2 pathway inhibits neuronal apoptosis and enhances neuronal cell proliferation after acute ischemia and hypoxia in the brain. These results support that IGF2 might participate in the pathological process of ischemic injury in IS.

Recent studies found that serum IGF2 level is related to metabolic risk factor of IS, such as blood glucose and lipid levels (25), BMI (26, 27) and weight gain in adults (28). FAIENZA et al. (18) discovered that variations in the IGF2 gene might affect SBP in obese children. These results suggest that IGF2 is relevant to the risk factors of IS and could indirectly contribute to the development of IS by influencing obesity, blood pressure, and metabolic syndrome. However, we did not find any significant association between IGF2 gene polymorphism and TC, TG, HDL-C, LDL-C, GLU, or blood pressure. This may be due to the widespread use of related medications among participants, which could have masked genetic effects. In interaction analysis, we found that the rs3741211 variant had a significant interaction with hypertension. Thus, further research on the mediate effect of these metabolic risk factors on IS would be warranted.

In our study, we observed that the rs3741211, rs10770125, and rs2585 variants were significantly associated with the risk of IS and stratified analyses showed that rs3741211 and rs2585 variants are associated with the higher risk of the LAA but not SAA. While haplotype A-C and G-T of rs10770125-rs2585 (vs. A-C haplotype) were significantly associated with an increased risk of both LAA and SAA. Previous study supports the association of IGF2 with the formation of atherosclerotic plaques the primary pathological mechanisms of LAA(29), and that with our findings together would help to well understand the molecular mechanism of LAA. In further subgroup analyses, we found that the rs3741211 variant was associated with the increased IS risk in males and had a negative interaction with females, while the rs2585 variant was associated with the increased IS risk in females. These results suggest that the association of IGF2 gene polymorphism with IS displays obvious differences. Further research would be warranted to explore these gender-specific mechanisms underline the etiology of IS and that it is necessary to control the potential bias of gender in the progress of study design and data analyses. Additionally, although previous studies found an association between IGF2 polymorphisms and blood pressure changes, we only found rs2585 associated with the risk of IS in hypertensive patients, and no significant results were observed in the heterogeneity test between subgroups. This may be due to different loci having distinct biological effects, and the impact of IGF2 variants on blood pressure may not consistently influence the risk of IS in hypertensive patients.

In further cohort studies, we did not observe any significant association between the three variants and the risk of IS. That may be due to several reasons. Firstly, we observed the significant association between rs2585 variants and the risk of IS in the case-control study with weak effect of 1.079 (P = 0.046), insufficient power resulted in this association not being validated in the cohort study. Secondly, there was no apparent effect of these variants on the probability of IS since the subjects of cohort study had a low residual risk of IS. Thirdly, the severity of IS in the cohort study's cases were milder than in the case-control study's cases since some of the cases in the cohort study were obtained from community or township hospitals, potentially affecting the results. Fourthly, some unpredictable potential biases may affect the accuracy and reliability of the observed associations. The impact of IGF2 variants may also be influenced by specific environmental or lifestyle factors not accounted for in the study. Therefore, further research based on those more homogeneous populations and taken the gene-environment interactions into consideration is needed to clarify the role of these variants played in IS incidence. Our study also found significant differences in IGF2 mRNA expression among the genotypes of rs3741211, rs10770125, and rs2585. IGF2 mRNA level increased with the variations of rs3741211, rs10770125, and rs2585 in the IS group. This finding suggests that these genotype variants may have potential biological functions in regulating IGF2 mRNA expression, and this regulatory effect is enhanced in IS patients. These mechanisms may affect mRNA stability, splicing, translation efficiency, and m6A methylation. Further research on how these variants influence IGF2 mRNA regulation is needed to elucidate the potential mechanism.

IGF2 also plays a pivotal role in protective function of post stroke. After a stroke, the antioxidative and neuroprotective effects of IGF2 mediated by IGF2R are particularly important in alleviating oxidative stress and secondary brain injury caused by mitochondrial dysfunction (30). Moreover, the role of IGF2 in promoting angiogenesis and its anti-inflammatory effects may positively influence stroke recovery (10, 31). In the middle cerebral artery occlusion (MCAO) model of stroke, post-treatment expression of IGF2 in the brain significantly increased, correlating with alleviated neurological deficits and improved cerebral blood flow (32). Direct injection of IGF2 into the ventricles significantly reduced the volume of ischemic damage (33). Similarly, injecting IGF2 into the hippocampus of rats significantly increased hippocampal volume and neuron numbers in a brain hemorrhage model(19). In this study, the IGF2 mRNA level in peripheral blood leukocytes of IS patients were lower than that of controls with a non-linear correlation with IS risk, and mRNA expression level was negatively correlated with post-discharge NIHSS and mRS scores. Therefore, IGF2 mRNA level may have predictive value in the utilization in diagnosis, treatment strategy formulation, and prognosis assessment of IS.

On the other hand, previous studies reported a negative correlation between IGF2 protein levels and mRS scores(34), and low plasma IGF2 levels in the acute phase were linked to increased post-stroke mortality, suggesting a positive role of IGF2 in stroke recovery (35). However, IS patients exhibited significantly higher plasma IGF2 protein levels during the acute phase and at a three-month follow-up compared to controls (34, 35). This observed discrepancy may be attributed to factors such as post-transcriptional regulation, compensatory responses of the body, and variances then biological sample types. Considering the crucial role of IGF2 in the recovery process of IS, this suggests that although variations in the IGF2 gene are associated with an increased risk of IS. The complete mechanism of this association is not limited solely to its effect on the expression levels of IGF2 mRNA. These might include post-transcriptional modifications and interactions with other molecular pathways.

Despite the novel findings in the present study, there are still some limitations to be clarified. First, we selected candidate SNPs in IGF2 with an MAF ≥ 0.05, while rare variations were neglected. Second, given that serum/plasma IGF2 protein level was not assessed in our study, the correlation analysis of IGF2 protein level, mRNA expression level and variations was not feasible. Therefore, further investigation would be warranted on the relationship between IGF2 mRNA and protein levels and their collective impact on stroke incidence and prognosis. Third, the association between IGF2 polymorphisms and IS found in case-control study was not replicated in cohort study, which might be caused susceptible bias in the cohort with reduced remain risk of IS and differential subtypes in cohort different from IS cases collected in hospital. Furthermore, potential information bias and rest confounding bias may be existed in this study. However, to our knowledge, this study is the first to investigate the relationship between IGF2 and IS from genomic and transcriptomic perspectives. Our findings would help provide a prior basis for further research on the relationship between IGFs and the development and prognosis of IS.

In conclusion, our study reveals a significant association between IGF2 polymorphisms with the risk and prognosis of IS and suggests that rs3741211, rs10770125, and rs2585 may affect the expression levels of IGF2 mRNA. Furthermore, IGF2 mRNA level of peripheral blood leukocytes was downregulated in IS patients and negatively correlated with poor prognosis.

5 Funding

This work was supported by the National Natural Science Foundation of China (81874280, 81673266, 82173611) and the Natural Science Major Research Project of Anhui Educational Committee (NO.KJ2021ZD0098).

6 Competing Interests

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

7 Authors' contributions

Chong Shen and Yingshui Yao conceptualized and designed the study and edited and proofed the manuscript. Haining Xie conducted data analysis, drafted the manuscript, and interpreted the research results. Changying Chen conducted experiments, participated in data organization and proofed the manuscript. Xu Han conducted data analysis, contributing to the robustness of the results and proofed the manuscript. Jing Wang, Nan Wu, and Mengxue Du performed the experimental work. Zhanyun Ren, Jie Li, Shiyuan Gu collected the samples and collected the data. Yuelong Jin, Yan Chen, Lijun Zhu, Zhengmei Fang and LW proofed the manuscript. All authors consent to take responsibility for the content of the work.

8 Availability of data and materials

The data supporting the findings of this study are provided within the article or attachment. For further inquiries or more detailed information, please do not hesitate to contact the corresponding author.

9 Ethics approval and consent to participate

The research protocol received approval from the Ethics Committee of Nanjing Medical University (#200803307 and #2018571). All study participants received comprehensive information about the research and voluntarily signed informed consent forms.

10 Acknowledgments

We would like to thank the collaborators worked at Xuzhou Medical University Affiliated Hospital, Jurong People's Hospital, Jiangning Hospital, and Jurong Center for Disease Control and Prevention for helping to collect the data of Jurong cohort.

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Table1. Demographic and clinical characteristics of study population

Characteristics	Group	Case-control study				Transcriptome case-control study				Population cohort study (n=4080)
Characteristics	Group	Control(n=3135)	IS(n=2497)	Z/χ²	P	Control(n=315)	IS(n=318)	Z/χ²	P	Population cohort study (n=4080)
Age (year)		64(58,71)	67(59,74)	6.88	<0.001^a	67(57,74)	68(57,74)	0.155	0.877^a	59(53,67)
Sex [n (%)]	Men	1219(38.9)	1491(59.7)	241.528	<0.001^b	182(57.8)	182(57.2)	0.019	0.890^b	1653(40.5)
	Women	1916(61.1)	1006(40.3)			133(42.2)	136(42.8)			2427(59.5)
SBP (mmHg)		140(128,153)	150(137,162)	16.53	<0.001^a	145(132,158)	153(137,168)	3.793	<0.001^a	134(123,141)
DBP (mmHg)		82(75,88)	89(80,96)	17.259	<0.001^a	83(75,90)	85(77,95)	2.543	0.011^a	82(78,89)
GLU (mmol/L)		5.36(4.89,5.98)	4.87(4.34,5.44)	21.007	<0.001^a	5.92(5.35,6.71)	5.51(4.88,6.36)	4.794	<0.001^a	5.28(4.85,5.80)
TC (mmol/L)		4.76(4.17,5.38)	4.57(3.91,5.34)	6.26	<0.001^a	4.76(4.16,5.32)	4.49(3.81,5.15)	3.659	<0.001^a	4.80(4.22,5.45)
TG (mmol/L)		1.36(0.95,2.01)	1.35(0.94,1.98)	0.854	0.393^a	1.29(0.93,1.85)	1.36(1.01,1.84)	0.713	0.476^a	1.32(0.90,2.00)
HDL-C (mmol/L)		1.28(1.07,1.53)	1.13(0.96,1.34)	17.156	<0.001^a	1.33(1.08,1.58)	1.12(0.94,1.31)	7.329	<0.001^a	1.33(1.13,1.55)
LDL-C (mmol/L)		2.79(2.31,3.31)	2.67(2.10,3.22)	3.07	<0.001^a	2.68(2.15,3.08)	2.7(2.16,3.24)	0.984	0.325^a	2.65(2.20,3.11)
Smoking [n (%)]	No	2376（75.8）	1928（77.2）	1.562	0.218^b	209(66.3)	270(84.9)	29.599	<0.001^b	3090(75.7)
	Yes	759（24.2）	569（22.8）			106(33.7)	48(15.1)			990(24.3)
Drinking [n (%)]	No	2437（77.7）	2138（85.7）	57.34	<0.001^b	219(69.5)	287(90.3)	42.393	<0.001^b	3201(78.5)
	Yes	698（22.3）	358（14.3）			96(30.5)	31(9.7)			879(21.5)
Hypertension [n(%)]	No	1851（59.0）	790（31.6）	419.176	<0.001^b	170(54.0)	120(37.7)	16.796	<0.001^b	2099(51.6)
	Yes	1284（41.0）	1707（68.4）			145(46.0)	198(62.3)			1981(48.4)
Diabetes [n (%)]	No	2803(89.4)	1847(74.0)	230.204	<0.001^b	266(84.4)	238(74.8)	8.991	0.003^b	3616(88.6)
	Yes	332(10.6)	650(26.0)			49(15.6)	80(25.2)			464(12.3)
Dyslipidemia [n (%)]	No	3001(95.7)	2308(92.4)	27.91	<0.001^b	306(97.1)	308(96.9)	0.045	0.832^b	1633(40.1)
	Yes	134(4.3)	189(7.6)			9(2.9)	10(3.1)			2447(59.9)

^a Mann-Whitney U test; ^b χ² test; IS, ischemic stroke; SBP, systolic blood pressure; DBP, diastolic blood pressure; GLU, glucose; TC, total cholesterol; TG, triglyceride; HDL-C, high density lipoprotein-cholesterol; LDL-C, low density lipoprotein-cholesterol.

Table 2. Additive interaction analysis of age group, sex, hypertension and IGF2 polymorphisms with the risk of IS

Interactive item	Estimates (95% CI)	Model 1	Model 2
Sex * rs3741211	RERI (95% CI)	-0.248(-0.471, -0.025)	-0.326(-0.824, 0.025) ^a
	AP (95% CI)	-0.519(-0.977, -0.127)	-0.843(-2.121, 0.081) ^a
Hypertension * rs3741211	RERI (95% CI)	0.824(0.340, 1.326)	0.826(-0.002, 1.745) ^b
	AP (95% CI)	0.234(0.100, 0.345)	0.278(-0.014, 0.474) ^b
	SI (95% CI)	1.485(1.161, 1.899)	1.721(0.956, 3.097) ^b
Age group * rs10770125	RERI (95% CI)	0.069(-0.205, 0.176)	0.045(-0.301, 0.159) ^c
	AP (95% CI)	0.046(-0.055, 0.187)	0.037(-0.118, 0.258) ^c
	SI (95% CI)	1.163(0.721, 1.875)	1.266(0.251, 6.395) ^c

Model 1, unadjusted, Model 2, adjusted for covariates.^a adjusted for age, drinking, smoking, hypertension, dyslipidemia, and diabetes. ^b adjusted for sex, age, drinking, smoking, dyslipidemia, and diabetes. ^c adjusted sex, drinking, smoking, hypertension, dyslipidemia, and diabetes.

Table 3. Frequencies of haplotypes rs10770125-rs2585 and their association analysis with TOAST subtypes of IS

TOAST subtype	Haplotype	All (n=5632)	Control (n=3135)	Cases (n=2497)	OR (95% CI)	P	OR (95% CI) ^b	P ^b
IS (n=2497)	A-T ^a	0.528	0.512	0.542	Reference	-	Reference	-
	G-C	0.320	0.32	0.319	1.063(0.977-1.155)	0.768	1.073(0.978-1.176)	0.596
	A-C	0.127	0.137	0.119	1.219(1.083-1.371)	0.01	1.211(1.064-1.378)	0.025
	G-T	0.025	0.031	0.02	1.614(1.250-2.083)	0.001	1.547(1.170-2.045)	0.009
LAA(n=1005)	A-T ^a	0.535	0.541	0.514	Reference	-	Reference	-
	G-C	0.318	0.320	0.314	1.044(0.932-1.169)	0.809	1.074(0.948-1.217)	0.812
	A-C	0.022	0.020	0.030	1.544(1.109-2.150)	0.030	1.593(1.103-2.301)	0.039
	G-T	0.125	0.119	0.142	1.255(1.074,1.167)	0.013	1.259(1.060-1.495)	0.032
SAA(n=1353)	A-T ^a	0.533	0.541	0.515	Reference	-	Reference	-
	G-C	0.320	0.320	0.319	1.053(0.952-1.166)	0.854	1.066(0.955-1.190)	0.636
	A-C	0.124	0.119	0.134	1.179(1.022-1.358)	0.090	1.163(0.997-1.358)	0.170
	G-T	0.023	0.020	0.032	1.643(1.226-2.200)	0.018	1.566(1.139-2.154)	0.014

^a A-T was selected as the reference. ^b Adjusted for sex, age, drinking, smoking, hypertension, dyslipidemia, and diabetes.

No competing interests reported.

SupplementaryMaterial.xlsx

IGF2 confers genetic susceptibility to the risk of ischemic stroke with differential mRNA expression in leukocytes

Status:

Version 1

Abstract

Figures

1 Background

2 Methods

2.1 Study population

2.2 Selection of tagSNPs

2.3 Blood collection and SNP genotyping

2.4 IGF2 mRNA measurement

2.5 Statistical analysis

3 Results

3.1 Characteristics of the participants

3.2 Association analysis of tagSNPs and IS in the case‐control study

3.3 Interaction analysis

3.4 Haplotype analyses of rs10770125–rs2585 and IS

3.5 Association analysis of tagSNPs and IS incidence risk in cohort study

3.6 Association analyses of tagSNPs and the long-term death risk after IS

3.7 Comparison of IGF2 mRNA expression level between IS cases and controls

3.8 RCS analysis of IGF2 mRNA expression level with IS

3.9 Correlation analysis of IGF2 mRNA expression level with NIHSS score and mRS score

4 Discussion

Declarations

References

Tables

Additional Declarations

Supplementary Files

Status:

Version 1