Profiling of Insulin-Like Growth Factor Binding Proteins (IGFBPs) in Obesity and Their Association With Ox-LDL and Hs-CRP in Adolescents

IGFBP7 promotes endothelial cell repair in the recovery phase of acute lung injury

IGFBP7 has been found to play an important role in inflammatory diseases, such as acute lung injury (ALI). However, the role of IGFBP7 in different stages of inflammation remains unclear. Transcriptome sequencing was used to identify the regulatory genes of IGFBP7, and endothelial IGFBP7 expression was knocked down using Aplnr-Dre mice to evaluate the endothelial proliferation capacity. The expression of proliferation-related genes was detected by Western blotting and RT-PCR assays. In the present study, we found that knockdown of IGFBP7 in endothelial cells significantly decreases the expression of endothelial cell proliferation-related genes and cell number in the recovery phase but not in the acute phase of ALI. Mechanistically, using bulk-RNA sequencing and CO-IP, we found that IGFBP7 promotes phosphorylation of FOS and subsequently up-regulates YAP1 molecules, thereby promoting endothelial cell proliferation. This study indicated that IGFBP7 has diverse roles in different stages of ALI, which extends the understanding of IGFBP7 in different stages of ALI and suggests that IGFBP7 as a potential therapeutic target in ALI needs to take into account the period specificity of ALI.

Canonical correlation analysis for multi-omics: Application to cross-cohort analysis

Integrative approaches that simultaneously model multi-omics data have gained increasing popularity because they provide holistic system biology views of multiple or all components in a biological system of interest. Canonical correlation analysis (CCA) is a correlation-based integrative method designed to extract latent features shared between multiple assays by finding the linear combinations of features-referred to as canonical variables (CVs)-within each assay that achieve maximal across-assay correlation. Although widely acknowledged as a powerful approach for multi-omics data, CCA has not been systematically applied to multi-omics data in large cohort studies, which has only recently become available. Here, we adapted sparse multiple CCA (SMCCA), a widely-used derivative of CCA, to proteomics and methylomics data from the Multi-Ethnic Study of Atherosclerosis (MESA) and Jackson Heart Study (JHS). To tackle challenges encountered when applying SMCCA to MESA and JHS, our adaptations include the incorporation of the Gram-Schmidt (GS) algorithm with SMCCA to improve orthogonality among CVs, and the development of Sparse Supervised Multiple CCA (SSMCCA) to allow supervised integration analysis for more than two assays. Effective application of SMCCA to the two real datasets reveals important findings. Applying our SMCCA-GS to MESA and JHS, we identified strong associations between blood cell counts and protein abundance, suggesting that adjustment of blood cell composition should be considered in protein-based association studies. Importantly, CVs obtained from two independent cohorts also demonstrate transferability across the cohorts. For example, proteomic CVs learned from JHS, when transferred to MESA, explain similar amounts of blood cell count phenotypic variance in MESA, explaining 39.0% ~ 50.0% variation in JHS and 38.9% ~ 49.1% in MESA. Similar transferability was observed for other omics-CV-trait pairs. This suggests that biologically meaningful and cohort-agnostic variation is captured by CVs. We anticipate that applying our SMCCA-GS and SSMCCA on various cohorts would help identify cohort-agnostic biologically meaningful relationships between multi-omics data and phenotypic traits.

Association of Body Mass Index with Insulin-like Growth Factor-1 Levels among 3227 Chinese Children Aged 2-18 Years

OBJECTIVES

Insulin-like growth factor-1 (IGF-1) levels are affected by nutritional status, yet there is limited research exploring the association between body mass index (BMI) and IGF-1 levels among children.

METHODS

This cross-sectional study included 3227 children aged 2-18 years without specific diseases, whose height, weight, and pubertal stages were measured and assessed by pediatricians. BMI standard deviation scores (BMISDS) were used to categorize children as underweight (BMISDS < -2); normal-weight (-2 ≤ BMISDS ≤ 1); overweight (1 < BMISDS ≤ 2); and obese (BMISDS > 2). Children were divided into low-level (<-0.67 SD) and nonlow-level (≥-0.67 SD) groups based on IGF-1 standard deviation scores (IGF-1SDS). The association between IGF-1 and BMI as categorical and continuous variables was explored by Binary logistic regression, the restrictive cubic spline model, and the generalized additive model. Models were adjusted by height and pubertal development. Recursive algorithm and multivariate piecewise linear regression were further utilized to assess the threshold of the smooth curve.

RESULTS

IGF-1 levels varied by BMI categories, with the highest levels observed in the overweight group. The proportion of low IGF-1 levels in underweight, normal-weight, overweight, and obese groups was 32.1%, 14.2%, 8.4%, and 6.5%, respectively. The risk odds of low IGF-1 levels in underweight children were 2.86-, 2.20-, and 2.25-fold higher than in children with normal weight before adjustment, after adjustment for height, and after adjustment for height and puberty, respectively. When analyzing the association between BMI and low IGF-1 levels, dose-response analysis demonstrated an inverted J-shaped relationship between BMISDS and low IGF-1 levels. Lower or higher BMISDS increased the odds of low IGF-1 levels, and significance was retained in underweight children but not in obese children. When BMI and IGF-1 levels were used as continuous variables, the relationship between the BMISDS and IGF-1SDS followed a nonlinear inverted U shape. IGF-1SDS increased with the increase of BMISDS (β = 0.174, 95% CI: 0.141 to 0.208, p < 0.01) when BMISDS was less than 1.71 standard deviation (SD) and decreased with the increase of BMISDS (β = -0.358, 95% CI: -0.474 to -0.241, p < 0.01) when BMISDS was greater than 1.71 SD.

CONCLUSIONS

The relationship between BMI and IGF-1 levels was found to depend on the type of variable, and extremely low or high BMI values could result in a tendency toward low IGF-1 levels, emphasizing the importance of maintaining a normal BMI range for normal IGF-1 levels.

Comprehensive Analysis of Prognostic Value and Immune Infiltration of IGFBP Family Members in Glioblastoma

Glioblastoma (GBM) is the most common primary malignant brain tumor in adults. The insulin-like growth factor-binding protein (IGFBP) family is involved in tumorigenesis and the development of multiple cancers. However, little is known about the prognostic value and regulatory mechanisms of IGFBPs in GBM. Oncomine, Gene Expression Profiling Interactive Analysis, PrognoScan, cBioPortal, LinkedOmics, TIMER, and TISIDB were used to analyze the differential expression, prognostic value, genetic alteration, biological function, and immune cell infiltration of IGFBPs in GBM. We observed that IGFBP1, IGFBP2, IGFBP3, IGFBP4, and IGFBP5 mRNA expression was significantly upregulated in patients with GBM, whereas IGFBP6 was downregulated; this difference in mRNA expression was statistically insignificant. Subsequent investigations showed that IGFBP4 and IGFBP6 mRNA levels were significantly associated with overall survival in patients with GBM. Functional Gene Ontology Annotation and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis revealed that genes coexpressed with IGFBP4 and IGFBP6 were mainly enriched in immune-related pathways. These results were validated using the TIMER and TSMIDB databases. This study demonstrated that the IGFBP family has prognostic value in patients with GBM. IGFBP4 and IGFBP6 are two members of the IGFBP family that had the highest prognostic value; thus, they have the potential to serve as survival predictors and immunotherapeutic targets in GBM.

Fasting Serum IGFBP-1 as a Marker of Insulin Resistance in Diverse School Age Groups

Introduction

The known markers of insulin resistance in obese children are well studied. However, they require serial measurements and complicated calculations. The objective is to study IGFBP-1 and its relation with other known risk measures.

Materials and Methods

The study included 98 New York City school students of diverse ethnic/racial backgrounds (57 males and 41 females), 11-15 years of age. Subjects were enrolled in a cross-sectional study, and anthropometric measures were collected. They underwent fasting intravenous glucose tolerance tests (IVGTT), and glucose, insulin, lipids, IGFBP-1, adiponectin and inflammatory markers were collected.

Results

The subjects were stratified into 3 groups based upon the BMI Z-score. Out of all the subjects, 65.3% were in the group with a BMI Z-score <1 SDS, 16.3% subjects were in the group with a BMI Z-score of 1 to 2 SDS, and 18.4% of the subjects were in the group with a BMI Z-score of more than 2 SDS. The group with a BMI Z-score of more than 2 SDS had increased waist circumference (WC), body fat, increased fasting insulin, and triglycerides (TG). This group had decreased levels of adiponectin and HDL and low IGFBP-1 as compared to the group with BMI <1 SDS. The group with a BMI Z-score of 1 to 2 SDS had a decreased level of IGFBP-1 as compared to the group with a BMI Z-score less than 1 SDS. IGFBP-1 inversely correlated with age, WC, BMI, body fat, TG, and insulin levels. IGFBP-1 positively correlated with adiponectin and HDL levels.

Conclusion

IGFBP-1 in children can identify the presence of insulin resistance in the group with BMI 1 to 2 SDS, even before the known markers of insulin resistance such as elevated triglycerides and even before decreased HDL and adiponectin levels are identified.