Associations of circulating plasma microRNAs with age, body mass index and sex in a population-based study

Abstract

Background: Non-cellular blood circulating microRNAs (plasma miRNAs) represent a promising source for the development of prognostic and diagnostic tools owing to their minimally invasive sampling, high stability, and simple quantification by standard techniques such as RT-qPCR. So far, the majority of association studies involving plasma miRNAs were disease-specific case-control analyses. In contrast, in the present study, plasma miRNAs were analysed in a sample of 372 individuals from a population-based cohort study, the Study of Health in Pomerania (SHIP).

Methods: Quantification of miRNA levels was performed by RT-qPCR using the Exiqon Serum/Plasma Focus microRNA PCR Panel V3.M covering 179 different miRNAs. Of these, 155 were included in our analyses after quality-control. Associations between plasma miRNAs and the phenotypes age, body mass index (BMI), and sex were assessed via a two-step linear regression approach per miRNA. The first step regressed out the technical parameters and the second step determined the remaining associations between the respective plasma miRNA and the phenotypes of interest.

Results: After regressing out technical parameters and adjusting for the respective other two phenotypes, 7, 15, and 35 plasma miRNAs were significantly (q < 0.05) associated with age, BMI, and sex, respectively. Additional adjustment for the blood cell parameters identified 12 and 19 miRNAs to be significantly associated with age and BMI, respectively. Most of the BMI-associated miRNAs likely originate from liver. Sex-associated differences in miRNA levels were largely determined by differences in blood cell parameters. Thus, only 7 as compared to originally 35 sex-associated miRNAs displayed sex-specific differences after adjustment for blood cell parameters.

Conclusions: These findings emphasize that circulating miRNAs are strongly impacted by age, BMI, and sex. Hence, these parameters should be considered as covariates in association studies based on plasma miRNA levels. The established experimental and computational workflow can now be used in future screening studies to determine associations of plasma miRNAs with defined disease phenotypes.

Fig. 1

Association q-values of miRNAs in two-step regression models with adjustment for technical and biological parameters. The -log₁₀(q) values of the linear regression analysis of miRNA levels and phenotypes age (blue rectangle), BMI (green triangle) and sex (red circle) are depicted. Q-values were obtained via Benjamini-Hochberg (BH) multiple testing correction of raw p-values. The dotted line marks the significance threshold of q = 0.05. Plasma miRNAs are lexicographically arranged on the x-axis (though not labelled individually)

Fig. 2

Associations of miRNAs and direction of effect. The effect direction is shown for each miRNA significantly associated with age, BMI and sex after adjustment for a) technical covariates and the respective other two phenotypes and b) after additional adjustment for blood composition. Each row represents a miRNA and each column shows the association with a specific phenotype. The magnitude of the Benjamini-Hochberg corrected p-values (q-values) is indicated by the colour tone. Darker colour indicates a lower q-value. Positive correlations of miRNA levels (β_∆Ct < 0, since smaller ∆Ct values indicate higher miRNA levels) are indicated by red upward triangles while negative correlations (β_∆Ct > 0, since larger ∆Ct values indicate lower miRNA levels) are indicated by blue downward triangles. Sex has been numerically encoded as the number of X-chromosomes. Hence, positive correlation here indicates a female-specific miRNA while negative correlation indicates a male-specific miRNA. A grey dot indicates no significant association

Fig. 3

Overlap of associations of miRNAs for different phenotypes. Venn diagram of miRNAs significantly (q < 0.05) associated with the three phenotypes age, BMI, and sex, in two-step regression models incorporating technical and biological parameters

Fig. 4

Overlap of associations of miRNAs for different phenotypes after adjustment for blood cell parameters. Overlap of miRNAs significantly (q < 0.05) associated with the three phenotypes age, BMI, and sex in two-step regression models incorporating technical parameters, all three phenotypes, and blood cell parameters