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

Abstract

BackgroundNon-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).MethodsQuantification 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.ResultsAfter 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.ConclusionsThese 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.Electronic supplementary materialThe online version of this article (doi:10.1186/s12920-015-0136-7) contains supplementary material, which is available to authorized users.

miRNAs Differentially Expressed by Next-Generation Sequencing in Cord Blood Buffy Coat Samples of Boys and Girls

“…Previous miRNA studies [36,85], as well as our findings reported here, indicate the importance of accounting for sex differences when analyzing miRNA data. Similar observations have been reported for other epigenetic marks including DNA methylation and histone acetylation [28,[86][87].…”

“…For example, one study found no differences by sex in miRNA expression of 108 miRNAs characterized in 20 adults [39]. In another human study, (n = 18) four miRNAs (out of 534 miRNAs) were significantly upregulated in women [38], while yet another study reported seven out of 179 candidate miRNAs to be differentially expressed by sex [36]. To our knowledge, no data are available on genomewide miRNA expression (miRNAome) differences by sex in children.…”

“…One small study (n = 18) reported 40 miRNAs that were differentially expressed by sex in the human prefrontal cortex [20]. A few studies have examined sex-related differences in profiles of circulating plasma miRNAs in adults but were limited either by small sample size or low number of miRNAs assessed [36][37][38][39]. For example, one study found no differences by sex in miRNA expression of 108 miRNAs characterized in 20 adults [39].…”

MicroRNAs for the Prediction of Early Response to Sorafenib Treatment in Human Hepatocellular Carcinoma

“…The qPCR was performed using the StepOne real-time detection system (Applied Biosystems, Foster City, Calif., USA). The expression level of five reference miRNAs abundantly detected in serum (miR-95-5p, miR-103a-3p, miR-191-5p, miR-423-3p, and miR-425-5p) was measured, and their mean value was applied as an internal control for quantification [17,18]. We applied the comparative cycle threshold method for relative quantification of miRNAs within the input according to the manufacturer's instruction.…”

Variability in, variability out: best practice recommendations to standardize pre-analytical variables in the detection of circulating and tissue microRNAs

“…Biological and environmental factors are predominantly common to both tissue and circulating miRNAs. Multiple studies have highlighted that both external and patient-intrinsic -Study design -Develop standard procedures for specimen selection and handling -Review miRNA databases to select appropriate miRNAs for study -Avoid blood cell miRNAs -Account for environmental and biological preanalytic variables with appropriate case and control subjects -Document relevant preanalytic variables and deviations from standard procedures -Extraction -Common extraction method for all specimens -Normalization -Use exogenous "spike in" miRNA or validated endogenous miRNA factors such as age, sex, race, body mass index (BMI), diet, fasting state, underlying illnesses/organ dysfunction, blood cell count, exercise, vitamin supplementation, medications, smoking, diurnal variation, chemical exposure and even altitude can influence miRNA concentrations and are often difficult to standardize [2,13,[16][17][18][19][20][21][22][23][24][25][26][27].…”