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Additional file 1: Supplementary Information. Supplementary Table 1. Primer sequences for mitochondrial DNA qPCR. Supplementary Table 2. Inflammatory proteins detected in plasma EVs. Supplementary Table 3. Significant interactions of EV inflammatory proteins. Supplementary Figure 1. Schematic workflow and primer design for quantifying EV mtDNA levels. Supplementary Figure 2. Positive correlation between EV mtDNA levels.

Additional file 1: Figure S1 Cell area of endothelial cells treated with EVs from euglycemic or diabetic individuals.

Figure S1. Comparison of the performances of different DNA methylation data normalization and preprocessing methods: (a) correlation between technical replicates; (b) probe variance between technical replicates. (PPTX 100 kb)

Figure S2. Scatter plots and Pearsonâ s correlation coefficients of methylation M values of top ten age-associated differentially methylated CpG positions (aDMPs) and chronological age in (a) African Americans and (b) whites. (PPTX 121 kb)

Background: Body mass index (BMI) based on self-reported height and weight has been criticized as being biased because of an observed tendency for overweight and obese people to overestimate height and underestimate weight, resulting in higher misclassification for these groups. We examined the validity of BMI based on self-reported values in a sample of Norwegian women aged 44–64 years. Methods: The study sample of 1,837 participants in the Norwegian Women and Cancer study self-reported height and weight, and then, within 1 year, either self-reported anthropometric again, or were measured by medical staff. Demographic and anthropometric were compared using t-tests and chi-square tests of independence. Misclassification of BMI categories was assessed by weighted Cohen’s kappa and Bland–Altman plot. Results: On average, the two measurements were taken 8 months apart, and self-reported weight increased by 0.6 kg (P,0.05), and BMI by 0.2 kg/m2 (P,0.05). The distribution of BMI categories did not differ between self-reported and measured values. There was substantial agreement between self-reported values and those measured by medical staff (weighted kappa 0.73). Under-reporting resulting in misclassification of BMI category was most common among overweight women (36%), but the highest proportion of extreme under-reporting was found in obese women (18% outside the 95% limits of agreement). The cumulative distribution curves for the measured and self-reported values closely followed each other, but measurements by medical staff were shifted slightly toward higher BMI values. Conclusion: While there was substantial agreement between self-reported and measured BMI values, there was small but statistically significant under-reporting of weight and thus self-reported BMI. The tendency to under-report was largest among overweight women, while the largest degree of under-reporting was found in the obese group. Self-reported weight and height provide a valid ranking of BMI for middle-aged Norwegian women. Keywords: body mass index, Norway, repeated measurements, self-report, validity, women © 2015 Skeie et al. This work is published by Dove Medical Press Limited, and licensed under Creative Commons Attribution – Non Commercial (unported, v3.0) License. The full terms of the License are available at http://creativecommons.org/licenses/by-nc/3.0/. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. Permissions beyond the scope of the License are administered by Dove Medical Press Limited. Information on how to request permission may be found at: http://www.dovepress.com/permissions.php

The key goal of metabolomic studies is to identify relevant individual biomarkers or composite metabolic patterns associated with particular disease status or patho-physiological conditions. There are currently very few approaches to evaluate the variability of metabolomic data in terms of characteristics of individuals or aspects pertaining to technical processing. To address this issue, a method was developed to identify and quantify the contribution of relevant sources of variation in metabolomic data prior to investigation of etiological hypotheses. The Principal Component Partial R-square (PC-PR2) method combines features of principal component and of multivariable linear regression analyses. Within the European Prospective Investigation into Cancer and nutrition (EPIC), metabolic profiles were determined by H-1 NMR analysis on 807 serum samples originating from a nested liver cancer case-control study. PC-PR2 was used to quantify the variability of metabolomic profiles in terms of study subjects age, sex, body mass index, country of origin, smoking status, diabetes and fasting status, as well as factors related to sample processing. PC-PR2 enables the evaluation of important sources of variations in metabolomic studies within large-scale epidemiological investigations.

Background: There is increasing ambiguity towards national mammographic screening programs due to varying publicized estimates of overdiagnosis, i.e., breast cancer that would not have been diagnosed in the women’s lifetime outside screening. This analysis compares the cumulative incidence of breast cancer in screened and unscreened women in Norway from the start of the fully implemented Norwegian Breast Cancer Screening Program (NBCSP) in 2005. Methods: Subjects were 53 363 women in the Norwegian Women and Cancer (NOWAC) study, aged 52–79 years, with follow-up through 2010. Mammogram and breast cancer risk factor information were taken from the most recent questionnaire (2002–07) before the start of individual follow-up. The analysis differentiated screening into incidence (52–69 years) and post screening (70–79 years). Relative risks (RR) were estimated by Poisson regression. Results: The analysis failed to detect a significantly increased cumulative incidence rate in screened versus other women 52–79 years. RR of breast cancer among women outside the NBCSP, the “control group”, was non-significantly reduced by 7% (RR = 0∙93; 95% confidence interval 0∙79 to 1∙10) compared to those in the program. The RR was attenuated when adjusted for risk factors; RRadj = 0∙97 (0∙82 to 1∙15). The control group consisted of two subpopulations, those who only had a mammogram outside the program (RRadj =1∙04; 0∙86 to 1∙26) and those who never had a mammogram (RRadj= 0∙77; 0∙59 to 1∙01). These groups differed significantly with respect to risk factors for breast cancer, partly as a consequence of the prescription rules for hormone therapy which indicate a mammogram. Conclusions: In the fully implemented NBCSP, no significant difference was found in cumulative incidence rates of breast cancer between NOWAC women screened and not screened. Naïve comparisons of screened and unscreened women may be affected by important differences in risk factors. The current challenge for the screening program is to improve the diagnostics used at prevalence screenings (ages 50–51).