Epidemiological studies have established that obesity and diabetes are associated with markedly decreased concentrations of N-terminal proANP (NT-proANP) compared to normal body weight as defined by body mass index (BMI) . The bioactive region of proANP, e.g. the C-terminal ANP-28, is known to be implicated in a range of physiological effects in terms of cardiac remodeling and metabolic control . Notably, the increased risk of cardiovascular disease among obese individuals might be partly explained by the relative ANP deficiency seen in obesity, and decreased MR-proANP concentrations have even been suggested to predict the development of type 2 diabetes .
The main diagnostic interest for proANP and ANP plasma measurement has so far been to quantitate increased concentrations associated with cardiac disease , . However, in the light of the potential importance of reduced proANP concentrations in metabolic disease, there is a need for optimizing methods to accurately reflect changes also in the low concentration range. This new need is underscored by the fact that coexistence of cardiac and metabolic disease is frequent  and therefore might interfere with the diagnostic usefulness of atrial and b-type natriuretic peptides in heart failure assessment.
The objective of the present study was partly to optimize our previously reported processing-independent assay (PIA) for proANP  and calculate gender- and age-specific reference intervals using established reference material from the Scandinavian countries , . Notably, this type of translational analysis quantitates the total amount of the proANP-derived peptide products irrespective of modifications, i.e. all processing products are measured as one quantity in the form of a trypsin-generated fragment (proANP 1–16). For each of these in vitro generated fragments, one ANP mRNA to propeptide translation can be determined. In this way, we are able to report on cardiac expression and secretion without analytical bias from possible modifications and cleavages . Furthermore, we wanted to examine the effect of BMI and fasting plasma glucose on proANP concentrations in this largely non-obese population by simple and multiple regression analyses. In this regard, an impact of glucose concentrations was suggested in a recent report that provided mechanistic insight into the effect of glucose concentrations on cardiac ANP gene expression .
Materials and methods
The PIA technology has previously been extensively reported . Briefly, plasma is subjected to treatment with a protease, here trypsin, to enzymatically release a defined fragment from the precursor. This fragment is carefully chosen to be without post-translational modifications or endoproteolytic cleavage sites, where after the fragment is quantified with a conventional immunoassay . For the present proANP method, we used the setup as reported with two major changes: (a) we increased the amount of radioactive tracer from 1000 to 10,000 cpm per test tube, and (b) we changed the mixture for the preanalytical trypsin treatment after the ethanol extraction to one part resuspended plasma extract to one part buffer containing trypsin (concentration). The limit of blank (LoB) was calculated as the mean binding of ten blank replicates+1.645 standard deviations of blanks, limit of detection (LoD) as LoB+1.645 standard deviations of 50-pmol samples and limit of quantification (LoQ) as LoQ≥LoD  Imprecision was calculated from samples with different concentrations of proANP covering the calibration curve.
Plasma samples and subject characteristics
We used plasma samples obtained in the Nordic Reference Interval Project Biobank and Database (NOBIDA) . From this material originally consisting of 3002 subjects, plasma from 853 subjects was randomly selected and sought to represent country, gender, and age equally. Due to missing values on all body size parameters and plasma glucose or problems with the proANP analysis, 160 subjects were excluded, and thus 693 subjects (337 men; 356 women) were included in the study. Table 1 shows the clinical and subject characteristics of our study sample and the NOBIDA population. Data on individual gender, age, body height, body weight, plasma glucose in fasting subjects, and proANP concentrations were applied in statistical analyses. BMI was calculated as body weight in kilograms divided by height in meters squared. Subjects are referred to as normal weight, overweight and obese based on a BMI of <25, 25–30, and >30 kg/m2, respectively.
The subjects were divided into sex-specific age groups of <50, 50–70, and >70 years. This division was chosen from an earlier population-based study on pro-B type natriuretic peptide (proBNP) . We visually inspected the distribution pattern of sex- and age-specific proANP concentrations (shown in Figure 1) and excluded four outliers from this evaluation , two men aged 27 (2340 pmol/L) and 76 years (1906 pmol/L) and two women aged 34 (1114 pmol/L) and 79 years (2396 pmol/L). Two subjects were removed from the height analysis, two from the weight analyses, and four from the BMI analysis due to missing information. Thirty-four subjects were removed from the plasma glucose analysis (14 subjects were non-fasting and 20 had missing values). The RefVal software  based on recommendations of the International Federation of Clinical Chemistry and Laboratory Medicine was used to calculate the 95% reference intervals using the non-parametric bootstrap method. Non-parametric independent tests for comparison of gender-specific median proANP-values in all three age groups and multiple regression analyses for prediction of proANP concentrations were run using SPSS 22. For the comparison between plasma proANP concentrations and body weight, body height, BMI, and fasting plasma glucose concentrations, scatterplots and linear regression analyses were performed by GraphPad Prism 4. A p-value <0.05 was considered statistically significant.
Assay precision was improved considerably by the new setup for the PIA assay. The interassay imprecision coefficients of variation over the lower part of the calibration curve were 3.5% for 50 pmol/L, 4.1% for 100 pmol/L, 4.6% for 250 pmol/L, 6.2% for 500 pmol/L and 10.2% for 1000 pmol/L. Total imprecision was chosen from these data, and accept limit was set to <10%. LoB was 16.9 pmol/L, LoD was 58.6 pmol/L and LoQ was ≥58.6 pmol/L.
The number of subjects and calculated reference intervals including median and ranges for the gender-specific age-groups are shown in the Table 2. Statistical tests of differences of median plasma proANP concentrations are shown in Table 3. Women <50 years displayed significantly higher median proANP concentrations compared to men of the same age group, whereas no differences were observed among 50–70 years and >70 years. No significant association was observed between proANP and BMI or between proANP and plasma glucose. Statistical tests for correlation between phenotype parameters are shown in Table 3. A modest but significant negative correlation is found between proANP concentrations and both height and weight. When gender-specific correlations of proANP and height and weight, respectively, were analyzed, correlations proved insignificant. Two multiple regression analyses with proANP as dependent variable and age, sex, plasma glucose, height and weight as independent variables is shown in Table 3. These results show that the addition of plasma glucose, height and weight to age and sex as independent variables contributes modestly to the explanation of the variation of proANP concentrations in our study population (25% vs. 21%).
In the present report, we show that proANP concentrations in plasma are not associated with BMI and plasma glucose concentrations, respectively. Also, height and weight have no significant correlation with proANP concentrations in sex-specific analyses. These results are supported by the multiple regression analyses, where the investigated parameters (plasma glucose, height and weight) do not increase the explanatory potential substantially on top of the previously established effects of age and sex.
A negative association between BMI and proANP concentrations in plasma has been described previously . However, the NOBIDA population used in this study differs from the general populations in previous studies in several ways. The NOBIDA population was included based on health criteria, where subjects were excluded for known diabetes or fasting plasma glucose values above levels of diagnostic values for diabetes. In addition, the majority of NOBIDA subjects did not smoke, had no chronic disease, did not receive medication, and had no heredity history of diabetes . As shown in Figure 1, only a small fraction of NOBIDA subjects were obese in terms of BMI. It is therefore a selected population and does not reflect the general Scandinavian population. In this perspective, our results show that, in generally healthy subjects, overweight (until a BMI of 30) per se is not associated with a decrease in proANP concentrations, whereas overweight subjects in non-selected western populations with a higher prevalence of lifestyle risk factors do display decreased proANP concentrations. Taken together, the present results imply that BMI is not the single factor causing a relatively reduced proANP in overweight (BMI <30), but suggest that other factors related to overweight and obesity may be involved.
The individual decrease in the proANP concentration in plasma recently described in relation to an acute rise in plasma glucose  cannot be directly translated into our observational study with a non-diabetic range of fasting plasma glucose concentrations. However, among healthy subjects, fasting plasma glucose does apparently not affect the proANP plasma concentration. Whether plasma glucose serves as a predictor of relative proANP deficiency among patients with manifest diabetes still needs to be clarified.
In a comparison of gender in age groups, the median values of proANP concentrations are only significantly higher in women <50 years. Higher concentrations of mid-regional (MR) proANP concentrations in women have been reported previously , , . The mixed gender-effect on proANP concentrations observed in our study might be partly explained by the lower number of subjects in the age groups of 50–70 and >70 years, which decreases the power of the statistical analyses. Moreover, age may affect proANP concentrations in men and women differently, where the effect of menopause seems particularly relevant .
A main advantage of the present study is the optimized PIA method for accurate measurement of plasma proANP. Based on the processing-independent principle, this method comes closer to determine the total translational expression of ANP than assays targeted ANP (a labile analyte), and the method has proven to determine reliable proANP concentrations. Besides, the optimization of the lower concentration range of plasma proANP makes measurement more precise than assays developed to investigate patients with heart failure. Our study design benefits from the inclusion of a largely non-obese and non-smoking population with a low frequency of chronic disease and heredity of diabetes. When these apparent confounders are reduced, the isolated effects of BMI and plasma glucose on proANP values can be interpreted on a more reliable basis.
A limitation to this study is the non-diabetic range of fasting plasma glucose values. Any association between elevated values of fasting glucose among patients with diabetes and proANP expression can therefore not be assessed in this study. Moreover, our study is limited by the paucity of individual demographic and clinical characteristics. The minor proportion of our study population with chronic disease or tobacco consumption can therefore not be excluded and exact comparisons of these characteristics to the characteristics of other reference materials are hampered. Lastly, the number of available samples of subjects >70 years in the NOBIDA population was limited and their reference intervals should be treated with some caution. We, therefore, consider the results as preliminary and should be corroborated in a larger cohort.
In conclusion, we report gender- and age-specific reference intervals for proANP in plasma measured with an optimized PIA for the lower concentration range. In the examined healthy, non-obese population, no association between proANP and BMI and fasting plasma glucose was found. It should therefore be stressed that optimized methods for quantitating low proANP concentrations should be used when performing studies on proANP concentrations in the lower concentration range.
We are grateful to laboratory technician Anne Truesen Asanovski for her expertise regarding assay techniques.
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About the article
Published Online: 2017-07-08
Published in Print: 2017-11-27
Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.
Research funding: An unrestricted research grant from the Novo Nordisk Foundation.
Employment or leadership: None declared.
Honorarium: None declared.
Competing interests: The funding organization(s) played no role in the study design; in the collection, analysis, and interpretation of data; in the writing of the report; or in the decision to submit the report for publication.