van der Linden et al. Clinical Epigenetics (2022) 14:159 https://doi.org/10.1186/s13148-022-01378-5 RESEARCH Open Access An explorative epigenome-wide association study of plasma renin and aldosterone concentration in a Ghanaian population: the RODAM study Eva L. van der Linden1,2*, Adrienne Halley1, Karlijn A. C. Meeks1,3, Felix Chilunga1, Charles Hayfron‑Benjamin4,5, Andrea Venema6, Ingrid M. Garrelds7, A. H. Jan Danser7, Bert‑Jan van den Born1,2, Peter Henneman6† and Charles Agyemang1† Abstract Background: The epigenetic regulation of the renin–angiotensin–aldosterone system (RAAS) potentially plays a role in the pathophysiology underlying the high burden of hypertension in sub‑Saharan Africans (SSA). Here we report the first epigenome‑wide association study (EWAS) of plasma renin and aldosterone concentrations and the aldosterone‑ to‑renin ratio (ARR). Methods: Epigenome‑wide DNA methylation was measured using the Illumina 450K array on whole blood samples of 68 Ghanaians. Differentially methylated positions (DMPs) were assessed for plasma renin concentration, aldos‑ terone, and ARR using linear regression models adjusted for age, sex, body mass index, diabetes mellitus, hyperten‑ sion, and technical covariates. Additionally, we extracted methylation loci previously associated with hypertension, kidney function, or that were annotated to RAAS‑related genes and associated these with renin and aldosterone concentration. Results: We identified one DMP for renin, ten DMPs for aldosterone, and one DMP associated with ARR. Top DMPs were annotated to the PTPRN2, SKIL, and KCNT1 genes, which have been reported in relation to cardiometabolic risk factors, atherosclerosis, and sodium‑potassium handling. Moreover, EWAS loci previously associated with hyperten‑ sion, kidney function, or RAAS‑related genes were also associated with renin, aldosterone, and ARR. Conclusion: In this first EWAS on RAAS hormones, we identified DMPs associated with renin, aldosterone, and ARR in a SSA population. These findings are a first step in understanding the role of DNA methylation in regulation of the RAAS in general and in a SSA population specifically. Replication and translational studies are needed to establish the role of these DMPs in the hypertension burden in SSA populations. †Peter Henneman and Charles Agyemang contributed equally to this work *Correspondence: e.l.vanderlinden@amsterdamumc.nl 1 Department of Public and Occupational Health, Amsterdam UMC, University of Amsterdam, Amsterdam Public Health Research Institute, Location AMC, Meibergdreef 9, 1105AZ Amsterdam, The Netherlands Full list of author information is available at the end of the article © The Author(s) 2022. 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The Creative Commons Public Domain Dedication waiver (http:// creat iveco mmons. org/ publi cdoma in/ zero/1. 0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. van der Linden et al. Clinical Epigenetics (2022) 14:159 Page 2 of 11 Keywords: DNA methylation, Epigenome‑wide association study, Renin, Aldosterone, RAAS, Hypertension, Sub‑ Saharan Africa, RODAM Background RODAM study have been published before [14] and will Sub-Saharan African (SSA) origin populations face a be briefly described here. higher burden of hypertension compared with other ethnic In the period 2012–2015, 6385 Ghanaian men and groups [1], and African migrants in Europe have a much women were recruited. The participants resided in rural higher prevalence of hypertension than their non-migrat- Ghana, urban Ghana, and in the European cities of Lon- ing counterparts in Africa [2]. Salt sensitivity (SS) is one of don, Amsterdam, and Berlin. The majority of participants the best studied pathophysiological mechanisms to explain were of Akan ethnicity, and Ghanaians in Europe were the high prevalence of hypertension among SSA origin first-generation migrants. populations [3]. SS refers to an increase in blood pressure Of participants aged 25 years and older with complete upon salt intake and relates to excessive sodium and water data on physical examination and blood samples pro- retention by the kidneys, and suppression of the renin- file, 736 participants were selected for DNA methylation angiotensin-aldosterone system (RAAS) [4], a physiologi- profiling. These participants were selected based on a cal system regulating circulating blood volume and blood case-control design, including about 300 cases with non- pressure [5]. West African descent populations have higher drug-treated diabetes, 300 controls without diabetes, prevalence of salt-sensitive hypertension [6], with a profile and 135 controls with neither diabetes nor obesity. After of suppressed renin, accompanied by an increased aldos- excluding participants with sex discordances (n = 11), terone-to-renin ratio [7]. SS is affected by multiple factors duplicates (n = 8), and those not meeting the qual- including genetics [8], and phenotypical aspects such as ity control thresholds (n = 12), 713 eligible participants age, obesity, insulin resistance, and hypertension [9]. How- remained. Of these participants, those without aldos- ever, key underlying determinants and exact molecular terone and renin measurements were excluded (n = 644, mechanisms of SS among SSA populations still need to be excluding all participants residing in London and Ber- elucidated. In this light, studying epigenetic modifications lin as they were not included in aldosterone and renin in relation to RAAS can be of interest. ‘Epigenetics’ refers analysis), as was one participant with an outlier in renin to changes in readability and transcription of the genome, concentration (77 pg/mL), which was > 3 times the SD of without alterations to the DNA sequence itself [10]. Epi- the log-transformed renin concentration. This resulted genetic modifications, of which DNA methylation is the in 68 participants in the current analysis, all residing in most widely studied epigenetic feature, are influenced by rural and urban Ghana, or Amsterdam (Additional file 1: both genetic and environmental factors. Several studies FigureS1). have identified differentially methylated positions (DMPs) located in RAAS-related genes to be associated with hyper- Phenotypic measurements tension [11, 12], including the AGT, AGTR1, ACE, NOS3, Data collection procedures for questionnaire and physi- and SCNN1A genes [5, 13]. However, none of these studies cal examination were highly standardised across the have examined the association between epigenome-wide different study locations. Questionnaires were used DNA methylation and concentration of RAAS hormones. to collect data on gender, age, level of education, and Moreover, studies that identified RAAS-related genes were length of stay in Europe. Physical examination was per- mainly conducted in European and African American pop- formed using validated devices. Weight was measured ulations, whereas these groups differ from SSA based pop- in light clothing without shoes with a SECA 877 scale to ulations in terms of genetics and environment. Therefore, the nearest 0.1  kg. Height was measured without shoes the aim of this proof-of-principle study was to assess the using a SECA 2017 portable stadiometer to the nearest association between DNA methylation and plasma renin 0.1 cm. Anthropometric measures were taken twice and and aldosterone concentrations, and aldosterone-to-renin the mean of the two measures was used in analyses. Body ratio (ARR) in Ghanaians. mass index (BMI) was calculated by dividing the weight in kilograms by the height in metres squared. After at Methods least 5  min of rest, three blood pressure (BP) readings Study population and study design were taken in a sitting position, with a cuff fixed on the For this study, baseline data of the longitudinal, multi- left arm. The mean of the second and third reading was centre Research on Obesity and Diabetes among Afri- used in the analyses. Hypertension was defined as having can Migrants (RODAM) study were used. Details on the a BP of systolic ≥ 140 mmHg and/or diastolic ≥ 90 mmHg v an der Linden et al. Clinical Epigenetics (2022) 14:159 Page 3 of 11 and/or the use of BP-lowering medication. BP-lowering the methylated and unmethylated probes for each CpG medication was categorised based on Anatomical Thera- site on the array. These intensities were expressed as peutic Chemical classification of medication that partici- methylation Beta values, which is a value between zero pants brought with them to the research location. (unmethylated) and one (methylated). A log2 ratio of the Venous blood samples were collected in the sitting intensities of methylated versus unmethylated probes position, after an overnight fast of at least ten hours. was calculated, which is referred to as M values. Using All samples were collected around the same time of the R statistical software (version 4.2.0), quality control was day (morning) to control for the influence of circadian performed using the MethylAid package (version 1.30.0), rhythm. After samples collection, samples were imme- using default thresholds of 10.5 for methylated and diately processed, aliquoted and cryopreserved, before unmethylated intensities, 11.75 for overall quality con- storage in −  80˚C freezers. Samples collected in Ghana trol, 12.75 for bisulfite conversion, 12.50 for hybridisa- were shipped to Europe while kept frozen at −  80 ˚C. tion control, and 0.95 for detection p-value. Functional Fasting plasma glucose concentrations were measured normalisation of the raw 450 K data was conducted using using the hexokinase method by colorimetry, in the lab- the minfi package (version 1.42.0). After the removal of oratory of the Institute of Tropical Medicine and Inter- probes annotated to the X and Y chromosomes, known national Health, Berlin, Germany. Diabetes mellitus was to involve cross-hybridisation or to contain common sin- defined according to self-reported diabetes and/or fasting gle-nucleotide polymorphisms (SNPs) with a minor allele glucose ≥ 7.0 mmol/L. Aldosterone and renin concentra- frequency of ≥ 5%, a set of 429,449 CpG sites remained tion (pg/mL) were measured in heparin plasma samples, for analysis [16]. Blood cell type distribution was esti- and analyses were performed at the Department of Inter- mated based on the method of Houseman et al. [17]. nal Medicine of the Erasmus MC, Rotterdam, the Neth- erlands. All samples, including those from Ghana, were Statistical analyses analysed in the same laboratory, to prevent inter-labo- Association between renin, aldosterone, and ARR and DNA ratory differences affecting the results. After thawing of methylation the samples, renin concentration was determined by an To assess differentially methylated positions (DMPs), immunoradiometric assay (Cisbio, Saclay, France) using multivariate linear regression was performed between an active site-directed radiolabelled antibody binding to renin concentration, aldosterone, and ARR (independ- renin only. The lower detection limit of this assay was ent variables) and DNA methylation M values (depend- 2  pg/mL. Aldosterone concentrations were measured ent variable), using the Limma package (version 3.52.0). by solid-phase radioimmunoassay (Demeditec Diag- Methylation M values were used in statistical analysis to nostics, Kiel, Germany), with a lower detection limit of ensure normal distribution, whereas methylation Beta 12  pg/ml. Aldosterone-to-renin ratio (ARR, pg/mL/pg/ values were used to facilitate interpretation and visuali- mL) was calculated by dividing aldosterone by the renin sation. Models were adjusted for sex, age, BMI, diabetes concentration. The distributions of aldosterone, renin, mellitus, hypertension, estimated blood cell type counts and ARR were assessed using histograms and the Shap- (CD8 + T lymphocytes, CD4 + T lymphocytes, natural iro-Wilkinson test. To ensure normal distribution of the killer cells, B cells, monocytes, granulocytes), and tech- traits, aldosterone concentration was transformed using nical covariates (hybridisation batch and array posi- Box-Cox transformation, and renin and ARR were log- tion), because of correlation with DNA methylation in transformed. Renin, aldosterone and ARR were chosen the principal components analysis, as well as because of for analysis, because of their relevance in the context of an overrepresentation of participants with diabetes and SS and salt-sensitive hypertension. high BMI in the sample. Model fit was assessed using QQ plots, as well as the genomic inflation factor lambda. DNA methylation profiling, processing, and quality control Because of improvement in model fit after bias and Previous RODAM publications have elaborated upon inflation correction using the R package bacon (version the DNA methylation profiling, processing, and quality 1.24.0) [18], we applied this adjustment to all analyses control on whole blood samples [15], and this process (lambda with inflation correction for renin 1.046, aldos- will be summarised here. In the lab of Source BioSci- terone 0.995, ARR 1.026) (Additional file  1: FigureS2). ence, Nottingham, UK, the Zymo EZ DNAm™ kit was False discovery rate (FDR) adjustment of the p-values used for bisulfite conversion of DNA. Using the Infin- was applied, to reduce the risk of false positive findings in ium® HumanMethylation450 BeadChip, the converted multiple testing. An FDR-adjusted p-value of < 0.05 was DNA was amplified and hybridised, hereby quantifying considered epigenome-wide significant. DNAm levels of approximately 485,000 CpG sites. Meth- To identify the contribution of the top DMPs to the ylation levels were measured based on the intensities of variance in renin and aldosterone concentration, linear van der Linden et al. Clinical Epigenetics (2022) 14:159 Page 4 of 11 regression was performed using z-standardised meth- a candidate-gene DMP analysis on these for each of our ylation M values of the identified DMPs as independ- traits of interest. For these candidate-gene analyses, the ent variable and the (untransformed) trait of interest as same models, including adjustment for bias and infla- dependent variable. The R squared statistics from the lin- tion, were used as for the main analysis. All CpGs with ear regression analyses with and without covariates were an FDR-adjusted p-value of < 0.05 were considered to be used to calculate trait variance explained by each DMP. statistically significant. Similar methods were used to assess the explained vari- ance in systolic and diastolic BP, with the z-standardised Biological relevance M values of the top DMPs associated with renin, aldos- Gene expression levels for the epigenome-wide signifi- terone, and ARR as independent variables in the linear cant DMPs were assessed using the publicly available regression model, adjusted for age, sex, BMI, diabetes iMethyl database, in which the DNA methylation lev- mellitus, blood cell distribution, and technical variates. els of CD4 + T lymphocytes as well as gene expression As several types of BP-lowering medication can inter- levels, denoted per kilo base of transcript per million fere with the RAAS system, a sensitivity analysis exclud- mapped fragments (FPKM), are reported for 100 appar- ing those on BP-lowering medication (n = 10) was ently healthy individuals living in Japan [22]. performed, to assess the impact of medication use on the Enrichment analysis using the Kyoto Encyclopedia association. of Genes and Genomes (KEGG) catalogue in package For the top DMPs per trait, we extracted the median R missMethyl (version 1.30.0) was performed to gain methylation Beta values with accompanying interquartile insight into the function and biological pathways of our ranges, and stratified these per geographical location, to findings. The top 5000 CpGs with the smallest p-values examine whether differences in methylation levels existed per trait were used as input. between participants residing in rural and urban Ghana, The gaphunter gfunction of the minfi package was used and Amsterdam. These median values were compared to examine whether the significant DMPs were poten- between the location using nonparametric the Kruskal– tially under the influence of genetic variation. The func- Wallis test. A two-sided p-value < 0.05 was considered tion was run with a threshold of 0.05, reflecting a gap of statistically significant. 5% in beta value, suggestive of genetic influence. Differentially methylated regions Results To assess whether DNA methylation of genomic regions, Population characteristics rather than on single positions, was associated with Table 1 shows the population characteristics. Males and the traits of interest, the R package bumphunter (ver- females were included at nearly equal proportions, and sion 1.38.0) was used to assess differentially methylated the mean age was 52 years. The majority of the partici- regions (DMRs), using similar models as used in the pants resided in rural and urban Ghana, and all Ghanaian DMP analysis. Methylation M value cut-offs of 0.2 was migrants resided in Amsterdam. One-third of the partici- chosen for the input, which limited the analysis to 100 pants had diabetes mellitus and close to 40% of the par- candidate regions and 20% difference in regression coef- ticipants were classified as hypertensive. Ten participants ficient beta between candidate probes. The analysis was reported taking prescribed BP-lowering medication, of run with bootstrapping with 500 permutations. DMRs which calcium channel blockers (n = 9) and angioten- with more than two CpGs, and a family-wise error rate sin receptor blockers (n = 4) were the most used types (FWER) < 0.2 were considered statistically significant. of drugs. The median renin concentration was 7.62  pg/ DMRs were visualised using coMET package (version mL, the median aldosterone was 103.44 pg/mL, and the 1.27.2). median ARR was 14.16 pg/mL/pg/mL. Replication Differentially methylated positions We used the EWAS atlas [19] to extract all CpGs previ- Renin ously reported to be associated with BP, systolic blood We found one intergenic CpG, cg02105843, epigenome- pressure (SBP), diastolic blood pressure (DBP), and wide significantly associated with renin concentration hypertension and performed a candidate DMP analysis. (Table 2, Fig. 1A). One standard deviation (SD) increase Additionally, we tried to replicate CpG sites significantly in methylation M-value of this DMP was associated with associated with eGFR in a large, multi-ethnic, meta-anal- 9.08 pg/mL lower renin levels and this DMP contributed ysis of EWAS [20]. Lastly, we extracted the probes previ- to 15% of the variance in renin concentration (Table 3). ously annotated to RAAS-related genes (ACE, AGT, REN, The top 5 DMPs associated with renin concentration CYP11B2, HDS11B2, and NR3C2) [21] and performed explained 6.4% in variance in SBP and 8.0% in variance in v an der Linden et al. Clinical Epigenetics (2022) 14:159 Page 5 of 11 Table 1 Population characteristics Aldosterone N 68 Ten CpGs were associated with aldosterone concentra- tion on an epigenome-wide level (Table 2, Fig.  1B). The Sex, male (%) 32 (47.1) DMPs with the smallest p-values were located in an inter- Age, years (mean (SD)) 52.6 (9.0) genic region (cg06215643), in the transcription start site Site (%) (TSS) of the RPL41 gene (cg03123773), and in the body of Rural Ghana 24 (35.3) the KCNT1 gene. DMPs with the highest explained vari- Urban Ghana 32 (47.1) ance in aldosterone concentration were cg09869144 and Amsterdam 12 (17.6) cg15627834, located in the body of the MLYCD and SKIL BMI, kg/m2 (mean (SD)) 25.1 (5.5) genes, respectively, contributing to 73.58 and − 56.33 pg/ Diabetes mellitus (%) 22 (32.4) mL change in aldosterone level with one SD increase in Hypertension (%) 27 (39.7) M value. The combined ten DMPs explained 46% of the Systolic blood pressure, mmHg (mean (SD)) 131.78 (23.98) variance in aldosterone concentration (Table 3), 19.6% in Diastolic blood pressure, mmHg (mean (SD)) 82.28 (15.53) SBP, and 24.2% in DBP levels. Prescribed BP‑lowering medication (%) 10 (14.7) In the sensitivity analysis excluding ten participants Diuretics (%) 2 (2.9) with prescribed BP-lowering medication, four of the ten Beta blockers (%) 2 (2.9) CpGs did no longer reach epigenome-wide significance. Calcium channel blockers (%) 9 (13.2) However, these four CpGs, cg17876128, cg14771658, Angiotensin receptor blocker (%) 4 (5.9) cg18784903, and cg24500222, did show a similar direc- Renin, pg/mL (median [IQR]) 7.62 [4.26, 11.62] tion of effect and effect size as in the main analysis Aldosterone, pg/mL (median [IQR]) 103.44 [70.85, 163.21] including participants with BP-lowering medication. ARR (pg/mL/pg/mL) (median [IQR]) 14.16 [7.45, 28.05] For the top DMPs, there was no significant difference in SD Standard deviation, BMI Body mass index, BP Blood pressure, ARR methylation levels between the geographical locations. Aldosterone-to-renin ratio, IQR Interquartile range ARR ARR was epigenome-wide significantly associ- ated with methylation of intergenic CpG cg15602420 DBP. Excluding participants on BP-lowering medication (Table  2, Fig.  1C). An increase of one SD in methyla- did not impact this result, and there was no difference tion level of this DMP was associated with 13.25 pg/mL/ in methylation levels for this DMP between participants pg/mL lower ARR and explained 19% of the variance in residing in rural and urban Ghana or in Amsterdam. this trait (Table 3). 6.2% of the variance in SBP and DBP Table 2 Epigenome‑wide significant differentially methylated positions associated with renin, aldosterone and aldosterone/renin ratio Regres.Coeff* p-value FDR adj. p-val chr pos Gene symbol** Gene group Methylation, % (sd)*** Renin cg02105843 − 0.149 1.65E‑08 0.0071 chr11 522,809 intergenic 90.46 (2.06) Aldosterone cg06215643 0.209 2.73E‑08 0.0084 chr22 50,119,046 intergenic 57.05 (5.13) cg03123773 − 0.613 3.90E‑08 0.0084 chr12 56,510,201 RPL41 TSS200 5.83 (3.24) cg21178689 0.291 1.08E‑07 0.0154 chr9 138,594,307 KCNT1 Body 64.4 (7.16) cg17876128 − 0.247 1.46E‑07 0.0156 chr1 4,068,541 intergenic 86.45 (4.01) cg14771658 0.196 2.16E‑07 0.0185 chr2 167,350,883 intergenic 37.57 (5.31) cg18784903 0.270 2.90E‑07 0.0185 chr7 1,337,353 intergenic 65.02 (6.38) cg09869144 0.150 3.01E‑07 0.0185 chr16 83,948,818 MLYCD Body 98.18 (0.36) cg15627834 − 0.489 4.55E‑07 0.0245 chr3 170,078,469 SKIL Body 91.89 (9.01) cg06124066 − 0.148 8.83E‑07 0.0421 chr7 157,885,905 PTPRN2 Body 91.01 (1.78) cg24500222 − 0.173 1.10E‑06 0.0472 chr7 71,747,028 CALN1 5’UTR 76.47 (4.74) ARR cg15602420 − 0.217 1.12E‑07 0.0481 chr1 20,205,059 Intergenic 77.9 (5.53) * For M-values, model adjusted for covariates: sex, age, BMI, diabetes, hypertension, CD8T, CD4T, NK, Bcell, Monocytes, Granulocytes, batch, position; ARR, aldosterone- to-renin-ratio ** Annotated using UCSC genome browser, genome build hg37 *** Methylation level calculated as: methylation beta*100 van der Linden et al. Clinical Epigenetics (2022) 14:159 Page 6 of 11 Fig. 1 Manhattan plots for bacon adjusted p‑values of the EWAS of renin A aldosterone B and aldosterone‑to‑renin ratio C. Dotted lines indicates epigenome‑wide significant p‑value were explained by the top 5 DMPs associated with ARR. HDL-DRB1 gene, was associated with aldosterone con- Excluding participants with BP-lowering medication centration (Additional file  1: Figure S5). No DMR was did not affect our findings. No difference in methylation identified for the ARR. There was no overlap between level between the different locations of residence could the significant DMPs and DMRs. be observed. Replication Differentially methylated regions Through the EWAS atlas we found 16 probes that had For renin, we identified two DMRs at an FWER < 0.2, previously been associated with BP, 142 for SBP, 191 for annotated to the NFYA (Additional file  1: Figure S3) DBP, and 151 probes for hypertension. Intergenic CpGs and the RNF39 gene (Additional file 1: Figure S4), both cg18264657 and cg11710912 annotated to the ARGLU1 located on chromosome 6. One DMR, annotated to the gene that were previously associated with hypertension v an der Linden et al. Clinical Epigenetics (2022) 14:159 Page 7 of 11 Table 3 Association between z‑transformed DNA methylation levels of top differentially methylated positions (independent variable) and renin, aldosterone, and aldosterone‑to‑renin‑ratio concentration (dependent variable) chr pos Gene symbol Gene group Regression coeff Explained variance (%) Renin cg02105843 chr11 522,809 Intergenic − 9.08 15.24 Aldosterone cg06215643 chr22 50,119,046 44.16 18.45 cg03123773 chr12 56,510,201 RPL41 TSS200 − 59.72 20.43 cg21178689 chr9 138,594,307 KCNT1 Body 45.22 17.54 cg17876128 chr1 4,068,541 Intergenic − 53.90 19.69 cg14771658 chr2 167,350,883 Intergenic 50.93 20.03 cg18784903 chr7 1,337,353 Intergenic 45.30 20.12 cg09869144 chr16 83,948,818 MLYCD Body 73.58 23.4 cg15627834 chr3 170,078,469 SKIL Body − 56.33 23.15 cg06124066 chr7 157,885,905 PTPRN2 Body − 63.38 17.62 cg24500222 chr7 71,747,028 CALN1 5’UTR − 60.77 16.42 ARR cg15602420 chr1 20,205,059 intergenic − 13.25 19.49 Model: trait (untransformed) ~ DNAm(Mval) + sex + age + BMI + diabetes + h yper te nsion +​ CD8T + CD4T + N K + Bce ll + M ono + G ran + b atc h + ​positio n; A RR , al do ste ron e-to-renin-ratio in other study populations of European and South Asian three were associated with renin concentration and one ancestry were associated with renin concentration and with ARR. Intergenic CpG cg01695994, cg02059849 ARR in our Ghanaian study population (Table 4). Inter- located in the body of PTP4A3 gene and cg22623033 genic cg05376465, previously associated with SBP in annotated to the PIP5K1C gene were significantly asso- European and South Asian ancestry population, was ciated with renin concentration, as was cg02059849 associated with aldosterone concentration in our study. (PTP4A3) to ARR. 65 CpGs previously reported to be associated with eGFR Next, we performed association analysis on a subset of in populations of European and African American ances- 45 probes annotated to ACE, REN, CYP11B2, HSD11B2, try were assessed in our Ghanaian population, of which and NR3C2 genes, which are all involved in the RAAS Table 4 Replicated differentially methylated positions associated with plasma renin, aldosterone concentration and aldosterone/renin ratio, based on candidate‑gene approach Number CpG chr pos Gene symbol Gene group Regression.Coeff.** FDR adj. p-value of CpGs replicated* Renin Hypertension 1/148 cg18264657 chr18 4,455,862 intergenic − 0.1812 0.0169 eGFR 3/65 cg01695994 chr17 80,246,403 intergenic 0.1498 0.0012 cg02059849 chr8 142,437,898 PTP4A3 Body 0.0937 0.0102 cg22623033 chr19 3,653,592 PIP5K1C Body 0.0736 0.0142 Aldosterone SBP 1/136 cg05376465 chr14 76,734,327 intergenic − 0.4734 0.0496 ARR HTN 1/148 cg11710912 chr13 107,217,605 ARGLU1 Body 0.1496 0.0389 eGFR 1/65 cg02059849 chr8 142,437,898 PTP4A3 Body − 0.1016 0.0073 RAAS 1/45 cg12841684 chr4 149,251,768 NR3C2 Body − 0.1276 0.0113 * Number of CpGs replicated of total number of CpGs included in candidate-gene approach ** For M values, adjusted for covariates eGFR Estimated glomerular filtration rate, ARR aldosterone-to-renin-ratio, SBP Systolic blood pressure, HTN Hypertension, RAAS Renin–angiotensin–aldosterone system van der Linden et al. Clinical Epigenetics (2022) 14:159 Page 8 of 11 system. One of these CpGs, cg12841684 located in the been associated with metabolic syndrome in African body of the NR3C2 gene, was associated with ARR at an American population [23], and hypermethylation of FDR adjusted p-value of 0.01. the SKIL gene has been associated with atherosclerosis [24]. We identified one DMP, cg21178689, annotated to Biological relevance the KCNT1 gene, which codes for potassium sodium- Generally, methylation levels as reported in the iMethyl activated channel subfamily T, that, among others, reg- database were similar compared to average methyla- ulates insulin secretion, heart rate, and smooth muscle tion betas in our population (Additional file 1: Table S1). contraction [25]. Methylation in this gene has previ- Additionally, for three CpGs associated with aldosterone, ously been associated with BMI [26] and type 2 diabe- higher levels of methylation in the body of the SKIL and tes mellitus [27]. For renin, the significant DMP was PTPRN2 gene were associated with higher gene expres- located in an intergenic region; however, the nearby sion, whereas higher levels of methylation in the body located gene PTDSS2, has previously been shown to of the MLYCD gene were associated with lower gene relate to fat mass if expressed in adipose tissue [28]. expression. DNA methylation of cg15602420, located downstream In the pathway analysis using the KEGG catalogue, the of the OTUD3 gene, was associated with ARR. Methyla- top 20 pathways associated with renin, aldosterone, and tion in the OTUD3 gene has previously been associated ARR were involved in fatty acid metabolism, diabetes car- with glucocorticoid exposure [29]. Additionally, the sig- diomyopathy, and immunological processes (Additional nificant DMRs were annotated to genes associated with file  1: Table  S2). However, none of the pathways were BMI [30], diabetes mellitus and coronary heart disease, statically significant at an FDR-adjusted p-value < 0.05. and the pathway analysis suggested enrichment of path- None of the identified significant DMPs showed a gap ways involved in CVD pathophysiology. These findings in Beta value distribution in the gaphunter analysis, indi- link hormones of the RAAS to other metabolic systems. cating no signs of genetic variation influencing methyla- As this is the first EWAS on the RAAS, we could tion levels of these DMPs. not replicate our findings nor could we replicate find- ings from previous studies. However, in our candidate Discussion approach, we found that several DMPs previously asso- Key findings ciated with hypertension and kidney function were also In this first EWAS on plasma aldosterone and renin con- associated with concentrations of renin, aldosterone, centrations, we identified one DMP associated with renin and ARR. This suggests the potential shared regula- concentration, ten DMPs associated with aldosterone tory mechanism. This is also supported by our findings concentration, and one DMP associated with ARR in a that the top DMPs associated with plasma renin and Ghanaian population. These DMPs contributed to 15%, aldosterone concentration explained a substantial per- 46%, and 19% of the explained variance in renin, aldos- centage of variance in SBP and DBP. Remarkably, how- terone, and ARR concentration, respectively. Addition- ever, was that we were able to replicate only one CpG ally, DMPs associated with renin, aldosterone, and ARR annotated to genes involved in the RAAS, namely explained a substantial percentage of the variance in SBP cg12841684 annotated to the NR3C2 gene, which and DBP. For the genome-wide significant DMPs associ- encodes for the mineralocorticoid receptor mediating ated with renin and aldosterone concentration, no dif- the actions of aldosterone on salt and water balance ferences in methylation levels were observed between [31]. Previous findings from animal studies have shown participants residing in rural and urban Ghana and Gha- the impact of salt intake, hypertension, and proinflam- naian migrants in Europe. Two DMRs associated with matory cytokines on methylation and expression of the renin and one DMR associated with aldosterone con- AGT and CYP11B2 gene [32]. It could therefore be that centration were identified. We found CpGs previously the association between DNA methylation of RAAS associated with hypertension and kidney function to be genes and the aldosterone and renin concentration are associated with concentrations of renin and aldosterone, masked in our study, because of the complex regulation and we found one DMP annotated to the NR3C2 gene of of these concentrations by other factors like salt intake, the RAAS system to be associated with ARR. serum potassium concentrations, and sympathetic nervous system activity, or because of the small sample Discussion of the key findings size of this study (i.e. false negative findings). We found several DMPs associated with concentrations Hypertension is more prevalent among SSA migrants of aldosterone annotated to genes that have previously in Europe compared to their non-migrating counter- been reported to be related to CVD risk factors. For parts in SSA [2], and DNA methylation has been shown instance, hypermethylation in the PTPRN2 gene has to differ between migrant and non-migrant populations v an der Linden et al. Clinical Epigenetics (2022) 14:159 Page 9 of 11 [33]. However, the mean methylation levels for our DMPs from our study with an underlying genetic defect known associated with aldosterone, renin, and ARR did not dif- to be associated with changes in RAAS, such as Liddle fer between participants residing in rural and urban syndrome or familial hyperaldosteronism (37). However, Ghana, and Ghanaian migrants in Amsterdam (Addi- as these syndromes are rare, we do not expect these to tional file 1: Table S3). This is noteworthy, as the median have substantially impacted our findings. Additionally, levels of renin seem to be lower in Amsterdam than in results from the   gaphunter analysis did not indicate urban Ghana and rural Ghana, whereas aldosterone potential genetic variations underlying our significant showed an opposite trend, being highest in Amsterdam DMPs. Lastly, as this study used cross-sectional data, and lowest in rural Ghana. This may suggest that other inference on causality should be made with caution. factors like BMI or BP impact renin and aldosterone more directly than DNA methylation does. Perspectives Hypertension in SSA origin populations is often char- The findings of this proof-of-principle study suggest a acterised by suppressed renin and high ARR [29]. Fitting role of DNA methylation in the regulation of plasma the EWAS for suppressed versus unsuppressed renin, and renin and aldosterone concentration, with a potential for high versus low ARR, classified based on median split link to blood pressure and other CVD risk factors. This of both distributions, showed the same DMPs that were study can serve as a starting point to further elucidate the epigenome-wide significant in the EWAS for continu- regulation of RAAS and the pathophysiology of (salt-sen- ous traits (data available on request). This supports the sitive) hypertension, and the complex gene-environment robustness of our findings and indicates that the asso- interaction affecting these mechanisms. Specifically, ciation between renin concentration and ARR and DNA future research should include larger sample size, of dif- methylation is linear. ferent ethnic origins, and preferably of longitudinal study design, in order to replicate our findings, determine eth- Strengths and limitations nic-specific differences, and establish causality in relation This is the first EWAS on hormones of the RAAS and it to incidence of hypertension and CVD risk. Ultimately, was conducted in a genetically homogenous populations this information could inform targeted interventions of Ghanaians, a SSA population with a high prevalence aiming to reduce the burden of hypertension in general, of hypertension. Data collection was highly standardised, and among SSA populations specifically. allowing for high-quality data and comparison of popula- tions residing in different locations. Conclusions Our study has some limitations. Firstly, as this was an In this first EWAS on renin and aldosterone concentra- explorative study, the sample size and study design were tions in general, and specifically in a SSA population, we not based on specific statistical power to detect cer- detected several epigenome-wide significant DMPs. Our tain effect. However, the goal of this proof-of-principle results need to be replicated in large cohorts of differ- study was to lay a foundation, which can lead to further ent ethnic origins. Additionally, translational and longi- research. Secondly, we used whole blood samples to tudinal studies are needed to better understand the role assess DNA methylation associated with aldosterone and of DNA methylation in the regulation of hormones of renin concentration. DNA methylation is tissue-specific, RAAS, thereby disentangling the pathophysiology of salt- and even though renin and aldosterone are hormones sensitive hypertension in SSA populations. that are secreted into the blood, it is possible that DNA methylation is different in the tissues where these hor- Supplementary Information mones are produced and/or act, i.e. in the kidney and The online version contains supplementary material available at https://d oi. org/1 0.1 186/ s13148‑0 22‑ 01378‑5. adrenal glands. The DNA methylation levels for our top DMPs, however, generally showed concordance between Additional file 1: Table S1 Relationship between DNA methylation of blood, kidney, and adrenal gland tissue [34]. Thirdly, it differentially methylated positions and gene expression as reported in the has been shown that renin’s precursor prorenin can be iMEHTYL database. Table S2 Enrichment analysis for the top 20 pathways associated with renin, aldosterone and ARR. Table S3 Median methylation activated if samples are not frozen quickly after collec- beta levels of differentially methylated positions, stratified by geographical tion, or thawed slowly before analysis [35, 36]. This cry- location of residence. Fig. S1. Participants inclusion flow chart. Fig. S2 QQ oactivation of prorenin could result in increased renin plots of bacon adjusted p‑values for renin, aldosterone and ARR popula‑ tion. Fig. S3 Differentially methylated region (DMR) annotated to chromo‑ concentration. However, as samples were frozen quickly some 6 (NFYA) associated with renin concentration. Fig. S4 Differentially after collection, and were thawed according to standard methylated region (DMR) annotated to chromosome 6 (RNF39) associated operating procedures, the impact of activating prorenin with renin concentration. Fig. S5 Differentially methylated region (DMR) annotated to chromosome 6 (HLA-DRB1) associated with aldosterone is likely to be minimal. Fourthly, since our study lacks concentration. genetic profiles, we were not able to exclude participants van der Linden et al. Clinical Epigenetics (2022) 14:159 Page 10 of 11 Acknowledgements Medicine, Department of Internal Medicine, Erasmus MC, University Medical The authors are very grateful to the advisory board members for their valuable Center Rotterdam, Amsterdam, The Netherlands. support in shaping the methods, to the research assistants, interviewers and other staff of the five research sites, who have taken part in gathering the data Received: 6 October 2022 Accepted: 16 November 2022 and, most of all, to the Ghanaian volunteers participating in this project. We gratefully acknowledge Jan van Straalen from the Academic Medical Centre for his valuable support with standardisation of the laboratory procedures and the AMC Biobank for support in biobank management and storage of collected samples. References 1. Agyemang C, Kieft S, Snijder MB, Beune EJ, van den Born BJ, Brewster LM, Author contributions et al. 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