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Biogerontology

, Volume 20, Issue 5, pp 627–647 | Cite as

Epigenome-wide exploratory study of monozygotic twins suggests differentially methylated regions to associate with hand grip strength

  • Mette SoerensenEmail author
  • Weilong Li
  • Birgit Debrabant
  • Marianne Nygaard
  • Jonas Mengel-From
  • Morten Frost
  • Kaare Christensen
  • Lene Christiansen
  • Qihua Tan
Open Access
Research Article

Abstract

Hand grip strength is a measure of muscular strength and is used to study age-related loss of physical capacity. In order to explore the biological mechanisms that influence hand grip strength variation, an epigenome-wide association study (EWAS) of hand grip strength in 672 middle-aged and elderly monozygotic twins (age 55–90 years) was performed, using both individual and twin pair level analyses, the latter controlling the influence of genetic variation. Moreover, as measurements of hand grip strength performed over 8 years were available in the elderly twins (age 73–90 at intake), a longitudinal EWAS was conducted for this subsample. No genome-wide significant CpG sites or pathways were found, however two of the suggestive top CpG sites were mapped to the COL6A1 and CACNA1B genes, known to be related to muscular dysfunction. By investigating genomic regions using the comb-p algorithm, several differentially methylated regions in regulatory domains were identified as significantly associated to hand grip strength, and pathway analyses of these regions revealed significant pathways related to the immune system, autoimmune disorders, including diabetes type 1 and viral myocarditis, as well as negative regulation of cell differentiation. The genes contributing to the immunological pathways were HLA-B, HLA-C, HLA-DMA, HLA-DPB1, MYH10, ERAP1 and IRF8, while the genes implicated in the negative regulation of cell differentiation were IRF8, CEBPD, ID2 and BRCA1. In conclusion, this exploratory study suggests hand grip strength to associate with differentially methylated regions enriched in immunological and cell differentiation pathways, and hence merits further investigations.

Keywords

Epigenome-wide association study Hand grip strength Twin data Longitudinal data Pathway analyses Comb-p 

Abbreviations

EWAS

Epigenome-wide association study

GWAS

Genome-wide association study

TWAS

Transcriptome-wide association study

MADT

The Study of Middle Aged Danish Twins

LSADT

The Longitudinal Study of Aging Danish Twins

BWD

The Study of Birth Weight Discordant Twins

PCA

Principle component analysis

DMRs

Differentially methylated regions

FDR

False discovery rate

TSS

Transcription start site

KEGG

The Kyoto Encyclopedia of Genes and Genomes database

WebGestalt

The WEB-based GEne SeT AnaLysis Toolkit

MSigDB

Molecular Signatures Database

GREAT

Genomic regions enrichment annotation tool

ORA

Overrepresentation enrichment analysis

GREA

Genomic regions enrichment analysis

Introduction

Aging is accompanied by a general loss of physical capacity that is often associated with adverse age-related outcomes and thus may have significant influences on the quality of life. For example, age-related decline in skeletal muscle strength, which is an important component of sarcopenia and frailty, is associated with physical disability, morbidity and mortality (Beaudart et al. 2017).

Hand grip strength is a simple and easily administered measurement of muscle strength that correlates well with overall muscle strength (Visser et al. 2000; Wang et al. 2005; Bohannon et al. 2012). In accordance, the average hand grip strength peaks in early adulthood, followed by a period of continuance, and then starts declining from midlife and throughout life (Frederiksen et al. 2006; Dodds et al. 2014). Hand grip strength has been associated with a number of adverse health outcomes, such as depression (van Milligen et al. 2011; Fukumori et al. 2015), cardio-metabolic risk markers and diseases (Mainous et al. 2015; Lawman et al. 2016; Lee et al. 2016), and multi-morbidity (Cheung et al. 2013; Yorke et al. 2015), as well as with an increased overall mortality risk (Newman et al. 2006; Arvandi et al. 2016). In a study of almost 140,000 individuals from 17 different countries, Leong et al. (2015) found associations between hand grip strength and incident cardiovascular disease and further confirmed that hand grip strength is a strong predictor of all-cause and in particular cardiovascular mortality. Recently, a study of 502,293 participants from the UK Biobank confirms an association to all cause mortality and cause specific mortality from cardiovascular disease, yet also puts forward associations to cause specific mortality from respiratory disease, chronic obstructive pulmonary disease and cancers, as well as to incidences of cardiovascular disease, respiratory diseases, chronic obstructive pulmonary disease and cancers (Celis-Morales et al. 2018). In addition, recent literature reviews firmly validate the associations between hand grip strength and cognitive decline (Fritz et al. 2017; Rijk et al. 2016) and functional status such as activity of daily living (Rijk et al. 2016).

Previous studies have estimated that the heritability of hand grip strength is approximately 50–60% (Frederiksen et al. 2002). However, very few consistent genetic associations have been found, and large genome-wide association studies (GWAS) of hand grip strength are scarce compared to other traits, thus leaving the molecular background of hand grip strength largely unaccounted for (Garatachea and Lucía 2013; Chan et al. 2015). The very first hand grip strength GWAS included 2088 adults (aged 55–85 years) and revealed no genome-wide significant results (Chan et al. 2015). Later, a meta-analysis of 27,581 individuals over 65 years of age from 14 different cohorts discovered one genome-wide significant association, rs752045, in an intergenic region on chromosome 8, which was replicated in 6363 individuals from three additional cohorts (Matteini et al. 2016). rs752045 is located in a DNase hypersensitivity site known to affect a binding motif of the CCAAT/enhancer-binding protein-b (CEBPB), which has been implicated in muscle cell differentiation and muscle repair (Matteini et al. 2016). Recently, an even larger multi-center GWAS of 195,180 individuals identified 16 loci including genes involved in processes such as structure and function of skeletal muscle fibers (ACTG1), neuronal maintenance and signal transduction (PEX14, TGFA, SYT1), and monogenic syndromes with involvement of psychomotor impairment (PEX14, LRPPRC and KANSL1) (Willems et al. 2017).

In addition to genetic studies, blood-based studies of molecular signatures may prove valuable for determining systemic factors and pathways involved in muscle strength maintenance and age-related decline. A gene expression analysis of blood messenger RNA from 698 people found the expression of the CEBPB gene to be significantly associated with hand grip strength as well as with physical performance (Harries et al. 2012). In a larger transcriptome-wide association study (TWAS), Pilling et al. (2016) investigated the association between hand grip strength and gene expression levels in a total of 7781 individuals aged 20–104 years from 4 independent cohorts. They identified 208 differentially expressed genes, half of which were novel in muscle-related literature, thus warranting future work on mechanisms underlying these associations (Pilling et al. 2016). Recently, Murabito et al. (2017) examined the association of 229 whole-blood microRNAs (miRNAs) with hand grip strength in a population of 5668 individuals aged 24–90 years. The study identified 93 significant miRNAs, for which the potential gene targets were involved in biological pathways of importance for muscle and aging (e.g. metabolism) (Murabito et al. 2017).

To our knowledge, only one study has compared blood-based DNA methylation with hand grip strength. In 2015, Marioni et al. performed an epigenome-wide association study (EWAS) of hand grip strength in 1085 individuals from the Lothian Birth Cohort (mean age of 69.5 years) and found no CpG sites with a p value < 10−5. However, looking at the related phenotype skeletal muscle mass, a blood-based EWAS in 1550 middle-aged women, including 50 monozygotic twin pairs discordant for hand grip strength, identified a number of significantly associated differentially methylated genomic regions, of which some showed replication in 1196 individuals: these included the DNAH12, ZFP64, TP63 and GUSBP11 genes, which have previously been linked to muscle differentiation and function (Livshits et al. 2016). Hence, the latter study suggests the importance of epigenetic approaches for understanding the molecular basis of muscle phenotypes such as hand grip strength, and furthermore supports the use of blood as an informative tissue in studies of muscle function.

With the aim of exploring the association between hand grip strength and DNA methylation profiles in blood cells, we studied a sample of 672 monozygotic twins with an age span of 55–90 years. The study took advantage of the twin data by applying an intra-pair approach, controlling the influence of genetic variation and lowering the influence of potential confounders such as differences in rearing environment. Furthermore, we performed a longitudinal EWAS in 192 elderly individuals (age 73–90 years at intake), for whom data on hand grip strength were available from up to five survey waves conducted over 8 years.

Materials and methods

Study populations

The study population comprised 672 monozygotic twins drawn from population-based, nation-wide surveys conducted within the framework of the Danish Twin Registry (Skytthe et al. 2013). Of these, 480 were participants from the Study of Middle Aged Danish Twins (MADT) and 192 were participants from the Longitudinal Study of Aging Danish Twins (LSADT) (Skytthe et al. 2013).

MADT was initiated in 1998 as a Danish nation-wide study of 4314 twins randomly selected from birth cohorts spanning 1931–1952. In 2008–2011 a follow-up study was conducted of all eligible twin pairs originally enrolled (Skytthe et al. 2013). The present study included all intact monozygotic (MZ) twin pairs from the follow-up study. LSADT is a cohort-sequential study of Danish twins aged 70 years or more, initiated in 1995. Surviving twins were followed-up every second year until 2005 (McGue and Christensen 2007). In 1997, whole blood samples were collected from 689 same-sex twins and the present study included all MZ pairs from this wave. For replication purpose we used data on 275 participants from the Study of Birth Weight Discordant Twins (BWD), a nation-wide study conducted in 2008–2010 of the most birth weight discordant twins in the twin registry (Frost et al. 2012). These samples were previously analyzed for epigenome-wide association with birth-weight discordance by Tan et al. (2014), who reported no significant findings. The demographics of the study sample are shown in Table 1.
Table 1

Descriptives of the study population

Sample

Survey

No. individuals

No. complete twin pairs (no. individuals)

Sex distribution [females (%)]

Mean age at blood sampling [years (SD) (range)]

Mean hand grip strength [kg (SD) (range)]

Mean height [cm (SD) (range)]

Study population (MADT + LSADT)

MADT

480

237 (474)

219 (45.6)

66.3 (6.0) (55.9–79.9)

37.2 (11.6) (15–70)

168.4 (8.8) (147–194)

LSADT

192

84 (168)

124 (64.6)

78.5 (3.7) (73.2–90.5)

24.3 (8.3) (7–45)

165 (8.2) (145–183)

Total

672

321 (642)

343 (51.0)

69.8 (7.8) (55.9–90.5)

33.5 (12.2) (7–70)

167.5 (8.8) (145–194)

Replication sample

BWD (young)

BWD (middle-aged)

136

139

66 (132)

66 (132)

61 (44.9)

68 (48.9)

33.6 (2.0) (30.1–37.3)

63.5 (3.8) (57.4–74.1)

46.0 (12.0) (17–79)

38.5 (11.5) (14–66)

175.4 (10.2) (154–205)

170.1 (8.1) (151–188)

Total

275

132 (264)

132 (48.0)

48.7 (15.3) (30.1–74.1)

42.2 (12.3) (14–79)

172.7 (9.5) (151–205)

As the BWD is made up by two age groups, they are listed separately here

No. number of, SD standard deviation

Informed consents were obtained from all participants and all surveys were approved by The Regional Committees on Health Research Ethics for Southern Denmark (S-VF-19980072, S-VF-20040241 and S-20090033) and were conducted in accordance with the Helsinki II declaration.

Phenotype data

Hand grip strength was measured by a hand-held dynamometer (SMEDLEY’S dynamometer, Scandidact, Kvistgaard, Denmark), using a maximum of three measurements with the strongest hand. For LSADT the measurements were performed in 1999, while the blood sample was drawn in 1997, giving a mean age difference of 1.79 years (standard deviation (SD) = 0.28). In analysis of the LSADT data, the co-variate values (including age) were from 1997. For MADT and BWD all data were collected in the same survey wave, yet not necessarily on the same date. On average, the difference between blood sampling and measurement of hand grip strength was less than 2 weeks.

For the 192 LSADT individuals longitudinal data were available. Survey waves were performed in 1999, 2001, 2003, 2005 and 2007. In summary, 39 individuals took part in the baseline wave (1999) only, while 46, 36 and 34 individuals took part in 2, 3 or 4 waves, respectively, and 37 individuals took part in all 5 waves. The individual-level hand grip strength values decreased in the study population as a whole (see Online Resource 1, Supplementary Fig. 1), with an average decline from intake to the last measurement of − 0.49 kg/year (SD 1.42, range − 5.9; 7.3).

DNA methylation data

DNA was isolated from buffy coat by salt precipitation, using either a manual protocol (Miller et al. 1988) or a semi-automated protocol with the Autopure System (Qiagen, Hilden, Germany). Bisulfite conversion of 500 ng genomic DNA was performed using the EZ Methylation Gold kit (Zymo Research, Orange County, CA, USA) and DNA methylation was measured using the Infinium HumanMethylation450K BeadChip (Illumina, San Diego, CA, USA) at Leiden University Medical Center or GenomeScan B.V., Leiden, The Netherlands, according to the manufacturer’s protocols. The BeadChip images were scanned using the iScan system.

The DNA methylation data were prepared on four different occasions giving four datasets; the BWD dataset (N = 296), the MADT dataset (N = 498) and two separate LSADT datasets (N = 224 and N = 86, respectively). Quality control and normalization were performed similarly to Tobi et al. 2015 and separately for each dataset. Data pre-processing was done using the free R packages MethylAid (van Iterson et al. 2014) and minfi (Aryee et al. 2014). Samples were excluded, first if less than 95% of the probes had a detection P-value < 0.01, or second if the samples failed based on the internal quality control probes used by MethylAid (van Iterson et al. 2014). The sex of the individuals was confirmed by considering the values of the X chromosome probes by multidimensional scaling. The DNA methylation data were annotated using GRCh37/hg19. In total, 4 BWD twin pairs and 6 MADT twin pairs were excluded due to poor sample quality (8) or sex discrepancy (2). Probes were excluded if they had a detection P-value > 0.01, a raw intensity value of zero, a low bead count (< 3 beads), a measurement success rate below 95% or were identified as being cross-reactive (Chen et al. 2013). In total, between 32,055 and 33,316 CpGs out of the 485,512 CpGs on the array were excluded for of the four datasets. After probe filtering, a sample success rate of 0.95 was applied; no samples were excluded. Normalization was carried out by Functional normalization (Fortin et al. 2014) with four principle components (PCs) for the MADT and LSADT datasets and 5 PCs for the BWD dataset. The present study includes CpGs overlapping between the MADT and LSADT datasets (452,178 CpGs). Before statistical analysis, the β-values were transformed to M-values by logit transformation, presenting a distribution more suitable for statistical testing (Du et al. 2010).

Blood leukocyte subtypes (monocytes, lymphocytes, basophils, neutrophils and eosinophils) counted using a Coulter LH 750 Haematology Analyser were available for 483 of the 492 MADT individuals and for 212 of the 292 BWD individuals. For the remaining individuals the blood counts were imputed by a modified version of the PredictCellComposition method (www.github.com/mvaniterson/predictcellcomposition), employing partial least squares regression after setting values of 0 to 0.0001, i.e. inflating percentages below the minimum value observed (0.0099), and transforming the cell composition using an isometric log ratio transformation.

Statistical analysis

Cross-sectional analyses

A principle component analysis (PCA) of the methylation data found the top 4 PCs to describe the majority of the variance in the data, as the subsequent PCs accounted for less than 3% of the variance (data not shown). Still, as PC1 correlated highly (correlation coefficient: 0.99) with sex, only PC2–4 were included in the statistical models. Furthermore, as the technical co-variates DNA plate number, 450K array number and dataset reflected much of the same variance, only the dataset co-variate was included. Cell counts for monocytes, lymphocytes and eosinophils were included in all statistical models, while basophil counts were excluded due to low variability, and neutrophil cell counts were excluded as neutrophil and lymphocyte cell counts reflected much of the same variance. Finally, age and height were included in all statistical models, as they associate with hand grip strength (Frederiksen et al. 2006), which was also found for the present data (data not shown).

Cross-sectional EWAS with single CpG methylation levels as the outcome and hand grip strength as the explanatory variable, were performed with the use of linear regression models, first on an individual level (individual level EWAS) and next on a twin pair level using an intra-pair design (twin pair level EWAS).

The individual level analysis was carried out with a mixed effect model using twin pairing as a random factor to accommodate for correlations between twins in a pair. Sex, age at blood sampling, height, cell counts (monocytes, lymphocytes and eosinophils) and PC2–4 were included as fixed effects and a dataset variable as a further random effect.

In the twin pair level analysis, a linear regression model was applied (similarly to Starnawska et al. 2017) to test the association between intra-pair DNA methylation difference and intra-pair difference in hand grip strength, while adjusting for sex, mean age of the twin pair at time of blood sampling, as well as for the intra-pair difference in cell counts (monocytes, lymphocytes and eosinophils), height and PCs 2–4. For the 321 intact twin pairs of the present study population the average hand grip strength was 34.0 kg (SD 12.2, range 7–70), while the average intra-pair difference was 4.4 kg (SD 3.8, range 0–21). The intra-pair difference for a given co-variate was calculated by subtracting the co-variate value of the twin with the lower hand grip strength from the co-variate value of the co-twin with the higher hand grip strength. In a sub-analysis, the twin pair level model was applied to the 50% most discordant twin pairs with respect to hand grip strength, thus removing twin pairs with no or little intra-pair difference.

The replication analysis in the 275 BWD individuals was performed as described above, with additional adjustment for birth weight or intra-pair difference in birth weight.

All statistical analyses were carried out in R version 3.3.1.

Longitudinal analyses

An EWAS of hand grip strength over time was carried out by a growth curve model, which employs a linear mixed-effects model with hand grip strength at the different time points as the outcome and the single CpG methylation level, time from baseline to survey, sex, age at blood sampling, cell counts (monocytes, lymphocytes and eosinophils), height and PCs 2–4 as fixed effects. A dataset variable, a twin pairing variable and a twin ID variable (allows individual varying baseline levels) were included as random effects, whereby the twin ID specific effect was nested within the twin pair specific effect. This initial random intercept model was extended by a random slope model (allows individual varying changes in hand grip strength over time). Considering that the two models gave very similar results, although slightly higher p-values in the random slope model (data not shown), only the results from the random intercept model are reported. All statistical analyses were carried out in R version 3.3.1.

Pathways analyses of EWAS findings

CpGs with an uncorrected p-value < 10−3 were annotated using the information by Illumina and the corresponding genes were used for overrepresentation enrichment analysis (ORA) against the genes on the 450K array using the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Reactome databases available through the WEB-based GEne SeT AnaLysis Toolkit (WebGestalt) (Wang et al. 2017). Moreover, an ORA sub-analysis restricted to the 134,070 CpG sites annotated to the Function Feature Type TSS200 (transcription start site (TSS) to − 200 nt upstream of TSS) or TSS1500 (− 200 to − 1500 nt upstream of TSS) was performed. The number of genes investigated and annotated to the databases can be seen in the Online Resource 1, Supplementary Table 1.

Differentially methylated regions

In order to identify differentially methylated regions (DMR), the comb-p approach was applied (Pedersen et al. 2012) for the results obtained in the cross-sectional and longitudinal EWAS. The comb-p has been reported to have high control of false-positive rate and the best sensitivity compared to other popular DMR tools, such as bumphunter, DMRcate and probe lasso (Mallik et al. 2018). The following settings were used: p-value was set to 0.1 to seed a region; maximum 500 bps to skip before finding next seed, if new seed was not found, the region is ended. Gene regions identified were annotated using the BioMart (www.biomart.org) and Ensemble (www.ensembl.org) databases. The gene regions passing correction for multiple testing by false discovery rate (FDR) (see below) were investigated by MSigDB genomic regions enrichment analysis (GREA) by the Genomic Regions Enrichment of Annotations Tools (GREAT) (http://great.stanford.edu) using the BED file output from comb-p as input. The hypergeometric test over genes was applied.

Adjustment for multiple testing

In all analyses adjustment for multiple testing was performed by the Benjamini–Hochberg FDR correction method (Benjamini and Hochberg 1995). The suggested genome-wide significance threshold for EWAS analyses using 450K data has been proposed to be p < 10−6 (Tsai and Bell 2015). However, as we consider this an exploratory study, and in order to give an overview of the top results, we report all findings with an unadjusted p < 10−5 for the EWAS results.

Results

The demographics of the study sample are shown in Table 1. All Supplementary results can be found in the Online Resource 1.

Single CpG epigenome-wide association studies of hand grip strength

Epigenome-wide association studies (EWAS) were performed both on the intake hand grip values for the entire study population, as well as on longitudinal hand grip measurements, which were available for the elderly twin sample (LSADT). The qq-plots of the analyses are shown in the Online Resource 1, Supplementary Fig. 2, while the EWAS results are listed in the Online Resource 1, Supplementary Tables 2–5.

No genome-wide significant associations were observed in the cross-sectional analyses of the 672 individuals (see Table 2A–C). Five CpG sites displayed a p-value below the cut-off of 1 × 10−5 in the individual level EWAS: four of these were located in gene regions, i.e. in the d-glutamate cyclase (DGLUCY), NLR family CARD domain containing (NLRC5), tec protein tyrosine kinase (TEC) and iroquois homeobox 4 (IRX4) genes, respectively, and 1 CpG site was in an intragenic region, the nearest gene being MT-RNR2 Like 1 (MTRNR2L1). Similarly, the twin pair level EWAS displayed 5 CpG sites with a p-value < 1 × 10−5: four were located in gene regions, i.e. in the solute carrier organic anion transporter family member 3A1 (SLCO3A1), calcium voltage-gated channel subunit alpha1 (CACNA1B), RNA binding motif protein 33 (RBM33) and LIM domain binding 2 (LDB2) genes, respectively and 1 CpG site was in an intragenic region, the nearest gene being Lengsin (LGSN). The CpG site cg01763947 in SLCO3A1 was also found to be associated in the analysis of the 50% most discordant twin pairs, together with 6 additional CpG sites.
Table 2

Overview of the results (p-value < 10−5) from the (A–C) cross-sectional EWAS and the (D) longitudinal EWAS (LSADT sample) of hand grip strength

 

CpG

Estimate

SE

P-value

Chr

Position (bp)

Gene

Distance to gene (bp)

Gene—CGI feature

(A) Individual level analysis

cg14304073

− 0.0029

0.0006

2.53E−06

14

91,691,190

DGLUCY

0

3′UTR—open sea

cg16411857

0.0068

0.0015

5.87E−06

16

57,023,191

NLRC5

0

TSS1500—island

cg14057711

− 0.0083

0.0018

6.71E−06

17

22,203,489

MTRNR2L1

− 179,498

IGR—open sea

cg13469425

0.0113

0.0025

7.39E−06

4

48,175,353

TEC

0

Body—open sea

cg03398865

− 0.0058

0.0013

9.20E−06

5

1,879,554

IRX4

0

Body—shore

(B) Intra-pair analysis all

cg01763947

0.0160

0.0032

1.09E−06

15

92,459,894

SLCO3A1

0

Body—shore

cg11680027

0.0123

0.0026

2.76E−06

6

63,921,394

LGSN

64,462

IGR—open sea

cg14140691

0.0100

0.0022

4.97E−06

9

141,012,312

CACNA1B

0

Body—shelf

cg18809570

0.0116

0.0025

5.25E−06

7

155,437,147

RBM33

0

TSS200—island

cg14187877

0.0253

0.0055

5.75E−06

4

16,679,554

LDB2

0

Body—open sea

(C) Intra-pair analysis top 50% discordant

cg01763947

0.0274

0.0055

1.47E−06

15

92,459,894

SLCO3A1

0

Body—shore

cg06663068

0.0176

0.0036

2.11E−06

3

9,994,061

PRRT3; PRRT3*

0

1st Exon; 5′UTR—island

cg15549075

0.0140

0.0029

2.77E−06

2

46,098,874

PRKCE

0

Body—open sea

cg27653831

0.0318

0.0067

4.26E−06

16

67,700,515

C16orf86;ENKD1; ENKD1*

0

TSS1500; 5′UTR; 1stExon—island

cg01874084

0.0314

0.0066

4.86E−06

12

56,320,851

WIBG

0

Body—island

cg05870108

− 0.0466

0.0100

6.80E−06

X

106,749,745

FRMPD3-AS1

6468

IGR—shore

cg10314909

− 0.0278

0.0060

7.46E−06

1

2,524,890

MMEL1

0

Body—shelf

(D) Longitudinal analysis (LSADT sample)

cg15245520

2.2127

0.4781

5.00E−06

21

47,411,200

COL6A1

0

Body—shelf

cg04927341

1.4535

0.3190

6.96E−06

1

86,576,497

COL24A1

0

Body

cg09714181

− 0.6239

0.1371

7.20E−06

5

126,853,350

PRRC1

0

5′UTR;1stExon—island

cg20569588

− 1.5613

0.3448

7.79E−06

1

55,267,285

TTC22

0

TSS1500;TSS1500—shore

cg17070108

2.1001

0.4649

8.22E−06

12

8,758,006

AICDA

0

Body—island

SE standard error, Chr chromosome, bp base pairs, CGI CpG island, UTR untranslated region, TSS transcription start site, IGR intergenic region (CpG is assigned to nearest gene)

*CpG holds different gene feature depending on transcript. Annotation is based on Build 37

None of the 16 top-ranked CpG sites listed in Table 2A–C replicated convincingly in the BWD twins (see the Online Resource 1, Supplementary Table 6).

The longitudinal analysis of hand grip strength in the LSADT sample revealed 5 CpG sites in 5 different genes with a p-value below the cut-off of 1 × 10−5: the collagen type VI alpha 1 chain (COL6A1), collagen type XXIV alpha 1 chain (COL24A1), proline rich coiled-coil 1 (PRRC1), tetratricopeptide repeat domain 22 (TTC22) and activation induced cytidine deaminase (AICDA) genes, respectively (see Table 2D).

In the overrepresentation enrichment analyses (ORA) against the KEGG and Reactome databases of the CpG sites with an uncorrected p-value below 1 × 10−3, none of the identified pathways reached statistical significance after correction for multiple testing. All ORA results with an uncorrected p-value < 0.05 are summarized in the Online Resource 1, Supplementary Table 7.

Analysis of differentially methylated regions

The EWAS results obtained were used for identification of differentially methylation regions (DMRs) by the comb-p algorithm. In total, 70, 58, 51 and 63 DMRs displayed a FDR p-value below 0.05 for the individual level EWAS, the twin pair level EWAS, the twin pair level EWAS for the 50% most discordant pairs and the longitudinal EWAS, respectively (see Tables 3, 4, 5 and 6). Of these, 60, 49, 41 and 54 DMRs, respectively, were annotated to gene regions, while the remaining DMRs were annotated to intergenic regions.
Table 3

DMRs associated to hand grip strength (FDR < 0.05) in the individual level EWAS

DMR position

Lowest p-value

Length

Region p-value

FDR p-value

Gene name/nearest gene (distance in bp)

chr8:48675647–48676093

2.61E−11

7

2.61E−11

2.58E−08

PRKDC (− 10022)

chr17:37024020–37024761

1.01E−09

6

1.15E−09

6.85E−07

LASP1 (− 2092)

chr4:5021084–5021695

4.55E−10

9

6.60E−09

4.77E−06

CYTL1

chr6:32223076–32223431

1.39E−08

10

1.39E−08

1.73E−05

TSBP1-AS1 (− 412)

chr6:33040535–33041584

1.69E−10

11

5.63E−08

2.37E−05

HLA-DPA1

chr6:32920102–32921234

1.89E−08

12

5.79E−08

2.26E−05

AL645941.2

chr6:32920102–32921234

1.89E−08

12

5.79E−08

2.26E−05

HLA-DMA

chr1:223566127–223567174

4.65E−07

11

1.65E−07

6.94E−05

C1orf65

chr6:32120203–32121556

4.03E−10

35

2.43E−07

7.95E−05

PRRT1

chr6:32120203–32121556

4.03E−10

35

2.43E−07

7.95E−05

PPT2

chr5:96078430–96079434

5.05E−06

5

3.31E−07

1.46E−04

CAST

chr1:16163479–16164123

7.29E−08

7

3.49E−07

2.40E−04

FLJ37453

chr6:27482641–27483070

3.69E−07

4

3.70E−07

3.80E−04

AL021918.1

chr6:28972937–28973829

1.94E−08

15

4.85E−07

2.40E−04

ZNF311

chr7:2801424–2802977

4.47E−08

11

1.06E−06

3.03E−04

AMZ1

chr7:2801424–2802977

4.47E−08

11

1.06E−06

3.03E−04

GNA12

chr3:42977777–42978249

3.03E−07

8

1.44E−06

1.35E−03

AC092042.3

chr3:42977777–42978249

3.03E−07

8

1.44E−06

1.35E−03

KRBOX1

chr3:42977777–42978249

3.03E−07

8

1.44E−06

1.35E−03

KRBOX1-AS1

chr4:46126066–46126374

2.85E−06

6

2.85E−06

4.07E−03

GABRG1

chr2:731073–731595

3.54E−06

6

3.65E−06

3.09E−03

AC092159.1

chr5:177412586–177413022

4.73E−06

5

4.73E−06

4.78E−03

AC106795.3 (− 2967)

chr7:156400033–156400991

3.38E−06

7

4.93E−06

2.27E−03

LINC01006

chr2:8596908–8597924

1.13E−04

6

5.15E−06

2.24E−03

LINC00299 (+ 74173)

chr21:47604052–47605175

3.68E−05

8

5.52E−06

2.17E−03

AP001468.1

chr21:47604052–47605175

3.68E−05

8

5.52E−06

2.17E−03

SPATC1L

chr6:33039396–33039805

5.77E−06

7

5.77E−06

6.22E−03

HLA-DPA1

chr19:12831659–12832213

5.73E−06

4

5.90E−06

4.69E−03

TNPO2

chr16:83171068–83171315

6.35E−06

4

6.35E−06

1.13E−02

CDH13

chr6:31323397–31323857

6.54E−06

5

6.77E−06

6.49E−03

HLA-B

chr16:85878940–85879146

6.81E−06

4

6.81E−06

1.45E−02

AC018695.1 (+ 29004)

chr1:202172535–202172913

8.84E−06

5

8.84E−06

1.03E−02

LGR6

chr19:12983876–12984646

1.54E−04

3

9.53E−06

5.45E−03

MAST1

chr1:117529478–117530010

9.46E−06

5

9.81E−06

8.11E−03

PTGFRN

chr1:870791–872236

4.39E−06

10

1.14E−05

3.48E−03

SAMD11

chr1:109204168–109204822

5.39E−05

6

1.18E−05

7.94E−03

HENMT1 (+ 674)

chr20:42142224–42143490

5.30E−06

27

1.25E−05

4.35E−03

L3MBTL1

chr6:159238463–159238745

1.31E−05

3

1.31E−05

2.03E−02

EZR

chr7:16890431–16891080

1.04E−05

6

1.46E−05

9.89E−03

AC073333.2 (+ 731)

chr6:31431312–31431903

4.83E−05

5

1.52E−05

1.13E−02

HCP5

chr6:149772150–149772893

4.66E−05

6

1.54E−05

9.11E−03

ZC3H12D

chr1:161008127–161008978

6.03E−06

11

1.58E−05

8.17E−03

AL591806.3

chr1:161008127–161008978

6.03E−06

11

1.58E−05

8.17E−03

TSTD1

chr10:102278918–102280552

1.89E−05

13

1.62E−05

4.38E−03

SEC31B

chr10:102278918–102280552

1.89E−05

13

1.62E−05

4.38E−03

AL133352.1

chr10:102278918–102280552

1.89E−05

13

1.62E−05

4.38E−03

NDUFB8

chr17:8378756–8379226

3.07E−06

5

1.64E−05

1.53E−02

NDEL1

chr17:8378756–8379226

3.07E−06

5

1.64E−05

1.53E−02

MYH10

chr15:50474069–50474622

9.07E−06

9

2.33E−05

1.85E−02

ATP8B4

chr15:50474069–50474622

9.07E−06

9

2.33E−05

1.85E−02

SLC27A2

chr7:1893710–1894689

1.57E−04

7

2.81E−05

1.26E−02

MAD1L1

chr1:3806715–3808170

9.38E−05

7

2.97E−05

8.98E−03

C1orf174

chr3:21792248–21792992

6.99E−06

8

4.26E−05

2.50E−02

ZNF385D

chr1:156260918–156261404

1.72E−05

6

4.64E−05

4.13E−02

TMEM79

chr1:156260918–156261404

1.72E−05

6

4.64E−05

4.13E−02

C1orf85

chr2:75425832–75426299

1.50E−05

3

4.80E−05

4.44E−02

TACR1

chr1:202130344–202131185

1.03E−04

5

4.90E−05

2.54E−02

PTPN7

chr5:321681–322817

1.07E−03

6

5.21E−05

2.01E−02

PDCD6

chr5:321681–322817

1.07E−03

6

5.21E−05

2.01E−02

AHRR

chr6:28828946–28829765

1.57E−05

19

5.81E−05

3.09E−02

LINC01623

chr6:28828946–28829765

1.57E−05

19

5.81E−05

3.09E−02

RPL13P

chr19:55549414–55550251

3.34E−05

9

6.19E−05

3.21E−02

GP6

chr19:55549414–55550251

3.34E−05

9

6.19E−05

3.21E−02

AC011476.3

chr19:55549414–55550251

3.34E−05

9

6.19E−05

3.21E−02

AC011476.2

chr17:41277847–41278713

4.05E−05

15

6.22E−05

3.12E−02

NBR2

chr17:1944831–1945955

5.84E−05

9

9.94E−05

3.83E−02

DPH1

chr17:1944831–1945955

5.84E−05

9

9.94E−05

3.83E−02

OVCA2

chr19:58569986–58570996

5.42E−05

10

1.09E−04

4.67E−02

ZNF135

chr6:41375881–41377289

5.66E−05

6

1.41E−04

4.33E−02

FOXP4-AS1 (− 86710)

chr12:115130855–115132762

3.38E−03

13

1.63E−04

3.71E−02

TBX3 (+ 10793)

FDR false discovery rate corrected p-value, bp base pairs

Table 4

DMRs associated to hand grip strength (FDR < 0.05) in the twin pair level EWAS

DMR position

Lowest p-value

Length

Region p-value

FDR p-value

Gene name/nearest gene (distance in bp)

chr6:28972891–28973829

2.02E−17

16

2.04E−12

9.60E−10

ZNF311

chr3:182816738–182817627

6.16E−11

13

8.21E−11

4.08E−08

MCCC1

chr8:125462982–125463683

2.28E−09

7

2.38E−09

1.50E−06

TRMT12

chr14:24422368–24423200

2.80E−10

9

1.05E−08

5.56E−06

DHRS4-AS1

chr14:24422368–24423200

2.80E−10

9

1.05E−08

5.56E−06

DHRS4

chr6:33040535–33041584

5.23E−09

11

2.87E−07

1.21E−04

HLA-DPA1

chr19:55972504–55973339

4.14E−08

10

4.52E−07

2.39E−04

ISOC2

chr3:5137549–5138171

6.81E−08

5

6.47E−07

4.60E−04

AC090955.1 (− 3763)

chr2:8597159–8597924

3.98E−04

5

1.48E−06

8.53E−04

LINC00299 (+ 74173)

chr1:24229232–24229683

1.96E−06

5

1.96E−06

1.92E−03

CNR2

chr2:47382287–47382740

2.31E−06

7

2.31E−06

2.25E−03

C2orf61

chr2:47382287–47382740

2.31E−06

7

2.31E−06

2.25E−03

AC073283.3

chr6:30881112–30882214

5.09E−09

31

2.39E−06

9.57E−04

GTF2H4

chr6:30881112–30882214

5.09E−09

31

2.39E−06

9.57E−04

VARS2

chr12:49937852–49938019

2.83E−06

3

2.83E−06

7.46E−03

KCNH3

chr6:33039396–33039805

3.14E−06

7

3.14E−06

3.38E−03

HLA-DPA1

chr18:60051870–60052465

3.45E−06

5

3.84E−06

2.85E−03

TNFRSF11A

chr6:170589483–170589898

4.44E−06

4

4.44E−06

4.71E−03

LINC01624 (+ 923)

chr5:138211043–138211185

5.19E−06

6

5.19E−06

1.60E−02

CTNNA1

chr5:138211043–138211185

5.19E−06

6

5.19E−06

1.60E−02

LRRTM2

chr21:47604052–47605175

2.36E−04

8

7.00E−06

2.75E−03

AP001468.1

chr21:47604052–47605175

2.36E−04

8

7.00E−06

2.75E−03

SPATC1L

chr1:1564422–1565028

1.45E−04

6

7.68E−06

5.58E−03

MIB2

chr15:98195808–98196334

7.46E−06

6

7.69E−06

6.44E−03

LINC00923 (− 89640)

chr2:165811816–165812298

7.39E−06

6

7.75E−06

7.08E−03

SLC38A11

chr7:27182493–27184031

4.51E−06

23

9.47E−06

2.72E−03

AC004080.6

chr7:27182493–27184031

4.51E−06

23

9.47E−06

2.72E−03

HOXA-AS3

chr7:27182493–27184031

4.51E−06

23

9.47E−06

2.72E−03

HOXA3

chr7:27182493–27184031

4.51E−06

23

9.47E−06

2.72E−03

HOXA5

chr12:7315351–7315566

1.02E−05

4

1.02E−05

2.07E−02

AC018653.1 (− 3919)

chr12:54446019–54447350

2.28E−06

18

1.24E−05

4.12E−03

HOXC4

chr7:27231204–27232151

1.02E−04

5

1.67E−05

7.74E−03

AC004080.2

chr9:133306495–133306792

1.98E−05

2

1.98E−05

2.90E−02

HMCN2

chr2:3704363–3705179

2.23E−05

8

2.29E−05

1.23E−02

ALLC (− 1422)

chr7:130131480–130132791

3.38E−06

33

2.37E−05

7.97E−03

MEST

chr22:46449430–46450115

2.22E−05

11

2.56E−05

1.64E−02

AL121672.3

chr22:46449430–46450115

2.22E−05

11

2.56E−05

1.64E−02

PRR34-AS1

chr22:46449430–46450115

2.22E−05

11

2.56E−05

1.64E−02

C22orf26

chr22:46449430–46450115

2.22E−05

11

2.56E−05

1.64E−02

MIRLET7BHG

chr5:134879326–134880437

1.98E−04

6

2.74E−05

1.08E−02

NEUROG1 (+ 8798)

chr2:121776314–121776605

3.25E−05

3

3.25E−05

4.81E−02

GLI2 (+ 26376)

chr10:14372431–14372914

3.79E−05

5

3.97E−05

3.57E−02

FRMD4A

chr17:6899085–6899889

6.27E−06

14

4.10E−05

2.23E−02

ALOX12-AS1

chr17:6899085–6899889

6.27E−06

14

4.10E−05

2.23E−02

ALOX12

chr17:6899085–6899889

6.27E−06

14

4.10E−05

2.23E−02

AC040977.2

chr19:45737208–45738116

2.59E−05

10

4.20E−05

2.02E−02

MARK4

chr19:45737208–45738116

2.59E−05

10

4.20E−05

2.02E−02

EXOC3L2

chr7:56515510–56516505

2.14E−06

12

4.33E−05

1.91E−02

AC092447.7

chr1:223566278–223567174

3.24E−06

10

4.80E−05

2.34E−02

C1orf65

chr6:42927940–42928921

7.20E−06

27

4.84E−05

2.16E−02

GNMT

chr11:61594965–61596756

1.79E−03

12

5.83E−05

1.43E−02

FADS2

chr11:61594965–61596756

1.79E−03

12

5.83E−05

1.43E−02

FADS1

chr1:64668576–64669600

1.02E−04

12

6.45E−05

2.74E−02

UBE2U

chr5:118603742–118604834

1.75E−04

10

7.03E−05

2.80E−02

TNFAIP8

chr6:32976805–32977675

3.32E−05

12

7.45E−05

3.72E−02

HLA-DOA

chr6:30069659–30070404

5.78E−06

17

8.13E−05

4.71E−02

TRIM31 (− 1015)

chr5:140535392–140536497

4.73E−06

9

9.87E−05

3.87E−02

PCDHB17P

chr1:18967020–18968001

9.30E−05

6

1.04E−04

4.56E−02

PAX7

FDR false discovery rate corrected p-value, bp base pairs

Applying the comb-p output for functional enrichment analysis using the GREAT tool (http://great.stanford.edu), revealed 8 significant pathways for the individual level EWAS; of these pathways, 7 were related to immune function or (auto)immune diseases, while 1 was related to C-MYC transcriptional repression (see Table 7). No pathways passed correction for multiple testing for the twin pair EWAS, the twin pair level EWAS for the 50% most discordant pairs and the longitudinal EWAS. All GREAT results with an uncorrected p-value < 0.05 are summarized in the Online Resource 1, Supplementary Table 8.

Discussion

In this study we explored the association between variation in the epigenome and physical functioning represented by hand grip strength using both cross-sectional and longitudinal measures of hand grip strength. The use of twins enabled us to explore epigenetic association independent of the genetic background of the individuals.

Although we did not identify any epigenome-wide significant associations of single CpG sites, some biologically plausible genes were observed (Table 2): the top CpG site of the longitudinal EWAS was located in COL6A1 encoding the alpha 1 subunit of type VI collagen. Collagens are extracellular matrix proteins, maintaining the integrity of various tissues. Mutations in COL6A1 have been reported to cause, among others, Bethlem myopathy (omim.org/entry/158810) and Ullrich Congenital Muscular Dystrophy 1 (omim.org/entry/254090) characterized by increased skeletal muscle weakness and joint stiffness in infancy. Furthermore, a top CpGs of the twin pair EWAS was located in the calcium voltage-gated channel subunit alpha1B (CACNA1B) gene, encoding the alpha-1B pore-forming subunit of an N-type voltage-dependent calcium channel. Voltage-dependent calcium channels are involved in calcium-dependent processes, including muscle contraction, hormone or neurotransmitter release, gene expression and cell death. Mutations in CACNA1B have been reported for, among others, dystonia 23, characterized by involuntary muscle contraction (omim.org/entry/614860). Both genes have been reported to be expressed across a broad range of tissues, including whole blood and skeletal muscle (www.genecards.org).

Only one study on the epigenetics of hand grip strength has been published so far (Marioni et al. 2015) with no significant CpG sites or pathways reported. However, 3 GWAS (Chan et al. 2015, Matteini et al. 2016 and Willems et al. 2017), 2 TWAS (Harries et al. 2012 and Pilling et al. 2016) and 1 genome-wide miRNA study (Murabito et al. 2017) have explored hand grip strength. None of these studies report overlapping genes among their top findings, and none of the suggestive genes of the present study were reported in these studies, neither among the top findings nor among the nominal significant genes reported by Harries et al. (2012), Chan et al. (2015), and Matteini et al. (2016). Furthermore, no significant pathways were identified in the ORA of the present study; 4 pathways did relate to muscle contraction, yet to cardiac and vascular smooth (blood vessel) muscle contraction, not skeletal muscle function (Online Resource 1, Supplementary Table 7). Of the previous studies, a TWAS (Pilling et al. 2016), a genome-wide miRNA study (Murabito et al. 2017) and a GWAS (Willems et al. 2017) reported pathway analyses. Due to differences in methodology a complete comparison was not possible, nevertheless, if grouping pathways by the overall hierarchical level of functional events as defined by the databases, some overlap between the present ORA and the previous studies was seen: pathways related to gene expression (Pilling et al. 2016), signal transduction, the neuronal system and cell cycle/death (Murabito et al. 2017), the immune system (Pilling et al. 2016 and Murabito et al. 2017), DNA repair (Willems et al. 2017), RNA metabolism (Murabito et al. 2017) and protein metabolism (Pilling et al. 2016, Murabito et al. 2017 and Willems et al. 2017). Such overlap could indicate that these processes might relate to hand grip strength, although it must be stressed that these overall categories are broad and hence can reflect a rather broad array of biological functions.

Contrary to the single CpG analysis, the analysis of differentially methylated regions (DMRs), with enriched power by combining multiple CpGs, did show significant findings; of the 203 unique gene regions identified (Tables 3, 4, 5, 6), 5 have been reported by previous studies on hand grip strength: the PLEKHB1 pleckstrin homology domain containing B1 (PLEKHB1) and O-6-methylguanine-DNA methyltransferase (MGMT) genes in the GWAS of Matteini et al. (2016) and Willems et al. (2017), respectively, and the tachykinin receptor 1 (TACR1), G protein-coupled receptor 19 (GPR19) and prostaglandin F2 receptor inhibitor (PTGFRN) genes in the TWAS by Harries et al. (2012). Interestingly, mutations in PLEKHB1 have been reported to cause amyotrophic lateral sclerosis, characterized by problems with muscle control and movement (omim.org/entry/105400).
Table 5

DMRs associated to hand grip strength (FDR < 0.05) in the twin pair level EWAS the 50% most discordant pairs

DMR position

Lowest p-value

Length

Region p-value

FDR p-value

Gene name/nearest gene (distance in bp)

chr7:27182493–27185137

7.03E−21

49

1.44E−20

2.40E−18

AC004080.6

chr7:27182493–27185137

7.03E−21

49

1.44E−20

2.40E−18

HOXA-AS3

chr7:27182493–27185137

7.03E−21

49

1.44E−20

2.40E−18

HOXA3

chr7:27182493–27185137

7.03E−21

49

1.44E−20

2.40E−18

HOXA5

chr7:27182493–27185137

7.03E−21

49

1.44E−20

2.40E−18

HOXA6

chr19:55972504–55973779

4.24E−13

11

1.37E−11

4.74E−09

ISOC2

chr6:28972937–28973829

8.00E−14

15

5.60E−10

2.77E−07

ZNF311

chr19:10735482–10736449

3.87E−08

11

2.56E−08

1.17E−05

SLC44A2

chr8:125462982–125463683

1.85E−08

7

2.40E−08

1.51E−05

TRMT12

chr12:54446019–54446577

4.07E−08

10

4.51E−08

3.57E−05

HOXC4

chr6:84418433–84419361

2.57E−07

13

1.70E−07

8.10E−05

SNAP91

chr16:89118603–89119710

3.98E−07

8

3.77E−07

1.50E−04

AC135782.1

chr17:7311282–7312082

3.96E−08

8

3.04E−07

1.68E−04

NLGN2

chr11:503807–504938

1.25E−06

8

7.47E−07

2.92E−04

RNH1

chr10:134600089–134601342

2.38E−09

21

1.19E−06

4.18E−04

NKX6-2 (+ 1786)

chr3:5137549–5138171

5.51E−07

5

6.00E−07

4.26E−04

AC090955.1 (− 3763)

chr21:47604052–47605175

8.51E−05

8

2.06E−06

8.10E−04

AP001468.1

chr21:47604052–47605175

8.51E−05

8

2.06E−06

8.10E−04

SPATC1L

chr1:6204000–6204225

5.34E−07

2

5.34E−07

1.05E−03

CHD5

chr5:178986131–178986907

1.42E−06

9

2.23E−06

1.27E−03

RUFY1

chr11:315751–316457

1.74E−06

6

2.03E−06

1.27E−03

IFITM2 (+ 1185)

chr5:149669368–149669814

1.99E−06

6

1.99E−06

1.97E−03

CAMK2A

chr18:60051870–60052465

3.08E−06

5

3.28E−06

2.43E−03

TNFRSF11A

chr5:134879326–134880437

7.73E−05

6

6.71E−06

2.66E−03

NEUROG1 (+ 8798)

chr18:7567426–7568892

3.67E−05

11

1.43E−05

4.29E−03

PTPRM

chr1:35586358–35586650

3.29E−06

5

3.29E−06

4.97E−03

EFCAB14P1 (− 1265)

chr14:24422419–24423200

5.16E−06

8

9.16E−06

5.17E−03

DHRS4-AS1

chr14:24422419–24423200

5.16E−06

8

9.16E−06

5.17E−03

DHRS4

chr7:52341469–52342266

8.54E−06

5

9.85E−06

5.45E−03

RF00322 (+ 5001)

chr22:34316316–34317025

1.66E−05

4

1.79E−05

1.12E−02

LARGE

chr6:30881112–30882261

1.12E−05

33

2.94E−05

1.12E−02

GTF2H4

chr6:30881112–30882261

1.12E−05

33

2.94E−05

1.12E−02

VARS2

chr21:46238781–46238958

5.09E−06

3

5.09E−06

1.26E−02

SUMO3 (+ 264)

chr6:164393165–164393861

9.11E−06

7

2.30E−05

1.45E−02

Z97205.2 (− 111890)

chr17:6899085–6899889

9.36E−07

14

3.81E−05

2.07E−02

ALOX12-AS1

chr17:6899085–6899889

9.36E−07

14

3.81E−05

2.07E−02

ALOX12

chr17:6899085–6899889

9.36E−07

14

3.81E−05

2.07E−02

AC040977.2

chr2:165811816–165812298

3.40E−06

6

2.72E−05

2.46E−02

SLC38A11

chr12:12848977–12849589

3.40E−05

10

3.55E−05

2.53E−02

GPR19

chr2:242823275–242824715

6.37E−04

8

8.72E−05

2.64E−02

LINC01237

chr7:27231204–27232151

6.53E−04

5

6.12E−05

2.82E−02

AC004080.2

chr17:4689284–4689894

4.00E−05

7

4.19E−05

2.99E−02

VMO1

chr11:73356744–73358108

1.59E−05

11

9.52E−05

3.04E−02

PLEKHB1

chr1:223566278–223567174

3.35E−05

10

7.17E−05

3.47E−02

C1orf65

chr12:49937852–49938019

1.35E−05

3

1.35E−05

3.50E−02

KCNH3

chr10:102880841–102881546

7.91E−06

7

6.20E−05

3.81E−02

TLX1NB

chr2:121776314–121776605

2.58E−05

3

2.58E−05

3.84E−02

GLI2 (+ 26376)

chr3:182816873–182817627

7.39E−05

12

7.76E−05

4.45E−02

MCCC1

chr12:132293329–132293703

4.01E−05

5

4.01E−05

4.63E−02

RNU6-1017P (− 6479)

chr13:95363755–95364334

1.56E−03

4

6.32E−05

4.71E−02

SOX21

chr1:64668576–64669600

3.28E−04

12

1.17E−04

4.91E−02

UBE2U

FDR false discovery rate corrected p-value, bp base pairs

Table 6

DMRs associated to hand grip strength (FDR < 0.05) in the longitudinal EWAS (LSADT sample)

DMR position

Lowest p-value

Length

Region p-value

FDR p-value

Gene name/nearest gene (distance in bp)

chr13:97646341–97647223

5.71E−14

10

1.51E−12

7.55E−10

OXGR1

chr5:126852955–126853955

6.40E−14

16

1.54E−11

6.79E−09

PRRC1

chr19:48894694–48895031

3.46E−11

9

3.46E−11

4.53E−08

KDELR1

chr7:30544055–30545075

4.40E−13

14

3.51E−10

1.52E−07

GGCT

chr5:135414858–135416614

1.50E−07

20

8.51E−10

2.14E−07

VTRNA2-1

chr7:27141774–27143253

4.48E−10

19

3.16E−09

9.42E−07

HOXA2

chr1:16163479–16164123

6.11E−09

7

1.33E−08

9.15E−06

FLJ37453

chr22:32598479–32598571

4.18E−08

2

4.18E−08

2.01E−04

RFPL2

chr6:28956226–28956990

1.83E−08

22

8.13E−08

4.70E−05

HCG16

chr6:170054730–170055333

7.11E−08

3

9.50E−08

6.96E−05

WDR27

chr5:92956474–92957401

6.54E−08

11

1.73E−07

8.25E−05

MIR2277

chr5:92956474–92957401

6.54E−08

11

1.73E−07

8.25E−05

FAM172A

chr3:185911208–185912487

5.59E−06

9

2.06E−07

7.10E−05

DGKG

chr7:130130122–130132791

5.75E−06

60

4.19E−07

6.93E−05

MEST

chr10:135039948–135040251

5.60E−07

4

5.60E−07

8.16E−04

KNDC1 (+ 335)

chr17:35423405–35424060

4.76E−07

6

9.87E−07

6.65E−04

AATF (+ 9889)

chr14:101290717–101293451

3.36E−05

30

1.02E−06

1.65E−04

MEG3

chr11:128693567–128694680

1.14E−08

10

1.52E−06

6.03E−04

FLI1 (+ 11518)

chr18:77280171–77280587

1.92E−06

4

1.92E−06

2.04E−03

NFATC1

chr8:1094484–1094905

2.02E−06

6

2.02E−06

2.11E−03

ERICH1-AS1 (+ 7128)

chr5:162864268–162865237

1.03E−06

13

2.14E−06

9.77E−04

CCNG1 (− 307)

chr5:162864268–162865237

1.03E−06

13

2.14E−06

9.77E−04

CCNG1

chr11:15095017–15095979

2.35E−06

10

2.25E−06

1.03E−04

CALCB

chr5:180045597–180046053

2.15E−06

3

2.74E−06

2.65E−03

FLT4

chr4:39448160–39449131

2.45E−06

7

3.40E−06

1.55E−03

KLB

chr5:134362967–134364718

1.05E−05

11

5.66E−06

1.43E−03

PITX1

chr12:247963–249287

4.05E−05

6

5.71E−06

1.90E−03

IQSEC3

chr12:247963–249287

4.05E−05

6

5.71E−06

1.90E−03

AC026369.1

chr6:170589483–170589898

7.25E−06

4

7.25E−06

7.69E−03

LINC01624 (+ 923)

chr8:61626185–61627282

5.49E−09

6

7.69E−06

3.09E−03

CHD7

chr1:203320190–203320542

1.41E−05

6

1.41E−05

1.75E−02

FMOD

chr20:57463265–57465140

1.36E−05

41

1.44E−05

3.38E−03

AL121917.1

chr20:57463265–57465140

1.36E−05

41

1.44E−05

3.38E−03

GNAS

chr10:131263962–131265797

6.68E−06

27

1.64E−05

3.95E−03

MGMT

chr13:112209007–112209222

1.67E−05

3

1.67E−05

3.37E−02

AL359649.1 (− 31541)

chr13:100217962–100219014

8.56E−05

6

1.70E−05

7.10E−03

TM9SF2 (+ 2754)

chr14:23623480–23624378

1.44E−05

8

1.80E−05

8.79E−03

SLC7A8

chr18:77905119–77905948

1.37E−05

10

1.88E−05

9.95E−03

ADNP2

chr18:77905119–77905948

1.37E−05

10

1.88E−05

9.95E−03

PARD6G-AS1

chr3:67704629–67705286

9.14E−06

7

2.26E−05

1.51E−02

SUCLG2-AS1

chr3:67704629–67705286

9.14E−06

7

2.26E−05

1.51E−02

SUCLG2

chr6:33872206–33872862

5.35E−05

7

2.98E−05

1.98E−02

AL138889.1

chr7:157981849–157982590

2.24E−05

8

3.21E−05

1.89E−02

PTPRN2

chr2:61371880–61372317

3.56E−05

9

3.56E−05

3.53E−02

AC016747.1

chr2:61371880–61372317

3.56E−05

9

3.56E−05

3.53E−02

KIAA1841

chr2:61371880–61372317

3.56E−05

9

3.56E−05

3.53E−02

C2orf74

chr22:38092643–38093208

2.42E−05

11

3.80E−05

2.93E−02

NOL12

chr22:38092643–38093208

2.42E−05

11

3.80E−05

2.93E−02

TRIOBP

chr2:177013630–177015126

5.13E−05

15

3.82E−05

1.12E−02

MIR10B

chr2:177013630–177015126

5.13E−05

15

3.82E−05

1.12E−02

HOXD3

chr11:2019129–2020561

8.85E−05

38

3.93E−05

1.20E−02

H19

chr4:10020360–10021356

1.80E−04

6

4.06E−05

1.78E−02

SLC2A9

chr6:109611667–109612230

4.59E−05

4

5.66E−05

4.35E−02

PTCHD3P3

chr14:103366987–103367859

9.99E−05

6

5.82E−05

2.91E−02

TRAF3

chr8:1365049–1365907

4.61E−05

6

6.88E−05

3.48E−02

AF067845.1 (+ 47908)

chr22:31317764–31318547

1.47E−05

12

6.94E−05

3.84E−02

MORC2-AS1

chr6:32144667–32146780

2.34E−05

39

7.14E−05

1.48E−02

AGPAT1

chr6:32144667–32146780

2.34E−05

39

7.14E−05

1.48E−02

RNF5

chr7:90895894–90896702

6.45E−05

4

7.36E−05

3.95E−02

FZD1

chr11:6290951–6292897

1.12E−04

13

8.42E−05

1.89E−02

CCKBR

chr1:206858537–206859687

3.68E−05

9

9.04E−05

3.42E−02

MAPKAPK2

chr16:68482144–68483195

5.76E−05

12

1.05E−04

4.31E−02

SMPD3

chr12:19592184–19593685

5.58E−04

10

1.26E−04

3.63E−02

AEBP2

FDR false discovery rate corrected p-value, bp base pairs

Moreover, a number of genes were observed in more than one comb-p analysis (Online Resource 1, Supplementary Table 8): the major histocompatibility complex, class II, DP alpha 1 (HLA-DPA1), AP001468.1 (RNA antisense gene), coiled-coil domain containing 185 (C1orf65), long intergenic non-protein coding RNA 299 (LINC00299), zinc finger protein 311 (ZNF311) and spermatogenesis and centriole associated 1 like (SPATC1L) genes were seen in the individual level EWAS and the twin pair level EWAS, while the uncharacterized LOC729614 (FLJ37453), mesoderm specific transcript (MEST) and long intergenic non-protein coding RNA 1624 (LINC01624) genes were observed in the longitudinal EWAS, as well as in a cross sectional EWAS. The latter finding might indicate that FLJ37453, MEST and LINC01624 could be relevant for both the level of hand grip strength at one point in time and the spanning over several time points. The biological functions of FLJ37453 and LINC01624 are largely unknown, while MEST is known to encode an alpha/beta hydrolase protein. Mutations in MEST cause among others the Russell–Silver syndrome (omim.org/entry/180860), characteristics including short stature.

Major histocompatibility complex genes were also revealed in the gene sets passing correction for multiple testing in the genomic regions enrichment analysis (GREA) (Table 7): the major histocompatibility complex, class I, C (HLA-C), major histocompatibility complex, class I, B (HLA-B), major histocompatibility complex, class II, DM alpha (HLA-DMA) and major histocompatibility complex, class II, DP beta 1 (HLA-DPB1) genes did, together with the (non-muscular) myosin heavy chain 10 (MYH10), endoplasmic reticulum aminopeptidase 1 (ERAP1), and interferon regulatory factor 8 (IRF8) genes, constitute the 7 pathways related to the immune system or (auto)immune diseases. Some of these genes have been reported to be mutated in diseases related to the immune system, e.g. HLA-DMA: rheumatoid arthritis and lupus, HLA-C: psoriasis and HIV, HLA-B: ankylosing spondylitis (arthritis of the spine) and IRF8 and HLA-DPB1: immune deficiencies (www.genecards.org). Moreover, when considering all nominally significant findings from the GREA (Online Resource 1, Supplementary Table 8), 31 out of 80 unique gene sets were related to immune function. These findings point to immune function as important for hand grip strength, which might not be surprising considering the well-known role of the immune system in muscle growth and regeneration (Tidball 2017), a process which declines during aging, contributing to the occurrence of sarcopenia (Domingues-Faria et al. 2016).
Table 7

Pathways passing correction for multiple testing (FDR < 0.05) in GREAT genomics regions enrichment analysis (GREA) of comb-p identified DMRs (individual level EWAS)

Overall level of functional class

Name (ID)

P-value

FDR

p-value

Fold enrichment

Expected

Observed gene hits

Total genes

Gene set coverage

Term gene coverage

Human diseases; Cardiovascular diseases

Viral myocarditis (KEGG hsa05416)

1.80E−05

0.024

15.42

0.32

HLA-DMA, HLA-DPB1, HLA-C, HLA-B, MYH10

68

0.058

0.074

Human diseases; Immune diseases

Allograft rejection (KEGG hsa05330)

2.25E−05

0.015

23.97

0.17

HLA-DMA, HLA-DPB1, HLA-C, HLA-B

35

0.047

0.114

Human diseases; Immune diseases

Graft-versus-host disease (KEGG hsa05332)

2.82E−05

0.012

22.68

0.18

HLA-DMA, HLA-DPB1, HLA-C, HLA-B

37

0.047

0.108

Human diseases; Endocrine and metabolic diseases

Type I diabetes mellitus (KEGG hsa04940)

4.26E−05

0.014

20.47

0.20

HLA-DMA, HLA-DPB1, HLA-C, HLA-B

41

0.047

0.098

Human diseases; Immune diseases

Autoimmune thyroid disease (KEGG hsa05320)

9.38E−05

0.025

16.78

0.24

HLA-DMA, HLA-DPB1, HLA-C, HLA-B

50

0.047

0.080

Immune system

Genes involved in Antigen Presentation: Folding, assembly and peptide loading of class I MHC (Reactome R-HSA-983170)

1.12E−04

0.025

31.47

0.10

ERAP-1, HLA-C, HLA-B

20

0.035

0.150

Immune system

Genes involved in Interferon gamma signaling (Reactome R-HSA-877300)

1.91E−04

0.036

13.99

0.29

HLA-DPB1, HLA-C, HLA-B, IRF8

60

0.047

0.067

NA

Validated targets of C-MYC transcriptional repression (PID_MYC_REPRESS_PATHWAY)

2.31E−04

0.038

13.32

0.30

CEBPD, ID2, IRF8, BRCA1

63

0.047

0.064

FDR false discovery rate corrected p value, Fold enrichment fold enrichment of number of genomic regions in the test set with the annotation, Expected expected number of genomic regions in the test set with the annotation, Observed gene hits actual number of genomic regions in the test set with the annotation, Total Genes number of genes in the genome with the annotation, Set Coverage the fraction of all genes in the test set with the annotation, Term Coverage fraction of all genes with the annotation that are tagged by the test set

The last gene set found in the GREA related to negative regulation of cell differentiation and was composed of the IRF8, the CCAAT enhancer binding protein delta (CEBPD), inhibitor of DNA binding 2 (ID2) and BRCA1 DNA repair associated (BRCA1) genes. The MYC10, ID2 and BRCA1 genes are involved in cancer, while CEBPD is involved in juvenile diseases of the central nervous system, as well as in regulation of genes involved in immune and inflammatory responses (www.genecards.org). Furthermore, as mentioned in the introduction, an interaction partner of CEBPD, CEBPB, has been implicated in muscle cell differentiation and muscle repair (Matteini et al. 2016), and was reported in the TWAS by Harries et al. (2012). Also, ID2 has been related to muscle cell differentiation (www.genecards.org), indicating a role of these two genes in muscle biology, potentially mediated via repression of cell differentiation or immune function.

Finally, in the GREA the individual level EWAS displayed significant findings (Table 7), while the twin pair level EWAS and the longitudinal EWAS did not. One reason could be that the latter holds lower power, as it only included the elderly part of the study population. Moreover, the twin pair EWAS controls for genetic variation by analyzing intra-pair differences by linear regression, while the individual level EWAS accommodate the intra-pair correlation by inclusion of twin pairing as a random factor, which relies on the fulfillment of the assumptions of the model. Hence, it is possible that the individual level EWAS results are, least in part, influenced by genetic variations left unaccounted for.

A possible limitation of the present study is the use of DNA methylation data from whole blood rather than a tissue more intuitively relevant for a muscle related trait. It is well known that epigenetics is important for differentiation of and aging in muscle cells (Carrió and Suelves 2015; Zykovich et al. 2014), and that, although there is some overlap, differences in the epigenetic signature of muscle tissue and blood cells have been reported (Slieker et al. 2013). The findings by Livshits et al. (2016) on muscle mass do, however, indicate that the use of blood samples for epigenetic studies is a feasible approach for identification of muscle-related epigenetic changes, although obviously restricted to changes that are mirrored in blood. This line of thought is supported by the blood-based gene expression study performed by Pilling et al. (2016) on hand grip strength, which found several genes previously reported to be associated to muscle function (Pilling et al. 2016). In any case, to decipher the muscle cell specific epigenetic signatures related to muscle strength, future studies on the less accessible muscle cell derived DNA methylation data and hand grip strength are highly relevant. Furthermore, as the age-related decline in muscle strength is a multifactorial and complex process affected by for instance cognitive, neural and hormonal processes (Manini and Clark 2012; Tieland et al. 2018), we cannot exclude that other physiological aging-related phenomena are reflected in the blood samples under study here, and that the results obtained might reflect such phenomena. Specifically, we can not exclude that the findings might, at least in part, reflect frailty, especially in the elderly part of the study population, as decreased hand grip strength is one component of the frailty phenotype (e.g. Li et al. 2019). Most importantly, this point is supported by the fact that the most significant biological pathways found in this study are highly implicated in immune function. In the literature, the strong connection between immune function and physical frailty has been extensively studied and discussed (Lu et al. 2016; Fuentes et al. 2017). In this sense, findings from this exploratory study might provide potential references for exploring the epigenetics of age-related loss of physical capacity.

In conclusion, in this exploratory study we identified several epigenetic markers of potential importance to physical functioning, as reflected by hand grip strength, using both cross-sectional and longitudinal data from twins. Furthermore, investigation of differentially methylated regions identified significant genomic regions and pathways related to the immune system and autoimmune diseases. However, additional studies are needed to further clarify the potential importance of these markers and regions. Such clarification could elucidate the mechanisms underlining maintenance and loss of muscle strength and age-related loss of physical capacity.

Notes

Acknowledgements

This study was supported by The Danish Council for Independent Research—Medical Sciences (DFF-6110-00016), the European Union’s Seventh Framework Programme (FP7/2007–2011) under grant Agreement No. 259679 and The Danish National Program for Research Infrastructure 2007 (09-063256).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Informed consent

Informed consents to participate in the surveys were obtained from all participants in the surveys.

Supplementary material

10522_2019_9818_MOESM1_ESM.xlsx (93.5 mb)
Supplementary material 1 (XLSX 95703 kb)

References

  1. Arvandi M, Strasser B, Meisinger C, Volaklis K, Gothe RM, Siebert U, Ladwig KH, Grill E, Horsch A, Laxy M, Peters A, Thorand B (2016) Gender differences in the association between grip strength and mortality in older adults: results from the KORA-age study. BMC Geriatr 16(1):201.  https://doi.org/10.1186/s12877-016-0381-4 CrossRefGoogle Scholar
  2. Aryee MJ, Jaffe AE, Corrada-Bravo H, Ladd-Acosta C, Feinberg AP, Hansen KD, Irizarry RA (2014) Minfi: a flexible and comprehensive Bioconductor package for the analysis of Infinium DNA methylation microarrays. Bioinformatics 30(10):1363–1369.  https://doi.org/10.1093/bioinformatics/btu049 CrossRefGoogle Scholar
  3. Beaudart C, Zaaria M, Pasleau F, Reginster JY, Bruyère O (2017) Health outcomes of sarcopenia: a systematic review and meta-analysis. PLoS ONE 12(1):e0169548.  https://doi.org/10.1371/journal.pone.0169548 CrossRefGoogle Scholar
  4. Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate a practical and powerful approach to multiple testing. J R Stat Soc B 57:289–300.  https://doi.org/10.2307/2346101 Google Scholar
  5. Bohannon RW, Magasi SR, Bubela DJ, Wang YC, Gershon RC (2012) Grip and knee extension muscle strength reflect a common construct among adults. Muscle Nerve 46(4):555–558.  https://doi.org/10.1002/mus.23350 CrossRefGoogle Scholar
  6. Carrió E, Suelves M (2015) DNA methylation dynamics in muscle development and disease. Front Aging Neurosci 7:19.  https://doi.org/10.3389/fnagi.2015.00019 CrossRefGoogle Scholar
  7. Celis-Morales CA, Welsh P, Lyall DM, Steell L, Petermann F, Anderson J, Iliodromiti S, Sillars A, Graham N, Mackay DF, Pell JP, Gill JMR, Sattar N, Gray SR (2018) Associations of grip strength with cardiovascular, respiratory, and cancer outcomes and all cause mortality: prospective cohort study of half a million UK Biobank participants. BMJ 361:k1651.  https://doi.org/10.1136/bmj.k1651 Google Scholar
  8. Chan JP, Thalamuthu A, Oldmeadow C, Armstrong NJ, Holliday EG, McEvoy M, Kwok JB, Assareh AA, Peel R, Hancock SJ, Reppermund S, Menant J, Trollor JN, Brodaty H, Schofield PR, Attia JR, Sachdev PS, Scott RJ, Mather KA (2015) Genetics of hand grip strength in mid to late life. Age (Dordr) 37(1):9745.  https://doi.org/10.1007/s11357-015-9745-5 CrossRefGoogle Scholar
  9. Chen YA, Lemire M, Choufani S, Butcher DT, Grafodatskaya D, Zanke BW, Gallinger S, Hudson TJ, Weksberg R (2013) Discovery of cross-reactive probes and polymorphic CpGs in the Illumina Infinium HumanMethylation450 microarray. Epigenetics 8(2):203–209.  https://doi.org/10.4161/epi.23470 CrossRefGoogle Scholar
  10. Cheung CL, Nguyen US, Au E, Tan KC, Kung AW (2013) Association of handgrip strength with chronic diseases and multimorbidity: a cross-sectional study. Age (Dordr) 35(3):929–941.  https://doi.org/10.1007/s11357-012-9385-y CrossRefGoogle Scholar
  11. Dodds RM, Syddall HE, Cooper R, Benzeval M, Deary IJ, Dennison EM, Der G, Gale CR, Inskip HM, Jagger C, Kirkwood TB, Lawlor DA, Robinson SM, Starr JM, Steptoe A, Tilling K, Kuh D, Cooper C, Sayer AA (2014) Grip strength across the life course: normative data from twelve British studies. PLoS ONE 9(12):e113637CrossRefGoogle Scholar
  12. Domingues-Faria C, Vasson MP, Goncalves-Mendes N, Boirie Y, Walrand S (2016) Skeletal muscle regeneration and impact of aging and nutrition. Ageing Res Rev 26:22–36.  https://doi.org/10.1016/j.arr.2015.12.004 CrossRefGoogle Scholar
  13. Du P, Zhang X, Huang CC, Jafari N, Kibbe WA, Hou L, Lin SM (2010) Comparison of Beta-value and M-value methods for quantifying methylation levels by microarray analysis. BMC Bioinform 11:587.  https://doi.org/10.1186/1471-2105-11-587 CrossRefGoogle Scholar
  14. Fortin JP, Labbe A, Lemire M, Zanke BW, Hudson TJ, Fertig EJ, Greenwood CM, Hansen KD (2014) Functional normalization of 450 k methylation array data improves replication in large cancer studies. Genome Biol 15(12):503.  https://doi.org/10.1186/s13059-014-0503-2 CrossRefGoogle Scholar
  15. Frederiksen H, Gaist D, Petersen HC, Hjelmborg J, McGue M, Vaupel JW, Christensen K (2002) Hand grip strength: a phenotype suitable for identifying genetic variants affecting mid- and late-life physical functioning. Genet Epidemiol 23(2):110–122.  https://doi.org/10.1002/gepi.1127 CrossRefGoogle Scholar
  16. Frederiksen H, Hjelmborg J, Mortensen J, McGue M, Vaupel JW, Christensen K (2006) Age trajectories of grip strength: cross-sectional and longitudinal data among 8,342 Danes aged 46 to 102. Ann Epidemiol 16(7):554–562.  https://doi.org/10.1016/j.annepidem.2005.10.006 CrossRefGoogle Scholar
  17. Fritz NE, McCarthy CJ, Adamo DE (2017) Handgrip strength as a means of monitoring progression of cognitive decline—a scoping review. Ageing Res Rev 35:112–123.  https://doi.org/10.1016/j.arr.2017.01.004 CrossRefGoogle Scholar
  18. Frost M, Petersen I, Brixen K, Beck-Nielsen H, Holst JJ, Christiansen L, Højlund K, Christensen K (2012) Adult glucose metabolism in extremely birthweight-discordant monozygotic twins. Diabetologia 55(12):3204–3212.  https://doi.org/10.1007/s00125-012-2695-x CrossRefGoogle Scholar
  19. Fuentes E, Fuentes M, Alarcón M, Palomo I (2017) Immune system dysfunction in the elderly. An Acad Bras Cienc 89(1):285–299.  https://doi.org/10.1590/0001-3765201720160487 CrossRefGoogle Scholar
  20. Fukumori N, Yamamoto Y, Takegami M, Yamazaki S, Onishi Y, Sekiguchi M, Otani K, Konno S, Kikuchi S, Fukuhara S (2015) Association between hand-grip strength and depressive symptoms: Locomotive Syndrome and Health Outcomes in Aizu Cohort Study (LOHAS). Age Ageing 44(4):592–598.  https://doi.org/10.1093/ageing/afv013 CrossRefGoogle Scholar
  21. Garatachea N, Lucía A (2013) Genes and the ageing muscle: a review on genetic association studies. Age (Dordr) 35(1):207–233.  https://doi.org/10.1007/s11357-011-9327-0 CrossRefGoogle Scholar
  22. Harries LW, Pilling LC, Hernandez LD, Bradley-Smith R, Henley W, Singleton AB, Guralnik JM, Bandinelli S, Ferrucci L, Melzer D (2012) CCAAT-enhancer-binding protein-beta expression in vivo is associated with muscle strength. Aging Cell 11(2):262–268.  https://doi.org/10.1111/j.1474-9726.2011.00782.x CrossRefGoogle Scholar
  23. Lawman HG, Troiano RP, Perna FM, Wang CY, Fryar CD, Ogden CL (2016) Associations of relative handgrip strength and cardiovascular disease biomarkers in U.S. adults, 2011-2012. Am J Prev Med 50(6):677–683.  https://doi.org/10.1016/j.amepre.2015.10.022 CrossRefGoogle Scholar
  24. Lee WJ, Peng LN, Chiou ST, Chen LK (2016) Relative handgrip strength is a simple indicator of cardiometabolic risk among middle-aged and older people: a nationwide population-based study in Taiwan. PLoS ONE 11(8):e0160876.  https://doi.org/10.1371/journal.pone.0160876 CrossRefGoogle Scholar
  25. Leong DP, Teo KK, Rangarajan S, Lopez-Jaramillo P, Avezum A Jr, Orlandini A, Seron P, Ahmed SH, Rosengren A, Kelishadi R, Rahman O, Swaminathan S, Iqbal R, Gupta R, Lear SA, Oguz A, Yusoff K, Zatonska K, Chifamba J, Igumbor E, Mohan V, Anjana RM, Gu H, Li W, Yusuf S, Prospective Urban Rural Epidemiology (PURE) Study investigators (2015) Prognostic value of grip strength: findings from the Prospective Urban Rural Epidemiology (PURE) study. Lancet 386(9990):266–273.  https://doi.org/10.1016/S0140-6736(14)62000-6 CrossRefGoogle Scholar
  26. Li X, Ploner A, Karlsson IK, Liu X, Magnusson PKE, Pedersen NL, Hägg S, Jylhävä J (2019) The frailty index is a predictor of cause-specific mortality independent of familial effects from midlife onwards: a large cohort study. BMC Med. 17(1):94.  https://doi.org/10.1186/s12916-019-1331-8 CrossRefGoogle Scholar
  27. Livshits G, Gao F, Malkin I, Needhamsen M, Xia Y, Yuan W, Bell CG, Ward K, Liu Y, Wang J, Bell JT, Spector TD (2016) Contribution of heritability and epigenetic factors to skeletal muscle mass variation in United Kingdom twins. J Clin Endocrinol Metab 101(6):2450–2459.  https://doi.org/10.1210/jc.2016-1219 CrossRefGoogle Scholar
  28. Lu Y, Tan CT, Nyunt MS, Mok EW, Camous X, Kared H, Fulop T, Feng L, Ng TP, Larbi A (2016) Inflammatory and immune markers associated with physical frailty syndrome: findings from Singapore longitudinal aging studies. Oncotarget 7(20):28783–28795.  https://doi.org/10.18632/oncotarget.8939 CrossRefGoogle Scholar
  29. Mainous AG 3rd, Tanner RJ, Anton SD, Jo A (2015) Grip strength as a marker of hypertension and diabetes in healthy weight adults. Am J Prev Med 49(6):850–858.  https://doi.org/10.1016/j.amepre.2015.05.025 CrossRefGoogle Scholar
  30. Mallik S, Odom GJ, Gao Z, Gomez L, Chen X, Wang L (2018) An evaluation of supervised methods for identifying differentially methylated regions in Illumina methylation arrays. Brief Bioinform.  https://doi.org/10.1093/bib/bby085 Google Scholar
  31. Manini TM, Clark BC (2012) Dynapenia and aging: an update. J Gerontol A Biol Sci Med Sci 67(1):28–40.  https://doi.org/10.1093/gerona/glr010 CrossRefGoogle Scholar
  32. Marioni RE, Shah S, McRae AF, Ritchie SJ, Muniz-Terrera G, Harris SE, Gibson J, Redmond P, Cox SR, Pattie A, Corley J, Taylor A, Murphy L, Starr JM, Horvath S, Visscher PM, Wray NR, Deary IJ (2015) The epigenetic clock is correlated with physical and cognitive fitness in the Lothian Birth Cohort 1936. Int J Epidemiol 44(4):1388–1396.  https://doi.org/10.1093/ije/dyu277 CrossRefGoogle Scholar
  33. Matteini AM, Tanaka T, Karasik D, Atzmon G, Chou WC, Eicher JD, Johnson AD, Arnold AM, Callisaya ML, Davies G, Evans DS, Holtfreter B, Lohman K, Lunetta KL, Mangino M, Smith AV, Smith JA, Teumer A, Yu L, Arking DE, Buchman AS, Chibinik LB, De Jager PL, Evans DA, Faul JD, Garcia ME, Gillham-Nasenya I, Gudnason V, Hofman A, Hsu YH, Ittermann T, Lahousse L, Liewald DC, Liu Y, Lopez L, Rivadeneira F, Rotter JI, Siggeirsdottir K, Starr JM, Thomson R, Tranah GJ, Uitterlinden AG, Völker U, Völzke H, Weir DR, Yaffe K, Zhao W, Zhuang WV, Zmuda JM, Bennett DA, Cummings SR, Deary IJ, Ferrucci L, Harris TB, Kardia SL, Kocher T, Kritchevsky SB, Psaty BM, Seshadri S, Spector TD, Srikanth VK, Windham BG, Zillikens MC, Newman AB, Walston JD, Kiel DP, Murabito JM (2016) GWAS analysis of handgrip and lower body strength in older adults in the CHARGE consortium. Aging Cell 15(5):792–800.  https://doi.org/10.1111/acel.12468 CrossRefGoogle Scholar
  34. McGue M, Christensen K (2007) Social activity and healthy aging: a study of aging Danish twins. Twin Res Hum Genet 10(2):255–265.  https://doi.org/10.1375/twin.10.2.255 CrossRefGoogle Scholar
  35. Miller SA, Dykes DD, Polesky HF (1988) A simple salting out procedure for extracting DNA from human nucleated cells. Nucleic Acids Res 16(3):1215CrossRefGoogle Scholar
  36. Murabito JM, Rong J, Lunetta KL, Huan T, Lin H, Zhao Q, Freedman JE, Tanriverdi K, Levy D, Larson MG (2017) Cross-sectional relations of whole-blood miRNA expression levels and hand grip strength in a community sample. Aging Cell 16(4):888–894.  https://doi.org/10.1111/acel.12622 CrossRefGoogle Scholar
  37. Newman AB, Kupelian V, Visser M, Simonsick EM, Goodpaster BH, Kritchevsky SB, Tylavsky FA, Rubin SM, Harris TB (2006) Strength, but not muscle mass, is associated with mortality in the health, aging and body composition study cohort. J Gerontol A 61(1):72–77CrossRefGoogle Scholar
  38. Pedersen BS, Schwartz DA, Yang IV, Kechris KJ (2012) Comb-p: software for combining, analyzing, grouping and correcting spatially correlated P-values. Bioinformatics 28(22):2986–2988.  https://doi.org/10.1093/bioinformatics/bts545 CrossRefGoogle Scholar
  39. Pilling LC, Joehanes R, Kacprowski T, Peters M, Jansen R, Karasik D, Kiel DP, Harries LW, Teumer A, Powell J, Levy D, Lin H, Lunetta K, Munson P, Bandinelli S, Henley W, Hernandez D, Singleton A, Tanaka T, van Grootheest G, Hofman A, Uitterlinden AG, Biffar R, Gläser S, Homuth G, Malsch C, Völker U, Penninx B, van Meurs JB, Ferrucci L, Kocher T, Murabito J, Melzer D (2016) Gene transcripts associated with muscle strength: a CHARGE meta-analysis of 7,781 persons. Physiol Genomics 48(1):1–11.  https://doi.org/10.1152/physiolgenomics.00054.2015 CrossRefGoogle Scholar
  40. Rijk JM, Roos PR, Deckx L, van den Akker M, Buntinx F (2016) Prognostic value of handgrip strength in people aged 60 years and older: a systematic review and meta-analysis. Geriatr Gerontol Int 16(1):5–20.  https://doi.org/10.1111/ggi.12508 CrossRefGoogle Scholar
  41. Skytthe A, Christiansen L, Kyvik KO, Bødker FL, Hvidberg L, Petersen I, Nielsen MM, Bingley P, Hjelmborg J, Tan Q, Holm NV, Vaupel JW, McGue M, Christensen K (2013) The Danish Twin Registry: linking surveys, national registers, and biological information. Twin Res Hum Genet 16(1):104–111.  https://doi.org/10.1017/thg.2012.77 CrossRefGoogle Scholar
  42. Slieker RC, Bos SD, Goeman JJ, Bovée JV, Talens RP, van der Breggen R, Suchiman HE, Lameijer EW, Putter H, van den Akker EB, Zhang Y, Jukema JW, Slagboom PE, Meulenbelt I, Heijmans BT (2013) Identification and systematic annotation of tissue-specific differentially methylated regions using the Illumina 450 k array. Epigenet Chromatin 6(1):26.  https://doi.org/10.1186/1756-8935-6-26 CrossRefGoogle Scholar
  43. Starnawska A, Tan Q, McGue M, Mors O, Børglum AD, Christensen K, Nyegaard M, Christiansen L (2017) Epigenome-wide association study of cognitive functioning in middle-aged monozygotic twins. Front Aging Neurosci 9:413.  https://doi.org/10.3389/fnagi.2017.00413 CrossRefGoogle Scholar
  44. Tan Q, Frost M, Heijmans BT, von Bornemann Hjelmborg J, Tobi EW, Christensen K, Christiansen L (2014) Epigenetic signature of birth weight discordance in adult twins. BMC Genomics 15:1062.  https://doi.org/10.1186/1471-2164-15-1062 CrossRefGoogle Scholar
  45. Tidball JG (2017) Regulation of muscle growth and regeneration by the immune system. Nat Rev Immunol 17(3):165–178.  https://doi.org/10.1038/nri.2016.150 CrossRefGoogle Scholar
  46. Tieland M, Trouwborst I, Clark BC (2018) Skeletal muscle performance and ageing. J Cachexia Sarcopenia Muscle 9(1):3–19.  https://doi.org/10.1002/jcsm.12238 CrossRefGoogle Scholar
  47. Tobi EW, Slieker RC, Stein AD, Suchiman HE, Slagboom PE, van Zwet EW, Heijmans BT, Lumey LH (2015) Early gestation as the critical time-window for changes in the prenatal environment to affect the adult human blood methylome. Int J Epidemiol 44(4):1211–1223.  https://doi.org/10.1093/ije/dyv043 CrossRefGoogle Scholar
  48. Tsai PC, Bell JT (2015) Power and sample size estimation for epigenomewide association scans to detect differential DNA methylation. Int J Epidemiol 44:1429–1441.  https://doi.org/10.1093/ije/dyv041 CrossRefGoogle Scholar
  49. van Iterson M, Tobi EW, Slieker RC, den Hollander W, Luijk R, Slagboom PE, Heijmans BT (2014) MethylAid: visual and interactive quality control of large Illumina 450 k datasets. Bioinformatics 30(23):3435–3437.  https://doi.org/10.1093/bioinformatics/btu566 CrossRefGoogle Scholar
  50. van Milligen BA, Lamers F, de Hoop GT, Smit JH, Penninx BW (2011) Objective physical functioning in patients with depressive and/or anxiety disorders. J Affect Disord 131(1–3):193–199.  https://doi.org/10.1016/j.jad.2010.12.005 CrossRefGoogle Scholar
  51. Visser M, Deeg DJ, Lips P, Harris TB, Bouter LM (2000) Skeletal muscle mass and muscle strength in relation to lower-extremity performance in older men and women. J Am Geriatr Soc 48(4):381–386.  https://doi.org/10.1111/j.1532-5415.2000.tb04694.x CrossRefGoogle Scholar
  52. Wang M, Leger AB, Dumas GA (2005) Prediction of back strength using anthropometric and strength measurements in healthy females. Clin Biomech (Bristol, Avon) 20(7):685–692.  https://doi.org/10.1016/j.clinbiomech.2005.03.003 CrossRefGoogle Scholar
  53. Wang J, Vasaikar S, Shi Z, Greer M, Zhang B (2017) WebGestalt 2017: a more comprehensive, powerful, flexible and interactive gene set enrichment analysis toolkit. Nucleic Acids Res 45(W1):W130–W137.  https://doi.org/10.1093/nar/gkx356 CrossRefGoogle Scholar
  54. Willems SM, Wright DJ, Day FR, Trajanoska K, Joshi PK, Morris JA, Matteini AM, Garton FC, Grarup N, Oskolkov N, Thalamuthu A, Mangino M, Liu J, Demirkan A, Lek M, Xu L, Wang G, Oldmeadow C, Gaulton KJ, Lotta LA, Miyamoto-Mikami E, Rivas MA, White T, Loh PR, Aadahl M, Amin N, Attia JR, Austin K, Benyamin B, Brage S, Cheng YC, Cięszczyk P, Derave W, Eriksson KF, Eynon N, Linneberg A, Lucia A, Massidda M, Mitchell BD, Miyachi M, Murakami H, Padmanabhan S, Pandey A, Papadimitriou I, Rajpal DK, Sale C, Schnurr TM, Sessa F, Shrine N, Tobin MD, Varley I, Wain LV, Wray NR, Lindgren CM, MacArthur DG, Waterworth DM, McCarthy MI, Pedersen O, Khaw KT, Kiel DP, GEFOS Any-Type of Fracture Consortium, Pitsiladis Y, Fuku N, Franks PW, North KN, van Duijn CM, Mather KA, Hansen T, Hansson O, Spector T, Murabito JM, Richards JB, Rivadeneira F, Langenberg C, Perry JRB, Wareham NJ, Scott RA (2017) Large-scale GWAS identifies multiple loci for hand grip strength providing biological insights into muscular fitness. Nat Commun 8:16015.  https://doi.org/10.1038/ncomms16015 CrossRefGoogle Scholar
  55. Yorke AM, Curtis AB, Shoemaker M, Vangsnes E (2015) Grip strength values stratified by age, gender, and chronic disease status in adults aged 50 years and older. J Geriatr Phys Ther 38(3):115–121.  https://doi.org/10.1519/JPT.0000000000000037 CrossRefGoogle Scholar
  56. Zykovich A, Hubbard A, Flynn JM, Tarnopolsky M, Fraga MF, Kerksick C, Ogborn D, MacNeil L, Mooney SD, Melov S (2014) Genome-wide DNA methylation changes with age in disease-free human skeletal muscle. Aging Cell 13(2):360–366.  https://doi.org/10.1111/acel.12180 CrossRefGoogle Scholar

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© The Author(s) 2019

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors and Affiliations

  1. 1.Epidemiology, Biostatistics and Biodemography, Department of Public HealthUniversity of Southern DenmarkOdense CDenmark
  2. 2.Department of Clinical Biochemistry and Pharmacology, Center for Individualized Medicine in Arterial DiseasesOdense University HospitalOdense CDenmark
  3. 3.Department of Clinical GeneticsOdense University HospitalOdense CDenmark
  4. 4.Endocrine Research Unit, KMEBUniversity of Southern DenmarkOdense CDenmark
  5. 5.Department of Clinical ImmunologyCopenhagen University HospitalCopenhagen ØDenmark

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