DNA methylation in genes of longevity-regulating pathways: association with obesity and metabolic complications

Results

Demographic, anthropometric and metabolic characteristics

Demographic, anthropometric and metabolic characteristics of the whole population ordered by median age are shown (Table 1). Population was divided according to median age (45 years) and as expected, older subjects (≥ 45 years) presented increased body mass index (BMI), waist circumference (WC), systolic and diastolic blood pressure (HT), insulin resistance (IR), and circulating cholesterol and triglyceride levels when compared to younger individuals. Differences in cholesterol levels were associated mainly to an increase in low-density lipoprotein cholesterol (LDL-c) and no significant differences were detected in high-density lipoprotein cholesterol (HDL-c) (Figure 1).

Table 1. Demographic, anthropometric and metabolic characteristics of the whole population and stratified by median age.

Variable		All (n=474)	<45 years (n=224)		≥45 years (n=250)
Age (years)	47.2±14.1			34.5±7.6	58.3±8.3*
Men/women	171/303			60/164	111/139*
Body weight (kg)	81.6±19.1			80.2±20.0	83.0±18.2
BMI (kg/m2)	30.1±5.6			29.0±6.0	30.9±5.2*
WC (cm)	95.7±16.1			91.4±17.6	99.7±13.4*
SBP (mm Hg)	121±28			101±35	124±42*
DBP (mm Hg)	89±25			75±32	90±32*
Glucose (mg/dl)	102±30			90±19	113±33*
Insulin (mIU/L)	9.6±7.0			9.0±6.4	10.7±7.8*
HOMA-IR index	2.44±2.28			2.13±1.98	2.96±2.64*
Total cholesterol (mg/dl)	205±40			191±37	216±39*
HDL-c (mg/dl)	53±13			54±14	54±13
LDL-c (mg/dl)	127±37			117±36	137±35*
Triglycerides (mg/dl)	120 ±72			105±70	132±73*
Categorical variables are presented as number of cases and continuous variables as means ± standard deviations. BMI: body mass index; WC: waist circumference; SBP: systolic blood pressure; DBP: diastolic blood pressure; HOMA-IR index: homeostatic model assessment-insulin resistance index; HDL-c: high-density lipoprotein cholesterol; LDL-c: low-density lipoprotein cholesterol. *<45 years vs ≥45 years, p<0.05.

Figure 1. Methylation levels (beta values mean ± SEM) of CpGs located at genes of the longevity-regulating pathway in relation to waist circumference categories after age and sex adjustments. Normal waist circumference vs High waist circumference levels, p<0.01**; p<0.001***; p<0.0001****. Cut-off value between both groups was 102 cm for men and 88 cm for women.

DNA methylation in genes of longevity-regulating pathways in relation to BMI

Longevity and lifespan are influenced by genetics, the environment, and lifestyle. As phenotypes can vary substantially among individuals, we analyzed if DNA methylation of genes involved in longevity-regulating pathways is deregulated in presence of metabolic alterations. These genes were identified by using KEGG database (“Longevity-regulating pathway”). A first analysis was performed by correlating all DNA methylation sites across the 450K array with BMI. Among the 13,268 CpGs significantly associated with BMI (p<0.05), we focused on the 58 methylation sites that located in genes participating in the longevity-regulating pathway (FDR-adjusted p-value <0.0001) (Supplementary Table 2, Supplementary Table 1). Hereafter, 25 of these CpG sites were statistically significant (p<0.05) between younger (<45 y) and older subjects (≥45 y), and genomic and statistical data of these 25 CpG sites are shown (Tables 2-3). In a second step, we decided to perform a search of this 25 CpG sites in public repositories such as Gene Expression Omnibus (GEO) databases with metabolic phenotypes related to our study population (Supplementary Table 2). This analysis was conducted in four tissues such as PBMC (peripheral blood mononuclear cells), liver biopsies, subcutaneous and visceral fat to evaluate the methylation state in selected CpG sites (Table 4). GSE76399 compared insulin resistant and insulin sensitive individuals in PBMC, whereas GSE65057 compared obese and non-obese individuals in liver samples. In liver tissue from obese subjects, 13 CpG sites were hypomethylated showing a similar pattern in comparison to our results (blue lines in Table 4). In adipose tissue, we found only a hypermethylation for cg07199894 in ULK1 gene in omental fat and hypomethylation for cg11322849 (INS), cg14844401 (ADCY5) and cg14323456 (RHEB) in subcutaneous fat, but it is important to note that sample size, gender and BMI were very different among studies. It is noteworthy that we found more coincidence between our results (that were based on the CpGs that were differential according to BMI) and the study comparing the methylation patterns between obese and non-obese subjects in liver, than to the study performed in PBMC (similar to our cells) but which compared insulin resistant versus insulin sensitive individuals. Another important difference between our study and the GSE76285 performed also in PBMC is that the population studied in GSE76285 was composed by subjects with extreme obesity (BMI >35), which makes difficult to find the same CpGs in both studies.

Table 2. Genomic data of the CpG sites located at longevity-regulating pathway genes that were statistically associated with BMI.

	Illumina_ID	Gene name	Gene symbol	Chromosomal (CHR) position2	Genomic region	p-value	FDR adjusted p-value	Limma B
1	cg08862778	Mechanistic target of rapamycin kinase	MTOR	1:11322643	TSS200	1.8E-13	3.3E-10	18.8
2	cg11322849	Insulin, transcript variant 1	INS	11:2182783	TSS1500	1.5E-10	5.1E-08	12.2
3	cg07199894	Unc-51 like autophagy activating kinase 1	ULK1	12:132379104	TSS200	1.3E-08	1.4E-06	7.83
4	cg11658986	Adenylate cyclase 6	ADCY6	12:49177605	1stExon	1.5E-08	1.5E-06	7.7
5	cg14862787	cAMP responsive element binding protein 5	CREB5	7:28507879	5'UTR	4.1E-08	3.1E-06	6.7
6	cg05792022	Forkhead box O1	FOXO1	13:41239732	1stExon	4.7E-08	3.5E-06	6.6
7	cg04149773	Adenylate cyclase 6	ADCY6	17:78518437	TSS200	1.4E-07	7.8E-06	5.5
8	cg14113970	Calcium/calmodulin dependent protein kinase IV, transcript variant 1	CAMK4	5:110789928	Body	2.1E-07	1.0E-05	5.1
9	cg14844401	Adenylate cyclase 5, transcript variant 1	ADCY5	3:123133741	Body	2.3E-07	1.1E-05	5.0
10	cg14267811	TSC complex subunit 1, transcript variant 4	TSC1	9:135819425	5'UTR	3.2E-07	1.4E-05	4.7
11	cg01284192	Insulin like growth factor 1 receptor, transcript variant 1	IGF1R	15:99500473	Body	4.1E-07	1.6E-05	4.5
12	cg24061580	Protein kinase AMP-activated non-catalytic subunit gamma 2, transcript variant a,	PRKAG2	7:151573966	5'UTR	4.7E-07	1.8E-05	4.4
13	cg06223834	Adenylate cyclase 9	ADCY9	16:4103161	Body	6.2E-07	2.2E-05	4.1
14	cg19503731	AKT serine/threonine kinase 3, transcript variant 2	AKT3	1:244007303	TSS1500	1.3E-06	3.8E-05	3.4
15	cg13154908	Phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha	PIK3CA	3:178869001	5'UTR	1.5E-06	4.3E-05	3.2
16	cg18237616	Ras homolog, mTORC1 binding	RHEB	7:151191667	Body	2.0E-06	5.2E-05	3.0
17	cg14323456	Ras homolog, mTORC1 binding	RHEB	7:151205434	Body	2.0E-06	5.3E-05	2.9
18	cg01781374	Calcium/calmodulin dependent protein kinase IV, transcript variant 1	CAMK4	5:110777659	Body	2.1E-06	5.4E-05	2.9
19	cg11301281	cAMP responsive element binding protein 5, transcript variant 2	CREB5	7:28513286	5'UTR	2.4E-06	6.0E-05	2.8
20	cg02823066	Insulin like growth factor 1, transcript variant 3	IGF1	12:102819748	Body	2.5E-06	6.1E-05	2.7
21	cg20300093	Adenylate cyclase 5, transcript variant 1	ADCY5	3:123138250	Body	2.7E-06	6.6E-05	2.6
22	cg14077232	Euchromatic histone lysine methyltransferase 1, transcript variant 1	EHMT1	9:140656200	Body	2.7E-06	6.6E-05	2.6
23	cg08128650	RELA proto-oncogene, NF-kB subunit, transcript variant 1	RELA	11:65426704	Body	3.3E-06	7.6E-05	2.5
24	cg19418273	cAMP responsive element binding protein 3 like 2, transcript variant 1	CREB3L2	7:137562440	3'UTR	3.8E-06	8.3E-05	2.3
25	cg14072989	Ribosomal protein S6 kinase B1, transcript variant 1	RPS6KB1	17:57974225	Body	4.5E-06	9.3E-05	2.2
Data are sorted by FDR values. 1CpG identifier, 2CpG locations were mapped using GRCh37 version of the genome from Ensembl platform. BMI: body mass index; CHR: chromosome; FDR: False Discovery rate; B: LIMMA B-statistic from LIMMA.

Table 3. Methylation levels of CpG sites located at genes of the longevity-regulating pathways and differences between both age groups.

	CpG	Gene	<45 years	≥ 45 years	p-value
1	cg08862778	MTOR	0.111 ± 0.001	0.106 ± 0.001	0.0422
2	cg11322849	INS	0.576 ± 0.003	0.565 ± 0.003	0.0115
3	cg07199894	ULK1	0.227 ± 0.004	0.243 ± 0.003	0.0068
4	cg11658986	ADCY6	0.721 ± 0.002	0.709 ± 0,002	0.0016
5	cg14862787	CREB5	0.833 ± 0.001	0.822 ± 0.001	< 0.0001
6	cg05792022	FOXO1	0.213 ± 0.002	0.201 ± 0.002	0.0002
7	cg04149773	ADCY6	0.741 ± 0.002	0.732 ± 0.002	0.0019
8	cg14113970	CAMK4	0.774 ± 0.003	0.759 ± 0.004	0.0198
9	cg14844401	ADCY5	0.649 ± 0.002	0.641 ± 0.002	0.0343
10	cg14267811	TSC1	0.190 ± 0.003	0.177 ± 0.003	0.0049
11	cg01284192	IGF1R	0.801 ± 0.002	0.792 ± 0.002	0.0051
12	cg24061580	PRKAG2	0.217 ± 0.002	0.209 ± 0.001	0.0067
13	cg06223834	ADCY9	0.773 ± 0.003	0.758 ± 0.003	0.0009
14	cg19503731	AKT3	0.865 ± 0.001	0.860 ± 0.001	0.0401
15	cg13154908	PIK3CA	0.798 ± 0.003	0.788 ± 0.003	0.0497
16	cg18237616	RHEB	0.840 ± 0.002	0.830 ± 0.002	0.0039
17	cg14323456	RHEB	0.821 ± 0.003	0.810 ± 0.004	0.0456
18	cg01781374	CAMK4	0.831 ± 0.002	0.822 ± 0.002	0.0099
19	cg11301281	CREB5	0.763 ± 0.001	0.752 ± 0.001	< 0.0001
20	cg02823066	IGF1	0.658 ± 0.002	0.647 ± 0.002	0.0028
21	cg20300093	ADCY5	0.618 ± 0.002	0.609 ± 0.002	0.0032
22	cg14077232	EHMT1	0.823 ± 0.002	0.812 ± 0.002	0.0026
23	cg08128650	RELA	0.777 ± 0.001	0.766 ± 0.002	0.0001
24	cg19418273	CREB3L2	0.844 ± 0.002	0.835 ± 0.003	0.0316
25	cg14072989	RPS6KB1	0.825 ± 0.002	0.814 ± 0.003	0.0308
CpG beta values are grouped by age and p values are shown. Data are expressed as beta values mean ± SEM.

Table 4. Methylation levels of 25 CpG sites located at genes of the longevity-regulating pathways that were differentially methylated between younger (<45 y) and older subjects (≥45 y) in GEO datasets. GSE76399 compared insulin resistant and insulin sensitive individuals in PBMC, whereas GSE65057 compared obese and non-obese individuals in liver samples.

		PMBC (GSE76399)			Liver tissue (GSE65057)
CpG	Gene	Ins- Sens (n=40)	Ins- Resist (n=40)	P value	Non-Obese (n=7)	Obese (n=8)	P value
cg08862778	MTOR	0.022 ± 0.001	0.024 ± 0.001	0.145	0.069 ± 0.004	0.079 ± 0.009	0.357
cg11322849	INS	0.692 ± 0.006	0.668 ± 0.016	0.154	0.676 ± 0.008	0.626 ± 0.017	0.025
cg07199894	ULK1	0.199 ± 0.006	0.206 ± 0.007	0.447	0.319 ± 0.011	0.311 ± 0.014	0.661
cg11658986	ADCY6	0.675 ± 0.008	0.677 ± 0.007	0.893	0.621 ± 0.010	0.566 ± 0.010	0.003
cg14862787	CREB5	0.875 ± 0.003	0.856 ± 0.016	0.255	0.719 ± 0.011	0.720 ± 0.020	0.968
cg05792022	FOXO1	0.209 ± 0.004	0.204 ± 0.005	0.470	0.261 ± 0.007	0.237 ± 0.014	0.170
cg04149773	ADCY6	0.724 ± 0.018	0.726 ± 0.019	0.959	0.797 ± 0.006	0.748 ± 0.019	0.035
cg14113970	CAMK4	0.881 ± 0.027	0.827 ± 0.037	0.236	0.850 ± 0.008	0.795 ± 0.026	0.078
cg14844401	ADCY5	0.698 ± 0.010	0.658 ± 0.028	0.186	0.657 ± 0.011	0.631 ± 0.016	0.202
cg14267811	TSC1	0.209 ± 0.005	0.211 ± 0.005	0.775	0.219 ± 0.006	0.212 ± 0.012	0.605
cg01284192	IGF1R	0.842 ± 0.005	0.837 ± 0.005	0.471	0.815 ± 0.009	0.774 ± 0.009	0.008
cg24061580	PRKAG2	0.199 ± 0.004	0.186 ± 0.005	0.060	0.247 ± 0.004	0.244 ± 0.008	0.760
cg06223834	ADCY9	0.867 ± 0.006	0.821 ± 0.030	0.143	0.838 ± 0.010	0.795 ± 0.015	0.042
cg19503731	AKT3	0.953 ± 0.003	0.946 ± 0.003	0.086	0.902 ± 0.006	0.895 ± 0.010	0.578
cg13154908	PIK3CA	0.955 ± 0.003	0.949 ± 0.003	0.087	0.927 ± 0.004	0.909 ± 0.006	0.029
cg18237616	RHEB	0.919 ± 0.018	0.914 ± 0.018	0.860	0.712 ± 0.017	0.717 ± 0.015	0.819
cg14323456	RHEB	0.956 ± 0.004	0.956 ± 0.003	0.958	0.929 ± 0.010	0.886 ± 0.015	0.039
cg01781374	CAMK4	0.926 ± 0.016	0.878 ± 0.023	0.097	0.839 ± 0.012	0.852 ± 0.012	0.457
cg11301281	CREB5	0.768 ± 0.019	0.733 ± 0.024	0.256	0.636 ± 0.011	0.584 ± 0.016	0.024
cg02823066	IGF1	0.649 ± 0.021	0.619 ± 0.037	0.481	0.319 ± 0.015	0.323 ± 0.013	0.830
cg20300093	ADCY5	0.639 ± 0.017	0.594 ± 0.028	0.172	0.708 ± 0.009	0.647 ± 0.027	0.068
cg14077232	EHMT1	0.954 ± 0.002	0.956 ± 0.002	0.479	0.905 ± 0.005	0.901 ± 0.013	0.806
cg08128650	RELA	0.799 ± 0.011	0.768 ± 0.017	0.138	0.698 ± 0.006	0.666 ± 0.015	0.077
cg19418273	CREB3L2	0.964 ± 0.003	0.960 ± 0.004	0.527	0.940 ± 0.005	0.907 ± 0.007	0.003
cg14072989	RPS6KB1	0.948 ± 0.006	0.947 ± 0.006	0.994	0.904 ± 0.013	0.839 ± 0.023	0.033
Ins-Sens: Insulin sensitive; Ins-Resist: Insulin Resistant. PBMC: Peripheral Blood Mononuclear Cells. Blue boxes represent significant changes in DNAm (p<0.05).

Methylation levels in longevity-regulating pathways and metabolic alterations

From those 25 CpG sites that were differentially methylated among age categories, a second filter was applied. Subjects were categorized according the presence of metabolic alterations and the new selection was made based in those CpGs that were either hypo- or hypermethylated according to age and metabolic traits, in the same direction: those CpGs that were hypomethylated in the disease must be also hypomethylated with age, and vice versa. To perform this analysis, binary categories were created (absence or presence of the condition) as previously described: Abdominal obesity, hypercholesterolemia, insulin resistance and metabolic syndrome. Fifteen CpG sites were positively associated with abdominal obesity: cg08862778 (MTOR), cg11322849 (INS), cg07199894 (ULK1), cg14844401 (ADCY5), cg20300093 (ADCY5), cg11658986 (ADCY6), cg04149773 (ADCY6), cg14862787 (CREB5), cg11301281 (CREB5), cg05792022 (FOXO1), cg14267811 (TSC1), cg02823066 (IGF1), cg01284192 (IGF1R), cg08128650 (RELA), cg24061580 (PRKAG2) (Figure 1). Of these 15 CpG sites, cg07199894 (ULK1) was the only hypermethylated.

Thirteen CpG were positively associated with hypercholesterolemia: cg08862778 (MTOR), cg07199894 (ULK1), cg11658986 (ADCY6), cg04149773 (ADCY6), cg06223834 (ADCY9), cg18237616 (RHEB), cg05792022 (FOXO1), cg01781374 (CAMK4), cg11301281 (CREB5), cg14267811 (TSC1), cg08128650 (RELA), cg01284192 (IGF1R), cg24061580 (PRKAG2) (Figure 2).

Figure 2. Methylation levels (beta values mean ± SEM) of CPGs located at genes of the longevity-regulating pathway in relation to total cholesterol categories after age and sex adjustments. Normal cholesterol vs High cholesterol levels, p<0.05*; p<0.01**; p<0.001***; p<0.0001****. Cut-off value between both groups was 200 mg/dl of total cholesterol in plasma.

Twelve CpG sites were associated with HOMA index: cg08862778 (MTOR), cg11322849 (INS), cg07199894 (ULK1), cg11658986- cg04149773 (ADCY6), cg14862787-cg11301281 (CREB5), cg05792022 (FOXO1), cg14844401 (ADCY5), cg01284192 (IGF1R), cg02823066 (IGF1), cg08128650 (RELA) (Figure 3).

Figure 3. Methylation levels (beta values mean ± SEM) of CPGs located at genes of the longevity-regulating pathway in relation to HOMA-index categories after age and sex adjustments. Normal HOMA-index vs High HOMA-index levels, p<0.05*; p<0.01**; p<0.001***; p<0.0001****. Cut-off value between both groups was a 2.4 HOMA-IR index, higher levels was considered insulin resistant.

Finally, eleven CpG sites were associated with metabolic syndrome: cg08862778 (MTOR), cg11322849 (INS), cg07199894 (ULK1), cg11658986-cg04149773 (ADCY6), cg14862787-11301281 (CREB5), cg14844401-cg20300093 (ADCY5), cg01284192 (IGF1R) and cg08128650 (RELA) (Figure 4).

Figure 4. Methylation levels (beta values mean ± SEM) of CPGs located at genes of the longevity-regulating pathway in relation to Metabolic Syndrome categories after age and sex adjustments. Non-Metabolic syndrome vs Metabolic syndrome, p<0.05*; p<0.01**; p<0.001***; p<0.0001****. Metabolic syndrome was defined as the presence of three of five criteria: large waist circumference reduced HDL-c, hypertriglyceridemia, hypertension and fasting hyperglycemia.

In an attempt to define age-specific CpG sites, we decided to perform a characterization in the older subjects according to their metabolic phenotype. Starting from the 58 CpG sites associated with BMI residing in longevity regulating genes in the whole population (FDR<0.0001), and using the same criteria to split the population based on metabolic parameters as previously described, we observed that new CpG sites (blue lines in Table 5) appear differentially methylated in hyperglycemia, insulin resistance and hypertriglyceridemia but not in hypercholesterolemia. Interestingly, 8 CpGs (cg11322849, cg07199894, cg14862787, cgcg14844401, cg01091261, cg06223834, cg08128650, cg04149773 in gray lines, Table 5) coincide when the whole population and the old sub-population are analyzed, which suggests that these genes could have a greater impact in metabolic regulation (Table 5). Moreover, PRKAG2 (cg20406576) and EHMT2 (cg00210002) are hypomethylated in subjects with hyperglycemia, HOMA-IR ≥2.5 and hypertriglyceridemia. In the case of IRS1 (cg21511036), AKT1S1 (cg03813033), ADCY2 (cg12566890 and EHMT2 (cg00210002) are hypomethylated, whereas AKT1 (cg01749142), FOXO3 (15283498), are hypermethylated in subjects with insulin resistance and hypertriglyceridemia.

Table 5. Methylation levels of CpG sites associated with BMI in the whole population and located at genes of the longevity-regulating pathways, according to metabolic phenotype in the older sub-population (≥ 45 years).

CpG site	Gene			Normoglycemia (n=112)	Hyperglycemia (n=127)			P value
cg20406576	PRKAG2		0.802 ± 0.003		0.789 ± 0.003			0.005
cg00210002	EHMT2		0.150 ± 0.004		0.136 ± 0.004			0.009
CpG site	Gene		HOMA-IR < 2.5 (n=75)		HOMA-IR >= 2.5 (n=53)			P value
cg21511036	IRS1		0.129 ± 0.002		0.119 ± 0.003			0.014
cg20406576	PRKAG2		0.791 ± 0.003		0.776 ± 0.005			0.023
cg01749142	AKT1		0.140 ± 0.004		0.158 ± 0.005			0.005
cg15283498	FOXO3		0.799 ± 0.006		0.825 ± 0.004			0.003
cg17848496	IRS1		0.818 ± 0.004		0.832 ± 0.003			0.037
cg03813033	AKT1S1		0.111 ± 0.003		0.093 ± 0.002			0.000
cg07012178	PRKAG2		0.755 ± 0.003		0.737 ± 0.003			0.001
cg18028483	SESN1		0.751 ± 0.006		0.772 ± 0.006			0.033
cg12566890	ADCY2		0.715 ± 0.003		0.702 ± 0.003			0.005
cg13574337	ADCY9		0.665 ± 0.006		0.638 ± 0.005			0.003
cg13796676	SIRT1		0.813 ± 0.006		0.834 ± 0.005			0.012
cg06772578	PPARGC1A		0.783 ± 0.006		0.805 ± 0.005			0.015
cg10421188	CREB5		0.637 ± 0.004		0.614 ± 0.004			0.001
cg24937356	PRKAA2		0.792 ± 0.006		0.814 ± 0.004			0.013
cg04932465	CAMKK2		0.666 ± 0.004		0.647 ± 0.005			0.006
cg08315825	IGF1R		0.795 ± 0.007		0.819 ± 0.006			0.018
cg00210002	EHMT2		0.136 ± 0.004		0.114 ± 0.004			0.001
cg11322849	INS		0.557 ± 0.005		0.539 ± 0.006			0.044
cg07199894	ULK1		0.254 ± 0.007		0.284 ± 0.006			0.006
cg14862787	CREB5		0.819 ± 0.002		0.810 ± 0.003			0.042
cg14844401	ADCY5		0.631 ± 0.005		0.614 ± 0.005			0.025
cg01091261	ADCY9		0.799 ± 0.003		0.788 ± 0.004			0.048
cg06223834	ADCY9		0.770 ± 0.004		0.786 ± 0.005			0.018
cg08128650	RELA		0.762 ± 0.003		0.751 ± 0.003			0.042
CpG site		Gene	Non-Hypertriglyceridemia			Hypertriglyceridemia	P value
			(n=177)			(n=55)
cg21511036		IRS1	0.138 ± 0.001			0.130 ± 0.003	0.030
cg20406576		PRKAG2	0.798 ± 0.002			0.786 ± 0.004	0.025
cg01749142		AKT1	0.127 ± 0.002			0.139 ± 0.004	0.014
cg15283498		FOXO3	0.783 ± 0.004			0.805 ± 0.006	0.013
cg03813033		AKT1S1	0.122 ± 0.002			0.110 ± 0.004	0.027
cg12566890		ADCY2	0.721 ± 0.002			0.710 ± 0.004	0.019
cg13613346		EHMT2	0.865 ± 0.004			0.881 ± 0.003	0.032
cg00210002		EHMT2	0.149 ± 0.003			0.127 ± 0.004	0.000
cg11322849		INS	0.568 ± 0.003			0.550 ± 0.006	0.010
cg04149773		ADCY6	0.734 ± 0.002			0.725 ± 0.003	0.044

In a second step, PathDIP was used to perform a pathway enrichment analysis and address the impact of these epigenetic marks on longevity-related molecular processes. This analysis showed that those CpG sites associated with the occurrence of metabolic alterations contributed significantly to the regulation of longevity-regulating pathways (Table 6). Moreover, multiple linear regressions adjusted by LARS (least-angle regression) were performed to determine which CpG sites contributed in a significant way to the metabolic traits. The resulting models show that the identified CpG sites have an important impact in abdominal obesity measured as waist circumference (adjusted r²:0.341), and also in insulin resistance expressed as HOMA-index (adjusted r²:0.153) (Table 6).

Table 6. Pathway enrichment analysis results ('q-value (FDR: BH-method) less than 0.05').

Nº CpG	Pathway Source	Pathway Name	P value	Category	Genes	r2 Adj
15	KEGG	Longevity regulating pathway	2.27E-26	All	ADCY5, ADCY6, ADCY9, CAMK4, CREB5, FOXO1, IGF1, IGF1R, INS, MTOR, PRKAG2, RELA, RHEB, TSC1, ULK1	0
12	KEGG	Longevity regulating pathway	3.23E-21	Cholesterol	ADCY6, ADCY9, CAMK4, CREB5, FOXO1, IGF1R, MTOR, PRKAG2, RELA, RHEB, TSC1, ULK1	0.072
10	KEGG	Longevity regulating pathway	8.67E-18	HOMA-Index	ADCY5, ADCY6, CREB5, FOXO1, IGF1, IGF1R, INS, MTOR, RELA,ULK1	0.153
12	KEGG	Longevity regulating pathway	3.23E-21	Waist Circumference	ADCY5, ADCY6, CREB5, FOXO1, IGF1, IGF1R, INS, MTOR, PRKAG2, RELA, TSC1, ULK1	0.341
8	KEGG	Longevity regulating pathway	2.30E-14	Metabolic Syndrome	ADCY5, ADCY6, CREB5, IGF1R, INS, MTOR, RELA, ULK1	0.092
KEGG: Kyoto Encyclopedia of Genes and Genomes. HOMA: homeostatic model assessment BMI: Body mass index.

As mentioned before, a total of 15 CpG sites are hypo- or hypermethylated in older subjects, but only six of these CpG sites are common for all the metabolic alterations previously described (Figure 5). In a second approach, correlations between methylation levels and age were made for the six CpG sites located at the genes MTOR, ULK1, ADCY6, IGFR1, CREB5 and RELA. In the case of cg08128650 (RELA) and cg11301281 (CREB5), methylation levels negative correlated with age, while cg07199984 (ULK1) showed a significant positive correlation (Figure 6). Considering that a small number of common CpG sites are significant associated with age and r² adjusted values are small, we decided to create a z-score that includes those 25 CpG sites differentially methylated according to age previously described (Table 3) and a linear regression was performed analyzing this z-score with age, adjusted by BMI (Figure 7). This analysis showed a statistically significant association with age (r²:0.098, p<0.0001), where older subjects presented hypomethylation in CpG sites related to genes involved in longevity-regulating pathways.

Figure 5. Venn diagram showing the common CpG sites differentially methylated between the different metabolic disturbances.

Figure 6. Correlations between DNA methylation levels (beta values) at CpGs located at genes of the longevity-regulating pathway and age, after sex adjustment. In (A) cg08128650, RELA, (B) cg11301281, CREB5, and (C) cg07199894, ULK1 (n=474 subjects).

Figure 7. Relation between Z-scores of methylation levels and age. Values reflect the significant change in Z-scores from CpG methylation (25 CpG sites, n=474) according age, after BMI adjustment.

Analysis of DNA methylation in the older subpopulation (≥45 y)

DNA methylation patterns were analyzed using the whole population to define CpG sites related to BMI, but it is plausible to think that using a mixed population could mask a specific pattern related to age and whether BMI-associated CpGs differ significantly in young and older individuals. To address this, starting from normalized beta values, a new analysis was performed to identify CpG sites associated with BMI in young and old subjects, separately. Using the same criteria as described above (p-value <0.05), a total of 470 CpG sites were identified in <45 y subjects (n=220) while in ≥45 y subjects, (n=250) a total of 52,991 CpG sites. In a second step, we selected CpGs residing in longevity genes with a FDR value (p<0.0001) and this only could be possible in the older group where a total of 14 CpG sites were identified (Table 7). When the old sub-population is compared with the whole population, 6 CpGs (FDR<0.0001) are coincident in these two groups (cg14113970, cg13551841, cg15283498, cg21511036, cg06772578, cg07012178, in gray lines, Table 7).

Table 7. CpG sites residing in longevity genes identified in older subjects (FDR_≥ 45 y) in comparison to the whole population (FDR_Total).

ID	Gene	FDR_≥45	FDR_Total
cg04566392	ADIPOR1	1.89E-05	NA
cg08779982	TSC2	4.31E-05	NA
cg24773542	EHMT2	3.43E-05	NA
cg13804196	PRKACG	1.91E-05	NA
cg08048831	IGF1R	2.93E-05	1.00E-04
cg03310087	IGF1R	9.18E-05	5.00E-04
cg07340599	CREB5	8.48E-05	1.00E-04
cg21184115	FOXO3	1.29E-05	2.00E-04
cg14113970	CAMK4	8.66E-05	1.00E-05
cg13551841	EHMT1	4.79E-05	5.12E-07
cg15283498	FOXO3	2.76E-05	2.01E-06
cg21511036	IRS1	2.72E-06	5.01E-11
cg06772578	PPARGC1A	3.35E-05	3.83E-05
cg07012178	PRKAG2	1.35E-05	6.28E-05
FDR: False Discovery Rate

Four of these CpGs that were found in the older population (blue and green lines) had not appeared before (cg04566392, cg08779982, cg24773542, cg13804196) but two of them belong to the genes EHMT2 and PRKACG, previously described in the whole population. Finally, four of these CpGs found in the older population also became significant (cg08048831, cg03310087, cg07340599, cg21184115, in purple lines, Table 7). These four CpGs in the whole population did not reach statistical significance, maybe because we are mixing both young and older populations, and when the older sub-population is analyzed alone, this effect disappears.

In a similar manner to that performed in the whole population, in the pursuit to obtain CpGs associated to metabolic disturbances in the sub-old population, we decided to split the old population (≥45 y) according to their metabolic phenotype and analyze these CpGs. In the old sub-population, from the 14 CpG sites that were associated with BMI in Table 7, we observed that three CpG sites were hypomethylated in hyperglycemia (cg13551841, cg08779982, cg21511036), two CpG sites were hypomethylated (cg13551841 and cg21511036) and one CpG residing in ADIPOR1 (cg04566392) was hypermethylated in insulin resistance, and the CpG residing in IRS1 (cg21511036) was hypomethylated in subjects with hypertriglyceridemia (Table 8). In this analysis, cg21511036 (IRS1) was hypomethylated in subjects with hyperglycemia, insulin resistance and hypertriglyceridemia. Those CpG sites were not previously described (blue lines). It is noteworthy that cg21511036 (IRS1) appears as a conserved hypomethylated CpG in the whole population and also, in the old sub-population (Tables 5 and 8), which suggests an important role in lipid and glucose disturbances.

Table 8. DNA methylation of CpG sites associated with BMI (FDR<0.0001) and residing in longevity regulating pathways according to metabolic phenotype in ≥ 45 y.

CpG site	Gene	Normoglycemia	Hyperglycemia	P value
		(n=112)	(n=127)
cg13551841	EHMT1	0.708 ± 0.004	0.695 ± 0.004	0.023
cg08779982	TSC2	0.856 ± 0.002	0.848 ± 0.002	0.013
cg21511036	IRS1	0.137 ± 0.002	0.129 ± 0.002	0.013
CpG site	Gene	HOMA-IR < 2.5	HOMA-IR >= 2.5	P value
		(n=75)	(n=53)
cg04566392	ADIPOR1	0.067 ± 0.002	0.076 ± 0.002	0.003
cg13551841	EHMT1	0.717 ± 0.004	0.690 ± 0.006	0.000
cg21511036	IRS1	0.146 ± 0.002	0.135 ± 0.003	0.002
CpG site	Gene	Non-Hypertriglyceridemia	Hypertriglyceridemia	P value
		(n=177)	(n=55)
cg21511036	IRS1	0.135 ± 0.002	0.124 ± 0.003	0.004

We were not able to validate these results with other techniques due to the lack of DNA, but in an attempt to contrast these results, we decided to search these identified CpGs in GEO database. We did not find significant differences in CpG sites in selected datasets (Supplementary Table 2) in tissues as blood or visceral fat. In liver biopsies, two CpG sites (cg15283498-FOXO3; cg07012178-PRKAG2) were hypomethylated, whereas in subcutaneous adipose tissue from subjects with or without metabolic syndrome, cg07340599 (CREB5) and cg08779982 (TSC2) were hypomethylated.

Abbreviations

ADCY: adenylate cyclase; APOE: apolipoprotein E; AMP: adenosine monophosphate; ATP: adenosine triphosphate; BMI: body mass index; CpG: cytosine-phosphate-guanine site; CREB5: CAMP responsive element binding protein 5; DNAm: DNA methylation; DXA: dual-energy X-ray absorptiometry; FDR: false discovery rate; FOXO1: forkhead box protein O1; GEO: gene expression omnibus database; GHR: growth hormone receptor; GWAS: genome-wide association study; HDL-c: high-density lipoprotein cholesterol; HOMA-IR: homeostatic model assessment-insulin resistance index; HT: hypertension; IGF1: insulin-like growth factor 1; IG1R: insulin-like growth factor 1 receptor; INS: insulin; IR: insulin resistance; KEGG: Kyoto encyclopedia of genes and genomes; LARS: least-angle regression; LDL-c: low-density lipoprotein cholesterol; LIMMA: linear models for microarray data; MENA: methyl epigenome network association project; MetS: Metabolic syndrome; mTOR: mammalian target of rapamycin; NFκB: nuclear factor κB; PathDIP: pathway data integration portal; PBMC: peripheral blood mononuclear cell; PCR: polymerase chain reaction; PRKAG2: 5´-AMP-activated protein kinase subunit gamma-2; r: regression, r²: squared regression; RELA: transcription factor p65; SBP: systolic blood pressure; SNP: single nucleotide polymorphism; SPSS: statistical package for the social sciences; TC: total cholesterol; TG: triglycerides; TSC1: hamartin; TSS: transcription start site; ULK1: Unc-51 like autophagy activating kinase; UTR: untranslated region; WC: waist circumference.

Author Contributions

F.S.P., O.R.L., F.M. and J.A.M. conceived and designed the study. F.S.P., O.R.L. and M.L.M. performed statistical analyses. J.L.S and J.R.B., analyzed the information. F.S.P. and O.R.L., wrote the manuscript and all authors edited and approved the final manuscript.

Acknowledgements

The authors gratefully acknowledge to all participants and technical support from Asuncion Redin, Laura Olazaran, Iosune Zubieta, Maria Hernandez, Salome Perez, Blanca Martinez de Morentin, Ana Lorente, Veronica Ciaurriz and Maria Zabala is gratefully acknowledged.

Conflicts of Interest

The authors declare that they have no conflicts of interest concerning this investigation.

Funding

This research was supported by NUTRIGENIO Grant (AGL2013-45554-R) and CIBERobn (CB12/03/30002). F.S.P., Postdoctoral Fellowship, Becas Chile-CONICYT (Nº19980/2017). O.R.L. (CONACyT, Nº CVU. 444175, PNPC 000091).

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