Still trying to figure out what’s “interesting” but I’d say that ELOVL2 and TMEM98 are among the more interesting sites\
mine for EVOVL2 is 0.53/0.34 and TMEM98 is 0.19. Can’t find values for the more “control-level” “meta level” genes like DNMT or the yamanaka factors or ERCC or most DNA repair genes.
| cg15966757 SLC6A13 0.91 | ||
| cg16085042 | HSP90B1 | 0.07 |
no FOXO genes either.
this is depleted in names of genes I’ve listed as “central to aging” and I’ve seen A LOT
The malin gene encodes a RING type E3-ubiquitin ligase which forms a functional complex with laforin, a glucan phosphatase [19]. Mutations in either malin or laforin in humans lead to the development of Lafora progressive myoclonus epilepsy, a rare fatal neurodegenerative disease with early manifestations in the early childhood. Brain damage is incurred due to deposition underbranched and hyperphosphorylated insoluble glycogen in the brain and peripheral tissues [20,21,22]. It is notable that glucan deposits have been described in the setting of aging animals and humans [23,24,25], unrelated to Lafora disease, which raises the possibility of lesser malin activity with age. Indeed, malin appears to participate in a delicate homeostatic network linking neuronal glycogen synthesis and energetic utilization, interacting with autophagy, mitochondrial function, and response to thermal stress, which could collectively affect lifespan [19, 25,26,27,28]. The possibility that malin expression, which is critical for inhibition of polyglucan deposits in neurons, plays a role in healthful longevity in humans is intriguing and requires targeted research. In animal studies malin deficiency can lead to impaired autophagy and accumulation of dysfunctional mitochondria, which eventually promote neurodegeneration, immune disorders, cancer, and accelerated aging [27].
Secretagogin is an intracellular calcium sensor and facilitator of insulin secretion by pancreatic islet beta cells [29]. Recently it was shown that secretagogin plays a critical role in the second phase of glucose-stimulated insulin secretion [30], protects against insulin aggregation, and enhances peripheral response to insulin [31]. Concordant with this broad role in carbohydrate handling, secretagogin knockout leads to hyperglycemia [32]. Secretagogin is also expressed in neuroendocrine cells where it likely regulates exocytosis and hormone release [33, 34]. Concordantly, it is also involved in danger avoidance behavior through the control of post synaptic cell surface availability of NMDA receptors in the central amygdala [35]. We are not aware, however, of published reports examining the relation between induced changes in secretagogin expression and lifespan or longevity.
Of major interest in the Horvath algorithm are CpG sites with a negative contribution to the epigenetic age, such as frataxin. Frataxin is a nuclear-encoded mitochondrial protein which is part of the Fe-S-cluster-containing proteins acting as an iron chaperone, thereby allowing normal function of the mitochondrial respiratory chain [36]. In our analysis frataxin shows both high inter-personal variability and also partly explains some (~ 8%) of the calculated age difference between epigenetically old and average subjects (Fig. 3A). The fact that higher methylation of frataxin can extend life, as indirectly suggested by its epigenetic age lowering effect is somewhat counterintuitive: defects in the expression of this mitochondrial protein cause the neurodegenerative syndrome of Friedreich’s ataxia [37, 38], which is also accompanied by cardiomyopathy, diabetes mellitus, and reduced life expectancy [39]. However, inactivation of many mitochondrial genes in the nematode Caenorhabditis elegans by RNAi was actually shown to extend lifespan [40]. Ventura et al. reported that suppression of the frataxin homolog gene (frh-1) prolonged lifespan in the nematode, along with an altered phenotype of smaller size, diminished fertility, and variant responses to oxidative stress. Thus, whereas sizable inactivation of frataxin causes a disabling disease, a more moderate frataxin suppression, such as achieved by RNAi, could lead to higher lifespan as seen in C. elegans [41]. There is evidence that frataxin silencing induces mitochondrial autophagy as an evolutionarily conserved response to the ensuing iron starvation [36]. In a broader sense, lesser frataxin availability might comprise a surmountable challenge which elicits mitophagy that eventually preconditions the cell’s capacity to sustain future stress, thereby increasing the likelihood of extended lifespan.
Our findings of inter-personal variabilities in the epigenetic age components of the healthy population have raised our interest in discovering epigenetic age patterns of individuals with biological age accelerating diseases, such as diabetic patients. Our results did not reflect epigenetic age acceleration for T2D and showed rather the opposite, for T1D subjects which had lower epigenetic age than the average of the healthy population (under the red curve in Fig. 5). Nevertheless, these results are in agreement with earlier publications which have also used chronological age-based epigenetic age calculators, such as Horvath’s epigenetic clock [8, 42]. This may indicate that the CpG probes chosen for the construction of such epigenetic age clocks do not reflect variations in DNA methylation leading to epigenetic age drifting in diabetes or its complications. What makes the T1D population “epigenetically younger” according to the aging clock used in this study would be an interesting question for future investigations. Notably, a different epigenetic clock type, the “DNAm GrimAge,” which incorporates DNA methylation sites related to surrogate biomarkers of smoking level and of selected plasma proteins, that are strongly associated with mortality and morbidity, may be a better choice for predicting age acceleration in diabetics [3, 4, 42].
Of the top five most variable age components that are found solely in the diabetic cohort, PAWR is a tumor suppressor gene, inducing selective apoptosis of cancer cells [43,44,45], and may thus be related to association of aging with higher cancer rates. Another components is related to PIPOX which catalyze the oxidation of L-pipecolate, an intermediate step in the catabolic process of L-lysine to acetyl-CoA, produced in the pipecolate pathway [46, 47]. Elevated levels of lysine were found to be associated with higher risk for the development of T2D and for T2D-concomitant cardiovascular disease (CVD) [48]. In addition, PIPOX promotes sarcosine oxidative N-demethylation, yielding glycine [49], as part of the sarcosine pathway, which is involved in the methionine cycle [50,51,52,53]. The methionine cycle is responsible for the production of S-adenosylmethionine (SAM), the methyl donor substrate in the process of cytosine DNA methylation by the family of DNA methyl transferase (DNMT) enzymes. Differential levels of PIPOX and sarcosine were observed in several types of cancers [49, 54]. In addition, methionine cycle restriction and regulation of SAM production were shown to extend lifespan in various animal models [55].
| Related gene symbol | Related gene definition/product | Illumina’s CpG ID | Contribution to epigenetic age (years)* | SD of the age contribution (years) |
|---|---|---|---|---|
| SCGN | Secretagogin | cg06493994 | 7.1 | 2.1 |
| NHLRC1 | Malin | cg22736354 | 9.3 | 1.8 |
| MIR7-3HG | MIR7-3 Host Gene | cg02479575 | 1.8 | 1.0 |
| FZD9 | Frizzled 9 | cg20692569 | 6.8 | 0.9 |
| SCAP | SREBP cleavage-active protein | cg26614073 | − 4.7 | 0.7 |
| REEP1 | Receptor expression enhancing protein 1 | cg01968178 | 2.5 | 0.7 |
| CSNK1D | Casein kinase 1 delta | cg19761273 | − 4.3 | 0.7 |
| FXN | Frataxin | cg07158339 | − 4.6 | 0.7 |
| NDUFS5 | NADH dehydrogenase Fe-S protein 5 | cg07388493 | − 4.8 | 0.7 |
my SCGN is 0.14, NHLRC1 is 0.18, MIR7-3 at cg02479575 is 0.01, and FZD9 is 0.44
SCAP is 0.52, REEP1 is 0.10, CSNK1D is 0.26, FXN is 0.44, NDUFS5 is 0.49.
Histogram of the 𝛽-values of the study population of the ArrayExpress data set E-GEOD-68379. This study in particular shows a high number of methylation sites close to 0 and 1, which could be of interest and a problem in modeling
(there are more cg values from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8853656/ supplementary table but i can’t find their corresponding equivalent in my trudiagnostic report - for later§0
A vegan diet was associated predominantly with hypomethylation of genes, most notably methyltransferase-like 1 (METTL1). Although a limited number of differentially methylated features were detected in the current study, the false discovery method revealed that a much larger proportion of differentially methylated genes and sites exist, and could be detected with a larger sample size. Our findings suggest modest differences in DNA methylation in vegans and non-vegetarians, with a much greater number of detectable significant differences expected with a larger sample.
The only one CpG site that reached statistical significance in both datasets after multiple correction and adjustment for age and gender was cg05228408, which is associated with the gene for the chloride transport protein 6 (CLCN6; LBC1921 [HR = 1.16; 95% CI 1.06–1.26; P = 0.00072]; LBC1936 [HR = 1.26; 95% CI 1.12–1.42; P = 0.00013]). This genomic region is of specific interest because single-nucleotide polymorphisms identified in its vicinity were found to be associated with blood pressure and hypertension [26–28]. Therefore, we have now trained a model for the ESTHER discovery group based on the beta values of cg05228408. Upon the adjustment for chronological age, gender, batch, and leucocyte distribution, this model revealed significant association with all-cause mortality in the discovery (P = 0.0011) and in the overall population (P = 0.0148; Additional file 1: Table S2).
I have 0.38 on this site…
The CpGs that were overlapping associated with life expectancy in both datasets were cg05294455 (MYL4), cg08598221 (SNTB1), cg09462576 (MRPL55), cg15804973 (MAP3K5), cg20654468 (LPXN), cg25268718 (PSME1), cg26581729 (NPDC1), and cg02867102 (no gene). Please note that the number of individual CpGs that reached statistical significance in the three predictors is not a quality measure for these age predictors. The CpGs of the Hannum and Horvath predictors were selected by Elastic Net algorithms—they were therefore selected to work together, rather than individually.
from Individual CpG sites that are associated with age and life expectancy become hypomethylated upon aging | Clinical Epigenetics | Full Text (note almost no overlap with above). also most are not in the trudiagnostic report
Table 2. Ten top genes wittabh the greatest differential CpG methylation status in vegans vs. nonvegetarians.
| Entrez Gene Name | Symbol | Fold Change | Location | Type(s) |
|---|---|---|---|---|
| mitochondrial ribosomal protein L19 | MRPL19 | 2.517 | Cytoplasm | other |
| proline rich 7, synaptic | PRR7 | 2.071 | Other | other |
| glutathione S-transferase C-terminal domain containing | GSTCD | 1.879 | Cytoplasm | enzyme |
| chromosome 7 open reading frame 50 | C7orf50 | 1.743 | Other | other |
| dynein axonemal heavy chain 10 | DNAH10 | 1.636 | Cytoplasm | other |
| solute carrier family 38 member 6 | SLC38A6 | 1.619 | Plasma Membrane | transporter |
| glutathione S-transferase theta 1 | GSTT1 | 1.594 | Cytoplasm | enzyme |
| calcium voltage-gated channel auxiliary subunit beta 2 | CACNB2 | 1.541 | Plasma Membrane | ion channel |
| family with sequence similarity 19 member A5, C-C motif chemokine like | FAM19A5 | 1.524 | Extracellular Space | other |
| transmembrane protein 229A | TMEM229A | 1.523 | Other | other |
Genes highlighted in grey are common for both nonvegetarians and pescatarians vs. vegans comparisons.
My PRR7 is 0.05. Most of the others are not in the report