Skip to main content
NCBI home page
GeroScience logoLink to GeroScience
. 2025 Jan 23;47(3):4041–4054. doi: 10.1007/s11357-025-01516-w

Painful diabetic neuropathy is associated with accelerated epigenetic aging

Katarzyna Malgorzata Kwiatkowska 1,✉,#, Paolo Garagnani 1,2,✉,#, Massimiliano Bonafé 1,2, Maria Giulia Bacalini 3, Luciano Calzari 4, Davide Gentilini 4,5, Dan Ziegler 6, Monique M Gerrits 7, Catharina G Faber 8, Rayaz A Malik 9,10, Margherita Marchi 11, Erika Salvi 11, Giuseppe Lauria 11,12,#, Chiara Pirazzini 1,#
PMCID: PMC12181573  PMID: 39847262

Abstract

About one out of two diabetic patients develop diabetic neuropathy (DN), of these 20% experience neuropathic pain (NP) leading to individual, social, and health-economic burden. Risk factors for NP are largely unknown; however, premature aging was recently associated with several chronic pain disorders. DNA methylation-based biological age (DNAm) is associated with disease risk, morbidity, and mortality in different clinical settings. The purpose of this work was to study, for the first time, whether biological age is involved in pain development in a huge cohort of DN patients with neuropathy assessed by anatomopathological assay (99 painful (PDN), 132 painless (PLDN) patients, 84 controls (CTRL)). Six subsets of DNAm biomarkers were calculated to evaluate NP-associated changes in epigenetic aging, telomere shortening, blood cell count estimates, and plasma protein surrogates. We observed pain-related acceleration of epigenetic age (DNAmAgeHannum, DNAmGrimAgeBasedOnPredictedAge, DNAmAgeSkinBloodClock), pace of aging (DunedinPoAm), and shortening of telomeres between PDN and PLDN patients. PDN showed decreased predicted counts of B lymphocytes, naive and absolute CD8 T cells, and increased granulocyte counts. Several surrogates of plasma proteins were significantly different (GHR, MMP1, THBS2, PAPPA, TGF-α, GDF8, EDA, MPL, CCL21) in PDNs compared to PLDNs. These results provide the first evidence of an acceleration of biological aging in patients with painful compared to painless DN. This achievement has been possible thanks to the state of the art clinical phenotyping of the enrolled patients. Our findings indicate that the aging process may be directly involved in the PDN progression and in general health degeneration in the T2DM patients. Therefore, it is possible to hypothesize that the administration of effective antiaging drugs could slow down or even block the disease advancement.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11357-025-01516-w.

Keywords: Biological aging, Aging biomarker, Epigenetic clock, Neuropathic pain, Diabetic neuropathy, DNA methylation

Introduction

Neuropathic pain (NP) is caused by a lesion or disease of the somatosensory nervous system [1] and is distinguished from other types of pain by simultaneous sensory loss and pain, with or without sensory hypersensitivity with allodynia or hyperalgesia [2]. NP often affects patients with acquired peripheral neuropathies—a group of disorders characterized by the degeneration of sensory and motor nerve fibers due to systemic diseases like diabetes or chemotherapy [3, 4]. These pathological changes lead to somatosensory pathway impairment with loss of perception to stimuli such as touch, pressure, temperature, and nociception mainly in the feet. Moreover, sensory nerve damage causes spontaneous positive symptoms, such as paresthesia, tingling, burning pain, sensation of tightness or paroxysmal shooting, and allodynia, namely painful sensations caused by non-painful stimuli.

The diagnosis of peripheral neuropathy is achieved by bedside clinical examination, nerve conduction studies, and skin biopsy. Due to its high prevalence, type 2 diabetes mellitus (T2DM) is the most common cause of painful neuropathy worldwide. However, despite a similar degree of peripheral nerve degeneration, only 20% of patients develop painful diabetic neuropathy (DN). The mechanisms underlying this variability remain unknown and cannot be explained by genetic alterations [5].

The pathological acceleration of biological aging has been directly correlated with chronological age, is linked to several age-related health conditions and complications, and can predict lifespan. Biological aging can be measured through a wide range of predictors including telomere length [6], the transcriptome of a specific set of genes [7, 8], glycan composition [9], and specific changes in the proteome [10], metabolome [11, 12], and methylome [13–17]. Moreover, structural neuroimaging allows brain-predicted aging estimation [18]. Some of these signatures, particularly those related to epigenetics, telomere, and neuroimaging changes, were previously reported to be altered in chronic pain disorders [19–23].

The purpose of the present work was to investigate epigenetic aging expressed by DNA methylation-based models in patients diagnosed with painful or painless DN, and sex- and age-matched healthy subjects.

Methods

Study cohorts

The two independent cohorts used for the experimental design, PROPGER and PROPENG, originated from the European funded project “Molecule-to-man pain network” (PAIN-Net, H2020-MSCA-ITN-2016, Grant Agreement number: 721841) focused on diabetic neuropathy. The PROPGER cohort was provided by the University of Maastricht (NL) and consisted of painful and painless DN individuals recruited by the German Diabetes Center in Düsseldorf (DE). Individuals were 93% of German ethnicity, 3% of non-German European ethnicity, 3% of Asian, 0.6% of African, and 0.3% mixed origin. The PROPENG cohort consisted of painful and painless DN subjects provided by Fondazione IRCCS Istituto Neurologico Carlo Besta in Milan (IT) and Manchester University (UK), and were 79% of European ethnicity, 18% of Asian, 2% of Afrocaribbean, and 1% mixed origin.

Additionally, a control group (CTRL) was recruited from healthy Italian subjects from a collection at the Department of Experimental, Diagnostic and Specialty Medicine (DIMES) of the University of Bologna (IT). All study participants gave their signed informed consent.

All patients underwent clinical examination and phenotype characterization within the PAIN-Net consortium uniform diagnostic approach and shared protocols to guarantee homogeneous subgroup clustering. Neuropathy was determined from the result of skin biopsy [24] and patients were considered as “painful” if they experienced neuropathic pain for more than 1 year and if their pain intensity reached a Numeric Rating Scale NRS of 4 or greater [25].

Sample selection

The population genomic background is heterogeneous being shaped by demographic history, neutral and adaptive evolution, and their complex interplay [26]. Genetic variations are closely linked to DNA methylation and can easily influence the epigenetic patterns, introducing confounding variability in the data that should not be neglected [27]. Therefore, in order to reduce the bias coming from population genetics, we included exclusively individuals of European ethnicity. Type 1 and 2 diabetes induces different biological changes influenced by differing genetic predisposition [28], which could be a source of additional variability. Thus, only T2DM subjects were selected. Whenever possible, it was attempted to match painful, painless, and control samples regarding the chronological age and sex since both factors were shown to alter the DNA methylation levels [29, 30].

DNA extraction and methylation experiment

DNA was extracted from whole blood of DN patients and healthy controls using respectively Puregene Blood kit (Qiagen) and QIAamp DNA Blood Mini kit (Qiagen), following the steps of corresponding protocols. The use of different DNA extraction kits is unlikely to have a significant effect on observed differences between the groups, and it should not falsify or distort the detected methylation patterns associated with phenotype of interest [31]. All the samples were quantified with Qubit dsDNA Broad Range Assay kit on Qubit Fluorometer by Thermo Fisher Scientific and verified to contain ≥ 1000 ng of DNA, i.e. the amount required for a methylation assay. A total of 1000 ng of genomic DNA was normalized in 50µL of H2O and were used in bisulfite conversion with EZ DNA Methylation Kit (Zymo Research) following the manufacturer’s instructions. Genome-wide DNA methylation experiment was performed with Infinium HumanMethylationEPIC BeadChip (Illumina) according to original protocol. Within each array the samples and phenotypic groups were accurately randomized.

Preprocessing of raw data

All the handling and manipulation of files were performed in Linux environment. Illumina output data (files in.idat format) from the experiments were parsed and preprocessed using minfi package within R software (version 3.6.3). Low-quality samples with mean probe detection p-value above 0.05 were excluded from the analysis and the probes that failed (i.e. presented detection p-value > 0.01) in at least one of the samples were removed. The raw values of intensities in green and red channels were normalized using functional approach removing undesired variation with a regression model of explained variability based on the control probes included in the array (using preprocessQuantile function from minfi package). Beta values estimating the methylation levels as a ratio of methylated to unmethylated alleles intensities, ranging between 0 (totally unmethylated) and 1 (totally methylated), were calculated for all the samples and used in subsequent biological age estimation.

Estimation of DNAm-based biomarkers

We assessed six subsets of biomarkers based on DNA methylation: (a) epigenetic clocks and the component parts of DNAmGrimAge clock, (b) bolstered clock models (PC-Clocks), (c) surrogates of blood cell counts, (d) predictions of biological traits, (e) signature of chronic low-grade inflammation as measured by serum levels of C-reactive protein (CRP-associated risk score), and (f) plasma protein surrogates (EpiScores). List of all calculated DNAm-based variables with the corresponding references and computation methods used is provided in Supplementary TableS1. Respective assessments were performed online or implemented in R (v4.2.2) or Python (v3.9.16) environments installed on Linux OS using normalized methylation Beta values as an input. Eventual outliers (samples for which DNAm-based biomarkers were below Q1–1.5IQR or above Q3 + 1.5IQR, where Q1 is the first quartile, Q3 is the third quartile, and IQR is the interquartile range) were removed prior to two-stage residual-outcome regression analysis.

Two-stage residual-outcome regression model

The variations in DNAm-based estimates in studied phenotypes were investigated applying a two-stage residual-outcome regression approach. Particularly, for each biomarker, we built a reference linear regression model lm(y ~ x) with the calculated estimate as dependent variable y, with chronological age as independent variable x whilst correcting for sex covariate, using in the fitting step exclusively data from the subjects with PLDN. This generated model was used to predict the respective value of epigenetic variables in the PDN and CTRL groups, and to subsequently calculate the residuals—i.e. the distance between experimental values and modeled regression line—corrected for chronological age. The different phenotypic groups were compared using Student’s t-test on age-adjusted residuals with a 0.05 statistical significance level.

Results

We analyzed T2DM patients with painful or painless DN and a group of healthy individuals as controls. For all the groups, six subsets of DNAm-based biomarkers were estimated, including methylation age measures according to different available epigenetic clocks with the component parts of GrimAge clock and telomere length estimator, PC bolstered models of classic clocks, surrogates of blood cell counts, predictions of biological traits, CRP-associated risk score, and EpiScores of plasma protein surrogates. The differences in the parameters among phenotypic groups within studied populations were examined with two-stage residual-outcome regression approach.

Cohort profiles

After removal of low-quality samples, there were 99 PDN cases (mean age 67 years; age range 46–83), 132 PLDN patients (mean age 67 years; age range 41–84), and 84 CTRL subjects (mean age 64 years; age range 41–78) as summarized in Table 1. Groups did not differ significantly in mean age (p-value from ANOVA test = 0.051). The average duration of T2DM was 13 years (ranging between 0 and 46) and 11 years (ranging between 0 and 44) in PDN and PLDN patients, respectively. Although the PDN patients had a longer duration of diabetes than the PLDN patients, this difference was not statistically significant (p-value from Student’s two-sided t-test for samples with equal variances = 0.080).

Table 1.

Characteristics of studied cohort. Sample size, sex distribution, mean age, and duration of T2DM for each phenotypic group of studied cohorts are reported. PDN painful diabetic neuropathy, PLDN painless diabetic neuropathy, CTRL controls, T2DM type 2 diabetes mellitus, SD standard deviation, NA not applicable

PDN PLDN CTRL
Number of samples (%) 99 (31.4%) 132 (41.9%) 84 (26.7%)
Sex (females/males) 24/75 33/99 26/58
Age average ± SD (years) 67.0 ± 9.5 67.3 ± 9.3 64.3 ± 9.0
T2DM duration (years) 13.5 ± 10.1 11.3 ± 8.5 NA
Open in a new tab

NP-related DNAm-based biomarkers

DNAm-based estimates were analyzed with a two-stage residual-outcome regression approach in 99 PDN and 132 PLDN patients, and 84 healthy controls to identify the DNA methylation-based changes in epigenetic aging and in other biological surrogates linked to PDN.

Analysis of DNAm biomarkers from subset A demonstrated significant pain-related accelerated epigenetic aging in the PDN compared to PLDN group. Details of results from two-stage residual-outcome regression are provided in Supplementary Table S2. In particular, telomere shortening, a well-known hallmark of both cellular senescence and biological aging, estimated with DNAmTL model in this work, was significantly increased (p-value = 0.002) in PDN as illustrated in Fig. 1A. There was an acceleration of biological age expressed by DNAmAgeHannum (p-value = 0.011) and DNAmAgeSkinBloodClock (p-value = 0.028) clocks. Although the predictor of DNAmGrimAgeBasedOnPredictedAge was increased (p-value = 0.028) in PDN patients, none of the separate components of the model reached statistical significance. Subjects with PDN also had an increased pace of aging (p-value = 0.038) calculated using DunedInPoAm algorithm. Figure 1B–E visualize residuals of estimated values of significant epigenetic clocks.

Fig. 1.

Fig. 1

Open in a new tab

Alterations in epigenetic biomarkers expressed by A DNAmTL, B DNAmAgeHannum, C DNAmAgeSkinBloodClock, D DNAmGrimAgeBasedOnPredictedAge, and E DunedInPoAm models, observed in PDN, PLDN, and CTRL groups (X axis). Residuals from the two-stage residual-outcome regression approach with the PLDN group as the reference fit are reported on the Y axis. For each phenotype contrast, p-values from Student’s t-test are disclosed

Biomarkers of subset B included models improved with principal component analysis of several standard predictors present in subset A: Horvath’s DNAmAge, DNAmAgeHannum, DNAmGrimAge and its components, DNAmPhenoAge, and DNAmTL. Results of the regression analysis are reported in Supplementary Table S3. The outcome of bolstered algorithms replicated a significant acceleration of epigenetic clocks in the PDN group for the following models: PC-DNAmGrimAge (p-value = 0.005), PC-DNAmTL (p-value = 0.016), PC-DNAmPhenoAge (p-value = 0.039), and PC-DNAmAgeHannum (p-value = 0.044) as shown in Fig. 2A–D. Additionally, using amended models, we found increased predicted plasma levels of plasminogen activator inhibitor antigen type 1 (PC-PAI1) and a higher predicted number of cigarette packs smoked/year (PC-PACKYRS) which are both component parts of DNAmGrimAge (Fig. 2E and 2F).

Fig. 2.

Fig. 2

Open in a new tab

Alterations in epigenetic biomarkers expressed by A PC-DNAmGrimAge, B PC-DNAmTL, C PC-DNAmPhenoAge, D PC-DNAmHannum, E PC-PAI1, and F PC-PACKYRS models, observed in PDN, PLDN, and CTRL groups (X axis). Residuals from the two-stage residual-outcome regression approach with the PLDN group as the reference fit are reported on the Y axis. For each phenotype contrast, p-values from Student’s t-test are disclosed

Our DNAm biomarker subset C included estimates of blood cell counts. Results of differential analysis are reported in Supplementary TableS4. B lymphocytes and naïve and absolute CD8 T cell surrogates were decreased in PDN compared to PDLN patients, with p-values of 0.008, 0.015, and 0.038, respectively. Moreover, a methylation-based predictor of the granulocyte count was significantly higher (p-value = 0.047) in PDN compared to PLDN patients. Pain-related alterations in estimates of blood cell counts are visualized on Fig. 3.

Fig. 3.

Fig. 3

Open in a new tab

Alterations of DNAm-based blood cell counts estimates of A B lymphocytes, B naive CD8 T cells, C CD8 T cells, and D granulocytes, observed in PDN, PLDN, and CTRL groups (X axis). Residuals from the two-stage residual-outcome regression approach with the PLDN group as the reference fit are reported on the Y axis. For each phenotype contrast, p-values from Student’s t-test are disclosed

No significant differences between the two phenotypes were found for the subset D biomarkers including biological traits such as waist-to-hip ratio, HDL cholesterol levels, body fat levels, body mass index, and predicted alcohol and cigarette use (Supplementary Table S5). Similarly, analysis of subset E involving a signature of the risk score for chronic low-grade inflammation measured by serum levels of CRP did not differ between PDN and PLDN patients (Supplementary Table S6 ).

Finally, analysis of DNAm biomarker subset F of EpiScores which are plasma protein surrogates returned ten estimates that were related to pain. A comprehensive list of all EpiScore results is provided in Supplementary Table S7 . Predicted levels of growth hormone receptor (GHR; p-value 0.003), interstitial collagenase (MMP1; p-value 0.003), thrombospondin-2 (THBS2; p-value 0.009), pappalysin-1 (PAPPA; p-value 0.011), and transforming growth factor alpha (TGF-α; p-value = 0.046) proteins were significantly increased in patients with PDN compared to PLDN (Fig. 4). Plasma growth/differentiation factor 8 (GDF8; p-value 0.010), ectodysplasin-A (EDA; p-value 0.016), thrombopoietin receptor (MPL; p-value 0.021), C–C motif chemokine 21 (6-Ckine/CCL21; p-value 0.026), and granzyme A (GZMA; p-value = 0.048) levels were lower in the patients with PDN compared to PLDN (Fig. 5). We investigated possible correlations between the above parameters with biological ageing in order to uncover a direct influence of ageing speed on these results. Pearson’s correlation between significant DNAm-based clocks and EpiScores was analyzed (Supplementary Figures S1-S10). Even if there were several significant correlations, r coefficient ranged between 0 and 0.5 indicating weak to moderate strength of correlations (the highest correlation was found between THBS2 and DNAmAgeHannum: r = 0.505 and p-value < 0.001).

Fig. 4.

Fig. 4

Open in a new tab

Alterations of DNAm-based plasma protein surrogates of A growth hormone receptor (GHR), B interstitial collagenase (MMP1), C thrombospondin-2 (THBS2), D pappalysin-1 (PAPPA), and E transforming growth factor alpha (TGF-α), observed in PDN, PLDN, and CTRL groups (X axis). Residuals from the two-stage residual-outcome regression approach with the PLDN group as the reference fit are reported on the Y axis. For each phenotype contrast, p-values from Student’s t-test are disclosed

Fig. 5.

Fig. 5

Open in a new tab

Alterations of DNAm-based plasma protein surrogates of A growth/differentiation factor 8 (GDF8), B ectodysplasin-A (EDA), C thrombopoietin receptor (MPL), D C–C motif chemokine 21 (6-Ckine; CCL21), and E granzyme A (GZMA), observed in PDN, PLDN, and CTRL groups (X axis). Residuals from the two-stage residual-outcome regression approach with the PLDN group as the reference fit are reported on the Y axis. For each phenotype contrast, p-values from Student’s t-test are disclosed

Notably, all DNAm-based biomarkers revealed a consistent phenotype-related trend, where the PLDN group oscillated at the baseline levels since it was used as the reference group to build a regression model, and PDN and CTRL groups presented opposite shifts corresponding to alterations in epigenetic surrogates.

Discussion

To the best of our knowledge, this is the first study evaluating epigenetic aging in a cohort of T2DM patients with painful and painless diabetic neuropathy. We provide evidence of significant differences in epigenetic clocks and in a battery of DNAm-based biomarkers between T2DM patients with painful and painless diabetic neuropathy.

We found consistent NP-related acceleration in DNAmAgeHannum, DNAmGrimAgeBasedOnPredictedAge, and DNAmAgeSkinBloodClock models, as well as in DunedinPoAm that captures individual variation in the pace of biological aging which was increased in PDN patients. Several studies previously found associations between epigenetic aging and chronic pain despite differences in experimental designs, cohort numbers, subject phenotype, and evaluation tools used. Acceleration of DNAmAge was observed in self-reported chronic pain in a cohort of healthy community-dwelling adults [20]. DunedinPACE was associated with chronic low back pain, its intensity and interference [19, 32]. DNAmGrimAge was positively correlated with self-reported knee pain [21, 23, 33] and knee osteoarthritis pain [22, 34].

DNAm-based prediction of telomere length further confirmed acceleration of epigenetic aging in patients with NP and indeed we observed an increased shortening of telomeres in the PDN group. DNA methylation-based estimation of telomere length is highly robust and provides a more accurate prediction of disease outcomes and all-cause mortality risk [35]. Our findings are consistent with a previous study indicating a relationship between chronic pain and reduced telomere length in women with fibromyalgia comparing to healthy controls [36]. Another study in patients with fibromyalgia showed a negative correlation between pain measured with the McGill Pain Questionnaire and DNAmTL [37]. A study in women with migraine and matched controls also showed increased telomere shortening [38]. Subjects with chronic knee osteoarthritis pain had significantly shorter telomeres than individuals without or with low pain intensity [39, 40].

Advanced epigenetic age in the PDN group was reproduced with principal-component-based versions of the standard models. PC-DNAmGrimAge, PC-DNAmPhenoAge, PC-DNAmAgeHannum confirmed the acceleration of methylation clocks, and PC-DNAmTL replicated increased shortening of telomeres. These alternative algorithms offer improved control of the estimation noise and enhanced reliability of predicted clocks [41, 42].

Amongst DNA methylation-based estimates of blood cell counts, we observed decreased surrogate values of B lymphocytes, naive and absolute CD8 T cells, and increased granulocyte counts in PDN patients compared to the PLDN group. During aging, the production of B and T lymphocytes drastically declines affecting efficiency of the immune system, increasing the risk of infections and autoimmune diseases [43, 44]. An increased estimated granulocyte count was found in patients with Parkinson’s disease and it correlated with intrinsic and extrinsic epigenetic age acceleration [45]. To our knowledge, this is the first study showing that alterations in blood cell counts are related to NP-related age acceleration demonstrated in the DNAm clock analysis.

The EWAS data also enabled the calculation of EpiScores to estimate relative levels of several plasma proteins, providing interesting insights. Analysis of plasma protein surrogates indicated a pain-related reduction in predicted levels of plasma C–C motif chemokine 21 (CCL21) in the PDN group. CCL21was shown to evoke hypersensitivity [46, 47], to contribute to neuropathic pain [48–50], and to play a role in inflammation and associated degeneration [51–53]. Our analysis demonstrated increased DNAm-based surrogate of growth hormone (GHR) in the PDN group. GHR was previously associated with fibromyalgia [54, 55], chronic pain [56–58], diabetes, and age-related pathologies including inflammatory disorders, stroke, and neurodegenerative diseases [59, 60]. In our study, DNAm-based predictor of plasma thrombospondin-2 (THBS2) was increased in the PDN group. Increased THBS2 glycoprotein has recently been observed in diabetic nephrophaty patients [61] and in accelerated aging conditions [62, 63]. Estimates of pappalysin 1 (PAPPA) were higher in both PLDN and PDN patients. Protein PAPPA was shown to be associated with risk of diabetes [64], to contribute to the development and progression of age-related degenerative changes [65–67], and to promote the longevity in case of its deficiency [65, 68, 69]. DNAm-based surrogate of TGF-α was decreased in studied PDN patients. This protein was previously reported to be involved in the progression of diabetic neprophaty [70], in cognitive decline and neuropathological aging [71]. Growth differentiation factor 8 (GDF8), also known as myostatin, was significantly decreased in PDN patients. GDF8 was previously negatively associated with diabetes [72] and it was proposed as a potential target for rejuvenation (not confirmed in experimental studies) [73]. In our cohort, low estimated plasma levels of granzyme A (GZMA) were associated with PDN. GZMA was previously reported in pediatric type 1 diabetes [74] and it was also recognized as a proinflammatory mediator contributing to overreaction of the immune system with a reduced inflammatory response [75–77]. Predicted levels of ectodysplasin A (EDA) were lower in the PDN compared to PLDN group. EDA hepatokine was found to be overexpressed in T2DM [78, 79] and to be involved in bone homeostasis and osteopetrosis-like skeletal changes [80–82]. EpiScore estimating plasma level of interstitial collagenase (MMP1) was significantly higher in the PDN compared to the PLDN group. MMP1 was previously reported in painful joint pathologies [83], rheumatoid arthritis, and osteoarthritis [84–86]. EpiScore of thrombopoietin receptor (MPL) was lower in PDN compared to PLDN. MPL has been related to hematological disorders, where its deficiency led to thrombocytopenia and bone marrow failure [87–89] and its enhanced functioning drove the development of myeloproliferative neoplasms [90–92]. Analysis of correlation between DNAm-based clocks and EpiScores confirmed that altered plasma protein levels are linked to a phenotype rather than to an accelerated aging.

Interestingly, for a great part of the estimates, significant differences were found not only between PDN and PLDN phenotypes, but also between both PDN/PLDN and healthy controls. The three groups could reflect the evolving stages of diabetic neuropathy, where CTRL represents a biological state in which diabetic neuropathy may or may not occur, PLDN embodies the state of development and progression, and PDN corresponds to a severe form in which the pattern of DNA methylation-based estimates captures the biological aging associated with its progression.

We have also observed a large set of epigenetic variables that did not differ between PDN and PLDN, but that varied when compared between PDN/PLDN groups and controls. These findings indicate that traits characterizing DN, independently of the development of NP, must exist. This is not surprising, because DN patients presented with accelerated biological age expressed by Horvath’s DNAmAge, PhenoAge, and AltumAge models when compared to healthy subjects. Several previously published studies have shown a positive association between epigenetic clocks and diabetes [93–95] despite differences in experimental designs and statistical approaches. On the other hand, Roshandel and colleagues [96] investigated four epigenetic clocks (DNAmAge, DNAmAgeSkinBloodClock, PhenoAge, and DNAmGrimAge) in relation to complications in type 1 diabetes and confirmed a positive association between DNAmGrimAge and neuropathy. Telomere shortening, another surrogate of biological aging, was also accelerated in diabetic neuropathy [97], although McCartney et al. [98], Horvath et al. [99], and Vetter et al. [100] did not provide support for this hypothesis. Overall, the relationship between NP and biological age could have bidirectional dynamics, with chronic pain being a symptom of aging and a driver of accelerated aging via epigenetic pathways.

Conclusions

We have comprehensively described alterations in DNA methylation-based clocks, cell count estimates, and surrogates of plasma protein levels in painful and painless DN, and identified significant correlations between these epigenetic biomarkers and neuropathic pain. Our study provides the first evidence that biological age acceleration is associated with the development of pain in diabetic neuropathy. These findings indicate that the aging process may be directly involved in the progression towards the diabetic neuropathy with pain and in general in a health degeneration in T2DM. With that said, it is possible to hypothesize that the administration of effective anti-aging therapeutics could slow down or even block the progression towards neuropathic pain and fitness derangement of the patients. Therefore, presented epigenetic signatures could be useful to better profile patients at risk of developing painful DN and also lead to the development of potential new avenues of treatment.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (PDF 372 KB) (371.5KB, pdf)
Supplementary file2 (PDF 367 KB) (367.3KB, pdf)
Supplementary file3 (PDF 371 KB) (370.6KB, pdf)
Supplementary file4 (PDF 367 KB) (367KB, pdf)
Supplementary file5 (PDF 367 KB) (367.1KB, pdf)
Supplementary file6 (PDF 364 KB) (364.4KB, pdf)
Supplementary file7 (PDF 367 KB) (367.3KB, pdf)
Supplementary file8 (PDF 370 KB) (370.4KB, pdf)
Supplementary file9 (PDF 369 KB) (369.3KB, pdf)
Supplementary file10 (PDF 368 KB) (368.4KB, pdf)
Supplementary file11 (XLSX 23 KB) (23.2KB, xlsx)
Supplementary file12 (XLSX 11 KB) (11.1KB, xlsx)
Supplementary file13 (XLSX 9 KB) (9.2KB, xlsx)
Supplementary file14 (XLSX 9 KB) (8.7KB, xlsx)
Supplementary file15 (XLSX 8 KB) (8.3KB, xlsx)
Supplementary file16 (XLSX 8 KB) (7.7KB, xlsx)
Supplementary file17 (XLSX 19 KB) (19KB, xlsx)

Acknowledgements

The authors thank the Ministry of University and Research (MUR), NextGenerationEU, National Recovery and Resilience Plan, project MNESYS (PE0000006): “A Multiscale integrated approach to the study of the nervous system in health and disease” (DN.1553 11.10.2022).

Abbreviations

NP

Neuropathic pain

DN

Diabetic neuropathy

T2DM

Type 2 diabetes mellitus

PDN

Painful diabetic neuropathy

PLDN

Painless diabetic neuropathy

CTRL

Healthy controls

Author contribution

Conceptualization: PG, KMK, CP, GL, CGF.

Methodology: KMK, CP, MGB, DG, LC.

Statistical and mathematical analysis: KMK, ES.

Formal analysis, investigation, literature search: KMK, PG, CP, MM.

Writing—original draft preparation: KMK, PG, CP.

Writing—review and editing: KMK, PG, CP, GL, RAM.

Funding acquisition: GL, PG.

Resources: PG, CP, GL, DZ, MMG, CGF, MB.

Supervision: PG, CP, GL.

Funding

This work was supported by the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie Grant Agreement No. 721841 PainNET and the Italian Ministry of Health (RRC).

Data availability

The datasets generated and analyzed during the current study are available in the GEO NCBI repository under accession number GSE286347.

Declarations

Ethics approval and consent to participate

Informed consent was given by all patients to participate in the study. The study was approved by the Lombardy Region Ethic Committee Section of the FONDAZIONE IRCCS ISTITUTO NEUROLOGICO “CARLO BESTA” (n. 56, November 7th, 2018) and by the Ethics Committee of Maastricht University (NL36128.06S.11 / METC 11–2-030).

Competing interests

The authors declare no competing interests.

Footnotes

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Katarzyna Malgorzata Kwiatkowska and Paolo Garagnani contributed equally to this work. Giuseppe Lauria and Chiara Pirazzini contributed equally to this work.

Contributor Information

Katarzyna Malgorzata Kwiatkowska, Email: katarzyn.kwiatkowsk2@unibo.it.

Paolo Garagnani, Email: paolo.garagnani2@unibo.it.

References

  • 1.Online Mendelian Inheritance in Man, OMIM [Internet]. McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University (Baltimore, MD). [cited 2024 Mar 27]. Available from: https://omim.org/
  • 2.Feldman EL, Bennett DLH, Nave KA, Jensen TS. New horizons in diabetic neuropathy: mechanisms, bioenergetics, and pain. Neuron. 2017;93(6):1296–313. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.Bae EH, Greenwald MK, Schwartz AG. Chemotherapy-induced peripheral neuropathy: mechanisms and therapeutic avenues. Neurotherapeutics. 2021;18(4):2384–96. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Calcutt NA. Diabetic neuropathy and neuropathic pain: a (con)fusion of pathogenic mechanisms? Pain. 2020;161(Supplement 1):S65-86. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.Almomani R, Sopacua M, Marchi M, Ślęczkowska M, Lindsey P, De Greef BTA, et al. Genetic profiling of sodium channels in diabetic painful and painless and idiopathic painful and painless neuropathies. Int J Mol Sci. 2023;24(9):8278. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.Blackburn EH, Greider CW, Szostak JW. Telomeres and telomerase: the path from maize, Tetrahymena and yeast to human cancer and aging. Nat Med. 2006;12(10):1133–8. [DOI] [PubMed] [Google Scholar]
  • 7.Holly AC, Melzer D, Pilling LC, Henley W, Hernandez DG, Singleton AB, et al. Towards a gene expression biomarker set for human biological age. Aging Cell. 2013;12(2):324–6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Peters MJ, Joehanes R, Pilling LC, Schurmann C, Conneely KN, Powell J, et al. The transcriptional landscape of age in human peripheral blood. Nat Commun [Internet]. 2015 Oct 22 [cited 2021 Jan 19];6. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4639797/ [DOI] [PMC free article] [PubMed]
  • 9.Krištić J, Vučković F, Menni C, Klarić L, Keser T, Beceheli I, et al. Glycans are a novel biomarker of chronological and biological ages. J Gerontol A Biol Sci Med Sci. 2014;69(7):779–89. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Menni C, Kiddle SJ, Mangino M, Viñuela A, Psatha M, Steves C, et al. Circulating proteomic signatures of chronological age. J Gerontol A Biol Sci Med Sci. 2015;70(7):809–16. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Hertel J, Friedrich N, Wittfeld K, Pietzner M, Budde K, Van der Auwera S, et al. Measuring biological age via metabonomics: the metabolic age score. J Proteome Res. 2015;15(2):400–10. [DOI] [PubMed] [Google Scholar]
  • 12.Menni C, Kastenmüller G, Petersen AK, Bell JT, Psatha M, Tsai PC, et al. Metabolomic markers reveal novel pathways of ageing and early development in human populations. Int J Epidemiol. 2013;42(4):1111–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Hannum G, Guinney J, Zhao L, Zhang L, Hughes G, Sadda S, et al. Genome-wide methylation profiles reveal quantitative views of human aging rates. Mol Cell. 2013;49(2):359–67. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Horvath S. DNA methylation age of human tissues and cell types. Genome Biol. 2013;14(10):R115. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Horvath S, Oshima J, Martin GM, Lu AT, Quach A, Cohen H, et al. Epigenetic clock for skin and blood cells applied to Hutchinson Gilford Progeria Syndrome and ex vivo studies. Aging. 2018;10(7):1758–75. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Levine ME, Lu AT, Quach A, Chen BH, Assimes TL, Bandinelli S, et al. An epigenetic biomarker of aging for lifespan and healthspan [Internet]. 2018 [cited 2024 Mar 27]. Available from: http://biorxiv.org/lookup/doi/10.1101/276162 [DOI] [PMC free article] [PubMed]
  • 17.Lu AT, Quach A, Wilson JG, Reiner AP, Aviv A, Raj K, et al. DNA methylation GrimAge strongly predicts lifespan and healthspan. Aging. 2019;11(2):303–27. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Cole JH, Ritchie SJ, Bastin ME, Valdés Hernández MC, Muñoz Maniega S, Royle N, et al. Brain age predicts mortality. Mol Psychiatry. 2018;23(5):1385–92. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Aroke EN, Wiggins AM, Hobson JM, Srinivasasainagendra V, Quinn TL, Kottae P, et al. The pace of biological aging helps explain the association between insomnia and chronic low back pain. Mol Pain. 2023;19:17448069231210648. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Cruz-Almeida Y, Sinha P, Rani A, Huo Z, Fillingim RB, Foster T. Epigenetic aging is associated with clinical and experimental pain in community-dwelling older adults. Mol Pain. 2019;15:174480691987181. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Peterson JA, Strath LJ, Nodarse CL, Rani A, Huo Z, Meng L, et al. Epigenetic aging mediates the association between pain impact and brain aging in middle to older age individuals with knee pain. Epigenetics. 2022;17(13):2178–87. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Jackson P, Spector AL, Strath LJ, Antoine LH, Li P, Goodin BR, et al. Epigenetic age acceleration mediates the relationship between neighborhood deprivation and pain severity in adults with or at risk for knee osteoarthritis pain. Soc Sci Med. 2023;331:116088. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Peterson JA, Crow JA, Johnson AJ, Meng L, Rani A, Huo Z, et al. Pain interference mediates the association between epigenetic aging and grip strength in middle to older aged males and females with chronic pain. Front Aging Neurosci. 2023;23(15):1122364. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Lauria G, Cornblath DR, Johansson O, McArthur JC, Mellgren SI, Nolano M, et al. EFNS guidelines on the use of skin biopsy in the diagnosis of peripheral neuropathy. Eur J Neurol. 2005;12(10):747–58. [DOI] [PubMed] [Google Scholar]
  • 25.Downie WW, Leatham PA, Rhind VM, Wright V, Branco JA, Anderson JA. Studies with pain rating scales. Ann Rheum Dis. 1978;37(4):378–81. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Sazzini M, Gnecchi Ruscone GA, Giuliani C, Sarno S, Quagliariello A, De Fanti S, et al. Complex interplay between neutral and adaptive evolution shaped differential genomic background and disease susceptibility along the Italian peninsula. Sci Rep [Internet]. 2016 Sep 1 [cited 2020 Oct 5];6. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5007512/ [DOI] [PMC free article] [PubMed]
  • 27.Gutierrez-Arcelus M, Lappalainen T, Montgomery SB, Buil A, Ongen H, Yurovsky A, et al. Passive and active DNA methylation and the interplay with genetic variation in gene regulation. eLife. 2013 Jun 4;2:e00523. [DOI] [PMC free article] [PubMed]
  • 28.Grant SFA, Hakonarson H, Schwartz S. Can the genetics of type 1 and type 2 diabetes shed light on the genetics of latent autoimmune diabetes in adults? Endocr Rev. 2010;31(2):183–93. [DOI] [PubMed] [Google Scholar]
  • 29.Bacalini MG, Boattini A, Gentilini D, Giampieri E, Pirazzini C, Giuliani C, et al. A meta-analysis on age-associated changes in blood DNA methylation: results from an original analysis pipeline for Infinium 450k data. Aging. 2015;7(2):97–109. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Davegårdh C, Hall Wedin E, Broholm C, Henriksen TI, Pedersen M, Pedersen BK, et al. Sex influences DNA methylation and gene expression in human skeletal muscle myoblasts and myotubes. Stem Cell Res Ther [Internet]. 2019 Jan 15 [cited 2020 Oct 5];10. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6332625/ [DOI] [PMC free article] [PubMed]
  • 31.Hjorthaug HS, Gervin K, Mowinckel P, Munthe-Kaas MC. Exploring the influence from whole blood DNA extraction methods on Infinium 450K DNA methylation. PLoS ONE. 2018;13(12):e0208699. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Aroke EN, Srinivasasainagendra V, Kottae P, Quinn TL, Wiggins AM, Hobson J, et al. The pace of biological aging predicts nonspecific chronic low back pain severity. J Pain. 2024;25(4):974–83. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Peterson JA, Meng L, Rani A, Sinha P, Johnson AJ, Huo Z, et al. Epigenetic aging, knee pain and physical performance in community-dwelling middle-to-older age adults. Exp Gerontol. 2022;166:111861. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.Strath LJ, Meng L, Rani A, Sinha P, Johnson AJ, Huo Z, et al. Accelerated epigenetic aging mediates the association between vitamin D levels and knee pain in community-dwelling individuals. J Nutr Health Aging. 2022;26(4):318–23. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Lu AT, Seeboth A, Tsai PC, Sun D, Quach A, Reiner AP, et al. DNA methylation-based estimator of telomere length. Aging. 2019;11(16):5895–923. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.Hassett AL, Epel E, Clauw DJ, Harris RE, Harte SE, Kairys A, et al. Pain is associated with short leukocyte telomere length in women with fibromyalgia. J Pain Off J Am Pain Soc. 2012;13(10):959–69. [DOI] [PubMed] [Google Scholar]
  • 37.Kwiatkowska KM, Bacalini MG, Sala C, Kaziyama H, de Andrade DC, Terlizzi R, et al. Analysis of epigenetic age predictors in pain-related conditions. Front Public Health [Internet]. 2020 Jun 9 [cited 2020 Sep 30];8. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7296181/ [DOI] [PMC free article] [PubMed]
  • 38.Ren H, Collins V, Fernandez-Enright F, Quinlan S, Griffiths L, Choo K. Shorter telomere length in peripheral blood cells associated with migraine in women. Headache. 2010;1(50):965–72. [DOI] [PubMed] [Google Scholar]
  • 39.Sibille KT, Langaee T, Burkley B, Gong Y, Glover TL, King C, et al. Chronic pain, perceived stress, and cellular aging: an exploratory study. Mol Pain. 2012;12(8):12. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40.Sibille KT, Chen H, Bartley EJ, Riley J, Glover TL, King CD, et al. Accelerated aging in adults with knee osteoarthritis pain: consideration for frequency, intensity, time, and total pain sites. Pain Rep. 2017;2(3):e591. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 41.Higgins-Chen AT, Thrush KL, Wang Y, Minteer CJ, Kuo PL, Wang M, et al. A computational solution for bolstering reliability of epigenetic clocks: implications for clinical trials and longitudinal tracking. Nat Aging. 2022;2(7):644–61. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42.Noroozi R, Rudnicka J, Pisarek A, Wysocka B, Masny A, Boroń M, et al. Analysis of epigenetic clocks links yoga, sleep, education, reduced meat intake, coffee, and a SOCS2 gene variant to slower epigenetic aging. GeroScience. 2023;46(2):2583–604. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 43.De Mol J, Kuiper J, Tsiantoulas D, Foks AC. The dynamics of B cell aging in health and disease. Front Immunol. 2021;5(12):733566. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44.Quinn KM, Fox A, Harland KL, Russ BE, Li J, Nguyen THO, et al. Age-related decline in primary CD8+ T cell responses is associated with the development of senescence in virtual memory CD8+ T cells. Cell Rep. 2018;23(12):3512–24. [DOI] [PubMed] [Google Scholar]
  • 45.Horvath S, Ritz BR. Increased epigenetic age and granulocyte counts in the blood of Parkinson’s disease patients. Aging. 2015;7(12):1130–42. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 46.Pawlik K, Mika J. Targeting members of the chemokine family as a novel approach to treating neuropathic pain. Mol Basel Switz. 2023;28(15):5766. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 47.Biber K, Tsuda M, Tozaki-Saitoh H, Tsukamoto K, Toyomitsu E, Masuda T, et al. Neuronal CCL21 up-regulates microglia P2X4 expression and initiates neuropathic pain development: neuronal CCL21 is causing neuropathic pain. EMBO J. 2011;30(9):1864–73. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 48.Boakye PA, Tang SJ, Smith PA. Mediators of neuropathic pain; focus on spinal microglia, CSF-1, BDNF, CCL21, TNF-α, Wnt ligands, and interleukin 1β. Front Pain Res Lausanne Switz. 2021;2:698157. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 49.Piotrowska A, Rojewska E, Pawlik K, Kreiner G, Ciechanowska A, Makuch W, et al. Pharmacological blockade of spinal CXCL3/CXCR2 signaling by NVP CXCR2 20, a selective CXCR2 antagonist, reduces neuropathic pain following peripheral nerve injury. Front Immunol. 2019;26(10):2198. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 50.Schmitz K, Pickert G, Wijnvoord N, Häussler A, Tegeder I. Dichotomy of CCL21 and CXCR3 in nerve injury-evoked and autoimmunity-evoked hyperalgesia. Brain Behav Immun. 2013;32:186–200. [DOI] [PubMed] [Google Scholar]
  • 51.Subburaman M, Edderkaoui B. Evaluation of CCL21 role in post-knee injury inflammation and early cartilage degeneration. Heymann D, editor. PLOS ONE. 2021 Mar 2;16(3):e0247913. [DOI] [PMC free article] [PubMed]
  • 52.Damås JK, Smith C, Øie E, Fevang B, Halvorsen B, Wæhre T, et al. Enhanced expression of the homeostatic chemokines CCL19 and CCL21 in clinical and experimental atherosclerosis: possible pathogenic role in plaque destabilization. Arterioscler Thromb Vasc Biol. 2007;27(3):614–20. [DOI] [PubMed] [Google Scholar]
  • 53.Damås JK, Landrø L, Fevang B, Heggelund L, Tjønnfjord GE, Fløisand Y, et al. Homeostatic chemokines CCL19 and CCL21 promote inflammation in human immunodeficiency virus-infected patients with ongoing viral replication. Clin Exp Immunol. 2009;157(3):400–7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 54.Cuatrecasas G, Gonzalez MJ, Alegre C, Sesmilo G, Fernandez-Solà J, Casanueva FF, et al. High prevalence of growth hormone deficiency in severe fibromyalgia syndromes. J Clin Endocrinol Metab. 2010;95(9):4331–7. [DOI] [PubMed] [Google Scholar]
  • 55.Landis CA, Lentz MJ, Rothermel J, Riffle SC, Chapman D, Buchwald D, et al. Decreased nocturnal levels of prolactin and growth hormone in women with fibromyalgia 1. J Clin Endocrinol Metab. 2001;86(4):1672–8. [DOI] [PubMed] [Google Scholar]
  • 56.Dubick M, Ravin T, Michel Y, Morrisette D. Use of localized human growth hormone and testosterone injections in addition to manual therapy and exercise for lower back pain: a case series with 12-month follow-up. J Pain Res. 2015 Jun;295. [DOI] [PMC free article] [PubMed]
  • 57.Bennett RM, Clark SC, Walczyk J. A Randomized, Double-Blind, Placebo-controlled study of growth hormone in the treatment of fibromyalgia 11supported in part by a research grant from Genentech Inc. San Francisco Am J Med. 1998;104(3):227–31. [DOI] [PubMed] [Google Scholar]
  • 58.Cuatrecasas G, Alegre C, Fernandez-Solà J, Gonzalez MJ, Garcia-Fructuoso F, Poca-Dias V, et al. Growth hormone treatment for sustained pain reduction and improvement in quality of life in severe fibromyalgia. Pain. 2012;153(7):1382–9. [DOI] [PubMed] [Google Scholar]
  • 59.Guevara-Aguirre J, Balasubramanian P, Guevara-Aguirre M, Wei M, Madia F, Cheng CW, et al. Growth hormone receptor deficiency is associated with a major reduction in pro-aging signaling, cancer, and diabetes in humans. Sci Transl Med. 2011 Feb 16;3(70):70ra13. [DOI] [PMC free article] [PubMed]
  • 60.Bartke A, Hascup E, Hascup K, Masternak MM. Growth hormone and aging: new findings. World J Mens Health. 2021;39(3):454. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 61.Lin Z, Zhang D, Zhang X, Guo W, Wang W, Zhang Y, et al. Extracellular status of thrombospondin-2 in type 2 diabetes mellitus and utility as a biomarker in the determination of early diabetic kidney disease. BMC Nephrol. 2023;24(1):154. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 62.Csoka AB, English SB, Simkevich CP, Ginzinger DG, Butte AJ, Schatten GP, et al. Genome-scale expression profiling of Hutchinson-Gilford progeria syndrome reveals widespread transcriptional misregulation leading to mesodermal/mesenchymal defects and accelerated atherosclerosis. Aging Cell. 2004;3(4):235–43. [DOI] [PubMed] [Google Scholar]
  • 63.Miedel E, Dishowitz MI, Myers MH, Dopkin D, Yu Y, Miclau TS, et al. Disruption of thrombospondin-2 accelerates ischemic fracture healing. J Orthop Res. 2013;31(6):935–43. [DOI] [PubMed] [Google Scholar]
  • 64.Ferreira JP, Lamiral Z, Xhaard C, Duarte K, Bresso E, Devignes MD, et al. Circulating plasma proteins and new-onset diabetes in a population-based study: proteomic and genomic insights from the STANISLAS cohort. Eur J Endocrinol. 2020;183(3):285–95. [DOI] [PubMed] [Google Scholar]
  • 65.Conover CA, Bale LK, Mader JR, Mason MA, Keenan KP, Marler RJ. Longevity and age-related pathology of mice deficient in pregnancy-associated plasma protein-a. J Gerontol Ser A. 2010;65A(6):590–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 66.Vallejo AN, Michel JJ, Bale LK, Lemster BH, Borghesi L, Conover CA. Resistance to age-dependent thymic atrophy in long-lived mice that are deficient in pregnancy-associated plasma protein A. Proc Natl Acad Sci. 2009;106(27):11252–7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 67.Conover CA, Bale LK, Nair KS. Comparative gene expression and phenotype analyses of skeletal muscle from aged wild-type and PAPP-A-deficient mice. Exp Gerontol. 2016;80:36–42. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 68.Conover CA, Bale LK, Marler RJ. Pregnancy-associated plasma protein-A deficiency improves survival of mice on a high fat diet. Exp Gerontol. 2015;70:131–4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 69.Conover CA, Harstad SL, Tchkonia T, Kirkland JL. Preferential impact of pregnancy-associated plasma protein-A deficiency on visceral fat in mice on high-fat diet. Am J Physiol-Endocrinol Metab. 2013;305(9):E1145–53. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 70.Heuer JG, Harlan SM, Yang DD, Jaqua DL, Boyles JS, Wilson JM, et al. Role of TGF-alpha in the progression of diabetic kidney disease. Am J Physiol-Ren Physiol. 2017;312(6):F951–62. [DOI] [PubMed] [Google Scholar]
  • 71.Gómez-Oliva R, Martínez-Ortega S, Atienza-Navarro I, Domínguez-García S, Bernal-Utrera C, Geribaldi-Doldán N, et al. Rescue of neurogenesis and age-associated cognitive decline in SAMP8 mouse: role of transforming growth factor-alpha. Aging Cell. 2023;22(6):e13829. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 72.Meloux A, Rochette L, Maza M, Bichat F, Tribouillard L, Cottin Y, et al. Growth differentiation factor-8 (GDF8)/myostatin is a predictor of troponin I peak and a marker of clinical severity after acute myocardial infarction. J Clin Med. 2019;9(1):116. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 73.Semba RD, Zhang P, Zhu M, Fabbri E, Gonzalez-Freire M, Carlson OD, et al. Relationship of circulating growth and differentiation factors 8 and 11 and their antagonists as measured using liquid chromatography–tandem mass spectrometry with age and skeletal muscle strength in healthy adults. J Gerontol Ser A. 2019;74(1):129–36. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 74.Hamel Y, Mauvais FX, Pham HP, Kratzer R, Marchi C, Barilleau É, et al. A unique CD8+ T lymphocyte signature in pediatric type 1 diabetes. J Autoimmun. 2016;73:54–63. [DOI] [PubMed] [Google Scholar]
  • 75.Garzón-Tituaña M, Sierra-Monzón JL, Comas L, Santiago L, Khaliulina-Ushakova T, Uranga-Murillo I, et al. Granzyme A inhibition reduces inflammation and increases survival during abdominal sepsis. Theranostics. 2021;11(8):3781–95. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 76.Metkar SS, Menaa C, Pardo J, Wang B, Wallich R, Freudenberg M, et al. Human and mouse granzyme A induce a proinflammatory cytokine response. Immunity. 2008;29(5):720–33. [DOI] [PubMed] [Google Scholar]
  • 77.Van Daalen KR, Reijneveld JF, Bovenschen N. Modulation of inflammation by extracellular granzyme A. Front Immunol. 2020;19(11):931. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 78.Deng X, Cai Z, Li Y, Wu X, Zhao L, Li H, et al. Increased circulating levels of ectodysplasin A in newly diagnosed type 2 diabetic patients. Front Endocrinol. 2021;9(12):737624. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 79.Deng X, Guo C, Qin H, Zhao L, Li Y, Zhao Z, et al. Association between circulating ectodysplasin A and diabetic kidney disease. Foti D, editor. J Diabetes Res. 2023 Apr 12;2023:1–10. [DOI] [PMC free article] [PubMed]
  • 80.Schweikl C, Maier-Wohlfart S, Schneider H, Park J. Ectodysplasin A1 Deficiency leads to osteopetrosis-like changes in bones of the skull associated with diminished osteoclastic activity. Int J Mol Sci. 2022;23(20):12189. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 81.Hill NL, Laib A, Duncan MK. Mutation of the Ectodysplasin-A gene results in bone defects in mice. J Comp Pathol. 2002;126(2–3):220–5. [DOI] [PubMed] [Google Scholar]
  • 82.Bornert F, Choquet P, Gros CI, Aubertin G, Perrin-Schmitt F, Clauss F, et al. Subtle morphological changes in the mandible of tabby mice revealed by micro-CT imaging and elliptical Fourier quantification. Front Physiol [Internet]. 2011 [cited 2024 Mar 27];2. Available from: http://journal.frontiersin.org/article/10.3389/fphys.2011.00015/abstract [DOI] [PMC free article] [PubMed]
  • 83.Ita ME, Singh S, Troche HR, Welch RL, Winkelstein BA. Intra-articular MMP-1 in the spinal facet joint induces sustained pain and neuronal dysregulation in the DRG and spinal cord, and alters ligament kinematics under tensile loading. Front Bioeng Biotechnol. 2022;3(10):926675. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 84.Vincenti MP, Brinckerhoff CE. Transcriptional regulation of collagenase (MMP-1, MMP-13) genes in arthritis: integration of complex signaling pathways for the recruitment of gene-specific transcription factors. Arthritis Res Ther. 2002;4(3):157. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 85.Barlas IÖ, Sezgin M, Erdal ME, Sahin G, Ankarali HC, Altintas ZM, et al. Association of (−1,607) 1G/2G polymorphism of matrix metalloproteinase-1 gene with knee osteoarthritis in the Turkish population (knee osteoarthritis and MMPs gene polymorphisms). Rheumatol Int. 2009;29(4):383–8. [DOI] [PubMed] [Google Scholar]
  • 86.Geng R, Xu Y, Hu W, Zhao H. The association between MMP-1 gene rs1799750 polymorphism and knee osteoarthritis risk. Biosci Rep. 2018 Oct 31;38(5):BSR20181257. [DOI] [PMC free article] [PubMed]
  • 87.Cornish N, Aungraheeta MR, FitzGibbon L, Burley K, Alibhai D, Collins J, et al. Monoallelic loss-of-function THPO variants cause heritable thrombocytopenia. Blood Adv. 2020;4(5):920–4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 88.Noris P, Marconi C, De Rocco D, Melazzini F, Pippucci T, Loffredo G, et al. A new form of inherited thrombocytopenia due to monoallelic loss of function mutation in the thrombopoietin gene. Br J Haematol. 2018;181(5):698–701. [DOI] [PubMed] [Google Scholar]
  • 89.Pecci A, Ragab I, Bozzi V, De Rocco D, Barozzi S, Giangregorio T, et al. Thrombopoietin mutation in congenital amegakaryocytic thrombocytopenia treatable with romiplostim. EMBO Mol Med. 2018;10(1):63–75. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 90.Pikman Y, Lee BH, Mercher T, McDowell E, Ebert BL, Gozo M, et al. MPLW515L is a novel somatic activating mutation in myelofibrosis with myeloid metaplasia. Sawyers C, editor. PLoS Med. 2006 Jul 18;3(7):e270. [DOI] [PMC free article] [PubMed]
  • 91.Ding J, Komatsu H, Wakita A, Kato-Uranishi M, Ito M, Satoh A, et al. Familial essential thrombocythemia associated with a dominant-positive activating mutation of the c-MPL gene, which encodes for the receptor for thrombopoietin. Blood. 2004;103(11):4198–200. [DOI] [PubMed] [Google Scholar]
  • 92.Beer PA, Campbell PJ, Scott LM, Bench AJ, Erber WN, Bareford D, et al. MPL mutations in myeloproliferative disorders: analysis of the PT-1 cohort. Blood. 2008;112(1):141–9. [DOI] [PubMed] [Google Scholar]
  • 93.Irvin MR, Aslibekyan S, Do A, Zhi D, Hidalgo B, Claas SA, et al. Metabolic and inflammatory biomarkers are associated with epigenetic aging acceleration estimates in the GOLDN study. Clin Epigenetics. 2018;10(1):56. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 94.Roetker NS, Pankow JS, Bressler J, Morrison AC, Boerwinkle E. Prospective study of epigenetic age acceleration and incidence of cardiovascular disease outcomes in the ARIC study (Atherosclerosis Risk in Communities). Circ Genomic Precis Med. 2018;11(3):e001937. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 95.Kim K, Joyce BT, Zheng Y, Schreiner PJ, Jacobs DR, Catov JM, et al. DNA methylation grimage and incident diabetes: the Coronary Artery Risk Development in Young Adults (CARDIA) Study. Diabetes. 2021;70(6):1404–13. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 96.DCCT/EDIC Research Group, Roshandel D, Chen Z, Canty AJ, Bull SB, Natarajan R, et al. DNA methylation age calculators reveal association with diabetic neuropathy in type 1 diabetes. Clin Epigenetics. 2020 Dec;12(1):52 [DOI] [PMC free article] [PubMed]
  • 97.Fyhrquist F, Tiitu A, Saijonmaa O, Forsblom C, Groop P ‐H., on behalf of the FinnDiane Study Group. Telomere length and progression of diabetic nephropathy in patients with type 1 diabetes. J Intern Med. 2010 Mar;267(3):278–86. [DOI] [PubMed]
  • 98.McCartney DL, Stevenson AJ, Walker RM, Gibson J, Morris SW, Campbell A, et al. Investigating the relationship between DNA methylation age acceleration and risk factors for Alzheimer’s disease. Alzheimers Dement Diagn Assess Dis Monit. 2018;10(1):429–37. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 99.Horvath S, Gurven M, Levine ME, Trumble BC, Kaplan H, Allayee H, et al. An epigenetic clock analysis of race/ethnicity, sex, and coronary heart disease. Genome Biol. 2016;17(1):171. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 100.Vetter VM, Spieker J, Sommerer Y, Buchmann N, Kalies CH, Regitz-Zagrosek V, et al. DNA methylation age acceleration is associated with risk of diabetes complications. Commun Med. 2023;3(1):21. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary file1 (PDF 372 KB) (371.5KB, pdf)
Supplementary file2 (PDF 367 KB) (367.3KB, pdf)
Supplementary file3 (PDF 371 KB) (370.6KB, pdf)
Supplementary file4 (PDF 367 KB) (367KB, pdf)
Supplementary file5 (PDF 367 KB) (367.1KB, pdf)
Supplementary file6 (PDF 364 KB) (364.4KB, pdf)
Supplementary file7 (PDF 367 KB) (367.3KB, pdf)
Supplementary file8 (PDF 370 KB) (370.4KB, pdf)
Supplementary file9 (PDF 369 KB) (369.3KB, pdf)
Supplementary file10 (PDF 368 KB) (368.4KB, pdf)
Supplementary file11 (XLSX 23 KB) (23.2KB, xlsx)
Supplementary file12 (XLSX 11 KB) (11.1KB, xlsx)
Supplementary file13 (XLSX 9 KB) (9.2KB, xlsx)
Supplementary file14 (XLSX 9 KB) (8.7KB, xlsx)
Supplementary file15 (XLSX 8 KB) (8.3KB, xlsx)
Supplementary file16 (XLSX 8 KB) (7.7KB, xlsx)
Supplementary file17 (XLSX 19 KB) (19KB, xlsx)

Data Availability Statement

The datasets generated and analyzed during the current study are available in the GEO NCBI repository under accession number GSE286347.

Articles from GeroScience are provided here courtesy of Springer

RESOURCES