An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging
Open accessAbstract
While many diseases of aging have been linked to the immunological system, immune metrics capable of identifying the most at-risk individuals are lacking. From the blood immunome of 1,001 individuals aged 8–96 years, we developed a deep-learning method based on patterns of systemic age-related inflammation. The resulting inflammatory clock of aging (iAge) tracked with multimorbidity, immunosenescence, frailty and cardiovascular aging, and is also associated with exceptional longevity in centenarians. The strongest contributor to iAge was the chemokine CXCL9, which was involved in cardiac aging, adverse cardiac remodeling and poor vascular function. Furthermore, aging endothelial cells in human and mice show loss of function, cellular senescence and hallmark phenotypes of arterial stiffness, all of which are reversed by silencing CXCL9. In conclusion, we identify a key role of CXCL9 in age-related chronic inflammation and derive a metric for multimorbidity that can be utilized for the early detection of age-related clinical phenotypes. From the blood immunome of 1,001 individuals aged 8–96 years, the authors used deep learning to develop an inflammatory clock of aging (iAge) that tracks with multimorbidity, immunosenescence, frailty and cardiovascular aging, and is also associated with exceptional longevity in centenarians. The main contributor to iAge is the chemokine CXCL9, which is shown to control endothelial cell senescence and function.
AI snapshot
Written from the paper’s full text.
TL;DR
Using blood immune protein data from 1,001 people aged 8–96, the authors built a deep-learning inflammatory aging clock (iAge) that predicts chronological age and tracks multimorbidity, frailty, immunosenescence, and cardiovascular aging. CXCL9 emerged as the strongest positive contributor, and silencing it reversed endothelial dysfunction and senescence phenotypes in human and mouse models.
Key findings
- iAge correlated with chronological age at R = 0.78 (P < 10−16); the guided auto-encoder outperformed linear methods (P < 0.05) except a classic neural network (P = 0.54), with average test reconstruction errors of 15.2 years for age and 0.26 normalized for protein data.
- iAge was associated with multimorbidity in adults over 60 (n = 285, P < 0.01); Elastic Net prediction of multimorbidity had MAE = 0.41, with iAge, high cholesterol, and BMI among the largest coefficients. In 29 older adults, iAge in 2010 predicted 2017 frailty score (R² = 0.81, P < 0.001), stronger than calendar age. Among 19 centenarians and 18 controls, 13/19 centenarians were in the low iAge-index group (P = 0.028), and the iAge gene signature was associated with all-cause mortality in Framingham (P = 0.02, n = 2,290).
- iAge correlated with immunosenescence markers: naive CD8+ T-cell frequency and phospho-STAT responses across 818 individuals and 96 conditions (combined P < 10−5).
- CXCL9 was the top positive iAge contributor and rose with age (P < 10−15, starting around age 60). In 97 healthy adults aged 25–90, CXCL9 correlated with pulse wave velocity (R = 0.22) and relative wall thickness (R = 0.3) at P < 0.01, while LIF correlated negatively (R = −0.27 and −0.22). CXCL9 knockdown (~75% reduction) rescued tube formation, nitric oxide production, and Ac-LDL uptake in late-passage iPSC-ECs; RNA-seq showed ~fourfold CXCL9 increase at passage 8, inflammatory pathway upregulation, proliferation pathway downregulation, and reversal of arterial-stiffness-related CAM/MMP/COL gene changes.
Methods
- The Stanford 1000 Immunomes Project enrolled 1,001 ambulatory participants (339 males, 662 females) aged 8–96 between 2007 and 2016; serum cytokines were measured in 1,001, CMV status in 748, gene expression in 397, cellular phenotypes in 935, in vitro cytokine stimulations in 818, and a 53-item clinical questionnaire in 902. Only healthy controls from the chronic fatigue syndrome study were used.
- iAge was derived with a guided auto-encoder (GAE), a two-layer neural network balancing reconstruction and chronological-age prediction losses; fivefold cross-validation with nested threefold hyperparameter selection chose code length 5, depth 2, guidance ratio 0.2, and L2 = 0.001. Comparisons included Elastic Net, gradient boosting, PCA, plain auto-encoders, and neural networks.
- Validation used 37 Italian centenarians/controls, a Framingham Heart Study gene-expression signature with Cox regression (n = 2,290), a 97-person cardiovascular cohort, human iPSC-derived endothelial cells, mouse aortic rings, RNA-seq, flow/CyTOF immunophenotyping, Luminex/SOMAscan assays, and isometric myography.
Limitations
- The paper states that biological clocks generally do not directly reveal mechanism, so individual biomarkers require experimental testing; it also notes that the cytokine stimulation assay may not fully mirror in vivo stimuli and activates naive T cells and some helper T-cell subsets poorly. The authors also note that the iAge–chronological-age correlation (R = 0.78) is lower than a reported proteomic age metric (R = 0.92).
- The paper states that no statistical method predetermined sample size, no data were excluded, and no randomization or blinding was used; mouse experiments were not blinded. The iAge model used cross-sectional baseline data even though some participants had longitudinal samples.
- Evident from the text: several validation cohorts were modest in size (37 centenarian/control participants; 97 cardiovascular participants), and the Framingham analysis relied on a derived gene signature because sufficient proteomics data were unavailable, which may limit direct comparability and generalizability.
Model-written summary. Check it against the paper before relying on it.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
0 of 66 cited sources checked · 0 supported · 0 flagged
- Findingown data
The chronic inflammation metric accurately predicts chronological age in the population using total inflammatory burden measured by circulating immune protein levels.
“This metric for chronic inflammation accurately predicts cAge in the population (Fig. 1a) using the total inflammatory burden as measured by the level of circulating immune proteins (Extended Data Fig. 4).”
Quote in the paperSubstantial claim - Findingown data
The largest coefficients for predicting multimorbidity included iAge, high cholesterol and BMI.
“The top features with the largest coefficients included iAge, high cholesterol and BMI (Extended Data Fig. 5c).”
Quote in the paperNot a substantial claim - Findingown data
In older adults over 60 years old, iAge was significantly correlated with multimorbidity.
“We observed a significant correlation between iAge and multimorbidity in the older adults in this study (>60 years old) (n = 285, P < 0.01) (Fig. 1b).”
Quote in the paperSubstantial claim - Findingown data
In 29 older adults, iAge measured in 2010 predicted frailty score in 2017 after adjustment for chronological age, sex, BMI and CMV status.
“Using a linear regression model where frailty score in 2017 was regressed onto iAge calculated in 2010 and controlled for cAge, sex, BMI and CMV status, we found that iAge from 2010 was predictive of frailty score in 2017 (R2 = 0.81, P < 0.001; Fig. 1c).”
Quote in the paperSubstantial claim - Findingown data
The iAge gene expression signature was significantly associated with all-cause mortality in the Framingham Heart Study after adjustment for multiple mortality-related covariates.
“We observed that the iAge gene signature was significantly associated with all-cause mortality following adjustment to multiple covariates associated with mortality, including age, sex, smoking, cholesterol levels, blood pressure, diabetes and existence of a cardiovascular disease (P = 0.02, Cox proportional hazards model, n = 2,290).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [36] Mahmood, S. S., Levy, D., Vasan, R. S. & Wang, T. J. The Framingham Heart Study and the epidemiology of cardiovascular disease: a historical perspective. Lancet 383, 999–1008 (2014). source
Not checked in the free run.
- Rests on an assumption: The iAge gene expression signature derived from the Stanford cohort can serve as a proxy for iAge in the Framingham Heart Study cohort.
- Findingown data
The top negative contributors among the most variable Jacobians were TRAIL, IFN-γ, CXCL1, IL-2, TGF-α, PAI-1 and LIF.
“and TNF-related apoptosis-inducing ligand (TRAIL), IFN-γ, CXCL1, IL-2, transforming growth factor (TGF)-α, plasminogen activator inhibitor (PAI)-1 and leukemia inhibitory factor (LIF) (negative contributors).”
Quote in the paperNot a substantial claim - Findingown data
The top positive contributors among the most variable Jacobians were CXCL9, EOTAXIN, Mip-1α, LEPTIN, IL-1β, IL-5, IFN-α and IL-4.
“where the top 15 most variable Jacobians were CXCL9, EOTAXIN, Mip-1α, LEPTIN, IL-1β, IL-5, IFN-α and IL-4 (positive contributors)”
Quote in the paperNot a substantial claim - Findingown data
There was a statistically significant difference in tube formation, nitric oxide production and acetylated LDL uptake between Scramble cells at passage 0 and CXCL9-knockdown cells at passage 8.
“It is also noteworthy that when comparing tube formation, NO production and uptake of Ac-LDL in Scramble at passage 0 versus CXCL9-KD at passage 8, there is a statistically significant difference in all three metrics (P < 0.01).”
Quote in the paperSubstantial claim - Findingown data
The contribution of iAge to frailty score was significantly stronger than that of calendar age.
“Notably, the contribution of iAge to frailty score was significantly stronger than that of calendar age (Fig. 1d).”
Quote in the paperSubstantial claim - Findingown data
Endothelial cells from old mice showed impaired function, evident as decreased tube formation.
“while at the same time showed impaired EC function as evident by decreased tube formation (Extended Data Fig. 8e,f).”
Quote in the paperSubstantial claim - Findingown data
CXCL9 was the most positive contributor to iAge.
“Given that the most positive contributor to iAge was CXCL9”
Quote in the paperNot a substantial claim - Findingown data
Endothelial cells isolated from old mice showed higher levels of CXCL9.
“As expected, ECs isolated from old mice showed higher levels of CXCL9 (Extended Data Fig. 8d)”
Quote in the paperNot a substantial claim - Findingown data
CXCR3 was expressed in endothelial cells but not in cardiomyocytes.
“The CXCL9 receptor, Gαi protein-coupled protein CXCR3, was expressed in ECs but not in cardiomyocytes (Extended Data Fig. 9).”
Quote in the paperSubstantial claim - Findingown data
CXCL9 had a modest positive correlation with pulse wave velocity.
“At P < 0.01, we found a modest positive correlation between CXCL9 and PWV (R = 0.22) and RWT (R = 0.3) (Fig. 3c–f).”
Quote in the paperSubstantial claim - Findingown data
In the healthy validation cohort, only 6 of 48 circulating immune proteins were significantly correlated with age.
“In this healthy cohort, inflammation markers were measured using a 48-plex cytokine panel and only 6 out of 48 circulating immune proteins were significantly correlated with age (P < 0.05).”
Quote in the paperNot a substantial claim - Findingown data
IL-6 and tumor necrosis factor-α were not major contributors to iAge.
“Notably, canonical markers of acute infection such as IL-6 and tumor necrosis factor-α were not major contributors to iAge”
Quote in the paperSubstantial claim - Findingown data
CXCL9 levels were significantly higher in blood endothelial progenitor cells from older individuals than from younger individuals.
“Quantitative PCR analysis of BECs from young and old individuals showed a significant increase in CXCL9 levels in older compared to younger subjects (Fig. 4b).”
Quote in the paperNot a substantial claim - Findingown data
iPSC-ECs at passage 8 had significantly impaired tube formation compared with early passages 0 and 2.
“iPSC-ECs at passage 8 showed significantly impaired tube formation when compared to early passages of iPSC-ECs, including passage 0 and 2.”
Quote in the paperSubstantial claim - Findingown data
The age-related changes in the validation cohort were in the same direction as those in the 1KIP cohort.
“These changes were in the same direction as those observed in the 1KIP cohort.”
Quote in the paperNot a substantial claim - Findingown data
CXCL9 expression was reduced by about 75% in CXCL9-knockdown hiPSC-ECs compared with Scramble hiPSC-ECs.
“CXCL9 expression, as analyzed by quantitative PCR, was reduced by ~75% in CXCL9-KD hiPSC-ECs compared to Scramble hiPSC-ECs (not shown).”
Quote in the paperSubstantial claim - Findingown data
LIF had a negative correlation with relative wall thickness.
“We also found a negative correlation between LIF and PWV (R = −0.27) and RWT (R = −0.22).”
Quote in the paperNot a substantial claim - Findingown data
LIF had a negative correlation with pulse wave velocity.
“We also found a negative correlation between LIF and PWV (R = −0.27) and RWT (R = −0.22).”
Quote in the paperSubstantial claim - Findingown data
Knockdown of CXCL9 in iPSC-ECs rescued endothelial cell dysfunction in late passages P6 and P8.
“Notably, the knockdown of CXCL9 (CXCL9-KD) in iPSC-ECs rescued the EC dysfunction in late passages of iPSC-ECs (P6 and P8), suggesting an important role of CXCL9 and the EC phenotype.”
Quote in the paperNot a substantial claim - Findingown data
CXCL9 levels increased significantly with age, beginning at age 60.
“we compared CXCL9 levels between different age groups and found significant increases in this protein with age (P < 10−15, by one-way analysis of variance (ANOVA) test) starting at the age of 60 years (Fig. 3b and Extended Data Fig. 6).”
Quote in the paperNot a substantial claim - Findingown data
In the validation cohort, CXCL9 was again the largest contributor to age-related inflammation.
“Among these, CXCL9 was again the largest contributor to age-related inflammation (Extended Data Fig. 7), supporting the findings observed in the 1KIP cohort.”
Quote in the paperNot a substantial claim - Findingown data
CXCL9 had a modest positive correlation with relative wall thickness.
“At P < 0.01, we found a modest positive correlation between CXCL9 and PWV (R = 0.22) and RWT (R = 0.3) (Fig. 3c–f).”
Quote in the paperSubstantial claim - Findingown data
Silencing CXCL9 in endothelial cells reversed the high inflammation/low proliferation early senescence phenotype.
“silencing of CXCL9 in ECs resulted in a reversal of the high inflammation/low proliferation early senescence phenotype”
Quote in the paperNot a substantial claim - Findingown data
In Scramble iPSC-ECs, SA-β-gal activity increased with passage number.
“As expected, Scramble iPSC-ECs showed a passage-dependent increase in SA-β-gal activity, suggesting an increase in cellular senescence.”
Quote in the paperSubstantial claim - Findingown data
Endothelial cells, but not cardiomyocytes, express the CXCL9 receptor CXCR3.
“As ECs but not cardiomyocytes expressed the CXCL9 receptor, CXCR3 (Extended Data Fig. 9)”
Quote in the paperSubstantial claim - Findingown data
Aortic rings from old mice showed impaired vascular relaxation in response to acetylcholine compared with young mice.
“As seen in Fig. 6d, aortic rings excised from old mice showed impaired vascular relaxation when compared to young mice in response to acetylcholine.”
Quote in the paperNot a substantial claim - Findingown data
IFN-γ did not increase in expression in the cellular aging RNA-seq experiment.
“IFN-γ, a direct agonist to CXCL9, did not increase in expression in our cellular aging RNA-seq experiment”
Quote in the paperSubstantial claim - Findingown data
Removing most features did not significantly affect prediction accuracy.
“Removal of most features did not significantly affect prediction accuracy (Extended Data Fig. 2).”
Quote in the paperSubstantial claim - Findingown data
iAge was associated with several aging phenotypes in the general population.
“This metric tracked with multiple aging phenotypes in the general population”
Quote in the paperSubstantial claim- Rests on an assumption: The Stanford 1KIP cohort reflects the general population for aging phenotypes.
- Findingown data
At passage 8, Scramble iPSC-ECs did not sprout in vivo.
“however, P8 (late passaged) Scramble iPSC-ECs failed to show sprouting in vivo (Fig. 7c,d).”
Quote in the paperSubstantial claim - Findingown data
Except for some COL genes, the vascular stiffness gene profile was reversed in CXCL9-knockdown cells.
“Except for some COL genes, this vascular stiffness gene profile is reversed in CXCL9-KD cells”
Quote in the paperSubstantial claim - Findingown data
CXCL9 had a dose-dependent effect on vasorelaxation in treated mouse aortas compared with controls.
“As shown in Fig. 6c, a dose-dependent effect of CXCL9 is observed on vasorelaxation in treated aortas versus controls, which validates our findings of the effect of CXCL9 on the arterial stiffness gene expression phenotypes.”
Quote in the paperSubstantial claim - Findingown data
In the Stanford 1KIP data, a model excluding TRAIL but containing EOTAXIN, MIP-1α, CXCL1, CXCL9, IFN-γ, IL-1β, IL-2, LEPTIN and PAI-1 had prediction accuracy not different from model 1 including TRAIL.
“Using Stanford 1KIP data, we found that the prediction accuracy of a model when TRAIL is removed but containing EOTAXIN, MIP-1α, CXCL1, CXCL9, IFN-γ, IL-1β, IL-2, LEPTIN and PAI-1 was not different from that of model 1 (in which TRAIL is included) (by likelihood ratio test, P < 0.01).”
Quote in the paperSubstantial claim - Findingown data
At passage 0, both Scramble and CXCL9-knockdown iPSC-ECs formed capillaries in vivo.
“As seen in Fig. 7c, immunohistochemical images showed formation of capillaries in Scramble and CXCL9-KD iPSC-ECs at P0”
Quote in the paperNot a substantial claim - Findingown data
In the 1KIP study, IFN-γ was the second-most important negative contributor to iAge.
“in our 1KIP study, IFN-γ was in fact the second-most important negative contributor to iAge”
Quote in the paperNot a substantial claim - Findingown data
The cross-validation error for the five-feature model (EOTAXIN, IFNG, GROA, TRAIL, CXCL9) is not significantly different from the error for the all-feature model.
“The cross-validation error using only five features (model 1) (EOTAXIN, IFNG, GROA, TRAIL and CXCL9 (which are not removed as they are the last feature)), is not significantly different from the error obtained using all features (model 2)”
Quote in the paperSubstantial claim - Findingown data
In CXCL9-knockdown iPSC-ECs, SA-β-gal activity at later passages was significantly lower than in Scramble cells.
“However, in CXCL9-KD iPSC-ECs the SA-β-gal activity at later passages was significantly reduced when compared to Scramble, suggesting a direct link between CXCL9 expression and cellular senescence (Fig. 7b).”
Quote in the paperSubstantial claim - Findingown data
CXCL9 was validated as an indicator of cardiovascular pathology independently of age.
“A major contributor to the inflammatory clock, CXCL9, was validated as an indicator of cardiovascular pathology independently of age.”
Quote in the paperSubstantial claim - Findingown data
Aortic rings from both young and old mice incubated with CXCL9 exhibited impaired vascular relaxation.
“However, aortic rings from both young and old mice when incubated with CXCL9 exhibited impaired vascular relaxation.”
Quote in the paperSubstantial claim - Findingown data
CAM, MMP and COL genes related to arterial stiffness were substantially upregulated at passage 8.
“We found a substantial upregulation of CAM, MMP and COL genes related to arterial stiffness at passage 8 (Fig. 6a).”
Quote in the paperSubstantial claim - Findingown data
The correlation between chronological age and iAge was 0.78 (P < 10−16).
“The correlation between chronological age and iAge was 0.78 (P < 10−16) (Fig. 1a)”
Quote in the paperSubstantial claim- Rests on an assumption: Chronological age serves as a valid benchmark for biological aging.
- Findingown data
At passage 8, CXCL9-knockdown iPSC-ECs showed significantly better in vivo angiogenesis than Scramble cells.
“In contrast, P8 CXCL9-KD iPSC-ECs showed significantly improved in vivo angiogenesis, suggesting a critical role of CXCL9 in EC senescence.”
Quote in the paperSubstantial claim - Findingown data
The proliferation rate was significantly higher in passage 0 iPSC-ECs than in passage 8 iPSC-ECs over 4 days.
“As seen in Fig. 7a, the kinetic profile of iPSC-EC proliferation over 4 d showed a significant increase in the proliferation rate in P0 iPSC-ECs when compared to P8 iPSC-ECs.”
Quote in the paperSubstantial claim - Findingown data
When CXCL9 was inhibited in passage 8 iPSC-ECs, the proliferation rate increased significantly compared with Scramble-treated cells.
“Notably, when CXCL9 was inhibited in P8 iPSC-ECs (CXCL9-KD), the proliferation rate showed a significant increase when compared to Scramble-treated cells.”
Quote in the paperSubstantial claim - Findingnone given
The nonlinear GAE method was optimal for identifying iAge and its contributors.
“Our nonlinear GAE method was optimal for the identification of iAge and its contributors.”
Quote in the paperSubstantial claim - Findingown data
No significant correlation was found between BMI, smoking, dyslipidemia and CXCL9 gene or protein expression.
“Notably, we did not find any significant correlation between known disease risk factors reported in the study (BMI, smoking, dyslipidemia) and levels of CXCL9 gene or protein expression.”
Quote in the paperSubstantial claim - Findingown data
Endothelial cells isolated from old mice showed impaired function, evident as decreased tube formation (P = 0.042).
“while at the same time showed impaired EC function as evident by decreased tube formation (P value = 0.042) (a, f).”
Quote in the paperNot a substantial claim - Findingown data
CXCL9, HGF, CXCL1, and LIF changed in the same direction in both the Stanford 1KIP and the validation cohort.
“CXCL9, HGF, CXCL1, and LIF were found to change in the same direction in both the Stanford 1KIP and the validation cohort.”
Quote in the paperNot a substantial claim - Findingown data
Capillary-like networks showed impaired tube formation by human BECs from old individuals compared to young individuals.
“Representative images of capillary-like networks show impaired tube formation by human BECs of old individuals compared to young.”
Quote in the paperNot a substantial claim - Findingown data
In raw data, Spearman correlation between immune protein features and batch ID showed a strong dependency of data source on the top four components, reaching a steady state after component five.
“a, Spearman correlation between immune protein features and batch ID shows a strong dependency of data source on top 4 components (raw data, green line), which reaches a steady state after component 5.”
Quote in the paperNot a substantial claim - Findingown data
The mean absolute error for predicting comorbidity was 0.41.
“A Mean Absolute Error (MAE) for the prediction of comorbidity of 0.41 is observed (b).”
Quote in the paperSubstantial claim - Findingown data
Endothelial cells isolated from old mice had higher levels of CXCL9 (P = 0.023).
“ECs isolated from old mice showed higher levels of CXCL9 (P value = 0.023) (d)”
Quote in the paperNot a substantial claim - Interpretationown data
iAge has a key role in the accumulation of physiological damage during aging.
“This highlights the key role of iAge in the accumulation of physiological damage during aging.”
Quote in the paperSubstantial claim - Premisecitation
The nine established hallmarks of aging have all been shown to be connected with sustained systemic inflammation.
“have all been shown to be linked to sustained systemic inflammation7–16.”
Quote in the paperSubstantial claim0 of 10 sources checked- cites [7] Cavadas, C., Aveleira, C. A., Souza, G. F. P. & Velloso, L. A. The pathophysiology of defective proteostasis in the hypothalamus — from obesity to ageing. Nat. Rev. Endocrinol. 12, 723–733 (2016). source
Not checked in the free run.
- cites [8] Efeyan, A., Comb, W. C. & Sabatini, D. M. Nutrient-sensing mechanisms and pathways. Nature 517, 302–310 (2015). source
Not checked in the free run.
- cites [9] Grivennikov, S. I., Greten, F. R. & Karin, M. Immunity, inflammation, and cancer. Cell 140, 883–899 (2010). source
Not checked in the free run.
- cites [10] Hunter, R. L. et al. Inflammation induces mitochondrial dysfunction and dopaminergic neurodegeneration in the nigrostriatal system. J. Neurochem. 100, 1375–1386 (2007). source
Not checked in the free run.
- cites [11] Jurk, D. et al. Chronic inflammation induces telomere dysfunction and accelerates ageing in mice. Nat. Commun. 5, 4172 (2014). source
Not checked in the free run.
- cites [12] Lasry, A. & Ben-Neriah, Y. Senescence-associated inflammatory responses: aging and cancer perspectives. Trends Immunol. 36, 217–228 (2015). source
Not checked in the free run.
- cites [13] Nathan, C. & Cunningham-Bussel, A. Beyond oxidative stress: an immunologist’s guide to reactive oxygen species. Nat. Rev. Immunol. 13, 349–361 (2013). source
Not checked in the free run.
- cites [14] Oh, J., Lee, Y. D. & Wagers, A. J. Stem cell aging: mechanisms, regulators and therapeutic opportunities. Nat. Med. 20, 870–880 (2014). source
Not checked in the free run.
- cites [15] Thevaranjan, N. et al. Age-associated microbial dysbiosis promotes intestinal permeability, systemic inflammation, and macrophage dysfunction. Cell Host Microbe 21, 455–466 (2017). source
Not checked in the free run.
- cites [16] Alpert, A. et al. A clinically meaningful metric of immune age derived from high-dimensional longitudinal monitoring. Nat. Med. 25, 487–495 (2019). source
Not checked in the free run.
- Interpretationown data
Low-grade chronic inflammation in aging humans is best modeled by nonlinear methods that account for network structure and redundancy among immunological protein biomarkers.
“These results indicate that the phenomenon of low-grade chronic inflammation in aging humans is best modeled using nonlinear methods, which take into account network structure and redundancy in immunological protein biomarkers.”
Quote in the paperSubstantial claim - Interpretationown data
The inflammatory clock is a metric for overall health linked to multiple age-associated diseases.
“Collectively, these results show that the inflammatory clock is a metric for overall health linked to multiple diseases associated with aging.”
Quote in the paperSubstantial claim - Study design and conductown data
The analysis of naive CD8+ T-cell frequency estimated the contribution of iAge after controlling for age, CMV and sex in a multiple regression model.
“estimated the contribution of iAge after controlling for Age, CMV and sex by a multiple regression model.”
Quote in the paperSubstantial claim - Premisecitation
Endothelial cell dysfunction strongly affects arterial stiffness through cellular adhesion molecules, matrix metalloproteinases and collagen molecules.
“EC dysfunction has been shown to strongly affect arterial stiffness via cellular adhesion molecules (CAMs), matrix metalloproteinases (MMPs) and collagen molecules (COLs)67–71.”
Quote in the paperSubstantial claim0 of 5 sources checked- cites [67] Zieman, S. J., Melenovsky, V. & Kass, D. A. Mechanisms, pathophysiology, and therapy of arterial stiffness. Arter. Thromb. Vasc. Biol. 25, 932–943 (2005). source
Not checked in the free run.
- cites [68] Kohn, J. C., Lampi, M. C. & Reinhart-King, C. A. Age-related vascular stiffening: causes and consequences. Front. Genet. 6, 112 (2015). source
Not checked in the free run.
- cites [69] Lu, P., Takai, K., Weaver, V. M. & Werb, Z. Extracellular matrix degradation and remodeling in development and disease. Cold Spring Harb. Perspect. Biol. 3, a005082 (2011). source
Not checked in the free run.
- cites [70] Kuzuya, M. et al. Glycation cross-links inhibit matrix metalloproteinase-2 activation in vascular smooth muscle cells cultured on collagen lattice. Diabetologia 44, 433–436 (2001). source
Not checked in the free run.
- cites [71] González-Amaro, R., Diaz-González, F. & Sánchez-Madrid, F. Adhesion molecules in inflammatory diseases. Drugs 56, 977–988 (1998). source
Not checked in the free run.
- Interpretationreasoning
Increased inflammation and decreased proliferation in aged endothelial cells could contribute to the impaired tube formation and endothelial dysfunction observed earlier.
“Such functional impact of increased inflammation and decreased proliferation in endothelial aged cells could contribute to the impaired tube formation and endothelial dysfunction observed in the experiments described previously.”
Quote in the paperNot a substantial claim - Interpretationown data
Centenarians have a protective iAge index phenotype regardless of chronological age.
“which indicates that regardless of cAge, centenarians have a protective iAge index phenotype.”
Quote in the paperSubstantial claim - Interpretationown data
CXCL9 is an important factor in age-related chronic inflammation.
“Taken together, these results suggest that CXCL9 is an important factor in age-related chronic inflammation.”
Quote in the paperSubstantial claim - Interpretationcitation
This expression profile is indicative of an early cellular senescence phenotype.
“This profile is indicative of an early cellular senescence phenotype62–66.”
Quote in the paperSubstantial claim0 of 5 sources checked- cites [62] Minamino, T. & Komuro, I. Vascular cell senescence: contribution to atherosclerosis. Circ. Res. 100, 15–26 (2007). source
Not checked in the free run.
- cites [63] Yin, H. & Pickering, J. G. Cellular senescence and vascular disease: novel routes to better understanding and therapy. Can. J. Cardiol. 32, 612–623 (2016). source
Not checked in the free run.
- cites [64] Uryga, A. K. & Bennett, M. R. Ageing induced vascular smooth muscle cell senescence in atherosclerosis. J. Physiol. 594, 2115–2124 (2016). source
Not checked in the free run.
- cites [65] Jia, G., Aroor, A. R., Jia, C. & Sowers, J. R. Endothelial cell senescence in aging-related vascular dysfunction. Biochim. Biophys. Acta, Mol. Basis Dis. 1865, 1802–1809 (2019). source
Not checked in the free run.
- cites [66] Stojanovic, S. D., Fiedler, J., Bauersachs, J., Thum, T. & Sedding, D. G. Senescence-induced inflammation: an important player and key therapeutic target in atherosclerosis. Eur. Heart J. 41, 2983–2996 (2020). source
Not checked in the free run.
- Interpretationown data
Except for IL-1β, infection-driven inflammatory markers of the acute inflammatory response do not contribute to age-related chronic inflammation.
“indicating that, except for IL-1β, infection-driven inflammatory markers of the acute inflammatory response do not contribute to age-related chronic inflammation.”
Quote in the paperSubstantial claim - Study design and conductcitation
The assays used to measure endothelial function robustly assess endothelial cell health.
“together, the assays robustly assess the health of ECs.”
Quote in the paperSubstantial claim0 of 4 sources checked- cites [53] Arnaoutova, I. & Kleinman, H. K. In vitro angiogenesis: endothelial cell tube formation on gelled basement membrane extract. Nat. Protoc. 5, 628–635 (2010). source
Not checked in the free run.
- cites [54] Mukai, N. et al. A comparison of the tube forming potentials of early and late endothelial progenitor cells Exp. Cell. Res. 314, 430–440 (2008). source
Not checked in the free run.
- cites [55] Tousoulis, D., Kampoli, A. M., Tentolouris, C., Papageorgiou, N. & Stefanadis, C. The role of nitric oxide on endothelial function. Curr. Vasc. Pharmacol. 10, 4–18 (2012). source
Not checked in the free run.
- cites [56] Voyta, J. C., Via, D. P., Butterfield, C. E. & Zetter, B. R. Identification and isolation of endothelial cells based on their increased uptake of acetylated low-density lipoprotein. J. Cell Biol. 99, 2034–2040 (1984). source
Not checked in the free run.
- Interpretationown data
iAge is associated with exceptional longevity.
“This indicates that iAge is associated with exceptional longevity.”
Quote in the paperSubstantial claim - Interpretationown data
CXCL9 and LIF could serve as early blood biomarkers for cardiovascular disease risk in otherwise healthy individuals.
“taken together, these results show that soluble blood markers CXCL9 and LIF could be used as early biomarkers to assess cardiovascular disease risk in otherwise healthy individuals.”
Quote in the paperSubstantial claim - Interpretationown data
CXCL9 expression has a direct relationship with cellular senescence.
“However, in CXCL9-KD iPSC-ECs the SA-β-gal activity at later passages was significantly reduced when compared to Scramble, suggesting a direct link between CXCL9 expression and cellular senescence (Fig. 7b).”
Quote in the paperSubstantial claim - Interpretationown data
The inflammatory clock can be estimated with the reduced five-feature set as accurately as with all 50 features.
“indicating that the inflammatory clock can be estimated with this reduced set, as accurately as by using all 50 features.”
Quote in the paperSubstantial claim - Interpretationown data
There are triggers of CXCL9 other than IFN-γ that play a role in endothelial cellular senescence.
“suggesting that there are triggers of CXCL9 (other than IFN-γ) that play a role in cellular senescence in the endothelium that are currently unknown.”
Quote in the paperNot a substantial claim - Interpretationreasoning
Therapies targeting CXCL9 could prevent age-related deterioration of the vascular system and other physiological systems.
“which indicates that therapies targeting CXCL9 could be used to prevent age-related deterioration of the vascular system and other physiological systems as well.”
Quote in the paperSubstantial claim - Interpretationown data
Targeting CXCL9 may delay the onset of endothelial cell senescence.
“which suggests by tackling CXCL9 it may be possible to delay onset of EC senescence.”
Quote in the paperSubstantial claim - Interpretationreasoning
iAge can serve as a biological surrogate of age-related health versus disease.
“and therefore can be used as a biological surrogate of age-related health versus disease.”
Quote in the paperSubstantial claim - Interpretationown data
Silencing CXCL9 can restore the endothelial cell phenotype.
“which suggests that silencing of this single gene can restore the EC phenotype”
Quote in the paperSubstantial claim - Interpretationreasoning
iAge can identify healthy older adults with no clinical or laboratory evidence of cardiovascular disease who are at risk for early cardiovascular aging.
“our immune metric for human health can identify within healthy older adults with no clinical or laboratory evidence of cardiovascular disease, those at risk for early cardiovascular aging.”
Quote in the paperSubstantial claim - Premisecitation
Sustained levels of inflammatory mediators lead to nonfunctional constitutive phosphorylation of signaling pathways with saturation of phosphorylation sites, resulting in lowered δ phosphorylation and dampened immune response.
“we show that sustained levels of inflammatory mediators lead to nonfunctional constitutive phosphorylation of signaling pathways with saturation of phosphorylation sites in signaling proteins (such as the JAK–STAT system), which results in a lowered δ phosphorylation in response to acute stimuli and subsequent dampening of the immune response to infections or vaccination28.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [28] Shen-Orr Shai, S. et al. Defective signaling in the JAK–STAT pathway tracks with chronic inflammation and cardiovascular risk in aging humans. Cell Syst. 3, 374–384 (2016). source
Not checked in the free run.
- Interpretationown data
The endothelium is a central player in cardiovascular aging.
“Our data also place the endothelium as a central player in cardiovascular aging”
Quote in the paperSubstantial claim - Interpretationown data
CXCL9 is critical for endothelial cell senescence.
“In contrast, P8 CXCL9-KD iPSC-ECs showed significantly improved in vivo angiogenesis, suggesting a critical role of CXCL9 in EC senescence.”
Quote in the paperSubstantial claim - Interpretationreasoning
iAge can be used as a companion diagnostic to inform physicians about inflammatory burden and overall health status.
“we generate an inflammatory clock of aging, which can be used as a companion diagnostic to inform physicians about patient’s inflammatory burden and overall health status, especially in those with chronic diseases.”
Quote in the paperSubstantial claim - Interpretationown data
The authors conclude that GAE is superior to traditional machine learning methods.
“We conclude that GAE is superior to traditional machine learning methods.”
Quote in the paperSubstantial claim
- May combine two claims. 50
- Classified as probably not a substantial claim. 59
- The quote only partly states this claim. 1
May combine two claims.
Findingown dataOn the test set, the average reconstruction error was 15.2 years for age prediction and 0.26 normalized for circulating immune protein data.
“The average reconstruction errors on the test set for prediction of age and circulating immune protein data were 15.2 years and 0.26 (normalized), respectively.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataThe GAE method outperformed other dimensionality-reduction methods in predicting chronological age, except that it did not differ significantly from the classic neural network.
“Overall, the GAE method utilized (a two-layer fully connected neural network with five nodes in each layer) outperformed other methods in predicting chronological age (cAge) (P < 0.05) with the exception of the classic neural network (P = 0.54) (Extended Data Fig. 3b).”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataSeveral immune parameters predicted multimorbidity: total CD8+ T cells, plasmablasts and transitional B cells were negative predictors, while effector CD8+ T cells, total lymphocytes, monocytes and central memory T cells were positive predictors.
“In addition, immune parameters such as total CD8+ T cells, plasmablasts, transitional B cells such as IgD+CD27− and IgD−CD27− B cells (negative predictors), effector CD8+ T cells, total lymphocytes, monocytes and central memory T cells (positive predictors) were predictive of multimorbidity (Extended Data Fig. 5d).”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataCXCL9 knockdown completely reversed the early cellular senescence phenotype, with upregulation of proliferative pathways and downregulation of inflammatory pathways compared with Scramble hiPSC-ECs.
“CXCL9-KD showed a complete reversal of this early cellular senescence phenotype with upregulation of proliferative pathways and downregulation of inflammatory pathways when compared to Scramble hiPSC-ECs (Fig. 5c–e).”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataAmong controls, 77% were in the high iAge index rank group and 23% were in the low rank group.
“In contrast, there were 77% (14 out of 18) of controls in the high rank versus 23% in the low rank group (Fig. 1e)”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataIL-11Rα, CXCL10 and HGF increased with age, while CXCL1 and LIF decreased.
“In addition, IL-11Rα, CXCL10 and hepatocyte growth factor (HGF) increased with age, whereas CXCL1 and LIF decreased (Extended Data Fig. 7).”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataAmong centenarians, 68% were in the low iAge index rank group and 31% were in the high rank group.
“Sixty-eight percent (13 out of 19) centenarians were in the low rank group (P = 0.028), whereas only 31% (6 out of 19) were in the high rank group.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataAge was the strongest contributor to changes in naive CD8+ T cells, followed by iAge and CMV as negative contributors, and sex.
“Not surprisingly, age was the strongest contributor to changes in naive CD8+ T cells followed by iAge, CMV (negative contributors) and sex (frequency of total CD8+ T cells in females was 24% versus 30% in males; Fig. 2a).”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataBlood endothelial progenitor cells from older individuals showed reduced tube formation, reduced nitric oxide production and reduced acetylated LDL uptake.
“Comparing ECs from older and younger individuals, we found that BECs from older patients showed reduced capacity to form networks of tubular structures (Extended Data Fig. 8b and Fig. 4b), reduced capacity to produce NO (Fig. 4c) and a reduced capacity to incorporate Ac-LDL (Fig. 4d).”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataCXCL9 transcript levels increased in a time-dependent manner up to about fourfold at passage 8 compared with day 0 cultures, and CXCL9 expression was substantially reduced in CXCL9-knockdown hiPSC-ECs.
“We observed a time-dependent increase in CXCL9 transcript levels up to ~fourfold at passage 8 compared to cells obtained from cultures at day 0 and a substantial reduction of CXCL9 expression in CXCL9-KD hiPSC-ECs (Fig. 5a).”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Findingown dataTotal CD8+ T-cell frequency was 24% in females and 30% in males.
“frequency of total CD8+ T cells in females was 24% versus 30% in males”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataBy passage 4, endothelial cells lost their capacity to form tubes, and this was partially restored when CXCL9 was knocked down.
“As early as passage 4, ECs lose their capacity to form tubes, which can be partially restored when CXCL9 is knocked down (Fig. 4e and Extended Data Fig. 10).”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataIncreasing iAge was associated with a general decrease in B-cell and T-cell responses to stimuli and an overall potentiation of monocyte response.
“Notably, there was a general decrease of B-cell and T-cell responses to stimuli and an overall potentiation of monocyte response associated with increasing iAge (combined P < 10−5; Fig. 2b).”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataFGSEA in aged cells showed upregulation of hallmark inflammatory pathways and downregulation of hallmark cell proliferation pathways.
“Fast gene set enrichment analysis (FGSEA) in aged cells revealed upregulation of genes in hallmark inflammatory pathways and downregulation of genes in hallmark cell proliferation pathways (Fig. 5b).”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataLate-passaged iPSC-ECs failed to produce nitric oxide in response to acetylcholine or to take up acetylated LDL compared with early passages.
“Late-passaged iPSC-ECs failed to produce NO in response to acetylcholine or uptake Ac-LDL respectively, when compared to early passages of iPSC-ECs (Fig. 4f,g).”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Findingown dataThe best hyperparameter setting for GAE with code length 5 was depth 2, guidance ratio 0.2, and L2 0.001.
“we used fivefold cross validation to select the best hyper-parameter setting (depth = 2, guidance ratio = 0.2, L2 = 0.001) on all GAEs with a code length of 5.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataiAge predicts multimorbidity and mortality.
“we demonstrate that iAge predicts multimorbidity and mortality”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataCXCL9 is mainly produced by aged endothelium and predicts subclinical cardiovascular aging in nominally healthy individuals.
“we find that CXCL9 is mainly produced by aged endothelium and predicts subclinical levels of cardiovascular aging in nominally healthy individuals.”
Quote in the paperSubstantial claim- Rests on an assumption: CXCL9 attracts T cells, is induced by IFN-γ, and is primarily secreted by neutrophils, macrophages, and endothelial cells.
Probably not a substantial claim: may not pass S3 (load-bearing).
Findingown dataThe best code length for the GAE was 5.
“After obtaining the best code length as 5 (Extended Data Fig. 3a)”
Quote in the paperNot a substantial claimMay combine two claims.
Findingnone giveniAge is directly associated with multiple disease phenotypes, including cardiovascular aging, frailty, immune decline, and exceptional longevity.
“iAge is directly associated with multiple disease phenotypes, including cardiovascular aging, frailty, immune decline and exceptional longevity.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataGAE outperformed Elastic Net, PCA, and plain auto-encoder.
“In our case, this method outperformed other commonly used linear modeling methods such as Elastic Net and PCA and also other nonlinear approaches such as plain auto-encoder89 (Extended Data Fig. 3b).”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataiAge is associated with multiple diseases and immunosenescence.
“we find that iAge tracks with multiple diseases and immunosenescence.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Findingown dataThe best hyper-parameter setting for GAE with code length 5 was depth = 2, guidance-ratio = 0.2, and L2 = 0.001, selected using 5-fold cross-validation.
“After obtaining the best code length as 5, we used the 5-fold-cross-validation to select the best hyper-parameter setting (depth = 2, guidance-ratio = 0.2, L2 = 0.001) on all GAE with code length 5.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Findingown dataCXCR3 was not expressed in iPSC-induced cardiomyocytes, fibroblasts, or iPSC.
“CXCR3 was not expressed in iPSC induced cardiomyocytes (iPSC-CM), Fibroblast, or iPSC.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataCXCL9-knockdown hiPSC-ECs retained their capacity to form tubes even at later passages, whereas scramble hiPSC-ECs showed impaired tube formation at later passages.
“Representative images of capillary-like networks from scramble- and CXCL9-KD hiPSC-ECs show that CXCL9-KD hiPSC-ECs retain their capacity to form tubes even at later passages when compared to scramble that showed impaired tube formation towards later passages of hiPSC-ECs.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataThe GAE method performed better than linear methods for reconstructing protein data and predicting chronological age.
“The GAE method outperforms linear methods for protein data reconstruction and prediction of chronological age (b).”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Findingown dataThere was a significant correlation between age and CXCL9 in univariate regression analysis.
“Significant correlation between age and CXCL9 using univariate regression analysis.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim. May combine two claims.
Findingown dataThe Stanford 1000 Immunomes Project consists of 1001 ambulatory subjects aged 8 to 96 (34% male, 66% female) recruited between 2007 and 2016 for a longitudinal aging and vaccination study, and for an independent chronic fatigue syndrome study from which only healthy controls were included.
“The Stanford 1000 Immunomes Project consist of 1001 ambulatory subjects age 8 to 96 (34% males, 66% females) recruited during the years 2007 to 2016 for a longitudinal study of aging and vaccination, and for an independent study of chronic fatigue syndrome from which only healthy controls were included.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataIn the authors' experiment, the optimal code length for the GAE was 5, because a sixth code gave no significant improvement in total loss (p = 0.18).
“In our experiment, the best code length is 5 (a) as adding one more code (6) does not significantly improve the total loss (p = 0.18).”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Findingown dataThe predictive performance of gradient boosting decision tree (GBDT) was similar to that of PCA.
“In (c), we found that the predictive performance of gradient boosting decision tree (GBDT) has similar performance as PCA.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataEighteen features were selected, including the inflammatory clock, high cholesterol, BMI, and immune parameters such as total CD8+ T cells, plasmablasts, and transitional B cells as negative predictors, and IgD+CD27- and IgD-CD27- B cells, effector CD8+ T cells, total lymphocytes, monocytes, and central memory T cells as positive predictors.
“Eighteen features are selected including inflammatory clock, high cholesterol and BMI (c) and immune parameters such as total CD8 (+) T cells, plasmablasts and transitional B cells (negative predictors) and IgD+CD27- and IgD-CD27-B cells, effector CD8 (+) T cells, total lymphocytes and monocytes, and central memory T cells (positive predictors) (d)”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataNormalization and batch correction removed batch effect, as shown by a lower mean absolute Spearman correlation between all features and batch ID, indicating that sample source could not be distinguished from corrected data.
“Data normalization and batch correction removes batch effect as indicated by lower mean absolute Spearman correlation between all features and batch id (blue line), which indicates impossibility to distinguish sample source from corrected data.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Findingown dataCXCR3 was highly expressed in iPSC-induced endothelial cells and Human Umbilical Vein Endothelial Cells (HUVEC).
“However, it is highly expressed in iPSC induced endothelial cells and Human Umbilical Vein Endothelial Cells (HUVEC).”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
FindingreasoningBecause LASSO regression is stochastic, each implementation produced a slightly different list of genes predictive of iAge.
“Due to the stochastic nature of LASSO regression, each implementation produced a slightly different list of genes that were predictive of iAge.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing). May combine two claims.
PremisecitationMultimorbidity is the top priority for global health research and is a gold standard in aging research because it reflects accumulated physiological damage.
“Multimorbidity is the number one priority for global health research and has become the gold standard in aging research as it represents the accumulation of physiological damage in an individual33.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [33] Barnett, K. et al. Epidemiology of multimorbidity and implications for health care, research, and medical education: a cross-sectional study. Lancet 380, 37–43 (2012). source
Not checked in the free run.
May combine two claims.
PremisecitationThere are no standard biomarkers for chronic inflammatory state, and studies have generally produced conflicting results.
“Despite the importance of this type of inflammatory reaction, there are currently no standard biomarkers to characterize a chronic inflammatory state and studies have generally yielded conflicting results18,19.”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [18] Franceschi, C., Garagnani, P., Vitale, G., Capri, M. & Salvioli, S. Inflammaging and garb-aging. Trends Endocrinol. Metab. 28, 199–212 (2017). source
Not checked in the free run.
- cites [19] Morrisette-Thomas, V. et al. Inflamm-aging does not simply reflect increases in pro-inflammatory markers. Mechanisms Ageing Dev. 139, 49–57 (2014). source
Not checked in the free run.
May combine two claims.
PremisecitationInflammatory components of the immune system are often chronically elevated in older people and are linked to higher incidence of cancer, cardiovascular disease, neurodegenerative disorders and other conditions.
“inflammatory components of the immune system are often chronically elevated in aged individuals and associated with an increased incidence of cancer, cardiovascular disease, neurodegenerative disorders and others1–3”
Quote in the paperSubstantial claim0 of 3 sources checked- cites [1] Kotas, M. E. & Medzhitov, R. Homeostasis, inflammation, and disease susceptibility. Cell 160, 816–827 (2015). source
Not checked in the free run.
- cites [2] Liu, F. et al. Mechanosignaling through YAP and TAZ drives fibroblast activation and fibrosis. Am. J. Physiol. Lung Cell Mol. Physiol. 308, L344–L357 (2015). source
Not checked in the free run.
- cites [3] Crusz, S. M. & Balkwill, F. R. Inflammation and cancer: advances and new agents. Nat. Rev. Clin. Oncol. 12, 584–596 (2015). source
Not checked in the free run.
Classified as probably not a substantial claim.
Study design and conductreasoningThe multimorbidity analysis adjusted for age, BMI, sex, CMV and high cholesterol because each has a reported effect on age-related pathologies.
“For these analyses, we controlled for age, body mass index (BMI), sex, CMV and high cholesterol (also a binary category), because of the reported effect of each of these variables in the etiology of age-related pathologies.”
Quote in the paperSubstantial claimClassified as probably not a substantial claim.
Study design and conductown dataFivefold cross validation was used to measure predictive performance of each dimensionality-reduction method on the test set.
“We employed fivefold cross validation and measured the predictive performances of each method on the test set.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationThe authors' earlier cellular composite metric for immune aging, IMM-AGE, was strongly associated with all-cause mortality.
“Recent work from our group identified a cellular composite metric for immune aging (IMM-AGE), which was strongly associated with all-cause mortality16.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [16] Alpert, A. et al. A clinically meaningful metric of immune age derived from high-dimensional longitudinal monitoring. Nat. Med. 25, 487–495 (2019). source
Not checked in the free run.
Classified as probably not a substantial claim.
Study design and conductcitationDeep immune phenotyping for the Stanford 1KIP samples was performed at the Stanford HIMC using rigorously standardized procedures.
“For all samples of the Stanford 1KIP, deep immune phenotyping was conducted at the Stanford HIMC, where peripheral blood specimens were processed and analyzed using rigorously standardized procedures31.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [31] Maecker, H. T. et al. New tools for classification and monitoring of autoimmune diseases. Nat. Rev. Rheumatol. 8, 317–328 (2012). source
Not checked in the free run.
May combine two claims.
PremisecitationChronic systemic inflammation is associated with aging, is low-grade and persistent, and ultimately causes collateral damage to tissues and organs.
“This type of inflammation is associated with aging and is characterized by being low-grade and persistent, ultimately leading to collateral damage to tissues and organs1,17.”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [1] Kotas, M. E. & Medzhitov, R. Homeostasis, inflammation, and disease susceptibility. Cell 160, 816–827 (2015). source
Not checked in the free run.
- cites [17] Goldberg, E. L. & Dixit, V. D. Drivers of age-related inflammation and strategies for healthspan extension. Immunol. Rev. 265, 63–74 (2015). source
Not checked in the free run.
May combine two claims.
Interpretationown dataKnockdown of CXCL9 rescues endothelial dysfunction by passage 8 but does not fully restore endothelial cell function to the level of healthy passage 0 cells.
“This suggests that while knockdown of CXCL9 rescues endothelial dysfunction by passage 8, it cannot restore EC function completely to the level of healthy ECs at passage 0.”
Quote in the paperSubstantial claimMay combine two claims.
Interpretationown dataCXCL9 has a profound effect in the cardiovascular system and indicates a new role in angiogenesis and endothelial cell function during cardiovascular aging.
“Taken together these results demonstrate that CXCL9 has a profound effect in the cardiovascular system and indicates a new role for this chemokine in angiogenesis and EC function during cardiovascular aging.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductown dataThe validation cohort was selected from 151 recruited participants using strict selection criteria.
“selected from a total of 151 recruited participants using strict selection criteria (Methods).”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationC-reactive protein and IL-6 have been linked to immunosenescence in prior studies, but their relationship with systemic chronic inflammation had not been established.
“Canonical acute inflammation proteins such as C-reactive protein and interleukin (IL)-6 have been associated with immunosenescence in previous studies37,38, but the relationship with systemic chronic inflammation (SCI) has not yet been established.”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [37] Bertsch, T. et al. C-reactive protein and the acute phase reaction in geriatric patients. Z. Gerontologie Geriatr. 48, 595–600 (2015). source
Not checked in the free run.
- cites [38] Scheller, J., Chalaris, A., Schmidt-Arras, D. & Rose-John, S. The pro- and anti-inflammatory properties of the cytokine interleukin-6. Biochim. Biophys. Acta 1813, 878–888 (2011). source
Not checked in the free run.
Classified as probably not a substantial claim. May combine two claims.
PremisecitationHigh relative wall thickness indicates concentric cardiac remodeling, and elevated pulse wave velocity reflects organ damage and predicts future cardiovascular events and all-cause mortality better than conventional risk factors.
“As high RWT indicates concentric cardiac remodeling and elevated PWV is reflective of organ damage and predicts future cardiovascular events and all-cause mortality better than conventional cardiovascular disease risk factors45–47”
Quote in the paperSubstantial claim0 of 3 sources checked- cites [45] Vlachopoulos, C., Aznaouridis, K. & Stefanadis, C. Prediction of cardiovascular events and all-cause mortality with arterial stiffness: a systematic review and meta-analysis. J. Am. Coll. Cardiol. 55, 1318–1327 (2010). source
Not checked in the free run.
- cites [46] Ben-Shlomo, Y. et al. Aortic pulse wave velocity improves cardiovascular event prediction: an individual participant meta-analysis of prospective observational data from 17,635 subjects. J. Am. Coll. Cardiol. 63, 636–646 (2014). source
Not checked in the free run.
- cites [47] Chirinos, J. A. Large artery stiffness, microvascular function, and cardiovascular risk. Circ. Cardiovasc Imaging 9, e005903 (2016). source
Not checked in the free run.
Classified as probably not a substantial claim.
Study design and conductcitationThe study used a well-established endothelial aging model by generating hiPSCs from five independent human donors and differentiating them into endothelial cells.
“we used a well-established model for endothelial aging57,58 by generating human induced pluripotent stem cells (hiPSCs) from fibroblasts obtained from five independent human donors59 and subsequently differentiated them into endothelial cells (hiPSC-ECs)60.”
Quote in the paperSubstantial claim0 of 4 sources checked- cites [57] Boisen, L., Drasbek, K. R., Pedersen, A. S. & Kristensen, P. Evaluation of endothelial cell culture as a model system of vascular ageing. Exp. Gerontol. 45, 779–787 (2010). source
Not checked in the free run.
- cites [58] Brandes, R. P., Fleming, I. & Busse, R. Endothelial aging. Cardiovascular Res. 66, 286–294 (2005). source
Not checked in the free run.
- cites [59] Takahashi, K. & Yamanaka, S. Induction of pluripotent stem cells from mouse embryonic and adult fibroblast cultures by defined factors. Cell 126, 663–676 (2006). source
Not checked in the free run.
- cites [60] Sayed, N. et al. Clinical trial in a dish using iPSCs shows lovastatin improves endothelial dysfunction and cellular cross-talk in LMNA cardiomyopathy. Sci. Transl. Med. 12, eaax9276 (2020). source
Not checked in the free run.
May combine two claims.
Interpretationown dataiAge correlates with an established marker of immune senescence, naive CD8+ T-cell frequency, and with PBMC signaling responses in vitro.
“These results demonstrate that iAge correlates with an established biomarker of immune senescence (naive CD8+ T-cell frequency) and with PBMC signaling responses in vitro.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductown dataThe phospho-STAT signaling analysis used multiple regression controlling for age, CMV and sex.
“We conducted multiple regression analysis controlling age, CMV and sex (Methods).”
Quote in the paperNot a substantial claimMay combine two claims.
Interpretationown dataCXCL9 increases in endothelia and endothelial cell dysfunction are associated with aging in both humans and mice.
“These results demonstrate a concomitant increase in CXCL9 in the endothelia and EC dysfunction associated with aging both in humans and mice.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationThe endothelium has a role in the etiology of hypertension and arterial stiffness.
“Long-standing evidence has suggested a role for the endothelium in the etiology of hypertension and arterial stiffness48,49.”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [48] Figueiredo, V. N. et al. Vascular stiffness and endothelial dysfunction: correlations at different levels of blood pressure. Blood Press. 21, 31–38 (2012). source
Not checked in the free run.
- cites [49] Vanhoutte, P. M., Feletou, M. & Taddei, S. Endothelium-dependent contractions in hypertension. Br. J. Pharmacol. 144, 449–458 (2005). source
Not checked in the free run.
Classified as probably not a substantial claim.
Study design and conductown dataThe cardiovascular analysis used multiple regression hierarchical analysis controlling for age, sex, BMI, heart rate, systolic blood pressure, fasting glucose and total cholesterol to HDL ratio.
“We then performed multiple regression hierarchical analysis using the six selected inflammatory markers associated with aging in this cohort and cardiovascular measurements (PWV and RWT) controlling for age, sex, BMI, heart rate, systolic blood pressure, fasting glucose and total cholesterol to high-density lipoprotein (HDL) ratio.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationAdvanced signs of cardiovascular aging such as tissue remodeling and cardiac hypertrophy are often preceded and may be initiated by malfunctioning aged endothelia.
“More recent work has also shown that advanced signs of cardiovascular aging such as tissue remodeling and cardiac hypertrophy are often preceded and may be initiated by the malfunctioning of aged endothelia50–52.”
Quote in the paperNot a substantial claim0 of 3 sources checked- cites [50] Castellon, X. & Bogdanova, V. Chronic inflammatory diseases and endothelial dysfunction. Aging Dis. 7, 81–89 (2016). source
Not checked in the free run.
- cites [51] Harvey, A., Montezano, A. C. & Touyz, R. M. Vascular biology of ageing: implications in hypertension. J. Mol. Cell Cardiol. 83, 112–121 (2015). source
Not checked in the free run.
- cites [52] Kamo, T., Akazawa, H. & Komuro, I. Cardiac nonmyocytes in the hub of cardiac hypertrophy. Circ. Res. 117, 89–98 (2015). source
Not checked in the free run.
May combine two claims.
Interpretationown dataThere are at least two sources of CXCL9-mediated inflammation with aging: one age-intrinsic in aging endothelial cells and one age-independent in the validation cohort.
“At least two sources of CXCL9-mediated inflammation can ensue with aging based on our findings; one that is age-intrinsic and observed in aging ECs and one that is independent of age (likely as a response to cumulative exposure to environmental insults) and found in the validation cohort of 97 apparently healthy adults.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Study design and conductown dataiPSC cardiomyocytes were differentiated to >85% purity and endothelial cells to >90% purity.
“These iPSCs were differentiated into cardiomyocytes to purities of >85% and ECs to purities of >90%.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Study design and conductnone givenThe study was conducted in accordance with ethical regulations and approved by Stanford IRB, with written informed consent from all cohorts.
“This study was conducted in accordance with current relevant ethical regulations on human participant research. Written informed consent was obtained from all the study cohorts used and the study protocol was approved by the Stanford University Administrative Panels on Human Subjects in Medical Research, Institutional Review Board.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationCXCL9 is associated with falls in the older population.
“Notably, CXCL9 has also been shown to be associated with falls in the older population101,102”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [101] Amorim, J. S. C. et al. Inflammatory markers and occurrence of falls: Bambui cohort study of aging. Rev. Saude Publica 53, 35 (2019). source
Not checked in the free run.
- cites [102] Chiu, B. C. et al. Differential effects of ageing on cytokine and chemokine responses during type-1 (mycobacterial) and type-2 (schistosomal) pulmonary granulomatous inflammation in mice. Mech. Ageing Dev. 123, 313–326 (2002). source
Not checked in the free run.
Probably not a substantial claim: may not pass S1 (asserted). May combine two claims.
InterpretationreasoningCXCL9 acts in both paracrine and autocrine fashion, creating a positive feedback loop.
“we hypothesize that this chemokine acts both in a paracrine fashion (when it is produced by macrophages to attract T cells to the site of injury) and in an autocrine fashion (when it is produced by the endothelium) creating a positive feedback loop.”
Quote in the paperNot a substantial claim- Rests on an assumption: CXCL9 attracts T cells, is induced by IFN-γ, and is primarily secreted by neutrophils, macrophages, and endothelial cells.
Probably not a substantial claim: may not pass S3 (load-bearing).
Interpretationown dataEndothelial cells may be one source of inflammation.
“they also suggest that ECs may be one source of inflammation”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductnone givenThe stimuli strongly activate the memory compartment of bulk CD8+ and CD4+ T cells but act weakly on naive T cells.
“while the stimuli we used strongly activate the memory compartment of bulk CD8+ and CD4+ T cells, these act relatively weakly on naive T cells.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductreasoningThe stimuli used in the cytokine stimulation assay may not fully mirror physiological stimuli acting on specific immune cell subsets in vivo.
“one limitation of the assay relates to the stimuli used here which may not completely mirror the physiological stimuli that act on specific immune cell subsets in vivo.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductnone givenAll echocardiographic parameters were measured in triplicate and averaged.
“All parameters were measured in triplicate and averaged.”
Quote in the paperNot a substantial claimMay combine two claims.
Interpretationown dataThe lack of angiogenesis, impaired nitric oxide production and dysfunctional acetylated LDL uptake indirectly suggested a cellular senescence phenotype that could be rescued by knocking down CXCL9 during iPSC-EC passaging.
“The lack of angiogenesis, impaired production of NO and dysfunctional uptake of Ac-LDL indirectly suggested a cellular senescence phenotype that could be rescued by knocking down CXCL9 as iPSC-EC is passaged.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductown dataEchocardiographs were interpreted by one physician blinded to age and clinical/biological data.
“All studies were interpreted by one physician (F.H.) who was blinded to age as well as clinical and biological data.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationCXCL9 increases with age in humans.
“Some studies in humans have found CXCL9 to increase with age93–98”
Quote in the paperNot a substantial claim0 of 6 sources checked- cites [93] Bonfante, H. L. et al. CCL2, CXCL8, CXCL9 and CXCL10 serum levels increase with age but are not altered by treatment with hydroxychloroquine in patients with osteoarthritis of the knees. Int J. Rheum. Dis. 20, 1958–1964 (2017). source
Not checked in the free run.
- cites [94] Roubenoff, R. et al. Monocyte cytokine production in an elderly population: effect of age and inflammation. J. Gerontol. A Biol. Sci. Med Sci. 53, M20–M26 (1998). source
Not checked in the free run.
- cites [95] Torres, K. C. L. et al. Immune senescence and biomarkers profile of Bambui aged population-based cohort. Exp. Gerontol. 103, 47–56 (2018). source
Not checked in the free run.
- cites [96] Shurin, G. V. et al. Dynamic alteration of soluble serum biomarkers in healthy aging. Cytokine 39, 123–129 (2007). source
Not checked in the free run.
- cites [97] Zheng, Y. et al. Age-related pro-inflammatory and pro-angiogenic changes in human aqueous humor. Int J. Ophthalmol. 11, 196–200 (2018).
Not checked in the free run.
- cites [98] Sandovici, I. et al. Ageing is associated with molecular signatures of inflammation and type 2 diabetes in rat pancreatic islets. Diabetologia 59, 502–511 (2016). source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductown dataInter-observer variability for PWV measurement had an intraclass correlation coefficient of 0.94.
“Inter-observer variability was calculated on 50 samples in our laboratory and the intraclass correlation coefficient was 0.94 for PWV measured by two independent observers”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Study design and conductnone givenAll PCR reactions were performed in triplicate, normalized to GAPDH, and assessed using comparative Ct or ΔΔCt.
“All PCR reactions were performed in triplicate, normalized to the GAPDH endogenous control gene and assessed using the comparative Ct method.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S1 (asserted), S3 (load-bearing), S4 (based). May combine two claims. The quote only partly states this claim.
InterpretationreasoningOne root cause of CXCL9 overproduction is cellular aging itself, which can trigger metabolic dysfunction with production of DAMPs.
“We thus hypothesize that one root cause of CXCL9 overproduction is cellular aging per se, which can trigger metabolic dysfunction”
Quote in the paperNot a substantial claim- Rests on an assumption: CXCL9 attracts T cells, is induced by IFN-γ, and is primarily secreted by neutrophils, macrophages, and endothelial cells.
Probably not a substantial claim: may not pass S3 (load-bearing).
InterpretationreasoningMonotonic transformations of cAge or iAge do not affect ranks, so the findings are robust to transformations such as log and exp.
“Any monotonic transformation of cAge or iAge does not affect ranks, hence findings are robust to transformations such as log and exp.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Study design and conductown dataiPSCs were derived from five healthy individuals and passed assessments for pluripotency and genomic stability.
“We derived iPSCs from five healthy individuals and cell lines passed common assessments for pluripotency such as expression of pluripotent markers (Oct4 and Nanog) and genomic stability such as karyotyping.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationCytokine stimulation assays have proven utility for assessing overall immune competence.
“Despite the proven utility of cytokine stimulation assays used in our study with respect to an individual’s overall immune competence5,24,25”
Quote in the paperNot a substantial claim0 of 3 sources checked- cites [5] Furman, D. et al. Expression of specific inflammasome gene modules stratifies older individuals into two extreme clinical and immunological states. Nat. Med. 23, 174–184 (2017). source
Not checked in the free run.
- cites [24] Furman, D. et al. Apoptosis and other immune biomarkers predict influenza vaccine responsiveness. Mol. Syst. Biol. 9, 659 (2013). source
Not checked in the free run.
- cites [25] Furman, D. et al. Cytomegalovirus infection enhances the immune response to influenza. Sci. Transl. Med. 7, 281ra43 (2015). source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationThe proteomic age metric had a correlation of R = 0.92 with chronological age.
“which is lower than that of the recently reported ‘proteomic age’ metric (R = 0.92)90.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [90] Tanaka, T. et al. Plasma proteomic signature of age in healthy humans. Aging Cell. 17, e12799 (2018). source
Not checked in the free run.
May combine two claims.
Interpretationown dataCXCL9 has a central role in vascular dysfunction and likely contributes to arterial stiffness and premature aging in vivo.
“These results demonstrate a central role for CXCL9 in vascular dysfunction, which likely contributes to arterial stiffness and premature aging in vivo.”
Quote in the paperSubstantial claimMay combine two claims.
Interpretationown dataCXCL9 is a master regulator of vascular function and cellular senescence.
“we demonstrate that CXCL9 is a master regulator of vascular function and cellular senescence”
Quote in the paperSubstantial claim- Rests on an assumption: CXCL9 attracts T cells, is induced by IFN-γ, and is primarily secreted by neutrophils, macrophages, and endothelial cells.
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationInflammatory markers are at least partly responsible for reduced JAK-STAT response to cytokine stimulations in various leukocyte populations.
“inflammatory markers have been shown to be, at least in part, responsible for a reduced JAK–STAT response to cytokine stimulations in various leukocyte populations in our previous studies of aging28.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [28] Shen-Orr Shai, S. et al. Defective signaling in the JAK–STAT pathway tracks with chronic inflammation and cardiovascular risk in aging humans. Cell Syst. 3, 374–384 (2016). source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationHigh baseline inflammatory markers correlate with weaker responses to hepatitis B and herpes zoster vaccine formulations.
“previous reports showing that high levels of baseline inflammatory markers correlate with weaker responses to hepatitis B and herpes zoster vaccine formulations15,91.”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [15] Thevaranjan, N. et al. Age-associated microbial dysbiosis promotes intestinal permeability, systemic inflammation, and macrophage dysfunction. Cell Host Microbe 21, 455–466 (2017). source
Not checked in the free run.
- cites [91] Fourati, S. et al. Pre-vaccination inflammation and B-cell signalling predict age-related hyporesponse to hepatitis B vaccination. Nat. Commun. 7, 10369 (2016). source
Not checked in the free run.
Classified as probably not a substantial claim.
InterpretationreasoningIncreasing doses of CXCL9 and expression of its receptor lead to cumulative deterioration of endothelial function in aging.
“In this model, increasing doses of CXCL9 and expression of its receptor in these cells leads to cumulative deterioration of endothelial function in aging.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductnone givenThe experiment was repeated three times.
“Experiment was repeated 3 times.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Study design and conductown dataThe study did not use randomization or blinding.
“No randomization or blinding was conducted for this study.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Study design and conductnone givenPRIN06 and PRIN09 samples were measured in two batches and the datasets were bridged using SomaLogic calibrators.
“PRIN06 and PRIN09 samples were measured in two batches. Datasets were bridged to each other using SomaLogic calibrators.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductown dataFive-fold cross-validation was used to identify the best code length among lengths from 1 to 10.
“We used 5-fold cross-validation to identify the best code length, among lengths from 1 to 10.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductnone givenWithin each fold, nested 3-fold cross-validation was performed to select hyper-parameters (depth, weight decay, and guidance-ratio).
“Within each fold we performed nested 3-fold cross-validation to select hyper-parameters (depth, weight decay and guidance-ratio).”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductnone givenNo statistical method was used to predetermine sample size for the experiments.
“For our experiments, no statistical method was used to predetermine sample size.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductnone givenFor the cardiovascular study, one physician interpreted the data while blinded to age, clinical and biological data.
“For the cardiovascular study the data were interpreted by one physician (F.H.) who was blinded to age as well as clinical and biological data.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductnone givenIn a validation study, 97 healthy adults aged 25-90, well matched for cardiovascular risk factors, were selected from 151 recruited subjects.
“In a validation study, 97 healthy adults (aged 25–90) well matched for cardiovascular risk factors, were selected from a total of 151 recruited subjects.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim. May combine two claims.
Study design and conductnone givenDifferential penalty values were applied to each regressor, forcing the age variable in while imposing a stringent lasso penalty on all other features, so that selected variables would not correlate with age.
“By applying differential penalty values for each regressor, age variable is ‘forced in’, while imposing a stringent penalty (the lasso penalty) to all other features, so that selected variables do not correlate with age.”
Quote in the paperSubstantial claimClassified as probably not a substantial claim.
Study design and conductnone givenCovariates including age, BMI, CMV detection and sex were controlled by inclusion in all regression analyses.
“Covariates such as age, BMI, detection of CMV and sex were controlled by their inclusion in all regression analysis.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductown dataIn the SOMAscan assay, no samples or probe data were excluded.
“Data in relative fluorescent units for 1,305 SOMAmer probes were obtained for these samples and no samples or probe data were excluded.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
Study design and conductown dataSerum immune protein data were normalized and batch-corrected so that data from different sources could be combined and used together.
“Immune protein data from serum samples were subjected to normalization and batch correction procedures (See Methods) to ensure data from different sources can be combined and used as a whole.”
Quote in the paperSubstantial claimClassified as probably not a substantial claim.
Study design and conductown dataAge-adjusted cross-validation was performed on available data from all 902 subjects to select predictors of comorbidity without bias while controlling for age.
“To select for predictors of comorbidity without bias, based on available data for all 902 subjects while controlling for the age effect, age-adjusted cross-validation was performed (a).”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductnone givenBlinding was not performed in the mouse experiments.
“Blinding was not performed in mice experiments.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim. May combine two claims.
PremisecitationAuto-encoders use a nonlinear transformation of data and can model complex processes.
“Auto-encoders use a nonlinear transformation of data and hence, can model complex processes119.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [119] Behzadi-Khormouji, H. et al. Deep learning, reusable and problem-based architectures for detection of consolidation on chest X-ray images. Comput. Methods Prog. Biomed. 185, 105162 (2020). source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductnone givenThe analyses did not exclude any data.
“No data were excluded from the analyses.”
Quote in the paperNot a substantial claim
The paper relies on this without support.
The iAge gene expression signature derived from the Stanford cohort can serve as a proxy for iAge in the Framingham Heart Study cohort.
“As there were no sufficient proteomics data to directly estimate iAge in the cohort, we derived a gene expression signature of iAge using available data from 397 participants in our study and performed an enrichment analysis of the derived gene signature on each sample in the Framingham Heart Study (Methods).”
The paper relies on this without support.
CXCL9 attracts T cells, is induced by IFN-γ, and is primarily secreted by neutrophils, macrophages, and endothelial cells.
“CXCL9 is a T-cell chemoattractant induced by IFN-γ and is mostly produced by neutrophils, macrophages and ECs.”
The paper relies on this without support.
The Stanford 1KIP cohort reflects the general population for aging phenotypes.
“This metric tracked with multiple aging phenotypes in the general population”
The paper relies on this without support.
Chronological age serves as a valid benchmark for biological aging.
“The correlation between chronological age and iAge was 0.78”
The paper relies on this without support.
Auto-encoders can produce different results with different initialization (the re-parameterization problem).
“One problem with auto-encoders is re-parameterization. With different initialization, it could have different results.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Many immune cells from an inflammageing environment are hyporesponsive to acute ex vivo stimuli, while monocytes appear hyperresponsive.
“many immune cells (including B cells, T cells, and DCs) isolated from an inflammageing environment have been shown to be hyporesponsive to acute ex vivo stimuli, although monocytes appear to standout as being hyperresponsive81.”
From SARS-CoV-2, COVID-19 and the aging immune system (2021) · cites this paper as [81]
Basal inflammageing is driven by NF-κB signaling and generally renders immune cells initially hyporesponsive to acute activation, with the possible exception of monocyte subsets.
“Basal inflammageing is driven by NF-κB signaling and generally renders immune cells initially hyporesponsive to acute activation, with the possible exception of the monocyte subsets71,81.”
From SARS-CoV-2, COVID-19 and the aging immune system (2021) · cites this paper as [81]
A recent set of publications profiled immune cell and inflammatory markers to develop an immune ageing clock that predicted mortality, cardiovascular outcomes and immunosenescence.
“A recent set of publications did this by profiling immune cell and inflammatory markers to develop an immune ageing clock that predicted mortality, cardiovascular outcomes and immunosenescence171,172.”
From Measuring biological age using omics data (2022) · cites this paper as [171]
The authors used information from the clock to identify CXCL9 as a potential causal regulator of immune ageing and validated this result through functional follow-up in cellular models.
“The authors used information from the clock to identify CXCL9 as a potential causal regulator of immune ageing and validated this result through functional follow-up in cellular models, which further supports the ability of these hybrid approaches to discover causal regulators of ageing.”
From Measuring biological age using omics data (2022) · cites this paper as [171]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Reported test significance conflicts with a no-difference claim
MinorThe methods say the two models' prediction accuracies were not different, yet give P < 0.01 for the likelihood-ratio test cited for that comparison.
“was not different from that of model 1 (in which TRAIL is included)”
“(by likelihood ratio test, P < 0.01)”
B-cell subsets have opposite predictor signs
MinorThe results describe the IgD+CD27− and IgD−CD27− B-cell subsets as negative predictors of multimorbidity, whereas the extended-data caption lists those subsets as positive predictors.
“transitional B cells such as IgD+CD27− and IgD−CD27− B cells (negative predictors)”
“and IgD+CD27- and IgD-CD27-B cells, effector CD8 (+) T cells, total lymphocytes and monocytes, and central memory T cells (positive predictors) (d)”
Cites
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946 · see citing works
Takahashi, K., & Yamanaka, S. (2006) · Cell · doi:10.1016/j.cell.2006.07.024 · Cited by 26,673 · see citing works
Regularization and Variable Selection Via the Elastic Net
Zou, H., & Hastie, T. (2005) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.1467-9868.2005.00503.x · Cited by 20,957 · see citing works
Self-citationRegularization Paths for Generalized Linear Models via Coordinate Descent
Friedman, J., Hastie, T., & Tibshirani, R. (2010) · Journal of Statistical Software · doi:10.18637/jss.v033.i01 · Cited by 19,927 · see citing works
Self-citationLópez-Otín, C., Blasco, M. A., Partridge, L., Serrano, M., & Kroemer, G. (2013) · Cell · doi:10.1016/j.cell.2013.05.039 · Cited by 14,982 · see citing works
Lang, R. M., Bierig, M., Devereux, R. B., Flachskampf, F. A., Foster, E., Pellikka, P. A., Picard, M. H., Roman, M. J., Seward, J., Shanewise, J. S., et al. (2005) · Journal of the American Society of Echocardiography · doi:10.1016/j.echo.2005.10.005 · Cited by 11,318 · see citing works
Immunity, Inflammation, and Cancer
Grivennikov, S. I., Greten, F. R., & Karin, M. (2010) · Cell · doi:10.1016/j.cell.2010.01.025 · Cited by 10,755 · see citing works
Antiinflammatory Therapy with Canakinumab for Atherosclerotic Disease
Ridker, P. M., Everett, B. M., Thuren, T., MacFadyen, J. G., Chang, W. H., Ballantyne, C., Fonseca, F., Nicolau, J., Koenig, W., Anker, S. D., et al. (2017) · New England Journal of Medicine · doi:10.1056/nejmoa1707914 · Cited by 9,482 · see citing works
DNA methylation age of human tissues and cell types
Horvath, S. (2013) · Genome Biology · doi:10.1186/gb-2013-14-10-r115 · Cited by 7,075 · see citing works
Learning Deep Architectures for AI
Bengio, Y. (2009) · Foundations and Trends® in Machine Learning · doi:10.1561/2200000006 · Cited by 7,048 · see citing works
Barnett, K., Mercer, S. W., Norbury, M., Watt, G., Wyke, S., & Guthrie, B. (2012) · The Lancet · doi:10.1016/s0140-6736(12)60240-2 · Cited by 6,972 · see citing works
Learning Deep Architectures for AI
Bengio, Y. (2009) · now publishers, Inc. eBooks · doi:10.1561/9781601982957 · Cited by 5,037 · see citing works
Chronic inflammation in the etiology of disease across the life span
Furman, D., Campisi, J., Verdin, E., Carrera-Bastos, P., Targ, S., Franceschi, C., Ferrucci, L., Gilroy, D. W., Fasano, A., Miller, G. W., et al. (2019) · Nature Medicine · doi:10.1038/s41591-019-0675-0 · Cited by 4,915 · see citing works
Self-citationRecommendations for the Evaluation of Left Ventricular Diastolic Function by Echocardiography
Nagueh, S. F., Appleton, C. P., Gillebert, T. C., Marino, P. N., Oh, J. K., Smiseth, O. A., Waggoner, A. D., Flachskampf, F. A., Pellikka, P. A., & Evangelista, A. (2009) · Journal of the American Society of Echocardiography · doi:10.1016/j.echo.2008.11.023 · Cited by 4,367 · see citing works
Genome-wide methylation profiles reveal quantitative views of human aging rates
Hannum, G., Guinney, J., Zhao, L., Zhang, L., Hughes, G., Sadda, S., Klotzle, B., Bibikova, M., Fan, J.-B., Gao, Y., et al. (2013) · Molecular Cell · doi:10.1016/j.molcel.2012.10.016 · Cited by 4,270 · see citing works
Prediction of Cardiovascular Events and All-Cause Mortality With Arterial Stiffness
Vlachopoulos, C., Aznaouridis, K., & Stefanadis, C. (2010) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2009.10.061 · Cited by 4,165 · see citing works
Chronic Inflammation (Inflammaging) and Its Potential Contribution to Age-Associated Diseases
Franceschi, C., & Campisi, J. (2014) · The Journals of Gerontology Series A: Biological Sciences and Medical Sciences · doi:10.1093/gerona/glu057 · Cited by 3,891 · see citing works
Self-citationRecommendations for the evaluation of left ventricular diastolic function by echocardiography
Nagueh, S. F., Appleton, C. P., Gillebert, T. C., Marino, P. N., Oh, J. K., Smiseth, O. A., Waggoner, A. D., Flachskampf, F. A., Pellikka, P. A., & Evangelisa, A. (2008) · European Journal of Echocardiography · doi:10.1093/ejechocard/jep007 · Cited by 3,351 · see citing works
The pro- and anti-inflammatory properties of the cytokine interleukin-6
Scheller, J., Chalaris, A., Schmidt-Arras, D., & Rose-John, S. (2011) · Biochimica et Biophysica Acta (BBA) - Molecular Cell Research · doi:10.1016/j.bbamcr.2011.01.034 · Cited by 3,212 · see citing works
Extracellular Matrix Degradation and Remodeling in Development and Disease
Lu, P., Takai, K., Weaver, V. M., & Werb, Z. (2011) · Cold Spring Harbor Perspectives in Biology · doi:10.1101/cshperspect.a005058 · Cited by 2,265 · see citing works
The JAK/STAT signaling pathway
Rawlings, J. S., Rosler, K. M., & Harrison, D. A. (2004) · Journal of Cell Science · doi:10.1242/jcs.00963 · Cited by 1,858 · see citing works
Mechanisms, Pathophysiology, and Therapy of Arterial Stiffness
Zieman, S. J., Melenovsky, V., & Kass, D. A. (2005) · Arteriosclerosis, Thrombosis, and Vascular Biology · doi:10.1161/01.atv.0000160548.78317.29 · Cited by 1,854 · see citing works
Aortic Pulse Wave Velocity Improves Cardiovascular Event Prediction
Ben-Shlomo, Y., Spears, M., Boustred, C., May, M., Anderson, S. G., Benjamin, E. J., Boutouyrie, P., Cameron, J., Chen, C.-H., Cruickshank, J. K., et al. (2014) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2013.09.063 · Cited by 1,835 · see citing works
Aptamer-Based Multiplexed Proteomic Technology for Biomarker Discovery
Gold, L., Ayers, D., Bertino, J., Bock, C., Bock, A., Brody, E. N., Carter, J., Dalby, A. B., Eaton, B. E., Fitzwater, T., et al. (2010) · PLoS ONE · doi:10.1371/journal.pone.0015004 · Cited by 1,754 · see citing works
Lian, X., Hsiao, C., Wilson, G., Zhu, K., Hazeltine, L. B., Azarin, S. M., Raval, K. K., Zhang, J., Kamp, T. J., & Palecek, S. P. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1200250109 · Cited by 1,717 · see citing works
The Framingham Heart Study and the epidemiology of cardiovascular disease: a historical perspective
Mahmood, S. S., Levy, D., Vasan, R. S., & Wang, T. J. (2014) · The Lancet · doi:10.1016/s0140-6736(13)61752-3 · Cited by 1,622 · see citing works
Beyond oxidative stress: an immunologist's guide to reactive oxygen species
Nathan, C., & Cunningham-Bussel, A. (2013) · Nature Reviews Immunology · doi:10.1038/nri3423 · Cited by 1,619 · see citing works
Thevaranjan, N., Puchta, A., Schulz, C., Naidoo, A., Szamosi, J. C., Verschoor, C. P., Loukov, D., Schenck, L. P., Jury, J., Foley, K. P., et al. (2017) · Cell Host & Microbe · doi:10.1016/j.chom.2017.03.002 · Cited by 1,322 · see citing works
Voyta, J. C., Via, D. P., Butterfield, C. E., & Zetter, B. R. (1984) · The Journal of cell biology · doi:10.1083/jcb.99.6.2034 · Cited by 1,239 · see citing works
Homeostasis, Inflammation, and Disease Susceptibility
Kotas, M. E., & Medzhitov, R. (2015) · Cell · doi:10.1016/j.cell.2015.02.010 · Cited by 1,213 · see citing works
Inflammation and cancer: advances and new agents
Crusz, S. M., & Balkwill, F. R. (2015) · Nature Reviews Clinical Oncology · doi:10.1038/nrclinonc.2015.105 · Cited by 1,184 · see citing works
Signaling through the JAK/STAT pathway, recent advances and future challenges
Kisseleva, T., Bhattacharya, S., Braunstein, J., & Schindler, C. (2002) · Gene · doi:10.1016/s0378-1119(02)00398-0 · Cited by 1,158 · see citing works
Nutrient-sensing mechanisms and pathways
Efeyan, A., Comb, W. C., & Sabatini, D. M. (2015) · Nature · doi:10.1038/nature14190 · Cited by 1,110 · see citing works
Variation in the Human Immune System Is Largely Driven by Non-Heritable Influences
Brodin, P., Jojic, V., Gao, T., Bhattacharya, S., Angel, C. J. L., Furman, D., Shen-Orr, S., Dekker, C. L., Swan, G. E., Butte, A. J., et al. (2015) · Cell · doi:10.1016/j.cell.2014.12.020 · Cited by 1,065 · see citing works
Self-citationThe Role of Nitric Oxide on Endothelial Function
Tousoulis, D., Kampoli, A.-M., Tentolouris Nikolaos Papageorgiou, C., & Stefanadis, C. (2012) · Current Vascular Pharmacology · doi:10.2174/157016112798829760 · Cited by 1,012 · see citing works
Franceschi, C., Garagnani, P., Vitale, G., Capri, M., & Salvioli, S. (2017) · Trends in Endocrinology & Metabolism · doi:10.1016/j.tem.2016.09.005 · Cited by 997 · see citing works
Self-citationUndulating changes in human plasma proteome profiles across the lifespan
Lehallier, B., Gate, D., Schaum, N., Nanasi, T., Lee, S. E., Yousef, H., Moran Losada, P., Berdnik, D., Keller, A., Verghese, J., et al. (2019) · Nature Medicine · doi:10.1038/s41591-019-0673-2 · Cited by 981 · see citing works
Self-citationMechanosignaling through YAP and TAZ drives fibroblast activation and fibrosis
Liu, F., Lagares, D., Choi, K. M., Stopfer, L., Marinković, A., Vrbanac, V., Probst, C. K., Hiemer, S. E., Sisson, T. H., Horowitz, J. C., et al. (2015) · American Journal of Physiology-Lung Cellular and Molecular Physiology · doi:10.1152/ajplung.00300.2014 · Cited by 874 · see citing works
Chronic inflammation induces telomere dysfunction and accelerates ageing in mice
Jurk, D., Wilson, C., Passos, J. F., Oakley, F., Correia-Melo, C., Greaves, L., Saretzki, G., Fox, C., Lawless, C., Anderson, R., et al. (2014) · Nature Communications · doi:10.1038/ncomms5172 · Cited by 814 · see citing works
Age- and Gender-Related Ventricular-Vascular Stiffening
Redfield, M. M., Jacobsen, S. J., Borlaug, B. A., Rodeheffer, R. J., & Kass, D. A. (2005) · Circulation · doi:10.1161/circulationaha.105.541078 · Cited by 810 · see citing works
Stem cell aging: mechanisms, regulators and therapeutic opportunities
Oh, J., Lee, Y. D., & Wagers, A. J. (2014) · Nature Medicine · doi:10.1038/nm.3651 · Cited by 796 · see citing works
Age-Dependent Impairment of Angiogenesis
Rivard, A., Fabre, J., Silver, M., Chen, D., Murohara, T., Kearney, M., Magner, M., Asahara, T., & Isner, J. M. (1999) · Circulation · doi:10.1161/01.cir.99.1.111 · Cited by 774 · see citing works
In vitro angiogenesis: endothelial cell tube formation on gelled basement membrane extract
Arnaoutova, I., & Kleinman, H. K. (2010) · Nature Protocols · doi:10.1038/nprot.2010.6 · Cited by 760 · see citing works
Furman, D., Hejblum, B. P., Simon, N., Jojic, V., Dekker, C. L., Thiébaut, R., Tibshirani, R. J., & Davis, M. M. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1321060111 · Cited by 678 · see citing works
Self-citationMezzaroma, E., Toldo, S. A., Farkas, D., Seropian, I. M., Tassell, B. W. V., Salloum, F. N., Kannan, H. R., Menna, A. C., Voelkel, N. F., & Abbate, A. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1108586108 · Cited by 631 · see citing works
BRANDES, R., FLEMING, I., & BUSSE, R. (2005) · Cardiovascular Research · doi:10.1016/j.cardiores.2004.12.027 · Cited by 563 · see citing works
Plasma proteomic signature of age in healthy humans
Tanaka, T., Biancotto, A., Moaddel, R., Moore, A. Z., Gonzalez‐Freire, M., Aon, M. A., Candia, J., Zhang, P., Cheung, F., Fantoni, G., et al. (2018) · Aging Cell · doi:10.1111/acel.12799 · Cited by 562 · see citing works
Single sample scoring of molecular phenotypes
Foroutan, M., Bhuva, D. D., Horan, K., Lyu, R., Cursons, J., & Davis, M. J. (2018) · BMC Bioinformatics · doi:10.1186/s12859-018-2435-4 · Cited by 548 · see citing works
Self-citationVascular Cell Senescence: Contribution to Atherosclerosis
Minamino, T., & Komuro, I. (2007) · Circulation Research · doi:10.1161/01.res.0000256837.40544.4a · Cited by 542 · see citing works
Complex modulation of cell type-specific signaling in response to type I interferons
van Boxel-Dezaire, A. H. H., Rani, M. R. S., & Stark, G. R. (2006) · Immunity · doi:10.1016/j.immuni.2006.08.014 · Cited by 530 · see citing works
Endothelial Dysfunction as a Target for Prevention of Cardiovascular Disease
Versari, D., Daghini, E., Virdis, A., Ghiadoni, L., & Taddei, S. (2009) · Diabetes Care · doi:10.2337/dc09-s330 · Cited by 520 · see citing works
A clinically meaningful metric of immune age derived from high-dimensional longitudinal monitoring
Alpert, A., Pickman, Y., Leipold, M., Rosenberg-Hasson, Y., Ji, X., Gaujoux, R., Rabani, H., Starosvetsky, E., Kveler, K., Schaffert, S., et al. (2019) · Nature Medicine · doi:10.1038/s41591-019-0381-y · Cited by 513 · see citing works
Self-citationAnalysis of the Adult Human Plasma Metabolome
Lawton, K. A., Berger, A., Mitchell, M., Milgram, K. E., Evans, A. M., Guo, L., Hanson, R. W., Kalhan, S. C., Ryals, J. A., & Milburn, M. V. (2008) · Pharmacogenomics · doi:10.2217/14622416.9.4.383 · Cited by 490 · see citing works
Endothelial cell senescence in aging-related vascular dysfunction
Jia, G., Aroor, A. R., Jia, C., & Sowers, J. R. (2019) · Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease · doi:10.1016/j.bbadis.2018.08.008 · Cited by 454 · see citing works
Regulation of gene-activation pathways by PIAS proteins in the immune system
Shuai, K., & Liu, B. (2005) · Nature reviews. Immunology · doi:10.1038/nri1667 · Cited by 447 · see citing works
Age-related vascular stiffening: causes and consequences
Kohn, J. C., Lampi, M. C., & Reinhart-King, C. A. (2015) · Frontiers in Genetics · doi:10.3389/fgene.2015.00112 · Cited by 440 · see citing works
Monocyte Cytokine Production in an Elderly Population: Effect of Age and Inflammation
Roubenoff, R., Harris, T. B., Abad, L. W., Wilson, P. W. F., Dallal, G. E., & Dinarello, C. A. (1998) · The Journals of Gerontology Series A: Biological Sciences and Medical Sciences · doi:10.1093/gerona/53a.1.m20 · Cited by 418 · see citing works
Senescence-associated inflammatory responses: aging and cancer perspectives
Lasry, A., & Ben-Neriah, Y. (2015) · Trends in Immunology · doi:10.1016/j.it.2015.02.009 · Cited by 408 · see citing works
Cytokine signature associated with disease severity in chronic fatigue syndrome patients
Montoya, J. G., Holmes, T. H., Anderson, J. N., Maecker, H. T., Rosenberg‐Hasson, Y., Valencia, I. J., Chu, L., Younger, J., Tato, C. M., & Davis, M. M. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1710519114 · Cited by 397 · see citing works
Self-citationFurman, D., Chang, J., Lartigue, L., Bolen, C. R., Haddad, F., Gaudilliere, B., Ganio, E. A., Fragiadakis, G. K., Spitzer, M. H., Douchet, I., et al. (2017) · Nature Medicine · doi:10.1038/nm.4267 · Cited by 389 · see citing works
Self-citationDeep biomarkers of human aging: Application of deep neural networks to biomarker development
Putin, E., Mamoshina, P., Aliper, A. M., Korzinkin, M., Moskalev, A., Kolosov, A., Ostrovskiy, A., Cantor, C. R., Vijg, J., & Zhavoronkov, A. A. (2016) · Aging · doi:10.18632/aging.100968 · Cited by 373 · see citing works
Human serum metabolic profiles are age dependent
Yu, Z., Zhai, G., Singmann, P., He, Y., Xu, T., Prehn, C., Römisch‐Margl, W., Lattka, E., Gieger, C., Soranzo, N., et al. (2012) · Aging Cell · doi:10.1111/j.1474-9726.2012.00865.x · Cited by 340 · see citing works
Krutzik, P. O., Irish, J. M., Nolan, G. P., & Perez, O. D. (2004) · Clinical Immunology · doi:10.1016/j.clim.2003.11.009 · Cited by 336 · see citing works
Cytomegalovirus infection enhances the immune response to influenza
Furman, D., Jojic, V., Sharma, S., Shen-Orr, S. S., L. Angel, C. J., Onengut-Gumuscu, S., Kidd, B. A., Maecker, H. T., Concannon, P., Dekker, C. L., et al. (2015) · Science Translational Medicine · doi:10.1126/scitranslmed.aaa2293 · Cited by 332 · see citing works
Self-citationVascular biology of ageing—Implications in hypertension
Harvey, A., Montezano, A. C., & Touyz, R. M. (2015) · Journal of Molecular and Cellular Cardiology · doi:10.1016/j.yjmcc.2015.04.011 · Cited by 330 · see citing works
Hunter, R. L., Dragicevic, N., Seifert, K., Choi, D. Y., Liu, M., Kim, H., Cass, W. A., Sullivan, P. G., & Bing, G. (2007) · Journal of Neurochemistry · doi:10.1111/j.1471-4159.2006.04327.x · Cited by 322 · see citing works
Godec, J., Tan, Y. Q., Liberzon, A., Tamayo, P., Bhattacharya, S., Butte, A. J., Mesirov, J. P., & Haining, W. N. (2016) · Immunity · doi:10.1016/j.immuni.2015.12.006 · Cited by 288 · see citing works
Endothelium‐dependent contractions in hypertension
Vanhoutte, P. M., Feletou, M., & Taddei, S. (2005) · British Journal of Pharmacology · doi:10.1038/sj.bjp.0706042 · Cited by 284 · see citing works
Inflamm-aging does not simply reflect increases in pro-inflammatory markers
Morrisette-Thomas, V., Cohen, A. A., Fülöp, T., Riesco, É., Legault, V., Li, Q., Milot, E., Dusseault-Bélanger, F., & Ferrucci, L. (2014) · Mechanisms of Ageing and Development · doi:10.1016/j.mad.2014.06.005 · Cited by 283 · see citing works
Metabolomic markers reveal novel pathways of ageing and early development in human populations
Menni, C., Kastenmüller, G., Petersen, A. K., Bell, J. T., Psatha, M., Tsai, P.-C., Gieger, C., Schulz, H., Erte, I., John, S., et al. (2013) · International Journal of Epidemiology · doi:10.1093/ije/dyt094 · Cited by 282 · see citing works
Drivers of age‐related inflammation and strategies for healthspan extension
Goldberg, E. L., & Dixit, V. D. (2015) · Immunological Reviews · doi:10.1111/imr.12295 · Cited by 274 · see citing works
Left Ventricular T-Cell Recruitment Contributes to the Pathogenesis of Heart Failure
Nevers, T. A., Salvador, A. M., Grodecki‐Pena, A., Knapp, A., Velázquez, F., Aronovitz, M. J., Kapur, N. K., Karas, R. H., Blanton, R. M., & Alcaide, P. (2015) · Circulation Heart Failure · doi:10.1161/circheartfailure.115.002225 · Cited by 272 · see citing works
Collino, S., Montoliu, I., Martin, F.-P. J., Scherer, M., Mari, D., Salvioli, S., Bucci, L., Ostan, R., Monti, D., Biagi, E., et al. (2013) · PLoS ONE · doi:10.1371/journal.pone.0056564 · Cited by 270 · see citing works
Self-citationFace Aging with Identity-Preserved Conditional Generative Adversarial Networks
Tang, X., Wang, Z., Luo, W.-X., & Gao, S.-H. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00828 · Cited by 216 · see citing works
Gu, M., Shao, N., Sa, S., Li, D., Termglinchan, V., Ameen, M., Karakikes, I., Sosa, G., Grubert, F., Lee, J., et al. (2016) · Cell stem cell · doi:10.1016/j.stem.2016.08.019 · Cited by 213 · see citing works
Self-citationMamoshina, P., Kochetov, K., Lane, E., Cortese, F., Aliper, A. M., Lee, W., Ahn, S., Lee, U., Skjodt, N. M., Kovalchuk, O., et al. (2018) · The Journals of Gerontology Series A · doi:10.1093/gerona/gly005 · Cited by 198 · see citing works
Chronic Inflammatory Diseases and Endothelial Dysfunction
Castellon, X., & Bogdanova, V. (2016) · Aging and disease · doi:10.14336/ad.2015.0803 · Cited by 191 · see citing works
Senescence-induced inflammation: an important player and key therapeutic target in atherosclerosis
Stojanović, S. D., Fiedler, J., Bauersachs, J., Thum, T., & Sedding, D. G. (2020) · European Heart Journal · doi:10.1093/eurheartj/ehz919 · Cited by 188 · see citing works
Ishikawa, M., Maekawa, K., Saito, K., Senoo, Y., Urata, M., Murayama, M., Tajima, Y., Kumagai, Y., & Saito, Y. (2014) · PLoS ONE · doi:10.1371/journal.pone.0091806 · Cited by 187 · see citing works
Apoptosis and other immune biomarkers predict influenza vaccine responsiveness
Furman, D., Jojic, V., Kidd, B., Shen‐Orr, S., Price, J., Jarrell, J., Tse, T., Huang, H., Lund, P., Maecker, H. T., et al. (2013) · Molecular Systems Biology · doi:10.1038/msb.2013.15 · Cited by 184 · see citing works
Self-citationFourati, S., Cristescu, R., Loboda, A., Talla, A., Filali, A., Railkar, R., Schaeffer, A. K., Favre, D., Gagnon, D., Peretz, Y., et al. (2016) · Nature Communications · doi:10.1038/ncomms10369 · Cited by 183 · see citing works
Measuring Biological Age via Metabonomics: The Metabolic Age Score
Hertel, J., Friedrich, N., Wittfeld, K., Pietzner, M., Budde, K., Van der Auwera, S., Lohmann, T., Teumer, A., Völzke, H., Nauck, M., et al. (2016) · Journal of Proteome Research · doi:10.1021/acs.jproteome.5b00561 · Cited by 181 · see citing works
Effects of Aging, Cytomegalovirus Infection, and EBV Infection on Human B Cell Repertoires
Wang, C., Liu, Y., Xu, L. T., Jackson, K., Roskin, K. M., Pham, T. D., Laserson, J., Marshall, E. L., Seo, K., Lee, J.-Y., et al. (2013) · The Journal of Immunology · doi:10.4049/jimmunol.1301384 · Cited by 179 · see citing works
Self-citationTransdifferentiation of Human Fibroblasts to Endothelial Cells
Sayed, N., Wong, W. T., Ospino, F., Meng, S., Lee, J. E., Jha, A., Dexheimer, P. J., Aronow, B. J., & Cooke, J. P. (2014) · Circulation · doi:10.1161/circulationaha.113.007394 · Cited by 179 · see citing works
Self-citationCardiac Nonmyocytes in the Hub of Cardiac Hypertrophy
Kamo, T., Akazawa, H., & Komuro, I. (2015) · Circulation Research · doi:10.1161/circresaha.117.305349 · Cited by 171 · see citing works
Matsa, E., Burridge, P. W., Yu, K.-H., Ahrens, J. H., Termglinchan, V., Wu, H., Liu, C., Shukla, P., Sayed, N., Churko, J. M., et al. (2016) · Cell stem cell · doi:10.1016/j.stem.2016.07.006 · Cited by 164 · see citing works
Self-citationAgeing induced vascular smooth muscle cell senescence in atherosclerosis
Uryga, A. K., & Bennett, M. R. (2016) · The Journal of Physiology · doi:10.1113/jp270923 · Cited by 160 · see citing works
A comparison of the tube forming potentials of early and late endothelial progenitor cells
Mukai, N., Akahori, T., Komaki, M., Li, Q., Kanayasu-Toyoda, T., Ishii-Watabe, A., Kobayashi, A., Yamaguchi, T., Abe, M., Amagasa, T., et al. (2008) · Experimental Cell Research · doi:10.1016/j.yexcr.2007.11.016 · Cited by 157 · see citing works
Shen-Orr, S. S., Furman, D., Kidd, B. A., Hadad, F., Lovelace, P., Huang, Y.-W., Rosenberg-Hasson, Y., Mackey, S., Grisar, F. A. G., Pickman, Y., et al. (2016) · Cell Systems · doi:10.1016/j.cels.2016.09.009 · Cited by 154 · see citing works
Self-citationRockwood, K. J., Rockwood, M. R. H., & Mitnitski, A. (2010) · Journal of the American Geriatrics Society · doi:10.1111/j.1532-5415.2009.02667.x · Cited by 151 · see citing works
Zhou, L., Leonard, A., Pavel, A. B., Malik, K., Raja, A., Glickman, J., Estrada, Y. D., Peng, X., del Duca, E., Sanz-Cabanillas, J., et al. (2019) · Journal of Allergy and Clinical Immunology · doi:10.1016/j.jaci.2019.01.015 · Cited by 146 · see citing works
Fine-Grained Age Estimation in the Wild With Attention LSTM Networks
Zhang, K., Liu, N., Yuan, X., Guo, X., Gao, C., Zhao, Z., & Ma, Z. (2019) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2019.2936410 · Cited by 128 · see citing works
Behzadi-khormouji, H., Rostami, H., Salehi, S., Derakhshande-Rishehri, T., Masoumi, M., Salemi, S., Keshavarz, A., Gholamrezanezhad, A., Assadi, M., & Batouli, A. (2020) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2019.105162 · Cited by 112 · see citing works
Blažková, J., Gupta, S., Liu, Y., Gaudilliere, B. L., Ganio, E. A., Bolen, C. R., Dover, R. S., Fragiadakis, G. K., Angst, M. S., Hasni, S., et al. (2017) · The Journal of Immunology · doi:10.4049/jimmunol.1601855 · Cited by 112 · see citing works
Self-citationSayed, N., Liu, C., Ameen, M., Himmati, F., Zhang, J. Z., Khanamiri, S., Moonen, J.-R., Wnorowski, A., Cheng, L., Rhee, J.-W., et al. (2020) · Science Translational Medicine · doi:10.1126/scitranslmed.aax9276 · Cited by 111 · see citing works
Self-citationThe pathophysiology of defective proteostasis in the hypothalamus — from obesity to ageing
Cavadas, C., Aveleira, C. A., Souza, G. F. P., & Velloso, L. A. (2016) · Nature Reviews Endocrinology · doi:10.1038/nrendo.2016.107 · Cited by 105 · see citing works
Network representations of immune system complexity
Subramanian, N., Torabi‐Parizi, P., Gottschalk, R. A., Germain, R. N., & Dutta, B. (2015) · WIREs Systems Biology and Medicine · doi:10.1002/wsbm.1288 · Cited by 100 · see citing works
Adhesion Molecules in Inflammatory Diseases
Gonz??lez-Amaro, R., D??az-Gonz??lez, F., & S??nchez-Madrid, F. (1998) · Drugs · doi:10.2165/00003495-199856060-00003 · Cited by 98 · see citing works
Vascular stiffness and endothelial dysfunction: Correlations at different levels of blood pressure
Figueiredo, V. N., Yugar-Toledo, J. C., Martins, L. C., Martins, L. B., de Faria, A. P. C., de Haro Moraes, C., Sierra, C., Coca, A., & Moreno, H. (2012) · Blood Pressure · doi:10.3109/08037051.2011.617045 · Cited by 98 · see citing works
Altara, R., Mallat, Z., Booz, G. W., & Zouein, F. A. (2016) · Journal of Immunology Research · doi:10.1155/2016/4396368 · Cited by 96 · see citing works
Dynamic alteration of soluble serum biomarkers in healthy aging
Shurin, G. V., Yurkovetsky, Z. R., Chatta, G. S., Tourkova, I. L., Shurin, M. R., & Lokshin, A. E. (2007) · Cytokine · doi:10.1016/j.cyto.2007.06.006 · Cited by 95 · see citing works
New tools for classification and monitoring of autoimmune diseases
Maecker, H. T., Lindstrom, T. M., Robinson, W. H., Utz, P. J., Hale, M., Boyd, S. D., Shen-Orr, S. S., & Fathman, C. G. (2012) · Nature Reviews Rheumatology · doi:10.1038/nrrheum.2012.66 · Cited by 95 · see citing works
Self-citationCXCL10 Is a Circulating Inflammatory Marker in Patients with Advanced Heart Failure: a Pilot Study
Altara, R., Manca, M., Hessel, M. H. M., Gu, Y., van Vark, L. C., Akkerhuis, K. M., Staessen, J. A., Struijker-Boudier, H. A. J., Booz, G. W., & Blankesteijn, W. M. (2016) · Journal of Cardiovascular Translational Research · doi:10.1007/s12265-016-9703-3 · Cited by 93 · see citing works
Deep Learning using Convolutional LSTM estimates Biological Age from Physical Activity
Rahman, S. A., & Adjeroh, D. (2019) · Scientific Reports · doi:10.1038/s41598-019-46850-0 · Cited by 92 · see citing works
IgH sequences in common variable immune deficiency reveal altered B cell development and selection
Roskin, K. M., Simchoni, N., Liu, Y., Lee, J.-Y., Seo, K., Hoh, R. A., Pham, T. D., Park, J. H., Furman, D., Dekker, C. L., et al. (2015) · Science Translational Medicine · doi:10.1126/scitranslmed.aab1216 · Cited by 88 · see citing works
Self-citationNLRC4 suppresses melanoma tumor progression independently of inflammasome activation
Janowski, A. M., Colegio, O. R., Hornick, E. E., McNiff, J. M., Martin, M. D., Badovinac, V. P., Norian, L. A., Zhang, W., Cassel, S. L., & Sutterwala, F. S. (2016) · Journal of Clinical Investigation · doi:10.1172/jci86953 · Cited by 88 · see citing works
Cellular Senescence and Vascular Disease: Novel Routes to Better Understanding and Therapy
Yin, H., & Pickering, J. G. (2016) · Canadian Journal of Cardiology · doi:10.1016/j.cjca.2016.02.051 · Cited by 82 · see citing works
Kuzuya, M., Asai, T., Kanda, S., Maeda, K., Cheng, X. W., & Iguchi, A. (2001) · Diabetologia · doi:10.1007/s001250051640 · Cited by 79 · see citing works
Towards a gene expression biomarker set for human biological age
Holly, A. C., Melzer, D. R., Pilling, L. C., Henley, W., Hernandez, D. G., Singleton, A., Bandinelli, S., Guralnik, J. M., Ferrucci, L., & Harries, L. W. (2013) · Aging Cell · doi:10.1111/acel.12044 · Cited by 79 · see citing works
A modified generalized Fisher method for combining probabilities from dependent tests
Dai, H. D., Leeder, J. S., & Cui, Y. (2014) · Frontiers in Genetics · doi:10.3389/fgene.2014.00032 · Cited by 68 · see citing works
Hardison, J. L., Wrightsman, R. A., Carpenter, P. M., Lane, T. E., & Manning, J. E. (2005) · Infection and Immunity · doi:10.1128/iai.74.1.125-134.2006 · Cited by 66 · see citing works
Altara, R., Gu, Y., Struijker-Boudier, H. A. J., Thijs, L., Staessen, J. A., & Blankesteijn, W. M. (2015) · PLoS ONE · doi:10.1371/journal.pone.0141394 · Cited by 54 · see citing works
Characterization of Influenza Vaccine Immunogenicity Using Influenza Antigen Microarrays
Price, J. V., Jarrell, J. A., Furman, D., Kattah, N. H., Newell, E. W., Dekker, C. L., Davis, M. M., & Utz, P. J. (2013) · PLoS ONE · doi:10.1371/journal.pone.0064555 · Cited by 50 · see citing works
Self-citationEvaluation of endothelial cell culture as a model system of vascular ageing
Boisen, L., Drasbek, K. R., Pedersen, A. S., & Kristensen, P. (2010) · Experimental Gerontology · doi:10.1016/j.exger.2010.06.003 · Cited by 39 · see citing works
Bonfante, H. D. L., Almeida, C. D. S., Abramo, C., Grunewald, S. T. F., Levy, R. A., & Teixeira, H. C. (2017) · International Journal of Rheumatic Diseases · doi:10.1111/1756-185x.12589 · Cited by 33 · see citing works
Sandovici, I., Hammerle, C. M., Cooper, W. N., Smith, N. H., Tarry-Adkins, J. L., Dunmore, B. J., Bauer, J., Andrews, S. R., Yeo, G. S. H., Ozanne, S. E., et al. (2016) · Diabetologia · doi:10.1007/s00125-015-3837-8 · Cited by 31 · see citing works
Immune senescence and biomarkers profile of Bambuí aged population-based cohort
Torres, K. C. L., Rezende, V. B. D., Lima-Silva, M. L., Santos, L. J. D. S., Costa, C. G., Mambrini, J. V. D. M., Peixoto, S. V., Tarazona-Santos, E., Martins Filho, O. A., Lima-Costa, M. F., et al. (2018) · Experimental Gerontology · doi:10.1016/j.exger.2017.12.006 · Cited by 30 · see citing works
C-reactive protein and the acute phase reaction in geriatric patients
Bertsch, T., Triebel, J., Bollheimer, C., Christ, M., Sieber, C., Fassbender, K., & Heppner, H. J. (2015) · Zeitschrift für Gerontologie und Geriatrie · doi:10.1007/s00391-015-0938-4 · Cited by 24 · see citing works
Large Artery Stiffness, Microvascular Function, and Cardiovascular Risk
Chirinos, J. A. (2016) · Circulation: Cardiovascular Imaging · doi:10.1161/circimaging.116.005903 · Cited by 24 · see citing works
Age-related pro-inflammatory and pro-angiogenic changes in human aqueous humor
Yan, Z., Rao, Y., Li, J.-K., Huang, Y., Zhao, P., & Li, J. (2018) · International Journal of Ophthalmology · doi:10.18240/ijo.2018.02.03 · Cited by 21 · see citing works
Self-citationde Araújo, F. F., Torres, K., Peixoto, S. V., Ribeiro, A. L. P., Mambrini, J. V. D. M., de Rezende, V. B., Silva, M. L. L., Filho, A. I. D. L., Teixeira‐Carvalho, A., Lima‐Costa, M. F., et al. (2020) · Infectious Diseases of Poverty · doi:10.1186/s40249-020-00663-w · Cited by 21 · see citing works
Chiu, B. C., Shang, X., Frait, K. A., Hu, J. S., Komuniecki, E., Miller, R. A., & Chensue, S. W. (2002) · Mechanisms of Ageing and Development · doi:10.1016/s0047-6374(01)00372-4 · Cited by 19 · see citing works
Inflammatory markers and occurrence of falls
Amorim, J. S. C. D., Torres, K. C. L., Teixeira-Carvalho, A., Martins-Filho, O. A., Lima-Costa, M. F., & Peixoto, S. V. (2019) · Revista de Saúde Pública · doi:10.11606/s1518-8787.2019053000855 · Cited by 16 · see citing works
Marked Vascular Dysfunction in a Case of Peripartum Cardiomyopathy
Khanamiri, S., Rhee, J., Paik, D. T., Chen, I. Y., Liu, C., & Sayed, N. (2019) · Journal of Vascular Research · doi:10.1159/000496163 · Cited by 6 · see citing works
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
Inflammation and aging: signaling pathways and intervention therapies
Li, X., Li, C., Zhang, W., Wang, Y., Qian, P., & Huang, H. (2023) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-023-01502-8 · Cited by 1,443 · see citing works
“In addition, centenarians have been found to possess stronger anti-inflammatory abilities, suggesting that inflammation and immunity may a significant impact on the process of aging.(6,7) Considering the complexity of aging, multi-modal and multiperspective studies are important.”
BackgroundSocial Determinants of Cardiovascular Disease
Powell-Wiley, T. M., Baumer, Y., Baah, F. O., Baez, A. S., Farmer, N., Mahlobo, C. T., Pita, M. A., Potharaju, K. A., Tamura, K., & Wallen, G. R. (2022) · Circulation Research · doi:10.1161/circresaha.121.319811 · Cited by 904 · see citing works
Immunosenescence and inflammaging in the aging process: age-related diseases or longevity?
Santoro, A., Bientinesi, E., & Monti, D. (2021) · Ageing Research Reviews · doi:10.1016/j.arr.2021.101422 · Cited by 603 · see citing works
Self-citationMeasuring biological age using omics data
Rutledge, J., Oh, H., & Wyss-Coray, T. (2022) · Nature Reviews Genetics · doi:10.1038/s41576-022-00511-7 · Cited by 570 · see citing works
Self-citationInflammation, Aging, and Cardiovascular Disease
Liberale, L., Badimón, L., Montecucco, F., Lüscher, T. F., Libby, P., & Camici, G. G. (2022) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2021.12.017 · Cited by 472 · see citing works
Chronic inflammation and the hallmarks of aging
Baechle, J. J., Chen, N., Makhijani, P., Winer, S., Furman, D., & Winer, D. A. (2023) · Molecular Metabolism · doi:10.1016/j.molmet.2023.101755 · Cited by 471 · see citing works
Self-citationAging Hallmarks and the Role of Oxidative Stress
Maldonado, E., Morales, S., Urbina, F., & Solari, A. (2023) · Antioxidants · doi:10.3390/antiox12030651 · Cited by 454 · see citing works
Cai, Y., Song, W., Li, J., Jing, Y., Liang, C., Zhang, L., Zhang, X., Zhang, W., Liu, B., An, Y., et al. (2022) · Science China Life Sciences · doi:10.1007/s11427-022-2161-3 · Cited by 436 · see citing works
Bao, H., Cao, J., Chen, M., Min, C., Chen, W., Chen, X., Chen, Y., Chen, Y., Chen, Y., Chen, Z., et al. (2023) · Science China Life Sciences · doi:10.1007/s11427-023-2305-0 · Cited by 389 · see citing works
Argentieri, M. A., Xiao, S., Bennett, D., Winchester, L. M., Nevado‐Holgado, A. J., Ghose, U., Albukhari, A., Yao, P., Mazidi, M., Lv, J., et al. (2024) · Nature Medicine · doi:10.1038/s41591-024-03164-7 · Cited by 378 · see citing works
SARS-CoV-2, COVID-19 and the aging immune system
Bartleson, J. M., Radenkovic, D., Covarrubias, A. J., Furman, D., Winer, D. A., & Verdin, E. (2021) · Nature Aging · doi:10.1038/s43587-021-00114-7 · Cited by 359 · see citing works
Self-citationVascular Cognitive Impairment (VCI)
Rundek, T., Tolea, M. I., Ariko, T., Fagerli, E. A., & Camargo, C. J. (2021) · Neurotherapeutics · doi:10.1007/s13311-021-01170-y · Cited by 325 · see citing works
Interconnections between Inflammageing and Immunosenescence during Ageing
Teissier, T., Boulanger, É., & Cox, L. S. (2022) · Cells · doi:10.3390/cells11030359 · Cited by 310 · see citing works
Osteoarthritis year in review 2021: biology
Jiang, Y. (2021) · Osteoarthritis and Cartilage · doi:10.1016/j.joca.2021.11.009 · Cited by 268 · see citing works
Rasmussen, L. J. H., Petersen, J. E. V., & Eugen‐Olsen, J. (2021) · Frontiers in Immunology · doi:10.3389/fimmu.2021.780641 · Cited by 256 · see citing works
WHO working definition of vitality capacity for healthy longevity monitoring
Bautmans, I., Knoop, V., Thiyagarajan, J. A., Maier, A. B., Beard, J., Freiberger, E., Belsky, D. W., Aubertin‐Leheudre, M., Mikton, C. R., Cesari, M., et al. (2022) · The Lancet Healthy Longevity · doi:10.1016/s2666-7568(22)00200-8 · Cited by 240 · see citing works
Targeting immune dysfunction in aging
Borgoni, S., Kudryashova, K. S., Burka, K., & de Magalhães, J. P. (2021) · Ageing Research Reviews · doi:10.1016/j.arr.2021.101410 · Cited by 227 · see citing works
Aging and InflammationPreprint
Singh, A., Schurman, S. H., Bektas, A., Kaileh, M., Roy, R., Wilson, D. M., Sen, R., & Ferrucci, L. (2023) · Cold Spring Harbor Perspectives in Medicine · doi:10.1101/cshperspect.a041197 · Cited by 194 · see citing works
Zhu, H., Chen, J., Liu, K., Gao, L., Wu, H., Ma, L., Zhou, J., Liu, Z., & Han, J. J. (2023) · Science Advances · doi:10.1126/sciadv.abq7599 · Cited by 147 · see citing works
Inflammaging as a target for healthy ageing
Dugan, B., Conway, J., & Duggal, N. A. (2023) · Age and Ageing · doi:10.1093/ageing/afac328 · Cited by 142 · see citing works
Stress, epigenetics, and aging: Unraveling the intricate crosstalk
Wu, Z., Qu, J. T., Zhang, W., & Liu, G. (2023) · Molecular Cell · doi:10.1016/j.molcel.2023.10.006 · Cited by 142 · see citing works
Wang, Y., Li, R., Tong, R., Chen, T., Sun, M., Luo, L., Li, Z., Chen, Y., Zhao, Y., Zhang, C., et al. (2025) · Nature Immunology · doi:10.1038/s41590-024-02059-6 · Cited by 139 · see citing works
Disease diagnostics using machine learning of B cell and T cell receptor sequences
Zaslavsky, M., Craig, E., Michuda, J., Sehgal, N., Ram-Mohan, N., Lee, J.-Y., Nguyen, K. D., Hoh, R. A., Pham, T. D., Röltgen, K., et al. (2025) · Science · doi:10.1126/science.adp2407 · Cited by 123 · see citing works
Self-citationThe Role of Immune Cells in Oxi-Inflamm-Aging
de Toda, I. M., Ceprián, N., Cerro, E. D., & la Fuente, M. D. (2021) · Cells · doi:10.3390/cells10112974 · Cited by 121 · see citing works
The matricellular protein SPARC induces inflammatory interferon-response in macrophages during aging
Ryu, S., Sidorov, S., Ravussin, É., Artyomov, M. N., Iwasaki, A., Wang, A., & Dixit, V. D. (2022) · Immunity · doi:10.1016/j.immuni.2022.07.007 · Cited by 121 · see citing works
Multi-omic profiling reveals age-related immune dynamics in healthy adults
Gong, Q., Sharma, M., Glass, M. C., Kuan, E. L., Chander, A., Singh, M., Graybuck, L. T., Thomson, Z., LaFrance, C. M., Zaim, S. R., et al. (2025) · Nature · doi:10.1038/s41586-025-09686-5 · Cited by 121 · see citing works
Risk Factors of Severe COVID-19: A Review of Host, Viral and Environmental Factors
Zsichla, L., & Müller, V. (2023) · Viruses · doi:10.3390/v15010175 · Cited by 113 · see citing works
Spatial transcriptomic clocks reveal cell proximity effects in brain ageing
Sun, E., Zhou, O. Y., Hauptschein, M., Rappoport, N., Xu, L., Negredo, P. N., Liu, L. A., Rando, T. A., Zou, J., & Brunet, A. (2024) · Nature · doi:10.1038/s41586-024-08334-8 · Cited by 98 · see citing works
Longitudinal phenotypic aging metrics in the Baltimore Longitudinal Study of Aging
Kuo, P., Schrack, J. A., Levine, M. E., Shardell, M. D., Simonsick, E. M., Chia, C. W., Moore, A. Z., Tanaka, T., An, Y., Karikkineth, A. C., et al. (2022) · Nature Aging · doi:10.1038/s43587-022-00243-7 · Cited by 94 · see citing works
Biological ageing with HIV infection: evaluating the geroscience hypothesis
Montano, M. A., Oursler, K. K., Xu, K., Sun, Y. V., & Marconi, V. C. (2022) · The Lancet Healthy Longevity · doi:10.1016/s2666-7568(21)00278-6 · Cited by 89 · see citing works
Karagiannis, T. T., Dowrey, T. W., Villacorta-Martín, C., Montano, M. A., Reed, E. R., Belkina, A. C., Andersen, S. L., Perls, T. T., Monti, S., Murphy, G. J., et al. (2023) · EBioMedicine · doi:10.1016/j.ebiom.2023.104514 · Cited by 86 · see citing works
Fong, S., Pabis, K., Latumalea, D., Dugersuren, N., Unfried, M., Tolwinski, N. S., Kennedy, B. K., & Gruber, J. (2024) · Nature Aging · doi:10.1038/s43587-024-00646-8 · Cited by 86 · see citing works
The hormetic and hermetic role of IL-6
Forcina, L., Franceschi, C., & Musarò, A. (2022) · Ageing Research Reviews · doi:10.1016/j.arr.2022.101697 · Cited by 85 · see citing works
Self-citationLong-term human spaceflight and inflammaging: Does it promote aging?
Capri, M., Conte, M., Ciurca, E., Pirazzini, C., Garagnani, P., Santoro, A., Longo, F., Salvioli, S., Lau, P., Moeller, R., et al. (2023) · Ageing Research Reviews · doi:10.1016/j.arr.2023.101909 · Cited by 85 · see citing works
Self-citationBiomarkers of aging in frailty and age-associated disorders: State of the art and future perspective
Salvioli, S., Basile, M. S., Bencivenga, L., Carrino, S., Conte, M., Damanti, S., LORENZO, R. D., Fiorenzato, E., Gialluisi, A., Ingannato, A., et al. (2023) · Ageing Research Reviews · doi:10.1016/j.arr.2023.102044 · Cited by 85 · see citing works
Qiu, W., Chen, H., Kaeberlein, M. R., & Lee, S. (2023) · The Lancet Healthy Longevity · doi:10.1016/s2666-7568(23)00189-7 · Cited by 85 · see citing works
Anti-Aging Effects of Flavonoids from Plant Extracts
Păcularu‐Burada, B., Cîrîc, A., & Begea, M. (2024) · Foods · doi:10.3390/foods13152441 · Cited by 78 · see citing works
Organ-specific biological clocks: Ageotyping for personalized anti-aging medicine
Prattichizzo, F., Frigé, C., Pellegrini, V., Scisciola, L., Santoro, A., Monti, D., Rippo, M. R., Ivanchenko, M., Olivieri, F., & Franceschi, C. (2024) · Ageing Research Reviews · doi:10.1016/j.arr.2024.102253 · Cited by 75 · see citing works
Self-citationProteomic aging clock ( PAC ) predicts age‐related outcomes in middle‐aged and older adults
Kuo, C., Chen, Z., Liu, P., Pilling, L. C., Atkins, J. L., Fortinsky, R. H., Kuchel, G. A., & Diniz, B. S. (2024) · Aging Cell · doi:10.1111/acel.14195 · Cited by 73 · see citing works
Development of an epigenetic clock resistant to changes in immune cell composition
Tomusiak, A., Floro, A., Tiwari, R., Riley, R. R., Matsui, H., Andrews, N., Kasler, H. G., & Verdin, E. M. (2024) · Communications Biology · doi:10.1038/s42003-024-06609-4 · Cited by 73 · see citing works
Giunta, S., Xia, S., Pelliccioni, G., & Olivieri, F. (2023) · GeroScience · doi:10.1007/s11357-023-00947-7 · Cited by 72 · see citing works
Pervizaj-Oruqaj, L., Ferrero, M. R., Matt, U., & Herold, S. (2024) · European Respiratory Review · doi:10.1183/16000617.0263-2023 · Cited by 72 · see citing works
Mechanisms underpinning poor antibody responses to vaccines in ageing
Lee, J. L., & Linterman, M. A. (2021) · Immunology Letters · doi:10.1016/j.imlet.2021.11.001 · Cited by 71 · see citing works
Immunoengineering strategies to enhance vascularization and tissue regeneration
Zárubová, J., Hasani‐Sadrabadi, M. M., Ardehali, R., & Li, S. (2022) · Advanced Drug Delivery Reviews · doi:10.1016/j.addr.2022.114233 · Cited by 71 · see citing works
Inflammaging: mechanisms and role in the cardiac and vasculature
Ajoolabady, A., Praticò, D., Vinciguerra, M., Lip, G. Y. H., Franceschi, C., & Ren, J. T. (2023) · Trends in Endocrinology and Metabolism · doi:10.1016/j.tem.2023.03.005 · Cited by 71 · see citing works
Self-citationSkin health and biological aging
Furman, D., Auwerx, J. H., Bulteau, A., Church, G. M., Couturaud, V., Crabbé, L., Davies, K. J. A., Decottignies, A., Gladyshev, V. N., Kennedy, B. K., et al. (2025) · Nature Aging · doi:10.1038/s43587-025-00901-6 · Cited by 72 · see citing works
Self-citationGrowth Differentiation Factor-15 in Immunity and Aging
Pence, B. D. (2022) · Frontiers in Aging · doi:10.3389/fragi.2022.837575 · Cited by 67 · see citing works
Immunosenescence: How Aging Increases Susceptibility to Bacterial Infections and Virulence Factors
Theodorakis, N., Feretzakis, G., Hitas, C., Kreouzi, M., Kalantzi, S., Spyridaki, A., Kollia, Z., Verykios, V. S., & Nikolaou, M. (2024) · Microorganisms · doi:10.3390/microorganisms12102052 · Cited by 68 · see citing works
Li, W., Zhang, Z., Kumar, S., Botey-Bataller, J., Zoodsma, M., Ehsani, A., Zhan, Q., Alaswad, A., Zhou, L., Grondman, I., et al. (2025) · Nature Aging · doi:10.1038/s43587-025-00819-z · Cited by 66 · see citing works
Nonuniversality of inflammaging across human populations
Franck, M., Tanner, K., Tennyson, R. L., Daunizeau, C., Ferrucci, L., Bandinelli, S., Trumble, B. C., Kaplan, H. S., Aronoff, J. E., Stieglitz, J., et al. (2025) · Nature Aging · doi:10.1038/s43587-025-00888-0 · Cited by 66 · see citing works
Self-citationThe infrapatellar fat pad in inflammaging, knee joint health, and osteoarthritis
Wang, M. G., Seale, P., & Furman, D. (2024) · npj Aging · doi:10.1038/s41514-024-00159-z · Cited by 64 · see citing works
Self-citationImmunosenescence and inflamm-ageing in COVID-19
Zinatizadeh, M. R., Zarandi, P. K., Ghiasi, M., Kooshki, H., Mohammadi, M., Amani, J., & Rezaei, N. (2022) · Ageing Research Reviews · doi:10.1016/j.arr.2022.101818 · Cited by 62 · see citing works
Extracellular Vesicles in Aging: An Emerging Hallmark?
Manni, G., Buratta, S., Pallotta, M. T., Chiasserini, D., Michele, A. D., Emiliani, C., Giovagnoli, S., Pascucci, L., Romani, R., Bellezza, I., et al. (2023) · Cells · doi:10.3390/cells12040527 · Cited by 63 · see citing works
Impact of Geroscience on Therapeutic Strategies for Older Adults With Cardiovascular Disease
Forman, D. E., Kuchel, G. A., Newman, J. C., Kirkland, J. L., Volpi, E., Taffet, G. E., Barzilai, N., Pandey, A., Kitzman, D. W., Libby, P., et al. (2023) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2023.05.038 · Cited by 63 · see citing works
Singh, S. T., Giron, L. B., Shaikh, M. W., Shankaran, S., Engen, P. A., Bogin, Z. R., Bambi, S. A., Goldman, A. R., Azevedo, J. L. L. C., Orgaz, L., et al. (2024) · Microbiome · doi:10.1186/s40168-024-01758-4 · Cited by 62 · see citing works
Heterogeneity and transcriptome changes of human CD8+ T cells across nine decades of life
Lü, J., Ahmad, R., Nguyen, T. B. V., Cifello, J., Hemani, H., Li, J., Chen, J., Li, S., Wang, J., Achour, A., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-32869-x · Cited by 61 · see citing works
Marcon, G., Tettamanti, M., Capacci, G., Fontanel, G., Spanò, M., Nobili, A., Forloni, G., & Franceschi, C. (2020) · Aging · doi:10.18632/aging.103872 · Cited by 60 · see citing works
Self-citationA unified metric of human immune health
Sparks, R., Rachmaninoff, N., Lau, W. W., Hirsch, D., Bansal, N., Martins, A. J., Chen, J., Liu, C. C., Cheung, F., Failla, L. E., et al. (2024) · Nature Medicine · doi:10.1038/s41591-024-03092-6 · Cited by 60 · see citing works
Roadmap for alleviating the manifestations of ageing in the cardiovascular system
Liberale, L., Tual-Chalot, S., Sedej, S., Ministrini, S., Georgiopoulos, G., Grunewald, M., Bäck, M., Bochaton-Piallat, M.-L., Boon, R. A., Ramos, G. C., et al. (2025) · Nature Reviews Cardiology · doi:10.1038/s41569-025-01130-5 · Cited by 59 · see citing works
Self-citationBiomarkers of ageing: Current state‐of‐art, challenges, and opportunities
Chen, R., Wang, Y., Zhang, S., Bulloch, G., Zhang, J., Liao, H., Shang, X., Clark, M., Peng, Q., Ge, Z., et al. (2023) · MedComm – Future Medicine · doi:10.1002/mef2.50 · Cited by 59 · see citing works
Toward precision interventions and metrics of inflammaging
Franceschi, C., Olivieri, F., Moskalev, A., Ivanchenko, M., & Santoro, A. (2025) · Nature Aging · doi:10.1038/s43587-025-00938-7 · Cited by 59 · see citing works
Self-citationInflammageing, a targetable pathway for preventing cardiovascular diseases
Aranda, J. F., Ramírez, C. M., & Mittelbrunn, M. (2024) · Cardiovascular Research · doi:10.1093/cvr/cvae240 · Cited by 58 · see citing works
Inflammatory Diseases, Inflammatory Biomarkers, and Alzheimer Disease
Huang, J. V., Su, B., Karhunen, V., Gill, D., Zuber, V., Ahola‐Olli, A., Palaniswamy, S., AUVINEN, J. P., Herzig, K., Keinanen-Kiukaanniemi, S. M., et al. (2022) · Neurology · doi:10.1212/wnl.0000000000201489 · Cited by 56 · see citing works
Wu, F., Du, H., Overbey, E. G., Kim, J., Makhijani, P., Martin, N., Lerner, C. A., Nguyen, K., Baechle, J. J., Valentino, T. R., et al. (2024) · Nature Communications · doi:10.1038/s41467-023-42013-y · Cited by 56 · see citing works
Self-citationHematopoietic Stem Cells and the Immune System in Development and Aging
Shevyrev, D., Tereshchenko, V., Berezina, T. N., & Александрович, Р. С. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms24065862 · Cited by 54 · see citing works
Current Trends in Anti-Aging Strategies
Rosén, R., & Yarmush, M. L. (2023) · Annual Review of Biomedical Engineering · doi:10.1146/annurev-bioeng-120122-123054 · Cited by 54 · see citing works
Zhang, W., Xu, M., Chen, F., Su, Y., Yu, M., Xing, L., Chang, Y. E., & Yan, T. (2023) · Experimental Neurology · doi:10.1016/j.expneurol.2023.114474 · Cited by 54 · see citing works
Ergothioneine promotes longevity and healthy aging in male mice
Katsube, M., Ishimoto, T., Fukushima, Y., Kagami, A., Shuto, T., & Kato, Y. (2024) · GeroScience · doi:10.1007/s11357-024-01111-5 · Cited by 55 · see citing works
An immunologist’s guide to immunosenescence and its treatment
Caruso, C., Ligotti, M. E., Accardi, G., Aiello, A., & Candore, G. (2022) · Expert Review of Clinical Immunology · doi:10.1080/1744666x.2022.2106217 · Cited by 53 · see citing works
Fuentealba, M., Rouch, L., Guyonnet, S., Lemaitre, J.-M., de Souto Barreto, P., Vellas, B., Andrieu, S., & Furman, D. (2025) · Nature Aging · doi:10.1038/s43587-025-00883-5 · Cited by 51 · see citing works
Self-citationYusipov, I., Kondakova, E. V., Kalyakulina, A., Krivonosov, M., Lobanova, N., Bacalini, M. G., Franceschi, C., Vedunova, M. V., & Ivanchenko, M. (2022) · GeroScience · doi:10.1007/s11357-022-00540-4 · Cited by 50 · see citing works
Self-citationAgeing, immune fitness and cancer
Dolan, M., Libby, K. A., Ringel, A. E., van Galen, P., & McAllister, S. S. (2025) · Nature reviews. Cancer · doi:10.1038/s41568-025-00858-z · Cited by 50 · see citing works
Pathogenic mitochondrial dysfunction and metabolic abnormalities
Moos, W. H., Faller, D. V., Glavas, I. P., Harpp, D. N., Kamperi, N., Kanara, I., Kodukula, K., Mavrakis, A. N., Pernokas, J., Pernokas, M., et al. (2021) · Biochemical Pharmacology · doi:10.1016/j.bcp.2021.114809 · Cited by 49 · see citing works
Emerging rejuvenation strategies—Reducing the biological age
Zhang, B., Trapp, A., Kerepesi, C., & Gladyshev, V. N. (2021) · Aging Cell · doi:10.1111/acel.13538 · Cited by 49 · see citing works
Human organ rejuvenation by VEGF-A: Lessons from the skin
Keren, A., Bertolini, M., Keren, Y., Ullmann, Y., Paus, R., & Gilhar, A. (2022) · Science Advances · doi:10.1126/sciadv.abm6756 · Cited by 47 · see citing works
Ramasubramanian, R., Meier, H. C. S., Vivek, S., Klopack, E. T., Crimmins, E. M., Faul, J. D., Nikolich‐Žugich, J., & Thyagarajan, B. (2022) · Immunity & Ageing · doi:10.1186/s12979-022-00290-z · Cited by 47 · see citing works
Briand, M., Raffin, J., González-Bautista, E., Ritz, P., van Kan, G. A., Pillard, F., Faruch, M., Guyonnet, S., Dray, C., Vellas, B. J., et al. (2024) · GeroScience · doi:10.1007/s11357-024-01245-6 · Cited by 46 · see citing works
A gut aging clock using microbiome multi-view profiles is associated with health and frail risk
Wang, H., Chen, Y., Feng, L., Lu, S., Zhu, J., Zhao, J., Zhang, H., Chen, W., & Lu, W. (2024) · Gut Microbes · doi:10.1080/19490976.2023.2297852 · Cited by 44 · see citing works
Markov, N. T., Lindbergh, C. A., Staffaroni, A. M., Pérez, K., Stevens, M., Nguyen, K., Murad, N. F., Fonseca, C. S., Campisi, J., Kramer, J. H., et al. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2207181119 · Cited by 43 · see citing works
Self-citationVerschoor, C. P., Vlasschaert, C., Rauh, M. J., & Paré, G. (2023) · Aging Cell · doi:10.1111/acel.13863 · Cited by 42 · see citing works
Корчажкина, Н. Б., Aa, M., Reshetova, I. V., Dimova, O. V., & Kotenko, K. V. (2023) · Problems of Balneology Physiotherapy and Exercise Therapy · doi:10.17116/kurort20231000616 · Cited by 42 · see citing works
Critical review of aging clocks and factors that may influence the pace of aging
Min, M., Egli, C., Dulai, A. S., & Sivamani, R. K. (2024) · Frontiers in Aging · doi:10.3389/fragi.2024.1487260 · Cited by 40 · see citing works
Enhancing immunity during ageing by targeting interactions within the tissue environment
Bracken, O. V., Maeyer, R. P. H. D., & Akbar, A. N. (2025) · Nature Reviews Drug Discovery · doi:10.1038/s41573-024-01126-9 · Cited by 40 · see citing works
An interferon gamma response signature links myocardial aging and immunosenescence
Ashour, D., Rebs, S., Arampatzi, P., Saliba, A.-E., Dudek, J., Schulz, R., Hofmann, U., Frantz, S., Cochain, C., Streckfuß-Bömeke, K., et al. (2023) · Cardiovascular Research · doi:10.1093/cvr/cvad068 · Cited by 39 · see citing works
Advancing nutrition science to meet evolving global health needs
Neufeld, L. M., Ho, E., Obeid, R., Tzoulis, C., Green, M., Huber, L. G., Stout, M., & Griffiths, J. C. (2023) · European Journal of Nutrition · doi:10.1007/s00394-023-03276-9 · Cited by 39 · see citing works
Kritchevsky, S. B., & Cummings, S. R. (2025) · JAMA · doi:10.1001/jama.2025.11289 · Cited by 38 · see citing works
Aging-related biomarker discovery in the era of immune checkpoint inhibitors for cancer patients
Al-Danakh, A. Y., Safi, M., Jian, Y., Yang, L., Zhu, X., Chen, Q., Yang, K., Wang, S., Zhang, J., & Yang, D. (2024) · Frontiers in Immunology · doi:10.3389/fimmu.2024.1348189 · Cited by 36 · see citing works
Wadhwa, A., Moreno‐Villanueva, M., Crucian, B. E., & Wu, H. (2024) · Immunity & Ageing · doi:10.1186/s12979-024-00449-w · Cited by 37 · see citing works
Franceschi, C., Santoro, A., & Capri, M. (2020) · Seminars in Immunopathology · doi:10.1007/s00281-020-00823-y · Cited by 36 · see citing works
Self-citationNaigeon, M., Dugage, M. R., Danlos, F., Boselli, L., Jouniaux, J.-M., de Oliveira, C., Ferrara, R., Duchemann, B., Berthot, C., Girard, L., et al. (2023) · Science Advances · doi:10.1126/sciadv.adh0708 · Cited by 35 · see citing works
Human immune phenotyping reveals accelerated aging in type 1 diabetes
Shapiro, M. R., Dong, X., Perry, D. J., McNichols, J., Thirawatananond, P., Posgai, A. L., Peters, L. D., Motwani, K., Musca, R. S., MUIR, A. B., et al. (2023) · JCI Insight · doi:10.1172/jci.insight.170767 · Cited by 35 · see citing works
Application of AI in biological age prediction
Meng, D., Zhang, S., Huang, Y., Mao, K., & Han, J. J. (2024) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2024.102777 · Cited by 34 · see citing works
Self-citationHOMA-IR is positively correlated with biological age and advanced aging in the US adult population
Yang, H., Gong, R., Liu, M., Deng, Y., Zheng, X., & Hu, T. (2023) · European journal of medical research · doi:10.1186/s40001-023-01448-1 · Cited by 33 · see citing works
McGee, K. C., Sullivan, J., Hazeldine, J., Schmunk, L. J., Martin-Herranz, D. E., Jackson, T., & Lord, J. M. (2024) · GeroScience · doi:10.1007/s11357-024-01138-8 · Cited by 33 · see citing works
Shue, F., White, L. J., Hendrix, R. D., Ulrich, J. D., Henson, R. L., Knight, W., Martens, Y. A., Wang, N., Roy, B., Starling, S. C., et al. (2024) · Science Advances · doi:10.1126/sciadv.adk3674 · Cited by 33 · see citing works
Immunoglobulin G N-glycan markers of accelerated biological aging during chronic HIV infection
Giron, L. B., Liu, Q., Adeniji, O. S., Yin, X., Kannan, T., Ding, J., Lu, D. Y., Langan, S. J., Zhang, J., Azevedo, J. L. L. C., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-47279-4 · Cited by 33 · see citing works
Angel, C. J. L., Pham, E. A., Du, H., Vallania, F., Fram, B. J., Pérez, K., Nguyen, T., Rosenberg‐Hasson, Y., Ahmed, A., Dekker, C. L., et al. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2022928118 · Cited by 33 · see citing works
Self-citationXu, D., & Xu, Z. (2024) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2024.102950 · Cited by 32 · see citing works
B cells drive CD4 T cell immunosenescence and age-associated health decline
Khan, S., Chakraborty, M., Wu, F., Chen, N., Wang, T., Chan, Y. T., Sayad, A., Vásquez, J. D. S., Kotlyar, M., Nguyen, K., et al. (2026) · Science Immunology · doi:10.1126/sciimmunol.adv7615 · Cited by 32 · see citing works
Self-citationThe ageing immune system as a driver of systemic ageing
Jang, I. H., Niedernhofer, L. J., Robbins, P. D., & Camell, C. D. (2026) · Nature reviews. Immunology · doi:10.1038/s41577-026-01269-3 · Cited by 33 · see citing works
- Authors
- Sayed, N., Huang, Y., Nguyen, K., Krejciova-Rajaniemi, Z., Grawe, A. P., Gao, T., Tibshirani, R., Hastie, T., Alpert, A., Cui, L., Kuznetsova, T., Rosenberg-Hasson, Y., Ostan, R., Monti, D., Lehallier, B., Shen-Orr, S. S., Maecker, H. T., Dekker, C. L., Wyss-Coray, T., Franceschi, C., Jojic, V., Haddad, F., Montoya, J. G., Wu, J. C., Davis, M. M., & Furman, D.
- Affiliations
- Stanford Medicine (US)
- Buck Institute for Research on Aging (US)
- University of North Carolina at Chapel Hill (US)
- Technion – Israel Institute of Technology (IL)
- KU Leuven (BE)
- University of Bologna (IT)
- University of Florence (IT)
- N. I. Lobachevsky State University of Nizhny Novgorod (RU)
- Howard Hughes Medical Institute (US)
- Corresponding author
- Furman, David
- ORCID
- Venue
- Nature Aging
- Published
- 2021-07-12
- Volume
- 1
- Issue
- 7
- Pages
- 598-615
- ISSN
- 2662-8465 (ISSN-L)
- PMC
- PMC8654267
- OpenAlex
- W3182997864
- Semantic Scholar
- 367ef4249a61343109670699611b8d663e163fe7
- Cited by
- 572
- References
- 124
- Citations by year
- 2026: 118 · 2025: 117 · 2024: 159 · 2023: 86 · 2022: 71 · 2021: 21
- FWCI
- 31.11
- Type
- journal article
- Language
- en
- Update notices
- correction (2021-07-26)
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- Locations
- Nature Aging (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Lirias (KU Leuven) (submittedVersion, other-oa)
- Carolina Digital Repository (University of North Carolina at Chapel Hill) (submittedVersion)
- Florence Research (University of Florence) (submittedVersion)
- UNC Libraries
- Funders
- Glenn Foundation for Medical Research
- European Commission
- Ministry of Education and Science of the Russian Federation
- National Institutes of Health (UL1TR003142)
- National Institute on Aging (P50AG047366, P30AG059307, P30AG066515)
- National Heart, Lung, and Blood Institute (K01HL135455)
- National Institute of Allergy and Infectious Diseases (U19AI090019, U19AI057229)
- National Institute of Diabetes and Digestive and Kidney Diseases (P30DK116074)
- National Center for Research Resources (UL1RR025744)
- National Center for Advancing Translational Sciences
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Aging › Genetics, Aging, and Longevity in Model Organisms
- Health Sciences › Medicine › Physiology › Telomeres, Telomerase, and Senescence
- Health Sciences › Medicine › Geriatrics and Gerontology › Frailty in Older Adults
- Keywords
- age-related inflammation, multimorbidity, immunosenescence, frailty, cardiovascular aging, centenarians, longevity, CXCL9, chemokines, endothelial cell senescence, arterial stiffness, cardiac remodeling, vascular function, deep learning
- MeSH terms
- Immunosenescence, Frailty, Multimorbidity, Deep Learning, Aged, 80 and over, Aging, Animals, Humans, Inflammation, Endothelial Cells, Mice
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Claims read by deepseek-v4.1-flash under definition version 3 (extract-v3) on 2026-10-06.
Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-10-06.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review