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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.
Overview
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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.
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Claims the paper makes
86 Own result · 34 Own interpretation · 30 Own method · 22 Not checked yet · 3 No basis given · 2 Partly supported · accuracy not yet measured
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“have all been shown to be linked to sustained systemic inflammation7–16.”
§ An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging > Abstract
What the sources say
- Not checked yetcites [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
- Partly supportedcites [8] Efeyan, A., Comb, W. C. & Sabatini, D. M. Nutrient-sensing mechanisms and pathways. Nature 517, 302–310 (2015). sourceExperimental — accuracy not yet measured
The Concluding Remarks link chronic inflammation in obesity to deregulated nutrient sensing and separately discuss nutrient sensing in aging. They do not establish that all nine hallmarks of aging are linked to sustained systemic inflammation; no page number is available in our copy. (LLM, conf=0.78)
- Partly supportedcites [9] Grivennikov, S. I., Greten, F. R. & Karin, M. Immunity, inflammation, and cancer. Cell 140, 883–899 (2010). sourceExperimental — accuracy not yet measured
In “Types of inflammation and general mechanisms” (page unavailable), the review links damaged DNA and cell senescence to chronic inflammation. “Inflammation and tumor initiation” also discusses inflammation and genomic instability. These passages support links involving some aging-related processes, but not the claim that all nine hallmarks are linked to sustained systemic inflammation. (LLM, conf=0.87)
- Not checked yetcites [10] Hunter, R. L. et al. Inflammation induces mitochondrial dysfunction and dopaminergic neurodegeneration in the nigrostriatal system. J. Neurochem. 100, 1375–1386 (2007). source
- Partly supportedcites [11] Jurk, D. et al. Chronic inflammation induces telomere dysfunction and accelerates ageing in mice. Nat. Commun. 5, 4172 (2014). sourceExperimental — accuracy not yet measured
The Discussion (page unavailable) links chronic inflammation to telomere dysfunction, DNA damage, senescence and reduced tissue regeneration, and describes connections with nutrient signalling and mitochondrial function. It does not establish that all nine hallmarks of aging have been linked to sustained systemic inflammation; the claim’s universal scope is unsupported by this paper. (LLM, conf=0.88)
- Not checked yetcites [12] Lasry, A. & Ben-Neriah, Y. Senescence-associated inflammatory responses: aging and cancer perspectives. Trends Immunol. 36, 217–228 (2015). source
- Could not verifycites [13] Nathan, C. & Cunningham-Bussel, A. Beyond oxidative stress: an immunologist’s guide to reactive oxygen species. Nat. Rev. Immunol. 13, 349–361 (2013). sourceExperimental — accuracy not yet measured
The passage headed “ROS and inflammasomes” (indexed as Abstract; page unavailable) discusses ROS and inflammasome activation, not the nine hallmarks of aging. Searches for the hallmarks and for sustained systemic inflammation in aging found no passage connecting all nine to inflammation. (LLM, conf=0.75)
- Partly supportedcites [14] Oh, J., Lee, Y. D. & Wagers, A. J. Stem cell aging: mechanisms, regulators and therapeutic opportunities. Nat. Med. 20, 870–880 (2014). sourceExperimental — accuracy not yet measured
The “Systemic factors” section links aging tissues to chronic inflammation and identifies senescent cells as one possible driver. It does not show that all nine established hallmarks of aging are linked to sustained systemic inflammation; its discussion concerns particular tissues and mechanisms. (LLM, conf=0.85)
- Partly supportedcites [15] Thevaranjan, N. et al. Age-associated microbial dysbiosis promotes intestinal permeability, systemic inflammation, and macrophage dysfunction. Cell Host Microbe 21, 455–466 (2017). sourceExperimental — accuracy not yet measured
The Introduction states that aging is characterized by chronic, low-grade systemic inflammation and reports that age-related microbial dysbiosis triggers systemic inflammation in mice. It does not examine the nine established hallmarks individually or show that all nine are linked to sustained systemic inflammation. (LLM, conf=0.86)
- Partly supportedcites [16] Alpert, A. et al. A clinically meaningful metric of immune age derived from high-dimensional longitudinal monitoring. Nat. Med. 25, 487–495 (2019). sourceExperimental — accuracy not yet measured
The introductory passage under “Abstract > Reporting Summary” says aging is often accompanied by a rise in circulating inflammatory mediators, termed inflammaging. It does not say that each of the nine hallmarks of aging has been shown to be linked to sustained systemic inflammation; the hallmarks appear in the References as the title of another work. (LLM, conf=0.82)
boostedcorrelational- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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).”
§ An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging > Results > Deep-learning analysis of circulating immune biomarkers.
plaincorrelational- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The top features with the largest coefficients included iAge, high cholesterol and BMI (Extended Data Fig. 5c).”
§ An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging > Results > iAge predicts multimorbidity and frailty.
plaincorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“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).”
§ An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging > Results > iAge predicts multimorbidity and frailty.
plaincorrelationaln = 285- Asserted: passes — The authors put it forward as true.
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In the paper
“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).”
§ An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging > Results > iAge predicts multimorbidity and frailty.
plaincorrelationaln = 29- Asserted: passes — The authors put it forward as true.
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In the paper
“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).”
§ An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging > Results > Lower inflammatory clock index in centenarians.
What the sources say
- Not checked yetcites [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
plaincorrelationaln = 2,290 ·- Asserted: passes — The authors put it forward as true.
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- 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.
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In the paper
“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).”
§ An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging > Results > CXCL9 is an important component of iAge and correlates with cardiovascular aging in healthy adults.
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“where the top 15 most variable Jacobians were CXCL9, EOTAXIN, Mip-1α, LEPTIN, IL-1β, IL-5, IFN-α and IL-4 (positive contributors)”
§ An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging > Results > CXCL9 is an important component of iAge and correlates with cardiovascular aging in healthy adults.
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“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).”
§ An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging > Results > Inhibition of CXCL9 rescues endothelial cell dysfunction.
plaincorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“Notably, the contribution of iAge to frailty score was significantly stronger than that of calendar age (Fig. 1d).”
§ An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging > Results > iAge predicts multimorbidity and frailty.
boostedcorrelationaln = 29- Asserted: passes — The authors put it forward as true.
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The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“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”
§ An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging > Abstract
What the sources say
- Partly supportedcites [1] Kotas, M. E. & Medzhitov, R. Homeostasis, inflammation, and disease susceptibility. Cell 160, 816–827 (2015). sourceExperimental — accuracy not yet measured
The “Inflammation and homeostatic circuits” section (page not supplied) links chronic inflammation to atherosclerosis and neurodegenerative diseases. It does not establish that inflammatory components are often chronically elevated in older people or link them to cancer incidence. (LLM, conf=0.86)
- Could not verifycites [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). sourceExperimental — accuracy not yet measured
The Abstract discusses fibrotic extracellular matrix and fibroblast activation, while the Results and Discussion address YAP/TAZ mechanosignaling in fibrosis; no page numbers are available in our copy. Searches for chronic immune inflammation in aging and its association with cancer, cardiovascular disease, or neurodegenerative disorders found no passage addressing the claim. (LLM, conf=0.75)
- Not checked yetcites [3] Crusz, S. M. & Balkwill, F. R. Inflammation and cancer: advances and new agents. Nat. Rev. Clin. Oncol. 12, 584–596 (2015). source
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- Partly supported
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In the paper
“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.”
§ An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging > Results > Deep-learning analysis of circulating immune biomarkers.
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“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).”
§ An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging > Results > Deep-learning analysis of circulating immune biomarkers.
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“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).”
§ An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging > Results > iAge predicts multimorbidity and frailty.
plaincorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“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).”
§ An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging > Results > CXCL9 governs inflammation and proliferation in aged EC.
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“In contrast, there were 77% (14 out of 18) of controls in the high rank versus 23% in the low rank group (Fig. 1e)”
§ An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging > Results > Lower inflammatory clock index in centenarians.
plainn = 37- Asserted: passes — The authors put it forward as true.
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In the paper
“In addition, IL-11Rα, CXCL10 and hepatocyte growth factor (HGF) increased with age, whereas CXCL1 and LIF decreased (Extended Data Fig. 7).”
§ An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging > Results > CXCL9 is an important component of iAge and correlates with cardiovascular aging in healthy adults.
plaindirectn = 97- Asserted: passes — The authors put it forward as true.
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In the paper
“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.”
§ An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging > Results > Lower inflammatory clock index in centenarians.
plainSingle case ·- Asserted: passes — The authors put it forward as true.
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In the paper
“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).”
§ An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging > Results > iAge is correlated with immunosenescence.
plaincorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“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).”
§ An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging > Results > CXCL9 increases with age in human blood endothelial cells.
plaincorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“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).”
§ An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging > Results > CXCL9 governs inflammation and proliferation in aged EC.
plaindirect- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“frequency of total CD8+ T cells in females was 24% versus 30% in males”
§ An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging > Results > iAge is correlated with immunosenescence.
plain- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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).”
§ An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging > Results > Inhibition of CXCL9 rescues endothelial cell dysfunction.
plaindirect- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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).”
§ An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging > Results > iAge is correlated with immunosenescence.
plaincorrelationaln = 818 ·- Asserted: passes — The authors put it forward as true.
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In the paper
“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).”
§ An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging > Results > CXCL9 governs inflammation and proliferation in aged EC.
plaincorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“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).”
§ An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging > Results > Inhibition of CXCL9 rescues endothelial cell dysfunction.
plain- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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.”
§ An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging > Methods > Extraction of an inflammatory clock.
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“we demonstrate that iAge predicts multimorbidity and mortality”
§ An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging > Discussion
boostedcorrelational- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“we find that CXCL9 is mainly produced by aged endothelium and predicts subclinical levels of cardiovascular aging in nominally healthy individuals.”
§ An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging > Discussion
plaincorrelationaln = 97- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
- Rests on an assumption: CXCL9 attracts T cells, is induced by IFN-γ, and is primarily secreted by neutrophils, macrophages, and endothelial cells.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“After obtaining the best code length as 5 (Extended Data Fig. 3a)”
§ An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging > Methods > Extraction of an inflammatory clock.
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“iAge is directly associated with multiple disease phenotypes, including cardiovascular aging, frailty, immune decline and exceptional longevity.”
§ An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging > Discussion
plaincorrelational- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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).”
§ An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging > Discussion
plain- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“we find that iAge tracks with multiple diseases and immunosenescence.”
§ An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging > Discussion
plaincorrelational- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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.”
§ An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging > Extended Data
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“CXCR3 was not expressed in iPSC induced cardiomyocytes (iPSC-CM), Fibroblast, or iPSC.”
§ An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging > Extended Data
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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.”
§ An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging > Extended Data
plaindirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The GAE method outperforms linear methods for protein data reconstruction and prediction of chronological age (b).”
§ An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging > Extended Data
plain- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Significant correlation between age and CXCL9 using univariate regression analysis.”
§ An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging > Extended Data
plaincorrelational- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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.”
§ An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging > Extended Data
plainn = 1,001- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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).”
§ An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging > Extended Data
plain- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In (c), we found that the predictive performance of gradient boosting decision tree (GBDT) has similar performance as PCA.”
§ An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging > Extended Data
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: fails — The paper’s conclusions may not depend on it.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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)”
§ An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging > Extended Data
plaincorrelationaln = 902- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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.”
§ An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging > Extended Data
hedgeddirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“However, it is highly expressed in iPSC induced endothelial cells and Human Umbilical Vein Endothelial Cells (HUVEC).”
§ An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging > Extended Data
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Due to the stochastic nature of LASSO regression, each implementation produced a slightly different list of genes that were predictive of iAge.”
§ An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging > Methods > Selecting the most important genes predictive of iAge.
plaindirectn = 397- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“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.”
§ An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging > Results > iAge predicts multimorbidity and frailty.
What the sources say
- Not checked yetcites [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
boostedn = 285- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
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Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“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.”
§ An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging > Abstract
What the sources say
- Not checked yetcites [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 yetcites [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
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
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- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“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.”
§ An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging > Results > iAge predicts multimorbidity and frailty.
plainn = 285- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“We employed fivefold cross validation and measured the predictive performances of each method on the test set.”
§ An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging > Results > Deep-learning analysis of circulating immune biomarkers.
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
In the paper
“Recent work from our group identified a cellular composite metric for immune aging (IMM-AGE), which was strongly associated with all-cause mortality16.”
§ An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging > Abstract
What the sources say
- Not checked yetcites [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
boostedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“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.”
§ An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging > Results > Study design of the Stanford 1000 Immunomes project.
What the sources say
- Not checked yetcites [31] Maecker, H. T. et al. New tools for classification and monitoring of autoimmune diseases. Nat. Rev. Rheumatol. 8, 317–328 (2012). source
plainn = 1,001- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
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- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
None of the sources cited for this claim have been checked yet.
In the paper
“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.”
§ An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging > Abstract
What the sources say
- Not checked yetcites [1] Kotas, M. E. & Medzhitov, R. Homeostasis, inflammation, and disease susceptibility. Cell 160, 816–827 (2015). source
- Not checked yetcites [17] Goldberg, E. L. & Dixit, V. D. Drivers of age-related inflammation and strategies for healthspan extension. Immunol. Rev. 265, 63–74 (2015). source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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.”
§ An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging > Results > Inhibition of CXCL9 rescues endothelial cell dysfunction.
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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.”
§ An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging > Results > Inhibition of CXCL9 rescues endothelial cell dysfunction.
boosteddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“selected from a total of 151 recruited participants using strict selection criteria (Methods).”
§ An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging > Results > CXCL9 is an important component of iAge and correlates with cardiovascular aging in healthy adults.
plainn = 151- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“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.”
§ An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging > Results > iAge is correlated with immunosenescence.
What the sources say
- Not checked yetcites [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 yetcites [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
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“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”
§ An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging > Results > CXCL9 is an important component of iAge and correlates with cardiovascular aging in healthy adults.
What the sources say
- Not checked yetcites [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 yetcites [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 yetcites [47] Chirinos, J. A. Large artery stiffness, microvascular function, and cardiovascular risk. Circ. Cardiovasc Imaging 9, e005903 (2016). source
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“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.”
§ An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging > Results > Inhibition of CXCL9 rescues endothelial cell dysfunction.
What the sources say
- Not checked yetcites [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 yetcites [58] Brandes, R. P., Fleming, I. & Busse, R. Endothelial aging. Cardiovascular Res. 66, 286–294 (2005). source
- Not checked yetcites [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 yetcites [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
boostedn = 5- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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.”
§ An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging > Results > iAge is correlated with immunosenescence.
boostedcorrelationaln = 818- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“We conducted multiple regression analysis controlling age, CMV and sex (Methods).”
§ An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging > Results > iAge is correlated with immunosenescence.
plainn = 818 ·- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“These results demonstrate a concomitant increase in CXCL9 in the endothelia and EC dysfunction associated with aging both in humans and mice.”
§ An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging > Results > CXCL9 increases with age in human blood endothelial cells.
boostedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Long-standing evidence has suggested a role for the endothelium in the etiology of hypertension and arterial stiffness48,49.”
§ An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging > Results > CXCL9 increases with age in human blood endothelial cells.
What the sources say
- Not checked yetcites [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 yetcites [49] Vanhoutte, P. M., Feletou, M. & Taddei, S. Endothelium-dependent contractions in hypertension. Br. J. Pharmacol. 144, 449–458 (2005). source
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“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.”
§ An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging > Results > CXCL9 is an important component of iAge and correlates with cardiovascular aging in healthy adults.
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
None of the sources cited for this claim have been checked yet.
In the paper
“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.”
§ An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging > Results > CXCL9 increases with age in human blood endothelial cells.
What the sources say
- Not checked yetcites [50] Castellon, X. & Bogdanova, V. Chronic inflammatory diseases and endothelial dysfunction. Aging Dis. 7, 81–89 (2016). source
- Not checked yetcites [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 yetcites [52] Kamo, T., Akazawa, H. & Komuro, I. Cardiac nonmyocytes in the hub of cardiac hypertrophy. Circ. Res. 117, 89–98 (2015). source
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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.”
§ An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging > Discussion
hedgeddirectn = 97- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“These iPSCs were differentiated into cardiomyocytes to purities of >85% and ECs to purities of >90%.”
§ An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging > Methods > Induced human pluripotent stem cell-derived cardiomyocytes and endothelial cells.
plainn = 5- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“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.”
§ An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging > Methods > Ethics declaration.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Notably, CXCL9 has also been shown to be associated with falls in the older population101,102”
§ An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging > Discussion
What the sources say
- Not checked yetcites [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 yetcites [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
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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.”
§ An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging > Discussion
hedgeddirect- Asserted: fails — The authors may not put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The authors may not put it forward as true. It may state two separate claims as one.
- Rests on an assumption: CXCL9 attracts T cells, is induced by IFN-γ, and is primarily secreted by neutrophils, macrophages, and endothelial cells.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“they also suggest that ECs may be one source of inflammation”
§ An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging > Discussion
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“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.”
§ An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging > Discussion
boosteddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“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.”
§ An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging > Discussion
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“All parameters were measured in triplicate and averaged.”
§ An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging > Methods > Cardiovascular phenotyping.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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.”
§ An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging > Results > Age-related elevation in CXCL9 leads to endothelial cell senescence.
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“All studies were interpreted by one physician (F.H.) who was blinded to age as well as clinical and biological data.”
§ An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging > Methods > Cardiovascular phenotyping.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“Some studies in humans have found CXCL9 to increase with age93–98”
§ An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging > Discussion
What the sources say
- Not checked yetcites [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 yetcites [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 yetcites [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 yetcites [96] Shurin, G. V. et al. Dynamic alteration of soluble serum biomarkers in healthy aging. Cytokine 39, 123–129 (2007). source
- Not checked yetcites [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 yetcites [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
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“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”
§ An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging > Methods > Cardiovascular phenotyping.
plaincorrelationaln = 50- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“All PCR reactions were performed in triplicate, normalized to the GAPDH endogenous control gene and assessed using the comparative Ct method.”
§ An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging > Methods > Real-time PCR.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We thus hypothesize that one root cause of CXCL9 overproduction is cellular aging per se, which can trigger metabolic dysfunction”
§ An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging > Discussion
hedgeddirect- Asserted: fails — The authors may not put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The authors may not put it forward as true. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one. The quote only partly states this claim.
- Rests on an assumption: CXCL9 attracts T cells, is induced by IFN-γ, and is primarily secreted by neutrophils, macrophages, and endothelial cells.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Any monotonic transformation of cAge or iAge does not affect ranks, hence findings are robust to transformations such as log and exp.”
§ An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging > Methods > Extraction of an inflammatory clock.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“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.”
§ An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging > Methods > Induced human pluripotent stem cell-derived cardiomyocytes and endothelial cells.
plainn = 5- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Despite the proven utility of cytokine stimulation assays used in our study with respect to an individual’s overall immune competence5,24,25”
§ An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging > Discussion
What the sources say
- Not checked yetcites [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 yetcites [24] Furman, D. et al. Apoptosis and other immune biomarkers predict influenza vaccine responsiveness. Mol. Syst. Biol. 9, 659 (2013). source
- Not checked yetcites [25] Furman, D. et al. Cytomegalovirus infection enhances the immune response to influenza. Sci. Transl. Med. 7, 281ra43 (2015). source
boostedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“which is lower than that of the recently reported ‘proteomic age’ metric (R = 0.92)90.”
§ An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging > Discussion
What the sources say
- Not checked yetcites [90] Tanaka, T. et al. Plasma proteomic signature of age in healthy humans. Aging Cell. 17, e12799 (2018). source
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“These results demonstrate a central role for CXCL9 in vascular dysfunction, which likely contributes to arterial stiffness and premature aging in vivo.”
§ An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging > Results > CXCL9 impairs vascular function and contributes to arterial stiffness.
boostedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“we demonstrate that CXCL9 is a master regulator of vascular function and cellular senescence”
§ An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging > Discussion
boosteddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Rests on an assumption: CXCL9 attracts T cells, is induced by IFN-γ, and is primarily secreted by neutrophils, macrophages, and endothelial cells.
In the paper
“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.”
§ An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging > Discussion
What the sources say
- Not checked yetcites [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
plainconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“previous reports showing that high levels of baseline inflammatory markers correlate with weaker responses to hepatitis B and herpes zoster vaccine formulations15,91.”
§ An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging > Discussion
What the sources say
- Not checked yetcites [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 yetcites [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
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In this model, increasing doses of CXCL9 and expression of its receptor in these cells leads to cumulative deterioration of endothelial function in aging.”
§ An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging > Discussion
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Experiment was repeated 3 times.”
§ An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging > Extended Data
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“No randomization or blinding was conducted for this study.”
§ An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging > Methods > Statistics and reproducibility.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“PRIN06 and PRIN09 samples were measured in two batches. Datasets were bridged to each other using SomaLogic calibrators.”
§ An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging > Methods > SOMAscan assay.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“We used 5-fold cross-validation to identify the best code length, among lengths from 1 to 10.”
§ An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging > Extended Data
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Within each fold we performed nested 3-fold cross-validation to select hyper-parameters (depth, weight decay and guidance-ratio).”
§ An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging > Extended Data
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“For our experiments, no statistical method was used to predetermine sample size.”
§ An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging > Methods > Statistics and reproducibility.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“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.”
§ An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging > Methods > Statistics and reproducibility.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“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.”
§ An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging > Extended Data
plainn = 97- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“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.”
§ An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging > Extended Data
plainn = 902- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Covariates such as age, BMI, detection of CMV and sex were controlled by their inclusion in all regression analysis.”
§ An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging > Methods > Statistics and reproducibility.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Data in relative fluorescent units for 1,305 SOMAmer probes were obtained for these samples and no samples or probe data were excluded.”
§ An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging > Methods > SOMAscan assay.
plainn = 7- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“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.”
§ An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging > Extended Data
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“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).”
§ An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging > Extended Data
plainn = 902- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Blinding was not performed in mice experiments.”
§ An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging > Methods > Statistics and reproducibility.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Auto-encoders use a nonlinear transformation of data and hence, can model complex processes119.”
§ An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging > Methods > Guided auto-encoder and the inflammatory clock.
What the sources say
- Not checked yetcites [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
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“No data were excluded from the analyses.”
§ An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging > Methods > Statistics and reproducibility.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
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?
In the citing paper
“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]
In the citing paper
“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]
In the citing paper
“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]
In the citing paper
“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.
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging > Methods > Estimation of the inflammatory clock in centenarians and older control cohorts.
“was not different from that of model 1 (in which TRAIL is included)”
§ An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging > Methods > Estimation of the inflammatory clock in centenarians and older control cohorts.
“(by likelihood ratio test, P < 0.01)”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging > Results > iAge predicts multimorbidity and frailty.
“transitional B cells such as IgD+CD27− and IgD−CD27− B cells (negative predictors)”
§ An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging > Extended Data
“and IgD+CD27- and IgD-CD27-B cells, effector CD8 (+) T cells, total lymphocytes and monocytes, and central memory T cells (positive predictors) (d)”
Citations
Cites
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
Takahashi, K., & Yamanaka, S. (2006) · Cell · doi:10.1016/j.cell.2006.07.024 · Cited by 26,673
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
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
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
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
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
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
Learning Deep Architectures for AI
Bengio, Y. (2009) · Foundations and Trends® in Machine Learning · doi:10.1561/2200000006 · Cited by 7,330
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
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
“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 934
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
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
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 481
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
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 491
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 450
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 402
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 421
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
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 347
Interconnections between Inflammageing and Immunosenescence during Ageing
Teissier, T., Boulanger, É., & Cox, L. S. (2022) · Cells · doi:10.3390/cells11030359 · Cited by 323
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Jiang, Y. (2021) · Osteoarthritis and Cartilage · doi:10.1016/j.joca.2021.11.009 · Cited by 268
Rasmussen, L. J. H., Petersen, J. E. V., & Eugen‐Olsen, J. (2021) · Frontiers in Immunology · doi:10.3389/fimmu.2021.780641 · Cited by 261
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., Cesari, M., et al. (2022) · The Lancet Healthy Longevity · doi:10.1016/s2666-7568(22)00200-8 · Cited by 251
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 232
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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 201
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 157
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 154
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Wu, Z., Qu, J. T., Zhang, W., & Liu, G. (2023) · Molecular Cell · doi:10.1016/j.molcel.2023.10.006 · Cited by 145
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 149
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 132
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 124
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 124
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 142
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Zsichla, L., & Müller, V. (2023) · Viruses · doi:10.3390/v15010175 · Cited by 121
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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 106
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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 94
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 88
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 97
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Forcina, L., Franceschi, C., & Musarò, A. (2022) · Ageing Research Reviews · doi:10.1016/j.arr.2022.101697 · Cited by 85
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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 88
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 91
Qiu, W., Chen, H., Kaeberlein, M. R., & Lee, S. (2023) · The Lancet Healthy Longevity · doi:10.1016/s2666-7568(23)00189-7 · Cited by 90
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Păcularu‐Burada, B., Cîrîc, A., & Begea, M. (2024) · Foods · doi:10.3390/foods13152441 · Cited by 80
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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 76
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 80
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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 77
Giunta, S., Xia, S., Pelliccioni, G., & Olivieri, F. (2023) · GeroScience · doi:10.1007/s11357-023-00947-7 · Cited by 75
Pervizaj-Oruqaj, L., Ferrero, M. R., Matt, U., & Herold, S. (2024) · European Respiratory Review · doi:10.1183/16000617.0263-2023 · Cited by 78
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Lee, J. L., & Linterman, M. A. (2021) · Immunology Letters · doi:10.1016/j.imlet.2021.11.001 · Cited by 73
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Zárubová, J., Hasani‐Sadrabadi, M. M., Ardehali, R., & Li, S. (2022) · Advanced Drug Delivery Reviews · doi:10.1016/j.addr.2022.114233 · Cited by 75
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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 72
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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 69
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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 64
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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 64
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 63
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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 64
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 62
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Franceschi, C., Olivieri, F., Moskalev, A., Ivanchenko, M., & Santoro, A. (2025) · Nature Aging · doi:10.1038/s43587-025-00938-7 · Cited by 62
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Aranda, J. F., Ramírez, C. M., & Mittelbrunn, M. (2024) · Cardiovascular Research · doi:10.1093/cvr/cvae240 · Cited by 64
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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 59
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 58
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 58
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 56
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
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 56
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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
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
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 51
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 55
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 50
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 53
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
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
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 48
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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 45
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
Self-citationVerschoor, C. P., Vlasschaert, C., Rauh, M. J., & Paré, G. (2023) · Aging Cell · doi:10.1111/acel.13863 · Cited by 45
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
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 42
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 43
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
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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 40
Kritchevsky, S. B., & Cummings, S. R. (2025) · JAMA · doi:10.1001/jama.2025.11289 · Cited by 40
Aging-related biomarker discovery in the era of immune checkpoint inhibitors for cancer patients
Al-Danakh, A., 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 37
Wadhwa, A., Moreno‐Villanueva, M., Crucian, B. E., & Wu, H. (2024) · Immunity & Ageing · doi:10.1186/s12979-024-00449-w · Cited by 38
Franceschi, C., Santoro, A., & Capri, M. (2020) · Seminars in Immunopathology · doi:10.1007/s00281-020-00823-y · Cited by 36
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
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 36
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 35
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
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 35
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
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
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 35
Self-citationXu, D., & Xu, Z. (2024) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2024.102950 · Cited by 32
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 37
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 35
Metadata
- 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
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cell Biology › 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.
A language model drew the claims from the paper on 2026-10-06, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-06 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Cited sources are checked with a language model’s help (gpt-6-sol).
Statistics and arithmetic are recomputed, not judged.
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