Abstract

   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.
     __________________________________________________________________

   The important role of the immune system in the maintenance of human
   health and protection against infections has been recognized for over a
   hundred years. However, it was only in the past few decades that it has
   become apparent that 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 others^[77]1–[78]3. From these
   observations, it has been postulated that inflammation plays a critical
   role in regulating physiological aging^[79]4,[80]5. Furthermore, the
   well-established nine hallmarks of aging^[81]6: (1) genomic
   instability, (2) shortening telomere length, (3) epigenetic
   modifications, (4) loss of proteostasis, (5) deregulated nutrient
   sensing, (6) mitochondrial dysfunction, (7) cellular senescence, (8)
   stem cell exhaustion and (9) altered intracellular communication, have
   all been shown to be linked to sustained systemic
   inflammation^[82]7–[83]16.

   Contrary to the acute response, which is typically triggered by
   infection, chronic and systemic inflammation is thought to be triggered
   by physical, chemical or metabolic stimuli (‘sterile’ agents) such as
   those released by damaged cells and environmental insults, generally
   termed ‘damage-associated molecular patterns’ (DAMPs). 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 organs^[84]1,[85]17. 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 results^[86]18,[87]19.

   Recent work from our group identified a cellular composite metric for
   immune aging (IMM-AGE), which was strongly associated with all-cause
   mortality^[88]16. Here, we have extended our studies to focus on
   soluble immune biomarkers and define the relationship between systemic
   chronic inflammation and disease. We set out to establish a broad
   survey of immunity in over 1,000 individuals (the Stanford 1000
   Immunomes Project (1KIP)), wherein biological samples from 1,001
   individuals were obtained between 2007 and 2016 and comprehensively
   measured in a single facility, the Stanford Human Immune Monitoring
   Center (HIMC). At this center, peripheral blood specimens were
   processed and analyzed using multiple technological platforms for gene
   expression, serum cytokine level, cell subset composition, cellular
   responses to multiple stimuli and seropositivity to cytomegalovirus
   (CMV) infection. For 902 individuals, a comprehensive health assessment
   using a 53-feature clinical questionnaire was also obtained.

   Given the well-established importance of chronic inflammation for many
   human diseases and the lack of standard measures^[89]20, we used
   deep-learning methods on blood immune biomarkers to construct a metric
   for age-related chronic inflammation (iAge). iAge predicts important
   aging phenotypes and provides insights into the mechanisms leading to
   vascular aging. In addition, this metric was associated with
   exceptional longevity and with all-cause mortality in the Framingham
   Heart Study. We demonstrate that the most robust contributor to iAge,
   the interferon (IFN)-related chemokine CXCL9, was associated with an
   upregulation of multiple inflammatory pathway genes, downregulation of
   proliferation pathways and endothelial cellular senescence. Moreover,
   CXCL9 suppressed vascular function in aortic tissue from mice and
   correlated with subclinical cardiac remodeling and arterial stiffness
   in a validation study of healthy older adults.

   Therefore, we have identified a metric for systemic chronic age-related
   sterile inflammation which tracks with multiple disease phenotypes in
   multiple cohorts and thus, could be used as a metric for healthy versus
   unhealthy aging. Our results also demonstrate a link between
   inflammatory molecules of the immune system and vascular biology.

Results

Study design of the Stanford 1000 Immunomes project.

   Between 2007 and 2016, blood samples were drawn from ambulatory
   participants (n = 1,001) (339 males and 662 females) aged 8 to 96 years
   ([90]Extended Data Figs. 1 and [91]2) who had been recruited at
   Stanford University (Stanford 1KIP) for a longitudinal study of aging
   and vaccination^[92]5,[93]21–[94]29 and for an independent study of
   chronic fatigue syndrome^[95]30. Only healthy controls were included
   ([96]Methods). 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
   procedures^[97]31. Serum samples were obtained and used for protein
   content determination (including a total of 50 cytokines, chemokines
   and growth factors) (n = 1,001) and to assess CMV status (n = 748), a
   major determinant of immune system variation^[98]22,[99]25. Peripheral
   blood mononuclear cells (PBMCs) or whole-blood samples were used for
   the determination of gene expression (n = 397), cellular phenotypes and
   frequencies (n = 935) and for investigation of in vitro cellular
   responses to a variety of cytokine stimulations (n = 818). Extended
   clinical report forms were collected from 902 out of 1,001 individuals,
   of which 299 were males and 603 were females ([100]Extended Data Fig.
   1). A total of 37 additional older adults (19 centenarians and 18
   control participants) from Bologna, Italy, were included and screened
   for serum proteins to derive iAge on these extremely long-lived humans.

Deep-learning analysis of circulating immune biomarkers.

   Given the increasingly recognized effect of systemic chronic
   inflammation in the development of a wide variety of diseases
   associated with aging, especially in cardiovascular
   disease^[101]5,[102]32, we set out to construct a metric for
   age-related chronic inflammation that could summarize an individual’s
   inflammatory burden. We undertook an unbiased approach to compactly
   represent the nonlinear structure of the cytokine network. To do so, we
   developed a deep-learning method called guided auto-encoder (GAE). The
   GAE method is a type of deep neural network that utilizes nonlinear
   equations and effectively eliminates noise and redundancy in data, yet
   retaining the most relevant biological information from circulating
   immune protein data.

   To test the robustness and quality of the GAE method, we compared the
   accuracy of GAE against other widely used dimensionality reduction
   methods that use linear equations, such as the Elastic Net, Gradient
   Boosting Decision Tree (GBDT) and principal-component analysis (PCA),
   as well as those involving nonlinear equations, such as plain
   auto-encoders and neural networks ([103]Extended Data Fig. 3a–[104]c).
   We employed fivefold cross validation and measured the predictive
   performances of each method on the test set. 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) ([105]Extended Data Fig. 3b). 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. These results
   indicate that the phenomenon of low-grade chronic inflammation in aging
   humans is best modeled using nonlinear methods, which take into account
   network structure and redundancy in immunological protein biomarkers.
   This metric for chronic inflammation accurately predicts cAge in the
   population ([106]Fig. 1a) using the total inflammatory burden as
   measured by the level of circulating immune proteins ([107]Extended
   Data Fig. 4).

Fig. 1 |. The inflammatory clock of aging tracks with multimorbidity, frailty
and exceptional longevity.

   Fig. 1 |
   [108]Open in a new tab

   a, Using a GAE method on 50 circulating immune proteins, we derived
   iAge to predict cAge. Ten age-related disease items were selected to
   characterize the clinical significance of iAge. The items analyzed
   included different diseases and physiological systems: cancer,
   cardiovascular, respiratory, gastrointestinal, urologic, neurologic,
   endocrine–metabolic, musculoskeletal, genital–reproductive and
   psychiatric. All these disease features were binary. b, After adjusting
   for covariates, iAge was significantly correlated with multimorbidity
   in the older population analyzed (>60 years old, n = 285) (boxes
   represent 25th and 75th percentiles around the median (line); whiskers
   represent 1.5× interquartile range). c, For a subset of older adults (n
   = 29), frailty was assessed in 2017 using a modified frailty score
   ([109]Methods). iAge measured in 2010 predicted the frailty score 7
   years in advance. d, We applied linear regression where predicted
   frailty scores from 2010 were regressed onto observed frailty scores
   from 2017. Correlation coefficient (R^2) and P value of F-test of
   overall significance are reported. iAge was shown to be better than
   calendar age (P < 0.05 by likelihood ratio test for model comparison).
   P values are derived from univariate linear regression and inferential
   statistics where the P value for the independent variable tests the
   null hypothesis that the variable has no correlation with the dependent
   variable. NS, not significant. e, Comparison of the inflammatory index
   (rank cAge minus rank iAge) was computed between a healthy group of
   older adults (n = 18, age range 50–79 years) and centenarian
   participants (n = 19, age range 99–107 years). Centenarians were
   over-represented in individuals with low iAge index (protective
   phenotype), whereas the control older adults group were
   over-represented in individuals with high iAge index.

iAge predicts multimorbidity and frailty.

   To gain further insights into the effect of age-related chronic
   inflammation on age-related pathology, we computed a regression
   analysis between the total number of age-related diseases
   (multimorbidity) and iAge. 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 individual^[110]33. The items analyzed included different diseases
   of different physiological systems: cancer, cardiovascular,
   respiratory, gastrointestinal, urologic, neurologic,
   endocrine–metabolic, musculoskeletal, genital–reproductive and
   psychiatric dysfunctions. All these disease features were binary. 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. We observed a significant correlation between iAge and
   multimorbidity in the older adults in this study (>60 years old) (n =
   285, P < 0.01) ([111]Fig. 1b). This highlights the key role of iAge in
   the accumulation of physiological damage during aging.

   Next, we envisioned an unbiased approach to select predictors of
   multimorbidity based on available data for a total of 902 Stanford 1KIP
   participants while controlling for the age effect. To do so, we used a
   shrinkage method for variable selection by cross validation, called
   Elastic Net, which has been increasingly used in immunology, aging and
   other medical fields over the past years^[112]34. We applied
   differential penalties for each potential predictor such that the
   machine-learning procedure would ‘force’ age to be selected, while
   imposing a stringent penalty to all other features so that the
   variables selected do not correlate with age ([113]Extended Data Fig.
   5a). The mean absolute error (MAE) for prediction of multimorbidity was
   0.41 ([114]Extended Data Fig. 5b). The top features with the largest
   coefficients included iAge, high cholesterol and BMI ([115]Extended
   Data Fig. 5c). 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 ([116]Extended Data Fig.
   5d). Collectively, these results show that the inflammatory clock is a
   metric for overall health linked to multiple diseases associated with
   aging.

   To longitudinally assess the importance of iAge in age-related
   functional deterioration, we calculated iAge in a subgroup of 29 older
   adults in 2010 and a frailty score including the time-up-and-go
   test^[117]35 was measured in 2017 for the same participants. 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 (R^2 = 0.81, P < 0.001; [118]Fig. 1c). Notably, the contribution
   of iAge to frailty score was significantly stronger than that of
   calendar age ([119]Fig. 1d).

Lower inflammatory clock index in centenarians.

   Next, we explored the relationship between inflammatory age and
   exceptional longevity. We computed an inflammatory index in an
   additional cohort of 37 individuals, 18 of which were 50–79 years old
   and 19 were centenarians, except for 1 individual who was 99 years old
   at the time of blood extraction. To do so, we first ranked both cohorts
   in terms of cAge and iAge. For each participant, we then computed the
   difference of their cAge rank and iAge rank and used this difference
   (iAge index) to stratify participants into high and low, if they were
   above or below the population rank mean, respectively. Last, we
   calculated enrichment for exceptional longevity in the low iAge index
   group (individuals with most protective phenotypes) by hypergeometric
   test. 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. In contrast, there were 77% (14 out of 18) of controls in
   the high rank versus 23% in the low rank group ([120]Fig. 1e), which
   indicates that regardless of cAge, centenarians have a protective iAge
   index phenotype. This indicates that iAge is associated with
   exceptional longevity.

   To further validate the clinical implication of the iAge score, we
   leveraged data from the Framingham Heart Study^[121]36, a longitudinal
   cohort tracking thousands of individuals for decades. 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
   ([122]Methods). 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).

iAge is correlated with immunosenescence.

   Canonical acute inflammation proteins such as C-reactive protein and
   interleukin (IL)-6 have been associated with immunosenescence in
   previous studies^[123]37,[124]38, but the relationship with systemic
   chronic inflammation (SCI) has not yet been established. To investigate
   this link, we first used the frequency of naive CD8^+ T cells, which
   are well-known markers for immunosenescence, and estimated the
   contribution of iAge after controlling for Age, CMV and sex by a
   multiple regression model. 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; [125]Fig. 2a).

Fig. 2 |. The inflammatory clock of aging correlates with immunosenescence.

   Fig. 2 |
   [126]Open in a new tab

   a, A hallmark of immunosenescence (naive CD8^+ T cells) was used to
   examine the potential contribution of iAge to this condition. In a
   multiple regression model, iAge was significantly correlated with the
   frequency of naive CD8^+ T cells to a similar extent to CMV positivity.
   cAge was the strongest contributor (P < 10^−15), followed by CMV (P <
   10^−5), iAge (P < 10^−3) and sex (P = 0.012). ***P <0.001, **P < 0.01,
   *P < 0.05. P values are derived from hypothesis testing, where the null
   hypothesis is that the variable has no correlation with the dependent
   variable. b, The activation of multiple intracellular pathways was
   measured using the phosphoflow method in B cells, CD4^+ T cells
   (CD45RA^+ and CD45RA^− subsets), CD8^+ T cells (CD45RA^+ and CD45RA^−
   subsets) and in monocytes. In this method, PBMCs are plated ex vivo and
   activated with a variety of cytokine stimuli to measure phosphorylation
   events in STAT proteins (specifically STAT1, STAT3 and STAT5). iAge is
   consistently negatively correlated with B-cell and T-cell responses to
   cytokine stimuli and positively correlated with monocyte responses (P <
   10^−5 by self-contained test of modified Fisher’s combined
   probability).

   To examine the effect of iAge in the immune response, we used a
   well-established multiplexed assay of phosphorylated STAT molecules in
   PBMCs following different stimulations in vitro^[127]39,[128]40. PBMCs
   were stimulated with the cytokines IFN-α, IL-6, IL-10 and IL-2 and
   subsequently stained with antibodies specific for phosphorylated forms
   of STAT proteins. The fold increase of phospho-STAT1, phospho-STAT3 and
   phospho-STAT5 was calculated from a variety of immune cells of 818
   individuals, totaling 96 conditions. We conducted multiple regression
   analysis controlling age, CMV and sex ([129]Methods). 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; [130]Fig. 2b). 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.

CXCL9 is an important component of iAge and correlates with cardiovascular
aging in healthy adults.

   To isolate the factors contributing the most to iAge, we computed the
   most variable Jacobians (first-order partial derivative of iAge). We
   found both positive and negative contributors to iAge ([131]Fig. 3a),
   where the top 15 most variable Jacobians were CXCL9, EOTAXIN, Mip-1α,
   LEPTIN, IL-1β, IL-5, IFN-α and IL-4 (positive contributors) 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).
   Notably, canonical markers of acute infection such as IL-6 and tumor
   necrosis factor-α were not major contributors to iAge, indicating that,
   except for IL-1β, infection-driven inflammatory markers of the acute
   inflammatory response do not contribute to age-related chronic
   inflammation. Given that the most positive contributor to iAge was
   CXCL9, we compared CXCL9 levels between different age groups and found
   significant increases in this protein with age (P < 10^−15, by one-way
   analysis of variance (ANOVA) test) starting at the age of 60 years
   ([132]Fig. 3b and [133]Extended Data Fig. 6). Taken together, these
   results suggest that CXCL9 is an important factor in age-related
   chronic inflammation.

Fig. 3 |. CXCL9 is a major contributor to iAge.

   Fig. 3 |
   [134]Open in a new tab

   a, Decomposition of the inflammatory score was conducted by estimating
   the most variable Jacobians (first-order partial derivative of the
   inflammatory clock). Boxes represent 25th and 75th percentiles around
   the median (line); whiskers represent 1.5× interquartile range. Both
   positive and negative contributors to the inflammatory clock are
   observed. b, The top 15 most variable Jacobians were CXCL9, EOTAXIN,
   Mip-1α, LEPTIN, IL-1β, IL-5, IFN-α and IL-4 (positive contributors),
   and TRAIL, IFN-γ, CXCL1, IL-2, TGF-α, PAI-1 and LIF (negative
   contributors). Significant differences in the levels of CXCL9 were
   observed between age groups (P < 0.001, by one-way ANOVA). The pairwise
   differences between groups were evaluated with the Tukey’s honest
   significant differences test. Significant differences were shown for
   older age groups (60–80 years and >80 years) and younger age groups
   (<20 years, 20–40 years, 40–60 years). ***P < 0.001; **P < 0.01; *P <
   0.05; ^#P < 0.1. Exact P values for each pairwise comparisons are as
   follows: <20 versus 20–40, 0.72; <20 versus 40–60, 0.99; <20 versus
   60–80, 0.09; <20 versus >80, 0; 20–40 versus 40–60, 0.13; 20–40 versus
   60–80, 3.5 × 10^−6; 20–40 versus >80, 0; 40–60 versus 60–80, 0.023;
   40–60 versus >80, 0; 60–80 versus >80, 7.7 × 10^−6. Boxes represent
   25th and 75th percentiles around the median (line); whiskers denote
   1.5× interquartile range. c,d, In a validation study, 97 healthy adults
   (aged 25–90 years) well matched for cardiovascular risk factors were
   selected from a total of 151 recruited participants. Cardiovascular age
   was estimated using aortic PWV and RWT. Using multiple linear
   regression analysis after adjusting for age, sex, BMI, heart rate,
   systolic blood pressure, fasting glucose and total cholesterol to HDL
   ratio, positive correlations were obtained between CXCL9 and PWV (R =
   0.22) and RWT (R = 0.3) (P < 0.01), and negative correlations were
   observed between LIF and PWV (R = −0.27) (c) and RWT (R = −0.22) (d). P
   values are derived from hypothesis testing, where the null hypothesis
   is that the variable has no correlation with the dependent variable.
   e,f, Direct comparisons between CXCL9 and the two cardiovascular aging
   phenotypes (PWV (e) and RWT (f)) are depicted. No other variable
   included in the models had high co-linearity as suggested by variance
   inflation factors (VIF) <3 for each factor.

   To validate these results and investigate the previously reported role
   CXCL9 in cardiovascular aging^[135]41–[136]44, we conducted a follow-up
   study in an independent cohort of 97 extremely healthy adults (aged
   25–90 years) matched for cardiovascular risk factors (including
   conserved levels of high-sensitivity C-reactive protein;
   [137]Supplementary Table 1), selected from a total of 151 recruited
   participants using strict selection criteria ([138]Methods). In this
   healthy cohort, inflammation markers were measured using a 48-plex
   cytokine panel and only 6 out of 48 circulating immune proteins were
   significantly correlated with age (P < 0.05). Among these, CXCL9 was
   again the largest contributor to age-related inflammation
   ([139]Extended Data Fig. 7), supporting the findings observed in the
   1KIP cohort. In addition, IL-11Rα, CXCL10 and hepatocyte growth factor
   (HGF) increased with age, whereas CXCL1 and LIF decreased
   ([140]Extended Data Fig. 7). These changes were in the same direction
   as those observed in the 1KIP cohort.

   Individuals in the validation cohort were subjected to cardiovascular
   assessment, including pulse wave velocity (PWV) testing, a measure of
   vascular stiffness and relative wall thickness (RWT), a surrogate
   measure of cardiac remodeling ([141]Methods). 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. At P < 0.01, we
   found a modest positive correlation between CXCL9 and PWV (R = 0.22)
   and RWT (R = 0.3) ([142]Fig. 3c–[143]f). We also found a negative
   correlation between LIF and PWV (R = −0.27) and RWT (R = −0.22).

   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 factors^[144]45–[145]47, taken together, these results show that
   soluble blood markers CXCL9 and LIF could be used as early biomarkers
   to assess cardiovascular disease risk in otherwise healthy individuals.

CXCL9 increases with age in human blood endothelial cells.

   Long-standing evidence has suggested a role for the endothelium in the
   etiology of hypertension and arterial stiffness^[146]48,[147]49. 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
   endothelia^[148]50–[149]52. We explored the potential contribution of
   CXCL9 toward cardiovascular aging through endothelial cells. First, we
   assessed levels of CXCL9 in young and old individuals by isolating
   their blood endothelial progenitor cells (BECs) ([150]Extended Data
   Fig. 8a). Quantitative PCR analysis of BECs from young and old
   individuals showed a significant increase in CXCL9 levels in older
   compared to younger subjects ([151]Fig. 4b). Furthermore, a
   comprehensive characterization of BECs from both cohorts showed
   impairment of endothelial function in older individuals when compared
   to younger individuals. To measure endothelial function, we examined
   the endothelial cells’ (ECs) ability to form networks of tubular
   structures^[152]53,[153]54, produce nitric oxide (NO)^[154]55 and
   incorporate acetylated low-density lipoprotein (Ac-LDL)^[155]56;
   together, the assays robustly assess the health of ECs. Comparing ECs
   from older and younger individuals, we found that BECs from older
   patients showed reduced capacity to form networks of tubular structures
   ([156]Extended Data Fig. 8b and [157]Fig. 4b), reduced capacity to
   produce NO ([158]Fig. 4c) and a reduced capacity to incorporate Ac-LDL
   ([159]Fig. 4d).

Fig. 4 |. CXCL9 is an important regulator of endothelial cell aging.

   Fig. 4 |
   [160]Open in a new tab

   a, Quantitative PCR data show increased expression of CXCL9 in BECs of
   older individuals compared to younger individuals (P = 0.0075). b,
   Significant differences in tube formation capacity are observed in BECs
   from older and younger individuals (P = 0.0323). c, Quantification of
   NO production shows impaired capacity of BECs from older individuals to
   produce NO when compared to younger individuals in response to
   acetylcholine (Ach) (adjusted P value (P[adj]) of BECs (young) versus
   BECs (old), P <0.0001; P[adj] value of BECs (young) Ach versus BECs
   (old) Ach, 0.0002). d, Quantification of LDL uptake show impaired
   capacity of BECs from older individuals to uptake Ac-LDL when compared
   to younger individuals (P[adj] = 0.0002). e–g, Quantification of number
   of tubes, LDL uptake and NO production in response to Ach in Scramble
   and CXCL9-KD iPSC-ECs shows a significant improvement in aging
   phenotypes in ECs at passage 6 and 8 with silencing of the CXCL9 gene.
   P[adj] values for P6 (Scramble) versus P6 (CXCL9 shRNA) = 0.008 (e); P8
   (Scramble) versus P8 (CXCL9 shRNA) = 0.0475. P[adj] values for P6
   (Scramble) versus P6 (CXCL9 shRNA) = 0.044; P8 (Scramble) versus P8
   (CXCL9 shRNA) = 0.001 (f). P[adj] values for P6 (Scramble) Ach versus
   P6 (CXCL9-KD) Ach = 0.0116; P8 (Scramble) Ach versus P8 (CXCL9-KD) Ach
   = 0.0001 (g). Scramble are hiPSCs infected with lentivirus carrying
   nonsense-sequence shRNA. CXCL9-KD are hiPSCs infected with lentivirus
   carrying sequence-specific shRNA to knockdown expression of CXCL9. All
   data are represented as mean ± s.e.m., n = 3, *P < 0.05, **P < 0.01,
   ***P < 0.001; ****P < 0.0001; NS, not significant. Statistical analyses
   were performed using Student’s t-test or one-way ANOVA corrected with
   the Bonferroni method.

   Similar experiments were conducted in mice. Aortas from young (3–4
   months) and old mice (2 years) were excised, digested and cultured in
   EC medium ([161]Extended Data Figure 8c). Once confluent, ECs from both
   young and old mice were assessed for CXCL9 expression and function. As
   expected, ECs isolated from old mice showed higher levels of CXCL9
   ([162]Extended Data Fig. 8d), while at the same time showed impaired EC
   function as evident by decreased tube formation ([163]Extended Data
   Fig. 8e,[164]f). These results demonstrate a concomitant increase in
   CXCL9 in the endothelia and EC dysfunction associated with aging both
   in humans and mice.

Inhibition of CXCL9 rescues endothelial cell dysfunction.

   Next, we investigated how the increase in CXCL9 in older ECs is related
   to endothelial dysfunction. In these experiments, we used a
   well-established model for endothelial aging^[165]57,[166]58 by
   generating human induced pluripotent stem cells (hiPSCs) from
   fibroblasts obtained from five independent human donors^[167]59 and
   subsequently differentiated them into endothelial cells
   (hiPSC-ECs)^[168]60. The CXCL9 receptor, Gα[i] protein-coupled protein
   CXCR3, was expressed in ECs but not in cardiomyocytes ([169]Extended
   Data Fig. 9). We used lentiviral infection of CXCL9 sequence-specific
   short hairpin (sh)RNA to knockdown expression of CXCL9 in hiPSCs
   (CXCL9-KD). As a control, we also infected hiPSCs with
   nonsense-sequence shRNA (Scramble) and subsequently, both cultures were
   differentiated to ECs. CXCL9 expression, as analyzed by quantitative
   PCR, was reduced by ~75% in CXCL9-KD hiPSC-ECs compared to Scramble
   hiPSC-ECs (not shown). CXCL9-KD and Scramble hiPSC-ECs were serially
   cultured to passage 8 in a time-course experiment to mimic cellular
   aging.

   We then investigated the functional impact of the observed phenotype in
   a model for angiogenesis by measuring EC capacity to form networks of
   tubular structures^[170]54, the production of NO and uptake of Ac-LDL.
   iPSC-ECs at passage 8 showed significantly impaired tube formation when
   compared to early passages of iPSC-ECs, including passage 0 and 2. As
   early as passage 4, ECs lose their capacity to form tubes, which can be
   partially restored when CXCL9 is knocked down ([171]Fig. 4e and
   [172]Extended Data Fig. 10). Next, we assessed the capacity of these
   early- or late-passaged iPSC-ECs to produce NO or uptake acetylated
   LDL. 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 ([173]Fig. 4f,[174]g).

   Notably, the knockdown of CXCL9 (CXCL9-KD) in iPSC-ECs rescued the EC
   dysfunction in late passages of iPSC-ECs (P6 and P8), suggesting an
   important role of CXCL9 and the EC phenotype. 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).
   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. Altogether, these results are
   consistent with previous findings showing age-dependent endothelial
   dysfunction, fewer T cells and impaired vasodilation with advanced age
   in animal models, and requirement of angiogenesis in migration and
   proliferation of ECs^[175]61. 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.

CXCL9 governs inflammation and proliferation in aged EC.

   In a time-course experiment where CXCL9-KD and Scramble hiPSC-ECs were
   serially cultured to passage 8, RNA was also extracted at every other
   passage for bulk RNA-sequencing (RNA-seq) transcriptome analysis
   ([176]Methods). 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 ([177]Fig. 5a). 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 ([178]Fig. 5b). This profile is
   indicative of an early cellular senescence phenotype^[179]62–[180]66.
   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 ([181]Fig. 5c–[182]e). Examples of these inflammatory and
   proliferation hallmark pathways include the IFN-γ response and E2F
   targets, respectively ([183]Fig. 5d (Scramble) and [184]Fig. 5e
   (CXCL9-KD)). Such functional impact of increased inflammation and
   decreased proliferation in endothelial aged cells could contribute to
   the impaired tube formation and endothelial dysfunction observed in the
   experiments described previously.

Fig. 5 |. Early cellular senescence and loss of angiogenesis capacity in
iPSC-derived aging endothelia is reversed by silencing CXCL9.

   Fig. 5 |
   [185]Open in a new tab

   a, Pathway enrichment analysis and tube network formation of Scramble
   versus CXCL9-KD were analyzed. hiPSCs infected with lentivirus carrying
   nonsense-sequence shRNA (Scramble) and hiPSCs infected with lentivirus
   carrying sequence-specific shRNA to knockdown expression of CXCL9
   (CXCL9-KD) were both induced to ECs ([186]Methods). RNA-seq analysis
   was conducted on cells at passage 0, 2, 4, 6 and 8 for both conditions.
   CXCL9 messenger RNA in Scramble was highly upregulated as early as
   passage 4, whereas CXCL9 mRNA expression in CXCL9-KD did not
   significantly change with in vitro cellular aging. b, Pathway
   enrichment comparing Scramble at passage 0 and passage 8. Upregulated
   inflammatory pathways and downregulated proliferation pathways are
   depicted (P8 versus P0). c, Comparing Scramble at P8 with CXCL9-KD at
   P8 shows that silencing of CXCL9 leads to a complete reversal of the
   early EC senescence phenotype. An example of inflammatory pathway
   (IFN-γ) and an example of proliferation pathway (E2F targets) is shown
   in d. d, Relative expression of genes in the hallmark pathways for
   Scramble at passage 0, 2, 4, 6 and 8 (S0, S2, S4, S6 and S8) are shown.
   e, Example of inflammatory pathway (IFN-γ) and an example of
   proliferation pathway (E2F targets) for CXCL9-KD at passage 0, 2, 4, 6
   and 8 (KD0, KD2, KD4, KD6 and KD8) are shown. ***P < 0.001; **P < 0.01;
   *P < 0.05.

CXCL9 impairs vascular function and contributes to arterial stiffness.

   To further explore the role of CXCL9 in cardiovascular aging in our in
   vitro EC aging model, we focused on molecules that are related to a
   surrogate of cardiovascular risk, arterial stiffness. EC dysfunction
   has been shown to strongly affect arterial stiffness via cellular
   adhesion molecules (CAMs), matrix metalloproteinases (MMPs) and
   collagen molecules (COLs)^[187]67–[188]71. We compared gene expression
   levels of all CAM genes (n = 13), MMP genes (n = 12) and COL genes (n =
   23) in Scramble hiPSC-ECs at passage 0 versus passage 8. We found a
   substantial upregulation of CAM, MMP and COL genes related to arterial
   stiffness at passage 8 ([189]Fig. 6a). Except for some COL genes, this
   vascular stiffness gene profile is reversed in CXCL9-KD cells, which
   suggests that silencing of this single gene can restore the EC
   phenotype ([190]Fig. 6b).

Fig. 6 |. CXCL9 promotes a vascular stiffness gene expression signature in
the aging endothelium and impairs endothelial function.

   Fig. 6 |
   [191]Open in a new tab

   The expression levels of hallmark vascular stiffness genes—CAMs, MMPs
   and COLs—were analyzed in Scramble and CXCL9-KD aging cells. a, CAMs,
   MMPs and COLs are highly expressed in Scramble passage 8 compared to
   passage 0. b, Knockdown of CXCL9 completely restores the expression of
   CAMs and MMPs, but not COLs. c, Line graph of percent relaxation of
   mouse thoracic aortic sections incubated with increasing concentrations
   of CXCL9 shows impaired vascular reactivity to acetylcholine,
   suggesting that CXCL9 dampens vascular function. d, A similar trend is
   observed when CXCL9 is given to either young or old mice. CXCL9
   disrupts the relaxation supposedly induced by acetylcholine. All data
   are represented as mean ± s.e.m., n = 3, *P[adj] value of young mice
   (PBS) versus young mice (CXCL9) = 0.0237; ^#P[adj] value of young mice
   (PBS) versus old mice (PBS) = 0.0003, ^$P[adj] value of young mice
   (PBS) versus young mice (CXCL9) < 0.0001. Statistical analyses were
   performed using two-way ANOVA followed by a Bonferroni post hoc test; n
   = 3 (three separate segments of aortas).

   As genes related to arterial stiffness are upregulated in Scramble
   passage 8 but their expression is largely attenuated in CXCL9-KD, we
   hypothesized that there might be a causal effect between arterial
   stiffness and increase expression of CXCL9. To test this, we incubated
   mouse thoracic aortic sections with increasing concentrations of
   recombinant mouse CXCL9 and assessed cellular contractibility by
   incubating vessels with the prostaglandin agonist U46619 and measured
   relaxation curves by isometric myography^[192]72. As shown in [193]Fig.
   6c, [194]a dose-dependent effect of CXCL9 is observed on vasorelaxation
   in treated aortas versus controls, which validates our findings of the
   effect of CXCL9 on the arterial stiffness gene expression phenotypes.
   The same experiment was conducted in young versus old mice using only
   one dose of CXCL9 (1 ng ml^−1). As seen in [195]Fig. 6d, aortic rings
   excised from old mice showed impaired vascular relaxation when compared
   to young mice in response to acetylcholine. However, aortic rings from
   both young and old mice when incubated with CXCL9 exhibited impaired
   vascular relaxation. These results demonstrate a central role for CXCL9
   in vascular dysfunction, which likely contributes to arterial stiffness
   and premature aging in vivo.

Age-related elevation in CXCL9 leads to endothelial cell senescence.

   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. To
   directly explore the role of CXCL9 in cellular senescence, we assessed
   the proliferation rate and cellular senescence markers in Scramble and
   CXCL9-KD iPSC-ECs at different passages. First, we assessed the kinetic
   profile of iPSC-ECs from Scramble and CXCL9-KD cells every 24 h for up
   to 4 d. Briefly, equal numbers of Scramble and CXCL9-KD iPSC-ECs from
   passage 0 and passage 8 were seeded in a 96-well plate and cells were
   quantified using a Cytation five-cell imaging multimode reader, where
   individual cells were counted every 24 h by imaging
   4′,6-diamidino-2-phenylindole (DAPI)-positive cells. As seen in
   [196]Fig. 7a, the kinetic profile of iPSC-EC proliferation over 4 d
   showed a significant increase in the proliferation rate in P0 iPSC-ECs
   when compared to P8 iPSC-ECs. Notably, when CXCL9 was inhibited in P8
   iPSC-ECs (CXCL9-KD), the proliferation rate showed a significant
   increase when compared to Scramble-treated cells.

Fig. 7 |. CXCL9 regulates endothelial cell senescence and capillary network
formation in vivo.

   Fig. 7 |
   [197]Open in a new tab

   a, Growth curves over 4 d show recovery of cell proliferation in
   CXCL9-KD iPSC-ECs in later passages when compared to Scramble iPSC-ECs
   (P[adj] value of P8 Scramble (day 4) versus P8 CXCL9-KD (day 4) =
   0.0232). b, Cellular senescence activity assay shows restoration of
   SA-β-gal activity in CXCL9-KD iPSC-ECs at later passages when compared
   to Scramble iPSC-ECs (P[adj] value of P6 (Scramble) versus P6 (CXCL9
   shRNA) = 0.0406; P[adj] value of P8 (Scramble) versus P8 (CXCL9 shRNA)
   = 0.0278). c, Representative immunohistochemical images showing CD31^+
   human capillaries from serially passaged Scramble and CXCL9-KD
   iPSC-ECs. Arrows denote CD31 staining on iPSC-EC indicating capillary
   formation. d, Quantification of CD31^+ capillaries show improved
   capacity of late passaged CXCL9-KD iPSC-ECs to form in vivo capillary
   networks (P[adj] value of P0 (Scramble) versus P8 (Scramble) <0.0001;
   P[adj] value of P8 (Scramble) versus P8 (CXCL9 shRNA) = 0.0487). All
   data are represented as mean ± s.e.m., n = 3, *P < 0.05, ****P < 0.001.
   Statistical analyses were performed using one-way ANOVA corrected with
   the Bonferroni method. Scale bars, 100 μm.

   Next, we assessed the senescence-associated β-galactosidase (SA-β-gal)
   activity in Scramble or CXCL9-KD iPSC-ECs at different passages to
   determine cellular senescence in these cells. Cell lysates were
   collected and SA-β-gal activity measured using a standard fluorometric
   substrate. As expected, Scramble iPSC-ECs showed a passage-dependent
   increase in SA-β-gal activity, suggesting an increase in cellular
   senescence. However, in CXCL9-KD iPSC-ECs the SA-β-gal activity at
   later passages was significantly reduced when compared to Scramble,
   suggesting a direct link between CXCL9 expression and cellular
   senescence ([198]Fig. 7b).

   Finally, we examined the capacity of Scramble and CXCL9-KD iPSC-ECs to
   form capillaries in vivo when injected subcutaneously in
   immunodeficient mice^[199]73. Early and late-passaged iPSC-ECs from
   both Scramble and CXCL9-KD groups were placed in Matrigel and injected
   subcutaneously into the lower abdominal region of SCID mice. Following
   2 weeks, Matrigel plugs were excised, fixed and stained for human CD31.
   As seen in [200]Fig. 7c, immunohistochemical images showed formation of
   capillaries in Scramble and CXCL9-KD iPSC-ECs at P0; however, P8 (late
   passaged) Scramble iPSC-ECs failed to show sprouting in vivo ([201]Fig.
   7c,[202]d). In contrast, P8 CXCL9-KD iPSC-ECs showed significantly
   improved in vivo angiogenesis, suggesting a critical role of CXCL9 in
   EC senescence.

Discussion

   In this study, we conducted extensive immune monitoring in a large
   cohort of 1,001 individuals to identify immune biomarkers of aging and
   establish reference values for age-related systemic chronic
   inflammation. We used artificial intelligence to create a compact
   representation of these biomarkers and derived an ‘inflammatory clock’
   of aging, which takes into account the nonlinear relationship and
   redundancy of the cytokine network. This metric tracked with multiple
   aging phenotypes in the general population and thus, has strong
   potential for translational medicine, as it could be used as a
   diagnostic tool for identifying those at risk for both noncommunicable
   and infectious diseases.

   Our nonlinear GAE method was optimal for the identification of iAge and
   its contributors. As with other deep-learning methods, GAE is capable
   of capturing complex relationships between analytes. Similar methods
   striving to extract signatures of aging have been described in
   different systems ranging from genome-wide association studies to
   proteomics. We summarize a few notable aging clocks in
   [203]Supplementary Table 3. In brief, an epigenetic clock using markers
   measuring DNA methylation on CpG sites has been used to calculate an
   epigenetic age that was able to predict all-cause
   mortality^[204]74,[205]75. It has also been associated with age-related
   diseases such as frailty, Alzheimer’s disease, Parkinson’s disease and
   cancer. Other clocks such as transcriptomic and microRNA clocks have
   also been shown to successfully capture aspects of the aging process
   that are different from epigenetic clocks. Instead of being associated
   with all-cause mortality or disease, transcriptomic clocks are
   associated with IL-6, albumin, lipids and glucose levels^[206]76. There
   have also been attempts to derive proteomic clocks and metabolomic
   clocks^[207]77–[208]82 of clinical relevance; however, iAge allows for
   new discoveries in the immune system. iAge derived from immunological
   cytokines gives us an insight into the salient cytokines that are
   related to aging and disease. A notable difference compared to other
   clocks is that iAge is clearly actionable as shown by our experiments
   in CXCL9 where we can reverse aging phenotypes. More practical
   approaches range from altering a person’s exposomes (lifestyle) and or
   the use of interventions to target CXCL9 and other biomarkers described
   here.

   Recent advancements in deep learning beyond traditional
   machine-learning methods have provided enormous opportunities to model
   biological age. Some of the most popular deep-learning architectures
   used to estimate biological age have been recurrent neural networks
   (RNNs), convolutional neural networks (CNNs), generative adversarial
   networks (GANs) and deep artificial neural networks (ANNs). RNNs have
   been used on face attributes and physical activities to estimate
   biological age^[209]83. Although the modality is not in the realm of
   biological markers, RNNs have potential to garner results in biological
   data that require positional relationships such as epigenetic age. CNNs
   and GANs have both been used to abstract facial attributes to predict
   chronological age^[210]84,[211]85. GANs and CNNs are exceptional in
   abstracting images to distill useful information. Future applications
   of GANs and CNNs can be applied in other biological images such as
   magnetic resonance imaging; however, for now, these models are proof of
   concepts that they can accurately estimate cAge; they might not
   necessarily predict the health or lifespan of individuals. The
   deep-learning models that have been applied to modality used in this
   paper are the deep ANNs. ANNs have been applied to blood biochemistry
   markers and cell counts to derive biological age^[212]86,[213]87. The
   results showed that such clocks are able to predict all-cause
   mortality, potentially finding biomarkers to intervene and steer
   individuals toward a healthier life.

   Some of the limitations of biological clocks in general is that they do
   not directly provide the mechanism by which they work. While it is
   possible to infer causality between aging and molecular biomarkers
   especially in the context of longitudinal or time-series data,
   individual biomarkers selected from biological aging clocks need to be
   experimentally tested to elucidate the underlying mechanism, as we have
   done in this study. Our GAE algorithm, a deep-learning method that
   efficiently deals with the network structure and nonlinear behavior of
   the inflammatory response, can extract high-level complex abstractions
   as ‘data representations’ using nonlinear functions and is well suited
   for the analysis of complex systems where most behaviors are nonlinear,
   context-dependent and organized in a distributed hierarchical
   fashion^[214]88. 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-encoder^[215]89 ([216]Extended
   Data Fig. 3b). The correlation between chronological age and iAge was
   0.78 (P < 10^−16) ([217]Fig. 1a), which is lower than that of the
   recently reported ‘proteomic age’ metric (R = 0.92)^[218]90. However,
   in contrast with proteomic age, which did not report disease
   associations, we find that iAge tracks with multiple diseases and
   immunosenescence. In particular, we find a strong association between
   elevated iAge and poor acute ex vivo immune responses, which is
   consistent with previous reports showing that high levels of baseline
   inflammatory markers correlate with weaker responses to hepatitis B and
   herpes zoster vaccine formulations^[219]15,[220]91. Similarly,
   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 aging^[221]28.
   Despite the proven utility of cytokine stimulation assays used in our
   study with respect to an individual’s overall immune
   competence^[222]5,[223]24,[224]25, 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.
   For example, 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. Additional cell subsets that are poorly
   activated by the cytokines used in our study are type 1 helper T cells
   CD4^+ T cells that can be activated by IL-12 and IL-18 or type 17
   helper CD4^+ T cells, which respond to other cytokine stimulations such
   as IL-1β or IL-18 in concert with IL-23 to produce type 17 helper
   T-cell-associated cytokines.

   Recent findings from our group^[225]16,[226]28 placed the immune system
   in the center of aging phenotypes. Similar to our previous findings,
   our inflammatory clock metric specifically hones in on the crucial role
   that the immune system and SCI play in the accumulation of diseases of
   aging, with a focus on cardiovascular aging. Unlike other metrics of
   ‘biological’ age, which do not offer a clinically relevant
   metric^[227]92, we demonstrate that iAge predicts multimorbidity and
   mortality and therefore can be used as a biological surrogate of
   age-related health versus disease. iAge is directly associated with
   multiple disease phenotypes, including cardiovascular aging, frailty,
   immune decline and exceptional longevity. In our recent work^[228]16,
   we combined cellular phenotypes to describe subject- and
   population-level immune aging phenotypes (IMM-AGE), which correlated
   with iAge. This suggests that future research should leverage both
   immune-age scores to propose a unified metric that reflects multiple
   aspects of immune aging, thus potentially providing a better clinical
   predictive value.

   A major contributor to the inflammatory clock, CXCL9, was validated as
   an indicator of cardiovascular pathology independently of age. CXCL9 is
   a T-cell chemoattractant induced by IFN-γ and is mostly produced by
   neutrophils, macrophages and ECs. Despite previous data showing that
   CXCL9 and other CXCR3 ligands are significantly elevated in
   hypertension and in patients with left ventricular dysfunction^[229]41,
   we find that CXCL9 is mainly produced by aged endothelium and predicts
   subclinical levels of cardiovascular aging in nominally healthy
   individuals. Some studies in humans have found CXCL9 to increase with
   age^[230]93–[231]98 and an age-dependent profile has also been observed
   in Chagas disease^[232]99 and atopic dermatitis^[233]100. Notably,
   CXCL9 has also been shown to be associated with falls in the older
   population^[234]101,[235]102, which parallels our results predicting
   frailty. 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. Notably, we did
   not find any significant correlation between known disease risk factors
   reported in the study (BMI, smoking, dyslipidemia) and levels of CXCL9
   gene or protein expression. We thus hypothesize that one root cause of
   CXCL9 overproduction is cellular aging per se, which can trigger
   metabolic dysfunction (as shown in many previous studies of aging) with
   production of DAMPs. Examples of these include adenosine, adenine and
   N4-acetylcytidine as demonstrated in our previous longitudinal studies
   of aging^[236]5. These DAMPs can then act through the inflammasome
   machinery, such as NLRC4, to regulate multiple inflammatory signals,
   including IL-1β and CXCL9 (ref. ^[237]103).

   Our data also place the endothelium as a central player in
   cardiovascular aging, consistent with previous findings^[238]104 and
   they also suggest that ECs may be one source of inflammation, but it is
   also possible that cardiomyocytes play a role as in models of acute
   myocardial infarction there is activation of the inflammasome NLRP3 in
   these cells^[239]105,[240]106. As ECs but not cardiomyocytes expressed
   the CXCL9 receptor, CXCR3 ([241]Extended Data Fig. 9), 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. In this model, increasing doses of
   CXCL9 and expression of its receptor in these cells leads to cumulative
   deterioration of endothelial function in aging. Moreover, silencing of
   CXCL9 in ECs resulted in a reversal of the high inflammation/low
   proliferation early senescence phenotype, which suggests by tackling
   CXCL9 it may be possible to delay onset of EC senescence. It is also
   notable that IFN-γ, a direct agonist to CXCL9, did not increase in
   expression in our cellular aging RNA-seq experiment, suggesting that
   there are triggers of CXCL9 (other than IFN-γ) that play a role in
   cellular senescence in the endothelium that are currently unknown.
   However, in our 1KIP study, IFN-γ was in fact the second-most important
   negative contributor to iAge, which could be explained by the
   cell-priming effect of cytokines, where the effect of a first cytokine
   alters the response to a different one^[242]107–[243]109. In a more
   recent and refined version of this model (the high baseline-low output
   model for chronic inflammation and the acute response) we show that
   sustained levels of inflammatory mediators lead to nonfunctional
   constitutive phosphorylation of signaling pathways with saturation of
   phosphorylation sites in signaling proteins (such as the JAK–STAT
   system), which results in a lowered δ phosphorylation in response to
   acute stimuli and subsequent dampening of the immune response to
   infections or vaccination^[244]28.

   In conclusion, by applying artificial intelligence methods to deep
   immune monitoring of human blood we generate an inflammatory clock of
   aging, which can be used as a companion diagnostic to inform physicians
   about patient’s inflammatory burden and overall health status,
   especially in those with chronic diseases. Furthermore, our immune
   metric for human health can identify within healthy older adults with
   no clinical or laboratory evidence of cardiovascular disease, those at
   risk for early cardiovascular aging. Lastly, we demonstrate that CXCL9
   is a master regulator of vascular function and cellular senescence,
   which indicates that therapies targeting CXCL9 could be used to prevent
   age-related deterioration of the vascular system and other
   physiological systems as well.

Methods

Ethics declaration.

   [245]ClinicalTrials.gov identifiers for the vaccine studies are
   [246]NCT01827462, [247]NCT02133781, [248]NCT03020498, [249]NCT03020537,
   [250]NCT01987349, [251]NCT03022396, [252]NCT03022422, [253]NCT03022435,
   [254]NCT03023176 and [255]NCT02141581. 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.

The Stanford 1000 Immunomes study cohort.

   The Stanford 1KIP consists of 1,001 ambulatory individuals (339 males
   and 662 females) recruited at Stanford University between the years
   2007 and 2016 for various studies of aging and vaccination (n =
   605)^[256]5,[257]21–[258]29 and for an independent study of chronic
   fatigue syndrome^[259]30, from which we utilized data from the control
   set of participants only (n = 397). The current study uses blood
   samples collected before vaccination and where results of the flu
   vaccine trial have been published^[260]24.

Aging and vaccination study cohort.

   Study participants were enrolled in an influenza vaccine study at the
   Stanford-LPCH Vaccine Program between 2007 and 2016. Baseline samples
   were obtained from all individuals before vaccination with influenza
   vaccine. The protocol for this study was approved by the Institutional
   Review Board of the Research Compliance Office at Stanford University.
   Informed consent was obtained from all participants. All individuals
   were ambulatory. At the time of initial enrollment volunteers had no
   acute systemic or serious concurrent illness, no history of
   immunodeficiency, nor any known or suspected impairment of immunologic
   function, including clinically observed liver disease, diabetes
   mellitus treated with insulin, moderate to severe renal disease, blood
   pressure >150/95 at screening, chronic hepatitis B or C or recent or
   current use of immunosuppressive medication. In addition, on each
   annual vaccination day, none of the volunteers had been recipients or
   donors of blood or blood products within the past 6 months and 6 weeks,
   respectively and none showed any signs of febrile illness on the day of
   baseline blood draw. Peripheral blood samples were obtained from
   venipuncture and mononuclear cells were separated and stored at the
   Stanford Clinical and Translational Research Unit. Whole blood was used
   for gene expression analysis. Serum was separated by centrifugation of
   clotted blood and stored at −80 °C before CMV serology, cytokine and
   chemokine determination.

Chronic Fatigue Syndrome Study cohort.

   Study participants were recruited from Northern California from 2 March
   2010 to 1 September 2011. Their peripheral blood was drawn between 8:30
   am and 3:30 pm on the day of enrollment. Samples were collected at
   baseline for each participant (no exercise before blood sampling). In
   addition, as each patient with myalgic encephalomyelitis (ME)/chronic
   fatigue syndrome (CFS) was being recruited into the study, two
   corresponding, age and sex-matched controls, were contemporaneously
   enrolled until the target sample size of 200 patients and 400 controls
   was obtained. This approach resulted in patients and controls being
   intercalated in their time of entry into the study. Eight milliliters
   of blood were drawn into a red-topped serum tube (Thermo Fisher
   Scientific) by the Clinical and Translational Research Unit’s
   phlebotomy team. Serum was obtained by allowing blood to clot for 40
   min. Once clotted, the blood tube was centrifuged in a refrigerated (4
   °C) centrifuge (Allegra X-15R, Beckman Coulter) at 2,000g for 15 min.
   Serum was isolated and mixed thoroughly in a tube using a 2-ml sterile,
   serological pipette (Thermo Fisher Scientific) to obtain a homogenous
   solution before dispensing to storage tubes. Serum was distributed into
   aliquots per the Stanford HIMC ([261]http://iti.stanford.edu/himc.html)
   aliquot guidelines and frozen at −80 °C. For the day of the cytokine
   assay, matched sets of patients with ME/CFS and healthy controls were
   mixed in all plates to reduce confounding case status with plate
   artifacts. In summary, patients with ME/CFS and controls were treated
   identically in terms of recruitment and serum handling protocols. To be
   included in the CFS Study, participants had to be 14 years of age or
   older, reside in Northern California and provide written informed
   consent and Health Insurance Portability and Accountability Act of 1996
   authorization as required by the Stanford University Institutional
   Review Board (protocol nos. 18068 and 18155). Only healthy controls
   were used for this study.

Validation cohort and centenarians.

   A total of 37 individuals were enrolled by two Italian study centers
   (Bologna and Florence) and surrounding areas. The group of centenarians
   consisted of 19 individuals (10 men, mean age 102.8 ± 2.3 years and 9
   women, mean age 103.7 ± 2.6 years) and the group of controls consisted
   of 18 individuals (9 men, mean age 64.8 ± 7.9 years and 9 women, mean
   age 67.1 ± 7.3 years). The lists of individuals recruited here were
   obtained by the Office of Vital Statistics. All participants signed
   informed consent before undergoing the questionnaires (functional and
   cognitive status, depression, self-perceived health), measurements
   (anthropometric measures, blood pressure, physical performance) and
   blood sampling. History of past and current diseases was accurately
   collected by checking the participants’ medical documentation and
   addressing major age-related pathologies. The current use of medication
   (including inspection of drugs by the interviewer) was recorded. The
   study protocol was approved by the Ethical Committee of
   Sant’Orsola-Malpighi University Hospital. Overnight fasting blood
   samples were obtained in the morning. Plasma was obtained within 2 h
   from venipuncture by centrifugation at 2,000g for 20 min at 4 °C,
   rapidly frozen and stored at −80 °C.

Cardiovascular Study cohort.

   After approval by Stanford’s Institutional Review Board, 151
   individuals participating in the National Institute of Health sponsored
   5 U19 AI05086 Study and Stanford Cardiovascular Institute Aging Study
   were screened for inclusion in this study. The screening process
   included a comprehensive health questionnaire, including the London
   School of Hygiene cardiovascular questionnaire. Exclusion criteria
   included history of acute or chronic illness such as atherosclerosis,
   systemic hypertension, diabetes mellitus or dementia, familial history
   of early cardiovascular disease (<55 years old), on nonsteroidal
   anti-inflammatory drugs or on inhaled steroids on a regular basis,
   history of malignancies, history of surgery within the last year,
   history of atopic skin disease, history of infection within the last 3
   months, including upper respiratory infections or urinary infections
   and history of vaccination within the past 3 months. Patients older
   than 80 years who had a previous history of mild systemic hypertension
   but with normal blood pressure at the time of the visit (blood pressure
   <140/90 mm Hg) were not excluded from the study. On the basis of the
   inclusion and exclusion criteria, 97 individuals were included in the
   study. We divided the patients into four groups according to
   pre-specified age boundaries (25–44, 45–59, 60–74 and 75–90 years old).

Human iPSC generation and culture.

   Protocols for isolation and use of patient blood were approved by the
   Stanford University Human Subjects Research Institutional Review Board.
   The iPSCs were generated using the OSKM CytoTune-iPS 2.0 Sendai
   Reprogramming kit viral particle factors (Life Technologies). Colonies
   that resembled human embryonic stem cell-like morphology were picked
   and seeded at one colony per 12-well plate well (Matrigel coated) in E8
   medium supplemented with 10 μM Y27632. iPSCs used for this study were
   at passage 20–25. Details regarding the characterization of human iPSCs
   have been previously published^[262]110.

Human iPSC differentiation to endothelial cells.

   Human iPSCs (hiPSCs) were seeded on Matrigel plates and grown in hiPSC
   medium for 4 d to 75–80% confluency. Differentiation to ECs was
   initiated by treating the hiPSCs with 6 μM CHIR99021 in RPMI-B27
   without insulin medium (Life Technologies) for 2 d, followed by another
   treatment of 2 μM CHIR99021 in RPMI-B27 without insulin medium for 2 d.
   Following these treatments, differentiating hiPSCs were subjected to
   endothelial medium EGM2 (Lonza) supplemented with 50 ng ml^−1 vascular
   endothelial growth factor (VEGF), 20 ng ml^−1 BMP4 and 20 ng ml^−1
   basic fibroblast growth factor for 7 d, with the medium being changed
   every 2 d. On day 12, induced ECs were isolated using
   magnetic-activated cell sorting (MACS), where cells were first
   dispersed by trypsin, then incubated with CD144 antibody and finally
   passed through a MACS column containing CD144-conjugated magnetic
   microbeads (Miltenyi Biotec). The sorted cells were then seeded on 0.2%
   gelatin-coated plates and maintained in EGM2 medium supplemented with
   10 μM SB431542 (TGF-β inhibitor). hiPSC-ECs were passaged on confluence
   and maintained in EGM2 medium.

In vitro monolayer cardiomyocyte differentiation of human iPCSs.

   To induce cardiomyocyte differentiation, approximately 1 × 10^5
   undifferentiated hiPCSs were seeded in each well of Matrigel-coated
   six-well plates and cultured in differentiation
   medium^[263]111,[264]112. Glucose-free MEM-α supplemented with fetal
   bovine serum (FBS) and lactate was employed to enrich cells to 98.0%
   α-actinin-positive at 37 °C, 20% O[2] and 5% CO[2] in a humidified
   incubator with a change of medium every 48 h and cells were passaged
   once they reached 80–90% confluence. The hiPSC-induced cardiomyocytes
   were treated immediately after enrichment.

Cell lines.

   Human umbilical vein ECs were purchased from Lonza and cultured in EGM2
   medium (Lonza) with changes of the medium every 2 d. Human fibroblasts
   were purchased from ScienCell Research Lab and cultured in Dulbecco’s
   modified Eagle’s medium (Gibco), supplemented with 20% FBS and 1%
   filter-sterilized penicillin–streptomycin.^[265]59.

Cardiovascular phenotyping.

   Cardiovascular age was assessed using three parameters: (1) aortic PWV,
   a measure of vascular stiffness; (2) RWT, a measure of ventricular
   remodeling and (3) early diastolic mitral annular velocities (e′), a
   measure of ventricular relaxation. In addition, we measured the ratio
   of early mitral inflow velocity (E) to e′, a surrogate marker of
   end-diastolic filling pressures^[266]113,[267]114.

   Aortic PWV was calculated as the ratio of the pulse wave distance (in
   meters) to the transit time (in seconds). A 9.0-MHz Philips linear
   array probe was used to assess the carotid arteries (main common, bulb
   and internal carotid artery) and proximal femoral arteries. Pulse wave
   distance (D) was measured as the distance from the sternal notch to the
   femoral artery (x[direct]) from which we subtracted the distance from
   the sternal notch to proximal descending aorta (D = x[direct] −
   x[notch−aorta]). The intersecting tangent method was used to measure
   the time from a reference echocardiograph signal and the foot of the
   pulse wave. Heart rate had to be within 2 b.p.m. between the carotid
   and femoral signal. All Doppler signals were recorded at 150 mm s^−1.
   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 (independent measures of path
   length and transit time).

   Echocardiographs were performed using the Philips IE33 system according
   to recommendations^[268]115. All studies were interpreted by one
   physician (F.H.) who was blinded to age as well as clinical and
   biological data. All parameters were measured in triplicate and
   averaged. Ventricular dimensions and wall thickness were measured using
   M-mode derived measures; we excluded the septal band from the
   measurement of the septum and chordates from the measurements of the
   posterior wall. RWT was calculated as the sum of the septal and
   posterior wall divided by left ventricular internal dimensions.
   Ventricular mass was estimated using the American Society of
   Echocardiography’s recommended formula based on modeling the left
   ventricle as a prolate ellipse^[269]115. Left ventricular ejection
   fraction was estimated using the Simpson biplane method. The tissue
   Doppler e′ velocity represents an average of the septal and lateral
   annulus^[270]113,[271]114. Inter-observer variability was calculated on
   50 samples; the intraclass correlation in our laboratory is 0.93 for
   left ventricle mass measurements.

Induced human pluripotent stem cell-derived cardiomyocytes and endothelial
cells.

   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.
   These iPSCs were differentiated into cardiomyocytes to purities of >85%
   and ECs to purities of >90%. iPSC cardiomyocytes were differentiated on
   day 30 and iPSC-ECs were differentiated on day 14. Both types of cells
   expressed mature cell markers such as PECAM1 for ECs and MYH6 for
   cardiomyocytes.

Real-time PCR.

   To analyze gene expression of CXCR3, RNA was isolated using a RNeasy
   Plus kit (QIAGEN), complementary DNA was produced using a High-Capacity
   RNA-to-cDNA kit (Life Technologies) and real-time PCR was performed
   using TaqMan Gene Expression Assays, TaqMan Gene Expression Master Mix
   and a StepOnePlusTM Real-Time PCR System (Life Technologies). All PCR
   reactions were performed in triplicate, normalized to the GAPDH
   endogenous control gene and assessed using the comparative Ct method.

   To analyze the gene expression pattern for CXCL9, RNA was extracted
   using a QIAGEN RNA isolation kit (QIAGEN 74104) and cDNA was
   synthesized using qScript cDNA SuperMix (QuantaBio). Real-time PCR was
   performed using TaqMan Gene

   We used expression assays (GAPDH, Hs02758991_g1, CXCL9, Hs00171065_m1),
   TaqMan Master Mix using a 7900HT Real-Time PCR System (Thermo Fisher
   Scientific). All PCR reactions were performed in triplicate, normalized
   to the GAPDH housekeeping gene and assessed using the ΔΔCt relative
   quantification method.

RNA sequencing.

   To understand the gene expression landscape in aging iPSC-ECs, we
   performed bulk RNA-seq on iPSC-ECs at different passages including P0,
   P2, P4, P6 and P8 iPSC-ECs. Similarly, to confirm our findings that
   aging ECs express elevated levels of CXCL9 and its downstream effects,
   we included CXCL9-KD hiPSC-ECs for RNA sequencing. Briefly, Scramble or
   CXCL9-KD hiPSCs were differentiated to ECs using our established
   protocol and once sorted, iPSC-ECs were collected at these specific
   passages for RNA extraction. Total RNA was extracted using an RNeasy
   mini kit (QIAGEN) and shipped to Novogene for RNA sequencing, where RNA
   samples were converted into individual cDNA libraries (250–300-bp
   insert cDNA library). TruSeq methods used single reads of 50 base
   lengths sequenced at 20–30 million read depths with the use of the
   Illumina Platform PE150. Trimmed sequences were generated as FASTQ
   outputs and mapped to the human reference genome (hg38) using HISAT2
   and raw counts of transcripts were obtained using featureCounts. The
   counts were further imported to R-studio as input for normalization and
   differential expressed gene analysis using DESeq2. The R package,
   FGSEA, was used to conduct pathway enrichments using Hallmark gene sets
   from the Broad Institute’s MSigDB collections^[272]116.

Vascular tube-like formation.

   The functions of the generated hiPSC-ECs were characterized in
   angiogenic assays and compared to hiPSCs. The generated hiPSC-EC were
   assessed for their ability to form tube-like structures by seeding 1 ×
   10^4 cells in wells coated with Matrigel (Corning Matrigel Matrix)
   containing EGM2 medium supplemented with 50 ng ml^−1 VEGF and incubated
   for 16–24 h.

Isometric tension recordings.

   Mouse thoracic aortas were carefully dissected and the vessels were
   transferred to a dish with ice-cold Krebs solution (in mmol l^−1, 133
   NaCl, 4.6 KCl, 2.5 CaCl[2],16.3 NaHCO[3], 1.75 Na[2]HPO[4], 0.6
   MgSO[4], 10 glucose). The vessels were cut into small rings and mounted
   on an isometric wire myograph chambers (Danish Myo Technology) and
   subjected to a normalization protocol. Following normalization, vessels
   were incubated with either PBS or different concentrations of
   recombinant mouse CXCL9 protein (R&D systems, catalog no. 492-MM) for
   3–4 h. A concentration-dependent contraction curve was created by the
   cumulative application of the prostaglandin agonist U46619.
   Subsequently, concentration-dependent relaxation curves of
   acetylcholine were conducted on these vessels and percentage relaxation
   was calculated for each dose.

CXCL9 knockdown.

   Gene knockdown experiments were performed using the GIPZ CXCL9 shRNA
   Viral Particle Starter kit (Dharmacon) containing a pool of select
   shRNA. These included V3LHS_368350 (TAGACATGTTTGAACTCCA), V3LHS_409682
   (AGTTATATACTGTCTACCT) and V3LHS_409683 (AGAAGAACAAAGACAATCA). The
   multiplicity of infection of CXCL9 shRNA was assessed after 72 h of
   infection by puromycin selection and green fluorescent protein analysis
   according to the manufacturer’s instructions. iPSCs were transfected
   with CXCL9 shRNA lentivirus at a multiplicity of infection >0.9 and
   knockdown efficiency was measured by real-time PCR with reverse
   transcription.

Whole-blood gene expression.

   Five hundred nanograms of high-quality total RNA was used for the
   Illumina gene expression microarray (HumanHT-12 BeadChip, v4)
   experiment. The Illumina Direct Hyb labeling method performs 3′-based
   gene expression measurements through reverse transcription and in vitro
   transcription techniques that incorporate biotin-labeled nucleotides
   into nascent products. Labeled cRNA products are hybridized onto bead
   arrays, washed and stained with streptavidin-Cy3. Each array on the
   HumanHT-12 BeadChip targets >25,000 annotated genes with >48,000
   probes. Hybridization and scanning was performed using the Illumina
   BeadArray reader at the Stanford Functional Genomics Facility as
   described in the Whole-Genome Gene Expression Direct Hybridization
   Assay Guide (catalog no. BD-901-1002, 11322355 rev. A). Data were
   extracted using the Illumina BeadStudio for further analysis.

Selecting the most important genes predictive of iAge.

   iAges were calculated for our cohort. In that cohort, 397 individuals
   had gene expression data. We regressed iAge onto gene expression data
   using a LASSO regression (glmnet R)^[273]117 and implemented 100 of
   such regressions. Due to the stochastic nature of LASSO regression,
   each implementation produced a slightly different list of genes that
   were predictive of iAge. For the final list of selected genes, we
   filtered for genes that were selected 100 out of 100 times from the
   regressions.

Flow cytometry immunophenotyping.

   This assay was performed by the HIMC at Stanford University. PBMCs were
   thawed in warm medium, washed twice and resuspended at 1 × 10^7 viable
   cells ml^−1. Then, 50 μl cells per well were stained for 45 min at room
   temperature with the relevant antibodies (all reagents from BD
   Biosciences). Cells were washed three times with FACS buffer (PBS
   supplemented with 2% FBS and 0.1% sodium azide) and resuspended in 200
   μl FACS buffer. Then, 100,000 lymphocytes per sample were collected
   using DIVA 6.0 software on an LSRII flow cytometer (BD Biosciences).
   Data analysis was performed using FlowJo v.9.3 by gating on live cells
   based on forward versus side-scatter profiles, then on singlets using
   forward scatter area versus height, followed by cell subset-specific
   gating.

Phosphoepitope flow cytometry (cytokine stimulation, pSTAT readouts).

   This assay was performed by the HIMC at Stanford University. PBMCs were
   thawed in warm medium, washed twice and resuspended at 0.5 × 10^6
   viable cells ml^−1. Then, 200 μl of cells were plated per well in
   96-well deep-well plates. After resting for 1 h at 37 °C, cells were
   stimulated by adding 50 μl of cytokine (IFN-α, IL-6, IL-10 or IL-2) and
   incubated at 37 °C for 15 min. PBMCs were then fixed with
   paraformaldehyde (PFA), permeabilized with methanol and stored at −80
   °C overnight. Each well was barcoded using a combination of Pacific
   Orange and Alexa-750 dyes (Invitrogen) and pooled in tubes. Cells were
   washed with FACS buffer (PBS supplemented with 2% FBS and 0.1% sodium
   azide) and stained with the following antibodies (all from BD
   Biosciences): CD3 Pacific blue, CD4 PerCP-Cy5.5, CD20 PerCp-Cy5.5, CD33
   PE-Cy7, CD45RA Qdot 605, pSTAT-1 AlexaFluor488, pSTAT-3 AlexaFluor647
   and pSTAT-5 PE. The samples were then washed and resuspended in FACS
   buffer. Then, 100,000 cells per stimulation condition were collected
   using DIVA 6.0 software on an LSRII flow cytometer (BD Biosciences).
   Data analysis was performed using FlowJo v.9.3 by gating on live cells
   based on forward versus side-scatter profiles, then on singlets using
   forward scatter area versus height, followed by cell subset-specific
   gating.

CyTOF immunophenotyping.

   This assay was performed in the HIMC at Stanford University. PBMCs were
   thawed in warm medium, washed twice, resuspended in CyFACS buffer (PBS
   supplemented with 2% BSA, 2 mM EDTA and 0.1% sodium azide) and viable
   cells were counted by Vi-cell. Cells were added to a V-bottom
   microtiter plate at 1.5 × 10^6 viable cells per well and washed once by
   pelleting and resuspension in fresh CyFACS buffer. The cells were
   stained for 60 min on ice with 50 μl of the relevant antibody–polymer
   conjugate cocktail. All antibodies were from purified unconjugated,
   carrier-protein-free stocks from BD Biosciences, BioLegend or R&D
   Systems. The polymer and metal isotopes were from DVS Sciences. The
   cells were washed twice by pelleting and resuspension with 250 μl FACS
   buffer. Cells were resuspended in 100 μl PBS buffer containing 2 μg
   ml^−1 live-dead (DOTA-maleimide (Macrocyclics) containing
   natural-abundance indium). The cells were washed twice by pelleting and
   resuspension with 250 μl PBS. The cells were resuspended in 100 μl 2%
   PFA in PBS and placed at 4 °C overnight. The next day, cells were
   pelleted and washed by resuspension in fresh PBS. Cells were
   resuspended in 100 μl eBiosciences permeabilization buffer (1× in PBS)
   and placed on ice for 45 min before washing twice with 250 μl PBS. If
   intracellular staining was performed, cells were resuspended in 50 μl
   antibody cocktail in CyFACS for 1 h on ice before washing twice in
   CyFACS. Cells were resuspended in 100 μl iridium-containing DNA
   intercalator (1:2,000 dilution in PBS; DVS Sciences) and incubated at
   room temperature for 20 min. Cells were washed twice in 250 μl MilliQ
   water and then diluted in a total volume of 700 μl in MilliQ water
   before injection into the CyTOF (DVS Sciences). Data analysis was
   performed using FlowJo v.9.3 (CyTOF settings) by gating on intact cells
   based on iridium isotopes from the intercalator, then on singlets by
   Ir191 versus cell length, then on live cells (Indium-live-dead minus
   population), followed by cell subset-specific gating.

Phosphoepitope CyTOF (cytokine stimulation, pSTAT readouts).

   This assay was performed by the HIMC at Stanford University. PBMCs were
   thawed in warm medium, washed twice, counted by Vi-cell and resuspended
   at 5 × 10^6 viable cells ml^−1. Then, 100 μl of cells were plated per
   well in 96-well deep-well plates. After resting for 1 h at 37 °C, cells
   were stimulated by adding 25 μl each of IFN-α, IL-6, IL-10 or IL-2 and
   incubated at 37 °C for 15 min. Cells were then fixed with PFA, washed
   twice with CyFACS buffer (PBS supplemented with 2% BSA, 2 mM EDTA and
   0.1% sodium azide) and stained for 30 min at room temperature with 20
   μl of surface antibody cocktail. Cells were washed twice with cyFACS
   buffer, permeabilized with 100% methanol and stored at −80 °C
   overnight. On the next day, cells were washed with cyFACS buffer and
   resuspended in 20 μl intracellular antibody cocktail in CyFACS buffer
   for 30 min at room temperature before washing twice in CyFACS buffer.
   Cells were resuspended in 100 μl iridium-containing DNA intercalator
   (1:2,000 dilution in 2% PFA in PBS) and incubated at room temperature
   for 20 min. Cells were washed once with cyFACS buffer and twice with
   MilliQ water, then were diluted to 7.5 × 105 cells ml^−1 in MilliQ
   water before injection into the CyTOF. Data analysis was performed
   using FlowJo v.9.3 (CyTOF settings) by gating on intact cells based on
   the iridium isotopes from the intercalator, then on singlets by Ir191
   versus cell length followed by cell subset-specific gating.

Determination of serum immune proteins.

   This assay was performed in the HIMC at Stanford University. Human 50-
   or 51-plex Luminex polystyrene bead kits were purchased from
   Panomics/Affymetrix and were used according to the manufacturer’s
   recommendations with modifications as described below. Briefly, samples
   were mixed with antibody-linked polystyrene beads on 96-well
   filter-bottom plates and incubated at room temperature for 2 h followed
   by overnight incubation at 4 °C. Room temperature incubation steps were
   performed on an orbital shaker at 500–600 r.p.m. Plates were vacuum
   filtered and washed twice with wash buffer, then incubated with
   biotinylated detection antibody for 2 h at room temperature. Samples
   were then filtered and washed twice as above and resuspended in
   streptavidin-PE. After incubation for 40 min at room temperature, two
   additional vacuum washes were performed and the samples were
   resuspended in Reading Buffer. Each sample was measured in duplicate.
   Plates were read using a Luminex 200 instrument with a lower bound of
   100 beads per sample per cytokine. Custom assay control beads by Radix
   Biosolutions were added to all wells.

SOMAscan assay.

   Thirty-seven plasma samples (from 19 centenarians and 18 others aged
   50–79 years) from two different cohorts (PRIN06 and PRIN09) were used
   in this study. Samples were stored at −80 °C and sent on dry ice to
   SomaLogic. The SOMAscan platform was used to quantify levels of plasma
   proteins^[274]118. Briefly, this platform is based on modified
   single-stranded DNA (SOMAmers) that are used to bind to specific
   protein targets. Data in relative fluorescent units for 1,305 SOMAmer
   probes were obtained for these samples and no samples or probe data
   were excluded. PRIN06 and PRIN09 samples were measured in two batches.
   Datasets were bridged to each other using SomaLogic calibrators.

Quantification and statistical analysis.

Normalization procedures for Luminex assays.

   We conducted a three-step normalization procedure. First, we used an
   internal control (CON-S) that was run on each batch to plate-normalize
   the data. We considered the regression model
   [MATH:
   <mrow><msub><mi>Y</mi><mrow><mi>i</mi><mi>j</mi></mrow></msub><mo>=</mo
   ><mi>β</mi><mo>+</mo><mstyle
   mathvariant="bold"><mi>x</mi></mstyle><mi>i</mi><mi>j</mi><mo>′</mo><mi
   >β</mi><mo>+</mo><mi>z</mi><mi>i</mi><mi>j</mi><mo>′</mo><mi>Y</mi><mo>
   +</mo><mi>c</mi><mi>i</mi><mi>j</mi><mo>′</mo><mi>α</mi><mo>+</mo><mtex
   t>p</mtext><mi>i</mi><mo>′</mo><mi>θ</mi><mo>+</mo><mi>E</mi><mi>i</mi>
   <mi>j</mi><mo>,</mo></mrow> :MATH]

   where outcome Yij is the cytokine’s median fluorescence intensity
   averaged over duplicate wells (aMFI) for the jth participant on the ith
   plate, x is the design vector of variables of interest with
   corresponding regression coefficient β, and z is the design vector of
   nuisance variables of corresponding regression coefficients γ. These
   may include baseline covariates and random coefficients to model
   longitudinal data. Covariance among repeated observations within
   participants (for example, longitudinal aMFI) was modeled via C(Eijk,
   Eijk′) for k ≠ k′. Parameter β0 is the intercept. This model also
   adjusts for nonspecific binding c (used CHEX4) (with corresponding
   regression coefficients α)^[275]30. The vector form permits modeling of
   any nonlinear effects of nonspecific binding on the outcome. For plate
   effects, we used the indicator-variable vector, p, of plate effects has
   regression coefficients θ. The variance of regression residual E is
   allowed to vary among plates, such as Var(Eij) ≠ Var(Ei′j) for i ≠ i′.
   Together, p′θ and variance Var(Eij) account, respectively, for location
   and scale effects of plates.

   Second, for source normalization of the influenza vaccine studies
   versus chronic fatigue study, we used a naive correction in which PCA
   is conducted on all data and the effect of top components is removed by
   regression analysis on the data source until the batch effect is no
   longer significant. The mean absolute correlation is then computed as a
   function of the number of PCA components in batch-corrected versus raw
   data and heat maps for before and after PCA correction are also shown
   ([276]Extended Data Fig. 4).

Guided auto-encoder and the inflammatory clock.

   When dealing with data with a large number of dimensions and complex
   network structures, we aimed to find a nonlinear method to summarize
   the data possibly to a compact representation. This compact
   representation can be further used for feature extraction,
   visualization or classification purpose. To obtain an informative
   representation, we proposed a model called GAE. The method is built
   based on Auto-Encoder with a combined objective. Auto-encoders use a
   nonlinear transformation of data and hence, can model complex
   processes^[277]119. One problem with auto-encoders is
   re-parameterization. With different initialization, it could have
   different results. Among the different types of visualizations with
   similar summarization levels, one usually wants a representation that
   is informative of a specific target. Hence, we can construct a
   representation with two focuses: (1) the learned compact representation
   can be recovered from the original data as much as possible
   (reconstruction loss) and (2) the learned compact representation should
   be as informative of the desired target as possible (prediction loss).
   Therefore, we proposed a structure, GAE, which balances the two
   objectives, to provide an informative representation. We applied GAE to
   extract an immunology score or inflammatory clock. It is a nonlinear
   transformation of the cytokine data in a person that both approximates
   the true age, while preserving the information of cytokine level.

Auto-encoder.

   Given the input data vector x, an auto-encoder aims to reconstruct the
   input data vector x. We consider that an auto-encoder with L encoding
   layers and L decoding layers has a depth of L and each layer has a
   fixed number of hidden nodes, m.

   For convenience, the input layer is defined as h[0](x) = x and the
   output of the lth hidden layer is defined as h[l](x). The number of
   nodes in layer l is m[l]. The input into the lth layer of the network
   is defined as:
   [MATH: <mrow><msub><mi>a</mi><mi>l</mi></msub><mo
   stretchy="false">(</mo><mi>x</mi><mo
   stretchy="false">)</mo><mo>=</mo><msub><mi>h</mi><mrow><mi>l</mi><mo>−<
   /mo><mn>1</mn></mrow></msub><msup><mrow><mo
   stretchy="false">(</mo><mi>x</mi><mo
   stretchy="false">)</mo></mrow><mi>T</mi></msup><msub><mi>W</mi><mi>l</m
   i></msub><mo>+</mo><msub><mi>β</mi><mi>l</mi></msub><mo>,</mo></mrow>
   :MATH]

   where W[l] is a real value weight matrix of m[l−1] by m[l] and β[l] is
   a vector of length m[l-1]. The output of lth hidden layer is:
   [MATH: <mrow><msub><mi>h</mi><mi>l</mi></msub><mo
   stretchy="false">(</mo><mi>x</mi><mo
   stretchy="false">)</mo><mo>=</mo><mtext
   mathvariant="italic">tanh</mtext><mrow><mo>(</mo><mrow><msub><mi>a</mi>
   <mi>l</mi></msub><mo stretchy="false">(</mo><mi>x</mi><mo
   stretchy="false">)</mo></mrow><mo>)</mo></mrow></mrow> :MATH]

   where tanh is the hyperbolic tangent function:
   [MATH: <mrow><mtext>tanh</mtext><mo
   stretchy="false">(</mo><mi>x</mi><mo
   stretchy="false">)</mo><mo>=</mo><mfrac><mrow><mn>1</mn><mo>−</mo><msup
   ><mi>e</mi><mrow><mo>−</mo><mn>2</mn><mi>x</mi></mrow></msup></mrow><mr
   ow><mn>1</mn><mo>+</mo><msup><mi>e</mi><mrow><mo>−</mo><mn>2</mn><mi>x<
   /mi></mrow></msup></mrow></mfrac></mrow> :MATH]

   We define the output of the Lth layer h[L](x) as the coding layer. The
   decoding layers are from L + 1 to 2L − 1 layer with the same setting.
   Finally, a linear output layer is on top of the last decoding layer:
   [MATH:
   <mrow><msub><mi>f</mi><mrow><mi>A</mi><mi>E</mi></mrow></msub><mo
   stretchy="false">(</mo><mi>x</mi><mo
   stretchy="false">)</mo><mo>=</mo><msub><mi>h</mi><mrow><mn>2</mn><mi>L<
   /mi><mo>−</mo><mn>1</mn></mrow></msub><msup><mrow><mo
   stretchy="false">(</mo><mi>x</mi><mo
   stretchy="false">)</mo></mrow><mi>T</mi></msup><msub><mi>W</mi><mrow><m
   n>2</mn><mi>L</mi><mo>−</mo><mn>1</mn></mrow></msub><mo>+</mo><msub><mi
   >β</mi><mrow><mn>2</mn><mi>L</mi></mrow></msub><mo>,</mo></mrow> :MATH]

   Given data vectors x, we train an auto-encoder and minimize the
   reconstruction loss on the data:
   [MATH:
   <mrow><msub><mtext>minimize</mtext><mi>θ</mi></msub><munder><mo>∑</mo><
   mi>i</mi></munder><msub><mi>f</mi><mrow><mi>A</mi><mi>E</mi></mrow></ms
   ub><mrow><mo>(</mo><mrow><msup><mi>x</mi><mi>i</mi></msup><mo>,</mo><mi
   >θ</mi></mrow><mo>)</mo></mrow><mo>−</mo><msubsup><mi>x</mi><mn>2</mn><
   mrow><mi>i</mi><mn>2</mn></mrow></msubsup><mo>+</mo><mi>λ</mi><msubsup>
   <mi>θ</mi><mn>2</mn><mn>2</mn></msubsup></mrow> :MATH]

   where i is the range of the number of samples, θ represents all the
   parameters used in the auto-encoder and λ is the weight decay penalty
   used for regularization. To optimize objective (1), we used a
   stochastic optimization method ADAM^[278]120.

Guided auto-encoder.

   A GAE aims to reduce both reconstruction loss and predictive loss.
   Given the input x, a side-phenotype y and an auto-encoder f[AE], the
   GAE incorporates a predictive function on the coding layer:
   [MATH: <mrow><msub><mi>f</mi><mi>G</mi></msub><mo
   stretchy="false">(</mo><mi>x</mi><mo
   stretchy="false">)</mo><mo>=</mo><msub><mi>h</mi><mi>L</mi></msub><msup
   ><mrow><mo stretchy="false">(</mo><mi>x</mi><mo
   stretchy="false">)</mo></mrow><mi>T</mi></msup><msub><mi>w</mi><mi>G</m
   i></msub><mo>+</mo><msub><mi>β</mi><mi>G</mi></msub><mo>,</mo></mrow>
   :MATH]

   with its own set of parameters w[G] and β[G]

   Let θ be the set of all parameters of a GAE, the training objective is:
   [MATH:
   <mrow><msub><mtext>minimize</mtext><mi>θ</mi></msub><munder><mo>∑</mo><
   mi>i</mi></munder><mrow><mo>(</mo><mrow><mi>α</mi><msub><mi>f</mi><mi>G
   </mi></msub><mrow><mo>(</mo><mrow><msup><mi>x</mi><mi>i</mi></msup><mo>
   ,</mo><mi>θ</mi></mrow><mo>)</mo></mrow><mo>−</mo><msubsup><mi>y</mi><m
   n>2</mn><mrow><mi>i</mi><mn>2</mn></mrow></msubsup><mo>+</mo><mo
   stretchy="false">(</mo><mn>1</mn><mo>−</mo><mi>α</mi><mo
   stretchy="false">)</mo><msub><mi>f</mi><mrow><mi>A</mi><mi>E</mi></mrow
   ></msub><mrow><mo>(</mo><mrow><msup><mi>x</mi><mi>i</mi></msup><mo>,</m
   o><mi>θ</mi></mrow><mo>)</mo></mrow><mo>−</mo><msubsup><mi>x</mi><mn>2<
   /mn><mrow><mi>i</mi><mn>2</mn></mrow></msubsup></mrow><mo>)</mo></mrow>
   <mo>+</mo><mi>λ</mi><msubsup><mi>θ</mi><mn>2</mn><mn>2</mn></msubsup><m
   o>,</mo></mrow> :MATH]
   (2)

   where α is a real value number between 0 and 1, which is called the
   guidance ratio. An example GAE with depth 2 and width 3 is shown in
   [279]Supplementary Fig. 1. We use the optimization method ADAM^[280]120
   to minimize objective. By choosing different guidance ratios, we can
   reach different levels of balance between prediction loss and
   reconstruction loss.

Extraction of an inflammatory clock.

   To provide a marker summarization of a patient’s immune system health
   state, we present the inflammatory clock. This is the age of patient
   that is predictable from the inflammatory state of the immune system.
   To obtain this quantity, we focused on cytokine measurements. By
   construction, the inflammatory clock is a nonlinear function of
   cytokine measurements, but also an estimate of the patient’s true age.

   To construct this quantity, we used GAE aimed to compactly represent
   cytokine measurements and predict side-phenotype chronological age. We
   identified best code length, among lengths from 1 to 10, using fivefold
   cross validation. We selected the length of code k, whose performance
   was not statistically significantly worse than that of longer codes
   (paired Student’s t-test P value >0.05). Within each fold, we performed
   nested threefold cross validation to select hyper-parameters (depth,
   weight decay and guidance ratio).

   After obtaining the best code length as 5 ([281]Extended Data Fig. 3a),
   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. Finally, we trained the GAE on the whole dataset
   with the selected best hyper-parameter setting and obtained the
   predictive function as the inflammatory clock predictor. To derive the
   inflammatory clock index, we computed rank differences between
   exceptional longevity participants and adult controls. To do so, we
   first ranked both cohorts in terms of cAge and iAge. For each
   participant, we then computed the difference of their cAge rank and
   iAge rank and used this difference (iAge index) to stratify
   participants into high and low, if they were above or below the
   population mean, respectively. Any monotonic transformation of cAge or
   iAge does not affect ranks, hence findings are robust to
   transformations such as log and exp.

   The model to construct iAge utilizes cross-sectional data, where the
   first instance for each individual record (baseline) is selected.
   Despite that a number of individuals’ samples were collected in a
   longitudinal manner between 2007 and 2016 ([282]Supplementary Fig. 1).
   To assess feature dependency to the iAge metric, a fivefold
   cross-validation procedure was conducted, which effectively split the
   dataset 80–20% repeatedly and absolute error was obtained from
   averaging model error obtained on each of the test sets.

Prediction of multimorbidity using cyclical coordinate descent and
correlation with immunosenescence.

   We hypothesized that important immune components would emerge from
   fitting a linear regression model with l1 and l2 penalties, the Elastic
   Net penalty, a regularization algorithm that uses cyclical coordinate
   descent in a path-wise fashion. We envisioned an unbiased approach to
   select predictors of multimorbidity based on available data for all 902
   participants while controlling for the age effect. A total of 127
   features were included in the prediction model. We assume all of our
   predictors are standardized to a mean of 0 and s.d. of 1. The result of
   our fitting procedure is the set of predictor weights β and intercept α
   for the linear regression model. In practice, penalty weights are set
   by a data-driven procedure, such as tenfold cross validation. The
   minimum λ was chosen to yield the lowest MAE with the minimum set of
   features. We envisioned age-controlled feature selection by imposing a
   feature-specific penalty. In this procedure feature age is ‘forced in’
   the model and the l1 penalty is able to choose from all other features.
   In practice, we created a vector of size 127 and chose α = 0 for
   feature age and α = 1 for the remaining 126 features. To investigate
   the effect of iAge on immunosenescence, multiple regression analysis
   was conducted using iAge as a predictor variable (controlled by age,
   sex and CMV) and the frequency of naive CD8^+ T cells, as a target
   variable (a surrogate of immunosenescence). Similarly, the effect of
   iAge (after controlling for age, sex and CMV) was estimated on a total
   of 92 cell stimulations. Adjusted P values (by permutation tests) were
   combined by using a modified Fisher’s combined probability
   test^[283]121.

Estimation of the inflammatory clock in centenarians and older control
cohorts.

   We first aimed at estimating the minimum set of features required for
   accurate prediction of the inflammatory clock. To do so, we used the
   results from our previous analysis in which we investigated the
   composition of the inflammatory clock based on the first-order partial
   derivative of the inflammatory clock (Jacobians). We sorted the immune
   features based on their absolute Jacobian and subsequently generated n
   − feature set models, each with a different feature number and removed
   one feature at a time starting from the least to the most important,
   which is never fully discarded. With the removal of each feature, a P
   value (two sample Student’s t-test) on the cross-validation errors
   between the ‘feature set model’ and the ‘all-feature-set model’ is
   computed. Removal of most features did not significantly affect
   prediction accuracy ([284]Extended Data Fig. 2). The cross-validation
   error using only five features (model 1) (EOTAXIN, IFNG, GROA, TRAIL
   and CXCL9 (which are not removed as they are the last feature)), is not
   significantly different from the error obtained using all features
   (model 2), indicating that the inflammatory clock can be estimated with
   this reduced set, as accurately as by using all 50 features.

   As one important immune protein was not measured in the SOMAscan assay
   (TRAIL), we aimed to build a regression model to predict inflammation,
   excluding TRAIL, which yields the same accuracy as model 1. Hence, we
   compared the inflammatory clock prediction accuracy of model 1, to the
   accuracy of a series of models (TRAIL excluded) including an increasing
   number of features based on feature contribution to the inflammatory
   clock, as performed previously. Using Stanford 1KIP data, we found that
   the prediction accuracy of a model when TRAIL is removed but containing
   EOTAXIN, MIP-1α, CXCL1, CXCL9, IFN-γ, IL-1β, IL-2, LEPTIN and PAI-1 was
   not different from that of model 1 (in which TRAIL is included) (by
   likelihood ratio test, P < 0.01).

   We then directly estimated the inflammatory clock as the predicted age
   of participants in the aging control cohort and centenarians based on
   standardized coefficients from the previous analysis on the Stanford
   1KIP dataset and normalized RTU values for EOTAXIN, MIP-1α, CXCL1,
   CXCL9, IFN-γ, IL-1β, IL-2, LEPTIN and PAI-1. The inflammatory clock was
   then used to compute an inflammatory clock index (rank cAge minus rank
   iAge) in these cohorts.

Enrichment analysis of iAge gene signature on Framingham Heart Study.

   The Framingham Heart Study gene expression, phenotypic clinical data
   and longitudinal survival data were downloaded from dbGap and
   preprocessed as detailed in Alpert et al.^[285]16. The enrichment of
   the gene signature in the Framingham Heart Study samples was calculated
   using single-sample gene-set enrichment analysis^[286]122. For survival
   analysis, we calculated a multivariate Cox regression model (n = 2,290)
   regressing all-cause mortality against the clinical covariates: age,
   sex, smoking status, diabetes, total cholesterol, HDL cholesterol,
   blood pressure, a cardiovascular disease status assessed on the date of
   the eighth exam and the iAge score.

Isolation of blood endothelial cells.

   Blood collected from young and old individuals was centrifuged to
   isolate the buffy coat, which was washed with PBS as described
   previously^[287]60. Following centrifugation, cell pellets were
   resuspended in blood outgrowth EC medium containing 20% FBS in EGM2
   medium and seeded on collagen-coated plates. Medium was changed every 2
   d and BECs were characterized once confluent.

Isolation of mouse aortic endothelial cells.

   Aortas from young (3–4 months) and old (2 years) mice were dissected,
   cut and digested using freshly prepared 1 μg ml^−1 Liberase solution
   (R&D) for 30 min. Following digestion, cell pellets were resuspended in
   EGM2 medium with 5% FBS. Once confluent, ECs were isolated by MACS
   using CD144-conjugated magnetic beads.

Endothelial functional assays.

Tube formation.

   The functions of the generated hiPSC-ECs and blood ECs were
   characterized in angiogenic assays and compared to hiPSCs. Briefly,
   cultured iPSC-ECs were dissociated using 1× trypsin and 1 × 10^4 cells
   were resuspended in EGM2 medium supplemented with 50 ng ml^−1 VEGF.
   Following this, cells were seeded on 24-well plates, precoated with
   Matrigel (Corning Matrigel Matrix) for 16–24 h.

Nitric oxide production.

   The capacity of iPSC-ECs and blood ECs to produce NO was assessed by
   measuring the concentration of NO in culture supernatants using a NO
   detection kit (Molecular Probe) in basal and acetylcholine-stimulated
   conditions. Briefly, nitrates in culture supernatants were converted
   into nitrite and the total amount of nitrite was determined by
   colorimetric Griess reaction. Readings were recorded by measuring the
   absorbance at 540 nm using a microplate reader.

Ac-LDL uptake.

   The capacity of iPSC-ECs and blood ECs to uptake Ac-LDL was assessed
   using fluorescently labeled LDL. Briefly, cells were incubated in
   96-well white clear-bottom cell culture plates for 24 h and
   fluorescence was measured at Ex/Em = 540/575 nm. Results were
   calculated using a standard curve according to the manufacturer’s
   instructions (Biovision). A wash-off step was performed to determine
   nonspecific fluorescently labeled LDL.

In vivo angiogenesis assay.

   The capacity of iPSC-ECs to form functional capillaries in vivo was
   assessed by injecting 5 × 10^5 cells mixed in Matrigel to make a final
   volume of 200 μl. Cells were injected subcutaneously in SCID mice and
   after 2 weeks, Matrigel plugs were excised for immunohistochemical
   analysis. Plugs were fixed in 4% PFA, sectioned and stained for human
   CD31. CD31^+ human capillaries were quantified per field using a
   fluorescent microscope.

Cellular senescence activity assay.

   Cellular senescence assay was performed to detect SA-β-gal activity
   using a fluorometric format (Enzo, catalog no. ENZ-KIT129). Briefly,
   cell lysates were collected and SA-β-gal activity was measured using a
   fluorometric substrate. Fluorescence was measured at 360 nm
   (excitation)/465 nm (emission).

Statistics and reproducibility.

   For our experiments, no statistical method was used to predetermine
   sample size. No data were excluded from the analyses. No randomization
   or blinding was conducted for this study. Covariates such as age, BMI,
   detection of CMV and sex were controlled by their inclusion in all
   regression analysis. 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. Blinding was not performed in mice
   experiments.

Reporting Summary.

   Further information on research design is available in the Nature
   Research Reporting Summary linked to this article.

Extended Data

Extended Data Fig. 1 |. 1000 Immunomes Study design: systematic analysis of
immune systems via ‘OMiCS’ approaches.

   Extended Data Fig. 1 |
   [288]Open in a new tab

   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. For all samples of the Stanford 1KIP, deep
   immune phenotyping was conducted at the Stanford Human Immune
   Monitoring Center, where peripheral blood specimens were isolated and
   analyzed using standard procedures. Peripheral blood samples were
   obtained by venipuncture and peripheral blood mononuclear cells or
   whole blood samples were used for determination of cellular phenotypes
   and frequencies (N = 935) and for investigation of in vitro cellular
   responses to a variety of cytokine stimulations (N = 818); serum
   samples were obtained and used for protein content determination
   (including a total of 50 cytokines, chemokines and growth factors) (N =
   1001). Clinical characterization was assessed via clinical
   questionnaire in a total of 902 subjects who completed the full set of
   53 clinical items. From a total of 97 healthy young and older adults,
   comprehensive cardiovascular phenotyping was also conducted

Extended Data Fig. 2 |.

   Extended Data Fig. 2 |
   [289]Open in a new tab

   Age distribution of the Stanford 1KIP cohort.

Extended Data Fig. 3 |. Estimation of the GAE code length and accuracy of age
prediction.

   Extended Data Fig. 3 |
   [290]Open in a new tab

   We used 5-fold cross-validation to identify the best code length, among
   lengths from 1 to 10. We selected the length of code k, whose
   performance was not statistically significantly worse than that of
   longer codes (paired t-test p-value > 0.05). Within each fold we
   performed nested 3-fold cross-validation to select hyper-parameters
   (depth, weight decay and guidance-ratio). 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). 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. Finally, we trained the GAE on the whole
   dataset with the selected best hyper-parameter setting and obtained the
   predictive function as the inflammatory clock predictor. GAE was
   compared to other machine learning methods such as autoencoder, neural
   networks, PCA, and RAW in (b). For the neural network, 2 fully
   connected layers with 5 nodes in each layer and tanh activation
   function were used. For PCA and RAW, we used elastic net to predict
   age. The GAE method outperforms linear methods for protein data
   reconstruction and prediction of chronological age (b). In (c), we
   found that the predictive performance of gradient boosting decision
   tree (GBDT) has similar performance as PCA. We conclude that GAE is
   superior to traditional machine learning methods.

Extended Data Fig. 4 |. Elimination of batch effect for serum immune protein
data. .

   Extended Data Fig. 4 |
   [291]Open in a new tab

   Immune protein data from serum samples were subjected to normalization
   and batch correction procedures (See [292]Methods) to ensure data from
   different sources can be combined and used as a whole. a, Spearman
   correlation between immune protein features and batch ID shows a strong
   dependency of data source on top 4 components (raw data, green line),
   which reaches a steady state after component 5. 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. b, Upper panel: immune protein expression heatmap of
   uncorrected data, Lower panel: immune protein expression heatmap of
   corrected data. The two batches come from two study cohorts, the
   Chronic Fatigue Syndrome Study (CFS) and Aging and vaccination study
   cohort (Flu).

Extended Data Fig. 5 |. iAge predictive of multi-mordity.

   Extended Data Fig. 5 |
   [293]Open in a new tab

   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). 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. A
   Mean Absolute Error (MAE) for the prediction of comorbidity of 0.41 is
   observed (b). 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)

Extended Data Fig. 6 |. Univariate Regression between Age and CXCL9.

   Extended Data Fig. 6 |
   [294]Open in a new tab

   Significant correlation between age and CXCL9 using univariate
   regression analysis. We used linear regression where CXCL9 were
   regressed onto age. Correlation coefficient (R^2) and p-value of F-test
   of overall significance are reported.

Extended Data Fig. 7 |. Luminex data for cardiovascular validation cohort.

   Extended Data Fig. 7 |
   [295]Open in a new tab

   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. Immune protein analysis was conducted in samples
   from these subjects. CXCL9, HGF, CXCL1, and LIF were found to change in
   the same direction in both the Stanford 1KIP and the validation cohort.

Extended Data Fig. 8 |. Human blood endothelial progenitor cells and mice
endothelial cells.

   Extended Data Fig. 8 |
   [296]Open in a new tab

   a, Representative images of human blood progenitor endothelial cells
   from young (left) and old (right) individuals. b, Representative images
   of capillary-like networks show impaired tube formation by human BECs
   of old individuals compared to young. To further confirm the potential
   contribution of CXCL9 in cardiovascular aging, we assessed its
   expression in young (3–4 month) and old mice (2 yr.) endothelial cells
   (c). ECs isolated from old mice showed higher levels of CXCL9 (P value
   = 0.023) (d), while at the same time showed impaired EC function as
   evident by decreased tube formation (P value = 0.042) (a, f).
   [297]Figure S8: All data represented as mean ± SEM, n = 3, *P < 0.05.
   Statistical analyses were performed using Student’s t-test (paired).
   Scale bar: 50 μm.

Extended Data Fig. 9 |. Expression of CXCR3 RNA in different tissue types.

   Extended Data Fig. 9 |
   [298]Open in a new tab

   CXCR3 was not expressed in iPSC induced cardiomyocytes (iPSC-CM),
   Fibroblast, or iPSC. However, it is highly expressed in iPSC induced
   endothelial cells and Human Umbilical Vein Endothelial Cells (HUVEC).
   All data represented as mean ± SEM.

Extended Data Fig. 10 |. Validation of the effects of CXCL9 on endothelial
function.

   Extended Data Fig. 10 |
   [299]Open in a new tab

   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.
   Scale bar: 50 μm. Experiment was repeated 3 times.

Supplementary Material

   Supplementary Information
   [300]NIHMS1750030-supplement-Supplementary_Information.pdf^ (656.3KB,
   pdf)
   Source Data Fig. 1
   [301]NIHMS1750030-supplement-Source_Data_Fig__1.xlsx^ (73.4KB, xlsx)
   Source Data Fig. 3
   [302]NIHMS1750030-supplement-Source_Data_Fig__3.xlsx^ (364.7KB, xlsx)
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   [303]NIHMS1750030-supplement-Source_Data_Fig__4.xlsx^ (12.1KB, xlsx)
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   Source Data Fig. 5
   [305]NIHMS1750030-supplement-Source_Data_Fig__5.xlsx^ (83.1KB, xlsx)
   Source Data Extended Data Fig. 6
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   (29.8KB, xlsx)
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   Source Data Fig. 7
   [308]NIHMS1750030-supplement-Source_Data_Fig__7.xlsx^ (10.9KB, xlsx)
   Source Data CXCL9 expression in mouse EC (relative fold change).
   [309]NIHMS1750030-supplement-Source_Data_CXCL9_expression_in_mouse_EC__
   relative_fold_change__.xlsx^ (9.2KB, xlsx)
   Source Data Extended Data Fig. 5
   [310]NIHMS1750030-supplement-Source_Data_Extended_Data_Fig__5.xlsx^
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   Source Data Extended Data Fig. 10 Validation of the effects of CXCL9 on
   endothelial function.
   [311]NIHMS1750030-supplement-Source_Data_Extended_Data_Fig__10_Validati
   on_of_the_effects_of_CXCL9_on_endothelial_function_.csv^ (195B, csv)
   Source Data Extended Data Fig. 2
   [312]NIHMS1750030-supplement-Source_Data_Extended_Data_Fig__2.xlsx^
   (1.6MB, xlsx)
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   [313]NIHMS1750030-supplement-Source_Data_Fig__6.xlsx^ (4.3MB, xlsx)
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   [315]NIHMS1750030-supplement-Source_Data_Extended_Data_Fig__4.xlsx^
   (1.8MB, xlsx)

Acknowledgements