Detection of a Novel,Integrative Aging Process Suggests Complex Physiological Integration

Many studies of aging examine biomarkers one at a time, but complex systems theory and network theory suggest that interpretations of individual markers may be context-dependent. Here, we attempted to detect underlying processes governing the levels of many biomarkers simultaneously by applying principal components analysis to 43 common clinical biomarkers measured longitudinally in 3694 humans from three longitudinal cohort studies on two continents (Women’s Health and Aging I & II, InCHIANTI, and the Baltimore Longitudinal Study on Aging). The first axis was associated with anemia, inflammation, and low levels of calcium and albumin. The axis structure was precisely reproduced in all three populations and in all demographic sub-populations (by sex, race, etc.); we call the process represented by the axis “integrated albunemia.” Integrated albunemia increases and accelerates with age in all populations, and predicts mortality and frailty – but not chronic disease – even after controlling for age. This suggests a role in the aging process, though causality is not yet clear. Integrated albunemia behaves more stably across populations than its component biomarkers, and thus appears to represent a higher-order physiological process emerging from the structure of underlying regulatory networks. If this is correct, detection of this process has substantial implications for physiological organization more generally.     

Proteomic architecture of frailty across the spectrum of cardiovascular disease

While frailty is a prominent risk factor in an aging population, the underlying biology of frailty is incompletely described. Here, we integrate 979 circulating proteins across a wide range of physiologies with 12 measures of frailty in a prospective discovery cohort of 809 individuals with severe aortic stenosis (AS) undergoing transcatheter aortic valve implantation. Our aim was to characterize the proteomic architecture of frailty in a highly susceptible population and study its relation to clinical outcome and systems-wide phenotypes to define potential novel, clinically relevant frailty biology. Proteomic signatures (specifically of physical function) were related to post-intervention outcome in AS, specifying pathways of innate immunity, cell growth/senescence, fibrosis/metabolism, and a host of proteins not widely described in human aging. In published cohorts, the “frailty proteome” displayed heterogeneous trajectories across age (20–100 years, age only explaining a small fraction of variance) and were associated with cardiac and non-cardiac phenotypes and outcomes across two broad validation cohorts (N > 35,000) over ≈2–3 decades. These findings suggest the importance of precision biomarkers of underlying multi-organ health status in age-related morbidity and frailty.     

Unraveling the metabolic underpinnings of frailty using multicohort observational and Mendelian randomization analyses

Identifying metabolic biomarkers of frailty, an age-related state of physiological decline, is important for understanding its metabolic underpinnings and developing preventive strategies. Here, we systematically examined 168 nuclear magnetic resonance-based metabolomic biomarkers and 32 clinical biomarkers for their associations with frailty. In up to 90,573 UK Biobank participants, we identified 59 biomarkers robustly and independently associated with the frailty index (FI). Of these, 34 associations were replicated in the Swedish TwinGene study (n = 11,025) and the Finnish Health 2000 Survey (n = 6073). Using two-sample Mendelian randomization, we showed that the genetically predicted level of glycoprotein acetyls, an inflammatory marker, was statistically significantly associated with an increased FI (β per SD increase = 0.37%, 95% confidence interval: 0.12–0.61). Creatinine and several lipoprotein lipids were also associated with increased FI, yet their effects were mostly driven by kidney and cardiometabolic diseases, respectively. Our findings provide new insights into the causal effects of metabolites on frailty and highlight the role of chronic inflammation underlying frailty development.     

PTPN22 gene functional polymorphism (rs2476601) in older adults with frailty syndrome

The frailty syndrome is a common clinical marker of vulnerability in older adults conducive to an overall decline in inflammatory stress responsiveness; yet little is known about the genetic risk factors for frailty in elderly. Our aim was to investigate the association between the rs2476601 polymorphism in PTPN22 gene and susceptibility to frailty in Mexican older adults. Data included 630 subjects 70 and older from The Coyoacán cohort, classified as frail, pre-frail, and non-frail following Fried’s criteria. Sociodemographic and clinical characteristics were compared between groups at baseline and after a multivariate analysis. The rs2476601 polymorphism was genotyped by TaqMan genotyping assay using real-time PCR and genotype frequencies were determined for each frailty phenotype in all participants and subsets by age range. Genetic association was examined using stratified and interaction analyses adjusting for age, sex and variables selected in the multivariate analysis. Disability for day-life activities, depression and cognitive impairment were associated with the risk of pre-frailty and frailty at baseline and after adjustment. Carrying the T allele increased significantly the risk of frailty in patients 76 and older (OR 5.64, 95% CI 4.112–7.165) and decreased the risk of pre-frailty under no clinical signs of depression (OR 0.53; 95% CI 0.17–1.71). The PTPN22 polymorphism, rs2476601, could be a genetic risk factor for frailty as subject to quality of life. This is the first study analyzing such relationship in Mexican older adults. Confirming these findings requires additional association studies on wider age ranges in populations of older adults with frailty syndrome.

     

Identification of a seven glycopeptide signature for malignant pleural mesothelioma in human serum by selected reaction monitoring

Background

Serum biomarkers can improve diagnosis and treatment of malignant pleural mesothelioma (MPM). However, the evaluation of potential new serum biomarker candidates is hampered by a lack of assay technologies for their clinical evaluation. Here we followed a hypothesis-driven targeted proteomics strategy for the identification and clinical evaluation of MPM candidate biomarkers in serum of patient cohorts.

Results

Based on the hypothesis that cell surface exposed glycoproteins are prone to be released from tumor-cells to the circulatory system, we screened the surfaceome of model cell lines for potential MPM candidate biomarkers. Selected Reaction Monitoring (SRM) assay technology allowed for the direct evaluation of the newly identified candidates in serum. Our evaluation of 51 candidate biomarkers in the context of a training and an independent validation set revealed a reproducible glycopeptide signature of MPM in serum which complemented the MPM biomarker mesothelin.

Conclusions

Our study shows that SRM assay technology enables the direct clinical evaluation of protein-derived candidate biomarker panels for which clinically reliable ELISA’s currently do not exist.     

Frailty has a stronger association with inflammation than age in older veterans

Background

Upregulation of pro-inflammatory cytokines has not only been associated with increased morbidity and mortality in older adults but also has been linked to frailty. In the current study we aimed to compare the relative relationship of age and frailty on inflammation and thrombosis in older veterans.

Results

We analyzed 117 subjects (age range 62–95 years; median 81) divided into 3 cohorts: non-frail, pre-frail and frail based on the Fried phenotype of frailty. Serum inflammatory markers were determined using commercially available ELISA kits. Frail and pre-frail (PF) subjects had higher levels than non-frail (NF) subjects of IL-6 (NF vs. PF: p?=?0.002; NF vs. F: p?<?0.001), TNFR1 (NF vs. F: p?=?0.012), TNFRII (NF vs. F: 0.002; NF vs. PF: p?=?0.005) and inflammatory index: = 0.333*log(IL-6)?+?0.666*log(sTNFR1) (NF vs. F: p?=?0.009; NF vs. PF: p?<?0.001). Frailty status explained a greater percent of variability in markers of inflammation than age: IL-6 (12 % vs. 0.3 %), TNFR1 (5 % vs. 4 %), TNFR2 (11 % vs. 6 %), inflammatory index (16 % vs. 8 %). Aging was significantly associated with higher fibrinogen (p?=?0.04) and D-dimer levels (p?=?0.01) but only among NF subjects.

Conclusion

In conclusion, these data suggest that among older veterans, frailty status has a stronger association with inflammation and the inflammatory index than age does. Larger studies, in more diverse populations are needed to confirm these findings.

     

Genome-wide methylation profiling reveals Zinc finger protein 516 (ZNF516) and FK-506-binding protein 6 (FKBP6) promoters frequently methylated in cervical neoplasia,associated with HPV status and ethnicity in a Chilean population

Cervical cancer is a major health concern among women in Latin America due to its high incidence and mortality. Therefore, the discovery of molecular markers for cervical cancer screening and triage is imperative. The aim of this study was to use a genome wide DNA methylation approach to identify novel methylation biomarkers in cervical cancer. DNA from normal cervical mucosa and cervical cancer tissue samples from Chile was enriched with Methylated DNA Immunoprecipitation (MeDIP), hybridized to oligonucleotide methylation microarrays and analyzed with a stringent bioinformatics pipeline to identify differentially methylated regions (DMRs) as candidate biomarkers. Quantitative Methylation Specific PCR (qMSP) was used to study promoter methylation of candidate DMRs in clinical samples from two independent cohorts. HPV detection and genotyping were performed by Reverse Line Blot analysis. Bioinformatics analysis revealed GGTLA4, FKBP6, ZNF516, SAP130, and INTS1 to be differentially methylated in cancer and normal tissues in the Discovery cohort. In the Validation cohort FKBP6 promoter methylation had 73% sensitivity and 80% specificity (AUC = 0.80). ZNF516 promoter methylation was the best biomarker, with both sensitivity and specificity of 90% (AUC = 0.92), results subsequently corroborated in a Prevalence cohort. Together, ZNF516 and FKBP6 exhibited a sensitivity of 84% and specificity of 81%, when considering both cohorts. Our genome wide DNA methylation assessment approach (MeDIP-chip) successfully identified novel biomarkers that differentiate between cervical cancer and normal samples, after adjusting for age and HPV status. These biomarkers need to be further explored in case-control and prospective cohorts to validate them as cervical cancer biomarkers.     

Olfactory Dysfunction Predicts 5-Year Mortality in Older Adults

Prediction of mortality has focused on disease and frailty, although antecedent biomarkers may herald broad physiological decline. Olfaction, an ancestral chemical system, is a strong candidate biomarker because it is linked to diverse physiological processes. We sought to determine if olfactory dysfunction is a harbinger of 5-year mortality in the National Social Life, Health and Aging Project [NSHAP], a nationally representative sample of older U.S. adults. 3,005 community-dwelling adults aged 57–85 were studied in 2005–6 (Wave 1) and their mortality determined in 2010–11 (Wave 2). Olfactory dysfunction, determined objectively at Wave 1, was used to estimate the odds of 5-year, all cause mortality via logistic regression, controlling for demographics and health factors. Mortality for anosmic older adults was four times that of normosmic individuals while hyposmic individuals had intermediate mortality (p<0.001), a “dose-dependent” effect present across the age range. In a comprehensive model that included potential confounding factors, anosmic older adults had over three times the odds of death compared to normosmic individuals (OR, 3.37 [95%CI 2.04, 5.57]), higher than and independent of known leading causes of death, and did not result from the following mechanisms: nutrition, cognitive function, mental health, smoking and alcohol abuse or frailty. Olfactory function is thus one of the strongest predictors of 5-year mortality and may serve as a bellwether for slowed cellular regeneration or as a marker of cumulative toxic environmental exposures. This finding provides clues for pinpointing an underlying mechanism related to a fundamental component of the aging process.     

Dominance relationships in male Nepal gray langurs (Semnopithecus schistaceus)

Stress plays an important role in the etiology of multiple morbid and mortal outcomes among the living. Drawing on health paradigms constructed among the living augments our evolving knowledge of relationships between stress and health. Therefore, elucidating relationships between stress and both chronic and acute skeletal lesions may help clarify our understanding of long‐term health trends in the past. In this study, we propose an index of “skeletal frailty,” based on models of frailty used to evaluate the life‐long effects of stress on health among living populations. Here, we assess the possible applicability of frailty to archaeological populations. The skeletal frailty index (SFI) is proposed as a methodological liaison between advances made by biological anthropologists studying relationships between stress and health among the living and bioarchaeologists studying stress and health among the dead. In a case study examining skeletal stress in Medieval London, the SFI is applied to nonmonastic (N = 60) and monastic (N = 74) samples. We used analysis of variance/analysis of covariance to compare SFI values between nonmonastic‐monastic groups, sexes, and age cohorts. Results indicate higher lifetime morbidity among monastic groups. These results complement previous bioarchaeological findings on the same London populations, wherein lower risks of mortality and longer lifespans were observed for monastic populations. SFI data reflect the morbidity‐mortality paradox observed in modern populations and accompany recent findings in bioarchaeology of variation in Medieval monastic and nonmonastic “health.” Ultimately, this study demonstrates the SFI's utility in bioarchaeology, through its application of commonly assessed skeletal biomarkers, its ease of applicability, and its potential usefulness for assessing changes in skeletal health over time and across specific geographies.     

Reduced health-related quality of life in elders with frailty: a cross-sectional study of community-dwelling elders in Taiwan

Purpose

Exploring the domains and degrees of health-related quality of life (HRQOL) that are affected by the frailty of elders will help clinicians understand the impact of frailty. This association has not been investigated in community-dwelling elders. Therefore, we examined the domains and degree of HRQOL of elders with frailty in the community in Taiwan.

Methods

A total of 933 subjects aged 65 years and over were recruited in 2009 from a metropolitan city in Taiwan. Using an adoption of the Fried criteria, frailty was defined by five components: shrinking, weakness, poor endurance and energy, slowness, and low physical activity level. HRQOL was assessed by the short form 36 (SF-36). The multiple linear regression model was used to test the independent effects of frailty on HRQOL.

Results

After multivariate adjustment, elders without frailty reported significantly better health than did the pre-frail and frail elders on all scales, and the pre-frail elders reported better health than did the frail elders for all scales except the scales of role limitation due to physical and emotional problems and the Mental Component Summary (MCS). The significantly negative differences between frail and robust elders ranged from 3.58 points for the MCS to 22.92 points for the physical functioning scale. The magnitude of the effects of frail components was largest for poor endurance and energy, and next was for slowness. The percentages of the variations of these 10 scales explained by all factors in the models ranged from 11.1% (scale of role limitation due to emotional problems) to 49.1% (scale of bodily pain).

Conclusions

Our study demonstrates that the disabilities in physical health inherent in frailty are linked to a reduction in HRQOL. Such an association between clinical measures and a generic measure of the HRQOL may offer clinicians new information to understand frailty and to conceptualize it within the broader context of disability.     

A Versatile Microparticle-Based Immunoaggregation Assay for Macromolecular Biomarker Detection and Quantification

The rapid, sensitive and low-cost detection of macromolecular biomarkers is critical in clinical diagnostics, environmental monitoring, research, etc. Conventional assay methods usually require bulky, expensive and designated instruments and relative long assay time. For hospitals and laboratories that lack immediate access to analytical instruments, fast and low-cost assay methods for the detection of macromolecular biomarkers are urgently needed. In this work, we developed a versatile microparticle (MP)-based immunoaggregation method for the detection and quantification of macromolecular biomarkers. Antibodies (Abs) were firstly conjugated to MP through streptavidin-biotin interaction; the addition of macromolecular biomarkers caused the aggregation of Ab-MPs, which were subsequently detected by an optical microscope or optical particle sizer. The invisible nanometer-scale macromolecular biomarkers caused detectable change of micrometer-scale particle size distributions. Goat anti-rabbit immunoglobulin and human ferritin were used as model biomarkers to demonstrate MP-based immunoaggregation assay in PBS and 10% FBS to mimic real biomarker assay in the complex medium. It was found that both the number ratio and the volume ratio of Ab-MP aggregates caused by biomarker to all particles were directly correlated to the biomarker concentration. In addition, we found that the detection range could be tuned by adjusting the Ab-MP concentration. We envision that this novel MP-based immunoaggregation assay can be combined with multiple detection methods to detect and quantify macromolecular biomarkers at the nanogram per milliliter level.     

Performance characteristics of combinations of host biomarkers to identify women with occult placental malaria: a case-control study from Malawi

Background

Because of its propensity to sequester in the placental intervillous space, Plasmodium falciparum can evade detection by peripheral smear in women with placental malaria (PM). We evaluated host biomarkers as potential indicators of occult PM infections.

Methods and Findings

Using a case-control design, we evaluated the ability of biomarkers to identify PM in the absence of circulating peripheral parasites (n = 24) compared to placental smear-negative controls (n = 326). We measured levels of biomarkers (C3a, C5a, CRP, angiopoietin-1, angiopoietin-2, sTie-2, sEndoglin, VEGF, sFlt-1, tissue factor, and leptin) in maternal peripheral plasma at delivery. Using ROC curve analysis, we assessed the ability of clinical parameters and biomarkers to accurately detect PM infections identified by placental smear. We show that decreases in sFlt-1 and leptin and increases in CRP were associated with occult PM infections (p<0.01) and correlated with placental parasitaemia (p<0.01). Individually, all markers had moderate ability to diagnose occult PM infections with areas under the ROC between 0.62 and 0.72. In order to improve diagnostic performance, we generated simple scoring systems to identify PM infections using either a clinical score (0–2), a biomarker score (0–3) or a clinical plus biomarker score (0–5). The combinatorial model that incorporated both clinical parameters and biomarkers had an area under curve (AUC) of 0.85 (95% CI, 0.81-0.89), which was significantly better at identifying occult PM infections than the clinical score alone (p = 0.001).

Conclusion

These data suggest that host biomarkers in the maternal peripheral blood may improve the detection of PM in the absence of peripheral parasitaemia.     

Leveraging nonlinear dynamic models to predict progression of neuroimaging biomarkers

Using biomarkers to model disease course effectively and make early prediction is a challenging but critical path to improving diagnostic accuracy and designing preventive trials for neurological disorders. Leveraging the domain knowledge that certain neuroimaging biomarkers may reflect the disease pathology, we propose a model inspired by the neural mass model from cognitive neuroscience to jointly model nonlinear dynamic trajectories of the biomarkers. Under a nonlinear mixed‐effects model framework, we introduce subject‐ and biomarker‐specific random inflection points to characterize the critical time of underlying disease progression as reflected in the biomarkers. A latent liability score is shared across biomarkers to pool information. Our model allows assessing how the underlying disease progression will affect the trajectories of the biomarkers, and, thus, is potentially useful for individual disease management or preventive therapeutics. We propose an EM algorithm for maximum likelihood estimation, where in the E step, a normal approximation is used to facilitate numerical integration. We perform extensive simulation studies and apply the method to analyze data from a large multisite natural history study of Huntington's Disease (HD). The results show that some neuroimaging biomarker inflection points are early signs of the HD onset. Finally, we develop an online tool to provide the individual prediction of the biomarker trajectories given the medical history and baseline measurements.     

An association study of cyclase-associated protein 2 and frailty

Frailty is a geriatric syndrome that results from multisystem impairment caused by age-associated accumulation of deficits. The frailty index is used to define the level of frailty. Several studies have searched for molecular biomarkers associated with frailty, to meet the needs for personalized care. Cyclase-associated protein 2 (CAP2) is a multifunctional actin-binding protein involved in various physiological and pathological processes, that might reflect frailty's intrinsic complexity. This study aimed to investigate the association between frailty index and circulating CAP2 concentration in 467 community-dwelling older adults (median age: 79; range: 65–92 years) from Milan, Italy. The selected robust regression model showed that circulating CAP2 concentration was not associated with chronological age, as well as sex and education. However, circulating CAP2 concentration was significantly and inversely associated with the frailty index: a 0.1-unit increase in frailty index leads to ~0.5-point mean decrease in CAP2 concentration. Furthermore, mean CAP2 concentration was significantly lower in frail participants (i.e., frailty index ≥0.25) than in non-frail participants. This study shows the association between serum CAP2 concentration and frailty status for the first time, highlighting the potential of CAP2 as a biomarker for age-associated accumulation of deficits.     

An in vivo platform for tumor biomarker assessment

Tumor biomarkers provide a quantitative tool for following tumor progression and response to therapy. However, investigations of clinically useful tumor biomarkers are time-consuming, costly, and limited by patient and tumor heterogeneity. In addition, assessment of biomarkers as indicators of therapy response is confounded by the concomitant use of multiple therapeutic interventions. Herein we report our use of a clinically relevant orthotopic animal model of malignant pleural mesothelioma for investigating tumor biomarkers. Utilizing multi-modality imaging with correlative histopathology, we demonstrate the utility and accuracy of the mouse model in investigating tumor biomarkers--serum soluble mesothelin-related peptide (SMRP) and osteopontin (OPN). This model revealed percentage change in SMRP level to be an accurate biomarker of tumor progression and therapeutic response--a finding consistent with recent clinical studies. This in vivo platform demonstrates the advantages of a validated mouse model for the timely and cost-effective acceleration of human biomarker translational research.     

Biomarker signatures of aging

Because people age differently, age is not a sufficient marker of susceptibility to disabilities, morbidities, and mortality. We measured nineteen blood biomarkers that include constituents of standard hematological measures, lipid biomarkers, and markers of inflammation and frailty in 4704 participants of the Long Life Family Study (LLFS), age range 30–110 years, and used an agglomerative algorithm to group LLFS participants into clusters thus yielding 26 different biomarker signatures. To test whether these signatures were associated with differences in biological aging, we correlated them with longitudinal changes in physiological functions and incident risk of cancer, cardiovascular disease, type 2 diabetes, and mortality using longitudinal data collected in the LLFS. Signature 2 was associated with significantly lower mortality, morbidity, and better physical function relative to the most common biomarker signature in LLFS, while nine other signatures were associated with less successful aging, characterized by higher risks for frailty, morbidity, and mortality. The predictive values of seven signatures were replicated in an independent data set from the Framingham Heart Study with comparable significant effects, and an additional three signatures showed consistent effects. This analysis shows that various biomarker signatures exist, and their significant associations with physical function, morbidity, and mortality suggest that these patterns represent differences in biological aging. The signatures show that dysregulation of a single biomarker can change with patterns of other biomarkers, and age‐related changes of individual biomarkers alone do not necessarily indicate disease or functional decline.     

Why have so few proteomic biomarkers “survived” validation? (Sample size and independent validation considerations)

Proteomic biomarker discovery has led to the identification of numerous potential candidates for disease diagnosis, prognosis, and prediction of response to therapy. However, very few of these identified candidate biomarkers reach clinical validation and go on to be routinely used in clinical practice. One particular issue with biomarker discovery is the identification of significantly changing proteins in the initial discovery experiment that do not validate when subsequently tested on separate patient sample cohorts. Here, we seek to highlight some of the statistical challenges surrounding the analysis of LC‐MS proteomic data for biomarker candidate discovery. We show that common statistical algorithms run on data with low sample sizes can overfit and yield misleading misclassification rates and AUC values. A common solution to this problem is to prefilter variables (via, e.g. ANOVA and or use of correction methods such as Bonferonni or false discovery rate) to give a smaller dataset and reduce the size of the apparent statistical challenge. However, we show that this exacerbates the problem yielding even higher performance metrics while reducing the predictive accuracy of the biomarker panel. To illustrate some of these limitations, we have run simulation analyses with known biomarkers. For our chosen algorithm (random forests), we show that the above problems are substantially reduced if a sufficient number of samples are analyzed and the data are not prefiltered. Our view is that LC‐MS proteomic biomarker discovery data should be analyzed without prefiltering and that increasing the sample size in biomarker discovery experiments should be a very high priority.     

Blood autoantibody and cytokine profiles predict response to anti-tumor necrosis factor therapy in rheumatoid arthritis

Introduction

Anti-TNF therapies have revolutionized the treatment of rheumatoid arthritis (RA), a common systemic autoimmune disease involving destruction of the synovial joints. However, in the practice of rheumatology approximately one-third of patients demonstrate no clinical improvement in response to treatment with anti-TNF therapies, while another third demonstrate a partial response, and one-third an excellent and sustained response. Since no clinical or laboratory tests are available to predict response to anti-TNF therapies, great need exists for predictive biomarkers.

Methods

Here we present a multi-step proteomics approach using arthritis antigen arrays, a multiplex cytokine assay, and conventional ELISA, with the objective to identify a biomarker signature in three ethnically diverse cohorts of RA patients treated with the anti-TNF therapy etanercept.

Results

We identified a 24-biomarker signature that enabled prediction of a positive clinical response to etanercept in all three cohorts (positive predictive values 58 to 72%; negative predictive values 63 to 78%).

Conclusions

We identified a multi-parameter protein biomarker that enables pretreatment classification and prediction of etanercept responders, and tested this biomarker using three independent cohorts of RA patients. Although further validation in prospective and larger cohorts is needed, our observations demonstrate that multiplex characterization of autoantibodies and cytokines provides clinical utility for predicting response to the anti-TNF therapy etanercept in RA patients.