Protein profiling reveals consequences of lifestyle choices on predicted biological aging

Perinatal Exposure to Lead or Diethylhexyl Phthalate in Mice: Sex-Specific Effects on Cardiac DNA Methylation and Gene Expression across Time

BACKGROUND

Global and site-specific changes in DNA methylation and gene expression are associated with cardiovascular development, aging, and disease, but how the transcriptome and epigenome of the heart change across the life course in males vs. females and how chemical exposures early in life influence this programming have not yet been investigated.

OBJECTIVES

We used an established mouse model of developmental exposures to investigate the effects of perinatal exposure to either lead (Pb) or diethylhexyl phthalate (DEHP), two ubiquitous environmental contaminants that are both strongly associated with cardiovascular diseases (CVDs), on DNA methylation and gene expression across the life course in whole hearts.

METHODS

Dams were randomly assigned to receive human physiologically relevant levels of Pb (32  ppm in water), DEHP (25 mg / kg chow), or control water and chow. Exposures started 2 weeks prior to mating and continued until weaning at postnatal day 21 (3 wk of age). Approximately 1 male and 1 female offspring per litter were followed to 3 wk, 5 months, or 10 months of age, at which time whole hearts were collected (n ≥ 5 per sex per exposure). Enhanced reduced representation bisulfite sequencing (ERRBS) was used to assess the cardiac DNA methylome at 3 wk and 10 months, and RNA-Seq was conducted at all three time points. MethylSig and edgeR were used to identify age-related differentially methylated regions (DMRs) and differentially expressed genes (DEGs), respectively, within each sex and exposure group. Cell type deconvolution of bulk RNA-Seq data was conducted using the MuSiC algorithm and publicly available single-cell RNA-Seq data.

RESULTS

Thousands of DMRs and hundreds of DEGs were identified in control, DEHP, and Pb-exposed hearts across time between 3 wk and 10 months of age. A closer look at the genes and pathways showing differential DNA methylation revealed that the majority were unique to each sex and exposure group. Overall, pathways governing development and differentiation changed across time in all conditions. A small number of genes in each group showed significant differences in DNA methylation and gene expression with life stage, including several that were different in toxicant-exposed but not control mice. We also observed subtle but significant differences in the proportion of several cell types that were associated with life stage, sex, or developmental exposure.

DISCUSSION

Together these data suggest that gene expression and DNA methylation programs, as well as cellular composition, may differ across the life course long after cessation of exposure in perinatal Pb- or DEHP-exposed mice compared to controls and highlight potential biomarkers of developmental toxicant exposures; however, additional studies are required for confirmation. Further studies are also needed to investigate how epigenetic and transcriptional differences impact cardiovascular health across the life course, particularly in old age when the risk of cardiovascular diseases is markedly increased. https://doi.org/10.1289/EHP15503.

Acute Impact of Nonoptimal Ambient Temperatures on Plasma Levels of 3000 Proteins in Chinese Adults

Nonoptimal ambient temperatures (i.e., cold and heat) are leading environmental determinants of major diseases worldwide, but the underlying pathological mechanisms are still poorly understood. We used distributed-lag nonlinear models to examine the associations of cold (5th percentile: -2.1 °C) and heat (95th percentile: 29.5 °C) with 2923 plasma proteins in 3926 adults from 10 areas across China. Overall, 949 proteins were significantly (5% false discovery rate) associated with ambient temperature, including 387 (216/171 down/upregulated) with cold, 770 (656/114 down/upregulated) with heat, and 208 with both cold and heat. Above the median reference temperature (17.7 °C), the associations were largely linear, while below it, they were nonlinear with attenuation below 5 °C, potentially reflecting mediation by heating. Among the 949 proteins, >80% were also associated with systolic blood pressure and incident ischemic heart disease risk and enriched in relevant pathological pathways (e.g., inflammation, immunity, and platelet aggregation). Our study provided a novel atlas of plasma proteins associated with nonoptimal temperatures in Chinese adults.

Model Organisms in Aging Research: Evolution of Database Annotation and Ortholog Discovery

BACKGROUND

This study aims to analyze the exploration degree of popular model organisms by utilizing annotations from the UniProtKB (Swiss-Prot) knowledge base. The research focuses on understanding the genomic and post-genomic data of various organisms, particularly in relation to aging as an integral model for studying the molecular mechanisms underlying pathological processes and physiological states.

METHODS

Having characterized the organisms by selected parameters (numbers of gene splice variants, post-translational modifications, etc.) using previously developed information models, we calculated proteome sizes: the number of possible proteoforms for each species. Our analysis also involved searching for orthologs of human aging genes within these model species.

RESULTS

Our findings indicate that genomic and post-genomic data for more primitive species, such as bacteria and fungi, are more comprehensively characterized compared to other organisms. This is attributed to their experimental accessibility and simplicity. Additionally, we discovered that the genomes of the most studied model organisms allow for a detailed analysis of the aging process, revealing a greater number of orthologous genes related to aging.

CONCLUSIONS

The results highlight the importance of annotating the genomes of less-studied species to identify orthologs of marker genes associated with complex physiological processes, including aging. Species that potentially possess unique traits associated with longevity and resilience to age-related changes require comprehensive genomic studies.

Impact of coffee intake on human aging: Epidemiology and cellular mechanisms

The conception of coffee consumption has undergone a profound modification, evolving from a noxious habit into a safe lifestyle actually preserving human health. The last 20 years also provided strikingly consistent epidemiological evidence showing that the regular consumption of moderate doses of coffee attenuates all-cause mortality, an effect observed in over 50 studies in different geographic regions and different ethnicities. Coffee intake attenuates the major causes of mortality, dampening cardiovascular-, cerebrovascular-, cancer- and respiratory diseases-associated mortality, as well as some of the major causes of functional deterioration in the elderly such as loss of memory, depression and frailty. The amplitude of the benefit seems discrete (17 % reduction) but nonetheless corresponds to an average increase in healthspan of 1.8 years of lifetime. This review explores evidence from studies in humans and human tissues supporting an ability of coffee and of its main components (caffeine and chlorogenic acids) to preserve the main biological mechanisms responsible for the aging process, namely genomic instability, macromolecular damage, metabolic and proteostatic impairments with particularly robust effects on the control of stress adaptation and inflammation and unclear effects on stem cells and regeneration. Further studies are required to detail these mechanistic benefits in aged individuals, which may offer new insights into understanding of the biology of aging and the development of new senostatic strategies. Additionally, the safety of this lifestyle factor in the elderly prompts a renewed attention to recommending the maintenance of coffee consumption throughout life as a healthy lifestyle and to further exploring who gets the greater benefit with what schedules of which particular types and doses of coffee.

Proteomic Markers of Aging and Longevity: A Systematic Review

This article provides a systematic review of research conducted on the proteomic composition of blood as part of a complex biological age estimation. We performed a comprehensive analysis of 17 publicly available datasets and compiled an integral list of proteins. These proteins were sorted based on their detection probability using mass spectrometry in human plasma. We propose this list as a basis for creating a panel of peptides and quantifying the content of selected proteins in the format of a proteomic aging clock. The selected proteins are especially notable for their roles in inflammatory processes and lipid metabolism. Our findings suggest, for the first time, that proteins associated with systemic disorders, including those approved by the FDA for clinical use, could serve as potential markers of aging.

Sex-Specific Deflection of Age-Related DNA Methylation and Gene Expression in Mouse Heart by Perinatal Toxicant Exposures

Background

Global and site-specific changes in DNA methylation and gene expression are associated with cardiovascular aging and disease, but how toxicant exposures during early development influence the normal trajectory of these age-related molecular changes, and whether there are sex differences, has not yet been investigated.

Objectives

We used an established mouse model of developmental exposures to investigate the effects of perinatal exposure to either lead (Pb) or diethylhexyl phthalate (DEHP), two ubiquitous environmental contaminants strongly associated with CVD, on age-related cardiac DNA methylation and gene expression.

Methods

Dams were randomly assigned to receive human physiologically relevant levels of Pb (32 ppm in water), DEHP (25 mg/kg chow), or control water and chow. Exposures started two weeks prior to mating and continued until weaning at postnatal day 21 (3 weeks of age). Approximately one male and one female offspring per litter were followed to 3 weeks, 5 months, or 10 months of age, at which time whole hearts were collected (n ≥ 5 per sex per exposure). Enhanced reduced representation bisulfite sequencing (ERRBS) was used to assess the cardiac DNA methylome at 3 weeks and 10 months, and RNA-seq was conducted at all 3 time points. MethylSig and edgeR were used to identify age-related differentially methylated regions (DMRs) and differentially expressed genes (DEGs), respectively, within each sex and exposure group. Cell type deconvolution of bulk RNA-seq data was conducted using the MuSiC algorithm and publicly available single cell RNA-seq data.

Results

Thousands of DMRs and hundreds of DEGs were identified in control, DEHP, and Pb-exposed hearts across time between 3 weeks and 10 months of age. A closer look at the genes and pathways showing differential DNA methylation revealed that the majority were unique to each sex and exposure group. Overall, pathways governing development and differentiation were most frequently altered with age in all conditions. A small number of genes in each group showed significant changes in DNA methylation and gene expression with age, including several that were altered by both toxicants but were unchanged in control. We also observed subtle, but significant changes in the proportion of several cell types due to age, sex, and developmental exposure.

Discussion

Together these data show that perinatal Pb or DEHP exposures deflect normal age-related gene expression, DNA methylation programs, and cellular composition across the life course, long after cessation of exposure, and highlight potential biomarkers of developmental toxicant exposures. Further studies are needed to investigate how these epigenetic and transcriptional changes impact cardiovascular health across the life course.

Plasma proteins as potential biomarkers of aging of single tissue and cell type

Plasma proteins serve as biomarkers of aging and various age-related diseases. While a number of plasma proteins have been identified that increase or decrease with age, the interpretation of each protein is challenging. This is due to the nature of plasma, which is a mixture of factors secreted by many different tissues and cells. Therefore, the catalog of age-related proteins secreted by a single cell type in a single tissue would be useful for understanding tissue-specific aging patterns. In this study, the author addressed this challenge by integrative data mining of the Human Protein Atlas and the recently published result of large-scale aging proteomics research. Finally, we identified the 17 age-related proteins produced by a single tissue and a single cell type: MBL2 and HP in the liver (hepatocytes), SFTPC in the lung (type II alveolar cells), PRL and POMC in the pituitary (anterior cells), GCG, CUZD1 and CPA2 in the pancreas (pancreatic cells), MYBPC1 in skeletal muscle (myocytes), PTH in the parathyroid gland (glandular cells), LPO and AMY1A in the salivary gland (glandular cells), INSL3 in the male testis (Leydig cells), KLK3 and KLK4 in the male prostate (glandular cells), MPO and ACP5 in immune cells. This list of proteins would be potentially useful for understanding age-related changes in the plasma proteome and inter-tissue networks.

OMICmAge: An integrative multi-omics approach to quantify biological age with electronic medical records

Biological aging is a multifactorial process involving complex interactions of cellular and biochemical processes that is reflected in omic profiles. Using common clinical laboratory measures in ~30,000 individuals from the MGB-Biobank, we developed a robust, predictive biological aging phenotype, EMRAge, that balances clinical biomarkers with overall mortality risk and can be broadly recapitulated across EMRs. We then applied elastic-net regression to model EMRAge with DNA-methylation (DNAm) and multiple omics, generating DNAmEMRAge and OMICmAge, respectively. Both biomarkers demonstrated strong associations with chronic diseases and mortality that outperform current biomarkers across our discovery (MGB-ABC, n=3,451) and validation (TruDiagnostic, n=12,666) cohorts. Through the use of epigenetic biomarker proxies, OMICmAge has the unique advantage of expanding the predictive search space to include epigenomic, proteomic, metabolomic, and clinical data while distilling this in a measure with DNAm alone, providing opportunities to identify clinically-relevant interconnections central to the aging process.

Age prediction from human blood plasma using proteomic and small RNA data: a comparative analysis

Aging clocks, built from comprehensive molecular data, have emerged as promising tools in medicine, forensics, and ecological research. However, few studies have compared the suitability of different molecular data types to predict age in the same cohort and whether combining them would improve predictions. Here, we explored this at the level of proteins and small RNAs in 103 human blood plasma samples. First, we used a two-step mass spectrometry approach measuring 612 proteins to select and quantify 21 proteins that changed in abundance with age. Notably, proteins increasing with age were enriched for components of the complement system. Next, we used small RNA sequencing to select and quantify a set of 315 small RNAs that changed in abundance with age. Most of these were microRNAs (miRNAs), downregulated with age, and predicted to target genes related to growth, cancer, and senescence. Finally, we used the collected data to build age-predictive models. Among the different types of molecules, proteins yielded the most accurate model (R² = 0.59 ± 0.02), followed by miRNAs as the best-performing class of small RNAs (R² = 0.54 ± 0.02). Interestingly, the use of protein and miRNA data together improved predictions (R2 = 0.70 ± 0.01). Future work using larger sample sizes and a validation dataset will be necessary to confirm these results. Nevertheless, our study suggests that combining proteomic and miRNA data yields superior age predictions, possibly by capturing a broader range of age-related physiological changes. It will be interesting to determine if combining different molecular data types works as a general strategy to improve future aging clocks.

Pre-diagnostic blood biomarkers for adult glioma

Glioma is one of the most common malignant primary brain tumours in adults, of which, glioblastoma is the most prevalent and malignant entity. Glioma is often diagnosed at a later stage of disease progression, which means it is associated with significant mortality and morbidity. Therefore, there is a need for earlier diagnosis of these tumours, which would require sensitive and specific biomarkers. These biomarkers could better predict glioma onset to improve diagnosis and therapeutic options for patients. While liquid biopsies could provide a cheap and non-invasive test to improve the earlier detection of glioma, there is little known on pre-diagnostic biomarkers which predate disease detection. In this review, we examine the evidence in the literature for pre-diagnostic biomarkers in glioma, including metabolomics and proteomics. We also consider the limitations of these approaches and future research directions of pre-diagnostic biomarkers for glioma.

Effect of mHealth plus occupational therapy on cognitive function, mood and physical function in people after cancer: Secondary analysis of a randomized controlled trial

BACKGROUND

Medical and surgical treatments for breast cancer have various adverse effects. Both mobile health and supervised intervention strategies have been implemented to overcome these effects, but some gaps remain to be addressed. Scientific evidence for the effectiveness of occupational therapy in cancer is limited.

OBJECTIVE

To compare the clinical effectiveness of the BENECA mHealth app used alone or combined with an integral supervised rehabilitation strategy that focused on cognitive performance, mood state, functional capacity, and cancer-related pain and fatigue in overweight women after breast cancer.

METHODS

In this secondary analysis of an assessor-blinded randomized controlled clinical trial, 80 overweight women after breast cancer (stage I-IIIA) were randomly allocated to an integral approach group (IA; n=40) or a control group (CG; n=40). All participants participated in an 8-week intervention. Assessments were performed at baseline, 8 weeks, and 6 months and included cognitive performance (Trial Making Test and Wechsler Adult Intelligence Scale), psychological state (Hospital Anxiety and Depression Scale), pain (Brief Pain Inventory), fatigue (Piper Fatigue Scale), and physical function (6 min walk test). An intention-to-treat analysis was conducted with analysis of covariance.

RESULTS

Selective attention (TMT) was significantly higher in the IA group, with a moderate to large effect size for TMT A (T2: d=1.1; T 3: d=1.2), working memory and processing speed (WAIS), anxiety and general HADS score (d=1.6), and functional capacity at 8 weeks and 6 months (d=1.5). Fatigue perception (mean difference, -0.6; 95% CI -1.4 to 0.04; p=0.009) and pain (intensity level p<0.001; interference level p=0.002) were also significantly more improved in the IA group.

CONCLUSIONS

An integral strategy involving the BENECA mHealth app with a supervised, multimodal intervention improved cognitive, psychological, and functional performance in women after breast cancer more than mHealth alone. Occupational therapy has a role to play in breast cancer rehabilitation.

Human age reversal: Fact or fiction?

Although chronological age correlates with various age-related diseases and conditions, it does not adequately reflect an individual's functional capacity, well-being, or mortality risk. In contrast, biological age provides information about overall health and indicates how rapidly or slowly a person is aging. Estimates of biological age are thought to be provided by aging clocks, which are computational models (e.g., elastic net) that use a set of inputs (e.g., DNA methylation sites) to make a prediction. In the past decade, aging clock studies have shown that several age-related diseases, social variables, and mental health conditions associate with an increase in predicted biological age relative to chronological age. This phenomenon of age acceleration is linked to a higher risk of premature mortality. More recent research has demonstrated that predicted biological age is sensitive to specific interventions. Human trials have reported that caloric restriction, a plant-based diet, lifestyle changes involving exercise, a drug regime including metformin, and vitamin D3 supplementation are all capable of slowing down or reversing an aging clock. Non-interventional studies have connected high-quality sleep, physical activity, a healthy diet, and other factors to age deceleration. Specific molecules have been associated with the reduction or reversal of predicted biological age, such as the antihypertensive drug doxazosin or the metabolite alpha-ketoglutarate. Although rigorous clinical trials are needed to validate these initial findings, existing data suggest that aging clocks are malleable in humans. Additional research is warranted to better understand these computational models and the clinical significance of lowering or reversing their outputs.

A catalogue of omics biological ageing clocks reveals substantial commonality and associations with disease risk

Biological age (BA), a measure of functional capacity and prognostic of health outcomes that discriminates between individuals of the same chronological age (chronAge), has been estimated using a variety of biomarkers. Previous comparative studies have mainly used epigenetic models (clocks), we use ~1000 participants to compare fifteen omics ageing clocks, with correlations of 0.21-0.97 with chronAge, even with substantial sub-setting of biomarkers. These clocks track common aspects of ageing with 95% of the variance in chronAge being shared among clocks. The difference between BA and chronAge - omics clock age acceleration (OCAA) - often associates with health measures. One year’s OCAA typically has the same effect on risk factors/10-year disease incidence as 0.09/0.25 years of chronAge. Epigenetic and IgG glycomics clocks appeared to track generalised ageing while others capture specific risks. We conclude BA is measurable and prognostic and that future work should prioritise health outcomes over chronAge.

The Role of Immune Cells in Oxi-Inflamm-Aging

Aging is the result of the deterioration of the homeostatic systems (nervous, endocrine, and immune systems), which preserve the organism's health. We propose that the age-related impairment of these systems is due to the establishment of a chronic oxidative stress situation that leads to low-grade chronic inflammation throughout the immune system's activity. It is known that the immune system weakens with age, which increases morbidity and mortality. In this context, we describe how the function of immune cells can be used as an indicator of the rate of aging of an individual. In addition to this passive role as a marker, we describe how the immune system can work as a driver of aging by amplifying the oxidative-inflammatory stress associated with aging (oxi-inflamm-aging) and inducing senescence in far tissue cells. Further supporting our theory, we discuss how certain lifestyle conditions (such as social environment, nutrition, or exercise) can have an impact on longevity by affecting the oxidative and inflammatory state of immune cells, regulating immunosenescence and its contribution to oxi-inflamm-aging.

Predictive models of aging of the human eye based on ocular anterior segment morphology

Aging is a major risk factor for various eye diseases, such as cataract, glaucoma, and age-related macular degeneration. Age-related changes are observed in almost all structures of the human eye. Considerable individual variations exist within a group of similarly aged individuals, indicating the need for more informative biomarkers for assessing the aging of the eyes. The morphology of the ocular anterior segment has been reported to vary across age groups, focusing on only a few corneal parameters, such as keratometry and thickness of the cornea, which could not provide accurate estimation of age. Thus, the association between eye aging and the morphology of the anterior segment remains elusive. In this study, we aimed to develop a predictive model of age based on a large number of anterior segment morphology-related features, measured via the high-resolution ocular anterior segment analysis system (Pentacam). This approach allows for an integrated assessment of age-related changes in corneal morphology, and the identification of important morphological features associated with different eye aging patterns. Three machine learning methods (neural networks, Lasso regression and extreme gradient boosting) were employed to build predictive models using 276 anterior segment features of 63,753 participants from 10 ophthalmic centers in 10 different cities of China. The best performing age prediction model achieved a median absolute error of 2.80 years and a mean absolute error of 3.89 years in the validation set. An external cohort of 100 volunteers was used to test the performance of the prediction model. The developed neural network model achieved a median absolute error of 3.03 years and a mean absolute error of 3.40 years in the external cohort. In summary, our study revealed that the anterior segment morphology of the human eye may be an informative and non-invasive indicator of eye aging. This could prompt doctors to focus on age-related medical interventions on ocular health.

The Immunity Clock

The immune system has been for long considered a marker of health. The age-related decline in its function results in a greater incidence of infections, autoimmune diseases and cancer. Nevertheless, it is still not known if immune function can be used to accurately estimate the rate of aging of an individual. A set of 14 immune function variables were measured in 214 healthy individuals ranging from 19 to 88 years old. All immune variables were selected as independent variables for the prediction of age by multiple linear regression (MLR). The Immunity Clock was constructed including the following 5 immune variables: natural killer activity, phagocytosis and chemotaxis of neutrophils and chemotaxis and proliferative capacity of lymphocytes reaching an adjusted R 2 of 80.3% and a standard error of the estimate of 4.74 years. The Immunity Clock was validated in a different group of healthy individuals (N=106) obtaining a Pearson´s correlation coefficient of 0.898 (p < 0.001) between chronological age and the age estimated by the Immunity Clock, the ImmunolAge. Moreover, we demonstrate that women with anxiety (N=10) show a higher ImmunolAge than their chronological age whereas healthy centenarians (N=8) show a lower one. In addition, the Immunity Clock provided here proves to be useful for monitoring the effectiveness of a nutritional intervention lasting one month, by detecting a diminished ImmunolAge in the same individuals. Further research will be needed to ascertain if the Immunity Clock is a passive marker of the aging process or it plays an active role in it.

Proteomics in aging research: A roadmap to clinical, translational research

The identification of plasma proteins that systematically change with age and, independent of chronological age, predict accelerated decline of health is an expanding area of research. Circulating proteins are ideal translational "omics" since they are final effectors of physiological pathways and because physicians are accustomed to use information of plasma proteins as biomarkers for diagnosis, prognosis, and tracking the effectiveness of treatments. Recent technological advancements, including mass spectrometry (MS)-based proteomics, multiplexed proteomic assay using modified aptamers (SOMAscan), and Proximity Extension Assay (PEA, O-Link), have allowed for the assessment of thousands of proteins in plasma or other biological matrices, which are potentially translatable into new clinical biomarkers and provide new clues about the mechanisms by which aging is associated with health deterioration and functional decline. We carried out a detailed literature search for proteomic studies performed in different matrices (plasma, serum, urine, saliva, tissues) and species using multiple platforms. Herein, we identified 232 proteins that were age-associated across studies. Enrichment analysis of the 232 age-associated proteins revealed metabolic pathways previously connected with biological aging both in animal models and in humans, most remarkably insulin-like growth factor (IGF) signaling, mitogen-activated protein kinases (MAPK), hypoxia-inducible factor 1 (HIF1), cytokine signaling, Forkhead Box O (FOXO) metabolic pathways, folate metabolism, advance glycation end products (AGE), and receptor AGE (RAGE) metabolic pathway. Information on these age-relevant proteins, likely expanded and validated in longitudinal studies and examined in mechanistic studies, will be essential for patient stratification and the development of new treatments aimed at improving health expectancy.

A randomized trial of physical activity for cognitive functioning in breast cancer survivors: Rationale and study design of I Can! Improving Cognition After Cancer

INTRODUCTION

Difficulties with cognition are extremely common among breast cancer survivors and can significantly impact quality of life, daily functioning, and ability to return to work. One promising intervention is increasing physical activity, as it has been effective in improving cognition in non-cancer populations. Few physical activity intervention trials with cognition outcomes have included cancer survivors. This project builds upon our previous work indicating that increased physical activity can improve objectively measured processing speed and self-reported cognition among breast cancer survivors.

METHODS

The I Can! study will examine whether a physical activity intervention improves cognition among 250 post-treatment breast cancer survivors (Stages I-III, <5 years post-treatment) who are reporting cognitive difficulties. This 2-arm randomized controlled trial comparing a 6-month physical activity intervention (Exercise Group) to a health & wellness attention-comparison condition (Health & Wellness Group) will examine intervention effects on cognition (at 3 and 6 months) and maintenance of effects at 12 months. The primary aim is to investigate the impact of exercise on objectively measured processing speed and self-reported cognition. Secondary aims are to investigate maintenance of cognitive changes and examine candidate biological mechanisms and psychological mediators.

CONCLUSION

The I Can! study will contribute to the scientific, public health, and survivorship intervention literature by providing new information on the impact of physical activity for cognitive impairment in breast cancer survivors. Findings from this study will inform guidelines for physical activity to improve the lives of millions of breast cancer survivors.

Plasma proteins associated with cardiovascular death in patients with chronic coronary heart disease: A retrospective study

BACKGROUND

Circulating biomarkers are associated with the development of coronary heart disease (CHD) and its complications by reflecting pathophysiological pathways and/or organ dysfunction. We explored the associations between 157 cardiovascular (CV) and inflammatory biomarkers and CV death using proximity extension assays (PEA) in patients with chronic CHD.

METHODS AND FINDINGS

The derivation cohort consisted of 605 cases with CV death and 2,788 randomly selected non-cases during 3-5 years follow-up included in the STabilization of Atherosclerotic plaque By Initiation of darapLadIb TherapY (STABILITY) trial between 2008 and 2010. The replication cohort consisted of 245 cases and 1,042 non-cases during 12 years follow-up included in the Ludwigshafen Risk and Cardiovascular Health (LURIC) study between 1997 and 2000. Biomarker levels were measured with conventional immunoassays and/or with the OLINK PEA panels CVD I and Inflammation. Associations with CV death were evaluated by Random Survival Forest (RF) and Cox regression analyses. Both cohorts had the same median age (65 years) and 20% smokers, while there were slight differences in male sex (82% and 76%), hypertension (70% and 78%), and diabetes (39% and 30%) in the respective STABILITY and LURIC cohorts. The analyses identified 18 biomarkers with confirmed independent association with CV death by Boruta analyses and statistical significance (all p < 0.0001) by Cox regression when adjusted for clinical characteristics in both cohorts. Most prognostic information was carried by N-terminal prohormone of brain natriuretic peptide (NTproBNP), hazard ratio (HR for 1 standard deviation [SD] increase of the log scale of the distribution of the biomarker in the replication cohort) 2.079 (95% confidence interval [CI] 1.799-2.402), and high-sensitivity troponin T (cTnT-hs) HR 1.715 (95% CI 1.491-1.973). The other proteins with independent associations were growth differentiation factor 15 (GDF-15) HR 1.728 (95% CI 1.527-1.955), transmembrane immunoglobulin and mucin domain protein (TIM-1) HR 1.555 (95% CI 1.362-1.775), renin HR 1.501 (95% CI 1.305-1.727), osteoprotegerin (OPG) HR 1.488 (95% CI 1.297-1.708), soluble suppression of tumorigenesis 2 protein (sST2) HR 1.478 (95% CI 1.307-1.672), cystatin-C (Cys-C) HR 1.370 (95% CI 1.243-1.510), tumor necrosis factor-related apoptosis-inducing ligand receptor 2 (TRAIL-R2) HR 1.205 (95% CI 1.131-1.285), carbohydrate antigen 125 (CA-125) HR 1.347 (95% CI 1.226-1.479), brain natriuretic peptide (BNP) HR 1.399 (95% CI 1.255-1.561), interleukin 6 (IL-6) HR 1.478 (95% CI 1.316-1.659), hepatocyte growth factor (HGF) HR 1.259 (95% CI 1.134-1.396), spondin-1 HR 1.295 (95% CI 1.156-1.450), fibroblast growth factor 23 (FGF-23) HR 1.349 (95% CI 1.237-1.472), chitinase-3 like protein 1 (CHI3L1) HR 1.284 (95% CI 1.129-1.461), tumor necrosis factor receptor 1 (TNF-R1) HR 1.486 (95% CI 1.307-1.689), and adrenomedullin (AM) HR 1.750 (95% CI 1.490-2.056). The study is limited by the differences in design, size, and length of follow-up of the 2 studies and the lack of results from coronary angiograms and follow-up of nonfatal events.

CONCLUSIONS

Profiles of levels of multiple plasma proteins might be useful for the identification of different pathophysiological pathways associated with an increased risk of CV death in patients with chronic CHD.

TRIAL REGISTRATION

ClinicalTrials.gov NCT00799903.

AI-based investigation of molecular biomarkers of longevity

In this paper, I build deep neural networks of various structures and hyperparameters in order to predict human chronological age based on open-access biochemical indicators and their specifications from the NHANES database. In total, 1152 neural networks are trained and tested. The algorithms are trained and tested on incomplete data: missing values in data records are extrapolated by mean or median values for each parameter. I select the best neural networks in terms of validation accuracy (coefficient of determination and mean absolute error). It turns out that the most accurate results are delivered by multilayer networks (6 layers) with recurrent layers. Neural network types are selected by trial and error. The algorithms reached an accuracy of 78% in terms of coefficient of determination and 6.5 in terms of mean absolute error. I also list empirically determined features of neural networks that increase accuracy for the task of chronological age prediction. Obtained results can be considered as an approximation of human biological age. Parameters in training datasets are selected the most broadly: all potentially relevant parameters (926) from the NHANES database are used. Although the networks are trained on the incomplete data, they demonstrated the ability to make reasonable predictions (with R² > 0.7) based on no more than 100 biochemical indicators. Hence, for practical reasons the full data on each of 926 indicators are not required, although the analysis of the impact of each indicator is useful for theoretical developments.

Depression, GABA, and Age Correlate with Plasma Levels of Inflammatory Markers

Immunomodulation is increasingly being recognised as a part of mental diseases. Here, we examined whether levels of immunological protein markers changed with depression, age, or the inhibitory neurotransmitter gamma-aminobutyric acid (GABA). An analysis of plasma samples from patients with a major depressive episode and control blood donors (CBD) revealed the expression of 67 inflammatory markers. Thirteen of these markers displayed augmented levels in patients compared to CBD. Twenty-one markers correlated with the age of the patients, whereas 10 markers correlated with the age of CBD. Interestingly, CST5 and CDCP1 showed the strongest correlation with age in the patients and CBD, respectively. IL-18 was the only marker that correlated with the MADRS-S scores of the patients. Neuronal growth factors (NGFs) were significantly enhanced in plasma from the patients, as was the average plasma GABA concentration. GABA modulated the release of seven cytokines in anti-CD3-stimulated peripheral blood mononuclear cells (PBMCs) from the patients. The study reveals significant changes in the plasma composition of small molecules during depression and identifies potential peripheral biomarkers of the disease.

Improved power and precision with whole genome sequencing data in genome-wide association studies of inflammatory biomarkers

Genome-wide association studies (GWAS) have identified associations between thousands of common genetic variants and human traits. However, common variants usually explain a limited fraction of the heritability of a trait. A powerful resource for identifying trait-associated variants is whole genome sequencing (WGS) data in cohorts comprised of families or individuals from a limited geographical area. To evaluate the power of WGS compared to imputations, we performed GWAS on WGS data for 72 inflammatory biomarkers, in a kinship-structured cohort. When using WGS data, we identified 18 novel associations that were not detected when analyzing the same biomarkers with genotyped or imputed SNPs. Five of the novel top variants were low frequency variants with a minor allele frequency (MAF) of <5%. Our results suggest that, even when applying a GWAS approach, we gain power and precision using WGS data, presumably due to more accurate determination of genotypes. The lack of a comparable dataset for replication of our results is a limitation in our study. However, this further highlights that there is a need for more genetic epidemiological studies based on WGS data.

Association between Biomarkers of Low-grade Inflammation and Sex Hormones in Women with Polycystic Ovary Syndrome

Objective Women with polycystic ovary syndrome (PCOS) have higher circulating levels of C-reactive protein, but the relationship between inflammation and endocrine function in PCOS remains poorly understood. Thus, this study aimed to investigate the association between low-grade inflammation and sex hormones in women with PCOS.

Design and Patients A comprehensive panel of biomarkers of inflammation was measured in serum of 63 women with PCOS using proximity extension assay technology. Associations of 65 biomarkers with sex hormones were assessed without and with adjustment for age and body mass index (BMI).

Results In the unadjusted analysis, 20 biomarkers were positively correlated with 17-OH-progesterone (17-OH-P), 14 with prolactin and 6 with free testosterone, whereas inverse associations were found for 16 biomarkers with sex hormone-binding globulin (SHBG), 6 with luteinizing hormone (LH) and 6 with estrogen (all p<0.05). Among the positive associations, correlations were mainly found for five chemokines (CXCL11, CCL4, MCP-4/CCL13, CXCL5, CXCL6) and for VEGF-A, LAP-TGFβ1, TNFSF14 and MMP-1. Inverse associations with sex hormones were mainly present for two chemokines (CXCL1, MCP-2/CCL8), CDCP1, CST5 and CSF-1. Adjustment for age and BMI reduced the number of biomarker associations for SHBG and estrogen, but had hardly any impact on associations with 17-OH-P, prolactin, free testosterone and LH.

Conclusion Women with PCOS feature BMI-independent associations between biomarkers of inflammation and certain sex steroid and hypophyseal hormones. Most of these inflammation-related biomarkers were chemokines, which may be relevant as potential mediators of the increased cardiometabolic risk of women with PCOS.

General and Abdominal Obesity and Incident Distal Sensorimotor Polyneuropathy: Insights Into Inflammatory Biomarkers as Potential Mediators in the KORA F4/FF4 Cohort

OBJECTIVE

To investigate the associations between different anthropometric measurements and development of distal sensorimotor polyneuropathy (DSPN) considering interaction effects with prediabetes/diabetes and to evaluate subclinical inflammation as a potential mediator.

RESEARCH DESIGN AND METHODS

This study was conducted among 513 participants from the Cooperative Health Research in the Region of Augsburg (KORA) F4/FF4 cohort (aged 62-81 years). Anthropometry was measured at baseline. Incident DSPN was defined by neuropathic impairments using the Michigan Neuropathy Screening Instrument at baseline and follow-up. Associations between anthropometric measurements and DSPN were estimated by multivariable logistic regression. Potential differences by diabetes status were assessed using interaction terms. Mediation analysis was conducted to determine the mediation effect of subclinical inflammation in these associations.

RESULTS

After a mean follow-up of 6.5 years, 127 cases with incident DSPN were detected. Both general and abdominal obesity were associated with development of DSPN. The odds ratios (95% CI) of DSPN were 3.06 (1.57; 5.97) for overweight, 3.47 (1.72; 7.00) for obesity (reference: normal BMI), and 1.22 (1.07; 1.38) for 5-cm differences in waist circumference, respectively. Interaction analyses did not indicate any differences by diabetes status. Two chemokines (C-C motif chemokine ligand 7 [CCL7] and C-X-C motif chemokine ligand 10 [CXCL10]) and one neuron-specific marker (Delta/Notch-like epidermal growth factor-related receptor [DNER]) were identified as potential mediators, which explained a proportion of the total effect up to 11% per biomarker.

CONCLUSIONS

General and abdominal obesity were associated with incident DSPN among individuals with and without diabetes, and this association was partly mediated by inflammatory markers. However, further mechanisms and biomarkers should be investigated as additional mediators to explain the remainder of this association.

A Systemic Inflammatory Signature Reflecting Cross Talk Between Innate and Adaptive Immunity Is Associated With Incident Polyneuropathy: KORA F4/FF4 Study

Prospective analyses of biomarkers of inflammation and distal sensorimotor polyneuropathy (DSPN) are scarce and limited to innate immunity. We therefore aimed to assess associations between biomarkers reflecting multiple aspects of immune activation and DSPN. The study was based on 127 case subjects with incident DSPN and 386 noncase subjects from the population-based Cooperative Health Research in the Region of Augsburg (KORA) F4/FF4 cohort (follow-up 6.5 years). Proximity extension assay technology was used to measure serum levels of biomarkers of inflammation. Of 71 biomarkers assessed, 26 were associated with incident DSPN. After adjustment for multiple testing, higher levels of six biomarkers remained related to incident DSPN. Three of these proteins (MCP-3/CCL7, MIG/CXCL9, IP-10/CXCL10) were chemokines, and the other three (DNER, CD40, TNFRSF9) were soluble forms of transmembrane receptors. The chemokines had neurotoxic effects on neuroblastoma cells in vitro. Addition of all six biomarkers improved the C statistic of a clinical risk model from 0.748 to 0.783 (P = 0.011). Pathway analyses indicated that multiple cell types from innate and adaptive immunity are involved in the development of DSPN. We thus identified novel associations between biomarkers of inflammation and incident DSPN pointing to a complex cross talk between innate and adaptive immunity in the pathogenesis of the disease.

High plasma organochlorine pesticide levels are related to increased biological age as calculated by DNA methylation analysis

BACKGROUND

Organochlorine pesticides (OCPs) have been shown in the experimental setting to alter DNA methylation. Since DNA methylation changes during the life-span, formulas have been presented to calculate "DNA methylation age" as a measure of biological age.

OBJECTIVES

We aimed to investigate if circulating levels of three OCPs were related to increased DNA methylation age METHODS: 71CpG DNA methylation age (Hannum formula) was calculated based on data from the Illumina 450 k Bead Methylation chip in 1000 subjects in the Prospective Study of the Vasculature in Uppsala Seniors (PIVUS) study (50% women, all aged 70 years at the examination). The difference between DNA methylation age and chronological age was calculated (DiffAge). 2,2-bis (4-chlorophenyl)-1,1-dichloroethene (p,p'-DDE), hexachlorobenzene (HCB), and transnonachlor (TNC) levels were measured in plasma by high-resolution gas chromatography coupled mass spectrometry (HRGC-HRMS).

RESULTS

Increased p,p'-DDE and TNC, but not HCB, levels were related to increased DiffAge both in sex and BMI-adjusted models, as well as in multiple adjusted models (sex, education level, exercise habits, smoking, energy and alcohol consumption and BMI) (p = 0.0051 and p = 0.011, respectively). No significant interactions between the OCPs and sex or BMI regarding DiffAge were found.

CONCLUSION

In this cross-sectional study, increased levels of two out of three OCPs were related to increased DNA methylation age, further suggesting negative health effects in humans of these widespread environmental contaminants.

Extracting biological age from biomedical data via deep learning: too much of a good thing?

Age-related physiological changes in humans are linearly associated with age. Naturally, linear combinations of physiological measures trained to estimate chronological age have recently emerged as a practical way to quantify aging in the form of biological age. In this work, we used one-week long physical activity records from a 2003-2006 National Health and Nutrition Examination Survey (NHANES) to compare three increasingly accurate biological age models: the unsupervised Principal Components Analysis (PCA) score, a multivariate linear regression, and a state-of-the-art deep convolutional neural network (CNN). We found that the supervised approaches produce better chronological age estimations at the expense of a loss of the association between the aging acceleration and all-cause mortality. Consequently, we turned to the NHANES death register directly and introduced a novel way to train parametric proportional hazards models suitable for out-of-the-box implementation with any modern machine learning software. As a demonstration, we produced a separate deep CNN for mortality risks prediction that outperformed any of the biological age or a simple linear proportional hazards model. Altogether, our findings demonstrate the emerging potential of combined wearable sensors and deep learning technologies for applications involving continuous health risk monitoring and real-time feedback to patients and care providers.

Biomarkers in cancer therapy related cardiac dysfunction (CTRCD)

Biomarkers are at the cornerstone of preventive measures and contribute to the screening process. More recently, biomarkers have been used to gauge the biological response to the employed therapies. Since it is ubiquitously used to detect subclinical disease process, biomarkers also have found its place in cancer therapy related cardiac dysfunction (CTRCD). The aim of this review is to comprehensively present up-to-date knowledge of biomarkers in CTRCD and highlight some of the future biomedical technologies that may strengthen the screening process, and/or provide new insight in pathological mechanisms behind CTRCD.

The Human Plasma Proteome Draft of 2017: Building on the Human Plasma PeptideAtlas from Mass Spectrometry and Complementary Assays

Human blood plasma provides a highly accessible window to the proteome of any individual in health and disease. Since its inception in 2002, the Human Proteome Organization's Human Plasma Proteome Project (HPPP) has been promoting advances in the study and understanding of the full protein complement of human plasma and on determining the abundance and modifications of its components. In 2017, we review the history of the HPPP and the advances of human plasma proteomics in general, including several recent achievements. We then present the latest 2017-04 build of Human Plasma PeptideAtlas, which yields ∼43 million peptide-spectrum matches and 122,730 distinct peptide sequences from 178 individual experiments at a 1% protein-level FDR globally across all experiments. Applying the latest Human Proteome Project Data Interpretation Guidelines, we catalog 3509 proteins that have at least two non-nested uniquely mapping peptides of nine amino acids or more and >1300 additional proteins with ambiguous evidence. We apply the same two-peptide guideline to historical PeptideAtlas builds going back to 2006 and examine the progress made in the past ten years in plasma proteome coverage. We also compare the distribution of proteins in historical PeptideAtlas builds in various RNA abundance and cellular localization categories. We then discuss advances in plasma proteomics based on targeted mass spectrometry as well as affinity assays, which during early 2017 target ∼2000 proteins. Finally, we describe considerations about sample handling and study design, concluding with an outlook for future advances in deciphering the human plasma proteome.

The relative contribution of DNA methylation and genetic variants on protein biomarkers for human diseases

Associations between epigenetic alterations and disease status have been identified for many diseases. However, there is no strong evidence that epigenetic alterations are directly causal for disease pathogenesis. In this study, we combined SNP and DNA methylation data with measurements of protein biomarkers for cancer, inflammation or cardiovascular disease, to investigate the relative contribution of genetic and epigenetic variation on biomarker levels. A total of 121 protein biomarkers were measured and analyzed in relation to DNA methylation at 470,000 genomic positions and to over 10 million SNPs. We performed epigenome-wide association study (EWAS) and genome-wide association study (GWAS) analyses, and integrated biomarker, DNA methylation and SNP data using between 698 and 1033 samples depending on data availability for the different analyses. We identified 124 and 45 loci (Bonferroni adjusted P < 0.05) with effect sizes up to 0.22 standard units' change per 1% change in DNA methylation levels and up to four standard units' change per copy of the effective allele in the EWAS and GWAS respectively. Most GWAS loci were cis-regulatory whereas most EWAS loci were located in trans. Eleven EWAS loci were associated with multiple biomarkers, including one in NLRC5 associated with CXCL11, CXCL9, IL-12, and IL-18 levels. All EWAS signals that overlapped with a GWAS locus were driven by underlying genetic variants and three EWAS signals were confounded by smoking. While some cis-regulatory SNPs for biomarkers appeared to have an effect also on DNA methylation levels, cis-regulatory SNPs for DNA methylation were not observed to affect biomarker levels. We present associations between protein biomarker and DNA methylation levels at numerous loci in the genome. The associations are likely to reflect the underlying pattern of genetic variants, specific environmental exposures, or represent secondary effects to the pathogenesis of disease.

Effects of Long-Term Storage Time and Original Sampling Month on Biobank Plasma Protein Concentrations

The quality of clinical biobank samples is crucial to their value for life sciences research. A number of factors related to the collection and storage of samples may affect the biomolecular composition. We have studied the effect of long-time freezer storage, chronological age at sampling, season and month of the year and on the abundance levels of 108 proteins in 380 plasma samples collected from 106 Swedish women. Storage time affected 18 proteins and explained 4.8–34.9% of the observed variance. Chronological age at sample collection after adjustment for storage-time affected 70 proteins and explained 1.1–33.5% of the variance. Seasonal variation had an effect on 15 proteins and month (number of sun hours) affected 36 proteins and explained up to 4.5% of the variance after adjustment for storage-time and age. The results show that freezer storage time and collection date (month and season) exerted similar effect sizes as age on the protein abundance levels. This implies that information on the sample handling history, in particular storage time, should be regarded as equally prominent covariates as age or gender and need to be included in epidemiological studies involving protein levels.

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Storage time explains up to 35 % of plasma protein concentration variation in frozen biobank samples from healthy women.

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Storage time exert similar effect sizes as individual age and should be included as a covariate in epidemiological studies.

One basic requirement of life science research is the quality of samples. Proper handling and rigorous biobanking of clinical samples is crucial for collection of samples for rare diseases, for monitoring individual variation in longitudinal studies and for prospective studies of biomarkers and risk of developing for instance cardiovascular disease. We have studied the effect of long-time storage, individual age and sampling month and conclude that storage-time has similar impact on protein levels as age. The results emphasize the need to include sample parameters as covariates in future epidemiological studies, which may facilitate future discoveries of novel biomarkers for disease.

Reliability of DNA methylation measures from dried blood spots and mononuclear cells using the HumanMethylation450k BeadArray

The reliability of methylation measures from the widely used HumanMethylation450 (HM450K) microarray has not been assessed for DNA from dried blood spots (DBS) or peripheral blood mononuclear cells (PBMC), nor for combined data from different studies. Repeated HM450K methylation measures in DNA from DBS and PBMC samples were available from participants in six case-control studies nested within the Melbourne Collaborative Cohort Study. Reliability was assessed for individual CpGs by calculating the intraclass correlation coefficient (ICC) based on technical replicates (samples repeated in a single study; 126 PBMC, 136 DBS) and study duplicates (samples repeated across studies; 280 PBMC, 769 DBS) using mixed-effects models. Reliability based on technical replicates was moderate for PBMC (median ICC = 0.42), but lower for DBS (median ICC = 0.20). Study duplicates gave lower ICCs than technical replicates. CpGs that were either highly methylated or unmethylated generally had lower ICCs, which appeared to be mostly related to their lower variability. The ICCs for global methylation measures were high, typically greater than 0.70. The reliability of methylation measures determined by the HM450K microarray is wide-ranging and depends primarily on the variability in methylation at individual CpG sites. The power of association studies is low for a substantial proportion of CpGs in the HM450K assay.