Protein Biomarkers of Early Menopause and Incident Cardiovascular Disease

Background Premature and early menopause are independently associated with greater risk of cardiovascular disease (CVD). However, mechanisms linking age of menopause with CVD remain poorly characterized. Methods and Results We measured 71 circulating CVD protein biomarkers in 1565 postmenopausal women enrolled in the FHS (Framingham Heart Study). We examined the association of early menopause with biomarkers and tested whether early menopause modified the association of biomarkers with incident cardiovascular outcomes (heart failure, major CVD, and all-cause death) using multivariable-adjusted linear regression and Cox models, respectively. Among 1565 postmenopausal women included (mean age 62 years), 395 (25%) had a history of early menopause. Of 71 biomarkers examined, we identified 7 biomarkers that were significantly associated with early menopause, of which 5 were higher in women with early menopause including adrenomedullin and resistin, and 2 were higher in women without early menopause including insulin growth factor-1 and CNTN1 (contactin-1) (Benjamini-Hochberg adjusted P<0.1 for all). Early menopause also modified the association of specific biomarkers with incident cardiovascular outcomes including adrenomedullin (Pint<0.05). Conclusions Early menopause is associated with circulating levels of CVD protein biomarkers and appears to modify the association between select biomarkers with incident cardiovascular outcomes. Identified biomarkers reflect several distinct biological pathways, including inflammation, adiposity, and neurohormonal regulation. Further investigation of these pathways may provide mechanistic insights into the pathogenesis, prevention, and treatment of early menopause-associated CVD.

Expression quantitative trait methylation analysis elucidates gene regulatory effects of DNA methylation: the Framingham Heart Study

Expression quantitative trait methylation (eQTM) analysis identifies DNA CpG sites at which methylation is associated with gene expression. The present study describes an eQTM resource of CpG-transcript pairs derived from whole blood DNA methylation and RNA sequencing gene expression data in 2115 Framingham Heart Study participants. We identified 70,047 significant cis CpG-transcript pairs at p < 1E-7 where the top most significant eGenes (i.e., gene transcripts associated with a CpG) were enriched in biological pathways related to cell signaling, and for 1208 clinical traits (enrichment false discovery rate [FDR] ≤ 0.05). We also identified 246,667 significant trans CpG-transcript pairs at p < 1E-14 where the top most significant eGenes were enriched in biological pathways related to activation of the immune response, and for 1191 clinical traits (enrichment FDR ≤ 0.05). Independent and external replication of the top 1000 significant cis and trans CpG-transcript pairs was completed in the Women's Health Initiative and Jackson Heart Study cohorts. Using significant cis CpG-transcript pairs, we identified significant mediation of the association between CpG sites and cardiometabolic traits through gene expression and identified shared genetic regulation between CpGs and transcripts associated with cardiometabolic traits. In conclusion, we developed a robust and powerful resource of whole blood eQTM CpG-transcript pairs that can help inform future functional studies that seek to understand the molecular basis of disease.

The Association of Protein Biomarkers With Incident Heart Failure With Preserved and Reduced Ejection Fraction

BACKGROUND

Heart failure with preserved ejection fraction (HFpEF) and heart failure with reduced ejection fraction (HFrEF) are distinct clinical entities, yet there is scant evidence for associations of proteomic signatures with future development of HFpEF versus HFrEF.

METHODS

We evaluated the association of 71 protein biomarkers with incident HFpEF versus HFrEF (left ventricular ejection fraction ≥ versus <50%) among Framingham Heart Study participants using multivariable Cox models.

RESULTS

Among 7038 participants (mean age 49 years; 54% women), 5 biomarkers were associated with increased risk of incident HFpEF (false discovery rate q<0.05): NT-proBNP (N-terminal pro-B-type natriuretic peptide; hazard ratio [HR], 2.13; 95% CI, 1.52-2.99; P<0.001), growth differentiation factor-15 (HR, 1.67; 95% CI, 1.32-2.12; P<0.001), adrenomedullin (HR, 1.58; 95% CI, 1.23-2.04; P<0.001), uncarboxylated matrix Gla protein (HR, 1.55; 95% CI 1.23-1.95; P<0.001), and C-reactive protein (HR, 1.46; 95% CI, 1.17-1.83; P=0.001). Fourteen biomarkers were associated with incident HFrEF (multivariable P<0.001, q<0.05 for all). Of these, 3 biomarkers were associated with both HF subtypes (NT-proBNP, growth differentiation factor-15, and C-reactive protein). When compared directly, myeloperoxidase, resistin, and paraoxanase-1 were more strongly associated with HFrEF than HFpEF.

CONCLUSIONS

We identified 5 protein biomarkers of new-onset HFpEF representing pathways of inflammation, cardiac stress, and vascular stiffness, which partly overlapped with HFrEF. We found 14 biomarkers associated with new-onset HFrEF, with some distinct associations including myeloperoxidase, resistin, and paraoxanase-1. Taken together, these findings provide insights into similarities and differences in the development of HF subtypes.

REGISTRATION

URL: https://clinicaltrials.gov/ct2/show/NCT00005121; Unique identifier: NCT0005121.

Association of 71 cardiovascular disease-related plasma proteins with pulmonary function in the community

RATIONALE

It has been speculated that shared mechanisms underlie respiratory and cardiovascular diseases (CVD) including systemic inflammation or mutual risk factors. In this context, we sought to examine the associations of CVD-related plasma proteins with lung function as measured by spirometry in a large community-based cohort of adults.

METHODS

The study included 5777 Framingham Heart Study participants who had spirometry and measurement of 71 CVD-related plasma proteins. The association of plasma proteins with lung function was assessed cross-sectionally and longitudinally using models accounting for familial correlations. Linear mixed models were used for the following measurements: FEV1%predicted, FVC%predicted, and FEV1/FVC ratio with secondary analyses examining obstructive and restrictive physiology at baseline and their new onset during follow up.

MEASUREMENTS AND MAIN RESULTS

Among the 71 CVD-related plasma proteins, 13 proteins were associated in cross-sectional analyses with FEV1%predicted, 17 proteins were associated with FVC%predicted, and 1 protein was associated with FEV1/FVC. The proteins with the greatest inverse relations to FEV1%predicted and FVC%predicted included leptin, adrenomedullin, and plasminogen activator inhibitor-1; in contrast there were three proteins with positive relations to FEV1%predicted and FVC%predicted including insulin growth factor binding protein 2, tetranectin, and soluble receptor for advanced glycation end products. In longitudinal analyses, three proteins were associated with longitudinal change in FEV1 (ΔFEV1) and four with ΔFVC; no proteins were associated with ΔFEV1/FVC.

CONCLUSION

Our findings highlight CVD-related plasma proteins that are associated with lung function including markers of inflammation, adiposity, and fibrosis, representing proteins that may contribute both to respiratory and CVD risk.

An Integrative Genomic Strategy Identifies sRAGE as a Causal and Protective Biomarker of Lung Function

BACKGROUND

There are few clinically useful circulating biomarkers of lung function and lung disease. We hypothesized that genome-wide association studies (GWAS) of circulating proteins in conjunction with GWAS of pulmonary traits represents a clinically relevant approach to identifying causal proteins and therapeutically useful insights into mechanisms related to lung function and disease.

STUDY QUESTION

Can an integrative genomic strategy using GWAS of plasma soluble receptor for advanced glycation end-products (sRAGE) levels in conjunction with GWAS of lung function traits identify putatively causal relations of sRAGE to lung function?

STUDY DESIGN AND METHODS

Plasma sRAGE levels were measured in 6,861 Framingham Heart Study participants and GWAS of sRAGE was conducted to identify protein quantitative trait loci (pQTL), including cis-pQTL variants at the sRAGE protein-coding gene locus (AGER). We integrated sRAGE pQTL variants with variants from GWAS of lung traits. Colocalization of sRAGE pQTL variants with lung trait GWAS variants was conducted, and Mendelian randomization was performed using sRAGE cis-pQTL variants to infer causality of sRAGE for pulmonary traits. Cross-sectional and longitudinal protein-trait association analyses were conducted for sRAGE in relation to lung traits.

RESULTS

Colocalization identified shared genetic signals for sRAGE with lung traits. Mendelian randomization analyses suggested protective causal relations of sRAGE to several pulmonary traits. Protein-trait association analyses demonstrated higher sRAGE levels to be cross-sectionally and longitudinally associated with preserved lung function.

INTERPRETATION

sRAGE is produced by type I alveolar cells, and it acts as a decoy receptor to block the inflammatory cascade. Our integrative genomics approach provides evidence for sRAGE as a causal and protective biomarker of lung function, and the pattern of associations is suggestive of a protective role of sRAGE against restrictive lung physiology. We speculate that targeting the AGER/sRAGE axis may be therapeutically beneficial for the treatment and prevention of inflammation-related lung disease.

Plasma EFEMP1 Is Associated with Brain Aging and Dementia: The Framingham Heart Study

BACKGROUND

Epidermal growth factor containing fibulin extracellular matrix protein-1 (EFEMP1) has been associated with increased white matter hyperintensities (WMH) burden and disorders of premature aging and may have a shared pathophysiological role in the development of WMH and dementia.

OBJECTIVE

To determine the association between plasma EFEMP1 levels and MRI markers of vascular brain injury and incident all-cause and Alzheimer's disease (AD) dementia.

METHODS

We measured plasma EFEMP1 levels in 1597 [53% women, mean age 68.7 (SD 5.7) years] dementia-free Framingham Offspring cohort participants between 1998-2001 and subsequently followed them for incident dementia. Secondary outcomes included stroke, structural MRI brain measures and neurocognitive test performance.

RESULTS

During a median 11.8 [Q1, Q3 : 7.1, 13.3] year follow-up, 131 participants developed dementia. The highest quintile of plasma EFEMP1, compared to the bottom four quintiles, was associated with an increased risk of time to incident all-cause dementia (HR 1.77, 95% CI 1.18-2.64) and AD dementia (HR 1.76, 95% CI 1.11-2.81) but not with markers of vascular brain injury (WMH, covert brain infarcts or stroke). Higher circulating EFEMP1 concentrations were also cross-sectionally associated with lower total brain (β±SE, -0.28±0.11, p = 0.01) and hippocampal volumes (-0.006±0.003, p = 0.04) and impaired abstract reasoning (Similarities test, -0.18±0.08, p = 0.018 per standard deviation increment in EFEMP1).

CONCLUSION

Elevated circulating EFEMP1 is associated with an increased risk of all-cause and AD dementia, smaller hippocampal and total brain volumes, and poorer cognitive performance. EFEMP1 may play an important biological role in the development of AD dementia. Further studies to validate these findings are warranted.

Cardiovascular disease related circulating biomarkers and cancer incidence and mortality: is there an association?

AIMS

Recent studies suggest an association between cardiovascular disease (CVD) and cancer incidence/mortality, but the pathophysiological mechanisms underlying these associations are unclear. We aimed to examine biomarkers previously associated with CVD and study their association with incident cancer and cancer-related death in a prospective cohort study.

METHODS AND RESULTS

We used a proteomic platform to measure 71 cardiovascular biomarkers among 5,032 participants in the Framingham Heart Study who were free of cancer at baseline. We used multivariable-adjusted Cox models to examine the association of circulating protein biomarkers with risk of cancer incidence and mortality. To account for multiple testing, we set a 2-sided false discovery rate (FDR Q-value) <0.05.Growth differentiation factor-15 (GDF15; also known as macrophage inhibitory cytokine-1 [MIC1])) was associated with increased risk of incident cancer (hazards ratio [HR] per 1 standard deviation increment 1.31, 95% CI 1.17-1.47), incident gastrointestinal cancer (HR 1.85, 95% CI 1.37-2.50), incident colorectal cancer (HR 1.94, 95% CI 1.29-2.91) and cancer-related death (HR 2.15, 95% CI 1.72-2.70). Stromal cell-derived factor-1 (SFD1) showed an inverse association with cancer-related death (HR 0.75, 95% CI 0.65-0.86). Fibroblast growth factor-23 (FGF23) showed an association with colorectal cancer (HR 1.55, 95% CI 1.20-2.00), and granulin (GRN) was associated with hematologic cancer (HR 1.61, 95% CI 1.30-1.99). Other circulating biomarkers of inflammation, immune activation, metabolism, and fibrosis showed suggestive associations with future cancer diagnosis.

CONCLUSION

We observed several significant associations between circulating CVD biomarkers and cancer, supporting the idea that shared biological pathways underlie both diseases. Further investigations of specific mechanisms that lead to both CVD and cancer are warranted.

TRANSLATIONAL PERSPECTIVE

In our prospective cohort study, baseline levels of biomarkers previously associated with CVD were found to be associated with future development of cancer. In particular, GDF15 was associated with increased risk of cancer incidence and mortality, including gastrointestinal and colorectal cancers; SDF1 was inversely associated with cancer-related death, and FGF23 and GRN were associated with increased risk of colorectal and hematologic cancers, respectively. Other biomarkers of inflammation, immune activation, metabolism, and fibrosis showed suggestive associations. These results suggest potential shared biological pathways that underlie both development of cancer and CVD.

Cardiovascular Biomarkers of Obesity and Overlap With Cardiometabolic Dysfunction

Background Obesity may be associated with a range of cardiometabolic manifestations. We hypothesized that proteomic profiling may provide insights into the biological pathways that contribute to various obesity-associated cardiometabolic traits. We sought to identify proteomic signatures of obesity and examine overlap with related cardiometabolic traits, including abdominal adiposity, insulin resistance, and adipose depots. Methods and Results We measured 71 circulating cardiovascular disease protein biomarkers in 6981 participants (54% women; mean age, 49 years). We examined the associations of obesity, computed tomography measures of adiposity, cardiometabolic traits, and incident metabolic syndrome with biomarkers using multivariable regression models. Of the 71 biomarkers examined, 45 were significantly associated with obesity, of which 32 were positively associated and 13 were negatively associated with obesity (false discovery rate q<0.05 for all). There was significant overlap of biomarker profiles of obesity and cardiometabolic traits, but 23 biomarkers, including melanoma cell adhesion molecule (MCAM), growth differentiation factor-15 (GDF15), and lipoprotein(a) (LPA) were unique to metabolic traits only. Using hierarchical clustering, we found that the protein biomarkers clustered along 3 main trait axes: adipose, metabolic, and lipid traits. In longitudinal analyses, 6 biomarkers were significantly associated with incident metabolic syndrome: apolipoprotein B (apoB), insulin-like growth factor-binding protein 2 (IGFBP2), plasma kallikrein (KLKB1), complement C2 (C2), fibrinogen (FBN), and N-terminal pro-B-type natriuretic peptide (NT-proBNP); false discovery rate q<0.05 for all. Conclusions We found that the proteomic architecture of obesity overlaps considerably with associated cardiometabolic traits, implying shared pathways. Despite overlap, hierarchical clustering of proteomic profiles identified 3 distinct clusters of cardiometabolic traits: adipose, metabolic, and lipid. Further exploration of these novel protein targets and associated pathways may provide insight into the mechanisms responsible for the progression from obesity to cardiometabolic disease.

Associations of Alcohol Consumption with Cardiovascular Disease-Related Proteomic Biomarkers: The Framingham Heart Study

BACKGROUND

Alcohol consumption and cardiovascular disease (CVD) have a complex relation.

OBJECTIVES

We examined the associations between alcohol consumption, fasting plasma proteins, and CVD risk.

METHODS

We performed cross-sectional association analyses of alcohol consumption with 71 CVD-related plasma proteins, and also performed prospective association analyses of alcohol consumption and protein concentrations with 3 CVD risk factors (obesity, hypertension, and diabetes) in 6745 Framingham Heart Study (FHS) participants (mean age 49 y; 53% women).

RESULTS

A unit increase in log10 transformed alcohol consumption (g/d) was associated with an increased risk of hypertension (HR = 1.14; 95% CI: 1.04, 1.26; P = 0.007), and decreased risks of obesity (HR = 0.80; 95% CI: 0.71, 0.91; P = 4.6 × 10-4) and diabetes (HR: 0.68; 95% CI: 0.58, 0.80; P = 5.1 × 10-6) in a median of 13-y (interquartile = 7, 14) of follow-up. We identified 43 alcohol-associated proteins in a discovery sample (n = 4348, false discovery rate <0.05) and 20 of them were significant (P <0.05/43) in an independent validation sample (n = 2397). Eighteen of the 20 proteins were inversely associated with alcohol consumption. Four of the 20 proteins demonstrated 3-way associations, as expected, with alcohol consumption and CVD risk factors. For example, a greater concentration of APOA1 was associated with higher alcohol consumption (P = 1.2 × 10-65), and it was also associated with a lower risk of diabetes (P = 8.5 × 10-6). However, several others showed unexpected 3-way associations.

CONCLUSIONS

We identified 20 alcohol-associated proteins in 6745 FHS samples. These alcohol-associated proteins demonstrated complex relations with the 3 CVD risk factors. Future studies with integration of more proteomic markers and larger sample size are warranted to unravel the complex relation between alcohol consumption and CVD risk.

Growth Differentiation Factor 15 and NT-proBNP as Blood-Based Markers of Vascular Brain Injury and Dementia

Background

GDF15 (growth differentiation factor 15) and NT‐proBNP (N‐terminal pro‐B‐type natriuretic peptide) may offer promise as biomarkers for cognitive outcomes, including dementia. We determined the association of these biomarkers with cognitive outcomes in a community‐based cohort.

Methods and Results

Plasma GDF15 (n=1603) and NT‐proBNP levels (n=1590) (53% women; mean age, 68.7 years) were measured in dementia‐free Framingham Offspring cohort participants at examination 7 (1998–2001). Participants were followed up for incident dementia. Secondary outcomes included Alzheimer disease dementia, magnetic resonance imaging structural brain measures, and neurocognitive performance. During a median 11.8‐year follow‐up, 131 participants developed dementia. On multivariable Cox proportional‐hazards analysis, higher circulating GDF15 was associated with an increased risk of incident all‐cause and Alzheimer disease dementia (hazard ratio [HR] per SD increment in natural log‐transformed biomarker value, 1.54 [95% CI, 1.22–1.95] and 1.37 [95% CI, 1.03–1.81], respectively), whereas higher plasma NT‐proBNP was also associated with an increased risk of all‐cause dementia (HR, 1.32; 95% CI, 1.05–1.65). Elevated GDF15 was associated with lower total brain and hippocampal volumes, greater white matter hyperintensity volume, and poorer cognitive performance. Elevated NT‐proBNP was associated with greater white matter hyperintensity volume and poorer cognitive performance. Addition of both biomarkers to a conventional risk factor model improved dementia risk classification (net reclassification improvement index, 0.25; 95% CI, 0.05–0.45).

Conclusions

Elevated plasma GDF15 and NT‐proBNP were associated with vascular brain injury on magnetic resonance imaging, poorer neurocognitive performance, and increased risk of incident dementia in individuals aged >60 years. Both biomarkers improved dementia risk classification beyond that of traditional clinical risk factors, indicating their potential value in predicting incident dementia.

Sex Differences in Circulating Biomarkers of Cardiovascular Disease

BACKGROUND

Differences in proteomic profiles between men and women may provide insights into the biological pathways that contribute to known sex differences in cardiovascular disease (CVD).

OBJECTIVES

This study sought to investigate sex differences in circulating biomarkers representative of biological pathways implicated in the development of CVD among Framingham Heart Study participants.

METHODS

The authors measured 71 circulating CVD protein biomarkers in 7,184 participants (54% women, mean age 49 years). Multivariable models were used to evaluate the associations of sex, menopause, and hormone status with biomarkers. Cox models were used to examine whether sex modified the association of biomarkers with incident CVD.

RESULTS

Of 71 biomarkers examined, 61 (86%) differed significantly between men and women, of which 37 were higher in women (including adipokines and inflammatory markers such as leptin and C-reactive protein), and 24 were higher in men (including fibrosis and platelet markers such as MMP-8 (matrix metalloproteinase-8) and TIMP-1 (tissue inhibitor of metalloproteinases 1); false discovery rate q < 0.05 for all). Sex differences in biomarker profiles were most pronounced between pre-menopausal women versus men, with attenuated sex differences among post-menopausal women not taking hormone replacement therapy. Sex modified the association of specific biomarkers with incident CVD, including CD14 and apolipoprotein B (p_interaction <0.05 for all).

CONCLUSIONS

In a predominantly Caucasian population, the authors identified widespread sex differences in circulating biomarkers that reflect distinct pathways implicated in CVD, including inflammation, adiposity, fibrosis, and platelet homeostasis. Menopause and hormone status accounted for some, but not all, of the observed sex differences. Further investigation into factors underlying sex-based differences may provide mechanistic insight into CVD development.

Lipidomic profiling identifies signatures of metabolic risk

BACKGROUND

Metabolic syndrome (MetS), the clustering of metabolic risk factors, is associated with cardiovascular disease risk. We sought to determine if dysregulation of the lipidome may contribute to metabolic risk factors.

METHODS

We measured 154 circulating lipid species in 658 participants from the Framingham Heart Study (FHS) using liquid chromatography-tandem mass spectrometry and tested for associations with obesity, dysglycemia, and dyslipidemia. Independent external validation was sought in three independent cohorts. Follow-up data from the FHS were used to test for lipid metabolites associated with longitudinal changes in metabolic risk factors.

RESULTS

Thirty-nine lipids were associated with obesity and eight with dysglycemia in the FHS. Of 32 lipids that were available for replication for obesity and six for dyslipidemia, 28 (88%) replicated for obesity and five (83%) for dysglycemia. Four lipids were associated with longitudinal changes in body mass index and four were associated with changes in fasting blood glucose in the FHS.

CONCLUSIONS

We identified and replicated several novel lipid biomarkers of key metabolic traits. The lipid moieties identified in this study are involved in biological pathways of metabolic risk and can be explored for prognostic and therapeutic utility.

Evidence for a Causal Role of the SH2B3-β2M Axis in Blood Pressure Regulation

Genetic variants at SH2B3 are associated with blood pressure and circulating β₂M (β-2 microglobulin), a well-characterized kidney filtration biomarker. We hypothesize that circulating β₂M is an independent risk predictor of hypertension and may causally contribute to its development. The study sample consisted of 7 065 Framingham Heart Study participants with measurements of plasma β₂M. Generalized estimating equations were used to test the association of β₂M with prevalent and new-onset hypertension. There were 2 145 (30%) cases of prevalent hypertension at baseline and 886 (21%) cases of incident hypertension during 6 years of follow-up. A 1-SD increase in baseline plasma β₂M was associated with a greater risk of prevalent (odds ratio 1.14, 95% CI 1.05-1.24) and new-onset (odds ratio 1.18, 95% CI 1.07-1.32) hypertension. Individuals within the top β₂M quartile had a greater risk than the bottom quartile for prevalent (odds ratio 1.29, 95% CI 1.05-1.57) and new-onset (odds ratio 1.59, 95% CI 1.20-2.11) hypertension. These associations remained essentially unchanged in analyses restricted to participants free of albuminuria and chronic kidney disease. Mendelian randomization demonstrated that lower SH2B3 expression is causal for increased circulating β₂M levels, and in a hypertensive mouse model, knockout of Sh2b3 increased β ₂ M gene expression. In a community-based study of healthy individuals, higher plasma β₂M levels are associated with increased risk of prevalent and incident hypertension independent of chronic kidney disease status. Overlapping genetic signals for hypertension and β₂M, in conjunction with mouse knockout experiments, suggest that the SH2B3-β₂M axis plays a causal role in hypertension.

Circulating IGFBP-2: a novel biomarker for incident dementia

Objective

To determine the association between plasma insulin‐like growth factor binding protein 2 (IGFBP‐2) and cognitive outcomes.

Methods

We measured plasma IGFBP‐2 levels in 1596 (53% women, mean age 68.7 [SD 5.7] years) dementia‐free Framingham Offspring cohort participants between 1998 and 2001. Multivariable Cox proportional hazards models related plasma IGFBP‐2 to subsequent risk of incident dementia and Alzheimer’s disease. MRI brain measures and cognitive performance were included as secondary outcomes.

Results

During a median follow‐up of 11.8 (Q1, Q3: 7.1, 13.3) years, 131 participants developed incident dementia, of whom 98 were diagnosed with Alzheimer’s disease. The highest tertile of IGFBP‐2, compared to the lowest tertile, was associated with an increased risk of incident all‐cause dementia (hazard ratio [HR] 2.89, 95% CI 1.63–5.13) and Alzheimer’s disease (HR 3.63, 95% CI 1.76–7.50) in multivariable analysis. Higher circulating IGFBP2 levels were also cross‐sectionally associated with poorer performance on tests of abstract reasoning but not with MRI‐based outcomes. After adding plasma IGFBP‐2 levels to a conventional dementia prediction model, 32% of individuals with dementia were correctly assigned a higher predicted risk, while 8% of individuals without dementia were correctly assigned a lower predicted risk (overall net reclassification improvement index, 0.40, 95% CI 0.22–0.59).

Interpretation

Elevated circulating IGFBP‐2 levels were associated with an increased risk of both all‐cause dementia and Alzheimer’s disease. Addition of IGFBP2 plasma levels to a model of traditional risk factors significantly improved dementia risk classification. Manipulation of insulin‐like growth factor signaling via IGFBP‐2 may be a promising therapeutic target for dementia.

Epigenome-wide association study of DNA methylation and microRNA expression highlights novel pathways for human complex traits

DNA methylation (DNAm) and microRNAs (miRNAs) have been implicated in a wide-range of human diseases. While often studied in isolation, DNAm and miRNAs are not independent. We analyzed associations of expression of 283 miRNAs with DNAm at >400K CpG sites in whole blood obtained from 3565 individuals and identified 227 CpGs at which differential methylation was associated with the expression of 40 nearby miRNAs (cis-miR-eQTMs) at FDR<0.01, including 91 independent CpG sites at r² < 0.2. cis-miR-eQTMs were enriched for CpGs in promoter and polycomb-repressed state regions, and 60% were inversely associated with miRNA expression. Bidirectional Mendelian randomization (MR) analysis further identified 58 cis-miR-eQTMCpG-miRNA pairs where DNAm changes appeared to drive miRNA expression changes and opposite directional effects were unlikely. Integration of genetic variants in joint analyses revealed an average partial between cis-miR-eQTM CpGs and miRNAs of 2% after conditioning on site-specific genetic variation, suggesting that DNAm is an important epigenetic regulator of miRNA expression. Finally, two-step MR analysis was performed to identify putatively causal CpGs driving miRNA expression in relation to human complex traits. We found that an imprinted region on 14q32 that was previously identified in relation to age at menarche is enriched with cis-miR-eQTMs. Nine CpGs and three miRNAs at this locus tested causal for age at menarche, reflecting novel epigenetic-driven molecular pathways underlying this complex trait. Our study sheds light on the joint genetic and epigenetic regulation of miRNA expression and provides insights into the relations of miRNAs to their targets and to complex phenotypes.

Circulating fibroblast growth factor 23 levels and incident dementia: The Framingham heart study

BACKGROUND

Fibroblast growth factor 23 is an emerging vascular biomarker, recently associated with cerebral small vessel disease and poor cognition in patients on dialysis. It also interacts with klotho, an anti-aging and cognition enhancing protein.

OBJECTIVE

To determine if circulating Fibroblast growth factor 23 (FGF23) is associated with new-onset cognitive outcomes in a community-based cohort of cognitively healthy adults with long-term follow-up.

METHODS

We measured serum FGF23 levels in 1537 [53% women, mean age 68.7 (SD 5.7)] dementia-free Framingham Offspring participants at their 7th quadrennial examination (1998-2001), and followed these participants for the development of clinical all-cause dementia and Alzheimer's disease (AD). Secondary outcomes included MRI-based structural brain measures, and neurocognitive test performance at exam 7.

RESULTS

During a median (Q1, Q3) 12-year (7.0, 13.3) follow up, 122 (7.9%) participants developed dementia, of whom 91 (5.9%) had AD. Proportional-hazards regression analysis, adjusted for age, sex, education, systolic blood pressure, antihypertensive medication, prevalent cardiovascular disease, diabetes mellitus, smoking status and apoE ε4 carrier status, revealed that higher serum FGF23 levels were associated with an increased risk of incident dementia and AD (Hazard ratio [HR] per 1 standard deviation increment in inverse transformed FGF23 level 1.25, 95% CI 1.02-1.53, and 1.32, 95% CI 1.04-1.69, respectively). There was no significant interaction according to presence/absence of significant renal impairment (eGFR <30 versus ≥30ml/min) and risk of dementia (based on 1537; p = 0.97).

CONCLUSIONS

Higher circulating FGF23 is associated with an increased risk of dementia, suggesting that FGF23-related biological pathways may play a role in the development of dementia.

Genome-wide mapping of plasma protein QTLs identifies putatively causal genes and pathways for cardiovascular disease

Identifying genetic variants associated with circulating protein concentrations (protein quantitative trait loci; pQTLs) and integrating them with variants from genome-wide association studies (GWAS) may illuminate the proteome's causal role in disease and bridge a knowledge gap regarding SNP-disease associations. We provide the results of GWAS of 71 high-value cardiovascular disease proteins in 6861 Framingham Heart Study participants and independent external replication. We report the mapping of over 16,000 pQTL variants and their functional relevance. We provide an integrated plasma protein-QTL database. Thirteen proteins harbor pQTL variants that match coronary disease-risk variants from GWAS or test causal for coronary disease by Mendelian randomization. Eight of these proteins predict new-onset cardiovascular disease events in Framingham participants. We demonstrate that identifying pQTLs, integrating them with GWAS results, employing Mendelian randomization, and prospectively testing protein-trait associations holds potential for elucidating causal genes, proteins, and pathways for cardiovascular disease and may identify targets for its prevention and treatment.

Protein Biomarkers of Cardiovascular Disease and Mortality in the Community

BACKGROUND

The discovery of novel and highly predictive biomarkers of cardiovascular disease (CVD) has the potential to improve risk-stratification methods and may be informative regarding biological pathways contributing to disease.

METHODS AND RESULTS

We used a discovery proteomic platform that targeted high-value proteins for CVD to ascertain 85 circulating protein biomarkers in 3523 Framingham Heart Study participants (mean age, 62 years; 53% women). Using multivariable-adjusted Cox models to account for clinical variables, we found 8 biomarkers associated with incident atherosclerotic CVD, 18 with incident heart failure, 38 with all-cause mortality, and 35 with CVD death (false discovery rate, q<0.05 for all; P-value ranges, 9.8×10-34 to 3.6×10-2). Notably, a number of regulators of metabolic and adipocyte homeostasis were associated with cardiovascular events, including insulin-like growth factor 1 (IGF1), insulin-like growth factor binding protein 1 (IGFBP1), insulin-like growth factor binding protein 2 (IGFBP2), leptin, and adipsin. In a multimarker approach that accounted for clinical factors, growth differentiation factor 15 (GDF15) was associated with all outcomes. In addition, N-terminal pro-b-type natriuretic peptide, C-reactive protein, and leptin were associated with incident heart failure, and C-type lectin domain family 3 member B (CLEC3B; tetranectin), N-terminal pro-b-type natriuretic peptide, arabinogalactan protein 1 (AGP1), soluble receptor for advanced glycation end products (sRAGE), peripheral myelin protein 2 (PMP2), uncarboxylated matrix Gla protein (UCMGP), kallikrein B1 (KLKB1), IGFBP2, IGF1, leptin receptor, and cystatin-C were associated with all-cause mortality in a multimarker model.

CONCLUSIONS

We identified numerous protein biomarkers that predicted cardiovascular outcomes and all-cause mortality, including biomarkers representing regulators of metabolic homeostasis and inflammatory pathways. Further studies are needed to validate our findings and define clinical utility, with the ultimate goal of improving strategies for CVD prevention.

Association of Body Mass Index with DNA Methylation and Gene Expression in Blood Cells and Relations to Cardiometabolic Disease: A Mendelian Randomization Approach

BACKGROUND

The link between DNA methylation, obesity, and adiposity-related diseases in the general population remains uncertain.

METHODS AND FINDINGS

We conducted an association study of body mass index (BMI) and differential methylation for over 400,000 CpGs assayed by microarray in whole-blood-derived DNA from 3,743 participants in the Framingham Heart Study and the Lothian Birth Cohorts, with independent replication in three external cohorts of 4,055 participants. We examined variations in whole blood gene expression and conducted Mendelian randomization analyses to investigate the functional and clinical relevance of the findings. We identified novel and previously reported BMI-related differential methylation at 83 CpGs that replicated across cohorts; BMI-related differential methylation was associated with concurrent changes in the expression of genes in lipid metabolism pathways. Genetic instrumental variable analysis of alterations in methylation at one of the 83 replicated CpGs, cg11024682 (intronic to sterol regulatory element binding transcription factor 1 [SREBF1]), demonstrated links to BMI, adiposity-related traits, and coronary artery disease. Independent genetic instruments for expression of SREBF1 supported the findings linking methylation to adiposity and cardiometabolic disease. Methylation at a substantial proportion (16 of 83) of the identified loci was found to be secondary to differences in BMI. However, the cross-sectional nature of the data limits definitive causal determination.

CONCLUSIONS

We present robust associations of BMI with differential DNA methylation at numerous loci in blood cells. BMI-related DNA methylation and gene expression provide mechanistic insights into the relationship between DNA methylation, obesity, and adiposity-related diseases.

Plasma clusterin levels and risk of dementia, Alzheimer's disease, and stroke

INTRODUCTION

Genetic variation in the clusterin gene has been associated with Alzheimer Disease (AD), and the clusterin protein is thought to play a mechanistic role. We explored the associations of clusterin plasma levels with incident dementia, AD, and stroke.

METHODS

Plasma clusterin was assessed in 1532 nondemented participants from the Framingham Study Offspring cohort between 1998 and 2001 (mean age, 69 ± 6; 53% women). We related clusterin levels to risk of incident dementia, AD, and stroke using Cox-proportional hazards models and examined potential interactions.

RESULTS

A significant interaction of plasma clusterin levels with age was observed. Clusterin was significantly associated with increased risk of dementia among elderly persons (>80 years; hazard ratio [HR], 95% confidence interval = 6.25, 1.64-23.89; P = .007) and with decreased risk of dementia (HR = 0.53, 0.32-0.88; P = .013) and stroke (HR = 0.78, 0.63-0.97; P = .029) among younger participants.

DISCUSSION

The association between plasma clusterin levels and risk of dementia and stroke may be modified by age or an age-related factor.

Metabolite Signatures of Metabolic Risk Factors and their Longitudinal Changes

CONTEXT

Metabolic dysregulation underlies key metabolic risk factors—obesity, dyslipidemia, and dysglycemia.

OBJECTIVE

To uncover mechanistic links between metabolomic dysregulation and metabolic risk by testing metabolite associations with risk factors cross-sectionally and with risk factor changes over time.

DESIGN

Cross-sectional—discovery samples (n = 650; age, 36–69 years) from the Framingham Heart Study (FHS) and replication samples (n = 670; age, 61–76 years) from the BioImage Study, both following a factorial design sampled from high vs low strata of body mass index, lipids, and glucose. Longitudinal—FHS participants (n = 554) with 5–7 years of follow-up for risk factor changes.

SETTING

Observational studies.

PARTICIPANTS

Cross-sectional samples with or without obesity, dysglycemia, and dyslipidemia, excluding prevalent cardiovascular disease and diabetes or dyslipidemia treatment. Age- and sex-matched by group.

INTERVENTIONS

None.

MAIN OUTCOME MEASURE(S)

Gas chromatography-mass spectrometry detected 119 plasma metabolites. Cross-sectional associations with obesity, dyslipidemia, and dysglycemia were tested in discovery, with external replication of 37 metabolites. Single- and multi-metabolite markers were tested for association with longitudinal changes in risk factors.

RESULTS

Cross-sectional metabolite associations were identified with obesity (n = 26), dyslipidemia (n = 21), and dysglycemia (n = 11) in discovery. Glutamic acid, lactic acid, and sitosterol associated with all three risk factors in meta-analysis (P < 4.5 × 10−4). Metabolites associated with longitudinal risk factor changes were enriched for bioactive lipids. Multi-metabolite panels explained 2.5–15.3% of longitudinal changes in metabolic traits.

CONCLUSIONS

Cross-sectional results implicated dysregulated glutamate cycling and amino acid metabolism in metabolic risk. Certain bioactive lipids were associated with risk factors cross-sectionally and over time, suggesting their upstream role in risk factor progression. Functional studies are needed to validate findings and facilitate translation into treatments or preventive measures.

Integrative network analysis reveals molecular mechanisms of blood pressure regulation

Genome‐wide association studies (GWAS) have identified numerous loci associated with blood pressure (BP). The molecular mechanisms underlying BP regulation, however, remain unclear. We investigated BP‐associated molecular mechanisms by integrating BP GWAS with whole blood mRNA expression profiles in 3,679 individuals, using network approaches. BP transcriptomic signatures at the single‐gene and the coexpression network module levels were identified. Four coexpression modules were identified as potentially causal based on genetic inference because expression‐related SNPs for their corresponding genes demonstrated enrichment for BP GWAS signals. Genes from the four modules were further projected onto predefined molecular interaction networks, revealing key drivers. Gene subnetworks entailing molecular interactions between key drivers and BP‐related genes were uncovered. As proof‐of‐concept, we validated SH2B3, one of the top key drivers, using Sh2b3^−/− mice. We found that a significant number of genes predicted to be regulated by SH2B3 in gene networks are perturbed in Sh2b3^−/− mice, which demonstrate an exaggerated pressor response to angiotensin II infusion. Our findings may help to identify novel targets for the prevention or treatment of hypertension.

A meta-analysis of gene expression signatures of blood pressure and hypertension

Genome-wide association studies (GWAS) have uncovered numerous genetic variants (SNPs) that are associated with blood pressure (BP). Genetic variants may lead to BP changes by acting on intermediate molecular phenotypes such as coded protein sequence or gene expression, which in turn affect BP variability. Therefore, characterizing genes whose expression is associated with BP may reveal cellular processes involved in BP regulation and uncover how transcripts mediate genetic and environmental effects on BP variability. A meta-analysis of results from six studies of global gene expression profiles of BP and hypertension in whole blood was performed in 7017 individuals who were not receiving antihypertensive drug treatment. We identified 34 genes that were differentially expressed in relation to BP (Bonferroni-corrected p<0.05). Among these genes, FOS and PTGS2 have been previously reported to be involved in BP-related processes; the others are novel. The top BP signature genes in aggregate explain 5%–9% of inter-individual variance in BP. Of note, rs3184504 in SH2B3, which was also reported in GWAS to be associated with BP, was found to be a trans regulator of the expression of 6 of the transcripts we found to be associated with BP (FOS, MYADM, PP1R15A, TAGAP, S100A10, and FGBP2). Gene set enrichment analysis suggested that the BP-related global gene expression changes include genes involved in inflammatory response and apoptosis pathways. Our study provides new insights into molecular mechanisms underlying BP regulation, and suggests novel transcriptomic markers for the treatment and prevention of hypertension.

The focus of blood pressure (BP) GWAS has been the identification of common DNA sequence variants associated with the phenotype; this approach provides only one dimension of molecular information about BP. While it is a critical dimension, analyzing DNA variation alone is not sufficient for achieving an understanding of the multidimensional complexity of BP physiology. The top loci identified by GWAS explain only about 1 percent of inter-individual BP variability. In this study, we performed a meta-analysis of gene expression profiles in relation to BP and hypertension in 7017 individuals from six studies. We identified 34 differentially expressed genes for BP, and discovered that the top BP signature genes explain 5%–9% of BP variability. We further linked BP gene expression signature genes with BP GWAS results by integrating expression associated SNPs (eSNPs) and discovered that one of the top BP loci from GWAS, rs3184504 in SH2B3, is a trans regulator of expression of 6 of the top 34 BP signature genes. Our study, in conjunction with prior GWAS, provides a deeper understanding of the molecular and genetic basis of BP regulation, and identifies several potential targets and pathways for the treatment and prevention of hypertension and its sequelae.

Dissecting the roles of microRNAs in coronary heart disease via integrative genomic analyses

OBJECTIVE

The roles of microRNAs (miRNAs) in coronary heart disease (CHD) have not been well characterized. This study sought to systematically characterize the complex genomic architecture of CHD by integrating whole blood miRNA and mRNA expression with genetic variation in 186 CHD cases and 186 controls.

APPROACH AND RESULTS

At false discovery rate <0.2, 15 miRNAs were differentially expressed between CHD cases and controls. To explore regulatory mechanisms, we integrated miRNA and mRNA expression with genome-wide genotype data to investigate miRNA and mRNA associations and relationships of genetic variation with miRNAs. We identified a large number of correlated miRNA-mRNA pairs and genetic loci that seem to regulate miRNA levels. Subsequently, we explored the relationships of these complex molecular associations with CHD status. We identified a large difference in miRNA-mRNA associations between CHD cases and controls, as demonstrated by a significantly higher proportion of inversely correlated miRNA-mRNA pairs in cases versus controls (80% versus 30%; P<1×10(-16)), suggesting a genome-wide shift in the regulatory structure of the transcriptome in CHD. The differentially coexpressed miRNA-mRNA pairs showed enrichment for CHD risk genetic variants affecting both miRNA and mRNA expression levels, implicating a putatively causal role in CHD. Furthermore, 3 miRNAs (miR-1275, miR-365a-3p, and miR-150-5p) were associated with an mRNA coexpression module that was causally linked to CHD and reflected the dysregulation of B-cell centered immune function.

CONCLUSIONS

Our results provide novel evidence that miRNAs are important regulators of biological processes involved in CHD via genetic control and via their tight coexpression with mRNAs.

A systematic heritability analysis of the human whole blood transcriptome

Genome-wide expression quantitative trait locus (eQTL) mapping may reveal common genetic variants regulating gene expression. In addition to mapping eQTLs, we systematically evaluated the heritability of the whole blood transcriptome in 5,626 participants from the Framingham Heart Study. Of all gene expression measurements, about 40 % exhibit evidence of being heritable [hgeneExp(2) > 0, (p < 0.05)], the average heritability was estimated to be 0.13, and 10 % display hgeneExp(2) > 0.2. To identify the role of eQTLs in promoting phenotype differences and disease susceptibility, we investigated the proportion of cis/trans eQTLs in different heritability categories and discovered that genes with higher heritability are more likely to have cis eQTLs that explain large proportions of variance in the expression of the corresponding genes. Single cis eQTLs explain 0.33-0.53 of variance in transcripts on average, whereas single trans eQTLs only explain 0.02-0.07. The top cis eQTLs tend to explain more variance in the corresponding gene when its hgeneExp(2) is greater. Taking body mass index (BMI) as a case study, we cross-linked cis/trans eQTLs with both GWAS SNPs and differentially expressed genes for BMI. We discovered that BMI GWAS SNPs in 16p11.2 (e.g., rs7359397) are associated with several BMI differentially expressed genes in a cis manner (e.g. SULT1A1, SPNS1, and TUFM). These BMI signature genes explain a much larger proportion of variance in BMI than do the GWAS SNPs. Our results shed light on the impact of eQTLs on the heritability of the human whole blood transcriptome and its relations to phenotype differences.

Integromic analysis of genetic variation and gene expression identifies networks for cardiovascular disease phenotypes

BACKGROUND

Cardiovascular disease (CVD) reflects a highly coordinated complex of traits. Although genome-wide association studies have reported numerous single nucleotide polymorphisms (SNPs) to be associated with CVD, the role of most of these variants in disease processes remains unknown.

METHODS AND RESULTS

We built a CVD network using 1512 SNPs associated with 21 CVD traits in genome-wide association studies (at P≤5×10(-8)) and cross-linked different traits by virtue of their shared SNP associations. We then explored whole blood gene expression in relation to these SNPs in 5257 participants in the Framingham Heart Study. At a false discovery rate <0.05, we identified 370 cis-expression quantitative trait loci (eQTLs; SNPs associated with altered expression of nearby genes) and 44 trans-eQTLs (SNPs associated with altered expression of remote genes). The eQTL network revealed 13 CVD-related modules. Searching for association of eQTL genes with CVD risk factors (lipids, blood pressure, fasting blood glucose, and body mass index) in the same individuals, we found examples in which the expression of eQTL genes was significantly associated with these CVD phenotypes. In addition, mediation tests suggested that a subset of SNPs previously associated with CVD phenotypes in genome-wide association studies may exert their function by altering expression of eQTL genes (eg, LDLR and PCSK7), which in turn may promote interindividual variation in phenotypes.

CONCLUSIONS

Using a network approach to analyze CVD traits, we identified complex networks of SNP-phenotype and SNP-transcript connections. Integrating the CVD network with phenotypic data, we identified biological pathways that may provide insights into potential drug targets for treatment or prevention of CVD.

Stromal cell-derived factor 1 as a biomarker of heart failure and mortality risk

OBJECTIVE

CXCL12 encodes stromal cell-derived factor 1α (SDF-1), which binds to the receptor encoded by CXCR4. Variation at the CXCL12 locus is associated with coronary artery disease and endothelial progenitor cell numbers, whereas variation at the CXCR4 locus is associated with leukocyte telomere length, which has been shown to be associated with coronary artery disease. Therefore, we examined the relationships of plasma SDF-1 levels to cardiovascular disease (CVD)-related outcomes, risk factors, leukocyte telomere length, and endothelial progenitor cells.

APPROACH AND RESULTS

SDF-1 was measured in 3359 Framingham Heart Study participants. We used Cox regression to examine relationships of SDF-1 to new-onset CVD, myocardial infarction, heart failure, and all-cause mortality; we used linear regression to evaluate associations of SDF-1 with risk factors, leukocyte telomere length, and CD34+ cell phenotypes. In multivariable models, higher SDF-1 levels were associated with older age, lower levels of high-density lipoprotein-cholesterol and cigarette smoking. Higher SDF-1 levels were associated with lower CD34+ cell frequency (P=0.02) but not with leukocyte telomere length. During follow-up (median, 9.3 years), there were 263 new-onset CVD events, 160 myocardial infarctions, 200 heart failure events, and 385 deaths. After adjusting for clinical risk factors, SDF-1 levels were associated with heart failure (P=0.04) and all-cause mortality (P=0.003) but not with CVD (P=0.39) or myocardial infarction (P=0.10). The association of SDF-1 levels with myocardial infarction was attenuated after adjustment for high-density lipoprotein-cholesterol.

CONCLUSIONS

After adjusting for traditional CVD risk factors, SDF-1 is associated with heart failure and all-cause mortality risk. Additional studies are needed to determine whether measurement of SDF-1 levels has clinical use.

Protein biomarkers of new-onset cardiovascular disease: prospective study from the systems approach to biomarker research in cardiovascular disease initiative

OBJECTIVE

Incorporation of novel plasma protein biomarkers may improve current models for prediction of atherosclerotic cardiovascular disease (ASCVD) risk.

APPROACH AND RESULTS

We used discovery mass spectrometry (MS) to determine plasma concentrations of 861 proteins in 135 myocardial infarction (MI) cases and 135 matched controls. Then, we measured 59 markers by targeted MS in 336 ASCVD case-control pairs. Associations with MI or ASCVD were tested in single-marker and multiple-marker analyses adjusted for established ASCVD risk factors. Twelve single markers from discovery MS were associated with MI incidence (at P<0.01), adjusting for clinical risk factors. Seven proteins in aggregate (cyclophilin A, cluster of differentiation 5 molecule [CD5] antigen-like, cell-surface glycoprotein mucin cell surface associated protein 18 [MUC-18], collagen-α 1 [XVIII] chain, salivary α-amylase 1, C-reactive protein, and multimerin-2) were highly associated with MI (P<0.0001) and significantly improved its prediction compared with a model with clinical risk factors alone (C-statistic of 0.71 versus 0.84). Through targeted MS, 12 single proteins were predictors of ASCVD (at P<0.05) after adjusting for established risk factors. In multiple-marker analyses, 4 proteins in combination (α-1-acid glycoprotein 1, paraoxonase 1, tetranectin, and CD5 antigen-like) predicted incident ASCVD (P<0.0001) and moderately improved the C-statistic from the model with clinical covariates alone (C-statistic of 0.69 versus 0.73).

CONCLUSIONS

Proteomics profiling identified single- and multiple-marker protein panels that are associated with new-onset ASCVD and may lead to a better understanding of underlying disease mechanisms. Our findings include many novel protein biomarkers that, if externally validated, may improve risk assessment for MI and ASCVD.

A systems biology framework identifies molecular underpinnings of coronary heart disease

OBJECTIVE

Genetic approaches have identified numerous loci associated with coronary heart disease (CHD). The molecular mechanisms underlying CHD gene-disease associations, however, remain unclear. We hypothesized that genetic variants with both strong and subtle effects drive gene subnetworks that in turn affect CHD.

APPROACH AND RESULTS

We surveyed CHD-associated molecular interactions by constructing coexpression networks using whole blood gene expression profiles from 188 CHD cases and 188 age- and sex-matched controls. Twenty-four coexpression modules were identified, including 1 case-specific and 1 control-specific differential module (DM). The DMs were enriched for genes involved in B-cell activation, immune response, and ion transport. By integrating the DMs with gene expression-associated single-nucleotide polymorphisms and with results of genome-wide association studies of CHD and its risk factors, the control-specific DM was implicated as CHD causal based on its significant enrichment for both CHD and lipid expression-associated single-nucleotide polymorphisms. This causal DM was further integrated with tissue-specific Bayesian networks and protein-protein interaction networks to identify regulatory key driver genes. Multitissue key drivers (SPIB and TNFRSF13C) and tissue-specific key drivers (eg, EBF1) were identified.

CONCLUSIONS

Our network-driven integrative analysis not only identified CHD-related genes, but also defined network structure that sheds light on the molecular interactions of genes associated with CHD risk.

Gene expression signatures of coronary heart disease

OBJECTIVE

To identify transcriptomic biomarkers of coronary heart disease (CHD) in 188 cases with CHD and 188 age- and sex-matched controls who were participants in the Framingham Heart Study.

APPROACH AND RESULTS

A total of 35 genes were differentially expressed in cases with CHD versus controls at false discovery rate<0.5, including GZMB, TMEM56, and GUK1. Cluster analysis revealed 3 gene clusters associated with CHD, 2 linked to increased erythrocyte production and a third to reduced natural killer and T cell activity in cases with CHD. Exon-level results corroborated and extended the gene-level results. Alternative splicing analysis suggested that GUK1 and 38 other genes were differentially spliced in cases with CHD versus controls. Gene Ontology analysis linked ubiquitination and T-cell-related pathways with CHD.

CONCLUSIONS

Two bioinformatically defined groups of genes show consistent associations with CHD. Our findings are consistent with the hypotheses that hematopoesis is upregulated in CHD, possibly reflecting a compensatory mechanism, and that innate immune activity is disrupted in CHD or altered by its treatment. Transcriptomic signatures may be useful in identifying pathways associated with CHD and point toward novel therapeutic targets for its treatment and prevention.

Abstract 88: Systems Biology Approaches to Exploring Molecular Mechanisms Underlying Blood Pressure Regulation

Blood pressure (BP) is a complex trait that is influenced by both genetic and environment factors. Recent genome-wide association studies (GWAS) have identified about 30 genetic loci that are associated with systolic and diastolic blood pressure (SBP, DBP) and hypertension. However, the molecular mechanisms underlying the GWAS associations remain unclear. We hypothesize that BP-associated genetic variants with both strong and subtle effects drive shifts in gene subnetworks that in turn affect BP. We surveyed BP-associated molecular interactions in Framingham offspring cohort participants (n=2461; 55% women; age range 40-92 yrs.) by integrating gene expression profiles, expression-associated SNPs (eSNPs), and BP GWAS with network approaches. Peripheral whole blood samples were collected and large-scale transcriptomic microarray analysis was performed on all available participants who attended a clinic visit in 2005-2008. Based on pedigrees the samples were split into a discovery set (n=1421) and a replication set (n=820). The expression levels of 661 genes (FDR < 20%) and the eigengenes of 3 coexpression modules (CoEMs), which were found to be significantly correlated with SBP or DBP in the discovery set, were tested in the replication set. Seventy-two genes were replicated at FDR < 20%. The 72-gene BP-correlated signature and 2 of the three CoEMs were found to be significantly enriched for eSNPs with low p value associations with BP in the International Consortium of Blood Pressure (ICBP) GWAS at p<0.001, suggesting that these gene sets play causal roles in BP regulation. The putative causal BP gene sets were in turn integrated with tissue-specific Bayesian networks and a protein-protein interaction network to identify central network nodes (key drivers or KDs) that drive these BP-related gene sets. One hundred and ten KDs including TSPAN2, ECT2 and KCNK3 were identified. The BP causal gene sets and their KDs are enriched for BP-related biological processes including ion transport, nervous system development, and blood coagulation. Our systems biology analysis not only predicts novel BP risk genes, but also uncovers a network structure that entails the molecular interactions among BP and cardiovascular risk genes in a tissue-specific fashion.

Galectin-3, a marker of cardiac fibrosis, predicts incident heart failure in the community

OBJECTIVES

The aim of this study was to examine the relation of galectin-3 (Gal-3), a marker of cardiac fibrosis, with incident heart failure (HF) in the community.

BACKGROUND

Gal-3 is an emerging prognostic biomarker in HF, and experimental studies suggest that Gal-3 is an important mediator of cardiac fibrosis. Whether elevated Gal-3 concentrations precede the development of HF is unknown.

METHODS

Gal-3 concentrations were measured in 3,353 participants in the Framingham Offspring Cohort (mean age 59 years; 53% women). The relation of Gal-3 to incident HF was assessed using proportional hazards regression.

RESULTS

Gal-3 was associated with increased left ventricular mass in age-adjusted and sex-adjusted analyses (p = 0.001); this association was attenuated in multivariate analyses (p = 0.06). A total of 166 participants developed incident HF and 468 died during a mean follow-up period of 11.2 years. Gal-3 was associated with risk for incident HF (hazard ratio [HR]: 1.28 per 1 SD increase in log Gal-3; 95% confidence interval [CI]: 1.14 to 1.43; p < 0.0001) and remained significant after adjustment for clinical variables and B-type natriuretic peptide (HR: 1.23; 95% CI: 1.04 to 1.47; p = 0.02). Gal-3 was also associated with risk for all-cause mortality (multivariable-adjusted HR: 1.15; 95% CI: 1.04 to 1.28; p = 0.01). The addition of Gal-3 to clinical factors resulted in negligible changes to the C-statistic and minor improvements in net reclassification improvement.

CONCLUSIONS

Higher concentration of Gal-3, a marker of cardiac fibrosis, is associated with increased risk for incident HF and mortality. Future studies evaluating the role of Gal-3 in cardiac remodeling may provide further insights into the role of Gal-3 in the pathophysiology of HF.

Abstract 37: Metabolomic Signatures of Metabolic Risk Factors for Atherosclerotic Cardiovascular Disease

Background: Multiple cardiovascular disease (CVD) risk factors cluster in the same individuals and their concurrence is used to diagnose metabolic syndrome (MetSyn), which carries substantial risk for CVD. We hypothesized that MetSyn is associated with multiple metabolomic derangements.

Methods: As part of the SABRe CVD initiative, a multi-project investigation of biomarkers of CVD and its risk factors, we designed a 2x2x2 factorial study of MetSyn risk factors that included 650 individuals from the Framingham Heart Study (out of a total N of ∼3200) assigned to 8 unique groups of approximately 81 individuals each, sampled from high vs. low strata of BMI, lipids, and glucose. We conducted gas chromatography-mass spectroscopy (GC/MS) on plasma samples from 650 eligible individuals. General linear modeling was used to identify biomarkers that differed across all 8 groups or differed in their main effects on individual risk factors.

Results: Characteristics of the study sample (mean±SD), according to group assignment, are presented in the Table. GC/MS characterized 149 metabolites; of these 18 differed across all groups at P<5x10 -8 and 36 differed at P<0.00001. The top 3 most highly significant metabolites across all groups were glucose (P=2x10 -40 ), glutamic acid (P=4x10 -26 ), and sphingomyelins (lowest P=8x10 -25 ). The top 3 most highly significant main effects of metabolites for BMI were: glutamic acid (P=2x10 -18 ), sitosterol (P=2x10 -10 ), and uric acid (P=3x10 -10 ), for dyslipidemia: sphingomyelins (lowest P=1x10 -27 ), glutamic acid (P=5x10 -20 ), and lactic acid (P=7x10 -12 ), and for dysglycemia: glucose (P=1x10 -42 ), fructose (P=3x10 -7 ), and 2-hydroxybutanoic acid (P=6x10 -7 ).

Conclusions: Metabolomic profiling identified multiple biomarker signatures of MetSyn and its major metabolic risk factors. These novel findings warrant external replication. Understanding the pathways represented by our results may help to unravel the molecular derangements contributing to MetSyn and its constituent risk factors. This knowledge may identify therapeutic targets for the prevention and treatment of CVD.

Chicken macrophage stimulating protein is a ligand of the receptor protein-tyrosine kinase Sea

Affinity chromatography, employing the extracellular domain of the Sea receptor, was used to enrich Sea-binding proteins from chicken serum. One isolated protein bound both a Sea-immunoglobulin fusion protein and an antisera raised against murine macrophage stimulating protein. Amino-terminal sequencing of the dual-reactive protein yielded sequences which were identical to the predicted alpha and beta subunits of chicken macrophage stimulating protein. The partially purified chicken macrophage stimulating protein caused autophosphorylation of the Sea receptor. Previous work showed that recombinant expression of fully activatible human or mouse macrophage stimulating protein required a specific Cys to Ala substitution (Wahl, R. C., Costigan, V. J., Batac, J. P., Chen, K., Cam, L., Courchesne, P. L., Patterson, S. D. Zhang, K., and Pacifici, R. E. (1997) J. Biol. Chem. 272, 1-4). Therefore, we expressed both the wild type and the specific Cys to Ala form of chicken macrophage stimulating protein as recombinant proteins. After proteolytic activation, only conditioned media from COS cells transfected with the C665A chicken macrophage stimulating protein, but not from wild type chicken macrophage-stimulating protein, or control vector, was detected by the Sea-immunoglobulin fusion protein in Western blotting experiments. Conditioned media containing the C665A chicken macrophage-stimulating protein readily caused Sea phosphorylation, while conditioned media containing the wild type chicken macrophage-stimulating protein was only effective at inducing receptor phosphorylation at high concentrations. In addition to receptor phosphorylation, the C665A chicken macrophage-stimulating protein induced phosphorylation of Shc, Erk1, and Erk 2. We conclude that macrophage-stimulating protein is a ligand of the Sea receptor protein-tyrosine kinase.