Integrated Analys of High-Fat Challenge-Induced Changes in Blood Cell Whole-Genome Gene Expression.

SCOPE: Several studies have examined the whole-genome gene expression response in blood cells to high-fat challenges with differing results. The study aims to identify consistently up- or downregulated genes and pathways in response to a high-fat challenge using several integration methods. METHODS AND RESULTS: Three studies measuring the gene expression response to a high-fat challenge in white blood cells are evaluated for common trends using several integration methods. Overlap in differentially expressed genes between separate studies is examined, p-values of each separate study are combined, and data are analyzed as one merged dataset. Differentially expressed genes and pathways are compared between these methods. Selecting genes differentially expressed in the three separate studies result in 67 differentially expressed genes, primarily involved in circadian pathways. Using the Fishers p-value method and a merged dataset analysis, changes in 1097 and 1182 genes, respectively, are observed. The upregulated genes upon a high-fat challenge are related to inflammation, whereas downregulated genes are related to unfolded protein response, protein processing, cholesterol biosynthesis, and translation. CONCLUSION: A general gene expression response to a high-fat challenge is identified. Compared to separate analyses, integrated analysis provides added value for the discovery of a consistent gene expression response.

Genome-Wide Association Study of Diabetic Kidney Disease Highlights Biology Involved in Glomerular Basement Membrane Collagen.

BACKGROUND: Although diabetic kidney disease demonstrates both familial clustering and single nucleotide polymorphism heritability, the specific genetic factors influencing risk remain largely unknown. METHODS: To identify genetic variants predisposing to diabetic kidney disease, we performed genome-wide association study (GWAS) analyses. Through collaboration with the Diabetes Nephropathy Collaborative Research Initiative, we assembled a large collection of type 1 diabetes cohorts with harmonized diabetic kidney disease phenotypes. We used a spectrum of ten diabetic kidney disease definitions based on albuminuria and renal function. RESULTS: Our GWAS meta-analysis included association results for up to 19,406 individuals of European descent with type 1 diabetes. We identified 16 genome-wide significant risk loci. The variant with the strongest association (rs55703767) is a common missense mutation in the collagen type IV alpha 3 chain (COL4A3) gene, which encodes a major structural component of the glomerular basement membrane (GBM). Mutations in COL4A3 are implicated in heritable nephropathies, including the progressive inherited nephropathy Alport syndrome. The rs55703767 minor allele (Asp326Tyr) is protective against several definitions of diabetic kidney disease, including albuminuria and ESKD, and demonstrated a significant association with GBM width; protective allele carriers had thinner GBM before any signs of kidney disease, and its effect was dependent on glycemia. Three other loci are in or near genes with known or suggestive involvement in this condition (BMP7) or renal biology (COLEC11 and DDR1). CONCLUSIONS: The 16 diabetic kidney disease-associated loci may provide novel insights into the pathogenesis of this condition and help identify potential biologic targets for prevention and treatment.

Exploring Coronary Artery Disease GWAs Targets With Functional Links to Immunometabolism.

Finding genetic variants that cause functional disruption or regulatory change among the many implicated GWAs variants remains a key challenge to translating the findings from GWAs to therapeutic treatments. Defining the causal mechanisms behind the variants require functional screening experiments that can be complex and costly. Prioritizing variants for functional characterization using techniques that capture important functional and regulatory elements can assist this. The genetic architecture of complex traits such as cardiovascular disease and type II diabetes comprise an enormously large number of variants of small effect contributing to heritability and spread throughout the genome. This makes it difficult to distinguish which variants or core genes are most relevant for prioritization and how they contribute to the regulatory networks that become dysregulated leading to disease. Despite these challenges, recent GWAs for CAD prioritized genes associated with lipid metabolism, coagulation and adhesion along with novel signals related to innate immunity, adipose tissue and, vascular function as important core drivers of risk. We focus on three examples of novel signals associated with CAD which affect risk through missense or UTR mutations indicating their potential for therapeutic modification. These variants play roles in adipose tissue function vascular function and innate immunity which form the cornerstones of immuno-metabolism. In addition we have explored the putative, but potentially important interactions between the environment, specifically food and nutrition, with respect to key processes.

Personalized Cardio-Metabolic Responses to an Anti-Inflammatory Nutrition Intervention in Obese Adolescents: A Randomized Controlled Crossover Trial.

SCOPE: Chronic inflammation and hypoadiponectinemia are characteristics of obesity-induced insulin resistance (IR). The effect of an anti-inflammatory nutrition supplement (AINS) on IR and adiponectin biology in overweight adolescents was investigated. The secondary objective was to examine the extent to which individuals' biomarker profiles, derived from baseline phenotypes, predicted response or not to the AINS. Additionally, the impact of DNA methylation on intervention efficacy was assessed. METHODS AND RESULTS: Seventy overweight adolescents (13-18 years) were recruited to this randomized controlled crossover trial. Participants received an AINS (long chain n-3 PUFA, vitamin C, α-tocopherol, green tea extract, and lycopene) and placebo for 8 weeks each. Homeostatic model assessment (HOMA)-IR, adiponectin, inflammatory profiles, and DNA methylation were assessed. HOMA-IR was unchanged in the total cohort. High-molecular-weight (HMW) adiponectin was maintained following the AINS while it decreased over time following the placebo intervention. HOMA-IR decreased in 40% of subjects (responders) following the AINS. Responders' pretreatment phenotype was characterized by higher HOMA-IR, total and LDL cholesterol, but similar BMI in comparison to nonresponders. HMW adiponectin response to the AINS was associated with bidirectional modulation of adipogenic gene methylation. CONCLUSION: The AINS modulated adiponectin biology, an early predictor of type 2 diabetes risk, was associated with bidirectional modulation of adipogenic gene methylation in weight-stable overweight adolescents. HOMA-IR decreased in a sub-cohort of adolescents with an adverse metabolic phenotype. Thus, suggesting that more stratified or personalized nutrition approaches may enhance efficacy of dietary interventions.

Association of Repeatedly Measured High-Sensitivity-Assayed Troponin I with Cardiovascular Disease Events in a General Population from the MORGAM/BiomarCaRE Study.

BACKGROUND: High-sensitivity troponin I (hs-cTnI) concentrations reflect myocardial stress. The role of hs-cTnI in predicting long-term changes in the risk of cardiovascular disease (CVD) in general populations is not clearly defined. METHODS: We investigated whether the change in 3 repeated measures of hs-cTnI collected 5 years apart in a prospective Danish study (3875 participants, initially aged 30-60 years, 51% female, disease free at baseline) improves 10-year prediction of incident CVD compared to using a single most recent hs-cTnI measurement. The change process was modelled using a joint (longitudinal and survival) model and compared to a Cox model using a single hs-cTnI measure adjusted for classic CVD risk factors, and evaluated using discrimination statistics. RESULTS: Median hs-cTnI concentrations changed from 2.6 ng/L to 3.4 ng/L over 10 years. The change in hs-cTnI predicts 10-year risk of CVD (581 events); the joint model gave a hazard ratio of 1.31 per interquartile difference in hs-cTnI (95% CI 1.15-1.48) after adjustment for CVD risk factors. However, the joint model performed only marginally better (c-index improvement 0.0041, P = 0.03) than using a single hs-cTnI measure (c-index improvement 0.0052, P = 0.04) for prediction of CVD, compared to a model incorporating CVD risk factors without hs-cTnI (c-index 0.744). CONCLUSIONS: The change in hs-cTnI in 5-year intervals better predicts risk of CVD in the general population, but the most recent measure of hs-cTnI, (at 10 years) is as effective in predicting CVD risk. This simplifies the use of hs-cTnI as a prognostic marker for primary prevention of CVD in the general population.

The genetics of blood pressure regulation and its target organs from association studies in 342,415 individuals.

To dissect the genetic architecture of blood pressure and assess effects on target organ damage, we analyzed 128,272 SNPs from targeted and genome-wide arrays in 201,529 individuals of European ancestry, and genotypes from an additional 140,886 individuals were used for validation. We identified 66 blood pressure-associated loci, of which 17 were new; 15 harbored multiple distinct association signals. The 66 index SNPs were enriched for cis-regulatory elements, particularly in vascular endothelial cells, consistent with a primary role in blood pressure control through modulation of vascular tone across multiple tissues. The 66 index SNPs combined in a risk score showed comparable effects in 64,421 individuals of non-European descent. The 66-SNP blood pressure risk score was significantly associated with target organ damage in multiple tissues but with minor effects in the kidney. Our findings expand current knowledge of blood pressure-related pathways and highlight tissues beyond the classical renal system in blood pressure regulation.

The Predictive Value of Depressive Symptoms for All-Cause Mortality: Findings From the PRIME Belfast Study Examining the Role of Inflammation and Cardiovascular Risk Markers.

OBJECTIVES: To improve understanding about the potential underlying biological mechanisms in the link between depression and all-cause mortality and to investigate the role that inflammatory and other cardiovascular risk factors may play in the relationship between depressive symptoms and mortality. METHODS: Depression and blood-based biological markers were assessed in the Belfast PRIME prospective cohort study (N = 2389 men, aged 50-59 years) in which participants were followed up for 18 years. Depression was measured using the 10-item Welsh Pure Depression Inventory. Inflammation markers (C-reactive protein [CRP], neopterin, interleukin [IL]-1 receptor antagonist [IL-1Ra], and IL-18) and cardiovascular-specific risk factors (N-terminal pro-b-type natriuretic peptide, midregion pro-atrial natriuretic peptide, midregion pro-adrenomedullin, C-terminal pro-endothelin-1 [CT-proET]) were obtained at baseline. We used Cox proportional hazards modeling to examine the association between depression and biological measures in relation to all-cause mortality and explore the mediating effects. RESULTS: During follow-up, 418 participants died. Higher levels of depressive symptoms were associated with higher levels of CRP, IL-1Ra, and CT-proET. After adjustment for socioeconomic and life-style risk factors, depressive symptoms were significantly associated with all-cause mortality (hazard ratio = 1.10 per scale unit, 95% confidence interval = 1.04-1.16). This association was partly explained by CRP (7.3%) suggesting a minimal mediation effect. IL-1Ra, N-terminal pro-b-type natriuretic peptide, midregion pro-atrial natriuretic peptide, midregion pro-adrenomedullin, and CT-proET contributed marginally to the association between depression and subsequent mortality. CONCLUSIONS: Inflammatory and cardiovascular risk markers are associated with depression and with increased mortality. However, depression and biological measures show additive effects rather than a pattern of meditation of biological factors in the association between depression and mortality.

Predictive value of galectin-3 for incident cardiovascular disease and heart failure in the population-based FINRISK 1997 cohort.

OBJECTIVES: Galectin-3 is an emerging biomarker playing an important, complex role in intracellular pathways of cardiovascular diseases and heart failure. We aimed therefore to investigate the predictive value of galectin-3 for incident cardiovascular disease and heart failure. METHODS: Galectin-3 levels were measured in 8444 participants of the general population-based FINRISK 1997 cohort. Cox proportional hazards regression analyses, adjusting for traditional Framingham risk factors, prevalent valvular heart disease, eGFR (estimated glomerular filtration rate) as well as NT-proBNP, were used to examine the predictive power of galectin-3. Measurements of discrimination and reclassification using 10-fold cross-validation were performed to control for over-optimism. Cardiovascular death (CD), all-cause mortality, myocardial infarction (MI), ischemic stroke (hemorrhagic strokes were excluded) and heart failure (HF) were used as endpoints. RESULTS: During the follow-up of up to 15 years there were in total 1136 deaths from any cause, 383 cardiac deaths, 359 myocardial infarctions, 401 ischemic strokes and 641 cases of incident heart failure. Hazard ratios (HR) were statistically significant for all-cause mortality (1.12, p < 0.001), cardiac death (1.15, p = 0.033) and heart failure (1.10, p = 0.049). Statistical significance was lost when analyzing by gender except for all-cause mortality. No significant improvements were observed in model discrimination or overall reclassification upon inclusion of galectin-3. Compared to NT-proBNP, the predictive power of galectin-3 was weaker but both remained significant, independently of each other. CONCLUSION: Galectin-3 levels were predictive for future cardiovascular events but improvements in discrimination and reclassifications were modest.

ST2 may not be a useful predictor for incident cardiovascular events, heart failure and mortality.

OBJECTIVES: We hypothesised that soluble ST2 (sST2) levels can identify people with elevated risk of subsequent cardiovascular disease (CVD) and add to existing risk prediction algorithms. BACKGROUND: ST2 is a receptor for the inflammatory cytokine IL33. Increased sST2 levels have been associated with heart failure and death in acute myocardial infarction patients and in the general population. METHODS: We measured high-sensitivity sST2 in 8444 men and women (25-74 years) from the FINRISK97 prospective population cohort. Cox proportional hazards modelling evaluated the ability of sST2 to predict fatal and non-fatal heart failure, CVD (coronary heart disease, stroke), diabetes, and death over 15 years follow-up. Discrimination and reclassification statistics for 10-year absolute risks compared the ability of sST2 to improve upon Framingham risk factors (FRF), N-terminal pro-brain natriuretic peptide (NT-proBNP), renal function (eGFR) and prevalent valvular heart disease (VHD). RESULTS: sST2 showed suggestive but non-significant associations with heart failure {(HR per 1 SD of log sST2 1.06; 95% CI 0.96 to 1.17 (562 events))}, and with CVD (1.01 95% CI 0.94 to 1.08) (914 events) after adjustment for FRF, NT-proBNP, eGFR and VHD. sST2 significantly predicted death from all causes following similar adjustment ({HR 1.09 (95% CI 1.01 to 1.19) (974 events))}. No improvement in the c-index was observed for models adding sST2 to the risk factors. CONCLUSIONS: In a healthy general population from Finland, sST2 did not improve long-term prediction of cardiovascular events including heart failure or all-cause mortality.

Genetic markers enhance coronary risk prediction in men: the MORGAM prospective cohorts.

BACKGROUND: More accurate coronary heart disease (CHD) prediction, specifically in middle-aged men, is needed to reduce the burden of disease more effectively. We hypothesised that a multilocus genetic risk score could refine CHD prediction beyond classic risk scores and obtain more precise risk estimates using a prospective cohort design. METHODS: Using data from nine prospective European cohorts, including 26,221 men, we selected in a case-cohort setting 4,818 healthy men at baseline, and used Cox proportional hazards models to examine associations between CHD and risk scores based on genetic variants representing 13 genomic regions. Over follow-up (range: 5-18 years), 1,736 incident CHD events occurred. Genetic risk scores were validated in men with at least 10 years of follow-up (632 cases, 1361 non-cases). Genetic risk score 1 (GRS1) combined 11 SNPs and two haplotypes, with effect estimates from previous genome-wide association studies. GRS2 combined 11 SNPs plus 4 SNPs from the haplotypes with coefficients estimated from these prospective cohorts using 10-fold cross-validation. Scores were added to a model adjusted for classic risk factors comprising the Framingham risk score and 10-year risks were derived. RESULTS: Both scores improved net reclassification (NRI) over the Framingham score (7.5%, p = 0.017 for GRS1, 6.5%, p = 0.044 for GRS2) but GRS2 also improved discrimination (c-index improvement 1.11%, p = 0.048). Subgroup analysis on men aged 50-59 (436 cases, 603 non-cases) improved net reclassification for GRS1 (13.8%) and GRS2 (12.5%). Net reclassification improvement remained significant for both scores when family history of CHD was added to the baseline model for this male subgroup improving prediction of early onset CHD events. CONCLUSIONS: Genetic risk scores add precision to risk estimates for CHD and improve prediction beyond classic risk factors, particularly for middle aged men.

A multiple biomarker risk score for guiding clinical decisions using a decision curve approach.

AIMS: We assessed whether a cardiovascular risk model based on classic risk factors (e.g. cholesterol, blood pressure) could refine disease prediction if it included novel biomarkers (C-reactive protein, N-terminal pro-B-type natriuretic peptide, troponin I) using a decision curve approach which can incorporate clinical consequences. METHODS AND RESULTS: We evaluated whether a model including biomarkers and classic risk factors could improve prediction of 10 year risk of cardiovascular disease (CVD; chronic heart disease and ischaemic stroke) against a classic risk factor model using a decision curve approach in two prospective MORGAM cohorts. This included 7739 men and women with 457 CVD cases from the FINRISK97 cohort; and 2524 men with 259 CVD cases from PRIME Belfast. The biomarker model improved disease prediction in FINRISK across the high-risk group (20-40%) but not in the intermediate risk group, at the 23% risk threshold net benefit was 0.0033 (95% CI 0.0013-0.0052). However, in PRIME Belfast the net benefit of decisions guided by the decision curve was improved across intermediate risk thresholds (10-20%). At p(t) = 10% in PRIME, the net benefit was 0.0059 (95% CI 0.0007-0.0112) with a net increase in 6 true positive cases per 1000 people screened and net decrease of 53 false positive cases per 1000 potentially leading to 5% fewer treatments in patients not destined for an event. CONCLUSION: The biomarker model improves 10-year CVD prediction at intermediate and high-risk thresholds and in particular, could be clinically useful at advising middle-aged European males of their CVD risk.