US20110318726A1 - Predicting coronary artery disease and risk of cardiovascular events - Google Patents

Predicting coronary artery disease and risk of cardiovascular events Download PDF

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US20110318726A1
US20110318726A1 US13/255,568 US201013255568A US2011318726A1 US 20110318726 A1 US20110318726 A1 US 20110318726A1 US 201013255568 A US201013255568 A US 201013255568A US 2011318726 A1 US2011318726 A1 US 2011318726A1
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metabolite
metabolites
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cad
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Svati H. Shah
Christopher B. Newgard
William E. Kraus
Elizabeth R. Hauser
Geoffrey S. Ginsburg
L. Kristin Newby
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    • A—HUMAN NECESSITIES
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    • A—HUMAN NECESSITIES
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    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
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    • A61B5/7271—Specific aspects of physiological measurement analysis
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    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
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    • G01N21/75—Systems in which material is subjected to a chemical reaction, the progress or the result of the reaction being investigated
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    • G01N2800/00—Detection or diagnosis of diseases
    • G01N2800/32—Cardiovascular disorders
    • G01N2800/324—Coronary artery diseases, e.g. angina pectoris, myocardial infarction
    • G—PHYSICS
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    • G01N2800/50—Determining the risk of developing a disease
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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    • Y10T436/17—Nitrogen containing
    • Y10T436/173845—Amine and quaternary ammonium
    • Y10T436/174614—Tertiary amine
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    • Y10T436/20—Oxygen containing
    • Y10T436/200833—Carbonyl, ether, aldehyde or ketone containing
    • Y10T436/201666—Carboxylic acid

Definitions

  • Coronary artery disease is the leading cause of death in industrialized countries, and in concert with the epidemic of obesity and diabetes, is rapidly becoming the leading cause of death in developing countries.
  • the genetic predilection of CAD is well-established; family history has been shown to be an independent risk factor for CAD, especially in early onset forms. Despite this, the genetic architecture of CAD remains largely unknown.
  • the risk assessment may include predicting the likelihood a subsequent cardiovascular event such as a myocardial infarction, predicting development of CAD, or discriminating the presence of CAD in a subject.
  • the methods include detecting at least one metabolite in a sample from the subject.
  • the metabolite may be an acylcarnitine, an amino acid, a ketone, a free fatty acid or ⁇ -hydroxybutyrate.
  • the levels of metabolites are then compared to a standard or to control subjects and can be used to determine the level of risk of a cardiovascular event, the risk of development of CAD or the presence of CAD in the subject.
  • methods of developing a treatment plan for a subject with or at risk of developing CAD or a subject at risk for a cardiovascular event are also provided.
  • the methods include using the level of detected metabolite in the subject to develop a treatment plan based on the risk of cardiovascular disease in the subject.
  • the plan may include diet, exercise and pharmaceutical treatment options.
  • methods for assessing the risk of cardiovascular disease in a subject in which a sample is obtained from the subject.
  • the sample is provided to a laboratory for detection of metabolite levels in the sample.
  • the metabolites detected may be acylcarnitines, amino acids, ketones, fatty acids or hydroxybutyrate.
  • the laboratory returns a report indicating metabolite levels in the sample, which are indicative of the risk of cardiovascular disease in the subject.
  • FIG. 1 is a set of graphs showing the receiver operating characteristic (ROC) curves for metabolite factors and CAD.
  • ROC curves and measures of model fit are presented for three models: a clinical model inclusive of traditional CAD risk factors (diabetes, hypertension, dyslipidemia, smoking, BMI, family history; and for the replication group, age, race and sex are also included) (black line); a model inclusive of all traditional risk factors plus metabolite factors 4 and 9 (gray line); and a model inclusive of all traditional risk factors plus all metabolite factors (dashed black line).
  • the top graph shows the initial group and the bottom graph shows the replication group.
  • FIG. 2 is a set of graphs showing the cox proportional hazards model for predictive capability of metabolite Factor 8 for cardiovascular events. Unadjusted (left panel) and adjusted (right panel) survival curves (adjusted for BMI, severity of CAD, hypertension, dyslipidemia, diabetes, smoking, family history, ejection fraction, serum creatinine, subsequent CABG, age, race and sex) are presented for metabolite factor 8.
  • FIG. 3 is a pedigree of the eight multiplex GENECARD families. Black filled in symbols signify affected with premature CAD; smaller gray circles signify blood profiling performed for this study. Note that the majority of the family members profiled are as-of-yet unaffected offspring of the original affected-sibling pairs.
  • FIG. 4 is a graph showing the heritabilities of conventional metabolites.
  • the Y-axis is the negative log 10 of the p-value for the heritability estimate (X-axis). Error bars around heritability point estimates are in light grey.
  • FIG. 5 is a graph showing the heritabilities of amino acids and free fatty acids. Displayed are heritabilities of amino acids and free fatty acids.
  • the Y-axis is the negative log 10 of the p-value for the heritability estimate (X-axis). Error bars around heritability point estimates are in light grey.
  • FIG. 6 is a graph showing the heritabilities of acylcarnitines.
  • the Y-axis is the negative log 10 of the p-value for the heritability estimate (X-axis). Error bars around heritability point estimates are in light grey.
  • Metabolomics the study of small-molecule metabolites, may be useful for diagnosis of human disease. Studies have demonstrated heritability of metabolites in plants and mice. As described in the Examples, metabolite profiles are heritable in human families with early-onset CAD, suggesting that the known heritability of CAD may be mediated at least in part through metabolic components measurable in blood.
  • the Examples describe quantitative profiling of 69 metabolites, including acylcarnitine species (byproducts of mitochondrial fatty acid, carbohydrate and amino acid oxidation), amino acids and conventional metabolites such as free fatty acids, ketones and ⁇ -hydroxybutyrate, in participants enrolled in the Duke CATHGEN biorepository and in families selected from the Duke GENECARD study.
  • the capability of metabolite profiles to assess the risk of cardiovascular disease in a subject is provided herein. The Examples demonstrate that the levels of particular metabolites, alone or in combination, discriminate the likelihood of developing CAD, the presence of CAD and the risk of subsequent cardiovascular events.
  • the methods include detecting the level of at least one metabolite in a sample from the subject.
  • the amount or relative level of the metabolite may be detected.
  • the metabolites detected may be acylcarnitines, amino acids, ketones, free fatty acids (FFA), or hydroxybutyrate.
  • the level of the metabolite in the sample from the subject is then compared to a standard to assess the risk of cardiovascular disease.
  • the standard may be an empirically derived number for each metabolite indicating a normal range and/or a range indicative of cardiovascular disease or may be direct comparison to the levels of metabolite in individuals with known cardiovascular disease status.
  • Methods for assessing the risk of cardiovascular disease in a subject by obtaining a sample from the subject and providing the sample to a laboratory for detection of metabolite levels in the sample are also provided.
  • the metabolite detected by the laboratory may include acylcarnitines, amino acids, ketones, fatty acids and hydroxybutyrate.
  • a report indicating metabolite levels in the sample is then received from the laboratory. The report indicates the level of the metabolite in the subject and the level can be used to compare to standard values to indicate the risk of cardiovascular disease in the subject.
  • the risk of cardiovascular disease includes assessing the risk of a subject without CAD developing CAD over time due to heritable factors, assessing the presence or absence of CAD in a subject and assessing the risk of having a cardiovascular event.
  • Cardiovascular events include myocardial infarction, stroke and death.
  • Subjects may be any mammal, suitably the subject is human.
  • Subjects identified as having or at risk of developing CAD may be further assessed to determine their risk of a cardiovascular event using the methods provided herein.
  • the methods may be used to help diagnose the presence of CAD in a non-invasive fashion and/or to develop a treatment plan for subjects identified as at risk for CAD, having CAD or at risk for a cardiovascular event.
  • the treatment plan may include provision of dietary, exercise, and pharmaceutical therapies to the subject.
  • a cardiovascular event includes, but is not limited to, myocardial infarction (MI), stroke and death.
  • MI myocardial infarction
  • the metabolite may be detected using a variety of samples, several of which will be apparent to those skilled in the art.
  • peripheral blood was obtained from the subject and processed in order to detect the level of metabolites in the subject.
  • Other tissues or fluids from the subject may also be used, including but not limited to blood, plasma, urine, serum, saliva, and tissue biopsies.
  • any method may be used to detect the metabolite.
  • the method is quantitative such that the level or amount of the metabolite in the subject or a sample from the subject may be determined.
  • the level of the metabolites was detected by mass spectrometry. Other methods of measurement may be used, including nuclear magnetic resonance (NMR).
  • NMR nuclear magnetic resonance
  • the metabolites may also be detected using colorimetric or fluorometric assays based on detection of the metabolite by an assay such as a binding or enzymatic assay. Any suitable assay method for the metabolites may be used. Such methods will be apparent to those skilled in the art.
  • the level of the metabolite in the subject may be reported as ng/ml of metabolite in blood or tissue, by the mM or ⁇ M concentration of the metabolite in the blood or tissue or by using arbitrary units to show relative levels amongst subjects. In the Examples, the mM or ⁇ M of metabolite in the blood are reported.
  • detection of a single metabolite is sufficient to assess risk of cardiovascular disease.
  • 2, 3, 4, 5, 7, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60 or 65 metabolites may be detected and used in the methods to assess the risk of cardiovascular disease.
  • the metabolites detected may be related in a factor by principal component analysis of a population of subjects. The factors, or groups of metabolites, useful for assessing heritability of CAD and for the presence of CAD or risk of having a cardiovascular event are presented in the Examples below.
  • the level of metabolite in the subject is used to determine whether the subject has CAD, risk of the subject developing CAD and/or the risk of the subject experiencing a cardiovascular event in the future.
  • the level of risk determination may be based on a standard level of the metabolite present in the blood. Such a standard is used for the relationship between HDL and LDL cholesterol measurements in which risk for CAD is predicted when cholesterol levels reach certain level in the blood after fasting and the ratio of HDL to LDL is beyond standard limits. Such standards are generally developed based on a large population study.
  • the determination of risk may be based on direct comparison to one or more control subjects. For example, a set of control subjects lacking CAD and with no cardiovascular events in the two years following sample procurement and a set of control subjects with CAD and with or without a cardiovascular event in the two years following sample procurement could be used as a comparison.
  • the risk of cardiovascular disease in the subject may be expressed in relative terms. For instance a normal level of a metabolite may be referred to as 1.0 in subjects at low to average risk for cardiovascular disease such as CAD or a cardiovascular event. Any numbers below 1.0 could indicate the subject has a lower risk than the general population risk. A number greater than 1.0 would indicate that the subject has a greater than average risk level and the actual number could relate to the level of risk. For example, a subject whose metabolite level is 2.0 may be two times as likely to experience a cardiovascular event in the next two years as compared to an average individual.
  • the assessment of risk of cardiovascular disease includes but is not limited to, developing a risk profile.
  • the assessment or prediction may indicate that the subject is 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 100%, 200%, 300%, 400%, 500%, 750% or 1000% more likely to have or develop a cardiovascular disease, such as CAD or have cardiovascular event, than a control subject.
  • a control subject is an individual that does not have CAD and possesses levels of the metabolite that do not correlate with an increased risk of CAD or a cardiovascular event.
  • the metabolites predictive of risk of developing cardiovascular disease CAD include metabolites involved in many of the major pathways of lipid, protein and carbohydrate metabolism.
  • the acylcarnitines include acetyl carnitine (C2), a by-product of glucose, fatty acid and amino acid metabolism, Propionyl carnitine (C3) and Isoveleryl carnitine (C5), which provide information on amino acid catabolism, the dicarboxylated acylcarnitine, which report on peroxisomal fatty acid metabolism, and the medium-long chain acylcarnitines, which are intermediates in long-chain fatty acid beta-oxidation.
  • the amino acids serve as important intermediates in protein turnover and catabolism and the ketones are an index of fatty acid beta-oxidation.
  • Table 1 shows the short and full-names of the metabolites tested in the Examples.
  • Table 2 shows the biological functions, if available, of each of the tested metabolites.
  • Methods of predicting the risk of a cardiovascular event (death or myocardial infarction) in a subject by detecting at least one metabolite in the subject are also provided.
  • the metabolites predictive of risk of a cardiovascular event are presumed products of peroxisomal fatty acid metabolism, in particular the short-chain dicarboxyl acylcarnitines, and citrulline.
  • the specific metabolites are listed in Table 9 of the Examples and are identified as factor 8.
  • Table 10 shows the individual metabolites within Factor 8 and provides the Factor load data for each metabolite.
  • the data in the Examples demonstrate that citrulline and the short-chain dicarboxyl acylcarnitines are predictive of the risk of a cardiovascular event.
  • metabolites may also be predictive of the risk of a cardiovascular event. These metabolites include Gly, Ala, Ser, Pro, Met, His, Phe, Tyr, Asx, Glx, Ornithine, Citrulline, arg, C2, C3, C4:C14; C5:1, C5, C4:OH, C14-DC:C4DC, C5-DC, C6-DC, C10:3, C10, C10-OH:C8DC, C12:1, C12, C12-OH:C10DC, C14:1-OH, C14-OH:C12-DC, C16, C16-OH/C14-DC, C18:2, C18-OH/C16-DC, C20, C20:1-OH/C18:1-DC, C20-OH/C18-DC, C8:1-OH/C6:1-DC, C8:1-DC, C16:1, C16:1-OH/C14:1-DC, C20:4, FFA, HBUT, and
  • the levels of citrulline, C5-DC, C6-DC, C8:1-OH/C6:1-DC, and C8:1-DC are predictive of cardiovascular events.
  • the levels of ornithine, citrulline, C5, C14-DC:C4DC, C5-DC, C6-DC, C10-OH:C8DC, C8:1-OH/C6:1-DC, C8:1-DC, C20:4 and FFA are also useful for assessing the risk of a cardiovascular event.
  • metabolites useful for assessing the presence of CAD are medium-chain acylcarnitine, a branched chain amino acid or associated metabolite, or a metabolite associated with the urea cycle.
  • Table 6 The specific metabolites are listed in Table 6, 7 and 8 of the Examples and are identified as factors 1, 4, and 9 in Table 9.
  • Table 10 shows the individual metabolites within Factors 1, 4 and 9 and provides the Factor load data for each metabolite. Only those metabolites with factor loads greater than or equal to 0.04 are included in the factor.
  • metabolites may also be predictive of the risk of a cardiovascular event. These metabolites include Pro, Leu/Ile, Met, Val, Glx, Citrulline, C2, C3, C4:Ci4; C5, C8, C8:1-OH/C6:1-DC, C10:1, C14:2, C14:1-OH, C16:2, C16:1, C16:2, C16:1, C16:1-OH/C14:1-DC, C18-OH/C16-DC, HBUT, and Ket. In particular, the levels of Leu/Ile, Glx, C2, C14:1-OH and C16:1-OH/C14:1-DC are indicative of the presence of CAD in a subject.
  • Increased levels of Leu/Ile or Glx as compared to normal controls or a normal standard are indicative of CAD in the subject.
  • Decreases levels of C2, C14:1-OH and C16:1-OH/C14:1-DC are indicative of the presence of CAD in a subject.
  • a level of Leu/Ile greater than 165 mM, 170 mM or 175 mM is indicative of coronary artery disease.
  • a level of Glx greater than 127 mM, 128 mM, 129 mM, 130 mM, 132 mM, 135 mM or 140 mM is indicative of coronary artery disease.
  • a level of C14:1-OH less than 0.014 ⁇ M, 0.013 ⁇ M or 0.012 ⁇ M is indicative of coronary artery disease.
  • a level of C16:1-OH/C14:1-DC less than 0.009 ⁇ M, 0.0089 ⁇ M, or 0.0088 ⁇ M is indicative of coronary artery disease.
  • metabolites useful for assessing the likelihood of developing CAD in a subject by detecting the level of at least one metabolite in a sample from the subject are also provided.
  • the metabolites useful for assessing the likely development of CAD are the short- and medium-chain acylcarnitine metabolites, branched chain amino acids and urea cycle related metabolites.
  • the specific metabolites are listed in Table 14 of the Examples. Table 15 shows the individual metabolites within the identified Factors. Only those metabolites with factor loads greater than or equal to 0.04 are included in the factor. Individual metabolites may also be predictive of the risk of a cardiovascular event. These metabolites include ketones, arg, ornithine, citrulline, glx, ala, val, leu/Ile, pro, C2, C14:1, C18:1, C5:1, C4-i4, C18, C10:1 and FFA.
  • the CATHGEN biorepository consists of subjects recruited sequentially through the cardiac catheterization laboratories at Duke University Medical Center (Durham, N.C.). After informed consent, blood was obtained from the femoral artery at time of arterial access for catheterization, immediately processed to separate plasma, and frozen at ⁇ 80° C. All subjects were fasting for a minimum of six hours prior to collection. Clinical data were provided by the DDCD, a database of patients undergoing cardiac catheterization at Duke University since 1969. Medication data were collected for medications used chronically, i.e. medications at admission (inpatients) or from a clinic note within one month prior (outpatients). Following-up data, including occurrence of myocardial infarction (MI) and death were collected at six months after catheterization, then annually thereafter. Vital status was confirmed through the National Death Index. The indication for catheterization for all subjects was clinical concern for ischemic heart disease. Patients with severe pulmonary hypertension or organ transplant were excluded.
  • MI myocardial infarction
  • CAD index is a numerical summary of angiographic data.
  • Age-of-onset was defined as age at first MI, percutaneous coronary intervention (PCI), coronary artery bypass grafting (CABG), or age at first catheterization meeting CADindex threshold. Sex- and race-matched controls meeting the following criteria were selected: CADindex ⁇ 23; no coronary artery with >50% stenosis; age-at-catheterization ⁇ 61 years; and no history of MI, PCI, CABG, or transplant. Given the differences in age based on these criteria, results could be confounded by age.
  • PCI percutaneous coronary intervention
  • CABG coronary artery bypass grafting
  • profiling was performed in an independent cardiovascular event case-control group (event-replication') composed of unique individuals from CATHGEN meeting the following criteria: ejection fraction >40%; no history of PCI or CABG; and no subsequent CABG.
  • the Duke Institutional Review Board approved the protocols for CATHGEN and the current study. Informed consent was obtained from each subject.
  • Standard clinical chemistry methods were used for conventional metabolites with reagents from Roche Diagnostics (Indianapolis, Ind.), and for free fatty acids (total) and ketones (total and ⁇ -hydroxybutyrate) with reagents from Wako. All assays were performed on a Hitachi 911 clinical chemistry analyzer.
  • MS-profiled metabolites acylcarnitines, amino acids
  • acylcarnitines amino acids
  • the following protocol was used.
  • Proteins were first removed by precipitation with methanol. Aliquoted supernatants were dried, and then esterified with hot, acidic methanol (acylcarnitines) or n-butanol (amino acids). Analysis was done using tandem MS with a Quattro Micro instrument (Waters Corporation, Milford, Mass.).
  • Leucine/isoleucine (LEU/ILE) are reported as a single analyte because they are not resolved by our MS/MS method, and include contributions from allo-isoleucine and hydroxyproline. Under normal circumstances these isobaric amino acids contribute little to the signal attributed to LEU/ILE.
  • the acidic conditions used to form butyl esters results in partial hydrolysis of glutamine to glutamic acid and of asparagine to aspartate.
  • GLX GLU/GLN
  • ASX ASP/ASN
  • Metabolite levels reported as “0” i.e., below the lower limits of quantification (LOQ)
  • LOQ lower limits of quantification
  • Metabolites with >25% of values as “0” were not analyzed (five acylcarnitines). All metabolites were natural log-transformed to approximate a normal distribution.
  • generalized linear regression models were used to assess differences in metabolite levels between CAD cases and controls, both unadjusted and adjusted for traditional CAD risk factors not constrained by matching: diabetes, hypertension, dyslipidemia, body-mass-index (BMI), family history of CAD, and smoking Analyses of the replication group were further adjusted for race, sex and age.
  • scoring coefficients from PCA-derived factors constructed in the initial CAD group were used to calculate factor scores in the event-replication group; logistic regression was used to assess the association between factors and case/control status (unadjusted and adjusted for BMI, dyslipidemia, hypertension, diabetes, family history, smoking, creatinine, and ejection fraction).
  • PCA identified 12 factors comprised of collinear metabolites (Table 9), grouping in biologically plausible factors.
  • factor 1 medium-chain acylcarnitines
  • factor 4 branched-chain amino acids and related metabolites
  • factor 9 arginine, histidine, citrulline, Ci4-DC:C4DC.
  • the factor load for each metabolite is presented in Table 10.
  • peripheral blood metabolite profiles are independently associated with the presence of CAD, and add to the discriminative capability for CAD compared with models containing only clinical variables. Further, we report a specific metabolite cluster that independently predicts subsequent cardiovascular events in individuals with CAD.
  • the GENECARD study enrolled 920 families to perform affected-sibling-pair linkage for identification of genes for early-onset CAD (before age 51 for men, age 56 for women) (Hauser et al., 2003 , Am Heart J, 145, 602-613). Families with at least two siblings each of whom met the criteria for early-onset CAD (before age 51 for men, age 56 for women) were recruited. Unaffected family members were defined as no clinical evidence of CAD and age greater than 55 years for men (greater than 60 years for women). From this cohort, we selected eight representative families we believed would be particularly informative, based on availability of a relatively large number of family members and a heavy burden of CAD in the proband and surrounding generations ( FIG. 3 ).
  • Blood samples were promptly processed after collection via peripheral venous phlebotomy (within minutes), frozen as soon as possible thereafter (at most within 12 hours with the majority of samples being frozen within 1-2 hours of collection), and stored as plasma samples in EDTA-treated tubes at ⁇ 80° C. Samples were collected as often as possible in a fasting state; however, the consistency of this could not be determined. Institutional Review Boards approved study protocols; informed consent was obtained from each subject.
  • Biochemical measurements Frozen plasma samples were used to quantitatively measure targeted metabolites, including 37 acylcarnitine species, 15 amino acids, nine free fatty acids and conventional analytes, ketones and C-reactive protein (CRP). Sample preparation and coefficients of variation have been reported (Haqq et al., 2005 Contemp Clin Trials, 26, 616-625). The laboratory was blinded to family identifiers and case-control status. Assay ranges are 0.05-40 micromolar ( ⁇ M) (acylcarnitines); 5-1000 ⁇ M (amino acids); and 1-1000 mmol/L (fatty acids). For simplicity, the clinical shorthand of metabolites is used (Table 1). Intra-individual variability was assessed in samples from five individuals for which repeat profiling was performed on the same sample on five separate days. Coefficients-of-variation and correlation confirmed minimal inter-assay variability (Table 1).
  • Standard clinical chemistry methods were used for conventional metabolites, including glucose, total cholesterol, high-density-lipoprotein (HDL)- and low-density-lipoprotein (LDL) cholesterol, and triglycerides with reagents from Roche Diagnostics (Indianapolis, Ind.); and free fatty acids (total) and ketones (total and 3-hydroxybutyrate) with reagents from Wako (Richmond, Va.). All measurements were performed using a Hitachi 911 clinical chemistry analyzer.
  • HDL high-density-lipoprotein
  • LDL low-density-lipoprotein
  • Free fatty acids Free fatty acids. Free fatty acids were gently methylated using iodomethane and purified by solid-phase extraction (Patterson et al., 1999 , J Lipid Res, 40, 2118-2124). Derivatized fatty acids were analyzed by capillary gas chromatography/mass spectrometry (GC/MS) using a Trace DSQ instrument (Thermo Electron Corporation, Austin, Tex.). Due to sample volume considerations, only 80 of the 117 individuals (five out of eight families) had free fatty acid measurements performed.
  • GC/MS capillary gas chromatography/mass spectrometry
  • Heritability analysis Heritabilities were calculated using the Sequential Oligogenic Linkage Analysis Routines (SOLAR) software version 4.0.7 (Almasy and Blangero, 1998 , Am J Hum Genet, 62, 1198-1211), which uses maximum-likelihood methods to estimate variance components, allowing incorporation of fixed effects for known covariates and variance components for genetic effects. This approach appropriately accounts for correlation between all family members and allows incorporation of extended pedigrees such as is present in the current study. The total variation is partitioned into components for additive genetic variance and environmental variance, as well as a residual (unexplained) variability. The program uses the pedigree covariance matrix
  • ⁇ is the covariance matrix
  • ⁇ is the matrix of kinship values
  • i ⁇ g 2 is the additive genetic variance
  • I represents the identity matrix
  • ⁇ e 2 is the random environmental variance (Almasy et al., 1998, supra).
  • This model allows for complex pedigree data (i.e. beyond parent-offspring pairs) and hence, the resulting heritability estimates are more accurate than those obtained using only nuclear family members.
  • all sampled individuals from the pedigree were entered into the variance components models, including unaffected offspring, cousins, and married-in family members. Incorporation of married-in family members (i.e. genetically unrelated but with shared environment) allows for better estimation of the environmental component of intrafamilial clustering of traits.
  • Values considered outliers were excluded from heritability analyses, defined as values falling outside of the mean ⁇ 4SD (one-two outliers for each of 24 of the metabolites). Metabolite measurements below the lower limits of quantification (LOQ) were given a value of LOQ/2. Four metabolites having >25% of samples below LOQ were not further analyzed (C6, C5-OH:C3-DC, C4DC, and C10:2 acylcarnitines). All measurements were natural log-transformed prior to analysis, resulting in most metabolites approximating a normal distribution, an important consideration for variance components analysis.
  • polygenic heritability models were then constructed. For the normally distributed metabolites (the majority of metabolites), polygenic heritability models were calculated using the log-transformed values, adjusting for age, sex, BMI, DM, dyslipidemia, hypertension and CAD. The proband and family members were not selected based on any metabolite values; however, the potential for ascertainment bias exists. Therefore, analyses were corrected based on which of the family members (proband) was the index member for ascertainment of the family for early-onset CAD.
  • PCA principal components analysis
  • Heritabilities, clinical covariates and household effects for individual metabolites are presented, including: heritability point estimates, standard error for the heritability estimate, clinical covariates found to be significant in the polygenic model, the proportion of variance explained by household effects, the p-value for the household effects, the proportion of variance in the metabolite explained by those clinical covariates, and the p-value for the heritabilities.
  • Proportion House- Proportion Var hold Variance Heritability Short Name Heritability SE Covariates* Household p-value Covariates ⁇ p-value** C2 0.50 0.17 Age 0.06 0.3 0.18 0.00008 C3 0.35 0.13 HTN, Sex 0.08 0.06 0.18 0.0003 C4:Ci4 0.56 0.17 CAD, Age, 0.01 0.4 0.29 0.00003 HTN, Dys, Sex C5:1 0.67 0.14 None 0.02 0.4 N/A 0.000003 C5 0.34 0.16 Sex 0.00 N/A 0.22 0.003 C4-OH 0.37 0.16 Age 0.04 0.2 0.05 0.001 C8:1 0.27 0.18 BMI, Age 0.00 N/A 0.20 0.03 C8 0.45 0.23 Age 0.09 0.2 0.10 0.01 C5-DC 0.45 0.18 Age 0.00 N/A 0.05 0.003 C6-DC 0.51 0.20 HTN, Age, 0.08 0.2 0.20 0.004 Sex C10:3 0.16 0.13 Age 0.00 N/
  • DM diabetes mellitus
  • HTN hypertension
  • BMI body-mass-index
  • CAD affected with premature CAD
  • DYS dyslipidemia.
  • acylcarnitines the C18 (C18, C18:1, and C18:2) and the C14 acylcarnitines (C14, C14:1) (all p ⁇ 0.0001), along with C5:1 (p ⁇ 0.0001), and C2 (p ⁇ 0.0001) acylcarnitines best differentiated families.
  • ketones p ⁇ 0.0001

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