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Posts Tagged ‘Framingham Heart Study’


9:20AM 11/12/2014 – 10th Annual Personalized Medicine Conference at the Harvard Medical School, Boston

REAL TIME Coverage of this Conference by Dr. Aviva Lev-Ari, PhD, RN – Director and Founder of LEADERS in PHARMACEUTICAL BUSINESS INTELLIGENCE, Boston http://pharmaceuticalintelligence.com

9:20 a.m. Panel Discussion – Genomic Technologies

Genomic Technologies

The greatest impetus for personalized medicine is the initial sequencing of the human genome at the beginning of this Century. As we began to recognize the importance of genetic factors in human health and disease, efforts to understand genetic variation and its impact on health have accelerated. It was estimated that it cost more than two billion dollars to sequence the first human genome and reduction in the cost of sequence became an imperative to apply this technology to many facets of risk assessment, diagnosis, prognosis and therapeutic intervention. This panel will take a brief historical look back at how the technologies have evolved over the last 15 years and what the future holds and how these technologies are being applied to patient care.

Genomic Technologies

Opening Speaker and Moderator:

George Church, Ph.D.
Professor of Genetics, Harvard Medical School; Director, Personal Genomics

Genomic Technologies and Sequencing

  • highly predictive, preventative
  • non predictive

Shareable Human Genomes Omics Standards

$800 Human Genome Sequence – Moore’s Law does not account for the rapid decrease in cost of Genome Sequencing

Genome Technologies and Applications

  • Genia nanopore – battery operated device
  • RNA & protein traffic
  • Molecular Stratification Methods – more than one read, sequence ties
  • Brain Atlas  – transcriptome of mouse brains
  • Multigenics – 700 genes: hGH therapies

Therapies

  • vaccine
  • hygiene
  • age

~1970 Gene Therapy in Clinical Trials

Is Omic technologies — a Commodity?

  • Some practices will have protocols
  • other will never become a commodity

 

Panelists:

Sam Hanash, M.D., Ph.D. @MDAndersonNews

Director, Red & Charline McCombs Institute for Early Detection & Treatment of Cancer MD Anderson Cancer Center

Heterogeneity among Cancer cells. Data analysis and interpretation is very difficult, back up technology

Proteins and Peptides before analysis with spectrometry:

  • PM  – Immunotherapy approaches need be combined with other techniques
  • How modification in protein type affects disease
  • amplification of an aberrant protein – when that happens cancer developed. Modeling on a CHip of peptide synthesizer

Mark Stevenson @servingscience

Executive Vice President and President, Life Sciences Solutions
Thermo Fisher Scientific

Issues of a Diagnostics Developer:

  • FDA regulation, need to test on several tissues
  • computational environment
  • PCR, qPCR – cost effective
  • BGI – competitiveness

Robert Green, MD @BrighamWomens

Partners, Health Care Personalized Medicine — >>Disclosure: Illumina and three Pharmas

Innovative Clinical Trial: Alzheimer’s Disease, integration of sequencing with drug development

  • Population based screening with diagnosis
  • Cancer predisposition: Cost, Value, BRCA
  • epigenomics technologies to be integrated
  • Real-time diagnostics
  • Screening makes assumption on Predisposition
  • Public Health view: Phenotypes in the Framingham Studies: 64% pathogenic genes were prevalent – complication based in sequencing.

Questions from the Podium:

  • Variants analysis
  • Metastasis different than solid tumor itself – Genomics will not answer issues related to tumor in special tissues variability

 

 

 

 

– See more at: http://personalizedmedicine.partners.org/Education/Personalized-Medicine-Conference/Program.aspx#sthash.qGbGZXXf.dpuf

@HarvardPMConf

#PMConf

@SachsAssociates

 

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Heart and Aging Research in Genomic Epidemiology: 1700 MIs and 2300 coronary heart disease events among about 29 000 eligible patients: Design of Prospective Meta-Analyses of Genome-Wide Association Studies From 5 Cohorts

Reporter: Aviva Lev-Ari, PhD, RN

 

Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium

Heart and Aging Research in Genomic Epidemiology: 1700 MIs and 2300 coronary heart disease events among about 29 000 eligible patients: Design of Prospective Meta-Analyses of Genome-Wide Association Studies From 5 Cohorts

Bruce M. Psaty, MD, PhD, Christopher J. O’Donnell, MD, MPH, Vilmundur Gudnason, MD, PhD, Kathryn L. Lunetta, PhD, Aaron R. Folsom, MD, Jerome I. Rotter, MD,André G. Uitterlinden, PhD, Tamara B. Harris, MD, Jacqueline C.M. Witteman, PhD,Eric Boerwinkle, PhD and on Behalf of the CHARGE Consortium

Author Affiliations

From the Cardiovascular Health Research Unit, Departments of Medicine, Epidemiology, and Health Services (B.M.P.), University of Wash; Center for Health Studies, Group Health (B.M.P.), Seattle, Wash; the National Heart, Lung and Blood Institute and the Framingham Heart Study (C.J.O.D.), Framingham, Mass; Icelandic Heart Association and the Department of Cardiovascular Genetics (Y.G.), University of Iceland, Reykjavik, Iceland; Department of Biostatistics (K.L.), Boston University School of Public Health, Mass; Division of Epidemiology and Community Health (A.R.F.), University of Minnesota, Minneapolis; Medical Genetics Institute (J.I.R.), Cedars-Sinai Medical Center, Los Angeles, Calif; Departments of Internal Medicine (A.G.U.) and Epidemiology (A.G.U., J.C.M.W.), Erasmus Medical Center, Rotterdam, The Netherlands; Laboratory of Epidemiology, Demography, and Biometry (T.B.H.), Intramural Research Program, National Institute on Aging, Bethesda, Md; and Human Genetics Center and Division of Epidemiology (E.B.), University of Texas, Houston.

Guest editor for this article was Elizabeth R. Hauser, PhD.

Abstract

Background— The primary aim of genome-wide association studies is to identify novel genetic loci associated with interindividual variation in the levels of risk factors, the degree of subclinical disease, or the risk of clinical disease. The requirement for large sample sizes and the importance of replication have served as powerful incentives for scientific collaboration.

Methods— The Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium was formed to facilitate genome-wide association studies meta-analyses and replication opportunities among multiple large population-based cohort studies, which collect data in a standardized fashion and represent the preferred method for estimating disease incidence. The design of the Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium includes 5 prospective cohort studies from the United States and Europe: the Age, Gene/Environment Susceptibility—Reykjavik Study, the Atherosclerosis Risk in Communities Study, the Cardiovascular Health Study, the Framingham Heart Study, and the Rotterdam Study. With genome-wide data on a total of about 38 000 individuals, these cohort studies have a large number of health-related phenotypes measured in similar ways. For each harmonized trait, within-cohort genome-wide association study analyses are combined by meta-analysis. A prospective meta-analysis of data from all 5 cohorts, with a properly selected level of genome-wide statistical significance, is a powerful approach to finding genuine phenotypic associations with novel genetic loci.

Conclusions— The Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium and collaborating non-member studies or consortia provide an excellent framework for the identification of the genetic determinants of risk factors, subclinical-disease measures, and clinical events.

Example of Coronary Heart Disease

The cohort-study methods papers provide detail about many of the phenotypes listed in Table 2. For coronary heart disease, investigators knowledgeable about the phenotype in each study decided to focus on fatal and nonfatal myocardial infarction (MI) as the primary outcome because the MI criteria differed in only trivial ways among the studies. There were some minor differences in the definition of the composite outcome of MI, fatal coronary heart disease, and sudden death, which became the secondary outcome. Only subjects at risk for an incident event were included in the analysis. MI survivors whose DNA was drawn after the event were not eligible. The primary analysis was restricted to Europeans or European Americans. Patients entered the analysis at the time of the DNA blood draw, and were followed until an event, death, loss to follow up, or the last visit. The main recommendations of the Analysis Committee were adopted, and a threshold of 5×10−8 was selected for genome-wide statistical significance. Analyses in progress include about 1700 MIs and 2300 coronary heart disease events among about 29 000 eligible patients. Each cohort conducted its own analysis, and results were uploaded to a secure share site for the fixed-effects meta-analysis. Even with this number of events (Supplemental Figure 2), power is good for only for relatively high minor allele frequencies (>0.25) and large relative risks (>1.3).

The authors had full access to and take full responsibility for the integrity of the data. All authors have read and agree to the manuscript as written.

Discussion

In thousands of published papers, the 5 CHARGE cohort studies and many of the collaborating studies have already characterized the risk factors for and the incidence and prognosis of a variety of aging-related and cardiovascular conditions. The analysis of the incident MI, for instance, is free from the survival bias typically associated with cross-sectional or case-control studies. The methodologic advantages of the prospective population-based cohort design, the similarity of phenotypes across 5 studies, the availability of genome-wide genotyping data in each cohort, and the need for large sample sizes to provide reliable estimates of genotype-phenotype associations have served as the primary incentives for the formation of the CHARGE consortium, which includes GWAS data on about 38 000 individuals. The consortium effort relies on collaborative methods that are similar to those used by the individual contributing cohorts.

Phenotype experts who know the studies and the data well are responsible for phenotype-standardization across cohorts. The coordinated prospectively planned meta-analyses of CHARGE provide results that are virtually identical to a cohort-adjusted pooled analysis of individual level data. This approach–the within-study analysis followed by a between-study meta-analysis–avoids the human subjects issues associated with individual-level data sharing.

Editors, reviewers, and readers expect replication as the standard in science.6 The finding of a genetic association in one population with evidence for replication in multiple independent populations provides moderate assurance against false-positive reports and helps to establish the validity of the original finding. In a single experiment, the discovery-replication structure is traditionally embodied in a 2-stage design. The CHARGE consortium includes up to 5 independent replicate samples as well as additional collaborating studies for some phenotype working groups, so that it would have been possible to set up analysis plans within CHARGE to mimic the traditional 2-stage design for replication. For instance, the 2 largest cohorts could have served as the discovery set and the others as the replication set. However, attaining the extremely small probability values expected in GWAS requires large sample sizes. For any phenotype, a prospective meta-analysis of all participating cohorts, with a properly selected level of genome-wide statistical significance to minimize the chance of false-positives, is the most powerful approach to finding new genuine associations for genetic loci.25 When findings narrowly miss the prespecified significance threshold, genotyping individuals in other independent populations provides additional evidence about the association. For findings that substantially exceed pre-established significance thresholds, the results of a CHARGE meta-analysis effectively provide evidence of a multistudy replication.

The effort to assemble and manage the CHARGE consortium has provided some interesting and unanticipated challenges. Participating cohorts often had relationships with outside study groups that predated the formation of CHARGE. Timelines for genotyping and imputation have shifted. Purchases of new computer systems for the volume of work were sometimes necessary. Each cohort came to the consortium with their own traditions for methods of analysis, organization, and authorship policies that, while appropriate for their own work, were not always optimal for collaboration with multiple external groups. Within each cohort, the investigators had often formed working groups that divided up the large number of available phenotypes in ways that made sense locally but did not necessarily match the configuration that had been adopted by other cohorts. The Research Steering Committee has attempted to create a set of CHARGE working groups that accommodate the needs and the conventions of the various cohorts. Transparency, disclosure, and professional collaborative behavior by all participating investigators have been essential to the process.

Resource limitations are another challenge. Grant applications that funded the original single-study genome-wide genotyping effort typically imagined a much simpler design. The CHS whole-genome study had as its primary aim, for instance, the analysis of data on 3 endpoints, coronary disease, stroke and heart failure. With a score of active phenotype working groups, the CHARGE collaboration broadened the scope of the short-term work well beyond initial expectations for all the participating cohorts.

One of the premier challenges has been communications among scores of investigators at a dozen sites. CHS and ARIC are themselves multi-site studies. To be successful, the CHARGE collaboration has required effective communications: (1) within each cohort; (2) between cohorts; (3) within the CHARGE working groups; and (4) among the major CHARGE committees. In addition to the traditional methods of conference calls and email, the CHARGE “wiki,” set up by Dr J. Bis (Seattle, Wash), has provided a crucial and highly functional user-driven website for calendars, minutes, guidelines, working group analysis plans, manuscript proposals, and other documents. In the end, there is no substitute for face-to-face meetings, especially at the beginning of the collaboration, and this complex meta-organization has benefited from several CHARGE-wide meetings.

The major emerging opportunity is the collaboration with other studies and consortia. Many working groups have already incorporated nonmember studies into their efforts. Several working groups have coordinated submissions of initial manuscripts with the parallel submission of manuscripts from other studies or consortia. Several working groups have embarked on plans for joint meta-analyses between CHARGE and other consortia. CHARGE has tried to acknowledge and reward the efforts of champions, who assume leadership responsibility for moving these large complex projects forward and who are often hard-working young investigators, the key to the future success of population science.

The CHARGE Consortium represents an innovative model of collaborative research conducted by research teams that know well the strengths, the limitations, and the data from 5 prospective population-based cohort studies. By leveraging the dense genotyping, deep phenotyping and the diverse expertise, prospective meta-analyses are underway to identify and replicate the major common genetic determinants of risk factors, measures of subclinical disease, and clinical events for cardiovascular disease and aging.

SOURCE:

Circulation: Cardiovascular Genetics.2009; 2: 73-80

doi: 10.1161/ CIRCGENETICS.108.829747

 

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Comorbidity of Diabetes and Aging in Cardiovascular Diseases

Reporter: Aviva Lev-Ari, PhD, RN

Heart and Aging Research in Genomic Epidemiology1700 MIs and 2300 coronary heart disease events among about 29 000 eligible patients

Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) ConsortiumDesign of Prospective Meta-Analyses of Genome-Wide Association Studies From 5 Cohorts

Bruce M. Psaty, MD, PhD, Christopher J. O’Donnell, MD, MPH, Vilmundur Gudnason, MD, PhD, Kathryn L. Lunetta, PhD, Aaron R. Folsom, MD, Jerome I. Rotter, MD, André G. Uitterlinden, PhD, Tamara B. Harris, MD, Jacqueline C.M. Witteman, PhD, Eric Boerwinkle, PhD and on Behalf of the CHARGE Consortium

Author Affiliations

From the Cardiovascular Health Research Unit, Departments of Medicine, Epidemiology, and Health Services (B.M.P.), University of Wash; Center for Health Studies, Group Health (B.M.P.), Seattle, Wash; the National Heart, Lung and Blood Institute and the Framingham Heart Study (C.J.O.D.), Framingham, Mass; Icelandic Heart Association and the Department of Cardiovascular Genetics (Y.G.), University of Iceland, Reykjavik, Iceland; Department of Biostatistics (K.L.), Boston University School of Public Health, Mass; Division of Epidemiology and Community Health (A.R.F.), University of Minnesota, Minneapolis; Medical Genetics Institute (J.I.R.), Cedars-Sinai Medical Center, Los Angeles, Calif; Departments of Internal Medicine (A.G.U.) and Epidemiology (A.G.U., J.C.M.W.), Erasmus Medical Center, Rotterdam, The Netherlands; Laboratory of Epidemiology, Demography, and Biometry (T.B.H.), Intramural Research Program, National Institute on Aging, Bethesda, Md; and Human Genetics Center and Division of Epidemiology (E.B.), University of Texas, Houston.

Guest editor for this article was Elizabeth R. Hauser, PhD.

Abstract

Background— The primary aim of genome-wide association studies is to identify novel genetic loci associated with interindividual variation in the levels of risk factors, the degree of subclinical disease, or the risk of clinical disease. The requirement for large sample sizes and the importance of replication have served as powerful incentives for scientific collaboration.

Methods— The Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium was formed to facilitate genome-wide association studies meta-analyses and replication opportunities among multiple large population-based cohort studies, which collect data in a standardized fashion and represent the preferred method for estimating disease incidence. The design of the Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium includes 5 prospective cohort studies from the United States and Europe: the Age, Gene/Environment Susceptibility—Reykjavik Study, the Atherosclerosis Risk in Communities Study, the Cardiovascular Health Study, the Framingham Heart Study, and the Rotterdam Study. With genome-wide data on a total of about 38 000 individuals, these cohort studies have a large number of health-related phenotypes measured in similar ways. For each harmonized trait, within-cohort genome-wide association study analyses are combined by meta-analysis. A prospective meta-analysis of data from all 5 cohorts, with a properly selected level of genome-wide statistical significance, is a powerful approach to finding genuine phenotypic associations with novel genetic loci.

Conclusions— The Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium and collaborating non-member studies or consortia provide an excellent framework for the identification of the genetic determinants of risk factors, subclinical-disease measures, and clinical events.

Example of Coronary Heart Disease

The cohort-study methods papers provide detail about many of the phenotypes listed in Table 2. For coronary heart disease, investigators knowledgeable about the phenotype in each study decided to focus on fatal and nonfatal myocardial infarction (MI) as the primary outcome because the MI criteria differed in only trivial ways among the studies. There were some minor differences in the definition of the composite outcome of MI, fatal coronary heart disease, and sudden death, which became the secondary outcome. Only subjects at risk for an incident event were included in the analysis. MI survivors whose DNA was drawn after the event were not eligible. The primary analysis was restricted to Europeans or European Americans. Patients entered the analysis at the time of the DNA blood draw, and were followed until an event, death, loss to follow up, or the last visit. The main recommendations of the Analysis Committee were adopted, and a threshold of 5×108 was selected for genome-wide statistical significance. Analyses in progress include about 1700 MIs and 2300 coronary heart disease events among about 29 000 eligible patients. Each cohort conducted its own analysis, and results were uploaded to a secure share site for the fixed-effects meta-analysis. Even with this number of events (Supplemental Figure 2), power is good for only for relatively high minor allele frequencies (>0.25) and large relative risks (>1.3).

The authors had full access to and take full responsibility for the integrity of the data. All authors have read and agree to the manuscript as written.

Discussion

In thousands of published papers, the 5 CHARGE cohort studies and many of the collaborating studies have already characterized the risk factors for and the incidence and prognosis of a variety of aging-related and cardiovascular conditions. The analysis of the incident MI, for instance, is free from the survival bias typically associated with cross-sectional or case-control studies. The methodologic advantages of the prospective population-based cohort design, the similarity of phenotypes across 5 studies, the availability of genome-wide genotyping data in each cohort, and the need for large sample sizes to provide reliable estimates of genotype-phenotype associations have served as the primary incentives for the formation of the CHARGE consortium, which includes GWAS data on about 38 000 individuals. The consortium effort relies on collaborative methods that are similar to those used by the individual contributing cohorts.

Phenotype experts who know the studies and the data well are responsible for phenotype-standardization across cohorts. The coordinated prospectively planned meta-analyses of CHARGE provide results that are virtually identical to a cohort-adjusted pooled analysis of individual level data. This approach–the within-study analysis followed by a between-study meta-analysis–avoids the human subjects issues associated with individual-level data sharing.

Editors, reviewers, and readers expect replication as the standard in science.6 The finding of a genetic association in one population with evidence for replication in multiple independent populations provides moderate assurance against false-positive reports and helps to establish the validity of the original finding. In a single experiment, the discovery-replication structure is traditionally embodied in a 2-stage design. The CHARGE consortium includes up to 5 independent replicate samples as well as additional collaborating studies for some phenotype working groups, so that it would have been possible to set up analysis plans within CHARGE to mimic the traditional 2-stage design for replication. For instance, the 2 largest cohorts could have served as the discovery set and the others as the replication set. However, attaining the extremely small probability values expected in GWAS requires large sample sizes. For any phenotype, a prospective meta-analysis of all participating cohorts, with a properly selected level of genome-wide statistical significance to minimize the chance of false-positives, is the most powerful approach to finding new genuine associations for genetic loci.25 When findings narrowly miss the prespecified significance threshold, genotyping individuals in other independent populations provides additional evidence about the association. For findings that substantially exceed pre-established significance thresholds, the results of a CHARGE meta-analysis effectively provide evidence of a multistudy replication.

The effort to assemble and manage the CHARGE consortium has provided some interesting and unanticipated challenges. Participating cohorts often had relationships with outside study groups that predated the formation of CHARGE. Timelines for genotyping and imputation have shifted. Purchases of new computer systems for the volume of work were sometimes necessary. Each cohort came to the consortium with their own traditions for methods of analysis, organization, and authorship policies that, while appropriate for their own work, were not always optimal for collaboration with multiple external groups. Within each cohort, the investigators had often formed working groups that divided up the large number of available phenotypes in ways that made sense locally but did not necessarily match the configuration that had been adopted by other cohorts. The Research Steering Committee has attempted to create a set of CHARGE working groups that accommodate the needs and the conventions of the various cohorts. Transparency, disclosure, and professional collaborative behavior by all participating investigators have been essential to the process.

Resource limitations are another challenge. Grant applications that funded the original single-study genome-wide genotyping effort typically imagined a much simpler design. The CHS whole-genome study had as its primary aim, for instance, the analysis of data on 3 endpoints, coronary disease, stroke and heart failure. With a score of active phenotype working groups, the CHARGE collaboration broadened the scope of the short-term work well beyond initial expectations for all the participating cohorts.

One of the premier challenges has been communications among scores of investigators at a dozen sites. CHS and ARIC are themselves multi-site studies. To be successful, the CHARGE collaboration has required effective communications: (1) within each cohort; (2) between cohorts; (3) within the CHARGE working groups; and (4) among the major CHARGE committees. In addition to the traditional methods of conference calls and email, the CHARGE “wiki,” set up by Dr J. Bis (Seattle, Wash), has provided a crucial and highly functional user-driven website for calendars, minutes, guidelines, working group analysis plans, manuscript proposals, and other documents. In the end, there is no substitute for face-to-face meetings, especially at the beginning of the collaboration, and this complex meta-organization has benefited from several CHARGE-wide meetings.

The major emerging opportunity is the collaboration with other studies and consortia. Many working groups have already incorporated nonmember studies into their efforts. Several working groups have coordinated submissions of initial manuscripts with the parallel submission of manuscripts from other studies or consortia. Several working groups have embarked on plans for joint meta-analyses between CHARGE and other consortia. CHARGE has tried to acknowledge and reward the efforts of champions, who assume leadership responsibility for moving these large complex projects forward and who are often hard-working young investigators, the key to the future success of population science.

The CHARGE Consortium represents an innovative model of collaborative research conducted by research teams that know well the strengths, the limitations, and the data from 5 prospective population-based cohort studies. By leveraging the dense genotyping, deep phenotyping and the diverse expertise, prospective meta-analyses are underway to identify and replicate the major common genetic determinants of risk factors, measures of subclinical disease, and clinical events for cardiovascular disease and aging.

SOURCE:

Circulation: Cardiovascular Genetics.2009; 2: 73-80

doi: 10.1161/ CIRCGENETICS.108.82974

 

Read Full Post »


Myocardial Strain and Segmental Synchrony: Age and Gender in Speckle-tracking-based Echocardiographic Study

Reporter: Aviva Lev-Ari, PhD, RN

  • Original Article

Age- and Sex-based Reference Limits and Clinical Correlates of Myocardial Strain and Synchrony: The Framingham Heart Study

  1. Susan Cheng1*,
  2. Martin G. Larson2,
  3. Elizabeth L. McCabe3,
  4. Ewa Osypiuk4,
  5. Birgitta T. Lehman4,
  6. Plamen Stanchev4,
  7. Jayashri Aragam5,
  8. Emelia J. Benjamin6,
  9. Scott D. Solomon7 and
  10. Ramachandran S. Vasan8

+Author Affiliations


  1. 1Framingham Heart Study, Framingham, MA; Brigham and Women’s Hospital, Boston, MA

  2. 2Framingham Heart Study, Framingham, MA; Boston University, Boston, MA

  3. 3Boston University, Boston, MA

  4. 4Framingham Heart Study, Framingham, MA

  5. 5Veterans Administration Hospital, West Roxbury, MA

  6. 6Framingham Heart Study, Framingham, MA; Whitaker Cardiovascular Institute, Boston University School of Medicine, Boston, MA; Boston University School of Public Health, Boston, MA

  7. 7Brigham and Women’s Hospital, Boston, MA

  8. 8Framingham Heart Study, Framingham, MA; Whitaker Cardiovascular Institute, Boston University School of Medicine, Boston, MA
  1. * Brigham and Women’s Hospital, 75 Francis Street, PBB-119 Boston, MA 02115scheng3@partners.org

Abstract

Background—There is rapidly growing interest in applying measures of myocardial strain and segmental synchrony in clinical investigations and in practice, but data are limited regarding their reference ranges in healthy individuals.

Methods and Results—We performed speckle-tracking-based echocardiographic measures of left ventricular (LV) myocardial strain and segmental synchrony in healthy Framingham Heart Study participants (n=738, mean age 63 years, 64% women) who were free of cardiovascular disease. Reference values (2.5th, 50th, 97.5th quantiles) were estimated using quantile regression. Age- and sex-based upper (97.5th quantile) limits were as follows:

  • 15.5% to -16.9% (women) and -14.5 to -15.4% (men) for longitudinal strain;
  • -21.9% to -24.3% (women) and -18.9% to -25.0% (men) for circumferential strain;
  • 114-158 msec (women) and 133-206 msec (men) for longitudinal segmental synchrony (SD of regional time-to-peak strains); and,
  • 204-224 msec (women) and 201-288 msec (men) for transverse segmental synchrony.

In multivariable analyses, women compared to men had

  • ~1.7% greater longitudinal strain,
  • ~2.2% greater transverse strain, and
  • ~3.2% greater circumferential strain (P<0.0001 for all).

Older age and higher diastolic blood pressure, even within the normal range, was associated with worse transverse segmental synchrony (P<0.001). Overall, clinical covariates contributed ≤12% of the variation in myocardial strain or synchrony in this healthy sample.

Conclusions—We estimated age- and sex-specific reference limits for echocardiographic measures of LV strain and synchrony in a healthy community-based sample, wherein clinical covariates contributed only a modest proportion of the variation. These data may facilitate interpretation of LV strain-based measures obtained in future clinical research and practice.

Key Words:

  • Received November 19, 2012.
  • Accepted July 18, 2013.

SOURCE

CIRCIMAGING.112.000627Published online before print August 5, 2013,doi: 10.1161/​CIRCIMAGING.112.000627

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