Managing differential performance of polygenic risk scores across groups: Real-world experience of the eMERGE Network

The differential performance of polygenic risk scores (PRSs) by group is one of the major ethical barriers to their clinical use. It is also one of the main practical challenges for any implementation effort. The social repercussions of how people are grouped in PRS research must be considered in communications with research participants, including return of results. Here, we outline the decisions faced and choices made by a large multi-site clinical implementation study returning PRSs to diverse participants in handling this issue of differential performance. Our approach to managing the complexities associated with the differential performance of PRSs serves as a case study that can help future implementers of PRSs to plot an anticipatory course in response to this issue.

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

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

Integrating genetics and metabolomics from multi-ethnic and multi-fluid data reveals putative mechanisms for age-related macular degeneration

Age-related macular degeneration (AMD) is a leading cause of blindness in older adults. Investigating shared genetic components between metabolites and AMD can enhance our understanding of its pathogenesis. We conduct metabolite genome-wide association studies (mGWASs) using multi-ethnic genetic and metabolomic data from up to 28,000 participants. With bidirectional Mendelian randomization analysis involving 16,144 advanced AMD cases and 17,832 controls, we identify 108 putatively causal relationships between plasma metabolites and advanced AMD. These metabolites are enriched in glycerophospholipid metabolism, lysophospholipid, triradylcglycerol, and long chain polyunsaturated fatty acid pathways. Bayesian genetic colocalization analysis and a customized metabolome-wide association approach prioritize putative causal AMD-associated metabolites. We find limited evidence linking urine metabolites to AMD risk. Our study emphasizes the contribution of plasma metabolites, particularly lipid-related pathways and genes, to AMD risk and uncovers numerous putative causal associations between metabolites and AMD risk.

Abstract P341: A Flexible, Interpretable Approach to Recalibrate Risk Prediction Models: Adapting the Pooled Cohorts Equation to a Local New England Contemporary Population

Introduction: Atherosclerotic cardiovascular disease (ASCVD) prognostic models have well-documented poor generalizability in contemporary populations. Sustained, accurate performance of risk prediction models is challenging given demographic shifts, changes in practice patterns, and geographic variation. We propose an approach to tailor known risk prediction models for local settings while preserving known disease risk mechanisms. Here, as a case study, we recalibrate the Pooled Cohort Equations (PCE) in a contemporary (1997-2018), local electronic health records (EHR) cohort in New England.

Methods: We conducted a retrospective cohort study using EHR from large Boston-area hospitals. 61,808 patients 45+ years old, without ASCVD in 1997-2006 and complete data to calculate PCE model were split into training (50%), validation (10%), and test sets (40%). We used XGBoost to recalibrate PCE model (XGB-PCE) using PCE risk score, age, race/ethnicity, and sex (Figure). We constrained the XGB-PCE model to preserve known physiologic trends i.e. increase in ASCVD risk with increase in total cholesterol and systolic blood pressure. Models’ discrimination and calibration were calculated in a set aside test set using the Harrell’s c-index and modified Nam D’Agostino χ2 test.

Results: In the test set, XGB-PCE exhibited improved calibration across ASCVD risk categories (p=0.37) and race/ethnicity populations (p=0.73). In contrast, even after recalibration in the large, PCE was poorly calibrated across risk categories (p<0.001) and race/ethnicity populations (p<0.001). For model discrimination, both models produced similar c-index (Δc-index of ±.01).

Conclusions: Our recalibration approach maintained comparable discriminative performance and demonstrated improved calibration relative to the PCE while preserving known disease risk mechanisms. Methods that are flexible and preserve disease mechanisms have the potential to locally tailor existing models to be better calibrated for contemporary populations.

Genome-wide polygenic score to predict chronic kidney disease across ancestries

Chronic kidney disease (CKD) is a common complex condition associated with high morbidity and mortality. Polygenic prediction could enhance CKD screening and prevention; however, this approach has not been optimized for ancestrally diverse populations. By combining APOL1 risk genotypes with genome-wide association studies (GWAS) of kidney function, we designed, optimized and validated a genome-wide polygenic score (GPS) for CKD. The new GPS was tested in 15 independent cohorts, including 3 cohorts of European ancestry (n = 97,050), 6 cohorts of African ancestry (n = 14,544), 4 cohorts of Asian ancestry (n = 8,625) and 2 admixed Latinx cohorts (n = 3,625). We demonstrated score transferability with reproducible performance across all tested cohorts. The top 2% of the GPS was associated with nearly threefold increased risk of CKD across ancestries. In African ancestry cohorts, the APOL1 risk genotype and polygenic component of the GPS had additive effects on the risk of CKD.

0700 Prospective and Cross-sectional Associations Between Sleep Apnea and Disease in a Phenome-wide Analysis of a Clinical Biobank

Introduction

The potential contributions of sleep apnea (SA) to the risk of developing many diseases remain unidentified due to survey limits in cohort studies. We aimed to identify novel associations between common comorbidities in participants with SA compared to matched controls in a clinical biobank by leveraging electronic health record (EHR) information informed by natural language processing (NLP)-based phenotyping. We also investigated the relationship between polysomnography (PSG) statistics and these diseases.

Methods

The sample from the Mass General Brigham Biobank was comprised of 4,876 NLP-defined SA adult cases and 12,314 controls matched on age, biological sex, BMI, race/ethnicity, and healthcare utilization with a “data floor” of minimal EHR information. Prospective (&gt;1 year post SA diagnosis) and cross-sectional analyses considered 527 merged PheCode groups of NLP-defined diseases with ≥1% prevalence. Sex-stratified analyses were also performed. Associations with case/control status were further analyzed using rank-normalized Apnea-hypopnea Index (AHI3p) and the percentage of sleep with hypoxemia &lt;88% (Per88) in 4,544 participants, adjusting for age, biological sex, BMI, and race/ethnicity in a smaller single-night clinic sample (57% with AHI3p ≥15).

Results

In prospective analyses, 170 diseases had significant odds ratios (OR) when comparing participants with SA versus controls following Bonferroni adjustment (p &lt; 3.3 × 10-5), 8 of which were confirmed using PSG. Lead associations included a broad range of pathophysiology (e.g. chronic pulmonary heart disease, tension headache, and chronic fatigue syndrome). Diseases with higher ORs in women compared to men included chronic pulmonary heart disease, chronic renal failure, congestive heart failure not otherwise specified (NOS), and gout (p for sex interaction ≤3.2 × 10-3). Significant associations with PSG traits included hypertensive heart disease, fluid overload, and heart failure NOS (p ≤7.8 × 10-5). 281 diseases significantly differed in cross-sectional analyses, 41 of which were confirmed using PSG. 37 of these 41 associated diseases had lower p-values for Per88 compared to the AHI3p.

Conclusion

Sleep apnea may contribute to disease risk across a broad range of pathophysiology, with increased sex-specific risks for multiple common comorbidities. Future work should investigate the specific role of hypoxemia in these associations.

Support (If Any)

NIH (K01-HL135405, R35-HL135818, U01-HG008685 and P30-AR070253).

Prevalence of monoclonal gammopathies and clinical outcomes in a high-risk US population screened by mass spectrometry: a multicentre cohort study

BACKGROUND

Prevalence estimates for monoclonal gammopathy of undetermined significance (MGUS) are based on predominantly White study populations screened by serum protein electrophoresis supplemented with immunofixation electrophoresis. A prevalence of 3% is reported for MGUS in the general population of European ancestry aged 50 years or older. MGUS prevalence is two times higher in individuals of African descent or with a family history of conditions related to multiple myeloma. We aimed to evaluate the prevalence and clinical implications of monoclonal gammopathies in a high-risk US population screened by quantitative mass spectrometry.

METHODS

We used quantitative matrix-assisted laser desorption ionisation-time of flight (MALDI-TOF) mass spectrometry and EXENT-iQ software to screen for and quantify monoclonal gammopathies in serum from 7622 individuals who consented to the PROMISE screening study between Feb 26, 2019, and Nov 4, 2021, and the Mass General Brigham Biobank (MGBB) between July 28, 2010, and July 1, 2021. M-protein concentrations at the monoclonal gammopathy of indeterminate potential (MGIP) level were confirmed by liquid chromatography mass spectrometry testing. 6305 (83%; 2211 from PROMISE, 4094 from MGBB) of 7622 participants in the cohorts were at high risk for developing a monoclonal gammopathy on the basis of Black race or a family history of haematological malignancies and fell within the eligible high-risk age range (30 years or older for PROMISE cohort and 18 years or older for MGBB cohort); those over 18 years were also eligible if they had two or more family members with a blood cancer (PROMISE cohort). Participants with a plasma cell malignancy diagnosed before screening were excluded. Longitudinal clinical data were available for MGBB participants with a median follow-up time from serum sample screening of 4·5 years (IQR 2·4-6·7). The PROMISE study is registered with ClinicalTrials.gov, NCT03689595.

FINDINGS

The median age at time of screening was 56·0 years (IQR 46·8-64·1). 5013 (66%) of 7622 participants were female, 2570 (34%) male, and 39 (<1%) unknown. 2439 (32%) self-identified as Black, 4986 (65%) as White, 119 (2%) as other, and 78 (1%) unknown. Using serum protein electrophoresis with immunofixation electrophoresis, the MGUS prevalence was 6% (101 of 1714) in high-risk individuals aged 50 years or older. Using mass spectrometry, we observed a total prevalence of monoclonal gammopathies of 43% (1788 of 4207) in this group. We termed monoclonal gammopathies below the clinical immunofixation electrophoresis detection level (<0·2 g/L) MGIPs, to differentiate them from those with higher concentrations, termed mass-spectrometry MGUS, which had a 13% (592 of 4207) prevalence by mass spectrometry in high-risk individuals aged 50 years or older. MGIP was predominantly of immunoglobulin M isotype, and its prevalence increased with age (19% [488 of 2564] for individuals aged <50 years, 29% [1464 of 5058] for those aged ≥50 years, and 37% [347 of 946] for those aged ≥70 years). Mass-spectrometry MGUS prevalence increased with age (5% [127 of 2564] for individuals aged <50 years, 13% [678 of 5058] for those aged ≥50 years, and 18% [173 of 946] for those aged ≥70 years) and was higher in men (314 [12%] of 2570) compared with women (485 [10%] 5013; p=0·0002), whereas MGIP prevalence did not differ significantly by gender. In those aged 50 years or older, the prevalence of mass spectrometry was significantly higher in Black participants (224 [17%] of 1356) compared with the controls (p=0·0012) but not in those with family history (368 [13%] of 2851) compared with the controls (p=0·1008). Screen-detected monoclonal gammopathies correlated with increased all-cause mortality in MGBB participants (hazard ratio 1·55, 95% CI 1·16-2·08; p=0·0035). All monoclonal gammopathies were associated with an increased likelihood of comorbidities, including myocardial infarction (odds ratio 1·60, 95% CI 1·26-2·02; p=0·00016 for MGIP-high and 1·39, 1·07-1·80; p=0·015 for mass-spectrometry MGUS).

INTERPRETATION

We detected a high prevalence of monoclonal gammopathies, including age-associated MGIP, and made more precise estimates of mass-spectrometry MGUS compared with conventional gel-based methods. The use of mass spectrometry also highlighted the potential hidden clinical significance of MGIP. Our study suggests the association of monoclonal gammopathies with a variety of clinical phenotypes and decreased overall survival.

FUNDING

Stand Up To Cancer Dream Team, the Multiple Myeloma Research Foundation, and National Institutes of Health.

Hematopoietic mosaic chromosomal alterations and risk for infection among 767,891 individuals without blood cancer

Age is the dominant risk factor for infectious diseases, but the mechanisms linking the two are incompletely understood1,2. Age-related mosaic chromosomal alterations (mCAs) detected from blood-derived DNA genotyping, are structural somatic variants associated with aberrant leukocyte cell counts, hematological malignancy, and mortality3-11. Whether mCAs represent independent risk factors for infection is unknown. Here we use genome-wide genotyping of blood DNA to show that mCAs predispose to diverse infectious diseases. We analyzed mCAs from 767,891 individuals without hematological cancer at DNA acquisition across four countries. Expanded mCA (cell fraction >10%) prevalence approached 4% by 60 years of age and was associated with diverse incident infections, including sepsis, pneumonia, and coronavirus disease 2019 (COVID-19) hospitalization. A genome-wide association study of expanded mCAs identified 63 significant loci. Germline genetic alleles associated with expanded mCAs were enriched at transcriptional regulatory sites for immune cells. Our results link mCAs with impaired immunity and predisposition to infections. Furthermore, these findings may also have important implications for the ongoing COVID-19 pandemic, particularly in prioritizing individual preventive strategies and evaluating immunization responses.

OP0111 RHEUMATOID ARTHRITIS SEROLOGIC PHENOTYPE AT DIAGNOSIS AND SUBSEQUENT RISK FOR PNEUMONIA IDENTIFIED USING MACHINE LEARNING APPROACHES

Background:

Patients with rheumatoid arthritis (RA) are at increased risk of serious infections, with considerable excess morbidity and mortality after pneumonia. RA-related autoantibodies such as anti-cyclic citrullinated peptide (CCP) and rheumatoid factor (RF) may be generated at inflamed pulmonary mucosa prior to clinical RA onset. Therefore, patients with seropositive RA may be at increased risk for pneumonia after RA diagnosis due to subclinical pulmonary injury.

Objectives:

We investigated whether seropositive RA was associated with increased pneumonia risk compared to seronegative RA.

Methods:

We performed a retrospective cohort study among RA patients seen at a health care system in Boston, MA. RA patients were identified using a previously validated electronic health record (EHR) algorithm incorporating billing codes, natural language processing (NLP) of notes, medications, and laboratory results at 97% specificity1. We constructed an incident RA cohort using NLP for the index date of initial mention of RA. All patients were required to have both CCP and RF data from clinical care to determine serologic RA phenotype. We used semi-supervised machine learning approaches to identify pneumonia using billing codes and terms extracted using NLP, with the Centers for Disease Control definition of pneumonia from medical record review as a gold standard. The area under the receiver operating curve (AUROC) for this billing code+NLP pneumonia algorithm was 0.94 compared to the standard rule-based pneumonia algorithm (billing code on inpatient discharge) AUROC of 0.86 (p<0.001). Smoking status was extracted using NLP methods. Other covariates, including a previous validated weighted RA multimorbidity score2, were determined using structured EHR data. We used Cox regression to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for pneumonia adjusting for potential confounders.

Results:

We analyzed a total of 4,110 patients with incident RA and both CCP/RF data available. Mean age at index date was 53.0 years (SD 14.8), 77.2% were female, and 79.8% were CCP+ or RF+. During 32,248 patient-years of follow-up (mean 7.8 years/patient), we identified 240 pneumonia cases. Patients with seropositive RA had a HR of 1.99 (95%CI 1.30-3.01, Table) for pneumonia compared to patients with seronegative RA, adjusted for age, sex, smoking, index year, ESR level, glucocorticoid use, DMARD use, and weighted RA multimorbidity score. While CCP+ RA (HR 1.91, 95%CI 1.23-2.97) and RF+ RA (HR 2.07, 95%CI 1.35-3.16) had increased pneumonia risk compared to seronegative RA, the CCP+RF- RA subgroup had no association with pneumonia (HR 0.67, 95%CI 0.23-1.93).

Conclusion:

Patients with incident seropositive RA, particularly RF+ RA, had increased risk for pneumonia throughout the RA disease course that was not explained by measured confounders including smoking status, multimorbidity, medications, and ESR level. Further studies should investigate how RF+ may predispose RA patients to later develop pneumonia after clinical RA diagnosis.

References:

[1]Liao KP, Cai T, Gainer V, et al. Electronic medical records for discovery research in rheumatoid arthritis. Arthritis Care Res. 2010;62(8):1120–1127.

[2]Radner H, Yoshida K, Mjaavatten MD, et al. Development of a multimorbidity index: Impact on quality of life using a rheumatoid arthritis cohort. Semin Arthritis Rheum. 2015;45(2):167–173.

Disclosure of Interests:

Jeffrey Sparks Consultant of: Bristol-Myers Squibb, Optum, Janssen, Gilead, Weixing Huang: None declared, Bing Lu: None declared, Sicong Huang: None declared, Andrew Cagan: None declared, Vivian Gainer: None declared, Sean Finan: None declared, Guergana Savova: None declared, Daniel Solomon Grant/research support from: Funding from Abbvie and Amgen unrelated to this work, Elizabeth Karlson: None declared, Katherine Liao: None declared

FRI0585 HIGH-THROUGHPUT METHODOLOGY FOR EMR-BASED IDENTIFICATION OF CLINICAL SUB-PHENOTYPES IN COMPLEX PATIENT POPULATIONS

Background:

Heterogeneity in disease populations complicates discovery of risk factors. To identify risk factors for subpopulations of diseases, we need analytical methods that can deal with unidentified disease subgroups.

Objectives:

Inspired by successful approaches from the Big Data field, we developed a high-throughput approach to identify subpopulations within patients with heterogeneous, complex diseases using the wealth of information available in Electronic Medical Records (EMRs).

Methods:

We extracted longitudinal healthcare-interaction records coded by 1,853 PheCodes[1] of the 64,819 patients from the Boston’s Partners-Biobank. Through dimensionality reduction using t-SNE[2] we created a 2D embedding of 32,424 of these patients (set A). We then identified distinct clusters post-t-SNE using DBscan[3] and visualized the relative importance of individual PheCodes within them using specialized spectrographs. We replicated this procedure in the remaining 32,395 records (set B).

Results:

Summary statistics of both sets were comparable (Table 1).

Table 1.

Summary statistics of the total Partners Biobank dataset and the 2 partitions.

Set-Aset-BTotalEntries12,200,31112,177,13124,377,442Patients32,42432,39564,819Patientyears369,546.33368,597.92738,144.2unique ICD codes25,05624,95326,305unique Phecodes1,8511,8531,853

We found 284 clusters in set A and 295 in set B, of which 63.4% from set A could be mapped to a cluster in set B with a median (range) correlation of 0.24 (0.03 – 0.58).

Clusters represented similar yet distinct clinical phenotypes; e.g. patients diagnosed with “other headache syndrome” were separated into four distinct clusters characterized by migraines, neurofibromatosis, epilepsy or brain cancer, all resulting in patients presenting with headaches (Fig. 1 & 2). Though EMR databases tend to be noisy, our method was also able to differentiate misclassification from true cases; SLE patients with RA codes clustered separately from true RA cases.

Figure 1.

Two dimensional representation of Set A generated using dimensionality reduction (tSNE) and clustering (DBScan).

Figure 2.

Phenotype Spectrographs (PheSpecs) of four clusters characterized by “Other headache syndromes”, driven by codes relating to migraine, epilepsy, neurofibromatosis or brain cancer.

Conclusion:

We have shown that EMR data can be used to identify and visualize latent structure in patient categorizations, using an approach based on dimension reduction and clustering machine learning techniques. Our method can identify misclassified patients as well as separate patients with similar problems into subsets with different associated medical problems. Our approach adds a new and powerful tool to aid in the discovery of novel risk factors in complex, heterogeneous diseases.

References:

[1] Denny, J.C. et al. Bioinformatics (2010)

[2]van der Maaten et al. Journal of Machine Learning Research (2008)

[3] Ester, M. et al. Proceedings of the Second International Conference on Knowledge Discovery and Data Mining. (1996)

Disclosure of Interests:

Marc Maurits: None declared, Thomas Huizinga Grant/research support from: Ablynx, Bristol-Myers Squibb, Roche, Sanofi, Consultant of: Ablynx, Bristol-Myers Squibb, Roche, Sanofi, Marcel Reinders: None declared, Soumya Raychaudhuri: None declared, Elizabeth Karlson: None declared, Erik van den Akker: None declared, Rachel Knevel: None declared

1164 Prospective Associations Between Sleep Duration, Variability and Timing and Diseases from an Electronic Health Record Biobank in 24,065 Individuals

Introduction

Implementation of electronic health records (EHR) across healthcare systems linking clinical to survey data has enabled systematic assessments of longitudinal relationships between sleep traits and diseases classified by PheWAS codes where ICD-9/10 codes are collapsed to categories based on clinical similarity. In the Partners Biobank, a hospital-based virtual cohort from Mass General Brigham in greater Boston, MA, we aimed to assess associations between sleep traits and incident diseases.

Methods

Self-reported weekday/weekend bed and wake times from a survey at consent were used to derive sleep traits. Incident diseases were defined as two incident PheWAS codes on separate dates ≥1y after consent. Cox proportional hazards models compared short (&lt;7h) and long (≥9h) sleep duration, with 7-8h (referent group), adjusted for age, gender, race/ethnicity, and employment status, then further adjusted for BMI. Similarly, sleep midpoint (midpoint between weekend wake/bed times), sleep debt (difference in weekend/weekday sleep duration), and social jetlag (difference in weekend/weekday sleep midpoint) were assessed.

Results

The analytical sample consisted of 24,065 adults (mean sleep duration =8.12h) seeking regular care with sleep data. Participants had a total of 7,513,649 ICD codes of which incident 323,946 ICD codes mapped to 137,137 PheWAS codes. Over a median follow-up of 2.73 years (interquartile range: 1.82-3.98), participants sleeping &lt;7h had a significantly higher risk of incident Acute pain [hazard ratio(95% confidence interval)=1.46(1.2-1.78)], Tobacco use disorder [1.42(1.18-1.71)], Sciatica [1.72(1.3-2.27)], and Edema [1.69(1.25-2.28)]. Each additional hour of later sleep midpoint and increased sleep debt and social jetlag associated with higher risk of incident Major depressive disorder [midpoint:1.30(1.14-1.49); debt:1.23(1.09-1.38); jetlag:1.54(1.27-1.84)]. Associations retained significance upon further adjustment for BMI, except for Edema, and no other associations were observed at the Bonferroni threshold (P=0.0125).

Conclusion

Our findings in a large hospital-based virtual cohort support unique inter-relationships between sleep duration/timing on somatic, behavioral, and mental health outcomes.

Support

H.S.D. and R.S. are supported by NIDDK grant R01DK107859. B.C. is supported by K01-HL135405-01. S.R. and R.S. are partially supported by R35 NHLBI HL 135816.

Harmonizing Clinical Sequencing and Interpretation for the eMERGE III Network

The advancement of precision medicine requires new methods to coordinate and deliver genetic data from heterogeneous sources to physicians and patients. The eMERGE III Network enrolled >25,000 participants from biobank and prospective cohorts of predominantly healthy individuals for clinical genetic testing to determine clinically actionable findings. The network developed protocols linking together the 11 participant collection sites and 2 clinical genetic testing laboratories. DNA capture panels targeting 109 genes were used for testing of DNA and sample collection, data generation, interpretation, reporting, delivery, and storage were each harmonized. A compliant and secure network enabled ongoing review and reconciliation of clinical interpretations, while maintaining communication and data sharing between clinicians and investigators. A total of 202 individuals had positive diagnostic findings relevant to the indication for testing and 1,294 had additional/secondary findings of medical significance deemed to be returnable, establishing data return rates for other testing endeavors. This study accomplished integration of structured genomic results into multiple electronic health record (EHR) systems, setting the stage for clinical decision support to enable genomic medicine. Further, the established processes enable different sequencing sites to harmonize technical and interpretive aspects of sequencing tests, a critical achievement toward global standardization of genomic testing. The eMERGE protocols and tools are available for widespread dissemination.

Factors determining participation in prevention trials among systemic lupus erythematosus patients: a qualitative study

OBJECTIVE

A feasibility study for a trial of strategies for the prevention of atherosclerosis in patients with systemic lupus erythematosus (SLE) was stopped because of inadequate recruitment. There is little understanding of the factors influencing patients' decisions about participation in prevention trials. Our goal was to determine factors that patients with SLE consider in deciding about participating in prevention trials, to uncover concerns about SLE trials, and to investigate how study design and purpose affect participation decisions.

METHODS

We conducted focus groups with trial participants (n = 13), trial nonparticipants (n = 8), and a group of patients with diabetes (n = 9). We conducted telephone interviews with SLE patients who refused participation in the trial and the focus groups (n = 10). A trained facilitator elicited factors influencing participation decisions. Transcripts were coded and grouped into themes using grounded theory.

RESULTS

Demographic characteristics of the groups were similar. Seven factors emerged as important in decision making: current health status, study design, physician involvement, personal benefit, altruism, time, and incentives. These factors were considered by individuals who elected to participate and those who did not, but weighed differently. Among the trial participants, good health status, encouragement from one's physician, and desires to learn and to contribute stimulated participation. Reasons for nonparticipation included current health status, medication and randomization concerns, and personal factors.

CONCLUSION

We observed that similar factors were weighed differently by participants and nonparticipants. Our results suggest that strategies such as health education, enlistment of personal physicians, and limitation of time requirements may enhance recruitment of patients with SLE into clinical prevention trials.

Atherosclerotic vascular disease in systemic lupus erythematosus

In the United States, systemic lupus erythematosus (SLE) disproportionately affects African Americans. It has become a chronic disease with long-term morbidity including chronic renal disease, osteoporosis, cataracts, psychosocial impairment, and importantly, atherosclerotic vascular disease (ASVD). The latter (myocardial infarction, angina, peripheral vascular disease and stroke) are strikingly accelerated, occurring in subjects who are predominantly premenopausal women at an age when ASVD is rare or unusual. Although much is known about the biology, risk factors, and the prevention of atherosclerosis in normal individuals, little work has been done in SLE. In fact, ASVD in people with SLE may be a different disease. Approximately 1.5% of SLE patients per year will have a myocardial infarction or equivalent; about 0.5% of SLE patients per year will have a stroke. The risk factors for ASVD in SLE are based on small, retrospective, single center studies. These suggest that the risk factors known for the general population (i.e., smoking, obesity, sedentary lifestyle, high LDL cholesterol, etc.) are also observed in SLE. The best study of risk factors shows that even accounting for the known factors, SLE and/or its treatment (glucocorticoids) is by far the most important. Our current management of cardiovascular risk factors in SLE patients with ASVD is substandard and our adherence to national guidelines for prevention is substandard. It is not known whether improving either will prevent these disastrous outcomes. Very little is known about the risk factors in African Americans with SLE, although there is data to suggest that they may not be identical to those seen in Caucasian populations. The study of the best and most effective means to prevent ASVD in SLE and in African Americans with SLE and in African Americans with SLE should be a major priority.

Maximal voluntary force of bilateral and unilateral leg extension

The aims were: (1) to investigate whether the 10-20% lower force during bilateral (BL) as compared to unilateral (UL) leg extension could be due to a general inability to activate fully a large number of muscles simultaneously, (2) to analyse the EMG signal of the quadriceps femoris during leg extensions, (3) to study the BL/UL force ratio in extension of the knee, and (4) to study the BL/UL leg extension force ratio in untrained and trained subjects. A 10% lower maximal voluntary isometric force was demonstrated during BL as compared to UL leg extension. This force discrepancy did not change when a total arm load of 250 N was applied simultaneously. Nor did the absolute force levels change, which indicates that the lower BL leg extension force is not due to a general mechanism of reduced activation with an increased number of muscles recruited in maximal voluntary contractions. Integrated EMG activity, mean power frequency and root mean square value of the EMG amplitude did not differ between UL and BL leg extensions. The knee extension force was slightly greater (4%) during BL than UL contractions. These findings are arguments against a reduced activation of the knee extensor muscles being the cause of the lower bilateral leg extension force. No differences in BL/UL force ratio were noted between groups of untrained and trained subjects despite the fact that several of the trained groups do different forms of BL leg extensions regularly. Thus, it does not appear that training readily affects the BL/UL leg extension force ratio.

In-vitro stimulation of the rat epitrochlearis muscle. I. Contractile activity per se affects myofibrillar protein degradation and amino acid metabolism

The influence of contractile activity on protein degradation and amino acid metabolism in skeletal muscle was investigated by utilizing an in-vitro electrical stimulation model with the rat epitrochlearis muscle preparation. Graded decreases in contraction force and in the muscle content of ATP and PCr, and increases in lactate were recorded with different rates of stimulation (1 h) and with both isometric twitches and tetanic contractions. 3-Methylhistidine and phenylalanine were chosen as indicators of myofibrillar and total protein degradation, respectively. The release of 3-methylhistidine was significantly stimulated by contractile activity, but a significant increase in the total amount of this amino acid (released amount + tissue content) occurred only at the most intense contraction rates. The release rate, tissue content and total amount of phenylalanine were not influenced by the contractions. Glutamate formation was generally inhibited, but its release was increased. Alanine synthesis was increased in moderately and intensely stimulated muscles. Glutamine and glycine were not influenced by the contractions, however. Inhibition of protein synthesis did not significantly influence protein degradation or amino acid release. The data suggest that in the absence of anabolic factors in the medium, myofibrillar protein degradation is increased in heavily activated muscle. This takes place without total protein breakdown being affected.

A new dynamometer measuring concentric and eccentric muscle strength in accelerated, decelerated, or isokinetic movements. Validity and reproducibility

A new computerized dynamometer (the SPARK System) is described. The system can measure concentric and eccentric muscle strength (torque) during linear or nonlinear acceleration or deceleration, isokinetic movements up to 400 degrees.s-1, and isometric torque. Studies were performed to assess: I. validity and reproducibility of torque measurements; II. control of lever arm position; III. control of different velocity patterns; IV. control of velocity during subject testing; and, V. intra-individual reproducibility. No significant difference was found between torque values computed by the system and known torque values (p greater than 0.05). No difference was present between programmed and external measurement of the lever arm position. Accelerating, decelerating and isokinetic velocity patterns were highly reproducible, with differences in elapsed time among 10 trials being never greater than 0.001 s. Velocity during concentric and eccentric isokinetic quadriceps contractions at 30 degrees.s-1, 120 degrees.s-1 and 270 degrees.s-1 never varied by more than 3 degrees.s-1 among subjects (N = 21). Over three days of testing, the overall error for concentric and eccentric quadriceps contraction peak torque values for 5 angular velocities between 30 degrees.s-1 and 270 degrees.s-1 ranged from 5.8% to 9.0% and 5.8% to 9.6% respectively (N = 25). The results indicate that the SPARK System provides valid and reproducible torque measurements and strict control of velocity. In addition, the intra-individual error is in accordance with those reported for other similar devices.

Eccentric and concentric torque-velocity characteristics of the quadriceps femoris in man

The primary purpose of this investigation was to study the eccentric and concentric torque-velocity characteristics of the quadriceps femoris in man using a recently developed combined isometric, concentric and eccentric controlled velocity dynamometer (the SPARK System). A secondary purpose was to compare the method error associated with maximal voluntary concentric and eccentric torque output over a range of testing velocities. 21 males (21-32 years) performed on two separate days maximal voluntary isometric, concentric and eccentric contractions of the quadriceps femoris at 4 isokinetic lever arm velocities of 0 degree.s-1 (isometric), 30 degrees.s-1, 120 degrees.s-1 and 270 degrees.s-1. Eccentric peak torque and angle-specific torques (measured every 10 degrees from 30 degrees to 70 degrees) did not significantly change from 0 degrees.s-1 to 270 degrees.s-1 (p greater than 0.005) with the exception of angle-specific 40 degrees torque, which significantly increased; p less than 0.05). The mean method error was significantly higher for the eccentric tests (10.6% +/- 1.6%) than for the concentric tests (8.1% +/- 1.7%) (p less than 0.05). The mean method error decreased slightly with increasing concentric velocity (p greater than 0.05), and increased slightly with increasing eccentric velocity (p greater than 0.05). A tension restricting neural mechanism, if active during maximal eccentric contractions, could possibly account for the large difference seen between the present eccentric torque-velocity results and the classic results obtained from isolated animal muscle.

Electrolyte versus blood cardioplegia: randomized clinical and myocardial ultrastructural study

In 40 consecutive patients undergoing coronary artery bypass, one of two solutions for cardioplegia, each containing 30 mEq/L of K+ was used randomly. The groups were comparable except for intramyocardial temperature. With electrolyte solution (Group A), it was 16.5 degrees +/- 0.34 degrees C, while with blood from the pump-oxygenator (Group B) it was 20.3 degrees +/- 0.41 degrees C (p less than 0.001). After bypass left atrial pressure (LAP) was 11.9 +/- 0.67 torr in Group A and 8.1 +/- 0.49 torr in Group B (p less than 0.001). CPK-MB was elevated in 45% of Group A patients versus 15% in Group B (p less than 0.05). No patient died. Two myocardial infarctions occurred in Group A and one in Group B. Stereological morphometric electron microscopy was performed on biopsy specimens taken from the left ventricle (1) before perfusion, (2) after cardioplegia, and (3) 30 minutes after reperfusion. Group A showed marked intracellular edema, mitochondrial swelling, pronounced depletion of glycogen stores, and focal myofibrillary disorganization. Group B showed near normal myocardial ultrastructure with increased glycogen stores and minimal mitochondrial swelling. Morphometric analysis revealed a statistically significant increase in the degree of mitochondrial swelling (51%) in Group A compared with Group B after reperfusion (p less than 0.001). Thus, blood K+ cardioplegia resulted in better preservation of myocardial ultrastructure, lower ventricular filling pressure, and lesser CPK-MB release compared with this particular electrolyte cardioplegia.