Exertional rhabdomyolysis (ER) occurs in young, otherwise healthy, individuals principally during strenuous exercise, athletic, and military training. Although many risk factors have been offered, it is unclear why some individuals develop ER when participating in comparable levels of physical exertion under identical environmental conditions and others do not. This study investigated possible genetic polymorphisms that might help explain ER. DNA samples derived from a laboratory-based study of persons who had never experienced an episode of ER (controls) and clinical ER cases referred for testing over the past several years were analyzed for single nucleotide polymorphisms (SNPs) in candidate genes. These included angiotensin I converting enzyme (ACE), α-actinin-3 (ACTN3), creatine kinase muscle isoform (CKMM), heat shock protein A1B (HSPA1B), interleukin 6 (IL6), myosin light chain kinase (MYLK), adenosine monophosphate deaminase 1 (AMPD1), and sickle cell trait (HbS). Population included 134 controls and 47 ER cases. The majority of ER cases were men (n = 42/47, 89.4 %); the five women with ER were Caucasian. Eighteen African Americans (56.3 %) were ER cases. Three SNPs were associated with ER: CKMM Ncol, ACTN3 R577X, and MYLK C37885A. ER cases were 3.1 times more likely to have the GG genotype of CKMM (odds ratio/OR = 3.1, confidence interval/CI 1.33-7.10), 3.0 times for the XX genotype of ACTN3 SNP (OR = 2.97, CI 1.30-3.37), and 5.7 times for an A allele of MYLK (OR = 21.35, CI 2.60-12.30). All persons with HbS were also ER cases. Three distinct polymorphisms were associated with ER. Further work will be required to replicate these findings and determine the mechanism(s) whereby these variants might confer susceptibility.
A three-pool model was used to improve white-matter T 2 relaxometry in low signal-to-noise (SNR) data. To verify the model very high SNR T 2 relaxometry experiments were performed on myelinated tissue samples and three-pool fractions were consistently found. Relaxation curves based on the in vitro results were simulated with multiple SNRs and fit using the three-pool model and three less constraining nonnegative least squaresbased methods. In recent years T 2 relaxation has been shown to be multiexponential for myelinated tissue (1,2). While the exact number, fractional size, and T 2 values of these components are the subject of ongoing investigation, several studies have shown evidence that one of these exponentials (T 2 ϳ 10 -15 ms) corresponds approximately to the water pool within the myelin sheath (2). By accurately determining the T 2 values and pool fractions of relaxation components, it is possible to track microanatomical changes both during normal development and during the progression of disease states (e.g., multiple sclerosis).To avoid assumptions in the number of pools or compartments in white matter, previous studies were performed with multiexponential fitting of T 2 relaxation curves based on a nonnegative least squares (NNLS) fit (3). Whittall and MacKay improved upon the NNLS method (NNLS W method) by imposing an additional constraint so that the fit would be less sensitive to noise (4). This constraint favors less complex spectra and causes the fit to preferentially choose broader, even overlapping, T 2 peaks. Graham et al. (5) refined these constraints to obtain further improvement (NNLS G method). These methods, however, did not demonstrate robust performance at clinically achievable signal-to-noise ratio (SNR) values.At low SNR values NNLS-based methods often do not consistently find the correct number of peaks. It is our contention that the number of exponentials must be the same in each repetition of an experiment for pool fractions to be meaningfully discussed. This work expands upon earlier papers that have studied the details of curve fitting (6) and model selection (7) in multiexponential data using nonlinear least-squares procedures.In addition to improving the mathematical analysis at low SNR, the three-pool model clearly and directly relates the results to a physical model for multiexponential T 2. The most likely cause of multiexponential relaxation in myelinated tissue is restricted diffusion at barriers between separate tissue compartments. When such barriers impede the mixing of water between compartments to the point that the mixing time is long compared to the withincompartment relaxation time, then the slow exchange model is valid, and the total relaxation curve is a weighted sum of the relaxation curves of the individual compartments. Myelin is the chief restrictive barrier to water diffusion in myelinated tissue, so water can be seen as being divided into three compartments or pools: water within the myelin sheaths, water within myelinated axons, and all other water (8,9)...
Genetic polymorphisms may explain why certain individuals will develop exertional rhabdomyolysis (ER) or markedly elevated serum creatine kinase (CK) levels following exertion, while others in the same environment, performing the same exertion, do not. Prospectively, 499 recruits were evaluated during the initial fortnight of Army basic training. Serum CK levels were determined before and during that time. Eleven candidate genetic polymorphisms were studied and compared to CK levels. No subjects developed ER. Baseline CK was significantly greater in interleukin-6 G174C GG and myosin light chain kinase 2 (MLCK 2) AA subjects. Intertraining levels were significantly greater in angiotensin I-converting enzyme D/D and interleukin-6 GG subjects. Among African-Americans, those with MLCK2 AA had greater baseline CK (1,352 +/- 1,102.8 IU/L) than AC and CC genotypes (536.9 +/- 500.6). African-American men have the highest baseline levels and are more likely to have MLCK AA genotype. Whether this finding is associated with an increased incidence of ER requires further study.
Heat shock proteins act as molecular chaperones, assist in peptide maturation, and transport nascent peptides across membranes. One commonly studied single nucleotide polymorphism (SNP) for one of the proteins is HSPA1B (+A1538G). However, several studies of this polymorphism have failed to achieve Hardy-Weinberg equilibrium (HWE) for their sample. We compared various published procedures for analyzing the HSPA1B +A1538G SNP and report reasons for HWE discrepancies. Samples from 141 apparently healthy, physically active, volunteers (99 men and 42 women) were analyzed. The first protocol, initially described by Schröder et al., resulted in a genotypic distribution of 22 GG (15.6%), 119 AG (84.4%), and 0 AA; results were confirmed by reanalysis and sequencing. Two other published protocols, one described by Klausz et al. and another by Fekete et al., were used to confirm these results: both resulted in 22 GG (15.6%), 46 AA (32.6%), and 73 AG (51.7%). Additionally, the results were within HWE and confirmed by sequence analysis. Of the original 119 subjects genotyped as AG by the Schröder protocol, 46 of those were confirmed as AA with the Klausz and Fekete methods. Mixing primers from the Schröder and Klausz protocol resulted in 100% concordance with the data generated by the Klausz and Fekete protocols. Some published data on HSP genotyping deviate from HWE; thus, primers used for analyzing these highly homologous genes must be carefully considered. Our results highlight the importance of reinvestigating data when HWE is not achieved for the HSPA1B, or another, polymorphism.
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