News about Health (Part 7)

Risk and Severity of COPD Is Associated With the Group-Specific Component of Serum Globulin 1F Allele: Statistical Analysis

Risk and Severity of COPD Is Associated With the Group-Specific Component of Serum Globulin 1F Allele: Statistical AnalysisTo test Gc-globulin phenotypic variance between patients with COPD and healthy smokers, and to compare the frequency of individuals over or under the threshold (dFEV1 > 90 mL/yr, LAA% > 60%, or mean CT score < — 940 HU) in the two populations, asymptotic normal tests for the equality of two population probabilities were performed. A Welch test was employed to test the difference of population averages in continuous variates between the two groups. StatView Version 5.0 (SAS Institute; Cary, NC) was used for the statistical calculations; p < 0.05 was considered statistically significant. more

Risk and Severity of COPD Is Associated With the Group-Specific Component of Serum Globulin 1F Allele: Genotyping

Risk and Severity of COPD Is Associated With the Group-Specific Component of Serum Globulin 1F Allele: GenotypingDNA for genotyping was extracted from blood using standard phenolchloroform method. To detect point mutations in exon XI (Glu/Asp 416 and Thr/Lys 420) of the Gc-globulin gene, polymerase chain reaction (PCR) was performed followed by restriction fragment-length polymorphism analysis. To amplify the region of interest that contains the point mutations, we used the upstream primer described by Schellenberg et al (5’TAAT-GAGCAAATGAAAGAAG3′), and we designed a downstream primer (5’TGAGTAGATTGGAGTGCATAC3′) according to the published Gc-globulin gene sequence. The PCR product is a fragment of 462 base-pairs (bp). review

Risk and Severity of COPD Is Associated With the Group-Specific Component of Serum Globulin 1F Allele: Materials and Methods

We also considered it important to test the contribution of Gc-globulin genotypes to the disease progression or severity in patients with COPD because, to our knowledge, this has not yet been investigated. The annual decline of FEV1 (dFEV1) is often taken to represent progressive airway obstruction and physiologic deterioration in smokers, and as an indication of disease progression in patients with COPD. Sandford et al investigated the relationship between various candidate gene genotypes, including Gc-globulin, and dFEV1 in a population of smokers; no relation was seen between Gc-globulin genotypes and decline in lung function. In addition to the evaluation of airway obstruction, it is also important to evaluate in patients with COPD the severity of parenchymal injury that is termed emphysema. Parameters such as low-attenuation area percentage (LAA%) and mean CT score in high-resolu-tion CT (HRCT) have been shown to be useful in the assessment of emphysema. To test the hypothesis that Gc-globulin polymorphism has an important role in the susceptibility to COPD, we analyzed the polymorphism in patients with COPD and healthy smokers in a sample drawn from the Japanese population. We further examined the correlation between the genotypes and the extent of deterioration in the rate of airflow in patients with COPD represented by dFEV1. The correlation between the genotypes and the extent of emphysema was evaluated by several radiologic parameters assessed by HRCT.

Risk and Severity of COPD Is Associated With the Group-Specific Component of Serum Globulin 1F Allele

Risk and Severity of COPD Is Associated With the Group-Specific Component of Serum Globulin 1F AlleleAlthough the most critical factor for acquiring COPD is cigarette smoking, only 15 to 20% of chronic smokers get this disease. Several epidemiologic studies have suggested familial clustering of the disease. This suggests that the genetic factors are likely to have a role in determining an individual’s susceptibility to COPD. Polymorphisms of several candidate genes have been investigated in relation to the development of COPD. One such candidate is the gene encoding the group-specific component of serum globulin (Gc-globulin), also called vitamin-D-binding protein.

Comparison of Continuous and Discrete Measurements of Dyspnea During Exercise in Patients With COPD and Normal Subjects: Ekman Fraction

Comparison of Continuous and Discrete Measurements of Dyspnea During Exercise in Patients With COPD and Normal Subjects: Ekman FractionFor all sensory modalities, Weber’s law states that the physical change in the size of a JND must be increased by a constant fraction of its original or background value to produce a perceived change in sensory experience. Whereas the Weber fraction relates to a physical or physiologic variable, the Ekman fraction refers to a sensory sensation such as breathlessness. With the continuous, but not the discrete, method for measuring breathlessness, the change in breathlessness can be determined over the course of exercise (ie, Ekman fraction). Our results showed similar mean Ekman fractions between patients with COPD (33%) and healthy, age-matched subjects (29%). Of interest, these values are higher than the 23% that we observed in 14 healthy, female college subjects, and the 18% observed in 14 healthy, male college students. Whether the observed differences in the Ekman fractions are real and, if so, are related to the age of the subjects will require future testing in a larger population. Here

Comparison of Continuous and Discrete Measurements of Dyspnea During Exercise in Patients With COPD and Normal Subjects: VE-dyspnea

However, the x-intercept for the VE-dyspnea relationship was significantly higher in patients with COPD compared with healthy subjects. These findings are consistent with previous observations that VE is higher at similar exercise intensities in patients with COPD compared to normal subjects, due, at least in part, to an increase in dead space ventilation.
The absolute threshold may be an important parameter to consider in evaluating breathlessness during exercise testing. For example, Killian and colleagues2 estimated that the thresholds for leg effort and dyspnea occurred “between 20% and 40% of maximal power output” based on discrete perceptual ratings. Although Teramoto et al termed the V02 on the x-intercept as the “threshold load of dyspnea,” it is not possible to calculate an absolute threshold based on the discrete method for rating breathlessness. storehealthmall.eu

Comparison of Continuous and Discrete Measurements of Dyspnea During Exercise in Patients With COPD and Normal Subjects: Healthy Subjects

Comparison of Continuous and Discrete Measurements of Dyspnea During Exercise in Patients With COPD and Normal Subjects: Healthy SubjectsThe continuous method also permits calculation of an absolute threshold corresponding to the onset of breathlessness in a manner similar to standard procedures followed throughout other areas of sensory psychophysics. In other words, there are no statistical assumptions required in determining the threshold for a single subject because the value is inherent in the rating method. We have defined the threshold as the physiologic value corresponding to the “first dyspnea rating that surpasses the No. 0.5 (just noticeable)” on the Borg scale. As with all such measures of threshold obtained by classical psychophysical methods, this particular criterion is arbitrary in that other cutoff values could have been selected. What is important here is that the same statistical criterion be applied for all comparisons among conditions. www.mycanadianfamilypharmacy.net

Comparison of Continuous and Discrete Measurements of Dyspnea During Exercise in Patients With COPD and Normal Subjects: Statistical Analysis Considerations

Both linear, and power2, function equations have been used to summarize the relationships between physiologic variables during exercise and the perceptual response of dyspnea. In simple terms, both approaches involve the matching of one continuum to another continuum. In 1992, Killian and colleagues2 published results of perceived magnitude of dyspnea and leg effort during cycle ergom-etry in 460 normal subjects using power function equations. However, the data shown in Figure 2 of an individual patient with COPD clearly illustrate that linear relationships can also be used to describe such results. Based on correlation coefficients as an indicator of goodness of fit, the physiologic/perceptual relationships in our subjects were similar for linear and power function analyses, except that linear regression for VE-breathlessness fit the data significantly better than did the power function. Moreover, numerous investigators” have used linear regression to report their data. For these reasons, we used linear function analyses to summarize the findings in this report.

Comparison of Continuous and Discrete Measurements of Dyspnea During Exercise in Patients With COPD and Normal Subjects: Discussion

Comparison of Continuous and Discrete Measurements of Dyspnea During Exercise in Patients With COPD and Normal Subjects: DiscussionTable 3 presents summary data for the parameters of the linear regression analyses and absolute thresholds for the independent variables of power production, V02, and VE, respectively. There were no significant differences for the slopes and intercepts for the three independent variables and breathlessness ratings between the methods. Absolute thresholds could only be calculated with the continuous method.
Comparison Between Patients With COPD and Healthy Subjects
As expected, the peak exercise values for all three independent variables attained by the patients with COPD were significantly lower than that attained by the healthy subjects (Table 1). However, both the patient group and the healthy control subjects reported similar peak breathlessness ratings (approximately 6 on the Borg scale) [Table 2].

Comparison of Continuous and Discrete Measurements of Dyspnea During Exercise in Patients With COPD and Normal Subjects: Test-Retest Reliability

The best-fitting linear equation is shown for each relationship. For each group, there were high Pearson correlation coefficients for power production (r = 0.96 to 0.98), Vo2 (r = 0.95 to 0.96), and Ve (r = 0.95 to 0.97) and breathlessness ratings. There were no differences in the magnitude of the correlations between the discrete and continuous methods. A power function was also fit to the same data sets, but in all but one case there was no significant difference between the correlation coefficients (as an indicator of goodness of fit) obtained from the two models. The only exception was that linear function for VE-breathlessness fit the data significantly better (p < 0.05) than did the power function. Based on the above results, and because various authors’ have previously reported linear regression analyses to describe similar data, we present our current results as linear functions. Moreover, the differences between the continuous and discrete methods, and the differences between patients with COPD and healthy control subjects, were the same whether the data were fit by a linear or power function.

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