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Multivariate Statistical Analysis as a Supplementary Tool for Interpretation of Variations in Salivary Cortisol Level in Women with Major Depressive Disorder.

Dziurkowska E, Wesolowski M - ScientificWorldJournal (2015)

Bottom Line: The cortisol contents in saliva of depressed women were quantified by HPLC with UV detection day-to-day during the whole period of hospitalization.The SSRIs, SNRIs, and the polypragmasy reduce most effectively the hormone secretion.Thus, both unsupervised pattern recognition methods, HCA and PCA, can be used as complementary tools for interpretation of the results obtained by laboratory diagnostic methods.

View Article: PubMed Central - PubMed

Affiliation: Department of Analytical Chemistry, Medical University of Gdansk, Gen. J. Hallera 107, 80-416 Gdansk, Poland.

ABSTRACT
Multivariate statistical analysis is widely used in medical studies as a profitable tool facilitating diagnosis of some diseases, for instance, cancer, allergy, pneumonia, or Alzheimer's and psychiatric diseases. Taking this in consideration, the aim of this study was to use two multivariate techniques, hierarchical cluster analysis (HCA) and principal component analysis (PCA), to disclose the relationship between the drugs used in the therapy of major depressive disorder and the salivary cortisol level and the period of hospitalization. The cortisol contents in saliva of depressed women were quantified by HPLC with UV detection day-to-day during the whole period of hospitalization. A data set with 16 variables (e.g., the patients' age, multiplicity and period of hospitalization, initial and final cortisol level, highest and lowest hormone level, mean contents, and medians) characterizing 97 subjects was used for HCA and PCA calculations. Multivariate statistical analysis reveals that various groups of antidepressants affect at the varying degree the salivary cortisol level. The SSRIs, SNRIs, and the polypragmasy reduce most effectively the hormone secretion. Thus, both unsupervised pattern recognition methods, HCA and PCA, can be used as complementary tools for interpretation of the results obtained by laboratory diagnostic methods.

No MeSH data available.


Related in: MedlinePlus

PCA loadings plot illustrating the impact of fourteen raw variables on the scattering of ninety-seven patients under antidepressant therapy. The Arabic digits denote the raw variables as follows: 1: patients age, 2: multiplicity of hospitalization, 3: period of hospitalization, 4: initial cortisol level, 5: final cortisol level, 6: the highest cortisol concentration, 7: the lowest cortisol concentration, 8: difference between the highest and lowest cortisol concentration, 9: mean concentration, 10: median determined during the whole period of hospitalization, 11: mean level of hormone during the 30% of the hospitalization period, 12: mean level of hormone during the 60% of the hospitalization period, 13: mean level of hormone during the 90% of the hospitalization period, 14: standard deviation of the mean concentration, 15: relative standard deviation of the mean concentration, and 16: antidepressant.
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fig3: PCA loadings plot illustrating the impact of fourteen raw variables on the scattering of ninety-seven patients under antidepressant therapy. The Arabic digits denote the raw variables as follows: 1: patients age, 2: multiplicity of hospitalization, 3: period of hospitalization, 4: initial cortisol level, 5: final cortisol level, 6: the highest cortisol concentration, 7: the lowest cortisol concentration, 8: difference between the highest and lowest cortisol concentration, 9: mean concentration, 10: median determined during the whole period of hospitalization, 11: mean level of hormone during the 30% of the hospitalization period, 12: mean level of hormone during the 60% of the hospitalization period, 13: mean level of hormone during the 90% of the hospitalization period, 14: standard deviation of the mean concentration, 15: relative standard deviation of the mean concentration, and 16: antidepressant.

Mentions: Figure 3 shows the PCA loadings, that is, the relationship between the raw variables and calculated principal components. The raw variables, which located the subjects according to the PC1 axis, were the mean, initial, and the highest salivary cortisol levels, the difference between highest and lowest cortisol concentration, the mean level of hormone during the 30% of hospitalization period, and the standard deviation of mean cortisol concentration. The most significant impact on the characteristic scattering of the subjects according to PC2 axis had the median, the lowest, and the mean levels of cortisol during the 60% of hospitalization as well as the relative standard deviation of mean cortisol concentration, which is negatively correlated with this axis.


Multivariate Statistical Analysis as a Supplementary Tool for Interpretation of Variations in Salivary Cortisol Level in Women with Major Depressive Disorder.

Dziurkowska E, Wesolowski M - ScientificWorldJournal (2015)

PCA loadings plot illustrating the impact of fourteen raw variables on the scattering of ninety-seven patients under antidepressant therapy. The Arabic digits denote the raw variables as follows: 1: patients age, 2: multiplicity of hospitalization, 3: period of hospitalization, 4: initial cortisol level, 5: final cortisol level, 6: the highest cortisol concentration, 7: the lowest cortisol concentration, 8: difference between the highest and lowest cortisol concentration, 9: mean concentration, 10: median determined during the whole period of hospitalization, 11: mean level of hormone during the 30% of the hospitalization period, 12: mean level of hormone during the 60% of the hospitalization period, 13: mean level of hormone during the 90% of the hospitalization period, 14: standard deviation of the mean concentration, 15: relative standard deviation of the mean concentration, and 16: antidepressant.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4562094&req=5

fig3: PCA loadings plot illustrating the impact of fourteen raw variables on the scattering of ninety-seven patients under antidepressant therapy. The Arabic digits denote the raw variables as follows: 1: patients age, 2: multiplicity of hospitalization, 3: period of hospitalization, 4: initial cortisol level, 5: final cortisol level, 6: the highest cortisol concentration, 7: the lowest cortisol concentration, 8: difference between the highest and lowest cortisol concentration, 9: mean concentration, 10: median determined during the whole period of hospitalization, 11: mean level of hormone during the 30% of the hospitalization period, 12: mean level of hormone during the 60% of the hospitalization period, 13: mean level of hormone during the 90% of the hospitalization period, 14: standard deviation of the mean concentration, 15: relative standard deviation of the mean concentration, and 16: antidepressant.
Mentions: Figure 3 shows the PCA loadings, that is, the relationship between the raw variables and calculated principal components. The raw variables, which located the subjects according to the PC1 axis, were the mean, initial, and the highest salivary cortisol levels, the difference between highest and lowest cortisol concentration, the mean level of hormone during the 30% of hospitalization period, and the standard deviation of mean cortisol concentration. The most significant impact on the characteristic scattering of the subjects according to PC2 axis had the median, the lowest, and the mean levels of cortisol during the 60% of hospitalization as well as the relative standard deviation of mean cortisol concentration, which is negatively correlated with this axis.

Bottom Line: The cortisol contents in saliva of depressed women were quantified by HPLC with UV detection day-to-day during the whole period of hospitalization.The SSRIs, SNRIs, and the polypragmasy reduce most effectively the hormone secretion.Thus, both unsupervised pattern recognition methods, HCA and PCA, can be used as complementary tools for interpretation of the results obtained by laboratory diagnostic methods.

View Article: PubMed Central - PubMed

Affiliation: Department of Analytical Chemistry, Medical University of Gdansk, Gen. J. Hallera 107, 80-416 Gdansk, Poland.

ABSTRACT
Multivariate statistical analysis is widely used in medical studies as a profitable tool facilitating diagnosis of some diseases, for instance, cancer, allergy, pneumonia, or Alzheimer's and psychiatric diseases. Taking this in consideration, the aim of this study was to use two multivariate techniques, hierarchical cluster analysis (HCA) and principal component analysis (PCA), to disclose the relationship between the drugs used in the therapy of major depressive disorder and the salivary cortisol level and the period of hospitalization. The cortisol contents in saliva of depressed women were quantified by HPLC with UV detection day-to-day during the whole period of hospitalization. A data set with 16 variables (e.g., the patients' age, multiplicity and period of hospitalization, initial and final cortisol level, highest and lowest hormone level, mean contents, and medians) characterizing 97 subjects was used for HCA and PCA calculations. Multivariate statistical analysis reveals that various groups of antidepressants affect at the varying degree the salivary cortisol level. The SSRIs, SNRIs, and the polypragmasy reduce most effectively the hormone secretion. Thus, both unsupervised pattern recognition methods, HCA and PCA, can be used as complementary tools for interpretation of the results obtained by laboratory diagnostic methods.

No MeSH data available.


Related in: MedlinePlus