siMS Score: Simple Method for Quantifying Metabolic Syndrome.
OBJECTIVE:To evaluate siMS score and siMS risk score, novel continuous metabolic syndrome scores as methods for quantification of metabolic status and risk. MATERIALS AND METHODS:Developed siMS score was calculated using formula: siMS score = 2*Waist/Height + Gly/5.6 + Tg/1.7 + TAsystolic/130-HDL/1....
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2016-01-01
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| Series: | PLoS ONE |
| Online Access: | http://europepmc.org/articles/PMC4706421?pdf=render |
| _version_ | 1828865357338640384 |
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| author | Ivan Soldatovic Rade Vukovic Djordje Culafic Milan Gajic Vesna Dimitrijevic-Sreckovic |
| author_facet | Ivan Soldatovic Rade Vukovic Djordje Culafic Milan Gajic Vesna Dimitrijevic-Sreckovic |
| author_sort | Ivan Soldatovic |
| collection | DOAJ |
| description | OBJECTIVE:To evaluate siMS score and siMS risk score, novel continuous metabolic syndrome scores as methods for quantification of metabolic status and risk. MATERIALS AND METHODS:Developed siMS score was calculated using formula: siMS score = 2*Waist/Height + Gly/5.6 + Tg/1.7 + TAsystolic/130-HDL/1.02 or 1.28 (for male or female subjects, respectively). siMS risk score was calculated using formula: siMS risk score = siMS score * age/45 or 50 (for male or female subjects, respectively) * family history of cardio/cerebro-vascular events (event = 1.2, no event = 1). A sample of 528 obese and non-obese participants was used to validate siMS score and siMS risk score. Scores calculated as sum of z-scores (each component of metabolic syndrome regressed with age and gender) and sum of scores derived from principal component analysis (PCA) were used for evaluation of siMS score. Variants were made by replacing glucose with HOMA in calculations. Framingham score was used for evaluation of siMS risk score. RESULTS:Correlation between siMS score with sum of z-scores and weighted sum of factors of PCA was high (r = 0.866 and r = 0.822, respectively). Correlation between siMS risk score and log transformed Framingham score was medium to high for age groups 18+,30+ and 35+ (0.835, 0.707 and 0.667, respectively). CONCLUSIONS:siMS score and siMS risk score showed high correlation with more complex scores. Demonstrated accuracy together with superior simplicity and the ability to evaluate and follow-up individual patients makes siMS and siMS risk scores very convenient for use in clinical practice and research as well. |
| first_indexed | 2024-12-13T04:27:54Z |
| format | Article |
| id | doaj.art-7cba0be859224698bce57d7630a2591b |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-13T04:27:54Z |
| publishDate | 2016-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-7cba0be859224698bce57d7630a2591b2022-12-21T23:59:39ZengPublic Library of Science (PLoS)PLoS ONE1932-62032016-01-01111e014614310.1371/journal.pone.0146143siMS Score: Simple Method for Quantifying Metabolic Syndrome.Ivan SoldatovicRade VukovicDjordje CulaficMilan GajicVesna Dimitrijevic-SreckovicOBJECTIVE:To evaluate siMS score and siMS risk score, novel continuous metabolic syndrome scores as methods for quantification of metabolic status and risk. MATERIALS AND METHODS:Developed siMS score was calculated using formula: siMS score = 2*Waist/Height + Gly/5.6 + Tg/1.7 + TAsystolic/130-HDL/1.02 or 1.28 (for male or female subjects, respectively). siMS risk score was calculated using formula: siMS risk score = siMS score * age/45 or 50 (for male or female subjects, respectively) * family history of cardio/cerebro-vascular events (event = 1.2, no event = 1). A sample of 528 obese and non-obese participants was used to validate siMS score and siMS risk score. Scores calculated as sum of z-scores (each component of metabolic syndrome regressed with age and gender) and sum of scores derived from principal component analysis (PCA) were used for evaluation of siMS score. Variants were made by replacing glucose with HOMA in calculations. Framingham score was used for evaluation of siMS risk score. RESULTS:Correlation between siMS score with sum of z-scores and weighted sum of factors of PCA was high (r = 0.866 and r = 0.822, respectively). Correlation between siMS risk score and log transformed Framingham score was medium to high for age groups 18+,30+ and 35+ (0.835, 0.707 and 0.667, respectively). CONCLUSIONS:siMS score and siMS risk score showed high correlation with more complex scores. Demonstrated accuracy together with superior simplicity and the ability to evaluate and follow-up individual patients makes siMS and siMS risk scores very convenient for use in clinical practice and research as well.http://europepmc.org/articles/PMC4706421?pdf=render |
| spellingShingle | Ivan Soldatovic Rade Vukovic Djordje Culafic Milan Gajic Vesna Dimitrijevic-Sreckovic siMS Score: Simple Method for Quantifying Metabolic Syndrome. PLoS ONE |
| title | siMS Score: Simple Method for Quantifying Metabolic Syndrome. |
| title_full | siMS Score: Simple Method for Quantifying Metabolic Syndrome. |
| title_fullStr | siMS Score: Simple Method for Quantifying Metabolic Syndrome. |
| title_full_unstemmed | siMS Score: Simple Method for Quantifying Metabolic Syndrome. |
| title_short | siMS Score: Simple Method for Quantifying Metabolic Syndrome. |
| title_sort | sims score simple method for quantifying metabolic syndrome |
| url | http://europepmc.org/articles/PMC4706421?pdf=render |
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