Which anthropometric measurements including visceral fat, subcutaneous fat, body mass index, and waist circumference could predict the urinary stone composition most?
Background Although there is growing evidence of relationship between obesity and some specific stone compositions, results were inconsistent. Due to a greater relationship between metabolic syndrome and some specific stone type, obesity measured by body mass index (BMI) has limitation in determinin...
Ausführliche Beschreibung
Autor*in: |
Kim, Jae Heon [verfasserIn] |
---|
Format: |
E-Artikel |
---|---|
Sprache: |
Englisch |
Erschienen: |
2015 |
---|
Schlagwörter: |
---|
Anmerkung: |
© Kim et al.; licensee BioMed Central. 2015. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( |
---|
Übergeordnetes Werk: |
Enthalten in: BMC urology - London : BioMed Central, 2001, 15(2015), 1 vom: 14. März |
---|---|
Übergeordnetes Werk: |
volume:15 ; year:2015 ; number:1 ; day:14 ; month:03 |
Links: |
---|
DOI / URN: |
10.1186/s12894-015-0013-x |
---|
Katalog-ID: |
SPR028227557 |
---|
LEADER | 01000caa a22002652 4500 | ||
---|---|---|---|
001 | SPR028227557 | ||
003 | DE-627 | ||
005 | 20230519201902.0 | ||
007 | cr uuu---uuuuu | ||
008 | 201007s2015 xx |||||o 00| ||eng c | ||
024 | 7 | |a 10.1186/s12894-015-0013-x |2 doi | |
035 | |a (DE-627)SPR028227557 | ||
035 | |a (SPR)s12894-015-0013-x-e | ||
040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
041 | |a eng | ||
100 | 1 | |a Kim, Jae Heon |e verfasserin |4 aut | |
245 | 1 | 0 | |a Which anthropometric measurements including visceral fat, subcutaneous fat, body mass index, and waist circumference could predict the urinary stone composition most? |
264 | 1 | |c 2015 | |
336 | |a Text |b txt |2 rdacontent | ||
337 | |a Computermedien |b c |2 rdamedia | ||
338 | |a Online-Ressource |b cr |2 rdacarrier | ||
500 | |a © Kim et al.; licensee BioMed Central. 2015. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( | ||
520 | |a Background Although there is growing evidence of relationship between obesity and some specific stone compositions, results were inconsistent. Due to a greater relationship between metabolic syndrome and some specific stone type, obesity measured by body mass index (BMI) has limitation in determining relationship between obesity and stone compositions. The aim of this study was to determine the relationship among BMI, visceral fat, and stone compositions. Methods We retrospectively reviewed data of patients with urinary stone removed over a 5 year period (2011–2014). Data on patient age, gender, BMI, urinary pH, stone composition, fat volumes (including visceral fat, subcutaneous fat, total fat, waist circumference), and ratio for visceral to total fat using computed tomography based delineation were collected. To figure out the predicting factor while adjusting other confounding factors, discriminant analysis was used. Results Among 262 cases, average age was 52.21 years. Average BMI and visceral fat were 25.03 $ cm^{2} $ and 124.75 $ cm^{2} $, respectively. By chi square test, there was significant (p < 0.001) difference in stone types according to sex. By ANOVA test, BMI, visceral fat, visceral to subcutaneous fat ratio, the percentage of visceral fat and total fat showed significant association with stone types. By discriminant analysis, visceral fat was proved to be a powerful factor to predict stone composition (structure matrix of visceral fat = −0.735) with 42.0% of predictive value. Conclusion Visceral fat adiposity strongly related with uric acid stone and has better predictive value than BMI or urinary pH to classify the types of stone. | ||
650 | 4 | |a Urinary calculi |7 (dpeaa)DE-He213 | |
650 | 4 | |a Obesity |7 (dpeaa)DE-He213 | |
650 | 4 | |a Body mass index |7 (dpeaa)DE-He213 | |
650 | 4 | |a Visceral fat |7 (dpeaa)DE-He213 | |
650 | 4 | |a Computed tomography |7 (dpeaa)DE-He213 | |
700 | 1 | |a Doo, Seung Whan |4 aut | |
700 | 1 | |a Cho, Kang Su |4 aut | |
700 | 1 | |a Yang, Won Jae |4 aut | |
700 | 1 | |a Song, Yun Seob |4 aut | |
700 | 1 | |a Hwang, Jiyoung |4 aut | |
700 | 1 | |a Hong, Seong Sook |4 aut | |
700 | 1 | |a Kwon, Soon-Sun |4 aut | |
773 | 0 | 8 | |i Enthalten in |t BMC urology |d London : BioMed Central, 2001 |g 15(2015), 1 vom: 14. März |w (DE-627)335488811 |w (DE-600)2059857-9 |x 1471-2490 |7 nnns |
773 | 1 | 8 | |g volume:15 |g year:2015 |g number:1 |g day:14 |g month:03 |
856 | 4 | 0 | |u https://dx.doi.org/10.1186/s12894-015-0013-x |z kostenfrei |3 Volltext |
912 | |a GBV_USEFLAG_A | ||
912 | |a SYSFLAG_A | ||
912 | |a GBV_SPRINGER | ||
912 | |a SSG-OLC-PHA | ||
912 | |a GBV_ILN_11 | ||
912 | |a GBV_ILN_20 | ||
912 | |a GBV_ILN_22 | ||
912 | |a GBV_ILN_23 | ||
912 | |a GBV_ILN_24 | ||
912 | |a GBV_ILN_39 | ||
912 | |a GBV_ILN_40 | ||
912 | |a GBV_ILN_60 | ||
912 | |a GBV_ILN_62 | ||
912 | |a GBV_ILN_63 | ||
912 | |a GBV_ILN_65 | ||
912 | |a GBV_ILN_69 | ||
912 | |a GBV_ILN_73 | ||
912 | |a GBV_ILN_74 | ||
912 | |a GBV_ILN_95 | ||
912 | |a GBV_ILN_105 | ||
912 | |a GBV_ILN_110 | ||
912 | |a GBV_ILN_151 | ||
912 | |a GBV_ILN_161 | ||
912 | |a GBV_ILN_170 | ||
912 | |a GBV_ILN_206 | ||
912 | |a GBV_ILN_213 | ||
912 | |a GBV_ILN_230 | ||
912 | |a GBV_ILN_285 | ||
912 | |a GBV_ILN_293 | ||
912 | |a GBV_ILN_602 | ||
912 | |a GBV_ILN_2003 | ||
912 | |a GBV_ILN_2014 | ||
912 | |a GBV_ILN_4012 | ||
912 | |a GBV_ILN_4037 | ||
912 | |a GBV_ILN_4112 | ||
912 | |a GBV_ILN_4125 | ||
912 | |a GBV_ILN_4126 | ||
912 | |a GBV_ILN_4249 | ||
912 | |a GBV_ILN_4305 | ||
912 | |a GBV_ILN_4306 | ||
912 | |a GBV_ILN_4307 | ||
912 | |a GBV_ILN_4313 | ||
912 | |a GBV_ILN_4322 | ||
912 | |a GBV_ILN_4323 | ||
912 | |a GBV_ILN_4324 | ||
912 | |a GBV_ILN_4325 | ||
912 | |a GBV_ILN_4338 | ||
912 | |a GBV_ILN_4367 | ||
912 | |a GBV_ILN_4700 | ||
951 | |a AR | ||
952 | |d 15 |j 2015 |e 1 |b 14 |c 03 |
author_variant |
j h k jh jhk s w d sw swd k s c ks ksc w j y wj wjy y s s ys yss j h jh s s h ss ssh s s k ssk |
---|---|
matchkey_str |
article:14712490:2015----::hcatrpmtimaueeticuigicrlasbuaeuftoyasneadascrufrneol |
hierarchy_sort_str |
2015 |
publishDate |
2015 |
allfields |
10.1186/s12894-015-0013-x doi (DE-627)SPR028227557 (SPR)s12894-015-0013-x-e DE-627 ger DE-627 rakwb eng Kim, Jae Heon verfasserin aut Which anthropometric measurements including visceral fat, subcutaneous fat, body mass index, and waist circumference could predict the urinary stone composition most? 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Kim et al.; licensee BioMed Central. 2015. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( Background Although there is growing evidence of relationship between obesity and some specific stone compositions, results were inconsistent. Due to a greater relationship between metabolic syndrome and some specific stone type, obesity measured by body mass index (BMI) has limitation in determining relationship between obesity and stone compositions. The aim of this study was to determine the relationship among BMI, visceral fat, and stone compositions. Methods We retrospectively reviewed data of patients with urinary stone removed over a 5 year period (2011–2014). Data on patient age, gender, BMI, urinary pH, stone composition, fat volumes (including visceral fat, subcutaneous fat, total fat, waist circumference), and ratio for visceral to total fat using computed tomography based delineation were collected. To figure out the predicting factor while adjusting other confounding factors, discriminant analysis was used. Results Among 262 cases, average age was 52.21 years. Average BMI and visceral fat were 25.03 $ cm^{2} $ and 124.75 $ cm^{2} $, respectively. By chi square test, there was significant (p < 0.001) difference in stone types according to sex. By ANOVA test, BMI, visceral fat, visceral to subcutaneous fat ratio, the percentage of visceral fat and total fat showed significant association with stone types. By discriminant analysis, visceral fat was proved to be a powerful factor to predict stone composition (structure matrix of visceral fat = −0.735) with 42.0% of predictive value. Conclusion Visceral fat adiposity strongly related with uric acid stone and has better predictive value than BMI or urinary pH to classify the types of stone. Urinary calculi (dpeaa)DE-He213 Obesity (dpeaa)DE-He213 Body mass index (dpeaa)DE-He213 Visceral fat (dpeaa)DE-He213 Computed tomography (dpeaa)DE-He213 Doo, Seung Whan aut Cho, Kang Su aut Yang, Won Jae aut Song, Yun Seob aut Hwang, Jiyoung aut Hong, Seong Sook aut Kwon, Soon-Sun aut Enthalten in BMC urology London : BioMed Central, 2001 15(2015), 1 vom: 14. März (DE-627)335488811 (DE-600)2059857-9 1471-2490 nnns volume:15 year:2015 number:1 day:14 month:03 https://dx.doi.org/10.1186/s12894-015-0013-x kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 15 2015 1 14 03 |
spelling |
10.1186/s12894-015-0013-x doi (DE-627)SPR028227557 (SPR)s12894-015-0013-x-e DE-627 ger DE-627 rakwb eng Kim, Jae Heon verfasserin aut Which anthropometric measurements including visceral fat, subcutaneous fat, body mass index, and waist circumference could predict the urinary stone composition most? 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Kim et al.; licensee BioMed Central. 2015. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( Background Although there is growing evidence of relationship between obesity and some specific stone compositions, results were inconsistent. Due to a greater relationship between metabolic syndrome and some specific stone type, obesity measured by body mass index (BMI) has limitation in determining relationship between obesity and stone compositions. The aim of this study was to determine the relationship among BMI, visceral fat, and stone compositions. Methods We retrospectively reviewed data of patients with urinary stone removed over a 5 year period (2011–2014). Data on patient age, gender, BMI, urinary pH, stone composition, fat volumes (including visceral fat, subcutaneous fat, total fat, waist circumference), and ratio for visceral to total fat using computed tomography based delineation were collected. To figure out the predicting factor while adjusting other confounding factors, discriminant analysis was used. Results Among 262 cases, average age was 52.21 years. Average BMI and visceral fat were 25.03 $ cm^{2} $ and 124.75 $ cm^{2} $, respectively. By chi square test, there was significant (p < 0.001) difference in stone types according to sex. By ANOVA test, BMI, visceral fat, visceral to subcutaneous fat ratio, the percentage of visceral fat and total fat showed significant association with stone types. By discriminant analysis, visceral fat was proved to be a powerful factor to predict stone composition (structure matrix of visceral fat = −0.735) with 42.0% of predictive value. Conclusion Visceral fat adiposity strongly related with uric acid stone and has better predictive value than BMI or urinary pH to classify the types of stone. Urinary calculi (dpeaa)DE-He213 Obesity (dpeaa)DE-He213 Body mass index (dpeaa)DE-He213 Visceral fat (dpeaa)DE-He213 Computed tomography (dpeaa)DE-He213 Doo, Seung Whan aut Cho, Kang Su aut Yang, Won Jae aut Song, Yun Seob aut Hwang, Jiyoung aut Hong, Seong Sook aut Kwon, Soon-Sun aut Enthalten in BMC urology London : BioMed Central, 2001 15(2015), 1 vom: 14. März (DE-627)335488811 (DE-600)2059857-9 1471-2490 nnns volume:15 year:2015 number:1 day:14 month:03 https://dx.doi.org/10.1186/s12894-015-0013-x kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 15 2015 1 14 03 |
allfields_unstemmed |
10.1186/s12894-015-0013-x doi (DE-627)SPR028227557 (SPR)s12894-015-0013-x-e DE-627 ger DE-627 rakwb eng Kim, Jae Heon verfasserin aut Which anthropometric measurements including visceral fat, subcutaneous fat, body mass index, and waist circumference could predict the urinary stone composition most? 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Kim et al.; licensee BioMed Central. 2015. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( Background Although there is growing evidence of relationship between obesity and some specific stone compositions, results were inconsistent. Due to a greater relationship between metabolic syndrome and some specific stone type, obesity measured by body mass index (BMI) has limitation in determining relationship between obesity and stone compositions. The aim of this study was to determine the relationship among BMI, visceral fat, and stone compositions. Methods We retrospectively reviewed data of patients with urinary stone removed over a 5 year period (2011–2014). Data on patient age, gender, BMI, urinary pH, stone composition, fat volumes (including visceral fat, subcutaneous fat, total fat, waist circumference), and ratio for visceral to total fat using computed tomography based delineation were collected. To figure out the predicting factor while adjusting other confounding factors, discriminant analysis was used. Results Among 262 cases, average age was 52.21 years. Average BMI and visceral fat were 25.03 $ cm^{2} $ and 124.75 $ cm^{2} $, respectively. By chi square test, there was significant (p < 0.001) difference in stone types according to sex. By ANOVA test, BMI, visceral fat, visceral to subcutaneous fat ratio, the percentage of visceral fat and total fat showed significant association with stone types. By discriminant analysis, visceral fat was proved to be a powerful factor to predict stone composition (structure matrix of visceral fat = −0.735) with 42.0% of predictive value. Conclusion Visceral fat adiposity strongly related with uric acid stone and has better predictive value than BMI or urinary pH to classify the types of stone. Urinary calculi (dpeaa)DE-He213 Obesity (dpeaa)DE-He213 Body mass index (dpeaa)DE-He213 Visceral fat (dpeaa)DE-He213 Computed tomography (dpeaa)DE-He213 Doo, Seung Whan aut Cho, Kang Su aut Yang, Won Jae aut Song, Yun Seob aut Hwang, Jiyoung aut Hong, Seong Sook aut Kwon, Soon-Sun aut Enthalten in BMC urology London : BioMed Central, 2001 15(2015), 1 vom: 14. März (DE-627)335488811 (DE-600)2059857-9 1471-2490 nnns volume:15 year:2015 number:1 day:14 month:03 https://dx.doi.org/10.1186/s12894-015-0013-x kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 15 2015 1 14 03 |
allfieldsGer |
10.1186/s12894-015-0013-x doi (DE-627)SPR028227557 (SPR)s12894-015-0013-x-e DE-627 ger DE-627 rakwb eng Kim, Jae Heon verfasserin aut Which anthropometric measurements including visceral fat, subcutaneous fat, body mass index, and waist circumference could predict the urinary stone composition most? 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Kim et al.; licensee BioMed Central. 2015. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( Background Although there is growing evidence of relationship between obesity and some specific stone compositions, results were inconsistent. Due to a greater relationship between metabolic syndrome and some specific stone type, obesity measured by body mass index (BMI) has limitation in determining relationship between obesity and stone compositions. The aim of this study was to determine the relationship among BMI, visceral fat, and stone compositions. Methods We retrospectively reviewed data of patients with urinary stone removed over a 5 year period (2011–2014). Data on patient age, gender, BMI, urinary pH, stone composition, fat volumes (including visceral fat, subcutaneous fat, total fat, waist circumference), and ratio for visceral to total fat using computed tomography based delineation were collected. To figure out the predicting factor while adjusting other confounding factors, discriminant analysis was used. Results Among 262 cases, average age was 52.21 years. Average BMI and visceral fat were 25.03 $ cm^{2} $ and 124.75 $ cm^{2} $, respectively. By chi square test, there was significant (p < 0.001) difference in stone types according to sex. By ANOVA test, BMI, visceral fat, visceral to subcutaneous fat ratio, the percentage of visceral fat and total fat showed significant association with stone types. By discriminant analysis, visceral fat was proved to be a powerful factor to predict stone composition (structure matrix of visceral fat = −0.735) with 42.0% of predictive value. Conclusion Visceral fat adiposity strongly related with uric acid stone and has better predictive value than BMI or urinary pH to classify the types of stone. Urinary calculi (dpeaa)DE-He213 Obesity (dpeaa)DE-He213 Body mass index (dpeaa)DE-He213 Visceral fat (dpeaa)DE-He213 Computed tomography (dpeaa)DE-He213 Doo, Seung Whan aut Cho, Kang Su aut Yang, Won Jae aut Song, Yun Seob aut Hwang, Jiyoung aut Hong, Seong Sook aut Kwon, Soon-Sun aut Enthalten in BMC urology London : BioMed Central, 2001 15(2015), 1 vom: 14. März (DE-627)335488811 (DE-600)2059857-9 1471-2490 nnns volume:15 year:2015 number:1 day:14 month:03 https://dx.doi.org/10.1186/s12894-015-0013-x kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 15 2015 1 14 03 |
allfieldsSound |
10.1186/s12894-015-0013-x doi (DE-627)SPR028227557 (SPR)s12894-015-0013-x-e DE-627 ger DE-627 rakwb eng Kim, Jae Heon verfasserin aut Which anthropometric measurements including visceral fat, subcutaneous fat, body mass index, and waist circumference could predict the urinary stone composition most? 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Kim et al.; licensee BioMed Central. 2015. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( Background Although there is growing evidence of relationship between obesity and some specific stone compositions, results were inconsistent. Due to a greater relationship between metabolic syndrome and some specific stone type, obesity measured by body mass index (BMI) has limitation in determining relationship between obesity and stone compositions. The aim of this study was to determine the relationship among BMI, visceral fat, and stone compositions. Methods We retrospectively reviewed data of patients with urinary stone removed over a 5 year period (2011–2014). Data on patient age, gender, BMI, urinary pH, stone composition, fat volumes (including visceral fat, subcutaneous fat, total fat, waist circumference), and ratio for visceral to total fat using computed tomography based delineation were collected. To figure out the predicting factor while adjusting other confounding factors, discriminant analysis was used. Results Among 262 cases, average age was 52.21 years. Average BMI and visceral fat were 25.03 $ cm^{2} $ and 124.75 $ cm^{2} $, respectively. By chi square test, there was significant (p < 0.001) difference in stone types according to sex. By ANOVA test, BMI, visceral fat, visceral to subcutaneous fat ratio, the percentage of visceral fat and total fat showed significant association with stone types. By discriminant analysis, visceral fat was proved to be a powerful factor to predict stone composition (structure matrix of visceral fat = −0.735) with 42.0% of predictive value. Conclusion Visceral fat adiposity strongly related with uric acid stone and has better predictive value than BMI or urinary pH to classify the types of stone. Urinary calculi (dpeaa)DE-He213 Obesity (dpeaa)DE-He213 Body mass index (dpeaa)DE-He213 Visceral fat (dpeaa)DE-He213 Computed tomography (dpeaa)DE-He213 Doo, Seung Whan aut Cho, Kang Su aut Yang, Won Jae aut Song, Yun Seob aut Hwang, Jiyoung aut Hong, Seong Sook aut Kwon, Soon-Sun aut Enthalten in BMC urology London : BioMed Central, 2001 15(2015), 1 vom: 14. März (DE-627)335488811 (DE-600)2059857-9 1471-2490 nnns volume:15 year:2015 number:1 day:14 month:03 https://dx.doi.org/10.1186/s12894-015-0013-x kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 15 2015 1 14 03 |
language |
English |
source |
Enthalten in BMC urology 15(2015), 1 vom: 14. März volume:15 year:2015 number:1 day:14 month:03 |
sourceStr |
Enthalten in BMC urology 15(2015), 1 vom: 14. März volume:15 year:2015 number:1 day:14 month:03 |
format_phy_str_mv |
Article |
institution |
findex.gbv.de |
topic_facet |
Urinary calculi Obesity Body mass index Visceral fat Computed tomography |
isfreeaccess_bool |
true |
container_title |
BMC urology |
authorswithroles_txt_mv |
Kim, Jae Heon @@aut@@ Doo, Seung Whan @@aut@@ Cho, Kang Su @@aut@@ Yang, Won Jae @@aut@@ Song, Yun Seob @@aut@@ Hwang, Jiyoung @@aut@@ Hong, Seong Sook @@aut@@ Kwon, Soon-Sun @@aut@@ |
publishDateDaySort_date |
2015-03-14T00:00:00Z |
hierarchy_top_id |
335488811 |
id |
SPR028227557 |
language_de |
englisch |
fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR028227557</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230519201902.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201007s2015 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1186/s12894-015-0013-x</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR028227557</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s12894-015-0013-x-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Kim, Jae Heon</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Which anthropometric measurements including visceral fat, subcutaneous fat, body mass index, and waist circumference could predict the urinary stone composition most?</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2015</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© Kim et al.; licensee BioMed Central. 2015. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Background Although there is growing evidence of relationship between obesity and some specific stone compositions, results were inconsistent. Due to a greater relationship between metabolic syndrome and some specific stone type, obesity measured by body mass index (BMI) has limitation in determining relationship between obesity and stone compositions. The aim of this study was to determine the relationship among BMI, visceral fat, and stone compositions. Methods We retrospectively reviewed data of patients with urinary stone removed over a 5 year period (2011–2014). Data on patient age, gender, BMI, urinary pH, stone composition, fat volumes (including visceral fat, subcutaneous fat, total fat, waist circumference), and ratio for visceral to total fat using computed tomography based delineation were collected. To figure out the predicting factor while adjusting other confounding factors, discriminant analysis was used. Results Among 262 cases, average age was 52.21 years. Average BMI and visceral fat were 25.03 $ cm^{2} $ and 124.75 $ cm^{2} $, respectively. By chi square test, there was significant (p < 0.001) difference in stone types according to sex. By ANOVA test, BMI, visceral fat, visceral to subcutaneous fat ratio, the percentage of visceral fat and total fat showed significant association with stone types. By discriminant analysis, visceral fat was proved to be a powerful factor to predict stone composition (structure matrix of visceral fat = −0.735) with 42.0% of predictive value. Conclusion Visceral fat adiposity strongly related with uric acid stone and has better predictive value than BMI or urinary pH to classify the types of stone.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Urinary calculi</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Obesity</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Body mass index</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Visceral fat</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Computed tomography</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Doo, Seung Whan</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Cho, Kang Su</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Yang, Won Jae</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Song, Yun Seob</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Hwang, Jiyoung</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Hong, Seong Sook</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Kwon, Soon-Sun</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">BMC urology</subfield><subfield code="d">London : BioMed Central, 2001</subfield><subfield code="g">15(2015), 1 vom: 14. März</subfield><subfield code="w">(DE-627)335488811</subfield><subfield code="w">(DE-600)2059857-9</subfield><subfield code="x">1471-2490</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:15</subfield><subfield code="g">year:2015</subfield><subfield code="g">number:1</subfield><subfield code="g">day:14</subfield><subfield code="g">month:03</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://dx.doi.org/10.1186/s12894-015-0013-x</subfield><subfield code="z">kostenfrei</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_SPRINGER</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_11</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_74</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_206</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2003</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">15</subfield><subfield code="j">2015</subfield><subfield code="e">1</subfield><subfield code="b">14</subfield><subfield code="c">03</subfield></datafield></record></collection>
|
author |
Kim, Jae Heon |
spellingShingle |
Kim, Jae Heon misc Urinary calculi misc Obesity misc Body mass index misc Visceral fat misc Computed tomography Which anthropometric measurements including visceral fat, subcutaneous fat, body mass index, and waist circumference could predict the urinary stone composition most? |
authorStr |
Kim, Jae Heon |
ppnlink_with_tag_str_mv |
@@773@@(DE-627)335488811 |
format |
electronic Article |
delete_txt_mv |
keep |
author_role |
aut aut aut aut aut aut aut aut |
collection |
springer |
remote_str |
true |
illustrated |
Not Illustrated |
issn |
1471-2490 |
topic_title |
Which anthropometric measurements including visceral fat, subcutaneous fat, body mass index, and waist circumference could predict the urinary stone composition most? Urinary calculi (dpeaa)DE-He213 Obesity (dpeaa)DE-He213 Body mass index (dpeaa)DE-He213 Visceral fat (dpeaa)DE-He213 Computed tomography (dpeaa)DE-He213 |
topic |
misc Urinary calculi misc Obesity misc Body mass index misc Visceral fat misc Computed tomography |
topic_unstemmed |
misc Urinary calculi misc Obesity misc Body mass index misc Visceral fat misc Computed tomography |
topic_browse |
misc Urinary calculi misc Obesity misc Body mass index misc Visceral fat misc Computed tomography |
format_facet |
Elektronische Aufsätze Aufsätze Elektronische Ressource |
format_main_str_mv |
Text Zeitschrift/Artikel |
carriertype_str_mv |
cr |
hierarchy_parent_title |
BMC urology |
hierarchy_parent_id |
335488811 |
hierarchy_top_title |
BMC urology |
isfreeaccess_txt |
true |
familylinks_str_mv |
(DE-627)335488811 (DE-600)2059857-9 |
title |
Which anthropometric measurements including visceral fat, subcutaneous fat, body mass index, and waist circumference could predict the urinary stone composition most? |
ctrlnum |
(DE-627)SPR028227557 (SPR)s12894-015-0013-x-e |
title_full |
Which anthropometric measurements including visceral fat, subcutaneous fat, body mass index, and waist circumference could predict the urinary stone composition most? |
author_sort |
Kim, Jae Heon |
journal |
BMC urology |
journalStr |
BMC urology |
lang_code |
eng |
isOA_bool |
true |
recordtype |
marc |
publishDateSort |
2015 |
contenttype_str_mv |
txt |
author_browse |
Kim, Jae Heon Doo, Seung Whan Cho, Kang Su Yang, Won Jae Song, Yun Seob Hwang, Jiyoung Hong, Seong Sook Kwon, Soon-Sun |
container_volume |
15 |
format_se |
Elektronische Aufsätze |
author-letter |
Kim, Jae Heon |
doi_str_mv |
10.1186/s12894-015-0013-x |
title_sort |
which anthropometric measurements including visceral fat, subcutaneous fat, body mass index, and waist circumference could predict the urinary stone composition most? |
title_auth |
Which anthropometric measurements including visceral fat, subcutaneous fat, body mass index, and waist circumference could predict the urinary stone composition most? |
abstract |
Background Although there is growing evidence of relationship between obesity and some specific stone compositions, results were inconsistent. Due to a greater relationship between metabolic syndrome and some specific stone type, obesity measured by body mass index (BMI) has limitation in determining relationship between obesity and stone compositions. The aim of this study was to determine the relationship among BMI, visceral fat, and stone compositions. Methods We retrospectively reviewed data of patients with urinary stone removed over a 5 year period (2011–2014). Data on patient age, gender, BMI, urinary pH, stone composition, fat volumes (including visceral fat, subcutaneous fat, total fat, waist circumference), and ratio for visceral to total fat using computed tomography based delineation were collected. To figure out the predicting factor while adjusting other confounding factors, discriminant analysis was used. Results Among 262 cases, average age was 52.21 years. Average BMI and visceral fat were 25.03 $ cm^{2} $ and 124.75 $ cm^{2} $, respectively. By chi square test, there was significant (p < 0.001) difference in stone types according to sex. By ANOVA test, BMI, visceral fat, visceral to subcutaneous fat ratio, the percentage of visceral fat and total fat showed significant association with stone types. By discriminant analysis, visceral fat was proved to be a powerful factor to predict stone composition (structure matrix of visceral fat = −0.735) with 42.0% of predictive value. Conclusion Visceral fat adiposity strongly related with uric acid stone and has better predictive value than BMI or urinary pH to classify the types of stone. © Kim et al.; licensee BioMed Central. 2015. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( |
abstractGer |
Background Although there is growing evidence of relationship between obesity and some specific stone compositions, results were inconsistent. Due to a greater relationship between metabolic syndrome and some specific stone type, obesity measured by body mass index (BMI) has limitation in determining relationship between obesity and stone compositions. The aim of this study was to determine the relationship among BMI, visceral fat, and stone compositions. Methods We retrospectively reviewed data of patients with urinary stone removed over a 5 year period (2011–2014). Data on patient age, gender, BMI, urinary pH, stone composition, fat volumes (including visceral fat, subcutaneous fat, total fat, waist circumference), and ratio for visceral to total fat using computed tomography based delineation were collected. To figure out the predicting factor while adjusting other confounding factors, discriminant analysis was used. Results Among 262 cases, average age was 52.21 years. Average BMI and visceral fat were 25.03 $ cm^{2} $ and 124.75 $ cm^{2} $, respectively. By chi square test, there was significant (p < 0.001) difference in stone types according to sex. By ANOVA test, BMI, visceral fat, visceral to subcutaneous fat ratio, the percentage of visceral fat and total fat showed significant association with stone types. By discriminant analysis, visceral fat was proved to be a powerful factor to predict stone composition (structure matrix of visceral fat = −0.735) with 42.0% of predictive value. Conclusion Visceral fat adiposity strongly related with uric acid stone and has better predictive value than BMI or urinary pH to classify the types of stone. © Kim et al.; licensee BioMed Central. 2015. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( |
abstract_unstemmed |
Background Although there is growing evidence of relationship between obesity and some specific stone compositions, results were inconsistent. Due to a greater relationship between metabolic syndrome and some specific stone type, obesity measured by body mass index (BMI) has limitation in determining relationship between obesity and stone compositions. The aim of this study was to determine the relationship among BMI, visceral fat, and stone compositions. Methods We retrospectively reviewed data of patients with urinary stone removed over a 5 year period (2011–2014). Data on patient age, gender, BMI, urinary pH, stone composition, fat volumes (including visceral fat, subcutaneous fat, total fat, waist circumference), and ratio for visceral to total fat using computed tomography based delineation were collected. To figure out the predicting factor while adjusting other confounding factors, discriminant analysis was used. Results Among 262 cases, average age was 52.21 years. Average BMI and visceral fat were 25.03 $ cm^{2} $ and 124.75 $ cm^{2} $, respectively. By chi square test, there was significant (p < 0.001) difference in stone types according to sex. By ANOVA test, BMI, visceral fat, visceral to subcutaneous fat ratio, the percentage of visceral fat and total fat showed significant association with stone types. By discriminant analysis, visceral fat was proved to be a powerful factor to predict stone composition (structure matrix of visceral fat = −0.735) with 42.0% of predictive value. Conclusion Visceral fat adiposity strongly related with uric acid stone and has better predictive value than BMI or urinary pH to classify the types of stone. © Kim et al.; licensee BioMed Central. 2015. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( |
collection_details |
GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 |
container_issue |
1 |
title_short |
Which anthropometric measurements including visceral fat, subcutaneous fat, body mass index, and waist circumference could predict the urinary stone composition most? |
url |
https://dx.doi.org/10.1186/s12894-015-0013-x |
remote_bool |
true |
author2 |
Doo, Seung Whan Cho, Kang Su Yang, Won Jae Song, Yun Seob Hwang, Jiyoung Hong, Seong Sook Kwon, Soon-Sun |
author2Str |
Doo, Seung Whan Cho, Kang Su Yang, Won Jae Song, Yun Seob Hwang, Jiyoung Hong, Seong Sook Kwon, Soon-Sun |
ppnlink |
335488811 |
mediatype_str_mv |
c |
isOA_txt |
true |
hochschulschrift_bool |
false |
doi_str |
10.1186/s12894-015-0013-x |
up_date |
2024-07-03T18:06:07.617Z |
_version_ |
1803582143326060544 |
fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR028227557</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230519201902.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201007s2015 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1186/s12894-015-0013-x</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR028227557</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s12894-015-0013-x-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Kim, Jae Heon</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Which anthropometric measurements including visceral fat, subcutaneous fat, body mass index, and waist circumference could predict the urinary stone composition most?</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2015</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© Kim et al.; licensee BioMed Central. 2015. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Background Although there is growing evidence of relationship between obesity and some specific stone compositions, results were inconsistent. Due to a greater relationship between metabolic syndrome and some specific stone type, obesity measured by body mass index (BMI) has limitation in determining relationship between obesity and stone compositions. The aim of this study was to determine the relationship among BMI, visceral fat, and stone compositions. Methods We retrospectively reviewed data of patients with urinary stone removed over a 5 year period (2011–2014). Data on patient age, gender, BMI, urinary pH, stone composition, fat volumes (including visceral fat, subcutaneous fat, total fat, waist circumference), and ratio for visceral to total fat using computed tomography based delineation were collected. To figure out the predicting factor while adjusting other confounding factors, discriminant analysis was used. Results Among 262 cases, average age was 52.21 years. Average BMI and visceral fat were 25.03 $ cm^{2} $ and 124.75 $ cm^{2} $, respectively. By chi square test, there was significant (p < 0.001) difference in stone types according to sex. By ANOVA test, BMI, visceral fat, visceral to subcutaneous fat ratio, the percentage of visceral fat and total fat showed significant association with stone types. By discriminant analysis, visceral fat was proved to be a powerful factor to predict stone composition (structure matrix of visceral fat = −0.735) with 42.0% of predictive value. Conclusion Visceral fat adiposity strongly related with uric acid stone and has better predictive value than BMI or urinary pH to classify the types of stone.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Urinary calculi</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Obesity</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Body mass index</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Visceral fat</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Computed tomography</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Doo, Seung Whan</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Cho, Kang Su</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Yang, Won Jae</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Song, Yun Seob</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Hwang, Jiyoung</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Hong, Seong Sook</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Kwon, Soon-Sun</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">BMC urology</subfield><subfield code="d">London : BioMed Central, 2001</subfield><subfield code="g">15(2015), 1 vom: 14. März</subfield><subfield code="w">(DE-627)335488811</subfield><subfield code="w">(DE-600)2059857-9</subfield><subfield code="x">1471-2490</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:15</subfield><subfield code="g">year:2015</subfield><subfield code="g">number:1</subfield><subfield code="g">day:14</subfield><subfield code="g">month:03</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://dx.doi.org/10.1186/s12894-015-0013-x</subfield><subfield code="z">kostenfrei</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_SPRINGER</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_11</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_74</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_206</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2003</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">15</subfield><subfield code="j">2015</subfield><subfield code="e">1</subfield><subfield code="b">14</subfield><subfield code="c">03</subfield></datafield></record></collection>
|
score |
7.399617 |