Using multivariate quantile regression analysis to explore cardiovascular risk differences in subjects with chronic kidney disease by race and ethnicity: Findings from the U.S. Chronic Renal Insufficiency Cohort Study
Background and Aims: Adults with chronic kidney disease (CKD) carry an extraordinarily high risk for cardiovascular disease (CVD). The present study aimed to test two hypotheses that: (1) CVD risk factors disproportionately affect non-Hispanic black (NHB) with CKD compared to non-Hispanic white (NHW...
Ausführliche Beschreibung
Autor*in: |
Longjian Liu [verfasserIn] |
---|
Format: |
E-Artikel |
---|---|
Sprache: |
Englisch |
Erschienen: |
2015 |
---|
Schlagwörter: |
---|
Übergeordnetes Werk: |
In: International Cardiovascular Forum Journal - Barcaray International, 2017, 2(2015), 1, Seite 20-26 |
---|---|
Übergeordnetes Werk: |
volume:2 ; year:2015 ; number:1 ; pages:20-26 |
Links: |
Link aufrufen |
---|
Katalog-ID: |
DOAJ049403796 |
---|
LEADER | 01000caa a22002652 4500 | ||
---|---|---|---|
001 | DOAJ049403796 | ||
003 | DE-627 | ||
005 | 20230308143041.0 | ||
007 | cr uuu---uuuuu | ||
008 | 230227s2015 xx |||||o 00| ||eng c | ||
035 | |a (DE-627)DOAJ049403796 | ||
035 | |a (DE-599)DOAJf3cf81a000fe497db513d71334252621 | ||
040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
041 | |a eng | ||
050 | 0 | |a RC666-701 | |
100 | 0 | |a Longjian Liu |e verfasserin |4 aut | |
245 | 1 | 0 | |a Using multivariate quantile regression analysis to explore cardiovascular risk differences in subjects with chronic kidney disease by race and ethnicity: Findings from the U.S. Chronic Renal Insufficiency Cohort Study |
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 | ||
520 | |a Background and Aims: Adults with chronic kidney disease (CKD) carry an extraordinarily high risk for cardiovascular disease (CVD). The present study aimed to test two hypotheses that: (1) CVD risk factors disproportionately affect non-Hispanic black (NHB) with CKD compared to non-Hispanic white (NHW). (2) This difference significantly contributes to an excess risk of CVD in NHB versus NHW. Methods: A total of 3,939 aged 21-74 years old participating in the Chronic Renal Insufficiency Cohort Study was analyzed. A sum weighted CVDRisk score was constructed from well-established CVD risk factors. Differences in CVD Risk score by race/ethnicity were tested using quantile regression (Qreg) analysis. Results: The prevalence of CVD was 30.7% in NHW and 38.2% in NHB (p<0.001). The means (SD) of CVD Risk score were 12.6 (5.7) in NHW and 14.6 (6.4) in NHB (p<0.001). Qreg analysis indicated that NHB with estimate glomerular filtration rate (eGFR) 30-59.9 ml/min/1.73m2 had significantly higher (worse) CVD Risk scores across all quantiles (Qs) than NHW. This race differences in CVD Risk were also significantly higher in NHB with eGFR 60-70 ml/min/1.73m2 in Qs 1 and 2 as compared to their NHW counterparts. An estimated 35.8% of the excess prevalent CVD could be attributable to the difference in CVD Risk for NHB versus NHW. Conclusion: NHB have a significantly higher CVD risk factor score in those with moderate and mild CKD than NHW. | ||
650 | 4 | |a CVD Risk | |
650 | 4 | |a CKD | |
650 | 4 | |a quantile regression | |
653 | 0 | |a Medicine | |
653 | 0 | |a R | |
653 | 0 | |a Diseases of the circulatory (Cardiovascular) system | |
773 | 0 | 8 | |i In |t International Cardiovascular Forum Journal |d Barcaray International, 2017 |g 2(2015), 1, Seite 20-26 |w (DE-627)859466345 |w (DE-600)2855971-X |x 24093424 |7 nnns |
773 | 1 | 8 | |g volume:2 |g year:2015 |g number:1 |g pages:20-26 |
856 | 4 | 0 | |u https://doi.org/10.17987/icfj.v2i1.70 |z kostenfrei |
856 | 4 | 0 | |u https://doaj.org/article/f3cf81a000fe497db513d71334252621 |z kostenfrei |
856 | 4 | 0 | |u http://icfjournal.org/index.php/icfj/article/view/70/89 |z kostenfrei |
856 | 4 | 2 | |u https://doaj.org/toc/2410-2636 |y Journal toc |z kostenfrei |
856 | 4 | 2 | |u https://doaj.org/toc/2409-3424 |y Journal toc |z kostenfrei |
912 | |a GBV_USEFLAG_A | ||
912 | |a SYSFLAG_A | ||
912 | |a GBV_DOAJ | ||
912 | |a GBV_ILN_20 | ||
912 | |a GBV_ILN_22 | ||
912 | |a GBV_ILN_23 | ||
912 | |a GBV_ILN_24 | ||
912 | |a GBV_ILN_31 | ||
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_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 2 |j 2015 |e 1 |h 20-26 |
author_variant |
l l ll |
---|---|
matchkey_str |
article:24093424:2015----::snmliaitqatlrgesoaayitepoeadoaclrikifrneisbetwtcrncinyiesbrcadtnctfni |
hierarchy_sort_str |
2015 |
callnumber-subject-code |
RC |
publishDate |
2015 |
allfields |
(DE-627)DOAJ049403796 (DE-599)DOAJf3cf81a000fe497db513d71334252621 DE-627 ger DE-627 rakwb eng RC666-701 Longjian Liu verfasserin aut Using multivariate quantile regression analysis to explore cardiovascular risk differences in subjects with chronic kidney disease by race and ethnicity: Findings from the U.S. Chronic Renal Insufficiency Cohort Study 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background and Aims: Adults with chronic kidney disease (CKD) carry an extraordinarily high risk for cardiovascular disease (CVD). The present study aimed to test two hypotheses that: (1) CVD risk factors disproportionately affect non-Hispanic black (NHB) with CKD compared to non-Hispanic white (NHW). (2) This difference significantly contributes to an excess risk of CVD in NHB versus NHW. Methods: A total of 3,939 aged 21-74 years old participating in the Chronic Renal Insufficiency Cohort Study was analyzed. A sum weighted CVDRisk score was constructed from well-established CVD risk factors. Differences in CVD Risk score by race/ethnicity were tested using quantile regression (Qreg) analysis. Results: The prevalence of CVD was 30.7% in NHW and 38.2% in NHB (p<0.001). The means (SD) of CVD Risk score were 12.6 (5.7) in NHW and 14.6 (6.4) in NHB (p<0.001). Qreg analysis indicated that NHB with estimate glomerular filtration rate (eGFR) 30-59.9 ml/min/1.73m2 had significantly higher (worse) CVD Risk scores across all quantiles (Qs) than NHW. This race differences in CVD Risk were also significantly higher in NHB with eGFR 60-70 ml/min/1.73m2 in Qs 1 and 2 as compared to their NHW counterparts. An estimated 35.8% of the excess prevalent CVD could be attributable to the difference in CVD Risk for NHB versus NHW. Conclusion: NHB have a significantly higher CVD risk factor score in those with moderate and mild CKD than NHW. CVD Risk CKD quantile regression Medicine R Diseases of the circulatory (Cardiovascular) system In International Cardiovascular Forum Journal Barcaray International, 2017 2(2015), 1, Seite 20-26 (DE-627)859466345 (DE-600)2855971-X 24093424 nnns volume:2 year:2015 number:1 pages:20-26 https://doi.org/10.17987/icfj.v2i1.70 kostenfrei https://doaj.org/article/f3cf81a000fe497db513d71334252621 kostenfrei http://icfjournal.org/index.php/icfj/article/view/70/89 kostenfrei https://doaj.org/toc/2410-2636 Journal toc kostenfrei https://doaj.org/toc/2409-3424 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_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 2 2015 1 20-26 |
spelling |
(DE-627)DOAJ049403796 (DE-599)DOAJf3cf81a000fe497db513d71334252621 DE-627 ger DE-627 rakwb eng RC666-701 Longjian Liu verfasserin aut Using multivariate quantile regression analysis to explore cardiovascular risk differences in subjects with chronic kidney disease by race and ethnicity: Findings from the U.S. Chronic Renal Insufficiency Cohort Study 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background and Aims: Adults with chronic kidney disease (CKD) carry an extraordinarily high risk for cardiovascular disease (CVD). The present study aimed to test two hypotheses that: (1) CVD risk factors disproportionately affect non-Hispanic black (NHB) with CKD compared to non-Hispanic white (NHW). (2) This difference significantly contributes to an excess risk of CVD in NHB versus NHW. Methods: A total of 3,939 aged 21-74 years old participating in the Chronic Renal Insufficiency Cohort Study was analyzed. A sum weighted CVDRisk score was constructed from well-established CVD risk factors. Differences in CVD Risk score by race/ethnicity were tested using quantile regression (Qreg) analysis. Results: The prevalence of CVD was 30.7% in NHW and 38.2% in NHB (p<0.001). The means (SD) of CVD Risk score were 12.6 (5.7) in NHW and 14.6 (6.4) in NHB (p<0.001). Qreg analysis indicated that NHB with estimate glomerular filtration rate (eGFR) 30-59.9 ml/min/1.73m2 had significantly higher (worse) CVD Risk scores across all quantiles (Qs) than NHW. This race differences in CVD Risk were also significantly higher in NHB with eGFR 60-70 ml/min/1.73m2 in Qs 1 and 2 as compared to their NHW counterparts. An estimated 35.8% of the excess prevalent CVD could be attributable to the difference in CVD Risk for NHB versus NHW. Conclusion: NHB have a significantly higher CVD risk factor score in those with moderate and mild CKD than NHW. CVD Risk CKD quantile regression Medicine R Diseases of the circulatory (Cardiovascular) system In International Cardiovascular Forum Journal Barcaray International, 2017 2(2015), 1, Seite 20-26 (DE-627)859466345 (DE-600)2855971-X 24093424 nnns volume:2 year:2015 number:1 pages:20-26 https://doi.org/10.17987/icfj.v2i1.70 kostenfrei https://doaj.org/article/f3cf81a000fe497db513d71334252621 kostenfrei http://icfjournal.org/index.php/icfj/article/view/70/89 kostenfrei https://doaj.org/toc/2410-2636 Journal toc kostenfrei https://doaj.org/toc/2409-3424 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_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 2 2015 1 20-26 |
allfields_unstemmed |
(DE-627)DOAJ049403796 (DE-599)DOAJf3cf81a000fe497db513d71334252621 DE-627 ger DE-627 rakwb eng RC666-701 Longjian Liu verfasserin aut Using multivariate quantile regression analysis to explore cardiovascular risk differences in subjects with chronic kidney disease by race and ethnicity: Findings from the U.S. Chronic Renal Insufficiency Cohort Study 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background and Aims: Adults with chronic kidney disease (CKD) carry an extraordinarily high risk for cardiovascular disease (CVD). The present study aimed to test two hypotheses that: (1) CVD risk factors disproportionately affect non-Hispanic black (NHB) with CKD compared to non-Hispanic white (NHW). (2) This difference significantly contributes to an excess risk of CVD in NHB versus NHW. Methods: A total of 3,939 aged 21-74 years old participating in the Chronic Renal Insufficiency Cohort Study was analyzed. A sum weighted CVDRisk score was constructed from well-established CVD risk factors. Differences in CVD Risk score by race/ethnicity were tested using quantile regression (Qreg) analysis. Results: The prevalence of CVD was 30.7% in NHW and 38.2% in NHB (p<0.001). The means (SD) of CVD Risk score were 12.6 (5.7) in NHW and 14.6 (6.4) in NHB (p<0.001). Qreg analysis indicated that NHB with estimate glomerular filtration rate (eGFR) 30-59.9 ml/min/1.73m2 had significantly higher (worse) CVD Risk scores across all quantiles (Qs) than NHW. This race differences in CVD Risk were also significantly higher in NHB with eGFR 60-70 ml/min/1.73m2 in Qs 1 and 2 as compared to their NHW counterparts. An estimated 35.8% of the excess prevalent CVD could be attributable to the difference in CVD Risk for NHB versus NHW. Conclusion: NHB have a significantly higher CVD risk factor score in those with moderate and mild CKD than NHW. CVD Risk CKD quantile regression Medicine R Diseases of the circulatory (Cardiovascular) system In International Cardiovascular Forum Journal Barcaray International, 2017 2(2015), 1, Seite 20-26 (DE-627)859466345 (DE-600)2855971-X 24093424 nnns volume:2 year:2015 number:1 pages:20-26 https://doi.org/10.17987/icfj.v2i1.70 kostenfrei https://doaj.org/article/f3cf81a000fe497db513d71334252621 kostenfrei http://icfjournal.org/index.php/icfj/article/view/70/89 kostenfrei https://doaj.org/toc/2410-2636 Journal toc kostenfrei https://doaj.org/toc/2409-3424 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_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 2 2015 1 20-26 |
allfieldsGer |
(DE-627)DOAJ049403796 (DE-599)DOAJf3cf81a000fe497db513d71334252621 DE-627 ger DE-627 rakwb eng RC666-701 Longjian Liu verfasserin aut Using multivariate quantile regression analysis to explore cardiovascular risk differences in subjects with chronic kidney disease by race and ethnicity: Findings from the U.S. Chronic Renal Insufficiency Cohort Study 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background and Aims: Adults with chronic kidney disease (CKD) carry an extraordinarily high risk for cardiovascular disease (CVD). The present study aimed to test two hypotheses that: (1) CVD risk factors disproportionately affect non-Hispanic black (NHB) with CKD compared to non-Hispanic white (NHW). (2) This difference significantly contributes to an excess risk of CVD in NHB versus NHW. Methods: A total of 3,939 aged 21-74 years old participating in the Chronic Renal Insufficiency Cohort Study was analyzed. A sum weighted CVDRisk score was constructed from well-established CVD risk factors. Differences in CVD Risk score by race/ethnicity were tested using quantile regression (Qreg) analysis. Results: The prevalence of CVD was 30.7% in NHW and 38.2% in NHB (p<0.001). The means (SD) of CVD Risk score were 12.6 (5.7) in NHW and 14.6 (6.4) in NHB (p<0.001). Qreg analysis indicated that NHB with estimate glomerular filtration rate (eGFR) 30-59.9 ml/min/1.73m2 had significantly higher (worse) CVD Risk scores across all quantiles (Qs) than NHW. This race differences in CVD Risk were also significantly higher in NHB with eGFR 60-70 ml/min/1.73m2 in Qs 1 and 2 as compared to their NHW counterparts. An estimated 35.8% of the excess prevalent CVD could be attributable to the difference in CVD Risk for NHB versus NHW. Conclusion: NHB have a significantly higher CVD risk factor score in those with moderate and mild CKD than NHW. CVD Risk CKD quantile regression Medicine R Diseases of the circulatory (Cardiovascular) system In International Cardiovascular Forum Journal Barcaray International, 2017 2(2015), 1, Seite 20-26 (DE-627)859466345 (DE-600)2855971-X 24093424 nnns volume:2 year:2015 number:1 pages:20-26 https://doi.org/10.17987/icfj.v2i1.70 kostenfrei https://doaj.org/article/f3cf81a000fe497db513d71334252621 kostenfrei http://icfjournal.org/index.php/icfj/article/view/70/89 kostenfrei https://doaj.org/toc/2410-2636 Journal toc kostenfrei https://doaj.org/toc/2409-3424 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_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 2 2015 1 20-26 |
allfieldsSound |
(DE-627)DOAJ049403796 (DE-599)DOAJf3cf81a000fe497db513d71334252621 DE-627 ger DE-627 rakwb eng RC666-701 Longjian Liu verfasserin aut Using multivariate quantile regression analysis to explore cardiovascular risk differences in subjects with chronic kidney disease by race and ethnicity: Findings from the U.S. Chronic Renal Insufficiency Cohort Study 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background and Aims: Adults with chronic kidney disease (CKD) carry an extraordinarily high risk for cardiovascular disease (CVD). The present study aimed to test two hypotheses that: (1) CVD risk factors disproportionately affect non-Hispanic black (NHB) with CKD compared to non-Hispanic white (NHW). (2) This difference significantly contributes to an excess risk of CVD in NHB versus NHW. Methods: A total of 3,939 aged 21-74 years old participating in the Chronic Renal Insufficiency Cohort Study was analyzed. A sum weighted CVDRisk score was constructed from well-established CVD risk factors. Differences in CVD Risk score by race/ethnicity were tested using quantile regression (Qreg) analysis. Results: The prevalence of CVD was 30.7% in NHW and 38.2% in NHB (p<0.001). The means (SD) of CVD Risk score were 12.6 (5.7) in NHW and 14.6 (6.4) in NHB (p<0.001). Qreg analysis indicated that NHB with estimate glomerular filtration rate (eGFR) 30-59.9 ml/min/1.73m2 had significantly higher (worse) CVD Risk scores across all quantiles (Qs) than NHW. This race differences in CVD Risk were also significantly higher in NHB with eGFR 60-70 ml/min/1.73m2 in Qs 1 and 2 as compared to their NHW counterparts. An estimated 35.8% of the excess prevalent CVD could be attributable to the difference in CVD Risk for NHB versus NHW. Conclusion: NHB have a significantly higher CVD risk factor score in those with moderate and mild CKD than NHW. CVD Risk CKD quantile regression Medicine R Diseases of the circulatory (Cardiovascular) system In International Cardiovascular Forum Journal Barcaray International, 2017 2(2015), 1, Seite 20-26 (DE-627)859466345 (DE-600)2855971-X 24093424 nnns volume:2 year:2015 number:1 pages:20-26 https://doi.org/10.17987/icfj.v2i1.70 kostenfrei https://doaj.org/article/f3cf81a000fe497db513d71334252621 kostenfrei http://icfjournal.org/index.php/icfj/article/view/70/89 kostenfrei https://doaj.org/toc/2410-2636 Journal toc kostenfrei https://doaj.org/toc/2409-3424 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_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 2 2015 1 20-26 |
language |
English |
source |
In International Cardiovascular Forum Journal 2(2015), 1, Seite 20-26 volume:2 year:2015 number:1 pages:20-26 |
sourceStr |
In International Cardiovascular Forum Journal 2(2015), 1, Seite 20-26 volume:2 year:2015 number:1 pages:20-26 |
format_phy_str_mv |
Article |
institution |
findex.gbv.de |
topic_facet |
CVD Risk CKD quantile regression Medicine R Diseases of the circulatory (Cardiovascular) system |
isfreeaccess_bool |
true |
container_title |
International Cardiovascular Forum Journal |
authorswithroles_txt_mv |
Longjian Liu @@aut@@ |
publishDateDaySort_date |
2015-01-01T00:00:00Z |
hierarchy_top_id |
859466345 |
id |
DOAJ049403796 |
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">DOAJ049403796</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230308143041.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230227s2015 xx |||||o 00| ||eng c</controlfield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ049403796</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJf3cf81a000fe497db513d71334252621</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="050" ind1=" " ind2="0"><subfield code="a">RC666-701</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Longjian Liu</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Using multivariate quantile regression analysis to explore cardiovascular risk differences in subjects with chronic kidney disease by race and ethnicity: Findings from the U.S. Chronic Renal Insufficiency Cohort Study</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="520" ind1=" " ind2=" "><subfield code="a">Background and Aims: Adults with chronic kidney disease (CKD) carry an extraordinarily high risk for cardiovascular disease (CVD). The present study aimed to test two hypotheses that: (1) CVD risk factors disproportionately affect non-Hispanic black (NHB) with CKD compared to non-Hispanic white (NHW). (2) This difference significantly contributes to an excess risk of CVD in NHB versus NHW. Methods: A total of 3,939 aged 21-74 years old participating in the Chronic Renal Insufficiency Cohort Study was analyzed. A sum weighted CVDRisk score was constructed from well-established CVD risk factors. Differences in CVD Risk score by race/ethnicity were tested using quantile regression (Qreg) analysis. Results: The prevalence of CVD was 30.7% in NHW and 38.2% in NHB (p<0.001). The means (SD) of CVD Risk score were 12.6 (5.7) in NHW and 14.6 (6.4) in NHB (p<0.001). Qreg analysis indicated that NHB with estimate glomerular filtration rate (eGFR) 30-59.9 ml/min/1.73m2 had significantly higher (worse) CVD Risk scores across all quantiles (Qs) than NHW. This race differences in CVD Risk were also significantly higher in NHB with eGFR 60-70 ml/min/1.73m2 in Qs 1 and 2 as compared to their NHW counterparts. An estimated 35.8% of the excess prevalent CVD could be attributable to the difference in CVD Risk for NHB versus NHW. Conclusion: NHB have a significantly higher CVD risk factor score in those with moderate and mild CKD than NHW.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">CVD Risk</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">CKD</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">quantile regression</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Medicine</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">R</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Diseases of the circulatory (Cardiovascular) system</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">International Cardiovascular Forum Journal</subfield><subfield code="d">Barcaray International, 2017</subfield><subfield code="g">2(2015), 1, Seite 20-26</subfield><subfield code="w">(DE-627)859466345</subfield><subfield code="w">(DE-600)2855971-X</subfield><subfield code="x">24093424</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:2</subfield><subfield code="g">year:2015</subfield><subfield code="g">number:1</subfield><subfield code="g">pages:20-26</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.17987/icfj.v2i1.70</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/f3cf81a000fe497db513d71334252621</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">http://icfjournal.org/index.php/icfj/article/view/70/89</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/2410-2636</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/2409-3424</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</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_DOAJ</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_31</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_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">2</subfield><subfield code="j">2015</subfield><subfield code="e">1</subfield><subfield code="h">20-26</subfield></datafield></record></collection>
|
callnumber-first |
R - Medicine |
author |
Longjian Liu |
spellingShingle |
Longjian Liu misc RC666-701 misc CVD Risk misc CKD misc quantile regression misc Medicine misc R misc Diseases of the circulatory (Cardiovascular) system Using multivariate quantile regression analysis to explore cardiovascular risk differences in subjects with chronic kidney disease by race and ethnicity: Findings from the U.S. Chronic Renal Insufficiency Cohort Study |
authorStr |
Longjian Liu |
ppnlink_with_tag_str_mv |
@@773@@(DE-627)859466345 |
format |
electronic Article |
delete_txt_mv |
keep |
author_role |
aut |
collection |
DOAJ |
remote_str |
true |
callnumber-label |
RC666-701 |
illustrated |
Not Illustrated |
issn |
24093424 |
topic_title |
RC666-701 Using multivariate quantile regression analysis to explore cardiovascular risk differences in subjects with chronic kidney disease by race and ethnicity: Findings from the U.S. Chronic Renal Insufficiency Cohort Study CVD Risk CKD quantile regression |
topic |
misc RC666-701 misc CVD Risk misc CKD misc quantile regression misc Medicine misc R misc Diseases of the circulatory (Cardiovascular) system |
topic_unstemmed |
misc RC666-701 misc CVD Risk misc CKD misc quantile regression misc Medicine misc R misc Diseases of the circulatory (Cardiovascular) system |
topic_browse |
misc RC666-701 misc CVD Risk misc CKD misc quantile regression misc Medicine misc R misc Diseases of the circulatory (Cardiovascular) system |
format_facet |
Elektronische Aufsätze Aufsätze Elektronische Ressource |
format_main_str_mv |
Text Zeitschrift/Artikel |
carriertype_str_mv |
cr |
hierarchy_parent_title |
International Cardiovascular Forum Journal |
hierarchy_parent_id |
859466345 |
hierarchy_top_title |
International Cardiovascular Forum Journal |
isfreeaccess_txt |
true |
familylinks_str_mv |
(DE-627)859466345 (DE-600)2855971-X |
title |
Using multivariate quantile regression analysis to explore cardiovascular risk differences in subjects with chronic kidney disease by race and ethnicity: Findings from the U.S. Chronic Renal Insufficiency Cohort Study |
ctrlnum |
(DE-627)DOAJ049403796 (DE-599)DOAJf3cf81a000fe497db513d71334252621 |
title_full |
Using multivariate quantile regression analysis to explore cardiovascular risk differences in subjects with chronic kidney disease by race and ethnicity: Findings from the U.S. Chronic Renal Insufficiency Cohort Study |
author_sort |
Longjian Liu |
journal |
International Cardiovascular Forum Journal |
journalStr |
International Cardiovascular Forum Journal |
callnumber-first-code |
R |
lang_code |
eng |
isOA_bool |
true |
recordtype |
marc |
publishDateSort |
2015 |
contenttype_str_mv |
txt |
container_start_page |
20 |
author_browse |
Longjian Liu |
container_volume |
2 |
class |
RC666-701 |
format_se |
Elektronische Aufsätze |
author-letter |
Longjian Liu |
title_sort |
using multivariate quantile regression analysis to explore cardiovascular risk differences in subjects with chronic kidney disease by race and ethnicity: findings from the u.s. chronic renal insufficiency cohort study |
callnumber |
RC666-701 |
title_auth |
Using multivariate quantile regression analysis to explore cardiovascular risk differences in subjects with chronic kidney disease by race and ethnicity: Findings from the U.S. Chronic Renal Insufficiency Cohort Study |
abstract |
Background and Aims: Adults with chronic kidney disease (CKD) carry an extraordinarily high risk for cardiovascular disease (CVD). The present study aimed to test two hypotheses that: (1) CVD risk factors disproportionately affect non-Hispanic black (NHB) with CKD compared to non-Hispanic white (NHW). (2) This difference significantly contributes to an excess risk of CVD in NHB versus NHW. Methods: A total of 3,939 aged 21-74 years old participating in the Chronic Renal Insufficiency Cohort Study was analyzed. A sum weighted CVDRisk score was constructed from well-established CVD risk factors. Differences in CVD Risk score by race/ethnicity were tested using quantile regression (Qreg) analysis. Results: The prevalence of CVD was 30.7% in NHW and 38.2% in NHB (p<0.001). The means (SD) of CVD Risk score were 12.6 (5.7) in NHW and 14.6 (6.4) in NHB (p<0.001). Qreg analysis indicated that NHB with estimate glomerular filtration rate (eGFR) 30-59.9 ml/min/1.73m2 had significantly higher (worse) CVD Risk scores across all quantiles (Qs) than NHW. This race differences in CVD Risk were also significantly higher in NHB with eGFR 60-70 ml/min/1.73m2 in Qs 1 and 2 as compared to their NHW counterparts. An estimated 35.8% of the excess prevalent CVD could be attributable to the difference in CVD Risk for NHB versus NHW. Conclusion: NHB have a significantly higher CVD risk factor score in those with moderate and mild CKD than NHW. |
abstractGer |
Background and Aims: Adults with chronic kidney disease (CKD) carry an extraordinarily high risk for cardiovascular disease (CVD). The present study aimed to test two hypotheses that: (1) CVD risk factors disproportionately affect non-Hispanic black (NHB) with CKD compared to non-Hispanic white (NHW). (2) This difference significantly contributes to an excess risk of CVD in NHB versus NHW. Methods: A total of 3,939 aged 21-74 years old participating in the Chronic Renal Insufficiency Cohort Study was analyzed. A sum weighted CVDRisk score was constructed from well-established CVD risk factors. Differences in CVD Risk score by race/ethnicity were tested using quantile regression (Qreg) analysis. Results: The prevalence of CVD was 30.7% in NHW and 38.2% in NHB (p<0.001). The means (SD) of CVD Risk score were 12.6 (5.7) in NHW and 14.6 (6.4) in NHB (p<0.001). Qreg analysis indicated that NHB with estimate glomerular filtration rate (eGFR) 30-59.9 ml/min/1.73m2 had significantly higher (worse) CVD Risk scores across all quantiles (Qs) than NHW. This race differences in CVD Risk were also significantly higher in NHB with eGFR 60-70 ml/min/1.73m2 in Qs 1 and 2 as compared to their NHW counterparts. An estimated 35.8% of the excess prevalent CVD could be attributable to the difference in CVD Risk for NHB versus NHW. Conclusion: NHB have a significantly higher CVD risk factor score in those with moderate and mild CKD than NHW. |
abstract_unstemmed |
Background and Aims: Adults with chronic kidney disease (CKD) carry an extraordinarily high risk for cardiovascular disease (CVD). The present study aimed to test two hypotheses that: (1) CVD risk factors disproportionately affect non-Hispanic black (NHB) with CKD compared to non-Hispanic white (NHW). (2) This difference significantly contributes to an excess risk of CVD in NHB versus NHW. Methods: A total of 3,939 aged 21-74 years old participating in the Chronic Renal Insufficiency Cohort Study was analyzed. A sum weighted CVDRisk score was constructed from well-established CVD risk factors. Differences in CVD Risk score by race/ethnicity were tested using quantile regression (Qreg) analysis. Results: The prevalence of CVD was 30.7% in NHW and 38.2% in NHB (p<0.001). The means (SD) of CVD Risk score were 12.6 (5.7) in NHW and 14.6 (6.4) in NHB (p<0.001). Qreg analysis indicated that NHB with estimate glomerular filtration rate (eGFR) 30-59.9 ml/min/1.73m2 had significantly higher (worse) CVD Risk scores across all quantiles (Qs) than NHW. This race differences in CVD Risk were also significantly higher in NHB with eGFR 60-70 ml/min/1.73m2 in Qs 1 and 2 as compared to their NHW counterparts. An estimated 35.8% of the excess prevalent CVD could be attributable to the difference in CVD Risk for NHB versus NHW. Conclusion: NHB have a significantly higher CVD risk factor score in those with moderate and mild CKD than NHW. |
collection_details |
GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_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 |
Using multivariate quantile regression analysis to explore cardiovascular risk differences in subjects with chronic kidney disease by race and ethnicity: Findings from the U.S. Chronic Renal Insufficiency Cohort Study |
url |
https://doi.org/10.17987/icfj.v2i1.70 https://doaj.org/article/f3cf81a000fe497db513d71334252621 http://icfjournal.org/index.php/icfj/article/view/70/89 https://doaj.org/toc/2410-2636 https://doaj.org/toc/2409-3424 |
remote_bool |
true |
ppnlink |
859466345 |
callnumber-subject |
RC - Internal Medicine |
mediatype_str_mv |
c |
isOA_txt |
true |
hochschulschrift_bool |
false |
callnumber-a |
RC666-701 |
up_date |
2024-07-03T23:10:02.749Z |
_version_ |
1803601264250978304 |
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">DOAJ049403796</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230308143041.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230227s2015 xx |||||o 00| ||eng c</controlfield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ049403796</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJf3cf81a000fe497db513d71334252621</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="050" ind1=" " ind2="0"><subfield code="a">RC666-701</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Longjian Liu</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Using multivariate quantile regression analysis to explore cardiovascular risk differences in subjects with chronic kidney disease by race and ethnicity: Findings from the U.S. Chronic Renal Insufficiency Cohort Study</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="520" ind1=" " ind2=" "><subfield code="a">Background and Aims: Adults with chronic kidney disease (CKD) carry an extraordinarily high risk for cardiovascular disease (CVD). The present study aimed to test two hypotheses that: (1) CVD risk factors disproportionately affect non-Hispanic black (NHB) with CKD compared to non-Hispanic white (NHW). (2) This difference significantly contributes to an excess risk of CVD in NHB versus NHW. Methods: A total of 3,939 aged 21-74 years old participating in the Chronic Renal Insufficiency Cohort Study was analyzed. A sum weighted CVDRisk score was constructed from well-established CVD risk factors. Differences in CVD Risk score by race/ethnicity were tested using quantile regression (Qreg) analysis. Results: The prevalence of CVD was 30.7% in NHW and 38.2% in NHB (p<0.001). The means (SD) of CVD Risk score were 12.6 (5.7) in NHW and 14.6 (6.4) in NHB (p<0.001). Qreg analysis indicated that NHB with estimate glomerular filtration rate (eGFR) 30-59.9 ml/min/1.73m2 had significantly higher (worse) CVD Risk scores across all quantiles (Qs) than NHW. This race differences in CVD Risk were also significantly higher in NHB with eGFR 60-70 ml/min/1.73m2 in Qs 1 and 2 as compared to their NHW counterparts. An estimated 35.8% of the excess prevalent CVD could be attributable to the difference in CVD Risk for NHB versus NHW. Conclusion: NHB have a significantly higher CVD risk factor score in those with moderate and mild CKD than NHW.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">CVD Risk</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">CKD</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">quantile regression</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Medicine</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">R</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Diseases of the circulatory (Cardiovascular) system</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">International Cardiovascular Forum Journal</subfield><subfield code="d">Barcaray International, 2017</subfield><subfield code="g">2(2015), 1, Seite 20-26</subfield><subfield code="w">(DE-627)859466345</subfield><subfield code="w">(DE-600)2855971-X</subfield><subfield code="x">24093424</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:2</subfield><subfield code="g">year:2015</subfield><subfield code="g">number:1</subfield><subfield code="g">pages:20-26</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.17987/icfj.v2i1.70</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/f3cf81a000fe497db513d71334252621</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">http://icfjournal.org/index.php/icfj/article/view/70/89</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/2410-2636</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/2409-3424</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</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_DOAJ</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_31</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_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">2</subfield><subfield code="j">2015</subfield><subfield code="e">1</subfield><subfield code="h">20-26</subfield></datafield></record></collection>
|
score |
7.3998404 |