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Association between the trajectory of ideal cardiovascular health metrics and incident chronic kidney disease among 27,635 older adults in northern China-a prospective cohort study
There is a lack of relevant studies evaluating the long-term impact of cardiovascular health factor (CVH) metrics on chronic kidney disease (CKD). This study investigates the long-term change in CVH metrics in older people and explores the relationship between CVH metrics trajectory and CKD. In tota...
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| Published in: | BMC geriatrics 2024-02, Vol.24 (1), p.193-9, Article 193 |
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| creator | Bai, Pufei Shao, Xian Ning, Xiaoqun Jiang, Xi Liu, Hongyan Lin, Yao Hou, Fang Zhang, Yourui Zhou, Saijun Yu, Pei |
| description | There is a lack of relevant studies evaluating the long-term impact of cardiovascular health factor (CVH) metrics on chronic kidney disease (CKD).
This study investigates the long-term change in CVH metrics in older people and explores the relationship between CVH metrics trajectory and CKD.
In total, 27,635 older people aged over 60 from the community-based Tianjin Chronic Kidney Disease Cohort study were enrolled. The participants completed five annual physical examinations between January 01, 2014, and December 31, 2018, and a subsequent follow-up between January 01, 2019, and December 31, 2021. CVH metrics trajectories were established by the group-based trajectory model to predict CKD risk. The relationships between baseline CVH, CVH change (ΔCVH), and CKD risk were also explored by logistic regression and restricted cubic spline regression model. In addition, likelihood ratio tests were used to compare the goodness of fit of the different models.
Six distinct CVH metrics trajectories were identified among the participants: low-stable (11.19%), low-medium-stable (30.58%), medium-stable (30.54%), medium-high-decreased (5.46%), medium-high-stable (18.93%), and high-stable (3.25%). After adjustment for potential confounders, higher CVH metrics trajectory was associated with decreased risk of CKD (P for trend |
| doi_str_mv | 10.1186/s12877-024-04760-5 |
| format | article |
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This study investigates the long-term change in CVH metrics in older people and explores the relationship between CVH metrics trajectory and CKD.
In total, 27,635 older people aged over 60 from the community-based Tianjin Chronic Kidney Disease Cohort study were enrolled. The participants completed five annual physical examinations between January 01, 2014, and December 31, 2018, and a subsequent follow-up between January 01, 2019, and December 31, 2021. CVH metrics trajectories were established by the group-based trajectory model to predict CKD risk. The relationships between baseline CVH, CVH change (ΔCVH), and CKD risk were also explored by logistic regression and restricted cubic spline regression model. In addition, likelihood ratio tests were used to compare the goodness of fit of the different models.
Six distinct CVH metrics trajectories were identified among the participants: low-stable (11.19%), low-medium-stable (30.58%), medium-stable (30.54%), medium-high-decreased (5.46%), medium-high-stable (18.93%), and high-stable (3.25%). After adjustment for potential confounders, higher CVH metrics trajectory was associated with decreased risk of CKD (P for trend < 0.001). Comparing the high-stable with the low-stable group, the risk of CKD decreased by 46%. All sensitivity analyses, including adjusting for baseline CVH and removing each CVH component from the total CVH, produced consistent results. Furthermore, the likelihood ratio test revealed that the model established by the CVH trajectory fit better than the baseline CVH and Δ CVH.
The higher CVH metrics trajectory and improvement of CVH metrics were associated with decreased risk of CKD. This study emphasized the importance of improving CVH to achieve primary prevention of CKD in older people.</description><identifier>ISSN: 1471-2318</identifier><identifier>EISSN: 1471-2318</identifier><identifier>DOI: 10.1186/s12877-024-04760-5</identifier><identifier>PMID: 38408910</identifier><language>eng</language><publisher>England: BioMed Central Ltd</publisher><subject>Aged ; Blood pressure ; Body mass index ; Cardiovascular diseases ; Cardiovascular Diseases - diagnosis ; Cardiovascular Diseases - epidemiology ; Cardiovascular Diseases - prevention & control ; Cardiovascular health metrics ; Care and treatment ; China - epidemiology ; Cholesterol ; Chronic kidney disease ; Chronic kidney failure ; Cohort analysis ; Cohort Studies ; Confounding (Statistics) ; Demographic aspects ; Development and progression ; Exercise ; Glucose ; Group-based trajectory model ; Health aspects ; Health behavior ; Health Status ; Humans ; Kidney diseases ; Life style ; Middle Aged ; Older people ; Physical fitness ; Prevention ; Prospective cohort study ; Prospective Studies ; Quality Indicators, Health Care ; Questionnaires ; Renal Insufficiency, Chronic - diagnosis ; Renal Insufficiency, Chronic - epidemiology ; Risk Factors ; Sensitivity analysis ; Urine ; Variables</subject><ispartof>BMC geriatrics, 2024-02, Vol.24 (1), p.193-9, Article 193</ispartof><rights>2024. The Author(s).</rights><rights>COPYRIGHT 2024 BioMed Central Ltd.</rights><rights>2024. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>The Author(s) 2024</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c515t-5c6b95fe2eb352cd4fb5273313364d1313b68cd627f6082e865982a29cd54dc73</cites><orcidid>0000-0001-6137-5824</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC10898137/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2956854881?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,885,25753,27924,27925,37012,37013,44590,53791,53793</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/38408910$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Bai, Pufei</creatorcontrib><creatorcontrib>Shao, Xian</creatorcontrib><creatorcontrib>Ning, Xiaoqun</creatorcontrib><creatorcontrib>Jiang, Xi</creatorcontrib><creatorcontrib>Liu, Hongyan</creatorcontrib><creatorcontrib>Lin, Yao</creatorcontrib><creatorcontrib>Hou, Fang</creatorcontrib><creatorcontrib>Zhang, Yourui</creatorcontrib><creatorcontrib>Zhou, Saijun</creatorcontrib><creatorcontrib>Yu, Pei</creatorcontrib><title>Association between the trajectory of ideal cardiovascular health metrics and incident chronic kidney disease among 27,635 older adults in northern China-a prospective cohort study</title><title>BMC geriatrics</title><addtitle>BMC Geriatr</addtitle><description>There is a lack of relevant studies evaluating the long-term impact of cardiovascular health factor (CVH) metrics on chronic kidney disease (CKD).
This study investigates the long-term change in CVH metrics in older people and explores the relationship between CVH metrics trajectory and CKD.
In total, 27,635 older people aged over 60 from the community-based Tianjin Chronic Kidney Disease Cohort study were enrolled. The participants completed five annual physical examinations between January 01, 2014, and December 31, 2018, and a subsequent follow-up between January 01, 2019, and December 31, 2021. CVH metrics trajectories were established by the group-based trajectory model to predict CKD risk. The relationships between baseline CVH, CVH change (ΔCVH), and CKD risk were also explored by logistic regression and restricted cubic spline regression model. In addition, likelihood ratio tests were used to compare the goodness of fit of the different models.
Six distinct CVH metrics trajectories were identified among the participants: low-stable (11.19%), low-medium-stable (30.58%), medium-stable (30.54%), medium-high-decreased (5.46%), medium-high-stable (18.93%), and high-stable (3.25%). After adjustment for potential confounders, higher CVH metrics trajectory was associated with decreased risk of CKD (P for trend < 0.001). Comparing the high-stable with the low-stable group, the risk of CKD decreased by 46%. All sensitivity analyses, including adjusting for baseline CVH and removing each CVH component from the total CVH, produced consistent results. Furthermore, the likelihood ratio test revealed that the model established by the CVH trajectory fit better than the baseline CVH and Δ CVH.
The higher CVH metrics trajectory and improvement of CVH metrics were associated with decreased risk of CKD. This study emphasized the importance of improving CVH to achieve primary prevention of CKD in older people.</description><subject>Aged</subject><subject>Blood pressure</subject><subject>Body mass index</subject><subject>Cardiovascular diseases</subject><subject>Cardiovascular Diseases - diagnosis</subject><subject>Cardiovascular Diseases - epidemiology</subject><subject>Cardiovascular Diseases - prevention & control</subject><subject>Cardiovascular health metrics</subject><subject>Care and treatment</subject><subject>China - epidemiology</subject><subject>Cholesterol</subject><subject>Chronic kidney disease</subject><subject>Chronic kidney failure</subject><subject>Cohort analysis</subject><subject>Cohort Studies</subject><subject>Confounding (Statistics)</subject><subject>Demographic aspects</subject><subject>Development and progression</subject><subject>Exercise</subject><subject>Glucose</subject><subject>Group-based trajectory model</subject><subject>Health aspects</subject><subject>Health behavior</subject><subject>Health Status</subject><subject>Humans</subject><subject>Kidney diseases</subject><subject>Life style</subject><subject>Middle Aged</subject><subject>Older people</subject><subject>Physical fitness</subject><subject>Prevention</subject><subject>Prospective cohort study</subject><subject>Prospective Studies</subject><subject>Quality Indicators, Health Care</subject><subject>Questionnaires</subject><subject>Renal Insufficiency, Chronic - diagnosis</subject><subject>Renal Insufficiency, Chronic - epidemiology</subject><subject>Risk Factors</subject><subject>Sensitivity analysis</subject><subject>Urine</subject><subject>Variables</subject><issn>1471-2318</issn><issn>1471-2318</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>PIMPY</sourceid><sourceid>DOA</sourceid><recordid>eNptUstu1DAUjRCIlsIPsECW2LAgxW87K1RVPCpVYgNry7FvJh4y9mAnU81_8YG4nVJ1EPLC1r3nnPvwaZrXBJ8TouWHQqhWqsWUt5griVvxpDklXJGWMqKfPnqfNC9KWWNMlKbyeXPCNMe6I_i0-X1RSnLBziFF1MN8AxDRPAKas12Dm1PeozSg4MFOyNnsQ9rZ4pbJZjTW2DyiDcw5uIJs9ChEV6FxRm7MKQaHfgYfYY98KGALILtJcYWoei-ZQGnykJH1yzSXykQx5Vo5R3Q5hmhbi7Y5lW1tIuwAuTTWNCrz4vcvm2eDnQq8ur_Pmh-fP32__Npef_tydXlx3TpBxNwKJ_tODEChZ4I6z4deUMUYYUxyT-rdS-28pGqQWFPQUnSaWto5L7h3ip01Vwddn-zabHPY2Lw3yQZzF0h5ZWyeg5vAdAoPQhIlajHeg-qV81hJArzTRFtctT4etLZLvwHv6pKynY5EjzMxjGaVdobUn9KE3Xbz7l4hp18LlNlsQnEwTTZCWoqhHaOcKaVohb79B7pOS451VxUlpBZcV8kH1MrWCUIcUi3sbkXNhdKcaKy5rqjz_6Dq8bAJLkUYQo0fEeiB4Or3lQzDw5AEm1vjmoNxTTWuuTOuEZX05vF6Hih_ncr-AG7d6ls</recordid><startdate>20240226</startdate><enddate>20240226</enddate><creator>Bai, Pufei</creator><creator>Shao, Xian</creator><creator>Ning, Xiaoqun</creator><creator>Jiang, Xi</creator><creator>Liu, Hongyan</creator><creator>Lin, Yao</creator><creator>Hou, Fang</creator><creator>Zhang, Yourui</creator><creator>Zhou, Saijun</creator><creator>Yu, Pei</creator><general>BioMed Central Ltd</general><general>BioMed Central</general><general>BMC</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7QP</scope><scope>7TK</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>K9.</scope><scope>M0S</scope><scope>M1P</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0001-6137-5824</orcidid></search><sort><creationdate>20240226</creationdate><title>Association between the trajectory of ideal cardiovascular health metrics and incident chronic kidney disease among 27,635 older adults in northern China-a prospective cohort study</title><author>Bai, Pufei ; Shao, Xian ; Ning, Xiaoqun ; Jiang, Xi ; Liu, Hongyan ; Lin, Yao ; Hou, Fang ; Zhang, Yourui ; Zhou, Saijun ; Yu, Pei</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c515t-5c6b95fe2eb352cd4fb5273313364d1313b68cd627f6082e865982a29cd54dc73</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Aged</topic><topic>Blood pressure</topic><topic>Body mass index</topic><topic>Cardiovascular diseases</topic><topic>Cardiovascular Diseases - 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Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>BMC geriatrics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Bai, Pufei</au><au>Shao, Xian</au><au>Ning, Xiaoqun</au><au>Jiang, Xi</au><au>Liu, Hongyan</au><au>Lin, Yao</au><au>Hou, Fang</au><au>Zhang, Yourui</au><au>Zhou, Saijun</au><au>Yu, Pei</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Association between the trajectory of ideal cardiovascular health metrics and incident chronic kidney disease among 27,635 older adults in northern China-a prospective cohort study</atitle><jtitle>BMC geriatrics</jtitle><addtitle>BMC Geriatr</addtitle><date>2024-02-26</date><risdate>2024</risdate><volume>24</volume><issue>1</issue><spage>193</spage><epage>9</epage><pages>193-9</pages><artnum>193</artnum><issn>1471-2318</issn><eissn>1471-2318</eissn><abstract>There is a lack of relevant studies evaluating the long-term impact of cardiovascular health factor (CVH) metrics on chronic kidney disease (CKD).
This study investigates the long-term change in CVH metrics in older people and explores the relationship between CVH metrics trajectory and CKD.
In total, 27,635 older people aged over 60 from the community-based Tianjin Chronic Kidney Disease Cohort study were enrolled. The participants completed five annual physical examinations between January 01, 2014, and December 31, 2018, and a subsequent follow-up between January 01, 2019, and December 31, 2021. CVH metrics trajectories were established by the group-based trajectory model to predict CKD risk. The relationships between baseline CVH, CVH change (ΔCVH), and CKD risk were also explored by logistic regression and restricted cubic spline regression model. In addition, likelihood ratio tests were used to compare the goodness of fit of the different models.
Six distinct CVH metrics trajectories were identified among the participants: low-stable (11.19%), low-medium-stable (30.58%), medium-stable (30.54%), medium-high-decreased (5.46%), medium-high-stable (18.93%), and high-stable (3.25%). After adjustment for potential confounders, higher CVH metrics trajectory was associated with decreased risk of CKD (P for trend < 0.001). Comparing the high-stable with the low-stable group, the risk of CKD decreased by 46%. All sensitivity analyses, including adjusting for baseline CVH and removing each CVH component from the total CVH, produced consistent results. Furthermore, the likelihood ratio test revealed that the model established by the CVH trajectory fit better than the baseline CVH and Δ CVH.
The higher CVH metrics trajectory and improvement of CVH metrics were associated with decreased risk of CKD. This study emphasized the importance of improving CVH to achieve primary prevention of CKD in older people.</abstract><cop>England</cop><pub>BioMed Central Ltd</pub><pmid>38408910</pmid><doi>10.1186/s12877-024-04760-5</doi><tpages>9</tpages><orcidid>https://orcid.org/0000-0001-6137-5824</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Aged Blood pressure Body mass index Cardiovascular diseases Cardiovascular Diseases - diagnosis Cardiovascular Diseases - epidemiology Cardiovascular Diseases - prevention & control Cardiovascular health metrics Care and treatment China - epidemiology Cholesterol Chronic kidney disease Chronic kidney failure Cohort analysis Cohort Studies Confounding (Statistics) Demographic aspects Development and progression Exercise Glucose Group-based trajectory model Health aspects Health behavior Health Status Humans Kidney diseases Life style Middle Aged Older people Physical fitness Prevention Prospective cohort study Prospective Studies Quality Indicators, Health Care Questionnaires Renal Insufficiency, Chronic - diagnosis Renal Insufficiency, Chronic - epidemiology Risk Factors Sensitivity analysis Urine Variables |
| title | Association between the trajectory of ideal cardiovascular health metrics and incident chronic kidney disease among 27,635 older adults in northern China-a prospective cohort study |
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