Yang Chen, Ying Gue, Maciej Banach, Dimitri Mikhailidis, Peter P. Toth, Marek Gierlotka, Tadeusz Osadnik, Marcin Golawski, Tomasz Tomasik, Adam Windak, Jacek Jozwiak, Gregory Y. H. Lip, the LIPIDOGRAM Investigators
{"title":"利用分层聚类技术分析波兰初级保健患者的表型:探索 LIPIDOGEN2015 研究队列中的死亡风险。","authors":"Yang Chen, Ying Gue, Maciej Banach, Dimitri Mikhailidis, Peter P. Toth, Marek Gierlotka, Tadeusz Osadnik, Marcin Golawski, Tomasz Tomasik, Adam Windak, Jacek Jozwiak, Gregory Y. H. Lip, the LIPIDOGRAM Investigators","doi":"10.1111/eci.14261","DOIUrl":null,"url":null,"abstract":"<div>\n \n \n <section>\n \n <h3> Background</h3>\n \n <p>Comorbidities in primary care do not occur in isolation but tend to cluster together causing various clinically complex phenotypes. This study aimed to distinguish phenotype clusters and identify the risks of all-cause mortality in primary care.</p>\n </section>\n \n <section>\n \n <h3> Methods</h3>\n \n <p>The baseline cohort of the LIPIDOGEN2015 sub-study involved 1779 patients recruited by 438 primary care physicians. To identify different phenotype clusters, we used hierarchical clustering and investigated differences between clinical characteristics and mortality between clusters. We then performed causal analyses using causal mediation analysis to explore potential mediators between different clusters and all-cause mortality.</p>\n </section>\n \n <section>\n \n <h3> Results</h3>\n \n <p>A total of 1756 patients were included (mean age 51.2, SD 13.0; 60.3% female), with a median follow-up of 5.7 years. Three clusters were identified: Cluster 1 (<i>n</i> = 543) was characterised by overweight/obesity (body mass index ≥ 25 kg/m<sup>2</sup>), older (age ≥ 65 years), more comorbidities; Cluster 2 (<i>n</i> = 459) was characterised by non-overweight/obesity, younger, fewer comorbidities; Cluster 3 (<i>n</i> = 754) was characterised by overweight/obesity, younger, fewer comorbidities. Adjusted Cox regression showed that compared with Cluster 2, Cluster 1 had a significantly higher risk of all-cause mortality (HR 3.87, 95% CI: 1.24–15.91), whereas this was insignificantly different for Cluster 3. Causal mediation analyses showed that decreased protein thiol groups mediated the hazard effect of all-cause mortality in Cluster 1 compared with Cluster 2, but not between Clusters 1 and 3.</p>\n </section>\n \n <section>\n \n <h3> Conclusion</h3>\n \n <p>Overweight/obesity older patients with more comorbidities had the highest risk of long-term all-cause mortality, and in the young group population overweight/obesity insignificantly increased the risk in the long-term follow-up, providing a basis for stratified phenotypic risk management.</p>\n </section>\n </div>","PeriodicalId":12013,"journal":{"name":"European Journal of Clinical Investigation","volume":null,"pages":null},"PeriodicalIF":4.4000,"publicationDate":"2024-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/eci.14261","citationCount":"0","resultStr":"{\"title\":\"Phenotypes of Polish primary care patients using hierarchical clustering: Exploring the risk of mortality in the LIPIDOGEN2015 study cohort\",\"authors\":\"Yang Chen, Ying Gue, Maciej Banach, Dimitri Mikhailidis, Peter P. Toth, Marek Gierlotka, Tadeusz Osadnik, Marcin Golawski, Tomasz Tomasik, Adam Windak, Jacek Jozwiak, Gregory Y. H. Lip, the LIPIDOGRAM Investigators\",\"doi\":\"10.1111/eci.14261\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n \\n <section>\\n \\n <h3> Background</h3>\\n \\n <p>Comorbidities in primary care do not occur in isolation but tend to cluster together causing various clinically complex phenotypes. This study aimed to distinguish phenotype clusters and identify the risks of all-cause mortality in primary care.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Methods</h3>\\n \\n <p>The baseline cohort of the LIPIDOGEN2015 sub-study involved 1779 patients recruited by 438 primary care physicians. To identify different phenotype clusters, we used hierarchical clustering and investigated differences between clinical characteristics and mortality between clusters. We then performed causal analyses using causal mediation analysis to explore potential mediators between different clusters and all-cause mortality.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Results</h3>\\n \\n <p>A total of 1756 patients were included (mean age 51.2, SD 13.0; 60.3% female), with a median follow-up of 5.7 years. Three clusters were identified: Cluster 1 (<i>n</i> = 543) was characterised by overweight/obesity (body mass index ≥ 25 kg/m<sup>2</sup>), older (age ≥ 65 years), more comorbidities; Cluster 2 (<i>n</i> = 459) was characterised by non-overweight/obesity, younger, fewer comorbidities; Cluster 3 (<i>n</i> = 754) was characterised by overweight/obesity, younger, fewer comorbidities. Adjusted Cox regression showed that compared with Cluster 2, Cluster 1 had a significantly higher risk of all-cause mortality (HR 3.87, 95% CI: 1.24–15.91), whereas this was insignificantly different for Cluster 3. 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Phenotypes of Polish primary care patients using hierarchical clustering: Exploring the risk of mortality in the LIPIDOGEN2015 study cohort
Background
Comorbidities in primary care do not occur in isolation but tend to cluster together causing various clinically complex phenotypes. This study aimed to distinguish phenotype clusters and identify the risks of all-cause mortality in primary care.
Methods
The baseline cohort of the LIPIDOGEN2015 sub-study involved 1779 patients recruited by 438 primary care physicians. To identify different phenotype clusters, we used hierarchical clustering and investigated differences between clinical characteristics and mortality between clusters. We then performed causal analyses using causal mediation analysis to explore potential mediators between different clusters and all-cause mortality.
Results
A total of 1756 patients were included (mean age 51.2, SD 13.0; 60.3% female), with a median follow-up of 5.7 years. Three clusters were identified: Cluster 1 (n = 543) was characterised by overweight/obesity (body mass index ≥ 25 kg/m2), older (age ≥ 65 years), more comorbidities; Cluster 2 (n = 459) was characterised by non-overweight/obesity, younger, fewer comorbidities; Cluster 3 (n = 754) was characterised by overweight/obesity, younger, fewer comorbidities. Adjusted Cox regression showed that compared with Cluster 2, Cluster 1 had a significantly higher risk of all-cause mortality (HR 3.87, 95% CI: 1.24–15.91), whereas this was insignificantly different for Cluster 3. Causal mediation analyses showed that decreased protein thiol groups mediated the hazard effect of all-cause mortality in Cluster 1 compared with Cluster 2, but not between Clusters 1 and 3.
Conclusion
Overweight/obesity older patients with more comorbidities had the highest risk of long-term all-cause mortality, and in the young group population overweight/obesity insignificantly increased the risk in the long-term follow-up, providing a basis for stratified phenotypic risk management.
期刊介绍:
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