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Cardiometabolic risk factor clusters in older adults using latent class analysis on the Bushehr elderly health program
Metabolic syndrome (MetS), comprising obesity, insulin resistance, hypertension, and dyslipidemia, increases the risk of type II diabetes mellitus and cardiovascular disease. This study aimed to identify the prevalence and determinants of specific clusters of the MetS components and tobacco consumpt...
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description | Metabolic syndrome (MetS), comprising obesity, insulin resistance, hypertension, and dyslipidemia, increases the risk of type II diabetes mellitus and cardiovascular disease. This study aimed to identify the prevalence and determinants of specific clusters of the MetS components and tobacco consumption among older adults in Iran. The current study was conducted in the second stage of the Bushehr Elderly Health (BEH) program in southern Iran—a population-based cohort including 2424 subjects aged ≥ 60 years. Latent class analysis (LCA) was used to identify MetS and tobacco consumption patterns. Multinomial logistic regression was conducted to investigate factors associated with each MetS class, including sociodemographic and behavioral variables. Out of 2424 individuals, the overall percentage of people with one or more components of MetS or current tobacco use was 57.8% and 20.8%, respectively. The mean (SD) age of all participants was 69.3(6.4) years. LCA ascertained the presence of four latent classes: class 1 (“low risk”; with a prevalence of 35.3%), class 2 (“MetS with medication-controlled diabetes”; 11.1%), class 3 (“high risk of MetS and associated medication use”; 27.1%), and class 4 (“central obesity and treated hypertension”; 26.4%). Compared to participants with a body mass index (BMI) |
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This study aimed to identify the prevalence and determinants of specific clusters of the MetS components and tobacco consumption among older adults in Iran. The current study was conducted in the second stage of the Bushehr Elderly Health (BEH) program in southern Iran—a population-based cohort including 2424 subjects aged ≥ 60 years. Latent class analysis (LCA) was used to identify MetS and tobacco consumption patterns. Multinomial logistic regression was conducted to investigate factors associated with each MetS class, including sociodemographic and behavioral variables. Out of 2424 individuals, the overall percentage of people with one or more components of MetS or current tobacco use was 57.8% and 20.8%, respectively. The mean (SD) age of all participants was 69.3(6.4) years. LCA ascertained the presence of four latent classes: class 1 (“low risk”; with a prevalence of 35.3%), class 2 (“MetS with medication-controlled diabetes”; 11.1%), class 3 (“high risk of MetS and associated medication use”; 27.1%), and class 4 (“central obesity and treated hypertension”; 26.4%). Compared to participants with a body mass index (BMI) < 30, participants with BMI ≥ 30 were more likely to belong to class 3 (OR 1.91, 95% CI 1.31–2.79) and class 4 (OR 1.49, 95% CI 1.06–2.08). Polypharmacy was associated with membership in class 2 (OR 2.07, 95% CI 1.12–3.81), class 3 (OR 9.77, 95% CI 6.12–15.59), and class 4 (OR 1.76, 95% CI 1.07–2.91). The elevated triglyceride-glucose index was associated with membership in class 2 (OR 12.33, 95% CI 7.75–19.61) and class 3 (OR 12.04, 95% CI 8.31–17.45). Individuals with poor self-related health were more likely to belong to class 3 (OR 1.43; 95% CI 1.08–1.93). Four classes were identified among older adults in Iran with distinct patterns of cardiometabolic risk factors. Segmenting elderly individuals into these cardiometabolic categories has the potential to enhance the monitoring and management of cardiometabolic risk factors. This strategy may help reduce the severe outcomes of metabolic syndrome in this susceptible population.</description><identifier>ISSN: 2045-2322</identifier><identifier>EISSN: 2045-2322</identifier><identifier>DOI: 10.1038/s41598-024-73997-2</identifier><identifier>PMID: 39468091</identifier><language>eng</language><publisher>London: Nature Publishing Group UK</publisher><subject>692/499 ; 692/700 ; Aged ; Body mass index ; Cardiometabolic ; Cardiometabolic Risk Factors ; Cardiovascular diseases ; Cardiovascular Diseases - epidemiology ; Consumption patterns ; Diabetes ; Diabetes mellitus ; Diabetes Mellitus, Type 2 - epidemiology ; Disease resistance ; Dyslipidemia ; Female ; Health risks ; Humanities and Social Sciences ; Humans ; Hypertension ; Hypertension - epidemiology ; Insulin resistance ; Iran - epidemiology ; Latent Class Analysis ; Male ; Metabolic disorders ; Metabolic syndrome ; Metabolic Syndrome - epidemiology ; Middle Aged ; multidisciplinary ; Obesity ; Older adults ; Older people ; Population studies ; Prevalence ; Risk Factors ; Science ; Science (multidisciplinary) ; Tobacco</subject><ispartof>Scientific reports, 2024-10, Vol.14 (1), p.25736-10, Article 25736</ispartof><rights>The Author(s) 2024</rights><rights>2024. The Author(s).</rights><rights>The Author(s) 2024. This work is published under http://creativecommons.org/licenses/by-nc-nd/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 2024</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c422t-b35d52fc62826a100620cc01f355360004a04bc6488f6a6bd6cd008e0c0f8a533</cites><orcidid>0000-0003-4333-4126</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/3121470574/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/3121470574?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,885,25751,27922,27923,37010,37011,44588,53789,53791,74896</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/39468091$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Afrashteh, Sima</creatorcontrib><creatorcontrib>Jalalian, Zahrasadat</creatorcontrib><creatorcontrib>Daneshi, Nima</creatorcontrib><creatorcontrib>Jamshidi, Ali</creatorcontrib><creatorcontrib>Batty, Jonathan A.</creatorcontrib><creatorcontrib>Mahdavizade, Haniye</creatorcontrib><creatorcontrib>Farhadi, Akram</creatorcontrib><creatorcontrib>Malekizadeh, Hasan</creatorcontrib><creatorcontrib>Nabipour, Iraj</creatorcontrib><creatorcontrib>Larijani, Bagher</creatorcontrib><title>Cardiometabolic risk factor clusters in older adults using latent class analysis on the Bushehr elderly health program</title><title>Scientific reports</title><addtitle>Sci Rep</addtitle><addtitle>Sci Rep</addtitle><description>Metabolic syndrome (MetS), comprising obesity, insulin resistance, hypertension, and dyslipidemia, increases the risk of type II diabetes mellitus and cardiovascular disease. This study aimed to identify the prevalence and determinants of specific clusters of the MetS components and tobacco consumption among older adults in Iran. The current study was conducted in the second stage of the Bushehr Elderly Health (BEH) program in southern Iran—a population-based cohort including 2424 subjects aged ≥ 60 years. Latent class analysis (LCA) was used to identify MetS and tobacco consumption patterns. Multinomial logistic regression was conducted to investigate factors associated with each MetS class, including sociodemographic and behavioral variables. Out of 2424 individuals, the overall percentage of people with one or more components of MetS or current tobacco use was 57.8% and 20.8%, respectively. The mean (SD) age of all participants was 69.3(6.4) years. LCA ascertained the presence of four latent classes: class 1 (“low risk”; with a prevalence of 35.3%), class 2 (“MetS with medication-controlled diabetes”; 11.1%), class 3 (“high risk of MetS and associated medication use”; 27.1%), and class 4 (“central obesity and treated hypertension”; 26.4%). Compared to participants with a body mass index (BMI) < 30, participants with BMI ≥ 30 were more likely to belong to class 3 (OR 1.91, 95% CI 1.31–2.79) and class 4 (OR 1.49, 95% CI 1.06–2.08). Polypharmacy was associated with membership in class 2 (OR 2.07, 95% CI 1.12–3.81), class 3 (OR 9.77, 95% CI 6.12–15.59), and class 4 (OR 1.76, 95% CI 1.07–2.91). The elevated triglyceride-glucose index was associated with membership in class 2 (OR 12.33, 95% CI 7.75–19.61) and class 3 (OR 12.04, 95% CI 8.31–17.45). Individuals with poor self-related health were more likely to belong to class 3 (OR 1.43; 95% CI 1.08–1.93). Four classes were identified among older adults in Iran with distinct patterns of cardiometabolic risk factors. Segmenting elderly individuals into these cardiometabolic categories has the potential to enhance the monitoring and management of cardiometabolic risk factors. This strategy may help reduce the severe outcomes of metabolic syndrome in this susceptible population.</description><subject>692/499</subject><subject>692/700</subject><subject>Aged</subject><subject>Body mass index</subject><subject>Cardiometabolic</subject><subject>Cardiometabolic Risk Factors</subject><subject>Cardiovascular diseases</subject><subject>Cardiovascular Diseases - epidemiology</subject><subject>Consumption patterns</subject><subject>Diabetes</subject><subject>Diabetes mellitus</subject><subject>Diabetes Mellitus, Type 2 - epidemiology</subject><subject>Disease resistance</subject><subject>Dyslipidemia</subject><subject>Female</subject><subject>Health risks</subject><subject>Humanities and Social Sciences</subject><subject>Humans</subject><subject>Hypertension</subject><subject>Hypertension - epidemiology</subject><subject>Insulin resistance</subject><subject>Iran - epidemiology</subject><subject>Latent Class Analysis</subject><subject>Male</subject><subject>Metabolic disorders</subject><subject>Metabolic syndrome</subject><subject>Metabolic Syndrome - epidemiology</subject><subject>Middle Aged</subject><subject>multidisciplinary</subject><subject>Obesity</subject><subject>Older adults</subject><subject>Older people</subject><subject>Population studies</subject><subject>Prevalence</subject><subject>Risk Factors</subject><subject>Science</subject><subject>Science (multidisciplinary)</subject><subject>Tobacco</subject><issn>2045-2322</issn><issn>2045-2322</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>PIMPY</sourceid><sourceid>DOA</sourceid><recordid>eNp9kktv1DAURiMEolXpH2CBLLFhE_B7nBWCEY9KldjA2rqxncSDJx5sp9L8ezxNKS0LvLGVe3zia39N85LgtwQz9S5zIjrVYsrbDeu6TUufNOcUc9FSRunTB-uz5jLnHa5D0I6T7nlzxjouFe7IeXOzhWR93LsCfQzeoOTzTzSAKTEhE5ZcXMrIzygG6xICu4SS0ZL9PKIAxc2lUpAzghnCMfuM4ozK5NDHJU9uSsid9oUjmhyEMqFDimOC_Yvm2QAhu8u7-aL58fnT9-3X9vrbl6vth-vWcEpL2zNhBR2MpIpKIBhLio3BZGBCMFk74oB5byRXapAgeyuNxVg5bPCgQDB20VytXhthpw_J7yEddQSvbz_ENGpIxZvgtLGDJVhR7lTPCZPgrBkMwVJwK1wH1fV-dR2Wfl-LtfcE4ZH0cWX2kx7jjSZEkI5xVQ1v7gwp_lpcLnrvs3EhwOzikjUjtD4qVZxW9PU_6C4uqd7xSvENFhteKbpSJsWckxvuT0OwPsVErzHRNSb6Nib6pH71sI_7LX9CUQG2ArmW5tGlv__-j_Y3pyrJ2g</recordid><startdate>20241028</startdate><enddate>20241028</enddate><creator>Afrashteh, Sima</creator><creator>Jalalian, Zahrasadat</creator><creator>Daneshi, Nima</creator><creator>Jamshidi, Ali</creator><creator>Batty, Jonathan A.</creator><creator>Mahdavizade, Haniye</creator><creator>Farhadi, Akram</creator><creator>Malekizadeh, Hasan</creator><creator>Nabipour, Iraj</creator><creator>Larijani, Bagher</creator><general>Nature Publishing Group UK</general><general>Nature Publishing Group</general><general>Nature Portfolio</general><scope>C6C</scope><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>7X7</scope><scope>7XB</scope><scope>88A</scope><scope>88E</scope><scope>88I</scope><scope>8FE</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>LK8</scope><scope>M0S</scope><scope>M1P</scope><scope>M2P</scope><scope>M7P</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>Q9U</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0003-4333-4126</orcidid></search><sort><creationdate>20241028</creationdate><title>Cardiometabolic risk factor clusters in older adults using latent class analysis on the Bushehr elderly health program</title><author>Afrashteh, Sima ; Jalalian, Zahrasadat ; Daneshi, Nima ; Jamshidi, Ali ; Batty, Jonathan A. ; Mahdavizade, Haniye ; Farhadi, Akram ; Malekizadeh, Hasan ; Nabipour, Iraj ; Larijani, Bagher</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c422t-b35d52fc62826a100620cc01f355360004a04bc6488f6a6bd6cd008e0c0f8a533</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>692/499</topic><topic>692/700</topic><topic>Aged</topic><topic>Body mass index</topic><topic>Cardiometabolic</topic><topic>Cardiometabolic Risk Factors</topic><topic>Cardiovascular diseases</topic><topic>Cardiovascular Diseases - epidemiology</topic><topic>Consumption patterns</topic><topic>Diabetes</topic><topic>Diabetes mellitus</topic><topic>Diabetes Mellitus, Type 2 - epidemiology</topic><topic>Disease resistance</topic><topic>Dyslipidemia</topic><topic>Female</topic><topic>Health risks</topic><topic>Humanities and Social Sciences</topic><topic>Humans</topic><topic>Hypertension</topic><topic>Hypertension - epidemiology</topic><topic>Insulin resistance</topic><topic>Iran - epidemiology</topic><topic>Latent Class Analysis</topic><topic>Male</topic><topic>Metabolic disorders</topic><topic>Metabolic syndrome</topic><topic>Metabolic Syndrome - epidemiology</topic><topic>Middle Aged</topic><topic>multidisciplinary</topic><topic>Obesity</topic><topic>Older adults</topic><topic>Older people</topic><topic>Population studies</topic><topic>Prevalence</topic><topic>Risk Factors</topic><topic>Science</topic><topic>Science (multidisciplinary)</topic><topic>Tobacco</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Afrashteh, Sima</creatorcontrib><creatorcontrib>Jalalian, Zahrasadat</creatorcontrib><creatorcontrib>Daneshi, Nima</creatorcontrib><creatorcontrib>Jamshidi, Ali</creatorcontrib><creatorcontrib>Batty, Jonathan A.</creatorcontrib><creatorcontrib>Mahdavizade, Haniye</creatorcontrib><creatorcontrib>Farhadi, Akram</creatorcontrib><creatorcontrib>Malekizadeh, Hasan</creatorcontrib><creatorcontrib>Nabipour, Iraj</creatorcontrib><creatorcontrib>Larijani, Bagher</creatorcontrib><collection>Springer_OA刊</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>ProQuest_Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Biology Database (Alumni Edition)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Science Database (Alumni Edition)</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>ProQuest Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>ProQuest Biological Science Collection</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>ProQuest Science Journals</collection><collection>Biological Science Database</collection><collection>Publicly Available Content Database (Proquest) (PQ_SDU_P3)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central Basic</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>Scientific reports</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Afrashteh, Sima</au><au>Jalalian, Zahrasadat</au><au>Daneshi, Nima</au><au>Jamshidi, Ali</au><au>Batty, Jonathan A.</au><au>Mahdavizade, Haniye</au><au>Farhadi, Akram</au><au>Malekizadeh, Hasan</au><au>Nabipour, Iraj</au><au>Larijani, Bagher</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Cardiometabolic risk factor clusters in older adults using latent class analysis on the Bushehr elderly health program</atitle><jtitle>Scientific reports</jtitle><stitle>Sci Rep</stitle><addtitle>Sci Rep</addtitle><date>2024-10-28</date><risdate>2024</risdate><volume>14</volume><issue>1</issue><spage>25736</spage><epage>10</epage><pages>25736-10</pages><artnum>25736</artnum><issn>2045-2322</issn><eissn>2045-2322</eissn><abstract>Metabolic syndrome (MetS), comprising obesity, insulin resistance, hypertension, and dyslipidemia, increases the risk of type II diabetes mellitus and cardiovascular disease. This study aimed to identify the prevalence and determinants of specific clusters of the MetS components and tobacco consumption among older adults in Iran. The current study was conducted in the second stage of the Bushehr Elderly Health (BEH) program in southern Iran—a population-based cohort including 2424 subjects aged ≥ 60 years. Latent class analysis (LCA) was used to identify MetS and tobacco consumption patterns. Multinomial logistic regression was conducted to investigate factors associated with each MetS class, including sociodemographic and behavioral variables. Out of 2424 individuals, the overall percentage of people with one or more components of MetS or current tobacco use was 57.8% and 20.8%, respectively. The mean (SD) age of all participants was 69.3(6.4) years. LCA ascertained the presence of four latent classes: class 1 (“low risk”; with a prevalence of 35.3%), class 2 (“MetS with medication-controlled diabetes”; 11.1%), class 3 (“high risk of MetS and associated medication use”; 27.1%), and class 4 (“central obesity and treated hypertension”; 26.4%). Compared to participants with a body mass index (BMI) < 30, participants with BMI ≥ 30 were more likely to belong to class 3 (OR 1.91, 95% CI 1.31–2.79) and class 4 (OR 1.49, 95% CI 1.06–2.08). Polypharmacy was associated with membership in class 2 (OR 2.07, 95% CI 1.12–3.81), class 3 (OR 9.77, 95% CI 6.12–15.59), and class 4 (OR 1.76, 95% CI 1.07–2.91). The elevated triglyceride-glucose index was associated with membership in class 2 (OR 12.33, 95% CI 7.75–19.61) and class 3 (OR 12.04, 95% CI 8.31–17.45). Individuals with poor self-related health were more likely to belong to class 3 (OR 1.43; 95% CI 1.08–1.93). Four classes were identified among older adults in Iran with distinct patterns of cardiometabolic risk factors. Segmenting elderly individuals into these cardiometabolic categories has the potential to enhance the monitoring and management of cardiometabolic risk factors. This strategy may help reduce the severe outcomes of metabolic syndrome in this susceptible population.</abstract><cop>London</cop><pub>Nature Publishing Group UK</pub><pmid>39468091</pmid><doi>10.1038/s41598-024-73997-2</doi><tpages>10</tpages><orcidid>https://orcid.org/0000-0003-4333-4126</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | 692/499 692/700 Aged Body mass index Cardiometabolic Cardiometabolic Risk Factors Cardiovascular diseases Cardiovascular Diseases - epidemiology Consumption patterns Diabetes Diabetes mellitus Diabetes Mellitus, Type 2 - epidemiology Disease resistance Dyslipidemia Female Health risks Humanities and Social Sciences Humans Hypertension Hypertension - epidemiology Insulin resistance Iran - epidemiology Latent Class Analysis Male Metabolic disorders Metabolic syndrome Metabolic Syndrome - epidemiology Middle Aged multidisciplinary Obesity Older adults Older people Population studies Prevalence Risk Factors Science Science (multidisciplinary) Tobacco |
title | Cardiometabolic risk factor clusters in older adults using latent class analysis on the Bushehr elderly health program |
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