Metabolic profile‐based subgroups can identify differences in brain volumes and brain iron deposition
Aims To evaluate associations of metabolic profiles and biomarkers with brain atrophy, lesions, and iron deposition to understand the early risk factors associated with dementia. Materials and methods Using data from 26 239 UK Biobank participants free from dementia and stroke, we assessed the assoc...
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| Veröffentlicht in: | Diabetes, obesity & metabolism obesity & metabolism, 2023-01, Vol.25 (1), p.121-131 |
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| creator | Lumsden, Amanda L. Mulugeta, Anwar Mäkinen, Ville‐Petteri Hyppönen, Elina |
| description | Aims
To evaluate associations of metabolic profiles and biomarkers with brain atrophy, lesions, and iron deposition to understand the early risk factors associated with dementia.
Materials and methods
Using data from 26 239 UK Biobank participants free from dementia and stroke, we assessed the associations of metabolic subgroups, derived using an artificial neural network approach (self‐organizing map), and 39 individual biomarkers with brain MRI measures: total brain volume (TBV), grey matter volume (GMV), white matter volume (WMV), hippocampal volume (HV), white matter hyperintensity (WMH) volume, and caudate iron deposition.
Results
In metabolic subgroup analyses, participants characterized by high triglycerides and liver enzymes showed the most adverse brain outcomes compared to the healthy reference subgroup with high‐density lipoprotein cholesterol and low body mass index (BMI) including associations with GMV (βstandardized −0.20, 95% confidence interval [CI] −0.24 to −0.16), HV (βstandardized −0.09, 95% CI −0.13 to −0.04), WMH volume (βstandardized 0.22, 95% CI 0.18 to 0.26), and caudate iron deposition (βstandardized 0.30, 95% CI 0.25 to 0.34), with similar adverse associations for the subgroup with high BMI, C‐reactive protein and cystatin C, and the subgroup with high blood pressure (BP) and apolipoprotein B. Among the biomarkers, striking associations were seen between basal metabolic rate (BMR) and caudate iron deposition (βstandardized 0.23, 95% CI 0.22 to 0.24 per 1 SD increase), GMV (βstandardized −0.15, 95% CI −0.16 to −0.14) and HV (βstandardized −0.11, 95% CI −0.12 to −0.10), and between BP and WMH volume (βstandardized 0.13, 95% CI 0.12 to 0.14 for diastolic BP).
Conclusions
Metabolic profiles were associated differentially with brain neuroimaging characteristics. Associations of BMR, BP and other individual biomarkers may provide insights into actionable mechanisms driving these brain associations. |
| doi_str_mv | 10.1111/dom.14853 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_pubme</sourceid><recordid>TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_10946804</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2709736128</sourcerecordid><originalsourceid>FETCH-LOGICAL-c4443-4290b072e4f3a267eafb924e92ae83c1908d5e91040bd3c7d46dee7b4e42770c3</originalsourceid><addsrcrecordid>eNp1kctuFDEQRS0EImFgwQ-gltjAopPyY9ruFYrCU0qUDawtP6oHR912Y08HzY5P4Bv5EkxmiAAJL8qlqqOra19CnlI4ofWc-jSdUKHW_B45pqLjLeWsu3_bs1b1wI7Io1KuAUBwJR-SI97BmiuQx2RziVtj0xhcM-c0hBF_fPtuTUHflMVuclrm0jgTm-AxbsOwa3wYBswYHZYmxMZmU-tNGpepDkz0h0nIKTYe51TCNqT4mDwYzFjwyeFekU9v33w8f99eXL37cH520TohBG8F68GCZCgGblgn0Qy2ZwJ7ZlBxR3tQfo09BQHWcye96DyitAIFkxIcX5FXe915sRN6V01nM-o5h8nknU4m6L83MXzWm3SjKfSiU_WDVuTFQSGnLwuWrZ5CcTiOJmJaimYSesk7ylRFn_-DXqclx_q-SgkFqlpaV-rlnnI5lZJxuHNDQf_KT9f89G1-lX32p_078ndgFTjdA19rVLv_K-nXV5d7yZ9bfKdF</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2748087705</pqid></control><display><type>article</type><title>Metabolic profile‐based subgroups can identify differences in brain volumes and brain iron deposition</title><source>MEDLINE</source><source>Wiley Online Library Journals Frontfile Complete</source><creator>Lumsden, Amanda L. ; Mulugeta, Anwar ; Mäkinen, Ville‐Petteri ; Hyppönen, Elina</creator><creatorcontrib>Lumsden, Amanda L. ; Mulugeta, Anwar ; Mäkinen, Ville‐Petteri ; Hyppönen, Elina</creatorcontrib><description>Aims
To evaluate associations of metabolic profiles and biomarkers with brain atrophy, lesions, and iron deposition to understand the early risk factors associated with dementia.
Materials and methods
Using data from 26 239 UK Biobank participants free from dementia and stroke, we assessed the associations of metabolic subgroups, derived using an artificial neural network approach (self‐organizing map), and 39 individual biomarkers with brain MRI measures: total brain volume (TBV), grey matter volume (GMV), white matter volume (WMV), hippocampal volume (HV), white matter hyperintensity (WMH) volume, and caudate iron deposition.
Results
In metabolic subgroup analyses, participants characterized by high triglycerides and liver enzymes showed the most adverse brain outcomes compared to the healthy reference subgroup with high‐density lipoprotein cholesterol and low body mass index (BMI) including associations with GMV (βstandardized −0.20, 95% confidence interval [CI] −0.24 to −0.16), HV (βstandardized −0.09, 95% CI −0.13 to −0.04), WMH volume (βstandardized 0.22, 95% CI 0.18 to 0.26), and caudate iron deposition (βstandardized 0.30, 95% CI 0.25 to 0.34), with similar adverse associations for the subgroup with high BMI, C‐reactive protein and cystatin C, and the subgroup with high blood pressure (BP) and apolipoprotein B. Among the biomarkers, striking associations were seen between basal metabolic rate (BMR) and caudate iron deposition (βstandardized 0.23, 95% CI 0.22 to 0.24 per 1 SD increase), GMV (βstandardized −0.15, 95% CI −0.16 to −0.14) and HV (βstandardized −0.11, 95% CI −0.12 to −0.10), and between BP and WMH volume (βstandardized 0.13, 95% CI 0.12 to 0.14 for diastolic BP).
Conclusions
Metabolic profiles were associated differentially with brain neuroimaging characteristics. Associations of BMR, BP and other individual biomarkers may provide insights into actionable mechanisms driving these brain associations.</description><identifier>ISSN: 1462-8902</identifier><identifier>EISSN: 1463-1326</identifier><identifier>DOI: 10.1111/dom.14853</identifier><identifier>PMID: 36053807</identifier><language>eng</language><publisher>Oxford, UK: Blackwell Publishing Ltd</publisher><subject>Apolipoprotein B ; Atrophy ; Biomarkers ; Blood pressure ; Body mass index ; Brain - diagnostic imaging ; brain iron ; brain volume ; Cholesterol ; Cystatin C ; Dementia ; Dementia disorders ; Hippocampus ; Humans ; Hypertension ; Iron ; metabolic profiling ; Metabolic rate ; Metabolism ; Metabolome ; Neural networks ; Neuroimaging ; Original ; Risk factors ; self‐organizing map ; Substantia alba ; Substantia grisea ; Triglycerides ; white matter hyperintensities</subject><ispartof>Diabetes, obesity & metabolism, 2023-01, Vol.25 (1), p.121-131</ispartof><rights>2022 The Authors. published by John Wiley & Sons Ltd.</rights><rights>2022 The Authors. Diabetes, Obesity and Metabolism published by John Wiley & Sons Ltd.</rights><rights>2022. This article 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><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c4443-4290b072e4f3a267eafb924e92ae83c1908d5e91040bd3c7d46dee7b4e42770c3</citedby><cites>FETCH-LOGICAL-c4443-4290b072e4f3a267eafb924e92ae83c1908d5e91040bd3c7d46dee7b4e42770c3</cites><orcidid>0000-0003-3670-9399 ; 0000-0002-8018-3454 ; 0000-0002-0214-6498 ; 0000-0002-7262-2656</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1111%2Fdom.14853$$EPDF$$P50$$Gwiley$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1111%2Fdom.14853$$EHTML$$P50$$Gwiley$$Hfree_for_read</linktohtml><link.rule.ids>230,314,776,780,881,1411,27901,27902,45550,45551</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/36053807$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Lumsden, Amanda L.</creatorcontrib><creatorcontrib>Mulugeta, Anwar</creatorcontrib><creatorcontrib>Mäkinen, Ville‐Petteri</creatorcontrib><creatorcontrib>Hyppönen, Elina</creatorcontrib><title>Metabolic profile‐based subgroups can identify differences in brain volumes and brain iron deposition</title><title>Diabetes, obesity & metabolism</title><addtitle>Diabetes Obes Metab</addtitle><description>Aims
To evaluate associations of metabolic profiles and biomarkers with brain atrophy, lesions, and iron deposition to understand the early risk factors associated with dementia.
Materials and methods
Using data from 26 239 UK Biobank participants free from dementia and stroke, we assessed the associations of metabolic subgroups, derived using an artificial neural network approach (self‐organizing map), and 39 individual biomarkers with brain MRI measures: total brain volume (TBV), grey matter volume (GMV), white matter volume (WMV), hippocampal volume (HV), white matter hyperintensity (WMH) volume, and caudate iron deposition.
Results
In metabolic subgroup analyses, participants characterized by high triglycerides and liver enzymes showed the most adverse brain outcomes compared to the healthy reference subgroup with high‐density lipoprotein cholesterol and low body mass index (BMI) including associations with GMV (βstandardized −0.20, 95% confidence interval [CI] −0.24 to −0.16), HV (βstandardized −0.09, 95% CI −0.13 to −0.04), WMH volume (βstandardized 0.22, 95% CI 0.18 to 0.26), and caudate iron deposition (βstandardized 0.30, 95% CI 0.25 to 0.34), with similar adverse associations for the subgroup with high BMI, C‐reactive protein and cystatin C, and the subgroup with high blood pressure (BP) and apolipoprotein B. Among the biomarkers, striking associations were seen between basal metabolic rate (BMR) and caudate iron deposition (βstandardized 0.23, 95% CI 0.22 to 0.24 per 1 SD increase), GMV (βstandardized −0.15, 95% CI −0.16 to −0.14) and HV (βstandardized −0.11, 95% CI −0.12 to −0.10), and between BP and WMH volume (βstandardized 0.13, 95% CI 0.12 to 0.14 for diastolic BP).
Conclusions
Metabolic profiles were associated differentially with brain neuroimaging characteristics. Associations of BMR, BP and other individual biomarkers may provide insights into actionable mechanisms driving these brain associations.</description><subject>Apolipoprotein B</subject><subject>Atrophy</subject><subject>Biomarkers</subject><subject>Blood pressure</subject><subject>Body mass index</subject><subject>Brain - diagnostic imaging</subject><subject>brain iron</subject><subject>brain volume</subject><subject>Cholesterol</subject><subject>Cystatin C</subject><subject>Dementia</subject><subject>Dementia disorders</subject><subject>Hippocampus</subject><subject>Humans</subject><subject>Hypertension</subject><subject>Iron</subject><subject>metabolic profiling</subject><subject>Metabolic rate</subject><subject>Metabolism</subject><subject>Metabolome</subject><subject>Neural networks</subject><subject>Neuroimaging</subject><subject>Original</subject><subject>Risk factors</subject><subject>self‐organizing map</subject><subject>Substantia alba</subject><subject>Substantia grisea</subject><subject>Triglycerides</subject><subject>white matter hyperintensities</subject><issn>1462-8902</issn><issn>1463-1326</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>24P</sourceid><sourceid>EIF</sourceid><recordid>eNp1kctuFDEQRS0EImFgwQ-gltjAopPyY9ruFYrCU0qUDawtP6oHR912Y08HzY5P4Bv5EkxmiAAJL8qlqqOra19CnlI4ofWc-jSdUKHW_B45pqLjLeWsu3_bs1b1wI7Io1KuAUBwJR-SI97BmiuQx2RziVtj0xhcM-c0hBF_fPtuTUHflMVuclrm0jgTm-AxbsOwa3wYBswYHZYmxMZmU-tNGpepDkz0h0nIKTYe51TCNqT4mDwYzFjwyeFekU9v33w8f99eXL37cH520TohBG8F68GCZCgGblgn0Qy2ZwJ7ZlBxR3tQfo09BQHWcye96DyitAIFkxIcX5FXe915sRN6V01nM-o5h8nknU4m6L83MXzWm3SjKfSiU_WDVuTFQSGnLwuWrZ5CcTiOJmJaimYSesk7ylRFn_-DXqclx_q-SgkFqlpaV-rlnnI5lZJxuHNDQf_KT9f89G1-lX32p_078ndgFTjdA19rVLv_K-nXV5d7yZ9bfKdF</recordid><startdate>202301</startdate><enddate>202301</enddate><creator>Lumsden, Amanda L.</creator><creator>Mulugeta, Anwar</creator><creator>Mäkinen, Ville‐Petteri</creator><creator>Hyppönen, Elina</creator><general>Blackwell Publishing Ltd</general><general>Wiley Subscription Services, Inc</general><scope>24P</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>7T5</scope><scope>7TK</scope><scope>H94</scope><scope>K9.</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0003-3670-9399</orcidid><orcidid>https://orcid.org/0000-0002-8018-3454</orcidid><orcidid>https://orcid.org/0000-0002-0214-6498</orcidid><orcidid>https://orcid.org/0000-0002-7262-2656</orcidid></search><sort><creationdate>202301</creationdate><title>Metabolic profile‐based subgroups can identify differences in brain volumes and brain iron deposition</title><author>Lumsden, Amanda L. ; Mulugeta, Anwar ; Mäkinen, Ville‐Petteri ; Hyppönen, Elina</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c4443-4290b072e4f3a267eafb924e92ae83c1908d5e91040bd3c7d46dee7b4e42770c3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Apolipoprotein B</topic><topic>Atrophy</topic><topic>Biomarkers</topic><topic>Blood pressure</topic><topic>Body mass index</topic><topic>Brain - diagnostic imaging</topic><topic>brain iron</topic><topic>brain volume</topic><topic>Cholesterol</topic><topic>Cystatin C</topic><topic>Dementia</topic><topic>Dementia disorders</topic><topic>Hippocampus</topic><topic>Humans</topic><topic>Hypertension</topic><topic>Iron</topic><topic>metabolic profiling</topic><topic>Metabolic rate</topic><topic>Metabolism</topic><topic>Metabolome</topic><topic>Neural networks</topic><topic>Neuroimaging</topic><topic>Original</topic><topic>Risk factors</topic><topic>self‐organizing map</topic><topic>Substantia alba</topic><topic>Substantia grisea</topic><topic>Triglycerides</topic><topic>white matter hyperintensities</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Lumsden, Amanda L.</creatorcontrib><creatorcontrib>Mulugeta, Anwar</creatorcontrib><creatorcontrib>Mäkinen, Ville‐Petteri</creatorcontrib><creatorcontrib>Hyppönen, Elina</creatorcontrib><collection>Wiley Online Library Open Access</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Immunology Abstracts</collection><collection>Neurosciences Abstracts</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Diabetes, obesity & metabolism</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Lumsden, Amanda L.</au><au>Mulugeta, Anwar</au><au>Mäkinen, Ville‐Petteri</au><au>Hyppönen, Elina</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Metabolic profile‐based subgroups can identify differences in brain volumes and brain iron deposition</atitle><jtitle>Diabetes, obesity & metabolism</jtitle><addtitle>Diabetes Obes Metab</addtitle><date>2023-01</date><risdate>2023</risdate><volume>25</volume><issue>1</issue><spage>121</spage><epage>131</epage><pages>121-131</pages><issn>1462-8902</issn><eissn>1463-1326</eissn><abstract>Aims
To evaluate associations of metabolic profiles and biomarkers with brain atrophy, lesions, and iron deposition to understand the early risk factors associated with dementia.
Materials and methods
Using data from 26 239 UK Biobank participants free from dementia and stroke, we assessed the associations of metabolic subgroups, derived using an artificial neural network approach (self‐organizing map), and 39 individual biomarkers with brain MRI measures: total brain volume (TBV), grey matter volume (GMV), white matter volume (WMV), hippocampal volume (HV), white matter hyperintensity (WMH) volume, and caudate iron deposition.
Results
In metabolic subgroup analyses, participants characterized by high triglycerides and liver enzymes showed the most adverse brain outcomes compared to the healthy reference subgroup with high‐density lipoprotein cholesterol and low body mass index (BMI) including associations with GMV (βstandardized −0.20, 95% confidence interval [CI] −0.24 to −0.16), HV (βstandardized −0.09, 95% CI −0.13 to −0.04), WMH volume (βstandardized 0.22, 95% CI 0.18 to 0.26), and caudate iron deposition (βstandardized 0.30, 95% CI 0.25 to 0.34), with similar adverse associations for the subgroup with high BMI, C‐reactive protein and cystatin C, and the subgroup with high blood pressure (BP) and apolipoprotein B. Among the biomarkers, striking associations were seen between basal metabolic rate (BMR) and caudate iron deposition (βstandardized 0.23, 95% CI 0.22 to 0.24 per 1 SD increase), GMV (βstandardized −0.15, 95% CI −0.16 to −0.14) and HV (βstandardized −0.11, 95% CI −0.12 to −0.10), and between BP and WMH volume (βstandardized 0.13, 95% CI 0.12 to 0.14 for diastolic BP).
Conclusions
Metabolic profiles were associated differentially with brain neuroimaging characteristics. Associations of BMR, BP and other individual biomarkers may provide insights into actionable mechanisms driving these brain associations.</abstract><cop>Oxford, UK</cop><pub>Blackwell Publishing Ltd</pub><pmid>36053807</pmid><doi>10.1111/dom.14853</doi><tpages>11</tpages><orcidid>https://orcid.org/0000-0003-3670-9399</orcidid><orcidid>https://orcid.org/0000-0002-8018-3454</orcidid><orcidid>https://orcid.org/0000-0002-0214-6498</orcidid><orcidid>https://orcid.org/0000-0002-7262-2656</orcidid><oa>free_for_read</oa></addata></record> |
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| source | MEDLINE; Wiley Online Library Journals Frontfile Complete |
| subjects | Apolipoprotein B Atrophy Biomarkers Blood pressure Body mass index Brain - diagnostic imaging brain iron brain volume Cholesterol Cystatin C Dementia Dementia disorders Hippocampus Humans Hypertension Iron metabolic profiling Metabolic rate Metabolism Metabolome Neural networks Neuroimaging Original Risk factors self‐organizing map Substantia alba Substantia grisea Triglycerides white matter hyperintensities |
| title | Metabolic profile‐based subgroups can identify differences in brain volumes and brain iron deposition |
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