Determinants of a dietary pattern linked with greater metabolic risk and its tracking during adolescence
Background Although growing evidence suggests that dietary patterns associated with noncommunicable diseases in adulthood may develop early in life, when these are established, as well as their determinants, remains unclear. Methods We examined determinants and tracking of a dietary pattern (DP) ass...
Gespeichert in:
| Veröffentlicht in: | Journal of human nutrition and dietetics 2018-04, Vol.31 (2), p.218-227 |
|---|---|
| Hauptverfasser: | , , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 227 |
|---|---|
| container_issue | 2 |
| container_start_page | 218 |
| container_title | Journal of human nutrition and dietetics |
| container_volume | 31 |
| creator | Appannah, G. Pot, G. K. Oddy, W. H. Jebb, S. A. Ambrosini, G. L. |
| description | Background
Although growing evidence suggests that dietary patterns associated with noncommunicable diseases in adulthood may develop early in life, when these are established, as well as their determinants, remains unclear.
Methods
We examined determinants and tracking of a dietary pattern (DP) associated with metabolic risk and its key food groups among 860 adolescents in the Western Australian Pregnancy (Raine) Cohort study. Food intake was reported using a food frequency questionnaire (FFQ) at 14 and 17 years. Z‐scores for an ‘energy‐dense, high‐fat, low‐fibre’ DP were estimated by applying reduced rank regression at both ages. Tracking was based on the predictive value (PV) of remaining in the DPZ‐score or food intake quartile at 14 and 17 years. Early‐life exposures included: maternal age; maternal pre‐pregnancy body mass index; parent smoking status during pregnancy; and parent socio‐economic position (SEP) at 14 and 17 years. Associations between the DPZ‐scores, early‐life factors and SEP were analysed using regression analysis.
Results
Dietary tracking was strongest among boys with high DPZ‐scores, high intakes of processed meat, low‐fibre bread, crisps and savoury snacks (PV > 1) and the lowest intakes of vegetables, fruit and legumes. Lower maternal education (β = 0.09, P = 0.002 at 14 years; β = 0.14, P |
| doi_str_mv | 10.1111/jhn.12519 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_1947099887</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>1947099887</sourcerecordid><originalsourceid>FETCH-LOGICAL-c3889-dd95c3b29d30994d7068dafac462ffe6f19121cc9ff58d19edab23bece6447ab3</originalsourceid><addsrcrecordid>eNp1kMtOxCAUQInR6PhY-AOGxI0uqkAflKUZ3zG60cQdoXBxmGnpCG2Mfy866sJENjeBw8nNQWifkhOazul85k8oK6lYQxOaV2XGOH9eRxMiSpblNadbaDvGOSGkooRsoi1WC15WvJqg2TkMEDrnlR8i7i1W2DgYVHjHSzWkJ49b5xdg8JsbZvglgEqXuEtI07dO4-DiAitvsEv_h6D0wvkXbMbwOZTpW4gavIZdtGFVG2Hve-6gp8uLx-l1dvdwdTM9u8t0XtciM0aUOm-YMDkRojCcVLVRVumiYtZCZamgjGotrC1rQwUY1bC8AQ1VUXDV5DvoaOVdhv51hDjIzqUN2lZ56McoqSh4Mtc1T-jhH3Tej8Gn7SQjtCAF5wVL1PGK0qGPMYCVy-C6FEhSIj_zy5RffuVP7MG3cWw6ML_kT-8EnK6AN9fC-_8meXt9v1J-AMrqkF4</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2014047742</pqid></control><display><type>article</type><title>Determinants of a dietary pattern linked with greater metabolic risk and its tracking during adolescence</title><source>MEDLINE</source><source>Wiley Online Library All Journals</source><creator>Appannah, G. ; Pot, G. K. ; Oddy, W. H. ; Jebb, S. A. ; Ambrosini, G. L.</creator><creatorcontrib>Appannah, G. ; Pot, G. K. ; Oddy, W. H. ; Jebb, S. A. ; Ambrosini, G. L.</creatorcontrib><description>Background
Although growing evidence suggests that dietary patterns associated with noncommunicable diseases in adulthood may develop early in life, when these are established, as well as their determinants, remains unclear.
Methods
We examined determinants and tracking of a dietary pattern (DP) associated with metabolic risk and its key food groups among 860 adolescents in the Western Australian Pregnancy (Raine) Cohort study. Food intake was reported using a food frequency questionnaire (FFQ) at 14 and 17 years. Z‐scores for an ‘energy‐dense, high‐fat, low‐fibre’ DP were estimated by applying reduced rank regression at both ages. Tracking was based on the predictive value (PV) of remaining in the DPZ‐score or food intake quartile at 14 and 17 years. Early‐life exposures included: maternal age; maternal pre‐pregnancy body mass index; parent smoking status during pregnancy; and parent socio‐economic position (SEP) at 14 and 17 years. Associations between the DPZ‐scores, early‐life factors and SEP were analysed using regression analysis.
Results
Dietary tracking was strongest among boys with high DPZ‐scores, high intakes of processed meat, low‐fibre bread, crisps and savoury snacks (PV > 1) and the lowest intakes of vegetables, fruit and legumes. Lower maternal education (β = 0.09, P = 0.002 at 14 years; β = 0.14, P < 0.001 at 17 years) and lower maternal age at birth (β = 0.09, P = 0.003 at 14 years; β = 0.11, P = 0.004 at 17 years) were positively associated with higher DPZ‐scores.
Conclusions
An energy‐dense, high‐fat, low‐fibre dietary pattern tracks more strongly among adolescent boys who have high scores for this pattern at 14 years of age. These findings highlight target foods and population subgroups for early interventions aiming to improve dietary behaviours.</description><identifier>ISSN: 0952-3871</identifier><identifier>EISSN: 1365-277X</identifier><identifier>DOI: 10.1111/jhn.12519</identifier><identifier>PMID: 28975676</identifier><language>eng</language><publisher>England: Blackwell Publishing Ltd</publisher><subject>Adolescence ; Adolescent ; Adolescent Behavior ; Adolescents ; Age ; Australia ; Body Mass Index ; Body size ; Bread ; Cohort Studies ; Diet ; Diet Surveys ; Diet, Western ; dietary patterns ; Family ; Feeding Behavior ; Female ; Food ; Food groups ; Food intake ; Humans ; Legumes ; Male ; Maternal Age ; Meat ; Metabolic Diseases - etiology ; Metabolism ; Mothers ; Noncommunicable Diseases ; Pregnancy ; Raine Study ; Regression analysis ; Sex Factors ; Smoking ; Snack foods ; social determinants ; Socioeconomic Factors ; Subgroups ; Teenagers ; tracking ; Vegetables ; Western Australia</subject><ispartof>Journal of human nutrition and dietetics, 2018-04, Vol.31 (2), p.218-227</ispartof><rights>2017 The British Dietetic Association Ltd.</rights><rights>2018 The British Dietetic Association Ltd</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c3889-dd95c3b29d30994d7068dafac462ffe6f19121cc9ff58d19edab23bece6447ab3</citedby><cites>FETCH-LOGICAL-c3889-dd95c3b29d30994d7068dafac462ffe6f19121cc9ff58d19edab23bece6447ab3</cites><orcidid>0000-0003-4636-6529 ; 0000-0002-6119-7017 ; 0000-0003-1001-4265</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%2Fjhn.12519$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1111%2Fjhn.12519$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,776,780,1411,27901,27902,45550,45551</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/28975676$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Appannah, G.</creatorcontrib><creatorcontrib>Pot, G. K.</creatorcontrib><creatorcontrib>Oddy, W. H.</creatorcontrib><creatorcontrib>Jebb, S. A.</creatorcontrib><creatorcontrib>Ambrosini, G. L.</creatorcontrib><title>Determinants of a dietary pattern linked with greater metabolic risk and its tracking during adolescence</title><title>Journal of human nutrition and dietetics</title><addtitle>J Hum Nutr Diet</addtitle><description>Background
Although growing evidence suggests that dietary patterns associated with noncommunicable diseases in adulthood may develop early in life, when these are established, as well as their determinants, remains unclear.
Methods
We examined determinants and tracking of a dietary pattern (DP) associated with metabolic risk and its key food groups among 860 adolescents in the Western Australian Pregnancy (Raine) Cohort study. Food intake was reported using a food frequency questionnaire (FFQ) at 14 and 17 years. Z‐scores for an ‘energy‐dense, high‐fat, low‐fibre’ DP were estimated by applying reduced rank regression at both ages. Tracking was based on the predictive value (PV) of remaining in the DPZ‐score or food intake quartile at 14 and 17 years. Early‐life exposures included: maternal age; maternal pre‐pregnancy body mass index; parent smoking status during pregnancy; and parent socio‐economic position (SEP) at 14 and 17 years. Associations between the DPZ‐scores, early‐life factors and SEP were analysed using regression analysis.
Results
Dietary tracking was strongest among boys with high DPZ‐scores, high intakes of processed meat, low‐fibre bread, crisps and savoury snacks (PV > 1) and the lowest intakes of vegetables, fruit and legumes. Lower maternal education (β = 0.09, P = 0.002 at 14 years; β = 0.14, P < 0.001 at 17 years) and lower maternal age at birth (β = 0.09, P = 0.003 at 14 years; β = 0.11, P = 0.004 at 17 years) were positively associated with higher DPZ‐scores.
Conclusions
An energy‐dense, high‐fat, low‐fibre dietary pattern tracks more strongly among adolescent boys who have high scores for this pattern at 14 years of age. These findings highlight target foods and population subgroups for early interventions aiming to improve dietary behaviours.</description><subject>Adolescence</subject><subject>Adolescent</subject><subject>Adolescent Behavior</subject><subject>Adolescents</subject><subject>Age</subject><subject>Australia</subject><subject>Body Mass Index</subject><subject>Body size</subject><subject>Bread</subject><subject>Cohort Studies</subject><subject>Diet</subject><subject>Diet Surveys</subject><subject>Diet, Western</subject><subject>dietary patterns</subject><subject>Family</subject><subject>Feeding Behavior</subject><subject>Female</subject><subject>Food</subject><subject>Food groups</subject><subject>Food intake</subject><subject>Humans</subject><subject>Legumes</subject><subject>Male</subject><subject>Maternal Age</subject><subject>Meat</subject><subject>Metabolic Diseases - etiology</subject><subject>Metabolism</subject><subject>Mothers</subject><subject>Noncommunicable Diseases</subject><subject>Pregnancy</subject><subject>Raine Study</subject><subject>Regression analysis</subject><subject>Sex Factors</subject><subject>Smoking</subject><subject>Snack foods</subject><subject>social determinants</subject><subject>Socioeconomic Factors</subject><subject>Subgroups</subject><subject>Teenagers</subject><subject>tracking</subject><subject>Vegetables</subject><subject>Western Australia</subject><issn>0952-3871</issn><issn>1365-277X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp1kMtOxCAUQInR6PhY-AOGxI0uqkAflKUZ3zG60cQdoXBxmGnpCG2Mfy866sJENjeBw8nNQWifkhOazul85k8oK6lYQxOaV2XGOH9eRxMiSpblNadbaDvGOSGkooRsoi1WC15WvJqg2TkMEDrnlR8i7i1W2DgYVHjHSzWkJ49b5xdg8JsbZvglgEqXuEtI07dO4-DiAitvsEv_h6D0wvkXbMbwOZTpW4gavIZdtGFVG2Hve-6gp8uLx-l1dvdwdTM9u8t0XtciM0aUOm-YMDkRojCcVLVRVumiYtZCZamgjGotrC1rQwUY1bC8AQ1VUXDV5DvoaOVdhv51hDjIzqUN2lZ56McoqSh4Mtc1T-jhH3Tej8Gn7SQjtCAF5wVL1PGK0qGPMYCVy-C6FEhSIj_zy5RffuVP7MG3cWw6ML_kT-8EnK6AN9fC-_8meXt9v1J-AMrqkF4</recordid><startdate>201804</startdate><enddate>201804</enddate><creator>Appannah, G.</creator><creator>Pot, G. K.</creator><creator>Oddy, W. H.</creator><creator>Jebb, S. A.</creator><creator>Ambrosini, G. L.</creator><general>Blackwell Publishing Ltd</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>7T7</scope><scope>8FD</scope><scope>C1K</scope><scope>FR3</scope><scope>NAPCQ</scope><scope>P64</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0003-4636-6529</orcidid><orcidid>https://orcid.org/0000-0002-6119-7017</orcidid><orcidid>https://orcid.org/0000-0003-1001-4265</orcidid></search><sort><creationdate>201804</creationdate><title>Determinants of a dietary pattern linked with greater metabolic risk and its tracking during adolescence</title><author>Appannah, G. ; Pot, G. K. ; Oddy, W. H. ; Jebb, S. A. ; Ambrosini, G. L.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3889-dd95c3b29d30994d7068dafac462ffe6f19121cc9ff58d19edab23bece6447ab3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Adolescence</topic><topic>Adolescent</topic><topic>Adolescent Behavior</topic><topic>Adolescents</topic><topic>Age</topic><topic>Australia</topic><topic>Body Mass Index</topic><topic>Body size</topic><topic>Bread</topic><topic>Cohort Studies</topic><topic>Diet</topic><topic>Diet Surveys</topic><topic>Diet, Western</topic><topic>dietary patterns</topic><topic>Family</topic><topic>Feeding Behavior</topic><topic>Female</topic><topic>Food</topic><topic>Food groups</topic><topic>Food intake</topic><topic>Humans</topic><topic>Legumes</topic><topic>Male</topic><topic>Maternal Age</topic><topic>Meat</topic><topic>Metabolic Diseases - etiology</topic><topic>Metabolism</topic><topic>Mothers</topic><topic>Noncommunicable Diseases</topic><topic>Pregnancy</topic><topic>Raine Study</topic><topic>Regression analysis</topic><topic>Sex Factors</topic><topic>Smoking</topic><topic>Snack foods</topic><topic>social determinants</topic><topic>Socioeconomic Factors</topic><topic>Subgroups</topic><topic>Teenagers</topic><topic>tracking</topic><topic>Vegetables</topic><topic>Western Australia</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Appannah, G.</creatorcontrib><creatorcontrib>Pot, G. K.</creatorcontrib><creatorcontrib>Oddy, W. H.</creatorcontrib><creatorcontrib>Jebb, S. A.</creatorcontrib><creatorcontrib>Ambrosini, G. L.</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Industrial and Applied Microbiology Abstracts (Microbiology A)</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>Engineering Research Database</collection><collection>Nursing & Allied Health Premium</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>MEDLINE - Academic</collection><jtitle>Journal of human nutrition and dietetics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Appannah, G.</au><au>Pot, G. K.</au><au>Oddy, W. H.</au><au>Jebb, S. A.</au><au>Ambrosini, G. L.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Determinants of a dietary pattern linked with greater metabolic risk and its tracking during adolescence</atitle><jtitle>Journal of human nutrition and dietetics</jtitle><addtitle>J Hum Nutr Diet</addtitle><date>2018-04</date><risdate>2018</risdate><volume>31</volume><issue>2</issue><spage>218</spage><epage>227</epage><pages>218-227</pages><issn>0952-3871</issn><eissn>1365-277X</eissn><abstract>Background
Although growing evidence suggests that dietary patterns associated with noncommunicable diseases in adulthood may develop early in life, when these are established, as well as their determinants, remains unclear.
Methods
We examined determinants and tracking of a dietary pattern (DP) associated with metabolic risk and its key food groups among 860 adolescents in the Western Australian Pregnancy (Raine) Cohort study. Food intake was reported using a food frequency questionnaire (FFQ) at 14 and 17 years. Z‐scores for an ‘energy‐dense, high‐fat, low‐fibre’ DP were estimated by applying reduced rank regression at both ages. Tracking was based on the predictive value (PV) of remaining in the DPZ‐score or food intake quartile at 14 and 17 years. Early‐life exposures included: maternal age; maternal pre‐pregnancy body mass index; parent smoking status during pregnancy; and parent socio‐economic position (SEP) at 14 and 17 years. Associations between the DPZ‐scores, early‐life factors and SEP were analysed using regression analysis.
Results
Dietary tracking was strongest among boys with high DPZ‐scores, high intakes of processed meat, low‐fibre bread, crisps and savoury snacks (PV > 1) and the lowest intakes of vegetables, fruit and legumes. Lower maternal education (β = 0.09, P = 0.002 at 14 years; β = 0.14, P < 0.001 at 17 years) and lower maternal age at birth (β = 0.09, P = 0.003 at 14 years; β = 0.11, P = 0.004 at 17 years) were positively associated with higher DPZ‐scores.
Conclusions
An energy‐dense, high‐fat, low‐fibre dietary pattern tracks more strongly among adolescent boys who have high scores for this pattern at 14 years of age. These findings highlight target foods and population subgroups for early interventions aiming to improve dietary behaviours.</abstract><cop>England</cop><pub>Blackwell Publishing Ltd</pub><pmid>28975676</pmid><doi>10.1111/jhn.12519</doi><tpages>10</tpages><orcidid>https://orcid.org/0000-0003-4636-6529</orcidid><orcidid>https://orcid.org/0000-0002-6119-7017</orcidid><orcidid>https://orcid.org/0000-0003-1001-4265</orcidid><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0952-3871 |
| ispartof | Journal of human nutrition and dietetics, 2018-04, Vol.31 (2), p.218-227 |
| issn | 0952-3871 1365-277X |
| language | eng |
| recordid | cdi_proquest_miscellaneous_1947099887 |
| source | MEDLINE; Wiley Online Library All Journals |
| subjects | Adolescence Adolescent Adolescent Behavior Adolescents Age Australia Body Mass Index Body size Bread Cohort Studies Diet Diet Surveys Diet, Western dietary patterns Family Feeding Behavior Female Food Food groups Food intake Humans Legumes Male Maternal Age Meat Metabolic Diseases - etiology Metabolism Mothers Noncommunicable Diseases Pregnancy Raine Study Regression analysis Sex Factors Smoking Snack foods social determinants Socioeconomic Factors Subgroups Teenagers tracking Vegetables Western Australia |
| title | Determinants of a dietary pattern linked with greater metabolic risk and its tracking during adolescence |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-07T10%3A56%3A10IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Determinants%20of%20a%20dietary%20pattern%20linked%20with%20greater%20metabolic%20risk%20and%20its%20tracking%20during%20adolescence&rft.jtitle=Journal%20of%20human%20nutrition%20and%20dietetics&rft.au=Appannah,%20G.&rft.date=2018-04&rft.volume=31&rft.issue=2&rft.spage=218&rft.epage=227&rft.pages=218-227&rft.issn=0952-3871&rft.eissn=1365-277X&rft_id=info:doi/10.1111/jhn.12519&rft_dat=%3Cproquest_cross%3E1947099887%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2014047742&rft_id=info:pmid/28975676&rfr_iscdi=true |