{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,16]],"date-time":"2026-02-16T08:21:16Z","timestamp":1771230076787,"version":"3.50.1"},"reference-count":69,"publisher":"Cambridge University Press (CUP)","license":[{"start":{"date-parts":[[2020,3,23]],"date-time":"2020-03-23T00:00:00Z","timestamp":1584921600000},"content-version":"unspecified","delay-in-days":1177,"URL":"https:\/\/www.cambridge.org\/core\/terms"},{"start":{"date-parts":[[2017,1,1]],"date-time":"2017-01-01T00:00:00Z","timestamp":1483228800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"}],"content-domain":{"domain":["europsy-journal.com","clinicalkey.jp","clinicalkey.com","clinicalkey.es","clinicalkey.com.au","clinicalkey.fr","em-consulte.com","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Eur. psychiatr."],"published-print":{"date-parts":[[2017,1]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec id=\"S0924933800068334_abst0005\" sec-type=\"other\"><jats:title>Background<\/jats:title><jats:p>Key lifestyle-environ risk factors are operative for depression, but it is unclear how risk factors cluster. Machine-learning (ML) algorithms exist that learn, extract, identify and map underlying patterns to identify groupings of depressed individuals without constraints. The aim of this research was to use a large epidemiological study to identify and characterise depression clusters through \u201cGraphing lifestyle-environs using machine-learning methods\u201d (GLUMM).<\/jats:p><\/jats:sec><jats:sec id=\"S0924933800068334_abst0010\" sec-type=\"methods\"><jats:title>Methods<\/jats:title><jats:p>Two ML algorithms were implemented: unsupervised Self-organised mapping (SOM) to create GLUMM clusters and a supervised boosted regression algorithm to describe clusters. Ninety-six \u201clifestyle-environ\u201d variables were used from the National health and nutrition examination study (2009\u20132010). Multivariate logistic regression validated clusters and controlled for possible sociodemographic confounders.<\/jats:p><\/jats:sec><jats:sec id=\"S0924933800068334_abst0015\" sec-type=\"results\"><jats:title>Results<\/jats:title><jats:p>The SOM identified two GLUMM cluster solutions. These solutions contained one dominant depressed cluster (GLUMM5-1, GLUMM7-1). Equal proportions of members in each cluster rated as highly depressed (17%). Alcohol consumption and demographics validated clusters. Boosted regression identified GLUMM5-1 as more informative than GLUMM7-1. Members were more likely to: have problems sleeping; unhealthy eating; \u2264 2\u00a0years in their home; an old home; perceive themselves underweight; exposed to work fumes; experienced sex at \u2264 14\u00a0years; not perform moderate recreational activities. A positive relationship between GLUMM5-1 (OR: 7.50, <jats:italic>P<\/jats:italic> &lt; 0.001) and GLUMM7-1 (OR: 7.88, <jats:italic>P<\/jats:italic> &lt; 0.001) with depression was found, with significant interactions with those married\/living with partner (<jats:italic>P<\/jats:italic> = 0.001).<\/jats:p><\/jats:sec><jats:sec id=\"S0924933800068334_abst0020\" sec-type=\"conclusions\"><jats:title>Conclusion<\/jats:title><jats:p>Using ML based GLUMM to form ordered depressive clusters from multitudinous lifestyle-environ variables enabled a deeper exploration of the heterogeneous data to uncover better understandings into relationships between the complex mental health factors.<\/jats:p><\/jats:sec>","DOI":"10.1016\/j.eurpsy.2016.06.003","type":"journal-article","created":{"date-parts":[[2016,11,1]],"date-time":"2016-11-01T16:00:18Z","timestamp":1478016018000},"page":"40-50","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":25,"title":["Why so GLUMM? 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