{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,24]],"date-time":"2026-07-24T20:31:24Z","timestamp":1784925084589,"version":"3.55.0"},"reference-count":63,"publisher":"SAGE Publications","issue":"8","license":[{"start":{"date-parts":[[2022,10,1]],"date-time":"2022-10-01T00:00:00Z","timestamp":1664582400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Child and Adolescent Psychopharmacology"],"published-print":{"date-parts":[[2022,10]]},"abstract":"<jats:sec>\n                    <jats:title>Objectives:<\/jats:title>\n                    <jats:p>To assess characteristics and correlates of metabolic syndrome (MetS) in adolescents with major depressive disorder (MDD) or bipolar disorder-depressive episode (BP-d).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods:<\/jats:title>\n                    <jats:p>Case\u2013control study, using national inpatient sample data, including adolescents (age, 12\u201318 years) with a primary diagnosis of MDD or BP-d. Using propensity score matching (based on age, sex, and race\/ethnicity), we extracted cases with MetS (\u22653 of the following conditions: obesity, diabetes, hypercholesterolemia, and hypertension) and controls without MetS. We used a multivariable logistic regression model calculating adjusted odds ratios (aORs) for potential correlates of MetS, focusing on primary mood disorders and psychiatric comorbidities.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results:<\/jats:title>\n                    <jats:p>\n                      In 607 age-\/sex-\/race\/ethnicity-matched adolescents (MDD\u2009=\u200983.5%, BP-d\u2009=\u200916.5%), comparing those with (\n                      <jats:italic toggle=\"yes\">N<\/jats:italic>\n                      \u2009=\u2009332) versus without MetS (\n                      <jats:italic toggle=\"yes\">N<\/jats:italic>\n                      \u2009=\u2009275), MetS was most prevalent in later-age adolescents (mean age 16.3 years), females (58.1%), Whites (40.3%), and Blacks (31.5%). MetS was characterized by obesity (84.9% vs. 3.6%), hypertension (81% vs. 1.8%), diabetes (72.8% vs. 9.1%), and hypercholesterolemia (67.2% vs. 3.6%) (all\n                      <jats:italic toggle=\"yes\">p<\/jats:italic>\n                      \u2009&lt;\u20090.001). MetS was associated with a primary diagnosis of BP-d versus MDD (aOR 2.42, 95% confidence interval [CI] 1.47\u20133.97) and comorbid disruptive behavior disorders (DBD) (aOR 4.45, 95% CI 1.55\u201312.78), while comorbid substance use disorder reduced MetS risk (aOR 0.31, 95% CI 0.19\u20130.50).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion:<\/jats:title>\n                    <jats:p>In adolescents with MDD or BP-d, MetS was associated with a primary BP-d diagnosis, and comorbid DBD. MetS-related parameters should be screened for early in adolescents with depression-spectrum disorders aiming to prevent the development or effects of MetS.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1089\/cap.2022.0017","type":"journal-article","created":{"date-parts":[[2022,10,25]],"date-time":"2022-10-25T13:08:25Z","timestamp":1666703305000},"page":"426-433","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":3,"title":["Characteristics and Correlates of Metabolic Syndrome in Adolescents with Unipolar and Bipolar Depression: Results from a Cross-National Inpatient Case\u2013Control Study"],"prefix":"10.1177","volume":"32","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2895-9155","authenticated-orcid":false,"given":"Rikinkumar S.","family":"Patel","sequence":"first","affiliation":[{"name":"Department of Child and Adolescent Psychiatry, Duke University Medical Center, Durham, North Carolina, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pradipta","family":"Majumder","sequence":"additional","affiliation":[{"name":"Department of Psychiatry, WellSpan Health, York, Pennsylvania, USA."},{"name":"Department of Behavioral Health, Drexel University College of Medicine, Philadelphia, Pennsylvania, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christoph U.","family":"Correll","sequence":"additional","affiliation":[{"name":"Department of Psychiatry, The Zucker Hillside Hospital, Northwell Health, Glen Oaks, New York, USA."},{"name":"Department of Psychiatry and Molecular Medicine, Donald and Barbara Zucker School of Medicine at Hofstra\/Northwell, Hempstead, New York, USA."},{"name":"Department of Child and Adolescent Psychiatry, Charit\u00e9 Universit\u00e4tsmedizin Berlin, Berlin, Germany."}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2022,10,17]]},"reference":[{"key":"e_1_3_3_2_1","unstructured":"NIS Description of Data Elements. 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