Literature DB >> 1432845

A clinical and biological validation of the DSM-III melancholia diagnosis in men: results of pattern recognition methods.

M Maes1, L Maes, C Schotte, P Cosyns.   

Abstract

Pattern recognition methods were carried out on a sample of 80 depressed men, assessed by means of 14 items relevant to depressive symptomatology of the Structured Clinical Interview for DSM-III-R. 1985 edition (SCID). A cluster analysis generated two classes, which were described as a vital (n = 35) and a nonvital cluster (n = 45). Vital depressives were characterized by psychomotor disorders, loss of energy, cognitive disturbances, a distinct quality of mood, early morning awakening and nonreactivity (the "vital" symptoms). Our findings support the descriptive validity of the DSM-III melancholia diagnostic category, although the DSM-III criteria are too conservative and include nonrelevant symptoms (e.g., diurnal variation, anorexia-weight loss) whilst excluding some important items (e.g., loss of energy, cognitive disorders). Vital depressed men were significantly older, more severely depressed and they exhibited biological disturbances (abnormal dexamethasone suppression test, lower basal thyroid secreting hormone) as opposed to nonvital depressives. There are several arguments to support the possibility that both clusters constitute relevant stages in the overall severity of illness continuum, whilst showing qualitative differences with regard to the vital symptoms. In other words, both clusters are continuous categories within the overall severity of illness continuum and form discrete categories with regard to the vital symptoms. By merging the dimensional and categorical hypotheses, we were able to construct a new integrated threshold model: unipolar depression in men is probably a homogeneous disease with reference to overall severity of illness, but--as severity increases--vital symptoms emerge, grouping together into a distinct profile, i.e., vital depression.

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Year:  1992        PMID: 1432845     DOI: 10.1016/0022-3956(92)90022-g

Source DB:  PubMed          Journal:  J Psychiatr Res        ISSN: 0022-3956            Impact factor:   4.791


  9 in total

1.  Subtypes of major depression: latent class analysis in depressed Han Chinese women.

Authors:  Y Li; S Aggen; S Shi; J Gao; Y Li; M Tao; K Zhang; X Wang; C Gao; L Yang; Y Liu; K Li; J Shi; G Wang; L Liu; J Zhang; B Du; G Jiang; J Shen; Z Zhang; W Liang; J Sun; J Hu; T Liu; X Wang; G Miao; H Meng; Y Li; C Hu; Y Li; G Huang; G Li; B Ha; H Deng; Q Mei; H Zhong; S Gao; H Sang; Y Zhang; X Fang; F Yu; D Yang; T Liu; Y Chen; X Hong; W Wu; G Chen; M Cai; Y Song; J Pan; J Dong; R Pan; W Zhang; Z Shen; Z Liu; D Gu; X Wang; X Liu; Q Zhang; J Flint; K S Kendler
Journal:  Psychol Med       Date:  2014-04-09       Impact factor: 7.723

2.  Development of a Novel Neuro-immune and Opioid-Associated Fingerprint with a Cross-Validated Ability to Identify and Authenticate Unknown Patients with Major Depression: Far Beyond Differentiation, Discrimination, and Classification.

Authors:  Hussein Kadhem Al-Hakeim; Suhaer Zeki Al-Fadhel; Arafat Hussein Al-Dujaili; Andre Carvalho; Sira Sriswasdi; Michael Maes
Journal:  Mol Neurobiol       Date:  2019-05-23       Impact factor: 5.590

3.  Deficit schizophrenia is a discrete diagnostic category defined by neuro-immune and neurocognitive features: results of supervised machine learning.

Authors:  Buranee Kanchanatawan; Sira Sriswasdi; Supaksorn Thika; Sunee Sirivichayakul; André F Carvalho; Michel Geffard; Marta Kubera; Michael Maes
Journal:  Metab Brain Dis       Date:  2018-03-11       Impact factor: 3.584

4.  Complex Regional Pain Syndrome: Practical Diagnostic and Treatment Guidelines, 5th Edition.

Authors:  R Norman Harden; Candida S McCabe; Andreas Goebel; Michael Massey; Tolga Suvar; Sharon Grieve; Stephen Bruehl
Journal:  Pain Med       Date:  2022-06-10       Impact factor: 3.637

5.  Genome-wide polygenic scoring for a 14-year long-term average depression phenotype.

Authors:  Shun-Chiao Chang; M Maria Glymour; Stefan Walter; Liming Liang; Karestan C Koenen; Eric J Tchetgen; Marilyn C Cornelis; Ichiro Kawachi; Eric Rimm; Laura D Kubzansky
Journal:  Brain Behav       Date:  2014-02-12       Impact factor: 2.708

Review 6.  Beyond Lumping and Splitting: A Review of Computational Approaches for Stratifying Psychiatric Disorders.

Authors:  Andre F Marquand; Thomas Wolfers; Maarten Mennes; Jan Buitelaar; Christian F Beckmann
Journal:  Biol Psychiatry Cogn Neurosci Neuroimaging       Date:  2016-09

7.  Identifying subtypes of depression in clinician-annotated text: a retrospective cohort study.

Authors:  Benson Kung; Maurice Chiang; Gayan Perera; Megan Pritchard; Robert Stewart
Journal:  Sci Rep       Date:  2021-11-17       Impact factor: 4.379

8.  Identifying the Subtypes of Major Depressive Disorder Based on Somatic Symptoms: A Longitudinal Study Using Latent Profile Analysis.

Authors:  Xiaohui Wu; Yuncheng Zhu; Zhiguo Wu; Jia Huang; Lan Cao; Yun Wang; Yousong Su; Hongmei Liu; Maosheng Fang; Zhijian Yao; Zuowei Wang; Fan Wang; Yong Wang; Daihui Peng; Jun Chen; Yiru Fang
Journal:  Front Psychiatry       Date:  2022-07-12       Impact factor: 5.435

Review 9.  Data-driven subtypes of major depressive disorder: a systematic review.

Authors:  Hanna M van Loo; Peter de Jonge; Jan-Willem Romeijn; Ronald C Kessler; Robert A Schoevers
Journal:  BMC Med       Date:  2012-12-04       Impact factor: 8.775

  9 in total

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