Literature DB >> 17612419

Random-mood interpretation of determinants for major depression.

Kirsten I Kaptein1, Peter De Jonge, Jakob Korf, Jan Spijker, Ron De Graaf, Siebren Y Van Der Werf.   

Abstract

BACKGROUND: It has recently been proposed that major depression disorder (MDD) may, in a heterogeneous population-based cohort, be interpreted in terms of a random-mood model. Mood fluctuations are thought to result from stressors that occur randomly in time. We have investigated whether this concept also holds for more homogeneous groups, defined by known determinants for MDD, and whether the model's parameters, susceptibility (Z) and relaxation time (T), may be evaluated and used to differentiate between subcohorts.
METHOD: From a large epidemiological survey, the Netherlands Mental Health Survey and Incidence Study (NEMESIS), data on the duration of MDD were obtained for subcohorts, based on gender, severity of depression, recurrence and co-morbidity with dysthymia, anxiety and somatic disorder, and were compared with random-mood simulation calculations.
RESULTS: Susceptibility, Z, is empirically found to be proportional to incidence and may be identified with a risk ratio. A second scaling rule states the proportionality of mean duration with the product of Z and T. This Z-T classification proves to be more sensitive than conventional significance tests. Notably for men/women and for co-morbid anxiety, differences are seen that have previously gone unnoticed.
CONCLUSIONS: Depression may be conceptualized as a disorder resulting from random-mood fluctuations, the response to which is influenced by a large variety of determinants or risk factors. The model's parameters can be evaluated and may be used in differentiating between risk factor-defined subgroups.

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Year:  2007        PMID: 17612419     DOI: 10.1017/S0033291707001018

Source DB:  PubMed          Journal:  Psychol Med        ISSN: 0033-2917            Impact factor:   7.723


  2 in total

1.  The possibility of evidence-based psychiatry: depression as a case.

Authors:  Drozdstoy Stojanov; Jakob Korf; Peter de Jonge; Georgi Popov
Journal:  Clin Epigenetics       Date:  2010-11-30       Impact factor: 6.551

2.  Data-driven classification of bipolar I disorder from longitudinal course of mood.

Authors:  A L Cochran; M G McInnis; D B Forger
Journal:  Transl Psychiatry       Date:  2016-10-11       Impact factor: 6.222

  2 in total

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