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Femke Lamers

Researcher at Public Health Research Institute

Publications -  185
Citations -  8645

Femke Lamers is an academic researcher from Public Health Research Institute. The author has contributed to research in topics: Anxiety & Major depressive disorder. The author has an hindex of 43, co-authored 147 publications receiving 6028 citations. Previous affiliations of Femke Lamers include VU University Medical Center & Maastricht University.

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Comorbidity Patterns of Anxiety and Depressive Disorders in a Large Cohort Study: the Netherlands Study of Depression and Anxiety (NESDA)

TL;DR: Comorbidity rates in anxiety and depressive disorders were very high, indicating that it is advisable to assess both disorders routinely regardless of the primary reason for consultation, especially important since comorbid patients showed a specific vulnerability pattern, with more childhood trauma, neuroticism, and higher severity and duration of symptoms.
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Understanding the somatic consequences of depression: biological mechanisms and the role of depression symptom profile

TL;DR: The heterogeneity of the depression concept seems to play a differentiating role: metabolic syndrome and inflammation up-regulations appear more specific to the atypical depression subtype, whereas hypercortisolemia appears more specific for melancholic depression.
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Evidence for a differential role of HPA-axis function, inflammation and metabolic syndrome in melancholic versus atypical depression

TL;DR: It is confirmed that chronic forms of the two major subtypes of depression are associated with different biological correlates with inflammatory and metabolic dysregulation in atypical depression and HPA-axis hyperactivity in melancholic depression.
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Identifying depressive subtypes in a large cohort study: results from the Netherlands Study of Depression and Anxiety (NESDA).

TL;DR: In this article, the heterogeneity of depression in the current classification system remains a point of discussion in the psychiatric field, despite previous efforts to subclassify depressive disorders, and data-driven techniques may help to come to a more empirically based classification.