BACKGROUND: Common genetic variants, such as the brain-derived neurotrophic factor (BDNF) Val/66/Met polymorphism (rs6265), are known to interact with environmental factors such as early adversity to increase the risk of subsequent major depression. Much less is known about how they interact with individual differences in cortisol, although these also represent a risk for major depression. AIMS: To determine whether this BDNF variant moderated the risk represented by higher levels of morning salivary cortisol in adult women. METHOD: We recruited 279 premenopausal women who were at high risk of major depressive disorder because of either negative self-evaluation, unsupportive core relationship or chronic subclinical symptoms of depression or anxiety. Morning salivary cortisol was measured daily for up to 10 days at entry. Participants were followed up for about 12 months by telephone calls at 3-4 monthly intervals. Major depression and severe life events were assessed through interviews at baseline and follow-up; DNA was obtained from the saliva. RESULTS: There were 53 onsets (19%) of depressive episodes during follow-up. There was a significant U-shaped relationship between adjusted morning cortisol levels at baseline and the probability of depression onset during follow-up. In total, 51% experienced at least one severe life event/difficulty, and this strongly predicted subsequent onsets of depressive episodes. The BDNF Val/66/Met genotype was not directly associated with onsets of depression or with cortisol levels, but there was significant interaction between Val/66/Met and cortisol: the association between baseline cortisol and depression was limited to those with the Val/66/Val variant. There was no interaction between life events and either this BDNF polymorphism or cortisol levels. CONCLUSIONS: Morning salivary cortisol interacts with the BDNF Val/66/Met polymorphism in predicting new depressive episodes. This paper adds to the evidence that single gene polymorphisms interact with endogenous factors to predict depression.
BACKGROUND: Common genetic variants, such as the brain-derived neurotrophic factor (BDNF) Val/66/Met polymorphism (rs6265), are known to interact with environmental factors such as early adversity to increase the risk of subsequent major depression. Much less is known about how they interact with individual differences in cortisol, although these also represent a risk for major depression. AIMS: To determine whether this BDNF variant moderated the risk represented by higher levels of morning salivary cortisol in adult women. METHOD: We recruited 279 premenopausal women who were at high risk of major depressive disorder because of either negative self-evaluation, unsupportive core relationship or chronic subclinical symptoms of depression or anxiety. Morning salivary cortisol was measured daily for up to 10 days at entry. Participants were followed up for about 12 months by telephone calls at 3-4 monthly intervals. Major depression and severe life events were assessed through interviews at baseline and follow-up; DNA was obtained from the saliva. RESULTS: There were 53 onsets (19%) of depressive episodes during follow-up. There was a significant U-shaped relationship between adjusted morning cortisol levels at baseline and the probability of depression onset during follow-up. In total, 51% experienced at least one severe life event/difficulty, and this strongly predicted subsequent onsets of depressive episodes. The BDNFVal/66/Met genotype was not directly associated with onsets of depression or with cortisol levels, but there was significant interaction between Val/66/Met and cortisol: the association between baseline cortisol and depression was limited to those with the Val/66/Val variant. There was no interaction between life events and either this BDNF polymorphism or cortisol levels. CONCLUSIONS: Morning salivary cortisol interacts with the BDNFVal/66/Met polymorphism in predicting new depressive episodes. This paper adds to the evidence that single gene polymorphisms interact with endogenous factors to predict depression.
Authors: G De Foubert; S L Carney; C S Robinson; E J Destexhe; R Tomlinson; C A Hicks; T K Murray; J P Gaillard; C Deville; V Xhenseval; C E Thomas; M J O'Neill; T S C Zetterström Journal: Neuroscience Date: 2004 Impact factor: 3.590
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