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Blood Test Predicts Which Antidepressant Will Work

Blood Test Predicts Which Antidepressant Will Work post image

New blood tests could take the guessing out of antidepressant prescriptions.

Up until now doctors have essentially been guessing which antidepressant medication might work for patients.

But now researchers have identified a blood test that could help.

Dr Madhukar Trivedi, who led the research, said:

“Currently, our selection of depression medications is not any more superior than flipping a coin, and yet that is what we do.

Now we have a biological explanation to guide treatment of depression.”

The blood test measures the levels of C-reactive protein (CRP).

They tested the connection between CRP and two antidepressants called escitalopram (‎Cipralex, Lexapro etc.) and bupropion (Wellbutrin, Elontril, Zyban etc.).

They found that CRP levels could help predict which combination would work.

Dr Trivedi thinks the same approach could be used with other antidepressants.

CRP is a measure of inflammation in the body, which studies suggest is linked to depression.

CRP levels are not the only biological markers that might provide clues as to which antidepressant will work.

Dr Trivedi said:

“Both patients and primary-care providers are very desperately looking for markers that would indicate there is some biology involved in this disease.

Otherwise, we are talking about deciding treatments from question-and-answer from the patients, and that is not sufficient.”

Giving up

Currently, around one-third of patients do not improve after taking the first medication they are prescribed.

Fully 40% of people given antidepressants stop taking them within three months.

Dr Trivedi said:

“This outcome happens because they give up.

Giving up hope is really a central symptom of the disease.

However, if treatment selection is tied to a blood test and improves outcomes, patients are more likely to continue the treatment and achieve the benefit.”

The study was published in the journal Psychoneuroendocrinology (Jha et al., 2017).