Blood Test Predicts Personalized Depression Treatment
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By LabMedica International staff writers Posted on 22 Jun 2016 |

Image: The Agilent Bioanalyzer microfluidics-based platform (Photo courtesy of Agilent Technologies).
A blood test has been developed that accurately and reliably predicts whether depressed patients will respond to common antidepressants, which could herald a new era of personalized treatment for people with depression.
Guided by this test, patients with blood inflammation above a certain threshold could be directed towards earlier access to more assertive antidepressant strategies, such as a combination of antidepressants, before their condition worsens.
Scientists at King’s College London (UK) focused on two biomarkers in the blood that measure inflammation, as previous studies have already shown that elevated levels of inflammation are associated with poor response to antidepressants. They measured the quantity of two biomarkers, Macrophage Migration Inhibitory Factor (MIF) and interleukin (IL)-1β, in two independent clinical samples of depressed patients, before or after they took a range of commonly prescribed antidepressants.
The ribonucleic acid (RNA) quantity was assessed by evaluation of the A260/280 and A260/230 ratios using a Nanodrop spectrometer (NanoDrop Technologies, Wilmington, DE, USA), and RNA quality was determined using an Agilent Bioanalyzer (Agilent Technologies, Santa Clara, CA, USA). RNA samples were then stored at -80 °C until their processing for gene expression analyses.
The investigators r found that blood test results above a specified threshold level could precisely and reliably predict the probability of individuals responding to the treatments. Patients with levels of MIF and IL-1β above the thresholds showed a 100% chance of not responding to conventional, commonly prescribed antidepressants. Those with inflammation below the suggested threshold could be expected to respond to first-line antidepressants.
The two biomarkers examined in the study are both thought to be important in predicting how people with depression respond to antidepressants, as they are involved in several brain mechanisms relevant to depression. These include the birth of new brain cells and connections between them, as well as the death of brain cells through a process called oxidative stress. Oxidative stress occurs when the body both overproduces and then struggles to remove molecules called free radicals. These free radicals break down brain connections and disrupt the brain's chemical signaling, which in turn can lead to the development of depressive symptoms by reducing the brain's protective mechanisms.
Annamaria Cattaneo, PhD, the senior author of the study, said, “'This is the first time a blood test has been used to precisely predict, in two independent clinical groups of depressed patients, the response to a range of commonly prescribed antidepressants. These results also confirm and extend the mounting evidence that high levels of inflammation induce a more severe form of depression, which is less likely to respond to common antidepressants.” The study was published on May 11, 2016, in the journal The International Journal of Neuropsychopharmacology.
Related Links:
King’s College London
NanoDrop Technologies
Agilent Technologies
Guided by this test, patients with blood inflammation above a certain threshold could be directed towards earlier access to more assertive antidepressant strategies, such as a combination of antidepressants, before their condition worsens.
Scientists at King’s College London (UK) focused on two biomarkers in the blood that measure inflammation, as previous studies have already shown that elevated levels of inflammation are associated with poor response to antidepressants. They measured the quantity of two biomarkers, Macrophage Migration Inhibitory Factor (MIF) and interleukin (IL)-1β, in two independent clinical samples of depressed patients, before or after they took a range of commonly prescribed antidepressants.
The ribonucleic acid (RNA) quantity was assessed by evaluation of the A260/280 and A260/230 ratios using a Nanodrop spectrometer (NanoDrop Technologies, Wilmington, DE, USA), and RNA quality was determined using an Agilent Bioanalyzer (Agilent Technologies, Santa Clara, CA, USA). RNA samples were then stored at -80 °C until their processing for gene expression analyses.
The investigators r found that blood test results above a specified threshold level could precisely and reliably predict the probability of individuals responding to the treatments. Patients with levels of MIF and IL-1β above the thresholds showed a 100% chance of not responding to conventional, commonly prescribed antidepressants. Those with inflammation below the suggested threshold could be expected to respond to first-line antidepressants.
The two biomarkers examined in the study are both thought to be important in predicting how people with depression respond to antidepressants, as they are involved in several brain mechanisms relevant to depression. These include the birth of new brain cells and connections between them, as well as the death of brain cells through a process called oxidative stress. Oxidative stress occurs when the body both overproduces and then struggles to remove molecules called free radicals. These free radicals break down brain connections and disrupt the brain's chemical signaling, which in turn can lead to the development of depressive symptoms by reducing the brain's protective mechanisms.
Annamaria Cattaneo, PhD, the senior author of the study, said, “'This is the first time a blood test has been used to precisely predict, in two independent clinical groups of depressed patients, the response to a range of commonly prescribed antidepressants. These results also confirm and extend the mounting evidence that high levels of inflammation induce a more severe form of depression, which is less likely to respond to common antidepressants.” The study was published on May 11, 2016, in the journal The International Journal of Neuropsychopharmacology.
Related Links:
King’s College London
NanoDrop Technologies
Agilent Technologies
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