BiOmarkers tO perSonalise Treatment of depression (BOOST depression)

Major depressive disorder (MDD) is a severely debilitating psychiatric disorder
affecting 550.000 individuals in the Netherlands each year and is the leading
cause of disability worldwide. Treatment options (including pharmacological
treatment with a selective serotonin reuptake inhibitor (SSRI), cognitive
behavioural therapy (CBT), repetitive transcranial magnetic stimulation (rTMS),
intranasal esketamine, and electroconvulsive therapy (ECT) only result in
30-50% of patients achieving clinical response. Each subsequent treatment
attempt can become less successful, which increases the risk for chronic
depression and suicidality.
Uncovering biomarkers predictive of treatment outcome would help us circumvent
current trial and error care based on a one-size fits- all stepped care
protocols. While previous re-search has shown that various behavioral/clinical
and biological factors at baseline are related to eventual treatment response
across the five treatments mentioned above, these studies are performed on
small samples and/or not validated with independent methods, limit-ing the
reliability of the associations and preventing utilization in clinical care.
This study aims to discover and validate biological (MRI, EEG, (epi)genetics)
and clinical markers (questionnaires) (together henceforth called biomarkers)
obtained from patients with MDD that are related to treatment outcome. We will
develop a machine learning model that can perform differential predictions of
treatment outcome with these biomarkers for possible use in future care. This
could replace current trial-and-error treatment practice, which would have an
enormous benefit for patients and society, could reduce the number of
ineffective treatments and suffering of unneeded side-effects, and could
improve our understanding of the biological and clinical factors underlying
effective treatments in depression.

Our primary objective in this study is to predict treatment outcome for SSRI,
CBT, rTMS, esketamine, and ECT in patients with MDD with the use of biomarkers
and machine learning using data obtained before a new or follow-up treatment.
Our secondary objective is to identify the neurobiological mechanisms
underlying treatment response using data obtained before and after treatment.

We will use a longitudinal parallel group design. Patients with a verified
diagnosis of MDD will receive one of the five investigated new or follow-up
treatments as usual in accordance with national guidelines. We will acquire
biological and clinical data from each participant before and after treatment.
In order to control for test-retest effects in the longitudinal analysis of
neuroimaging data, we will additionally recruit a group of matched healthy
controls who will also be investigated twice at a comparable interval.

As this is a prospective non-interventional study, the burden for participants
is limited to time investment, which includes filling in questionnaires (40
minutes at baseline, 5 minutes every two weeks, ~0.5 hours at 3-month
follow-up, 5 minutes at 6-month follow-up) and obtaining a saliva sample at
home, and an on-site visit for additional questionnaires, physical
measurements, and neuroimaging (~2/2.5 hours at baseline and ~2 hours at
3-month follow-up).
In addition, participants can voluntarily decide if they want to invest
additional time (approximately 40 minutes) through opt-in procedures for the
ecological momentary assessment (EMA; 8 times a day for 6 days, 1 minute per
administration at home).
We have attempted to reduce the time burden where possible, and most of the
study procedures can be performed at home at the participant*s own pace. Study
procedures and associated time burden have been constructed and approved by
representatives from our consortium partner De Depressievereniging serving the
interest of patients with depression.
The study entails no physical risk, as the MRI scan will be performed by
trained personnel in hospitals/research institutions. There is no (clinical)
benefit for participation, but the data acquired from the study may greatly
improve personalized treatments for future patients.

Study procedures and the expected time burden are as follows:
• Questionnaires:
Participants will be asked to complete several standardised
questionnaires regarding participation eligibility, demographic measures, and
clinical measures of symptom occurrence and severity one week prior to the
start of their treatment. It is expected to take a total of 1.5 hours to fill
out these questionnaires (40 minutes at home, 50 minutes on site), of which 7
out of 12 questionnaires can be filled out at home in the participant*s own
time, spread out over the 7 days prior to treatment. The MINI-S, HDRS-17, HAM-A
DM-TRD and C-SSRS need to be assessed by a trained and qualified employee
during the first site visit. The biweekly repeated online questionnaire QIDS-SR
for three-month follow-up takes approximately 5 minutes. A final QIDS-SR will
be assessed after 6 month for long term efficacy assessment.
• Neuroimaging:
Neuroimaging data will be obtained by trained personnel with the use of
EEG and MRI. Acquisition of neuroimaging data including preparation time and
instructions is expected to take approximately one hour and fifteen minutes.
• Saliva sampling
Participants will be asked to spit in a sample tube for the collection
of genetic material. This will be done at home in the morning on an empty
stomach for accurate epigenetic measurement.
• Physical measurements
We will measure the participant*s height, weight, heart rate, and blood
pressure.


Opt-in study procedures:
• EMA:
The EMA measures daily affective fluctuations and will be performed
with the online smartphone app M-path (https://m-path.io/) installed on the
participant*s mobile phone. Participants will receive a notification to
complete a 10-Item PANAS short-form questionnaire at random timepoints 8 times
a day for 6 days. Affective dynamics have previously been associated with
depressive symptoms and treatment response in similar EMA designs [17, 18].
While the number of assessments per day may be a time burden for the
participants, the time it takes to complete the 10 items in each assessment is
short (approximately 1 minute).