Targeting the Microbiome to Improve Clinical Outcomes in Bipolar Disorder
Targeting the Microbiome to Improve Clinical Outcomes in Bipolar Disorder
The objective of this study is to determine how specific dietary control alters the microbiome composition to effect clinical outcome measures in a longitudinal study of individuals with bipolar disorder. Our central hypothesis is that a low carbohydrate (CHO) / high polyunsaturated fat (PUFA) diet will increase the fractional representation of specific butyrate producing members of the Firmicutes phylum in the gut microbiome, which will attenuate host inflammation, improve sleep quality and reduce anxiety in bipolar patients. The rationale for the proposed research is to take the first step in a continuum of studies to develop personalized novel approaches to treat mood disorders, including the need to address gut dysbiosis, which often co-occurs with mental illness. The investigators will test our hypothesis and achieve the objective of this proposal with the following Specific Aims: 1) Determine the taxonomical change in the stool microbiome following a low CHO / high PUFA diet; and 2) Determine the changes in sleep quality, anxiety, and depression following a low CHO / high PUFA diet. These aims will be achieved using the unique resources at the University of Michigan, including the Nutrition Assessment Laboratory for dietary intervention, the Host-Microbiome Laboratory for microbial assays, and the ongoing Prechter Longitudinal Study of Bipolar Disorder. At the end of the proposed studies the investigators expect to set the stage for future studies to assess neurochemical mechanisms. These data will provide a greater understanding of the mechanism by which diet controls the specific microbes in the gut microbiome to affect mood disorders and gut dysbiosis and improve response to psychiatric treatment paradigms.
RECRUITING AND INCENTIVIZING RESEARCH PARTICIPANTS
For the proposed study, the investigators will recruit research participants from the Prechter Longitudinal Study of Bipolar Disorder, housed at the University of Michigan under the direction of Dr. Melvin McInnis, mentor on this application. The Prechter study is in its 10th year with over 1200 participants, including psychiatric and healthy control individuals. the investigators have successfully recruited from this study in the past to perform an observational dietary study and single point microbiomic analysis described in the Previous Work section. The investigators will leverage the infrastructure of the study with 50% dedicated time for a research assistant to manage recruitment, scheduling, follow up and data entry duties. The investigators will also leverage 5% dedicated time for the study coordinator, Gloria Harrington, who oversees all administrative aspects of the Prechter Study, including assurance of regulatory compliance.
The investigators will recruit 20 males and 20 females from the Prechter cohort, aged 25-65 with a confirmed diagnosis of Bipolar I disorder from a diagnostic interview for genetic studies (DIGS), used as the baseline intake instrument for the Prechter study. Eligible subjects will be consented and scheduled for an intake interview and assigned a unique identification number for tracking with a secure laboratory information management system. Subjects will be offered a monetary incentive of $200, paid in installments after completion of study milestones activities.
DETERMINING HABITUAL DIETS
The investigators will determine the habitual baseline diets of all subjects using a 3 non consecutive days over 1 week with an automated online 24-hour diet recall (ASA24 from the National Cancer Institute). Subjects will meet with a certified nutritionist from the Nutrition Assessment Laboratory in the Nutrition and Obesity Research Center at the University of Michigan during the consent visit to explain how to use the recall appropriately. After 1 week, subjects will return for to the MCRU for baseline biological samples and dietary guidance as detailed below.
COLLECTING BIOLOGICAL SAMPLES, MORPHOMETRIC DATA AND STANDARD HEALTH MEASURES
During the baseline visit (following the week involving dietary recalls), all subjects will donate baseline fasted blood samples for free fatty acid analysis; a urine sample for analysis of oxidative stress markers; and have their height, weight, waist and hip circumference, and blood pressure recorded and be asked to complete the interviewer guided Hamilton Depression scale questionnaire, the Young Mania Rating Scale questionnaire and the Global Physical Activity Questionnaire. Participants will also complete a brief 25-35 minute neuropsychological test battery during their baseline and final visit. Subjects will also return a stool sample, which will be collected at home with a supplied collection kit. Subjects will repeat all procedures at the end of the 1st and 4th week of the dietary protocol. These procedures as well as the dietary interviews will occur in the Michigan Clinical Research Unit at the University of Michigan, staffed with phlebotomists, nurses, nutritionists and technicians with expertise in all proposed procedures; and equipment needed to process the samples. Finally, subjects will be asked about all prescription and non-prescription medications taken over the previous week.
DIETARY INTERVENTION
For the diet intervention, subjects will be guided by a certified nutritionist to achieve a target macronutrient composition (by total calorie contribution) of 15% protein, 45% carbohydrate, 40% fat, with <10% saturated fat, ~15% PUFA and ~15% monounsaturated fatty acids (MUFA). Specific targeted profiles are given in Table 4. For reference, The investigators also show the cognate values attained from a 7-day observational study of 91 individuals with bipolar disorder described above. Comparing the target and previously determined habitual values from the same population, the major change to the diet will be a near doubling of PUFA intake and a small increase in MUFA intake, at the expense of reducing carbohydrates and saturated fat, which is a realistic and achievable goal.
The investigators will achieve the diet with specific menus, considering personal preferences gleaned from the habitual diet analysis described. The investigators will provide subjects with specific cooking oils for addition to foods prepared at home, to achieve the oleic (OLA), LA and α-linolenic (LNA) targets; and fish-oil supplements to achieve the DHA and EPA targets. Subjects will also be provided a list of foods to eat and avoid (tailored from their habitual diet records), as well as several menu items from chain restaurants to eat for meals unavoidably eaten away from home. Subjects will be trained to report on their diets using the ASA24. Records will be reviewed at least weekly by research staff and assessed for deviation from the diet outside of threshold ranges (as average daily intake) of at least 12% PUFA and no more than 50% carbohydrate. In parallel to this project the investigators will develop python scripts to extract data to automate the process, however, the investigators will not depend on success of that goal in order to meet the objectives of this project. Values falling outside the set thresholds will be brought to the subjects' attention with suggestions for correction. Importantly, since plasma LA correlates strongly with dietary intake, objective measures of dietary adherence to PUFA intake will be available upon completion of free fatty acid analysis from fasting blood samples, which can be leveraged as covariates in statistical models. Finally, the investigators will avoid November - January for the dietary interventions, as diets tend to be non-habitual during this period.
Following the 4-week dietary manipulation, subjects will be encouraged to maintain the high PUFA diet for 6 months. Due to limited resources, The investigators will not continue to collect stool samples following the 4-week period (although other funding will be pursued to do so). However, the investigators will continue to encourage subjects to use the ASA24 weekly. The investigators will also continue to monitor self-report measures as a component of the longitudinal study, requiring no additional resources from this application.
Dr.Evans (Principal Investigator) has established a good relationship with the research staff in the Nutrition Assessment Laboratory (NAL) during past studies, described above, and has developed the plans for the proposed dietary manipulation with Theresa Han-Markey, RD, Bionutrition Manager of the NAL, who's services are budgeted into the proposed study. Dr. Evans has also recently completed formal training in nutritional sciences as a component of a K01 career development award and will be intimately involved in the dietary studies. Furthermore, Dr. Charles Burant, Director of the Nutrition and Obesity Research Center and previous mentor for Dr. Evans K01 award, will consult on these studies (see letter of support).
INTERROGATING THE TAXONOMICAL COMPOSITION OF THE STOOL MICROBIOME
Home OmniGene Gut stool collection kits from DNA Genotek (Ottawa, CA) will be used to collect samples at baseline, week 1 and week 4 of the dietary period. These kits were used successfully to generate preliminary data describe above, and have the advantage of maintaining sample DNA stability for 14 days without freezing. Subjects will return the completed kits at the scheduled study visits. Methods for identifying microbial complements in the samples have been previously described in a publication by the HMI Laboratory at the University of Michigan. Briefly, microbial DNA will be extracted from stool samples and processed for 16S ribosomal sequencing using a dual-indexing sequencing strategy. Sequencing will be done on the Illumina MiSeq platform, according to the manufacturer's instructions. Analysis of the 16S rRNA gene will be done using an SOP (www.mothur.org/wiki/MiSeq\_SOP) to process the MiSeq data. Sequence classifications will be determined by comparing sequences to the Ribosomal Database Project and sequences will be clustered into OTUs.
COLLECTION OF SELF-REPORTED HEALTH MEASURES
Longitudinal participants currently complete self-rated questionnaires by mail every 2 months. For the current study the investigators use average responses previously collected as baseline measures and new responses collected at the completion of the diet period as outcome measures. These questionnaires include the PHQ-9, the PSQI, the LFQ, the Altman Self-Rating Mania Scale (ASRM), the Short Form 12 Health Survey (SF-12), and the Generalized Anxiety Disorder Assessment (GAD-7).
DATA ANALYSIS
Data analysis has 2 major phases. The first is generating normalized summary scores from the various measures. The second is using statistical models to test for main effects of diet. Summary of questionnaire scores and anthropomorphic measures are standard and not further detailed here. Summary of microbiomic data sets will be done as described above and OTUs output in 2 ways: 1) as a fractional representation table, where each OTU is expressed as a fraction of the total OTUs per sample; and 2) as total sequence counts, where each OTU is represented as total abundance. Both will be tested in statistical analyses.
For the second phase the investigators will use two main statistical models: 1) regression analyses to test for main effects of diet on OTU level microbiome data and self-reported burden of disease measures, adjusting for age, sex and BMI; and 2) structured equation modeling analysis to test for mediation effects of OTUs on the relationship between diet and clinical outcomes. The investigators will achieve the latter approach as diagrammed in Figure 2. Main effect of diet will be tested on plasma PUFA (primarily LA), microbiome OTUs, and self-reported health measures, depicted by the dotted line. The cross-lag model, using SEM, will test for statistically significant reciprocal effects, shown by the bold arrows. For example, the investigators will test if increase in plasma LA accounts for the effect (fully or partially) in changes in microbiome OTUs, and if changes in OTUs account for effects on self-reported outcome measures.
Inclusion Criteria:
Exclusion Criteria: