M. D. Hill’s research while affiliated with Indiana University School of Medicine - Lafayette and other places

What is this page?


This page lists works of an author who doesn't have a ResearchGate profile or hasn't added the works to their profile yet. It is automatically generated from public (personal) data to further our legitimate goal of comprehensive and accurate scientific recordkeeping. If you are this author and want this page removed, please let us know.

Publications (1)


Steps 1-3: Discovery, Prioritization and Validation of Biomarkers for Hallucinations and Delusions
A, F Cohorts used in study, depicting flow of discovery, prioritization, and validation of biomarkers from each step. B, G Discovery cohort longitudinal within-subject analysis. Phchp### is study ID for each subject. V# denotes visit number. C, H Prioritization using Convergent Functional Genomics (CFG). D, I Validation -biomarkers are assessed for stepwise change from discovery subjects with no symptoms, high symptoms to the validation subjects with clinically severe symptoms, using ANOVA. The histograms depict a top increased and a top decreased biomarker (E, J). Number of probesets and scoring at each of the Steps. Step 1 -Discovery probesets are identified based on their score for tracking symptoms and ranked 33% (2 pt), 50% (4 pt) and 80% (6 pt). Step 2- Prioritization with CFG for prior evidence of involvement in psychotic disorders. Maximum of 12 pt. Genes scoring at least 6 pt out of a maximum possible of 18 pt after Discovery and Prioritization are carried forward to the validation step. Step 3- Validation in an independent cohort of psychiatric patients with clinically severe hallucination (P3 or P1 ≥ 4, PANSS ≥ 21) We selected the top CFE score ≥ 14 (n = 98 for hallucinations, n = 70 for delusions) for further testing and characterization.
Best single biomarkers predictors for state and trait
A–C Hallucinations (D, E, F). Delusions. From top candidate biomarkers after Steps 1–3 (Discovery, Prioritization, Validation) (n = 98 for hallucinations, n = 70 for delusions). Bar graph shows nominally significant predictive biomarkers in each group. Table underneath the figures displays the actual number of biomarkers for each group whose ROC AUC p values (A, B, D, E) and Cox Odds Ratio p values (C, F) are at least nominally significant. Some gender and diagnosis group are missing from the graph as they did not have any significant biomarkers. Cross-sectional is based on levels at one visit. Longitudinal is based on levels at multiple visits (integrates levels at most recent visit, maximum levels, slope into most recent visit, and maximum slope). Dividing lines represent the cutoffs for a test performing at chance levels (white), and at the same level as the best biomarkers for all subjects in cross-sectional (gray) and longitudinal (black) based predictions. Biomarkers perform better than chance. Biomarkers performed better when personalized by gender and diagnosis. *nominally significant with p < 0.05 ** survived Bonferroni correction for the number of candidate biomarkers tested.
Example of prototype report for physicians
A Hallucinations. B Delusions. Using a panel of the best predictive biomarkers for state and trait in all and by gender. The raw expression values of the biomarkers in the 794 microarrays gene expression database were Z-scored by gender. For state score, the Z-scored expression value of each increased biomarker was compared to the average value for the biomarker in the high hallucinations group (P3, hallucinations ≥4), and the average value of the no hallucinations group (P3, hallucination = 1) resulting in scores of 1 or 0 respectively, and 0.5 if it was in between. The reverse was done for decreased biomarkers. For trait chronic risk score, we calculated the average expression value for a biomarker in the first-year hospitalizations group, and in the not hospitalized in the first-year group, and for all future hospitalizations, and no future hospitalizations. The digitized biomarkers were then added into a polygenic risk score. A similar approach was done for delusions. The digitized biomarkers were also used for matching with existing psychiatric medications and alternative treatments (nutraceuticals and others). We used our large datasets and literature databases to match biomarkers to medications that had effects on gene expression opposite to their expression in high hallucinations or delusions. Each medication matched to a biomarker got the biomarker score of 1, 0.5 or 0. The scores for the medications were added, normalized for the number of biomarkers that were 1 or 0.5 in that patient, resulting in a percentile match. Subject 297v1 is a 54 year old African-American male with a diagnosis of schizoaffective disorder. He had a high CFI-SZ score of 90, indicating he had been severely ill in the past. At the time of testing, his PANSS P3 Hallucinations items was 1, and his P1 delusions item was 1, indicating no symptoms by clinical assessment. His psychiatric medications were divalproex 1250 mg at bedtime and fluphenazine 7.5 mg at bedtime. His nutraceuticals were cyanocobalamin 500 mcg daily, vitamin E 400 units daily, and a multivitamin. The reports indicate that he was a good match for valproate, a medication he was on, and while externally he was rated low on PANSS items, he still had significant internal levels of biomarkers of disease severity.
Precision medicine for psychotic disorders: objective assessment, risk prediction, and pharmacogenomics
  • Article
  • Publisher preview available

February 2024

·

254 Reads

·

9 Citations

Molecular Psychiatry

M. D. Hill

·

·

·

[...]

·

A. B. Niculescu

Psychosis occurs inside the brain, but may have external manifestations (peripheral molecular biomarkers, behaviors) that can be objectively and quantitatively measured. Blood biomarkers that track core psychotic manifestations such as hallucinations and delusions could provide a window into the biology of psychosis, as well as help with diagnosis and treatment. We endeavored to identify objective blood gene expression biomarkers for hallucinations and delusions, using a stepwise discovery, prioritization, validation, and testing in independent cohorts design. We were successful in identifying biomarkers that were predictive of high hallucinations and of high delusions states, and of future psychiatric hospitalizations related to them, more so when personalized by gender and diagnosis. Top biomarkers for hallucinations that survived discovery, prioritization, validation and testing include PPP3CB, DLG1, ENPP2, ZEB2, and RTN4. Top biomarkers for delusions include AUTS2, MACROD2, NR4A2, PDE4D, PDP1, and RORA. The top biological pathways uncovered by our work are glutamatergic synapse for hallucinations, as well as Rap1 signaling for delusions. Some of the biomarkers are targets of existing drugs, of potential utility in pharmacogenomics approaches (matching patients to medications, monitoring response to treatment). The top biomarkers gene expression signatures through bioinformatic analyses suggested a prioritization of existing medications such as clozapine and risperidone, as well as of lithium, fluoxetine, valproate, and the nutraceuticals omega-3 fatty acids and magnesium. Finally, we provide an example of how a personalized laboratory report for doctors would look. Overall, our work provides advances for the improved diagnosis and treatment for schizophrenia and other psychotic disorders.

View access options

Citations (1)


... Although efforts are being made to minimize bias and enhance objectivity through observation and standardized assessment procedures (e.g., Positive and Negative Syndrome Scale; PANSS [7]), the diagnosis process can be time-consuming and subject to potential errors based on the clinician's expertise and patient rapport. Moreover, while recent research has explored using blood biomarkers to diagnose psychiatric disorders, this approach requires costly laboratory testing, which may not be accessible in some areas of the world [32]. ...

Reference:

Heart2Mind: Human-Centered Contestable Psychiatric Disorder Diagnosis System using Wearable ECG Monitors
Precision medicine for psychotic disorders: objective assessment, risk prediction, and pharmacogenomics

Molecular Psychiatry