About

Nima Aghaeepour is a professor at Stanford University, leading the Aghaeepour Laboratory. His research focuses on immunology, with an emphasis on applying computational and data science approaches to understand immune system dynamics. His work involves integrating data analysis, machine learning, and systems biology to advance knowledge in immunological processes and improve clinical outcomes.

Research topics

• Medicine
• Internal medicine
• Biology
• Psychiatry
• Bioinformatics
• Physiology
• Computational biology
• Pediatrics
• Pharmacology
• Intensive care medicine
• Obstetrics
• Pathology
• Genetics

Selected publications

• Mortality Risk Among Patients With COVID-19 Prescribed Selective Serotonin Reuptake Inhibitor Antidepressants

  JAMA Network Open · 2021 · 139 citations

  • Medicine
  • Internal medicine
  • Psychiatry

  Importance: Antidepressant use may be associated with reduced levels of several proinflammatory cytokines suggested to be involved with the development of severe COVID-19. An association between the use of selective serotonin reuptake inhibitors (SSRIs)-specifically fluoxetine hydrochloride and fluvoxamine maleate-with decreased mortality among patients with COVID-19 has been reported in recent studies; however, these studies had limited power due to their small size. Objective: To investigate the association of SSRIs with outcomes in patients with COVID-19 by analyzing electronic health records (EHRs). Design, Setting, and Participants: This retrospective cohort study used propensity score matching by demographic characteristics, comorbidities, and medication indication to compare SSRI-treated patients with matched control patients not treated with SSRIs within a large EHR database representing a diverse population of 83 584 patients diagnosed with COVID-19 from January to September 2020 and with a duration of follow-up of as long as 8 months in 87 health care centers across the US. Exposures: Selective serotonin reuptake inhibitors and specifically (1) fluoxetine, (2) fluoxetine or fluvoxamine, and (3) other SSRIs (ie, not fluoxetine or fluvoxamine). Main Outcomes and Measures: Death. Results: A total of 3401 adult patients with COVID-19 prescribed SSRIs (2033 women [59.8%]; mean [SD] age, 63.8 [18.1] years) were identified, with 470 receiving fluoxetine only (280 women [59.6%]; mean [SD] age, 58.5 [18.1] years), 481 receiving fluoxetine or fluvoxamine (285 women [59.3%]; mean [SD] age, 58.7 [18.0] years), and 2898 receiving other SSRIs (1733 women [59.8%]; mean [SD] age, 64.7 [18.0] years) within a defined time frame. When compared with matched untreated control patients, relative risk (RR) of mortality was reduced among patients prescribed any SSRI (497 of 3401 [14.6%] vs 1130 of 6802 [16.6%]; RR, 0.92 [95% CI, 0.85-0.99]; adjusted P = .03); fluoxetine (46 of 470 [9.8%] vs 937 of 7050 [13.3%]; RR, 0.72 [95% CI, 0.54-0.97]; adjusted P = .03); and fluoxetine or fluvoxamine (48 of 481 [10.0%] vs 956 of 7215 [13.3%]; RR, 0.74 [95% CI, 0.55-0.99]; adjusted P = .04). The association between receiving any SSRI that is not fluoxetine or fluvoxamine and risk of death was not statistically significant (447 of 2898 [15.4%] vs 1474 of 8694 [17.0%]; RR, 0.92 [95% CI, 0.84-1.00]; adjusted P = .06). Conclusions and Relevance: These results support evidence that SSRIs may be associated with reduced severity of COVID-19 reflected in the reduced RR of mortality. Further research and randomized clinical trials are needed to elucidate the effect of SSRIs generally, or more specifically of fluoxetine and fluvoxamine, on the severity of COVID-19 outcomes.

  DOI

• Integrated trajectories of the maternal metabolome, proteome, and immunome predict labor onset

  Science Translational Medicine · 2021 · 142 citations

  • Biology
  • Medicine
  • Computational biology

  = 10, independent test set]. Coordinated alterations in maternal metabolome, proteome, and immunome marked a molecular shift from pregnancy maintenance to prelabor biology 2 to 4 weeks before delivery. A surge in steroid hormone metabolites and interleukin-1 receptor type 4 that preceded labor coincided with a switch from immune activation to regulation of inflammatory responses. Our study lays the groundwork for developing blood-based methods for predicting the day of labor, anchored in mechanisms shared in preterm and term pregnancies.

  DOI

• Multiomics Characterization of Preterm Birth in Low- and Middle-Income Countries

  JAMA Network Open · 2020 · 103 citations

  Senior authorCorresponding
  • Medicine
  • Obstetrics
  • Pediatrics

  Importance: Worldwide, preterm birth (PTB) is the single largest cause of deaths in the perinatal and neonatal period and is associated with increased morbidity in young children. The cause of PTB is multifactorial, and the development of generalizable biological models may enable early detection and guide therapeutic studies. Objective: To investigate the ability of transcriptomics and proteomics profiling of plasma and metabolomics analysis of urine to identify early biological measurements associated with PTB. Design, Setting, and Participants: This diagnostic/prognostic study analyzed plasma and urine samples collected from May 2014 to June 2017 from pregnant women in 5 biorepository cohorts in low- and middle-income countries (LMICs; ie, Matlab, Bangladesh; Lusaka, Zambia; Sylhet, Bangladesh; Karachi, Pakistan; and Pemba, Tanzania). These cohorts were established to study maternal and fetal outcomes and were supported by the Alliance for Maternal and Newborn Health Improvement and the Global Alliance to Prevent Prematurity and Stillbirth biorepositories. Data were analyzed from December 2018 to July 2019. Exposures: Blood and urine specimens that were collected early during pregnancy (median sampling time of 13.6 weeks of gestation, according to ultrasonography) were processed, stored, and shipped to the laboratories under uniform protocols. Plasma samples were assayed for targeted measurement of proteins and untargeted cell-free ribonucleic acid profiling; urine samples were assayed for metabolites. Main Outcomes and Measures: The PTB phenotype was defined as the delivery of a live infant before completing 37 weeks of gestation. Results: Of the 81 pregnant women included in this study, 39 had PTBs (48.1%) and 42 had term pregnancies (51.9%) (mean [SD] age of 24.8 [5.3] years). Univariate analysis demonstrated functional biological differences across the 5 cohorts. A cohort-adjusted machine learning algorithm was applied to each biological data set, and then a higher-level machine learning modeling combined the results into a final integrative model. The integrated model was more accurate, with an area under the receiver operating characteristic curve (AUROC) of 0.83 (95% CI, 0.72-0.91) compared with the models derived for each independent biological modality (transcriptomics AUROC, 0.73 [95% CI, 0.61-0.83]; metabolomics AUROC, 0.59 [95% CI, 0.47-0.72]; and proteomics AUROC, 0.75 [95% CI, 0.64-0.85]). Primary features associated with PTB included an inflammatory module as well as a metabolomic module measured in urine associated with the glutamine and glutamate metabolism and valine, leucine, and isoleucine biosynthesis pathways. Conclusions and Relevance: This study found that, in LMICs and high PTB settings, major biological adaptations during term pregnancy follow a generalizable model and the predictive accuracy for PTB was augmented by combining various omics data sets, suggesting that PTB is a condition that manifests within multiple biological systems. These data sets, with machine learning partnerships, may be a key step in developing valuable predictive tests and intervention candidates for preventing PTB.

  DOI

• Discovery and validation of biomarkers to aid the development of safe and effective pain therapeutics: challenges and opportunities

  Nature Reviews Neurology · 2020 · 475 citations

  • Medicine
  • Intensive care medicine
  • Pharmacology

  Pain medication plays an important role in the treatment of acute and chronic pain conditions, but some drugs, opioids in particular, have been overprescribed or prescribed without adequate safeguards, leading to an alarming rise in medication-related overdose deaths. The NIH Helping to End Addiction Long-term (HEAL) Initiative is a trans-agency effort to provide scientific solutions to stem the opioid crisis. One component of the initiative is to support biomarker discovery and rigorous validation in collaboration with industry leaders to accelerate high-quality clinical research into neurotherapeutics and pain. The use of objective biomarkers and clinical trial end points throughout the drug discovery and development process is crucial to help define pathophysiological subsets of pain, evaluate target engagement of new drugs and predict the analgesic efficacy of new drugs. In 2018, the NIH-led Discovery and Validation of Biomarkers to Develop Non-Addictive Therapeutics for Pain workshop convened scientific leaders from academia, industry, government and patient advocacy groups to discuss progress, challenges, gaps and ideas to facilitate the development of biomarkers and end points for pain. The outcomes of this workshop are outlined in this Consensus Statement.

  DOI

Recent grants

• Machine Learning for Integrative Modeling of the Immune System in Clinical Settings

  NIH · $2.2M · 2020–2025

• Neuropathology of synapses in AD and ADRD

  NIH · $2.2M · 2022–2025

Frequent coauthors

• Ryan R. Brinkman

  Terry Fox Research Institute

  202 shared

• Richard H. Scheuermann

  National Institutes of Health

  140 shared

• Raphaël Gottardo

  138 shared

• Tim R. Mosmann

  University of Rochester Medical Center

  138 shared

• Greg Finak

  Harvard University

  137 shared

• David Smith

  128 shared

• J Craig

  University of California, San Diego

  128 shared

• Brice Gaudillière

  Stanford University

  110 shared

Labs

• Aghaeepour LaboratoryPI

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