The ability of Interleukin-10 to negate haemozoin-related pro-inflammatory effects has the potential to restore impaired macrophage function associated with malaria infection.

BACKGROUND: Although pro-inflammatory cytokines are involved in the clearance of Plasmodium falciparum during the early stages of the infection, increased levels of these cytokines have been implicated in the pathogenesis of severe malaria. Amongst various parasite-derived inducers of inflammation, the malarial pigment haemozoin (Hz), which accumulates in monocytes, macrophages and other immune cells during infection, has been shown to significantly contribute to dysregulation of the normal inflammatory cascades. METHODS: The direct effect of Hz-loading on cytokine production by monocytes and the indirect effect of Hz on cytokine production by myeloid cells was investigated during acute malaria and convalescence using archived plasma samples from studies investigating P. falciparum malaria pathogenesis in Malawian subjects. Further, the possible inhibitory effect of IL-10 on Hz-loaded cells was examined, and the proportion of cytokine-producing T-cells and monocytes during acute malaria and in convalescence was characterized. RESULTS: Hz contributed towards an increase in the production of inflammatory cytokines, such as Interferon Gamma (IFN-γ), Tumor Necrosis Factor (TNF) and Interleukin 2 (IL-2) by various cells. In contrast, the cytokine IL-10 was observed to have a dose-dependent suppressive effect on the production of TNF among other cytokines. Cerebral malaria (CM) was characterized by impaired monocyte functions, which normalized in convalescence. CM was also characterized by reduced levels of IFN-γ-producing T cell subsets, and reduced expression of immune recognition receptors HLA-DR and CD 86, which also normalized in convalescence. However, CM and other clinical malaria groups were characterized by significantly higher plasma levels of pro-inflammatory cytokines than healthy controls, implicating anti-inflammatory cytokines in balancing the immune response. CONCLUSIONS: Acute CM was characterized by elevated plasma levels of pro-inflammatory cytokines and chemokines but lower proportions of cytokine-producing T-cells and monocytes that normalize during convalescence. IL-10 is also shown to have the potential to indirectly prevent excessive inflammation. Cytokine production dysregulated by the accumulation of Hz appears to impair the balance of the immune response to malaria and exacerbates pathology.

PheWAS analysis on large-scale biobank data with PheTK.

Summary: With the rapid growth of genetic data linked to electronic health record data in huge cohorts, large-scale phenome-wide association study (PheWAS), have become powerful discovery tools in biomedical research. PheWAS is an analysis method to study phenotype associations utilizing longitudinal electronic health record (EHR) data. Previous PheWAS packages were developed mostly in the days of smaller biobanks and with earlier PheWAS approaches. PheTK was designed to simplify analysis and efficiently handle biobank-scale data. PheTK uses multithreading and supports a full PheWAS workflow including extraction of data from OMOP databases and Hail matrix tables as well as PheWAS analysis for both phecode version 1.2 and phecodeX. Benchmarking results showed PheTK took 64% less time than the R PheWAS package to complete the same workflow. PheTK can be run locally or on cloud platforms such as the All of Us Researcher Workbench ( All of Us ) or the UK Biobank (UKB) Research Analysis Platform (RAP). Availability and implementation: The PheTK package is freely available on the Python Package Index (PyPi) and on GitHub under GNU Public License (GPL-3) at https://github.com/nhgritctran/PheTK . It is implemented in Python and platform independent. The demonstration workspace for All of Us will be made available in the future as a featured workspace. Contact: PheTK@mail.nih.gov.

Comparison of phenomic profiles in the All of Us Research Program against the US general population and the UK Biobank.

IMPORTANCE: Knowledge gained from cohort studies has dramatically advanced both public and precision health. The All of Us Research Program seeks to enroll 1 million diverse participants who share multiple sources of data, providing unique opportunities for research. It is important to understand the phenomic profiles of its participants to conduct research in this cohort. OBJECTIVES: More than 280 000 participants have shared their electronic health records (EHRs) in the All of Us Research Program. We aim to understand the phenomic profiles of this cohort through comparisons with those in the US general population and a well-established nation-wide cohort, UK Biobank, and to test whether association results of selected commonly studied diseases in the All of Us cohort were comparable to those in UK Biobank. MATERIALS AND METHODS: We included participants with EHRs in All of Us and participants with health records from UK Biobank. The estimates of prevalence of diseases in the US general population were obtained from the Global Burden of Diseases (GBD) study. We conducted phenome-wide association studies (PheWAS) of 9 commonly studied diseases in both cohorts. RESULTS: This study included 287 012 participants from the All of Us EHR cohort and 502 477 participants from the UK Biobank. A total of 314 diseases curated by the GBD were evaluated in All of Us, 80.9% (N = 254) of which were more common in All of Us than in the US general population [prevalence ratio (PR) >1.1, P < 2 × 10-5]. Among 2515 diseases and phenotypes evaluated in both All of Us and UK Biobank, 85.6% (N = 2152) were more common in All of Us (PR >1.1, P < 2 × 10-5). The Pearson correlation coefficients of effect sizes from PheWAS between All of Us and UK Biobank were 0.61, 0.50, 0.60, 0.57, 0.40, 0.53, 0.46, 0.47, and 0.24 for ischemic heart diseases, lung cancer, chronic obstructive pulmonary disease, dementia, colorectal cancer, lower back pain, multiple sclerosis, lupus, and cystic fibrosis, respectively. DISCUSSION: Despite the differences in prevalence of diseases in All of Us compared to the US general population or the UK Biobank, our study supports that All of Us can facilitate rapid investigation of a broad range of diseases. CONCLUSION: Most diseases were more common in All of Us than in the general US population or the UK Biobank. Results of disease-disease association tests from All of Us are comparable to those estimated in another well-studied national cohort.

Drug-Induced Liver Injury with Commonly Used Antibiotics in the All of Us Research Program.

Antibiotics are a known cause of idiosyncratic drug-induced liver injury (DILI). According to the Centers for Disease Control and Prevention, the five most commonly prescribed antibiotics in the United States are azithromycin, ciprofloxacin, cephalexin, amoxicillin, and amoxicillin-clavulanate. We quantified the frequency of acute DILI for these common antibiotics in the All of Us Research Program, one of the largest electronic health record (EHR)-linked research cohorts in the United States. Retrospective analyses were conducted applying a standardized phenotyping algorithm to de-identified clinical data available in the All of Us database for 318,598 study participants. Between February 1984 and December 2022, more than 30% of All of Us participants (n = 119,812 individuals) had been exposed to at least 1 of our 5 study drugs. Initial screening identified 591 potential case patients that met our preselected laboratory-based phenotyping criteria. Because DILI is a diagnosis of exclusion, we then used phenome scanning to narrow the case counts by (i) scanning all EHRs to identify all alternative diagnostic explanations for the laboratory abnormalities, and (ii) leveraging International Classification of Disease 9th revision (ICD)-9 and ICD 10th revision (ICD)-10 codes as exclusion criteria to eliminate misclassification. Our final case counts were 30 DILI cases with amoxicillin-clavulanate, 24 cases with azithromycin, 24 cases with ciprofloxacin, 22 cases with amoxicillin alone, and < 20 cases with cephalexin. These findings demonstrate that data from EHR-linked research cohorts can be efficiently mined to identify DILI cases related to the use of common antibiotics.

Systematic replication of smoking disease associations using survey responses and EHR data in the All of Us Research Program.

OBJECTIVE: The All of Us Research Program (All of Us) aims to recruit over a million participants to further precision medicine. Essential to the verification of biobanks is a replication of known associations to establish validity. Here, we evaluated how well All of Us data replicated known cigarette smoking associations. MATERIALS AND METHODS: We defined smoking exposure as follows: (1) an EHR Smoking exposure that used International Classification of Disease codes; (2) participant provided information (PPI) Ever Smoking; and, (3) PPI Current Smoking, both from the lifestyle survey. We performed a phenome-wide association study (PheWAS) for each smoking exposure measurement type. For each, we compared the effect sizes derived from the PheWAS to published meta-analyses that studied cigarette smoking from PubMed. We defined two levels of replication of meta-analyses: (1) nominally replicated: which required agreement of direction of effect size, and (2) fully replicated: which required overlap of confidence intervals. RESULTS: PheWASes with EHR Smoking, PPI Ever Smoking, and PPI Current Smoking revealed 736, 492, and 639 phenome-wide significant associations, respectively. We identified 165 meta-analyses representing 99 distinct phenotypes that could be matched to EHR phenotypes. At P < .05, 74 were nominally replicated and 55 were fully replicated. At P < 2.68 × 10-5 (Bonferroni threshold), 58 were nominally replicated and 40 were fully replicated. DISCUSSION: Most phenotypes found in published meta-analyses associated with smoking were nominally replicated in All of Us. Both survey and EHR definitions for smoking produced similar results. CONCLUSION: This study demonstrated the feasibility of studying common exposures using All of Us data.