Publications by authors named "Iain R Konigsberg"

Objectives: To construct multi-trait polygenic scores (PRS) predicting chronic obstructive pulmonary disease (COPD) and exacerbations, validate their performance in diverse cohorts, and identify PRS-related proteins for potential therapeutic targeting.

Design: Prospective cohort studies.

Setting: Genetic Epidemiology of COPD (COPDGene; 2007-present), Evaluation of COPD Longitudinally to Identify Predictive Surrogate Endpoints (ECLIPSE; 2005-2008), Mass General Brigham Biobank (MGBB; 2010-present), All of Us (2016-present), and UK Biobank (UKB; 2006-present).

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Adoptive cell transfers (ACTs) constitute an emerging platform for improving the systemic delivery of nano- and microparticle systems. Macrophages (Mφ) are an attractive cell type for particle-carrying ACTs because their attachment, phagocytosis, and chemotaxis can improve pharmacokinetics and reduce off-target effects. However, little is known about how macrophage transport and function change when carrying particles of different shapes, or whether these changes can be leveraged for improving ACTs.

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Background: Hypertension (HT) and chronic kidney diseases (CKD) are complex conditions having both genetic and environmental contributions, disproportionately affecting African American (AA) individuals. Recent evidence is contradictory regarding the directionality of the relationship between the two conditions. This study investigates the relationship between CKD and blood pressure (BP)-related traits with CKD and BP by generating polygenic risk scores (PRSs) for CKD and BP-related traits in 2,995 AA participants of the Jackson Heart Study.

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Polygenic risk score (PRS) distributions vary across populations, complicating PRS risk assessment. We evaluated the impact of PRS calibration according to individualized genetic ancestry estimates on PRS performance using two large multi-ethnic PRS for type 2 diabetes (T2D) (PRS) and height (PRS), in 8,841 African American (AA) individuals from the Reasons for Geographic and Racial Differences in Stroke (REGARDS) study. We calibrated each participant's score as a function of estimated genetic similarity to the Yoruba (GSYRI) cohort in the 1000 Genomes Project.

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Population descriptors used in genetic studies have broad social and translational implications. There are no globally agreed-upon definitions or usages of common population descriptors (e.g.

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Background: Chronic obstructive pulmonary disease (COPD) exhibits marked heterogeneity in lung function decline, mortality, exacerbations, and other disease-related outcomes. Omic risk scores (ORS) estimate the cumulative contribution of omics, such as the transcriptome, proteome, and metabolome, to a particular trait. This study evaluates the predictive value of ORS for COPD-related traits in both smoking-enriched and general population cohorts.

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Objective: Exposure to maternal gestational diabetes mellitus (GDM) is associated with childhood BMI. Among youth, we explored whether three different glucagon-like peptide 1 receptor gene (GLP-1R) polymorphisms modified the associations between 1) GDM and BMI trajectories and 2) GDM and markers of glucose-insulin homeostasis.

Research Design And Methods: For 464 participants from the Exploring Perinatal Outcomes Among Children (EPOCH) study, microarray genotyping was performed during childhood (∼10 years).

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Most genetic variants identified through genome-wide association studies (GWASs) are suspected to be regulatory in nature, but only a small fraction colocalize with expression quantitative trait loci (eQTLs, variants associated with expression of a gene). Therefore, it is hypothesized but largely untested that integration of disease GWAS with context-specific eQTLs will reveal the underlying genes driving disease associations. We used colocalization and transcriptomic analyses to identify shared genetic variants and likely causal genes associated with critically ill COVID-19 and idiopathic pulmonary fibrosis.

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Despite considerable advances in identifying risk factors for obesity development, there remains substantial gaps in our knowledge about its etiology. Variation in obesity (defined by BMI) is thought to be due in part to heritable factors; however, obesity-associated genetic variants only account for a small portion of heritability. Epigenetic regulation defined by genetic and/or environmental factors with changes in gene expression, may account for some of this "missing heritability".

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Context: This is the first study to examine the association between variants of the glucagon-like-peptide-1 receptor gene (GLP-1R) and metabolic characteristics among youth.

Objective: We explored separate associations of 3 GLP-1R polymorphisms (rs10305420, rs6923761, and rs1042044) with body mass index (BMI) trajectories and markers of glucose-insulin homeostasis.

Methods: Mixed models examined associations between GLP-1R polymorphisms and trajectories of BMI.

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Our understanding of the origins of noncommunicable diseases has evolved over the years with greater consideration given to the lasting influence exposures and experiences during the preconceptional and prenatal periods can have. Research highlights the associations of parental exposures (e.g.

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Background: Genetic variants and gene expression predict risk of chronic obstructive pulmonary disease (COPD), but their effect on COPD heterogeneity is unclear. We aimed to define high-risk COPD subtypes using genetics (polygenic risk score, PRS) and blood gene expression (transcriptional risk score, TRS) and assess differences in clinical and molecular characteristics.

Methods: We defined high-risk groups based on PRS and TRS quantiles by maximising differences in protein biomarkers in a COPDGene training set and identified these groups in COPDGene and ECLIPSE test sets.

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Article Synopsis
  • Researchers investigated blood protein networks in chronic obstructive pulmonary disease (COPD) using data from over 3,000 participants to better understand complex interconnections rather than just individual biomarker changes.
  • They applied advanced techniques to analyze 4,776 proteins, identifying significant networks linked to factors like smoking status and emphysema.
  • The study found both known and new proteins associated with COPD, highlighting the importance of these networks in understanding the disease across different ethnic groups, with some results replicating in another study cohort.
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Sparse multiple canonical correlation network analysis (SmCCNet) is a machine learning technique for integrating omics data along with a variable of interest (e.g., phenotype of complex disease), and reconstructing multi-omics networks that are specific to this variable.

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Background: Sarcoidosis is a heterogeneous granulomatous disease with no accurate biomarkers of disease progression. Therefore, we profiled and integrated the DNA methylome, mRNAs, and microRNAs to identify molecular changes associated with sarcoidosis and disease progression that might illuminate underlying mechanisms of disease and potential biomarkers.

Methods: Bronchoalveolar lavage cells from 64 sarcoidosis subjects and 16 healthy controls were used.

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Most genetic variants identified through genome-wide association studies (GWAS) are suspected to be regulatory in nature, but only a small fraction colocalize with expression quantitative trait loci (eQTLs, variants associated with expression of a gene). Therefore, it is hypothesized but largely untested that integration of disease GWAS with context-specific eQTLs will reveal the underlying genes driving disease associations. We used colocalization and transcriptomic analyses to identify shared genetic variants and likely causal genes associated with critically ill COVID-19 and idiopathic pulmonary fibrosis.

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Rationale: Genetic variants and gene expression predict risk of chronic obstructive pulmonary disease (COPD), but their effect on COPD heterogeneity is unclear.

Objectives: Define high-risk COPD subtypes using both genetics (polygenic risk score, PRS) and blood gene expression (transcriptional risk score, TRS) and assess differences in clinical and molecular characteristics.

Methods: We defined high-risk groups based on PRS and TRS quantiles by maximizing differences in protein biomarkers in a COPDGene training set and identified these groups in COPDGene and ECLIPSE test sets.

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Background: Studies have identified individual blood biomarkers associated with chronic obstructive pulmonary disease (COPD) and related phenotypes. However, complex diseases such as COPD typically involve changes in multiple molecules with interconnections that may not be captured when considering single molecular features.

Methods: Leveraging proteomic data from 3,173 COPDGene Non-Hispanic White (NHW) and African American (AA) participants, we applied sparse multiple canonical correlation network analysis (SmCCNet) to 4,776 proteins assayed on the SomaScan v4.

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Article Synopsis
  • Aging, HIV infection, and antiretroviral therapy influence the epigenetic profile of skeletal muscle, potentially leading to quicker frailty onset in individuals living with HIV (PWH) right after infection.
  • A study with 24 weeks of aerobic and resistance exercise training found specific DNA methylation changes in the skeletal muscle of older PWH and uninfected controls, highlighting that the impact of HIV on DNA methylation was more significant than that of exercise.
  • Results showed some epigenetic modifications after training but indicated that more extensive research is needed to confirm these findings, especially with larger groups and additional analyses.
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Objective: The degree of sexual dimorphism in certain traits between males and females differ from one sample to another. Although trait differences by sex are often reported in bioanthropological research, few studies test for statistical significance or make raw data available. TestDimorph is the first R package dedicated to testing and comparing the degree of sexual dimorphism among different samples by leveraging summary statistics.

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Obesity has been linked to the gut microbiome, epigenome, and diet, yet these factors have not been studied together during obesity treatment. Our objective was to evaluate associations among gut microbiota (MB), DNA methylation (DNAme), and diet prior to and during a behavioral weight loss intervention. Adults ( = 47, age 40.

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Privacy protection is a core principle of genomic but not proteomic research. We identified independent single nucleotide polymorphism (SNP) quantitative trait loci (pQTL) from COPDGene and Jackson Heart Study (JHS), calculated continuous protein level genotype probabilities, and then applied a naïve Bayesian approach to link SomaScan 1.3K proteomes to genomes for 2812 independent subjects from COPDGene, JHS, SubPopulations and InteRmediate Outcome Measures In COPD Study (SPIROMICS) and Multi-Ethnic Study of Atherosclerosis (MESA).

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Article Synopsis
  • - The study focuses on sarcoidosis, a complex disease that lacks clear diagnostic markers, through the analysis of DNA methylation, mRNA, and microRNA to uncover molecular changes related to the disease and its progression.
  • - Researchers analyzed bronchoalveolar lavage cells from 64 sarcoidosis patients and 16 healthy individuals, using advanced genomic techniques to identify key changes, including 46,812 CpGs, 1,842 mRNAs, and several miRNAs linked to the disease.
  • - The findings highlighted the importance of the PI3K/AKT1 signaling pathway and discovered potential biomarkers that could aid in diagnosing and understanding sarcoidosis, paving the way for future research to verify these results.
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Background/objectives: Obesity, defined as excessive fat accumulation that represents a health risk, is increasing in adults and children, reaching global epidemic proportions. Body mass index (BMI) correlates with body fat and future health risk, yet differs in prediction by fat distribution, across populations and by age. Nonetheless, few genetic studies of BMI have been conducted in ancestrally diverse populations.

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