Genome-wide association study identifies multiple HLA loci for sarcoidosis susceptibility.

Sarcoidosis is a complex systemic disease. Our study aimed to (1) identify novel alleles associated with sarcoidosis susceptibility; (2) provide an in-depth evaluation of HLA alleles and sarcoidosis susceptibility and (3) integrate genetic and transcription data to identify risk loci that may more directly impact disease pathogenesis. We report a genome-wide association study of 1335 sarcoidosis cases and 1264 controls of European descent (EA) and investigate associated alleles in a study of African Americans (AA: 1487 cases and 1504 controls). The EA and AA cohort was recruited from multiple United States sites. HLA alleles were imputed and tested for association with sarcoidosis susceptibility. Expression quantitative locus and colocalization analysis were performed using a subset of subjects with transcriptome data. Forty-nine SNPs in the HLA region in HLA-DRA, -DRB9, -DRB5, -DQA1 and BRD2 genes were significantly associated with sarcoidosis susceptibility in EA, rs3129888 was also a risk variant for sarcoidosis in AA. Classical HLA alleles DRB1*0101, DQA1*0101 and DQB1*0501, which are highly correlated, were also associated with sarcoidosis. rs3135287 near HLA-DRA was associated with HLA-DRA expression in peripheral blood mononuclear cells and bronchoalveolar lavage from subjects and lung tissue and whole blood from GTEx. We identified six novel SNPs (out of the seven SNPs representing the 49 significant SNPs) and nine HLA alleles associated with sarcoidosis susceptibility in the largest EA population. We also replicated our findings in an AA population. Our study reiterates the potential role of antigen recognition and/or presentation HLA class II genes in sarcoidosis pathogenesis.

The neutrophil to lymphocyte ratio associates with markers of Alzheimer's disease pathology in cognitively unimpaired elderly people.

BACKGROUND: An elevated neutrophil-lymphocyte ratio (NLR) in blood has been associated with Alzheimer's disease (AD). However, an elevated NLR has also been implicated in many other conditions that are risk factors for AD, prompting investigation into whether the NLR is directly linked with AD pathology or a result of underlying comorbidities. Herein, we explored the relationship between the NLR and AD biomarkers in the cerebrospinal fluid (CSF) of cognitively unimpaired (CU) subjects. Adjusting for sociodemographics, APOE4, and common comorbidities, we investigated these associations in two cohorts: the Alzheimer's Disease Neuroimaging Initiative (ADNI) and the M.J. de Leon CSF repository at NYU. Specifically, we examined associations between the NLR and cross-sectional measures of amyloid-ß42 (Aß42), total tau (t-tau), and phosphorylated tau181 (p-tau), as well as the trajectories of these CSF measures obtained longitudinally. RESULTS: A total of 111 ADNI and 190 NYU participants classified as CU with available NLR, CSF, and covariate data were included. Compared to NYU, ADNI participants were older (73.79 vs. 61.53, p < 0.001), had a higher proportion of males (49.5% vs. 36.8%, p = 0.042), higher BMIs (27.94 vs. 25.79, p < 0.001), higher prevalence of hypertensive history (47.7% vs. 16.3%, p < 0.001), and a greater percentage of Aß-positivity (34.2% vs. 20.0%, p = 0.009). In the ADNI cohort, we found cross-sectional associations between the NLR and CSF Aß42 (ß = -12.193, p = 0.021), but not t-tau or p-tau. In the NYU cohort, we found cross-sectional associations between the NLR and CSF t-tau (ß = 26.812, p = 0.019) and p-tau (ß = 3.441, p = 0.015), but not Aß42. In the NYU cohort alone, subjects classified as Aß + (n = 38) displayed a stronger association between the NLR and t-tau (ß = 100.476, p = 0.037) compared to Aß- subjects or the non-stratified cohort. In both cohorts, the same associations observed in the cross-sectional analyses were observed after incorporating longitudinal CSF data. CONCLUSIONS: We report associations between the NLR and Aß42 in the older ADNI cohort, and between the NLR and t-tau and p-tau in the younger NYU cohort. Associations persisted after adjusting for comorbidities, suggesting a direct link between the NLR and AD. However, changes in associations between the NLR and specific AD biomarkers may occur as part of immunosenescence.

Antidepressant exposure and long-term dementia risk in a nationwide retrospective study on US veterans with midlife major depressive disorder.

INTRODUCTION: The use of antidepressants in major depressive disorder (MDD) has been reported to influence long-term risk of Alzheimer's disease (AD) and AD-related dementias (AD/ADRD), but studies are conflicting. METHODS: We used inverse probability weighted (IPW) Cox models with time-varying covariates in a retrospective cohort study among midlife veterans with MDD within the US Veterans Affairs healthcare system from January 1, 2000 to June 1, 2022. RESULTS: A total of 35,200 patients with MDD were identified. No associations were seen regarding the effect of being exposed to any antidepressant versus no exposure on AD/ADRD risk (events = 1,056, hazard ratio = 0.94, 95% confidence interval: 0.81 to 1.09) or the exposure to specific antidepressant classes versus no exposure. A risk reduction was observed for female patients in a stratified analysis; however, the number of cases was small. DISCUSSION: Our study suggests that antidepressant exposure has no effect on AD/ADRD risk. The association in female patients should be interpreted with caution and requires further attention. HIGHLIGHTS: We studied whether antidepressant use was associated with future dementia risk. We specifically focused on patients after their first-ever diagnosis of depression. We used IPW Cox models with time-varying covariates and a large observation window. Our study did not identify an effect of antidepressant use on dementia risk. A risk reduction was observed in female patients, but the number of cases was small.

Soluble receptor for advanced glycation end products (sRAGE) as a biomarker of COPD.

BACKGROUND: Soluble receptor for advanced glycation end products (sRAGE) is a proposed emphysema and airflow obstruction biomarker; however, previous publications have shown inconsistent associations and only one study has investigate the association between sRAGE and emphysema. No cohorts have examined the association between sRAGE and progressive decline of lung function. There have also been no evaluation of assay compatibility, receiver operating characteristics, and little examination of the effect of genetic variability in non-white population. This manuscript addresses these deficiencies and introduces novel data from Pittsburgh COPD SCCOR and as well as novel work on airflow obstruction. A meta-analysis is used to quantify sRAGE associations with clinical phenotypes. METHODS: sRAGE was measured in four independent longitudinal cohorts on different analytic assays: COPDGene (n = 1443); SPIROMICS (n = 1623); ECLIPSE (n = 2349); Pittsburgh COPD SCCOR (n = 399). We constructed adjusted linear mixed models to determine associations of sRAGE with baseline and follow up forced expiratory volume at one second (FEV1) and emphysema by quantitative high-resolution CT lung density at the 15th percentile (adjusted for total lung capacity). RESULTS: Lower plasma or serum sRAGE values were associated with a COPD diagnosis (P < 0.001), reduced FEV1 (P < 0.001), and emphysema severity (P < 0.001). In an inverse-variance weighted meta-analysis, one SD lower log10-transformed sRAGE was associated with 105 ± 22 mL lower FEV1 and 4.14 ± 0.55 g/L lower adjusted lung density. After adjusting for covariates, lower sRAGE at baseline was associated with greater FEV1 decline and emphysema progression only in the ECLIPSE cohort. Non-Hispanic white subjects carrying the rs2070600 minor allele (A) and non-Hispanic African Americans carrying the rs2071288 minor allele (A) had lower sRAGE measurements compare to those with the major allele, but their emphysema-sRAGE regression slopes were similar. CONCLUSIONS: Lower blood sRAGE is associated with more severe airflow obstruction and emphysema, but associations with progression are inconsistent in the cohorts analyzed. In these cohorts, genotype influenced sRAGE measurements and strengthened variance modelling. Thus, genotype should be included in sRAGE evaluations.

Comparison of Proteomic Assessment Methods in Multiple Cohort Studies.

Novel proteomics platforms, such as the aptamer-based SOMAscan platform, can quantify large numbers of proteins efficiently and cost-effectively and are rapidly growing in popularity. However, comparisons to conventional immunoassays remain underexplored, leaving investigators unsure when cross-assay comparisons are appropriate. The correlation of results from immunoassays with relative protein quantification is explored by SOMAscan. For 63 proteins assessed in two chronic obstructive pulmonary disease (COPD) cohorts, subpopulations and intermediate outcome measures in COPD Study (SPIROMICS), and COPDGene, using myriad rules based medicine multiplex immunoassays and SOMAscan, Spearman correlation coefficients range from -0.13 to 0.97, with a median correlation coefficient of ≈0.5 and consistent results across cohorts. A similar range is observed for immunoassays in the population-based Multi-Ethnic Study of Atherosclerosis and for other assays in COPDGene and SPIROMICS. Comparisons of relative quantification from the antibody-based Olink platform and SOMAscan in a small cohort of myocardial infarction patients also show a wide correlation range. Finally, cis pQTL data, mass spectrometry aptamer confirmation, and other publicly available data are integrated to assess relationships with observed correlations. Correlation between proteomics assays shows a wide range and should be carefully considered when comparing and meta-analyzing proteomics data across assays and studies.

Power and Sample Size Calculations for Genetic Association Studies in the Presence of Genetic Model Misspecification.

INTRODUCTION: When analyzing data from large-scale genetic association studies, such as targeted or genome-wide resequencing studies, it is common to assume a single genetic model, such as dominant or additive, for all tests of association between a given genetic variant and the phenotype. However, for many variants, the chosen model will result in poor model fit and may lack statistical power due to model misspecification. OBJECTIVE: We develop power and sample size calculations for tests of gene and gene × environment interaction, allowing for misspecification of the true mode of genetic susceptibility. METHODS: The power calculations are based on a likelihood ratio test framework and are implemented in an open-source R package ("genpwr"). RESULTS: We use these methods to develop an analysis plan for a resequencing study in idiopathic pulmonary fibrosis and show that using a 2-degree of freedom test can increase power to detect recessive genetic effects while maintaining power to detect dominant and additive effects. CONCLUSIONS: Understanding the impact of model misspecification can aid in study design and developing analysis plans that maximize power to detect a range of true underlying genetic effects. In particular, these calculations help identify when a multiple degree of freedom test or other robust test of association may be advantageous.

Common Genetic Polymorphisms Influence Blood Biomarker Measurements in COPD.

Implementing precision medicine for complex diseases such as chronic obstructive lung disease (COPD) will require extensive use of biomarkers and an in-depth understanding of how genetic, epigenetic, and environmental variations contribute to phenotypic diversity and disease progression. A meta-analysis from two large cohorts of current and former smokers with and without COPD [SPIROMICS (N = 750); COPDGene (N = 590)] was used to identify single nucleotide polymorphisms (SNPs) associated with measurement of 88 blood proteins (protein quantitative trait loci; pQTLs). PQTLs consistently replicated between the two cohorts. Features of pQTLs were compared to previously reported expression QTLs (eQTLs). Inference of causal relations of pQTL genotypes, biomarker measurements, and four clinical COPD phenotypes (airflow obstruction, emphysema, exacerbation history, and chronic bronchitis) were explored using conditional independence tests. We identified 527 highly significant (p < 8 X 10-10) pQTLs in 38 (43%) of blood proteins tested. Most pQTL SNPs were novel with low overlap to eQTL SNPs. The pQTL SNPs explained >10% of measured variation in 13 protein biomarkers, with a single SNP (rs7041; p = 10-392) explaining 71%-75% of the measured variation in vitamin D binding protein (gene = GC). Some of these pQTLs [e.g., pQTLs for VDBP, sRAGE (gene = AGER), surfactant protein D (gene = SFTPD), and TNFRSF10C] have been previously associated with COPD phenotypes. Most pQTLs were local (cis), but distant (trans) pQTL SNPs in the ABO blood group locus were the top pQTL SNPs for five proteins. The inclusion of pQTL SNPs improved the clinical predictive value for the established association of sRAGE and emphysema, and the explanation of variance (R2) for emphysema improved from 0.3 to 0.4 when the pQTL SNP was included in the model along with clinical covariates. Causal modeling provided insight into specific pQTL-disease relationships for airflow obstruction and emphysema. In conclusion, given the frequency of highly significant local pQTLs, the large amount of variance potentially explained by pQTL, and the differences observed between pQTLs and eQTLs SNPs, we recommend that protein biomarker-disease association studies take into account the potential effect of common local SNPs and that pQTLs be integrated along with eQTLs to uncover disease mechanisms. Large-scale blood biomarker studies would also benefit from close attention to the ABO blood group.

Biomarkers Predictive of Exacerbations in the SPIROMICS and COPDGene Cohorts.

RATIONALE: Chronic obstructive pulmonary disease exacerbations are associated with disease progression, higher healthcare cost, and increased mortality. Published predictors of future exacerbations include previous exacerbation, airflow obstruction, poor overall health, home oxygen use, and gastroesophageal reflux. OBJECTIVES: To determine the value of adding blood biomarkers to clinical variables to predict exacerbations. METHODS: Subjects from the SPIROMICS (Subpopulations and Intermediate Outcomes Measures in COPD Study) (n = 1,544) and COPDGene (Genetic Epidemiology of COPD) (n = 602) cohorts had 90 plasma or serum candidate proteins measured on study entry using Myriad-RBM multiplex panels. We defined total exacerbations as subject-reported worsening in respiratory health requiring therapy with corticosteroids and/or antibiotics, and severe exacerbations as those leading to hospitalizations or emergency room visits. We assessed retrospective exacerbations during the 12 months before enrollment and then documented prospective exacerbations in each cohort. Exacerbations were modeled for biomarker associations with negative binomial regression including clinical covariates (age, sex, percent predicted FEV1, self-reported gastroesophageal reflux, St. George's Respiratory Questionnaire score, smoking status). We used the Stouffer-Liptak test to combine P values for metaanalysis. MEASUREMENTS AND MAIN RESULTS: Between the two cohorts, 3,471 total exacerbations (1,044 severe) were reported. We identified biomarkers within each cohort that were significantly associated with a history of exacerbation and with a future exacerbation, but there was minimal replication between the cohorts. Although established clinical features were predictive of exacerbations, of the blood biomarkers only decorin and α2-macroglobulin increased predictive value for future severe exacerbations. CONCLUSIONS: Blood biomarkers were significantly associated with the occurrence of exacerbations but were not robust between cohorts and added little to the predictive value of clinical covariates for exacerbations.

Multiple biomarkers predict disease severity, progression and mortality in COPD.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) is a heterogeneous disease characterized by multiple subtypes and variable disease progression. Blood biomarkers have been variably associated with subtype, severity, and disease progression. Just as combined clinical variables are more highly predictive of outcomes than individual clinical variables, we hypothesized that multiple biomarkers may be more informative than individual biomarkers to predict subtypes, disease severity, disease progression, and mortality. METHODS: Fibrinogen, C-Reactive Protein (CRP), surfactant protein D (SP-D), soluble Receptor for Advanced Glycation Endproducts (sRAGE), and Club Cell Secretory Protein (CC16) were measured in the plasma of 1465 subjects from the COPDGene cohort and 2746 subjects from the ECLIPSE cohort. Regression analysis was performed to determine whether these biomarkers, individually or in combination, were predictive of subtypes, disease severity, disease progression, or mortality, after adjustment for clinical covariates. RESULTS: In COPDGene, the best combinations of biomarkers were: CC16, sRAGE, fibrinogen, CRP, and SP-D for airflow limitation (p < 10-4), SP-D, CRP, sRAGE and fibrinogen for emphysema (p < 10-3), CC16, fibrinogen, and sRAGE for decline in FEV1 (p < 0.05) and progression of emphysema (p < 10-3), and all five biomarkers together for mortality (p < 0.05). All associations except mortality were validated in ECLIPSE. The combination of SP-D, CRP, and fibrinogen was the best model for mortality in ECLIPSE (p < 0.05), and this combination was also significant in COPDGene. CONCLUSION: This comprehensive analysis of two large cohorts revealed that combinations of biomarkers improve predictive value compared with clinical variables and individual biomarkers for relevant cross-sectional and longitudinal COPD outcomes.

The value of blood cytokines and chemokines in assessing COPD.

BACKGROUND: Blood biomarkers are increasingly used to stratify high risk chronic obstructive pulmonary disease (COPD) patients; however, there are fewer studies that have investigated multiple biomarkers and replicated in multiple large well-characterized cohorts of susceptible current and former smokers. METHODS: We used two MSD multiplex panels to measure 9 cytokines and chemokines in 2123 subjects from COPDGene and 1117 subjects from SPIROMICS. These biomarkers included: interleukin (IL)-2, IL-6, IL-8, IL-10, tumor necrosis factor (TNF)-α, interferon (IFN)-γ, eotaxin/CCL-11, eotaxin-3/CCL-26, and thymus and activation-regulated chemokine (TARC)/CCL-17. Regression models adjusted for clinical covariates were used to determine which biomarkers were associated with the following COPD phenotypes: airflow obstruction (forced expiratory flow at 1 s (FEV1%) and FEV1/forced vital capacity (FEV1/FVC), chronic bronchitis, COPD exacerbations, and emphysema. Biomarker-genotype associations were assessed by genome-wide association of single nucleotide polymorphisms (SNPs). RESULTS: Eotaxin and IL-6 were strongly associated with airflow obstruction and accounted for 3-5% of the measurement variance on top of clinical variables. IL-6 was associated with progressive airflow obstruction over 5 years and both IL-6 and IL-8 were associated with progressive emphysema over 5 years. None of the biomarkers were consistently associated with chronic bronchitis or COPD exacerbations. We identified one novel SNP (rs9302690 SNP) that was associated with CCL17 plasma measurements. CONCLUSION: When assessing smoking related pulmonary disease, biomarkers of inflammation such as IL-2, IL-6, IL-8, and eotaxin may add additional modest predictive value on top of clinical variables alone. TRIAL REGISTRATION: COPDGene (ClinicalTrials.gov Identifier: NCT02445183 ). Subpopulations and Intermediate Outcomes Measures in COPD Study (SPIROMICS) ( ClinicalTrials.gov Identifier: NCT 01969344 ).

Electronic Cigarette Use in US Adults at Risk for or with COPD: Analysis from Two Observational Cohorts.

BACKGROUND: Electronic cigarettes (e-cigarettes) are battery-operated nicotine-delivery devices used by some smokers as a cessation tool as well as by never smokers. OBJECTIVE: To determine the usage of e-cigarettes in older adults at risk for or with chronic obstructive pulmonary disease (COPD). DESIGN: Prospective cohorts. PARTICIPANTS: COPDGene (N = 3536) and SPIROMICS (N = 1060) subjects who were current or former smokers aged 45-80. MAIN MEASURES: Participants were surveyed to determine whether e-cigarette use was associated with longitudinal changes in COPD progression or smoking habits. KEY RESULTS: From 2010 to 2016, participants who had ever used e-cigarettes steadily increased to 12-16%, but from 2014 to 2016 current use was stable at ~5%. E-cigarette use in African-Americans (AA) and whites was similar; however, AA were 1.8-2.9 times as likely to use menthol-flavored e-cigarettes. Current e-cigarette and conventional cigarette users had higher nicotine dependence and consumed more nicotine than those who smoked only conventional cigarettes. E-cigarette users had a heavier conventional cigarette smoking history and worse respiratory health, were less likely to reduce or quit conventional cigarette smoking, had higher nicotine dependence, and were more likely to report chronic bronchitis and exacerbations. Ever e-cigarette users had more rapid decline in lung function, but this trend did not persist after adjustment for persistent conventional cigarette smoking. CONCLUSIONS: E-cigarette use, which is common in adults with or at risk for COPD, was associated with worse pulmonary-related health outcomes, but not with cessation of smoking conventional cigarettes. Although this was an observational study, we find no evidence supporting the use of e-cigarettes as a harm reduction strategy among current smokers with or at risk for COPD.

Plasma sphingolipids associated with chronic obstructive pulmonary disease phenotypes.

RATIONALE: Chronic obstructive pulmonary disease (COPD) occurs in a minority of smokers and is characterized by intermittent exacerbations and clinical subphenotypes such as emphysema and chronic bronchitis. Although sphingolipids as a class are implicated in the pathogenesis of COPD, the particular sphingolipid species associated with COPD subphenotypes remain unknown. OBJECTIVES: To use mass spectrometry to determine which plasma sphingolipids are associated with subphenotypes of COPD. METHODS: One hundred twenty-nine current and former smokers from the COPDGene cohort had 69 distinct sphingolipid species detected in plasma by targeted mass spectrometry. Of these, 23 were also measured in 131 plasma samples (117 independent subjects) using an untargeted platform in an independent laboratory. Regression analysis with adjustment for clinical covariates, correction for false discovery rate, and metaanalysis were used to test associations between COPD subphenotypes and sphingolipids. Peripheral blood mononuclear cells were used to test associations between sphingolipid gene expression and plasma sphingolipids. MEASUREMENTS AND MAIN RESULTS: Of the measured plasma sphingolipids, five sphingomyelins were associated with emphysema; four trihexosylceramides and three dihexosylceramides were associated with COPD exacerbations. Three sphingolipids were strongly associated with sphingolipid gene expression, and 15 sphingolipid gene/metabolite pairs were differentially regulated between COPD cases and control subjects. CONCLUSIONS: There is evidence of systemic dysregulation of sphingolipid metabolism in patients with COPD. Subphenotyping suggests that sphingomyelins are strongly associated with emphysema and glycosphingolipids are associated with COPD exacerbations.

Dissecting childhood asthma with nasal transcriptomics distinguishes subphenotypes of disease.

BACKGROUND: Bronchial airway expression profiling has identified inflammatory subphenotypes of asthma, but the invasiveness of this technique has limited its application to childhood asthma. OBJECTIVES: We sought to determine whether the nasal transcriptome can proxy expression changes in the lung airway transcriptome in asthmatic patients. We also sought to determine whether the nasal transcriptome can distinguish subphenotypes of asthma. METHODS: Whole-transcriptome RNA sequencing was performed on nasal airway brushings from 10 control subjects and 10 asthmatic subjects, which were compared with established bronchial and small-airway transcriptomes. Targeted RNA sequencing nasal expression analysis was used to profile 105 genes in 50 asthmatic subjects and 50 control subjects for differential expression and clustering analyses. RESULTS: We found 90.2% overlap in expressed genes and strong correlation in gene expression (ρ = .87) between the nasal and bronchial transcriptomes. Previously observed asthmatic bronchial differential expression was strongly correlated with asthmatic nasal differential expression (ρ = 0.77, P = 5.6 × 10(-9)). Clustering analysis identified TH2-high and TH2-low subjects differentiated by expression of 70 genes, including IL13, IL5, periostin (POSTN), calcium-activated chloride channel regulator 1 (CLCA1), and serpin peptidase inhibitor, clade B (SERPINB2). TH2-high subjects were more likely to have atopy (odds ratio, 10.3; P = 3.5 × 10(-6)), atopic asthma (odds ratio, 32.6; P = 6.9 × 10(-7)), high blood eosinophil counts (odds ratio, 9.1; P = 2.6 × 10(-6)), and rhinitis (odds ratio, 8.3; P = 4.1 × 10(-6)) compared with TH2-low subjects. Nasal IL13 expression levels were 3.9-fold higher in asthmatic participants who experienced an asthma exacerbation in the past year (P = .01). Several differentially expressed nasal genes were specific to asthma and independent of atopic status. CONCLUSION: Nasal airway gene expression profiles largely recapitulate expression profiles in the lung airways. Nasal expression profiling can be used to identify subjects with IL13-driven asthma and a TH2-skewed systemic immune response.

The neutrophil to lymphocyte ratio associates with markers of Alzheimer's disease pathology in cognitively unimpaired elderly people.

Background: An elevated neutrophil-lymphocyte ratio (NLR) in blood has been associated with Alzheimer's disease (AD). However, an elevated NLR has also been implicated in many other conditions that are risk factors for AD, prompting investigation into whether the NLR is directly linked with AD pathology or a result of underlying comorbidities. Herein, we explored the relationship between the NLR and AD biomarkers in the cerebrospinal fluid (CSF) of cognitively unimpaired (CU) subjects. Adjusting for sociodemographics, APOE4, and common comorbidities, we investigated these associations in two cohorts: the Alzheimer's Disease Neuroimaging Initiative (ADNI) and the M.J. de Leon CSF repository at NYU. Specifically, we examined associations between the NLR and cross-sectional measures of amyloid-ß42 (Aß42), total tau (t-tau), and phosphorylated tau181 (p-tau), as well as the trajectories of these CSF measures obtained longitudinally. Results: A total of 111 ADNI and 190 NYU participants classified as CU with available NLR, CSF, and covariate data were included. Compared to NYU, ADNI participants were older (73.79 vs. 61.53, p < 0.001), had a higher proportion of males (49.5% vs. 36.8%, p = 0.042), higher BMIs (27.94 vs. 25.79, p < 0.001), higher prevalence of hypertensive history (47.7% vs. 16.3%, p < 0.001), and a greater percentage of Aß-positivity (34.2% vs. 20.0%, p = 0.009). In the ADNI cohort, we found cross-sectional associations between the NLR and CSF Aß42 (ß=-12.193, p = 0.021), but not t-tau or p-tau. In the NYU cohort, we found cross-sectional associations between the NLR and CSF t-tau (ß = 26.812, p = 0.019) and p-tau (ß = 3.441, p = 0.015), but not Aß42. In the NYU cohort alone, subjects classified as Aß+ (n = 38) displayed a stronger association between the NLR and t-tau (ß = 100.476, p = 0.037) compared to Aß- subjects or the non-stratified cohort. In both cohorts, the same associations observed in the cross-sectional analyses were observed after incorporating longitudinal CSF data. Conclusions: We report associations between the NLR and Aß42 in the older ADNI cohort, and between the NLR and t-tau and p-tau181 in the younger NYU cohort. Associations persisted after adjusting for comorbidities, suggesting a direct link between the NLR and AD. However, changes in associations between the NLR and specific AD biomarkers may occur as part of immunosenescence.

Impact of Marijuana Smoking on COPD Progression in a Cohort of Middle-Aged and Older Persons.

Background: Limited data are available regarding marijuana smoking's impact on the development or progression of chronic obstructive pulmonary disease (COPD) in middle-aged or older adults with a variable history of tobacco cigarette smoking. Methods: We divided ever-tobacco smoking participants in the SubPopulations and InteRmediate Outcomes In COPD Study (SPIROMICS) into 3 groups based on self-reported marijuana use: current, former, or never marijuana smokers (CMSs, FMSs or NMSs, respectively). Longitudinal data were analyzed in participants with ≥2 visits over a period of ≥52 weeks. Measurements: We compared CMSs, FMSs, and NMSs, and those with varying amounts of lifetime marijuana use. Mixed effects linear regression models were used to analyze changes in spirometry, symptoms, health status, and radiographic metrics; zero-inflated negative binomial models were used for exacerbation rates. All models were adjusted for age, sex, race, baseline tobacco smoking amount, and forced expiratory volume in 1 second (FEV1) %predicted. Results: Most participants were followed for ≥4 years. Annual rates of change in FEV1, incident COPD, respiratory symptoms, health status, radiographic extent of emphysema or air trapping, and total or severe exacerbations were not different between CMSs or FMSs versus NMSs or between those with any lifetime amount of marijuana use versus NMSs. Conclusions: Among SPIROMICS participants with or without COPD, neither former nor current marijuana smoking of any lifetime amount was associated with evidence of COPD progression or its development. Because of our study's limitations, these findings underscore the need for further studies to better understand longer-term effects of marijuana smoking in COPD.

Genomic biomarkers in chronic beryllium disease and sarcoidosis.

Background Previous gene expression studies have identified genes IFNγ, TNFα, RNase 3, CXCL9, and CD55 as potential biomarkers for sarcoidosis and/or chronic beryllium disease (CBD). We hypothesized that differential expression of these genes could function as diagnostic biomarkers for sarcoidosis and CBD, and prognostic biomarkers for sarcoidosis. Study Design/Methods We performed RT-qPCR on whole blood samples from CBD (n = 132), beryllium sensitized (BeS) (n = 109), and sarcoidosis (n = 99) cases and non-diseased controls (n = 97) to determine differential expression of target genes. We then performed logistic regression modeling and generated ROC curves to determine which genes could most accurately differentiate: 1) CBD versus sarcoidosis 2) CBD versus BeS 3) sarcoidosis versus controls 4) non-progressive versus progressive sarcoidosis. Results CD55 and TNFα were significantly upregulated, while CXCL9 was significantly downregulated in CBD compared to sarcoidosis (p < 0.05). The ROC curve from the logistic regression model demonstrated high discriminatory ability of the combination of CD55, TNFα, and CXCL9 to distinguish between CBD and sarcoidosis with an AUC of 0.98. CD55 and TNFα were significantly downregulated in sarcoidosis compared to controls (p < 0.05). The ROC curve from the model showed a reasonable discriminatory ability of CD55 and TNFα to distinguish between sarcoidosis and controls with an AUC of 0.86. There was no combination of genes that could accurately differentiate between CBD and BeS or sarcoidosis phenotypes. Interpretation CD55, TNFα and CXCL9 expression levels can accurately differentiate between CBD and sarcoidosis, while CD55 and TNFα expression levels can accurately differentiate sarcoidosis and controls.

Plasma Metabolomic Signatures of Chronic Obstructive Pulmonary Disease and the Impact of Genetic Variants on Phenotype-Driven Modules.

Background: Small studies have recently suggested that there are specific plasma metabolic signatures in chronic obstructive pulmonary disease (COPD), but there have been no large comprehensive study of metabolomic signatures in COPD that also integrate genetic variants. Materials and Methods: Fresh frozen plasma from 957 non-Hispanic white subjects in COPDGene was used to quantify 995 metabolites with Metabolon's global metabolomics platform. Metabolite associations with five COPD phenotypes (chronic bronchitis, exacerbation frequency, percent emphysema, post-bronchodilator forced expiratory volume at one second [FEV1]/forced vital capacity [FVC], and FEV1 percent predicted) were assessed. A metabolome-wide association study was performed to find genetic associations with metabolite levels. Significantly associated single-nucleotide polymorphisms were tested for replication with independent metabolomic platforms and independent cohorts. COPD phenotype-driven modules were identified in network analysis integrated with genetic associations to assess gene-metabolite-phenotype interactions. Results: Of metabolites tested, 147 (14.8%) were significantly associated with at least 1 COPD phenotype. Associations with airflow obstruction were enriched for diacylglycerols and branched chain amino acids. Genetic associations were observed with 109 (11%) metabolites, 72 (66%) of which replicated in an independent cohort. For 20 metabolites, more than 20% of variance was explained by genetics. A sparse network of COPD phenotype-driven modules was identified, often containing metabolites missed in previous testing. Of the 26 COPD phenotype-driven modules, 6 contained metabolites with significant met-QTLs, although little module variance was explained by genetics. Conclusion: A dysregulation of systemic metabolism was predominantly found in COPD phenotypes characterized by airflow obstruction, where we identified robust heritable effects on individual metabolite abundances. However, network analysis, which increased the statistical power to detect associations missed previously in classic regression analyses, revealed that the genetic influence on COPD phenotype-driven metabolomic modules was modest when compared with clinical and environmental factors.

Pre-analytic Considerations for Mass Spectrometry-Based Untargeted Metabolomics Data.

Metabolomics is the science of characterizing and quantifying small molecule metabolites in biological systems. These metabolites give organisms their biochemical characteristics, providing a link between genotype, environment, and phenotype. With these opportunities also come data challenges, such as compound annotation, missing values, and batch effects. We present the steps of a general pipeline to process untargeted mass spectrometry data to alleviate the latter two challenges. We assume to have a matrix with metabolite abundances, with metabolites in rows and samples in columns. The steps in the pipeline include summarizing technical replicates (if available), filtering, imputing, transforming, and normalizing the data. In each of these steps, a method and parameters should be chosen based on assumptions one is willing to make, the question of interest, and diagnostic tools. Besides giving a general pipeline that can be adapted by the reader, our goal is to review diagnostic tools and criteria that are helpful when making decisions in each step of the pipeline and assessing the effectiveness of normalization and batch correction. We conclude by giving a list of useful packages and discuss some alternative approaches that might be more appropriate for the reader's data.

Real-world use of rescue inhaler sensors, electronic symptom questionnaires and physical activity monitors in COPD.

Background: Chronic obstructive pulmonary disease (COPD) is a heterogeneous disease characterised by airflow obstruction and other morbidities such as respiratory symptoms, reduced physical activity and frequent bronchodilator use. Recent advances in personal digital monitoring devices can permit continuous collection of these data in COPD patients, but the relationships among them are not well understood. Methods: 184 individuals from a single centre of the COPDGene cohort agreed to participate in this 3-week observational study. Each participant used a smartphone to complete a daily symptom diary (EXAcerbations of Chronic pulmonary disease Tool, EXACT), wore a wrist-worn accelerometer to record continuously physical activity and completed the Clinical Visit PROactive Physical Activity in COPD questionnaire. 58 users of metered dose inhalers for rescue (albuterol) were provided with an inhaler sensor, which time stamped each inhaler actuation. Results: Rescue inhaler use was strongly correlated with E-RS:COPD score, while step counts were correlated with neither rescue use nor E-RS:COPD score. Frequent, unpatterned inhaler use pattern was associated with worse respiratory symptoms and less physical activity compared with frequent inhaler use with a regular daily pattern. There was a strong week-by-week correlation among measurements, suggesting that 1 week of monitoring is sufficient to characterise stable patients with COPD. Discussion: The study highlights the interaction and relevance of personal real-time monitoring of respiratory symptoms, physical activity and rescue medication in patients with COPD. Additionally, visual displays of longitudinal data may be helpful for disease management to help drive conversations between patients and caregivers and for risk-based monitoring in clinical trials.