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Integrative factorization methods for multi-omic data estimate factors explaining biological variation. Factors can be treated as covariates to predict an outcome and the factorization can be used to impute missing values. However, no available methods provide a comprehensive framework for statistical inference and uncertainty quantification for these tasks. A novel framework, Bayesian Simultaneous Factorization (BSF), is proposed to decompose multi-omics variation into joint and individual structures simultaneously within a probabilistic framework. BSF uses conjugate normal priors and the posterior mode of this model can be estimated by solving a structured nuclear norm-penalized objective that also achieves rank selection and motivates the choice of hyperparameters. BSF is then extended to simultaneously predict a continuous or binary phenotype while estimating latent factors, termed Bayesian Simultaneous Factorization and Prediction (BSFP). BSF and BSFP accommodate concurrent imputation, i.e., imputation during the model-fitting process, and full posterior inference for missing data, including "blockwise" missingness. It is shown via simulation that BSFP is competitive in recovering latent variation structure, and demonstrate the importance of accounting for uncertainty in the estimated factorization within the predictive model. The imputation performance of BSF is examined via simulation under missing-at-random and missing-not-at-random assumptions. Finally, BSFP is used to predict lung function based on the bronchoalveolar lavage metabolome and proteome from a study of HIV-associated obstructive lung disease, revealing multi-omic patterns related to lung function decline and a cluster of patients with obstructive lung disease driven by shared metabolomic and proteomic abundance patterns.
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Human cytomegalovirus (CMV) is a highly prevalent herpesvirus that is often transmitted to the neonate via breast milk. Postnatal CMV transmission can have negative health consequences for preterm and immunocompromised infants, but any effects on healthy term infants are thought to be benign. Furthermore, the impact of CMV on the composition of the hundreds of bioactive factors in human milk has not been tested. Here, we utilize a cohort of exclusively breastfeeding full-term mother-infant pairs to test for differences in the milk transcriptome and metabolome associated with CMV, and the impact of CMV in breast milk on the infant gut microbiome and infant growth. We find upregulation of the indoleamine 2,3-dioxygenase (IDO) tryptophan-to-kynurenine metabolic pathway in CMV+ milk samples, and that CMV+ milk is associated with decreased Bifidobacterium in the infant gut. Our data indicate two opposing CMV-associated effects on infant growth; with kynurenine positively correlated, and CMV viral load negatively correlated, with infant weight-for-length at 1 month of age. These results suggest CMV transmission, CMV-related changes in milk composition, or both may be modulators of full-term infant development.
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Aleitamento Materno , Infecções por Citomegalovirus , Citomegalovirus , Microbioma Gastrointestinal , Cinurenina , Leite Humano , Humanos , Leite Humano/virologia , Leite Humano/microbiologia , Leite Humano/química , Feminino , Infecções por Citomegalovirus/transmissão , Infecções por Citomegalovirus/virologia , Lactente , Recém-Nascido , Cinurenina/metabolismo , Cinurenina/análise , Carga Viral , Masculino , Adulto , Indolamina-Pirrol 2,3,-Dioxigenase/metabolismo , Triptofano/metabolismo , Triptofano/análise , MetabolomaRESUMO
Background: Obstructive lung disease (OLD) is increasingly prevalent among persons living with HIV (PLWH). However, the role of proteases in HIV-associated OLD remains unclear. Methods: We combined proteomics and peptidomics to comprehensively characterize protease activities. We combined mass spectrometry (MS) analysis on bronchoalveolar lavage fluid (BALF) peptides and proteins from PLWH with OLD (n=25) and without OLD (n=26) with a targeted Somascan aptamer-based proteomic approach to quantify individual proteases and assess their correlation with lung function. Endogenous peptidomics mapped peptides to native proteins to identify substrates of protease activity. Using the MEROPS database, we identified candidate proteases linked to peptide generation based on binding site affinities which were assessed via z-scores. We used t-tests to compare average forced expiratory volume in 1 second per predicted value (FEV1pp) between samples with and without detection of each cleaved protein and adjusted for multiple comparisons by controlling the false discovery rate (FDR). Findings: We identified 101 proteases, of which 95 had functional network associations and 22 correlated with FEV1pp. These included cathepsins, metalloproteinases (MMP), caspases and neutrophil elastase. We discovered 31 proteins subject to proteolytic cleavage that associate with FEV1pp, with the top pathways involved in small ubiquitin-like modifier mediated modification (SUMOylation). Proteases linked to protein cleavage included neutrophil elastase, granzyme, and cathepsin D. Interpretations: In HIV-associated OLD, a significant number of proteases are up-regulated, many of which are involved in protein degradation. These proteases degrade proteins involved in cell cycle and protein stability, thereby disrupting critical biological functions.
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OBJECTIVE: To investigate the effects of gestational age (GA) and phototherapy on the plasma metabolite profile of preterm infants with neonatal hyperbilirubinemia (NHB). STUDY DESIGN: From a cohort of prospectively enrolled infants born preterm (n = 92), plasma samples of very preterm (VPT; GA, 28 + 0 to 31 + 6 weeks, n = 27) and moderate/late preterm (M/LPT; GA, 32 + 0 to 35 + 6 weeks, n = 33) infants requiring phototherapy for NHB were collected prior to the initiation of phototherapy and 24 hours after starting phototherapy. An additional sample was collected 48 hours after starting phototherapy in a randomly selected subset (n = 30; VPT n = 15; M/LPT n = 15). Metabolite profiles were determined using ultraperformance liquid chromatography tandem mass spectroscopy. Two-way ANCOVA was used to identify metabolites that differed between GA groups and timepoints after adjusting for total serum bilirubin levels (false discovery rate q-value < 0.05). Top impacted pathways were identified using pathway over-representation analysis. RESULTS: Phototherapy was initiated at lower total serum bilirubin (mean ± SD mg/dL) levels in VPT compared with M/LPT infants (7.3 ± 1.4 vs 9.9 ± 1.9, P < .01). We identified 664 metabolites that were significant for a phototherapy effect, 191 metabolites significant for GA, and 46 metabolites significant for GA × phototherapy interaction (false discovery rate q-value < 0.05). Longer duration phototherapy had a larger mean effect size (24 hours postphototherapy: d = 0.36; 48 hours postphototherapy: d = 0.43). Top pathways affected by phototherapy included membrane lipid metabolism, one-carbon metabolism, creatine biosynthesis, and oligodendrocyte differentiation. CONCLUSION: Phototherapy alters the plasma metabolite profile more than GA in preterm infants with NHB, affecting pathways related to lipid and one-carbon metabolism, energy biosynthesis, and oligodendrocyte differentiation.
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Rationale: Physical activity, lung function, and grip strength are associated with exacerbations, hospitalizations, and mortality in people with chronic obstructive pulmonary disease (COPD). We tested whether baseline inflammatory biomarkers were associated with longitudinal outcomes of these physiologic measurements. Methods: The COPD Activity: Serotonin Transporter, Cytokines, and Depression (CASCADE) study was a prospective observational study of individuals with COPD. A total of 14 inflammatory biomarkers were measured at baseline. Participants were followed for 2 years. We analyzed associations between baseline biomarkers and forced expiratory volume in 1 second (FEV1), physical activity, and grip strength. We used a hierarchical hypothesis testing procedure to reduce type I error. We used Pearson correlations to test associations between baseline biomarkers and longitudinal changes in the outcomes of interest. We used Fisher's linear discriminant analysis to test if linear combinations of baseline biomarkers predict rapid FEV1 decline. Finally, we used linear mixed modeling to test associations between baseline biomarkers and outcomes of interest at baseline, year 1, and year 2; models were adjusted for age, smoking status, baseline biomarkers, and FEV1. Results: A total of 302 participants (age 67.5 ± 8.5 years, 19.5% female, 28.5% currently smoking) were included. Baseline biomarkers were not associated with longitudinal changes in grip strength, physical activity, or rapid FEV1 decline. Higher interleukin-6 and C-reactive protein were associated with lower physical activity at baseline and these relationships persisted at year 1 and year 2. Conclusion: Baseline inflammatory biomarkers did not predict changes in lung function or physical activity, but higher inflammatory biomarkers were associated with persistently low levels of physical activity.
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Statistical approaches that successfully combine multiple datasets are more powerful, efficient, and scientifically informative than separate analyses. To address variation architectures correctly and comprehensively for high-dimensional data across multiple sample sets (ie, cohorts), we propose multiple augmented reduced rank regression (maRRR), a flexible matrix regression and factorization method to concurrently learn both covariate-driven and auxiliary structured variations. We consider a structured nuclear norm objective that is motivated by random matrix theory, in which the regression or factorization terms may be shared or specific to any number of cohorts. Our framework subsumes several existing methods, such as reduced rank regression and unsupervised multimatrix factorization approaches, and includes a promising novel approach to regression and factorization of a single dataset (aRRR) as a special case. Simulations demonstrate substantial gains in power from combining multiple datasets, and from parsimoniously accounting for all structured variations. We apply maRRR to gene expression data from multiple cancer types (ie, pan-cancer) from The Cancer Genome Atlas, with somatic mutations as covariates. The method performs well with respect to prediction and imputation of held-out data, and provides new insights into mutation-driven and auxiliary variations that are shared or specific to certain cancer types.
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Neoplasias , Humanos , Análise Multivariada , Neoplasias/genéticaRESUMO
Crossed random effects models (CREMs) are particularly useful in longitudinal data applications because they allow researchers to account for the impact of dynamic group membership on individual outcomes. However, no research has determined what data conditions need to be met to sufficiently identify these models, especially the group effects, in a longitudinal context. This is a significant gap in the current literature as future applications to real data may need to consider these conditions to yield accurate and precise model parameter estimates, specifically for the group effects on individual outcomes. Furthermore, there are no existing CREMs that can model intrinsically nonlinear growth. The goals of this study are to develop a Bayesian piecewise CREM to model intrinsically nonlinear growth and evaluate what data conditions are necessary to empirically identify both intrinsically linear and nonlinear longitudinal CREMs. This study includes an applied example that utilizes the piecewise CREM with real data and three simulation studies to assess the data conditions necessary to estimate linear, quadratic, and piecewise CREMs. Results show that the number of repeated measurements collected on groups impacts the ability to recover the group effects. Additionally, functional form complexity impacted data collection requirements for estimating longitudinal CREMs.
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Modelos Estatísticos , Dinâmica não Linear , Teorema de Bayes , Simulação por Computador , Modelos LinearesRESUMO
BACKGROUND: The current pediatric practice of monitoring for infantile iron deficiency (ID) via hemoglobin (Hgb) screening at one y of age does not identify preanemic ID nor protect against later neurocognitive deficits. OBJECTIVES: To identify biomarkers of iron-related metabolic alterations in the serum and brain and determine the sensitivity of conventional iron and heme indices for predicting risk of brain metabolic dysfunction using a nonhuman primate model of infantile ID. METHODS: Simultaneous serum iron and RBC indices, and serum and cerebrospinal fluid (CSF) metabolomic profiles were determined in 20 rhesus infants, comparing iron sufficient (IS; N = 10) and ID (N = 10) infants at 2 and 4 mo of age. RESULTS: Reticulocyte hemoglobin (RET-He) was lower at 2 wk in the ID group. Significant IS compared with ID differences in serum iron indices were present at 2 mo, but Hgb and RBC indices differed only at 4 mo (P < 0.05). Serum and CSF metabolomic profiles of the ID and IS groups differed at 2 and 4 mo (P < 0.05). Key metabolites, including homostachydrine and stachydrine (4-5-fold lower at 4 mo in ID group, P < 0.05), were altered in both serum and CSF. Iron indices and RET-He at 2 mo, but not Hgb or other RBC indices, were correlated with altered CSF metabolic profile at 4 mo and had comparable predictive accuracy (area under the receiver operating characteristic curve scores, 0.75-0.80). CONCLUSIONS: Preanemic ID at 2 mo was associated with metabolic alterations in serum and CSF in infant monkeys. Among the RBC indices, only RET-He predicted the future risk of abnormal CSF metabolic profile with a predictive accuracy comparable to serum iron indices. The concordance of homostachydrine and stachydrine changes in serum and CSF indicates their potential use as early biomarkers of brain metabolic dysfunction in infantile ID.
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Anemia Ferropriva , Encefalopatias , Deficiências de Ferro , Animais , Lactente , Humanos , Criança , Anemia Ferropriva/complicações , Anemia Ferropriva/diagnóstico , Macaca mulatta/metabolismo , Prognóstico , Ferro/metabolismo , Hemoglobinas/metabolismo , Encefalopatias/metabolismo , Biomarcadores , Encéfalo/metabolismoRESUMO
Statistical approaches that successfully combine multiple datasets are more powerful, efficient, and scientifically informative than separate analyses. To address variation architectures correctly and comprehensively for high-dimensional data across multiple sample sets (i.e., cohorts), we propose multiple augmented reduced rank regression (maRRR), a flexible matrix regression and factorization method to concurrently learn both covariate-driven and auxiliary structured variation. We consider a structured nuclear norm objective that is motivated by random matrix theory, in which the regression or factorization terms may be shared or specific to any number of cohorts. Our framework subsumes several existing methods, such as reduced rank regression and unsupervised multi-matrix factorization approaches, and includes a promising novel approach to regression and factorization of a single dataset (aRRR) as a special case. Simulations demonstrate substantial gains in power from combining multiple datasets, and from parsimoniously accounting for all structured variation. We apply maRRR to gene expression data from multiple cancer types (i.e., pan-cancer) from TCGA, with somatic mutations as covariates. The method performs well with respect to prediction and imputation of held-out data, and provides new insights into mutation-driven and auxiliary variation that is shared or specific to certain cancer types.
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Perinatal iron deficiency (FeD) targets the hippocampus and leads to long-term cognitive deficits. Intranasal insulin administration improves cognitive deficits in adult humans with Alzheimer's disease and type 2 diabetes and could provide benefits in FeD-induced hippocampal dysfunction. To objective was to assess the effects of intranasal insulin administration intranasal insulin administration on the hippocampal transcriptome in a developing rat model of perinatal FeD. Perinatal FeD was induced using low-iron diet from gestational day 3 until postnatal day (P) 7, followed by an iron sufficient (FeS) diet through P21. Intranasal insulin was administered at a dose of 0.3 IU twice daily from P8 to P21. Hippocampi were removed on P21 from FeS control, FeD control, FeS insulin, and FeD insulin groups. Total RNA was isolated and profiled using next-generation sequencing. Gene expression profiles were characterized using custom workflows and expression patterns examined using ingenuity pathways analysis (n = 7-9 per group). Select RNAseq results were confirmed via qPCR. Transcriptomic profiling revealed that mitochondrial biogenesis and flux, oxidative phosphorylation, quantity of neurons, CREB signaling in neurons, and RICTOR-based mTOR signaling were disrupted with FeD and positively affected by intranasal insulin treatment with the most benefit observed in the FeD insulin group. Both perinatal FeD and intranasal insulin administration altered gene expression profile in the developing hippocampus. Intranasal insulin treatment reversed the adverse effects of FeD on many molecular pathways and could be explored as an adjunct therapy in perinatal FeD.
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Diabetes Mellitus Tipo 2 , Deficiências de Ferro , Adulto , Humanos , Feminino , Gravidez , Animais , Ratos , Insulina , Transcriptoma , Hipocampo , Ferro , Alvo Mecanístico do Complexo 2 de RapamicinaRESUMO
Introduction: Anxiety is the most common manifestation of psychopathology in youth, negatively affecting academic, social, and adaptive functioning and increasing risk for mental health problems into adulthood. Anxiety disorders are diagnosed only after clinical symptoms emerge, potentially missing opportunities to intervene during critical early prodromal periods. In this study, we used a new empirical approach to extracting nonlinear features of the electroencephalogram (EEG), with the goal of discovering differences in brain electrodynamics that distinguish children with anxiety disorders from healthy children. Additionally, we examined whether this approach could distinguish children with externalizing disorders from healthy children and children with anxiety. Methods: We used a novel supervised tensor factorization method to extract latent factors from repeated multifrequency nonlinear EEG measures in a longitudinal sample of children assessed in infancy and at ages 3, 5, and 7 years of age. We first examined the validity of this method by showing that calendar age is highly correlated with latent EEG complexity factors (r = 0.77). We then computed latent factors separately for distinguishing children with anxiety disorders from healthy controls using a 5-fold cross validation scheme and similarly for distinguishing children with externalizing disorders from healthy controls. Results: We found that latent factors derived from EEG recordings at age 7 years were required to distinguish children with an anxiety disorder from healthy controls; recordings from infancy, 3 years, or 5 years alone were insufficient. However, recordings from two (5, 7 years) or three (3, 5, 7 years) recordings gave much better results than 7 year recordings alone. Externalizing disorders could be detected using 3- and 5 years EEG data, also giving better results with two or three recordings than any single snapshot. Further, sex assigned at birth was an important covariate that improved accuracy for both disorder groups, and birthweight as a covariate modestly improved accuracy for externalizing disorders. Recordings from infant EEG did not contribute to the classification accuracy for either anxiety or externalizing disorders. Conclusion: This study suggests that latent factors extracted from EEG recordings in childhood are promising candidate biomarkers for anxiety and for externalizing disorders if chosen at appropriate ages.
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Human cytomegalovirus (CMV) is a highly prevalent herpesvirus that is often transmitted to the neonate via breast milk. Postnatal CMV transmission can have negative health consequences for preterm and immunocompromised infants, but any effects on healthy term infants are thought to be benign. Furthermore, the impact of CMV on the composition of the hundreds of bioactive factors in human milk has not been tested. Here, we utilize a cohort of exclusively breastfeeding full term mother-infant pairs to test for differences in the milk transcriptome and metabolome associated with CMV, and the impact of CMV in breast milk on the infant gut microbiome and infant growth. We find upregulation of the indoleamine 2,3- dioxygenase (IDO) tryptophan-to-kynurenine metabolic pathway in CMV+ milk samples, and that CMV+ milk is associated with decreased Bifidobacterium in the infant gut. Our data indicate a complex relationship between milk CMV, milk kynurenine, and infant growth; with kynurenine positively correlated, and CMV viral load negatively correlated, with infant weight-for-length at 1 month of age. These results suggest CMV transmission, CMV-related changes in milk composition, or both may be modulators of full term infant development.
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BACKGROUND: Observational studies have shown an association between higher bilirubin levels and improved respiratory health outcomes. Targeting higher bilirubin levels has been proposed as a novel therapeutic strategy in COPD. However, bilirubin levels are influenced by multiple intrinsic and extrinsic factors, and these observational studies are prone to confounding. Genetic analyses are one approach to overcoming residual confounding in observational studies. OBJECTIVES: To test associations between a genetic determinant of bilirubin levels and respiratory health outcomes. METHODS: COPDGene participants underwent genotyping at the baseline visit. We confirmed established associations between homozygosity for rs6742078 and higher bilirubin, and between higher bilirubin and decreased risk of acute respiratory events within this cohort. For our primary analysis, we used negative binomial regression to test associations between homozygosity for rs6742078 and rate of acute respiratory events. RESULTS: 8,727 participants (n = 6,228 non-Hispanic white and 2,499 African American) were included. Higher bilirubin was associated with decreased rate of acute respiratory events [incidence rate ratio (IRR) 0.85, 95% CI 0.75 to 0.96 per SD increase in bilirubin intensity]. We did not find significant associations between homozygosity for rs6742078 and acute respiratory events (IRR 0.94, 95% CI 0.70 to 1.25 for non-Hispanic white and 1.09, 95% CI 0.91 to 1.31 for African American participants). CONCLUSIONS: A genetic determinant of higher bilirubin levels was not associated with better respiratory health outcomes. These results do not support targeting higher bilirubin levels as a therapeutic strategy in COPD.
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Polimorfismo de Nucleotídeo Único , Doença Pulmonar Obstrutiva Crônica , Humanos , Polimorfismo de Nucleotídeo Único/genética , Bilirrubina , Análise da Randomização Mendeliana/métodos , Incidência , Doença Pulmonar Obstrutiva Crônica/diagnóstico , Doença Pulmonar Obstrutiva Crônica/epidemiologia , Doença Pulmonar Obstrutiva Crônica/genéticaRESUMO
Modern data often take the form of a multiway array. However, most classification methods are designed for vectors, i.e., 1-way arrays. Distance weighted discrimination (DWD) is a popular high-dimensional classification method that has been extended to the multiway context, with dramatic improvements in performance when data have multiway structure. However, the previous implementation of multiway DWD was restricted to classification of matrices, and did not account for sparsity. In this paper, we develop a general framework for multiway classification which is applicable to any number of dimensions and any degree of sparsity. We conducted extensive simulation studies, showing that our model is robust to the degree of sparsity and improves classification accuracy when the data have multiway structure. For our motivating application, magnetic resonance spectroscopy (MRS) was used to measure the abundance of several metabolites across multiple neurological regions and across multiple time points in a mouse model of Friedreich's ataxia, yielding a four-way data array. Our method reveals a robust and interpretable multi-region metabolomic signal that discriminates the groups of interest. We also successfully apply our method to gene expression time course data for multiple sclerosis treatment. An R implementation is available in the package MultiwayClassification at http://github.com/lockEF/MultiwayClassification.
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A Bayesian approach to predict a continuous or binary outcome from data that are collected from multiple sources with a multi-way (i.e., multidimensional tensor) structure is described. As a motivating example, molecular data from multiple 'omics sources, each measured over multiple developmental time points, as predictors of early-life iron deficiency (ID) in a rhesus monkey model are considered. The method uses a linear model with a low-rank structure on the coefficients to capture multi-way dependence and model the variance of the coefficients separately across each source to infer their relative contributions. Conjugate priors facilitate an efficient Gibbs sampling algorithm for posterior inference, assuming a continuous outcome with normal errors or a binary outcome with a probit link. Simulations demonstrate that the model performs as expected in terms of misclassification rates and correlation of estimated coefficients with true coefficients, with large gains in performance by incorporating multi-way structure and modest gains when accounting for differing signal sizes across the different sources. Moreover, it provides robust classification of ID monkeys for the motivating application.
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OBJECTIVE: Identify the improvement in diabetes performance measures and population-based clinical outcomes resulting from changes in care management processes (CMP) in primary care practices over 3 years. RESEARCH DESIGN AND METHODS: This repeated cross-sectional study tracked clinical performance measures for all diabetes patients seen in a cohort of 330 primary care practices in 2017 and 2019. Unit of analysis was patient-year with practice-level CMP exposures. Causal inference is based on dynamic changes in individual CMPs between years by practice. We used the Bayesian method to simultaneously estimate a five-outcome model: A1c, systolic and diastolic blood pressure, guideline-based statin use, and Optimal Diabetes Care (ODC). We control for unobserved time-invariant practice characteristics and secular change. We modeled correlation of errors across outcomes. Statistical significance was identified using 99% Bayesian credible intervals (analogous to P < 0.01). RESULTS: Implementation of 18 of 62 CMPs was associated with statistically significant improvements in patient outcomes. Together, these resulted in 12.1% more patients meeting ODC performance measures. Different CMPs affected different outcomes. Three CMPs accounted for 47% of the total ODC improvement, 68% of A1c decrease, 21% of SBP reduction, and 55% of statin use increase: 1) systems for identifying and reminding patients due for testing, 2) after-visit follow-up by a nonclinician, and 3) guideline-based clinician reminders for preventive services during a clinic visit. CONCLUSIONS: Effective quality improvement in primary care focuses on practice redesign that clearly improves diabetes outcomes. Tailoring CMP adoption in primary care provides effective improvement in ODC performance through focused changes in diabetes outcomes.
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Diabetes Mellitus , Inibidores de Hidroximetilglutaril-CoA Redutases , Humanos , Estudos Transversais , Hemoglobinas Glicadas , Teorema de Bayes , Inibidores de Hidroximetilglutaril-CoA Redutases/uso terapêutico , Diabetes Mellitus/terapiaRESUMO
BACKGROUND: Infantile iron deficiency (ID) causes anemia and compromises neurodevelopment. Current screening relies on hemoglobin (Hgb) determination at 1 year of age, which lacks sensitivity and specificity for timely detection of infantile ID. Low reticulocyte Hgb equivalent (RET-He) indicates ID, but its predictive accuracy relative to conventional serum iron indices is unknown. OBJECTIVES: The objective was to compare diagnostic accuracies of iron indices, red blood cell (RBC) indices, and RET-He for predicting the risk of ID and IDA in a nonhuman primate model of infantile ID. METHODS: Serum iron, total iron binding capacity, unsaturated iron binding capacity, transferrin saturation (TSAT), Hgb, RET-He, and other RBC indices were determined at 2 wk and 2, 4, and 6 mo in breastfed male and female rhesus infants (N = 54). The diagnostic accuracies of RET-He, iron, and RBC indices for predicting the development of ID (TSAT < 20%) and IDA (Hgb < 10 g/dL + TSAT < 20%) were determined using t tests, area under the receiver operating characteristic curve (AUC) analysis, and multiple regression models. RESULTS: Twenty-three (42.6%) infants developed ID and 16 (29.6%) progressed to IDA. All 4 iron indices and RET-He, but not Hgb or RBC indices, predicted future risk of ID and IDA (P < 0.001). The predictive accuracy of RET-He (AUC = 0.78, SE = 0.07; P = 0.003) for IDA was comparable to that of the iron indices (AUC = 0.77-0.83, SE = 0.07; P ≤ 0.002). A RET-He threshold of 25.5 pg strongly correlated with TSAT < 20% and correctly predicted IDA in 10 of 16 infants (sensitivity: 62.5%) and falsely predicted possibility of IDA in only 4 of 38 unaffected infants (specificity: 89.5%). CONCLUSIONS: RET-He is a biomarker of impending ID/IDA in rhesus infants and can be used as a hematological parameter to screen for infantile ID.
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Anemia Ferropriva , Anemia , Deficiências de Ferro , Masculino , Feminino , Animais , Reticulócitos/química , Reticulócitos/metabolismo , Anemia/metabolismo , Hemoglobinas/metabolismo , Ferro/metabolismo , Primatas/metabolismoRESUMO
Purpose: Obstructive lung disease is increasingly common among persons with HIV, both smokers and nonsmokers. We used aptamer proteomics to identify proteins and associated pathways in HIV-associated obstructive lung disease. Methods: Bronchoalveolar lavage fluid (BALF) samples from 26 persons living with HIV with obstructive lung disease were matched to persons living with HIV without obstructive lung disease based on age, smoking status and antiretroviral treatment. 6414 proteins were measured using SomaScan® aptamer-based assay. We used sparse distance-weighted discrimination (sDWD) to test for a difference in protein expression and permutation tests to identify univariate associations between proteins and forced expiratory volume in 1â s % predicted (FEV1 % pred). Significant proteins were entered into a pathway over-representation analysis. We also constructed protein-driven endotypes using K-means clustering and performed over-representation analysis on the proteins that were significantly different between clusters. We compared protein-associated clusters to those obtained from BALF and plasma metabolomics data on the same patient cohort. Results: After filtering, we retained 3872 proteins for further analysis. Based on sDWD, protein expression was able to separate cases and controls. We found 575 proteins that were significantly correlated with FEV1 % pred after multiple comparisons adjustment. We identified two protein-driven endotypes, one of which was associated with poor lung function, and found that insulin and apoptosis pathways were differentially represented. We found similar clusters driven by metabolomics in BALF but not plasma. Conclusion: Protein expression differs in persons living with HIV with and without obstructive lung disease. We were not able to identify specific pathways differentially expressed among patients based on FEV1 % pred; however, we identified a unique protein endotype associated with insulin and apoptotic pathways.
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Chronic obstructive pulmonary disease (COPD) is among the leading causes of death worldwide and HIV is an independent risk factor for the development of COPD. However, the etiology of this increased risk and means to identify persons with HIV (PWH) at highest risk for COPD have remained elusive. Biomarkers may reveal etiologic pathways and allow better COPD risk stratification. We performed a matched case:control study of PWH in the Strategic Timing of Antiretoviral Treatment (START) pulmonary substudy. Cases had rapid lung function decline (> 40 mL/year FEV1 decline) and controls had stable lung function (+ 20 to - 20 mL/year). The analysis was performed in two distinct groups: (1) those who were virally suppressed for at least 6 months and (2) those with untreated HIV (from the START deferred treatment arm). We used linear mixed effects models to test the relationship between case:control status and blood concentrations of pneumoproteins (surfactant protein-D and club cell secretory protein), and biomarkers of inflammation (IL-6 and hsCRP) and coagulation (d-dimer and fibrinogen); concentrations were measured within ± 6 months of first included spirometry. We included an interaction with treatment group (untreated HIV vs viral suppression) to test if associations varied by treatment group. This analysis included 77 matched case:control pairs in the virally suppressed batch, and 42 matched case:control pairs in the untreated HIV batch (n = 238 total) who were followed for a median of 3 years. Median (IQR) CD4 + count was lowest in the controls with untreated HIV at 674 (580, 838). We found no significant associations between case:control status and pneumoprotein or biomarker concentrations in either virally suppressed or untreated PWH. In this cohort of relatively young, recently diagnosed PWH, concentrations of pneumoproteins and biomarkers of inflammation and coagulation were not associated with subsequent rapid lung function decline.Trial registration: NCT00867048 and NCT01797367.
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Infecções por HIV , Doença Pulmonar Obstrutiva Crônica , Humanos , Infecções por HIV/complicações , Infecções por HIV/tratamento farmacológico , Biomarcadores , Inflamação , PulmãoRESUMO
Human milk is a complex mix of nutritional and bioactive components that provide complete nutrition for the infant. However, we lack a systematic knowledge of the factors shaping milk composition and how milk variation influences infant health. Here, we used multi-omic profiling to characterize interactions between maternal genetics, milk gene expression, milk composition, and the infant fecal microbiome in 242 exclusively breastfeeding mother-infant pairs. We identified 487 genetic loci associated with milk gene expression unique to the lactating mammary gland, including loci that impacted breast cancer risk and human milk oligosaccharide concentration. Integrative analyses uncovered connections between milk gene expression and infant gut microbiome, including an association between the expression of inflammation-related genes with IL-6 concentration in milk and the abundance of Bifidobacteria in the infant gut. Our results show how an improved understanding of the genetics and genomics of human milk connects lactation biology with maternal and infant health.