A meta-analysis of immune-cell fractions at high resolution reveals novel associations with common phenotypes and health outcomes.

BACKGROUND: Changes in cell-type composition of tissues are associated with a wide range of diseases and environmental risk factors and may be causally implicated in disease development and progression. However, these shifts in cell-type fractions are often of a low magnitude, or involve similar cell subtypes, making their reliable identification challenging. DNA methylation profiling in a tissue like blood is a promising approach to discover shifts in cell-type abundance, yet studies have only been performed at a relatively low cellular resolution and in isolation, limiting their power to detect shifts in tissue composition. METHODS: Here we derive a DNA methylation reference matrix for 12 immune-cell types in human blood and extensively validate it with flow-cytometric count data and in whole-genome bisulfite sequencing data of sorted cells. Using this reference matrix, we perform a directional Stouffer and fixed effects meta-analysis comprising 23,053 blood samples from 22 different cohorts, to comprehensively map associations between the 12 immune-cell fractions and common phenotypes. In a separate cohort of 4386 blood samples, we assess associations between immune-cell fractions and health outcomes. RESULTS: Our meta-analysis reveals many associations of cell-type fractions with age, sex, smoking and obesity, many of which we validate with single-cell RNA sequencing. We discover that naïve and regulatory T-cell subsets are higher in women compared to men, while the reverse is true for monocyte, natural killer, basophil, and eosinophil fractions. Decreased natural killer counts associated with smoking, obesity, and stress levels, while an increased count correlates with exercise and sleep. Analysis of health outcomes revealed that increased naïve CD4 + T-cell and N-cell fractions associated with a reduced risk of all-cause mortality independently of all major epidemiological risk factors and baseline co-morbidity. A machine learning predictor built only with immune-cell fractions achieved a C-index value for all-cause mortality of 0.69 (95%CI 0.67-0.72), which increased to 0.83 (0.80-0.86) upon inclusion of epidemiological risk factors and baseline co-morbidity. CONCLUSIONS: This work contributes an extensively validated high-resolution DNAm reference matrix for blood, which is made freely available, and uses it to generate a comprehensive map of associations between immune-cell fractions and common phenotypes, including health outcomes.

Metabolomic differences in lung function metrics: evidence from two cohorts.

RATIONALE: The biochemical mechanisms underlying lung function are incompletely understood. OBJECTIVES: To identify and validate the plasma metabolome of lung function using two independent adult cohorts: discovery-the European Prospective Investigation into Cancer-Norfolk (EPIC-Norfolk, n=10 460) and validation-the VA Normative Aging Study (NAS) metabolomic cohort (n=437). METHODS: We ran linear regression models for 693 metabolites to identify associations with forced expiratory volume in one second (FEV1) and the ratio of FEV1 to forced vital capacity (FEV1/FVC), in EPIC-Norfolk then validated significant findings in NAS. Significance in EPIC-Norfolk was denoted using an effective number of tests threshold of 95%; a metabolite was considered validated in NAS if the direction of effect was consistent and p<0.05. MEASUREMENTS AND MAIN RESULTS: Of 156 metabolites that associated with FEV1 in EPIC-Norfolk after adjustment for age, sex, body mass index, height, smoking and asthma status, 34 (21.8%) validated in NAS, including several metabolites involved in oxidative stress. When restricting the discovery sample to men only, a similar percentage, 18 of 79 significant metabolites (22.8%) were validated. A smaller number of metabolites were validated for FEV1/FVC, 6 of 65 (9.2%) when including all EPIC-Norfolk as the discovery population, and 2 of 34 (5.9%) when restricting to men. These metabolites were characterised by involvement in respiratory track secretants. Interestingly, no metabolites were validated for both FEV1 and FEV1/FVC. CONCLUSIONS: The validation of metabolites associated with respiratory function can help to better understand mechanisms of lung health and may assist the development of biomarkers.

Procalcitonin metabolomics in the critically ill reveal relationships between inflammation intensity and energy utilization pathways.

Procalcitonin is a biomarker of systemic inflammation and may have importance in the immune response. The metabolic response to elevated procalcitonin in critical illness is not known. The response to inflammation is vitally important to understanding metabolism alterations during extreme stress. Our aim was to determine if patients with elevated procalcitonin have differences in the metabolomic response to early critical illness. We performed a metabolomics study of the VITdAL-ICU trial where subjects received high dose vitamin D3 or placebo. Mixed-effects modeling was used to study changes in metabolites over time relative to procalcitonin levels adjusted for age, Simplified Acute Physiology Score II, admission diagnosis, day 0 25-hydroxyvitamin D level, and the 25-hydroxyvitamin D response to intervention. With elevated procalcitonin, multiple members of the short and medium chain acylcarnitine, dicarboxylate fatty acid, branched-chain amino acid, and pentose phosphate pathway metabolite classes had significantly positive false discovery rate corrected associations. Further, multiple long chain acylcarnitines and lysophosphatidylcholines had significantly negative false discovery rate corrected associations with elevated procalcitonin. Gaussian graphical model analysis revealed functional modules specific to elevated procalcitonin. Our findings show that metabolite differences exist with increased procalcitonin indicating activation of branched chain amino acid dehydrogenase and a metabolic shift.

Association of Human Plasma Metabolomics with Delayed Dark Adaptation in Age-Related Macular Degeneration.

The purpose of this study was to analyze the association between plasma metabolite levels and dark adaptation (DA) in age-related macular degeneration (AMD). This was a cross-sectional study including patients with AMD (early, intermediate, and late) and control subjects older than 50 years without any vitreoretinal disease. Fasting blood samples were collected and used for metabolomic profiling with ultra-performance liquid chromatography-mass spectrometry (LC-MS). Patients were also tested with the AdaptDx (MacuLogix, Middletown, PA, USA) DA extended protocol (20 min). Two measures of dark adaptation were calculated and used: rod-intercept time (RIT) and area under the dark adaptation curve (AUDAC). Associations between dark adaption and metabolite levels were tested using multilevel mixed-effects linear modelling, adjusting for age, gender, body mass index (BMI), smoking, race, AMD stage, and Age-Related Eye Disease Study (AREDS) formulation supplementation. We included a total of 71 subjects: 53 with AMD (13 early AMD, 31 intermediate AMD, and 9 late AMD) and 18 controls. Our results revealed that fatty acid-related lipids and amino acids related to glutamate and leucine, isoleucine and valine metabolism were associated with RIT (p < 0.01). Similar results were found when AUDAC was used as the outcome. Fatty acid-related lipids and amino acids are associated with DA, thus suggesting that oxidative stress and mitochondrial dysfunction likely play a role in AMD and visual impairment in this condition.

Whole Genome Sequencing Identifies CRISPLD2 as a Lung Function Gene in Children With Asthma.

BACKGROUND: Asthma is a common respiratory disorder with a highly heterogeneous nature that remains poorly understood. The objective was to use whole genome sequencing (WGS) data to identify regions of common genetic variation contributing to lung function in individuals with a diagnosis of asthma. METHODS: WGS data were generated for 1,053 individuals from trios and extended pedigrees participating in the family-based Genetic Epidemiology of Asthma in Costa Rica study. Asthma affection status was defined through a physician's diagnosis of asthma, and most participants with asthma also had airway hyperresponsiveness (AHR) to methacholine. Family-based association tests for single variants were performed to assess the associations with lung function phenotypes. RESULTS: A genome-wide significant association was identified between baseline FEV1/FVC ratio and a single-nucleotide polymorphism in the top hit cysteine-rich secretory protein LCCL domain-containing 2 (CRISPLD2) (rs12051168; P = 3.6 × 10-8 in the unadjusted model) that retained suggestive significance in the covariate-adjusted model (P = 5.6 × 10-6). Rs12051168 was also nominally associated with other related phenotypes: baseline FEV1 (P = 3.3 × 10-3), postbronchodilator (PB) FEV1 (7.3 × 10-3), and PB FEV1/FVC ratio (P = 2.7 × 10-3). The identified baseline FEV1/FVC ratio and rs12051168 association was meta-analyzed and replicated in three independent cohorts in which most participants with asthma also had confirmed AHR (combined weighted z-score P = .015) but not in cohorts without information about AHR. CONCLUSIONS: These findings suggest that using specific asthma characteristics, such as AHR, can help identify more genetically homogeneous asthma subgroups with genotype-phenotype associations that may not be observed in all children with asthma. CRISPLD2 also may be important for baseline lung function in individuals with asthma who also may have AHR.

Clarification of the risk of chronic obstructive pulmonary disease in α1-antitrypsin deficiency PiMZ heterozygotes.

RATIONALE: Severe α1-antitrypsin deficiency (typically PiZZ homozygosity) is associated with a significantly increased risk of airflow obstruction and emphysema but the risk of chronic obstructive pulmonary disease (COPD) in PiMZ heterozygotes remains uncertain. OBJECTIVES: This was a family-based study to determine the risk of COPD in PiMZ individuals. METHODS: We compared 99 PiMM and 89 PiMZ nonindex subjects recruited from 51 index probands who were confirmed PiMZ heterozygotes and also had a diagnosis of COPD Global Initiative for Chronic Obstructive Lung Disease stage II-IV. The primary outcome measures of interest were quantitative variables of pre- and post-bronchodilator FEV1/FVC ratio, FEV1 (liters), FEV1 (% predicted), forced expiratory flow midexpiratory phase (FEF25-75; liters per second), FEF25-75 (% predicted), and a categorical outcome of COPD. MEASUREMENTS AND MAIN RESULTS: PiMZ heterozygotes compared with PiMM individuals had a reduced median (interquartile range) post-bronchodilator FEV1 (% predicted) (92.0 [75.6-105.4] vs. 98.6 [85.5-109.7]; P = 0.04), FEV1/FVC ratio (0.75 [0.66-0.79] vs. 0.78 [0.73-0.83]; P = 0.004), and FEF25-75 (% predicted) (63.84 [38.45-84.35] vs. 72.8 [55.5-97.7]; P = 0.0013) compared with PiMM individuals. This effect was abrogated in never-smoking and accentuated in ever-smoking PiMZ individuals. PiMZ heterozygosity was associated with an adjusted odds ratio for COPD of 5.18 (95% confidence interval, 1.27-21.15; P = 0.02) and this was higher (odds ratio, 10.65; 95% confidence interval, 2.17-52.29; P = 0.004) in ever-smoking individuals. CONCLUSIONS: These results indicate that PiMZ heterozygotes have significantly more airflow obstruction and COPD than PiMM individuals and cigarette smoke exposure exerts a significant modifier effect.

Genome-wide linkage analysis of heroin dependence in Han Chinese: results from Wave Two of a multi-stage study.

Previously we reported the results of Wave One of a genome-wide search for heroin dependence susceptibility loci in Han Chinese families from Yunnan Province, China, near Asia's "Golden Triangle". Our initial analysis of 194 independent affected sibling-pairs from 192 families identified two regions with nonparametric linkage (NPL) Z-scores greater than 2.0, which were suggestive of linkage. Presently we have supplemented our sample with additional individuals and families, bringing the total number of genotyped individuals to 1513 and the number of independent sibling-pairs to 397. Upon repeating our analyses with this larger sample, we found that the evidence for linkage at our most strongly implicated locus from Wave One (marker D17S1880; 53.4cM on 17q11.2; NPL Z=2.36; uncorrected p=0.009) was completely abolished (Z=-1.13; p=0.900). In contrast, the evidence for linkage at the second-most strongly implicated locus from Wave One (D4S1644; 143.3cM on 4q31.21; NPL Z=2.19; uncorrected p=0.014) increased in its magnitude and significance (Z=2.64; uncorrected p=0.004), becoming the most strongly implicated locus overall in our full sample. Other loci on chromosomes 1, 2, 4, 12, 16, and X also displayed nominally significant evidence for linkage (p< or =0.05). These loci appear to be entirely distinct from opioid-linked loci reported by other groups; however, meta-analyses of all available linkage data may reveal common sites of interest and promising candidate genes that can be further evaluated as risk factors for the illness.

Evaluation of OPRM1 variants in heroin dependence by family-based association testing and meta-analysis.

OPRM1, which codes for the mu-opioid receptor, is the most frequently studied candidate gene for opioid dependence. Despite numerous allelic association studies, no definitive conclusion has been reached regarding the role of OPRM1 polymorphisms in determining risk for opioid dependence. We attempted to resolve this by conducting a family-based association study and meta-analysis which may be more robust and powerful, respectively, than traditional case-control analyses. First, we genotyped three single nucleotide polymorphisms (SNPs) of OPRM1 in 1208 individuals from 473 Han Chinese families ascertained on the basis of having two or more siblings with DSM-IV-defined opioid dependence. The Val6Ala and Arg111His SNPs were detected, but with low minor allele frequencies (0.002 and 0.001, respectively). The Asn40Asp SNP was more informative (minor allele frequency: 0.419), but no significant evidence was observed for either a dominant (p=0.810) or additive (p=0.406) effect of this polymorphism on risk for opioid dependence. In addition, a meta-analysis of case-control studies of opioid dependence was performed, and found a similar lack of evidence for an association with the Asn40Asp SNP (p=0.859). Although a role of OPRM1 polymorphisms in determining risk for opioid dependence cannot be entirely discounted, a major contribution of the Asn40Asp polymorphism seems unlikely. Further analysis is warranted in samples from specific ancestral groups. In addition, it is critical that other OPRM1 variants, including all haplotype-tagging and amino-acid-coding SNPs, be tested for an influence on risk for opioid dependence, since the Asn40Asp polymorphism is only one of several hundred known mutations in the gene.