Network Analysis Reveals Protein Modules Associated with Childhood Respiratory Diseases.

Background: The first year of life is a period of rapid immune development that can impact health trajectories and the risk of developing respiratory-related diseases, such as asthma, recurrent infections, and eczema. However, the biology underlying subsequent disease development remains unknown. Methods: Using weighted gene correlation network analysis (WGCNA), we derived modules of highly correlated immune-related proteins in plasma samples from children at age 1 year (N=294) from the Vitamin D Antenatal Asthma Reduction Trial (VDAART). We applied regression analyses to assess relationships between protein modules and development of childhood respiratory diseases up to age 6 years. We then characterized genomic, environmental, and metabolomic factors associated with modules. Results: WGCNA identified four protein modules at age 1 year associated with incidence of childhood asthma and/or recurrent wheeze (Padj range: 0.02-0.03), respiratory infections (Padj range: 6.3×10-9-2.9×10-6), and eczema (Padj=0.01) by age 6 years; three modules were associated with at least one environmental exposure (Padj range: 2.8×10-10-0.03) and disrupted metabolomic pathway(s) (Padj range: 2.8×10-6-0.04). No genome-wide SNPs were identified as significant genetic risk factors for any protein module. Relationships between protein modules with clinical, environmental, and 'omic factors were temporally sensitive and could not be recapitulated in protein profiles at age 6 years. Conclusion: These findings suggested protein profiles as early as age 1 year predicted development of respiratory-related diseases through age 6 and were associated with changes in pathways related to amino acid and energy metabolism. These may inform new strategies to identify vulnerable individuals based on immune protein profiling.

Urinary eicosanoid levels in early life and risk of atopic disease in childhood.

BACKGROUND: Eicosanoids are lipid mediators including thromboxanes (TXs), prostaglandins (PGs), and leukotrienes with a pathophysiological role in established atopic disease. However, their role in the inception of disease is unclear. This study aimed to investigate the association between urinary eicosanoids in early life and development of atopic disease. METHODS: This study quantified the levels of 21 eicosanoids in urine from children from the COPSAC2010 (Copenhagen Prospective Studies on Asthma in Childhood 2010) (age 1 year, n = 450) and VDAART (Vitamin D Antenatal Asthma Reduction Trial) (age 3 years, n = 575) mother-child cohorts and analyzed the associations with development of wheeze/asthma, atopic dermatitis, and biomarkers of type-2 inflammation, applying false discovery rate of 5% (FDR5%) multiple testing correction. RESULTS: In both cohorts, analyses adjusted for environmental determinants showed that higher TXA2 eicosanoids in early life were associated with increased risk of developing atopic dermatitis (P < FDR5%) and type-2 inflammation (P < .05). In VDAART, lower PGE2 and PGI2 eicosanoids and higher isoprostanes were also associated with increased risk of atopic dermatitis (P < FDR5%). For wheeze/asthma, analyses in COPSAC2010 showed that lower isoprostanes and PGF2 eicosanoids and higher PGD2 eicosanoids at age 1 year associated with an increased risk at age 1-10 years (P < .05), whereas analyses in VDAART showed that lower PGE2 and higher TXA2 eicosanoids at age 3 years associated with an increased risk at 6 years (P < FDR5%). CONCLUSIONS: This study suggests that early life perturbations in the eicosanoid metabolism are present before the onset of atopic disease in childhood, which provides pathophysiological insight in the inception of atopic diseases.

A genome-wide association study of mass spectrometry proteomics using the Seer Proteograph platform.

Genome-wide association studies (GWAS) with proteomics are essential tools for drug discovery. To date, most studies have used affinity proteomics platforms, which have limited discovery to protein panels covered by the available affinity binders. Furthermore, it is not clear to which extent protein epitope changing variants interfere with the detection of protein quantitative trait loci (pQTLs). Mass spectrometry-based (MS) proteomics can overcome some of these limitations. Here we report a GWAS using the MS-based Seer Proteograph™ platform with blood samples from a discovery cohort of 1,260 American participants and a replication in 325 individuals from Asia, with diverse ethnic backgrounds. We analysed 1,980 proteins quantified in at least 80% of the samples, out of 5,753 proteins quantified across the discovery cohort. We identified 252 and replicated 90 pQTLs, where 30 of the replicated pQTLs have not been reported before. We further investigated 200 of the strongest associated cis-pQTLs previously identified using the SOMAscan and the Olink platforms and found that up to one third of the affinity proteomics pQTLs may be affected by epitope effects, while another third were confirmed by MS proteomics to be consistent with the hypothesis that genetic variants induce changes in protein expression. The present study demonstrates the complementarity of the different proteomics approaches and reports pQTLs not accessible to affinity proteomics, suggesting that many more pQTLs remain to be discovered using MS-based platforms.

Metabolomic-derived endotypes of age-related macular degeneration (AMD): a step towards identification of disease subgroups.

Age-related macular degeneration (AMD) is a leading cause of blindness worldwide, with a complex pathophysiology and phenotypic diversity. Here, we apply Similarity Network Fusion (SNF) to cluster AMD patients into putative metabolomics-derived endotypes. Using a discovery cohort of 163 AMD patients from Boston, US, and a validation cohort of 214 patients from Coimbra, Portugal, we identified four distinct metabolomics-derived endotypes with varying retinal structural and functional characteristics, confirmed across both cohorts. Patients clustered into Endotype 1 exhibited a milder form of AMD and were characterized by low levels of amino acids in specific metabolic pathways. Meanwhile, patients clustered into both Endotype 3 and 4 were associated with more severe AMD and exhibited low levels of fatty acid metabolites and elevated levels of sphingomyelins and fatty acid metabolites, respectively. These preliminary findings indicate that metabolomics-derived endotyping may offer a refined strategy for categorizing AMD patients based on their specific pathophysiological underpinnings, rather than relying solely on traditional observational clinical indicators.

Metabolite signatures associated with microRNA miR-143-3p serve as drivers of poor lung function trajectories in childhood asthma.

BACKGROUND: Lung function trajectories (LFTs) have been shown to be an important measure of long-term health in asthma. While there is a growing body of metabolomic studies on asthma status and other phenotypes, there are no prospective studies of the relationship between metabolomics and LFTs or their genomic determinants. METHODS: We utilized ordinal logistic regression to identify plasma metabolite principal components associated with four previously-published LFTs in children from the Childhood Asthma Management Program (CAMP) (n = 660). The top significant metabolite principal component (PCLF) was evaluated in an independent cross-sectional child cohort, the Genetic Epidemiology of Asthma in Costa Rica Study (GACRS) (n = 1151) and evaluated for association with spirometric measures. Using meta-analysis of CAMP and GACRS, we identified associations between PCLF and microRNA, and SNPs in their target genes. Statistical significance was determined using an false discovery rate-adjusted Q-value. FINDINGS: The top metabolite principal component, PCLF, was significantly associated with better LFTs after multiple-testing correction (Q-value = 0.03). PCLF is composed of the urea cycle, caffeine, corticosteroid, carnitine, and potential microbial (secondary bile acid, tryptophan, linoleate, histidine metabolism) metabolites. Higher levels of PCLF were also associated with increases in lung function measures and decreased circulating neutrophil percentage in both CAMP and GACRS. PCLF was also significantly associated with microRNA miR-143-3p, and SNPs in three miR-143-3p target genes; CCZ1 (P-value = 2.6 × 10-5), SLC8A1 (P-value = 3.9 × 10-5); and TENM4 (P-value = 4.9 × 10-5). INTERPRETATION: This study reveals associations between metabolites, miR-143-3p and LFTs in children with asthma, offering insights into asthma physiology and possible interventions to enhance lung function and long-term health. FUNDING: Molecular data for CAMP and GACRS via the Trans-Omics in Precision Medicine (TOPMed) program was supported by the National Heart, Lung, and Blood Institute (NHLBI).

Sphingolipid classes and the interrelationship with pediatric asthma and asthma risk factors.

BACKGROUND: While dysregulated sphingolipid metabolism has been associated with risk of childhood asthma, the specific sphingolipid classes and/or mechanisms driving this relationship remain unclear. We aimed to understand the multifaceted role between sphingolipids and other established asthma risk factors that complicate this relationship. METHODS: We performed targeted LC-MS/MS-based quantification of 77 sphingolipids in plasma from 997 children aged 6 years from two independent cohorts (VDAART and COPSAC2010 ). We examined associations of circulatory sphingolipids with childhood asthma, lung function, and three asthma risk factors: functional SNPs in ORMDL3, low vitamin D levels, and reduced gut microbial maturity. Given racial differences between these cohorts, association analyses were performed separately and then meta-analyzed together. RESULTS: We observed elevations in circulatory sphingolipids with asthma phenotypes and risk factors; however, there were differential associations of sphingolipid classes with clinical outcomes and/or risk factors. While elevations from metabolites involved in ceramide recycling and catabolic pathways were associated with asthma and worse lung function [meta p-value range: 1.863E-04 to 2.24E-3], increased ceramide levels were associated with asthma risk factors [meta p-value range: 7.75E-5 to .013], but not asthma. Further investigation identified that some ceramides acted as mediators while some interacted with risk factors in the associations with asthma outcomes. CONCLUSION: This study demonstrates the differential role that sphingolipid subclasses may play in asthma and its risk factors. While overall elevations in sphingolipids appeared to be deleterious overall; elevations in ceramides were uniquely associated with increases in asthma risk factors only; while elevations in asthma phenotypes were associated with recycling sphingolipids. Modification of asthma risk factors may play an important role in regulating sphingolipid homeostasis via ceramides to affect asthma. Further function work may validate the observed associations.

Plasma Metabolites Associated with OCT Features of Age-Related Macular Degeneration.

Purpose: The most widely used classifications of age-related macular degeneration (AMD) and its severity stages still rely on color fundus photographs (CFPs). However, AMD has a wide phenotypic variability that remains poorly understood and is better characterized by OCT. We and others have shown that patients with AMD have a distinct plasma metabolomic profile compared with controls. However, all studies to date have been performed solely based on CFP classifications. This study aimed to assess if plasma metabolomic profiles are associated with OCT features commonly seen in AMD. Design: Prospectively designed, cross-sectional study. Participants: Subjects with a diagnosis of AMD and a control group (> 50 years old) from Boston, United States, and Coimbra, Portugal. Methods: All participants were imaged with CFP, used for AMD staging (Age-Related Eye Disease Study 2 classification scheme), and with spectral domain OCT (Spectralis, Heidelberg). OCT images were graded by 2 independent graders for the presence of characteristic AMD features, according to a predefined protocol. Fasting blood samples were collected for metabolomic profiling (using nontargeted high-resolution mass spectrometry by Metabolon Inc). Analyses were conducted using logistic regression models including the worst eye of each patient (AREDS2 classification) and adjusting for confounding factors. Each cohort (United States and Portugal) was analyzed separately and then results were combined by meta-analyses. False discovery rate (FDR) was used to account for multiple comparisons. Main Outcome Measures: Plasma metabolite levels associated with OCT features. Results: We included data on 468 patients, 374 with AMD and 94 controls, and on 725 named endogenous metabolites. Meta-analysis identified significant associations (FDR < 0.05) between plasma metabolites and 3 OCT features: hyperreflective foci (6), atrophy (6), and ellipsoid zone disruption (3). Most associations were seen with amino acids, and all but 1 metabolite presented specific associations with the OCT features assessed. Conclusions: To our knowledge, we show for the first time that plasma metabolites have associations with specific OCT features seen in AMD. Our results support that the wide spectrum of presentations of AMD likely include different pathophysiologic mechanisms by identifying specific pathways associated with each OCT feature. Financial Disclosures: Proprietary or commercial disclosure may be found after the references.

OMICmAge: An integrative multi-omics approach to quantify biological age with electronic medical records.

Biological aging is a multifactorial process involving complex interactions of cellular and biochemical processes that is reflected in omic profiles. Using common clinical laboratory measures in ~30,000 individuals from the MGB-Biobank, we developed a robust, predictive biological aging phenotype, EMRAge, that balances clinical biomarkers with overall mortality risk and can be broadly recapitulated across EMRs. We then applied elastic-net regression to model EMRAge with DNA-methylation (DNAm) and multiple omics, generating DNAmEMRAge and OMICmAge, respectively. Both biomarkers demonstrated strong associations with chronic diseases and mortality that outperform current biomarkers across our discovery (MGB-ABC, n=3,451) and validation (TruDiagnostic, n=12,666) cohorts. Through the use of epigenetic biomarker proxies, OMICmAge has the unique advantage of expanding the predictive search space to include epigenomic, proteomic, metabolomic, and clinical data while distilling this in a measure with DNAm alone, providing opportunities to identify clinically-relevant interconnections central to the aging process.

The value of prospective metabolomic susceptibility endotypes: broad applicability for infectious diseases.

BACKGROUND: As new infectious diseases (ID) emerge and others continue to mutate, there remains an imminent threat, especially for vulnerable individuals. Yet no generalizable framework exists to identify the at-risk group prior to infection. Metabolomics has the advantage of capturing the existing physiologic state, unobserved via current clinical measures. Furthermore, metabolomics profiling during acute disease can be influenced by confounding factors such as indications, medical treatments, and lifestyles. METHODS: We employed metabolomic profiling to cluster infection-free individuals and assessed their relationship with COVID severity and influenza incidence/recurrence. FINDINGS: We identified a metabolomic susceptibility endotype that was strongly associated with both severe COVID (ORICUadmission = 6.7, p-value = 1.2 × 10-08, ORmortality = 4.7, p-value = 1.6 × 10-04) and influenza (ORincidence = 2.9; p-values = 2.2 × 10-4, ßrecurrence = 1.03; p-value = 5.1 × 10-3). We observed similar severity associations when recapitulating this susceptibility endotype using metabolomics from individuals during and after acute COVID infection. We demonstrate the value of using metabolomic endotyping to identify a metabolically susceptible group for two-and potentially more-IDs that are driven by increases in specific amino acids, including microbial-related metabolites such as tryptophan, bile acids, histidine, polyamine, phenylalanine, and tyrosine metabolism, as well as carbohydrates involved in glycolysis. INTERPRETATIONS: These metabolites may be identified prior to infection to enable protective measures for these individuals. FUNDING: The Longitudinal EMR and Omics COVID-19 Cohort (LEOCC) and metabolomic profiling were supported by the National Heart, Lung, and Blood Institute and the Intramural Research Program of the National Center for Advancing Translational Sciences, National Institutes of Health.

An epidemiological introduction to human metabolomic investigations.

Metabolomics holds great promise for uncovering insights around biological processes impacting disease in human epidemiological studies. Metabolites can be measured across biological samples, including plasma, serum, saliva, urine, stool, and whole organs and tissues, offering a means to characterize metabolic processes relevant to disease etiology and traits of interest. Metabolomic epidemiology studies face unique challenges, such as identifying metabolites from targeted and untargeted assays, defining standards for quality control, harmonizing results across platforms that often capture different metabolites, and developing statistical methods for high-dimensional and correlated metabolomic data. In this review, we introduce metabolomic epidemiology to the broader scientific community, discuss opportunities and challenges presented by these studies, and highlight emerging innovations that hold promise to uncover new biological insights.

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.

Elevated third trimester corticosteroid levels are associated with fewer offspring infections.

Respiratory infections are a leading cause of morbidity and mortality in early life, and recurrent infections increase the risk of developing chronic diseases. The maternal environment during pregnancy can impact offspring health, but the factors leading to increased infection proneness have not been well characterized during this period. Steroids have been implicated in respiratory health outcomes and may similarly influence infection susceptibility. Our objective was to describe relationships between maternal steroid levels and offspring infection proneness. Using adjusted Poisson regression models, we evaluated associations between sixteen androgenic and corticosteroid metabolites during pregnancy and offspring respiratory infection incidence across two pre-birth cohorts (N = 774 in VDAART and N = 729 in COPSAC). Steroid metabolites were measured in plasma samples from pregnant mothers across all trimesters of pregnancy by ultrahigh-performance-liquid-chromatography/mass-spectrometry. We conducted further inquiry into associations of steroids with related respiratory outcomes: asthma and lung function spirometry. Higher plasma corticosteroid levels in the third trimester of pregnancy were associated with lower incidence of offspring respiratory infections (P = 4.45 × 10-7 to 0.002) and improved lung function metrics (P = 0.020-0.036). Elevated maternal androgens were generally associated with increased offspring respiratory infections and worse lung function, with some associations demonstrating nominal significance at P < 0.05, but these trends were inconsistent across individual androgens. Increased maternal plasma corticosteroid levels in the late second and third trimesters were associated with lower infections and better lung function in offspring, which may represent a potential avenue for intervention through corticosteroid supplementation in late pregnancy to reduce offspring respiratory infection susceptibility in early life.Clinical Trial Registry information: VDAART and COPSAC were originally conducted as clinical trials; VDAART: ClinicalTrials.gov identifier NCT00920621; COPSAC: ClinicalTrials.gov identifier NCT00798226.

Whole-Genome Sequencing Analysis of Human Metabolome in Multi-Ethnic Populations.

Circulating metabolite levels may reflect the state of the human organism in health and disease, however, the genetic architecture of metabolites is not fully understood. We have performed a whole-genome sequencing association analysis of both common and rare variants in up to 11,840 multi-ethnic participants from five studies with up to 1666 circulating metabolites. We have discovered 1985 novel variant-metabolite associations, and validated 761 locus-metabolite associations reported previously. Seventy-nine novel variant-metabolite associations have been replicated, including three genetic loci located on the X chromosome that have demonstrated its involvement in metabolic regulation. Gene-based analysis have provided further support for seven metabolite-replicated loci pairs and their biologically plausible genes. Among those novel replicated variant-metabolite pairs, follow-up analyses have revealed that 26 metabolites have colocalized with 21 tissues, seven metabolite-disease outcome associations have been putatively causal, and 7 metabolites might be regulated by plasma protein levels. Our results have depicted the genetic contribution to circulating metabolite levels, providing additional insights into understanding human disease.

Phenotypically driven subgroups of ASD display distinct metabolomic profiles.

Autism Spectrum Disorder (ASD) is a heterogeneous condition that includes a broad range of characteristics and associated comorbidities; however, the biology underlying the variability in phenotypes is not well understood. As ASD impacts approximately 1 in 100 children globally, there is an urgent need to better understand the biological mechanisms that contribute to features of ASD. In this study, we leveraged rich phenotypic and diagnostic information related to ASD in 2001 individuals aged 4 to 17 years from the Simons Simplex Collection to derive phenotypically driven subgroups and investigate their respective metabolomes. We performed hierarchical clustering on 40 phenotypes spanning four ASD clinical domains, resulting in three subgroups with distinct phenotype patterns. Using global plasma metabolomic profiling generated by ultrahigh-performance liquid chromatography mass spectrometry, we characterized the metabolome of individuals in each subgroup to interrogate underlying biology related to the subgroups. Subgroup 1 included children with the least maladaptive behavioral traits (N = 862); global decreases in lipid metabolites and concomitant increases in amino acid and nucleotide pathways were observed for children in this subgroup. Subgroup 2 included children with the highest degree of challenges across all phenotype domains (N = 631), and their metabolome profiles demonstrated aberrant metabolism of membrane lipids and increases in lipid oxidation products. Subgroup 3 included children with maladaptive behaviors and co-occurring conditions that showed the highest IQ scores (N = 508); these individuals had increases in sphingolipid metabolites and fatty acid byproducts. Overall, these findings indicated distinct metabolic patterns within ASD subgroups, which may reflect the biological mechanisms giving rise to specific patterns of ASD characteristics. Our results may have important clinical applications relevant to personalized medicine approaches towards managing ASD symptoms.

Metabolomic profiling reveals extensive adrenal suppression due to inhaled corticosteroid therapy in asthma.

The application of large-scale metabolomic profiling provides new opportunities for realizing the potential of omics-based precision medicine for asthma. By leveraging data from over 14,000 individuals in four distinct cohorts, this study identifies and independently replicates 17 steroid metabolites whose levels were significantly reduced in individuals with prevalent asthma. Although steroid levels were reduced among all asthma cases regardless of medication use, the largest reductions were associated with inhaled corticosteroid (ICS) treatment, as confirmed in a 4-year low-dose ICS clinical trial. Effects of ICS treatment on steroid levels were dose dependent; however, significant reductions also occurred with low-dose ICS treatment. Using information from electronic medical records, we found that cortisol levels were substantially reduced throughout the entire 24-hour daily period in patients with asthma who were treated with ICS compared to those who were untreated and to patients without asthma. Moreover, patients with asthma who were treated with ICS showed significant increases in fatigue and anemia as compared to those without ICS treatment. Adrenal suppression in patients with asthma treated with ICS might, therefore, represent a larger public health problem than previously recognized. Regular cortisol monitoring of patients with asthma treated with ICS is needed to provide the optimal balance between minimizing adverse effects of adrenal suppression while capitalizing on the established benefits of ICS treatment.

Urinary Mass Spectrometry Profiles in Age-Related Macular Degeneration.

We and others have shown that patients with different severity stages of age-related macular degeneration (AMD) have distinct plasma metabolomic profiles compared to controls. Urine is a biofluid that can be obtained non-invasively and, in other fields, urine metabolomics has been proposed as a feasible alternative to plasma biomarkers. However, no studies have applied urinary mass spectrometry (MS) metabolomics to AMD. This study aimed to assess urinary metabolomic profiles of patients with different stages of AMD and a control group. We included two prospectively designed, multicenter, cross-sectional study cohorts: Boston, US (n = 185) and Coimbra, Portugal (n = 299). We collected fasting urine samples, which were used for metabolomic profiling (Ultrahigh Performance Liquid chromatography-Mass Spectrometry). Multivariable logistic and ordinal logistic regression models were used for analysis, accounting for gender, age, body mass index and use of AREDS supplementation. Results from both cohorts were then meta-analyzed. No significant differences in urine metabolites were seen when comparing patients with AMD and controls. When disease severity was considered as an outcome, six urinary metabolites differed significantly (p < 0.01). In particular, two of the metabolites identified have been previously shown by our group to also differ in the plasma of patients of AMD compared to controls and across severity stages. While there are fewer urinary metabolites associated with AMD than plasma metabolites, this study identified some differences across stages of disease that support previous work performed with plasma, thus highlighting the potential of these metabolites as future biomarkers for AMD.

Plasma Metabolomic Profiles Associated with Three-Year Progression of Age-Related Macular Degeneration.

Plasma metabolomic profiles have been shown to be associated with age-related macular degeneration (AMD) and its severity stages. However, all studies performed to date have been cross-sectional and have not assessed progression of AMD. This prospective, longitudinal, pilot study analyzes, for the first time, the association between plasma metabolomic profiles and progression of AMD over a 3-year period. At baseline and 3 years later, subjects with AMD (n = 108 eyes) and controls (n = 45 eyes) were imaged with color fundus photos for AMD staging and tested for retinal function with dark adaptation (DA). Fasting plasma samples were also collected for metabolomic profiling. AMD progression was considered present if AMD stage at 3 years was more advanced than at baseline (n = 26 eyes, 17%). Results showed that, of the metabolites measured at baseline, eight were associated with 3-year AMD progression (p < 0.01) and 19 (p < 0.01) with changes in DA. Additionally, changes in the levels (i.e., between 3 years and baseline) of 6 and 17 metabolites demonstrated significant associations (p < 0.01) with AMD progression and DA, respectively. In conclusion, plasma metabolomic profiles are associated with clinical and functional progression of AMD at 3 years. These findings contribute to our understanding of mechanisms of AMD progression and the identification of potential therapeutics for this blinding disease.

Area under the dark adaptation curve as a reliable alternate measure of dark adaptation response.

PURPOSE: Quantification of dark adaptation (DA) response using the conventional rod intercept time (RIT) requires very long testing time and may not be measurable in the presence of impairments due to diseases such as age-related macular degeneration (AMD). The goal of this study was to investigate the advantages of using area under the DA curve (AUDAC) as an alternative to the conventional parameters to quantify DA response. METHODS: Data on 136 eyes (AMD: 98, normal controls: 38) from an ongoing longitudinal study on AMD were used. DA was measured using the AdaptDx 20 min protocol. AUDAC was computed from the raw DA characteristic curve at different time points, including 6.5 min and 20 min (default). The presence of AMD in the given eye was predicted using a logistic regression model within the leave-one-out cross-validation framework, with DA response as the predictor while adjusting for age and gender. The DA response variable was either the AUDAC values computed at 6.5 min (AUDAC6.5) or at 20 min (AUDAC20) cut-off, or the conventional RIT. RESULTS: AUDAC6.5 was strongly correlated with AUDAC20 (ß=86, p<0.001, R2=0.87). The accuracy of predicting the presence of AMD using AUDAC20 was 76%, compared with 79% when using RIT, the current gold standard. In addition, when limiting AUDAC calculation to 6.5 min cut-off, the predictive accuracy of AUDAC6.5 was 80%. CONCLUSIONS: AUDAC can be a valuable measure to quantify the overall DA response and can potentially facilitate shorter testing duration while maintaining diagnostic accuracy.