Evidence map›Paper›PMID 37869026›Full record

ArticleOphthalmology science

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

Ines Lains, Xikun Han, João Gil, Joana Providencia, Archana Nigalye, Rodrigo Alvarez, Vivian Paraskevi Douglas, Kevin Mendez, Raviv Katz, Gregory Tsougranis and 10 more

Abstract read
In one paragraph

Article in Ophthalmology science. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

11 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

20 authors.

Ines LainsDepartment of Ophthalmology, Massachusetts Eye and Ear, Harvard Medical School, Boston, Massachusetts.
Xikun HanDepartment of Epidemiology, Harvard T H Chan School of Public Health, Boston, Massachusetts.
João GilFaculty of Medicine, University of Coimbra, Coimbra, Portugal.
Joana ProvidenciaFaculty of Medicine, University of Coimbra, Coimbra, Portugal.
Archana NigalyeDepartment of Ophthalmology, Massachusetts Eye and Ear, Harvard Medical School, Boston, Massachusetts.
Rodrigo AlvarezDepartment of Ophthalmology, Massachusetts Eye and Ear, Harvard Medical School, Boston, Massachusetts.
Vivian Paraskevi DouglasDepartment of Ophthalmology, Massachusetts Eye and Ear, Harvard Medical School, Boston, Massachusetts.
Kevin MendezSystems Genetics and Genomics Unit, Channing Division of Network Medicine Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts.
Raviv KatzDepartment of Ophthalmology, Massachusetts Eye and Ear, Harvard Medical School, Boston, Massachusetts.
Gregory TsougranisDepartment of Ophthalmology, Massachusetts Eye and Ear, Harvard Medical School, Boston, Massachusetts.
Jinglun LiDepartment of Biostatistics, Harvard T H Chan School of Public Health, Boston, Massachusetts.
Rachel S KellySystems Genetics and Genomics Unit, Channing Division of Network Medicine Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts.
Ivana K KimDepartment of Ophthalmology, Massachusetts Eye and Ear, Harvard Medical School, Boston, Massachusetts.
Jessica Lasky-SuSystems Genetics and Genomics Unit, Channing Division of Network Medicine Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts.
Rufino SilvaFaculty of Medicine, University of Coimbra, Coimbra, Portugal.
Joan W MillerDepartment of Ophthalmology, Massachusetts Eye and Ear, Harvard Medical School, Boston, Massachusetts.
Liming LiangDepartment of Biostatistics, Harvard T H Chan School of Public Health, Boston, Massachusetts.
Demetrios VavvasDepartment of Ophthalmology, Massachusetts Eye and Ear, Harvard Medical School, Boston, Massachusetts.
John B MillerDepartment of Ophthalmology, Massachusetts Eye and Ear, Harvard Medical School, Boston, Massachusetts.
Deeba HusainDepartment of Ophthalmology, Massachusetts Eye and Ear, Harvard Medical School, Boston, Massachusetts.

Funding

Metabolomics a Novel Tool for Investigating the Pathogenesis of Age-related Macular DegenerationR01EY030088 · NEI · MASSACHUSETTS EYE AND EAR INFIRMARY · PI Deeba Husain · 2020 to 2026
$3.5M
Multi-Omic Endotyping of AsthmaK01HL146980 · NHLBI · BRIGHAM AND WOMEN'S HOSPITAL · PI KELLY, RACHEL SABINE · 2019 to 2023
$891k
NEI NIH HHS R01 EY030088NHLBI NIH HHS K01 HL146980
6 · The paper itself

Abstract

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.

Indexed as

Age-related macular degeneration/AMDAtrophyHyperreflective fociMetabolomicsOCT

Identifiers

PMID37869026
PMCPMC10587636

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.