Evidence map›Paper›PMID 35918752›Full record

ArticleBMC medical genomics2022

Clinical genetics evaluation and testing of connective tissue disorders: a cross-sectional study.

Olivia J Veatch, Jacob Steinle, Waheeda A Hossain, Merlin G Butler

Open access · goldAbstract read
In one paragraph

Article in BMC medical genomics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
1.3field-weighted citation impact, top 18% of its field
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

8 citing papers in PubMed, 8 citations in OpenAlex.

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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

4 authors at 1 institution in 1 country.

Olivia J Veatch *Department of Psychiatry and Behavioral Sciences, University of Kansas Medical Center, 3901 Rainbow Blvd., MS-4015, Kansas City, KS, 66160, USA. oveatch@kumc.edu.
Jacob Steinle *Department of Psychiatry and Behavioral Sciences, University of Kansas Medical Center, 3901 Rainbow Blvd., MS-4015, Kansas City, KS, 66160, USA.
Waheeda A HossainDepartment of Psychiatry and Behavioral Sciences, University of Kansas Medical Center, 3901 Rainbow Blvd., MS-4015, Kansas City, KS, 66160, USA.
Merlin G ButlerDepartment of Psychiatry and Behavioral Sciences, University of Kansas Medical Center, 3901 Rainbow Blvd., MS-4015, Kansas City, KS, 66160, USA.
University of Kansas Medical Center · US

Funding

Research Design and Analysis Core (RDAC)P30HD002528 · NICHD · UNIVERSITY OF KANSAS LAWRENCE · PI LITTLE, TODD D. · 1985 to 2015
$22.1M
Efficient Translation of Genetics Research for Clinical Decision SupportK01LM012870 · NLM · UNIVERSITY OF PENNSYLVANIA · PI VEATCH, OLIVIA J · 2018 to 2020
$537k
Elucidating the causes and consequences of sleep disturbances in children with rare genetic syndromesR21HD107535 · NICHD · UNIVERSITY OF KANSAS MEDICAL CENTER · PI BUTLER, MERLIN G, VEATCH, OLIVIA J · 2022 to 2023
$426k
NICHD NIH HHS P30 HD002528NICHD NIH HHS R21 HD107535NLM NIH HHS K01 LM012870
6 · The paper itself

Abstract

backgroundHeritable connective tissue disorders (HCTDs) consist of heterogeneous syndromes. The diagnosis of HCTDs is aided by genomic biotechnologies (e.g., next-generation sequencing panels) facilitating the discovery of novel variants causing disease.

methodsDetailed clinical exam data and CLIA-approved genetic testing results from next generation sequencing of 74 genes known to play a role in HCTDs were manually reviewed and analyzed in one hundred consecutive, unrelated patients with phenotypic features indicative of a HCTD referred over a 3.5-year period (2016-2020) to a specialized academic genetics clinic. The prevalence of symptoms was evaluated in the context of genetic variants. We also determined if symptoms among different organ systems were related and performed latent class analysis to identify distinct groups of patients based on symptomatology.

resultsIn the cohort of 100 consecutive, unrelated individuals there were four pathogenic, six likely pathogenic and 35 classified potentially pathogenic variants of unknown clinical significance. Patients with potentially pathogenic variants exhibited similar symptom profiles when compared to patients with pathogenic/likely pathogenic variants in the same genes. Although results did not meet a multiple testing corrected threshold, patients with connective tissue symptoms had suggestive evidence of increased odds of having skin (odds ratio 2.18, 95% confidence interval 1.12 to 4.24) and eye symptoms (odds ratio 1.89, 95% confidence interval 0.98 to 3.66) requiring further studies. The best performing latent class analysis results were identified when dividing the dataset into three distinct groups based on age, gender and presence or absence of symptoms in the skeletal, connective tissue, nervous, gastrointestinal and cardiovascular systems. These distinct classes of patients included individuals with: (1) minimal skeletal symptoms, (2) more skeletal but fewer connective tissue, nervous or gastrointestinal symptoms and (3) more nervous system symptoms.

conclusionsWe used novel approaches to characterize phenotype-genotype relationships, including pinpointing potentially pathogenic variants, and detecting unique symptom profiles in patients with features of HCTDs. This study may guide future diagnosis and disease/organ system monitoring with continued improvement and surveillance by clinicians for patients and their families.

Indexed as

Connective Tissue DiseasesEhlers-Danlos SyndromeConnective TissueCross-Sectional StudiesHigh-Throughput Nucleotide SequencingHumansEhlers–Danlos syndromeGene variantsHeritable connective tissue disordersLatent class analysisNext-generation sequencingPhenotype-genotype relationships

Identifiers

PMID35918752
PMCPMC9344629
OpenAlexW4289518204

What OpenQuestion holds

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LicenceCC BY
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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.