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Longitudinal Changes In Telomere Length In Patients with Dyskeratosis Congenita

2010· article· en· W2556728679 on OpenAlexaff
Blanche P. Alter, Neelam Giri, Peter M. Lansdorp, Gabriela M. Baerlocher, Philip S. Rosenberg, Sharon A. Savage

Bibliographic record

VenueBlood · 2010
Typearticle
Languageen
FieldMedicine
TopicTelomeres, Telomerase, and Senescence
Canadian institutionsTerry Fox Research Institute
Fundersnot available
KeywordsDyskeratosis congenitaTelomereFluorescence in situ hybridizationBiologyBone marrow failureChromosome instabilityPathologyGeneticsChromosomeMedicineGeneStem cell

Abstract

fetched live from OpenAlex

Abstract Abstract 2230 Dyskeratosis congenita (DC) is an inherited bone marrow failure syndrome with a complex clinical phenotype, including dysplastic nails, lacy reticular pigmentation, and oral leukoplakia (the diagnostic triad). Numerous other physical abnormalities may be present, in addition to cytopenias due to bone marrow failure, and a high risk of leukemia or solid tumors. However, many patients have no physical findings at diagnosis. Patients with DC have very short telomeres, and approximately one-half have a mutation in one of six genes important in telomere biology. Telomere length in leukocyte subsets, measured by automated flow fluorescence in situ hybridization (flow-FISH), is both sensitive and specific for identifying individuals with DC. Telomeres consist of nucleotide repeats and a protein complex at chromosome ends that are critical in chromosomal stability which shorten during normal cell division. Cross-sectional studies of normal individuals suggest that telomere length decreases with age in a sigmoid pattern from birth to old age. In a cross-sectional analysis of 26 patients with DC, we previously observed that telomere length appeared to be stable or even to slightly increase with age (BP Alter et al, Blood 110:149, 2007). Similar results were shown in 23 different DC patients by others (M Bessler et al, FEBS Lett 2010 in press). We speculated that these data were influenced by early presentation (or recognition) of clinically more severe patients, while patients with similar telomere length who were clinically milder were identified at older ages. In this pilot study, we examined, for the first time, the longitudinal age-association of telomere attrition in nine patients with DC who were followed for five to seven years (currently 8 – 50 years of age). These include three patients with mutations in TERC, and two each with TINF2, TERT, and DKC1 mutations. When first studied, four had normal hematopoiesis, three moderate cytopenias, one was receiving androgens, and one was on transfusions. Subsequently, one with normal hematopoiesis developed mild thrombocytopenia, one who was on transfusions responded to androgens, and one with moderate aplastic anemia became severe. In all cases, telomere length decreased with age. In a linear regression model, the average annual decrease in telomere length in lymphocytes was 167 base pairs/year (bp/yr) + 104, similar to the rate in granulocytes, 159 + 92 bp/yr. According to the literature, the rate of telomere attrition in longitudinal studies in normal blood is ∼45-50 bp/yr, albeit by methods other than flow-FISH; the rate of telomere shortening appears to decrease with increasing age. The average patient Z-scores at the beginning of the study were -3.9 standard deviations below the median for age in healthy normal controls, and were -4.3 at the end, consistent with the impression that DC patient telomeres shorten somewhat more than expected from normal aging. These data support the hypothesis that the earlier cross-sectional results for patients with DC indeed were influenced by the cross-sectional rather than longitudinal nature of the data. The current longitudinal data suggest that telomere shortening could possibly be accelerated in patients with DC, but larger studies are required. Our results indicate that patients with DC have telomeres that are much shorter than normal for their age, and that over time they continue to shorten, consistent with DC being classified as a disorder of premature aging. Disclosures: Lansdorp: Repeat Diagnostics: Equity Ownership.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.008
GPT teacher head0.219
Teacher spread0.211 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2010
Admission routes1
Has abstractyes

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