Telomere Length Dynamics in Normal Individuals and in Patients with Hematopoietic Stem Cell‐Associated Disorders
Bibliographic record
Abstract
The telomere length in nucleated peripheral blood (PB) cells indirectly reflects the mitotic history of their precursors: the hematopoietic stem cells (HSCs). The average length of telomeres in PB leukocytes can be measured using fluorescence in situ hybridization and flow cytometry (flow FISH). We previously used flow FISH to characterize the age-related turnover of HSCs in healthy individuals. In this review, we describe results of recent flow FISH studies in patients with selected hematopoietic stem cell-associated disorders: chronic myelogenous leukemia (CML) and several bone marrow failure syndromes. CML is characterized by a marked expansion of myeloid Philadelphia chromosome positive (Ph+) cells. Nevertheless, nonmalignant (Ph-) HSCs typically coexist in the bone marrow of CML patients. We analyzed the telomere length in > 150 peripheral blood leukocytes (PBLs) and bone marrow samples of patients with CML as well as samples of Ph- T-lymphocytes. Compared to normal controls, the overall telomere fluorescence in PBLs of patients with CML was significantly reduced. However, no telomere shortening was observed in Ph- T-lymphocytes. Patients in late chronic phase (CP) had significantly shorter telomeres than those assessed earlier in CP. Our data suggest that progressive telomere shortening is correlated with disease progression in CML. Within the group of patients with bone marrow failure syndromes, we only found significantly shortened telomeres (compared to age-adjusted controls) in granulocytes from patients with aplastic anemia (AA). Strikingly, the telomere length in granulocytes from AA patients who had recovered after immunosuppressive therapy (recAA) did not differ significantly from controls, whereas untreated patients and nonresponders with persistent severe pancytopenia (sAANR) showed marked and significant telomere shortening compared to healthy donors and patients with recAA. Furthermore, an inverse correlation between age-adjusted telomere length and peripheral blood counts was found in support of a model in which the degree of cytopenia and the amount of telomere shortening are correlated. These results support the concept of extensive proliferation of HSCs in subgroups of AA patients and suggest a potential use of telomere-length measurements as a prognostic tool in this group of disorders as well.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".