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Record W4390225736 · doi:10.1101/2023.12.25.23300434

Evaluating the relationship between glycemic control and bone fragility within the UK biobank: Observational and one-sample Mendelian randomization analyses

2023· preprint· en· W4390225736 on OpenAlexaff
Samuel Ghatan, Fjorda Koromani, Katerina Trajanoska, Evert F S van Velsen, Maryam Kavousi, M. Carola Zillikens, Carolina Medina‐Gómez, Ling Oei, Fernando Rivadeneira

Bibliographic record

VenuemedRxiv · 2023
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMetabolism, Diabetes, and Cancer
Canadian institutionsMcGill University
Fundersnot available
KeywordsMendelian randomizationGlycemicMedicineType 2 diabetesInternal medicineDiabetes mellitusEndocrinologyBiology

Abstract

fetched live from OpenAlex

Abstract Aims/hypothesis This study aimed to: (1) examine the relationship between glycemic control, bone mineral density estimated from heel ultrasound (eBMD) and fracture risk in individuals with type 1 and type 2 diabetes and (2) perform a one-sample Mendelian randomization study to explore potential linear and non-linear associations between glycemic control, eBMD, and fractures. Methods This study comprised 452,131 individuals from the UK Biobank with glycated hemoglobin A1C (HbA1c) and eBMD levels. At baseline, 4,078 participants were diagnosed with type 1 diabetes and 23,682 with type 2 diabetes. HbA1c was used to classify patients into “adequately-” (ACD; n=17,078; HbA1c < 7.0%/53mmol/mol) and “inadequately-” (ICD; n=10,682; HbA1c ≥ 7.0%/53mmol/mol) controlled diabetes. The association between glycemic control (continuous and categorical) and eBMD was tested using linear regression, while fracture risk was estimated in Cox regression models, both controlling for covariates. Mendelian randomization (MR) was used to evaluate linear and non-linear causal relationships between HbA1c levels, fracture risk, and eBMD. Results In individuals with type 1 diabetes, a 1% unit (11mmol/mol) increase in HbA1c levels was associated with a 12% increase in fracture risk (HR: 1.12, 95% CI [1.05-1.19]). Individuals with type 1 diabetes had lower eBMD in both the ICD (beta = −0.08, 95% CI [−0.11, −0.04]) and ACD (beta = −0.05, 95% CI [-0.11,0.01]) groups, as compared to subjects without diabetes. Fracture risk was highest in individuals with type 1 diabetes and ICD (HR 2.84, 95%CI [2.53, 3.19]), followed by those with ACD (HR 2.26, 95%CI [1.91, 2.69]). Individuals with type 2 diabetes had higher eBMD in both ICD (beta=0.12SD, 95%CI [0.10, 0.14]) and ACD (beta=0.07SD, 95%CI [0.05, 0.08]) groups. Significant evidence for a non-linear association between HbA1c and fracture risk was observed (F-test ANOVA p-value = 0.002) in individuals with type 2 diabetes, with risk being increased at both low and high levels of HbA1c. Fracture risk between the type 2 diabetes ACD and ICD groups was not significantly different (HR: 0.97, 95%CI [0.91-1.16]), despite increased BMD. In MR analyses genetically predicted higher HbA1c levels were not significantly associated with fracture risk (Causal Risk Ratio: 1.04, 95%CI [0.95-1.14]). However, disease stratified analyses were underpowered. We did observe evidence of a non-linear causal association with eBMD (quadratic test P-value = 0.0002), indicating U-shaped relationship between HbA1c and eBMD. Conclusion/interpretation We obtained evidence that lower HbA1c levels will reduce fracture risk in patients with type 1 diabetes. In individuals with type 2 diabetes, lowering HbA1c levels can mitigate the risk of fractures up to a threshold, beyond which the risk may begin to rise once more. MR analyses demonstrated a causal relationship between genetically predicted HbA1c levels and eBMD, but not fracture risk.

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)0.048
metaresearch head score (Gemma)0.115
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 score0.048
Threshold uncertainty score0.253

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0480.115
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.276
GPT teacher head0.409
Teacher spread0.133 · 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
Published2023
Admission routes1
Has abstractyes

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