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Record W4409910748 · doi:10.1210/jendso/bvae163.2409

MON-148 Weight Gain in Patients with Acromegaly after Pituitary Surgery: Predictive Factors and Insights - A Large Single Center Experience

2024· article· en· W4409910748 on OpenAlexaff
M. Sharon Stack, Elena V Varlamov, Dae‐Soon Lim, Francis Langlois, Maria Fleseriu

Bibliographic record

VenueJournal of the Endocrine Society · 2024
Typearticle
Languageen
FieldMedicine
TopicPituitary Gland Disorders and Treatments
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsAcromegalyCenter (category theory)Single CenterMedicineInternal medicineChemistryGrowth hormone

Abstract

fetched live from OpenAlex

Abstract M. Stack: None. E.V. Varlamov: Grant Recipient; Self; Pfizer, Inc., Lumiio, Recordati. D.S. Lim: None. F. Langlois: Consulting Fee; Self; Recordati, Ipsen. M. Fleseriu: Consulting Fee; Self; Amryt, Camurus, Crinetics, Ipsen, Recordati. Grant Recipient; Self; Amryt, Crinetics, Ionis Pharmaceuticals Inc., Recordati. Background: Excessive growth hormone (GH) and insulin-like growth factor 1 (IGF-1) in patients (pts) with acromegaly (acro) leads to skeletal overgrowth and metabolic changes. GH lipolytic effect ↑ lean body mass, ↓ fat mass, and ↑ insulin resistance. Partial reversal of such changes can occur after GH adenoma surgery. At follow-up (f/u), weight outcomes postoperatively (postop) vary in studies from no change to significant weight gain. Objective: Enhance understanding and identify predictors of postop weight changes in pts with acro. Design & Method: IRB approved retrospective review of pts with acro who underwent pituitary surgery at OHSU (2006-2022) with >12 months (mo) f/u. Excluded: pts who started/stopped weight- altering drugs or were in a weight loss program (3 mo pre- to 12 mo postop), pregnancy, and uncontrolled hyper/hypothyroidism. Age, sex, weight, BMI, IGF-1, GH, pituitary function status, HbA1C, comorbidities, and medications were recorded. Data analysis: SPSS29, Excel. Results: 15 pts were excluded, for a final cohort of 93 pts (53 females), age 45.4 ± 17.1 years, BMI 29.5 ± 6.5 kg/m2, mean f/u 44.4 ± 23.5 mo. 53 pts were in remission at 12 mo. 65% gained weight at 12 mo postop; 43% > 3% and 32% > 5%. Mean weight gain was 2.6 ± 7.2 kg (2.7%) from baseline to 12 mo postop (89.9 ± 23.9 vs 92.5 ± 26.4 kg, p < 0.001), with no significant sex-based differences at 3, 6, and 12 mo f/u. Age, preop BMI, preop GH, preop IGF-1xULN, % ↓ in IGF-1, acro remission status, pituitary deficiencies, DM2, hyperlipidemia, hypertension and obstructive sleep apnea were not significantly associated with weight gain at 6 and 12 mo. Females with > 3% weight gain had higher preop IGF-1xULN vs those with < 3% weight gain at both 6 (p = 0.036) and 12 mo (p = 0.01). No difference in weight gain at 12 mo was observed in treatment-naïve vs previously treated pts. Postop medications for acro were not associated with weight change at 6 and 12 mo. Weight gain tended to be higher in pts with pituitary surgery pre-2016 (3.8% vs 1.3%, p = 0.057).Discussion: This large single center study highlights that 65% pts with acro gained weight at 12 mo postop, with a noteworthy proportion gaining > 3% and 5%. Weight gain is relatively similar with previous reports: 1-3 kg in pts with surgical/medical remission. Among multiple factors examined, higher preop IGF-1 was associated with weight gain in females only. The mechanism of observed sex difference needs further study. Pts with surgery since 2016 tended to gain less weight, possibly due to proactive discussion with pts of weight gain risk at our Center. Conclusion: Our findings emphasize a risk of significant postop weight gain in pts with acro; in females, weight gain of > 3 % was associated with preop IGF-1 levels. Given that obesity increases cardiovascular risk, lifestyle or pharmacological methods to prevent weight gain after pituitary surgery should be actively considered. Monday, June 3, 2024

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.006
Threshold uncertainty score0.336

Codex and Gemma teacher scores by category

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

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 head0.007
GPT teacher head0.223
Teacher spread0.217 · 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 teacher head, 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
Published2024
Admission routes1
Has abstractyes

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