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Age‐Associated Decline in Erectile Function: The Confounding Influence of Obesity

2010· article· en· W2289225164 on OpenAlexaff
M. Tina Maio, Marina Komolova, Corry Smallegange, Michael A. Adams

Bibliographic record

VenueThe FASEB Journal · 2010
Typearticle
Languageen
FieldMedicine
TopicSexual function and dysfunction studies
Canadian institutionsQueen's University
Fundersnot available
KeywordsErectile dysfunctionMedicineErectile functionInternal medicineObesityConfoundingEndocrinologyTestosterone (patch)ApomorphineAdipose tissueUrologyDopamine

Abstract

fetched live from OpenAlex

Although there is a well‐established link between aging and erectile dysfunction (ED), the differential contribution of obesity, specifically visceral adipose tissue (VAT), as a causal factor in ED at a given age has not been determined. Caloric restriction (CR) can prolong lifespan and is a powerful tool that we use in our research to delineate the effects of aging on erectile function distinct from obesity. Methods 3 groups of male Sprague‐Dawley rats (10–30 wks) were used: (10 and 20% CR), and ad libitum (control) Body weigh (Bwt), length, and abdominal girth (AG) were taken throughout. Full body MRI, was performed at 4 time points. The apomorphine bioassay (80 ug/kg, s.c.) was used to assess erectile function at 30wks of age in Study 1, whereas cavernous nerve stimulation was used in Study 2. Total VAT fat pads were weighed upon sacrifice. Results Final Bwt.'s (30wks) were Study 1/2: 659/660g (control), CR 10% 537g, and CR 20% 410/400g. VAT (% Bwt.) was Study 1/2: 6.2/7.5%(control), CR 10% 5.8%, CR 20% 3.8/2.2%. Erectile responses were 0.6±0.7 in controls, 0.9±1.1 in CR 10% and 1.8±1.0 in CR 20% . The CR‐induced reduction in VAT was well correlated with centrally mediated erectile responses (R 2 =0.7). Conclusions The age related decline in erectile responses was prevented via CR 20% . There was a positive correlation between a reduction in VAT and improvements in sexual function. (Funds: HSFO, MK:CIHR‐DRA)

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)0.001
metaresearch head score (Gemma)0.001
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 score0.255
Threshold uncertainty score0.596

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 integrity0.0000.001
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 head0.030
GPT teacher head0.301
Teacher spread0.272 · 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".

Quick stats

Citations0
Published2010
Admission routes1
Has abstractyes

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