Does Type 1 Diabetes Affect Male Infertility: Type 1 Diabetes Exchange Registry-Based Analysis
Bibliographic record
Abstract
Introduction: The prevalence of type 1 diabetes (T1D) has been increasing over the last few decades and is commonly believed to negatively impact male fertility. We aimed to estimate the prevalence of infertility among men with T1D and to characterize potential clinical predictors for male infertility among men with T1D. Methods: We used data collected from the T1D Exchange Registry from 2012 to 2017. Men with T1D completed an infertility questionnaire indicating whether they had ever had problems conceiving a child or had ever received abnormal results from infertility testing. Collected data included age at questionnaire, age at diagnosis of T1D, duration of T1D, race/ethnicity, insurance status, education level, annual household income, hemoglobin A1c (HbA1c), low density lipoprotein (LDL), diabetic retinopathy, micro/macroalbuminuria, and renal failure. Results: The survey was completed by 2171 registry members, 33 (1.5%) of whom reported male infertility. Mean age at questionnaire was 38 and 56 years in the fertile and infertile groups, respectively (P < 0.001). There was no statistically significant difference in the mean age at T1D diagnosis (16 and 27 years), mean duration of T1D at questionnaire (22 and 30 years), white non-Hispanic ethnicity (1906/2138, 89% versus 30/33, 91%), private insurance (1509/2138, 79% versus 30/33, 91%), and annual household income in US dollars ≥ $100,000 (757/2138, 45% versus 16/33, 55%) in the fertile and infertile men, respectively. On multivariate analysis, for each year of advancing age, men were 5% more likely to experience infertility. Age at questionnaire was the only significant predictor of infertility (OR 1.05; 95%CI 1.03 to 1.08). Age at T1D diagnosis (OR 1.01; 95%CI 0.99 to 1.04), duration of T1D (OR 0.99; 95%CI 0.96 to 1.01), mean HbA1C (OR 1.03; 95%CI 0.77 to 1.37), diabetic retinopathy (OR 1.04; 95%CI 0.50 to 2.15), and mean LDL (OR 1.01; 95%CI 0.99 to 1.02) failed to independently predict infertility; however, presence of renal failure (OR 3.38; 95%CI 0.94 to 12.13) and micro/macroalbuminuria (OR 1.27; 95%CI 0.42 to 3.82) trended toward increased odds of infertility. Conclusions: This study highlights the prevalence of male infertility among men with T1D. Beyond age, there were no independent clinical predictors for male infertility among men with T1D; however, men with clinical evidence of diabetes-associated renal compromise trended toward greater odds of infertility. Further studies of fertility in this growing, at-risk population are warranted.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".