Endometriosis Typology and Ovarian Cancer Risk.

Importance: Endometriosis has been associated with an increased risk of ovarian cancer; however, the associations between endometriosis subtypes and ovarian cancer histotypes have not been well-described. Objective: To evaluate the associations of endometriosis subtypes with incidence of ovarian cancer, both overall and by histotype. Design, Setting, and Participants: Population-based cohort study using data from the Utah Population Database. The cohort was assembled by matching 78 893 women with endometriosis in a 1:5 ratio to women without endometriosis. Exposures: Endometriosis cases were identified via electronic health records and categorized as superficial endometriosis, ovarian endometriomas, deep infiltrating endometriosis, or other. Main Outcomes and Measures: Estimated adjusted hazard ratios (aHRs), adjusted risk differences (aRDs) per 10 000 women, and 95% CIs for overall ovarian cancer, type I ovarian cancer, and type II ovarian cancer comparing women with each type of endometriosis with women without endometriosis. Models accounted for sociodemographic factors, reproductive history, and past gynecologic operations. Results: In this Utah-based cohort, the mean (SD) age at first endometriosis diagnosis was 36 (10) years. There were 597 women with ovarian cancer. Ovarian cancer risk was higher among women with endometriosis compared with women without endometriosis (aHR, 4.20 [95% CI, 3.59-4.91]; aRD, 9.90 [95% CI, 7.22-12.57]), and risk of type I ovarian cancer was especially high (aHR, 7.48 [95% CI, 5.80-9.65]; aRD, 7.53 [95% CI, 5.46-9.61]). Ovarian cancer risk was highest in women with deep infiltrating endometriosis and/or ovarian endometriomas for all ovarian cancers (aHR, 9.66 [95% CI, 7.77-12.00]; aRD, 26.71 [95% CI, 20.01-33.41]), type I ovarian cancer (aHR, 18.96 [95% CI, 13.78-26.08]; aRD, 19.57 [95% CI, 13.80-25.35]), and type II ovarian cancer (aHR, 3.72 [95% CI, 2.31-5.98]; aRD, 2.42 [95% CI, -0.01 to 4.85]). Conclusions and Relevance: Ovarian cancer risk was markedly increased among women with ovarian endometriomas and/or deep infiltrating endometriosis. This population may benefit from counseling regarding ovarian cancer risk and prevention and could be an important population for targeted screening and prevention studies.

Predicting the onset of Alzheimer's disease and related dementia using Electronic Health Records: Findings from the Cache County Study on Memory in Aging (1995-2008).

Introduction: Clinical notes, biomarkers, and neuroimaging have been proven valuable in dementia prediction models. Whether commonly available structured clinical data can predict dementia is an emerging area of research. We aimed to predict Alzheimer's disease (AD) and Alzheimer's disease related dementias (ADRD) in a well-phenotyped, population-based cohort using a machine learning approach. Methods: Administrative healthcare data (k=163 diagnostic features), in addition to Census/vital record sociodemographic data (k = 6 features), were linked to the Cache County Study (CCS, 1995-2008). Results: Among successfully linked UPDB-CCS participants (n=4206), 522 (12.4%) had incident AD/ADRD as per the CCS "gold standard" assessments. Random Forest models, with a 1-year prediction window, achieved the best performance with an Area Under the Curve (AUC) of 0.67. Accuracy declined for dementia subtypes: AD/ADRD (AUC = 0.65); ADRD (AUC = 0.49). DISCUSSION: Commonly available structured clinical data (without labs, notes, or prescription information) demonstrate modest ability to predict AD/ADRD, corroborated by prior research.

Low-dose aspirin, maternal cardiometabolic health, and offspring respiratory health 9 to 14 years after delivery: Findings from the EAGeR Follow-up Study.

BACKGROUND: Accumulating evidence shows that peri-conceptional and in-utero exposures have lifetime health impacts for mothers and their offspring. OBJECTIVES: We conducted a Follow-Up Study of the Effects of Aspirin in Gestation and Reproduction (EAGeR) trial with two objectives. First, we determined if women who enrolled at the Utah site (N = 1001) of the EAGeR trial (2007-2011, N = 1228) could successfully be contacted and agree to complete an online questionnaire on their reproductive, cardio-metabolic, and offspring respiratory health 9-14 years after original enrollment. Second, we evaluated if maternal exposure to low-dose aspirin (LDA) during pregnancy was associated with maternal cardio-metabolic health and offspring respiratory health. METHODS: The original EAGeR study population included women, 18-40 years of age, who had 1-2 prior pregnancy losses, and who were trying to become pregnant. At follow-up (2020-2021), participants from the Utah cohort completed a 13-item online questionnaire on reproductive and cardio-metabolic health, and those who had a live birth during EAGeR additionally completed a 7-item questionnaire on the index child's respiratory health. Primary maternal outcomes included hypertension and hypercholesterolemia; primary offspring outcomes included wheezing and asthma. RESULTS: Sixty-eight percent (n = 678) of participants enrolled in the follow-up study, with 10% and 15% reporting maternal hypertension and hypercholesterolemia, respectively; and 18% and 10% reporting offspring wheezing and asthma. We found no association between maternal LDA exposure and hypertension (risk difference [RD] -0.001, 95% confidence interval [CI] -0.05, 0.04) or hypercholesterolemia (RD -0.01, 95% CI -0.06, 0.05) at 9-14 years follow-up. Maternal LDA exposure was not associated with offspring wheezing (RD -0.002, 95% CI -0.08, 0.08) or asthma (RD 0.13, 95% CI 0.11, 0.37) at follow-up. Findings remained robust after considering potential confounding and selection bias. CONCLUSIONS: We observed no association between LDA exposure during pregnancy and maternal cardiometabolic or offspring respiratory health.

An artificial intelligence approach for investigating multifactorial pain-related features of endometriosis.

Endometriosis is a debilitating, chronic disease that is estimated to affect 11% of reproductive-age women. Diagnosis of endometriosis is difficult with diagnostic delays of up to 12 years reported. These delays can negatively impact health and quality of life. Vague, nonspecific symptoms, like pain, with multiple differential diagnoses contribute to the difficulty of diagnosis. By investigating previously imprecise symptoms of pain, we sought to clarify distinct pain symptoms indicative of endometriosis, using an artificial intelligence-based approach. We used data from 473 women undergoing laparoscopy or laparotomy for a variety of surgical indications. Multiple anatomical pain locations were clustered based on the associations across samples to increase the power in the probability calculations. A Bayesian network was developed using pain-related features, subfertility, and diagnoses. Univariable and multivariable analyses were performed by querying the network for the relative risk of a postoperative diagnosis, given the presence of different symptoms. Performance and sensitivity analyses demonstrated the advantages of Bayesian network analysis over traditional statistical techniques. Clustering grouped the 155 anatomical sites of pain into 15 pain locations. After pruning, the final Bayesian network included 18 nodes. The presence of any pain-related feature increased the relative risk of endometriosis (p-value < 0.001). The constellation of chronic pelvic pain, subfertility, and dyspareunia resulted in the greatest increase in the relative risk of endometriosis. The performance and sensitivity analyses demonstrated that the Bayesian network could identify and analyze more significant associations with endometriosis than traditional statistical techniques. Pelvic pain, frequently associated with endometriosis, is a common and vague symptom. Our Bayesian network for the study of pain-related features of endometriosis revealed specific pain locations and pain types that potentially forecast the diagnosis of endometriosis.