Abstract Background Studies evaluating the association between obesity and pelvic organ prolapse (POP) report estimates that range from negative to positive associations. Heterogeneous definitions ...for POP and variable choices for categorizing obesity measures have made it challenging to conduct meta-analysis. Objective We systematically evaluated evidence to provide quantitative summaries of association between degrees of obesity and POP, and identify sources of heterogeneity. Evidence acquisition and method of synthesis We searched for all indexed publications relevant to POP up until June 18, 2015 in PubMed/Medline to identify analytical observational studies published in English that reported risk ratios (relative risk, odds ratio or hazard ratio) for body mass index (BMI) categories in relation to POP. Random-effects meta-analyses were conducted to report associations with POP for overweight and obese BMI categories compared with women in the normal-weight category (referent: BMI <25 kg/m2 ). Results Of the seventy studies that reported evidence on obesity and POP, 22 eligible studies provided effect estimates for meta-analysis of the overweight and obese BMI categories. Compared with the referent category, women in the overweight and obese categories had meta-analysis risk ratios of at least 1.36 (95% confidence interval CI: 1.20, 1.53) and at least 1.47 (95% CI: 1.35, 1.59), respectively. Subgroup analyses showed effect estimates for objectively-measured clinically-significant POP were higher than for self-reported POP. Other potential sources of heterogeneity included proportion of post-menopausal women in study and reported study design. Conclusions Overweight and obese women are more likely to have POP compared with women with BMI in the normal range. The finding that the associations for obesity measures were strongest for objectively-measured, clinically-significant POP further strengthens this evidence. However, prospective investigations evaluating obesity and POP are few.
Diseases such as uterine leiomyomata (fibroids and benign tumors of the uterus) and keloids (raised scars) may share common etiology. Fibroids and keloids can co-occur in individuals, and both are ...highly heritable, suggesting they may share common genetic risk factors. Fibroproliferative diseases are common and characterized by scarring and overgrowth of connective tissue, impacting multiple organ systems. These conditions both have racial disparities in prevalence, with the highest prevalence observed among individuals of African ancestry. Several fibroproliferative diseases are more severe and common in populations of sub-Saharan Africa. This mini-review aims to provide a broad overview of the current knowledge of the evolutionary origins and causes of fibroproliferative diseases. We also discuss current hypotheses proposing that the increased prevalence of these diseases in African-derived populations is due to the selection for profibrotic alleles that are protective against helminth infections and provide examples from knowledge of uterine fibroid and keloid research.
Genetic association studies often examine features independently, potentially missing subpopulations with multiple phenotypes that share a single cause. We describe an approach that aggregates ...phenotypes on the basis of patterns described by Mendelian diseases. We mapped the clinical features of 1204 Mendelian diseases into phenotypes captured from the electronic health record (EHR) and summarized this evidence as phenotype risk scores (PheRSs). In an initial validation, PheRS distinguished cases and controls of five Mendelian diseases. Applying PheRS to 21,701 genotyped individuals uncovered 18 associations between rare variants and phenotypes consistent with Mendelian diseases. In 16 patients, the rare genetic variants were associated with severe outcomes such as organ transplants. PheRS can augment rare-variant interpretation and may identify subsets of patients with distinct genetic causes for common diseases.
Females with polycystic ovary syndrome (PCOS), the most common endocrine disorder in women, have an increased risk of developing cardiometabolic disorders such as insulin resistance, obesity, and ...type 2 diabetes (T2D). While only diagnosable in females, males with a family history of PCOS can also exhibit a poor cardiometabolic profile. Therefore, we aimed to elucidate the role of sex in the cardiometabolic comorbidities observed in PCOS by conducting bidirectional genetic risk score analyses in both sexes. We first conducted a phenome-wide association study (PheWAS) using PCOS polygenic risk scores (PCOSPRS) to identify potential pleiotropic effects of PCOSPRS across 1,380 medical conditions recorded in the Vanderbilt University Medical Center electronic health record (EHR) database, in females and males. After adjusting for age and genetic ancestry, we found that European (EUR)-ancestry males with higher PCOSPRS were significantly more likely to develop hypertensive diseases than females at the same level of genetic risk. We performed the same analysis in an African (AFR)-ancestry population, but observed no significant associations, likely due to poor trans-ancestry performance of the PRS. Based on observed significant associations in the EUR-ancestry population, we then tested whether the PRS for comorbid conditions (e.g., T2D, body mass index (BMI), hypertension, etc.) also increased the odds of a PCOS diagnosis. Only BMIPRS and T2DPRS were significantly associated with a PCOS diagnosis in EUR-ancestry females. We then further adjusted the T2DPRS for measured BMI and BMIresidual (regressed on the BMIPRS and enriched for the environmental contribution to BMI). Results demonstrated that genetically regulated BMI primarily accounted for the relationship between T2DPRS and PCOS. Overall, our findings show that the genetic architecture of PCOS has distinct sex differences in associations with genetically correlated cardiometabolic traits. It is possible that the cardiometabolic comorbidities observed in PCOS are primarily explained by their shared genetic risk factors, which can be further influenced by biological variables including sex and BMI.
Given current evidence supporting a genetic predisposition for pelvic organ prolapse, we conducted a systematic review of published literature on the genetic epidemiology of pelvic organ prolapse. ...Inclusion criteria were linkage studies, candidate gene association and genome-wide association studies in adult women published in English and indexed in PubMed through Dec. 2012, with no limit on date of publication. Methodology adhered to the PRISMA guidelines. Data were systematically extracted by 2 reviewers and graded by the Venice criteria for studies of genetic associations. A metaanalysis was performed on all single nucleotide polymorphisms evaluated by 2 or more studies with similar methodology. The metaanalysis suggests that collagen type 3 alpha 1 (COL3A1) rs1800255 genotype AA is associated with pelvic organ prolapse (odds ratio, 4.79; 95% confidence interval, 1.91–11.98; P = .001) compared with the reference genotype GG in populations of Asian and Dutch women. There was little evidence of heterogeneity for rs1800255 ( P value for heterogeneity = .94; proportion of variance because of heterogeneity, I2 = 0.00%). There was insufficient evidence to determine whether other single nucleotide polymorphisms evaluated by 2 or more papers were associated with pelvic organ prolapse. An association with pelvic organ prolapse was seen in individual studies for estrogen receptor alpha (ER-α) rs2228480 GA, COL3A1 exon 31, chromosome 9q21 (heterogeneity logarithm of the odds score 3.41) as well as 6 single nucleotide polymorphisms identified by a genome-wide association study. Overall, individual studies were of small sample size and often of poor quality. Future studies would benefit from more rigorous study design as outlined in the Venice recommendations.
Many adverse pregnancy outcomes differ by race. We examined the association between self-reported race and miscarriage (pregnancy loss at <20 weeks) in a community-based pregnancy cohort. Women from ...the southeastern United States (North Carolina, Texas, and Tennessee) were enrolled in "Right from the Start" from 2000 to 2009. They were recruited while trying to conceive or during early pregnancy. Participants completed study ultrasound examinations, interviews, and consent forms for review of medical records. We used proportional hazard models to examine miscarriage risk among black women compared with white women, adjusted for confounders. There were 537 observed miscarriages among 4,070 women, 23% of whom self-identified as black (n = 932). The life table-adjusted cumulative risk of loss after gestational week 5 was 21.3%. With adjustment for age and alcohol use, blacks had increased risk of miscarriage compared with whites (adjusted hazard ratio = 1.57, 95% confidence interval: 1.27, 1.93). When risk of loss before gestational week 10 was dichotomized at the median gestational age, there was little difference, but black women had a greater risk thereafter compared with white women (adjusted hazard ratio = 1.93, 95% confidence interval: 1.48, 2.51). Early pregnancy ultrasound examinations did not differ by race. In summary, self-reported race is independently associated with risk of miscarriage, and the higher risk for black women is concentrated in gestational weeks 10-20.
Chronic kidney disease (CKD), defined by low estimated glomerular filtration rate (eGFR), contributes to global morbidity and mortality. Here we conduct a transethnic Genome-Wide Association Study of ...eGFR in 280,722 participants of the Million Veteran Program (MVP), with replication in 765,289 participants from the Chronic Kidney Disease Genetics (CKDGen) Consortium. We identify 82 previously unreported variants, confirm 54 loci, and report interesting findings including association of the sickle cell allele of betaglobin among non-Hispanic blacks. Our transcriptome-wide association study of kidney function in healthy kidney tissue identifies 36 previously unreported and nine known genes, and maps gene expression to renal cell types. In a Phenome-Wide Association Study in 192,868 MVP participants using a weighted genetic score we detect associations with CKD stages and complications and kidney stones. This investigation reinterprets the genetic architecture of kidney function to identify the gene, tissue, and anatomical context of renal homeostasis and the clinical consequences of dysregulation.
Polycystic ovary syndrome is the most common endocrine disorder affecting women of reproductive age. A number of criteria have been developed for clinical diagnosis of polycystic ovary syndrome, with ...the Rotterdam criteria being the most inclusive. Evidence suggests that polycystic ovary syndrome is significantly heritable, and previous studies have identified genetic variants associated with polycystic ovary syndrome diagnosed using different criteria. The widely adopted electronic health record system provides an opportunity to identify patients with polycystic ovary syndrome using the Rotterdam criteria for genetic studies.
To identify novel associated genetic variants under the same phenotype definition, we extracted polycystic ovary syndrome cases and unaffected controls based on the Rotterdam criteria from the electronic health records and performed a discovery-validation genome-wide association study.
We developed a polycystic ovary syndrome phenotyping algorithm on the basis of the Rotterdam criteria and applied it to 3 electronic health record–linked biobanks to identify cases and controls for genetic study. In the discovery phase, we performed an individual genome-wide association study using the Geisinger MyCode and the Electronic Medical Records and Genomics cohorts, which were then meta-analyzed. We attempted validation of the significant association loci (P<1×10−6) in the BioVU cohort. All association analyses used logistic regression, assuming an additive genetic model, and adjusted for principal components to control for population stratification. An inverse-variance fixed-effect model was adopted for meta-analysis. In addition, we examined the top variants to evaluate their associations with each criterion in the phenotyping algorithm. We used the STRING database to characterize protein-protein interaction network.
Using the same algorithm based on the Rotterdam criteria, we identified 2995 patients with polycystic ovary syndrome and 53,599 population controls in total (2742 cases and 51,438 controls from the discovery phase; 253 cases and 2161 controls in the validation phase). We identified 1 novel genome-wide significant variant rs17186366 (odds ratio OR=1.37 1.23, 1.54, P=2.8×10−8) located near SOD2. In addition, 2 loci with suggestive association were also identified: rs113168128 (OR=1.72 1.42, 2.10, P=5.2×10−8), an intronic variant of ERBB4 that is independent from the previously published variants, and rs144248326 (OR=2.13 1.52, 2.86, P=8.45×10−7), a novel intronic variant in WWTR1. In the further association tests of the top 3 single-nucleotide polymorphisms with each criterion in the polycystic ovary syndrome algorithm, we found that rs17186366 (SOD2) was associated with polycystic ovaries and hyperandrogenism, whereas rs11316812 (ERBB4) and rs144248326 (WWTR1) were mainly associated with oligomenorrhea or infertility. We also validated the previously reported association with DENND1A1. Using the STRING database to characterize protein-protein interactions, we found both ERBB4 and WWTR1 can interact with YAP1, which has been previously associated with polycystic ovary syndrome.
Through a discovery-validation genome-wide association study on polycystic ovary syndrome identified from electronic health records using an algorithm based on Rotterdam criteria, we identified and validated a novel genome-wide significant association with a variant near SOD2. We also identified a novel independent variant within ERBB4 and a suggestive association with WWTR1. With previously identified polycystic ovary syndrome gene YAP1, the ERBB4-YAP1-WWTR1 network suggests involvement of the epidermal growth factor receptor and the Hippo pathway in the multifactorial etiology of polycystic ovary syndrome.
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•Extreme preterm birth (EPB) accounts for the majority of newborn deaths.•Deep learning models that consider temporal relations can predict EPB.•Deep learning ensemble models achieve ...a higher performance than individual models.•EPB is associated with significant morbidity, e.g., systemic lupus erythematosus.
Models for predicting preterm birth generally have focused on very preterm (28–32 weeks) and moderate to late preterm (32–37 weeks) settings. However, extreme preterm birth (EPB), before the 28th week of gestational age, accounts for the majority of newborn deaths. We investigated the extent to which deep learning models that consider temporal relations documented in electronic health records (EHRs) can predict EPB.
EHR data were subject to word embedding and a temporal deep learning model, in the form of recurrent neural networks (RNNs) to predict EPB. Due to the low prevalence of EPB, the models were trained on datasets where controls were undersampled to balance the case-control ratio. We then applied an ensemble approach to group the trained models to predict EPB in an evaluation setting with a nature EPB ratio. We evaluated the RNN ensemble models with 10 years of EHR data from 25,689 deliveries at Vanderbilt University Medical Center. We compared their performance with traditional machine learning models (logistical regression, support vector machine, gradient boosting) trained on the datasets with balanced and natural EPB ratio. Risk factors associated with EPB were identified using an adjusted odds ratio.
The RNN ensemble models trained on artificially balanced data achieved a higher AUC (0.827 vs. 0.744) and sensitivity (0.965 vs. 0.682) than those RNN models trained on the datasets with naturally imbalanced EPB ratio. In addition, the AUC (0.827) and sensitivity (0.965) of the RNN ensemble models were better than the AUC (0.777) and sensitivity (0.819) of the best baseline models trained on balanced data. Also, risk factors, including twin pregnancy, short cervical length, hypertensive disorder, systemic lupus erythematosus, and hydroxychloroquine sulfate, were found to be associated with EPB at a significant level.
Temporal deep learning can predict EPB up to 8 weeks earlier than its occurrence. Accurate prediction of EPB may allow healthcare organizations to allocate resources effectively and ensure patients receive appropriate care.
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