Predicting nutrition and environmental factors associated with female reproductive disorders using a knowledge graph and random forests.

OBJECTIVE: Female reproductive disorders (FRDs) are common health conditions that may present with significant symptoms. Diet and environment are potential areas for FRD interventions. We utilized a knowledge graph (KG) method to predict factors associated with common FRDs (for example, endometriosis, ovarian cyst, and uterine fibroids). MATERIALS AND METHODS: We harmonized survey data from the Personalized Environment and Genes Study (PEGS) on internal and external environmental exposures and health conditions with biomedical ontology content. We merged the harmonized data and ontologies with supplemental nutrient and agricultural chemical data to create a KG. We analyzed the KG by embedding edges and applying a random forest for edge prediction to identify variables potentially associated with FRDs. We also conducted logistic regression analysis for comparison. RESULTS: Across 9765 PEGS respondents, the KG analysis resulted in 8535 significant or suggestive predicted links between FRDs and chemicals, phenotypes, and diseases. Amongst these links, 32 were exact matches when compared with the logistic regression results, including comorbidities, medications, foods, and occupational exposures. DISCUSSION: Mechanistic underpinnings of predicted links documented in the literature may support some of our findings. Our KG methods are useful for predicting possible associations in large, survey-based datasets with added information on directionality and magnitude of effect from logistic regression. These results should not be construed as causal but can support hypothesis generation. CONCLUSION: This investigation enabled the generation of hypotheses on a variety of potential links between FRDs and exposures. Future investigations should prospectively evaluate the variables hypothesized to impact FRDs.

Contributions of common genetic variants to constitutional delay of puberty and idiopathic hypogonadotropic hypogonadism.

CONTEXT: Constitutional delay of puberty (CDP) is highly heritable, but the genetic basis for CDP is largely unknown. Idiopathic hypogonadotropic hypogonadism (IHH) can be caused by rare genetic variants, but in about half of cases, no rare-variant cause is found. OBJECTIVE: To determine whether common genetic variants that influence pubertal timing contribute to CDP and IHH. DESIGN: Case-control study. PARTICIPANTS: 80 individuals with CDP; 301 with normosmic IHH, and 348 with Kallmann syndrome; control genotyping data from unrelated studies. MAIN OUTCOME MEASURES: Polygenic scores (PGS) based on genome-wide association studies for timing of male pubertal hallmarks and age at menarche (AAM). RESULTS: The CDP cohort had higher PGS for male pubertal hallmarks and for AAM compared to controls (for male hallmarks, Cohen's d = 0.85, p = 1 × 10-16; for AAM, d = 0.67, p = 1 × 10-10). The normosmic IHH cohort also had higher PGS for male hallmarks compared to controls, but the difference was smaller (male hallmarks d = 0.20, p = 0.003; AAM d = 0.10, p = 0.055). No differences were seen for the KS cohort compared to controls (male hallmarks d = 0.04, p = 0.45; AAM d = -0.03, p = 0.86). CONCLUSIONS: Common genetic variants that influence pubertal timing in the general population contribute strongly to the genetics of CDP, weakly to normosmic IHH, and potentially not at all to KS. These findings demonstrate that the common-variant genetics of CDP and normosmic IHH are largely but not entirely distinct.

Questionnaire-based exposome-wide association studies for common diseases in the Personalized Environment and Genes Study.

The exposome collectively refers to all exposures, beginning in utero and continuing throughout life, and comprises not only standard environmental exposures such as point source pollution and ozone levels but also exposures from diet, medication, lifestyle factors, stress, and occupation. The exposome interacts with individual genetic and epigenetic characteristics to affect human health and disease, but large-scale studies that characterize the exposome and its relationships with human disease are limited. To address this gap, we used extensive questionnaire data from the diverse North Carolina-based Personalized Environment and Genes Study (PEGS, n = 9, 429) to evaluate exposure associations in relation to common diseases. We performed an exposome-wide association study (ExWAS) to examine single exposure models and their associations with 11 common complex diseases, namely allergic rhinitis, asthma, bone loss, fibroids, high cholesterol, hypertension, iron-deficient anemia, ovarian cysts, lower GI polyps, migraines, and type 2 diabetes. Across diseases, we found associations with lifestyle factors and socioeconomic status as well as asbestos, various dust types, biohazardous material, and textile-related exposures. We also found disease-specific associations such as fishing with lead weights and migraines. To differentiate between a replicated result and a novel finding, we used an AI-based literature search and database tool that allowed us to examine the current literature. We found both replicated findings, especially for lifestyle factors such as sleep and smoking across diseases, and novel findings, especially for occupational exposures and multiple diseases.

Interactive data sharing for multiple questionnaire-based exposome-wide association studies and exposome correlations in the Personalized Environment and Genes Study.

The correlations among individual exposures in the exposome, which refers to all exposures an individual encounters throughout life, are important for understanding the landscape of how exposures co-occur, and how this impacts health and disease. Exposome-wide association studies (ExWAS), which are analogous to genome-wide association studies (GWAS), are increasingly being used to elucidate links between the exposome and disease. Despite increased interest in the exposome, tools and publications that characterize exposure correlations and their relationships with human disease are limited, and there is a lack of data and results sharing in resources like the GWAS catalog. To address these gaps, we developed the PEGS Explorer web application to explore exposure correlations in data from the diverse North Carolina-based Personalized Environment and Genes Study (PEGS) that were rigorously calculated to account for differing data types and previously published results from ExWAS. Through globe visualizations, PEGS Explorer allows users to explore correlations between exposures found to be associated with complex diseases. The exposome data used for analysis includes not only standard environmental exposures such as point source pollution and ozone levels but also exposures from diet, medication, lifestyle factors, stress, and occupation. The web application addresses the lack of accessible data and results sharing, a major challenge in the field, and enables users to put results in context, generate hypotheses, and, importantly, replicate findings in other cohorts. PEGS Explorer will be updated with additional results as they become available, ensuring it is an up-to-date resource in exposome science.

Application of machine learning and artificial intelligence in the diagnosis and classification of polycystic ovarian syndrome: a systematic review.

Introduction: Polycystic Ovarian Syndrome (PCOS) is the most common endocrinopathy in women of reproductive age and remains widely underdiagnosed leading to significant morbidity. Artificial intelligence (AI) and machine learning (ML) hold promise in improving diagnostics. Thus, we performed a systematic review of literature to identify the utility of AI/ML in the diagnosis or classification of PCOS. Methods: We applied a search strategy using the following databases MEDLINE, Embase, the Cochrane Central Register of Controlled Trials, the Web of Science, and the IEEE Xplore Digital Library using relevant keywords. Eligible studies were identified, and results were extracted for their synthesis from inception until January 1, 2022. Results: 135 studies were screened and ultimately, 31 studies were included in this study. Data sources used by the AI/ML interventions included clinical data, electronic health records, and genetic and proteomic data. Ten studies (32%) employed standardized criteria (NIH, Rotterdam, or Revised International PCOS classification), while 17 (55%) used clinical information with/without imaging. The most common AI techniques employed were support vector machine (42% studies), K-nearest neighbor (26%), and regression models (23%) were the commonest AI/ML. Receiver operating curves (ROC) were employed to compare AI/ML with clinical diagnosis. Area under the ROC ranged from 73% to 100% (n=7 studies), diagnostic accuracy from 89% to 100% (n=4 studies), sensitivity from 41% to 100% (n=10 studies), specificity from 75% to 100% (n=10 studies), positive predictive value (PPV) from 68% to 95% (n=4 studies), and negative predictive value (NPV) from 94% to 99% (n=2 studies). Conclusion: Artificial intelligence and machine learning provide a high diagnostic and classification performance in detecting PCOS, thereby providing an avenue for early diagnosis of this disorder. However, AI-based studies should use standardized PCOS diagnostic criteria to enhance the clinical applicability of AI/ML in PCOS and improve adherence to methodological and reporting guidelines for maximum diagnostic utility. Systematic review registration: https://www.crd.york.ac.uk/prospero/, identifier CRD42022295287.

Predicting nutrition and environmental factors associated with female reproductive disorders using a knowledge graph and random forests.

Objective: Female reproductive disorders (FRDs) are common health conditions that may present with significant symptoms. Diet and environment are potential areas for FRD interventions. We utilized a knowledge graph (KG) method to predict factors associated with common FRDs (e.g., endometriosis, ovarian cyst, and uterine fibroids). Materials and Methods: We harmonized survey data from the Personalized Environment and Genes Study on internal and external environmental exposures and health conditions with biomedical ontology content. We merged the harmonized data and ontologies with supplemental nutrient and agricultural chemical data to create a KG. We analyzed the KG by embedding edges and applying a random forest for edge prediction to identify variables potentially associated with FRDs. We also conducted logistic regression analysis for comparison. Results: Across 9765 PEGS respondents, the KG analysis resulted in 8535 significant predicted links between FRDs and chemicals, phenotypes, and diseases. Amongst these links, 32 were exact matches when compared with the logistic regression results, including comorbidities, medications, foods, and occupational exposures. Discussion: Mechanistic underpinnings of predicted links documented in the literature may support some of our findings. Our KG methods are useful for predicting possible associations in large, survey-based datasets with added information on directionality and magnitude of effect from logistic regression. These results should not be construed as causal, but can support hypothesis generation. Conclusion: This investigation enabled the generation of hypotheses on a variety of potential links between FRDs and exposures. Future investigations should prospectively evaluate the variables hypothesized to impact FRDs.

Race/ethnicity-stratified fine-mapping of the MHC locus reveals genetic variants associated with late-onset asthma.

Introduction: Asthma is a chronic disease of the airways that impairs normal breathing. The etiology of asthma is complex and involves multiple factors, including the environment and genetics, especially the distinct genetic architecture associated with ancestry. Compared to early-onset asthma, little is known about genetic predisposition to late-onset asthma. We investigated the race/ethnicity-specific relationship among genetic variants within the major histocompatibility complex (MHC) region and late-onset asthma in a North Carolina-based multiracial cohort of adults. Methods: We stratified all analyses by self-reported race (i.e., White and Black) and adjusted all regression models for age, sex, and ancestry. We conducted association tests within the MHC region and performed fine-mapping analyses conditioned on the race/ethnicity-specific lead variant using whole-genome sequencing (WGS) data. We applied computational methods to infer human leukocyte antigen (HLA) alleles and residues at amino acid positions. We replicated findings in the UK Biobank. Results: The lead signals, rs9265901 on the 5' end of HLA-B, rs55888430 on HLA-DOB, and rs117953947 on HCG17, were significantly associated with late-onset asthma in all, White, and Black participants, respectively (OR = 1.73, 95%CI: 1.31 to 2.14, p = 3.62 × 10-5; OR = 3.05, 95%CI: 1.86 to 4.98, p = 8.85 × 10-6; OR = 19.5, 95%CI: 4.37 to 87.2, p = 9.97 × 10-5, respectively). For the HLA analysis, HLA-B*40:02 and HLA-DRB1*04:05, HLA-B*40:02, HLA-C*04:01, and HLA-DRB1*04:05, and HLA-DRB1*03:01 and HLA-DQB1 were significantly associated with late-onset asthma in all, White, and Black participants. Conclusion: Multiple genetic variants within the MHC region were significantly associated with late-onset asthma, and the associations were significantly different by race/ethnicity group.

Prevalence of Deleterious Variants in MC3R in Patients With Constitutional Delay of Growth and Puberty.

CONTEXT: The melanocortin 3 receptor (MC3R) has recently emerged as a critical regulator of pubertal timing, linear growth, and the acquisition of lean mass in humans and mice. In population-based studies, heterozygous carriers of deleterious variants in MC3R report a later onset of puberty than noncarriers. However, the frequency of such variants in patients who present with clinical disorders of pubertal development is currently unknown. OBJECTIVE: This work aimed to determine whether deleterious MC3R variants are more frequently found in patients clinically presenting with constitutional delay of growth and puberty (CDGP) or normosmic idiopathic hypogonadotropic hypogonadism (nIHH). METHODS: We examined the sequence of MC3R in 362 adolescents with a clinical diagnosis of CDGP and 657 patients with nIHH, experimentally characterized the signaling properties of all nonsynonymous variants found and compared their frequency to that in 5774 controls from a population-based cohort. Additionally, we established the relative frequency of predicted deleterious variants in individuals with self-reported delayed vs normally timed menarche/voice-breaking in the UK Biobank cohort. RESULTS: MC3R loss-of-function variants were infrequent but overrepresented in patients with CDGP (8/362 [2.2%]; OR = 4.17; P = .001). There was no strong evidence of overrepresentation in patients with nIHH (4/657 [0.6%]; OR = 1.15; P = .779). In 246 328 women from the UK Biobank, predicted deleterious variants were more frequently found in those self-reporting delayed (aged ≥16 years) vs normal age at menarche (OR = 1.66; P = 3.90E-07). CONCLUSION: We have found evidence that functionally damaging variants in MC3R are overrepresented in individuals with CDGP but are not a common cause of this phenotype.

The Thyroid Hormone Axis and Female Reproduction.

Thyroid function affects multiple sites of the female hypothalamic-pituitary gonadal (HPG) axis. Disruption of thyroid function has been linked to reproductive dysfunction in women and is associated with menstrual irregularity, infertility, poor pregnancy outcomes, and gynecological conditions such as premature ovarian insufficiency and polycystic ovarian syndrome. Thus, the complex molecular interplay between hormones involved in thyroid and reproductive functions is further compounded by the association of certain common autoimmune states with disorders of the thyroid and the HPG axes. Furthermore, in prepartum and intrapartum states, even relatively minor disruptions have been shown to adversely impact maternal and fetal outcomes, with some differences of opinion in the management of these conditions. In this review, we provide readers with a foundational understanding of the physiology and pathophysiology of thyroid hormone interactions with the female HPG axis. We also share clinical insights into the management of thyroid dysfunction in reproductive-aged women.

Heterozygous mutations in SOX2 may cause idiopathic hypogonadotropic hypogonadism via dominant-negative mechanisms.

Pathogenic SRY-box transcription factor 2 (SOX2) variants typically cause severe ocular defects within a SOX2 disorder spectrum that includes hypogonadotropic hypogonadism. We examined exome-sequencing data from a large, well-phenotyped cohort of patients with idiopathic hypogonadotropic hypogonadism (IHH) for pathogenic SOX2 variants to investigate the underlying pathogenic SOX2 spectrum and its associated phenotypes. We identified 8 IHH individuals harboring heterozygous pathogenic SOX2 variants with variable ocular phenotypes. These variant proteins were tested in vitro to determine whether a causal relationship between IHH and SOX2 exists. We found that Sox2 was highly expressed in the hypothalamus of adult mice and colocalized with kisspeptin 1 (KISS1) expression in the anteroventral periventricular nucleus of adult female mice. In vitro, shRNA suppression of mouse SOX2 protein in Kiss-expressing cell lines increased the levels of human kisspeptin luciferase (hKiss-luc) transcription, while SOX2 overexpression repressed hKiss-luc transcription. Further, 4 of the identified SOX2 variants prevented this SOX2-mediated repression of hKiss-luc. Together, these data suggest that pathogenic SOX2 variants contribute to both anosmic and normosmic forms of IHH, attesting to hypothalamic defects in the SOX2 disorder spectrum. Our study describes potentially novel mechanisms contributing to SOX2-related disease and highlights the necessity of SOX2 screening in IHH genetic evaluation irrespective of associated ocular defects.

Association of distance to swine concentrated animal feeding operations with immune-mediated diseases: An exploratory gene-environment study.

BACKGROUND: Concentrated animal feeding operations (CAFOs) are a source of environmental pollution and have been associated with a variety of health outcomes. Immune-mediated diseases (IMD) are characterized by dysregulation of the normal immune response and, while they may be affected by gene and environmental factors, their association with living in proximity to a CAFO is unknown. OBJECTIVES: We explored gene, environment, and gene-environment (GxE) relationships between IMD, CAFOs, and single nucleotide polymorphisms (SNPs) of prototypical xenobiotic response genes AHR, ARNT, and AHRR and prototypical immune response gene PTPN22. METHODS: The exposure analysis cohort consisted of 6,464 participants who completed the Personalized Environment and Genes Study Health and Exposure Survey and a subset of 1,541 participants who were genotyped. We assessed the association between participants' residential proximity to a CAFO in gene, environment, and GxE models. We recombined individual associations in a transethnic model using METAL meta-analysis. RESULTS: In White participants, ARNT SNP rs11204735 was associated with autoimmune diseases and rheumatoid arthritis (RA), and ARNT SNP rs1889740 was associated with RA. In a transethnic genetic analysis, ARNT SNPs rs11204735 and rs1889740 and PTPN22 SNP rs2476601 were associated with autoimmune diseases and RA. In participants living closer than one mile to a CAFO, the log-distance to a CAFO was associated with autoimmune diseases and RA. In a GxE interaction model, White participants with ARNT SNPs rs11204735 and rs1889740 living closer than eight miles to a CAFO had increased odds of RA and autoimmune diseases, respectively. The transethnic model revealed similar GxE interactions. CONCLUSIONS: Our results suggest increased risk of autoimmune diseases and RA in those living in proximity to a CAFO and a potential role of the AHR-ARNT pathway in conferring risk. We also report the first association of ARNT SNPs rs11204735 and rs1889740 with RA. Our findings, if confirmed, could allow for novel genetically-targeted or other preventive approaches for certain IMD.

The skin is no barrier to mixtures: Air pollutant mixtures and reported psoriasis or eczema in the Personalized Environment and Genes Study (PEGS).

BACKGROUND: Autoimmune (AI) diseases appear to be a product of genetic predisposition and environmental triggers. Disruption of the skin barrier causes exacerbation of psoriasis/eczema. Oxidative stress is a mechanistic pathway for pathogenesis of the disease and is also a primary mechanism for the detrimental effects of air pollution. METHODS: We evaluated the association between autoimmune skin diseases (psoriasis or eczema) and air pollutant mixtures in 9060 subjects from the Personalized Environment and Genes Study (PEGS) cohort. Pollutant exposure data on six criteria air pollutants are publicly available from the Center for Air, Climate, and Energy Solutions and the Atmospheric Composition Analysis Group. For increased spatial resolution, we included spatially cumulative exposure to volatile organic compounds from sites in the United States Environmental Protection Agency Toxic Release Inventory and the density of major roads within a 5 km radius of a participant's address from the United States Geological Survey. We applied logistic regression with quantile g-computation, adjusting for age, sex, diagnosis with an autoimmune disease in family or self, and smoking history to evaluate the relationship between self-reported diagnosis of an AI skin condition and air pollution mixtures. RESULTS: Only one air pollution variable, sulfate, was significant individually (OR = 1.06, p = 3.99E-2); however, the conditional odds ratio for the combined mixture components of PM2.5 (black carbon, sulfate, sea salt, and soil), CO, SO2, benzene, toluene, and ethylbenzene is 1.10 (p-value = 5.4E-3). SIGNIFICANCE: While the etiology of autoimmune skin disorders is not clear, this study provides evidence that air pollutants are associated with an increased prevalence of these disorders. The results provide further evidence of potential health impacts of air pollution exposures on life-altering diseases. SIGNIFICANCE AND IMPACT STATEMENT: The impact of air pollution on non-pulmonary and cardiovascular diseases is understudied and under-reported. We find that air pollution significantly increased the odds of psoriasis or eczema in our cohort and the magnitude is comparable to the risk associated with smoking exposure. Autoimmune diseases like psoriasis and eczema are likely impacted by air pollution, particularly complex mixtures and our study underscores the importance of quantifying air pollution-associated risks in autoimmune disease.

Determination of Half-lives of Circulating FSH and LH Glycoforms in Women During GnRH Receptor Blockade.

CONTEXT: Both FSH and LH circulate as 2 glycoforms, differing in number of glycans: low-N-glycosylated glycoforms, FSHtri and LHdi, and fully N-glycosylated glycoforms, FSHtetra and LHtri. OBJECTIVES: To determine the half-lives of endogenous circulating gonadotropin glycoforms in women during GnRH receptor blockade. DESIGN/PARTICIPANTS: Serum samples were collected in 8 healthy women before and up to 20 hours after administration of the NAL-GLU GnRH antagonist. Three women were in early follicular phase, 2 at mid-cycle phase, and 3 were postmenopausal. MAIN OUTCOME MEASURES: The half-life of each glycoform was estimated by monoexponential decay for FSH (n = 8) and LH (n = 5). Data were analyzed using paired t tests. RESULTS: Half-lives in the circulation of low-N-glycosylated glycoforms of both FSH and LH were shorter than those of the fully N-glycosylated glycoforms (mean; range, FSHtri 343; 116-686 minutes vs FSHtetra 757; 436-1038, minutes, P = 0.0003; LHdi 125, 84-198 minutes vs LHtri 164, 107-235 minutes, P = 0.004). The half-lives of low-and fully N-glycosylated forms of LH were shorter than the corresponding half-lives of FSH glycoforms, P = 0.0008. CONCLUSIONS: For both FSH and LH, low-N-glycosylated glycoforms disappeared from the circulation faster than the fully N-glycosylated. The half-lives of low and fully N-glycosylated forms of LH were shorter than the corresponding half-lives of FSH. The estimated values for half-life in the circulation of total FSH and total LH will depend on the relative amounts of the 2 glycoforms of each hormone and their individual disappearance rates in circulation.

Questionnaire-based exposome-wide association studies (ExWAS) reveal expected and novel risk factors associated with cardiovascular outcomes in the Personalized Environment and Genes Study.

While multiple factors are associated with cardiovascular disease (CVD), many environmental exposures that may contribute to CVD have not been examined. To understand environmental effects on cardiovascular health, we performed an exposome-wide association study (ExWAS), a hypothesis-free approach, using survey data on endogenous and exogenous exposures at home and work and data from health and medical histories from the North Carolina-based Personalized Environment and Genes Study (PEGS) (n = 5015). We performed ExWAS analyses separately on six cardiovascular outcomes (cardiac arrhythmia, congestive heart failure, coronary artery disease, heart attack, stroke, and a combined atherogenic-related outcome comprising angina, angioplasty, atherosclerosis, coronary artery disease, heart attack, and stroke) using logistic regression and a false discovery rate of 5%. For each CVD outcome, we tested 502 single exposures and built multi-exposure models using the deletion-substitution-addition (DSA) algorithm. To evaluate complex nonlinear relationships, we employed the knockoff boosted tree (KOBT) algorithm. We adjusted all analyses for age, sex, race, BMI, and annual household income. ExWAS analyses revealed novel associations that include blood type A (Rh-) with heart attack (OR[95%CI] = 8.2[2.2:29.7]); paint exposures with stroke (paint related chemicals: 6.1[2.2:16.0], acrylic paint: 8.1[2.6:22.9], primer: 6.7[2.2:18.6]); biohazardous materials exposure with arrhythmia (1.8[1.5:2.3]); and higher paternal education level with reduced risk of multiple CVD outcomes (stroke, heart attack, coronary artery disease, and combined atherogenic outcome). In multi-exposure models, trouble sleeping and smoking remained important risk factors. KOBT identified significant nonlinear effects of sleep disorder, regular intake of grapefruit, and a family history of blood clotting problems for multiple CVD outcomes (combined atherogenic outcome, congestive heart failure, and coronary artery disease). In conclusion, using statistics and machine learning, these findings identify novel potential risk factors for CVD, enable hypothesis generation, provide insights into the complex relationships between risk factors and CVD, and highlight the importance of considering multiple exposures when examining CVD outcomes.

Scoping review of COVID-19-related systematic reviews and meta-analyses: can we really have confidence in their results?

AIM: The aim of this study was to systematically appraise the quality of a sample of COVID-19-related systematic reviews (SRs) and discuss internal validity threats affecting the COVID-19 body of evidence. DESIGN: We conducted a scoping review of the literature. SRs with or without meta-analysis (MA) that evaluated clinical data, outcomes or treatments for patients with COVID-19 were included. MAIN OUTCOME MEASURES: We extracted quality characteristics guided by A Measurement Tool to Assess Systematic Reviews-2 to calculate a qualitative score. Complementary evaluation of the most prominent published limitations affecting the COVID-19 body of evidence was performed. RESULTS: A total of 63 SRs were included. The majority were judged as a critically low methodological quality. Most of the studies were not guided by a pre-established protocol (39, 62%). More than half (39, 62%) failed to address risk of bias when interpreting their results. A comprehensive literature search strategy was reported in most SRs (54, 86%). Appropriate use of statistical methods was evident in nearly all SRs with MAs (39, 95%). Only 16 (33%) studies recognised heterogeneity in the definition of severe COVID-19 as a limitation of the study, and 15 (24%) recognised repeated patient populations as a limitation. CONCLUSION: The methodological and reporting quality of current COVID-19 SR is far from optimal. In addition, most of the current SRs fail to address relevant threats to their internal validity, including repeated patients and heterogeneity in the definition of severe COVID-19. Adherence to proper study design and peer-review practices must remain to mitigate current limitations.

Prevalence of Hirsutism Among Reproductive-Aged African American Women.

Background: Hirsutism is the most common clinical symptom of hyperandrogenism, but racial and ethnic-specific thresholds have not been established. Our objective was to characterize hirsutism using self-report of hair growth in a large sample of African American women. Materials and Methods: The Study of Environment, Lifestyle, and Fibroids is a prospective community-based cohort study of African American women (23-34 at recruitment). A total of 1568 participants received the modified Ferriman-Gallwey (mFG) pictorial assessment and were asked if they were ever bothered by excess hair. We estimated the prevalence of hirsutism (mFG score ≥8) and associations of acne, polycystic ovary syndrome (PCOS), and menstrual cycle characteristics with hirsutism. We also explored hirsutism defined by the 95th percentile of scores in our cohort (mFG = 11) and a newly recommended criterion, mFG = 4. Results: We could determine hirsutism status in 1556 women. Thirty-seven percent reported being bothered by excess hair, and 10% met the mFG criterion for hirsutism. History of severe facial acne was positively associated with hirsutism (prevalence ratio: 1.90; 95% confidence interval [CI]: 1.30-2.76), as was physician-diagnosed PCOS (2.22, 95% CI: 1.30-3.81). Women with irregular menstrual cycles were also more likely to report hirsutism (1.78, 95% CI: 1.00-3.18). Results were similar using mFG ≥11 and attenuated using mFG ≥4. Conclusions: Hirsutism prevalence was 10% in this community sample of African American women and was associated with PCOS, severe acne, and irregular menstrual cycles suggesting this represented hirsutism caused by hyperandrogenism. Ethnically diverse, population-based studies assessing the association between mFG score and androgen levels are needed to better understand the hirsutism threshold as a clinical marker of hyperandrogenism.

The Hypothalamic Pituitary Thyroid Axis and Sleep.

Sleep has a bidirectional relationship with the hypothalamic-pituitary-thyroid (HPT) axis, and both these homeostatic processes are inter-dependent for robust physiological functioning. The quality and quantity of sleep influence the circadian pattern of TSH and thyroid hormone secretion. Short term sleep restriction significantly reduces the amplitude of nocturnal TSH secretion and may modulate active thyroid hormone secretion, likely through an increased sympathetic tone. Conversely, TSH and active thyroid hormone affect the quantity and architecture of sleep. For instance, low TSH values are permissive for slow wave sleep and maintenance of normal sleep architecture, while the hypo- or hyper-secretion of active thyroid hormones adversely affects the quality and quantity of sleep. Structural thyroid disorders may also be associated with an altered circadian clock - a phenomenon warranting further investigation. In this review, we aim to provide readers a comprehensive review on the associations between the HPT axis and sleep patterns.

Exploring the motivations of research participants who chose not to learn medically actionable secondary genetic findings about themselves.

PURPOSE: Proposals to return medically actionable secondary genetic findings (SFs) in the clinical and research settings have generated controversy regarding whether to solicit individuals' preferences about their "right not to know" genetic information. This study contributes to the debate by surveying research participants who have actively decided whether to accept or refuse SFs. METHODS: Participants were drawn from a large National Institutes of Health (NIH) environmental health study. Participants who had accepted SFs (n = 148) or refused SFs (n = 83) were given more detailed information about the types of SFs researchers could return and were given an opportunity to revise their original decision. RESULTS: Forty-one of 83 initial refusers (49.4%) opted to receive SFs following the informational intervention. Nearly 75% of these "reversible refusers" thought they had originally accepted SFs. The 50.6% of initial refusers who continued to refuse ("persistent refusers") demonstrated high levels of understanding of which SFs would be returned postintervention. The most prominent reason for refusing was concern about becoming worried or sad (43.8%). CONCLUSION: This study demonstrates the need for a more robust informed consent process when soliciting research participants' preferences about receiving SFs. We also suggest that our data support implementing a default practice of returning SFs without actively soliciting preferences.