Prevalence and knowledge of polycystic ovary syndrome (PCOS) and health-related practices among women of Syria: a cross-sectional study.

Polycystic Ovarian Syndrome (PCOS) is a prevalent metabolic and hormonal disorder affecting women of reproductive age. Limited data exists on Syrian women's PCOS awareness and health behaviors. This study aimed to gauge PCOS prevalence, knowledge, awareness, and health-related practices among Syrian women. A cross-sectional online survey was conducted from 11 February to 27 October 2022, targeting Syrian women aged 18-45. Collaborators from specific medical universities distributed a questionnaire adapted from a Malaysian paper through social media platforms. Out of 1840 surveyed Syrian women, 64.2% were aged 21-29, and 69.6% held bachelor's degrees. Those with a bachelor's degree exhibited the highest mean knowledge score (12.86), and women previously diagnosed with PCOS had a higher mean knowledge score (13.74) than those without. Approximately 27.4% were confirmed PCOS cases, and 38.9% had possible cases. Women with PCOS were 3.41 times more likely to possess knowledge about the condition. The findings suggest a moderate level of PCOS knowledge and health-related practices among Syrian women, emphasizing the need for increased awareness. Consistent local PCOS screening programs, in collaboration with obstetrics and gynecology professionals, are crucial for improving understanding and clinical symptom recognition of this condition among Syrian women.

Polycystic ovary syndrome.

Despite affecting ~11-13% of women globally, polycystic ovary syndrome (PCOS) is a substantially understudied condition. PCOS, possibly extending to men's health, imposes a considerable health and economic burden worldwide. Diagnosis in adults follows the International Evidence-based Guideline for the Assessment and Management of Polycystic Ovary Syndrome, requiring two out of three criteria - clinical or biochemical hyperandrogenism, ovulatory dysfunction, and/or specific ovarian morphological characteristics or elevated anti-Müllerian hormone. However, diagnosing adolescents omits ovarian morphology and anti-Müllerian hormone considerations. PCOS, marked by insulin resistance and hyperandrogenism, strongly contributes to early-onset type 2 diabetes, with increased odds for cardiovascular diseases. Reproduction-related implications include irregular menstrual cycles, anovulatory infertility, heightened risks of pregnancy complications and endometrial cancer. Beyond physiological manifestations, PCOS is associated with anxiety, depression, eating disorders, psychosexual dysfunction and negative body image, collectively contributing to diminished health-related quality of life in patients. Despite its high prevalence persisting into menopause, diagnosing PCOS often involves extended timelines and multiple health-care visits. Treatment remains ad hoc owing to limited understanding of underlying mechanisms, highlighting the need for research delineating the aetiology and pathophysiology of the syndrome. Identifying factors contributing to PCOS will pave the way for personalized medicine approaches. Additionally, exploring novel biomarkers, refining diagnostic criteria and advancing treatment modalities will be crucial in enhancing the precision and efficacy of interventions that will positively impact the lives of patients.

Biomarkers identification in follicular fluid in relation to live birth in in vitro fertilization of women with polycystic ovary syndrome in different subtypes by using UPLC-MS method.

BACKGROUND: Polycystic ovary syndrome (PCOS) is a common infertility disorder which affects reproductive-aged women. However, metabolic change profiles of follicular fluid (FF) in lean and obese women diagnosed with and without PCOS remains unclear. METHODS: 95 infertile women were divided into four subgroups: LC (lean control), OC (overweight control), LP (lean PCOS), and OP (overweight PCOS). The FF samples were collected during oocyte retrieval and assayed by ultra-performance liquid chromatography coupled with mass spectrometry (UPLC-MS) metabolomics. RESULTS: A total of 236 metabolites were identified by metabolic analysis. The pathway enrichment analysis revealed that the glycerophospholipid metabolism (impact = 0.11182), ether lipid metabolism (impact = 0.14458), and primary bile acid biosynthesis (impact = 0.03267) were related to metabolic pathway between PCOS and control. Correlation analyses showed that epitestosterone sulfate was found positively correlated with fertilization rate in PCOS, while falcarindione, lucidone C. and notoginsenoside I was found to be negatively correlated. The combined four biomarkers including lucidone C, epitestosterone sulfate, falcarindione, and notoginsenoside I was better in predicting live birth rate, with AUC of 0.779. CONCLUSION: The follicular fluid of women with PCOS showed unique metabolic characteristics. Our study provides better identification of PCOS follicular fluid metabolic dynamics, which may serve as potential biomarkers of live birth.

Association of anti-Müllerian hormone and insulin resistance in adolescent girls with polycystic ovary syndrome.

INTRODUCTION: Insulin resistance (IR) is confirmed as an important feature among polycystic ovary syndrome (PCOS) patients. Anti-Müllerian hormone (AMH), a vital marker of ovarian dysfunction, is proposed for inclusion in the diagnosis of PCOS in adolescents. We sought to investigate the relationship between the AMH level and IR in Chinese girls with PCOS. MATERIAL AND METHODS: 92 girls with PCOS aged 14-18 years were enrolled and divided into 2 subgroups: PCOS with IR group (n = 25) and PCOS without IR group (n = 67). A homeostasis model assessment-insulin resistance (HOMA-IR) value ≥ 2.5 was defined as IR. Clinical data and biochemical indexes were compared between the 2 groups. Multivariate logistic regression analysis and multivariate linear regression analysis were performed to determine which clinical variables were independently associated with IR and AMH level, respectively. RESULTS: PCOS girls with IR had higher levels of AMH than those of PCOS girls without IR (p < 0.01). Moreover, body mass index, triglyceride, and AMH values were shown to be independent risk factors for HOMA-IR after multivariate analysis. Meanwhile, age, insulin, and follicle-stimulating hormone levels were significantly related to AMH levels in those girls. CONCLUSIONS: Our findings show that AMH is an independent determinant of IR in PCOS adolescents, and the fasting insulin level is closely associated with the AMH level, which indicates that the AMH pathway might play a role in the development of IR in PCOS adolescents. The interaction between AMH and IR in PCOS girls warrants further large-scale evaluation.

Quantitative proteomics reveals pregnancy prognosis signature of polycystic ovary syndrome women based on machine learning.

OBJECTIVE: We aimed to screen and construct a predictive model for pregnancy loss in polycystic ovary syndrome (PCOS) patients through machine learning methods. METHODS: We obtained the endometrial samples from 33 PCOS patients and 7 healthy controls at the Reproductive Center of the Second Hospital of Lanzhou University from September 2019 to September 2020. Liquid chromatography tandem mass spectrometry (LCMS/MS) was conducted to identify the differentially expressed proteins (DEPs) of the two groups. Gene Ontology (GO) as well as Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were performed to analyze the related pathways and functions of the DEPs. Then, we used machine learning methods to screen the feature proteins. Multivariate Cox regression analysis was also conducted to establish the prognostic models. The performance of the prognostic model was then evaluated by the receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA). In addition, the Bootstrap method was conducted to verify the generalization ability of the model. Finally, linear correlation analysis was performed to figure out the correlation between the feature proteins and clinical data. RESULTS: Four hundred and fifty DEPs in PCOS and controls were screened out, and we obtained some pathways and functions. A prognostic model for the pregnancy loss of PCOS was established, which has good discrimination and generalization ability based on two feature proteins (TIA1, COL5A1). Strong correlation between clinical data and proteins were identified to predict the reproductive outcome in PCOS. CONCLUSION: The model based on the TIA1 and COL5A1 protein could effectively predict the occurrence of pregnancy loss in PCOS patients and provide a good theoretical foundation for subsequent research.

Women's use of online health and social media resources to make sense of their polycystic ovary syndrome (PCOS) diagnosis: a qualitative study.

BACKGROUND: With the growing availability of online health resources and the widespread use of social media to better understand health conditions, people are increasingly making sense of and managing their health conditions using resources beyond their health professionals and personal networks. However, where the condition is complex and poorly understood, this can involve extensive "patient work" to locate, interpret and test the information available. The overall purpose of this study was to investigate how women with polycystic ovary syndrome (PCOS) across two healthcare systems engage with online health resources and social media to better understand this complex and poorly understood lifelong endocrine disorder. METHODS: A semi-structured interview study was conducted with women from the US ( N = 8 ) and UK ( N = 7 ) who had been diagnosed with PCOS within the previous five years. Transcribed data was analysed using a reflexive thematic analysis method. RESULTS: We highlight the information needs and information-seeking strategies women use to make sense of how PCOS affects them, to gain emotional support, and to help them find an effective treatment. We also show how women with PCOS use online health and social media resources to compare themselves to women they view as "normal" and other women with PCOS, to find their sense of "normal for me" along a spectrum of this disorder. CONCLUSION: We draw on previous models of sense-making and finding normal for other complex and sensitive health conditions to capture the nuances of making sense of PCOS. We also discuss implications for the design and use of social media to support people managing PCOS.

Prevalence and Diagnosis of PCOS Using Electronic Health Records: A Scoping Review and a Database Analysis.

Polycystic ovary syndrome (PCOS) is the most common endocrine disorder affecting women of reproductive age. It increases the risk of type 2 diabetes, cardiovascular disease, endometrial cancer, infertility, gestational diabetes, preeclampsia, and preterm birth. Accurately identifying predictors of these health risks is crucial. Electronic health records (EHRs) offer an affordable approach, however, the validity and reliability of EHRs for PCOS diagnosis are unclear. A scoping review of the literature on the prevalence and reliability of the diagnosis of PCOS using EHRs was performed. An analysis of the feasibility of obtaining diagnostic variables from a PCOS patient database was also carried out. Eight studies met the criteria. The prevalence of PCOS ranged from 0.27% to 5.8%. Reliability varied, with one study reporting a sensitivity of 50% and a specificity of 29%. Another study found a 74.4% agreement between international classification of disease (ICD) codes and clinical criteria. The database analysis found only 13.7%, 8%, and 7.5% of women had all the necessary variables for an objective diagnosis of PCOS using the Rotterdam, National Institutes of Health (NIH), and Androgen Excess and PCOS Society (AEPCOS) criteria, respectively. Using EHRs results in an underestimation of PCOS prevalence compared to other diagnostic criteria, and many women identified may not meet the complete diagnostic criteria. These findings have implications for future research studies on PCOS prevalence and related health risks.

Distribution and anthropometric characteristics of Rotterdam criteria-based phenotypic forms of Polycystic ovaries syndrome in Ukraine.

OBJECTIVE: To explore the distribution of Rotterdam-based PCOS phenotypes and their associations with anthropometric parameters predictive of cardiometabolic risks in Ukrainian referral PCOS women. STUDY DESIGN: It was a cross-sectional study conducted by the Ukrainian Society of Gynecological Endocrinology between September 2021 and January 2022 involving 42 clinics in 10 regional centres representing the major geographical parts of Ukraine. Two hundred obstetrician-gynecologists whose practice facilities corresponded to study criteria were committed to entering records of their PCOS patients aged 20-45 years into the uniform data collection forms. The recorded parameters were: PCOS phenotype with the mandatory assessment of biochemical hyperandrogenism, age, BMI, waist circumference, and hyperandrogenism symptoms. RESULTS: 5254 patients' records were completed. Phenotype A was the most prevalent - 47.7 %, phenotypes B, C, and D were almost equally distributed in the studied population: 17.6 %, 17.4 %, and 17.3 % respectively. The total prevalence of androgenic phenotypes based on the presence of biochemical hyperandrogenism was 82.7 %. The incidence of obesity and hyperandrogenism symptoms, and mean BMI values were higher in phenotypes A and B compared to C and D. At the same time, the presence of 34.1 % and 46.2 % of normal-weight subjects in phenotypes A and B respectively, supports the fact that the excessive BMI is not a universal characteristic of androgenic phenotypes. In younger age groups, phenotypes C and D demonstrated the predominance of normal weight, but in older subgroups, the situation changed: in the age group of 36-45 y.o. compared to 18-25 y.o., the percentage of overweight and obese subjects for the non-classic phenotypes increased more than for the classic ones: C (OR = 3.91, 95 % CI: 2.41-6.38), D (OR = 4.14, 95 % CI: 2.64-6.52), A (OR = 2.30, 95 % CI:1.72-2.08), and B (OR = 2.56, 95 % CI:1.69-3.89). CONCLUSIONS: In thoroughly assessed Ukrainian referral PCOS population the classic phenotypes prevailed as in other clinical cohorts. The classic phenotypes were characterized by the higher rate of adiposity and severity of clinical hyperandrogenism. At the same time, obese, overweight, and normal-weight subjects were present in all phenotypes, and the risk of obesity in non-classic phenotypes was higher in older age groups.

Suicide Attempts After a Diagnosis of Polycystic Ovary Syndrome : A Cohort Study.

BACKGROUND: Limited evidence exists about suicide risk in persons with polycystic ovary syndrome (PCOS). OBJECTIVE: To assess suicide risk in persons with PCOS, accounting for psychiatric comorbid conditions and age group. DESIGN: Cohort study. SETTING: Data from the Taiwanese nationwide database from 1997 to 2012. PATIENTS: A cohort of 18 960 patients diagnosed with PCOS, each matched with control participants in a 1:10 ratio on the basis of age, psychiatric comorbid conditions, urbanization level, and income. Suicide attempts were evaluated using Cox regression models. MEASUREMENTS: Suicide risk with hazard ratios (HRs). RESULTS: Participants with PCOS had a notable 8.47-fold increase in risk for suicide attempt compared with the control group (HR, 8.47 [95% CI, 7.54 to 9.51]), after adjustment for demographic characteristics, psychiatric comorbid conditions, Charlson Comorbidity Index scores, and frequency of all-cause clinical visits. The elevated risk was evident across the adolescent (HR, 5.38 [CI, 3.93 to 7.37]), young adult (<40 years; HR, 9.15 [CI, 8.03 to 10.42]), and older adult (HR, 3.75 [CI, 2.23 to 6.28]) groups. Sensitivity analyses involving the exclusion of data from the first year or the first 3 years of observation yielded consistent results. LIMITATION: Potential underestimation of PCOS and mental disorder prevalence due to use of administrative claims data; lack of clinical data, such as body mass index and depressive symptoms; and no assessment of a confounding effect of valproic acid exposure. CONCLUSION: This study underscores the heightened risk for suicide attempt that persons with PCOS face, even after adjustment for demographics, psychiatric comorbid conditions, physical conditions, and all-cause clinical visits. This suggests the importance of routine monitoring of mental health and suicide risk in persons diagnosed with PCOS. PRIMARY FUNDING SOURCE: Taipei Veterans General Hospital, Yen Tjing Ling Medical Foundation, and Ministry of Science and Technology of Taiwan.

Predicting polycystic ovary syndrome with machine learning algorithms from electronic health records.

Introduction: Predictive models have been used to aid early diagnosis of PCOS, though existing models are based on small sample sizes and limited to fertility clinic populations. We built a predictive model using machine learning algorithms based on an outpatient population at risk for PCOS to predict risk and facilitate earlier diagnosis, particularly among those who meet diagnostic criteria but have not received a diagnosis. Methods: This is a retrospective cohort study from a SafetyNet hospital's electronic health records (EHR) from 2003-2016. The study population included 30,601 women aged 18-45 years without concurrent endocrinopathy who had any visit to Boston Medical Center for primary care, obstetrics and gynecology, endocrinology, family medicine, or general internal medicine. Four prediction outcomes were assessed for PCOS. The first outcome was PCOS ICD-9 diagnosis with additional model outcomes of algorithm-defined PCOS. The latter was based on Rotterdam criteria and merging laboratory values, radiographic imaging, and ICD data from the EHR to define irregular menstruation, hyperandrogenism, and polycystic ovarian morphology on ultrasound. Results: We developed predictive models using four machine learning methods: logistic regression, supported vector machine, gradient boosted trees, and random forests. Hormone values (follicle-stimulating hormone, luteinizing hormone, estradiol, and sex hormone binding globulin) were combined to create a multilayer perceptron score using a neural network classifier. Prediction of PCOS prior to clinical diagnosis in an out-of-sample test set of patients achieved an average AUC of 85%, 81%, 80%, and 82%, respectively in Models I, II, III and IV. Significant positive predictors of PCOS diagnosis across models included hormone levels and obesity; negative predictors included gravidity and positive bHCG. Conclusion: Machine learning algorithms were used to predict PCOS based on a large at-risk population. This approach may guide early detection of PCOS within EHR-interfaced populations to facilitate counseling and interventions that may reduce long-term health consequences. Our model illustrates the potential benefits of an artificial intelligence-enabled provider assistance tool that can be integrated into the EHR to reduce delays in diagnosis. However, model validation in other hospital-based populations is necessary.

Determination of urinary androgens in women with polycystic ovary syndrome using LC-QqQ/MS and the application of thin film solid-phase microextraction (TF-SPME).

Hyperandrogenism is one of the most pronounced symptoms of Polycystic Ovary Syndrome (PCOS) and seems to play a key role in the pathogenesis of this complex disorder. Nevertheless, there is still a lack of consistent results regarding common steroid predictors of PCOS. Therefore, a liquid chromatography tandem mass spectrometry (HPLC-QqQ/MS) method was developed and validated to determine the concentrations of four classic androgens: androstenedione (An-dione), testosterone (T), 5α-dihydrotestosterone (DHT) and androsterone (An) in urine samples obtained from women with PCOS and healthy controls. The limits of detection were between 0.04 and 0.09 ng/mL, while the limits of quantification ranged from 0.1 to 0.3 ng/mL respectively. As a pre-treatment procedure prior to analysis, hydrolysis using ß-glucuronidase and thin film solid-phase microextraction (TF-SPME) was applied. The methodology was employed to perform targeted metabolomics of urinary steroids in women with PCOS and healthy controls. All measured androgens: An-dione (p < 0.0001), T (p = 0.0001), DHT (p < 0.0001) and An (p = 0.0002) showed significantly higher concentrations in the urine of women with PCOS. The largest difference in the mean concentration was found for DHT, which was 2.8 times higher in the PCOS group (13.9 ± 14.1 ng/mg creatinine) in comparison to healthy controls (4.9 ± 3.4 ng/mg creatinine). The results of receiver operating characteristic curve indicated that determination of the panel of three urinary androgens: T+DHT+An-dione with, under the study assumptions, was the best predictor of PCOS diagnosis (AUC of ROC curve = 0.91 (95 % CI: 0.8212-0.9905). The application of an LC-MS/MS-based analysis, together with highly sensitive extraction techniques like TF-SPME, is a suitable approach to perform fast assays and obtain reliable results - crucial in the search for valuable and significant steroids predictors of PCOS.

The role of the visceral adiposity index in the assessment of metabolic syndrome of polycystic ovary syndrome patients: a new anthropometric index.

PURPOSE: Polycystic ovary syndrome (PCOS) is a common endocrine disorder often linked to metabolic syndrome (MS), raising the risk of cardiovascular disease and type II diabetes. Certain indicators, such as the lipid accumulation product (LAP) and homeostatic model assessment for insulin resistance (HOMA-IR), can predict MS in PCOS patients. This study aimed to assess the predictive power of the visceral adiposity index (VAI) in comparison to LAP and HOMA-IR as predictors of MS in PCOS patients. METHODS: In this cross-sectional observational study, data from 317 diagnosed PCOS women were analyzed. VAI, LAP, and HOMA-IR were computed as indexes. Participants were categorized into two groups for index accuracy comparison: PCOS patients with and without MS. The data were assessed using a ROC curve. RESULTS: Among PCOS women with MS, 92.3% had abnormal VAI results, 94.5% had abnormal LAP results, and only 50.5% had abnormal HOMA-IR results. Conversely, the majority of PCOS women without MS had normal HOMA-IR (64.6%). When comparing these indexes using the ROC curve, VAI displayed the highest accuracy, followed by LAP and HOMA-IR. CONCLUSION: The VAI index proved to be a superior predictor of metabolic MS in PCOS women when compared to other indexes.

Healthcare and research priorities for women with polycystic ovary syndrome in the UK National Health Service: A modified Delphi method.

OBJECTIVE: Polycystic ovary syndrome (PCOS) is a chronic lifelong condition affecting up to 20% of women worldwide. There is limited input from affected women to guide the provision of healthcare services and future research needs. Our objective was to scope the healthcare and research priorities of women with PCOS in the United Kingdom. DESIGN: A three-staged modified Delphi method, consisting of two questionnaires and a consensus meeting involving lay representatives and healthcare professionals. PATIENTS AND MEASUREMENTS: Lay patient representatives of women with PCOS. Participants were asked to identify and rank healthcare and research priorities for their importance. RESULTS: Six hundred and twenty-four lay participants took part in our Delphi method. Over 98% were diagnosed with PCOS (614/624, 98.4%). More than half experienced difficulties to receive a PCOS diagnosis (375/624, 60%), and the majority found it difficult to access specialised PCOS health services in the NHS (594/624, 95%). The top two healthcare priorities included better education for health professionals on the diagnosis and management of PCOS (238/273, 87.1%) and the need to set up specialist PCOS services (234/273, 85.7%). The top two research priorities focused on identifying better treatments for irregular periods (233/273, 85.3%) followed by better tests for early PCOS diagnosis (230/273, 84.2%). CONCLUSIONS: We identified 13 healthcare and 14 research priorities that reflect the current health needs of women with PCOS in the United Kingdom. Adopting these priorities in future healthcare and research planning will help to optimise the health of women with PCOS and increase patient satisfaction.

Deciphering the potential of proteomic-based biomarkers in women's reproductive diseases: empowering precision medicine in gynecology.

CONTEXT: Gynecological disorders represent a complex set of malignancies that result from a diverse array of molecular changes affecting the lives of over a million women worldwide. Ovarian, Endometrial, and Cervical cancers, Endometriosis, PCOS are the most prevalent ones that pose a grave threat to women's health. Proteomics has emerged as an invaluable tool for developing novel biomarkers, screening methods, and targeted therapeutic agents for gynecological disorders. Some of these biomarkers have been approved by the FDA, but regrettably, they have a constrained diagnostic accuracy in early-stage diagnosis as all of these biomarkers lack sensitivity and specificity. Lately, high-throughput proteomics technologies have made significant strides, allowing for identification of potential biomarkers with improved sensitivity and specificity. However, limited successes have been shown with translation of these discoveries into clinical practice. OBJECTIVE: This review aims to provide a comprehensive overview of the current and potential protein biomarkers for gynecological cancers, endometriosis and PCOS, discusses recent advances and challenges, and highlights future directions for the field. CONCLUSION: We propose that proteomics holds great promise as a powerful tool to revolutionize the fight against female reproductive diseases and can ultimately improve personalized patient outcomes in women's biomedicine.

Management of polycystic ovary syndrome must include assessment and treatment of mental health symptoms.

Polycystic ovary syndrome (PCOS) is an endocrine disorder with reproductive and metabolic manifestations affecting millions of women worldwide. The health risks associated with PCOS, however, go beyond physical health. Over the past decade, data have emerged demonstrating a high risk of concurrent mental health conditions, specifically depression and anxiety, but extending into other aspects of psychological health, including body image distress, eating disorders, and sexual dysfunction. International surveys suggest physician knowledge about the mental health associations with PCOS is poor and that patients are often dissatisfied regarding counseling-related psychological issues. We performed a review of mental health comorbidities in individuals with PCOS, including depression, anxiety, body image distress, eating disorders, psychosexual dysfunction, and decreased quality of life, as well as evaluated the impact of common PCOS treatments on these conditions. Most meta-analyses in reproductive age women demonstrate increased risks of these conditions, although data are more limited in adolescents and older adults. In addition, the impact of PCOS treatments on these conditions as well as data on first-line treatments in the PCOS population is limited. All providers involved in the multidimensional care of individuals with PCOS should be aware of these mental health risks to provide appropriate screening, counseling and referral options. Future studies should be designed to evaluate targeted treatment for individuals with PCOS.

Mental health disorders-should we screen all our patients?

Understanding the precise interplay between mental and physical health, as related to specific gynecological diseases, is crucial to providing high-quality, comprehensive, and effective care to our patients. A large body of literature provides evidence for the association of infertility, polycystic ovary syndrome, endometriosis and fibroids with anxiety, depression, eating disorders, sexual dysfunction, and/or reduced health related quality of life. Although the precise etiology of these associations is not clear, chronic pain, hormonal changes, body image distress, feelings of helplessness and high stress levels have all been described as possible mediators. Lack of early diagnosis and management of mental health conditions is known to impact compliance with office visits, diagnostic testing and treatment leading to overall reduced quality of life. As part of a holistic approach, we need to develop evidence-based guidelines for screening high-risk patients and increase collaboration between gynecologists and mental health professionals to offer seamless care. This goal may be aspirational as there are several patient- and provider-related challenges to offering comprehensive care to this patient population. Embracing novel technology-based opportunities and incorporating connected healthcare delivery models will help us meet these growing challenges.