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Study Question: Are empirically derived adolescent overweight/obesity phenotypes differentially associated with polycystic ovary syndrome (PCOS) in young adulthood?

Summary Answer: Self-reported PCOS diagnosis risk in young adulthood varied by empirically derived adolescent overweight/obesity phenotypes, with the highest risk observed among those in the 'mothers with obesity' and 'early puberty' phenotypes.

What Is Known Already: Overweight and obesity during puberty are postulated to promote the development of PCOS. Much of the prior literature in this area is cross-sectional and defines weight status based solely on BMI, yet emerging research suggests that not all people with overweight/obesity have the same risk for chronic health conditions, including PCOS.

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Unlabelled: Cardiorespiratory fitness can be assessed by direct, indirect, maximal, and moderate effort, running, cycling, or walking methods.

Objective: To predict maximum oxygen consumption (V O2max) from the six-minute walk test in schoolchildren.

Patients And Method: 459 students were included, 215 were male and 244 were female, aged 11.

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Introduction: Polycystic ovarian syndrome (PCOS) is a heterogeneous endocrinal physiological disorder characterized by chronic oligo-ovulation or an-ovulation, hyperandrogenism, and polycystic morphology in ovaries on transvaginal or abdominal ultrasound. Hyperandrogenism and insulin resistance are already well-documented pathophysiological mechanisms in PCOS. Besides this, autoimmunity has been hypothesized in its pathogenesis.

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Dietary Factors Associated with Depressive Symptoms in Midlife Women 40-50 Years of Age Living in the United States.

Womens Health Rep (New Rochelle)

December 2024

Department of Family Health Care Nursing, School of Nursing, University of California, San Francisco, California, USA.

Purpose: Women in the decade before menopause are at risk for depression. This study describes dietary factors associated with depression risk in late premenopausal women that could be modifiable with targeted interventions.

Methods: Descriptive cross-sectional study comparing a community-based sample of 342 healthy premenopausal women categorized as low-risk and high-risk for depression in a secondary analysis of dietary variables.

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The objectives of this study were to evaluate different machine learning algorithms for predicting body weight (BW) in Sujiang pigs using the following morphological traits: age, body length (BL), backfat thickness (BFT), chest circumference (CC), body height (BH), chest width (CW), and hip width (HW). Additionally, this study also investigated which machine learning algorithms could accurately and efficiently predict body weight in pigs using a limited set of morphological traits. For this purpose, morphological measurements of 365 mature (180 ± 5 days) Sujiang pigs from the Jiangsu Sujiang Pig Breeding Farm in Taizhou, Jiangsu Province, China were used.

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