David Geffen School of Medicine at UCLA

BMI may be most vital determinant of basal metabolic rate in PCOS.

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The BMI of patients with polycystic ovary syndrome appeared to be the most important factor in basal metabolic rate, independent of the polycystic ovary syndrome phenotype and insulin resistance, according to Margareta D. Pisarska, MD, who presented the data at the conjoint meeting of the International Federation of Fertility Societies and the American Society for Reproductive Medicine.

“Based on our study — since we do think obesity does play a significant role — we believe it is important for endocrinologists to help counsel these women in a fashion similar to those who are obese by emphasizing that weight loss and lowering BMI are important,” Pisarska, director of the division of reproductive endocrinology and infertility; director of the Fertility and Reproductive Medicine Center at Cedars-Sinai Medical Center; associate professor at Cedars-Sinai Medical Center and the David Geffen School of Medicine at UCLA, told Endocrine Today.

 

The researchers conducted the case-control study examining the metabolic changes (ie, lean body mass, body fat mass, body fat percentage, skeletal muscle mass, BMI and basal metabolic rate) in 128 patients with PCOS (mean age, 28.1 years) and 72 eumenorrheic, non-hirsute controls (mean age, 32.9 years).

In terms of hormonal profile, patients with PCOS had greater testosterone, dehydroepiandrosterone sulfate (DHEA-sulfate), fasting insulin and homeostasis model assessment of insulin resistance (HOMA-IR) levels compared with controls.

After controlling for age and BMI differences, there was no difference in body composition parameters between patients with PCOS and controls. There were no significant results regarding changes to the basal metabolic rate (P=.0162), lean body mass (P=.0153) or skeletal muscle mass (P=.0169), she said.

However, differences in fasting insulin and HOMA-IR remained significant. When looking at insulin resistance in women with PCOS as a potential factor affecting body composition and metabolic rates, there was also no difference between these groups.

“It is not necessarily PCOS; BMI and age are probably the more important determinants of basal metabolic rate, regardless of PCOS phenotype and insulin resistance,” Pisarska said.

BMI may be most vital determinant of basal metabolic rate in PCOS.

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The BMI of patients with polycystic ovary syndrome appeared to be the most important factor in basal metabolic rate, independent of the polycystic ovary syndrome phenotype and insulin resistance, according to Margareta D. Pisarska, MD, who presented the data at the conjoint meeting of the International Federation of Fertility Societies and the American Society for Reproductive Medicine.

“Based on our study — since we do think obesity does play a significant role — we believe it is important for endocrinologists to help counsel these women in a fashion similar to those who are obese by emphasizing that weight loss and lowering BMI are important,” Pisarska, director of the division of reproductive endocrinology and infertility; director of the Fertility and Reproductive Medicine Center at Cedars-Sinai Medical Center; associate professor at Cedars-Sinai Medical Center and the David Geffen School of Medicine at UCLA, told Endocrine Today.

The researchers conducted the case-control study examining the metabolic changes (ie, lean body mass, body fat mass, body fat percentage, skeletal muscle mass, BMI and basal metabolic rate) in 128 patients with PCOS (mean age, 28.1 years) and 72 eumenorrheic, non-hirsute controls (mean age, 32.9 years).

In terms of hormonal profile, patients with PCOS had greater testosterone, dehydroepiandrosterone sulfate (DHEA-sulfate), fasting insulin and homeostasis model assessment of insulin resistance (HOMA-IR) levels compared with controls.

After controlling for age and BMI differences, there was no difference in body composition parameters between patients with PCOS and controls. There were no significant results regarding changes to the basal metabolic rate (P=.0162), lean body mass (P=.0153) or skeletal muscle mass (P=.0169), she said.

However, differences in fasting insulin and HOMA-IR remained significant. When looking at insulin resistance in women with PCOS as a potential factor affecting body composition and metabolic rates, there was also no difference between these groups.

“It is not necessarily PCOS; BMI and age are probably the more important determinants of basal metabolic rate, regardless of PCOS phenotype and insulin resistance,” Pisarska said.

Model predicted final menses.

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Researchers at UCLA have developed a model to estimate the timing of a woman’s final menstrual period. According to researchers, the model has the potential to help physicians and patients determine when the menopausal transition is complete and estimate bone loss.

“We need a better way to answer women’s questions about when to expect the final menstrual period,” researcher Gail A. Greendale, MD, from UCLA’s David Geffen School of Medicine, said in a press release. “If further research bears out our approach, it could be the first step to developing Web-based calculators and other tools women can use to estimate where they are in the menopause transition and how far away their final period is.”

Greendale and colleagues included 554 women from the Study of Women’s Health Across the Nation (SWAN). They designed the probability of meeting specific landmarks: 2 years before, 1 year before and the final menstrual period (FMP).

“For example, some researchers have proposed that an intervention begun 1 or 2 years before the final menstrual period would greatly decrease future fracture risk by preventing the very rapid bone loss that occurs in the few years before and few years after the final menses,” Greendale said. “But before ideas such as this can be tested, we need to accurately predict where a woman is in her timeline to menopause.”

Therefore, the researchers assessed the probability of being in restricted intervals: 1 to 2 years before FMP, 2 years before FMP and FMP, or 1 year before FMP and FMP. Additionally, the markers that best predicted having crossed each landmark were determined, with the ideal markers defined as the greatest area under the receiver-operator curve (AUC).

Researchers wrote that the final models included the current estradiol and follicle-stimulating hormone (FSH), age, the stage of menopause transition, race/ethnicity and whether serum was collected during the early follicular phase.

Data indicate the AUC of final models predicted the probability of a woman having crossed 2 years before (0.902), 1 year before (0.926) and the FMP (0.945), researchers wrote. If they identified women as having crossed the 2 years before the FMP landmark when predicted probability extended beyond 0.3, the sensitivity was 85% and specificity 77%, they added.

Despite limitations, Greendale and colleagues conclude that the clinical practice implementation of their model is conceivable. However, further studies are warranted to determine validation of these findings.

Source: http://www.healio.com