Literature DB >> 16617213

Menstrual cycle characteristics and predictability of ovulation of Bhutia women in Sikkim, India.

Sharon R Williams1.   

Abstract

Although a woman's menstrual history can have significant implications for health outcomes, few studies have examined menstrual cycle variability in non-western, non-clinically based populations. This study presents menstrual cycle characteristics from Bhutia women living in Gangtok, Sikkim, India. The Bhutia are one of two indigenous populations residing in this small, northeastern state of India. A total of 1067 cycles were recorded by 200 Bhutia women over the course of 12 months. Mean cycle length in this population was similar to reported mean cycle lengths for populations in the U.S (30 days vs. 28 days). Menstrual cycles in this sample were highly variable with most women experiencing more than one short or long menstrual cycle. The frequency of irregular menstrual cycles experienced by individuals also varied significantly by season. A body mass index (BMI) above or below the WHO defined normal range was associated with higher rates of irregular cycles. Leutenizing hormone (LH) and follicle stimulating hormone (FSH) levels were also determined from urine samples collected just before mid-cycle, based on median cycle lengths. Although menstrual cycles in this sample were highly variable, median cycle length was still useful in predicting timing of the pre-ovulatory hormone surges of LH and FSH. Frequency of irregular cycles did impact the successful capture of the LH and FSH peak values.

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Year:  2006        PMID: 16617213     DOI: 10.2114/jpa2.25.85

Source DB:  PubMed          Journal:  J Physiol Anthropol        ISSN: 1880-6791            Impact factor:   2.867


  3 in total

1.  Does Ramadan fasting has any effects on menstrual cycles?

Authors:  Mahnaz Yavangi; Mohammad Ali Amirzargar; Nasibeh Amirzargar; Maryam Dadashpour
Journal:  Iran J Reprod Med       Date:  2013-02

2.  Characterizing physiological and symptomatic variation in menstrual cycles using self-tracked mobile-health data.

Authors:  Kathy Li; Iñigo Urteaga; Chris H Wiggins; Anna Druet; Amanda Shea; Virginia J Vitzthum; Noémie Elhadad
Journal:  NPJ Digit Med       Date:  2020-05-26

3.  A predictive model for next cycle start date that accounts for adherence in menstrual self-tracking.

Authors:  Kathy Li; Iñigo Urteaga; Amanda Shea; Virginia J Vitzthum; Chris H Wiggins; Noémie Elhadad
Journal:  J Am Med Inform Assoc       Date:  2021-12-28       Impact factor: 4.497

  3 in total

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