Impact of Body Mass Index on the Prevalence of Metabolic Syndrome among Infertile South Indian Women

Abstract

Aim and objective To study the prevalence of metabolic syndrome (MBS) in different body mass index (BMI) group infertile women in both polycystic ovarian syndrome (PCOS) and non-PCOS subgroups. Materials and methods A prospective cross-sectional study of all infertile women attending the outpatient in the department of reproductive medicine and surgery in a tertiary care center catering patients from South India was conducted. These women were categorized based on BMI into groups as ≤23 kg/m2, 23.1–25 kg/m2, 25.1–27 kg/m2, 27.1–30 kg/m2, and >30 kg/m2. They were further subdivided as PCOS and non-PCOS based on Rotterdam criteria (2003). These women underwent screening for MBS according to the modified American Heart Association/National Heart Lung Blood Institute AHA/NHLBI (ATP III 2005) definition. The results were analyzed for significance by the unpaired t test and the Chi-square test. Results A total of 1,030 infertile women were analyzed. The mean age was 28.8 ± 4.5 years; mean BMI was 26.9 ± 3.7 kg/m2. More than 90% of women had waist circumference >80 cm. The prevalence of MBS among the infertile women was 35.3% and among PCOS and non-PCOS women was 44.7 and 28.9%, respectively. Even though there was statistically no significant difference in the metabolic parameters among PCOS and non-PCOS subgroups in different BMI cutoff levels, there was an increase in abnormal metabolic parameters with increase in BMI. The prevalence of MBS in the BMI groups among PCOS women was 11.1, 28.1, 29.7, 51.8, and 73.6%, respectively (p = 0.0005) and among non-PCOS women was 4.9, 20.4, 24.6, 41.6, and 66.7%, respectively (p = 0.0005). Conclusion The BMI is an independent risk factor for MBS in both PCOS and non-PCOS infertile women. Clinical significance Screening for MBS in infertile women helps in early identification and primary prevention by lifestyle modification helps in delaying long-term consequences of type 2 diabetes mellitus and cardiovascular diseases. How to cite this article Vembu R, Nandini Devi M, Nellepalli SR, et al. Impact of Body Mass Index on the Prevalence of Metabolic Syndrome among Infertile South Indian Women. Int J Infertil Fetal Med 2019;10(3):42–45.

Association Between Anthropometric Risk Factors and Metabolic Syndrome Among Adults in India: A Systematic Review and Meta-Analysis of Observational Studies

“…Half of the included studies (13 of 26) were conducted in the southern states of Tamil Nadu, Kerala, Andhra Pradesh, and Telangana ( 18 , 22 – 24 , 28 , 29 , 31 , 34 , 36 – 40 ). Nineteen were community-based studies ( 16 , 17 , 19 , 21 – 26 , 28 – 34 , 36 , 38 , 41 ), 6 were facility-based ( 18 , 20 , 27 , 35 , 37 , 40 ), and 1 ( 39 ) was a workplace-based study. The sample sizes of the included studies ranged from 112 to 9,886 participants.…”

“…Most studies (17 of 26) used NCEP ATP-III criteria to diagnose metabolic syndrome ( 16 , 18 , 21 – 24 , 26 , 27 , 30 – 32 , 34 , 35 , 37 – 39 , 41 ). Twenty-one ( 17 – 23 , 25 – 28 , 30 – 35 , 37 , 38 , 40 , 41 ) reported an association between metabolic syndrome and obesity, and 15 ( 16 – 19 , 24 , 27 – 29 , 31 , 32 , 35 – 37 , 39 , 40 ) found the syndrome to be associated with overweight ( Table 2 ).…”

“…Under the selection bias domain, most studies (17 of 26) had high bias risk related to representativeness of the sample ( 17 , 18 , 20 – 22 , 25 , 26 , 29 , 31 – 37 , 39 , 40 ), about 15 had high bias risk related to sample size justification ( 18 , 20 , 21 , 26 , 28 , 29 , 33 – 41 ), and 16 had high bias risk related to nonresponse ( 16 – 22 , 25 , 28 , 31 , 33 , 34 , 38 – 41 ). However, none of the studies had high risk related to ascertainment of exposure.…”

“…However, none of the studies had high risk related to ascertainment of exposure. Under the comparability domain, 16 studies had high risk of bias related to control of confounding ( 17 – 21 , 23 , 26 , 29 – 32 , 34 , 35 , 37 , 40 , 41 ). Under the outcome domain, only 1 study had high bias risk related to ascertainment of outcome and statistical test reporting ( 40 ).…”

“…Under the comparability domain, 16 studies had high risk of bias related to control of confounding ( 17 – 21 , 23 , 26 , 29 – 32 , 34 , 35 , 37 , 40 , 41 ). Under the outcome domain, only 1 study had high bias risk related to ascertainment of outcome and statistical test reporting ( 40 ). More than half of the studies (14 of 26) had good to satisfactory quality ( 16 , 19 , 22 – 25 , 27 , 28 , 30 , 32 , 33 , 36 , 38 , 39 ) ( Table 3 ).…”