Challenges in Determining Body Fat in Pregnant Women

Robič, Tatjana; Benedik, Evgen; Mis, Nataša Fidler; Bratanič, Borut; Rogelj, Irena; Golja, Petra

doi:10.1159/000358339

Cited by 7 publications

(9 citation statements)

References 32 publications

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“…These study results point to using the Presley equation for estimating %BF as the Paxton equation significantly overestimated %BF in this study's overweight or obese, pregnant population. The tendency of the Paxton equation to yield higher %BF estimates is consistent with Robič et al's findings (Robič et al, 2014).…”

Section: Discussionsupporting

confidence: 90%

Pilot Study Examining Pregnancy-specific Equations to Estimate Percent Body Fat in an Overweight/obese Pregnant Hispanic Population

Reisenberg¹,

Mauldin²,

Sawrey‐Kubicek³

et al. 2016

CALIF J HEALTH PROMOT

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Background and Purpose: Over half of women entering pregnancy are overweight or obese, increasing metabolic risk. This pilot study investigated whether established equations for estimating maternal percent body fat using anthropometry are accurate for Hispanic, overweight or obese pregnant women. Methods: The Siri technique of calculating percent body fat from direct measurements of body density and total body water was the gold-standard. Other pregnancy-specific equations were also examined. The study population included 15 normoglycemic, pregnant Hispanic women in their third trimester (33.2±1.9 gestational week) with a pre-pregnancy body mass index ≥25 to

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Section: Discussionsupporting

confidence: 90%

Pilot Study Examining Pregnancy-specific Equations to Estimate Percent Body Fat in an Overweight/obese Pregnant Hispanic Population

Reisenberg¹,

Mauldin²,

Sawrey‐Kubicek³

et al. 2016

CALIF J HEALTH PROMOT

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“…However, our trial was pragmatic and objective measurements such as accelerometry and fitness data are less prone to be affected by the support given to participants or by a desirability bias. Fat percentage estimates were based on widely used equations although not validated during pregnancy, as no consensus exists on which anthropometric method should be used to reliably determine body composition during pregnancy [ 47 ]. Because it was not possible to have skinfold measures at 36 weeks made by the same assessor as for the first two visits and to avoid high inter-observer variability [ 48 ], this assessment was not performed.…”

Section: Discussionmentioning

confidence: 99%

A 12-Week Exercise Program for Pregnant Women with Obesity to Improve Physical Activity Levels: An Open Randomised Preliminary Study

et al. 2015

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ObjectiveTo evaluate whether a 12-week supervised exercise program promotes an active lifestyle throughout pregnancy in pregnant women with obesity.MethodsIn this preliminary randomised trial, pregnant women (body mass index ≥ 30 kg/m2) were allocated to either standard care or supervised training, from 15 to 27 weeks of gestation. Physical activity was measured by accelerometry at 14, 28 and 36 weeks, while fitness (oxygen consumption (VO2) at the anaerobic threshold), nutrition (caloric intake and macronutrients percentage) and anthropometry were assessed at 14 and 28 weeks of gestation. Analyses were performed using repeated measures ANOVA.ResultsA total of fifty (50) women were randomised, 25 in each group. There was no time-group interaction for time spent at moderate and vigorous activity (pinteraction = 0.064), but the exercise group’s levels were higher than controls’ at all times (pgroup effect = 0.014). A significant time-group interaction was found for daily physical activity (p = 0.023); similar at baseline ((22.0 ± 6.7 vs 21.8 ± 7.3) x 104 counts/day) the exercise group had higher levels than the control group following the intervention ((22.8 ± 8.3 vs 19.2 ± 4.5) x 104 counts/day, p = 0.020) and at 36 weeks of gestation ((19.2 ± 1.5 vs 14.9 ± 1.5) x 104 counts/day, p = 0.034). Exercisers also gained less weight than controls during the intervention period despite similar nutritional intakes (difference in weight change = -0.1 kg/week, 95% CI -0.2; -0.02, p = 0.016) and improved cardiorespiratory fitness (difference in fitness change = 8.1%, 95% CI 0.7; 9.5, p = 0.041).ConclusionsCompared with standard care, a supervised exercise program allows pregnant women with obesity to maintain fitness, limit weight gain and attenuate the decrease in physical activity levels observed in late pregnancy.Trial RegistrationClinicalTrials.gov NCT01610323

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“…Importantly, obesity can trigger GDM development. Although body mass index (BMI) is not appropriately descriptive for obesity during pregnancy, this anthropomorphic parameter was linked to GDM occurrence [40]. Adipose tissue and placenta can produce a similar pattern of cytokines, which explains the fact that obese women are at higher risk of GDM [41].…”

Section: Gdm Aetiology and Associated Risk Factorsmentioning

confidence: 99%

Predictive and diagnostic biomarkers for gestational diabetes and its associated metabolic and cardiovascular diseases

Lorenzo-Almorós

Hang

Peiró

et al. 2019

Cardiovasc Diabetol

120

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Gestational diabetes mellitus (GDM) is defined as the presence of high blood glucose levels with the onset, or detected for the first time during pregnancy, as a result of increased insulin resistance. GDM may be induced by dysregulation of pancreatic β-cell function and/or by alteration of secreted gestational hormones and peptides related with glucose homeostasis. It may affect one out of five pregnancies, leading to perinatal morbidity and adverse neonatal outcomes, and high risk of chronic metabolic and cardiovascular injuries in both mother and offspring. Currently, GDM diagnosis is based on evaluation of glucose homeostasis at late stages of pregnancy, but increased age and bodyweight , and familiar or previous occurrence of GDM, may conditionate this criteria. In addition, an earlier and more specific detection of GDM with associated metabolic and cardiovascular risk could improve GDM development and outcomes. In this sense, 1st-2nd trimester-released biomarkers found in maternal plasma including adipose tissue-derived factors such as adiponectin, visfatin, omentin-1, fatty acid-binding protein-4 and retinol binding-protein-4 have shown correlations with GDM development. Moreover, placenta-related factors such as sex hormone-binding globulin, afamin, fetuin-A, fibroblast growth factors-21/23, ficolin-3 and follistatin, or specific micro-RNAs may participate in GDM progression and be useful for its recognition. Finally, urine-excreted metabolites such as those related with serotonin system, non-polar amino-acids and ketone bodies, may complete a predictive or earlydiagnostic panel of biomarkers for GDM.

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Challenges in Determining Body Fat in Pregnant Women

Cited by 7 publications

References 32 publications

Pilot Study Examining Pregnancy-specific Equations to Estimate Percent Body Fat in an Overweight/obese Pregnant Hispanic Population

Pilot Study Examining Pregnancy-specific Equations to Estimate Percent Body Fat in an Overweight/obese Pregnant Hispanic Population

A 12-Week Exercise Program for Pregnant Women with Obesity to Improve Physical Activity Levels: An Open Randomised Preliminary Study

Predictive and diagnostic biomarkers for gestational diabetes and its associated metabolic and cardiovascular diseases

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