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Related Concept Videos

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Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again...
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Longitudinal studies are also widely used in other medical and social science fields. For instance, in cardiovascular research, they can monitor patients' health over decades to identify risk factors for heart disease, such as high cholesterol or smoking, and evaluate the long-term effectiveness of preventive measures. Similarly, in mental health studies, researchers might follow individuals from adolescence into adulthood to understand the development and progression of conditions like...
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Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
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The Use of Gas Chromatography to Analyze Compositional Changes of Fatty Acids in Rat Liver Tissue during Pregnancy
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Body Composition During Pregnancy: Longitudinal Changes and Method Comparisons.

Marja Bosaeus1, Ulrika Andersson-Hall2, Louise Andersson1

  • 1Department of Physiology, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Box 432, SE-405 30, Gothenburg, Sweden.

Reproductive Sciences (Thousand Oaks, Calif.)
|January 30, 2020
PubMed
Summary
This summary is machine-generated.

Quantitative magnetic resonance (QMR) and air displacement plethysmography (ADP) accurately measured fat mass in obese pregnant women. However, QMR overestimated fat mass in normal-weight women, and bioelectrical impedance analysis (BIA) showed inaccuracies in both groups.

Keywords:
Air displacement plethysmographyBioelectrical impedance analysisBody compositionPregnancyQuantitative magnetic resonance

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Area of Science:

  • Reproductive Health
  • Maternal Nutrition
  • Body Composition Analysis

Background:

  • Accurate assessment of body composition during pregnancy is crucial for maternal and fetal health.
  • Obesity in pregnancy presents unique challenges for body composition monitoring.
  • Existing methods for measuring fat mass may have varying accuracy in pregnant populations.

Purpose of the Study:

  • To compare the accuracy of quantitative magnetic resonance (QMR) and bioelectrical impedance analysis (BIA) against air displacement plethysmography (ADP) for assessing fat mass in pregnant women.
  • To evaluate these methods across different trimesters of pregnancy in both normal-weight and obese women.
  • To assess the longitudinal changes in fat mass using these techniques during pregnancy.

Main Methods:

  • Longitudinal study involving normal-weight (n=122) and obese (n=29) Swedish women.
  • Body composition analyzed using QMR, BIA (Tanita MC-180MA-III), and ADP during pregnancy weeks 8-12, 24-26, and 35-37.
  • ADP results were calculated using pregnancy-adjusted fat-free mass densities.

Main Results:

  • In obese women, QMR and ADP showed similar mean fat mass, but with wide limits of agreement.
  • QMR consistently overestimated fat mass in normal-weight women, particularly at lower values in trimesters 1 and 3.
  • BIA significantly underestimated fat mass in obese women and showed variable accuracy in normal-weight women across trimesters, also overestimating fat mass changes.

Conclusions:

  • QMR and pregnancy-adjusted ADP provide similar fat mass estimates and changes in pregnant obese women.
  • QMR overestimates fat mass in normal-weight pregnant women, indicating potential limitations.
  • BIA demonstrates significant inaccuracies and imprecision for assessing fat mass in both normal-weight and obese pregnant women.