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Maternal fat ultrasound measurement and nutritional assessment during pregnancy: A dataset centered in gestational outcomes

Alexandre da Silva Rocha Juliana Rombaldi Bernardi Alice Schoffel Daniela Kretzer Salete Matos José Antônio Magalhães Marcelo Goldani

Published: Dec. 4, 2020. Version: 1.0.0

When using this resource, please cite: (show more options)
Rocha, A. d. S., Rombaldi Bernardi, J., Schoffel, A., Kretzer, D., Matos, S., Magalhães, J. A., & Goldani, M. (2020). Maternal fat ultrasound measurement and nutritional assessment during pregnancy: A dataset centered in gestational outcomes (version 1.0.0). PhysioNet.

Please include the standard citation for PhysioNet: (show more options)
Goldberger, A., Amaral, L., Glass, L., Hausdorff, J., Ivanov, P. C., Mark, R., ... & Stanley, H. E. (2000). PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation [Online]. 101 (23), pp. e215–e220.


This dataset was collected as part of a prospective study in which abdominal maternal fat tissues ultrasound measurements were compared with outcomes during hospitalization for labor and delivery. Data was collected in a low-risk outpatient clinic from October 2016 to December 2017 in Porto Alegre, Brazil. Of the 272 patients initially included, 61 (22%) were lost to follow-up, resulting in a final sample of 211 cases.

At baseline, a routine obstetric ultrasound was performed with a maternal epigastric fat measurement conducted in all included cases. Two assessments were performed: the epigastric maternal visceral adipose tissue (preperitoneal m-VAT) and the epigastric maternal subcutaneous adipose tissue (preperitoneal m-SAT).  Cases included before 20 weeks had an additional periumbilical fat assessment performed; this included: the periumbilical maternal visceral adipose tissue (periumbilical m-VAT) and  the periumbilical maternal subcutaneous adipose tissue (periumbilical m-SAT). Data regarding anthropometric, nutritional and demographic characteristics were assessed at baseline. Pregnancy outcomes were determined through hospital charts in order to establish pregnancy, labor and newborn conditions.

The dataset has 116 variables that include demographic, ultrasound, nutritional and laboratory characteristics measured at baseline. Likewise, it presents data related to the birth process and newborn conditions during the postpartum period. The pregnancy abnormalities data obtained retrospectively are highlighted. The dataset may be helpful for future studies that seek to assess the predictive capacity of maternal abdominal fat for abnormal pregnancy outcomes.


There is robust data regarding the usefulness of ultrasound abdominal adipose tissue measurement during pregnancy. Correlations were found between the adipose tissue thickness and insulin resistance when the following outcomes were considered: gestational diabetes mellitus [1–3], fasting blood glucose abnormalities [4], glucose challenge test and oral glucose tolerance test abnormalities[5,6], and altered Homeostasis Model Assessment to Insulin Resistance (HOMA -IR) [5]. In addition, correlations were found with preeclampsia[7], preterm birth [4,8] and newborn weight [9]. When maternal fat is measured during early pregnancy, there is the possibility for predictive assessments of third trimester abnormalities, allowing prompt interventions to improve the perinatal outcome.

There are two possible sites to access maternal abdominal fat amounts during pregnancy: the periumbilical site [2,10–12]   and the epigastric site [3,8]. This project aimed to address the maternal abdominal fat ultrasound measurement during the three trimesters of pregnancy in a cohort design study. The outcomes were determined through a hospital chart review following childbirth. This study intended to address a gap within the existing literature, where most of the current research focuses on outcomes during pregnancy, such as laboratory abnormalities affiliated with hypertension or dysglycemia. The database contains prenatal anthropometric, nutritional and laboratory characteristics, alongside fetal ultrasound diagnosis related to biometry, fetal weight and growth, placenta position, and amniotic fluid volume.


The study was approved by the Research Ethics Committee of the municipality of Porto Alegre (approval number 2.132.090).


This prospective cohort study was performed at the Murialdo Teaching Health Center - Ultrasound Department, which provides fetal medicine services to the Public Health System in Porto Alegre City, Brazil. Data collection occurred from October 2016 to December 2017. Of the 272 patients initially included, 61 (22%) were lost to follow-up, resulting in a final sample of 211 participants. The inclusion criteria were women in any three trimesters of pregnancy. Participants were excluded if they had scar tissue within locations used for maternal fat assessment, multiple pregnancies, aneuploidy or major fetal abnormality.  

Procedure at inclusion

A routine ultrasound evaluation was performed by the author, ASR (Fetologist certified by the Brazilian College of Obstetrician and Gynaecology - FEBRASGO) to diagnose fetal growth as described by Hadlock [13].  The biometric result was compared with fetal age established from the last menstrual period or previous obstetric ultrasound. Placental position, fetal heartbeats, amniotic fluid level, and basic fetal anatomy were assessed in all cases. Pregnancies in the embryonic phase were evaluated through an endovaginal ultrasound to determine gestational age, placental position, heartbeat count and maternal adnexa evaluation. For cases between 11+0 to 13+6 weeks, the nuchal translucency and the nasal bone were assessed to estimate aneuploidy risk as described by Nicolaides [14].

Maternal preperitoneal fat evaluation

Maternal preperitoneal fat was measured in all trimesters with a convex probe placed in the middle sagittal epigastric region as described by Suzuki[15]. Attention was paid to avoid excessive pressure that could falsely compress surfaces of interest. The ultrasound caliper was placed from the anterior liver surface to the linea alba, appraising the epigastric maternal visceral adipose tissue (preperitoneal m-VAT). The electronic caliper was then passed from the superficial dermal edge to the linea alba in order to assess the epigastric maternal subcutaneous adipose tissue (preperitoneal m-SAT). 

Maternal periumbilical fat evaluation

In cases with a gestational age below 20 weeks, maternal periumbilical fat was additionally measured due the gravid uterus in a below position allowing  the measurements. The site was assessed with convex probe placed 2cm above the maternal umbilical scar in a middle sagittal position as described by Armellini[16]. An ultrasound electronic caliper was placed from the anterior aortic wall to the linea alba to establish the periumbilical maternal visceral adipose tissue (periumbilical m-VAT).  Subsequently, the electronic caliper was passed from the superficial dermal edge to the linea alba to demonstrate the periumbilical maternal subcutaneous adipose tissue (periumbilical m-SAT).  The mean of two measurements was calculated among both visceral fat assessment, the first at ended maternal inspiration and the second at ended maternal expiration.

Nutritional interview

Nutritional anamnesis was carried out by the authors SDM and DCK (nutritionists certified by the Brazilian Nutrition Council) at the inclusion process to ascertain data regarding the quantity, frequency, and quality of participants’ nutritional food consumption. Macro and micronutrients consumption was highlighted. Regarding foods considered to be harmful to nutritional health, such as multi-processed foods and those containing high sugar concentrations (i.e., soft drinks, candies, cookies, and others with high flour content) were highlighted in the anamnesis.

Maternal Anthropometry

The  maternal blood pressure evaluation was performed with an automatic wrist sphygmomanometer compatible with diameters between 13.5cm to 21.5cm (Omron HEM-6131®). The mean blood pressure was calculated from the left and right wrist scores. The pre-pregnant Body Mass Index (BMI) was calculated using maternal weight measurement recorded before the 12th week of pregnancy and maternal height measured at recruitment. For participants that had the first maternal weight measurement performed after 12 weeks of pregnancy, the authors used the maternal pre-gravid self-reported weight to calculate the BMI. The authors considered results above 30kg/m2 as pre-pregnant obesity. Nutritional maternal adequacy measurements were centered on maternal perimeters and skinfolds. Regarding the former, an anthropometric scale was used to assess brachial, waist, hip, calf and neck circumference. In respect to skinfold appraisal, the tricciptal, subscapular and supra iliac skinfold was utilized. The Lange® caliper was used for skinfolds measurements. Anthropometric data was measured in duplicate, where the mean value between measurement was used.

Demographic and laboratory data

In addition to the inclusion interview, the outpatient prenatal records were reviewed to determine: blood pressure scores; maternal weight gain; HIV, hepatitis C, and syphilis screening; hemoglobin and hematocrit; fasting blood glucose and glucose challenge test and urinalysis for proteinuria results. Medical records were also used to obtain previous ultrasound results, past pregnancy records, ethnicity information, and history of tobacco and drug use.

Gestational outcomes

Hospital charts were reviewed to document any abnormalities that occurred during the pregnancy, labor or delivery. Reports of gestational diabetes mellitus, hypertension in pregnancy, preeclampsia, anemia, infections, fetal conditions and growth were recorded. The labor and delivery conditions, birth mode, newborn weight, and Apgar score were assessed. Post-delivery conditions of interest included necessary newborn intensive care or intubation, birth trauma, neonatal jaundice, among others.

Data Description

The dataset comprises of two CSV files, (1) a data dictionary containing a summary of variables and (2) a record of observations:

  • data_dictionary.csv: a data dictionary containing a list of variables, descriptions, and coding information (for example, the variable alcohol_use captures self-reported use of alcohol, where 0indicates "No" and 1 indicates "yes".
  • observations.csv: contains 116 variables including demographic details, ultrasound measurements, nutritional and laboratory characteristics, and variables referring to the labor and birth process.

The variables should be self-explanatory and most use the “0” code when absent and “1” when present. A full explanation of each variable as well as the values ​​assigned when in categorical form are provided in the data dictionary. Continuous variables are identified in the dictionary.

Missing data

An empty field in observations.csv indicates that a response was missing in the inclusion questionnaire (i.e. no information was provided by the patient). no_answer indicates that the patient did not provide an answer when questioned by an interviewer. not_applicable indicates that the category was not applicable given a previous response (e.g. if a patient had not previously given birth, "past newborn weight" is not applicable).

Usage Notes

The authors emphasize that the data was loaded in full, excluding patient identifiers and personal information. This dataset is a superset of the project entitled "Visceral adipose tissue measurements during pregnancy" [17], which formed the basis for a study on maternal visceral adipose tissue during pregnancy [18].


There are limitations that should be highlighted. There were losses to follow-up due to the study design that used hospital records to determine outcomes. From the 272 patients initially included, 61 (22%) were lost to follow-up, resulting in a final sample of 211. The maternal fat ultrasound measurement was performed by a single certified sonologist physician instead of two, which may impact measurement bias. Laboratory results were obtained from varying laboratories belonging to the Brazilian Public Health System, which may have also contributed to measurement biases. The pre-pregnant BMI was calculated from self-reported weight before pregnancy in a limited group of cases without first trimester weight in the hospital charts, which may result in recall bias.

Release Notes

This dataset is a more complete version of the dataset available on PhysioNet entitled "Visceral adipose tissue measurements during pregnancy" [17]. The previously published dataset comprises information underpinning a cohort study entitled "Maternal visceral adipose tissue during the first half of pregnancy predicts gestational diabetes at the time of delivery" [18].


Financial support was provided by the Hospital de Clínicas de Porto Alegre Research and Event Incentive Fund (Fundo de Incentivo à Pesquisa e Eventos, FIPE). Additional support was provided by the Porto Alegre municipal government (ultrasound equipment, research facilities) and the Federal University of Rio Grande do Sul (technical and scientific support).

Conflicts of Interest

The authors declare that they have no competing financial interests.


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  2. Rocha A da S, Bernardi JR, Matos S, Kretzer DC, Schöffel AC, Goldani MZ, de Azevedo Magalhães JA. Maternal visceral adipose tissue during the first half of pregnancy predicts gestational diabetes at the time of delivery - a cohort study. Petry CJ, ed. PLoS One. 2020;15(4):e0232155.
  3. D’Ambrosi F, Crovetto F, Colosi E, Fabietti I, Carbone F, Tassis B, Motta S, Bulfoni A, Fedele L, Rossi G, Persico N. Maternal Subcutaneous and Visceral Adipose Ultrasound Thickness in Women with Gestational Diabetes Mellitus at 24-28 Weeks’ Gestation. Fetal Diagn Ther. 2018;43(2):143-147.
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  6. Alves JG, Souza ASR, Figueiroa JN, de Araújo CAL, Guimarães A, Ray JG. Visceral Adipose Tissue Depth in Early Pregnancy and Gestational Diabetes Mellitus - a Cohort Study. Sci Rep. 2020;10(1):2032.
  7. Ray JG, De Souza LR, Park AL, Connelly PW, Bujold E, Berger H. Preeclampsia and Preterm Birth Associated With Visceral Adiposity in Early Pregnancy. J Obstet Gynaecol Canada. 2017;39(2):78-81
  8. Gur EB, Ince O, Turan GA, Karadeniz M, Tatar S, Celik E, Yalcin M, Guclu S. Ultrasonographic visceral fat thickness in the first trimester can predict metabolic syndrome and gestational diabetes mellitus. Endocrine. 2014;47(2):478-484.
  9. Cisneiros RM, Dutra LP, Silveira FJC, Souza AR, Marques M, Amorim MM, Urquia ML, Ray JG, Alves JG. Visceral adiposity in the first half of pregnancy predicts newborn weight among adolescent mothers. J Obstet Gynaecol Can. 2013;35(8):704-709.
  10. De Souza LR, Berger H, Retnakaran R, Maguire JL, Nathens AB, Connelly PW, Ray JG. First-Trimester Maternal Abdominal Adiposity Predicts Dysglycemia and Gestational Diabetes Mellitus in Midpregnancy. Diabetes Care. 2016;39(1):61-64.
  11. Bourdages M, Demers MÉ, Dubé S, Gasse C, Girard M, Boutin A, Ray JG, Bujold E, Demers S. First-Trimester Abdominal Adipose Tissue Thickness to Predict Gestational Diabetes. J Obstet Gynaecol Canada. 2018;40(7):883-887.
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  15. Suzuki R, Watanabe S, Hirai Y, Akiyama K, Nishide T, Matsushima Y, Murayama H, Ohshima H, Shinomiya M, Shirai K, Saito Y, Yoshida S, Saisho H, Ohto M. Abdominal wall fat index, estimated by ultrasonography, for assessment of the ratio of visceral fat to subcutaneous fat in the abdomen. Am J Med. 1993;95(3):309-314.
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  17. Rocha, A. d. S., von Diemen, L., Kretzer, D., Matos, S., Rombaldi Bernardi, J., & Magalhães, J. A. (2020). Visceral adipose tissue measurements during pregnancy (version 1.0.0). PhysioNet.
  18. Rocha AdS, Bernardi JR, Matos S, Kretzer DC, Schöffel AC, et al. (2020) Maternal visceral adipose tissue during the first half of pregnancy predicts gestational diabetes at the time of delivery – a cohort study. PLOS ONE 15(4): e0232155.

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