RESUMEN

Objetivos:

Evaluar la capacidad predictiva de las recomendaciones de ganancia de peso gestacional respecto del bajo peso al nacer (BPN) y la macrosomía neonatal, propuestas por el Institute of Medicine (IOM) y el Centro Latinoamericano de Perinatología (CLAP).

Materiales y métodos:

La búsqueda bibliográfica se realizó en PubMed, Embase (vía Ovid), Cochrane Library, EBSCO host, Scopus, LILACS y SciELO. La calidad metodológica se evaluó utilizando QUADAS 2.

Resultados:

Se obtuvieron 1192 artícu los, cinco cumplieron los criterios de inclusión, ningún estudio evaluó las recomendaciones CLAP. La sensibilidad y especificidad para predecir el BPN y la macrosomía variaron largamente según el país de realización del estudio. En las cohortes de América Latina, la sensibilidad para predecir el BPN varió entre 62,8% y 74%, y la especificidad, entre 61,7% y 68%, mientras que la sensi bilidad para predecir macrosomía fue 28,8%, y la especificidad, 43,8%. En la mayoría de estudios el valor predictivo positivo fue inferior al 25%, y el valor predictivo negativo, superior al 90%. La mayoría de los estudios tuvo alto riesgo de sesgo y problemas de aplicabilidad en la selección de pacientes.

Conclusiones:

La limitada calidad metodológica y representatividad de las cohortes estudiadas, probables factores de confusión no ajustados y modestos valores de sensibilidad y especificidad sugieren la necesidad de desarrollar estudios para establecer recomendaciones que se ajusten a las características epidemiológicas de la población peruana.

Palabras clave:
Ganancia de Peso Gestacional; Sensibilidad y Especificidad; Recién Nacido de Bajo Peso; Macrosomía Fetal

ABSTRACT

Objectives:

To evaluate the predictive capacity of gestational weight gain recommendations regarding low birth weight (LBW) and neonatal macrosomia, proposed by the Institute of Medicine (IOM) and the Latin American Center of Perinatology (CLAP).

Mate rials and methods:

The bibliographic search was performed in PubMed, Embase (via Ovid), Cochrane Library, EBSCOhost, Sco pus, LILACS and SciELO. Methodological quality was evaluated using QUADAS 2.

Results:

A total of 1,192 articles were identified, only 5 articles met the inclusion criteria, no study evluated the CLAP recomendations. Sensitivity and specificity to predict LBW and macrosomia varied widely depending on which country the study took place. In the Latin American cohorts, the sensitivity for predicting LBW ranged from 62.8% to 74% and the specificity from 61.7% to 68%, while the sensitivity for predicting macrosomia was 28.8% and the specificity 43.8%. In most studies the positive predictive value was less than 25%, and the negative predictive va lue was more than 90%. Most studies had high risk of bias and applicability problems in patient selection.

Conclusions:

The limited methodological quality and representativeness of the studied cohorts, probable unadjusted confounding factors and modest values of sensitivity and specificity suggest the need to develop studies aimed at providing recommendations that fit the epidemiological characteristics of the Peruvian population.

Keywords:
Gestational Weight Gain; Sensitivity and Specificity; infant, Low birth weight; Fetal Macrosomia

INTRODUCTION

Weight gain during gestation is an important predictor of newborn outcomes ¹1. Rasmussen KM, Yaktine AL, Institute of Medicine (US) and National Research Council (US) Committee to Reexamine IOM Pregnancy Weight Guidelines. IOM (Institute of Medicine) and NRC (National Research Council). Weight Gain During Pregnancy: Reexamining the Guidelines [Internet]. Washington, DC: The National Academies Press (US); 2009 [citado el 20 de junio de 2019]. Disponible en: https://www.ncbi.nlm.nih.gov/books/NBK32813/.
https://www.ncbi.nlm.nih.gov/books/NBK32... . Insufficient gain increases the risk of preterm and low birth weight (LBW) births ²2. Kominiarek MA, Peaceman AM. Gestational weight gain. Am J Obstet Gynecol. 2017;217(6):642-51. doi: 10.1016/j.ajog.2017.05.040.
https://doi.org/10.1016/j.ajog.2017.05.0... ^,33. Han Z, Lutsiv O, Mulla S, McDonald SD, Knowledge Synthesis Group. Maternal height and the risk of preterm birth and low birth weight: a systematic review and meta-analyses. J Obstet Gynaecol Can. 2012;34(8):721-46. doi: 10.1016/S1701-2163(16)35337-3.
https://doi.org/10.1016/S1701-2163(16)35... , while excessive gain increases the risk of neonatal macrosomia ⁴4. Tian C, Hu C, He X, Zhu M, Qin F, Liu Y, et al. Excessive weight gain during pregnancy and risk of macrosomia: a meta-analysis. Arch Gynecol Obstet. 2016;293(1):29-35. doi: 10.1007/s00404-015-3825-8.
https://doi.org/10.1007/s00404-015-3825-... . In turn, both LBW and macrosomia increase neonatal mortality and morbidity ¹1. Rasmussen KM, Yaktine AL, Institute of Medicine (US) and National Research Council (US) Committee to Reexamine IOM Pregnancy Weight Guidelines. IOM (Institute of Medicine) and NRC (National Research Council). Weight Gain During Pregnancy: Reexamining the Guidelines [Internet]. Washington, DC: The National Academies Press (US); 2009 [citado el 20 de junio de 2019]. Disponible en: https://www.ncbi.nlm.nih.gov/books/NBK32813/.
https://www.ncbi.nlm.nih.gov/books/NBK32... , adverse maternal outcomes ²2. Kominiarek MA, Peaceman AM. Gestational weight gain. Am J Obstet Gynecol. 2017;217(6):642-51. doi: 10.1016/j.ajog.2017.05.040.
https://doi.org/10.1016/j.ajog.2017.05.0... , and the risk of chronic diseases during adulthood ²2. Kominiarek MA, Peaceman AM. Gestational weight gain. Am J Obstet Gynecol. 2017;217(6):642-51. doi: 10.1016/j.ajog.2017.05.040.
https://doi.org/10.1016/j.ajog.2017.05.0... ^,55. Law CM. Significance of birth weight for the future. Arch Dis Child Fetal Neonatal Ed. 2002;86(1):F7-8. doi: 10.1136/fn.86.1.f7.
https://doi.org/10.1136/fn.86.1.f7... , which means a significant economic burden on health services and society ⁶6. Petrou S. Economic consequences of preterm birth and low birthweight. BJOG Int J Obstet Gynaecol. 2003;110(20):17-23..

During the 1970s and 1980s, a single gestational weight gain of 12.5 kg was recommended in most countries, regardless of pre-pregnancy weight and height ⁷7. Mardones F, García-Huidobro T, Ralph C, Farías M, Domínguez A, Rojas I, et al. Influencia combinada del índice de masa corporal pregestacional y de la ganancia de peso en el embarazo sobre el crecimiento fetal. Rev Med Chil. 2011;139(6):710-6. doi: 10.4067/S0034-98872011000600003.
https://doi.org/10.4067/S0034-9887201100... . In 1983, the Latin American Center for Perinatology (CLAP) of the Pan American Health Organization developed weight gain patterns when pre-pregnancy weight was unknown ⁸8. Fescina R. Aumento de peso durante el embarazo. Método para su cálculo cuando se desconoce el peso habitual. Bol of Sanit Panam. 1983;95(2):156-62.. While in 1990, the Institute of Medicine (IOM) of the United States published recommendations for weight gain during pregnancy, adopted by most countries worldwide, and updated in 2009 ¹1. Rasmussen KM, Yaktine AL, Institute of Medicine (US) and National Research Council (US) Committee to Reexamine IOM Pregnancy Weight Guidelines. IOM (Institute of Medicine) and NRC (National Research Council). Weight Gain During Pregnancy: Reexamining the Guidelines [Internet]. Washington, DC: The National Academies Press (US); 2009 [citado el 20 de junio de 2019]. Disponible en: https://www.ncbi.nlm.nih.gov/books/NBK32813/.
https://www.ncbi.nlm.nih.gov/books/NBK32... .

In Peru, the recommendations from CLAP and the IOM are commonly used because of the absence of a national pattern of weight gain during pregnancy. However, both international patterns present limitations that affect their diagnostic accuracy, such as the origin and age of the basic epidemiological data, the representativeness of the reference population, and methodological aspects, such as the inclusion and source of information on pre-pregnancy body mass index (BMI) data. For this reason, a systematic review was carried out to evaluate the ability of both recommendations to predict LBW and neonatal macrosomia.

MATERIALS AND METHODS

This systematic review seeks to evaluate the best diagnostic parameter for pregnancy weight gain. The study was conducted following the recommendations of the PRISMA statement ⁹9. Hutton B, Salanti G, Caldwell DM, Chaimani A, Schmid CH, Cameron C, et al. The PRISMA extension statement for reporting of systematic reviews incorporating network meta-analyses of health care interventions: checklist and explanations. Ann Intern Med. 2015;162(11):777-84. doi: 10.7326/M14-2385.
https://doi.org/10.7326/M14-2385... , except for the protocol’s registration in a database, which is available upon request to the corresponding author.

Bibliographic search strategy

A systematic search for observational prospective or retrospective cohort and case-control studies was conducted in 7 electronic databases: PubMed, Embase (via OVID), Cochrane Library, EBSCOhost, Scopus, LILACS, and SciELO; by using a combination of the following search terms: pregnancy, gestation, pregnant women, gestational weight gain, accuracy, sensitivity, specificity, predictive value. The search for studies was limited to those published in Spanish and English, without being restricted by the publication date. The last search took place in May 3, 2019. The search strategies for each database consulted are found in the supplementary material. Additionally, a manual search was performed on Google Scholar and the reference lists of the selected studies were checked to include any relevant references.

Eligibility criteria

Studies that met the following criteria were considered: a) type of participants: apparently healthy pregnant women of any age and gestational condition (single or multiple pregnancy); b) index tests: total and/or trimester-specific gestational weight gain, as recommended by CLAP and IOM; c) standard: low birth weight and neonatal macrosomia diagnosis, according to criteria, such as low birth weight below 2,500 g, and neonatal macrosomia above 4,000 g; d) outcome measurement types: sensitivity, specificity, positive or negative predictive value.

Study selection and data extraction

Records obtained with the search strategy were initially evaluated by reading first the titles and abstracts, and then the full text of the selected articles. The information about the study methodology, participants, intervention, control and reported outcomes was registered in a data extraction form designed in Microsoft® Excel 2010.

Results presentation

Quantitative variables are presented as averages plus standard deviation. Qualitative variables are presented as proportions. For both types of variables, and if available, the 95% confidence intervals and the values of statistical significance are presented. When sensitivity, specificity, and positive or negative predictive values were not found in the study, these data were calculated using Review Manager 5.3 (The Cochrane Collaboration, Oxford, UK).

Quality assessment of the studies

The methodological quality of each included study was evaluated using the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS 2) tool ¹⁰10. Whiting PF, Rutjes AWS, Westwood ME, Mallett S, Deeks JJ, Reitsma JB, et al. QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Ann Intern Med. 2011;155(8):529-36. doi: 10.7326/0003-4819-155-8-201110180-00009.
https://doi.org/10.7326/0003-4819-155-8-... . Two independent reviewers participated in each stage. Any disagreement was resolved by group discussion or by consensus of the authors.

RESULTS

A total of 2,601 references were identified. The systematic review included 5 studies that met the previously established selection criteria ¹¹11. Alberico S, Montico M, Barresi V, Monasta L, Businelli C, Soini V, et al. The role of gestational diabetes, pre-pregnancy body mass index and gestational weight gain on the risk of newborn macrosomia: results from a prospective multicentre study. BMC Pregnancy Childbirth. 2014;14:23. doi: 10.1186/1471-2393-14-23.
https://doi.org/10.1186/1471-2393-14-23...

12. Asplund CA, Seehusen DA, Callahan TL, Olsen C. Percentage change in antenatal body mass index as a predictor of neonatal macrosomia. Ann Fam Med. 2008;6(6):550-4. doi: 10.1370/afm.903.
https://doi.org/10.1370/afm.903...

13. Barros DC, Saunders C, Santos MM, Libera BD, Gama SG, Leal MdoC. The performance of various anthropometric assessment methods for predicting low birth weight in adolescent pregnant women. Braz J Epidemiol. 2014;17(3):761-74. doi: 10.1590/1809-4503201400030015.
https://doi.org/10.1590/1809-45032014000...

14. Carvalho P, Barros DC, Campos ABF, Ayeta AC, Queiróz JA, Saunders C. Performance of an anthropometric assessment method as a predictor of low birthweight and being small for gestational age. J Hum Nutr Diet. 2015;28(3):292-9. doi: 10.1111/jhn.12235.
https://doi.org/10.1111/jhn.12235... ^-1515. Li N, Liu E, Guo J, Pan L, Li B, Wang P, et al. Maternal prepregnancy body mass index and gestational weight gain on pregnancy outcomes. PloS One. 2013;8(12):e82310. doi: 10.1371/journal.pone.0082310.
https://doi.org/10.1371/journal.pone.008... (Figure 1).

Characteristics of the included studies

The studies were conducted in public health facilities located in the United States ¹²12. Asplund CA, Seehusen DA, Callahan TL, Olsen C. Percentage change in antenatal body mass index as a predictor of neonatal macrosomia. Ann Fam Med. 2008;6(6):550-4. doi: 10.1370/afm.903.
https://doi.org/10.1370/afm.903... , Brazil¹³13. Barros DC, Saunders C, Santos MM, Libera BD, Gama SG, Leal MdoC. The performance of various anthropometric assessment methods for predicting low birth weight in adolescent pregnant women. Braz J Epidemiol. 2014;17(3):761-74. doi: 10.1590/1809-4503201400030015.
https://doi.org/10.1590/1809-45032014000... ^,1414. Carvalho P, Barros DC, Campos ABF, Ayeta AC, Queiróz JA, Saunders C. Performance of an anthropometric assessment method as a predictor of low birthweight and being small for gestational age. J Hum Nutr Diet. 2015;28(3):292-9. doi: 10.1111/jhn.12235.
https://doi.org/10.1111/jhn.12235... , Italy ¹¹11. Alberico S, Montico M, Barresi V, Monasta L, Businelli C, Soini V, et al. The role of gestational diabetes, pre-pregnancy body mass index and gestational weight gain on the risk of newborn macrosomia: results from a prospective multicentre study. BMC Pregnancy Childbirth. 2014;14:23. doi: 10.1186/1471-2393-14-23.
https://doi.org/10.1186/1471-2393-14-23... , and China ¹⁵15. Li N, Liu E, Guo J, Pan L, Li B, Wang P, et al. Maternal prepregnancy body mass index and gestational weight gain on pregnancy outcomes. PloS One. 2013;8(12):e82310. doi: 10.1371/journal.pone.0082310.
https://doi.org/10.1371/journal.pone.008... . Only 4 studies used a retrospective cohort design ¹²12. Asplund CA, Seehusen DA, Callahan TL, Olsen C. Percentage change in antenatal body mass index as a predictor of neonatal macrosomia. Ann Fam Med. 2008;6(6):550-4. doi: 10.1370/afm.903.
https://doi.org/10.1370/afm.903... ^,1515. Li N, Liu E, Guo J, Pan L, Li B, Wang P, et al. Maternal prepregnancy body mass index and gestational weight gain on pregnancy outcomes. PloS One. 2013;8(12):e82310. doi: 10.1371/journal.pone.0082310.
https://doi.org/10.1371/journal.pone.008... and 1 used a prospective cohort design ¹¹11. Alberico S, Montico M, Barresi V, Monasta L, Businelli C, Soini V, et al. The role of gestational diabetes, pre-pregnancy body mass index and gestational weight gain on the risk of newborn macrosomia: results from a prospective multicentre study. BMC Pregnancy Childbirth. 2014;14:23. doi: 10.1186/1471-2393-14-23.
https://doi.org/10.1186/1471-2393-14-23... . The data collection period ranged from 7 months to 9 years (Table 1).

One study was conducted on adolescents with an average age of 17.6 years ¹³13. Barros DC, Saunders C, Santos MM, Libera BD, Gama SG, Leal MdoC. The performance of various anthropometric assessment methods for predicting low birth weight in adolescent pregnant women. Braz J Epidemiol. 2014;17(3):761-74. doi: 10.1590/1809-4503201400030015.
https://doi.org/10.1590/1809-45032014000... , and 4 studies were conducted in adults with ages between 26.5 and 31.7 years on average ¹¹11. Alberico S, Montico M, Barresi V, Monasta L, Businelli C, Soini V, et al. The role of gestational diabetes, pre-pregnancy body mass index and gestational weight gain on the risk of newborn macrosomia: results from a prospective multicentre study. BMC Pregnancy Childbirth. 2014;14:23. doi: 10.1186/1471-2393-14-23.
https://doi.org/10.1186/1471-2393-14-23... ^,1212. Asplund CA, Seehusen DA, Callahan TL, Olsen C. Percentage change in antenatal body mass index as a predictor of neonatal macrosomia. Ann Fam Med. 2008;6(6):550-4. doi: 10.1370/afm.903.
https://doi.org/10.1370/afm.903... ^,1414. Carvalho P, Barros DC, Campos ABF, Ayeta AC, Queiróz JA, Saunders C. Performance of an anthropometric assessment method as a predictor of low birthweight and being small for gestational age. J Hum Nutr Diet. 2015;28(3):292-9. doi: 10.1111/jhn.12235.
https://doi.org/10.1111/jhn.12235... ^,1515. Li N, Liu E, Guo J, Pan L, Li B, Wang P, et al. Maternal prepregnancy body mass index and gestational weight gain on pregnancy outcomes. PloS One. 2013;8(12):e82310. doi: 10.1371/journal.pone.0082310.
https://doi.org/10.1371/journal.pone.008... . Two studies reported a greater predominance of white pregnant women ¹²12. Asplund CA, Seehusen DA, Callahan TL, Olsen C. Percentage change in antenatal body mass index as a predictor of neonatal macrosomia. Ann Fam Med. 2008;6(6):550-4. doi: 10.1370/afm.903.
https://doi.org/10.1370/afm.903... ^,1313. Barros DC, Saunders C, Santos MM, Libera BD, Gama SG, Leal MdoC. The performance of various anthropometric assessment methods for predicting low birth weight in adolescent pregnant women. Braz J Epidemiol. 2014;17(3):761-74. doi: 10.1590/1809-4503201400030015.
https://doi.org/10.1590/1809-45032014000... , and 3 studies did not report the race of the study population ¹¹11. Alberico S, Montico M, Barresi V, Monasta L, Businelli C, Soini V, et al. The role of gestational diabetes, pre-pregnancy body mass index and gestational weight gain on the risk of newborn macrosomia: results from a prospective multicentre study. BMC Pregnancy Childbirth. 2014;14:23. doi: 10.1186/1471-2393-14-23.
https://doi.org/10.1186/1471-2393-14-23... ^,1414. Carvalho P, Barros DC, Campos ABF, Ayeta AC, Queiróz JA, Saunders C. Performance of an anthropometric assessment method as a predictor of low birthweight and being small for gestational age. J Hum Nutr Diet. 2015;28(3):292-9. doi: 10.1111/jhn.12235.
https://doi.org/10.1111/jhn.12235... ^,1515. Li N, Liu E, Guo J, Pan L, Li B, Wang P, et al. Maternal prepregnancy body mass index and gestational weight gain on pregnancy outcomes. PloS One. 2013;8(12):e82310. doi: 10.1371/journal.pone.0082310.
https://doi.org/10.1371/journal.pone.008... . In the 2 studies that reported the newborn sex, a similar male to female ratio was observed ¹¹11. Alberico S, Montico M, Barresi V, Monasta L, Businelli C, Soini V, et al. The role of gestational diabetes, pre-pregnancy body mass index and gestational weight gain on the risk of newborn macrosomia: results from a prospective multicentre study. BMC Pregnancy Childbirth. 2014;14:23. doi: 10.1186/1471-2393-14-23.
https://doi.org/10.1186/1471-2393-14-23... ^,1515. Li N, Liu E, Guo J, Pan L, Li B, Wang P, et al. Maternal prepregnancy body mass index and gestational weight gain on pregnancy outcomes. PloS One. 2013;8(12):e82310. doi: 10.1371/journal.pone.0082310.
https://doi.org/10.1371/journal.pone.008... . All studies evaluated the diagnostic accuracy of the gestational weight gain recommendations suggested by the IOM (Table 2).

Interventions’ effect

Low birth weight

Barros ¹³13. Barros DC, Saunders C, Santos MM, Libera BD, Gama SG, Leal MdoC. The performance of various anthropometric assessment methods for predicting low birth weight in adolescent pregnant women. Braz J Epidemiol. 2014;17(3):761-74. doi: 10.1590/1809-4503201400030015.
https://doi.org/10.1590/1809-45032014000... , Carvallo ¹⁴14. Carvalho P, Barros DC, Campos ABF, Ayeta AC, Queiróz JA, Saunders C. Performance of an anthropometric assessment method as a predictor of low birthweight and being small for gestational age. J Hum Nutr Diet. 2015;28(3):292-9. doi: 10.1111/jhn.12235.
https://doi.org/10.1111/jhn.12235... , and Li ¹⁵15. Li N, Liu E, Guo J, Pan L, Li B, Wang P, et al. Maternal prepregnancy body mass index and gestational weight gain on pregnancy outcomes. PloS One. 2013;8(12):e82310. doi: 10.1371/journal.pone.0082310.
https://doi.org/10.1371/journal.pone.008... reported that the prevalence of LBW varied between 2.2% and 10.8%. The sensitivity was 16.9% in the Chinese population ¹⁵15. Li N, Liu E, Guo J, Pan L, Li B, Wang P, et al. Maternal prepregnancy body mass index and gestational weight gain on pregnancy outcomes. PloS One. 2013;8(12):e82310. doi: 10.1371/journal.pone.0082310.
https://doi.org/10.1371/journal.pone.008... , and varied between 62.8% and 74% in the Brazilian population ¹³13. Barros DC, Saunders C, Santos MM, Libera BD, Gama SG, Leal MdoC. The performance of various anthropometric assessment methods for predicting low birth weight in adolescent pregnant women. Braz J Epidemiol. 2014;17(3):761-74. doi: 10.1590/1809-4503201400030015.
https://doi.org/10.1590/1809-45032014000... ^,1414. Carvalho P, Barros DC, Campos ABF, Ayeta AC, Queiróz JA, Saunders C. Performance of an anthropometric assessment method as a predictor of low birthweight and being small for gestational age. J Hum Nutr Diet. 2015;28(3):292-9. doi: 10.1111/jhn.12235.
https://doi.org/10.1111/jhn.12235... . The specificity varied between 61.7% and 68.3% in the Brazilian population ¹³13. Barros DC, Saunders C, Santos MM, Libera BD, Gama SG, Leal MdoC. The performance of various anthropometric assessment methods for predicting low birth weight in adolescent pregnant women. Braz J Epidemiol. 2014;17(3):761-74. doi: 10.1590/1809-4503201400030015.
https://doi.org/10.1590/1809-45032014000... ^,1414. Carvalho P, Barros DC, Campos ABF, Ayeta AC, Queiróz JA, Saunders C. Performance of an anthropometric assessment method as a predictor of low birthweight and being small for gestational age. J Hum Nutr Diet. 2015;28(3):292-9. doi: 10.1111/jhn.12235.
https://doi.org/10.1111/jhn.12235... , and it was 90.3% in the Chinese population ¹⁵15. Li N, Liu E, Guo J, Pan L, Li B, Wang P, et al. Maternal prepregnancy body mass index and gestational weight gain on pregnancy outcomes. PloS One. 2013;8(12):e82310. doi: 10.1371/journal.pone.0082310.
https://doi.org/10.1371/journal.pone.008... . The positive predictive value varied between 3.8% and 19.2%, while the negative predictive value varied between 6.1% and 98% (Table 3).

Neonatal Macrosomia

Prevalence of neonatal macrosomia varied between 4% and 15.6%. Sensitivity, between 28.8% and 76%. The specificity, between 43.8% and 72.6%. The positive predictive value, between 12.6% and 92.6%, and the negative predictive value, between 89.1% and 97.6% (Table 3). The estimates correspond to 4 studies: Alberico ¹¹11. Alberico S, Montico M, Barresi V, Monasta L, Businelli C, Soini V, et al. The role of gestational diabetes, pre-pregnancy body mass index and gestational weight gain on the risk of newborn macrosomia: results from a prospective multicentre study. BMC Pregnancy Childbirth. 2014;14:23. doi: 10.1186/1471-2393-14-23.
https://doi.org/10.1186/1471-2393-14-23... , Asplund ¹²12. Asplund CA, Seehusen DA, Callahan TL, Olsen C. Percentage change in antenatal body mass index as a predictor of neonatal macrosomia. Ann Fam Med. 2008;6(6):550-4. doi: 10.1370/afm.903.
https://doi.org/10.1370/afm.903... , Barros¹³13. Barros DC, Saunders C, Santos MM, Libera BD, Gama SG, Leal MdoC. The performance of various anthropometric assessment methods for predicting low birth weight in adolescent pregnant women. Braz J Epidemiol. 2014;17(3):761-74. doi: 10.1590/1809-4503201400030015.
https://doi.org/10.1590/1809-45032014000... and Li ¹⁵15. Li N, Liu E, Guo J, Pan L, Li B, Wang P, et al. Maternal prepregnancy body mass index and gestational weight gain on pregnancy outcomes. PloS One. 2013;8(12):e82310. doi: 10.1371/journal.pone.0082310.
https://doi.org/10.1371/journal.pone.008... .

Quality evaluation

All studies presented a high risk of bias regarding patient selection and the reference test. Most studies showed a high risk of bias regarding the index, flow and time tests. As for the applicability of the studies, most of them presented high risk in the patient selection and low risk in the reference test; the results of the index test were variable (Table 4).

DISCUSSION

Nowadays, there is a greater understanding of the interaction between maternal diet, pre-pregnancy BMI, weight gain during pregnancy, maternal comorbidities, and immediate and long-term health outcomes ¹⁶16. Shenassa ED, Kinsey C, Moser M, Fahey J. Gestational Weight Gain: Historical Evolution of a Contested Health Outcome. Obstet Gynecol Surv. 2017;72(7):445-53. doi: 10.1097/OGX.0000000000000459.
https://doi.org/10.1097/OGX.000000000000... . However, optimal gestational weight gain has not yet been clearly defined and remains a controversial and critical issue ¹⁷17. Mestrovic Z, Roje D, Vulic M, Zec M. Calculation of optimal gestation weight gain in pre-pregnancy underweight women due to body mass index change in relation to mother's height. Arch Gynecol Obstet. 2017;295(1):81-6. doi: 10.1007/s00404-016-4218-3.
https://doi.org/10.1007/s00404-016-4218-... . There are weight gain recommendations from the IOM and CLAP, which are commonly used in prenatal care. In this context, the study aimed to evaluate the recommendations for gestational weight gain proposed by the IOM and CLAP based on their ability to predict birth weight and neonatal macrosomia knowing that they have been associated with a significant increase in the risk of adverse maternal and neonatal outcomes ¹⁸18. Goldstein RF, Abell SK, Ranasinha S, Misso M, Boyle JA, Black MH, et al. Association of Gestational Weight Gain With Maternal and Infant Outcomes: A Systematic Review and Meta-analysis. JAMA. 2017;317(21):2207-25. doi: 10.1001/jama.2017.3635.
https://doi.org/10.1001/jama.2017.3635... .

The findings suggest that the country where the study takes place influences both outcomes. With respect to low birth weight, the findings were consistent, showing similar values for sensitivity and specificity among the Brazilian cohort of adult pregnant women ¹⁴14. Carvalho P, Barros DC, Campos ABF, Ayeta AC, Queiróz JA, Saunders C. Performance of an anthropometric assessment method as a predictor of low birthweight and being small for gestational age. J Hum Nutr Diet. 2015;28(3):292-9. doi: 10.1111/jhn.12235.
https://doi.org/10.1111/jhn.12235... and adolescent pregnant women ¹³13. Barros DC, Saunders C, Santos MM, Libera BD, Gama SG, Leal MdoC. The performance of various anthropometric assessment methods for predicting low birth weight in adolescent pregnant women. Braz J Epidemiol. 2014;17(3):761-74. doi: 10.1590/1809-4503201400030015.
https://doi.org/10.1590/1809-45032014000... ; different from those obtained in a Chinese cohort, which presents lower sensitivity and higher specificity ¹⁵15. Li N, Liu E, Guo J, Pan L, Li B, Wang P, et al. Maternal prepregnancy body mass index and gestational weight gain on pregnancy outcomes. PloS One. 2013;8(12):e82310. doi: 10.1371/journal.pone.0082310.
https://doi.org/10.1371/journal.pone.008... . These differences were also observed in the outcome of neonatal macrosomia, where the Brazilian cohort obtained less sensitivity and specificity ¹³13. Barros DC, Saunders C, Santos MM, Libera BD, Gama SG, Leal MdoC. The performance of various anthropometric assessment methods for predicting low birth weight in adolescent pregnant women. Braz J Epidemiol. 2014;17(3):761-74. doi: 10.1590/1809-4503201400030015.
https://doi.org/10.1590/1809-45032014000... , compared with the Italian ¹¹11. Alberico S, Montico M, Barresi V, Monasta L, Businelli C, Soini V, et al. The role of gestational diabetes, pre-pregnancy body mass index and gestational weight gain on the risk of newborn macrosomia: results from a prospective multicentre study. BMC Pregnancy Childbirth. 2014;14:23. doi: 10.1186/1471-2393-14-23.
https://doi.org/10.1186/1471-2393-14-23... , American ¹²12. Asplund CA, Seehusen DA, Callahan TL, Olsen C. Percentage change in antenatal body mass index as a predictor of neonatal macrosomia. Ann Fam Med. 2008;6(6):550-4. doi: 10.1370/afm.903.
https://doi.org/10.1370/afm.903... , and Chinese ¹⁵15. Li N, Liu E, Guo J, Pan L, Li B, Wang P, et al. Maternal prepregnancy body mass index and gestational weight gain on pregnancy outcomes. PloS One. 2013;8(12):e82310. doi: 10.1371/journal.pone.0082310.
https://doi.org/10.1371/journal.pone.008... cohorts.

Some studies describe differences in pre-pregnancy nutritional status, weight gain during pregnancy, and the magnitude of risk of adverse gestational and neonatal outcomes around populations of different continents and ethnicities ¹⁹19. Headen IE, Davis EM, Mujahid MS, Abrams B. Racial-ethnic differences in pregnancy-related weight. Adv Nutr Bethesda Md. 2012;3(1):83-94. doi: 10.3945/an.111.000984.
https://doi.org/10.3945/an.111.000984... ^,2020. Goldstein RF, Abell SK, Ranasinha S, Misso ML, Boyle JA, Harrison CL, et al. Gestational weight gain across continents and ethnicity: systematic review and meta-analysis of maternal and infant outcomes in more than one million women. BMC Med. 2018;16(1):153. doi: 10.1186/s12916-018-1128-1.
https://doi.org/10.1186/s12916-018-1128-... . In that sense, the IOM proposed the development of additional studies to investigate whether the recommendations for weight gain during pregnancy differ according to the racial/ethnic group, suggesting that they could reflect not only the biological differences between the groups, but also the difference in their socio-economic conditions ¹1. Rasmussen KM, Yaktine AL, Institute of Medicine (US) and National Research Council (US) Committee to Reexamine IOM Pregnancy Weight Guidelines. IOM (Institute of Medicine) and NRC (National Research Council). Weight Gain During Pregnancy: Reexamining the Guidelines [Internet]. Washington, DC: The National Academies Press (US); 2009 [citado el 20 de junio de 2019]. Disponible en: https://www.ncbi.nlm.nih.gov/books/NBK32813/.
https://www.ncbi.nlm.nih.gov/books/NBK32... .

On the other hand, the studies included in our review reported a higher proportion of negative predictive values, mostly above 90%, compared to the proportion of positive predictive values, mostly below 25% for both outcomes. Positive and negative predictive values indicate the probability that a diagnostic test will obtain the correct diagnosis if it turns out to be positive or negative, while sensitivity and specificity assess the probability of obtaining a positive or negative result, depending on the patient’s true condition. In a scenario where this condition is previously unknown, predictive values could be relevant to support clinical decisions ²¹21. Fernández P, Pértegas-Díaz S. Pruebas diagnósticas: Sensibilidad y especificidad. Cad Aten Primaria. 2003;10:120-4.^,2222. Bravo-Grau S, Cruz QJP. Estudios de exactitud diagnóstica: Herramientas para su Interpretación. Rev Chil Radiol. 2015;21(4):158-64. doi: 10.4067/S0717-93082015000400007.
https://doi.org/10.4067/S0717-9308201500... .

However, the prevalence of the disease or condition to be evaluated influences the predictive values. When it is low, the negative predictive value is higher and, consequently, the positive predictive value is lower ²¹21. Fernández P, Pértegas-Díaz S. Pruebas diagnósticas: Sensibilidad y especificidad. Cad Aten Primaria. 2003;10:120-4.^,2323. Hernaez R, Thrift AP. High Negative Predictive Value, Low Prevalence, and Spectrum Effect: Caution in the Interpretation. Clin Gastroenterol Hepatol Off. 2017;15(9):1355-8. doi: 10.1016/j.cgh.2017.05.002.
https://doi.org/10.1016/j.cgh.2017.05.00... . In this context, this review reports higher negative predictive values, which could mean an important characteristic and would allow the presence of low birth weight and neonatal macrosomia to be ruled out with greater security and confidence in settings where both prevalences are relatively low.

The average age of the adult pregnancy cohorts included in our review ranged from 26 to 31 years. This epidemiological characteristic differs from the fecundity of Peruvian women. In Peru, the average age at the first birth is 22.3 years, and 12.6% of women between 15 and 19 years have been pregnant at some time ²⁴24. Instituto Nacional de Estadística e Informática. Perú: Encuesta Demográfica y de Salud Familiar 2018 [Internet]. Lima, Perú: INEI; 2018 [citado el 20 de junio de 2019]. Disponible en: https://www.inei.gob.pe/media/MenuRecursivo/publicaciones_digitales/Est/Lib1656/index1.html., which increases the risk of age-related adverse obstetric and neonatal outcomes ²⁵25. Pinheiro RL, Areia AL, Mota Pinto A, Donato H. Advanced Maternal Age: Adverse Outcomes of Pregnancy, A Meta-Analysis. Acta Med Port. 2019;32(3):219-26. doi: 10.20344/amp.11057.
https://doi.org/10.20344/amp.11057... . The exact origin of this relationship has not been determined. One hypothesis suggests that the older the mother, the greater the body weight before pregnancy, due to the cumulative weight increase in adulthood ²⁶26. McKoy JN, Hartmann KE, Jerome RN, Andrews JC, Penson DF. Future Research Needs for Outcomes of Weight Gain in Pregnancy [Internet]. Rockville (MD): Agency for Healthcare Research and Quality (US); 2010 [citado 20 de junio de 2019]. Disponible en: http://www.ncbi.nlm.nih.gov/books/NBK51337/.
http://www.ncbi.nlm.nih.gov/books/NBK513... , while in older pregnant women the influence of the natural aging process and the existence of comorbidities cannot be ruled out ²⁵25. Pinheiro RL, Areia AL, Mota Pinto A, Donato H. Advanced Maternal Age: Adverse Outcomes of Pregnancy, A Meta-Analysis. Acta Med Port. 2019;32(3):219-26. doi: 10.20344/amp.11057.
https://doi.org/10.20344/amp.11057... . Similarly, it has been demonstrated that adolescent pregnancy increases the obstetric and neonatal risk related to biological and social factors ²⁷27. Azevedo WF, Diniz MB, Fonseca ESVB, Azevedo LMR, Evangelista CB. Complications in adolescent pregnancy: systematic review of the literature. Einstein Sao Paulo Braz. 2015;13(4):618-26. doi: 10.1590/S1679-45082015RW3127.
https://doi.org/10.1590/S1679-45082015RW... .

Three studies reported the characteristics of maternal body mass index before pregnancy^(13-15). In these 3 studies, 2.5% to 11.2% of the patients presented with thinness, and 13.6% to 25.3% presented with overweight or obesity before gestation. Both conditions differ from the characteristics of women of childbearing age in Peru, where thinness measured by BMI is found in 1.6%, and excess weight (overweight or obesity) in 62.2% ²⁴24. Instituto Nacional de Estadística e Informática. Perú: Encuesta Demográfica y de Salud Familiar 2018 [Internet]. Lima, Perú: INEI; 2018 [citado el 20 de junio de 2019]. Disponible en: https://www.inei.gob.pe/media/MenuRecursivo/publicaciones_digitales/Est/Lib1656/index1.html.. We believe that these differences could be explained by the age of the cohorts, between 1999 and 2009. These discrepancies could influence in the accuracy of the results of our review, given that there are studies that show that pre-pregnancy thinness is associated with an increased risk of low birth weight ²⁸28. Rahman MM, Abe SK, Kanda M, Narita S, Rahman MS, Bilano V, et al. Maternal body mass index and risk of birth and maternal health outcomes in low- and middle-income countries: a systematic review and meta-analysis. Obes Rev. 2015;16(9):758-70. doi: 10.1111/obr.12293.
https://doi.org/10.1111/obr.12293... ^,2929. Yu Z, Han S, Zhu J, Sun X, Ji C, Guo X. Pre-Pregnancy Body Mass Index in Relation to Infant Birth Weight and Offspring Overweight/Obesity: A Systematic Review and Meta-Analysis. PLoS ONE. 2013;8(4):1-1. doi: 10.1371/journal.pone.0061627.
https://doi.org/10.1371/journal.pone.006... , while pre-pregnancy overweight and obesity is associated with an increased risk of neonatal macrosomia ²⁹29. Yu Z, Han S, Zhu J, Sun X, Ji C, Guo X. Pre-Pregnancy Body Mass Index in Relation to Infant Birth Weight and Offspring Overweight/Obesity: A Systematic Review and Meta-Analysis. PLoS ONE. 2013;8(4):1-1. doi: 10.1371/journal.pone.0061627.
https://doi.org/10.1371/journal.pone.006... . For this reason, it is important that recommendations on weight gain during pregnancy should consider the pre-pregnancy body mass index.

Three studies included pregnant women with gestational diabetes, with an average prevalence of 1.3% to 2.6%, and 1 study excluded pregnant women with this condition. Two studies did not report controlling this variable. In all cases, this situation limits the representativeness of the results, given that the estimated prevalence of gestational diabetes affects up to 16% of pregnant women in Peru ³⁰30. Villena JE. Diabetes Mellitus in Peru. Ann Glob Health. 2015;81(6):765-75. doi: 10.1016/j.aogh.2015.12.018.
https://doi.org/10.1016/j.aogh.2015.12.0... . Gestational diabetes triples the risk of macrosomic births, while treatment of this disease reduces the incidence of macrosomia in neonates. The proposed physiological mechanisms to explain the relationship between gestational diabetes and macrosomia are explained by fetal hyperinsulinemia, increased glucose use, and increased fetal adipose tissue³¹31. Kamana K, Shakya S, Zhang H. Gestational diabetes mellitus and macrosomia: a literature review. Ann Nutr Metab. 2015;66(2):14-20. doi: 10.1159/000371628.
https://doi.org/10.1159/000371628... .

There is no gold standard that properly assesses the fetal development and, at the same time, provides an error-free classification of all subjects assessed, verifies all index test results, and that can be performed within a short time interval that avoids changes in the target condition ³²32. Reitsma JB, Rutjes AWS, Khan KS, Coomarasamy A, Bossuyt PM. A review of solutions for diagnostic accuracy studies with an imperfect or missing reference standard. J Clin Epidemiol. 2009;62(8):797-806. doi: 10.1016/j.jclinepi.2009.02.005.
https://doi.org/10.1016/j.jclinepi.2009.... . Measuring fetal growth by using ultrasound or MRI does not comply with the conditions described above ³³33. Kadji C, Cannie MM, Resta S, Guez D, Abi-Khalil F, De Angelis R, et al. Magnetic resonance imaging for prenatal estimation of birthweight in pregnancy: review of available data, techniques, and future perspectives. Am J Obstet Gynecol. 2019;220(5):428-39. doi: 10.1016/j.ajog.2018.12.031.
https://doi.org/10.1016/j.ajog.2018.12.0... ^,3434. Milner J, Arezina J. The accuracy of ultrasound estimation of fetal weight in comparison to birth weight: A systematic review. Ultrasound Leeds Engl. 2018;26(1):32-41. doi: 10.1177/1742271X17732807.
https://doi.org/10.1177/1742271X17732807... ; this is why gestational weight gain is used for an approximate measure of fetal growth, it is simple to use and low cost. However, misclassification due to an imperfect standard inevitably leads to positive or negative biased accuracy.

Worldwide, it is estimated that 47% of pregnant women had a gestational weight gain higher than IOM’s recommendation, and 23% had a lower gain than those recommendations¹⁸18. Goldstein RF, Abell SK, Ranasinha S, Misso M, Boyle JA, Black MH, et al. Association of Gestational Weight Gain With Maternal and Infant Outcomes: A Systematic Review and Meta-analysis. JAMA. 2017;317(21):2207-25. doi: 10.1001/jama.2017.3635.
https://doi.org/10.1001/jama.2017.3635... . In this context, it has been identified that social determinants configure a complex problem that goes beyond solely biological issues. Within this set of social determinants, parity could influence the pre-pregnancy body mass index ³⁵35. Hill B, Bergmeier H, McPhie S, Fuller-Tyszkiewicz M, Teede H, Forster D, et al. Is parity a risk factor for excessive weight gain during pregnancy and postpartum weight retention? A systematic review and meta-analysis. Obes Rev. 2017;18(7):755-64. doi: 10.1111/obr.12538.
https://doi.org/10.1111/obr.12538... ; periods between pregnancies under 18 months and over 59 months could increase the risk of low birth weight ³⁶36. Conde-Agudelo A, Rosas-Bermúdez A, Kafury-Goeta AC. Birth spacing and risk of adverse perinatal outcomes: a meta-analysis. JAMA. 2006;295(15):1809-23. doi: 10.1001/jama.295.15.1809.
https://doi.org/10.1001/jama.295.15.1809... ; low educational level increases the risk of insufficient or excessive weight gain during pregnancy ³⁷37. O'Brien EC, Alberdi G, McAuliffe FM. The influence of socioeconomic status on gestational weight gain: a systematic review. J Public Health Oxf Engl. 2018;40(1):41-55. doi: 10.1093/pubmed/fdx038.
https://doi.org/10.1093/pubmed/fdx038... ; psychological factors, such as emotional distress or dissatisfaction with body image, influence in weight gain ³⁸38. Kapadia MZ, Gaston A, Van Blyderveen S, Schmidt L, Beyene J, McDonald H, et al. Psychological factors and trimester-specific gestational weight gain: a systematic review. J Psychosom Obstet Gynaecol. 2015;36(1):15-22. doi: 10.3109/0167482X.2014.993311.
https://doi.org/10.3109/0167482X.2014.99... , or low levels of physical activity during pregnancy increases the risk of excessive weight gain ³⁹39. Streuling I, Beyerlein A, Rosenfeld E, Hofmann H, Schulz T, von Kries R. Physical activity and gestational weight gain: a meta-analysis of intervention trials. BJOG Int J Obstet Gynaecol. 2011;118(3):278-84. doi: 10.1111/j.1471-0528.2010.02801.x.
https://doi.org/10.1111/j.1471-0528.2010... . Each of these social determinants is accompanied by underlying factors that interrelate to form a complex causal model.

One systematic review reported that quantifying weight as an isolated intervention in antenatal care is not effective in reducing weight gain during pregnancy ⁴⁰40. Fealy SM, Taylor RM, Foureur M, Attia J, Ebert L, Bisquera A, et al. Weighing as a stand-alone intervention does not reduce excessive gestational weight gain compared to routine antenatal care: a systematic review and meta-analysis of randomised controlled trials. BMC Pregnancy Childbirth. 2017;17(1):36. doi: 10.1186/s12884-016-1207-2.
https://doi.org/10.1186/s12884-016-1207-... . Thus, it should be part of the interventions made by health professionals in collaboration with mothers to achieve weight gain goals, such as to monitor and provide feedback on their progress ⁴⁰40. Fealy SM, Taylor RM, Foureur M, Attia J, Ebert L, Bisquera A, et al. Weighing as a stand-alone intervention does not reduce excessive gestational weight gain compared to routine antenatal care: a systematic review and meta-analysis of randomised controlled trials. BMC Pregnancy Childbirth. 2017;17(1):36. doi: 10.1186/s12884-016-1207-2.
https://doi.org/10.1186/s12884-016-1207-... , to have standardized and evidence-based approaches ⁴¹41. Flannery C, McHugh S, Kenny LC, O'Riordan MN, McAuliffe FM, Bradley C, et al. Exploring obstetricians', midwives' and general practitioners' approach to weight management in pregnant women with a BMI =25 kg/m2: a qualitative study. BMJ Open. 2019;9(1):e024808. doi: 10.1136/bmjopen-2018-024808.
https://doi.org/10.1136/bmjopen-2018-024... , to improve knowledge and beliefs about weight gain in women of childbearing age ⁴²42. Hill B, Hayden M, McPhie S, Bailey C, Skouteris H. Preconception and antenatal knowledge and beliefs about gestational weight gain. Aust N Z J Obstet Gynaecol. 2019; doi: 10.1111/ajo.12942.
https://doi.org/10.1111/ajo.12942... , to incorporate a holistic approach integrating not only the pregnant woman, but also the family or her social environment ⁴³43. Hamilton EAA, Nowell AK, Harden A, Thangaratinam S. Conduct and reporting of acceptability, attitudes, beliefs and experiences of pregnant women in randomised trials on diet and lifestyle interventions: A systematic review. Eur J Obstet Gynecol Reprod Biol. 2018;225:243-54. doi: 10.1016/j.ejogrb.2018.05.008.
https://doi.org/10.1016/j.ejogrb.2018.05... , to provide nutritional consultation on healthy food, oriented to calories, needed macronutrients and adequate levels of physical activity ⁴⁴44. Shieh C, Cullen DL, Pike C, Pressler SJ. Intervention strategies for preventing excessive gestational weight gain: systematic review and meta-analysis. Obes Rev. 2018;19(8):1093-109. doi: 10.1111/obr.12691.
https://doi.org/10.1111/obr.12691... , to accompany the dietary activities with strategies of behavior change, mainly in the provision of information, motivational interviews, self-monitoring and contingent rewards ⁴⁵45. Hill B, Skouteris H, Fuller-Tyszkiewicz M. Interventions designed to limit gestational weight gain: a systematic review of theory and me-ta-analysis of intervention components. Obes Rev. 2013;14(6):435-50. doi: 10.1111/obr.12022.
https://doi.org/10.1111/obr.12022... , to provide prenatal care with a multidisciplinary approach ⁴⁶46. Ghaffari N, Srinivas SK, Durnwald CP. The multidisciplinary approach to the care of the obese parturient. Am J Obstet Gynecol. 2015;213(3):318-25. doi: 10.1016/j.ajog.2015.03.001.
https://doi.org/10.1016/j.ajog.2015.03.0... ^,4747. Hodnett ED, Fredericks S, Weston J. Support during pregnancy for women at increased risk of low birthweight babies. Cochrane Database Syst Rev. 2010;(6):CD000198. doi: 10.1002/14651858.CD000198.pub2.
https://doi.org/10.1002/14651858.CD00019... . The exact duration, frequency, intensity or an optimal environment for providing these interventions has not been determined ⁴⁸48. Walker R, Bennett C, Blumfield M, Gwini S, Ma J, Wang F, et al. Attenuating Pregnancy Weight Gain-What Works and Why: A Systematic Review and Meta-Analysis. Nutrients. 2018;10(7). doi: 10.3390/nu10070944.
https://doi.org/10.3390/nu10070944... .

One of the most important limitations of this systematic review is that the majority of the included studies were retrospective cohorts that gathered information from medical records. Some other limitations, due to the study type: difficulty in controlling confounding factors (gestational age, mother’s age, socioeconomic level, pre-existing diseases, among others), often there are no variables that could narrow the analysis or that could be measured precisely; additionally, not ensuring a high percentage of participation and follow-up increases the risk of probable selection bias. For example, the included studies had between 20% and 40% of data loss, mostly related to the variables of interest. Only 1 study ¹³13. Barros DC, Saunders C, Santos MM, Libera BD, Gama SG, Leal MdoC. The performance of various anthropometric assessment methods for predicting low birth weight in adolescent pregnant women. Braz J Epidemiol. 2014;17(3):761-74. doi: 10.1590/1809-4503201400030015.
https://doi.org/10.1590/1809-45032014000... analyzed the related characteristics between the studied population and the losses, and found significant differences between both groups. The heterogeneous methodology for measuring the variables of interest may not have been adequately controlled, which increased the risk of bias.

Our review focused on the ability to predict low birth weight and neonatal macrosomia as recommended by the IOM on gestational weight gain. However, inadequate gestational weight gain is also related to maternal outcomes: the lower probability of starting or continuing breastfeeding ⁴⁹49. Huang Y, Ouyang Y-Q, Redding SR. Maternal Prepregnancy Body Mass Index, Gestational Weight Gain, and Cessation of Breastfeeding: A Systematic Review and Meta-Analysis. Breastfeed Med. 2019;14(6):366-374. doi: 10.1089/bfm.2018.0138.
https://doi.org/10.1089/bfm.2018.0138... , increased postpartum weight retention ⁵⁰50. Rong K, Yu K, Han X, Szeto IMY, Qin X, Wang J, et al. Pre-pregnancy BMI, gestational weight gain and postpartum weight retention: a meta-analysis of observational studies. Public Health Nutr. 2015;18(12):2172-82. doi: 10.1017/S1368980014002523.
https://doi.org/10.1017/S136898001400252... , the risk of obesity, diabetes, heart disease, endocrine disease, and psychiatric disease in later years ⁵¹51. Moll U, Olsson H, Landin-Olsson M. Impact of Pregestational Weight and Weight Gain during Pregnancy on Long-Term Risk for Diseases. PloS One. 2017;12(1):e0168543. doi: 10.1371/journal.pone.0168543.
https://doi.org/10.1371/journal.pone.016... . In the newborn, it is associated with an increased risk of obesity, metabolic and cardiovascular disease in later life⁵²52. Poston L. Gestational weight gain: influences on the long-term health of the child. Curr Opin Clin Nutr Metab Care. 2012;15(3):252-7. doi: 10.1097/MCO.0b013e3283527cf2.
https://doi.org/10.1097/MCO.0b013e328352... .

We did not find studies that evaluated the ability of CLAP weight gain recommendations to predict LBW and neonatal macrosomia. As for the IOM recommendations, sensitivity and specificity was low, especially in Latin American cohorts. However, the low methodological quality of the studies, the limited representativeness of the studied cohorts and the probable unadjusted confounding factors indicate that there is a need to develop studies with an adequate methodological quality to allow the establishment of recommendations that would fit the epidemiological characteristics of the Peruvian population. Likewise, it is absolutely necessary to prevent inadequate weight gain during pregnancy, providing quality antenatal care, based on a multidisciplinary approach, with interventions supported by scientific evidence and considering the different social determinants involved in its development.

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