Abstract
This study assessed the relationship between the body mass index (BMI) of HIV-1-infected women and their infants’ perinatal outcomes. The study population consisted of women enrolled in the NICHD International Site Development Initiative (NISDI) Perinatal Study with data allowing calculation of the BMI adjusted for length of gestation (adjBMI), who delivered singleton infants. Outcome variables included infant growth parameters at birth (weight, BMI, length and head circumference) and gestational age. Of 697 women from Argentina, the Bahamas, Brazil and Mexico who were included in the analysis, the adjBMI was classified as underweight for 109 (15.6%), normal for 418 (60.0%), overweight for 88 (12.6%) and obese for 82 (11.8%). Median infant birth weight, BMI, birth length and head circumference differed significantly according to maternal adjBMI (P≤0.0002). Underweight mothers gave birth to infants with lower weight, lower BMI, shorter length and smaller head circumference, while infants born to normal, overweight and obese mothers were of similar size.
Keywords: maternal BMI, low birth weight, preterm, HIV-exposed infants, HIV-infected pregnant women
1. Introduction
Increasing numbers of women have acquired HIV-1 infection over the past few years. With the feminization of the HIV-1 epidemic, women from different social and cultural settings are becoming infected every day, with consequent harm to their general health and family life. Malnutrition is a complex and frequent event during the course of HIV-1 infection and has multiple causes [1]. Maternal malnutrition is associated with increased risks of illness, disability and death for the fetus and infant [2].
Maternal HIV-1 infection and receipt of antiretrovirals (ARVs) during pregnancy have been reported to be associated with an increased risk of preterm birth and low birth weight [3, 4, 5, 6]. Although many cohorts of HIV-1-infected pregnant women have been followed in different parts of the world, published data regarding their nutritional status are limited. In 1999, a study from Africa reported that underweight, HIV-1-infected pregnant women have a greater risk of delivering low birth weight infants [7].
The objectives of this analysis were to describe the body mass index (BMI) adjusted for length of gestation (adjBMI) of HIV-1-infected pregnant women enrolled in the NICHD International Site Development Initiative (NISDI) Perinatal Study, and to investigate the association of adjBMI with infant outcomes (gestational age, infant BMI at birth, and birth weight, length, and head circumference).
2. Materials and Methods
2.1 Subjects and Definitions
The NISDI Perinatal Study is a prospective cohort study of HIV-1-infected women and their infants at sites in Latin America and the Caribbean [8]. At study entry, women are interviewed for social, clinical and obstetric history, and study visits include a physical examination and laboratory evaluations (hematology, biochemistry, CD4/CD8 and viral load measurements). The protocol was approved by the ethical review board at each site, the sponsoring institution (National Institute of Child Health and Human Development), and the data management center (Westat).
The study population comprised HIV-1-infected women who delivered live born, singleton infants between September 2002 and March 2005. BMI at enrollment was adjusted for length of gestation (adjBMI) using an algorithm available from the Ministry of Health of Argentina [9]. Categories of adjBMI (kg/m2) were: underweight (≥10 to <19.8), normal (≥19.8 to <26.1), overweight (≥26.1 to <29) and obese (≥29 to <50). Outcome variables were infant growth parameters at birth (weight, length, BMI and head circumference) and gestational age. The maternal ARV regimen received at the time of enrollment was categorized as follows: one or two nucleoside reverse transcriptase inhibitors (NRTIs), two NRTIs with one non-nucleoside reverse transcriptase inhibitor (NNRTI) (HAART/NNRTI), two NRTIs with one PI (HAART/PI), or ”Other”.
2.2 Statistical analysis
The association of maternal adjBMI at enrollment with categorical variables was evaluated using the Fisher-Freeman-Halton [10] exact test. The Fisher-Freeman-Halton exact test, a generalization of Fisher’s exact test for greater than 2 × 2 tables, was used to assess independence between maternal adjBMI and the categorical covariates. For variables measured on a continuous scale, the Kruskal-Wallis test was used to assess statistical significance [11]. The Kruskal-Wallis test, a non-parametric counterpart to analysis of variance (ANOVA), tests whether the sample means are from the same population against the alternative that they come from different populations. All analyses were carried out using SAS, Version 8, with P values of 0.05 or less used to define statistical significance [12].
3. Results
Of 803 women enrolled in the NISDI Perinatal Study, 723 had delivered as of March 2005. Of these 723 women, 697 delivered live born singleton infants, and these mother-infant pairs represent the study population. Sixty-six percent of these women were enrolled in Brazil, 25% percent in Argentina, 6% in the Bahamas and 3% in Mexico. At enrollment, 109 (15.6%) women were underweight, 418 (60.0%) had normal weight, 88 (12.6%) were overweight, and 82 (11.8%) were obese. There was a significant difference in adjBMI distribution (p<0.0001) according to country of origin, with a larger proportion of those from the Bahamas being obese and the majority of mothers from Argentina, Brazil and Mexico being of normal weight; 17.4% of women from Brazil and 14.0% from Argentina were underweight. Women from the Bahamas and Mexico were less likely to be underweight (7.3% and 8.3%, respectively).
The median age at enrollment was 27 years. Significant differences in the median age at enrollment were observed among adjBMI groups (P<0.0001); the median age of those who were underweight was the lowest (24 years) while that of obese women was highest (29 years). There were no significant associations between years of formal education completed or number of household members and maternal adjBMI (P>0.4), but gainful employment outside the home was significantly associated with maternal adjBMI. A larger proportion of gainfully employed women were overweight or obese when compared to those not employed, while a higher proportion of women not employed outside the home were underweight (P=0.012).
HIV-1-related characteristics according to maternal adjBMI are shown in Table 1. Although statistically significant differences in clinical and virologic measures of HIV-1 disease progression according to maternal adjBMI at enrollment were not observed, there were significant differences in immunologic measures of disease progression. Generally, those who were overweight or obese had the highest lymphocyte counts, as well as CD4+ and CD8+ counts, while those who were underweight or of normal weight had the lowest values (P<0.02). The maternal ARV regimen received at enrollment was not associated with maternal adjBMI at enrollment (P=0.13). Since plasma viral load can be directly affected by the receipt of antiretrovirals (ARVs), the plasma HIV-1 RNA concentration at enrollment also was examined among adjBMI groups according to whether or not the subject received ARVs prior to enrollment. Those who were not receiving ARVs at enrollment had higher viral loads than those receiving ARVs at enrollment, but the viral loads did not differ according to maternal adjBMI within these two strata (P>0.1) (data not shown).
Table 1.
HIV-1-related characteristics of pregnant women at enrollment, overall and according to maternal body mass index adjusted for length of gestation (adjBMI) at enrollment
| Maternal adjBMI (kg/m2) at Enrollment | ||||||
|---|---|---|---|---|---|---|
| Underweight | Normal | Over-Weight | Obese | |||
| Overall | (≥10.0 - <19.8) | (≥19.8 - <26.1) | (≥26.1 - <29.0) | (≥29.0 - <50.0) | ||
| Enrollment characteristic | N=697 | n=109 | n=418 | n=88 | n=82 | P value 1 |
| Duration of HIV-1 diagnosis prior to enrollment (months): | ||||||
| Mean (SD) | 18.9 (26.9) | 16.2 (23.3) | 20.2 (27.8) | 15.1 (22.4) | 19.6 (31.4) | |
| Median | 5.0 | 4.0 | 5.0 | 4.0 | 5.0 | 0.56 |
| CDC disease classification: n (%) | ||||||
| Category A | 599 | 91 (16.2) | 351 (58.6) | 83 (13.9) | 74 (12.4) | |
| Category B | 42 | 7 (16.7) | 29 (69.0) | 2 (4.8) | 4 (9.5) | |
| Category C | 56 | 11 (19.6) | 38 (67.9) | 3 (5.4) | 4 (7.1) | 0.21 |
| Plasma HIV-1 RNA concentration (copies/mL): | ||||||
| Mean (SD) | 19037 (78608) | 15179 (53015) | 19675 (84660) | 23851 (83524) | 15741 (69695) | |
| Median | 452.0 | 653.0 | 470.5 | 227.0 | 332.5 | 0.32 |
| Absolute lymphocyte count (cells/mL): | ||||||
| Mean (SD) | 1494 (502) | 1383 (474) | 1475 (469) | 1580 (574) | 1652 (574) | |
| Median | 1440 | 1321 | 1433 | 1575 | 1584 | 0.0011 |
| CD4+ absolute count (cells/mm3): | ||||||
| Mean (SD) | 428 (221) | 421 (208) | 409 (212) | 503 (271) | 465 (209) | |
| Median | 397 | 384 | 378 | 456 | 424 | 0.011 |
| Antiretrovial regimen received at enrollment 2: n (%) | ||||||
| None | 161 | 28 (17.4) | 97 (60.3) | 18 (11.2) | 18 (11.2) | |
| 1 NRTI | 57 | 11 (19.3) | 24 (42.1) | 12 (21.1) | 10 (17.5) | |
| 2 NRTIs | 30 | 4 (13.3) | 21 (70.0) | 2 (6.7) | 3 (10.0) | |
| 2 NRTIs + 1 NNRTI | 190 | 23 (12.1) | 112 (59.0) | 27 (14.2) | 28 (14.7) | |
| 2 NRTIs + 1 PI | 247 | 43 (17.4) | 156 (63.2) | 26 (10.5) | 22 (8.9) | |
| Other | 12 | 0 | 8 (66.7) | 3 (25.0) | 1 (8.3) | 0.13 |
The p-values for associations between maternal BMI at enrollment and categorical characteristics are based on the Fisher-Freeman-Halton exact test, while those for associations with continuous scaled characteristics are based on the non-parametric Kruskal-Wallis test of equality of means.
Maternal ARV regimen at the time of enrollment was categorized as: one or two nucleoside reverse transcriptase inhibitors (NRTIs), two NRTIs with one non-nucleoside reverse transcriptase inhibitor (NNRTI) (HAART/NNRTI), two NRTIs with one PI (HAART/PI), or ”Other”.
Laboratory measures at enrollment according to maternal adjBMI are shown in Table 2. Maternal adjBMI at enrollment was significantly associated with bicarbonate, blood urea nitrogen (BUN), cholesterol and hemoglobin (P<0.05). Median bicarbonate levels were lower among overweight and obese mothers than among those who were underweight or of normal weight, while the median BUN value was highest among normal weight mothers and lowest among obese mothers. The median total cholesterol level at enrollment was lowest among underweight and normal weight mothers and highest among overweight and obese mothers. The median hemoglobin value was lowest among underweight mothers compared to normal, overweight and obese mothers whose median levels were similar.
Table 2.
Laboratory measures of pregnant women at enrollment, overall and according to maternal body mass index adjusted for length of gestation (adjBMI) at enrollment
| Maternal adjBMI (kg/m2) at Enrollment | ||||||
|---|---|---|---|---|---|---|
| Underweight | Normal | Over-Weight | Obese | |||
| Enrollment laboratory measure | Overall N=697 | (≥10.0 - <19.8) n=109 | (≥19.8 - <26.1) n=418 | (≥26.1 - <29.0) n=88 | (≥29.0 - <50.0) n=82 | P value1 |
| Bicarbonate (mmol/l): | ||||||
| Mean (SD) | 22.64 (2.58) | 23.13 (2.06) | 22.64 (2.50) | 22.41 (3.54) | 22.11 (2.30) | |
| Median | 23.00 | 23.00 | 23.00 | 22.00 | 22.00 | 0.0066 |
| BUN (mmol/l): | ||||||
| Mean (SD) | 3.84 (2.55) | 3.50 (1.83) | 4.03 (2.69) | 3.77 (2.77) | 3.34 (2.30) | |
| Median | 2.86 | 2.86 | 3.21 | 2.86 | 2.50 | 0.023 |
| Glucose (mmol/l): | ||||||
| Mean (SD) | 4.27 (0.77) | 4.16 (0.92) | 4.28 (0.68) | 4.21 (0.83) | 4.42 (0.90) | |
| Median | 4.16 | 4.11 | 4.22 | 4.11 | 4.33 | 0.075 |
| AST/SGOT (µkat/l): | ||||||
| Mean (SD) | 0.35 (0.23) | 0.37 (0.29) | 0.36 (0.23) | 0.34 (0.15) | 0.33 (0.20) | |
| Median | 0.30 | 0.30 | 0.30 | 0.30 | 0.29 | 0.51 |
| ALT/SGPT (µkat/l): | ||||||
| Mean (SD) | 0.36 (0.26) | 0.40 (0.34) | 0.35 (0.24) | 0.35 (0.20) | 0.41 (0.30) | |
| Median | 0.32 | 0.32 | 0.32 | 0.32 | 0.38 | 0.13 |
| Albumin (g/dL): | ||||||
| Mean (SD) | 33.75 (4.31) | 33.28 (4.15) | 33.92 (4.32) | 34.11 (4.74) | 33.12 (3.95) | |
| Median | 34.00 | 33.00 | 34.00 | 35.00 | 33.00 | 0.24 |
| Total protein (g/dl): | ||||||
| Mean (SD) | 71.0 (6.1) | 71.2 (6.0) | 71.0 (5.9) | 70.6 (7.5) | 71.5 (5.7) | |
| Median | 70.0 | 71.0 | 70.0 | 69.0 | 71.0 | 0.59 |
| Cholesterol (mmol/l): | ||||||
| Mean (SD) | 5.32 (1.16) | 5.24 (1.25) | 5.24 (1.12) | 5.56 (1.15) | 5.61 (1.19) | |
| Median | 5.20 | 5.16 | 5.17 | 5.59 | 5.57 | 0.012 |
| Triacylglycerol (mmol/l): | ||||||
| Mean (SD) | 2.18 (0.98) | 2.14 (0.98) | 2.20 (1.04) | 2.11 (0.81) | 2.19 (0.86) | |
| Median | 2.01 | 1.90 | 2.03 | 1.96 | 2.07 | 0.86 |
| Hemoglobin (mmol/l): | ||||||
| Mean (SD) | 6.83 (0.78) | 6.66 (0.81) | 6.82 (0.79) | 6.91 (0.76) | 6.95 (0.71) | |
| Median | 6.83 | 6.70 | 6.86 | 6.95 | 7.01 | 0.049 |
The p-values for associations between maternal adjBMI and laboratory measures at enrollment are based on the non-parametric Kruskal-Wallis test of equality of means.
Overall, 63 infants (9.2%) were born preterm (<37 weeks) and 89 infants (12.9%) were born with low birth weight (<2500 grams). As shown in Table 3, median infant birth weight, BMI, length and head circumference at birth differed significantly according to maternal adjBMI (P≤0.0002). Underweight mothers delivered the lightest babies; birth weight was similar among those who were of normal weight or who were overweight, while the median birth weight was highest for obese women. Underweight mothers gave birth to babies with the lowest BMI, shortest length and smallest head circumference.
Table 3.
Infant outcomes, overall and according to maternal body mass index adjusted for length of gestation (adjBMI) at enrollment
| Maternal adjBMI (kg/m2) at Enrollment | ||||||
|---|---|---|---|---|---|---|
| Underweight | Normal | Over-Weight | Obese | |||
| Overall | (≥10.0 - <19.8) | (≥19.8 - <26.1) | (≥26.1 - <29.0) | (≥29.0 - <50.0) | ||
| Infant outcome | N=697 | n=109 | n=418 | n=88 | n=82 | P value1 |
| Gestational age at birth (weeks): | ||||||
| Mean (SD) | 38.4 (1.7) | 38.0 (2.0) | 38.5 (1.5) | 38.5 (1.6) | 38.4 (2.3) | |
| Median | 39.0 | 38.0 | 39.0 | 39.0 | 39.0 | 0.069 |
| Birth weight (grams): | ||||||
| Mean (SD) | 3005 (519) | 2753 (567) | 3022 (466) | 3142 (496) | 3105 (625) | |
| Median | 3000 | 2700 | 3000 | 3000 | 3200 | <0.0001 |
| BMI at birth: | ||||||
| Mean (SD) | 13.0 (1.6) | 12.4 (1.7) | 13.0 (1.6) | 13.3 (1.3) | 13.1 (2.0) | |
| Median | 13.0 | 12.5 | 13.1 | 13.2 | 13.1 | 0.0002 |
| Birth length (centimeters): | ||||||
| Mean (SD) | 48.0 (2.7) | 46.9 (3.0) | 48.2 (2.5) | 48.5 (2.5) | 48.5 (3.1) | |
| Median | 48.0 | 47.0 | 48.0 | 48.8 | 49.0 | <0.0001 |
| Head circumference at birth (centimeters): | ||||||
| Mean (SD) | 33.9 (1.7) | 33.2 (1.9) | 34.0 (1.5) | 34.3 (1.4) | 34.1 (2.1) | |
| Median | 34.0 | 33.5 | 34.0 | 34.5 | 34.0 | <0.0001 |
The p-values for associations between maternal adjBMI at enrollment and infant outcomes are based on the non-parametric Kruskal-Wallis test of equality of means.
4. Discussion
In this study, maternal BMI adjusted for gestational age was associated with infant birth weight, length, BMI, and head circumference. Underweight women tended to deliver smaller infants than other women.
The incidence of low birth weight and preterm birth has been previously investigated in the NISDI population [13]. In this published analysis, variables associated with the occurrence of preterm birth included country of residence, maternal clinical disease stage, maternal plasma viral load, diabetes, mode of delivery and hypertension. In addition to maternal underweight, risk factors for low birth weight included pre-eclampsia/eclampsia, non-elective cesarean section and maternal diabetes. The present analysis focused on the effect of maternal nutritional status, evaluated by BMI adjusted for gestational age, on infant outcomes treated as continuous measures.
The proportion of underweight HIV-1-infected women at enrollment (almost 16%) is much higher than the 5.7% found among HIV-1-uninfected, pregnant women in Brazil between 1991 and 1995 [14]. Unfortunately information is not available concerning the nutritional status of pregnant women in other Latin American countries.
The high proportion of underweight women in our study could represent a manifestation of HIV-1 disease itself. However, we did not observe a significant association between HIV-1 clinical stage and adjBMI. Alternatively, the high proportion of underweight women in the study likely reflects the fact that, in many parts of the world, the HIV-1 epidemic is spreading faster among people of lower socioeconomic status. Although women from all social strata in Brazil are being affected by the HIV-1 epidemic, infection is more prevalent among the most socially disenfranchised segments of the population, as measured by the number of years of formal education completed [15]. Our finding that HIV-1-infected pregnant women who were underweight tended to be younger is consistent with previously reported results regarding Brazilian pregnant women without HIV-1 infection [14] and is particularly relevant in countries where youths are initiating sexual activity early in life and socio-cultural factors lead to pregnancy at young ages. The importance of social aspects with regard to the high frequency of underweight pregnant women in our study population is underscored by the association of gainful employment during pregnancy and higher adjBMI.
Nutritional status has been identified as a predictor of levels of T-cell subclasses among HIV-1-uninfected women [16]. In this study, underweight and normal weight women had lower CD4+ count values when compared to obese women, but we found no association between maternal adjBMI and clinical stage or viral load. BMI assesses the body’s fat mass, and thus our results are consistent with the report by McDermott et al [17] in which a positive linear relationship was observed between fat mass and CD4+ counts among HIV-1-infected adults. Our observed association of adjBMI with CD4+ counts could be related to many known causes of nutritional impairment in patients with more advanced HIV-1 disease and must be interpreted with caution, since we do not have information about pre-pregnancy weight or possible weight loss in the recent past. Metabolic consequences of antiretroviral treatment also should be considered since women with lower CD4+ values could be more heavily exposed to these drugs and have altered body composition.
Maternal adjBMI was associated with several laboratory measures (bicarbonate, BUN, glucose, cholesterol, and hemoglobin). Bicarbonate was included in the biochemistry panel for these HIV-1-infected pregnant women to monitor lactic acidosis associated with ARV toxicity. The association of median bicarbonate levels and adjBMI is difficult to interpret since most of available literature in this field concerns patients with chronic kidney disease. For example, a French research group also found a negative correlation of bicarbonate with albumin, pre-albumin, BMI and lean body mass when evaluating chronic kidney disease patients receiving dialysis [18]. Since BUN is a laboratory marker of dietary protein intake, the finding of the lowest median BUN value among obese women raises a question about the protein content of diets among obese pregnant women in Latin America, although no difference in total protein or albumin was found among adjBMI groups. The higher median total cholesterol values among overweight and obese women emphasizes the importance of evaluating diet during antenatal clinic visits. Hemoglobin is frequently used as a parameter to evaluate the nutritional status of pregnant women and, in a study of pregnant adolescents in Tanzania, there was a strong correlation between maternal hemoglobin values and infant birth weight [19]. In Zimbabwe, maternal arm fat, used as a proxy for body fat mass, was a predictor of infant birth weight, while maternal age, arm muscle area, and hemoglobin level were not associated with infant birth weight [20].
The overall rate of preterm birth (9.2%) is lower than that of the general population in Brazil in 2004 (16.2%) [21], and also lower than that reported in other series of HIV-1-infected pregnant women: Rwanda (22.7%) [22], the U.S. (17%) [23], and Europe (25% between 2000–2004) [6]. However, our observed rate of low birth weight (12.9%) is similar to that of the HIV-1-uninfected black population in the U.S. (13%) [24], Latin America (10–20%) [25] and Brazil (10.4%) [21]. It is also similar to that reported in other studies of HIV-1-infected women in Tanzania (11%) [26,27], the U.S. (13%) [23], and Kenya (9.6%) [28].
Prematurity and low birth weight are associated with increased childhood morbidity and mortality, including hospitalization for [29] and mortality from infectious diseases [30]. In addition, fetal under-nutrition predisposes to adult hypertension, diabetes, obesity and coronary artery disease [reviewed in 31]. Our results confirm the importance of nutritional evaluation during antenatal care of HIV-1-infected women. Antiretroviral and obstetrical interventions have dramatically changed the life expectancy of infants born to HIV-1 infected women, but among populations of young people living in mid-developed countries, HIV-1 infection and social impairment occur in parallel and increase risk to family health.
Further research on micronutrient deficiency in HIV-1-infected pregnant women and its possible effect on fetus/infant growth and general health are needed. The use of nutrition interventions to correct possible deficiencies should improve the quality of life and the growth of HIV-1-exposed infants. Nutritional interventions may be particularly important among HIV-1-infected pregnant adolescents and younger women, especially in developing countries.
ACKNOWLEDGEMENTS
NICHD International Site Development Initiative: Perinatal Protocol Principal investigators, study coordinators, coordinating center representatives, and NICHD staff include: Argentina: Buenos Aires: Marcelo H. Losso, Adriana S. Durán, Silvina Ivalo (Hospital General de Agudos José María Ramos Mejía); Pedro Cahn, Maria Rolon (Hospital Juan Fernandez); Pedro Cahn, Edgardo Szyld, Eduardo Warley, Adriana Aguilar (Hospital Diego Paroissien); Pedro Cahn, Mariana Ceriotto, Susana Luciano, Maria Laura Collins (Hospital de Agudos Dra. Cecilia Grierson); Bahamas: Nassau: Perry Gomez, Percival McNeil, Marva Jervis, Chanelle Diggiss, Rosamae Bain (Princess Margaret Hospital); Brazil: Belo Horizonte: Jorge Pinto, Victor Melo, Fabiana Kakehasi (Universidade Federal de Minas Gerais); Caxias do Sul: Ricardo de Souza, Jose Mauro Madi (Universidade de Caxias do Sul /Hospital Geral de Caxias do Sul); Ricardo de Souza, Ruti Pipi, Rosangela Boff, (Universidade de Caxias do Sul / Ambulatório Municipal de DST/AIDS); Porto Alegre: Ricardo de Souza, Breno Riegel Santos, Marineide Melo, Rita Lira (Universidade de Caxias do Sul/Hospital Conceição); Ricardo de Souza, Mario Peixoto, Rita Lira (Universidade de Caxias do Sul/Hospital Femina); Ribeirão Preto: Marisa M. Mussi-Pinhata, Geraldo Duarte, Carolina Sales V. Macedo, Conrado Milani Coutinho (Hospital das Clínicas da Faculdade de Medicina de Ribeirão Preto da Universidade de São Paulo); Rio de Janeiro: Marcos Machado D’Ippolito, Esau Custodio Joao, Jacqueline Menezes, Guilherme Amaral Calvet (Hospital dos Servidores do Estado); São Paulo: Regina Celia de Menezes Succi, Prescilla Chow Lindsey (Federal University of São Paulo); Mexico: Mexico City: Noris Pavía-Ruz, Javier Ortiz Ibarra, Ricardo Figueroa-Damian, Noemi Guadalupe Plazola - Camacho (Instituto Nacional de Perinatología); Data Management and Statistical Center: Yolanda Bertucci, Julianne Byrne, Laura Freimanis, René Gonin, D. Robert Harris, Roslyn Hennessey, James Korelitz, Sonia Stoszek, Susan Truitt (Westat, Rockville, MD, USA); NICHD: Lynne Mofenson, Jack Moye, Jennifer S. Read, Leslie Serchuck, Heather Watts (National Institute of Child Health and Human Development, Bethesda, Maryland, USA). Supported by NICHD Contract #N01-HD-3-3345.
List of Abbreviations
- AdjBMI
body mass index adjusted for length of gestation
- ARV
antiretroviral
- BMI
body mass index
- BUN
blood urea nitrogen
- HAART
highly active antiretroviral therapy
- HIV-1
human immunodeficiency virus type 1
- NICHD
National Institute of Child Health and Human Development
- NISDI
NICHD International Site Development Initiative
- NNRTI
non-nucleoside reverse transcriptase inhibitor
- NRTI
nucleoside reverse transcriptase inhibitors
- PI
protease inhibitor
Footnotes
Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Presented in part: 43rd Annual Meeting of Infectious Diseases Society of America (San Francisco, USA; October 2005) (abstract 773).
References
- 1.Malvy D, Thiébeaut R, Marimoutou C, Dabis F Groupe d’Epidémiologie Clinique du Sida en Aquitaine. Weight loss and body mass index as predictors of HIV disease progression to AIDS in adults. Aquitaine Cohort, France, 1985–1997. J Am Col Nutr. 2001;20:609–615. doi: 10.1080/07315724.2001.10719065. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.World Health Organization. Maternal anthropometry and pregnancy outcomes. A WHO Collaborative Study. Bull World Health Organ. 1995;73(Suppl):1–98. [PMC free article] [PubMed] [Google Scholar]
- 3.Leroy V, Ladner J, Nyiraziraje M, et al. Effect of HIV-1 infection on pregnancy in Kigali, Rwanda, 1992–1994. AIDS. 1998;12:643–650. doi: 10.1097/00002030-199806000-00014. [DOI] [PubMed] [Google Scholar]
- 4.Brocklehurst P, French R. The association between maternal HIV infection and perinatal outcome: a systematic review of the literature and meta-analysis. Br J Obstet Gynaecol. 1998;105:836–848. doi: 10.1111/j.1471-0528.1998.tb10227.x. [DOI] [PubMed] [Google Scholar]
- 5.Ellis J, Williams H, Graves W, Lindsay MK. Human immunodeficiency virus infection is a risk factor for adverse perinatal outcome. Am J Obstet Gynecol. 2002;186(5):903–906. doi: 10.1067/mob.2002.123407. [DOI] [PubMed] [Google Scholar]
- 6.European Collaborative Study. Increased risk of adverse pregnancy outcomes in HAART treated HIV infected women in Europe. AIDS. 2004;18:2337–2339. doi: 10.1097/00002030-200411190-00019. [DOI] [PubMed] [Google Scholar]
- 7.Castetbon K, Lander J, Leroy V, Chauliac M, Karita E, De Clercq A, Van de Perre P. Dabis F for the Pregnancy and HIV Study Group. Low birthweight in infants born to African HIV-infected women: relationship with maternal body weight during pregnancy. J Trop Pediatr. 1999;45:152–157. doi: 10.1093/tropej/45.3.152. [DOI] [PubMed] [Google Scholar]
- 8.Read JS, Cahn P, Losso M, Pinto J, Joao E, Duarte G, Cardoso E, Freimanis-Hance L, Stoszek S NISDI Perinatal Study Group. Management of HIV-Infected Pregnant Women at Latin American and Caribbean Sites. Obstet Gynecol. 2007;109:1358–1367. doi: 10.1097/01.AOG.0000265211.76196.ac. [DOI] [PubMed] [Google Scholar]
- 9.Ministerio de Salud y Ambiente de la Nación, Dirección Nacional de Salud Materno Infantil, Programa Materno Infantil, Provincia de Buenos Aires y la Dirección de Información de la Provincia de Neuquén. [Ministry of Health and Environment of the Nation, National Directorate of Maternal and Infant Health, Maternal and Infant Program of the Buenos Aires Province and the Directorate of Information of the Province of Neuquén.] Programa Nutri Desarrollado en EpiInfo. [Nutri Program, Developed in EpiInfo.] http://www.msal.gov.ar/htm/Site/promin/UCMISALUD/Software/nutri13.zip. Available at: http://www.msal.gov.ar/ htm/Site/promin/UCMISALUD/ utilidades/ util.htm. Accessed September 5, 2007
- 10.Freeman GH, Halton JH. Note on an exact treatment of contingency, goodness of fit and other problems of significance. Biometrika. 1951;38:141–149. [PubMed] [Google Scholar]
- 11.Kruskal WH, Wallis WA. Use of ranks in one-criterion variance analysis. J Am Stat Assoc. 1952;47:583–621. [Google Scholar]
- 12.SAS Institute Inc. SAS Statistical Software, Version 8. Cary, NC: SAS Institute Inc.; 1999. [Google Scholar]
- 13.Szyld EG, Warley EM, Freimanis L, Gonin R, Cahn P, Calvet GA, Duarte G, Melo VH, Read JS NISDI Perinatal Study Group. Maternal antiretroviral drugs during pregnancy and infant low birth weight and preterm birth. AIDS. 2006;20:2345–2353. doi: 10.1097/01.aids.0000253362.01696.9d. [DOI] [PubMed] [Google Scholar]
- 14.Nucci LB, Schimidt MI, Duncan BB, Fuchs SC, Fleck ET, Santos Britto MM. Nutritional status of pregnant women: prevalence and associated pregnancy outcomes. Rev Saúde Pública. 2001;35:502–507. doi: 10.1590/s0034-89102001000600002. [DOI] [PubMed] [Google Scholar]
- 15.Ministério da Saúde, Brasil. [Accessed September 5, 2007];Plano estratégico programa nacional de DST e AIDS. 2005 Available at: http//www.aids.gov.br.
- 16.Molls RR, Ahluwalia N, Mastro AM, Smiciklas-Wright H, Handte GC. Nutritional status predicts primary subclasses of T cells and the lymphocyte proliferation response in healthy older women. J Nutr. 2005;135:2644–2650. doi: 10.1093/jn/135.11.2644. [DOI] [PubMed] [Google Scholar]
- 17.McDermott AY, Terrin N, Wanke C, Skinner S, Tchetgen E, Shevitz AH. CD4 cell count, viral Load, and highly active antiretroviral therapy use are independent predictors of body composition alterations in HIV-infected adults: A longitudinal study. Clin Infect Dis. 2005;41:1662–1670. doi: 10.1086/498022. [DOI] [PubMed] [Google Scholar]
- 18.Chauveau P, Fouque D, Combe C, Laville M, Canaud B, Azar R, Cano N, Aparicio M, Leverve X French Study Group for Nutrition in Dialysis. Acidosis and nutritional status in hemodialyzed patients. Semin Dial. 2000;13(4):241–246. doi: 10.1046/j.1525-139x.2000.00066.x. [DOI] [PubMed] [Google Scholar]
- 19.Shirima CP, Kinabo JL. Nutritional status and birth outcomes of adolescent pregnant girls in Morogono, Coast and Dar es Salaam regions, Tanzania. Nutrition. 2005;21:32–38. doi: 10.1016/j.nut.2004.09.006. [DOI] [PubMed] [Google Scholar]
- 20.Friis H, Gomo E, Nyazema N, Ndhlovu P, Krarup H, Kaestel P, Michaelsen KF. Maternal body composition, HIV infection and other predictors of gestation length and birth size in Zimbabwe. British J Nutr. 2004;92:833–840. doi: 10.1079/bjn20041275. [DOI] [PubMed] [Google Scholar]
- 21.Barros FC, Victora CG, Barros AJ, Santos IS, Albernaz E, Matijasevich A, Domingues MR, Sclowitz IKT, Hallal PC, Silveira MF, Vaughan JP. The challenge of reducing neonatal mortality in middle-income countries: findings from three Brazilian birth cohorts in 1982, 1993, and 2004. Lancet. 2005;365:847–854. doi: 10.1016/S0140-6736(05)71042-4. [DOI] [PubMed] [Google Scholar]
- 22.Leroy V, Ladner J, Nyiraziraje M, De Clercq A, Bazubagira A, Van de Perre P, Karita E, Dabis F Pregnancy and HIV Study Group. Effect of HIV-1 infection on pregnancy in Kigali, Rwanda, 1992–1994. AIDS. 1998;12:643–650. doi: 10.1097/00002030-199806000-00014. [DOI] [PubMed] [Google Scholar]
- 23.Lambert JS, Watts DH, Mofenson L, Stiehm ER, Harris DR, Bethel J, Whitehouse J, Jimenez E, Gandia J, Scott G, O’Sullivan MJ, Kovacs A, Stek A, Shearer WT, Hammill H, van Dyke R, Maupin R, Silio M, Fowler MG Pediatric AIDS Clinical Trials Group 185 Team. Risk factors for preterm birth, low birth weight, and intrauterine growth retardation in infants born to HIV-infected pregnant women receiving zidovudine. AIDS. 2000;14:1389–1399. doi: 10.1097/00002030-200007070-00012. [DOI] [PubMed] [Google Scholar]
- 24.Centers for Disease Control and Prevention. Infant mortality and low birth weight among black and white infants – United States, 1980–2000. MMWR. 2002;1251(27):589–592. [PubMed] [Google Scholar]
- 25.Kramer MS. Determinants of low birth weight: methodological assessment and meta-analysis. Bull World Health Organ. 1987;65:663–737. [PMC free article] [PubMed] [Google Scholar]
- 26.Dreyfuss ML, Msamanga GI, Spiegelman D, Hunter DJ, Urassa EJ, Hertzmark E, Fawzi WW. Determinants of low birth weight among HIV-infected pregnant women in Tanzania. Am J Clin Nutr. 2001;74:814–826. doi: 10.1093/ajcn/74.6.814. [DOI] [PubMed] [Google Scholar]
- 27.Wei R, Msamanga GI, Spiegelman D, et al. Association between low birth weight and infant mortality in children born to human immunodeficiency virus-1 infected mothers in Tanzania. Pediatr Infect Dis J. 2004;23(6):530–535. doi: 10.1097/01.inf.0000129691.42964.eb. [DOI] [PubMed] [Google Scholar]
- 28.Mwanyumba F, Claeys P, Gaillard P, Verhofstede C, Chohan V, Mandaliya K, Ndinya-Achola J, Bwayo J, Temmerman M. Correlation between maternal and infant HIV infection and low birth weight: a study in Mombasa, Kenya. J Obst Gynaecol. 2001;21:27–31. doi: 10.1080/01443610020022078. [DOI] [PubMed] [Google Scholar]
- 29.Hviid A, Melbye M. The impact of birth weight on infectious disease hospitalization in childhood. Am J Epidemiol. 2007;165(7):756–761. doi: 10.1093/aje/kwk064. [DOI] [PubMed] [Google Scholar]
- 30.Read JS, Clemens JD, Klebanoff MA. Moderate low birth weight and infectious disease mortality during infancy and childhood. Am J Epidemiol. 1994;140:721–733. doi: 10.1093/oxfordjournals.aje.a117320. [DOI] [PubMed] [Google Scholar]
- 31.Thompson JN. Fetal nutrition and adult hypertension, diabetes, obesity, and coronary artery disease. Neonatal Netw. 2007;26(4):235–240. doi: 10.1891/0730-0832.26.4.235. [DOI] [PubMed] [Google Scholar]