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Nutritional indicators of adverse pregnancy outcomes andmother-to-child transmission of HIV among HIV-infected women13

Saurabh Mehta, Karim P Manji, Alicia M Young, Elizabeth R Brown, Charles Chasela, Taha E Taha, Jennifer S Read,Robert L Goldenberg, and Wafaie W Fawzi

ABSTRACT

Background: Poor nutrition may be associated with mother-to-

child transmission (MTCT) of HIV and other adverse pregnancy

outcomes.

Objective: The objective was to examine the relation of nutritional

indicators with adverse pregnancy outcomes among HIV-infected

women in Tanzania, Zambia, and Malawi.

Design: Body mass index (BMI; in kg/m2

) and hemoglobin con-centrations at enrollment and weight change during pregnancy were

prospectively related to fetal loss, neonatal death, low birth weight,

preterm birth, and MTCT of HIV.

Results: In a multivariate analysis, having a BMI 21.8 was sig-

nificantly associated with preterm birth [odds ratio (OR): 1.82; 95%

CI: 1.34, 2.46] and low birthweight (OR: 2.09; 95% CI: 1.41, 3.08).

A U-shaped relation between weight change during pregnancy and

preterm birth was observed. Severe anemia was significantly asso-

ciated with fetal loss or stillbirth (OR: 3.67; 95% CI: 1.16, 11.66),

preterm birth (OR: 2.08; 95% CI:1.39, 3.10), low birth weight (OR:

1.76; 95% CI: 1.07, 2.90), and MTCT of HIV by the time of birth

(OR:2.26; 95% CI:1.18,4.34) and by4 6 wkamong those negative

at birth (OR: 2.33; 95% CI: 1.15, 4.73).

Conclusions: Anemia, poor weight gain during pregnancy, and low

BMI in HIV-infected pregnant women are associated with increased

risks of adverse infant outcomes and MTCT of HIV. Interventions

that reducethe risk of wasting or anemiaduringpregnancy shouldbe

evaluated to determine their possible effect on the incidence of

adverse pregnancy outcomes and MTCT of HIV. Am J Clin

Nutr2008;87:163949.

INTRODUCTION

The interplay between nutrition and infection is well estab-

lished (1); poor nutrition predisposes to disease acquisition and

progression, and disease leads to worsening of nutritional status.In the context of pregnancy and HIV infection, poor nutritional

status, preexistent or as a result of HIV-induced wasting or

weightloss, is likely to increase therisk of mother-to-child trans-

mission (MTCT) of HIV (2). Additionally, poor nutrition as

reflected by short stature, low body mass index (BMI), poor

weight gain during pregnancy, and a low hemoglobin concen-

tration is an established risk factor for adverse pregnancy out-

comes such as low birth weight, fetal loss, preterm birth, and

intrauterine growth retardation among HIV-uninfected women

(39).Wasting, defined as involuntary weightloss (10), is oneof

the hallmarks of HIV disease. Furthermore, anemia is known to

be the most frequent hematologic abnormality of HIV disease

(11). Hence, we expect HIV infection to lead to a greater risk of

adverse pregnancy outcomes in infected women, as has been

suggested in some studies (2, 12). However, these studies

were conducted in an era when no interventions for the pre-

vention of MTCT of HIV, such as nevirapine (NVP) prophy-

laxis, were available. Thus, the association between poor nu-

tritional status and MTCT of HIV and adverse pregnancyoutcomes is unknown in HIV-infected women who receive

such interventions.

We analyzed data from the HIVNET 024 trial (13), a random-

ized controlled trial of antepartum and peripartum antibiotics to

prevent chorioamnionitis-associated MTCT and preterm birth,

and examined the associations between maternal BMI and he-

moglobin at enrollment and subsequent weight gain or loss dur-

ing pregnancy and adverse pregnancy outcomes and MTCT of

HIV.

1 From the Departments of Nutrition and Epidemiology, Harvard School

ofPublicHealth, Boston, MA(SM andWWF);the Departmentof Pediatrics,

Muhimbili University College of Health Sciences, Dar es Salaam, Tanzania(KPM); the Statistical Center for HIV/AIDS Research and Prevention, Fred

Hutchinson Cancer Research Center, Seattle, WA (AMY and ERB); the

University of North Carolina Project, Lilongwe, Malawi (CC); the

Bloomberg School of Public Health, Johns Hopkins University, Baltimore,

MD (TET); the Pediatric, Adolescent, and Maternal AIDS Branch, NICHD,

NIH, DHHS, Bethesda, MD (JSR); and the Department of Obstetrics and

Gynecology, Drexel University College of Medicine, Philadelphia, PA

(RLG).2 Supported by the HIV Network for Prevention Trials and sponsored by

the US National Institute of Allergy and Infectious Diseases, National Insti-

tutes of Health, Department of Health andHumanServices, through contract

N01-AI-035173 with Family Health International; contract N01-AI-045200

with Fred Hutchinson Cancer Research Center; and subcontract N01-AI-

035173-117/412 with Johns Hopkins University. Also supportedby the HIV

Prevention Trials Network and sponsored by the National Institute of ChildHealthand HumanDevelopment, National Institute on DrugAbuse, National

Institute of Mental Health, and Office of AIDS Research of the National

Institutesof Health, US Department of Healthand Human Services, Harvard

University (U01-AI-048006), Johns Hopkins University (U01-AI-048005),

and theUniversityof Alabama at Birmingham(U01-AI-047972). Nevirapine

(Viramune, Boehringer Ingelheim GmbH, Ingelheim, Germany) was pro-

vided by Boehringer Ingelheim Pharmaceuticals, Inc.3 Address reprint requests and correspondence to S Mehta, Department of

Nutrition,HarvardSchool of PublicHealth,655 HuntingtonAvenue,Boston,

MA 02115. E-mail: [email protected] September 21, 2007.

Accepted for publication January 23, 2008.

1639Am J Clin Nutr2008;87:1639 49. Printed in USA. 2008 American Society for Nutrition

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SUBJECTS AND METHODS

HPTN 024 trial

The study design and population of the HPTN 024 trial were

described in detail elsewhere (13). Briefly, this trial was con-

ducted from July 2001 to August 2003 at 4 sites in 3 African

countries: Blantyre and Lilongwe, Malawi; Dar es Salaam, Tan-

zania;and Lusaka, Zambia. The study protocol was approved by

each of the US and foreign collaborating Institutional ReviewBoards. HIV-infected women were randomly assigned at 2024

wk of gestation to receive either antibiotics (metronidazole plus

erythromycin antenatally and metronidazole plus ampicillin in-

trapartum) or placebo. All women self-administered 200 mg

NVP at the onset of labor, and the infants were given a 2-mg/kg

dose of NVP syrup within the first 72 h of lifein accordance with

the HIVNET 012 antiretroviral prophylaxis regimen (14). After

enrollment, women were followed through the remainder of the

pregnancy, through delivery, and 1 y postpartum.

Information was collected on sociodemographic characteris-

tics, such as years of education; anthropometric measures, in-

cluding weight and height; and laboratory variables, namely,

CD4 cell counts, plasma HIV RNA concentrations (viral loads),and hemoglobin concentrations on enrollment into the study

(2024 wk of gestational age). Additionally, information on

weight also was collected during the second (2630 wk of ges-

tational age) and third (36 wk of gestational age) prenatal visits.

The infants gestational age in weeks, birth weight in grams, and

occurrence of adverse events such as neonatal mortality (death

before 29 d of age) were recorded. Gestational age was deter-

mined by using 3 methods: 1) fundal height (uterine size in

centimeters measured from the pubic symphysis to the uterine

fundus), 2) the modified Ballard examination (15) performed at

delivery or within48 h of birth,and 3) the calculation of expected

date of delivery from thedate of last menstrual period as recalled

by the participant.The infants HIV infection status was determined by testing

bloodsamplescollected asdriedblood spotsvia heel stick atbirth

and at6 wkand12 moof age.These dried blood spots weretested

by RNA polymerase chain reaction (PCR) from whole blood.

Organon-Teknika NucliSens (Organon-Teknika, Durham, NC)

was used for the Malawi and Zambia sites; whereas Roche

Amplicor v1.5 (Roche Diagnostics, Branchburg, NJ) was used

for samples from the Tanzania sites. All tests were performed at

a reference laboratory (University of North Carolina, Chapel

Hill, NC).

Study population and variables for this analysis

For the analyses reported in this article, we included all HIV-

infected women enrolled in the HIVNET 024 study for whom

delivery information was available. We identified 3 main mark-

ers of maternal nutritional status, namely BMI and hemoglobin

(both assessed at study enrollment) and weight change during

pregnancy. BMIwas calculated by dividing theweight (inkg) by

thesquare of height (in m). Because there areno standard cutoffs

for BMI for pregnant women at 2024 wk of gestation, and

because the conventional cutoffs from the US Centers for Dis-

easeControland Prevention (16)for nonpregnant adults are more

applicable to populations in developed countries, BMI was cat-

egorized on the basis of tertiles of BMI in the study population.

Furthermore, BMIis expected to vary by week of gestationat the

first visit, and hence an arbitrary cutoff point may not be appli-

cable. Hemoglobin concentrations were categorized into 3

groups: severe anemia (8.5 g/dL), mild-to-moderate anemia

(8.510.9 g/dL), and no anemia ( 11 g/dL), based on cutoffs

used for referral to district hospitals in Tanzania (17).

Because weight was measuredat 3 timepointsbefore delivery,

we performed 2 additional sets of analyses to estimate the asso-

ciations between weight change and birth outcome. The first

subset included all births after 30 wk, and the rate of weightchange was calculated as the difference between the weights at

enrollment and the second visit divided by the time between the

visits. Thesecond subsetincluded only birthsafter 36 wk,and the

rate of weight change was estimated for each woman using least

squares based on all 3 measurements (oronly 2 if a measurement

was missing). Associations between weight change and the out-

comes were assessed with weight change as a continuous as well

as a categorical covariate. Weight loss was defined as a rate of

weight change 0 kg/wk. Low, normal, and high weight gain

were defined as 25th percentile, 25th percentile to 75th

percentile, and75th percentile of the positive weight gain dis-

tribution.

The primary outcomes of interest were adverse pregnancyoutcomes, including fetal loss or stillbirth, neonatal mortality

(defined as death of the infant before 29 d of age), preterm birth

(defined as delivery before 37 wk of gestation determined by

fundal height), and low birth weight (defined as birth weight

2500 g).MTCTof HIVwas assessed at2 time points: 1)atbirth

and 2) at 4 6 wk after birth. Transmission was assumed to have

occurred in utero if thebirth RNA PCR assay resultwas positive.

If thebirthspecimen RNA PCR assay resultwas negative butthe

test at week 6 waspositive, theinfection was classified as having

occurred in the intrapartum/early postnatal period (18).

Statistical analyses

Chi-square tests were performed to test for associations be-tween categorical variables. One-way analysis of variance tests

were performed to compare the means of continuous character-

istics by the levels of categorical variables. To explore the overall

shape of the relation between each nutritional measure and each

adverse birth outcome, generalized additive models were fit and

plots of the nutritional measuresagainst the predictedprobability

of the outcome were generated. Logistic regression models were

fitto assessthe associations between adverse birth outcomes and

the nutritional markers. Censored multinomial models (19) were

fit to assess the associations between MTCT of HIV and the

nutritional markers. The multivariate models included the nutri-

tional measures and were adjusted for age, CD4 count and viral

load at enrollment, and site. Additionally, when a quadratic re-

lation was suggested by the generalized additive models, a

squared term for that predictor was included in the model. Pear-

son correlation coefficients between maternal weight and infant

birth weight were also calculated, and exploratory scatterplots

were created. Allanalyseswere conducted by using SAS version

9.1 (SAS Institute Inc, Cary, NC) (20).

RESULTS

A total of 2294 eligible HIV-infected women were randomly

assigned in theparent HIVNET 024 trial describedabove. Seven

women died beforedelivery, and160 women were lost to follow-

up. Of 2127 women who were known to have delivered, delivery

1640 MEHTA ET AL


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information was available for 2126 (ie, 92.7% of eligible

women). Therefore, the study population comprised 2126 HIV-

infected women and their infants.

The baseline maternal characteristics of the study population

of 2126 mothers, overall and according to BMI and hemoglobin

at enrollment and weight change during pregnancy, are summa-

rized in Table 1.BMI(kg/m2) was categorized intothe following

groups based on tertiles:21.8, 21.8 23.9, and 24.0. A higher

BMI was significantly associated with older age, higher hemo-globin, and lower cervical and plasma viral load. A higher he-

moglobin concentration was significantly associated with

younger age, lower gestational age at enrollment, higher weight,

higher BMI, higher CD4 cell count, and lower cervical and

plasma viral loadsat enrollment. Weight change was categorized

into the following groups: 0 kg/wk, 0.010.18 kg/wk ( 25th

percentile), 0.190.41 kg/wk (25th percentile to 75th per-

centile), and 0.42 kg/wk (75th percentile of the positive

weight gain distribution). Weight change was associated with

age, gestational age, weight, BMI, hemoglobin, and cervical and

plasma viral loads.

Infant outcomes, overall and according to maternal BMI and

hemoglobin at enrollment and weight change during pregnancy,are shown in Table 2. Lower maternal BMI was associated with

preterm birth and low birth weight. Lower maternal hemoglobin

concentrations were associated with preterm birth, low birth

weight, fetal loss or stillbirth, and neonatal death. Weight change

during pregnancy was associated with preterm birth, low birth

weight, and fetal loss or stillbirth. The associations of BMI,

maternal hemoglobin, and weight change during pregnancy with

preterm birth remained the same regardless of the measure of

gestational age used (Ballard examination and last menstrual

periodbased numbers not shown).

Lower maternal BMI and hemoglobin also were associated

with an increased risk of MTCT of HIV (Table 3). When as-

sessedat birth, MTCT of HIVwas significantly higherin womenwho were severely anemic (hemoglobin8.5 g/dL) at baseline

(13%; 95% CI: 9%, 17%) compared with women with hemoglo-

bin 11 g/dL at baseline (5%; 95%CI: 3%,7%). Theoverall risk

of HIV infection at 46 wk (including those testing positive at

birth) was 28% (95% CI: 22%, 34%) in women who had severe

anemia: 16% (95% CI: 14%, 18%) in women with moderate

anemia (8.5 hemoglobin 11 g/dL), and 9% (95% CI: 7%,

12%) in women with hemoglobin 11 g/dL at baseline. The

corresponding numbers by maternal BMI at enrollment were

17% (95% CI: 14%, 20%)in women in the lowesttertile of BMI,

17%(95% CI:15%, 19%) in women in themiddle tertile of BMI,

and 12% (95% CI: 9%, 14%) in women in the highest tertile of

BMI.

Wehad information on weightchange between 2 visits only on

thewomen whodeliveredafter30 wk.Hence, we excludedthe 67

women who delivered before 30 wk of gestational age from

further analysis. Furthermore, the results for deliveries after 30

wk and for deliveries after 36 wk were similar, except for some

outcomes, which are noted below. Therefore, the results pre-

sented in Tables 4, 5, and 6 and in the figures refer to deliveries

after 30 wk (n 2059) unless otherwise specified.

In multivariate analysis (Table 4), women with BMI in the

lowest tertile had 1.82 times greater odds of delivering a preterm

infant compared with women with BMI in the highest tertile

(95% CI: 1.34, 2.46; P 0.01). In models with continuous

predictors, a decrease in 1 kg/m2 in BMI was associated with a

1.10 times greater odds of preterm birth [95% CI:1.06,1.16; P

0.01; adjusted for age, CD4 count, plasma viral load (log), and

site]. Women with severe anemia had a 2.08 times greater odds

of delivering a preterm infantthandid women with a hemoglobin

concentration 11 g/dL (95% CI: 1.39, 3.10; P 0.01). In

models with continuous predictors, a 1 g/dL decrease in hemo-

globin was associated with 1.14 times greater odds of preterm

birth [95% CI:1.05,1.25; P 0.01; adjusted forage, CD4count,

plasma viral load (log), and site].Because the generalized additive models suggested a qua-

dratic relation between weight change and preterm birth and

between weight change and low birth weight, a squared weight

change term was included in the model. The log odds ratios

(ORs) of preterm birth for women losing 0.1 0.7 kg/wk or gain-

ing 0.11 kg/wk, compared with women with no weight change

(0 kg/wk), are shown in Figure 1. The values corresponding to

theORs andCIs shown in theplots areprovided in Table 4. These

plots indicate that women losing weight had a higher odds of

preterm birth than did women remaining at the same weight, and

the odds of preterm birth increased as weight loss increased.

Gaining0.9 kg/wk also was associated with an increased odds

of preterm birth compared with notgaining or losingany weight.Whenadjusted for maternalage, CD4 count, plasma viral load,

and site, bothBMI and hemoglobin were significantly associated

with lowbirthweight (Table5); women with a BMIin thelowest

tertile had a 2.09 times greater odds of delivering an infant with

low birth weight compared with women with BMI in the highest

tertile (95% CI:1.41, 3.08; P 0.01). In models with continuous

predictors, a 1-kg/m2 decrease in BMIwas associated with a 1.13

times greater odds of having an infant with a low birth weight

[95% CI: 1.06, 1.20; P 0.01; adjusted for age, CD4 count,

plasma viral load (log), and site]. Similarly, women with severe

anemia had a 1.76 times greater odds of delivering a low-birth-

weightinfant compared with women with a hemoglobin concen-

tration 11 g/dL (95% CI: 1.07, 2.90; P 0.03). When theanalysis wasrestricted to deliveries after 36 wk (data notshown),

the magnitude of the ORs of low birth weight for the covariates

was similar, although the relation with hemoglobin became non-

significant(P 0.06); these results suggest thatthe effect of BMI

and hemoglobin on low birth weight is independent of their effect

on preterm birth.

As with preterm birth, a quadratic relation between weight

change and low birth weight was indicated in generalized addi-

tive models. Hence, a weight change squared term was included

in the multivariate model. The results suggest that women losing

weight had a higher odds of low birth weight than did women

remaining at the same weight, and the odds of low birth weight

increased as weight loss increased (Figure 2). Women gaining

between 0.1 and 0.6 kg/wk had a lower odds of low birth weight

than did women whose weight remains the same. Similar results

were obtained when the analysis was restricted to deliveries after

36 wk (data not shown).

Although BMI was not a significant predictor of fetal loss or

stillbirth (Table 6), both weight change and hemoglobin were

significant predictors of fetal loss or stillbirth in the unadjusted

and adjusted models. In models with continuous predictors, a

1-g/dL decrease in hemoglobin was associated with a 1.5 times

greater odds of fetal loss or stillbirth [95% CI: 1.19, 1.89; P

0.01; adjusted for age, CD4 count, plasma viral load (log), and

site]. Women with severe anemia (hemoglobin 8.5 g/dL) had

a 3.67 times greater odds of fetal loss or stillbirth compared with

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women with hemoglobin 11 g/dL after adjustment for age,

CD4 cell count, viral load, and site. A one kilogram per week

decrease in weight change was associated with lower odds of

fetal loss or stillbirth [OR: 0.28; 95% CI: 0.09, 0.91; P 0.03;

adjusted for age, CD4 counts, plasma viral load (log), and site].

When the analysis was restricted to women delivering after 36

wk, the magnitude of the ORs with severe anemia was similar;

however, the associations became non-significant (data not

shown).In the adjusted model, only weight change was a significant

predictor of neonatal death (Table 6). A 1-kg/wk decrease in

weight change was associated with a 4.36-fold increase in the

odds of neonatal death [95% CI: 1.57, 12.11; P 0.01; adjusted

for age, CD4 count, plasma viral load (log), and site]. However,

none of the nutritional markers were significantly associated

with neonatal death when the analysis was limited to deliveries

after 36 wk (data not shown).

BMI was not significantly associated with MTCT at birth

(Table 6); however, women with severe anemia had a 2.26 times

higher odds of MTCT of HIV compared with women with he-

moglobin 11 g/dL. In models with continuous predictors, a

1-g/dL decrease in hemoglobin was associated with a 1.17 timesgreater odds of MTCT at birth [95% CI: 1.02, 1.34; P 0.03;

adjusted for age, CD4 count, plasma viral load (log) and site].

Only severeanemia was a significant predictor of MTCT at birth

inthe analysesrestricted to deliveries after36 wk (OR: 2.48;95%

CI: 1.17, 5.26; P 0.02; data not shown).

Neither BMI nor weight change was a significant predictor of

MTCTofHIVat46wkamongthosenegativeatbirth(Table6).

However, women with severe anemia had a 2.33 times greater

odds of MTCT of HIV compared with women with hemoglobin

11 g/dL. A similar relation was observed between severe ane-

mia and risk of MTCT when the analyses were restricted to

deliveries after 36 wk (data not shown).

DISCUSSION

In this prospective study of HIV-infected women, we found

that poor nutritional status as characterized by low BMI at the

first prenatalvisit was associated withpretermbirth and lowbirth

weight. The risk of preterm birth increased with increasing

weight loss or excessive weight gain. Weight loss during preg-

nancy also was associated with a significantly increased risk of

neonatal death. In this study, maternal anemia was associated

with increased risks of fetal loss or stillbirth, preterm birth, low

birth weight, and MTCT of HIV at birth and at 46 wk among

those negative at birth.

These findings are consistent with those of studies in HIV-

uninfected populations. For example, in a review of 46 national

surveys from 36 developing countries, low maternal BMI was

associated with low birth weight and neonatal mortality (21).

Poor weight gain during pregnancy also is known to be associ-

ated with risk of preterm birth (5, 22), low birth weight (3, 23),

and fetal loss (24, 25). On the other hand, maternal overweight

and obesity also are known to increase the risk of adverse ob-

stetric and neonatal outcomes (26, 27), whereas excessive ma-

ternal weight gain is associated with macrosomia (23) and large-

for-gestational age infants and pre-eclampsia (28).

Similar associations have been reported between maternal

anthropometric measures and MTCT of HIV. Low maternal

midupper arm circumference was found to be associated withTABLE

2

InfantoutcomesbymaternalBMIandhemo

globinatenrollmentandweightchangedurin

gpregnancy

Characteristic

Overall

BM

Iatenrollment(kg/m2)

P

Hemoglobinatenrollment(g/dL)

P

Weightchangeduringpregnancy(kg)

P

21.8

21.823.9

24.0

8.5

8.510.9

11

Weightloss

Lowweight

gain

Normalweight

gain

Highweightgain

Pretermbirth,

fundalheight

37wk[n(%)]

547/2126(25.7)

217/708(30.6)

190/695(27.3)

140/723(19.4)

0.0

11

100/257(38.9)

309/1269(24.3)

130/573(22.7)

0.011

121/330(36.7)

74/445(16.6)

164/841(19.5)

107/396

27.0

0.0

11

Lowbirthweight

[n(%)]

288/1969(14.6)

124/652(19.0)

93/638(14.6)

71/679(10.5)

0.0

11

52/225(23.1)

170/1184(14.4)

61/538(11.3)

0.011

67/300(22.3)

47/432(10.9)

85/798(10.7)

47/363(12.9)

0.0

11

Fetalloss/stillborn

[n(%)]

74/2126(3.5

)

28/708(4.0)

22/695(3.2

)

24/723(3.3

)

0.6

91

16/257(6.2

)

39/1269(3.1

)

17/573(3.0

)

0.031

9/330(2.7

)

4/445(0.9

)

18/841(2.1)

15/396(3.8

)

0.0

41

Neonataldeath

[n(%)]

89/1987(4.5

)

38/662(5.7)

25/654(3.8

)

26/671(3.9

)

0.1

61

16/229(7.0

)

56/1199(4.7

)

16/534(3.0

)

0.041

17/307(5.5

)

11/425(2.6

)

22/802(2.7)

9/373(2.4

)

0.0

61

Infantweightgain

at46wk

(g/wk)

271(2.5

2)

264(4.26

)

276(4.5

0)

274(4.3

4)

0.1

32

267(7.0

8)

269(3.3

3)

278(4.6

7)

0.222

278(7.1

8)

277(5.0

7)

269(3.96)

266(5.6

2)

0.3

12

1

Pearsonchi-squaretest.

2

One-wayANOVAtest.



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intrapartum and early postnatal transmission in Zimbabwe (29).

In Tanzania,maternal weight lossduring pregnancy was strongly

associated with the risk of MTCT of HIV, independent of ma-

ternal CD4 cell count and plasma viral load (2). In Malawi, no

association was reported between BMI at first prenatal visit,

which occurred during the second or third trimester, and MTCT;

however, this studyassessed HIVinfectionstatus at1 y afterbirth

(30).

Low baseline BMI is a marker of minimal maternal nutrient

reserves. Severe protein-energy malnutrition could be responsi-

ble for adverse pregnancy outcomes (2), as suggested by studies

in which protein-energy supplementation of HIV-uninfected

pregnant women improved maternal weight gain and fetal

growth and decreased the risks of stillbirth and low birth weight

(3133). Suboptimal intakes of micronutrients such as calcium,

vitamins B-6 and B-12, and folate also may play a role (34 37).

TABLE 3

Mother-to-child transmission of HIV by maternal BMI and hemoglobin at enrollment and weight change during pregnancy1

Time of HIVinfection

BMI at enrollment (kg/m2) Hemoglobin at enrollment (g/dL) Weight change during pregnancy (kg)

21.8(n 654)

21.823.9(n 652)

24.0(n 671)

8.5(n 230)

8.511(n 1191)

11(n 532)

Weight loss(n 310)

Low weightgain

(n 434)

Normalweight gain(n 806)

Highweight gain(n 367)

Birth

Probability of

HIV

0.08 0.09 0.06 0.13 0.09 0.05 0.09 0.08 0.07 0.11

9 5% C I ( 0. 06 , 0 .1 0) ( 0. 08 , 0 .1 1) ( 0. 04 , 0 .0 8) ( 0. 09, 0 .1 7) ( 0. 07 , 0 .1 0) ( 0. 03 , 0 .0 7) ( 0. 06 , 0 .1 2) ( 0. 05 , 0 .1 0) ( 0. 05 , 0 .0 9) ( 0. 08 , 0 .1 5)

46 wk (among thosenegative at birth)

Probability ofHIV

0.10 0.08 0.06 0.17 0.08 0.04 0.10 0.06 0.08 0.08

9 5% C I ( 0. 07 , 0 .1 2) ( 0. 07 , 0 .1 0) ( 0. 04 , 0 .0 8) ( 0. 12, 0 .2 3) ( 0. 07 , 0 .1 0) ( 0. 02 , 0 .0 6) ( 0. 06 , 0 .1 4) ( 0. 04 , 0 .0 9) ( 0. 06 , 0 .1 0) ( 0. 05 , 0 .1 1)

46 wk

Probability ofHIV

0.17 0.17 0.12 0.28 0.16 0.09 0.18 0.14 0.15 0.18

9 5% C I ( 0. 14 , 0 .2 0) ( 0. 15 , 0 .1 9) ( 0. 09 , 0 .1 4) ( 0. 22, 0 .3 4) ( 0. 14 , 0 .1 8) ( 0. 07 , 0 .1 2) ( 0. 14 , 0 .2 2) ( 0. 10 , 0 .1 7) ( 0. 12 , 0 .1 7) ( 0. 14 , 0 .2 2)

1 Estimates obtained from univariate censored multinomial models for HIV infection.

TABLE 4

Multivariate analysis of preterm birth

Risk factor

Unadjusted models1 Adjusted model2,3 Adjusted model2,4

Odds ratio 95% CI P Odds ratio 95% CI P Odds ratio 95% CI P

BMI (n 2059)

21.8 kg/m2 (n 677) 1.74 (1.35, 2.25) 0.01 1.82 (1.34, 2.46) 0.01

21.823.9 kg/m2 (n 672) 1.52 (1.17, 1.97) 1.57 (1.16, 2.12)

24 kg/m2 (n 710) 1.00 1.00

Hemoglobin (n 2033)

8.5 g/dL (n 243) 2.11 (1.51, 2.95) 0.01 2.08 (1.39, 3.10) 0.01

8.510.9 g/dL (n 1232) 1.09 (0.85, 1.39) 1.08 (0.80, 1.44)

11 g/dL (n 558) 1.00 1.00

Weight change during pregnancy (n 2012)

Weight loss (n 330) 2.39 (1.80, 3.17) 0.02 2.16 (1.55, 3.00) 0.23

Low weight gain (n 445) 0.82 (0.61, 1.11) 0.81 (0.58, 1.13)

Normal weight gain (n 841) 1.00 1.00

High weight gain (n 396) 1.53 (1.16, 2.02) 1.55 (1.14, 2.12)

Weight change (n 2012)

1.0 versus 0 kg/wk 11.60 (4.08, 32.9) 10.30 (3.14, 33.5) 0.8 versus 0 kg/wk 5.52 (2.63, 11.6) 5.03 (2.17, 11.6)

0.6 versus 0 kg/wk 2.98 (1.83, 4.85) 2.79 (1.61, 4.84)

0.4 versus 0 kg/wk 1.83 (1.38, 2.42) 1.75 (1.28, 2.41)

0.2 versus 0 kg/wk 1.27 (1.13, 1.43) 1.24 (1.09, 1.42)

0.2 versus 0 kg/wk 0.89 (0.82, 0.97) 0.91 (0.82, 1.00)

0.4 versus 0 kg/wk 0.91 (0.78, 1.06) 0.94 (0.78, 1.12)

0.6 versus 0 kg/wk 1.04 (0.83, 1.31) 1.09 (0.84, 1.42)

0.8 versus 0 kg/wk 1.36 (0.98, 1.90) 1.44 (0.98, 2.11)

1.0 versus 0 kg/wk 2.02 (1.24, 3.27) 2.14 (1.22, 3.75)

1 P values obtained from univariate logistic regression models.2 Adjusted for age, CD4 count, plasma viral load (log), and site.3 P values obtained from multivariate logistic regression models with continuous predictors.4 P values obtained from multivariate logistic regression models with categorical predictors.

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This may be especially true for HIV-infected women, as sug-

gested by results from micronutrient supplementation trials inTanzania, in which supplementation of HIV-infected pregnant

women with vitamins B, C, and E lowered the risks of low birth

weight, preterm birth, and fetal loss by almost 40% (38).

Poor weight gain again may partly be due to a lack of protein-

energy availability to the fetus, although the exact biological

mechanism is unknown (39). Weight loss during pregnancy is

likely to occur at the expense of maternal rather than fetal tissues

and may therefore constitute an indicator of maternal wasting in

the course of HIV disease. Wasting, defined as involuntary

weight loss, is one of the hallmarks of HIV disease. Metabolic

disturbances that lead to wasting appear to represent an adaptive

responseto a generalized inflammatorystate and are mediatedby

an increased secretion of pro-inflammatory cytokines, including

tumor necrosis factor-, interferon-, and interleukins-1 and -6

(40). The increase in these cytokines (41) could lead to an in-

crease in placental inflammation, which could lead to increased

susceptibility of the placenta to viral infection and replication

(42) anda loss ofintegrity of theplacentalbarrier.Wastingis also

a strong predictor of adverse outcomes among HIV-infected

individuals independent of other markers of disease progression

(43 46). This is supported by studies that have shown that wast-

ing increased viral shedding in genital secretions and was sig-

nificantly related to an increased rate of adverse birth outcomes,

including fetal loss, low birth weight, and preterm delivery (12).

We noted that a low level weight gain during pregnancy ap-

peared to be protective againstfetal loss. Themechanism forsuch

an effect is not clear; it might be that such low weight gain is

associated with improved care provided to women with moreadvanced HIV disease, thus resulting in a lower risk of fetal loss.

These surviving infants, however, might be at a higher risk of

dying in the neonatal period, which may explain the adverse

association observed between weight change and neonatal mor-

tality. Furthermore, mothers who have a relatively better health

statusare more likely to carry into term, which is likelyto explain

the absence of this adverse association in deliveries after 36 wk.

We also found that anemia was very common in this popula-

tion; 73% of the women had hemoglobin concentrations 11

g/dL. Severe anemia, defined as a hemoglobin concentration

8.5 g/dL, was present in 12% of the population. This is similar

to the prevalence of anemia observed in other studies in HIV-

infected pregnant women in developing countries (4749). Ane-

mia is an established riskfactor for higher maternalmortality and

morbidity and adverse perinatal outcomes in HIV-uninfected

women (5053). Many women, particularly in developing coun-

tries, enter pregnancy with little or no iron reserves, mainly

because of poor nutrition but also because of closely spaced

pregnancies, prolonged periods of lactation, and blood loss from

postpartum hemorrhage (54). Also, an increased incidence of

low birth weight in infants of anemic mothers has been observed

in several studies (50, 5557). Given that maternal hemoglobin

is required for oxygen transportation across the placenta, anemia

may compromise oxygen delivery to the growing fetus, which

potentially leads to intrauterine growth retardation and low birth

weight (58).

TABLE 5

Multivariate analysis of low birth weight (2500 g)

Risk factor

Unadjusted models1 Adjusted model2,3 Adjusted model2,4

Odds ratio 95% CI P Odds ratio 95% CI P Odds ratio 95% CI P

BMI (n 1930)

BMI21.8 kg/m2 (n 633) 1.94 (1.39, 2.71) 0.01 2.09 (1.41, 3.08) 0.01

BMI 21.824.0 kg/m2 (n 626) 1.43 (1.01, 2.03) 1.59 (1.07, 2.37)

BMI 24 kg/m2 (n 671) 1.00 1.00

Hemoglobin (g/dL) (n 1909)

Hb8.5 (n 218) 2.26 (1.47, 3.47) 0.04 1.76 (1.07, 2.90) 0.04

Hb 8.510.9 (n 1159) 1.25 (0.90, 1.74) 0.98 (0.67, 1.42)

Hb 11 (n 532) 1.00 1.00


Weight loss (n 300) 2.41 (1.70, 3.43) 0.01 2.30 (1.52, 3.47) 0.02

Low weight gain (n 432) 1.02 (0.70, 1.49) 0.98 (0.64, 1.49)



Weight change (n 1893)

1.0 versus 0 7.75 (2.62, 22.9) 15.90 (4.49, 56.1)

0.8 versus 0 4.43 (2.05, 9.56) 7.35 (2.99, 18.1)

0.6 versus 0 2.73 (1.64, 4.52) 3.80 (2.09, 6.89)

0.4 versus 0 1.81 (1.35, 2.43) 2.18 (1.54, 3.09) 0.2 versus 0 1.30 (1.14, 1.47) 1.40 (1.21, 1.63)

0.2 versus 0 0.83 (0.75, 0.92) 0.80 (0.71, 0.89)

0.4 versus 0 0.75 (0.62, 0.90) 0.71 (0.57, 0.87)

0.6 versus 0 0.72 (0.54, 0.97) 0.70 (0.51, 0.96)

0.8 versus 0 0.75 (0.49, 1.16) 0.77 (0.49, 1.21)

1.0 versus 0 0.84 (0.45, 1.58) 0.94 (0.49, 1.80)

1 P values obtained from univariate logistic regression models.2 Adjusted for age, CD4 count, plasma viral load (log), and site.3 P values obtained from a multivariate logistic regression model with continuous predictors.4 P values obtained from a multivariate logistic regression model with categorical predictors.



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TABLE 6

Multivariate analysis of fetal loss/stillbirth, neonatal death, and HIV infection

Outcome and risk factor

Unadjusted models1 Adjusted model1,2

Odds ratio 95% CI P Odds ratio 95% CI P

Fetal loss/stillbirth

BMI (n 2059)

21.8 kg/m2 (n 677) 0.94 (0.48, 1.82) 0.83 0.99 (0.45, 2.17) 0.98

21.823.9 kg/m2 (n 672) 0.66 (0.32, 1.37) 0.60 (0.25, 1.47)

24 kg/m2 (n 710) 1.00 1.00

Hemoglobin (n 2033)

8.5 g/dL (n 243) 2.95 (1.15, 7.57) 0.03 3.67 (1.16, 11.66) 0.02

8.510.9 g/dL (n 1232) 1.60 (0.72, 3.53) 1.69 (0.62, 4.62)

11 g/dL (n 558) 1.00 1.00


Weight loss (n 330) 1.28 (0.57, 2.88) 0.16 1.24 (0.48, 3.20) 0.17

Low weight gain (n 445) 0.41 (0.14, 1.23) 0.26 (0.06, 1.15)



Neonatal death

BMI (n 1947)

21.8 kg/m2 (n 643) 1.08 (0.60, 1.96) 0.80 1.13 (0.58, 2.18) 0.71

21.823.9 kg/m2

(n 641) 0.75 (0.39, 1.43) 0.67 (0.32, 1.38) 24 kg/m2 (n 663) 1.00 1.00

Hemoglobin (n 1923)

8.5 g/dL (n 222) 2.45 (1.05, 5.74) 0.04 1.96 (0.74, 5.20) 0.17

8.510.9 g/dL (n 1173) 1.62 (0.82, 3.18) 1.47 (0.68, 3.16)

11 g/dL (n 528) 1.00 1.00


Weight loss (n 307) 2.08 (1.09, 3.97) 0.04 2.05 (1.02, 4.13) 0.04

Low weight gain (n 425) 0.94 (0.45, 1.96) 0.81 (0.36, 1.80)



HIV infection at birth

BMI (n 1956)

21.8 kg/m2 (n 644) 1.43 (0.92, 2.22) 0.13 1.06 (0.65, 1.73) 0.87

21.823.9 kg/m2 (n 645) 2.05 (1.35, 3.10) 1.58 (1.00, 2.51)

24 kg/m2 (n 667) 1.00 1.00

Hemoglobin (n 1932)

8.5 g/dL (n 224) 2.78 (1.61, 4.82) 0.01 2.26 (1.18, 4.34) 0.01

8.510.9 g/dL (n 1178) 1.73 (1.11, 2.68) 1.57 (0.93, 2.64)

11 g/dL (n 530) 1.00 1.00


Weight loss (n 310) 1.26 (0.77, 2.04) 0.40 1.07 (0.62, 1.86) 0.08

Low weight gain (n 434) 1.10 (0.71, 1.72) 0.89 (0.53, 1.48)



HIV infection at 46 wk among those negative at birth

BMI (n 1956)

21.8 kg/m2 (n 644) 1.63 (1.05, 2.54) 0.03 1.14 (0.68, 1.91) 0.61

21.823.9 kg/m2 (n 645) 1.35 (0.85, 2.14) 0.96 (0.56, 1.65)

24 kg/m

2

(n 667) 1.00 1.00 Hemoglobin (n 1932)

8.5 g/dL (n 224) 4.84 (2.63, 8.90) 0.01 2.33 (1.15, 4.73) 0.02

8.510.9 g/dL (n 1178) 2.09 (1.25, 3.51) 1.29 (0.72, 2.29)

11 g/dL (n 530) 1.00 1.00


Weight loss (n 310) 1.23 (0.75, 2.03) 0.72 1.09 (0.60, 1.99) 0.82

Low weight gain (n 434) 0.72 (0.43, 1.19) 0.77 (0.43, 1.37)



1 P valuesobtained from univariate logistic regression modelsfor fetal loss/stillbirth and neonatal death and from univariate censored multinomial models

for HIV infection.2 Adjusted for age, CD4 count, plasma viral load (log), and site. P values obtained from a multivariate logistic regression model predicting fetal

loss/stillbirth and neonatal death and from a censored multinomial regression model predicting HIV infection with categorical predictors.

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Anemia is also a common hematologic abnormality in HIV

disease (11)and is an independent predictor of mortality (59,60),

progression to AIDS (61, 62), and decreased quality of life (63)

among HIV-infected individuals. An explanation for our results

could be that anemia is a marker of advanced HIV disease and

hence is associated withadversepregnancy outcomesand MTCT

of HIV.

The major limitation of this study is its observational design;

lower BMI, more severe anemia, or poorer weight gain during

pregnancy may be a consequence of advanced HIV disease,

which is more likely to be associated with increased risk of

adverse pregnancy outcomes and MTCT of HIV. However, we

adjusted for most known confounders such as CD4 cell counts

and viral load, which are determinants of HIV disease stage.

Our findingshave important implicationsfor the prevention of

adverse pregnancy outcomes and MTCT of HIV. The women in

this study received NVP prophylaxis to prevent MTCT of HIV;

however, poor nutritional status remained a marker of increased

Weight change (kg/wk)

logOddsratio

-1 -0.9 -0.8 -0.7 -0.6 -0.5 -0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

0

0.5

1

1.5

2

2.5

3

3.5Upper 95% Confidence Limitlog Odds RatioLower 95% Confidence Limit

FIGURE 1. Log odds ratios of preterm birth (deliveries after 30 wk) for weight change compared with no weight change (0 kg/wk). Values are estimatesfrom an adjusted model with continuous nutritional predictors.

Weight change (kg/wk)

lo

gOddsratio

-1 -0.9 -0.8 -0.7 -0.6 -0.5 -0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

-0.5

0

0.5

1

1.5

2

2.5

3

3.5

4Upper 95% Confidence Limitlog Odds RatioLower 95% Confidence Limit

FIGURE 2. Logodds ratiosof lowbirthweight(deliveries after 30 wk)for weightchangecompared with no weightchange(0 kg/wk). Values areestimates

from an adjusted model with continuous nutritional predictors.



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risk of MTCT ofHIV. Interventionsthat lower theriskof wasting

during HIV disease and treat and prevent anemia could be po-

tentially efficacious adjunct modalities to strategies such as an-

tiretroviralprophylaxisto decreasethe riskof MTCTof HIV,and

trials that randomly assign women to these interventions are

warranted. Such interventions could include protein-energy and

micronutrient supplementation and the treatment of infections

such as malaria, intestinal parasitic infections, and other oppor-

tunistic infections (64, 65)all cofactors in the etiology of mal-nutrition among HIV-infected women.

We are grateful to the mothers and children who participated in this study

and to the entire study team at each site for their dedication and excellent

collaboration.

The authors responsibilities were as followsSM, KPM, WWF, and

AMY: contributed to theplansfor dataanalysis; SM:interpretedthe dataand

wrote theinitialdraftof themanuscript;AMY:analyzedand assisted with the

data interpretation; ERB: provided statistical guidance and helped interpret

the data; and TET, RLG, WWF, JSR, KPM, and CC (trial investigators):

contributed to the study design and implementation. All coauthors partici-

pated in the manuscript preparation. None of the authors had a personal or

financial conflict of interest.

The HPTN 024 team consisted of the following personsProtocol Co-

chairs:Taha E Taha (JohnsHopkins University BloombergSchoolof Public

Health, Baltimore, MD) and Robert Goldenberg (University of Alabama at

Birmingham). In-Country Cochairs/Investigators of Record: Newton

Kumwenda and George Kafulafula (Blantyre, Malawi), Francis Martinson

(Lilongwe, Malawi), Gernard Msamanga (Dar es Salaam, Tanzania), and

Moses Sinkala and Jeffrey Stringer (Lusaka, Zambia). US Cochairs: Irving

Hoffman (University of North Carolina, Chapel Hill, NC)and Wafaie Fawzi

(Harvard School of Public Health, Boston, MA). In-Country Investigators,

Consultants, and Key Site Personnel: Robin Broadhead, George Liomba,

Johnstone Kumwenda, Tsedal Mebrahtu, Pauline Katunda, and Maysoon

Dahab (Blantyre, Malawi); Peter Kazembe, David Chilongozi, Charles

Chasela, George Joaki Willard Dzinyemba, and Sam Kamanga (Lilongwe,

Malawi); Elgius Lyamuya, Charles Kilewo, Karim Manji, Sylvia Kaaya,

Said Aboud, Muhsin Sheriff, Elmar Saathoff, Priya Satow, Iluminata

Ballonzi, Gretchen Antelman, and Edgar Basheka (Dar es Salaam,Tanzania); Victor Mudenda, Christine Kaseba, Maureen Njobvu, Makungu

Kabaso, Muzala Kapina, Anthony Yeta, Seraphine Kaminsa, Constantine

Malama,Dara Potter, MacleanUkwimi, AlisonTaylor, PatrickChipaila,and

Bernice Mwale (Lusaka, Zambia). US Investigators, Consultants, and Key

Site Personnel: Priya Joshi, Ada Cachafeiro, Shermalyn Greene, Marker

Turner, Melissa Kerkau, Paul Alabanza, Amy James, Som Siharath, and

Tiffany Tribull (University of North Carolina, Chapel Hill, NC); Saidi

Kapiga and George Seage (Harvard School of Public Health, Boston, MA);

StenVermund, WilliamAndrews, andDeedeeLyon(Universityof Alabama

at Birmingham). National Institute of Allergy and Infectious Diseases Med-

ical Officer: Samuel Adeniyi-Jones. National Institute of Child Health and

Human Development Medical Officer: Jennifer S Read. Protocol Pharma-

cologist: Scharla Estep. Protocol Statisticians: Elizabeth R Brown, Thomas

R Fleming, Anthony Mwatha, Lei Wang, and Ying Q Chen. Protocol Virol-

ogist: Susan Fiscus. Protocol Operations Coordinator: Lynda Emel. Data

Coordinators: Debra J Lands and Ceceilia J Dominique. Systems Analyst

Programmers: Alice H Fisher andMarthaDoyle. Protocol Specialist:Megan

Valentine.

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