|The Effects of Improved Nutrition in Early Childhood : The institute of Nutrition of Central America and Panama (INCAP) Follow-up Study; Proceedings of an IDECG workshop, July 1990, Bellagio, Italy, Supplement of The Journal of Nutrition (International Dietary Energy Consultative Group - IDECG, 1990, 113 pages)|
|Nutritional supplementation during the preschool years and physical work capacity in adolescent and adult Guatemalans(¹,²)|
The above hypothesis was tested in a sample of Guatemalan adolescents and young adults who were participants in a nutritional supplementation trial while they were children. From January 1969 to September 1977, the Institute of Nutrition of Central America and Panama (INCAP) carried out a longitudinal study of growth and development in four rural Ladino (i.e., Spanish speaking, mestizo population) communities in eastern Guatemala. The subjects of the study were all village children aged ³7 y and all pregnant and lactating women. Cohorts of newborns were included for study until February 1977. Data collection for individual children ceased when they reached 7 y of age. All field data collection terminated in September 1977. Martorell et al. (1995) have described the study design, sampling scheme and measurements taken in this study.
The principal hypothesis under study was that improved nutrition results in accelerated mental development and physical growth of preschool-aged children. Two of the villages (one large, one small) consumed a high protein-energy drink (Atole) provided as a supplement to the normal diet. In two other villages |one large, one small) a nonprotein low calorie drink (Fresco) was provided. Atole contained incaparina (a vegetable protein mixture developed by INCAP), dry skim milk and sugar and had 163 kcal/682 kJ and 11.5 g of protein per cup (180 mL) whereas the Fresco contained no protein and as little sugar and flavoring agents as necessary for palatability. The Fresco provided 59 kcal/247 kJ per cup. Both drinks were distributed in food supplementation centers and were available daily, on a voluntary basis, to all members of the community. A cup containing 180 mL was provided to each individual, but more was given if desired. The unique feature of this study was that individual intake was recorded carefully, on a daily basis, to the nearest 10 mL. A curative-preventive medical care program was also implemented in all four communities.
From 1977 to 1988 no research was conducted in any of these villages. In 1988 INCAP returned to the villages to conduct a follow-up study of the participants in the original intervention trial, by then ranging in age from 11 to 27 y. The design, methods and procedures of this follow-up study are described in detail by Martorell et al. (1995). All participants, who were exposed to the intervention at some time before 7 y of age were candidates for the follow-up study. Because subjects in the original supplementation trial had varying periods of exposure to the intervention depending on their birth cohort, it was necessary to divide the sample into exposure cohorts.
Figure 1 presents the three cohorts chosen on the basis of the ages at which they were exposed to the intervention. Cohort 2, 14- 18 years at follow-up, had complete exposure to the intervention throughout gestation and the first 3 y of life and is considered to be the cohort where the effect of the intervention should be most observable. Most of the impact of nutritional supplementation on physical growth in this population was seen before 3 y (Schroeder et al. l995), therefore, 3 y was considered to be an appropriate cutoff age. Subjects in Cohort 1, the youngest children at follow-up, were born before the intervention stopped but, depending on their birth dates, were exposed for. varying lengths of time up to 3 y. Subjects in Cohort 3, the oldest group, were all born before the intervention began in 1969 and had complete exposure from 4 to 7 y of age but variable exposure from birth to 3 y of age. The follow-up sample included approximately 1574 subjects or nearly 73% of all original participants (Martorell et al. 1995).
Sample. The assessment of physical performance is a time-consuming procedure, and not all 1574 subjects could be tested. A representative subsample was identified for the physical performance test. Approximately 25% of the subjects (n = 366) identified as residing in the original study villages at the time of the follow-up were selected at random after stratifying by treatment, sex and cohort. Of this subsample, 206 consented to participate, while 73 individuals (40%) from Atole villages and 87 (48%) from Fresco villages refused to participate, so replacements were selected with a second random selection and eventually with volunteers from the villages. The primary reasons stated for refusal to participate were similar between groups. Fifty-one percent "did not have the time" or "were not interested," 27% were working temporarily or residing permanently in Guatemala City, and a small number were pregnant or had recently delivered (5%), were physically unable to participate (12%) or refused to provide a blood sample (5%). A subsequent round of random sampling yielded a similar response rate (92/163 or 56%) and the remainder (71 subjects) of the subsample was filled by volunteers from the pool of nonsampled subjects. Five subjects were excluded because they did not achieve the criteria for maximal exertion on the exercise test, leaving 364 subjects for analysis.
Compared with the total sample from which it was drawn, the work capacity subsample is not significantly different in height, weight, FFM, percent body fat and body mass index (BMI). The subsample differed slightly but not significantly from the total sample in the distribution of the amount of nutritional supplement ingested. Among Atole subjects of both sexes, the subsample slightly overrepresents the higher consumers of supplement, while the subsample of Fresco males slightly underrepresent:s the higher consumers. The 71 volunteer subjects did not differ in their anthropometry and supplement ingestion from the 298 subjects drawn at random.
Anthropometry and body composition. All anthropometry was taken by trained personnel using standard procedures (Lohman et al.1988). All anthropometrists were trained together, which minimized interobserver error. Weight, height and bioelectrical impedance (BIA) were measured at the time of the physical exercise test while all other measurements were taken within the previous 3 wk during an examination conducted in the subjects' home villages. FFM was estimated for each subject from anthropometry and bioelectrical impedance analysis (model BIA101, RJL Systems, Mt. Clemens, MI) using regression prediction equations specifically developed for this population (Conlisk et al. 1992).
Skeletal maturity. Biological maturity was estimated for all subjects under 18 y of age by assessing skeletal age with the Tann.er-Whitehouse-2 (TW2) procedure (Tanner et al. 1983). Maturity is expressed in this study either as skeletal age (SA) or as the difference (SA-CA) between SA and chronological age (CA) where a negative value reflects a delay in maturity relative to expected skeletal development for chronological age. Rivera et al. (1995) also included SA in the analyses but called it ma turation. The method for computing SA is reported elsewhere (Pickett et al. 1995); mature girls>16 y of age were assigned SA equal to their CA.
Physical work capacity; Work capacity was determined as the oxygen consumption at maximum physical exertion (VO2max) on a motorized treadmill (model 18-54, Quinton Instruments, Seattle, WA)> VO2max was assessed by standard open-circuit spirometry techniques similar to those described by Spurr and Reina (1989). A continuous and progressive test modified from the Balke and Ware (1959) treadmill procedure was administered to all subjects, with oxygen consumption (VO2), carbon dioxide production (VCO2) and cardiac frequency (fH) determined at each work load. Preliminary to the actual test, all subjects were acclimated to the treadmill and face mask and were instructed on the testing procedures. Testing began with a 3-min warm-up on the treadmill at 5% grade and 3.5 mph. Heart rate (fH) during the last 30 s was used to determine the treadmill velocity to be used for the rest of the test (fH<125, 4.2 mph; fH = 125140, 3.5 mph; fH>140, 3.0 mph). Immediately after the warm-up the subject began the continuous test at the specified starting workload. The grade of the treadmill increased by 2.5% every 2 min until maximum effort was achieved. In most tests the final two or three workloads were reduced to 1 min each so that subject fatigue did not result in a premature cessation of the test before VO2max could be observed. Velocity was increased only if the subject had reached the maximum treadmill grade (25%) but had not reached the criteria for VO2max. Criteria for maximum effort was the failure to increase VO2 by>150 mL between two adjacent work grades on the treadmill while maintaining fH above 190 bpm. Maximum exertion was confirmed in 91% of the subjects who reached this plateau of VO2. The remaining 9% of subjects achieved maximum heart rates>-1 SD of the heart rate predicted for their age. Five subjects (1.3% of those tested) did not meet these requirements for maximum exertion and were excluded from the analysis. Pulmonary ventilation (VE), VO2 and VCO2 were determined during the last 30 s of each workload using a Parkinson-Cowen Dry Gas Meter (model CD4, Rayfield Equipment, Waitsfield, VT) and Ametek medical gas analyzers (models S-3A and CD-3A, Thermox Instruments, Pittsburgh, PA). Expiratory gas was sampled through a Respironics (Monroeville, PA) Speakeasy-II face mask-valve and a mixing chamber using a CostillWilmore apparatus (R-Pel, Los Altos, CA). Gas analyzers were calibrated after every second subject using room air and factory standardized calibration gases (Fisher Scientific, Springfield, NJ). Heart rate was monitored with a Burdick (Milton, WI) electrocardiograph (model CS-525) with precordial leads at the CM5 position, and backed-up with a Uniq CIC Heartwatch (model 8799, Creative Health Products, Plymouth, MI) remote digital heart rate recorder. Testing was conducted at two laboratory sites because the villages were spread over too large an area to allow for easy transport to one laboratory. Both laboratories were air conditioned to maintain temperatures within the range of 25 to 30°C. The average barometric pressure at the two labs during testing was 748 and 710 mmHg. Twenty-three subjects were retested on a different day within 3 wk to determine test reliability. The technical error of measurement was 0.015 L/min or 8% of the age- and sex-adjusted total variance for VO2max.
Statistical analysis. Analyses were conducted in two steps. Analysis of covariance controlling for age was conducted within cohorts to test for differences in work capacity and related measures of body size, composition and maturity between Atole and Fresco subjects. Additional analyses of covariance were carried out to control for possible confounding effects of village size (1 = large, 0 = small), socioeconomic status (SES) and level of individual participation in the supplementation. The SES measure used in these analyses was derived from a factor analysis of characteristics of the home and household possessions (Rivera et al. 19951). Total volume of supplement consumed was used as a proxy for partioipation. Because participation was dependent on the age when the subjects were born there is a clear effect of child's age and cohort assignment on the amount of supplement ingested during the first 3 y of life. Only Cohort 2 children were exposed to supplementation over the entire age range of birth to 3 y. Therefore, the statistical control for participation was applied only to this cohort. Subjects from Cohort 2 were ranked from lowest to highest volume of supplement consumed during the first 3 y of life. Based on the relative ranking within either Fresco or Atole groups, subjects were given a percentile score. Total supplement consumption ranged from 0 to 386 L over the 3 y. The mean daily intake of Fresco was 43 mL (range = 0-226 mL), while 114 mL (range = 0-350 mL) of Atole was consumed daily. The supplement-specific percentile score (volume ranking) was used as a covariate along with age and SES in regression models that tested for treatment group effects on VO2max.
To control for the effects of body size on VO2max and therefore to test for treatment effects on aerobic power, the data are presented in two ways. Tables and figures of mean values for various subgroups (sex X cohort X treatment) express VO2 either in L/min or in mL/kg body weight · mine-¹and mL/kg FFM · min -¹. However, the formal testing of treatment effects on VO2max (L/min) includes body weight and FFM as covariates along with potential confounders such as village size, age, SES and volume of supplement consumed in separate regression models. This allows body size to scale itself relative to VO2max and is statistically a preferred means of controlling for these variables because it avoids the restrictive assumptions of a variable computed as a ratio of two variables (Tanner 1949). Differences in VO2max between groups were considered statistically significant if the P value was <0.05 on a two-sided test. The P value criterion for inclusion of an interactive term in a regression was 0.20; however, a value of £0.10 was considered to reflect a strong statistically significant interaction. To maintain consistency across regression models other covariates were retained in all models even if they were not statistically significant.
The second step of analysis tested for the dose response relationship between amount of supplement ingested and various measures of work capacity. This analysis was limited to Cohort 2 Atole subjects because they were exposed to the intervention during a critical period and consumed a wide enough range of supplement. Multiple regression procedures were used with VO2max (L/min) as the dependent variable and energy consumed from Atole as the independent variable after age and SES were controlled as potential confounders. All statistical analysis was conducted using programs from SAS (SAS Institute, Inc., Cary, NC).