Effects of Anthropometric Indices on Cognitive Performance of Pre-School Children

Introduction

Infant growth is a process that involves changes in both size and behavior; physical growth contributes to the developmental process of the mental and cognitive abilities of infants and children that is demonstrated in motor and intellectual performance (Johnson & Blasco, 1997; Vaughan, 1992). Anthropometric measurements are commonly used as indices to assess physical growth and development in infants and children. Growth is evaluated by comparing individual measurements to standard references represented by percentile curves on a growth chart. Anthropometric deficits could contribute later to delays in cognitive and intellectual performance in children (Mendez & Adair, 1999).

Inadequate nutrition during infancy and childhood contributes to faltering in linear growth and to impaired mental and intellectual performance. In addition, maternal factors such as nutritional status, socio-economic status, and dietary habits such as consumption of alcohol and caffeine, might affect the growth of infants (Fahmida et al., 2008; Nolan, Schell, Stark, & Gomez, 2001). Micronutrient or energy supplement has beneficial effects on growth and development if the infants or children are malnourished or have deficits in growth (Ashworth, Morris, Lira, & McGregor, 1998; Black, Sazawal, Black, Khosla, Kumar, & Menor, 2004; Hamadani, Fuchs, Osendarp, Khatun, Huda, & Grantham-McGregor 2001; Torbjan, Lonnerdal, Stenlund, Gamayanti,Isamil, Seswandhana, & Perrson, 2004).

Anthropometric measurements are used as indices to assess short-term and long-term growth and predict functional outcomes. Several studies have investigated factors that affect the growth of infants such as nutritional status, feeding patterns (Agostoni, Grandi, Gianni, Torcoletti, Giovannini, & Rita 1990; Donma & Donma, 1999), socio-economic status, and maternal dietary habits as in the consumption of alcohol and caffeine (Day et al., 1990; Fahmida et al., 2008; Haste, Brooke, Anderson, & Balnd, 1991; Nolan, Schell, Stark, & Gomez, 2001; Shu, Hatch, Mills, Clemens & Susser, 1995).

Anthropometry and Intelligence

Growth monitoring during infancy allows for early detection of chronic illness or nutritional deficiency. It is also important for evaluating the efficacy of medical and nutritional intervention (Ross & English, 2005). The most common anthropometry measurements during infancy include weight and length. Length is an excellent indicator of growth, since it is not subject to daily variations as is the case with weight. Measurement of head circumference is an indicator of brain growth, and while it is a less sensitive indicator of growth, it reflects brain development and possible neurological disorders (Gokhale & Kirschner, 2003). Three different systems are commonly used for evaluating body size anthropometric data. All three systems compare an individual child’s growth to the growth of children in a reference population (WHO, 1995):

  1. The Z-score or standard deviation system, this system expresses the anthropometric value as the number of standard deviations that are below or above the median value.
  2. The percentile system, the percentile refers to the position of an individual on a given reference distribution.
  3. The percent of median system: The anthropometric measurements are expressed as a percentage of the median value of the expected reference.

It is important to repeat the measurements frequently to avoid errors in the interpretation of anthropometric indices (World Health Organization [WHO], 1995). Several studies have examined the effect of micronutrients and macronutrients on growth and development during infancy (defined as between birth and 12 months of age).

Torbjorn et al. (2004) conducted a study in Indonesian that tested the effect of zinc and iron on growth and psychomotor development in infants. They tested each nutrient as a single supplement and zinc and iron as a combined supplement. The results showed that giving the zinc and iron as two single supplements significantly improved growth and psychomotor development, but using them as one supplement did not affect growth or development, probably because of the antagonistic interactions between the two metals that may have affected their absorption. Another study showed that supplementation in pregnancy of under nourished mothers with multiple micronutrients which included 15 different vitamins and minerals (iodide, zinc, selenium, Cu, Vitamin A, B1, B2, B3, B6, B12, C, D, E) in addition to iron-folate supplements, had a significant benefit for infants in their motor development and activity levels compared to infants whose mothers received only an iron-folate supplement (Fahmida et al., 2008).

Infant feeding patterns can also affect the growth and development of infants. A study in India found that breast milk enhances nervous system development (Selvakumar & Vishnu Bhat, 2007). This development was quantified using the Trivandrum Development Screening Charts (TDSC). Anthropometric parameters were measured and analyzed with Z-score of weight, and weight-for-height using CDC 2000 anthropometric standards. The TDSC scores were significantly different between babies weaned before three months of age and ones that weaned around the ideal time of six months or more. A significantly lower development score was found in early weaned (< 3 months) infants compared to those exclusively breast fed for 6 months or more. Furthermore, significant difference in Z-scores of weight was observed. Infants exclusively breast fed beyond the ideal time (6 months) showed lower Z-score of weight compared to infants exclusively breast fed for 6 months or less. Infants who were exclusively breast fed for ≤3 months are more likely to have slower development, while infants weaned after 6 months are more likely to have lower weight than those weaned at 6 months.

Heinonn et al. (2008) conducted a study in Finland to investigate the relation between growth and cognitive abilities in infancy and early childhood. Anthropometric measures (weight, length, and head circumference) were measured at 5, 20, and 56 months of age, and there were four cognitive- ability tests administered to the children at 56 month of age: 1) general reasoning 2) visual-motor integration 3) verbal competence, and 4) language comprehension. The study revealed that children born at term with a relatively smaller body size at birth performed worse on cognitive performance tests later at 56 months of age. This suggests that different periods and measures of growth may be associated with different effects on later cognitive abilities. Low BMI and decreased head circumference might have negative effects on cognitive abilities that could extend from birth to the second year of age while a decrease in growth rate ( weight and height) could have negative effect on cognitive abilities during the early months of age (birth to 5 months).

A study in France investigated the relation between growth status and the development of cognitive and intellectual abilities (Vaivre-Douret et al., 2009). Information such as gestational age in weeks, birth weight, head circumference, and stature was recorded from the child’s medical file. A parent questionnaire was mailed to 1200 families covering different aspects of child and family history (pregnancy, neonatal period, psychomotor development, schooling and parental socio-economic status). Only 725 of the questionnaires were returned and the study distinguished three groups: preterm infants, full-term infants, and post-term infants.

The research showed that there was no significant difference between the preterm and full-term infants in regard to their motor abilities (sitting, crawling, walking), but the post-term were significantly earlier than the preterm and full-term infants in developing these abilities (Vaivre-Douret et al.,2009). Also the IQ was measured during primary or secondary schooling (mean age: 11.0± 4 years) by using the WISC-III. The IQ scores of children who were preterm at birth did not significantly differ from those who were full-term and post-term infants. This implies that children born preterm could catch up in growth when they are exposed to favorable  prenatal environment (few pregnancy complications) and favorable postnatal socio-economic environment.

Broekman et al. (2008) examined the association between birth length (BL), birth weight (BW), head circumference (HC), gestational age at birth (GA) and childhood IQ in a large cohort study of healthy Singapore children of normal birth size. Children aged 7 to 9 years, were recruited from 3 schools in different parts of Singapore. The children’s IQ was measured by completing the Raven’s Standard Progressive Matrices (RPM). This test assesses visual alertness and spatial and abstract pattern-recognition abilities, and the parents completed a baseline questionnaire. The finding showed that the children who were born with normal ranges of BL, BW, and HCs had higher IQ scores later in childhood. This also suggests that BL may better reflect the phases of fetal growth related to later cognitive function, because fetal length increases primarily during the second trimester which suggests that developmental process during this period has a lasting effect on cognitive ability.

The Avon longitudinal study was conducted in the United Kingdom to investigate the effect of head growth prenatally, during infancy, and during later periods of development on cognitive function of 633 full-term infants (Gale, O’Callaghan, Bredow, & Martyn, 2006). The cohort was asked to attend clinics for a follow up examination at 4, 8, 12, 18, 25, 31, 37, 43, 49, 61, and 96 months of age. Head circumference was measured after birth and during each visit to the clinic. Furthermore, standing heights at the ages of 4 and 8 years were measured. Cognitive function was evaluated with the Wechsler Preschool and Primary Scale of Intelligence at the age of 4 years and with Wechsler Intelligence Scale at the age of 8 years. The results showed that head circumference at birth was a significant indicator of performance IQ scores at the age of 4 years. It was also an important predictor of IQ at the age of 8 years. However, the findings showed that brain growth during infancy is the most significant period of postnatal brain growth for predicting later intelligence.

An Avon longitudinal study of parents and children (ALSPAC) investigated the association between poor weight gain in infancy and subsequent intellectual deficit (Edmond, Blair, Emmett, & Drewett, 2007). A total of 1406 live births were included in the cohort study. Weight measurements were taken at birth, at 8 weeks (range1-3 months), and 9 months (range 6- 12 months), Socioeconomic and health data were collected by using a series of postal questionnaires completed by the parents at 4 weeks, 6months, and 15 months after delivery. And at the age of 8 years, children were tested by using the Wechsler Intelligence Scale for Children. There was a positive linear relationship between infant growth from birth to 8 weeks and child IQ at 8 years of age. An improved weight gain in early infancy (first 8 weeks) was significantly associated with increase in child IQ scores. Also parental education and social class showed to be strong predictors of IQ scores, children whom performed well on the test were from better educated families. This suggests that the deficit in IQ is related to early rather than later growth faltering, and timely intervention is required to prevent any intellectual deficits that may occur.

The US Collaborative Prenatal Project (CPP) studied the impact of season of birth on various anthropometric and neurocognitive development variables from birth to 7 years of age (McGrath, Saha, Lieberman & Buka, 2006). Between 1960 and 1967, the CPP conducted a study, involving 50,000 women and their offspring from 12 different US sites. The main predictor variable of this study was season of birth, while the outcome variables included three anthropometric measurements (weight, length or height, and head circumference). Six measures of motor and cognitive developments were used during the study. Weight, length, and head circumference were examined at birth, 8 months, 4 and 7 years of age.

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The main findings showed that winter and spring births were associated with changed physical and neurocognitive outcomes (McGrath, Saha, Lieberman & Buka, 2006). At birth, winter/spring babies were significantly taller, heavier, and had larger head circumference. However, season of birth was not significantly associated with any of the anthropometric measures at the age 8 months or 4 years, although there was a remarkable relation between winter/spring birth and improved performance of the Bayley Scale motor component at 8 months of age. However, the relation was not so obvious on the Bender Gestalt Test that measures the visuo-constructive ability. (This suggests that exposure to seasonal changes influences brain developments more in some domains (fine and gross motor coordination) than others (sensory discrimination ability).

The National Collaborative Prenatal Project in the US examined the relation between variation in birth weight and IQ (Matte, Bresnahan, Begg, & Susser, 2003). A sibship sample was constructed from children who met specific inclusion criteria. The full sibship sample included 3484 children from 1683 families. Children from the sibship sample were from families of higher than average socioeconomic status. Furthermore, two samples were drawn from the full sibship sample, the first sib sample included one sibling and was chosen randomly from each family (n= 1683, 871 girls and 812 boys). The second sample included all siblings pair from families contributing only two children and were chosen randomly from larger sibships (n= 3366, 1742 girls and 1624 boys)

The intelligence tests were administered at age 7 years, including four of five verbal and three of five performance tests from the Wechsler Intelligence Scale for Children (Matte, Bresnahan, Begg, & Susser, 2003). The results showed a significant association between IQ and birth weight in both sexes, and it was stronger in boys than girls, a 1000 g increase in birth weight relates to a 4.6 point increase in IQ among boys but only 2.8 among girls. Within siblings pairs of the same sex, IQ differences were related to differences in birth weight (heavier sibling have higher IQ), but this association was significant only in boys. However, children with low birth weight had a slightly lower mean IQ than their same sex, normal birth weight siblings. This implies that IQ at age 7 years is linearly associated to birth weight among children of normal birth weight.

Evidence showed that IQ tends to be higher in those with normal birth weight. Gale, O’Callaghan, Godfrey, Law, and Martyn, 2004 investigated the relationship between brain growth in different periods of pre- and postnatal life and cognitive function of 9 year-old children. The study included singleton children born to Caucasian women who attended antenatal clinic at < 17 weeks gestation. Anthropometric measurements were taken at three time periods: 18 weeks gestation (fetal ultrasound), birth, and 9 months of age. Information about the pregnancy and delivery, parental social class, and maternal education were collected. At the 9 month postnatal visit, women were asked regarding infant feeding. A total of 559 children were followed up to the age of 9 months, and a total of 221 children were tested for their cognitive function at age 9 years.

The cognitive function of the child and his/her mother was assessed using the Wechsler Abbreviated Intelligence Scale; this provides age-adjusted IQ scores for full scale, verbal and performance intelligence (Gale, O’Callaghan, Godfrey, Law, & Martyn, 2004). The results showed that boys achieved higher scores than girls for full-scale IQ (108.7 compared with 104.2) and for performance IQ (107.4 compared with 101.7), but there was no significant difference between the sexes in verbal IQ. There was no significant relation between birth weight and length at birth with IQ at age 9 years, also no significant relation was found between IQ and weight and length at 9 months or weight and height at 9 years. But there were strong significant associations between measures of postnatal head growth and IQ. These findings imply that, increased postnatal brain growth is more important than fetal growth to attain a peak cognitive performance later in childhood.

In summary, infant growth, which is assessed by anthropometric measurements (weight, length, head circumference), contributes to cognitive and intellectual abilities later in childhood in typical weight infants in Singapore (Broekman et al., 2008), Finland (Heinonn et al., 2008), and France (Vaivre-Douret et al., 2009). Micronutrient supplement and exclusive breast feeding have been shown to improve growth and development of infants (Torbjorn et al., 2004; Selvakumar & Vishnu Bhat, 2007). Also season of birth showed impact on anthropometric and neurocognitive development (McGrath, Saha, Lieberman & Buka, 2006). Deficits in anthropometric indices in early infancy (poor weight, length, head circumference) could affect cognitive development later in childhood (Edmon et al., 2007). However, these studies looked at effects of infant growth on later cognitive performance. Using VIP procedures, cognitive development can be assessed in infancy.

Visual Information Processing (VIP) and Intelligence

Visual information processing assesses attention, memory formation, and ability to process information quickly and efficiently, which are fundamental aspects of cognitive functioning (Bornstein, 1985; Bornstein, Slater, Brown, Roberts, & Barrett, 1997). Measures of habituation refer to the decrease in visual attention that ultimately reflects memory formation of the familiar stimulus, and consequently the processing of information from the stimulus. Therefore measures of habituation have been noticed as potential predictors of intelligence (McCall, 1994). Several studies found evidence that measures of information processing in infancy are related to cognition and intelligence in later childhood (Dougherty & Haith, 1997; Rose, Feldman, Wallace, & McCarton, 1991; Thompson, Fagan & Fulker, 1991).

Kail (2000) described the developmental changes in speed of information processing, and the role of processing speed in the development of intelligence. He examined research that linked speed of information processing to intelligence and concluded that processing speed is an element of intelligence. That is, processing speed is not an independent factor that contributes to intelligence, but is thought to be linked to other elements of intelligence. More rapid processing enhances the memory, which in turn, enhances reasoning. Furthermore, processing speed can influence performance on intelligence tests directly and indirectly. The indirect effect is demonstrated by the impact of processing speed on memory and directly by, speeding retrieval of information from long-term memory, which eventually enhances performance on intelligence test.

Rose, Feldman, and Wallace (1992), examined infant information processing in relation to cognitive performance at 6 years of age. The cohort study consisted of 109 participants (63 preterms, 46 full terms infants) there were examined from 7 months to 6 years of age. At 7 15 months of age, visual recognition memory was assessed. At 1 year of age the infants first received seven paired comparison problems, three assessed visual recognition memory and four assessed cross-modal transfer (tactual-visual). Finally the Einstein Scale of object permanence was administered; this scale assesses the child’s progress through a series of cognitive stages. At 6 years of age, the Wechsler Intelligence Scales for Children was used to assess verbal and performance IQ and other tests that involved measures of language, reading, and quantitative skills were also used. Several tests were used to assess perceptual organization and reasoning.

The results showed that in general the scores were higher for the full-terms infants than those born preterm (Rose, Feldman, & Wallace, 1992). The study found that at 7-months of age the visual recognition score was positively and significantly correlated with all aspects of IQ, at age 6 measures including language, achievement (reading and quantitative skills), and the perceptual organization. The 1-year cross-modal score was related to 6-year IQ. These findings imply that measures of information processing that were obtained during the first year of life are reliable in predicting intelligence and several specific cognitive abilities at 6 years.

Rose and Feldman (1997) examined the role of speed and memory in infant information processing to 11 years old children’s IQ. The sample consisted of 90 children (50 preterm and 40 full terms).The participants had been followed from 7 months to 6 years of age, and then were contacted for an additional follow-up at 11 years of age. Two primary measures of information processing in infancy consisted of visual recognition memory and cross-modal transfer. At 11 years of age, children’s memory and processing speed were assessed by the Cognitive Ability Test (CAT) and the Specific Cognitive Abilities test (SCA). The IQ was assessed using the Wechsler Intelligence Scale. The results of the study provided evidence that speed and memory are among the major contributors to infant information processing for prediction of later IQ in children. The relation between 7 month visual recognition memory and 11 year IQ declined when speed and/or memory were statistically controlled at 7 months and 1 year to 11 years of age. Furthermore, infants who had better information processing, showed better performance on the (CAT, SCA) measures later in childhood.

A meta-analysis was conducted by McCall and Carriger (1993); it included studies that investigated infant habituation and recognition memory performance as predictors of later IQ. Some of the conclusions from this review suggested that prediction from habituation and recognition memory may be stronger when such assessments are made between 2 and 8 months of age rather than earlier or later. Infants, who perform tasks from paradigms (habituation and recognition memory) more rapidly which is representative of information processing, are more likely to have higher IQ. And the performance on these processes remains stable from infancy to childhood.

Sheppard and Vernon (2008) conducted a review that investigated the relationship between intelligence and speed of information processing during the past 50 years. Articles (n=172) that presented one or more correlations between mental speed measures and intelligence or effect sizes regarding age, sex, or racial differences for speeded task performance were included in this review. Measures of mental speed used in these studies were classified according to (reaction time, general speed of processing, speed of short-term memory processing, and speed of long-term memory retrieval). Measures of intelligence were classified as (general intelligence, fluid intelligence, and crystallized intelligence). Novel mental speed tasks were more correlated to fluid intelligence, while tasks that required subjects to retrieve learned information from long term memory were highly correlated with crystallized intelligence. In regard to group differences in mental speed, results showed that mental speed is slower among elderly adults and young children. For the sex differences a number of studies have reported that females tend to have faster mental speed than males. And for racial differences, mental speed was significantly faster in whites than black.

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Visual information processing is an aspect of the ability to convey information rapidly, through attention and memory formation to establish cognitive functioning (Bornstein, 1985) Speed of information processing is an important factor that contributes to intelligence later in childhood (Rose & Feldman, 1997; Rose, Feldman, & Wallace, 1992). Measures of visual habituation and recognition memory in infancy are potential elements of infant information processing for predicting later intelligence in children (McCall & Carriger, 1993). Also speed of information processing contributes to intelligence by enhancing memory and speeding retrieval of information from long-term memory, which in turn enhances the performance on intelligence tests (Kail, 2000).

Anthropometry and Visual Information Processing

Few studies have investigated the relation between anthropometry and visual information processing in infants (Kennedy et al., 2008; Rose, 1994). Both examined the relation between growth and visual information processing in generally malnourished infants. But no previous studies have investigated this relation in healthy breastfed infants.

Rose (1994) conducted a study in India that examined the relation between physical growth and visual information processing in infants. The sample consisted of 183 infants aged 5 to 12 months and whose weight was either adequate or low for their age. Anthropometric indices of weight-for-age, length-for-age, weight-for-length, and head circumference were used to index variation in physical growth. Also visual recognition memory and tactual-visual cross-modal transfer were measured in infants. The results showed that infants who are longer and heavier performed significantly better than infants who are underweight or have deficits in length growth. And head circumference, birth weight, and history of previous illness were positively associated with performance. Furthermore, maternal weight and height, and parental education were also associated with better infant performance.

Kennedy et al. (2008) examined growth and visual information processing in infants in Southern Ethiopia. Infants (n=100) aged 6 to 8 months were recruited; anthropometry measurements (weight, length, head circumference) and visual information processing (familiarization and test phases) trials were conducted. The final sample consisted of 69 infants, those who were able to complete at least three trials of VIP. The findings showed a correlation between total amount of familiarization and novelty quotient, infants who looked longer at the familiar stimuli had greater visual preference towards the novel one. Furthermore, three multiple regression analysis (longest look during familiarization, mean look duration, and novelty quotient), showed that none of the growth parameters (weight, length, and head circumference) related to novelty quotient, but weight for age Z score alone related significantly to longest look duration. Also head circumference showed a significant relation to the mean look duration. However, infants with lower weight-for-age Z-scores, and length-for-age Z-score had longest looks to stimuli, and slower mean shifts rates respectively during familiarization compared to better nourished infants. This suggests that malnourished infant physical growth is associated with visual information processing.

Infant physical growth is related to cognitive development, and impaired growth due to malnutrition or illness could consequently affect the cognitive process. Infants who are longer and heavier performed better on visual-recognition memory and cross-modal transfer (Rose, 1994). Also infants who are shorter and weigh less tend to look longer to stimuli and have less shift of looking between the familiar and novel stimuli (Kennedy et al., 2008). In general, infants who are adequately nourished have better information processing.

Demographic effects on Visual Information Processing / Anthropometry

Maternal smoking during pregnancy is associated with preterm delivery, low birth weight, and small head circumference (Kallen, 2000; Kyrklud-Blomberg & Cnattingins, 1998; Shah &Bracken, 2000). Also several studies reported that children born to mothers who smoke have deficits in IQ scores relative to those born to mothers who do not smoke (Fergusson & Lloyd, 1999; McGee & Stanton, 1994; Olds, Henderson, & Tatelbaum, 1994).

Kallen (2000) investigated the relation between maternal smoking during pregnancy and infant head circumference at birth. The study was based on all births of Swedish women in 1983- 1996. During the first prenatal visit (10-12 week gestation) each women was interviewed for personal information; smoking habits were among the information that was recorded. Three outcomes were considered: head circumference < 32 cm, head circumference < 2 standard deviation (-2 SD) below the expected for gestational age, and the ratio between the actual head circumference and the expected head circumference according to gestational age. The findings of the study showed a statistically significant association between maternal smoking and small head circumference.

Olds, Henderson and Tatelbaum (1994) conducted a study in a semirural county in New York State between April 1978 and September 1980. They interviewed 400 women at the 34th week of gestation, and at 6, 10, 22, 34, and 46 months of the child’s life. During the prenatal interview an assessment was made regarding smoking habits, diet, alcohol and drug use. Women, who smocked ten or more cigarettes per day during pregnancy, were less educated, lived in lower social class, and had fewer prenatal care visits compared to those who did not smoke. Children born to women who smoked heavily during pregnancy had IQ scores at 1 and 2 years of age that were 7 points lower and at 3 and 4 years of age that were 9 points lower than children born to women who did not smoke during pregnancy. These suggest the risk of neurodevelopment impairment among children born to women who smoke during pregnancy.

A Scandinavian study investigated if smoking during pregnancy may have an adverse effect on child’s mental and motor abilities (Trasti,Vik, Jacobsen, & Bakketeig, 1999). The sample consisted of 535 women (35% smokers, 64% non-smokers). Information about smoking habits and length of education was obtained from the women. And a total of 516 children were followed-up during the study, at age 13 months, children were assessed by using the Bayley Scales of infant development (BSID), and with the Wechsler Preschool and Primary Scales of Intelligence (WPPSI-R) at 5 years of age. There was no significant difference observed between children of smokers and non smokers on the BSID test, whereas the IQ scores obtained from WPPSI-R test were lower for children of smokers compared to those of the non smokers. There was a negative effect of smoking during pregnancy on cognitive function but it was not statistically significant.

Several studies have explored how poverty and low parental education are related to low levels of school achievement and IQ later in childhood (Alexander, Entwisle, & Danber, 1993; Duncan & Brooks-Gunn, 1997). Bradley and Corwyn (2002), found that socioeconomic status as an indicator of income, education, and occupation, was associated with better parenting and thus better cognitive outcome in children. Also maternal or parental education was found to be good predictors of later intellectual achievement (Davis-Kean, 2005).

Mayes and Bornstein (1995) conducted a study to investigate the relations between parental education and infant habituation and novelty recovery performance. The sample consisted of 94 term infants. Information on maternal education was classified as: 1) grade school, 2) high school graduate, 3) some college or college degree. Infants were seen at 3 months of age for habituation and novelty preference assessment. Infants who completed the habituation procedure (n= 76), showed no differences between boys and girls among the three maternal educational levels. According to the findings, early information processing is not correlated to maternal education. This suggests that other cognitive domains other than information processing, could still relate to parental education. Furthermore, genetic and behavioral factors may affect early measures of information processing.

Smoking during pregnancy can cause low birth weight and small head circumference (Kallen, 2000; Kyrklud-Blombeg & Cnattingins, 1998). Women who smoke during pregnancy tend to have less education, live in lower socioeconomic status. And children of smokers are more likely to have lower IQ Scores than children of non smokers (Olds, Hendrson, & Tatelbaum, 1994). Also smoking during pregnancy affected mental and motor abilities in infants and children (Trasti, Jacobsen, & Bakketeig, 1999). Although maternal education could contribute to cognitive function in infants and later in children, no significant association between maternal or parental education and information processing has been established (Mayes & Bornstein, 1995).

Breast feeding and Intelligence and Growth

Breast feeding during infancy has been associated with positive effect on later intelligence (Temboury, Otero, Polanco, & Arribas, 1994). Docosahexaenoic acid (DHA), which is a specific nutrient in human milk, was associated with significant improvement in mental, cognitive and motor development during infancy (Birch, Garfield, Hoffman, Uauy, & Birch, 2000). Furthermore, Slykerman et al. (2005) found breast feeding is particularly important for cognitive development of preschool children born small for gestational age. Children who were full term but small for gestational age and breastfed for longer than 12 months had higher IQ scores at 3.5 years of age than those who were not breastfed. Finally, a recent study based on a large randomized trial provided strong evidence that exclusive and prolonged breastfeeding improves children’s cognitive development as measured by IQ at teachers’ academic rating at age 6.5 years of age (Promotion of Breastfeeding Intervention Trial Study Group [PROBIT], 2008).

Conclusion

Infant growth is assessed by anthropometric measures, and evaluated by comparing individual measures to standard references, most common anthropometric measures used in infancy include (weight, length, and head circumference) (WHO, 2006). Physical growth contributes to cognitive and mental development (Johnson & Blasco, 1997; Vaughan, 1992). Also inadequate dietary intake could lead to deficits in growth and cognitive abilities (Mendez & Adair). Micronutrient supplements such as zinc and iron have been shown to improve growth and psychomotor development in infants and children. Infants exclusively breast fed for 6 months or more, appear to grow and develop intellectually better than those weaned at 3 months of age (Selvakumar & Vishnu Bhat, 2007). Also improved weight gain and head growth during infancy is significantly associated with higher IQ scores later in childhood (Edmond, Blair, Emmett, & Drewett, 2007; Gale, O’Callaghan, Bredow, & Martyn, 2006; Heinonn et al., 2008).

Later intellectual and cognitive abilities may be related to infant information processing (Dougherty & Haith, 1997; Rose, Feldman, Wallace, & McCarton, 1991; Thompson, Fagan, & Fulker, 1991). Attention and habituation are major components in information processing, and habituation is a potential predictor of intelligence later in childhood (McCall, 1994). Infants who process information more rapidly are more likely to have higher IQ (Kail, 2000), and infants who are longer and heavier perform better on visual information processing variables (Rose, Feldman, & Wallace, 1992).

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Maternal factors such as education, socioeconomic status, and smoking have been shown to influence cognitive outcomes later in childhood (Kyrklud-Blomberg & Cnattings, 1998; Shah & Bracken, 2000) Women who smoke during pregnancy are more likely to be less educated and thus have less income and low socioeconomic status which contributes to physical an cognitive deficits later in childhood ((Kallen, 2000; Olds, Henderson, & Tatelbaum, 1994; Trasti, Vik, Jacobsen, & Bakketeig, 1999). Also maternal or parental education and socioeconomic status have been found to be good predictors of intellectual achievement later in childhood (DavisKean, 2005), also low parental education levels have been shown to be related to low levels of school achievement later in childhood (Alexander, Entwisle,& Danber, 1993).

Finally, breast feeding has been shown to have a positive impact on later intellectual abilities (Temboury, Otero, Polanco, & Arribas, 1994). Children who continue breast feeding for prolonged time (> 12months), had better cognitive development and thus higher IQ scores (PROBIT, 2008).

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