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Record W1574595025 · doi:10.5772/33112

Can Breastfeeding Reduce the Risk of Childhood Obesity?

2012· book-chapter· en· W1574595025 on OpenAlexaff
Laurie Twells, Leigh Anne, Valerie Ludlow

Bibliographic record

VenueChildhood Obesity · 2012
Typebook-chapter
Languageen
FieldMedicine
TopicObesity, Physical Activity, Diet
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsBreastfeedingChildhood obesityMedicineObesityEnvironmental healthPediatricsEndocrinologyOverweight

Abstract

fetched live from OpenAlex

Childhood Obesity 54 how it differs in constituents and method of delivery from infant formula and 3) to summarize the high level research (i.e., systematic reviews and meta analysis) that examines the relationship between breastfeeding and childhood obesity. Definition of obesity/childhood obesityObesity is defined as excessive accumulation of fatty tissue that can hinder the effective functioning of the human body (WHO, 2000).As a result, health problems may ensue, leading to a poor quality of life and decreased lifespan.When seen in childhood, the issues of obesity can be far more extensive, as health problems occur earlier in life and place a burden on the child, family, community and health care system.The calculations that determine obesity in children are not as clear-cut as one might think.It has been suggested that the standard BMI (weight (kg)/height (m 2 )) should not be used as the sole measurement, since children's height and weight vary greatly during major growth spurts (Logue & Sattar, 2011).In addition, waist circumference should be included in the calculation, as central obesity is considered a major risk factor for Type 2 Diabetes Mellitus (DM) and Coronary Artery Disease (CAD) (Kovacs et al., 2010;Steene-Johannessen et al., 2010; Stevens et al., 2010).Currently, the classification of overweight and obesity varies among major agencies (Table 1).The US Centre for Disease Control (CDC), International Obesity Task Force (IOTF) and WHO base their determinations on different populations during different time periods.The CDC surveyed American children from 1963-1994 (National Centre for Health Statistics, 2002).In 2000, the IOTF based their criteria on children from US, Brazil, Great Britain, Hong Kong, Netherlands and Singapore (Cole et al., 2000).In comparison, in 2006, the WHO collected data on children from US, Brazil, Ghana, India, Oman and Norway (WHO Multicentre Growth Reference Study Group, 2006) who lived in optimal health conditions, and were exclusively/primarily breastfed for up to 4 months, with some breastfeeding extending until 12 months of age.Using information and extrapolating for children, the cutoff points are given below.These guidelines should be considered when reviewing data on obesity rates in various reports, as different definitions provide varying prevalence estimates.For example the CDC cut-offs tend to report a much higher prevalence of obesity compared to the other growth references (Twells & Newhook, 2011).Another point to consider is whether or not growth charts for infants (based on age) should be modified based on infant feeding method.Formula fed children grow faster than those who are breastfed (Dewey, 1998;Kramer et al., 2004;Victora et al., 1998), possibly due to the higher protein content of formula versus breast milk, which should be considered when referring to patterns of infant growth (Alexy et al., 1999;Koletzko et al., 2005).Even with guidelines, such as those from CDC, IOTF, and WHO, it is suggested that childhood overweight and obesity are often underdiagnosed.The development of a better diagnostic tool may be beneficial, since it is within childhood that interventions might prove to be more effective (Benson, 2009).US CDC IOTF WHO Overweight >85 th ≥91st >84th Obese ≥95th ≥99th >97.7thTable 1.Childhood obesity rankings (percentile) www.intechopen.comCan Breastfeeding Reduce the Risk of Childhood Obesity? 55 Global prevalence of childhood obesityThe prevalence of obesity is increasing throughout the world, particularly among children.In 2008, 1.5 billion adults (20 and older) were overweight; comparatively in 2010, 43 million children under the age of 5 were overweight (WHO, 2011a).Human obesity develops as a result of interactions between genes, environmental factors, and behaviour.It has been found that genetic disorders such as Bardet-Biedl and Prader-Willi syndromes can directly cause obesity and, as a result of the study of twins and adopted children, heritability estimates of 40-70% have been reported (Farooqi & O'Rahilly, 2006).The relatively recent rise in global obesity rates over the last 25 years however suggest that mainly environmental factors are involved.Current environmental and lifestyle issues (social, behavioural, cultural and community) which promote an imbalance between energy intake and energy expenditure are purported to be the main causes of increasing obesity rates.The sedentary lifestyle and high-caloric diet seen in many populations have led to alarming rates of childhood obesity.As shown in Figure 1, since 1976 the United States has seen a surge in obesity rates among all age groups of children; particularly, obesity rates among preschool children aged 2-5 increased from 5.0% to 10.4% over the 1976/1980 to the 2007/2008 time span (Ogden & Carroll, 2010).Similar increases have been reported in regional populations in Canada (Canning et al., 2007).

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0090.002

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.012
GPT teacher head0.228
Teacher spread0.216 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations4
Published2012
Admission routes1
Has abstractyes

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