A lot of research has identified the risk factors that increase the chance a newly born baby will become obese as a child or adult. Identifying which babies are at risk, may help develop various strategies for the more vulnerable babies. This would enable limited resources to be directed to the newborn children most at risk and so help prevent obesity developing.
A new tool has been developed as a formula that calculates a baby's risk of becoming obese when they are born. Entering a set of simple information about the parents weight and height as a BMI index, information about the mother's occupation, birth weight, whether the mother smoked during pregnancy and other data.
The tool was developed by a group of researches who examined a relatively large data set of more than 4032 people born in Finland in1986 to examine correlations between various factors and body weight over the last 25 years. Italian and US data were also used to develop three equations.
They then used the equations to develop an automatic calculator of risk for childhood obesity, showcased as an Excel application. The results for the model were very encouraging with a 75% success rate between the prediction and the incidence of obesity for the most at risk group.
This article discusses the causal factors linked to the risk of obesity, how the formula was developed and how it can be used to tackle the alarming growing rate of obesity in children.
The individual risk factors for childhood obesity are well known, and a lot of previous research has confirmed the correlations. Most of these risk factors were used in developing an overall obesity risk formula in the published research. A New Scientist publication discusses the implications of the findings. The key risk factors discussed in the publications are:
Research has shown a strong correlation between the BMI of the parents and child's BMI. This is probably the strongest predictor. Children of obese parents are more likely to be overweight or obese than children whose parents are either a healthy weight or underweight (about 40% compared with 20%). There are exceptions and the child's genetics may not necessarily mirror that of the parents through mutations and genetic recombinations. This can mean that there may be active genes present in the baby, but not in the parents. This may increase a child's obesity risk.
There is a lot of research that shows that women who put on too much extra weight during pregnancy tend to have babies with a higher birth weight than would be expected from their genetics. This appears to increase the child’s risk for long-term obesity.
Researcher reported in The Lancet looked for correlations by examining multiple single pregnancies for the same mother. This excluded genetics and allowed the effect of the mother's weight gain to be assessed. The conclusions from this study were:
Research has consistently shown a higher prevalence of overweight and obese children of mothers who smoked during pregnancy. The increased probability ratio was 1.43 for being overweight and 2.06 for obesity. This occurred, despite the fact that mothers who smoke tend to have babies with smaller birth weights. It has been suggested that this could be explained by a catch-up in growth in the first years of life. Children that demonstrate a catch-up in growth had a higher BMI and showed other signs of being overweight. Rapid catch-up growth occurs in many children of mothers who smoked during pregnancy.
However, despite much research, the catch-up growth has not been shown to explain the effect of maternal smoking during pregnancy. Whatever the cause, there is a clear correlation between smoking and the risk of children being obese.
There is evidence that for certain groups children with fewer siblings have a higher risk of obesity. However parenting style and family status also has other effects on the risk of childhood obesity.
There have been many studies which show that parents who are more stressed, or who regard themselves as stressed are more likely to have obese children. The severity and number of stressors are both important. Larger families often means more stress. The main causes of stress are health issues and financial strain, with links to the children's physical activity.
An Australian study using data on 4–5 year old children found that the father's parenting style was more important. Children with more controlling fathers had reduced risk of being overweight (in a higher BMI category).
Research has shown a strong correlation between low socio-economic status in early life and increased fatness in adulthood. In adults, education has often been linked to health outcomes, including adult obesity. For example a recent study in Australia of children at ages 4–5 years and 6–7 years found that the children of mothers worked part-time, watched less TV and were less likely to be overweight than children of mothers who were not employed or who worked full-time.
There is growing evidence that birth weights over 4 kg increases the risk of obesity in childhood and adolescence. The risk of obesity is twice as large for babies in the highest birth weight categories.
Shown below are examples of the risk predictions for various categories.
Example A shows the influence of parental BMI on risks for the least likely category where there is:
The maximum predicted risk of obesity is about 10% when both parents have a BMI of 35.
Example B shows the influence of parental BMI on risks for a moderately at risk category where there is:
The maximum predicted risk of obesity is about 30% when both parents have a BMI of 35. For parental BMIs of 30 the risk is about 10%, and for BMIs of 25 its about 2%.
Example C shows the influence of parental BMI on risks for a most at risk category where there is:
The maximum predicted risk of obesity is about 77% when both parents have a BMI of 35. For parental BMIs of 30 the risk is about 40%, and for BMIs of 25 its about 15%.
The study provides a predictive tool for evaluating the risk of babies developing childhood obesity using data available at birth. The tool uses readily available information on traditional risk factors for newborns. The calculators and require minimal time to calculate and could be easily added to existing record systems. The researchers hope that it will identify the children most at risk encourage health authorities and parents to take early action to improve their children's health. It could enable scarce resources to be better directed at the most vulnerable children.
Although the new formula approach to predicting the risk of childhood obesity has received widespread praise, several potential problems have been identified: