This paper investigated the impact of early life course risk factors on the risk of subsequent childhood obesity. For many of the investigated risk factors, we found a raw effect in our study. Our results regarding maternal smoking are confirmed by literature. In studies where maternal smoking was assessed, this factor is quite consistently reported to be a marked risk factor. Similarly, high gestational weight gain was previously shown to be a risk factor not only for overweight and obesity of the child, but also of the mother. Sometimes this effect is attributed to the influence of maternal adiposity, however in our study, gestational weight gain showed an independent effect, also after control for parental BMI. We were well in accordance with the literature, regarding high birth weight which was also a risk factor in our study. However, when we adjusted for fat-free mass, this effect was completely removed, corroborating the hypothesis that birth weight is mainly influencing lean body mass and not fat mass. Since the BMI is correlated with fat mass and with fat-free mass, one could argue that adjusting for fat-free mass leads generally to over-adjustment. Since the difference of fat-free mass between normal-weight and obese children is much lower than that of fat mass, we argue that adjustment for fat-free mass should not be able to mask any true effect on fat mass. Moreover, we checked for each of the other investigated factors whether adjustment for fat-free mass influences the OR. This was, apart from the effect for birth weight, not the case.
Caesarian section is another putative risk factor for childhood obesity that came into focus rather recently. We also did find an elevated risk for obesity in the offspring, also after controlling for gestational gain, maternal BMI and breastfeeding initiation. However, in the final multivariate model Caesarian section was not of particular importance, when judged by the Wald statistic, and no longer statistically significant. The impact breastfeeding exerts on overweight is controversial since it is influenced by several other risk factors, as e.g. socioeconomic status, maternal BMI and maternal smoking. One of the rare RCTs showed no effects of feeding breast milk on the prevalence of obesity until the age of 15, suggesting that the protective effects found in other studies were due to statistical artifacts. On breastfeeding promotion in preterm babies in Belarus showed no difference regarding obesity prevalence later in life in the two arms of the study. Many previous studies have investigated either ever versus never-breastfeeding, while fewer have studied the duration of breastfeeding. Moreover, duration of breast-feeding is frequently modeled as months of breastfeeding assuming thus a strictly linear effect without any upper limit. In our study, we categorized the duration of breastfeeding and found a u-shaped effect. Interestingly, a similar pattern was found in a cohort study in Spain and in a secondary analysis of several cohorts examining the risk of duration of breastfeeding on cardiovascular risk factors. However, in our study, the effects were no longer statistically significant when controlling for confounding factors. As many other studies, we also found a strong impact of parental weight status on the risk of childhood obesity. In a full model that included all investigated early life course risk factors for which we could establish an effect, gestational weight gain, smoking during pregnancy, Caesarian section were found to be risk factors for later childhood obesity, and breastfeeding 4 to 11 months was found to be protective. However, after additional adjustment for parental BMI and parental educational status, only gestational weight gain remained statistically significant. Both, maternal as well as paternal BMI were the strongest risk factors in our study, and they confounded several of the investigated associations.
The current study has several limitations. It has to be kept in mind that the study regions are not representative of the respective emirates of UAE, let alone of the UAE population as a whole. Moreover, due to the high number of Abu Dhabi obese children in the study baseline survey (and the low number in Dubai and Sharjah), the case-control sample was highly unbalanced with regard to country. As country was a matching variable this should not bias the results. We did not test for different effects by country, as we did for sex and age group, since the sample size was too small for most emirates. For most of the associations that are hypothesized to be of a physiologic nature, like e.g. the association between Caesarian section and obesity, generalizability should not be compromised by the choice of regions. However, the association of social factors, like parental education, with childhood obesity is strongly dependent on context and therefore of limited external validity.
The investigated risk factors all rely on parental self-report. The validity of parental recall of most of the investigated factors is quite high, and we did not find much evidence for differential misclassification, which means that recall bias leads to risk attenuation rather than to distorted results. Recall bias might play a role in the lacking effect for introduction of solid food, as this factors has probably lowest validity among all investigated factors. Parental BMI is calculated using self-reported height and weight. Self-reported BMI is known to underestimate the true BMI, especially in the obese. Assuming a familial clustering of obesity this produces a likely differential misclassification for parental BMI in our cases and controls. This misclassification also leads to attenuated Odds Ratios for paternal and maternal BMI, since especially among cases the proportion of overweight and obese parents is underestimated as opposed to the control group.
Our results show that parental BMI is of particular importance for childhood obesity. The parental BMI is a factor or an indicator for very different causal pathways, belonging to categories of shared genetics, a shared obesogenic environment or the process known as early programming. Currently many obesity intervention programs target children. However, reducing the prevalence of parental overweight and obesity would not only help preventing childhood obesity, but would in general lead to an improved health not only of the children, but also of their parents. Beyond this, our results indicate that especially maternal weight gain should be monitored closely during pregnancy. The mechanisms and possible prevention targets of maternal diet and other social and behavioral factors before and during pregnancy as risk factors for parental BMI and high gestational weight gain as well as for obesity in the offspring should be the subject of further studies.
Handling of missing and incomplete data
The fitness levels and sex of the participants in each school will be compared with the class averages. This will provide an estimate of the generalizability of the findings in each school according to the class they represent. Where participants are found to differ from the class average, weighted scores will be presented along with crude prevalence figures. Missing data for individuals will be imputed using measures on individuals with comparable scores in other known values. For example, waist measurement may be imputed from a different participant with the same BMI and height. Results based on data without and with imputed fields will be presented.
Each participant will be asked to complete the physical activity questionnaire for adolescents (PAQ-A). The questionnaire is a seven-day recall on physical activity; a validated questionnaire that has been used widely in research. The questionnaire will be administered and completed during the same session as the BP. Instructions will be explained to participants prior to starting the test and they will be encouraged to complete the questionnaire alone. It will take approximately 20 minutes to complete the questionnaire.
Even though the health consequences of obesity are most commonly seen during adulthood, the underlying factors of these diseases could originate during childhood. Evidence is now emerging that obesity-driven type 2 diabetes might become the most common form of diabetes in adolescents within the next ten years. It is therefore vital to know exactly how early the health consequences and risk factors for these serious diseases occur, and how early the can be detected if they are to be addressed successfully.
In recent years, childhood obesity has become a world wide issue with increasing poor dietary habits together with inactivity being associated with rising levels of obesity in children. Many studies have convincingly shown that overweight and obesity during adolescence is a determinant of a number of CVD risk factors in adulthood. Results from longitudinal studies have shown that adolescent adiposity tracks moderately into adulthood, implying that preventing obesity during childhood may be advantageous to health in later life. Patterns of fat distribution have also been shown to influence CVD risk. It has been discovered by many studies that abdominal obesity better predicts CVD risk compared to overall obesity. It seems that the elevated risk of abdominal obesity is related to the visceral fat that is stored around the internal organs.
Identifiable Risk Factors
Many risk factors have been recognised as contributors towards the development of CVD. These include unhealthy diets, physical inactivity, obesity, hypertension and hypercholesterolemia. More recently other risk factors have been shown to contribute towards the development of CVD, notably, elevated concentrations of fibrinogen (Fg), C-reactive protein (CRP), interleukin-6 (IL-6); and reduced levels of adiponectin (high molecular weight). Fibrinogen is the main coagulation protein in plasma and thus promotes activities such as platelet aggregation and increased blood viscosity. Elevated fibrinogen levels have been found in obese individuals and thus may have an indirect effect on CVD through levels of adiposity. Fg is also thought to be responsible for changes in other acute phase proteins, notably CRP. These are released as a result of active inflammation (an underlying cause of CVD), moreover, it appears that the inflammatory cytokine IL-6 is the underlying stimulator of these acute phase proteins. Adiponectin is a protein hormone that regulates the metabolism of lipids. It is the most abundant hormone released from fat cells and has been suggested to be anti-inflammatory. Concentrations of this protein have been found to be low in obese individuals, thus contributing to the pathogenesis of CVD and increased inflammation. It can therefore be seen that CVD risk may not solely be due to one factor but a combination of many which may originate through behavioural and lifestyle factors.
Socioeconomic status, ethnicity and other factors
It has also emerged that CVD risk may differ between people of differing socioeconomic status (SES), this is true for both developed, and developing countries. In developed countries, epidemiological evidence illustrates that SES is inversely linked with CVD morbidity and mortality; however, evidence of this relationship in developing countries, is sparse. This may suggest that behavioural factors have a higher influence on disease risk factor profiles that genetic factors. As previously highlighted, there is growing evidence that the risk for CVD originates during childhood, indeed, research has found that a very low or very high birth weight is associated with increased risk of CVD. Furthermore, there is evidence that birth weight varies according to ethnic background and thus individuals with Asian or African origins could be at a higher genetic risk of developing obesity and CVD. This protocol outlines the methods to examine lifestyle and behavioural factors associated with childhood obesity, diabetes and CVD risk in school children aged 11â€“13 years.
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