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Education and BMI: a genetic informed analysis

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Abstract

Nowadays obesity is one of the key health risk factor. The negative association between obesity and education is widely known and, although the mechanisms underlying this relationship are far less clear, it is often interpreted as a causal one. In this work we aim to investigate the relationship between education and Body Mass Index (BMI) applying behavioral genetics models to data coming from the Italian Twin Registry (more precisely, a subsample of 619 pairs of Mz and 335 pairs of same sex Dz twins). Variance decomposition models reveal that for both education and BMI heritability should not be overlooked. Above all, Cholesky decomposition model shows that around 30 % of the covariance between BMI and Title Page with all Author Contact Details education is due to common genetic factors. This result may suggest to rethink the relationship between education and BMI.

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Notes

  1. Body Mass Index, calculated by dividing the individual’s weight in kilograms by the square of his/her height in metres, is a measure of obesity commendable for its simplicity and generally reliable for adults (\(>\)18 years). On the basis of medical-health criteria, the WHO defines an adult person as (a) underweight if s/he has BMI less than 18.5; (b) normal weight if s/he has a BMI between 18.5 and 24.9; (c) overweight if s/he has a BMI between 25 and 29.9, and (d) obese if s/he has a BMI greater than 30.

  2. Some recent studies with the Instrumental Variable method (IV), which supposedly allows to correctly study causal effects, have strengthened the hypothesis of a causal relationship between education and obesity (Brunello et al. 2009; Grabner 2008).

  3. See the Journal of Twin Research and Human Genetics (2002, vol. 5, issue 5 and 2006, vol. 9, issue 6) for two special issues on twins registries around the world. As far as the Italian Twin Registry, see the article by Stazi and colleagues in Twin Research 2002, vol. 5, issue 5, pp. 382–386.

  4. The database used was obtained after a preliminary purging which eliminated the outlier pairs through examination of the distribution of the distances between the BMIs of the twins in the same pair (see Neale and Cardon 1992); in our case four pairs were removed from a total of 1,245 pairs. Finally, we considered only twins aged over 25 years in order to have twins who had completed full-time education.

  5. Notwithstanding the fact that people tend to underestimate their weight and overestimate their height, generally self-reported measures of weight and height are considered to be quite reliable (Boström and Diderichsen 1997; Roberts 1995). It seems that biases in these self-reported measures are linked to personal characteristics like gender, age and real weight and height rather than to individual SES (Roberts 1995; Rowland 1990; Shields et al. 2008). Hence, this kind of bias would affect prevalence estimate but would not significantly affect the relationship between SES and obesity (Roberts 1995).

  6. The fact that heritability is a population-specific statistic implies that caution is necessary when comparing heritability estimates obtained in distinct populations. It is above all necessary to avoid misleading conclusions concerning the nature of differences among groups: if two groups have very different values on a highly heritable trait, this does not entail that such observed differences are caused by genetic factors (Visscher et al. 2008).

  7. In this study both BMI and education are measured as continuous traits. It is true that BMI is not the same that obesity (a dichotomous trait) but the so called “threshold traits” require different methods of analysis (for instance, the DeFries–Fulker analysis of extreme, cf. Plomin 2001). In the case of BMI is not clear whether a dichotomization would be appropriate: the problem is to understand whether obesity represents, from an etiological point of view, a distinct phenomenon from BMI or it is just the extreme of a continuum (hence implying quantitative but not qualitative differences). Often a threshold is artificially imposed by a the scientific community for convenience, in order to easily identify the ones to be treated. However, many studies actually measure traits like depression on continuous scale because the dichotomization of such traits typically result in a loss of statistical power to detect genetic effects (Anholt and Mackay 2009; Neale et al. 1994). For these reasons we choose to consider BMI but we need to be aware that the heritability of BMI may not coincide with that of obesity.

  8. RMSEA is an absolute measure of fit, that presumes that the best fitting model has a fit of zero. Usually the value needs to be below 0.06 to indicate a good fit (Bollen 1989).

  9. As Plomin (2001) point out, it is desirable to maintain the variance component of unshared environment in each model because the measurement error is included in it.

  10. The AIC (Akaike Information’s Criterion) is a comparative measure of fit. In Amos is calculated as \(\chi ^{2}\)—2q where q stands for the number of parameters of the model. Lower values indicate a better fit.

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Della Bella, S., Lucchini, M. Education and BMI: a genetic informed analysis. Qual Quant 49, 2577–2593 (2015). https://doi.org/10.1007/s11135-014-0129-1

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