Eriksson, J. G.

Fall, C. H. D.

Osmond, C.

Victora, C.

PubMed

Helsingin yliopiston digitaalinen arkisto

Commentary : Disentangling the contributions of childhood and adult weight to cardiovascular disease risk

Fall, C.

Eriksson, J.G.

Southampton (e-Prints Soton)

CommentaryCommentary: Disentangling the contributionsof childhood and adult weight to cardiovasculardisease riskC.H.D. Fall,1,* C. Victora,2 J.G. Eriksson3 and C. Osmond11MRC Lifecourse Epidemiology Unit, University of Southampton, Southampton, UK, 2UniversidadeFederal de Pelotas, Pelotas, Brazil and 3Institute of Clinical Medicine, University of Helsinki, Helsinki,Finland*Corresponding author. MRC Lifecourse Epidemiology Unit, Southampton General Hospital, Tremona Road, SouthamptonSO16 6YD, UK. E-mail: chdf@mrc.soton.ac.ukAccepted 17 May 2015When Abraham et al. embarked on the study described intheir 1971 paper,1 it was already known that high adultbody weight is a risk factor for cardiovascular disease(CVD) and that childhood and adult weight tend to be pos-itively correlated. They wanted to find out if childhoodweight makes an independent contribution to CVD riskover and above its correlation with adult weight. They suc-cessfully re-traced  700 men who attended schools inHagerstown, MD, USA, between 1923 and 1928 and hadweight recorded at the age of 9–13 years, and measuredadult weight, CVD risk markers (blood pressure, glucoseand lipids) and four morbidities (‘diabetes’, ‘hypertensivevascular disease’, ‘arteriosclerotic heart disease’, ‘cardio-vascular renal disease’). They analysed the data by tabulat-ing these outcomes across categories of childhood andadult weight, and then cross-tabulating them according toboth childhood and adult weight simultaneously. Thismust have been one of the first studies to investigate therelationship of an early life characteristic to adult CVD.They started out with three possible ‘causal pathways’in mind (Figure 1): (i) childhood weight is positively associ-ated with adult weight, but most of the variability in CVDrisk is explained by adult weight; (ii) childhood weight ispositively associated with risk, and adult weight has noindependent additional effect; or (iii) both childhood andadult weight are positively and independently associatedwith risk, or in other words a component of higher child-hood weight that is not encapsulated in adult weight isassociated with increased risk. A fourth possibility (iv) wasthat childhood and adult weight are independently associ-ated with risk but in opposite directions, such that lowerchild weight, or a component of it, is associated withincreased risk.Abraham’s analysis confirmed a strong correlationbetween childhood and adult weights. Children in thehighest or lowest weight categories tended to remain in thesame categories as adults. The risk for most of the CVDoutcomes increased with increasing adult body weight.Though not so strongly, most of the outcomes alsoincreased with increasing childhood weight. However, inthe cross-tabulations, they found something unexpected.The highest morbidity appeared to be among those menwho were lightest as children but heaviest as adults.One problem with their analysis was that, because ofthe correlation between childhood and adult weight, thelightest child-heaviest adult cell in these tables containedvery few men (N< 10). They also tried grouping the menaccording to the degree of change in weight categorybetween childhood and adulthood. Again, small numbersin key cells substantially defeated them, but they concludedthat the highest morbidity was in men who had the greatestupward change in body weight. They could not test mostVC The Author 2016; all rights reserved. Published by Oxford University Press on behalf of the International Epidemiological Association 1This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permitsunrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.International Journal of Epidemiology, 2016, 1–6doi: 10.1093/ije/dyw157Commentary Int. J. Epidemiol. Advance Access published August 7, 2016 at University of Southampton on August 10, 2016http://ije.oxfordjournals.org/Downloaded from of these associations statistically with the tests available atthat time, due to small numbers and limited software.However, they concluded that the overweight adult whohad been a below average weight child had the highestadult CVD morbidity, and that morbidity was ‘a functionof adult acquired ‘fatness’.’ To paraphrase their conclu-sions: fat ‘thin’ men, who had been thin children andbecame fat adults, were at higher risk than fat ‘fat’ men,who had been fat all their lives.How would we tackle this question today? LikeAbraham et al., we might initially do some cross-tabulations. Despite the problems of low numbers inextreme cells, this is a good way of getting to know suchdata, but is too often left out nowadays in the rush to con-struct a regression model. A similar method, that adds fur-ther information, is to create contour plots, which can bedesigned in such a way that contours only appear wherethere are sufficient data. Figure 2 shows hypothetical con-tour plots for the scenarios depicted in Figure 1.We would probably then go on to use multiple regres-sion, and examine associations between childhood weightand adult risk unadjusted and then adjusted for adultweight. These two analyses would answer two separatequestions. The unadjusted model is ‘forward-looking’.Only childhood weight is known, and we are like a paedia-trician with a child standing in front of us, trying to predictits future CVD risk. The association between childhoodweight and the outcome is therefore the total or ‘net’ effectof childhood weight, including any effect acting through itspositive association with adult weight and any positive(Figure 1c) or negative (Figure 1d) effect that does not actthrough adult weight. The second question is ‘backward-looking’. We now know adult weight, and are like an adultphysician with the person standing in front of us, trying toisolate the contribution made by his or her childhoodweight. The effect of childhood weight in a model adjustedfor adult weight will reflect its independent contribution.It sounds simple, but in reality it is not easy to interpretthe results. We have been grappling with similar problemsover many years, in relation to ‘developmental origins ofadult health and disease’ (DOHaD) concepts, trying to dis-entangle associations between birthweight, adult bodymass index (BMI) and adult CVD. One issue is that whenthere are only two measurements to predict the outcome(childhood weight and adult weight), it is impossible toseparate the effects of childhood weight, adult weight andFigure 1 Alternative ‘causal pathways’ linking child and adult weight to CVD morbidity. Heavy black arrows represent main independent effects;dashed arrows represent correlations, but no/little independent effect on the outcome2 International Journal of Epidemiology, 2016, Vol. 0, No. 0 at University of Southampton on August 10, 2016http://ije.oxfordjournals.org/Downloaded from the growth that led from one to the other, which is just thedifference between them; a greater difference could resultfrom lower early weight or higher later weight, or both. Italso gives no information about the importance of the tim-ing of weight change. When both childhood weight andadult weight are included in the model, an inverse associa-tion between childhood weight and morbidity could meanthat morbidity is associated either with low childhoodweight or with a greater change in weight between child-hood and adulthood. This was pointed out by Lucas et al.in relation to the inverse association between birthweightand adult blood pressure.2 They argued that the inverseassociation between birthweight and adult blood pressureafter adjusting for adult BMI indicates that acceleratedpost-natal growth, rather than programming by fetalundernutrition, could be a cause of hypertension. The truthis that it is impossible to distinguish between these twopossibilities when these are the only data available. This isconceptually identical to the ‘age, period, cohort’ problemin epidemiology, where it is known to be impossible todisentangle the effects of year of birth, age at death andyear of death on disease mortality rates.3A helpful step is to examine whether the associationbetween adult weight and morbidity differs at different lev-els of childhood weight. If so, then there is a statisticalinteraction, which suggests that childhood weight modifiesthe effects of adult weight. An example, using risk con-tours, is shown using data from the 1923–33 Helsinki birthcohort study from Finland (Figure 3). In this example, theearlier measure is ponderal index at birth, and the latermeasure is BMI at age 11 years. A higher 11-year BMI isassociated with an increased risk of adult coronary heartdisease in adult life, but the increase in risk is greateramong men who were thin (had a low ponderal index) atbirth than among men who had a high ponderal index atbirth. This gives more confidence that thinness at birth isintrinsically important in relation to later CVD.Abraham et al. would have benefited from having dataat intervening time points between childhood and adultlife. Statistical methods have been developed to use serialFigure 2 Hypothetical contour plots corresponding to the ‘causal pathways’ shown in Figure 1. Increasing thickness of the contour lines representsincreasing riskInternational Journal of Epidemiology, 2016, Vol. 0, No. 0 3 at University of Southampton on August 10, 2016http://ije.oxfordjournals.org/Downloaded from childhood measurements to isolate associations of size andgrowth at specific ages with later outcomes. For example,conditional variables are standardized residuals derivedfrom regressing size (e.g. weight) at any age on previoussize measurements, producing independent, uncorrelatedvariables representing greater or lesser size at each agethan expected, given earlier size.5,6 They overcome theproblems caused by the strong correlations between serialmeasures of body size in an individual.The conditional method can be used to take both ‘for-ward-looking’ and ‘backward-looking’ approaches. Figure4 shows data from the 1934–44 Helsinki birth cohortstudy, in which birthweight, weight at 2 years and BMI at11 years were available. Adult BMI was based on actualmeasurements, and hypertension was defined by use ofmedications.7 In the forward-looking analysis, withoutadult weight (Figure 4a), lower birthweight and higherconditional BMI at age 11 years were associated with anincreased risk of adult hypertension. The data could alsobe interpreted as showing that lower birthweight andgreater weight gain between 2 and 11 years were associ-ated with adult hypertension. Conditional weight at 2years (or weight gain between birth and 2 years) was unre-lated to hypertension. These are the ‘net effects’ of earlysize.Now, if adult BMI is included in the model (Figure 4b)we get the ‘backward-looking view. Knowing adult BMI,conditional BMI at 11 years is no longer positively relatedto hypertension. This suggests that the positive associationof conditional 11-year BMI in the forward-looking analysiswas acting on adult hypertension risk mainly through adultBMI. The associations of 2-year conditional weight and11-year conditional BMI with hypertension have bothbecome, like that of birthweight, negative or inverse. Weconclude that a component of 11-year BMI tracks throughinto adult BMI and is a risk factor for adult hypertension.But there is also a component of weight at 11 years, 2 yearsand birth whereby a lower body weight is an independentrisk factor for later hypertension. These conclusions aresubstantially similar to those reached by Abraham, butgive more information about weight at different ages inFigure 3 Hazard ratios for death from coronary heart disease for menborn in Helsinki 1924–33 according to ponderal index at birth and BMIat age 11 years. Arrows indicate average values.Source: Eriksson et al. 19994Figure 4 Birthweight and conditional measures of childhood weight and BMI as predictors of adult hypertension in the 1934–44 Helsinki birth cohort;y, years4 International Journal of Epidemiology, 2016, Vol. 0, No. 0 at University of Southampton on August 10, 2016http://ije.oxfordjournals.org/Downloaded from childhood. The conditional growth approach has beenextended to include not only multiple ages, but also multi-ple measures of body size. For example, Adair et al. haveinvestigated independent effects of childhood soft tissueand linear growth on adult human capital and CVD riskmarkers;6 And Krishnaveni et al. have investigated inde-pendent effects of earlier growth in height, adiposity andlean body mass on CVD risk markers in adolescence.8 Thelatter study suggested that the positive net effect of child-hood weight gain on later blood pressure was mediated bygrowth in adiposity, and not by linear or lean-tissuegrowth. Other modelling approaches, in addition to theconditional approach, have been recently reviewed.9Apart from the above considerations, a full interpreta-tion of epidemiological data like that of Abraham et al.would also require evaluation of confounding (the possibil-ity that both childhood weight and CVD outcomes are cau-sally related to some other factor, such as: lowsocioeconomic status); selection bias (the possibility thatthe association between childhood weight and CVD dif-fered between people who were included in the analysisand those who were not); and the influence of missing dataand measurement error. In relation to selection bias,Abraham et al. compared childhood weights between thosewho were and were not re-traced/studied, but could nothave answered the more critical question as to whether theassociation between childhood weight and outcomes dif-fered between these two groups.Abraham et al., in their discussion, speculated about thebiology (‘different types of fat’) underlying their results. Itis worth bringing to bear on this the greater understandingof the biology linking factors in early life to adult disease,that has come from recent research. In the DOHaD world,nobody believes that weight per se (birthweight or child-hood weight) ‘causes’ anything. As Gillman wrote in rela-tion to birthweight, weight is not a ‘monolith’;10 weight ismade up of multiple components (muscle, bone, fat, differ-ent organs and tissues) whose development is influenced bymultiple environmental factors. It is the effects of these fac-tors on individual developing tissues, and the end result interms of their structure and function across the life course,which are thought to cause later disease.Another effect of the same environmental influencesmay be, though not invariably, to alter body weight.Hence we can start to see a scenario whereby some mecha-nisms linking early life weight to adult CVD may differfrom the mechanisms linking early life weight to adultweight. One component of early life weight may reflect anexposure that causes an individual to develop later CVD,whereas another may reflect the tracking of weight intoadult life, influencing CVD risk in a different way. To fleshthis out with an example, adult blood pressure could behigh because an individual has reduced numbers of neph-rons in the kidneys, resulting from a specific nutritionaldeficiency in the mother when the fetal kidney was devel-oping.11 This could also reduce birthweight. However,fetal adiposity may be unaffected by this maternal defi-ciency, and the newborn has a normal quantity of bodyfat. The usual relationship between newborn adiposity andadult adiposity pertains, and partly determines adultadiposity and body weight, which are positively associatedwith blood pressure. One component of birthweight (kid-ney weight) would be negatively associated with adultblood pressure, but another (body fat) would be positivelyassociated with blood pressure. The persuasive evidencethat early life factors are important in the causation ofadult CVD rests not so much on the associations with earlylife body weight as on new observations that these associa-tions stimulated. Animal experiments have shown thatadult blood pressure, insulin sensitivity, glucose toleranceand body composition can be influenced by manipulatingthe mother’s diet during pregnancy, and that these effectsare matched by changes in individual tissues and metabolicpathways.12 We are far from understanding early lifeeffects at this level in humans, and Abraham et al. wereeven further from that but their speculations, about‘acquired’ fat being different from ‘endogenous’ fat, high-light the need to delineate underlying mechanisms in orderto make sense of the body weight associations.The article by Abraham et al. reminds us how far wehave come in terms of the ‘tools’ of life course epidemiol-ogy. They had to trace their study subjects manually fromthe school records, using telephone directories, service listsand death certificates. They traced an impressive 717 outof 1963 schoolchildren, but paid a price in laboriousnessand missing data. The morbidities they studied were notdefined, and some of them are all but unrecognizablenow. We benefit today from computerized records, growthstandards, standardized disease definitions and a far moresophisticated statistical armamentarium. Theseadvances have revolutionized what we can learn fromdata, but cannot deal with all the limitations of the datathemselves.References1. Abraham S, Collins G, Nordsieck M. Relationship of childhoodweight status to morbidity in adults. HSMHA Health Rep1971;86:273–84.2. Lucas A, Fewtrell M, Cole T. Fetal origins of adult disease: thehypothesis revisited. BMJ 1999;319:245–49.3. Clayton DG, Schifflers E. Models for temporal variation in cancerrates. II: Age-period-cohort models. StatMed 1987;6:469–81.4. Eriksson JG, Forsen T, Tuomilehto J, Winter PD, Osmond C,Barker DJP. Catch-up growth in childhood and death from cor-onary heart disease: longitudinal study. BMJ 1999;318:427–31.International Journal of Epidemiology, 2016, Vol. 0, No. 0 5 at University of Southampton on August 10, 2016http://ije.oxfordjournals.org/Downloaded from 5. Gale CR, O’Callaghan FJ, Bredow M, Martyn CN; AvonLongitudinal Study of Parents and Children Study Team. The in-fluence of head growth in fetal life, infancy and childhood on in-telligence at the ages of 4 and 8 years. Pediatrics2006;118:1486–92.6. Adair LS, Fall CHD, Osmond C et al.; the COHORTS group.Associations of linear growth and relative weight gain duringearly life with adult health and human capital in countries of lowand middle income: findings from five birth cohort studies.Lancet 2013;382:525–34.7. Eriksson JG, Forse´n TJ, Kajantie E, Osmond C, Barker DJ.Childhood growth and hypertension in later life. Hypertension2007;49:1415–21.8. Krishnaveni GV, Veena SR, Srinivasan K, Osmond C, Fall CHD.Linear growth and fat and lean tissue gain during childhood:associations with cardiometabolic and cognitive outcomes inadolescent Indian children. PLoS One 2015;10:e0143231.9. Wills A, Tilling K. Modelling repeat exposures: some examplesfrom life course epidemiology. In: Kuh D, Cooper R, Hardy R,Richards M, Ben-Shlomo Y (eds). A Life Course Approach toHealthy Ageing. Oxford, UK: Oxford University Press, 2014.10. Gillman MW. Epidemiological challenges in studying the fetalorigins of adult chronic disease. Int J Epidemiol2002;31:294–99.11. Luyckx VA, Bertram JF, Brenner BM et al. Effect of fetal andchild health on kidney development and long-term risk of hyper-tension and kidney disease. Lancet 2013;382:273–83.12. Warner MJ, Ozanne SE. Mechanisms involved in the develop-mental programming of adulthood disease. Biochem J2010;427:333–47.6 International Journal of Epidemiology, 2016, Vol. 0, No. 0 at University of Southampton on August 10, 2016http://ije.oxfordjournals.org/Downloaded from 

Commentary: Disentangling the contributions of childhood and adult weight to cardiovascular disease risk

Commentary : Disentangling the contributions of childhood and adult weight to cardiovascular disease risk

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