Adverse childhood socioeconomic position is associated with increased coronary heart disease (CHD) risk in later life,(1,2,3) and it has been suggested that this may, at least in part, be mediated by adiposity and its associated adverse metabolic and vascular changes. As well as being associated with future risk of CHD, individuals from poorer socioeconomic backgrounds in childhood have also been found to be more obese, more dyslipidaemic and more insulin resistant in adulthood than those from higher socioeconomic groups.( 4,5) Greater BMI and obesity in childhood and early adulthood have been shown in two very large studies to predict CHD risk in adulthood,( 6,7) but it is unknown whether this association is because of the tracking of BMI from childhood to adulthood (in which case interventions to prevent/treat obesity in adulthood might be an appropriate option) or whether permanent changes in metabolic and vascular function occur in childhood as a result of greater adiposity (in which case interventions in childhood would be paramount). A major problem with life course studies that identify associations of risk factors in childhood / early adulthood (socioeconomic position or BMI in the examples above) with future risk of adult diseases such as CHD, is that by definition the populations being studied experienced their childhood at least 5-6 decades ago and the relevance of these findings to contemporary populations of children, who live in very different circumstances, is unclear.

If the association of childhood adverse socioeconomic position with adult CHD risk from older birth cohorts is at least in part because those from poorer backgrounds in childhood were more obese and therefore experienced adverse metabolic consequences which resulted in long-term damage and increased susceptibility to future CHD risk, and if socioeconomic differentials in contemporary children were likely to have similar long term effects on future CHD risk, then one would expect to find socioeconomic differentials in adiposity and its associated adverse metabolic outcomes in contemporary cohorts of children.

Studies in contemporary populations of children from high income countries have shown socioeconomic differentials in adiposity, with those from more deprived socioeconomic backgrounds being more adipose.( 8,9) For example, in the European Heart Health Study children aged 9 and 15 from Denmark (high income country) whose parents were least well educated and who came from houses with the lowest income had larger waist circumferences and skinfold thicknesses and greater BMI than those from more educated parents and higher income families, though in two lower income countries (Estonia & Portugal) associations were in the opposite direction.(8) In that study socioeconomic differentials in lipids (HDLc, LDLc and triglycerides) and circulating insulin were in the directions that one would anticipate from the associations with adiposity, but few other studies have been able to examine socioeconomic differentials in a wide range of metabolic markers in a contemporary population of children. Of particular relevance to this application, it has also been show in ALSPAC that there is a clear social gradient (based on head of household social class at birth) of fat mass (with children of higher social class having a lower fat mass), but no gradient in lean mass.(9) Comparing these results to published studies using BMI the authors concluded that social inequalities in childhood obesity may have been underestimated in previous studies.(9) Our aims here are to extend that work by looking at other adiposity markers within ALSPAC and also metabolic and vascular risk factors. The lead author of that published paper (Andy Ness) is a co-applicant on this application and we would be happy to involve others who worked on those data on the papers that result from this application.

The aim of this proposal is to examine the association of socioeconomic position at birth with adiposity and metabolic markers in childhood at age 9.

Our specific objectives are:

1. Determine the magnitude of the association of head of household occupational social class, maternal educational attainment and parental educational attainment (as determined at birth) with BMI, waist circumference and DXA assessed fat mass at age 9.

2. Determine the magnitude of the association of head of household occupational social class, maternal education and paternal education (at birth) with blood pressure and circulating levels of insulin, HDLc, LDLc, triglycerides, apolipoprotein, adiponectin, leptin, CRP and IL6 at age 9.

3. To determine whether any associations in objective 2 are mediated by adiposity measurements.

In a second paper we would like to build on this first paper (whatever the findings) by completing the following objectives:

1. Determine the magnitude of the association of head of household occupational social class, maternal educational attainment and parental educational attainment (as determine at birth) with BMI, waist circumference and DXA assessed fat mass at age 15.

2. Determine the magnitude of the association of head of household occupational social class, maternal education and paternal education (at birth) with blood pressure and circulating fasting levels of insulin, HDLc, LDLc, triglycerides, apolipoprotein at age 15.

3. To determine whether any associations in objective 2 are mediated by adiposity measurements.

The rationale for this second paper is that the association of childhood BMI/obesity with future CHD risk (as shown in two very large study populations (6,7)) strengthens with older age at BMI assessment, suggesting that later childhood/early adulthood may be a more important risk period and/or that tracking of obesity into adulthood might be an important mechanism for the association of childhood adiposity with future CHD risk. There is also some evidence that the associations of childhood socioeconomic position with adiposity and metabolic and vascular risk factors increase with age. In the European Heart Health Study point estimates are larger at age 15 than 9 but the sample size it too small to determine whether these differences are statistically robust.

Our rationale for treating these as two separate papers is that (a) we believe there is sufficient data for two papers each with distinct and clear messages, with the second clearly building on the first; (b) the measurements at age 15 were on fasting samples, whereas those at 9 were not, and at age 9 a wider range of measurements (including leptin, adiponecting and markers of inflammation) have been completed than at 15 and we do not wish to distract from the messages of the paper by having in one paper to discuss these differences in detail (they will be discussed in the second paper).

Plan for completing work

1. DAL will put dataset together

2. DAL, LH & BG will agree analysis plan

3. LH will complete initial analyses with input from DAL & BG

4. LH will draft initial paper with input from DAL & BG

5. Other applicants on this proposal (and others they suggest as appropriate) will comment on draft and further revisions

References:

1. Galobardes B, Lynch JW, Davey Smith G. Childhood socioeconomic circumstances and cause-specific mortality in adulthood: systematic review and interpretation. Epidemiol Rev. 2004;26:7-21.

2. Galobardes B, Lynch JW, Davey Smith G. Is the association between childhood socioeconomic circumstances and cause-specific mortality established? Update of a systematic review. J Epidemiol Community Health. 2008 May;62:387-90

3. Galobardes B, Davey Smith G, Lynch JW. Systematic review of the influence of childhood socioeconomic circumstances on risk for cardiovascular disease in adulthood. Ann Epidemiol. 2006 Feb;16:91-104.

4. Davey Smith G, Hart C. Insulin resistance syndrome and childhood social

conditions. Lancet 1997;349:2845.

5. Lawlor DA, Ebrahim S, Davey Smith G. Socioeconomic position in childhood and adulthood and insulin resistance: cross sectional survey using data from the British women's heart and health study. BMJ 2002; 325:805-807

6. Baker JL, Olsen LW, Sorensen TI. Childhood body-mass index and the risk of coronary heart disease in adulthood. N Engl J Med 2007;357:2329-2337

7. Bjorge T, Engeland A, Tverdal A, Smith GD. Body Mass Index in Adolescence in Relation to Cause-specific Mortality: A Follow-up of 230,000 Norwegian Adolescents. Am J Epidemiol 2008;168:30-37

8. Lawlor DA, Harro M, Wedderkopp N, Andersen LB, Sardinha LB, Riddoch CJ, Page AS, Anderssen SA, Froberg K, Stansbie D, Davey Smith G. The association of socioeconomic position with insulin resistance among children from northern (Denmark), eastern (Estonia) and southern (Portugal) Europe: findings from the European Youth Heart Study. BMJ 2005;331:183-86.

9. Ness AR, Leary S, Reilly J, Wells J, Tobias J, Clark E, Davey Smith G, the ALSPAC Study Team. The social patterning of fat and lean mass in a contemporary cohort of children. International Journal of Pediatric Obesity 2006; 1:1, 59-61

Data required

1. SEP at birth (exposure) - occupational social class mother & father; education mother; education father

2. Adiposity measurements at age 9 (outcome 1): month and year of clinic; age of child at clinic; weight; height; waist circumference; DXA fat mass & lean mass from 9 year clinic

3. Metabolic and vascular measurements at age 9 (outcome 2): BP, lipids, insulin, adiponectin, leptin, CRP, IL6 from samples taken at 9 year clinic

4. Adiposity measurements at age 15 (outcome 1, second paper): month and year of clinic; age of child at clinic; weight; height; waist circumference; DXA fat mass & lean mass from 15 year clinic

5. Metabolic and vascular measurements at age 15 (outcome 2, second paper): BP, fasting lipids, insulin and glucose from samples taken at 15 year clinic

6. Potential confounding factors: Maternal and Paternal age at birth, ethnicity, maternal parity, maternal smoking in pregnancy, paternal smoking around time of birth/pregnancy, maternal pre-pregnancy BMI, paternal BMI around time of birth/pregnancy; child's sex.