T. J. Wilkin

Fatness leads to inactivity, but inactivity does not lead to fatness: a longitudinal study in children (EarlyBird 45)

Objective To establish in children whether inactivity is the cause of fatness or fatness the cause of inactivity. Design A non-intervention prospective cohort study examining children annually from 7 to 10 years. Baseline versus change to follow-up associations were used to examine the direction of causality. Setting Plymouth, England. Participants 202 children (53% boys, 25% overweight/obese) recruited from 40 Plymouth primary schools as part of the EarlyBird study. Main outcome measures Physical activity (PA) was measured using Actigraph accelerometers. The children wore the accelerometers for 7 consecutive days at each annual time point. Two components of PA were analysed: the total volume of PA and the time spent at moderate and vigorous intensities. Body fat per cent (BF%) was measured annually by dual energy x ray absorptiometry. Results BF% was predictive of changes in PA over the following 3 years, but PA levels were not predictive of subsequent changes in BF% over the same follow-up period. Accordingly, a 10% higher BF% at age 7 years predicted a relative decrease in daily moderate and vigorous intensities of 4 min from age 7 to 10 years (r=−0.17, p=0.02), yet more PA at 7 years did not predict a relative decrease in BF% between 7 and 10 years (r=−0.01, p=0.8). Conclusions Physical inactivity appears to be the result of fatness rather than its cause. This reverse causality may explain why attempts to tackle childhood obesity by promoting PA have been largely unsuccessful.

Variation in physical activity lies with the child, not his environment: evidence for an 'activitystat' in young children (EarlyBird 16)

Objective:There is currently wide interest in the physical activity of children, but little understanding of its control. Here, we use accelerometers to test the hypothesis that habitual activity in young children is centrally, rather than environmentally, regulated. By central regulation we mean a classic biological feedback loop, with a set-point individual to the child, which controls his/her activity independently of external factors.Design:Non-intervention, observational and population-based, set in the home and at school.Results:Girls were systematically less active than boys, and both weekday/weekend day and year-on-year activities were correlated (r=0.43–0.56). A fivefold variation in timetabled PE explained less than 1% of the total variation in physical activity. The activity cost of transport to school was only 2% of total activity, but over 90% of it was recovered elsewhere in the day. The weekly activity recorded by children in Plymouth was the same (to within <0.3%) as that recorded independently in Glasgow, 800 km away. Total daily activity was unrelated to time reportedly spent watching TV.Interpretation:The correlations within groups and the similarities between them suggest that physical activity in children is under central biological regulation. There are implications both for public health planners and for the potentially novel signalling pathways involved.

A Common Haplotype of the Glucokinase Gene Alters Fasting Glucose and Birth Weight: Association in Six Studies and Population-Genetics Analyses

Fasting glucose is associated with future risk of type 2 diabetes and ischemic heart disease and is tightly regulated despite considerable variation in quantity, type, and timing of food intake. In pregnancy, maternal fasting glucose concentration is an important determinant of offspring birth weight. The key determinant of fasting glucose is the enzyme glucokinase (GCK). Rare mutations of GCK cause fasting hyperglycemia and alter birth weight. The extent to which common variation of GCK explains normal variation of fasting glucose and birth weight is not known. We aimed to comprehensively define the role of variation of GCK in determination of fasting glucose and birth weight, using a tagging SNP (tSNP) approach and studying 19,806 subjects from six population-based studies. Using 22 tSNPs, we showed that the variant rs1799884 is associated with fasting glucose at all ages in the normal population and exceeded genomewide levels of significance (P=10-9). rs3757840 was also highly significantly associated with fasting glucose (P=8x10-7), but haplotype analysis revealed that this is explained by linkage disequilibrium (r2=0.2) with rs1799884. A maternal A allele at rs1799884 was associated with a 32-g (95% confidence interval 11-53 g) increase in offspring birth weight (P=.002). Genetic variation influencing birth weight may have conferred a selective advantage in human populations. We performed extensive population-genetics analyses to look for evidence of recent positive natural selection on patterns of GCK variation. However, we found no strong signature of positive selection. In conclusion, a comprehensive analysis of common variation of the glucokinase gene shows that this is the first gene to be reproducibly associated with fasting glucose and fetal growth.

The impact of school-time activity on total physical activity: the activitystat hypothesis (EarlyBird 46).

Objectives:To explore the activitystat hypothesis in primary school children by asking whether more physical activity (PA) in school time is compensated for by less PA at other times.Study Design:Observational, repeated measures (four consecutive occasions over a 12-month period).Setting:South-west England.Participants:A total of 206 children (115 boys, aged 8–10 years) from 3 primary schools (S1, S2 and S3), which recorded large differences in PA during school time.Measurements:Total PA (TPA) and its moderate-and-vigorous component were recorded weekly by accelerometry, in school and out of school, and adjusted for local daily rainfall and daylight hours. Habitual PA was assessed by linear mixed-effects modelling on repeated measures.Results:S1 children recorded 64% more in-school PA, but S2 and S3 children compensated with correspondingly more out-of-school PA, so that TPA between the three schools was no different: 35.6 (34.3–36.9), 37.3 (36.0–38.6) and 36.2 (34.9–37.5)  Units, respectively (P=0.38).Conclusions:The PA of children seems to compensate in such a way that more activity at one time is met with less activity at another. The failure of PA programmes to reduce childhood obesity could be attributable to this compensation.

Changing perspectives in diabetes: their impact on its classification

Type 1 and type 2 diabetes are usually regarded as distinct disorders, but the convergence of their phenotypes over recent years, the relationship of body weight to the risk of type 1 diabetes, the diminishing importance of the type 1 susceptibility genes and the finding of autoantibodies in patients with type 2 diabetes, invite a different interpretation. The possibility that type 1 and type 2 diabetes, rather than being different, are merely poles of a single spectrum, where variation in the tempo of beta cell loss determines age at onset and symptoms at presentation, has important implications. Correct classification is crucial because it directs appropriate treatment and, where available, prevention. This article argues that type 1 diabetes is currently misclassified, provides evidence that insulin resistance drives type 1 diabetes as it does type 2, and proposes how the ‘accelerator hypothesis’ can be tested in a randomised controlled trial, which could demonstrate, for the first time, the safe and effective prevention of type 1 diabetes.

Metabolic risk in early childhood: the EarlyBird Study

OBJECTIVE: For a decade or more, poor nutrition during gestation, expressed as low weight at birth, was held to be the factor responsible for insulin resistance later in life. Birth weights, however, are rising and insulin-resistant states, such as diabetes, faster still. Alternative explanations are needed for insulin resistance in contemporary society. This review cites data from the EarlyBird study on the relationships of insulin resistance and metabolic disturbance in early childhood.DESIGN: EarlyBird is a nonintervention prospective cohort study that asks the question ‘Which children develop insulin resistance, and why?’ It is unique in taking serial blood samples from a young age with which to monitor the behaviour of insulin resistance and its metabolic correlates, and in its comprehensive assessment of factors known or thought to influence insulin resistanceSUBJECTS: In all, 307 randomly selected healthy school children at school entry (mean age 4.9 y) and at 12 and 24 months later.MEASUREMENTS: In the children: Birth weight and, at each time point height, weight, body mass index (BMI, kg/m2), skinfolds at five sites, circumferences, resting energy expenditure, physical activity, body composition, heart rate variability, diet, HOMA-IR and HOMA-ISC, blood pressure, full blood count, haemoglobin and haematocrit, HbA1C, total cholesterol, HDL cholesterol, LDL cholesterol, triglycerides, uric acid, IGF-1, gonadotrophins and SHBG. In their parents: At baseline height, weight, BMI, waist circumference, HOMA-IR and HOMA-ISC, full blood count, haematocrit, HbA1C, total cholesterol, HDL cholesterol, calculated LDL cholesterol, triglycerides, uric acid, gonadotrophins and SHBG.RESULTS: Four observations are reported here: (1) There are clear correlations in contemporary children between insulin resistance and weight at 5 y, but none with birth weight. (2) Females throughout life are intrinsically more insulin resistant than males. (3) The substantial variation of physical activity among young children is attributable to the child, and not to his environment. (4) There is dissociation in young children between fatness and insulin resistance.CONCLUSION: There is much yet to be learned about the development of obesity and insulin resistance in children. The notions of overnutrition and underactivity alone are too simplistic.

BMI was right all along: taller children really are fatter (implications of making childhood BMI independent of height) EarlyBird 48.

Objective:Several studies suggest that taller children may be wrongly labelled as ‘overweight’ because body mass index (BMI) is not independent of height (Ht) in childhood, and recommend adjustment to render the index Ht independent. We used objective measures of %body fat and hormonal/metabolic markers of fatness to investigate whether BMI and the corresponding fat mass index (FMI) mislead in childhood, or whether taller children really are fatter.Design:Longitudinal observational study measuring children annually from age 7 to 12 years.Subjects:Two hundred and eighty healthy children (56% boys) from the EarlyBird study.Measurements:BMI (body mass (BM)/Ht2), FMI (fat mass (FM)/Ht2), %body fat ((FM/BM) × 100, where FM was measured by dual-energy X-ray absorptiometry), fasting leptin (a hormonal measure of body fatness) and insulin resistance (a metabolic marker derived from the validated homeostasis model assessment program for insulin resistance - HOMA2-IR) were all analysed in relation to Ht. Alternative Ht-independent indices of BM and FM were compared with BMI and FMI as indicators of true fatness and related health risk.Results:BMI and FMI correlated with Ht at each annual time point (r∼0.47 and 0.46, respectively), yet these correlations were similar in strength to those between Ht and %fat (r∼0.47), leptin (r∼0.41) and insulin resistance (r∼0.40). Also, children who grew the most between 7 and 12 years showed greater increases in BMI, FMI, leptin and insulin resistance (tertile 1 vs 3, all p<0.05). BMI and FMI explained ∼20% more of the variation in %fat, ∼15% more in leptin and ∼10% more in insulin resistance than the respective Ht-independent reformulations (BM/Ht3.5 and FM/Ht7, both p<0.001).Conclusion:Taller children really are fatter than their shorter peers, have higher leptin levels and are more insulin resistant. Attempts to render indices of BM or FM independent of Ht in children seem inappropriate if the object of the index is to convey health risk.

Children from low-income families have less access to sports facilities, but are no less physically active: cross-sectional study (EarlyBird 35).

BACKGROUND Rising levels of childhood obesity have led to an increasing number of Government sponsored initiatives attempting to stem the problem. Much of the focus to date has been on physical activity and out-of-school activity in particular. There is an assumption that children from low-income families suffer most where there is a lack of structured physical education in school. Accordingly, provision of additional facilities for sport and other forms of active recreation tend to target areas of socio-economic deprivation. AIM We have assessed the relationship between parental income, the use of out-of-school sports facilities and the overall physical activity of young children across a wide socio-economic range. METHODS Total weekly physical activity was measured, objectively, over 7 days both at 7 years and 8 years in a healthy cohort of 121 boys and 93 girls using actigraph accelerometers. Questionnaires were used to establish parental income and parents reported the childs weekly use of out-of-school facilities for structured physical activity. RESULTS Children from low-income families attended significantly fewer sessions of structured out-of-school activities than those from wealthier families (r = 0.39), with a clear dose-response relationship across income groups. Nevertheless, total physical activity, measured objectively over seven continuous days, showed no relationship between parental income and the mean activity level of the children (r = -0.08). Nor did we find a relationship between parental income and time spent in higher intensity activity (r = -0.04). CONCLUSION Social inequality appears to have little impact on physical activity in young children. Those from poorer families make less use of facilities for structured activity out-of-school but they nevertheless record the same overall level of activity as others. What they lack in opportunity they appear to make up in the form of unstructured exercise. Improving provision for sport may not lead to the expected rise in activity levels in young children.

IOTF thresholds for overweight and obesity and their relation to metabolic risk in children (EarlyBird 20)

The International Obesity TaskForce has published paediatric cutoffs from the age of 2 years for overweight and obesity, based on adult thresholds. We question their rationale. The adult cutoffs were based on known health risk; the childrens were not. Data from the EarlyBird Study show that BMI category for overweight and obesity in young children are poor markers of insulin resistance and, by implication, of metabolic risk and diabetes. Moreover, BMI is known to track poorly from early childhood to adulthood. We know even less about the tracking of insulin resistance and other indices of metabolic risk from the earliest years. Until we understand more about which children acquire such risk factors, any such thresholds for overweight and obesity should be used with caution in the very young, as they may unnecessarily stigmatise the heavier child.

Reliability of energy expenditure prediction equations in the weight management clinic.

BACKGROUND Few weight management clinics have access to indirect calorimetry with which to measure energy expenditure. Instead, they use energy expenditure prediction equations, which were not designed for use in obesity. We aimed to establish the extent to which such equations overestimate and underestimate resting energy expenditure (REE) in overweight and obese individuals. METHODS We compared the Schofield, Harris & Benedict, James & Lean and World Health Organisation (WHO) REE prediction equations with the clinical gold standard of indirect calorimetry in 28 males and 168 females, with a mean (SD) age of 28.9 (6.4) years and body mass index (BMI) of 19-67 kg m(-2). RESULTS The mean REE estimated by indirect calorimetry, and the Schofield, Harris & Benedict, James & Lean and WHO equations were 8.09, 8.30, 8.09, 8.37 and 8.23 MJ day(-1) (1934, 1983, 1933, 2001 and 1966 kcal day(-1)), respectively. Although rising BMI exerted only a small effect on the mean differences between indirect calorimetry and the predicted REE [Schofield: +272 kJ (+65 kcal)/10 units BMI, P = 0.02; Harris & Benedict: +42 kJ (+10 kcal)/10 units BMI, P = 0.69; James & Lean: +217 kJ (+52 kcal) 10 units BMI, P = 0.06 and WHO: +42 kJ (+10 kcal) BMI, P = 0.11], the variance among overweight and obese patients of BMI >25 was substantially higher compared to that among normal weight subjects of BMI <25, on whom the equations were based. The estimated REE by Schofield for an individual of BMI 35 kg m(-2), for example, could lie anywhere from 2.78 MJ (661 kcal) above the indirect calorimetry value to 2.59 MJ (618) kcal below it. CONCLUSIONS Prediction equations offer a quick assessment of energy needs for hypocaloric diets although, in reality, they run the random risk of excessive restriction or further weight gain.