OBJECTIVE

To investigate whether associations of common genetic variants recently identified for fasting glucose or insulin levels in nondiabetic adults are detectable in healthy children and adolescents.

RESEARCH DESIGN AND METHODS

A total of 16 single nucleotide polymorphisms (SNPs) associated with fasting glucose were genotyped in six studies of children and adolescents of European origin, including over 6,000 boys and girls aged 9–16 years. We performed meta-analyses to test associations of individual SNPs and a weighted risk score of the 16 loci with fasting glucose.

RESULTS

Nine loci were associated with glucose levels in healthy children and adolescents, with four of these associations reported in previous studies and five reported here for the first time (GLIS3, PROX1, SLC2A2, ADCY5, and CRY2). Effect sizes were similar to those in adults, suggesting age-independent effects of these fasting glucose loci. Children and adolescents carrying glucose-raising alleles of G6PC2, MTNR1B, GCK, and GLIS3 also showed reduced β-cell function, as indicated by homeostasis model assessment of β-cell function. Analysis using a weighted risk score showed an increase [β (95% CI)] in fasting glucose level of 0.026 mmol/L (0.021–0.031) for each unit increase in the score.

CONCLUSIONS

Novel fasting glucose loci identified in genome-wide association studies of adults are associated with altered fasting glucose levels in healthy children and adolescents with effect sizes comparable to adults. In nondiabetic adults, fasting glucose changes little over time, and our results suggest that age-independent effects of fasting glucose loci contribute to long-term interindividual differences in glucose levels from childhood onwards.

Ken Ong and colleagues genotyped children from the ALSPAC birth cohort and showed an association between greater early infancy gains in weight and length and genetic markers for adult obesity risk.

Background

Genome-wide studies have identified several common genetic variants that are robustly associated with adult obesity risk. Exploration of these genotype associations in children may provide insights into the timing of weight changes leading to adult obesity.

Methods and Findings

Children from the Avon Longitudinal Study of Parents and Children (ALSPAC) birth cohort were genotyped for ten genetic variants previously associated with adult BMI. Eight variants that showed individual associations with childhood BMI (in/near: FTO, MC4R, TMEM18, GNPDA2, KCTD15, NEGR1, BDNF, and ETV5) were used to derive an “obesity-risk-allele score” comprising the total number of risk alleles (range: 2–15 alleles) in each child with complete genotype data (n = 7,146). Repeated measurements of weight, length/height, and body mass index from birth to age 11 years were expressed as standard deviation scores (SDS). Early infancy was defined as birth to age 6 weeks, and early infancy failure to thrive was defined as weight gain between below the 5th centile, adjusted for birth weight. The obesity-risk-allele score showed little association with birth weight (regression coefficient: 0.01 SDS per allele; 95% CI 0.00–0.02), but had an apparently much larger positive effect on early infancy weight gain (0.119 SDS/allele/year; 0.023–0.216) than on subsequent childhood weight gain (0.004 SDS/allele/year; 0.004–0.005). The obesity-risk-allele score was also positively associated with early infancy length gain (0.158 SDS/allele/year; 0.032–0.284) and with reduced risk of early infancy failure to thrive (odds ratio  = 0.92 per allele; 0.86–0.98; p = 0.009).

Conclusions

The use of robust genetic markers identified greater early infancy gains in weight and length as being on the pathway to adult obesity risk in a contemporary birth cohort.

Please see later in the article for the Editors' Summary

Editors' Summary

Background

The proportion of overweight and obese children is increasing across the globe. In the US, the Surgeon General estimates that, compared with 1980, twice as many children and three times the number of adolescents are now overweight. Worldwide, 22 million children under five years old are considered by the World Health Organization to be overweight.

Being overweight or obese in childhood is associated with poor physical and mental health. In addition, childhood obesity is considered a major risk factor for adult obesity, which is itself a major risk factor for cancer, heart disease, diabetes, osteoarthritis, and other chronic conditions.

The most commonly used measure of whether an adult is a healthy weight is body mass index (BMI), defined as weight in kilograms/(height in metres)2. However, adult categories of obese (>30) and overweight (>25) BMI are not directly applicable to children, whose BMI naturally varies as they grow. BMI can be used to screen children for being overweight and or obese but a diagnosis requires further information.

Why Was This Study Done?

As the numbers of obese and overweight children increase, a corresponding rise in future numbers of overweight and obese adults is also expected. This in turn is expected to lead to an increasing incidence of poor health. As a result, there is great interest among health professionals in possible pathways between childhood and adult obesity. It has been proposed that certain periods in childhood may be critical for the development of obesity.

In the last few years, ten genetic variants have been found to be more common in overweight or obese adults. Eight of these have also been linked to childhood BMI and/or obesity. The authors wanted to identify the timing of childhood weight changes that may be associated with adult obesity. Knowledge of obesity risk genetic variants gave them an opportunity to do so now, without following a set of children to adulthood.

What Did the Researchers Do and Find?

The authors analysed data gathered from a subset of 7,146 singleton white European children enrolled in the Avon Longitudinal Study of Parents and Children (ALSPAC) study, which is investigating associations between genetics, lifestyle, and health outcomes for a group of children in Bristol whose due date of birth fell between April 1991 and December 1992. They used knowledge of the children's genetic makeup to find associations between an obesity risk allele score—a measure of how many of the obesity risk genetic variants a child possessed—and the children's weight, height, BMI, levels of body fat (at nine years old), and rate of weight gain, up to age 11 years.

They found that, at birth, children with a higher obesity risk allele score were not any heavier, but in the immediate postnatal period they were less likely to be in the bottom 5% of the population for weight gain (adjusted for birthweight), often termed “failure to thrive.” At six weeks of age, children with a higher obesity risk allele score tended to be longer and heavier, even allowing for weight at birth.

After six weeks of age, the obesity risk allele score was not associated with any further increase in length/height, but it was associated with a more rapid weight gain between birth and age 11 years. BMI is derived from height and weight measurements, and the association between the obesity risk allele score and BMI was weak between birth and age three-and-a-half years, but after that age the association with BMI increased rapidly. By age nine, children with a higher obesity risk allele score tended to be heavier and taller, with more fat on their bodies.

What Do These Findings Mean?

The combined obesity allele risk score is associated with higher rates of weight gain and adult obesity, and so the authors conclude that weight gain and growth even in the first few weeks after birth may be the beginning of a pathway of greater adult obesity risk.

A study that tracks a population over time can find associations but it cannot show cause and effect. In addition, only a relatively small proportion (1.7%) of the variation in BMI at nine years of age is explained by the obesity risk allele score.

The authors' method of finding associations between childhood events and adult outcomes via genetic markers of risk of disease as an adult has a significant advantage: the authors did not have to follow the children themselves to adulthood, so their findings are more likely to be relevant to current populations. Despite this, this research does not yield advice for parents how to reduce their children's obesity risk. It does suggest that “failure to thrive” in the first six weeks of life is not simply due to a lack of provision of food by the baby's caregiver but that genetic factors also contribute to early weight gain and growth.

The study looked at the combined obesity risk allele score and the authors did not attempt to identify which individual alleles have greater or weaker associations with weight gain and overweight or obesity. This would require further research based on far larger numbers of babies and children. The findings may also not be relevant to children in other types of setting because of the effects of different nutrition and lifestyles.

Additional Information

Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1000284.

Further information is available on the ALSPAC study

The UK National Health Service and other partners provide guidance on establishing a healthy lifestyle for children and families in their Change4Life programme

The International Obesity Taskforce is a global network of expertise and the advocacy arm of the International Association for the Study of Obesity. It works with the World Health Organization, other NGOs, and stakeholders and provides information on overweight and obesity

The Centers for Disease Control and Prevention (CDC) in the US provide guidance and tips on maintaining a healthy weight, including BMI calculators in both metric and Imperial measurements for both adults and children. They also provide BMI growth charts for boys and girls showing how healthy ranges vary for each sex at with age

The Royal College of Paediatrics and Child Health provides growth charts for weight and length/height from birth to age 4 years that are based on WHO 2006 growth standards and have been adapted for use in the UK

The CDC Web site provides information on overweight and obesity in adults and children, including definitions, causes, and data

The CDC also provide information on the role of genes in causing obesity.

The World Health Organization publishes a fact sheet on obesity, overweight and weight management, including links to childhood overweight and obesity

Wikipedia includes an article on childhood obesity (note that Wikipedia is a free online encyclopedia that anyone can edit; available in several languages)

Obesity is a serious international health problem that increases the risk of several common diseases. The genetic factors predisposing to obesity are poorly understood. A genome-wide search for type 2 diabetes–susceptibility genes identified a common variant in the FTO (fat mass and obesity associated) gene that predisposes to diabetes through an effect on body mass index (BMI). An additive association of the variant with BMI was replicated in 13 cohorts with 38,759 participants. The 16% of adults who are homozygous for the risk allele weighed about 3 kilograms more and had 1.67-fold increased odds of obesity when compared with those not inheriting a risk allele. This association was observed from age 7 years upward and reflects a specific increase in fat mass.

Complex diseases are presumed to be the results of interactions of several genes and environmental factors, with each gene only having a small effect on the disease. Thus, the methods that can account for gene-gene interactions to search for a set of marker loci in different genes or across genome and to analyze these loci jointly are critical. In this article, we propose an ensemble learning approach (ELA) to detect a set of loci whose main and interaction effects jointly have a significant association with the trait. In the ELA, we first search for “base learners” and then combine the effects of the base learners by a linear model. Each base learner represents a main effect or an interaction effect. The result of the ELA is easy to interpret. When the ELA is applied to analyze a data set, we can get a final model, an overall P-value of the association test between the set of loci involved in the final model and the trait, and an importance measure for each base learner and each marker involved in the final model. The final model is a linear combination of some base learners. We know which base learner represents a main effect and which one represents an interaction effect. The importance measure of each base learner or marker can tell us the relative importance of the base learner or marker in the final model. We used intensive simulation studies as well as a real data set to evaluate the performance of the ELA. Our simulation studies demonstrated that the ELA is more powerful than the single-marker test in all the simulation scenarios. The ELA also outperformed the other three existing multi-locus methods in almost all cases. In an application to a large-scale case-control study for Type 2 diabetes, the ELA identified 11 single nucleotide polymorphisms that have a significant multi-locus effect (P-value = 0.01), while none of the single nucleotide polymorphisms showed significant marginal effects and none of the two-locus combinations showed significant two-locus interaction effects.

Context

The common C allele of rs314276 in LIN28B has been robustly associated with earlier age at menarche in girls and associated with earlier timing of other pubertal traits in both sexes.

Objective

Our objective was to explore the associations between rs314276, as a marker of earlier pubertal timing, and body mass index (BMI), weight, and height across the life course.

Methods

The rs314276 in LIN28B was genotyped in 1242 men and 1209 women born in 1946 and participating in the Medical Research Council National Survey of Health and Development. Birth weight was recorded, and height and weight were measured or self-reported repeatedly at 11 time points between ages 2 and 53 yr. Polynomial mixed models were used to test whether additive genetic associations with SD scores (SDS) for BMI and height changed with age between 0 and 53 yr.

Results

Longitudinal analyses revealed age-dependent associations between rs314276 genotype and BMI (P < 0.001 for genotype-by-age2 interaction) and body weight (P < 0.001 for genotype-by-age2 interaction) in women, but not in men. In women only, the C allele at rs314276 was associated with higher BMI SDS from ages 15–43 yr. In contrast, C allele associations with shorter height SDS were apparent in both men and women and did not vary with age.

Conclusion

A common genetic variant in LIN28B that confers earlier puberty was associated with a prolonged increase in BMI during adolescence and early to mid-adulthood in women only. Such genetic associations may provide insights into the direct effects of pubertal timing on obesity risk.

Percent mammographic breast density (PMD) is a strong heritable risk factor for breast cancer. However, the pathways through which this risk is mediated are still unclear. To explore whether PMD and breast cancer have a shared genetic basis, we identified genetic variants most strongly associated with PMD in a published meta-analysis of five genome-wide association studies (GWAS) and used these to construct risk scores for 3628 breast cancer cases and 5190 controls from the UK2 GWAS of breast cancer. The signed per-allele effect estimates of SNPs were multiplied with the respective allele counts in the individual and summed over all SNPs to derive the risk score for an individual. These scores were included as the exposure variable in a logistic regression model with breast cancer case-control status as the outcome. This analysis was repeated using ten different cut-offs for the most significant density SNPs (1-10% representing 5,222-50,899 SNPs). Permutation analysis was also performed across all 10 cut-offs. The association between risk score and breast cancer was significant for all cut-offs from 3-10% of top density SNPs, being most significant for the 6% (2-sided P=0.002) to 10% (P=0.001) cut-offs (overall permutation P=0.003). Women in the top 10% of the risk score distribution had a 31% increased risk of breast cancer [OR= 1.31 (95%CI 1.08-1.59)] compared to women in the bottom 10%. Together, our results demonstrate that PMD and breast cancer have a shared genetic basis that is mediated through a large number of common variants.

Objective

The association between quantity of fruit and vegetable intake and risk of type 2 diabetes (T2D) is not clear, and the relationship with variety of intake is unknown. The current study examined the association of both quantity and variety of fruit and vegetable intake and risk of T2D.

Research Design and Methods

We examined the 11-year incidence of T2D in relation to quantity and variety of fruit, vegetables and combined fruit and vegetable intake in a case-cohort study of 3,704 participants (n=653 T2D cases) nested within the European Prospective Investigation into Cancer and Nutrition-Norfolk Study, who completed 7-day prospective food diaries. Variety of intake was derived from the total number of different items consumed in a one week period. Multivariable Prentice-weighted Cox regression was used to estimate hazard ratios (HR) and 95% CI.

Results

Greater quantity of combined fruit and vegetable intake was associated with 21% lower hazard of T2D (HR 0.79; 0.62-1.00) comparing extreme tertiles, in adjusted analyses including variety. Separately, quantity of vegetable intake (HR 0.76; 0.60-0.97), but not fruit, was inversely associated with T2D in adjusted analysis. Greater variety in fruit (HR 0.70; 0.53-0.91), vegetable (HR 0.77; 0.61-0.98), and combined fruit and vegetable (HR 0.61; 0.48-0.78) intake was associated with a lower hazard of T2D, independent of known confounders and quantity of intake comparing extreme tertiles.

Conclusions

These findings suggest that a diet characterised by greater quantity of vegetable and greater variety of both fruit and vegetable intake is associated with a reduced risk of T2D.

OBJECTIVE

In the general, nondiabetic population, fasting glucose increases only slightly over time, whereas 2-h postload glucose shows a much steeper age-related rise. The reasons underlying these different age trajectories are unknown. We investigated whether common genetic variants associated with fasting and 2-h glucose contribute to age-related changes of these traits.

RESEARCH DESIGN AND METHODS

We studied 5,196 nondiabetic participants of the Whitehall II cohort (aged 40–78 years) attending up to four 5-yearly oral glucose tolerance tests. A genetic score was calculated separately for fasting and 2-h glucose, including 16 and 5 single nucleotide polymorphisms, respectively. Longitudinal modeling with age centered at 55 years was used to study the effects of each genotype and genetic score on fasting and 2-h glucose and their interactions with age, adjusting for sex and time-varying BMI.

RESULTS

The fasting glucose genetic score was significantly associated with fasting glucose with a 0.029 mmol/L (95% CI 0.023–0.034) difference (P = 2.76 × 10−21) per genetic score point, an association that remained constant over time (age interaction P = 0.17). Two-hour glucose levels differed by 0.076 mmol/L (0.047–0.105) per genetic score point (P = 3.1 × 10−7); notably, this effect became stronger with increasing age by 0.006 mmol/L (0.003–0.009) per genetic score point per year (age interaction P = 3.0 × 10−5), resulting in diverging age trajectories by genetic score.

CONCLUSIONS

Common genetic variants contribute to the age-related rise of 2-h glucose levels, whereas associations of variants for fasting glucose are constant over time, in line with stable age trajectories of fasting glucose.

The epidemiology of obesity suggests that, for the majority of individuals, the disorder arises from an interaction between genetic predisposition and lifestyle behaviors such as dietary intake and physical activity. Unravelling the molecular basis of such interactions is complex but is becoming a realistic proposition as evidence emerges from whole genome association studies of genetic variants that are definitively associated with obesity. A range of possible study designs is available for investigating gene–lifestyle interaction, and the strengths and weaknesses of each approach are discussed in this article. Given the likely small main effect of common genetic variants and the difficulties in demonstrating associations of lifestyle factors with future risk of obesity, we would favor an analytical approach based on the clear specification of prior probabilities to reduce the likelihood of false discovery. Mixed approaches combining data from large-scale observational studies with smaller intervention trials may be ideal. In designing new studies to investigate these issues, a key choice is how precisely to quantify the important, but difficult to measure lifestyle behaviors. It is clear from power calculations that an approach based on enhancing precision of measurement of diet and physical activity is critical.

The high heritability of obesity coupled with the rapid increase in prevalence suggests that a combination of genetic and behavioral factors is critical to the etiology of obesity (1). It is easy to propose such a model for the development of obesity, but it is altogether much harder to identify the molecular mechanisms that underlie such a model. In this article, we review overall strategies and possible epidemiological study designs for investigating how genetic and behavioral risk factors combine to lead to excess weight gain.

Objective

To investigate the relationship between Angiopoietin-like protein 4 (Angptl4) levels, CHD biomarkers and ANGPTL4 variants.

Methods and Results

Plasma Angptl4 was quantified in 666 subjects of the Northwick Park Heart Study II using a validated ELISA. Seven ANGPTL4 SNPs were genotyped and CHD biomarkers assessed in the whole cohort (n=2775). Weighted mean (±SD) plasma Angptl4 levels were 10.0(±11.0) ng/ml. Plasma Angptl4 concentration correlated positively with age (r=0.15, P<0.001), body fat mass (r=0.19, P=0.003) but negatively with plasma HDL-cholesterol (r=−0.13, P=0.01). No correlation with triglycerides was observed. T266M was independently associated with plasma Angptl4 levels (P<0.001), but not associated with triglycerides or with CHD risk in the meta-analysis of five studies (4,061 cases/15,395 controls). E40K showed no independent association with plasma Angptl4 levels. In HEK293 and Huh7 cells compared to wild-type, E40K and T266M showed significantly altered synthesis and secretion, respectively.

Conclusions

These data suggest that circulating Angptl4 levels do not influence triglyceride levels or CHD risk since (1) Angptl4 levels were not correlated with triglycerides, (2) T266M, although associated with Angptl4 levels, showed no association with plasma triglycerides (3) Triglyceride-lowering E40K did not influence Angptl4 levels. These results provide new insights into the role of Angptl4 in triglyceride metabolism.

OBJECTIVE

To evaluate the incidence and relative risk of type 2 diabetes defined by the newly proposed HbA1c diagnostic criteria in groups categorized by different baseline HbA1c levels.

RESEARCH DESIGN AND METHODS

Using data from the European Prospective Investigation of Cancer (EPIC)-Norfolk cohort with repeat HbA1c measurements, we estimated the prevalence of known and previously undiagnosed diabetes at baseline (baseline HbA1c ≥6.5%) and the incidence of diabetes over 3 years. We also examined the incidence and corresponding odds ratios (ORs) by different levels of baseline HbA1c. Incident diabetes was defined clinically (self-report at follow-up, prescribed diabetes medication, or inclusion on a diabetes register) or biochemically (HbA1c ≥6.5% at the second health assessment), or both.

RESULTS

The overall prevalence of diabetes was 4.7%; 41% of prevalent cases were previously undiagnosed. Among 5,735 participants without diabetes at baseline (identified clinically or using HbA1c criteria, or both), 72 developed diabetes over 3 years (1.3% [95% CI 1.0–1.5]), of which 49% were identified using the HbA1c criteria. In 6% of the total population, the baseline HbA1c was 6.0–6.4%; 36% of incident cases arose in this group. The incidence of diabetes in this group was 15 times higher than in those with a baseline HbA1c of <5.0% (OR 15.5 [95% CI 7.2–33.3]).

CONCLUSIONS

The cumulative incidence of diabetes defined using a newly proposed HbA1c threshold in this middle-aged British cohort was 1.3% over 3 years. Targeting interventions to individuals with an HbA1c of 6.0–6.4% might represent a feasible preventive strategy, although complementary population-based preventive strategies are also needed to reduce the growing burden of diabetes.

Background: Longitudinal growth associations with genetic variants identified for adult BMI may provide insights into the timing of obesity susceptibility.

Objective: The objective was to explore associations of known BMI loci with measures of body size from birth to adulthood.

Design: A total of 2537 individuals from a longitudinal British birth cohort were genotyped for 11 genetic variants robustly associated with adult BMI (in/near FTO, MC4R, TMEM18, GNPDA2, KCTD15, NEGR1, BDNF, ETV5, SEC16B, SH2B1, and MTCH2). We derived an obesity-risk-allele score, comprising the sum of BMI-increasing alleles in each individual, and examined this for an association with birth weight and repeated measures of weight, height, and BMI SD scores (SDS) at 11 time points between ages 2 and 53 y.

Results: The obesity-risk-allele score showed borderline significant association with birth weight (0.019 SDS/allele; P = 0.05) and was more clearly associated with higher weight and BMI at all time points between ages 2 and 53 y; the strongest associations with weight occurred at ages 11 and 20 y (both 0.056 SDS/allele). In longitudinal analyses, the score was positively associated with weight gain only between birth and 11 y (0.003 SDS/allele per year; 95% CI: 0.001, 0.004; P = 0.001). The risk-allele score was associated with taller height at 7 y (0.031 SDS/allele; P = 0.002) and greater height gains between 2 and 7 y (0.007 SDS/allele per year; P < 0.001), but not with adult height (P = 0.5).

Conclusions: The combined effect of adult obesity susceptibility variants on weight gain was confined to childhood. These variants conferred a faster tempo of height growth that was evident before the pubertal years.

OBJECTIVE

We examined the independent associations between objectively measured free-living physical activity energy expenditure (PAEE) and the metabolic syndrome in adults in rural and urban Cameroon.

RESEARCH DESIGN AND METHODS

PAEE was measured in 552 rural and urban dwellers using combined heart rate and movement sensing over 7 continuous days. The metabolic syndrome was defined using the National Cholesterol Education Program-Adult Treatment Panel III criteria.

RESULTS

Urban dwellers had a significantly lower PAEE than rural dwellers (44.2 ± 21.0 vs. 59.6 ± 23.7 kJ/kg/day, P < 0.001) and a higher prevalence of the metabolic syndrome (17.7 vs. 3.5%, P < 0.001). In multivariate regression models adjusted for possible confounders, each kJ/kg/day of PAEE was associated with a 2.1% lower risk of prevalent metabolic syndrome (odds ratio 0.98, P = 0.03). This implies a 6.5 kJ/kg/day difference in PAEE, equivalent to 30 min/day of brisk walking, corresponds to a 13.7% lower risk of prevalent metabolic syndrome. The population attributable fraction of prevalent metabolic syndrome due to being in the lowest quartile of PAEE was 26.3% (25.3% in women and 35.7% in men).

CONCLUSIONS

Urban compared with rural residence is associated with lower PAEE and higher prevalence of metabolic syndrome. PAEE is strongly independently associated with prevalent metabolic syndrome in adult Cameroonians. Modest population-wide changes in PAEE may have significant benefits in terms of reducing the emerging burden of metabolic diseases in sub-Saharan Africa.

Evidence for a major role of genetic factors in the determination of body mass index (BMI) comes from studies of related individuals. Despite consistent evidence for a heritable component of BMI, estimates of BMI heritability vary widely between studies and the reasons for this remain unclear. While some variation is natural due to differences between populations and settings, study design factors may also explain some of the heterogeneity. We performed a systematic review that identified 88 independent estimates of BMI heritability from twin studies (total 140,525 twins) and 27 estimates from family studies (42,968 family members). BMI heritability estimates from twin studies ranged from 0.47 to 0.90 (5th/50th/95th centiles: 0.58/0.75/0.87) and were generally higher than those from family studies (range: 0.24–0.81; 5th/50th/95th centiles: 0.25/0.46/0.68). Meta-regression of the results from twin studies showed that BMI heritability estimates were 0.07 (P = 0.001) higher in children than in adults; estimates increased with mean age among childhood studies (+0.012/year, P = 0.002), but decreased with mean age in adult studies (−0.002/year, P = 0.002). Heritability estimates derived from AE twin models (which assume no contribution of shared environment) were 0.12 higher than those from ACE models (P < 0.001), whilst lower estimates were associated with self reported versus DNA-based determination of zygosity (−0.04, P = 0.02), and with self reported versus measured BMI (−0.05, P = 0.03). Although the observed differences in heritability according to aspects of study design are relatively small, together, the above factors explained 47% of the heterogeneity in estimates of BMI heritability from twin studies. In summary, while some variation in BMI heritability is expected due to population-level differences, study design factors explained nearly half the heterogeneity reported in twin studies. The genetic contribution to BMI appears to vary with age and may have a greater influence during childhood than adult life.

Objective

To investigate trends in obesity prevalence in recent years and to predict the obesity prevalence in 2015 in European populations.

Methods

Data of 97 942 participants from seven cohorts involved in the European Prospective Investigation into Cancer and Nutrition (EPIC) study participating in the Diogenes project (named as “Diogenes cohort” in the following) with weight measurements at baseline and follow-up were used to predict future obesity prevalence with logistic linear and non-linear (leveling off) regression models. In addition, linear and leveling off models were fitted to the EPIC-Potsdam dataset with five weight measures during the observation period to find out which of these two models might provide the more realistic prediction.

Results

During a mean follow-up period of 6 years, the obesity prevalence in the Diogenes cohort increased from 13% to 17%. The linear prediction model predicted an overall obesity prevalence of about 30% in 2015, whereas the leveling off model predicted a prevalence of about 20%. In the EPIC-Potsdam cohort, the shape of obesity trend favors a leveling off model among men (R2 = 0.98), and a linear model among women (R2 = 0.99).

Conclusion

Our data show an increase in obesity prevalence since the 1990ies, and predictions by 2015 suggests a sizeable further increase in European populations. However, the estimates from the leveling off model were considerably lower.

OBJECTIVE

Large-scale genome-wide association (GWA) studies have thus far identified 16 loci incontrovertibly associated with obesity-related traits in adults. We examined associations of variants in these loci with anthropometric traits in children and adolescents.

RESEARCH DESIGN AND METHODS

Seventeen variants representing 16 obesity susceptibility loci were genotyped in 1,252 children (mean ± SD age 9.7 ± 0.4 years) and 790 adolescents (15.5 ± 0.5 years) from the European Youth Heart Study (EYHS). We tested for association of individual variants and a genetic predisposition score (GPS-17), calculated by summing the number of effect alleles, with anthropometric traits. For 13 variants, summary statistics for associations with BMI were meta-analyzed with previously reported data (Ntotal = 13,071 children and adolescents).

RESULTS

In EYHS, 15 variants showed associations or trends with anthropometric traits that were directionally consistent with earlier reports in adults. The meta-analysis showed directionally consistent associations with BMI for all 13 variants, of which 9 were significant (0.033–0.098 SD/allele; P < 0.05). The near-TMEM18 variant had the strongest effect (0.098 SD/allele P = 8.5 × 10−11). Effect sizes for BMI tended to be more pronounced in children and adolescents than reported earlier in adults for variants in or near SEC16B, TMEM18, and KCTD15, (0.028–0.035 SD/allele higher) and less pronounced for rs925946 in BDNF (0.028 SD/allele lower). Each additional effect allele in the GPS-17 was associated with an increase of 0.034 SD in BMI (P = 3.6 × 10−5), 0.039 SD, in sum of skinfolds (P = 1.7 × 10−7), and 0.022 SD in waist circumference (P = 1.7 × 10−4), which is comparable with reported results in adults (0.039 SD/allele for BMI and 0.033 SD/allele for waist circumference).

CONCLUSIONS

Most obesity susceptibility loci identified by GWA studies in adults are already associated with anthropometric traits in children/adolescents. Whereas the association of some variants may differ with age, the cumulative effect size is similar.

Using mendelian randomization, Roman Pfister and colleagues demonstrate a potentially causal link between low levels of B-type natriuretic peptide (BNP), a hormone released by damaged hearts, and the development of type 2 diabetes.

Background

Genetic and epidemiological evidence suggests an inverse association between B-type natriuretic peptide (BNP) levels in blood and risk of type 2 diabetes (T2D), but the prospective association of BNP with T2D is uncertain, and it is unclear whether the association is confounded.

Methods and Findings

We analysed the association between levels of the N-terminal fragment of pro-BNP (NT-pro-BNP) in blood and risk of incident T2D in a prospective case-cohort study and genotyped the variant rs198389 within the BNP locus in three T2D case-control studies. We combined our results with existing data in a meta-analysis of 11 case-control studies. Using a Mendelian randomization approach, we compared the observed association between rs198389 and T2D to that expected from the NT-pro-BNP level to T2D association and the NT-pro-BNP difference per C allele of rs198389. In participants of our case-cohort study who were free of T2D and cardiovascular disease at baseline, we observed a 21% (95% CI 3%–36%) decreased risk of incident T2D per one standard deviation (SD) higher log-transformed NT-pro-BNP levels in analysis adjusted for age, sex, body mass index, systolic blood pressure, smoking, family history of T2D, history of hypertension, and levels of triglycerides, high-density lipoprotein cholesterol, and low-density lipoprotein cholesterol. The association between rs198389 and T2D observed in case-control studies (odds ratio = 0.94 per C allele, 95% CI 0.91–0.97) was similar to that expected (0.96, 0.93–0.98) based on the pooled estimate for the log-NT-pro-BNP level to T2D association derived from a meta-analysis of our study and published data (hazard ratio = 0.82 per SD, 0.74–0.90) and the difference in NT-pro-BNP levels (0.22 SD, 0.15–0.29) per C allele of rs198389. No significant associations were observed between the rs198389 genotype and potential confounders.

Conclusions

Our results provide evidence for a potential causal role of the BNP system in the aetiology of T2D. Further studies are needed to investigate the mechanisms underlying this association and possibilities for preventive interventions.

Please see later in the article for the Editors' Summary

Editors' Summary

Background

Worldwide, nearly 250 million people have diabetes, and this number is increasing rapidly. Diabetes is characterized by dangerous amounts of sugar (glucose) in the blood. Blood sugar levels are normally controlled by insulin, a hormone that the pancreas releases after meals (digestion of food produces glucose). In people with type 2 diabetes (the most common form of diabetes), blood sugar control fails because the fat and muscle cells that usually respond to insulin by removing sugar from the blood become insulin resistant. Type 2 diabetes can be controlled with diet and exercise, and with drugs that help the pancreas make more insulin or that make cells more sensitive to insulin. The long-term complications of diabetes, which include kidney failure and an increased risk of cardiovascular problems such as heart disease and stroke, reduce the life expectancy of people with diabetes by about 10 years compared to people without diabetes.

Why Was This Study Done?

Because the causes of type 2 diabetes are poorly understood, it is hard to devise ways to prevent the condition. Recently, B-type natriuretic peptide (BNP, a hormone released by damaged hearts) has been implicated in type 2 diabetes development in cross-sectional studies (investigations in which data are collected at a single time point from a population to look for associations between an illness and potential risk factors). Although these studies suggest that high levels of BNP may protect against type 2 diabetes, they cannot prove a causal link between BNP levels and diabetes because the study participants with low BNP levels may share some another unknown factor (a confounding factor) that is the real cause of both diabetes and altered BNP levels. Here, the researchers use an approach called “Mendelian randomization” to examine whether reduced BNP levels contribute to causing type 2 diabetes. It is known that a common genetic variant (rs198389) within the genome region that encodes BNP is associated with a reduced risk of type 2 diabetes. Because gene variants are inherited randomly, they are not subject to confounding. So, by investigating the association between BNP gene variants that alter NT-pro-BNP (a molecule created when BNP is being produced) levels and the development of type 2 diabetes, the researchers can discover whether BNP is causally involved in this chronic condition.

What Did the Researchers Do and Find?

The researchers analyzed the association between blood levels of NT-pro-BNP at baseline in 440 participants of the EPIC-Norfolk study (a prospective population-based study of lifestyle factors and the risk of chronic diseases) who subsequently developed diabetes and in 740 participants who did not develop diabetes. In this prospective case-cohort study, the risk of developing type 2 diabetes was associated with lower NT-pro-BNP levels. They also genotyped (sequenced) rs198389 in the participants of three case-control studies of type 2 diabetes (studies in which potential risk factors for type 2 diabetes were examined in people with type 2 diabetes and matched controls living in the East of England), and combined these results with those of eight similar published case-control studies. Finally, the researchers showed that the association between rs198389 and type 2 diabetes measured in the case-control studies was similar to the expected association calculated from the association between NT-pro-BNP level and type 2 diabetes obtained from the prospective case-cohort study and the association between rs198389 and BNP levels obtained from the EPIC-Norfolk study and other published studies.

What Do These Findings Mean?

The results of this Mendelian randomization study provide evidence for a causal, protective role of the BNP hormone system in the development of type 2 diabetes. That is, these findings suggest that low levels of BNP are partly responsible for the development of type 2 diabetes. Because the participants in all the individual studies included in this analysis were of European descent, these findings may not be generalizable to other ethnicities. Moreover, they provide no explanation of how alterations in the BNP hormone system might affect the development of type 2 diabetes. Nevertheless, the demonstration of a causal link between the BNP hormone system and type 2 diabetes suggests that BNP may be a potential target for interventions designed to prevent type 2 diabetes, particularly since the feasibility of altering BNP levels with drugs has already been proven in patients with cardiovascular disease.

Additional Information

Please access these websites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1001112.

The International Diabetes Federation provides information about all aspects of diabetes

The US National Diabetes Information Clearinghouse provides detailed information about diabetes for patients, health-care professionals, and the general public (in English and Spanish)

The UK National Health Service Choices website also provides information for patients and carers about type 2 diabetes and includes people's stories about diabetes

MedlinePlus provides links to further resources and advice about diabetes (in English and Spanish)

Wikipedia has pages on BNP and on Mendelian randomization (note: Wikipedia is a free online encyclopedia that anyone can edit; available in several languages)

The charity Healthtalkonline has interviews with people about their experiences of diabetes; the charity Diabetes UK has a further selection of stories from people with diabetes

High percent mammographic density adjusted for age and body mass index (BMI) is one of the strongest risk factors for breast cancer. We conducted a meta-analysis of five genome-wide association studies of percent mammographic density and report an association with rs10995190 in ZNF365 (combined P=9×6·10−10). This finding might partly explain the underlying biology of the recently discovered association between common variants in ZNF365 and breast cancer risk.

Background

Population-based screening for cardiovascular disease (CVD) risk, incorporating blood tests, is proposed in several countries.

Aim

The aim of this study was to evaluate whether a simple approach to identifying individuals at high risk of CVD using routine data might be effective.

Design of study

Prospective cohort study (EPIC-Norfolk).

Setting

Norfolk area, UK.

Method

A total of 21 867 men and women aged 40–74 years, who were free from CVD and diabetes at baseline, participated in the study. The discrimination (the area under the receiver operating characteristic curve [aROC]), calibration, sensitivity/specificity, and positive/negative predictive value were evaluated for different risk thresholds of the Framingham risk equations and the Cambridge diabetes risk score (as an example of a simple risk score using routine data from electronic general practice records).

Results

During 203 664 person-years of follow-up, 2213 participants developed a first CVD event (10.9 per 1000 person-years). The Cambridge diabetes risk score predicted CVD events reasonably well (aROC 0.72; 95% confidence interval [CI] = 0.71 to 0.73), while the Framingham risk score had the best predictive ability (aROC 0.77; 95% CI = 0.76 to 0.78). The Framingham risk score overestimated risk of developing CVD in this representative British population by 60%.

Conclusion

A risk score incorporating routinely available data from GP records performed reasonably well at predicting CVD events. This suggests that it might be more efficient to use routine data as the first stage in a stepwise population screening programme to identify people at high risk of developing CVD before more time- and resource-consuming tests are used.

Background

Few studies have compared the validity of objective measures of physical activity energy expenditure (PAEE) in pregnant and non-pregnant women. PAEE is commonly estimated with accelerometers attached to the hip or waist, but little is known about the validity and participant acceptability of wrist attachment. The objectives of the current study were to assess the validity of a simple summary measure derived from a wrist-worn accelerometer (GENEA, Unilever Discover, UK) to estimate PAEE in pregnant and non-pregnant women, and to evaluate participant acceptability.

Methods

Non-pregnant (N = 73) and pregnant (N = 35) Swedish women (aged 20–35 yrs) wore the accelerometer on their wrist for 10 days during which total energy expenditure (TEE) was assessed using doubly-labelled water. PAEE was calculated as 0.9×TEE-REE. British participants (N = 99; aged 22–65 yrs) wore accelerometers on their non-dominant wrist and hip for seven days and were asked to score the acceptability of monitor placement (scored 1 [least] through 10 [most] acceptable).

Results

There was no significant correlation between body weight and PAEE. In non-pregnant women, acceleration explained 24% of the variation in PAEE, which decreased to 19% in leave-one-out cross-validation. In pregnant women, acceleration explained 11% of the variation in PAEE, which was not significant in leave-one-out cross-validation. Median (IQR) acceptability of wrist and hip placement was 9(8–10) and 9(7–10), respectively; there was a within-individual difference of 0.47 (p<.001).

Conclusions

A simple summary measure derived from a wrist-worn tri-axial accelerometer adds significantly to the prediction of energy expenditure in non-pregnant women and is scored acceptable by participants.

Summary

Background

Intensive treatment of multiple cardiovascular risk factors can halve mortality among people with established type 2 diabetes. We investigated the effect of early multifactorial treatment after diagnosis by screening.

Methods

In a pragmatic, cluster-randomised, parallel-group trial done in Denmark, the Netherlands, and the UK, 343 general practices were randomly assigned screening of registered patients aged 40–69 years without known diabetes followed by routine care of diabetes or screening followed by intensive treatment of multiple risk factors. The primary endpoint was first cardiovascular event, including cardiovascular mortality and morbidity, revascularisation, and non-traumatic amputation within 5 years. Patients and staff assessing outcomes were unaware of the practice's study group assignment. Analysis was done by intention to treat. This study is registered with ClinicalTrials.gov, number NCT00237549.

Findings

Primary endpoint data were available for 3055 (99·9%) of 3057 screen-detected patients. The mean age was 60·3 (SD 6·9) years and the mean duration of follow-up was 5·3 (SD 1·6) years. Improvements in cardiovascular risk factors (HbA1c and cholesterol concentrations and blood pressure) were slightly but significantly better in the intensive treatment group. The incidence of first cardiovascular event was 7·2% (13·5 per 1000 person-years) in the intensive treatment group and 8·5% (15·9 per 1000 person-years) in the routine care group (hazard ratio 0·83, 95% CI 0·65–1·05), and of all-cause mortality 6·2% (11·6 per 1000 person-years) and 6·7% (12·5 per 1000 person-years; 0·91, 0·69–1·21), respectively.

Interpretation

An intervention to promote early intensive management of patients with type 2 diabetes was associated with a small, non-significant reduction in the incidence of cardiovascular events and death.

Funding

National Health Service Denmark, Danish Council for Strategic Research, Danish Research Foundation for General Practice, Danish Centre for Evaluation and Health Technology Assessment, Danish National Board of Health, Danish Medical Research Council, Aarhus University Research Foundation, Wellcome Trust, UK Medical Research Council, UK NIHR Health Technology Assessment Programme, UK National Health Service R&D, UK National Institute for Health Research, Julius Center for Health Sciences and Primary Care, University Medical Center, Utrecht, Novo Nordisk, Astra, Pfizer, GlaxoSmithKline, Servier, HemoCue, Merck.

Background

To explore the relationship between leisure and commuter cycling with objectively measured levels of road traffic and whether any relationship was affected by traffic levels directly outside of home or in local neighbourhood.

Findings

We conducted a secondary analysis of data from the UK European Prospective Investigation of Cancer (EPIC) Norfolk cohort in 2009. We used a geographical information system (GIS) and gender specific multivariate models to relate 13 927 participants' reported levels of cycling with an index of road traffic volume (Road Traffic Volume Index Score - RTVIS). RTVIS were calculated around each participants home, using four distance based buffers, (0.5 km, 1 km, 2 km and 3.2 km). Models were adjusted for age, social status, education, car access and deprivation. Both genders had similar decreases in leisure cycling as traffic volumes increased at greater distances from home (OR 0.42, (95% CI 0.32-0.52, p < 0.001) for women and OR 0.41, (95% CI 0.33-0.50, p < 0.001) for men in the highest quartile at 3.2 km). There was no effect of traffic volumes at any distance on commuter cycling.

Conclusions

Traffic volumes appear to have greater impact on leisure cycling than commuter cycling. Future research should investigate the importance of traffic on different types of cycling and include psychosocial correlates.

To infer the causal association between childhood BMI and age at menarche, we performed a mendelian randomisation analysis using twelve established “BMI-increasing” genetic variants as an instrumental variable (IV) for higher BMI. In 8,156 women of European descent from the EPIC-Norfolk cohort, height was measured at age 39–77 years; age at menarche was self-recalled, as was body weight at age 20 years, and BMI at 20 was calculated as a proxy for childhood BMI. DNA was genotyped for twelve BMI-associated common variants (in/near FTO, MC4R, TMEM18, GNPDA2, KCTD15, NEGR1, BDNF, ETV5, MTCH2, SEC16B, FAIM2 and SH2B1), and for each individual a “BMI-increasing-allele-score” was calculated by summing the number of BMI-increasing alleles across all 12 loci. Using this BMI-increasing-allele-score as an instrumental variable for BMI, each 1 kg/m2 increase in childhood BMI was predicted to result in a 6.5% (95% CI: 4.6–8.5%) higher absolute risk of early menarche (before age 12 years). While mendelian randomisation analysis is dependent on a number of assumptions, our findings support a causal effect of BMI on early menarche and suggests that increasing prevalence of childhood obesity will lead to similar trends in the prevalence of early menarche.

OBJECTIVE

Wolfram syndrome 1 (WFS1) single nucleotide polymorphisms (SNPs) are associated with risk of type 2 diabetes. In this study we aimed to refine this association and investigate the role of low-frequency WFS1 variants in type 2 diabetes risk.

RESEARCH DESIGN AND METHODS

For fine-mapping, we sequenced WFS1 exons, splice junctions, and conserved noncoding sequences in samples from 24 type 2 diabetic case and 68 control subjects, selected tagging SNPs, and genotyped these in 959 U.K. type 2 diabetic case and 1,386 control subjects. The same genomic regions were sequenced in samples from 1,235 type 2 diabetic case and 1,668 control subjects to compare the frequency of rarer variants between case and control subjects.

RESULTS

Of 31 tagging SNPs, the strongest associated was the previously untested 3′ untranslated region rs1046320 (P = 0.008); odds ratio 0.84 and P = 6.59 × 10−7 on further replication in 3,753 case and 4,198 control subjects. High correlation between rs1046320 and the original strongest SNP (rs10010131) (r2 = 0.92) meant that we could not differentiate between their effects in our samples. There was no difference in the cumulative frequency of 82 rare (minor allele frequency [MAF] <0.01) nonsynonymous variants between type 2 diabetic case and control subjects (P = 0.79). Two intermediate frequency (MAF 0.01–0.05) nonsynonymous changes also showed no statistical association with type 2 diabetes.

CONCLUSIONS

We identified six highly correlated SNPs that show strong and comparable associations with risk of type 2 diabetes, but further refinement of these associations will require large sample sizes (>100,000) or studies in ethnically diverse populations. Low frequency variants in WFS1 are unlikely to have a large impact on type 2 diabetes risk in white U.K. populations, highlighting the complexities of undertaking association studies with low-frequency variants identified by resequencing.

OBJECTIVE

WFS1 (Wolfram Syndrome 1) SNPs are associated with risk of type 2 diabetes (T2D). Here, we aimed to refine this association and investigate the role of low frequency WFS1 variants in T2D risk.

RESEARCH DESIGN AND METHODS

For fine-mapping, we sequenced WFS1 exons, splice junctions and conserved non-coding sequences in 24 T2D cases and 68 controls, selected tagging SNPs, and genotyped these in 959 UK T2D cases and 1386 controls. The same genomic regions were sequenced in 1235 T2D cases and 1668 controls to compare the frequency of rarer variants between cases and controls.

RESULTS

Of 31 tagging SNPs, the strongest associated was the previously untested 3′ UTR rs1046320 (P=0.008); OR=0.84, P=6.59 × 10−7 on further replication in 3753 cases and 4198 controls. High correlation between rs1046320 and the original strongest SNP (rs10010131) (r2=0.92) meant that we could not differentiate between their effects in our samples. There was no difference in the cumulative frequency of 82 rare (MAF<0.01) non-synonymous variants between T2D cases and controls (P=0.79). Two intermediate frequency (MAF 0.01-0.05) non-synonymous changes also showed no statistical association with T2D.

CONCLUSION

We identified six highly correlated SNPs that show strong and comparable associations with risk of T2D association but further refinement of these associations will require large sample sizes (>100,000), or studies in ethnically diverse populations. Low frequency variants in WFS1 are unlikely to have a large impact on T2D risk in white UK populations, highlighting the complexities of undertaking association studies with low frequency variants identified by re-sequencing.