PMCC PMCC

Search tips
Search criteria

Advanced
Results 1-25 (1628807)

Clipboard (0)
None

Related Articles

1.  Genetic Markers of Adult Obesity Risk Are Associated with Greater Early Infancy Weight Gain and Growth 
PLoS Medicine  2010;7(5):e1000284.
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)
doi:10.1371/journal.pmed.1000284
PMCID: PMC2876048  PMID: 20520848
2.  Metabolic Signatures of Adiposity in Young Adults: Mendelian Randomization Analysis and Effects of Weight Change 
PLoS Medicine  2014;11(12):e1001765.
In this study, Wurtz and colleagues investigated to what extent elevated body mass index (BMI) within the normal weight range has causal influences on the detailed systemic metabolite profile in early adulthood using Mendelian randomization analysis.
Please see later in the article for the Editors' Summary
Background
Increased adiposity is linked with higher risk for cardiometabolic diseases. We aimed to determine to what extent elevated body mass index (BMI) within the normal weight range has causal effects on the detailed systemic metabolite profile in early adulthood.
Methods and Findings
We used Mendelian randomization to estimate causal effects of BMI on 82 metabolic measures in 12,664 adolescents and young adults from four population-based cohorts in Finland (mean age 26 y, range 16–39 y; 51% women; mean ± standard deviation BMI 24±4 kg/m2). Circulating metabolites were quantified by high-throughput nuclear magnetic resonance metabolomics and biochemical assays. In cross-sectional analyses, elevated BMI was adversely associated with cardiometabolic risk markers throughout the systemic metabolite profile, including lipoprotein subclasses, fatty acid composition, amino acids, inflammatory markers, and various hormones (p<0.0005 for 68 measures). Metabolite associations with BMI were generally stronger for men than for women (median 136%, interquartile range 125%–183%). A gene score for predisposition to elevated BMI, composed of 32 established genetic correlates, was used as the instrument to assess causality. Causal effects of elevated BMI closely matched observational estimates (correspondence 87%±3%; R2 = 0.89), suggesting causative influences of adiposity on the levels of numerous metabolites (p<0.0005 for 24 measures), including lipoprotein lipid subclasses and particle size, branched-chain and aromatic amino acids, and inflammation-related glycoprotein acetyls. Causal analyses of certain metabolites and potential sex differences warrant stronger statistical power. Metabolite changes associated with change in BMI during 6 y of follow-up were examined for 1,488 individuals. Change in BMI was accompanied by widespread metabolite changes, which had an association pattern similar to that of the cross-sectional observations, yet with greater metabolic effects (correspondence 160%±2%; R2 = 0.92).
Conclusions
Mendelian randomization indicates causal adverse effects of increased adiposity with multiple cardiometabolic risk markers across the metabolite profile in adolescents and young adults within the non-obese weight range. Consistent with the causal influences of adiposity, weight changes were paralleled by extensive metabolic changes, suggesting a broadly modifiable systemic metabolite profile in early adulthood.
Please see later in the article for the Editors' Summary
Editors' Summary
Background
Adiposity—having excessive body fat—is a growing global threat to public health. Body mass index (BMI, calculated by dividing a person's weight in kilograms by their height in meters squared) is a coarse indicator of excess body weight, but the measure is useful in large population studies. Compared to people with a lean body weight (a BMI of 18.5–24.9 kg/m2), individuals with higher BMI have an elevated risk of developing life-shortening cardiometabolic diseases—cardiovascular diseases that affect the heart and/or the blood vessels (for example, heart failure and stroke) and metabolic diseases that affect the cellular chemical reactions that sustain life (for example, diabetes). People become unhealthily fat by consuming food and drink that contains more energy (calories) than they need for their daily activities. So adiposity can be prevented and reversed by eating less and exercising more.
Why Was This Study Done?
Epidemiological studies, which record the patterns of risk factors and disease in populations, suggest that the illness and death associated with excess body weight is partly attributable to abnormalities in how individuals with high adiposity metabolize carbohydrates and fats, leading to higher blood sugar and cholesterol levels. Further, adiposity is also associated with many other deviations in the metabolic profile than these commonly measured risk factors. However, epidemiological studies cannot prove that adiposity causes specific changes in a person's systemic (overall) metabolic profile because individuals with high BMI may share other characteristics (confounding factors) that are the actual causes of both adiposity and metabolic abnormalities. Moreover, having a change in some aspect of metabolism could also lead to adiposity, rather than vice versa (reverse causation). Importantly, if there is a causal effect of adiposity on cardiometabolic risk factor levels, it might be possible to prevent the progression towards cardiometabolic diseases by weight loss. Here, the researchers use “Mendelian randomization” to examine whether increased BMI within the normal and overweight range is causally influencing the metabolic risk factors from many biological pathways during early adulthood. Because gene variants are inherited randomly, they are not prone to confounding and are free from reverse causation. Several gene variants are known to lead to modestly increased BMI. Thus, an investigation of the associations between these gene variants and risk factors across the systemic metabolite profile in a population of healthy individuals can indicate whether higher BMI is causally related to known and novel metabolic risk factors and higher cardiometabolic disease risk.
What Did the Researchers Do and Find?
The researchers measured the BMI of 12,664 adolescents and young adults (average BMI 24.7 kg/m2) living in Finland and the blood levels of 82 metabolites in these young individuals at a single time point. Statistical analysis of these data indicated that elevated BMI was adversely associated with numerous cardiometabolic risk factors. For example, elevated BMI was associated with raised levels of low-density lipoprotein, “bad” cholesterol that increases cardiovascular disease risk. Next, the researchers used a gene score for predisposition to increased BMI, composed of 32 gene variants correlated with increased BMI, as an “instrumental variable” to assess whether adiposity causes metabolite abnormalities. The effects on the systemic metabolite profile of a 1-kg/m2 increment in BMI due to genetic predisposition closely matched the effects of an observed 1-kg/m2 increment in adulthood BMI on the metabolic profile. That is, higher levels of adiposity had causal effects on the levels of numerous blood-based metabolic risk factors, including higher levels of low-density lipoprotein cholesterol and triglyceride-carrying lipoproteins, protein markers of chronic inflammation and adverse liver function, impaired insulin sensitivity, and elevated concentrations of several amino acids that have recently been linked with the risk for developing diabetes. Elevated BMI also causally led to lower levels of certain high-density lipoprotein lipids in the blood, a marker for the risk of future cardiovascular disease. Finally, an examination of the metabolic changes associated with changes in BMI in 1,488 young adults after a period of six years showed that those metabolic measures that were most strongly associated with BMI at a single time point likewise displayed the highest responsiveness to weight change over time.
What Do These Findings Mean?
These findings suggest that increased adiposity has causal adverse effects on multiple cardiometabolic risk markers in non-obese young adults beyond the effects on cholesterol and blood sugar. Like all Mendelian randomization studies, the reliability of the causal association reported here depends on several assumptions made by the researchers. Nevertheless, these findings suggest that increased adiposity has causal adverse effects on multiple cardiometabolic risk markers in non-obese young adults. Importantly, the results of both the causal effect analyses and the longitudinal study suggest that there is no threshold below which a BMI increase does not adversely affect the metabolic profile, and that a systemic metabolic profile linked with high cardiometabolic disease risk that becomes established during early adulthood can be reversed. Overall, these findings therefore highlight the importance of weight reduction as a key target for metabolic risk factor control among young adults.
Additional Information
Please access these websites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1001765.
The Computational Medicine Research Team of the University of Oulu has a webpage that provides further information on metabolite profiling by high-throughput NMR metabolomics
The World Health Organization provides information on obesity (in several languages)
The Global Burden of Disease Study website provides the latest details about global obesity trends
The UK National Health Service Choices website provides information about obesity, cardiovascular disease, and type 2 diabetes (including some personal stories)
The American Heart Association provides information on all aspects of cardiovascular disease and diabetes and on keeping healthy; its website includes personal stories about heart attacks, stroke, and diabetes
The US Centers for Disease Control and Prevention has information on all aspects of overweight and obesity and information about heart disease, stroke, and diabetes
MedlinePlus provides links to other sources of information on heart disease, vascular disease, and obesity (in English and Spanish)
Wikipedia has a page on Mendelian randomization (note: Wikipedia is a free online encyclopedia that anyone can edit; available in several languages)
doi:10.1371/journal.pmed.1001765
PMCID: PMC4260795  PMID: 25490400
3.  How Has the Age-Related Process of Overweight or Obesity Development Changed over Time? Co-ordinated Analyses of Individual Participant Data from Five United Kingdom Birth Cohorts 
PLoS Medicine  2015;12(5):e1001828.
Background
There is a paucity of information on secular trends in the age-related process by which people develop overweight or obesity. Utilizing longitudinal data in the United Kingdom birth cohort studies, we investigated shifts over the past nearly 70 years in the distribution of body mass index (BMI) and development of overweight or obesity across childhood and adulthood.
Methods and Findings
The sample comprised 56,632 participants with 273,843 BMI observations in the 1946 Medical Research Council National Survey of Health and Development (NSHD; ages 2–64 years), 1958 National Child Development Study (NCDS; 7–50), 1970 British Cohort Study (BCS; 10–42), 1991 Avon Longitudinal Study of Parents and Children (ALSPAC; 7–18), or 2001 Millennium Cohort Study (MCS; 3–11). Growth references showed a secular trend toward positive skewing of the BMI distribution at younger ages. During childhood, the 50th centiles for all studies lay in the middle of the International Obesity Task Force normal weight range, but during adulthood, the age when a 50th centile first entered the overweight range (i.e., 25–29.9 kg/m2) decreased across NSHD, NCDS, and BCS from 41 to 33 to 30 years in males and 48 to 44 to 41 years in females. Trajectories of overweight or obesity showed that more recently born cohorts developed greater probabilities of overweight or obesity at younger ages. Overweight or obesity became more probable in NCDS than NSHD in early adulthood, but more probable in BCS than NCDS and NSHD in adolescence, for example. By age 10 years, the estimated probabilities of overweight or obesity in cohorts born after the 1980s were 2–3 times greater than those born before the 1980s (e.g., 0.229 [95% CI 0.219–0.240] in MCS males; 0.071 [0.065–0.078] in NSHD males). It was not possible to (1) model separate trajectories for overweight and obesity, because there were few obesity cases at young ages in the earliest-born cohorts, or (2) consider ethnic minority groups. The end date for analyses was August 2014.
Conclusions
Our results demonstrate how younger generations are likely to accumulate greater exposure to overweight or obesity throughout their lives and, thus, increased risk for chronic health conditions such as coronary heart disease and type 2 diabetes mellitus. In the absence of effective intervention, overweight and obesity will have severe public health consequences in decades to come.
In a longitudinal analysis, William Johnson and colleagues examine how individual lifetime BMI trajectories among white citizens of the UK have changed from 1946 to 2014.
Editors' Summary
Background
Overweight and obesity are major threats to global health. The global prevalence of obesity (the proportion of the world's population that is obese) has more than doubled since 1980; 13% of the adult population, or 0.6 billion people, are now classified as obese, while an additional 1.3 billion adults are overweight. Both classifications are determined by body mass index (BMI), which is calculated by dividing a person's weight in kilograms by the square of their height in meters. Obese individuals have a BMI of 30 kg/m2 or more, while overweight individuals have a BMI of 25–30 kg/m2. BMI values above 25 kg/m2 increase the risk of developing non-communicable diseases (NCDs), including cardiovascular diseases, cancers and diabetes. Each year, NCDs kill 38 million people (including 28 million people in low- and middle-income countries and 9 million people under 60 years of age), thereby accounting for more than 75% of the world's annual deaths.
In the United Kingdom, studies report that roughly one quarter of adults are obese, and a further third or more are overweight. This “obesity epidemic” extends to children; according to the National Child Measurement Programme for England (NCMP), about 9% of 4–5-year-olds and 19% of 10–11-year-olds were obese in 2013. In parallel, the UK has not seen the improvements in child and young adult mortality seen in comparable European states.
Why Was This Study Done?
Cross-sectional surveys in the UK, United States, and elsewhere have documented the obesity epidemic, but longitudinal data—drawn from periodic BMI measurements from individuals over their lifetimes—are needed to clarify the time course, or trajectory, of overweight and obesity. Longitudinal data can answer practical questions important for designing health policy interventions. Is the age at which individuals develop overweight or obesity changing over time? In which individuals are the greatest increases in BMI occurring? The authors leveraged longitudinal data from five birth cohort studies (studies that follow a selected group of individuals born during a short window of time), incepted in 1946, 1958, 1970, 1991, and 2001. These large cohort projects were funded by the UK government for the purpose of providing data for long-term health analyses such as this one; in total, the current study’s included sample comprised 56,632 participants with 273,843 BMI observations from participants aged 2 through 64.
What Did the Researchers Do and Find?
The present study aimed to investigate (1) shifts from the 1940s to the 2000s in the distribution of BMI across age and (2) shifts over the same period in the probability of developing overweight or obesity across age. For each of the five cohorts, subdivided by sex and childhood versus adulthood (thus, a total of 20 datasets), the authors applied statistical models to produce trajectories for each BMI centile (subset that results from dividing the distribution of BMI measurements into 100 groups with equal frequency; here, the 90th centile is the group for which 90% of the relevant population has lower BMI). They then investigated secular trends (long-term, non-periodic variations) at different centiles of the BMI distribution. For example, by comparing the trajectories of the 50th centile for adult males across the five cohorts, the researchers could see how the age at which BMI values reached the obese range varied between eras among this group.
The data revealed that most of the between-cohort, and thus between-era, increases in BMI took place in the highest centiles, indicating that overall gains in BMI mainly comprised very high BMI individuals carrying even more weight. Across the 1946, 1958, and 1970 cohorts, the age at which the 50th centile of adults entered the overweight range decreased from 41 to 33 to 30 years in males and 48 to 44 to 41 years in females. The probabilities of overweight and obesity across adulthood also increased. While children in the 50th BMI centile have remained at normal weight through the decades, the overall childhood probability of developing overweight or obesity has increased 2–3-fold from before to after the 1980s.
What Do These Findings Mean?
These findings describe the changing pattern of age-related progression of overweight and obesity from early childhood in white citizens of the UK. The findings may not be generalizable because other populations have distinct genetic predispositions, environmental exposures, and access to health care. In addition, the accuracy of the findings may be affected by differences between cohorts in how weight and height (and thus BMI) were measured. Nevertheless, these findings—in particular, the increased risk of overweight and obesity at younger ages—suggest that compared to previous generations, current and future generations will accumulate greater overweight or obesity exposure across their lives, likely resulting in increased risk for NCDs. Further research is now needed to determine whether lifestyle factors in the UK have affected the trajectory of BMI and to discover the extent to which these shifting weight trajectories have contributed to morbidity and mortality.
Additional Information
This list of resources contains links that can be accessed when viewing the PDF on a device or via the online version of the article at http://dx.doi.org/10.1371/journal.pmed.1001828. The World Health Organization provides information on obesity and non-communicable diseases around the world (in several languages)The UK National Health Service Choices website also provides detailed information about obesity and a link to a personal story about losing weightThe International Obesity Taskforce provides information about the global obesity epidemicThe US Centers for Disease Control and Prevention provides information on non-communicable diseases around the world and on overweight and obesity and diabetes (including some information in Spanish)The US Department of Agriculture's ChooseMyPlate.gov website provides a personal healthy eating planThe Weight-control Information Network is an information service provided for the general public and health professionals by the US National Institute of Diabetes and Digestive and Kidney Diseases (in English and Spanish)MedlinePlus has links to further information about obesity (in English and Spanish)
doi:10.1371/journal.pmed.1001828
PMCID: PMC4437909  PMID: 25993005
4.  Interactions between genotype and depressive symptoms on obesity 
Behavior genetics  2009;39(3):296-305.
Background
Depression and Genetic variation in serotonin and monoamine transmission have both been associated with Body Mass Index (BMI), but their interaction effects are not well understood. We examined the interaction between depressive symptoms and functional polymorphisms of serotonin transporter (SLC6A4) and monoamine oxidase A (MAOA) on categories of BMI.
Methods
Participants were from the National Longitudinal Study of Adolescent Health. Multiple logistic regression was used to investigate interactions between candidate genes and depression on risk of obesity (BMI≥30) or overweight + obese combined (BMI≥25).
Results
Males with an MAOA active allele with high depressive symptoms were at decreased risk of obesity (OR, 0.22; 95% CI, 0.06 – 0.78) and overweight + obesity (OR, 0.48; 95% CI, 0.26 – 0.89). No similar effect was observed among females.
Conclusions
These findings highlight that the obesity-depression relationship may vary as a function of gender and genetic polymorphism, and suggest the need for further study.
doi:10.1007/s10519-009-9266-z
PMCID: PMC2884968  PMID: 19337825
5.  Association analyses for dopamine receptor gene polymorphisms and weight status in a longitudinal analysis in obese children before and after lifestyle intervention 
BMC Pediatrics  2013;13:197.
Background
Dopamine receptors are involved in midbrain reward circuit activation. Polymorphisms in two dopamine receptor genes, DRD2 and DRD4, have been associated with altered perception of food reward and weight gain. The objective of this study was to determine whether the same risk alleles were associated with overweight/obesity and with lower reduction of overweight after a 1-year lifestyle intervention.
Methods
In a longitudinal study the association of polymorphisms in DRD2 (rs18000497, risk allele: T, formerly A1 allele at the TaqI A1 polymorphism) and DRD4 (variable number of tandem repeats (VNTR); 48 bp repeat in exon III; risk alleles: 7 repeats or longer: 7R+) was tested on weight loss success following a 1-year lifestyle childhood obesity intervention (OBELDICKS). An additional exploratory cross-sectional case-control study was performed to compare the same DRD polymorphisms in these overweight/obese children and adolescents versus lean adult controls. Subjects were 423 obese and 28 overweight children participating in lifestyle intervention (203 males), age median 12.0 (interquartile range 10.0–13.7) years, body mass index - standard deviation score (BMI-SDS) 2.4 ± 0.5; 583 lean adults (232 males); age median 25.3 (interquartile range 22.5–26.8) years, BMI 19.1 ± 1.9 kg/m2. BMI, BMI-SDS and skinfold thickness measures were assessed at baseline and after 1 year; genotyping was performed for DRD2 risk variant rs1800497 and DRD4 exon III VNTR.
Results
The DRD2 genotype had a nominal effect on success in the weight loss intervention. The weakest BMI-SDS reduction was in children homozygous for two rs1800497 T-alleles (n = 11) compared to the combined group with zero (n = 308) or one (n = 132) rs1800497 T-allele (-0.08 ± 0.36 vs. -0.28 ± 0.34; p < 0.05). There was no association between the DRD4 VNTR alleles and genotypes and success in the weight loss intervention. No associations of the risk alleles of the DRD2 and DRD4 polymorphisms and obesity were observed in the cross-sectional part of the study.
Conclusions
We did not find association between polymorphisms in DRD2 and DRD4 genes and weight status. However, obese carriers of two DRD2 rs1800497 T-alleles may be at risk for weak responses to lifestyle interventions aimed at weight reduction.
Trial registration
Obesity intervention program “Obeldicks” is registered at clinicaltrials.gov (NCT00435734).
doi:10.1186/1471-2431-13-197
PMCID: PMC4219494  PMID: 24283216
Dopamine receptor polymorphisms; Obesity; Lifestyle intervention; Weight reduction
6.  Change in the Body Mass Index Distribution for Women: Analysis of Surveys from 37 Low- and Middle-Income Countries 
PLoS Medicine  2013;10(1):e1001367.
Using cross-sectional surveys, Fahad Razak and colleagues investigate how the BMI (body mass index) distribution is changing for women in low- and middle-income countries.
Background
There are well-documented global increases in mean body mass index (BMI) and prevalence of overweight (BMI≥25.0 kg/m2) and obese (BMI≥30.0 kg/m2). Previous analyses, however, have failed to report whether this weight gain is shared equally across the population. We examined the change in BMI across all segments of the BMI distribution in a wide range of countries, and assessed whether the BMI distribution is changing between cross-sectional surveys conducted at different time points.
Methods and Findings
We used nationally representative surveys of women between 1991–2008, in 37 low- and middle-income countries from the Demographic Health Surveys ([DHS] n = 732,784). There were a total of 96 country-survey cycles, and the number of survey cycles per country varied between two (21/37) and five (1/37). Using multilevel regression models, between countries and within countries over survey cycles, the change in mean BMI was used to predict the standard deviation of BMI, the prevalence of underweight, overweight, and obese. Changes in median BMI were used to predict the 5th and 95th percentile of the BMI distribution. Quantile-quantile plots were used to examine the change in the BMI distribution between surveys conducted at different times within countries. At the population level, increasing mean BMI is related to increasing standard deviation of BMI, with the BMI at the 95th percentile rising at approximately 2.5 times the rate of the 5th percentile. Similarly, there is an approximately 60% excess increase in prevalence of overweight and 40% excess in obese, relative to the decline in prevalence of underweight. Quantile-quantile plots demonstrate a consistent pattern of unequal weight gain across percentiles of the BMI distribution as mean BMI increases, with increased weight gain at high percentiles of the BMI distribution and little change at low percentiles. Major limitations of these results are that repeated population surveys cannot examine weight gain within an individual over time, most of the countries only had data from two surveys and the study sample only contains women in low- and middle-income countries, potentially limiting generalizability of findings.
Conclusions
Mean changes in BMI, or in single parameters such as percent overweight, do not capture the divergence in the degree of weight gain occurring between BMI at low and high percentiles. Population weight gain is occurring disproportionately among groups with already high baseline BMI levels. Studies that characterize population change should examine patterns of change across the entire distribution and not just average trends or single parameters.
Please see later in the article for the Editors' Summary
Editors' Summary
Background
The number of obese people (individuals who have an excessive amount of body fat) is rapidly increasing in many countries. Globally, there were about 200 million obese adults in 1995; by 2010, 475 million adults were obese and another billion were classified as overweight. Obesity is defined as having a body mass index (BMI, an indicator of body fat calculated by dividing a person's weight in kilograms by their height in meters squared) of more than 30.0 kg/m2. Compared to people with a healthy weight (a BMI between 18.5 and 24.9 kg/m2), obese individuals and overweight individuals (who have a BMI between 25.0 and 29.9 kg/m2) have an increased risk of developing diabetes, heart disease and stroke, and tend to die younger. At the same time in many developing countries substantial numbers of people are underweight (BMI <18.5 kg/m2) or have chronic energy deficiency (BMI <16.0 kg/m2) and are at risk of increased risk of dying due to infectious disease or respiratory problems.
Why Was This Study Done?
The global obesity epidemic is usually described in terms of increases in the average BMI or in the prevalence of obesity (the proportion of the population whose BMI is above 30.0 kg/m2). Such descriptions assume that the BMIs of fat and thin people are increasing at the same rate and that the shape of the population's BMI distribution curve remains constant. However, as average BMI and the prevalence of obesity can increase it is unclear how the prevalence of underweight changes. This is potentially important for the health of the population because underweight individuals, like obese individuals, tend to die younger than healthy weight individuals, particularly in low-income countries. In this study, the researchers use repeated cross-sectional survey data collected from low- and middle-income countries in the Demographic and Health Surveys (DHS) to examine changes in BMI in women across the BMI distribution between 1991 and 2008. Repeated cross-sectional surveys collect data from a population at multiple time points from different individuals drawn from the same population, DHS are a data collection and surveillance project that help developing countries track health and population trends.
What Did the Researchers Do and Find?
The researchers used statistical models to analyze data from DHS surveys of more than 730,000 women living in 37 low- and middle-income countries (two to five surveys per country). Increasing average BMI was associated with an increase in the standard deviation of BMI (a measure of the dispersion of BMI in the population) both across and within countries over time. With increasing average BMI, the BMI at both the 5th and 95th percentile increased; 90% of the BMIs in a population lie between these percentiles so these BMI values indicate the spread of the BMI distribution. However, the BMI at the 95th percentile increased about 2.5 times faster than the BMI at the 5th percentile. Moreover, with increasing average BMI, the prevalence of overweight and obesity increased faster than the decline in the prevalence of underweight. Finally, quantile-quantile plots for each country (a graphical method that compares two distributions) revealed a consistent pattern of unequal weight gain across the BMI distribution as average BMI increased, with pronounced weight gains at the obese end of the distribution and little change at the underweight end.
What Do These Findings Mean?
These findings show that increases in average BMI are associated with an increased spread of BMI across and within populations. Consequently, changes in average BMI or single measurements such as the prevalence of overweight do not capture the divergence in the degree of weight gain occurring between that part of the population that has a low BMI and that part that has a high BMI. In other words, at least for the low- and middle-income countries included in this study, population weight gain is occurring disproportionately among groups with high baseline BMI levels. The researchers suggest, therefore, that the characterization of the BMI of populations over time should examine the patterns of change across the whole BMI distribution. Moreover, rather than a single broad population strategy for weight control, optimum health outcomes, they suggest, might be achieved by a strategy that includes targeted interventions to reduce weight in high BMI segments of the population and to increase weight in low BMI segments.
Additional Information
Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1001367.
The US Centers for Disease Control and Prevention provides information on all aspects of overweight and obesity (in English and Spanish)
The World Health Organization provides information on obesity (in several languages); Malri's story describes the health risks faced by an obese child
The UK National Health Service Choices website also provides detailed information about obesity and a link to a personal story about losing weight
The International Obesity Taskforce provides information about the global obesity epidemic
The US Department of Agriculture's ChooseMyPlate.gov website provides a personal healthy eating plan; the Weight-control Information Network is an information service provided for the general public and health professionals by the US National Institute of Diabetes and Digestive and Kidney Diseases (in English and Spanish)
MedlinePlus has links to further information about obesity (in English and Spanish)
doi:10.1371/journal.pmed.1001367
PMCID: PMC3545870  PMID: 23335861
7.  Dopamine Polymorphisms and Depressive Symptoms Predict Foods Intake: Results from a Nationally Representative Sample 
Appetite  2011;57(2):339-348.
Depression and variation in dopamine related genes have both independently been associated with food consumption. Depressive symptoms could synergistically interact with genetic variation to influence food intake. We examined the interaction between high depressive symptoms and functional polymorphisms of dopamine transportor (SLC6A3), monoamine oxidase A (MAOA), dopamine receptor D2 (DRD2) and dopamine receptor D4 (DRD4) on intake of high-calorie sweet, high-calorie non-sweet, and low-calorie foods in the National Longitudinal Study of Adolescent Health (Add Health). Multivariate linear regression analyses were used to examine main effects of gene and depression symptoms and their interaction (genotype-by-high depression symptoms) on food categories. Applying a false discovery rate criterion for multiple comparisons indicated a statistically significant interaction for females with high depressive symptoms and the SLC6A3 gene, such that those with the SLC6A3 10/10 allele reported greater intake of high-calorie sweet foods than their counterparts high in depressive symptoms with the SLC6A3 any 9 allele (LS mean 10/10 allele = 2.5, SE = .13; LS mean any 9 allele = 1.8, SE = .13, p<.05). These findings highlight that the relationship between depression and food intake may vary as a function of genetic polymorphism. Further research is needed to confirm these findings.
doi:10.1016/j.appet.2011.05.325
PMCID: PMC3156384  PMID: 21672565
Adolescent; Diet; Dopamine; Depression
8.  Assessing Causality in the Association between Child Adiposity and Physical Activity Levels: A Mendelian Randomization Analysis 
PLoS Medicine  2014;11(3):e1001618.
Here, Timpson and colleagues performed a Mendelian Randomization analysis to determine whether childhood adiposity causally influences levels of physical activity. The results suggest that increased adiposity causes a reduction in physical activity in children; however, this study does not exclude lower physical activity also leading to increasing adiposity.
Please see later in the article for the Editors' Summary
Background
Cross-sectional studies have shown that objectively measured physical activity is associated with childhood adiposity, and a strong inverse dose–response association with body mass index (BMI) has been found. However, few studies have explored the extent to which this association reflects reverse causation. We aimed to determine whether childhood adiposity causally influences levels of physical activity using genetic variants reliably associated with adiposity to estimate causal effects.
Methods and Findings
The Avon Longitudinal Study of Parents and Children collected data on objectively assessed activity levels of 4,296 children at age 11 y with recorded BMI and genotypic data. We used 32 established genetic correlates of BMI combined in a weighted allelic score as an instrumental variable for adiposity to estimate the causal effect of adiposity on activity.
In observational analysis, a 3.3 kg/m2 (one standard deviation) higher BMI was associated with 22.3 (95% CI, 17.0, 27.6) movement counts/min less total physical activity (p = 1.6×10−16), 2.6 (2.1, 3.1) min/d less moderate-to-vigorous-intensity activity (p = 3.7×10−29), and 3.5 (1.5, 5.5) min/d more sedentary time (p = 5.0×10−4). In Mendelian randomization analyses, the same difference in BMI was associated with 32.4 (0.9, 63.9) movement counts/min less total physical activity (p = 0.04) (∼5.3% of the mean counts/minute), 2.8 (0.1, 5.5) min/d less moderate-to-vigorous-intensity activity (p = 0.04), and 13.2 (1.3, 25.2) min/d more sedentary time (p = 0.03). There was no strong evidence for a difference between variable estimates from observational estimates. Similar results were obtained using fat mass index. Low power and poor instrumentation of activity limited causal analysis of the influence of physical activity on BMI.
Conclusions
Our results suggest that increased adiposity causes a reduction in physical activity in children and support research into the targeting of BMI in efforts to increase childhood activity levels. Importantly, this does not exclude lower physical activity also leading to increased adiposity, i.e., bidirectional causation.
Please see later in the article for the Editors' Summary
Editors' Summary
Background
The World Health Organization estimates that globally at least 42 million children under the age of five are obese. The World Health Organization recommends that all children undertake at least one hour of physical activity daily, on the basis that increased physical activity will reduce or prevent excessive weight gain in children and adolescents. In practice, while numerous studies have shown that body mass index (BMI) shows a strong inverse correlation with physical activity (i.e., active children are thinner than sedentary ones), exercise programs specifically targeted at obese children have had only very limited success in reducing weight. The reasons for this are not clear, although environmental factors such as watching television and lack of exercise facilities are traditionally blamed.
Why Was This Study Done?
One of the reasons why obese children do not lose weight through exercise might be that being fat in itself leads to a decrease in physical activity. This is termed reverse causation, i.e., obesity causes sedentary behavior, rather than the other way around. The potential influence of environmental factors (e.g., lack of opportunity to exercise) makes it difficult to prove this argument. Recent research has demonstrated that specific genotypes are related to obesity in children. Specific variations within the DNA of individual genes (single nucleotide polymorphisms, or SNPs) are more common in obese individuals and predispose to greater adiposity across the weight distribution. While adiposity itself can be influenced by many environmental factors that complicate the interpretation of observed associations, at the population level, genetic variation is not related to the same factors, and over the life course cannot be changed. Investigations that exploit these properties of genetic associations to inform the interpretation of observed associations are termed Mendelian randomization studies. This research technique is used to reduce the influence of confounding environmental factors on an observed clinical condition. The authors of this study use Mendelian randomization to determine whether a genetic tendency towards high BMI and fat mass is correlated with reduced levels of physical activity in a large cohort of children.
What Did the Researchers Do and Find?
The researchers looked at a cohort of children from a large long-term health research project (the Avon Longitudinal Study of Parents and Children). BMI and total body fat were recorded. Total daily activity was measured via a small movement-counting device. In addition, the participants underwent genotyping to detect the presence of several SNPs known to be linked to obesity. For each child a total BMI allelic score was determined based on the number of obesity-related genetic variants carried by that individual. The association between obesity and reduced physical activity was then studied in two ways. Direct correlation between actual BMI and physical activity was measured (observational data). Separately, the link between BMI allelic score and physical activity was also determined (Mendelian randomization or instrumental variable analysis). The observational data showed that boys were more active than girls and had lower BMI. Across both sexes, a higher-than-average BMI was associated with lower daily activity. In genetic analyses, allelic score had a positive correlation with BMI, with one particular SNP being most strongly linked to high BMI and total fat mass. A high allelic score for BMI was also correlated with lower levels of daily physical activity. The authors conclude that children who are obese and have an inherent predisposition to high BMI also have a propensity to reduced levels of physical activity, which may compound their weight gain.
What Do These Findings Mean?
This study provides evidence that being fat is in itself a risk factor for low activity levels, separately from external environmental influences. This may be an example of “reverse causation,” i.e., high BMI causes a reduction in physical activity. Alternatively, there may be a bidirectional causality, so that those with a genetic predisposition to high fat mass exercise less, leading to higher BMI, and so on, in a vicious circle. A significant limitation of the study is that validated allelic scores for physical activity are not available. Thus, it is not possible to determine whether individuals with a high allelic score for BMI also have a propensity to exercise less, or whether it is simply the circumstance of being overweight that discourages activity. This study does suggest that trying to persuade obese children to lose weight by exercising more is likely to be ineffective unless additional strategies to reduce BMI, such as strict diet control, are also implemented.
Additional Information
Please access these websites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1001618.
The US Centers for Disease Control and Prevention provides obesity-related statistics, details of prevention programs, and an overview on public health strategy in the United States
A more worldwide view is given by the World Health Organization
The UK National Health Service website gives information on physical activity guidelines for different age groups
The International Obesity Task Force is a network of organizations that seeks to alert the world to the growing health crisis threatened by soaring levels of obesity
MedlinePlus—which brings together authoritative information from the US National Library of Medicine, National Institutes of Health, and other government agencies and health-related organizations—has a page on obesity
Additional information on the Avon Longitudinal Study of Parents and Children is available
The British Medical Journal has an article that describes Mendelian randomization
doi:10.1371/journal.pmed.1001618
PMCID: PMC3958348  PMID: 24642734
9.  The Effect of Elevated Body Mass Index on Ischemic Heart Disease Risk: Causal Estimates from a Mendelian Randomisation Approach 
PLoS Medicine  2012;9(5):e1001212.
A Mendelian randomization analysis conducted by Børge G. Nordestgaard and colleagues using data from observational studies supports a causal relationship between body mass index and risk for ischemic heart disease.
Background
Adiposity, assessed as elevated body mass index (BMI), is associated with increased risk of ischemic heart disease (IHD); however, whether this is causal is unknown. We tested the hypothesis that positive observational associations between BMI and IHD are causal.
Methods and Findings
In 75,627 individuals taken from two population-based and one case-control study in Copenhagen, we measured BMI, ascertained 11,056 IHD events, and genotyped FTO(rs9939609), MC4R(rs17782313), and TMEM18(rs6548238). Using genotypes as a combined allele score in instrumental variable analyses, the causal odds ratio (OR) between BMI and IHD was estimated and compared with observational estimates. The allele score-BMI and the allele score-IHD associations used to estimate the causal OR were also calculated individually. In observational analyses the OR for IHD was 1.26 (95% CI 1.19–1.34) for every 4 kg/m2 increase in BMI. A one-unit allele score increase associated with a 0.28 kg/m2 (95 CI% 0.20–0.36) increase in BMI and an OR for IHD of 1.03 (95% CI 1.01–1.05) (corresponding to an average 1.68 kg/m2 BMI increase and 18% increase in the odds of IHD for those carrying all six BMI increasing alleles). In instrumental variable analysis using the same allele score the causal IHD OR for a 4 kg/m2 increase in BMI was 1.52 (95% CI 1.12–2.05).
Conclusions
For every 4 kg/m2 increase in BMI, observational estimates suggested a 26% increase in odds for IHD while causal estimates suggested a 52% increase. These data add evidence to support a causal link between increased BMI and IHD risk, though the mechanism may ultimately be through intermediate factors like hypertension, dyslipidemia, and type 2 diabetes. This work has important policy implications for public health, given the continuous nature of the BMI-IHD association and the modifiable nature of BMI. This analysis demonstrates the value of observational studies and their ability to provide unbiased results through inclusion of genetic data avoiding confounding, reverse causation, and bias.
Please see later in the article for the Editors' Summary
Editors' Summary
Background
Ischemic heart disease (IHD; also known as coronary heart disease) is the leading cause of death among adults in developed countries. In the US alone, IHD kills nearly half a million people every year. With age, fatty deposits (atherosclerotic plaques) build up in the walls of the coronary arteries, the blood vessels that supply the heart with oxygen and nutrients. The resultant reduction in the heart's blood supply causes shortness of breath, angina (chest pains that are usually relieved by rest), and potentially fatal heart attacks (myocardial infarctions). Risk factors for IHD include smoking, high blood pressure (hypertension), abnormal amounts of cholesterol and other fat in the blood (dyslipidemia), type 2 diabetes, and being overweight or obese (having excess body fat). Treatments for IHD include lifestyle changes (for example, losing weight) and medications that lower blood pressure and blood cholesterol levels. The narrowed arteries can also be widened using a device called a stent or surgically bypassed.
Why Was This Study Done?
Prospective observational studies have shown an association between a high body mass index (BMI, a measure of body fat that is calculated by dividing a person's weight in kilograms by their height in meters squared; a BMI greater than 30 kg/m2 indicates obesity) and an increased risk of IHD. Observational studies, which ask whether people who are exposed to a suspected risk factor develop a specific disease more often than people who are not exposed to the risk factor, cannot prove, however, that changes in BMI/adiposity cause IHD. Obese individuals may share other characteristics that cause both IHD and obesity (confounding) or, rather than obesity causing IHD, IHD may cause obesity (reverse causation). Here, the researchers use “Mendelian randomization” to examine whether elevations in BMI across the lifecourse have a causal impact on IHD risk. Three common genetic variants—FTO(rs9939609), MC4R(rs17782313), and TMEM18(rs6548238)—which have the largest single genetic variant associations with BMI were used in this study. Given that gene variants are inherited essentially randomly with respect to conventional confounding factors and are not subject reverse causation, use of these as instruments (or proxy measures) for variation in BMI as a risk factor (as opposed to measuring BMI directly) allows researchers to comment on whether obesity is causally involved in IHD.
What Did the Researchers Do and Find?
The researchers analyzed data from two population-based studies in which adults were physically examined and answered a lifestyle questionnaire before being followed to see how many developed IDH. They also analyzed data from a case-control study on IDH (in a case-control study, people with a disease are matched with similar people without the disease and the occurrence of risk factors in the patients and controls is compared). Overall, the researchers measured the BMI of 75,627 white individuals, among whom 11,056 already had IDH or developed it, and determined which of the BMI-increasing genetic variants each participant carried. On the basis of the observational data, every 4 kg/m2 increase in BMI increased the odds of IDH by 26% (an odds ratio of 1.26). Using a score derived from the combination of the three genetic variants, the researchers confirmed an association between each BMI increasing allele and both BMI (as expected) and IHD (0.28 kg/m2 and an odds ratio for IHD of 1.03, respectively). On average, compared to people carrying no BMI-increasing gene variants, people carrying six BMI-increasing gene variants had a 1.68 kg/m2 increase in BMI and an 18% increase in IHD risk. To extend this and to essentially reassess the original, observational, relationship between BMI and IHD risk, an “instrumental variable analysis” was used to examine the causal effect of a lifetime change in BMI on the risk of IDH. In this, it was found that for every 4 kg/m2 increase in BMI increased the odds of IDH by 52%.
What Do These Findings Mean?
These findings support a causal link between increased BMI and IDH risk, although it may be that BMI affects IDH through intermediate factors such as hypertension, dyslipidemia, and diabetes. The findings also show that observational studies into the impact of elevated BMI on IHD risk were consistent with this, but also that the inclusion of genetic data increases the value of observational studies by making it possible to avoid issues such as confounding and reverse causation. Finally, these findings and those of recent, observational studies have important implications for public-health policy because they show that the association between BMI (which is modifiable by lifestyle changes) and IHD is continuous. That is, any increase in BMI increases the risk of IHD; there is no threshold below which a BMI increase has no effect on IDH risk. Thus, public-health policies that aim to reduce BMI by even moderate levels could substantially reduce the occurrence of IDH in populations.
Additional Information
Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1001212.
The American Heart Association provides information about IHD and tips on keeping the heart healthy, including weight management; it also provides personal stories about IHD
The UK National Health Service Choices website provides information about IHD, including information on prevention and personal stories about IHD
Information is available from the British Heart Foundation on heart disease and keeping the heart healthy
The US National Heart Lung and Blood Institute also provides information on IHD (in English and Spanish)
MedlinePlus provides links to many other sources of information on IHD (in English and Spanish)
Wikipedia has a page on Mendelian randomization (note: Wikipedia is a free online encyclopedia that anyone can edit; available in several languages)
doi:10.1371/journal.pmed.1001212
PMCID: PMC3341326  PMID: 22563304
10.  Exploring the Developmental Overnutrition Hypothesis Using Parental–Offspring Associations and FTO as an Instrumental Variable 
PLoS Medicine  2008;5(3):e33.
Background
The developmental overnutrition hypothesis suggests that greater maternal obesity during pregnancy results in increased offspring adiposity in later life. If true, this would result in the obesity epidemic progressing across generations irrespective of environmental or genetic changes. It is therefore important to robustly test this hypothesis.
Methods and Findings
We explored this hypothesis by comparing the associations of maternal and paternal pre-pregnancy body mass index (BMI) with offspring dual energy X-ray absorptiometry (DXA)–determined fat mass measured at 9 to 11 y (4,091 parent–offspring trios) and by using maternal FTO genotype, controlling for offspring FTO genotype, as an instrument for maternal adiposity. Both maternal and paternal BMI were positively associated with offspring fat mass, but the maternal association effect size was larger than that in the paternal association in all models: mean difference in offspring sex- and age-standardised fat mass z-score per 1 standard deviation BMI 0.24 (95% confidence interval [CI]: 0.22 to 0.26) for maternal BMI versus 0.13 (95% CI: 0.11, 0.15) for paternal BMI; p-value for difference in effect < 0.001. The stronger maternal association was robust to sensitivity analyses assuming levels of non-paternity up to 20%. When maternal FTO, controlling for offspring FTO, was used as an instrument for the effect of maternal adiposity, the mean difference in offspring fat mass z-score per 1 standard deviation maternal BMI was −0.08 (95% CI: −0.56 to 0.41), with no strong statistical evidence that this differed from the observational ordinary least squares analyses (p = 0.17).
Conclusions
Neither our parental comparisons nor the use of FTO genotype as an instrumental variable, suggest that greater maternal BMI during offspring development has a marked effect on offspring fat mass at age 9–11 y. Developmental overnutrition related to greater maternal BMI is unlikely to have driven the recent obesity epidemic.
Using parental-offspring associations and theFTO gene as an instrumental variable for maternal adiposity, Debbie Lawlor and colleagues found that greater maternal BMI during offspring development does not appear to have a marked effect on offspring fat mass at age 9-11.
Editors' Summary
Background.
Since the 1970s, the proportion of children and adults who are overweight or obese (people who have an unhealthy amount of body fat) has increased sharply in many countries. In the US, 1 in 3 adults is now obese; in the mid-1970s it was only 1 in 7. Similarly, the proportion of overweight children has risen from 1 in 20 to 1 in 5. An adult is considered to be overweight if their body mass index (BMI)—their weight in kilograms divided by their height in meters squared—is between 25 and 30, and obese if it is more than 30. For children, the healthy BMI depends on their age and gender. Compared to people with a healthy weight (a BMI between 18.5 and 25), overweight or obese individuals have an increased lifetime risk of developing diabetes and other adverse health conditions, sometimes becoming ill while they are still young. People become unhealthily fat when they consume food and drink that contains more energy than they need for their daily activities. It should, therefore, be possible to avoid becoming obese by having a healthy diet and exercising regularly.
Why Was This Study Done?
Some researchers think that “developmental overnutrition” may have caused the recent increase in waistline measurements. In other words, if a mother is overweight during pregnancy, high sugar and fat levels in her body might permanently affect her growing baby's appetite control and metabolism, and so her offspring might be at risk of becoming obese in later life. If this hypothesis is true, each generation will tend to be fatter than the previous one and it will be very hard to halt the obesity epidemic simply by encouraging people to eat less and exercise more. In this study, the researchers have used two approaches to test the developmental overnutrition hypothesis. First, they have asked whether offspring fat mass is more strongly related to maternal BMI than to paternal BMI; it should be if the hypothesis is true. Second, they have asked whether a genetic indicator of maternal fatness—the “A” variant of the FTO gene—is related to offspring fat mass. A statistical association between maternal FTO genotype (genetic make-up) and offspring fat mass would support the developmental nutrition hypothesis.
What Did the Researchers Do and Find?
In 1991–1992, the Avon Longitudinal Study of Parents and Children (ALSPAC) enrolled about 14,000 pregnant women and now examines their offspring at regular intervals. The researchers first used statistical methods to look for associations between the self-reported prepregnancy BMI of the parents of about 4,000 children and the children's fat mass at ages 9–11 years measured using a technique called dual energy X-ray absorptiometry. Both maternal and paternal BMI were positively associated with offspring fat mass (that is, fatter parents had fatter children) but the effect of maternal BMI was greater than the effect of paternal BMI. When the researchers examined maternal FTO genotypes and offspring fat mass (after allowing for the offspring's FTO genotype, which would directly affect their fat mass), there was no statistical evidence to suggest that differences in offspring fat mass were related to the maternal FTO genotype.
What Do These Findings Mean?
Although the findings from first approach provide some support for the development overnutrition hypothesis, the effect of maternal BMI on offspring fat mass is too weak to explain the recent obesity epidemic. Developmental overnutrition could, however, be responsible for the much slower increase in obesity that began a century ago. The findings from the second approach provide no support for the developmental overnutrition hypothesis, although these results have wide error margins and need confirming in a larger study. The researchers also note that the effects of developmental overnutrition on offspring fat mass, although weak at age 9–11, might become more important at later ages. Nevertheless, for now, it seems unlikely that developmental overnutrition has been a major driver of the recent obesity epidemic. Interventions that aim to improve people's diet and to increase their physical activity levels could therefore slow or even halt the epidemic.
Additional Information.
Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.0050033.
See a related PLoS Medicine Perspective article
The MedlinePlus encyclopedia has a page on obesity (in English and Spanish)
The US Centers for Disease Control and Prevention provides information on all aspects of obesity (in English and Spanish)
The UK National Health Service's health Web site (NHS Direct) provides information about obesity
The International Obesity Taskforce provides information about preventing obesity and on childhood obesity
The UK Foods Standards Agency, the United States Department of Agriculture, and Shaping America's Health all provide useful advice about healthy eating for adults and children
The ALSPAC Web site provides information about the Avon Longitudinal Study of Parents and Children and its results so far
doi:10.1371/journal.pmed.0050033
PMCID: PMC2265763  PMID: 18336062
11.  Genetic Effects on Longitudinal Changes from Healthy to Adverse Weight and Metabolic Status — The HUNT Study 
PLoS ONE  2015;10(10):e0139632.
Introduction
The complexity of obesity and onset and susceptibility of cardio-metabolic disorders are still poorly understood and is addressed here through studies of genetic influence on weight gain and increased metabolic risk longitudinally.
Subjects/Methods
Twenty seven previously identified obesity, eating disorder or metabolic risk susceptibility SNPs were tested for association with weight or metabolically related traits longitudinally in 3999 adults participating both in the HUNT2 (1995–97) and HUNT3 (2006–08) surveys. Regression analyses were performed with changes from normal weight to overweight/obesity or from metabolically healthy to adverse developments with regards to blood pressure, glucose, HDL cholesterol, triglycerides or metabolic syndrome as outcomes. Additionally, a sub-sample of 1380 adolescents was included for testing association of nine SNPs with longitudinal weight gain into young adulthood.
Results
The most substantial effect on BMI-based weight gain from normal to overweight/obesity in adults was observed for the DRD2 variant (rs6277)(OR: 0.79, 95% CI: 0.69–0.90, P = 3.9x10-4, adj. P = 0.015). DRD2 was not associated with BMI on a cross-sectional level. In the adolescent sample, FTO (rs1121980) was associated with change to overweight at adulthood in the combined male-female sample (OR: 1.27, 95% CI: 1.09–1.49, P = 3.0x10-3, adj. P = 0.019) and in females (OR: 1.53, 95% CI: 1.23–1.91, P = 1.8x10-4, adj. P = 0.003). When testing for association to longitudinal adverse developments with regard to blood pressure, blood lipids and glucose, only rs964184 (ZNF259/APOA5) was significantly associated to unfavourable triglyceride changes (OR: 1.66, 95% CI: 1.36–2.03, P = 5.7x10-7, adj. P = 0.001). Pleiotropic effects on metabolic traits, however, were observed for several genetic loci cross-sectionally, ZNF259/APOA5, LPL and GRB14 being the most important.
Conclusions
DRD2 exhibits effects on weight gain from normal weight to overweight/obesity in adults, while, FTO is associated to weight gain from adolescence to young adulthood. Unhealthy longitudinal triglyceride development is strongly affected by ZNF259/APOA. Our main finding, linking the DRD2 variant directly to the longitudinal weight gain observed, has not previously been identified. It suggests a genetic pre-disposition involving the dopaminergic signalling pathways known to play a role in food reward and satiety linked mechanisms.
doi:10.1371/journal.pone.0139632
PMCID: PMC4596824  PMID: 26445370
12.  Obesity and Multiple Sclerosis: A Mendelian Randomization Study 
PLoS Medicine  2016;13(6):e1002053.
Background
Observational studies have reported an association between obesity, as measured by elevated body mass index (BMI), in early adulthood and risk of multiple sclerosis (MS). However, bias potentially introduced by confounding and reverse causation may have influenced these findings. Therefore, we elected to perform Mendelian randomization (MR) analyses to evaluate whether genetically increased BMI is associated with an increased risk of MS.
Methods and Findings
Employing a two-sample MR approach, we used summary statistics from the Genetic Investigation of Anthropometric Traits (GIANT) consortium and the International MS Genetics Consortium (IMSGC), the largest genome-wide association studies for BMI and MS, respectively (GIANT: n = 322,105; IMSGC: n = 14,498 cases and 24,091 controls). Seventy single nucleotide polymorphisms (SNPs) were genome-wide significant (p < 5 x 10−8) for BMI in GIANT (n = 322,105) and were investigated for their association with MS risk in the IMSGC. The effect of each SNP on MS was weighted by its effect on BMI, and estimates were pooled to provide a summary measure for the effect of increased BMI upon risk of MS. Our results suggest that increased BMI influences MS susceptibility, where a 1 standard deviation increase in genetically determined BMI (kg/m2) increased odds of MS by 41% (odds ratio [OR]: 1.41, 95% CI 1.20–1.66, p = 2.7 x 10−5, I2 = 0%, 95% CI 0–29). Sensitivity analyses, including MR-Egger regression, and the weighted median approach provided no evidence of pleiotropic effects. The main study limitations are that, while these sensitivity analyses reduce the possibility that pleiotropy influenced our results, residual pleiotropy is difficult to exclude entirely.
Conclusion
Genetically elevated BMI is associated with risk of MS, providing evidence for a causal role for obesity in MS etiology. While obesity has been associated with many late-life outcomes, these findings suggest an important consequence of childhood and/or early adulthood obesity.
Using a Mendelian randomization approach, Brent Richards and colleagues examine the possibility that genetically raised body mass index could affect risk of multiple sclerosis.
Author Summary
Why Was This Study Done?
Multiple sclerosis (MS) is a debilitating disease that carries a large social and economic burden.
The risk factors that cause MS remain poorly understood.
Previous observational epidemiological studies have reported an association between elevated body mass index (BMI) in early adulthood and risk of MS; however, lifestyle factors that influence BMI may bias the relationship between BMI and MS.
What Did the Researchers Do and Find?
The researchers tested whether inherited genetic variation that influences BMI is associated with MS. Such analyses provide an estimate of the relationship between BMI and MS that is not influenced by confounding factors, with the exception of confounding by ancestry; since assignment to genotype at conception is a random process, it breaks associations with other potential confounding factors.
Using data from the largest genome-wide association study consortia for MS and BMI, the researchers provided evidence supporting elevated BMI as a causal risk factor for MS.
A genetically determined change in the BMI category from overweight to obese was associated with a substantially increased risk of MS in this study.
What Do These Findings Mean?
Elevated BMI could be an important, and potentially modifiable, risk factor for MS.
This provides further rationale to address rising obesity rates and to investigate whether interventions that promote a healthy lifestyle may help to mitigate MS risk.
doi:10.1371/journal.pmed.1002053
PMCID: PMC4924848  PMID: 27351487
13.  SLC6A3 and body mass index in the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial 
BMC Medical Genetics  2009;10:9.
Background
To investigate the contribution of the dopamine transporter to dopaminergic reward-related behaviors and anthropometry, we evaluated associations between polymorphisms at the dopamine transporter gene(SLC6A3) and body mass index (BMI), among participants in the Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial.
Methods
Four polymorphisms (rs6350, rs6413429, rs6347 and the 3' variable number of tandem repeat (3' VNTR) polymorphism) at the SLC6A3 gene were genotyped in 2,364 participants selected from the screening arm of PLCO randomly within strata of sex, age and smoking history. Height and weight at ages 20 and 50 years and baseline were assessed by questionnaire. BMI was calculated and categorized as underweight, normal, overweight and obese (<18.5, 18.5–24.9, 25.0–29.9, or ≥ 30 kg/m2, respectively). Odds ratios (ORs) and 95% confidence intervals (CIs) of SLC6A3 genotypes and haplotypes were computed using conditional logistic regression.
Results
Compared with individuals having a normal BMI, obese individuals at the time of the baseline study questionnaire were less likely to possess the 3' VNTR variant allele with 9 copies of the repeated sequence in a dose-dependent model (** is referent; OR*9 = 0.80, OR99 = 0.47, ptrend = 0.005). Compared with individuals having a normal BMI at age 50, overweight individuals (A-C-G-* is referent; ORA-C-G-9 = 0.80, 95% CI 0.65–0.99, p = 0.04) and obese individuals (A-C-G-* is referent; ORA-C-G-9 = 0.70, 95% CI 0.49–0.99, p = 0.04) were less likely to possess the haplotype with the 3'variant allele (A-C-G-9).
Conclusion
Our results support a role of genetic variation at the dopamine transporter gene, SLC6A3, as a modifier of BMI.
doi:10.1186/1471-2350-10-9
PMCID: PMC2640369  PMID: 19183461
14.  Causal Relationship between Obesity and Vitamin D Status: Bi-Directional Mendelian Randomization Analysis of Multiple Cohorts 
PLoS Medicine  2013;10(2):e1001383.
A mendelian randomization study based on data from multiple cohorts conducted by Karani Santhanakrishnan Vimaleswaran and colleagues re-examines the causal nature of the relationship between vitamin D levels and obesity.
Background
Obesity is associated with vitamin D deficiency, and both are areas of active public health concern. We explored the causality and direction of the relationship between body mass index (BMI) and 25-hydroxyvitamin D [25(OH)D] using genetic markers as instrumental variables (IVs) in bi-directional Mendelian randomization (MR) analysis.
Methods and Findings
We used information from 21 adult cohorts (up to 42,024 participants) with 12 BMI-related SNPs (combined in an allelic score) to produce an instrument for BMI and four SNPs associated with 25(OH)D (combined in two allelic scores, separately for genes encoding its synthesis or metabolism) as an instrument for vitamin D. Regression estimates for the IVs (allele scores) were generated within-study and pooled by meta-analysis to generate summary effects.
Associations between vitamin D scores and BMI were confirmed in the Genetic Investigation of Anthropometric Traits (GIANT) consortium (n = 123,864). Each 1 kg/m2 higher BMI was associated with 1.15% lower 25(OH)D (p = 6.52×10−27). The BMI allele score was associated both with BMI (p = 6.30×10−62) and 25(OH)D (−0.06% [95% CI −0.10 to −0.02], p = 0.004) in the cohorts that underwent meta-analysis. The two vitamin D allele scores were strongly associated with 25(OH)D (p≤8.07×10−57 for both scores) but not with BMI (synthesis score, p = 0.88; metabolism score, p = 0.08) in the meta-analysis. A 10% higher genetically instrumented BMI was associated with 4.2% lower 25(OH)D concentrations (IV ratio: −4.2 [95% CI −7.1 to −1.3], p = 0.005). No association was seen for genetically instrumented 25(OH)D with BMI, a finding that was confirmed using data from the GIANT consortium (p≥0.57 for both vitamin D scores).
Conclusions
On the basis of a bi-directional genetic approach that limits confounding, our study suggests that a higher BMI leads to lower 25(OH)D, while any effects of lower 25(OH)D increasing BMI are likely to be small. Population level interventions to reduce BMI are expected to decrease the prevalence of vitamin D deficiency.
Please see later in the article for the Editors' Summary
Editors' Summary
Background
Obesity—having an unhealthy amount of body fat—is increasing worldwide. In the US, for example, a third of the adult population is now obese. Obesity is defined as having a body mass index (BMI, an indicator of body fat calculated by dividing a person's weight in kilograms by their height in meters squared) of more than 30.0 kg/m2. Although there is a genetic contribution to obesity, people generally become obese by consuming food and drink that contains more energy than they need for their daily activities. Thus, obesity can be prevented by having a healthy diet and exercising regularly. Compared to people with a healthy weight, obese individuals have an increased risk of developing diabetes, heart disease and stroke, and tend to die younger. They also have a higher risk of vitamin D deficiency, another increasingly common public health concern. Vitamin D, which is essential for healthy bones as well as other functions, is made in the skin after exposure to sunlight but can also be obtained through the diet and through supplements.
Why Was This Study Done?
Observational studies cannot prove that obesity causes vitamin D deficiency because obese individuals may share other characteristics that reduce their circulating 25-hydroxy vitamin D [25(OH)D] levels (referred to as confounding). Moreover, observational studies cannot indicate whether the larger vitamin D storage capacity of obese individuals (vitamin D is stored in fatty tissues) lowers their 25(OH)D levels or whether 25(OH)D levels influence fat accumulation (reverse causation). If obesity causes vitamin D deficiency, monitoring and treating vitamin D deficiency might alleviate some of the adverse health effects of obesity. Conversely, if low vitamin D levels cause obesity, encouraging people to take vitamin D supplements might help to control the obesity epidemic. Here, the researchers use bi-directional “Mendelian randomization” to examine the direction and causality of the relationship between BMI and 25(OH)D. In Mendelian randomization, causality is inferred from associations between genetic variants that mimic the influence of a modifiable environmental exposure and the outcome of interest. Because gene variants do not change over time and are inherited randomly, they are not prone to confounding and are free from reverse causation. Thus, if a lower vitamin D status leads to obesity, genetic variants associated with lower 25(OH)D concentrations should be associated with higher BMI, and if obesity leads to a lower vitamin D status, then genetic variants associated with higher BMI should be associated with lower 25(OH)D concentrations.
What Did the Researchers Do and Find?
The researchers created a “BMI allele score” based on 12 BMI-related gene variants and two “25(OH)D allele scores,” which are based on gene variants that affect either 25(OH)D synthesis or breakdown. Using information on up to 42,024 participants from 21 studies, the researchers showed that the BMI allele score was associated with both BMI and with 25(OH)D levels among the study participants. Based on this information, they calculated that each 10% increase in BMI will lead to a 4.2% decrease in 25(OH)D concentrations. By contrast, although both 25(OH)D allele scores were strongly associated with 25(OH)D levels, neither score was associated with BMI. This lack of an association between 25(OH)D allele scores and obesity was confirmed using data from more than 100,000 individuals involved in 46 studies that has been collected by the GIANT (Genetic Investigation of Anthropometric Traits) consortium.
What Do These Findings Mean?
These findings suggest that a higher BMI leads to a lower vitamin D status whereas any effects of low vitamin D status on BMI are likely to be small. That is, these findings provide evidence for obesity as a causal factor in the development of vitamin D deficiency but not for vitamin D deficiency as a causal factor in the development of obesity. These findings suggest that population-level interventions to reduce obesity should lead to a reduction in the prevalence of vitamin D deficiency and highlight the importance of monitoring and treating vitamin D deficiency as a means of alleviating the adverse influences of obesity on health.
Additional Information
Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1001383.
The US Centers for Disease Control and Prevention provides information on all aspects of overweight and obesity (in English and Spanish); a data brief provides information about the vitamin D status of the US population
The World Health Organization provides information on obesity (in several languages)
The UK National Health Service Choices website provides detailed information about obesity and a link to a personal story about losing weight; it also provides information about vitamin D
The International Obesity Taskforce provides information about the global obesity epidemic
The US Department of Agriculture's ChooseMyPlate.gov website provides a personal healthy eating plan; the Weight-control Information Network is an information service provided for the general public and health professionals by the US National Institute of Diabetes and Digestive and Kidney Diseases (in English and Spanish)
The US Office of Dietary Supplements provides information about vitamin D (in English and Spanish)
MedlinePlus has links to further information about obesity and about vitamin D (in English and Spanish)
Wikipedia has a page on Mendelian randomization (note: Wikipedia is a free online encyclopedia that anyone can edit; available in several languages)
Overview and details of the collaborative large-scale genetic association study (D-CarDia) provide information about vitamin D and the risk of cardiovascular disease, diabetes and related traits
doi:10.1371/journal.pmed.1001383
PMCID: PMC3564800  PMID: 23393431
15.  Lipid levels are associated with a regulatory polymorphism of the monoamine oxidase-A gene promoter (MAOA-uVNTR) 
Summary
Background
The monoamine oxidase-A (MAOA) gene plays a vital role in the metabolism of neurotransmitters, e.g, serotonin, norepinephrine, and dopamine. A polymorphism in the promoter region (MAOA-uVNTR) affects transcriptional efficiency. Allelic variation in MAOA-uVNTR has been associated with body mass index (BMI). We extended previous work by examining relations among this polymorphism and serum lipid levels.
Material/Methods
The sample consisted of 74 males enrolled in a study of caregivers for relatives with dementia. Regression models, adjusted for age, race, group status (caregiver/control), and cholesterol lowering medication (yes/no), were used to examine associations between high verses low MAOA-uVNTR activity alleles and total cholesterol, HDL, LDL, VLDL, LDL/HDL ratio, triglycerides, and BMI.
Results
Higher total cholesterol (p<0.03), LDL/HDL ratio (p<0.01), triglycerides (p<0.02), and VLDL (p<0.02) were associated with low activity MAOA-uVNTR alleles. HDL and LDL were modestly related to MAOA-uVNTR activity, however, they did not reach the conventional significance level (p<0.07 and p<0.10, respectively). BMI (p<0.74) was unrelated to MAOA-uVNTR transcription.
Conclusions
The present findings suggest that MAOA-uVNTR may influence lipid levels and individuals with less active alleles are at increased health risk.
PMCID: PMC2759533  PMID: 18227761
stress; genetics; lipoprotien; allelic variation
16.  Association between Attention-Deficit/Hyperactivity Disorder (ADHD) symptoms and obesity and hypertension in early adulthood: A population-based study 
Objective
To examine associations between attention-deficit/hyperactivity disorder (ADHD) symptoms, obesity and hypertension in young adults in a large population-based cohort.
Design, Setting, and Participants
The study population consisted of 15,197 respondents from the National Longitudinal Study of Adolescent Health, a nationally representative sample of adolescents followed from 1995 – 2009 in the United States. Multinomial logistic and logistic models examined the odds of overweight, obesity, and hypertension in adulthood in relation to retrospectively reported ADHD symptoms. Latent curve modeling was used to assess the association between symptoms and naturally occurring changes in body mass index (BMI) from adolescence to adulthood.
Results
Linear association was identified between the number of inattentive (IN) and hyperactive/impulsive (HI) symptoms and waist-circumference, BMI, diastolic blood pressure, and systolic blood pressure (all ps for trend < .05). Controlling for demographic variables, physical activity, alcohol use, smoking, and depressive symptoms, those with 3 or more HI or IN symptoms had the highest odds of obesity (HI 3+ OR, 1.50; 95% CI, 1.22-2.83; IN 3+ OR, 1.21; 95% CI, 1.02-1.44) compared to those with no HI or IN symptoms. HI symptoms at the 3+ level were significantly associated with a higher OR of hypertension (HI 3+ OR, 1.24; 95% CI 1.01-1.51; HI continuous OR, 1.04; 95% CI 1.00-1.09), but associations were non-significant when models were adjusted for BMI. Latent growth modeling results indicated that compared to those reporting no HI or IN symptoms, those reporting more 3 or symptoms had higher initial levels of BMI during adolescence. Only HI symptoms were associated with change in BMI.
Conclusion
Self-reported ADHD symptoms were associated with adult BMI and change in BMI from adolescence to adulthood, providing further evidence of a link between ADHD symptoms and obesity.
doi:10.1038/ijo.2010.214
PMCID: PMC3391591  PMID: 20975727
attention-deficit/hyperactivity disorder; obesity; hypertension; young adult; risk factors
17.  Parenting Styles and Body Mass Index Trajectories From Adolescence to Adulthood 
Objective
Parenting styles such as authoritarian, disengaged, or permissive are thought to be associated with greater adolescent obesity risk than an authoritative style. This study assessed the relationship between parenting styles and changes in body mass index (BMI) from adolescence to young adulthood.
Methods
The study included self-reported data from adolescents in the National Longitudinal Study of Adolescent Health. Factor mixture modeling, a data-driven approach, was used to classify participants into parenting style groups based on measures of acceptance and control. Latent growth modeling (LGM) identified patterns of developmental changes in BMI. After a number of potential cofounders were controlled for, parenting style variables were entered as predictors of BMI trajectories. Analyses were also conducted for males and females of three racial/ethnic groups (Hispanic, black, white) to assess whether parenting styles were differentially associated with BMI trajectories in these 6 groups.
Results
Parenting styles were classified into 4 groups: authoritarian, disengaged, permissive, and balanced. Compared with the balanced parenting style, authoritarian and disengaged parenting styles were associated with a less steep average BMI increase (linear slope) over time, but also less leveling off (quadratic) of BMI over time. Differences in BMI trajectories were observed for various genders and races, but the differences did not reach statistical significance.
Conclusions
Adolescents who reported having parents with authoritarian or disengaged parenting styles had greater increases in BMI as they transitioned to young adulthood despite having a lower BMI trajectory through adolescence.
doi:10.1037/a0027927
PMCID: PMC3616616  PMID: 22545979
18.  Central adiposity, obesity during early adulthood, and pancreatic cancer mortality in a pooled analysis of cohort studies 
Annals of Oncology  2015;26(11):2257-2266.
This analysis within a large international consortium provides strong evidence that central obesity, independent of body mass index (BMI), and high BMI during early adulthood may increase pancreatic cancer mortality. Moreover, our results provide evidence that avoiding excess weight gain before early adulthood (during childhood) may be particularly important for pancreatic cancer prevention.
Background
Body mass index (BMI), a measure of obesity typically assessed in middle age or later, is known to be positively associated with pancreatic cancer. However, little evidence exists regarding the influence of central adiposity, a high BMI during early adulthood, and weight gain after early adulthood on pancreatic cancer risk.
Design
We conducted a pooled analysis of individual-level data from 20 prospective cohort studies in the National Cancer Institute BMI and Mortality Cohort Consortium to examine the association of pancreatic cancer mortality with measures of central adiposity (e.g. waist circumference; n = 647 478; 1947 pancreatic cancer deaths), BMI during early adulthood (ages 18–21 years) and BMI change between early adulthood and cohort enrollment, mostly in middle age or later (n = 1 096 492; 3223 pancreatic cancer deaths). Multivariable hazard ratios (HRs) and 95% confidence intervals (CIs) were calculated using Cox proportional hazards regression models.
Results
Higher waist-to-hip ratio (HR = 1.09, 95% CI 1.02–1.17 per 0.1 increment) and waist circumference (HR = 1.07, 95% CI 1.00–1.14 per 10 cm) were associated with increased risk of pancreatic cancer mortality, even when adjusted for BMI at baseline. BMI during early adulthood was associated with increased pancreatic cancer mortality (HR = 1.18, 95% CI 1.11–1.25 per 5 kg/m2), with increased risk observed in both overweight and obese individuals (compared with BMI of 21.0 to <23 kg/m2, HR = 1.36, 95% CI 1.20–1.55 for BMI 25.0 < 27.5 kg/m2, HR = 1.48, 95% CI 1.20–1.84 for BMI 27.5 to <30 kg/m2, HR = 1.43, 95% CI 1.11–1.85 for BMI ≥30 kg/m2). BMI gain after early adulthood, adjusted for early adult BMI, was less strongly associated with pancreatic cancer mortality (HR = 1.05, 95% CI 1.01–1.10 per 5 kg/m2).
Conclusions
Our results support an association between pancreatic cancer mortality and central obesity, independent of BMI, and also suggest that being overweight or obese during early adulthood may be important in influencing pancreatic cancer mortality risk later in life.
doi:10.1093/annonc/mdv355
PMCID: PMC4621029  PMID: 26347100
central adiposity; BMI; pancreatic cancer; pooled analysis
19.  Genotypes Do Not Confer Risk For Delinquency ut Rather Alter Susceptibility to Positive and Negative Environmental Factors: Gene-Environment Interactions of BDNF Val66Met, 5-HTTLPR, and MAOA-uVNTR 
Background:
Previous evidence of gene-by-environment interactions associated with emotional and behavioral disorders is contradictory. Differences in findings may result from variation in valence and dose of the environmental factor, and/or failure to take account of gene-by-gene interactions. The present study investigated interactions between the brain-derived neurotrophic factor gene (BDNF Val66Met), the serotonin transporter gene-linked polymorphic region (5-HTTLPR), the monoamine oxidase A (MAOA-uVNTR) polymorphisms, family conflict, sexual abuse, the quality of the child-parent relationship, and teenage delinquency.
Methods:
In 2006, as part of the Survey of Adolescent Life in Västmanland, Sweden, 1 337 high-school students, aged 17–18 years, anonymously completed questionnaires and provided saliva samples for DNA analyses.
Results:
Teenage delinquency was associated with two-, three-, and four-way interactions of each of the genotypes and the three environmental factors. Significant four-way interactions were found for BDNF Val66Met × 5-HTTLPR×MAOA-uVNTR × family conflicts and for BDNF Val66Met × 5-HTTLPR×MAOA-uVNTR × sexual abuse. Further, the two genotype combinations that differed the most in expression levels (BDNF Val66Met Val, 5-HTTLPR LL, MAOA-uVNTR LL [girls] and L [boys] vs BDNF Val66Met Val/Met, 5-HTTLPR S/LS, MAOA-uVNTR S/SS/LS) in interaction with family conflict and sexual abuse were associated with the highest delinquency scores. The genetic variants previously shown to confer vulnerability for delinquency (BDNF Val66Met Val/Met × 5-HTTLPR S × MAOA-uVNTR S) were associated with the lowest delinquency scores in interaction with a positive child-parent relationship.
Conclusions:
Functional variants of the MAOA-uVNTR, 5-HTTLPR, and BDNF Val66Met, either alone or in interaction with each other, may be best conceptualized as modifying sensitivity to environmental factors that confer either risk or protection for teenage delinquency.
doi:10.1093/ijnp/pyu107
PMCID: PMC4376552  PMID: 25522433
brain-derived neurotrophic factor; gene-environment interaction; juvenile delinquency; monoamine oxidase; serotonin plasma membrane transport proteins
20.  Interactions Between MAOA Genotype and Receipt of Public Assistance: Predicting Change in Depressive Symptoms and Body Mass Index 
Response to stress is determined in part by genetically-influenced regulation of the monoamine system. We examined the interaction of a stressor (receipt of public assistance) and a gene regulating the monoamine system (MAOA) in the prediction of change in adolescent depressive symptoms and body mass index (BMI). Participants were drawn from the National Longitudinal Study on Adolescent Health (AddHealth) genetically-informative subsample. We focused on males due to the fact that males only have one MAOA allele. Growth curve analyses were conducted to assess the association between public assistance, MAOA allele, and their interaction and the intercept and slope of depressive symptoms and BMI. The results indicated that among males, MAOA allele type interacted with receipt of public assistance in the prediction of rate of change in both depressive symptoms and BMI from early adolescence through early adulthood. Males with the short MAOA allele whose families received public assistance tended to experience increased growth in depressive symptoms and BMI. Implications of the findings for understanding the relations among stress, physiology, and development are discussed.
doi:10.1111/j.1532-7795.2010.00694.x
PMCID: PMC3178327  PMID: 21949471
21.  Gender Difference in Interactions between MAOA Promoter uVNTR Polymorphism and Negative Familial Stressors on Body Mass Index among Chinese Adolescents 
Pediatric obesity  2013;9(5):e80-e90.
Summary
Objectives
Monoamine oxidase A (MAOA) modulates metabolism of serotonin and dopamine metabolism, neurotransmitters involved in regulation of appetite and food intake. The gene coding for MAOA contains a 30-bp tandem repeat (uVNTR) polymorphism in its promoter region that has been previously identified to be associated with obesity with mixed findings in the literature. Our goals were to replicate the population effects of this functional polymorphism on obesity risk, and to further explore gender differences and interaction effects with negative stressors.
Methods
Analyses were conducted with data on genotypes, measured weight and height, and self-reported behavioral characteristics among 1,101 Chinese adolescents 11-15 years old living in Wuhan, China.
Results
Girls with the high activity allele had significantly lower BMI (β=-0.25±0.98, p=0.011) compared to those with the low activity allele. Experience of negative familial stressors(e.g., death or illness of family members, hit or scolded by parents and increased quarreling with parents, parents argued frequently) significantly weakened this protective genetic effect on BMI (p for interaction=0.043). Stratified analyses showed a significant protective genetic effect on BMI only within the stratum of low stress level (β=-0.44±0.14, p=0.002). No similar effect was observed among boys.
Conclusions
Our findings confirm the genetic effects of MAOA uVNTR polymorphism on BMI in a Chinese adolescent population and suggest potential genetic interactions with negative familial stressors.
doi:10.1111/j.2047-6310.2013.00181.x
PMCID: PMC4159439  PMID: 23761378
MAOA Polymorphism; Familial Stressors; BMI; Chinese Adolescents; Gene-Environment Interaction
22.  GENE AND GENE BY SEX ASSOCIATIONS WITH INITIAL SENSITIVITY TO NICOTINE IN NONSMOKERS 
Behavioural pharmacology  2008;19(5-6):630-640.
Genetic variation may influence initial sensitivity to nicotine (i.e. during early tobacco exposure), perhaps helping to explain differential vulnerability to nicotine dependence. This study explored associations of functional candidate gene polymorphisms with initial sensitivity to nicotine in 101 young adult nonsmokers of European ancestry. Nicotine (0, 5, 10 μg/kg) was administered via nasal spray followed by mood, nicotine reward (e.g. “liking”) and perception (e.g. “feel effects”) measures, physiological responses, sensory processing (pre-pulse inhibition of startle), and performance tasks. Nicotine reinforcement was assessed in a separate session using a nicotine vs. placebo spray choice procedure. For the dopamine D4 receptor (DRD4 VNTR), presence of the 7 repeat allele was associated with greater aversive responses to nicotine (decreases in “vigor”, positive affect, and rapid information processing; increased cortisol) and reduced nicotine choice. Individuals with at least one DRD4 7-repeat allele also reported increased “feel effects” and greater startle response, but in men only. Also observed in men but not women were other genetic associations, such as greater “feel effects” and anger, and reduced fatigue, in the dopamine D2 receptor (DRD2 C957T SNP) TT versus CT or CC genotypes. Very few or no significant associations were seen for the DRD2/ANKK1 TaqIA polymorphism, the serotonin transporter promoter VNTR or 5HTTLPR (SLC6A4), the dopamine transporter 3’ VNTR (SLC6A3), and the mu opioid receptor A118G SNP (OPRM1). Although these results are preliminary, this study is the first to suggest that genetic polymorphisms related to function in the dopamine D4, and perhaps D2, receptor may modulate initial sensitivity to nicotine prior to the onset of dependence and may do so differentially between men and women.
doi:10.1097/FBP.0b013e32830c3621
PMCID: PMC2743299  PMID: 18690117
nicotine; sensitivity; genetics; dopamine; reward; reinforcement
23.  Obesity-Susceptibility Loci and Their Influence on Adiposity-Related Traits in Transition from Adolescence to Adulthood - The HUNT Study 
PLoS ONE  2012;7(10):e46912.
Introduction
Obesity-susceptibility loci have been related to adiposity traits in adults and may affect body fat estimates in adolescence. There are indications that different sets of obesity-susceptibility loci influence level of and change in obesity-related traits from adolescence to adulthood.
Objectives
To investigate whether previously reported obesity-susceptible loci in adults influence adiposity traits in adolescence and change in BMI and waist circumference (WC) from adolescence into young adulthood. We also examined whether physical activity modifies the effects of these genetic loci on adiposity-related traits.
Methods
Nine obesity-susceptibility variants were genotyped in 1 643 adolescents (13–19 years old) from the HUNT study, Norway, who were followed-up into young adulthood. Lifestyle was assessed using questionnaires and anthropometric measurements were taken. The effects of genetic variants individually and combined in a genetic predisposition score (GPS) on obesity-related traits were studied cross-sectionally and longitudinally. A modifying effect of physical activity was tested.
Results
The GPS was significantly associated to BMI (B: 0.046 SD/allele [0.020, 0.073], p = 0.001) in adolescence and in young adulthood (B: 0.041 SD/allele [0.015, 0.067], p = 0.002) as it was to waist circumference (WC). The GPS was not associated to change in BMI (p = 0.762) or WC (p = 0.726). We found no significant interaction effect between the GPS and physical activity.
Conclusions
Our observations suggest that obesity-susceptibility loci established in adults affect BMI and WC already in adolescence. However, an association with change in adiposity-related traits from adolescence to adulthood could not be verified for these loci. Neither could an attenuating effect of physical activity on the association between the obesity-susceptibility genes and body fat estimates be revealed.
doi:10.1371/journal.pone.0046912
PMCID: PMC3477114  PMID: 23094032
24.  A Preliminary Analysis of Interactions Between Genotype, Retrospective ADHD Symptoms, and Initial Reactions to Smoking in a Sample of Young Adults 
Nicotine & Tobacco Research  2011;14(2):229-233.
Introduction:
Initial reactions to cigarettes predict later regular smoking. Symptoms of attention deficit hyperactivity disorder (ADHD) have also been shown to increase smoking risk and may moderate the relationship between genotype and smoking. We conducted an exploratory study to assess whether ADHD symptoms interact with genetic variation to predict self-reported initial reactions to smoking.
Methods:
Participants were a subsample of 1,900 unrelated individuals with genotype data drawn from the National Longitudinal Study of Adolescent Health (Add Health), a nationally representative sample of adolescents followed from 1995 to 2002. Linear regression was used to examine relationships among self-reported ADHD symptoms, genotype, and self-reported initial reactions to cigarettes (index scores reflecting pleasant and unpleasant reactions).
Results:
Polymorphisms in the DRD2 gene, SLC6A4 gene, and among males, the MAOA gene interacted with retrospective reports of ADHD symptoms in predicting pleasant initial reaction to cigarettes. Polymorphisms in the CYP2A6 gene and, among females, the MAOA gene interacted with retrospective reports of ADHD symptoms in predicting unpleasant initial reaction to cigarettes. No main effect for any of these polymorphisms was observed nor were any interactions with DRD4 and DAT genes.
Conclusions:
These findings suggest that genotypes associated with monoamine neurotransmission interact with ADHD symptoms to influence initial reactions to cigarette smoking. Given that an initial pleasant reaction to cigarettes increases risk for lifetime smoking, these results add to a growing body of literature that suggests that ADHD symptoms increase risk for smoking and should be accounted for in genetic studies of smoking.
doi:10.1093/ntr/ntr125
PMCID: PMC3265740  PMID: 21778150
25.  A Nested Case–Control Study of Metabolically Defined Body Size Phenotypes and Risk of Colorectal Cancer in the European Prospective Investigation into Cancer and Nutrition (EPIC) 
PLoS Medicine  2016;13(4):e1001988.
Background
Obesity is positively associated with colorectal cancer. Recently, body size subtypes categorised by the prevalence of hyperinsulinaemia have been defined, and metabolically healthy overweight/obese individuals (without hyperinsulinaemia) have been suggested to be at lower risk of cardiovascular disease than their metabolically unhealthy (hyperinsulinaemic) overweight/obese counterparts. Whether similarly variable relationships exist for metabolically defined body size phenotypes and colorectal cancer risk is unknown.
Methods and Findings
The association of metabolically defined body size phenotypes with colorectal cancer was investigated in a case–control study nested within the European Prospective Investigation into Cancer and Nutrition (EPIC) study. Metabolic health/body size phenotypes were defined according to hyperinsulinaemia status using serum concentrations of C-peptide, a marker of insulin secretion. A total of 737 incident colorectal cancer cases and 737 matched controls were divided into tertiles based on the distribution of C-peptide concentration amongst the control population, and participants were classified as metabolically healthy if below the first tertile of C-peptide and metabolically unhealthy if above the first tertile. These metabolic health definitions were then combined with body mass index (BMI) measurements to create four metabolic health/body size phenotype categories: (1) metabolically healthy/normal weight (BMI < 25 kg/m2), (2) metabolically healthy/overweight (BMI ≥ 25 kg/m2), (3) metabolically unhealthy/normal weight (BMI < 25 kg/m2), and (4) metabolically unhealthy/overweight (BMI ≥ 25 kg/m2). Additionally, in separate models, waist circumference measurements (using the International Diabetes Federation cut-points [≥80 cm for women and ≥94 cm for men]) were used (instead of BMI) to create the four metabolic health/body size phenotype categories. Statistical tests used in the analysis were all two-sided, and a p-value of <0.05 was considered statistically significant. In multivariable-adjusted conditional logistic regression models with BMI used to define adiposity, compared with metabolically healthy/normal weight individuals, we observed a higher colorectal cancer risk among metabolically unhealthy/normal weight (odds ratio [OR] = 1.59, 95% CI 1.10–2.28) and metabolically unhealthy/overweight (OR = 1.40, 95% CI 1.01–1.94) participants, but not among metabolically healthy/overweight individuals (OR = 0.96, 95% CI 0.65–1.42). Among the overweight individuals, lower colorectal cancer risk was observed for metabolically healthy/overweight individuals compared with metabolically unhealthy/overweight individuals (OR = 0.69, 95% CI 0.49–0.96). These associations were generally consistent when waist circumference was used as the measure of adiposity. To our knowledge, there is no universally accepted clinical definition for using C-peptide level as an indication of hyperinsulinaemia. Therefore, a possible limitation of our analysis was that the classification of individuals as being hyperinsulinaemic—based on their C-peptide level—was arbitrary. However, when we used quartiles or the median of C-peptide, instead of tertiles, as the cut-point of hyperinsulinaemia, a similar pattern of associations was observed.
Conclusions
These results support the idea that individuals with the metabolically healthy/overweight phenotype (with normal insulin levels) are at lower colorectal cancer risk than those with hyperinsulinaemia. The combination of anthropometric measures with metabolic parameters, such as C-peptide, may be useful for defining strata of the population at greater risk of colorectal cancer.
Gunter and colleagues analyse a large European dataset to determine how body size and metabolic profile associates with the risk of developing colorectal cancer.
Editors' Summary
Background
Colorectal cancer is the third most common cancer worldwide and is a leading cause of cancer-related death, killing around 700,000 people every year. It develops when cells in the colon (the final part of the digestive system, which is also known as the large intestine or large bowel) or the rectum (the lower end of the colon) acquire genetic changes that allow them to divide uncontrollably to form a tumor and to move around the body (metastasize). Symptoms of colorectal cancer include blood in the stool, a change in bowel habits, and unexplained weight loss. Treatments for colorectal cancer include surgery, chemotherapy, and radiation. As with other types of cancer, these treatments are more likely to be successful if started when the tumor is very small. Consequently, many countries run screening programs that use colonoscopy, the fecal occult blood test, and other tests to detect the earliest signs of colorectal cancer in apparently healthy people.
Why Was This Study Done?
Being obese—having too much body fat—is associated with an increased colorectal cancer risk (other risk factors include age, having a family history of colorectal cancer, and eating a high-fat, low-fiber diet). Obesity is also associated with several other chronic diseases, and recent evidence suggests that some obese individuals have a higher risk of developing these diseases than others. For example, overweight/obese individuals who have hyperinsulinemia (abnormally high blood levels of insulin; “metabolically unhealthy”) seem to have a higher risk of cardiovascular disease than their non-hyperinsulinemic (“metabolically healthy”) overweight counterparts. If certain combinations of metabolic health status and body size (“metabolically defined body size phenotypes”) are also associated with colorectal cancer, measurement of insulin levels in conjunction with body fat (adiposity) measurements such as body mass index (BMI; an indicator of body fat calculated by dividing a person’s weight in kilograms by their height in meters squared) might improve colorectal cancer risk assessment. In this nested case–control study, the researchers assess the associations between metabolically defined body size phenotypes and colorectal cancer risk. A nested case–control study identifies everyone in a group (here, participants in the European Prospective Investigation into Cancer and Nutrition [EPIC] study) who has a specific condition, identifies matched individuals in the same group without the condition, and asks whether these controls and the cases differ in terms of a specific characteristic or outcome.
What Did the Researchers Do and Find?
The researchers matched 737 participants in the EPIC study who developed colorectal cancer after study enrollment with 737 controls and used serum concentrations of C-peptide, a marker of insulin secretion, and BMI measurements to classify each individual as metabolically healthy/normal weight, metabolically healthy/overweight, metabolically unhealthy/normal weight, or metabolically unhealthy/overweight. Specifically, the researchers categorized people as metabolically unhealthy if they had a C-peptide level above an arbitrarily chosen cut-off value based on the distribution of C-peptide levels in the control participants and as overweight if they had a BMI of ≥25 kg/m2 (the standard definition of overweight). Compared to metabolically healthy normal weight individuals, metabolically unhealthy normal weight and overweight individuals had an increased colorectal cancer risk; metabolically healthy overweight individuals had a similar colorectal cancer risk to metabolically healthy normal weight individuals. Among overweight individuals, metabolically healthy individuals had a lower colorectal cancer risk than metabolically unhealthy individuals. Finally, similar associations were seen when the researchers used waist circumference instead of BMI as the measure of adiposity.
What Do These Findings Mean?
These findings suggest that normal weight individuals with hyperinsulinemia (the metabolically unhealthy normal weight phenotype) have a higher risk of colorectal cancer than normal weight individuals without hyperinsulinemia. They also suggest that metabolically unhealthy overweight individuals have a higher risk of colorectal cancer than metabolically healthy overweight individuals. The accuracy of these findings may be limited by the method the researchers used to classify individuals as hyperinsulinemic—there is no universally accepted clinical definition for using C-peptide level to diagnose hyperinsulinemia. Nevertheless, these findings suggest that the assessment of insulin levels in conjunction with adiposity measures might be a better way to assess an individual’s colorectal cancer risk than simply measuring adiposity, and might help to identify those individuals at high risk of colorectal cancer who are most likely to benefit from targeted interventions designed to prevent the onset of clinical disease.
Additional Information
This list of resources contains links that can be accessed when viewing the PDF on a device or via the online version of the article at http://dx.doi.org/10.1371/journal.pmed.1001988.
The US National Cancer Institute provides information for patients about all aspects of colorectal cancer; it also provides more detailed information colorectal cancer for health professionals and information on cancer risk and obesity
The UK National Health Service Choices website has information and personal stories about colorectal cancer and information on obesity
The not-for-profit organization Cancer Research UK provides information about colorectal cancer and about the association between cancer and obesity
MedlinePlus provides links to further resources about colorectal cancer and about obesity
Wikipedia has a page on hyperinsulinemia (note that Wikipedia is a free online encyclopedia that anyone can edit; available in several languages)
More information about the EPIC study is available
doi:10.1371/journal.pmed.1001988
PMCID: PMC4821615  PMID: 27046222

Results 1-25 (1628807)