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1.  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
2.  Reduction in BMI z-score and improvement in cardiometabolic risk factors in obese children and adolescents. The Oslo Adiposity Intervention Study - a hospital/public health nurse combined treatment  
BMC Pediatrics  2011;11:47.
Background
Weight loss and increased physical fitness are established approaches to reduce cardiovascular risk factors. We studied the reduction in BMI z-score associated with improvement in cardiometabolic risk factors in overweight and obese children and adolescents treated with a combined hospital/public health nurse model. We also examined how aerobic fitness influenced the results.
Methods
From 2004-2007, 307 overweight and obese children and adolescents aged 7-17 years were referred to an outpatient hospital pediatrics clinic and evaluated by a multidisciplinary team. Together with family members, they were counseled regarding diet and physical activity at biannual clinic visits. Visits with the public health nurse at local schools or at maternal and child health centres were scheduled between the hospital consultations. Fasting blood samples were taken at baseline and after one year, and aerobic fitness (VO2peak) was measured. In the analyses, 230 subjects completing one year of follow-up by December 2008 were divided into four groups according to changes in BMI z-score: Group 1: decrease in BMI z-score≥0.23, Group 2: decrease in BMI z-score≥0.1-< 0.23, Group 3: decrease in/stable BMI z-score≥0.0-< 0.1, Group 4: increase in BMI z-score (>0.00-0.55).
Results
230 participants were included in the analyses (75%). Mean (SD) BMI z-score was reduced from 2.18 (0.30) to 2.05 (0.39) (p < 0.001) in the group as a whole. After adjustment for BMI z-score, waist circumference and gender, the three groups with reduced BMI z-score had a significantly greater reduction in HOMA-IR, insulin, total cholesterol, LDL cholesterol and total/HDL cholesterol ratio than the group with increased BMI z-score. Adding change in aerobic fitness to the model had little influence on the results. Even a very small reduction in BMI z-score (group 3) was associated with significantly lower insulin, total cholesterol, LDL and total/HDL cholesterol ratio. The group with the largest reduction in BMI z-score had improvements in HOMA-IR and aerobic fitness as well. An increase in BMI z-score was associated with worsening of C-peptide and total/HDL cholesterol ratio.
Conclusions
Even a modest reduction in BMI z-score after one year of combined hospital/and public health nurse intervention was associated with improvement in several cardiovascular risk factors.
doi:10.1186/1471-2431-11-47
PMCID: PMC3121603  PMID: 21619652
3.  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
4.  The Role of Adiposity in Cardiometabolic Traits: A Mendelian Randomization Analysis 
Fall, Tove | Hägg, Sara | Mägi, Reedik | Ploner, Alexander | Fischer, Krista | Horikoshi, Momoko | Sarin, Antti-Pekka | Thorleifsson, Gudmar | Ladenvall, Claes | Kals, Mart | Kuningas, Maris | Draisma, Harmen H. M. | Ried, Janina S. | van Zuydam, Natalie R. | Huikari, Ville | Mangino, Massimo | Sonestedt, Emily | Benyamin, Beben | Nelson, Christopher P. | Rivera, Natalia V. | Kristiansson, Kati | Shen, Huei-yi | Havulinna, Aki S. | Dehghan, Abbas | Donnelly, Louise A. | Kaakinen, Marika | Nuotio, Marja-Liisa | Robertson, Neil | de Bruijn, Renée F. A. G. | Ikram, M. Arfan | Amin, Najaf | Balmforth, Anthony J. | Braund, Peter S. | Doney, Alexander S. F. | Döring, Angela | Elliott, Paul | Esko, Tõnu | Franco, Oscar H. | Gretarsdottir, Solveig | Hartikainen, Anna-Liisa | Heikkilä, Kauko | Herzig, Karl-Heinz | Holm, Hilma | Hottenga, Jouke Jan | Hyppönen, Elina | Illig, Thomas | Isaacs, Aaron | Isomaa, Bo | Karssen, Lennart C. | Kettunen, Johannes | Koenig, Wolfgang | Kuulasmaa, Kari | Laatikainen, Tiina | Laitinen, Jaana | Lindgren, Cecilia | Lyssenko, Valeriya | Läärä, Esa | Rayner, Nigel W. | Männistö, Satu | Pouta, Anneli | Rathmann, Wolfgang | Rivadeneira, Fernando | Ruokonen, Aimo | Savolainen, Markku J. | Sijbrands, Eric J. G. | Small, Kerrin S. | Smit, Jan H. | Steinthorsdottir, Valgerdur | Syvänen, Ann-Christine | Taanila, Anja | Tobin, Martin D. | Uitterlinden, Andre G. | Willems, Sara M. | Willemsen, Gonneke | Witteman, Jacqueline | Perola, Markus | Evans, Alun | Ferrières, Jean | Virtamo, Jarmo | Kee, Frank | Tregouet, David-Alexandre | Arveiler, Dominique | Amouyel, Philippe | Ferrario, Marco M. | Brambilla, Paolo | Hall, Alistair S. | Heath, Andrew C. | Madden, Pamela A. F. | Martin, Nicholas G. | Montgomery, Grant W. | Whitfield, John B. | Jula, Antti | Knekt, Paul | Oostra, Ben | van Duijn, Cornelia M. | Penninx, Brenda W. J. H. | Davey Smith, George | Kaprio, Jaakko | Samani, Nilesh J. | Gieger, Christian | Peters, Annette | Wichmann, H.-Erich | Boomsma, Dorret I. | de Geus, Eco J. C. | Tuomi, TiinaMaija | Power, Chris | Hammond, Christopher J. | Spector, Tim D. | Lind, Lars | Orho-Melander, Marju | Palmer, Colin Neil Alexander | Morris, Andrew D. | Groop, Leif | Järvelin, Marjo-Riitta | Salomaa, Veikko | Vartiainen, Erkki | Hofman, Albert | Ripatti, Samuli | Metspalu, Andres | Thorsteinsdottir, Unnur | Stefansson, Kari | Pedersen, Nancy L. | McCarthy, Mark I. | Ingelsson, Erik | Prokopenko, Inga
PLoS Medicine  2013;10(6):e1001474.
In this study, Prokopenko and colleagues provide novel evidence for causal relationship between adiposity and heart failure and increased liver enzymes using a Mendelian randomization study design.
Please see later in the article for the Editors' Summary
Background
The association between adiposity and cardiometabolic traits is well known from epidemiological studies. Whilst the causal relationship is clear for some of these traits, for others it is not. We aimed to determine whether adiposity is causally related to various cardiometabolic traits using the Mendelian randomization approach.
Methods and Findings
We used the adiposity-associated variant rs9939609 at the FTO locus as an instrumental variable (IV) for body mass index (BMI) in a Mendelian randomization design. Thirty-six population-based studies of individuals of European descent contributed to the analyses.
Age- and sex-adjusted regression models were fitted to test for association between (i) rs9939609 and BMI (n = 198,502), (ii) rs9939609 and 24 traits, and (iii) BMI and 24 traits. The causal effect of BMI on the outcome measures was quantified by IV estimators. The estimators were compared to the BMI–trait associations derived from the same individuals. In the IV analysis, we demonstrated novel evidence for a causal relationship between adiposity and incident heart failure (hazard ratio, 1.19 per BMI-unit increase; 95% CI, 1.03–1.39) and replicated earlier reports of a causal association with type 2 diabetes, metabolic syndrome, dyslipidemia, and hypertension (odds ratio for IV estimator, 1.1–1.4; all p<0.05). For quantitative traits, our results provide novel evidence for a causal effect of adiposity on the liver enzymes alanine aminotransferase and gamma-glutamyl transferase and confirm previous reports of a causal effect of adiposity on systolic and diastolic blood pressure, fasting insulin, 2-h post-load glucose from the oral glucose tolerance test, C-reactive protein, triglycerides, and high-density lipoprotein cholesterol levels (all p<0.05). The estimated causal effects were in agreement with traditional observational measures in all instances except for type 2 diabetes, where the causal estimate was larger than the observational estimate (p = 0.001).
Conclusions
We provide novel evidence for a causal relationship between adiposity and heart failure as well as between adiposity and increased liver enzymes.
Please see later in the article for the Editors' Summary
Editors' Summary
Cardiovascular disease (CVD)—disease that affects the heart and/or the blood vessels—is a major cause of illness and death worldwide. In the US, for example, coronary heart disease—a CVD in which narrowing of the heart's blood vessels by fatty deposits slows the blood supply to the heart and may eventually cause a heart attack—is the leading cause of death, and stroke—a CVD in which the brain's blood supply is interrupted—is the fourth leading cause of death. Globally, both the incidence of CVD (the number of new cases in a population every year) and its prevalence (the proportion of the population with CVD) are increasing, particularly in low- and middle-income countries. This increasing burden of CVD is occurring in parallel with a global increase in the incidence and prevalence of obesity—having an unhealthy amount of body fat (adiposity)—and of metabolic diseases—conditions such as diabetes in which metabolism (the processes that the body uses to make energy from food) is disrupted, with resulting high blood sugar and damage to the blood vessels.
Why Was This Study Done?
Epidemiological studies—investigations that record the patterns and causes of disease in populations—have reported an association between adiposity (indicated by an increased body mass index [BMI], which is calculated by dividing body weight in kilograms by height in meters squared) and cardiometabolic traits such as coronary heart disease, stroke, heart failure (a condition in which the heart is incapable of pumping sufficient amounts of blood around the body), diabetes, high blood pressure (hypertension), and high blood cholesterol (dyslipidemia). However, observational studies cannot prove that adiposity causes any particular cardiometabolic trait because overweight individuals may share other characteristics (confounding factors) that are the real causes of both obesity and the cardiometabolic disease. Moreover, it is possible that having CVD or a metabolic disease causes obesity (reverse causation). For example, individuals with heart failure cannot do much exercise, so heart failure may cause obesity rather than vice versa. Here, the researchers use “Mendelian randomization” to examine whether adiposity is causally related to various cardiometabolic traits. Because gene variants are inherited randomly, they are not prone to confounding and are free from reverse causation. It is known that a genetic variant (rs9939609) within the genome region that encodes the fat-mass- and obesity-associated gene (FTO) is associated with increased BMI. Thus, an investigation of the associations between rs9939609 and cardiometabolic traits can indicate whether obesity is causally related to these traits.
What Did the Researchers Do and Find?
The researchers analyzed the association between rs9939609 (the “instrumental variable,” or IV) and BMI, between rs9939609 and 24 cardiometabolic traits, and between BMI and the same traits using genetic and health data collected in 36 population-based studies of nearly 200,000 individuals of European descent. They then quantified the strength of the causal association between BMI and the cardiometabolic traits by calculating “IV estimators.” Higher BMI showed a causal relationship with heart failure, metabolic syndrome (a combination of medical disorders that increases the risk of developing CVD), type 2 diabetes, dyslipidemia, hypertension, increased blood levels of liver enzymes (an indicator of liver damage; some metabolic disorders involve liver damage), and several other cardiometabolic traits. All the IV estimators were similar to the BMI–cardiovascular trait associations (observational estimates) derived from the same individuals, with the exception of diabetes, where the causal estimate was higher than the observational estimate, probably because the observational estimate is based on a single BMI measurement, whereas the causal estimate considers lifetime changes in BMI.
What Do These Findings Mean?
Like all Mendelian randomization studies, the reliability of the causal associations reported here depends on several assumptions made by the researchers. Nevertheless, these findings provide support for many previously suspected and biologically plausible causal relationships, such as that between adiposity and hypertension. They also provide new insights into the causal effect of obesity on liver enzyme levels and on heart failure. In the latter case, these findings suggest that a one-unit increase in BMI might increase the incidence of heart failure by 17%. In the US, this corresponds to 113,000 additional cases of heart failure for every unit increase in BMI at the population level. Although additional studies are needed to confirm and extend these findings, these results suggest that global efforts to reduce the burden of obesity will likely also reduce the occurrence of CVD and metabolic disorders.
Additional Information
Please access these websites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1001474.
The American Heart Association provides information on all aspects of cardiovascular disease and tips on keeping the heart healthy, including weight management (in several languages); its website includes personal stories about stroke and heart attacks
The US Centers for Disease Control and Prevention has information on heart disease, stroke, and all aspects of overweight and obesity (in English and Spanish)
The UK National Health Service Choices website provides information about cardiovascular disease and obesity, including a personal story about losing weight
The World Health Organization provides information on obesity (in several languages)
The International Obesity Taskforce provides information about the global obesity epidemic
Wikipedia has a page on Mendelian randomization (note: Wikipedia is a free online encyclopedia that anyone can edit; available in several languages)
MedlinePlus provides links to other sources of information on heart disease, on vascular disease, on obesity, and on metabolic disorders (in English and Spanish)
The International Association for the Study of Obesity provides maps and information about obesity worldwide
The International Diabetes Federation has a web page that describes types, complications, and risk factors of diabetes
doi:10.1371/journal.pmed.1001474
PMCID: PMC3692470  PMID: 23824655
5.  Parents as Agents of Change (PAC) in pediatric weight management: The protocol for the PAC randomized clinical trial 
BMC Pediatrics  2012;12:114.
Background
There is an urgent need to develop and evaluate weight management interventions to address childhood obesity. Recent research suggests that interventions designed for parents exclusively, which have been named parents as agents of change (PAC) approaches, have yielded positive outcomes for managing pediatric obesity. To date, no research has combined a PAC intervention approach with cognitive behavioural therapy (CBT) to examine whether these combined elements enhance intervention effectiveness. This paper describes the protocol our team is using to examine two PAC-based interventions for pediatric weight management. We hypothesize that children with obesity whose parents complete a CBT-based PAC intervention will achieve greater reductions in adiposity and improvements in cardiometabolic risk factors, lifestyle behaviours, and psychosocial outcomes than children whose parents complete a psycho-education-based PAC intervention (PEP).
Methods/Design
This study is a pragmatic, two-armed, parallel, single-blinded, superiority, randomized clinical trial. The primary objective is to examine the differential effects of a CBT-based PAC vs PEP-based PAC intervention on children’s BMI z-score (primary outcome). Secondary objectives are to assess intervention-mediated changes in cardiometabolic, lifestyle, and psychosocial variables in children and parents. Both interventions are similar in frequency of contact, session duration, group facilitation, lifestyle behaviour goals, and educational content. However, the interventions differ insofar as the CBT-based intervention incorporates theory-based concepts to help parents link their thoughts, feelings, and behaviours; these cognitive activities are enabled by group leaders who possess formal training in CBT. Mothers and fathers of children (8–12 years of age; BMI ≥85th percentile) are eligible to participate if they are proficient in English (written and spoken) and agree for at least one parent to attend group-based sessions on a weekly basis. Anthropometry, cardiometabolic risk factors, lifestyle behaviours, and psychosocial health of children and parents are assessed at pre-intervention, post-intervention, 6-, and 12-months follow-up.
Discussion
This study is designed to extend findings from earlier efficacy studies and provide data on the effect of a CBT-based PAC intervention for managing pediatric obesity in a real-world, outpatient clinical setting.
Trial Registration
ClinicalTrials.gov identifier: NCT01267097
doi:10.1186/1471-2431-12-114
PMCID: PMC3469386  PMID: 22866998
Obesity; Pediatric; Treatment; Parents; Cognitive behavioral therapy; Canada
6.  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
7.  Urbanicity and Lifestyle Risk Factors for Cardiometabolic Diseases in Rural Uganda: A Cross-Sectional Study 
PLoS Medicine  2014;11(7):e1001683.
Johanna Riha and colleagues evaluate the association of lifestyle risk factors with elements of urbanicity, such as having a public telephone, a primary school, or a hospital, among individuals living in rural settings in Uganda.
Please see later in the article for the Editors' Summary
Background
Urban living is associated with unhealthy lifestyles that can increase the risk of cardiometabolic diseases. In sub-Saharan Africa (SSA), where the majority of people live in rural areas, it is still unclear if there is a corresponding increase in unhealthy lifestyles as rural areas adopt urban characteristics. This study examines the distribution of urban characteristics across rural communities in Uganda and their associations with lifestyle risk factors for chronic diseases.
Methods and Findings
Using data collected in 2011, we examined cross-sectional associations between urbanicity and lifestyle risk factors in rural communities in Uganda, with 7,340 participants aged 13 y and above across 25 villages. Urbanicity was defined according to a multi-component scale, and Poisson regression models were used to examine associations between urbanicity and lifestyle risk factors by quartile of urbanicity. Despite all of the villages not having paved roads and running water, there was marked variation in levels of urbanicity across the villages, largely attributable to differences in economic activity, civil infrastructure, and availability of educational and healthcare services. In regression models, after adjustment for clustering and potential confounders including socioeconomic status, increasing urbanicity was associated with an increase in lifestyle risk factors such as physical inactivity (risk ratio [RR]: 1.19; 95% CI: 1.14, 1.24), low fruit and vegetable consumption (RR: 1.17; 95% CI: 1.10, 1.23), and high body mass index (RR: 1.48; 95% CI: 1.24, 1.77).
Conclusions
This study indicates that even across rural communities in SSA, increasing urbanicity is associated with a higher prevalence of lifestyle risk factors for cardiometabolic diseases. This finding highlights the need to consider the health impact of urbanization in rural areas across SSA.
Please see later in the article for the Editors' Summary
Editors’ Summary
Background
Cardiometabolic diseases—cardiovascular diseases that affect the heart and/or the blood vessels and metabolic diseases that affect the cellular chemical reactions needed to sustain life—are a growing global health concern. In sub-Saharan Africa, for example, the prevalence (the proportion of a population that has a given disease) of adults with diabetes (a life-shortening metabolic disease that affects how the body handles sugars) is currently 3.8%. By 2030, it is estimated that the prevalence of diabetes among adults in this region will have risen to 4.6%. Similarly, in 2004, around 1.2 million deaths in sub-Saharan Africa were attributed to coronary heart disease, heart failure, stroke, and other cardiovascular diseases. By 2030, the number of deaths in this region attributable to cardiovascular disease is expected to double. Globally, cardiovascular disease and diabetes are now responsible for around 17.3 million and 1.3 million annual deaths, respectively, together accounting for about one-third of all deaths.
Why Was This Study Done?
Experts believe that increased consumption of saturated fats, sugar, and salt and reduced physical activity are partly responsible for the increasing global prevalence of cardiometabolic diseases. These lifestyle changes, they suggest, are related to urbanization—urban expansion into the countryside and migration from rural to urban areas. If this is true, the prevalence of unhealthy lifestyles should increase as rural areas adopt urban characteristics. Sub-Saharan Africa is the least urbanized region in the world, with about 60% of the population living in rural areas. However, rural settlements across the subcontinent are increasingly adopting urban characteristics. It is important to know whether urbanization is affecting the health of rural residents in sub-Saharan Africa to improve estimates of the future burden of cardiometabolic diseases in the region and to provide insights into ways to limit this burden. In this cross-sectional study (an investigation that studies participants at a single time point), the researchers examine the distribution of urban characteristics across rural communities in Uganda and the association of these characteristics with lifestyle risk factors for cardiometabolic diseases.
What Did the Researchers Do and Find?
For their study, the researchers used data collected in 2011 by the General Population Cohort study, a study initiated in 1989 to describe HIV infection trends among people living in 25 villages in rural southwestern Uganda that collects health-related and other information annually from its participants. The researchers quantified the “urbanicity” of the 25 villages using a multi-component scale that included information such as village size and economic activity. They then used statistical models to examine associations between urbanicity and lifestyle risk factors such as body mass index (BMI, a measure of obesity) and self-reported fruit and vegetable consumption for more than 7,000 study participants living in those villages. None of the villages had paved roads or running water. However, urbanicity varied markedly across the villages, largely because of differences in economic activity, civil infrastructure, and the availability of educational and healthcare services. Notably, increasing urbanicity was associated with an increase in lifestyle risk factors for cardiovascular diseases. So, for example, people living in villages with the highest urbanicity scores were nearly 20% more likely to be physically inactive and to eat less fruits and vegetables and nearly 50% more likely to have a high BMI than people living in villages with the lowest urbanicity scores.
What Do These Findings Mean?
These findings indicate that, across rural communities in Uganda, even a small increase in urbanicity is associated with a higher prevalence of potentially modifiable lifestyle risk factors for cardiometabolic diseases. These findings suggest, therefore, that simply classifying settlements as either rural or urban may not be adequate to capture the information needed to target strategies for cardiometabolic disease management and control in rural areas as they become more urbanized. Because this study was cross-sectional, it is not possible to say how long a rural population needs to experience a more urban environment before its risk of cardiometabolic diseases increases. Longitudinal studies are needed to obtain this information. Moreover, studies of other countries in sub-Saharan Africa are needed to show that these findings are generalizable across the region. However, based on these findings, and given that more than 553 million people live in rural areas across sub-Saharan Africa, it seems likely that increasing urbanization will have a substantial impact on the future health of populations throughout sub-Saharan Africa.
Additional Information
Please access these websites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1001683.
This study is further discussed in a PLOS Medicine Perspective by Fahad Razak and Lisa Berkman
The American Heart Association provides information on all aspects of cardiovascular disease and diabetes; its website includes personal stories about heart attacks, stroke, and diabetes
The US Centers for Disease Control and Prevention has information on heart disease, stroke, and diabetes (in English and Spanish)
The UK National Health Service Choices website provides information about cardiovascular disease and diabetes (including some personal stories)
The World Health Organization’s Global Noncommunicable Disease Network (NCDnet) aims to help low- and middle-income countries reduce illness and death caused by cardiometabolic and other non-communicable diseases
The World Heart Federation has recently produced a report entitled “Urbanization and Cardiovascular Disease”
Wikipedia has a page on urbanization (note that Wikipedia is a free online encyclopedia that anyone can edit; available in several languages)
doi:10.1371/journal.pmed.1001683
PMCID: PMC4114555  PMID: 25072243
8.  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
9.  Prevalence of cardiometabolic risk factors and metabolic syndrome in obese Kuwaiti adolescents 
Background
Childhood and adolescent obesity is associated with insulin resistance, abnormal glucose metabolism, hypertension, dyslipidemia, inflammation, liver disease, and compromised vascular function. The purpose of this pilot study was to determine the prevalence of cardiometabolic risk factor abnormalities and metabolic syndrome (MetS) in a sample of obese Kuwaiti adolescents, as prevalence data might be helpful in improving engagement with obesity treatment in future.
Methods
Eighty obese Kuwaiti adolescents (40 males) with a mean (standard deviation) age of 12.3 years (1.1 years) participated in the present study. All participants had a detailed clinical examination and anthropometry, blood pressure taken, and assessment of fasting levels of C-reactive protein, intracellular adhesion molecule, interleukin-6, fasting blood glucose, insulin, liver function tests (alanine aminotransferase, aspartate aminotransferase, gamma glutamyltransferase), lipid profile (cholesterol, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, triglycerides), insulin resistance by homeostasis model assessment, and adiponectin. MetS was assessed using two recognized criteria modified for use in younger individuals.
Results
The cardiometabolic risk factors with highest prevalence of abnormal values included aspartate aminotransferase (88.7% of the sample) and insulin resistance by homeostasis model assessment (67.5%), intracellular adhesion molecule (66.5%), fasting insulin (43.5%), C-reactive protein (42.5%), low-density lipoprotein cholesterol (35.0%), total cholesterol (33.5%), and systolic blood pressure (30.0%). Of all participants, 96.3% (77/80) had at least one impaired cardiometabolic risk factor as well as obesity. Prevalence of MetS was 21.3% according to the International Diabetes Federation definition and 30% using the Third Adult Treatment Panel definition.
Conclusion
The present study suggests that obese Kuwaiti adolescents have multiple cardiometabolic risk factor abnormalities. Future studies are needed to test the benefits of intervention in this high-risk group. They also suggest that prevention of obesity in children and adults should be a major public health goal in Kuwait.
doi:10.2147/DMSO.S66156
PMCID: PMC4216021  PMID: 25368527
obesity; adolescents; prevalence; cardiometabolic risk factors; metabolic syndrome
10.  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
11.  Cardiometabolic risk factors and quality of life in severely obese children and adolescents in the Netherlands 
BMC Pediatrics  2013;13:62.
Background
The prevalence of severe obesity in children and adolescents is increasing. However, little is known about cardiometabolic risk factors and quality of life of children with severe obesity.
Therefore, the aim of this study was to assess the demographic characteristics and the prevalence of cardiometabolic risk factors and quality of life in severely obese children and adolescents undergoing intensive inpatient treatment for obesity.
Methods
Data were collected between August 2009 and April 2011 on 16 children (8-13y) and 64 adolescents (13-19y) with severe obesity (SDS-BMI >= 3.0 or SDS-BMI >= 2.3 and comorbidity) participating in an RCT evaluating two intensive inpatient treatment programs for obesity. Demographic, anthropometric, clinical characteristics and two components of the EuroQol for the assessment of quality of life are described.
Results
Eighty percent of participants in this study had at least one cardiometabolic risk factor in addition to severe obesity. Low HDL-cholesterol and hypertension were most prevalent (65.0% respectively 31.2%). The highest significant correlations were found between SDS-BMI and SDS-waist circumference, fasting plasma insulin and HOMA-IR (correlation coefficients respectively 0.80, 0.49, and 0.48). With regard to quality of life, the mean utility score of the participants was 0.79 on a scale of 0.0 to 1.0 on the EuroQol questionnaire and their mean individual valuation was 69.1 on a scale of 0 to100.
Conclusion
Cardiometabolic risk factors are already highly prevalent in this group of severely obese children and adolescents. The score of 69.1 found for quality of life in this study suggests that participants experience important limitations in their quality of life. However, quality of life is not associated with the prevalence of cardiometabolic risk factors.
Trial registration
Netherlands Trial Register (NTR1678, registered 20-Feb-2009)
doi:10.1186/1471-2431-13-62
PMCID: PMC3639189  PMID: 23607651
Severe obesity; Child; Adolescent; Cardiometabolic risk factors; Quality of life
12.  Patterns of Obesity Development before the Diagnosis of Type 2 Diabetes: The Whitehall II Cohort Study 
PLoS Medicine  2014;11(2):e1001602.
Examining patterns of change in body mass index (BMI) and other cardiometabolic risk factors in individuals during the years before they were diagnosed with diabetes, Kristine Færch and colleagues report that few of them experienced dramatic BMI changes.
Please see later in the article for the Editors' Summary
Background
Patients with type 2 diabetes vary greatly with respect to degree of obesity at time of diagnosis. To address the heterogeneity of type 2 diabetes, we characterised patterns of change in body mass index (BMI) and other cardiometabolic risk factors before type 2 diabetes diagnosis.
Methods and Findings
We studied 6,705 participants from the Whitehall II study, an observational prospective cohort study of civil servants based in London. White men and women, initially free of diabetes, were followed with 5-yearly clinical examinations from 1991–2009 for a median of 14.1 years (interquartile range [IQR]: 8.7–16.2 years). Type 2 diabetes developed in 645 (1,209 person-examinations) and 6,060 remained free of diabetes during follow-up (14,060 person-examinations). Latent class trajectory analysis of incident diabetes cases was used to identify patterns of pre-disease BMI. Associated trajectories of cardiometabolic risk factors were studied using adjusted mixed-effects models. Three patterns of BMI changes were identified. Most participants belonged to the “stable overweight” group (n = 604, 94%) with a relatively constant BMI level within the overweight category throughout follow-up. They experienced slightly worsening of beta cell function and insulin sensitivity from 5 years prior to diagnosis. A small group of “progressive weight gainers” (n = 15) exhibited a pattern of consistent weight gain before diagnosis. Linear increases in blood pressure and an exponential increase in insulin resistance a few years before diagnosis accompanied the weight gain. The “persistently obese” (n = 26) were severely obese throughout the whole 18 years before diabetes diagnosis. They experienced an initial beta cell compensation followed by loss of beta cell function, whereas insulin sensitivity was relatively stable. Since the generalizability of these findings is limited, the results need confirmation in other study populations.
Conclusions
Three patterns of obesity changes prior to diabetes diagnosis were accompanied by distinct trajectories of insulin resistance and other cardiometabolic risk factors in a white, British population. While these results should be verified independently, the great majority of patients had modest weight gain prior to diagnosis. These results suggest that strategies focusing on small weight reductions for the entire population may be more beneficial than predominantly focusing on weight loss for high-risk individuals.
Please see later in the article for the Editors' Summary
Editors' Summary
Background
Worldwide, more than 350 million people have diabetes, a metabolic disorder characterized by high amounts of glucose (sugar) in the blood. Blood sugar levels are normally controlled by insulin, a hormone released by the pancreas after meals (digestion of food produces glucose). In people with type 2 diabetes (the commonest form of diabetes) blood sugar control fails because the fat and muscle cells that normally respond to insulin by removing sugar from the blood become insulin resistant. Type 2 diabetes, which was previously called adult-onset diabetes, can be controlled with diet and exercise, and with drugs that help the pancreas make more insulin or that make cells more sensitive to insulin. Long-term complications, which include an increased risk of heart disease and stroke, reduce the life expectancy of people with diabetes by about 10 years compared to people without diabetes. The number of people with diabetes is expected to increase dramatically over the next decades, coinciding with rising obesity rates in many countries. To better understand diabetes development, to identify people at risk, and to find ways to prevent the disease are urgent public health goals.
Why Was This Study Done?
It is known that people who are overweight or obese have a higher risk of developing diabetes. Because of this association, a common assumption is that people who experienced recent weight gain are more likely to be diagnosed with diabetes. In this prospective cohort study (an investigation that records the baseline characteristics of a group of people and then follows them to see who develops specific conditions), the researchers tested the hypothesis that substantial weight gain precedes a diagnosis of diabetes and explored more generally the patterns of body weight and composition in the years before people develop diabetes. They then examined whether changes in body weight corresponded with changes in other risk factors for diabetes (such as insulin resistance), lipid profiles and blood pressure.
What Did the Researchers Do and Find?
The researchers studied participants from the Whitehall II study, a prospective cohort study initiated in 1985 to investigate the socioeconomic inequalities in disease. Whitehall II enrolled more than 10,000 London-based government employees. Participants underwent regular health checks during which their weight and height were measured, blood tests were done, and they filled out questionnaires for other relevant information. From 1991 onwards, participants were tested every five years for diabetes. The 6,705 participants included in this study were initially free of diabetes, and most of them were followed for at least 14 years. During the follow-up, 645 participants developed diabetes, while 6,060 remained free of the disease.
The researchers used a statistical tool called “latent class trajectory analysis” to study patterns of changes in body mass index (BMI) in the years before people developed diabetes. BMI is a measure of human obesity based on a person's weight and height. Latent class trajectory analysis is an unbiased way to subdivide a number of people into groups that differ based on specified parameters. In this case, the researchers wanted to identify several groups among all the people who eventually developed diabetes each with a distinct pattern of BMI development. Having identified such groups, they also examined how a variety of tests associated with diabetes risk, and risks for heart disease and stroke changed in the identified groups over time.
They identified three different patterns of BMI changes in the 645 participants who developed diabetes. The vast majority (606 individuals, or 94%) belonged to a group they called “stable-overweight.” These people showed no dramatic change in their BMI in the years before they were diagnosed. They were overweight when they first entered the study and gained or lost little weight during the follow-up years. They showed only minor signs of insulin-resistance, starting five years before they developed diabetes. A second, much smaller group of 15 people gained weight consistently in the years before diagnosis. As they were gaining weight, these people also had raises in blood pressure and substantial gains in insulin resistance. The 26 remaining participants who formed the third group were persistently obese for the entire time they participated in the study, in some cases up to 18 years before they were diagnosed with diabetes. They had some signs of insulin resistance in the years before diagnosis, but not the substantial gain often seen as the hallmark of “pre-diabetes.”
What Do These Findings Mean?
These results suggest that diabetes development is a complicated process, and one that differs between individuals who end up with the disease. They call into question the common notion that most people who develop diabetes have recently gained a lot of weight or are obese. A substantial rise in insulin resistance, another established risk factor for diabetes, was only seen in the smallest of the groups, namely the people who gained weight consistently for years before they were diagnosed. When the scientists applied a commonly used predictor of diabetes called the “Framingham diabetes risk score” to their largest “stably overweight” group, they found that these people were not classified as having a particularly high risk, and that their risk scores actually declined in the last five years before their diabetes diagnosis. This suggests that predicting diabetes in this group might be difficult.
The researchers applied their methodology only to this one cohort of white civil servants in England. Before drawing more firm conclusions on the process of diabetes development, it will be important to test whether similar results are seen in other cohorts and among more diverse individuals. If the three groups identified here are found in other cohorts, another question is whether they are as unequal in size as in this example. And if they are, can the large group of stably overweight people be further subdivided in ways that suggest specific mechanisms of disease development? Even without knowing how generalizable the provocative findings of this study are, they should stimulate debate on how to identify people at risk for diabetes and how to prevent the disease or delay its onset.
Additional Information
Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1001602.
The US National Diabetes Information Clearinghouse provides information about diabetes for patients, health-care professionals, and the general public, including information on diabetes prevention (in English and Spanish)
The UK National Health Service Choices website provides information for patients and carers about type 2 diabetes; it includes people's stories about diabetes
The charity Diabetes UK also provides detailed information about diabetes for patients and carers, including information on healthy lifestyles for people with diabetes, and has a further selection of stories from people with diabetes; the charity Healthtalkonline has interviews with people about their experiences of diabetes
MedlinePlus provides links to further resources and advice about diabetes (in English and Spanish)
More information about the Whitehall II study is available
doi:10.1371/journal.pmed.1001602
PMCID: PMC3921118  PMID: 24523667
13.  Maternal Overweight and Obesity and Risks of Severe Birth-Asphyxia-Related Complications in Term Infants: A Population-Based Cohort Study in Sweden 
PLoS Medicine  2014;11(5):e1001648.
Martina Persson and colleagues use a Swedish national database to investigate the association between maternal body mass index in early pregnancy and severe asphyxia-related outcomes in infants delivered at term.
Please see later in the article for the Editors' Summary
Background
Maternal overweight and obesity increase risks of pregnancy and delivery complications and neonatal mortality, but the mechanisms are unclear. The objective of the study was to investigate associations between maternal body mass index (BMI) in early pregnancy and severe asphyxia-related outcomes in infants delivered at term (≥37 weeks).
Methods and Findings
A nation-wide Swedish cohort study based on data from the Medical Birth Register included all live singleton term births in Sweden between 1992 and 2010. Logistic regression analyses were used to obtain odds ratios (ORs) with 95% CIs for Apgar scores between 0 and 3 at 5 and 10 minutes, meconium aspiration syndrome, and neonatal seizures, adjusted for maternal height, maternal age, parity, mother's smoking habits, education, country of birth, and year of infant birth. Among 1,764,403 term births, 86% had data on early pregnancy BMI and Apgar scores. There were 1,380 infants who had Apgar score 0–3 at 5 minutes (absolute risk  = 0.8 per 1,000) and 894 had Apgar score 0–3 at 10 minutes (absolute risk  = 0.5 per 1,000). Compared with infants of mothers with normal BMI (18.5–24.9), the adjusted ORs (95% CI) for Apgar scores 0–3 at 10 minutes were as follows: BMI 25–29.9: 1.32 (1.10–1.58); BMI 30–34.9: 1.57 (1.20–2.07); BMI 35–39.9: 1.80 (1.15–2.82); and BMI ≥40: 3.41 (1.91–6.09). The ORs for Apgar scores 0–3 at 5 minutes, meconium aspiration, and neonatal seizures increased similarly with maternal BMI. A study limitation was lack of data on effects of obstetric interventions and neonatal resuscitation efforts.
Conclusion
Risks of severe asphyxia-related outcomes in term infants increase with maternal overweight and obesity. Given the high prevalence of the exposure and the severity of the outcomes studied, the results are of potential public health relevance and should be confirmed in other populations. Prevention of overweight and obesity in women of reproductive age is important to improve perinatal health.
Please see later in the article for the Editors' Summary
Editors' Summary
Background
Economic, technologic, and lifestyle changes over the past 30 years have created an abundance of cheap, accessible, high-calorie food. Combined with fewer demands for physical activity, this situation has lead to increasing body mass throughout most of the world. Consequently, being overweight or obese is much more common in many high-income and low-and middle-income countries compared to 1980. Worldwide estimates put the percentage of overweight or obese adults as increasing by over 10%, between 1980 and 2008.
As being overweight becomes a global epidemic, its prevalence in women of reproductive age has also increased. Pregnant women who are overweight or obese are a cause for concern because of the possible associated health risks to both the infant and mother. Research is necessary to more clearly define these risks.
Why Was This Study Done?
In this study, the researchers investigated the complications associated with excess maternal weight that could hinder an infant from obtaining enough oxygen during delivery (neonatal asphyxia). All fetuses experience a loss of oxygen during contractions, however, a prolonged loss of oxygen can impact an infant's long-term development. To explore this risk, the researchers relied on a universal scoring system known as the Apgar score. An Apgar score is routinely recorded at one, five, and ten minutes after birth and is calculated from an assessment of heart rate, respiratory effort, and color, along with reflexes and muscle tone. An oxygen deficit during delivery will have an impact on the score. A normal score is in the range of 7–10. Body mass index (BMI) a calculation that uses height and weight, was used to assess the weight status (i.e., normal, overweight, obese) of the mother during pregnancy.
What Did the Researchers Do and Find?
Using the Swedish medical birth registry (a database including nearly all the births occurring in Sweden since 1973) the researchers selected records for single births that took place between 1992 to 2010. The registry also incorporates prenatal care data and researchers further selected for records that included weight and height measurement taken during the first prenatal visit. BMI was calculated using the weight and height measurement. Based on BMI ranges that define weight groups as normal, overweight, and obesity grades I, II, and III, the researchers analyzed and compared the number of low Apgar scoring infants (Apgar 0–3) in each group. Mothers with normal weight gave birth to the majority of infants with Apgar 0–3. In comparison the proportion of low Apgar scores were greater in babies of overweight and obese mothers. The researchers found that the rates of low Apgar scores increased with maternal BMI: the authors found that rates of low Apgar score at 5 minutes increased from 0.4 per 1,000 among infants of underweight women (BMI <18.5) to 2.4 per 1,000 among infants of women with obesity class III (BMI ≥40). Furthermore, overweight (BMI 25.0–29.9) was associated with a 55% increased risk of low Apgar scores at 5 minutes; obesity grade I (BMI 30–34.9) and grade II (BMI 35.0–39.9) with an almost 2-fold and a more than 2-fold increased risk, respectively; and obesity grade ΙΙΙ (BMI ≥40.0) with a more than 3-fold increase in risk. Finally, maternal overweight and obesity also increase the risks for seizures and meconium aspiration in the neonate.
What Do These Findings Mean?
These findings suggest that the risk of experiencing an oxygen deficit increases for the babies of women who are overweight or obese. Given the high prevalence of overweight and obesity in many countries worldwide, these findings are important and suggest that preventing women of reproductive age from becoming overweight or obese is therefore important to the health of their children.
A limitation of this study is the lack of data on the effects of clinical interventions and neonatal resuscitation efforts that may have been performed at the time of birth. Also Apgar scoring is based on five variables and a low score is not the most direct way to determine if the infant has experienced an oxygen deficit. However, these findings suggest that early detection of perinatal asphyxia is particularly relevant among infants of overweight and obese women although more studies are necessary to confirm the results in other populations.
Additional Information
Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1001648.
The US National Institutes of Health explains and calculates body mass index
The NIH also defines the Apgar scoring system
The United Kingdom's National Health Service has information for pregnant woman who are overweight
The UK-based Overseas Development Institute discusses how changes in diet have led to a worldwide health crisis in its “Future Diets” publication
Information about the Swedish health care system is available
Information in English is available from the National Board of Health and Welfare in Sweden
doi:10.1371/journal.pmed.1001648
PMCID: PMC4028185  PMID: 24845218
14.  TV Viewing and Physical Activity Are Independently Associated with Metabolic Risk in Children: The European Youth Heart Study 
PLoS Medicine  2006;3(12):e488.
Background
TV viewing has been linked to metabolic-risk factors in youth. However, it is unclear whether this association is independent of physical activity (PA) and obesity.
Methods and Findings
We did a population-based, cross-sectional study in 9- to 10-y-old and 15- to 16-y-old boys and girls from three regions in Europe (n = 1,921). We examined the independent associations between TV viewing, PA measured by accelerometry, and metabolic-risk factors (body fatness, blood pressure, fasting triglycerides, inverted high-density lipoprotein (HDL) cholesterol, glucose, and insulin levels). Clustered metabolic risk was expressed as a continuously distributed score calculated as the average of the standardized values of the six subcomponents. There was a positive association between TV viewing and adiposity (p = 0.021). However, after adjustment for PA, gender, age group, study location, sexual maturity, smoking status, birth weight, and parental socio-economic status, the association of TV viewing with clustered metabolic risk was no longer significant (p = 0.053). PA was independently and inversely associated with systolic and diastolic blood pressure, fasting glucose, insulin (all p < 0.01), and triglycerides (p = 0.02). PA was also significantly and inversely associated with the clustered risk score (p < 0.0001), independently of obesity and other confounding factors.
Conclusions
TV viewing and PA may be separate entities and differently associated with adiposity and metabolic risk. The association between TV viewing and clustered metabolic risk is mediated by adiposity, whereas PA is associated with individual and clustered metabolic-risk indicators independently of obesity. Thus, preventive action against metabolic risk in children may need to target TV viewing and PA separately.
A study of over 1,900 European children showed that TV viewing and physical activity in children are separately associated with obesity and metabolic risk.
Editors' Summary
Background.
Childhood obesity is a rapidly growing problem. Twenty-five years ago, overweight children were rare. Now, 155 million of the world's children are overweight, and 30–45 million are obese. Both conditions are diagnosed by comparing a child's body mass index (BMI; weight divided by height squared) with the average BMI for their age and sex. Being overweight during childhood is worrying because it is one of the so-called metabolic-risk factors that increase the chances of developing diabetes, heart problems, or strokes later in life. Other metabolic-risk factors are fatness around the belly, blood-fat disorders, high blood pressure, and problems with how the body uses insulin and blood sugar. Until recently, like obesity, these other metabolic-risk factors were seen only in adults, but now they are becoming increasingly common in children. In the US, 1 in 20 adolescents has metabolic syndrome—three or more of these risk factors. Environmental and behavioural changes have probably contributed to the increase in metabolic syndrome in children. As a group, they tend to be less physically active nowadays and they eat bigger portions of energy-dense foods more often. Increased TV viewing during childhood (and the use of other media such as computer games) has also been linked to increased obesity and to poorer health as an adult.
Why Was This Study Done?
One popular theory is that TV viewing may affect obesity and other metabolic-risk factors by displacing PA. Instead of playing in the yard after school, the theory suggests, children laze about in front of the TV. However, there is limited evidence to support this idea, and health professionals need to know whether TV viewing and PA are related, and how they affect metabolic-risk factors, in order to improve children's health. In this study, the researchers examined the associations between TV viewing, PA, and metabolic-risk factors in European children.
What Did the Researchers Do and Find?
The researchers enrolled nearly 2,000 children in two age groups from three areas in Europe. They measured the children's height and weight, estimated how fat they were by measuring skin fold thickness, measured their blood pressure, and examined the levels of glucose, insulin, and different fats in their blood. The children completed a computer questionnaire about the lengths of time for which they watched TV and how often they ate while doing so, and their PA was measured using a device called an accelerometer that each child wore for four days. When these data were analyzed statistically, the researchers found that TV viewing was slightly associated with clustered metabolic risk (the average of the individual metabolic-risk factors). This association was due to an association between TV viewing and obesity—the children who watched most TV tended to be the fattest children. However, TV viewing was not related to PA. The most active children were not necessarily those who watched least TV. Most importantly, PA was related to all individual risk factors except for obesity and with clustered metabolic risk. These associations were independent of obesity.
What Do These Findings Mean?
These results suggest that TV viewing does not damage children's health by displacing PA as popularly believed. The finding that the association between TV viewing and clustered metabolic-risk factors is mediated by obesity suggests that targeting behaviours like eating while watching TV might be a good way to improve children's health. Indeed, the researchers provide some evidence that eating while watching TV is associated with being overweight, but the results of this post hoc analysis—one that was not planned in advance—need to be confirmed. Another limitation of the study is the possibility that the children inaccurately reported their TV watching habits. Also, because measurements of metabolic-risk factors were made only once, it is impossible to say whether TV viewing or lack of PA actually causes an increase in metabolic-risk factors.
Nevertheless, these results strongly suggest that promoting PA is beneficial in relation to metabolic-risk factors, but less so in relation to obesity in childhood. TV viewing and PA should be treated as separate targets in programs designed to reverse the obesity and metabolic-syndrome epidemic in children.
Additional Information.
Please access these Web sites via the online version of this summary at http://dx.doi.org/doi:10.1371/journal.pmed.0030488.
US Centers for Disease Control and Prevention, information on overweight and obesity
International Obesity Taskforce, information on obesity and its prevention, particularly in childhood
Global Prevention Alliance, details of international efforts to halt the obesity epidemic and its associated chronic diseases
American Heart Association, information for patients and professionals on metabolic syndrome and children's health
doi:10.1371/journal.pmed.0030488
PMCID: PMC1705825  PMID: 17194189
15.  Anthropometrics to Identify Overweight Children at Most Risk for the Development of Cardiometabolic Disease 
Background
Sagittal abdominal diameter (SAD) is a novel anthropometric that correlates more strongly with visceral adipose tissue (VAT) and cardiometabolic disease risk in adults compared with body mass index (BMI). However, little research has evaluated this measurement in children.
Objective
To evaluate SAD as a measure of cardiometabolic risk compared with other anthropometrics in overweight/obese children.
Methods
This study was a cross-sectional subset analysis of 8- to 12-year-old overweight/ obese children. SAD was compared to BMI, waist circumference (WC), BMI z-score, and percent body fat to determine which measurement was most closely associated with cardiometabolic risk factors. A total cardiometabolic risk score comprising all biochemical markers and blood pressure was also compared to these same anthropometrics.
Results
Overweight/obese children (n = 145, mean age 10 ± 1.4 years, mean BMI percentile 97.9 ± 0.02) were included in the analysis. SAD correlated with the greatest number of biochemical markers/blood pressure values including triglycerides (r = .18, P = .03), HgbA1c (r = .21, P = .01), and systolic blood pressure (r = .38, P < .0001). SAD was more strongly correlated to total risk score (r = .25, P = .002) than WC (r = .22, P = .006), BMI (r = .17, P = .04), BMI-z (r = .18, P = .03), and percent body fat (r = .18, P = .03).
Conclusion
This is the first study to evaluate SAD in overweight/obese American children as a marker of cardiometabolic disease risk. The results suggest a slightly stronger correlation between SAD and cardiometabolic risk factors in overweight/obese children; however, all correlations were weak. As this was a pilot study, additional research is needed prior to recommending the use of this measurement in clinical practice.
doi:10.1177/1941406413501379
PMCID: PMC4254738  PMID: 25485038
sagittal abdominal diameter; children; adolescents; overweight and obesity; cardiometabolic disease
16.  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
17.  Impact of obesity on glucose and lipid profiles in adolescents at different age groups in relation to adulthood 
BMC Family Practice  2002;3:18.
Background
As obesity is rapidly becoming a major medical and public health problem, the aim of our study was to determine: 1) if obesity in Caucasian adolescents at 5 different Tanner stages are associated with obesity in adulthood and its obesity-associated abnormal glucose and lipid profiles, 2) the type of fat distribution is associated with glucose and lipid profile abnormalities, and 3) the risk level and the age of appearance of these abnormalities.
Methods
For the first study, data analyses were from a case-control study of adolescents classified according to their BMI; a BMI ≥ 85th percentile for age and sex as overweight, and those with a BMI ≥ 95th percentile as obese. Subjects with a BMI < 85th percentile were classified as controls. WC:AC ratio of waist circumference to arm circumference was used as an indicator of a central pattern of adiposity. Two other indices of central adiposity were calculated from skinfolds: Central-peripheral (CPR) as subscapular skinfold + suprailliac skinfold)/ (triceps skinfold + thigh skinfold) and ratio of subscapular to triceps skinfold (STR). The sum of the four skinfolds (SUM) was calculated from triceps, subscapular, suprailliac and thigh skinfolds. SUM provides a single measure of subcutaneous adiposity. Representative adult subjects were used for comparison. Glucose and lipid profiles were also determined in these subjects. Abnormal glucose and lipid profiles were determined as being those with fasting glucose ≥ 6.1 mmol/l and lipid values ≥ 85th percentile adjusted for age and sex, respectively. Prevalence and odds ratio analysis were used to determine the impact of obesity on glucose and lipid profiles at each Tanner stages for both sexes. Correlation coefficient analyses were used to determine the association between glucose and lipid profiles and anthropometric measurements for both sexes. The second study evaluated in a retrospective-prospective longitudinal way if: 1) obesity in adolescence is associated with obesity in adulthood and 2) the nature of obesity-associated risk factors. Incidence and odds ratio analysis were used to determine the impact of obesity on glucose and lipid profiles at 7 different age groups from 9 to 38 years old in both sexes between 1974 to 2000.
Results
Overall, glucose and lipid profiles were significantly (P < 0.01) associated with all anthropometric measurements either in male and female adolescents. WC:AC, CPR, STR and SUM are stronger predictors of both glucose and lipid profiles than BMI. Obese and overweight adolescents of Tanner stages III and higher are at increased risk of having an impaired glucose and lipid profiles than normal subjects with odds ratios of 5.9 and higher. Obesity in adolescents of 13–15 years old group is significantly (P < 0.01) associated with obesity in adulthood (with odds ratios of at least 12 for both men and women) and abnormal glucose (odds ratio of ≥ 8.6) and lipid profiles (odds ratio of ≥ 11.4).
Conclusions
This study confirmed that adolescents aged between 13 and 15 years old of both sexes with a BMI ≥ 85th percentile are at increased risk of becoming overweight or obese adults and presenting abnormal glucose and lipid profiles as adults. This emphasizes the importance of early detection and intervention directed at treatment of obesity to avert the long-term consequences of obesity on the development of cardiovascular diseases.
doi:10.1186/1471-2296-3-18
PMCID: PMC134463  PMID: 12379160
18.  Cardiometabolic Risks During Anabolic Hormone Supplementation in Older Men 
Obesity (Silver Spring, Md.)  2013;21(5):968-975.
There is little prospective information on the cardiometabolic risks of testosterone and growth hormone (GH) replacement therapy to youthful levels during aging. We conducted a double-masked, partially placebo controlled study in 112 men 65–90 years-old. Transdermal testosterone (5g-vs-10g/day) using a Leydig Cell Clamp and subcutaneous recombinant GH (rhGH) (0-vs-3-vs-5ug/kg/day) were administered for 16-weeks. Measurements included testosterone and IGF-1 levels, body composition by DEXA, and cardiometabolic risk factors (upper body fat, blood pressure, insulin sensitivity, fasting triglycerides, HDL-cholesterol, and serum adiponectin) at baseline and after 16 weeks of treatment. Some cardiometabolic factors improved (total and trunk fat, triglycerides, HDL-cholesterol) and others worsened (systolic blood pressure, insulin sensitivity index [QUICKI], adiponectin). Cardiometabolic risk composite scores (CRCS) improved (−0.69±1.55, p<0.001). In multivariate analyses, QUICKI, triglycerides, and HDL-cholesterol contributed 33%, 16%, and 14% of the variance in CRCS, respectively. Pathway analyses indicated that changes in fat and lean mass were related to individual cardiometabolic variables and CRCS in a complex manner. Changes in BMI, reflecting composite effects of changes in fat and lean mass, were more robustly associated with cardiometabolic risks than changes in fat mass or LBM individually. In conclusion, testosterone and rhGH administration was associated with diverse changes in individual cardiometabolic risk factors, but in aggregate appeared not to worsen cardiometabolic risk in healthy older men after 4-months. The long term effects of these and similar anabolic therapies on cardiovascular events should be investigated in populations with greater funtional limitations along with important health disabilities including upper body obesity and other cardiometabolic risks.
doi:10.1002/oby.20081
PMCID: PMC3930448  PMID: 23784898
Cardiometabolic Risks; Testosterone; Growth Hormone; BMI; Aging
19.  Effects of BMI, Fat Mass, and Lean Mass on Asthma in Childhood: A Mendelian Randomization Study 
PLoS Medicine  2014;11(7):e1001669.
In this study, Granell and colleagues used Mendelian randomization to investigate causal effects of BMI, fat mass, and lean mass on current asthma at age 7½ years in the Avon Longitudinal Study of Parents and Children (ALSPAC) and found that higher BMI increases the risk of asthma in mid-childhood.
Please see later in the article for the Editors' Summary
Background
Observational studies have reported associations between body mass index (BMI) and asthma, but confounding and reverse causality remain plausible explanations. We aim to investigate evidence for a causal effect of BMI on asthma using a Mendelian randomization approach.
Methods and Findings
We used Mendelian randomization to investigate causal effects of BMI, fat mass, and lean mass on current asthma at age 7½ y in the Avon Longitudinal Study of Parents and Children (ALSPAC). A weighted allele score based on 32 independent BMI-related single nucleotide polymorphisms (SNPs) was derived from external data, and associations with BMI, fat mass, lean mass, and asthma were estimated. We derived instrumental variable (IV) estimates of causal risk ratios (RRs). 4,835 children had available data on BMI-associated SNPs, asthma, and BMI. The weighted allele score was strongly associated with BMI, fat mass, and lean mass (all p-values<0.001) and with childhood asthma (RR 2.56, 95% CI 1.38–4.76 per unit score, p = 0.003). The estimated causal RR for the effect of BMI on asthma was 1.55 (95% CI 1.16–2.07) per kg/m2, p = 0.003. This effect appeared stronger for non-atopic (1.90, 95% CI 1.19–3.03) than for atopic asthma (1.37, 95% CI 0.89–2.11) though there was little evidence of heterogeneity (p = 0.31). The estimated causal RRs for the effects of fat mass and lean mass on asthma were 1.41 (95% CI 1.11–1.79) per 0.5 kg and 2.25 (95% CI 1.23–4.11) per kg, respectively. The possibility of genetic pleiotropy could not be discounted completely; however, additional IV analyses using FTO variant rs1558902 and the other BMI-related SNPs separately provided similar causal effects with wider confidence intervals. Loss of follow-up was unlikely to bias the estimated effects.
Conclusions
Higher BMI increases the risk of asthma in mid-childhood. Higher BMI may have contributed to the increase in asthma risk toward the end of the 20th century.
Please see later in the article for the Editors' Summary
Editors' Summary
Background
The global burden of asthma, a chronic (long-term) condition caused by inflammation of the airways (the tubes that carry air in and out of the lungs), has been rising steadily over the past few decades. It is estimated that, nowadays, 200–300 million adults and children worldwide are affected by asthma. Although asthma can develop at any age, it is often diagnosed in childhood—asthma is the most common chronic disease in children. In people with asthma, the airways can react very strongly to allergens such as animal fur or to irritants such as cigarette smoke, becoming narrower so that less air can enter the lungs. Exercise, cold air, and infections can also trigger asthma attacks, which can be fatal. The symptoms of asthma include wheezing, coughing, chest tightness, and shortness of breath. Asthma cannot be cured, but drugs can relieve its symptoms and prevent acute asthma attacks.
Why Was This Study Done?
We cannot halt the ongoing rise in global asthma rates without understanding the causes of asthma. Some experts think obesity may be one cause of asthma. Obesity, like asthma, is increasingly common, and observational studies (investigations that ask whether individuals exposed to a suspected risk factor for a condition develop that condition more often than unexposed individuals) in children have reported that body mass index (BMI, an indicator of body fat calculated by dividing a person's weight in kilograms by their height in meters squared) is positively associated with asthma. Observational studies cannot prove that obesity causes asthma because of “confounding.” Overweight children with asthma may share another unknown characteristic (confounder) that actually causes both obesity and asthma. Moreover, children with asthma may be less active than unaffected children, so they become overweight (reverse causality). Here, the researchers use “Mendelian randomization” to assess whether BMI has a causal effect on asthma. In Mendelian randomization, causality is inferred from associations between genetic variants that mimic the effect of a modifiable risk factor and the outcome of interest. Because gene variants are inherited randomly, they are not prone to confounding and are free from reverse causation. So, if a higher BMI leads to asthma, genetic variants associated with increased BMI should be associated with an increased risk of asthma.
What Did the Researchers Do and Find?
The researchers investigated causal effects of BMI, fat mass, and lean mass on current asthma at age 7½ years in 4,835 children enrolled in the Avon Longitudinal Study of Parents and Children (ALSPAC, a long-term health project that started in 1991). They calculated an allele score for each child based on 32 BMI-related genetic variants, and estimated associations between this score and BMI, fat mass and lean mass (both measured using a special type of X-ray scanner; in children BMI is not a good indicator of “fatness”), and asthma. They report that the allele score was strongly associated with BMI, fat mass, and lean mass, and with childhood asthma. The estimated causal relative risk (risk ratio) for the effect of BMI on asthma was 1.55 per kg/m2. That is, the relative risk of asthma increased by 55% for every extra unit of BMI. The estimated causal relative risks for the effects of fat mass and lean mass on asthma were 1.41 per 0.5 kg and 2.25 per kg, respectively.
What Do These Findings Mean?
These findings suggest that a higher BMI increases the risk of asthma in mid-childhood and that global increases in BMI toward the end of the 20th century may have contributed to the global increase in asthma that occurred at the same time. It is possible that the observed association between BMI and asthma reported in this study is underpinned by “genetic pleiotropy” (a potential limitation of all Mendelian randomization analyses). That is, some of the genetic variants included in the BMI allele score could conceivably also increase the risk of asthma. Nevertheless, these findings suggest that public health interventions designed to reduce obesity may also help to limit the global rise in asthma.
Additional Information
Please access these websites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1001669.
The US Centers for Disease Control and Prevention provides information on asthma and on all aspects of overweight and obesity (in English and Spanish)
The World Health Organization provides information on asthma and on obesity (in several languages)
The UK National Health Service Choices website provides information about asthma, about asthma in children, and about obesity (including real stories)
The Global Asthma Report 2011 is available
The Global Initiative for Asthma released its updated Global Strategy for Asthma Management and Prevention on World Asthma Day 2014
Information about the Avon Longitudinal Study of Parents and Children is available
MedlinePlus provides links to further information on obesity in children, on asthma, and on asthma in children (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.1001669
PMCID: PMC4077660  PMID: 24983943
20.  Body Adiposity Index versus Body Mass Index and Other Anthropometric Traits as Correlates of Cardiometabolic Risk Factors 
PLoS ONE  2013;8(6):e65954.
Objective
The worldwide prevalence of obesity mandates a widely accessible tool to categorize adiposity that can best predict associated health risks. The body adiposity index (BAI) was designed as a single equation to predict body adiposity in pooled analysis of both genders. We compared body adiposity index (BAI), body mass index (BMI), and other anthropometric measures, including percent body fat (PBF), in their correlations with cardiometabolic risk factors. We also compared BAI with BMI to determine which index is a better predictor of PBF.
Methods
The cohort consisted of 698 Mexican Americans. We calculated correlations of BAI, BMI, and other anthropometric measurements (PBF measured by dual energy X-ray absorptiometry, waist and hip circumference, height, weight) with glucose homeostasis indices (including insulin sensitivity and insulin clearance from euglycemic clamp), lipid parameters, cardiovascular traits (including carotid intima-media thickness), and biomarkers (C-reactive protein, plasminogen activator inhibitor-1 and adiponectin). Correlations between each anthropometric measure and cardiometabolic trait were compared in both sex-pooled and sex-stratified groups.
Results
BMI was associated with all but two measured traits (carotid intima-media thickness and fasting glucose in men), while BAI lacked association with several variables. BAI did not outperform BMI in its associations with any cardiometabolic trait. BAI was correlated more strongly than BMI with PBF in sex-pooled analyses (r = 0.78 versus r = 0.51), but not in sex-stratified analyses (men, r = 0.63 versus r = 0.79; women, r = 0.69 versus r = 0.77). Additionally, PBF showed fewer correlations with cardiometabolic risk factors than BMI. Weight was more strongly correlated than hip with many of the cardiometabolic risk factors examined.
Conclusions
BAI is inferior to the widely used BMI as a correlate of the cardiometabolic risk factors studied. Additionally, BMI’s relationship with total adiposity may not be the sole determinate of its association with cardiometabolic risk.
doi:10.1371/journal.pone.0065954
PMCID: PMC3679008  PMID: 23776578
21.  Diet, Psychosocial Factors Related to Diet and Exercise, and Cardiometabolic Conditions in Southern Californian Native Hawaiians 
Hawaii Medical Journal  2010;69(5 suppl 2):16-20.
Objective
Native Hawaiians are at higher risk for cardiometabolic disease, including diabetes and cardiovascular disease compared with other ethnic groups. Diet, body mass index (BMI) and psychosocial, as well as cultural issues may influence risk for cardiometabolic disease. Our team conducted a community-based participatory research study and examined diet, height/weight, psychosocial factors, and community health concerns in Native Hawaiians living in Southern California.
Design and Methods
Cross-section of 55 participants, ≥ 18 years old. Dietary data were collected via three 24-hr dietary recalls, anthropometrics were measured, and psychosocial factors and cardiometabolic conditions were self-reported. Talk story related to diet and health was completed in a sub-sample. Means and frequencies were calculated on dietary intakes, cardiometabolic disease and BMI. Independent t-test and chi square analyses, as appropriate, were performed to assess differences in dietary intakes, obesity and psychosocial factors between those with and without a pre-existing cardiometabolic condition.
Results
Of those with pre-existing health conditions (n = 28), 72% reported being diagnosed with a cardiometabolic condition. For those with pre-existing cardiometabolic conditions, the daily vegetable consumption was 2.57 servings (± 1.66) and the mean fruit consumption was 1.43 servings (± 0.1.99). The mean fiber intake was 16.24 grams (± 6.92), the mean percentage energy from fat was 34.82% (± 6.40) and the mean % energy from carbohydrate was 47.15 (± 6.77). The psychosocial data showed significantly (p ≤ 0.05) lower social support, social interaction, self-monitoring and cognitive-behavioral strategies related to exercise for those with cardiometabolic disease compared with those without disease. All the talk story discussion groups expressed concern over diabetes and weight management, both as an individual and community issue.
Conclusions
The dietary data indicate that Native Hawaiians residing in Southern California should aim to increase their vegetable, fiber, and reduce % energy from fat and saturated fat. Additionally, the psychosocial data suggests that implementing physical activity programs based on socio-cultural values such as ohana, community gatherings, as well as individual self-monitoring and cognitive-behavioral strategies may improve cardiometabolic outcomes. In efforts to reduce cardiometabolic disease disparity, these data suggest that Native Hawaiians in Southern California are aware and concerned about cardiometabolic disease in the community, and that implementation of an effective energetic (diet plus physical activity) intervention that is socially, and culturally specific for Native Hawaiians in Southern California is critical.
PMCID: PMC3158438  PMID: 20544604
22.  Effects of exercise on BMI z-score in overweight and obese children and adolescents: a systematic review with meta-analysis 
BMC Pediatrics  2014;14(1):225.
Background
Overweight and obesity are major public health problems in children and adolescents. The purpose of this study was to conduct a systematic review with meta-analysis to determine the effects of exercise (aerobic, strength or both) on body mass index (BMI) z-score in overweight and obese children and adolescents.
Methods
Studies were included if they were randomized controlled exercise intervention trials ≥ 4 weeks in overweight and obese children and adolescents 2 to 18 years of age, published in any language between 1990–2012 and in which data were available for BMI z-score. Studies were retrieved by searching eleven electronic databases, cross-referencing and expert review. Two authors (GAK, KSK) selected and abstracted data. Bias was assessed using the Cochrane Risk of Bias Assessment Instrument. Exercise minus control group changes were calculated from each study and weighted by the inverse of the variance. All results were pooled using a random-effects model with non-overlapping 95% confidence intervals (CI) considered statistically significant. Heterogeneity was assessed using Q and I2 while funnel plots and Egger’s regression test were used to assess for small-study effects. Influence and cumulative meta-analysis were performed as well as moderator and meta-regression analyses.
Results
Of the 4,999 citations reviewed, 835 children and adolescents (456 exercise, 379 control) from 10 studies representing 21 groups (11 exercise, 10 control) were included. On average, exercise took place 4 x week for 43 minutes per session over 16 weeks. Overall, a statistically significant reduction equivalent to 3% was found for BMI z-score . No small-study effects were observed and results remained statistically significant when each study was deleted from the model once. Based on cumulative meta-analysis, results have been statistically significant since 2009. None of the moderator or meta-regression analyses were statistically significant. The number-needed-to treat was 107 with an estimated 116,822 million obese US children and adolescents and approximately 1 million overweight and obese children and adolescents worldwide potentially improving their BMI z-score by participating in exercise.
Conclusions
Exercise improves BMI z-score in overweight and obese children and adolescents and should be recommended in this population group. However, a need exists for additional studies on this topic.
Electronic supplementary material
The online version of this article (doi:10.1186/1471-2431-14-225) contains supplementary material, which is available to authorized users.
doi:10.1186/1471-2431-14-225
PMCID: PMC4180550  PMID: 25204857
Exercise; Physical activity; Overweight; Obesity; Adiposity; Body composition; Body mass index; Children; Adolescents; Meta-analysis; Systematic review
23.  Overweight prevention in adolescents and children (behavioural and environmental prevention) 
Health political background
In 2006, the prevalence of overweight and adiposity among children and adolescents aged three to 17 years is 15%, 6.3% (800,000) of these are obese.
Scientific background
Obese children and adolescents have an increased body fat ratio. The reasons for overweight are – among others – sociocultural factors, and a low social status as determined by income and educational level of the parents. The consequences of adiposity during childhood are a higher risk of metabolic and cardiovascular diseases and increased mortality in adulthood. Possible approaches to primary prevention in children and adolescents are measures taken in schools and kindergarten, as well as education and involvement of parents. Furthermore, preventive measures geared towards changing environmental and living conditions are of particular importance.
Research questions
What is the effectiveness and efficiency of different measures and programs (geared towards changing behaviour and environmental and living conditions) for primary prevention of adiposity in children and adolescents, with particular consideration of social aspects?
Methods
The systematic literature search yielded 1,649 abstracts. Following a two-part selection process with predefined criteria 31 publications were included in the assessment.
Results
The majority of interventions evaluated in primary studies take place in schools. As the measures are mostly multi-disciplinary and the interventions are often not described in detail, no criteria of success for the various interventions can be extrapolated from the reviews assessed. An economic model calculation for Australia, which compares the efficiency of different interventions (although on the basis of low evidence) comes to the conclusion that the intervention with the greatest impact on society is the reduction of TV-ads geared towards children for foods and drinks rich in fat and sugar. There is a significant correlation between adiposity and socioeconomic deprivation. The lack of interventions (especially preventive measures geared towards changing environmental and living conditions) and studies focusing on this population group is noticeable.
Discussion
There are only a few primary studies of high quality on adiposity prevention in children and adolescents. Especially studies which compare different measures are lacking. This holds also true for the economic analysis, which seems logical insofar, as the basis for economic analyses are usually primary studies (preferably randomized controlled trials (RCT)) due to their evidence level). Studies on interventions geared towards changing environmental and living conditions and towards specific population groups (i. e. the socially disadvantaged) are hardly available.
Conclusions
There are hardly any primary studies of high quality on adiposity prevention in children and adolescents, especially studies which compare different measures are lacking. Interventions geared towards specific population groups (particularly for the socioeconomically disadvantaged) are specifically underrepresented. Establishing such studies is an essential requirement of adiposity prevention. Recommended are a combination of measures geared towards changing environmental and living conditions and towards specific population groups. Furthermore, it is recommended to systematically register future programs (preferably online) in order to be able to draft criteria of success.
doi:10.3205/hta000067
PMCID: PMC3011287  PMID: 21289892
24.  The Causal Effect of Vitamin D Binding Protein (DBP) Levels on Calcemic and Cardiometabolic Diseases: A Mendelian Randomization Study 
PLoS Medicine  2014;11(10):e1001751.
In this study, Richards and colleagues undertook a Mendelian randomization study to determine whether vitamin D binding protein (DBP) levels have a causal effect on common calcemic and cardiometabolic diseases. They concluded that DBP has no demonstrable causal effect on any of the diseases or traits investigated here, except Vit D levels.
Please see later in the article for the Editors' Summary
Background
Observational studies have shown that vitamin D binding protein (DBP) levels, a key determinant of 25-hydroxy-vitamin D (25OHD) levels, and 25OHD levels themselves both associate with risk of disease. If 25OHD levels have a causal influence on disease, and DBP lies in this causal pathway, then DBP levels should likewise be causally associated with disease. We undertook a Mendelian randomization study to determine whether DBP levels have causal effects on common calcemic and cardiometabolic disease.
Methods and Findings
We measured DBP and 25OHD levels in 2,254 individuals, followed for up to 10 y, in the Canadian Multicentre Osteoporosis Study (CaMos). Using the single nucleotide polymorphism rs2282679 as an instrumental variable, we applied Mendelian randomization methods to determine the causal effect of DBP on calcemic (osteoporosis and hyperparathyroidism) and cardiometabolic diseases (hypertension, type 2 diabetes, coronary artery disease, and stroke) and related traits, first in CaMos and then in large-scale genome-wide association study consortia. The effect allele was associated with an age- and sex-adjusted decrease in DBP level of 27.4 mg/l (95% CI 24.7, 30.0; n = 2,254). DBP had a strong observational and causal association with 25OHD levels (p = 3.2×10−19). While DBP levels were observationally associated with calcium and body mass index (BMI), these associations were not supported by causal analyses. Despite well-powered sample sizes from consortia, there were no associations of rs2282679 with any other traits and diseases: fasting glucose (0.00 mmol/l [95% CI −0.01, 0.01]; p = 1.00; n = 46,186); fasting insulin (0.01 pmol/l [95% CI −0.00, 0.01,]; p = 0.22; n = 46,186); BMI (0.00 kg/m2 [95% CI −0.01, 0.01]; p = 0.80; n = 127,587); bone mineral density (0.01 g/cm2 [95% CI −0.01, 0.03]; p = 0.36; n = 32,961); mean arterial pressure (−0.06 mm Hg [95% CI −0.19, 0.07]); p = 0.36; n = 28,775); ischemic stroke (odds ratio [OR] = 1.00 [95% CI 0.97, 1.04]; p = 0.92; n = 12,389/62,004 cases/controls); coronary artery disease (OR = 1.02 [95% CI 0.99, 1.05]; p = 0.31; n = 22,233/64,762); or type 2 diabetes (OR = 1.01 [95% CI 0.97, 1.05]; p = 0.76; n = 9,580/53,810).
Conclusions
DBP has no demonstrable causal effect on any of the diseases or traits investigated here, except 25OHD levels. It remains to be determined whether 25OHD has a causal effect on these outcomes independent of DBP.
Please see later in the article for the Editors' Summary
Editors' Summary
Background
Vitamin D deficiency is an increasingly common public health concern. According to some estimates, more than a billion people worldwide may be vitamin D deficient. Indeed, many people living in the US and Europe (in particular, elderly people, breastfed infants, people with dark skin, and obese individuals) have serum (circulating) 25-hydroxy-vitamin D (25OHD) levels below 50 nmol/l, the threshold for vitamin D deficiency. Vitamin D helps the body absorb calcium, a mineral that is essential for healthy bones. Consequently, vitamin D deficiency can lead to calcemic diseases such as rickets (a condition that affects bone development in children), osteomalacia (soft bones in adults), and osteoporosis (a condition in which the bones weaken and become susceptible to fracture). We get most of our vitamin D needs from our skin, which makes vitamin D after exposure to sunlight. Vitamin D is also found naturally in oily fish and eggs, and is added to some other foods, including cereals and milk, but some people need to take vitamin D supplements to avoid vitamin D deficiency.
Why Was This Study Done?
Observational studies have reported that the low levels of serum 25OHD and serum vitamin D binding protein (DBP, a key determinant of serum 25OHD level) are both associated with the risk of several common diseases and traits. Such studies have implicated vitamin D deficiency in cardiometabolic disease (cardiovascular diseases that affect the heart and/or blood vessels and metabolic diseases that affect the cellular chemical reactions needed to sustain life), in some cancers, and in Alzheimer disease. But observational studies cannot prove that vitamin D deficiency or DBP levels actually cause any of these diseases. So, for example, an observational study might report an association between vitamin D deficiency and type 2 diabetes (a metabolic disease), but the individuals who develop type 2 diabetes might share another unknown characteristic that is actually responsible for disease development (a confounding factor). Alternatively, type 2 diabetes might reduce circulating vitamin D levels (reverse causation). Here, the researchers undertake a Mendelian randomization study to determine whether circulating DBP levels have causal effects on calcemic and cardiometabolic diseases. 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 are inherited randomly, they are not prone to confounding and are free from reverse causation. So, if low DBP levels lead to low serum 25OHD levels, and vitamin D levels have a causal effect on common diseases, genetic variants associated with low DBP levels should be associated with the development of common diseases.
What Did the Researchers Do and Find?
The researchers analyzed the association between a genetic variant called single nucleotide polymorphism (SNP) rs2282679, which is known to alter DBP levels, and calcemic and cardiometabolic diseases and related traits in 2,254 participants in the Canadian Multicentre Osteoporosis Study (CaMos). The researchers report that there was a strong association between SNP rs2282679 and both serum DBP and 25OHD levels among the CaMos participants. However, there were no significant associations (associations unlikely to have occurred by chance) between SNP rs2282679 and calcium level, osteoporosis, or several cardiometabolic diseases, including heart attacks and diabetes. Moreover, when the researchers examined publically available genome-wide association study data collected by several international consortia investigating genetic influences on disease, they found no significant associations between rs2282679 and a wide range of calcemic and cardiometabolic diseases.
What Do These Findings Mean?
In this Mendelian randomization study, DBP level had no demonstrable causal effect on any of the calcemic or cardiometabolic diseases or traits investigated, except 25OHD level. Because most of the participants in CaMos and the international consortia were of European descent, these findings are applicable only to people of European ancestry. Moreover, like all Mendelian randomization studies, the reliability of these findings depends on several assumptions made by the researchers. Notably, although this study strongly suggests that DBP level does not have a causal influence on several common diseases, it remains to be determined whether 25OHD has a causal effect on any calcemic or cardiometabolic outcomes independent of DBP level.
Additional Information
Please access these websites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1001751.
The UK National Health Service Choices website provides information about vitamin D and about how to get vitamin D from sunshine; “Behind the Headlines” articles describe a recent observational study that reported an association between vitamin D deficiency and Alzheimer disease and the media coverage of this study, other health claims made for vitamin D, and a randomized control trial that questioned the role of vitamin D in disease
The US National Institutes of Health Office of Dietary Supplements provides information about vitamin D (in English and Spanish)
The US Centers for Disease Control and Prevention provides information about the vitamin D status of the US population
MedlinePlus has links to further information about vitamin D (in English and Spanish)
Information about the Canadian Multicentre Osteoporosis Study is available
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.1001751
PMCID: PMC4211663  PMID: 25350643
25.  Waist-to-height ratio and cardiometabolic risk factors in adolescence: findings from a prospective birth cohort 
Pediatric Obesity  2013;9(5):327-338.
Objective
To examine the associations between body mass index (BMI) and waist-to-height ratio (WHtR) measured in childhood and adolescence and cardiometabolic risk factors in adolescence.
Methods
Secondary data analysis of the Avon Longitudinal Study of Parents and Children, a population based cohort. Data from 2858 adolescents aged 15.5 (standard deviation 0.4) years and 2710 of these participants as children aged 7–9 years were used in this analysis. Outcome measures were cardiometabolic risk factors, including triglycerides, low density lipoprotein cholesterol, high density lipoprotein cholesterol, insulin, glucose and blood pressure at 15 years of age.
Results
Both BMI and WHtR measured at ages 7–9 years and at age 15 years were associated with cardiometabolic risk factors in adolescents. A WHtR ≥0.5 at 7–9 years increased the odds by 4.6 [95% confidence interval 2.6 to 8.1] for males and 1.6 [0.7 to 3.9] for females of having three or more cardiometabolic risk factors in adolescence. Cross-sectional analysis indicated that adolescents who had a WHtR ≥0.5, the odds ratio of having three or more cardiometabolic risk factors was 6.8 [4.4 to 10.6] for males and 3.8 [2.3 to 6.3] for females. The WHtR cut-point was highly specific in identifying cardiometabolic risk co-occurrence in male children and adolescents as well as female children (90 to 95%), but had poor sensitivity (17 to 53%). Similar associations were observed when BMI was used to define excess adiposity.
Conclusions
WHtR is a simple alternative to age and sex adjusted BMI for assessing cardiometabolic risk in adolescents.
doi:10.1111/j.2047-6310.2013.00192.x
PMCID: PMC4238826  PMID: 23894119
Waist-to-height ratio; cardiometabolic risk; ALSPAC longitudinal study

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