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1.  Mendelian Randomization Studies do not Support a Role for Raised Circulating Triglyceride Levels influencing Type 2 Diabetes, Glucose Levels, or Insulin Resistance 
Diabetes  2011;60(3):1008-1018.
Objective
The causal nature of associations between circulating triglycerides, insulin resistance and type 2 diabetes is unclear. We aimed to use Mendelian randomization to test the hypothesis that raised circulating triglyceride levels causally influence the risk of type 2 diabetes, raised normal fasting glucose levels, and hepatic insulin resistance.
Research design and methods
We tested 10 common genetic variants robustly associated with circulating triglyceride levels against type 2 diabetes status in 5637 cases, 6860 controls, and four continuous outcomes (reflecting glycemia and hepatic insulin resistance) in 8271 non-diabetic individuals from four studies.
Results
Individuals carrying greater numbers of triglyceride-raising alleles had increased circulating triglyceride levels (0.59 SD [95% CI: 0.52, 0.65] difference between the 20% of individuals with the most alleles and the 20% with the fewest alleles). There was no evidence that carriers of greater numbers of triglyceride-raising alleles were at increased risk of type 2 diabetes (per weighted allele odds ratio (OR) 0.99 [95% CI: 0.97, 1.01]; P = 0.26). In non-diabetic individuals, there was no evidence that carriers of greater numbers of triglyceride-raising alleles had increased fasting insulin levels (0.00 SD per weighted allele [95% CI: −0.01, 0.02]; P = 0.72) or increased fasting glucose levels (0.00 SD per weighted allele [95% CI: −0.01, 0.01]; P = 0.88). Instrumental variable analyses confirmed that genetically raised circulating triglyceride levels were not associated with increased diabetes risk, fasting glucose or fasting insulin, and, for diabetes, showed a trend towards a protective association (OR per 1 SD increase in log10-triglycerides: 0.61 [95% CI: 0.45, 0.83]; P = 0.002).
Conclusion
Genetically raised circulating triglyceride levels do not increase the risk of type 2 diabetes, or raise fasting glucose or fasting insulin levels in non-diabetic individuals. One explanation for our results is that raised circulating triglycerides are predominantly secondary to the diabetes disease process rather than causal.
doi:10.2337/db10-1317
PMCID: PMC3046819  PMID: 21282362
2.  Early Emergence of Ethnic Differences in Type 2 Diabetes Precursors in the UK: The Child Heart and Health Study in England (CHASE Study) 
PLoS Medicine  2010;7(4):e1000263.
Peter Whincup and colleagues carry out a cross-sectional study examining ethnic differences in precursors of of type 2 diabetes among children aged 9–10 living in three UK cities.
Background
Adults of South Asian origin living in the United Kingdom have high risks of type 2 diabetes and central obesity; raised circulating insulin, triglyceride, and C-reactive protein concentrations; and low HDL-cholesterol when compared with white Europeans. Adults of African-Caribbean origin living in the UK have smaller increases in type 2 diabetes risk, raised circulating insulin and HDL-cholesterol, and low triglyceride and C-reactive protein concentrations. We examined whether corresponding ethnic differences were apparent in childhood.
Methods and Findings
We performed a cross-sectional survey of 4,796 children aged 9–10 y in three UK cities who had anthropometric measurements (68% response) and provided blood samples (58% response); ethnicity was based on parental definition. In age-adjusted comparisons with white Europeans (n = 1,153), South Asian children (n = 1,306) had higher glycated haemoglobin (HbA1c) (% difference: 2.1, 95% CI 1.6 to 2.7), fasting insulin (% difference 30.0, 95% CI 23.4 to 36.9), triglyceride (% difference 12.9, 95% CI 9.4 to 16.5), and C-reactive protein (% difference 43.3, 95% CI 28.6 to 59.7), and lower HDL-cholesterol (% difference −2.9, 95% CI −4.5 to −1.3). Higher adiposity levels among South Asians (based on skinfolds and bioimpedance) did not account for these patterns. Black African-Caribbean children (n = 1,215) had higher levels of HbA1c, insulin, and C-reactive protein than white Europeans, though the ethnic differences were not as marked as in South Asians. Black African-Caribbean children had higher HDL-cholesterol and lower triglyceride levels than white Europeans; adiposity markers were not increased.
Conclusions
Ethnic differences in type 2 diabetes precursors, mostly following adult patterns, are apparent in UK children in the first decade. Some key determinants operate before adult life and may provide scope for early prevention.
Please see later in the article for the Editors' Summary
Editors' Summary
Background
Worldwide, nearly 250 million people have diabetes, and the number of people affected by this chronic disease is increasing rapidly. Diabetes is characterized by dangerous amounts of sugar (glucose) in the blood. Blood sugar levels are normally controlled by insulin, a hormone that the pancreas releases when blood sugar levels rise after eating (digestion of food produces glucose). In people with type 2 diabetes (the most common type of diabetes), blood sugar control fails because the fat and muscle cells that usually respond to insulin by removing sugar from the blood become less responsive to insulin (insulin resistant). Type 2 diabetes can be controlled with diet and exercise, and with drugs that help the pancreas make more insulin or that make cells more sensitive to insulin. Long-term complications of diabetes include kidney failure, blindness, nerve damage, and an increased risk of developing cardiovascular problems, including heart disease and stroke.
Why Was This Study Done?
South Asians and African-Caribbeans living in Western countries tend to have higher rates of type 2 diabetes than host populations. South Asian adults living in the UK, for example, have a 3-fold higher risk of developing type 2 diabetes than white Europeans. They also have higher fasting blood levels of glucose, insulin and triglycerides (a type of fat), higher blood levels of “glycated hemoglobin” (HbA1c; an indicator of average of blood-sugar levels over time), more body fat (increased adiposity), raised levels of a molecule called C-reactive protein, and lower levels of HDL-cholesterol (another type of fat) than white Europeans. Most of these “diabetes precursors” (risk factors) are also seen in black African-Caribbean adults living in the UK except that individuals in this ethnic group often have raised HDL-cholesterol levels and low triglyceride levels. Ethnic differences in type 2 diabetes precursors are also present in adolescents, but the extent to which they are present in childhood remains unclear. Knowing this information could have implications for diabetes prevention. In this population-based study, therefore, the researchers investigate patterns of diabetes precursors in 9- to 10-year-old UK children of white European, South Asian, and black African-Caribbean origin.
What Did the Researchers Do and Find?
The researchers enrolled nearly 5,000 children (including 1,153 white European, 1,306 South Asian and 1,215 black African-Caribbean children) from primary schools with high prevalences of ethnic minority pupils in London, Birmingham, and Leicester in the Child Heart and Health study in England (CHASE). They measured and weighed more than two-thirds of the enrolled children and determined their adiposity. They also took blood samples for measurement of diabetes precursors from nearly two-thirds of the children. The recorded ethnicity of each child was based on parental definition. The researchers' analysis of these data showed that, compared with white Europeans, South Asian children had higher levels of HbA1c, insulin, triglycerides, and C-reactive protein but lower HDL-cholesterol levels. In addition, they had higher adiposity levels than the white European children, but this did not account for the observed differences in the other diabetes precursors. Black African-Caribbean children also had higher levels of HbA1c, insulin, and C-reactive protein than white European children, although the differences were smaller than those between South Asians and white Europeans. Similar to black African-Caribbean adults, however, children of this ethnic origin had higher HDL-cholesterol and lower triglyceride levels than white Europeans.
What Do These Findings Mean?
These findings indicate that ethnic differences in diabetes precursors are already present in apparently healthy children before they are 10 years old. Furthermore, most of the ethnic differences in diabetes precursors seen among the children follow the pattern seen in adults. Although these findings need confirming in more children, they suggest that the ethnic differences in type 2 diabetes susceptibility first described in immigrants to the UK are persisting in UK-born South Asian and black African-Caribbean children. Most importantly, these findings suggest that some of the factors thought to be responsible for ethnic differences in type 2 diabetes—for example, varying levels of physical activity and dietary differences—are operating well before adult life. Interventions that target these factors early could, therefore, offer good opportunities for diabetes prevention in high-risk ethnic groups, provided such interventions are carefully tailored to the needs of these groups.
Additional Information
Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1000263.
The International Diabetes Federation provides information about all aspects of diabetes (in English, French and Spanish)
The US National Diabetes Information Clearinghouse provides detailed information about diabetes for patients, health-care professionals and the general public, including information on diabetes in specific US populations (in English and Spanish)
The UK National Health Service also provides information for patients and carers about type 2 diabetes (in several languages)
MedlinePlus provides links to further resources and advice about diabetes (in English and Spanish)
The US Agency for Healthcare Research and Quality has a fact sheet on diabetes disparities among racial and ethnic minorities
doi:10.1371/journal.pmed.1000263
PMCID: PMC2857652  PMID: 20421924
3.  Mendelian Randomization Study of B-Type Natriuretic Peptide and Type 2 Diabetes: Evidence of Causal Association from Population Studies 
PLoS Medicine  2011;8(10):e1001112.
Using mendelian randomization, Roman Pfister and colleagues demonstrate a potentially causal link between low levels of B-type natriuretic peptide (BNP), a hormone released by damaged hearts, and the development of type 2 diabetes.
Background
Genetic and epidemiological evidence suggests an inverse association between B-type natriuretic peptide (BNP) levels in blood and risk of type 2 diabetes (T2D), but the prospective association of BNP with T2D is uncertain, and it is unclear whether the association is confounded.
Methods and Findings
We analysed the association between levels of the N-terminal fragment of pro-BNP (NT-pro-BNP) in blood and risk of incident T2D in a prospective case-cohort study and genotyped the variant rs198389 within the BNP locus in three T2D case-control studies. We combined our results with existing data in a meta-analysis of 11 case-control studies. Using a Mendelian randomization approach, we compared the observed association between rs198389 and T2D to that expected from the NT-pro-BNP level to T2D association and the NT-pro-BNP difference per C allele of rs198389. In participants of our case-cohort study who were free of T2D and cardiovascular disease at baseline, we observed a 21% (95% CI 3%–36%) decreased risk of incident T2D per one standard deviation (SD) higher log-transformed NT-pro-BNP levels in analysis adjusted for age, sex, body mass index, systolic blood pressure, smoking, family history of T2D, history of hypertension, and levels of triglycerides, high-density lipoprotein cholesterol, and low-density lipoprotein cholesterol. The association between rs198389 and T2D observed in case-control studies (odds ratio = 0.94 per C allele, 95% CI 0.91–0.97) was similar to that expected (0.96, 0.93–0.98) based on the pooled estimate for the log-NT-pro-BNP level to T2D association derived from a meta-analysis of our study and published data (hazard ratio = 0.82 per SD, 0.74–0.90) and the difference in NT-pro-BNP levels (0.22 SD, 0.15–0.29) per C allele of rs198389. No significant associations were observed between the rs198389 genotype and potential confounders.
Conclusions
Our results provide evidence for a potential causal role of the BNP system in the aetiology of T2D. Further studies are needed to investigate the mechanisms underlying this association and possibilities for preventive interventions.
Please see later in the article for the Editors' Summary
Editors' Summary
Background
Worldwide, nearly 250 million people have diabetes, and this number is increasing rapidly. Diabetes is characterized by dangerous amounts of sugar (glucose) in the blood. Blood sugar levels are normally controlled by insulin, a hormone that the pancreas releases after meals (digestion of food produces glucose). In people with type 2 diabetes (the most common form of diabetes), blood sugar control fails because the fat and muscle cells that usually respond to insulin by removing sugar from the blood become insulin resistant. Type 2 diabetes can be controlled with diet and exercise, and with drugs that help the pancreas make more insulin or that make cells more sensitive to insulin. The long-term complications of diabetes, which include kidney failure and an increased risk of cardiovascular problems such as heart disease and stroke, reduce the life expectancy of people with diabetes by about 10 years compared to people without diabetes.
Why Was This Study Done?
Because the causes of type 2 diabetes are poorly understood, it is hard to devise ways to prevent the condition. Recently, B-type natriuretic peptide (BNP, a hormone released by damaged hearts) has been implicated in type 2 diabetes development in cross-sectional studies (investigations in which data are collected at a single time point from a population to look for associations between an illness and potential risk factors). Although these studies suggest that high levels of BNP may protect against type 2 diabetes, they cannot prove a causal link between BNP levels and diabetes because the study participants with low BNP levels may share some another unknown factor (a confounding factor) that is the real cause of both diabetes and altered BNP levels. Here, the researchers use an approach called “Mendelian randomization” to examine whether reduced BNP levels contribute to causing type 2 diabetes. It is known that a common genetic variant (rs198389) within the genome region that encodes BNP is associated with a reduced risk of type 2 diabetes. Because gene variants are inherited randomly, they are not subject to confounding. So, by investigating the association between BNP gene variants that alter NT-pro-BNP (a molecule created when BNP is being produced) levels and the development of type 2 diabetes, the researchers can discover whether BNP is causally involved in this chronic condition.
What Did the Researchers Do and Find?
The researchers analyzed the association between blood levels of NT-pro-BNP at baseline in 440 participants of the EPIC-Norfolk study (a prospective population-based study of lifestyle factors and the risk of chronic diseases) who subsequently developed diabetes and in 740 participants who did not develop diabetes. In this prospective case-cohort study, the risk of developing type 2 diabetes was associated with lower NT-pro-BNP levels. They also genotyped (sequenced) rs198389 in the participants of three case-control studies of type 2 diabetes (studies in which potential risk factors for type 2 diabetes were examined in people with type 2 diabetes and matched controls living in the East of England), and combined these results with those of eight similar published case-control studies. Finally, the researchers showed that the association between rs198389 and type 2 diabetes measured in the case-control studies was similar to the expected association calculated from the association between NT-pro-BNP level and type 2 diabetes obtained from the prospective case-cohort study and the association between rs198389 and BNP levels obtained from the EPIC-Norfolk study and other published studies.
What Do These Findings Mean?
The results of this Mendelian randomization study provide evidence for a causal, protective role of the BNP hormone system in the development of type 2 diabetes. That is, these findings suggest that low levels of BNP are partly responsible for the development of type 2 diabetes. Because the participants in all the individual studies included in this analysis were of European descent, these findings may not be generalizable to other ethnicities. Moreover, they provide no explanation of how alterations in the BNP hormone system might affect the development of type 2 diabetes. Nevertheless, the demonstration of a causal link between the BNP hormone system and type 2 diabetes suggests that BNP may be a potential target for interventions designed to prevent type 2 diabetes, particularly since the feasibility of altering BNP levels with drugs has already been proven in patients with cardiovascular disease.
Additional Information
Please access these websites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1001112.
The International Diabetes Federation provides information about all aspects of diabetes
The US National Diabetes Information Clearinghouse provides detailed information about diabetes for patients, health-care professionals, and the general public (in English and Spanish)
The UK National Health Service Choices website also provides information for patients and carers about type 2 diabetes and includes people's stories about diabetes
MedlinePlus provides links to further resources and advice about diabetes (in English and Spanish)
Wikipedia has pages on BNP and on Mendelian randomization (note: Wikipedia is a free online encyclopedia that anyone can edit; available in several languages)
The charity Healthtalkonline has interviews with people about their experiences of diabetes; the charity Diabetes UK has a further selection of stories from people with diabetes
doi:10.1371/journal.pmed.1001112
PMCID: PMC3201934  PMID: 22039354
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.  Independent Associations of Fasting Insulin, Glucose, and Glycated Haemoglobin with Stroke and Coronary Heart Disease in Older Women 
PLoS Medicine  2007;4(8):e263.
Background
Evidence suggests that variations in fasting glucose and insulin amongst those without frank type 2 diabetes mellitus are important determinants of cardiovascular disease. However, the relative importance of variations in fasting insulin, glucose, and glycated haemoglobin as risk factors for cardiovascular disease in women without diabetes is unclear. Our aim was to determine the independent associations of fasting insulin, glucose, and glycated haemoglobin with coronary heart disease and stroke in older women.
Methods and Findings
We undertook a prospective cohort study of 3,246 British women aged 60–79 y, all of whom were free of baseline coronary heart disease, stroke, and diabetes, and all of whom had fasting glucose levels below 7 mmol/l. Fasting insulin and homeostasis model assessment for insulin sensitivity (HOMA-S) were linearly associated with a combined outcome of coronary heart disease or stroke (n = 219 events), but there was no association of fasting glucose or glycated haemoglobin with these outcomes. Results were similar for coronary heart disease and stroke as separate outcomes. The age, life-course socioeconomic position, smoking, and physical activity adjusted hazard ratio for a combined outcome of incident coronary heart disease or stroke per one standard deviation of fasting insulin was 1.14 (95% CI 1.02–1.33). Additional adjustment for other components of metabolic syndrome, low-density lipoprotein cholesterol, fasting glucose, and glycated haemoglobin had little effect on this result.
Conclusions
Our findings suggest that in women in the 60–79 y age range, insulin resistance, rather than insulin secretion or chronic hyperglycaemia, is a more important risk factor for coronary heart disease and stroke. Below currently used thresholds of fasting glucose for defining diabetes, neither fasting glucose nor glycated haemoglobin are associated with cardiovascular disease.
From a prospective study of women aged 60-79 years, Debbie Lawlor and colleagues conclude that insulin resistance is an important risk factor for coronary heart disease and stroke.
Editors' Summary
Background.
Narrowing of the vessels that take blood to the heart and brain is a common form of cardiovascular disease—i.e., a disorder of the heart and blood vessels. It is a major cause of illness and death. By starving the heart and brain of oxygen, this condition causes coronary heart disease (CHD; heart problems such as angina and heart attacks) and strokes. A major risk factor for CHD and strokes is diabetes, a common chronic disease characterized by high levels of sugar (glucose) in the blood. In people who don't have diabetes, the hormone insulin controls blood-sugar levels. Insulin, which is released by the pancreas after eating, “instructs” insulin-responsive muscle and fat cells to absorb the glucose (released from food) from the bloodstream. In the very early stages of type 2 diabetes (the commonest type of diabetes, also called “adult onset” or “noninsulin-dependent” diabetes”), muscle and fat cells become unresponsive to insulin, so blood-sugar levels increase. This is called “insulin resistance.” The pancreas responds by making more insulin. As a result, people with insulin resistance have high blood levels of both insulin (hyperinsulinemia) and glucose (hyperglycemia). Eventually, the insulin-producing cells in the pancreas start to malfunction, insulin secretion decreases, and type 2 diabetes is the result.
Why Was This Study Done?
It is not yet clear whether it is insulin resistance or reduced insulin secretion that is responsible for the association between diabetes and cardiovascular disease. Physicians would like to know this information to help them to prevent CHD and strokes in their patients. There is evidence that variations in fasting glucose levels (blood glucose measured more than 8 h after eating), which provide an indication of how well pancreatic cells are producing insulin, and in fasting insulin levels, which provide an indication of insulin resistance, determine cardiovascular disease risk among people without type 2 diabetes, but the relative importance of these risk factors is unclear. In this study, the researchers have investigated whether markers of insulin resistance (fasting hyperinsulinemia) and of altered insulin secretion (fasting hyperglycemia, and increased glycated hemoglobin, which indicates how much sugar has been in the blood over the past few months) are associated with CHD and strokes in elderly women without diabetes. Their aim is to gain new insights into how diabetes affects cardiovascular disease risk.
What Did the Researchers Do and Find?
The researchers measured glucose, insulin, and glycated hemoglobulin in fasting blood samples taken from about 3,000 women aged 60–79 y when they enrolled in the British Women's Heart and Health Study. None of the women had CHD at enrollment, none had had a stroke, none had diagnosed diabetes, and all had a fasting blood glucose below 7 mmol/l (a higher reading indicates diabetes). After monitoring the women for nearly 5 y for CHD and strokes, the researchers looked for statistical associations between the occurrence of cardiovascular disease and markers of insulin resistance and reduced insulin secretion. They found that fasting insulin levels, but not fasting glucose or glycated hemoglobin levels, were associated with CHD and stroke, even after allowing for other factors that affect cardiovascular disease risk such as smoking and physical activity. In other words, raised fasting insulin levels increased the women's risk of developing cardiovascular disease.
What Do These Findings Mean?
These results indicate that in elderly women without diabetes, fasting insulin (a marker of insulin resistance) is a better predictor of future cardiovascular disease risk than fasting glucose or glycated hemoglobin (markers of reduced insulin secretion). This suggests that insulin resistance might be the main mechanism linking type 2 diabetes to CHD and stroke in elderly women. (Elderly women are known to run a high risk of developing these conditions, but they have been relatively neglected in previous studies of the risk factors for cardiovascular disease.) However, because relatively few women developed CHD during the study and even fewer had a stroke, this conclusion needs confirming in larger studies, preferably ones that include more rigorous tests of insulin resistance and secretion and also include women from more ethnic backgrounds than this study did. If the association between fasting insulin levels and cardiovascular disease risk is confirmed, therapeutic interventions or lifestyle interventions (for example, increased physical activity or weight loss) that prevent or reverse insulin resistance might reduce cardiovascular disease risk better than interventions that prevent chronic hyperglycemia.
Additional Information.
Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.0040263.
MedlinePlus encyclopedia page on coronary heart disease, stroke, and diabetes (in English and Spanish)
Information for patients and caregivers from the US National Diabetes Information Clearinghouse on diabetes, including information on insulin resistance and on diabetes, heart disease, and stroke
Information on the British Women's Heart and Health Study
doi:10.1371/journal.pmed.0040263
PMCID: PMC1952205  PMID: 17760500
6.  Inflammation, Insulin Resistance, and Diabetes—Mendelian Randomization Using CRP Haplotypes Points Upstream 
PLoS Medicine  2008;5(8):e155.
Background
Raised C-reactive protein (CRP) is a risk factor for type 2 diabetes. According to the Mendelian randomization method, the association is likely to be causal if genetic variants that affect CRP level are associated with markers of diabetes development and diabetes. Our objective was to examine the nature of the association between CRP phenotype and diabetes development using CRP haplotypes as instrumental variables.
Methods and Findings
We genotyped three tagging SNPs (CRP + 2302G > A; CRP + 1444T > C; CRP + 4899T > G) in the CRP gene and measured serum CRP in 5,274 men and women at mean ages 49 and 61 y (Whitehall II Study). Homeostasis model assessment-insulin resistance (HOMA-IR) and hemoglobin A1c (HbA1c) were measured at age 61 y. Diabetes was ascertained by glucose tolerance test and self-report. Common major haplotypes were strongly associated with serum CRP levels, but unrelated to obesity, blood pressure, and socioeconomic position, which may confound the association between CRP and diabetes risk. Serum CRP was associated with these potential confounding factors. After adjustment for age and sex, baseline serum CRP was associated with incident diabetes (hazard ratio = 1.39 [95% confidence interval 1.29–1.51], HOMA-IR, and HbA1c, but the associations were considerably attenuated on adjustment for potential confounding factors. In contrast, CRP haplotypes were not associated with HOMA-IR or HbA1c (p = 0.52–0.92). The associations of CRP with HOMA-IR and HbA1c were all null when examined using instrumental variables analysis, with genetic variants as the instrument for serum CRP. Instrumental variables estimates differed from the directly observed associations (p = 0.007–0.11). Pooled analysis of CRP haplotypes and diabetes in Whitehall II and Northwick Park Heart Study II produced null findings (p = 0.25–0.88). Analyses based on the Wellcome Trust Case Control Consortium (1,923 diabetes cases, 2,932 controls) using three SNPs in tight linkage disequilibrium with our tagging SNPs also demonstrated null associations.
Conclusions
Observed associations between serum CRP and insulin resistance, glycemia, and diabetes are likely to be noncausal. Inflammation may play a causal role via upstream effectors rather than the downstream marker CRP.
Using a Mendelian randomization approach, Eric Brunner and colleagues show that the associations between serum C-reactive protein and insulin resistance, glycemia, and diabetes are likely to be noncausal.
Editors' Summary
Background.
Diabetes—a common, long-term (chronic) disease that causes heart, kidney, nerve, and eye problems and shortens life expectancy—is characterized by high levels of sugar (glucose) in the blood. In people without diabetes, blood sugar levels are controlled by the hormone insulin. Insulin is released by the pancreas after eating and “instructs” insulin-responsive muscle and fat cells to take up the glucose from the bloodstream that is produced by the digestion of food. In the early stages of type 2 diabetes (the commonest type of diabetes), the muscle and fat cells become nonresponsive to insulin (a condition called insulin resistance), and blood sugar levels increase. The pancreas responds by making more insulin—people with insulin resistance have high blood levels of both insulin and glucose. Eventually, however, the insulin-producing cells in the pancreas start to malfunction, insulin secretion decreases, and frank diabetes develops.
Why Was This Study Done?
Globally, about 200 million people have diabetes, but experts believe this number will double by 2030. Ways to prevent or delay the onset of diabetes are, therefore, urgently needed. One major risk factor for insulin resistance and diabetes is being overweight. According to one theory, increased body fat causes mild, chronic tissue inflammation, which leads to insulin resistance. Consistent with this idea, people with higher than normal amounts of the inflammatory protein C-reactive protein (CRP) in their blood have a high risk of developing diabetes. If inflammation does cause diabetes, then drugs that inhibit CRP might prevent diabetes. However, simply measuring CRP and determining whether the people with high levels develop diabetes cannot prove that CRP causes diabetes. Those people with high blood levels of CRP might have other unknown factors in common (confounding factors) that are the real causes of diabetes. In this study, the researchers use “Mendelian randomization” to examine whether increased blood CRP causes diabetes. Some variants of CRP (the gene that encodes CRP) increase the amount of CRP in the blood. Because these variants are inherited randomly, there is no likelihood of confounding factors, and an association between these variants and the development of insulin resistance and diabetes indicates, therefore, that increased CRP levels cause diabetes.
What Did the Researchers Do and Find?
The researchers measured blood CRP levels in more than 5,000 people enrolled in the Whitehall II study, which is investigating factors that affect disease development. They also used the “homeostasis model assessment-insulin resistance” (HOMA-IR) method to estimate insulin sensitivity from blood glucose and insulin measurements, and measured levels of hemoglobin A1c (HbA1c, hemoglobin with sugar attached—a measure of long-term blood sugar control) in these people. Finally, they looked at three “single polynucleotide polymorphisms” (SNPs, single nucleotide changes in a gene's DNA sequence; combinations of SNPs that are inherited as a block are called haplotypes) in CRP in each study participant. Common haplotypes of CRP were related to blood serum CRP levels and, as previously reported, increased blood CRP levels were associated with diabetes and with HOMA-IR and HbA1c values indicative of insulin resistance and poor blood sugar control, respectively. By contrast, CRP haplotypes were not related to HOMA-IR or HbA1c values. Similarly, pooled analysis of CRP haplotypes and diabetes in Whitehall II and another large study on health determinants (the Northwick Park Heart Study II) showed no association between CRP variants and diabetes risk. Finally, data from the Wellcome Trust Case Control Consortium also showed no association between CRP haplotypes and diabetes risk.
What Do These Findings Mean?
Together, these findings suggest that increased blood CRP levels are not responsible for the development of insulin resistance or diabetes, at least in European populations. It may be that there is a causal relationship between CRP levels and diabetes risk in other ethnic populations—further Mendelian randomization studies are needed to discover whether this is the case. For now, though, these findings suggest that drugs targeted against CRP are unlikely to prevent or delay the onset of diabetes. However, they do not discount the possibility that proteins involved earlier in the inflammatory process might cause diabetes and might thus represent good drug targets for diabetes prevention.
Additional Information.
Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.0050155.
This study is further discussed in a PLoS Medicine Perspective by Bernard Keavney
The MedlinePlus encyclopedia provides information about diabetes and about C-reactive protein (in English and Spanish)
US National Institute of Diabetes and Digestive and Kidney Diseases provides patient information on all aspects of diabetes, including information on insulin resistance (in English and Spanish)
The International Diabetes Federation provides information about diabetes, including information on the global diabetes epidemic
The US Centers for Disease Control and Prevention provides information for the public and professionals on all aspects of diabetes (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.0050155
PMCID: PMC2504484  PMID: 18700811
7.  Regular Breakfast Consumption and Type 2 Diabetes Risk Markers in 9- to 10-Year-Old Children in the Child Heart and Health Study in England (CHASE): A Cross-Sectional Analysis 
PLoS Medicine  2014;11(9):e1001703.
Angela Donin and colleagues evaluated the association between breakfast consumption and composition and risk markers for diabetes and cardiovascular disease in 9- and 10-year-olds.
Please see later in the article for the Editors' Summary
Background
Regular breakfast consumption may protect against type 2 diabetes risk in adults but little is known about its influence on type 2 diabetes risk markers in children. We investigated the associations between breakfast consumption (frequency and content) and risk markers for type 2 diabetes (particularly insulin resistance and glycaemia) and cardiovascular disease in children.
Methods and Findings
We conducted a cross-sectional study of 4,116 UK primary school children aged 9–10 years. Participants provided information on breakfast frequency, had measurements of body composition, and gave fasting blood samples for measurements of blood lipids, insulin, glucose, and glycated haemoglobin (HbA1c). A subgroup of 2,004 children also completed a 24-hour dietary recall. Among 4,116 children studied, 3,056 (74%) ate breakfast daily, 450 (11%) most days, 372 (9%) some days, and 238 (6%) not usually. Graded associations between breakfast frequency and risk markers were observed; children who reported not usually having breakfast had higher fasting insulin (percent difference 26.4%, 95% CI 16.6%–37.0%), insulin resistance (percent difference 26.7%, 95% CI 17.0%–37.2%), HbA1c (percent difference 1.2%, 95% CI 0.4%–2.0%), glucose (percent difference 1.0%, 95% CI 0.0%–2.0%), and urate (percent difference 6%, 95% CI 3%–10%) than those who reported having breakfast daily; these differences were little affected by adjustment for adiposity, socioeconomic status, and physical activity levels. When the higher levels of triglyceride, systolic blood pressure, and C-reactive protein for those who usually did not eat breakfast relative to those who ate breakfast daily were adjusted for adiposity, the differences were no longer significant. Children eating a high fibre cereal breakfast had lower insulin resistance than those eating other breakfast types (p for heterogeneity <0.01). Differences in nutrient intakes between breakfast frequency groups did not account for the differences in type 2 diabetes markers.
Conclusions
Children who ate breakfast daily, particularly a high fibre cereal breakfast, had a more favourable type 2 diabetes risk profile. Trials are needed to quantify the protective effect of breakfast on emerging type 2 diabetes risk.
Please see later in the article for the Editors' Summary
Editors' Summary
Background
Worldwide, more than 380 million people have diabetes, a disorder that is characterized by high levels of glucose (sugar) in the blood. Blood sugar levels are usually controlled by insulin, a hormone released by the pancreas after meals (digestion of food produces glucose). In people with type 2 diabetes (the commonest type of diabetes) blood sugar control fails because the fat and muscle cells that normally respond to insulin become insulin resistant. Type 2 diabetes can often be controlled initially with diet and exercise and with drugs such as metformin and sulfonylureas. However, many patients eventually need insulin injections to control their blood sugar levels. Long-term complications of diabetes, which include an increased risk of heart disease and stroke (cardiovascular disease), reduce the life expectancy of people with diabetes by about 10 years compared to people without diabetes. Risk factors for the condition include being over 40 years old and being overweight or obese.
Why Was This Study Done?
Experts predict that by 2035 nearly 600 million people will have diabetes so better strategies to prevent diabetes are urgently needed. Eating breakfast regularly—particularly a high fiber, cereal-based breakfast—has been associated with a reduced risk of type 2 diabetes (and a reduced risk of being overweight or obese) in adults. However, little is known about whether breakfast eating habits affect markers of type 2 diabetes risk in children. In this cross-sectional study (an observational investigation that studies a group of individuals at a single time point), the researchers examine the associations between breakfast consumption (both frequency and content) and risk markers for type 2 diabetes, particularly insulin resistance and glycemia (the presence of sugar in the blood), in an ethnically mixed population of children; insulin resistance and glycemia measurements in children provide important information about diabetes development later in life.
What Did the Researchers Do and Find?
The researchers invited 9–10 year old children attending 200 schools in London, Birmingham, and Leicester to participate in the Child Heart and Health Study in England (CHASE), a study examining risk factors for cardiovascular disease and type 2 diabetes in children of South Asian, black African-Caribbean, and white European origin. The researchers measured the body composition of the study participants and the levels of insulin, glucose, and other markers of diabetes risk in fasting blood samples (blood taken from the children 8–10 hours after their last meal or drink). All the participants (4,116 children) reported how often they ate breakfast; 2,004 children also completed a 24-hour dietary recall questionnaire. Seventy-four percent of the children reported that they ate breakfast every day, 11% and 9% reported that they ate breakfast most days and some days, respectively, whereas 6% reported that they rarely ate breakfast. Children who ate breakfast infrequently had higher fasting insulin levels and higher insulin resistance than children who ate breakfast every day. Moreover, the children who ate a high fiber, cereal-based breakfast had lower insulin resistance than children who ate other types of breakfast such as low fiber or toast-based breakfasts.
What Do These Findings Mean?
These findings indicate that children who ate breakfast every day, particularly those who ate a high fiber breakfast, had lower levels of risk markers for type 2 diabetes than children who rarely ate breakfast. Importantly, the association between eating breakfast and having a favorable type 2 diabetes risk profile remained after allowing for differences in socioeconomic status, physical activity levels, and amount of body fat (adiposity); in observational studies, it is important to allow for the possibility that individuals who share a measured characteristic and a health outcome also share another characteristic (a confounder) that is actually responsible for the outcome. Although trials are needed to establish whether altering the breakfast habits of children can alter their risk of developing type 2 diabetes, these findings are encouraging. Specifically, they suggest that if all the children in England who do not eat breakfast daily could be encouraged to do so, it might reduce population-wide fasting insulin levels by about 4%. Moreover, encouraging children to eat a high fiber breakfast instead of a low fiber breakfast might reduce population-wide fasting insulin levels by 11%–12%. Thus, persuading children to eat a high fiber breakfast regularly could be an important component in diabetes preventative strategies in England and potentially worldwide.
Additional Information
Please access these websites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1001703.
The US National Diabetes Information Clearinghouse provides information about diabetes for patients, health-care professionals, and the general public, including detailed 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 and about living with diabetes; it also provides people's stories about diabetes; Change4Life, a UK campaign that provides tips for healthy living, has a webpage about the importance of a healthy breakfast
The charity Diabetes UK provides detailed information for patients and carers in several languages, including information on healthy lifestyles for people with diabetes
The UK-based non-profit organization Healthtalkonline has interviews with people about their experiences of diabetes
MedlinePlus provides links to further resources and advice about diabetes and diabetes prevention (in English and Spanish)
Kidshealth, a US-based not-for-profit organization provides information for parents about the importance of breakfast and information for children
More information about the Child Heart and Health Study in England (CHASE) is available
doi:10.1371/journal.pmed.1001703
PMCID: PMC4151989  PMID: 25181492
8.  Mendelian Randomization Studies Do Not Support a Causal Role for Reduced Circulating Adiponectin Levels in Insulin Resistance and Type 2 Diabetes 
Yaghootkar, Hanieh | Lamina, Claudia | Scott, Robert A. | Dastani, Zari | Hivert, Marie-France | Warren, Liling L. | Stancáková, Alena | Buxbaum, Sarah G. | Lyytikäinen, Leo-Pekka | Henneman, Peter | Wu, Ying | Cheung, Chloe Y.Y. | Pankow, James S. | Jackson, Anne U. | Gustafsson, Stefan | Zhao, Jing Hua | Ballantyne, Christie M. | Xie, Weijia | Bergman, Richard N. | Boehnke, Michael | el Bouazzaoui, Fatiha | Collins, Francis S. | Dunn, Sandra H. | Dupuis, Josee | Forouhi, Nita G. | Gillson, Christopher | Hattersley, Andrew T. | Hong, Jaeyoung | Kähönen, Mika | Kuusisto, Johanna | Kedenko, Lyudmyla | Kronenberg, Florian | Doria, Alessandro | Assimes, Themistocles L. | Ferrannini, Ele | Hansen, Torben | Hao, Ke | Häring, Hans | Knowles, Joshua W. | Lindgren, Cecilia M. | Nolan, John J. | Paananen, Jussi | Pedersen, Oluf | Quertermous, Thomas | Smith, Ulf | Lehtimäki, Terho | Liu, Ching-Ti | Loos, Ruth J.F. | McCarthy, Mark I. | Morris, Andrew D. | Vasan, Ramachandran S. | Spector, Tim D. | Teslovich, Tanya M. | Tuomilehto, Jaakko | van Dijk, Ko Willems | Viikari, Jorma S. | Zhu, Na | Langenberg, Claudia | Ingelsson, Erik | Semple, Robert K. | Sinaiko, Alan R. | Palmer, Colin N.A. | Walker, Mark | Lam, Karen S.L. | Paulweber, Bernhard | Mohlke, Karen L. | van Duijn, Cornelia | Raitakari, Olli T. | Bidulescu, Aurelian | Wareham, Nick J. | Laakso, Markku | Waterworth, Dawn M. | Lawlor, Debbie A. | Meigs, James B. | Richards, J. Brent | Frayling, Timothy M.
Diabetes  2013;62(10):3589-3598.
Adiponectin is strongly inversely associated with insulin resistance and type 2 diabetes, but its causal role remains controversial. We used a Mendelian randomization approach to test the hypothesis that adiponectin causally influences insulin resistance and type 2 diabetes. We used genetic variants at the ADIPOQ gene as instruments to calculate a regression slope between adiponectin levels and metabolic traits (up to 31,000 individuals) and a combination of instrumental variables and summary statistics–based genetic risk scores to test the associations with gold-standard measures of insulin sensitivity (2,969 individuals) and type 2 diabetes (15,960 case subjects and 64,731 control subjects). In conventional regression analyses, a 1-SD decrease in adiponectin levels was correlated with a 0.31-SD (95% CI 0.26–0.35) increase in fasting insulin, a 0.34-SD (0.30–0.38) decrease in insulin sensitivity, and a type 2 diabetes odds ratio (OR) of 1.75 (1.47–2.13). The instrumental variable analysis revealed no evidence of a causal association between genetically lower circulating adiponectin and higher fasting insulin (0.02 SD; 95% CI −0.07 to 0.11; N = 29,771), nominal evidence of a causal relationship with lower insulin sensitivity (−0.20 SD; 95% CI −0.38 to −0.02; N = 1,860), and no evidence of a relationship with type 2 diabetes (OR 0.94; 95% CI 0.75–1.19; N = 2,777 case subjects and 13,011 control subjects). Using the ADIPOQ summary statistics genetic risk scores, we found no evidence of an association between adiponectin-lowering alleles and insulin sensitivity (effect per weighted adiponectin-lowering allele: −0.03 SD; 95% CI −0.07 to 0.01; N = 2,969) or type 2 diabetes (OR per weighted adiponectin-lowering allele: 0.99; 95% CI 0.95–1.04; 15,960 case subjects vs. 64,731 control subjects). These results do not provide any consistent evidence that interventions aimed at increasing adiponectin levels will improve insulin sensitivity or risk of type 2 diabetes.
doi:10.2337/db13-0128
PMCID: PMC3781444  PMID: 23835345
9.  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
10.  Genome-Wide Association Identifies Nine Common Variants Associated With Fasting Proinsulin Levels and Provides New Insights Into the Pathophysiology of Type 2 Diabetes 
Strawbridge, Rona J. | Dupuis, Josée | Prokopenko, Inga | Barker, Adam | Ahlqvist, Emma | Rybin, Denis | Petrie, John R. | Travers, Mary E. | Bouatia-Naji, Nabila | Dimas, Antigone S. | Nica, Alexandra | Wheeler, Eleanor | Chen, Han | Voight, Benjamin F. | Taneera, Jalal | Kanoni, Stavroula | Peden, John F. | Turrini, Fabiola | Gustafsson, Stefan | Zabena, Carina | Almgren, Peter | Barker, David J.P. | Barnes, Daniel | Dennison, Elaine M. | Eriksson, Johan G. | Eriksson, Per | Eury, Elodie | Folkersen, Lasse | Fox, Caroline S. | Frayling, Timothy M. | Goel, Anuj | Gu, Harvest F. | Horikoshi, Momoko | Isomaa, Bo | Jackson, Anne U. | Jameson, Karen A. | Kajantie, Eero | Kerr-Conte, Julie | Kuulasmaa, Teemu | Kuusisto, Johanna | Loos, Ruth J.F. | Luan, Jian'an | Makrilakis, Konstantinos | Manning, Alisa K. | Martínez-Larrad, María Teresa | Narisu, Narisu | Nastase Mannila, Maria | Öhrvik, John | Osmond, Clive | Pascoe, Laura | Payne, Felicity | Sayer, Avan A. | Sennblad, Bengt | Silveira, Angela | Stančáková, Alena | Stirrups, Kathy | Swift, Amy J. | Syvänen, Ann-Christine | Tuomi, Tiinamaija | van 't Hooft, Ferdinand M. | Walker, Mark | Weedon, Michael N. | Xie, Weijia | Zethelius, Björn | Ongen, Halit | Mälarstig, Anders | Hopewell, Jemma C. | Saleheen, Danish | Chambers, John | Parish, Sarah | Danesh, John | Kooner, Jaspal | Östenson, Claes-Göran | Lind, Lars | Cooper, Cyrus C. | Serrano-Ríos, Manuel | Ferrannini, Ele | Forsen, Tom J. | Clarke, Robert | Franzosi, Maria Grazia | Seedorf, Udo | Watkins, Hugh | Froguel, Philippe | Johnson, Paul | Deloukas, Panos | Collins, Francis S. | Laakso, Markku | Dermitzakis, Emmanouil T. | Boehnke, Michael | McCarthy, Mark I. | Wareham, Nicholas J. | Groop, Leif | Pattou, François | Gloyn, Anna L. | Dedoussis, George V. | Lyssenko, Valeriya | Meigs, James B. | Barroso, Inês | Watanabe, Richard M. | Ingelsson, Erik | Langenberg, Claudia | Hamsten, Anders | Florez, Jose C.
Diabetes  2011;60(10):2624-2634.
OBJECTIVE
Proinsulin is a precursor of mature insulin and C-peptide. Higher circulating proinsulin levels are associated with impaired β-cell function, raised glucose levels, insulin resistance, and type 2 diabetes (T2D). Studies of the insulin processing pathway could provide new insights about T2D pathophysiology.
RESEARCH DESIGN AND METHODS
We have conducted a meta-analysis of genome-wide association tests of ∼2.5 million genotyped or imputed single nucleotide polymorphisms (SNPs) and fasting proinsulin levels in 10,701 nondiabetic adults of European ancestry, with follow-up of 23 loci in up to 16,378 individuals, using additive genetic models adjusted for age, sex, fasting insulin, and study-specific covariates.
RESULTS
Nine SNPs at eight loci were associated with proinsulin levels (P < 5 × 10−8). Two loci (LARP6 and SGSM2) have not been previously related to metabolic traits, one (MADD) has been associated with fasting glucose, one (PCSK1) has been implicated in obesity, and four (TCF7L2, SLC30A8, VPS13C/C2CD4A/B, and ARAP1, formerly CENTD2) increase T2D risk. The proinsulin-raising allele of ARAP1 was associated with a lower fasting glucose (P = 1.7 × 10−4), improved β-cell function (P = 1.1 × 10−5), and lower risk of T2D (odds ratio 0.88; P = 7.8 × 10−6). Notably, PCSK1 encodes the protein prohormone convertase 1/3, the first enzyme in the insulin processing pathway. A genotype score composed of the nine proinsulin-raising alleles was not associated with coronary disease in two large case-control datasets.
CONCLUSIONS
We have identified nine genetic variants associated with fasting proinsulin. Our findings illuminate the biology underlying glucose homeostasis and T2D development in humans and argue against a direct role of proinsulin in coronary artery disease pathogenesis.
doi:10.2337/db11-0415
PMCID: PMC3178302  PMID: 21873549
11.  Genetic Predisposition to Increased Blood Cholesterol and Triglyceride Lipid Levels and Risk of Alzheimer Disease: A Mendelian Randomization Analysis 
PLoS Medicine  2014;11(9):e1001713.
In this study, Proitsi and colleagues use a Mendelian randomization approach to dissect the causal nature of the association between circulating lipid levels and late onset Alzheimer's Disease (LOAD) and find that genetic predisposition to increased plasma cholesterol and triglyceride lipid levels is not associated with elevated LOAD risk.
Please see later in the article for the Editors' Summary
Background
Although altered lipid metabolism has been extensively implicated in the pathogenesis of Alzheimer disease (AD) through cell biological, epidemiological, and genetic studies, the molecular mechanisms linking cholesterol and AD pathology are still not well understood and contradictory results have been reported. We have used a Mendelian randomization approach to dissect the causal nature of the association between circulating lipid levels and late onset AD (LOAD) and test the hypothesis that genetically raised lipid levels increase the risk of LOAD.
Methods and Findings
We included 3,914 patients with LOAD, 1,675 older individuals without LOAD, and 4,989 individuals from the general population from six genome wide studies drawn from a white population (total n = 10,578). We constructed weighted genotype risk scores (GRSs) for four blood lipid phenotypes (high-density lipoprotein cholesterol [HDL-c], low-density lipoprotein cholesterol [LDL-c], triglycerides, and total cholesterol) using well-established SNPs in 157 loci for blood lipids reported by Willer and colleagues (2013). Both full GRSs using all SNPs associated with each trait at p<5×10−8 and trait specific scores using SNPs associated exclusively with each trait at p<5×10−8 were developed. We used logistic regression to investigate whether the GRSs were associated with LOAD in each study and results were combined together by meta-analysis. We found no association between any of the full GRSs and LOAD (meta-analysis results: odds ratio [OR] = 1.005, 95% CI 0.82–1.24, p = 0.962 per 1 unit increase in HDL-c; OR = 0.901, 95% CI 0.65–1.25, p = 0.530 per 1 unit increase in LDL-c; OR = 1.104, 95% CI 0.89–1.37, p = 0.362 per 1 unit increase in triglycerides; and OR = 0.954, 95% CI 0.76–1.21, p = 0.688 per 1 unit increase in total cholesterol). Results for the trait specific scores were similar; however, the trait specific scores explained much smaller phenotypic variance.
Conclusions
Genetic predisposition to increased blood cholesterol and triglyceride lipid levels is not associated with elevated LOAD risk. The observed epidemiological associations between abnormal lipid levels and LOAD risk could therefore be attributed to the result of biological pleiotropy or could be secondary to LOAD. Limitations of this study include the small proportion of lipid variance explained by the GRS, biases in case-control ascertainment, and the limitations implicit to Mendelian randomization studies. Future studies should focus on larger LOAD datasets with longitudinal sampled peripheral lipid measures and other markers of lipid metabolism, which have been shown to be altered in LOAD.
Please see later in the article for the Editors' Summary
Editors' Summary
Background
Currently, about 44 million people worldwide have dementia, a group of brain disorders characterized by an irreversible decline in memory, communication, and other “cognitive” functions. Dementia mainly affects older people and, because people are living longer, experts estimate that more than 135 million people will have dementia by 2050. The commonest form of dementia is Alzheimer disease. In this type of dementia, protein clumps called plaques and neurofibrillary tangles form in the brain and cause its degeneration. The earliest sign of Alzheimer disease is usually increasing forgetfulness. As the disease progresses, affected individuals gradually lose their ability to deal with normal daily activities such as dressing. They may become anxious or aggressive or begin to wander. They may also eventually lose control of their bladder and of other physical functions. At present, there is no cure for Alzheimer disease although some of its symptoms can be managed with drugs. Most people with the disease are initially cared for at home by relatives and other unpaid carers, but many patients end their days in a care home or specialist nursing home.
Why Was This Study Done?
Several lines of evidence suggest that lipid metabolism (how the body handles cholesterol and other fats) is altered in patients whose Alzheimer disease develops after the age of 60 years (late onset Alzheimer disease, LOAD). In particular, epidemiological studies (observational investigations that examine the patterns and causes of disease in populations) have found an association between high amounts of cholesterol in the blood in midlife and the risk of LOAD. However, observational studies cannot prove that abnormal lipid metabolism (dyslipidemia) causes LOAD. People with dyslipidemia may share other characteristics that cause both dyslipidemia and LOAD (confounding) or LOAD might actually cause dyslipidemia (reverse causation). Here, the researchers use “Mendelian randomization” to examine whether lifetime changes in lipid metabolism caused by genes have a causal impact on LOAD risk. 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 dyslipidemia causes LOAD, genetic variants that affect lipid metabolism should be associated with an altered risk of LOAD.
What Did the Researchers Do and Find?
The researchers investigated whether genetic predisposition to raised lipid levels increased the risk of LOAD in 10,578 participants (3,914 patients with LOAD, 1,675 elderly people without LOAD, and 4,989 population controls) using data collected in six genome wide studies looking for gene variants associated with Alzheimer disease. The researchers constructed a genotype risk score (GRS) for each participant using genetic risk markers for four types of blood lipids on the basis of the presence of single nucleotide polymorphisms (SNPs, a type of gene variant) in their DNA. When the researchers used statistical methods to investigate the association between the GRS and LOAD among all the study participants, they found no association between the GRS and LOAD.
What Do These Findings Mean?
These findings suggest that the genetic predisposition to raised blood levels of four types of lipid is not causally associated with LOAD risk. The accuracy of this finding may be affected by several limitations of this study, including the small proportion of lipid variance explained by the GRS and the validity of several assumptions that underlie all Mendelian randomization studies. Moreover, because all the participants in this study were white, these findings may not apply to people of other ethnic backgrounds. Given their findings, the researchers suggest that the observed epidemiological associations between abnormal lipid levels in the blood and variation in lipid levels for reasons other than genetics, or to LOAD risk could be secondary to variation in lipid levels for reasons other than genetics, or to LOAD, a possibility that can be investigated by studying blood lipid levels and other markers of lipid metabolism over time in large groups of patients with LOAD. Importantly, however, these findings provide new information about the role of lipids in LOAD development that may eventually lead to new therapeutic and public-health interventions for Alzheimer disease.
Additional Information
Please access these websites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1001713.
The UK National Health Service Choices website provides information (including personal stories) about Alzheimer's disease
The UK not-for-profit organization Alzheimer's Society provides information for patients and carers about dementia, including personal experiences of living with Alzheimer's disease
The US not-for-profit organization Alzheimer's Association also provides information for patients and carers about dementia and personal stories about dementia
Alzheimer's Disease International is the international federation of Alzheimer disease associations around the world; it provides links to individual associations, information about dementia, and links to World Alzheimer Reports
MedlinePlus provides links to additional resources about Alzheimer's disease (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.1001713
PMCID: PMC4165594  PMID: 25226301
12.  Novel Loci for Adiponectin Levels and Their Influence on Type 2 Diabetes and Metabolic Traits: A Multi-Ethnic Meta-Analysis of 45,891 Individuals 
Dastani, Zari | Hivert, Marie-France | Timpson, Nicholas | Perry, John R. B. | Yuan, Xin | Scott, Robert A. | Henneman, Peter | Heid, Iris M. | Kizer, Jorge R. | Lyytikäinen, Leo-Pekka | Fuchsberger, Christian | Tanaka, Toshiko | Morris, Andrew P. | Small, Kerrin | Isaacs, Aaron | Beekman, Marian | Coassin, Stefan | Lohman, Kurt | Qi, Lu | Kanoni, Stavroula | Pankow, James S. | Uh, Hae-Won | Wu, Ying | Bidulescu, Aurelian | Rasmussen-Torvik, Laura J. | Greenwood, Celia M. T. | Ladouceur, Martin | Grimsby, Jonna | Manning, Alisa K. | Liu, Ching-Ti | Kooner, Jaspal | Mooser, Vincent E. | Vollenweider, Peter | Kapur, Karen A. | Chambers, John | Wareham, Nicholas J. | Langenberg, Claudia | Frants, Rune | Willems-vanDijk, Ko | Oostra, Ben A. | Willems, Sara M. | Lamina, Claudia | Winkler, Thomas W. | Psaty, Bruce M. | Tracy, Russell P. | Brody, Jennifer | Chen, Ida | Viikari, Jorma | Kähönen, Mika | Pramstaller, Peter P. | Evans, David M. | St. Pourcain, Beate | Sattar, Naveed | Wood, Andrew R. | Bandinelli, Stefania | Carlson, Olga D. | Egan, Josephine M. | Böhringer, Stefan | van Heemst, Diana | Kedenko, Lyudmyla | Kristiansson, Kati | Nuotio, Marja-Liisa | Loo, Britt-Marie | Harris, Tamara | Garcia, Melissa | Kanaya, Alka | Haun, Margot | Klopp, Norman | Wichmann, H.-Erich | Deloukas, Panos | Katsareli, Efi | Couper, David J. | Duncan, Bruce B. | Kloppenburg, Margreet | Adair, Linda S. | Borja, Judith B. | Wilson, James G. | Musani, Solomon | Guo, Xiuqing | Johnson, Toby | Semple, Robert | Teslovich, Tanya M. | Allison, Matthew A. | Redline, Susan | Buxbaum, Sarah G. | Mohlke, Karen L. | Meulenbelt, Ingrid | Ballantyne, Christie M. | Dedoussis, George V. | Hu, Frank B. | Liu, Yongmei | Paulweber, Bernhard | Spector, Timothy D. | Slagboom, P. Eline | Ferrucci, Luigi | Jula, Antti | Perola, Markus | Raitakari, Olli | Florez, Jose C. | Salomaa, Veikko | Eriksson, Johan G. | Frayling, Timothy M. | Hicks, Andrew A. | Lehtimäki, Terho | Smith, George Davey | Siscovick, David S. | Kronenberg, Florian | van Duijn, Cornelia | Loos, Ruth J. F. | Waterworth, Dawn M. | Meigs, James B. | Dupuis, Josee | Richards, J. Brent
PLoS Genetics  2012;8(3):e1002607.
Circulating levels of adiponectin, a hormone produced predominantly by adipocytes, are highly heritable and are inversely associated with type 2 diabetes mellitus (T2D) and other metabolic traits. We conducted a meta-analysis of genome-wide association studies in 39,883 individuals of European ancestry to identify genes associated with metabolic disease. We identified 8 novel loci associated with adiponectin levels and confirmed 2 previously reported loci (P = 4.5×10−8–1.2×10−43). Using a novel method to combine data across ethnicities (N = 4,232 African Americans, N = 1,776 Asians, and N = 29,347 Europeans), we identified two additional novel loci. Expression analyses of 436 human adipocyte samples revealed that mRNA levels of 18 genes at candidate regions were associated with adiponectin concentrations after accounting for multiple testing (p<3×10−4). We next developed a multi-SNP genotypic risk score to test the association of adiponectin decreasing risk alleles on metabolic traits and diseases using consortia-level meta-analytic data. This risk score was associated with increased risk of T2D (p = 4.3×10−3, n = 22,044), increased triglycerides (p = 2.6×10−14, n = 93,440), increased waist-to-hip ratio (p = 1.8×10−5, n = 77,167), increased glucose two hours post oral glucose tolerance testing (p = 4.4×10−3, n = 15,234), increased fasting insulin (p = 0.015, n = 48,238), but with lower in HDL-cholesterol concentrations (p = 4.5×10−13, n = 96,748) and decreased BMI (p = 1.4×10−4, n = 121,335). These findings identify novel genetic determinants of adiponectin levels, which, taken together, influence risk of T2D and markers of insulin resistance.
Author Summary
Serum adiponectin levels are highly heritable and are inversely correlated with the risk of type 2 diabetes (T2D), coronary artery disease, stroke, and several metabolic traits. To identify common genetic variants associated with adiponectin levels and risk of T2D and metabolic traits, we conducted a meta-analysis of genome-wide association studies of 45,891 multi-ethnic individuals. In addition to confirming that variants at the ADIPOQ and CDH13 loci influence adiponectin levels, our analyses revealed that 10 new loci also affecting circulating adiponectin levels. We demonstrated that expression levels of several genes in these candidate regions are associated with serum adiponectin levels. Using a powerful novel method to assess the contribution of the identified variants with other traits using summary-level results from large-scale GWAS consortia, we provide evidence that the risk alleles for adiponectin are associated with deleterious changes in T2D risk and metabolic syndrome traits (triglycerides, HDL, post-prandial glucose, insulin, and waist-to-hip ratio), demonstrating that the identified loci, taken together, impact upon metabolic disease.
doi:10.1371/journal.pgen.1002607
PMCID: PMC3315470  PMID: 22479202
13.  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
14.  Report of the Committee on the Classification and Diagnostic Criteria of Diabetes Mellitus 
Abstract
Concept of Diabetes Mellitus:
Diabetes mellitus is a group of diseases associated with various metabolic disorders, the main feature of which is chronic hyperglycemia due to insufficient insulin action. Its pathogenesis involves both genetic and environmental factors. The long‐term persistence of metabolic disorders can cause susceptibility to specific complications and also foster arteriosclerosis. Diabetes mellitus is associated with a broad range of clinical presentations, from being asymptomatic to ketoacidosis or coma, depending on the degree of metabolic disorder.
Classification (Tables 1 and 2, and Figure 1):
 Etiological classification of diabetes mellitus and glucose metabolism disorders
Note: Those that cannot at present be classified as any of the above are called unclassifiable.
The occurrence of diabetes‐specific complications has not been confirmed in some of these conditions.
 Diabetes mellitus and glucose metabolism disorders due to other specific mechanisms and diseases
The occurrence of diabetes‐specific complications has not been confirmed in some of these conditions.
 A scheme of the relationship between etiology (mechanism) and patho‐physiological stages (states) of diabetes mellitus. Arrows pointing right represent worsening of glucose metabolism disorders (including onset of diabetes mellitus). Among the arrow lines, indicates the condition classified as ‘diabetes mellitus’. Arrows pointing left represent improvement in the glucose metabolism disorder. The broken lines indicate events of low frequency. For example, in type 2 diabetes mellitus, infection can lead to ketoacidosis and require temporary insulin treatment for survival. Also, once diabetes mellitus has developed, it is treated as diabetes mellitus regardless of improvement in glucose metabolism, therefore, the arrow lines pointing left are filled in black. In such cases, a broken line is used, because complete normalization of glucose metabolism is rare.
The classification of glucose metabolism disorders is principally derived from etiology, and includes staging of pathophysiology based on the degree of deficiency of insulin action. These disorders are classified into four groups: (i) type 1 diabetes mellitus; (ii) type 2 diabetes mellitus; (iii) diabetes mellitus due to other specific mechanisms or diseases; and (iv) gestational diabetes mellitus. Type 1 diabetes is characterized by destruction of pancreatic β‐cells. Type 2 diabetes is characterized by combinations of decreased insulin secretion and decreased insulin sensitivity (insulin resistance). Glucose metabolism disorders in category (iii) are divided into two subgroups; subgroup A is diabetes in which a genetic abnormality has been identified, and subgroup B is diabetes associated with other pathologic disorders or clinical conditions. The staging of glucose metabolism includes normal, borderline and diabetic stages depending on the degree of hyperglycemia occurring as a result of the lack of insulin action or clinical condition. The diabetic stage is then subdivided into three substages: non‐insulin‐ requiring, insulin‐requiring for glycemic control, and insulin‐dependent for survival. The two former conditions are called non‐insulin‐dependent diabetes and the latter is known as insulin‐dependent diabetes. In each individual, these stages may vary according to the deterioration or the improvement of the metabolic state, either spontaneously or by treatment.
Diagnosis (Tables 3–7 and Figure 2):
 Criteria of fasting plasma glucose levels and 75 g oral glucose tolerance test 2‐h value
*Casual plasma glucose ≥200 mg/dL (≥11.1 mmol/L) and HbA1c≥6.5% are also regarded as to indicate diabetic type.
Even for normal type, if 1‐h value is 180 mg/dL (10.0 mmol/L), the risk of progression to diabetes mellitus is greater than for <180 mg/dL (10.0 mmol/L) and should be treated as with borderline type (follow‐up observation, etc.). Fasting plasma glucose level of 100–109 mg/dL (5.5–6.0 mmol/L) is called ‘high‐normal’: within the range of normal fasting plasma glucose.
Plasma glucose level after glucose load in oral glucose tolerance test (OGTT) is not included in casual plasma glucose levels. The value for HbA1c (%) is indicated with 0.4% added to HbA1c (JDS) (%).
 Procedures for diagnosing diabetes mellitus
*The value for HbA1c (%) is indicated with 0.4% added to HbA1c (JDS) (%). **Hyperglycemia must be confirmed in a non‐stressful condition. OGTT, oral glucose tolerance test.
 Disorders and conditions associated with low HbA1c values
 Situations where a 75‐g oral glucose tolerance test is recommended
*The value for HbA1c (%) is indicated with 0.4% added to HbA1c (JDS) (%).
 Definition and diagnostic criteria of gestational diabetes mellitus
(IADPSG Consensus Panel, Reference 42, partly modified with permission of Diabetes Care).
 Flow chart outlining steps in the clinical diagnosis of diabetes mellitus. *The value for HbA1c (%) is indicated with 0.4% added to HbA1c (JDS) (%).
Categories of the State of Glycemia:  Confirmation of chronic hyperglycemia is essential for the diagnosis of diabetes mellitus. When plasma glucose levels are used to determine the categories of glycemia, patients are classified as having a diabetic type if they meet one of the following criteria: (i) fasting plasma glucose level of ≥126 mg/dL (≥7.0 mmol/L); (ii) 2‐h value of ≥200 mg/dL (≥11.1 mmol/L) in 75 g oral glucose tolerance test (OGTT); or (iii) casual plasma glucose level of ≥200 mg/dL (≥11.1 mmol/L). Normal type is defined as fasting plasma glucose level of <110 mg/dL (<6.1 mmol/L) and 2‐h value of <140 mg/dL (<7.8 mmol/L) in OGTT. Borderline type (neither diabetic nor normal type) is defined as falling between the diabetic and normal values. According to the current revision, in addition to the earlier listed plasma glucose values, hemoglobin A1c (HbA1c) has been given a more prominent position as one of the diagnostic criteria. That is, (iv) HbA1c≥6.5% is now also considered to indicate diabetic type. The value of HbA1c, which is equivalent to the internationally used HbA1c (%) (HbA1c [NGSP]) defined by the NGSP (National Glycohemoglobin Standardization Program), is expressed by adding 0.4% to the HbA1c (JDS) (%) defined by the Japan Diabetes Society (JDS).
Subjects with borderline type have a high rate of developing diabetes mellitus, and correspond to the combination of impaired fasting glucose (IFG) and impaired glucose tolerance (IGT) noted by the American Diabetes Association (ADA) and WHO. Although borderline cases show few of the specific complications of diabetes mellitus, the risk of arteriosclerosis is higher than those of normal type. When HbA1c is 6.0–6.4%, suspected diabetes mellitus cannot be excluded, and when HbA1c of 5.6–5.9% is included, it forms a group with a high risk for developing diabetes mellitus in the future, even if they do not have it currently.
Clinical Diagnosis:  1 If any of the criteria for diabetic type (i) through to (iv) is observed at the initial examination, the patient is judged to be ‘diabetic type’. Re‐examination is conducted on another day, and if ‘diabetic type’ is reconfirmed, diabetes mellitus is diagnosed. However, a diagnosis cannot be made only by the re‐examination of HbA1c alone. Moreover, if the plasma glucose values (any of criteria [i], [ii], or [iii]) and the HbA1c (criterion [iv]) in the same blood sample both indicate diabetic type, diabetes mellitus is diagnosed based on the initial examination alone. If HbA1c is used, it is essential that the plasma glucose level (criteria [i], [ii] or [iii]) also indicates diabetic type for a diagnosis of diabetes mellitus. When diabetes mellitus is suspected, HbA1c should be measured at the same time as examination for plasma glucose.2 If the plasma glucose level indicates diabetic type (any of [i], [ii], or [iii]) and either of the following conditions exists, diabetes mellitus can be diagnosed immediately at the initial examination.• The presence of typical symptoms of diabetes mellitus (thirst, polydipsia, polyuria, weight loss)• The presence of definite diabetic retinopathy3 If it can be confirmed that the above conditions 1 or 2 existed in the past, diabetes mellitus can be diagnosed or suspected regardless of the current test results.4 If the diagnosis of diabetes cannot be established by these procedures, the patient is followed up and re‐examined after an appropriate interval.5 The physician should assess not only the presence or absence of diabetes, but also its etiology and glycemic stage, and the presence and absence of diabetic complications or associated conditions.
Epidemiological Study:  For the purpose of estimating the frequency of diabetes mellitus, ‘diabetes mellitus’ can be substituted for the determination of ‘diabetic type’ from a single examination. In this case, HbA1c≥6.5% alone can be defined as ‘diabetes mellitus’.
Health Screening:  It is important not to misdiagnose diabetes mellitus, and thus clinical information such as family history and obesity should be referred to at the time of screening in addition to an index for plasma glucose level.
Gestational Diabetes Mellitus:  There are two hyperglycemic disorders in pregnancy: (i) gestational diabetes mellitus (GDM); and (ii) diabetes mellitus. GDM is diagnosed if one or more of the following criteria is met in a 75 g OGTT during pregnancy:
1 Fasting plasma glucose level of ≥92 mg/dL (5.1 mmol/L)2 1‐h value of ≥180 mg/dL (10.0 mmol/L)3 2‐h value of ≥153 mg/dL (8.5 mmol/L)
However, diabetes mellitus that is diagnosed by the clinical diagnosis of diabetes mellitus defined earlier is excluded from GDM. (J Diabetes Invest, doi: 10.1111/j.2040‐1124.2010.00074.x, 2010)
doi:10.1111/j.2040-1124.2010.00074.x
PMCID: PMC4020724  PMID: 24843435
Diabetes mellitus; Clinical diagnosis; HbA1c
15.  TXNIP Regulates Peripheral Glucose Metabolism in Humans  
PLoS Medicine  2007;4(5):e158.
Background
Type 2 diabetes mellitus (T2DM) is characterized by defects in insulin secretion and action. Impaired glucose uptake in skeletal muscle is believed to be one of the earliest features in the natural history of T2DM, although underlying mechanisms remain obscure.
Methods and Findings
We combined human insulin/glucose clamp physiological studies with genome-wide expression profiling to identify thioredoxin interacting protein (TXNIP) as a gene whose expression is powerfully suppressed by insulin yet stimulated by glucose. In healthy individuals, its expression was inversely correlated to total body measures of glucose uptake. Forced expression of TXNIP in cultured adipocytes significantly reduced glucose uptake, while silencing with RNA interference in adipocytes and in skeletal muscle enhanced glucose uptake, confirming that the gene product is also a regulator of glucose uptake. TXNIP expression is consistently elevated in the muscle of prediabetics and diabetics, although in a panel of 4,450 Scandinavian individuals, we found no evidence for association between common genetic variation in the TXNIP gene and T2DM.
Conclusions
TXNIP regulates both insulin-dependent and insulin-independent pathways of glucose uptake in human skeletal muscle. Combined with recent studies that have implicated TXNIP in pancreatic β-cell glucose toxicity, our data suggest that TXNIP might play a key role in defective glucose homeostasis preceding overt T2DM.
Vamsi Mootha, Leif Groop, and colleagues report that TXNIP regulates insulin-dependent and -independent pathways of glucose uptake in human skeletal muscle and that its expression is elevated in individuals with prediabetes and type 2 diabetes.
Editors' Summary
Background.
An epidemic of diabetes mellitus is threatening world health. 246 million people (6% of the world's population) already have diabetes and it is estimated that within 20 years, 380 million people will have this chronic disease, most of them in developing countries. Diabetes is characterized by high blood sugar (glucose) levels. It arises when the pancreas does not make enough insulin (type 1 diabetes) or when the body responds poorly to insulin (type 2 diabetes). Insulin, which is released in response to high blood glucose levels, instructs muscle, fat, and liver cells to take glucose (a product of food digestion) out of the bloodstream; cells use glucose as a fuel. Type 2 diabetes, which accounts for 90% of all cases of diabetes, is characterized by impaired glucose uptake by target tissues in response to insulin (this “insulin resistance” is one of the first signs of type 2 diabetes) and inappropriate glucose release from liver cells. Over time, the pancreas may also make less insulin. These changes result in poor glucose homeostasis (inadequate control of blood sugar levels), which can cause life-threatening complications such as kidney failure and heart attacks.
Why Was This Study Done?
If the world diabetes epidemic is to be halted, researchers need a better understanding of glucose homeostasis and need to identify which parts of this complex control system go awry in type 2 diabetes. This information might suggest ways to prevent type 2 diabetes developing in the first place and might reveal targets for drugs that could slow or reverse the disease process. In this study, the researchers have used multiple approaches to identify a new mediator of glucose homeostasis and to investigate whether this mediator is causally involved in the development of type 2 diabetes.
What Did the Researchers Do and Find?
The researchers took small muscle samples from people who did not have diabetes before and after increasing their blood insulin levels and used a technique called “microarray expression profiling” to identify genes whose expression was induced or suppressed by insulin. One of the latter genes was thioredoxin interacting protein (TXNIP), a gene whose expression is strongly induced by glucose yet suppressed by insulin. They next used previously published microarray expression data to show that TXNIP expression was consistently higher in the muscles of patients with diabetes or prediabetes (a condition in which blood glucose levels are slightly raised) than in normal individuals. The researchers then examined whether TXNIP expression was correlated with glucose uptake, again using previously published data. In people with no diabetes and those with prediabetes, as glucose uptake rates increased, TXNIP expression decreased but this inverse correlation was missing in people with diabetes. Finally, by manipulating TXNIP expression levels in insulin-responsive cells grown in the laboratory, the researchers found that TXNIP overexpression reduced basal and insulin-stimulated glucose uptake but that reduced TXNIP expression had the opposite effect.
What Do These Findings Mean?
These results provide strong evidence that TXNIP is a regulator of glucose homeostasis in people. Specifically, the researchers propose that TXNIP regulates glucose uptake in the periphery of the human body by acting as a glucose- and insulin-sensitive switch. They also suggest how it might be involved in the development of type 2 diabetes. Early in the disease process, a small insulin deficiency or slightly raised blood sugar levels would increase TXNIP expression in muscles and suppress glucose uptake by these cells. Initially, the pancreas would compensate for this by producing more insulin, but this compensation would eventually fail, allowing blood sugar levels to rise sufficiently to increase TXNIP expression in the pancreas. Previously published results suggest that this would induce the loss of insulin-producing cells in the pancreas, thus further reducing insulin production and glucose uptake in the periphery and, ultimately, resulting in type 2 diabetes. Although there are many unanswered questions about the exact role of TXNIP in glucose homeostasis, these results help to explain many of the changes in glucose control that occur early in the development of diabetes. Furthermore, they suggest that interventions designed to modulate the activity of TXNIP might break the vicious cycle that eventually leads to type 2 diabetes.
Additional Information.
Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.0040158.
The MedlinePlus encyclopedia has pages on diabetes
The US National Institute of Diabetes and Digestive and Kidney Diseases has information for patients on diabetes
Information on diabetes is available for patients and professionals from the US Centers for Disease Control and Prevention
The American Diabetes Association provides information on diabetes for patients
International Diabetes Federation has information on diabetes and a recent press release on the global diabetes epidemic
doi:10.1371/journal.pmed.0040158
PMCID: PMC1858708  PMID: 17472435
16.  Consumption of whole grains and legumes modulates the genetic effect of the APOA5 -1131C variant on changes in triglyceride and apolipoprotein A-V concentrations in patients with impaired fasting glucose or newly diagnosed type 2 diabetes 
Trials  2014;15:100.
Background
The apolipoprotein A5 gene (APOA5) -1131 T > C polymorphism is associated with mild hypertriglyceridemia in type 2 diabetic subjects, and interacts with dietary fat in the determination of triglyceride concentrations. We examined whether a substitution of whole grains and legumes for refined rice in a high carbohydrate diet (about 65% of energy derived from carbohydrate) may modify the effect of this variant on changes in apolipoprotein A-V (apoA-V) and triglyceride concentrations.
Methods
We genotyped the APOA5 -1131 T > C in individuals with impaired fasting glucose (IFG) or newly diagnosed type 2 diabetes, who were randomly assigned to either a group ingesting whole grain and legume meals daily or a control group for 12 weeks.
Results
After dietary intervention, we observed significant interactions between the APOA5 -1131 T > C polymorphism and carbohydrate sources (whole grains and legumes versus refined rice) in the determination of mean percent changes in triglyceride and apoA-V (P interactions <0.001 and =0.038, respectively). In the refined rice group (n = 93), the carriers of the risk C allele (n = 50) showed a greater increase in the mean percent changes of triglyceride and apoA-V than noncarriers after adjusting for HOMA-IR (P = 0.004 and 0.021, respectively). The whole grain and legume group (n = 92), however, showed a decrease in fasting glucose, HOMA-IR, and triglyceride, and an increase in apoA-V, irrespective of genotype.
Conclusions
The data showed that the magnitude of the genetic effect of the APOA5 -1131C variant on triglyceride and apoA-V levels was modulated when substituting consumption of whole grains and legumes for refined rice as a carbohydrate source in IFG or diabetic subjects.
Trial registration
ClinicalTrials.gov: NCT01784952.
doi:10.1186/1745-6215-15-100
PMCID: PMC3974230  PMID: 24690159
APOA5 -1131 T > C; Whole grains and legumes; Triglycerides; Apolipoprotein A-V
17.  ANGPTL4 variants E40K and T266M are associated with lower fasting triglyceride levels in Non-Hispanic White Americans from the Look AHEAD Clinical Trial 
BMC Medical Genetics  2011;12:89.
Background
Elevated triglyceride levels are a risk factor for cardiovascular disease. Angiopoietin-like protein 4 (Angptl4) is a metabolic factor that raises plasma triglyceride levels by inhibiting lipoprotein lipase (LPL). In non-diabetic individuals, the ANGPTL4 coding variant E40K has been associated with lower plasma triglyceride levels while the T266M variant has been associated with more modest effects on triglyceride metabolism. The objective of this study was to determine whether ANGPTL4 E40K and T266M are associated with triglyceride levels in the setting of obesity and T2D, and whether modification of triglyceride levels by these genetic variants is altered by a lifestyle intervention designed to treat T2D.
Methods
The association of ANGPTL4 E40K and T266M with fasting triglyceride levels was investigated in 2,601 participants from the Look AHEAD Clinical Trial, all of whom had T2D and were at least overweight. Further, we tested for an interaction between genotype and treatment effects on triglyceride levels.
Results
Among non-Hispanic White Look AHEAD participants, ANGPTL4 K40 carriers had mean triglyceride levels of 1.61 ± 0.62 mmol/L, 0.33 mmol/L lower than E40 homozygotes (p = 0.001). Individuals homozygous for the minor M266 allele (MAF 30%) had triglyceride levels of 1.75 ± 0.58 mmol/L, 0.24 mmol/L lower than T266 homozygotes (p = 0.002). The association of the M266 with triglycerides remained significant even after removing K40 carriers from the analysis (p = 0.002). There was no interaction between the weight loss intervention and genotype on triglyceride levels.
Conclusions
This is the first study to demonstrate that the ANGPTL4 E40K and T266M variants are associated with lower triglyceride levels in the setting of T2D. In addition, our findings demonstrate that ANGPTL4 genotype status does not alter triglyceride response to a lifestyle intervention in the Look AHEAD study.
doi:10.1186/1471-2350-12-89
PMCID: PMC3146919  PMID: 21714923
18.  Studies of a genetic variant in HK1 in relation to quantitative metabolic traits and to the prevalence of type 2 diabetes 
BMC Medical Genetics  2011;12:99.
Background
Single nucleotide polymorphisms (SNPs) within the gene encoding Hexokinase 1 (HK1) are associated with changes in glycated haemoglobin (HbA1c) levels. Our aim was to investigate the effect of HK1 rs7072268 on measures of glucose- and lipid-metabolism in a Danish non-diabetic population and combine the outcome of these analyses in a meta-analysis with previously published results. Furthermore, our aim was to perform a type 2 diabetes case-control analysis and meta-analysis with two previous case-control studies.
Methods
SNP rs7072268 was genotyped in 9,724 Danes. The quantitative trait study included 5,604 non-diabetic individuals from the Inter99 cohort. The case-control study included 4,449 glucose tolerant individuals and 3,398 patients with type 2 diabetes. Meta-analyses on quantitative traits included 24,560 Caucasian individuals and 30,802 individuals were included in the combined analysis of present and previous type 2 diabetes case-control studies.
Results
Using an additive model, we confirmed that the T-allele of rs7072268 associates with increased HbA1c of 0.6% (CI: 0.4 - 0.9), p = 3*10-7 per allele. The same allele associated with an increased area under the curve (AUC) for glucose of 5.0 mmol/l*min (0.1 - 10.0), p = 0.045 following an oral glucose tolerance test (OGTT) and increased fasting levels of cholesterol of 0.06 mmol/l (0.03 - 1.0), p = 0.001 and triglycerides of 2.0% (0.2 - 3.8), p = 0.03 per allele in the same study sample of non-diabetic individuals from the Inter99 cohort. However, the T-allele did not show any association with estimates of insulin release or insulin sensitivity neither in Inter99 nor in combined analyses. The prevalence of type 2 diabetes was increased among carriers of the rs7072268 T-allele both in the Danish study-population with an OR of 1.11 (1.02-1.21) and in a meta-analysis including the two additional sample sets with an OR of 1.06 (1.02-1.11). However, after Bonferroni correction the T-allele only remained associated to HbA1c and fasting cholesterol.
Conclusions
The present study provides suggestive evidence of an association of the rs7072268 T-allele in HK1 with increased AUC glucose following an OGTT in non-diabetic individuals and a nominal association with type 2 diabetes prior to Bonferroni correction. The latter was confirmed in combined analyses involving 16,445 cases and 14,357 control subjects.
doi:10.1186/1471-2350-12-99
PMCID: PMC3161933  PMID: 21781351
Hexokinase 1; Glycated Haemoglobin A1c; Type 2 diabetes; Genetics
19.  Muscle Mitochondrial ATP Synthesis and Glucose Transport/Phosphorylation in Type 2 Diabetes 
PLoS Medicine  2007;4(5):e154.
Background
Muscular insulin resistance is frequently characterized by blunted increases in glucose-6-phosphate (G-6-P) reflecting impaired glucose transport/phosphorylation. These abnormalities likely relate to excessive intramyocellular lipids and mitochondrial dysfunction. We hypothesized that alterations in insulin action and mitochondrial function should be present even in nonobese patients with well-controlled type 2 diabetes mellitus (T2DM).
Methods and Findings
We measured G-6-P, ATP synthetic flux (i.e., synthesis) and lipid contents of skeletal muscle with 31P/1H magnetic resonance spectroscopy in ten patients with T2DM and in two control groups: ten sex-, age-, and body mass-matched elderly people; and 11 younger healthy individuals. Although insulin sensitivity was lower in patients with T2DM, muscle lipid contents were comparable and hyperinsulinemia increased G-6-P by 50% (95% confidence interval [CI] 39%–99%) in all groups. Patients with diabetes had 27% lower fasting ATP synthetic flux compared to younger controls (p = 0.031). Insulin stimulation increased ATP synthetic flux only in controls (younger: 26%, 95% CI 13%–42%; older: 11%, 95% CI 2%–25%), but failed to increase even during hyperglycemic hyperinsulinemia in patients with T2DM. Fasting free fatty acids and waist-to-hip ratios explained 44% of basal ATP synthetic flux. Insulin sensitivity explained 30% of insulin-stimulated ATP synthetic flux.
Conclusions
Patients with well-controlled T2DM feature slightly lower flux through muscle ATP synthesis, which occurs independently of glucose transport /phosphorylation and lipid deposition but is determined by lipid availability and insulin sensitivity. Furthermore, the reduction in insulin-stimulated glucose disposal despite normal glucose transport/phosphorylation suggests further abnormalities mainly in glycogen synthesis in these patients.
Michael Roden and colleagues report that even patients with well-controlled insulin-resistant type 2 diabetes have altered mitochondrial function.
Editors' Summary
Background.
Diabetes mellitus is an increasingly common chronic disease characterized by high blood sugar (glucose) levels. In normal individuals, blood sugar levels are maintained by the hormone insulin. Insulin is released by the pancreas when blood glucose levels rise after eating (glucose is produced by the digestion of food) and “instructs” insulin-responsive muscle and fat cells to take up glucose from the bloodstream. The cells then use glucose as a fuel or convert it into glycogen, a storage form of glucose. In type 2 diabetes, the commonest type of diabetes, the muscle and fat cells become nonresponsive to insulin (a condition called insulin resistance) and consequently blood glucose levels rise. Over time, this hyperglycemia increases the risk of heart attacks, kidney failure, and other life-threatening complications.
Why Was This Study Done?
Insulin resistance is often an early sign of type 2 diabetes, sometimes predating its development by many years, so understanding its causes might provide clues about how to stop the global diabetes epidemic. One theory is that mitochondria—cellular structures that produce the energy (in the form of a molecule called ATP) needed to keep cells functioning—do not work properly in people with insulin resistance. Mitochondria change (metabolize) fatty acids into energy, and recent studies have revealed that fat accumulation caused by poorly regulated fatty acid metabolism blocks insulin signaling, thus causing insulin resistance. Other studies using magnetic resonance spectroscopy (MRS) to study mitochondrial function noninvasively in human muscle indicate that mitochondria are dysfunctional in people with insulin resistance by showing that ATP synthesis is impaired in such individuals. In this study, the researchers have examined both baseline and insulin-stimulated mitochondrial function in nonobese patients with well-controlled type 2 diabetes and in normal controls to discover more about the relationship between mitochondrial dysfunction and insulin resistance.
What Did the Researchers Do and Find?
The researchers determined the insulin sensitivity of people with type 2 diabetes and two sets of people (the “controls”) who did not have diabetes: one in which the volunteers were age-matched to the people with diabetes, and the other containing younger individuals (insulin resistance increases with age). To study insulin sensitivity in all three groups, the researchers used a “hyperinsulinemic–euglycemic clamp.” For this, after an overnight fast, the participants' insulin levels were kept high with a continuous insulin infusion while blood glucose levels were kept normal using a variable glucose infusion. In this situation, the glucose infusion rate equals glucose uptake by the body and therefore measures tissue sensitivity to insulin. Before and during the clamp, the researchers used MRS to measure glucose-6-phosphate (an indicator of how effectively glucose is taken into cells and phosphorylated), ATP synthesis, and the fat content of the participants' muscle cells. Insulin sensitivity was lower in the patients with diabetes than in the controls, but muscle lipid content was comparable and hyperinsulinemia increased glucose-6-phosphate levels similarly in all the groups. Patients with diabetes and the older controls had lower fasting ATP synthesis rates than the young controls and, whereas insulin stimulation increased ATP synthesis in all the controls, it had no effect in the patients with diabetes. In addition, fasting blood fatty acid levels were inversely related to basal ATP synthesis, whereas insulin sensitivity was directly related to insulin-stimulated ATP synthesis.
What Do These Findings Mean?
These findings indicate that the impairment of muscle mitochondrial ATP synthesis in fasting conditions and after insulin stimulation in people with diabetes is not due to impaired glucose transport/phosphorylation or fat deposition in the muscles. Instead, it seems to be determined by lipid availability and insulin sensitivity. These results add to the evidence suggesting that mitochondrial function is disrupted in type 2 diabetes and in insulin resistance, but also suggest that there may be abnormalities in glycogen synthesis. More work is needed to determine the exact nature of these abnormalities and to discover whether they can be modulated to prevent the development of insulin resistance and type 2 diabetes. For now, though, these findings re-emphasize the need for people with type 2 diabetes or insulin resistance to reduce their food intake to compensate for the reduced energy needs of their muscles and to exercise to increase the ATP-generating capacity of their muscles. Both lifestyle changes could improve their overall health and life expectancy.
Additional Information.
Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.0040154.
The MedlinePlus encyclopedia has pages on diabetes
The US National Institute of Diabetes and Digestive and Kidney Diseases provides information for patients on diabetes and insulin resistance
The US Centers for Disease Control and Prevention has information on diabetes for patients and professionals
American Diabetes Association provides information for patients on diabetes and insulin resistance
Diabetes UK has information for patients and professionals on diabetes
doi:10.1371/journal.pmed.0040154
PMCID: PMC1858707  PMID: 17472434
20.  Detailed Physiologic Characterization Reveals Diverse Mechanisms for Novel Genetic Loci Regulating Glucose and Insulin Metabolism in Humans 
Diabetes  2010;59(5):1266-1275.
OBJECTIVE
Recent genome-wide association studies have revealed loci associated with glucose and insulin-related traits. We aimed to characterize 19 such loci using detailed measures of insulin processing, secretion, and sensitivity to help elucidate their role in regulation of glucose control, insulin secretion and/or action.
RESEARCH DESIGN AND METHODS
We investigated associations of loci identified by the Meta-Analyses of Glucose and Insulin-related traits Consortium (MAGIC) with circulating proinsulin, measures of insulin secretion and sensitivity from oral glucose tolerance tests (OGTTs), euglycemic clamps, insulin suppression tests, or frequently sampled intravenous glucose tolerance tests in nondiabetic humans (n = 29,084).
RESULTS
The glucose-raising allele in MADD was associated with abnormal insulin processing (a dramatic effect on higher proinsulin levels, but no association with insulinogenic index) at extremely persuasive levels of statistical significance (P = 2.1 × 10−71). Defects in insulin processing and insulin secretion were seen in glucose-raising allele carriers at TCF7L2, SCL30A8, GIPR, and C2CD4B. Abnormalities in early insulin secretion were suggested in glucose-raising allele carriers at MTNR1B, GCK, FADS1, DGKB, and PROX1 (lower insulinogenic index; no association with proinsulin or insulin sensitivity). Two loci previously associated with fasting insulin (GCKR and IGF1) were associated with OGTT-derived insulin sensitivity indices in a consistent direction.
CONCLUSIONS
Genetic loci identified through their effect on hyperglycemia and/or hyperinsulinemia demonstrate considerable heterogeneity in associations with measures of insulin processing, secretion, and sensitivity. Our findings emphasize the importance of detailed physiological characterization of such loci for improved understanding of pathways associated with alterations in glucose homeostasis and eventually type 2 diabetes.
doi:10.2337/db09-1568
PMCID: PMC2857908  PMID: 20185807
21.  Effect of Moderate Alcoholic Beverage Consumption on Insulin Sensitivity in Insulin Resistant, Nondiabetic individuals 
Although moderate alcohol consumption has been associated with a decrease in plasma insulin concentrations, relatively few studies have been conducted to evaluate the effect of alcohol on insulin sensitivity, particularly in nondiabetic, insulin resistant individuals. Since enhanced insulin sensitivity could contribute to the reported association between moderate alcohol consumption and reduced risk of heart disease and diabetes, we believed it important to address this issue. Consequently, we evaluated the ability of moderate alcohol consumption to improve insulin sensitivity, as measured by determining the steady-state plasma glucose (SSPG) concentration during the insulin suppression test, in 20 nondiabetic, insulin resistant individuals. Measurements were made of SSPG, glucose, insulin and lipoprotein concentrations before and after consuming 30 grams of alcohol for 8-weeks, either as vodka (n=9) or red wine (n=11). SSPG concentrations (insulin resistance) decreased by ~8% in the total group (p=0.08), and high-density lipoprotein cholesterol (HDL-C) concentration increased by a mean of 0.09 mmol/L (p=0.02). Trends were similar in individuals who consumed vodka or red wine. Men tended to have greater decline in SSPG and increase in HDL-C compared with women. There were no other metabolic changes in fasting plasma glucose, insulin, and triglyceride concentrations. These data demonstrate that 8-weeks of moderate alcohol consumption had minimal impact on enhancing insulin sensitivity in nondiabetic, insulin resistant individuals, raising questions as to the role, if any, of improved insulin sensitivity in the purported clinical benefits associated with moderate alcohol consumption.
doi:10.1016/j.metabol.2008.10.013
PMCID: PMC2676844  PMID: 19217456
22.  Gene-Lifestyle Interaction and Type 2 Diabetes: The EPIC InterAct Case-Cohort Study 
PLoS Medicine  2014;11(5):e1001647.
In this study, Wareham and colleagues quantified the combined effects of genetic and lifestyle factors on risk of T2D in order to inform strategies for prevention. The authors found that the relative effect of a type 2 diabetes genetic risk score is greater in younger and leaner participants, and the high absolute risk associated with obesity at any level of genetic risk highlights the importance of universal rather than targeted approaches to lifestyle intervention.
Please see later in the article for the Editors' Summary
Background
Understanding of the genetic basis of type 2 diabetes (T2D) has progressed rapidly, but the interactions between common genetic variants and lifestyle risk factors have not been systematically investigated in studies with adequate statistical power. Therefore, we aimed to quantify the combined effects of genetic and lifestyle factors on risk of T2D in order to inform strategies for prevention.
Methods and Findings
The InterAct study includes 12,403 incident T2D cases and a representative sub-cohort of 16,154 individuals from a cohort of 340,234 European participants with 3.99 million person-years of follow-up. We studied the combined effects of an additive genetic T2D risk score and modifiable and non-modifiable risk factors using Prentice-weighted Cox regression and random effects meta-analysis methods. The effect of the genetic score was significantly greater in younger individuals (p for interaction  = 1.20×10−4). Relative genetic risk (per standard deviation [4.4 risk alleles]) was also larger in participants who were leaner, both in terms of body mass index (p for interaction  = 1.50×10−3) and waist circumference (p for interaction  = 7.49×10−9). Examination of absolute risks by strata showed the importance of obesity for T2D risk. The 10-y cumulative incidence of T2D rose from 0.25% to 0.89% across extreme quartiles of the genetic score in normal weight individuals, compared to 4.22% to 7.99% in obese individuals. We detected no significant interactions between the genetic score and sex, diabetes family history, physical activity, or dietary habits assessed by a Mediterranean diet score.
Conclusions
The relative effect of a T2D genetic risk score is greater in younger and leaner participants. However, this sub-group is at low absolute risk and would not be a logical target for preventive interventions. The high absolute risk associated with obesity at any level of genetic risk highlights the importance of universal rather than targeted approaches to lifestyle intervention.
Please see later in the article for the Editors' Summary
Editors' Summary
Background
Worldwide, more than 380 million people currently have diabetes, and the condition is becoming increasingly common. Diabetes is characterized by high levels of glucose (sugar) in the blood. Blood sugar levels are usually controlled by insulin, a hormone released by the pancreas after meals (digestion of food produces glucose). In people with type 2 diabetes (the commonest type of diabetes), blood sugar control fails because the fat and muscle cells that normally respond to insulin by removing excess sugar from the blood become less responsive to insulin. Type 2 diabetes can often initially be controlled with diet and exercise (lifestyle changes) and with antidiabetic drugs such as metformin and sulfonylureas, but patients may eventually need insulin injections to control their blood sugar levels. Long-term complications of diabetes, which include an increased risk of heart disease and stroke, reduce the life expectancy of people with diabetes by about ten years compared to people without diabetes.
Why Was This Study Done?
Type 2 diabetes is thought to originate from the interplay between genetic and lifestyle factors. But although rapid progress is being made in understanding the genetic basis of type 2 diabetes, it is not known whether the consequences of adverse lifestyles (for example, being overweight and/or physically inactive) differ according to an individual's underlying genetic risk of diabetes. It is important to investigate this question to inform strategies for prevention. If, for example, obese individuals with a high level of genetic risk have a higher risk of developing diabetes than obese individuals with a low level of genetic risk, then preventative strategies that target lifestyle interventions to obese individuals with a high genetic risk would be more effective than strategies that target all obese individuals. In this case-cohort study, researchers from the InterAct consortium quantify the combined effects of genetic and lifestyle factors on the risk of type 2 diabetes. A case-cohort study measures exposure to potential risk factors in a group (cohort) of people and compares the occurrence of these risk factors in people who later develop the disease with those who remain disease free.
What Did the Researchers Do and Find?
The InterAct study involves 12,403 middle-aged individuals who developed type 2 diabetes after enrollment (incident cases) into the European Prospective Investigation into Cancer and Nutrition (EPIC) and a sub-cohort of 16,154 EPIC participants. The researchers calculated a genetic type 2 diabetes risk score for most of these individuals by determining which of 49 gene variants associated with type 2 diabetes each person carried, and collected baseline information about exposure to lifestyle risk factors for type 2 diabetes. They then used various statistical approaches to examine the combined effects of the genetic risk score and lifestyle factors on diabetes development. The effect of the genetic score was greater in younger individuals than in older individuals and greater in leaner participants than in participants with larger amounts of body fat. The absolute risk of type 2 diabetes, expressed as the ten-year cumulative incidence of type 2 diabetes (the percentage of participants who developed diabetes over a ten-year period) increased with increasing genetic score in normal weight individuals from 0.25% in people with the lowest genetic risk scores to 0.89% in those with the highest scores; in obese people, the ten-year cumulative incidence rose from 4.22% to 7.99% with increasing genetic risk score.
What Do These Findings Mean?
These findings show that in this middle-aged cohort, the relative association with type 2 diabetes of a genetic risk score comprised of a large number of gene variants is greatest in individuals who are younger and leaner at baseline. This finding may in part reflect the methods used to originally identify gene variants associated with type 2 diabetes, and future investigations that include other genetic variants, other lifestyle factors, and individuals living in other settings should be undertaken to confirm this finding. Importantly, however, this study shows that young, lean individuals with a high genetic risk score have a low absolute risk of developing type 2 diabetes. Thus, this sub-group of individuals is not a logical target for preventative interventions. Rather, suggest the researchers, the high absolute risk of type 2 diabetes associated with obesity at any level of genetic risk highlights the importance of universal rather than targeted approaches to lifestyle intervention.
Additional Information
Please access these websites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1001647.
The US National Diabetes Information Clearinghouse provides information about diabetes for patients, health-care professionals and the general public, including detailed 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 and about living with diabetes; it also provides people's stories about diabetes
The charity Diabetes UK provides detailed information for patients and carers in several languages, including information on healthy lifestyles for people with diabetes
The UK-based non-profit organization Healthtalkonline has interviews with people about their experiences of diabetes
The Genetic Landscape of Diabetes is published by the US National Center for Biotechnology Information
More information on the InterAct study is available
MedlinePlus provides links to further resources and advice about diabetes and diabetes prevention (in English and Spanish)
doi:10.1371/journal.pmed.1001647
PMCID: PMC4028183  PMID: 24845081
23.  Paradoxical Lower Serum Triglyceride Levels and Higher Type 2 Diabetes Mellitus Susceptibility in Obese Individuals with the PNPLA3 148M Variant 
PLoS ONE  2012;7(6):e39362.
Background
Obesity is highly associated with elevated serum triglycerides, hepatic steatosis and type 2 diabetes (T2D). The I148M (rs738409) genetic variant of patatin-like phospholipase domain-containing 3 gene (PNPLA3) is known to modulate hepatic triglyceride accumulation, leading to steatosis. No association between PNPLA3 I148M genotype and T2D in Europeans has been reported. Aim of this study is to examine the relationship between PNPLA3 I148M genotypes and serum triglycerides, insulin resistance and T2D susceptibility by testing a gene-environment interaction model with severe obesity.
Methods and Findings
PNPLA3 I148M was genotyped in a large obese cohort, the SOS study (n = 3,473) and in the Go-DARTS (n = 15,448), a T2D case-control study. Metabolic parameters were examined across the PNPLA3 I148M genotypes in participants of the SOS study at baseline and at 2- and 10-year follow up after bariatric surgery or conventional therapy. The associations with metabolic parameters were validated in the Go-DARTS study. Serum triglycerides were found to be lower in the PNPLA3 148M carriers from the SOS study at baseline and from the Go-DARTS T2D cohort. An increased risk for T2D conferred by the 148M allele was found in the SOS study (O.R. 1.09, 95% C.I. 1.01-1.39, P = 0.040) and in severely obese individuals in the Go-DARTS study (O.R. 1.37, 95% C.I. 1.13-1.66, P = 0.001). The 148M allele was no longer associated with insulin resistance or T2D after bariatric surgery in the SOS study and no association with the 148M allele was observed in the less obese (BMI<35) individuals in the Go-DARTS study (P for interaction  = 0.002). This provides evidence for the obesity interaction with I48M allele and T2D risk in a large-scale cross-sectional and a prospective interventional study.
Conclusions
Severely obese individuals carrying the PNPLA3 148M allele have lower serum triglyceride levels, are more insulin resistant and more susceptible to T2D. This study supports the hypothesis that obesity-driven hepatic lipid accumulation may contribute to T2D susceptibility.
doi:10.1371/journal.pone.0039362
PMCID: PMC3377675  PMID: 22724004
24.  The Common P446L Polymorphism in GCKR Inversely Modulates Fasting Glucose and Triglyceride Levels and Reduces Type 2 Diabetes Risk in the DESIR Prospective General French Population 
Diabetes  2008;57(8):2253-2257.
OBJECTIVE— Hepatic glucokinase (GCK) is a key regulator of glucose storage and disposal in the liver, where its activity is competitively modulated, with respect to glucose, by binding to glucokinase regulatory protein (GCKR) in the presence of fructose 6-phosphate. Genome-wide association studies for type 2 diabetes identified GCKR as a potential locus for modulating triglyceride levels. We evaluated, in a general French population, the contribution of the GCKR rs1260326-P446L polymorphism to quantitative metabolic parameters and to dyslipidemia and hyperglycemia risk.
RESEARCH DESIGN AND METHODS— Genotype effects of rs1260326 were studied in 4,833 participants from the prospective DESIR (Data from an Epidemiological Study on the Insulin Resistance syndrome) cohort both at inclusion and using the measurements at follow-up.
RESULTS— The minor T-allele of rs1260326 was strongly associated with lower fasting glucose (−1.43% per T-allele; P = 8 × 10−13) and fasting insulin levels (−4.23%; P = 3 × 10−7), lower homeostasis model assessment of insulin resistance index (−5.69%; P = 1 × 10−8), and, conversely, higher triglyceride levels (3.41%; P = 1 × 10−4) during the 9-year study. These effects relate to a lower risk of hyperglycemia (odds ratio [OR] 0.79 [95% CI 0.70–0.88]; P = 4 × 10−5) and of incident cases during the study (hazard ratio [HR] 0.83 [0.74–0.95]; P = 0.005). Moreover, an additive effect of GCKR rs1260326(T) and GCK (−30G) alleles conferred lower fasting glycemia (P = 1 × 10−13), insulinemia (P = 5 × 10−6), and hyperglycemia risk (P = 1 × 10−6).
CONCLUSIONS— GCKR-L446 carriers are protected against type 2 diabetes despite higher triglyceride levels and risk of dyslipidemia, which suggests a potential molecular mechanism by which these two components of the metabolic syndrome can be dissociated.
doi:10.2337/db07-1807
PMCID: PMC2494697  PMID: 18556336
25.  Stress Hyperglycaemia in Hospitalised Patients and Their 3-Year Risk of Diabetes: A Scottish Retrospective Cohort Study 
PLoS Medicine  2014;11(8):e1001708.
In a retrospective analysis of a national database of hospital admissions, David McAllister and colleagues identify the 3-year risk of diabetes of hospitalized patients with hyperglycemia in Scotland.
Please see later in the article for the Editors' Summary
Background
Hyperglycaemia during hospital admission is common in patients who are not known to have diabetes and is associated with adverse outcomes. The risk of subsequently developing type 2 diabetes, however, is not known.
We linked a national database of hospital admissions with a national register of diabetes to describe the association between admission glucose and the risk of subsequently developing type 2 diabetes.
Methods and Findings
In a retrospective cohort study, patients aged 30 years or older with an emergency admission to hospital between 2004 and 2008 were included. Prevalent and incident diabetes were identified through the Scottish Care Information (SCI)-Diabetes Collaboration national registry. Patients diagnosed prior to or up to 30 days after hospitalisation were defined as prevalent diabetes and were excluded.
The predicted risk of developing incident type 2 diabetes during the 3 years following hospital discharge by admission glucose, age, and sex was obtained from logistic regression models. We performed separate analyses for patients aged 40 and older, and patients aged 30 to 39 years.
Glucose was measured in 86,634 (71.0%) patients aged 40 and older on admission to hospital. The 3-year risk of developing type 2 diabetes was 2.3% (1,952/86,512) overall, was <1% for a glucose ≤5 mmol/l, and increased to approximately 15% at 15 mmol/l. The risks at 7 mmol/l and 11.1 mmol/l were 2.6% (95% CI 2.5–2.7) and 9.9% (95% CI 9.2–10.6), respectively, with one in four (21,828/86,512) and one in 40 (1,798/86,512) patients having glucose levels above each of these cut-points. For patients aged 30–39, the risks at 7 mmol/l and 11.1 mmol/l were 1.0% (95% CI 0.8–1.3) and 7.8% (95% CI 5.7–10.7), respectively, with one in eight (1,588/11,875) and one in 100 (120/11,875) having glucose levels above each of these cut-points.
The risk of diabetes was also associated with age, sex, and socio-economic deprivation, but not with specialty (medical versus surgical), raised white cell count, or co-morbidity. Similar results were obtained for pre-specified sub-groups admitted with myocardial infarction, chronic obstructive pulmonary disease, and stroke.
There were 25,193 deaths (85.8 per 1,000 person-years) over 297,122 person-years, of which 2,406 (8.1 per 1,000 person-years) were attributed to vascular disease. Patients with glucose levels of 11.1 to 15 mmol/l and >15 mmol/l had higher mortality than patients with a glucose of <6.1 mmol/l (hazard ratio 1.54; 95% CI 1.42–1.68 and 2.50; 95% CI 2.14–2.95, respectively) in models adjusting for age and sex.
Limitations of our study include that we did not have data on ethnicity or body mass index, which may have improved prediction and the results have not been validated in non-white populations or populations outside of Scotland.
Conclusion
Plasma glucose measured during an emergency hospital admission predicts subsequent risk of developing type 2 diabetes. Mortality was also 1.5-fold higher in patients with elevated glucose levels. Our findings can be used to inform patients of their long-term risk of type 2 diabetes, and to target lifestyle advice to those patients at highest risk.
Please see later in the article for the Editors' Summary
Editors' Summary
Background
Insulin—a hormone released by the pancreas after meals—controls blood glucose (sugar) levels in healthy individuals. However, many patients admitted to hospital because of an acute illness have hyperglycemia, an abnormally high blood glucose level. In this setting, hyperglycemia can be caused by the drugs that patients are taking for existing conditions or may be stress hyperglycemia, a reversible condition in which hormonal changes induced by acute illness stimulate glucose production by the liver. However, hyperglycemia detected during an acute illness may also indicate underlying or incipient type 2 diabetes, a common condition in which blood glucose control fails. Type 2 diabetes can initially be controlled by diet, exercise, and antidiabetic drugs but many patients eventually need insulin injections to control their blood sugar level. Long-term complications of type 2 diabetes, which include an increased risk of heart attacks and stroke, reduce the life expectancy of people with diabetes by about 10 years compared to people without diabetes
Why Was This Study Done?
Prompt diagnosis of type 2 diabetes can minimize its long-term complications, so experts have designed several scoring systems based on lifestyle and other characteristics that allow primary care clinicians to identify the patients who should be tested for diabetes because they are at high risk of developing the condition. Unfortunately, these scoring systems cannot be used to interpret a high blood glucose result obtained during an acute illness so clinicians cannot currently advise their patients on the clinical significance of this type of abnormal glucose reading or make an informed decision about whether follow-up testing is needed. In this retrospective cohort study, the researchers investigate the association between blood glucose levels measured during emergency hospital admissions in Scotland and the risk of developing type 2 diabetes by linking together national databases of hospital admissions, laboratory test results, and people with diabetes. A retrospective cohort study examines the medical histories of a group of patients.
What Did the Researchers Do and Find?
The researchers used the databases to identify more than 100,000 patients aged 30 years or older who were admitted to a hospital for an acute illness between 2004 and 2008 in Scotland, to obtain information on blood glucose levels on admission for nearly three-quarters of these patients, and to identify which patients subsequently developed diabetes. They then used statistical models to estimate the patients' risk of developing type 2 diabetes during the 3 years following hospital discharge. Among patients aged 40 years or older, the overall 3-year risk of developing diabetes was 2.3%. The risk of developing diabetes increased linearly with increasing blood glucose level at admission. Specifically, the 3-year risks at blood glucose levels of 7 mmol/l and 11.1 mmol/l were 2.6% and 9.9%, respectively; because glucose levels fluctuate according to when an individual last ate, fasting blood glucose levels of 7 mmol/l and non-fasting blood glucose levels of 11.1 mmol/l are used as thresholds for the diagnosis of diabetes. The diabetes risk associated with blood glucose levels on admission among 30–39-year-old patients followed a similar pattern but was less marked. Finally, high glucose levels on admission were associated with increased mortality.
What Do These Findings Mean?
These findings indicate that blood glucose measured during an emergency hospital admission predicts the subsequent risk of type 2 diabetes among patients aged 40 years or older (the analysis specified in the researchers' original protocol). Importantly, however, they also suggest that a high blood glucose reading in these circumstances usually indicates stress hyperglycemia rather than type 2 diabetes. The accuracy and generalizability of these findings may be limited by the lack of data on ethnicity or body mass index (a measure of obesity), both of which affect diabetes risk, and by other aspects of the study design. Nevertheless, given their findings, the researchers recommend that any patient with a blood glucose level above 11.1 mmol/l on hospital admission for an acute illness (one in 40 patients in this study) should be offered follow-up testing. In addition, the researchers constructed a risk calculator using their findings that should help clinicians to inform their patients about their long-term risk of diabetes following hyperglycemia during an acute hospital admission and to target lifestyle advice to those patients at the highest risk of type 2 diabetes.
Additional Information
Please access these websites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1001708.
The US National Diabetes Information Clearinghouse provides information about diabetes and about diabetes prevention (in English and Spanish)
The UK National Health Service Choices website provides information about type 2 diabetes and about living with diabetes; it also provides people's stories about diabetes
The charity Diabetes UK provides information about diabetes in several languages, including information on healthy lifestyles for people with diabetes
Wikipedia has a page on stress hyperglycemia (note that Wikipedia is a free online encyclopedia that anyone can edit; available in several languages)
More information about stress hyperglycemia is available in Diapedia, a living textbook of diabetes produced by the European Association for the Study of Diabetes
GUARD (Glucose on Unselected Admissions and Risk of Diabetes), a risk calculator that allows clinicians to estimate a patient's 3-year risk of diabetes following hyperglycemia at hospital admission for an acute illness, is available online
The UK-based non-profit organization Healthtalkonline has interviews with people about their experiences of diabetes
MedlinePlus provides links to further resources and advice about diabetes and diabetes prevention (in English and Spanish)
doi:10.1371/journal.pmed.1001708
PMCID: PMC4138030  PMID: 25136809

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