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1.  Metabolic Signatures of Adiposity in Young Adults: Mendelian Randomization Analysis and Effects of Weight Change 
PLoS Medicine  2014;11(12):e1001765.
In this study, Wurtz and colleagues investigated to what extent elevated body mass index (BMI) within the normal weight range has causal influences on the detailed systemic metabolite profile in early adulthood using Mendelian randomization analysis.
Please see later in the article for the Editors' Summary
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).
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
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
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)
PMCID: PMC4260795  PMID: 25490400
2.  Whole Grain Rye Intake, Reflected by a Biomarker, Is Associated with Favorable Blood Lipid Outcomes in Subjects with the Metabolic Syndrome – A Randomized Study 
PLoS ONE  2014;9(10):e110827.
Background and Aim
Few studies have explored the possible plasma cholesterol lowering effects of rye consumption. The aim of this secondary analysis in the SYSDIET study was to investigate the association between plasma alkylresorcinols (AR), a biomarker for whole grain wheat and rye intake, and blood lipid concentrations in a population with metabolic syndrome. Furthermore, we analyzed the associations between the AR C17∶0/C21∶0 ratio, a suggested marker of the relative intake of whole grain/bran rye, and blood lipid concentrations.
Participants were 30–65 years of age, with body mass index (BMI) 27–40 kg/m2 and had metabolic syndrome. Individuals were recruited through six centers in the Nordic countries and randomized either to a healthy Nordic diet (ND, n = 93), rich in whole grain rye and wheat, as well as berries, fruits and vegetables, rapeseed oil, three fish meals per week and low-fat dairy products, or a control diet (n = 65) for 18/24 weeks. Associations between total plasma AR concentration and C17∶0/C21∶0 homologue ratio and blood lipids were investigated in pooled (ND + control group) regression analyses at 18/24 weeks adjusted for baseline value for the dependent variable, age, BMI and statin use.
When adjusted for confounders, total plasma AR at 18/24 weeks was not significantly associated with blood lipids but the AR ratio C17∶0/C21∶0 was inversely associated with LDL cholesterol concentrations (B (95% CI): −0.41 (−0.80 to −0.02)), log LDL/HDL cholesterol ratio (−0.20 (−0.37 to −0.03)), log non-HDL cholesterol (−0.20 (−0.37 to −0.03)), log apolipoprotein B (−0.12 (−0.24 to 0.00)) and log triglyceride concentrations (−0.35 (−0.59 to −0.12)).
Increased proportion of whole grain rye, reflected by a biomarker, in the diet is associated with favorable blood lipid outcomes, a relationship that should be further investigated.
Trial Registration NCT00992641
PMCID: PMC4207773  PMID: 25340768
3.  Meal Frequencies Modify the Effect of Common Genetic Variants on Body Mass Index in Adolescents of the Northern Finland Birth Cohort 1986 
PLoS ONE  2013;8(9):e73802.
Recent studies suggest that meal frequencies influence the risk of obesity in children and adolescents. It has also been shown that multiple genetic loci predispose to obesity already in youth. However, it is unknown whether meal frequencies could modulate the association between single nucleotide polymorphisms (SNPs) and the risk of obesity. We examined the effect of two meal patterns on weekdays –5 meals including breakfast (regular) and ≤4 meals with or without breakfast (meal skipping) – on the genetic susceptibility to increased body mass index (BMI) in Finnish adolescents. Eight variants representing 8 early-life obesity-susceptibility loci, including FTO and MC4R, were genotyped in 2215 boys and 2449 girls aged 16 years from the population-based Northern Finland Birth Cohort 1986. A genetic risk score (GRS) was calculated for each individual by summing the number of BMI-increasing alleles across the 8 loci. Weight and height were measured and dietary data were collected using self-administered questionnaires. Among meal skippers, the difference in BMI between high-GRS and low-GRS (<8 and ≥8 BMI-increasing alleles) groups was 0.90 (95% CI 0.63,1.17) kg/m2, whereas in regular eaters, this difference was 0.32 (95% CI 0.06,0.57) kg/m2 (pinteraction  = 0.003). The effect of each MC4R rs17782313 risk allele on BMI in meal skippers (0.47 [95% CI 0.22,0.73] kg/m2) was nearly three-fold compared with regular eaters (0.18 [95% CI -0.06,0.41] kg/m2) (pinteraction  = 0.016). Further, the per-allele effect of the FTO rs1421085 was 0.24 (95% CI 0.05,0.42) kg/m2 in regular eaters and 0.46 (95% CI 0.27,0.66) kg/m2 in meal skippers but the interaction between FTO genotype and meal frequencies on BMI was significant only in boys (pinteraction  = 0.015). In summary, the regular five-meal pattern attenuated the increasing effect of common SNPs on BMI in adolescents. Considering the epidemic of obesity in youth, the promotion of regular eating may have substantial public health implications.
PMCID: PMC3769374  PMID: 24040077
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
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).
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
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
PMCID: PMC3692470  PMID: 23824655
5.  Metabolic Signatures of Insulin Resistance in 7,098 Young Adults 
Diabetes  2012;61(6):1372-1380.
Metabolite associations with insulin resistance were studied in 7,098 young Finns (age 31 ± 3 years; 52% women) to elucidate underlying metabolic pathways. Insulin resistance was assessed by the homeostasis model (HOMA-IR) and circulating metabolites quantified by high-throughput nuclear magnetic resonance spectroscopy in two population-based cohorts. Associations were analyzed using regression models adjusted for age, waist, and standard lipids. Branched-chain and aromatic amino acids, gluconeogenesis intermediates, ketone bodies, and fatty acid composition and saturation were associated with HOMA-IR (P < 0.0005 for 20 metabolite measures). Leu, Ile, Val, and Tyr displayed sex- and obesity-dependent interactions, with associations being significant for women only if they were abdominally obese. Origins of fasting metabolite levels were studied with dietary and physical activity data. Here, protein energy intake was associated with Val, Phe, Tyr, and Gln but not insulin resistance index. We further tested if 12 genetic variants regulating the metabolites also contributed to insulin resistance. The genetic determinants of metabolite levels were not associated with HOMA-IR, with the exception of a variant in GCKR associated with 12 metabolites, including amino acids (P < 0.0005). Nonetheless, metabolic signatures extending beyond obesity and lipid abnormalities reflected the degree of insulin resistance evidenced in young, normoglycemic adults with sex-specific fingerprints.
PMCID: PMC3357275  PMID: 22511205
6.  Genome-wide association study identifies multiple loci influencing human serum metabolite levels 
Nature genetics  2012;44(3):269-276.
Nuclear magnetic resonance assays allow for measurement of a wide range of metabolic phenotypes. We report here the results of a GWAS on 8,330 Finnish individuals genotyped and imputed at 7.7 million SNPs for a range of 216 serum metabolic phenotypes assessed by NMR of serum samples. We identified significant associations (P < 2.31 × 10−10) at 31 loci, including 11 for which there have not been previous reports of associations to a metabolic trait or disorder. Analyses of Finnish twin pairs suggested that the metabolic measures reported here show higher heritability than comparable conventional metabolic phenotypes. In accordance with our expectations, SNPs at the 31 loci associated with individual metabolites account for a greater proportion of the genetic component of trait variance (up to 40%) than is typically observed for conventional serum metabolic phenotypes. The identification of such associations may provide substantial insight into cardiometabolic disorders.
PMCID: PMC3605033  PMID: 22286219
7.  Long Telomeres in Blood Leukocytes Are Associated with a High Risk of Ascending Aortic Aneurysm 
PLoS ONE  2012;7(11):e50828.
Ascending aortic aneurysm is a connective tissue disorder. Even though multiple novel gene mutations have been identified, risk profiling and diagnosis before rupture still represent a challenge. There are studies demonstrating shorter telomere lengths in the blood leukocytes of abdominal aortic aneurysm patients. The aim of this study was to measure whether relative telomere lengths are changed in the blood leukocytes of ascending aortic aneurysm patients. We also studied the expression of telomerase in aortic tissue samples of ascending aortic aneurysms. Relative lengths of leukocyte telomeres were determined from blood samples of patients with ascending aortic aneurysms and compared with healthy controls. Telomerase expression, both at the level of mRNA and protein, was quantified from the aortic tissue samples. Mean relative telomere length was significantly longer in ascending aortic aneurysm blood samples compared with controls (T/S ratio 0.87 vs. 0.61, p<0.001). Expressions of telomerase mRNA and protein were elevated in the aortic aneurysm samples (p<0.05 and p<0.01). Our study reveals a significant difference in the mean length of blood leukocyte telomeres in ascending aortic aneurysm and controls. Furthermore, expression of telomerase, the main compensating factor for telomere loss, is elevated at both the mRNA and protein level in the samples of aneurysmal aorta. Further studies will be needed to confirm if this change in telomere length can serve as a tool for assessing the risk of ascending aortic aneurysm.
PMCID: PMC3510165  PMID: 23209831
8.  Genome-wide association study identifies loci influencing concentrations of liver enzymes in plasma 
Chambers, John C | Zhang, Weihua | Sehmi, Joban | Li, Xinzhong | Wass, Mark N | Van der Harst, Pim | Holm, Hilma | Sanna, Serena | Kavousi, Maryam | Baumeister, Sebastian E | Coin, Lachlan J | Deng, Guohong | Gieger, Christian | Heard-Costa, Nancy L | Hottenga, Jouke-Jan | Kühnel, Brigitte | Kumar, Vinod | Lagou, Vasiliki | Liang, Liming | Luan, Jian’an | Vidal, Pedro Marques | Leach, Irene Mateo | O’Reilly, Paul F | Peden, John F | Rahmioglu, Nilufer | Soininen, Pasi | Speliotes, Elizabeth K | Yuan, Xin | Thorleifsson, Gudmar | Alizadeh, Behrooz Z | Atwood, Larry D | Borecki, Ingrid B | Brown, Morris J | Charoen, Pimphen | Cucca, Francesco | Das, Debashish | de Geus, Eco J C | Dixon, Anna L | Döring, Angela | Ehret, Georg | Eyjolfsson, Gudmundur I | Farrall, Martin | Forouhi, Nita G | Friedrich, Nele | Goessling, Wolfram | Gudbjartsson, Daniel F | Harris, Tamara B | Hartikainen, Anna-Liisa | Heath, Simon | Hirschfield, Gideon M | Hofman, Albert | Homuth, Georg | Hyppönen, Elina | Janssen, Harry L A | Johnson, Toby | Kangas, Antti J | Kema, Ido P | Kühn, Jens P | Lai, Sandra | Lathrop, Mark | Lerch, Markus M | Li, Yun | Liang, T Jake | Lin, Jing-Ping | Loos, Ruth J F | Martin, Nicholas G | Moffatt, Miriam F | Montgomery, Grant W | Munroe, Patricia B | Musunuru, Kiran | Nakamura, Yusuke | O’Donnell, Christopher J | Olafsson, Isleifur | Penninx, Brenda W | Pouta, Anneli | Prins, Bram P | Prokopenko, Inga | Puls, Ralf | Ruokonen, Aimo | Savolainen, Markku J | Schlessinger, David | Schouten, Jeoffrey N L | Seedorf, Udo | Sen-Chowdhry, Srijita | Siminovitch, Katherine A | Smit, Johannes H | Spector, Timothy D | Tan, Wenting | Teslovich, Tanya M | Tukiainen, Taru | Uitterlinden, Andre G | Van der Klauw, Melanie M | Vasan, Ramachandran S | Wallace, Chris | Wallaschofski, Henri | Wichmann, H-Erich | Willemsen, Gonneke | Würtz, Peter | Xu, Chun | Yerges-Armstrong, Laura M | Abecasis, Goncalo R | Ahmadi, Kourosh R | Boomsma, Dorret I | Caulfield, Mark | Cookson, William O | van Duijn, Cornelia M | Froguel, Philippe | Matsuda, Koichi | McCarthy, Mark I | Meisinger, Christa | Mooser, Vincent | Pietiläinen, Kirsi H | Schumann, Gunter | Snieder, Harold | Sternberg, Michael J E | Stolk, Ronald P | Thomas, Howard C | Thorsteinsdottir, Unnur | Uda, Manuela | Waeber, Gérard | Wareham, Nicholas J | Waterworth, Dawn M | Watkins, Hugh | Whitfield, John B | Witteman, Jacqueline C M | Wolffenbuttel, Bruce H R | Fox, Caroline S | Ala-Korpela, Mika | Stefansson, Kari | Vollenweider, Peter | Völzke, Henry | Schadt, Eric E | Scott, James | Järvelin, Marjo-Riitta | Elliott, Paul | Kooner, Jaspal S
Nature genetics  2011;43(11):1131-1138.
Concentrations of liver enzymes in plasma are widely used as indicators of liver disease. We carried out a genome-wide association study in 61,089 individuals, identifying 42 loci associated with concentrations of liver enzymes in plasma, of which 32 are new associations (P = 10−8 to P = 10−190). We used functional genomic approaches including metabonomic profiling and gene expression analyses to identify probable candidate genes at these regions. We identified 69 candidate genes, including genes involved in biliary transport (ATP8B1 and ABCB11), glucose, carbohydrate and lipid metabolism (FADS1, FADS2, GCKR, JMJD1C, HNF1A, MLXIPL, PNPLA3, PPP1R3B, SLC2A2 and TRIB1), glycoprotein biosynthesis and cell surface glycobiology (ABO, ASGR1, FUT2, GPLD1 and ST3GAL4), inflammation and immunity (CD276, CDH6, GCKR, HNF1A, HPR, ITGA1, RORA and STAT4) and glutathione metabolism (GSTT1, GSTT2 and GGT), as well as several genes of uncertain or unknown function (including ABHD12, EFHD1, EFNA1, EPHA2, MICAL3 and ZNF827). Our results provide new insight into genetic mechanisms and pathways influencing markers of liver function.
PMCID: PMC3482372  PMID: 22001757
9.  Novel Loci for Metabolic Networks and Multi-Tissue Expression Studies Reveal Genes for Atherosclerosis 
PLoS Genetics  2012;8(8):e1002907.
Association testing of multiple correlated phenotypes offers better power than univariate analysis of single traits. We analyzed 6,600 individuals from two population-based cohorts with both genome-wide SNP data and serum metabolomic profiles. From the observed correlation structure of 130 metabolites measured by nuclear magnetic resonance, we identified 11 metabolic networks and performed a multivariate genome-wide association analysis. We identified 34 genomic loci at genome-wide significance, of which 7 are novel. In comparison to univariate tests, multivariate association analysis identified nearly twice as many significant associations in total. Multi-tissue gene expression studies identified variants in our top loci, SERPINA1 and AQP9, as eQTLs and showed that SERPINA1 and AQP9 expression in human blood was associated with metabolites from their corresponding metabolic networks. Finally, liver expression of AQP9 was associated with atherosclerotic lesion area in mice, and in human arterial tissue both SERPINA1 and AQP9 were shown to be upregulated (6.3-fold and 4.6-fold, respectively) in atherosclerotic plaques. Our study illustrates the power of multi-phenotype GWAS and highlights candidate genes for atherosclerosis.
Author Summary
In this study, we aim to identify novel genetic variants for metabolism, characterize their effects on nearby genes, and show that the nearby genes are associated with metabolism and atherosclerosis. To discover new genetic variants, we use an alternative approach to traditional genome-wide association studies: we leverage the information in phenotype covariance to increase our statistical power. We identify variants at seven novel loci and then show that our top signals drive expression of nearby genes AQP9 and SERPINA1 in multiple tissues. We demonstrate that AQP9 and SERPINA1 gene expression, in turn, is associated with metabolite levels. Finally, we show that the genes are associated with atherosclerosis using mouse atherosclerotic lesion size (AQP9) as well as tissue from healthy human arteries and atherosclerotic plaques (AQP9 and SERPINA1). This study illustrates that multivariate analysis of correlated metabolites can boost power for gene discovery substantially. Further functional work will need to be performed to elucidate the biological role of SERPINA1 and AQP9 in atherosclerosis.
PMCID: PMC3420921  PMID: 22916037
10.  Genome scan for loci regulating HDL cholesterol levels in Finnish extended pedigrees with early coronary heart disease 
Coronary heart disease (CHD) is the leading cause of mortality in Western societies. Its risk is inversely correlated with plasma high-density lipoprotein cholesterol (HDL-C) levels, and approximately 50% of the variability in these levels is genetically determined. In this study, the aim was to carry out a whole-genome scan for the loci regulating plasma HDL-C levels in 35 well-defined Finnish extended pedigrees (375 members genotyped) with probands having low HDL-C levels and premature CHD. The additive genetic heritability of HDL-C was 43%. A variance component analysis revealed four suggestive quantitative trait loci (QTLs) for HDL-C levels, with the highest LOD score, 3.1, at the chromosomal locus 4p12. Other suggestive LOD scores were 2.1 at 2q33, 2.1 at 6p24 and 2.0 at 17q25. Three suggestive loci for the qualitative low HDL-C trait were found, with a nonparametric multipoint score of 2.6 at the chromosomal locus 10p15.3, 2.5 at 22q11 and 2.1 at 6p12. After correction for statin use, the strongest evidence of linkage was shown on chromosomes 4p12, 6p24, 6p12, 15q22 and 22q11. To search for the underlying gene on chromosome 6, we analyzed two functional and positional candidate genes (peroxisome proliferator-activated receptor-delta (PPARD), and retinoid X receptor beta, (RXRB)), but found no significant evidence of association. In conclusion, we identified seven chromosomal regions for HDL-C regulation exceeding the level for suggestive evidence of linkage.
PMCID: PMC2987327  PMID: 19935834
atherosclerosis; complex trait; lipoprotein; linkage analysis
11.  Association analyses of 249,796 individuals reveal eighteen new loci associated with body mass index 
Speliotes, Elizabeth K. | Willer, Cristen J. | Berndt, Sonja I. | Monda, Keri L. | Thorleifsson, Gudmar | Jackson, Anne U. | Allen, Hana Lango | Lindgren, Cecilia M. | Luan, Jian’an | Mägi, Reedik | Randall, Joshua C. | Vedantam, Sailaja | Winkler, Thomas W. | Qi, Lu | Workalemahu, Tsegaselassie | Heid, Iris M. | Steinthorsdottir, Valgerdur | Stringham, Heather M. | Weedon, Michael N. | Wheeler, Eleanor | Wood, Andrew R. | Ferreira, Teresa | Weyant, Robert J. | Segré, Ayellet V. | Estrada, Karol | Liang, Liming | Nemesh, James | Park, Ju-Hyun | Gustafsson, Stefan | Kilpeläinen, Tuomas O. | Yang, Jian | Bouatia-Naji, Nabila | Esko, Tõnu | Feitosa, Mary F. | Kutalik, Zoltán | Mangino, Massimo | Raychaudhuri, Soumya | Scherag, Andre | Smith, Albert Vernon | Welch, Ryan | Zhao, Jing Hua | Aben, Katja K. | Absher, Devin M. | Amin, Najaf | Dixon, Anna L. | Fisher, Eva | Glazer, Nicole L. | Goddard, Michael E. | Heard-Costa, Nancy L. | Hoesel, Volker | Hottenga, Jouke-Jan | Johansson, Åsa | Johnson, Toby | Ketkar, Shamika | Lamina, Claudia | Li, Shengxu | Moffatt, Miriam F. | Myers, Richard H. | Narisu, Narisu | Perry, John R.B. | Peters, Marjolein J. | Preuss, Michael | Ripatti, Samuli | Rivadeneira, Fernando | Sandholt, Camilla | Scott, Laura J. | Timpson, Nicholas J. | Tyrer, Jonathan P. | van Wingerden, Sophie | Watanabe, Richard M. | White, Charles C. | Wiklund, Fredrik | Barlassina, Christina | Chasman, Daniel I. | Cooper, Matthew N. | Jansson, John-Olov | Lawrence, Robert W. | Pellikka, Niina | Prokopenko, Inga | Shi, Jianxin | Thiering, Elisabeth | Alavere, Helene | Alibrandi, Maria T. S. | Almgren, Peter | Arnold, Alice M. | Aspelund, Thor | Atwood, Larry D. | Balkau, Beverley | Balmforth, Anthony J. | Bennett, Amanda J. | Ben-Shlomo, Yoav | Bergman, Richard N. | Bergmann, Sven | Biebermann, Heike | Blakemore, Alexandra I.F. | Boes, Tanja | Bonnycastle, Lori L. | Bornstein, Stefan R. | Brown, Morris J. | Buchanan, Thomas A. | Busonero, Fabio | Campbell, Harry | Cappuccio, Francesco P. | Cavalcanti-Proença, Christine | Chen, Yii-Der Ida | Chen, Chih-Mei | Chines, Peter S. | Clarke, Robert | Coin, Lachlan | Connell, John | Day, Ian N.M. | Heijer, Martin den | Duan, Jubao | Ebrahim, Shah | Elliott, Paul | Elosua, Roberto | Eiriksdottir, Gudny | Erdos, Michael R. | Eriksson, Johan G. | Facheris, Maurizio F. | Felix, Stephan B. | Fischer-Posovszky, Pamela | Folsom, Aaron R. | Friedrich, Nele | Freimer, Nelson B. | Fu, Mao | Gaget, Stefan | Gejman, Pablo V. | Geus, Eco J.C. | Gieger, Christian | Gjesing, Anette P. | Goel, Anuj | Goyette, Philippe | Grallert, Harald | Gräßler, Jürgen | Greenawalt, Danielle M. | Groves, Christopher J. | Gudnason, Vilmundur | Guiducci, Candace | Hartikainen, Anna-Liisa | Hassanali, Neelam | Hall, Alistair S. | Havulinna, Aki S. | Hayward, Caroline | Heath, Andrew C. | Hengstenberg, Christian | Hicks, Andrew A. | Hinney, Anke | Hofman, Albert | Homuth, Georg | Hui, Jennie | Igl, Wilmar | Iribarren, Carlos | Isomaa, Bo | Jacobs, Kevin B. | Jarick, Ivonne | Jewell, Elizabeth | John, Ulrich | Jørgensen, Torben | Jousilahti, Pekka | Jula, Antti | Kaakinen, Marika | Kajantie, Eero | Kaplan, Lee M. | Kathiresan, Sekar | Kettunen, Johannes | Kinnunen, Leena | Knowles, Joshua W. | Kolcic, Ivana | König, Inke R. | Koskinen, Seppo | Kovacs, Peter | Kuusisto, Johanna | Kraft, Peter | Kvaløy, Kirsti | Laitinen, Jaana | Lantieri, Olivier | Lanzani, Chiara | Launer, Lenore J. | Lecoeur, Cecile | Lehtimäki, Terho | Lettre, Guillaume | Liu, Jianjun | Lokki, Marja-Liisa | Lorentzon, Mattias | Luben, Robert N. | Ludwig, Barbara | Manunta, Paolo | Marek, Diana | Marre, Michel | Martin, Nicholas G. | McArdle, Wendy L. | McCarthy, Anne | McKnight, Barbara | Meitinger, Thomas | Melander, Olle | Meyre, David | Midthjell, Kristian | Montgomery, Grant W. | Morken, Mario A. | Morris, Andrew P. | Mulic, Rosanda | Ngwa, Julius S. | Nelis, Mari | Neville, Matt J. | Nyholt, Dale R. | O’Donnell, Christopher J. | O’Rahilly, Stephen | Ong, Ken K. | Oostra, Ben | Paré, Guillaume | Parker, Alex N. | Perola, Markus | Pichler, Irene | Pietiläinen, Kirsi H. | Platou, Carl G.P. | Polasek, Ozren | Pouta, Anneli | Rafelt, Suzanne | Raitakari, Olli | Rayner, Nigel W. | Ridderstråle, Martin | Rief, Winfried | Ruokonen, Aimo | Robertson, Neil R. | Rzehak, Peter | Salomaa, Veikko | Sanders, Alan R. | Sandhu, Manjinder S. | Sanna, Serena | Saramies, Jouko | Savolainen, Markku J. | Scherag, Susann | Schipf, Sabine | Schreiber, Stefan | Schunkert, Heribert | Silander, Kaisa | Sinisalo, Juha | Siscovick, David S. | Smit, Jan H. | Soranzo, Nicole | Sovio, Ulla | Stephens, Jonathan | Surakka, Ida | Swift, Amy J. | Tammesoo, Mari-Liis | Tardif, Jean-Claude | Teder-Laving, Maris | Teslovich, Tanya M. | Thompson, John R. | Thomson, Brian | Tönjes, Anke | Tuomi, Tiinamaija | van Meurs, Joyce B.J. | van Ommen, Gert-Jan | Vatin, Vincent | Viikari, Jorma | Visvikis-Siest, Sophie | Vitart, Veronique | Vogel, Carla I. G. | Voight, Benjamin F. | Waite, Lindsay L. | Wallaschofski, Henri | Walters, G. Bragi | Widen, Elisabeth | Wiegand, Susanna | Wild, Sarah H. | Willemsen, Gonneke | Witte, Daniel R. | Witteman, Jacqueline C. | Xu, Jianfeng | Zhang, Qunyuan | Zgaga, Lina | Ziegler, Andreas | Zitting, Paavo | Beilby, John P. | Farooqi, I. Sadaf | Hebebrand, Johannes | Huikuri, Heikki V. | James, Alan L. | Kähönen, Mika | Levinson, Douglas F. | Macciardi, Fabio | Nieminen, Markku S. | Ohlsson, Claes | Palmer, Lyle J. | Ridker, Paul M. | Stumvoll, Michael | Beckmann, Jacques S. | Boeing, Heiner | Boerwinkle, Eric | Boomsma, Dorret I. | Caulfield, Mark J. | Chanock, Stephen J. | Collins, Francis S. | Cupples, L. Adrienne | Smith, George Davey | Erdmann, Jeanette | Froguel, Philippe | Grönberg, Henrik | Gyllensten, Ulf | Hall, Per | Hansen, Torben | Harris, Tamara B. | Hattersley, Andrew T. | Hayes, Richard B. | Heinrich, Joachim | Hu, Frank B. | Hveem, Kristian | Illig, Thomas | Jarvelin, Marjo-Riitta | Kaprio, Jaakko | Karpe, Fredrik | Khaw, Kay-Tee | Kiemeney, Lambertus A. | Krude, Heiko | Laakso, Markku | Lawlor, Debbie A. | Metspalu, Andres | Munroe, Patricia B. | Ouwehand, Willem H. | Pedersen, Oluf | Penninx, Brenda W. | Peters, Annette | Pramstaller, Peter P. | Quertermous, Thomas | Reinehr, Thomas | Rissanen, Aila | Rudan, Igor | Samani, Nilesh J. | Schwarz, Peter E.H. | Shuldiner, Alan R. | Spector, Timothy D. | Tuomilehto, Jaakko | Uda, Manuela | Uitterlinden, André | Valle, Timo T. | Wabitsch, Martin | Waeber, Gérard | Wareham, Nicholas J. | Watkins, Hugh | Wilson, James F. | Wright, Alan F. | Zillikens, M. Carola | Chatterjee, Nilanjan | McCarroll, Steven A. | Purcell, Shaun | Schadt, Eric E. | Visscher, Peter M. | Assimes, Themistocles L. | Borecki, Ingrid B. | Deloukas, Panos | Fox, Caroline S. | Groop, Leif C. | Haritunians, Talin | Hunter, David J. | Kaplan, Robert C. | Mohlke, Karen L. | O’Connell, Jeffrey R. | Peltonen, Leena | Schlessinger, David | Strachan, David P. | van Duijn, Cornelia M. | Wichmann, H.-Erich | Frayling, Timothy M. | Thorsteinsdottir, Unnur | Abecasis, Gonçalo R. | Barroso, Inês | Boehnke, Michael | Stefansson, Kari | North, Kari E. | McCarthy, Mark I. | Hirschhorn, Joel N. | Ingelsson, Erik | Loos, Ruth J.F.
Nature genetics  2010;42(11):937-948.
Obesity is globally prevalent and highly heritable, but the underlying genetic factors remain largely elusive. To identify genetic loci for obesity-susceptibility, we examined associations between body mass index (BMI) and ~2.8 million SNPs in up to 123,865 individuals, with targeted follow-up of 42 SNPs in up to 125,931 additional individuals. We confirmed 14 known obesity-susceptibility loci and identified 18 new loci associated with BMI (P<5×10−8), one of which includes a copy number variant near GPRC5B. Some loci (MC4R, POMC, SH2B1, BDNF) map near key hypothalamic regulators of energy balance, and one is near GIPR, an incretin receptor. Furthermore, genes in other newly-associated loci may provide novel insights into human body weight regulation.
PMCID: PMC3014648  PMID: 20935630
12.  Metabonomic, transcriptomic, and genomic variation of a population cohort 
The lipid–leukocyte (LL) module is associated with, and reactive to, a wide variety of serum metabolites.The LL module appears to be a link between metabolism, adiposity, and inflammation.Serum metabolite concentrations themselves determine the connectedness of LL module.
Comprehensive characterization of human tissues promises novel insights into the biological architecture of human diseases and traits. We assessed metabonomic, transcriptomic, and genomic variation for a large population-based cohort from the capital region of Finland. Network analyses identified a set of highly correlated genes, the lipid–leukocyte (LL) module, as having a prominent role in over 80 serum metabolites (of 134 measures quantified), including lipoprotein subclasses, lipids, and amino acids. Concurrent association with immune response markers suggested the LL module as a possible link between inflammation, metabolism, and adiposity. Further, genomic variation was used to generate a directed network and infer LL module's largely reactive nature to metabolites. Finally, gene co-expression in circulating leukocytes was shown to be dependent on serum metabolite concentrations, providing evidence for the hypothesis that the coherence of molecular networks themselves is conditional on environmental factors. These findings show the importance and opportunity of systematic molecular investigation of human population samples. To facilitate and encourage this investigation, the metabonomic, transcriptomic, and genomic data used in this study have been made available as a resource for the research community.
PMCID: PMC3018170  PMID: 21179014
bioinformatics; biological networks; integrative genomics; metabonomics; transcriptomics
13.  Separating the mechanism-based and off-target actions of CETP-inhibitors using CETP gene polymorphisms 
Circulation  2009;121(1):52-62.
Cholesteryl ester transfer protein (CETP) inhibitors raise HDL-cholesterol but torcetrapib, the first-in-class inhibitor tested in a large outcome trial caused unexpected blood pressure elevation and increased cardiovascular events. Whether the hypertensive effect resulted from CETP-inhibition or an off-target action of torcetrapib has been debated. We hypothesised that common single nucleotide polymorphisms (SNPs) in the CETP-gene could help distinguish mechanism-based from off-target actions of CETP-inhibitors to inform on the validity of CETP as a therapeutic target.
Methods and Results
We compared the effect of CETP SNPs and torcetrapib treatment on lipid fractions, blood pressure and electrolytes in up to 67,687 individuals from genetic studies and 17,911 from randomised trials. CETP SNPs and torcetrapib treatment reduced CETP activity and had directionally concordant effect on eight lipid and lipoprotein traits (total-, LDL- and HDL-cholesterol, HDL2, HDL3, apolipoproteins A-I, -B, and triglycerides), with the genetic effect on HDL-cholesterol (0.13 mmol/L; 95% CI: 0.11, 0.14) being consistent with that expected of a 10 mg dose of torcetrapib (0.13 mmol/L; 0.10, 0.15). In trials, 60mg torcetrapib elevated systolic and diastolic blood pressure by 4.47mmHg (4.10, 4.84) and 2.08mmHg (1.84, 2.31) respectively. However, the effect of CETP SNPs on systolic 0.16mmHg (−0.28, 0.60) and diastolic blood pressure −0.04mmHg (−0.36, 0.28) was null and significantly different from that expected of 10 mg torcetrapib.
Discordance in the effects of CETP SNPs and torcetrapib treatment on blood pressure despite the concordant effects on lipids indicates the hypertensive action of torcetrapib is unlikely to be due to CETP-inhibition, or shared by chemically dissimilar CETP inhibitors. Genetic studies could find use in drug development programmes as a new source of randomised evidence for drug target validation in man.
PMCID: PMC2811869  PMID: 20026784
genetics; pharmacology; epidemiology
14.  A novel Bayesian approach to quantify clinical variables and to determine their spectroscopic counterparts in 1H NMR metabonomic data 
BMC Bioinformatics  2007;8(Suppl 2):S8.
A key challenge in metabonomics is to uncover quantitative associations between multidimensional spectroscopic data and biochemical measures used for disease risk assessment and diagnostics. Here we focus on clinically relevant estimation of lipoprotein lipids by 1H NMR spectroscopy of serum.
A Bayesian methodology, with a biochemical motivation, is presented for a real 1H NMR metabonomics data set of 75 serum samples. Lipoprotein lipid concentrations were independently obtained for these samples via ultracentrifugation and specific biochemical assays. The Bayesian models were constructed by Markov chain Monte Carlo (MCMC) and they showed remarkably good quantitative performance, the predictive R-values being 0.985 for the very low density lipoprotein triglycerides (VLDL-TG), 0.787 for the intermediate, 0.943 for the low, and 0.933 for the high density lipoprotein cholesterol (IDL-C, LDL-C and HDL-C, respectively). The modelling produced a kernel-based reformulation of the data, the parameters of which coincided with the well-known biochemical characteristics of the 1H NMR spectra; particularly for VLDL-TG and HDL-C the Bayesian methodology was able to clearly identify the most characteristic resonances within the heavily overlapping information in the spectra. For IDL-C and LDL-C the resulting model kernels were more complex than those for VLDL-TG and HDL-C, probably reflecting the severe overlap of the IDL and LDL resonances in the 1H NMR spectra.
The systematic use of Bayesian MCMC analysis is computationally demanding. Nevertheless, the combination of high-quality quantification and the biochemical rationale of the resulting models is expected to be useful in the field of metabonomics.
PMCID: PMC1892077  PMID: 17493257

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