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1.  Patients with type 1 diabetes show signs of vascular dysfunction in response to multiple high-fat meals 
Background
A high-fat diet promotes postprandial systemic inflammation and metabolic endotoxemia. We investigated the effects of three consecutive high-fat meals on endotoxemia, inflammation, vascular function, and postprandial lipid metabolism in patients with type 1 diabetes.
Methods
Non-diabetic controls (n = 34) and patients with type 1 diabetes (n = 37) were given three high-caloric, fat-containing meals during one day. Blood samples were drawn at fasting (8:00) and every two hours thereafter until 18:00. Applanation tonometry was used to assess changes in the augmentation index during the investigation day.
Results
Three consecutive high-fat meals had only a modest effect on serum LPS-activity levels and inflammatory markers throughout the day in both groups. Of note, patients with type 1 diabetes were unable to decrease the augmentation index in response to the high-fat meals. The most profound effects of the consecutive fat loads were seen in chylomicron and HDL-metabolism. The triglyceride-rich lipoprotein remnant marker, apoB-48, was elevated in patients compared to controls both at fasting (p = 0.014) and postprandially (p = 0.035). The activities of the HDL-associated enzymes PLTP (p < 0.001), and CETP (p = 0.007) were higher and paraoxonase (PON-1) activity, an anti-oxidative enzyme bound to HDL, decreased in patients with type 1 diabetes (p = 0.027).
Conclusions
In response to high-fat meals, early signs of vascular dysfunction alongside accumulation of chylomicron remnants, higher augmentation index, and decreased PON-1 activity were observed in patients with type 1 diabetes. The high-fat meals had no significant impact on postprandial LPS-activity in non-diabetic subjects or patients with type 1 diabetes suggesting that metabolic endotoxemia may be more central in patients with chronic metabolic disturbances such as obesity, type 2 diabetes, or diabetic kidney disease.
doi:10.1186/1743-7075-11-28
PMCID: PMC4067102  PMID: 24959195
High-fat diet; Vascular dysfunction; Type 1 diabetes
2.  Genome metabolome integrated network analysis to uncover connections between genetic variants and complex traits: an application to obesity 
Current studies of phenotype diversity by genome-wide association studies (GWAS) are mainly focused on identifying genetic variants that influence level changes of individual traits without considering additional alterations at the system-level. However, in addition to level alterations of single phenotypes, differences in association between phenotype levels are observed across different physiological states. Such differences in molecular correlations between states can potentially reveal information about the system state beyond that reported by changes in mean levels alone. In this study, we describe a novel methodological approach, which we refer to as genome metabolome integrated network analysis (GEMINi) consisting of a combination of correlation network analysis and genome-wide correlation study. The proposed methodology exploits differences in molecular associations to uncover genetic variants involved in phenotype variation. We test the performance of the GEMINi approach in a simulation study and illustrate its use in the context of obesity and detailed quantitative metabolomics data on systemic metabolism. Application of GEMINi revealed a set of metabolic associations which differ between normal and obese individuals. While no significant associations were found between genetic variants and body mass index using a standard GWAS approach, further investigation of the identified differences in metabolic association revealed a number of loci, several of which have been previously implicated with obesity-related processes. This study highlights the advantage of using molecular associations as an alternative phenotype when studying the genetic basis of complex traits and diseases.
doi:10.1098/rsif.2013.0908
PMCID: PMC3973353  PMID: 24573330
correlation analysis; differential networks; genome-wide association analysis; metabolomics; GEMINi
3.  Upstream Transcription Factor 1 (USF1) allelic variants regulate lipoprotein metabolism in women and USF1 expression in atherosclerotic plaque 
Scientific Reports  2014;4:4650.
Upstream transcription factor 1 (USF1) allelic variants significantly influence future risk of cardiovascular disease and overall mortality in females. We investigated sex-specific effects of USF1 gene allelic variants on serum indices of lipoprotein metabolism, early markers of asymptomatic atherosclerosis and their changes during six years of follow-up. In addition, we investigated the cis-regulatory role of these USF1 variants in artery wall tissues in Caucasians. In the Cardiovascular Risk in Young Finns Study, 1,608 participants (56% women, aged 31.9 ± 4.9) with lipids and cIMT data were included. For functional study, whole genome mRNA expression profiling was performed in 91 histologically classified atherosclerotic samples. In females, serum total, LDL cholesterol and apoB levels increased gradually according to USF1 rs2516839 genotypes TT < CT < CC and rs1556259 AA < AG < GG as well as according to USF1 H3 (GCCCGG) copy number 0 < 1 < 2. Furthermore, the carriers of minor alleles of rs2516839 (C) and rs1556259 (G) of USF1 gene had decreased USF1 expression in atherosclerotic plaques (P = 0.028 and 0.08, respectively) as compared to non-carriers. The genetic variation in USF1 influence USF1 transcript expression in advanced atherosclerosis and regulates levels and metabolism of circulating apoB and apoB-containing lipoprotein particles in sex-dependent manner, but is not a major determinant of early markers of atherosclerosis.
doi:10.1038/srep04650
PMCID: PMC3983598  PMID: 24722012
4.  Assessing multivariate gene-metabolome associations with rare variants using Bayesian reduced rank regression 
Bioinformatics  2014;30(14):2026-2034.
Motivation: A typical genome-wide association study searches for associations between single nucleotide polymorphisms (SNPs) and a univariate phenotype. However, there is a growing interest to investigate associations between genomics data and multivariate phenotypes, for example, in gene expression or metabolomics studies. A common approach is to perform a univariate test between each genotype–phenotype pair, and then to apply a stringent significance cutoff to account for the large number of tests performed. However, this approach has limited ability to uncover dependencies involving multiple variables. Another trend in the current genetics is the investigation of the impact of rare variants on the phenotype, where the standard methods often fail owing to lack of power when the minor allele is present in only a limited number of individuals.
Results: We propose a new statistical approach based on Bayesian reduced rank regression to assess the impact of multiple SNPs on a high-dimensional phenotype. Because of the method’s ability to combine information over multiple SNPs and phenotypes, it is particularly suitable for detecting associations involving rare variants. We demonstrate the potential of our method and compare it with alternatives using the Northern Finland Birth Cohort with 4702 individuals, for whom genome-wide SNP data along with lipoprotein profiles comprising 74 traits are available. We discovered two genes (XRCC4 and MTHFD2L) without previously reported associations, which replicated in a combined analysis of two additional cohorts: 2390 individuals from the Cardiovascular Risk in Young Finns study and 3659 individuals from the FINRISK study.
Availability and implementation: R-code freely available for download at http://users.ics.aalto.fi/pemartti/gene_metabolome/.
Contact: samuli.ripatti@helsinki.fi; samuel.kaski@aalto.fi
Supplementary information: Supplementary data are available at Bioinformatics online.
doi:10.1093/bioinformatics/btu140
PMCID: PMC4080737  PMID: 24665129
5.  Branched-Chain and Aromatic Amino Acids Are Predictors of Insulin Resistance in Young Adults 
Diabetes Care  2013;36(3):648-655.
OBJECTIVE
Branched-chain and aromatic amino acids are associated with the risk for future type 2 diabetes; however, the underlying mechanisms remain elusive. We tested whether amino acids predict insulin resistance index in healthy young adults.
RESEARCH DESIGN AND METHODS
Circulating isoleucine, leucine, valine, phenylalanine, tyrosine, and six additional amino acids were quantified in 1,680 individuals from the population-based Cardiovascular Risk in Young Finns Study (baseline age 32 ± 5 years; 54% women). Insulin resistance was estimated by homeostasis model assessment (HOMA) at baseline and 6-year follow-up. Amino acid associations with HOMA of insulin resistance (HOMA-IR) and glucose were assessed using regression models adjusted for established risk factors. We further examined whether amino acid profiling could augment risk assessment of insulin resistance (defined as 6-year HOMA-IR >90th percentile) in early adulthood.
RESULTS
Isoleucine, leucine, valine, phenylalanine, and tyrosine were associated with HOMA-IR at baseline and for men at 6-year follow-up, while for women only leucine, valine, and phenylalanine predicted 6-year HOMA-IR (P < 0.05). None of the other amino acids were prospectively associated with HOMA-IR. The sum of branched-chain and aromatic amino acid concentrations was associated with 6-year insulin resistance for men (odds ratio 2.09 [95% CI 1.38–3.17]; P = 0.0005); however, including the amino acid score in prediction models did not improve risk discrimination.
CONCLUSIONS
Branched-chain and aromatic amino acids are markers of the development of insulin resistance in young, normoglycemic adults, with most pronounced associations for men. These findings suggest that the association of branched-chain and aromatic amino acids with the risk for future diabetes is at least partly mediated through insulin resistance.
doi:10.2337/dc12-0895
PMCID: PMC3579331  PMID: 23129134
6.  Effects of Whole Grain, Fish and Bilberries on Serum Metabolic Profile and Lipid Transfer Protein Activities: A Randomized Trial (Sysdimet) 
PLoS ONE  2014;9(2):e90352.
Objective
We studied the combined effects of wholegrain, fish and bilberries on serum metabolic profile and lipid transfer protein activities in subjects with the metabolic syndrome.
Methods
Altogether 131 subjects (40–70 y, BMI 26–39 kg/m2) with impaired glucose metabolism and features of the metabolic syndrome were randomized into three groups with 12-week periods according to a parallel study design. They consumed either: a) wholegrain and low postprandial insulin response grain products, fatty fish 3 times a week, and bilberries 3 portions per day (HealthyDiet), b) wholegrain and low postprandial insulin response grain products (WGED), or c) refined wheat breads as cereal products (Control). Altogether 106 subjects completed the study. Serum metabolic profile was studied using an NMR-based platform providing information on lipoprotein subclasses and lipids as well as low-molecular-weight metabolites.
Results
There were no significant differences in clinical characteristics between the groups at baseline or at the end of the intervention. Mixed model analyses revealed significant changes in lipid metabolites in the HealthyDiet group during the intervention compared to the Control group. All changes reflected increased polyunsaturation in plasma fatty acids, especially in n-3 PUFAs, while n-6 and n-7 fatty acids decreased. According to tertiles of changes in fish intake, a greater increase of fish intake was associated with increased concentration of large HDL particles, larger average diameter of HDL particles, and increased concentrations of large HDL lipid components, even though total levels of HDL cholesterol remained stable.
Conclusions
The results suggest that consumption of diet rich in whole grain, bilberries and especially fatty fish causes changes in HDL particles shifting their subclass distribution toward larger particles. These changes may be related to known protective functions of HDL such as reverse cholesterol transport and could partly explain the known protective effects of fish consumption against atherosclerosis.
Trial Registration
The study was registered at ClinicalTrials.gov NCT00573781.
doi:10.1371/journal.pone.0090352
PMCID: PMC3938672  PMID: 24587337
7.  Biomarker Profiling by Nuclear Magnetic Resonance Spectroscopy for the Prediction of All-Cause Mortality: An Observational Study of 17,345 Persons 
PLoS Medicine  2014;11(2):e1001606.
In this study, Würtz and colleagues conducted high-throughput profiling of blood specimens in two large population-based cohorts in order to identify biomarkers for all-cause mortality and enhance risk prediction. The authors found that biomarker profiling improved prediction of the short-term risk of death from all causes above established risk factors. However, further investigations are needed to clarify the biological mechanisms and the utility of these biomarkers to guide screening and prevention.
Please see later in the article for the Editors' Summary
Background
Early identification of ambulatory persons at high short-term risk of death could benefit targeted prevention. To identify biomarkers for all-cause mortality and enhance risk prediction, we conducted high-throughput profiling of blood specimens in two large population-based cohorts.
Methods and Findings
106 candidate biomarkers were quantified by nuclear magnetic resonance spectroscopy of non-fasting plasma samples from a random subset of the Estonian Biobank (n = 9,842; age range 18–103 y; 508 deaths during a median of 5.4 y of follow-up). Biomarkers for all-cause mortality were examined using stepwise proportional hazards models. Significant biomarkers were validated and incremental predictive utility assessed in a population-based cohort from Finland (n = 7,503; 176 deaths during 5 y of follow-up). Four circulating biomarkers predicted the risk of all-cause mortality among participants from the Estonian Biobank after adjusting for conventional risk factors: alpha-1-acid glycoprotein (hazard ratio [HR] 1.67 per 1–standard deviation increment, 95% CI 1.53–1.82, p = 5×10−31), albumin (HR 0.70, 95% CI 0.65–0.76, p = 2×10−18), very-low-density lipoprotein particle size (HR 0.69, 95% CI 0.62–0.77, p = 3×10−12), and citrate (HR 1.33, 95% CI 1.21–1.45, p = 5×10−10). All four biomarkers were predictive of cardiovascular mortality, as well as death from cancer and other nonvascular diseases. One in five participants in the Estonian Biobank cohort with a biomarker summary score within the highest percentile died during the first year of follow-up, indicating prominent systemic reflections of frailty. The biomarker associations all replicated in the Finnish validation cohort. Including the four biomarkers in a risk prediction score improved risk assessment for 5-y mortality (increase in C-statistics 0.031, p = 0.01; continuous reclassification improvement 26.3%, p = 0.001).
Conclusions
Biomarker associations with cardiovascular, nonvascular, and cancer mortality suggest novel systemic connectivities across seemingly disparate morbidities. The biomarker profiling improved prediction of the short-term risk of death from all causes above established risk factors. Further investigations are needed to clarify the biological mechanisms and the utility of these biomarkers for guiding screening and prevention.
Please see later in the article for the Editors' Summary
Editors' Summary
Background
A biomarker is a biological molecule found in blood, body fluids, or tissues that may signal an abnormal process, a condition, or a disease. The level of a particular biomarker may indicate a patient's risk of disease, or likely response to a treatment. For example, cholesterol levels are measured to assess the risk of heart disease. Most current biomarkers are used to test an individual's risk of developing a specific condition. There are none that accurately assess whether a person is at risk of ill health generally, or likely to die soon from a disease. Early and accurate identification of people who appear healthy but in fact have an underlying serious illness would provide valuable opportunities for preventative treatment.
While most tests measure the levels of a specific biomarker, there are some technologies that allow blood samples to be screened for a wide range of biomarkers. These include nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry. These tools have the potential to be used to screen the general population for a range of different biomarkers.
Why Was This Study Done?
Identifying new biomarkers that provide insight into the risk of death from all causes could be an important step in linking different diseases and assessing patient risk. The authors in this study screened patient samples using NMR spectroscopy for biomarkers that accurately predict the risk of death particularly amongst the general population, rather than amongst people already known to be ill.
What Did the Researchers Do and Find?
The researchers studied two large groups of people, one in Estonia and one in Finland. Both countries have set up health registries that collect and store blood samples and health records over many years. The registries include large numbers of people who are representative of the wider population.
The researchers first tested blood samples from a representative subset of the Estonian group, testing 9,842 samples in total. They looked at 106 different biomarkers in each sample using NMR spectroscopy. They also looked at the health records of this group and found that 508 people died during the follow-up period after the blood sample was taken, the majority from heart disease, cancer, and other diseases. Using statistical analysis, they looked for any links between the levels of different biomarkers in the blood and people's short-term risk of dying. They found that the levels of four biomarkers—plasma albumin, alpha-1-acid glycoprotein, very-low-density lipoprotein (VLDL) particle size, and citrate—appeared to accurately predict short-term risk of death. They repeated this study with the Finnish group, this time with 7,503 individuals (176 of whom died during the five-year follow-up period after giving a blood sample) and found similar results.
The researchers carried out further statistical analyses to take into account other known factors that might have contributed to the risk of life-threatening illness. These included factors such as age, weight, tobacco and alcohol use, cholesterol levels, and pre-existing illness, such as diabetes and cancer. The association between the four biomarkers and short-term risk of death remained the same even when controlling for these other factors.
The analysis also showed that combining the test results for all four biomarkers, to produce a biomarker score, provided a more accurate measure of risk than any of the biomarkers individually. This biomarker score also proved to be the strongest predictor of short-term risk of dying in the Estonian group. Individuals with a biomarker score in the top 20% had a risk of dying within five years that was 19 times greater than that of individuals with a score in the bottom 20% (288 versus 15 deaths).
What Do These Findings Mean?
This study suggests that there are four biomarkers in the blood—alpha-1-acid glycoprotein, albumin, VLDL particle size, and citrate—that can be measured by NMR spectroscopy to assess whether otherwise healthy people are at short-term risk of dying from heart disease, cancer, and other illnesses. However, further validation of these findings is still required, and additional studies should examine the biomarker specificity and associations in settings closer to clinical practice. The combined biomarker score appears to be a more accurate predictor of risk than tests for more commonly known risk factors. Identifying individuals who are at high risk using these biomarkers might help to target preventative medical treatments to those with the greatest need.
However, there are several limitations to this study. As an observational study, it provides evidence of only a correlation between a biomarker score and ill health. It does not identify any underlying causes. Other factors, not detectable by NMR spectroscopy, might be the true cause of serious health problems and would provide a more accurate assessment of risk. Nor does this study identify what kinds of treatment might prove successful in reducing the risks. Therefore, more research is needed to determine whether testing for these biomarkers would provide any clinical benefit.
There were also some technical limitations to the study. NMR spectroscopy does not detect as many biomarkers as mass spectrometry, which might therefore identify further biomarkers for a more accurate risk assessment. In addition, because both study groups were northern European, it is not yet known whether the results would be the same in other ethnic groups or populations with different lifestyles.
In spite of these limitations, the fact that the same four biomarkers are associated with a short-term risk of death from a variety of diseases does suggest that similar underlying mechanisms are taking place. This observation points to some potentially valuable areas of research to understand precisely what's contributing to the increased risk.
Additional Information
Please access these websites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1001606
The US National Institute of Environmental Health Sciences has information on biomarkers
The US Food and Drug Administration has a Biomarker Qualification Program to help researchers in identifying and evaluating new biomarkers
Further information on the Estonian Biobank is available
The Computational Medicine Research Team of the University of Oulu and the University of Bristol have a webpage that provides further information on high-throughput biomarker profiling by NMR spectroscopy
doi:10.1371/journal.pmed.1001606
PMCID: PMC3934819  PMID: 24586121
8.  Cross-sectional and longitudinal associations of circulating omega-3 and omega-6 fatty acids with lipoprotein particle concentrations and sizes: population-based cohort study with 6-year follow-up 
Background
Cross-sectional studies have suggested that serum omega-3 (n-3) and omega-6 (n-6) polyunsaturated fatty acids (PUFAs) are related to favorable lipoprotein particle concentrations. We explored the associations of serum n-3 and n-6 PUFAs with lipoprotein particle concentrations and sizes in a general population cohort at baseline and after 6 years.
Findings
The cohort included 665 adults (274 men) with a 6-year follow-up. Nutritional counseling was given at baseline. Serum n-3 and n-6 PUFAs and lipoprotein particle concentrations and the mean particle sizes of VLDL, LDL, and HDL were quantified by nuclear magnetic resonance (NMR) spectroscopy for all baseline and follow-up samples at the same time. Concentrations of n-3 and n-6 PUFAs were expressed relative to total fatty acids. At baseline, n-3 PUFAs were not associated with lipoprotein particle concentrations. A weak negative association was observed for VLDL (P = 0.021) and positive for HDL (P = 0.011) particle size. n-6 PUFA was negatively associated with VLDL particle concentration and positively with LDL (P < 0.001) and HDL particle size (P < 0.001). The 6-year change in n-3 PUFA correlated positively with the change in particle size for HDL and LDL lipoproteins but negatively with VLDL particle size. An increase in 6-year levels of n-6 PUFAs was negatively correlated with the change in VLDL particle concentration and size, and positively with LDL particle size.
Conclusion
Change in circulating levels of both n-3 and n-6 PUFAs, relative to total fatty acids, during 6 years of follow-up are associated with changes in lipoprotein particle size and concentrations at the population level.
doi:10.1186/1476-511X-13-28
PMCID: PMC3922432  PMID: 24507090
Lipoprotein profile; Fatty acid; Cohort study
9.  A Comparison of Anthropometric, Metabolic, and Reproductive Characteristics of Young Adult Women from Opposite-Sex and Same-Sex Twin Pairs 
Background: Prenatal exposure to androgens has been linked to masculinization of several traits. We aimed to determine whether putative female intra-uterine exposure to androgens influences anthropometric, metabolic, and reproductive parameters using a twin design.
Methods: Two cohorts of Finnish twins born in 1975–1979 and 1983–1987 formed the basis for the longitudinal FinnTwin16 (FT16) and FinnTwin12 (FT12) studies. Self-reported anthropometric characteristics, disease status, and reproductive history were compared between 679 same-sex (SS) and 789 opposite-sex (OS) female twins (mean age ± SD: 34 ± 1.1) from the wave 5 of data collection in FT16. Serum lipid and lipoprotein subclass concentrations measured by nuclear magnetic resonance spectroscopy were compared in 226 SS and 169 OS female twins (mean age ± SD: 24 ± 2.1) from the wave 4 of data collection in FT12 and FT16.
Results: Anthropometric measures, the prevalence of hypertension and diabetes mellitus type 2 did not differ significantly between females from SS and OS twin pairs at age 34. Similarly, the prevalence of infertility, age at first pregnancy and number of induced and spontaneous abortions did not differ significantly between these two groups of women. The serum lipid and lipoprotein profile did not differ between females from SS and OS twins at age 24.
Conclusion: We found no evidence that androgen overexposure of the female fetus affects obesity, metabolic profile, or reproductive health in young adult females. However, these results do not exclude the possibility that prenatal androgen exposure in females could be adversely associated with these phenotypes later in life.
doi:10.3389/fendo.2014.00028
PMCID: PMC3945783  PMID: 24639667
prenatal androgen exposure; twin testosterone transfer hypothesis; opposite-sex twin pairs; anthropometrics; reproductive history; lipoprotein profile
10.  Effects of sea buckthorn and bilberry on serum metabolites differ according to baseline metabolic profiles in overweight women: a randomized crossover trial1234 
Background: Berries are associated with health benefits. Little is known about the effect of baseline metabolome on the overall metabolic responses to berry intake.
Objective: We studied the effects of berries on serum metabolome.
Design: Eighty overweight women completed this randomized crossover study. During the interventions of 30 d, subjects consumed dried sea buckthorn berries (SBs), sea buckthorn oil (SBo), sea buckthorn phenolics ethanol extract mixed with maltodextrin (SBe+MD) (1:1), or frozen bilberries. Metabolic profiles were quantified from serum samples by using 1H nuclear magnetic resonance spectroscopy.
Results: All interventions induced a significant (P < 0.001–0.003) effect on the overall metabolic profiles. The effect was observed both in participants who had a metabolic profile that reflected higher cardiometabolic risk at baseline (group B: P = 0.001–0.008) and in participants who had a lower-risk profile (group A: P < 0.001–0.009). Although most of the changes in individual metabolites were not statistically significant after correction for multiplicity, clear trends were observed. SB-induced effects were mainly on serum triglycerides and very-low-density lipoprotein (VLDL) and its subclasses, which decreased in metabolic group B. SBo induced a decreasing trend in serum total, intermediate-density lipoprotein (IDL), and low-density lipoprotein (LDL) cholesterol and subfractions of IDL and LDL in group B. During the SBe+MD treatment, VLDL fractions and serum triglycerides increased. Bilberries caused beneficial changes in serum lipids and lipoproteins in group B, whereas the opposite was true in group A.
Conclusion: Berry intake has overall metabolic effects, which depend on the cardiometabolic risk profile at baseline. This trial was registered at clinicaltrials.gov as NCT01860547.
doi:10.3945/ajcn.113.060590
PMCID: PMC3778864  PMID: 23945716
11.  Circulating Metabolite Predictors of Glycemia in Middle-Aged Men and Women 
Diabetes Care  2012;35(8):1749-1756.
OBJECTIVE
Metabolite predictors of deteriorating glucose tolerance may elucidate the pathogenesis of type 2 diabetes. We investigated associations of circulating metabolites from high-throughput profiling with fasting and postload glycemia cross-sectionally and prospectively on the population level.
RESEARCH DESIGN AND METHODS
Oral glucose tolerance was assessed in two Finnish, population-based studies consisting of 1,873 individuals (mean age 52 years, 58% women) and reexamined after 6.5 years for 618 individuals in one of the cohorts. Metabolites were quantified by nuclear magnetic resonance spectroscopy from fasting serum samples. Associations were studied by linear regression models adjusted for established risk factors.
RESULTS
Nineteen circulating metabolites, including amino acids, gluconeogenic substrates, and fatty acid measures, were cross-sectionally associated with fasting and/or postload glucose (P < 0.001). Among these metabolic intermediates, branched-chain amino acids, phenylalanine, and α1-acid glycoprotein were predictors of both fasting and 2-h glucose at 6.5-year follow-up (P < 0.05), whereas alanine, lactate, pyruvate, and tyrosine were uniquely associated with 6.5-year postload glucose (P = 0.003–0.04). None of the fatty acid measures were prospectively associated with glycemia. Changes in fatty acid concentrations were associated with changes in fasting and postload glycemia during follow-up; however, changes in branched-chain amino acids did not follow glucose dynamics, and gluconeogenic substrates only paralleled changes in fasting glucose.
CONCLUSIONS
Alterations in branched-chain and aromatic amino acid metabolism precede hyperglycemia in the general population. Further, alanine, lactate, and pyruvate were predictive of postchallenge glucose exclusively. These gluconeogenic precursors are potential markers of long-term impaired insulin sensitivity that may relate to attenuated glucose tolerance later in life.
doi:10.2337/dc11-1838
PMCID: PMC3402262  PMID: 22563043
12.  Hyperglycemia and a Common Variant of GCKR Are Associated With the Levels of Eight Amino Acids in 9,369 Finnish Men 
Diabetes  2012;61(7):1895-1902.
We investigated the association of glycemia and 43 genetic risk variants for hyperglycemia/type 2 diabetes with amino acid levels in the population-based Metabolic Syndrome in Men (METSIM) Study, including 9,369 nondiabetic or newly diagnosed type 2 diabetic Finnish men. Plasma levels of eight amino acids were measured with proton nuclear magnetic resonance spectroscopy. Increasing fasting and 2-h plasma glucose levels were associated with increasing levels of several amino acids and decreasing levels of histidine and glutamine. Alanine, leucine, isoleucine, tyrosine, and glutamine predicted incident type 2 diabetes in a 4.7-year follow-up of the METSIM Study, and their effects were largely mediated by insulin resistance (except for glutamine). We also found significant correlations between insulin sensitivity (Matsuda insulin sensitivity index) and mRNA expression of genes regulating amino acid degradation in 200 subcutaneous adipose tissue samples. Only 1 of 43 risk single nucleotide polymorphisms for type 2 diabetes or hyperglycemia, the glucose-increasing major C allele of rs780094 of GCKR, was significantly associated with decreased levels of alanine and isoleucine and elevated levels of glutamine. In conclusion, the levels of branched-chain, aromatic amino acids and alanine increased and the levels of glutamine and histidine decreased with increasing glycemia, reflecting, at least in part, insulin resistance. Only one single nucleotide polymorphism regulating hyperglycemia was significantly associated with amino acid levels.
doi:10.2337/db11-1378
PMCID: PMC3379649  PMID: 22553379
13.  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.
doi:10.2337/db11-1355
PMCID: PMC3357275  PMID: 22511205
14.  Lipoprotein subclass profiles in young adults born preterm at very low birth weight 
Background
Adults born preterm at very low birth weight (VLBW ≤ 1500g) have increased risk factors for cardiovascular diseases including high blood pressure and impaired glucose regulation. Non-optimal lipoprotein profile is generally also likely to affect the increased cardiovascular risk, but lipoprotein subclass level data on adults born at VLBW are sparse.
Subjects and methods
We studied 162 subjects born at VLBW and 169 term-born controls, aged 19 to 27 years. Total lipid, triglyceride and cholesterol concentrations of 14 lipoprotein subclasses were determined by proton nuclear magnetic resonance spectroscopy in the fasting state and in 2-hour serum samples from an oral glucose tolerance test.
Findings
In comparison to controls, VLBW subjects had significantly higher fasting concentration of triglycerides in chylomicrons and largest very-low-density lipoprotein particles [XXL-VLDL-TG, difference 0.026 (95% CI: 0.004 to 0.049), P = 0.024], and of triglycerides in small high-density lipoprotein particles [S-HDL-TG, 0.026 (95% CI: 0.002 to 0.051), P = 0.037]. The seemingly important role of triglycerides was further supported by principal component analysis in which the first component was characterized by multiple lipoprotein triglyceride measures.
Conclusions
Young adults born at VLBW and their peers born at term had triglyceride-related differences in both VLDL and HDL subclasses. These differences suggest that the increased risk factors for cardiovascular diseases among the VLBW individuals in adulthood may partly relate to impaired triglyceride metabolism.
doi:10.1186/1476-511X-12-57
PMCID: PMC3661387  PMID: 23631373
Very low birth weight; Prematurity; Lipoprotein; Lipids; Subclass
15.  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.
doi:10.1038/ng.1073
PMCID: PMC3605033  PMID: 22286219
16.  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.
doi:10.1038/ng.970
PMCID: PMC3482372  PMID: 22001757
17.  Genome-Wide Screen for Metabolic Syndrome Susceptibility Loci Reveals Strong Lipid Gene Contribution but No Evidence for Common Genetic Basis for Clustering of Metabolic Syndrome Traits 
Background
Genome-wide association (GWA) studies have identified several susceptibility loci for metabolic syndrome (MetS) component traits, but have had variable success in identifying susceptibility loci to the syndrome as an entity. We conducted a GWA study on MetS and its component traits in four Finnish cohorts consisting of 2637 MetS cases and 7927 controls, both free of diabetes, and followed the top loci in an independent sample with transcriptome and NMR-based metabonomics data. Furthermore, we tested for loci associated with multiple MetS component traits using factor analysis and built a genetic risk score for MetS.
Methods and Results
A previously known lipid locus, APOA1/C3/A4/A5 gene cluster region (SNP rs964184), was associated with MetS in all four study samples (P=7.23×10−9 in meta-analysis). The association was further supported by serum metabolite analysis, where rs964184 associated with various VLDL, TG, and HDL metabolites (P=0.024-1.88×10−5). Twenty-two previously identified susceptibility loci for individual MetS component traits were replicated in our GWA and factor analysis. Most of these associated with lipid phenotypes and none with two or more uncorrelated MetS components. A genetic risk score, calculated as the number of alleles in loci associated with individual MetS traits, was strongly associated with MetS status.
Conclusions
Our findings suggest that genes from lipid metabolism pathways have the key role in the genetic background of MetS. We found little evidence for pleiotropy linking dyslipidemia and obesity to the other MetS component traits such as hypertension and glucose intolerance.
doi:10.1161/CIRCGENETICS.111.961482
PMCID: PMC3378651  PMID: 22399527
metabolic syndrome; risk factors; genome-wide association study; meta-analysis; lipids
18.  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.
doi:10.1371/journal.pgen.1002907
PMCID: PMC3420921  PMID: 22916037
19.  Evidence of mechanism how rs7575840 influences apolipoprotein B containing lipid particles 
Objective
Recent genome-wide association studies (GWAS) identified a variant rs7575840 in the apolipoprotein B (APOB) gene region to be associated with LDL-C. However, the underlying functional mechanism of this variant that resides 6.5 kb upstream of APOB has remained unknown. Our objective was to investigate rs7575840 for association with refined apoB containing lipid particles; for replication in a non-Caucasian Mexican population; and for underlying functional mechanism.
Methods and Results
Our data show that rs7575840 is associated with serum apoB levels (P=4.85×10−10) and apoB containing lipid particles, very small VLDL, IDL and LDL particles (P=2×10−5 - 9×10−7) in the Finnish METSIM study sample (n=7,710). Fine mapping of the APOB region using 43 SNPs replicated the association of rs7575840 with apoB in a Mexican study sample (n=2,666, P=3.33×10−05). Furthermore, our transcript analyses of adipose RNA samples from 175 Finnish METSIM subjects indicate that rs7575840 alters expression of APOB (P=1.13×10−10) and a regional non-coding RNA (BU630349) (P=7.86×10−6) in adipose tissue.
Conclusions
It has been difficult to convert GWAS associations into mechanistic insights. Our data show that rs7575840 is associated with serum apoB levels and apoB containing lipid particles as well as influences expression of APOB and a regional transcript BU630349 in adipose tissue. We thus provide evidence how a common genome-wide significant SNP rs7575840 may affect serum apoB, LDL-C, and TC levels.
doi:10.1161/ATVBAHA.111.224139
PMCID: PMC3081410  PMID: 21393584
Apolipoprotein B; association analysis; gene expression; adipose tissue; Mexicans
20.  Effects of 34 Risk Loci for Type 2 Diabetes or Hyperglycemia on Lipoprotein Subclasses and Their Composition in 6,580 Nondiabetic Finnish Men 
Diabetes  2011;60(5):1608-1616.
OBJECTIVE
We investigated the effects of 34 genetic risk variants for hyperglycemia/type 2 diabetes on lipoprotein subclasses and particle composition in a large population-based cohort.
RESEARCH DESIGN AND METHODS
The study included 6,580 nondiabetic Finnish men from the population-based Metabolic Syndrome in Men (METSIM) study (aged 57 ± 7 years; BMI 26.8 ± 3.7 kg/m2). Genotyping of 34 single nucleotide polymorphism (SNPs) for hyperglycemia/type 2 diabetes was performed. Proton nuclear magnetic resonance spectroscopy was used to measure particle concentrations of 14 lipoprotein subclasses and their composition in native serum samples.
RESULTS
The glucose-increasing allele of rs780094 in GCKR was significantly associated with low concentrations of VLDL particles (independently of their size) and small LDL and was nominally associated with low concentrations of intermediate-density lipoprotein, all LDL subclasses, and high concentrations of very large and large HDL particles. The glucose-increasing allele of rs174550 in FADS1 was significantly associated with high concentrations of very large and large HDL particles and nominally associated with low concentrations of all VLDL particles. SNPs rs10923931 in NOTCH2 and rs757210 in HNF1B genes showed nominal or significant associations with several lipoprotein traits. The genetic risk score of 34 SNPs was not associated with any of the lipoprotein subclasses.
CONCLUSIONS
Four of the 34 risk loci for type 2 diabetes or hyperglycemia (GCKR, FADS1, NOTCH2, and HNF1B) were significantly associated with lipoprotein traits. A GCKR variant predominantly affected the concentration of VLDL, and the FADS1 variant affected very large and large HDL particles. Only a limited number of risk loci for hyperglycemia/type 2 diabetes significantly affect lipoprotein metabolism.
doi:10.2337/db10-1655
PMCID: PMC3292337  PMID: 21421807
22.  A Genome-Wide Screen for Interactions Reveals a New Locus on 4p15 Modifying the Effect of Waist-to-Hip Ratio on Total Cholesterol 
Surakka, Ida | Isaacs, Aaron | Karssen, Lennart C. | Laurila, Pirkka-Pekka P. | Middelberg, Rita P. S. | Tikkanen, Emmi | Ried, Janina S. | Lamina, Claudia | Mangino, Massimo | Igl, Wilmar | Hottenga, Jouke-Jan | Lagou, Vasiliki | van der Harst, Pim | Mateo Leach, Irene | Esko, Tõnu | Kutalik, Zoltán | Wainwright, Nicholas W. | Struchalin, Maksim V. | Sarin, Antti-Pekka | Kangas, Antti J. | Viikari, Jorma S. | Perola, Markus | Rantanen, Taina | Petersen, Ann-Kristin | Soininen, Pasi | Johansson, Åsa | Soranzo, Nicole | Heath, Andrew C. | Papamarkou, Theodore | Prokopenko, Inga | Tönjes, Anke | Kronenberg, Florian | Döring, Angela | Rivadeneira, Fernando | Montgomery, Grant W. | Whitfield, John B. | Kähönen, Mika | Lehtimäki, Terho | Freimer, Nelson B. | Willemsen, Gonneke | de Geus, Eco J. C. | Palotie, Aarno | Sandhu, Manj S. | Waterworth, Dawn M. | Metspalu, Andres | Stumvoll, Michael | Uitterlinden, André G. | Jula, Antti | Navis, Gerjan | Wijmenga, Cisca | Wolffenbuttel, Bruce H. R. | Taskinen, Marja-Riitta | Ala-Korpela, Mika | Kaprio, Jaakko | Kyvik, Kirsten O. | Boomsma, Dorret I. | Pedersen, Nancy L. | Gyllensten, Ulf | Wilson, James F. | Rudan, Igor | Campbell, Harry | Pramstaller, Peter P. | Spector, Tim D. | Witteman, Jacqueline C. M. | Eriksson, Johan G. | Salomaa, Veikko | Oostra, Ben A. | Raitakari, Olli T. | Wichmann, H.-Erich | Gieger, Christian | Järvelin, Marjo-Riitta | Martin, Nicholas G. | Hofman, Albert | McCarthy, Mark I. | Peltonen, Leena | van Duijn, Cornelia M. | Aulchenko, Yurii S. | Ripatti, Samuli
PLoS Genetics  2011;7(10):e1002333.
Recent genome-wide association (GWA) studies described 95 loci controlling serum lipid levels. These common variants explain ∼25% of the heritability of the phenotypes. To date, no unbiased screen for gene–environment interactions for circulating lipids has been reported. We screened for variants that modify the relationship between known epidemiological risk factors and circulating lipid levels in a meta-analysis of genome-wide association (GWA) data from 18 population-based cohorts with European ancestry (maximum N = 32,225). We collected 8 further cohorts (N = 17,102) for replication, and rs6448771 on 4p15 demonstrated genome-wide significant interaction with waist-to-hip-ratio (WHR) on total cholesterol (TC) with a combined P-value of 4.79×10−9. There were two potential candidate genes in the region, PCDH7 and CCKAR, with differential expression levels for rs6448771 genotypes in adipose tissue. The effect of WHR on TC was strongest for individuals carrying two copies of G allele, for whom a one standard deviation (sd) difference in WHR corresponds to 0.19 sd difference in TC concentration, while for A allele homozygous the difference was 0.12 sd. Our findings may open up possibilities for targeted intervention strategies for people characterized by specific genomic profiles. However, more refined measures of both body-fat distribution and metabolic measures are needed to understand how their joint dynamics are modified by the newly found locus.
Author Summary
Circulating serum lipids contribute greatly to the global health by affecting the risk for cardiovascular diseases. Serum lipid levels are partly inherited, and already 95 loci affecting high- and low-density lipoprotein cholesterol, total cholesterol, and triglycerides have been found. Serum lipids are also known to be affected by multiple epidemiological risk factors like body composition, lifestyle, and sex. It has been hypothesized that there are loci modifying the effects between risk factors and serum lipids, but to date only candidate gene studies for interactions have been reported. We conducted a genome-wide screen with meta-analysis approach to identify loci having interactions with epidemiological risk factors on serum lipids with over 30,000 population-based samples. When combining results from our initial datasets and 8 additional replication cohorts (maximum N = 17,102), we found a genome-wide significant locus in chromosome 4p15 with a joint P-value of 4.79×10−9 modifying the effect of waist-to-hip ratio on total cholesterol. In the area surrounding this genetic variant, there were two genes having association between the genotypes and the gene expression in adipose tissue, and we also found enrichment of association in genes belonging to lipid metabolism related functions.
doi:10.1371/journal.pgen.1002333
PMCID: PMC3197672  PMID: 22028671
23.  A Differential Network Approach to Exploring Differences between Biological States: An Application to Prediabetes 
PLoS ONE  2011;6(9):e24702.
Background
Variations in the pattern of molecular associations are observed during disease development. The comprehensive analysis of molecular association patterns and their changes in relation to different physiological conditions can yield insight into the biological basis of disease-specific phenotype variation.
Methodology
Here, we introduce a formal statistical method for the differential analysis of molecular associations via network representation. We illustrate our approach with extensive data on lipoprotein subclasses measured by NMR spectroscopy in 4,406 individuals with normal fasting glucose, and 531 subjects with impaired fasting glucose (prediabetes). We estimate the pair-wise association between measures using shrinkage estimates of partial correlations and build the differential network based on this measure of association. We explore the topological properties of the inferred network to gain insight into important metabolic differences between individuals with normal fasting glucose and prediabetes.
Conclusions/Significance
Differential networks provide new insights characterizing differences in biological states. Based on conventional statistical methods, few differences in concentration levels of lipoprotein subclasses were found between individuals with normal fasting glucose and individuals with prediabetes. By performing the differential analysis of networks, several characteristic changes in lipoprotein metabolism known to be related to diabetic dyslipidemias were identified. The results demonstrate the applicability of the new approach to identify key molecular changes inaccessible to standard approaches.
doi:10.1371/journal.pone.0024702
PMCID: PMC3181317  PMID: 21980352
24.  Sphingomyelin is associated with kidney disease in type 1 diabetes (The FinnDiane Study) 
Metabolomics  2011;8(3):369-375.
Diabetic kidney disease, diagnosed by urinary albumin excretion rate (AER), is a critical symptom of chronic vascular injury in diabetes, and is associated with dyslipidemia and increased mortality. We investigated serum lipids in 326 subjects with type 1 diabetes: 56% of patients had normal AER, 17% had microalbuminuria (20 ≤ AER < 200 μg/min or 30 ≤ AER < 300 mg/24 h) and 26% had overt kidney disease (macroalbuminuria AER ≥ 200 μg/min or AER ≥ 300 mg/24 h). Lipoprotein subclass lipids and low-molecular-weight metabolites were quantified from native serum, and individual lipid species from the lipid extract of the native sample, using a proton NMR metabonomics platform. Sphingomyelin (odds ratio 2.53, P < 10−7), large VLDL cholesterol (odds ratio 2.36, P < 10−10), total triglycerides (odds ratio 1.88, P < 10−6), omega-9 and saturated fatty acids (odds ratio 1.82, P < 10−5), glucose disposal rate (odds ratio 0.44, P < 10−9), large HDL cholesterol (odds ratio 0.39, P < 10−9) and glomerular filtration rate (odds ratio 0.19, P < 10−10) were associated with kidney disease. No associations were found for polyunsaturated fatty acids or phospholipids. Sphingomyelin was a significant regressor of urinary albumin (P < 0.0001) in multivariate analysis with kidney function, glycemic control, body mass, blood pressure, triglycerides and HDL cholesterol. Kidney injury, sphingolipids and excess fatty acids have been linked in animal models—our exploratory approach provides independent support for this relationship in human patients with diabetes.
Electronic supplementary material
The online version of this article (doi:10.1007/s11306-011-0343-y) contains supplementary material, which is available to authorized users.
doi:10.1007/s11306-011-0343-y
PMCID: PMC3351624  PMID: 22661917
Diabetic nephropathy; NMR metabonomics; Fatty acids; Sphingolipids; Phospholipids; Lipoprotein subclasses
25.  Clinical and Epidemiological Metabonomics 
doi:10.1155/2011/843150
PMCID: PMC3142885  PMID: 21804760

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