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Aims: To evaluate associations of metabolic profiles and biomarkers with brain atrophy, lesions, and iron deposition to understand the early risk factors associated with dementia. Materials and methods Using data from 26 239 UK Biobank participants free from dementia and stroke, we assessed the associations of metabolic subgroups, derived using an artificial neural network approach (self-organizing map), and 39 individual biomarkers with brain MRI measures: total brain volume (TBV), grey matter volume (GMV), white matter volume (WMV), hippocampal volume (HV), white matter hyperintensity (WMH) volume, and caudate iron deposition. Results In metabolic subgroup analyses, participants characterized by high triglycerides and liver enzymes showed the most adverse brain outcomes compared to the healthy reference subgroup with high-density lipoprotein cholesterol and low body mass index (BMI) including associations with GMV (beta(standardized) -0.20, 95% confidence interval [CI] -0.24 to -0.16), HV (beta(standardized) -0.09, 95% CI -0.13 to -0.04), WMH volume (beta(standardized) 0.22, 95% CI 0.18 to 0.26), and caudate iron deposition (beta(standardized) 0.30, 95% CI 0.25 to 0.34), with similar adverse associations for the subgroup with high BMI, C-reactive protein and cystatin C, and the subgroup with high blood pressure (BP) and apolipoprotein B. Among the biomarkers, striking associations were seen between basal metabolic rate (BMR) and caudate iron deposition (beta(standardized) 0.23, 95% CI 0.22 to 0.24 per 1 SD increase), GMV (beta(standardized) -0.15, 95% CI -0.16 to -0.14) and HV (beta(standardized) -0.11, 95% CI -0.12 to -0.10), and between BP and WMH volume (beta(standardized) 0.13, 95% CI 0.12 to 0.14 for diastolic BP). Conclusions Metabolic profiles were associated differentially with brain neuroimaging characteristics. Associations of BMR, BP and other individual biomarkers may provide insights into actionable mechanisms driving these brain associations. Refereed/Peer-reviewed

Refereed/Peer-reviewed Background and aims: Analyses to predict the risk of cancer typically focus on single biomarkers, which do not capture their complex interrelations. We hypothesized that the use of metabolic profiles may provide new insights into cancer prediction. Methods: We used information from 290,888 UK Biobank participants aged 37 to 73 years at baseline. Metabolic subgroups were defined based on clustering of biochemical data using an artificial neural network approach and examined for their association with incident cancers identified through linkage to cancer registry. In addition, we evaluated associations between 38 individual biomarkers and cancer risk. Results: In total, 21,973 individuals developed cancer during the follow-up (median 3.87 years, interquartile range [IQR] = 2.03–5.58). Compared to the metabolically favorable subgroup (IV), subgroup III (defined as “high BMI, C-reactive protein & cystatin C") was associated with a higher risk of obesity-related cancers (hazard ratio [HR] = 1.26, 95 % CI = 1.21 to 1.32) and hematologic-malignancies (e.g., lymphoid leukemia: HR = 1.83, 95%CI = 1.44 to 2.33). Subgroup II (“high triglycerides & liver enzymes”) was strongly associated with liver cancer risk (HR = 5.70, 95%CI = 3.57 to 9.11). Analysis of individual biomarkers showed a positive association between testosterone and greater risks of hormone-sensitive cancers (HR per SD higher = 1.32, 95%CI = 1.23 to 1.44), and liver cancer (HR = 2.49, 95%CI =1.47 to 4.24). Many liver tests were individually associated with a greater risk of liver cancer with the strongest association observed for gamma-glutamyl transferase (HR = 2.40, 95%CI = 2.19 to 2.65). Conclusions: Metabolic profile in middle-to-older age can predict cancer incidence, in particular risk of obesity-related cancer, hematologic malignancies, and liver cancer. Elevated values from liver tests are strong predictors for later risk of liver cancer.

A systematic comparison is presented for the effects of seven different normalization schemes in quantitative urinary metabolomics. Morning spot urine samples were analyzed with nuclear magnetic resonance (NMR) spectroscopy from a population-based group of 994 individuals. Forty-four metabolites were quantified and the metabolite-metabolite associations and the associations of metabolite concentrations with two representative clinical measures, body mass index and mean arterial pressure, were analyzed. Distinct differences were observed when comparing the effects of normalization for the intra-urine metabolite associations with those for the clinical associations. The metabolite-metabolite associations show quite complex patterns of similarities and dissimilarities between the different normalization methods, while the epidemiological association patterns are consistent, leading to the same overall biological interpretations. The results indicate that, in general, the normalization method appears to have only minor influences on standard epidemiological regression analyses with clinical/physiological measures. Multimetabolite normalization schemes showed consistent results with the customary creatinine reference. Nevertheless, interpretations of intra-urine metabolite associations and nuanced understanding of the epidemiological associations call for comparisons with different normalizations and accounting for the physiology, metabolism and kidney function related to the normalization schemes.

We assigned 329,908 UK Biobank participants into six subgroups based on a self-organizing map of 51 biochemical measures (blinded for clinical outcomes). The subgroup with the most favorable metabolic traits was chosen as the reference. Hazard ratios (HR) for incident disease were modeled by Cox regression. Enrichment ratios (ER) of incident multi-morbidity versus randomly expected co-occurrence were evaluated by permutation tests; ER is like HR but captures co-occurrence rather than event frequency. The subgroup with high urinary excretion without kidney stress (HR = 1.24) and the subgroup with the highest apolipoprotein B and blood pressure (HR = 1.52) were associated with ischemic heart disease (IHD). The subgroup with kidney stress, high adiposity and inflammation was associated with IHD (HR = 2.11), cancer (HR = 1.29), dementia (HR = 1.70) and mortality (HR = 2.12). The subgroup with high liver enzymes and triglycerides was at risk of diabetes (HR = 15.6). Multimorbidity was enriched in metabolically favorable subgroups (3.4 ≤ ER ≤ 4.0) despite lower disease burden overall; the relative risk of co-occurring disease was higher in the absence of obvious metabolic dysfunction. These results provide synergistic insight into metabolic health and its associations with cardiovascular disease in a large population sample.

Acute lymphoblastic leukemia (ALL) is the most common childhood cancer and comprises multiple genetically distinguishable subtypes. To detect subtypes, current pipelines include fusion calling, polymorphisms, candidate gene copy numbers and cytogenetics but these approaches have limitations. RNA-seq provides a functional genome-wide snapshot that enables classification of ALL subtypes, however, typical mRNA-seq clustering analyses lack the rigor of quantitative modelling. Furthermore, high-dimensional gene expression data across cohorts and countries contain biases that previous transcriptomics studies have not addressed. Our aim was to integrate easy-to-interpret reliable transcriptome-wide biomarkers into subtyping pipelines. We analyzed 2,046 samples from two continents, carefully adjusted for biases and applied a rigorous machine learning design with independent replication. Six ALL subtypes that covered 32% of patients were robustly detected by mRNA-seq (PPV ≥ 87%). Five other frequent subtypes were distinguishable in 40% of patients, although overlapping transcriptional profiles led to lower accuracy (52% ≤ PPV ≤ 73%). Based on these findings, we developed the Allspice tool that predicts ALL subtypes and driver genes from unadjusted mRNA-seq read counts as encountered in real-world settings. Allspice also includes quantitative classification and safety metrics to help determine the most plausible genetic drivers for cases where other findings are inconclusive.

Analysis of data from the UK Biobank considering multiple hormone-sensitive cancers as a single disease suggests substantial SNP-based heritability, identifying 55 variants with genome-wide significance. Hormone-related cancers, including cancers of the breast, prostate, ovaries, uterine, and thyroid, globally contribute to the majority of cancer incidence. We hypothesize that hormone-sensitive cancers share common genetic risk factors that have rarely been investigated by previous genomic studies of site-specific cancers. Here, we show that considering hormone-sensitive cancers as a single disease in the UK Biobank reveals shared genetic aetiology. We observe that a significant proportion of variance in disease liability is explained by the genome-wide single nucleotide polymorphisms (SNPs), i.e., SNP-based heritability on the liability scale is estimated as 10.06% (SE 0.70%). Moreover, we find 55 genome-wide significant SNPs for the disease, using a genome-wide association study. Pair-wise analysis also estimates positive genetic correlations between some pairs of hormone-sensitive cancers although they are not statistically significant. Our finding suggests that heritable genetic factors may be a key driver in the mechanism of carcinogenesis shared by hormone-sensitive cancers. usc Refereed/Peer-reviewed

Large-scale epidemiological and population data provide opportunities to identify subgroups of people who are at risk of disease or exposed to adverse environments. Clustering algorithms are popular data-driven tools to identify these subgroups; however, relying exclusively on algorithms may not produce the best results if the dataset does not have a clustered structure. For this reason, we propose a framework (the R-library Numero) that combines the self-organizing map algorithm, permutation analysis for statistical evidence and a final expert-driven subgrouping step. We used Numero to define subgroups in two examples without an obvious clustering structure: a biomedical dataset of kidney disease and another dataset of community-level socioeconomic indicators. We benchmarked the Numero subgroupings against popular clustering algorithms (principal components, K-means and hierarchical clustering). The Numero subgroupings were more intuitive and easier to interpret without losing mathematical quality. Therefore, we expect Numero to be useful for exploratory analyses of population-based epidemiological datasets.

Aims/hypothesis Previously, we proposed that data-driven metabolic subtypes predict mortality in type 1 diabetes. Here, we analysed new clinical endpoints and revisited the subtypes after 7 years of additional follow-up. Methods Finnish individuals with type 1 diabetes (2059 men and 1924 women, insulin treatment before 35 years of age) were recruited by the national multicentre FinnDiane Study Group. The participants were assigned one of six metabolic subtypes according to a previously published self-organising map from 2008. Subtype-specific all-cause and cardiovascular mortality rates in the FinnDiane cohort were compared with registry data from the entire Finnish population. The rates of incident diabetic kidney disease and cardiovascular endpoints were estimated based on hospital records. Results The advanced kidney disease subtype was associated with the highest incidence of kidney disease progression (67.5% per decade, p

Introduction Meta-analysis is the cornerstone of robust biomedical evidence. Objectives We investigated whether statistical reporting practices facilitate metabolomics meta-analyses. Methods A literature review of 44 studies that used a comparable platform. Results Non-numeric formats were used in 31 studies. In half of the studies, less than a third of all measures were reported. Unadjusted P-values were missing from 12 studies and exact P-values from 9 studies. Conclusion Reporting practices can be improved. We recommend (i) publishing all results as numbers, (ii) reporting effect sizes of all measured metabolites and (iii) always reporting unadjusted exact P-values.

Context and objective. Lipoproteins are involved in the pathophysiology of several metabolic diseases. Here we focus on the interplay between lipoprotein metabolism and adiponectin with the extension of alcohol intake. Design and subjects. Eighty-three low-to-moderate and 80 heavy alcohol drinkers were studied. Plasma adiponectin, other biochemical and extensive lipoprotein data were measured. Self-organizing maps were applied to characterize lipoprotein phenotypes and their interrelationships with biochemical measures and alcohol consumption. Results. Alcohol consumption and plasma adiponectin had a strong positive association. Heavy alcohol consumption was associated with decreased low-density lipoprotein cholesterol (LDL-C). Nevertheless, two distinct lipoprotein phenotypes were identified, one with elevated high-density lipoprotein cholesterol (HDL-C) and decreased very-low-density lipoprotein triglycerides (VLDL-TG) together with low prevalence of metabolic syndrome, and the other vice versa. The HDL particles were enlarged in both phenotypes related to the heavy drinkers. The low-to-moderate alcohol drinkers were characterized with high LDL-C and C-enriched LDL particles. Conclusions. The analyses per se illustrated the multi-faceted and non-linear nature of lipoprotein metabolism. The heavy alcohol drinkers were characterized either by an anti-atherogenic lipoprotein phenotype (with also the highest adiponectin concentrations) or by a phenotype with pro-atherogenic and metabolic syndrome-like features. Clinically this underlines the need to distinguish the differing individual risk for lipid-related metabolic disturbances also in heavy alcohol drinkers.

Digital footprints, the automatically accumulated by-products of our technology-saturated lives, offer an exciting opportunity for psychiatric research. The commercial sector has already embraced the electronic trails of customers as an enabling tool for guiding consumer behaviour, and analogous efforts are ongoing to monitor and improve the mental health of psychiatric patients. The untargeted collection of digital footprints that may or may not be health orientated comprises a large untapped information resource for epidemiological scale research into psychiatric disorders. Real-time monitoring of mood, sleep and physical and social activity in a substantial portion of the affected population in a naturalistic setting is unprecedented in psychiatry. We propose that digital footprints can provide these measurements from real world setting unobtrusively and in a longitudinal fashion. In this perspective article, we outline the concept of digital footprints and the services and devices that create them, and present examples where digital footprints have been successfully used in research. We then critically discuss the opportunities and fundamental challenges associated digital footprints in psychiatric research, such as collecting data from different sources, analysis, ethical and research design challenges.

Late-onset Alzheimer’s disease is the most common dementia type, yet no treatment exists to stop the neurodegeneration. Evidence from monogenic lysosomal diseases, neuronal pathology and experimental models suggest that autophagic and endolysosomal dysfunction may contribute to neurodegeneration by disrupting the degradation of potentially neurotoxic molecules such as amyloid-β and tau. However, it is uncertain how well the evidence from rare disorders and experimental models capture causal processes in common forms of dementia, including late-onset Alzheimer’s disease. For this reason, we set out to investigate if autophagic and endolysosomal genes were enriched for genetic variants that convey increased risk of Alzheimer’s disease; such a finding would provide population-based support for the endolysosomal hypothesis of neurodegeneration. We quantified the collective genetic associations between the endolysosomal system and Alzheimer’s disease in three genome-wide associations studies (combined n = 62 415). We used the Mergeomics pathway enrichment algorithm that incorporates permutations of the full hierarchical cascade of SNP-gene-pathway to estimate enrichment. We used a previously published collection of 891 autophagic and endolysosomal genes (denoted as AphagEndoLyso, and derived from the Lysoplex sequencing platform) as a proxy for cellular processes related to autophagy, endocytosis and lysosomal function. We also investigated a subset of 142 genes of the 891 that have been implicated in Mendelian diseases (MenDisLyso). We found that both gene sets were enriched for genetic Alzheimer’s associations: an enrichment score 3.67 standard deviations from the null model (P = 0.00012) was detected for AphagEndoLyso, and a score 3.36 standard deviations from the null model (P = 0.00039) was detected for MenDisLyso. The high enrichment score was specific to the AphagEndoLyso gene set (stronger than 99.7% of other tested pathways) and to Alzheimer’s disease (stronger than all other tested diseases). The APOE locus explained most of the MenDisLyso signal (1.16 standard deviations after APOE removal, P = 0.12), but the AphagEndoLyso signal was less affected (3.35 standard deviations after APOE removal, P = 0.00040). Additional sensitivity analyses further indicated that the AphagEndoLyso Gene Set contained an aggregate genetic association that comprised a combination of subtle genetic signals in multiple genes. We also observed an enrichment of Parkinson’s disease signals for MenDisLyso (3.25 standard deviations) and for AphagEndoLyso (3.95 standard deviations from the null model), and a brain-specific pattern of gene expression for AphagEndoLyso in the Gene Tissue Expression Project dataset. These results provide evidence that a diffuse aggregation of genetic perturbations to the autophagy and endolysosomal system may mediate late-onset Alzheimer’s risk in human populations.

AIMS/HYPOTHESIS: An abnormal urinary albumin excretion rate (AER) is often the first clinically detectable manifestation of diabetic nephropathy. Our aim was to estimate the heritability and to detect genetic variation associated with elevated AER in patients with type 1 diabetes.METHODS: The discovery phase genome-wide association study (GWAS) included 1,925 patients with type 1 diabetes and with data on 24 h AER. AER was analysed as a continuous trait and the analysis was stratified by the use of antihypertensive medication. Signals with a p value RESULTS: The narrow-sense heritability, captured with our genotyping platform, was estimated to explain 27.3% of the total AER variability, and 37.6% after adjustment for covariates. In the discovery stage, five single nucleotide polymorphisms in the GLRA3 gene were strongly associated with albuminuria (p CONCLUSIONS/INTERPRETATION: This study suggests novel pathways and molecular mechanisms for the pathogenesis of albuminuria in type 1 diabetes.

Sex and genetic variation influence the risk of developing diabetic nephropathy and ESRD in patients with type 1 diabetes. We performed a genome-wide association study in a cohort of 3652 patients from the Finnish Diabetic Nephropathy (FinnDiane) Study with type 1 diabetes to determine whether sex-specific genetic risk factors for ESRD exist. A common variant, rs4972593 on chromosome 2q31.1, was associated with ESRD in women (P5×10(-8)) but not in men (P=0.77). This association was replicated in the meta-analysis of three independent type 1 diabetes cohorts (P=0.02) and remained significant for women (P5×10(-8); odds ratio, 1.81 [95% confidence interval, 1.47 to 2.24]) upon combined meta-analysis of the discovery and replication cohorts. rs4972593 is located between the genes that code for the Sp3 transcription factor, which interacts directly with estrogen receptor α and regulates the expression of genes linked to glomerular function and the pathogenesis of nephropathy, and the CDCA7 transcription factor, which regulates cell proliferation. Further examination revealed potential transcription factor-binding sites within rs4972593 and predicted eight estrogen-responsive elements within 5 kb of this locus. Moreover, we found sex-specific differences in the glomerular expression levels of SP3 (P=0.004). Overall, these results suggest that rs4972593 is a sex-specific genetic variant associated with ESRD in patients with type 1 diabetes and may underlie the sex-specific protection against ESRD.

Diabetic nephropathy is the most serious complication of type 1 diabetes. There is no treatment to protect the kidneys from poorly controlled diabetes, and therefore prevention of the initial metabolic insults is currently the only effective approach to reducing the high mortality related to diabetic nephropathy. Metabolic phenotyping brings us one step closer to understanding the unique set of regulatory perturbations that predispose to kidney injury and paves the way for multiparametric risk assessment. Clinical Pharmacology & Therapeutics (2013); 94 5, 566–569. doi:10.1038/clpt.2013.158

Background— Long-term physical inactivity seems to cause many health problems. We studied whether persistent physical activity compared with inactivity has a global effect on serum metabolome toward reduced cardiometabolic disease risk. Methods and Results— Sixteen same-sex twin pairs (mean age, 60 years) were selected from a cohort of twin pairs on the basis of their >30-year discordance for physical activity. Persistently (≥5 years) active and inactive groups in 3 population-based cohorts (mean ages, 31–52 years) were also studied (1037 age- and sex-matched pairs). Serum metabolome was quantified by nuclear magnetic resonance spectroscopy. We used permutation analysis to estimate the significance of the multivariate effect combined across all metabolic measures; univariate effects were estimated by paired testing in twins and in matched pairs in the cohorts, and by meta-analysis over all substudies. Persistent physical activity was associated with the multivariate metabolic profile in the twins ( P =0.003), and a similar pattern was observed in all 3 population cohorts with differing mean ages. Isoleucine, α1-acid glycoprotein, and glucose were lower in the physically active than in the inactive individuals ( P P P Conclusions— The numerous differences found between persistently physically active and inactive individuals in the circulating metabolome together indicate better metabolic health in the physically active than in inactive individuals.

Objective Apolipoproteins B (apoB) and A1 (apoA1) may be better markers of atherosclerosis than serum lipids. We used computational methods to estimate apoB and apoA1 from serum total cholesterol, HDL-cholesterol and triglycerides and tested their clinical value in comparison to measured apoB and apoA1 values. Methods ApoB and apoA1 were measured with standard methods and estimated based on neural network regression models in 2166 young adult with data on carotid artery intima-media thickness (cIMT). Results Correlations between estimated and measured apoB and apoA1 were r = 0.98 and r = 0.95, respectively. ApoB/apoA1-ratio (both measured and estimated) associated with cIMT in multivariable models, and predicted cIMT at all levels of LDL-cholesterol concentration. Strong correlations between the estimated apolipoproteins and those measured from fasting samples were replicated in over 15,000 Caucasian subjects ( r = 0.93–0.96 for apoB and r = 0.91–0.92 for apoA1). Correlations with cIMT were replicated in over 2000 individuals. Estimated apoB/apoA1-ratio calculated from non-fasting lipids in over 20,000 individuals in the INTERHEART study was better than any of the cholesterol measures for estimation of the myocardial risk. Conclusions Serum cholesterol, HDL-cholesterol and triglycerides can be used to compute clinically useful estimates of apoB and apoA1. Using this methodology, estimates of apolipoproteins could be routinely added to laboratory reports to complement lipoprotein lipids in risk assessment.

Background Circulating cholesterol (C) and triglyceride (TG) levels are associated with vascular injury in type 1 diabetes (T1DM). Lipoproteins are responsible for transporting lipids, and alterations in their subclass distributions may partly explain the increased mortality in individuals with T1DM. Design and subjects A cohort of 3544 individuals with T1DM was recruited by the nationwide multicentre FinnDiane Study Group. At baseline, six very low-density lipoprotein VLDL, one intermediate-density lipoprotein IDL, three low-density lipoprotein LDL and four higher high-density lipoprotein HDL subclasses were quantified by proton nuclear magnetic resonance spectroscopy. At follow-up, the baseline data were analysed for incident micro- or macroalbuminuria (117 cases in 5.3 years), progression from microalbuminuria (63 cases in 6.1 years), progression from macroalbuminuria (109 cases in 5.9 years) and mortality (385 deaths in 9.4 years). Univariate associations were tested by age-matched cases and controls and multivariate lipoprotein profiles were analysed using the self-organizing map (SOM). Results TG and C levels in large VLDL were associated with incident albuminuria, TG and C in medium VLDL were associated with progression from microalbuminuria, and TG and C in all VLDL subclasses were associated with mortality. Large HDL-C was inversely associated with mortality. Three extreme phenotypes emerged from SOM analysis: (i) low C (

Aims Although oxygen is commonly used to treat various medical conditions, it has recently been shown to worsen vascular function (arterial stiffness) in healthy volunteers and even more in patients in whom vascular function might already be impaired. The effects of oxygen on arterial function in patients with type 1 diabetes (T1D) are unknown, although such patients display disturbed vascular function already at rest. Therefore, we tested whether short-term oxygen administration may alter the arterial function in patients with T1D. Methods We estimated arterial stiffness by augmentation index (AIx) and the pulse wave velocity equivalent (SI-DVP) in 98 patients with T1D and 49 age-and sex-matched controls at baseline and during hyperoxia by obtaining continuous noninvasive finger pressure waveforms using a recently validated method. Results AIx and SI-DVP increased in patients (P

Background Human diseases are commonly the result of multidimensional changes at molecular, cellular, and systemic levels. Recent advances in genomic technologies have enabled an outpour of omics datasets that capture these changes. However, separate analyses of these various data only provide fragmented understanding and do not capture the holistic view of disease mechanisms. To meet the urgent needs for tools that effectively integrate multiple types of omics data to derive biological insights, we have developed Mergeomics, a computational pipeline that integrates multidimensional disease association data with functional genomics and molecular networks to retrieve biological pathways, gene networks, and central regulators critical for disease development. Results To make the Mergeomics pipeline available to a wider research community, we have implemented an online, user-friendly web server (http://mergeomics.research.idre.ucla.edu/). The web server features a modular implementation of the Mergeomics pipeline with detailed tutorials. Additionally, it provides curated genomic resources including tissue-specific expression quantitative trait loci, ENCODE functional annotations, biological pathways, and molecular networks, and offers interactive visualization of analytical results. Multiple computational tools including Marker Dependency Filtering (MDF), Marker Set Enrichment Analysis (MSEA), Meta-MSEA, and Weighted Key Driver Analysis (wKDA) can be used separately or in flexible combinations. User-defined summary-level genomic association datasets (e.g., genetic, transcriptomic, epigenomic) related to a particular disease or phenotype can be uploaded and computed real-time to yield biologically interpretable results, which can be viewed online and downloaded for later use. Conclusions Our Mergeomics web server offers researchers flexible and user-friendly tools to facilitate integration of multidimensional data into holistic views of disease mechanisms in the form of tissue-specific key regulators, biological pathways, and gene networks. Electronic supplementary material The online version of this article (doi:10.1186/s12864-016-3057-8) contains supplementary material, which is available to authorized users.

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.

We hypothesised that the blunted baroreflex sensitivity (BRS) typical of type 1 diabetes is caused by a higher degree of tissue hypoxia in diabetes, and tested whether oxygen increased BRS and ventilation less, equally or more than in healthy control participants (the latter suggesting higher tissue hypoxia). In addition, we also considered the possible interference between oxygen and breathing pattern. In 96 participants with type 1 diabetes and 40 age-matched healthy controls, we measured BRS (average of six different standard methods), oxygen saturation, end-tidal carbon dioxide and ventilation changes during spontaneous and controlled breathing at 15 and six breaths/min, in normoxia and during 5 l/min oxygen administration. BRS was blunted and blood pressure higher in diabetic participants during spontaneous breathing (p

Baroreflex sensitivity (BRS) is an important prognostic index in cardiovascular diseases, however, its use is complicated by different methods difficult to compare and standardize, often providing conflicting results. We tested whether the simple ratio of RR interval to systolic blood pressure global variabilities (assessed by standard deviations) is a reliable measure of BRS, by measuring the agreement with six established methods. In addition, we tested whether high-pass filtering of data, by removing slow non-baroreflex-mediated fluctuations, could improve the agreement between different BRS methods. In 1,409 subjects, we compared 6 established methods (derived by cross-spectral and sequence analysis) and the new method, supine and in response to tilting (1,175 subjects). Data were analyzed after linear detrending, high-pass filtering at 0.025 and 0.05 Hz. Although all seven methods showed a general agreement, the new method consistently showed the lowest distance from the median of the remaining methods (0.04 ± 0.06 ms/mmHg over 2,584 files, p

Plasma lipid concentrations cannot properly account for the complex interactions prevailing in lipoprotein (patho)physiology. Sequential ultracentrifugation (UCF) is the gold standard for physical lipoprotein isolations allowing for subsequent analyses of the molecular composition of the particles. Due to labor and cost issues, however, the UCF-based isolations are usually done only for VLDL, LDL, and HDL fractions; sometimes with the addition of intermediate density lipoprotein (IDL) particles and the fractionation of HDL into HDL(2) and HDL(3) (as done here; n = 302). We demonstrate via these data, with the lipoprotein lipid concentration and composition information combined, that the self-organizing map (SOM) analysis reveals a novel data-driven in silico phenotyping of lipoprotein metabolism beyond the experimentally available classifications. The SOM-based findings are biologically consistent with several well-known metabolic characteristics and also explain some apparent contradictions. The novelty is the inherent emergence of complex lipoprotein associations; e.g., the metabolic subgrouping of the associations between plasma LDL cholesterol concentrations and the structural subtypes of LDL particles. Importantly, lipoprotein concentrations cannot pinpoint lipoprotein phenotypes. It would generally be beneficial to computationally enhance the UCF-based lipoprotein data as illustrated here. Particularly, the compositional variations within the lipoprotein particles appear to be a fundamental issue with metabolic and clinical corollaries.

We studied the impact of baseline lipid variables on the progression of renal disease in a large nationwide prospective cohort of patients with type 1 diabetes.A total of 2,304 adult patients with type 1 diabetes and available lipid profiles participating in the Finnish Diabetic Nephropathy Study (FinnDiane) were evaluated. Data on progression of renal disease were verified from medical files and patients were followed for 5.4 +/- 2.0 (mean +/- SD) years.High triacylglycerol, apolipoprotein (Apo) B, ApoA-II and HDL(3)-cholesterol concentrations predicted incident microalbuminuria. Progression to macroalbuminuria was predicted by high triacylglycerol and ApoB. When AER was entered into the model, triacylglycerol was no longer an independent predictor, but when patients with normal AER and microalbuminuria at baseline were pooled, triacylglycerol, HbA(1c), male sex and AER were all independent predictors of renal disease. High total cholesterol, LDL-cholesterol, non-HDL-cholesterol and triacylglycerol as well as low HDL-cholesterol, HDL(2)-cholesterol, ApoA-I and ApoA-II concentrations were predictive of progression to end-stage renal disease. However, when estimated GFR was entered into the model, only total cholesterol remained an independent predictor of progression.Lipid abnormalities, particularly high triacylglycerol concentrations, increase the risk of progression of renal disease.

OBJECTIVES This study aimed to identify clinical features associated with premature mortality in a large contemporary cohort of adults with type 1 diabetes. RESEARCH DESIGN AND METHODS The Finnish Diabetic Nephropathy (FinnDiane) study is a national multicenter prospective follow-up study of 4,201 adults with type 1 diabetes from 21 university and central hospitals, 33 district hospitals, and 26 primary health care centers across Finland. RESULTS During a median 7 years of follow-up, there were 291 deaths (7%), 3.6-fold (95% CI 3.2–4.0) more than that observed in the age- and sex-matched general population. Excess mortality was only observed in individuals with chronic kidney disease. Individuals with normoalbuminuria showed no excess mortality beyond the general population (standardized mortality ratio [SMR] 0.8, 95% CI 0.5–1.1), independent of the duration of diabetes. The presence of microalbuminuria, macroalbuminuria, and end-stage kidney disease was associated with 2.8, 9.2, and 18.3 times higher SMR, respectively. The increase in mortality across each stage of albuminuria was equivalent to the risk conferred by preexisting macrovascular disease. In addition, the glomerular filtration rate was independently associated with mortality, such that individuals with impaired kidney function, as well as those demonstrating hyperfiltration, had an increased risk of death. CONCLUSIONS An independent graded association was observed between the presence and severity of kidney disease and mortality in a large contemporary cohort of individuals with type 1 diabetes. These findings highlight the clinical and public health importance of chronic kidney disease and its prevention in the management of type 1 diabetes.

A three-molecular-window approach for (1)H NMR spectroscopy of serum is presented to obtain specific molecular data on lipoproteins, various low-molecular-weight metabolites, and individual lipid molecules together with their degree of (poly)(un)saturation. The multiple data were analysed with self-organising maps, illustrating the strength of the approach as a holistic metabonomics framework in solely data-driven metabolic phenotyping. We studied 180 serum samples of which 30% were related to mild cognitive impairment (MCI), a neuropsychological diagnosis with severely increased risk for Alzheimer's disease (AD). The results underline the association between MCI and the metabolic syndrome (MetS). Additionally, the low relativeamount of omega-3 fatty acids appears more indicative of MCI than low serum omega-3 or polyunsaturated fatty acid concentration as such. The analyses also feature the role of elevated glycoproteins in the risk for AD, supporting the view that coexistence of inflammation and the MetS forms a high risk condition for cognitive decline.

Aims Evidence suggests that chronic hyperglycaemia predicts not only microvascular disease but also macrovascular disease, however it is not known whether it is the glucose variability per se or the total glucose exposure that confers risk. The objective of this study was to investigate whether daily glucose variability influence blood pressure and arterial stiffness, an early sign of macrovascular disease, at baseline and during a hyperglycaemic clamp in patients with type 1 diabetes. Methods Twenty-two non-smoking male patients with type 1 diabetes without any diabetic complications, participated in the study. The patients were monitored for 72-h using a continuous glucose monitoring system. Before and during a 2-h hyperglycaemic clamp, blood pressure as well as pulse wave analysis and pulse wave velocity (PWV) were performed to assess arterial stiffness. Results No correlation was observed between mean amplitude of glycaemic excursions (MAGE) and arterial stiffness at baseline. There was a correlation between mean daily glucose and aortic PWV even after adjusting for BMI, HbA1c, and duration of diabetes in a multiple regression analysis (r = 0.48; P

Substantial evidence exists for the involvement of the renin-angiotensin system (RAS) in diabetic nephropathy. Angiotensin I converting enzyme 2 (ACE2), a new component of the RAS, has been implicated in kidney disease, hypertension and cardiac function. Based on this, the aim of the present study was to evaluate whether variations in ACE2 are associated with diabetic nephropathy.We used a cross-sectional, case-control study design to investigate 823 Finnish type 1 diabetic patients (365 with and 458 without nephropathy). Five single-nucleotide polymorphisms (SNPs) were genotyped using TaqMan technology. Haplotypes were estimated using PHASE software, and haplotype frequency differences were analysed using a chi(2)-test-based tool.None of the ACE2 polymorphisms was associated with diabetic nephropathy, and this finding was supported by the haplotype analysis. The ACE2 polymorphisms were not associated with blood pressure, BMI or HbA(1)c.In Finnish type 1 diabetic patients, ACE2 polymorphisms are not associated with diabetic nephropathy or any studied risk factor for this complication. Further studies are necessary to assess a minor effect of ACE2.

Objective— Genome-wide association studies have identified multiple genetic variants affecting the risk of coronary artery disease (CAD). However, individually these explain only a small fraction of the heritability of CAD and for most, the causal biological mechanisms remain unclear. We sought to obtain further insights into potential causal processes of CAD by integrating large-scale GWA data with expertly curated databases of core human pathways and functional networks. Approaches and Results— Using pathways (gene sets) from Reactome, we carried out a 2-stage gene set enrichment analysis strategy. From a meta-analyzed discovery cohort of 7 CAD genome-wide association study data sets (9889 cases/11 089 controls), nominally significant gene sets were tested for replication in a meta-analysis of 9 additional studies (15 502 cases/55 730 controls) from the Coronary ARtery DIsease Genome wide Replication and Meta-analysis (CARDIoGRAM) Consortium. A total of 32 of 639 Reactome pathways tested showed convincing association with CAD (replication P P NCAM1 , FYN , FURIN , etc) likely to play critical roles in the maintenance and functioning of several of the replicated pathways. Conclusions— These findings provide novel insights into how genetic variation, interpreted in the context of biological processes and functional interactions among genes, may help define the genetic architecture of CAD.

OBJECTIVE To study the ability of lipid variables to predict incident coronary artery disease (CAD) events in patients with type 1 diabetes at different stages of nephropathy. RESEARCH DESIGN AND METHODS Patients (n = 3,520) with type 1 diabetes and available lipid profiles participating in the Finnish Diabetic Nephropathy Study (FinnDiane) were included in the study. During a follow-up period of 10.2 years (8.6–12.0), 310 patients suffered an incident CAD event. RESULTS Apolipoprotein B (ApoB)/ApoA-I ratio was the strongest predictor of CAD in normoalbuminuric patients (hazard ratio 1.43 [95% CI 1.17–1.76] per one SD increase), and ApoB was the strongest in macroalbuminuric patients (1.47 [1.19–1.81]). Similar results were seen when patients were stratified by sex or glycemic control. LDL cholesterol was a poor predictor of CAD in women, normoalbuminuric patients, and patients with HbA1c below the median (8.3%, 67 mmol/L). The current recommended triglyceride cutoff of 1.7 mmol/L failed to predict CAD in normoalbuminuric patients, whereas the cohort median 0.94 mmol/L predicted incident CAD events. CONCLUSIONS In patients with type 1 diabetes, the predictive ability of the lipid variables differed substantially depending on the patient’s sex, renal status, and glycemic control. In normoalbuminuric patients, the ratios of atherogenic and antiatherogenic lipoproteins and lipids were the strongest predictors of an incident CAD event, whereas in macroalbuminuric patients, no added benefit was gained from the ratios. Current treatment recommendations may need to be revised to capture residual CAD risk in patients with type 1 diabetes.

Dietary fats have been shown to promote the translocation of bacterial endotoxins from the gut into circulation, which may induce systemic inflammation and modulate the inflammatory response of circulating immune cells. The aim of this study was to determine the effect of the postprandial milieu on inflammation and the inflammatory response of antigen presenting cells in the context of type 1 diabetes (T1D). Eleven patients with T1D and eleven nondiabetic controls were recruited as part of the FinnDiane study and given two fatty meals during 1 day. Cytokine responses in monocytes and myeloid dendritic cells (mDCs) as well as serum lipopolysaccharide activity levels, triglyceride concentrations and cytokine concentrations were measured from fasting and postprandial blood samples. Postprandially, patients with T1D and controls showed significant increases in eight inflammatory cytokines (IL-6, TNF-α, IL-1β, IFN-α, IL-10, IFN-γ, IL-12 and MIP-1β) without concomitant increase in serum LPS activity. Serum cytokine production was similar in both groups. No postprandial change was seen in the IL-6, TNF-α or IL-1β production of mDCs or monocytes. At fasting, diabetic mDCs exhibited higher LPS-induced IL-6 and IL-1β production than controls. Acute high-fat meals increase circulating cytokines but have no effect on serum lipopolysaccharide activity levels or cytokine production in circulating mDCs or monocytes. Our results suggest that postprandial increase in serum cytokine levels is neither mediated by circulating endotoxins nor the activation of circulating innate cells. The production of high-fat meal-induced inflammatory markers is most likely regulated at the tissue level.

AIMS/HYPOTHESIS: Diabetic nephropathy is a major diabetic complication, and diabetes is the leading cause of end-stage renal disease (ESRD). Family studies suggest a hereditary component for diabetic nephropathy. However, only a few genes have been associated with diabetic nephropathy or ESRD in diabetic patients. Our aim was to detect novel genetic variants associated with diabetic nephropathy and ESRD.METHODS: We exploited a novel algorithm, 'Bag of Naive Bayes', whose marker selection strategy is complementary to that of conventional genome-wide association models based on univariate association tests. The analysis was performed on a genome-wide association study of 3,464 patients with type 1 diabetes from the Finnish Diabetic Nephropathy (FinnDiane) Study and subsequently replicated with 4,263 type 1 diabetes patients from the Steno Diabetes Centre, the All Ireland-Warren 3-Genetics of Kidneys in Diabetes UK collection (UK-Republic of Ireland) and the Genetics of Kidneys in Diabetes US Study (GoKinD US).RESULTS: Five genetic loci (WNT4/ZBTB40-rs12137135, RGMA/MCTP2-rs17709344, MAPRE1P2-rs1670754, SEMA6D/SLC24A5-rs12917114 and SIK1-rs2838302) were associated with ESRD in the FinnDiane study. An association between ESRD and rs17709344, tagging the previously identified rs12437854 and located between the RGMA and MCTP2 genes, was replicated in independent case-control cohorts. rs12917114 near SEMA6D was associated with ESRD in the replication cohorts under the genotypic model (p CONCLUSIONS/INTERPRETATION: This study supports the previously identified findings on the RGMA/MCTP2 region and suggests novel susceptibility loci for ESRD. This highlights the importance of applying complementary statistical methods to detect novel genetic variants in diabetic nephropathy and, in general, in complex diseases.

Family trees have long been a valuable visual tool for geneticists in identifying clusters of inherited traits and genotypes. As more data are collected, drawing the graphs by hand becomes impractical and, for this reason, we have developed the pedigree software CraneFoot. It can process any family graph with minimal computational cost by making a pedigree transformation that enables the use of a linear node positioning algorithm. The program is designed for automated drawing to printed media and efficient visual classification of genetically interesting families from large data sets. It also incorporates a robust pedigree topology check with detailed error messages.

Objectives The aim of this study was to investigate the associations between lipid profiles and retinopathy in the large nationwide FinnDiane Study and to examine interactions and correlations between retinopathy, nephropathy and lipid variables. Design and subjects A total of 1465 patients with type 1 diabetes, available lipid profiles, ophthalmic records and fundus photographs were included in the study. The Early Treatment of Diabetic Retinopathy Study scale was used to assess the severity of retinopathy. In an independent cohort of 1100 patients, laser treatment was used to define severe diabetic retinopathy. Results HDL cholesterol was associated with proliferative retinopathy (PDR), and triglycerides were associated with mild nonproliferative retinopathy (NPDR) independently of nephropathy and other conventional risk factors (P

Diabetic kidney disease, or diabetic nephropathy (DN), is a major complication of diabetes and the leading cause of end-stage renal disease (ESRD) that requires dialysis treatment or kidney transplantation. In addition to the decrease in the quality of life, DN accounts for a large proportion of the excess mortality associated with type 1 diabetes (T1D). Whereas the degree of glycemia plays a pivotal role in DN, a subset of individuals with poorly controlled T1D do not develop DN. Furthermore, strong familial aggregation supports genetic susceptibility to DN. However, the genes and the molecular mechanisms behind the disease remain poorly understood, and current therapeutic strategies rarely result in reversal of DN. In the GEnetics of Nephropathy: an International Effort (GENIE) consortium, we have undertaken a meta-analysis of genome-wide association studies (GWAS) of T1D DN comprising ∼2.4 million single nucleotide polymorphisms (SNPs) imputed in 6,691 individuals. After additional genotyping of 41 top ranked SNPs representing 24 independent signals in 5,873 individuals, combined meta-analysis revealed association of two SNPs with ESRD: rs7583877 in the AFF3 gene (P = 1.2×10−8) and an intergenic SNP on chromosome 15q26 between the genes RGMA and MCTP2, rs12437854 (P = 2.0×10−9). Functional data suggest that AFF3 influences renal tubule fibrosis via the transforming growth factor-beta (TGF-β1) pathway. The strongest association with DN as a primary phenotype was seen for an intronic SNP in the ERBB4 gene (rs7588550, P = 2.1×10−7), a gene with type 2 diabetes DN differential expression and in the same intron as a variant with cis-eQTL expression of ERBB4. All these detected associations represent new signals in the pathogenesis of DN., Author Summary The global prevalence of diabetes has reached epidemic proportions, constituting a major health care problem worldwide. Diabetic kidney disease, or diabetic nephropathy (DN)—the major long term microvascular complication of diabetes—is associated with excess mortality among patients with type 1 diabetes. Even though DN has been shown to cluster in families, the underlying genetic and molecular pathways remain poorly defined. We have undertaken the largest genome-wide association study and meta-analysis to date on DN and on its most severe form of kidney disease, end-stage renal disease (ESRD). We identified new loci significantly associated with diabetic ESRD: AFF3 and an intergenic locus on chromosome 15q26 residing between RGMA and MCTP2. Our functional analyses suggest that AFF3 influences renal tubule fibrosis, a pathological hallmark of severe DN. Another locus in ERBB4 was suggestively associated with DN and resides in the same intronic region as a variant affecting the expression of ERBB4. Subsequent pathway analysis of the genes co-expressed with ERBB4 indicated involvement of fibrosis.

Type 1 diabetic patients with varying severity of kidney disease were investigated to create multimetabolite models of the disease process. Urinary albumin excretion rate was measured for 3358 patients with type 1 diabetes. Prospective records were available for 1051 patients, of whom 163 showed progression of albuminuria (8.3-year follow-up), and 162 were selected as stable controls. At baseline, serum lipids, lipoprotein subclasses, and low-molecular weight metabolites were quantified by NMR spectroscopy (325 samples). The data were analyzed by the self-organizing map. In cross-sectional analyses, patients with no complications had low serum lipids, less inflammation, and better glycemic control, whereas patients with advanced kidney disease had high serum cystatin-C and sphingomyelin. These phenotype extremes shared low unsaturated fatty acids (UFAs) and phospholipids. Prospectively, progressive albuminuria was associated with high UFAs, phospholipids, and IDL and LDL lipids. Progression at longer duration was associated with high HDL lipids, whereas earlier progression was associated with poor glycemic control, increased saturated fatty acids (SFAs), and inflammation. Diabetic kidney disease consists of diverse metabolic phenotypes: UFAs, phospholipids, IDL, and LDL may be important in the subclinical phase, high SFAs and low HDL suggest accelerated progression, and the sphingolipid pathway in advanced kidney injury deserves further research.

Detailed molecular phenotyping gives insight into disease mechanisms and can individualize medical practice for improved risk assessment and treatment. We show in an epidemiological study (n = 4309) that the multi-metabolic profiles obtained by serum NMR metabonomics inherently associate with the extent of atherosclerosis already in preclinical stages. Data-driven analysis of the spectral profiles of healthy, young adults revealed three distinct metabolic phenotypes associated with high carotid intima-media thickness (IMT), a surrogate marker of cardiovascular disease. The phenotypes were characterized by varying combinations of well-known metabolic disturbances like elevated VLDL and LDL and low HDL levels. Low IMT was also associated with distinct metabolic phenotypes with lipoprotein as well as other biochemical characteristics partly opposing those found for the high IMT phenotypes. Profiles of low-molecular-weight metabolites quantified from the experimentation were also characteristic for the metabolic phenotypes and substantiate developments toward the use of multi-metabolic risk phenotypes. The methodology can be taken as a direct extension for the routine analytics used for the risk assessment of atherosclerosis; quantification of metabolites will complement and might even replace conventional lipid measurements. Serum NMR metabonomics is therefore anticipated as a rational option for comprehensive cardiovascular risk assessment.

Activation of the receptor for AGE (RAGE) is implicated in the development and progression of vascular complications of diabetes. In this study, we explore factors and mortality outcomes associated with soluble RAGE (sRAGE) in a multicentre nationwide cohort of Finnish adults with type 1 diabetes. Baseline sRAGE concentrations were estimated in 3,100 adults with type 1 diabetes. Clinical and biological variables independently associated with sRAGE were identified using multivariate regression analysis. Independent predictors of mortality were determined using Cox and Fine–Gray proportional-hazards models. The main independent determinants of sRAGE concentrations were estimated glomerular filtration rate, albuminuria, body mass index, age, duration of diabetes, HbA1c and insulin dose (all p

While patients with type 1 diabetes (T1D) are known to suffer from early cardiovascular disease (CVD), we examined associations between arterial stiffness and diabetic complications in a large patient group with T1D.This study included 807 subjects (622 T1D and 185 healthy volunteers (age 40.6 ± 0.7 versus 41.6 ± 1.2 years; P = NS)). Arterial stiffness was measured by pulse wave analysis from each participant. Furthermore, information on diabetic retinopathy, nephropathy, and CVD was collected. The renal status was verified from at least two out of three urine collections.Patients with T1D without signs of diabetic nephropathy had stiffer arteries measured as the augmentation index (AIx) than age-matched control subjects (17.3% ± 0.6% versus 10.0% ± 1.2%; P0.001). Moreover, AIx (OR 1.08; 95% CI 1.03-1.13; P = 0.002) was associated with diabetic laser-treated retinopathy in patients with normoalbuminuria in a multivariate logistic regression analysis. The same was true for AIx and diabetic nephropathy (1.04 (1.01-1.08); P = 0.004) as well as AIx and CVD (1.06 (1.00-1.12); P = 0.01) in patients with T1D.Arterial stiffness was associated with microvascular and macrovascular complications in patients with T1D.

Background Cardiovascular disease is the main cause of premature death in patients with type 1 diabetes. Patients with diabetic kidney disease have an increased risk of heart attack or stroke. Accurate knowledge of the complex inter-dependencies between the risk factors is critical for pinpointing the best targets for research and treatment. Therefore, the aim of this study was to describe the association patterns between clinical and biochemical features of diabetic complications. Methods Medical records and serum and urine samples of 4,197 patients with type 1 diabetes were collected from health care centers in Finland. At baseline, the mean diabetes duration was 22 years, 52% were male, 23% had kidney disease (urine albumin excretion over 300 mg/24 h or end-stage renal disease) and 8% had a history of macrovascular events. All-cause mortality was evaluated after an average of 6.5 years of follow-up (25,714 patient years). The dataset comprised 28 clinical and 25 biochemical variables that were regarded as the nodes of a network to assess their mutual relationships. Results The networks contained cliques that were densely inter-connected (r > 0.6), including cliques for high-density lipoprotein (HDL) markers, for triglycerides and cholesterol, for urinary excretion and for indices of body mass. The links between the cliques showed biologically relevant interactions: an inverse relationship between HDL cholesterol and the triglyceride clique (r < -0.3, P < 10-16), a connection between triglycerides and body mass via C-reactive protein (r > 0.3, P < 10-16) and intermediate-density cholesterol as the connector between lipoprotein metabolism and albuminuria (r > 0.3, P < 10-16). Aging and macrovascular disease were linked to death via working ability and retinopathy. Diabetic kidney disease, serum creatinine and potassium, retinopathy and blood pressure were inter-connected. Blood pressure correlations indicated accelerated vascular aging in individuals with kidney disease (P < 0.001). Conclusion The complex pattern of links between diverse characteristics and the lack of a single dominant factor suggests a need for multifactorial and multidisciplinary paradigms for the research, treatment and prevention of diabetic complications.

There is an unmet need for a straightforward and cost-effective assessment of multiple lipoprotein risk factors for vascular diseases.1) To study the relation of various lipoprotein lipid and apolipoprotein (apo) measures on the Friedewald inputs, i.e. plasma triglycerides (TG), cholesterol (TC), and high-density lipoprotein cholesterol (HDL-C). 2) To build up regression models for the appropriate measures based solely on the Friedewald inputs.Data were available for 1,775 plasma samples, from which very-low-density lipoprotein (VLDL), intermediate-density lipoprotein (IDL), low-density lipoprotein (LDL), and HDL were also isolated by ultracentrifugation. For HDL(2)-C and apolipoproteins, 343 and 247 samples were available, respectively.Accurate models were obtained for VLDL-TG (cross-validation r=0.98), LDL-C (r=0.91), HDL(2)-C (r=0.92), apoA-I (r=0.92), and apoB (r=0.95). A semi-quantitative model was obtained for IDL-C (r=0.78). Due to the anticipated role of IDL-C in atherosclerosis, it was still kept within the accepted models and pursued further. The associations of the estimates with premature deaths were studied in 4,084 patients with type 1 diabetes. The associations of IDL-C and LDL-C were markedly different, the best predictors of mortality being apoB, apoB to apoA-I ratio, and IDL-C.The new models allow identification of clinically relevant lipoprotein profiles with no added cost to the conventional Friedewald formula.

OBJECTIVE—Poor glycemic control, elevated triglycerides, and albuminuria are associated with vascular complications in diabetes. However, few studies have investigated combined associations between metabolic markers, diabetic kidney disease, retinopathy, hypertension, obesity, and mortality. Here, the goal was to reveal previously undetected association patterns between clinical diagnoses and biochemistry in the FinnDiane dataset. RESEARCH DESIGN AND METHODS—At baseline, clinical records, serum, and 24-h urine samples of 2,173 men and 2,024 women with type 1 diabetes were collected. The data were analyzed by the self-organizing map, which is an unsupervised pattern recognition algorithm that produces a two-dimensional layout of the patients based on their multivariate biochemical profiles. At follow-up, the results were compared against all-cause mortality during 6.5 years (295 deaths). RESULTS—The highest mortality was associated with advanced kidney disease. Other risk factors included 1) a profile of insulin resistance, abdominal obesity, high cholesterol, triglycerides, and low HDL2 cholesterol, and 2) high adiponectin and high LDL cholesterol for older patients. The highest population-adjusted risk of death was 10.1-fold (95% CI 7.3–13.1) for men and 10.7-fold (7.9–13.7) for women. Nonsignificant risk was observed for a profile with good glycemic control and high HDL2 cholesterol and for a low cholesterol profile with a short diabetes duration. CONCLUSIONS—The self-organizing map analysis enabled detailed risk estimates, described the associations between known risk factors and complications, and uncovered statistical patterns difficult to detect by classical methods. The results also suggest that diabetes per se, without an adverse metabolic phenotype, does not contribute to increased mortality.

Object: The most severe complication of type 1 diabetes (T1DM) is diabetic nephropathy. It is associated with a high risk of cardiovascular complications and premature death and requires early detection to be efficiently treated. The clinical practice to diagnose diabetic nephropathy is also a non-optimal and tedious set up based on albumin excretion rate in multiple overnight or 24h urine samples. Conversely, in this study, these independent diagnostic data are used to provide a realistic testing case for applying 1H NMR metabonomics of serum in a diagnostic fashion. Materials and Methods: 182 T1DM and 21 non-diabetic (non-T1DM) individuals were studied. The 1H NMR of serum at 500 MHz was targeted at two molecular windows: lipoprotein lipids and low-molecular-weight metabolites. Results: T1DM and non-T1DM individuals were exclusively separated by 1H NMR. For diabetic nephropathy diagnosis in the T1DM patients, 1H NMR data (and clinical biochemistry data) gave a sensitivity of 87.1% (83.9%) and a specificity of 87.7% (95.9%). The predictive values of positive and negative tests were 89.0% (95.5%) and 83.6% (79.2%), respectively. Conclusions: 1H NMR metabonomics clearly distinguishes metabolic characteristics of T1DM and appears approximately as good a means to diagnose diabetic nephropathy from serum as an advanced set of biochemical variables.

Subtle metabolic changes precede and accompany chronic vascular complications, which are the primary causes of premature death in diabetes. To obtain a multimetabolite characterization of these high-risk individuals, we measured proton nuclear magnetic resonance (1H NMR) data from the serum of 613 patients with type I diabetes and a diverse spread of complications. We developed a new metabonomics framework to visualize and interpret the data and to link the metabolic profiles to the underlying diagnostic and biochemical variables. Our results indicate complex interactions between diabetic kidney disease, insulin resistance and the metabolic syndrome. We illustrate how a single 1H NMR protocol is able to identify the polydiagnostic metabolite manifold of type I diabetes and how its alterations translate to clinical phenotypes, clustering of micro- and macrovascular complications, and mortality during several years of follow-up. This work demonstrates the diffuse nature of complex vascular diseases and the limitations of single diagnostic biomarkers. However, it also promises cost-effective solutions through high-throughput analytics and advanced computational methods, as applied here in a case that is representative of the real clinical situation.

Background Pregnancy triggers well-known alterations in maternal glucose and lipid balance but its overall effects on systemic metabolism remain incompletely understood. Methods Detailed molecular profiles (87 metabolic measures and 37 cytokines) were measured for up to 4260 women (24–49 years, 322 pregnant) from three population-based cohorts in Finland. Circulating molecular concentrations in pregnant women were compared to those in non-pregnant women. Metabolic profiles were also reassessed for 583 women 6 years later to uncover the longitudinal metabolic changes in response to change in the pregnancy status. Results Compared to non-pregnant women, all lipoprotein subclasses and lipids were markedly increased in pregnant women. The most pronounced differences were observed for the intermediate-density, low-density and high-density lipoprotein triglyceride concentrations. Large differences were also seen for many fatty acids and amino acids. Pregnant women also had higher concentrations of low-grade inflammatory marker glycoprotein acetyls, higher concentrations of interleukin-18 and lower concentrations of interleukin-12p70. The changes in metabolic concentrations for women who were not pregnant at baseline but pregnant 6 years later (or vice versa) matched (or were mirror-images of) the cross-sectional association pattern. Cross-sectional results were consistent across the three cohorts and similar longitudinal changes were seen for 653 women in 4-year and 497 women in 10-year follow-up. For multiple metabolic measures, the changes increased in magnitude across the three trimesters. Conclusions Pregnancy initiates substantial metabolic and inflammatory changes in the mothers. Comprehensive characterisation of normal pregnancy is important for gaining understanding of the key nutrients for fetal growth and development. These findings also provide a valuable molecular reference in relation to studies of adverse pregnancy outcomes. Electronic supplementary material The online version of this article (doi:10.1186/s12916-016-0733-0) contains supplementary material, which is available to authorized users.

Background Complex diseases are characterized by multiple subtle perturbations to biological processes. New omics platforms can detect these perturbations, but translating the diverse molecular and statistical information into testable mechanistic hypotheses is challenging. Therefore, we set out to create a public tool that integrates these data across multiple datasets, platforms, study designs and species in order to detect the most promising targets for further mechanistic studies. Results We developed Mergeomics, a computational pipeline consisting of independent modules that 1) leverage multi-omics association data to identify biological processes that are perturbed in disease, and 2) overlay the disease-associated processes onto molecular interaction networks to pinpoint hubs as potential key regulators. Unlike existing tools that are mostly dedicated to specific data type or settings, the Mergeomics pipeline accepts and integrates datasets across platforms, data types and species. We optimized and evaluated the performance of Mergeomics using simulation and multiple independent datasets, and benchmarked the results against alternative methods. We also demonstrate the versatility of Mergeomics in two case studies that include genome-wide, epigenome-wide and transcriptome-wide datasets from human and mouse studies of total cholesterol and fasting glucose. In both cases, the Mergeomics pipeline provided statistical and contextual evidence to prioritize further investigations in the wet lab. The software implementation of Mergeomics is freely available as a Bioconductor R package. Conclusion Mergeomics is a flexible and robust computational pipeline for multidimensional data integration. It outperforms existing tools, and is easily applicable to datasets from different studies, species and omics data types for the study of complex traits. Electronic supplementary material The online version of this article (doi:10.1186/s12864-016-3198-9) contains supplementary material, which is available to authorized users.

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.

Background 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. Results 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. Conclusion 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.