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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

Context Aging varies between individuals, with profound consequences for chronic diseases and longevity. One hypothesis to explain the diversity is a genetically regulated molecular clock that runs differently between individuals. Large human studies with long enough follow-up to test the hypothesis are rare due to practical challenges, but statistical models of aging are built as proxies for the molecular clock by comparing young and old individuals cross-sectionally. These models remain untested against longitudinal data. Objective We applied novel methodology to test if cross-sectional modeling can distinguish slow vs accelerated aging in a human population. Methods We trained a machine learning model to predict age from 153 clinical and cardiometabolic traits. The model was tested against longitudinal data from another cohort. The training data came from cross-sectional surveys of the Finnish population (n = 9708; ages 25-74 years). The validation data included 3 time points across 10 years in the Young Finns Study (YFS; n = 1009; ages 24-49 years). Predicted metabolic age in 2007 was compared against observed aging rate from the 2001 visit to the 2011 visit in the YFS dataset and correlation between predicted vs observed metabolic aging was determined. Results The cross-sectional proxy failed to predict longitudinal observations (R2 = 0.018%, P = 0.67). Conclusion The finding is unexpected under the clock hypothesis that would produce a positive correlation between predicted and observed aging. Our results are better explained by a stratified model where aging rates per se are similar in adulthood but differences in starting points explain diverging metabolic fates.

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.

Background/Objective This observational study dissects the complex temporal associations between body-mass index (BMI), waist-hip ratio (WHR) and circulating metabolomics using a combination of longitudinal and cross-sectional population-based datasets and new systems epidemiology tools. Subjects/Methods Firstly, a data-driven subgrouping algorithm was employed to simplify high-dimensional metabolic profiling data into a single categorical variable: a self-organizing map (SOM) was created from 174 metabolic measures from cross-sectional surveys (FINRISK, n = 9708, ages 25–74) and a birth cohort (NFBC1966, n = 3117, age 31 at baseline, age 46 at follow-up) and an expert committee defined four subgroups of individuals based on visual inspection of the SOM. Secondly, the subgroups were compared regarding BMI and WHR trajectories in an independent longitudinal dataset: participants of the Young Finns Study (YFS, n = 1286, ages 24–39 at baseline, 10 years follow-up, three visits) were categorized into the four subgroups and subgroup-specific age-dependent trajectories of BMI, WHR and metabolic measures were modelled by linear regression. Results The four subgroups were characterised at age 39 by high BMI, WHR and dyslipidemia (designated TG-rich); low BMI, WHR and favourable lipids (TG-poor); low lipids in general (Low lipid) and high low-density-lipoprotein cholesterol (High LDL-C). Trajectory modelling of the YFS dataset revealed a dynamic BMI divergence pattern: despite overlapping starting points at age 24, the subgroups diverged in BMI, fasting insulin (three-fold difference at age 49 between TG-rich and TG-poor) and insulin-associated measures such as triglyceride-cholesterol ratio. Trajectories also revealed a WHR progression pattern: despite different starting points at the age of 24 in WHR, LDL-C and cholesterol-associated measures, all subgroups exhibited similar rates of change in these measures, i.e. WHR progression was uniform regardless of the cross-sectional metabolic profile. Conclusions Age-associated weight variation in adults between 24 and 49 manifests as temporal divergence in BMI and uniform progression of WHR across metabolic health strata.

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.

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

Background Quantification of metabolic changes over the human life course is essential to understanding ageing processes. Yet longitudinal metabolomics data are rare and long gaps between visits can introduce biases that mask true trends. We introduce new ways to process quantitative time-series population data and elucidate metabolic ageing trends in two large cohorts. Methods Eligible participants included 1672 individuals from the Cardiovascular Risk in Young Finns Study and 3117 from the Northern Finland Birth Cohort 1966. Up to three time points (ages 24–49 years) were analysed by nuclear magnetic resonance metabolomics and clinical biochemistry (236 measures). Temporal trends were quantified as median change per decade. Sample quality was verified by consistency of shared biomarkers between metabolomics and clinical assays. Batch effects between visits were mitigated by a new algorithm introduced in this report. The results below satisfy multiple testing threshold of P Results Women gained more weight than men (+6.5% vs +5.0%) but showed milder metabolic changes overall. Temporal sex differences were observed for C-reactive protein (women +5.1%, men +21.1%), glycine (women +5.2%, men +1.9%) and phenylalanine (women +0.6%, men +3.5%). In 566 individuals with ≥+3% weight gain vs 561 with weight change ≤−3%, divergent patterns were observed for insulin (+24% vs −10%), very-low-density-lipoprotein triglycerides (+32% vs −6%), high-density-lipoprotein2 cholesterol (−6.5% vs +4.7%), isoleucine (+5.7% vs −6.0%) and C-reactive protein (+25% vs −22%). Conclusion We report absolute and proportional trends for 236 metabolic measures as new reference material for overall age-associated and specific weight-driven changes in real-world populations.

Genome-wide association studies (GWASs) have implicated ∼380 genetic loci for plasma lipid regulation. However, these loci only explain 17–27% of the trait variance, and a comprehensive understanding of the molecular mechanisms has not been achieved. In this study, we utilized an integrative genomics approach leveraging diverse genomic data from human populations to investigate whether genetic variants associated with various plasma lipid traits, namely, total cholesterol, high and low density lipoprotein cholesterol (HDL and LDL), and triglycerides, from GWASs were concentrated on specific parts of tissue-specific gene regulatory networks. In addition to the expected lipid metabolism pathways, gene subnetworks involved in “interferon signaling,” “autoimmune/immune activation,” “visual transduction,” and “protein catabolism” were significantly associated with all lipid traits. In addition, we detected trait-specific subnetworks, including cadherin-associated subnetworks for LDL; glutathione metabolism for HDL; valine, leucine, and isoleucine biosynthesis for total cholesterol; and insulin signaling and complement pathways for triglyceride. Finally, by using gene-gene relations revealed by tissue-specific gene regulatory networks, we detected both known (e.g., APOH, APOA4, and ABCA1) and novel (e.g., F2 in adipose tissue) key regulator genes in these lipid-associated subnetworks. Knockdown of the F2 gene (coagulation factor II, thrombin) in 3T3-L1 and C3H10T1/2 adipocytes altered gene expression of Abcb11, Apoa5, Apof, Fabp1, Lipc, and Cd36; reduced intracellular adipocyte lipid content; and increased extracellular lipid content, supporting a link between adipose thrombin and lipid regulation. Our results shed light on the complex mechanisms underlying lipid metabolism and highlight potential novel targets for lipid regulation and lipid-associated diseases.

Genome-wide association studies (GWAS) have implicated ∼380 genetic loci for plasma lipid regulation. However, these loci only explain 17-27% of the trait variance and a comprehensive understanding of the molecular mechanisms has not been achieved. In this study, we utilized an integrative genomics approach leveraging diverse genomic data from human populations to investigate whether genetic variants associated with various plasma lipid traits, namely total cholesterol (TC), high and low density lipoprotein cholesterol (HDL and LDL), and triglycerides (TG), from GWAS were concentrated on specific parts of tissue-specific gene regulatory networks. In addition to the expected lipid metabolism pathways, gene subnetworks involved in ‘interferon signaling’, ‘autoimmune/immune activation’, ‘visual transduction’, and ‘protein catabolism’ were significantly associated with all lipid traits. Additionally, we detected trait-specific subnetworks, including cadherin-associated subnetworks for LDL, glutathione metabolism for HDL, valine, leucine and isoleucine biosynthesis for TC, and insulin signaling and complement pathways for TG. Finally, utilizing gene-gene relations revealed by tissue-specific gene regulatory networks, we detected both known (e.g. APOH, APOA4, and ABCA1) and novel (e.g. F2 in adipose tissue) key regulator genes in these lipid-associated subnetworks. Knockdown of the F2 gene (Coagulation Factor II, Thrombin) in 3T3-L1 and C3H10T1/2 adipocytes reduced gene expression of Abcb11, Apoa5, Apof, Fabp1, Lipc, and Cd36, reduced intracellular adipocyte lipid content, and increased extracellular lipid content, supporting a link between adipose thrombin and lipid regulation. Our results shed light on the complex mechanisms underlying lipid metabolism and highlight potential novel targets for lipid regulation and lipid-associated diseases.

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.

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

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 (

Aging is a complex biological process with remarkable individual variation and manifests also as changes in systemic metabolism. In a recent paper in PNAS, Chaleckis et al. (1) studied a set of blood metabolites in old and young individuals. The work was thorough in the methodological aspects regarding the mass spectrometry of circulating molecules in plasma, red blood cells, and whole blood. However, we find it alarming that the results and interpretations on the suggested age-related metabolic differences were not properly put into epidemiological context. We therefore draw attention to four key issues: ( i ) metabolic heterogeneity of … [↵][1]1To whom correspondence should be addressed. Email: ville-petteri.makinen{at}sahmri.com. [1]: #xref-corresp-1-1

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.

Mergeomics is a computational pipeline (http://mergeomics.research.idre.ucla.edu/Download/Package/) that integrates multidimensional omics-disease associations, functional genomics, canonical pathways and gene-gene interaction networks to generate mechanistic hypotheses. It first identifies biological pathways and tissue-specific gene subnetworks that are perturbed by disease-associated molecular entities. The disease-associated subnetworks are then projected onto tissue-specific gene-gene interaction networks to identify local hubs as potential key drivers of pathological perturbations. The pipeline is modular and can be applied across species and platform boundaries, and uniquely conducts pathway/network level meta-analysis of multiple genomic studies of various data types. Application of Mergeomics to cholesterol datasets revealed novel regulators of cholesterol metabolism.

Supplementary materials including detailed information on study populations, generalised data supporting our findings, and results from sensitivity analyses. (DOCX 2656 kb)

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.

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.

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

Arterial function, expressed as augmentation index (AIx) and pulse wave velocity (PWV), predicts cardiovascular disease [1–4] and can be assessed by specific validated devices that subsequently record peripheral (carotid, radial, femoral) arterial waveforms. It could have practical implications to obtain the same information from easily applicable finger blood pressure monitors, which are commonly used to test autonomic function.Methods to obtain PWV from finger photoplethysmogram (PPG, [5]), or AIx from finger blood pressure [6] have been proposed.We tested whether reliable AIx and PWV data could be obtained from 5-minute recordings of noninvasive finger blood pressure (Finapres® 2300, Ohmeda, USA), and compared them with data simultaneously obtained from applanation tonometry (SphygmoCor®, Atcor, Australia). The protocol was approved by Helsinki University Ethical Committee, and written consent was obtained from each subject. To compare the twomethods over awide range of values we studied 46 consecutive unselected subjects of different ages (31 male, age 44 ± 16 yr, range 24–90, SBP: 129 ± 21 mmHg, DBP: 74 ± 16 mmHg, BMI: 24.8 ± 4.5 kg/m), in supine position, under spontaneous breathing at rest. Two reliable measurements were obtained from 29 (AIx) and 31 (PWV) participants by applanation tonometry (AIx-at, PWV-at), and one measurement in the remaining subjects. When two values were obtained their average was used for comparison. Measurementof AIx by transfer functionoffingerbloodpressure (AIx-f). In theory, thebestmethod toderivenoninvasiveAIx is throughsimultaneous recording of peripheral noninvasive and central intra-arterial blood pressure, with subsequent evaluation of the mathematical relationship (transfer function, TF) linking these two signals together (TF1). The TF1 can then be used to reconstruct the central blood pressure profile and obtain the AIx [2–4,6,7]. This rather complex approach requires expensive high-fidelitymicro-tiparterial catheters [7], butnowthisprocedure canbe simplified since our tonometer provided both the radial artery and the reconstructed central pressure waveforms (hence also TF1). Since the recordings of the radial artery and finger blood pressures were simultaneous, it was possible to obtain the TF that links the finger pressure to the central pressure (TF2)with the samedegree of precision as for TF1. By using TF2, the AIx-f can then be calculated (on-line supplement). The obtained TF1 was almost identical in all subjects, confirming that a single TF1 was used by the Sphygmocor® to link the central and radial pressures [7] (Fig. 1). Measurement of AIx by direct analysis of finger blood pressure contour (AIx-fbpc). From the previous considerations it follows that the blood pressure profile alone provides all the necessary information to directly derive AIx. To verify this we normalised the averaged amplitude of the finger blood pressure pulses (first systolic peak= 100, lowest point = 0) and determined the location of the dicrotic notch. Then we calculated the normalised amplitude at 21 equally-spaced points from the first systolic peak to the dicrotic notch (on-line supplement). Each of the amplitudes was comparedwith the AIx-at by regression analysis. The regression equation linking the PWV-at with the point showing the best correlation was used to an AIx estimate: AIx-fbpc. Pulse wave velocity from the finger blood pressure. Millasseau [5] described and validated a simple technique to estimate the PWV from the PPG without the need of two separate recordings at two different International Journal of Cardiology 175 (2014) 169–217

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