Publications by authors named "Erik B van den Akker"

34 Publications

Cerebral small vessel disease genomics and its implications across the lifespan.

Nat Commun 2020 12 8;11(1):6285. Epub 2020 Dec 8.

University of Alabama at Birmingham School of Medicine, Birmingham, AL, 35233, USA.

White matter hyperintensities (WMH) are the most common brain-imaging feature of cerebral small vessel disease (SVD), hypertension being the main known risk factor. Here, we identify 27 genome-wide loci for WMH-volume in a cohort of 50,970 older individuals, accounting for modification/confounding by hypertension. Aggregated WMH risk variants were associated with altered white matter integrity (p = 2.5×10-7) in brain images from 1,738 young healthy adults, providing insight into the lifetime impact of SVD genetic risk. Mendelian randomization suggested causal association of increasing WMH-volume with stroke, Alzheimer-type dementia, and of increasing blood pressure (BP) with larger WMH-volume, notably also in persons without clinical hypertension. Transcriptome-wide colocalization analyses showed association of WMH-volume with expression of 39 genes, of which four encode known drug targets. Finally, we provide insight into BP-independent biological pathways underlying SVD and suggest potential for genetic stratification of high-risk individuals and for genetically-informed prioritization of drug targets for prevention trials.
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http://dx.doi.org/10.1038/s41467-020-19111-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7722866PMC
December 2020

Machine Learning Electronic Health Record Identification of Patients with Rheumatoid Arthritis: Algorithm Pipeline Development and Validation Study.

JMIR Med Inform 2020 Nov 30;8(11):e23930. Epub 2020 Nov 30.

Department of Rheumatology, Leiden University Medical Center, Leiden, Netherlands.

Background: Financial codes are often used to extract diagnoses from electronic health records. This approach is prone to false positives. Alternatively, queries are constructed, but these are highly center and language specific. A tantalizing alternative is the automatic identification of patients by employing machine learning on format-free text entries.

Objective: The aim of this study was to develop an easily implementable workflow that builds a machine learning algorithm capable of accurately identifying patients with rheumatoid arthritis from format-free text fields in electronic health records.

Methods: Two electronic health record data sets were employed: Leiden (n=3000) and Erlangen (n=4771). Using a portion of the Leiden data (n=2000), we compared 6 different machine learning methods and a naïve word-matching algorithm using 10-fold cross-validation. Performances were compared using the area under the receiver operating characteristic curve (AUROC) and the area under the precision recall curve (AUPRC), and F1 score was used as the primary criterion for selecting the best method to build a classifying algorithm. We selected the optimal threshold of positive predictive value for case identification based on the output of the best method in the training data. This validation workflow was subsequently applied to a portion of the Erlangen data (n=4293). For testing, the best performing methods were applied to remaining data (Leiden n=1000; Erlangen n=478) for an unbiased evaluation.

Results: For the Leiden data set, the word-matching algorithm demonstrated mixed performance (AUROC 0.90; AUPRC 0.33; F1 score 0.55), and 4 methods significantly outperformed word-matching, with support vector machines performing best (AUROC 0.98; AUPRC 0.88; F1 score 0.83). Applying this support vector machine classifier to the test data resulted in a similarly high performance (F1 score 0.81; positive predictive value [PPV] 0.94), and with this method, we could identify 2873 patients with rheumatoid arthritis in less than 7 seconds out of the complete collection of 23,300 patients in the Leiden electronic health record system. For the Erlangen data set, gradient boosting performed best (AUROC 0.94; AUPRC 0.85; F1 score 0.82) in the training set, and applied to the test data, resulted once again in good results (F1 score 0.67; PPV 0.97).

Conclusions: We demonstrate that machine learning methods can extract the records of patients with rheumatoid arthritis from electronic health record data with high precision, allowing research on very large populations for limited costs. Our approach is language and center independent and could be applied to any type of diagnosis. We have developed our pipeline into a universally applicable and easy-to-implement workflow to equip centers with their own high-performing algorithm. This allows the creation of observational studies of unprecedented size covering different countries for low cost from already available data in electronic health record systems.
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http://dx.doi.org/10.2196/23930DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7735897PMC
November 2020

Gene expression identifies patients who develop inflammatory arthritis in a clinically suspect arthralgia cohort.

Arthritis Res Ther 2020 11 9;22(1):266. Epub 2020 Nov 9.

Department of Rheumatology, Leiden University Medical Center, PO Box 9600, Leiden, 2300 RC, The Netherlands.

Background: Established rheumatoid arthritis (RA) patients display differentially expressed genes coding for cytokine/chemokine-mediated immunity compared to healthy controls. It is unclear, however, if in the pre-arthritis phase of clinically suspect arthralgia (CSA) expression of immune genes differ between patients who do or do not develop clinically evident inflammatory arthritis (IA).

Methods: Two hundred thirty-six consecutive patients presenting with arthralgia clinically suspected for progression to RA were followed until IA development or else for median 24 months (IQR 12-26). Baseline whole blood RNA expression was determined for a previously identified set of 133 genes associated with the innate and adaptive immune system by dual-color reverse-transcription multiplex ligation-dependent probe amplification (dcRT-MLPA) profiling. Cox proportional hazard models were used.

Results: Twenty percent of CSA patients developed IA. After correction for multiple testing, expression levels of six genes (IFNG, PHEX, IGF-1, IL-7R, CD19, CCR7) at the time of presentation were associated with progression to IA. PHEX and IGF-1 were highly correlated with each other (ρ = 0.97). In multivariable analysis correcting for the different genes, expressions of IL-7R and IGF-1 were independently associated with IA development (p = 0.025, p = 0.046, respectively). Moreover, IL-7R and IGF-1 remained significantly associated even after correction for known predictors (ACPA, CRP, imaging-detected subclinical joint inflammation; p = 0.039, p = 0.005, respectively). These genes are also associated with RA development.

Conclusions: IL-7R and IGF-1 were differentially expressed between CSA patients who did or did not progress to IA, independent from regularly used predictors. These biomarkers may become helpful in prognostication of CSA patients. Furthermore, because both genes are associated with T cell functioning, T cell dysregulation may mediate progression from arthralgia to arthritis.
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http://dx.doi.org/10.1186/s13075-020-02361-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7653888PMC
November 2020

Metabolic Age Based on the BBMRI-NL H-NMR Metabolomics Repository as Biomarker of Age-related Disease.

Circ Genom Precis Med 2020 10 14;13(5):541-547. Epub 2020 Aug 14.

Department of Internal Medicine, Maastricht University Medical Center, the Netherlands (C.D.A.S., C.J.H.v.d.K., M.M.J.v.G.).

Background: The blood metabolome incorporates cues from the environment and the host's genetic background, potentially offering a holistic view of an individual's health status.

Methods: We have compiled a vast resource of proton nuclear magnetic resonance metabolomics and phenotypic data encompassing over 25 000 samples derived from 26 community and hospital-based cohorts.

Results: Using this resource, we constructed a metabolomics-based age predictor (metaboAge) to calculate an individual's biological age. Exploration in independent cohorts demonstrates that being judged older by one's metabolome, as compared with one's chronological age, confers an increased risk on future cardiovascular disease, mortality, and functionality in older individuals. A web-based tool for calculating metaboAge (metaboage.researchlumc.nl) allows easy incorporation in other epidemiological studies. Access to data can be requested at bbmri.nl/samples-images-data.

Conclusions: In summary, we present a vast resource of metabolomics data and illustrate its merit by constructing a metabolomics-based score for biological age that captures aspects of current and future cardiometabolic health.
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http://dx.doi.org/10.1161/CIRCGEN.119.002610DOI Listing
October 2020

Association of common genetic variants with brain microbleeds: A genome-wide association study.

Neurology 2020 12 10;95(24):e3331-e3343. Epub 2020 Sep 10.

From the Departments of Epidemiology (M.J.K., H.H.H.A., D.V., S.J.v.d.L., P.Y., M.W.V., N.A., C.M.v.D., M.A.I.), Radiology and Nuclear Medicine (H.H.H.A., P.Y., A.v.d.L., M.W.V.), and Clinical Genetics (H.H.H.A.), Erasmus MC University Medical Center, Rotterdam, the Netherlands; Stroke Research Group, Department of Clinical Neurosciences (D.L., M.T., J.L., D.J.T., H.S.M.), University of Cambridge, UK; Department of Neurology (J.R.J.R., C.L.S., J.J.H., A.S.B., C.D., S. Seshadri), Boston University School of Medicine; The Framingham Heart Study (J.R.J.R., C.L.S., J.J.H., A.S.B., S. Seshadri), MA; Department of Biostatistics (A.V.S.), University of Michigan, Ann Arbor; Icelandic Heart Association (A.V.S., S. Sigurdsson, V.G.), Kopavogur, Iceland; Brown Foundation Institute of Molecular Medicine, McGovern Medical School (M.F.), and Human Genetics Center, School of Public Health (M.F.), University of Texas Health Science Center at Houston; Clinical Division of Neurogeriatrics, Department of Neurology (E.H., L.P., R.S.), Institute for Medical Informatics, Statistics and Documentation (E.H.), and Gottfried Schatz Research Center, Department of Molecular Biology and Biochemistry (Y.S., H.S.), Medical University of Graz, Austria; Center of Cerebrovascular Diseases, Department of Neurology (J.L.), West China Hospital, Sichuan University, Chengdu; Stroke Research Centre, Queen Square Institute of Neurology (I.C.H., D.W., H.H., D.J.W.), University College London, UK; Department of Neurosurgery (I.C.H.), Klinikum rechts der Isar, University of Munich, Germany; Centre for Cognitive Ageing and Cognitive Epidemiology, Psychology (M.L., D.C.M.L., M.E.B., I.J.D., J.M.W.), and Centre for Clinical Brain Sciences, Edinburgh Imaging, UK Dementia Research Institute (M.E.B., J.M.W.), University of Edinburgh, UK; Department of Internal Medicine, Section of Gerontology and Geriatrics (S.T.), Department of Cardiology (S.T., J.v.d.G., J.W.J.), Section of Molecular Epidemiology, Biomedical Data Sciences (E.B.v.d.A., M.B., P.E.S.), Leiden Computational Biology Center, Biomedical Data Sciences (E.B.v.d.A.), Department of Radiology (J.v.d.G.), and Einthoven Laboratory for Experimental Vascular Medicine (J.W.J.), Leiden University Medical Center, the Netherlands; Department of Neurology (A.-K.G., N.S.R.), Massachusetts General Hospital, Harvard Medical School, Boston; Memory Aging and Cognition Center (S.H., C.C.), National University Health System, Singapore; Department of Pharmacology (S.H., C.C.) and Saw Swee Hock School of Public Health (S.H.), National University of Singapore and National University Health System, Singapore; Pattern Recognition & Bioinformatics (E.B.v.d.A.), Delft University of Technology, the Netherlands; Department of Biostatistics (S.L., J.J.H., Q.Y., A.S.B.), Boston University School of Public Health, MA; Department of Radiology (C.R.J., K.K.), Mayo Clinic, Rochester, MN; Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases (C.L.S., S. Seshadri), UT Health San Antonio, TX; Department of Medicine, Division of Geriatrics (B.G.W., T.H.M), and Memory Impairment and Neurodegenerative Dementia (MIND) Center (T.H.M.), University of Mississippi Medical Center, Jackson; Singapore Eye Research Institute (C.Y.C., J.Y.K., T.Y.W.); Department of Neuroradiology (Z.M., J.M.W.), NHS Lothian, Edinburgh; Institute of Cardiovascular and Medical Sciences (D.J.S.), College of Medical, Veterinary and Life Sciences, University of Glasgow, UK; Division of Cerebrovascular Neurology (R.F.G.), Johns Hopkins University, Baltimore, MD; Department of Neuroradiology (A.D.M.), Atkinson Morley Neurosciences Centre, St George's NHS Foundation Trust, London, UK; Department of Neurology (C.D.), University of California at Davis; Nuffield Department of Population Health (C.M.v.D.), University of Oxford, UK; Laboratory of Epidemiology and Population Sciences (L.J.L.), National Institute on Aging, Baltimore, MD; and Faculty of Medicine (V.G.), University of Iceland, Reykjavik, Iceland.

Objective: To identify common genetic variants associated with the presence of brain microbleeds (BMBs).

Methods: We performed genome-wide association studies in 11 population-based cohort studies and 3 case-control or case-only stroke cohorts. Genotypes were imputed to the Haplotype Reference Consortium or 1000 Genomes reference panel. BMBs were rated on susceptibility-weighted or T2*-weighted gradient echo MRI sequences, and further classified as lobar or mixed (including strictly deep and infratentorial, possibly with lobar BMB). In a subset, we assessed the effects of ε2 and ε4 alleles on BMB counts. We also related previously identified cerebral small vessel disease variants to BMBs.

Results: BMBs were detected in 3,556 of the 25,862 participants, of which 2,179 were strictly lobar and 1,293 mixed. One locus in the region reached genome-wide significance for its association with BMB (lead rs769449; odds ratio [OR] [95% confidence interval (CI)] 1.33 [1.21-1.45]; = 2.5 × 10). ε4 alleles were associated with strictly lobar (OR [95% CI] 1.34 [1.19-1.50]; = 1.0 × 10) but not with mixed BMB counts (OR [95% CI] 1.04 [0.86-1.25]; = 0.68). ε2 alleles did not show associations with BMB counts. Variants previously related to deep intracerebral hemorrhage and lacunar stroke, and a risk score of cerebral white matter hyperintensity variants, were associated with BMB.

Conclusions: Genetic variants in the region are associated with the presence of BMB, most likely due to the ε4 allele count related to a higher number of strictly lobar BMBs. Genetic predisposition to small vessel disease confers risk of BMB, indicating genetic overlap with other cerebral small vessel disease markers.
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http://dx.doi.org/10.1212/WNL.0000000000010852DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7836652PMC
December 2020

The Role of Age-Related Clonal Hematopoiesis in Genetic Sequencing Studies.

Am J Hum Genet 2020 09;107(3):575-576

Pattern Recognition & Bioinformatics, Delft University of Technology, Delft 2628CD, the Netherlands; Leiden Computational Biology Center, Leiden University Medical Center, Leiden 2300RC, the Netherlands; Section of Molecular Epidemiology, Leiden University Medical Center, Leiden 2300RC, the Netherlands.

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http://dx.doi.org/10.1016/j.ajhg.2020.07.011DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7477003PMC
September 2020

Broad phenotype of cysteine-altering variants in UK Biobank: CADASIL to nonpenetrance.

Neurology 2020 09 30;95(13):e1835-e1843. Epub 2020 Jul 30.

From the Center for Hereditary Small Vessel Disease, Department of Clinical Genetics (J.W.R., R.J.H., G.G., J.G.D., S.A.J.L.O.), Department of Human Genetics (M.O.), Department of Biomedical Data Sciences (E.B.v.d.A.), and Department of Biomedical Data Sciences (E.S.), Leiden University Medical Center, the Netherlands; Institute for Stroke and Dementia Research (M.D., M.D., R.M.), University Hospital, LMU Munich, Germany; Pattern Recognition & Bioinformatics (E.B.v.d.A., H.H.), Delft University of Technology; Alzheimer Center Amsterdam (H.H.), Department of Neurology, Amsterdam Neuroscience, and Department of Clinical Genetics (H.H.), Vrije Universiteit Amsterdam, Amsterdam UMC, the Netherlands; and Department of Radiology and Imaging Sciences (K.N., A.S.), Indiana Alzheimer Disease Center, Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis.

Objective: To determine the small vessel disease spectrum associated with cysteine-altering variants in community-dwelling individuals by analyzing the clinical and neuroimaging features of UK Biobank participants harboring such variants.

Methods: The exome and genome sequencing datasets of the UK Biobank (n = 50,000) and cohorts of cognitively healthy elderly (n = 751) were queried for cysteine-altering variants. Brain MRIs of individuals harboring such variants were scored according to Standards for Reporting Vascular Changes on Neuroimaging criteria, and clinical information was extracted with ICD-10 codes. Clinical and neuroimaging data were compared to age- and sex-matched UK Biobank controls and clinically diagnosed patients from the Dutch cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL) registry.

Results: We identified 108 individuals harboring a cysteine-altering variant (2.2 of 1,000), of whom 75% have a variant that has previously been reported in CADASIL pedigrees. Almost all variants were located in 1 of the NOTCH3 protein epidermal growth factor-like repeat domains 7 to 34. White matter hyperintensity lesion load was higher in individuals with variants than in controls ( = 0.006) but lower than in patients with CADASIL with the same variants ( < 0.001). Almost half of the 24 individuals with brain MRI had a Fazekas score of 0 or 1 up to age 70 years. There was no increased risk of stroke.

Conclusions: Although community-dwelling individuals harboring a cysteine-altering variant have a higher small vessel disease MRI burden than controls, almost half have no MRI abnormalities up to age 70 years. This shows that cysteine altering variants are associated with an extremely broad phenotypic spectrum, ranging from CADASIL to nonpenetrance.
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http://dx.doi.org/10.1212/WNL.0000000000010525DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7682826PMC
September 2020

A Comprehensive Workflow for Applying Single-Cell Clustering and Pseudotime Analysis to Flow Cytometry Data.

J Immunol 2020 08 26;205(3):864-871. Epub 2020 Jun 26.

Department of Biomedical Data Sciences, Leiden University Medical Center, 2333 ZC Leiden, the Netherlands; and.

The introduction of single-cell platforms inspired the development of high-dimensional single-cell analysis tools to comprehensively characterize the underlying cellular heterogeneity. Flow cytometry data are traditionally analyzed by (subjective) gating of subpopulations on two-dimensional plots. However, the increasing number of parameters measured by conventional and spectral flow cytometry reinforces the need to apply many of the recently developed tools for single-cell analysis on flow cytometry data, as well. However, the myriads of analysis options offered by the continuously released novel packages can be overwhelming to the immunologist with limited computational background. In this article, we explain the main concepts of such analyses and provide a detailed workflow to illustrate their implications and additional prerequisites when applied on flow cytometry data. Moreover, we provide readily applicable R code covering transformation, normalization, dimensionality reduction, clustering, and pseudotime analysis that can serve as a template for future analyses. We demonstrate the merit of our workflow by reanalyzing a public human dataset. Compared with standard gating, the results of our workflow provide new insights in cellular subsets, alternative classifications, and hypothetical trajectories. Taken together, we present a well-documented workflow, which utilizes existing high-dimensional single-cell analysis tools to reveal cellular heterogeneity and intercellular relationships in flow cytometry data.
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http://dx.doi.org/10.4049/jimmunol.1901530DOI Listing
August 2020

Lifestyle-Intervention-Induced Reduction of Abdominal Fat Is Reflected by a Decreased Circulating Glycerol Level and an Increased HDL Diameter.

Mol Nutr Food Res 2020 05 23;64(10):e1900818. Epub 2020 Apr 23.

Department of Molecular Epidemiology, Leiden University Medical Center, Leiden, 2333ZC, The Netherlands.

Scope: Abdominal obesity is one of the main modifiable risk factors of age-related cardiometabolic disease. Cardiometabolic disease risk and its associated high abdominal fat mass, cholesterol, and glucose concentrations can be reduced by a healthier lifestyle. Hence, the aim is to understand the relation between lifestyle-induced changes in body composition, and specifically abdominal fat, and accompanying changes in circulating metabolic biomarkers.

Methods And Results: Data from the Growing Old Together (GOTO) study was used, which is a single arm lifestyle intervention in which 164 older adults (mean age 63 years, BMI 23-35 kg/m ) changed their lifestyle during 13 weeks by 12.5% caloric restriction plus 12.5% increase in energy expenditure. It is shown here that levels of circulating metabolic biomarkers, even after adjustment for body mass index, specifically associate with abdominal fat mass. The applied lifestyle intervention mainly reduces abdominal fat mass (-2.6%, SD = 3.0) and this reduction, when adjusted for general weight loss, is highly associated with decreased circulating glycerol concentrations and increased HDL diameter.

Conclusion: The lifestyle-induced reduction of abdominal fat mass is particularly associated, independent of body mass index or general weight loss, with decreased circulating glycerol concentrations and increased HDL diameter.
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http://dx.doi.org/10.1002/mnfr.201900818DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7317364PMC
May 2020

A data-driven methodology reveals novel myofiber clusters in older human muscles.

FASEB J 2020 04 5;34(4):5525-5537. Epub 2020 Mar 5.

Section of Molecular Epidemiology, Leiden University Medical Center, Leiden, the Netherlands.

Skeletal muscles control posture, mobility and strength, and influence whole-body metabolism. Muscles are built of different types of myofibers, each having specific metabolic, molecular, and contractile properties. Fiber classification is, therefore, regarded the key for understanding muscle biology, (patho-) physiology. The expression of three myosin heavy chain (MyHC) isoforms, MyHC-1, MyHC-2A, and MyHC-2X, marks myofibers in humans. Typically, myofiber classification is performed by an eye-based histological analysis. This classical approach is insufficient to capture complex fiber classes, expressing more than one MyHC-isoform. We, therefore, developed a methodological procedure for high-throughput characterization of myofibers on the basis of multiple isoforms. The mean fluorescence intensity of the three most abundant MyHC isoforms was measured per myofiber in muscle biopsies of 56 healthy elderly adults, and myofiber classes were identified using computational biology tools. Unsupervised clustering revealed the existence of six distinct myofiber clusters. A comparison with the visual assessment of myofibers using the same images showed that some of these myofiber clusters could not be detected or were frequently misclassified. The presence of these six clusters was reinforced by RNA expressions levels of sarcomeric genes. In addition, one of the clusters, expressing all three MyHC isoforms, correlated with histological measures of muscle health. To conclude, this methodological procedure enables deep characterization of the complex muscle heterogeneity. This study opens opportunities to further investigate myofiber composition in comparative studies.
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http://dx.doi.org/10.1096/fj.201902350RDOI Listing
April 2020

Comprehensive diagnostics of acute myeloid leukemia by whole transcriptome RNA sequencing.

Leukemia 2021 01 3;35(1):47-61. Epub 2020 Mar 3.

Department of Hematology, Leiden University Medical Center, 2300RC, Leiden, The Netherlands.

Acute myeloid leukemia (AML) is caused by genetic aberrations that also govern the prognosis of patients and guide risk-adapted and targeted therapy. Genetic aberrations in AML are structurally diverse and currently detected by different diagnostic assays. This study sought to establish whole transcriptome RNA sequencing as single, comprehensive, and flexible platform for AML diagnostics. We developed HAMLET (Human AML Expedited Transcriptomics) as bioinformatics pipeline for simultaneous detection of fusion genes, small variants, tandem duplications, and gene expression with all information assembled in an annotated, user-friendly output file. Whole transcriptome RNA sequencing was performed on 100 AML cases and HAMLET results were validated by reference assays and targeted resequencing. The data showed that HAMLET accurately detected all fusion genes and overexpression of EVI1 irrespective of 3q26 aberrations. In addition, small variants in 13 genes that are often mutated in AML were called with 99.2% sensitivity and 100% specificity, and tandem duplications in FLT3 and KMT2A were detected by a novel algorithm based on soft-clipped reads with 100% sensitivity and 97.1% specificity. In conclusion, HAMLET has the potential to provide accurate comprehensive diagnostic information relevant for AML classification, risk assessment and targeted therapy on a single technology platform.
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http://dx.doi.org/10.1038/s41375-020-0762-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7787979PMC
January 2021

A metabolic profile of all-cause mortality risk identified in an observational study of 44,168 individuals.

Nat Commun 2019 08 20;10(1):3346. Epub 2019 Aug 20.

Division of Human Nutrition, Wageningen University, PO Box 17, 6700 AA, Wageningen, The Netherlands.

Predicting longer-term mortality risk requires collection of clinical data, which is often cumbersome. Therefore, we use a well-standardized metabolomics platform to identify metabolic predictors of long-term mortality in the circulation of 44,168 individuals (age at baseline 18-109), of whom 5512 died during follow-up. We apply a stepwise (forward-backward) procedure based on meta-analysis results and identify 14 circulating biomarkers independently associating with all-cause mortality. Overall, these associations are similar in men and women and across different age strata. We subsequently show that the prediction accuracy of 5- and 10-year mortality based on a model containing the identified biomarkers and sex (C-statistic = 0.837 and 0.830, respectively) is better than that of a model containing conventional risk factors for mortality (C-statistic = 0.772 and 0.790, respectively). The use of the identified metabolic profile as a predictor of mortality or surrogate endpoint in clinical studies needs further investigation.
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http://dx.doi.org/10.1038/s41467-019-11311-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6702196PMC
August 2019

A genome-wide association study identifies genetic loci associated with specific lobar brain volumes.

Commun Biol 2019 2;2:285. Epub 2019 Aug 2.

17Department of Biomedical Data Sciences, Statistical Genetics, Leiden University Medical Center, Leiden, 2333ZA the Netherlands.

Brain lobar volumes are heritable but genetic studies are limited. We performed genome-wide association studies of frontal, occipital, parietal and temporal lobe volumes in 16,016 individuals, and replicated our findings in 8,789 individuals. We identified six genetic loci associated with specific lobar volumes independent of intracranial volume. Two loci, associated with occipital (6q22.32) and temporal lobe volume (12q14.3), were previously reported to associate with intracranial and hippocampal volume, respectively. We identified four loci previously unknown to affect brain volumes: 3q24 for parietal lobe volume, and 1q22, 4p16.3 and 14q23.1 for occipital lobe volume. The associated variants were located in regions enriched for histone modifications ( and ), or close to genes causing Mendelian brain-related diseases ( and ). No genetic overlap between lobar volumes and neurological or psychiatric diseases was observed. Our findings reveal part of the complex genetics underlying brain development and suggest a role for regulatory regions in determining brain volumes.
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http://dx.doi.org/10.1038/s42003-019-0537-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6677735PMC
April 2020

The effect of standardized food intake on the association between BMI and H-NMR metabolites.

Sci Rep 2016 12 14;6:38980. Epub 2016 Dec 14.

Department of Molecular Epidemiology, Leiden University Medical Center, 2300 RC Leiden, The Netherlands.

Multiple studies have shown that levels of H-NMR metabolites are associated with disease and risk factors of disease such as BMI. While most previous investigations have been performed in fasting samples, meta-analysis often includes both cohorts with fasting and non-fasting blood samples. In the present study comprising 153 participants (mean age 63 years; mean BMI 27 kg/m) we analyzed the effect of a standardized liquid meal (SLM) on metabolite levels and how the SLM influenced the association between metabolites and BMI. We observed that many metabolites, including glycolysis related metabolites, multiple amino acids, LDL diameter, VLDL and HDL lipid concentration changed within 35 minutes after a standardized liquid meal (SLM), similarly for all individuals. Remarkable, however, is that the correlations of metabolite levels with BMI remained highly similar before and after the SLM. Hence, as exemplified with the disease risk factor BMI, our results suggest that the applicability of H-NMR metabolites as disease biomarkers depends on the standardization of the fasting status rather than on the fasting status itself. Future studies are required to investigate the dependency of metabolite biomarkers for other disease risk factors on the fasting status.
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http://dx.doi.org/10.1038/srep38980DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5155225PMC
December 2016

Human Plasma N-glycosylation as Analyzed by Matrix-Assisted Laser Desorption/Ionization-Fourier Transform Ion Cyclotron Resonance-MS Associates with Markers of Inflammation and Metabolic Health.

Mol Cell Proteomics 2017 02 8;16(2):228-242. Epub 2016 Dec 8.

From the ‡Center for Proteomics and Metabolomics, Leiden University Medical Center, 2300 RC Leiden, The Netherlands;

Glycosylation is an abundant co- and post-translational protein modification of importance to protein processing and activity. Although not template-defined, glycosylation does reflect the biological state of an organism and is a high-potential biomarker for disease and patient stratification. However, to interpret a complex but informative sample like the total plasma N-glycome, it is important to establish its baseline association with plasma protein levels and systemic processes. Thus far, large-scale studies (n >200) of the total plasma N-glycome have been performed with methods of chromatographic and electrophoretic separation, which, although being informative, are limited in resolving the structural complexity of plasma N-glycans. MS has the opportunity to contribute additional information on, among others, antennarity, sialylation, and the identity of high-mannose type species.Here, we have used matrix-assisted laser desorption/ionization (MALDI)-Fourier transform ion cyclotron resonance (FTICR)-MS to study the total plasma N-glycome of 2144 healthy middle-aged individuals from the Leiden Longevity Study, to allow association analysis with markers of metabolic health and inflammation. To achieve this, N-glycans were enzymatically released from their protein backbones, labeled at the reducing end with 2-aminobenzoic acid, and following purification analyzed by negative ion mode intermediate pressure MALDI-FTICR-MS. In doing so, we achieved the relative quantification of 61 glycan compositions, ranging from HexHexNAc to HexHexNAcdHexNeu5Ac, as well as that of 39 glycosylation traits derived thereof. Next to confirming known associations of glycosylation with age and sex by MALDI-FTICR-MS, we report novel associations with C-reactive protein (CRP), interleukin 6 (IL-6), body mass index (BMI), leptin, adiponectin, HDL cholesterol, triglycerides (TG), insulin, gamma-glutamyl transferase (GGT), alanine aminotransferase (ALT), and smoking. Overall, the bisection, galactosylation, and sialylation of diantennary species, the sialylation of tetraantennary species, and the size of high-mannose species proved to be important plasma characteristics associated with inflammation and metabolic health.
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http://dx.doi.org/10.1074/mcp.M116.065250DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5294210PMC
February 2017

Fibroblast growth factor 21 reflects liver fat accumulation and dysregulation of signalling pathways in the liver of C57BL/6J mice.

Sci Rep 2016 07 29;6:30484. Epub 2016 Jul 29.

Nutrition, Metabolism &Genomics Group, Division of Human Nutrition, Wageningen University, 6700 EV Wageningen, The Netherlands.

Fibroblast growth factor 21 (Fgf21) has emerged as a potential plasma marker to diagnose non-alcoholic fatty liver disease (NAFLD). To study the molecular processes underlying the association of plasma Fgf21 with NAFLD, we explored the liver transcriptome data of a mild NAFLD model of aging C57BL/6J mice at 12, 24, and 28 months of age. The plasma Fgf21 level significantly correlated with intrahepatic triglyceride content. At the molecular level, elevated plasma Fgf21 levels were associated with dysregulated metabolic and cancer-related pathways. The up-regulated Fgf21 levels in NAFLD were implied to be a protective response against the NAFLD-induced adverse effects, e.g. lipotoxicity, oxidative stress and endoplasmic reticulum stress. An in vivo PPARα challenge demonstrated the dysregulation of PPARα signalling in the presence of NAFLD, which resulted in a stochastically increasing hepatic expression of Fgf21. Notably, elevated plasma Fgf21 was associated with declining expression of Klb, Fgf21's crucial co-receptor, which suggests a resistance to Fgf21. Therefore, although liver fat accumulation is a benign stage of NAFLD, the elevated plasma Fgf21 likely indicated vulnerability to metabolic stressors that may contribute towards progression to end-stage NAFLD. In conclusion, plasma levels of Fgf21 reflect liver fat accumulation and dysregulation of metabolic pathways in the liver.
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http://dx.doi.org/10.1038/srep30484DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4965761PMC
July 2016

Employing biomarkers of healthy ageing for leveraging genetic studies into human longevity.

Exp Gerontol 2016 09 29;82:166-74. Epub 2016 Jun 29.

Department of Molecular Epidemiology, Leiden University Medical Center, P.O. Box 9600, 2300 RC Leiden, The Netherlands. Electronic address:

Genetic studies have thus far identified a limited number of loci associated with human longevity by applying age at death or survival up to advanced ages as phenotype. As an alternative approach, one could first try to identify biomarkers of healthy ageing and the genetic variants associated with these traits and subsequently determine the association of these variants with human longevity. In the present study, we used this approach by testing whether the 35 baseline serum parameters measured in the Leiden Longevity Study (LLS) meet the proposed criteria for a biomarker of healthy ageing. The LLS consists of 421 families with long-lived siblings of European descent, who were recruited together with their offspring and the spouses of the offspring (controls). To test the four criteria for a biomarker of healthy ageing in the LLS, we determined the association of the serum parameters with chronological age, familial longevity, general practitioner-reported general health, and mortality. Out of the 35 serum parameters, we identified glucose, insulin, and triglycerides as biomarkers of healthy ageing, meeting all four criteria in the LLS. We subsequently showed that the genetic variants previously associated with these parameters are significantly enriched in the largest genome-wide association study for human longevity. In conclusion, we showed that biomarkers of healthy ageing can be used to leverage genetic studies into human longevity. We identified several genetic variants influencing the variation in glucose, insulin and triglycerides that contribute to human longevity.
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http://dx.doi.org/10.1016/j.exger.2016.06.013DOI Listing
September 2016

Metabolic effects of a 13-weeks lifestyle intervention in older adults: The Growing Old Together Study.

Aging (Albany NY) 2016 Jan;8(1):111-26

Department of Molecular Epidemiology, Leiden University Medical Center, 2300 RC Leiden, The Netherlands.

For people in their 40s and 50s, lifestyle programs have been shown to improve metabolic health. For older adults, however, it is not clear whether these programs are equally healthy. In the Growing Old Together study, we applied a 13-weeks lifestyle program, with a target of 12.5% caloric restriction and 12.5% increase in energy expenditure through an increase in physical activity, in 164 older adults (mean age=63.2 years; BMI=23-35 kg/m2). Mean weight loss was 4.2% (SE=2.8%) of baseline weight, which is comparable to a previous study in younger adults. Fasting insulin levels, however, showed a much smaller decrease (0.30 mU/L (SE=3.21)) and a more heterogeneous response (range=2.0-29.6 mU/L). Many other parameters of metabolic health, such as blood pressure, and thyroid, glucose and lipid metabolism improved significantly. Many 1H-NMR metabolites changed in a direction previously associated with a low risk of type 2 diabetes and cardiovascular disease and partially independently of weight loss. In conclusion, 25% reduction in energy balance for 13 weeks induced a metabolic health benefit in older adults, monitored by traditional and novel metabolic markers.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4761717PMC
http://dx.doi.org/10.18632/aging.100877DOI Listing
January 2016

IL7R gene expression network associates with human healthy ageing.

Immun Ageing 2015 11;12:21. Epub 2015 Nov 11.

Section of Molecular Epidemiology, Leiden University Medical Center, Zone S5-P, P.O. Box 9600, 2300 RC Leiden, The Netherlands ; Netherlands Consortium for Healthy Ageing, Leiden University Medical Center, P.O. Box 9600, 2300 RC Leiden, The Netherlands.

Background: The level of expression of the interleukin 7 receptor (IL7R) gene in blood has recently been found to be associated with familial longevity and healthy ageing. IL7R is crucial for T cell development and important for immune competence. To further investigate the IL7R pathway in ageing, we identified the closest interacting genes to construct an IL7R gene network that consisted of IL7R and six interacting genes: IL2RG, IL7, TSLP, CRLF2, JAK1 and JAK3. This network was explored for association with chronological age, familial longevity and immune-related diseases (type 2 diabetes, chronic obstructive pulmonary disease and rheumatoid arthritis) in 87 nonagenarians, 337 of their middle-aged offspring and 321 middle-aged controls from the Leiden Longevity Study (LLS).

Results: We observed that expression levels within the IL7R gene network were significantly different between the nonagenarians and middle-aged controls (P = 4.6 × 10(-4)), being driven by significantly lower levels of expression in the elderly of IL7, IL2RG and IL7R. After adjustment for multiple testing and white blood cell composition and in comparison with similarly aged controls, middle-aged offspring of nonagenarian siblings exhibit a lower expression level of IL7R only (P = 0.006). Higher IL7R gene expression in the combined group of middle-aged offspring and controls is associated with a higher prevalence of immune-related disease (P = 0.001). On the one hand, our results indicate that lower IL7R expression levels, as exhibited by the members of long-lived families that can be considered as 'healthy agers', are beneficial in middle age. This is augmented by the observation that higher IL7R gene expression associates with immune-related disease. On the other hand, IL7R gene expression in blood is lower in older individuals, indicating that low IL7R gene expression might associate with reduced health. Interestingly, this contradictory result is supported by the observation that a higher IL7R gene expression level is associated with better prospective survival, both in the nonagenarians (Hazard ratio (HR) = 0.63, P = 0.037) and the middle-aged individuals (HR = 0.33, P = 1.9 × 10(-4)).

Conclusions: Overall, we conclude that the IL7R network reflected by gene expression levels in blood may be involved in the rate of ageing and health status of elderly individuals.
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http://dx.doi.org/10.1186/s12979-015-0048-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4642670PMC
November 2015

Exome and whole genome sequencing in aging and longevity.

Adv Exp Med Biol 2015 ;847:127-39

Department of Medical Statistics and Bioinformatics, Section of Molecular Epidemiology, Leiden University Medical Center, Postal zone S-05-P, 2300 RC, PO Box 9600, Leiden, Netherlands,

Calendar age is the major risk factor for common disease. It is therefore expected that understanding the aging process will eventually lead to promotion of better health conditions in elderly populations. Such insight may be obtained by identifying the genetic determinants of familial and exceptional longevity and age-related disease. Research of these determinants has been performed in candidate gene, genome-wide association and linkage studies. Because exploration of the common variation in the genome did not explain much of the variation in the rate of aging and longevity, researchers in the field have only recently started to investigate the contribution of rare genetic variants to these traits. The increased throughput and decreased costs of next generation sequencing (NGS) have resulted in a great deal of novel applications for sequencing sets of candidate genes, whole exomes, and whole genomes of individuals. Most of the successful NGS applications are as yet those focused on genetic syndromes and cancers for which causal mutations are readily being identified. In this chapter, we discuss the genetic and phenomic aspects of human aging research and the use of NGS data to identify genes relevant for age-related diseases and lifespan regulation, and to investigate the accumulation of somatic genetic variation during the course of life.
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http://dx.doi.org/10.1007/978-1-4939-2404-2_6DOI Listing
August 2015

Underlying molecular mechanisms of DIO2 susceptibility in symptomatic osteoarthritis.

Ann Rheum Dis 2015 Aug 2;74(8):1571-9. Epub 2014 Apr 2.

Department of Molecular Epidemiology, LUMC, Leiden, The Netherlands Genomics Initiative, sponsored by the NCHA, Leiden, The Netherlands.

Objectives: To investigate how the genetic susceptibility gene DIO2 confers risk to osteoarthritis (OA) onset in humans and to explore whether counteracting the deleterious effect could contribute to novel therapeutic approaches.

Methods: Epigenetically regulated expression of DIO2 was explored by assessing methylation of positional CpG-dinucleotides and the respective DIO2 expression in OA-affected and macroscopically preserved articular cartilage from end-stage OA patients. In a human in vitro chondrogenesis model, we measured the effects when thyroid signalling during culturing was either enhanced (excess T3 or lentiviral induced DIO2 overexpression) or decreased (iopanoic acid).

Results: OA-related changes in methylation at a specific CpG dinucleotide upstream of DIO2 caused significant upregulation of its expression (β=4.96; p=0.0016). This effect was enhanced and appeared driven specifically by DIO2 rs225014 risk allele carriers (β=5.58, p=0.0006). During in vitro chondrogenesis, DIO2 overexpression resulted in a significant reduced capacity of chondrocytes to deposit extracellular matrix (ECM) components, concurrent with significant induction of ECM degrading enzymes (ADAMTS5, MMP13) and markers of mineralisation (ALPL, COL1A1). Given their concurrent and significant upregulation of expression, this process is likely mediated via HIF-2α/RUNX2 signalling. In contrast, we showed that inhibiting deiodinases during in vitro chondrogenesis contributed to prolonged cartilage homeostasis as reflected by significant increased deposition of ECM components and attenuated upregulation of matrix degrading enzymes.

Conclusions: Our findings show how genetic variation at DIO2 could confer risk to OA and raised the possibility that counteracting thyroid signalling may be a novel therapeutic approach.
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http://dx.doi.org/10.1136/annrheumdis-2013-204739DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4516000PMC
August 2015

Genome-wide association meta-analysis of human longevity identifies a novel locus conferring survival beyond 90 years of age.

Hum Mol Genet 2014 Aug 31;23(16):4420-32. Epub 2014 Mar 31.

Department of Experimental, Diagnostic and Specialty Medicine and.

The genetic contribution to the variation in human lifespan is ∼ 25%. Despite the large number of identified disease-susceptibility loci, it is not known which loci influence population mortality. We performed a genome-wide association meta-analysis of 7729 long-lived individuals of European descent (≥ 85 years) and 16 121 younger controls (<65 years) followed by replication in an additional set of 13 060 long-lived individuals and 61 156 controls. In addition, we performed a subset analysis in cases aged ≥ 90 years. We observed genome-wide significant association with longevity, as reflected by survival to ages beyond 90 years, at a novel locus, rs2149954, on chromosome 5q33.3 (OR = 1.10, P = 1.74 × 10(-8)). We also confirmed association of rs4420638 on chromosome 19q13.32 (OR = 0.72, P = 3.40 × 10(-36)), representing the TOMM40/APOE/APOC1 locus. In a prospective meta-analysis (n = 34 103), the minor allele of rs2149954 (T) on chromosome 5q33.3 associates with increased survival (HR = 0.95, P = 0.003). This allele has previously been reported to associate with low blood pressure in middle age. Interestingly, the minor allele (T) associates with decreased cardiovascular mortality risk, independent of blood pressure. We report on the first GWAS-identified longevity locus on chromosome 5q33.3 influencing survival in the general European population. The minor allele of this locus associates with low blood pressure in middle age, although the contribution of this allele to survival may be less dependent on blood pressure. Hence, the pleiotropic mechanisms by which this intragenic variation contributes to lifespan regulation have to be elucidated.
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http://dx.doi.org/10.1093/hmg/ddu139DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4103672PMC
August 2014

Aging as accelerated accumulation of somatic variants: whole-genome sequencing of centenarian and middle-aged monozygotic twin pairs.

Twin Res Hum Genet 2013 Dec 4;16(6):1026-32. Epub 2013 Nov 4.

Molecular Epidemiology, Medical Statistics and Bioinformatics, Leiden University Medical Center, Leiden, the Netherlands.

It has been postulated that aging is the consequence of an accelerated accumulation of somatic DNA mutations and that subsequent errors in the primary structure of proteins ultimately reach levels sufficient to affect organismal functions. The technical limitations of detecting somatic changes and the lack of insight about the minimum level of erroneous proteins to cause an error catastrophe hampered any firm conclusions on these theories. In this study, we sequenced the whole genome of DNA in whole blood of two pairs of monozygotic (MZ) twins, 40 and 100 years old, by two independent next-generation sequencing (NGS) platforms (Illumina and Complete Genomics). Potentially discordant single-base substitutions supported by both platforms were validated extensively by Sanger, Roche 454, and Ion Torrent sequencing. We demonstrate that the genomes of the two twin pairs are germ-line identical between co-twins, and that the genomes of the 100-year-old MZ twins are discerned by eight confirmed somatic single-base substitutions, five of which are within introns. Putative somatic variation between the 40-year-old twins was not confirmed in the validation phase. We conclude from this systematic effort that by using two independent NGS platforms, somatic single nucleotide substitutions can be detected, and that a century of life did not result in a large number of detectable somatic mutations in blood. The low number of somatic variants observed by using two NGS platforms might provide a framework for detecting disease-related somatic variants in phenotypically discordant MZ twins.
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http://dx.doi.org/10.1017/thg.2013.73DOI Listing
December 2013

Meta-analysis on blood transcriptomic studies identifies consistently coexpressed protein-protein interaction modules as robust markers of human aging.

Aging Cell 2014 Apr 19;13(2):216-25. Epub 2013 Nov 19.

Department of Molecular Epidemiology, Leiden University Medical Center, PO Box 9600, 2300 RC, Leiden, The Netherlands; The Delft Bioinformatics Lab, Delft University of Technology, PO Box 5031, 2600 GA, Delft, The Netherlands.

The bodily decline that occurs with advancing age strongly impacts on the prospects for future health and life expectancy. Despite the profound role of age in disease etiology, knowledge about the molecular mechanisms driving the process of aging in humans is limited. Here, we used an integrative network-based approach for combining multiple large-scale expression studies in blood (2539 individuals) with protein-protein Interaction (PPI) data for the detection of consistently coexpressed PPI modules that may reflect key processes that change throughout the course of normative aging. Module detection followed by a meta-analysis on chronological age identified fifteen consistently coexpressed PPI modules associated with chronological age, including a highly significant module (P = 3.5 × 10(-38)) enriched for 'T-cell activation' marking age-associated shifts in lymphocyte blood cell counts (R(2) = 0.603; P = 1.9 × 10(-10)). Adjusting the analysis in the compendium for the 'T-cell activation' module showed five consistently coexpressed PPI modules that robustly associated with chronological age and included modules enriched for 'Translational elongation', 'Cytolysis' and 'DNA metabolic process'. In an independent study of 3535 individuals, four of five modules consistently associated with chronological age, underpinning the robustness of the approach. We found three of five modules to be significantly enriched with aging-related genes, as defined by the GenAge database, and association with prospective survival at high ages for one of the modules including ASF1A. The hereby-detected age-associated and consistently coexpressed PPI modules therefore may provide a molecular basis for future research into mechanisms underlying human aging.
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http://dx.doi.org/10.1111/acel.12160DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4331790PMC
April 2014

Identification and systematic annotation of tissue-specific differentially methylated regions using the Illumina 450k array.

Epigenetics Chromatin 2013 Aug 6;6(1):26. Epub 2013 Aug 6.

Molecular Epidemiology, Leiden University Medical Center, Leiden, The Netherlands.

Background: DNA methylation has been recognized as a key mechanism in cell differentiation. Various studies have compared tissues to characterize epigenetically regulated genomic regions, but due to differences in study design and focus there still is no consensus as to the annotation of genomic regions predominantly involved in tissue-specific methylation. We used a new algorithm to identify and annotate tissue-specific differentially methylated regions (tDMRs) from Illumina 450k chip data for four peripheral tissues (blood, saliva, buccal swabs and hair follicles) and six internal tissues (liver, muscle, pancreas, subcutaneous fat, omentum and spleen with matched blood samples).

Results: The majority of tDMRs, in both relative and absolute terms, occurred in CpG-poor regions. Further analysis revealed that these regions were associated with alternative transcription events (alternative first exons, mutually exclusive exons and cassette exons). Only a minority of tDMRs mapped to gene-body CpG islands (13%) or CpG islands shores (25%) suggesting a less prominent role for these regions than indicated previously. Implementation of ENCODE annotations showed enrichment of tDMRs in DNase hypersensitive sites and transcription factor binding sites. Despite the predominance of tissue differences, inter-individual differences in DNA methylation in internal tissues were correlated with those for blood for a subset of CpG sites in a locus- and tissue-specific manner.

Conclusions: We conclude that tDMRs preferentially occur in CpG-poor regions and are associated with alternative transcription. Furthermore, our data suggest the utility of creating an atlas cataloguing variably methylated regions in internal tissues that correlate to DNA methylation measured in easy accessible peripheral tissues.
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http://dx.doi.org/10.1186/1756-8935-6-26DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3750594PMC
August 2013

Genome-wide linkage analysis for human longevity: Genetics of Healthy Aging Study.

Aging Cell 2013 Apr 6;12(2):184-93. Epub 2013 Feb 6.

Molecular Epidemiology, Leiden University Medical Centre, Leiden, ZC, 2333, The Netherlands.

Clear evidence exists for heritability of human longevity, and much interest is focused on identifying genes associated with longer lives. To identify such longevity alleles, we performed the largest genome-wide linkage scan thus far reported. Linkage analyses included 2118 nonagenarian Caucasian sibling pairs that have been enrolled in 15 study centers of 11 European countries as part of the Genetics of Healthy Aging (GEHA) project. In the joint linkage analyses, we observed four regions that show linkage with longevity; chromosome 14q11.2 (LOD = 3.47), chromosome 17q12-q22 (LOD = 2.95), chromosome 19p13.3-p13.11 (LOD = 3.76), and chromosome 19q13.11-q13.32 (LOD = 3.57). To fine map these regions linked to longevity, we performed association analysis using GWAS data in a subgroup of 1228 unrelated nonagenarian and 1907 geographically matched controls. Using a fixed-effect meta-analysis approach, rs4420638 at the TOMM40/APOE/APOC1 gene locus showed significant association with longevity (P-value = 9.6 × 10(-8) ). By combined modeling of linkage and association, we showed that association of longevity with APOEε4 and APOEε2 alleles explain the linkage at 19q13.11-q13.32 with P-value = 0.02 and P-value = 1.0 × 10(-5) , respectively. In the largest linkage scan thus far performed for human familial longevity, we confirm that the APOE locus is a longevity gene and that additional longevity loci may be identified at 14q11.2, 17q12-q22, and 19p13.3-p13.11. As the latter linkage results are not explained by common variants, we suggest that rare variants play an important role in human familial longevity.
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http://dx.doi.org/10.1111/acel.12039DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3725963PMC
April 2013

Transcriptional profiling of human familial longevity indicates a role for ASF1A and IL7R.

PLoS One 2012 11;7(1):e27759. Epub 2012 Jan 11.

Section of Molecular Epidemiology, Leiden University Medical Center, Leiden, The Netherlands.

The Leiden Longevity Study consists of families that express extended survival across generations, decreased morbidity in middle-age, and beneficial metabolic profiles. To identify which pathways drive this complex phenotype of familial longevity and healthy aging, we performed a genome-wide gene expression study within this cohort to screen for mRNAs whose expression changes with age and associates with longevity. We first compared gene expression profiles from whole blood samples between 50 nonagenarians and 50 middle-aged controls, resulting in identification of 2,953 probes that associated with age. Next, we determined which of these probes associated with longevity by comparing the offspring of the nonagenarians (50 subjects) and the middle-aged controls. The expression of 360 probes was found to change differentially with age in members of the long-lived families. In a RT-qPCR replication experiment utilizing 312 controls, 332 offspring and 79 nonagenarians, we confirmed a nonagenarian specific expression profile for 21 genes out of 25 tested. Since only some of the offspring will have inherited the beneficial longevity profile from their long-lived parents, the contrast between offspring and controls is expected to be weak. Despite this dilution of the longevity effects, reduced expression levels of two genes, ASF1A and IL7R, involved in maintenance of chromatin structure and the immune system, associated with familial longevity already in middle-age. The size of this association increased when controls were compared to a subfraction of the offspring that had the highest probability to age healthily and become long-lived according to beneficial metabolic parameters. In conclusion, an "aging-signature" formed of 21 genes was identified, of which reduced expression of ASF1A and IL7R marked familial longevity already in middle-age. This indicates that expression changes of genes involved in metabolism, epigenetic control and immune function occur as a function of age, and some of these, like ASF1A and IL7R, represent early features of familial longevity and healthy ageing.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0027759PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3256132PMC
May 2012