SummaryAn epigenetic profile defining the DNA methylation age (DNAm age) of an individual has been suggested to be a biomarker of aging, and thus possibly providing a tool for assessment of health and mortality. In this study, we estimated the DNAm age of 378 Danish twins, age 30-82 years, and furthermore included a 10-year longitudinal study of the 86 oldest-old twins (mean age of 86.1 at follow-up), which subsequently were followed for mortality for 8 years. We found that the DNAm age is highly correlated with chronological age across all age groups (r = 0.97), but that the rate of change of DNAm age decreases with age. The results may in part be explained by selective mortality of those with a high DNAm age. This hypothesis was supported by a classical survival analysis showing a 35% (4-77%) increased mortality risk for each 5-year increase in the DNAm age vs. chronological age. Furthermore, the intrapair twin analysis revealed a more-than-double mortality risk for the DNAm oldest twin compared to the co-twin and a 'doseresponse pattern' with the odds of dying first increasing 3.2 (1.05-10.1) times per 5-year DNAm age difference within twin pairs, thus showing a stronger association of DNAm age with mortality in the oldest-old when controlling for familial factors. In conclusion, our results support that DNAm age qualifies as a biomarker of aging.
Expression of HOX transcript antisense intergenic RNA (HOTAIR)--a long non-coding RNA--has been examined in a variety of human cancers, and overexpression of HOTAIR is correlated with poor survival among breast, colon, and liver cancer patients. In this retrospective study, we examine HOTAIR expression in 164 primary breast tumors, from patients who do not receive adjuvant treatment, in a design that is paired with respect to the traditional prognostic markers. We show that HOTAIR expression differs between patients with or without a metastatic endpoint, respectively. Survival analysis shows that high HOTAIR expression in primary tumors is significantly associated with worse prognosis independent of prognostic markers (P = 0.012, hazard ratio (HR) 1.747). This association is even stronger when looking only at estrogen receptor (ER)-positive tumor samples (P = 0.0086, HR 1.985). In ER-negative tumor samples, we are not able to detect a prognostic value of HOTAIR expression, probably due to the limited sample size. These results are successfully validated in an independent dataset with similar associations (P = 0.018, HR 1.825). In conclusion, our findings suggest that HOTAIR expression may serve as an independent biomarker for the prediction of the risk of metastasis in ER-positive breast cancer patients.
In this large cohort of surgically resected lung adenocarcinomas, the prevalence of ALK positivity was 6.2% using IHC and at least 2.2% using FISH. A screening strategy based on IHC or H-score could be envisaged. ALK positivity (by either IHC or FISH) was related to better OS.
Insulin resistance in skeletal muscle is a major risk factor for the development of type 2 diabetes in women with polycystic ovary syndrome (PCOS). In patients with type 2 diabetes, insulin resistance in skeletal muscle is associated with abnormalities in insulin signaling, fatty acid metabolism, and mitochondrial oxidative phosphorylation (OX-PHOS). In PCOS patients, the molecular mechanisms of insulin resistance are, however, less well characterized. To identify biological pathways of importance for the pathogenesis of insulin resistance in PCOS, we compared gene expression in skeletal muscle of metabolically characterized PCOS patients (n ؍ 16) and healthy control subjects (n ؍ 13) using two different approaches for global pathway analysis: gene set enrichment analysis (GSEA 1.0) and gene map annotator and pathway profiler (GenMAPP 2.0). We demonstrate that impaired insulin-stimulated total, oxidative and nonoxidative glucose disposal in PCOS patients are associated with a consistent downregulation of OXPHOS gene expression using GSEA and GenMAPP analysis. Quantitative real-time PCR analysis validated these findings and showed that reduced levels of peroxisome proliferator-activated receptor ␥ coactivator ␣ (PGC-1␣) could play a role in the downregulation of OXPHOS genes in PCOS. In these women with PCOS, the decrease in OXPHOS gene expression in skeletal muscle cannot be ascribed to obesity and diabetes. This supports the hypothesis of an early association between insulin resistance and impaired mitochondrial oxidative metabolism, which is, in part, mediated by reduced PGC-1␣ levels. These abnormalities may contribute to the increased risk of type 2 diabetes observed in women with PCOS. Diabetes 56:2349-2355, 2007
Only two genome-wide significant loci associated with longevity have been identified so far, probably because of insufficient sample sizes of centenarians, whose genomes may harbor genetic variants associated with health and longevity. Here we report a genome-wide association study (GWAS) of Han Chinese with a sample size 2.7 times the largest previously published GWAS on centenarians. We identified 11 independent loci associated with longevity replicated in Southern-Northern regions of China, including two novel loci (rs2069837-IL6; rs2440012-ANKRD20A9P) with genome-wide significance and the rest with suggestive significance (P < 3.65 × 10−5). Eight independent SNPs overlapped across Han Chinese, European and U.S. populations, and APOE and 5q33.3 were replicated as longevity loci. Integrated analysis indicates four pathways (starch, sucrose and xenobiotic metabolism; immune response and inflammation; MAPK; calcium signaling) highly associated with longevity (P ≤ 0.006) in Han Chinese. The association with longevity of three of these four pathways (MAPK; immunity; calcium signaling) is supported by findings in other human cohorts. Our novel finding on the association of starch, sucrose and xenobiotic metabolism pathway with longevity is consistent with the previous results from Drosophilia. This study suggests protective mechanisms including immunity and nutrient metabolism and their interactions with environmental stress play key roles in human longevity.
In population studies on aging, the data on genetic markers are often collected for individuals from different age groups. The purpose of such studies is to identify, by comparison of the frequencies of selected genotypes, "longevity" or "frailty" genes in the oldest and in younger groups of individuals. To address questions about more-complicated aspects of genetic influence on longevity, additional information must be used. In this article, we show that the use of demographic information, together with data on genetic markers, allows us to calculate hazard rates, relative risks, and survival functions for respective genes or genotypes. New methods of combining genetic and demographic information are discussed. These methods are tested on simulated data and then are applied to the analysis of data on genetic markers for two haplogroups of human mtDNA. The approaches suggested in this article provide a powerful tool for analyzing the influence of candidate genes on longevity and survival. We also show how factors such as changes in the initial frequencies of candidate genes in subsequent cohorts, or secular trends in cohort mortality, may influence the results of an analysis.
BackgroundA lack of a sufficiently large sample at single sites causes poor generalizability in automatic diagnosis classification of heterogeneous psychiatric disorders such as schizophrenia based on brain imaging scans. Advanced deep learning methods may be capable of learning subtle hidden patterns from high dimensional imaging data, overcome potential site-related variation, and achieve reproducible cross-site classification. However, deep learning-based cross-site transfer classification, despite less imaging site-specificity and more generalizability of diagnostic models, has not been investigated in schizophrenia.MethodsA large multi-site functional MRI sample (n = 734, including 357 schizophrenic patients from seven imaging resources) was collected, and a deep discriminant autoencoder network, aimed at learning imaging site-shared functional connectivity features, was developed to discriminate schizophrenic individuals from healthy controls.FindingsAccuracies of approximately 85·0% and 81·0% were obtained in multi-site pooling classification and leave-site-out transfer classification, respectively. The learned functional connectivity features revealed dysregulation of the cortical-striatal-cerebellar circuit in schizophrenia, and the most discriminating functional connections were primarily located within and across the default, salience, and control networks.InterpretationThe findings imply that dysfunctional integration of the cortical-striatal-cerebellar circuit across the default, salience, and control networks may play an important role in the “disconnectivity” model underlying the pathophysiology of schizophrenia. The proposed discriminant deep learning method may be capable of learning reliable connectome patterns and help in understanding the pathophysiology and achieving accurate prediction of schizophrenia across multiple independent imaging sites.
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