scholarly journals Sporadic, late-onset, and multistage diseases

Author(s):  
Anthony Webster ◽  
Robert Clarke

Somatic mutations can cause cancer and have recently been linked with a range of non-malignant diseases. Multistage models can characterise how mutations lead to cancer, and may also be applicable to these other diseases. Here we found the incidence of over 60% of common diseases in UK Biobank were consistent with a multistage model with an ordered sequence of stages, as approximated by a Weibull distribution, with the log of incidence linearly related to the log of age and the slope often interpreted as the number of stages. A model where the stages can occur in any order was also explored, as was stratification by smoking and diabetes status. Most importantly, we find that many diseases are low risk when young but then become inevitable in old age, but many other diseases do not, being more sporadic with a modest and modifiable risk that slowly increases with age.

2020 ◽  
Author(s):  
Marit de Jong ◽  
Mark Woodward ◽  
Sanne A.E Peters

<b>Objective:</b> Diabetes has shown to be a stronger risk factor for myocardial infarction (MI) in women than men. Whether sex differences exist across the glycaemic spectrum is unknown. We investigated sex differences in the associations of diabetes status and glycated haemoglobin (HbA1c) with the risk of MI. <br> <b>Research Design and Methods:</b> Data were used from 471,399 (56% women) individuals without cardiovascular disease (CVD) included in the UK Biobank. Sex-specific incidence rates were calculated by diabetes status and across levels of HbA1c, using Poisson regression. Cox proportional hazards analyses estimated sex-specific hazard ratios (HR) and women-to-men ratios by diabetes status and HbA1c for MI during a mean follow-up of 9 years. <br> <b>Results:</b> Women had lower incidence rates of MI than men, regardless of diabetes status or HbA1c level. Compared with individuals without diabetes, prediabetes, undiagnosed diabetes, and previously diagnosed diabetes were associated with increased risk of MI in both sexes. Previously diagnosed diabetes was more strongly associated with MI in women (HR 2∙33 [95%CI 1∙96;2∙78]) than men (1∙81 [1∙63;2∙02]), with a women-to-men ratio of HRs of 1∙29 (1∙05;1∙58). Each 1% higher HbA1c, independent of diabetes status, was associated with an 18% greater risk of MI in both women and men.<br> <b>Conclusions:</b> Although the incidence of MI was higher in men than women, the presence of diabetes is associated with a greater excess relative risk of MI in women. However, each 1% higher HbA1c was associated with an 18% greater risk of MI in both women and men.<br> <br>


2020 ◽  
Author(s):  
Marit de Jong ◽  
Mark Woodward ◽  
Sanne A.E Peters

<b>Objective:</b> Diabetes has shown to be a stronger risk factor for myocardial infarction (MI) in women than men. Whether sex differences exist across the glycaemic spectrum is unknown. We investigated sex differences in the associations of diabetes status and glycated haemoglobin (HbA1c) with the risk of MI. <br> <b>Research Design and Methods:</b> Data were used from 471,399 (56% women) individuals without cardiovascular disease (CVD) included in the UK Biobank. Sex-specific incidence rates were calculated by diabetes status and across levels of HbA1c, using Poisson regression. Cox proportional hazards analyses estimated sex-specific hazard ratios (HR) and women-to-men ratios by diabetes status and HbA1c for MI during a mean follow-up of 9 years. <br> <b>Results:</b> Women had lower incidence rates of MI than men, regardless of diabetes status or HbA1c level. Compared with individuals without diabetes, prediabetes, undiagnosed diabetes, and previously diagnosed diabetes were associated with increased risk of MI in both sexes. Previously diagnosed diabetes was more strongly associated with MI in women (HR 2∙33 [95%CI 1∙96;2∙78]) than men (1∙81 [1∙63;2∙02]), with a women-to-men ratio of HRs of 1∙29 (1∙05;1∙58). Each 1% higher HbA1c, independent of diabetes status, was associated with an 18% greater risk of MI in both women and men.<br> <b>Conclusions:</b> Although the incidence of MI was higher in men than women, the presence of diabetes is associated with a greater excess relative risk of MI in women. However, each 1% higher HbA1c was associated with an 18% greater risk of MI in both women and men.<br> <br>


2018 ◽  
Author(s):  
Roman Teo Oliynyk

AbstractBackgroundGenome-wide association studies and other computational biology techniques are gradually discovering the causal gene variants that contribute to late-onset human diseases. After more than a decade of genome-wide association study efforts, these can account for only a fraction of the heritability implied by familial studies, the so-called “missing heritability” problem.MethodsComputer simulations of polygenic late-onset diseases in an aging population have quantified the risk allele frequency decrease at older ages caused by individuals with higher polygenic risk scores becoming ill proportionately earlier. This effect is most prominent for diseases characterized by high cumulative incidence and high heritability, examples of which include Alzheimer’s disease, coronary artery disease, cerebral stroke, and type 2 diabetes.ResultsThe incidence rate for late-onset diseases grows exponentially for decades after early onset ages, guaranteeing that the cohorts used for genome-wide association studies overrepresent older individuals with lower polygenic risk scores, whose disease cases are disproportionately due to environmental causes such as old age itself. This mechanism explains the decline in clinical predictive power with age and the lower discovery power of familial studies of heritability and genome-wide association studies. It also explains the relatively constant-with-age heritability found for late-onset diseases of lower prevalence, exemplified by cancers.ConclusionsFor late-onset polygenic diseases showing high cumulative incidence together with high initial heritability, rather than using relatively old age-matched cohorts, study cohorts combining the youngest possible cases with the oldest possible controls may significantly improve the discovery power of genome-wide association studies.


2019 ◽  
Vol 28 (R2) ◽  
pp. R197-R206 ◽  
Author(s):  
Michael A Lodato ◽  
Christopher A Walsh

AbstractAging is a mysterious process, not only controlled genetically but also subject to random damage that can accumulate over time. While DNA damage and subsequent mutation in somatic cells were first proposed as drivers of aging more than 60 years ago, whether and to what degree these processes shape the neuronal genome in the human brain could not be tested until recent technological breakthroughs related to single-cell whole-genome sequencing. Indeed, somatic single-nucleotide variants (SNVs) increase with age in the human brain, in a somewhat stochastic process that may nonetheless be controlled by underlying genetic programs. Evidence from the literature suggests that in addition to demonstrated increases in somatic SNVs during aging in normal brains, somatic mutation may also play a role in late-onset, sporadic neurodegenerative diseases, such as Alzheimer’s disease and Parkinson’s disease. In this review, we will discuss somatic mutation in the human brain, mechanisms by which somatic mutations occur and can be controlled, and how this process can impact human health.


Blood ◽  
2013 ◽  
Vol 122 (21) ◽  
pp. 518-518 ◽  
Author(s):  
Hideki Makishima ◽  
Thomas LaFramboise ◽  
Bartlomiej P Przychodzen ◽  
Kenichi Yoshida ◽  
Matthew Ruffalo ◽  
...  

Abstract Chromosomal aberrations and somatic mutations constitute key elements of the pathogenesis of myelodysplastic syndromes (MDS), a clonal hematologic malignancy characterized by cytopenias, a dysplastic bone marrow and propensity to clonal evolution. Next generation sequencing (NGS) enables definition of somatic mutational patterns and clonal architecture as a discovery platform, and for clinical applications. We systematically applied NGS to 707 cases of MDS and MDS-related disorders. 205 cases (low-risk MDS: N=78, high-risk MDS: N=42, MDS/MPN: N=48 and sAML: N=37) were tested by whole exome sequencing (WES). For validation in an additional 502 patients (low-risk MDS: N=192, high-risk MDS: N=104, MDS/MPN: N=111 and sAML: N=95), targeted deep NGS was applied for 60 index genes which were most commonly affected in the cohort analyzed by WES. For NGS data analysis a statistical pipeline was developed to focus on: i) identification of the most relevant somatic mutations, and ii) minimization of false positive results. We studied serial samples from 21 exemplary informative patients. We also compared somatic mutational patterns to those seen in primary AML TCGA cohort (N=201). Given the size of the cohort, there was, for example, a 87% chance of seeing mutations at a frequency of 1% and a 98% of seeing those with a frequency of 2%. While focusing on the most common events, we observed 1117 somatic mutations in 199 genes. The 88 genes mutated mutated in >1% of cases with MDS carried 388 mutations in MDS+sAML (2.5/case), 128 in MDS/MPN (2.7/case) and 398 in pAML (2.0/case). The average number of mutations per case increased during progression (2.2 in lower-risk, 2.8 in higher-risk MDS, 3.4 in sAML). In MDS, the 30 most frequently affected genes were present at least once in 70% of patients. The 30 most frequently mutated genes in MDS/MPN were mutated in 82% of patients. Individual mutations were also sub-grouped according to their function. When we compared three MDS subcategories (lower-risk, higher-risk MDS and sAML) in a cross-sectional view, RTK family, RAS family, IDH family and cohesin family mutations were more frequently detected in the sAML group than in the MDS group. In contrast, the frequency of the DNMT family, TET2 and ASXL family gene mutations did not increase in frequency in the sAML cohort. In addition to better definition of mutational patterns of known genes, we have also defined new mutations, including in the RNA helicase family and the BRCC3pathway. Clonal architecture analysis indicates that mutations of TET2, DNMT3A, ASXL1, and U2AF1 most likely represent ancestral/originator events, while those of the IDH family, RTK family and cohesin family are typical secondary events. Establishment of mutational patterns may improve the precision of morphologically-based diagnosis. The comparison between MDS-related diseases (MDS+sAML) and pAML revealed a notably different mutational pattern suggestive of a distinct molecular derivation of these two disease groups. While RTK, IDH family and NPM1 mutations were more frequently observed in the pAML cohort, mutations of SF3B1 and SRSF2, were more common in MDS+sAML. With regard to the connections between individual mutation combinations, RTK mutations were strongly associated with DNMT, but not with RAS family mutations in the pAML cohort, while the mutual association between TET2 and PRC2 family, cohesin family and RUNX1were encountered in the MDS+sAML cohort. Individual mutations may have prognostic significance, including having an impact on survival, either within the entire cohort or within specific subgroups. In the combined MDS cohort, TP53 family mutations were associated with a poor prognosis (HR; 3.65, 95%CI; 1.90-7.01, P<.0001) by univariate analysis. Similar results were found for mutations in TCF4(HR; 7.98, 95%CI; 1.58-10.1, P<.0007). Such an individual approach does not allow for assessment of the impact of less common mutational events. In conclusion, our study continues to indicate the power of NGS in the molecular analysis of MDS. MDS and related disorders show a great deal of pathogenetic molecular overlap, consistent with their morphologic and clinical pictures, but also distinct molecular differences in mutational patterns. Some of the specific mutations are pathognomonic for specific subtypes while some may convey a prognostic rather than discriminatory value. Disclosures: Makishima: Scott Hamilton CARES grant: Research Funding; AA & MDS international foundation: Research Funding. Polprasert:MDS foundation: Research Funding.


1996 ◽  
Vol 2 (3) ◽  
pp. 133-139
Author(s):  
A. Phanjoo

Psychotic disorders in the elderly can be divided into three types: disorders that have started in earlier life and persist into old age; disorders that start de novo after the age of 60, and psychoses associated with brain disease, including the dementias. The classification of psychoses in late life has provoked controversy for nearly a century. The debate concerns whether schizophrenia can present at any stage of life or whether functional psychoses, arising for the first time in late life, represent different illnesses. The nomenclature of such disorders consists of numerous terms including late onset schizophrenia, late paraphrenia, paranoid psychosis of late life and schizophreniform psychosis. This plethora of terms has made research difficult to interpret.


Placenta ◽  
2013 ◽  
Vol 34 (9) ◽  
pp. A42 ◽  
Author(s):  
Yeonkyung Cho ◽  
Hee Jin Park ◽  
Soo Hyun Kim ◽  
Ji Yeon Kim ◽  
Kyoung Jin Lee ◽  
...  

2017 ◽  
Author(s):  
Guillaume Paré ◽  
Shihong Mao ◽  
Wei Q. Deng

AbstractMachine-learning techniques have helped solve a broad range of prediction problems, yet are not widely used to build polygenic risk scores for the prediction of complex traits. We propose a novel heuristic based on machine-learning techniques (GraBLD) to boost the predictive performance of polygenic risk scores. Gradient boosted regression trees were first used to optimize the weights of SNPs included in the score, followed by a novel regional adjustment for linkage disequilibrium. A calibration set with sample size of ~200 individuals was sufficient for optimal performance. GraBLD yielded prediction R2 of 0.239 and 0.082 using GIANT summary association statistics for height and BMI in the UK Biobank study (N=130K; 1.98M SNPs), explaining 46.9% and 32.7% of the overall polygenic variance, respectively. For diabetes status, the area under the receiver operating characteristic curve was 0.602 in the UK Biobank study using summary-level association statistics from the DIAGRAM consortium. GraBLD outperformed other polygenic score heuristics for the prediction of height (p<2.2x10−16) and BMI (p<1.57x10−4), and was equivalent to LDpred for diabetes. Results were independently validated in the Health and Retirement Study (N=8,292; 688,398 SNPs). Our report demonstrates the use of machine-learning techniques, coupled with summary-level data from large genome-wide meta-analyses to improve the prediction of polygenic traits.


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