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- Kaptoge, S., et al.
(författare)
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World Health Organization cardiovascular disease risk charts: revised models to estimate risk in 21 global regions
- 2019
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Ingår i: Lancet Global Health. - : Elsevier BV. - 2214-109X. ; 7:10
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Tidskriftsartikel (refereegranskat)abstract
- Background To help adapt cardiovascular disease risk prediction approaches to low-income and middle-income countries, WHO has convened an effort to develop, evaluate, and illustrate revised risk models. Here, we report the derivation, validation, and illustration of the revised WHO cardiovascular disease risk prediction charts that have been adapted to the circumstances of 21 global regions. Methods In this model revision initiative, we derived 10-year risk prediction models for fatal and non-fatal cardiovascular disease (ie, myocardial infarction and stroke) using individual participant data from the Emerging Risk Factors Collaboration. Models included information on age, smoking status, systolic blood pressure, history of diabetes, and total cholesterol. For derivation, we included participants aged 40-80 years without a known baseline history of cardiovascular disease, who were followed up until the first myocardial infarction, fatal coronary heart disease, or stroke event. We recalibrated models using age-specific and sex-specific incidences and risk factor values available from 21 global regions. For external validation, we analysed individual participant data from studies distinct from those used in model derivation. We illustrated models by analysing data on a further 123 743 individuals from surveys in 79 countries collected with the WHO STEPwise Approach to Surveillance. Findings Our risk model derivation involved 376 177 individuals from 85 cohorts, and 19 333 incident cardiovascular events recorded during 10 years of follow-up. The derived risk prediction models discriminated well in external validation cohorts (19 cohorts, 1 096 061 individuals, 25 950 cardiovascular disease events), with Harrell's C indices ranging from 0.685 (95% CI 0 . 629-0 741) to 0.833 (0 . 783-0- 882). For a given risk factor profile, we found substantial variation across global regions in the estimated 10-year predicted risk. For example, estimated cardiovascular disease risk for a 60-year-old male smoker without diabetes and with systolic blood pressure of 140 mm Hg and total cholesterol of 5 mmol/L ranged from 11% in Andean Latin America to 30% in central Asia. When applied to data from 79 countries (mostly low-income and middle-income countries), the proportion of individuals aged 40-64 years estimated to be at greater than 20% risk ranged from less than 1% in Uganda to more than 16% in Egypt. Interpretation We have derived, calibrated, and validated new WHO risk prediction models to estimate cardiovascular disease risk in 21 Global Burden of Disease regions. The widespread use of these models could enhance the accuracy, practicability, and sustainability of efforts to reduce the burden of cardiovascular disease worldwide. Copyright (C) 2019 The Author(s). Published by Elsevier Ltd.
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- Klionsky, Daniel J., et al.
(författare)
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Guidelines for the use and interpretation of assays for monitoring autophagy
- 2012
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Ingår i: Autophagy. - : Informa UK Limited. - 1554-8635 .- 1554-8627. ; 8:4, s. 445-544
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Forskningsöversikt (refereegranskat)abstract
- In 2008 we published the first set of guidelines for standardizing research in autophagy. Since then, research on this topic has continued to accelerate, and many new scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Accordingly, it is important to update these guidelines for monitoring autophagy in different organisms. Various reviews have described the range of assays that have been used for this purpose. Nevertheless, there continues to be confusion regarding acceptable methods to measure autophagy, especially in multicellular eukaryotes. A key point that needs to be emphasized is that there is a difference between measurements that monitor the numbers or volume of autophagic elements (e.g., autophagosomes or autolysosomes) at any stage of the autophagic process vs. those that measure flux through the autophagy pathway (i.e., the complete process); thus, a block in macroautophagy that results in autophagosome accumulation needs to be differentiated from stimuli that result in increased autophagic activity, defined as increased autophagy induction coupled with increased delivery to, and degradation within, lysosomes (in most higher eukaryotes and some protists such as Dictyostelium) or the vacuole (in plants and fungi). In other words, it is especially important that investigators new to the field understand that the appearance of more autophagosomes does not necessarily equate with more autophagy. In fact, in many cases, autophagosomes accumulate because of a block in trafficking to lysosomes without a concomitant change in autophagosome biogenesis, whereas an increase in autolysosomes may reflect a reduction in degradative activity. Here, we present a set of guidelines for the selection and interpretation of methods for use by investigators who aim to examine macroautophagy and related processes, as well as for reviewers who need to provide realistic and reasonable critiques of papers that are focused on these processes. These guidelines are not meant to be a formulaic set of rules, because the appropriate assays depend in part on the question being asked and the system being used. In addition, we emphasize that no individual assay is guaranteed to be the most appropriate one in every situation, and we strongly recommend the use of multiple assays to monitor autophagy. In these guidelines, we consider these various methods of assessing autophagy and what information can, or cannot, be obtained from them. Finally, by discussing the merits and limits of particular autophagy assays, we hope to encourage technical innovation in the field.
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- Reinbold, C. S., et al.
(författare)
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Analysis of the Influence of microRNAs in Lithium Response in Bipolar Disorder
- 2018
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Ingår i: Frontiers in Psychiatry. - : Frontiers Media SA. - 1664-0640. ; 9
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Tidskriftsartikel (refereegranskat)abstract
- Bipolar disorder (BD) is a common, highly heritable neuropsychiatric disease characterized by recurrent episodes of mania and depression. Lithium is the best-established long-term treatment for BD, even though individual response is highly variable Evidence suggests that some of this variability has a genetic basis. This is supported by the largest genome-wide association study (GWAS) of lithium response to date conducted by the International Consortium on Lithium Genetics (ConLiGen) Recently, we performed the first genome-wide analysis of the involvement of miRNAs in BD and identified nine BD associated miRNAs However, it is unknown whether these miRNAs are also associated with lithium response in BD. In the present study, we therefore tested whether common variants at these nine candidate miRNAs contribute to the variance in lithium response in BD. Furthermore, we systematically analyzed whether any other miRNA in the genome is implicated in the response to lithium. For this purpose, we performed gene-based tests for all known miRNA coding genes in the ConLiGen GWAS dataset (n = 2,563 patients) using a set-based testing approach adapted from the versatile gene based test for GWAS (VEGAS2). In the candidate approach, miR-499a showed a nominally significant association with lithium response, providing some evidence for involvement in both development and treatment of BD. In the genome-wide miRNA analysis, 71 miRNAs showed nominally significant associations with the dichotomous phenotype and 106 with the continuous trait for treatment response. A total of 15 miRNAs revealed nominal significance in both phenotypes with miR-633 showing the strongest association with the continuous trait (p = 9.80E-04) and miR-607 with the dichotomous phenotype (p = 5.79E-04). No association between miRNAs and treatment response to lithium in BD in either of the tested conditions withstood multiple testing correction. Given the limited power of our study, the investigation of miRNAs in larger GWAS samples of BD and lithium response is warranted.
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| 9. |
- Amare, A. T., et al.
(författare)
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Association of polygenic score for major depression with response to lithium in patients with bipolar disorder
- 2021
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Ingår i: Molecular Psychiatry. - : Springer Science and Business Media LLC. - 1359-4184 .- 1476-5578. ; 26, s. 2457-2470
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Tidskriftsartikel (refereegranskat)abstract
- Lithium is a first-line medication for bipolar disorder (BD), but only one in three patients respond optimally to the drug. Since evidence shows a strong clinical and genetic overlap between depression and bipolar disorder, we investigated whether a polygenic susceptibility to major depression is associated with response to lithium treatment in patients with BD. Weighted polygenic scores (PGSs) were computed for major depression (MD) at different GWAS p value thresholds using genetic data obtained from 2586 bipolar patients who received lithium treatment and took part in the Consortium on Lithium Genetics (ConLi(+)Gen) study. Summary statistics from genome-wide association studies in MD (135,458 cases and 344,901 controls) from the Psychiatric Genomics Consortium (PGC) were used for PGS weighting. Response to lithium treatment was defined by continuous scores and categorical outcome (responders versus non-responders) using measurements on the Alda scale. Associations between PGSs of MD and lithium treatment response were assessed using a linear and binary logistic regression modeling for the continuous and categorical outcomes, respectively. The analysis was performed for the entire cohort, and for European and Asian sub-samples. The PGSs for MD were significantly associated with lithium treatment response in multi-ethnic, European or Asian populations, at various p value thresholds. Bipolar patients with a low polygenic load for MD were more likely to respond well to lithium, compared to those patients with high polygenic load [lowest vs highest PGS quartiles, multi-ethnic sample: OR = 1.54 (95% CI: 1.18-2.01) and European sample: OR = 1.75 (95% CI: 1.30-2.36)]. While our analysis in the Asian sample found equivalent effect size in the same direction: OR = 1.71 (95% CI: 0.61-4.90), this was not statistically significant. Using PGS decile comparison, we found a similar trend of association between a high genetic loading for MD and lower response to lithium. Our findings underscore the genetic contribution to lithium response in BD and support the emerging concept of a lithium-responsive biotype in BD.
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| 10. |
- Cearns, M., et al.
(författare)
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Using polygenic scores and clinical data for bipolar disorder patient stratification and lithium response prediction: machine learning approach
- 2022
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Ingår i: British Journal of Psychiatry. - : Royal College of Psychiatrists. - 0007-1250 .- 1472-1465. ; 220:4, s. 219-228
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Tidskriftsartikel (refereegranskat)abstract
- Background Response to lithium in patients with bipolar disorder is associated with clinical and transdiagnostic genetic factors. The predictive combination of these variables might help clinicians better predict which patients will respond to lithium treatment. Aims To use a combination of transdiagnostic genetic and clinical factors to predict lithium response in patients with bipolar disorder. Method This study utilised genetic and clinical data (n = 1034) collected as part of the International Consortium on Lithium Genetics (ConLi(+)Gen) project. Polygenic risk scores (PRS) were computed for schizophrenia and major depressive disorder, and then combined with clinical variables using a cross-validated machine-learning regression approach. Unimodal, multimodal and genetically stratified models were trained and validated using ridge, elastic net and random forest regression on 692 patients with bipolar disorder from ten study sites using leave-site-out cross-validation. All models were then tested on an independent test set of 342 patients. The best performing models were then tested in a classification framework. Results The best performing linear model explained 5.1% (P = 0.0001) of variance in lithium response and was composed of clinical variables, PRS variables and interaction terms between them. The best performing non-linear model used only clinical variables and explained 8.1% (P = 0.0001) of variance in lithium response. A priori genomic stratification improved non-linear model performance to 13.7% (P = 0.0001) and improved the binary classification of lithium response. This model stratified patients based on their meta-polygenic loadings for major depressive disorder and schizophrenia and was then trained using clinical data. Conclusions Using PRS to first stratify patients genetically and then train machine-learning models with clinical predictors led to large improvements in lithium response prediction. When used with other PRS and biological markers in the future this approach may help inform which patients are most likely to respond to lithium treatment.
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