Individual Risk Characteristics Research Articles

Polygenic risk scores and the prediction of onset of mood and psychotic disorders in adolescents and young adults.

To examine whether polygenic risk scores (PRS) for neuroticism, depression, bipolar disorder and schizophrenia are higher in individuals manifesting trans-diagnostic risk factors for the development of major mental disorders and whether PRS enhance prediction of early onset full-threshold disorders. Using data from the Brisbane Longitudinal Twin Study, we examined individual PRS for neuroticism, depression, bipolar disorder and schizophrenia, recorded evidence of subthreshold syndromes and family history of mood and/or psychotic disorders and noted progression to trans-diagnostic clinical caseness (onset of major mental disorders) at follow-up. We undertook multivariate, receiver operating curve and logistic regression analyses that were adjusted for known variables of influence (age, twin status, and so on). Of 1473 eligible participants (female = 866, 59%; mean age 26.3 years), 28% (n = 409) met caseness criteria for a mood and/or psychotic disorder. All PRS were higher in cases versus non-cases but associations with different levels of risk were inconsistent. The prediction of caseness (reported as area under the curve with 95% confidence intervals [CI]) improved from 0.68 (95% CI: 0.65, 0.71) when estimated using clinical risk factors alone up to 0.71 (95% CI: 0.69, 0.73) when PRS were added to the model. Logistic regression identified five variables that optimally classified individuals according to caseness: age, sex, individual risk characteristics, PRS for depression and mental health case status of cotwins or siblings. The findings need replication. However, this exploratory study suggests that combining PRS with other risk factors has the potential to improve outcome prediction in youth.

Generalised decision curve analysis for explicit comparison of treatment effects.

Decision curve analysis (DCA) helps integrate prediction models with treatment assessments to guide personalised therapeutic choices among multiple treatment options. However, the current versions of DCA do not explicitly model treatment effects in the analysis but implicitly or holistically assess therapeutic benefits and harms. In addition, the existing DCA cannot allow the comparison of multiple treatments using a standard metric. To develop a generalised version of DCA (gDCA) by decomposing holistically assessed net benefits and harms into patient preferences versus empirical evidence (as obtained in the trials, meta-analyses of clinical studies, etc.) to allow individualised comparison of single or multiple treatments using a common metric. We reformulated DCA by (1) decomposing holistic, implicit utilities into specific utilities related to treatment effects and patient's relative values (RV) about disease outcomes versus treatment harms, (2) explicitly modelling each treatment effect at the level of probabilities and/or utilities (outcomes) in a decision tree, and (3) avoiding scaling effects employed in the original DCA to enable comparison of treatment effects against the common metrics. We used data from a published network meta-analysis of randomised trials to inform the use of statin treatment according to Framingham Risk Model. We illustrate the analysis by modelling the effects of three statins in the primary prevention of cardiovascular disease. We performed simultaneous comparisons against standard metrics (RV) for all treatments. We examined for which RV values, a predictive model for guiding personalised treatment, outperformed the strategies of treating everyone or treating no one. We found that the magnitude of benefits (efficacy) seems more important than the simple ratio of efficacy/harms. We describe gDCA for evaluating single or multiple treatments to help tailor therapy toward individual risk characteristics. gDCA further helps integrate the principles of evidence-based medicine with decision analysis.

Efficacy and safety of anti-androgens in the management of polycystic ovary syndrome: a systematic review and meta-analysis of randomised controlled trials

Anti-androgens and combined oral contraceptive pills (COCPs) may mitigate hyperandrogenism-related symptoms of polycystic ovary syndrome (PCOS). However, their efficacy and safety in PCOS remain unclear as previous reviews have focused on non-PCOS populations. To inform the 2023 International Evidence-based Guideline in PCOS, we conducted the first systematic review and meta-analysis investigating the efficacy and safety of anti-androgens in the management of hormonal and clinical features of PCOS. We systematically searched MEDLINE, Embase, PsycInfo, All EBM reviews, and CINAHL up to 28th June 2023 for randomised controlled trials (RCTs) examining oral anti-androgen use, alone or in combination with metformin, COCPs, lifestyle, or other interventions, in women of any age, with PCOS diagnosed by Rotterdam, National Institutes of Health or Androgen Excess & PCOS Society criteria, and using a form of contraception. Non-English studies and studies of less than 6 months duration or which used the same anti-androgen regimen in both/all groups were excluded in order to establish efficacy for the clinical outcomes of interest. Three authors screened articles against selection criteria and assessed risk of bias and quality using the Grading of Recommendations, Assessment, Development and Evaluations (GRADE) framework. Critical outcomes (prioritised during guideline development for GRADE purposes) included weight, body mass index (BMI), irregular cycles, hirsutism, liver function, and quality of life. Random effects meta-analyses were conducted where appropriate. This study is registered with PROSPERO, CRD42022345640. From 1660 studies identified in the search, 27 articles comprising 20 unique studies were included. Of these, 13 studies (n=961) were pooled in meta-analysis. Seven studies had a high risk of bias, nine moderate and four low. Anti-androgens included finasteride, flutamide, spironolactone, or bicalutamide. In meta-analysis, anti-androgens+lifestyle were superior to metformin+lifestyle for hirsutism (weighted mean difference [WMD] [95% CI]:-1.59 [-3.06,-0.12], p=0.03; I2=74%), SHBG (7.70nmol/l [0.75, 14.66], p=0.03; I2=0%), fasting insulin and fasting insulin: glucose ratio (-2.11 μU/ml [-3.97,-0.26], p=0.03; I2=0% and-1.12 [-1.44,-0.79], p<0.0001, I2=0%, respectively), but were not superior to placebo+lifestyle for hirsutism (-0.93, [-3.37, 1.51], p=0.45; I2=76%) or SHBG (9.72nmol/l [-0.71, 20.14], p=0.07; I2=31%). Daily use was more effective for hirsutism than use every three days (-3.48 [-4.58,-2.39], p<0.0001, I2=1%), and resulted in lower androstenedione levels (-0.30ng/ml [-0.50,-0.10], p=0.004; I2=0%). Combination treatment with anti-androgens+metformin+lifestyle resulted in lower testosterone compared with metformin+lifestyle (-0.29nmol/l [-0.52,-0.06], p=0.01; I2=61%), but there were no differences in hirsutism when anti-androgens+metformin+lifestyle were compared with either anti-androgens+lifestyle or metformin+lifestyle. In limited meta-analyses (n=2 trials), combining anti-androgens with COCP resulted in poorer lipid profiles compared with COCP±placebo, with no differences in other outcomes. Current evidence does not support the use of anti-androgens preferentially to COCPs to treat hyperandrogenism in PCOS. Anti-androgens could be considered to treat hirsutism in PCOS, where COCPs are contraindicated, poorly tolerated, or present a sub-optimal response after a minimum 6-month period, with consideration of clinical context and individual risk factors and characteristics. National Health and Medical Research Council (NHMRC) of Australia Monash University.

Identification of cuproptosis-related lncRNAs for prognosis and immunotherapy in glioma.

Glioma is a highly invasive primary brain tumour, making it challenging to accurately predict prognosis for glioma patients. Cuproptosis is a recently discovered cell death attracting significant attention in the tumour field. Whether cuproptosis-related genes have prognostic predictive value has not been clarified. In this study, uni-/multi-variate Cox and Lasso regression analyses were applied to construct a risk model based on cuproptosis-related lncRNAs using TCGA and CGGA cohorts. A nomogram was constructed to quantify individual risk, including clinical and genic characteristics and risk. GO and KEGG analyses were used to define functional enrichment of DEGs. Tumour mutation burden (TMB) and immune checkpoint analyses were performed to evaluate potential responses to ICI therapy. Ten prognostic lncRNAs were obtained from Cox regression. Based on the median risk score, patients were divided into high- and low-risk groups. Either for grade 2-3 or for grade 4, glioma patients with high-risk exhibited significant poorer prognoses. The risk was an independent risk factor associated with overall survival. The high-risk group was functionally associated with immune responses and cancer-related pathways. The high-risk group was associated with higher TMB scores. The expression levels of many immune checkpoints in the high-risk group were significantly higher than those in the low-risk group. Differentiated immune pathways were primarily enriched in the IFN response, immune checkpoint and T-cell co-stimulation pathways. In conclusion, we established a risk model based on cuproptosis-related lncRNAs showing excellent prognostic prediction ability but also indicating the immuno-microenvironment status of glioma.

How Do Healthy Women Perceive the Risk of Breast Cancer? The Role of Illness Perceptions and Compared Risk between Portugal and the U.A.E.

(1) Background: Breast cancer (BC) shows significant epidemiological differences between Eastern and Western countries. These may arise from socio-cultural factors influencing how healthy young women perceive this condition, their risk of getting cancer, and the implications for preventive screening behaviors. In this study, the illness perceptions, individual risk perception, compared risk, and beliefs about preventive behaviors for BC of female university students were compared using an anonymous online survey between a European country (Portugal) and the United Arab Emirates. (2) Method: A structural equation model (SEM) was developed to investigate the hypothetical relationship between illness perceptions and compared risk as predictors of perceived risk for BC. (3) Results: There were significant differences between the study variables. The SEM was invariant, but the differences between regression coefficients in both countries were highly statistically significant. Mediation analyses revealed a significant indirect effect of compared risk on individual risk and a significantly stronger direct effect for the Emirati sample. (4) Conclusions: These findings suggest that cultural research may help to explain factors that may shape social comparison of individual risk characteristics and influence perceived risk. Moreover, providing culturally appropriate strategies to be designed and implemented can promote early detection behaviors for BC.

FINANCIAL RISK METER FOR CRYPTOCURRENCIES AND TAIL RISK NETWORK-BASED PORTFOLIO CONSTRUCTION

Cryptocurrencies have emerged as a new asset class. In order to provide a thorough understanding of this new asset class, we study the dependencies in tail risk events within cryptocurrencies, and provide a hedging alternative in this paper. First, we adopt the Financial Risk Meter approach for cryptocurrencies, which is able to identify individual risk characteristics and indicate systemic risk in a network topology. Next, we detect the interdependencies across digital coins and study the spillover effects. Finally, we construct tail event sensitive portfolios and test the performance versus traditional approaches from January 2019 to May 2022.

Individual and Contextual Protective and Risk Characteristics for Residents of Recovery Homes

ABSTRACT Some recovery homes have facilitating relationships and organizational characteristics, and there are also social capital differences among residents of these recovery homes. It is important to better understand the impact of protective and risk individual and house factors on recovery issues among residents of these community-based settings. Individuals from 42 recovery homes were followed for up to six data collection periods over two years. House level latent class analyses tapped relationship and organizational domains and individual level latent class analyses were from derived from elements of recovery capital. Houses that manifested protective factors provided most residents positive outcomes, except those with elevated self-esteem. Houses that were less facilitating had more negative exits, except for those residents who were the highest functioning. Both individual and house characteristics are of importance in helping to understand risk factors associated with eviction outcomes for residents in recovery homes.

Financial Risk Meter FRM based on Expectiles

The Financial Risk Meter (FRM) is an established quantitative tool that, based on conditional Value at Risk (VaR) ideas, yields insight into the dynamics of network risk. Originally, the FRM has been composed via Lasso based quantile regression, but we here extend it by incorporating the idea of expectiles, thus indicating not only the tail probability but rather the actual tail loss given a stress situation in the network. The expectile variant of the FRM enjoys several advantages: Firstly, the multivariate tail risk indicator conditional expectile-based VaR (CoEVaR) can be derived, which is sensitive to the magnitude of extreme losses. Next, FRM index is not restricted to an index compared to the quantile based FRM mechanisms, but can be expanded to a set of systemic tail risk indicators, which provide investors with numerous tools in terms of diverse risk preferences. The power of FRM also lies in displaying the FRM distribution across various entities every day. In a functional data context, the FRM identifies outlying curves and serves as a signal box to display aberrant functional behavior. Two distinct patterns can be discovered under high stress and during stable periods from the empirical results in the United States stock market. Furthermore, the framework is able to identify individual risk characteristics and to capture spillover effects in a network.

Financial Risk Meter Based on Expectiles

The Financial Risk Meter (FRM) is an established quantitative tool that, based on conditional Value at Risk (VaR) ideas, yields insight into the dynamics of network risk. Originally, the FRM has been composed via Lasso based quantile regression, but we here extend it by incorporating the idea of expectiles, thus indicating not only the tail probability but rather the actual tail loss given a stress situation in the network. The expectile variant of the FRM enjoys several advantages: Firstly, the multivariate tail risk indicator conditional expectile-based VaR (CoEVaR) can be derived, which is sensitive to the magnitude of extreme losses. Next, FRM index is not restricted to an index compared to the quantile based FRM mechanisms, but can be expanded to a set of systemic tail risk indicators, which provide investors with numerous tools in terms of diverse risk preferences. The power of FRM also lies in displaying the FRM distribution across various entities every day. Two distinct patterns can be discovered under high stress and during stable periods from the empirical results in the United States stock market. Furthermore, the framework is able to identify individual risk characteristics and to capture spillover effects in a network.

Anxiety sensitivity and pain intensity independently predict opioid misuse and dependence in chronic pain patients

The United States (US) population consumes an estimated 68% of the world's prescribed opioids each year, and over 2 million adults in the US suffer from an opioid use disorder. Although chronic pain populations are among the highest risk segments of the general population for opioid misuse and dependence, there is little understanding of individual risk characteristics that may contribute to greater risk for these outcomes among this group. The present investigation explored the concurrent role of anxiety sensitivity and pain intensity and their interaction in relation to opioid misuse and dependence among 429 adults with chronic pain (73.9% female, Mage = 38.32 years, SD = 11.07). Results revealed that both anxiety sensitivity and pain intensity were associated with opioid misuse and dependence. There was no evidence of an interaction for either outcome. Post-hoc analyses indicated that of the lower-order anxiety sensitivity facets, physical and mental incapacitation concerns contributed to variance in opioid misuse and only mental incapacitation concerns contributed to variance in opioid dependence. Overall, the current findings suggest the importance of assessing anxiety sensitivity in screening for opioid-related problems among persons with chronic pain, as it may represent a distinct pathway to poorer opioid-related outcomes among this group.

Tail Risk Network Effects in the Cryptocurrency Market during the COVID-19 Crisis

Cryptocurrencies are gaining momentum in investor attention, are about to become a new asset class, and may provide a hedging alternative against the risk of devaluation of fiat currencies following the COVID-19 crisis. In order to provide a thorough understanding of this new asset class, risk indicators need to consider tail risk behaviour and the interdependencies between the cryptocurrencies not only for risk management but also for portfolio optimization. The tail risk network analysis framework proposed in the paper is able to identify individual risk characteristics and capture spillover effect in a network topology. Finally we construct tail event sensitive portfolios and consequently test the performance during an unforeseen COVID-19 pandemic.

Clinical Correlation Between Computerized Tomography Findings and Pathologic Diagnosis in Patients Surgically Treated for Complex Renal Cysts in a Colombian Tertiary Center.

IntroductionRecent data have reexamined the historical rates of malignancy observed in Bosniak IIF and III cystic lesions, and this has led to an endorsement of the use of active surveillance as the standard of care for Bosniak III renal cysts by affirming that overtreatment rates for this subgroup are close to 50%. In light of this, the present study describes the correlation between imaging studies and pathologic diagnosis in patients surgically treated for complex renal cysts in Colombia.Materials and methodsThis is a retrospective, descriptive, and observational study. We analyzed the medical histories of patients who had been surgically treated for complex renal cysts between 2010 and 2018 in the urology department of a tertiary center in Bogota, Colombia. The exclusion criteria were incomplete clinical histories and absent diagnostic images or their official interpretation.ResultsOut of the 235 urological procedures performed, nine were excluded. And 6.19% (n = 14) were cases of surgically treated complex renal cysts; 38.46% were compatible with Bosniak IIF, 46.15% were Bosniak III, and 15.38% were Bosniak IV. The percentage of renal cancer as revealed by the histopathological study was 60, 66.7 and 100 for renal cysts Bosniak IIF, III, and IV, respectively; 77.7% of these confirmed oncological cases had received a diagnosis of clear cell renal carcinoma.ConclusionsIn our study, the percentage of malignancy in patients with renal cysts Bosniak IIF and III was found to be greater compared to the findings in the recent medical literature. We believe that the management offered to the population with complex renal cysts in Colombia should be tailored to the individual risk characteristics of each patient.

Systematic Mortality Improvement Trends and Mortality Heterogeneity: Insights from Individual-Level HRS Data

Providers of life annuities and pensions need to consider both systematic mortality improvement trends and mortality heterogeneity. Although how mortality improvement varies with age and gender at the population level is well studied, how trends vary with risk factors remains relatively unexplored. This article assesses how systematic mortality improvement trends vary with individual risk characteristics using individual-level longitudinal data from the U.S. Health and Retirement Study between 1994 and 2009. Initially a Lee-Carter model is used to assess mortality improvement trends by grouping individuals with similar risk characteristics of gender, education, and race. We then fit a longitudinal mortality model to individual-level data allowing for heterogeneity and time trends in individual-level risk factors. Our results show how survey data can provide valuable insights into both mortality heterogeneity and improvement trends more effectively than commonly used aggregate models. We show how mortality improvement differs across individuals with different risk factors. Significantly, at an individual level, mortality improvement trends have been driven by changes in health history such as high blood pressure, cancer, and heart problems rather than risk factors such as education, marital status, body mass index, and smoker status.

Performance Measures Adjusted for the Risk Situation (PARS)

We introduce the new class of Performance measures Adjusted for the Risk Situation (PARS), which incorporate individual risk characteristics in the financial performance measure. The (risk) situation of an individual or company is represented by all of its future cash flows including (financial) consumption preferences; due to the effected risk transformation, PARS have zero volatility under the investment strategy replicating these future cash flows. We give several examples of cash flow structures for individuals and companies, showing how their PARS could be defined. In the context of a debt manager, we demonstrate how the PARS can be applied to the dynamic control of bond portfolios via sensitivities.

Systemic risk in the European sovereign and banking system

We investigate the systemic risk of the European sovereign and banking system during 2008–2013. We utilize a conditional measure of systemic risk that reflects market perceptions and can be intuitively interpreted as an entity’s conditional joint probability of default, given the hypothetical default of other entities. The measure of systemic risk is applicable to high dimensions and not only incorporates individual default risk characteristics but also captures the underlying interdependent relations between sovereigns and banks in a multivariate setting. In empirical applications, our results reveal significant time variation in systemic risk spillover effects for the sovereign and banking system. We find that systemic risk is mainly driven by risk premiums coupled with a steady increase in physical default risk.

Prognostic Classification Factors Associated With Development of Multiple Autoantibodies, Dysglycemia, and Type 1 Diabetes-A Recursive Partitioning Analysis.

To define prognostic classification factors associated with the progression from single to multiple autoantibodies, multiple autoantibodies to dysglycemia, and dysglycemia to type 1 diabetes onset in relatives of individuals with type 1 diabetes. Three distinct cohorts of subjects from the Type 1 Diabetes TrialNet Pathway to Prevention Study were investigated separately. A recursive partitioning analysis (RPA) was used to determine the risk classes. Clinical characteristics, including genotype, antibody titers, and metabolic markers were analyzed. Age and GAD65 autoantibody (GAD65Ab) titers defined three risk classes for progression from single to multiple autoantibodies. The 5-year risk was 11% for those subjects >16 years of age with low GAD65Ab titers, 29% for those ≤16 years of age with low GAD65Ab titers, and 45% for those subjects with high GAD65Ab titers regardless of age. Progression to dysglycemia was associated with islet antigen 2 Ab titers, and 2-h glucose and fasting C-peptide levels. The 5-year risk is 28%, 39%, and 51% for respective risk classes defined by the three predictors. Progression to type 1 diabetes was associated with the number of positive autoantibodies, peak C-peptide level, HbA1c level, and age. Four risk classes defined by RPA had a 5-year risk of 9%, 33%, 62%, and 80%, respectively. The use of RPA offered a new classification approach that could predict the timing of transitions from one preclinical stage to the next in the development of type 1 diabetes. Using these RPA classes, new prevention techniques can be tailored based on the individual prognostic risk characteristics at different preclinical stages.

Mortality Heterogeneity and Systematic Mortality Improvement

Insurers and pension funds provide life annuities and pensions that are impacted by both aggregate mortality improvement and individual mortality heterogeneity. Aggregate population mortality trends have shown significant improvement over long periods of time. Individual mortality heterogeneity arises from differing risk characteristics across individuals. This paper assesses the extent that systematic mortality improvement varies with individual risk characteristics. To do this, a Lee-Carter model is used to assess if mortality improvement varies for groups of individuals with similar risk characteristics along with an individual mortality model that allows for heterogeneity with time trends to assess systematic risk. Data from the U.S. Health and Retirement Study (HRS) is used since this provides longitudinal, individual level data. Our results are highly relevant to life insurers, pension funds and regulators assessing the future impact of improvement trends in mortality on their premiums and liabilities. Mortality trends differ across individuals reflecting the different risk factors and particularly the prevalence of different diseases such as high blood pressure, cancer and heart problems. Models that are based on aggregate population level trends and differing only by gender and age are not adequate in quantifying mortality trends and risks.

Time-varying systematic and idiosyncratic risk exposures of US bank holding companies

We study the time-varying risk exposures of US bank holding companies for the 1986–2012 period by decomposing total bank risk into systematic banking-industry risk, systematic market-wide risk, and idiosyncratic bank risk. Banking-industry risk factors directly relate to the banks’ financial intermediation functions while market-wide risk factors affect all stocks. In contrast, idiosyncratic risk is driven by individual bank risk characteristics. We analyze the systematic bank risk factors in time series regressions, determine their importance with a democratic orthogonalization technique, and explore the idiosyncratic risk in a panel regression framework. Our results suggest that corporate credit risk and real estate risk are most detrimental in crisis periods, while banks’ interest rate risk sensitivity has changed over the last decade. The banks’ equity ratio, loan-loss provisions, fraction of real estate loans, and proportion of non-interest income relate to the differences in individual bank risk. Moreover, banks’ idiosyncratic risk contains a strong state-level business cycle component. Our results are robust to alternative risk factor specifications. Overall, our study contributes to understanding the structure and time-variation of banks’ systematic and idiosyncratic risks.

Time-Varying Systematic and Idiosyncratic Risk Exposures of US Bank Holding Companies

We study the time-varying risk exposures of US bank holding companies for the 1986 to 2012 period by decomposing total bank risk into systematic banking-industry risk, systematic market-wide risk, and idiosyncratic bank risk. Banking-industry risk factors directly relate to the banks’ financial intermediation functions while market-wide risk factors affect all stocks. In contrast, idiosyncratic risk reflects individual bank risk characteristics. We analyze the systematic bank risk factors in time series regressions, determine their importance with a democratic orthogonalization technique, and explore the idiosyncratic risk in a panel regression framework. Our results suggest that corporate credit risk and real estate risk are most detrimental in crisis periods, while banks’ interest-rate-risk sensitivity changes over the last decade. The banks’ leverage, loan-loss provisions, fraction of real estate loans, and proportion of non-interest income relate to the differences in individual bank risk. Moreover, banks’ idiosyncratic risk contains a strong state-level business cycle component. Our results are robust to alternative risk factor specifications. Overall, our study contributes to understanding the structure and time-variation of banks’ systematic and idiosyncratic risks.

Ethnic density is not associated with psychological distress in Turkish-Dutch, Moroccan-Dutch and Surinamese-Dutch ethnic minorities in the Netherlands

Ethnic density, the proportion of people of the same ethnic group in the neighbourhood, has been identified as a protective factor with regard to mental health in ethnic minorities. Research on the putative intermediating factors, exposure to discrimination and improved social support, has not yielded conclusive evidence. We investigated the association between ethnic density and psychological well-being in three ethnic minority groups in the Netherlands. We also assessed whether a protective ethnic density effect is related to the degree to which each group experiences discrimination and social support at group level. Using multi-level linear regression modelling, we studied the influence of ethnic density at neighbourhood level on psychological distress, measured with the Kessler Psychological Distress scale (K10), in 13,864 native Dutch, 1,206 Surinamese-Dutch, 978 Turkish-Dutch and 784 Moroccan-Dutch citizens of the four major cities in the Netherlands. Based on a nationwide survey among ethnic minorities on social integration, ethnic groups were ordered with respect to the intermediating factors. Ethnic density was not associated with psychological distress in any of the three ethnic minority groups. As a consequence, we found no support for either experiences of discrimination or for own-group social interactions at group level as intermediating factors. In all three ethnic minority groups, as well as in the native Dutch group, individual demographic and socio-economic factors emerged as the main explanations for individuals' mental well-being. These results suggest that individual demographic and socio-economic risk characteristics outweigh the influence of neighbourhood attributes on mental health.