Strong Predictor Of Risk Research Articles

Hair color, family history of melanoma, and the risk of Parkinson's disease: An analysis update

BackgroundA shared biological component between melanoma and Parkinson's disease (PD) has been suggested. Yet, epidemiological evidence is scarce. ObjectiveTo examine the association of hair color and family history of melanoma, two strong predictors of melanoma risk, with the occurrence of PD. MethodsWe followed 131,342 women and men for ∼30 years for the development of PD. We calculated the cumulative incidence of PD from ages 40 to 90 according to hair color, and estimated the hazard ratio of PD according to hair color and family history of melanoma. ResultsHair color was not strongly associated with the risk of PD, especially at advanced ages. In contrast, individuals with a family history of melanoma had a 1.4-fold higher risk of PD compared to those without a history. ConclusionsOur results support the hypothesis of a shared biological component between PD and melanoma. Both pigmentary and non-pigmentary pathways may play a role.

The effect of thiazolidinediones on body fat redistribution in adults: A systematic review and meta-analysis of randomized controlled trials.

Visceral adiposity is a strong predictor of cardiometabolic risk. Thiazolidinediones (TZDs) are associated with a shift in fat redistribution from visceral adipose tissue (VAT) to subcutaneous adipose tissue (SAT). We aimed to compare the effects of TZD and other interventions on fat remodeling in adults in randomized controlled trials. Among the 1331 retrieved studies, 39 trials with 1765 participants were included in the meta-analysis. The standardized mean difference in VAT change was not significantly different between TZD and comparators across the overall studies. Intriguingly, TZD treatment resulted in significant decreases in VAT compared with placebo and sulfonylureas (p < 0.05), although recombinant human growth hormone was superior to TZD regarding VAT reduction (p < 0.05). Data from 216 participants showed TZD leading to a greater reduction in liver fat percentage than comparators (p < 0.05). Compared with the controls, TZD significantly increased SAT, total body fat, weight, waist circumference, and body mass index (p < 0.05). However, TZD pronouncedly improved glucose control, insulin resistance, adiponectin, and lipid profile (p < 0.05). TZD provides a favorable effect on fat redistribution and benefits insulin sensitivity, suggesting a potentially valuable approach in cardiometabolic risk management.

Effect of LPA gene on CAD risk among diabetic patients

Abstract Introduction Previous research reported that the LPA gene is a strong and independent predictor of CAD in non-diabetic patients but not in patients with type 2 diabetes mellitus (T2DM). These results suggest that the LPA gene might contribute less to CAD risk in patients with T2DM than in the general population. Objective Investigate, in our population, the association between the LPA gene variant rs3798220 T&amp;gt;C and CAD risk among the total, diabetic and non-diabetic populations. Methods 3161 individuals (1724 coronary patients and 1437 controls) were genotyped for LPA rs3798220 T&amp;gt;C. This variant has a MAF &amp;lt;2%; hence the risk homozygous CC is a rare genotype, and we used the heterozygous CT in our analysis. Bivariate and multivariate logistic regression evaluated LPA heterozygous genotype (CT) and CAD risk in the whole population, in the group with T2DM (n=782) and without T2DM (n=2379). The multivariate model was adjusted to the main traditional risk factors for CAD. Results After multivariate analysis, LPA CT remained a strong and independent predictor of CAD risk in the whole and non-diabetic populations (OR=2.12; 95%CI: 1.43-3.14; p&amp;lt;0.0001 and OR=2.56; 95%CI: 1.64-4.00; p&amp;lt;0.0001, respectively). However, this association was less pronounced in the diabetic population (OR= 1.12; 95%CI: 0.47-2.65; p=0.799). Conclusions Our results show that the effect of the homozygous LPA genotype on CAD risk among the diabetic population might differ from that in non-diabetics, with less impact on diabetic patients. Many aspects of the genetic regulation of Lp(a) in diabetics are not fully understood, and other genetic and environmental factors may be crucial to CAD risk in diabetes, such as those linked to insulin resistance and hyperglycemia.

Combining hypothesis-driven ECG indices with machine learning improves ventricular arrhythmic risk prediction in a low-risk population

Abstract Background Life-threatening ventricular arrhythmias (LTVA) are a leading cause of mortality, but early identification of high-risk individuals remains a major challenge. An ECG index, TMV (T-wave morphology variations), predicts LTVA in 51,794 individuals without known cardiovascular disease from the UK Biobank (UKB) within a 10-year follow up (cohort 1), with an area under the ROC curve (AUC) of 0.558 and a hazard ratio (HR) of 1.57. Machine learning on the ECG has shown a high accuracy in detecting patients with cardiovascular disease, but its LTVA predictive value in a low-risk population and comparisons with hypothesis-driven risk markers, like TMV, is unknown. Purpose We tested the LTVA predictive value of a score combining a multi-layer convolutional neural network (CNN) model and TMV in individuals without known cardiovascular disease. Methods Using UKB we split the individuals from cohort 1 into training (90%) and internal test (10%) sets (Figure 1). In the training set, we applied 10-fold cross validation to train a multi-layer CNN with an attention layer, where the input was a 15-second ECG at rest (lead I), and the output was occurrence (or not) of LTVA within a 10-year follow-up. We used 10-second ECG data (lead I) from an independent cohort of 32,209 individuals without cardiovascular disease from UKB (cohort 2) as an external test set (median follow-up was 3.4 years), where we also calculated TMV. We next combined them into a score (weighted by their relative contribution to LTVA in a logistic regression). The AUC, and Cox regression HRs were calculated to estimate the performance of the CNN, TMV and the combined score. Results In cohort 1, 217 subjects had a LTVA during the follow-up period. In the internal test set, the AUC of the CNN was 0.707. In the external test set (60 LTVA events during the follow-up period), the CNN’s prediction and TMV led to AUCs of 0.610 and 0.604, respectively. Interestingly, the CNN’s prediction and TMV were not correlated (ρ = 0.005), and the combined score led to an AUC of 0.627. We set a threshold at the score value that maximised sensitivity and specificity, and survival analyses showed a HR of 3.007 (P &amp;lt; 0.0001) for individuals in the score+ (score &amp;gt; threshold) versus those in the score- (score &amp;lt; threshold) group, after adjusting for age and gender (Figure 2). The continuous score also predicted LTVA independently from age and gender. Conclusions A CNN-based model predicts LTVA in a low-risk population independently from TMV, a strong ECG risk predictor derived from knowledge on the underlying electrophysiology. When TMV and the CNN are combined into a score, the LTVA risk stratification improves, independently from age and gender. Our findings support the combination of hypothesis-based risk markers and CNN approaches to optimise LTVA prediction in a low-risk population.Study designSurvival curves

Ascending aortic length associates to acute aortic dissection

Abstract Background Acute Stanford type A aortic dissection is a life-threatening condition that is challenging to predict and prevent. The risk for aortic dissection is known to increase with ascending aortic (AA) dilatation, which sets indication for preventive aortic replacement. Anyhow, most of the dissections occur before aortic diameter reaches the diameter indications of preventive surgery. Purpose To investigate whether, alongside with aortic dilatation, AA elongation affects the increased risk for aortic dissection. Methods This retrospective study included patients treated for Stanford type A aortic dissection (n=102), patients with AA dilatation (max. diameter &amp;gt; 40 mm) (n=134) and healthy controls (n=191). AA length and dimensions were measured from aortic computed tomography angiography (CTA) images. The AA length was defined as a distance between the aortic annulus to the origin of the brachiocephalic trunk. Results AA length was significantly increased in patients with aortic dissection when compared to patients with AA dilatation and to healthy controls (106.2 ± 15.1 mm vs. 96.8 ± 11.2 mm vs. 82.5 ± 8.1 mm; p&amp;lt;0.001). After adjusting AA length to patient's height, BSA, sex and maximal diameter, adjusted length of AA was 99.8 mm in the dissection group, 94.4 mm in the AA dilatation group and 86.0 mm in healthy controls. Thus, when comparing adjusted AA length between the groups, dissected aortas were 13.8 mm longer than in healthy controls and 5.4 mm longer than in dilated non-dissected aortas (p&amp;lt;0.001). Dilated non-dissected aortas were 8.4 mm longer than in healthy controls (p&amp;lt;0.001). In the dissection cohort, 25/102 (24.5%) patients had maximal AA diameter &amp;lt;55 mm and 16/102 (12.7%) patients had maximal AA diameter ≥55 mm alongside with AA length &amp;gt; 110 mm (Table). By combining 110 mm AA length and &amp;lt;55 mm diameter, 58% of the dissection patients would have been identified instead of using only AA diameter ≥55 mm as a cut-off criteria, based on which only 33% patients met the indication for elective surgery. Conclusions To conclude, AA length seems to be a strong predictor of risk of type A aortic dissection and thus might be used as an additional tool in identifying patients for preventive surgery.Cut off - limits

Abstract 13873: Deep Learning-Based GWAS of Aortic Valve Function Links 81 Loci to Aortic Stenosis Risk

Background &amp; Aims: The genetic influences on normal aortic valve (AV) function and their impact on aortic stenosis (AS) risk are of significant interest. We sought to identify common genetic determinants of normal variation in AV function, and to understand their relationship with clinical disease. Methods: We used deep learning to measure peak velocity, mean gradient, and aortic valve area from MRI in UK Biobank participants. We conducted genome-wide association studies (GWAS) in a subset of 44,780 participants without cardiovascular disease. We performed multi-trait analysis of GWAS (MTAG) to incorporate data from AV measurements and AS diagnoses in UK Biobank and FinnGen, and applied PRScs to construct polygenic scores (PGS). Results: In the unadjusted analysis, 26 loci were associated with at least one AV measurement. Incorporating the AS GWAS using MTAG, we identified 81 distinct loci (62 with AV traits, 54 with AS, 35 overlapping), including PCSK9 (β=0.032, P=5.8E-09) and LDLR (β=0.018, P=2.3E-10). Additional loci were associated with lipid metabolism (LPA, FADS2, SORT1), atherosclerosis (CDKN2B, ARHGEF26, OTUD7B, PRDM16), inflammation (IL6, KLF2, TRAF1), and cardiac development and aortic root function (SMAD3, GATA4, ELN, TBX20, TEX41). The PGS for the unadjusted mean gradient was predictive of AS in FinnGen (HR 1.36 for the top 5% of participants vs all others, P=1.8E-14). All of Us participants in the top 5% of the MTAG-adjusted peak velocity PGS had an HR of 2.5 for incident AS (P=4.4E-08). Mendelian randomization showed association between Lp(a) and LDL—but not ApoA—on greater peak velocity (P=2.7E-13, P=1.6E-16, and P=0.60, respectively). Conclusion: We identified 81 genetic loci linked to AV function or AS. AV measurement PGS were strong predictors of AS risk. ApoB and Lp(a) showed causal association with higher peak velocity. These findings have implications for the early pathogenesis of AS and suggest modifiable pathways as targets for prevention.

Circulating Platelets in Thrombotic Antiphospholipid Syndrome Display High Procoagulant Activity and Surface Complement Deposition: Correlation with Thrombotic Risk

Background Antiphospholipid syndrome (APS) is characterized by circulating antiphospholipid antibodies (aPL) accompanied by arterial and/or venous thrombosis, and/or recurrent pregnancy loss. It commonly affects young women in whom it dramatically increases their risk of myocardial infarction, ischemic stroke, deep vein thrombosis and pulmonary embolism. Although the thrombotic risk associated with aPL is established, the underlying mechanisms are still incompletely defined and there are no definitive predictive biomarkers. As a result the management of APS involves indefinite anticoagulation. Increased activation of vascular cells, including platelets is considered to underlie the pathogenesis of APS, and may be mediated in some cases by complement. There is a considerable crosstalk between platelets and complement at various levels during thrombosis. First, activated complement proteins stimulate platelets through their cognate receptors on the platelet surface, and genetic deficiency of complement proteins in mice leads to decreased platelet responsiveness. Second, platelets secrete complement proteins into the medium upon activation. Third, agonist-stimulated platelets contribute to activation of both the classical and alternative pathways of the complement cascade. Hence, we sought to investigate the association between the complement-platelet axis and the prothrombotic pathology observed in APS. Methods Venous blood was drawn from patients with APS and healthy controls, and washed human platelets were prepared by differential centrifugation. Platelets either remained unstimulated or were treated with thrombin (0.1, 0.25, 0.5 U/ml) for 5 min and then labelled with FITC-PAC1, PE-anti-CD62P antibody, Alexa Fluor 488-anti-C4d conjugate, PE-Annexin V, Mitotracker Red, MitoSOX Red, Fluo4-AM, Rhod2-AM, Cell Event Green Caspase 3/7 detection reagent and FITC-IETD-FMK for 30 min in the dark, and then analyzed by flow cytometry to measure integrin activation, P-selectin expression (α-granule secretion), complement C4d binding, phosphatidylserine (PS) exposure, mitochondrial membrane potential, mitochondrial ROS, cytosolic calcium, mitochondrial calcium, caspase 3/7 activity (apoptosis) and caspase 8 activity (extrinsic apoptosis pathway), respectively. Platelet markers were correlated with complement binding and clinical characteristics of APS patients including number of thrombotic events and circulating levels of aPL. Results Thrombin-induced platelet integrin activation and P-selectin expression were identical in APS patients and healthy controls. However, platelets isolated from APS patients had significantly higher procoagulant activity (PS exposure) as well as complement C4d binding in both unstimulated and thrombin-stimulated states compared to healthy controls. There was a strong positive correlation between platelet PS exposure and C4d binding in APS patients, but not healthy individuals, suggesting a role for complement activation in formation of procoagulant platelets in APS. A subset of APS patients with high platelet procoagulant activity (PS exposure &amp;gt; mean + 2SD observed in healthy individuals) had lower mitochondrial membrane potential and increased mitochondrial calcium transients compared to healthy individuals, suggesting mitochondrial permeability transition pore (mPTP)-driven PS exposure. Another subset of APS patients with PS exposing platelets showed caspase activation indicating apoptosis. Platelet PS exposure and C4d binding correlated strongly with the number of thrombotic events as well as levels of circulating aPL, which identifies complement-induced procoagulant platelets as predictors and possible underlying mechanisms for thrombosis in APS. Conclusion Our preliminary studies suggest that PS-exposing platelets are generated in APS by multiple mechanisms including agonist-induced mPTP formation and apoptosis-associated caspase activation. Platelet PS exposure in APS is positively correlated with C4d binding, suggesting a role for complement activation in driving platelet procoagulant activity. Complement activation and PS exposure on platelets are strong predictors of thrombotic risk in APS and could serve as promising targets for novel antithrombotic agents in APS management.

Computed Tomography (CT) Calcium Scoring in Primary Prevention of Acute Coronary Syndrome and Future Cardiac Events in Patients With Systemic Lupus Erythematosus.

Systemic lupus erythematosus (SLE) is a complex and chronic autoimmune disease that impacts multiple organ systems and presents with varying symptomatology that makes targeting treatment extremely difficult. The cardiovascular system and more specifically the coronary arteries are heavily affected by SLE causing increased atherosclerosis and subsequently increased acute coronary syndrome (ACS) and increased future cardiac events. ACS is a common occurrence in patients with SLE due to the premature development of atherosclerosis due to the dysregulation of pro-inflammatory cytokines. Calcium scoring has been effectively utilized to identify plaque burden in patients with coronary artery calcification (CAC). Calcium scoring is a score obtained from a computed tomography (CT) image using non-contrast imaging, which provides quantitative information regarding CAC and aids in assessing cardiovascular risk. A calcium score of zero Hounsfeild unitscan be obtained using CT calcium scoring which indicates no calcium is identified in the coronary arteries and is a strong negative risk predictor for coronary artery disease. Early screening of SLE patients with CT calcium scoring could aid in early detection and treatment subsequently leading to delay of premature coronary atherosclerosis and future cardiac events in this patient population. Multiple studies have used calcium scoring as a method to measure arterial calcification in SLE patients. The Society of Cardiovascular Imaging has now endorsed the idea of obtaining a baseline calcium artery score with a repeat progression scan in 3-5 years. Calcium scoring has also been identified as an effective initial tool for stratification and identification of possible ACS. The various advantages of early calcium scoring signify the further research needed to fully understand and implement the advantages calcium scoring has to offer patients with SLE.

Serum uric acid is related to liver and kidney disease and 12-year mortality risk after myocardial infarction.

To study the associations of non-alcoholic fatty liver disease (NAFLD), chronic kidney disease (CKD), and serum uric acid (SUA) in patients with post-myocardial infarction (MI) patients, and the relationship of SUA with 12-year mortality risk. We included 3,396 patients (60-80 years old, 78% men) of the Alpha Omega Cohort. Multivariable prevalence ratios (PRs) were obtained for the association of NAFLD [fatty liver index (FLI), ≥77 (women) and ≥79 (men)] with CKD [estimated glomerular filtration rate (eGFR), <60 mL/min per 1.73 m2]. We calculated sensitivity and specificity of SUA to detect the (combined) presence and absence of NAFLD and CKD. Cause-specific mortality was monitored from enrolment (2002-2006) through December 2018. Hazard ratios (HRs) for all-cause and cardiovascular disease (CVD) mortality in SUA categories were obtained from multivariable Cox models. Median baseline FLI was 67 (men, 68; women, 64), and mean ± SD eGFR was 81 ± 20 mL/min per 1.73 m2 (17% with CKD). Sex-specific FLI was associated with higher CKD prevalence (PRtertile3 vs. tertile1, 1.94; 95% confidence interval: 1.57, 2.39). Baseline SUA was 0.36 ± 0.09 mmol/L. With increasing SUA concentrations, specificity for the presence of NAFLD, CKD, or both increased, and sensitivity decreased. During 12 (interquartile range, 9-14) years of follow-up, 1,592 patients died (713 from CVD). HRs ranged from 1.08 (0.88, 1.32) for SUA ≤0.25 mmol/L to 2.13 (1.75, 2.60) for SUA >0.50 mmol/L vs. SUA >0.30-0.35 mmol/L for all-cause mortality. For CVD mortality, HRs ranged from 1.05 (0.77, 1.44) to 2.43 (1.83, 3.25). NAFLD and CKD were strongly associated, which was reflected by higher SUA concentrations. SUA was a strong predictor of 12-year mortality risk after MI.

Coronary Event Risk Test (CERT) as a Risk Predictor for the 10-Year Clinical Outcome of Patients with Peripheral Artery Disease.

(1) Background: Ceramides are a new kind of lipid biomarker and have already been demonstrated to be valuable risk predictors in coronary patients. Patients with peripheral artery disease (PAD) are a population with a worse prognosis and higher mortality risk compared to coronary artery disease (CAD) patients. However, the value of ceramides for risk prediction in PAD patients is still vague, as addressed in the present study. (2)Methods: This observational study included 379 PAD patients. The primary endpoint was all-cause mortality at 10 years of follow-up. A set of ceramides was measured by LC-MS/MS and combined according to the Coronary Event Risk Test (CERT) score, which categorizes patients into one of four risk groups (low risk, moderate risk, high risk, very high risk). (3) Results: Kaplan-Meier survival curves revealed that the overall survival of patients decreased with the increasing risk predicted by the four CERT categories, advancing from low risk to very high risk. Cox regression analysis demonstrated that each one-category increase resulted in a 35% rise in overall mortality risk (HR = 1.35 [1.16-1.58]). Multivariable adjustment, including, among others, age, LDL-cholesterol, type 2 diabetes, and statin treatment before the baseline, did not abrogate this significant association (HR = 1.22 [1.04-1.43]). Moreover, we found that the beneficial effect of statin treatment is significantly stronger in patients with a higher risk, according to CERT. (4) Conclusions: We conclude that the ceramide-based risk score CERT is a strong predictor of the 10-year mortality risk in patients with PAD.

Assessment of machine learning algorithms in national data to classify the risk of self-harm among young adults in hospital: A retrospective study

Self-harm is one of the most common presentations at accident and emergency departments in the UK and is a strong predictor of suicide risk. The UK Government has prioritised identifying risk factors and developing preventative strategies for self-harm. Machine learning offers a potential method to identify complex patterns with predictive value for the risk of self-harm. National data in the UK Mental Health Services Data Set were isolated for patients aged 18-30years who started a mental health hospital admission between Aug 1, 2020 and Aug 1, 2021, and had been discharged by Jan 1, 2022. Data were obtained on age group, gender, ethnicity, employment status, marital status, accommodation status and source of admission to hospital and used to construct seven machine learning models that were used individually and as an ensemble to predict hospital stays that would be associated with a risk of self-harm. The training dataset included 23 808 items (including 1081 episodes of self-harm) and the testing dataset 5951 items (including 270 episodes of self-harm). The best performing algorithms were the random forest model (AUC-ROC 0.70, 95%CI:0.66-0.74) and the ensemble model (AUC-ROC 0.77 95%CI:0.75-0.79). Machine learning algorithms could predict hospital stays with a high risk of self-harm based on readily available data that are routinely collected by health providers and recorded in the Mental Health Services Data Set. The findings should be validated externally with other real-world, prospective data. This study was supported by the Midlands and Lancashire Commissioning Support Unit.

The association between disability progression, relapses, and treatment in early relapse onset MS: an observational, multi-centre, longitudinal cohort study

The indirect contribution of multiple sclerosis (MS) relapses to disability worsening outcomes, and vice-versa, remains unclear. Disease modifying therapies (DMTs) are potential modulators of this association. Understanding how these endo-phenotypes interact may provide insights into disease pathogenesis and treatment practice in relapse-onset MS (ROMS). Utilising a unique, prospectively collected clinical data from a longitudinal cohort of 279 first demyelinating event cases followed for up to 15 years post-onset, we examined indirect associations between relapses and treatment and the risk of disability worsening, and vice-versa. Indirect association parameters were estimated using joint models for longitudinal and survival data. Early relapses within 2.5 years of MS onset predicted early disability worsening outcomes (HR = 3.45, C.I 2.29–3.61) per relapse, but did not contribute to long-term disability worsening thereinafter (HR = 0.21, C.I 0.15–0.28). Conversely, disability worsening outcomes significantly contributed to relapse risk each year (HR = 2.96, C.I 2.91–3.02), and persisted over time (HR = 3.34, C.I 2.90–3.86), regardless of DMT treatments. The duration of DMTs significantly reduced the hazards of relapses (1st-line DMTs: HR = 0.68, C.I 0.58–0.79; 3rd-line DMTs: HR = 0.37, C.I 0.32–0.44) and disability worsening events (1st-line DMTs: HR = 0.74, C.I 0.69–0.79; 3rd-line DMTs: HR = 0.90, C.I 0.85–0.95), respectively. Results from time-dynamic survival probabilities further revealed individuals having higher risk of future relapses and disability worsening outcomes, respectively. The study provided evidence that in ROMS, relapses accrued within 2.5 years of MS onset are strong indicators of disability worsening outcomes, but late relapses accrued 2.5 years post onset are not overt risk factors for further disability worsening. In contrast, disability worsening outcomes are strong positive predictors of current and subsequent relapse risk. Long-term DMT use and older age strongly influence the individual outcomes and their associations.

Nonfasting remnant cholesterol and cardiovascular disease risk prediction in Albertans: a prospective cohort study.

European studies have shown that nonfasting remnant cholesterol can be a strong predictor of cardiovascular disease risk and may contribute to identifying residual risk; however, Canadian data are lacking on nonfasting remnant cholesterol. In this study, we aimed to determine the relation between nonfasting remnant cholesterol, low-density lipoprotein (LDL) cholesterol and cardiovascular disease among people in Alberta. In this retrospective analysis, we used data from Alberta's Tomorrow Project, a large prospective cohort that enrolled Albertans aged 35-69 years (2000-2015). Participants with consent to data linkage, with complete nonfasting lipid data and without existing cardiovascular disease were included. The nonfasting remnant cholesterol and LDL cholesterol relation with a composite cardiovascular disease outcome of major incident cardiovascular diagnoses, ascertained by linking to Alberta Health databases, was determined by multivariable logistic regression, adjusting for age, sex, statin use, comorbidities, and LDL cholesterol or remnant cholesterol. The final sample of 13 988 participants was 69.4% female, and the mean age was 61.8 (standard deviation [SD] 9.7) years. Follow-up time was approximately 15 years. Mean remnant cholesterol was significantly higher among individuals with versus without cardiovascular disease (0.87 [SD 0.40] mmol/L v. 0.78 [SD 0.38] mmol/L, standardized mean difference [SMD] -0.24), and mean LDL cholesterol was significantly lower (2.69 [SD 0.93] mmol/L v. 2.88 [SD 0.84] mmol/L, SMD 0.21). The odds of incident composite cardiovascular disease were significantly increased per mmol/L increase in remnant cholesterol (adjusted odds ratio [OR] 1.48, 95% confidence interval [CI] 1.27-1.73) but significantly decreased per mmol/L increase in LDL cholesterol (adjusted OR 0.73, 95% CI 0.68-0.79). In this large Albertan cohort of predominantly older females, nonfasting remnant cholesterol had a positive relation with cardiovascular disease incidence, whereas LDL cholesterol did not. These findings support the clinical utility of measuring non-fasting remnant cholesterol to detect cardiovascular disease risk.

Nutritional status of chronic kidney disease patients undergoing hemodialysis in Colombo, Sri Lanka

Introduction: Malnutrition is a common condition among patients receiving hemodialysis and one of the principal indicators of mortality and morbidity.Objectives: The main aim of the study was to assess the nutritional status of hemodialysis recipients.Methods: A cross-sectional study was conducted on fiftyfive patients in two nephrology clinics in Colombo. Data were collected under four categories; an interviewer administered questionnaire, anthropometric measurements, biochemical investigations and derived calculations. Urea reduction ratio and Kt/V were calculated to assess the dialysis efficiency. Nutritional Risk Index was employed to evaluate the malnutrition risk.Results: In the study population, 70.9% were males and mean age, mean dialysis duration were recorded as 56.05±13.16 years and 13±12 months respectively. According to BMI, 10.9% of the patients were underweight, 38.2%; normal, 14.5%; overweight and 36.4% were obese. Low levels of serum albumin, total protein, iron and hemoglobin were reported among 30.9%, 14.5%, 41.8% and 89.1% of patients respectively. More than 50% of the population had an increased CRP level, depicting an inflammatory reaction. Mean URR and Kt/V of the population were 65.8±9.6% and 1.3±0.3 respectively, which were above the recommended level. Malnutrition risk was negatively correlated with serum total calcium level (r=-0.49, p&lt;0.001), total protein level (r=-0.415, p&lt;0.05), dialysis duration (r=-0.351, p&lt;0.05), diastolic blood pressure (r=-0.35, p&lt;0.05) and MUAC (r=-0.332, p&lt;0.05) and positively correlated with absolute lymphocyte count (r=0.332, p&lt;0.05).Conclusion: Majority of the patients had an efficient dialysis session. High, moderate and low risk of malnutrition were analyzed as 7.3%, 29.1% and 63.6%. In a binomial logistic regression, total calcium level (p&lt;0.001, Exp(B); 0.027) and appetite of patients (p=0.04, Exp(B);13.816) were identified as strong predictors of malnutrition risk.

460-P: Pulse Pressure Predicts Future Cardiovascular Events in Cardiovascular Disease Patients with Type 2 Diabetes

Pulse pressure, i.e. the difference between systolic and diastolic blood pressure is a measure of arterial stiffness, which is a strong predictor of cardiovascular risk. Its impact on the risk of cardiovascular events in patients with type 2 diabetes (T2DM) is unclear and is addressed in the present study. We enrolled a large high risk cohort of 1327 patients with established cardiovascular disease (1009 patients with angiographically proven stable CAD and 318 patients with sonographically proven peripheral artery disease). T2DM was diagnosed according to ADA criteria. Prospectively, cardiovascular events were recorded over a mean follow-up period of 9.3±4.6 years. At baseline, pulse pressure was 60±17 mmHg in patients with T2DM (n=481) and 57±17 mmHg in subjects who did not have T2DM (p=0.002). During follow-up, 614 patients suffered cardiovascular events. The event rate was higher in patients with T2DM than in those who did not have diabetes (54.8% vs. 42.2% p&amp;lt;0.001). Pulse pressure significantly predicted cardiovascular events after multivariate adjustment including diabetes status in the total study cohort, with a standardized adjusted hazard ratio (HR) of 1.14 [1.05-1.24]; p=0.003. Importantly, pulse pressure also predicted cardiovascular events in the very high risk subgroup of cardiovascular disease patients with T2DM (HR 1.20 [1.06-1.37]; p=0.006). We conclude that pulse pressure predicts cardiovascular events in cardiovascular disease patients with T2DM. Disclosure T.Plattner: None. C.H.Saely: None. A.Vonbank: None. A.Mader: None. L.Sprenger: None. M.Maechler: None. B.Larcher: None. A.Leiherer: None. A.Muendlein: None. H.Drexel: None.

Evaluation of an AI Model to Assess Future Breast Cancer Risk.

Background Accurate breast cancer risk assessment after a negative screening result could enable better strategies for early detection. Purpose To evaluate a deep learning algorithm for risk assessment based on digital mammograms. Materials and Methods A retrospective observational matched case-control study was designed using the OPTIMAM Mammography Image Database from the National Health Service Breast Screening Programme in the United Kingdom from February 2010 to September 2019. Patients with breast cancer (cases) were diagnosed following a mammographic screening or between two triannual screening rounds. Controls were matched based on mammography device, screening site, and age. The artificial intelligence (AI) model only used mammograms at screening before diagnosis. The primary objective was to assess model performance, with a secondary objective to assess heterogeneity and calibration slope. The area under the receiver operating characteristic curve (AUC) was estimated for 3-year risk. Heterogeneity according to cancer subtype was assessed using a likelihood ratio interaction test. Statistical significance was set at P < .05. Results Analysis included patients with screen-detected (median age, 60 years [IQR, 55-65 years]; 2044 female, including 1528 with invasive cancer and 503 with ductal carcinoma in situ [DCIS]) or interval (median age, 59 years [IQR, 53-65 years]; 696 female, including 636 with invasive cancer and 54 with DCIS) breast cancer and 1:1 matched controls, each with a complete set of mammograms at the screening preceding diagnosis. The AI model had an overall AUC of 0.68 (95% CI: 0.66, 0.70), with no evidence of a significant difference between interval and screen-detected (AUC, 0.69 vs 0.67; P = .085) cancer. The calibration slope was 1.13 (95% CI: 1.01, 1.26). There was similar performance for the detection of invasive cancer versus DCIS (AUC, 0.68 vs 0.66; P = .057). The model had higher performance for advanced cancer risk (AUC, 0.72 ≥stage II vs 0.66 <stage II; P = .037). The AUC for detecting breast cancer in mammograms at diagnosis was 0.89 (95% CI: 0.88, 0.91). Conclusion The AI model was found to be a strong predictor of breast cancer risk for 3-6 years following a negative mammographic screening. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Mann and Sechopoulos in this issue.

Geosexual Archetype, Preventive Behaviors, and Sexually Transmitted Infections Among High-Risk Men Who Have Sex With Men.

Social geography plays an important role in transmission of sexually transmitted infections (STIs) among men who have sex with men. Previous qualitative work had identified 7 "geosexual archetypes," each with distinct travel patterns for sex and potentially important differences in STI rates. The objective of this article was to explore what could be learned about STI transmission by looking at STI prevention strategies (condom use and preexposure prophylaxis use) and prevalence of STIs among these geosexual archetypes. We analyzed data from the Sex Now 2019 online survey in Canada. Men who have sex with men who reported 3 or more partners in the past 6 months were included in the analysis (n = 3649). The most common archetype was "geoflexible" (sex at home, partner's home, and other places; 35.6%), followed by "privates" (sex only at own/partner's home; 23.0%); the least common archetypes was "rover" (sex not at home or partner's place; 4.0%). There were significant variations in both STI prevention strategies and prevalence of bacterial STIs in the past year by geosexual archetype. In particular, among those who were HIV negative, those who reported a geoflexible archetype and used preexposure prophylaxis but did not use condoms consistently had a 52.6% prevalence of bacterial STIs, which was much higher compared with all other groups. Within other archetypes, those living with HIV had the highest prevalence of bacterial STIs. Geosexual archetype together with participant's STI prevention strategies was a strong predictor of bacterial STI risk. Understanding how place is connected to bacterial STIs is key in prevention as individuals do not live in isolation.

Assessing the ecological vulnerability of Western Atlantic marine benthic gastropods

Assessing the extinction risk in marine invertebrates poses serious challenges to conservation biology, due to the magnitude of marine biodiversity, the inaccessibility of most of the marine realm, and the lack of appropriate data on population dynamics and ecology for most species. However, simple life history traits have a huge potential for preliminary screening criteria for assessing large numbers of species whose status is harsh or impossible to evaluate. Body size and trophic position could be strong predictors of extinction risk providing a general framework for the assessment of species vulnerability. We analyzed the Body Size-Trophic Position (BS-TP) relationship along 1,067 genera representing 4,256 nominal species of western Atlantic benthic gastropods. We found that a carnivore diet characterizes 67% of the genera and that, supporting theoretical predictions, the probability of being carnivores as a function of size showed a unimodal trend. For species with adult body sizes larger than 5 cm, a negative association between trophic position and body size was detected. This result points to an energetic restriction for the viability of large species, implying that organisms placed near the BS-TP boundary are extremely vulnerable to environmental changes. With this result, 109 genera from 42 families of carnivore gastropods and 33 genera from 19 families of herbivore gastropods that may be more vulnerable from the analyzed perspective were identified and ranked. Supporting these results, while the most vulnerable genera are not represented in global IUCN assessments, all our ‘top 10’ vulnerable families are being considered in National or Regional Red Lists. Prior to conducting regional or global conservation assessments for invertebrate taxa, screening methods should be strongly considered.

Abstract 1185: Mosaic chromosomal alterations are independent predictors of chronic lymphocytic leukemia risk

Abstract Chronic lymphocytic leukemia (CLL) is strongly associated with mosaic chromosomal alterations (mCAs), a type of clonal hematopoiesis characterized by large structural chromosomal changes (e.g., gains, losses and copy neutral loss of heterozygosity). We aim to identify specific chromosomal regions that, when impacted by mCAs, are most predictive of CLL risk and evaluate their predictive utility in models that include established CLL risk factors. We utilized genotype array data from 436,784 participants in the UK Biobank (UKBB) and 35,382 participants in the Prostate, Lung, Colorectal, and Ovarian (PLCO) screening trial to identify mCAs. UKBB was split (90/10) into training and test sets, and PLCO served as an external validation set. We developed sequential regression models to predict risk of CLL, with a baseline model consisting of age, age-squared, sex, smoking status, and ancestry. Additional models evaluated mCAs, a CLL polygenic risk score (PRS), and blood cell counts. Area under the receiver operating characteristic curve (AUC) was used to evaluate the predictive utility for both 5-year and 10-year CLL risk. Any detectable mCA was associated with incident CLL (Hazards Ratio (HR)=26, 95% Confidence interval (CI)=21-31), and mCAs on each individual chromosome were associated with increased CLL risk. mCAs on segments of chromosomes 13, 12, 22, 14, and 11 had the largest effect size (e.g., chr 13q14 HR=199, 95%CI=155-257). The CLL prediction model, including baseline factors, CLL PRS, and any autosomal mCA status, showed strong predictive utility in independent UKBB (AUC=0.88) and PLCO (AUC=0.88) samples. Restricting to a subset of mCAs strongly associated with CLL did not improve performance over including any detectable autosomal mCA. After the inclusion of blood cell traits, predictive utility further increased in UKBB (AUC=0.91; no count data available in PLCO). We observed the strongest CLL predictive utility in 5-year risk compared to 10-year risk across all models. mCAs genome-wide are associated with CLL risk, with variable effects across chromosomes and specific chromosomal regions. Autosomal mCAs are strong independent predictors of CLL risk and substantially add to predictive utility over standard clinical measures. The predictive utility of autosomal mCAs is strongest within the first 1-5 years of sample collection. Evaluation of mCAs could provide added utility for risk stratification in populations at high risk of CLL. Citation Format: Aubrey K. Hubbard, Alexander DePaulis, Sruthi Srinivasan, Ian D. Buller, Shu-Hong Lin, Weiyin Zhou, Stephen J. Chanock, Neal D. Freedman, Derek W. Brown, Mitchell J. Machiela. Mosaic chromosomal alterations are independent predictors of chronic lymphocytic leukemia risk [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 1185.

Novel machine learning model for predicting multiple unplanned hospitalisations

BackgroundIn the Australian public healthcare system, hospitals are funded based on the number of inpatient discharges and types of conditions treated (casemix). Demand for services is increasing faster than public...