Machine learning-based classifiers to predict metastasis in colorectal cancer patients.

Background: The increasing prevalence of colorectal cancer (CRC) in Iran over the past three decades has made it a key public health burden. This study aimed to predict metastasis in CRC patients using machine learning (ML) approaches in terms of demographic and clinical factors. Methods: This study focuses on 1,127 CRC patients who underwent appropriate treatments at Taleghani Hospital, a tertiary care facility. The patients were divided into training and test datasets in an 80:20 ratio. Various ML methods, including Naive Bayes (NB), random rorest (RF), support vector machine (SVM), neural network (NN), decision tree (DT), and logistic regression (LR), were used for predicting metastasis in CRC patients. Model performance was evaluated using 5-fold cross-validation, reporting sensitivity, specificity, the area under the curve (AUC), and other indexes. Results: Among the 1,127 patients, 183 (16%) had experienced metastasis. In the predictionof metastasis, both the NN and RF algorithms had the highest AUC, while SVM ranked third in both the original and balanced datasets. The NN and RF algorithms achieved the highest AUC (100%), sensitivity (100% and 100%, respectively), and accuracy (99.2% and 99.3%, respectively) on the balanced dataset, followed by the SVM with an AUC of 98.8%, a sensitivity of 97.5%, and an accuracy of 97%. Moreover, lower false negative rate (FNR), false positive rate (FPR), and higher negative predictive value (NPV) can be confirmed by these two methods. The results also showed that all methods exhibited good performance in the test datasets, and the balanced dataset improved the performance of most ML methods. The most important variables for predicting metastasis were the tumor stage, the number of involved lymph nodes, and the treatment type. In a separate analysis of patients with tumor stages I-III, it was identified that tumor grade, tumor size, and tumor stage are the most important features. Conclusion: This study indicated that NN and RF were the best among ML-based approaches for predicting metastasis in CRC patients. Both the tumor stage and the number of involved lymph nodes were considered the most important features.

Association between a lifestyle score and all-cause mortality: a prospective analysis of the Chilean National Health Survey 2009-2010.

OBJECTIVE: To investigate the association between a lifestyle score and all-cause mortality in the Chilean population. DESIGN: Prospective study. SETTINGS: The score was based on seven modifiable behaviours: salt intake, fruit and vegetable intake, alcohol consumption, sleep duration, smoking, physical activity and sedentary behaviours. 1-point was assigned for each healthy recommendation. Points were summed to create an unweighted score from 0 (less healthy) to 7 (healthiest). According to their score, participants were then classified into: less healthy (0-2 points), moderately healthy (3-4 points) and the healthiest (5-7 points). Associations between the categories of lifestyle score and all-cause mortality were investigated using Cox proportional hazard models adjusted for confounders. Nonlinear associations were also investigated. PARTICIPANTS: 2706 participants from the Chilean National Health Survey 2009-2010. RESULTS: After a median follow-up of 10·9 years, 286 (10·6 %) participants died. In the maximally adjusted model, and compared with the healthiest participants, those less healthy had 2·55 (95 % CI 1·75, 3·71) times higher mortality risk due to any cause. Similar trends were identified for the moderately healthy group. Moreover, there was a significant trend towards increasing the mortality risk when increasing unhealthy behaviours (hazard ratio model 3: 1·61 (95 % CI 1·34, 1·94)). There was no evidence of nonlinearity between the lifestyle score and all-cause mortality. CONCLUSION: Individuals in the less healthy lifestyle category had higher mortality risk than the healthiest group. Therefore, public health strategies should be implemented to promote adherence to a healthy lifestyle across the Chilean population.

Association between menstrual cycle phase and metabolites in healthy, regularly menstruating women in UK Biobank, and effect modification by inflammatory markers and risk factors for metabolic disease.

BACKGROUND: Preliminary evidence demonstrates some parameters of metabolic control, including glycaemic control, lipid control and insulin resistance, vary across the menstrual cycle. However, the literature is inconsistent, and the underlying mechanisms remain uncertain. This study aimed to investigate the association between the menstrual cycle phase and metabolites and to explore potential mediators and moderators of these associations. METHODS: We undertook a cross-sectional cohort study using UK Biobank. The outcome variables were glucose; triglyceride; triglyceride to glucose index (TyG index); total, HDL and LDL cholesterol; and total to HDL cholesterol ratio. Generalised additive models (GAM) were used to investigate non-linear associations between the menstrual cycle phase and outcome variables. Anthropometric, lifestyle, fitness and inflammatory markers were explored as potential mediators and moderators of the associations between the menstrual cycle phase and outcome variables. RESULTS: Data from 8694 regularly menstruating women in UK Biobank were analysed. Non-linear associations were observed between the menstrual cycle phase and total (p < 0.001), HDL (p < 0.001), LDL (p = 0.012) and total to HDL cholesterol (p < 0.001), but not glucose (p = 0.072), triglyceride (p = 0.066) or TyG index (p = 0.100). Neither anthropometric, physical fitness, physical activity, nor inflammatory markers mediated the associations between the menstrual cycle phase and metabolites. Moderator analysis demonstrated a greater magnitude of variation for all metabolites across the menstrual cycle in the highest and lowest two quartiles of fat mass and physical activity, respectively. CONCLUSIONS: Cholesterol profiles exhibit a non-linear relationship with the menstrual cycle phase. Physical activity, anthropometric and fitness variables moderate the associations between the menstrual cycle phase and metabolite concentration. These findings indicate the potential importance of physical activity and fat mass as modifiable risk factors of the intra-individual variation in metabolic control across the menstrual cycle in pre-menopausal women.

Social connection and mortality in UK Biobank: a prospective cohort analysis.

BACKGROUND: Components of social connection are associated with mortality, but research examining their independent and combined effects in the same dataset is lacking. This study aimed to examine the independent and combined associations between functional and structural components of social connection and mortality. METHODS: Analysis of 458,146 participants with full data from the UK Biobank cohort linked to mortality registers. Social connection was assessed using two functional (frequency of ability to confide in someone close and often feeling lonely) and three structural (frequency of friends/family visits, weekly group activities, and living alone) component measures. Cox proportional hazard models were used to examine the associations with all-cause and cardiovascular disease (CVD) mortality. RESULTS: Over a median of 12.6 years (IQR 11.9-13.3) follow-up, 33,135 (7.2%) participants died, including 5112 (1.1%) CVD deaths. All social connection measures were independently associated with both outcomes. Friends/family visit frequencies < monthly were associated with a higher risk of mortality indicating a threshold effect. There were interactions between living alone and friends/family visits and between living alone and weekly group activity. For example, compared with daily friends/family visits-not living alone, there was higher all-cause mortality for daily visits-living alone (HR 1.19 [95% CI 1.12-1.26]), for never having visits-not living alone (1.33 [1.22-1.46]), and for never having visits-living alone (1.77 [1.61-1.95]). Never having friends/family visits whilst living alone potentially counteracted benefits from other components as mortality risks were highest for those reporting both never having visits and living alone regardless of weekly group activity or functional components. When all measures were combined into overall functional and structural components, there was an interaction between components: compared with participants defined as not isolated by both components, those considered isolated by both components had higher CVD mortality (HR 1.63 [1.51-1.76]) than each component alone (functional isolation 1.17 [1.06-1.29]; structural isolation 1.27 [1.18-1.36]). CONCLUSIONS: This work suggests (1) a potential threshold effect for friends/family visits, (2) that those who live alone with additional concurrent markers of structural isolation may represent a high-risk population, (3) that beneficial associations for some types of social connection might not be felt when other types of social connection are absent, and (4) considering both functional and structural components of social connection may help to identify the most isolated in society.

Association between walking pace and incident type 2 diabetes by adiposity level: A prospective cohort study from the UK Biobank.

AIMS: To investigate the combined association of adiposity and walking pace with incident type 2 diabetes. METHODS: We undertook a prospective cohort study in 194 304 White-European participants (mean age 56.5 years, 55.9% women). Participants' walking pace was self-reported as brisk, average or slow. Adiposity measures included body mass index (BMI), waist circumference (WC) and body fat percentage (BF%). Associations were investigated using Cox proportional hazard models, with a 2-year landmark analysis. A four-way decomposition analysis was used for mediation and additive interaction. RESULTS: The median (interquartile range) follow-up was 5.4 (4.8-6.3) years. During the follow-up period, 4564 participants developed type 2 diabetes. Compared to brisk-walking participants with normal BMI, those with obesity who walked briskly were at an approximately 10- to 12-fold higher risk of type 2 diabetes (hazard ratio [HR] 9.64, 95% confidence interval [CI] 7.24-12.84, in women; HR 11.91, 95% CI 8.80-16.12, in men), whereas those with obesity and walked slowly had an approximately 12- to 15-fold higher risk (HR 12.68, 95% CI 9.62-16.71, in women; HR 15.41, 95% CI 11.27-21.06, in men). There was evidence of an additive interaction between WC and BF% and walking pace among women, explaining 17.8% and 47.9% excess risk respectively. Obesity mediated the association in women and men, accounting for 60.1% and 44.9%, respectively. CONCLUSIONS: Slow walking pace is a risk factor for type 2 diabetes independent of adiposity. Promoting brisk walking as well as weight management might be an effective type 2 diabetes prevention strategy given their synergistic effects.

Predicting metastasis in gastric cancer patients: machine learning-based approaches.

Gastric cancer (GC), with a 5-year survival rate of less than 40%, is known as the fourth principal reason of cancer-related mortality over the world. This study aims to develop predictive models using different machine learning (ML) classifiers based on both demographic and clinical variables to predict metastasis status of patients with GC. The data applied in this study including 733 of GC patients, divided into a train and test groups at a ratio of 8:2, diagnosed at Taleghani tertiary hospital. In order to predict metastasis in GC, ML-based algorithms, including Naive Bayes (NB), Random Forest (RF), Support Vector Machine (SVM), Neural Network (NN), Decision Tree (RT) and Logistic Regression (LR), with 5-fold cross validation were performed. To assess the model performance, F1 score, precision, sensitivity, specificity, area under the curve (AUC) of receiver operating characteristic (ROC) curve and precision-recall AUC (PR-AUC) were obtained. 262 (36%) experienced metastasis among 733 patients with GC. Although all models have optimal performance, the indices of SVM model seems to be more appropiate (training set: AUC: 0.94, Sensitivity: 0.94; testing set: AUC: 0.85, Sensitivity: 0.92). Then, NN has the higher AUC among ML approaches (training set: AUC: 0.98; testing set: AUC: 0.86). The RF of ML-based models, which determine size of tumor and age as two essential variables, is considered as the third efficient model, because of higher specificity and AUC (84% and 87%). Based on the demographic and clinical characteristics, ML approaches can predict the metastasis status in GC patients. According to AUC, sensitivity and specificity in both SVM and NN can be regarded as better algorithms among 6 applied ML-based methods.

C-reactive protein partially mediates the inverse association between coffee consumption and risk of type 2 diabetes: The UK Biobank and the Rotterdam study cohorts.

BACKGROUND: Coffee is among the most consumed beverages worldwide. Coffee consumption has been associated with lower risk of type 2 diabetes mellitus (T2D), but underlying mechanisms are not well understood. We aimed to study the role of classic and novel-T2D biomarkers with anti- or pro-inflammatory activity in the association between habitual coffee intake and T2D risk. Furthermore, we studied differences by coffee types and smoking status in this association. METHODS: Using two large population-based cohorts, the UK-Biobank (UKB; n = 145,368) and the Rotterdam Study (RS; n = 7111), we investigated associations of habitual coffee consumption with incident T2D and repeated measures of insulin resistance (HOMA-IR), using Cox proportional hazards and mixed effect models, respectively. Additionally, we studied associations between coffee and subclinical inflammation biomarkers including C-reactive protein (CRP) and IL-13, and adipokines, such as adiponectin and leptin, using linear regression models. Next, we performed formal causal mediation analyses to investigate the role of coffee-associated biomarkers in the association of coffee with T2D. Finally, we evaluated effect modification by coffee type and smoking. All models were adjusted for sociodemographic, lifestyle and health-related factors. RESULTS: During a median follow-up of 13.9 (RS) and 7.4 (UKB) years, 843 and 2290 incident T2D cases occurred, respectively. A 1 cup/day increase in coffee consumption was associated with 4% lower T2D risk (RS, HR = 0.96 [95%CI 0.92; 0.99], p = 0.045; UKB, HR = 0.96 [0.94; 0.98], p < 0.001), with lower HOMA-IR (RS, log-transformed ß = -0.017 [-0.024;-0.010], p < 0.001), and with lower CRP (RS, log-transformed ß = -0.014 [-0.022;-0.005], p = 0.002; UKB, ß = -0.011 [-0.012;-0.009], p < 0.001). We also observed associations of higher coffee consumption with higher serum adiponectin and IL-13 concentrations, and with lower leptin concentrations. Coffee-related CRP levels partially mediated the inverse association of coffee intake with T2D incidence (average mediation effect RS ß = 0.105 (0.014; 0.240), p = 0.016; UKB ß = 6.484 (4.265; 9.339), p < 0.001), with a proportion mediated by CRP from 3.7% [-0.012%; 24.4%] (RS) to 9.8% [5,7%; 25.8%] (UKB). No mediation effect was observed for the other biomarkers. Coffee-T2D and coffee-CRP associations were generally stronger among consumers of ground (filtered or espresso) coffee and among never and former smokers. CONCLUSIONS: Lower subclinical inflammation may partially mediate the beneficial association between coffee consumption and lower T2D risk. Consumers of ground coffee and non-smokers may benefit the most. KEYWORDS (MESH TERMS): coffee consumptions; diabetes mellitus, type 2; inflammation; adipokines; biomarkers; mediation analysis; follow-up studies.

Diet-Related Inflammation Is Associated with Worse COVID-19 Outcomes in the UK Biobank Cohort.

Diet, the most important modulator of inflammatory and immune responses, may affect COVID-19 incidence and disease severity. Data from 196,154 members of the UK biobank had at least one 24 h dietary recall. COVID-19 outcomes were based on PCR testing, hospital admissions, and death certificates. Adjusted Poisson regression analyses were performed to estimate the risk ratios (RR) and their 95% confidence intervals (CI) for dietary inflammatory index (DII)/energy-adjusted DII (E-DII) scores. Models were adjusted for sociodemographic factors, comorbidities, smoking status, physical activity, and sleep duration. Between January 2020 and March 2021, there were 11,288 incident COVID-19 cases, 1270 COVID-19-related hospitalizations, and 315 COVID-19-related deaths. The fully adjusted model showed that participants in the highest (vs. lowest) DII/E-DII quintile were at 10-17% increased risk of COVID-19 (DII: RR Q5 vs. Q1 = 1.10, 95% CI 1.04-1.17, Ptrend < 0.001; E-DII: RR Q5 vs. Q1 = 1.17, 95% CI 1.10-1.24, Ptrend < 0.001) and ≈40% higher risk was observed for disease severity (DII: RR Q5 vs. Q1 = 1.40, 95% CI 1.18-1.67, Ptrend < 0.001; E-DII: RR Q5 vs. Q1 = 1.39, 95% CI 1.16-1.66, Ptrend < 0.001). There was a 43% increased risk of COVID-19-related death in the highest DII quintile (RR Q5 vs. Q1 = 1.43, 95% CI 1.01-2.01, Ptrend = 0.04). About one-quarter of the observed positive associations between DII and COVID-19-related outcomes were mediated by body mass index (25.8% for incidence, 21.6% for severity, and 19.8% for death). Diet-associated inflammation increased the risk of COVID-19 infection, severe disease, and death.

Association of Self-reported Walking Pace With Type 2 Diabetes Incidence in the UK Biobank Prospective Cohort Study.

OBJECTIVE: To investigate the association between self-reported walking pace and type 2 diabetes (T2D) incidence and whether it differed by physical activity levels and walking time. METHODS: There were 162,155 participants (mean age, 57.1 years; 54.9% women) from the UK Biobank prospective study, recruited between 2006 and 2010, included in the study. Walking pace was self-reported and classified as brisk, average, or slow. Total physical activity and walking time were self-reported using the International Physical Activity Questionnaire. Association between walking pace and T2D incidence and the potential moderating role of physical activity and walking time were investigated using Cox proportional hazards models. RESULTS: The median follow-up was 7.4 (interquartile range, 6.7 to 8.2) years. There were 4442 participants in whom T2D developed during the follow-up period. In the fully adjusted model (sociodemographic factors, diet, body mass index, and physical activity), average walking pace (hazard ratio [HR], 1.28; 95% CI, 1.14 to 1.44) and slow walking pace (HR, 1.91; 95% CI, 1.62 to 2.24) were associated with a higher T2D risk compared with brisk walking among women. Among men, average walking pace (HR, 1.28; 95% CI, 1.17 to 1.40) and slow walking pace (HR, 1.73; 95% CI, 1.50 to 1.99) were also associated with higher T2D risk. Compared with slow walkers, brisk walkers have the same diabetes incidence rate 18.6 and 16.0 years later, for women and men, respectively. CONCLUSION: Average and slow walking pace was associated with a higher risk of incident T2D in both men and women, independent of major confounding factors. The associations were consistent across different physical activity levels and walking time.

Association of gamma-glutamyltransferase levels with total mortality, liver-related and cardiovascular outcomes: A prospective cohort study in the UK Biobank.

Background: Gamma-glutamyltransferase (GGT) levels in the blood can be a sensitive marker of liver injury but the extent to which they give insight into risk across multiple outcomes in a clinically useful way remains uncertain. Methods: Using data from 293,667 UK Biobank participants, the relationship of GGT concentrations to self-reported alcohol intake and adiposity markers were investigated. We next investigated whether GGT predicted liver-related, cardiovascular (CV) or all-cause mortality, and potentially improved CV risk prediction. Findings: Higher alcohol intake and greater waist circumference (WC) were associated with higher GGT; the association was stronger for alcohol with evidence of a synergistic effect of WC. Higher GGT concentrations were associated with multiple outcomes. Compared to a GGT of 14.5 U/L (lowest decile), values of 48 U/L for women and 60 U/L for men (common upper limits of 'normal') had hazard ratios (HRs) for liver-related mortality of 1.83 (95% CI 1.60-2.11) and 3.25 (95% CI 2.38-4.42) respectively, for CV mortality of 1.21 (95% CI 1.14-1.28) and 1.43 (95% CI 1.27-1.60) and for all-cause mortality of 1.15 (95% CI 1.12-1.18) and 1.31 (95% CI 1.24-1.38). Adding GGT to a risk algorithm for CV mortality reclassified an additional 1.24% (95% CI 0.14-2.34) of participants across a binary 5% 10-year risk threshold. Interpretation: Our study suggests that a modest elevation in GGT levels should trigger a discussion with the individual to review diet and lifestyle including alcohol intake and consideration of formal liver disease and CV risk assessment if not previously done. Funding: British Heart Foundation Centre of Research Excellence Grant (grant number RE/18/6/34217), NHS Research Scotland (grant number SCAF/15/02), the Medical Research Council (grant number MC_UU_00022/2); and the Scottish Government Chief Scientist Office (grant number SPHSU17).

Type 2 Diabetes, Glycemic Control, and Their Association With Dementia and Its Major Subtypes: Findings From the Swedish National Diabetes Register.

OBJECTIVE: Type 2 diabetes has been associated with high dementia risk. However, the links to different dementia subtypes is unclear. We examined to what extent type 2 diabetes is associated with dementia subtypes and whether such associations differed by glycemic control. RESEARCH DESIGN AND METHODS: We used data from the Swedish National Diabetes Register and included 378,299 patients with type 2 diabetes and 1,886,022 control subjects matched for age, sex, and county randomly selected from the Swedish Total Population Register. The outcomes were incidence of Alzheimer disease, vascular dementia, and nonvascular dementia. The association of type 2 diabetes with dementia was stratified by baseline glycated hemoglobin (HbA1c) in patients with type 2 diabetes only. Cox regression was used to study the excess risk of outcomes. RESULTS: Over the follow-up (median 6.8 years), dementia developed in 11,508 (3.0%) patients with type 2 diabetes and 52,244 (2.7%) control subjects. The strongest association was observed for vascular dementia, with patients with type 2 diabetes compared with control subjects having a hazard ratio [HR] of 1.34 (95% CI 1.28, 1.41). The association of type 2 diabetes with nonvascular dementia was more modest (HR 1.10 [95% CI 1.07, 1.13]). However, risk for Alzheimer disease was lower in patients with type 2 diabetes than in control subjects (HR 0.94 [95% CI 0.90, 0.99]). When the analyses were stratified by circulating concentrations of HbA1c, a dose-response association was observed. CONCLUSIONS: The association of type 2 diabetes with dementia differs by subtypes of dementia. The strongest detrimental association is observed for vascular dementia. Moreover, patients with type 2 diabetes with poor glycemic control have an increased risk of developing vascular and nonvascular dementia.

Lipoprotein(a) and cardiovascular disease: prediction, attributable risk fraction, and estimating benefits from novel interventions.

AIMS: To investigate the population attributable fraction due to elevated lipoprotein (a) (Lp(a)) and the utility of measuring Lp(a) in cardiovascular disease (CVD) risk prediction. METHODS AND RESULTS: In 413 734 participants from UK Biobank, associations of serum Lp(a) with composite fatal/non-fatal CVD (n = 10 066 events), fatal CVD (n = 3247), coronary heart disease (CHD; n = 18 292), peripheral vascular disease (PVD; n = 2716), and aortic stenosis (n = 901) were compared using Cox models. Median Lp(a) was 19.7 nmol/L (interquartile interval 7.6-75.3 nmol/L). About 20.8% had Lp(a) values >100 nmol/L; 9.2% had values >175 nmol/L. After adjustment for classical risk factors, 1 SD increment in log Lp(a) was associated with a hazard ratio for fatal/non-fatal CVD of 1.12 [95% confidence interval (CI) 1.10-1.15]. Similar associations were observed with fatal CVD, CHD, PVD, and aortic stenosis. Adding Lp(a) to a prediction model containing traditional CVD risk factors in a primary prevention group improved the C-index by +0.0017 (95% CI 0.0008-0.0026). In the whole cohort, Lp(a) above 100 nmol/L was associated with a population attributable fraction (PAF) of 5.8% (95% CI 4.9-6.7%), and for Lp(a) above 175 nmol/L the PAF was 3.0% (2.4-3.6%). Assuming causality and an achieved Lp(a) reduction of 80%, an ongoing trial to lower Lp(a) in patients with CVD and Lp(a) above 175 nmol/L may reduce CVD risk by 20.0% and CHD by 24.4%. Similar benefits were also modelled in the whole cohort, regardless of baseline CVD. CONCLUSION: Population screening for elevated Lp(a) may help to predict CVD and target Lp(a) lowering drugs, if such drugs prove efficacious, to those with markedly elevated levels.

Family history of diabetes and risk of SARS-COV-2 in UK Biobank: A prospective cohort study.

INTRODUCTION: The aim of this study was to determine risk of being SARS-CoV-2 positive and severe infection (associated with hospitalization/mortality) in those with family history of diabetes. METHODS: We used UK Biobank, an observational cohort recruited between 2006 and 2010. We compared the risk of being SARS-CoV-2 positive and severe infection for those with family history of diabetes (mother/father/sibling) against those without. RESULTS: Of 401,268 participants in total, 13,331 tested positive for SARS-CoV-2 and 2282 had severe infection by end of January 2021. In unadjusted models, participants with ≥2 family members with diabetes were more likely to be SARS-CoV-2 positive (risk ratio-RR 1.35; 95% confidence interval-CI 1.24-1.47) and severe infection (RR 1.30; 95% CI 1.04-1.59), compared to those without. The excess risk of being tested positive for SARS-CoV-2 was attenuated but significant after adjusting for demographics, lifestyle factors, multimorbidity and presence of cardiometabolic conditions. The excess risk for severe infection was no longer significant after adjusting for demographics, lifestyle factors, multimorbidity and presence of cardiometabolic conditions, and was absent when excluding incident diabetes. CONCLUSION: The totality of the results suggests that good lifestyle and not developing incident diabetes may lessen risks of severe infections in people with a strong family of diabetes.

Risk of mortality among inpatients with COVID-19 and type 2 diabetes: National data from Kuwait.

INTRODUCTION: To investigate type 2 diabetes as a risk factor for COVID-19 death following hospital admission in Kuwait. METHODS: A retrospective cohort study using data from a central hospital that cared for all hospitalized COVID-19 patients in Kuwait. We investigated the association between type 2 diabetes, with COVID-19 mortality using multiply imputed logistic regression and calculated the population attributable fraction. RESULTS: A total of 5333 patients were admitted with COVID-19, of whom 244 died (4.6%). Diabetes prevalence was 24.8%, but 53.7% of those who died had diabetes. After adjusting for age, sex, ethnicity and other comorbidities, diabetes was associated with death (OR 1.70 [95% CI 1.23, 2.34]) and admission to the intensive care unit more than 3 days after initial admission (OR 1.78 [95% CI 1.17, 2.70]). Assuming causality, the population attributable fraction for type 2 diabetes in COVID-19 death was 19.6% (95% CI 10.8, 35.6). CONCLUSION: Type 2 diabetes is a strong risk factor for COVID-19 death in the Middle East. Given the high prevalence of type 2 diabetes in the Middle East, as well as many Western countries, the public health implications are considerable.

Derivation and Validation of a 10-Year Risk Score for Symptomatic Abdominal Aortic Aneurysm: Cohort Study of Nearly 500 000 Individuals.

BACKGROUND: Abdominal aortic aneurysm (AAA) can occur in patients who are ineligible for routine ultrasound screening. A simple AAA risk score was derived and compared with current guidelines used for ultrasound screening of AAA. METHODS: United Kingdom Biobank participants without previous AAA were split into a derivation cohort (n=401 820, 54.6% women, mean age 56.4 years, 95.5% White race) and validation cohort (n=83 816). Incident AAA was defined as first hospital inpatient diagnosis of AAA, death from AAA, or an AAA-related surgical procedure. A multivariable Cox model was developed in the derivation cohort into an AAA risk score that did not require blood biomarkers. To illustrate the sensitivity and specificity of the risk score for AAA, a theoretical threshold to refer patients for ultrasound at 0.25% 10-year risk was modeled. Discrimination of the risk score was compared with a model of US Preventive Services Task Force (USPSTF) AAA screening guidelines. RESULTS: In the derivation cohort, there were 1570 (0.40%) cases of AAA over a median 11.3 years of follow-up. Components of the AAA risk score were age (stratified by smoking status), weight (stratified by smoking status), antihypertensive and cholesterol-lowering medication use, height, diastolic blood pressure, baseline cardiovascular disease, and diabetes. In the validation cohort, over 10 years of follow-up, the C-index for the model of the USPSTF guidelines was 0.705 (95% CI, 0.678-0.733). The C-index of the risk score as a continuous variable was 0.856 (95% CI, 0.837-0.878). In the validation cohort, the USPSTF model yielded sensitivity 63.9% and specificity 71.3%. At the 0.25% 10-year risk threshold, the risk score yielded sensitivity 82.1% and specificity 70.7% while also improving the net reclassification index compared with the USPSTF model +0.176 (95% CI, 0.120-0.232). A combined model, whereby risk scoring was combined with the USPSTF model, also improved prediction compared with USPSTF alone (net reclassification index +0.101 [95% CI, 0.055-0.147]). CONCLUSIONS: In an asymptomatic general population, a risk score based on patient age, height, weight, and medical history may improve identification of asymptomatic patients at risk for clinical events from AAA. Further development and validation of risk scores to detect asymptomatic AAA are needed.

Is older age associated with COVID-19 mortality in the absence of other risk factors? General population cohort study of 470,034 participants.

INTRODUCTION: Older people have been reported to be at higher risk of COVID-19 mortality. This study explored the factors mediating this association and whether older age was associated with increased mortality risk in the absence of other risk factors. METHODS: In UK Biobank, a population cohort study, baseline data were linked to COVID-19 deaths. Poisson regression was used to study the association between current age and COVID-19 mortality. RESULTS: Among eligible participants, 438 (0.09%) died of COVID-19. Current age was associated exponentially with COVID-19 mortality. Overall, participants aged ≥75 years were at 13-fold (95% CI 9.13-17.85) mortality risk compared with those <65 years. Low forced expiratory volume in 1 second, high systolic blood pressure, low handgrip strength, and multiple long-term conditions were significant mediators, and collectively explained 39.3% of their excess risk. The associations between these risk factors and COVID-19 mortality were stronger among older participants. Participants aged ≥75 without additional risk factors were at 4-fold risk (95% CI 1.57-9.96, P = 0.004) compared with all participants aged <65 years. CONCLUSIONS: Higher COVID-19 mortality among older adults was partially explained by other risk factors. 'Healthy' older adults were at much lower risk. Nonetheless, older age was an independent risk factor for COVID-19 mortality.