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Importance - Polygenic risk scores comprising millions of single-nucleotide polymorphisms (SNPs) could be useful for population-wide coronary heart disease (CHD) screening.
Objective - To determine whether a polygenic risk score improves prediction of CHD compared with a guideline-recommended clinical risk equation.
Design, Setting, and Participants - A retrospective cohort study of the predictive accuracy of a previously validated polygenic risk score was assessed among 4847 adults of white European ancestry, aged 45 through 79 years, participating in the Atherosclerosis Risk in Communities (ARIC) study and 2390 participating in the Multi-Ethnic Study of Atherosclerosis (MESA) from 1996 through December 31, 2015, the final day of follow-up. The performance of the polygenic risk score was compared with that of the 2013 American College of Cardiology and American Heart Association pooled cohort equations.
Exposures - Genetic risk was computed for each participant by summing the product of the weights and allele dosage across 6 630 149 SNPs. Weights were based on an international genome-wide association study.
Main Outcomes and Measures - Prediction of 10-year first CHD events (including myocardial infarctions, fatal coronary events, silent infarctions, revascularization procedures, or resuscitated cardiac arrest) assessed using measures of model discrimination, calibration, and net reclassification improvement (NRI).
Results - The study population included 4847 adults from the ARIC study (mean [SD] age, 62.9 [5.6] years; 56.4% women) and 2390 adults from the MESA cohort (mean [SD] age, 61.8 [9.6] years; 52.2% women). Incident CHD events occurred in 696 participants (14.4%) and 227 participants (9.5%), respectively, over median follow-up of 15.5 years (interquartile range [IQR], 6.3 years) and 14.2 (IQR, 2.5 years) years. The polygenic risk score was significantly associated with 10-year CHD incidence in ARIC with hazard ratios per SD increment of 1.24 (95% CI, 1.15 to 1.34) and in MESA, 1.38 (95% CI, 1.21 to 1.58). Addition of the polygenic risk score to the pooled cohort equations did not significantly increase the C statistic in either cohort (ARIC, change in C statistic, -0.001; 95% CI, -0.009 to 0.006; MESA, 0.021; 95% CI, -0.0004 to 0.043). At the 10-year risk threshold of 7.5%, the addition of the polygenic risk score to the pooled cohort equations did not provide significant improvement in reclassification in either ARIC (NRI, 0.018, 95% CI, -0.012 to 0.036) or MESA (NRI, 0.001, 95% CI, -0.038 to 0.076). The polygenic risk score did not significantly improve calibration in either cohort.
Conclusions and Relevance - In this analysis of 2 cohorts of US adults, the polygenic risk score was associated with incident coronary heart disease events but did not significantly improve discrimination, calibration, or risk reclassification compared with conventional predictors. These findings suggest that a polygenic risk score may not enhance risk prediction in a general, white middle-aged population.
OBJECTIVES - Physical frailty (or loss of physiologic reserve) is associated with cognitive impairment and dementia. Subjective cognitive decline (SCD) may represent early pathologic changes of dementia. The association between these disease markers is unclear.
DESIGN - Cross-sectional analysis.
SETTING - Community-based participants from the Vanderbilt Memory & Aging Project.
PARTICIPANTS - A total of 306 older adults with normal cognition (NC; n = 174) or mild cognitive impairment (MCI; n = 132).
MEASUREMENTS - Frailty was measured using standard methods, and a composite frailty score was calculated. SCD was quantified using the Everyday Cognition Scale (ECog; total score and four domain scores). Objective cognition was assessed with the Montreal Cognitive Assessment (MoCA). Proportional odds models, stratified by sex, related the frailty composite to MoCA and total ECog score adjusting for age, education, body mass index, cognitive diagnosis, depressed mood, Framingham Stroke Risk Profile, apolipoprotein E (APOE ε4) carrier status, and height (for gait speed models). Secondary models related individual frailty components to SCD domains and explored associations in NC only.
RESULTS - In women, frailty composite was related to MoCA (odds ratio [OR] = .56; P = .04), a finding attenuated in sensitivity analysis (OR = .59; P = .08). Frailty composite related to ECog total (OR = 2.27; P = .02), planning (OR = 2.63; P = .02), and organization scores (OR = 2.39; P = .03). Increasing gait speed related to lower ECog total (OR = .06; P = .003) and memory scores (OR = .03; P < .001). Grip strength related to lower ECog planning score (OR = .91; P = .04). In men, frailty was unrelated to objective and subjective cognition (P values >.07). Findings were consistent in the NC group.
CONCLUSION - Frailty component and composite scores are related to SCD before the presence of overt dementia. Results suggest that this association is present before overt cognitive impairment. Results suggest a possible sex difference in the clinical manifestation of frailty, with primary associations noted in women. Further studies should investigate mechanisms linking early changes among frailty, SCD, and cognition. J Am Geriatr Soc, 1-9, 2019. J Am Geriatr Soc 67:1803-1811, 2019.
© 2019 The American Geriatrics Society.
BACKGROUND - Proteomic approaches allow measurement of thousands of proteins in a single specimen, which can accelerate biomarker discovery. However, applying these technologies to massive biobanks is not currently feasible because of the practical barriers and costs of implementing such assays at scale. To overcome these challenges, we used a "virtual proteomic" approach, linking genetically predicted protein levels to clinical diagnoses in >40 000 individuals.
METHODS - We used genome-wide association data from the Framingham Heart Study (n=759) to construct genetic predictors for 1129 plasma protein levels. We validated the genetic predictors for 268 proteins and used them to compute predicted protein levels in 41 288 genotyped individuals in the Electronic Medical Records and Genomics (eMERGE) cohort. We tested associations for each predicted protein with 1128 clinical phenotypes. Lead associations were validated with directly measured protein levels and either low-density lipoprotein cholesterol or subclinical atherosclerosis in the MDCS (Malmö Diet and Cancer Study; n=651).
RESULTS - In the virtual proteomic analysis in eMERGE, 55 proteins were associated with 89 distinct diagnoses at a false discovery rate q<0.1. Among these, 13 associations involved lipid (n=7) or atherosclerosis (n=6) phenotypes. We tested each association for validation in MDCS using directly measured protein levels. At Bonferroni-adjusted significance thresholds, levels of apolipoprotein E isoforms were associated with hyperlipidemia, and circulating C-type lectin domain family 1 member B and platelet-derived growth factor receptor-β predicted subclinical atherosclerosis. Odds ratios for carotid atherosclerosis were 1.31 (95% CI, 1.08-1.58; P=0.006) per 1-SD increment in C-type lectin domain family 1 member B and 0.79 (0.66-0.94; P=0.008) per 1-SD increment in platelet-derived growth factor receptor-β.
CONCLUSIONS - We demonstrate a biomarker discovery paradigm to identify candidate biomarkers of cardiovascular and other diseases.
OBJECTIVE - Patients with fibromyalgia (FM) are 10 times more likely to die by suicide than the general population. The purpose of this study was to externally validate published models predicting suicidal ideation and suicide attempts in patients with FM and to identify interpretable risk and protective factors for suicidality unique to FM.
METHODS - This was a case-control study of large-scale electronic health record data collected from 1998 to 2017, identifying FM cases with validated Phenotype KnowledgeBase criteria. Model performance was measured through discrimination, including the receiver operating area under the curve (AUC), sensitivity, and specificity, and through calibration, including calibration plots. Risk factors were selected by L1 penalized regression with bootstrapping for both outcomes. Secondary utilization analyses converted time-based billing codes to equivalent minutes to estimate face-to-face provider contact.
RESULTS - We identified 8,879 patients with FM, with 34 known suicide attempts and 96 documented cases of suicidal ideation. External validity was good for both suicidal ideation (AUC 0.80) and attempts (AUC 0.82) with excellent calibration. Risk factors specific to suicidal ideation included polysomatic symptoms such as fatigue (odds ratio [OR] 1.29 [95% confidence interval (95% CI) 1.25-1.32]), dizziness (OR 1.25 [95% CI 1.22-1.28]), and weakness (OR 1.17 [95% CI 1.15-1.19]). Risk factors specific to suicide attempt included obesity (OR 1.18 [95% CI 1.10-1.27]) and drug dependence (OR 1.15 [95% CI 1.12-1.18]). Per utilization analyses, those patients with FM and no suicidal ideation spent 3.5 times more time in follow-up annually, and those without documented suicide attempts spent more than 40 times more time face-to-face with providers annually.
CONCLUSION - This is the first study to successfully apply machine learning to reliably detect suicidality in patients with FM, identifying novel risk factors for suicidality and highlighting outpatient engagement as a protective factor against suicide.
© 2018, American College of Rheumatology.
BACKGROUND - Observations from statin clinical trials and from Mendelian randomization studies suggest that low low-density lipoprotein cholesterol (LDL-C) concentrations may be associated with increased risk of type 2 diabetes mellitus (T2DM). Despite the findings from statin clinical trials and genetic studies, there is little direct evidence implicating low LDL-C concentrations in increased risk of T2DM.
METHODS AND FINDINGS - We used de-identified electronic health records (EHRs) at Vanderbilt University Medical Center to compare the risk of T2DM in a cross-sectional study among individuals with very low (≤60 mg/dl, N = 8,943) and normal (90-130 mg/dl, N = 71,343) LDL-C levels calculated using the Friedewald formula. LDL-C levels associated with statin use, hospitalization, or a serum albumin level < 3 g/dl were excluded. We used a 2-phase approach: in 1/3 of the sample (discovery) we used T2DM phenome-wide association study codes (phecodes) to identify cases and controls, and in the remaining 2/3 (validation) we identified T2DM cases and controls using a validated algorithm. The analysis plan for the validation phase was constructed at the time of the design of that component of the study. The prevalence of T2DM in the very low and normal LDL-C groups was compared using logistic regression with adjustment for age, race, sex, body mass index (BMI), high-density lipoprotein cholesterol, triglycerides, and duration of care. Secondary analyses included prespecified stratification by sex, race, BMI, and LDL-C level. In the discovery cohort, phecodes related to T2DM were significantly more frequent in the very low LDL-C group. In the validation cohort (N = 33,039 after applying the T2DM algorithm to identify cases and controls), the risk of T2DM was increased in the very low compared to normal LDL-C group (odds ratio [OR] 2.06, 95% CI 1.80-2.37; P < 2 × 10-16). The findings remained significant in sensitivity analyses. The association between low LDL-C levels and T2DM was significant in males (OR 2.43, 95% CI 2.00-2.95; P < 2 × 10-16) and females (OR 1.74, 95% CI 1.42-2.12; P = 6.88 × 10-8); in normal weight (OR 2.18, 95% CI 1.59-2.98; P = 1.1× 10-6), overweight (OR 2.17, 95% CI 1.65-2.83; P = 1.73× 10-8), and obese (OR 2.00, 95% CI 1.65-2.41; P = 8 × 10-13) categories; and in individuals with LDL-C < 40 mg/dl (OR 2.31, 95% CI 1.71-3.10; P = 3.01× 10-8) and LDL-C 40-60 mg/dl (OR 1.99, 95% CI 1.71-2.32; P < 2.0× 10-16). The association was significant in individuals of European ancestry (OR 2.67, 95% CI 2.25-3.17; P < 2 × 10-16) but not in those of African ancestry (OR 1.09, 95% CI 0.81-1.46; P = 0.56). A limitation was that we only compared groups with very low and normal LDL-C levels; also, since this was not an inception cohort, we cannot exclude the possibility of reverse causation.
CONCLUSIONS - Very low LDL-C concentrations occurring in the absence of statin treatment were significantly associated with T2DM risk in a large EHR population; this increased risk was present in both sexes and all BMI categories, and in individuals of European ancestry but not of African ancestry. Longitudinal cohort studies to assess the relationship between very low LDL-C levels not associated with lipid-lowering therapy and risk of developing T2DM will be important.
BACKGROUND & AIMS - The presence of specific single nucleotide polymorphisms (SNPs) can be used to calculate an individual's risk for colorectal cancer (CRC), called a genetic risk score (GRS). We investigated whether GRS can identify individuals with clinically relevant neoplasms in a screening colonoscopy population.
METHODS - We derived a GRS based on 48 SNPs associated with CRC, identified in a comprehensive literature search. We obtained genetic data from 1043 participants (50-79 years old) in a screening colonoscopy study in Germany, recruited from 2005 through 2013 (294 with advanced neoplasms, 249 with non-advanced adenoma (NAAs), and 500 without neoplasms). Each participant was assigned a GRS by aggregating their risk alleles (0, 1, or 2). Risk of advanced neoplasms and NAA according to GRS was calculated by multiple logistic regression. Risk advancement periods were calculated. We replicated our findings using data from a subset of the Tennessee Colorectal Polyp Study.
RESULTS - An increased GRS was associated with higher prevalence of advanced neoplasms, but not NAAs. Participants in the middle and upper tertiles of GRS had a 2.2-fold and 2.7-fold increase in risk, respectively, of advanced neoplasms compared to those in the lower tertile. Adjusted odds ratios (ORs) were 1.09 (95% confidence interval [CI], 0.76-1.57) for NAA in the middle tertile and 1.05 (95% CI, 0.70-1.55) for NAA in the upper tertile. The ORs were largest for proximal advanced neoplasms for participants in the middle tertile (OR, 3.55; 95% CI 1.85-6.82) and the upper tertile (OR, 3.61; 95% CI 1.84-7.10). The risk advancement period for medium vs low GRS was 13.4 years (95% CI 4.8-22.0) and for high vs low GRS was 17.5 years (95% CI, 7.8-27.3).
CONCLUSIONS - In a genetic analysis of participants in a CRC screening study in Germany, an increased GRS (based on CRC-associated SNPs) was associated with increased prevalence of advanced neoplasms. These findings might be used in defining risk-adapted screening ages.
Copyright © 2018 AGA Institute. Published by Elsevier Inc. All rights reserved.
BACKGROUND/OBJECTIVE - Despite effective therapies, rheumatoid arthritis (RA) can result in joint destruction requiring total joint arthroplasty to maintain patient function. An estimated 16% to 70% of those undergoing total joint arthroplasty of the hip or knee will receive a blood transfusion. Few studies have described risk factors for blood transfusion following total joint arthroplasty in patients with RA. The aim of this study was to identify demographic and clinical risk factors associated with receiving a blood transfusion following total joint arthroplasty among patients with RA.
METHODS - A retrospective study (n = 3270) was conducted using deidentified patient health claims information from a commercially insured, US data set (2007-2009). Data analysis included descriptive statistics and multivariate logistic regression.
RESULTS - Females were more likely to receive a blood transfusion (odds ratio [OR], 1.48; 95% confidence interval [CI], 1.16-1.87; p = 0.001). When compared with those in the South, patients residing the Midwest were less likely to receive a blood transfusion following total joint arthroplasty (OR, 0.56; 95% CI, 0.44-0.71). Relative to those receiving total knee arthroplasty, patients who underwent total hip arthroplasty were more likely to receive a blood transfusion (OR, 1.39; 95% CI, 1.14-1.70), and patients who underwent a total shoulder arthroplasty were less likely to receive a blood transfusion (OR, 0.14; 95% CI, 0.05-0.38; p < 0.001). Patients with a history of anemia were more likely to receive a blood transfusion compared with those who did not have this diagnosis (OR, 3.30; 95% CI, 2.62-4.14; p < 0.001).
CONCLUSIONS - Risk factors for the receipt of blood transfusions among RA patients who have undergone total joint arthroplasty were identified.
OBJECTIVE - Hepatorenal Syndrome (HRS) is a devastating form of acute kidney injury (AKI) in advanced liver disease patients with high morbidity and mortality, but phenotyping algorithms have not yet been developed using large electronic health record (EHR) databases. We evaluated and compared multiple phenotyping methods to achieve an accurate algorithm for HRS identification.
MATERIALS AND METHODS - A national retrospective cohort of patients with cirrhosis and AKI admitted to 124 Veterans Affairs hospitals was assembled from electronic health record data collected from 2005 to 2013. AKI was defined by the Kidney Disease: Improving Global Outcomes criteria. Five hundred and four hospitalizations were selected for manual chart review and served as the gold standard. Electronic Health Record based predictors were identified using structured and free text clinical data, subjected through NLP from the clinical Text Analysis Knowledge Extraction System. We explored several dimension reduction techniques for the NLP data, including newer high-throughput phenotyping and word embedding methods, and ascertained their effectiveness in identifying the phenotype without structured predictor variables. With the combined structured and NLP variables, we analyzed five phenotyping algorithms: penalized logistic regression, naïve Bayes, support vector machines, random forest, and gradient boosting. Calibration and discrimination metrics were calculated using 100 bootstrap iterations. In the final model, we report odds ratios and 95% confidence intervals.
RESULTS - The area under the receiver operating characteristic curve (AUC) for the different models ranged from 0.73 to 0.93; with penalized logistic regression having the best discriminatory performance. Calibration for logistic regression was modest, but gradient boosting and support vector machines were superior. NLP identified 6985 variables; a priori variable selection performed similarly to dimensionality reduction using high-throughput phenotyping and semantic similarity informed clustering (AUC of 0.81 - 0.82).
CONCLUSION - This study demonstrated improved phenotyping of a challenging AKI etiology, HRS, over ICD-9 coding. We also compared performance among multiple approaches to EHR-derived phenotyping, and found similar results between methods. Lastly, we showed that automated NLP dimension reduction is viable for acute illness.
Copyright © 2018 Elsevier Inc. All rights reserved.
Background - Age-related gait speed decline is accelerated in men with human immunodeficiency virus (HIV). Mitochondrial genetic variation is associated with frailty and mortality in the general population and may provide insight into mechanisms of functional decline in people aging with HIV.
Methods - Gait speed was assessed semiannually in the Multicenter AIDS Cohort Study. Mitochondrial DNA (mtDNA) haplogroups were extracted from genome-wide genotyping data, classifying men aged ≥50 years into 5 groups: mtDNA haplogroup H, J, T, Uk, and other. Differences in gait speed by haplogroups were assessed as rate of gait speed decline per year, probability of slow gait speed (<1.0 m/s), and hazard of slow gait using multivariable linear mixed-effects models, mixed-effects logistic regression models, and the Andersen-Gill model, controlling for hepatitis C virus infection, previous AIDS diagnosis, thymidine analogues exposure, education, body composition, smoking, and peripheral neuropathy. Age was further controlled for in the mixed-effects logistic regression models.
Results - A total of 455 HIV-positive white men aged ≥50 years contributed 3283 person-years of follow-up. Among them, 70% had achieved HIV viral suppression. In fully adjusted models, individuals with haplogroup J had more rapid decline in gait speed (adjusted slopes, 0.018 m/s/year vs 0.011 m/s/year, pinteraction = 0.012) and increased risk of developing slow gait (adjusted odds ratio, 2.97; 95% confidence interval, 1.24-7.08) compared to those with other haplogroups.
Conclusions - Among older, HIV-infected men, mtDNA haplogroup J was an independent risk factor for more rapid age-related gait speed decline.
Few prospective studies, and none in Asians, have systematically evaluated the relationship between blood metabolites and colorectal cancer risk. We conducted a nested case-control study to search for risk-associated metabolite biomarkers for colorectal cancer in an Asian population using blood samples collected prior to cancer diagnosis. Conditional logistic regression was performed to assess associations of metabolites with cancer risk. In this study, we included 250 incident cases with colorectal cancer and individually matched controls nested within two prospective Shanghai cohorts. We found 35 metabolites associated with risk of colorectal cancer after adjusting for multiple comparisons. Among them, 12 metabolites were glycerophospholipids including nine associated with reduced risk of colorectal cancer and three with increased risk [odds ratios per standard deviation increase of transformed metabolites: 0.31-1.98; p values: 0.002-1.25 × 10 ]. The other 23 metabolites associated with colorectal cancer risk included nine lipids other than glycerophospholipid, seven aromatic compounds, five organic acids and four other organic compounds. After mutual adjustment, nine metabolites remained statistically significant for colorectal cancer. Together, these independently associated metabolites can separate cancer cases from controls with an area under the curve of 0.76 for colorectal cancer. We have identified that dysregulation of glycerophospholipids may contribute to risk of colorectal cancer.
© 2018 UICC.