Hepatocellular carcinoma (HCC) and secondary liver tumors, such as colorectal cancer liver metastases are significant contributors to the overall burden of cancer-related morality. Current biomarkers, such as alpha-fetoprotein (AFP) for HCC, result in too many false negatives, necessitating noninvasive approaches with improved sensitivity. Volatile organic compounds (VOCs) detected in the breath of patients can provide valuable insight into disease processes and can differentiate patients by disease status. Here, we investigate whether 22 VOCs from the breath of 296 patients can distinguish those with no liver disease (n = 54), cirrhosis (n = 30), HCC (n = 112), pulmonary hypertension (n = 49), or colorectal cancer liver metastases (n = 51). This work extends previous studies by evaluating the ability for VOC signatures to differentiate multiple diseases in a large cohort of patients. Pairwise disease comparisons demonstrated that most of the VOCs tested are present in significantly different relative abundances (false discovery rate P < 0.1), indicating broad impacts on the breath metabolome across diseases. A predictive model developed using random forest machine learning and cross validation classified patients with 85% classification accuracy and 75% balanced accuracy. Importantly, the model detected HCC with 73% sensitivity compared with 53% for AFP in the same cohort. An added value of this approach is that influential VOCs in the predictive model may provide insight into disease etiology. Acetaldehyde and acetone, both of which have roles in tumor promotion, were considered important VOCs for differentiating disease groups in the predictive model and were increased in patients with cirrhosis and HCC compared to patients with no liver disease (false discovery rate P < 0.1). Conclusion: The use of machine learning and breath VOCs shows promise as an approach to develop improved, noninvasive screening tools for chronic liver disease and primary and secondary liver tumors. (Hepatology Communications 2020;4:1041-1055).

Current type 2 diabetes (T2D) management contraindicates intensive glycemia treatment in patients with high cardiovascular disease (CVD) risk and is partially motivated by evidence of harms in the Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial. Heterogeneity in response to intensive glycemia treatment has been observed, suggesting potential benefit for some individuals. RESEARCH DESIGN AND METHODSACCORD was a randomized controlled trial that investigated whether intensively treating glycemia in individuals with T2D would reduce CVD outcomes. Using a novel approach to cluster HbA 1c trajectories, we identified groups in the intensive glycemia arm with modified CVD risk. Genome-wide analysis and polygenic score (PS) were developed to predict group membership. Mendelian randomization was performed to infer causality. RESULTSWe identified four clinical groupings in the intensive glycemia arm, and clinical group 4 (C4) displayed fewer CVD (hazard ratio [HR] 0.34; P 5 2.01 × 10 −3 ) and microvascular outcomes (HR 0.86; P 5 0.015) than those receiving standard treatment. A singlenucleotide polymorphism, rs220721, in MAS1 reached suggestive significance in C4 (P 5 4.34 3 10 −7 ). PS predicted C4 with high accuracy (area under the receiver operating characteristic curve 0.98), and this predicted C4 displayed reduced CVD risk with intensive versus standard glycemia treatment (HR 0.53; P 5 4.02 × 10 −6 ), but not reduced risk of microvascular outcomes (P < 0.05). Mendelian randomization indicated causality between PS, on-trial HbA 1c , and reduction in CVD outcomes (P < 0.05). CONCLUSIONSWe found evidence of a T2D clinical group in ACCORD that benefited from intensive glycemia treatment, and membership in this group could be predicted using genetic variants. This study generates new hypotheses with implications for precision medicine in T2D and represents an important development in this landmark clinical trial warranting further investigation.The Action to Control Cardiovascular Risk in Diabetes (ACCORD) was a landmark trial to examine the effect of intensive glycemia treatment targeting glycated hemoglobin A 1c (HbA 1c ) <6% versus more modest therapy targeting HbA 1c 7.0-7.9%. The study was conducted in patients with type 2 diabetes (T2D) at high cardiovascular risk, with a primary end point of time to first occurrence of major adverse cardiovascular events (MACE), specifically nonfatal myocardial infarction (MI), nonfatal stroke, or

Background Improved detection of hepatocellular carcinoma (HCC) is needed, as current detection methods, such as alpha fetoprotein (AFP) and ultrasound, suffer from poor sensitivity. MicroRNAs (miRNAs) are small, non-coding RNAs that regulate many cellular functions and impact cancer development and progression. Notably, miRNAs are detectable in saliva and have shown potential as non-invasive biomarkers for a number of cancers including breast, oral, and lung cancers. Here, we present, to our knowledge, the first report of salivary miRNAs in HCC and compare these findings to patients with cirrhosis, a high-risk cohort for HCC. Methods We performed small RNA sequencing in 20 patients with HCC and 19 with cirrhosis. Eleven patients with HCC had chronic liver disease, and analyses were performed with these samples combined and stratified by the presence of chronic liver disease. P values were adjusted for multiple comparisons using a false discovery rate (FDR) approach and miRNA with FDR P < 0.05 were considered statistically significant. Differential expression of salivary miRNAs was compared to a previously published report of miRNAs in liver tissue of patients with HCC vs cirrhosis. Support vector machines and leave-one-out cross-validation were performed to determine if salivary miRNAs have predictive potential for detecting HCC. Results A total of 4,565 precursor and mature miRNAs were detected in saliva and 365 were significantly different between those with HCC compared to cirrhosis (FDR P < 0.05). Interestingly, 283 of these miRNAs were significantly downregulated in patients with HCC. Machine-learning identified a combination of 10 miRNAs and covariates that accurately classified patients with HCC (AUC = 0.87). In addition, we identified three miRNAs that were differentially expressed in HCC saliva samples and in a previously published study of miRNAs in HCC tissue compared to cirrhotic liver tissue. Conclusions This study demonstrates, for the first time, that miRNAs relevant to HCC are detectable in saliva, that salivary miRNA signatures show potential to be highly sensitive and specific non-invasive biomarkers of HCC, and that additional studies utilizing larger cohorts are needed.

Breath analysis is the study of volatile organic compounds (VOC’s) in exhaled breath. This analysis provides information on the body’s condition. In this study we investigated the relationship between 22 VOC’s detected in exhaled breath and plasma headspace using a selected ion flow tube mass spectrometer (SYFT-MS). We compared pairs of exhaled breath and plasma samples from patients with pulmonary hypertension inflammatory bowel disease (IBD), and IBD patients after J-pouch surgery (pouch group). Half of the measured VOC’s from exhaled breath were significantly associated with the VOC’s from plasma headspace. Interestingly, six breath VOC’s (trimethyl amine (FDR p = 0.02), hydrogen sulfide (FDR p = 7.64 × 10−30), ethanol (FDR p = 1.56 × 10−4), dimethyl sulfide (FDR p = 5.70 × 10−19), benzene (FDR p = 8.40 × 10−27), and acetaldehyde (FDR p = 4.27 × 10−17)) and two plasma headspace VOC’s (1-Octene (FDR p = 0.02) and 2-propanol (FDR p = 2.47 × 10−9)) were able to differentiate between the three groups. Breath and plasma headspace share a similar signature with significant association in half of the measured VOCs. The disease discriminatory capacity of breath and plasma headspace appear to be different. Therefore, using the VOC’s print from both breath and plasma headspace may better help diagnose patients.

Aims To determine the health outcomes associated with weight loss in individuals with obesity, and to better understand the relationship between disease burden (disease burden; ie, prior comorbidities, healthcare utilization) and weight loss in individuals with obesity by analysing electronic health records (EHRs). Materials and Methods We conducted a case‐control study using deidentified EHR‐derived information from 204 921 patients seen at the Cleveland Clinic between 2000 and 2018. Patients were aged ≥20 years with body mass index ≥30 kg/m2 and had ≥7 weight measurements, over ≥3 years. Thirty outcomes were investigated, including chronic and acute diseases, as well as psychological and metabolic disorders. Weight change was investigated 3, 5 and 10 years prior to an event. Results Weight loss was associated with reduced incidence of many outcomes (eg, type 2 diabetes, nonalcoholic steatohepatitis/nonalcoholic fatty liver disease, obstructive sleep apnoea, hypertension; P < 0.05). Weight loss >10% was associated with increased incidence of certain outcomes including stroke and substance abuse. However, many outcomes that increased with weight loss were attenuated by disease burden adjustments. Conclusions This study provides the most comprehensive real‐world evaluation of the health impacts of weight change to date. After comorbidity burden and healthcare utilization adjustments, weight loss was associated with an overall reduction in risk of many adverse outcomes.