Reports indicate that COVID-19 may impact pancreatic function and increase type 2 diabetes (T2D) risk, although real-world COVID-19 impacts on HbA1c and T2D are unknown. We tested whether COVID-19 increased HbA1c, risk of T2D, or diabetic ketoacidosis (DKA). We compared pre- and post-COVID-19 HbA1c, and T2D risk in a large real-world clinical cohort of 8,755 COVID-19(+) patients and 11,998 COVID-19(−) matched controls. We investigated if DKA risk was modified in COVID-19(+) patients with type 1 diabetes (T1D) (N=701) or T2D (N=21,830), or by race and sex. We observed a statistically significant, albeit clinically insignificant, HbA1c increase post-COVID-19 (all patients △HbA1c=0.06%; with T2D △HbA1c=0.1%), and no increase among COVID-19(−) patients. COVID-19(+) patients were 40% more likely to be diagnosed with T2D compared to COVID-19(−) patients and 28% more likely for the same HbA1c change as COVID-19(−) patients, indicating that COVID-19 attributed T2D risk may be due to increased recognition during COVID-19 management. DKA in COVID-19(+) patients with T1D was not increased. COVID-19(+) Black patients with T2D displayed disproportionately increased DKA risk (HR:2.46[1.48-6.09], P=0.004) compared to White patients, suggesting a need for further clinical awareness and investigation.

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.

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

Deep learning (DL) from electronic health records holds promise for disease prediction, but systematic methods for learning from simulated longitudinal clinical measurements have yet to be reported. We compared nine DL frameworks using simulated body mass index (BMI), glucose, and systolic blood pressure trajectories, independently isolated shape and magnitude changes, and evaluated model performance across various parameters (e.g., irregularity, missingness). Overall, discrimination based on variation in shape was more challenging than magnitude. Time-series forest-convolutional neural networks (TSF-CNN) and Gramian angular field(GAF)-CNN outperformed other approaches (P < 0.05) with overall area-under-the-curve (AUCs) of 0.93 for both models, and 0.92 and 0.89 for variation in magnitude and shape with up to 50% missing data. Furthermore, in a real-world assessment, the TSF-CNN model predicted T2D with AUCs reaching 0.72 using only BMI trajectories. In conclusion, we performed an extensive evaluation of DL approaches and identified robust modeling frameworks for disease prediction based on longitudinal clinical measurements.

Uncontrolled chemotherapy-induced nausea and vomiting can reduce patients' quality of life and may result in premature discontinuation of chemotherapy. Although nausea and vomiting are commonly grouped together, research has shown that antiemetics are clinically effective against chemotherapy-induced vomiting (CIV) but less so against chemotherapy-induced nausea (CIN). Nausea remains a problem for up to 68% of patients who are prescribed guideline-consistent antiemetics. Despite the high prevalence of CIN, relatively little is known regarding its etiology independent of CIV. This review summarizes a metagenomics approach to the study and treatment of CIN with the goal of encouraging future research. Metagenomics focuses on genetic risk factors and encompasses both human (ie, host) and gut microbial genetic variation. Little work to date has focused on metagenomics as a putative biological mechanism of CIN. Metagenomics has the potential to be a powerful tool in advancing scientific understanding of CIN by identifying new biological pathways and intervention targets. The investigation of metagenomics in the context of well-established demographic, clinical, and patientreported risk factors may help to identify patients at risk and facilitate the prevention and management of CIN.