Complexation Mechanisms of Aqueous Amylose: Molecular Dynamics Study Using 3-Pentadecylphenol.

Molecular dynamics (MD) simulations of linear amylose fragments containing 10 to 40 glucose units were used to study the complexation of the prototypical compound, 3-pentadecylphenol (PDP)─a natural product with surfactant-like properties─in aqueous solution. The amylose-PDP binding leverages mainly hydrophobic interactions together with excluded volume effects. It was found that while the most stable complexes contained PDP inside the helical structure of the amylose in the expected guest-host (inclusion) complexation manner, at higher temperatures, the commonly observed PDP-amylose complexes often involved more nonspecific interactions than inclusion complexation. In the case where a stoichiometric excess of PDP was added to the simulation box, self-aggregation of the small molecule precluded its ability to enter the internal helical part of the oligosaccharide, and as a result, inclusion complexation became ineffective. MD simulation trajectories were analyzed preliminarily using cluster analysis (CA), followed by more rigorous solvent accessible surface area (SASA) determination over the temperature range spanning from 277 to 433 K. It was found that using the SASA of PDP corrected for its intrinsic conformational changes, together with a generic hidden Markov model (HMM), an adequate quantification of the different types of PDP-amylose aggregates was obtained to allow further analysis. The enthalpy change associated with the guest-host binding equilibrium constant (Kgh) in aqueous solution was estimated to be -75 kJ/mol, which is about twice as high as one might expect based on experimentally measured values of similar complexes in the solid state where the (unsolvated) helical structure of amylose remains rigid. On the other hand, the nonspecific binding (Kns) enthalpy change associated with PDP-amylose interactions in the same solution environment was found to be about half of the inclusion complexation value.

Transcriptome-wide association analysis identifies candidate susceptibility genes for prostate-specific antigen levels in men without prostate cancer.

Deciphering the genetic basis of prostate-specific antigen (PSA) levels may improve their utility for prostate cancer (PCa) screening. Using genome-wide association study (GWAS) summary statistics from 95,768 PCa-free men, we conducted a transcriptome-wide association study (TWAS) to examine impacts of genetically predicted gene expression on PSA. Analyses identified 41 statistically significant (p < 0.05/12,192 = 4.10 × 10-6) associations in whole blood and 39 statistically significant (p < 0.05/13,844 = 3.61 × 10-6) associations in prostate tissue, with 18 genes associated in both tissues. Cross-tissue analyses identified 155 statistically significantly (p < 0.05/22,249 = 2.25 × 10-6) genes. Out of 173 unique PSA-associated genes across analyses, we replicated 151 (87.3%) in a TWAS of 209,318 PCa-free individuals from the Million Veteran Program. Based on conditional analyses, we found 20 genes (11 single tissue, nine cross-tissue) that were associated with PSA levels in the discovery TWAS that were not attributable to a lead variant from a GWAS. Ten of these 20 genes replicated, and two of the replicated genes had colocalization probability of >0.5: CCNA2 and HIST1H2BN. Six of the 20 identified genes are not known to impact PCa risk. Fine-mapping based on whole blood and prostate tissue revealed five protein-coding genes with evidence of causal relationships with PSA levels. Of these five genes, four exhibited evidence of colocalization and one was conditionally independent of previous GWAS findings. These results yield hypotheses that should be further explored to improve understanding of genetic factors underlying PSA levels.

Clinical associations with a polygenic predisposition to benign lower white blood cell counts.

Polygenic variation unrelated to disease contributes to interindividual variation in baseline white blood cell (WBC) counts, but its clinical significance is uncharacterized. We investigated the clinical consequences of a genetic predisposition toward lower WBC counts among 89,559 biobank participants from tertiary care centers using a polygenic score for WBC count (PGSWBC) comprising single nucleotide polymorphisms not associated with disease. A predisposition to lower WBC counts was associated with a decreased risk of identifying pathology on a bone marrow biopsy performed for a low WBC count (odds-ratio = 0.55 per standard deviation increase in PGSWBC [95%CI, 0.30-0.94], p = 0.04), an increased risk of leukopenia (a low WBC count) when treated with a chemotherapeutic (n = 1724, hazard ratio [HR] = 0.78 [0.69-0.88], p = 4.0 × 10-5) or immunosuppressant (n = 354, HR = 0.61 [0.38-0.99], p = 0.04). A predisposition to benign lower WBC counts was associated with an increased risk of discontinuing azathioprine treatment (n = 1,466, HR = 0.62 [0.44-0.87], p = 0.006). Collectively, these findings suggest that there are genetically predisposed individuals who are susceptible to escalations or alterations in clinical care that may be harmful or of little benefit.

Flavobacterium covae is the predominant species of columnaris-causing bacteria impacting the Channel Catfish industry in the southeastern United States.

OBJECTIVE: Columnaris disease is a leading cause of disease-related losses in the catfish industry of the southeastern United States. The term "columnaris-causing bacteria" (CCB) has been coined in reference to the four described species that cause columnaris disease: Flavobacterium columnare, F. covae, F. davisii, and F. oreochromis. Historically, F. columnare, F. covae, and F. davisii have been isolated from columnaris disease cases in the catfish industry; however, there is a lack of knowledge of which CCB species are most prevalent in farm-raised catfish. The current research objectives were to (1) sample columnaris disease cases from the U.S. catfish industry and identify the species of CCB involved and (2) determine the virulence of the four CCB species in Channel Catfish Ictalurus punctatus in controlled laboratory challenges. METHODS: Bacterial isolates or swabs of external lesions from catfish were collected from 259 columnaris disease cases in Mississippi and Alabama during 2015-2019. The DNA extracted from the samples was analyzed using a CCB-specific multiplex polymerase chain reaction to identify the CCB present in each diagnostic case. Channel Catfish were challenged by immersion with isolates belonging to each CCB species to determine virulence at ~28°C and 20°C. RESULT: Flavobacterium covae was identified as the predominant CCB species impacting the U.S. catfish industry, as it was present in 94.2% (n = 244) of diagnostic case submissions. Challenge experiments demonstrated that F. covae and F. oreochromis were highly virulent to Channel Catfish, with most isolates resulting in near 100% mortality. In contrast, F. columnare and F. davisii were less virulent, with most isolates resulting in less than 40% mortality. CONCLUSION: Collectively, these results demonstrate that F. covae is the predominant CCB in the U.S. catfish industry, and research aimed at developing new control and prevention strategies should target this bacterial species. The methods described herein can be used to continue monitoring the prevalence of CCB in the catfish industry and can be easily applied to other industries to identify which Flavobacterium species have the greatest impact.

Genome-wide association study of prostate-specific antigen levels in 392,522 men identifies new loci and improves cross-ancestry prediction.

We conducted a multi-ancestry genome-wide association study of prostate-specific antigen (PSA) levels in 296,754 men (211,342 European ancestry; 58,236 African ancestry; 23,546 Hispanic/Latino; 3,630 Asian ancestry; 96.5% of participants were from the Million Veteran Program). We identified 318 independent genome-wide significant (p≤5e-8) variants, 184 of which were novel. Most demonstrated evidence of replication in an independent cohort (n=95,768). Meta-analyzing discovery and replication (n=392,522) identified 447 variants, of which a further 111 were novel. Out-of-sample variance in PSA explained by our new polygenic risk score reached 16.9% (95% CI=16.1%-17.8%) in European ancestry, 9.5% (95% CI=7.0%-12.2%) in African ancestry, 18.6% (95% CI=15.8%-21.4%) in Hispanic/Latino, and 15.3% (95% CI=12.7%-18.1%) in Asian ancestry, and lower for higher age. Our study highlights how including proportionally more participants from underrepresented populations improves genetic prediction of PSA levels, with potential to personalize prostate cancer screening.

Clinical consequences of a genetic predisposition toward higher benign prostate-specific antigen levels.

BACKGROUND: Prostate-specific antigen (PSA) levels are influenced by genetic variation unrelated to prostate cancer risk. Whether a genetic predisposition to a higher PSA level predisposes to a diagnostic work-up for prostate cancer is not known. METHODS: Participants were 3110 men of African and European ancestries ages 45-70, without prostate cancer and with a baseline PSA < 4 ng/mL, undergoing routine clinical PSA screening. The exposure was a polygenic score (PGS) comprising 111 single nucleotide polymorphisms associated with PSA level, but not prostate cancer. We tested whether the PGS was associated with a: 1) PSA value > 4 ng/mL, 2) International Classification of Diseases (ICD) code for an elevated PSA, 3) encounter with a urologist, or 4) prostate biopsy. Multivariable Cox proportional hazards models were adjusted for age and genetic principal components. Analyses were stratified by age (45-59 years, and 60-70 years old). Association estimates are per standard deviation change in the PGS. FINDINGS: The median age was 56.6 years, and 2118 (68%) participants were 45-59 years. The median (IQR) baseline PSA level was 1.0 (0.6-1.7) ng/mL. Among men ages 45-59, the PGS was associated with a PSA > 4 (hazard ratio [HR] = 1.35 [95% CI, 1.17-1.57], p = 4.5 × 10-5), an ICD code for elevated PSA (HR = 1.30 [1.12-1.52], p = 8.0 × 10-4), a urological evaluation (HR = 1.34 [1.14-1.57], p = 4.8 × 10-4), and undergoing a prostate biopsy (HR = 1.35 [1.11-1.64], p = 0.002). Among men ages 60-70, association effect sizes were smaller and not significant. INTERPRETATION: A predisposition toward higher PSA levels was associated with clinical evaluations of an elevated PSA among men ages 45-59 years. FUNDING: National Institutes of Health (NIH).

Drug Aggregation of Sparingly-Soluble Ionizable Drugs: Molecular Dynamics Simulations of Papaverine and Prostaglandin F2α.

Aggregation in aqueous solution can have important implications on both the in vivo exposure of a drug and its pharmaceutical manufacturability. However, the drug aggregates formed can be very small and, thus, difficult to interrogate experimentally. On the other hand, at higher supersaturations where larger aggregates are supported, the chemical system is inherently metastable and therefore likewise challenging to study from an experimental standpoint. Understanding aggregation behavior is further complicated in the case of ionizable drugs where, unlike neutral compounds, there can be uncertainty in the kinds of drug molecules (i.e., charged, neutral, or both) that become incorporated into various clusters, particularly at pH values near the pKa. In this paper, we apply physics-based all-atom molecular dynamics (MD) simulations to study aggregation in the weakly basic drug papaverine and in the weakly acidic drug prostaglandin F2α. We employ in silico tools to construct simulation workflows and comprehensive cluster analysis protocols to elucidate the size distributions and dynamics of the drug aggregates formed at both an experimentally relevant concentration and at high supersaturation. We build on a previously published treatment [Solubility of sparingly soluble ionizable drugs. Adv. Drug Deliv. Rev. 2007, 59, 568-590, DOI: 10.1016/j.addr.2007.05.008] to translate the predicted aggregate distributions of each ionized drug into corresponding pH-solubility curves that can be compared directly to experiment. Our findings show that the assumption of a single predominant (charged) aggregate can be misleading in interpreting experimental pH-solubility curves, as it does not adequately reflect the rich diversity revealed in our simulations. Beyond not accounting for the distribution of ionized drug-containing clusters actually observed in solution, for both drugs we find evidence that neutral drug molecules can also participate in the aggregation phenomena. Notably, we observe that many drug molecules remain as free monomers in solution even under simulated conditions designed to mimic those where there is significant deviation of the experimental pH-solubility curve from the Henderson-Hasselbalch (H-H) equation, often taken to be a clear signpost of drug aggregation.

Clinical consequences of a polygenic predisposition to benign lower white blood cell counts: Consequences of benign WBC count genetics.

Polygenic variation unrelated to disease contributes to interindividual variation in baseline white blood cell (WBC) counts, but its clinical significance is undefined. We investigated the clinical consequences of a genetic predisposition toward lower WBC counts among 89,559 biobank participants from tertiary care centers using a polygenic score for WBC count (PGSWBC) comprising single nucleotide polymorphisms not associated with disease. A predisposition to lower WBC counts was associated with a decreased risk of identifying pathology on a bone marrow biopsy performed for a low WBC count (odds-ratio=0.55 per standard deviation increase in PGSWBC [95%CI, 0.30 - 0.94], p=0.04), an increased risk of leukopenia (a low WBC count) when treated with a chemotherapeutic (n=1,724, hazard ratio [HR]=0.78 [0.69 - 0.88], p=4.0×10-5) or immunosuppressant (n=354, HR=0.61 [0.38 - 0.99], p=0.04). A predisposition to benign lower WBC counts was associated with an increased risk of discontinuing azathioprine treatment (n=1,466, HR=0.62 [0.44 - 0.87], p=0.006). Collectively, these findings suggest that a WBC count polygenic score identifies individuals who are susceptible to escalations or alterations in clinical care that may be harmful or of little benefit.

Genetically adjusted PSA levels for prostate cancer screening.

Prostate-specific antigen (PSA) screening for prostate cancer remains controversial because it increases overdiagnosis and overtreatment of clinically insignificant tumors. Accounting for genetic determinants of constitutive, non-cancer-related PSA variation has potential to improve screening utility. In this study, we discovered 128 genome-wide significant associations (P < 5 × 10-8) in a multi-ancestry meta-analysis of 95,768 men and developed a PSA polygenic score (PGSPSA) that explains 9.61% of constitutive PSA variation. We found that, in men of European ancestry, using PGS-adjusted PSA would avoid up to 31% of negative prostate biopsies but also result in 12% fewer biopsies in patients with prostate cancer, mostly with Gleason score <7 tumors. Genetically adjusted PSA was more predictive of aggressive prostate cancer (odds ratio (OR) = 3.44, P = 6.2 × 10-14, area under the curve (AUC) = 0.755) than unadjusted PSA (OR = 3.31, P = 1.1 × 10-12, AUC = 0.738) in 106 cases and 23,667 controls. Compared to a prostate cancer PGS alone (AUC = 0.712), including genetically adjusted PSA improved detection of aggressive disease (AUC = 0.786, P = 7.2 × 10-4). Our findings highlight the potential utility of incorporating PGS for personalized biomarkers in prostate cancer screening.

Transcriptome-Wide Association Analysis Identifies Novel Candidate Susceptibility Genes for Prostate-Specific Antigen Levels in Men Without Prostate Cancer.

Deciphering the genetic basis of prostate-specific antigen (PSA) levels may improve their utility to screen for prostate cancer (PCa). We thus conducted a transcriptome-wide association study (TWAS) of PSA levels using genome-wide summary statistics from 95,768 PCa-free men, the MetaXcan framework, and gene prediction models trained in Genotype-Tissue Expression (GTEx) project data. Tissue-specific analyses identified 41 statistically significant (p < 0.05/12,192 = 4.10e-6) associations in whole blood and 39 statistically significant (p < 0.05/13,844 = 3.61e-6) associations in prostate tissue, with 18 genes associated in both tissues. Cross-tissue analyses that combined associations across 45 tissues identified 155 genes that were statistically significantly (p < 0.05/22,249 = 2.25e-6) associated with PSA levels. Based on conditional analyses that assessed whether TWAS associations were attributable to a lead GWAS variant, we found 20 novel genes (11 single-tissue, 9 cross-tissue) that were associated with PSA levels in the TWAS. Of these novel genes, five showed evidence of colocalization (colocalization probability > 0.5): EXOSC9, CCNA2, HIST1H2BN, RP11-182L21.6, and RP11-327J17.2. Six of the 20 novel genes are not known to impact PCa risk. These findings yield new hypotheses for genetic factors underlying PSA levels that should be further explored toward improving our understanding of PSA biology.

Epik: pKa and Protonation State Prediction through Machine Learning.

Epik version 7 is a software program that uses machine learning for predicting the pKa values and protonation state distribution of complex, druglike molecules. Using an ensemble of atomic graph convolutional neural networks (GCNNs) trained on over 42,000 pKa values across broad chemical space from both experimental and computed origins, the model predicts pKa values with 0.42 and 0.72 pKa unit median absolute and root mean square errors, respectively, across seven test sets. Epik version 7 also generates protonation states and recovers 95% of the most populated protonation states compared to previous versions. Requiring on average only 47 ms per ligand, Epik version 7 is rapid and accurate enough to evaluate protonation states for crucial molecules and prepare ultra-large libraries of compounds to explore vast regions of chemical space. The simplicity and time required for the training allow for the generation of highly accurate models customized to a program's specific chemistry.

A Polygenic Risk Score for Prostate Cancer Risk Prediction.

This retrospective cohort study compares 2 risk calculator systems that compute the probabilities of finding high-grade or any cancer on biopsy results in men undergoing a first prostate biopsy.

Shearing friction behaviour of synthetic polymers compared to a functionalized polysaccharide on biomimetic surfaces: models for the prediction of performance of eco-designed formulations.

The substitution of natural, bio-based and/or biodegradable polymers for those of petrochemical origin in consumer formulations has become an active area of research and development as the sourcing and destiny of material components becomes a more critical factor in product design. These polymers often differ from their petroleum-based counterparts in topology, raw material composition and solution behaviour. Effective and efficient reformulation that maintains comparable cosmetic performance to existing products requires a deep understanding of the differences in frictional behaviour between polymers as a function of their molecular structure. In this work, we simulate the tribological behaviour of three topologically distinct polymers in solution with surfactants and in contact with hair-biomimetic patterned surfaces. We compare a generic functionalized polysaccharide to two performant polymers used in shampoo formulations: a strongly positively charged polyelectrolyte and a zwitterionic copolymer. Topological differences are expected to affect rheological properties, as well as their direct interaction with structured biological substrates. Using a refined Martini-style coarse-grained model we describe the polymer-dependent differences in aggregation behaviour as well as selective interactions with a biomimetic model hair surface. Additionally, we introduce a formalism to characterize the response of the solution to shear as an initial study on lubrication properties, which define the sensorial performance of these systems in cosmetics (i.e., manageability, touch, etc.). The tools and techniques presented in this work illustrate the strength of molecular simulation in eco-design of formulation as a complement to experiment. These efforts help advance our understanding of how we can relate complex atomic-scale solution behaviour to relevant macroscopic properties. We expect these techniques to play an increasingly important role in advancing strategies for green polymer formulation design by providing an understanding for how new polymers could reach and even exceed the level of performance of existing polymers.

Exploring Risk Factors in Suicidal Ideation and Attempt Concept Cooccurrence Networks.

Suicide is a significant and rising threat to public health. In the United States, 47,500 people died from suicide in 2019, a 10-year increase of 30%. Many researchers are interested in studying the risk factors associated with suicidal ideation and suicide attempt to help inform clinical screening, intervention, and prevention efforts. Many suicide risk factor analyses draw from clinical subdomains and quantify risk factors independently. While traditional modeling approaches might assume independence between risk factors, current suicide research suggests that the development of suicidal intent is a complex, multifactorial process. Thus, it may be beneficial to how suicide risk-factors interact with one another. In this study, we used network analysis to generate visual suicidality risk relationship diagrams. We extract medical concepts from free-text clinical notes and generate cooccurrence-based risk networks for suicidal ideation and suicide attempt. In addition, we generate a network of risk factors for suicidal ideation which evolves into a suicide attempt. Our networks were able to replicate existing risk factor findings and provide additional insight into the degree to which risk factors behave as independent morbidities or as interacting comorbidities with other risk factors. These results highlight potential avenues for risk factor analyses of complex outcomes using network analysis.

Primary care visits and ambulatory care sensitive diabetes hospitalizations among adult Alabama Medicaid beneficiaries.

PURPOSE: To describe patterns of care use for Alabama Medicaid adult beneficiaries with diabetes and the association between primary care utilization and ambulatory care sensitive (ACS) diabetes hospitalizations. METHODS: This retrospective cohort study analyzes Alabama Medicaid claims data from January 2010 to April 2018 for 52,549 covered adults ages 19-64 with diabetes. Individuals were characterized by demographics, comorbidities, and health care use including primary, specialty, mental health and hospital care. Characteristics of those with and without any ACS diabetes hospitalization are reported. A set of 118,758 observations was created, pairing information on primary care use in one year with ACS hospitalizations in the following year. Logistic regression analysis was used to assess the impact of primary care use on the occurrence of an ACS hospitalization. RESULTS: One third of the cohort had at least one ACS diabetes hospitalization over their observed periods; hospital users tended to have multiple ACS hospitalizations. Hospital users had more comorbidities and pharmaceutical and other types of care use than those with no ACS hospitalizations. Controlling for other types of care use, comorbidities and demographics, having a primary care visit in one year was significantly associated with a reduced likelihood of ACS hospitalization in the following year (odds ratio comparing 1-2 visits versus none 0.79, 95% confidence interval 0.73-0.85). CONCLUSIONS: Program and population health interventions that increase access to primary care can have a beneficial effect of reducing excess inpatient hospital use for Medicaid covered adults with diabetes.

Molecular-Level Examination of Amorphous Solid Dispersion Dissolution.

Amorphous solid dispersions (ASDs) are commonly used to orally deliver small-molecule drugs that are poorly water-soluble. ASDs consist of drug molecules in the amorphous form which are dispersed in a hydrophilic polymer matrix. Producing a high-performance ASD is critical for effective drug delivery and depends on many factors such as solubility of the drug in the matrix and the rate of drug release in aqueous medium (dissolution), which is linked to bioperformance. Often, researchers perform a large number of design iterations to achieve this objective. A detailed molecular-level understanding of the mechanisms behind ASD dissolution behavior would aid in the screening, designing, and optimization of ASD formulations and would minimize the need for testing a wide variety of prototype formulations. Molecular dynamics and related types of simulations, which model the collective behavior of molecules in condensed phase systems, can provide unique insights into these mechanisms. To study the effectiveness of these simulation techniques in ASD formulation dissolution, we carried out dissipative particle dynamics simulations, which are particularly an efficient form of molecular dynamics calculations. We studied two stages of the dissolution process: the early-stage of the dissolution process, which focuses on the dissolution at the ASD/water interface, and the late-stage of the dissolution process, where significant drug release would have occurred and there would be a mixture of drug and polymer molecules in a predominantly aqueous environment. Experimentally, we used Fourier transform infrared spectroscopy to study the interactions between drugs, polymers, and water in the dry and wet states and the chromatographic technique to study the rate of drug and polymer release. Both experiments and simulations provided evidence of polymer microstructures and drug-polymer interactions as important factors for the dissolution behavior of the investigated ASDs, consistent with previous work by Pudlas et al. (Eur. J. Pharm. Sci.2015, 67, 21-31). As experimental and simulation results are consistent and complementary, it is clear that there is significant potential for combined experimental and computational research for a detailed understanding of ASD formulations and, hence, formulation optimization.

Characterizing moisture uptake and plasticization effects of water on amorphous amylose starch models using molecular dynamics methods.

Dynamics and thermophysical properties of amorphous starch were explored using molecular dynamics (MD) simulations. Using the OPLS3e force field, simulations of short amylose chains in water were performed to determine force field accuracy. Using well-tempered metadynamics, a free energy map of the two glycosidic angles of an amylose molecule was constructed and compared with other modern force fields. Good agreement of torsional sampling for both solvated and amorphous amylose starch models was observed. Using combined grand canonical Monte Carlo (GCMC)/MD simulations, a moisture sorption isotherm curve is predicted along with temperature dependence. Concentration-dependent activation energies for water transport agree quantitatively with previous experiments. Finally, the plasticization effect of moisture content on amorphous starch was investigated. Predicted glass transition temperature (Tg) depression as a function of moisture content is in line with experimental trends. Further, our calculations provide a value for the dry Tg for amorphous starch, a value which no experimental value is available.

Multiplex PCR for genotyping Flavobacterium columnare.

Recent research has identified four distinct genetic groups among isolates of Flavobacterium columnare through multilocus phylogenetic analyses; however, there are no quick methods to determine the genotype of an isolate. The objective of this research was to develop a multiplex PCR to rapidly genotype F. columnare to genetic group. Comparative bacterial genomics was used to identify regions in the genomes unique to each genetic group, and primers were designed to specifically amplify different sized amplicons for each genetic group. The optimized assay was demonstrated to be specific for each genetic group and F. columnare, and no specific amplicons were generated using gDNA from a panel of other Flavobacterium spp. and bacterial fish pathogens. The analytical sensitivity of the assay ranged from 209 to 883 genome equivalents depending on the genetic group. The multiplex PCR was evaluated by genotyping a panel of 22 unknown F. columnare isolates and performing DNA sequencing of the dnaK gene in parallel. The results demonstrated 100% accordance between multiplex PCR results and assignment to genetic group via phylogenetic analysis. The multiplex PCR provides a useful tool for assigning an unknown isolate to genetic group and may be used to determine which genetic groups of F. columnare are circulating and most predominant in different aquaculture industries.

Medicaid Expansion and Hospitalization for Ambulatory Care-Sensitive Conditions Among Nonelderly Adults With Diabetes.

Among nonelderly adults with diabetes, we compared hospitalizations for ambulatory care-sensitive conditions from 2013 (pre-Medicaid expansion) and 2014 (post-Medicaid expansion) for 13 expansion and 4 nonexpansion states using State Inpatient Databases. Medicaid expansion was associated with decreases in proportions of hospitalizations for chronic conditions (difference between 2014 and 2013 -0.17 percentage points in expansion and 0.37 in nonexpansion states, P = .04), specifically diabetes short-term complications (difference between 2014 and 2013 -0.05 percentage points in expansion and 0.21 in nonexpansion states, P = .04). Increased access to care through Medicaid expansion may improve disease management in nonelderly adults with diabetes.