Background: In order to manage a class of diseases as broad as congenital heart disease (CHD), multiple “manually generated” classification systems defining CHDs as mild, moderate and severe have been developed and used to good effect. As databases have grown, however, such “manual” complexity scoring has become infeasible. Though past attempts have been made to determine CHD complexity algorithmically using a list of diagnoses alone, missing data and lack of procedural information have been significant limitations. Methods: We built an algorithm that can stratify the complexity of patients with CHD by integrating their diagnoses with a list of their previous procedures. Specific procedures which address a missing diagnosis or imply a certain operative status were used to supplement the diagnosis list. To verify this algorithm, CHD specialists manually checked the classification of 100 children and 100 adults across four hospitals in Australia. Results: Our algorithm was 99.5% accurate in the manually checked cohort (100% in children and 99% in adults) and was able to automatically classify more than 90% of a cohort of over 24,000 CHD patients, including 92.5% of children (vs 84.4% without procedures, p < 0.0001) and 91.1% of adults (vs 70.4% without procedures; p < 0.0001). Conclusions: CHD complexity scoring is significantly improved by access to procedural history and can be automatically calculated with high accuracy.

Background: Brady- and tachyarrhythmias commonly complicate adult congenital heart disease (ACHD). Permanent pacemakers (PPMs) or implantable cardioverter-defibrillators (ICDs) are often utilised to prevent morbidity or mortality related to arrhythmia, but can also be associated with significant morbidity themselves. Methods: We analysed outcomes from patients in our comprehensive ACHD database who were seen at least twice since 2000 and once since 2018. Of 1953 ACHD patients, 134 had a PPM and 78 had an ICD (47 for primary and 31 for secondary prevention). Results: For PPM patients, 41% had a pacing percentage below 33%, 13% had 33-66%, and 46% had above 66%. One fifth required PPM upgrade, most to cardiac resynchronisation therapy, the rest to ICD. There were 33 appropriate ICD shocks in 15 patients (19%) and 34 inappropriate shocks in 13 patients (17%) over a median follow up of 4.6 years (IQR 0.9-8.3 years). Anti-tachycardia pacing was delivered appropriately for 28% of patients and inappropriately for 9%. Apart from inappropriate therapy, one third of PPM and ICD patients had other device-related complications. Acute PPM complications included lead dysfunction requiring revision (2%), pneumothorax (2%), pleural effusion (2%) and pocket infection (2%). ICDs were also acutely complicated by lead dysfunction (4%) as well as pocket hematoma (3%). The most common long-term complication overall was lead dysfunction, affecting one sixth of both PPM and ICD patients. Finally, the rate of device insertion increased significantly with disease severity.Conclusions: Anti-arrhythmic devices can be lifesaving in ACHD patients, but inappropriate therapy and devicerelated complications are very common.

Background: Around one million individuals in the UK have heart failure (HF), a chronic disease that causes significant morbidity and mortality. N-terminal pro-B-type natriuretic peptide (NT-proBNP) monitoring could help improve the care of patients with HF in the community. Aim: The aim of this study is to provide evidence to support the routine use of point-of-care (POC) NT-proBNP monitoring in primary care. Design & setting: In this observational cohort study, the Roche Cobas h 232 POC device was used to measure NT-proBNP in 27 patients with HF at 0, 6, and 12 months, with a subset reanalysed in the laboratory for comparison. Method: Data were analysed for within-person and between-person variability and concordance with laboratory readings using Passing–Bablok regression. GPs reported whether POC results impacted clinical decisionmaking, and patients indicated their willingness to participate in long-term cohort studies using the Likert acceptability scale. Results: Within-person variability in POC NT-proBNP over 12 months was 881 pg/mL (95% confidence interval [CI] = 380 to 1382 pg/mL). Between-person variability was 1972 pg/mL (95% CI = 1,525 to 2791 pg/mL). Passing–Bablok regression showed no significant systematic difference between POC and laboratory measurements. Patients indicated a high level of acceptability, and GP decisionmaking was affected for at least one visit in a third of patients. Conclusion: Within-person variability in POC NT-proBNP is around half of between-person variability, so detecting changes could be of use in HF management. High patient acceptability and impact on clinical decisionmaking warrant further investigation in a larger long-term cohort study.

Background: Hospital discharge codes are relied upon for research, accounting/invoicing and health systems planning. Congenital heart disease (CHD), however, is uniquely difficult for non-cardiologists to code due to the rarity, variety and complexity of lesions. It is therefore important that the accuracy of hospital discharge codes is regularly checked to ensure that the prevalence and burden of CHD is being correctly estimated and recorded.Methods and results: We identified all inpatient admissions of adults with CHD to Royal Prince Alfred Hospital in Sydney, Australia from January 2018 to March 2021 (257 admissions, 106 unique patients). The associated discharge coding summaries were extracted and compared to the codes in the separately collected and audited Adult CHD database. Only a quarter of discharge coding summaries contained any diagnosis of CHD, and just one -tenth accurately recorded all appropriate CHD diagnoses. Patients with simple lesions were most likely to have a coded diagnosis of CHD, while those with moderate and complex lesions were much less likely. Moreover, patients admitted under a cardiovascular specialty were twice as likely to have a coded diagnosis of CHD, compared with those admitted under non-cardiovascular specialties (p 1/4 0.006). Overall, less than half of patients had any hospital-coded diagnosis of CHD in any admission over the three-year study period.Conclusions: Hospital discharge coding dramatically underreports CHD, especially for patients with moderate and severe CHD lesions and for admissions under non-cardiovascular specialties. This suggests that discharge coding -based estimates of the burden of CHD on hospitals and health systems may be substantially underestimated.

Background: Congenital Heart Disease (CHD) encompasses a huge variety of rare diagnoses that range in complexity and comorbidity. To help build clinical guidelines, plan health services and conduct statistically powerful research on such a disparate set of diseases there have been various attempts to group pathologies into mild, moderate, or severe disease. So far, however, these complexity scores have required manual specialist input for every case, and are therefore missing in large databases where this is impractical, or quickly outdated when guidelines are revised.Methods: We used the up-to-date European Society of Cardiology guidelines to create an algorithm to assign complexity scores to CHD patients using only their diagnosis list. Two CHD specialists then independently assigned complexity scores to a random sample of patients.Results: Our algorithm was 96% accurate where both specialists agreed on a complexity score; this occurred 68% of the time overall, and 79% of the time in moderate or complex CHD. The algorithm "failed" mainly when diagnoses were insufficiently specific, usually for septal defects (where size was unspecified), or where complexity depends on the procedure performed (e.g. atrial/arterial switch for transposition of the great arteries).Conclusions: We were able to algorithmically determine the complexity scores of a majority of patients with CHD based on their diagnosis list alone. This could allow for automatic complexity scoring of most patients in large CHD databases, for example our own Registry of the Congenital Heart Alliance of Australia and New Zealand. This will facilitate targeted research into the management, outcomes and burden of CHD.

Genome-wide association studies have identified SLC16A13 as a novel susceptibility gene for type 2 diabetes. The SLC16A13 gene encodes SLC16A13/MCT13, a member of the solute carrier 16 family of monocarboxylate transporters. Despite its potential importance to diabetes development, the physiological function of SLC16A13 is unknown. Here, we validate Slc16a13 as a lactate transporter expressed at the plasma membrane and report on the effect of Slc16a13 deletion in a mouse model. We show that loss of Slc16a13 increases mitochondrial respiration in the liver, leading to reduced hepatic lipid accumulation and increased hepatic insulin sensitivity in high-fat diet fed Slc16a13 knockout mice. We propose a mechanism for improved hepatic insulin sensitivity in the context of Slc16a13 deficiency in which reduced intrahepatocellular lactate availability drives increased AMPK activation and increased mitochondrial respiration, while reducing hepatic lipid content. Slc16a13 deficiency thereby attenuates hepatic diacylglycerol-PKCε mediated insulin resistance in obese mice. Together, these data suggest that SLC16A13 is a potential target for the treatment of type 2 diabetes and non-alcoholic fatty liver disease.

Background: We aim to establish a new and informative bi-national Registry for Congenital Heart Disease (CHD) patients in Australia and New Zealand, to document the burden of disease and clinical outcomes for patients with CHDs across the lifespan. When planning for the implementation of this Registry, we sought to evaluate the strengths and weaknesses of existing national and large regional CHD databases. Methods: We characterised 15 large multi-institutional databases of pediatric and/or adult patients with CHD, documenting the richness of their datasets, the ease of linkage to other databases, the coverage of the target cohort and the strategies utilised for quality control. Results: The best databases contained demographic, clinical, physical, laboratory and patient-reported data, and were linked at least to the national/regional death registry. They also employed automatic data verification and regular manual audits. Coverage ranged from around 25% of all eligible CHD cases for larger databases to near 100% for some smaller registries of patients with specific CHD lesions, such as the Australia and New Zealand Fontan Registry. Conclusions: Existing national and regional CHD databases have strengths and weaknesses; few combine complete coverage with high quality and regularly audited data, across the broad range of CHDs. (c) 2021 Elsevier B.V. All rights reserved.

HomeCirculation: Cardiovascular Quality and OutcomesVol. 14, No. 7Towards a Unified Coding System for Congenital Heart Diseases Free AccessArticle CommentaryPDF/EPUBAboutView PDFView EPUBSections ToolsAdd to favoritesDownload citationsTrack citationsPermissions ShareShare onFacebookTwitterLinked InMendeleyRedditDiggEmail Jump toSupplementary MaterialsFree AccessArticle CommentaryPDF/EPUBTowards a Unified Coding System for Congenital Heart Diseases Jason Chami, BSc, Geoff Strange, MD, PhD, Calum Nicholson, BSc (Hons) and David S. Celermajer, MBBS (Hons), PhD, DSc Jason ChamiJason Chami https://orcid.org/0000-0002-1683-2800 Sydney Medical School, University of Sydney, Camperdown, NSW, Australia (J.C.). , Geoff StrangeGeoff Strange School of Medicine, University of Notre Dame Australia, Freemantle, WA, Australia (G.S.). , Calum NicholsonCalum Nicholson Heart Research Institute, Newtown, NSW, Australia (C.N.). and David S. CelermajerDavid S. Celermajer Correspondence to: David Celermajer, MBBS (Hons), PhD, DSc, Royal Prince Alfred Hospital, Missenden Rd, Camperdown 2050, Sydney, Australia. Email E-mail Address: [email protected] https://orcid.org/0000-0001-7640-0439 Royal Prince Alfred Hospital, Camperdown NSW, Australia (D.S.C.). Originally published28 Jun 2021https://doi.org/10.1161/CIRCOUTCOMES.121.008216Circulation: Cardiovascular Quality and Outcomes. 2021;14Other version(s) of this articleYou are viewing the most recent version of this article. Previous versions: June 28, 2021: Ahead of Print As congenital heart diseases (CHDs) are heterogenous, often complex and sometimes rare, many constantly evolving coding systems have been developed and used by CHD specialists, health care systems and databases. Unfortunately, these disparate coding systems mean that patient data are often siloed in small institutional databases that are unable to be easily combined or compared. To ensure continuity of care for patients and improve our understanding of the burden and outcomes of CHD, it is important to unify patient data from different sources under a single classification system. In the process of building a bi-national CHD registry in Australia and New Zealand, we aimed to identify the best coding system for our purposes and create translation tables from all other coding systems in regular use worldwide.Throughout the 1950s and 1960s, as pediatric cardiology emerged as a sub-speciality, many countries established their own pediatric cardiology and cardiac surgery services. Soon after, to record and refer to standardized diagnoses, a variety of logical coding systems for classification of CHDs were developed.1 The rarity of many CHD lesions makes informative epidemiological study difficult in single institutions, so regional and national databases were set up, using the database technology available in the era.Since then, technological advances in imaging (eg, echocardiography) have led to greater understanding of the anatomy of complex CHDs, necessitating more specific diagnostic coding systems to capture newly understood variations. Thus, as small databases have swelled in size and complexity, so too have the coding systems they use. The World Health Organization's International Classification of Diseases (ICD), for example, which contained no codes for CHD in its first version in 1900, contained 73 codes in its tenth revision, and will contain 318 in its upcoming eleventh revision (Figure).1Download figureDownload PowerPointFigure. Number of base congenital heart disease (CHD) codes in a selection of popular international CHD coding systems. EPCC indicates European Paediatric Cardiac Code; ICD, International Classification of Diseases; and STS-ECATS, Society of Thoracic Surgeons and the European Association for Cardio-Thoracic Surgery.Unfortunately, despite the efforts of the World Health Organization to promote the use of ICD, the rapid pace of research and the increasing digitization of medical records quickly render each new revision insufficient for the demands of modern electronic medical record systems. Instead of being limited to the internationally accepted standards, many national health services have developed extensive addenda to the established lists.2–6 Therefore, with increasing technological progress, the various coding systems for CHD lesions have grown further apart.When patients, doctors, nurses, and other health workers across the health care system use different codes to describe the same disease, this poses a risk to appropriate continuity of care. Indeed, without uniform diagnostic codes, data cannot be easily shared or understood across institutions or borders. For example, in Australia and New Zealand, there are currently tens of thousands of CHD cases siloed separately in institutional and regional databases, classified using different systems of CHD coding. This prevents combination or comparison and impedes research and health services planning. Finally, databases with patient information stored in free text without any stated coding system are neither searchable by diagnosis, nor able to automatically manage follow-up schedules based on disease severity, nor able to automatically generate comprehensive patient summaries.Unfortunately, CHD diagnostic coding is among the most challenging in medicine because of the very large number of conditions, many of which overlap, combine or are otherwise heterogeneous in nature. As an example, a relatively straightforward CHD lesion is the ventricular septal defect: the commonest inborn structural heart abnormality. Anatomically, there are at least 5 different subtypes of ventricular septal defect, according to different anatomic locations within the ventricular septum. Physiologically, the consequences of ventricular septal defect vary according to size and the coexistence of up to 10 different other CHDs, such as atrial septal defect, transposition of the great arteries and coarctation of the aorta. Many such combinations are common, but some are exceedingly rare. Some combinations occur in recognized patterns, such as Tetralogy of Fallot, while others do not. This example serves to underscore the complexities of CHD classification and coding, even before one considers the coding of operative interventions.The ideal CHD coding system would be appropriately detailed, universally adopted, and structured to allow grouping of related defects. There should be enough codes to describe defects with great specificity, but not so much detail that excessive expertise or time commitment is required on the part of coders themselves—a system that is too cumbersome can lead various institutions to create local Short Lists for their own purposes, making comparison difficult. For research purposes, miscellaneous codes should be avoided, as they encourage nonspecific coding where more accurate codes are available. Conversion tables from existing coding systems should be available and easy to automate.Based on these criteria, we identified and evaluated 5 coding systems for CHDs in widespread use internationally: The International Statistical Classification of Diseases and Related Health Problems, Ninth Revision (ICD-9); ICD-10 and its national modifications; the European Paediatric Cardiac Code (EPCC),7 the Nomenclature System of the International Congenital Heart Surgery Nomenclature and Database Project of the Society of Thoracic Surgeons and the European Association for Cardio-Thoracic Surgery (STS–EACTS),8,9 and the International Paediatric and Congenital Cardiac Code (IPCCC).1ICD-9 was ratified by the World Health Organization in 1978 and grew in popularity along with some of the very first large-scale electronic health databases. It had only 29 CHD codes. ICD-9 was replaced by ICD-10 in 1994 in response to a growing need for more detailed clinical data as electronic medical records grew in size and importance. With 73 codes for CHD, it is more detailed than ICD-9 but not detailed enough for modern research and administrative use. The Clinical Modification (ICD-10-CM), adopted in the United States in 2015, has >70 000 codes, several times more than standard ICD-10.4 The Australian Modification (ICD-10-AM) used since 1998 in Australia and New Zealand also has added detail for clinical databases.3 Due to these regional modifications, international comparison and collaboration remains difficult.EPCC and STS–EACTS were published near-simultaneously in 2000, both of which included several hundred base codes and the ability to code much more detailed diagnoses than ICD-10.7–9 The codes were organized in a branching tree structure, meaning that highly detailed diagnoses could be grouped together in cases of low patient numbers—a common scenario for CHD research. For convenience, they both came with premade Short Lists to ensure uniformity across use cases, but as direct competitors the codes needed to be harmonized to achieve the aim of a universal CHD coding system. By 2005, IPCCC had been created as a one-to-one map between EPCC and STS–EACTS.1 Over time, as IPCCC has been updated with newly described defects, these updates were pushed through to the EPCC and STS–EACTS Short Lists, which are now described as short lists of IPCCC itself, with the EPCC Short List (EPCC-SL) having a more clinical focus, while the STS–EACTS Short List (STS–EACTS-SL) is surgically oriented.Currently in development, ICD-11 will contain a CHD section based directly upon the IPCCC, with 318 codes for CHD alongside extension codes for clinical and administrative use that allow institutions to tailor the system to their needs without affecting the base codes.When considering all aspects, including clarity, detail, structure, and universality, we formed a view that EPCC-SL has the optimal combination of features. With a tree-structured code system, EPCC-SL avoids the pitfalls of miscellaneous codes that are found in droves in ICD-10-CM and ICD-10-AM, obscuring true diagnoses in research databases. Unlike EACTS–STS-SL, which is more surgically oriented, EACTS–SL has a clinical focus that is ideal for use in our large-scale clinical database. Furthermore, EPCC-SL is mapped to the IPCCC Long List, which will form the basis of the upcoming ICD-11. This allows not only for easy upgrade when the time comes, but also the potential for true universality. Finally, with around 600 codes relevant to a CHD database, EPCC-SL contains sufficient but not excessive detail for use in both hospital administration and research, while allowing for fast data entry and recall in a clinical setting. IPCCC Long List, in contrast, is extremely detailed, containing >11 000 unique codes—this is clearly too cumbersome for routine use and requires considerable expertise by the coders.For these reasons, EPCC-SL will serve as the base code for the upcoming bi-national ANZ CHD Registry, which is being implemented as a relational database in FileMaker Pro (Claris International Inc, Cupertino USA). This centralized database will be populated periodically with data from each participating institution, each of which will use the locally favored CHD coding system. To harmonize the incoming data, we have developed a lookup table which can be used to instantly translate codes from ICD-9, ICD-10, ICD-10-AM, and STS–EACTS-SL into EPCC-SL that is used in the Registry itself. This lookup table is easily accessible and modifiable, so any mistakes in translation can be easily corrected throughout the database. Furthermore, extra translation protocols (eg, to ICD-11 when it is published) can easily be added and pushed through the whole database. The entire translation table is provided in Table I in the Data Supplement.In general, we faced 2 types of problems when creating the translation table. One to many translations were very common when translating from coding systems lacking detail into EPCC-SL. For example, the ICD-10-AM diagnosis of double outlet right ventricle (Q20.1) could match up to 5 more specific codes in EPCC-SL. Nonetheless, the tree structure of EPCC-SL allows us to simply select a coarser description that matches the level of detail of the imported code (Table II in the Data Supplement). Many to one problems were less common, given the relatively high level of detail in EPCC-SL. These generally occurred when the source code had redundant miscellaneous codes that had no equivalent in EPCC-SL (Table III in the Data Supplement). In some cases, the dividing line between certain very closely related conditions is difficult to draw with certainty, such as with the variants of hypoplastic left heart syndromes. Despite these difficulties, it was possible to create a translation table that accurately converts ICD-9, ICD-10, ICD-10-AM, and STS–EACTS-SL codes into EPCC-SL.In conclusion, we propose that EPCC-SL is the most suitable coding system for national or international CHD registries. Furthermore, it is possible to create translation tables that match up any common congenital cardiac codes to EPCC-SL for storage in a central database with both clinical and research capabilities. Future directions include the possibility of coding for common operations (individual or serial) and developing more sophisticated coding approaches in patients with multiple coexisting CHD lesions. We acknowledge that almost all research and health institutions are currently committed to 1 or 2 locally favored coding systems and that change would be difficult. We hope that the translation protocols included in the Data Supplement could ease this transition, and that the clear benefits of coding harmonization for international cooperation make this endeavour worthwhile for health care systems.AcknowledgmentsWe would like to acknowledge Andrew McCallum for his work on the FileMaker Pro database housing the new bi-national ANZ CHD Registry. We also thank Drs Clare O'Donnell, Robert Weintraub, Mark Dennis, David Baker, and Michael Cheung for their work producing the coding system translation table available in the Data Supplement.Sources of FundingThe development of a comprehensive ANZ CHD Registry, and the diagnosis coding solutions described, was initially funded by philanthropic donations from HeartKids Australia and the Kinghorn Foundation. Additional funding has been provided by an Australian Department of Health grant through the Medical Research Future Fund, grant code is ARGCHDG0000028.Supplemental MaterialsSupplemental Tables I–IIIDisclosures None.FootnotesThe opinions expressed in this article are not necessarily those of the editors or of the American Heart Association.The Data Supplement is available at https://www.ahajournals.org/doi/suppl/10.1161/CIRCOUTCOMES.121.008216.For Sources of Funding and Disclosures, see page 759.Correspondence to: David Celermajer, MBBS (Hons), PhD, DSc, Royal Prince Alfred Hospital, Missenden Rd, Camperdown 2050, Sydney, Australia. Email david.[email protected]nsw.gov.auReferences1. Franklin RCG, Béland MJ, Colan SD, Walters HL, Aiello VD, Anderson RH, Bailliard F, Boris JR, Cohen MS, Gaynor JW, et al.. Nomenclature for congenital and paediatric cardiac disease: the International Paediatric and Congenital Cardiac Code (IPCCC) and the Eleventh Iteration of the International Classification of Diseases (ICD-11).Cardiol Young. 2017; 27:1872–1938. doi: 10.1017/S1047951117002244CrossrefMedlineGoogle Scholar2. Graubner B. ICD-10-GM 2014 Systematisches Verzeichnis: Internationale statistische Klassifikation der Krankheiten und verwandter Gesundheitsprobleme 11. Revision - German Modification Version 2014. Deutscher Ärzteverlag; 2013.Google Scholar3. Roberts RF, Innes KC, Walker SM. Introducing ICD-10-AM in Australian hospitals.Med J Aust. 1998; 169(S1):S32–S35. doi: 10.5694/j.1326-5377.1998.tb123473.xCrossrefMedlineGoogle Scholar4. National Center for Health Statistics. ICD-10-CM Official Guidelines for Coding and Reporting.Center for Disease Control and Prevention. Hyattsville, MD; 2021.Google Scholar5. Canadian Institute for Health Information.The Canadian Enhancement of ICD-10.2001.Google Scholar6. Nitsuwat S, Paoin W. Development of ICD-10-TM ontology for a semi-automated morbidity coding system in Thailand.Methods Inf Med. 2012; 51:519–528. doi: 10.3414/ME11-02-0024CrossrefMedlineGoogle Scholar7. Franklin R. The European paediatric cardiac code long list: structure and function — the first revision.Cardiol Young. 2002; 12(S2):9–17.CrossrefGoogle Scholar8. Mavroudis C, Jacobs JP. Congenital heart surgery nomenclature and database project: overview and minimum dataset.Ann Thorac Surg. 2000; 69(4 suppl):S2–17. doi: 10.1016/s0003-4975(99)01321-1CrossrefMedlineGoogle Scholar9. Béland MJ, Jacobs JP, Tchervenkov CI, Franklin RC; International Working Group for Mapping and Coding of Nomenclatures for Paediatric and Congenital Heart Disease. Report from the executive of The International Working Group for Mapping and Coding of Nomenclatures for Paediatric and Congenital Heart Disease.Cardiol Young. 2002; 12:425–430. doi: 10.1017/s1047951102000732CrossrefMedlineGoogle Scholar Previous Back to top Next FiguresReferencesRelatedDetails July 2021Vol 14, Issue 7Article InformationMetrics © 2021 American Heart Association, Inc.https://doi.org/10.1161/CIRCOUTCOMES.121.008216PMID: 34176294 Originally publishedJune 28, 2021 Keywordscardiologyregistriestechnologycongenital heart diseasePDF download Advertisement SubjectsCongenital Heart Disease

Dimethylguanidino valeric acid (DMGV) is a marker of fatty liver disease, incident coronary artery disease, cardiovascular mortality, and incident diabetes. Recently, it was reported that circulating DMGV levels correlated positively with consumption of sugary beverages and negatively with intake of fruits and vegetables in three Swedish community-based cohorts. Here, we validate these results in the Framingham Heart Study Third Generation Cohort. Furthermore, in mice, diets rich in sucrose or fat significantly increased plasma DMGV concentrations. DMGV is the product of metabolism of asymmetric dimethylarginine (ADMA) by the hepatic enzyme AGXT2. ADMA can also be metabolized to citrulline by the cytoplasmic enzyme DDAH1. We report that a high-sucrose diet induced conversion of ADMA exclusively into DMGV (supporting the relationship with sugary beverage intake in humans), while a high-fat diet promoted conversion of ADMA to both DMGV and citrulline. On the contrary, replacing dietary native starch with high-fiber-resistant starch increased ADMA concentrations and induced its conversion to citrulline, without altering DMGV concentrations. In a cohort of obese nondiabetic adults, circulating DMGV concentrations increased and ADMA levels decreased in those with either liver or muscle insulin resistance. This was similar to changes in DMGV and ADMA concentrations found in mice fed a high-sucrose diet. Sucrose is a disaccharide of glucose and fructose. Compared with glucose, incubation of hepatocytes with fructose significantly increased DMGV production. Overall, we provide a comprehensive picture of the dietary determinants of DMGV levels and association with insulin resistance.

Functional-anatomical changes in reward related brain circuits are described in chronic pain patients who report anhedonia or depressed mood. In pre-clinical rodent models of neuropathic pain there are varying reports of the effects of nerve injury on the motivation to consume sucrose, although hedonic responses to sucrose appear unchanged. These observations are derived from brief periods of exposure to sucrose. When sucrose is available ad libitum over a period of 21 days, there are marked individual differences in consumption. The motivation for, and hedonic experience of, drinking sucrose is mediated in part by dopamine-D2 and μ-opioid receptors in the nucleus accumbens (NAc). This study investigated the effects of chronic constriction injury (CCI) on ad libitum sucrose consumption in male Sprague Dawley rats and the expression of accumbal dopamine D2 and μ-opioid receptors. Nerve injury reduced sucrose drinking predominantly in rats with the highest pre-injury consumption levels. Despite these reductions in consumption, sucrose preferences were stable. In the NAc of rats whose sucrose consumption was affected by CCI, immunohistochemical analyses revealed bilateral reductions of dopamine D2-receptor expression in the core and shell; and a lateralised reduction of μ-opioid receptor expression in the core and dorsomedial shell of the right NAc. These alterations in receptor expression are located in regions which have been identified as hedonic hot and coldspots along an affective-motivational keyboard which directs behaviours either towards, or away from salient stimuli. These changes likely underlie the reduction in sucrose consumption observed in a subgroup of rats following nerve injury.