Cerebello-cerebral Functional Connectivity Networks in Major Depressive Disorder: a CAN-BIND-1 Study Report.

Neuroimaging studies have demonstrated aberrant structure and function of the "cognitive-affective cerebellum" in major depressive disorder (MDD), although the specific role of the cerebello-cerebral circuitry in this population remains largely uninvestigated. The objective of this study was to delineate the role of cerebellar functional networks in depression. A total of 308 unmedicated participants completed resting-state functional magnetic resonance imaging scans, of which 247 (148 MDD; 99 healthy controls, HC) were suitable for this study. Seed-based resting-state functional connectivity (RsFc) analysis was performed using three cerebellar regions of interest (ROIs): ROI1 corresponded to default mode network (DMN)/inattentive processing; ROI2 corresponded to attentional networks, including frontoparietal, dorsal attention, and ventral attention; ROI3 corresponded to motor processing. These ROIs were delineated based on prior functional gradient analyses of the cerebellum. A general linear model was used to perform within-group and between-group comparisons. In comparison to HC, participants with MDD displayed increased RsFc within the cerebello-cerebral DMN (ROI1) and significantly elevated RsFc between the cerebellar ROI1 and bilateral angular gyrus at a voxel threshold (p < 0.001, two-tailed) and at a cluster level (p < 0.05, FDR-corrected). Group differences were non-significant for ROI2 and ROI3. These results contribute to the development of a systems neuroscience approach to the diagnosis and treatment of MDD. Specifically, our findings confirm previously reported associations between MDD, DMN, and cerebellum, and highlight the promising role of these functional and anatomical locations for the development of novel imaging-based biomarkers and targets for neuromodulation therapies. ClinicalTrials.gov TRN: NCT01655706; Date of Registration: August 2nd, 2012.

Artificial intelligence assessment for early detection and prediction of renal impairment using electrocardiography.

PURPOSE: Although renal failure is a major healthcare burden globally and the cornerstone for preventing its irreversible progression is an early diagnosis, an adequate and noninvasive tool to screen renal impairment (RI) reliably and economically does not exist. We developed an interpretable deep learning model (DLM) using electrocardiography (ECG) and validated its performance. METHODS: This retrospective cohort study included two hospitals. We included 115,361 patients who had at least one ECG taken with an estimated glomerular filtration rate measurement within 30 min of the index ECG. A DLM was developed using 96,549 ECGs of 55,222 patients. The internal validation included 22,949 ECGs of 22,949 patients. Furthermore, we conducted an external validation with 37,190 ECGs of 37,190 patients from another hospital. The endpoint was to detect a moderate to severe RI (estimated glomerular filtration rate < 45 ml/min/1.73m2). RESULTS: The area under the receiver operating characteristic curve (AUC) of a DLM using a 12-lead ECG for detecting RI during the internal and external validation was 0.858 (95% confidence interval 0.851-0.866) and 0.906 (0.900-0.912), respectively. In the initial evaluation of 25,536 individuals without RI patients whose DLM was defined as having a higher risk had a significantly higher chance of developing RI than those in the low-risk group (17.2% vs. 2.4%, p < 0.001). The sensitivity map indicated that the DLM focused on the QRS complex and T-wave for detecting RI. CONCLUSION: The DLM demonstrated high performance for RI detection and prediction using 12-, 6-, single-lead ECGs.

Deep-learning model for screening sepsis using electrocardiography.

BACKGROUND: Sepsis is a life-threatening organ dysfunction and a major healthcare burden worldwide. Although sepsis is a medical emergency that requires immediate management, screening for the occurrence of sepsis is difficult. Herein, we propose a deep learning-based model (DLM) for screening sepsis using electrocardiography (ECG). METHODS: This retrospective cohort study included 46,017 patients who were admitted to two hospitals. A total of 1,548 and 639 patients had sepsis and septic shock, respectively. The DLM was developed using 73,727 ECGs from 18,142 patients, and internal validation was conducted using 7774 ECGs from 7,774 patients. Furthermore, we conducted an external validation with 20,101 ECGs from 20,101 patients from another hospital to verify the applicability of the DLM across centers. RESULTS: During the internal and external validations, the area under the receiver operating characteristic curve (AUC) of the DLM using 12-lead ECG was 0.901 (95% confidence interval, 0.882-0.920) and 0.863 (0.846-0.879), respectively, for screening sepsis and 0.906 (95% confidence interval (CI), 0.877-0.936) and 0.899 (95% CI, 0.872-0.925), respectively, for detecting septic shock. The AUC of the DLM for detecting sepsis using 6-lead and single-lead ECGs was 0.845-0.882. A sensitivity map revealed that the QRS complex and T waves were associated with sepsis. Subgroup analysis was conducted using ECGs from 4,609 patients who were admitted with an infectious disease, and the AUC of the DLM for predicting in-hospital mortality was 0.817 (0.793-0.840). There was a significant difference in the prediction score of DLM using ECG according to the presence of infection in the validation dataset (0.277 vs. 0.574, p < 0.001), including severe acute respiratory syndrome coronavirus 2 (0.260 vs. 0.725, p = 0.018). CONCLUSIONS: The DLM delivered reasonable performance for sepsis screening using 12-, 6-, and single-lead ECGs. The results suggest that sepsis can be screened using not only conventional ECG devices but also diverse life-type ECG machines employing the DLM, thereby preventing irreversible disease progression and mortality.

Detection and classification of arrhythmia using an explainable deep learning model.

BACKGROUND: Early detection and intervention is the cornerstone for appropriate treatment of arrhythmia and prevention of complications and mortality. Although diverse deep learning models have been developed to detect arrhythmia, they have been criticized due to their unexplainable nature. In this study, we developed an explainable deep learning model (XDM) to classify arrhythmia, and validated its performance using diverse external validation data. METHODS: In this retrospective study, the Sejong dataset comprising 86,802 electrocardiograms (ECGs) was used to develop and internally variate the XDM. The XDM based on a neural network-backed ensemble tree was developed with six feature modules that are able to explain the reasons for its decisions. The model was externally validated using data from 36,961 ECGs from four non-restricted datasets. RESULTS: During internal and external validation of the XDM, the average area under the receiver operating characteristic curves (AUCs) using a 12­lead ECG for arrhythmia classification were 0.976 and 0.966, respectively. The XDM outperformed a previous simple multi-classification deep learning model that used the same method. During internal and external validation, the AUCs of explainability were 0.925-0.991. CONCLUSION: Our XDM successfully classified arrhythmia using diverse formats of ECGs and could effectively describe the reason for the decisions. Therefore, an explainable deep learning methodology could improve accuracy compared to conventional deep learning methods, and that the transparency of XDM can be enhanced for its application in clinical practice.

Artificial intelligence for detecting electrolyte imbalance using electrocardiography.

INTRODUCTION: The detection and monitoring of electrolyte imbalance is essential for appropriate management of many metabolic diseases; however, there is no tool that detects such imbalances reliably and noninvasively. In this study, we developed a deep learning model (DLM) using electrocardiography (ECG) for detecting electrolyte imbalance and validated its performance in a multicenter study. METHODS AND RESULTS: This retrospective cohort study included two hospitals: 92,140 patients who underwent a laboratory electrolyte examination and an ECG within 30 min were included in this study. A DLM was developed using 83,449 ECGs of 48,356 patients; the internal validation included 12,091 ECGs of 12,091 patients. We conducted an external validation with 31,693 ECGs of 31,693 patients from another hospital, and the result was electrolyte imbalance detection. During internal, the area under the receiving operating characteristic curve (AUC) of a DLM using a 12-lead ECG for detecting hyperkalemia, hypokalemia, hypernatremia, hyponatremia, hypercalcemia, and hypocalcemia were 0.945, 0.866, 0.944, 0.885, 0.905, and 0.901, respectively. The values during external validation of the AUC of hyperkalemia, hypokalemia, hypernatremia, hyponatremia, hypercalcemia, and hypocalcemia were 0.873, 0.857, 0.839, 0.856, 0.831, and 0.813 respectively. The DLM helped to visualize the important ECG region for detecting each electrolyte imbalance, and it showed how the P wave, QRS complex, or T wave differs in importance in detecting each electrolyte imbalance. CONCLUSION: The proposed DLM demonstrated high performance in detecting electrolyte imbalance. These results suggest that a DLM can be used for detecting and monitoring electrolyte imbalance using ECG on a daily basis.

Artificial intelligence to diagnose paroxysmal supraventricular tachycardia using electrocardiography during normal sinus rhythm.

Aims: Paroxysmal supraventricular tachycardia (PSVT) is not detected owing to its paroxysmal nature, but it is associated with the risk of cardiovascular disease and worsens the patient quality of life. A deep learning model (DLM) was developed and validated to identify patients with PSVT during normal sinus rhythm in this multicentre retrospective study. Methods and results: This study included 12 955 patients with normal sinus rhythm, confirmed by a cardiologist. A DLM was developed using 31 147 electrocardiograms (ECGs) of 9069 patients from one hospital. We conducted an accuracy test with 13 753 ECGs of 3886 patients from another hospital. The DLM was developed based on residual neural network. Digitally stored ECG were used as predictor variables and the outcome of the study was ability of the DLM to identify patients with PSVT using an ECG during sinus rhythm. We employed a sensitivity map method to identify an ECG region that had a significant effect on developing PSVT. During accuracy test, the area under the receiver operating characteristic curve of a DLM using a 12-lead ECG for identifying PSVT patients during sinus rhythm was 0.966 (0.948-0.984). The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of DLM were 0.970, 0.868, 0.972, 0.255, and 0.998, respectively. The DLM showed delta wave and QT interval were important to identify the PSVT. Conclusion: The proposed DLM demonstrated a high performance in identifying PSVT during normal sinus rhythm. Thus, it can be used as a rapid, inexpensive, point-of-care means of identifying PSVT in patients.

Functional Alterations in Cerebellar Functional Connectivity in Anxiety Disorders.

Adolescents with anxiety disorders exhibit excessive emotional and somatic arousal. Neuroimaging studies have shown abnormal cerebral cortical activation and connectivity in this patient population. The specific role of cerebellar output circuitry, specifically the dentate nuclei (DN), in adolescent anxiety disorders remains largely unexplored. Resting-state functional connectivity analyses have parcellated the DN, the major output nuclei of the cerebellum, into three functional territories (FTs) that include default-mode, salience-motor, and visual networks. The objective of this study was to understand whether FTs of the DN are implicated in adolescent anxiety disorders. Forty-one adolescents (mean age 15.19 ± 0.82, 26 females) with one or more anxiety disorders and 55 age- and gender-matched healthy controls completed resting-state fMRI scans and a self-report survey on anxiety symptoms. Seed-to-voxel functional connectivity analyses were performed using the FTs from DN parcellation. Brain connectivity metrics were then correlated with State-Trait Anxiety Inventory (STAI) measures within each group. Adolescents with an anxiety disorder showed significant hyperconnectivity between salience-motor DN FT and cerebral cortical salience-motor regions compared to controls. Salience-motor FT connectivity with cerebral cortical sensorimotor regions was significantly correlated with STAI-trait scores in HC (R2 = 0.41). Here, we report DN functional connectivity differences in adolescents diagnosed with anxiety, as well as in HC with variable degrees of anxiety traits. These observations highlight the relevance of DN as a potential clinical and sub-clinical marker of anxiety.

Discriminatory performance of insulin-like growth factor 1 and insulin-like growth factor binding protein-3 by correlating values to chronological age, bone age, and pubertal status for diagnosis of isolated growth hormone deficiency.

PURPOSE: The discriminatory performance of insulin-like growth factor 1 (IGF-1) and insulin-like growth factor binding protein-3 (IGFBP-3) was investigated by correlating their values with chronological age (CA), bone age (BA), and pubertal status (PS) for diagnosis of isolated growth hormone deficiency (IGHD). METHODS: We evaluated IGF-1 and IGFBP-3 levels in 310 short-stature subjects subdivided into 2 groups: IGHD (n=31) and non-IGHD (n=279). IGF-1 and IGFBP-3 were assayed using immune-radiometric assay and transformed into standard deviation score (SDS) according to CA, BA, and PS. RESULTS: The highest sensitivity was found in IGF-1-SDS for CA and IGFBP-3-SDS for CA (22.6% and 30.0%, respectively). The highest specificity was found in IGF-1-SDS for PS and IGFBP-3-SDS for PS (98.2% and 94.4%, respectively). Groups with the highest positive predictive values were IGF-1-SDS for BA and IGFBP-3-SDS for BA (10.9% and 5.1%, respectively). Highest negative predictive values were seen in IGF-1-SDS for CA and IGFBP-3-SDS for CA (98.4% and 98.4%, respectively). CONCLUSION: IGF-1-SDS for CA, instead of IGF-1-SDS for BA or PS, could be used as a standard variable for IGHD screening. The sufficiently high specificity of IGF-1-SDS for PS suggests that this value is a useful tool for identification of IGHD.

Polymorphisms of IRAK1 Gene on X Chromosome Is Associated with Hashimoto Thyroiditis in Korean Children.

Autoimmune thyroid disease (AITD) is predominant in females and has been focused on the sexual diploid in immune response. The IL-1 receptor-associated kinase 1 (IRAK1) gene on the X chromosome was recently suggested as strong autoimmune disease-susceptible loci, second to the major histocompatibility complex region. We investigated the frequency of IRAK1 single-nucleotide polymorphisms (SNPs) in children with AITD. In this study, we observed that SNPs of IRAK1 including rs3027898, rs1059703, and rs1059702 in 115 Korean AITD pediatric patients (Graves' disease = 74 [females = 52/males = 22]; Hashimoto disease [HD] = 41 [females = 38/males = 3]; thyroid-associated ophthalmopathy [TAO] = 40 (females = 27/males = 13); without TAO = 75 (females = 63/males = 12); total males = 25, total females = 90; mean age = 11.9 years) and 204 healthy Korean individuals (males = 104/females = 100). The data from cases and controls were analyzed from separate sex-stratified or all combined by χ 2 test for categorical variables and Student t test for numerical variables. Our study revealed that SNPs of IRAK1-associated HD and without TAO but Graves' disease and TAO were not found significant. When cases and controls were analyzed by separate sex, we found that rs3027898 AA, rs1059703 AA, and rs1059702 GG showed disease susceptibility in female AITD, HD, and without TAO. Also, all rs3027898, rs1059703, and rs1059702 were found to be in strong linkage disequilibrium (D' = 0.96-0.98, r2 = 0.83-0.97). The haplotype of 3 SNPs was higher in AITD than in controls (CGA, r2 = 5.42, P = 0.019). Our results suggest that IRAK1 polymorphisms may contribute to the pathogenesis of HD, AITD, and without thyroid-associated ophthalmopathy for females.

Real-time fMRI feedback impacts brain activation, results in auditory hallucinations reduction: Part 1: Superior temporal gyrus -Preliminary evidence.

Auditory hallucinations (AH) are one of the core symptoms of schizophrenia (SZ) and constitute a significant source of suffering and disability. One third of SZ patients experience pharmacology-resistant AH, so an alternative/complementary treatment strategy is needed to alleviate this debilitating condition. In this study, real-time functional Magnetic Resonance Imaging neurofeedback (rt-fMRI NFB), a non-invasive technique, was used to teach 10 SZ patients with pharmacology-resistant AH to modulate their brain activity in the superior temporal gyrus (STG), a key area in the neurophysiology of AH. A functional task was designed in order to provide patients with a specific strategy to help them modify their brain activity in the desired direction. Specifically, they received neurofeedback from their own STG and were trained to upregulate it while listening to their own voice recording and downregulate it while ignoring a stranger's voice recording. This guided performance neurofeedback training resulted in a) a significant reduction in STG activation while ignoring a stranger's voice, and b) reductions in AH scores after the neurofeedback session. A single, 21-minute session of rt-fMRI NFB was enough to produce these effects, suggesting that this approach may be an efficient and clinically viable alternative for the treatment of pharmacology-resistant AH.

Real-time fMRI neurofeedback reduces auditory hallucinations and modulates resting state connectivity of involved brain regions: Part 2: Default mode network -preliminary evidence.

Auditory hallucinations (AHs) are one of the most distressing symptoms of schizophrenia (SZ) and are often resistant to medication. Imaging studies of individuals with SZ show hyperactivation of the default mode network (DMN) and the superior temporal gyrus (STG). Studies in SZ show DMN hyperconnectivity and reduced anticorrelation between DMN and the central executive network (CEN). DMN hyperconnectivity has been associated with positive symptoms such as AHs while reduced DMN anticorrelations with cognitive impairment. Using real-time fMRI neurofeedback (rt-fMRI-NFB) we trained SZ patients to modulate DMN and CEN networks. Meditation is effective in reducing AHs in SZ and to modulate brain network integration and increase DMN anticorrelations. Consequently, patients were provided with meditation strategies to enhance their abilities to modulate DMN/CEN. Results show a reduction of DMN hyperconnectivity and increase in DMNCEN anticorrelation. Furthermore, the change in individual DMN connectivity significantly correlated with reductions in AHs. This is the first time that meditation enhanced through rt-fMRI-NFB is used to reduce AHs in SZ. Moreover, it provides the first empirical evidence for a direct causal relation between meditation enhanced rt-fMRI-NFB modulation of DMNCEN activity and post-intervention modulation of resting state networks ensuing in reductions in frequency and severity of AHs.

Association of Intrinsic Brain Architecture With Changes in Attentional and Mood Symptoms During Development.

Importance: Understanding the neurodevelopmental trajectory of psychiatric symptoms is important for improving early identification, intervention, and prevention of mental disorders. Objective: To test whether the strength of the coupling of activation between specific brain regions, as measured by resting-state functional magnetic resonance imaging (fMRI), predicted individual children's developmental trajectories in terms of attentional problems characteristic of attention-deficit/hyperactivity disorder and internalizing problems characteristics of major depressive disorder (MDD). Design, Setting, and Participants: A community cohort of 94 children was recruited from Vanderbilt University between 2010 and 2013. They were followed up longitudinally for 4 years and the data were analyzed from 2016 to 2019. Based on preregistered hypotheses and an analytic plan, we examined whether specific brain connectivity patterns would be associated with longitudinal changes in scores on the Child Behavior Checklist (CBCL), a parental-report assessment used to screen for emotional, behavioral, and social problems and to predict psychiatric illnesses. Main Outcomes and Measures: We used the strength of resting-state fMRI connectivity at age 7 years to predict subsequent changes in CBCL measures 4 years later and investigated the mechanisms of change by associating brain connectivity changes with changes in the CBCL. Results: We analyzed data from a longitudinal brain development study involving children assessed at age 7 years (n = 94; 41 girls [43.6%]) and 11 years (n = 54; 32 girls [59.3%]). As predicted, less positive coupling at age 7 years between the medial prefrontal cortex and dorsolateral prefrontal cortex (DLPFC) was associated with a decrease in attentional symptoms by age 11 years (t49 = 2.38; P = .01; ß = 0.32). By contrast, a less positive coupling between a region implicated in mood, the subgenual anterior cingulate cortex (sgACC), and DLPFC at age 7 years was associated with an increase in internalizing (eg, anxiety/depression) behaviors by age 11 years (t49 = -2.4; P = .01; ß = -0.30). Logistic regression analyses revealed that sgACC-DLPFC connectivity was a more accurate predictor than baseline CBCL measures for progression to a subclinical score on internalization (t50 = -2.61; P = .01; ß = -0.29). We then replicated and extended the sgACC-DLPFC result in an independent sample of children with (n = 25) or without (n = 18) familial risk for MDD. Conclusions and Relevance: These resting-state fMRI metrics are promising biomarkers for the early identification of children at risk of developing MDD or attention-deficit/hyperactivity disorder.

Thyroid dysfunction in children with leukemia over the first year after hematopoietic stem cell transplantation.

Background Thyroid function in children with leukemia during the first year after hematopoietic stem cell transplantation (HSCT) was investigated. Methods The medical records of 186 subjects [111 boys and 75 girls; lymphoid=75, myeloid=111; median age at HSCT was 10.7 (0.8-21.8) years old] were reviewed retrospectively. Results In children with leukemia, T3 decreased at 1 month (p<0.001) and recovered 9 months to the levels before HSCT. TSH decreased at 1 month (p<0.001), recovered at 3 months and increased at 12 months (p<0.001) to the levels before HSCT. The incidence of euthyroid sick syndrome (ESS, 23.2%, 15.5%, 5.9%, 5.2%, 3.9%, p for trend <0.001) decreased and subclinical hypothyroidism (SH, 0%, 3.9%, 14.8%, 22.1%, 21.3%, p for trend <0.001) increased at 1, 3, 6, 9 and 12 months after HSCT. Out of 55 patients developing ESS during 3 months after HSCT, 54 recovered to normal thyroid function within 5 months without medication. Among the total 186 subjects, 21 patients have been treated with levothyroxine. Both height and weight standard deviation scores continued to decrease over 1 year after HSCT. Conclusions In children with leukemia, one-quarter had ESS at 1 month and one-fifth had SH at 12 months and continued growth impairments were observed during 1 year after HSCT. Most of the ESS patients recovered to normal within 5 months without medication. More long-term follow-up of thyroid function and growth in children with leukemia after HSCT is crucial.

The effect of overweight on the luteinizing hormone level after gonadorelin stimulation test in girls with idiopathic central precocious puberty.

PURPOSE: We investigated the effect of overweight on luteinizing hormone (LH) levels after a gonadorelin stimulation test in Korean girls with idiopathic central precocious puberty (CPP). METHODS: Medical records of 234 girls diagnosed with idiopathic CPP were reviewed retrospectively. CPP was diagnosed when the peak LH levels after gonadorelin stimulation was >5.0 U/L. The enrolled girls had a peak LH level >5.0 U/L after a gonadorelin stimulation test. Selected girls were classified as normoweight (body mass index [BMI] below the 85th percentile with respect to age) and overweight (BMI greater than the 85th percentile with respect to age). RESULTS: The peak LH (8.95±2.85 U/L vs. 11.97±8.42 U/L, P<0.01) and peak follicle-stimulating hormone (9.60±2.91 U/L vs. 11.17±7.77 U/L, P=0.04) after gonadorelin stimulation were lower in overweight girls with idiopathic CPP than in normoweight girls with idiopathic CPP. Being overweight was negatively associated with peak LH levels after gonadorelin stimulation test (odds ratio, 0.89; 95 % confidence interval, 0.81-0.98, P=0.02). CONCLUSION: In girls with idiopathic CPP, being overweight led to a lower LH peak after gonadorelin stimulation. Further research is needed to better understand the role of overweight on gonadotropin secretion in precocious puberty.

PEGylated hyaluronic acid-coated liposome for enhanced in vivo efficacy of sorafenib via active tumor cell targeting and prolonged systemic exposure.

This study aimed to design an effective formulation for enhancing the tumor-targeted delivery of sorafenib. Three sorafenib-loaded liposomal formulations including uncoated liposome (SF-Lip), hyaluronic acid-coated liposome (HA-SF-Lip), and PEGylated hyaluronic acid-coated liposome (PEG-HA-SF-Lip) were developed with narrow size distribution and high encapsulation efficiency. The cellular uptake and cytotoxicity of HA-SF-Lip and PEG-HA-SF-Lip were greater than those of SF-Lip in MDA-MB-231 cells overexpressing CD44, whereas there were no significant differences in MCF-7 cells with low CD44 expression, indicating the CD44-mediated cellular uptake of coated liposomes. In comparison with sorafenib solution, PEG-HA-SF-Lip increased the systemic exposure and plasma half-life in rats by 3-fold and 2-fold, respectively. Consistently, PEG-HA-SF-Lip was the most effective for tumor growth inhibition through CD44 targeting in the MDA-MB-231 tumor xenograft mouse model. Taken together, the present study suggests that PEG-HA-SF-Lip might be effective for the tumor-targeted delivery of sorafenib with enhanced systemic exposure and longer blood circulation.

Utilities for Type 2 Diabetes Treatment-Related Attributes in a South Korean and Taiwanese Population.

OBJECTIVES: To elicit utilities associated with type 2 diabetes medication-related attributes from South Korean and Taiwanese populations and to identify key drivers of preferences. METHODS: Data from 59 respondents from the general population in South Korea and Taiwan were analyzed. Respondents' preferences were elicited using a paper-based standard gamble questionnaire. Health states were designed to identify the utility or disutility of type 2 diabetes medication-related attributes, including dose frequency, nausea/vomiting (hereafter referred to as nausea), and weight change. RESULTS: The mean utility for the basic health state (encompassing current body weight and no nausea) was 0.754 ± 0.155 with weekly dose administration. Respondents showed a preference for weekly over daily administration (average increase in utility of 0.043 across all health states with weekly, vs. daily, administration). Nausea was associated with a decrease in utility (average decrease of -0.034 across all health states with, vs. without, nausea). Weight gain had little effect on utility (average decrease of 0.000 and 0.001 across all health states with, vs. without, 3% and 5% gain, respectively), although weight loss was associated with a small increase in utility (average increase of 0.028 and 0.029 across all health states with, vs. without, 3% and 5% loss, respectively). CONCLUSIONS: Utilities associated with type 2 diabetes medication-related attributes were elicited from a general population sample from South Korea and Taiwan. Treatment-related attributes, in particular dose frequency and nausea, had a measurable effect on utility and should be considered when selecting treatment regimens for South Korean or Taiwanese patients with type 2 diabetes.