The incidence of diabetes mellitus is increasing among companion animals. This disease has similar characteristics in both humans and animals. Diabetes is frequently identified as an independent risk factor for infections associated with increased mortality. In the present study, homozygous diabetic (db/db) mice were infected with Listeria (L.) monocytogenes and then treated with the anti-diabetic drug exendin-4, a glucagon-like peptide 1 analogue. In aged db/db mice, decreased CD11b+ macrophage populations with higher lipid content and lower phagocytic activity were observed. Exendin-4 lowered high lipid levels and enhanced phagocytosis in macrophages from db/db mice infected with L. monocytogenes. Exendin-4 also ameliorated obesity and hyperglycemia, and improved ex vivo bacteria clearance by macrophages in the animals. Liver histology examined during L. monocytogenes infection indicated that abscess formation was much milder in exendin-4-treated db/db mice than in the control animals. Moreover, mechanistic studies demonstrated that expression of ATP binding cassette transporter 1, a sterol transporter, was higher in macrophages isolated from the exendin-4-treated db/db mice. Overall, our results suggest that exendin-4 decreases the risk of infection in diabetic animals by modifying the interaction between intracellular lipids and phagocytic macrophages.
ATP-Binding Cassette Transporters/metabolism ; Age Factors ; Animals ; Blood Chemical Analysis ; Cholesterol/metabolism ; Diabetes Mellitus, Type 2/*drug therapy/genetics ; Dyslipidemias/drug therapy/genetics ; Female ; Hyperglycemia/drug therapy/genetics ; Hypoglycemic Agents/*therapeutic use ; Injections, Intraperitoneal ; *Lipid Metabolism/drug effects ; Listeria monocytogenes/*drug effects/immunology ; Listeriosis/*drug therapy/immunology/microbiology ; Macrophages/drug effects/*metabolism ; Mice ; Obesity/drug therapy/genetics ; Peptides/*therapeutic use ; Phagocytosis/drug effects ; Venoms/*therapeutic use

Background: and Purpose Migraine is a condition that is often observed to run in families, but its complex genetic background remains unclear. This study aimed to identify the genetic factors influencing migraines and their potential association with the family medical history. Methods: We performed a comprehensive genome-wide association study of a cohort of 1,561 outpatients with migraine and 473 individuals without migraine in Taiwan, including Han Chinese individuals with or without a family history of migraine. By analyzing the detailed headache history of the patients and their relatives we aimed to isolate potential genetic markers associated with migraine while considering factors such as sex, episodic vs. chronic migraine, and the presence of aura. Results: We revealed novel genetic risk loci, including rs2287637 in DEAD-Box helicase 1 and long intergenic non-protein coding RNA 1804 and rs12055943 in engulfment and cell motility 1, that were correlated with the family history of migraine. We also found a genetic location downstream of mesoderm posterior BHLH transcription factor 2 associated with episodic migraine, whereas loci within the ubiquitin-specific peptidase 26 exonic region, dual specificity phosphatase 9 and pregnancy-upregulated non-ubiquitous CaM kinase intergenic regions, and poly (ADP-ribose) polymerase 1 and STUM were linked to chronic migraine. We additionally identified genetic regionsassociated with the presence or absence of aura. A locus between LINC02561 and urocortin 3 was predominantly observed in female patients. Moreover, three different single-nucleotide polymorphisms were associated with the family history of migraine in the control group. Conclusions This study has identified new genetic locations associated with migraine and its family history in a Han Chinese population, reinforcing the genetic background of migraine. The findings point to potential candidate genes that should be investigated further.

Interleukin (IL)-20, a proinflammatory cytokine of the IL-10 family, is involved in acute and chronic renal failure. The aim of this study was to elucidate the role of IL-20 during diabetic nephropathy development. We found that IL-20 and its receptor IL-20R1 were upregulated in the kidneys of mice and rats with STZ-induced diabetes. In vitro, IL-20 induced MMP-9, MCP-1, TGF-β1 and VEGF expression in podocytes. IL-20 was upregulated by hydrogen peroxide, high-dose glucose and TGF-β1. In addition, IL-20 induced apoptosis in podocytes by activating caspase-8. In STZ-induced early diabetic nephropathy, IL-20R1-deficient mice had lower blood glucose and serum BUN levels and a smaller glomerular area than did wild-type controls. Anti-IL-20 monoclonal antibody (7E) treatment reduced blood glucose and the glomerular area and improved renal functions in mice in the early stage of STZ-induced diabetic nephropathy. ELISA showed that the serum IL-20 level was higher in patients with diabetes mellitus than in healthy controls. The findings of this study suggest that IL-20 induces cell apoptosis of podocytes and plays a role in the pathogenesis of early diabetic nephropathy.
Animals ; Apoptosis ; Blood Glucose ; Caspase 8 ; Diabetes Mellitus ; Diabetic Nephropathies* ; Enzyme-Linked Immunosorbent Assay ; Glucose ; Humans ; Hydrogen Peroxide ; In Vitro Techniques ; Interleukin-10 ; Interleukins ; Kidney ; Kidney Failure, Chronic ; Mice ; Podocytes* ; Rats ; Vascular Endothelial Growth Factor A

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

OBJECTIVE: Postoperative delirium (POD) is a highly prevalent complex neuropsychiatric syndrome in elderly patients. However, its pathophysiology is currently unknown. Early detection and prevention of POD is important; therefore, the aim of this study was to investigate the link between preoperative insulin growth factor 1 (IGF-1) levels in the serum and POD in the Chinese elderly patients. METHODS: One hundred and three patients who were undergoing an orthopedic operation took part in the study. Preoperative serum IGF-1 levels were measured. POD was determined daily using the Confusion Assessment Method (CAM) and DSM-IV TR. Baseline serum IGF-1 levels were compared between patients who did and did not develop POD. Correlation coefficients were calculated to evaluate relationship between baseline characteristics and serum IGF-1 levels. The relationship between baseline biomarkers and delirium status was investigated using logistic regression analysis, adjusting for potential confounding variables. RESULTS: Twenty-three patients developed POD. The POD group had lower MMSE scores and higher CCI scores and proportions of acute admission. Preoperative serum IGF-1 levels were correlated with MMSE scores and age (MMSE: r=0.230, p<0.05; age: r=-0.419, p<0.001). Baseline serum IGF-1 levels did not differ between patients who did and did not develop POD, even after adjusting for potential confounding factors, MMSE score, and age. CONCLUSION: No association was found between preoperative IGF-1 levels and POD, suggesting that they are not direct biomarkers of the incidence of POD among the Chinese elderly population. Further research with larger sample sizes is warranted to clarify the relationship.
Aged* ; Asian Continental Ancestry Group* ; Biomarkers ; Confounding Factors (Epidemiology) ; Delirium* ; Diagnostic and Statistical Manual of Mental Disorders ; Humans ; Incidence ; Insulin ; Insulin-Like Growth Factor I ; Logistic Models ; Orthopedics ; Sample Size

BACKGROUND AND PURPOSE: The requirement for neurology liaison is increasing in accordance with the growing health care demands associated with aging populations. The aim of this study was to characterize the nature of neurological inpatient liaisons (NILs) to help plan for the appropriate use of neurology resources. METHODS: This was a retrospective cross-sectional study of NILs in a secondary referral hospital over a 12-month period. RESULTS: There were 853 neurological consultations with a liaison rate of 3% per admission case. Chest medicine, gastroenterology, and infectious disease were the three most frequent specialties requesting liaison, and altered consciousness, seizure, and stroke were the three most frequent disorders for which a NIL was requested. Infection was the most common cause of altered consciousness. Epilepsy, infection, and previous stroke were common causes of seizure disorders. Acute stroke accounted for 44% of all stroke disorders. Electroencephalography was the most recommended study, and was also the most frequently performed. Ninety-five percent of emergency consultations were completed within 2 hours, and 85% of regular consultations were completed within 24 hours. The consult-to-visit times for emergency and regular consultations were 44+/-47 minutes (mean+/-standard deviation) and 730+/-768 minutes, respectively, and were shorter for regular consultations at intensive care units (p=0.0151) and for seizure and stroke disorders (p=0.0032). CONCLUSIONS: Altered consciousness, seizure, and stroke were the most common reasons for NILs. Half of the patients had acute neurological diseases warranting immediate diagnosis and treatment by the consulting neurologists. Balancing increasing neurologist workloads and appropriate health-care resources remains a challenge.
Aging ; Communicable Diseases ; Consciousness ; Cross-Sectional Studies* ; Delivery of Health Care ; Diagnosis ; Electroencephalography ; Emergencies ; Epilepsy ; Gastroenterology ; Humans ; Inpatients* ; Intensive Care Units ; Nervous System Diseases* ; Neurology ; Referral and Consultation ; Retrospective Studies* ; Secondary Care Centers* ; Seizures ; Stroke ; Taiwan* ; Thorax