Cardiovascular Risk Awareness, Exercise Practices and Metabolic Outcomes among Patients with Diabetes Mellitus in Northern Nigeria: A Cross-sectional, Multicentre Study.

BACKGROUND: Physical exercise helps to mitigate cardiovascular risks in people with diabetes mellitus (DM), but there are limited data in Nigeria. This study aimed to assess cardiovascular risk awareness, exercise practices and metabolic outcomes among Nigerians with diabetes. MATERIALS AND METHODS: We conducted a cross-sectional study at five tertiary hospitals using questionnaire interviews and clinical assessments. Participants' knowledge of cardiovascular risk factors and knowledge of exercise were assessed on 12- and 5-item scores, while exercise practices were classed as adequate if performed regularly on 3 or more days weekly for a total of 150 min or more based on the American Diabetes Association recommendations. Mean body mass index (BMI), blood pressure (BP), fasting blood glucose, serum haemoglobin A1C (HbA1c), lipid profile, urea, creatinine and uric acid were then compared among participant groups. RESULTS: We studied 426 participants with DM, 58.7% females. The mean age was 52.9 ± 13.1 years, with males significantly older than females (54.6 ± 12.2 vs. 51.8 ± 13.5 years; 95% confidence interval: 0.27-5.28, P = 0.03). The mean age at diabetes diagnosis was 44.8 ± 11.7 years, and the median duration of diabetes was 84 months. There was low knowledge of cardiovascular risk factors and low knowledge of exercise (mean scores of 2.94 and 2.31, respectively). Forty-three per cent of participants reported adequate exercise, which was significantly associated with younger age (P = 0.007), male gender (P = 0.001) and formal education (P = 0.021). Participants with adequate exercise had lower systolic BP and serum urea compared to those with inadequate exercise, but there were no significant differences in BMI, fasting glucose, HbA1c, serum lipids, creatinine or uric acid. CONCLUSION: Participants had low knowledge of cardiovascular risks and the appropriate exercise practices for diabetes patients. There is a need for better patient education on diabetes self-care and exercise at clinic visits.

The worldwide trend in diabetes awareness, treatment, and control from 1985 to 2022: a systematic review and meta-analysis of 233 population-representative studies.

Background: With the rapid increase in the prevalence of DM, studies on the awareness, treatment, and control of this condition are essential. Therefore, this study aimed to review the literature and pool the awareness, treatment, and control of diabetes at the global, regional, and national levels. Methods: In this systematic review and meta-analysis, several databases, including MEDLINE/PubMed, Institute of Scientific Information (ISI), Scopus, and Google Scholar, were searched using appropriate keywords up to June 2022. Observational studies investigating the awareness, treatment, and control of glucose levels among diabetic individuals were included. Awareness, treatment, and control were defined as the proportion of participants who were aware of their diabetes condition, treated pharmacologically, and achieved adequate glucose control, respectively. Two investigators independently conducted the study selection, data extraction, and quality assessment. Heterogeneity among studies was calculated using Chi-square, and a random-effect meta-analysis was used to pool the rates. Results: A total of 233 studies published between 1985 and 2022 met the inclusion criteria. The included studies had a combined population of 12,537,968. The pooled awareness of DM was 60% (95%CI: 56-63) and ranged from 41% (25-57) in low-income countries to 68% (64-72) in high-income countries, with no significant trend observed over the assessed periods at the global level. The pooled treatment of DM globally was 45% (42-48) and varied from 37% (31-43) in lower-middle-income countries to 53% (47-59) in high-income countries, showing variation over the examined time period. Before 2000, the proportion of adequate DM control was 16% (12-20), which significantly improved and reached 22% (19-25) after 2010. The pooled awareness, treatment, and control of DM were higher in females, high-income countries, and urban areas compared to males, upper and lower-middle-income countries, and rural areas, respectively. The older adults population had higher awareness and treatment rates than the adult population, but their DM control did not differ significantly. Conclusion: Despite the high level of awareness and treatment among the diabetic population, treatment success (control) is considerably low, particularly in low-income countries and rural areas. It is crucial to improve awareness, treatment, and control by strengthening the primary care system in all countries.

Diabetes technology: A primer for clinicians.

Diabetes technology is evolving rapidly and is changing the way both patients and clinicians approach the management of diabetes. With more devices gaining US Food and Drug Administration approval and insurance coverage expanding, these new technologies are being widely adopted by people living with diabetes. We provide a summary of the commonly available devices in the market today that clinicians will likely encounter. This includes continuous glucose monitors (CGMs); connected insulin pens, caps, and buttons; and insulin pumps. Clinicians' awareness of and familiarity with this technology will enhance its accessibility for patients with diabetes.

Identifying risk factors and mortality rate of premature coronary artery disease in young Saudi population.

Coronary artery disease is a leading cause of morbidity and mortality worldwide. It occurs due to a combination of genetics, lifestyle, and environmental factors. Premature coronary artery disease (PCAD) is a neglected clinical entity despite the rising number of cases worldwide. This study aimed to investigate the risk factors of premature coronary artery disease. In this study, we searched articles that had studied the risk factors of premature coronary artery diseases from January 2000 to July 2022 in Saudi Arabia in Web of Science, Pub Med, Scopus, Springer, and Wiley databases. The final analysis is based on seven articles. The smoking prevalence was 39%, diabetes mellitus 41%, hypertension 33%, overweight and obesity 18%, family history of coronary artery disease (CAD) 19%, dyslipidemia 37%, and the prevalence range of low-density lipoprotein cholesterol was 33.8-55.0%. The results revealed a mortality prevalence of 4% ranging from 2 to 8% which is similar to the prevalence in older patients which was 2-10%. Smoking, diabetes mellitus, hypertension, family history of CAD, dyslipidemia, and overweight/obesity are significantly and positively associated with premature coronary artery diseases. The health authorities should design and implement an intensive and effective prophylactic plan to minimize the subsequent impact of PCAD on the young population. In addition, early diagnosis of PCAD has great value in providing timely treatment, managing the patients, and minimizing the burden of the disease.

Incidence, Risk, and Clinical Course of New-Onset Diabetes in Long COVID: Protocol for a Systematic Review and Meta-Analysis of Cohort Studies.

BACKGROUND: COVID-19, an infectious disease pandemic, affected millions of people globally, resulting in high morbidity and mortality. Causing further concern, significant proportions of COVID-19 survivors endure the lingering health effects of SARS-CoV-2, the pathogen that causes COVID-19. One of the diseases manifesting as a postacute sequela of COVID-19 (also known as "long COVID") is new-onset diabetes. OBJECTIVE: The aim of this study is to examine the incidence of new-onset diabetes in patients with long COVID and assess the excess risk compared with individuals who tested negative for COVID-19. The study also aims to estimate the population-attributable fraction for COVID-19 as a risk factor for new-onset diabetes in long COVID and investigate the clinical course of new-onset diabetes cases. METHODS: This is a protocol for a systematic review and meta-analysis. PubMed, MEDLINE, Embase, Scopus, and Web of Science databases will be systematically searched to identify articles published between December 2019 and July 2024. A comprehensive search strategy for each database will be developed using a combination of Medical Subject Headings terms, subject headings, and text words to identify eligible studies. Cohort studies and randomized controlled trials (only control arms) involving patients with COVID-19 of any age, with follow-up data on new-onset diabetes in long COVID, will be considered for inclusion. Controls will comprise individuals who tested negative for COVID-19, with or without other respiratory tract infections. Three independent reviewers (AST, NB, and TT) will perform article selection, data extraction, and quality assessment of the studies. A fourth reviewer (ST) will review the identified studies for final inclusion in the analysis. The random-effects DerSimonian-Laird models will be used to estimate the pooled incidence proportion (%), incidence rate of diabetes (per 1000 person-years), and risk ratio (with 95% CIs) for diabetes incidence. RESULTS: A total of 1972 articles were identified through the initial search conducted in August 2023. After excluding duplicates, conducting title and abstract screening, and completing full-text reviews, 41 articles were found to be eligible for inclusion. The search will be updated in July 2024. Currently, data extraction is underway, and the meta-analysis is expected to be completed in August 2024. Publication of the study findings is anticipated by the end of 2024. CONCLUSIONS: The study findings should provide valuable insights to inform both clinical practice and public health policies regarding the effective management of new-onset diabetes in patients with long COVID. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/54853.

Investigation of geographic disparities of diabetes-related hospitalizations in Florida using flexible spatial scan statistics: An ecological study.

BACKGROUND: Hospitalizations due to diabetes complications are potentially preventable with effective management of the condition in the outpatient setting. Diabetes-related hospitalization (DRH) rates can provide valuable information about access, utilization, and efficacy of healthcare services. However, little is known about the local geographic distribution of DRH rates in Florida. Therefore, the objectives of this study were to investigate the geographic distribution of DRH rates at the ZIP code tabulation area (ZCTA) level in Florida, identify significant local clusters of high hospitalization rates, and describe characteristics of ZCTAs within the observed spatial clusters. METHODS: Hospital discharge data from 2016 to 2019 were obtained from the Florida Agency for Health Care Administration through a Data Use Agreement with the Florida Department of Health. Raw and spatial empirical Bayes smoothed DRH rates were computed at the ZCTA level. High-rate DRH clusters were identified using Tango's flexible spatial scan statistic. Choropleth maps were used to display smoothed DRH rates and significant high-rate spatial clusters. Demographic, socioeconomic, and healthcare-related characteristics of cluster and non-cluster ZCTAs were compared using the Wilcoxon rank sum test for continuous variables and Chi-square test for categorical variables. RESULTS: There was a total of 554,133 diabetes-related hospitalizations during the study period. The statewide DRH rate was 8.5 per 1,000 person-years, but smoothed rates at the ZCTA level ranged from 0 to 101.9. A total of 24 significant high-rate spatial clusters were identified. High-rate clusters had a higher percentage of rural ZCTAs (60.9%) than non-cluster ZCTAs (41.8%). The median percent of non-Hispanic Black residents was significantly (p < 0.0001) higher in cluster ZCTAs than in non-cluster ZCTAs. Populations of cluster ZCTAs also had significantly (p < 0.0001) lower median income and educational attainment, and higher levels of unemployment and poverty compared to the rest of the state. In addition, median percent of the population with health insurance coverage and number of primary care physicians per capita were significantly (p < 0.0001) lower in cluster ZCTAs than in non-cluster ZCTAs. CONCLUSIONS: This study identified geographic disparities of DRH rates at the ZCTA level in Florida. The identification of high-rate DRH clusters provides useful information to guide resource allocation such that communities with the highest burdens are prioritized to reduce the observed disparities. Future research will investigate determinants of hospitalization rates to inform public health planning, resource allocation and interventions.

Smartphone based wearable sweat glucose sensing device correlated with machine learning for real-time diabetes screening.

BACKGROUND: Diabetes is a significant health threat, with its prevalence and burden increasing worldwide indicating its challenge for global healthcare management. To decrease the disease severity, the diabetic patients are recommended to regularly check their blood glucose levels. The conventional finger-pricking test possesses some drawbacks, including painfulness and infection risk. Nowadays, smartphone has become a part of our lives offering an important benefit in self-health monitoring. Thus, non-invasive wearable sweat glucose sensor connected with a smartphone readout is of interest for real-time glucose detection. RESULTS: Wearable sweat glucose sensing device is fabricated for self-monitoring of diabetes. This device is designed as a body strap consisting of a sensing strip and a portable potentiostat connected with a smartphone readout via Bluetooth. The sensing strip is modified by carbon nanotubes (CNTs)-cellulose nanofibers (CNFs), followed by electrodeposition of Prussian blue. To preserve the activity of glucose oxidase (GOx) immobilized on the modified sensing strip, chitosan is coated on the top layer of the electrode strip. Herein, machine learning is implemented to correlate between the electrochemical results and the nanomaterial content along with deposition cycle of prussian blue, which provide the highest current response signal. The optimized regression models provide an insight, establishing a robust framework for design of high-performance glucose sensor. SIGNIFICANCE: This wearable glucose sensing device connected with a smartphone readout offers a user-friendly platform for real-time sweat glucose monitoring. This device provides a linear range of 0.1-1.5 mM with a detection limit of 0.1 mM that is sufficient enough for distinguishing between normal and diabetes patient with a cut-off level of 0.3 mM. This platform might be an alternative tool for improving health management for diabetes patients.

Microstrip isoelectric focusing with deep learning for simultaneous screening of diabetes, anemia, and thalassemia.

BACKGROUND: Hemoglobin (Hb) is an important protein in red blood cells and a crucial diagnostic indicator of diseases, e.g., diabetes, thalassemia, and anemia. However, there is a rare report on methods for the simultaneous screening of diabetes, anemia, and thalassemia. Isoelectric focusing (IEF) is a common separative tool for the separation and analysis of Hb. However, the current analysis of IEF images is time-consuming and cannot be used for simultaneous screening. Therefore, an artificial intelligence (AI) of IEF image recognition is desirable for accurate, sensitive, and low-cost screening. RESULTS: Herein, we proposed a novel comprehensive method based on microstrip isoelectric focusing (mIEF) for detecting the relative content of Hb species. There was a good coincidence between the quantitation of Hb via a conventional automated hematology analyzer and the one via mIEF with R2 = 0.9898. Nevertheless, our results showed that the accuracy of disease diagnosis based on the quantification of Hb species alone is as low as 69.33 %, especially for the simultaneous screening of multiple diseases of diabetes, anemia, alpha-thalassemia, and beta-thalassemia. Therefore, we introduced a ResNet1D-based diagnosis model for the improvement of screening accuracy of multiple diseases. The results showed that the proposed model could achieve a high accuracy of more than 90 % and a good sensitivity of more than 96 % for each disease, indicating the overwhelming advantage of the mIEF method combined with deep learning in contrast to the pure mIEF method. SIGNIFICANCE: Overall, the presented method of mIEF with deep learning enabled, for the first time, the absolute quantitative detection of Hb, relative quantitation of Hb species, and simultaneous screening of diabetes, anemia, alpha-thalassemia, and beta-thalassemia. The AI-based diagnosis assistant system combined with mIEF, we believe, will help doctors and specialists perform fast and precise disease screening in the future.

Association between blood lipids and diabetes mellitus in older Chinese adults aged 65 years or older: a cross-sectional analysis of residents' electronic health records.

AIM: This study aimed to investigate how blood lipids are associated with diabetes among older Chinese adults. METHODS: 3,268,928 older Chinese adults without known diabetes were included. Logistic regression and restricted cubic spline (RCS) models were conducted to study associations between blood lipids (total cholesterol [TC], triglycerides [TG], low-density lipoprotein cholesterol [LDL-C], and high-density lipoprotein cholesterol [HDL-C]) and diabetes. RESULTS: 202,832 diabetes cases were included. Compared with the lowest quintiles, TC, TG, and LDL-C in the highest quintiles showed a higher diabetes prevalence risk and HDL-C presented a lower risk in multivariate-adjusted logistic regression models. Odds ratios (ORs) and 95% confidence intervals (95% CIs) for the highest quintiles of TC, TG, and HDL-C were 1.39 (1.37-1.41), 2.56 (2.52-2.60), and 0.73 (0.72-0.74), respectively. For LDL-C, 3-5% lower risk was found in the second and third quintiles, and 4-23% higher risk was found in the fourth and fifth quintiles. RCS curves showed a non-linear relationship between each blood lipid parameters and diabetes (P-non-linear < 0.001). TG and HDL-C curves presented monotonically increasing and L-shaped patterns, respectively, whereas TC and LDL-C curves exhibited a J-shaped pattern. When TC < 4.04 mmol/L or LDL-C < 2.33 mmol/L, ORs of diabetes increased with the decrease of corresponding indexes. However, after excluding participants with lower LDL-C, the J-shaped association with TC disappeared. CONCLUSIONS: This study demonstrates non-linear associations between lipids and diabetes. Low cholesterol levels are associated with a high risk of diabetes. The cholesterol paradox should be considered during lipid-lowering treatments.

Comparison of two propensity score-based methods for balancing covariates: the overlap weighting and fine stratification methods in real-world claims data.

BACKGROUND: Two propensity score (PS) based balancing covariate methods, the overlap weighting method (OW) and the fine stratification method (FS), produce superb covariate balance. OW has been compared with various weighting methods while FS has been compared with the traditional stratification method and various matching methods. However, no study has yet compared OW and FS. In addition, OW has not yet been evaluated in large claims data with low prevalence exposure and with low frequency outcomes, a context in which optimal use of balancing methods is critical. In the study, we aimed to compare OW and FS using real-world data and simulations with low prevalence exposure and with low frequency outcomes. METHODS: We used the Texas State Medicaid claims data on adult beneficiaries with diabetes in 2012 as an empirical example (N = 42,628). Based on its real-world research question, we estimated an average treatment effect of health center vs. non-health center attendance in the total population. We also performed simulations to evaluate their relative performance. To preserve associations between covariates, we used the plasmode approach to simulate outcomes and/or exposures with N = 4,000. We simulated both homogeneous and heterogeneous treatment effects with various outcome risks (1-30% or observed: 27.75%) and/or exposure prevalence (2.5-30% or observed:10.55%). We used a weighted generalized linear model to estimate the exposure effect and the cluster-robust standard error (SE) method to estimate its SE. RESULTS: In the empirical example, we found that OW had smaller standardized mean differences in all covariates (range: OW: 0.0-0.02 vs. FS: 0.22-3.26) and Mahalanobis balance distance (MB) (< 0.001 vs. > 0.049) than FS. In simulations, OW also achieved smaller MB (homogeneity: <0.04 vs. > 0.04; heterogeneity: 0.0-0.11 vs. 0.07-0.29), relative bias (homogeneity: 4.04-56.20 vs. 20-61.63; heterogeneity: 7.85-57.6 vs. 15.0-60.4), square root of mean squared error (homogeneity: 0.332-1.308 vs. 0.385-1.365; heterogeneity: 0.263-0.526 vs 0.313-0.620), and coverage probability (homogeneity: 0.0-80.4% vs. 0.0-69.8%; heterogeneity: 0.0-97.6% vs. 0.0-92.8%), than FS, in most cases. CONCLUSIONS: These findings suggest that OW can yield nearly perfect covariate balance and therefore enhance the accuracy of average treatment effect estimation in the total population.

Corrigendum: Diet and exercise knowledge and practices for diabetes care within families in Senwabarwana.

No abstract available.

Diabetes and Infectious Diseases with a Focus on Melioidosis.

Diabetes mellitus (DM) leads to impaired innate and adaptive immune responses. This renders individuals with DM highly susceptible to microbial infections such as COVID-19, tuberculosis and melioidosis. Melioidosis is a tropical disease caused by the bacterial pathogen Burkholderia pseudomallei, where diabetes is consistently reported as the most significant risk factor associated with the disease. Type-2 diabetes is observed in 39% of melioidosis patients where the risk of infection is 13-fold higher than non-diabetic individuals. B. pseudomallei is found in the environment and is an opportunistic pathogen in humans, often exhibiting severe clinical manifestations in immunocompromised patients. The pathophysiology of diabetes significantly affects the host immune responses that play a critical role in fighting the infection, such as leukocyte and neutrophil impairment, macrophage and monocyte inhibition and natural killer cell dysfunction. These defects result in delayed recruitment as well as activation of immune cells to target the invading B. pseudomallei. This provides an advantage for the pathogen to survive and adapt within the immunocompromised diabetic patients. Nevertheless, knowledge gaps on diabetes-infectious disease comorbidity, in particular, melioidosis-diabetes comorbidity, need to be filled to fully understand the dysfunctional host immune responses and adaptation of the pathogen under diabetic conditions to guide therapeutic options.

Prevalence of Diabetes Mellitus among Haitian Americans: A Hidden Epidemic.

The prevalence of diabetes mellitus in the Haitian American population remains an important question. A recent study revealed an alarming prevalence of 39.9%. To corroborate these data, between November 2021 and September 2023 a representative sample was collected among 401 Haitian Americans in Florida, Maryland, New Jersey, and New York. Results revealed a crude prevalence rate of 36.6% (95% CI 31.85, 41.55%). The age-adjusted prevalence was 29.7% (CI 19.71%, 39.63%). This study's prevalence is nearly double the 16.8% (Z=10.48, p<.0001) rate in non-Hispanic African Americans and nearly two and a half times the 12.0% (Z=14.99, p<.0001) rate in all Americans. The crude prevalence for undiagnosed diabetes mellitus was 13.38% (CI 10.19%, 17.14%), with 17.11% age-adjusted prevalence (CI 7.53%, 26.70%). The scope of the diabetes burden, especially the high rate of undiagnosed cases, indicates a need for better strategies for the prevention, screening, treatment, and management of diabetes among Haitian Americans.

Prevalence of thyroid disorders among the diabetic population in Arar, Saudi Arabia.

OBJECTIVES: To study the prevalence of thyroid disorders (TDs) among the diabetic population in Arar, Saudi Arabia. METHODS: A cross-sectional design study carried out in Arar, northern province of Saudi Arabia, from October 2023 to January 2024. A structured questionnaire was used to collect the data. From the diabetic population aged over 18 years old. RESULTS: A total of 501 participants were enrolled. Most fall within the 20-35 age range, comprising 36.5% of the sample. Vitamin D deficiency appears to be the most prevalent comorbid condition. Following closely behind is vitamin B12 deficiency; hypertension and high blood lipids also show notable prevalence rates, affecting 10.5-22.1% of the population. In terms of diabetes, 42.8% of the population has been diagnosed with the condition. Among those with diabetes, the majority (67.6%) have been diagnosed with the second type, while 32.4% have the first type. There is an association between diabetes and TDs, with 51.3% of participants reporting this. CONCLUSION: The findings indicate that the adults in Arar, Saudi Arabia, lack some knowledge of TDs and their relationship to diabetes.

The Effect of Nursing Approach on the Self-Management and Adjustment to the Illness among Newly Diagnosed Diabetic Patients According to Meleis's Transition Theory.

This study aimed to determine how the nursing approach based on Meleis's transition theory affects the self-management and adjustment to the illness among newly diagnosed diabetic patients. The study was conducted as one-group and pretest-posttest quasi-experimental design. The data were collected using the Introductory Questionnaire, the Diabetes Self-Management Questionnaire (DSMQ), and the Psychosocial Adjustment to Illness Scale-Self Report (PAIS-SR). It was determined that there was a positive increase in the total score of the DSMQ after the intervention, and a positive decrease in the total score of the PAIS-SR, and the difference between the scores were statistically significant (p < .05).

Explainable hypoglycemia prediction models through dynamic structured grammatical evolution.

Effective blood glucose management is crucial for people with diabetes to avoid acute complications. Predicting extreme values accurately and in a timely manner is of vital importance to them. People with diabetes are particularly concerned about suffering a hypoglycemia (low value) event and, moreover, that the event will be prolonged in time. It is crucial to predict hyperglycemia (high value) and hypoglycemia events that may cause health damages in the short term and potential permanent damages in the long term. This paper describes our research on predicting hypoglycemia events at 30, 60, 90, and 120 minutes using machine learning methods. We propose using structured Grammatical Evolution and dynamic structured Grammatical Evolution to produce interpretable mathematical expressions that predict a hypoglycemia event. Our proposal generates white-box models induced by a grammar based on if-then-else conditions using blood glucose, heart rate, number of steps, and burned calories as the inputs for the machine learning technique. We apply these techniques to create three types of models: individualized, cluster, and population-based. They all are then compared with the predictions of eleven machine learning techniques. We apply these techniques to a dataset of 24 real patients of the Hospital Universitario Principe de Asturias, Madrid, Spain. The resulting models, presented as if-then-else statements that incorporate numeric, relational, and logical operations between variables and constants, are inherently interpretable. The True Positive Rate and True Negative Rate metrics are above 0.90 for 30-minute predictions, 0.80 for 60 min, and 0.70 for 90 min and 120 min for the three types of models. Individualized models exhibit the best metrics, while cluster and population-based models perform similarly. Structured and dynamic structured grammatical evolution techniques perform similarly for all forecasting horizons. Regarding the comparison of different machine learning techniques, on the shorter forecasting horizons, our proposals have a high probability of winning, a probability that diminishes on the longer time horizons. Structured grammatical evolution provides advanced forecasting models that facilitate model explanation, modification, and retesting, offering flexibility for refining solutions post-creation and a deeper understanding of blood glucose behavior. These models have been integrated into the glUCModel application, designed to serve people with diabetes.

Asociación entre Glucemia inestable, tiempo de estancia y mortalidad de pacientes en estado críticos: Estudio de cohorte / Associação Entre Glicemia Instável, Tempo de Permanência e Mortalidade de Pacientes Críticos: Estudo de Coorte / Relationships between unstable Glycemia, Hospital Length Of Stay, and mortality In Critically Ill patients: A cohort study

Resumen Introducción: El control y la evaluación de los niveles glucémicos de pacientes en estado críticos es un desafío y una competencia del equipo de enfermería. Por lo que, determinar las consecuencias de esta durante la hospitalización es clave para evidenciar la importancia del oportuno manejo. Objetivo: Determinar la asociación entre la glucemia inestable (hiperglucemia e hipoglucemia), el resultado de la hospitalización y la duración de la estancia de los pacientes en una unidad de cuidados intensivos. Metodología: Estudio de cohorte prospectivo realizado con 62 pacientes a conveniencia en estado crítico entre marzo y julio de 2017. Se recogieron muestras diarias de sangre para medir la glucemia. Se evaluó la asociación de la glucemia inestable con la duración de la estancia y el resultado de la hospitalización mediante ji al cuadrado de Pearson. El valor de p<0.05 fue considerado significativo. Resultados: De las 62 personas participantes, 50 % eran hombres y 50 % mujeres. La edad media fue de 63.3 años (±21.4 años). La incidencia de glucemia inestable fue del 45.2 % y se asoció con una mayor duración de la estancia en la UCI (p<0.001) y una progresión a la muerte como resultado de la hospitalización (p=0.03). Conclusión: Entre quienes participaron, la glucemia inestable se asoció con una mayor duración de la estancia más prolongada y con progresión hacia la muerte, lo que refuerza la importancia de la actuación de enfermería para prevenir su aparición.

Resumo Introdução: O controle e avaliação dos níveis glicêmicos em pacientes críticos é um desafio e uma competência da equipe de enfermagem. Portanto, determinar as consequências da glicemia instável durante a hospitalização é chave para evidenciar a importância da gestão oportuna. Objetivo: Determinar a associação entre glicemia instável (hiperglicemia e hipoglicemia), os desfechos hospitalares e o tempo de permanência dos pacientes em uma unidade de terapia intensiva. Métodos: Um estudo de coorte prospectivo realizado com 62 pacientes a conveniência em estado crítico entre março e julho de 2017. Foram coletadas amostras diariamente de sangue para medir a glicemia. A associação entre a glicemia instável com o tempo de permanência e o desfecho da hospitalização foi avaliada pelo teste qui-quadrado de Pearson. O valor de p <0,05 foi considerado significativo. Resultados: Das 62 pessoas participantes, 50% eram homens e 50% mulheres. A idade média foi de 63,3 anos (±21,4 anos). A incidência de glicemia instável foi de 45,2% e se associou a um tempo de permanência mais prolongado na UTI (p <0,001) e uma progressão para óbito como desfecho da hospitalização (p = 0,03). Conclusão: Entre os participantes, a glicemia instável se associou a um tempo mais longo de permanência e com progressão para óbito, enfatizando a importância da actuação da equipe de enfermagem para prevenir sua ocorrência.

Abstract Introduction: The control and evaluation of glycemic levels in critically ill patients is a challenge and a responsibility of the nursing team; therefore, determining the consequences of this during hospitalization is key to demonstrate the importance of timely management. Objective: To determine the relationship between unstable glycemia (hyperglycemia and hypoglycemia), hospital length of stay, and the hospitalization outcome of patients in an Intensive Care Unit (ICU). Methods: A prospective cohort study conducted with 62 critically ill patients by convenience sampling between March and July 2017. Daily blood samples were collected to measure glycemia. The correlation of unstable glycemia with the hospital length of stay and the hospitalization outcome was assessed using Pearson's chi-square. A p-value <0.05 was considered significant. Results: Among the 62 patients, 50% were male and 50% were female. The mean age was 63.3 years (±21.4 years). The incidence of unstable glycemia was 45.2% and was associated with a longer ICU stay (p<0.001) and a progression to death as a hospitalization outcome (p=0.03). Conclusion: Among critically ill patients, unstable glycemia was associated with an extended hospital length of stay and a progression to death, emphasizing the importance of nursing intervention to prevent its occurrence.

TA-Cloning for Diabetes Treatment: Expressing Corynebacterium Malic Enzyme Gene in E. coli.

Diabetes mellitus represents a persistent metabolic condition marked by heightened levels of blood glucose, presenting a considerable worldwide health concern, and finding targeted treatment for it is a crucial priority for global health. Gram-positive aerobic bacteria, predominantly inhabiting water and soil, are known carriers of various enzyme-encoding genetic material, which includes the malic enzyme gene that plays a role in insulin secretion. Corynebacterium glutamicum bacteria (ATCC 21799) were acquired from the Pasteur Institute and confirmed using microbiological and molecular tests, including DNA extraction. After identification, gene purification and cloning of the maeB gene were performed using the TA Cloning method. Additionally, the enhancement of enzyme expression was assessed using the expression vector pET-28a, and validation of simulation results was monitored through a real-time PCR analysis. Based on previous studies, the malic enzyme plays a pivotal role in maintaining glucose homeostasis, and increased expression of this enzyme has been associated with enhanced insulin sensitivity. However, the production of malic enzyme has encountered numerous challenges and difficulties. This study successfully isolated the malic enzyme genes via Corynebacterium glutamicum and introduced them into Escherichia coli for high-yield production. According to the results, the optimum temperature for the activity of enzymes has been identified as 39 °C.

Metabolomics Signature in Prediabetes and Diabetes: Insights From Tandem Mass Spectrometry Analysis.

OBJECTIVE: This study investigates the metabolic differences between normal, prediabetic and diabetic patients with good and poor glycaemic control (GGC and PGC). DESIGN: In this study, 1102 individuals were included, and 50 metabolites were analysed using tandem mass spectrometry. The diabetes diagnosis and treatment standards of the American Diabetes Association (ADA) were used to classify patients. METHODS: The nearest neighbour method was used to match controls and cases in each group on the basis of age, sex and BMI. Factor analysis was used to reduce the number of variables and find influential underlying factors. Finally, Pearson's correlation coefficient was used to check the correlation between both glucose and HbAc1 as independent factors with binary classes. RESULTS: Amino acids such as glycine, serine and proline, and acylcarnitines (AcylCs) such as C16 and C18 showed significant differences between the prediabetes and normal groups. Additionally, several metabolites, including C0, C5, C8 and C16, showed significant differences between the diabetes and normal groups. Moreover, the study found that several metabolites significantly differed between the GGC and PGC diabetes groups, such as C2, C6, C10, C16 and C18. The correlation analysis revealed that glucose and HbA1c levels significantly correlated with several metabolites, including glycine, serine and C16, in both the prediabetes and diabetes groups. Additionally, the correlation analysis showed that HbA1c significantly correlated with several metabolites, such as C2, C5 and C18, in the controlled and uncontrolled diabetes groups. CONCLUSIONS: These findings could help identify new biomarkers or underlying markers for the early detection and management of diabetes.