Impact of diabetes mellitus on osteoarthritis: a scoping review on biomarkers.

Osteoarthritis (OA) commonly affects the knee and hip joints and accounts for 19.3% of disability-adjusted life years and years lived with disability worldwide (Refs , ). Early management is important in order to avoid disability uphold quality of life (Ref. ). However, a lack of awareness of subclinical and early symptomatic stages of OA often hampers early management (Ref. ). Moreover, late diagnosis of OA among those with severe disease, at a stage when OA management becomes more complicated is common (Refs , , , ). Established risk factors for the development and progression of OA include increasing age, female, history of trauma and obesity (Ref. ). Recent studies have also drawn a link between OA and metabolic syndrome, which is characterized by insulin resistance, dyslipidaemia and hypertension (Refs , ).

A novel electronic health record-based, machine-learning model to predict severe hypoglycemia leading to hospitalizations in older adults with diabetes: A territory-wide cohort and modeling study.

BACKGROUND: Older adults with diabetes are at high risk of severe hypoglycemia (SH). Many machine-learning (ML) models predict short-term hypoglycemia are not specific for older adults and show poor precision-recall. We aimed to develop a multidimensional, electronic health record (EHR)-based ML model to predict one-year risk of SH requiring hospitalization in older adults with diabetes. METHODS AND FINDINGS: We adopted a case-control design for a retrospective territory-wide cohort of 1,456,618 records from 364,863 unique older adults (age ≥65 years) with diabetes and at least 1 Hong Kong Hospital Authority attendance from 2013 to 2018. We used 258 predictors including demographics, admissions, diagnoses, medications, and routine laboratory tests in a one-year period to predict SH events requiring hospitalization in the following 12 months. The cohort was randomly split into training, testing, and internal validation sets in a 7:2:1 ratio. Six ML algorithms were evaluated including logistic-regression, random forest, gradient boost machine, deep neural network (DNN), XGBoost, and Rulefit. We tested our model in a temporal validation cohort in the Hong Kong Diabetes Register with predictors defined in 2018 and outcome events defined in 2019. Predictive performance was assessed using area under the receiver operating characteristic curve (AUROC), area under the precision-recall curve (AUPRC) statistics, and positive predictive value (PPV). We identified 11,128 SH events requiring hospitalization during the observation periods. The XGBoost model yielded the best performance (AUROC = 0.978 [95% CI 0.972 to 0.984]; AUPRC = 0.670 [95% CI 0.652 to 0.688]; PPV = 0.721 [95% CI 0.703 to 0.739]). This was superior to an 11-variable conventional logistic-regression model comprised of age, sex, history of SH, hypertension, blood glucose, kidney function measurements, and use of oral glucose-lowering drugs (GLDs) (AUROC = 0.906; AUPRC = 0.085; PPV = 0.468). Top impactful predictors included non-use of lipid-regulating drugs, in-patient admission, urgent emergency triage, insulin use, and history of SH. External validation in the HKDR cohort yielded AUROC of 0.856 [95% CI 0.838 to 0.873]. Main limitations of this study included limited transportability of the model and lack of geographically independent validation. CONCLUSIONS: Our novel-ML model demonstrated good discrimination and high precision in predicting one-year risk of SH requiring hospitalization. This may be integrated into EHR decision support systems for preemptive intervention in older adults at highest risk.

In-depth correlation analysis between tear glucose and blood glucose using a wireless smart contact lens.

Tears have emerged as a promising alternative to blood for diagnosing diabetes. Despite increasing attempts to measure tear glucose using smart contact lenses, the controversy surrounding the correlation between tear glucose and blood glucose still limits the clinical usage of tears. Herein, we present an in-depth investigation of the correlation between tear glucose and blood glucose using a wireless and soft smart contact lens for continuous monitoring of tear glucose. This smart contact lens is capable of quantitatively monitoring the tear glucose levels in basal tears excluding the effect of reflex tears which might weaken the relationship with blood glucose. Furthermore, this smart contact lens can provide an unprecedented level of continuous tear glucose data acquisition at sub-minute intervals. These advantages allow the precise estimation of lag time, enabling the establishment of the concept called 'personalized lag time'. This demonstration considers individual differences and is successfully applied to both non-diabetic and diabetic humans, as well as in animal models, resulting in a high correlation.

A Combined Measure of the Triglyceride Glucose Index and Trimethylamine N-Oxide in Risk Stratification of ST-Segment Elevation Myocardial Infarction Patients with High-Risk Plaque Features Defined by Optical Coherence Tomography: A Substudy of the OCTAMI Registry Study.

Background and Aim: An elevated triglyceride-glucose (TyG) level is associated with increased risk of mortality in patients with CAD. Trimethylamine N-oxide (TMAO) has mechanistic links to atherosclerotic coronary artery disease (CAD) pathogenesis and is correlated with adverse outcomes. However, the incremental prognostic value of TMAO and TyG in the cohort of optical coherence tomography (OCT)-defined high-risk ST-segment elevation myocardial infarction (STEMI) patients is unknown. Methods: We studied 274 consecutive aged ≥18 years patients with evidence of STEMI and detected on pre-intervention OCT imaging of culprit lesions between March 2017 and March 2019. Outcomes: There were 22 (22.68%), 27 (27.84%), 26 (26.80%), and 22 (22.68%) patients in groups A-D, respectively. The baseline characteristics according to the level of TMAO and TyG showed that patients with higher level in both indicators were more likely to have higher triglycerides (p < 0.001), fasting glucose (p < 0.001) and higher incidence of diabetes (p = 0.008). The group with TMAO > median and TyG ≤ median was associated with higher rates of MACEs significantly (p = 0.009) in fully adjusted analyses. During a median follow-up of 2.027 years, 20 (20.6%) patients experienced MACEs. To evaluate the diagnostic value of the TyG index combined with TMAO, the area under the receiver operating characteristic curve for predicting MACEs after full adjustment was 0.815 (95% confidence interval, 0.723-0.887; sensitivity, 85.00%; specificity, 72.73%; cut-off level, 0.577). Among the group of patients with TMAO > median and TyG ≤ median, there was a significantly higher incidence of MACEs (p=0.033). A similar tendency was found in the cohort with hyperlipidemia (p=0.016) and diabetes mellitus (p=0.036). Conclusion: This study demonstrated the usefulness of combined measures of the TyG index and TMAO in enhancing risk stratification in STEMI patients with OCT-defined high-risk plaque characteristics. Trial Registration: This study was registered at ClinicalTrials.gov as NCT03593928.

Coin-sized, fully integrated, and minimally invasive continuous glucose monitoring system based on organic electrochemical transistors.

Continuous glucose monitoring systems (CGMs) are critical toward closed-loop diabetes management. The field's progress urges next-generation CGMs with enhanced antinoise ability, reliability, and wearability. Here, we propose a coin-sized, fully integrated, and wearable CGM, achieved by holistically synergizing state-of-the-art interdisciplinary technologies of biosensors, minimally invasive tools, and hydrogels. The proposed CGM consists of three major parts: (i) an emerging biochemical signal amplifier, the organic electrochemical transistor (OECT), improving the signal-to-noise ratio (SNR) beyond traditional electrochemical sensors; (ii) a microneedle array to facilitate subcutaneous glucose sampling with minimized pain; and (iii) a soft hydrogel to stabilize the skin-device interface. Compared to conventional CGMs, the OECT-CGM offers a high antinoise ability, tunable sensitivity and resolution, and comfort wearability, enabling personalized glucose sensing for future precision diabetes health care. Last, we discuss how OECT technology can help push the limit of detection of current wearable electrochemical biosensors, especially when operating in complicated conditions.

Identifying a group of factors predicting cognitive impairment among older adults.

BACKGROUND: Cognitive impairment has multiple risk factors spanning several domains, but few studies have evaluated risk factor clusters. We aimed to identify naturally occurring clusters of risk factors of poor cognition among middle-aged and older adults and evaluate associations between measures of cognition and these risk factor clusters. METHODS: We used data from the National Health and Nutrition Examination Survey (NHANES) III (training dataset, n = 4074) and the NHANES 2011-2014 (validation dataset, n = 2510). Risk factors were selected based on the literature. We used both traditional logistic models and support vector machine methods to construct a composite score of risk factor clusters. We evaluated associations between the risk score and cognitive performance using the logistic model by estimating odds ratios (OR) and 95% confidence intervals (CI). RESULTS: Using the training dataset, we developed a composite risk score that predicted undiagnosed cognitive decline based on ten selected predictive risk factors including age, waist circumference, healthy eating index, race, education, income, physical activity, diabetes, hypercholesterolemia, and annual visit to dentist. The risk score was significantly associated with poor cognitive performance both in the training dataset (OR Tertile 3 verse tertile 1 = 8.15, 95% CI: 5.36-12.4) and validation dataset (OR Tertile 3 verse tertile 1 = 4.31, 95% CI: 2.62-7.08). The area under the receiver operating characteristics curve for the predictive model was 0.74 and 0.77 for crude model and model adjusted for age, sex, and race. CONCLUSION: The model based on selected risk factors may be used to identify high risk individuals with cognitive impairment.

Predictive Role of Platelet and Inflammation Markers for Severe COVID-19 by Propensity Score Matching.

BACKGROUND: This study aims to ascertain the predictive value of platelet and inflammation markers in severe cases of COVID-19. METHODS: A retrospective real-world cohort study was conducted using propensity score matching (PSM). Patients were classified into severe and non-severe COVID-19 groups based on the severity of the disease, and the correlation between severe COVID-19 and laboratory parameters at admission was analyzed. RESULTS: The study included 397 adult patients, comprising 212 (53%) males and 185 (47%) females. Among these, 309 were non-severe and 88 were severe cases. The severe group had a higher median age than the non-severe group (60 vs. 42 years, p < 0.001). Independent risk factors for severe COVID-19 included age, diabetes comorbidity, fever, respiratory symptoms, platelet count, high-sensitivity C-reactive protein (hsCRP), interleukin-6 (IL-6), and the ratio of arterial oxygen partial pressure (PaO2) to the fraction of inspired oxygen (FiO2) (P/F ratio). After one-to-one PSM, adjusted for age, diabetes comorbidities, fever, and respiratory symptoms, significant differences in laboratory parameters at admission were observed. Compared to the non-severe group (n = 71), in the severe group (n = 71), elevated levels of hsCRP (median: 27.1 mg/L vs. 14.6 mg/L, p = 0.005) and IL-6 (median: 16.2 pg/mL vs. 15.3 pg/mL, p = 0.005) were observed, while platelet count (164 ± 36 × 109 vs. 180 ± 50 × 109, p = 0.02) and P/F ratio (median: 351 vs. 397, p = 0.001) were reduced. CONCLUSIONS: Elevated levels of hsCRP and IL-6, along with reduced platelet count and P/F ratio at admission, were significantly associated with severe COVID-19 and may serve as predictive indicators.

A new nomogram to predict in-hospital mortality in patients with acute decompensated chronic heart failure and diabetes after 48 Hours of Intensive Care Unit.

BACKGROUND: The study set out to develop an accurate and clinically valuable prognostic nomogram to assess the risk of in-hospital death in patients with acute decompensated chronic heart failure (ADCHF) and diabetes. METHODS: We extracted clinical data of patients diagnosed with ADCHF and diabetes from the Medical Information Mart for Intensive Care III database. Risk variables were selected utilizing least absolute shrinkage and selection operator regression analysis, and were included in multivariate logistic regression and presented in nomogram. bootstrap was used for internal validation. The discriminative power and predictive accuracy of the nomogram were estimated using the area under the receiver operating characteristic curve (AUC), calibration curve and decision curve analysis (DCA). RESULTS: Among 867 patients with ADCHF and diabetes, In-hospital death occurred in 81 (9.3%) patients. Age, heart rate, systolic blood pressure, red blood cell distribution width, shock, ß-blockers, angiotensin converting enzyme inhibitors or angiotensin receptor blockers, assisted ventilation, and blood urea nitrogen were brought into the nomogram model. The calibration curves suggested that the nomogram was well calibrated. The AUC of the nomogram was 0.873 (95% CI: 0.834-0.911), which was higher that of the Simplified Acute Physiology Score II [0.761 (95% CI: 0.711-0.810)] and sequential organ failure assessment score [0.699 (95% CI: 0.642-0.756)], and Guidelines-Heart Failure score [0.782 (95% CI: 0.731-0.835)], indicating that the nomogram had better ability to predict in-hospital mortality. In addition, the internally validated C-index was 0.857 (95% CI: 0.825-0.891), which again verified the validity of this model. CONCLUSIONS: This study constructed a simple and accurate nomogram for predicting in-hospital mortality in patients with ADCHF and diabetes, especially in those who admitted to the intensive care unit for more than 48 hours, which contributed clinicians to assess the risk and individualize the treatment of patients, thereby reducing in-hospital mortality.

Variation in the relationship between fasting glucose and HbA1c: implications for the diagnosis of diabetes in different age and ethnic groups.

INTRODUCTION: Identify non-glycemic factors affecting the relationship between fasting plasma glucose (FPG) and glycated hemoglobin (HbA1c), in order to refine diabetes diagnostic criteria. RESEARCH DESIGN AND METHODS: Relationship between FPG-HbA1c was assessed in 12 531 individuals from 2001 to 2018 US National Health and Nutrition Examination Survey. Using a recently described method, FPG and HbA1c were used to calculate apparent glycation ratio (AGR) of red blood cells for different subgroups based on age, race, and gender. RESULTS: At an FPG of 7 mmol/L, black individuals had a higher HbA1c (p<0.001, mean: 50.2 mmol/mol, 95% CI (49.8 to 50.4)) compared with white individuals (47.4 mmol/mol (47.2 to 47.5)). This corresponds to NGSP (National Glycohemoglobin Standardization Program) units of 6.7% and 6.5% for black versus white individuals, respectively. Similarly, individuals under 21 years had lower HbA1c (p<0.001, 47.9 mmol/mol (47.7 to 48.1), 6.5%) compared with those over 50 years (48.3 mmol/mol (48.2 to 48.5), 6.6%). Differences were also observed between women (p<0.001, 49.2 mmol/mol (49.1 to 49.3), 6.7%) and men (47.0 mmol/mol (46.8 to 47.1), 6.5%). Of note, the difference in HbA1c at FPG of 7 mmol/L in black females over 50 and white males under 21 years was 5 mmol/mol (0.46%). AGR differences according to race (p<0.001), age (p<0.001), and gender (p<0.001) explained altered glucose-HbA1c relationship in the analyzed groups. CONCLUSIONS: FPG-HbA1c relationship is affected by non-glycemic factors leading to incorrect diagnosis of diabetes in some individuals and ethnic groups. Assessment of AGR helps understand individual-specific relationship between glucose levels and HbA1c, which has the potential to more accurately diagnose and manage diabetes.

Evidence that tirzepatide protects against diabetes-related cardiac damages.

BACKGROUND: Glucagon-like peptide-1 receptor agonists (GLP-1RAs) are effective antidiabetic drugs with potential cardiovascular benefits. Despite their well-established role in reducing the risk of major adverse cardiovascular events (MACE), their impact on heart failure (HF) remains unclear. Therefore, our study examined the cardioprotective effects of tirzepatide (TZT), a novel glucose-dependent insulinotropic polypeptide (GIP) and glucagon-like peptide 1 (GLP-1) receptor agonist. METHODS: A three-steps approach was designed: (i) Meta-analysis investigation with the primary objective of assessing major adverse cardiovascular events (MACE) occurrence from major randomized clinical trials.; (ii) TZT effects on a human cardiac AC16 cell line exposed to normal (5 mM) and high (33 mM) glucose concentrations for 7 days. The gene expression and protein levels of primary markers related to cardiac fibrosis, hypertrophy, and calcium modulation were evaluated. (iii) In silico data from bioinformatic analyses for generating an interaction map that delineates the potential mechanism of action of TZT. RESULTS: Meta-analysis showed a reduced risk for MACE events by TZT therapy (HR was 0.59 (95% CI 0.40-0.79, Heterogeneity: r2 = 0.01, I2 = 23.45%, H2 = 1.31). In the human AC16 cardiac cell line treatment with 100 nM TZT contrasted high glucose (HG) levels increase in the expression of markers associated with fibrosis, hypertrophy, and cell death (p < 0.05 for all investigated markers). Bioinformatics analysis confirmed the interaction between the analyzed markers and the associated pathways found in AC16 cells by which TZT affects apoptosis, fibrosis, and contractility, thus reducing the risk of heart failure. CONCLUSION: Our findings indicate that TZT has beneficial effects on cardiac cells by positively modulating cardiomyocyte death, fibrosis, and hypertrophy in the presence of high glucose concentrations. This suggests that TZT may reduce the risk of diabetes-related cardiac damage, highlighting its potential as a therapeutic option for heart failure management clinical trials. Our study strongly supports the rationale behind the clinical trials currently underway, the results of which will be further investigated to gain insights into the cardiovascular safety and efficacy of TZT.

Sex-specific trends in incidence of first myocardial infarction among people with and without diabetes between 1985 and 2016 in a German region.

BACKGROUND: The reduction of myocardial infarction (MI) and narrowing the gap between the populations with and without diabetes are important goals of diabetes care. We analyzed time trends for sex-specific incidence rates (IR) of first MI (both non-fatal MI and fatal MI) as well as separately for first non-fatal MI and fatal MI in the population with and without diabetes. METHODS: Using data from the KORA myocardial infarction registry (Augsburg, Germany), we estimated age-adjusted IR in people with and without diabetes, corresponding relative risks (RR), and time trends from 1985 to 2016 using Poisson regression. RESULTS: There were 19,683 people with first MI (34% fatal MI, 71% men, 30% with diabetes) between 1985 and 2016. In the entire study population, the IR of first MI decreased from 359 (95% CI: 345-374) to 236 (226-245) per 100,000 person years. In men with diabetes, IR decreased only in 2013-2016. This was due to first non-fatal MI, where IR in men with diabetes increased until 2009-2012, and slightly decreased in 2013-2016. Overall, fatal MI declined stronger than first non-fatal MI corresponding to IRs. The RR of first MI substantially increased among men from 1.40 (1.22-1.61) in 1985-1988 to 2.60 (2.26-2.99) in 1997-2000 and moderately decreased in 2013-2016: RR: 1.75 (1.47-2.09). Among women no consistent time trend for RR was observed. Time trends for RR were similar regarding first non-fatal MI and fatal MI. CONCLUSIONS: Over the study period, we found a decreased incidence of first MI and fatal MI in the entire study population. The initial increase of first non-fatal MI in men with diabetes needs further research. The gap between populations with and without diabetes remained.

Clinical, glycometric features and treatment in a family with monogenic diabetes due to a new mutation in the insulin gene.

Monogenic diabetes caused by changes in the gene that encodes insulin (INS) is a very rare form of monogenic diabetes (<1%). The aim of this work is to describe the clinical and glycaemic control characteristics over time from four members of a family diagnosed with monogenic diabetes with the novel mutation: c.206del,p.(Gly69Aalfs*62) located in exon 3 of the gene INS. 75% are females, with debut in adolescence and negative autoimmunity. In all cases, C-peptide is detectable decades after diagnosis (>0.6ng/ml). Currently, patients are being treated either with insulin in a bolus-basal regimen, oral antidiabetics or hybrid closed loop system. Monogenic diabetes due to mutation in the INS is an entity with heterogeneous presentation, whose diagnosis requires high suspicion and presents an important clinical impact. Given the lack of standards in this regard, therapy must be individualized, although insulin therapy could help preserve beta cell functionality in these subjects.

Co-creation process of an intervention to implement a multiparameter point-of-care testing device in a primary healthcare setting for non-communicable diseases in Peru.

BACKGROUND: Point-of-care testing (POCT) devices are diagnostic tools that can provide quick and accurate results within minutes, making them suitable for diagnosing non-communicable diseases (NCDs). However, these devices are not widely implemented in healthcare systems and for this reason is relevant to understand the implementation process. AIM: To describe the process and define a strategy to implement a multiparameter POCT device for diagnosing and managing NCDs in one region of Peru. METHODS: A descriptive and non-experimental study, using the participatory methodologies of co-creation process. It was conducted in one region of Peru (Tumbes) to design an intervention for implementing a multiparameter POCT device. Two co-creation sessions were conducted involving five groups: community members, primary healthcare workers, these groups in both rural and urban settings, and regional decision-makers. These sessions included activities to understand patient journeys in receiving care for NCDs, identify facilitators and barriers to POCT devices usage, and define an implementation strategy for POCT devices in both rural and urban settings of Tumbes. The research team analysed the data and summarized key topics for discussion after each session. RESULTS: A total of 78 participants were enrolled across the five groups. Among community members: 22.2% had only diabetes, 24.1% had only hypertension, and 18.5% had both diagnoses. In the patient journey, community members mentioned that it took at least three days to receive a diagnosis and treatment for an NCD. Most of the participants agreed that the POCT devices would be beneficial for their communities, but they also identified some concerns. The strategy for POCT devices implementation included healthcare workers training, POCT devices must be placed in the laboratory area and must be able to perform tests for glucose, glycated haemoglobin, cholesterol, and creatinine. Advertising about POCT devices should be displayed at the healthcare centres and the municipality using billboards and flyers. CONCLUSIONS: The co-creation process was useful to develop strategies for the implementation of multiparameter POCT devices for NCDs, involving the participation of different groups of stakeholders guided by moderators in both, rural and urban, settings in Peru.

Epidemiology of diabetes mellitus in Pakistan: a systematic review protocol.

INTRODUCTION: Diabetes mellitus (DM) is a chronic metabolic disorder characterised by hyperglycaemia resulting from defects in insulin secretion, insulin action or both. As a major global health concern, its prevalence has been steadily increasing. Pakistan, is no exception to this trend, facing a growing burden of non-communicable diseases including DM. This research aims to comprehensively assess the prevalence of DM, and disparities between rural and urban populations as well as between men and women in Pakistan. METHODS AND ANALYSIS: The systematic review will follow Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines and will aim to assess DM prevalence in Pakistan. A comprehensive search strategy will be applied to databases like PubMed, Scopus, Cochrane, PakMediNet and CINAHL from inception up to 1st April 2024. We will include studies that focus on diabetes prevalence in the general population, employing WHO or American Diabetes Association criteria for diagnosis of DM. Cross-sectional studies, cohort studies and population-based surveys with a sample size ≥500, in English will be considered. Data extraction will be done as per a predefined proforma which will include study details such as demographics, prevalence data and methodology. A meta-analysis will be performed using a random effect model with an inverse variance weighted method. I2 statistics will be used to examine heterogeneity, and subgroup analyses will be performed. ETHICS AND DISSEMINATION: The findings from the systematic review will be shared by publishing them in a peer-reviewed journal and showcasing them at pertinent conferences. Our analysis will be based on aggregated data and will not involve individual patient information, thus eliminating the need for ethical clearance. PROSPERO REGISTRATION NUMBER: CRD42023453085.

Glycated albumin in the detection of diabetes during COVID-19 hospitalization.

BACKGROUND: Diabetes has emerged as an important risk factor for COVID-19 adverse outcomes during hospitalization. We investigated whether the measurement of glycated albumin (GA) may be useful in detecting newly diagnosed diabetes during COVID-19 hospitalization. METHODS: In this cross-sectional test accuracy study we evaluated HCPA Biobank data and samples from consecutive in-patients, from 30 March 2020 to 20 December 2020. ROC curves were used to analyse the performance of GA to detect newly diagnosed diabetes (patients without a previous diagnosis of diabetes and admission HbA1c ≥6.5%). RESULTS: A total of 184 adults (age 58.6 ± 16.6years) were enrolled, including 31 with newly diagnosed diabetes. GA presented AUCs of 0.739 (95% CI 0.642-0.948) to detect newly diagnosed diabetes. The GA cut-offs of 19.0% was adequate to identify newly diagnosed diabetes with high specificity (85.0%) but low sensitivity (48.4%). CONCLUSIONS: GA showed good performance to identify newly diagnosed diabetes and may be useful for identifying adults with the condition in COVID-19-related hospitalization.

Knowledge, attitude, and practice of pre-diabetic older people regarding pre-diabetes.

BACKGROUND: One of the risk factors of diabetes is the pre-diabetes stage which is significantly prevalent in older people. Knowledge, attitude, and practice of the pre-diabetic stage are of great importance and can decrease complications. The present study aimed to determine the knowledge, attitude, and practice of the pre-diabetic older people. METHODS: This cross-sectional study was conducted from April 2022 to August 2022 on 219 pre-diabetic older people referring to Sina Hospital in Tabriz, one of the most populated cities in the northwest of Iran. Data were collected using questionnaires of Knowledge, Attitude, Practice-Prediabetes Assessment Questionnaire (KAP-PAQ). The data were analyzed by SPSS 21. RESULTS: The mean scores of knowledge (in the range of 0-17), attitude (in the range of -10, + 10), and practice (in the range of 0-26) were 1.72 ± 1.0, 2.24 ± 1.92, and 5.76 ± 2.61, respectively. The older people's knowledge and practice levels in the pre-diabetes stage were low and about 50% of them had negative views. According to the Spearman correlation test, there was a positive significant relationship between the older people's knowledge and practice (p < 0.001, r = 0.234). CONCLUSIONS: The older people in the pre-diabetes stage had low knowledge and attitude and a negative viewpoint towards correcting lifestyle on diet, exercising and physical activity, weight control, diagnostic and screening methods. Increased knowledge about pre-diabetes and strengthened positive attitude towards correcting lifestyle through counseling as well as empowering the pre-diabetic older people can increase the efficiency of pre-diabetes prevention and control programs and prevent its progression to the diabetes stage.

The association between quality of life and diabetes: the Bushehr Elderly Health Program.

BACKGROUND AND OBJECTIVE: Considering the importance of diabetes and its increased prevalence with aging, this study aimed to evaluate the association between diabetes status and quality of life (QOL) and the determining factors in individuals over 60. METHODS: Two thousand three hundred seventy-five individuals including 819 (34.5%) with diabetes, aged 69.4 ± 6.4, from Bushehr Elderly Health Program (BEHP) were enrolled. We categorized the participants as non-diabetic, controlled diabetic, and poorly controlled diabetic. The QOL was assessed using the SF-12 questionnaire. The physical (PCS) and mental (MCS) component summaries of QOL were estimated. We compared the SF-12 domains and components between the categories using ANOVA. Further, the association of diabetes status with PCS and MCS was assessed after adjustment for possible confounders including age, sex, depression, cognitive impairment, physical activity, and other relevant factors using linear regression analysis. RESULTS: Individuals with diabetes had lower PCS (40.9 ± 8.8 vs. 42.7 ± 8.6, p-value < 0.001), and MCS scores (45.0 ± 10.2 vs. 46.4 ± 9.4, p-value < 0.001) compared to participants without diabetes. No significant differences were observed in PCS or MCS scores between controlled or poorly controlled individuals with diabetes. Diabetes status was associated with PCS and MCS scores in univariable analysis. Regarding physical component of QOL, after adjusting for other confounders, poorly controlled diabetes was significantly associated with PCS [beta: -1.27 (-2.02, -0.52)]; some other determinants include depression [-7.66 (-8.51, -6.80)], male sex [3.90 (3.24,4.57)], and good physical activity [1.87 (1.17,2.57)]. As for the mental component, controlled diabetes was significantly associated with MCS [-1.17 (-2.13, -0.22)]; other contributing factors include depression [-14.35 (-15.34, -13.37)], male sex [1.97 (1.20,2.73)], good physical activity [-1.55 (-2.35, -0.75)], and smoking [-1.42 (-2.24, -0.59)]. BMI had an inverse association with PCS [-0.19 (-0.26, -0.13)] and a direct association with MCS [0.14 (0.07,0.21)]. CONCLUSION: Individuals with diabetes exhibited reduced QOL scores. Upon adjusting for other variables, it was found that uncontrolled diabetes correlated with decreased PCS scores, whereas controlled diabetes was linked to lower MCS scores. Factors such as depression and being female were identified as contributors to diminished QOL in both physical and mental aspects. These results have the potential to guide healthcare decision-making, facilitating the creation of tailored interventions aimed at improving the QOL for individuals with diabetes, with a specific focus on women and depression.

Technological Advances of Wearable Device for Continuous Monitoring of In Vivo Glucose.

Managing diabetes is a chronic challenge today, requiring monitoring and timely insulin injections to maintain stable blood glucose levels. Traditional clinical testing relies on fingertip or venous blood collection, which has facilitated the emergence of continuous glucose monitoring (CGM) technology to address data limitations. Continuous glucose monitoring technology is recognized for tracking long-term blood glucose fluctuations, and its development, particularly in wearable devices, has given rise to compact and portable continuous glucose monitoring devices, which facilitates the measurement of blood glucose and adjustment of medication. This review introduces the development of wearable CGM-based technologies, including noninvasive methods using body fluids and invasive methods using implantable electrodes. The advantages and disadvantages of these approaches are discussed as well as the use of microneedle arrays in minimally invasive CGM. Microneedle arrays allow for painless transdermal puncture and are expected to facilitate the development of wearable CGM devices. Finally, we discuss the challenges and opportunities and look forward to the biomedical applications and future directions of wearable CGM-based technologies in biological research.