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 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.

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.

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.

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.

Association between diabetes mellitus and primary restenosis following endovascular treatment: a comprehensive meta-analysis of randomized controlled trials.

IMPORTANCE: Diabetes mellitus (DM) is thought to be closely related to arterial stenotic or occlusive disease caused by atherosclerosis. However, there is still no definitive clinical evidence to confirm that patients with diabetes have a higher risk of restenosis. OBJECTIVE: This meta-analysis was conducted to determine the effect of DM on restenosis among patients undergoing endovascular treatment, such as percutaneous transluminal angioplasty (PTA) or stenting. DATA SOURCES AND STUDY SELECTION: The PubMed/Medline, EMBASE and Cochrane Library electronic databases were searched from 01/1990 to 12/2022, without language restrictions. Trials were included if they satisfied the following eligibility criteria: (1) RCTs of patients with or without DM; (2) lesions confined to the coronary arteries or femoral popliteal artery; (3) endovascular treatment via PTA or stenting; and (4) an outcome of restenosis at the target lesion site. The exclusion criteria included the following: (1) greater than 20% of patients lost to follow-up and (2) a secondary restenosis operation. DATA EXTRACTION AND SYNTHESIS: Two researchers independently screened the titles and abstracts for relevance, obtained full texts of potentially eligible studies, and assessed suitability based on inclusion and exclusion criteria.. Disagreements were resolved through consultation with a third researcher. Treatment effects were measured by relative ratios (RRs) with 95% confidence intervals (CIs) using random effects models. The quality of the evidence was assessed using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) criteria. MAIN OUTCOMES AND MEASURES: The main observation endpoint was restenosis, including > 50% stenosis at angiography, or TLR of the primary operation lesion during the follow-up period. RESULTS: A total of 31,066 patients from 20 RCTs were included. Patients with DM had a higher risk of primary restenosis after endovascular treatment (RR = 1.43, 95% CI: 1.25-1.62; p = 0.001). CONCLUSIONS AND RELEVANCE: This meta-analysis of all currently available RCTs showed that patients with DM are more prone to primary restenosis after endovascular treatment.

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.

Role of remnant cholesterol in the relationship between physical activity and diabetes mellitus: an intermediary analysis.

Objective: The purpose of this investigation was to evaluate the potential link between physical activity (PA) and the heightened susceptibility to diabetes mellitus (DM), by examining whether remnant cholesterol (RC) might act as a mediator in this correlation. Methods: The research utilized data from the National Health and Nutrition Examination Survey, spanning from 2005 to 2018. Various statistical analyses were conducted for continuous and categorical variables, including the t-test, ANOVA, and χ2 test. Logistic regression was employed to analyze the association between PA and DM across three distinct models. Mediation analysis was also conducted to assess the potential mediation effects of RC. Results: The study encompassed a total of 9,149 participants, and it was observed that individuals with DM exhibited lower levels of PA. Furthermore, PA levels were found to be associated with all participant characteristics except poverty income ratio, fasting blood glucose, and HOMA-IR (p < 0.05). After adjusting for covariates (Model 3), individuals with high PA levels demonstrated a decreased likelihood of developing DM compared to those in the low PA group (OR: 0.73, 95%CI: 0.54-0.99). A significant dose-response relationship was identified (p < 0.05). No interaction between PA and RC in relation to DM risk was detected, and RC was found to serve as a mediator in the connection between PA and DM. After considering covariates, the mediating effect of RC between PA and DM weakens. Discussion: Our findings suggest that higher levels of PA are linked to a reduced risk of DM in U.S. adults, with RC likely playing a mediating role.

Construction of Biosensing System for Glycated Albumin Using an Electron Transfer Peptide-Modified Protein Probe.

Glycated albumin (GA) is one of the proteins that replaces several sugar moieties and can be used as an indicator of diabetes mellitus. We developed a sensing system that uses GA in the early detection of diabetes mellitus. In this study, H6Y4C acetylated (Ac-) at the N-terminals of the peptide was combined with wheat germ agglutinin (WGA) to recognize glucose moieties. The Ac-H6Y4C-WGA was constructed as a GA-sensing probe. The tyrosine residues of Y4C exhibited an oxidation peak, and His-tag moieties were introduced to separate Ac-H6Y4C-WGA in the synthesis of the probe. The Ac-H6Y4C-WGA probe binds with the 1-2 molecules of Ac-H6Y4C per WGA using matrix assisted laser desorption/ionization-time of flight (MALDI-TOF)-MS. Next, the functions of Ac-H6Y4C-WGA were evaluated using voltammetry. The number of electron-transfers was calculated based on the relationship between the peak potential and logarithm of scan rate and was 3.03. In the electrochemical measurements with mannose and bovine serum albumin, the peak currents were similar to that of GA alone. By contrast, a decrease in the peak current was suppressed when glucose was added to the solution containing the probe. As a result, Ac-H6Y4C-WGA was selectively bound to the glucose moieties of GA. The calibration curve via differential pulse voltammetry was proportional to the concentrations of GA and ranged from 1.0 × 10-12 to 2.0 × 10-11 M with a detection limit of 3.3 × 10-13 M.

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.

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.

Interventions for Pancreatitis-New Approaches, Knowledge Gaps, and Research Opportunities: Summary of a National Institute of Diabetes and Digestive and Kidney Diseases Workshop.

ABSTRACT: There exists no cure for acute, recurrent acute or chronic pancreatitis and treatments to date have been focused on managing symptoms. A recent workshop held by the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) focused on interventions that might disrupt or perhaps even reverse the natural course of this heterogenous disease, aiming to identify knowledge gaps and research opportunities that might inform future funding initiatives for NIDDK. The breadth and variety of identified active or planned clinical trials traverses the spectrum of the disease and was conceptually grouped for the workshop into behavioral, nutritional, pharmacologic and biologic, and mechanical interventions. Cognitive and other behavioral therapies are proven interventions for pain and addiction, but barriers exist to their use. Whilst a disease specific instrument quantifying pain is now validated, an equivalent is lacking for nutrition - and both face challenges in ease and frequency of administration. Multiple pharmacologic agents hold promise. Ongoing development of Patient Reported Outcome (PRO) measurements can satisfy Investigative New Drug (IND) regulatory assessments. Despite multiple randomized clinical trials demonstrating benefit, great uncertainty remains regarding patient selection, timing of intervention, and type of mechanical intervention (endoscopic versus surgery). Challenges and opportunities to establish beneficial interventions for patients were identified.

Contact Allergy to Allergens in the Swedish Baseline Series Overrepresented in Diabetes Patients with Skin Reactions to Medical Devices - A Retrospective Study from Southern Sweden.

Allergic contact dermatitis is reported among individuals using continuous glucose monitoring systems and insulin pumps. The aim of this study was to describe contact allergy patterns for allergens in the Swedish baseline series and medical device-related allergens among users. Contact allergy to baseline series allergens and isobornyl acrylate was compared between diabetes patients and dermatitis patients patch-tested at the Department of Occupational and Environmental Dermatology during 2017 to 2020. Fifty- four diabetes patients and 2,567 dermatitis patients were included. The prevalence of contact allergy to fragrance mix II and sesquiterpene lactone mix was significantly higher in diabetes patients compared with dermatitis patients. Of the diabetes patients 13.0% and of the dermatitis patients 0.5% tested positive to sesquiterpene lactone mix (p < 0.001). Of the diabetes patients 7.4% and of the dermatitis patients 2.3% tested positive to fragrance mix II (p = 0.041). Of the diabetes patients 70.4% tested positive to medical device-related allergens. Of the diabetes patients 63.0% and of the dermatitis patients 0.2% were allergic to isobornyl acrylate (p < 0.001). In conclusion, not only medical device-related contact allergies, but also contact allergy to baseline series allergens (fragrance mix II and sesquiterpene lactone mix), is overrepresented in diabetes patients who use medical devices.

Determinants of health and mortality in undiagnosed diabetes: A nationally representative US adult, 2011-2020.

AIM: This study examines the determinants of health and mortality associated with undiagnosed diabetes among a nationally representative sample of US adults. METHODS: Data are from the National Health and Nutrition Examination Survey between 2011 and 2012 and 2019-2020. Diabetes status is categorized into three groups: undiagnosed diabetes, diagnosed diabetes, and no diabetes. Multiple logistic regression is used to estimate the association between undiagnosed diabetes and three domains of risk factors, including sociodemographic and health behavioral and clinical factors. Cox proportional hazards models are performed to compare excess mortality risk between the three groups. RESULTS: Young adults, racial minorities, the foreign-born, and individuals with limited access to health care are more likely to be unaware of their diabetes. Moreover, adults without a family history of diabetes and chronic conditions have a higher chance of undiagnosed diabetes. No health behavioral factors are found to be associated with undiagnosed diabetes. Adults with undiagnosed diabetes have a lower risk of all-cause and cardiovascular disease mortality compared to those with diagnosed diabetes, but a higher risk of all-cause mortality than those with no diabetes. CONCLUSION: Targeted public health approaches should address sociodemographic and clinical factors to reduce the burden of undiagnosed diabetes.

Early glucose abnormalities revealed by continuous glucose monitoring associate with lung function decline in cystic fibrosis: A five-year prospective study.

BACKGROUND: Cystic fibrosis related diabetes (CFRD) is commonly associated with declining lung function and nutritional status. We aimed to evaluate the pulmonary impact of early glucose abnormalities by using 2-h standard oral glucose tolerance testing (OGTT) and continuous glucose monitoring (CGM) in people with cystic fibrosis (PwCF). METHODS: PwCF aged ≥10 years old without known CFRD were included in a five-year prospective multicentre study. Annual evaluation of nutritional status, lung function, OGTT and CGM was set up. Associations between annual rate changes (Δ) in lung function, ΔFEV1 (forced expiratory volume in 1 s) percentage predicted (pp) and ΔFVC (forced vital capacity) pp., and annual rate changes in OGTT or CGM variables were estimated with a mixed model with a random effect for subject. RESULTS: From 2009 to 2016, 112 PwCF (age: 21 ± 11 years, BMI (body mass index) z-score: -0.55 ± 1.09, FEV1pp: 77 ± 24 %, 2-h OGTT glucose: 122 ± 44 mg/dL, AUC (area under curve) >140 mg/dL: 1 mg/dL/day (0.2, 3.0) were included. A total of 428 OGTTs and 480 CGMs were collected. The participants presented annual decline of FVCpp and FEV1pp at -1.0 % per year (-1.6, -0.4), p < 0.001 and - 1.9 % per year (-2.5, -1.3), p < 0.001 respectively without change in BMI z-score during the study. Variation of two-hour OGTT glucose was not associated with declining lung function, as measured by ΔFEV1pp (p = 0.94) and ΔFVCpp (p = 0.90). Among CGM variables, only increase in AUC >140 mg/dL between two annual visits was associated with a decrease in ΔFVCpp (p < 0.05) and ΔFEV1pp (p < 0.05). CONCLUSIONS: This prospective study supports the fact that early glucose abnormalities revealed by CGM predict pulmonary function decline in PwCF, while 2-h standard OGTT glucose is not associated with pulmonary impairment.

Screening for Coronary Artery Disease in Asymptomatic Pilots with Diabetes Mellitus.

INTRODUCTION: Coronary artery disease (CAD) is a cause of death in 75% of patients with diabetes. Its often asymptomatic nature delays diagnosis. In aeronautics, it can cause in-flight incapacitation, beyond which it represents a major fear for the medical expert. Screening for CAD is still a topical subject with the advent of new cardiovascular (CV) risk biomarkers and more effective screening tests. We report the experience of the Aeromedical Expertise Center of Rabat in this screening of diabetic pilots, with a recommendations review.METHODS: A prospective study over 1 yr included diabetic pilots who benefited from systematic screening for CAD after a CV risk stratification. Coronary angiography is performed if a screening test is positive. Subsequent follow-up is carried out in consultation with the attending physician with regular evaluation in our center.RESULTS: There were 38 pilots included in our study. The average age was 55 ± 4.19 yr and about 73% had a high CV risk. CAD was detected in 4 cases (10.52%) who had abnormal resting electrocardiograms and required revascularization with the placement of active stents. Approximately 75% of pilots with CAD returned to fly through a waiver with restrictions.DISCUSSION: Screening for coronary disease in diabetics is controversial, and current recommendations are not unanimous. In our study, the screening did not identify coronary diabetic pilots who could benefit from bypass surgery. Nevertheless, coronary disease was diagnosed, justifying grounding to preserve flight safety, which is an absolute priority in aviation medicine.Zerrik M, Moumen A, El Ghazi M, Smiress FB, Iloughmane Z, El M'hadi C, Chemsi M. Screening for coronary artery disease in asymptomatic pilot with diabetes mellitus. Aerosp Med Hum Perform. 2024; 95(4):200-205.