In the field of human health research, the homeostasis of copper (Cu) is receiving increased attention due to its connection to pathological conditions, including diabetes mellitus (DM). Recent studies have demonstrated that proteins associated with Cu homeostasis, such as ATOX1, FDX1, ATP7A, ATPB, SLC31A1, p53, and UPS, also contribute to DM. Cuproptosis, characterized by Cu homeostasis dysregulation and Cu overload, has been found to cause the oligomerization of lipoylated proteins in mitochondria, loss of iron-sulfur protein, depletion of glutathione, production of reactive oxygen species, and cell death. Further research into how cuproptosis affects DM is essential to uncover its mechanism of action and identify effective interventions. In this article, we review the molecular mechanism of Cu homeostasis and the role of cuproptosis in the pathogenesis of DM. The study of small-molecule drugs that affect these proteins offers the possibility of moving from symptomatic treatment to treating the underlying causes of DM.

BACKGROUND: The COVID-19 pandemic created unprecedented challenges for people with type 2 diabetes (T2D) and prediabetes to access in-person health care support. Primary care teams accelerated plans to implement digital health technologies (DHTs), such as remote consultations and digital self-management. There is limited evidence about whether there were inequalities in how people with T2D and prediabetes adjusted to these changes.
OBJECTIVE: This study aimed to explore how people with T2D and prediabetes adapted to the reduction in in-person health support and the increased provision of support through DHTs during the COVID-19 pandemic and beyond.
METHODS: A purposive sample of people with T2D and prediabetes was recruited by text message from primary care practices that served low-income areas. Semistructured interviews were conducted by phone or video call, and data were analyzed thematically using a hybrid inductive and deductive approach.
RESULTS: A diverse sample of 30 participants was interviewed. There was a feeling that primary care had become harder to access. Participants responded to the challenge of accessing support by rationing or delaying seeking support or by proactively requesting appointments. Barriers to accessing health care support were associated with issues with using the total triage system, a passive interaction style with health care services, or being diagnosed with prediabetes at the beginning of the pandemic. Some participants were able to adapt to the increased delivery of support through DHTs. Others had lower capacity to use DHTs, which was caused by lower digital skills, fewer financial resources, and a lack of support to use the tools.
CONCLUSIONS: Inequalities in motivation, opportunity, and capacity to engage in health services and DHTs lead to unequal possibilities for people with T2D and prediabetes to self-care and receive care during the COVID-19 pandemic. These issues can be addressed by proactive arrangement of regular checkups by primary care services and improving capacity for people with lower digital skills to engage with DHTs.

BACKGROUND: Pasteurized human donor milk (DM) is frequently used for feeding preterm newborns and extrauterine growth-restricted (EUGR) infants. Most human milk banks performed a pasteurization of DM using the standard method of Holder pasteurization (HoP) which consists of heating milk at 62.5°C for 30 min. High hydrostatic pressure (HHP) processing was proposed to be an innovative nonthermal method to pasteurize DM. However, the effect of different modes of DM pasteurization on body growth, intestinal maturation, and microbiota has never been investigated in vivo during the lactation.
OBJECTIVE: We aimed to study these effects in postnatally growth-restricted (PNGR) mice pups daily supplemented with HoP-DM or HHP-DM.
METHODS: PNGR was induced by increasing the number of pups per litter (15 pups/mother) at postnatal Day 4 (PND4). From PND8 to PND20, mice pups were supplemented with HoP-DM or HHP-DM. At PND21, the intestinal permeability was measured in vivo, the intestinal mucosal histology, gut microbiota, and short-chain fatty acids (SCFAs) level were analyzed.
RESULTS: HHP-DM pups displayed a significantly higher body weight gain than HoP-DM pups during lactation. At PND21, these two types of human milk supplementations did not differentially alter intestinal morphology and permeability, the gene-expression level of several mucosal intestinal markers, gut microbiota, and the caecal SCFAs level.
CONCLUSIONS: Our data suggest that HHP could be an attractive alternative to HoP and that HHP-DM may ensure a better body growth of preterm and/or EUGR infants.

UNASSIGNED: Regular physical activity and exercise are fundamental components of a healthy lifestyle for youth living with type 1 diabetes (T1D). Yet, few youth living with T1D achieve the daily minimum recommended levels of physical activity. For all youth, regardless of their disease status, minutes of physical activity compete with other daily activities, including digital gaming. There is an emerging area of research exploring whether digital games could be displacing other physical activities and exercise among youth, though, to date, no studies have examined this question in the context of youth living with T1D.
UNASSIGNED: We examined characteristics of digital gaming versus nondigital gaming (other exercise) sessions and whether youth with T1D who play digital games (gamers) engaged in less other exercise than youth who do not (nongamers), using data from the Type 1 Diabetes Exercise Initiative Pediatric study.
UNASSIGNED: During a 10-day observation period, youth self-reported exercise sessions, digital gaming sessions, and insulin use. We also collected data from activity wearables, continuous glucose monitors, and insulin pumps (if available).
UNASSIGNED: The sample included 251 youths with T1D (age: mean 14, SD 2 y; self-reported glycated hemoglobin A1c level: mean 7.1%, SD 1.3%), of whom 105 (41.8%) were female. Youth logged 123 digital gaming sessions and 3658 other exercise (nondigital gaming) sessions during the 10-day observation period. Digital gaming sessions lasted longer, and youth had less changes in glucose and lower mean heart rates during these sessions than during other exercise sessions. Youth described a greater percentage of digital gaming sessions as low intensity (82/123, 66.7%) when compared to other exercise sessions (1104/3658, 30.2%). We had 31 youths with T1D who reported at least 1 digital gaming session (gamers) and 220 youths who reported no digital gaming (nongamers). Notably, gamers engaged in a mean of 86 (SD 43) minutes of other exercise per day, which was similar to the minutes of other exercise per day reported by nongamers (mean 80, SD 47 min).
UNASSIGNED: Digital gaming sessions were longer in duration, and youth had less changes in glucose and lower mean heart rates during these sessions when compared to other exercise sessions. Nevertheless, gamers reported similar levels of other exercise per day as nongamers, suggesting that digital gaming may not fully displace other exercise among youth with T1D.

UNASSIGNED: Gestational diabetes mellitus (GDM), a chronic condition leading to glucose intolerance during pregnancy, is common in low- and middle-income countries, posing health risks to both the mother and fetus. Limited studies have been done in Ethiopia, especially using WHO\'s 2013 universal screening criteria. Therefore, this study aimed to evaluate the risk factors linked to GDM in women attending antenatal (ANC) clinics in Hawassa town public health institutions, located in the Sidama regional state of Ethiopia.
UNASSIGNED: An Unmatched case-control study was carried out in Ethiopia\'s Sidama Region from April 1st to June 10th, 2023, involving 510 pregnant women. The Oral Glucose Tolerance Test (OGTT) was utilized for universal screening and diagnosing GDM based on the updated 2013 WHO diagnostic criteria. Data analysis included descriptive and analytical statistics, with variables having p-values below 0.1 deemed suitable for bivariate analysis. Statistical significance was assessed using the adjusted odds ratio (AOR) with a 95% confidence interval and a p-value < 0.05.
UNASSIGNED: The study involved 633 participants (255 cases and 378 controls), resulting in a 100% response rate, with women having an average age of 29.03 years.Variables such as: age at first conception (AOR=0.97, P=0.01, 95% CI (0.95,0.99)), urban residency (AOR=1.66, P<0.01, 95% CI(01.14,2.40)), widowed marital status (AOR=0.30, P=0.02, 95% CI (0.30,0.90)), parity (AOR=1.10, P<0.01, 95% CI (1.03,1.17)), history of stillbirth (AOR=1.15, P=0.03, 95% CI(1.04,2.30)), and previous cesarean section (AOR=1.86, P=0.01, 95% CI (1.13,2.66)) were identified as independent factors associated with GDM.
UNASSIGNED: The study concluded that factors like age at first conception, place of residence, marital status, parity, history of Caesarian section, and stillbirth were independently associated with GDM. Surprisingly, upper arm circumference (MUAC), a proxy for pre-gestational BMI, was not identified as a risk factor for GDM. It is recommended that healthcare providers conduct comprehensive GDM risk assessments in pregnant women to identify and address risk factors, and propose specific screening and intervention strategies.

BACKGROUND: Diabetic retinopathy (DR) is one of the most common complications of diabetes mellitus. The global burden is immense with a worldwide prevalence of 8.5%. Recent advancements in artificial intelligence (AI) have demonstrated the potential to transform the landscape of ophthalmology with earlier detection and management of DR.
OBJECTIVE: This study seeks to provide an update and evaluate the accuracy and current diagnostic ability of AI in detecting DR versus ophthalmologists. Additionally, this review will highlight the potential of AI integration to enhance DR screening, management, and disease progression.
METHODS: A systematic review of the current landscape of AI\'s role in DR will be undertaken, guided by the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) model. Relevant peer-reviewed papers published in English will be identified by searching 4 international databases: PubMed, Embase, CINAHL, and the Cochrane Central Register of Controlled Trials. Eligible studies will include randomized controlled trials, observational studies, and cohort studies published on or after 2022 that evaluate AI\'s performance in retinal imaging detection of DR in diverse adult populations. Studies that focus on specific comorbid conditions, nonimage-based applications of AI, or those lacking a direct comparison group or clear methodology will be excluded. Selected papers will be independently assessed for bias by 2 review authors (JS and DM) using the Quality Assessment of Diagnostic Accuracy Studies tool for systematic reviews. Upon systematic review completion, if it is determined that there are sufficient data, a meta-analysis will be performed. Data synthesis will use a quantitative model. Statistical software such as RevMan and STATA will be used to produce a random-effects meta-regression model to pool data from selected studies.
RESULTS: Using selected search queries across multiple databases, we accumulated 3494 studies regarding our topic of interest, of which 1588 were duplicates, leaving 1906 unique research papers to review and analyze.
CONCLUSIONS: This systematic review and meta-analysis protocol outlines a comprehensive evaluation of AI for DR detection. This active study is anticipated to assess the current accuracy of AI methods in detecting DR.
UNASSIGNED: DERR1-10.2196/57292.

Diabetes mellitus (DM) is known as the first non-communicable global epidemic. It is estimated that 537 million people have DM, but the condition has been properly diagnosed in less than half of these patients. Despite numerous preventive measures, the number of DM cases is steadily increasing. The state of chronic hyperglycaemia in the body leads to numerous complications, including diabetic cardiomyopathy (DCM). A number of pathophysiological mechanisms are behind the development and progression of cardiomyopathy, including increased oxidative stress, chronic inflammation, increased synthesis of advanced glycation products and overexpression of the biosynthetic pathway of certain compounds, such as hexosamine. There is extensive research on the treatment of DCM, and there are a number of therapies that can stop the development of this complication. Among the compounds used to treat DCM are antiglycaemic drugs, hypoglycaemic drugs and drugs used to treat myocardial failure. An important element in combating DCM that should be kept in mind is a healthy lifestyle-a well-balanced diet and physical activity. There is also a group of compounds-including coenzyme Q10, antioxidants and modulators of signalling pathways and inflammatory processes, among others-that are being researched continuously, and their introduction into routine therapies is likely to result in greater control and more effective treatment of DM in the future. This paper summarises the latest recommendations for lifestyle and pharmacological treatment of cardiomyopathy in patients with DM.

Diabetes mellitus (DM) predisposes individuals to vascular injury, leading to poor outcomes after ischemic stroke and symptomatic hemorrhagic transformation (SHT) after thrombolytic and endovascular treatment (EVT). Metformin (MET), an oral antidiabetic drug, has shown potential neuroprotective effects, but its impact on stroke prognosis in DM patients undergoing EVT remains unclear. In a multicenter study, 231 patients with DM undergoing EVT for acute ischemic stroke were enrolled. Prior MET use was identified, and patients were stratified into MET+ and MET- groups. Demographics, clinical data, and outcomes were compared between groups. Multivariate analysis was used to assess the effect of MET on stroke prognosis. Of the enrolled patients, 59.3% were previously on MET. MET+ patients had lower initial infarct volumes and NIHSS scores compared to MET-taking patients. Multivariate analysis showed that MET+ was associated with a lower risk of stroke progression and SHT (with stroke progression as follows: odd ratio [OR] 0.24, 95% confidence interval [CI] [0.12-0.48], p < 0.001; SHT: OR 0.33, 95% CI [0.14-0.75], p = 0.01) and was also associated with better 3-month functional outcomes (mRS 0-2) after EVT. Prestroke MET use in DM patients undergoing EVT is associated with improved stroke prognosis, including reduced risk of stroke progression and SHT and better functional outcomes. These findings suggest the potential neuroprotective role of MET in this population and highlight its clinical utility as an adjunctive therapy in the management of ischemic stroke. Further research is warranted to elucidate the underlying mechanisms and to optimize MET therapy in this setting.

BACKGROUND: The growth in the capabilities of telehealth have made it possible to identify individuals with a higher risk of uncontrolled diabetes and provide them with targeted support and resources to help them manage their condition. Thus, predictive modeling has emerged as a valuable tool for the advancement of diabetes management.
OBJECTIVE: This study aimed to conceptualize and develop a novel machine learning (ML) approach to proactively identify participants enrolled in a remote diabetes monitoring program (RDMP) who were at risk of uncontrolled diabetes at 12 months in the program.
METHODS: Registry data from the Livongo for Diabetes RDMP were used to design separate dynamic predictive ML models to predict participant outcomes at each monthly checkpoint of the participants\' program journey (month-n models) from the first day of onboarding (month-0 model) up to the 11th month (month-11 model). A participant\'s program journey began upon onboarding into the RDMP and monitoring their own blood glucose (BG) levels through the RDMP-provided BG meter. Each participant passed through 12 predicative models through their first year enrolled in the RDMP. Four categories of participant attributes (ie, survey data, BG data, medication fills, and health signals) were used for feature construction. The models were trained using the light gradient boosting machine and underwent hyperparameter tuning. The performance of the models was evaluated using standard metrics, including precision, recall, specificity, the area under the curve, the F1-score, and accuracy.
RESULTS: The ML models exhibited strong performance, accurately identifying observable at-risk participants, with recall ranging from 70% to 94% and precision from 40% to 88% across the 12-month program journey. Unobservable at-risk participants also showed promising performance, with recall ranging from 61% to 82% and precision from 42% to 61%. Overall, model performance improved as participants progressed through their program journey, demonstrating the importance of engagement data in predicting long-term clinical outcomes.
CONCLUSIONS: This study explored the Livongo for Diabetes RDMP participants\' temporal and static attributes, identification of diabetes management patterns and characteristics, and their relationship to predict diabetes management outcomes. Proactive targeting ML models accurately identified participants at risk of uncontrolled diabetes with a high level of precision that was generalizable through future years within the RDMP. The ability to identify participants who are at risk at various time points throughout the program journey allows for personalized interventions to improve outcomes. This approach offers significant advancements in the feasibility of large-scale implementation in remote monitoring programs and can help prevent uncontrolled glycemic levels and diabetes-related complications. Future research should include the impact of significant changes that can affect a participant\'s diabetes management.

BACKGROUND: Type 2 diabetes disproportionately affects South Asian subgroups. Lifestyle prevention programs help prevent and manage diabetes; however, there is a need to tailor these programs for mobile health (mHealth).
OBJECTIVE: This study examined technology access, current use, and preferences for health communication among South Asian immigrants diagnosed with or at risk for diabetes, overall and by sex. We examined factors associated with interest in receiving diabetes information by (1) text message, (2) online (videos, voice notes, online forums), and (3) none or skipped, adjusting for sociodemographic characteristics and technology access.
METHODS: We used baseline data collected in 2019-2021 from two clinical trials among South Asian immigrants in New York City (NYC), with one trial focused on diabetes prevention and the other focused on diabetes management. Descriptive statistics were used to examine overall and sex-stratified impacts of sociodemographics on technology use. Overall logistic regression was used to examine the preference for diabetes information by text message, online (videos, voice notes, or forums), and no interest/skipped response.
RESULTS: The overall sample (N=816) had a mean age of 51.8 years (SD 11.0), and was mostly female (462/816, 56.6%), married (756/816, 92.6%), with below high school education (476/816, 58.3%) and limited English proficiency (731/816, 89.6%). Most participants had a smartphone (611/816, 74.9%) and reported interest in receiving diabetes information via text message (609/816, 74.6%). Compared to male participants, female participants were significantly less likely to own smartphones (317/462, 68.6% vs 294/354, 83.1%) or use social media apps (Viber: 102/462, 22.1% vs 111/354, 31.4%; WhatsApp: 279/462, 60.4% vs 255/354, 72.0%; Facebook: Messenger 72/462, 15.6% vs 150/354, 42.4%). A preference for receiving diabetes information via text messaging was associated with male sex (adjusted odds ratio [AOR] 1.63, 95% CI 1.01-2.55; P=.04), current unemployment (AOR 1.62, 95% CI 1.03-2.53; P=.04), above high school education (AOR 2.17, 95% CI 1.41-3.32; P<.001), and owning a smart device (AOR 3.35, 95% CI 2.17-5.18; P<.001). A preference for videos, voice notes, or online forums was associated with male sex (AOR 2.38, 95% CI 1.59-3.57; P<.001) and ownership of a smart device (AOR 5.19, 95% CI 2.83-9.51; P<.001). No interest/skipping the question was associated with female sex (AOR 2.66, 95% CI 1.55-4.56; P<.001), high school education or below (AOR 2.02, 95% CI 1.22-3.36; P=.01), not being married (AOR 2.26, 95% CI 1.13-4.52; P=.02), current employment (AOR 1.96, 95% CI 1.18-3.29; P=.01), and not owning a smart device (AOR 2.06, 95% CI 2.06-5.44; P<.001).
CONCLUSIONS: Technology access and social media usage were moderately high in primarily low-income South Asian immigrants in NYC with prediabetes or diabetes. Sex, education, marital status, and employment were associated with interest in mHealth interventions. Additional support to South Asian women may be required when designing and developing mHealth interventions.
BACKGROUND: ClinicalTrials.gov NCT03333044; https://classic.clinicaltrials.gov/ct2/show/NCT03333044, ClinicalTrials.gov NCT03188094; https://classic.clinicaltrials.gov/ct2/show/NCT03188094.
UNASSIGNED: RR2-10.1186/s13063-019-3711-y.