Older adults aged ≥ 75 years with type 2 diabetes (T2D) experience high healthcare utilization, yet evidence on treatment patterns and outcomes in this age group is limited.

To assess associations between glycemic control, kidney function, and hospitalization among individuals aged ≥ 75 years with T2D.

Individuals aged ≥ 75 years with T2D were identified in 2018 using data from a population‑based healthcare database covering all levels of care in Region Halland. Healthcare utilization, including hospital admissions, hospital days, primary care encounters and mortality, was evaluated during 2019. Inclusion required at least one measurement of glycated hemoglobin (HbA1c) and estimated glomerular filtration rate (eGFR). Kidney function was classified by eGFR ≥ 60; 30-59; <30 mL/min/1.73 m², and HbA1c as < 52, 52-70, or > 70 mmol/mol. Associations with hospital days were analyzed using Poisson regression adjusted for demographic, clinical, and treatment variables.

The cohort comprised 1,446 individuals (49% women); 88 patients (6%) died during follow‑up. HbA1c was < 52 mmol/mol in 43%, 52-70 mmol/mol in 45%, and > 70 mmol/mol in 12%. CKD was common, with 56% classified as stage 3 and 10% as stage 4-5. Hospital days were higher among individuals with CKD stage 4-5 (RR 1.93, 95% CI:1.71-2.17) and HbA1c > 70 mmol/mol (RR 1.44, 95% CI:1.28-1.61). Primary care encounters were frequent (physician visits: 4.6 [SD 3.7]; nurse visits: 9.0 [SD 9.3]) and associated with CKD but not with glucose dysregulation. Insulin use was associated with more hospital days, whereas metformin and incretin‑based therapy were associated with fewer.

In adults aged ≥ 75 years with T2D, hospitalization burden was substantial and was associated with advanced CKD and poor glycemic control, while primary care encounters were frequent. These findings demonstrate an association between kidney function, glycemic status, and healthcare utilization in older adults with T2D.

Tractional retinal detachment caused by fibroproliferative membrane formation is a vision-threatening complication of proliferative diabetic retinopathy (PDR). Müller cells can acquire myofibroblast-like features during retinal fibroproliferative remodeling, yet the metabolic mechanisms governing these phenotypic changes remain poorly understood. Here, we report that lactate is a metabolic regulator of myofibroblast-like remodeling in Müller cells. Metabolomic profiling revealed elevated vitreous lactate levels in patients with PDR. Consistently, lactate concentrations were increased in the vitreous and retinal tissues of diabetic rats, whereas high-glucose exposure promoted lactate accumulation in primary Müller cells. Increased lactate availability enhanced Müller cell proliferative activity and migration, as well as the expression of myofibroblast-associated proteins, including α-smooth muscle actin, smooth muscle protein 22, and collagen I, whereas oxamate, an inhibitor of lactate dehydrogenase, attenuated these responses. Lactylome profiling and molecular validation revealed K186 of methyl-CpG-binding domain protein 2 (MBD2) as a functionally relevant lactylation site. RNA sequencing of high-glucose-treated Müller cells expressing MBD2-K186R versus MBD2-WT revealed reduced YAP1 expression in the K186R group, suggesting that YAP1 is a downstream effector of MBD2 K186 lactylation. Mechanistically, increased lactate availability enhanced MBD2 K186 lactylation and YAP1 activation, as evidenced by reduced inhibitory phosphorylation of YAP1 and increased nuclear accumulation of YAP1. MBD2 K186R attenuated YAP1 activation and myofibroblast-like changes, whereas YAP1 overexpression partially reversed these effects. Collectively, these findings reveal a lactate-MBD2 K186 lactylation-YAP1 signaling axis that promotes myofibroblast-like remodeling of Müller cells and may contribute to fibroproliferative pathology in PDR.

To prospectively investigate the association between baseline body mass index (BMI) and the risk of incident cerebral microbleeds (CMBs) in middle-aged and elderly patients with type 2 diabetes mellitus (T2DM), and to explore potential gender-specific risk thresholds in an exploratory analysis.

This single-center prospective observational cohort study consecutively enrolled T2DM patients without baseline CMBs from April 2022 to March 2023. Baseline demographic, clinical, and biochemical data were collected. BMI was categorized according to Chinese criteria (normal weight BMI < 24 kg/m², overweight 24-28 kg/m², and obesity BMI ≥ 28 kg/m²). All participants underwent follow-up 3.0T magnetic resonance imaging (including susceptibility-weighted imaging sequence) every 12-18 months or earlier if clinically indicated to detect incident CMBs. The primary outcome was the occurrence of ≥1 incident CMBs during follow-up. Cox proportional hazards models were used to calculate hazard ratios (HRs). Restricted cubic spline (RCS) models and two-piecewise linear regression were employed to analyze the nonlinear relationship and threshold effects between BMI and CMBs risk.

The study ultimately included 936 T2DM patients without baseline CMBs, with a median follow-up of 30.00 months (range: 6-42 months; IQR: 12.0, 36.0 months). During follow-up, 100 patients (10.68%) developed incident CMBs. Multivariable Cox regression analysis revealed that compared with the normal weight group, the obesity group (BMI ≥ 28 kg/m²) had a significantly higher risk of incident CMBs (HR = 2.58, 95% CI: 1.46-4.54, p < 0.001). Each 1 kg/m² increase in baseline BMI was associated with a 15% increased risk of incident CMBs (HR = 1.15, 95% CI: 1.09-1.22, p < 0.001). Threshold effect analysis showed a nonlinear association between BMI and CMBs risk (p for nonlinearity < 0.001). The exploratory inflection point was 21.10 kg/m² for males; beyond this threshold, each unit increase in BMI increased CMBs risk by 18% (HR = 1.18, 95% CI: 1.10-1.26, p < 0.001). For females, the exploratory inflection point was 26.80 kg/m², beyond which risk increased by 25% per unit increase (HR = 1.25, 95% CI: 1.02-1.54, p = 0.029).

In this single-center Chinese cohort, higher baseline BMI was associated with incident CMBs in middle-aged and elderly T2DM patients. Exploratory analysis suggested potential sex-specific inflection points (male: 21.10 kg/m²; female: 26.80 kg/m²). This study may inform future research on gender-differentiated weight management strategies, but these findings are not yet ready for clinical implementation.

Metabolic dysfunction-associated steatotic liver disease (MASLD) is highly prevalent in patients with type 2 diabetes mellitus (T2DM), yet effective pharmacological options remain limited. Imeglimin, a novel antidiabetic agent targeting mitochondrial bioenergetics, has shown potential hepatoprotective effects in preclinical studies; however, its impact on liver function and body composition in MASLD remains unclear.

This retrospective, single-center pilot study analyzed prospectively collected data from 28 patients with MASLD and T2DM who were treated with imeglimin for 6 months. Clinical and biochemical parameters were assessed at baseline and at 3 and 6 months. Body composition was evaluated using bioelectrical impedance analysis.

Over 6 months, fasting blood glucose decreased from 148.5 to 131.0 mg/dL (p = 0.015), accompanied by reductions in liver enzymes (aspartate aminotransferase: 33-26 U/L, p < 0.001; alanine aminotransferase: 28.5-23.5 U/L, p = 0.001). Body weight and body fat percentage also decreased (p < 0.001 and p = 0.018, respectively), whereas no statistically significant changes were observed in skeletal muscle mass. No serious adverse events were reported.

Imeglimin therapy was associated with favorable changes in glycemic parameters, liver enzymes, and body composition in this pilot cohort, without a statistically significant reduction in skeletal muscle mass. These findings suggest a potential metabolic and hepatic effect of imeglimin and may be particularly relevant for patients with MASLD at risk of sarcopenia. Larger prospective studies with histological or validated non-invasive endpoints are warranted.

The online version contains supplementary material available at https://doi.org/10.1007/s13340-026-00931-4.

Type 2 diabetes mellitus (T2DM) complicated by sarcopenia represents a considerable public health challenge, yet early detection remains challenging due to a lack of practical screening tools. The aim of this study was to develop and internally validate a clinically feasible, mechanism-based machine learning model for predicting sarcopenia risk in patients with T2DM.

A total of 904 patients with T2DM treated at the First Affiliated Hospital of Xinjiang Medical University from May 2024 to May 2026 were retrospectively enrolled. Sarcopenia was diagnosed according to the Asian Working Group for Sarcopenia (AWGS) 2025 consensus. The dataset was randomly partitioned into training (70%) and internal validation (30%) sets. Key predictive features were identified by intersecting the variables selected via least absolute shrinkage and selection operator (LASSO) regression and the Boruta algorithm. Eight machine learning algorithms were subsequently developed and evaluated.

The prevalence of sarcopenia among the patients with T2DM was 29.20%. Eight predictors were selected: age, sex, body mass index, serum albumin, alanine aminotransferase, hemoglobin, metabolic score for insulin resistance, and monocyte-to-high-density lipoprotein cholesterol ratio. LightGBM demonstrated the best overall predictive performance in the validation set, yielding an area under the receiver operating characteristic curve (AUC) of 0.928, an accuracy of 0.882, a sensitivity of 0.897, a specificity of 0.876, a precision of 0.745, and an F1 score of 0.814. Calibration and decision curve analysis indicated that the model yielded clinical net benefit. A web-based calculator developed using LightGBM enabled three-tier risk stratification: low (<15%), moderate (15%-40%), and high (>40%).

A machine learning model for predicting sarcopenia risk in patients with T2DM was developed and internally validated, resulting in the implementation of an online risk calculator. This mechanism-based model demonstrated promising internal predictive performance and may provide a practical and cost-effective screening approach for diabetes management.

Adaptations are common in implementation studies, yet they are rarely documented systematically. Understanding when, why, and how adaptations occur can improve implementation processes, support interpretation of study outcomes, and inform scaling in diverse settings. Using comprehensive frameworks such as reach, effectiveness, adoption, implementation, and maintenance (RE-AIM) and expanded framework for reporting adaptations and modifications-enhanced can guide systematic documentation, but practical, standardized approaches remain underdeveloped.

This study describes a multi-method, framework-guided approach to documenting adaptations across all phases of the accelerating colorectal cancer screening and follow-up through implementation science San Diego study, which implemented multilevel, evidence-based interventions to reduce colorectal cancer in community health center (CHC) systems.

We documented 44 adaptations over the pre-implementation (n = 22), implementation (n = 19), and sustainment (n = 3) phases. Most were verified through emails/meetings, with research staff/CHC staff as primary informants. Adaptations most frequently involved intervention materials, content, and format, and were primarily driven by pragmatic considerations to align with CHC workflows, patient needs, and contextual factors. While most adaptations had no major impact on RE-AIM outcomes, several improved implementations (e.g. centralizing FIT kit procurement to address COVID-19-related supply chain issues). Negative impacts were rare.

A multi-method, framework-based approach to adaptation documentation is feasible and valuable in complex, real-world implementation studies. Early and ongoing tracking supports proactive problem-solving, enhances contextual fit, and informs sustainability planning. This methodology can serve as a model for systematic adaptation tracking in safety-net and community-based healthcare settings, strengthening the evidence base for how adaptations influence implementation and effectiveness outcomes.

The Clinical Trials Registration #NCT04941300.

Plexiform neurofibromas arise in up to half of people with neurofibromatosis type 1 (NF1), causing pain, disfigurement and functional loss. Loss of neurofibromin releases RAS-MAPK signaling, making MEK1/2 a rational target. Two agents are now approved in the United States, the adult indication only since 2025. We evaluated the efficacy and safety of MEK inhibitor monotherapy in NF1-associated symptomatic, inoperable plexiform neurofibroma across children and adults. We searched six databases and registries from inception to 10 July 2026, reporting per PRISMA 2020. The primary outcome was objective response rate (≥20% tumor volume reduction, REiNS criteria). Two reviewers screened, extracted and appraised studies (RoB 2, ROBINS-I); certainty was rated with GRADE. Proportions were pooled using random-effects models after Freeman-Tukey transformation, with subgrouping by age. Eleven reports of nine studies (528 participants) met eligibility, covering selumetinib, mirdametinib, trametinib and binimetinib. The pooled descriptive response rate was 53.7% (95% CI 40.5-66.6; I2 = 84.0%) and varied by age: 62.9% in children versus 40.8% in adults. Because eight of the nine strata were single-arm, these are descriptive pooled proportions rather than controlled treatment effects. Adult heterogeneity was driven almost entirely by KOMET, the only randomized trial (20% versus 5% with placebo, p = 0.011); excluding it, the adult estimate was 49.0%. Responses were partial but usually durable, with improvements in pain and quality of life. Toxicity was mostly low grade, with uncommon cardiac and ocular events. MEK inhibition shrinks symptomatic inoperable plexiform neurofibromas in children and adults and is now supported by randomized evidence, although pooled estimates represent a weighted average across four pharmacologically distinct agents rather than a single class effect. Benefit is partial and depends on continued treatment. Optimal duration, comparative effectiveness and any effect on malignant transformation remain undefined.

Effective monitoring of thyroid cancer treatment requires timely detection of clinically meaningful changes across complex, multistage care pathways. Traditional monitoring approaches often rely on static thresholds and periodic assessments, limiting their sensitivity to subtle yet important process deviations and potentially delaying clinical intervention. This study aimed to develop and evaluate an integrated, data-driven framework that combines deep learning-based risk prediction with genetic algorithm-optimized statistical process monitoring to enhance thyroid cancer treatment surveillance. Informative clinical variables were first identified using the Information Functions algorithm, and a Multilayer Perceptron (MLP) neural network was then employed to predict patient-specific treatment risk, capturing nonlinear relationships within longitudinal electronic medical record data. The resulting risk estimates were sequentially incorporated into Exponentially Weighted Moving Average (EWMA) control charts to enable continuous process monitoring. To further improve chart performance, a genetic algorithm was used to optimize EWMA parameters, enhancing accuracy and sensitivity while maintaining chart stability. The results demonstrate that the MLP model outperforms conventional machine learning approaches in risk prediction across multiple evaluation metrics. Genetic algorithm-optimized EWMA charts exhibit smoother trajectories, improved stability, and more reliable detection of clinically meaningful deviations compared with non-optimized charts. The consistency of these improvements across multiple features suggests that GA-based optimization enhances overall control chart performance rather than feature-specific behavior. This hybrid framework demonstrates the feasibility of integrating machine learning-derived risk prediction with optimized statistical process control for longitudinal treatment monitoring. The proposed approach supports proactive treatment surveillance, risk-stratified follow-up, timely clinical intervention, and data-driven decision-making in thyroid cancer care. Furthermore, the framework has the potential to enable continuous and adaptive monitoring across complex multi-stage treatment pathways if integrated with electronic medical record (EMR) systems in future implementations.

Gastrointestinal (GI) cancers account for approximately 24% of all cancer deaths in the United States (US). Demographic and socioeconomic status (SES) factors have been associated with mortality in GI cancers. However, the associations between GI cancer mortality and neighborhood SES (nSES) among non-Hispanic Asian American, Native Hawaiian, and Pacific Islander (AANHPI) populations in California have not been investigated. This study examined GI cancer mortality rates by nSES among AANHPI adults in California.

The California Cancer Registry, part of the population-based Surveillance, Epidemiology and End Results (SEER) program of the National Cancer Institute, was used to identify AANHPI males and females who died of GI cancers from 2008 through 2012. GI cancer deaths were defined by the following sites: colorectum, stomach, esophagus, small intestine, and anus or anal canal. Each death was assigned a nSES index using the 2010 US Census data. Age-adjusted mortality rates of GI cancer and mortality rate ratios (MRR) as well as 95% confidence intervals by nSES quintiles were calculated for a 5-year period from 2008 through 2012. Subgroup analyses were conducted by sex and cancer site.

A total of 5,153 cases were included in the final study population for analysis. Among all AANHPI males and females, those residing in the lowest nSES quintile had a 1.31-fold higher MRR of GI cancers compared to those in the highest nSES quintile (Q1 vs. Q5; 95% confidence interval (CI): 1.19-1.45). This positive association was observed in both males and females, although not statistically significant in females (males: nSES Q1 vs. Q5 MRR: 1.50; 95% CI: 1.31-1.71; females: nSES Q1 vs. Q5 MRR: 1.11; 95% CI: 0.96-1.29). Similar patterns of associations were seen across specific GI cancer sites.

AANHPI adults in California residing in lower nSES neighborhoods experienced higher GI cancer mortality compared with those in higher nSES areas. Future studies are needed to clarify the underlying mechanisms and identify actionable strategies to mitigate nSES-related inequities in GI cancer outcomes in the AANHPI population.

Breast cancer remains one of the main causes of morbidity and mortality among females worldwide. Despite the advancement of several treatment options such as surgery, chemotherapy, and radiotherapy to tackle this disease, several women still refuse these allopathic treatment options due to adverse effects posed by these treatment modalities which usually remain poorly identified and managed particularly in low-resourced-constrained settings like those in Sub-Saharan Africa.

We explored the experiences of Ghanaian women with breast cancer being nursed after undergoing radiotherapy.

This was an exploratory descriptive qualitative study involving seventeen (17) women diagnosed with breast cancer who had undergone radiotherapy at a Teaching Hospital in Kumasi, Ghana. The participants were engaged in in-depth interviews using a piloted interview guide. The interviews were audio-taped, transcribed verbatim, and analyzed using thematic analysis with the help of Nvivo 10.

Two main themes emerged from this study; physical experiences and social experiences. The sub-themes under the physical experiences included pain, breast and skin changes, physical exhaustion, and gastrointestinal symptoms. Regarding social experiences, we identified three sub-themes, mainly, family and partner support, support from employers, churches, and friends, and finally sexual life challenges.

Several physical experiences post radiation therapy deter women diagnosed with breast cancer from consenting to undergo radiotherapy or recommending radiotherapy as a treatment option for breast cancer in Ghana. However, the social networks of women with breast cancer undergoing radiation therapy could serve as useful motivators in promoting radiation therapy. We recommend that nurses/midwives and other health professionals handle the negative physical symptoms experienced with tact and professionalism through supportive care services post radiation therapy to improve acceptance of this treatment option. Similarly, the Ghana Health Service should ensure that nurses at breast cancer care units utilize the social networks of women diagnosed with breast cancer undergoing radiation therapy as social capital to reduce the psychological stress associated with radiation therapy.