Type 2 diabetes mellitus (T2DM) is a fast-growing chronic disease, with at least 1.3 million people diagnosed in Australia. In the Western Sydney Local Health District (WSLHD), an estimated 13.1% of all adults have T2DM. The condition significantly contributes to cardiovascular, heart and kidney diseases and causes a large disease burden. Lifestyle modifications, such as improved nutrition, increased physical activity and stress reduction, are recommended as first-line treatments for T2DM management. However, the current primary care system cannot meet the growing demands for diabetes care, necessitating the development of innovative, scalable, cost-effective solutions. Digital health technologies present a promising approach for promoting self-management in individuals with T2DM.

This cluster-randomised controlled trial aims to evaluate the feasibility and effectiveness of Gro-AUS, a localised version of the Gro Health app in Australia, to support T2DM management in Australian primary care settings. The trial will be conducted across multiple general practice clinics within the WSLHD, an area with a high prevalence of T2DM and significant cultural diversity in patient populations. Participants will be randomly assigned by clinic to either the intervention group (digital health programme) or control group (standard care). Primary outcomes include improvements in glycaemic control, cardiovascular risk factors and diabetes remission, with secondary outcomes such as weight loss, physical activity and mental well-being. Data will be collected using electronic and paper methods, with secure storage and de-identification ensuring participant privacy. The study's mixed-method approach ensures inclusivity for patients with varying levels of digital literacy. Data will be securely stored, de-identified and used to assess the effectiveness of the intervention. Findings are expected to inform future models of diabetes care in Australia, providing evidence for the scalability of digital health technologies in chronic disease management.

This trial is by nature unblinded. The recruitment style for a stepped-wedge trial may also bias participant engagement. However, it has direct implications for clinical practice as an effectiveness implementation trial. The design also allows for a much larger sample and more statistical power to examine outcomes.

This trial has been prospectively registered with the Australian New Zealand Clinical Trials Registry. Ethical approval has been granted by the WSLHD Human Research Ethics Committee prior to data collection. Results will be disseminated through publication in a peer-reviewed medical journal and shared via the Agency for Clinical Innovation, the Primary Care Health Network and through community engagement initiatives.

ANZCTR388639.

To examine chronic kidney disease (CKD) prevalence, incidence, prognosis, kidney function decline and associated risk factors among people with diabetes and/or hypertension.

Cross-sectional multicentre study.

14 primary care centres across Jakarta.

Adults (≥18 years) with diabetes and/or hypertension were included. Exclusion criteria were receiving kidney replacement therapy, language barrier, cognitive impairments, refusal to consent and pregnancy. Participants were grouped into three categories: hypertension only, diabetes only and both.

None.

Primary outcomes included CKD prevalence, incidence, number-needed-to-screen, KDIGO-based prognosis and annual kidney function decline. Secondary outcomes were risk factors for CKD, uncontrolled blood glucose, blood pressure and albuminuria.

A total of 1263 participants were enrolled: 51% had hypertension, 17.6% diabetes and 31.4% both. Mean age: 57.1±10.2 years, 72.2% female and 76% obese. Renin angiotensin aldosterone system inhibitors were prescribed in 32.3%, and only 1.2% used insulin despite a median glycated haemoglobin of 7.5% (IQR: 6.5-9.1). CKD prevalence was 14.8%, with an incidence rate of 9.1 per 100 person-years; number-needed-to-screen was 7. Based on KDIGO criteria, 48.9% were at moderate-to-very high risk of adverse outcomes. Baseline estimated glomerular filtration rate was 80.9 (SE=10.1), declining by 4.7 (SE=9.9) mL/min/1.73 m² annually. CKD incidence was higher with albuminuria (OR 3.6, p=0.007) in the combined group; older age (OR 4.5, p<0.001), male (OR 2.3, p=0.026) and haematuria (OR 2.5, p=0.034) in the hypertension group and cardiovascular disease (OR 14.9, p=0.004) in the diabetes group.

CKD burden is high among people with diabetes and hypertension. Nearly half were at elevated risk despite preserved kidney function, highlighting the need for targeted early screening.

Diabetic nephropathy (DN), a severe complication of diabetes, is characterized by proteinuria, hypertension, and progressive renal function decline, potentially leading to end-stage renal disease. The International Diabetes Federation projects that by 2045, 783 million people will have diabetes, with 30%-40% of them developing DN. Current diagnostic approaches lack sufficient sensitivity and specificity for early detection and diagnosis, underscoring the need for an accurate, interpretable predictive model to enable timely intervention, reduce cardiovascular risks, and optimize health care costs.

This study aimed to develop and validate a machine learning-based predictive model for DN in patients with type 2 diabetes, with a focus on achieving high predictive accuracy while ensuring transparency and interpretability through explainable artificial intelligence techniques, thereby supporting early diagnosis, risk assessment, and personalized clinical decision-making.

Our retrospective cohort study investigated 1000 patients with type 2 diabetes using data from electronic medical records collected between 2015 and 2020. The study design incorporated a sample of 444 patients with DN and 556 without, focusing on demographics, clinical metrics such as blood pressure and glucose levels, and renal function markers. Data collection relied on electronic records, with missing values handled via multiple imputation and dataset balance achieved using Synthetic Minority Oversampling Technique (SMOTE). In this study, advanced machine learning algorithms, namely Extreme Gradient Boosting (XGBoost), CatBoost, and Light Gradient-Boosting Machine (LightGBM), were used due to their robustness in handling complex datasets. Key metrics, including accuracy, precision, recall, F1-score, specificity, and area under the curve, were used to provide a comprehensive assessment of model performance. In addition, explainable machine learning techniques, such as Local Interpretable Model-Agnostic Explanations (LIME) and Shapley Additive Explanations (SHAP), were applied to enhance the transparency and interpretability of the models, offering valuable insights into their decision-making processes.

XGBoost and LightGBM demonstrated superior performance, with XGBoost achieving the highest accuracy of 86.87%, a precision of 88.90%, a recall of 84.40%, an F1-score of 86.44%, and a specificity of 89.12%. LIME and SHAP analyses provided insights into the contribution of individual features to elucidate the decision-making processes of these models, identifying serum creatinine, albumin, and lipoproteins as significant predictors.

The developed machine learning model not only provides a robust predictive tool for early diagnosis and risk assessment of DN but also ensures transparency and interpretability, crucial for clinical integration. By enabling early intervention and personalized treatment strategies, this model has the potential to improve patient outcomes and optimize health care resource usage.

To evaluate quality of life, sarcopenia and proteinuria, six and 12 months after infection with mild and moderate COVID-19.

We evaluated 253 individuals with mild (n = 119) and moderate (n = 134) clinical presentation for COVID-19 (reverse transcription-polymerase chain reaction-RT-PCR) after six (T6) and 12 (T12) months from the date of acute infection (T0). Quality of life, pain, risk for sarcopenia, muscle strength and proteinuria were assessed by the Short Form Health Survey 36 (SF-36) questionnaire; visual analogue scale (VAS); the Simple Questionnaire to Rapidly Diagnose Sarcopenia (SARC-F); hand grip and sit-up and the urinalysis strip, respectively.

The average age was 44 ± 10 and 43 ± 12 years; female 68 and 59% for the mild and moderate groups, respectively. Seventy-five percent or more of patients were vaccinated with at least two doses before acquiring COVID-19 infection. Individuals with a moderate clinical presentation in relation to mild cases were hypertensive (23 and 6%, p < 0.001) and had diabetes mellitus (9 and 2%; p = 0.01) at the time of COVID-19 acute infection. The moderate group at T6 presented lower functional capacity (SF36: 46 ± 20 vs. 61 ± 24); more pain (SF36: 45 ± 29 vs. 67 ± 32 and VAS: 55 vs. 32%); greater dysfunctionality for daily activities (Duke Activity Status Index-DASI: 40 ± 11 vs. 45 ± 10); lower limb muscle strength (sit-up: 9 ± 2 vs. 11 ± 2); higher risk for sarcopenia (SARC-F: 6 ± 4 vs. 4 ± 3) and higher proteinuria ≥ 1"+": 59 vs. 42%) compared to the mild group. After 12 months, the moderate group remained with greater pain (SF36+VAS) and more dysfunctionality in daily activities (DASI) compared to the mild group.

Comparing T12 to T6, we observed that the mild group had worse functional capacity; more pain (SF36+VAS); lower upper limb strength and higher proteinuria ≥ 1"+": 63 vs. 42%). We observed a negative correlation between SARC-F score and sit-up; functional capacity (SF36).

Explicit definitions for potentially inappropriate prescriptions (PIPs) are useful for optimizing drug use. The objective of the present study was to validate a list of definitions of PIPs for antidiabetic drugs in a Delphi survey with general practitioners, diabetologists, community pharmacists, hospital pharmacists and pharmacologists from mainland France, Belgium, and Switzerland.

The experts gave their opinion on each explicit definition and could suggest new definitions. Definitions with a 1-to-9 Likert score of between 7 and 9 from at least 75% of the participants were validated. The results were discussed during consensus meetings after each round.

46 participants were recruited, and 38 (82.6%) completed the survey. The Delphi survey resulted in a consensus list of 41 explicit definitions of PIPs for antidiabetic drugs in four groups: (i) the need to temporarily discontinue a medication in the event of acute illness (n = 9; 22%), (ii) the need to review and adjust the dosing regimen (n = 26; 36.6%), (iii) the initiation of an inappropriate drug (n = 3; 7.3%), and (iv) the need for further monitoring of a people with type 2 diabetes (n = 3; 7.3%).

The list is specific for antidiabetic drugs (other than insulin) for people with type 2 diabetes. This explicit list could be implemented in a clinical decision support system for the automatic detection of PIPs and might help healthcare professionals involved in the management of people living with type 2 diabetes.

Glycemic control in type 1 diabetes (T1D) remains a challenge, with 20-30% of adults achieving an A1c target of <7%. Glucagon-like peptide 1 receptor agonist (GLP-1 RA) and dual glucose-dependent insulinotropic polypeptide (GIP) and GLP-1 RA (GIP/GLP-1 RA) have emerged as a promising therapy in T1D. Previous studies have shown that patients with T1D can significantly improve glycemic control while experiencing a reduction in insulin dose and body weight when long-acting GLP-1RAs or GIP/GLP-1RAs are added to insulin therapy. However, randomized controlled trials (RCT) are still insufficient.

This is a prospective, randomized, parallel-group, open-label, superiority-controlled design that evaluates the safety and efficacy of adding tirzepatide to insulin therapy in participants with T1D under automated insulin delivery (AID) control. We will enroll 42 participants aged 18-65 years with confirmed T1D diagnosis ≥12 months, currently on AID insulin therapy for at least three months, with A1C ≥ 6.5% and ≤ 10%, and BMI ≥ 23 kg/m2. Participants will be randomized in a 1:1 ratio to either tirzepatide with a target dosage of 5.0 mg (after titration) or standard of care (SoC) for 16 weeks. The primary endpoint is continuous glucose monitoring (CGM)-measured percent time spent between 3.9 and 10.0 mmol/L (TIR) from baseline to follow-up after 16 weeks of treatment. Secondary endpoints include: the CGM-measured change in 24/7 percent time >10.0 mmol/L, > 13.9 mmol/L, < 3.9 mmol/L, < 3.0 mmol/L. The exploratory endpoints include: the change in body mass index (BMI), liver steatosis (MASLD), and body composition. Safety outcomes include severe hypoglycemia, diabetic ketoacidosis (DKA), and refractory gastrointestinal side effects.

This is the first prospective study to investigate the safety and efficacy of tirzepatide (GIP/GLP1-RAs) as an adjuvant therapy to AID in T1D. This study may contribute unique data to significantly improving glucose and cardio-metabolic outcomes, re-directing attention to further treatment in T1D beyond insulin therapies.

Predictors of type 2 diabetes (T2D) remission following intensive lifestyle intervention (ILI) are poorly characterized, especially in high-risk populations, such as India. This study aimed to identify the key predictors of T2D remission after an ILI in an Indian population. This retrospective analysis included 2384 patients with T2D (age 30-75 years; body mass index (BMI) ≥23 kg/m² enrolled in an online one-year ILI program at the Freedom from Diabetes Clinic, India, between May 2021 and August 2023. The intervention included personalized plant-based diet, physical activity, stress management, and medical support. Remission was defined as maintaining glycated hemoglobin (HbA1c) < 48 mmol/mol (6.5%) for ≥3 months without glucose-lowering medications. Anthropometric and biochemical data were extracted from clinical records. Predictors were assessed using logistic regression analysis. Post- intervention, 744 patients (31.2%) achieved remission The remission group showed significantly greater improvements in weight (-8.5% vs. -5.2%), BMI (-8.6% vs. -5.2%), HbA1c (-15.3% vs. -12.4%), fasting insulin (-26.6% vs. -11.4%), and homeostatic model assessment of insulin resistance (HOMA-IR) (-37.3% vs. -19.7%), than the non-remission group (p <0.05). The predictors of remission included age (≤50 years), higher BMI (≥25 kg/m²), drug-naïve status, shorter disease duration (≤6 years), juice fasting, baseline HbA1c <7%, weight loss >10%, and post-intervention HOMA-IR <2.5 (p <0.05). Our findings demonstrate that a significant proportion of individuals with T2D can achieve remission through a comprehensive culturally adapted lifestyle program. The identification of both baseline and post-intervention predictors underscores the importance of early, personalized, and holistic care in diabetes management.

Diabetes disproportionately impacts communities across the US and in Southern California, where there is a large Hispanic population. Diabetes prevalence continues to increase, and more studies are needed to understand behaviors and socioecological factors contributing to diabetes risk regionally and nationally.

To identify social and behavioral correlates (SBC) of diabetes in Southern California and to contrast these findings with national data to help ensure that public health strategies are appropriately targeted and regionally responsive.

This ecological and cross-sectional study used modeled estimates from census tracts in Southern California and nationwide, drawing from the Centers for Disease Control and Prevention's 2024 Population Level Analysis and Community Estimates (PLACES) dataset.

Based on PLACES data availability and prior evidence, 24 SBC were selected and were grouped into health outcomes and conditions, prevention, health risk behaviors, health-related social needs, and broader social determinants of health.

Diagnosed diabetes prevalence, also from the PLACES dataset, represented the percentage of adults ever diagnosed with diabetes by a health care professional, excluding gestational diabetes. A total of 24 indicators were tested for their association with diabetes prevalence using a gradient-boosted regression tree method.

In Southern California (5420 census tracts composed of approximately 18.5 million adults), diagnosed diabetes had a mean (SE) prevalence of 11.29% (0.06), ranging from 1.4% (0.05) to 33.6% (1.37), compared with 11.52% (0.02) nationally (62 480 tracts composed of approximately 253 million adults). In Southern California, leveraging an explainable machine learning approach, 5 key correlates were identified accounting for 67% of the estimate in the model: physical inactivity (31%), routine check-ups (14%), binge drinking (11%), lack of insurance (6%), and food insecurity (5%). While some key correlates overlapped regionally and nationally, their estimated contributions differed. Additionally, obesity, receipt of food stamps, being aged 65 years or older, and persons of ethnic or racial minority background (all except White, non-Hispanic) were key correlates nationally but not in Southern California.

In this cross-sectional study, key correlates of diagnosed diabetes in Southern California included physical inactivity, access to health care, and food insecurity, while nationally, obesity, food stamp participation, older age (≥65 years), and racial or ethnic minority status were also key. These findings point to possible regional differences in the factors linked to diabetes prevalence and highlight the need for further research to determine their significance and potential for guiding targeted interventions.

The Wound-QoL-17 and its short version, the Wound-QoL-14, measure health-related quality of life in patients with chronic wounds. This study assessed the content validity of this questionnaire.

We recruited adult patients with chronic wounds in outpatient and inpatient settings in Germany. We conducted semi-structured interviews, which were audio-recorded, transcribed verbatim and analysed using qualitative content analysis.

Almost all of the 21 patients (mean age 63 years, n = 16 male) had leg ulcers (n = 11) or diabetic foot ulcers (n = 8). The analysis resulted in six main categories: items; relevance; comprehensibility; comprehensiveness; version Wound-QoL-17 vs. Wound-QoL-14; further aspects. Participants mostly understood the distinct items well and found them easy to answer and relevant to their situation. The overall questionnaire was mostly rated relevant, comprehensible and comprehensive, including instructions, response scale, and recall period.

This study confirms the content validity of the Wound-QoL for patients with leg ulcers or diabetic foot ulcers and shows that it adequately reflects the construct of wound-specific quality of life. The Wound-QoL-17 should be used in clinical settings where differentiated assessment is appropriate. In research contexts where the calculation of scores is paramount, the Wound-QoL-14 should be used.

Long non-coding RNAs (lncRNAs) do not encode proteins and are transcripts longer than 200 nucleotides. The precise involvement of lncRNAs in type 1 diabetes mellitus (T1DM) pathogenesis remains unclear. Therefore, this study aimed to analyze the expressions of five lncRNAs in peripheral blood mononuclear cells of individuals with T1DM and without DM.

This study comprised 27 patients with T1DM (cases) and 13 individuals without DM (controls). The case group was divided into two subgroups based on T1DM duration: < 5 years of diagnosis group and long-term diabetes group (≥5 years). LncRNA expression was evaluated by qPCR.

MALAT1 and TUG1 were upregulated in patients within the first five years of diagnosis of T1DM compared to the other groups. MEG3 was upregulated in the case group of < 5 years of diagnosis compared to controls. TUG1 and MALAT1 levels were negatively correlated with the duration of T1DM, while TUG1 and MEG3 were positively correlated with glycated hemoglobin levels. Bioinformatics analysis revealed that MALAT1, MEG3, and TUG1 regulate and interact with protein-codifying genes and microRNAs involved in T1DM-related pathways.

Our study revealed MALAT1, MEG3, and TUG1 upregulation in patients within the first five years of diagnosis of T1DM.