Current evidence on incidence and progression rate of diabetic retinopathy reveals disparities across Australasia, and no pooled estimates are available to guide region-wide planning. This review and meta-analysis assesses the overall incidence, determinants and progression of diabetic retinopathy in Australasia.

This systematic review followed the Joanna Briggs Institute manual for incidence synthesis. Articles were sourced from Ovid MEDLINE, Scopus, Web of Science, CINAHL, ProQuest and PsycINFO until October 2024. Articles reporting incidence, progression, and determinants of type 2 diabetes in Australia and surrounding islands were included.

Nine cohorts, including seven population-based and two institution-based cohort studies, predominantly from Australia, were analysed. The pooled annual incidence of diabetic retinopathy was 4.22% (95% CI 2.29-6.15), while individual studies reported annual progression from 3.08% to 18.22%. Incident diabetic retinopathy was associated with elevated fasting blood glucose, haemoglobin A1c, systolic blood pressure and increasing connecting peptide levels. Diabetic retinopathy progression was associated with increasing age, higher haemoglobin A1c levels, fasting blood glucose, longer duration of diabetes, an increased albumin-creatinine ratio and no fenofibrate treatment.

Diabetic retinopathy incidence in Australasia is highly variable, highest in institution-based cohorts and among Indigenous Australians. HbA1c is the only determinant consistently and significantly associated with both onset and progression. Because most evidence predates modern diabetes care, future prospective cohorts should adopt standardised, multifactorial designs to generate contemporary, comparable data and reduce persistent inequities in eye-care access.

Trehalose, a naturally occurring disaccharide, is gaining recognition for its therapeutic potential across a wide range of disorders. This review highlights the increasing importance of trehalose in clinical research and its applications in disease management. The unique biochemical properties of trehalose, including its ability to increase stress resistance and protect cellular structures, make it a promising therapeutic agent for various pathological conditions. Trehalose has demonstrated potential in addressing metabolic disorders such as diabetes and obesity by regulating glucose homeostasis, improving insulin sensitivity, and modulating lipid metabolism. Additionally, it has shown promise in cancer treatment by inhibiting tumor growth and promoting cell death. Beneficial effects have also been observed in models of cardiovascular diseases, ocular and dental conditions, osteoarthritis, Niemann-Pick disease, and genetic disorders. While existing clinical studies provide valuable insights into the therapeutic potential of trehalose, further research is needed to optimize its dosage, elucidate its mechanisms of action, and conduct large-scale trials to establish its efficacy and safety. In conclusion, trehalose represents a promising therapeutic compound with broad applications, offering a compelling avenue for future research and the development of innovative treatment strategies.

Diabetes mellitus (DM) screening remains a major global health concern, with over 60% of cases undiagnosed. While breath analysis offers a noninvasive alternative to blood-based methods, current studies focus solely on acetone and lack diagnostic specificity. Here, we report an integrated diagnostic strategy combining metabolomics-driven biomarker discovery, portable solid electrolyte gas sensors (SEGS), and cellular-level metabolic investigation. Using GC-MS profiling of 130 DM patients and 122 healthy controls, we identified nine discriminative volatile organic compounds (VOCs). A random forest (RF) model achieves a cross-validated AUC of 0.93. The SEGS analyzer detects target VOCs at ppb levels within 30 s, enabling point-of-care (POC) screening. Clinical validation demonstrates 100% accuracy (n = 10) for diabetic ketoacidosis (DKA) detection and 83.3% (n = 30) for DM. Insulin-resistant (IR) cell models uncover breath VOCs' association with nonvolatile metabolite (NVM) pathways, supporting biological interpretability. This work establishes a biologically interpretable, clinically validated, and field-deployable diagnostic platform for scalable, low-cost DM screening in community and resource-limited settings.

Flash glucose monitoring systems (FGMS; FreeStyle Libre) are useful devices for managing diabetic patients. The FGMS is reportedly accurate for diabetic dogs with hyperglycemia and euglycemia but might underestimate glucose concentrations during hypoglycemia.

Assess the frequency of low and high interstitial glucose (IG) concentrations recorded in healthy, nondiabetic dogs using FGMS.

Twenty-three hospital employee-owned dogs.

Prospective, observational study. The FGMS was placed on all dogs to record ≥488 readings each over up to 14 days. Interstitial glucose concentrations were analyzed to identify the frequency of low, normal, and high IG concentrations. Descriptive statistics were calculated, and comparisons between demographic cohorts were performed.

During monitoring, 73.7% (14/19) of participants had at least one low IG concentration (<70 mg/dL), whereas 26.3% (5/19) had at least one high IG concentration (>180 mg/dL). The mean (±SD) percentage of low and high IG concentrations per dog was 2.8 ± 4.3% and 0.8 ± 2.2%, respectively. Markedly decreased IG (<55 mg/dL) and markedly increased (>250 mg/dL) IG concentrations occurred in 63.2% (12/19) and 10.5% (2/19) of dogs, respectively. The frequency of low IG concentrations in dogs weighing 2.5-20.5 kg (2.2%; interquartile range [IQR], 1.1-5.0) was higher (P = .02) than in dogs weighing 20.6-41.4 kg (0.1%; IQR, 0.0-0.7%); the median difference was 2.1% (95% confidence interval, 0.6-10.1).

Low and high IG concentrations were recorded in healthy, nondiabetic dogs, providing a context for interpreting FGMS results in diabetic dogs.

Diabetic foot ulcer (DFU) is a common and debilitating complication of diabetes mellitus, representing a significant clinical challenge. This article delves into the intricate pathophysiology underlying DFU, aiming to enhance our understanding of this complex wound healing process. We explore the interplay of multifactorial aspects, including peripheral neuropathy, vascular insufficiency, and impaired immune response, which contribute to the development and progression of DFU. Moreover, the dysregulation of key cellular and molecular mechanisms involved in inflammation, angiogenesis, extracellular matrix remodeling, and infection are examined. A comprehensive understanding of the pathophysiology of DFU including oxidative stress, neuropathy, dysregulated angiogenesis, impaired immune response, and key molecular pathways supports the development of targeted therapeutic strategies beyond current treatments to improve wound healing, reduce complications, and enhance patient quality of care.

Sepsis causes an uncontrollable activation of pro- and anti-inflammatory responses, leading to metabolic derangements, notably glucose variability (GV). Both hyperglycaemia and hypoglycaemia can occur in septic patients, regardless of the diabetes status. Hyperglycaemia control using insulin has been shown to reduce morbidity and mortality. Current guidelines suggest maintaining glucose levels between 140 and 180 mg/dL for better clinical outcomes. Managing glycaemic variability is crucial in reducing mortality in intensive care unit (ICU) patients with sepsis.

The study included patients aged ≥ 18 years, admitted for ≥ 24 h, focussing on the first 5 days of ICU stay. Data on patient characteristics, glucose values, comorbidities, organ failures, and outcomes were collected. GV was assessed, and comorbidities were determined using diagnostic codes.

Among a group of 100 patients (mean age 54.16 ± 18.5 years; 66% male), diabetes mellitus (84%) and hypertension (57%) were the most common comorbidities. Pneumonia (25%) and urosepsis (22%) were the primary sources of sepsis. Patients with multiple organ dysfunction syndrome (MODS) had significantly higher mean glucose levels (MGLs) than those without (P < 0.05). Higher glucose levels were also observed in non-survivors compared to survivors (P < 0.05). Glycaemic variability, measured by the coefficient of variation, was significantly higher in non-survivors. Insulin requirements were higher in unresolved cases (P < 0.05).

Higher glycaemic variability and MGLs were associated with increased mortality and MODS in ICU patients with sepsis. Improved outcomes were observed in patients with lower glycaemic variability, highlighting the need for insulin protocols to maintain optimal glucose control and reduce variability in critical care settings.

This systematic review examines the impact of dietary fiber intake on insulin resistance in individuals with type 2 diabetes mellitus (T2DM). Given the global rise in T2DM prevalence and the central role of insulin resistance in its pathophysiology, there is an increasing emphasis on nonpharmacological interventions, such as dietary fiber, to manage glycemic outcomes. A comprehensive search was conducted across six databases including PubMed, Scopus, Web of Science, ProQuest, CINAHL, and CENTRAL, focusing exclusively on randomized controlled trials (RCTs) up to June 2022. Thirteen RCTs, involving a total of 641 participants, were included in the final synthesis. The interventions varied in fiber type (soluble, insoluble, and mixed), source (whole grains, legumes, flaxseed, composite flour), and dosage (5-50 g/day), with durations ranging from 1 to 6 months. Findings consistently demonstrated that dietary fiber, especially soluble and mixed types, significantly improved insulin resistance as measured by HOMA-IR and related indices. Additionally, notable improvements were observed in fasting glucose, HbA1c, LDL cholesterol, and body weight in several trials. While the results are promising, limitations, such as short study durations, small sample sizes, heterogeneity in intervention protocols, and limited long-term data, constrain broader generalization. Despite these challenges, the evidence strongly supports dietary fiber as an effective adjunct in managing insulin resistance in T2DM. The review underscores the need for longer-duration, multicenter RCTs with standardized fiber interventions to confirm findings and inform clinical practice. Dietary fiber should be integrated into individualized diabetes management strategies to enhance metabolic outcomes and overall health.

To investigate postpartum weight retention (PPWR) in women with gestational diabetes mellitus (GDM) during the early postpartum period and to identify its key determinants.

This study enrolled 342 women with GDM who delivered at Fujian Maternity and Child Health Hospital between January 2024 and June 2025. Data on sociodemographic characteristics, pre-pregnancy body mass index (BMI), gestational weight gain (GWG), and postpartum weight were collected using questionnaires and telephone follow-ups at 42 days and 3 months postpartum. Generalized Estimating Equation (GEE) models were applied to examine factors influencing PPWR.

Mean PPWR at 42 days and 3 months postpartum was 5.09 ± 4.55 kg and 4.12 ± 4.17 kg, respectively. GEE analysis showed that postpartum time, monthly household income per capita, pre-pregnancy BMI, GWG, and parity significantly influenced PPWR (all P < 0.05). PPWR at 3 months postpartum was significantly lower than at 42 days (β = -0.970, P < 0.001). Higher household income (>5000 CNY/month) was associated with lower PPWR. Compared with women with normal pre-pregnancy BMI, overweight and obese women had lower PPWR, whereas underweight women had higher PPWR. Excessive GWG was a risk factor for PPWR (β = 3.312, P < 0.001), and multiparous women had lower PPWR than primiparous women (β = -1.057, P = 0.009). No significant associations were observed between PPWR and age, education level, mode of delivery, infant feeding method, or sleep quality (all P > 0.05).

Women with GDM experience significant PPWR in the early postpartum period, although weight retention decreases over time. GWG, pre-pregnancy BMI, household economic status, and parity are major determinants of early PPWR. Targeted weight management interventions should prioritize women with low income, pre-pregnancy underweight, and primiparity, with interventions initiated during pregnancy to reduce long-term metabolic risk.

Type 1 diabetes mellitus (T1DM) is a chronic autoimmune disorder associated with a substantial metabolic burden. However, large-scale, real-world studies assessing sex-specific multimorbidity patterns in T1DM remain limited.

To characterize the clinical profiles and multimorbidity patterns in patients with T1DM, using age- and sex-matched individuals with type 2 diabetes mellitus (T2DM) as comparators.

Using the Shanghai Hospital Link Database, we identified patients aged 10-79 years with a first T1DM-coded diagnosis between 2014 and 2023 and selected 1:1 sex- and index-age-matched T2DM comparators. Clinical characteristics were compared between groups overall and stratified by sex. Sex-stratified multimorbidity networks were constructed and analyzed using standard network metrics. Subgroup-unique comorbid disease pairs were identified using the Salton Cosine Index.

We included 18,971 patients with T1DM and 18,971 matched T2DM comparators. Compared with T2DM, T1DM showed shorter observed follow-up, higher glycated hemoglobin, higher high-density lipoprotein cholesterol, and lower triglyceride, with more pronounced differences among females. Hypertension and dyslipidemia were the most central comorbidities across all networks, closely linked to cardiovascular and cerebrovascular conditions. The female T1DM network exhibited greater complexity and connectivity than the male T1DM network. Subgroup-unique pairs were more frequent in T1DM, including peripheral vascular disease-nonalcoholic fatty liver disease and diabetic eye complications-stroke in males, and neuropathy-osteoporosis, nephropathy-chronic gastritis, and chronic gastritis-asthma in females.

We identified shared cardiometabolic comorbidities (hypertension and dyslipidemia) across diabetes types and sexes, and sex-specific multimorbidity co-occurrence signatures in T1DM. These findings support integrating blood pressure and lipid optimization with glycemic management, and underscore the need for sex-sensitive surveillance of subgroup-specific clusters, highlighting broader multisystem clustering in females and vascular-risk co-occurrence signals in males that may warrant intensified multifactorial atherosclerotic cardiovascular disease prevention.

Hypertension and diabetes are major drivers of cardiovascular disease (CVD), and their coexistence confers excess risk. This study aimed to develop a hemoglobin A1c (HbA1c)-systolic blood pressure (SBP) index (HSI) to simultaneously capture glucose and blood pressure status and investigate the associations of baseline and cumulative HSI with incident CVD.

Data were drawn from three population-based cohorts: the China Health and Retirement Longitudinal Study (CHARLS), the English Longitudinal Study of Ageing (ELSA), and the US Health and Retirement Study (HRS). Baseline HSI was calculated as HbA1c (%) × SBP (mmHg)/100. Cumulative HSI was derived from repeated measurements weighted by time intervals. Cause-specific Cox proportional hazards models were used to investigate linear associations of baseline and cumulative HSI with incident CVD. Additionally, restricted cubic splines were used to assess nonlinear relationships.

A total of 6,822 participants from CHARLS, 3,640 from ELSA, and 5,709 from HRS were included, with median follow-up of 9.0, 10.0, and 12.3 years, respectively. Across all three cohorts, the combination of elevated HbA1c and SBP was associated with the highest CVD risk. Higher baseline HSI were significantly associated with increased risks of CVD in the CHARLS (hazard ratio [HR] per 1 standard deviation [SD] increase =1.16, 95% confidence interval [CI] 1.11-1.22), ELSA (1.13, 95% CI 1.06-1.21), and HRS (1.14, 95% CI 1.10-1.19). Cumulative HSI levels were also significantly associated with elevated CVD risk (CHARLS: HR per 1 SD increase = 1.19, 95% CI 1.12-1.26; ELSA: 1.14, 95% CI 1.04-1.26; HRS, 1.15, 95% CI 1.08-1.22). No evidence of nonlinearity between baseline HSI and CVD was detected. The associations were almost consistent across demographic and clinical subgroups. The predictive performance of HSI was superior to HbA1c or SBP alone.

HSI, a simple composite of HbA1c and SBP, was consistently associated with incident CVD across three international cohorts. Its predictive ability exceeded that of HbA1c or SBP alone, highlighting it as a pragmatic tool for integrated cardiometabolic risk assessment. The findings warrant further clinical validation.