Diabetes, Obesity & Metabolism最新文献

Aims: The aim of this study was to quantify the interaction between hypertension and diabetes on cardiovascular disease risk in elderly adults, using data from the CHARLS and HRS cohorts. Competing risk models accounting for mortality were employed in the analysis.

Materials and methods: This is a prospective analysis of 18 647 participants aged ≥60 years, drawn from two major longitudinal studies: the China Health and Retirement Longitudinal Study (CHARLS, n = 9823, 2011-2020) and the Health and Retirement Study (HRS, n = 8824, 2010-2020). Participants had no baseline cardiovascular disease. The primary endpoints included stroke, myocardial infarction, heart failure, and cardiovascular mortality. Interactions between hypertension and diabetes were assessed using Cox proportional hazards and Fine-Grey competing risk models, with both multiplicative and additive approaches applied.

Results: Over median follow-up of 7.8 years (CHARLS) and 8.2 years (HRS), 1909 cardiovascular events occurred. Comorbid hypertension-diabetes showed elevated risk versus neither condition. Significant additive interactions emerged consistently: RERI 0.75 (95% CI: 0.19-1.31) in CHARLS and 0.84 (95% CI: 0.21-1.47) in HRS, with 28-31% excess risk attributable to interaction. Synergy indices confirmed super-additive effects. Stroke showed strongest interaction (RERI ~0.9, SI ~1.8), while myocardial infarction demonstrated minimal synergy. Effects were amplified in participants aged 60-74 and females.

Conclusions: This analysis shows that hypertension and diabetes together increase cardiovascular risk in elderly individuals by about 30%. The findings are consistent across different ethnic groups and healthcare systems, suggesting universal biological mechanisms. This supports updating risk assessments and enhancing preventive strategies, especially for cerebrovascular risks in the elderly.

Background: The burden attributable to body mass index (BMI) remains a major public health concern in China and imposes substantial socio-economic costs.

Methods: Data from the Global Burden of Disease Study 2023 and economic projections were integrated to estimate and project the burden among Chinese adults aged 20-64 years. A multi-method approach was utilised, including burden estimation, joinpoint regression for trend analysis, demographic decomposition, age-period-cohort (APC) modelling, and Bayesian age-period-cohort (BAPC) modelling for future projections. This study introduced and applied the B-PALY framework, which integrates the BAPC model with productivity-adjusted life years (PALYs), to systematically predict the productivity impact and macroeconomic burden associated with high BMI-related diseases.

Results: In 2023, high BMI-related diseases caused 118 511 deaths and 9.7 million disability-adjusted life years (DALYs), accounting for approximately 20% of all deaths and 25% of all DALYs among China's working-age population. From 1990 to 2023, the burden showed significant upward trends. Demographic decomposition identified population aging and growth as primary drivers, partially offset by epidemiological changes. APC analysis revealed an increasing disease burden with age and elevated DALY risks in recent birth cohorts. Projections indicated a substantial rise in the burden of diseases attributable to high BMI, in terms of absolute deaths and DALYs, through 2050. The B-PALY framework estimated that, in 2023 alone, these diseases imposed a macroeconomic burden of approximately 129.77 billion Intl$ (about 0.03% of China's 2023 GDP). Projected cumulative losses from 2023 to 2029 amount to Intl $1.14 trillion.

Conclusion: High BMI-related diseases caused a substantial health and macroeconomic burden in China's working-age population, with rising trends projected to continue.

Aims: Impaired insulin sensitivity is an under-recognised risk in Type 1 diabetes but is challenging to measure with 'gold-standard' euglycaemic clamps. Adding stable-isotope glucose distinguishes hepatic and muscle insulin action (assessed by endogenous glucose production [EGP] and glucose infusion rate [GIR], respectively). We therefore searched for a blood-biomarker alternative.

Methods: Two-step clamps were conducted in 40 adults with Type 1 diabetes, participating in the INTIMET trial (INsulin resistance in Type 1 diabetes managed with METformin, ACTRN12619001440112). Participants were characterised with 33 baseline biomarkers.

Results: Exhaustive search analyses derived a formula predicting an 'unfavourable GIR' (dichotomous variable: below median of 60.4 μmol/kg fat-free mass [FFM]/min) using: total daily insulin dose (TDI), fasting triglycerides (TGs), insulin-like growth factor 1 and aspartate aminotransferase levels (area under the receiver operating characteristic curve (AUROC) 0.97, p < 0.0001, R² [Nagelkerke] = 0.83, 92.5% accuracy). An 'unfavourable EGP level' (above median of 6.2 μmol/kg FFM/min) during low-dose clamp was predicted by TDI, TGs, alkaline phosphatase and uric acid levels (AUROC 0.86, p = 0.001, R² (Nagelkerke) = 0.50, 80% accuracy). A free online tool (https://bit.ly/EGP-GIR-calculator) converts these variables into dichotomised EGP and GIR estimates.

Conclusions: We demonstrate that clinical and research biomarkers can be used to estimate tissue specific insulin sensitivity in adults with Type 1 diabetes.

Aims: Adipose tissue insulin resistance, reflecting impaired insulin-mediated suppression of lipolysis in adipose tissue, is recognized as an early metabolic abnormality. However, its associations with fasting blood glucose (FBG) and haemoglobin A1c (HbA1c) have not been well characterized in adults without diabetes. The study aims to evaluate these associations under conditions of normoglycaemia and prediabetes.

Materials and methods: The study enrolled 9011 adults without diabetes. Adipose tissue insulin resistance index (Adipo-IR) was calculated by the product of fasting insulin and free fatty acid levels. We categorized FBGand HbA1c separately into three groups. We conducted logistic regression after adjustment for potential confounders.

Results: Adipo-IR gradually increased with the increase of FBG categories and HbA1c categories. The positive association between Adipo-IR and FBG levels varied by body mass index (BMI) (p for interaction <0.001), with a strengthened relationship in the condition of BMI <24 kg/m². One unit increase in log-Adipo-IR was associated with 2.74 (95% CI: 2.02, 3.73, p < 0.001) times odds of having high FBG levels (denoted as FBG ≥5.6 mmol/L) after adjustment for other risk factors in participants with BMI <24 kg/m². However, the ORs turned out to be 1.67 (95% CI: 1.36, 2.07, p < 0.001) and 1.30 (95% CI: 0.98, 1.74, p = 0.07) in participants with BMI 24 to <28 and ≥28 kg/m², respectively. The positive associations between Adipo-IR and HbA1c levels were also observed among participants with body mass index <24, 24 to <28, and ≥28 kg/m², and among females and males, respectively.

Conclusions: Adipose tissue insulin resistance demonstrated a significant dose-response relationship with both FBG and HbA1c levels in individuals without diabetes, which may reflect early glycaemic alterations. The association between Adipo-IR and FBG was more pronounced under conditions of normal or low body weight.

Aims: To assess whether preoperative sodium-glucose co-transporter 2 inhibitor (SGLT2i) use reduces the odds of postoperative acute kidney injury (AKI) in patients with type 2 diabetes undergoing surgery.

Methods: We conducted a target trial emulation using inverse probability of treatment weighting, utilising routinely collected data from a tertiary centre in Australia. Patients with type 2 diabetes were included. We compared patients undergoing surgery under general anaesthesia who were taking SGLT2i preoperatively with patients undergoing surgery who were not taking SGLT2i preoperatively. The primary outcome was postoperative AKI within 7 days of surgery. We estimated the average treatment effect on the treated and calculated adjusted odds ratios (ORs) and standardised risk differences.

Results: We included 2499 patients (738 taking SGLT2i preoperatively and 1761 not taking SGLT2i preoperatively). Over half of patients underwent emergency surgery. Postoperative AKI occurred in 18.6% taking SGLT2i preoperatively and 25.8% not taking SGLT2i preoperatively. Odds of postoperative AKI were significantly lower in those who used SGLT2i preoperatively (adjusted OR 0.71, 95% CI 0.56-0.91, p = 0.007). Effects were consistent across subgroup and sensitivity analyses.

Conclusions: In patients with type 2 diabetes who used SGLT2i preoperatively, its use reduced the odds of postoperative acute kidney injury.

Control-IQ+ is an automated insulin delivery (AID) algorithm approved for people with type 1 diabetes aged 2+ years and adults aged 18+ years with type 2 diabetes. While numerous publications support improved glycaemia and quality of life for people with diabetes, this practice paper is intended to encourage uptake for healthcare professionals (HCPs) who are less familiar with AID systems. This includes recommendations for initializing settings, as well as research and practice-based approaches to optimizing glycaemia with stronger settings (e.g., strong correction factor settings). In addition to automated insulin adjustments every 5 min, Control-IQ+ delivers a large Autobolus up to once per hour, which can improve glycaemia in people who have challenges with routine bolusing. Simple bolus strategies and tips and tricks also highlight how to make Control-IQ+ easy to use for HCPs and the person with diabetes.

Aims: Sotagliflozin, an inhibitor of sodium-glucose co-transporter (SGLT)-1 and 2, reduces albuminuria, slows GFR decline, and may have diuretic and osmoregulatory effects. The effect of sotagliflozin added to insulin was assessed on markers of neurohormone activation and volume homeostasis in patients with type 1 diabetes (T1D).

Materials and methods: This is a post-hoc analysis of the randomised, double-blinded, placebo-controlled inTandem3 trial, which assessed efficacy of sotagliflozin 400 mg/d versus placebo as an adjunct to insulin. The present biomarker analysis included 362 participants (26%) who had biological samples collected at baseline and week 24. Plasma renin, copeptin, serum N-terminal pro b-type natriuretic peptide (NT-proBNP), and markers of tubular injury, inflammation, haematopoiesis, and iron homeostasis were measured at baseline and following week 24 treatment; fractional excretion of lithium (FE_Li) and glucose (FE_Glucose) were calculated.

Results: Participants were 46 years of age (60% female) with a mean eGFR and median UACR of 88.4 mL/min/1.73 m² and 7.5 mg/g, respectively. Sotagliflozin increased copeptin, FE_Li, and FE_Glucose by 33%, 14%, and 60-fold, respectively (all p < 0.01), but did not change renin or NT-proBNP (both p ≥ 0.11) compared to placebo. Further, urinary kidney injury molecule-1 decreased by 19% (p = 0.03). Haemoglobin and haematocrit increased (both p < 0.01), without altering inflammatory, erythropoietic, or iron biomarkers (all p ≥ 0.11).

Conclusions: In this T1D population, sotagliflozin increased copeptin levels, FE_Li, and FE_Glucose without altering renin, reflecting adaptive mechanisms that preserve sodium and fluid balance and protect against volume depletion. Sotagliflozin also decreased markers of kidney injury and increased haematocrit.

Aims: The precise mechanism of sodium glucose co-transporter 2 (SGLT2) inhibitor on reno-protective effect has been still unclear. In this study, we hypothesised that SGLT2 inhibitor prevents diabetic kidney disease via reduction of hypoxia-induced factors.

Materials and methods: In this multicenter, prospective, randomised, double blinded clinical trial, people with type 2 diabetes and microalbuminuria were randomised equally to empagliflozin (10 mg/day) (n = 40) and placebo (n = 39) and followed 24 weeks. The primary endpoint was change in urinary albumin creatinine ratio (ACR) and urinary liver type fatty acid binding protein (L-FABP) excretion from baseline to 24 weeks. Major secondary outcome was change in serum vascular endothelial growth factor (VEGF), angiopoietin-like proteins 2 (ANGPTL2), angiopoietin-like proteins 4 (ANGPTL4), and adrenomedullin (AM) levels.

Results: Although the reduction of ACR was significantly greater in the empagliflozin group than the placebo group at 4 and 12 weeks, the difference of change at 24 weeks between the two groups was not statistically significant (Empagliflozin group-Placebo group: -0.3643, 95% CI: -0.7571 to 0.0285, p = 0.0686). There was no difference in urinary L-FABP excretion between the empagliflozin and placebo groups. Serum VEGF and ANGPTL2 decreased significantly more in the empagliflozin group, whereas there were no significant differences in AM and ANGPTL4.

Conclusions: These results demonstrated that empagliflozin partially suppressed the hypoxia-induced angiogenic factors overproduction in addition to a declining trend in ACR in the early stage of diabetic kidney disease, which might contribute to the mechanisms of reno-protective effects of this agent (jRCTs051200147).

Aims: Drug-gene interactions (DGIs) modify drug response and safety, yet their influence on diabetic microvascular complications remains unclear. This study aimed to elucidate the role of DGIs in these complications.

Materials and methods: Using UK Biobank (UKB) data, we identified medications frequently prescribed to individuals with diabetes and defined DGIs based on the Food and Drug Administration (FDA) and the Clinical Pharmacogenetics Implementation Consortium (CPIC) guidelines. Associations between DGIs and diabetic microvascular complications were evaluated using Cox proportional hazards models, which are suited for longitudinal time-to-event data. Two complementary analyses were performed: (1) a therapeutic class-level analysis among medication users, and (2) a genotype-level analysis among individuals with non-normal metabolizer phenotypes who used the corresponding medications.

Results: We identified 368 medications preferentially used among participants with diabetes, primarily cardiovascular agents and detected 55 clinically relevant DGIs implicating 30 medications and 7 genes. Among users of antithrombotic agents, the presence of DGIs was associated with diabetic kidney disease (DKD) (hazard ratio [HR]: 1.44, 95% confidence interval [CI]: 1.12-1.86) and diabetic neuropathy (DN) (HR: 2.13, 95% CI: 1.39-3.28). Likewise, among individuals with non-normal metabolizer status for CYP2C19 or CYP2D6, DGIs conferred elevated risks for DKD and DN (HR range: 1.26-2.11). However, no significant association was found between DGI and DR.

Conclusion: This study provides the first comprehensive assessment of DGIs and diabetic microvascular complications. DGIs involving antithrombotic agents and non-normal CYP2C19 or CYP2D6 metabolizers were significantly linked to higher risks of DKD and DN. These findings underscore the potential of pharmacogenomic-guided prescribing to enhance drug safety.

Continuous glucose monitoring (CGM) is now central to diabetes management, yet variation in how respective medical products are evaluated limits meaningful comparison between CGM systems. Three barriers currently constrain reliable interpretation of glucose-derived measures. The first is limited transparency: in several regulatory settings, particularly those using Conformité Européenne marking, clinical-study reports, reference-method information and analytical documentation required for market authorisation are not publicly accessible. The second barrier is heterogeneity in study procedures. Existing evaluations use different reference-glucose methods, sampling strategies, glucose-manipulation protocols and participant characteristics, leading to accuracy estimates that cannot be interpreted consistently across systems. The third barrier is calibration alignment. Even with full transparency and aligned procedures, CGM systems may differ because their calibration algorithms are trained on distinct reference-glucose datasets, influencing reported glucose ranges, automated insulin-delivery behaviour and interpretation during device transitions. A modified Delphi process involving clinicians, laboratory scientists, and researchers identified these issues as the principal determinants of comparability. During this process, the International Federation of Clinical Chemistry and Laboratory Medicine released a validated framework for performance evaluation of CGM systems, providing a unified approach to reference-method selection, dynamic in-clinic testing, and structured reporting. Adoption would reduce procedural variability but does not resolve calibration-alignment differences. This international clinical opinion proposes a pathway towards internationally interpretable CGM evaluation: immediate transparency of clinical evidence, routine declaration of calibration alignment, and progressive adoption of validated standardised procedures. These steps provide a foundation for reliable interpretation and globally comparable assessment of CGM technologies.