Diabetes, Obesity & Metabolism最新文献

Aim: To explore the association between the non-high-density lipoprotein cholesterol (HDL-C)/HDL-C ratio (NHHR) and the risk of major adverse cardiovascular events (MACEs) and overall mortality in patients with type 2 diabetes mellitus (T2DM).

Materials and methods: NHHR, calculated as (total cholesterol - HDL-C)/HDL-C, was evaluated in 10,188 participants. Cox proportional hazard regression models were employed to assess the association of NHHR with future risk of MACEs and overall mortality. Restricted cubic spline analysis, smooth curve fitting and piecewise regression models were utilized to explore the non-linear correlation and establish the threshold. Subgroup and interaction analyses verified the robustness of the findings. The area under the receiver operating characteristic area under the curve assessed the additional predictive value of NHHR beyond conventional risk factors.

Results: After adjusting for confounding factors, each 1-unit increase in NHHR was associated with a 12% increased risk of MACEs (hazard ratio [HR]: 1.12, 95% confidence interval [CI]: 1.07-1.16; p < 0.0001), a 5% increase in overall mortality (HR: 1.05, 95% CI: 1.01-1.10; p = 0.0256), a 10% increase in cardiovascular disease mortality (HR 1.10, 95% CI: 1.03-1.18; p = 0.0074), an 12% increase in non-fatal myocardial infarction (HR: 1.12, 95% CI: 1.05-1.18; p = 0.0002), and an 11% increase in non-fatal stroke (HR: 1.11, 95% CI: 1.02-1.20; p = 0.0123). Analyses showed a non-linear relationship between NHHR and MACEs in patients with T2DM (non-linearity p < 0.001). A two-stage linear regression model identified a threshold for MACEs at 6.28. Integration NHHR into the conventional model significantly enhanced predictive accuracy for MACEs.

Conclusions: NHHR is a predictor of the risk of developing MACEs and overall mortality in patients with T2DM, with higher NHHR values independently associated with increased future MACE risks after full adjustment for confounders.

Continuous glucose monitoring (CGM) has led to a paradigm shift in the management of pregnant women with type 1 diabetes (T1D), with improved glycaemic control, less hypoglycaemia and fewer pregnancy complications. Data on CGM use in pregnant women with type 2 diabetes (T2D) are limited. A large randomized controlled trial (RCT) on CGM use in people with T2D in pregnancy is ongoing. Small studies on CGM use in women with gestational diabetes (GDM) have suggested improved glycaemic control and better qualification when insulin is needed. However, none of these studies was powered to evaluate pregnancy outcomes. Several large RCTs are ongoing in women with GDM. In addition to CGM, other technologies, such as advanced hybrid closed-loop (AHCL) systems have further improved glycaemic management in people with T1D. AHCL therapy adapts insulin delivery via a predictive algorithm integrated with CGM and an insulin pump. A large RCT with the AHCL CamAPS® FX demonstrated a 10% increase in time in range compared to standard insulin therapy in a pregnant population with T1D. Recently, an RCT of an AHCL system not approved for use in pregnancy (780G MiniMed) has also demonstrated additional benefits of AHCL therapy compared to standard insulin therapy, with improved time in range overnight, less hypoglycaemia and improved treatment satisfaction. More evidence is needed on the impact of AHCL therapy on maternal and neonatal outcomes and on which glycaemic targets with CGM should be used in pregnant women with T2D and GDM. We review the current evidence on the use of CGM and AHCL therapy in pregnancy.

The global prevalence of chronic kidney disease (CKD) is approximately 9%. CKD is predicted to become the fifth largest global cause of death by 2040. Moreover, CKD causes disability, diminished quality of life and poses a high cost to healthcare systems. Delaying the development and progression of CKD is therefore of the utmost importance. Several kidney-specific outcome trials on sodium-glucose co-transporter-2 inhibitors (SGLT-2s) have recently provided a paradigm shift in the treatment of people with CKD, with or without diabetes, as these agents have been shown to reduce the progression of CKD on top of maximally tolerated renin-angiotensin-aldosterone system (RAAS) blockade. The relative benefit and safety of SGLT-2is seems to be consistent across ethnicities, ages and frailty categories; however, this needs to be tested in dedicated clinical trials. Guidelines make clear recommendations for the prescription of SGLT-2is and RAAS inhibitors as standard of care for people with CKD. Their combination with other newer antidiabetic agents may provide further benefits by targeting different components of CKD mechanisms. Dedicated randomized controlled trials are needed to test whether combination with other agents could extend the use of SGLT2is and identify people in whom a combination of drugs may be most effective. Increased efforts to implement the guidelines on treatment with SGLT-2is for people with CKD are needed, particularly in those at the highest risk of adverse outcomes and without type 2 diabetes. Moreover, strategies to target the equitable use of SGLT-2is are needed.

Aims: Roux-en-Y gastric bypass (RYGB) surgery alters postprandial glucose profiles, causing post-bariatric hypoglycaemia (PBH) in some individuals. Due to the liver's central role in glucose homeostasis, hepatic glucose handling might differ in RYGB-operated patients with PBH compared to non-operated healthy controls (HC).

Materials and methods: We enrolled RYGB-operated adults with PBH and HCs (n = 10 each). Participants ingested 60 g of [6,6'-²H₂]-glucose (d-glucose) after an overnight fast. Deuterium metabolic imaging (DMI) with interleaved ¹³C magnetic resonance spectroscopy was performed before and until 150 min post-d-glucose intake, with frequent blood sampling to quantify glucose enrichment and gluco-regulatory hormones until 180 min. Glucose fluxes were assessed by mathematical modelling. Outcome trajectories were described using generalized additive models.

Results: In RYGB subjects, the hepatic d-glucose signal increased early, followed by a decrease, whereas HCs exhibited a gradual increase and consecutive stabilization. Postprandial hepatic glycogen accumulation and the suppression of endogenous glucose production were lower in RYGB patients than in HCs, despite higher insulin exposure, indicating lower hepatic insulin sensitivity. The systemic rate of ingested d-glucose was faster in RYGB, leading to a higher, earlier plasma glucose peak and increased insulin secretion. Postprandial glucose disposal increased in RYGB patients, without between-group differences in peripheral insulin sensitivity.

Conclusions: Exploiting DMI with stable isotope flux analysis, we observed distinct postprandial hepatic glucose trajectories and parameters of glucose-insulin homeostasis in RYGB patients with PBH versus HCs. Despite altered postprandial glucose kinetics and higher insulin exposure, there was no evidence of impaired hepatic glucose uptake or output predisposing to PBH in RYGB patients.

Aim: The aim was to investigate the causal relationship between puberty timing and plasma metabolites, accounting for birth weight, childhood and adulthood adiposity.

Materials and methods: The meta-analysis of genome-wide association studies (GWAS) for puberty timing was extracted from the ReproGen Consortium, involving 329 345 women of European ancestry. Summary data for 174 plasma metabolites were retrieved from a recently conducted cross-platform GWAS that involved a meta-analysis of three cohort studies (i.e. the Fenland, European Prospective Investigation into Cancer-Norfolk and INTERVAL studies) and three publicly available studies and included up to 86 507 participants. We conducted a two-sample Mendelian randomization (MR) analysis to infer the causal relationship of puberty timing on 174 plasma metabolites, complemented by a two-step and multivariable Mendelian randomization (MVMR) analysis to assess direct and indirect effects. Additionally, summary-level data from the UK Biobank were used for our replication analysis.

Results: The results of the two-sample MR provide moderate evidence supporting a causal relationship between puberty timing and 23 of 174 plasma metabolites (i.e. 7 acylcarnitines, 8 amino acids, 2 biogenic amines and 6 lysophosphatidylcholines). Even after single-nucleotide polymorphisms associated with birth weight and childhood adiposity were excluded, causal effects persisted for 16 metabolites (i.e. 8 acylcarnitines, 4 amino acids, 2 biogenic amines and 2 lysophosphatidylcholines). The two-step MR analysis provided evidence that the relationship between puberty timing and plasma metabolites was mediated by adulthood adiposity. Additionally, moderate evidence emerged for an independent causal effect of puberty timing on 10 metabolites through an MVMR analysis (i.e. 5 acylcarnitines, 2 amino acids, 1 biogenic amine, 1 lysophosphatidylcholine and 1 phosphatidylcholine). Furthermore, the replication analysis suggested the robustness of our results.

Conclusions: In summary, our study provides compelling evidence that puberty timing has a causal influence on certain plasma metabolites, although this influence is largely mediated by adulthood adiposity.

Aims: The aim was to evaluate the effect of extended use of the Omnipod® 5 automated insulin delivery (AID) system in adults with type 2 diabetes and suboptimal glycaemic control.

Materials and methods: Following an 8-week single-arm, multicentre, outpatient trial of AID in adults with type 2 diabetes and baseline ≥ 64 mmol/mol, participants were given the opportunity to continue use of the AID system in a 26-week (~6 month) extension phase. The primary safety endpoints were percentage of time with sensor glucose ≥ 250 mg/dL and < 54 mg/dL. Additional glycaemic measures, including percentage of time in range (TIR) (70-180 mg/dL) and HbA1c, were evaluated. The use of non-insulin anti-hyperglycaemic medications was permitted throughout the entire study.

Results: During the initial 8-week study, participants (N = 22) achieved a decrease in percentage of time ≥ 250 mg/dL from 27.4% ± 21.0% to 10.5% ± 8.8% (p < 0.0001), which further decreased to 9.7% ± 9.2% during the extension phase (p = 0.0002 vs. standard therapy). Percentage of time < 54 mg/dL remained low from standard therapy through extension (median [interquartile range] 0.00% [0.00%, 0.06%] vs. 0.02% [0.00%, 0.05%], p > 0.05). HbA1c decreased by 1.6% ± 1.2% (15.5 ± 13.1 mmol/mol, p < 0.0001) and TIR increased by 22.4% ± 19.2% (p < 0.0001) from standard therapy through extension with no significant change in body mass index and without an observed increase in total daily insulin requirements.

Conclusions: These longer-term findings of Omnipod 5 AID system use demonstrate the potential value of AID in helping people with type 2 diabetes reach glycaemic targets.

Aims: About 10%-30% of individuals with obesity are metabolically healthy, but the specific characteristics of the metabolically healthy obesity (MHO) phenotype remain unclear. We aimed to examine how physical activity, education, depressive symptoms and genetic predisposition to obesity differ between individuals with MHO and those with metabolically unhealthy obesity (MUO), and whether these factors predict stability in MHO or conversion to a metabolically unhealthy state.

Materials and methods: We retrieved data on 9809 individuals with obesity from the Health and Retirement Study collected between 2006 and 2016. We compared how physical activity, education, depressive symptoms and a polygenic score for higher body mass index (BMI) (PGS_BMI) differed cross-sectionally between MHO and MUO using logistic regression. We then examined if the same factors predict conversion to a metabolically unhealthy state over 4 years in individuals with MHO.

Results: Individuals with MHO had higher physical activity (odds ratio [OR] = 0.81), higher education (OR = 0.83) and lower depressive symptoms (OR = 1.14) compared to those with MUO but did not differ in the PGS_BMI. The associations were slightly attenuated in mutually adjusted models. None of the factors were associated with conversion from MHO to a metabolically unhealthy state. However, a higher PGS_BMI indicated 24% lower risk of conversion to a metabolically unhealthy state (p = 0.07).

Conclusions: Physical activity, education and depressive symptoms differed between MHO and MUO, even when mutually adjusted for, but did not predict conversion from a metabolically healthy to unhealthy state. Although not statistically significant, the results indicated that those with genetically predicted high BMI are more likely to maintain MHO and not convert to a metabolically unhealthy state.