Non-alcoholic fatty liver disease (NAFLD) is an increasingly common condition associated with type 2 diabetes (T2DM) and cardiovascular disease (CVD). Since systemic metabolic dysfunction underlies NAFLD, the current nomenclature has been revised, and the term metabolic-associated fatty liver disease (MAFLD) has been proposed. The new definition emphasizes the bidirectional relationships and increases awareness in looking for fatty liver disease among patients with T2DM and CVD or its risk factors, as well as looking for these diseases among patients with NAFLD. The most recommended treatment method of NAFLD is lifestyle changes, including dietary fructose limitation, although other treatment methods of NAFLD have recently emerged and are being studied. Given the focus on the liver–gut axis targeting, bacteria may also be a future aim of NAFLD treatment given the microbiome signatures discriminating healthy individuals from those with NAFLD. In this review article, we will provide an overview of the associations of fructose consumption, gut microbiota, diabetes, and CVD in patients with NAFLD.

Microvascular complications of diabetes seem to be clustered and put patients at higher risk of developing cardiovascular disease (CVD). This was a questionnaire-based study designed to screen for the presence of diabetic peripheral neuropathy (DPN), defined as the score in the Michigan Neuropathy Screening Instrument (MNSI) above 2, and to evaluate its association with other complication of diabetes, including CVD. There were 184 patients included into the study. The prevalence of DPN in the study group was 37.5%. The regression model analysis revealed that the presence of DPN was significantly associated with the presence of diabetic kidney disease (DKD) (P = 0.0034;) and patient's age (P < 0.0001). Thirty-four patients (49.3%) with MNSI score >2 were diagnosed with CVD in comparison to 24 (20.1%) subjects with MNSI score ≤ 2 (P = 0.00006). In case of having one diabetes complication diagnosed, it is important to screen for others, including macrovascular ones.

For decades, metformin has been the drug of first choice in the management of type 2 diabetes. However, approximately 2-13% of patients do not tolerate metformin due to gastrointestinal (GI) side effects. Since metformin influences the gut microbiota, we hypothesized that a multi-strain probiotics supplementation would mitigate the gastrointestinal symptoms associated with metformin usage. This randomized, double-blind, placebo-controlled, single-center, cross-over trial (ProGasMet study) assessed the efficacy of a multi-strain probiotic in 37 patients with metformin intolerance. Patients were randomly allocated (1:1) to receive probiotic (PRO-PLA) or placebo (PLA-PRO) at baseline and, after 12 weeks (period 1), they crossed-over to the other treatment arm (period 2). The primary outcome was the reduction of GI adverse events of metformin. 37 out of 82 eligible patients were enrolled in the final analysis of whom 35 completed the 32 weeks study period and 2 patients resigned at visit 5. Regardless of the treatment arm allocation, while on probiotic supplementation, there was a significant reduction of incidence (for the probiotic period in PRO-PLA/PLA-PRO: P = 0.017/P = 0.054), quantity and severity of nausea (P = 0.016/P = 0.024), frequency (P = 0.009/P = 0.015) and severity (P = 0.019/P = 0.005) of abdominal bloating/pain as well as significant improvement in self-assessed tolerability of metformin (P < 0.01/P = 0.005). Moreover, there was significant reduction of incidence of diarrhea while on probiotic supplementation in PRO-PLA treatment arm (P = 0.036). A multi-strain probiotic diminishes the incidence of gastrointestinal adverse effects in patients with type 2 diabetes and metformin intolerance.

Context: The presence of metabolic dysfunction–associated steatotic liver disease (MASLD) in patients with diabetes mellitus (DM) is associated with a high risk of cardiovascular disease, but is often underdiagnosed.Objective: To develop machine learning (ML) models for risk assessment of MASLD occurrence in patients with DM.Methods: Feature selection determined the discriminative parameters, utilized to classify DM patients as those with and without MASLD. The performance of the multiple logistic regression model was quantified by sensitivity, specificity, and percentage of correctly classified patients, and receiver operating characteristic (ROC) curve analysis. Decision curve analysis (DCA) assessed the model's net benefit for alternative treatments.Results: We studied 2000 patients with DM (mean age 58.85 ± 17.37 years; 48% women). Eight parameters: age, body mass index, type of DM, alanine aminotransferase, aspartate aminotransferase, platelet count, hyperuricaemia, and treatment with metformin were identified as discriminative. The experiments for 1735 patients show that 744/991 (75.08%) and 586/744 (78.76%) patients with/without MASLD were correctly identified (sensitivity/specificity: 0.75/0.79). The area under ROC (AUC) was 0.84 (95% CI, 0.82-0.86), while DCA showed a higher clinical utility of the model, ranging from 30% to 84% threshold probability. Results for 265 test patients confirm the model's generalizability (sensitivity/specificity: 0.80/0.74; AUC: 0.81 [95% CI, 0.76-0.87]), whereas unsupervised clustering identified high-risk patients.Conclusion: A ML approach demonstrated high performance in identifying MASLD in patients with DM. This approach may facilitate better risk stratification and cardiovascular risk prevention strategies for high-risk patients with DM at risk of MASLD.

Sodium‐glucose cotransporter 2 inhibitors (SGLT2i) have been recognized as potent antioxidant agents. Since SGLT2i are nephroprotective drugs, we aimed to examine the urine antioxidant status in patients with type 2 diabetes mellitus (T2DM). One hundred and one subjects participated in this study, including 37 T2DM patients treated with SGLT2i, 31 T2DM patients not using SGLT2i, and 33 healthy individuals serving as a control group. Total antioxidant capacity (TAC), superoxide dismutase (SOD), manganese superoxide dismutase (MnSOD), free thiol groups (R‐SH, sulfhydryl groups), and catalase (CAT) activity, as well as glucose concentration, were assessed in the urine of all participants. Urine SOD and MnSOD activity were significantly higher among T2DM patients treated with SGLT2i than T2DM patients without SGLT2i treatment (p = 0.009 and p = 0.003, respectively) and to the healthy controls (p = 0.002 and p = 0.001, respectively). TAC was significantly lower in patients with T2DM treated with SGLT2i when compared to those not treated and healthy subjects (p = 0.036 and p = 0.019, respectively). It could be hypothesized that the mechanism by which SGLT2i provides nephroprotective effects involves improvement of the SOD antioxidant activity. However, lower TAC might impose higher OS (oxidative stress), and elevation of SOD activity might be a compensatory mechanism.

The impact of diabetic kidney disease (DKD) phenotype on the cardiovascular (CV) risk remains ambiguous. This was a prospective, single-center study (The Silesia Diabetes-Heart Project, NCT05626413) of people with diabetes mellitus (DM). The primary outcome was a composite of CV events during a median follow up of 2.8 years (IQR 1.7; 4.0). Individuals were divided into groups based on DKD phenotypes [reduced estimated glomerular filtration rate (eGFR) (< 60 mL/min/1.73 m2), and/or elevated urinary albumin creatinine ratio (UACR) (≥ 30 mg/g)] and compared to those without DKD. Among 2306 people with DM (mean age 58 ± 18 years, 51.9% males) those with DKD had higher CV events risk (aHR 1.02, 95% CI 1.01–1.03) compared to those without DKD. People with reduced eGFR (aHR 1.78, 95% CI 1.19–2.67) and those with reduced eGFR and elevated UACR (aHR 1.60, 95% CI 1.08–2.37) were at higher risk of CV events compared to people with normal eGFR and UACR, while people with elevated UACR only (aHR 0.91, 95% CI 0.62–1.32) did not had the risk heightened. Among people with DKD less frequent prescriptions with sodium-glucose co-transporter 2 inhibitors and renin–angiotensin–aldosterone system inhibitors were observed (OR 0.38, 95% CI 0.29–0.49, p < 0.001 and OR 0.39, 95% CI 0.30–0.50, p < 0.001, respectively) compared with the ones without DKD. People with DKD, with reduced eGFR or both reduced eGFR and elevated UACR, but not with elevated UACR alone, are at higher CV risk. Personalizing therapy based on clinic-based renal phenotyping can facilitate the initiation of CV disease modifying therapy.