Metabolic Dysfunction Biomarkers as Predictors of Early Diabetes

Distinguishing health-related parameters between metabolically healthy and metabolically unhealthy obesity in women

BACKGROUND

Obesity represents a global health crisis, yet a dichotomy is emerging with classification according to the metabolic state into metabolically healthy obesity (MHO) and metabolically unhealthy obesity (MUO). This study aimed to identify distinctive systemic clinical/endocrinological parameters between MHO individuals, employing a comprehensive comparative analysis of 50 biomarkers. Our emphasis was on routine analytes, ensuring cost-effectiveness for widespread use in diagnosing metabolic health.

SUBJECTS/METHODS

The study included 182 women diagnosed with obesity referred for bariatric surgery at the Endocrinology, Diabetes, and Metabolism Service of São João Hospital and University Centre in Portugal. MUO was defined by the presence of at least one of the following metabolic disorders: diabetes, hypertension, or dyslipidemia. Patients were stratified based on the diagnosis of these pathologies.

RESULTS

Significantly divergent health-related parameters were observed between MHO and MUO patients. Notable differences included: albumin (40.1 ± 2.2 vs 40,98 ± 2.6 g/L, p value = 0.017), triglycerides (110.7 ± 51.1 vs 137.57 ± 82.6 mg/dL, p value = 0.008), glucose (99.49 ± 13.0 vs 119.17 ± 38.9 mg/dL, p value < 0.001), glycated hemoglobin (5.58 ± 0.4 vs 6.15 ± 1.0%, p value < 0.001), urea (31.40 ± 10.0 vs 34.61 ± 10.2 mg/dL, p value = 0.014), total calcium (4.64 ± 0.15 vs 4.74 ± 0.17 mEq/L, 1 mEq/L = 1 mg/L, p value < 0.001), ferritin (100.04 ± 129.1 vs 128.55 ± 102.1 ng/mL, p value = 0.005), chloride (104.68 ± 1.5 vs 103.04 ± 2.6 mEq/L, p value < 0.001), prolactin (13.57 ± 6.3 vs 12.47 ± 7.1 ng/mL, p value = 0.041), insulin (20.36 ± 24.4 vs 23.87 ± 19.6 μU/mL, p value = 0.021), c peptide (3.78 ± 1.8 vs 4.28 ± 1.7 ng/mL, p value = 0.003), albumin/creatinine ratio (15.41 ± 31.0 vs 48.12 ± 158.7 mg/g creatinine, p value = 0.015), and whole-body mineral density (1.27 ± 0.1 vs 1.23 ± 0.1 g/cm2, p value = 0.016).

CONCLUSIONS

Our findings highlight potential additional parameters that should be taken into consideration alongside the commonly used biomarkers for classifying metabolic health in women. These include albumin, urea, total calcium, ferritin, chloride, prolactin, c-peptide, albumin-creatinine ratio, and whole-body mineral density. Moreover, our results also suggest that MHO may represent a transitional phase preceding the development of the MUO phenotype.

NMR-based metabolomic profiling identifies inflammation and muscle-related metabolites as predictors of incident type 2 diabetes mellitus beyond glucose: the Di@bet.es study

AIMS

The aim of this study was to combine nuclear magnetic resonance-based metabolomics and machine learning to find a glucose-independent molecular signature associated with future type 2 diabetes mellitus development in a subgroup of individuals from the Di@bet.es study.

METHODS

The study group included 145 individuals developing type 2 diabetes mellitus during the 8-year follow-up, 145 individuals matched by age, sex and BMI who did not develop diabetes during the follow-up but had equal glucose concentrations to those who did and 145 controls matched by age and sex. A metabolomic analysis of serum was performed to obtain the lipoprotein and glycoprotein profiles and 15 low molecular weight metabolites. Several machine learning-based models were trained.

RESULTS

Logistic regression performed the best classification between individuals developing type 2 diabetes during the follow-up and glucose-matched individuals. The area under the curve was 0.628, and its 95% confidence interval was 0.510-0.746. Glycoprotein-related variables, creatinine, creatine, small HDL particles and the Johnson-Neyman intervals of the interaction of Glyc A and Glyc B were statistically significant.

CONCLUSIONS

The model highlighted a relevant contribution of inflammation (glycosylation pattern and HDL) and muscle (creatinine and creatine) in the development of type 2 diabetes as independent factors of hyperglycemia.

Curcuminoid supplementation in canine diabetic mellitus and its complications using proteomic analysis

Introduction

Inflammation and oxidative stress contribute to diabetes pathogenesis and consequences. Therapeutic approaches for canine diabetes remain a challenge. Curcumin has anti-inflammatory and anti-oxidative effects and is beneficial for humans with diabetes mellitus (DM); however, data on its impact on canine diabetes is limited. This study aimed to evaluate the potential for causing adverse effects, anti-inflammatory effects, anti-oxidative effects and proteomic patterns of curcuminoid supplementation on canine DM.

Methods

Altogether, 18 dogs were divided into two groups: DM (n = 6) and healthy (n = 12). Curcuminoid 250 mg was given to the DM group orally daily for 180 days. Blood and urine sample collection for hematological parameters, blood biochemistry, urinalysis, oxidative stress parameters, inflammatory markers and proteomics were performed every 6 weeks.

Results and discussion

Curcuminoid supplementation with standard therapy significantly decreased oxidative stress with the increased glutathione/oxidized glutathione ratio, but cytokine levels were unaffected. According to the proteomic analysis, curcuminoid altered the expression of alpha-2-HS-glycoprotein, transthyretin, apolipoprotein A-I and apolipoprotein A-IV, suggesting that curcuminoid improves insulin sensitivity and reduces cardiovascular complications. No negative impact on clinical symptoms, kidneys or liver markers was identified. This study proposed that curcuminoids might be used as a targeted antioxidant strategy as an adjunctive treatment to minimize diabetes complications in dogs.

Underestimated Prediabetic Biomarkers: Are We Blind to Their Strategy?

Type 2 Diabetes (T2D) is currently one of the fastest growing health challenging, a non-communicable disease result of the XXI century lifestyle. Given its growing incidence and prevalence, it became increasingly imperative to develop new technologies and implement new biomarkers for early diagnosis in order to promote lifestyle changes and thus cause a setback of the disease. Promising biomarkers have been identified as predictive of T2D development; however, none of them have yet been implemented in clinical practice routine. Moreover, many prediabetic biomarkers can also represent potential therapeutical targets in disease management. Previous studies have identified the most popular biomarkers, which are being thoroughly investigated. However, there are some biomarkers with promising preliminary results with limited associated studies; hence there is still much to be understood about its mechanisms and associations in T2D pathophysiology. This work identifies and discusses the promising results of Galectin-3, Ophthalmate and Fetuin-A.