COVID19 biomarkers: What did we learn from systematic reviews?

The coronavirus disease 2019 (COVID19) pandemic continues to represent a substantial public health concern. It can rapidly progress to severe disease, with poor prognosis and a high mortality risk. An early diagnosis and specific prognostic tools can help healthcare providers to start interventions promptly, understand the likely prognosis and to identify and treat timely individuals likely to develop severe disease with enhanced mortality risk. Here we focused on an impressive set of systematic reviews and meta-analyses that were performed since the start of the COVID19 pandemic and summarized their results related to the levels of hematologic, inflammatory, immunologic biomarkers as well as markers of cardiac, respiratory, hepatic, gastrointestinal and renal systems and their association with the disease progression, severity and mortality. The evidence outlines the significance of specific biomarkers, including inflammatory and immunological parameters (C-reactive protein, procalcitonin, interleukin-6), hematological (lymphocytes count, neutrophil-to-lymphocyte ratio, D-dimer, ferritin, red blood cell distribution width), cardiac (troponin, CK-MB, myoglobin), liver (AST, ALT, total bilirubin, albumin) and lung injury (Krebs von den Lungen-6) that can be used as prognostic biomarkers to aid the identification of high-risk patients and the prediction of serious outcomes, including mortality, in COVID19. Thus, these parameters should be used as essential tools for an early risk stratification and adequate intervention in improving disease outcomes in COVID19 patients

Effects of combined treatment of probiotics and metformin in management of type 2 diabetes:A systematic review and meta-analysis

Background: Lifestyle changes and dietary intervention, including the use of probiotics, can modulate dysbiosis of gut microbiome and contribute to the management of type 2 diabetes mellitus (T2DM). This systematic review and meta-analysis aim to assess the efficacy of metformin plus probiotics versus metformin alone on outcomes in patients with T2DM. Methods: We searched MEDLINE and EMBASE from inception to February 2023 to identify all randomized controlled trials (RCTs), which compared the use of metformin plus probiotics versus metformin alone in adult patients with T2DM. Data were summarized as mean differences (MD) with 95 % confidence interval (CI) and pooled under the random effects model. Findings: Fourteen RCTs (17 comparisons, 1009 patients) were included in this systematic review. Pooled results show a significant decrease in fasting glucose (FG) (MD = −0.64, 95 % CI = −1.06, −0.22) and HbA1c (MD = −0.29, 95 % CI = −0.47, −0.10) levels in patients with T2DM treated with metformin plus probiotics versus metformin alone. The addition of probiotics to metformin resulted in lower odds of gastrointestinal adverse events (Odds ratio = 0.18, 95 % CI = 0.09, 0.3.8; I2 = 0 %). Conclusions: The addition of probiotics to metformin therapy is associated with improvement in T2DM outcomes. However, high-quality and adequately reported RCTs are needed in the future to confirm our findings.</p

PPAR agonists as add-on treatment with metformin in management of type 2 diabetes:a systematic review and meta-analysis

The combination of metformin and the peroxisome proliferator-activated receptors (PPAR) agonists offers a promising avenue for managing type 2 diabetes (T2D) through their potential complementary mechanisms of action. The results from randomized controlled trials (RCT) assessing the efficacy of PPAR agonists plus metformin versus metformin alone in T2D are inconsistent, which prompted the conduct of the systematic review and meta-analysis. We searched MEDLINE and EMBASE from inception (1966) to March 2023 to identify all RCTs comparing any PPAR agonists plus metformin versus metformin alone in T2D. Categorical variables were summarized as relative risk along with 95% confidence interval (CI). Twenty RCTs enrolling a total of 6058 patients met the inclusion criteria. The certainty of evidence ranged from moderate to very low. Pooled results show that using PPAR agonist plus metformin, as compared to metformin alone, results in lower concentrations of fasting glucose [MD = - 22.07 mg/dl (95% CI - 27.17, - 16.97), HbA1c [MD = - 0.53% (95% CI - 0.67, - 0.38)], HOMA-IR [MD = - 1.26 (95% CI - 2.16, - 0.37)], and fasting insulin [MD = - 19.83 pmol/L (95% CI - 29.54, - 10.13)] without significant increase in any adverse events. Thus, synthesized evidence from RCTs demonstrates the beneficial effects of PPAR agonist add-on treatment versus metformin alone in T2D patients. In particular, novel dual PPARα/γ agonist (tesaglitazar) demonstrate efficacy in improving glycaemic and lipid concentrations, so further RCTs should be performed to elucidate the long-term outcomes and safety profile of these novel combined and personalized therapeutic strategies in the management of T2D.PROSPERO registration no. CRD42023412603.</p

Association between 11β-hydroxysteroid dehydrogenase type 1 gene polymorphisms and metabolic syndrome

ntroduction: The enzyme 11β-hydroxysteroid dehydrogenase type 1 (11β-HSD1) catalyzes the con-version of the hormonally inactive cortisone to active cortisol, thus facilitating glucocorticoid receptor activation in target tissues. Increased expression of 11β-HSD1 in adipose tissue has been associated with obesity and insulin resistance. In this study, we investigated the association of two 11β-HSD1 gene (HSD11B1) polymorphisms with the metabolic syndrome (MetS) and its characteristics in the Bosnian population. Materials and methods: The study included 86 participants: 43 patients diagnosed with MetS and 43 healthy controls. Subjects were genotyped for two HSD11B1 gene polymorphisms: rs846910: G>A and rs45487298: insA, by the high resolution melting curve analysis. Genotype distribution and an influence of genotypes on clinical and biochemical parameters were assessed. Results: There was no significant difference in the mutated allele frequencies for the two HSD11B1 gene polymorphisms between MetS patients and controls. In MetS patients, no significant associati-ons between disease-associated traits and rs45487298: insA were found. Regarding rs846910: G>A variant, heterozygous patients (G/A) had significantly lower systolic (P = 0.017) and diastolic blood pressure (P = 0.015), lower HOMA-IR index (P = 0.011) and higher LDL-cholesterol levels (P = 0.049), compared to the wild-type homozygotes. In the control group, rs45487298: insA polymorphism was associated with lower fasting plasma insulin levels (P = 0.041), lower homeostasis model asses-sment insulin resistance (HOMA-IR) index (P = 0.041) and lower diastolic blood pressure (P = 0.048). Significant differences between rs846910: G>A genotypes in controls were not detected. Haplotype analysis confirmed the association of rs45487298: insA with markers of insulin resistance in the con-trol subjects. Conclusions: Our results indicate that a common rs45487298: insA polymorphism in HSD11B1 gene may have a protective effect against insulin resistance

Effects of TCF7L2 rs7903146 variant on metformin response in patients with type 2 diabetes

The response to metformin, the most commonly used drug for the treatment of type 2 diabetes (T2D), is highly variable. The common variant rs7903146 C>T within the transcription factor 7 like 2 gene (TCF7L2) is the strongest genetic risk factor associated with T2D to date. In this study we explored the effects of TCF7L2 rs7903146 genotype on metformin response in T2D. The study included 86 newly diagnosed patients with T2D, incident users of metformin. Levels of fasting glucose, insulin, HbA1c, total cholesterol, HDL-cholesterol, LDL-cholesterol, triglycerides, and anthropometric parameters were measured prior to metformin therapy, and 6 and 12 months after the treatment. Genotyping of TCF7L2 rs7903146 was performed by the Sequenom MassARRAY® iPLEX® platform. At baseline, the diabetes risk allele (T) showed an association with lower triglyceride levels (p = 0.037). After 12 months of metformin treatment, the T allele was associated with 25.9% lower fasting insulin levels (95% CI 10.9-38.3%, p = 0.002) and 29.1% lower HOMA-IR index (95% CI 10.1-44.1%, p = 0.005), after adjustment for baseline values. Moreover, the T allele was associated with 6.7% lower fasting glucose levels (95% CI 1.1-12.0%, p = 0.021), adjusted for baseline glucose and baseline HOMA-%B levels, after 6 months of metformin treatment. This effect was more pronounced in TT carriers who had 16.8% lower fasting glucose levels (95% CI 7.0-25.6%, p = 0.002) compared to the patients with CC genotype. Our results suggest that TCF7L2 rs7903146 variant affects markers of insulin resistance and glycemic response to metformin in newly diagnosed patients with T2D within the first year of metformin treatment

Variation in the Glucose Transporter gene <i>SLC2A2 </i>is associated with glycaemic response to metformin

Metformin is the first-line antidiabetic drug with over 100 million users worldwide, yet its mechanism of action remains unclear1. Here the Metformin Genetics (MetGen) Consortium reports a three-stage genome-wide association study (GWAS), consisting of 13,123 participants of different ancestries. The C allele of rs8192675 in the intron of SLC2A2, which encodes the facilitated glucose transporter GLUT2, was associated with a 0.17% (P = 6.6 × 10−14) greater metformin-induced reduction in hemoglobin A1c (HbA1c) in 10,577 participants of European ancestry. rs8192675 was the top cis expression quantitative trait locus (cis-eQTL) for SLC2A2 in 1,226 human liver samples, suggesting a key role for hepatic GLUT2 in regulation of metformin action. Among obese individuals, C-allele homozygotes at rs8192675 had a 0.33% (3.6 mmol/mol) greater absolute HbA1c reduction than T-allele homozygotes. This was about half the effect seen with the addition of a DPP-4 inhibitor, and equated to a dose difference of 550 mg of metformin, suggesting rs8192675 as a potential biomarker for stratified medicine

Effects of diabetes, insulin, and vanadium on regulation of glycogen synthesis : roles of glycogen synthase kinase-3 and protein phosphatase-1

Although the activation of muscle glycogen synthase by insulin was recognized 40 years ago (Villar-Palasi and Larner 1960), the molecular mechanisms of this insulin effect are still unclear. In the present study, we examined the in vivo effects of insulin and vanadium treatment on glycogen synthase (GS) activation in two animal models of diabetes. Wistar rats with streptozotocin (STZ)-induced (60 mg/kg i.v.) diabetes were used as an animal model of poorly controlled type 1 diabetes, while Zucker fatty rats were used as a model ofthe prediabetic state of type 2 diabetes. The GS fractional activity (GSFA), as well as the activity of its two proposed upstream regulating enzymes in the insulin-signaling cascade, glycogen synthase kinase-3 (GSK-3) and protein phosphatase-1 (PP1), were determined in control and STZ-diabetic rats with either short-term (4-week) or long-term (7- or 9-week) diabetes following vanadium treatment, which started one week after STZ-injection. Treated Wistar rats received either bis(maltolato)oxovanadium (IV) (BMOV) or bis(ethylmaltolato)oxovanadium (IV) (BEOV) at a final dose of 0.3-0.4 mmol/kg/day administered in drinking water. The Zucker rats were treated with the same dose of BMOV for 3 or 10 weeks. Treated animals were euglycemic at the time of termination. The skeletal muscle, liver and heart were removed quickly either before or following an insulin injection (5 U/kg i.v.), freeze-clamped, powdered using liquid nitrogen and homogenized. Neither diabetes, nor vanadium or insulin in vivo treatment affected GSK-3β activity in STZ-diabetic rats skeletal muscle, liver, and heart, nor in the Zucker fatty rat muscle as compared to controls. In skeletal muscle no difference in basal GSFA between either short- or long-term STZ-diabetic rats and their age-matched controls was shown. Following insulin stimulation in the short-term STZ-diabetic rats muscle GSFA was increased, while in the long-term diabetic rats it remained unchanged. Taken together with plasma glucose levels, these data suggest that STZ-diabetic rats become refractory to the effects of insulin on GS activity after a longer duration of diabetes. PP1 activity in skeletal muscle was increased by diabetes and returned to normal by vanadium treatment. However, this treatment did not stimulate GSFA in the skeletal muscle of STZ-diabetic animals. Interestingly, in the liver from long-term STZ-diabetic rats, the activities of both total and active GS were decreased compared to controls, and then restored by vanadium treatment, suggesting a tissue specific regulation of glycogen synthesis. Importantly, in the Zucker fatty rats vanadium treatment improved insulin sensitivity and mimicked insulin effects on GSFA and PP1 activity in skeletal muscle of fatty rats. Furthermore, insulin-stimulated PP1 activity in skeletal muscle of fatty rats was restored by vanadium treatment. In conclusion, the observed glucoregulatory effect of vanadium treatment in STZ-diabetic rats may be related, at least in part, to the regulation of hepatic glycogen synthesis. The discordance between GS and PP1 activity in skeletal muscle of two different animal models of diabetes may imply the involvement of alternative signaling pathways in the regulation of glycogen synthesis.Medicine, Faculty ofAnesthesiology, Pharmacology and Therapeutics, Department ofGraduat

Pharmacogenetics and personalized treatment of type 2 diabetes

Type 2 diabetes mellitus (T2DM) is a worldwide epidemic with considerable health and economic consequences. T2DM patients are often treated with more than one drug, including oral antidiabetic drugs (OAD) and drugs used to treat diabetic complications, such as dyslipidemia and hypertension. If genetic testing could be employed to predict treatment outcome, appropriate measures could be ta-ken to treat T2DM more efficiently. Here we provide a review of pharmacogenetic studies focused on OAD and a role of common drug-metabolizing enzymes (DME) and drug-transporters (DT) variants in therapy outcomes. For example, genetic variations of several membrane transporters, including SLC22A1/2 and SLC47A1/2 genes, are implicated in the highly variable glycemic response to met-formin, a first-line drug used to treat newly diagnosed T2DM. Furthermore, cytochrome P450 (CYP) enzymes are implicated in variation of sulphonylurea and meglitinide metabolism. Additional variants related to drug target and diabetes risk genes have been also linked to interindividual differences in the efficacy and toxicity of OAD. Thus, in addition to promoting safe and cost-effective individualized diabetes treatment, pharmacogenomics has a great potential to complement current efforts to optimi-ze treatment of diabetes and lead towards its effective and personalized care