Metabolomics, with its wealth of data, offers a valuable avenue for enhancing predictions and decision-making in diabetes. This observational study aimed to leverage machine learning (ML) algorithms to predict the 4-year risk of developing type 2 diabetes mellitus (T2DM) using targeted quantitative metabolomics data. A cohort of 279 cardiovascular risk patients who underwent coronary angiography and who were initially free of T2DM according to American Diabetes Association (ADA) criteria was analyzed at baseline, including anthropometric data and targeted metabolomics, using liquid chromatography (LC)–mass spectroscopy (MS) and flow injection analysis (FIA)–MS, respectively. All patients were followed for four years. During this time, 11.5% of the patients developed T2DM. After data preprocessing, 362 variables were used for ML, employing the Caret package in R. The dataset was divided into training and test sets (75:25 ratio) and we used an oversampling approach to address the classifier imbalance of T2DM incidence. After an additional recursive feature elimination step, identifying a set of 77 variables that were the most valuable for model generation, a Support Vector Machine (SVM) model with a linear kernel demonstrated the most promising predictive capabilities, exhibiting an F1 score of 50%, a specificity of 93%, and balanced and unbalanced accuracies of 72% and 88%, respectively. The top-ranked features were bile acids, ceramides, amino acids, and hexoses, whereas anthropometric features such as age, sex, waist circumference, or body mass index had no contribution. In conclusion, ML analysis of metabolomics data is a promising tool for identifying individuals at risk of developing T2DM and opens avenues for personalized and early intervention strategies.

Obesity is characterized by the rapid expansion of visceral adipose tissue, resulting in a hypoxic environment in adipose tissue which leads to a profound change of gene expression in adipocytes. As a consequence, there is a dysregulation of metabolism and adipokine secretion in adipose tissue leading to the development of systemic inflammation and finally resulting in the onset of metabolic diseases. The flavonoid quercetin as well as other secondary plant metabolites also referred to as phytochemicals have anti-oxidant, anti-inflammatory, and anti-diabetic effects known to be protective in view of obesity-related-diseases. Nevertheless, its underlying molecular mechanism is still obscure and thus the focus of this study was to explore the influence of quercetin on human SGBS (Simpson Golabi Behmel Syndrome) adipocytes’ gene expression. We revealed for the first time that quercetin significantly changed expression of adipokine (Angptl4, adipsin, irisin and PAI-1) and glycolysis-involved (ENO2, PFKP and PFKFB4) genes, and that this effect not only antagonized but in part even overcompensated the effect mediated by hypoxia in adipocytes. Thus, these results are explained by the recently proposed hypothesis that the protective effect of quercetin is not solely due to its free radical-scavenging activity but also to a direct effect on mitochondrial processes, and they demonstrate that quercetin might have the potential to counteract the development of obesity-associated complications.

Serum cholesterol procalcitonin (PCT) and C-reactive protein (CRP) levels were measured consecutively in 76 critically ill patients at admission to the intensive care unit. The presence of infection was defined according to the CDC (Centers for Disease Control and Prevention) criteria; in-house mortality, underlying diseases, and severity of sepsis were monitored. Nonsurvivors had significantly lower cholesterol levels compared with survivors (69 mg/dL [range, 37–88 mg/dL] vs. 96 mg/dL [range, 71–132 mg/dL], P = 0.006) whereas no significant differences were noted for serum PCT and CRP levels. In a cohort of patients with cholesterol levels of 50 mg/dL or less, 82% did not survive as compared with patients with cholesterol levels of 100 mg/dL or greater (mortality, 21%). In a control group without infection, no difference of cholesterol, PCT, or CRP was found between survivors and nonsurvivors. Our data show that low cholesterol levels in patients with infectious disease have a prognostic value and may be useful markers to identify high-risk patients already at admission.

Statins, which are effective lipid-lowering drugs, also possess anti-inflammatory potential. However, circulating lipoproteins may also play a protective role during acute inflammatory diseases because of their ability to bind bacterial toxins. Low cholesterol levels have been reported in inflammatory conditions, and plasma cholesterol concentrations inversely correlate with severity and clinical outcome in septic patients. It is thus paradoxical that statins, which drastically reduce circulating cholesterol levels, should be beneficial in patients with inflammatory disease who are already hypocholesterolemic. We investigated the effect of simvastatin on LPS-induced nuclear factor kappaB (NF-kappaB) activation, TNF release, and mortality in guinea pigs, an animal model with a lipoprotein profile and pattern similar to humans. In the present study, simvastatin reduced circulating total and low-density lipoprotein cholesterol levels by 68% and 76%, respectively, and LPS-induced mortality from 73% to 20%. This reduction was accompanied by a significant reduction of NF-kappaB activation in the liver tissue, splenocytes, and plasma TNF levels by about 80%, 50%, and 77%, respectively. Our data suggest that simvastatin, despite lowering circulating low-density lipoprotein cholesterol, decreased LPS toxicity by reduction of NF-kappaB activation and subsequent release of TNF by modulating 3-hydroxy-3-methylglutaryl coenzyme A reductase activity and therefore deserves consideration as a possible adjuvant therapy in acute inflammatory disease.

Since the onset of the COVID-19 pandemic in March 2020, over 769 million confirmed COVID-19 cases, including close to 7 million COVID-19-related deaths, have been reported. Although mortality rates have dropped notably compared to the first months of the pandemic, spikes in reported cases and mortality rates continue to be registered. Both recent spikes in case numbers and the continued emergence of new variants suggest that vulnerable patient groups, including older adults, immunocompromised patients, and patients with severe comorbidities, are going to continue to be affected by COVID-19. In order to curb the pandemic, relieve the pressure on primary care facilities, and reduce mortality rates, global vaccination programs have been established by the WHO, with over 13.5 billion vaccine doses having been administered globally. In most immunocompetent individuals, vaccination against COVID-19 results in the production of anti-SARS-CoV-2 spike antibodies. However, certain patient subsets have inadequate or reduced immune responses, and immune responses are known to decrease with age. General recommendations on the timing of booster vaccinations may therefore be insufficient to protect vulnerable patients. This review aims to evaluate the clinical role of anti-SARS-CoV-2 antibodies, focusing on measurement indications, prognostic value, and potential as a correlate of protection to guide future booster vaccination strategies.

BACKGROUND: Despite the currently prevailing, milder Omicron variant of COVID-19, older adults remain at elevated risk of hospital admission, critical illness, and death. Loss of efficacy of the immune system, including reduced strength, quality, and durability of antibody responses, may render generalized recommendations on booster vaccinations inadequate. There is a lack of data on the efficacy of antibody levels in older adults and on the utility of vaccination status versus antibody levels as a correlate of protection. It is further unclear whether antibody levels may be used to guide the timing of booster vaccinations in older adults. METHODS: We conducted a prospective multicenter cohort study comprising hospitalized patients with COVID-19. Anti–SARS-CoV-2 spike antibodies were measured on hospital admission. The primary endpoint was in-hospital mortality. Patients were stratified by age, antibody levels, and vaccination status. Multiple logistic regression and Cox regression analyses were conducted. RESULTS: In total, 785 older patients (≥60 years of age [a]) and 367 controls (<60a) were included. After adjusting for confounders, risk of mortality, ICU admission, endotracheal intubation, and oxygen administration was 4.9, 2.6, 6.5, and 2.3 times higher, respectively, if antibody levels were < 1,200 BAU/mL (aOR, 4.92 [95%CI, 2.59–9.34], P < 0.0001; aOR, 2.64 [95%CI, 1.52–4.62], P = 0.0006; aOR, 6.50 [95%CI, 1.48–28.47], P = 0.013; aOR, 2.34 [95%CI, 1.60–3.343], P < 0.0001). Older adults infected with the Omicron variant were approximately 6 times more likely to die if antibody levels were < 1,200 BAU/mL (aOR, 6.3 [95% CI, 2.43–16.40], P = 0.0002). CONCLUSION: Antibody levels were a stronger predictor of in-hospital mortality than vaccination status. Monitoring antibody levels may constitute a better and more direct approach for safeguarding older adults from adverse COVID-19 outcomes.