Obesity is characterized by an excessive accumulation of fat leading to a plethora of medical complications, including coronary artery disease, hypertension, type 2 diabetes mellitus or impaired glucose tolerance and dyslipidemia. Formerly, several physiological roles of organokines, including adipokines, hepatokines, myokines and gut hormones have been described in obesity, especially in the regulation of glucose and lipid metabolism, insulin sensitivity, oxidative stress, and low-grade inflammation. The canonical effect of these biologically active peptides and proteins may serve as an intermediate regulatory level that connects the central nervous system and the endocrine, autocrine, and paracrine actions of organs responsible for metabolic and inflammatory processes. Better understanding of the function of this delicately tuned network may provide an explanation for the wide range of obesity phenotypes with remarkable inter-individual differences regarding comorbidities and therapeutic responses. The aim of this review is to demonstrate the role of organokines in the lipid and glucose metabolism focusing on the obese non-diabetic subgroup. We also discuss the latest findings about sarcopenic obesity, which has recently become one of the most relevant metabolic disturbances in the aging population.

Altered organokine expression contributes to increased cardiometabolic risk in obesity. Our aim was to evaluate the associations of serum afamin with glucose homeostasis, atherogenic dyslipidemia, and other adipokines in severe obesity to clarify the early metabolic alterations. 106 non-diabetic obese (NDO) subjects and 62 obese patients with type 2 diabetes matched for age, gender, and body mass index (BMI) were enrolled in this study. We compared their data with 49 healthy, lean controls. Serum afamin and retinol-binding protein 4 (RBP4), as well as plasma plasminogen activator inhibitor-1 (PAI-1), were measured with ELISA, and lipoprotein subfractions were analyzed using Lipoprint gel electrophoresis. Afamin and PAI-1 found to be significantly higher in the NDO and T2M group (p < 0.001 and p < 0.001, respectively) than in the controls. In contrast, RBP4 was unexpectedly lower in the NDO and T2DM group compared to controls (p < 0.001). Afamin showed negative correlations with mean LDL size and RBP4, but positive correlations with anthropometric, glucose/lipid parameters, and PAI-1 in both the overall patients and the in NDO + T2DM groups. BMI, glucose, intermediate HDL, and small HDL were predictors of afamin. Afamin may serve as a biomarker for the severity of cardiometabolic disturbances in obesity. The complexity of organokine patterns in NDO subjects draws attention to the diverse spectrum of obesity-related comorbidities.

Betatrophin, also known as angiopoietin-like protein 8 (ANGPTL8), mainly plays a role in lipid metabolism. To date, associations between betatrophin and lipoprotein subfractions are poorly investigated. For this study, 50 obese patients with type 2 diabetes (T2D) and 70 nondiabetic obese (NDO) subjects matched in gender, age, and body mass index (BMI) as well as 49 gender- and age-matched healthy, normal-weight controls were enrolled. Serum betatrophin levels were measured with ELISA, and lipoprotein subfractions were analyzed using Lipoprint gel electrophoresis. Betatrophin concentrations were found to be significantly higher in the T2D and NDO groups compared to the controls in all subjects and in females, but not in males. We found significant positive correlations between triglyceride, very low density lipoprotein (VLDL), large LDL (low density lipoprotein), small LDL, high density lipoprotein (HDL) -6-10 subfractions, and betatrophin, while negative correlations were detected between betatrophin and IDL, mean LDL size, and HDL-1-5. Proportion of small HDL was the best predictor of betatrophin in all subjects. Small LDL and large HDL subfractions were found to be the best predictors in females, while in males, VLDL was found to be the best predictor of betatrophin. Our results underline the significance of serum betatrophin measurement in the cardiovascular risk assessment of obese patients with and without T2D, but gender differences might be taken into consideration.

Kallistatin is an endogenous serine proteinase inhibitor with various functions, including antioxidative, anti-inflammatory, and anti-atherosclerotic properties. To date, associations between kallistatin and lipoprotein subfractions are poorly investigated. In this study, we enrolled 62 obese patients with type 2 diabetes (T2D), 106 nondiabetic obese (NDO) subjects matched in gender, age, and body mass index, as well as 49 gender- and age-matched healthy, normal-weight controls. Serum kallistatin levels were measured with ELISA, and lipoprotein subfractions were analyzed using Lipoprint^® (Quantimetrix Corp., Redondo Beach, CA, USA) gel electrophoresis. Kallistatin concentrations were significantly higher in T2D patients compared to NDO and control groups. We found significant positive correlations between very-low-density lipoprotein (VLDL), small high-density lipoprotein (HDL) subfractions, glucose, hemoglobin A_1c (HbA_1c), betatrophin, and kallistatin, while negative correlations were detected between mean low-density lipoprotein (LDL) size, large and intermediate HDL subfractions, and kallistatin in the whole study population. The best predictor of kallistatin was HbA_1c in T2D patients, high-sensitivity C-reactive protein (hsCRP) and betatrophin in NDO patients, and hsCRP in controls. Our results indicate that kallistatin expression might be induced by persistent hyperglycemia in T2D, while in nondiabetic subjects, its production might be associated with systemic inflammation. The correlation of kallistatin with lipid subfractions may suggest its putative role in atherogenesis.

Brugada syndrome (BrS) is a rare but potentially life-threatening condition that may lead to sudden cardiac death. Among the causes, dysfunctions of ion channels involved in the cardiac action potential (specifically in SCN5A and SCN10A genes) are particularly significant. Among diagnosed Brugada patients, fever-induced episodes occur in 20–30% of cases. Fever worsens sodium channel dysfunction, as elevated temperature further reduces their conductance. First clinical manifestation of BrS occurs usually during a febrile episode, especially in young people. We performed a multiparametric examination in addition to genetic analysis. We treated a 19-year-old man presenting with subfebrility. During the patient’s subfebrile episodes, 12-lead ECG recordings revealed ST-segment elevations in leads V1–V3. Notably, the patient remained asymptomatic. Targeted genetic testing of SCN5A did not reveal any disease-causing variants as an underlying cause of the syndrome, but the temperature-inducing effect was demonstrated. The occurrence of the Brugada type 1 pattern has also been observed at subfebrile episodes, although significantly rarely. This case demonstrates that in susceptible patients, even a relatively mild elevation in body temperature can trigger ion channel dysfunctions. Timely diagnosis and follow-up are important in preserving quality of life and preventing fatal outcomes.

Background/Objectives: Autoimmune inflammation enhances the electrical instability of the atrial myocardium in patients with systemic sclerosis (SSc); thus, atrial arrhythmia risk is increased, which might be predicted by evaluating the P wave interval and dispersion of a 12-lead surface electrocardiogram (ECG). Methods: We examined 26 SSc patients and 36 healthy controls and measured the P wave interval and P wave dispersion of the 12-lead surface ECG in each patient. Furthermore, echocardiography and 24-h Holter ECG were performed and levels of inflammatory laboratory parameters, including serum progranulin (PGRN), sVCAM-1, sICAM-1, leptin and C-reactive protein (CRP), were determined. Lipid parameters, such as Apo A-I, LDL-cholesterol (LDL-C), oxidized LDL (oxLDL) and the LDL and HDL subfractions were also evaluated. Results: The P wave interval showed a significant positive correlation with the levels of Apo A-I, LDL-C, CRP, sVCAM-1, sICAM-1 and leptin. The oxLDL level correlated positively with P wave dispersion. Of note, significant positive correlation was also found between the large HDL percentage and the P wave interval. Conclusions: Our results suggest that PGRN, sVCAM-1, sICAM-1, leptin, CRP, LDL-C and oxLDL, along with LDL and HDL subfractions, might have a role in atrial arrhythmogenesis in patients with SSc.