Abstract
Background: Medical Subject Headings (MeSH) in PubMed has a subsection No 18 called endophenotypes with subheadings, including metabolisms. Endophenotypes - measurable, biologic (physiologic, biochemical, and anatomical features), behavioural (psychometric pattern) or cognitive markers that are found more often in people with a disease than in the general population. Because many endophenotypes are present before the disease onset and in individuals with heritable risk for disease such as unaffected family members, they can be used to help diagnose and search for causative genes. Another characteristic of metabolism that should be mentioned is metabolic flexibility, i.e., ability to efficiently adapt metabolism by substrate sensing, trafficking, storage and utilization depending on energy availability and need. Humans have evolved with the capacity for metabolic flexibility, and the ability to switching energy sources from carbohydrate to fat and use ketones for fuel. These conditions promote the breakdown of excess fat stores, sparing of lean muscle, and improvement in insulin sensitivity. Metabolic health or unhealthy are assured by the participation of various organs such as the liver, intestines, body fat, muscles, heart and brain tissue, i.e., body mass components, that provide the body's metabolic potential
Aim: To clarify the characteristics of the metabolically unhealthy endophenotype with an emphasis on systemic and local (organ) metabolic disorders and to substantiate the possibility of using non-invasive tests to verify variants of metabolically unhealthy phenotypes.
Material and methods. Systemic dysmetabolism assess by the determination of individual body mass components (30 in total) and metabolic (biological) age using bioimpedance (BIA-V). For local dysmetabolism: We used the ketones determination in exhaled air (breath test) before and after ketosis induction in the liver with the amino acid L-lysine (2 grams) and the liver steatosis degree measurement by the Controlled attenuation parameter (CAP) by transient elastography.
Results and discussion. Ketosis index gave (20 practically healthy) the following results in ppm/minute: in 30% cases-1,0 and more (fast inductors), 0,1-0,4 (medium inductors) and with the absence of ketosis-slow inductors. Analyses using ROC (Receiver Operating Characteristic Curve) allowed us to establish: minimal cut-off value ketones AUC below 615 (ketosis-slow inductors) as the indicator an older Metabiological age (MET-age) vs. chronological ages(CHR-age), with sensitivity - 0,81, specificity - 0,91 and Likelihood Ratio = 9,0. Fast inductors with MET-age younger to CHR-age has significantly more Body Cells Mass (BCM) proportion - 50,5 (95%CI = 50,0-51,1) vs. 43,9(95%CI = 42,8-45,0) and less content of Fat Mass (in kg) fixed -14,7 (95%CI =13,7-15,6) vs. 27,9(95%CI =25,3-30,5. Slow inductors revealed a significantly more frequent increase in blood ALT activity (more than 30 IU) - 41% vs. 5% in fast inductors, what does it show more frequent metabolic dysfunction of the liver (local dysmetabolism). The parallel comparison analysis the MET-age, steatosis (STe) and liver fibrosis(F) show following. In practically healthy people MET-age oldest 2 years or more than CHR-age (Age Diff) was found in the subgroup with severe steatosis (S=3) in 88,9% (95%CI=51,8 – 99,7) and in 9,1% at 95%CI = 0,2 – 41,31 of individuals with mild steatosis (S0+S1). Single Binary Sample Diagnostic Test for MET-age oldest 2 years has sensitivity- 86% (95%CI= 49 – 97); specificity-83% (95%CI= 44 – 97); Likelihood ratio for positive test =5,14 (95% C.I. = 1,34 – 87,52); for negative test = 0,17% (95% C.I. = 0,05 – 0,71).
In patients with hepatomegaly. As follows from the presented data, there is fibrosis (from F1 to F2-3) in group, and there is severe steatosis(S=3). Analysis of individual values: 16% at 95% CI = 5,3 - 32.8) did not have steatosis, CAP equal to or less than 244 dB/m. Severe steatosis was found in18 people (56,3% at 95% CI = 37.7 – 73,6), the CAP index was more than 296 dB/m. In 11 persons (35,5% (95%CI=19,2-54,6) indicated fibrosis F3-4 (more than 12 kPa), from its 3 persons have cirrhosis (F4, more than 18 kPa). Single Binary Sample Diagnostic Test (for Age Diff as test for liver steatosis): sensitivity- 86% (95%CI= 49 – 97); specificity-83% (95%CI= 44 – 97); Likelihood ratio for positive test =5,14 (95% C.I. = 1,34 – 87,52); for negative test = 0,17% (95% C.I. = 0,05 – 0,71).
Conclusions: The results obtained deepen scientific understanding of metabolic dysfunction based on the assessment of metabolic flexibility according to the original indicator - the induction of physiological ketosis by an amino acid metabolized in the liver. In this article, we have shown for the first time that practically healthy individuals who are teetotallers have liver steatosis, and that it correlates with liver fibrosis. At the same time, the possibility of identifying such individuals using tetrapolar multifrequency biological impedance with a vector component (BIA-V) based on metabolic age (Met-age) and active body cell mass (BCM) indices has been demonstrated. Second, in addition, we were able to establish additional properties of a drug with a prokinetic effect in the form of the presence of metabolic components with an effect on the degree of steatosis, combined with the possibility of predicting the inefficiency of the drug based on the BCM value.
Our study had some additional strengths. It was the first study to assess the association among metabolic factors, body mass composition, and steatosis/fibrosis in patients with hepatomegaly without other evidence suggestive of specific liver pathology on routine clinical examination. Severe steatosis was found in 56,3% at 95% CI = 37.7 – 73,6), the CAP index was more than 296 dB/m. In 35,5% (95%CI=19,2-54,6) indicated fibrosis F3-4 (more than 12 kPa), from its 3 persons have cirrhosis (F4, more than 18 kPa). At the same time, neither BMI nor waist circumference have not diagnostic value for detecting steatosis. The article demonstrates a reliable possibility of predicting the presence of steatosis and fibrosis in patients with hepatomegaly based on the difference in metabolic and chronological ages with specificity (83%, 95%CI= 44 – 97) and sensitivity (81,3%, 95%CI= 57 – 93) sufficient for practical use and identification of such cases.
The authors formulated a hypothesis and presented evidence for it: metabolic disorders (dysmetabolism) are formed both in the form of a general or systemic (on a body-wide scale) and in the form of local metabolic disorders (individual organs, an example is fatty liver dysfunction).