What are the real associations of homeostasis model assessment (HOMA) with body mass index and age?

INTRODUCTION: Insulin resistance (IR), a key pathogenesis mechanism of metabolic disorders, can be tested using homeostatic model assessment (HOMA). HOMA-IR quantifies peripheral tissue IR, whereas HOMA-ß determines insulin secretion. The cross-sectional study aimed to examine non-linear associations of HOMA indices with age when adjusting for body mass index (BMI), and thus to investigate the indices' ability to reflect the real development of glucose metabolism disorders over time. MATERIAL AND METHODS: The sample comprised 3406 individuals without diabetes mellitus (DM) divided into those with normal glucose metabolism (NGT, n = 1947) and prediabetes (n = 1459) after undergoing biochemical analyses. Polynomial multiple multivariate regression was applied to objectify associations of HOMA with both age and BMI. RESULTS: Mean values of HOMA-IR and HOMA-ß in individuals with NGT were 1.5 and 82.8, respectively, while in prediabetics they were 2.2 and 74.3, respectively. The regression proved an inverse non-linear dependence of pancreatic b dysfunction, expressed by HOMA-ß, on age, but did not prove a dependence on age for HOMA-IR. Both indices were positively, statistically significantly related to BMI, with a unit increase in BMI representing an increase in HOMA-IR by 0.1 and in HOMA-ß by 3.2. CONCLUSIONS: The mean values of HOMA indices showed that, compared with NGT, prediabetes is associated with more developed IR but lower insulin secretion. Both HOMA-IR and HOMA-b are predicted by BMI, but only HOMA-ß is predicted by age. HOMA indices can reflect non-linear, closer-to-reality dependencies on age, which in many epidemiological studies are simplified to linear ones. The assessment of glucose metabolism using HOMA indices is beneficial for the primary prevention of IR and thus DM.

Cholesterol and glucose profiles according to different fasting C-peptide levels: a cross-sectional analysis in a healthy cohort from the Czech Republic.

The relationship between glycaemia and lipoprotein metabolism has not been completely clarified, and slight differences may be found between local authors, trials and evaluated parameters. Therefore this cross-sectional study investigated fasting cholesterol and glucose levels along with the determination of atherogenic index in a cohort of healthy individuals from the Czech Republic in relation to their fasting C-peptide levels. Data were collected between 2009 and 2018 and a total of 3189 individuals were stratified by C-peptide reference range (260-1730 pmol/l) into three groups - below (n = 111), within (n = 2952) and above (n = 126). Total, HDL, LDL cholesterol and atherogenic index were used to compare lipoprotein levels by relevant C-peptide concentrations. Participants using the supplements to affect lipid or glycaemia metabolism were excluded from this study. The evaluation of blood parameters in a fasting state included correlations between C-peptide and cholesterols, differences of variances (F-test) and the comparison of lipoprotein mean values (t-test) between the groups created by the C-peptide reference range. Mean values of total (4.9, 5.1, 5.3 mmol/l), LDL (2.6, 3.1, 3.4 mmol/l) cholesterol and atherogenic index (2.1, 2.8, 3.7) were higher with increasing C-peptide levels, whereas HDL was inversely associated with fasting C-peptide concentration. A positive and negative correlation between atherogenic index (rxy = 0.36) and HDL level (rxy = -0.36) with C-peptide values was found. Differences of HDL, LDL and atherogenic index were, in particular, recorded between the groups below and above the reference range of C-peptide (p ≤ 0.001). Considerable differences (p ≤ 0.001) were also observed for the same lipoprotein characteristics between the groups above and within the C-peptide reference. Generally, the type of cholesterol is crucial for the evaluation of specific changes concerning the C-peptide range. Lipoprotein concentrations differ in relation to C-peptide - not only below and above the physiological range, but also inside and outside of it. Conclusions: Fasting levels of cholesterol, plasma glucose, and atherogenic index were strongly associated with fasting C-peptide levels in healthy individuals. Our data suggest that fasting C-peptide could serve as a biomarker for the early detection of metabolic syndrome and/or insulin resistance prior to the manifestation of type 2 diabetes.

The Changes of Cholesterol Profile at the Different Insulin Resistance Range in the Czech Republic.

Background and Objectives: The mechanism of the relationship between glycemia and lipid metabolism has not been completely clarified, and slight differences may be found between authors and the kinds of evaluated parameters. Therefore, this study focused on possible changes of lipoprotein profile with regards to HOMA IR (Homeostatic Model Assessment for Insulin Resistance) cut-off 3.63, considered a signal of glucose metabolism alterations. Materials and Methods: The metabolic profiles of 3051 individuals were divided by HOMA IR values into two groups below cut-off 3.63, including (n = 2627) and above cut-off (n = 424). Patients taking medication or supplements to affect lipid, insulin, or glucose metabolism were excluded. Fasting glucose levels, insulin, and lipoproteins (total, HDL-high density and LDL-low density lipoprotein cholesterol) were compared between the groups with different HOMA IR. After analysis of data distribution, F-test and t-test were provided to compare variances and mean values. Results: The evaluation shows that the kind of cholesterol is crucial for a possible relationship with glucose metabolism and consequently confirms the changes of lipoproteins (HDL and LDL) by HOMA IR cut-off 3.63. Conclusions: The results of patients divided by HOMA IR cut-off 3.63 also suggest possible changes in the regulation of glucose metabolism and lipoprotein concentrations (HDL and LDL).

Onion Peel Powder as an Antioxidant-Rich Material for Sausages Prepared from Mechanically Separated Fish Meat.

Mechanically separated fish meat (MSFM) can be used for the manufacturing of ready-to-eat products, such as sausages; however, it is highly perishable. Several plant by-products, including onion peel, which is rich in polyphenol antioxidants, can be added to food to extend shelf life. This study investigated the effects of the addition of onion peel powder (OPP) to sausage made from MSFM. Sausages were divided into four groups with different amounts of added OPP: 0% (control), 1%, 2%, and 3%. Cooked sausages were stored for 28 days at 5 °C. Samples were analyzed for thiobarbituric acid reactive substances, antioxidant activity, total polyphenol content, pH, and organoleptic properties. The addition of OPP significantly increased antioxidant activity and total polyphenol content and decreased pH, indicating acidic nature of OPP. Polyphenols from OPP effectively suppressed lipid oxidation. A 1-2% addition of OPP enhanced sensory properties. After the 28-day storage, the control samples received the lowest sensory score, due to the presence of a strong fishy odor, which was not present in samples with OPP. HPLC-MS/MS analysis revealed that quercetin is the most dominant compound in OPP. Overall, the results indicate that the addition of OPP in amounts of 1-2% can extend shelf life, without the deterioration of sensory properties.

Alterations of glycaemia, insulin resistance and body mass index within the C-peptide optimal range in non-diabetic patients.

The study focused on changes or cut-offs of glycaemia, insulin resistance and body mass index within the C-peptide reference range (260-1730 pmol/l). The metabolic profile of individuals in the Czech Republic without diabetes (n = 3186) was classified by whiskers and quartiles of C-peptide into four groups with the following ranges: 290-510 (n = 694), 511-710 (n = 780), 711-950 (n = 720) and 951-1560 pmol/l (n = 673). Fasting levels of glucose, insulin, HOMA IR (Homeostasis Model Assessment for Insulin Resistance) and BMI (body mass index) were compared by a relevant C-peptide range. Participants taking medication to control glycaemia were excluded. The evaluation involved correlations between C-peptides and the above parameters, F-test and t-test. Changes in glucose levels (from 5.3 to 5.6 mmol/l) between the groups were lower in comparison to insulin, which reached relatively greater changes (from 4.0 to 14.2 mIU/l). HOMA IR increased considerably with growing C-peptide concentrations (0.9, 1.5, 2.2 and 3.5) and BMI values showed a similar trend (28.3, 31.0, 33.6 and 37.4). Considerable changes were observed for insulin (5.2 mIU/l, 57.8%) and HOMA IR (1.3, 61.3%) between groups with C-peptide ranges of 711-950 and 951-1560 pmol/l. Although correlations involving C-peptide, insulin, glucose and BMI seemed to be non-significant (up to rxy = 0.25), the mean values of insulin, HOMA IR and BMI showed statistically significant changes between all groups with various C-peptide concentrations (p ≤ 0.001). Generally, most important differences appeared in glucose metabolism and body mass index between C-peptide ranges of 711-950 and 951-1560 pmol/l. Absolute and relative changes of C-peptide concentrations are possible to use for the assessment of glucose regulatory mechanism. The spectrum of investigated parameters could be a useful tool to prevent the risks linked with the alterations of glycaemia.