Effect size and inferential statistical techniques coupled with machine learning for assessing the association between prolactin concentration and metabolic homeostasis

Research on Bitter Peptides in the Field of Bioinformatics: A Comprehensive Review

Bitter peptides are small molecular peptides produced by the hydrolysis of proteins under acidic, alkaline, or enzymatic conditions. These peptides can enhance food flavor and offer various health benefits, with attributes such as antihypertensive, antidiabetic, antioxidant, antibacterial, and immune-regulating properties. They show significant potential in the development of functional foods and the prevention and treatment of diseases. This review introduces the diverse sources of bitter peptides and discusses the mechanisms of bitterness generation and their physiological functions in the taste system. Additionally, it emphasizes the application of bioinformatics in bitter peptide research, including the establishment and improvement of bitter peptide databases, the use of quantitative structure-activity relationship (QSAR) models to predict bitterness thresholds, and the latest advancements in classification prediction models built using machine learning and deep learning algorithms for bitter peptide identification. Future research directions include enhancing databases, diversifying models, and applying generative models to advance bitter peptide research towards deepening and discovering more practical applications.

Diagnosis of hypoprolactinemia

Prolactin is a polypeptide hormone composed of 199 amino acids, synthesized by lactotroph cells. Its primary effects are on the mammary gland and gonadal axes, but it also influences different organs and systems, particularly metabolic functions. Current literature has mainly focused on the diagnosis, monitoring, and treatment of hyperprolactinemia. Due to the lack of a well-established effective treatment for hypoprolactinemia, it is not clinically emphasized. Therefore, data on its diagnosis is limited. Hypoprolactinemia has been associated with metabolic dysfunctions such as type 2 diabetes mellitus, fatty liver, dyslipidemia, fertility problems, sexual dysfunction, and increased cardiovascular disease. While often seen as a part of combined hormone deficiencies due to pituitary damage, isolated prolactin deficiency is rare. Hypoprolactinemia can serve as a marker for extensive pituitary gland damage and dysfunction.Low or undetectable serum prolactin levels and the absence of a sufficient prolactin peak in the thyrotropin-releasing hormone (TRH) stimulation test are considered diagnostic for hypoprolactinemia. Gender appears to influence both basal prolactin levels and TRH stimulation test responses. Basal prolactin levels of, at least, 5 ng/mL for males and 7 ng/mL for females can be used as cut-off levels for normal prolactin reserve. Minimum peak prolactin responses of 18 ng/mL for males and 41 ng/mL for females to TRH stimulation can exclude hypoprolactinemia. However, larger population studies across different age groups and sexes are needed to better define normal basal prolactin levels and prolactin responses to the TRH stimulation test.

Prolactin effects on the pathogenesis of diabetes mellitus

BACKGROUND

Prolactin (PRL) is a pituitary hormone promoting lactation in response to the suckling reflex. Beyond its well-known effects, novel tissue-specific and metabolic functions of PRL are emerging.

AIMS

To dissect PRL as a critical mediator of whole-body gluco-insulinemic sensitivity.

METHODS

PubMed-based search with the following terms 'prolactin', 'glucose metabolism', 'type 2 diabetes mellitus', 'type 1 diabetes mellitus', 'gestational diabetes mellitus' was performed.

DISCUSSION

The identification of the PRL-glucose metabolism network poses the basis for unprecedented avenues of research in the pathogenesis of diabetes mellitus type 1 or 2, as well as of gestational diabetes. In this regard, it is of timely relevance to define properly the homeostatic PRL serum levels since glucose metabolism could be influenced by the circulating amount of the hormone.

RESULTS

This review underscores the basic mechanisms of regulation of pancreatic β-cell functions by PRL and provides a revision of articles which have investigated the connection between PRL unbalancing and diabetes mellitus. Future studies are needed to elucidate the burden and the role of PRL in the regulation of glucose metabolism and determine the specific PRL threshold that may impact the management of diabetes.

CONCLUSION

A careful evaluation and context-driven interpretation of PRL levels (e.g., pregnancy, PRL-secreting pituitary adenomas, drug-related hyper- and hypoprolactinemia) could be critical for the correct screening and management of glucometabolic disorders, such as type 1 or 2 as well as gestational diabetes mellitus.

Effect size and inferential statistical techniques coupled with machine learning for assessing the association between prolactin concentration and metabolic homeostasis

BACKGROUND AND OBJECTIVE

Recent guidelines classify low prolactin levels as low as <7 ng/mL and high levels as >25 ng/mL, while the "Homeostatically Functionally Increased Transient Prolactinemia" (HomeoFIT-PRL) range (25-100 ng/mL) suggests that a temporary increase in prolactin could be metabolically beneficial if no related health issues are present. The aim of this study was to investigate the association between mean prolactin concentrations and disturbances in glycidic and lipidic metabolism and to identify the gray zone associated with prolactin inflection points that correlate with these metabolic changes.

METHODS

This cross-sectional study involved 65,795 adults who underwent HOMA-IR, glucose, insulin, total cholesterol, HDL-c, LDL-c, and triglyceride tests. Data was categorized into 106 partitions based on prolactin results. Employing an approach referred to in this study as "Hierarchical Multicriteria Analysis of Differences Between Groups - Statistical and Effect Size Approach" (HiMADiG-SESA) comparing the mean concentrations of metabolic tests across prolactin ranges. A machine learning model was utilized to determine inflection points and their corresponding confidence intervals (CIs). These CIs helped establish gray zones in mean prolactin results related to metabolic changes.

RESULTS

Statistically and clinically, metabolic test means differed for prolactin <7 ng/mL, except insulin. In the HomeoFIT-PRL range, means were lower except for HDL-c. The gray zones of the mean prolactin results associated with changes in glycidic and lipidic metabolism were 9.58-12.87 ng/mL and 13.81-18.73 ng/mL, respectively.

CONCLUSION

A strong correlation was identified between mean prolactin concentrations and the results of metabolism tests below the gray zones associated with inflection points, indicating the potential role of prolactin in the appearance of metabolic disorders. Mean prolactin results can provide deeper insight into metabolic balance.