Myo-inositol, D-chiro-inositol and Glucomannan in PCOS
Condition: PCOS
Study type: Observational

Summary: 
      The aim of this study is to analyze the metabolic profiles in women with PCOS before and
      after 3 months of therapy with a combination of myo-inositol, D-chiro-inositol and
      glucomannan, and compare these data with a group of healthy control women.
    

Detailed description: 
      1. RATIONALE & BACKGROUND INFORMATION Polycystic ovary syndrome (PCOS) is a heterogeneous
           syndrome and one of the most common female endocrine disorders, affecting 5-20% of women
           in reproductive age . Clinical expression is highly variable, but typically includes
           oligo-ovulation or anovulation, hyperandrogenism and polycystic ovaries . PCOS is
           associated with an increased risk of type 2 diabetes, cardiovascular events and
           endometrial cancer. Insulin Resistance (IR) plays a central role in approximately 70-80%
           of obese women and in 15-30% of lean women with PCOS, and represents the pathogenic link
           between metabolic and reproductive disorders in PCOS. According to recent guidelines,
           insulin-sensitizer drugs, like inositols, are the first-line therapy in women with
           metabolic abnormalities and irregular menstrual cycle with the purpose to improve
           fertility, whereas a lifestyle change with weight loss and physical activity is the
           first step in overweight and obese PCOS patients . Moreover, the association
           inositols-glucomannan may represent a good therapeutic strategy in the treatment of PCOS
           women with insulin resistance. Metabolomic approach is used to better define the
           pathophysiology of PCOS and to describe how different therapies can modify metabolic
           profiles. Although the association inositols-glucomannan may represent a good
           therapeutic strategy in the treatment of PCOS women with insulin resistance, the effect
           of inositols on the metabolomic profile of these women has not been described yet.

        2. STUDY GOALDS AND OBJECTIVES The aim of this study is to analyze the metabolic profiles
           in women with PCOS before and after 3 months of therapy with a combination of
           myo-inositol, D-chiro-inositol and glucomannan, and compare these data with a group of
           healthy control women.

        3. STUDY DESIGN The study is a prospective and observation. The inclusion criteria are: age
           between 18 and 35 years, overweight/obesity (BMI > 25 kg/m2), absence of any other acute
           intercurrent or chronic illness, a positive diagnosis of PCOS according to Rotterdam
           criteria. Exclusion criterion is using hormonal medications or drugs that affect insulin
           sensitivity (e.g., inositols or metformin) before enrollment.

           The use of myo-inositol (1.75 g), D-chiro-inositol (0.25 g) and glucomannan (4.0 g)/die
           must precede the recruitment of no more than 30 days. The decision to start treatment
           must have already been made before and independently of the start of the study. The use
           of inositol and glucomannan must take place according to the technical data sheet. In
           particular, myo-inositol (1.75 g), D-chiro-inositol (0.25 g) and glucomannan (4.0 g) are
           expected to be subdivided into two doses before main meals.

        4. METHODOLOGY 4.1 Admission visit (V0) Once the eligibility criteria have been checked,
           the investigator will inform the patient about the objectives of the study during the
           initial visit (V0) and obtain written informed consent form.

           The compilation of a clinical card includes general information, anamnesis, BMI, the
           characteristics of the menstrual cycle, the amount of menstrual loss, the degree of
           hirsutism according to the Ferriman-Gallwey index and the degree of acne in agreement to
           the Global evaluation scale proposed in 2002 by FDA.

           The investigator will collect from the clinical documentation available the baseline
           glycaemia, insulin, triglycerides, cholesterol values before the start of the treatment.
           Furthermore, information on the ultrasound picture will be collected in terms of ovary
           volumes and antral follicles.

           A sample of 2-3 ml of basal blood will be collected for metabolomic evaluations, using a
           BD vacutainer (Becton Dickinson, Oxfordshire, UK) blood collection red tube (with no
           additives). After centrifugation, the sample will immediately freeze to -80 °C until the
           time of analysis.

           The patient will then be invited to continue treatment with myo-inositol (1.75 g),
           D-chiro-inositol (0.25 g) and glucomannan (4 g) die and to show up for control after 90
           days (V1).

           4.2 Follow-up visit 90th day (±15) after enrollment (V1) During the V1 the patient will
           be interviewed on the regular therapy and clinical symptoms, any adverse events and the
           course of the menstrual cycle.

           Furthermore, all patients will be re-evaluated regarding the anthropometric, biochemical
           and ultrasound parameters.

           At V1 a second blood sample of 2-3 ml will be collected with the same methods describe
           above.

           4.3 Biochemical and metabolomics samples analysis Blood concentration of glucose,
           insulin, triglycerides and cholesterol is evaluated for control subjects and for cases
           at baseline and after 3 months of treatment. HOMA-IR si also calculated. Ovary volumes
           and the antral follicles count were evaluated by a vaginal ultrasound performed by a
           trained gynecologist.

           Metabolome extraction, purification and derivatization is carried out with the
           MetaboPrep GC kit (Theoreo srl, Montecorvino Pugliano [SA], Italy) according to the
           manufacturer's instructions. Details regarding metabolite extraction and the overall
           analytical scheme, including QA/QC sample analyses, were reported in Troisi et al.
           (2017, 2018)

        5. Follow-Up Three months.

        6. Data Management and Statistical Analysis At the end of the treatments the data
           collection of all patients is scheduled and the introduction of the same, in coded or
           clear form, in a database (Excel for windows) appropriately structured to contain all
           the expected items. In order to comply with the privacy law, the sensitive nominal data
           will be appropriately replaced by the numerical codes assigned to each patient, so that
           from the simple consultation of the data it will not be possible to deduce any
           individual direct reference. Moreover, the only data that can be extracted from the
           database are related to aggregated sets to be published for scientific purposes.

           Data is reported as mean±standard deviation for continuous variables and number
           (percentage) for categorical variables.

           Statistical analysis are performed using Statistica software (StatSoft, Oklahoma, USA)
           and Minitab (Minitab Inc, Pennsylvania, USA). Normal distribution of data is verified
           using the Shapiro-Wilks test. Since the data are normally distributed, the investigators
           use one-way ANOVA with the Tukey post hoc test for inter-group comparisons. The alpha
           (ɑ) value is set to 0.05. Pearson's chi-squared test is used to determine differences
           among groups for the categorical variables.

           For multivariate data analysis, the chromatographic data are tabulated with one sample
           per row and one variable (metabolite) per column. Data pre-treatment consists of
           normalizing each metabolite peak area to that of the internal standard followed by
           generalized log transformation and data scaling by autoscaling (mean-centered and divide
           it by standard deviation of each variable). PLS-DA is performed using the statistical
           software package R (Foundation for Statistical Computing, Vienna, Austria). Class
           separation is achieved by PLS-DA, which is a supervised method that uses multivariate
           regression techniques to extract, via linear combinations of original variables (X), the
           information that can predict class membership (Y). PLS regression is performed using the
           plsr function included in the R pls package. Classification and cross-validation is
           performed using the corresponding wrapper function included in the caret package. A
           permutation test is performed to assess the significance of class discrimination. In
           each permutation, a PLS-DA model is built between the data (X) and the permuted class
           labels (Y) using the optimal number of components determined by cross validation for the
           model based on the original class assignment. Variable Importance in Projection (VIP)
           scores are calculated for each metabolite. The VIP score is a weighted sum of squares of
           the PLS loadings, taking into account the amount of explained Y-variation in each
           dimension. The highest scoring VIP metabolites are compared in terms of fold changes
           (FC). FC is the ratio of the mean abundances between any two classes and is a measure
           describing how much a quantity changes going from an initial to a final value.

           The metabolic pathways are constructed using MetScape application of the software
           Cytoscape.

        7. Expected Outcomes of the Study The goal of this pilot study is to identify a complex
           network of serum molecules that appear to be correlated with PCOS, and with a combined
           treatment with inositols and glucomannan.

        8. Duration of the Project 24 months
    

Intervention name(s): Serum Metabolomics profiling
Intervention type(s): Other
Intervention description(s): Untargeted serum metabolomics profiling
Observational model: Case-Control
Time perspective: Prospective
Sampling method: Non-Probability Sample
Study population: 
        Young women with a PCOS diagnosis without intercurrent chronic illness and a matched
        healthy population.
      
Biospecimen description: 
      Serum
    
Keywords: Metabolomics