Metabotyping in the Postmenopausal Stage

NCT05397015 | Completed

• 1 other identifier: SHE-HEALTH
• Study Type: observational
• Target: 200
• Locations: 1 country
• Sites: 1

Brief Summary

Menopause is defined as the absence of menstrual periods for twelve consecutive months. Although the onset may vary, natural menopause occurs between the ages of 45 and 55 and is considered a stage in the aging process for women. Menopause is a stage strongly conditioned by hormonal modulations with effects on the cardiovascular system associated with abdominal obesity, insulin resistance, decreased energy expenditure, endothelial dysfunction, hypertension, and dyslipidemia. Furthermore, an increase in the production of proinflammatory cytokines involved in numerous pathologies such as osteoporosis has been observed. The results of several studies suggest that intestinal microbiota (IM) profile may be related to menopause condition by several means, although the data are stil inconclusive. Estrogen reduction leads to a progressive loss of bone density, a reduction in the bone formation/resorption balance and an increased risk of bone fractures among postmenopausal women. Recently, the alternative to estrogen therapies to reduce the risk of fractures are nutritional strategies fundamentally based on the use of probiotics, whose effect are associated with beneficial modulations of IM. SHE-HEALTH is a study in which, in a cohort of postmenopausal women, metabolomics, transcriptomics and metagenomics will be combined with the analysis of usual anthropometric and clinical biomarkers and also with genetic and epigenetic analyses to identify population groups (clusters). This study will allow establishing solid scientific bases to define, in future projects, effective nutritional strategies based on group nutrition in postmenopausal women. The main objective of the present study is to obtain clusters of postmenopausal women, identifying metabotypes (similar metabolic profiles) and enterotypes (similar IM profiles), and combining complementary variables such as classical anthropometric, biochemical and clinical biomarkers. The secondary objectives of the study are to characterize: 1) The genetic profile of the study cohort; 2) The epigenetic profile of the study cohort; 3) The gene expression profile of the study cohort.

Conditions

Menopause; Postmenopausal Disorder; Postmenopausal Symptoms

Keywords

Metabolomics; Transcriptomics; Metagenomics; Personalized Nutrition; Metabotypes; Enterotypes

Outcome Measures

Primary Outcomes (33)

• Metabolomics in serum

  Non-targeted metabolomics of serum samples measured using proton nuclear magnetic resonance. Data will be analysed together with the other primary outcomes for cluster identification. Data will be scaled using unit variance scaling. Principal components analysis, Partial Least-Squares Discriminant Analysis and hierarchical clustering will be used to identify clusters and to detect differences among metabotypes. The quality of the model will be judged by the goodness-of-fit parameter, the predictive ability parameter and cross-validation test.

  At day 1

• Metabolomics in erythrocytes

  Non-targeted metabolomics of erythrocytes samples measured using proton nuclear magnetic resonance. Data will be analysed together with the other primary outcomes for cluster identification. Data will be scaled using unit variance scaling. Principal components analysis, Partial Least-Squares Discriminant Analysis and hierarchical clustering will be used to identify clusters and to detect differences among metabotypes. The quality of the model will be judged by the goodness-of-fit parameter, the predictive ability parameter and cross-validation test.

  At day 1

• Metabolomics in urine

  Non-targeted metabolomics of urine samples measured using proton nuclear magnetic resonance. Data will be analysed together with the other primary outcomes for cluster identification. Data will be scaled using unit variance scaling. Principal components analysis, Partial Least-Squares Discriminant Analysis and hierarchical clustering will be used to identify clusters and to detect differences among metabotypes. The quality of the model will be judged by the goodness-of-fit parameter, the predictive ability parameter and cross-validation test.

  At day 1

• Metagenomics in faeces

  Faecal intestinal microbiota analysis will be done by 16sRNA sequencing. Data will be analysed together with the other primary outcomes for cluster identification. Data will be scaled using unit variance scaling. Principal components analysis, Partial Least-Squares Discriminant Analysis and hierarchical clustering will be used to identify clusters and to detect differences among metabotypes. The quality of the model will be judged by the goodness-of-fit parameter, the predictive ability parameter and cross-validation test.

  At day 1

• Serum hsCRP levels

  Serum hsCRP levels will be measured by human ELISA kits. Data will be analysed together with the other primary outcomes for cluster identification. Data will be scaled using unit variance scaling. Principal components analysis, Partial Least-Squares Discriminant Analysis and hierarchical clustering will be used to identify clusters and to detect differences among metabotypes. The quality of the model will be judged by the goodness-of-fit parameter, the predictive ability parameter and cross-validation test.

  At day 1

• Serum IL-6 levels

  Serum IL-6 levels will be measured by human ELISA kits. Data will be analysed together with the other primary outcomes for cluster identification. Data will be scaled using unit variance scaling. Principal components analysis, Partial Least-Squares Discriminant Analysis and hierarchical clustering will be used to identify clusters and to detect differences among metabotypes. The quality of the model will be judged by the goodness-of-fit parameter, the predictive ability parameter and cross-validation test.

  At day 1

• Serum TNFalpha levels

  Serum TNFalpha levels will be measured by human ELISA kits. Data will be analysed together with the other primary outcomes for cluster identification. Data will be scaled using unit variance scaling. Principal components analysis, Partial Least-Squares Discriminant Analysis and hierarchical clustering will be used to identify clusters and to detect differences among metabotypes. The quality of the model will be judged by the goodness-of-fit parameter, the predictive ability parameter and cross-validation test.

  At day 1

• Serum BALP levels

  Serum BALP levels will be measured by human ELISA kits. Data will be analysed together with the other primary outcomes for cluster identification. Data will be scaled using unit variance scaling. Principal components analysis, Partial Least-Squares Discriminant Analysis and hierarchical clustering will be used to identify clusters and to detect differences among metabotypes. The quality of the model will be judged by the goodness-of-fit parameter, the predictive ability parameter and cross-validation test.

  At day 1

• Serum osteocalcin levels

  Serum osteocalcin levels will be measured by human ELISA kits. Data will be analysed together with the other primary outcomes for cluster identification. Data will be scaled using unit variance scaling. Principal components analysis, Partial Least-Squares Discriminant Analysis and hierarchical clustering will be used to identify clusters and to detect differences among metabotypes. The quality of the model will be judged by the goodness-of-fit parameter, the predictive ability parameter and cross-validation test.

  At day 1

• Serum TRAP5b levels

  Serum TRAP5b levels will be measured by human ELISA kits. Data will be analysed together with the other primary outcomes for cluster identification. Data will be scaled using unit variance scaling. Principal components analysis, Partial Least-Squares Discriminant Analysis and hierarchical clustering will be used to identify clusters and to detect differences among metabotypes. The quality of the model will be judged by the goodness-of-fit parameter, the predictive ability parameter and cross-validation test.

  At day 1

• Serum CTX-I levels

  Serum CTX-I levels will be measured by human ELISA kits. Data will be analysed together with the other primary outcomes for cluster identification. Data will be scaled using unit variance scaling. Principal components analysis, Partial Least-Squares Discriminant Analysis and hierarchical clustering will be used to identify clusters and to detect differences among metabotypes. The quality of the model will be judged by the goodness-of-fit parameter, the predictive ability parameter and cross-validation test.

  At day 1

• Serum PINP levels

  Serum PINP levels will be measured by human ELISA kits. Data will be analysed together with the other primary outcomes for cluster identification. Data will be scaled using unit variance scaling. Principal components analysis, Partial Least-Squares Discriminant Analysis and hierarchical clustering will be used to identify clusters and to detect differences among metabotypes. The quality of the model will be judged by the goodness-of-fit parameter, the predictive ability parameter and cross-validation test.

  At day 1

• Serum FSH levels

  Serum FSH levels will be measured by human ELISA kits. Data will be analysed together with the other primary outcomes for cluster identification. Data will be scaled using unit variance scaling. Principal components analysis, Partial Least-Squares Discriminant Analysis and hierarchical clustering will be used to identify clusters and to detect differences among metabotypes. The quality of the model will be judged by the goodness-of-fit parameter, the predictive ability parameter and cross-validation test.

  At day 1

• Serum 17beta E2 levels

  Serum 17beta E2 levels will be measured by human ELISA kits. Data will be analysed together with the other primary outcomes for cluster identification. Data will be scaled using unit variance scaling. Principal components analysis, Partial Least-Squares Discriminant Analysis and hierarchical clustering will be used to identify clusters and to detect differences among metabotypes. The quality of the model will be judged by the goodness-of-fit parameter, the predictive ability parameter and cross-validation test.

  At day 1

• Serum inhibin B levels

  Serum inhibin B levels will be measured by human ELISA kits. Data will be analysed together with the other primary outcomes for cluster identification. Data will be scaled using unit variance scaling. Principal components analysis, Partial Least-Squares Discriminant Analysis and hierarchical clustering will be used to identify clusters and to detect differences among metabotypes. The quality of the model will be judged by the goodness-of-fit parameter, the predictive ability parameter and cross-validation test.

  At day 1

• Serum testosterone levels

  Serum testosterone levels will be measured by human ELISA kits. Data will be analysed together with the other primary outcomes for cluster identification. Data will be scaled using unit variance scaling. Principal components analysis, Partial Least-Squares Discriminant Analysis and hierarchical clustering will be used to identify clusters and to detect differences among metabotypes. The quality of the model will be judged by the goodness-of-fit parameter, the predictive ability parameter and cross-validation test.

  At day 1

• Serum AMH levels

  Serum AMH levels will be measured by human ELISA kits. Data will be analysed together with the other primary outcomes for cluster identification. Data will be scaled using unit variance scaling. Principal components analysis, Partial Least-Squares Discriminant Analysis and hierarchical clustering will be used to identify clusters and to detect differences among metabotypes. The quality of the model will be judged by the goodness-of-fit parameter, the predictive ability parameter and cross-validation test.

  At day 1

• Serum SHBG levels

  Serum SHBG levels will be measured by human ELISA kits. Data will be analysed together with the other primary outcomes for cluster identification. Data will be scaled using unit variance scaling. Principal components analysis, Partial Least-Squares Discriminant Analysis and hierarchical clustering will be used to identify clusters and to detect differences among metabotypes. The quality of the model will be judged by the goodness-of-fit parameter, the predictive ability parameter and cross-validation test.

  At day 1

• Serum triglycerides levels

  Serum triglycerides levels will be measured by Cobas Mira Plus autoanalyzer (Roche Diagnostics Systems, Madrid, Spain). Data will be analysed together with the other primary outcomes for cluster identification. Data will be scaled using unit variance scaling. Principal components analysis, Partial Least-Squares Discriminant Analysis and hierarchical clustering will be used to identify clusters and to detect differences among metabotypes. The quality of the model will be judged by the goodness-of-fit parameter, the predictive ability parameter and cross-validation test.

  At day 1

• Serum total cholesterol levels

  Serum total cholesterol levels will be measured by Cobas Mira Plus autoanalyzer (Roche Diagnostics Systems, Madrid, Spain). Data will be analysed together with the other primary outcomes for cluster identification. Data will be scaled using unit variance scaling. Principal components analysis, Partial Least-Squares Discriminant Analysis and hierarchical clustering will be used to identify clusters and to detect differences among metabotypes. The quality of the model will be judged by the goodness-of-fit parameter, the predictive ability parameter and cross-validation test.

  At day 1

• Serum LDL-cholesterol levels

  Serum LDL-cholesterol levels will be measured by Cobas Mira Plus autoanalyzer (Roche Diagnostics Systems, Madrid, Spain). Data will be analysed together with the other primary outcomes for cluster identification. Data will be scaled using unit variance scaling. Principal components analysis, Partial Least-Squares Discriminant Analysis and hierarchical clustering will be used to identify clusters and to detect differences among metabotypes. The quality of the model will be judged by the goodness-of-fit parameter, the predictive ability parameter and cross-validation test.

  At day 1

• Serum HDL-cholesterol levels

  Serum HDL-cholesterol levels will be measured by Cobas Mira Plus autoanalyzer (Roche Diagnostics Systems, Madrid, Spain). Data will be analysed together with the other primary outcomes for cluster identification. Data will be scaled using unit variance scaling. Principal components analysis, Partial Least-Squares Discriminant Analysis and hierarchical clustering will be used to identify clusters and to detect differences among metabotypes. The quality of the model will be judged by the goodness-of-fit parameter, the predictive ability parameter and cross-validation test.

  At day 1

• Serum glucose levels

  Serum glucose levels will be measured by Cobas Mira Plus autoanalyzer (Roche Diagnostics Systems, Madrid, Spain). Data will be analysed together with the other primary outcomes for cluster identification. Data will be scaled using unit variance scaling. Principal components analysis, Partial Least-Squares Discriminant Analysis and hierarchical clustering will be used to identify clusters and to detect differences among metabotypes. The quality of the model will be judged by the goodness-of-fit parameter, the predictive ability parameter and cross-validation test.

  At day 1

• Serum insulin levels

  Serum insulin levels will be measured by Cobas Mira Plus autoanalyzer (Roche Diagnostics Systems, Madrid, Spain). Data will be analysed together with the other primary outcomes for cluster identification. Data will be scaled using unit variance scaling. Principal components analysis, Partial Least-Squares Discriminant Analysis and hierarchical clustering will be used to identify clusters and to detect differences among metabotypes. The quality of the model will be judged by the goodness-of-fit parameter, the predictive ability parameter and cross-validation test.

  At day 1

• Homeostatic Model Assessment from Insulin Resistance index (HOMA-IR)

  HOMA-IR will be calculated using serum glucose and insulin levels. Data will be analysed together with the other primary outcomes for cluster identification. Data will be scaled using unit variance scaling. Principal components analysis, Partial Least-Squares Discriminant Analysis and hierarchical clustering will be used to identify clusters and to detect differences among metabotypes. The quality of the model will be judged by the goodness-of-fit parameter, the predictive ability parameter and cross-validation test.

  At day 1

• Serum ALT levels

  Serum ALT levels will be measured by Cobas Mira Plus autoanalyzer (RocheDiagnostics Systems, Madrid, Spain). Data will be analysed together with the other primary outcomes for cluster identification. Data will be scaled using unit variance scaling. Principal components analysis, Partial Least-Squares Discriminant Analysis and hierarchical clustering will be used to identify clusters and to detect differences among metabotypes. The quality of the model will be judged by the goodness-of-fit parameter, the predictive ability parameter and cross-validation test.

  At day 1

• Serum AST levels

  Serum AST levels will be measured by Cobas Mira Plus autoanalyzer (RocheDiagnostics Systems, Madrid, Spain). Data will be analysed together with the other primary outcomes for cluster identification. Data will be scaled using unit variance scaling. Principal components analysis, Partial Least-Squares Discriminant Analysis and hierarchical clustering will be used to identify clusters and to detect differences among metabotypes. The quality of the model will be judged by the goodness-of-fit parameter, the predictive ability parameter and cross-validation test.

  At day 1

• Serum creatinine levels

  Serum creatinine levels will be measured by Cobas Mira Plus autoanalyzer (RocheDiagnostics Systems, Madrid, Spain). Data will be analysed together with the other primary outcomes for cluster identification. Data will be scaled using unit variance scaling. Principal components analysis, Partial Least-Squares Discriminant Analysis and hierarchical clustering will be used to identify clusters and to detect differences among metabotypes. The quality of the model will be judged by the goodness-of-fit parameter, the predictive ability parameter and cross-validation test.

  At day 1

• Serum uric acid levels

  Serum uric acid levels will be measured by Cobas Mira Plus autoanalyzer (RocheDiagnostics Systems, Madrid, Spain). Data will be analysed together with the other primary outcomes for cluster identification. Data will be scaled using unit variance scaling. Principal components analysis, Partial Least-Squares Discriminant Analysis and hierarchical clustering will be used to identify clusters and to detect differences among metabotypes. The quality of the model will be judged by the goodness-of-fit parameter, the predictive ability parameter and cross-validation test.

  At day 1

• Serum urea levels

  Serum urea levels will be measured by Cobas Mira Plus autoanalyzer (RocheDiagnostics Systems, Madrid, Spain). Data will be analysed together with the other primary outcomes for cluster identification. Data will be scaled using unit variance scaling. Principal components analysis, Partial Least-Squares Discriminant Analysis and hierarchical clustering will be used to identify clusters and to detect differences among metabotypes. The quality of the model will be judged by the goodness-of-fit parameter, the predictive ability parameter and cross-validation test.

  At day 1

• Urine 8-OHdG levels

  Urine 8-OHdG levels will be measured by human ELISA kits. Data will be analysed together with the other primary outcomes for cluster identification. Data will be scaled using unit variance scaling. Principal components analysis, Partial Least-Squares Discriminant Analysis and hierarchical clustering will be used to identify clusters and to detect differences among metabotypes. The quality of the model will be judged by the goodness-of-fit parameter, the predictive ability parameter and cross-validation test.

  At day 1

• Urine F2-isoprostanes levels

  Urine F2-isoprostanes levels will be measured by human ELISA kits. Data will be analysed together with the other primary outcomes for cluster identification. Data will be scaled using unit variance scaling. Principal components analysis, Partial Least-Squares Discriminant Analysis and hierarchical clustering will be used to identify clusters and to detect differences among metabotypes. The quality of the model will be judged by the goodness-of-fit parameter, the predictive ability parameter and cross-validation test.

  At day 1

• Urine NTX levels

  Urine NTX levels will be measured by human ELISA kits. Data will be analysed together with the other primary outcomes for cluster identification. Data will be scaled using unit variance scaling. Principal components analysis, Partial Least-Squares Discriminant Analysis and hierarchical clustering will be used to identify clusters and to detect differences among metabotypes. The quality of the model will be judged by the goodness-of-fit parameter, the predictive ability parameter and cross-validation test.

  At day 1

Secondary Outcomes (12)

• Body weight

  At day 1

• Height

  At day 1

• Body mass index

  At day 1

• Waist circumference

  At day 1

• Blood pressure (in mmHg)

  At day 1

Study Arms (1)

postmenopausal women

A cohort of 200 postmenopausal women

Other: No intervention will be done

Interventions

No intervention will be done OTHER

No intervention will be done

postmenopausal women

Eligibility Criteria

• Age: 40 Years - 63 Years
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64)
• Sampling Method: Non-Probability Sample

Study Population

The cohort of the study will be selected from the general population.

You may qualify if:

• Women between 40 and 63 years old with amenorrhea for a period of time equal or greater than 12 months.
• Without hormone replacement therapy.
• Sign the informed consent.

You may not qualify if:

• Women diagnosed with diabetes (or serum glucose ≥ 126 mg/dL) or other chronic pathologies (coronary, cardiovascular, celiac disease, Crohn's disease and chronic kidney diseases (or serum creatinine ≥ 1.5 mg/dL).
• Women taking medications prescribed for hypertension and dyslipidemia. Women who have consumed during the week prior to start to start of the study anti-inflammatory drugs.
• women with chronic gastrointestinal problems.
• Women with a body mass index (in kg/m2) \<18 or ≥35.
• Women who are participating in another clinical trial or following a prescribed diet for any reason, including weigh loss, during the last month.
• Women who consume more than 14 alcoholic beverages per week.
• Women current smokers.

Sponsors & Collaborators

• Fundació Eurecat: lead

Study Sites (1)

Eurecat

Reus, 43204, Spain

Location

Related Links

• Technological Centre of Nutrition and Health. Eurecat\_Reus.

Biospecimen

Retention: SAMPLES WITH DNA

Samples of blood, faeces, urine, hair and hair follicles will be collected

Study Officials

• Antoni Caimari, PhD

  Eurecat-Reus

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: CROSS SECTIONAL
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: April 1, 2022
• First Posted: May 31, 2022
• Study Start: April 16, 2021
• Primary Completion: November 29, 2022
• Study Completion: November 29, 2022
• Last Updated: March 16, 2023

Record last verified: 2023-03

Locations

ES (1)