Outcome
Type |
Measure |
Description |
Time frame |
Safety issue |
Primary |
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 |
|
Primary |
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 |
|
Primary |
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 |
|
Primary |
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 |
|
Primary |
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 |
|
Primary |
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 |
|
Primary |
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 |
|
Primary |
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 |
|
Primary |
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 |
|
Primary |
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 |
|
Primary |
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 |
|
Primary |
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 |
|
Primary |
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 |
|
Primary |
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 |
|
Primary |
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 |
|
Primary |
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 |
|
Primary |
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 |
|
Primary |
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 |
|
Primary |
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 |
|
Primary |
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 |
|
Primary |
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 |
|
Primary |
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 |
|
Primary |
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 |
|
Primary |
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 |
|
Primary |
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 |
|
Primary |
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 |
|
Primary |
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 |
|
Primary |
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 |
|
Primary |
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 |
|
Primary |
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 |
|
Primary |
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 |
|
Primary |
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 |
|
Primary |
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 |
Body weight |
Body weight measured by TANITA SC 330 S portable scale (Peroxfarma, Barcelona, Spain) . |
At day 1 |
|
Secondary |
Height |
Height measured by TANITA Leicester Portable (Tanita Corp., Barcelona, Spain) |
At day 1 |
|
Secondary |
Body mass index |
Weight and height will be combined to report body mass index in kg/m^2 |
At day 1 |
|
Secondary |
Waist circumference |
Waist circumference will be measured using a 150 cm anthropometric steel measuring tape |
At day 1 |
|
Secondary |
Blood pressure (in mmHg) |
Systolic and diastolic pressure will be measured twice after 2-5 minutes of patient respite, seated, with one minute interval in between, using an automatic sphygmomanometer (OMRON HEM-907; Peroxfarma, Barcelona, Spain). |
At day 1 |
|
Secondary |
Waist circumference to height ratio |
Waist circumference and height will be combined to report waist circumference to height ratio. |
At day 1 |
|
Secondary |
Body composition |
Body fat mass and body lean mass will be measured using TANITA SC 330 S Body Composition Analyzer (Peroxfarma, Barcelona, Spain) |
At day 1 |
|
Secondary |
Dietary intake |
Dietary intake will be measured using 3-day dietary record. |
At day 1 |
|
Secondary |
Transcriptomics analysis in hair follicles. |
Transcriptomics analysis in hair follicles samples will be done by RNA-seq. |
At day 1 |
|
Secondary |
Transcriptomics analysis in total blood. |
Transcriptomic analysis will be performed with blood samples collected in PAXgene tubes by microarray technology (Agilent Technologies). This analysis will be carried out with a sub-cohort of post-menopausal women from each of the different clusters obtained with a total of 64 samples. |
At day 1 |
|
Secondary |
MicroRNAs analysis in total blood. |
MicroRNAs will be analyzed in blood samples collected in PAX gene tubes using RNA-seq technology. This analysis will be carried out with a sub-cohort of post-menopausal women from each of the different clusters obtained with a total of 64 samples. |
At day 1 |
|
Secondary |
DNA methylation analysis in total blood. |
DNA methylation analysis will be performed with blood samples collected in PAXgene tubes by bisulfite conversion of the DNA combined with targeted amplification of regions of interest, library construction and next-generation sequencing. This analysis will be carried out with a sub-cohort of post-menopausal women from each of the different clusters obtained with a total of 64 samples. |
At day 1 |
|