linear ordinary least square (ols) regression  (STATA Corporation)

Journal: Nature metabolism

Article Title: Gut microbiome pattern reflects healthy aging and predicts survival in humans

doi: 10.1038/s42255-021-00348-0

Figure Lengend Snippet: (a) Age ß-coefficients and corresponding P-values from OLS models predicting Bray-Curtis uniqueness at the ASV- and genus-level in the American Gut Project (AGP) and two vendors in the Arivale cohort. In the AGP dataset, the analysis was performed on all samples, and then repeated on the subset of samples who had available sex and BMI data for covariate adjustment. P-values reported are derived from OLS linear regression models and result from a two-sided hypothesis. (b) Spearman correlations of different ß-diversity metrics with age on both the ASV- and genus-level independently in each vendor used for gut microbiome processing in the Arivale cohort.

Article Snippet: The relationship between the calculated uniqueness measure and age in the Arivale cohort was modeled using Ordinary Least Square (OLS) linear regression (Python) where square root transformed Bray-Curtis uniqueness was modeled as the dependent variable and each age decade was compared to the youngest reference group (<30 years), adjusting for sex, BMI, and either genus or ASV-level Shannon diversity, depending on what level the uniqueness measure was calculated.

Techniques: Derivative Assay

Journal: Nature metabolism

Article Title: Gut microbiome pattern reflects healthy aging and predicts survival in humans

doi: 10.1038/s42255-021-00348-0

Figure Lengend Snippet: ‘pvalue’ corresponds to the unadjusted P-Value of the ß-coefficient (B-coef column) for each analyte from an OLS model adjusted for gut microbiome vendor. ‘r_squared’ reflects the percent of variance explained beyond microbiome vendor for each analyte independently for the Genus-level Bray-Curtis measure. ‘age_adjusted_coeff’ and ‘age_adjusted_corr_pvalue’ correspond to the ß-coefficient and the Bonferroni corrected P-Value (two-sided) for each analyte predicting Genus-level Bray-Curtis Uniqueness, adjusting for gut microbiome vendor and age. The ‘age_adj_coeff (ASV-level)’ and the ‘age_adj_corr_pvalue (ASV-level)’ correspond to analysis done on the ASV-level Bray-Curtis Uniqueness measure, where models were adjusted for vendor and age. ‘Missing’ shows the number of missing observations for each analyte. Values highlighted in red are statistically significant after multiple-hypothesis correction (Bonferroni P-Value<0.05, two-sided).

Article Snippet: The relationship between the calculated uniqueness measure and age in the Arivale cohort was modeled using Ordinary Least Square (OLS) linear regression (Python) where square root transformed Bray-Curtis uniqueness was modeled as the dependent variable and each age decade was compared to the youngest reference group (<30 years), adjusting for sex, BMI, and either genus or ASV-level Shannon diversity, depending on what level the uniqueness measure was calculated.

Techniques:

Journal: Nature metabolism

Article Title: Gut microbiome pattern reflects healthy aging and predicts survival in humans

doi: 10.1038/s42255-021-00348-0

Figure Lengend Snippet: (a) A plot of -log10 p-values for each of the 653 plasma metabolites measured in the Arivale cohort, from OLS regression models predicting genus-level Bray-Curtis uniqueness adjusted for microbiome vendor, sex, age, age , a sex*age interaction term, BMI, and Shannon diversity. Metabolites are color-coded by their super-family. All metabolites above the light red line are significant after multiple-hypothesis correction (Bonferroni P<0.05, two-sided), while the blue line indicates the unadjusted P-value threshold. Asterisks (*) indicate metabolites that were confidently identified on the basis of mass spectrometry data, but for which no reference standards are available to verify the identity. (b) Spearman correlation coefficients for each of the metabolites significantly associated with genus-level Bray-Curtis uniqueness after adjusting for covariates and multiple-hypothesis correction (Bonferroni P<0.05 two-sided). (c) Spearman correlation coefficients for each of the metabolites significantly associated with the ASV-level Bray-Curtis uniqueness measure after adjusting for covariates and multiple-hypothesis correction (Bonferroni P<0.05 two-sided). For both subfigures b) and c), bars are color-coded as in a). (d) Scatter plot of genus-level Bray-Curtis Uniqueness and the strongest metabolite predictor, phenylacetylglutamine, adjusted for vendor. (e) Scatter plot of ASV-level Bray-Curtis uniqueness and the strongest metabolite predictor, phenylacetylglutamine, adjusted for vendor. The lines shown are the y∼x regression lines, and the shaded regions are 95% confidence intervals for the slope of the line. The p-values reported in (d) and (e) are a result of two-sided statistical tests.

Article Snippet: The relationship between the calculated uniqueness measure and age in the Arivale cohort was modeled using Ordinary Least Square (OLS) linear regression (Python) where square root transformed Bray-Curtis uniqueness was modeled as the dependent variable and each age decade was compared to the youngest reference group (<30 years), adjusting for sex, BMI, and either genus or ASV-level Shannon diversity, depending on what level the uniqueness measure was calculated.

Techniques: Clinical Proteomics, Mass Spectrometry

Journal: Nature metabolism

Article Title: Gut microbiome pattern reflects healthy aging and predicts survival in humans

doi: 10.1038/s42255-021-00348-0

Figure Lengend Snippet: ‘pvalue’ corresponds to the unadjusted P-Value of the ß-coefficient (covariate_adj. Beta_coeff column) for each analyte from an OLS model adjusted for age, age , sex, a sex*age interaction term, BMI, Shannon diversity, and vendor with Genus-level Bray-Curtis uniqueness as the dependent variable. ‘corr_pval’ corresponds to the Bonferoni corrected P-value. ‘SUPER_PATHWAY’ indicates what pathway the metabolite belongs to. The last three columns are the same as the first three, but for Bray-Curtis uniqueness calculated on the ASV level. All metabolites with an unadjusted P-Value<0.01 are shown. Values highlighted in red are statistically significant after multiple-hypothesis correction (Bonferroni P-Value<0.05, two-sided).

Article Snippet: The relationship between the calculated uniqueness measure and age in the Arivale cohort was modeled using Ordinary Least Square (OLS) linear regression (Python) where square root transformed Bray-Curtis uniqueness was modeled as the dependent variable and each age decade was compared to the youngest reference group (<30 years), adjusting for sex, BMI, and either genus or ASV-level Shannon diversity, depending on what level the uniqueness measure was calculated.

Techniques:

Journal: Nature metabolism

Article Title: Gut microbiome pattern reflects healthy aging and predicts survival in humans

doi: 10.1038/s42255-021-00348-0

Figure Lengend Snippet: (a-c) PCoA of the MrOS discovery cohort color-coded by (a) genus-level Bray-Curtis uniqueness, (b) relative Bacteroides abundance, and (c) relative Prevotella abundance. (d) Barplot demonstrating the correlation of strongest taxa associated with genus-level gut microbiome uniqueness in the MrOS discovery cohort, color-coded by phylum. ( e) Correlation of genus-level Bray-Curtis uniqueness scores with age across the MrOS discovery and validation cohorts under different health stratifications. Also shown are age β-coefficients (slopes) with 95% confidence intervals from (OLS) linear regression models predicting genus-level Bray-Curtis uniqueness adjusted for BMI across the same stratifications. ‘Excellent’ corresponds to individuals who self-reported their health to be excellent, while ‘

Article Snippet: The relationship between the calculated uniqueness measure and age in the Arivale cohort was modeled using Ordinary Least Square (OLS) linear regression (Python) where square root transformed Bray-Curtis uniqueness was modeled as the dependent variable and each age decade was compared to the youngest reference group (<30 years), adjusting for sex, BMI, and either genus or ASV-level Shannon diversity, depending on what level the uniqueness measure was calculated.

Techniques: Biomarker Discovery

Journal: Nature metabolism

Article Title: Gut microbiome pattern reflects healthy aging and predicts survival in humans

doi: 10.1038/s42255-021-00348-0

Figure Lengend Snippet: (a-e) Plots demonstrating the strength of Spearman correlation between age and gut microbiome measures at different taxonomic resolutions. (a) The blue/red panel corresponds to the calculated Weighted UniFrac (ß-diversity) uniqueness score at the genus level, while (b) the grey/green and (c) grey/yellow panels correspond to Shannon diversity and Observed species (α-diversity measures) at the ASV level, respectively. Significant correlations (two-sided) are indicated with asterisks. Exact correlation coefficients and corresponding p-values for (a) are provided in . (d-e) The same plots as in (b-c), with α-diversity calculated at the genus level. (f) Comparison of ASV level and genus-level analysis in healthy aging in the MrOS cohort. Barplots represent correlation coefficients comparing age and uniqueness at the ASV level across composite healthy MrOS individuals, and the remainder of the cohort in both the discovery and validation groups. (g) ß -coefficients for age from OLS regression models predicting genus-level Bray-Curtis uniqueness in healthy composite individuals and remainder of the cohort, adjusted individually for the most commonly reported supplements and medications in the MrOS cohort.

Article Snippet: The relationship between the calculated uniqueness measure and age in the Arivale cohort was modeled using Ordinary Least Square (OLS) linear regression (Python) where square root transformed Bray-Curtis uniqueness was modeled as the dependent variable and each age decade was compared to the youngest reference group (<30 years), adjusting for sex, BMI, and either genus or ASV-level Shannon diversity, depending on what level the uniqueness measure was calculated.

Techniques: Comparison, Biomarker Discovery, Medications