Journal: Nature Medicine

Article Title: Multiomic signatures of body mass index identify heterogeneous health phenotypes and responses to a lifestyle intervention

doi: 10.1038/s41591-023-02248-0

Figure Lengend Snippet: a , Overview of study cohorts and the omics-based BMI model generation. CV, cross-validation. b , Correlation between the measured and predicted BMIs. The solid line is the OLS linear regression line with 95% confidence interval, and the dotted line is measured BMI = predicted BMI. Standard measures: OLS linear regression model with sex, age, triglycerides, HDL cholesterol, LDL cholesterol, glucose, insulin and HOMA-IR as regressors; P adj : adjusted P value of two-sided Pearson’s correlation test with the Benjamini–Hochberg method across the five categories ( n = 1,277 participants). c , d , Model performance of each fitted BMI model. Out-of-sample R 2 was calculated from each corresponding hold-out testing set (Arivale: c , d ) or from the external testing set (TwinsUK: d ). Metabolomics (full): LASSO model trained by all 766 metabolites of the Arivale dataset; Metabolomics (restricted): LASSO model trained by the common 489 metabolites in the Arivale and TwinsUK datasets (Extended Data Fig. and ); P adj : adjusted P value of two-sided Welch’s t -test with the Benjamini–Hochberg method across the four ( c ) or three ( d ) comparisons. Data: mean with 95% confidence interval, n = 10 models. Note that Standard measures and Metabolomics (full) of Arivale in d are the same with corresponding ones in c . e , Association between omics-inferred BMI and physiological feature. For each of the 51 numeric physiological features (Supplementary Data ), β -coefficient was estimated using OLS linear regression model with the measured or omics-inferred BMI as a dependent variable and sex, age and ancestry principal components as covariates. Presented are the 30 features that were significantly associated with at least one of the BMI types after multiple testing adjustment with the Benjamini–Hochberg method across the 255 (51 features × 5 BMI types) regressions. n, number of assessed participants. Data: estimate with 95% confidence interval. *Adjusted P < 0.05, **adjusted P < 0.01, ***adjusted P < 0.001. All exact values of test summaries are found in Supplementary Data and .

Article Snippet: Full version: LASSO model trained by all 766 metabolites in the Arivale dataset, Restricted version: LASSO model trained by the common 489 metabolites in the Arivale and TwinsUK datasets ( ). a , The number of the variables that were robustly retained across all ten MetBMI models.

Techniques: Biomarker Discovery

Journal: Nature Medicine

Article Title: Multiomic signatures of body mass index identify heterogeneous health phenotypes and responses to a lifestyle intervention

doi: 10.1038/s41591-023-02248-0

Figure Lengend Snippet: a – c , Comparison of the MetBMI model between the main analyses (Arivale cohort) and the validation analyses (TwinsUK cohort). Full version: LASSO model trained by all 766 metabolites in the Arivale dataset, Restricted version: LASSO model trained by the common 489 metabolites in the Arivale and TwinsUK datasets . a , The number of the variables that were robustly retained across all ten MetBMI models. The number in square brackets indicates the number of the robustly retained metabolites that were derived from the common 489 metabolites. b , Correlation of the mean of β -coefficients in the ten MetBMI models. Only the robustly retained metabolites in either full version (37 metabolites) or restricted version (74 metabolites) were analyzed. c , Correlation of the MetBMI prediction. b , c , The solid line is the OLS linear regression line with 95% confidence interval, and the dotted line in c is the value in full version = the value in restricted version. P : P value of two-sided Pearson’s correlation test. n = 76 metabolites ( b ) or 1,277 participants ( c ). d , Correlation between the measured and predicted BMIs. The solid line is the OLS linear regression line with 95% confidence interval, and the dotted line is measured BMI = predicted BMI. Standard measures: OLS linear regression model with sex, age, triglycerides, HDL cholesterol, LDL cholesterol, glucose, insulin and HOMA-IR as regressors; Metabolomics: the restricted version of MetBMI model, corresponding to Metabolomics (restricted) in Fig. ; P adj : adjusted P value of two-sided Pearson’s correlation test with the Benjamini–Hochberg method across the four (two categories × two cohorts) tests. n = 1,277 (Arivale) or 1,834 (TwinsUK) participants. All exact values of test summaries are found in Supplementary Data .

Article Snippet: Full version: LASSO model trained by all 766 metabolites in the Arivale dataset, Restricted version: LASSO model trained by the common 489 metabolites in the Arivale and TwinsUK datasets ( ). a , The number of the variables that were robustly retained across all ten MetBMI models.

Techniques: Comparison, Biomarker Discovery, Derivative Assay

Journal: Nature Medicine

Article Title: Multiomic signatures of body mass index identify heterogeneous health phenotypes and responses to a lifestyle intervention

doi: 10.1038/s41591-023-02248-0

Figure Lengend Snippet: a , b , Pairwise correlation of all plasma analytes ( a ; Metabolomics: 766 metabolites, Proteomics: 274 proteins, Clinical labs: 71 clinical laboratory tests, Combined omics: 1,111 analytes) or the analytes that were retained across all ten LASSO models ( b ; Metabolomics: 62 metabolites, Proteomics: 30 proteins, Clinical labs: 20 clinical laboratory tests, Combined omics: 132 analytes). Each violin is scaled to have same width between the omics categories and represents the kernel density distribution with the standard boxplot . c , Hierarchical clustering and heatmap for the pairwise correlations of the analytes that were retained across all ten CombiBMI models (132 analytes: 77 metabolites, 51 proteins and four clinical laboratory tests). Of note, both upper and lower triangular sides of the symmetric matrix are visualized. d , Model performance of each fitted BMI model with sex stratification. Out-of-sample R 2 was calculated from each corresponding hold-out testing set. Standard measures: OLS linear regression model with sex, age, triglycerides, HDL cholesterol, LDL cholesterol, glucose, insulin and HOMA-IR as regressors; P adj : adjusted P value of two-sided Welch’s t -test with the Benjamini–Hochberg method across the eight (four comparisons × two sexes) comparisons. Data: mean with 95% confidence interval, n = 10 models. All exact values of test summaries are found in Supplementary Data . Note that the sample size for modeling was different between female and male (Female: 821 participants versus Male: 456 participants). e – h , Transition of out-of-sample R 2 in the LASSO-modeling iteration analysis for metabolomics ( e ), proteomics ( f ), clinical labs ( g ) or combined omics ( h ). The iteration is highlighted with shading color when the removed analyte is the variable that was retained across all the original ten models. Data: mean with 95% confidence interval, n = 10 models.

Article Snippet: Full version: LASSO model trained by all 766 metabolites in the Arivale dataset, Restricted version: LASSO model trained by the common 489 metabolites in the Arivale and TwinsUK datasets ( ). a , The number of the variables that were robustly retained across all ten MetBMI models.

Techniques: Clinical Proteomics

Journal: Nature Medicine

Article Title: Multiomic signatures of body mass index identify heterogeneous health phenotypes and responses to a lifestyle intervention

doi: 10.1038/s41591-023-02248-0

Figure Lengend Snippet: a , Model performance of each fitted BMI model. P adj : adjusted P value of two-sided Welch’s t -test with the Benjamini–Hochberg method across the 12 (3 methods × 4 categories) comparisons. Data: mean with 95% confidence interval, n = 10 models. b , Correlation of the predicted BMI between LASSO and the other methods. The solid line is the OLS linear regression line with 95% confidence interval, and the dotted line is LASSO = the other method. P adj : adjusted P value of two-sided Pearson’s correlation test with the Benjamini–Hochberg method across the 12 (3 methods × 4 categories) combinations. n = 1,277 participants. c – f , Comparison of the omics-based BMI model between LASSO and elastic net. c – e , The number of the variables that were robustly retained across all ten models. f , Correlation of the mean of β -coefficients in the ten models. Only the robustly retained analytes in either LASSO models or elastic net models were analyzed. The solid line is the OLS linear regression line with 95% confidence interval. P adj : adjusted P value of two-sided Pearson’s correlation test with the Benjamini–Hochberg method across the four categories. n = 62 metabolites, 30 proteins, 20 clinical laboratory tests or 134 analytes. a , b , f , All exact values of test summaries are found in Supplementary Data . g , The top 30 variables that had the highest absolute value for the mean of β -coefficients in the ten ridge CombiBMI models. β -coefficient was obtained from the fitted CombiBMI model with ridge linear regression. Data: the standard box plot , n = 10 models. h , The top 30 variables that had the highest mean of feature importance in the ten random forest CombiBMI models. Feature importance was calculated as the normalized total reduction of the mean squared error. Data: mean with 95% confidence interval, n = 10 models.

Article Snippet: Full version: LASSO model trained by all 766 metabolites in the Arivale dataset, Restricted version: LASSO model trained by the common 489 metabolites in the Arivale and TwinsUK datasets ( ). a , The number of the variables that were robustly retained across all ten MetBMI models.

Techniques: Comparison

Journal: Nature Medicine

Article Title: Multiomic signatures of body mass index identify heterogeneous health phenotypes and responses to a lifestyle intervention

doi: 10.1038/s41591-023-02248-0

Figure Lengend Snippet: a , The variables that were retained across all ten CombiBMI models (132 analytes: 77 metabolites, 51 proteins and four clinical laboratory tests). β -coefficient was obtained from the fitted CombiBMI model with LASSO linear regression (Supplementary Data ). Each background color corresponds to the analyte category. Data: the standard box plot , n = 10 models. b – d , Univariate explained variance in BMI by each metabolite ( b ), protein ( c ) or clinical laboratory test ( d ). BMI was independently regressed on each of the analytes that were retained in at least one of the ten LASSO models (209 metabolites, 74 proteins and 41 clinical laboratory tests; Supplementary Data ), using OLS linear regression with sex, age and ancestry principal components as covariates. Multiple testing was adjusted with the Benjamini–Hochberg method across the 210 ( b ), 75 ( c ) or 42 ( d ) regressions, including each omics-based BMI model as reference. Among the analytes that were significantly associated with BMI (180 metabolites, 63 proteins and 30 clinical laboratory tests), only the top 30 significant analytes are presented with their univariate variances. All exact values of test summaries are found in Supplementary Data .

Article Snippet: Full version: LASSO model trained by all 766 metabolites in the Arivale dataset, Restricted version: LASSO model trained by the common 489 metabolites in the Arivale and TwinsUK datasets ( ). a , The number of the variables that were robustly retained across all ten MetBMI models.

Techniques:

Journal: Nature Medicine

Article Title: Multiomic signatures of body mass index identify heterogeneous health phenotypes and responses to a lifestyle intervention

doi: 10.1038/s41591-023-02248-0

Figure Lengend Snippet: a , Overview of study cohort and the omics-based WHtR model generation. CV,cross-validation. b , Distribution of the baseline WHtR. c , Correlation between the measured WHtR and BMI. d , Correlation between the measured and predicted WHtRs. e , Model performance of each fitted WHtR model. f – i , Transition of out-of-sample R 2 in the LASSO-modeling iteration analysis for metabolomics ( f ), proteomics ( g ), clinical labs ( h ) or combined omics ( i ). The iteration is highlighted with shading color when the removed analyte is the variable that was retained across all the original ten models. j , The variables that were retained across all ten CombiWHtR models (37 analytes: 18 metabolites, 15 proteins and four clinical laboratory tests). β -coefficient was obtained from the fitted CombiWHtR model (Supplementary Data ). k , Univariate explained variance in WHtR by each analyte. Among the analytes that were significantly associated with WHtR (212 analytes; ), only the top 30 significant analytes are presented with their univariate variances. l , Difference of the omics-inferred WHtR from the measured WHtR (ΔWHtR). m , Difference in ΔWHtR between clinically-defined metabolic health conditions. Each comparison value indicates adjusted P value, calculated from OLS linear regression with WHtR, sex, age and ancestry principal components as covariates, while adjusting multiple testing with the Benjamini–Hochberg method across the eight (two BMI classes × four omics categories) regressions. P adj : adjusted P value of two-sided Pearson’s correlation test ( c , d , l ) or Welch’s t -test ( e ) with the Benjamini–Hochberg method across the two sexes ( c ), five categories ( d ), four comparisons ( e ) or six combinations ( l ). Data: mean with 95% confidence interval ( e – i ) or the standard boxplot ( j , m ), n = 10 models ( e – i , j ) (see Supplementary Data for each number of participants in m ). All exact values of test summaries are found in Supplementary Data and .

Article Snippet: Full version: LASSO model trained by all 766 metabolites in the Arivale dataset, Restricted version: LASSO model trained by the common 489 metabolites in the Arivale and TwinsUK datasets ( ). a , The number of the variables that were robustly retained across all ten MetBMI models.

Techniques: Biomarker Discovery, Comparison

Journal: Nature Medicine

Article Title: Multiomic signatures of body mass index identify heterogeneous health phenotypes and responses to a lifestyle intervention

doi: 10.1038/s41591-023-02248-0

Figure Lengend Snippet: a – d , Comparison of the omics-based LASSO model between BMI and WHtR. a – c , The number of the variables that were robustly retained across all ten LASSO models. d , Correlation of the mean of β -coefficients in the ten LASSO models. Only the robustly retained analytes in either BMI models or WHtR models were analyzed. e , Correlation between ΔBMI (that is, difference of the omics-inferred BMI from the measured BMI) and ΔWHtR (that is, difference of the omics-inferred WHtR from the measured WHtR). Only the participants having both BMI and WHtR were analyzed. d , e , The solid line is the OLS linear regression line with 95% confidence interval. P adj : adjusted P value of two-sided Pearson’s correlation test with the Benjamini–Hochberg method across the four categories. n = 92 metabolites ( d , Metabolomics), 36 proteins ( d , Proteomics), 26 clinical laboratory tests ( d , Clinical labs), 146 analytes ( d , Combined omics) or 1,078 participants ( e ). All exact values of test summaries are found in Supplementary Data .

Article Snippet: Full version: LASSO model trained by all 766 metabolites in the Arivale dataset, Restricted version: LASSO model trained by the common 489 metabolites in the Arivale and TwinsUK datasets ( ). a , The number of the variables that were robustly retained across all ten MetBMI models.

Techniques: Comparison