df_linear_associations: Linear Associations of Blood Biomarkers to BMI, HOMA-IR and...
In NightingaleHealth/ggforestplot: Forestplots of Measures of Effects and Their Confidence Intervals
Description
Usage
Format
Details
Source
Description
A data frame containing cross-sectional associations of the Nightingale blood
biomarkers to Body Mass Index (BMI), insulin resistance (log(HOMA-IR)) and
fasting glucose. For these values a linear regression model was used, adjusted
for age and sex.
Usage
1
Format
A data frame (tibble) with 687 rows and 5 columns:
- name
Blood biomarker name. Note: glucose is missing as the results
are adjusted for this biomarker.
- trait
The response variable of the regression model, either BMI,
log(HOMA-IR) or fasting glucose.
- beta
Linear regression coefficient β.
- se
Standard error.
- pvalue
P-value.
Details
"Values are beta-correlations from cross-sectional metabolite associations
with BMI, log(HOMA-IR) and fasting glucose. For comparison of the patterns of
associations, magnitudes are scaled to 1-SD in each of the outcomes
(corresponding to 4.2 kg/m2 for BMI, 0.57 for log(HOMA-IR) and 0.56 mmol/l
for glucose) per 1-SD log-transformed metabolite concentration. Results were
adjusted for sex and age, and meta-analyzed for 11,896 individuals from the
four cohorts. Error bars denote 95% confidence intervals; the large sample
size and consistency across cohorts make confidence intervals narrow for the
cross-sectional linear regression analyses."
The values are shown in Figure S5 of A. V. Ahola-Olli et al. (2019):
https://www.biorxiv.org/content/early/2019/01/08/513648
Source
These data are taken from the Supplementary material of
A. V. Ahola-Olli et al. (2019).
https://www.biorxiv.org/content/early/2019/01/08/513648
NightingaleHealth/ggforestplot documentation built on April 10, 2020, 7:01 p.m.
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Description Usage Format Details Source
Description
A data frame containing cross-sectional associations of the Nightingale blood biomarkers to Body Mass Index (BMI), insulin resistance (log(HOMA-IR)) and fasting glucose. For these values a linear regression model was used, adjusted for age and sex.
Usage
1 |
Format
A data frame (tibble) with 687 rows and 5 columns:
- name
Blood biomarker name. Note: glucose is missing as the results are adjusted for this biomarker.
- trait
The response variable of the regression model, either BMI, log(HOMA-IR) or fasting glucose.
- beta
Linear regression coefficient β.
- se
Standard error.
- pvalue
P-value.
Details
"Values are beta-correlations from cross-sectional metabolite associations with BMI, log(HOMA-IR) and fasting glucose. For comparison of the patterns of associations, magnitudes are scaled to 1-SD in each of the outcomes (corresponding to 4.2 kg/m2 for BMI, 0.57 for log(HOMA-IR) and 0.56 mmol/l for glucose) per 1-SD log-transformed metabolite concentration. Results were adjusted for sex and age, and meta-analyzed for 11,896 individuals from the four cohorts. Error bars denote 95% confidence intervals; the large sample size and consistency across cohorts make confidence intervals narrow for the cross-sectional linear regression analyses." The values are shown in Figure S5 of A. V. Ahola-Olli et al. (2019): https://www.biorxiv.org/content/early/2019/01/08/513648
Source
These data are taken from the Supplementary material of A. V. Ahola-Olli et al. (2019). https://www.biorxiv.org/content/early/2019/01/08/513648
R Package Documentation
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We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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