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Associations of fat and carbohydrate intake with cardiovascular disease and mortality: prospective cohort study of UK Biobank participants - March 2020

Associations of fat and carbohydrate intake with cardiovascular disease and mortality: prospective cohort study of UK Biobank participants - March 2020
Frederick K Ho, research associate,1 Stuart R Gray, senior lecturer,2 Paul Welsh, senior lecturer,2 Fanny Petermann-Rocha, PhD student,1,2 Hamish Foster, clinical academic GP fellow,1 Heather Waddell, PhD student,2 Jana Anderson, research fellow,1 Donald Lyall, lecturer,1 Naveed Sattar, professor,2 Jason M R Gill, professor,2 John C Mathers, professor,3 Jill P Pell, professor,1 and Carlos Celis-Morales, research fellow1,2,4,5
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7190059/

Abstract

Objective

To investigate the association of macronutrient intake with all cause mortality and cardiovascular disease (CVD), and the implications for dietary advice.

Design

Prospective population based study.

Setting

UK Biobank.

Participants

195 658 of the 502 536 participants in UK Biobank completed at least one dietary questionnaire and were included in the analyses. Diet was assessed using Oxford WebQ, a web based 24 hour recall questionnaire, and nutrient intakes were estimated using standard methodology. Cox proportional models with penalised cubic splines were used to study non-linear associations.

Main outcome measures

All cause mortality and incidence of CVD.

Results

4780 (2.4%) participants died over a mean 10.6 (range 9.4-13.9) years of follow-up, and 948 (0.5%) and 9776 (5.0%) experienced fatal and non-fatal CVD events, respectively, over a mean 9.7 (range 8.5-13.0) years of follow-up. Non-linear associations were found for many macronutrients. Carbohydrate intake showed a non-linear association with mortality; no association at 20-50% of total energy intake but a positive association at 50-70% of energy intake (3.14 v 2.75 per 1000 person years, average hazard ratio 1.14, 95% confidence interval 1.03 to 1.28 (60-70% v 50% of energy)). A similar pattern was observed for sugar but not for starch or fibre. A higher intake of monounsaturated fat (2.94 v 3.50 per 1000 person years, average hazard ratio 0.58, 0.51 to 0.66 (20-25% v 5% of energy)) and lower intake of polyunsaturated fat (2.66 v 3.04 per 1000 person years, 0.78, 0.75 to 0.81 (5-7% v 12% of energy)) and saturated fat (2.66 v 3.59 per 1000 person years, 0.67, 0.62 to 0.73 (5-10% v 20% of energy)) were associated with a lower risk of mortality. A dietary risk matrix was developed to illustrate how dietary advice can be given based on current intake.

Conclusion

Many associations between macronutrient intake and health outcomes are non-linear. Thus dietary advice could be tailored to current intake. Dietary guidelines on macronutrients (eg, carbohydrate) should also take account of differential associations of its components (eg, sugar and starch).
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https://preview.redd.it/wgngpelueij51.png?width=1002&format=png&auto=webp&s=5ee8efcb1b02b036dfbe180fca4abc82f4da77d4
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https://preview.redd.it/iz1wgw65fij51.png?width=1002&format=png&auto=webp&s=4ec39eddcc7d0bf9bddd7264b61a1a4b1d9678c7

Strengths and limitations of this study

A strength of this study is that we did not assume linearity between intakes of macronutrients and health outcomes and we adjusted mutually for macronutrient components. We also explored associations with constituent components of macronutrients—for example, starch, sugar, and dietary fibre are components of carbohydrates, each of which has distinctive relations with health outcomes. The possibility of confounding was dealt with through statistical adjustment for a wide range of covariates and through a series of sensitivity analyses. As with any observational study, however, residual confounding is possible, and causation cannot be tested. Also, summary statistics and estimates of absolute risk from this study might not be generalisable even though the personal characteristics of the cohort and estimated effect sizes are similar to those of the general population.36 37 38 As the dietary information used in this study was provided by around half of UK Biobank participants, selection bias is possible. Dietary measurements in our study were derived from 24 hour recall so might not portray participants’ typical intake precisely and could be subject to recall bias.39 Owing to limited statistical power, we did not exclude participants who did not provide multiple dietary records, and some analyses might be underpowered. Further, we were not able to reliably test whether some associations were sex specific. Similarly, associations at the extreme ends of intake (particularly intakes with wide confidence intervals) should be interpreted with caution. Isocaloric replacement analysis is based on comparisons between participants and might not represent real life changes as occurs in randomised controlled trials. We were unable to investigate associations with added sugars, trans fat, types of polyunsaturated fat (omega-3 and omega-6), and animal based versus plant based protein because these data were not available. Also, food source (eg, whole grain versus refined carbohydrate sources) might modify the associations between macronutrient intake and outcomes. The dietary risk matrix was constructed for illustrative purposes rather than as a tool ready for implementation, and the cut-off values have not been validated.
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interpreting interaction effect in Cox models

Hi community,
I'm playing around (mostly for the first time) running Cox regression models on a attrition dataset. My models predict teacher attrition from a school district.
The outcome is the duration of employment (in years). My models control for school type (high school; middle school; elem omitted); job position (bunch of dummy variables); age (year of birth, meaning higher numbers are younger people); and school poverty (a school- variable, defined as the % of students on reduced or free lunch [rfl]).
My main IV of interest is school climate, which reflects the degree to which a principal is supportive. Like poverty, school climate is school level variable (same value assigned to all teachers in the same site). It it takes the school average of teachers responses to some survey questions (about principal supportiveness), where responses were measured on a 4 point agree-to-disagree scale).
The school climate variable is called collab_fac in my models. I was hoping to get a direct effect for collab_fac but it hasn't been supported in the data. However, I am finding an interesting and sensible interaction between school climate and poverty. The negative interaction suggests that the positive effect (i.e., higher hazard rate) of poverty on teacher attrition is attenuated by having a supportive principal. Sensible, I think.
I think I understand how to interpret the direct effects in the model. Yet, while I understand the interaction is negative (since the hazard ratio is < 1), I'm struggling to put the interaction coefficient (.09) into more concrete terms -- e.g., "for every 1 point increase in collab_fac (I probably should have standardized it), the effect of poverty on attrition goes down by X%."
I'm hoping someone will graciously provide some insight here. You can see my Stata code and model output (I include descriptive statistics) at the following Google Docs link.
(https://docs.google.com/document/d/13Hda-JJm0i67B8fasyODKtdedkOUqipnQLcVzb-eFlw/edit?usp=sharing)
Thanks, Poobiedog
submitted by Poobiedog to statistics [link] [comments]

how to interpret hazard ratio in cox model video

Survival Analysis Cox proportinal hazards model using SPSS (survival ... Survival Analysis in R, part 6, Cox Proportional Hazards ... Fit a Cox proportional hazards model and check ... Cox Proportional Hazards Model - YouTube Interpreting Hazard Ratios - YouTube Webinar Overview of Cox Proportional Hazard Models Cox ... How to interpret a survival plot - YouTube Survival analysis in SPSS using Cox regression (v2) - YouTube R: Cox proportional hazard model - interaction term - YouTube

$\begingroup$ If there is more than one coefficient for the predictor in the model, you can't interpret any single coefficient very well. A simple case would be having $x$ and $x^2$ in the model; you need to vary $\beta_{1}x + \beta_{2}x^{2}$ to get a hazard ratio of interest. $\endgroup$ – Frank Harrell Sep 23 '13 at 15:30 Hazard ratio is reported most commonly in time-to-event analysis or survival analysis (i.e. when we are interested in knowing how long it takes for a particular event/outcome to occur). Hazard ratio can be obtained and calculated from the Cox regression - or Cox proportional hazard regression model. The Cox PH model • is a semiparametric model • makes no assumptions about the form of h(t) (non-parametric part of model) • assumes parametric form for the effect of the predictors on the hazard In most situations, we are more interested in the parameter estimates than the shape of the hazard. The Cox PH model is well-suited to this goal. The Hazard ratio (HR) is one of the measures that in clinical research are most often difficult to interpret for students and researchers. In this post we will try to explain this measure in terms of its practical use. You should know what the Hazard Ratio is, but we will repeat it again. Let’s take […] We argue that the term "relative risk" should not be used as a synonym for "hazard ratio" and encourage to use the probabilistic index as an alternative effect measure for Cox regression. The probabilistic index is the probability that the event time of an exposed or treated subject exceeds the event time of an unexposed or untreated subject conditional on the other covariates. Using hazard ratio statements in SAS 9.4, ... How can I interpret the Beta coefficient (B) from Cox regression model ... How can I validate a cox proportional hazard's model made in SPSS v22. ... The Cox model can be written as a multiple linear regression of the logarithm of the hazard on the variables \(x_i\), with the baseline hazard being an ‘intercept’ term that varies with time. The quantities \(exp(b_i)\) are called hazard ratios (HR). The Cox model can be written as a multiple linear regression of the logarithm of the hazard on the variables \(x_i\), with the baseline hazard being an ‘intercept’ term that varies with time. The quantities \(exp(b_i)\) are called hazard ratios (HR). When reporting hazard ratios for Cox regression analysis, is it common to report the hazard ratio for the interaction term itself? For example, I have a model with 3 terms: a Hazard ratio: Similar to how odds is used in logistic regression, the equivalent for odds in cox proportional hazard model is hazard. The hazard ratio look into comparing the hazards occurring in ...

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Survival Analysis

This short video describes how to interpret a survival plot. Please post any comments or questions below, or at our Statistics for Citizen Scientists group: ... Explore how to fit a Cox proportional hazards model using Stata. We also describes how to check the proportional-hazards assumption statistically using -esta... Webinar Overview of Cox Proportional Hazard Models Cox Regression 11 29 18 - Duration: 1 ... How to interpret a survival plot - Duration: 4:05. Darren Dahly 29,499 views. 4:05. Tristan Boudreault ... Survival analysisTitle: Interpreting coefficients in a multiple explanatory variable Cox proportional hazard model: confounding variableHosmer & Lemeshow Cha... Ryan Womack, Data LibrarianRutgers Universityhttps://ryanwomack.comtwitter: @ryandatahttps://github.com/ryandata/Survival/or http://libguides.rutgers.edu/dat... To request the .pdf of the handout please contact us with the name of this presentation at:https://www.omegastatistics.com/contact/An overview of Kaplan-Meie... This video provides a demonstration of the use of Cox Proportional Hazards (regression) model based on example data provided in Luke & Homan (1998). A copy ... About Press Copyright Contact us Creators Advertise Developers Terms Privacy Policy & Safety How YouTube works Test new features Press Copyright Contact us Creators ... This video provides a demonstration of the use of the Cox proportional hazards model using SPSS. The data comes from a demonstration of this model within the... This video wil help students and clinicians understand how to interpret hazard ratios.

how to interpret hazard ratio in cox model

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