Coffee drinking timing and mortality in US adults

Table of Contents

Overall Summary

Study Background and Main Findings

This study investigated the association between coffee drinking timing patterns and mortality risk using data from the NHANES, WLVS, and MLVS. Two distinct patterns were identified: "morning-type" (36% of participants), characterized by coffee consumption concentrated in the morning, and "all-day-type" (16%), with consumption spread throughout the day. The morning-type pattern was significantly associated with a lower risk of all-cause mortality (HR: 0.88; 95% CI: 0.81-0.96) and CVD-specific mortality (HR: 0.69; 95% CI: 0.55-0.87) compared to non-coffee drinking. The all-day-type pattern did not show a significant association with mortality risk. A significant interaction was found between coffee drinking timing and coffee intake amounts for all-cause mortality (P-interaction = 0.031), with moderate to heavy consumption associated with lower risk only among morning-type drinkers.

Research Impact and Future Directions

This study provides compelling evidence for an association between coffee drinking timing and mortality risk, suggesting that a morning-type pattern is associated with lower all-cause and CVD-specific mortality. The use of robust statistical methods and multiple datasets strengthens these findings. However, the observational nature of the study precludes causal inferences, and the observed associations could be influenced by unmeasured confounding factors or reverse causation.

The practical utility of these findings lies in their potential to inform public health recommendations regarding coffee consumption. The study suggests that concentrating coffee intake in the morning may be more beneficial than consuming it throughout the day. This aligns with the growing body of research on chrononutrition, which emphasizes the importance of meal timing in optimizing health outcomes. However, it is important to note that these findings are specific to the US population and may not be generalizable to other populations with different coffee consumption habits and cultural contexts.

While this study provides valuable insights, it is crucial to acknowledge the uncertainties that remain. The precise mechanisms underlying the observed associations are not fully understood, although circadian rhythm disruption and the anti-inflammatory effects of coffee are proposed as potential explanations. Further research is needed to confirm these mechanisms and to explore the potential role of other factors, such as genetics and lifestyle, in modifying the relationship between coffee drinking timing and mortality.

A critical unanswered question is whether the observed associations are truly causal. Future research should employ study designs that can establish causality, such as randomized controlled trials or Mendelian randomization studies. Additionally, the long-term effects of different coffee drinking timing patterns on various health outcomes, beyond mortality, need to be investigated. While the methodological limitations, such as the reliance on self-reported data and the potential for residual confounding, are acknowledged, they do not fundamentally undermine the study's conclusions. However, they do highlight the need for cautious interpretation and further research to validate these findings.

Critical Analysis and Recommendations

Comprehensive Data and Validation (written-content)
The study utilizes multiple large, well-established datasets (NHANES, WLVS, MLVS) and includes extensive validation efforts, including sensitivity analyses. This enhances the generalizability and robustness of the findings, increasing confidence in the observed associations.
Section: Methods
Rigorous Statistical Approach (written-content)
The application of two-step cluster analysis and Cox proportional hazards models demonstrates a rigorous statistical approach to identifying patterns and assessing associations. This strengthens the credibility of the identified coffee drinking patterns and their relationship with mortality.
Section: Methods
Novel Investigation of Coffee Timing (written-content)
The study is the first to investigate the association between patterns of coffee drinking timing and mortality risk. This novel approach provides valuable insights into the potential importance of timing in the relationship between coffee consumption and health outcomes.
Section: Discussion
Significant Association of Morning-Type Pattern with Lower Mortality (written-content)
The finding that a morning-type coffee drinking pattern is significantly associated with lower all-cause and CVD-specific mortality, even after adjusting for confounders, is a major strength. This suggests that timing of coffee consumption may be an important factor in its health effects, independent of the amount consumed.
Section: Results
Lack of Rationale for Time Period Categorization (written-content)
The Methods section does not provide a clear rationale for the chosen time periods (morning, afternoon, evening) used to categorize coffee drinking timing. Providing a justification based on scientific reasoning or prior evidence would strengthen the methodological rigor and ensure the categorization is not perceived as arbitrary.
Section: Methods
Limited Discussion of Limitations (written-content)
While the Discussion acknowledges limitations, it could benefit from a more in-depth discussion of their potential impact on the findings, such as recall bias, residual confounding, and generalizability. A more thorough discussion would enhance transparency and allow readers to better evaluate the strength of the evidence.
Section: Discussion
Alternative Explanations Not Fully Explored (written-content)
The Discussion could benefit from exploring alternative explanations for the observed associations, such as the morning-type pattern being a marker for other healthy lifestyle factors or the role of unmeasured confounders. This would provide a more comprehensive and balanced interpretation of the findings.
Section: Discussion
Missing Context for Cluster Characteristics (written-content)
The Results section could provide more context for the characteristics of the identified clusters (morning-type and all-day-type). Describing the demographic, lifestyle, and health characteristics that differentiate these clusters would aid in interpreting the observed associations and identifying potential confounders.
Section: Results

Section Analysis

Introduction

Key Aspects

Strengths

Suggestions for Improvement

Methods

Key Aspects

Strengths

Suggestions for Improvement

Results

Key Aspects

Strengths

Suggestions for Improvement

Non-Text Elements

Figure 1 Distribution and characteristics of study participants. (A)...
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Figure 1 Distribution and characteristics of study participants. (A) Distribution of participants according to two-step clustering for the total population in the National Health and Nutrition Examination Survey. (B) Distribution of participants provided the first-day dietary data in the National Health and Nutrition Examination Survey. (C) Distribution of participants provided the second-day dietary data in the National Health and Nutrition Examination Survey. (D) Distribution of participants according to two-step clustering in Women's Lifestyle Validation Study. (E) Distribution of participants according to two-step clustering in Women's Lifestyle Validation Study. Morning was defined as from 4 a.m. to 11:59 a.m., afternoon from 12 p.m. to 4:59 p.m., and evening from 5 p.m. to 3:59 a.m. NHANES, National Health and Nutrition Examination Survey; WLVS, Women's Lifestyle Validation Study; MLVS, Momen's Lifestyle Validation Study

Figure/Table Image (Page 5)
Figure 1 Distribution and characteristics of study participants. (A) Distribution of participants according to two-step clustering for the total population in the National Health and Nutrition Examination Survey. (B) Distribution of participants provided the first-day dietary data in the National Health and Nutrition Examination Survey. (C) Distribution of participants provided the second-day dietary data in the National Health and Nutrition Examination Survey. (D) Distribution of participants according to two-step clustering in Women's Lifestyle Validation Study. (E) Distribution of participants according to two-step clustering in Women's Lifestyle Validation Study. Morning was defined as from 4 a.m. to 11:59 a.m., afternoon from 12 p.m. to 4:59 p.m., and evening from 5 p.m. to 3:59 a.m. NHANES, National Health and Nutrition Examination Survey; WLVS, Women's Lifestyle Validation Study; MLVS, Momen's Lifestyle Validation Study
First Reference in Text
In the present study, two distinct patterns of coffee drinking timing were identified: Cluster 1, morning-type and Cluster 2, all-day-type (Figure 1A).
Description
  • Overview of Figure 1: Figure 1 presents a visual analysis of coffee drinking habits among different groups of people, divided into five distinct sections labeled A through E. These sections illustrate how often and when people consume coffee. The data is categorized using a statistical method called "two-step clustering." Two-step clustering is a type of statistical analysis that groups similar items together; in this case, it groups people with similar coffee-drinking habits. The analysis separates participants into those who mostly drink coffee in the morning ("morning-type") and those who drink coffee throughout the day ("all-day-type"). It also shows a group that does not drink coffee ("non-drinker").
  • Details of Figure 1A: Part A of the figure displays the coffee-drinking patterns for a large group of people from a study called the National Health and Nutrition Examination Survey (NHANES). It shows the percentage of people who fall into each coffee-drinking category: morning-type, all-day-type, and non-drinkers. The specific percentages are represented visually, likely through a bar graph or pie chart, allowing for a quick comparison of the sizes of each group.
  • Details of Figures 1B and 1C: Parts B and C also use data from the NHANES but differentiate between the first and second day of dietary information collected. This means that the researchers looked at people's coffee habits on two separate days to see if they were consistent. Like Part A, these sections show the distribution of morning-type, all-day-type, and non-drinkers, but separately for each day. This helps to confirm whether the coffee-drinking patterns observed are stable over these two days.
  • Details of Figures 1D and 1E: Parts D and E shift to data from different studies: the Women's Lifestyle Validation Study (WLVS) and the Men's Lifestyle Validation Study (MLVS). These sections use the same two-step clustering method to categorize participants into the same three groups: morning-type, all-day-type, and non-drinkers. By including these additional studies, the researchers aim to show that the coffee-drinking patterns identified in NHANES are not unique to that group but are also found in other populations.
  • Definition of Time Periods: The caption provides a clear definition of what is meant by "morning," "afternoon," and "evening" in the context of this study. "Morning" is defined as the period from 4 a.m. to 11:59 a.m., "afternoon" from 12 p.m. to 4:59 p.m., and "evening" from 5 p.m. to 3:59 a.m. These definitions are important because they set the boundaries for what constitutes morning-type versus all-day-type coffee consumption. The specificity of these time frames allows for a precise classification of participants' coffee-drinking habits.
Scientific Validity
  • Use of Two-Step Clustering: The application of two-step clustering for identifying patterns in coffee drinking timing is appropriate given its ability to handle large datasets with categorical variables. However, the validity of these patterns depends on the robustness of the clustering algorithm's assumptions and parameters. The authors should provide more details on the cluster validation process, including the choice of the number of clusters and the stability of cluster assignments.
  • Consistency Across Datasets: Presenting data from multiple datasets (NHANES, WLVS, MLVS) strengthens the generalizability of the identified patterns. The consistency in findings across these datasets suggests that the morning-type and all-day-type patterns are not artifacts of a particular population or study design. However, it is important to note that the WLVS and MLVS are validation studies, which may differ in their participant characteristics and data collection methods compared to the primary NHANES dataset.
  • Temporal Stability: The comparison of coffee drinking patterns between the first and second-day dietary data in NHANES (Figures 1B and 1C) provides insight into the temporal stability of these patterns. If the distributions are similar between the two days, it suggests that the identified patterns are relatively stable within individuals over a short period. However, longer-term stability cannot be inferred from this data alone.
Communication
  • Clarity of Figure Presentation: The figure effectively communicates the distribution of participants into different coffee-drinking patterns. The use of separate panels for each dataset and time point allows for a clear comparison between groups. However, the specific percentages within each category are not labeled directly on the graphs, which could make it difficult for readers to quickly grasp the exact proportions.
  • Definition of Time Periods: The caption clearly defines the time periods used to classify coffee drinking timing (morning, afternoon, evening). This is crucial for understanding the distinction between morning-type and all-day-type patterns. However, it would be helpful to include a rationale for these specific time boundaries, as they may not align with conventional definitions of these periods.
  • Acronym Definitions: The caption provides full names for the acronyms used (NHANES, WLVS, MLVS), which is essential for readers who may be unfamiliar with these studies. This enhances the accessibility of the figure to a broader audience.
  • Caption Length and Detail: While the caption is detailed, it is also quite lengthy. The authors could consider moving some of the more detailed information, such as the exact time boundaries for each period, to the main text or supplementary materials to improve the readability of the figure caption.
Table 1 Characteristics of participants by patterns of coffee drinking timing...
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Table 1 Characteristics of participants by patterns of coffee drinking timing in National Health and Nutrition Examination Survey

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Table 1 Characteristics of participants by patterns of coffee drinking timing in National Health and Nutrition Examination Survey
First Reference in Text
The baseline characteristics of participants according to the patterns of coffee drinking timing are presented in Table 1.
Description
  • Overall Purpose of Table 1: Table 1 describes the characteristics of people who participated in the National Health and Nutrition Examination Survey (NHANES). It divides them into three groups based on their coffee drinking habits: those who don't drink coffee ("non-drinkers"), those who mostly drink it in the morning ("morning type"), and those who drink it throughout the day ("all-day-type"). The table then compares these groups across various factors like age, gender, race, income, education, health conditions, and lifestyle choices. Essentially, it's like a detailed profile of each group, allowing us to see if there are any noticeable differences between them beyond just when they drink their coffee.
  • Structure and Organization of Table 1: The table is organized with the three coffee-drinking groups as columns and various characteristics as rows. Each row represents a different factor, such as age, gender, or whether they have a certain health condition like diabetes. For each factor, the table shows the data for each of the three groups. For example, under the row "Age, years," we see the average age of people in each group. The table uses statistical measures like mean (average) and standard deviation (a measure of how spread out the data is) for continuous variables like age and percentages for categorical variables like race or education level.
  • Specific Characteristics Listed: The table includes a wide range of characteristics. Some are demographic, like age, gender, race, family income, and education level. Others are health-related, like body mass index (BMI), diabetes, hypertension, and high cholesterol. There are also lifestyle factors like smoking status, physical activity, and dietary habits, including the AHEI diet score, which stands for Alternative Healthy Eating Index. This is a measure of diet quality; a higher score indicates a healthier diet. Finally, the table provides details about their coffee and other beverage consumption, including total coffee intake, caffeinated and decaffeinated coffee intake, tea intake, and caffeinated soda intake.
  • Statistical Measures Used: The table uses different statistical measures depending on the type of data. For continuous variables like age or coffee intake, it shows the mean (average) and standard deviation. The standard deviation is a measure of how spread out the data is around the mean. A smaller standard deviation means the data points are closer to the average, while a larger one means they are more spread out. For categorical variables like race or smoking status, it shows percentages. For example, under "Non-Hispanic White," it shows the percentage of people in each group who identify as Non-Hispanic White. The table also includes the median and interquartile range (IQR) for some variables. The median is the middle value when the data is arranged in order, and the IQR is the range between the 25th and 75th percentiles, giving an idea of the spread of the middle 50% of the data.
Scientific Validity
  • Relevance of Characteristics: The characteristics presented in Table 1 are relevant to the study's aim of understanding the relationship between coffee drinking timing and health outcomes. The inclusion of demographic, health-related, and lifestyle factors allows for the assessment of potential confounders and effect modifiers. However, the authors should justify the selection of these specific characteristics based on prior research or hypothesized mechanisms.
  • Representativeness of Sample: As the data is derived from the NHANES, a nationally representative survey, the characteristics presented in Table 1 are likely to be representative of the US adult population. This enhances the generalizability of the study findings. However, it is important to acknowledge that the specific coffee-drinking patterns identified may not be directly applicable to other populations with different cultural or lifestyle contexts.
  • Statistical Analysis: The use of appropriate statistical measures (mean, standard deviation, percentages, median, IQR) for different variable types is commendable. However, the table lacks information on the statistical significance of the differences between the groups. Adding p-values or confidence intervals would strengthen the interpretation of the observed differences.
Communication
  • Clarity of Column Headings: The column headings clearly identify the three coffee-drinking groups, making it easy to compare their characteristics. The use of descriptive terms like "Non-drinkers," "Morning type," and "All-day-type" is intuitive and enhances the readability of the table.
  • Organization of Characteristics: The characteristics are organized in a logical manner, grouping related factors together (e.g., demographic, health-related, lifestyle). This facilitates the identification of patterns and differences between the groups. However, the table is quite dense, and the authors could consider using visual cues like bolding or shading to highlight key findings.
  • Footnotes and Abbreviations: The table includes footnotes that explain abbreviations and define specific variables, such as family income categories and short sleep duration. This is helpful for readers who may be unfamiliar with these terms. However, the footnote for "AHEI diet score" could be more descriptive, explaining that it is a measure of diet quality.
  • Missing Data: The table does not explicitly address how missing data was handled. It is important to know the extent of missing data for each characteristic and whether any imputation methods were used, as this could affect the observed differences between the groups.
Table 2 Association of coffee drinking timing with mortality in National Health...
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Table 2 Association of coffee drinking timing with mortality in National Health and Nutrition Examination Survey

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Table 2 Association of coffee drinking timing with mortality in National Health and Nutrition Examination Survey
First Reference in Text
Compared with non-coffee drinking, a morning-type coffee drinking pattern was significantly associated with a lower risk of all-cause mortality (HR: .88; 95% CI: .81–.96), whereas an all-day-type pattern was not associated with the risk of all-cause mortality (HR: .99; 95% CI: .90-1.10), after adjustment for age, sex, race, NHANES cycles, family income, education levels, diabetes, hypertension, high cholesterol, smoking, physical activity, Alternative Healthy Eating Index score, total calorie intake, and the amount of caffeinated coffee and decaffeinated coffee (Table 2).
Description
  • Overall Purpose of Table 2: Table 2 explores the relationship between when people drink coffee and their risk of dying from any cause (all-cause mortality), from heart disease (CVD-specific mortality), or from cancer (cancer-specific mortality). It compares three groups: people who don't drink coffee, those who drink it mostly in the morning ("morning type"), and those who drink it throughout the day ("all-day-type"). The table shows how each group's risk of death compares to the non-coffee drinkers, using a measure called the hazard ratio (HR).
  • Understanding Hazard Ratio (HR): The hazard ratio (HR) is a way of comparing the risk of an event (in this case, death) between two groups. An HR of 1 means there's no difference in risk between the groups. An HR less than 1 means the group of interest (e.g., morning-type coffee drinkers) has a lower risk compared to the reference group (non-coffee drinkers). An HR greater than 1 means the group of interest has a higher risk. For example, if the HR for morning-type drinkers is 0.88, it means they have a 12% lower risk of death compared to non-coffee drinkers (1 - 0.88 = 0.12, or 12%).
  • Understanding Confidence Intervals (CI): The 95% confidence interval (CI) gives a range of values within which the true HR likely falls. It's like a margin of error. A narrower CI means we're more confident about the estimated HR. A wider CI means there's more uncertainty. If the CI includes 1, it means we can't be sure there's a real difference between the groups because it's possible the true HR is 1 (no difference).
  • Statistical Adjustments: The table shows results that have been "adjusted" for various factors. This means the researchers used statistical methods to account for differences between the groups that could affect the risk of death, such as age, sex, race, income, education, health conditions, smoking, and diet. By adjusting for these factors, they try to isolate the effect of coffee drinking timing on mortality. The table shows results for three different levels of adjustment: a "multivariable-adjusted model," further adjusted for tea and caffeinated soda intake, and further adjusted for short sleep and trouble sleeping.
  • Structure of the Table: The table is organized with the three coffee-drinking groups as columns (Non-drinker, Morning type, All-day-type) and different models of statistical adjustments as rows. For each type of mortality (all-cause, CVD-specific, cancer-specific), the table shows the number of deaths and the total number of people in each group (Events/total). It then shows the HR and 95% CI for each group compared to the non-coffee drinkers, for each level of adjustment. This allows us to see how the association between coffee drinking timing and mortality changes as more factors are accounted for.
Scientific Validity
  • Appropriateness of Statistical Models: The use of Cox proportional hazards models, as indicated by the reporting of hazard ratios (HRs), is appropriate for analyzing time-to-event data like mortality. The progressive adjustment for potential confounders is a strength, as it allows for the examination of the independent association between coffee drinking timing and mortality. However, the authors should provide a more detailed rationale for the selection of covariates in each model and discuss the potential for residual confounding.
  • Interpretation of Hazard Ratios: The interpretation of HRs and their associated 95% confidence intervals is generally accurate. The finding that morning-type coffee drinking is associated with a lower risk of all-cause and CVD-specific mortality, even after adjusting for multiple confounders, is noteworthy. However, the authors should be cautious in implying causality, as this is an observational study. The lack of association between all-day-type coffee drinking and mortality should also be interpreted in the context of the study's limitations.
  • Consideration of Competing Risks: The authors mention in the supplementary material that they performed sensitivity analyses accounting for competing risks in the analyses of CVD-specific and cancer-specific mortality. This is important, as deaths from other causes can affect the observed association between coffee drinking timing and the specific cause of death. The consistency of the findings in these sensitivity analyses strengthens the overall results.
Communication
  • Clarity of Column and Row Headings: The column headings clearly identify the three coffee-drinking groups, and the row headings describe the different mortality outcomes and adjustment models. The use of terms like "Events/total" is standard in survival analysis and is appropriate for a scientific audience.
  • Presentation of Hazard Ratios and Confidence Intervals: The table clearly presents the HRs and 95% CIs for each group and adjustment model. The use of bolding for statistically significant results (although not explicitly defined) helps to draw attention to the key findings. However, the table could be improved by including a more explicit definition of statistical significance (e.g., p < 0.05) in the footnote.
  • Footnote Clarity: The footnote provides essential information about the statistical models used and the covariates adjusted for in each model. However, the descriptions could be more precise. For example, instead of simply stating "Models adjusted for...," the authors could briefly explain that these factors were included as covariates in the Cox regression models.
  • Accessibility to Non-Expert Readers: While the table is appropriate for a scientific audience, it may be challenging for non-expert readers to fully understand. The concepts of hazard ratios, confidence intervals, and statistical adjustments are not intuitively obvious. The authors could consider providing a brief explanation of these concepts in the main text or supplementary materials to enhance the accessibility of the findings.

Discussion

Key Aspects

Strengths

Suggestions for Improvement

Non-Text Elements

Figure 2 Joint association between coffee intake amounts and coffee drinking...
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Figure 2 Joint association between coffee intake amounts and coffee drinking timing on the risk of mortality. One cup equal to 8 ounces (1 ounce 28.3 g); models adjusted for age, sex, race, and ethnicity, National Health and Nutrition Examination Survey cycles, family income, education levels, body mass index, diabetes, hypertension, high cholesterol, smoking status, time of smoking cessation, physical activity, Alternative Healthy Eating Index, total calorie intake, tea intake, caffeinated soda intake, percentage of decaf intake, short sleep duration, and trouble sleeping. HR, hazard ratio; CI, confidential interval; CVD, cardiovascular disease

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Figure 2 Joint association between coffee intake amounts and coffee drinking timing on the risk of mortality. One cup equal to 8 ounces (1 ounce 28.3 g); models adjusted for age, sex, race, and ethnicity, National Health and Nutrition Examination Survey cycles, family income, education levels, body mass index, diabetes, hypertension, high cholesterol, smoking status, time of smoking cessation, physical activity, Alternative Healthy Eating Index, total calorie intake, tea intake, caffeinated soda intake, percentage of decaf intake, short sleep duration, and trouble sleeping. HR, hazard ratio; CI, confidential interval; CVD, cardiovascular disease
First Reference in Text
Similar interaction pattern was observed for CVD-specific mortality, but the interaction term was not significant (Figures 2B and 3B).
Description
  • Overall Purpose of Figure 2: Figure 2 examines how both the amount of coffee people drink and when they drink it (morning vs. throughout the day) together relate to their risk of death. It looks at two types of mortality: all-cause mortality (death from any cause) and CVD-specific mortality (death from heart disease). The figure likely uses graphs to show how the risk of death changes as coffee intake increases, separately for those who drink it mostly in the morning and those who drink it throughout the day.
  • Understanding "Joint Association": "Joint association" means that the figure considers the combined effect of two factors: coffee intake amount and coffee drinking timing. It's not just looking at how much coffee people drink or when they drink it in isolation, but how these two things together influence the risk of death. For example, it might show that drinking a lot of coffee in the morning has a different effect on mortality risk compared to drinking the same amount spread throughout the day.
  • Definition of "One Cup": The caption clarifies that "one cup" of coffee is defined as 8 ounces (or about 28.3 grams). This is important for standardizing the measurement of coffee intake across the study. It ensures that everyone is on the same page when talking about how much coffee people are drinking.
  • Statistical Adjustments: The caption lists a wide range of factors that were adjusted for in the statistical models. This means that the researchers used statistical methods to account for differences between the groups that could affect the risk of death, such as age, sex, race, income, education, health conditions, smoking, and diet. By adjusting for these factors, they try to isolate the effect of coffee intake and timing on mortality. This is like making sure they are comparing apples to apples, rather than apples to oranges.
  • Use of Hazard Ratio (HR) and Confidence Interval (CI): The caption mentions "HR" (hazard ratio) and "CI" (confidence interval). The hazard ratio is a way of comparing the risk of death between two groups (e.g., morning coffee drinkers vs. non-coffee drinkers). A hazard ratio of 1 means there's no difference in risk. A hazard ratio less than 1 means the group of interest has a lower risk, and a hazard ratio greater than 1 means they have a higher risk. The confidence interval gives a range of values within which the true hazard ratio likely falls. It's like a margin of error. If the confidence interval includes 1, it means we can't be sure there's a real difference between the groups.
Scientific Validity
  • Assessment of Interaction: The analysis of the joint association between coffee intake amounts and coffee drinking timing on mortality risk is a sophisticated approach to understanding the complex relationship between these factors. The reference text highlights that an interaction was observed for CVD-specific mortality, albeit non-significant. The authors should elaborate on the implications of this finding and discuss potential reasons for the lack of statistical significance, such as limited statistical power or the need for larger sample sizes in specific subgroups.
  • Adjustment for Confounders: The extensive list of covariates adjusted for in the models is commendable, as it reduces the potential for confounding. However, the authors should provide a more detailed rationale for the selection of these specific covariates and acknowledge the possibility of residual confounding from unmeasured or inadequately measured factors.
  • Use of Appropriate Statistical Models: While the caption does not explicitly state the type of statistical models used, the reference to "HR" suggests that Cox proportional hazards models were likely employed. This is appropriate for analyzing time-to-event data like mortality. However, the authors should explicitly state the type of models used and provide details on model diagnostics and goodness-of-fit in the methods section or supplementary materials.
Communication
  • Clarity of Figure Presentation: Although the actual figure is not provided, the caption suggests that it presents the joint association between coffee intake and timing on mortality risk, likely through graphs. The clarity of these graphs would depend on factors such as the labeling of axes, the use of appropriate scales, and the visual distinction between different groups (e.g., morning vs. all-day drinkers). Clear visual presentation is crucial for effectively communicating the complex interaction being investigated.
  • Definition of "One Cup": The caption clearly defines "one cup" as 8 ounces, which is important for standardizing coffee intake measurements. This allows readers to accurately interpret the coffee intake amounts presented in the figure.
  • Explanation of Statistical Adjustments: The caption lists the numerous factors adjusted for in the models, which is important for understanding the rigor of the analysis. However, it could be improved by briefly explaining why these adjustments are necessary (i.e., to account for potential confounding). Additionally, the phrase "percentage of decaf intake" could be clarified as "percentage of total coffee intake that was decaffeinated."
  • Use of Abbreviations: The caption defines the abbreviations HR, CI, and CVD, which is helpful for readers who may be unfamiliar with these terms. However, the term "confidential interval" should be corrected to "confidence interval."
  • Accessibility to Non-Expert Readers: While the caption provides a detailed description of the analysis, it may be challenging for non-expert readers to fully understand. Concepts like joint association, interaction, hazard ratios, and statistical adjustments are not intuitive. The authors could consider providing a simplified explanation of these concepts in the main text or supplementary materials to make the findings more accessible.
Figure 3 Dose-response relationships between coffee intake amounts and the risk...
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Figure 3 Dose-response relationships between coffee intake amounts and the risk of mortality according to patterns of coffee drinking timing. Models adjusted for age, sex, race, and ethnicity, National Health and Nutrition Examination Survey cycles, family income, education levels, body mass index, diabetes, hypertension, high cholesterol, smoking status, time of smoking cessation, physical activity, Alternative Healthy Eating Index, total calorie intake, tea intake, caffeinated soda intake, percentage of decaf intake, short sleep duration, and trouble sleeping. HR, hazard ratio; CI, confidential interval; CVD, cardiovascular disease

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Figure 3 Dose-response relationships between coffee intake amounts and the risk of mortality according to patterns of coffee drinking timing. Models adjusted for age, sex, race, and ethnicity, National Health and Nutrition Examination Survey cycles, family income, education levels, body mass index, diabetes, hypertension, high cholesterol, smoking status, time of smoking cessation, physical activity, Alternative Healthy Eating Index, total calorie intake, tea intake, caffeinated soda intake, percentage of decaf intake, short sleep duration, and trouble sleeping. HR, hazard ratio; CI, confidential interval; CVD, cardiovascular disease
First Reference in Text
Similar interaction pattern was observed for CVD-specific mortality, but the interaction term was not significant (Figures 2B and 3B).
Description
  • Overall Purpose of Figure 3: Figure 3 illustrates how different amounts of coffee consumption are linked to the risk of death, and how this link varies depending on whether someone drinks coffee mostly in the morning or throughout the day. This is known as a "dose-response relationship," which basically means how the effect (risk of death) changes with increasing doses (amount of coffee). The figure likely uses lines or curves to show these relationships, separately for morning-type and all-day-type coffee drinkers.
  • Understanding "Dose-Response Relationship": A "dose-response relationship" is a fundamental concept in many scientific fields. It describes how the effect of something changes as the "dose" or amount of exposure increases. In this case, the "dose" is the amount of coffee consumed, and the "effect" is the risk of death. For example, the figure might show that as coffee intake increases, the risk of death decreases, but this decrease might be more pronounced for those who drink coffee mostly in the morning compared to those who drink it throughout the day.
  • Statistical Adjustments: Similar to Figure 2, the caption for Figure 3 lists a wide range of factors that were adjusted for in the statistical models. This means that the researchers used statistical methods to account for differences between the groups that could affect the risk of death, such as age, sex, race, income, education, health conditions, smoking, and diet. By adjusting for these factors, they try to isolate the effect of coffee intake and timing on mortality, making sure they are comparing like with like.
  • Use of Hazard Ratio (HR) and Confidence Interval (CI): The caption mentions "HR" (hazard ratio) and "CI" (confidence interval). The hazard ratio is a way of comparing the risk of death between two groups (e.g., those who drink 1 cup of coffee per day vs. those who drink none). A hazard ratio of 1 means there's no difference in risk. A hazard ratio less than 1 means the group of interest has a lower risk, and a hazard ratio greater than 1 means they have a higher risk. The confidence interval gives a range of values within which the true hazard ratio likely falls. It's like a margin of error. If the confidence interval includes 1, it means we can't be sure there's a real difference between the groups.
Scientific Validity
  • Assessment of Dose-Response: The evaluation of dose-response relationships is crucial for understanding the nature of the association between coffee intake and mortality. The authors should clearly describe the methods used to model these relationships, such as whether they used linear or non-linear models, and provide justification for their choices. They should also discuss the potential for non-linearity and threshold effects, as these can have important implications for public health recommendations.
  • Stratification by Coffee Drinking Timing: The stratification of the analysis by coffee drinking timing patterns (morning-type vs. all-day-type) is a strength, as it allows for the examination of how the dose-response relationship may differ between these groups. This addresses the central hypothesis of the study regarding the importance of timing in the coffee-mortality association. However, the authors should ensure that the sample sizes within each stratum are sufficient to provide stable estimates of the dose-response relationship.
  • Adjustment for Confounders and Model Specification: The extensive adjustment for potential confounders is commendable. However, as with previous elements, the authors should provide a more detailed rationale for the selection of these covariates and acknowledge the possibility of residual confounding. They should also explicitly state the type of statistical models used (e.g., Cox regression) and provide details on model diagnostics and goodness-of-fit in the methods section or supplementary materials.
Communication
  • Clarity of Figure Presentation: While the actual figure is not provided, the caption suggests that it presents dose-response relationships, likely through graphs. The clarity of these graphs would depend on factors such as the labeling of axes (e.g., coffee intake amount, hazard ratio), the use of appropriate scales, and the visual distinction between different groups (e.g., morning vs. all-day drinkers). Clear visual presentation is crucial for effectively communicating the potentially complex dose-response relationships.
  • Explanation of Statistical Adjustments: The caption lists the numerous factors adjusted for in the models, which is important for understanding the rigor of the analysis. However, it could be improved by briefly explaining why these adjustments are necessary (i.e., to account for potential confounding). As with Figure 2, the phrase "percentage of decaf intake" could be clarified as "percentage of total coffee intake that was decaffeinated."
  • Use of Abbreviations: The caption defines the abbreviations HR, CI, and CVD, which is helpful for readers who may be unfamiliar with these terms. However, the term "confidential interval" should be corrected to "confidence interval."
  • Accessibility to Non-Expert Readers: The concept of dose-response relationships may be challenging for non-expert readers to grasp. The authors could consider providing a simplified explanation of this concept in the main text or supplementary materials, perhaps using a real-world analogy to illustrate the idea. Additionally, the use of technical terms like "hazard ratio" and "statistical adjustments" may further limit accessibility. Providing lay-friendly definitions or explanations of these terms would enhance the overall communication of the findings.

Conclusions

Key Aspects

Strengths

Suggestions for Improvement

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