Coffee drinking timing and mortality in US adults

Introduction

Key Aspects

• Coffee Consumption and Health: The Introduction establishes the widespread consumption of coffee globally and highlights its association with potential health benefits. Moderate coffee consumption has been linked to reduced risks of Type 2 diabetes, cardiovascular diseases (CVDs), and overall mortality. This is supported by the 2015-20 US Dietary Guidelines, which include moderate coffee consumption as part of a healthy dietary pattern.
• Controversy on Heavy Coffee Consumption: The paper acknowledges the ongoing debate surrounding the health effects of heavy coffee consumption, defined as more than 3-5 cups per day. While moderate consumption is generally associated with positive outcomes, the impact of heavy consumption on mortality risk remains controversial and requires further investigation.
• Modifying Factors of Coffee's Health Effects: The authors introduce the concept that various clinical and behavioral factors might modify the relationship between coffee intake, particularly heavy intake, and health outcomes. These factors include smoking, decaffeinated coffee intake, genetic variations in caffeine metabolism, sweeteners added to coffee, and the brewing method. However, the paper states that there is currently no consistent evidence to support the role of these factors as modifiers.
• Circadian Rhythm and Food Intake Timing: The Introduction highlights the emerging importance of circadian rhythm in regulating human food intake behaviors and metabolism. It connects this concept to previous studies showing that the timing of food intake can modify the association between food intake and health outcomes. This sets the stage for the paper's focus on coffee drinking timing.
• Coffee's Stimulating Effects and Circadian Disruption: The authors note that coffee is often used to enhance wakefulness due to caffeine's stimulating effects on the central nervous system. They propose that consuming coffee later in the day might disrupt daily circadian rhythms, potentially altering the relationship between coffee intake and health outcomes. This establishes the central research question regarding the existence of distinct coffee drinking timing patterns and their association with mortality risk.
• Study Aim and Approach: The Introduction concludes by outlining the study's aim: to identify patterns of coffee drinking timing in the population using cluster analysis in the National Health and Nutrition Examination Survey (NHANES). These patterns will be validated in the Women's and Men's Lifestyle Validation Study (WLVS and MLVS) and subsequently used to investigate the joint association between coffee drinking timing, coffee intake amounts, and mortality risk in NHANES.

Strengths

• Clear Contextualization

  The Introduction effectively establishes the context of the research by highlighting the global popularity of coffee and its potential health implications.

  "Coffee is one of the most commonly consumed beverages in the world." (Page 2)
• Logical Progression of Ideas

  The section logically progresses from general coffee consumption to the specific research focus on timing, building a clear rationale for the study.

  "Notably, increasing evidence has indicated the importance of circadian rhythm in regulating human food intake behaviours and metabolism" (Page 2)
• Well-Defined Research Gap

  The Introduction clearly identifies the research gap by stating that the impact of coffee drinking timing on health outcomes is not well understood.

  "It is unclear whether there are distinct patterns of coffee drinking timing in the population, and if so, whether these patterns are associated with mortality risk." (Page 3)

Suggestions for Improvement

• Elaborate on Circadian Rhythm

  This medium-impact improvement would enhance the reader's understanding of the study's theoretical underpinnings. The Introduction section is the appropriate place to introduce this concept as it provides the foundation for the research question regarding coffee drinking timing. Expanding on the concept of circadian rhythm would strengthen the paper by providing a more comprehensive background for readers unfamiliar with this area. This would also help to emphasize the novelty and significance of investigating the role of timing in coffee consumption's health effects. Ultimately, providing a more detailed explanation of circadian rhythm would improve the study's scientific contribution by ensuring that the research question is properly contextualized and its importance is fully appreciated.

  "Notably, increasing evidence has indicated the importance of circadian rhythm in regulating human food intake behaviours and metabolism" (Page 2)

  Implementation: 1. Briefly define circadian rhythm and its role in regulating physiological processes. 2. Provide examples of how disruption of circadian rhythm can impact health. 3. Explain how coffee consumption, particularly the timing, might interact with circadian rhythm.

• Strengthen Rationale for Studying Coffee Timing

  This medium-impact improvement would further solidify the justification for the study's focus. While the Introduction mentions the potential for coffee timing to modify health outcomes, it could more explicitly connect this to the broader implications for public health and personalized nutrition. This belongs in the Introduction as it reinforces the motivation behind the research. Strengthening the rationale would enhance the paper by highlighting the potential impact of the findings on dietary recommendations and public health guidelines related to coffee consumption. This would also underscore the study's contribution to the growing field of chrononutrition. Ultimately, a more compelling rationale would increase the study's significance by emphasizing the practical implications of understanding the role of coffee drinking timing in health outcomes.

  "Intriguingly, coffee has long been used to improve wakefulness and relieve drowsiness due to the stimulating effects of caffeine on the central" (Page 2)

  Implementation: 1. Briefly discuss the potential public health implications of understanding how coffee timing affects health. 2. Mention the possibility of personalized dietary recommendations based on coffee drinking patterns. 3. Connect the research to the broader field of chrononutrition.

• Clarify Definition of Heavy Consumption

  This low-impact improvement would enhance the clarity and precision of the Introduction. While the section mentions "heavy coffee consumption," providing a more precise definition early on would benefit readers. This is appropriate for the Introduction as it sets the context for the research. Clarifying the definition would improve the paper by ensuring consistency and avoiding ambiguity throughout the study. This would also facilitate comparisons with other research on coffee consumption. Ultimately, a clear definition of heavy consumption would enhance the study's methodological rigor by ensuring that the research question is clearly defined.

  "However, the association between heavy coffee consumption (more than 3–5 cups/day) and mortality risk is still controversial." (Page 2)

  Implementation: 1. Provide a specific range for heavy coffee consumption (e.g., "more than 4 cups per day"). 2. Briefly explain the rationale for this definition, if applicable.

Methods

Key Aspects

• Study Population and Data Source: The study utilized data from the National Health and Nutrition Examination Survey (NHANES), a nationally representative study in the United States. Participants aged 18 years or older with available dietary recall and mortality data from 10 NHANES cycles (1999-2000 to 2017-18) were included. The study also incorporated data from the Women's and Men's Lifestyle Validation Study (WLVS and MLVS) to externally validate the patterns of coffee drinking timing identified in NHANES. The WLVS and MLVS are sub-studies conducted within the Nurses' Health Study (NHS), NHS II, and the Health Professionals Follow-up Study (HPFS).
• Dietary Data Collection and Coffee Timing Categorization: NHANES collected dietary data using 24-hour dietary recalls, where participants reported the timing, types, and amounts of all food and beverages consumed. Coffee consumption was determined by searching USDA food code descriptions. Coffee drinking timing was categorized into three periods: morning (4 a.m. to 11:59 a.m.), afternoon (12 p.m. to 4:59 p.m.), and evening (5 p.m. to 3:59 a.m.). The average frequencies of coffee consumption within these periods were used to define patterns of coffee drinking timing. WLVS and MLVS collected two 7-day dietary records (7DDRs) approximately 6 months apart. Participants reported the timing of food consumption and actual intake computed by measuring gram weights of the foods before and after eating.
• Cluster Analysis for Identifying Coffee Drinking Patterns: Two-step cluster analysis, a hybrid approach involving pre-clustering followed by hierarchical clustering, was employed to identify patterns of coffee drinking timing in the population. The optimal number of clusters was estimated using the silhouette width method, distances were calculated using log-likelihood, and clustering was performed according to Schwarz's Bayesian criterion. This statistical technique allows for the identification of distinct groups within a population based on similarities in their characteristics, in this case, coffee drinking timing.
• Mortality Assessment and Statistical Analysis: Information on death and death date was identified through linkage to the National Death Index. Causes of deaths were classified as cardiovascular disease (CVD) or cancer using the International Classification of Diseases, Tenth Revision codes. Cox proportional hazards models were used to evaluate the associations between patterns of coffee drinking timing, coffee intake amounts, and the risk of mortality. Hazard ratios (HRs) and 95% confidence intervals (CIs) were estimated, adjusting for various covariates such as age, sex, race/ethnicity, family income, education, diabetes, hypertension, high cholesterol, smoking status, physical activity, and dietary factors.
• Robustness and Validation: To ensure the robustness of the findings, several sensitivity analyses were performed. These included repeating the cluster analysis using dietary recall data from Day 1 or Day 2 only, using caffeinated or decaffeinated coffee only, and performing cluster analyses using WLVS and MLVS as external validations. Stratified analyses were conducted based on age, sex, family income, smoking status, chronic conditions, and sleep duration. The study also investigated the joint association of coffee drinking timing and coffee intake amounts with mortality risk.

Strengths

• Comprehensive Data Sources

  The study utilizes multiple large, well-established datasets, including NHANES, WLVS, and MLVS, which enhances the generalizability and validity of the findings.

  "This study included participants from a prospective cohort in the NHANES" (Page 3)
• Detailed Dietary Assessment

  The use of 24-hour dietary recalls in NHANES and 7-day dietary records in WLVS and MLVS provides detailed information on the timing and amount of coffee consumption.

  "NHANES collected dietary data using 24-h dietary recalls; participants re-ported the timing, types, and amounts of all food and beverages consumed" (Page 3)
• Rigorous Statistical Methods

  The application of two-step cluster analysis and Cox proportional hazards models demonstrates a rigorous statistical approach to identifying patterns and assessing associations.

  "We used two-step clustering to identify patterns of coffee drinking timing in population." (Page 3)
• Extensive Validation Efforts

  The study includes multiple sensitivity analyses and external validation using different datasets, which strengthens the robustness of the findings.

  "To test the robustness of our findings, we (i) repeated the cluster analysis by using the dietary recall for Day 1 or Day 2 only" (Page 3)

Suggestions for Improvement

• Clarify Rationale for Time Period Categorization

  This medium-impact improvement would enhance the methodological transparency of the study. The Methods section is the appropriate place to provide this justification as it directly relates to how the key exposure variable is defined. Providing a clear rationale for the chosen time periods would strengthen the paper by ensuring that the categorization is not perceived as arbitrary and by demonstrating that it is based on sound scientific reasoning or prior evidence. This would also allow readers to better evaluate the appropriateness of the categorization and its potential impact on the study's findings. Ultimately, clarifying the rationale for the time period categorization would improve the study's methodological rigor by providing a more transparent and well-justified approach to defining coffee drinking timing.

  "The coffee drinking timing was categorized in three time peri-ods, including morning (from 4 a.m. to 11:59 a.m.), afternoon (from 12" (Page 3)

  Implementation: 1. Briefly explain the rationale for choosing the specific time periods (morning, afternoon, evening). 2. Cite any relevant literature or prior research that supports this categorization. 3. If the categorization is based on physiological considerations (e.g., circadian rhythm), briefly explain the connection.

• Elaborate on Cluster Analysis Parameters

  This medium-impact improvement would enhance the reproducibility and transparency of the study. The Methods section is the ideal location for this information as it pertains to the core statistical technique used to identify coffee drinking patterns. Providing more details about the cluster analysis parameters would strengthen the paper by allowing other researchers to fully understand and potentially replicate the analysis. This would also enable readers to better assess the validity of the identified clusters and their robustness to different parameter choices. Ultimately, elaborating on the cluster analysis parameters would improve the study's methodological rigor by ensuring that the pattern identification process is transparent, reproducible, and well-justified.

  "Two-step cluster analysis is a hybrid approach that first performs pre-clustering and is then followed by hierarchical clustering." (Page 3)

  Implementation: 1. Specify the exact criteria used for pre-clustering. 2. Describe in more detail how the silhouette width method was applied to determine the optimal number of clusters. 3. Provide the specific log-likelihood distance measure used. 4. Explain any other relevant parameter settings or choices made during the cluster analysis.

• Address Potential Misclassification of Coffee Timing

  This low-impact improvement would enhance the study's robustness by acknowledging and addressing a potential limitation. The Methods section is the appropriate place to discuss this as it relates to the measurement of the primary exposure variable. Acknowledging the potential for misclassification due to reliance on self-reported data would strengthen the paper by demonstrating a critical awareness of the limitations inherent in dietary recall methods. Discussing how this potential misclassification might affect the results would further enhance the study's transparency and allow readers to interpret the findings with appropriate caution. Ultimately, addressing the potential for misclassification of coffee timing would improve the study's scientific rigor by acknowledging and mitigating a potential source of bias.

  "NHANES collected dietary data using 24-h dietary recalls; participants re-ported the timing, types, and amounts of all food and beverages consumed" (Page 3)

  Implementation: 1. Briefly acknowledge that self-reported dietary data may be subject to recall bias, potentially leading to misclassification of coffee drinking timing. 2. Discuss the potential direction and magnitude of this bias. 3. Suggest ways in which future studies could minimize this bias (e.g., using more objective measures of coffee consumption or timing).

Results

Key Aspects

• Identification of Coffee Drinking Timing Patterns: The study identified two primary patterns of coffee drinking timing using cluster analysis: "morning-type" and "all-day-type." The morning-type pattern, characterized by coffee consumption mainly concentrated in the morning hours (4 a.m. to 11:59 a.m.), was observed in 36% of the NHANES participants. The all-day-type pattern, with coffee consumption spread throughout the day, was found in 16% of participants. The remaining 48% were non-coffee drinkers. These patterns were validated in the WLVS and MLVS cohorts, showing similar distributions.
• Baseline Characteristics of Participants: Participants with the morning-type and all-day-type patterns differed from non-coffee drinkers in several baseline characteristics. They were generally older, more likely to be White, had higher family income, and exhibited a higher prevalence of diabetes, hypertension, and high cholesterol. Among coffee drinkers, those with the morning-type pattern tended to consume more tea and caffeinated soda but less overall coffee compared to those with the all-day-type pattern.
• Association Between Coffee Drinking Timing and Mortality Risk: A significant association was observed between the morning-type coffee drinking pattern and a lower risk of all-cause mortality (Hazard Ratio (HR): 0.88; 95% Confidence Interval (CI): 0.81-0.96) compared to non-coffee drinking, after adjusting for various potential confounders. This association remained significant even after further adjustments. The all-day-type pattern did not show a significant association with all-cause mortality risk (HR: 0.99; 95% CI: 0.90-1.10). For cause-specific mortality, the morning-type pattern was associated with a reduced risk of cardiovascular disease (CVD)-specific mortality (HR: 0.69; 95% CI: 0.55-0.87) but not cancer mortality.
• Joint Association of Coffee Intake and Timing with Mortality: The study found that the association between coffee intake amounts and all-cause mortality risk differed significantly by coffee drinking timing patterns (P-interaction = 0.031). Among participants with the morning-type pattern, moderate to heavy coffee consumption was associated with a lower risk of all-cause mortality. However, this association was not observed among those with the all-day-type pattern. A similar interaction pattern was observed for CVD-specific mortality, although the interaction term did not reach statistical significance.
• Sensitivity and Stratified Analyses: The study performed several sensitivity analyses to ensure the robustness of the findings. These included internal validation using different dietary recall days, analyses based on caffeinated or decaffeinated coffee only, and consideration of competing risks for cause-specific mortality. The results remained consistent across these analyses. Stratified analyses based on age, sex, family income, smoking status, chronic conditions, and sleep duration did not reveal significant interactions with coffee drinking timing patterns in relation to all-cause mortality risk.

Strengths

• Clear Presentation of Findings

  The Results section clearly presents the identified coffee drinking patterns and their associations with mortality, supported by specific hazard ratios and confidence intervals.

  "Compared with non-coffee drinking, a morning-type coffee drinking pattern was significantly associated with a lower risk of all-cause mor-tality" (Page 4)
• Comprehensive Statistical Analysis

  The study employs robust statistical methods, including Cox proportional hazards models and interaction analyses, to assess the relationship between coffee drinking patterns and mortality.

  "The association between coffee intake amounts and the risk of all-cause mortality dif-fered by patterns of coffee drinking timing" (Page 4)
• Thorough Validation and Sensitivity Analyses

  The researchers conducted multiple sensitivity analyses and validated their findings using different datasets and approaches, strengthening the credibility of the results.

  "Similar patterns were verified in the following series of sensitivity analyses" (Page 4)

Suggestions for Improvement

• Provide More Context for Cluster Characteristics

  This medium-impact improvement would enhance the reader's understanding of the identified clusters. The Results section is the appropriate place for this contextualization as it directly relates to the characteristics of the groups being compared in the subsequent analyses. Providing more context for the cluster characteristics would strengthen the paper by allowing readers to better interpret the observed associations between coffee drinking patterns and mortality. This would also help to identify potential confounding factors or effect modifiers that might be related to the differences between the clusters. Ultimately, providing more context for the cluster characteristics would improve the study's scientific contribution by ensuring that the findings are properly interpreted and their implications are fully understood.

  "Of the 40 725 participants in NHANES, 36% (n = 14 643) were identified as having a morning-type pattern" (Page 4)

  Implementation: 1. Briefly describe the demographic, lifestyle, and health characteristics that most strongly differentiate the morning-type and all-day-type clusters. 2. Discuss any notable differences in these characteristics between the two clusters. 3. Consider adding a supplementary table that provides a more detailed comparison of the cluster characteristics.

• Elaborate on the Implications of Null Findings

  This medium-impact improvement would provide a more nuanced interpretation of the study's results. The Results section is the appropriate place to discuss the implications of null findings as it is where the results are presented and interpreted. Elaborating on the implications of the null findings would strengthen the paper by providing a more balanced and comprehensive discussion of the results. This would also help to guide future research by highlighting areas where further investigation is needed. Ultimately, elaborating on the implications of the null findings would improve the study's scientific contribution by ensuring that all results, both positive and negative, are given appropriate consideration.

  "whereas an all-day-type pattern was not associated with the risk of all-cause mortality" (Page 4)

  Implementation: 1. Briefly discuss the implications of the lack of association between the all-day-type pattern and mortality risk. 2. Consider potential explanations for this null finding. 3. Discuss whether this finding aligns with or contradicts previous research.

• Clarify the Rationale for Choosing Specific Covariates

  This low-impact improvement would enhance the transparency of the study's methodology. The Results section is the appropriate place to briefly mention this rationale, as it directly relates to the statistical models used to analyze the data. Clarifying the rationale for choosing specific covariates would strengthen the paper by demonstrating that the selection was based on sound scientific reasoning and prior research. This would also allow readers to better evaluate the appropriateness of the statistical models and the potential for residual confounding. Ultimately, clarifying the rationale for covariate selection would improve the study's methodological rigor by providing a more transparent and well-justified approach to the statistical analysis.

  "after adjustment for age, sex, race, NHANES cycles, family income, education levels, diabetes, hypertension" (Page 4)

  Implementation: 1. Briefly explain why the chosen covariates were included in the statistical models. 2. Cite any relevant literature or prior research that supports the inclusion of these covariates. 3. If certain covariates were chosen based on their potential to confound the association between coffee drinking timing and mortality, briefly explain the hypothesized confounding mechanism.

Non-Text Elements

Figure 1 Distribution and characteristics of study participants. (A)...

Full Caption

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)

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

Figure/Table Image (Page 6)

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

Figure/Table Image (Page 7)

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

• Primary Findings and Their Significance: The study's core finding is the identification of two distinct coffee drinking timing patterns, labeled "morning-type" and "all-day-type," and their differential associations with mortality risk. Notably, the morning-type pattern, characterized by coffee consumption concentrated in the morning, was significantly associated with reduced risks of all-cause and cardiovascular disease (CVD)-specific mortality. This association persisted even after adjusting for potential confounders, including the total amount of coffee consumed. In contrast, the all-day-type pattern, where coffee intake was spread throughout the day, did not show a significant association with mortality risk compared to non-coffee drinking. These findings suggest that the timing of coffee consumption may be a crucial factor in its relationship with health outcomes, independent of the quantity consumed.
• Interaction Between Coffee Timing and Intake Amounts: A key finding of this study is the significant interaction between coffee drinking timing patterns and coffee intake amounts in relation to all-cause mortality risk. Specifically, among individuals with a morning-type pattern, moderate to heavy coffee consumption was associated with a lower risk of all-cause mortality. However, this association was not observed among those with an all-day-type pattern. This suggests that the potential health benefits of coffee consumption may be more pronounced when intake is concentrated in the morning hours. This interaction highlights the importance of considering both the timing and amount of coffee consumption when evaluating its impact on health.
• Potential Mechanisms: Circadian Rhythm Disruption: The authors propose two potential mechanisms to explain the observed associations. The first mechanism involves the disruption of circadian rhythms. Consuming coffee, particularly in the afternoon or evening, may interfere with the body's natural circadian clock. This is supported by evidence showing that heavy coffee consumption later in the day can reduce nighttime melatonin production. Melatonin, a hormone crucial for regulating the sleep-wake cycle, has been linked to oxidative stress, blood pressure, and CVD risk. Thus, disrupting circadian rhythms through late-day coffee consumption could potentially negate the beneficial effects of coffee observed with morning consumption.
• Potential Mechanisms: Anti-inflammatory Effects: The second proposed mechanism relates to the anti-inflammatory properties of coffee. Coffee contains bioactive compounds that exert anti-inflammatory effects. Interestingly, some pro-inflammatory markers in the body follow a circadian pattern, typically peaking in the morning and declining throughout the day. The authors hypothesize that the anti-inflammatory effects of coffee may be more beneficial when consumption is concentrated in the morning, coinciding with the peak of these inflammatory markers. This could explain why the morning-type pattern was associated with a lower risk of mortality, while the all-day-type pattern, with its more dispersed coffee intake, was not.
• Comparison with Existing Literature: The authors contextualize their findings within the existing literature on coffee consumption and mortality. They acknowledge the ongoing debate regarding the association between heavy coffee consumption and mortality risk, noting inconsistencies across previous studies. The present study's findings, particularly the interaction between coffee timing and intake amounts, offer a potential explanation for these inconsistencies. The authors suggest that differences in coffee drinking timing patterns across populations might contribute to the varying results observed in previous research. This highlights the novelty of their study in considering the role of timing, which has been largely overlooked in prior investigations.
• Strengths and Limitations: The authors acknowledge the strengths of their study, including the use of cluster analysis to identify coffee drinking patterns, the large sample size from a nationally representative cohort (NHANES), and the validation of the identified patterns in two independent cohorts (WLVS and MLVS). They also discuss limitations, such as the observational nature of the study, which precludes causal inferences, the reliance on self-reported data, the potential for residual confounding, and the lack of generalizability beyond the US population. They also mention the lack of genetic information on caffeine metabolism and the need for external validation of the association between coffee drinking timing patterns and mortality risk.
• Implications and Future Research: The authors highlight the potential implications of their findings, suggesting that coffee drinking timing may be an important factor to consider in the association between coffee intake and health outcomes. They propose that concentrating coffee consumption in the morning may be more strongly associated with lower mortality risk than consuming coffee throughout the day. They call for future research to further explore the role of coffee drinking timing in the relationship between coffee consumption and health, particularly through studies that can establish causality and investigate the underlying mechanisms.

Strengths

• Novelty and Significance

  The Discussion effectively highlights the novel contribution of the study, emphasizing that it is the first to investigate the association between patterns of coffee drinking timing and mortality risk.

  "To our knowledge, this is the first study to investigate the association between patterns of coffee drinking timing and the risk of mortality." (Page 5)
• Clear Explanation of Findings

  The authors effectively articulate the main findings of the study, particularly the identification of two distinct coffee drinking patterns (morning-type and all-day-type) and their differential associations with mortality risk. The explanation is clear and concise, making it easy for readers to grasp the key results.

  "Compared with non-coffee drinking, we found that a morning-type coffee drinking pattern was significantly associated with lower risks of all-cause mortality and CVD-specific mortality" (Page 5)
• Proposed Mechanisms

  The Discussion provides plausible biological mechanisms to explain the observed associations, focusing on circadian rhythm disruption and the anti-inflammatory effects of coffee.

  "Two potential mechanisms could explain our findings. First, consuming coffee in the afternoon or evening may disrupt circadian rhythms." (Page 5)

Suggestions for Improvement

• Expand on Alternative Explanations

  This medium-impact improvement would provide a more comprehensive and balanced discussion of the study's findings. While the Discussion section mentions potential mechanisms, it could benefit from exploring alternative explanations for the observed associations. This belongs in the Discussion as it is where the results are interpreted and their implications are considered. Expanding on alternative explanations would strengthen the paper by demonstrating a more critical and thorough consideration of the findings. This would also help to identify potential areas for future research and ensure that the study's conclusions are not overstated. Ultimately, exploring alternative explanations would improve the study's scientific rigor by providing a more nuanced and comprehensive interpretation of the results.

  "Two potential mechanisms could explain our findings." (Page 5)

  Implementation: 1. Discuss the possibility that the morning-type pattern is a marker for other healthy lifestyle factors not fully accounted for by the adjustments. 2. Explore the potential role of unmeasured confounders related to socioeconomic status, stress levels, or other factors that might influence both coffee drinking timing and mortality risk. 3. Consider the possibility that the observed associations are due to reverse causation, where individuals with certain health conditions might alter their coffee drinking habits.

• Strengthen the Discussion of Limitations

  This high-impact improvement would enhance the transparency and credibility of the study. While the Discussion acknowledges some limitations, it could benefit from a more in-depth discussion of their potential impact on the findings. This is crucial in the Discussion section as it provides the context for interpreting the results and assessing their validity. Strengthening the discussion of limitations would enhance the paper by demonstrating a more critical awareness of the study's shortcomings and their potential implications. This would also help readers to better evaluate the strength of the evidence and the generalizability of the findings. Ultimately, a more thorough discussion of limitations would improve the study's scientific rigor by providing a more balanced and transparent assessment of the research.

  "The present study also has several limitations." (Page 9)

  Implementation: 1. Elaborate on the potential for recall bias and measurement error due to the reliance on self-reported dietary data, and discuss how this might have affected the results. 2. Discuss in more detail the potential for residual confounding, even after adjusting for a wide range of covariates. 3. Explicitly address the limitation that the study population was restricted to US adults, and discuss the implications for generalizability to other populations. 4. Acknowledge the lack of data on other factors that might influence coffee drinking timing, such as shift work or chronotype.

• Provide More Specific Recommendations for Future Research

  This medium-impact improvement would enhance the study's contribution to the field by providing a clearer roadmap for future investigations. The Discussion section is the appropriate place for this as it is where the implications of the findings are considered and future directions are proposed. Providing more specific recommendations for future research would strengthen the paper by guiding researchers in building upon the current study's findings. This would also help to address the limitations of the present study and advance the understanding of the relationship between coffee drinking timing and health outcomes. Ultimately, more specific recommendations for future research would improve the study's impact by facilitating further investigation and contributing to the development of evidence-based dietary guidelines.

  "our findings suggest that coffee drinking in the morning may be more strongly associated with lower mortality than coffee drinking later in the day." (Page 9)

  Implementation: 1. Suggest specific research designs that could help to establish causality, such as randomized controlled trials or Mendelian randomization studies. 2. Recommend specific areas for future research to explore the underlying mechanisms, such as studies investigating the effects of coffee timing on circadian biomarkers or inflammatory markers. 3. Propose research to examine the association between coffee drinking timing and mortality risk in diverse populations, including different age groups, ethnicities, and countries. 4. Suggest studies that investigate the potential interaction between coffee drinking timing and other lifestyle factors, such as diet, exercise, and sleep patterns.

Non-Text Elements

Figure 2 Joint association between coffee intake amounts and coffee drinking...

Full Caption

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

Figure/Table Image (Page 8)

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...

Full Caption

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

Figure/Table Image (Page 9)

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

• Coffee Drinking Timing and Mortality Risk: This study discovered that the timing of coffee consumption is independently associated with all-cause and cardiovascular disease (CVD)-specific mortality risks, even after accounting for the amount of coffee consumed. Specifically, a "morning-type" pattern, where coffee intake is concentrated in the morning hours, was linked to a reduced risk of mortality. In contrast, an "all-day-type" pattern, characterized by coffee consumption spread throughout the day, did not demonstrate a significant association with mortality risk compared to not drinking coffee.
• Significance of Morning Coffee Consumption: The findings suggest that consuming coffee in the morning may be more strongly associated with a lower risk of mortality than consuming coffee later in the day. This implies that the timing of coffee intake could be a crucial factor to consider when evaluating the relationship between coffee consumption and health outcomes. The study highlights that the potential health benefits of coffee might be more pronounced when consumption is limited to the morning.
• Methodological Approach: The researchers used cluster analysis, a statistical technique for grouping individuals based on similarities in their characteristics, to identify distinct patterns of coffee drinking timing within the population. This is a novel application of cluster analysis in the context of coffee consumption and mortality research. They further validated these patterns using data from multiple, well-established studies, enhancing the robustness of their findings.
• Study Limitations: The authors acknowledge several limitations of their study. The observational nature of the research prevents them from establishing a direct causal relationship between coffee drinking timing and mortality. Additionally, the reliance on self-reported data for coffee consumption and other factors introduces the possibility of recall bias and measurement errors. They also note that unmeasured confounding factors, such as shift work or individual chronotypes (natural sleep-wake preferences), could potentially influence the results. Furthermore, the study's focus on the US population limits the generalizability of the findings to other populations with different coffee consumption habits and cultural contexts.
• Implications and Future Research: The study underscores the importance of considering the timing of coffee intake, in addition to the amount, when assessing its association with health outcomes. The authors suggest that future research should focus on investigating the underlying mechanisms that link coffee drinking timing to mortality risk. They also call for studies that can establish causality and explore the generalizability of these findings to diverse populations.

Strengths

• Concise Summary of Findings

  The Conclusions section effectively summarizes the key findings, highlighting the association between coffee drinking timing and mortality risk.

  "We found that coffee drinking timing was associated with all-cause mortality risk and CVD-specific mortality risk" (Page 9)
• Clear Statement of Implications

  The section clearly states the main implication of the study, emphasizing the importance of considering drinking timing in the association between coffee intake and health outcomes.

  "our findings suggest that coffee drinking in the morning may be more strongly associated with lower mortality" (Page 9)
• Acknowledgement of Limitations

  The authors appropriately acknowledge the limitations of the study, including its observational nature and the potential for confounding.

  "given the observational nature of the study, the associations observed cannot be interpreted as causality" (Page 9)

Suggestions for Improvement

• Expand on Practical Implications

  This medium-impact improvement would enhance the real-world relevance of the study. The Conclusions section is the ideal place for this discussion as it is where the overall significance of the findings is presented. Expanding on the practical implications would strengthen the paper by bridging the gap between research findings and their potential application in public health and individual behavior. This would also help readers understand the broader context of the study and its potential impact on dietary recommendations. Ultimately, discussing the practical implications would enhance the study's impact by highlighting its relevance beyond the academic realm.

  "our findings suggest that coffee drinking in the morning may be more strongly associated with lower mortality" (Page 9)

  Implementation: 1. Briefly discuss the potential implications of the findings for public health recommendations regarding coffee consumption. 2. Suggest how individuals might consider adjusting their coffee drinking habits based on the study's results. 3. Mention the potential for personalized dietary advice based on coffee drinking patterns.

• Strengthen Connection to Previous Sections

  This medium-impact improvement would enhance the cohesiveness of the paper and reinforce the study's key message. The Conclusions section is where the findings are synthesized and their broader significance is discussed, making it the appropriate place to explicitly connect back to previous sections. Strengthening the connection to previous sections would enhance the paper by providing a more integrated and holistic view of the research. This would also help readers to better understand how the different parts of the study fit together and contribute to the overall conclusions. Ultimately, explicitly linking the conclusions to previous sections would improve the paper's narrative flow and reinforce the study's main findings.

  "our findings suggest that coffee drinking in the morning may be more strongly associated with lower mortality than coffee drinking later in the day" (Page 9)

  Implementation: 1. Briefly reiterate how the findings build upon the research gap identified in the Introduction. 2. Mention how the Results and Discussion sections support the conclusions drawn. 3. Briefly summarize how the identified mechanisms in the Discussion relate to the observed associations in the Conclusions.

• Reiterate Novelty and Contribution More Forcefully

  This low-impact improvement would further emphasize the study's unique contribution to the field. The conclusion section is the appropriate place to do this as it provides the final takeaway message for the reader. Reiterate novelty and contribution more forcefully would enhance the paper by solidifying the study's significance and its impact on the existing literature. This would also help to position the research within the broader scientific context and highlight its value to the field. Ultimately, more forcefully emphasizing the study's novelty and contribution would improve the paper's impact by leaving a stronger impression on the reader and underscoring the study's importance.

  "although the use of cluster analysis to identify latent categories within a population is not novel" (Page 9)

  Implementation: 1. Restate that this is the first study to examine the association between coffee drinking timing patterns and mortality risk. 2. Briefly highlight the unique methodological approach used. 3. Emphasize the potential of the findings to advance the understanding of the relationship between coffee consumption and health.