Association Between Handgrip Strength and Mortality in Individuals Aged 90+

Table of Contents

Overall Summary

Overview

This study investigates the relationship between handgrip strength and mortality in individuals aged 90 years and older, using data from the Survey of Health, Ageing, and Retirement in Europe (SHARE). Handgrip strength, a measure of overall muscle strength, was assessed from 1890 participants across 28 countries. The study found a curvilinear association between handgrip strength and mortality, suggesting that both very low and very high strength are linked to increased mortality risk compared to the median. The analysis utilized time-varying Cox regression with restricted cubic splines to model non-linear relationships, accounting for various demographic and health factors to isolate the effect of muscle strength.

Key Findings

Strengths

Areas for Improvement

Significant Elements

Figure

Description: FIGURE 2 shows the association of handgrip strength with mortality risk, using a line graph to depict the hazard ratios and 95% confidence intervals.

Relevance: This figure visually demonstrates the U-shaped relationship, highlighting the varying mortality risks across different levels of handgrip strength.

Table

Description: TABLE 1 provides baseline characteristics of the study population, including demographic and health-related variables.

Relevance: The table is crucial for understanding the study sample's makeup, ensuring transparency about the population analyzed.

Conclusion

The study concludes that maintaining or enhancing handgrip strength is associated with decreased mortality risk in individuals aged 90 and older. This finding underscores the potential health benefits of interventions like resistance training in the oldest old. While the study highlights the gradual nature of this association, it also points to the need for comprehensive muscle assessments in future research. The results have significant implications for healthcare strategies targeting this rapidly growing demographic, suggesting that even modest improvements in muscle strength can contribute to better health outcomes. Further research should explore the mechanisms underpinning this relationship and evaluate targeted interventions to optimize muscle health in older adults.

Section Analysis

Abstract

Overview

This abstract investigates the relationship between muscle strength and mortality in the oldest old (90+ years) using data from the Survey of Health, Ageing and Retirement in Europe (SHARE). Handgrip strength, a common measure of overall muscle strength, was measured for 1890 participants. Researchers used time-varying Cox regression, a statistical method that accounts for changes in variables over time and estimates the risk of death, to analyze the association between handgrip strength and all-cause mortality. They found a curvilinear relationship: both very low and very high handgrip strength were associated with increased mortality risk, with the lowest risk observed at moderate strength levels. This pattern held true for both men and women, though the specific strength values associated with increased or decreased risk differed between sexes.

Key Aspects

Strengths

Suggestions for Improvement

Introduction

Overview

This introduction establishes the importance of studying the relationship between muscle strength and mortality in the oldest old (90+ years). It highlights the age-related decline in muscle mass and strength, emphasizing its impact on daily activities and overall health. The introduction also notes skeletal muscle's endocrine function, releasing myokines that influence various physiological processes. Handgrip strength is presented as an accessible measure of overall muscle strength, linked to various health outcomes. The section underscores the knowledge gap in understanding this relationship specifically in the oldest old, a rapidly growing demographic with unique health challenges. It points out limitations of previous studies, such as small sample sizes, categorical analyses, and a lack of representation from diverse populations. Finally, it states the study's aim: to determine the prospective association between handgrip strength and all-cause mortality in the oldest old using a large, multinational dataset and advanced statistical methods like restricted cubic splines to model potential non-linear relationships.

Key Aspects

Strengths

Suggestions for Improvement

Methods

Overview

This section details the methodology used to investigate the association between handgrip strength and mortality in the oldest old. The study uses data from the Survey of Health, Ageing and Retirement in Europe (SHARE), a multi-country survey conducted in 27 European countries and Israel. The study includes 1890 participants aged 90 and older, with data collected across eight waves. Handgrip strength, measured in kilograms using a dynamometer, serves as the predictor variable. All-cause mortality, determined through end-of-life interviews with proxies (individuals close to the deceased), is the outcome. The analysis employs time-varying Cox regression, a statistical method used to analyze time-to-event data, accounting for changes in variables over time. Restricted cubic splines, a technique for modeling non-linear relationships, are used to explore the association between handgrip strength and mortality. Several covariates, including demographic factors, lifestyle indicators, and health status, are controlled for in the analysis. Sensitivity analyses are conducted to assess the robustness of the findings.

Key Aspects

Strengths

Suggestions for Improvement

Non-Text Elements

FIGURE 1 | Flow of participants through the study.
First Reference in Text
Figure 1 shows the flow of participants through the study.
Description
  • Initial Dataset: The flowchart starts by indicating the initial dataset comprised of 691,096 observations taken from 238,950 individuals participating in eight waves of the SHARE study. Think of an "observation" as a single data point collected from a person during one of the study waves. Since individuals could participate in multiple waves, there are more observations than individuals.
  • Age Criterion: The first filtering step involves selecting individuals aged 90 or older. Anyone younger than 90 or with missing age information is removed. This reduces the dataset to 7,385 observations from 4,820 individuals.
  • Multiple Observations Criterion: The next filter requires individuals to have participated in at least two waves of the study. This is important for longitudinal analysis, which examines changes over time. Individuals with only one observation are excluded.
  • Complete Data Criterion: The final filter removes individuals with missing data for any of the variables used in the analysis: the predictor variable (muscle strength), the outcome variable (mortality), or any of the control variables (e.g., age, sex, smoking status). This ensures that the statistical analysis includes only complete cases.
  • Final Sample: After applying all filters, the final sample consists of 1,890 individuals. This is the group used for the statistical analysis presented in the paper.
Scientific Validity
  • Clarity of Inclusion/Exclusion: The flowchart clearly presents the inclusion and exclusion criteria, which is crucial for the reproducibility and transparency of the study. The rationale for each filtering step is explicitly stated, allowing readers to understand the sample selection process.
  • Potential Selection Bias: The substantial reduction in sample size from the initial dataset to the final analytical sample raises concerns about potential selection bias. The authors should discuss the potential impact of this attrition on the generalizability of the findings. Do the excluded individuals differ systematically from the included individuals in ways that might affect the results?
  • Missing Data Handling: While excluding individuals with missing data is a common approach, the authors should provide more information about the extent and nature of missing data in the original dataset. A complete case analysis can introduce bias if the missing data are not missing completely at random. The authors should consider exploring alternative methods for handling missing data, such as imputation techniques.
Communication
  • Visual Representation: The flowchart effectively uses a visual format to communicate the participant selection process. This makes it easy for readers to grasp the flow of data and understand how the final sample was derived.
  • Clarity and Conciseness: The flowchart is clear and concise, presenting the information in a straightforward manner. The labels and annotations are easy to understand.
  • Integration with Text: The figure is well-integrated with the text, with a clear reference in the Methods section. The caption provides a concise summary of the flowchart's content.
  • Visual Enhancements: While the flowchart is effective in its current form, some visual enhancements could further improve its clarity. For example, using different colors or shapes for the boxes could help visually distinguish the different filtering stages. Adding percentages to each step would provide a clearer picture of the attrition at each stage.

Results

Overview

This section presents the findings of the study investigating the association between handgrip strength and mortality in individuals aged 90 and older. The baseline characteristics of the study population are described, including demographics and handgrip strength measurements. The primary results focus on the relationship between handgrip strength and mortality risk, stratified by sex. The analysis reveals a gradual and inverse association, meaning that higher handgrip strength is associated with lower mortality risk. Specific hazard ratios (HRs) are presented for the 10th and 90th percentiles of handgrip strength for both men and women. A hazard ratio is a measure of how often a particular event (in this case, death) happens in one group compared to another group over a period of time. An HR greater than 1 indicates a higher risk, while an HR less than 1 indicates a lower risk. The results show that lower handgrip strength is associated with a higher risk of mortality, while higher handgrip strength is associated with a lower risk.

Key Aspects

Strengths

Suggestions for Improvement

Non-Text Elements

FIGURE 2 | Association of handgrip strength with mortality risk during...
Full Caption

FIGURE 2 | Association of handgrip strength with mortality risk during follow-up in adults older than 90 years (n=1890). The full line shows the hazard ratios and the dotted lines show the 95% CI.

First Reference in Text
Figure 2 shows the gradual association of handgrip strength with mortality risk during follow-up in the total sample of men and women older than 90 years.
Description
  • Graph Type: This is a line graph showing the relationship between handgrip strength and mortality risk. Handgrip strength (in kilograms) is plotted on the x-axis, and the hazard ratio (HR) is on the y-axis.
  • Hazard Ratio (HR): The hazard ratio represents the relative risk of death. An HR of 1 means the risk is the same as the reference group. An HR greater than 1 means a higher risk of death, and an HR less than 1 means a lower risk of death, compared to the reference group.
  • 95% Confidence Interval (CI): The dotted lines around the solid line represent the 95% confidence interval. This is a range of values within which we are 95% confident that the true hazard ratio lies. A wider interval indicates more uncertainty in the estimated HR.
  • Reference Value: The median handgrip strength of 18 kg is used as the reference point (HR = 1). Hazard ratios for other handgrip strengths are relative to the risk at this median strength. For example, an HR of 2 for a given strength would mean double the mortality risk compared to someone with the median strength.
  • Curvilinear Relationship: The graph shows a U-shaped curve. This means that both very low and very high handgrip strength, relative to the median, are associated with an increased risk of mortality. The lowest point on the curve represents the handgrip strength associated with the lowest mortality risk.
Scientific Validity
  • Statistical Methodology: The use of Cox regression with restricted cubic splines is appropriate for analyzing time-to-event data like mortality and exploring non-linear relationships with continuous predictors like handgrip strength. This approach is well-suited to the research question.
  • Confidence Intervals: Presenting the 95% confidence intervals is essential for conveying the precision of the estimated hazard ratios. The authors should discuss the width of the intervals and their implications for the interpretation of the findings. Wide intervals indicate less certainty about the true HR.
  • Reference Value Justification: While using the median as a reference is acceptable, the authors should justify this choice. Consider presenting results with other reference points, such as clinically relevant cut-off values for handgrip strength, to provide more context and practical implications.
  • Control for Confounding: It's crucial that the Cox regression model adequately controls for potential confounding variables (e.g., age, sex, comorbidities). The authors should clearly state which confounders were included in the model and justify their selection.
Communication
  • Clarity of Axes and Labels: The axes are clearly labeled, and the caption provides essential information about the sample size and the meaning of the lines. This makes the graph easy to understand.
  • Visual Representation of Uncertainty: Using dotted lines for the confidence intervals effectively communicates the uncertainty associated with the hazard ratios.
  • Clinical Implications: The authors should expand on the clinical implications of the U-shaped curve. What does this mean for interventions aimed at improving handgrip strength? Is there an optimal range of handgrip strength to target?
  • Visual Appeal: The graph is visually simple and easy to interpret. Consider using color or shading to highlight the area within the confidence intervals, further emphasizing the range of plausible values for the HR.
FIGURE 3 | Association of handgrip strength with mortality risk during...
Full Caption

FIGURE 3 | Association of handgrip strength with mortality risk during follow-up in men older than 90years (n=725). The full line shows the hazard ratios and the dotted lines show the 95% CI.

First Reference in Text
Sex and handgrip strength did not interact significantly (p=0.40), and we there excluded the interaction term from the final model. However, as muscle strength levels differ substantially between sexes, we report the sex-stratified analyses here. Figures 3 and 4 show the gradual association of hand-grip strength with mortality risk during follow-up in men and women, respectively, older than 90 years.
Description
  • Graph Type: Figure 3 is a line graph depicting the association between handgrip strength and mortality risk specifically for men over 90 years old. The x-axis represents handgrip strength measured in kilograms, while the y-axis represents the hazard ratio (HR).
  • Hazard Ratio (HR): The hazard ratio is a measure of relative risk. An HR of 1 indicates that the risk of death is the same as the reference group. An HR greater than 1 signifies a higher risk, while an HR less than 1 signifies a lower risk compared to the reference.
  • 95% Confidence Interval (CI): The dotted lines around the solid line represent the 95% confidence interval. This interval provides a range within which the true hazard ratio is likely to fall 95% of the time. A wider CI suggests greater uncertainty in the estimate.
  • Reference Value: The graph uses a median handgrip strength of 26 kg for men as the reference point (HR = 1). Hazard ratios for other handgrip strength values are relative to the risk at this median strength.
  • Shape of the Curve: The graph shows a generally decreasing trend, suggesting that higher handgrip strength is associated with lower mortality risk in men over 90. The curve appears somewhat curvilinear, with a steeper decline in risk at lower handgrip strength values.
Scientific Validity
  • Sex-Stratified Analysis: Presenting sex-stratified results is justified given the substantial differences in muscle strength between men and women. This approach allows for a more nuanced understanding of the relationship between handgrip strength and mortality within each sex.
  • Interaction Term: The authors correctly tested for an interaction between sex and handgrip strength. The non-significant interaction (p=0.40) supports their decision to present sex-stratified results without an interaction term in the model.
  • Confidence Intervals: The inclusion of confidence intervals is crucial. The authors should discuss the width of the CIs, especially in regions where they are wider, and address the implications for the interpretation of the findings.
  • Sample Size: The sample size for men (n=725) is relatively small, which might limit the statistical power to detect subtle effects or interactions. This should be acknowledged as a limitation.
Communication
  • Clarity and Labeling: The graph is clearly labeled, and the caption provides essential information. The axes are well-defined, and the meaning of the lines is explained.
  • Visual Presentation of Uncertainty: The use of dotted lines for confidence intervals effectively communicates the uncertainty associated with the hazard ratios.
  • Comparison with Women: Since this figure presents results for men, it would be helpful to visually compare these findings with the corresponding results for women (Figure 4). Consider presenting both graphs side-by-side or in a panel for easier comparison.
  • Clinical Implications: The authors should discuss the clinical implications of these findings specifically for men. What are the recommended handgrip strength targets for this population, and how can these targets be achieved?
FIGURE 4 | Association of handgrip strength with mortality risk during...
Full Caption

FIGURE 4 | Association of handgrip strength with mortality risk during follow-up in women older than 90 years (n=1165). The full line shows the hazard ratios and the dotted lines show the 95% CI.

First Reference in Text
Sex and handgrip strength did not interact significantly (p=0.40), and we there excluded the interaction term from the final model. However, as muscle strength levels differ substantially between sexes, we report the sex-stratified analyses here. Figures 3 and 4 show the gradual association of hand-grip strength with mortality risk during follow-up in men and women, respectively, older than 90 years.
Description
  • Graph Type: Figure 4 presents a line graph illustrating the relationship between handgrip strength and mortality risk specifically in women older than 90 years. Handgrip strength, measured in kilograms, is on the x-axis, and the hazard ratio (HR) is on the y-axis.
  • Hazard Ratio (HR): The hazard ratio represents the relative risk of death compared to a reference group. An HR of 1 means the risk is the same as the reference. An HR greater than 1 indicates a higher risk, and an HR less than 1 indicates a lower risk.
  • 95% Confidence Interval (CI): The dotted lines around the solid line represent the 95% confidence interval. This interval gives a range of values within which we are 95% confident that the true hazard ratio lies. A wider interval suggests more uncertainty in the estimated HR.
  • Reference Value: The median handgrip strength for women (16 kg) is used as the reference point, corresponding to a hazard ratio of 1. All other hazard ratios are relative to the risk at this median strength.
  • Shape of the Curve: The graph shows a generally decreasing trend, indicating that higher handgrip strength is associated with a lower mortality risk in women over 90. Similar to the graph for men (Figure 3), the curve appears somewhat curvilinear, with a steeper decline in risk at lower handgrip strength values.
Scientific Validity
  • Sex-Stratified Analysis: Presenting sex-stratified analyses is justified given the known differences in muscle strength between sexes. This approach allows for a more accurate assessment of the relationship between handgrip strength and mortality within each sex.
  • Confidence Intervals: The inclusion of 95% confidence intervals is essential. The authors should discuss the width of the CIs and their implications, particularly in areas where the intervals are wider, indicating greater uncertainty in the estimated HRs.
  • Comparison with Men: Direct comparison with the results for men (Figure 3) is crucial for understanding the sex-specific effects of handgrip strength on mortality. Presenting both graphs together would facilitate this comparison.
  • Sample Size: While the sample size for women (n=1165) is larger than that for men, it is still important to consider the potential impact of sample size on the precision of the estimates and the ability to detect smaller effects.
Communication
  • Clarity and Labeling: The graph is clearly labeled, and the caption provides essential information. The axes are well-defined, and the meaning of the lines is explained.
  • Visual Presentation of Uncertainty: The use of dotted lines for confidence intervals effectively communicates the uncertainty associated with the hazard ratios.
  • Direct Comparison: Presenting Figures 3 and 4 side-by-side or in a panel would greatly enhance the comparison between sexes and allow readers to quickly grasp the similarities and differences in the relationships.
  • Clinical Implications: The authors should discuss the specific clinical implications of these findings for women. What handgrip strength targets are recommended for this population, and what interventions are most effective in improving handgrip strength and potentially reducing mortality risk?
TABLE 1 | Descriptive baseline characteristics of the study population.
First Reference in Text
Table 1 shows the baseline characteristics of the study population of participants aged 90 years or older.
Description
  • Structure: The table presents descriptive statistics for various characteristics of the study population. It's organized into columns representing the entire study population ('All participants'), men, and women. Each row corresponds to a specific characteristic.
  • Data Presentation: For continuous variables like handgrip strength, age, and BMI, the table provides the number of participants (n), the mean (average), and the standard deviation (SD). The standard deviation shows how spread out the data is around the mean. For categorical variables like sex, smoking status, education, and marital status, the table shows the number of participants and the frequency (percentage) within each category.
  • Characteristics: The table includes a range of characteristics relevant to the study, including demographics (age, sex), physical measures (handgrip strength, BMI), lifestyle factors (smoking status), socioeconomic factors (education), and social factors (marital status). It also includes geographic region and self-perceived health.
Scientific Validity
  • Baseline Characteristics: Presenting baseline characteristics is crucial in observational studies like this one. It allows readers to understand the composition of the study population and assess the potential for confounding. This information helps determine whether the observed associations between handgrip strength and mortality might be influenced by other factors.
  • Descriptive Statistics: The choice of descriptive statistics is appropriate for the different types of variables. Means and standard deviations are suitable for continuous variables, while frequencies and percentages are appropriate for categorical variables.
  • Missing Data: The table should ideally include information about missing data for each variable. How many participants had missing data for each characteristic? This is important for transparency and understanding the potential impact of missing data on the results.
Communication
  • Clarity and Organization: The table is generally well-organized and easy to read. The column headings clearly indicate the different groups (all, men, women), and the row labels clearly identify each characteristic.
  • Units of Measurement: The table clearly indicates the units of measurement for each variable (e.g., kg for handgrip strength, kg/m^2 for BMI). This is essential for proper interpretation of the data.
  • Abbreviations: While common abbreviations like SD and BMI are likely to be understood by the target audience, any less common abbreviations should be defined in a table footnote or the main text.
  • Formatting: Consider using clearer formatting to visually separate the different sections of the table (all participants, men, women). For instance, adding horizontal lines or shading could improve readability.

Discussion

Overview

This discussion section interprets the study's findings on the relationship between handgrip strength and mortality in the oldest old (90+ years). The study found a gradual inverse association, where greater handgrip strength correlates with lower mortality risk. This supports the idea that every increment of muscle strength contributes to longevity in this age group. The discussion compares these findings with previous research, noting that earlier studies often had lower age limits (80-90 years) and used categorical analyses, making it difficult to observe the gradual association found in this study. The authors discuss potential mechanisms linking muscle strength to mortality, including vascular function, inflammatory markers (cytokines and myokines), brain health, and cognitive function. The discussion also highlights the practical implications of these findings, emphasizing the potential benefits of strength training interventions for the oldest old, while acknowledging the need for personalized and supervised programs to minimize risks. The section concludes by summarizing the study's strengths and limitations and reiterating the main conclusion: a graded association exists between muscle strength and survival in the oldest old, suggesting the importance of maintaining or improving muscle strength through interventions like resistance training.

Key Aspects

Strengths

Suggestions for Improvement

Conclusion

Overview

This conclusion reiterates the study's main finding: a gradual inverse association between handgrip strength and mortality in the oldest old (90+ years). This means that higher handgrip strength, a measure of overall muscle strength, is associated with a lower risk of death in this age group. The conclusion emphasizes that this association is observed across the entire range of handgrip strength, suggesting that every increment of strength contributes to increased survival. The authors highlight the practical implications of this finding, advocating for interventions like resistance training to improve muscle strength in the oldest old. They also acknowledge the study's limitations, such as using handgrip strength as the sole measure of muscle function and potential residual confounding factors.

Key Aspects

Strengths

Suggestions for Improvement

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