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
- Curvilinear Association: The study found that both low and high handgrip strength, compared to the median, were associated with increased mortality risk, using hazard ratios to quantify this relationship. This pattern was consistent for both men and women, highlighting the nuanced role of muscle strength in mortality.
- Sex-Stratified Analysis: Analysis revealed consistent patterns of association across sexes, with men and women demonstrating similar trends in the relationship between handgrip strength and mortality.
- Clinical Implications: The study suggests that maintaining or improving muscle strength, possibly through resistance training, could benefit the oldest old by potentially lowering mortality risk.
- Potential Biological Mechanisms: The study discusses how handgrip strength might influence mortality through vascular function, inflammation, and cognitive health.
- Robustness of Findings: Sensitivity analyses confirmed the main findings, demonstrating that the results held up under various modeling conditions and assumptions.
Strengths
- Comprehensive Dataset: The study uses a large, diverse sample across 28 countries, enhancing the generalizability of the findings to a wide population of the oldest old.
- Advanced Statistical Methods: The use of time-varying Cox regression with restricted cubic splines allows for sophisticated modeling of non-linear relationships, providing a nuanced understanding of the association.
- Clear Presentation of Results: The results are clearly communicated through hazard ratios and confidence intervals, offering a quantifiable measure of the association between handgrip strength and mortality.
- Sex-Stratified Analysis: By considering sex differences in muscle strength, the study provides a more accurate assessment of the association for each gender.
Areas for Improvement
- Elaborate on U-Shaped Relationship: The study should further discuss the U-shaped relationship observed, exploring the reasons behind increased mortality risk at both low and high handgrip strength levels.
- Comprehensive Muscle Function Assessment: Future research should incorporate additional measures of muscle function, such as gait speed or lower limb strength, to provide a more holistic view of muscle health.
- Address Missing Data: The study could improve by detailing the extent and impact of missing data, possibly employing imputation methods to handle incomplete datasets more effectively.
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
- Study Population: The study included 1890 individuals aged 90 years or older from 27 European countries and Israel participating in the SHARE study. The majority of participants were women (61.6%), and the mean age was 91.0 ± 1.5 years.
- Muscle Strength Measurement: Muscle strength was assessed using handgrip dynamometry, a device that measures the force a person can exert with their hand. Measurements were recorded in kilograms.
- Statistical Analysis: The association between muscle strength and mortality was analyzed using time-varying Cox regression with restricted cubic splines. This statistical technique allows for modeling non-linear relationships between variables. Restricted cubic splines fit a smooth curve to the data, allowing for flexibility in the shape of the relationship between handgrip strength and mortality. The model controlled for potential confounding variables such as age, sex, smoking status, BMI, marital status, education level, geographical region, and self-perceived health.
- Key Findings: The study found a curvilinear association between muscle strength and mortality. Both very low and very high levels of muscle strength were associated with increased mortality risk compared to the median strength level. This association was observed in both men and women.
Strengths
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Clear Research Question
The abstract clearly states the research question: investigating the association between muscle strength and all-cause mortality in the oldest old.
"This study investigates the prospective association of muscle strength with all- cause mortality in the oldest old." (Page 1)
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Appropriate Methodology
The use of time-varying Cox regression with restricted cubic splines is appropriate for analyzing the relationship between a time-dependent variable like muscle strength and mortality, allowing for non-linear relationships.
"Using time- varying Cox regression with restricted cubic splines, we determined the prospective association of muscle strength with mortality" (Page 1)
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Comprehensive Reporting
The abstract provides key details about the study population, methodology, and findings, including specific hazard ratios and confidence intervals.
"The 10th percentile of muscle strength (10 kg) showed a hazard ratio (HR) of 1.27 (95% CI 1.13–1.43, p < 0.001). The 90th percentile (31 kg) showed an HR of 0.69 (95% CI 0.58–0.82, p < 0.001)." (Page 1)
Suggestions for Improvement
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Clarify Curvilinear Relationship
While the abstract mentions a 'curvilinear fashion,' it could be more explicit about the nature of this relationship. Because readers may not immediately grasp the concept of a U-shaped or inverted U-shaped association, clarifying this point would enhance understanding.
Implementation: Replace 'in a gradual and curvilinear fashion' with 'in a U-shaped curve, indicating that both lower and higher levels of strength compared to the median were associated with increased mortality.'
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Elaborate on Sensitivity Analyses
The abstract mentions sensitivity analyses but doesn't specify what these analyses entailed. Because transparency is crucial for evaluating the robustness of the findings, providing more detail about the sensitivity analyses would strengthen the abstract.
Implementation: Expand the sentence on sensitivity analyses to specify the methods used, e.g., 'Sensitivity analyses, which excluded individuals who died within the first 2 years of follow-up to address potential reverse causality, confirmed the main findings.'
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
- Age-Related Muscle Decline: Muscle mass and strength naturally decrease with age, impacting essential daily activities and overall health. This decline typically begins after age 40 and accelerates in the oldest old.
- Skeletal Muscle's Endocrine Role: Skeletal muscles act as endocrine organs, releasing myokines, signaling proteins that influence processes like body weight regulation, insulin sensitivity, and inflammation levels. Physical activity stimulates myokine release, contributing to overall health.
- Handgrip Strength as a Measure: Handgrip strength, easily measured with a dynamometer, serves as a reliable indicator of overall muscle strength and is associated with skeletal muscle mass. It has been linked to various health conditions and mortality risk.
- Knowledge Gap in the Oldest Old: Existing research on the muscle strength-mortality relationship is limited in the oldest old, a rapidly expanding population segment with distinct health characteristics and high prevalence of multimorbidity (multiple chronic conditions) and polypharmacy (use of multiple medications).
- Study Aim: The study aims to investigate the prospective association (future risk) between handgrip strength and all-cause mortality in individuals aged 90+ years. It will use a large, multinational sample and restricted cubic splines, a statistical technique for modeling non-linear relationships, to address limitations of previous research.
Strengths
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Clear Rationale
The introduction effectively establishes the importance of studying the muscle strength-mortality relationship in the oldest old by highlighting the age-related decline in muscle function and its impact on health outcomes. The rationale for focusing on this specific demographic is well-articulated.
"Ageing is associated with a range of physiological changes... musculoskeletal changes include a decline in muscle mass and strength..." (Page 2)
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Highlighting Knowledge Gaps
The introduction clearly identifies the limitations of previous research, such as small sample sizes and the use of categorical analyses, justifying the need for the current study. This strengthens the study's contribution to the field.
"However, few studies have assessed the importance of handgrip strength on survival in the 'oldest old'... previous studies primarily used categorical analyses..." (Page 2)
Suggestions for Improvement
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Expand on the Significance of Non-linear Relationships
The introduction mentions the use of restricted cubic splines but doesn't fully explain why exploring non-linear relationships is crucial in this context, because understanding the specific shape of the association between muscle strength and mortality can inform targeted interventions.
"...we use...restricted cubic splines analyses...This approach allows us to examine the potentially nonlinear relationship..." (Page 2)
Implementation: Add a sentence explaining that a non-linear relationship might suggest optimal strength ranges for longevity, which would be missed by linear models.
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Strengthen the Connection to the Abstract
The introduction could better connect to the abstract by explicitly mentioning the curvilinear relationship found, because this reinforces the rationale for using restricted cubic splines.
Implementation: Briefly mention that preliminary findings suggest a curvilinear association, further emphasizing the need for a flexible modeling approach.
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
- Study Design and Participants: Data from eight waves of the SHARE study, spanning 2004-2022, were used. The study included 1890 participants aged 90+ with data from at least two waves. SHARE employs a multistage stratified sampling design, dividing countries into strata based on geographical area, with municipalities or zip codes as primary sampling units. Data collection involves computer-assisted personal interviews conducted every other year.
- Predictor: Muscle Strength: Handgrip strength was measured using a dynamometer, with two measurements taken for each hand. The maximum value from either hand was used for analysis. Participants were instructed to squeeze the dynamometer with maximal effort for 2 seconds while maintaining a standardized posture (elbow at 90° flexion, wrist in neutral position, upper arm against the trunk).
- Outcome: All-Cause Mortality: Mortality data were obtained from end-of-life interviews with proxy respondents. The date of death was recorded, and all-cause mortality was the outcome of interest. Missing dates of death were imputed using the mean of the last participant interview and the proxy interview date.
- Covariates: The analyses controlled for several covariates: sex, age, smoking status (current, ex-, never), body mass index (BMI), marital status, education level (ISCED-1997 categories), geographical region (based on UN classification), and self-perceived health. These were selected based on their known associations with both muscle strength and mortality in older adults.
- Statistical Analyses: Time-varying Cox regression was used to analyze the association between handgrip strength and mortality, with time-on-study in months as the timescale. Restricted cubic splines were used to model potential non-linear relationships. Knots for the splines were placed at the 10th, 50th, and 90th percentiles of handgrip strength distribution. Sensitivity analyses were conducted with alternative knot placements and by excluding participants with less than 2 years of follow-up. Sex and geographical region interaction terms were tested.
Strengths
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Comprehensive Covariate Selection
The authors have included a wide range of relevant covariates, addressing potential confounding factors and increasing the validity of the findings.
"We controlled the analyses for sex, age, smoking status, body mass index (BMI), marital status, education, geographical region and self-perceived health." (Page 3)
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Appropriate Statistical Methodology
The use of time-varying Cox regression with restricted cubic splines is appropriate for analyzing time-to-event data with a time-dependent predictor and potential non-linear relationships.
"Using the phreg procedure, we performed Cox regression to determine the prospective association of handgrip strength with mortality...we used restricted cubic splines..." (Page 4)
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Robustness Checks
The inclusion of sensitivity analyses with alternative knot placements and exclusion of participants with short follow-up strengthens the study by assessing the robustness of the results to different modeling choices.
"To test the robustness of the results, we also performed sensitivity analyses with alternative knot placements...and exclusion of those with less than 2 years follow-up." (Page 4)
Suggestions for Improvement
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Justification for Knot Placement
While the authors mention using the 10th, 50th, and 90th percentiles for knot placement, they haven't provided a clear justification for this choice, because different knot placements can influence the shape of the spline and the interpretation of the results.
"Based on the recommendations by Harrell [29], pre-specified that knots were placed at the 10th, 50th and 90th percentiles of the exposure distribution..." (Page 4)
Implementation: Include a brief explanation for the chosen knot placement, perhaps referencing Harrell's recommendations or explaining that these percentiles provide adequate representation of the handgrip strength distribution.
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Missing Data Handling
The authors state they performed complete case analyses, but it's important to elaborate on the extent and potential impact of missing data, because substantial missingness can introduce bias.
"Analyses were performed as complete case analyses." (Page 4)
Implementation: Provide details on the proportion of missing data for each variable and discuss any potential biases arising from the complete case approach. Consider exploring alternative imputation methods if missingness is substantial.
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Clarify Time-Varying Covariates
The methods section mentions using time-varying Cox regression to account for changes in muscle strength and covariates, but it lacks specifics on which covariates were treated as time-varying and how their changes were handled, because this information is crucial for understanding the model's construction and interpretation.
"We used time-varying Cox regression to account for changes in both muscle strength and covariates over time..." (Page 4)
Implementation: Explicitly state which covariates were time-varying (e.g., BMI, smoking status) and describe how their temporal changes were incorporated into the Cox model (e.g., updated at each wave, linear interpolation).
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
- Baseline Characteristics: The study population consisted of more women (61.6%) than men (38.4%), with a mean age of 91.0 years. Men had a higher mean handgrip strength (26.7 kg) than women (16.4 kg). Few participants were current smokers (2.9%). The majority had a lower education level (65.7%), and more women were widowed (76.7%) compared to men (37.9%). Self-perceived health was mostly rated as fair (40.3%) or good (32.5%).
- Handgrip Strength and Mortality: The analysis showed a gradual and inverse association between handgrip strength and mortality risk in both men and women. The median handgrip strength was 26 kg for men and 16 kg for women. At the 10th percentile of handgrip strength (15 kg for men, 10 kg for women), the hazard ratios (HRs) for mortality were 1.33 and 1.19, respectively. At the 90th percentile (35 kg for men, 23 kg for women), the HRs were 0.75 for both sexes. This indicates that individuals with handgrip strength at the 10th percentile had a higher risk of mortality compared to those at the median, while those at the 90th percentile had a lower risk.
Strengths
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Clear Presentation of Key Findings
The results section clearly presents the key findings regarding the association between handgrip strength and mortality, stratified by sex. The use of hazard ratios with confidence intervals provides a quantifiable measure of this association.
"Stratified for sex, the median levels of muscle strength were 26 kg for men and 16 kg for women...The 10th percentile of muscle strength showed HRs of 1.33 (95% CI 1.10-1.61) at 15 kg for men and 1.19 (95% CI 1.05-1.35) at 10 kg for women." (Page 4)
Suggestions for Improvement
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Include Results of Interaction Term Test
While the abstract mentions a non-significant interaction between sex and handgrip strength, the results section should explicitly state the p-value for this interaction term, because this information is crucial for understanding why the interaction term was excluded from the final model.
"Sex and handgrip strength did not interact significantly (p = 0.40), and we there excluded the interaction term from the final model." (Page 4)
Implementation: Include a sentence stating the p-value for the sex-by-handgrip strength interaction, e.g., "The interaction term between sex and handgrip strength was not statistically significant (p=0.40)."
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Provide Context for Figures 3 and 4
The results section mentions Figures 3 and 4 but doesn't describe what these figures depict, because readers need this information to interpret the visual representation of the data.
"Figures 3 and 4 show the gradual association of handgrip strength with mortality risk during follow-up in men and women, respectively, older than 90 years." (Page 4)
Implementation: Briefly describe the content of Figures 3 and 4, e.g., "Figures 3 and 4 illustrate the gradual association between handgrip strength and mortality risk in men and women, respectively, using restricted cubic splines."
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Clarify the Rationale for Sex-Stratified Analysis
While the results are presented stratified by sex, the rationale for this approach could be more clearly articulated, because it's important to justify why sex-specific analyses are necessary despite the non-significant interaction.
"However, as muscle strength levels differ substantially between sexes, we report the sex-stratified analyses here." (Page 4)
Implementation: Elaborate on the substantial difference in muscle strength levels between sexes mentioned as the reason for stratification, providing specific examples or data to support this claim. For instance, state the mean or median handgrip strength for each sex and explain how this difference could influence the relationship with mortality.
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
- Gradual Inverse Association: The study found a gradual, inverse relationship between handgrip strength and mortality risk, meaning that for every increase in handgrip strength, the risk of death decreases. This contrasts with some previous studies that suggested a threshold effect, where only strength levels below a certain point increased mortality risk.
- Comparison with Previous Research: The discussion compares the findings with previous studies on handgrip strength and mortality in older adults. It notes that while some studies found associations, they often had lower age limits (80-90 years) and used categorical analyses (grouping participants into strength categories), which may have obscured the gradual nature of the association observed in this study.
- Potential Mechanisms: The discussion explores several potential biological mechanisms that could explain the link between muscle strength and mortality. These include improved vascular function (how blood vessels work), the release of anti-inflammatory signaling proteins (cytokines and myokines) from muscles, and better brain health and cognitive function.
- Practical Implications: The discussion highlights the practical implications of the findings, suggesting that interventions aimed at increasing muscle strength, such as resistance training, could be beneficial for extending life expectancy in the oldest old. It also emphasizes the need for careful consideration of individual health status and the importance of personalized and supervised exercise programs to minimize risks.
- Strengths and Limitations: The discussion acknowledges the study's strengths, including the large, diverse sample, the use of time-varying Cox regression with restricted cubic splines, and the sensitivity analyses conducted. It also addresses limitations, such as the use of handgrip strength as the sole measure of muscle function, potential residual confounding factors, the use of complete case analysis, and the reliance on proxy respondents for mortality data.
Strengths
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Comprehensive Discussion of Findings
The discussion provides a thorough interpretation of the results, placing them in the context of existing literature and exploring potential underlying mechanisms.
"Using time-varying Cox regression with restricted cubic splines, the present study demonstrates that muscle strength is gradually and inversely associated with mortality risk in the oldest old." (Page 4)
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Practical and Clinical Implications
The discussion effectively translates the research findings into practical recommendations for interventions, emphasizing the potential benefits of strength training for the oldest old while acknowledging the need for cautious implementation.
"As muscle strength at all ages is highly adaptive to resistance training, the present findings highlight the importance of improving muscle strength in both men and women among the oldest old." (Page 7)
Suggestions for Improvement
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Elaborate on the Curvilinear Association in the Abstract
While the abstract mentions a curvilinear association, the discussion could elaborate on the observed shape and its implications, because the abstract's brevity might not fully convey the nuanced relationship found.
"Using time-varying Cox regression with restricted cubic splines, the present study demonstrates that muscle strength is gradually and inversely associated with mortality risk in the oldest old." (Page 4)
Implementation: Briefly describe the shape of the curve (e.g., U-shaped, inverted U-shaped) and discuss whether the study supports an optimal range of handgrip strength for longevity.
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Discuss the Limitations of Using Handgrip Strength
While the discussion acknowledges the limitations of handgrip strength as a sole measure of muscle function, it could further elaborate on the specific challenges in the oldest old population, because this age group may experience differential decline in various muscle groups, and handgrip strength might not fully capture overall muscle health.
"Although handgrip strength is widely used as a proxy for overall muscle strength...it is important to acknowledge its limitations, particularly in the oldest old population." (Page 4)
Implementation: Expand on the limitations by discussing the potential for different rates of decline in upper and lower limb strength and the lack of information on muscle power and endurance, which are also important aspects of muscle function in older adults.
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Strengthen the Discussion of Residual Confounding
The discussion mentions potential residual confounding but could strengthen this point by providing specific examples and their potential impact, because simply listing potential confounders without explaining their potential influence on the observed association limits the reader's understanding of the study's limitations.
"Several potential residual confounding factors may exist, including physical activity levels, dietary factors, access to healthcare and genetic factors." (Page 7)
Implementation: Elaborate on specific residual confounders, such as physical activity levels, dietary factors, or genetic predisposition, and discuss how these factors might affect both handgrip strength and mortality, potentially biasing the observed association.
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
- Gradual Inverse Association: The study's primary finding is a gradual inverse relationship between handgrip strength and mortality. This means that as handgrip strength increases, the risk of death decreases proportionally. This is important because it suggests that even small improvements in muscle strength can have a positive impact on survival in the oldest old.
- Practical Implications: The authors suggest that interventions aimed at increasing muscle strength, such as resistance training, could be beneficial for the oldest old. Resistance training involves exercises that work against a force, like weights or resistance bands, to build muscle strength. They emphasize the need for personalized and supervised exercise programs to ensure safety and effectiveness in this frail population.
- Limitations: The conclusion acknowledges several limitations. Using handgrip strength as the sole measure of muscle function might not fully capture overall muscle health. Residual confounding, meaning other factors that could influence both handgrip strength and mortality, might not be fully accounted for. For example, individuals with better access to healthcare might have both higher handgrip strength and lower mortality risk, independent of the direct effect of strength.
Strengths
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Clear and Concise Summary of Findings
The conclusion effectively summarizes the main finding of the study, highlighting the gradual inverse association between handgrip strength and mortality in the oldest old. This allows readers to quickly grasp the key takeaway message.
"In conclusion, the present study demonstrates a gradual association between muscle strength and survival in the oldest old." (Page 8)
Suggestions for Improvement
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Expand on the Clinical Implications of the Gradual Association
While the conclusion mentions the practical implications of the findings, it could further elaborate on the clinical significance of the gradual inverse association, because this nuanced relationship has important implications for designing and implementing interventions.
"As muscle strength at all ages is highly adaptive to resistance training, the present findings highlight the importance of improving muscle strength in both men and women among the oldest old." (Page 8)
Implementation: Discuss how the gradual nature of the association suggests that even small gains in muscle strength can be beneficial, potentially motivating older adults and healthcare professionals to prioritize strength training even when substantial improvements are difficult to achieve.
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Discuss Future Research Directions
The conclusion would benefit from a brief discussion of future research directions, because identifying unanswered questions and potential avenues for further investigation helps advance the field and build upon the current study's findings.
Implementation: Add a paragraph outlining potential future research, such as investigating the specific types of resistance training most effective for the oldest old, exploring the combined effects of strength training with other interventions like nutritional support, or examining the role of specific myokines in mediating the relationship between muscle strength and mortality.
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Strengthen the Connection to the Introduction
The conclusion could strengthen its connection to the introduction by explicitly revisiting the knowledge gap identified earlier and explaining how the current study addresses it, because this reinforces the study's contribution to the field.
Implementation: Briefly reiterate the limitations of previous research mentioned in the introduction, such as the use of categorical analyses or the lack of focus on the oldest old, and then explicitly state how the current study's findings, using restricted cubic splines and a large sample of nonagenarians and centenarians, provide new insights into the muscle strength-mortality relationship in this understudied population.