Study analysis · European Journal of Nutrition · 2019
Why does the same diet linked to healthy aging on a Greek island do absolutely nothing in the city? The answer could be about more than just protein.
In older Greek islanders, eating more protein was linked to better healthy-aging scores, but the same link didn't show up in city dwellers.
Overview
What the study found
The study in plain English — the bottom line, every takeaway we extracted, and what to do with them.
In simple terms
This study is like taking a picture at one moment. It asked older people what they usually eat and also checked how healthy they were at that same time. But because it didn't follow people over time or assign their diets, we can't tell if the food made them healthier or if some other reason explains the link. So we can only say the two things are connected, not that one caused the other.
What’s the bottom line?
Scientists wanted to know if the amount of protein and carbs people eat is linked to how healthy they are as they get older. They asked older Greek people about their eating habits and gave them a 'healthy aging' score.
How strong is this study?
Imagine comparing two groups of kids: those who eat vegetables and those who don't, but you just look at them once. Even if veggie-eaters are taller, there might be other reasons they're taller, like having parents who are tall. This study is similar—it looks at many people at one time, so there could be other reasons for the healthy aging besides diet. It's not a strong experiment where you randomly give people different diets, so we should be cautious about trusting the results.
0 / 100
- COI disclosureconflicts of interest not disclosed
- Data availabilitydata not shared
- Code availabilitycode not shared
0 / 100
- Randomizationnot randomized
- Blindingblinding unclear
- Control groupno control group
- Sample sizeno sample size reported
- Follow-upno follow-up reported
100 / 100
54 / 100
- P-values+15/15
- Effect size+20/20
- Confidence intervalsno confidence intervals
- Pre-registrationnot pre-registered
Each component is scored out of 100 and then capped by the study design — a case series cannot reach the ceiling a randomised trial can, however well it is reported.
Where it sits
RCT reviewsReviews of RCTs (Meta-analyses)
Max 100Randomized TrialsRandomized Trials
Max 90Reviews of Cohort StudiesReviews of Cohort Studies
Max 85Cohort StudiesCohort Studies
Max 72Reviews of Case-Control StudiesReviews of Case-Control Studies
Max 63Case-Control StudiesCase-Control Studies
Max 58Cross-Sectional & Case SeriesCross-Sectional & Case Series
Max 50Expert OpinionExpert Opinion
Max 533 / 100
Probability of being correct
Snapshots of a population at a single point in time, or descriptions of small groups. Can identify correlations and prevalence, but cannot determine cause and effect.
This design cannot establish causation — the findings describe an association, not a cause. Cross-sectional design assesses exposure and outcome simultaneously, so temporal sequence cannot be determined. There is no randomization or control group, and unmeasured confounding may explain observed associations. Therefore, causal inference is not possible.
COI Unknown
Could not determine conflict of interest status
No conflict of interest or funding statements are available in the provided text. The study appears to be a cross-sectional analysis of two epidemiological studies, but full disclosures are not accessible.
The article is behind a paywall; only abstract and references were provided. No funding or COI declarations were visible.
Key takeaways
- 01
For older people living on Greek islands, those who ate low protein and lots of carbs had slightly lower healthy aging scores.
- 02
Those who ate high protein and lots of carbs had slightly higher scores.
- 03
For people in cities, there was no clear link.
- 04
The differences were small, could be due to chance or other factors, and this study cannot prove that eating more protein causes healthier aging.
Surprising findings
- A high-protein, high-carbohydrate diet was associated with higher successful aging scores among older Greek islanders, while a low-protein, high-carbohydrate diet was associated with lower scores—but neither association existed in urban participants.Most diet advice pits protein against carbs. This study found that combining protein with carbohydrates was beneficial in one group. And the geographic difference is unexpected—the same dietary pattern had no effect in cities.
- The diet-by-area interaction was highly significant (p < 0.001), even though the individual association p-values were only borderline (p = 0.04).This means the difference between island and city is likely more meaningful than the simple association in the island group, which is a statistical nuance that many casual readers overlook.
Practical takeaways
If you're an older adult, don't drastically cut carbs or protein based on fads. Instead, focus on getting enough protein from quality sources—like fish, dairy, and legumes—especially if you live in a rural or island community where traditional food is accessible.
This study is cross-sectional and cannot prove that a high-protein diet causes healthier aging. The association was only found in insular Greeks, not urban ones, so it may not apply to you.
low confidenceWhen reading nutrition studies, pay attention to the population and the context. A finding in one region or demographic may not generalize to everyone, even if it's statistically significant.
The study's marginal p-values (0.04) and small effect sizes mean the findings are fragile and could be due to chance or unmeasured confounding.
medium confidenceConsider a broader definition of healthy aging—not just avoiding disease but staying socially engaged, physically active, and mentally sharp. Diet might influence all these, not just blood markers.
The Successful Aging Index is validated, but it's still a self-reported and composite measure, and the study used food frequency questionnaires, which have memory and accuracy limitations.
medium confidenceWhy this study matters
Island vs. city: The protein paradox
The study combined data from two Greek studies (ATTICA for urban, MEDIS for insular) and found that among islanders, a low-protein/high-carb diet was linked to lower successful aging scores (B = -0.08, p = 0.04), while a high-protein/high-carb diet was linked to higher scores (B = 0.06, p = 0.04). But in urban participants, no significant associations were seen (all p > 0.05). The diet-by-area interaction was highly significant (p < 0.001), suggesting the effect really did differ between islands and cities.
This challenges the one-size-fits-all diet advice. What works for one population may not work for another, even within the same country and similar Mediterranean background.
The 'food quality' hypothesis: Is it the protein or where it comes from?
The authors propose that the island-city difference might be due to a 'diet-environmental interaction' related to the quality of foods consumed and nutrient sources. Islanders may eat more fresh fish, local dairy, and traditional Mediterranean foods, while urban dwellers may rely on processed or lower-quality protein and carbs. The study's data couldn't measure this directly, but it's a compelling explanation for the geographic discrepancy.
People often obsess over macro percentages (protein/carb/fat), but this study suggests the source and quality of those nutrients might matter as much—or more—than the quantity.
The Successful Aging Index: More than just avoiding disease
The study didn't just look at chronic diseases. The Successful Aging Index (SAI) includes 10 health-related social, lifestyle, and clinical characteristics—like social engagement, cognitive function, and mobility. The link with protein/carb patterns was found for this broader measure of aging well, not just avoiding illness.
This moves the conversation from 'surviving' to 'thriving' in old age. People want to not just live longer, but live better, and diet may play a role in that broader picture.
The effect sizes are tiny—so why does it matter?
The regression coefficients (B = -0.08 and B = 0.06) are small, and the p-values are borderline (0.04). The authors themselves note the cross-sectional design can't prove causation. Yet the highly significant interaction (p < 0.001) suggests the pattern is real, even if the individual effects are modest. It's a clue, not a conclusion.
In nutrition research, when a small effect appears consistently in a subgroup and there's a plausible biological mechanism, it can still generate valuable hypotheses for future studies—and for individuals, even small changes in aging outcomes can be meaningful.
Want the whole report?
Detailed mode opens the full scientific breakdown — every score component, the methodology, conflicts of interest, the evidence analysis behind each claim, and the raw study data.
Overview
What the study found
The study in plain English — the bottom line, every takeaway we extracted, and what to do with them.
Not medical advice. For informational purposes only. Always consult a healthcare professional. Terms
Scientists wanted to know if the amount of protein and carbs people eat is linked to how healthy they are as they get older. They asked older Greek people about their eating habits and gave them a 'healthy aging' score.
Research results
For older people living on Greek islands, those who ate low protein and lots of carbs had slightly lower healthy aging scores. Those who ate high protein and lots of carbs had slightly higher scores. For people in cities, there was no clear link.
What this means - more context
The differences were small, could be due to chance or other factors, and this study cannot prove that eating more protein causes healthier aging.
To investigate the association between protein and carbohydrate intake levels and successful aging in older Greek adults.
In a combined cross-sectional analysis of the ATTICA (urban) and MEDIS (insular) studies, the association between protein-carbohydrate dietary patterns and a validated Successful Aging Index (SAI) was assessed. Among older adults living in insular areas, a low-protein/high-carbohydrate diet was associated with lower SAI scores (B = -0.08, p = 0.04) and a high-protein/high-carbohydrate diet was associated with higher SAI scores (B = 0.06, p = 0.04), compared to a low-protein/low-carbohydrate diet. No significant associations were observed in urban participants (all p > 0.05), and the diet-by-area interaction was significant (p < 0.001). The authors concluded that a high-protein diet appears beneficial for older islanders and hypothesized a diet-environmental interaction related to food quality.
Methods Used
Combined cross-sectional analysis of two epidemiological studies (ATTICA and MEDIS). Dietary intake was assessed via food frequency questionnaires, and successful aging was measured using a validated 10-item Successful Aging Index (SAI). Linear regression models examined associations of protein-carbohydrate dietary patterns with SAI, stratified by study area (insular vs. urban).
Main Finding
In insular areas, a low-protein/high-carbohydrate diet was associated with lower SAI (B = -0.08, p = 0.04) and a high-protein/high-carbohydrate diet with higher SAI (B = 0.06, p = 0.04) versus low-protein/low-carbohydrate. No significant associations were found in urban areas (all p > 0.05); diet-by-area interaction p < 0.001.
Confidence Level
Moderate. Cross-sectional design cannot establish causality; self-reported dietary intake; subgroup-specific findings with marginal p-values.
Study Flags
Red Flags
- •Cross-sectional design cannot establish causality
- •Subgroup-specific findings may not generalize
- •Potential confounding by lifestyle and health status
No biological mechanisms were identified in this study. This may be an epidemiological, observational, or survey-based study that reports associations rather than proposing causal biological pathways.
Surprising Findings
A high-protein, high-carbohydrate diet was associated with higher successful aging scores among older Greek islanders, while a low-protein, high-carbohydrate diet was associated with lower scores—but neither association existed in urban participants.
Most diet advice pits protein against carbs. This study found that combining protein with carbohydrates was beneficial in one group. And the geographic difference is unexpected—the same dietary pattern had no effect in cities.
Practical Takeaways
If you're an older adult, don't drastically cut carbs or protein based on fads. Instead, focus on getting enough protein from quality sources—like fish, dairy, and legumes—especially if you live in a rural or island community where traditional food is accessible.
RCT reviewsReviews of RCTs (Meta-analyses)
Max 100Randomized TrialsRandomized Trials
Max 90Reviews of Cohort StudiesReviews of Cohort Studies
Max 85Cohort StudiesCohort Studies
Max 72Reviews of Case-Control StudiesReviews of Case-Control Studies
Max 63Case-Control StudiesCase-Control Studies
Max 58Cross-Sectional & Case SeriesCross-Sectional & Case Series
Max 50Expert OpinionExpert Opinion
Max 533 / 100
Probability of being correct
Snapshots of a population at a single point in time, or descriptions of small groups. Can identify correlations and prevalence, but cannot determine cause and effect.
Human Cross-Sectional
Subject
Lower probability
on the GRADE evidence scale
This study is like taking a picture at one moment. It asked older people what they usually eat and also checked how healthy they were at that same time. But because it didn't follow people over time or assign their diets, we can't tell if the food made them healthier or if some other reason explains the link. So we can only say the two things are connected, not that one caused the other.
Strengths
- Combined analysis of two well-established epidemiological cohorts (ATTICA and MEDIS), increasing sample size and diversity.
- Use of a validated multi-component Successful Aging Index (SAI) based on health, social, and lifestyle characteristics.
- Explicitly tested for interaction between diet and study area (insular/urban), providing nuance.
Weaknesses
- Cross-sectional design so no temporal relationship can be established.
- Dietary intake measured at a single time point, likely leading to measurement error and recall bias.
- Residual confounding by unmeasured lifestyle, socioeconomic, and health factors.
Methodology
Evidence Keywords
Statistical Reporting
Not medical advice. For informational purposes only. Always consult a healthcare professional. Terms
Scientists wanted to know if the amount of protein and carbs people eat is linked to how healthy they are as they get older. They asked older Greek people about their eating habits and gave them a 'healthy aging' score.
Research results
For older people living on Greek islands, those who ate low protein and lots of carbs had slightly lower healthy aging scores. Those who ate high protein and lots of carbs had slightly higher scores. For people in cities, there was no clear link.
What this means - more context
The differences were small, could be due to chance or other factors, and this study cannot prove that eating more protein causes healthier aging.
To investigate the association between protein and carbohydrate intake levels and successful aging in older Greek adults.
In a combined cross-sectional analysis of the ATTICA (urban) and MEDIS (insular) studies, the association between protein-carbohydrate dietary patterns and a validated Successful Aging Index (SAI) was assessed. Among older adults living in insular areas, a low-protein/high-carbohydrate diet was associated with lower SAI scores (B = -0.08, p = 0.04) and a high-protein/high-carbohydrate diet was associated with higher SAI scores (B = 0.06, p = 0.04), compared to a low-protein/low-carbohydrate diet. No significant associations were observed in urban participants (all p > 0.05), and the diet-by-area interaction was significant (p < 0.001). The authors concluded that a high-protein diet appears beneficial for older islanders and hypothesized a diet-environmental interaction related to food quality.
Methods Used
Combined cross-sectional analysis of two epidemiological studies (ATTICA and MEDIS). Dietary intake was assessed via food frequency questionnaires, and successful aging was measured using a validated 10-item Successful Aging Index (SAI). Linear regression models examined associations of protein-carbohydrate dietary patterns with SAI, stratified by study area (insular vs. urban).
Main Finding
In insular areas, a low-protein/high-carbohydrate diet was associated with lower SAI (B = -0.08, p = 0.04) and a high-protein/high-carbohydrate diet with higher SAI (B = 0.06, p = 0.04) versus low-protein/low-carbohydrate. No significant associations were found in urban areas (all p > 0.05); diet-by-area interaction p < 0.001.
Confidence Level
Moderate. Cross-sectional design cannot establish causality; self-reported dietary intake; subgroup-specific findings with marginal p-values.
Study Flags
Red Flags
- •Cross-sectional design cannot establish causality
- •Subgroup-specific findings may not generalize
- •Potential confounding by lifestyle and health status
No biological mechanisms were identified in this study. This may be an epidemiological, observational, or survey-based study that reports associations rather than proposing causal biological pathways.
Surprising Findings
A high-protein, high-carbohydrate diet was associated with higher successful aging scores among older Greek islanders, while a low-protein, high-carbohydrate diet was associated with lower scores—but neither association existed in urban participants.
Most diet advice pits protein against carbs. This study found that combining protein with carbohydrates was beneficial in one group. And the geographic difference is unexpected—the same dietary pattern had no effect in cities.
Practical Takeaways
If you're an older adult, don't drastically cut carbs or protein based on fads. Instead, focus on getting enough protein from quality sources—like fish, dairy, and legumes—especially if you live in a rural or island community where traditional food is accessible.
RCT reviewsReviews of RCTs (Meta-analyses)
Max 100Randomized TrialsRandomized Trials
Max 90Reviews of Cohort StudiesReviews of Cohort Studies
Max 85Cohort StudiesCohort Studies
Max 72Reviews of Case-Control StudiesReviews of Case-Control Studies
Max 63Case-Control StudiesCase-Control Studies
Max 58Cross-Sectional & Case SeriesCross-Sectional & Case Series
Max 50Expert OpinionExpert Opinion
Max 533 / 100
Probability of being correct
Snapshots of a population at a single point in time, or descriptions of small groups. Can identify correlations and prevalence, but cannot determine cause and effect.
Human Cross-Sectional
Subject
Lower probability
on the GRADE evidence scale
This study is like taking a picture at one moment. It asked older people what they usually eat and also checked how healthy they were at that same time. But because it didn't follow people over time or assign their diets, we can't tell if the food made them healthier or if some other reason explains the link. So we can only say the two things are connected, not that one caused the other.
Strengths
- Combined analysis of two well-established epidemiological cohorts (ATTICA and MEDIS), increasing sample size and diversity.
- Use of a validated multi-component Successful Aging Index (SAI) based on health, social, and lifestyle characteristics.
- Explicitly tested for interaction between diet and study area (insular/urban), providing nuance.
Weaknesses
- Cross-sectional design so no temporal relationship can be established.
- Dietary intake measured at a single time point, likely leading to measurement error and recall bias.
- Residual confounding by unmeasured lifestyle, socioeconomic, and health factors.
Methodology
Evidence Keywords
Statistical Reporting
Scoring
How strong is this study?
Imagine comparing two groups of kids: those who eat vegetables and those who don't, but you just look at them once. Even if veggie-eaters are taller, there might be other reasons they're taller, like having parents who are tall. This study is similar—it looks at many people at one time, so there could be other reasons for the healthy aging besides diet. It's not a strong experiment where you randomly give people different diets, so we should be cautious about trusting the results.
0 / 100
- COI disclosureconflicts of interest not disclosed
- Data availabilitydata not shared
- Code availabilitycode not shared
0 / 100
- Randomizationnot randomized
- Blindingblinding unclear
- Control groupno control group
- Sample sizeno sample size reported
- Follow-upno follow-up reported
100 / 100
54 / 100
- P-values+15/15
- Effect size+20/20
- Confidence intervalsno confidence intervals
- Pre-registrationnot pre-registered
Each component is scored out of 100 and then capped by the study design — a case series cannot reach the ceiling a randomised trial can, however well it is reported.
Where it sits
RCT reviewsReviews of RCTs (Meta-analyses)
Max 100Randomized TrialsRandomized Trials
Max 90Reviews of Cohort StudiesReviews of Cohort Studies
Max 85Cohort StudiesCohort Studies
Max 72Reviews of Case-Control StudiesReviews of Case-Control Studies
Max 63Case-Control StudiesCase-Control Studies
Max 58Cross-Sectional & Case SeriesCross-Sectional & Case Series
Max 50Expert OpinionExpert Opinion
Max 533 / 100
Probability of being correct
Snapshots of a population at a single point in time, or descriptions of small groups. Can identify correlations and prevalence, but cannot determine cause and effect.
This design cannot establish causation — the findings describe an association, not a cause. Cross-sectional design assesses exposure and outcome simultaneously, so temporal sequence cannot be determined. There is no randomization or control group, and unmeasured confounding may explain observed associations. Therefore, causal inference is not possible.
COI Unknown
Could not determine conflict of interest status
No conflict of interest or funding statements are available in the provided text. The study appears to be a cross-sectional analysis of two epidemiological studies, but full disclosures are not accessible.
The article is behind a paywall; only abstract and references were provided. No funding or COI declarations were visible.