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Healthy Aging with NutritionProtein for healthy aging -
Recruitment was based on 42 randomly selected clusters using census collector districts and stratified by Australian states and territories. Participants with incomplete data were excluded.
A flowchart of the final sample analysed is shown in Fig. The AusDiab study was approved by the International Diabetes Institute ethics committee and Alfred Health ethics committees.
All AusDiab participants provided written informed consent. The current analysis was approved by Deakin University Human Research Ethics Committee Project number — Habitual dietary intake was assessed via self-administration of a item food frequency questionnaire FFQ. The FFQ was developed in Australia to assess the habitual dietary intakes of an ethnically diverse cohort aged 40—years [ 34 ].
Plant protein was calculated by combining soy and vegetable. Dairy protein was calculated by combining full-fat dairy and low-fat dairy. Animal protein was calculated by combining red meat, dairy, fish steamed, grilled, baked, fried, tinned , chicken, butter, eggs, flavoured milk and ice-cream.
Total protein intake was calculated by combining protein from all foods. Health-related quality of life HRQoL was collected via the self-administered SF Version 1 questionnaire used with permission from the Medical Outcomes Trust Boston, MA, USA [ 38 ].
Based on the answers from this question survey, the two summary scores of PCS and MCS were calculated using published guidelines [ 39 , 40 ]. Summary scores were then normalised to have a mean of 50 and standard deviation of 10 across the Australian general population [ 39 , 40 ].
Higher scores indicate better HRQoL. Positive results indicate improved HRQoL. The SF has demonstrated good construct validity, test-retest reliability and internal consistency, and has been validated for use in older adults [ 41 , 42 , 43 ].
Prior history of cardiovascular disease CVD; angina, coronary heart disease, or stroke was obtained by self-reported medical history [ 44 ]. Diet quality was assessed using the Dietary Guideline Index DGI [ 45 ] based on food intakes collected from the item FFQ.
The DGI is a food-based dietary index which assesses dietary intake against the Australian Dietary Guidelines [ 46 ]. Indicators of each dietary guideline were identified, with age and sex specific cut-offs developed.
Two items usually included in the DGI fluid intake and limiting intake of salty food were not included, as the FFQ did not collect this data. Adherence was scored from 0 not meeting recommendation to 10 fully meeting recommendation. Total scores ranged from 0 to , with higher scores indicating greater diet quality.
Height was measured without shoes to the nearest 0. Weight was measured to the nearest 0. Body mass index BMI was calculated as weight in kilograms divided by height in meters squared. Physical activity level was assessed using the validated Active Australia survey [ 48 , 49 ].
Time spent performing leisure time physical activity duration and frequency was reported over the preceding week. Because vigorous-intensity activity is commonly considered to contribute additional health benefits, double the time spent in vigorous physical activity is used when creating insufficient and sufficient categories of physical activity.
Data for all potential confounders were collected at baseline, except household type which was collected at the year follow-up. Differences between included and excluded participants were assessed using independent sample t —tests for continuous variables and chi-squared tests for categorical variables.
Changes in HRQoL from baseline to the year follow-up were assessed using paired t -tests. The interaction of the relationship between protein intakes and HRQoL by sex was assessed using linear regression.
Protein intake cut-points were chosen based on the recommendation from the PROT-AGE study group that adults aged over years consume dietary protein of at least 1. Directed acyclic graphs [ 51 ] were used to assist with the identification of key confounders based on assumed directions of associations between covariates, the exposure and the outcome Supplemental Fig.
Model 2 included all confounders included in model 1 plus BMI the direction of the relationship between protein intake and BMI is unclear i.
protein intake may influence BMI or BMI may influence protein intake. Based on the literature, the presence of diabetes [ 52 ] and CVD [ 53 ] were considered to be on the causal pathway between intakes of dietary protein total protein and different sources of protein and HRQoL, as was diet quality as protein intake and protein source are components of diet quality [ 45 ] and therefore not included as confounding factors in the main model [ 54 ].
However, sensitivity analysis was performed including diet quality and the presence of diabetes and CVD. To adjust for possible over and under reporting of energy intake, the model also included EI:EE.
The possibility of non-linear relationships between protein intakes and year changes in HRQoL was assessed using squared protein intakes.
No evidence of non-linearity was found. Residuals from regression models were assessed for normality and heteroscedasticity using P-P plots and plots of residuals against fitted values, respectively. To determine the robustness of our findings, the following sensitivity analyses were performed.
In the second sensitivity analysis, baseline HRQoL was included in the model as a covariate. In the third sensitivity analysis, participants baseline CVD and diabetes status, together with diet quality, were included in the model as confounders. Statistical analysis was performed using SPSS Software version 25, , IBM Corp.
Baseline characteristics and nutrient intakes of the participants are shown in Table 1. The mean ±SD age of participants was Compared to the participants included in this study, the excluded were older, had a higher BMI, were more likely to be from a rural location, had a higher prevalence of CVD and diabetes, had lower PCS and MCS scores, had lower levels of education, had a higher proportion of current smokers and had lower levels of physical activity Supplemental Table 1.
Analysis of the interaction between sex, protein intake and HRQoL found limited interactions significant in only two of the 20 relationships assessed data not shown.
Therefore, data for males and females were pooled. In the fully adjusted model, higher intakes of animal protein, red meat protein and processed animal protein were associated with detrimental changes in PCS scores. Higher intakes of red meat protein were also associated with detrimental changes in MCS in the fully adjusted model.
Sensitivity analysis supported results from the main analysis. In all sensitivity analyses, detrimental associations between animal and red meat proteins and PCS were confirmed.
The detrimental association between red meat protein and MCS was confirmed in two of the three sensitivity analyses Supplemental Table 2. There were no other changes in results between total protein and HRQoL results not shown. Total dietary protein, dairy protein and plant protein were not associated with changes in HRQoL.
Moreover, there was no difference in changes in HRQoL between participants who exceeded the total recommended protein intake compared with those who met the recommendation and those consuming below the recommended intake.
In this year longitudinal study we found that total dietary protein was not associated with changes in HRQoL. To our knowledge, our study is the first to investigate the long-term association between habitual dietary protein intake with changes in HRQoL.
For instance, Ten Haaf et al. This is of relevance to our study as there is evidence to support a strong association between depression and HRQoL [ 57 ]. A novel finding from our study was that meat-based proteins red meat protein and processed animal protein were associated with detrimental changes in PCS.
Recent evidence suggests several detrimental health outcomes associated with higher meat-based protein intakes. Consumption of processed meat has also been associated with numerous chronic health conditions, including colorectal cancer, coronary heart disease and diabetes [ 59 , 60 , 61 ].
Thus, the presence of chronic conditions could explain, at least in part, the relationships observed between meat-based proteins and the deterioration in PCS in the current study. Another possible explanation for the associations detected between meat-based proteins and detrimental changes in PCS is that the saturated fat associated with meat-based proteins has caused the detrimental effect on PCS.
Diets high in saturated fat produce a less diverse and more inflammatory gut microbiome [ 62 ], and increased systemic inflammation which has been linked to many age-related diseases such as rheumatoid arthritis, sarcopenia muscle loss and osteoporosis [ 63 ]. Thus, it could be hypothesized that higher consumption of saturated fat by consuming higher meat-based proteins may have increased rates of these age-related diseases.
The association between higher saturated fat intake and lower PCS has been observed in previous observational studies [ 64 , 65 ]. The inability to control for saturated fat is a limitation of the findings.
Nevertheless, it is worth noting that despite the significant adverse relationships between increased meat-based protein intake and changes in HRQoL, the associations were modest.
In our study, we found that changes in HRQoL in participants with total protein intakes below recommendations did not differ from those with protein intakes at or above recommendations.
A number of limitations must be considered when interpreting these findings. Firstly, a limitation of this study, as well as previous observational studies on this topic [ 19 , 20 , 21 , 22 ], is the modest sample size of Secondly, although this study included a range of confounders, it is possible residual confounding remained because of unmeasured confounders.
Thirdly, associations were only assessed using baseline protein intakes and confounders. Fourthly, only community-dwelling adults were eligible to participate in the AusDiab study, and thus the results cannot be generalised to other populations.
Fifthly, the study was exploratory in its analysis of a range of protein sources and therefore correction for multiple comparisons was not employed.
Caution should be used when interpreting the results of this study due to the number of associations assessed with no adjustments made for multiple comparisons, which may increase the likelihood of a type I error.
The results of this study provide a hypothesis of associations which need to be corroborated by future research. This suggests our results may only be generalizable to healthier participants. However, it should also be noted that sensitivity analysis revealed only a marginal decrease in the association between protein intake and HRQoL when baseline HRQoL was included in the model.
Despite the low number of participants available for our analysis, there are a number of strengths to the original AusDiab study which is why it was used for our secondary analysis. In addition, the AusDiab study used validated tools to measure dietary data and HRQoL.
We found that meeting recommended daily total protein intakes when expressed as grams per kg did not influence year HRQoL. Our results suggest that clinical advice, to potentially minimise long-term detrimental effects to HRQoL, include recommendations on avoiding animal protein, red meat protein and processed animal protein when choosing proteins to consume.
Dietary guidelines for older adults should consider protein source when advising older adults on protein consumption. The data that support the findings of this study are available from the Australian Diabetes, Obesity and Lifestyle study, contact Prof.
Jonathan Shaw Baker Heart and Diabetes Institute , but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. United Nations Department of Economics and Social Affairs. World Population Prospects: The Revision [Internet].
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Sullivan PW, Ghushchyan V, Wyatt HR, Wu EQ, Hill JO. By Emily Lachtrupp is a registered dietitian experienced in nutritional counseling, recipe analysis and meal plans.
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By Robby Berman on June 13, — Fact checked by Hannah Flynn. Share on Pinterest Moderate protein intake from a range of plant- or animal-based sources could help you live longer, a new study suggests. How does protein affect biological age?
Could protein help humans live longer? Older adults need more protein. The importance of eating protein. How much protein do you need? Share this article.
Close Protein for healthy aging Protwin of people in this group Sports nutrition and mental health get enough of this vital Protein for healthy aging. But hfalthy of those agingg especially important for older adults. Protein helps us maintain and even add muscle. Getting sufficient protein can help reduce the resultant increase in fall risk. Protein also plays a role in creating hormones, enzymes, and neurotransmitters, which your body uses in many ways. The risk of chronic conditions, Immune system vitality as hypertensiondiabetes mellituscancer, chronic Proteon pulmonary disease COPDand coronary heart xgingcan Agiing mitigated through diet Protein for healthy aging lifestyle changes. A new study Proyein Tufts University in Boston suggested Proteih including more protein, particularly Protekn protein, in a dietary pattern during midlife is linked to healthier aging in females. The study, led by researchers at the Jean Mayer USDA Human Nutrition Research Center on Aging HNRCAwas published January 17 in The American Journal of Clinical Nutrition. Kelsey Costaa registered dietitian nutritionist and nutrition consultant for the National Coalition on Healthcare, not involved in the study, commented on the findings to Medical News Today :. The research indicates that plant protein is the most effective in promoting healthy aging and maintaining a positive health status.
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