Graphical abstract
Keywords: Protein intake, Major adverse cardiovascular events, Age
Abstract
Background
The association between high protein intake and cardiovascular diseases (CVD) was conflicting and the role of age in this relationship was rarely studied. This study aimed to examine the association of high protein diet with the risk of CVD and the interaction of age in this association.
Methods
Participants from UK biobank (2007–2023) with at least 1 dietary questionnaire and without history of chronic diseases at baseline were included. High-protein diet was defined as protein intake ≥1.8 g·kg−1·day−1. The primary outcome was major adverse cardiovascular events (MACE). Cox regression analyses and restricted cubic spline (RCS) regression analyses were performed.
Results
Among 19420 participants, the median (IQR) age was 54 (14) years and 14137 (72.8%) were women. With a follow-up of 256258.9 person-years, 967 MACEs occurred. After adjusting for sociodemographic and cardiovascular risk factors, participants with high-protein intake had higher risk of MACE compared with participants with low-protein intake (HR = 1.21, 95% CI, 1.02–1.44, P = 0.027). High-protein diet was also associated with higher risk of all-cause mortality, heart failure, myocardial infarction and CVD death (HR = 1.39, 95% CI, 1.17–1.65, P < 0.001; HR = 1.43, 95% CI, 1.07–1.92, P = 0.015; HR = 1.50, 95% CI, 1.07–2.10, P = 0.019; HR = 1.73, 95% CI, 1.12–2.65, P = 0.013, respectively). Among participants aged over 55 years, high protein intake was associated with higher risk of MACE (HR = 1.36, 95% CI, 1.13-1.63, P = 0.001). Whereas, among participants younger than 55 years, the association was not significant (HR = 0.75, 95% CI, 0.51–1.11, P = 0.099, Pinteraction = 0.003). Similar interaction between age and high protein diet was witnessed in the association of high protein intake and stroke (Pinteraction = 0.019).
Conclusions
Higher protein intake was related to higher incidence of MACE in participants over 55 years old, but the association was not evident in their counterparts younger than 55 years old.
1. Introduction
The prevalence of cardiovascular disease (CVD) nearly doubled from 271 million in 1990 to 523 million in 2019 around the world [1]. Although great improvements have been achieved in cardiovascular care under active prevention and treatment in recent decades, CVD remains the leading cause of death worldwide [2]. Therefore, the prevention and treatment of CVD is still urgent. Despite control of traditional risk factors including cigarette smoking, hypertension, hypercholesterolemia, etc., diet patterns should also be taken into account [3,4]. It is widely acknowledged that high carbohydrate and fat diet is an important risk factor in the incidence of CVD and CVD mortality [4,5], in which excessive intake of protein may play a crucial role.
Prior studies showed a favorable effect of high protein diets (energy from protein/total energy >20%) on body weight, blood lipid, blood pressure and blood glucose [[6], [7], [8]]. It was proved that moderately increasing consumption of protein to 1.6 g/kg body weight per day was good for improving physical performance [9]. However, the impact of high protein intake on long-term health outcomes was inconsistent. Some studies showed a favorable association between intake of high protein and cardiovascular disease [10,11], while others didn’t get the positive results [12,13], even the fully contrary [14,15]. Moreover, Narasaki, et al. observed that a higher dietary protein intake was associated with a decreased mortality risk in younger participants (<65 years old) but not in older participants (≥65 years old) with normal kidney function [16]. However, few studies have clarified the interaction between age and protein intake in the incidence of CVD. These knowledge gaps impeded a more detailed guidance on dietary protein to prevent CVD.
Therefore, by analyzing data from UK biobank, we prospectively investigate the relationship between high protein diet and incident of cardiovascular events, and whether these associations vary between age groups.
2. Methods
2.1. Study design
UK biobank (UKB) was a prospective cohort study that recruited 502,131 participants who were aged 37–73 years from general population from 2007 to 2010. Participants completed a self-administered, touch screen questionnaire and face-to-face interview in one of 22 assessment centers across England, Scotland and Wales. Details of the UKB cohort have been described elsewhere [17].
Age was recorded based on the date of birth and date of attending an initial assessment center. Physical measurements including height, weight, and blood pressure were taken by trained staff, while ethnicity, smoking status, alcohol intake and marital status were self-reported. Physical activity was measured by self-reported total metabolic equivalent of task (MET) in a week [18]. Townsend deprivation index (TDI) was derived from postcode of residence using aggregated data on unemployment, car and home ownership, and household overcrowding [19]. Low density lipoprotein-Cholesterol (LDL-C) was measured by enzymatic protective selection analysis using an automatic biochemical analyzer. Body mass index (BMI) was calculated as weight (kg)/height (m)2. Only the assessment of above variables at baseline was used.
UK Biobank received ethical approval from the North West Centre for Research Ethics Committee and all participants provided electronically signed consent for their data to be used in health-related research.
2.2. Assessment of protein intake
Information of dietary intake was collected using the Oxford WebQ (www.ceu.ox.ac.uk/research/oxford-webq), a web-based 24 -h dietary assessment tool used for collecting information on the quantities of all food and drinks consumed over the previous day and automatically calculates daily nutrient intakes. Participants were invited to complete the Oxford WebQ on five occasions between 2009 and 2012. We used the average measure for individuals. The assessment of typical diet was collected by questionnaire "Would you say that what you ate and drank yesterday was fairly typical for you?" [20]. Those whose answer was “Yes” were identified as typical diet. Previous study showed the protein intake of 1.6-1.8 g·kg−1·day−1 was recommended for the athlete [21]. Thus, we defined high protein intake as dietary protein equal or more than 1.8 g·kg−1·day−1, and low protein intake as dietary protein less than 1.8 g·kg−1·day−1.
2.3. Participants
Among participants who returned questionnaires, we included those who completed at least one typical dietary questionnaire. We excluded: (1) who reported implausible energy intake level (less than 800 kcal/d or more than 2.5 times estimated energy requirement calculated by Jeor Equation); (2) who died within one year after recruitment; (3) 17.7% participants with physical activity missing; (4) who had a history of heart failure (HF), myocardial infarction (MI), stroke and chronic kidney disease (CKD) at baseline. Disease history was identified with international classification of diseases, 10th revision (ICD-10) (Table S1 in Supplementary material 1).
2.4. Outcomes
The primary outcome was major adverse cardiovascular events (MACE), which was defined as a component of HF, MI, stroke and cardiovascular death. Information on the MACE was obtained from cumulative hospital inpatient records, death certificates in the national death registries, and self-reports from interviews during follow-up. These events were identified with ICD-10 (Table S1 in Supplementary material 1). The secondary outcomes were all-cause mortality and the individual component of MACE.
2.5. Statistical analysis
To address potential confounding by indication, we used propensity score matching (PSM). Propensity scores were derived from a logistic regression model that included daily carbohydrate and fat intake, age, sex, ethnicity, BMI, marital status, alcohol drinking, smoking status, TDI, physical activity, systolic blood pressure (SBP), LDL-C, medical history (diabetes and cancer). We performed 1:4 nearest-neighbor matching. Balance was assessed using standardized mean differences (SMD < 0.10 indicating good balance). All outcome analyses were performed on the matched cohort.
Patients were classified as high and low protein group. Continuous variables were expressed as means and standard deviations (SDs) and compared by the t-test or medians and interquartile range (IQRs) and compared by the Kruskal–Wallis test. Categorical variables were summarized by frequencies with percentages and compared by the chi-square test for binary variables and the chi-square test for multiclass variables.
We calculated person-time of follow-up for each participant from the age in days at the date of recruitment (April 2007) until the date of outcome, loss to follow-up, or end of follow-up (July 1st, 2023 in this analysis), whichever came first. Kaplan–Meier curve was used for describing the cumulative incidence of Outcomes and MACE in different age groups during the follow-up period. Cox proportional hazards regression models were used with exposure variables fitted on restricted cubic splines to estimate the hazard ratio (HR) and 95% confidence internal (CI) of MACE associated with protein intake.
In a minimal adjusted model, we adjusted Cox proportional hazard model for age and sex. In the fully adjusted model, carbohydrate and fat intake, total energy, intake of added sugar, ethnicity, BMI, marital status, alcohol drinking, smoking status, TDI, physical activity, SBP, LDL-C and medical history of diabetes and cancer were further adjusted. In the analysis of components of MACE, we excluded individuals who reported prevalent history of relevant diseases at baseline. To explore the interaction effect between age and daily protein intake, an interaction model with covariables fully adjusted was used. Restricted Cubic Splines was used to show the association between high protein intake and incidence of MACE.
Sensitivity analysis was performed based on different cutoff values (1.5, 1.6, 1.7, 1.9, 2.0) of protein intake (g·kg−1·day−1) to evaluate the association. We also explore the influence of plant protein ratio on incidence of MACE by using bubble heat map and Restricted Cubic Splines. Multivariable Cox proportional hazards regression analyses were repeated after stratifying patients into different subgroups as follows: (1) age ≤55 years or >55 years; (2) men or women; (3) BMI ≥ 18.5 kg/m2 or <18.5 kg/m2; (4) participants with or without chronic diseases (including cancer, COPD and diabetes); (5) proportion of plant protein (<50% or ≥50%); (6) physical activity per week (<1746 min. per week or ≥1746 min. per week). Furthermore, we included those who completed at least two dietary questionnaires to explore the relationship between daily protein intake and MACE.
To explore the role of age in the association of protein intake and outcomes, we stratified participants into aged over 55 years or younger than 55 years. We tested the possible effect modification by implementing the “protein*age” into the multivariate model. Restricted Cubic Splines were used to show the association between high protein intake and incident of MACE in different age groups.
The rates of missing values for variables ranged from 0.04% (alcohol drinking) to 5.73% (LDL-C). We used multiple imputation by the Markov chain Monte Carlo method based on various demographic and clinical variables. All statistical tests were 2-tailed (α = 0.05). Statistical analyses were performed using R Statistical Software (version 4.3.2), with the package survival and GraphPad Prism Software (version 10.0.0).
3. Results
3.1. Baseline characteristics
A total of 149251 participants met inclusion criteria. Among them, 3884 participants had high protein intake. After PSM, 15536 participants were identified in the low protein intake group (Figure S1 in Supplementary material). Table S2 and Table 1 showed the baseline characteristics of participants according to daily protein intake before and after PSM. Good balance was achieved after PSM (Figure S2 in Supplementary material). There were 14137 (72.8%) females and 9841 (50.7%) participants younger than 55 years old. Daily protein intake (Mean ± SD) was 1.29 ± 0.27 g/kg BW for low protein intake group, and 2.03 ± 0.24 g/kg BW for high protein intake group after matching. Distribution of the number of completed dietary questionnaires per participant was shown in Figure S3 in Supplementary material. Classification of protein sources were shown in Table S3 in Supplementary material. Compared with low protein intake group, participants in high protein intake group consumed more carbohydrate and fat, and had more total energy intake.
Table 1.
Baseline characteristics of participants with high and low protein intake after propensity score matching.
| Low protein intake group a (N = 15536) | High protein intake group (N = 3884) | SMD | |
|---|---|---|---|
| Sex (%) | 0.017 | ||
| Female | 11286 (72.6) | 2851 (73.4) | |
| Male | 4250 (27.4) | 1033 (26.6) | |
| Age (years, %) | 0.019 | ||
| ≤55 | 7843 (50.5) | 1998 (51.4) | |
| >55 | 7693 (49.5) | 1886 (48.6) | |
| BMI, mean (SD), kg/m [2] | 23.5 (3.1) | 23.4 (3.5) | 0.017 |
| Married (%) | 5431 (35.0) | 1344 (34.6) | 0.007 |
| Ethnicity (%) | 0.033 | ||
| White | 14218 (91.5) | 3518 (90.6) | |
| Other | 1318 (8.5) | 366 (9.4) | |
| TDI, mean (SD) | −1.63 (2.83) | −1.62 (2.88) | 0.004 |
| Smoking status (%) | 0.016 | ||
| Never | 8692 (55.9) | 2156 (55.5) | |
| Previous | 5780 (37.2) | 1447 (37.3) | |
| Current | 1064 (6.8) | 281 (7.2) | |
| Alcohol drinking (%) | 0.008 | ||
| Never | 429 (2.8) | 110 (2.8) | |
| Previous | 469 (3.0) | 113 (2.9) | |
| Current | 14638 (94.2) | 3661 (94.3) | |
| SBP, mean (SD), mmHg | 136.5 (18.2) | 136.4 (18.4) | 0.006 |
| DBP, mean (SD), mmHg | 78.6 (9.8) | 78.4 (9.9) | 0.022 |
| LDL-C, mean (SD), mmol/L | 3.57 (0.85) | 3.57 (0.87) | < 0.001 |
| Physical activity, mean (SD), min | 2998.6 (2760.6) | 3054.9 (2793.4) | 0.020 |
| Medical History (%) | |||
| Diabetes | 409 (2.6) | 109 (2.8) | 0.011 |
| Cancer | 1381 (8.9) | 344 (8.9) | 0.001 |
| COPD | 135 (0.9) | 56 (1.4) | 0.054 |
| Energy, mean (SD), kcal/d | 2423.0 (552.4) | 2631.3 (519.6) | 0.388 |
| Protein (absolute), mean (SD), g/day | 86.1 (20.7) | 129.4 (28.8) | 1.722 |
| Protein, (mean (SD), g·kg−1·day−1 | 1.29 (0.27) | 2.03 (0.24) | 2.858 |
| Fat, mean (SD), g/day | 96.0 (29.9) | 98.4 (30.0) | 0.081 |
| Carbohydrate, mean (SD), g/day | 296.6 (78.0) | 298.8 (83.7) | 0.027 |
| Added sugar, median (IQR), g/day | 6 (18) | 0 (18) | 0.091 |
Abbreviations: BMI, body mass index; COPD, chronic obstructive pulmonary disease; DBP, diastolic blood pressure; LDL-C, low-density lipoprotein-cholesterol; SBP, systolic blood pressure; DBP diastolic blood pressure, TDI, Townsend deprivation index.
Low protein intake group: daily protein intake <1.8 g/kg body weight. High protein intake group: daily protein intake ≥1.8 g/kg body weight.
3.2. Association of protein intake and outcomes
During 256,258.9 person-years of follow-up, 211 MACEs occurred in the high-protein group and 756 MACEs occurred in the low-protein group. In the Kaplan-Meier analysis, participants with high-protein diet had higher risk of HF, CVD death and all-cause death than their counterparts with low-protein diet (Fig. 1). High protein diet was associated with higher risk of MACE than low protein diet in the fully adjusted model (HR = 1.21, 95%CI 1.02–1.44, P = 0.027) (Table 2). In the Restricted Cubic Splines, higher protein intake was associated with higher risk of MACE when daily protein intake was beyond 1.8 g/kg BW (Fig. 2). Participants with high-protein intake witnessed higher risk of HF (HR = 1.43, 95%CI 1.07–1.92, P = 0.015), MI (HR = 1.50, 95%CI 1.07–2.10, P = 0.019), CVD death (HR = 1.73, 95%CI 1.12–2.65, P = 0.013) and all-cause death (HR = 1.39, 95%CI 1.17–1.65, P < 0.001) compared with participants with low-protein intake (Table 2).
Fig. 1.
Kaplan-Meier curves for outcomes between high and low protein intake groups.
Participants were divided into two groups based on daily protein intake (cutoff value: 1.8 g/kg BW). High protein diet was associated with higher incidence of HF, CVD death and all-cause death (B, C and F), while no significant association was found between high protein diet and incidence of MACE, MI and stroke (A, D and E).
Table 2.
The associations between high protein intake and outcomes.
| Outcome | Total (n) | Event (n) | HR (95% CI) | P value |
|---|---|---|---|---|
| MACE | ||||
| Crude | 19420 | 967 | 1.13 (0.97,1.31) | 0.121 |
| Minimal | 19420 | 967 | 1.18 (1.01,1.37) | 0.035 |
| Multivariate | 19420 | 967 | 1.21 (1.02,1.44) | 0.027 |
| All-cause mortality | ||||
| Crude | 19760 | 903 | 1.27 (1.09,1.48) | 0.002 |
| Minimal | 19760 | 903 | 1.33 (1.14,1.54) | <0.001 |
| Multivariate | 19760 | 903 | 1.39 (1.17,1.65) | <0.001 |
| HF | ||||
| Crude | 19730 | 321 | 1.32 (1.02,1.70) | 0.035 |
| Minimal | 19730 | 321 | 1.38 (1.07,1.77) | 0.014 |
| Multivariate | 19730 | 321 | 1.43 (1.07,1.92) | 0.015 |
| MI | ||||
| Crude | 19610 | 263 | 1.14 (0.85,1.52) | 0.393 |
| Minimal | 19610 | 263 | 1.17 (0.87,1.57) | 0.291 |
| Multivariate | 19610 | 263 | 1.50 (1.07,2.10) | 0.019 |
| Stroke | ||||
| Crude | 19580 | 538 | 1.14 (0.93,1.40) | 0.209 |
| Minimal | 19580 | 538 | 1.18 (0.96,1.45) | 0.113 |
| Multivariate | 19580 | 538 | 1.17 (0.93,1.47) | 0.190 |
| CVD death | ||||
| Crude | 19760 | 134 | 1.60 (1.10,2.33) | 0.014 |
| Minimal | 19760 | 134 | 1.70 (1.17,2.47) | 0.006 |
| Multivariate | 19760 | 134 | 1.73 (1.12,2.65) | 0.013 |
Abbreviations: MI, myocardial infarction; CVD, cardiovascular disease; HF, heart failure; MACE, major adverse cardiovascular event.
In each outcome except CVD death and all-cause death, we exclude corresponding medical history and use propensity score matching again.
In crude model, no covariable was adjusted.
In minimal model, age (categorical) and sex was adjusted.
In multivariate model, age (categorical), sex, BMI, ethnicity, marital status, smoking status, alcohol drinking, carbohydrate intake, fat intake, total energy intake, added sugar intake, TDI, SBP, LDL-C, physical activity and medical history (diabetes and cancer) were adjusted.
Fig. 2.
Influence of high protein diet on MACE in overall population.
The reference for hazard ratio was used when protein intake was 1.8 g/kg BW. When daily protein intake was over 1.8 g/kg, risk of MACE was elevated in overall population. Abbreviation: BW, body weight.
In the sensitivity analysis, high protein diet was associated with higher risk of MACE was detected in participant whose BMI was over 18.5 kg/m2 (HR = 1.19, 95% CI 1.00–1.43, P = 0.048) and participants without any chronic disease (HR = 1.27, 95% CI 1.05–1.55, P = 0.014) (Table S4 in Supplementary material). When cutoff value of dietary protein intake was set as 1.6 and 1.9 g/kg BW, participants with high-protein intake was still associated with higher risk of MACE compared with participants with low-protein intake (HR = 1.13, 95% CI 1.00–1.28, P = 0.043; HR = 1.25, 95% CI 1.02–1.54, P = 0.035, respectively) (Table S5 in Supplementary material). However, we didn’t find clear relationship in protein source analysis, in which the association was not statistically significant between plant protein ratio and MACE (Figure S4 and Figure S5 in Supplementary material).
3.3. Age differences in the association of protein intake and outcomes
In the Kaplan–Meier analysis of participants aged 55 years or older, higher risk of MACE was witnessed in participants with high-protein diet (Figure S6 in Supplementary material). In the fully adjusted interaction model, association between high-protein intake and MACE was not significant among participants younger than 55 years old (HR = 0.75; 95% CI, 0.51–1.11, P = 0.099). Among participants over 55 years old, high protein diet was associated with higher risk of MACE (HR = 1.36; 95% CI, 1.13–1.63, P = 0.001) (Fig. 3). However, in age subgroup analysis, the association was not significant in participants over 55 years old in the Cox regression (Table S4 in Supplementary material) and the Restricted Cubic Splines (Figure S7A and S7B in Supplementary material). We found an interaction effect of age between daily protein intake and MACE (P for interaction = 0.003) (Fig. 3). Interaction effect of age was also found in the association of protein intake and stroke (P for interaction = 0.019) (Fig. 3).
Fig. 3.
Interaction of age in the association of protein intake and outcomes.
Abbreviations: MI, myocardial infarction; CVD, cardiovascular disease; HF, heart failure; MACE, major adverse cardiovascular event.
In the sensitive analysis, the above interaction effect of age in the association of protein intake and MACE was observed in subgroups of female (P for interaction = 0.008), participants whose BMI was over 18.5 kg/m2 (P for interaction = 0.018), participants without chronic diseases (P for interaction = 0.021) and participants whose weekly physical activity time was over 1746 min per week (P for interaction = 0.018) (Table S6 in Supplementary material). Among participants completed at least 2 diet questionnaires, the interaction effect between age and protein intake was also significant (P for interaction = 0.004) (Table S6 in Supplementary material). Finally, similar interaction effect between age and dietary protein was also witnessed when the cutoff value of dietary protein was set as 1.9 g/kg BW (P for interaction = 0.010) (Table S7 in Supplementary material).
4. Discussion
In this large prospective cohort study of 19420 participants from UKB followed for 13.2 years on average, we found that high protein intake was associated with higher risk of MACE in the fully adjusted model. Of note, the risk of HF, MI, CVD death and all-cause death were significantly higher in participants with high-protein diet than participants with low-protein diet. Moreover, we observed an interaction effect between age and protein intake, with high protein intake associated with 36% higher risk of MACE in participants over 55 years old, but the association was not evident in the counterparts younger than 55 years old. The results were largely consistent in a series of sensitivity and subgroup analysis.
The strengths of this study included the large sample size from a community-based cohort, repeated dietary assessments and the long-term follow-up. With a wide range of age, we have the capacity to detect the interaction effect of age in the association of protein intake and MACE. Besides, baseline characteristics are sufficiently comparable between the two groups in the cohort after PSM, which can largely avoid heterogeneity between groups. The study provides a reliable threshold of high protein intake for reference, which is essential in nutrition recommendation for the general population.
Prior studies on the association between high protein diet and cardiovascular diseases have yielded conflicting results [10,11,14,[22], [23], [24], [25], [26], [27]]. In a Swedish cohort, women with high protein score were associated with higher incidence of total CVD, ischemic heart disease and ischemic stroke [11]. Nynke Halbesma, et al. also found high protein intake was associated with a higher incidence of cardiovascular events in the general population [10]. These results were in consistent with our findings and we further identified the harmful effect of high protein diet (over 1.8 g/kg BW) in the incidence of MACE. Notably, high-protein diet was associated with 73% higher risk of CVD death and 43% higher risk of HF compared with low-protein diet. However, in a cohort of community-dwelling adults, no significant association was found between total protein intake and CVD motality [23]. The narrow range of age of the cited study may account for its negative finding. In this study, we found an interaction effect between age and protein intake in MACE, which is less reported in prior studies. In a study of 8461 individuals who did not have renal disease, no interaction was found between age and protein intake in the combined incidence of cardiovascular morbidity and cardiovascular mortality [10]. Its evaluation of daily protein intake, which was estimated by the 24-h urea nitrogen appearance and relatively small sample size may lead to the negative finding. However, in the Third National Health and Nutrition Examination Survey (NHANES III), a strongly interaction was found between age and the percentage of calorie from proteins in all-cause mortality [28]. In our study, we firstly identified a significant interaction between age and protein intake in MACE. Individuals aged over 55 years exhibited a pronounced risk of MACE with high protein intake, whereas no significant association of high-protein intake and MACE was observed in younger participants.
The age-stratified harmful effect of high protein intake may involve multiple pathways. Based on the metabolic processes of protein and results from previous literatures, we hypothesized that potential mechanisms may involve the following standpoints. First, high protein diet could increase the concentration of branched-chain amino acids (BCAAs), which was related to inflammation and oxidative stress levels [29], activation of mTOR signaling in macrophage [30,31] and therefore increasing the risk of cardiovascular disease [32,33]. The increased prevalence of constipation with aging [34] may influence the absorption and metabolism of protein in the bowel, which could also enhance the harmful effect of high protein diet. Second, in our cohort, high protein diet was associated with higher risk of COPD (Multivariate HR = 1.29, 95% CI 1.00–1.66, P = 0.047), which was one of the leading causes of heart failure. Third, high protein intake was associated with higher level of serum purine and uric acid [35,36] and hyperuricemia was an essential risk factor of cardiovascular disease [37,38]. Forth, a randomized controlled trial in older man showed increased circulatory concentrations of the microbiome metabolite Trimethylamine-N-Oxide (TMAO) in the high-protein diet group (over 1.6 g/kg BW) [39]. Moreover, advancing age was strongly associated with the level of TMAO [40,41]. It was proved that TMAO were positively associated with risk of MACE [42], by promoting the progression of atherosclerosis [43]. The situation could be worsened because of the difficulty in the clearance of TMAO in the elder [44].
Our findings provide fundamental evidence for recommendations on age-specific protein intake. In previous nutrition guidelines, daily 0.8–1.0 g/kg BW of protein was recommended for the general population [45]. We provided a reliable upper limit of 1.8 g/kg BW for population over 55 years old but we didn’t detect an upper limit for population less than 55 years old. The non-significant association observed in the younger subgroup may be partly attributable to a lower number of events (n = 201), which limited the statistical power to draw definitive conclusions for this specific population. Nutritionist and clinicians should assess dietary patterns holistically, considering the demand of protein, and provide individualized recommendation for protein intake for populations with different age. For specific population who need more protein, especially the elder and cachexia patients, high-protein diet should balance sarcopenia prevention and cardiovascular safety. For those who have already had high-protein diet, early intervention should be taken in the population under high risk. This study is of great importance for future randomized trials on the optimal amount of protein intake.
Our study had also several limitations. First, the generalizability of the findings is limited to a predominantly White (over 90%), healthy baseline population without CKD. Second, the collection of diet information was through 24-h recall questionnaire and recall bias was unavoidable and 36.3% participants had completed only one dietary questionnaire. However, we included participants with at least two completed dietary recalls in the sensitivity analyses, which yielded similar results. Finally, residual and unmeasured confounding factors could be not excluded. Nevertheless, we used the approach of PSM to minimize the confounding and the results were consistent among wide range of sensitivity and subgroup analysis.
In conclusion, high protein diet (daily protein intake ≥1.8 g/kg BW) was related to higher incidence of MACE in participants over 55 years old, but this association is not evident in counterparts younger than 55 years old. Therefore, nutritionists and clinicians should make individualized recommendation for protein intake by taking age into consideration. Additional clinical trials are necessary to validate the optimal protein intake for different populations.
Funding
This study was funded by Key R&D Projects of Guangzhou Science and Technology Program (No. 2023B01J1011 and No. 2023B03J1243), the National Natural Science Foundation of China (No. 82170384, No. 82100273, No. 82100387, No. 82270399, No. 82200415, No. 82370383, No. 82304491, No. 82300429, No. 82400447, No. 82470401 and No. 82000260), Guangdong Basic and Applied Basic Research Foundation (No. 2023A1515010627, No. 2021A1515010755, No. 2022A1515012161 and No. 2020A1515111094), “Kelion New Star” talent plan of The First Affiliated Hospital of Sun Yat-sen University (R08017) and the Non-profit Central Research Institute Fund of Chinese Academy of Medical Sciences (2023-PT320-03).
Ethics approval and consent to participate
UK Biobank received ethical approval from the North West Centre for Research Ethics Committee and all participants provided electronically signed consent for their data to be used in health-related research. All data collection and research use is in accordance with the Declaration of Helsinki.
CRediT authorship contribution statement
Dr Huang and Dr Liu have full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.
Concept and design: Peisen Huang, Chen Liu, Yuan Yu.
Acquisition, analysis, or interpretation of data: All authors.
Drafting of the manuscript: Cheng Huang, Yuan Yu.
Critical review of the manuscript for important intellectual content: All authors.
Statistical analysis: Cheng Huang, Weihao Liang.
Obtained funding: Chen Liu, Peisen Huang, Yuan Yu, Weihao Liang, Yilong Wang, Fangfei Wei, Tianyu Xu, Yu Ning, Zhe Zhen, Wengen Zhu, Yugang Dong.
Supervision: Yuan Yu, Chen Liu, Peisen Huang.
Declaration of Generative AI and AI-assisted technologies in the writing process
Not applicable.
Availability of data and materials
The data used in this study can be derived from the UK biobank (Available at: https://biobank.ctsu.ox.ac.uk/).
Declaration of competing interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgements
Not applicable.
Footnotes
Supplementary material related to this article can be found, in the online version, at doi:https://doi.org/10.1016/j.jnha.2025.100727.
Contributor Information
Chen Liu, Email: liuch75@mail.sysu.edu.cn.
Peisen Huang, Email: huangps3@mail.sysu.edu.cn.
Appendix A. Supplementary data
The following are Supplementary data to this article:
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The data used in this study can be derived from the UK biobank (Available at: https://biobank.ctsu.ox.ac.uk/).