Abstract
The health benefits of the Planetary Health Diet (PHD) require further validation. We examined associations between PHD adherence and risks of mortality and chronic diseases using data from two prospective cohorts—the US NHANES (1999–2018, 42,947 participants) and the UKB (125,372 participants)—and a meta-analysis of 37 published cohort studies (3,244,263 participants). Higher adherence to PHD was associated with lower all-cause mortality in both cohorts. In the UKB, it was also associated with reduced the risk of cancer and respiratory disease mortality. In the meta-analysis, higher adherence to the PHD was associated with lower risks of all-cause, cancer, and cardiovascular disease (CVD) mortality and reduced risks of colorectal cancer, lung cancer, CVDs, coronary heart disease, stroke, and diabetes. This analysis suggests that higher adherence to the PHD may offer substantial health benefits.
Adherence to the Planetary Health Diet is associated with lower risks of mortality and major chronic diseases.
INTRODUCTION
Contemporary dietary patterns substantially contribute to climate change and public health issues, exerting considerable stress on the Earth’s ecosystems. The global food system accounts for ~30% of total anthropogenic greenhouse gas emissions (GHGe) (1, 2), with high meat and dairy consumption being a major contributor (3). These high GHGe foods not only impose environmental pressure but are also associated with increased health risks (4, 5). These findings underscore the impact of the food system on global environmental and health issues, thereby compelling urgent consideration and action of sustainable food production and consumption practices.
To achieve mutually beneficial outcomes for both human health and the environment, the EAT-Lancet Commission proposed the Planetary Health Diet (PHD) as a reference diet (6). The PHD aims to mitigate diet-related GHGe and other environmental impacts while promoting human health (6). In a simulation analysis of dietary guidelines across 85 countries, Springmann et al. (7) found that the PHD outperforms the existing national and World Health Organization (WHO) guidelines in both health benefits and GHGe reduction. A study on the Chinese population showed that adherence to PHD was associated with lower GHGe and lower risk of all-cause mortality and other cause-specific mortality (8). Research from the Netherlands corroborated these findings (9).
However, some studies present contradictory evidence. In the analysis of NutriNet-Santé cohort, Berthy et al. (10) observed no clear association between the PHD diet and the combined or separate risks of cancer and cardiovascular disease (CVD). Additional research suggested that the PHD might not be related to the risks of CVD mortality and cancer mortality (8, 11). These conflicting findings may limit large-scale adoption of PHD, necessitating further research on the association between PHD and health outcomes.
To address this issue, this study analyzed two prospective cohort studies from the US National Health and Nutrition Examination Survey (NHANES, 1999–2018) and the UK Biobank (UKB) to investigate the associations between the PHD and the risk of all-cause and cause-specific mortality. Building upon the mortality findings from our two prospective cohorts, we conducted a meta-analysis integrating these results with those from previous cohort studies. In addition, our analysis also explored associations between the PHD and key chronic diseases, such as cancer and CVDs. This expanded scope strengthens the scientific basis for both research and public health application of the PHD.
RESULTS
Population characteristics in US NHANES and UKB
A total of 42,947 participants from the US NHANES and 125,372 from the UKB were included in the analysis. In US NHANES, 20,622 (48.0%) were male, with a median age of 47 years [interquartile range (IQR): 34 to 60]. In UKB, 55,456 (44.2%) were male, with a median age of 57 years (IQR: 50 to 62).
In US NHANES, the median PHD score was 30, ranging from 10 to 95. Participants with higher adherence to PHD were likely to be older, predominantly female; have a lower body mass index (BMI), higher levels of education and income; refrain from smoking but consume alcohol more than 10 times per month; and have a higher daily intake of total energy and macronutrients.
In UKB, the median PHD score was 59, with a range from 17 to 110. Participants with higher adherence to PHD were likely to be older, predominantly female; have a lower BMI and higher levels of education; and consume more total energy and macronutrients per day. Detailed baseline characteristics of the two prospective cohort studies populations can be found in Table 1 and figs. S1 to S3.
Table 1. Baseline characteristics of the study population by quintiles of PHD in US NHANES and UKB.
BMI, body mass index; PIR: ratio of family income to poverty; GHGe, greenhouse gas emissions.
| Characteristic | Planetary health diet score quintiles in US NHANES | Planetary health diet score quintiles in UKB | ||||||
|---|---|---|---|---|---|---|---|---|
| Quintile 1 (10–35), N = 10,737 | Quintile 2 (35–43), N = 10,737 | Quintile 3 (43–51), N = 10,736 | Quintile 4 (51–95), N = 10,737 | Quintile 1 (17–52), N = 31,343 | Quintile 2 (52–60), N = 31,343 | Quintile 3 (60–68), N = 31,343 | Quintile 4 (68–110), N = 31,343 | |
| Planetary health diet score, median (IQR) | 30 (26, 32) | 39 (37, 41) | 47 (45, 49) | 58 (54, 63) | 47 (43, 50) | 57 (54, 58) | 64 (62, 66) | 74 (71, 78) |
| Age, median (IQR), year | 43 (31, 57) | 46 (33, 60) | 48 (34, 62) | 51 (37, 63) | 57 (49, 62) | 57 (50, 62) | 57 (50, 62) | 57 (51, 62) |
| BMI, median (IQR), kg/m2 | 28 (24, 33) | 28 (24, 32) | 28 (24, 32) | 27 (24, 31) | 26.5 (24.0, 29.5) | 26.2 (23.7, 29.2) | 25.9 (23.5, 28.9) | 25.4 (23.1, 28.4) |
| Total energy, median (IQR), kcal/day | 1,937 (1,485, 2,654) | 1,915 (1,427, 2,505) | 1,906 (1,416, 2,470) | 2,005 (1,542, 2,515) | 1,978 (1,668, 2,358) | 2,004 (1,705, 2,351) | 2,025 (1,730, 2,350) | 2,036 (1,759, 2,342) |
| Total protein, median (IQR), g/day | 72 (52, 99) | 72 (51, 97) | 72 (51, 97) | 77 (58, 100) | 78 (65, 92) | 79 (67, 92) | 79 (67, 93) | 79 (68, 92) |
| Total fat, median (IQR), g/day | 72 (51, 104) | 71 (48, 99) | 71 (47, 97) | 76 (53, 101) | 69 (55, 86) | 70 (56, 87) | 71 (56, 87) | 73 (57, 88) |
| Carbohydrate, median (IQR), g/day | 235 (173, 316) | 226 (164, 301) | 227 (165, 301) | 236 (179, 307) | 243 (198, 295) | 247 (205, 293) | 249 (210, 292) | 250 (214, 290) |
| GHGe, median (IQR), kg CO2 | 4.06 (2.89, 5.66) | 3.85 (2.50, 5.31) | 3.55 (2.32, 5.12) | 3.56 (2.64, 4.81) | 5.60 (4.52, 6.82) | 5.59 (4.50, 6.76) | 5.44 (4.42, 6.61) | 5.26 (4.48, 6.28) |
| Sex | ||||||||
| Male | 6,007 (55) | 5,160 (47) | 4,772 (44) | 4,683 (42) | 16,867 (54) | 14,502 (46) | 13,084 (42) | 11,003 (35) |
| Female | 4,730 (45) | 5,577 (53) | 5,964 (56) | 6,054 (58) | 14,476 (46) | 16,841 (54) | 18,259 (58) | 20,340 (65) |
| Race* | ||||||||
| Non-Hispanic White | 5,108 (70) | 4,987 (70) | 4,662 (68) | 4,661 (69) | 30,486 (97) | 30,523 (97) | 30,500 (97) | 30,431 (97) |
| Non-Hispanic Black | 2,502 (13) | 2,249 (11) | 2,119 (11) | 1,939 (8.9) | 857 (2.7) | 820 (2.6) | 843 (2.7) | 912 (2.9) |
| Mexican American | 1,761 (7.8) | 1,908 (8.3) | 1,988 (8.4) | 1,780 (7.6) | ||||
| Others | 1,366 (10) | 1,593 (11) | 1,967 (13) | 2,357 (15) | ||||
| Education level† | ||||||||
| <11 grade | 3,211 (20) | 2,971 (18) | 2,755 (15) | 2,369 (13) | 10,773 (34) | 9,997 (32) | 9,440 (30) | 8,754 (28) |
| High school | 2,939 (30) | 2,620 (25) | 2,371 (22) | 2,037 (18) | 1,456 (4.6) | 1,426 (4.5) | 1,512 (4.8) | 1,473 (4.7) |
| More than high school | 4,587 (50) | 5,146 (57) | 5,610 (62) | 6,331 (69) | 11,207 (36) | 13,516 (43) | 14,775 (47) | 16,178 (52) |
| 7,907 (25) | 6,404 (20) | 5,616 (18) | 4,938 (16) | |||||
| Drinking behavior‡ | ||||||||
| Nondrinker | 2,844 (23) | 3,223 (25) | 3,447 (26) | 3,347 (25) | 1,037 (3.3) | 925 (3.0) | 796 (2.5) | 806 (2.6) |
| 1–5 drinks/month | 5,635 (53) | 5,338 (50) | 5,160 (48) | 5,016 (46) | 1,005 (3.2) | 914 (2.9) | 831 (2.7) | 851 (2.7) |
| 5–10 drinks/month | 840 (9.3) | 781 (8.9) | 722 (8.7) | 797 (9.2) | 29,301 (93) | 29,504 (94) | 29,716 (95) | 29,686 (95) |
| 10+ drinks/month | 1,418 (15) | 1,395 (16) | 1,407 (16) | 1,577 (19) | ||||
| Smoking behavior | ||||||||
| Never | 4,903 (46) | 5,667 (52) | 6,201 (57) | 6,547 (60) | 17,004 (54) | 18,012 (57) | 18,190 (58) | 18,554 (59) |
| Former | 2,442 (21) | 2,728 (25) | 2,774 (26) | 2,931 (28) | 11,085 (35) | 11,171 (36) | 11,356 (36) | 11,317 (36) |
| Current | 3,392 (33) | 2,342 (22) | 1,761 (17) | 1,259 (12) | 3,254 (10) | 2,160 (6.9) | 1,797 (5.7) | 1,472 (4.7) |
| Income level§ | ||||||||
| PIR < 1 | 2,543 (18) | 2,238 (15) | 2,081 (13) | 1,699 (11) | 10,736 (34) | 10,386 (33) | 10,482 (33) | 10,879 (35) |
| PIR (1–4) | 6,041 (54) | 5,959 (51) | 5,724 (49) | 5,362 (44) | 10,317 (33) | 10,484 (33) | 10,406 (33) | 10,193 (33) |
| PIR ≥ 4 | 2,153 (28) | 2,540 (34) | 2,931 (38) | 3,676 (45) | 10,290 (33) | 10,473 (33) | 10,455 (33) | 10,271 (33) |
| History of hypertension | ||||||||
| No | 7,078 (70) | 6,847 (68) | 6,832 (69) | 6,829 (68) | 23,119 (74) | 23,309 (74) | 23,719 (76) | 24,119 (77) |
| Yes | 3,659 (30) | 3,890 (32) | 3,904 (31) | 3,908 (32) | 8,224 (26) | 8,034 (26) | 7,624 (24) | 7,224 (23) |
| History of diabetes | ||||||||
| No | 9,570 (92) | 9,428 (91) | 9,302 (90) | 9,316 (91) | 30,113 (96) | 30,061 (96) | 30,212 (96) | 30,319 (97) |
| Yes | 1,167 (8.0) | 1,309 (9.2) | 1,434 (9.9) | 1,421 (9.5) | 1,230 (3.9) | 1,282 (4.1) | 1,131 (3.6) | 1,024 (3.3) |
*Race: NHANES (non-Hispanic White, non-Hispanic Black, Mexican American, and others) and UKB (white and non-white).
†Education level: NHANES (11 grade and below, high school, more than high school) and UKB (O levels/GCSEs or equivalent, A/AS level or equivalent, college or university degree, and others).
‡NHANES (nondrinker, 1 to 5 drinks/month, 5 to 10 drinks/month, and 10+ drinks/month) and UKB (never drink; former drink, current drink).
§NHANES [tertiles based on the ratio of household income to poverty: (PIR < 1, PIR (1 to 4), PIR ≥ 4 (34)] and UKB (tertiles based on the Townsend deprivation index).
PHD scores and environmental impact in US NHANES and UKB
Figure 1 and fig. S4 displayed the average contributions of GHGe for the reported diets across the PHD quartiles in both cohort studies. In US NHANES, the four dietary components that contributed the most to GHGe were red meat and its products, dairy and its products, cereals, and fruits. After stratification by quartiles, red meat and its products, along with dairy and its products, continued to have the highest contributions.
Fig. 1. Estimated contribution of each food group of PHD to GHGe.
In UKB, the four dietary components contributing the most to GHGe included red meat and its products, dairy and its products, saturated fats and unsaturated fats. Following stratification by quartiles, red meat and its products, as well as dairy and its products, remained the leading contributors.
Association of PHD with mortality in US NHANES and UKB
During 413,907.3 person-years of follow-up [median (IQR) follow-up of 9.3 (5.0, 13.8) years] in US NHANES, a total of 6795 deaths were recorded. In the UKB, more than 1,537,738.58 person-years follow-up [median (IQR) follow-up of 12.27 (11.66, 13.03) years], a total of 6853 deaths, were recorded.
In the fully adjusted model (model 3) of the US NHANES, higher adherence to PHD was associated with reduced the risk of all-cause mortality [hazard ratio (HR)Q4vs Q1: 0.77, 95% confidence interval (CI): 0.71 to 0.84], heart disease mortality (HRQ4vs.Q1: 0.81, 95% CI: 0.66 to 0.98) and other-related mortality (HRQ4vs.Q1: 0.81, 95% CI: 0.69 to 0.96) by 23, 19, and 19%, respectively (Fig. 2 and table S1). Restricted cubic spline modeling showed a reduced linear relationship between PHD scores and risk of all-cause mortality (P < 0.05), shown in fig. S5.
Fig. 2. Association between PHD scores and mortality from major diseases in US NHANES.
* denotes model adjusted for age and sex; # denotes model further adjusted for total energy intake, race, education level, smoking, alcohol, income level, history of hypertension, and history of diabetes; $ denotes model further adjusted for BMI.
In the fully adjusted model (model 3) of the UKB, higher adherence to PHD was associated with lower risk of all-cause mortality (HRQ4vs.Q1: 0.84, 95% CI: 0.78 to 0.90), cancer mortality (HRQ4vs.Q1: 0.84, 95% CI: 0.76 to 0.92), and respiratory diseases mortality (HRQ4vs.Q1: 0.39, 95% CI: 0.26 to 0.59) by 16, 16, and 61%, respectively, shown in Fig. 3 and table S1. Restricted cubic spline modeling showed an inverse linear relationship between PHD score and risk of all-cause and cancer mortality (P < 0.05, Pnonlinear > 0.05), shown in fig. S6.
Fig. 3. Association between PHD scores and mortality from major diseases in UKB.
* denotes model adjusted for age and sex; # denotes model further adjusted for total energy intake, race, education level, smoking, alcohol, income level, history of hypertension, and history of diabetes; $ denotes model further adjusted for BMI.
In US NHANES, the results of the sensitivity analyses shown that higher adherence to PHD demonstrated a negative association with cancer mortality in the models that excluded individuals who died within 2 and 5 years of follow-up. However, the negative association of high PHD adherence with risk of mortality from heart disease was no longer observed in the model that excluded individuals who died within 2 years and the raw model. In UKB, the results of the sensitivity analyses were broadly consistent with the results of the fully adjusted model (model 3), shown in table S2 and figs. S7 and S8.
Meta-analysis
Through a comprehensive literature search, we identified a total of 3286 articles by April 2025 [1001 from Web of Science (WOS), 544 from PubMed, 37 from Cochrane, 870 from Scopus, and 834 from Embase]. After excluding 1800 duplicate articles and 867 articles that were irrelevant to this study, we thoroughly checked the complete text of the remaining 251 potentially relevant articles. Ultimately, 37 articles were included in the meta-analysis, as detailed in tables S3 to S5.
Among the 37 cohort studies (including 3,244,263 participants), we obtained 9 cancer mortality studies, 9 CVD mortality studies, and 17 all-cause mortality studies. According to a pooled analysis, compared with the lowest category of PHD adherence, the highest category was associated with a 11% lower risk of cancer mortality (HR: 0.89, 95% CI: 0.85 to 0.92, I2: 1.8%), a 17% lower risk of CVD mortality (HR: 0.83, 95% CI: 0.79 to 0.87, I2: 21.2%) and a 21% lower risk of all-cause mortality (HR: 0.79, 95% CI: 0.72 to 0.82, I2: 70.0%). The Begg and Egger tests indicated that there were no significant publication bias (Table 2 and figs. S9 to S12).
Table 2. Meta-analysis of PHD scores and major health outcomes (mortality and chronic diseases) in prospective cohort studies.
CI: confidence interval; CVDs: cardiovascular diseases; CHD: coronary heart disease.
| Outcome | Numbers | HR (95% CI) | I2% | PBegg value | PEgger value | Model |
|---|---|---|---|---|---|---|
| Colorectal cancer | 3 | 0.87 (0.78–0.97) | 0.0 | 1.000 | 0.901 | Fixed-effects model |
| Lung cancer | 3 | 0.68 (0.59, 0.78) | 0.0 | 1.000 | 0.685 | Fixed-effects mode |
| CVDs | 9 | 0.83 (0.76–0.90) | 62.7 | 0.466 | 0.335 | Random-effects model |
| CHD | 6 | 0.83 (0.78–0.88) | 0.0 | 0.452 | 0.162 | Fixed-effects model |
| Diabetes | 7 | 0.74 (0.62–0.87) | 93.4 | 0.072 | 0.119 | Random-effects model |
| Total stroke | 9 | 0.84 (0.76–0.91) | 59.1 | 0.466 | 0.575 | Random-effects model |
| Ischemic stroke | 7 | 0.94 (0.86–1.03) | 0.0 | 0.764 | 0.809 | Fixed-effects model |
| Hemorrhagic stroke | 3 | 1.02 (0.79–1.32) | 0.0 | 1.000 | 0.909 | Fixed-effects model |
| Cancer mortality | 9 | 0.89 (0.85–0.92) | 1.8 | 0.677 | 0.253 | Fixed-effects model |
| CVD mortality | 9 | 0.83 (0.79–0.87) | 21.2 | 0.466 | 0.373 | Fixed-effects model |
| Respiratory disease mortality | 4 | 0.57 (0.42, 0.77) | 93.2 | 0.734 | 0.243 | Random-effects model |
| All-cause mortality | 17 | 0.79 (0.75–0.82) | 70.0 | 1.000 | 0.743 | Random-effects model |
Furthermore, we conducted additional analyses on the relationship between the PHD and colorectal cancer (n = 3), lung cancer (n = 3), CVDs (n = 9), CHD (n = 6), stroke (n = 9), ischemic stroke (n = 7), hemorrhagic stroke (n = 3), and diabetes (n = 7). The findings showed that higher adherence to PHD was associated with a reduced the risk of colorectal cancer (HR: 0.87, 95% CI: 0.78 to 0.97, I2: 0), lung cancer (HR: 0.68, 95% CI: 0.59 to 0.78, I2: 0), CVDs (HR: 0.83, 95% CI: 0.76 to 0.90, I2: 62.7%), CHD (HR: 0.83, 95% CI: 0.78 to 0.88, I2: 0), diabetes (HR: 0.74, 95% CI: 0.62 to 0.87, I2: 93.4%), and total stroke (HR: 0.84, 95% CI: 0.76 to 0.91, I2: 59.1%) by 15, 32, 17, 17, 26, and 16%, respectively. The Begg and Egger tests indicated no significant publication bias (Table 2 and figs. S9 and S13 to S20).
In addition, data from colorectal cancer (n = 3), lung cancer (n = 3), CVDs (n = 6), CHD (n = 4), diabetes (n = 6), total stroke (n = 3), ischemic stroke (n = 3), cancer mortality (n = 7), CVD mortality (n = 6), and all-cause mortality (n = 13) were included in the subsequent dose-response analysis. The results showed a linear association between higher PHD scores and increased risk of CVDs, CHD, diabetes, lung cancer, cancer mortality, CVD mortality, and all-cause mortality (P < 0.05; Pnonlinear > 0.05), shown in Fig. 4 and fig. S21. In the sensitivity analyses, the results remained the same regardless of whether we included the relevant results from these two cohort stuides, shown in table S6 and fig. S22.
Fig. 4. Spline analyses from the dose-response meta-analysis of cohort studies of PHD scores and major health outcomes.
DISCUSSION
The study examines the relationship between PHD adherence and risk of mortality using two large prospective cohorts. In both US NHANES and UKB, higher PHD adherence was associated with a reduced the risk of all-cause mortality. The UKB study further revealed that in the British population, higher adherence to PHD lessened the risk of death from cancer and respiratory disease. Meanwhile, higher adherence to PHD may be related to the reduction in GHGe. In meta-analyses, we found that higher adherence to PHD effectively lowered the risk of cancer mortality, CVD mortality, respiratory disease mortality, and all-cause mortality. Besides, higher adherence to PHD can reduce the risk of colorectal cancer, lung cancer, CVDs, CHD, stroke, and diabetes.
The results from these two prospective cohort studies and the meta-analysis, which suggested that adherence to PHD could reduce the risk of all-cause mortality, were largely consistent with previous research findings. A study reported that adherence to PHD was effective in reducing the risk of all-cause mortality by 25% (12). Karavasiloglou et al. (13) constructed a PHD based on consumption frequency over the past year (29 questions), which also showed a reduction in the risk of all-cause mortality. In contrast, we used dietary data from participants who completed two or more 24-hour dietary recalls in the UKB, which is more credible than diet frequency based on only 29 questions. Nevertheless, both studies from the UKB indicated that adherence to the PHD is associated with a lower risk of all-cause mortality (13). However, two cohort of Swedish found gender-specific differences in the association between higher adherence to PHD and reduced risk of all-cause mortality, with notable differences observed only in women (11). Our study further revealed that higher adherence to PHD had higher intake of total energy. This finding differs from other healthy dietary patterns, such as the Mediterranean diet, where higher adherence is often associated with lower total energy intake (14). Further studies are needed to confirm and better understand this relationship. However, it is worth noting that the current meta-analysis of PHD and total mortality exhibited a relatively high level of heterogeneity. This may be explained by several factors, including differences in dietary assessment methods—some studies used food frequency questionnaire, while others relied on 24-hour dietary recalls. In addition, certain studies had shorter follow-up periods and smaller sample sizes, which may have limited statistical power. Another important source of heterogeneity is the variation in how the PHD score was calculated, as different studies may have used different scoring algorithms.
In the analysis of specific mortality, we found that adherence to the PHD was not associated with cancer mortality in US NHANES, which is consistent with studies in China (8). However, this contrasts with the findings from the UKB and the Malmö Diet and Cancer Study (12). The difference may be due to the use of different cancer mortality codes across cohorts. In the NHANES and Chinese studies, cancer mortality codes were restricted to C00-C97, while the UKB and Malmö Diet and Cancer Study used code C00-D48. In the subsequent meta-analysis, we found that adherence to the PHD diet could reduce the risk of cancer mortality by 13%. Nevertheless, the interpretation of this result should be cautious, not only because of the variation in cancer mortality coding but also because of differences in the distribution of key risk factors, such as age, smoking, and alcohol consumption-across cohorts. Moreover, we also conducted a meta-analysis on chronic disease outcomes associated with the PHD. Higher adherence to the PHD was associated with a lower risk of colorectal cancer, lung cancer, CVDs, coronary heart disease, total stroke, and diabetes, consistent with findings from previous studies (9, 13, 15). However, as most existing evidence is derived from observational studies, further research—particularly randomized controlled trials—is warranted to establish causality and strengthen the evidence base for the health benefits of the PHD.
We also examined GHGe for each food group in PHD. The results found that high intake of PHD were associated with lower GHGe, which is consistent with other studies (5, 8). In US NHANES and UKB studies, red meat and its products, as well as dairy and its products, were found to be the two food categories with the highest GHGe, which is consistent with the results of a previous meta-analysis (3). However, we must approach these findings cautiously, as GHGe for each food group were obtained through the life cycle assessment (LCA) of food environment method, which has its limitations, such as unclear system boundaries definitions, the challenge of inventory analysis, and the uncertainty of selecting appropriate environmental impacts (16, 17). In addition, the specificity of diets and LCA in individual countries precludes extrapolation of the results.
This study represents a large-scale prospective analysis of the association between PHD and mortality in the general population, and its design helps minimize the potential for information bias. In addition, we conducted a comprehensive meta-analysis of the available evidence on PHD and major health outcomes. However, there are some limitations to this study. First, the UKB may not represent the general population in terms of certain participant characteristics; therefore, it cannot be used to provide generalizable disease prevalence and incidence rates. Specifically, UKB participants are typically from a more socioeconomically advantaged areas and tend to engage in healthier lifestyle behaviors compared to the general population. Therefore, the generalizability for disease prevalence and incidence rates was potentially limited. Second, dietary intake in these two cohort studies was assessed at baseline but may change during the follow-up period, so baseline measurements may not accurately reflect exposure levels throughout the study, potentially introducing bias. Third, the role of residual or unmeasured confounding cannot be completely ruled out. Specifically, the association between the PHD and mortality could be influenced by potential confounders, so the results and causal inferences of this study should be interpreted cautiously. Fourth, the main limitation of the current meta-analysis is the limited number of studies and differences in disease definitions. However, the low heterogeneity observed in the current studies suggests that the results are reliable.
Overall, our research findings demonstrated that adherence to PHD reduces the risk of mortality and colorectal cancer, lung cancer, CVDs, CHD, stroke, and diabetes and simultaneously mitigating GHGe. This analysis highlights the importance of promoting PHD for improving public health and combating global climate change. Further research should focus more on the implementation of PHD across diverse populations and on exploring its environmental benefits.
MATERIALS AND METHODS
Study population
The US NHANES is a 2-year-cycle project conducted by the Centers for Disease Control and Prevention (CDC) in the United States. The US NHANES enrolls a nationally representative sample of 5000 non-institutionalized individuals each year, selected from 15 counties nationwide consisting two main components: household interviews and health examinations (18, 19). We used data from 1999 to 2018, comprising 101,316 individuals. After excluded individuals under 20 years of age (n = 49,700) and those with implausible energy intake (less than 500 kcal/day or more than 3500 kcal/day for women and less than 800 kcal/day or more than 4000 kcal/day for men; n = 3450), 42,947 participants remained to be analyzed. Detailed participant selection process is shown in fig. S23. The US NHANES was approved by the NHANES Institutional Review Board, and all participants submitted written informed consent at enrollment.
The UKB is an ongoing prospective cohort study of UK individuals aged 40 to 69 years who underwent the initial assessment between 2006 and 2010 (20). We excluded participants who completed fewer than two 24-hour recalls (n = 375,555), those with implausible energy intake (women less than 500 kcal/day or more than 3500 kcal/day and men less than 800 kcal/day or more than 4,000 kcal/day; n = 667), resulting in 125,372 individuals for analysis, as detailed in fig. S24. The UKB was approved by the North West Multi-Centre Research Ethics Committee (16/NW/0274). All participants provided written and informed consent for data collection, analysis, and record linkage. This study was performed under UKB application number 103394. Details of the assessment for the remaining covariates are provided in the Supplementary Materials.
Calculation of the PHD scores
In US NHANES, dietary data were obtained from 24-hour dietary recalls. Since 2003, in addition to the initial face-to-face recall, a second 24-hour recall was typically conducted via telephone interview 3–10 days later. In this study, the 24-hour dietary recall from the first day was used (21). Dietary intake data were collected in each cycle of the NHANES dietary survey and the Food Patterns Equivalents Database (FPED) for the corresponding year (22, 23). As the FPED uses equivalents for public accessibility, conversion factors established by Blackstone and Conrad were applied to convert the equivalents of each FPED food group consumed by each NHANES participant into grams (24).
In UKB, dietary data were primarily derived from the 24-hour self-assessment dietary questionnaire. UKB used an online self-administered format, with the first assessment taking place in 2009, followed by four additional assessments between February and April 2011, June and August 2011, October and December 2011, and April to June 2012 (25, 26). To account for daily variability and potential seasonality effects, the daily intake was averaged for participants who completed two or more 24-hour dietary (27).
The PHD scores were calculated following the methods provided in the EAT-Lancet report (6) and by Ye et al. (8). In summary, 14 dietary components from the EAT-Lancet report were grouped into three categories: adequate, optimal, and moderate. Each dietary component was scored on a scale of 0 to 10, with the total PHD score ranging from 0 to 140 (details in table S7). To calculate the PHD scores, we standardized each participant’s intake of nutrients and other dietary components to a 2500 kcal/day level based on their actual total energy intake.
Estimation of environmental impacts
We calculated dietary GHGe for both cohort studies. In US NHANES, we used the Diet-Related Food Impacts on the Environment Database (dataFIELD). This database provides GHGe value for each food item reported by NHANES participants, estimated using a LCA methodology (28, 29). Dietary GHGe data for UKB were obtained from the study by Green et al. (30). This value was calculated using LCA data from the literature (mainly from the United Kingdom and Europe) and encompassing all stages from food production to consumer disposal (30). GHGe was expressed in kilograms of carbon dioxide equivalent (CO2eq). The dataFIELD database and the Green et al. (30) dataset have both been widely applied in previous studies to quantify diet-related environmental impacts.
Mortality ascertainment
Mortality was classified using the International Classification of Diseases, ninth and tenth versions (ICD-9 and ICD-10). In the US NHANES, mortality data were taken from the National Death Index (NDI) ending 31 December 2019. In UKB, the date and cause of death were included in the death certificate through links to the National Health Service (NHS) Information Centre for England and Wales (England and Wales) and the NHS Central Register for Scotland (Scotland). Further details of the linking process are available online (https://biobank.ndph.ox.ac.uk/showcase/ukb/docs/DeathLinkage.pdf). Censoring dates varied by country (England and Wales, 30 September 2021; Scotland, 31 October 2021). The follow-up period was defined as the time from each participant’s interview date to the date of death or the end of follow-up, whichever occurred first. Specific causes of mortality were identified with the following ICD codes: in US NHANES, heart disease (I00-I09, I11, I13, I20-I51) and cancer (C00-C97); in UKB, heart disease (I00-I09, I11, I13, I20-I51), ischemic heart disease (I20-I25), stroke (I60-I69), CVDs (I00-I79), cancer (C00-D48), respiratory disease (J09-J18 and J40-J47), and influenza and pneumonia (J09-J18). Stroke or CVD-related mortalities were not considered in the US NHANES because the NDI-matched mortality dataset ceased updating cerebrovascular disease mortality after 31 December 2011.
Statistical analysis
Baseline characteristics were stratified by quartiles of the PHD scores. Continuous variables with normal distributions were represented as means ± SD. Skewed distribution variables were shown as median (IQR). Categorical variables were presented as counts and percentages. Multiple estimation of chained equations was used to estimate missing covariate data. The HR and 95% CI for the association between quartiles of PHD scores and risk of mortality were derived using Cox proportional hazard regression modeling. Person-years were calculated from recruitment until death, loss to follow-up, or the end of follow-up. The proportional hazards assumption was validated using the Schoenfeld test, with no violations detected. Three main models were included in the Cox analysis. Model 1 was adjusted for age and sex; model 2 was further adjusted for total energy intake, race, education level, smoking, alcohol, income level, history of hypertension, and history of diabetes to model 1; model 3 was further adjusted for BMI to model 2.
A series of sensitivity analyses were conducted: (i) participants who died within 2 years of recruitment were excluded; (ii) participants who died within 5 years of recruitment were excluded; (iii) analyses were carried out on the raw data; and (iv) participants under the age of 40 years were excluded; (v) for the US NHANES, participants who only completed one 24-hour dietary recall were excluded; (vi) for the US NHANES, we further adjusted for the potential influence of survey cycles. For the US NHANES, we adjusted the sampling weights to account for the unequal probabilities of selection arising from the complex sampling design. In addition, Cox proportional hazard models with restricted cubic spline were applied to assess the dose-response relationship between PHD scores and major health outcomes. Three knots (10th, 50th, and 90th percentiles) were used as reference points to construct the curves.
Meta-analysis
We searched WOS, PubMed, Cochrane, Scopus, and Embase for studies published up to April 2025 examining the relationship between PHD and its related derivatives of dietary pattern and health outcomes on. The specific literature screening approach and process can be found in the Supplementary Materials and fig. S25. The meta-analysis was conducted using a fixed-effects model when heterogeneity was low (I2 < 50%) and a random-effects model when substantial heterogeneity was present (I2 ≥ 50%). HR was extracted on the basis of comparisons between the highest and lowest categories of PHD adherence. Egger’s test and Begg’s test were performed to assess the potential bias. We also performed linear and nonlinear dose-response relationships between PHD and mortality and other chronic disease outcomes using the method described by Greenland and Longnecker (31). This meta-analysis was prospectively registered in the search strategy (PROSPERO registration number CRD42024508887).
Data collection, organization, and analysis were conducted using R version 4.1.3 and Stata version 17.0 (Stata Corporation, College Station, TX, US). All significance tests were two-tailed, with P values < 0.05 considered statistically significant. This study followed STROBE and PRISMA reporting guidelines, shown in table S8 and S9 (32, 33).
Acknowledgments
We thank all US NHANES, UKB participants, and all staff for the contributions to these studies.
Funding: This research was funded by National Key R&D Program of China (no. 2023YFF1104402) and the National Natural Science Foundation of China (no. 82173509).
Author contributions: Y.W. provided research ideas and wrote. Y.W. and G.S. analyzed UKB data. C.Z., D.P., D.X., and Y.L. analyzed NHANES data. L.D., S.Y., P.W., J.X., and J.Y. collected the meta-analysis data. G.S. designed and supervised the work. All authors read and approved of the final manuscript.
Competing interests: All authors state that they have no competing interests and declare no financial relationships with any organization that might have an interest in the submitted work in the previous 3 years and no other relationships or activities that could appear to have influenced the submitted work.
Data and materials availability: All data needed to evaluate the conclusions of the paper is present in the paper and/or the Supplementary Materials. The US NHANES data are publicly available at www.cdc.gov/nchs/nhanes. The UKB data are publicly available on application at www.ukbiobank.ac.uk.
Supplementary Materials
This PDF file includes:
Supplementary Text
Tables S1 to S9
Figs. S1 to S25
References
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Associated Data
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Supplementary Materials
Supplementary Text
Tables S1 to S9
Figs. S1 to S25
References