Dietary quality and cardiometabolic indicators in the USA: A comparison of the Planetary Health Diet Index, Healthy Eating Index-2015, and Dietary Approaches to Stop Hypertension

Sarah M Frank; Lindsay M Jaacks; Christy L Avery; Linda S Adair; Katie Meyer; Donald Rose; Lindsey Smith Taillie

doi:10.1371/journal.pone.0296069

. 2024 Jan 10;19(1):e0296069. doi: 10.1371/journal.pone.0296069

Dietary quality and cardiometabolic indicators in the USA: A comparison of the Planetary Health Diet Index, Healthy Eating Index-2015, and Dietary Approaches to Stop Hypertension

Sarah M Frank ^1,², Lindsay M Jaacks ², Christy L Avery ^1,³, Linda S Adair ^1,⁴, Katie Meyer ^4,⁵, Donald Rose ⁶, Lindsey Smith Taillie ^1,^4,^*

Editor: Mohammad Reza Mahmoodi⁷

PMCID: PMC10781024 PMID: 38198440

Abstract

Background

The Planetary Health Diet Index (PHDI) measures adherence to the sustainable dietary guidance proposed by the EAT-Lancet Commission on Food, Planet, Health. To justify incorporating sustainable dietary guidance such as the PHDI in the US, the index needs to be compared to health-focused dietary recommendations already in use. The objectives of this study were to compare the how the Planetary Health Diet Index (PHDI), the Healthy Eating Index-2015 (HEI-2015) and Dietary Approaches to Stop Hypertension (DASH) relate to cardiometabolic risk factors.

Methods and findings

Participants from the National Health and Nutrition Examination Survey (2015–2018) were assigned a score for each dietary index. We examined disparities in dietary quality for each index. We used linear and logistic regression to assess the association of standardized dietary index values with waist circumference, blood pressure, HDL-C, fasting plasma glucose (FPG) and triglycerides (TG). We also dichotomized the cardiometabolic indicators using the cutoffs for the Metabolic Syndrome and used logistic regression to assess the relationship of the standardized dietary index values with binary cardiometabolic risk factors. We observed diet quality disparities for populations that were Black, Hispanic, low-income, and low-education. Higher diet quality was associated with improved continuous and binary cardiometabolic risk factors, although higher PHDI was not associated with high FPG and was the only index associated with lower TG. These patterns remained consistent in sensitivity analyses.

Conclusions

Sustainability-focused dietary recommendations such as the PHDI have similar cross-sectional associations with cardiometabolic risk as HEI-2015 or DASH. Health-focused dietary guidelines such as the forthcoming 2025–2030 Dietary Guidelines for Americans can consider the environmental impact of diet and still promote cardiometabolic health.

Introduction

Cardiovascular disease (CVD) is the number one cause of morbidity [1] and mortality [2] in the US. Poor dietary quality, in turn, is the number one risk factor for CVD [1]. Thus, improvements in dietary quality could significantly lessen the burden of CVD in the US.

Dietary guidelines are a set of recommendations designed to promote health and are often used as the basis for food policies. In 2019, the EAT-Lancet Commission on Food, Planet, Health introduced a “universal healthy reference diet,” [3] to jointly address diet-related disease and the environmental impact of food production. The diet emphasizes one rich in plant-sourced foods and low in animal-sourced foods using suggested amounts for a diet of 2500 kilocalories per day.

The Planetary Health Diet Index (PHDI) is a relatively new measure of dietary quality that incorporates recommendations on and is innovative in its consideration of sustainability and health from the EAT-Lancet reference diet into a numerical index [4–7]. To justify incorporating the EAT-Lancet Commission’s climate-focused recommendations into US food policies, there is a need to assess the PHDI’s performance as a predictor of cardiometabolic health and see how it compares to dietary recommendations already in use. Two commonly used dietary indices in the US are the Healthy Eating Index-2015 (HEI-2015) and an index based on Dietary Approaches to Stop Hypertension (DASH). Like PHDI, HEI-2015 uses pre-defined thresholds to quantify adherence to the Dietary Guidelines for Americans (DGAs) but does not discourage animal-sourced foods [8]. DASH is designed to prevent and control hypertension, but unlike PHDI and HEI-2015, DASH is scored on the distribution of component intake within the target population [9]. Both HEI-2015 and the DASH index are associated with decreased risk of cardiometabolic morbidity and mortality in the US [10, 11].

Additionally, there are well-documented dietary disparities by sex, income, education, and race/ethnicity for both HEI-2015 [12] and DASH [13]. To our knowledge, there have been no analyses of disparities in PHDI in the US. There is therefore a need to quantify the disparities in dietary quality as measured by PHDI and compare to disparities in HEI-2015 and DASH.

The objectives of this study were to see how the PHDI correlates with HEI-2015 and DASH. compare the performance of the three dietary indices in terms of prediction of binary cardiometabolic risk factors. We further examine socioeconomic disparities in diet quality as measured by the three indices.

Materials and methods

Study population

The US National Health and Nutrition Examination Survey (NHANES) is a repeated cross-sectional survey that uses a multistage probability design to sample the civilian, non-institutionalized population residing in the 50 states and District of Columbia [14]. Two cycles of NHANES are required to obtain reliable estimates of population-level means [15, 16], so we included data from the two most recently available NHANES cycles unaffected by the COVID-19 pandemic. The study protocols of the NHANES are approved by the Research Ethics Review Board at the National Center for Health Statistics (NCHS) [14]. This is a retrospective study of data that were fully-anonymized before the authors accessed them. Because the de-identified observational data from the National Health and Nutrition Examination Survey are publicly available for download, this study received a determination of Not Human Subjects Research by the Institutional Review Board at [First Author’s Home University].

Eligible participants were non-pregnant or lactating individuals aged 20 years or older who participated in the 2015–2016 or 2017–2018 NHANES cycle and for whom two days of valid dietary intake data were available. Participants whose mean total energy intake was <500kcal or >8000kcal/day were excluded [17].

Assessment of dietary intake

Trained interviewers used the US Department of Agriculture Automated Multiple Pass Method to gather 24-hour dietary recall data [18]. Participants were asked to recall all foods and beverages they consumed the previous day. Measuring guides were used to assist with estimating portion sizes. The second dietary interview was conducted unannounced via phone 3–10 days after the initial face-to-face interview.

Dietary recall data were merged to the Food Patterns Equivalent Database (FPED), which assigns foods to the 37 USDA Food Pattern Components using a food composition table. For single-ingredient food items, FPED assigns foods directly to the corresponding component. For foods with ingredients from more than one component, FPED disaggregates these items into their component ingredients’ gram weights using standard recipe files [19].

Dietary recall data were also used to derive total energy intake [20].

Planetary Health Diet Index, PHDI

The Planetary Health Diet Index (PHDI) measures adherence to the recommendations of the EAT-Lancet Commission Scientific Report [3] and is designed to provide 2500 kilocalories/day. The index consists of 14 equally-weighted components worth 10 points each (Table 1, S1 Table). Six of these components (whole grains; whole fruits; non-starchy vegetables; nuts and seeds; legumes; and unsaturated oils) were encouraged and eight (starchy vegetables; dairy; red and processed meat; poultry; eggs; fish; saturated oils and trans fats; added sugar and fruit juice) were discouraged. The theoretical range of the PHDI is 0 to 140, with a higher score indicating better adherence.

Table 1. Comparison of the dietary components of the Planetary Health Diet Index (PHDI), Healthy Eating Index-2015 (HEI-2015) and Dietary Approaches to Stop Hypertension (DASH).

Dietary Components	PHDI^*	HEI-2015^*	DASH^‡
Encouraged components
Grains	Whole grains	Whole grains	Whole grains
Fruits	Whole fruit (excluding juice)	Whole fruit^† (excluding juice)	Total fruit (including juice)
Fruits	Whole fruit (excluding juice)	Total fruit^† (including juice)	Total fruit (including juice)
Vegetables	Vegetables (excluding starchy)	Total vegetables^†	Total vegetables
Vegetables	Vegetables (excluding starchy)	Greens and beans^†	Total vegetables
Proteins	Nuts	Total protein foods^†	Total nuts and legumes
Proteins	Legumes	Seafood and plant proteins^†	Total nuts and legumes
Dairy		Total dairy	Low-fat dairy
Fats & oils	Unsaturated oils	Fatty acids (PUFAs + MUFAS)/ SFAs
Discouraged components
Grains		Refined grains
Vegetables	Starchy vegetables
Proteins	Red/processed meat		Red/processed meat
	Poultry
	Eggs
	Fish
Dairy	Total dairy
Fats & oils	Saturated oils and trans fat	Saturated fats
Sugar	Added sugar and fruit juice	Added sugars (excludes fruit juice)	Sugar-sweetened beverages
Sodium		Sodium	Sodium

Open in a new tab

* All dietary pattern component scores range 0–10 unless otherwise noted

^† Component score range: 0–5

^‡ All component score range: 1–5

Healthy Eating Index, HEI-2015

The Healthy Eating Index (HEI-2015) is a quantitative measure of adherence to the US DGAs, which are dietary recommendations published by the federal government and used as the basis for federal food and nutrition policy [21]. HEI-2015 was calculated based on scores for 13 food components (Table 1): nine adequacy components, for which intake was encouraged (total fruits including fruit juice; whole fruits; total vegetables; greens and beans; dairy; total protein foods; seafood and plant proteins; and ratio of unsaturated: saturated fatty acids) and four moderation components for which intake was discouraged (refined grains; sodium; added sugars; and saturated fats). Participant intakes for each food group were scored based on energy-adjusted food intake (amount per 1000 kilocalories). The minimum and maximum scoring criteria for each food group are described in detail elsewhere, and participant intakes between the minimum and maximum were scored proportionately [22, 23]. Unlike PHDI and DASH, these components are not weighted equally, with seven components (whole grains; dairy; ratio of unsaturated: saturated fatty acids; refined grains; sodium; added sugars; saturated fats) assigned a range of 0–10 points, and six components (total fruits; whole fruits; total vegetables; greens and beans; total protein foods; seafood and plant proteins) assigned a range of 0–5 points. Scores were then summed to create the total score (theoretical range: 0 to 100, with a higher score indicating better adherence) [8].

Dietary Approaches to Stop Hypertension, DASH

Dietary Approaches to Stop Hypertension (DASH) is specifically designed to maintain a healthy blood pressure and has been adapted in settings throughout the globe. The scoring criteria for DASH is based on a total of eight categories (Table 1), five of which were encouraged (fruits; vegetables; whole grains; nuts and legumes; and low-fat dairy) and three of which were discouraged (sodium; sugar-sweetened beverages; and red and processed meat). Scores for each category were assigned by quintile of energy-adjusted food group intake. DASH scores can range from 8 to 40, with a higher score indicating better adherence [11, 23].

Cardiometabolic risk factors

We examined the cardiometabolic risk factors that are used as the constituent criteria for the clinical definition of Metabolic Syndrome [24]. These cardiometabolic risk factors were: high waist circumference, high blood pressure, reduced high-density lipoprotein cholesterol (HDL-C), high fasting plasma glucose, and elevated fasting triglycerides.

Anthropometrics and blood samples were taken in the Mobile Examination Center (MEC) according to standardized protocol. NHANES has survey weights that apply to the subsample of participants who participated in the MEC exams. The NHANES anthropometric survey collected data on waist circumference (in centimeters, cm) and blood pressure (in mm Hg) [25]. Blood pressure was measured three consecutive times after a five-minute rest. We used the average of the second and third readings [26] to calculate systolic and diastolic blood pressure. High density lipoprotein (HDL-C, mg/dL) was measured in venous blood.

Additionally, in the laboratory subsample fasting blood-based biomarkers were collected from participants who reported in the morning session after an overnight fast; additional survey weights account for the fasted laboratory subsample. Fasting plasma glucose (FPG) and fasting triglycerides were measured in this blood panel and were available in mg/dL [26].

In addition to the continuous values, all variables were dichotomized using the criteria of cardiometabolic risk in the definition of Metabolic Syndrome (MetS) [24] (Table 2).

Table 2. Criteria used to define binary cardiometabolic risk factor outcomes.

Cardiometabolic Risk Factor	Threshold
High waist circumference	≥102 centimeters in males
High waist circumference	≥88 centimeters in females
High blood pressure	Systolic blood pressure ≥130 and/or diastolic blood pressure ≥85 mm Hg
High blood pressure	OR use of antihypertensive medication
Low high-density lipoprotein cholesterol	<40 mg/dL (1.0 mmol/L) in males
	<50 mg/dL (1.3 mmol/L) in females
	OR use of cholesterol medication
High fasting plasma glucose	≥100 mg/dL
High fasting plasma glucose	OR use of insulin or other antidiabetic medication
High fasting triglycerides^†	≥150 mg/dL

Open in a new tab

^†Analyses of elevated fasting triglycerides restricted to participants that did not report current cholesterol medication use

Covariates

All sociodemographic information was self-reported as part of a standardized questionnaire. Age data were modeled in ten-year age categories. Income data were classified using the Poverty Income Ratio (PIR), a measure of family income relative to the Federal Poverty Level that accounts for household size. Income was categorized as PIR 0–185%, PIR 186–399%, PIR ≥ 400%, and Missing (due to high missingness in self-reported income, 8.1%) [27]. Education was reported in continuous years and classified as high school equivalent or lower, some college, and college degree or higher [28]. Race/ethnicity data were self-reported via categorical selection and classified as Non-Hispanic white, Non-Hispanic Black, Hispanic, Non-Hispanic Asian, or Other race/ethnicity (including Multiracial) [27, 29].

Statistical analyses

Because the three indices have different value ranges, in descriptive analyses, we rescaled each index to have a range of 0 to 100. Bland-Altman plots were used to evaluate systematic differences in the continuous index values [30]. Pearson’s correlation coefficient was used to assess correlation of continuous values, and radar plots were used to visually inspect how individual components contributed to overall index values. To examine differences in index score by sociodemographic characteristics, we used survey-weighted regression with the standardized index scores as the dependent variable and dummy variables for each level of a given sociodemographic characteristic (sex, age, income, education, race/ethnicity) as the independent predictor variables.

In additional descriptive analyses, participants were classified into quintiles for each diet index (PHDI, HEI-2015, and DASH). Survey-weighted tables were used to examine percent agreement between quintiles of the three dietary indices and to examine the distribution of sociodemographic characteristics across quintile of each dietary index.

To directly compare the dietary indices and to test for linear trends, we created a standardized Z-score variable for each index (mean of zero, standard deviation of 1) and included this variable as a continuous exposure in survey-weighted linear regression models. We also used survey-weighted logistic regression models to estimate the association between diet Z-score and each cardiometabolic risk factor dichotomized according to the Metabolic Syndrome criteria (high waist circumference, high blood pressure, low HDL-C, high fasting plasma glucose, high triglycerides). For both linear and logistic regressions, models were adjusted for age, sex, income, education, race/ethnicity, and total energy intake.

In addition to our main analyses, we conducted several sensitivity analyses. We repeated the main analyses using quintile of dietary pattern as the exposure. Stata’s postestimation margins, dydx command was used to estimate the change in probability of outcome by quintile of dietary index. In additional sensitivity analyses, we systematically tested adding smoking behavior, alcohol use, and physical activity into our final model (S1 Methods). No combination of these additional covariates had a significant effect on model estimates, so they were excluded from the final models.

To mitigate concerns about reverse causality in participants who made dietary changes or began medication use after receiving advice from a physician, we conducted additional sensitivity analyses for all blood pressure, HDL-C, and FPG models restricted to participants who were not currently taking medication and who had never been diagnosed with the respective risk factor (i.e., high blood pressure, low HDL-C, and high FPG) by a doctor.

All analyses were conducted in Stata 17.0 and p<0.05 was considered significant.

Results and discussion

Results

The final sample size was 8,128 participants for the laboratory-based sample and 3,933 participants for the fasted subsample (Table 3). The survey-weighted prevalence of cardiometabolic risk factors ranged from 36.6% (95% CI: 34.1, 39.1%) for low HDL-C to 62.4% (59.8, 65.0%) for high FPG. The range of PHDI values was 21–125 on a scale from 0 to 140, whereas HEI-2015 values ranged from 15 to 99 on a scale of 0–100, and DASH spanned the theoretical range of 8 to 40. All three dietary indices were approximately normally distributed.

Table 3. Characteristics of eligible participants with two days of dietary recall data, NHANES 2015–2018^*.

Sex
Male	49.1 (3954)
Female	50.9 (4174)
Mean age (SD), years	48.6 (15.6)
Educational attainment
High school equivalent or lower	35.5 (3425)
Some college	32.1 (2575)
College degree or greater	32.4 (2121)
Income
Poverty-to-Income Ratio < 185%	28.6 (3212)
Poverty-to-Income Ratio 185–399%	28.3 (2217)
Poverty-to-Income Ratio ≥ 400%	35.0 (1874)
Missing income information	8.1 (825)
Race/ethnicity
Non-Hispanic white	64.1 (2949)
Non-Hispanic Black	11.1 (1873)
Hispanic	14.8 (2054)
Asian, Multiracial, and Other Non-Hispanic race/ethnicities	10.0 (1252)
Mean (SD) PHDI	62 (54–70)
Mean (SD) HEI-2015	53 (44–63)
Mean (SD) DASH	24 (19–28)
Prevalence of cardiometabolic risk factors
Elevated waist circumference	61.0 (4815)
Elevated blood pressure	43.8 (4132)
Reduced high density lipoprotein cholesterol (HDL-C)	41.7 (3535)
Elevated fasting triglycerides^†	36.6 (1672)
Elevated fasting glucose^†	62.4 (2460)

Open in a new tab

* Values are weighted % (unweighted N) unless otherwise noted. Weighted % accounts for complex survey weights.

^† Results are from fasted subsample only and reflect use of fasted analytic weights.

For continuous index values, the unweighted correlation between HEI-2015 and DASH (ρ = 0.78) was slightly stronger than that of PHDI and DASH (ρ = 0.66) or PHDI and HEI-2015 (ρ = 0.65). The Bland-Altman plots of differences for each pairwise comparison of values are shown in Fig 1. In survey-weighted tables, 45.8% (41.4, 50.4%) of those in the lowest quintile of HEI-2015 were in the lowest quintile of PHDI, 50.7% (44.1, 57.3%) in the lowest quintile of DASH and PHDI, and 62.8% (57.4, 67.9%) of those in the lowest quintile of HEI-2015 were also in the lowest quintile of DASH (Fig 2). For the highest quintile, the concordance was 61.6% (57.2, 65.9%) for PHDI and DASH, 54.4% (49.1, 59.5%) for PHDI and DASH, and 69.0% (62.0, 75.1%) for HEI-2015 and DASH. When looking at all three indices, concordance was 34.7% (30.5, 39.2%) for the lowest quintile–meaning that of participants in quintile 1, 34.7% of participants were in the quintile 1 for all three dietary values–and 41.4% (36.6, 46.4%) for the highest quintile.

Fig 1 — Planetary Health Diet Index, Healthy Eating Index-2015, and Dietary Approaches to Stop Hypertension scores were rescaled from 0 to 100 for comparability.

Fig 2 — Values are percent in a given quintile of one index that are in the same quintile of the other index. Perfect correlation would be 20.00% down the diagonal.

We observed several disparities in diet quality (Table 4). For all three rescaled dietary indices, mean dietary quality was lower for men than for women, and tended to be lower for younger individuals. People with low income and low education, as well as individuals who identified as Non-Hispanic Black, also had lower dietary quality as measured by all three indices. For PHDI and DASH only, there was also a significant gradient in dietary quality across income category. Finally, individuals who identified as Hispanic had lower dietary scores as measured by PHDI or DASH, but not for HEI-2015.

Table 4. Predicted standardized PHDI, HEI-2015, and DASH value by sociodemographic characteristics, NHANES 2015–2018^*,^†.

	PHDI	HEI-2015	DASH
Sex
Male^‡	44.0 (42.8, 45.1)	44.2 (42.8, 45.5)	46.0 (44.4, 47.5)
Female	47.1*** (46.1, 48.2)	47.2*** (45.6, 48.7)	51.7*** (49.8, 53.5)
Age category
20–29^‡	43.0 (41.5, 44.5)	41.4 (39.4, 43.4)	43.5 (41.4, 45.6)
30–39	45.0 (43.3, 46.6)	43.9** (42.1, 45.7)	45.9* (43.8, 48.1)
40–49	44.9* (43.8, 46.0)	45.5*** (44.4, 46.7)	48.1*** (46.3, 49.8)
50–59	46.1** (44.2, 48.1)	46.8*** (44.6, 49.1)	49.6*** (46.9, 52.3)
60–69	47.1*** (45.7, 48.5)	48.2*** (46.3, 50.0)	52.2*** (50.4, 54.0)
70–79	48.3*** (46.8, 49.8)	49.7*** (48.0, 51.3)	55.3*** (53.5, 57.1)
80 or older	47.1*** (45.6, 48.6)	48.5*** (46.3, 50.6)	56.1*** (54.0, 58.3)
Income
PIR < 185% ^‡	42.5 (41.4, 43.6)	42.3 (41.0, 43.6)	44.5 (42.9, 46.1)
PIR 185–399%	45.0*** (43.7, 46.3)	44.9*** (43.3, 46.6)	48.1*** (46.2, 50.0)
PIR ≥ 400%	48.5*** (47.3, 49.7)	48.9*** (47.1, 50.7)	53.0*** (50.9, 55.1)
Missing	45.7** (43.7, 47.6)	46.4*** (44.0, 48.8)	49.2*** (46.6, 51.2)
Education
High school or lower^‡	42.4 (41.5, 43.4)	42.0 (40.6, 43.4)	43.8 (42.1, 45.4)
Some college	44.1** (42.9, 45.3)	43.9* (42.3, 45.5)	46.9*** (45.0, 48.8)
College degree or greater	50.5*** (49.3, 51.7)	51.5*** (50.0, 53.1)	56.5*** (54.8, 58.1)
Race/ethnicity
Non-Hispanic white^‡	46.2 (45.1, 47.3)	45.7 (44.2, 47.2)	49.9 (48.2, 51.6)
Non-Hispanic Black	40.3*** (39.3, 41.4)	42.5*** (40.9, 44.2)	41.8*** (40.0, 43.6)
Hispanic	44.4** (43.3, 45.4)	45.3 (43.8, 46.9)	47.9* (46.3, 49.5)
Asian, Multiracial, and Other Non-Hispanic	49.0*** (47.3, 50.7)	49.7*** (47.9, 51.6)	51.6 (49.2, 53.9)

Open in a new tab

* Distribution of dietary scores were standardized to 0 to 100 scale for each index.

^† Values are from linear regression with standardized continuous score (range: 0–100) as the dependent variable and dummy indicators for sociodemographic category as independent variables.

^‡ Indicates reference category

* p<0.05, **p<0.01, ***p<0.001 for the difference from the referent category

A higher score on all three dietary indices was associated with health-promoting differences in cardiometabolic risk factors. Waist circumference decreased by a range of 1.5 (0.5, 2.5) centimeters per 1-SD increase in PHDI to 2.5 (1.8, 3.2) centimeters per 1-SD increase in DASH (Table 5). We observed comparable results using the binary risk factor thresholds: risk of high waist circumference decreased by 3.8 (1.9, 5.7), 4.4 (2.2, 6.5) and 4.7 (2.5, 7.0) percentage points per 1-SD increase in the PHDI, HEI-2015, and DASH values, respectively.

Table 5. Predicted change in continuous and binary cardiometabolic risk factors per one standard-deviation change in PHDI, HEI-2015, and DASH, NHANES 2003–2018^*.

	PHDI	HEI-2015	DASH	p-value^‡
Waist circumference
Centimeters	-1.9*** (-2.5, -1.2)	-2.3*** (-3.0, -1.5)	-2.5*** (-3.2, -1.8)	0.03
Predicted probability of high waist circumference	-3.8*** (-5.7, -1.9)	-4.4*** (-6.5, -2.2)	-4.7*** (-7.0, -2.5)	0.54
Blood pressure
Systolic blood pressure, mm Hg	-0.5 (-1.2, -0.1)	-0.9** (-1.5, -0.4)	-1.2*** (-1.7, -0.6)	0.34
Diastolic blood pressure, mm Hg	-0.2 (-0.7, 0.2)	-0.5 (-1.1, 0.1)	-0.7* (-1.3, -0.2)	0.49
Predicted probability of high blood pressure	-2.9* (-5.2, -0.6)	-3.7** (-5.7, -1.7)	3.9*** (-5.6, -2.1)	0.60
High-density lipoprotein cholesterol, HDL-C
mg/dL	1.9*** (1.3, 2.5)	2.1*** (1.6, 2.5)	1.5*** (0.9, 2.1)	0.20
Predicted probability of low HDL-C	-4.2*** (-5.8, 2.6)	-4.3*** (-5.8, -2.8)	-2.9** (-4.8, -1.0)	0.19
Fasting plasma glucose, FPG
mg/dL	-0.2 (-1.2, 0.8)	-0.3 (-1.7, 1.1)	0.0 (-1.6, 1.6)	0.64
Predicted probability of high FPG	-2.3 (-4.8, 0.0)	-2.8** (-4.8, -0.1)	-2.4* (-4.5, -0.3)	0.71
Fasting triglycerides
mg/dL^†	-4.6* (-9.2, -0.1)	-3.7* (-8.0, -0.5)	-5.4* (-9.3, -1.4)	0.59
Predicted probability of high fasting triglycerides	-1.8 (-4.1, 0.0)	0.9 (-3.6, 1.8)	-1.0 (-3.4, 1.4,)	0.66

Open in a new tab

* Survey-weighted regression models were adjusted for age, sex, income, education, race/ethnicity, and total energy intake.

^† *p<0.05, **p<0.01, ***p<0.001 for the difference from 0 as estimated by a Wald test.

^‡ P value for the joint comparison of the three indices as estimated by a Wald test.

For blood pressure, a 1-SD increase in PHDI and HEI-2015 scores were associated with lower systolic blood pressure, but not with lower diastolic blood pressure (Table 5). Higher DASH z-score was associated with lower systolic and diastolic blood pressure. In logistic regression, the predicted probability of high blood pressure decreased across the three indices, ranging from a reduction of 2.9 (0.6, 5.2) percentage points for a 1-SD increase in PHDI to 3.9 (2.2, 5.6) percentage points for DASH.

All three dietary indices were associated with higher HDL-C, ranging from 1.5 (0.9, 2.1) mg/dL higher for a 1-SD increase in DASH to 2.1 (1.6, 2.5) mg/dL higher for HEI-2015 (Table 5). The predicted probability of low HDL-C decreased by a range of 2.9 (1.0, 4.8) percentage points for a 1-SD increase in DASH to 4.3 (2.5, 5.8) percentage points for every 1-SD increase in HEI-2015.

In the fasted subsample, there were no significant associations between dietary index z-score and FPG (Table 5). For the logistic regression analyses using the MetS cutoffs, the predicted probability of high FPG decreased by 2.8 (0.1, 4.8) percentage points for a 1-SD increase in HEI-2015 and 2.4 (0.3, 4.5) percentage points per 1-SD increase in DASH. We did not observe a significant association between PHDI and the binary high FPG outcome.

For fasting triglycerides, a 1-SD increase in DASH was associated with lower fasting triglycerides (Table 5). PHDI and HEI-2015 were not associated with continuous fasting triglycerides. We did not observe a significant association between any of the dietary indices and predicted probability of elevated fasting triglycerides.

In sensitivity analyses of participants who had not been previously diagnosed with the given risk factor, the pattern of results was consistent with the main analyses for blood pressure (N = 4921) and HDL-C (N = 4580, S2 Table). For continuous results of FPG (N = 3094), there was still a negative association between higher dietary index score and lower FPG for all three indices, although the magnitude of the results was attenuated. Additionally, in the sensitivity analyses for FPG, higher PHDI was associated with a lower predicted probability of high FPG (S2 Table). Logistic regression using quintiles of PHDI as the exposure did not substantively impact our conclusions (S2 Fig, S3 Table).

Discussion

To our knowledge, this is the first study to compare a dietary index created with both health and environmental considerations, the PHDI, to two frequently used dietary indices created with health considerations only (HEI-2015 and DASH). We found a moderate correlation between the indices, with HEI-2015 and DASH more strongly correlated with each other than with PHDI. As expected, across the indices, higher diet quality was correlated with lower predicted probability of cardiometabolic risk across the risk factors examined here. Importantly, our results from the US are consistent with analyses of EAT-Lancet style dietary patterns in other countries that have found that a higher intake of this dietary pattern was associated with lower risk of type II diabetes in Mexico [31], the UK [5], and Denmark [32] and lower prevalence of cardiometabolic risk in the UK [5] and Brazil [33]. Finally, we find that disparities in diet quality are consistent across the three indices, highlighting the need for policies to promote access to healthy diets for vulnerable populations in the US.

This study is among the first to examine how a dietary pattern that measures adherence to the EAT-Lancet guidelines, the PHDI, compares to two well-established ways of measuring healthy diets. All three dietary indices share some common elements, such as encouraging high intakes of fruit, vegetables, and whole grains, and discouraging intake of added sugar and saturated fat. Yet of the three indices examined here, population-level distribution of PHDI values was lowest, and on the Bland-Altman plots were consistently lower than either HEI-2015 or DASH. This is likely because HEI-2015 is designed to reflect adherence to the Dietary Guidelines for Americans that were created to promote health within the American cultural context, and because DASH is designed to reflect hypertension risk, but its values are derived based on the distribution of intake in the underlying NHANES population. In contrast, PHDI is intended as a global reference diet that incorporates both diet and environmental risk using pre-defined cutpoints.

With this context in mind, the different ways that HEI-2015, DASH, and PHDI treat food groups makes the same diet score differently. For example, PHDI discourages starchy vegetables, emphasizes a high intake of plant sources of proteins such as legumes, nuts and seeds and has stricter scoring criteria for added sugars and saturated/trans fats than do HEI-2015 or DASH, such that the median value for these components was zero on the PHDI. Both HEI-2015 and DASH consider starchy vegetables under the encouraged total vegetable component. HEI-2015 scoring does not use mutually-exclusive categories and triple counts beans and legumes in the total vegetables, greens and beans, and seafood and plant proteins components [8], leading to higher HEI-2015 values for the same quantity of food. Additionally, PHDI recommends a maximum of 14 grams of red and processed meat intake per day. But the median value on the PHDI red and processed meat component was 5 out of 10, and the median intake of red and processed meat was over four times that of the PHDI recommendations, at 62 grams. HEI-2015, on the other hand, does not place an upper limit on meat intake and in fact encourages it in the total protein foods component, whose median value was the maximum 5 out of 5 points. Taken together, the differences in index construction, in scoring criteria for added sugars and saturated/trans fats, and in the conceptualization of red and processed meat as a discouraged or an encouraged component could explain the differences in the distribution of PHDI, HEI-2015, and DASH scores observed in our descriptive analyses.

Despite these differences, PHDI, HEI-2015, and DASH performed comparably in our primary analyses. First, American dietary quality according to each index was well below the theoretical maximum, aligning with other studies which similarly find that the average diets of Americans do not conform to dietary recommendations. Second, and most importantly, higher dietary quality as measured by each of these indexes is associated with lower cardiometabolic risk factors [10, 34]. Third, the indices performed comparably with respect to correlations with the cardiometabolic risk factors we examined, although PHDI was the only index that was associated with lower risk of elevated fasting triglycerides and was not as strongly associated with blood pressure when comparing intake quintiles. For triglycerides, this could be due to the inclusion of starchy vegetables as a separate, discouraged component in PHDI as well as a lower maximum saturated fat value. Both high intake of low-glycemic foods and saturated fats are associated with high triglycerides [35, 36]. On the other hand, PHDI does not have a sodium component where the other two indices do, and high sodium intake is a strong predictor of high blood pressure [37]. Despite these differences, all three diets have healthy plant-based options, which have not only been associated with lower cardiometabolic risk in a large US-based cohort study, but also have significant benefits for environmental sustainability [38].

We also observed disparities in diet quality across the three indices, such that populations that were Black or that had low levels of income or education had poorer diet quality. The disparities for PHDI were consistent with those observed for HEI-2015 and for DASH. Indeed, diet disparities in the US have been well-documented [12, 39, 40] and are tied to a combination of physical, social, economic, and political factors that make it difficult to access and afford healthy food [41]. Due to these structural factors, vulnerable populations in the US will also be disproportionately impacted by increases in food prices caused by climate change, exacerbating disparities in both food security and dietary quality [42]. These populations are also more susceptible to other threats to health and livelihood caused by climate change, again due to systematic inequalities that increase their risk of exposure to climate events and negatively impact their capacity to adapt [43, 44]. Ideally, policy solutions would address upstream determinants of health disparities and would lead to improvements in dietary quality measured by PHDI, HEI-2015, and DASH. But from a holistic health perspective, addressing disparities in PHDI–which is designed to address both nutritional and environmental aspects of long-term health–could have even greater benefits than using an index that considers nutrition alone.

Limitations and strengths

The present study has several limitations. Twenty-four hour recall data are subject to measurement error and do not represent usual intake. However, we use data from two days of dietary recall to obtain more information on participants’ diets and restricted our sample to participants with plausible total energy intakes. Additionally, PHDI is scored based on fixed intakes for a 2500 kilocalorie/day diet, while HEI-2015 and DASH use the energy density approach for scoring. NHANES is a cross-sectional survey, so we cannot establish causal inference for long-term disease risk, and reverse causality is possible. We did, however, conduct rigorous sensitivity analyses in undiagnosed participants, which mitigate concerns about dietary changes made at the advice of a physician.

This study also has several strengths. It is the first to use nationally-representative data to examine the correlation between the EAT-Lancet Commission’s dietary recommendations and cardiometabolic risk factors in the US. It also provides valuable context by directly comparing the PHDI with two other well-established dietary indices.

Conclusion

Our analysis suggests that sustainability-focused dietary recommendations, which we operationalized using the PHDI, have similar benefits for cardiometabolic risk factors as HEI-2015 and DASH. There is a need for effective policy solutions to support healthy diets overall, and particularly for populations suffering from a high burden of diet-related disease. Including sustainability in dietary guidelines can have environmental co-benefits while promoting population-level cardiometabolic health.

Supporting information

S1 Methods

(DOCX)

Click here for additional data file.^{(17.4KB, docx)}

S1 Table. Scoring criteria for the Planetary Health Diet Index (PHDI).

* Grams per day calculated from dry weight. ^† To calculate the score for the legumes component, the non-soy and soy subcomponents are each weighted at 0.5.

(DOCX)

Click here for additional data file.^{(18.5KB, docx)}

S2 Table. Predicted change in continuous and binary cardiometabolic risk factors per one standard-deviation score in Planetary Health Diet Index, Healthy Eating Index-2015, and Dietary Approaches to Stop Hypertension score among undiagnosed participants only, National Health and Nutrition Examination Survey 2003–2018*.

* Survey-weighted regression models were adjusted for age, sex, income, education, race/ethnicity, and total energy intake. ^† mg/dL = milligrams per deciliter.

(DOCX)

Click here for additional data file.^{(18.8KB, docx)}

S3 Table. Predicted probability of cardiometabolic risk factor by quintile of Planetary Health Diet Index, Healthy Eating Index-2015, and Dietary Approaches to Stop Hypertension score, National Health and Nutrition Examination Survey 2003–2018*,^†.

* Survey-weighted logistic regression models were adjusted for age, sex, income, education, race/ethnicity, and total energy intake. ^† * p<0.05, ** p<0.01, *** p<0.001. ^‡ Contrast is from Stata’s postestimation margins, dydx command and represents percentage point reduction in predicted probability from Quintile 1 to Quintile 5.

(DOCX)

Click here for additional data file.^{(21.4KB, docx)}

S4 Table. Predicted probability of cardiometabolic risk factor by quintile of Planetary Health Diet Index, Healthy Eating Index-2015, and Dietary Approaches to Stop Hypertension value among undiagnosed participants only, National Health and Nutrition Examination Survey 2003–2018*,^†.

* Survey-weighted logistic regression models were adjusted for age, sex, income, education, race/ethnicity, and total energy intake.^† * p<0.05, ** p<0.01, *** p<0.001. ^‡ Contrast is from Stata’s postestimation margins, dydx command and represents percentage point reduction in predicted probability from Quintile 1 to Quintile 5.

(DOCX)

Click here for additional data file.^{(19.6KB, docx)}

S1 Fig. Radar plots of median component scores for Planetary Health Diet Index (PHDI), Healthy Eating Index-2015 (HEI-2015), and Dietary Approaches to Stop Hypertension (DASH), National Health and Nutrition Examination Survey 2015–2018.

* All dietary pattern component scores range 0–10 unless otherwise noted. ^† Component score range: 0–5.

(TIF)

Click here for additional data file.^{(804.7KB, tif)}

S2 Fig. Estimated change in predicted probability of cardiometabolic risk factors between Quintiles 1 and 5 of Planetary Health Diet Index, Healthy Eating Index-2015, and Dietary Approaches to Stop Hypertension score*,^†.

* Logistic regression models were adjusted for age, sex, income, education, and race/ethnicity. * p<0.05, ** p<0.01, *** p<0.001 for the estimated contrast between Quintile 1 and Quintile 5.

(TIF)

Click here for additional data file.^{(309.7KB, tif)}

S1 Checklist. STROBE statement—Checklist of items that should be included in reports of observational studies.

(DOCX)

Click here for additional data file.^{(35.9KB, docx)}

Data Availability

The data underlying the results presented in the study are available from the National Health and Nutrition Examination Survey website, https://www.cdc.gov/nchs/nhanes/index.htm.

Funding Statement

SMF, LMJ, and LST received funding from Wellcome Trust Award Number 216042/Z/19/Z, https://https://wellcome.org/ The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

References

1.GBD Compare. In: Institute for Health Metrics and Evaluation [Internet]. Seattle, WA: IHME, University of Washington; 2007 -. [cited 2 February 2022]. http://vizhub.healthdata.org/gbd-compare.
2.Murphy SL, Kochanek KD, Xu J, Arias E. NCHS Data Brief No. 427: Mortality in the United States, 2020. Hyattesvilee, MD: Centers for Disease Control and Prevention, National Center for Health Statistics; 2021.
3.Willett W, Rockström J, Loken B, Springmann M, Lang T, Vermeulen S, et al. Food in the Anthropocene: the EAT–Lancet Commission on healthy diets from sustainable food systems. Lancet. 2019;393(10170): 447–92. doi: 10.1016/S0140-6736(18)31788-4 [DOI] [PubMed] [Google Scholar]
4.Frank SM, Jaacks LM, Adair LS, Avery CL, Rose D, Taillie LS. Adherence to the Planetary Health Diet Index and Correlation with Nutrients of Public Health Concern: An analysis of NHANES 2003–2018. Am J Clin Nutr. 2023; Under review. [DOI] [PMC free article] [PubMed] [Google Scholar]
5.Knuppel A, Papier K, Key TJ, Travis RC. EAT-Lancet score and major health outcomes: the EPIC-Oxford study. Lancet. 2019;394(10194): 213–4. doi: 10.1016/S0140-6736(19)31236-X [DOI] [PubMed] [Google Scholar]
6.Kesse-Guyot E, Rebouillat P, Brunin J, Langevin B, Allès B, Touvier M, et al. Environmental and nutritional analysis of the EAT-Lancet diet at the individual level: insights from the NutriNet-Santé study. J Clean Prod. 2021;296: 126555. [Google Scholar]
7.Cacau LT, De Carli E, De Carvalho AM, Lotufo PA, Moreno LA, Bensenor IM, et al. Development and Validation of an Index Based on EAT-Lancet Recommendations: The Planetary Health Diet Index. Nutrients. 2021;13(5): 1698. doi: 10.3390/nu13051698 [DOI] [PMC free article] [PubMed] [Google Scholar]
8.Krebs-Smith SM, Pannucci TE, Subar AF, Kirkpatrick SI, Lerman JL, Tooze JA, et al. Update of the Healthy Eating Index-2015. J Acad Nutr Diet 2018;118(9): 1591–1602. [DOI] [PMC free article] [PubMed] [Google Scholar]
9.Fung TT, Chiuve SE, McCullough ML, Rexrode KM, Logroscino G, Hu FB. Adherence to a DASH-style diet and risk of coronary heart disease and stroke in women. Arch Intern Med. 2008;168(7): 713–20. doi: 10.1001/archinte.168.7.713 [DOI] [PubMed] [Google Scholar]
10.Morze J, Danielewicz A, Hoffmann G, Schwingshackl L. Diet Quality as Assessed by the Healthy Eating Index, Alternate Healthy Eating Index, Dietary Approaches to Stop Hypertension Score, and Health Outcomes: A Second Update of a Systematic Review and Meta-Analysis of Cohort Studies. J Acad Nutr Diet. 2020;120(12):1998–2031.e15. doi: 10.1016/j.jand.2020.08.076 [DOI] [PubMed] [Google Scholar]
11.Hu EA, Steffen LM, Coresh J, Appel LJ, Rebholz CM. Adherence to the Healthy Eating Index–2015 and Other Dietary Patterns May Reduce Risk of Cardiovascular Disease, Cardiovascular Mortality, and All-Cause Mortality. J Nutr. 2020;150(2): 312–21. doi: 10.1093/jn/nxz218 [DOI] [PMC free article] [PubMed] [Google Scholar]
12.Liu J, Micha R, Li Y, Mozaffarian D. Trends in Food Sources and Diet Quality Among US Children and Adults, 2003–2018. JAMA Network Open. 2021;4(4): e215262. doi: 10.1001/jamanetworkopen.2021.5262 [DOI] [PMC free article] [PubMed] [Google Scholar]
13.Monsivais P, Rehm CD, Drewnowski A. The DASH diet and diet costs among ethnic and racial groups in the United States. JAMA internal medicine. 2013;173(20): 1922–4. doi: 10.1001/jamainternmed.2013.9479 [DOI] [PMC free article] [PubMed] [Google Scholar]
14.National Health and Nutrition Examination Survey (NHANES) MEC In-Person Dietary Interviewers Procedures Manual. Hyattsville, MD: Centers for Disease Control, National Center for Health Statistics; 2017.
15.National Health and Nutrition Examination Survey—Module 5: Reliability of Estimates. Hyattsville, MD: Centers for Disease Control, National Center for Health Statistics; 2021.
16.Parker J, Talih M, Malec DJ, Beresovsky V, Carroll MD, Gonzalez JF, et al. National Center for Health Statistics Data Presentation Standards for Proportions. Hyattsville, MD: Centers for Disease Control and Prevention, National Center for Health Statistics; 2017.
17.Willett W. Nutritional Epidemiology: Oxford University Press; 2012.
18.Steinfeldt L, Anand J, Murayi T. Food reporting patterns in the USDA Automated Multiple-Pass method. Procedia Food Sci. 2013;2: 145–56. [Google Scholar]
19.Bowman SA, Clemens JC, Friday JE, Moshfegh AJ. Food Patterns Equivalents Database 2017–2018: Methodology and User Guide. Beltsville, MD: US Department of Agriculture, Agricultural Research Service, Beltsville Human Nutrition Research Center; 2020.
20.Willett WC, Howe GR, Kushi LH. Adjustment for total energy intake in epidemiologic studies. Am J Clin Nutr. 1997;65(4): 1220S–8S. doi: 10.1093/ajcn/65.4.1220S [DOI] [PubMed] [Google Scholar]
21.Dietary Gudelines for Americans, 2020–2025. 9th edition: US Department of Agriculture and US. Department of Health and Human Services; 2020.
22.Frank SM, Webster J, McKenzie B, Geldsetzer P, Manne-Goehler J, Andall-Brereton G, et al. Consumption of Fruits and Vegetables Among Individuals 15 Years and Older in 28 Low- and Middle-Income Countries. J Nutr. 2019;149(7): 1252–9. doi: 10.1093/jn/nxz040 [DOI] [PubMed] [Google Scholar]
23.Struijk EA, Hagan KA, Fung TT, Hu FB, Rodríguez-Artalejo F, Lopez-Garcia E. Diet quality and risk of frailty among older women in the Nurses’ Health Study. Am J Clin Nutr. 2020;111(4): 877–83. doi: 10.1093/ajcn/nqaa028 [DOI] [PMC free article] [PubMed] [Google Scholar]
24.Alberti KGMM Eckel RH, Grundy SM Zimmet PZ, Cleeman JI Donato KA, et al. Harmonizing the Metabolic Syndrome. Circulation. 2009;120(16):1 640–5. [DOI] [PubMed] [Google Scholar]
25.National Health and Nutrition Examination Survey (NHANES) Anthropometry Procedures Manal. Hyattsville, MD: Centers for Disease Control and Prevention, National Center for Health Statistics;. 2017.
26.National Health and Nutrition Examination Survey (NHANES) Laboratory MEC Manual. Hyattsville, MD: Centers for Disease Control and Prevention, National Center for Health Statistics;. 2017.
27.Lacko AM, Maselko J, Popkin B, Ng SW. Socio-economic and racial/ethnic disparities in the nutritional quality of packaged food purchases in the USA, 2008–2018. Public Health Nutr. 2021: 1–13. doi: 10.1017/S1368980021000367 [DOI] [PMC free article] [PubMed] [Google Scholar]
28.Frank SM, Jaacks LM, Batis C, Vanderlee L, Taillie LS. Patterns of Red and Processed Meat Consumption across North America: A Nationally Representative Cross-Sectional Comparison of Dietary Recalls from Canada, Mexico, and the United States. Int J Environ Res Public Health. 2021;18(1):3 57. doi: 10.3390/ijerph18010357 [DOI] [PMC free article] [PubMed] [Google Scholar]
29.Health National and Nutrition Examination Survey (NHANES) Interviewer Procedures Manual. Hyattsville, MD: Centers for Disease Control, National Center for Health Statistics; 2017. [Google Scholar]
30.Bland JM, Altman D. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet. 1986;327(8476): 307–10. [PubMed] [Google Scholar]
31.López GE, Batis C, González C, Chávez M, Cortés-Valencia A, López-Ridaura R, et al. EAT-Lancet Healthy Reference Diet score and diabetes incidence in a cohort of Mexican women. Eur J Clin Nutr. 2022;77(3):348–355: 1–8. doi: 10.1038/s41430-022-01246-8 [DOI] [PubMed] [Google Scholar]
32.Langmann F, Ibsen DB, Tjønneland A, Olsen A, Overvad K, Dahm CC. Adherence to the EAT-Lancet diet is associated with a lower risk of type 2 diabetes: the Danish Diet, Cancer and Health cohort. Eur J Nutr. 2023;62(3): 1493–1502. doi: 10.1007/s00394-023-03090-3 [DOI] [PubMed] [Google Scholar]
33.Cacau LT, Benseñor IM, Goulart AC, Cardoso LdO, Santos IdS, Lotufo PA, et al. Adherence to the EAT-Lancet sustainable reference diet and cardiometabolic risk profile: cross-sectional results from the ELSA-Brasil cohort study. Eur J Nutr. 2023;62(2): 807–17. doi: 10.1007/s00394-022-03032-5 [DOI] [PubMed] [Google Scholar]
34.Jacobs S, Boushey CJ, Franke AA, Shvetsov YB, Monroe KR, Haiman CA, et al. A priori-defined diet quality indices, biomarkers and risk for type 2 diabetes in five ethnic groups: the Multiethnic Cohort. Br J Nutr. 2017;118(4): 312–20. doi: 10.1017/S0007114517002033 [DOI] [PMC free article] [PubMed] [Google Scholar]
35.Parks EJ, Hellerstein MK. Carbohydrate-induced hypertriacylglycerolemia: historical perspective and review of biological mechanisms. Am J Clin Nutr. 2000;71(2): 412–33. doi: 10.1093/ajcn/71.2.412 [DOI] [PubMed] [Google Scholar]
36.Appel LJ, Sacks FM, Carey VJ, Obarzanek E, Swain JF, Miller ER, et al. Effects of protein, monounsaturated fat, and carbohydrate intake on blood pressure and serum lipids: results of the OmniHeart randomized trial. JAMA. 2005;294(19): 2455–64. doi: 10.1001/jama.294.19.2455 [DOI] [PubMed] [Google Scholar]
37.Aburto NJ, Ziolkovska A, Hooper L, Elliott P, Cappuccio FP, Meerpohl JJ. Effect of lower sodium intake on health: systematic review and meta-analyses. BMJ. 2013;346: f1326. doi: 10.1136/bmj.f1326 [DOI] [PMC free article] [PubMed] [Google Scholar]
38.Musicus AA, Wang DD, Janiszewski M, Eshel G, Blondin SA, Willett W, et al. Health and environmental impacts of plant-rich dietary patterns: a US prospective cohort study. Lancet Planet Health. 2022;6(11): e892–e900. doi: 10.1016/S2542-5196(22)00243-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
39.Leung CW, Tester JM. The association between food insecurity and diet quality varies by race/ethnicity: an analysis of national health and nutrition examination survey 2011–2014 results. J Acad Nutr Diet. 2019;119(10): 1676–86. doi: 10.1016/j.jand.2018.10.011 [DOI] [PMC free article] [PubMed] [Google Scholar]
40.Zhang FF, Liu J, Rehm CD, Wilde P, Mande JR, Mozaffarian D. Trends and Disparities in Diet Quality Among US Adults by Supplementary Nutrition Assistance Program Participation Status. JAMA Network Open. 2018;1(2): e180237. [DOI] [PMC free article] [PubMed] [Google Scholar]
41.Neff RA, Palmer AM, McKenzie SE, Lawrence RS. Food systems and public health disparities. J Hunger Environ Nutr. 2009;4(3–4): 282–314. doi: 10.1080/19320240903337041 [DOI] [PMC free article] [PubMed] [Google Scholar]
42.Hasegawa T, Sakurai G, Fujimori S, Takahashi K, Hijioka Y, Masui T. Extreme climate events increase risk of global food insecurity and adaptation needs. Nature Food. 2021;2(8): 587–95. doi: 10.1038/s43016-021-00335-4 [DOI] [PubMed] [Google Scholar]
43.Adepoju OE, Han D, Chae M, Smith KL, Gilbert L, Choudhury S, et al. Health Disparities and Climate Change: The Intersection of Three Disaster Events on Vulnerable Communities in Houston, Texas. Int J Environ Res Public Health. 2021;19(1): 35. doi: 10.3390/ijerph19010035 [DOI] [PMC free article] [PubMed] [Google Scholar]
44.Shepherd M, KC B. Climate change and African Americans in the USA. Geography Compass. 2015;9(11): 579–91. [Google Scholar]

PLoS One. doi: 10.1371/journal.pone.0296069.r001

Decision Letter 0

Mohammad Reza Mahmoodi

Transfer Alert

This paper was transferred from another journal. As a result, its full editorial history (including decision letters, peer reviews and author responses) may not be present.

11 Jul 2023

PONE-D-23-16525Dietary quality and cardiometabolic indicators in the USA: A comparison of the Planetary Health Diet Index, Healthy Eating Index-2015, and Dietary Approaches to Stop HypertensionPLOS ONE

Dear Dr. Frank,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process. Kindly exert all the suggestions of the reviewers and academic editor of the manuscript so as not to be in the process of re-review or rejection of the manuscript. All applied changes should be yellow highlighted.

Please submit your revised manuscript by Aug 25 2023 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
A marked-up copy of your manuscript that highlights changes in YELLOW made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols.

We look forward to receiving your revised manuscript.

Kind regards,

Mohammad Reza Mahmoodi, Ph.D.

Academic Editor

PLOS ONE

Journal Requirements:

When submitting your revision, we need you to address these additional requirements.

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at

https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and

https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf

2. Please provide additional details regarding participant consent. In the ethics statement in the Methods and online submission information, please ensure that you have specified (1) whether consent was informed and (2) what type you obtained (for instance, written or verbal, and if verbal, how it was documented and witnessed). If your study included minors, state whether you obtained consent from parents or guardians. If the need for consent was waived by the ethics committee, please include this information.

If you are reporting a retrospective study of medical records or archived samples, please ensure that you have discussed whether all data were fully anonymized before you accessed them and/or whether the IRB or ethics committee waived the requirement for informed consent. If patients provided informed written consent to have data from their medical records used in research, please include this information

Once you have amended this/these statement(s) in the Methods section of the manuscript, please add the same text to the “Ethics Statement” field of the submission form (via “Edit Submission”).

For additional information about PLOS ONE ethical requirements for human subjects research, please refer to http://journals.plos.org/plosone/s/submission-guidelines#loc-human-subjects-research.

3. Please ensure that you include a title page within your main document.

4. We note that you have included the phrase “data not shown” in your manuscript. Unfortunately, this does not meet our data sharing requirements. PLOS does not permit references to inaccessible data. We require that authors provide all relevant data within the paper, Supporting Information files, or in an acceptable, public repository. Please add a citation to support this phrase or upload the data that corresponds with these findings to a stable repository (such as Figshare or Dryad) and provide and URLs, DOIs, or accession numbers that may be used to access these data. Or, if the data are not a core part of the research being presented in your study, we ask that you remove the phrase that refers to these data.

5. Please include captions for your Supporting Information files at the end of your manuscript, and update any in-text citations to match accordingly. Please see our Supporting Information guidelines for more information: http://journals.plos.org/plosone/s/supporting-information.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Partly

Reviewer #2: Yes

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: Comments to authors:

The main objective of this study was to explore the association between PHDI, HEI, and DASH with cardiometabolic biomarkers. The authors revealed that the association between HEI and DASH with the majority of cardiometabolic biomarkers was stronger than the association between PHDI with cardiometabolic biomarkers.

1. Another very interesting topic that has been addressed by dear authors but not deeply focused on has been socio-economic disparities. Other researchers have shown that the more people deviate from a healthy eating pattern, the more cardiometabolic biomarkers worsen both in people with risk factors such as diabetes and in healthy people. This has been well-established in many studies on food insecurity. In many communities where people are food insecure, their cardiometabolic biomarkers have worsened. In very simple language, food insecurity means insufficiency and imbalance in receiving all food groups in the daily food pattern.

2. Authors should know another point well that all parts of a manuscript/article must follow a certain homogeneity and respond to the main objectives of the study. Therefore, the authors are strongly requested to do the same in the discussion of the manuscript as they showed the correlation difference between the three indicators with selected biomarkers in the results of the study.

3. Another point that the authors know very well is that more than 26 years have been studied on the healthy eating index and more than 20 years on the DASH model, and these two patterns have well proven their role in the nutritional health of the people of a society. Therefore, avoid one-sidedness in the discussion which is the most essential part of the article.

4. The researchers who initially designed, implemented, and presented the healthy eating index and DASH aimed to implement the policies to promote access to healthy diets for vulnerable populations in the US.

5. The strengths and limitations section should be presented in a separate section under the subtitle of “the strengths and limitations” before the conclusion.

6. The last point is that the conclusion at the end of the article should be modified according to the obtained results.

Reviewer #2: I would thank the authors for this valuable article. I have some questions:

1- In Table 1, please say why you report the three indices (PHDI, HEI, and DASH) with median (IQR) instead of Mean (SD).

2- In Table 5, You did not report any p-values for linear and logistic regressions. Please add p-values to test whether, for example, high waist circumference risk significantly differs in the three indices.

**********

6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

**********

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

PLoS One. 2024 Jan 10;19(1):e0296069. doi: 10.1371/journal.pone.0296069.r002

Author response to Decision Letter 0

12 Sep 2023

Journal Requirements:

When submitting your revision, we need you to address these additional requirements.

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at

https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and

https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiaffiliat.pdf

Thank you. We have followed the example formatting above for the manuscript and for the title page.

For additional information about PLOS ONE ethical requirements for human subjects research, please refer to http://journals.plos.org/plosone/s/submission-guidelines#loc-human-subjects-research.

Thank you. We have updated the language as follows: “The study protocols of the NHANES are approved by the Research Ethics Review Board at the National Center for Health Statistics (NCHS) [14]. This is a retrospective study of data that were fully-anonymized before the authors accessed them. Because the de-identified observational data from the National Health and Nutrition Examination Survey are publicly available for download, this study received a determination of Not Human Subjects Research by the Institutional Review Board at [First Author’s Home University]."

3. Please ensure that you include a title page within your main document.

Thank you. We have included a title page in the main document and formatted according to the guidelines available at the link provided by the Journal.

Thank you for this feedback. The cited results are not a core part of the research being presented in the study and the phrase “data not shown” has been removed.

Thank you. We have included the captions for our Supporting Information files at the end of the manuscript. We have additionally uploaded each Supporting Information file individually per the guidelines on the PLOS ONE website.

Reviewer #1: Comments to authors:

Thank you for this comment. We wholeheartedly agree that food insecurity contributes to dietary disparities and to poorer cardiometabolic outcomes, particularly for historically disadvantaged populations. An analysis of food insecurity was beyond the scope of the present manuscript. We did find disparities in dietary quality for all three indices (PHDI, HEI-2015, and DASH) by key sociodemographic indicators, including income, education, and race/ethnicity, and reported on these in the results section. In the discussion we talk about the disparities in dietary quality that were observed in our study and that are consistent with dietary disparities that have been documented repeatedly in the literature (lines 331-332). We also mention that these disparities are due to a variety of structural factors that make accessibility and affordability of health food difficult, and that climate change has the potential to exacerbate these disparities (lines 332-339). We then mention that policy solutions are needed to address upstream determinants of health disparities and improve dietary quality (lines 339-341).

In this way, we hope that our study contributes to the important conversation about disparities in dietary quality, particularly for historically disadvantaged populations, and adds to the calls for policy solutions to ensure equitable access to healthy diets for all Americans.

Thank you for this comment. We believe that the discussion does follow a standard manuscript format, as we first summarize our results, then qualitatively compare the indices (which aligns with our descriptive analyses), summarize and discuss the implications of the similar correlations with cardiometabolic (which aligns with our primary objective and main regression analyses), discuss SD (which aligns with our secondary objective), and then move on to strengths and limitations. We have added some language to the discussion to signal which part of the results we are referring to in a given paragraph (e.g. “our descriptive analyses”, “our primary analyses”).

Please note that because of the similarities between the indices for our primary results, and because of the sheer quantity of results, we did not use discussion space to go through each biomarker individually. We believe such an approach would have been redundant for a reader and distracting from the other points we raised in the discussion.

Thank you for your comment. We agree with the reviewer that HEI and especially DASH are valuable public health nutrition tools. We spend more time discussing the PHDI since it is a novel index and may not be familiar to readers as HEI or DASH, whose benefits are already well-established.

In the revision, we present a more balanced discussion of the three indices in several ways:

• First, in the second and third paragraphs that compare PHDI, HEI-2015, and DASH scoring, we have emphasized that we are comparing the PHDI to two well-established ways of measuring healthy diets (lines 284-285). We have also reworded to emphasize “differences” between the dietary indices (lines 295-296) rather than making comparisons of “worse quality” as in the previous version.

• Second, we have taken out the sentence “Despite these differences, overall healthy plant-based diets – such as the PHDI - have been associated with lower cardiometabolic risk in a large US-based cohort study [40], suggesting that improved long-term adherence to the PHDI would similarly be associated with decreased cardiometabolic risk over time.” We replaced this sentence with “Despite these differences, all three diets have healthy plant-based options, which have not only been associated with lower cardiometabolic risk in a large US-based cohort study, but also have significant benefits for environmental sustainability [40].” (lines 326-328).

• Finally, in the last paragraph of the discussion, we mention that ideal policy solutions “would address upstream determinants of health disparities and would lead to improvements in dietary quality measured by PHDI, HEI-2015, and DASH (lines 339-341).”

Thank you for this comment. We agree that both HEI and DASH could be leveraged to reduce dietary disparities, and acknowledge in both our Introduction section (lines 75-75) and Discussion section (lines 331-332) that there are well-documented disparities for both of these indices. We further mention that policies could lead to improvements in both HEI and DASH as well as in PHDI (lines 339-341).

5. The strengths and limitations section should be presented in a separate section under the subtitle of “the strengths and limitations” before the conclusion.

We appreciate this suggestion. We have delimited “Limitations and strengths” as a subsection under the Results and discussion main heading. Please note that Results and discussion is now one heading rather than two per journal formatting guidelines (available from https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf).

6. The last point is that the conclusion at the end of the article should be modified according to the obtained results.

Thank you. We agree with the reviewer’s comment. We have modified the conclusion to first focus on the primary results of the study (i.e., the similar correlations with cardiometabolic indicators) at a high-level, and then have a high-level summary of the need for policy solutions. The conclusion now reads: “Our analysis suggests that sustainability-focused dietary recommendations, which we operationalized using the PHDI, have similar benefits for cardiometabolic risk factors as HEI-2015 and DASH. There is a need for effective policy solutions to support healthy diets overall, and particularly for populations suffering from a high burden of diet-related disease. Including sustainability in dietary guidelines can have environmental co-benefits while promoting population-level cardiometabolic health.” (lines 359-364).

Reviewer #2: I would thank the authors for this valuable article. I have some questions:

1- In Table 1, please say why you report the three indices (PHDI, HEI, and DASH) with median (IQR) instead of Mean (SD).

Thank you for this comment. We have updated the table to report the mean (SD) of the scores rather than the median (IQR).

Thank you for this comment. We have added columns for the pairwise test of the beta coefficients from each regression model into table 5. We have also added asterisks indicating the difference from zero for each predicted difference provided in table 5, and updated the table legend accordingly.

Attachment

Submitted filename: ReviewerResponses.docx

Click here for additional data file.^{(24.4KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0296069.r003

Decision Letter 1

Mohammad Reza Mahmoodi

10 Oct 2023

PONE-D-23-16525R1Dietary quality and cardiometabolic indicators in the USA: A comparison of the Planetary Health Diet Index, Healthy Eating Index-2015, and Dietary Approaches to Stop HypertensionPLOS ONE

Dear Dr. Frank,

Please submit your revised manuscript by Nov 24 2023 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
A marked-up copy of your manuscript that yellow highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

Please apply the suggestions of the respected reviewer to present the p-values for the logistic regression statistical test in cardiometabolic biomarkers between the three dietary guidelines. The consent of the statistical expert reviewer of your manuscript will be the condition of acceptance of your article.

We look forward to receiving your revised manuscript.

Kind regards,

Mohammad Reza Mahmoodi, Ph.D.

Academic Editor

PLOS ONE

Journal Requirements:

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #1: Yes

Reviewer #2: Yes

**********

6. Review Comments to the Author

Reviewer #1: (No Response)

Reviewer #2: Thank you for your responses to the comments. I ask you for a minor correction to your response.

With comment 2 ("In Table 5, You did not report any p-values for linear and logistic regressions. Please add p-values to test whether, for example, high waist circumference risk significantly differs in the three indices.") I want you to perform a test to compare three indices simultaneously not pair-wise comparisons.

Notice: I have a statistician's point of view on your results. If it is not necessary to compare these three indices (PHDI, HEI, and DASH) from a nutritional point of view, please do not change Table 5.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: Yes: Dr. Mohammad Reza Mahmoodi

Reviewer #2: No

**********

PLoS One. 2024 Jan 10;19(1):e0296069. doi: 10.1371/journal.pone.0296069.r004

Author response to Decision Letter 1

16 Nov 2023

Journal comments:

Statistical expert reviewer comments:

Authors' reply:

We thank the reviewer and the journal for their comments and the opportunity to revise the submission. We have now jointly compared the PHDI, HEI-2015, and DASH indices using a Wald test and report a single p-value. These were done for the linear and logistic regression models whose results are reported in Table 5.

Attachment

Submitted filename: ReviewerResponses.docx

Click here for additional data file.^{(16.6KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0296069.r005

Decision Letter 2

Mohammad Reza Mahmoodi

6 Dec 2023

Dietary quality and cardiometabolic indicators in the USA: A comparison of the Planetary Health Diet Index, Healthy Eating Index-2015, and Dietary Approaches to Stop Hypertension

PONE-D-23-16525R2

Dear Dr. Frank,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.

If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Mohammad Reza Mahmoodi, Ph.D.

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

Reviewer #2: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #2: Yes

**********

6. Review Comments to the Author

Reviewer #2: I would like to thank to the authors for this valuable study and for their efforts to improve the results by addressing reviewers' comments.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #2: No

**********

PLoS One. doi: 10.1371/journal.pone.0296069.r006

Acceptance letter

Mohammad Reza Mahmoodi

14 Dec 2023

PONE-D-23-16525R2

PLOS ONE

Dear Dr. Frank,

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now being handed over to our production team.

At this stage, our production department will prepare your paper for publication. This includes ensuring the following:

* All references, tables, and figures are properly cited

* All relevant supporting information is included in the manuscript submission,

* There are no issues that prevent the paper from being properly typeset

If revisions are needed, the production department will contact you directly to resolve them. If no revisions are needed, you will receive an email when the publication date has been set. At this time, we do not offer pre-publication proofs to authors during production of the accepted work. Please keep in mind that we are working through a large volume of accepted articles, so please give us a few weeks to review your paper and let you know the next and final steps.

Lastly, if your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

If we can help with anything else, please email us at customercare@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Mohammad Reza Mahmoodi

Academic Editor

PLOS ONE

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Methods

(DOCX)

Click here for additional data file.^{(17.4KB, docx)}

S1 Table. Scoring criteria for the Planetary Health Diet Index (PHDI).

* Grams per day calculated from dry weight. ^† To calculate the score for the legumes component, the non-soy and soy subcomponents are each weighted at 0.5.

(DOCX)

Click here for additional data file.^{(18.5KB, docx)}

* Survey-weighted regression models were adjusted for age, sex, income, education, race/ethnicity, and total energy intake. ^† mg/dL = milligrams per deciliter.

(DOCX)

Click here for additional data file.^{(18.8KB, docx)}

(DOCX)

Click here for additional data file.^{(21.4KB, docx)}

* Survey-weighted logistic regression models were adjusted for age, sex, income, education, race/ethnicity, and total energy intake.^† * p<0.05, ** p<0.01, *** p<0.001. ^‡ Contrast is from Stata’s postestimation margins, dydx command and represents percentage point reduction in predicted probability from Quintile 1 to Quintile 5.

(DOCX)

Click here for additional data file.^{(19.6KB, docx)}

* All dietary pattern component scores range 0–10 unless otherwise noted. ^† Component score range: 0–5.

(TIF)

Click here for additional data file.^{(804.7KB, tif)}

* Logistic regression models were adjusted for age, sex, income, education, and race/ethnicity. * p<0.05, ** p<0.01, *** p<0.001 for the estimated contrast between Quintile 1 and Quintile 5.

(TIF)

Click here for additional data file.^{(309.7KB, tif)}

S1 Checklist. STROBE statement—Checklist of items that should be included in reports of observational studies.

(DOCX)

Click here for additional data file.^{(35.9KB, docx)}

Attachment

Submitted filename: ReviewerResponses.docx

Click here for additional data file.^{(24.4KB, docx)}

Attachment

Submitted filename: ReviewerResponses.docx

Click here for additional data file.^{(16.6KB, docx)}

Data Availability Statement

The data underlying the results presented in the study are available from the National Health and Nutrition Examination Survey website, https://www.cdc.gov/nchs/nhanes/index.htm.

[pone.0296069.ref001] 1.GBD Compare. In: Institute for Health Metrics and Evaluation [Internet]. Seattle, WA: IHME, University of Washington; 2007 -. [cited 2 February 2022]. http://vizhub.healthdata.org/gbd-compare.

[pone.0296069.ref002] 2.Murphy SL, Kochanek KD, Xu J, Arias E. NCHS Data Brief No. 427: Mortality in the United States, 2020. Hyattesvilee, MD: Centers for Disease Control and Prevention, National Center for Health Statistics; 2021.

[pone.0296069.ref003] 3.Willett W, Rockström J, Loken B, Springmann M, Lang T, Vermeulen S, et al. Food in the Anthropocene: the EAT–Lancet Commission on healthy diets from sustainable food systems. Lancet. 2019;393(10170): 447–92. doi: 10.1016/S0140-6736(18)31788-4 [DOI] [PubMed] [Google Scholar]

[pone.0296069.ref004] 4.Frank SM, Jaacks LM, Adair LS, Avery CL, Rose D, Taillie LS. Adherence to the Planetary Health Diet Index and Correlation with Nutrients of Public Health Concern: An analysis of NHANES 2003–2018. Am J Clin Nutr. 2023; Under review. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0296069.ref005] 5.Knuppel A, Papier K, Key TJ, Travis RC. EAT-Lancet score and major health outcomes: the EPIC-Oxford study. Lancet. 2019;394(10194): 213–4. doi: 10.1016/S0140-6736(19)31236-X [DOI] [PubMed] [Google Scholar]

[pone.0296069.ref006] 6.Kesse-Guyot E, Rebouillat P, Brunin J, Langevin B, Allès B, Touvier M, et al. Environmental and nutritional analysis of the EAT-Lancet diet at the individual level: insights from the NutriNet-Santé study. J Clean Prod. 2021;296: 126555. [Google Scholar]

[pone.0296069.ref007] 7.Cacau LT, De Carli E, De Carvalho AM, Lotufo PA, Moreno LA, Bensenor IM, et al. Development and Validation of an Index Based on EAT-Lancet Recommendations: The Planetary Health Diet Index. Nutrients. 2021;13(5): 1698. doi: 10.3390/nu13051698 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0296069.ref008] 8.Krebs-Smith SM, Pannucci TE, Subar AF, Kirkpatrick SI, Lerman JL, Tooze JA, et al. Update of the Healthy Eating Index-2015. J Acad Nutr Diet 2018;118(9): 1591–1602. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0296069.ref009] 9.Fung TT, Chiuve SE, McCullough ML, Rexrode KM, Logroscino G, Hu FB. Adherence to a DASH-style diet and risk of coronary heart disease and stroke in women. Arch Intern Med. 2008;168(7): 713–20. doi: 10.1001/archinte.168.7.713 [DOI] [PubMed] [Google Scholar]

[pone.0296069.ref010] 10.Morze J, Danielewicz A, Hoffmann G, Schwingshackl L. Diet Quality as Assessed by the Healthy Eating Index, Alternate Healthy Eating Index, Dietary Approaches to Stop Hypertension Score, and Health Outcomes: A Second Update of a Systematic Review and Meta-Analysis of Cohort Studies. J Acad Nutr Diet. 2020;120(12):1998–2031.e15. doi: 10.1016/j.jand.2020.08.076 [DOI] [PubMed] [Google Scholar]

[pone.0296069.ref011] 11.Hu EA, Steffen LM, Coresh J, Appel LJ, Rebholz CM. Adherence to the Healthy Eating Index–2015 and Other Dietary Patterns May Reduce Risk of Cardiovascular Disease, Cardiovascular Mortality, and All-Cause Mortality. J Nutr. 2020;150(2): 312–21. doi: 10.1093/jn/nxz218 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0296069.ref012] 12.Liu J, Micha R, Li Y, Mozaffarian D. Trends in Food Sources and Diet Quality Among US Children and Adults, 2003–2018. JAMA Network Open. 2021;4(4): e215262. doi: 10.1001/jamanetworkopen.2021.5262 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0296069.ref013] 13.Monsivais P, Rehm CD, Drewnowski A. The DASH diet and diet costs among ethnic and racial groups in the United States. JAMA internal medicine. 2013;173(20): 1922–4. doi: 10.1001/jamainternmed.2013.9479 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0296069.ref014] 14.National Health and Nutrition Examination Survey (NHANES) MEC In-Person Dietary Interviewers Procedures Manual. Hyattsville, MD: Centers for Disease Control, National Center for Health Statistics; 2017.

[pone.0296069.ref015] 15.National Health and Nutrition Examination Survey—Module 5: Reliability of Estimates. Hyattsville, MD: Centers for Disease Control, National Center for Health Statistics; 2021.

[pone.0296069.ref016] 16.Parker J, Talih M, Malec DJ, Beresovsky V, Carroll MD, Gonzalez JF, et al. National Center for Health Statistics Data Presentation Standards for Proportions. Hyattsville, MD: Centers for Disease Control and Prevention, National Center for Health Statistics; 2017.

[pone.0296069.ref017] 17.Willett W. Nutritional Epidemiology: Oxford University Press; 2012.

[pone.0296069.ref018] 18.Steinfeldt L, Anand J, Murayi T. Food reporting patterns in the USDA Automated Multiple-Pass method. Procedia Food Sci. 2013;2: 145–56. [Google Scholar]

[pone.0296069.ref019] 19.Bowman SA, Clemens JC, Friday JE, Moshfegh AJ. Food Patterns Equivalents Database 2017–2018: Methodology and User Guide. Beltsville, MD: US Department of Agriculture, Agricultural Research Service, Beltsville Human Nutrition Research Center; 2020.

[pone.0296069.ref020] 20.Willett WC, Howe GR, Kushi LH. Adjustment for total energy intake in epidemiologic studies. Am J Clin Nutr. 1997;65(4): 1220S–8S. doi: 10.1093/ajcn/65.4.1220S [DOI] [PubMed] [Google Scholar]

[pone.0296069.ref021] 21.Dietary Gudelines for Americans, 2020–2025. 9th edition: US Department of Agriculture and US. Department of Health and Human Services; 2020.

[pone.0296069.ref022] 22.Frank SM, Webster J, McKenzie B, Geldsetzer P, Manne-Goehler J, Andall-Brereton G, et al. Consumption of Fruits and Vegetables Among Individuals 15 Years and Older in 28 Low- and Middle-Income Countries. J Nutr. 2019;149(7): 1252–9. doi: 10.1093/jn/nxz040 [DOI] [PubMed] [Google Scholar]

[pone.0296069.ref023] 23.Struijk EA, Hagan KA, Fung TT, Hu FB, Rodríguez-Artalejo F, Lopez-Garcia E. Diet quality and risk of frailty among older women in the Nurses’ Health Study. Am J Clin Nutr. 2020;111(4): 877–83. doi: 10.1093/ajcn/nqaa028 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0296069.ref024] 24.Alberti KGMM Eckel RH, Grundy SM Zimmet PZ, Cleeman JI Donato KA, et al. Harmonizing the Metabolic Syndrome. Circulation. 2009;120(16):1 640–5. [DOI] [PubMed] [Google Scholar]

[pone.0296069.ref025] 25.National Health and Nutrition Examination Survey (NHANES) Anthropometry Procedures Manal. Hyattsville, MD: Centers for Disease Control and Prevention, National Center for Health Statistics;. 2017.

[pone.0296069.ref026] 26.National Health and Nutrition Examination Survey (NHANES) Laboratory MEC Manual. Hyattsville, MD: Centers for Disease Control and Prevention, National Center for Health Statistics;. 2017.

[pone.0296069.ref027] 27.Lacko AM, Maselko J, Popkin B, Ng SW. Socio-economic and racial/ethnic disparities in the nutritional quality of packaged food purchases in the USA, 2008–2018. Public Health Nutr. 2021: 1–13. doi: 10.1017/S1368980021000367 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0296069.ref028] 28.Frank SM, Jaacks LM, Batis C, Vanderlee L, Taillie LS. Patterns of Red and Processed Meat Consumption across North America: A Nationally Representative Cross-Sectional Comparison of Dietary Recalls from Canada, Mexico, and the United States. Int J Environ Res Public Health. 2021;18(1):3 57. doi: 10.3390/ijerph18010357 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0296069.ref029] 29.Health National and Nutrition Examination Survey (NHANES) Interviewer Procedures Manual. Hyattsville, MD: Centers for Disease Control, National Center for Health Statistics; 2017. [Google Scholar]

[pone.0296069.ref030] 30.Bland JM, Altman D. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet. 1986;327(8476): 307–10. [PubMed] [Google Scholar]

[pone.0296069.ref031] 31.López GE, Batis C, González C, Chávez M, Cortés-Valencia A, López-Ridaura R, et al. EAT-Lancet Healthy Reference Diet score and diabetes incidence in a cohort of Mexican women. Eur J Clin Nutr. 2022;77(3):348–355: 1–8. doi: 10.1038/s41430-022-01246-8 [DOI] [PubMed] [Google Scholar]

[pone.0296069.ref032] 32.Langmann F, Ibsen DB, Tjønneland A, Olsen A, Overvad K, Dahm CC. Adherence to the EAT-Lancet diet is associated with a lower risk of type 2 diabetes: the Danish Diet, Cancer and Health cohort. Eur J Nutr. 2023;62(3): 1493–1502. doi: 10.1007/s00394-023-03090-3 [DOI] [PubMed] [Google Scholar]

[pone.0296069.ref033] 33.Cacau LT, Benseñor IM, Goulart AC, Cardoso LdO, Santos IdS, Lotufo PA, et al. Adherence to the EAT-Lancet sustainable reference diet and cardiometabolic risk profile: cross-sectional results from the ELSA-Brasil cohort study. Eur J Nutr. 2023;62(2): 807–17. doi: 10.1007/s00394-022-03032-5 [DOI] [PubMed] [Google Scholar]

[pone.0296069.ref034] 34.Jacobs S, Boushey CJ, Franke AA, Shvetsov YB, Monroe KR, Haiman CA, et al. A priori-defined diet quality indices, biomarkers and risk for type 2 diabetes in five ethnic groups: the Multiethnic Cohort. Br J Nutr. 2017;118(4): 312–20. doi: 10.1017/S0007114517002033 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0296069.ref035] 35.Parks EJ, Hellerstein MK. Carbohydrate-induced hypertriacylglycerolemia: historical perspective and review of biological mechanisms. Am J Clin Nutr. 2000;71(2): 412–33. doi: 10.1093/ajcn/71.2.412 [DOI] [PubMed] [Google Scholar]

[pone.0296069.ref036] 36.Appel LJ, Sacks FM, Carey VJ, Obarzanek E, Swain JF, Miller ER, et al. Effects of protein, monounsaturated fat, and carbohydrate intake on blood pressure and serum lipids: results of the OmniHeart randomized trial. JAMA. 2005;294(19): 2455–64. doi: 10.1001/jama.294.19.2455 [DOI] [PubMed] [Google Scholar]

[pone.0296069.ref037] 37.Aburto NJ, Ziolkovska A, Hooper L, Elliott P, Cappuccio FP, Meerpohl JJ. Effect of lower sodium intake on health: systematic review and meta-analyses. BMJ. 2013;346: f1326. doi: 10.1136/bmj.f1326 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0296069.ref038] 38.Musicus AA, Wang DD, Janiszewski M, Eshel G, Blondin SA, Willett W, et al. Health and environmental impacts of plant-rich dietary patterns: a US prospective cohort study. Lancet Planet Health. 2022;6(11): e892–e900. doi: 10.1016/S2542-5196(22)00243-1 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0296069.ref039] 39.Leung CW, Tester JM. The association between food insecurity and diet quality varies by race/ethnicity: an analysis of national health and nutrition examination survey 2011–2014 results. J Acad Nutr Diet. 2019;119(10): 1676–86. doi: 10.1016/j.jand.2018.10.011 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0296069.ref040] 40.Zhang FF, Liu J, Rehm CD, Wilde P, Mande JR, Mozaffarian D. Trends and Disparities in Diet Quality Among US Adults by Supplementary Nutrition Assistance Program Participation Status. JAMA Network Open. 2018;1(2): e180237. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0296069.ref041] 41.Neff RA, Palmer AM, McKenzie SE, Lawrence RS. Food systems and public health disparities. J Hunger Environ Nutr. 2009;4(3–4): 282–314. doi: 10.1080/19320240903337041 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0296069.ref042] 42.Hasegawa T, Sakurai G, Fujimori S, Takahashi K, Hijioka Y, Masui T. Extreme climate events increase risk of global food insecurity and adaptation needs. Nature Food. 2021;2(8): 587–95. doi: 10.1038/s43016-021-00335-4 [DOI] [PubMed] [Google Scholar]

[pone.0296069.ref043] 43.Adepoju OE, Han D, Chae M, Smith KL, Gilbert L, Choudhury S, et al. Health Disparities and Climate Change: The Intersection of Three Disaster Events on Vulnerable Communities in Houston, Texas. Int J Environ Res Public Health. 2021;19(1): 35. doi: 10.3390/ijerph19010035 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0296069.ref044] 44.Shepherd M, KC B. Climate change and African Americans in the USA. Geography Compass. 2015;9(11): 579–91. [Google Scholar]

PERMALINK

Dietary quality and cardiometabolic indicators in the USA: A comparison of the Planetary Health Diet Index, Healthy Eating Index-2015, and Dietary Approaches to Stop Hypertension

Sarah M Frank

Lindsay M Jaacks

Christy L Avery

Linda S Adair

Katie Meyer

Donald Rose

Lindsey Smith Taillie

Roles

Abstract

Background

Methods and findings

Conclusions

Introduction

Materials and methods

Study population

Assessment of dietary intake

Planetary Health Diet Index, PHDI

Table 1. Comparison of the dietary components of the Planetary Health Diet Index (PHDI), Healthy Eating Index-2015 (HEI-2015) and Dietary Approaches to Stop Hypertension (DASH).

Healthy Eating Index, HEI-2015

Dietary Approaches to Stop Hypertension, DASH

Cardiometabolic risk factors

Table 2. Criteria used to define binary cardiometabolic risk factor outcomes.

Covariates

Statistical analyses

Results and discussion

Results

Table 3. Characteristics of eligible participants with two days of dietary recall data, NHANES 2015–2018*.

Fig 1. Bland-Altman plots comparing rescaled PHDI, HEI-2015, and DASH values.

Fig 2. Percent agreement for quintiles of PHDI, HEI-2015 and DASH, NHANES 2015–2018.

Table 4. Predicted standardized PHDI, HEI-2015, and DASH value by sociodemographic characteristics, NHANES 2015–2018*,†.

Table 5. Predicted change in continuous and binary cardiometabolic risk factors per one standard-deviation change in PHDI, HEI-2015, and DASH, NHANES 2003–2018*.

Discussion

Limitations and strengths

Conclusion

Supporting information

Data Availability

Funding Statement

References

Decision Letter 0

Mohammad Reza Mahmoodi

Roles

Transfer Alert

Author response to Decision Letter 0

Decision Letter 1

Mohammad Reza Mahmoodi

Roles

Author response to Decision Letter 1

Decision Letter 2

Mohammad Reza Mahmoodi

Roles

Acceptance letter

Mohammad Reza Mahmoodi

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

Similar articles

Cited by other articles

Links to NCBI Databases

Table 3. Characteristics of eligible participants with two days of dietary recall data, NHANES 2015–2018^*.

Table 4. Predicted standardized PHDI, HEI-2015, and DASH value by sociodemographic characteristics, NHANES 2015–2018^*,^†.

Table 5. Predicted change in continuous and binary cardiometabolic risk factors per one standard-deviation change in PHDI, HEI-2015, and DASH, NHANES 2003–2018^*.