Association between diet protein score comprising plants to animal protein ratio and body composition in an Iranian population

Abstract

Obesity is increasingly recognized as a leading cause of death and is associated with various comorbidities. This study evaluates the relationship between protein score, characterized by the plant-to-animal protein ratio (PAR) and total protein per calorie (Pro%), and body composition: fat percentage (FATP), fat mass (FATM), and fat-free mass (FFM). We categorized 4512 individuals (55.2% female) into tertiles based on their protein score and its components. Male participants in the highest and middle protein score tertiles exhibited significantly greater FFM in both adjusted and crude models, and lower FATP and FATM in adjusted model 2. FFM was elevated in the top (P < 0.001) and middle (P = 0.002) Pro% tertiles in males in both adjusted models and only in the top tertile of all models in females (P = 0.003). The analysis of male participants revealed significantly lower FATP and FATM in the highest tertiles of Pro% in adjusted models. Among female participants, only the highest PAR tertile was associated with significantly lower FATM in adjusted model 1 (P = 0.042). Our findings indicate that protein score and its components are associated with favorable body composition differences. Health administrators may leverage these insights to refine dietary guidelines.

Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-024-79982-z.

Introduction

Protein has been widely known as one of the most important elements in a healthy dietary regimen. Different Recommended Dietary Allowances have been proposed based on the subject’s nitrogen balance, ranging from 0.8 g/kg of body weight per day in healthy subjects with minimal physical activity to 2 g/kg, which is believed to be safe for healthy subjects, and up to its upper limits at 3.5 g/kg of body weight per day¹. These protein intakes provide amino acids (AA) that aid in normal physiological and biochemical pathways. Besides this well-known contribution to human health, protein might help regulate body weight and thus reduce overweight and obesity².

Reports have shown an alarming threefold worldwide prevalence of obesity, with the highest rates among individuals aged between 50 and 65 and in regions of America and Europe³. In Iranian populations, studies estimate the prevalence of overweight and obesity to be 20.1% and 13.44%, respectively⁴. Some leading causes of deaths and Disability-adjusted Life Years (DALYs) related to high BMI include ischemic heart disease, stroke, diabetes mellitus, chronic kidney disease, hypertensive heart disease, and low back pain. Additionally, global DALYs related to high body mass index (BMI) have doubled from 1990 to 2017⁵.

Body weight regulation involves complex pathways, including hunger and satiety regulation, energy homeostasis, hormonal disturbances^6–8. While some aspect of these regulations can’t be directly modified⁹ some aspects of body weight regulation can be effectively modified to counter weight gain and obesity, including protein intake.

Protein is widely accepted as the most effective macronutrient to trigger the satiation response through various anorexigenic pathways that suppress hunger and induce satiation¹⁰. Studies have shown that a diet rich in protein can contribute to weight reduction¹¹. Protein affects the energy expenditure of the body via a U-shaped pattern; the body expends some energy to regulate the energy supply of essential nutrients by increasing thermogenesis in a state of severely low protein intake, and higher protein intake increases energy expenditure through various mechanisms, including its turnover, metabolism, and gluconeogenesis^12,13. Severely low protein intake is not an optimal solution as it can cause muscle wasting. In contrast, the advantage of higher protein intake on muscle mass has been widely investigated¹⁴. Type and quantity of protein sources is another important aspect of these favorable effects of protein intake on body composition, as some amino acids, including branched-chain amino acids and tryptophan, aid in reducing weight, while others, like methionine, which is higher in animal-based protein sources compared to plant-based protein, might mitigate these favorable effects¹⁵.

Møller et al.¹⁶ recently developed a protein score based on dietary protein intake and the plant-based to animal-based protein ratio. This method is useful as it contains not only the total amount of protein consumed per calorie, but also the proportion of protein sources. This allows us to further investigate the relationship between a diet with higher plant-derived protein relative to animal sources, total protein per calorie consumed, and body composition. The idea is that a diet higher in plant-based protein could be beneficial. Previous studies have shown a favorable relationship between this score and human health outcomes, including cardiovascular, chronic kidney diseases, and inflammatory bowel disease^17–19. We aimed to assess the association between this protein score and body composition tests, including fat percentage (FATP), fat mass (FATM), and fat-free mass (FFM).

Materials and methods

Study population

The current investigation is a part of the Fasa PERSIAN cohort study (FACS), which is a branch of the PERSIAN cohort that investigates the health of Iranians. The study took place in Sheshdeh and its surrounding villages (in Fasa County, Iran) and followed up with people aged 35–70 for 15 years^20,21. We included data from the enrollment phase conducted from November 2014 to June 2019. The study collected detailed information on the demographics, socioeconomic status, nutrition, and health of each person (10,136 in total). All the participants gave their written and informed consent before the study. The data were collected by interviewers categorized into general, medical, and nutrition groups. Each interviewer possesses an academic degree, ranging from a bachelor’s to a master’s, relevant to their field of work. An average of 25 people per day were enrolled, and data were collected in a sequential manner²². Out of the included participants, the first 4662 had a body composition test and entered our study. This investigation aimed to examine the link between protein score and body composition. We excluded participants who had cancer (N = 20) and regular alcohol drinkers (N = 127) (Fig. 1). To exclude outlier nutritional data, we excluded participants who had less than 800 or more than 4200 calories per day (N = 3). This range of extreme low and high calorie intake was used in previous studies²³. In the end, 4512 participants (55.2% female) were included in the analyses. The study followed the Declaration of Helsinki guidelines and received approval from the IRB (Institutional Review Board) of Fasa University of Medical Sciences (Code: IR.FUMS.REC.1395.177). It was verified that all the methods complied with the appropriate guidelines and rules.

Fig. 1.

Flowchart of the study population for inclusion in the final analysis.

Assessment of dietary intakes

The study used a 125-item FFQ based on the Willett-type questionnaire with some modifications for the Iranian food pattern to measure the diet and food intake of participants in the past year. This FFQ was validated for Iranian adults and showed a good correlation between dietary intakes measured by a similar FFQ and multiple days of 24-hour dietary recalls²⁴. The food size standard is based on the United States Department of Agriculture (USDA) values. A trained nutritionist asked the participants to report the amount, frequency, and portion size of each food item they had been consuming over the past 12 months (on a daily, weekly, monthly, and annual basis) based on this FFQ. The United States Department of Agriculture (USDA) and Iranian food composition tables were used to calculate energy and protein composition^25,26. We categorized each food item as either plant- or animal-based and accordingly summed up the protein content for further calculations.

Assessment of protein score

The premise behind protein scoring is that better health indicators result from higher protein intake and a higher ratio of plant to animal protein. The protein score¹⁶ was computed by calculating the plant-to-animal protein ratio (PAR) and total protein intake as a percentage of total energy (Pro%). We calculate PAR by dividing plant-based protein by animal-based protein and Pro% by summing up animal and plant-based protein calories and then dividing by the total calories of each participant. PAR and Pro% were used to categorize the study population into 11 strata. Participants in the highest category of these components received 10 points, subjects in the next stratum received 9 points, and so on, down to subjects in the lowest category, who received 0 points. The total protein score, which ranged from 0 to 20, was then calculated by summing the points from the two components. As a result, a higher score corresponds to a higher proportion of energy from total protein as well as a higher ratio of plant to animal protein, whereas a lower score corresponds to a lower proportion of protein. Additionally, each score component was taken into account independently. The range of the protein score and its sub-scores are reported in Supplementary Table 1.

Assessment of body composition

Body composition analysis was measured by bioelectric impedance analysis (BIA) using the Tanita BC-418 MA Segmental Body Composition Analyzer (Tanita, Japan), which uses eight electrodes. Measurements were conducted using a consistent sine wave current of 0.8 mA at a frequency of 50 kHz. Participants received instructions on preparations for body composition analysis to minimize variability in data collection, and tests were conducted after 8–12 h of fasting. Accordingly, FATM and FFM were measured. FATP was calculated as [(FATM/weight) × 100]²⁷.

Demographic, anthropometric, and physical activity assessments

A pretested questionnaire was utilized for collecting demographic data, such as age, sex, smoking status, level of education, medical history, and medication use. A SECA digital weighing scale (Seca 707; Seca Corporation, Hanover, Maryland) was used to measure weight over light clothing with an accuracy of up to 100 g. Using a stadiometer with a minimum increment of 1 mm, height was measured while the subject was standing with their shoulders in normal alignment and without shoes. BMI was computed as weight (kg) divided by the square of height (m²). The Modifiable Activity Questionnaire (MAQ), which has been updated and validated among Iranians previously, was used to measure physical activity²⁷. The wealth score index (WSI)²⁸, which is used to assess relative wealth or socioeconomic status, is estimated by Multiple Correspondence Analysis (MCA) of the following variables: access to a freezer, access to a washing machine, access to a dishwasher, access to a computer, access to the internet, access to a motorcycle, access to a car (no access, access to a car with a price of < 50 million Tomans, and access to a car with a price of > 50 million Tomans), access to a vacuum cleaner, type of color TV (no color TV or regular color TV vs. plasma color TV), owning a mobile phone, owning a PC or laptop, and international trips in a lifetime (never, just pilgrimage, both pilgrimage and non-pilgrimage trip.

Statistical analyses

All statistical analyses were performed using IBM SPSS Statistics 27. The study populations were divided into protein score tertiles using a simple rank method based on their individual components (the mean and standard deviation (SD) of each component are summarized in Supplementary Table 2). The mean ± SD for continuous variables and percentages for categorical variables across protein score tertiles are used to represent the baseline characteristics of the participants. Chi-square tests were applied for categorical variables and one-way analysis of variance (ANOVA) for continuous variables. Estimated marginal means were used to determine mean differences in body composition components (FATP, FATM, and FFM) across categories of the protein score and its components in adjusted models. In the crude model (using one-way ANOVA), we conducted Tukey post-hoc analysis. The first tertile was utilized as the reference in all models. Absolute values of each component in the total population and gender-specific subgroups were also computed using ANOVA. Potential confounding variables include age (years), energy intake (Cal/day), physical activity (MET-h/week), WSI, smoking status (yes/no), opioid usage status (yes/no), and hypothyroidism status (yes/no), which were adjusted for in adjusted model (1) To account for physical differences between genders, we added BMI, weight, and gender to the previous confounders in adjusted model (2) Diagnosing collinearity was performed using linear regression to calculate the Variance Inflation Factor (VIF) and Tolerance for each individual confounder. Using gender-specific subgroup analyses, potential effect modification was evaluated (male/female). P-values less than 0.05 were deemed statistically significant.

Results

A total of 4512 participants were included in the analysis. Overall, 55.2% of participants were female, and the mean ± SD age of participants was 47.74 ± 9.34 years. The characteristics of participants in the tertiles are reported in Table 1. Participants in the lower categories of protein score were less physically active, had slightly higher BMI, higher calorie intake, and WSI, and were also slightly older compared to those in the higher categories of protein score. Based on BMI cut-offs, the prevalence of obesity and overweight was higher in the lower tertiles, whereas the percentage of participants with normal body weight was higher in the higher category of protein score. Moreover, the prevalence of diabetes was higher in the lower categories of protein score, while there were no significant differences in the prevalence of hypertension and hypothyroidism.

Table 1.

Descriptive characteristics of participants according to tertiles (T) of protein score.

	Total	Protein score			p-value
		T1 (N = 1350)	T2 (N = 1802)	T3 (N = 1360)
Woman (%)	55.2	61.3	55.5	48.8	< 0.001
Age (year)	47.74 ± 9.34	48.29 ± 9.49	47.78 ± 9.40	47.13 ± 9.08	0.005
BMI (kg/m2)	25.62 ± 4.90	25.90 ± 5.06	25.61 ± 4.89	25.34 ± 4.74	0.012
Energy (Cal)	2870 ± 1115	3327 ± 1201	2826 ± 1060	2475 ± 917	< 0.001
Physical activity (MET)	41.94 ± 11.01	40.38 ± 9.84	41.94 ± 11.23	43.49 ± 11.58	< 0.001
Married (%)	89.4	87.5	89.8	90.8	0.015
WSI	0.01 ± 2.08	0.32 ± 2.09	0.008 ± 2.10	-0.28 ± 1.99	< 0.001
Opium consumption (%)	22.0	19.9	22.3	23.8	0.052
Active smoker (%)	26.4	23.3	26.3	29.6	< 0.001
Obesity status
Obese (BMI ≥ 30) (%)	17.7	19.6	17.7	15.9	0.043
Overweight (25–29.9) (%)	34.9	35.8	34.9	34	0.638
Normal weight (18.5–24.9) (%)	40.2	37.3	40.2	42.9	0.012
Underweight (BMI ≤ 18.4) (%)	6.2	6.1	6.3	6.2	0.989
Systolic blood pressure (mmHg)	108.96 ± 17.92	108.80 ± 18.35	108.87 ± 18.27	109.22 ± 17.00	0.804
Diastolic blood pressure (mmHg)	72.55 ± 11.94	72.38 ± 12.21	72.55 ± 12.08	74.73 ± 11.47	0.734
Diabetes (%)	12.4	14.4	12.3	10.7	0.013
Hypertension (%)	18.8	19.0	19.6	17.5	0.318
Hypothyroidism (%)	8.4	9.4	8.2	7.8	0.288

Abbreviations: BMI, Body Mass Index; WSI, Wealth Score Index.

Crude and adjusted models of mean difference and corresponding SD of body composition components, including FATP, FATM, and FFM, across tertiles of protein score and its components, including Pro% and PAR, are summarized in Tables 2, 3 and 4. The absolute mean ± SD of each component in the total population and gender-specific subgroups is reported in Supplementary Table 2. Results were reported as mean difference (MD) and 95% confidence interval (CI). The collinearity diagnostics are summarized in Supplementary Table 3. All VIF values were less than the threshold of 10 and Tolerance was more than 0.1, indicating no severe multicollinearity issues.

Table 2.

Mean differences of FATP (%), FATM (kg), and FFM (kg) across tertiles of protein score in crude and adjusted models. Adjustments were based on age, physical activity, energy intake, WSI, smoking status, opioid consumption, and hypothyroidism in adjusted model 1, with BMI, weight, and gender added in adjusted model 2.

TERTILE	Crude model (TUKEY)		Adjusted model 1		Adjusted model 2		N
	MD (95%CI)	p-value	MD (95%CI)	p-value	MD (95%CI)	p-value
FATP (total)
1	0		0		0		1350
2	-1.16 (-2.01, -0.32)	0.003	-0.66 (-1.24, -0.09)	0.023	-0.16 (-0.43, 0.09)	0.042	1802
3	-2.34 (-3.24, -1.44)	< 0.001	-1.33 (-1.97, -0.69)	< 0.001	-0.25 (-0.54, 0.04)	0.095	1360
Total							4512
FATP (Male)
1	0		0		0		523
2	-0.05 (-0.99, 0.89)	0.990	0.27 (-0.48, 1.02)	0.483	-0.55 (-0.98, -0.12)	0.011	801
3	-0.23 (-1.20, 0.73)	0.838	0.39 (-0.41, 1.20)	0.411	-0.53 (-0.99, -0.07)	0.024	696
Total							2020
FATP (Female)
1	0		0		0		827
2	-0.53 (-1.29, 0.21)	0.218	-0.18 (-0.82, 0.44)	0.559	0.08 (-0.24, 0.40)	0.624	1001
3	-0.80 (-1.63, 0.03)	0.063	-0.05 (-0.78, 0.67)	0.883	-0.07 (-0.45, 0.29)	0.680	664
Total							2492
FATM (total)
1	0		0		0		1350
2	-0.80 (-1.56, -0.03)	0.037	-0.28 (-0.87, 0.29)	0.331	-0.20 (-0.40, -0.01)	0.034	1802
3	-1.49 (-2.30, -0.67)	< 0.001	-0.48 (-1.13, 0.16)	0.141	-0.29 (-0.51, -0.07)	0.007	1360
Total							4512
FATM (Male)
1	0		0		0		523
2	0.26 (-0.71, 1.23)	0.804	0.63 (-0.15, 1.42)	0.114	-0.42 (-0.78, -0.05)	0.016	801
3	0.07 (-1.08, 0.70)	0.985	0.75 (-0.89, 1.60)	0.079	-0.48 (-0.87, -0.09)	0.009	696
Total							2020
FATM (Female)
1	0		0		0		827
2	-0.68 (-1.60, 0.23)	0.189	0.38 (-1.07, 0.48)	0.456	0.03 (-0.24, 0.31)	1	1001
3	-0.73 (-1.76, 0.28)	0.208	0.09 (-0.80, 0.99)	0.838	-0.07 (-0.39, 0.24)	1	664
Total							2492
FFM (Total)
1	0		0		0		1350
2	1.01 (0.27, 1.74)	0.003	1.30 (0.75, 1.84)	< 0.001	0.22 (0.04, 0.40)	0.013	1802
3	2.20 (1.42, 2.98)	< 0.001	2.68 (2.07, 3.28)	< 0.001	0.39 (0.19, 0.59	< 0.001	1360
Total							4512
FFM (Male)
1	0		0		0		523
2	1.18 (0.18, 2.18)	0.015	1.53 (0.73, 2.33)	< 0.001	0.39 (0.03, 0.79)	0.023	801
3	1.52 (0.49, 2.55)	0.002	2.02 (1.16, 2.89)	< 0.001	0.57 (0.19, 0.95)	< 0.001	696
Total							2020
FFM (Female)
1	0		0		0		827
2	-0.27 (-0.81, 0.26)	0.450	-0.11 (-0.55, 0.31)	0.594	-0.006 (-0.24, 0.23)	1	1001
3	0.09 (-0.49, 0.69)	0.921	0.40 (-0.10, 0.90)	0.121	0.14 (-0.13, 0.40)	0.636	664
Total							2492

Abbreviations: FATP, Fat Percentage; FATM, Fat Mass; FFM, Fat-Free Mass; BMI, Body Mass Index; MD, Mean Difference; CI, Confidence Interval.

Table 3.

Mean differences of FATP (%), FATM (kg), and FFM (kg) across tertiles of plant to animal protein ratio (PAR) in crude and adjusted models. Adjustments were based on age, physical activity, energy intake, WSI, smoking status, opioid consumption, and hypothyroidism in adjusted model 1, with BMI, weight, and gender added in adjusted model 2.

TERTILE	Crude model (TUKEY)		Adjusted model 1		Adjusted model 2		N
	MD (95%CI)	p-value	MD (95%CI)	p-value	MD (95%CI)	p-value
FATP (total)
1	0		0				1641
2	0.10 (-0.78, 0.98)	0.960	0.29 (-0.29, 0.89)	0.323	0.15 (-0.11, 0.43)	0.250	1230
3	0.05 (-0.76, 0.87)	0.988	-0.30 (-0.87, 0.25)	0.281	-0.03 (-0.29, 0.22)	0.777	1641
Total							4512
FATP (Male)
1	0		0				792
2	-0.06 (-0.98, 0.85)	0.987	0.29 (-0.42, 1.01)	0.424	0.06 (-0.34, 0.47)	0.748	573
3	-0.81 (-1.70, 0.070)	0.078	-0.23 (-0.93, 0.47)	0.520	-0.05 (-0.46, 0.34)	0.778	655
Total							2020
FATP (Female)
1	0		0				849
2	-0.21 (-0.91, 0.47)	0.537	0.10 (-0.57, 0.78)	0.764	0.15 (-0.19, 0.50)	0.373	657
3	-1.42 (-2.05, -0.79)	< 0.001	-0.73 (-1.36, -0.094)	0.024	-0.04 (-0.37, 0.28)	0.774	986
Total							2492
FATM (total)
1	0		0				1641
2	0.04 (-0.75, 0.84)	0.991	0.25 (-0.34, 0.84)	0.408	0.10 (-0.09, 0.29)	0.312	1230
3	-0.59 (-1.34, 0.14)	0.140	-0.52 (-1.08, 0.04)	0.072	-0.08 (-0.27, 0.37)	0.383	1641
Total							4512
FATM (Male)
1	0		0				792
2	-0.13 (-1.08, 0.81)	0.938	0.20 (-0.54, 0.95)	0.590	-0.04 (-0.32, 0.24)	0.765	573
3	-0.96 (-1.88, -0.05)	0.035	-0.32 (-1.06, 0.41)	0.386	-0.15 (-0.42, 0.12)	0.291	655
Total							2020
FATM (Female)
1	0		0				849
2	-0.08 (-1.09, 0.93)	0.980	0.19 (-0.64, 1.02)	0.653	0.16 (-0.07, 0.41)	0.177	657
3	-1.61 (-2.53, -0.70)	< 0.001	− 0.81 (-1.59, -0.03)	0.042	0.008 (-0.22, 0.23)	0.947	986
Total							2492
FFM (Total)
1	0		0				1641
2	-0.14 (-0.91, 0.61)	0.893	0.00 (-0.57, 0.55)	0.976	-0.07 (-0.25, 0.11)	0.454	1230
3	-1.55 (-2.26, -0.84)	< 0.001	-0.32 (-0.85, 0.21)	0.238	0.08 (-0.08, 0.26)	0.324	1641
Total							4512
FFM (Male)
1	0		0				792
2	0.01 (-0.96, 0.99)	0.999	0.19 (-0.57, 0.96)	0.627	0.01 (-0.26, 0.29)	0.910	573
3	-0.47 (-1.41, 0.47)	0.471	0.06 (-0.69, 0.81)	0.874	0.14 (-0.12, 0.41)	0.299	655
Total							2020
FFM (Female)
1	0		0				849
2	0.09 (-0.49, 0.68)	0.929	0.03 (-0.43, 0.50)	0.881	-0.9 (-0.31, 0.11)	0.356	657
3	-0.59 (-1.12, -0.06)	0.023	-0.27 (-0.71, 0.16)	0.215	-0.01 (-0.21, 0.18)	0.865	986
Total							2492

Abbreviations: FATP, Fat Percentage; FATM, Fat Mass; FFM, Fat-Free Mass; BMI, Body Mass Index; MD, Mean Difference; CI, Confidence Interval.

Table 4.

Mean differences of FATP (%), FATM (kg), and FFM (kg) across tertiles of total protein per calorie (Pro%) in crude and adjusted models. Adjustments were based on age, physical activity, energy intake, WSI, smoking status, opioid consumption, and hypothyroidism in adjusted model 1, with BMI, weight, and gender added in adjusted model 2.

TERTILE	Crude model (TUKEY)		Adjusted model 1		Adjusted model 2		N
	MD (95%CI)	p-value	MD (95%CI)	p-value	MD (95%CI)	p-value
FATP (total)
1	0		0				1641
2	-1.85 (-2.72, -0.97)	< 0.001	-0.96 (-1.57, -0.34)	0.002	-0.65 (-1.02, -0.28)	< 0.001	1230
3	-3.30 (-4.11, -2.49)	< 0.001	-1.82 (-2.45, -1.18)	< 0.001	-1.60 (-1.99, -1.21)	< 0.001	1641
Total							4512
FATP (Male)
1	0		0				530
2	0.05 (-0.96, 1.07)	0.991	0.57 (-0.24, 1.39)	0.172	-0.19 (-0.66, 0.27)	0.426	551
3	0.44 (-0.46, 1.35)	0.0.484	1.09 (0.26, 1.92)	0.010	-0.62 (-1.10, -0.14)	0.010	939
Total							2020
FATP (Female)
1	0		0				1111
2	-0.09 (-0.84, 0.69)	0.959	0.08 (-0.57, 0.75)	0.795	0.04 (-0.29, 0.39)	0.777	679
3	0.19 (-0.58, 0.96)	0.830	0.51 (-0.22, 1.24)	0.171	-0.24 (-0.61, 0.12)	0.200	702
Total							2492
FATM (total)
1	0		0				1641
2	-1.18 (-1.98, -0.38)	0.002	-0.40 (-1.02, 0.21)	0.199	-0.13 (-0.34, 0.06)	0.186	1230
3	-1.51 (-2.25, -0.77)	< 0.001	-0.23 (-0.87, 0.41)	0.476	-0.37 (-0.59, -0.15)	< 0.001	1641
Total							4512
FATM (Male)
1	0		0				530
2	0.23 (-0.81, 1.29)	0.857	0.79 (-0.05, 1.65)	0.068	-0.18 (-0.50, 0.14)	0.276	551
3	0.95 (0.01, 1.89)	0.047	1.69 (0.82, 2.56)	< 0.001	-0.46 (-0.79, -0.13)	0.005	939
Total							2020
FATM (Female)
1	0		0				1111
2	0.25 (-1.20, 0.70)	0.812	-0.03 (-0.85, 0.78)	0.930	-0.09 (-0.33, 0.14)	0.435	679
3	0.55 (-0.38, 1.50)	0.350	0.88 (-0.01, 1.77)	0.054	-0.25 (-0.51, 0.01)	0.062	702
Total							2492
FFM (Total)
1	0		0				1641
2	1.84 (1.09, 2.59)	< 0.001	1.91 (1.34, 2.48)	< 0.001	0.67 (0.42, 0.92)	< 0.001	1230
3	4.45 (3.75, 5.14)	< 0.001	4.51 (3.92, 5.10)	< 0.001	1.32 (1.06, 1.59)	< 0.001	1641
Total							4512
FFM (Male)
1	0		0				530
2	0.93 (-0.08, 2.07)	0.078	1.39 (0.52, 2.27)	0.002	0.34 (0.02, 0.65)	0.034	551
3	2.29 (1.32, 3.25)	< 0.001	2.86 (1.98, 3.74)	< 0.001	0.62 (0.30, 0.94)	< 0.001	939
Total							2020
FFM (Female)
1	0		0				1111
2	0.02 (-0.53, 0.57)	0.996	0.18 (-0.27, 0.64)	0.441	0.14 (-0.06, 0.35)	0.171	679
3	0.87 (0.33, 1.42)	< 0.001	1.02 (0.52, 1.52)	0.005	0.33 (0.11, 0.41)	0.003	702
Total							2492

Abbreviations: FATP, Fat Percentage; FATM, Fat Mass; FFM, Fat-Free Mass; BMI, Body Mass Index; MD, Mean Difference; CI, Confidence Interval.

Association between protein score and body composition

According to the results, participants in the third tertile (MD [95% CI]: -1.33% [-1.97, -0.69]; P < 0.001) and second tertile (MD [95% CI]: -0.66% [-1.24, -0.09]; P = 0.023) of protein score had significantly lower FATP in both crude and adjusted model 1. In adjusted model 2, only the second tertile of protein score showed significantly lower FATP (MD [95% CI]: -0.16% [-0.43, 0.09]; P = 0.042). Subgroup analysis based on gender in adjusted model 1 showed significantly higher FFM across categories of protein score, while adjusted model 2 showed significant mean differences in FATP, FATM, and FFM in male participants. In contrast, our investigations failed to demonstrate any significant differences in body composition across categories of protein score in female participants (Table 2).

Association between PAR and body composition

PAR was also investigated. Our results failed to show any significant difference in FATP among tertiles of PAR. However, subgroup analysis based on gender revealed that female participants in the highest tertiles of PAR had significantly lower FATP only in crude and adjusted models 1 (MD [95% CI]: -0.73% [-1.36, -0.09]; P = 0.024) (Table 3). Similarly, FATM was significantly lower in the highest tertile of PAR (MD [95% CI]: -0.81 kg [-1.59, -0.03]; P = 0.042) in female participants in adjusted model 1, in contrast to adjusted model 2. In male participants, FATM was significantly lower in the third tertile (MD [95% CI]: -0.96 kg [-1.88, -0.05]; P = 0.035) of PAR in the crude model, while adjusting for confounders showed insignificant results (P = 0.386). The mean difference of FFM across all participants in the crude model revealed a significant difference in the third tertile of PAR (MD [95% CI]: -1.55 kg [-2.26, -0.84]; P < 0.001), but after adjusting for confounders, the results failed to show any significance. Similarly, in female participants, the results only showed a significant difference in the third category of PAR in the crude model (MD [95% CI]: -0.59 kg [-1.12, -0.06]; P = 0.023) (Table 3).

Association between Pro% and body composition

We also analyzed body composition tests across tertiles of Pro%. As shown in Table 4, the FATP of participants in higher categories of Pro% was significantly lower in both tertiles compared to the reference in crude and adjusted models. However, subgroup analysis of the results based on gender demonstrates a significantly higher FATP in male participants in the third tertile compared to the reference in adjusted model 1 (MD [95% CI]: 1.09% [0.26, 1.92]; P = 0.010), while adjusted model 2 demonstrated lower FATP in the third category compared to the reference (MD [95% CI]: -0.62% [-1.10, -0.14]; P = 0.010). This difference might be due to inadequate confounding adjustment in adjusted model 1, as we observed a reversal in total and gender subgroups in adjusted model 1. FATM differences in tertiles were visible in the crude model, with significantly lower results in both tertiles compared to the reference and the third tertile in adjusted model 2 in the total population (MD [95% CI]: -0.37 [-0.59, -0.15]; P < 0.001) (Table 4). We observed that the third tertile of Pro% in male participants had significantly lower FATM compared to the reference (MD [95% CI]: -0.46 [-0.79, -0.13]; P = 0.005). The results of the analyses revealed a significantly higher FFM in total in all models. Similarly, we found a higher FFM in both tertiles of Pro% in male participants compared to the reference in both adjusted models, which was only visible in the highest category in the crude model (Table 4). In female participants, we found that FFM was significantly higher in the third tertile of Pro% in the crude model (MD [95% CI]: 0.87 kg [0.33, 1.42]; P < 0.001), adjusted model 1 (MD [95% CI]: 1.02 kg [0.52, 1.52]; P = 0.005), and adjusted model 2 (MD [95% CI]: 0.33 [0.11, 0.41]; P = 0.003).

Discussion

To our knowledge, this is the first investigation assessing the association between protein score and body composition. Summing up the results of PAR and Pro% in the protein score showed that male participants in higher categories have significantly lower FATM, FATP, and higher FFM. In contrast, female participants didn’t demonstrate any significant mean differences in body composition at higher tertiles compared to the first tertile of the protein score.

The present study showed that only female participants in the highest categories of PAR have significantly lower FATP and FATM. It has been shown that there are differences in amino acid metabolism between males and females. In certain scenarios, such as prolonged physical activity, females generally have a lower reliance on amino acids and a higher reliance on fat compared to their male counterparts. This is especially true for essential amino acids like leucine, which plant-derived proteins might lack^29–31. As a result, higher plant-based protein intake compared to higher quality animal protein sources might not favor regular metabolism in men compared to women. From a physiological point of view, higher plant-based protein intake over animal-based protein might lower free fatty acid accumulation, as they are generally much lower in fats compared to animal protein sources. Zhao et al., in a meta-analysis conducted in 2020, revealed no significant difference in body weight and BMI in a pooled effect model comparing participants receiving plant (soy, beans, etc.) versus animal (milk, whey, etc.) proteins³². Lang et al. (1998) found that there is no difference between plant- and animal-based protein intake in terms of satiety, 24-hour energy or macronutrient intakes, or postprandial plasma glucose and insulin concentrations³³. It should be considered that some dietary plant-based protein sources, which contain high amounts of refined carbohydrates, including white rice, should be taken into account, as the Iranian population generally has a very high consumption of these food groups³⁴.

Results of this investigation failed to demonstrate any significant differences in FFM in both genders across tertiles of PAR. In line with our results, Isanejad at al³⁵ and Sahni et al.³⁶ found that, compared to plant-based protein sources, total protein and animal protein sources were positively associated with lean mass. Furthermore, plant-based protein sources usually have less anabolic effect and generally would be oxidized rather than utilized for muscle synthesis due to lower protein quality³⁷. Thus, a higher proportion of plant-based protein to animal protein sources might not be able to cause significant changes in fat-free mass composition.

In contrast to PAR, FATP and FATM were significantly lower in male participants with the highest category of Pro% when gender physical differences were taken into account (adjusted model 2). In contrast, female participants didn’t show any significant differences in FATP and FATM between categories of Pro%. Evans et al., in a randomized clinical trial in 2012, found that a diet higher in protein could be more effective in reducing total body fat in men compared to women³⁸. Moreover, studies have shown a gender-based difference in protein intake, as adult males consume higher levels of meat compared to their female counterparts³⁹. Higher dietary protein intake has been shown to have a favorable effect on weight loss compared to control diets⁴⁰. Generally, the prevalence of obesity is higher in females⁴¹. Furthermore, the lack of any significant difference in fat composition in Pro% categories in the current investigation might also be due to protein intake being lower than the effective threshold and dietary reference intakes, as well as poor dietary patterns and physical inactivity in females⁴².

We also found that higher categories of Pro% revealed higher fat-free mass in both male and female participants compared to baseline. Previous studies have shown a protective effect of higher protein intake on lean body mass, whether during calorie restriction or resistance training^43,44. Higher protein intakes could reverse muscle breakdown and also promote muscle synthesis¹⁴. While we observed a significant mean difference in FFM in both categories of Pro% in male participants, only the third tertile in females was significantly higher. Similar to FATM and FATP, there might be a threshold of protein content that explains these disparities.

According to these results, higher protein intakes relative to calories are crucial for favorable body composition parameters. While the ratio of plant to animal protein sources might contribute to other health benefits¹⁴, body composition in the investigated population doesn’t follow a similar pattern.

Low protein intake is a major concern in human health^45–48. Several studies have been conducted to investigate the association between dietary proteins and the risk of chronic disorders and mortalities^49–51. Farvid et al. in 2017 demonstrated lower cancer mortality with higher consumption of fish and legumes, and lower all-cause mortality with higher egg consumption in an Iranian population⁵⁰. Although these results were based on specific food groups and might not be solely due to their protein content, they indicate the importance of higher protein intake from various sources for favorable health status in the Iranian population. Furthermore, in 1999, Hu et al. showed that replacing carbohydrates with dietary protein sources with the same fat and calorie content in a regimen mitigates the risk of ischemic heart disease⁵². interestingly, smith et al.⁵³., in an investigation conducted in 2015, showed that the association between dietary protein and weight changes is affected when protein is exchanged with carbohydrates. Given the fact that in Iranian populations, carbohydrate intake is relatively high and generally comes from refined carbohydrates (staple foods including refined bread and white rice), exchanging protein with carbohydrates might result in better health outcomes⁵⁴.

In conclusion, we found that protein score is associated with higher fat-free mass and basal metabolic rate in male participants in an Iranian population. Similarly, total protein per calorie intake was associated with higher fat-free mass and basal metabolic rate in both male and female participants. Furthermore, we found a negative association between Pro% and FATM in males. Moreover, PAR was shown to be associated with lower FATM in female participants. These results indicate the important role of protein score and its components (PAR and Pro%) in weight control.

Our study contains some limitations that should be considered when interpreting the results. One drawback is using the FFQ to assess the dietary intake of participants. While the questionnaire used in this investigation was valid and nutritionists conducted face-to-face interviews with each participant, this method of collecting nutritional data might cause some recall bias. However, using a relatively large sample size in our study might mitigate this error by lowering the margin of error. Furthermore, as this investigation was conducted in Iran with a specific food culture and we excluded participants with alcohol consumption, the results might not be generalizable to other populations. Finally, the present study is unable to conclude causality due to the methodology used. To confirm these results, we suggest more studies. Researchers might conduct further studies in different populations, including those with various dietary regimens, especially Western and Mediterranean diets, to investigate how different food sources of protein influence the relationship between protein score and body composition and whether higher protein intakes reveal similar favorable results. Health administrators might be able to utilize the results of the present study to control overweight and obesity among Iranian populations and aim to optimize the population’s dietary regimen by enhancing nutrition and the food system towards more protein intake and an optimal plant-to-animal-based protein ratio to promote better body composition in the Iranian population.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Author contributions

M.A. conceptualized the study, analyzed the data, and created the tables; A.J. and M.H. equally participated in writing the original draft and editing the manuscript; R.H. revised the article and supervised the project; M.F. provided the data and reviewed the article; A.A. revised the article.

Funding

This investigation received no external funding.

Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.

Declarations

Ethics approval

The study followed the Declaration of Helsinki guidelines and received approval from the IRB (Institutional Review Board) of Fasa University of Medical Sciences (Code: IR.FUMS.REC.1395.177).

Competing interests

The authors declare no competing interests.