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. 2023 Jul 25;23:158. doi: 10.1186/s12902-023-01413-3

The interactive relationship of dietary choline and betaine with physical activity on circulating creatine kinase (CK), metabolic and glycemic markers, and anthropometric characteristics in physically active young individuals

Ensiye Soleimani 1, Abnoos Mokhtari Ardekani 2, Ehsan Fayyazishishavan 3, Mahdieh Abbasalizad Farhangi 4,✉
PMCID: PMC10367233  PMID: 37491240

Abstract

Background

There is conflicting evidence on the relationship between dietary choline and betaine with metabolic markers and anthropometric characteristics. The aim of this study is to investigate the relationship between the interaction effects of dietary choline and betaine and physical activity (PA) on circulating creatine kinase (CK), metabolic and glycemic markers, and anthropometric characteristics in active youth.

Methods

In this cross-sectional study, data were collected from 120 to 18 to 35-year-old people. The food frequency questionnaire was used to assess dietary data; United States Department of Agriculture website was used to calculate choline and betaine in foods. CK, fasting blood sugar (FBS) and lipid profile markers were measured with ELISA kits. Low-density lipoprotein, and insulin sensitivity markers were calculated. Sociodemographic status, physical activity, and anthropometric characteristics were assessed based on a valid and reliable method. Analysis of co-variance (ANCOVA) tests adjusted for sex, PA, age, energy, and body mass index were used.

Results

Increasing dietary betaine and total choline and betaine was positively related to weight, waist-to-hip ratio, fat-free mass and bone mass (P < 0.05). Increasing dietary betaine lowered total cholesterol (P = 0.032) and increased high density lipoprotein (HDL) (P = 0.049). The interaction effect of dietary choline and physical activity improved insulin resistance (P < 0.05). As well as dietary betaine interacted with physical activity increased HDL (P = 0.049). In addition, dietary total choline and betaine interacted with physical activity decreased FBS (P = 0.047).

Conclusions

In general, increasing dietary choline and betaine along with moderate and high physical activity improved insulin resistance, increased HDL, and lowered FBS in the higher tertiles of dietary choline and betaine.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12902-023-01413-3.

Keywords: Choline, Betaine, Physical activity, Insulin resistance, Lipid profile

Background

The evidence and results of medical research show that having regular physical activity as one of several components of a healthy lifestyle is the most effective and accepted way to prevent the rise of noncommunicable diseases [1, 2]. For adults to earn health benefits, 75 min/week of intensive activity or 150 min/week of normal-intensity aerobic physical activity is suggested. There is evidence that recommended amounts of activity can reduce the hazard of some kinds of cancer, obesity, high blood pressure, osteoporosis, cardiovascular disease, diabetes, anxiety, stress, and depression [3–5]. Because of the importance of proper dietary choices in people who engage in regular physical activity, some nutrients have received special attention [4, 6–8]. Choline and betaine are essential nutrients obtained from the diet or de novo synthesis, and they are rich in different kinds of foods. Foods high in choline include liver, eggs, pork, seafood, milk, and beef, while seeds, grains, spinach, and beets are high in betaine [9–12]. As a methyl donor and precursor for phospholipids, lipoproteins, and acetylcholine, choline plays a critical role in cell membrane signaling, lipid transport, and neurotransmitter synthesis [9, 13, 14]. The derivative of choline, betaine, can serve as a methyl donor in many ways, including methylating homocysteine [15]. As part of a neurotransmitter, choline is effective in transmitting messages to skeletal muscles and thus plays a role in physical activity [16]. Inadequate dietary choline leads to decreased choline levels, followed by several changes in myoblasts, including muscle wasting, and finally increased serum creatine kinase (CK). There may be a relationship between insufficient of dietary choline and increase serum CK [17–19].

Studies have demonstrated that reducing weight, increasing activity, and changing eating habits or dietary components can increase glucose tolerance and modify lipid profiles [4, 20–23]. There has been conflicting evidence regarding the effects of dietary choline and betaine on glucose tolerance. Choline deficiency may modify glucose tolerance in some studies, but increasing dietary choline and betaine may also increase insulin sensitivity [6, 24–26]. The liver needs choline to transport triglycerides and very low-density lipoproteins (VLDL) into the blood. Although some evidence has been established regarding choline’s role in lipid metabolism, few studies have examined its association with human lipid profiles [27, 28]. To date, the relationship between the interaction effects of dietary choline and betaine with physical activity on changes in blood sugar and lipid profiles has not been investigated in any study.

The identification of anthropometric measurements is a useful method for studying the nutritional status of adults. Regular physical activity is related to better anthropometric measurements in youth [29, 30]. Among them are: weight loss, fat reduction, decreased waist-to-hip ratio, and waist circumference [31]. The relationship between higher dietary choline and betaine with favorable anthropometric characteristics, such as lower waist circumference (WC), lower body fat %, and weight loss, has been investigated in different studies [32–35]. However, additional investigation is required to understand the interaction effects of dietary choline and betaine and physical activity on these parameters. The aim of this study is to investigate the relationship between the interacted effects of dietary choline and betaine and physical activity (PA) on circulating CK, metabolic and glycemic markers, and anthropometric traits in active youth.

Methods

Participant population

A total of 120 young people from Tabriz participated in this cross-sectional study (Fig. 1). HOMA-IR (homeostatic model assessment for insulin resistance) was used to calculate the sample size [6], Z = 1.96, E = 8% mean, SD = 1.49 using the following formula: n = (z2×sd2)/e2 the total number of samples reached 120, considering 10% missing. Inclusion criteria are as follows: preparing to participate in the project, moderate physical activity for at least 4 h per week; age range 18–35 years; blood sampling was done 24 h after the last exercise to prevent acute changes in plasma volume caused by exercise that affect serum creatine kinase levels [18]; and having the physical ability to complete the measurement process and fill out the questionnaires. Exclusion criteria also included chronic conditions affecting food intake, including digestive problems and cancer, anorexia, alcohol and drug use, and the use of drugs and dietary supplements containing choline and betaine.

Fig. 1.

Fig. 1

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Study flowchart

Dietary intake and choline estimate

Food data were collected through a food frequency questionnaire whose validity and reliability was proven by Nik Niaz et al.; this questionnaire was adjusted for the Iranian population [36]. Every participant is asked to report accurately and honestly how much and how often they consume each food item on a yearly, monthly, weekly, or daily basis. Based on household standards, participants’ dietary intakes were converted into grams. An Iranian diet was adapted using NUTRITIONIST 4 software (version 7.0; N Squared Computing, Salem, OR), and calorie and nutrient intakes were calculated. Each gram of each food was multiplied by the amount of choline and betaine in each gram based on tables in the USDA database [37–39]. To calculate the total dietary choline, different forms of choline (phosphatidylcholine, phosphocholine, free choline, and glycerophosphocholine) were considered. For total choline and betaine, we summed the columns of total dietary choline and betaine. The table provided by USDA includes different types of foods with different preparation methods and indicates the choline content per 100 g of the food. We calculated this amount for each gram of a food that is closest to Iranian food culture in terms of cooking and storage methods and used it in the next steps. For example, in beef and beef products, there are about 37 different types of beef with different fat percentages and preparation methods. In Iranian culture, this type of meat is mostly used as a stew, so for the further calculations, the moderately fat-cooked type was used, which was selected based on the closest similarity. For eggs, the USDA food table lists 5 items, two of which are fried and two of which are boiled, and in this case, we calculated the average of these two and used it for the next steps [37, 38, 40–42].

Sociodemographic, anthropometric, and physical activity measurements

Sociodemographic information such as marital status, age, gender, and education level was gathered by questionnaire. Anthropometric features such as participant body mass index (BMI), weight, height, waist-to-hip ratio (WHR), hip circumference (HC), WC, mid-arm circumference (MAC), thigh circumference, and leg circumference were assessed. A Seca 753E electronic scale was used to precisely measure the subject’s weight (down to 0.1 kg) over a minimal range. The standing height of the participants was measured in a stationary position and without shoes (accurate to 0.1 cm), and then the BMI was computed (kg/m2) based on the formula. While the participants were lightly dressed, WC was measured as the distance between the smallest area below the ribs and above the iliac spine. Maximum HC was measured as hip circumference. The middle point between the elbow and shoulder distance was measured as the MAC. Approximately 10 cm below the groin was measured as the largest circumference of the thigh. The largest calf circumference was measured as calf circumference. All measurements will be measured with the help of an inflexible tape measure without any pressure on the meter with an accuracy of 0.1 cm. Body composition measurements were recorded while the participants were fasting for 12 h. While wearing light clothing, subjects were weighed after emptying their bladders using a bioelectrical impedance analysis (BIA) machine (Tanita, BC-418 MA, Tokyo, Japan). AIPAC or the short 7-item international physical activity questionnaire (IPAQ) was used to determine physical activity levels worldwide [30]. The short form of this questionnaire consists of 7 simple questions (validity and reliability having already been measured).

Biochemical measurements

First, 5 cc of venous blood samples from the participants were drawn by a laboratory technician while fasting for 8 to12 hours and then centrifuged for 10 min to separate serum and plasma. Creatine kinase (CK), fasting insulin, total cholesterol (TC), triglycerides (TG), and high-density lipoprotein cholesterol (HDL-C) were tested by spectrophotometry and ELISA (enzyme-linked immunosorbent assay). Low-density lipoprotein (LDL-C) was also calculated using the Friedwald Eq. [43]. The homeostatic model of insulin resistance (HOMA-IR) was assessed using the formula fasting insulin (µIU/mL), fasting blood glucose (mmol/L)/22.5, and quantitative insulin sensitivity index (QUICKI) as 1/[log fasting insulin (mU/L) + log (fasting plasma glucose (mmol/L)×18.0182)] [44–46].

Analyzing data statistically

Statistical analysis of the collected data was performed using SPSS software (version 21.0; SPSS Inc, Chicago, IL). A P value of less than 0.05 was considered significant. Normality was tested with the Shapiro-Wilk test (P > 0.05), and Levene’s test (P > 0.05) for equality of error variance was used to test for equality of variance. The mean standard deviation (SD) was used to indicate quantitative data, and numbers and percentages (%) were used for qualitative data. Differences between discrete and continuous variables in the different tertiles of dietary choline, betaine, and total choline and betaine were compared using the chi-square test and one-way analysis of variance (ANOVA), respectively. Bonferroni’s post hoc multiple comparison analysis revealed a significant mean difference between groups. Analysis of covariance (ANCOVA), general linear model (GLM), and univariate analysis were used to compare the biochemical and anthropometric variables after adjustment for confounding factors such as age and sex, BMI, PA, and energy intake. Additionally, the same method was used to investigate the interaction effect of dietary choline and betaine and physical activity and biochemical characteristics.

Results

A total of 250 individuals were screened for eligibility and 130 were found to be eligible for participation in the current study. However, 10 individuals were excluded due to incomplete biochemical, anthropometric or dietary data. Therefore, 120 individuals were completed the study (Fig. 1). General characteristics and anthropometric measurements of study participants across different tertiles of dietary choline, betaine, and total choline and betaine are shown in Table 1. After controlling for potentially confounding variables (age, sex, BMI, energy intake, PA), an increase in dietary betaine was significantly related to an increase in weight (P = 0.045), height (P = 0.001), MAC (P = 0.038) and WHR (P = 0.044). According to Bonferroni’s post hoc test, the average WHR difference was between T3 and T2 dietary betaine (P = 0.019). Additionally, there was a significant direct relationship between total dietary choline and betaine and weight (P = 0.038), WC (P = 0.005), and WHR (P = 0.011). According to Bonferroni’s post hoc test, the average weight (P = 0.021), WHR (P = 0.034), and HC (P = 0.005) differences were between T3 and T2 dietary total choline and betaine and about MAC (P = 0.007) the difference was between T3 and T1 (Table 1). Additionally, after considering the confounding variables, the increase in dietary betaine was related to the increase in FFM (P = 0.002), MM (P = 0.002), and BM (P = 0.001). Additionally, the increase in dietary choline and betaine was directly related to the increase in FFM (P = 0.047) and BM (P = 0.041). The average difference in FFM (P = 0.049), MM (P = 0.033) and BM (P = 0.044) between T3 and T1 was determined by Bonferroni’s post hoc test (Table 2). Additionally, after considering the confounding variables, the increase in dietary choline was related to the increase in the intake of calcium (P = 0.015), vitamin B8 (P = 0.043) and vitamin D (P = 0.037). Additionally, the increase in dietary betaine was related to protein (P = 0.007) and fat (P < 0.001), vitamin B1 (P < 0.001), vitamin B3 (P < 0.001), polyunsaturated fatty acid (PUFA) (P = 0.044), monounsaturated fatty acid (MUFA) (P < 0.001), and saturated fatty acid (SAFA) (P = 0.007) intake. There was also a direct relationship between dietary choline and betaine and vitamin B3. (P = 0.044) (Table 3). After considering the confounding variables, the increase in dietary betaine was related to the decrease in total cholesterol (P = 0.032) and increase in HDL (P = 0.049). According to Bonferroni’s post hoc test, the average HDL in the T2 dietary betaine had significant differences compared to T1 (P = 0.009) and TC in the T3 dietary betaine had a significant difference compared to T2 (P = 0.051). After considering all the confounding variables, the increase in dietary choline was associated with the increase in serum creatine kinase (P = 0.063), although this relationship was not significant (Table 4). The interaction between dietary choline and physical activity on biochemical markers is presented (Fig. 2). Subjects were divided into three categories based on the duration of their physical activity per week: less than 5 h (low), between 5 and 8 h (moderate), and more than 8 h (high). At moderate and high physical activity levels, increased dietary choline decreased insulin (P = 0.029) and HOMA-IR (P = 0.029) and increased QUICKI (P = 0.034). The interaction between dietary betaine and physical activity on biochemical markers is shown (Fig. 3). At all levels of physical activity, HDL levels were significantly higher in the moderate betaine intake group than in the lowest intake group. Even in the moderate physical activity group, the HDL trend was completely increasing with increasing dietary betaine (P = 0.049). The interaction between dietary choline and betaine and physical activity on biochemical markers is shown (Fig. 4). In the group that had the most physical activity, the highest level of dietary choline and betaine decreased FBS levels (P = 0.047). For the other biochemical variables, the interaction between dietary choline, betaine or combination of both was non-significant (Sup. Figures 1, 2, 3).

Table 1.

General characteristics and anthropometric measurements of study participants across different tertiels of dietary choline, betaine and total choline and betaine intake

Variables Total choline Total betaine Total choline and betaine
T1 (n = 40) T2 (n = 40) T3 (n = 40) *P **P T1 (n = 40) T2 (n = 40) T3 (n = 40) *P **P T1 (n = 40) T2 (n = 40) T3 (n = 40) *P **P
Age (year) 25.13(4.02) 22.97(4.07) 22.48(3.48) 0.007 0.104 24.39(4.32) 23.22(4.03) 22.92(3.58) 0.233 0.743 25.10(4.46) 22.67(3.49) 22.75(3.60) 0.008 0.067
Gender (male %) 15(39.5) 20(50.0) 28(68.3) 0.010 - 11(28.9) 19(47.5) 33(80.5) < 0.001 - 12(31.6) 22(56.4) 29(70.7) 0.001 -
Marital status (single %) 34(89.5) 36(90.0) 38(92.7) 0.621 - 34(89.5) 37(92.5) 37(90.2) 0.915 - 34(89.5) 36(92.3) 37(90.2) 0.914 -
Education (university graduate %) 21(55.3) 6(15.0) 7(17.1) < 0.001 - 14(36.8) 12(30.0) 8(19.5) 0.089 - 19(50.0) 8(20.5) 6(14.6) 0.001 -
Occupation status (student %) 32(84.2) 35(87.5) 35(85.4) 0.891 - 31(81.6) 36(90.0) 35(85.4) 0.647 - 31(81.6) 34(87.2) 36(87.8) 0.438 -
Weight (kg) 68.43(15.11) 68.01(13.45) 74.68(13.91) 0.064 0.622 65.38(14.91) 69.31(12.63) 76.24(13.71) 0.002 0.045b 66.30(14.62) 67.08(11.34) 77.85(14.25) < 0.001 0.038b
Height (cm) 169.80(9.35) 169.26(10.00) 175.93(8.38) 0.002 0.347 166.96(8.81) 170.41(8.22) 177.45(9.04) < 0.001 0.001b 167.94(8.76) 170.53(9.35) 176.62(9.00) < 0.001 0.183
BMI (kg/m2) 23.42(3.77) 23.45(3.42) 23.82(3.83) 0.862 0.967 23.11(3.74) 23.62(3.69) 23.96(3.57) 0.586 0.670 23.20(3.65) 22.90(3.37) 24.61(3.78) 0.083 0.161
MAC (cm) 28.23(3.96) 28.33(3.25) 29.54(3.67) 0.202 0.883 27.36(3.68) 28.65(3.41) 30.04(3.45) 0.004 0.038a 27.78(3.81) 28.03(3.40) 30.32(3.27) 0.002 0.110
WC (cm) 77.76(9.83) 76.33(8.48) 80.28(8.18) 0.131 0.208 75.42(9.24) 79.20(9.21) 79.65(7.89) 0.070 0.115 76.73(9.42) 75.68(7.89) 82.00(8.27) 0.003 0.005b
HC (cm) 100.39(7.12) 99.51(6.82) 100.54(8.04) 0.794 0.817 99.46(7.02) 99.62(7.50) 101.30(7.41) 0.461 0.429 99.89(6.97) 98.40(6.70) 102.15(7.87) 0.068 0.149
THC (cm) 57.27(5.09) 56.43(5.80) 56.70(6.36) 0.809 0.480 57.46(5.40) 56.45(6.04) 56.52(5.86) 0.694 0.853 57.24(5.14) 55.73(5.80) 57.42(6.25) 0.359 0.162
CC (cm) 36.78(3.56) 36.75(3.75) 37.28(3.40) 0.760 0.810 36.53(3.69) 36.81(3.68) 37.45(3.31) 0.505 0.525 36.56(3.42) 36.55(3.66) 37.71(3.52) 0.284 0.333
WHR (cm) 0.77(0.06) 0.76(0.07) 0.79(0.04) 0.057 0.114 0.75(0.05) 0.79(0.06) 0.78(0.05) 0.020 0.044b 0.76(0.06) 0.77(0.06) 0.80(0.04) 0.012 0.011b
PA (met-hour/week) 5.52 (2.03) 7.82 (6.29) 11.22 (8.48) < 0.001 0.703 6.13 (3.56) 7.99 (6.30) 10.48 (8.41) 0.013 0.874 5.65 (2.09) 7.94 (6.70) 11.11 (8.34) 0.001 0.839
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BMI Body mass index, MAC Mid-arm circumference, WC Waist circumference, HC Hip circumference, THC Thigh circumference, CC Calf circumference, WHR Waist-to-hip ratio, PA Physical activity. Data are presented as mean ± SD or percent; *Obtained from the one-way analysis of variance (ANOVA) or Chi-squared tests, where appropriate; P Significant at P < 0.05; 95th confidence intervals of the difference in parentheses **Obtained from ANCOVA model after adjustment for the confounding effects of age, sex, BMI and physical activity, calorie intake; P Significant at P < 0.05; 95th confidence intervals of the difference in parentheses

apost hoc Tukey signature difference between 1st tertile and 3rd tertile

bpost hoc Tukey signature difference between 2nd tertile and 3rd tertile

Table 2.

Body composition parameters of study participants across different tertiels of dietary choline, betaine and total choline and betaine intake

Variables Total choline Total betaine Total choline and betaine
T1 (n = 40) T2 (n = 40) T3 (n = 40) *P **P T1 (n = 40) T2 (n = 40) T3 (n = 40) *P **P T1 (n = 40) T2 (n = 40) T3 (n = 40) *P **P
FM (kg) 16.51 (7.84) 14.60 (6.25) 14.09 (7.48) 0.289 0.834 16.13 (7.85) 15.28 (6.52) 13.78 (7.26) 0.334 0.857 16.67 (7.84) 13.36 (5.21) 15.18 (8.10) 0.119 0.092
FFM (kg) 51.62 (10.67) 52.62 (11.12) 59.77 (11.11) 0.002 0.665 49.04 (10.43) 53.26 (9.68) 61.66 (10.71) < 0.001 0.002a 49.79 (10.05) 53.14 (9.93) 61.04 (11.55) < 0.001 0.047a
FFM (%) 76.28 (7.29) 78.37 (7.72) 81.30 (8.51) 0.018 0.901 75.79 (7.40) 78.02 (7.46) 82.13 (8.17) 0.001 0.116 75.49 (7.30) 80.14 (7.14) 80.53 (8.82) 0.007 0.160
MM (kg) 49.02 (10.17) 49.97 (10.60) 56.73 (10.59) 0.002 0.685 46.56 (9.95) 50.58 (9.22) 58.52 (10.23) < 0.001 0.002a 47.28 (9.58) 50.46 (9.47) 57.94 (11.02) < 0.001 0.064
BM (kg) 2.59 (0.49) 2.65 (0.51) 2.98 (0.51) 0.002 0.696 2.47 (0.48) 2.68 (0.45) 3.07 (0.49) < 0.001 0.001a 2.51 (0.47) 2.67 (0.46) 3.04 (0.53) < 0.001 0.041a
SMM (kg) 29.30 (6.46) 30.32 (7.16) 40.21 (32.27) 0.023 0.110 27.84 (6.47) 35.82 (32.76) 36.14 (6.99) 0.107 0.318 28.16 (6.11) 35.97 (32.78) 35.67 (7.56) 0.139 0.341
FM (%) 23.71 (7.29) 21.62 (7.71) 18.67 (8.53) 0.017 0.902 24.21 (7.39) 21.97 (7.45) 17.84 (8.20) 0.001 0.114 24.50 (7.29) 20.09 (7.19) 19.44 (8.85) 0.008 0.163
MA (year) 23.70 (12.46) 21.29 (9.26) 20.40 (9.41) 0.349 0.850 22.47 (12.21) 22.39 (10.03) 20.50 (9.10) 0.638 0.857 23.87 (12.45) 19.80 (8.71) 21.75 (9.81) 0.219 0.155
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FM Fat mass, FFM Fat free mass, MM Muscle mass, BM Bone mass, SMM Skeletal muscle mass, FP Fat percent, SMM Skeletal muscle mass, MA Metabolic age; **P values are obtained from ANCOVA model after adjustment for the confounding effects of age, sex, BMI and physical activity, calorie intake *P Significant at P < 0.05; 95th confidence intervals of the difference in parentheses

apost hoc Tukey signature difference between 1st tertile and 3rd tertile

Table 3.

Energy adjusted dietary intakes of study participants across different tertiels of dietary choline, betaine and total choline and betaine intake

Variables Total choline Total betaine Total choline and betaine
T1 (n = 40) T2 (n = 40) T3 (n = 40) *P **P T1 (n = 40) T2 (n = 40) T3 (n = 40) *P **P T1 (n = 40) T2 (n = 40) T3 (n = 40) *P **P
Energy (kcal) 1659.85 (383.49) 2329.22 (535.13) 3809.73 (1279.29) < 0.001 < 0.00a 1826.25 (704.35) 2515.91 (978.81) 3473.38 (1309.25) < 0.001 < 19460.001a 1643.90 (389.20) 2384.81 (642.68) 3764.81 (1275.96) < 0.001 < 0.001a
Choline (mg/d) 180.49 (33.38) 273.29 (30.94) 535.14 (318.71) < 0.001 < 0.001a 220.62 (75.09) 302.18 (117.06) 469.75 (346.97) < 0.001 0.002a 187.61 (38.56) 281.87 (61.01) 520.95 (327.35) < 0.001 < 0.001a
Betaine (mg/d) 87.70 (40.83) 145.87 (85.23) 231.64 (149.73) < 0.001 < 0.001a 65.98 (15.43) 120.49 (18.88) 276.52 (130.50) < 0.001 < 0.001a 72.49 (23.24) 131.37 (42.18) 261.28 (143.21) < 0.001 < 0.001a
Protein (g/day) 53.89 (11.59) 76.91 (13.71) 146.75 (64.34) < 0.001 0.062 59.57 (19.71) 82.96 (26.92) 135.16 (71.18) < 0.001 0.007b 53.34 (11.42) 78.99 (15.33) 145.21 (65.49) < 0.001 0.086
Fat (g/day) 55.67 (16.97) 75.00 (21.50) 136.86 (73.31) < 0.001 0.376 65.19 (27.96) 90.46 (49.41) 112.12 (73.87) 0.001 < 0.001a,b 56.93 (17.08) 78.17 (27.53) 131.74 (75.68) < 0.001 0.196
CHO (g/day) 248.33 (71.34) 349.82 (97.69) 520.19 (178.15) < 0.001 0.382 262.94 (105.91) 359.13 (141.34) 496.63 (160.16) < 0.001 0.051 242.56 (67.73) 354.76 (108.08) 521.06 (169.85) < 0.001 0.468
Total Fiber (g/day) 11.96 (3.56) 15.45 (5.10) 25.09 (13.11) < 0.001 0.487 1318 (6.30) 17.35 (10.26) 22.01 (10.99) < 0.001 0.125 11.97 (3.57) 15.87 (6.26) 24.65 (12.98) < 0.001 0.355
SFA (g/day) 15.97 (4.63) 23.18 (6.93) 44.78 (30.22) < 0.001 0.479 19.77 (8.89) 27.64 (14.55) 36.62 (31.72) 0.002 0.007 16.40 (5.11) 24.88 (8.66) 42.57 (31.29) < 0.001 0.981
MUFA (g/day) 17.26 (6.29) 23.50 (8.16) 44.91 (26.36) < 0.001 0.210 20.54 (10.09) 28.84 (16.37) 36.37 (27.09) 0.002 < 0.001a,b 17.82 (6.33) 24.43 (10.27) 43.21 (27.07) < 0.001 0.070
PUFA(g/day) 14.75 (6.56) 17.73 (8.08) 27.33 (19.88) < 0.001 0.146 16.29 (8.98) 21.96 (16.78) 21.60 (14.50) 0.125 0.044a,b 14.90 (6.53) 18.11 (9.44) 26.43 (19.64) < 0.001 0.141
Cholesterol (mg/day) 107.77 (34.99) 150.32 (42.56) 371.78 (308.38) < 0.001 0.410 127.54 (54.82) 175.54 (75.54) 327.28 < 0.001 0.472 106.98 (35.13) 161.29 (48.45) 360.32 (315.26) < 0.001 0.508
Sodium (mg/day) 2164.46 (1159.97) 2829.14 (1233.60) 4116.87 (2422.48) < 0.001 0.639 2358.54 (1135.83) 3132.64 (1647.18) 3624.03 (2430.84) 0.009 0.705 2267.61 (1168.74) 2732.41 (1268.54) 4080.90 (2444.37) < 0.001 0.722
Iron (mg/day) 17.07 (9.14) 19.85 (5.29) 34.11 (20.00) < 0.001 0.105 17.92 (8.42) 23.20 (19.05) 29.94 (13.16) 0.001 0.425 16.55 (9.03) 20.25 (5.60) 34.22 (19.82) < 0.001 0.119
Magnesium (mg/day) 222.08 (40.13) 292.55 (61.42) 463.34 (211.35) < 0.001 0.756 255.97 (74.08) 322.77 (136.72) 400.05 (216.14) < 0.001 0.134 231.12 (45.49) 298.01 (78.24) 448.19 (219.58) < 0.001 0.724
Zinc (mg/day) 6.13 (1.63) 8.28 (1.27) 15.81 (7.40)) < 0.001 0.065 7.33 (2.76) 9.71 (4.70) 13.21 (8.01) < 0.001 0.262 6.28 (1.70) 8.70 (2.15) 15.18 (7.82) < 0.001 0.546
Phosphorus (mg/day) 840.58 (213.10) 1161.90 (249.39) 2065.52 (926.56) < 0.001 0.109 1017.93 (421.36) 1330.39 (620.42) 1724.01 (986.64) < 0.001 0.165 867.00 (239.23) 1218.31 (348.64) 1968.70 (981.20) < 0.001 0.924
Calcium (mg/day) 762.55 (221.64) 1112.60 (318.26) 1690.15 (767.92) < 0.001 0.015a 928.56 (425.27) 1145.00 (491.60) 1495.79 (774.50) < 0.001 0.916 797.70 (268.28) 1123.80 (419.93) 1639.44 (759.05) < 0.001 0.132
Potassium (mg/day) 2418.14 (594.46) 3233.57 (841.41) 5217.09 (2191.57) < 0.001 0.296 2903.89 (1062.14) 3647.57 (1785.09) 4329.18 (2179.88) 0.002 0.226 2541.16 (633.21) 3296.87 (1081.96) 5023.45 (2290.79) < 0.001 0.881
Copper (mg/day) 2.29 (0.73) 2.78 (0.94) 4.62 (2.86) < 0.001 0.078 2.51 (0.82) 2.98 (1.39) 4.21 (2.92) < 0.001 0.366 2.35 (0.74) 2.83 (1.04) 4.51 (2.91) < 0.001 0.149
Manganese (mg/day) 2.69 (0.55) 3.30 (0.90) 4.68 (1.90) < 0.001 0.931 2.92 (0.71) 3.42 (1.32) 4.34 (1.89) < 0.001 0.868 2.76 (0.61) 3.33 (0.98) 4.57 (1.95) < 0.001 0.825
Selenium (mg/day) 0.03 (0.01) 0.05 (0.04) 0.08 (0.06) < 0.001 0.929 0.03 (0.01) 0.05 (0.02) 0.08 (0.07) < 0.001 0.172 0.0.03 (0.01) 0.05 (0.02) 0.08 (0.07) < 0.001 0.738
Fluorine (mg/day) 7350.58 (6449.84) 10402.47 (9419.36) 8917.95 (8091.78) 0.241 0.057 8348.76 (6825.60) 8540.42 (8620.91) 9806.17 (8857.45) 0.682 0.390 8226.08 (6979.17) 9491.05 (9490.82) 9174.36 (7864.30) 0.772 0.438
Chromium (mg/day) 0.06 (0.03) 0.06 (0.04) 0.08 (0.05) 0.039 0.629 0.06 (0.03) 0.06 (0.04) 0.08 0.05 0.009 0.120 0.06 (0.03) 0.06 (0.04) 0.08 (0.05) 0.029 0.466
Vitamin C (mg/day) 106.87 (46.85) 144.64 (76.33) 211.48 (123.92) < 0.001 0.811 127.85 (59.49) 162.74 (115.56) 172.91 (105.29) 0.095 0.137 115.46 (46.56) 140.23 (73.41) 207.82 (130.34) < 0.001 0.431
VitaminB1 (mg/day) 1.30 (0.37) 1.82 (0.60) 2.7859 (1.11668) < 0.001 0.799 1.30 (0.46) 1.75 (0.44) 2.85 (1.09) < 0.001 < 0.001a,b 1.26 (0.35) 1.79 (0.56) 2.86 (1.05) < 0.001 0.178
VitaminB2 (mg/day) 1.08 (0.32) 1.60 (0.46) 2.98 (1.66) < 0.001 0.267 1.35 (0.65) 1.79 (0.83) 2.52 (1.79) < 0.001 0.596 1.12 (0.39) 1.67 (0.58) 2.86 (1.71) < 0.001 0.875
VitaminB3 (mg/day) 15.01 (3.47) 21.90 (5.99) 37.16 (16.17) < 0.001 0.639 15.37 (4.84) 22.19 (6.46) 36.53 (16.39) < 0.001 < 0.001a, b 14.61 (3.28) 21.53 (5.20) 37.94 (15.54) < 0.001 0.044b
VitaminB6 (mg/day) 0.94 (0.29) 1.28 (0.40) 2.22 (1.18) < 0.001 0.846 1.09 (0.46) 1.50 (0.87) 1.85 (1.13) 0.001 0.208 0.96 (0.29) 1.31 (0.43) 2.17 (1.21) < 0.001 0.628
VitaminB9 (µg/day) 192.46 (50.72) 257.53 (69.75) 426.89 < 0.001 0.841 218.78 (76.56) 299.82 (146.93) 358.92 (208.72) < 0.001 0.364 198.56 (53.05) 263.52 (79.62) 413.87 (217.23) < 0.001 0.727
VitaminB12 (µg/day) 3.35 (1.53) 5.18 (2.45) 15.32 (24.45) < 0.001 0.094 4.33 (2.58) 5.80 (3.20) 13.95 (24.74). 0.009 0.379 3.28 (1.34) 5.73 (2.89) 14.80 (24.62) 0.001 0.098
VitaminB5 (mg/day) 3.91 (1.00) 5.36 (1.27) 9.78 (5.09) < 0.001 0.596 4.80 (2.16) 6.20 (3.51) 8.08 (5.01) 0.001 0.127 4.02 (1.10) 5.65 (1.83) 9.3 (5.30) < 0.001 0.475
VitaminB8 (mg/day) 14.28 (4.93) 18.84 (7.35) 27.28 (11.41) < 0.001 0.043a 16.81 (7.65) 20.81 (8.19) 22.84 (12.37) 0.019 0.371 15.06 (6.12) 19.67 (7.74) 25.48 (12.02) < 0.001 0.731
Vitamin A (RAE/day) 806.61 (405.91) 1080.66 (678.02) 2418.41 (2234.99) < 0.001 0.444 979.98 (579.36) 1219.61 (827.64). 2110.32 (2322.94) 0.002 0.321 833.60 (407.69) 1096.98 (642.50) 2368.61 (2279.56) < 0.001 0.278
Vitamin D (µg/day) 1.31 (0.94) 1.93 (1.51) 3.21 (3.20) < 0.001 0.037a 1.79 (1.63) 2.16 (1.88) 2.51 (2.98) 0.356 0.881 1.50 (1.21) 1.98 (1.77) 2.96 (3.11) 0.012 0.535
Vitamin K (µg/day) 168.54 (78.52) 232.93 (122.07) 305.61 (187.45) < 0.001 0.167 188.90 (91.08) 221.52 (114.02) 297.16 (195.50) 0.003 0.205 181.70 (90.94) 221.66 (123.84) 306.90 (183.90) < 0.001 0.352
Vitamin E (mg/day) 2.24 (0.57) 2.92 (0.98) 4.27 (1.41) < 0.001 0.567 2.43 (0.87) 3.15 (1.08) 3.85 (1.58) < 0.001 0.734 2.30 (0.68) 2.96 (0.97) 4.14 (1.51) < 0.001 0.900
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*P Significant at P < 0.05; 95th confidence intervals of the difference in parentheses. **P values are obtained from ANCOVA model after adjustment for the confounding effects of age, sex, BMI and physical activity, calorie intake

apost hoc Tukey signature difference between 1st tertile and 3rd tertile

bpost hoc Tukey signature difference between 2nd tertile and 3rd tertile

Table 4.

Biochemical parameters of study participants across different tertiels of dietary choline, betaine and total choline and betaine intake

Variables Total choline Total betaine Total choline and betaine
T1 (n = 40) T2 (n = 40) T3 (n = 40) *P **P T1 (n = 40) T2 (n = 40) T3 (n = 40) *P **P T1 (n = 40) T2 (n = 40) T3 (n = 40) *P **P
FBS (mg/dl) 70.83 (8.33) 70.97 (7.80) 69.70 (6.86) 0.832 0.538 70.59 (8.00) 70.65 (7.94) 70.26 (7.15) 0.969 0.499 70.08 (8.20) 72.16 (7.23) 69.25 (7.36) 0.209 0.075
Insulin (µ IU/ml) 10.45 (10.09) 8.94 (5.01) 9.41 (5.97) 0.881 0.572 11.54 (10.37) 8.86 (4.17) 8.43 (5.08) 0.116 0.175 10.47 (10.13) 9.33 (5.94) 9.00 (4.97) 0.641 0.620
TC (mg/dl) 208.06 (61.71) 205.19 (56.79) 185.95 (53.51) 0.172 0.671 209.40 (44.93) 218.17 (65.20) 171.67 (51.25) < 0.001 0.032b 206.44 (52.65) 208.44 (63.26) 184.29 (54.97) 0.110 0.150
HDL (mg/dl) 47.76 (11.36) 50.73 (11.88) 48.40 (11.07) 0.469 0.524 45.43 (9.34) 52.16 (11.89) 49.25 (12.05) 0.028 0.049a 46.48 (11.39) 50.93 (10.91) 49.45 (11.78) 0.205 0.545
LDL (mg/dl) 135.21 (67.72) 127.34 (59.52) 113.67 (53.70) 0.272 0.784 141.30 (46.56) 138.84 (70.47) 96.23 (52.44) 0.001 0.183 136.82 (60.70) 130.66 (61.88) 10,878 (57.24) 0.090 0.428
TG (mg/dl) 125.44 (80.83) 135.54 (64.16) 119.39 (63.97) 0.399 0.272 113.35 (66.02) 135.79 (75.95) 130.94 (66.45) 0.317 0.312 115.65 (81.38) 134.19 (57.36) 130.29 (69.11) 0.456 0.325
CK (U/L) 209.51 (67.49) 201.80 (76.43) 345.40 (543.41) 0.174 0.092 198.03 (77.09) 270.60 (333.84) 287.80 (443.93) 0.423 0.582 200.28 (69.20) 219.00 (78.44) 337.20 (544.84) 0.118 0.142
HOMA-IR 1.86 (1.89) 1.57 (0.91) 1.64 (1.07) 0.912 0.573 2.05 (2.00) 1.56 (0.80) 1.46 (0.87) 0.111 0.143 1.85 (1.90) 1.63 (1.07) 1.55 (0.88) 0.591 0.544
QUICKI 0.36 (0.03) 0.36 (0.03) 0.37 (0.04) 0.912 0.624 0.36 (0.03) 0.36 (0.03) 0.37 (0.04) 0.179 0.226 0.36 (0.03) 0.36 (0.03) 0.37 (0.04) 0.623 0.425
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FBS Fasting blood sugar, TC Total cholesterol, HDL High-density lipoprotein, LDL Low-density lipoprotein, TG Triglycerides, CK Creatine kinase. *P Significant at P < 0.05; 95th confidence intervals of the difference in parentheses. **P values are obtained from ANCOVA model after adjustment for the confounding effects of age, sex, BMI and physical activity, calorie intake and further adjusted for muscle mass for CK level

apost hoc Tukey signature difference between 1st tertile and 2nd tertile

bpost hoc Tukey signature difference between 2nd tertile and 3rd tertile

Fig. 2.

Fig. 2

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The interaction between dietary choline and physical activity on biochemical markers. HOMA-IR; Homeostatic model assessment for insulin resistance; QUICKI; quantitative insulin-sensitivity check index. P values of interaction are obtained from ANCOVA model after adjustment for the confounding effects of age, sex, BMI and calorie intake. *P Significant at P < .05; 95th confidence intervals of the difference in parentheses

Fig. 3.

Fig. 3

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The interaction between dietary betaine and physical activity on biochemical markers. HDL; high density lipoprotein. P values of interaction are obtained from ANCOVA model after adjustment for the confounding effects of age, sex, BMI and calorie intake*P Significant at P < .05; 95th confidence intervals of the difference in parentheses

Fig. 4.

Fig. 4

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The interaction effect of dietary choline + betaine and physical activity on biochemical markers. FBS; fasting blood sugar. P values of interaction are obtained from ANCOVA model after adjustment for the confounding effects of age, sex, BMI and calorie intake. *P Significant at P < .05; 95th confidence intervals of the difference in parentheses

Discussion

As far as we know, there are no other studies that have examined the interactive relationship between dietary choline and betaine and physical activity on circulating creatine kinase (CK), metabolic and glycemic markers, and anthropometric characteristics in physically active young people.

An increase in dietary betaine is associated with a decrease in TC and an increase in HDL and an increase in height, weight, MAC, WHR, FFM, MM, BM in anthropometric measurements (P < 0.05). In addition, there was a significant direct relationship between the total amount of dietary choline and betaine and weight, WC, WHR, FFM, and BM (P < 0.05). The interaction effect of dietary choline and moderate or high physical activity improved insulin resistance (P < 0.05). Higher dietary betaine along with high physical activity increased HDL in the highest tertile of dietary choline content compared with the lowest tertile (P < 0.05).

An important strength of the current study is the systematic control of all confounding factors and the accurate measurement of all anthropometric and biochemical characteristics. In population-based studies, in order to obtain more reliable results, it is essential to recognize and control the most important confounding factors. The factors that influence anthropometric and biochemical markers as confounding factors include age, sex, body mass index, physical activity, and energy intake [47, 48]. In the present study, all these confounders were adjusted for in all analyses.

Similar to our findings, in the study by Azad Bakht et al. dietary betaine was significantly related to MAC (r = 0.093, P = 0.009) and obesity risk (P < 0.05), but there was no association between choline and anthropometric measurements [49]. in general population. Similar to our study, Gao X et al. showed that increasing dietary betaine and dietary total choline and betaine increases FFM [35].Unlike the present study, increased WHR by increased dietary choline were observed in the study by Dibaba D et al. [24]. like the present study a study by Xiang Gao et al. showed that serum choline was positively associated with weight, BMI, and WC (r ranged from 0,09 to 0.10, and p < 0.05 for all) [50]. In two different studies conducted in obese adults and men, supplementation with betaine had no significant effect on the body composition of the participants [27, 51]. Increasing dietary choline and betaine significantly correlated with a reduction in BMI, waist circumference, and weight loss [52].

The results of the various studies appear to be inconsistent because of differences in the general and demographic characteristics of their populations. Some studies also differ in terms of the mode of investigation; some have used dietary choline and betaine, others have used choline or betaine supplements, and still others have examined serum levels of these micronutrients.

Insulin resistance is a multifaceted pathophysiologic condition whose development and progression are influenced by many factors. Recognizing, assessing, and monitoring these characteristics in analyzes is critical to uncovering meaningful associations. Age, gender and total daily caloric intake are among the factors that influence insulin sensitivity and should be monitored [53–56]. The interaction effect of dietary choline and moderate or high physical activity improved insulin resistance (decreasing insulin and HOMAIR and increasing QUICKI) (P < 0.05). In addition, increased dietary choline and betaine along with high physical activity decreased FBS in the third tertile of dietary choline and betaine compared with the first tertile (P < 0.05). The results of the study by X. Gao et al. showed an inverse correlation between dietary choline and betaine and fasting glucose and insulin levels and HOMA-IR (r = -0.08 to -0.27 for choline and r = -0.06 to -0.16 for betaine; P < 0.05), indicating that higher intake was associated with lower insulin resistance. Choline and betaine were positively related to QUICKI (r = 0.16–0.25 for choline and r = 0.11–0.16 for betaine; P < 0.01), indicating higher insulin sensitivity [6]. Choline supplement could alleviate inflammation and suppress oxidative stress, which play important roles in the development of IR, they noted, before adding that choline can also be metabolized to betaine, which could impact IR via a couple of different routes, including improving signaling pathways for glycogen synthesis and enhancing insulin sensitivity in fat tissue. Betaine may also reduce inflammatory markers levels [57–59].

In the present investigation, Increasing dietary betaine decreased serum levels TC and increased HDL (P < 0.05). Higher dietary betaine along with high physical activity increased HDL in the highest tertile of dietary choline compared with the lowest tertile (P < 0.05). In other research, there was an adverse association between serum betaine and some lipid profile factors (triglycerides, non-HDL cholesterol, and HDL cholesterol) [60]. Participants who were involved in a lipid clinic showed that betaine had an adverse relationship with apolipoprotein B (Apo B) and body fat [61, 62]. Current research demonstrates the function of BHMT (betaine homocysteine S-methyltransferase) in lipid metabolism, such that dietary induction of BHMT in mice causes elevated liver VLDL secretion, ApoB, ApoB mRNA, and triglyceride production [63]. Betaine homocysteine S-methyltransferase, as the most abundant protein in the liver of mammals, in addition to homocysteine remethylation, binds to membranes and is related to various proteins. Therefore, it can be concluded that BHMT probably indicates a metabolic link between 1-carbon metabolism and lipids [60, 64].

Creatine phosphokinase is an enzyme that is found in skeletal and cardiac muscle cells (MM and MB isoform) and brain tissue (BB isoform). Higher CK in the blood is typical because of muscle damage or muscular dystrophy [65–67]. Adequate dietary choline is important for maintaining muscle cell integrity, as a choline-deficient diet is associated with increased serum creatine phosphokinase in humans [17]. Progressive muscular dystrophy in rabbits is associated with choline deficiency [65, 68–70]. Deficiency of choline through the reduction of phosphatidylcholine in muscle cells induces apoptosis, increases the fragility of the membrane, and eventually leaks creatine phosphokinase to the outside of the cell. Additionally, receiving a low choline diet in men significantly increased the activity of serum creatine phosphokinase [17]. In various studies, it has been shown that there is a relationship between serum creatine kinase levels and some metabolic and glycemic indicators. In other words, elevated serum creatine kinase is associated with obesity, increased WC and BMI, WC, WHR and increased insulin resistance, and increased risk of heart disease [71–73]. However, serum creatine kinase levels depend on various factors such as age, sex, muscle mass, and physical activity [74–76]. In the present study, the group with the highest tertile of dietary choline and betaine had higher serum creatine kinase levels, although this association was not significant. This relationship can be explained by the fact that the group that received the most choline and betaine had the highest physical activity and muscle mass.

This is a cross-sectional study that cannot establish a cause-and-effect relationship.There is a need for another longitudinal study to fill this knowledge gap. In addition, although several factors were fully controlled in our analysis, including age, sex, BMI, physical activity, and caloric intake, genetic factors and unknown or poorly measured factors were not fully eliminated. The cross-sectional design of the current study makes it difficult to draw causal conclusions; In addition, a semiquantitative dietary assessment questionnaire was used that could introduce recall bias because of its subjective nature; however, the validity and reliability of the questionnaire have been confirmed in previous studies. Other strengths of this study include the numerous variables examined.

Conclusions

In our cross-sectional study of Iranian youth, the interaction effect of dietary choline and moderate or high physical activity improved insulin resistance (P < 0.05). Higher dietary betaine along with high physical activity increased HDL in the highest tertile of dietary choline compared with the lowest tertile (P < 0.05). Additionally, increased dietary choline and betaine together with high physical activity decreased FBS in the third tertile of dietary choline and betaine compared with the first tertile (P < 0.05).

Supplementary Information

Additional file 1. (252.7KB, docx)

Acknowledgements

We are thankful from all of the participants in the current study. Also, we thank Research undersecretary of Tabriz University of Medical Sciences for their financial support (Grant no. 69507).

Abbreviations

FFQ

Food frequency questionnaire

PA

Physical activity

BMI

Body mass index

ANOVA

Analysis of variance

ANCOVA

Analysis of covariance

BIA

Bioelectric impedance analysis

MAC

Mid arm circumference

WC

Waist circumference

WHR

Waist to hip ratio

FFA

Free fatty acids

CK

Creatin kinase

TC

Total cholesterol

LDL

Low density lipoprotein

HDL

High density lipoprotein

TG

Trygliserid

Authors' contributions

All authors approved the final version of the article. MAF and AMA designed the project and supervised it. ES also contributed to statistical analysis and manuscript writing. ES was also involved in hypothesis generation and the statistical approach. EF was involved in statistical approach and edition of the article. All of the authors were involved in manuscript writing. MAF also was involved in article revision.

Funding

The present study was financially supported by a grant from Tabriz University of Medical Sciences. (Code: IR.TBZMED.REC.1401.033). The funders had no role in hypothesis generation, recruiting, and designing the study. Their role was only financial support.

Availability of data and materials

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

Declarations

Ethics approval and consent to participate

This study protocol was approved by the ethics committee of the Tabriz University of Medical Sciences (Code: IR.TBZMED.REC.1401.033). Written informed consent was obtained from all of the participants before participation in the study. All methods in the current research were performed according to the Declaration of Helsinki's guidelines and regulations.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Footnotes

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Associated Data

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

Supplementary Materials

Additional file 1. (252.7KB, docx)

Data Availability Statement

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

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