Dietary pattern related to physical examination indicators in Tibetan migrants：A cross-sectional study

doi:10.21203/rs.3.rs-1618926/v1

Download PDF

Research Article

Dietary pattern related to physical examination indicators in Tibetan migrants：A cross-sectional study

https://doi.org/10.21203/rs.3.rs-1618926/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted

You are reading this latest preprint version

Background: Although dietary patterns of highlander have been widely investigated, no study has explored the relationship between dietary patterns and physical examination indicators in high-altitude migrants. This cross-sectional study aimed to investigate the relationship between dietary and physiological indexes and explore the suitable dietary patterns of Tibetan migrants.

Methods: 116 participants who had migrated to Tibet for > 2 years were recruited. Dietary patterns and physical examination indicators were assessed using a self-adjusted food frequency questionnaire and blood gas analysis, and six principal dietary patterns were obtained using principal component analysis.

Results: The following results were obtained: adherence to a coarse grain dietary pattern was positively associated with mean corpuscular hemoglobin concentration (odds ratio (OR) = 1.077, p = 0.054) and negatively associated with urea level (OR = 0.601, p = 0.013). High meat dietary pattern is positively associated with creatinine level (OR = 1.050, p = 0.023) but negatively associated with glomerular filtration rate (OR = 0.960, p = 0.020). Adherence to the beverage dietary pattern was positively associated with uric acid levels (OR = 1.005, p = 0.044). High fruit intake is positively correlated with aspartate transaminase/alanine transaminase level (OR = 5.271, p = 0.005) and red blood cell count (OR = 4.805, p = 0.033). An unhealthy dietary pattern was positively correlated with lymphocyte counts (OR = 2.904, p = 0.011).

Conclusions: In summary, a coarse grain-rich diet is suitable for Tibetan migrants. However, meat and fruit should not be consumed in excessive amounts, and sweetened drinks and alcohol should be avoided.

food frequency questionnaire

dietary patterns

physical examination indicators

high-altitude migrants

24-hour dietary recall method

Most previous studies examining the diets of individuals living in highland areas have focused on plateau-dwelling individuals, who have developed specific dietary patterns as a result of living on the plateau for generations ^(1–5). For example, a diet based on tsampa is accompanied by a high intake of meat and dairy products. Owing to the specific dietary patterns, Tibetans have developed a unique adaptation mechanism at high altitudes ⁽⁶⁾. However, studies investigating the dietary structure of migrants and whether it alleviates their physiological acclimatization at high altitudes are limited.

Exposure to high altitudes influences both the diet and physiological function of migrants. Studies on acclimatization to plateaus reported an increased basal metabolic rate and reduction in food intake in migrants due to maladaptation ^{(7, 8)}. This can have negative effects, such as depletion of stored muscle glycogen, leading to a negative nitrogen balance and even malnutrition^{(7, 8)}. A high-altitude environment can affect physiological functions, such as oxygen transport capacity, immune function, and liver and kidney functions. First, prolonged exposure to high-altitude conditions results in higher red blood cell (RBC) count, hemoglobin (HGB) level, and hematocrit (HCT) in migrants. Elevation in these indicators also leads to an increase in blood viscosity^(9–11). Second, the hypoxic environment influences the immune system, affecting the immune function of T lymphocytes and neutrophils and thus the immune response ^(12–14). Moreover, kidney function is affected by long-term exposure to high altitudes. An increase in creatinine (CREA) level and decrease in the glomerular filtration rate (GFR) may occur in first-time migrants to the plateau^{(15, 16)}. A slight improvement in the GFR was observed in individuals who had lived in high-altitude areas for a longer period ⁽¹⁷⁾. However, prolonged exposure to high altitudes is associated with an increase in blood uric acid (UA) level, even leading to the development of diseases, such as hyperuricemia and proteinuria ⁽¹⁸⁾. Finally, the hypoxic environment affects liver function. Animal studies have shown that chronic hypoxia is associated with liver damage⁽¹⁹⁾. However, the number of human-based studies investigating this association is limited. Aspartate aminotransferase (AST), alanine aminotransferase (ALT), and bilirubin levels have been considered indicators of liver function ⁽²⁰⁾. Migrants exhibited a significant increase in ALT levels after arrival at high-altitude areas ⁽²¹⁾. Although no significant change in the AST level was observed, the plasma volume decreased, while the concentration remained unchanged, leading to a decrease in the actual AST level⁽²²⁾. All of the abovementioned blood parameters can be evaluated through physical examination. It is vital to explore the relationship between diet and physical examination indicators to predict the risk of related diseases ⁽²³⁾. Similarly, in the case of highland migrants, exploring the relationship between their dietary patterns and physical examination indicators can provide a basis for subsequent beneficial dietary adjustments and facilitate their acclimatization to the highland environment.

Previous studies examining the relationship between dietary and physical examination indicators of highland migrants focused on specific groups, such as military personnel, athletes, mountaineers, and limited nutrients⁽²⁴⁾. For athletes, carbohydrate supplementation is important to increase the glycogen stores in the muscles and liver, and ingestion after training can rapidly restore glycogen and reduce muscle soreness ^{(25, 26)}. Researchers believe that supplementation with natural foods rich in antioxidants is safer and more effective than supplementation for altitude acclimatization ^(27–29). Nitrate is beneficial for migrant acclimatization ⁽³⁰⁾. Thus, foods rich in these nutrients may be beneficial for plateau acclimatization, but it remains inconclusive whether individuals with such dietary habits are better acclimatized physiologically.

In summary, no previous studies have addressed the dietary structure of highland migrants and even fewer have explored the overall acclimatization of the diet of highland migrants to the local environment. Therefore, this study aimed to investigate the dietary patterns of Tibetan migrants and relationship between dietary and physical examination indicators.

2.1 Participants

Altogether, 116 Han Chinese college students from Tibet University who were born and had grown up in a plain area (< 1,500 m) and had been living in Tibet (3,680 m) for > 2 years were recruited; all of these students were men. The average age of the participants was 20.65 years, with a standard deviation of 0.85 years. All participants provided written informed consent and were paid for their participation in the study. Institutional ethical approval was obtained before data collection, and the study conformed to the principles of the Declaration of Helsinki.

2.2 Study design

In this study, a self-adjusted food frequency questionnaire (FFQ) was used to collect the participants’ dietary intake data. The physical examination indicators were also obtained from fasting blood samples. Among the 116 participants, 95 completed the pretest FFQ, 59 valid questionnaires were recovered during the retest, and 56 participants underwent 24-h dietary recall (24HDR). Fasting blood samples were obtained from 95 participants. The flowchart of this study is shown in Fig. 1.

2.3 Food frequency questionnaire

The FFQ used in this study was modified and supplemented according to that in previous studies ⁽³¹⁾. Local traditional Tibetan foods, such as sweet tea and tsampa, were considered. The FFQ used in this study comprised 40 items and examined the dietary intake of each participant within 1 year. The FFQ included 35 specific food categories and five condiments. The participants were asked two questions for each food item to calculate the intake of each food: the frequency of eating a specific food and the amount of food consumed each time. Retest reliability was evaluated at a 1-month interval, the efficacy standard was collected using the 24HDR method for 3 consecutive days, and the average value of dietary intake for 3 days was used as the criterion for evaluating the validity. The participants did not understand the process of filling out the questionnaire and had disputes that were explained consistently.

2.4 Physical examination indicators

Physical examination indicators were obtained by fasting blood sampling and mainly included 19 physiological indexes, oxygen transport capacity, immune function, and liver and kidney function. The physiological indexes were as follows: RBC, HGB, HCT, mean corpuscular volume (MCV), mean corpuscular hemoglobin (MCH), mean corpuscular hemoglobin concentration (MCHC), neutrophil count (NEUT#), neutrophil ratio (NEUT%), lymphocyte count (LYMPH#), lymphocyte ratio (LYMPH%), albumin (ALB), globulin (GLB), albumin/globulin (A/G), ALT, AST, AST/ALT, serum urea (UREA), CREA, and UA. These indexes were analyzed using the Mindray SAL-9000 automatic biochemical analyzer (SAL-9000, Mindray) and Mindray BC-5390 automatic blood analyzer (BC-5390, Mindray, Shenzhen, China). Blood samples were analyzed in a standardized environment at Fokind Medical Care. Moreover, to determine the mechanistic changes in renal function, GFR was calculated based on the method used in previous studies combined with CREA ⁽³²⁾.

2.5 Statistical analysis

All statistical analyses were conducted using IBM SPSS 25.0. The specific statistical analysis methods used were as follows. The reliability and validity of the questionnaire were measured using Pearson’s product moment correlation. In addition, the Bland–Altman approach was used to visually compare the consistency between the two dietary tools^{(33, 34)}. The plots included lines for the mean difference and the limits of agreement (LOA), defined as the mean difference ± 1.96 × SD. Therefore, this method was also used in this study to assess the validity of the questionnaire. Due to the large number of items in the questionnaire, it was consolidated into 12 food groups to assess the consistency between the two methods. Furthermore, the differences between various food intakes obtained using the pre-measured FFQ and standard values of the 2021 and 2016 versions were compared. The dietary patterns of the migrants were extracted by principal component analysis, while Pearson’s correlation and binary logistic regression were used to explore the relationship between dietary patterns and physical examination indicators. A binary logistic regression analysis was conducted by dividing the principal component scores into two groups based on the median. The forward maximum likelihood ratio estimation method was used to analyze the independent variables in the regression analysis. The criterion for statistical significance was set at α < 0.05.

3.1 Descriptive statistics

Descriptive statistics were used to calculate the means and standard deviations of the demographic variables and physical examination indicators (Table 1).

Table 1

Descriptive statistics of participants’ age, body mass index, and physical examination indicators

Variables	mean	Sd.	Number of people in the normal range (percentage)
age	20.65	0.85
BMI	21.47	3.23
ALB	50.93	1.90	95(100%)
GLB	23.70	3.42	83(87.3%)
A/G	2.19	0.31	71(74.7%)
ALT	21.92	16.30	82(86.3%)
AST	21.80	9.96	87(91.6%)
AST/ALT	1.21	0.49	40(42.1%)
UREA	4.97	1.19	90(94.7%)
CREA	97.66	17.65	71(74.7%0
UA	457.22	81.51	33(34.7%)
NEUT#	3.16	0.96	86(90.5%)
LYMPH#	2.62	0.54	95(100%)
NEUT%	49.39	7.82	40(42.1%)
LYMPH%	42.14	8.33	31(32.6%)
RBC	5.80	0.36	26(27.4%)
HGB	181.07	8.87	1(1.1%)
HCT	53.69	2.82	93(97.9%)
MCV	92.71	3.43	93(97.9%)
MCH	31.27	1.21	42(44.2%)
MCHC	337.38	6.02	95(100%)

BMI, body mass index ALB, albumin; GLB, globulin; A/G, albumin/globulin; ALT, alanine aminotransferase; AST, aspartate aminotransferase; AST/ALT, aspartate aminotransferase/ alanine aminotransferase; CREA, creatinine; UA, uric acid; NEUT#, neutrophil count; LYMPH#, lymphocyte count; NEUT%, neutrophil ratio; LYMPH%, lymphocyte ratio; RBC, red blood cell count; HGB, hemoglobin; HCT, hematocrit; MCV, mean corpuscular volume; MCH, mean corpuscular hemoglobin; MCHC, mean corpuscular hemoglobin concentration.

BMI is a person’s weight in kilograms divided by the square of height in meters.

3.2 Reliability and validity of the FFQ

The retest reliability of the FFQ was calculated, and the Pearson’s correlation coefficient was used to calculate the reliability. The reliability ranged between 0.187 and 0.897, with an average value of 0.541. The category with the highest reliability was sweets (reliability coefficient r = 0.897), and the category with the lowest reliability was dry goods (reliability coefficient r = 0.187). The calculated Pearson’s correlation before and after measurement was significant, except for dry goods and dry beans. This finding shows that the FFQ has good reliability and can be used in follow-up studies and analyses within an acceptable range. Details of the reliability results are presented in Table 2.

Table 2

Retest reliability of FFQ

items	r	p	items	r	p
rice	0.545	0.000	bean products	0.309	0.019
wheat	0.398	0.002	vegetables	0.451	0.000
cereal	0.536	0.000	pickled vegetables	0.759	0.000
tuber	0.518	0.000	dried food	0.187	0.163
tsampa	0.635	0.000	fruit	0.730	0.000
fried food	0.365	0.005	carbonated	0.607	0.000
porridge	0.650	0.000	juice	0.308	0.020
livestock meat	0.261	0.050	tea & coffee	0.548	0.000
poultry meat	0.622	0.000	beer	0.589	0.000
processed meat	0.777	0.000	Baijiu	0.838	0.000
aquatic product	0.640	0.000	wine	0.744	0.000
egg	0.639	0.000	chang	0.789	0.000
milk	0.387	0.003	sweet food	0.897	0.000
yogurt	0.590	0.000	nut	0.363	0.006
sweet tea	0.701	0.000	snacks	0.445	0.001
buttered tea	0.587	0.000	oil	0.536	0.000
powdered milk	0.269	0.043	suger	0.799	0.000
tofu	0.444	0.001	salt	0.515	0.000
soybean milk	0.308	0.020	soy sauce	0.542	0.000
bean	0.213	0.111	vinegar	0.614	0.000

To analyze validity, participants who completed the pre- and posttest questionnaires were asked to take pictures and record their intake for 3 consecutive days from October 21 to 23. The collected pictures and dietary records were also converted into data, and Pearson’s correlation was calculated using the post-test FFQ. Some foods not consumed by the participants during the 3-day period, such as tsampa, ghee tea, and alcoholic products, were not found to be correlated with Pearson’s product correlation, which explains the drawbacks of this method. For the remainder, the Pearson’s product correlation ranged from −0.176 to 0.491. The details of the validity results are presented in Table 3. Bland-Altman plots show the valid comparability of dietary intakes estimated from post-test FFQ and 24HDR. Figure 2 shows the Bland-Altman plots for the different food groups. The Figure reveals the consistency of the two food measures across the 12 food groups, with most values within 95% of the LOA.

Table 3

Validity of the FFQ

items	r	p	items	r	p
rice	0.160	0.237	bean products	0.041	0.763
wheat	0.079	0.561	vegetables	0.009	0.945
cereal	-0.021	0.877	pickled vegetables	0.027	0.843
tuber	-0.051	0.711	dried food	0.004	0.974
tsampa	.a		fruit	0.112	0.412
fried food	0.199	0.141	carbonated	0.209	0.121
porridge	0.491	0.000	juice	0.176	0.194
livestock meat	0.026	0.846	tea & coffee	0.029	0.833
poultry meat	-0.139	0.308	beer	.a
processed meat	-0.044	0.748	Baujiu	0.087	0.526
aquatic product	0.137	0.313	wine	.a
egg	0.367	0.005	chang	.a
milk	0.198	0.144	sweet food	0.035	0.799
yogurt	-0.015	0.910	nut	0.049	0.722
sweet tea	0.265	0.049	snacks	0.007	0.957
buttered tea	.a		oil	.b
powdered milk	.a		suger	.b
tofu	0.069	0.614	salt	.b
soybean milk	0.169	0.212	soy sauce	.b
bean	0.025	0.855	vinegar	.b

“a”: Indicates no intake within 3 days.

“b”: Not presented due to difficulties in calculating condiments

The average intakes from both the post test of food frequency questionnaire and 3-day 24-hour dietary recall are plotted on the X-axis, and the difference is plotted on the Y-axis. The orange line means the mean of difference. Two green lines mean the limits of agreement (LOA), defined as mean difference ± 1.96 × SD. The black points mean dietary intakes data of participants.

3.3 Gap of actual dietary intakes and recommended intakes

Specific food intake was assessed based on the CDP (2021 version), which is the national standard for Chinese people. One-sample t-test was used to compare the means of specific food intake against the reference values. As the recommended values of condiments were not included in the recommended dietary intake of Chinese residents in 2021, the 2016 version of the Balanced Dietary Pagoda for Chinese Residents was used as a reference standard for comparison. As the units do not coincide, they were initially converted, and comparisons were made only afterward. The intake of livestock and poultry meat was significantly higher than the intake recommended in the 2021 version (t = 6.279, p = 0.000); the staple foods included in the FFQ were rice, wheat, cereals, potatoes, tsampa, and porridge. The intake of staple foods was higher than that in the 2021 version of the recommended value, and the difference between the two was not significant. For the remaining specific foods, the intake was significantly lower than that in the 2021 version of the standard recommendations (p = 0.000). As regards the intake of condiments, salt intake was greater than the maximum value required by the 2016 Chinese Residents’ Balanced Diet Pagoda (m = 4.316, μ = 3.6, t = 1.812, p = 0.073), and the difference between the two was marginally significant. Oil intake was higher than the standard value of the 2016 Chinese Residents’ Balanced Diet Pagoda, but the difference was not significant (t = 0.297, p = 0.767). The results are presented in Table 4.

Table 4

Gap of actual dietary and standard intakes

Category	Actual intake（liang/month）	Standard intake (liang/month)	t	P
staple food	209.790	195	1.095	0.276
livestock and poultry meat	93.295	34.5	6.279	0.000
vegetables	72.968	240	-24.969	0.000
egg	17.411	27	-3.914	0.000
dairy	51.768	180	-20.100	0.000
aquatic product	3.084	34.5	-76.927	0.000
fruit	30.221	165	-31.370	0.000
soybean and nut	6.947	18	-10.999	0.000
salt	4.316	<3.6	1.812	0.073
oil	17.179	16.5	0.297	0.767

1 liang = 50 g

3.4 Dietary patterns of Tibetan migrants

After taking the pretest, the results of the completed questionnaire were subjected to principal component analysis, and six principal components were retained based on the parallel analysis. The total variance explained by all six principal components reached 74.72%. The component matrix was rotated using the variance maximization method, and the principal components were named according to the rotation results. During the process of naming the principal components, the absolute value of the factor loadings ≥ 0.30 was mainly considered. Thus, six principal components were obtained: coarse grain dietary pattern, which included potatoes, cereals, ghee tea, and dried beans; meat dietary pattern, which included livestock meat and poultry meat; beverage dietary pattern, which included tea and coffee, carbonated drinks, and yogurt; dairy dietary pattern, which included milk, pickled vegetables, and yogurt; fruit dietary pattern, which included fruits, nuts, and fruit juices; and unhealthy dietary pattern, which included carbonated drinks, porridge, eggs, and beer. However, the factor loadings of porridge and eggs were negative; therefore, it was classified as an unhealthy dietary pattern. The specific dietary patterns are shown in Figure 3.

3.5 Correlation of dietary patterns with physical examination indicators

Principal component 1 (coarse grain dietary pattern) was positively correlated with NEUT% (r = 0.240, p = 0.019), negatively correlated with LYMPH% (r = −0.225, p = 0.028), significantly positively correlated with MCV (r = 0.202, p = 0.049), and positively correlated with MCH (r = 0.268, p = 0.009). Principal component 2 (meat dietary pattern) showed a significant positive correlation with AST levels (r = 0.202, p = 0.050). Principal component 3 (beverage dietary pattern) was positively correlated with both ALT and UA levels (r = 0.253, p = 0.014). Principal component 5 (fruit dietary pattern) was positively correlated with A/G (r = 0.256, p = 0.012) and negatively correlated with MCHC (r = -0.216, p = 0.036). The results of the correlation analysis are presented in Table 5 and Figure 4.

Table 5

Correlation of dietary pattern with physical examination indicators

Principal components	physiological index	r	p
coarse grain dietary pattern	NEUT%	0.240	0.019
coarse grain dietary pattern	LYMPH%	-0.225	0.028
coarse grain dietary pattern	MCV	0.202	0.049
coarse grain dietary pattern	MCH	0.268	0.009
meat dietary pattern	AST	0.202	0.050
beverage dietary pattern,	ALT	0.253	0.014
beverage dietary pattern,	UA	0.253	0.014
fruit dietary pattern	A/G	0.256	0.012
fruit dietary pattern	MCHC	0.216	0.036

NEUT#,neutrophil count;LYMPH%,lymphocyte ratio;

MCV,mean corpuscular volume;MCH,mean corpuscular hemoglobin;

AST, aspartate aminotransferase;ALT,alanine aminotransferase;

UA, uric acid;A/G,albumin/globulin;

MCHC,mean corpuscular hemoglobin concentration.

PCA1, coarse grain dietary pattern; PCA2, meat dietary pattern; PCA3, beverage dietary pattern; PCA4, dairy dietary pattern; PCA5, fruit dietary pattern; PCA6, unhealthy dietary pattern

ALB, albumin; GLB, globulin; A/G, albumin/globulin; ALT, alanine aminotransferase; AST, aspartate aminotransferase; AST/ALT, aspartate aminotransferase/ alanine aminotransferase; CREA, creatinine; UA, uric acid; NEUT#, neutrophil count; LYMPH#, lymphocyte count; NEUT%, neutrophil ratio; LYMPH%, lymphocyte ratio; RBC, red blood cell count; HGB, hemoglobin; HCT, hematocrit; MCV, mean corpuscular volume; MCH, mean corpuscular hemoglobin; MCHC, mean corpuscular hemoglobin concentration.

3.6 Binary logistic regression of dietary patterns with physical examination indicators

The abovementioned correlation analysis illustrates the association of some dietary patterns with physical examination indicators; however, owing to the large standard deviation of the principal component scores, the statistical method of binary logistic regression was more appropriate. Results were obtained using the method described in the Statistical Analysis section. Individuals with high MCHC were likely to consume more coarse-grained foods (OR = 1.077, p = 0.054). The opposite result was observed for urea level, which was negatively correlated with adherence to coarse-grained foods; individuals with high urea level typically consumed less coarse-grained foods (OR = 0.601, p = 0.013). The final independent variable that was retained for high or low adherence to meat dietary pattern that had an impact was CREA, which was positively correlated; individuals with high CREA level consumed more meat (OR = 1.050, p = 0.023). At the mechanistic level, adherence to the meat dietary pattern was associated with lower GFR (OR = 0.960, p = 0.020). Adherence to beverage dietary pattern was positively associated with serum UA levels; individuals with high UA levels may have consumed more drinks (OR = 1.005, p = 0.044). For dairy dietary pattern, the physical examination indicators had no significant effect. For principal component 5, the fruit dietary pattern was associated with two physiological indicators: a positive correlation between AST/ALT ratio (OR = 5.271, p = 0.005) and a positive correlation between RBC level and fruit dietary pattern (OR = 4.805, p = 0.033). Adherence to an unhealthy dietary pattern was positively correlated with LYMPH# (OR = 2.904, p = 0.011). The results of the binary logistic regression analysis are shown in Figure 5.

To the best of our knowledge, this study was the first to report the dietary structure of Tibetan highland migrants. The main findings are summarized in the following three points. First, the dietary patterns of Tibetan highland migrants were unbalanced. Second, six dietary patterns were extracted using a principal component analysis: coarse grain, meat, beverage, dairy, fruit, and unhealthy dietary patterns. Third, the dietary advice for migrants was as follows: coarse grain dietary pattern that included cereals and tubers are associated with healthier physical examination indicators. The coarse grain dietary pattern is a suitable option for migrants in the Tibetan highlands. Based on the results of the remaining relationship studies, meat and fruits should not be consumed in excessive amounts, and sweet drinks and alcohol should be avoided.

4.1 Unbalanced dietary intake of Tibetan migrants

The information collected using the FFQ, with the exception of staple food and livestock and poultry meat exceeded the recommended dietary intake for Chinese residents in 2021, while the intake of all other foods was insufficient and significantly below the recommended values. Because studies on migrant populations are limited, comparisons were made with previous studies conducted in Tibetan populations. Zhou et al. ⁽³⁾ concluded that highland natives had a higher intake of meat, soybeans, and nuts and lower intake of all other items. The results of the present study are relatively consistent with the findings of Zhou et al., who showed that meat intake exceeded the standard requirements for both migrant and Tibetan populations. This may be due to the specificities of the Tibetan region, such as the relatively developed livestock industry and greater energy expenditure due to the high altitude. Similarly, the intake of vegetables and fruits did not meet the recommended intake levels. In another study on the dietary intake of the national population conducted between 1982 and 2012, the intake of vegetables and fruits never reached the levels recommended in the Chinese Dietary Guidelines⁽³⁵⁾. Therefore, the reasons for the lower intake of vegetables and fruits among Tibetan migrants can be attributed to the long-term habits of the migrants, in addition to the special environment of the plateau, which is not suitable for the growth of these crops and makes it difficult to transport these crops⁽³⁶⁾. As insufficient intake of fruits and vegetables is common among the Chinese population, education regarding proper nutrition intake should be provided. The low intake of aquatic products can also be explained based on the abovementioned perspective; a previous survey on fish consumption in both plain port cities and non-port cities in China showed that the consumption of this type of food has been increasing annually and is significantly higher in port cities than in non-port cities ⁽³⁷⁾. However, according to the results of the 1982–2012 study ⁽³⁵⁾, the recommended intake standards are not met; therefore, difficulties in transportation and economic levels may limit the consumption of these foods among migrants. Higher intake of cereal and potato staple foods has also been reported in previous studies on native Tibetans by Dermience et al. ⁽¹⁾. Tibetan children have a higher intake of staple foods that provide adequate energy for individuals ⁽³⁸⁾. Although both migrants and Tibetan residents maintain high intake of staple food items, the specific food items consumed vary. Tibetan residents prefer barley, while migrants prefer wheat and rice.

In addition to the environmental and transport difficulties, the unbalanced diet of migrants mentioned above can be attributed to the lack of adequate nutritional awareness, which can be improved by providing sufficient information and education.

After comparing the magnitude of intake of different diet categories, dietary patterns were extracted using a principal component analysis based on the results of the questionnaires already collected. Traditional nutritional research has focused mostly on the effects of a single or a few nutrients or foods on the development of other diseases, but there are some limitations. In contrast with the people’s normal diet, the population’s diet involves not only a single food and nutrient but a synergy and association of several foods ⁽³⁹⁾. Therefore, the principal component analysis method of factor analysis was used to extract the principal components from the information collected from FFQs or dietary records to summarize the dietary patterns of a particular group. This statistical approach is widely used in the study on dietary structure^{(40, 41)}. This analysis yielded six main components: coarse grain, meat, beverage, dairy, fruit, and unhealthy dietary patterns. The cumulative contribution of variance was 74.72%.

4.2 Dietary pattern with negative effect for Tibetan migrants

Combining the findings of the study revealed that meat, beverage, and unhealthy dietary pattern had a negative impact on migrants’ acclimatization to high altitudes. Logistic regression analysis found a positive correlation between meat dietary pattern and serum CREA levels, suggesting that participants with higher meat intake may have higher CREA levels; this finding is consistent with those of previous studies ^{(42, 43)}. However, the high-altitude environment can affect the renal function and reduce the CREA clearance, which is detrimental to human health. The negative association of meat dietary pattern with GFR supports at a mechanistic level, the health hazards of meat consumption among migrants. A decrease in GFR leads to the reduction in CREA clearance and affects kidney function ⁽¹⁵⁾. The results of the correlation analysis revealed that the meat dietary pattern was positively associated with AST levels. As previously mentioned, AST is commonly used as an indicator of liver function to distinguish abnormal liver function ⁽⁴⁴⁾. Excessive consumption of animal protein can affect the liver function and lead to an increase in serum liver enzyme levels⁽⁴⁵⁾. Excessive meat intake may have adverse consequences on the participants involved in this study. Therefore, for migrants living in Tibetan highlands, excessive intake should be avoided after ensuring a basic supply of animal protein.

Both the regression and correlation results revealed a positive association between beverage dietary pattern and uric acid levels, in addition to the results of the correlation analysis, which revealed a positive association between beverage consumption and ALT. Participants who consumed more beverages had higher UA levels. Similar results were obtained in previous studies. Long-term consumption may increase the risk of developing metabolic arthritis and diabetes mellitus^{(46, 47)}. Meanwhile, the highland environment can affect the kidney function of migrants and increase the risk of hyperuricemia ⁽¹⁷⁾. Therefore, excessive consumption of sweetened beverages can be detrimental to the health of migrants, and excessive intake of sugar-sweetened beverages can lead to elevated ALT, which is harmful to human liver health ⁽⁴⁸⁾. For highland migrants, beverage intake must be controlled and kept low. Unhealthy dietary pattern, such as consumption of carbonated drinks and beer, were positively correlated with LYMPH# levels. People with high alcohol intake have higher LYMPH#⁽⁴⁹⁾. However, this physiological indicator has not been adequately studied in high-altitude environments, and the underlying mechanisms remain uncertain ⁽⁵⁰⁾. According to one empirical study, LYMPH# was elevated when participants reached high altitudes, but no consistent conclusion was obtained. This may be attributed to the differences in participants, environments, and experimental conditions between the studies⁽⁵¹⁾. According to the available literature, unhealthy dietary intake has negative effects on migrants.

4.3 Dietary pattern with positive effect for highland migrants

Participants with a more supportive coarse grain dietary pattern may have a higher MCHC, which is a potentially detrimental change for migrants; simultaneously, participants with a high intake of coarse-grained foods have lower serum urea levels. According to previous studies, hypoxia at high altitudes leads to a decrease in kidney function ⁽²⁰⁾, which in turn reduces the urea clearance and leads to an increase in blood urea level. Therefore, the consumption of coarse grains and tubers can mitigate the effects of high-altitude hypoxia on blood urea level, which is beneficial for migrants. In conjunction with the nutrient-level studies mentioned earlier, the results of this study are consistent with the beneficial effects of carbohydrate supplementation on migrant plateau acclimatization ^{(23,(52–54)}. At the specific food level, both migrants and Tibetans maintain a high intake of cereals and tubers, but their choice of foods varies. For Tibetans, in addition to the long evolution and adaptation, dietary factors are crucial ⁽⁶⁾. In addition to maintaining a basic intake of cereals and tubers and obtaining sufficient carbohydrates, migrants can also attempt the Tibetan specialties, such as tsampa and other highland barley products, to make their acclimatization to the altitude more effective.

4.4 Impact of fruit dietary pattern on migrants requires follow-up research

Based on the regression analysis, those adhering to a fruit dietary pattern may have higher RBC level and AST/ALT ratios. However, as highland migrants have higher RBC level due to the hypoxic environment, 69 participants in this study had higher than normal RBC range and even 26 participants had higher RBC level than that in erythrocytosis ^{(9, 10)}. Accordingly, fruit and nut intake should be maintained at an appropriate level. AST/ALT ratio is also a commonly used indicator of liver function ⁽⁵⁵⁾; however, it remains unclear how high altitude affects this indicator. Therefore, it is difficult to make a follow-up judgement. According to the participants, more than half had above normal AST/ALT ratio. This finding suggests that the consumption of such foods may be not conducive to acclimatization to high altitudes. According to the results of the correlation analysis, fruit dietary pattern was positively correlated with A/G and negatively correlated with MCHC. Previous studies have emphasized the importance of lower A/G levels for acclimatization to high-altitude environments ⁽²¹⁾. However, the results of analyses related to fruit dietary patterns were inconsistent. The mechanism behind this is difficult to explain based on the current study and may be due to the differences in participant groups. Furthermore, few studies have examined the relationship between altitude and A/G; therefore, to draw definitive conclusions further exploration will be needed through in-depth studies. Owing to the hypoxic environment of the plateau, there may be an increase in MCHC to maintain adequate oxygen intake. However, an excessive increase is detrimental to health. From this perspective, the fruit dietary pattern is beneficial. However, MCHCs were normal in all participants in this study. The effect of such foods on the MCHC needs to be investigated on a specific population to obtain more accurate results.

In summary, the results of the studies on fruits, nuts, and other fruit-foods differ from previous perceptions. Previous studies have concluded that the vitamins in fruits, with their antioxidant function, contribute to the high-altitude acclimatization of migrants ^{(23, 26–28)}. Consumption of healthy fats from nut species may have a beneficial effect on the individual ⁽⁵⁶⁾. This inconsistent result may be explained by the following: First, the FFQ in this study only covered a broad category of fruits, and no further specific information was collected. Hence, the results were possibly influenced by the choice of the fruit. A follow-up study could refine the questionnaire content. Second, as Tibet is a high-altitude region, the relatively slow logistics of transportation can be attributed to the loss of nutrients from fruits during transportation. Finally, the AST/ALT ratio can be influenced by dietary factors and physical activity levels. Its elevation can be attributed to increased activity levels and fatigue owing to the plateau environment ⁽⁵⁷⁾. Therefore, a more in-depth research is required to obtain deeper conclusions.

4.5 Summary and prospects

For Tibetan migrants, a coarse grain dietary pattern based on cereals and tubers is a more suitable option. Meat and fruits should not be consumed in excessive amounts, and sweetened and alcoholic drinks should not be consumed. The fruit dietary pattern is not suitable for excessive supplementation, this result is inconsistent with common sense and the results of previous studies. In contrast, the results of dairy product consumption were not significantly related to physical examination indicators in this study, possibly owing to the low intake of this food item among migrants or small sample of participants in this study.

This is the first study to investigate the dietary patterns of Tibetan migrants, and the results are valuable for understanding the changes in cognitive function and mood caused by high-altitude exposure. Existing studies have confirmed that the change in cognitive and emotional function from high-altitude exposure is closely related to that of human blood index ⁽⁵⁸⁾, and this study found that there is an association between dietary patterns and blood indexes, which indicates that the diet of residents of high-altitude areas may be associated with cognitive and emotional function, providing a new research direction for the future and showing scientific evidence for the high-altitude residents’ physical and mental health care.

However, some limitations should be addressed in future studies. First, the small sample size may have led to underrepresentation. This is reflected in the following aspects: (1) According to the internationally accepted criteria for classifying altitude, < 1,000 m is considered low altitude, 1,500–3,500 m is considered high altitude, and 3,500–5,500 m is considered ultra-high altitude. The study participants lived in Lhasa at an altitude of 3,680 m, which is an ultra-high altitude. As other altitudes were not covered, the representativeness of the study was insufficient. (2) The study participants were university students, and the age range was not sufficiently generalized. (3) In terms of sex, only male participants were involved. This choice avoids excessive differences in physical examination indicators based on sex but lacks generalization. As a preliminary study, this lacks generalizability; however, this study opens ideas and perspectives for subsequent studies. Subsequent studies could employ larger sample size and coverage to improve the study’s generalizability. Second, as liver and kidney functions are assessed based on the physiological parameters, this may be inadequate. However, it is more convenient to obtain this information using blood indicators.

In future studies, a wider group of participants could be used, considering the ethnicity, sex, age, and altitude differences, to improve the representativeness of the study. Second, more in-depth studies should be conducted to reveal the efficacy of diet in terms of nutrient levels to achieve high-altitude acclimatization based on the relationship between nutrients and physical examination indicators. The mechanisms by which different nutrient supplements can improve the levels of the physical examination indicators of migrants should be explored. Finally, future research could explore the relationship between nutrition with cognition and mood and explore physical and mental health care measures for high-altitude residents.

This study found that the diet of young participants who had moved to Tibet for 2 years was unbalanced; data on coarse grain, meat, beverage, dairy, fruit, and unhealthy dietary patterns were extracted. Coarse grain dietary pattern that included cereals and tubers are associated with healthier physical examination indicators. The coarse grain dietary pattern is a suitable option for Tibetan migrants. Based on the results of the remaining relationship studies, it can be deduced that meat and fruits should not be consumed in excessive amounts, and sweet and alcohol drinks should be avoided. Follow-up studies should be conducted to further reveal the representative nutrients of different dietary patterns and explore deeper physiological mechanisms.

24HDR: 24-hour dietary recall; ALB: albumin; A/G: albumin/globulin; ALT: alanine aminotransferase; AST: Aspartate aminotransferase; AST/ALT: Aspartate aminotransferase/ alanine aminotransferase; CREA: creatinine; FFQ: self-adjusted food frequency questionnaire; GFR: glomerular filtration rate; GLB: globulin; HCT: hematocrit; HGB: hemoglobin; LYMPH#: lymphocyte count; LYMPH%: lymphocyte ratio; MCH: mean corpuscular hemoglobin; MCHC: mean corpuscular hemoglobin concentration; MCV: mean corpuscular volume; NEUT#: neutrophil count; NEUT%: neutrophil ratio; RBC: red blood cell count; UA: uric acid.

Ethics approval and consent to participate:

The study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of Tibet University (protocol code: XZDXLL20210101. Lhasa, Tibet, China). Informed consent was obtained from all subjects involved in the study.

Consent for publication:

Not applicable

Availability of data and materials:

The datasets generated or analyzed during the current study are not publicly available due to the data management requirements of our institution but are available from the corresponding author upon reasonable request.

Conflicts of Interest Statement:

The authors have no conflicts of interest to declare that are relevant to the content of this article.

Funding:

This work was supported by the Scientific Development funds for Local Region from the Chinese Government in 2022 (XZ202201YD0018C), the Scientific Development funds for Local Region from the Chinese Government in 2022 (XZ202201YD0026C), Reformation and Development Funds for Local Region Universities from the Chinese Government in 2021 (zf21003002), and Department of Tibet Education Fund (xzjykt521004).

Author Contributions:

Conceptualization, H.M., D.Z., R.S., H.L. and Y.L.; Investigation and data analysis, J.H., J.F. P.P. and W.Z.; Writing – original draft preparation, R.S. and W.Z.; Writing – review and editing, Z.L., L.N. and H.M. All authors have read and agreed to the published version of the manuscript.

Acknowledgments:

Dermience M, Mathieu F, Li XW, Vandevijvere S, Claus W, De Maertelaer V, et al., editors. Minerals and trace elements intakes and food consumption patterns of young children living in rural areas of Tibet autonomous region, PR China: a cross-sectional survey. Healthcare; 2017: Multidisciplinary Digital Publishing Institute.
Peng W, Liu Y, Liu Y, Zhao H, Chen H. Major dietary patterns and their relationship to obesity among urbanized adult Tibetan pastoralists: HEC Press; 2019. 507–19 p.
Zhou C, Li M, Liu L, Zhao F, Cong W, Zhang F. Food Consumption and Dietary Patterns of Local Adults Living on the Tibetan Plateau: Results from 14 Countries along the Yarlung Tsangpo River. Nutrients. 2021;13(7):2444.
Zhen L, Cao S, Cheng S, Xie G, Wei Y, Liu X, et al. Arable land requirements based on food consumption patterns: Case study in rural Guyuan District, Western China. Ecol Econ. 2010;69(7):1443-53.
Xiao Z, Sun X, Zhaxi D, Zhang F, Ji Y, Cheng T, et al. Distinct Nutrient Intake Style in Inhabitants of Ultra-High-Altitude Areas in North of Tibet, China: A Cross-Sectional Study Based on Newly Developed Tibetan Food Frequency Questionnaires. Front. Nutr. 2021;8.
Guo X, Long R, Kreuzer M, Ding L, Shang Z, Zhang Y, et al. Importance of functional ingredients in yak milk-derived food on health of Tibetan nomads living under high-altitude stress: a review. Crit. Rev. Food Sci. Nutr. 2014;54(3):292-302.
Reynolds RD, Lickteig JA, Deuster PA, Howard MP, Conway JM, Pietersma A, et al. Energy metabolism increases and regional body fat decreases while regional muscle mass is spared in humans climbing Mt. Everest. J Nutr. 1999;129(7):1307-14.
Kechijan D. Optimizing nutrition for performance at altitude: a literature review. J Spec Oper Med. 2011;11(1):12-17.
Zhang R, Yu X, Shen Y, Yang C, Liu F, Ye S, et al. Correlation between RBC changes and coagulation parameters in high altitude population. Hematology. 2019;24(1):325-30.
Zhong R, Liu H, Wang H, Li X, He Z, Gangla M, et al. Adaption to high altitude: an evaluation of the storage quality of suspended red blood cells prepared from the whole blood of Tibetan plateau migrants. PLoS One. 2015;10(12):e0144201.
Wu T, Wang X, Wei C, Cheng H, Wang X, Li Y, et al. Hemoglobin levels in Qinghai-Tibet: different effects of gender for Tibetans vs. Han. J. Appl. Physiol. (1985). 2005.
Caris A, Santos R. Performance and altitude: is there anything that nutrition can do for it. Nutrition. 2019;60:35-40.
Mishra K, Ganju L, Singh SB. Hypoxia modulates innate immune factors: A review. Int Immunopharmacol. 2015;28(1):425-28.
Mazzeo RS, Donovan D, Fleshner M, Butterfield GE, Zamudio S, Wolfel EE, et al. Interleukin-6 response to exercise and high-altitude exposure: influence of α-adrenergic blockade. J. Appl. Physiol. (1985). 2001;91(5):2143-49.
Bestle MH, Olsen NV, Poulsen TD, Roach R, Fogh-Andersen N, Bie P. Prolonged hypobaric hypoxemia attenuates vasopressin secretion and renal response to osmostimulation in men. J. Appl. Physiol. (1985). 2002;92(5):1911-22.
Palubiski LM, O'Halloran KD, O'Neill J. Renal Physiological Adaptation to High Altitude: A Systematic Review. Front Physiol. 2020;11:756.
Arestegui AH, Fuquay R, Sirota J, Swenson ER, Schoene RB, Jefferson JA, et al. High altitude renal syndrome (HARS). J Am Soc Nephrol. 2011;22(11):1963-68.
Jefferson JA, Escudero E, Hurtado M-E, Kelly JP, Swenson ER, Wener MH, et al. Hyperuricemia, hypertension, and proteinuria associated with high-altitude polycythemia. Am J Kidney Dis. 2002;39(6):1135-42.
Luks AM, Swenson ER. Evaluating the risks of high altitude travel in chronic liver disease patients. High Alt. Med. Biol. 2015;16(2):80-88.
Tirabassi JN, Olewinski L, Khodaee M. Variation of traditional biomarkers of liver injury after an ultramarathon at altitude. Sports health. 2018;10(4):361-65.
Shivastava K, Malhotra M. Effect of adaptation to high altitude on components of blood and urine of lowlanders compared with high altitude natives. Int J Biometeorol. 1974;18(4):279-87.
Kametas N, McAuliffe F, Krampl E, Sherwood R, Nicolaides KH. Maternal electrolyte and liver function changes during pregnancy at high altitude. Clin. Chim. Acta. 2003;328(1-2):21-29.
Cheng Z, Shuai P, Qiao Q, Li T. Validity and reliability of a simplified food frequency questionnaire: a cross sectional study among physical health examination adults in southwest region of China. Nutr J. 2020;19(1):1-10.
Stobdan T, Chaurasia OP, Korekar G, Mundra S, Ali Z, Yadav A, et al. Attributes of Seabuckthorn (Hippophae rhamnoides L.) to Meet Nutritional Requirements in High Altitude. Def Sci J. 2010;60(2):226-30.
Michalczyk M, Czuba M, Zydek G, Zając A, Langfort J. Dietary recommendations for cyclists during altitude training. Nutrients. 2016;8(6):377.
Brouns F, Saris W, Stroecken J, Beckers E, Thijssen R, Rehrer N, et al. Eating, drinking, and cycling. A controlled Tour de France simulation study, part 1. Int J Sports Med. 1989;10(S 1):S32-S40.
Koivisto A, Paulsen G, Paur I, Garthe I, Tønnessen E, Raastad T, et al. Antioxidant‐rich foods and response to altitude training: A randomized controlled trial in elite endurance athletes. Scand. J. Med. Sci. Sports. 2018;28(9):1982-95.
Gomez-Cabrera M-C, Domenech E, Romagnoli M, Arduini A, Borras C, Pallardo FV, et al. Oral administration of vitamin C decreases muscle mitochondrial biogenesis and hampers training-induced adaptations in endurance performance. Am J Clin Nutr. 2008;87(1):142-49.
Paulsen G, Cumming KT, Holden G, Hallén J, Rønnestad BR, Sveen O, et al. Vitamin C and E supplementation hampers cellular adaptation to endurance training in humans: a double‐blind, randomised, controlled trial. J Physiol. 2014;592(8):1887-901.
Patrician A, Engan H, Lundsten D, Grote L, Vigetun-Haughey H, Schagatay E. The effect of dietary nitrate on nocturnal sleep-disordered breathing and arterial oxygen desaturation at high altitude. High Alt. Med. Biol. 2018;19(1):21-27.
Hong X, Ye Q, Wang Z, Yang H, Chen X, Zhou H, et al. Reproducibility and validity of dietary patterns identified using factor analysis among Chinese populations. Br J Nutr. 2016;116(5):842-52.
Levey AS, Stevens LA, Schmid CH, Zhang Y, Castro III AF, Feldman HI, et al. A new equation to estimate glomerular filtration rate. Ann Intern Med. 2009;150(9):604-12.
Bland JM, Altman D. Statistical methods for assessing agreement between two methods of clinical measurement. The lancet. 1986;327(8476):307-10.
Ding Y, Li F, Hu P, Ye M, Xu F, Jiang W, et al. Reproducibility and relative validity of a semi-quantitative food frequency questionnaire for the Chinese lactating mothers. Nutr J. 2021;20(1):1-12.
Zhang N, Du S, Ma G. Current lifestyle factors that increase risk of T2DM in China. Eur J Clin Nutr. 2017;71(7):832-38.
Rooze S, Dramaix-Wilmet M, Mathieu F, Bally P, Yangzom D, Li JZ, et al. Growth, nutritional status, and signs of rickets in 0–5-year-old children in a Kashin–Beck disease endemic area of Central Tibet. Eur J Pediatr. 2012;171(8):1185-91.
Hu Y, Yuan C, Yu K, Qu Y, Chen S, Wang X, et al. An online survey study of consumer preferences on aquatic products in China: Current seafood consumption patterns and trends. Fish. Aquacult. J. 2014;5(2):1.
Wang Z, Dang S, Yan H. Nutrient intakes of rural Tibetan mothers: a cross-sectional survey. BMC Public Health. 2010;10(1):1-8.
Hu FB. Dietary pattern analysis: a new direction in nutritional epidemiology. Curr Opin Lipidol. 2002;13(1):3-9.
Hu FB, Rimm E, Smith-Warner SA, Feskanich D, Stampfer MJ, Ascherio A, et al. Reproducibility and validity of dietary patterns assessed with a food-frequency questionnaire. Am J Clin Nutr. 1999;69(2):243-49.
Randall E, Marshall J, Graham S, Brasure J. Patterns in food use and their associations with nutrient intakes. Am J Clin Nutr. 1990;52(4):739-45.
Shinaberger CS, Kilpatrick RD, Regidor DL, McAllister CJ, Greenland S, Kopple JD, et al. Longitudinal associations between dietary protein intake and survival in hemodialysis patients. Am J Kidney Dis. 2006;48(1):37-49.
Patel SS, Molnar MZ, Tayek JA, Ix JH, Noori N, Benner D, et al. Serum creatinine as a marker of muscle mass in chronic kidney disease: results of a cross-sectional study and review of literature. J. Cachexia Sarcopenia Muscle. 2013;4(1):19-29.
Whitehead M, Hawkes N, Hainsworth I, Kingham J. A prospective study of the causes of notably raised aspartate aminotransferase of liver origin. Gut. 1999;45(1):129-33.
Lin L-Y, Hsu C-Y, Chiou H-Y, Lee H-A, Hsu L-M, Chang P-Y, et al. Association between dietary patterns and serum hepatic enzyme levels in adults with dyslipidemia and impaired fasting plasma glucose. Nutrients. 2021;13(3):987.
Curhan GC, Forman JP. Sugar-sweetened beverages and chronic disease. Kidney Int. 2010;77(7):569-70.
Ebrahimpour-koujan S, Saneei P, Larijani B, Esmaillzadeh A. Consumption of sugar-sweetened beverages and serum uric acid concentrations: a systematic review and meta-analysis. J Hum Nutr Diet. 2021;34(2):305-13.
Paramastri R, Hsu C-Y, Chuang Y-K, Lee H-A, Wiratama BS, Chao JC-J. Synergistic interaction of dietary pattern and concordance lifestyle with abnormal liver function among young adults in Taiwan: A population-based longitudinal study. Nutrients. 2021;13(10):3591.
Parry H, Cohen S, Schlarb JE, Tyrrel DA, Fisher A, Russell MA, et al. Smoking, alcohol consumption, and leukocyte counts. Am J Clin Pathol. 1997;107(1):64-67.
Mishra K, Ganju L. Influence of high altitude exposure on the immune system: a review. Immunol Invest. 2010;39(3):219-34.
Mariggio MA, Falone S, Morabito C, Guarnieri S, Mirabilio A, Pilla R, et al. Peripheral blood lymphocytes: a model for monitoring physiological adaptation to high altitude. High Alt. Med. Biol. 2010;11(4):333-42.
Oliver SJ, Golja P, Macdonald JH. Carbohydrate supplementation and exercise performance at high altitude: a randomized controlled trial. High Alt. Med. Biol. 2012;13(1):22-31.
Pesta D, Burtscher J, Burtscher M. High-Carbohydrate Ingestion in High Altitude. High Alt. Med. Biol. 2020.
Cf C, Lo M, Hl J, Hj K, Ta D, Gj I. Effects of high-carbohydrate diets on performance and clinical symptomatology after rapid ascent to high altitude. Fed Proc. 1969.
Zhang L-x, Lv Y, Xu A, Wang H-z. The prognostic significance of serum gamma-glutamyltransferase levels and AST/ALT in primary hepatic carcinoma. Bmc Cancer. 2019;19(1):1-9.
Shoaib S, Iqbal RK. An Overview of Effects of Carbonated Drinks. Natl J. Health Sci. 2019;4(2):80-84.
Bürger-Mendonça M, Bielavsky M, Barbosa FC. Liver overload in Brazilian triathletes after half-ironman competition is related muscle fatigue. Ann Hepatol. 2008;7(3):245-48.
Gao YX, Li P, Jiang CH, Liu C, Chen Y, Chen L, et al. Psychological and cognitive impairment of long‐term migrators to high altitudes and the relationship to physiological and biochemical changes. Eur J Neurol. 2015;22(10):1363-69.

No competing interests reported.

Download PDF

Version 1

posted

You are reading this latest preprint version

Dietary pattern related to physical examination indicators in Tibetan migrants：A cross-sectional study

Status:

Version 1

Abstract

Figures

1. Introduction

2. Materials And Methods

2.1 Participants

2.2 Study design

2.3 Food frequency questionnaire

2.4 Physical examination indicators

2.5 Statistical analysis

3. Results

3.1 Descriptive statistics

3.2 Reliability and validity of the FFQ

3.3 Gap of actual dietary intakes and recommended intakes

3.4 Dietary patterns of Tibetan migrants

3.5 Correlation of dietary patterns with physical examination indicators

3.6 Binary logistic regression of dietary patterns with physical examination indicators

4. Discussion

4.1 Unbalanced dietary intake of Tibetan migrants

4.2 Dietary pattern with negative effect for Tibetan migrants

4.3 Dietary pattern with positive effect for highland migrants

4.4 Impact of fruit dietary pattern on migrants requires follow-up research

4.5 Summary and prospects

5. Conclusion

Abbreviations

Declarations

References

Additional Declarations

Status:

Version 1