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Article

Associations between Dietary Factors and Self-Reported Physical Health in Chinese Scientific Workers

1
Department of Nutrition, Southwest Hospital, Third Military Medical University, Chongqing 400038, China
2
Department of Health Statistics, College of Preventive Medicine, Third Military Medical University, Chongqing 400038, China
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2015, 12(12), 16060-16069; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph121215041
Submission received: 22 July 2015 / Revised: 20 November 2015 / Accepted: 15 December 2015 / Published: 18 December 2015

Abstract

:
Background: Scientific workers play an important role in the development of science and technology. However, evidence is lacking with regard to the associations between their dietary factors and their health-related quality of life (HRQOL). Methods: A cross-sectional survey was conducted among 775 scientific workers from multiple universities and institutes in the Southwest region of China. A self-administered food-frequency questionnaire was used to collect the food consumption information, and the 36-item Short-Form Health Survey was used to assess physical HRQOL. Hierarchical multiple regression analysis was used to identify the factors associated with scientific workers’ HRQOL. Results: Physical HRQOL was negatively associated with age and intake of fresh pork (fat) and animal viscera, whereas consumption of vegetables, fruits, refined cereals and dairy products were positively correlated with physical HRQOL. Participants with daily intake of vegetable oils or mixed oils showed higher physical HRQOL scores than those with intake of animal oils. Conclusions: Dietary habits are closely associated with the physical HRQOL of scientific workers. The dietary patterns that had more vegetables and fruits, less fresh pork (fat) and animal viscera, and used vegetable oils during cooking corresponded to higher physical HRQOL scores. These findings are important for planning dietary strategies to improve physical health in scientific workers.

1. Introduction

Scientific workers, defined as persons whose major professional activity is to conduct scientific research, play a large role in the rapid development of science and technology. Scientific workers experience high occupational stress, irregular lifestyles, increased cognitive demands, and decreased physical activity. These problems threaten their health related quality of life (HRQOL), which is concerning for both the government and the general population. Staying healthy, both mentally and physically, is an important but difficult topic not only in the general public but also in occupational populations worldwide. Numerous efforts have been made to explore the factors related to HRQOL, including social factors [1], physical factors [2], psychological factors [3], and nutritional factors [4].
HRQOL, which was developed to assess a person’s health status, is an individual’s satisfaction or happiness as measured by a multidimensional concept referring to the physical, psychological and social domains of health. Many questionnaires have been used to evaluate HRQOL to date. Among them, the 36-item Short Form Health Survey (SF-36) is widely considered to be the most common [5]. The questionnaire consists of 36 items in eight dimensions of health, each of which has different components that evaluate both subjective (perceptions) and objective (functioning and health status) dimensions of health. HRQOL measurements have been frequently applied to patients, but they are also used among healthy populations. There are several well-known determinants of HRQOL [6], some of which can be modified because they are based on individual behaviors [7]. Among these behavioral determinants, dietary habits have attracted great interest from scientists and the general public.
The application of dietary patterns has been of particular interest in the field of nutritional epidemiology [8]. In healthy populations, dietary patterns appear to be associated with different health conditions. Results from a cohort study showed that participants with the highest baseline diet scores had higher adjusted mean scores in several SF-36 domains 5 years later, including physical function, general health, vitality, and physical composite score. Therefore, higher diet quality is prospectively associated with better quality of life and functional ability [9]. Another cohort study demonstrated that baseline adherence to a Western dietary pattern was inversely associated with self-perceived quality of life after 4 years of follow-up, whereas baseline adherence to a Mediterranean dietary pattern was positively associated with quality of life scores four years later [10]. Moreover, in a population-based cross-sectional study, increasing daily intake of fruit and vegetables by two portions has been shown to be associated with an 11% higher likelihood of good functional health. Thus, higher fruit and vegetable consumption is associated with better self-reported physical functional health within a general population [11]. Moreover, dietary pattern changes are highly associated with HRQOL in patients with cancer [12,13], inflammatory bowel disease [14], celiac disease [15], meibomian gland dysfunction [16], and others.
However, to our knowledge, there is a lack of evidence regarding the dietary factors affecting HRQOL among Chinese scientific workers. Accordingly, we conducted the present cross-sectional study to assess the associations between dietary intake and HRQOL in Chinese scientific workers and to explore its predictive factors. This study may provide evidence and a theoretical basis for developing strategies to improve QOL in Chinese scientific workers.

2. Materials and Methods

2.1. Ethics Statement

The study protocol was approved by the ethical committee on human experimentation of the Third Military Medical University, Chongqing, China (2014(133)). Interviewers read consent forms to the participants and participants signed to give informed consent.

2.2. Study Design and Sample

Data used in this cross-sectional study came from a baseline survey of 775 scientific workers from multiple universities or institutes in Southwest China, including the Third Military Medical University, Chongqing Medical University, Chongqing University, and the China Academy of Engineering Physics. The participants included 585 men and 190 women, aged from 30 to 65 years old. Eligibility criteria of this population included the following: (1) at least five years of experience in scientific research; (2) absence of any chronic diseases, such as hypertension, diabetes, etc.; and (3) clinically proven absence of cancer. Participants with no documentation, or incomplete documentation, of these criteria were excluded.

2.3. Measurements of General Characteristics

General characteristics included gender, age in years, body mass index (BMI), educational level, monthly income, marital status, smoking, drinking, exercise, work/rest cycle and sleep quality. Age was categorized as ≤45, 46–60 and >60 years old. BMI was calculated from the values of body weight (kg) divided by the square of body length (m2) and was categorized into three groups: Normal (18.5–23.9), Overweight (24–28), and Obese (>28). Educational level was divided into Bachelor’s or lower, Master’s, and Doctoral. Monthly income was categorized as <5000, 5000–10,000 and >10,000 Yuan. Marital status was either “Married” or “Unmarried or Divorced”. Responses to the Smoking and Drinking variables were either Yes or No. Participant level of exercise was categorized as Never, Once per week, and Twice per week or more. Work/rest cycle was defined as Regular or Irregular according to the regularity of the participants’ Work/rest cycle. Sleep quality was categorized as Good and Poor. Poor sleep quality was defined as meeting any of the following criteria: <5 h of sleep per night, insomnia, use of sleeping medication, or daytime dysfunction.

2.4. Dietary Intake Assessment

Food consumption information was collected by a modified validated self-administered food-frequency questionnaire (FFQ), which assessed self-reported intake of food in the previous 12 months [17]. The validity and reproducibility of this questionnaire have been evaluated in a previous study [18]. The FFQ included eighty-one food items and covered most of the commonly consumed foods in the southwest of China. The survey included nine validated short questions on food habits concerning: (1) refined cereals, including rice, pasta, white bread, cold breakfast cereals, 1 unit = 250 g; (2) legumes and tubers, including lentils, chickpeas, beans, peas, potatoes, sweet potatoes and yam, 1 unit = 50 g; (3) dairy products, including whole milk, condensed milk, yogurt, custard, cream, milk shake, cheese, or other dairy products, 1 unit = 250 mL; (4) fresh pork (lean), 1 unit = 100 g; (5) fresh pork (fat), 1 unit = 100 g; (6) animal viscera, including the brain, liver, kidney, heart, stomach, or intestines of cattle, pig, sheep or other animals, 1 unit = 100 g; (7) vegetables, including tomatoes, carrots, cauliflower, lettuce, green beans, eggplant, swiss chard, peppers, asparagus, spinach and other fresh vegetables, 1 unit = 500 g; (8) fruits, including banana, pear, melon, watermelon, citrus, strawberry, peach, cherry, fig, grapes, kiwi, mango, 1 unit = 300 g, and (9) cooking oils, including three dimensions: animal oils, such as butter, lard, vegetable oils, such as salad oil, sunflower oil, corn oil, nut oils, spray oils, margarines, or olive oil, and mixed oils, both animal and vegetable oils that were used in daily cooking. For each food item or food group, participants were asked how frequently (daily, weekly, monthly) they consumed the food or food group, followed by a question on the amount of consumption in units over the past 12 months.

2.5. Measurements of Physical HRQOL

The 36-item Short-Form Health Survey (SF-36) was used to measure physical HRQOL. The Mandarin version of the SF-36 has been shown to be a valid and reliable assessment of quality of life with a Cronbach’s alpha ranging from 0.75 to 0.90 for the eight dimensions [5]. Therefore, the SF-36 was used to assess quality of life in this study. This instrument contained 36 items and measured eight dimensions of HRQOL: physical functioning (PF), role-physical (RP), bodily pain (BP), general health (GH), vitality (VT), social functioning (SF), role limitation due to emotional problems (RE), and mental health (MH). PF, RP, BP and GH scores were used to create the physical component summary (PCS); a score was calculated for each dimension and was transformed to obtain a value ranging from 0 to 100, with higher scores indicating better health [19]. These five parameters (PF, RP, BP, GH and PCS) were described in detail and further analyzed to explore the dietary factors associated with physical HRQOL in our study.

2.6. Statistical Analysis

SPSS 18.0 statistical program (SPSS Inc., Chicago, IL, USA) was used to perform two sample t-test, one-way ANOVA or Kruskal-Wallis H test, and multivariate stepwise regression to evaluate the influencing factors on quality of life. The distributions of HRQOL in categorical variables were evaluated using t-test and ANOVA. If equal variances were not assumed when compared the difference between two samples, t- test was used to weight data to reduce variances. Whereas one-way ANOVA was used in the comparison among multiple variables, if equal variances were not assumed, Kruskal-Wallis H test was used to conduct this comparison. The correlation between HRQOL scores and continuous variables was assessed by Pearson’s correlation. If the correlation between two variables was more than 0.5, these variables were regarded as co-line variables. In this study, no co-line variables were found, multivariate stepwise regression were then performed using each cluster of HRQOL as dependent variables, and general characteristics as well as dietary factors as independent variables. A p < 0.05 was considered statistically significant, results were presented as means ± standard deviations (SD).

3. Results

3.1. Basic Characteristics of Participants

Data were obtained from 775 scientific workers. The basic characteristics of the participants are provided in Table 1. The age of participants ranged from 30 to 65 years. Most participants were men (N = 585, 75.5%). The majority of participants had a normal BMI (N = 564, 72.8%), 155 participants were overweight, and 56 participants were obese. Of the 775 participants, 35.6% (N = 276) had a Doctoral degree, 27.0% (N = 209) had a Master’s degree, and the rest of the participants (N = 290) had a bachelor’s degree or did not have any degree. The monthly income of 49.0% of the participants (N = 380) was lower than 5000 Yuan; just 9.3% of participants (N = 72) earned more than 10,000 Yuan every month. Regarding participant marital status, the majority (79.7%) of participants were married and 20.3% were single or divorced. A number of participants were smokers (N = 452, 58.3%) or drinkers (N = 164, 21.2%). With respect to their exercising habits, 30.7% of participants (N = 238) never exercised, 43.1% of participants (N = 334) exercised once per week, and 26.2% participants (N = 203) exercised twice or more per week. Four hundred fifty-three participants (58.5%) had a regular work/rest cycle, whereas the rest had an irregular cycle, and 314 participants (40.5%) had good sleep quality, while the rest did not. More baseline characteristics of participants are presented in Table 1.
Table 1. PF, RP, BP, GH and PCS scores based on the characteristics of scientific workers (Mean ± SD).
Table 1. PF, RP, BP, GH and PCS scores based on the characteristics of scientific workers (Mean ± SD).
VariableNPFRPBPGHPCS
Gender
Male58582.63 ± 18.22 *83.88 ± 19.9576.84 ± 19.38 *60.26 ± 19.0875.90 ± 14.17 *
Female19077.25 ± 16.68 *83.80 ± 17.8772.43 ± 20.48 *59.69 ± 19.0873.29 ± 13.97 *
Age
≤4522689.27 ± 15.49 *86.12 ± 17.85 *80.53 ± 17.05 *68.19 ± 18.95 *81.03 ± 12.85 *
46–6038580.64 ± 16.16 *83.83 ± 19.45 *75.20 ± 19.87 *58.15 ± 18.21 *74.45 ± 13.39 *
>6016472.04 ± 20.37 *80.90 ± 21.22 *70.49 ± 21.33 *53.68 ± 17.54 *69.28 ± 14.73 *
BMI (kg/m2)
Normal (18.5–23.9)56482.61 ± 17.53 *84.08 ± 19.2375.73 ± 19.6961.41 ± 19.08 *75.95 ± 14.13 *
Overweight (24–28)15579.45 ± 17.65 *84.92 ± 19.4176.21 ± 19.7057.75 ± 18.23 *74.58 ± 13.61 *
Obese (>28)5673.64 ± 21.33 *79.02 ± 21.3374.84 ± 20.4253.84 ± 19.56 *70.36 ± 15.07 *
Educational level
Bachelor or lower29083.57 ± 16.48 *85.53 ± 20.0175.80 ± 19.3364.75 ± 18.76 *77.41 ± 13.32 *
Master’s20983.16 ± 17.75 *84.75 ± 18.2877.37 ± 20.2460.00 ± 18.68 *76.32 ± 14.32 *
Doctoral27680.02 ± 17.46 *82.84 ± 20.4975.36 ± 19.3858.47 ± 18.61 *74.17 ± 13.58 *
Monthly income
<5000 Yuan38081.88 ± 18.6584.26 ± 19.7576.24 ± 19.9461.48 ± 18.7875.97 ± 14.43
5000–10,000 Yuan32380.53 ± 16.9483.50 ± 19.3974.63 ± 19.3558.83 ± 18.7374.39 ± 13.72
>10,000 Yuan7282.08 ± 19.1583.59 ± 18.3678.29 ± 20.1258.39 ± 21.6175.59 ± 14.54
Marital status
Married61881.26 ± 17.6883.82 ± 19.5775.76 ± 19.6259.92 ± 19.1575.19 ± 14.11
Unmarried/Divorced15781.62 ± 19.2384.12 ± 19.0275.77 ± 20.1960.97 ± 19.7675.62 ± 14.35
Smoking
No32380.84 ± 19.0683.48 ± 19.4973.76 ± 21.0159.01 ± 18.8374.27 ± 15.07
Yes45280.12 ± 18.0485.52 ± 18.9374.88 ± 14.1359.24 ± 17.6674.94 ± 11.70
Drinking
No61181.24 ± 18.3683.42 ± 19.5175.54 ± 19.9759.38 ± 19.3474.89 ± 14.35
Yes16482.20 ± 15.5884.55 ± 19.7276.85 ± 18.0162.70 ± 17.4776.57 ± 12.66
Exercise
Never23880.59 ± 18.2082.46 ± 20.5472.23 ± 20.20 *58.81 ± 18.9673.52 ± 14.19 *
Once per week33482.44 ± 16.7184.69 ± 18.7278.24 ± 19.07 *61.01 ± 18.6976.60 ± 13.58 *
Twice per week or more20383.39 ± 19.7084.21 ± 19.3275.82 ± 19.66 *60.23 ± 19.7775.16 ± 14.86 *
Work/rest cycle
Regular45381.28 ± 17.8784.26 ± 18.7875.83 ± 19.4359.96 ± 19.4875.33 ± 13.70
Irregular32180.84 ± 18.5483.18 ± 20.3575.30 ± 20.2559.71 ± 18.7474.76 ± 14.77
Sleep quality
Good31481.40 ± 17.3484.28 ± 18.3974.89 ± 19.0959.39 ± 19.3474.99 ± 13.83
Poor41280.73 ± 19.4083.42 ± 20.5176.25 ± 20.4159.74 ± 18.8975.04 ± 14.86
Notes: * p < 0.05, BMI: body mass index, PF: physical functioning, RP: role-physical, BP: bodily pain, GH: general health, PCS: physical component summary, SD: standard deviation.

3.2. Description of Physical HRQOL

In this study, mean ± SD scores of PF, RP, BP, GH and PCS based on the characteristics of participants are shown in Table 1. There were gender differences in RF, BP and PCS scores; male scientific workers had higher scores than their female counterparts. Scores of PF, RP, BP, GH and PCS significantly decreased with age, especially in the population above 60 years old. Scientific workers with a higher BMI had lower scores in PF, GH and PCS. As for educational level, although there were no differences between Bachelor’s or lower and Master’s levels, scientific workers with Doctoral degrees had lower PF, GH and PCS scores. Furthermore, participants who regularly exercised scored higher in PF and PCS than those who never exercised. However, significant differences in monthly income, marital status, smoking, drinking, work/rest cycle and sleep quality were not found. Because there was no difference of PF, RP, BP, GH and PCS scores in work/rest cycle and sleep quality, samples who just only omit the information of work/rest cycle and sleep quality have not been excluded, so that there were missing sample sizes for work/rest cycle and sleep quality.
Table 2 shows the physical HRQOL outcomes in scientific workers with different dietary patterns. Scientific workers who consumed more than 1 unit of refined cereals or fresh pork (lean) per day had significantly higher scores in PF and RP, but not BP, GH, or PCS, compared with workers consuming 1–2 units of refined cereal or lean pork per month. Moreover, participants with higher rates of legumes and tubers consumption had higher RP, BP, and PCS scores. A higher frequency of dairy product, vegetable, and fruit intake also resulted in higher PF, BP, GH and PCS scores. As for the cooking oils, participants with intake of vegetable oils or mixed oils scored higher in RP, BP and PCS than those who used animal oils. However, participants with a higher frequent consumption of fresh pork (fat) and animal viscera had lower scores in PF, RP, PCS, and/or BP, and GH.
Table 2. Scores of PF, RP, BP, GH and PCS based on dietary factors of scientific workers (Mean ± SD).
Table 2. Scores of PF, RP, BP, GH and PCS based on dietary factors of scientific workers (Mean ± SD).
VariableNPFRPBPGHPCS
Refined cereals
1–2 units/month5678.84 ± 22.64 *80.58 ± 23.77 *73.86 ± 16.8461.34 ± 17.7873.65 ± 14.56
1–2 units/week26780.26 ± 17.96 *82.26 ± 19.67 *74.64 ± 19.2060.85 ± 19.4274.50 ± 14.47
>1 unit/day39682.91 ± 16.72 *86.03 ± 17.80 *77.00 ± 20.0160.26 ± 18.8276.55 ± 13.81
Legumes and tubers
1–2 units/month38782.50 ± 16.8983.37 ± 19.18 *75.01 ± 18.83 *60.43 ± 18.7775.32 ± 13.50 *
1–2 units/week18282.64 ± 15.9988.53 ± 17.36 *78.30 ± 19.90 *61.14 ± 18.7977.65 ± 13.68 *
>1 unit/day11179.32 ± 20.9481.36 ± 19.92 *73.79 ± 20.93 *60.81 ± 20.0773.82 ± 16.37 *
Dairy products
1–2 units/month19175.26 ± 22.52 *83.18 ± 19.8173.10 ± 19.53 *59.43 ± 19.11 *72.75 ± 15.23 *
1–2 units/week22679.45 ± 16.43 *84.57 ± 18.3175.18 ± 19.58 *56.50 ± 16.65 *73.92 ± 13.31 *
>1 unit/day28887.65 ± 17.80 *85.13 ± 18.4677.84 ± 19.29 *64.46 ± 19.10 *78.70 ± 13.46 *
Fresh pork (lean)
1–2 units/month6677.35 ± 20.63 *79.83 ± 22.11 *75.12 ± 19.0259.55 ± 17.8472.96 ± 14.64
1–2 units/week22782.69 ± 16.70 *84.22 ± 19.31 *75.45 ± 18.6060.35 ± 17.0975.68 ± 13.56
>1 unit/day40482.26 ± 17.28 *85.83 ± 17.83 *76.44 ± 20.0361.01 ± 20.1676.38 ± 14.27
Fresh pork (fat)
1–2 units/month34787.62 ± 14.69 *85.84 ± 17.86 *78.28 ± 18.32 *65.11 ± 18.14 *79.21 ± 12.61 *
1–2 units/week14279.82±15.53 *79.95 ± 20.47 *75.17 ± 19.15 *58.10 ± 17.20 *74.91 ± 12.58 *
>1 unit/day16772.63 ± 20.33 *79.95 ± 20.47 *71.22 ± 20.82 *53.21 ± 19.63 *69.26 ± 15.73 *
Animal viscera
1–2 units/month58282.49 ± 16.44 *85.02 ± 18.42 *76.28 ± 18.9560.88 ± 18.9476.17 ± 13.67 *
1–2 units/week5182.55 ± 19.58 *83.58 ± 21.79 *76.55 ± 21.0060.51 ± 19.8275.80 ± 15.32 *
>1 unit/day1771.76 ± 24.23 *71.32 ± 20.62 *67.82 ± 18.8751.82 ± 20.6965.70 ± 18.03 *
Vegetables
1–2 units/month4674.89 ± 24.21 *69.70 ± 23.50 *74.28 ± 21.8558.46 ± 19.99 *69.33 ± 17.87 *
1–2 units/week5875.09 ± 21.16 *82.87 ± 18.72 *73.84 ± 19.3052.47 ± 19.81 *71.07 ± 16.23 *
>1 unit/day59682.96 ± 16.21 *85.61 ± 18.21 *76.22 ± 19.2361.52 ± 18.79 *76.58 ± 13.42 *
Fruits
1–2 units/month12379.51 ± 20.4577.79 ± 22.49 *74.67 ± 19.0756.98 ± 18.16 *72.24 ± 15.26 *
1–2 units/week24782.89 ± 15.5984.51 ± 18.55 *77.21 ± 18.3459.74 ± 19.20 *76.10 ± 13.39 *
>1 unit/day34581.52 ± 18.3486.47 ± 17.55 *75.51 ± 20.3062.40 ± 19.00 *76.48 ± 14.16 *
Cooking Oils
Animal oils1175.91 ± 21.6672.16 ± 32.04 *72.36 ± 17.39 *57.45 ± 9.4069.47 ± 16.95 *
Vegetable oils58981.14 ± 18.0383.72 ± 19.39 *74.64 ± 19.70 *59.85 ± 18.8674.84 ± 13.98 *
Mixed oils14682.43 ± 18.0385.10 ± 18.09 *80.21 ± 19.34 *62.08 ± 19.8777.46 ± 14.21 *
Notes: * p < 0.05, PF: physical functioning, RP: role-physical, BP: bodily pain, GH: general health, PCS: physical component summary, SD: standard deviation.

3.3. Correlations between Variables and Predictors of Physical HRQOL

The results of the hierarchical multiple regression analysis of PF, RP, BP, GH and PCS are presented in Table 3, Table 4, Table 5, Table 6 and Table 7. In the regression model, age was the only demographic characteristic of scientific workers that significantly correlated with physical HRQOL, whereas the dietary factors that affected the physical HRQOL scores included refined cereals, fresh pork (fat), dairy products, vegetables, fruits, animal viscera, and cooking oils. Dietary intake of fresh pork (fat) and animal viscera as well as participants’ age were negatively associated with PF, whereas intake of dairy products and fruits were positively associated with PF (Table 3). Consumption of vegetables and refined cereals was positively correlated with RP, whereas animal viscera consumption and age were negatively associated with RP (Table 4). BP scores were negatively associated with age, but positively associated with the types of cooking oils used (Table 5). Age and fresh pork (fat) intake showed negative associations with GH, whereas fruit consumption was positively correlated with GH (Table 6). PCS, which was the sum of PF, RP, BP and GH, was significantly negatively associated with age and intake of fresh pork (fat) and animal viscera, but positively correlated with vegetable and fruit consumption (Table 7).
Table 3. Stepwise regression predicting the PF scores.
Table 3. Stepwise regression predicting the PF scores.
VariablePhysical Functioning (PF)
Pearson’s rStep 1 (β)Step 2 (β)Step 3 (β)Step 4 (β)Step 5 (β)
Fresh pork (fat)0.358−7.056 ***−6.323 ***−4.772 ***−5.313 ***−4.950 ***
Dairy products0.4184.451 ***4.153 ***3.631 ***3.742 ***
Age0.451−4.470 ***−4.070 ***−4.304 ***
Fruits0.4713.466 ***3.584 ***
Animal viscera0.478−3.292 *
Notes: * p < 0.05, *** p < 0.001.
Table 4. Stepwise regression predicting the PF scores.
Table 4. Stepwise regression predicting the PF scores.
VariableRole-Physical (RP)
Pearson’s rStep 1 (β)Step 2 (β)Step 3 (β)Step 4 (β)
Vegetables0.2107.134 ***7.274 ***7.275 ***6.813 ***
Animal viscera0.242−5.443 **−5.603 **−5.696 **
Age0.268−3.100 **−3.117 **
Refined cereals0.2832.697 *
Notes: * p < 0.05, ** p < 0.01, *** p < 0.001.
Table 5. Stepwise regression predicting the BP scores.
Table 5. Stepwise regression predicting the BP scores.
VariableBody Pain (BP)
Pearson’s rStep 1 (β)Step 2 (β)
Age0.204−5.718 ***−5.603 ***
Cooking oils0.2193.445 *
Notes: * p < 0.05, *** p < 0.001.
Table 6. Stepwise regression predicting the GH scores.
Table 6. Stepwise regression predicting the GH scores.
VariablePhysical Component Summary (PCS)
Pearson’s rStep 1 (β)Step 2 (β)Step 3 (β)Step 4 (β)Step 5 (β)
Age0.311−6.199 ***−6.280 ***−4.718 ***−4.998 ***−5.026 ***
Fruits0.3563.125 ***3.159 ***3.337 ***2.743 ***
Fresh pork (fat)0.392−2.614 ***−3.582 ***−3.565 ***
Animal viscera0.405−3.582 ***−3.565 **
Vegetables0.4152.383 *
Note: * p < 0.05, ** p < 0.01, *** p < 0.001.
Table 7. Stepwise regression predicting the PCS scores.
Table 7. Stepwise regression predicting the PCS scores.
VariableGeneral Health (GH)
Pearson’s rStep 1 (β)Step 2 (β)Step 3 (β)
Age0.316−8.586 ***−8.685 ***−6.788 ***
Fruits0.3674.651 ***4.645 ***
Fresh pork (fat)0.393−3.556 ***
Note: *** p < 0.001.

4. Discussion

In this population-based cross-sectional study, we demonstrated that physical HRQOL was negatively associated with age and consumption of fresh pork (fat) and animal viscera but positively associated with consumption of vegetables, fruits, refined cereals and dairy products. In addition, the types of cooking oils used were found to be associated with BP; participants with daily intake of vegetable oils or mixed oils were more physically healthy than those with intake of animal oils.
HRQOL may be affected by many factors. To explore the associations between these factors, hierarchical multiple regression analysis was used to identify factors significantly related to physical HRQOL. Gender, age, BMI, educational level with a Doctoral degree, and the frequency of exercise have significant associations with physical HRQOL scores. Although other studies have suggested that these factors were associated with HRQOL [20,21,22], following regression analysis, only age was significantly related to physical HRQOL in our study. This is consistent with other findings on age patterns, with younger people reporting greater HRQOL than older people [23].
In the present study, the dietary factors that were positively correlated to physical HRQOL included vegetables, fruits, refined cereals and dairy products. This dietary pattern is similar to the Mediterranean diet, which is characterized by plant foods, cereals, legumes, fish and olive oil as the main source of nutrition and is widely considered to be a healthy eating pattern related to reduced risk of cardiovascular and neurodegenerative diseases and some cancers [24]. A previous study found that adherence to a Mediterranean diet was associated with a better HRQOL [25]. The results from a cohort study also demonstrated that baseline adherence to a Mediterranean dietary pattern was directly associated with better scores in quality of life four years later in comparison to the Western dietary pattern, which was rich in red meats, processed pastries and fast-food [10]. Our results were consistent with a previous study that found higher fruit and vegetable consumption to be associated with better self-reported physical functional health within a general population [11]. Antioxidants and polyphenols, which are largely present in vegetables and fruits, have been reported to have anti-inflammatory properties and have also been found to play a protective role against cardiovascular diseases and cancer [26]. Moreover, intake of monounsaturated fatty acids, which is the major component in vegetable oil, has been found to be associated with a reduced prevalence of risk factors for major chronic disease [27,28]. Therefore, the higher physical HRQOL scores found in scientific workers who frequently consumed vegetables, fruits and vegetable oil in this study may be attributed to the role of antioxidants and monounsaturated fatty acids in their dietary intake.
Our data also suggested that frequent consumption of fresh pork (fat) and animal viscera, which are rich in lipid, cholestenone and polyunsaturated fatty acids, seemed to be deleterious to quality of life. Both epidemiological and experimental studies have reported a detrimental effect of this dietary habit on weight gain, obesity and diabetes [29,30]. Furthermore, this pattern has also been associated with an increasing risk of cardiovascular diseases, endothelial dysfunction and a higher level of pro-inflammatory cytokines [31,32,33]. The high content of saturated, trans-unsaturated fatty acids usually present in these foods is thought to be responsible for the reported correlations [34]. The adverse effects of saturated, trans-unsaturated fats on cardiovascular diseases are considered to be associated with reductions in plasma levels of high density lipoprotein (HDL)-cholesterol, increases in low density lipoprotein (LDL)-cholesterol, endothelial dysfunction, pro-inflammatory changes, and displacement of essential fatty acids from membranes [35]. These changes may account for the negative associations between frequent consumption of fresh pork (fat) and animal viscera and physical HRQOL in the present study.
Although HRQOL is a subjective health measurement rather than a biological measure, self-reported physical health status has been reported to be a powerful predictor of long-term mortality [36,37]. Although age has been shown to be a significant predictor of physical HRQOL, it may be impossible to reverse. Dietary habits, on the other hand, can be changed to improve physical HRQOL. Therefore, we recommend that it is important for scientific workers to consume more vegetables and fruits, less fresh pork (fat) and animal viscera, and to use vegetable oils during cooking as much as possible to stay healthy. There were some limitations to the present study, which has all the limitations of a cross-sectional study. Firstly, the cross-sectional design did not allow us to make any causal statements about the relationships between the variables investigated. Secondly, although the reliability and validity of the FFQ and SF-36 questionnaire have been widely evaluated, misclassifications may have existed in the dietary and outcomes assessments, meaning over- or underestimation of true intake and health status may have introduced bias during the data analysis. Thirdly, caution is needed in generalizing the conclusions to larger contexts because participants were from just one province and represented only one career.

5. Conclusions

Our data found an association between dietary habits and self-reported physical health status in scientific workers. The dietary patterns that had more vegetables and fruits, less fresh pork (fat) and animal viscera, and used vegetable oils during cooking were linked with higher scores of physical HRQOL. These findings are important for planning dietary strategies to improve physical health in scientific workers. Further investigations should be conducted with a large survey including random samples of scientific workers around the world.

Acknowledgments

This work was supported by a grant from the project of special medicine of PLA (Grant No. 2010ZYZ232). Special thanks to all of the scientific workers for their participation and to the scientific institutes for their cooperation.

Author Contributions

Qianfeng Gong was responsible for the study design. Ling Tu and Hong Chen were responsible for data collection. Liang Zhou conducted the data analyses. Qianfeng Gong drafted the manuscript. All authors contributed to the development of the study framework, interpretation of the results, revisions of successive drafts of the manuscript, and approved the version submitted for publication.

Conflicts of interest

The authors declare no conflict of interest.

References

  1. Cohen-Carneiro, F.; Souza-Santos, R.; Rebelo, M.A. Quality of life related to oral health: Contribution from social factors. Cien. Saude. Colet. 2011, 16, 1007–1015. [Google Scholar] [CrossRef] [PubMed]
  2. Vathesatogkit, P.; Sritara, P.; Kimman, M.; Hengprasith, B.; E-Shyong, T.; Wee, H.L.; Woodward, M. Associations of lifestyle factors, disease history and awareness with health-related quality of life in a Thai population. PLoS ONE 2012, 7. [Google Scholar] [CrossRef] [PubMed]
  3. Van Mierlo, M.L.; Schroder, C.; van Heugten, C.M.; Post, M.W.; de Kort, P.L.; Visser-Meily, J.M. The influence of psychological factors on health-related quality of life after stroke: A systematic review. Int. J. Stroke 2014, 9, 341–348. [Google Scholar] [CrossRef] [PubMed]
  4. Shibata, H. Nutritional factors on longevity and quality of life in japan. J. Nutr. Health Aging 2001, 5, 97–102. [Google Scholar] [PubMed]
  5. Wang, R.; Wu, C.; Zhao, Y.; Yan, X.; Ma, X.; Wu, M.; Liu, W.; Gu, Z.; Zhao, J.; He, J. Health related quality of life measured by SF-36: A population-based study in Shanghai, China. BMC Public Health 2008, 8. [Google Scholar] [CrossRef] [PubMed]
  6. Serrano-Aguilar, P.; Munoz-Navarro, S.R.; Ramallo-Farina, Y.; Trujillo-Martin, M.M. Obesity and health related quality of life in the general adult population of the Canary islands. Qual. Life Res. 2009, 18, 171–177. [Google Scholar] [CrossRef] [PubMed]
  7. Brotherton, C.S.; Taylor, A.G.; Bourguignon, C.; Anderson, J.G. A high-fiber diet may improve bowel function and health-related quality of life in patients with crohn disease. Gastroenterol. Nurs. 2014, 37, 206–216. [Google Scholar] [CrossRef] [PubMed]
  8. Frazier-Wood, A.C. Dietary patterns, genes, and health: Challenges and obstacles to be overcome. Curr. Nutr. Rep. 2015, 4, 82–87. [Google Scholar] [CrossRef] [PubMed]
  9. Gopinath, B.; Russell, J.; Flood, V.M.; Burlutsky, G.; Mitchell, P. Adherence to dietary guidelines positively affects quality of life and functional status of older adults. J. Acad. Nutr. Diet. 2014, 114, 220–229. [Google Scholar] [CrossRef] [PubMed]
  10. Ruano, C.; Henriquez, P.; Martinez-Gonzalez, M.A.; Bes-Rastrollo, M.; Ruiz-Canela, M.; Sanchez-Villegas, A. Empirically derived dietary patterns and health-related quality of life in the sun project. PLoS ONE 2013, 8. [Google Scholar] [CrossRef] [PubMed]
  11. Myint, P.K.; Welch, A.A.; Bingham, S.A.; Surtees, P.G.; Wainwright, N.W.; Luben, R.N.; Wareham, N.J.; Smith, R.D.; Harvey, I.M.; Day, N.E.; et al. Fruit and vegetable consumption and self-reported functional health in men and women in the European prospective investigation into cancer-norfolk (epic-norfolk): A population-based cross-sectional study. Public Health Nutr. 2007, 10, 34–41. [Google Scholar] [CrossRef] [PubMed]
  12. Kassianos, A.P.; Raats, M.M.; Gage, H.; Peacock, M. Quality of life and dietary changes among cancer patients: A systematic review. Qual. Life Res. 2015, 24, 705–719. [Google Scholar] [CrossRef] [PubMed]
  13. Uster, A.; Ruefenacht, U.; Ruehlin, M.; Pless, M.; Siano, M.; Haefner, M.; Imoberdorf, R.; Ballmer, P.E. Influence of a nutritional intervention on dietary intake and quality of life in cancer patients: A randomized controlled trial. Nutrition 2013, 29, 1342–1349. [Google Scholar] [CrossRef] [PubMed]
  14. Powell, J.J.; Cook, W.B.; Hutchinson, C.; Tolkien, Z.; Chatfield, M.; Pereira, D.; Lomer, M.C. Dietary fortificant iron intake is negatively associated with quality of life in patients with mildly active inflammatory bowel disease. Nutr. Metab. 2013, 10. [Google Scholar] [CrossRef] [PubMed]
  15. Barratt, S.M.; Leeds, J.S.; Sanders, D.S. Quality of life in coeliac disease is determined by perceived degree of difficulty adhering to a gluten-free diet, not the level of dietary adherence ultimately achieved. J. Gastrointestin. Liver Dis. 2011, 20, 241–245. [Google Scholar] [PubMed]
  16. Olenik, A.; Mahillo-Fernandez, I.; Alejandre-Alba, N.; Fernandez-Sanz, G.; Perez, M.A.; Luxan, S.; Quintana, S.; de Carneros Llorente, A.M.; Garcia-Sandoval, B.; Jimenez-Alfaro, I. Benefits of omega-3 fatty acid dietary supplementation on health-related quality of life in patients with meibomian gland dysfunction. Clin. Ophthalmol. 2014, 8, 831–836. [Google Scholar] [CrossRef] [PubMed]
  17. Villegas, R.; Yang, G.; Liu, D.; Xiang, Y.B.; Cai, H.; Zheng, W.; Shu, X.O. Validity and reproducibility of the food-frequency questionnaire used in the shanghai men’s health study. Br. J. Nutr. 2007, 97, 993–1000. [Google Scholar] [CrossRef] [PubMed]
  18. Cai, H.; Zheng, W.; Xiang, Y.B.; Xu, W.H.; Yang, G.; Li, H.; Shu, X.O. Dietary patterns and their correlates among middle-aged and elderly chinese men: A report from the shanghai men’s health study. Br. J. Nutr. 2007, 98, 1006–1013. [Google Scholar] [CrossRef] [PubMed]
  19. Ware, J.E.; Kosinski, M.; Gandek, B.; Aaronson, N.K.; Apolone, G.; Bech, P.; Brazier, J.; Bullinger, M.; Kaasa, S.; Leplege, A.; et al. The factor structure of the SF-36 health survey in 10 countries: Results from the IQOLA project. J. Clin. Epidemiol. 1998, 51, 1159–1165. [Google Scholar] [CrossRef]
  20. Zhu, Y.; Wang, Q.; Pang, G.; Lin, L.; Origasa, H.; Wang, Y.; Di, J.; Shi, M.; Fan, C.; Shi, H. Association between body mass index and health-related quality of life: The “obesity paradox” in 21,218 adults of the Chinese general population. PLoS ONE 2015, 10. [Google Scholar] [CrossRef] [PubMed]
  21. Ose, D.; Rochon, J.; Campbell, S.M.; Wensing, M.; Freund, T.; van Lieshout, J.; Langst, G.; Szecsenyi, J.; Ludt, S. Health-related quality of life and risk factor control: The importance of educational level in prevention of cardiovascular diseases. Eur. J. Public Health 2014, 24, 679–684. [Google Scholar] [CrossRef] [PubMed]
  22. Knowles, A.M.; Herbert, P.; Easton, C.; Sculthorpe, N.; Grace, F.M. Impact of low-volume, high-intensity interval training on maximal aerobic capacity, health-related quality of life and motivation to exercise in ageing men. Age 2015, 37. [Google Scholar] [CrossRef] [PubMed]
  23. Meade, T.; Dowswell, E. Health-related quality of life in a sample of australian adolescents: Gender and age comparison. Qual. Life Res. 2015, 24, 2933–2938. [Google Scholar] [CrossRef] [PubMed]
  24. Sofi, F.; Abbate, R.; Gensini, G.F.; Casini, A. Accruing evidence on benefits of adherence to the mediterranean diet on health: An updated systematic review and meta-analysis. Am. J. Clin. Nutr. 2010, 92, 1189–1196. [Google Scholar] [CrossRef]
  25. Bonaccio, M.; di Castelnuovo, A.; Bonanni, A.; Costanzo, S.; de Lucia, F.; Pounis, G.; Zito, F.; Donati, M.B.; de Gaetano, G.; Iacoviello, L.; et al. Adherence to a mediterranean diet is associated with a better health-related quality of life: A possible role of high dietary antioxidant content. BMJ Open 2013, 3. [Google Scholar] [CrossRef] [PubMed]
  26. Scoditti, E.; Calabriso, N.; Massaro, M.; Pellegrino, M.; Storelli, C.; Martines, G.; de Caterina, R.; Carluccio, M.A. Mediterranean diet polyphenols reduce inflammatory angiogenesis through MMP-9 and COX-2 inhibition in human vascular endothelial cells: A potentially protective mechanism in atherosclerotic vascular disease and cancer. Arch. Biochem. Biophys. 2012, 527, 81–89. [Google Scholar] [CrossRef] [PubMed]
  27. Brehm, B.J.; Lattin, B.L.; Summer, S.S.; Boback, J.A.; Gilchrist, G.M.; Jandacek, R.J.; D’Alessio, D.A. One-year comparison of a high-monounsaturated fat diet with a high-carbohydrate diet in type 2 diabetes. Diabetes Care 2009, 32, 215–220. [Google Scholar] [CrossRef] [PubMed]
  28. Baum, S.J.; Kris-Etherton, P.M.; Willett, W.C.; Lichtenstein, A.H.; Rudel, L.L.; Maki, K.C.; Whelan, J.; Ramsden, C.E.; Block, R.C. Fatty acids in cardiovascular health and disease: A comprehensive update. J. Clin. Lipidol. 2012, 6, 216–234. [Google Scholar] [CrossRef] [PubMed]
  29. Black, M.H.; Watanabe, R.M.; Trigo, E.; Takayanagi, M.; Lawrence, J.M.; Buchanan, T.A.; Xiang, A.H. High-fat diet is associated with obesity-mediated insulin resistance and beta-cell dysfunction in mexicanamericans. J. Nutr. 2013, 143, 479–485. [Google Scholar] [CrossRef] [PubMed]
  30. Matsubara, T.; Mita, A.; Minami, K.; Hosooka, T.; Kitazawa, S.; Takahashi, K.; Tamori, Y.; Yokoi, N.; Watanabe, M.; Matsuo, E.; et al. PGRN is a key adipokine mediating high fat diet-induced insulin resistance and obesity through IL-6 in adipose tissue. Cell. Metab. 2012, 15, 38–50. [Google Scholar] [CrossRef] [PubMed]
  31. Schwingshackl, L.; Hoffmann, G. Comparison of the long-term effects of high-fat v. Low-fat diet consumption on cardiometabolic risk factors in subjects with abnormal glucose metabolism: A systematic review and meta-analysis. Br. J. Nutr. 2014, 111, 2047–2058. [Google Scholar] [CrossRef] [PubMed]
  32. Kelley, E.E.; Baust, J.; Bonacci, G.; Golin-Bisello, F.; Devlin, J.E.; St Croix, C.M.; Watkins, S.C.; Gor, S.; Cantu-Medellin, N.; Weidert, E.R.; et al. Fatty acid nitroalkenes ameliorate glucose intolerance and pulmonary hypertension in high-fat diet-induced obesity. Cardiovasc. Res. 2014, 101, 352–363. [Google Scholar] [CrossRef] [PubMed]
  33. Choi, M.S.; Kim, Y.J.; Kwon, E.Y.; Ryoo, J.Y.; Kim, S.R.; Jung, U.J. High-fat diet decreases energy expenditure and expression of genes controlling lipid metabolism, mitochondrial function and skeletal system development in the adipose tissue, along with increased expression of extracellular matrix remodelling- and inflammation-related genes. Br. J. Nutr. 2015, 113, 867–877. [Google Scholar] [PubMed]
  34. Fernandez-San, J.P.M. Trans fatty acids (TFA): Sources and intake levels, biological effects and content in commercial Spanish food. Nutr. Hosp. 2009, 24, 515–520. [Google Scholar]
  35. Woodside, J.V.; McKinley, M.C.; Young, I.S. Saturated and trans fatty acids and coronary heart disease. Curr. Atheroscler. Rep. 2008, 10, 460–466. [Google Scholar] [CrossRef] [PubMed]
  36. Chamberlain, A.M.; McNallan, S.M.; Dunlay, S.M.; Spertus, J.A.; Redfield, M.M.; Moser, D.K.; Kane, R.L.; Weston, S.A.; Roger, V.L. Physical health status measures predict all-cause mortality in patients with heart failure. Circ. Heart Fail. 2013, 6, 669–675. [Google Scholar] [CrossRef] [PubMed]
  37. Cesari, M.; Onder, G.; Zamboni, V.; Manini, T.; Shorr, R.I.; Russo, A.; Bernabei, R.; Pahor, M.; Landi, F. Physical function and self-rated health status as predictors of mortality: Results from longitudinal analysis in the ilsirente study. BMC Geriatr. 2008, 8. [Google Scholar] [CrossRef] [PubMed]

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MDPI and ACS Style

Gong, Q.-f.; Tu, L.; Zhou, L.; Chen, H. Associations between Dietary Factors and Self-Reported Physical Health in Chinese Scientific Workers. Int. J. Environ. Res. Public Health 2015, 12, 16060-16069. https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph121215041

AMA Style

Gong Q-f, Tu L, Zhou L, Chen H. Associations between Dietary Factors and Self-Reported Physical Health in Chinese Scientific Workers. International Journal of Environmental Research and Public Health. 2015; 12(12):16060-16069. https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph121215041

Chicago/Turabian Style

Gong, Qian-fen, Ling Tu, Liang Zhou, and Hong Chen. 2015. "Associations between Dietary Factors and Self-Reported Physical Health in Chinese Scientific Workers" International Journal of Environmental Research and Public Health 12, no. 12: 16060-16069. https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph121215041

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