ABSTRACT
The interactive effects of obesity and physical inactivity on lipid metabolism and prevalent dyslipidemia are scarcely reported in rural regions. 39029 subjects were obtained from the Henan Rural Cohort, and their metabolic equivalents (METs) of physical activity (PA) were computed. Independent associations of the obesity indices and PA with either lipid indices or prevalent dyslipidemia were analyzed by generalized linear models, and additive effects of obesity and PA on prevalent dyslipidemia were further quantified. Each obesity index was positively associated with total cholesterol, triglyceride, low-density lipoprotein or prevalent dyslipidemia but negatively associated with high-density lipoprotein, whereas the opposite association of PA with either each lipid index or prevalent dyslipidemia was observed. Joint association of PA and each obesity index with each lipid index and prevalent dyslipidemia was observed. Furthermore, the association of each obesity index in association with each lipid index was attenuated by increased PA levels.
Introduction
Dyslipidemia is a risk factor for multiple negative effects on human health, especially among middle-aged and older people. Dyslipidemia is comprised of at least one abnormal total cholesterol (TC), low- or high-density lipoprotein (LDL or HDL) and triglyceride (TG). (Opoku et al. Citation2019) According to a recent study, the high LDL has been controlled by effective prevention strategies in the past decades, but it increases steadily across moderate social demographic index countries, which has caused an increase in the number of deaths. (Du et al. Citation2022) Meanwhile, high LDL is ranked as the fifth risk factor for death. (Zhou et al. Citation2019) Furthermore, a Chinese national study shows that the overall prevalence of dyslipidemia is 33.8%. (Lu et al. Citation2021) And the comparable prevalent dyslipidemia (32.21%) is suggested by the baseline-survey of another cohort study. (Liu et al. Citation2018) Cardiovascular diseases are closely related to dyslipidemia, which is the major cause of the increased number of deaths in China and globally. (Kopin and Lowenstein Citation2017; Zhou et al. Citation2019; Du et al. Citation2022) Therefore, the economic burden attributed to dyslipidemia should be not negligible, (Ferrara et al. Citation2021) and it is necessary to identify modifiable factors of dyslipidemia, which is critical to prevent dyslipidemia and its related disease burden.
Obesity is recognized as a major problem that is noticed worldwide. Since rapid economic transition and dietary pattern changes, the obesity that has existed universally in the past few decades has increased significantly. (Pan et al. Citation2021) As a result of a cross-sectional study, the prevalence of general obesity increased from 31.9% to 37.2%, and central obesity increased from 25.9% to 35.4% during the period of 2007–2017. (Li et al. Citation2021) Obesity is closely related to dyslipidemia in the modern world. (Vekic et al. Citation2019) Accumulated evidence shows that obesity is related to an increased risk for dyslipidemia across different countries. According to a cross-sectional study, both general (body mass index, BMI) and abdominal obesity (waist circumference, WC) were related to increased blood lipid indices and universal blood lipid abnormalities among Chinese. (Zhu et al. Citation2022) Another study indicated that obesity classified by BMI was a positive correlation with lipid abnormalities in the United States. (Nguyen et al. Citation2008) Moreover, Sangrós et al. reported that universal blood lipid abnormalities were observed in central type obese classified by WC or waist-height to ratio (WHtR) in comparison to the ones with general obesity. (Sangrós et al. Citation2018) However, the above-mentioned studies are conducted in urban settings and developed countries, and further research is still needed to explore obesity indices and dyslipidemia in rural regions, which may provide a scientific basis for taking effective measures to reduce the dyslipidemia epidemic and its related diseases.
The beneficial effects of physical activity (PA) on human health are well documented. (Muscella et al. Citation2020b) Associations of PA with lipid metabolism are controversial. The literature shows the beneficial relationship between PA and blood lipid levels, especially HDL and TG. (Mitchell et al. Citation2019; Muscella et al. Citation2020a) However, no association of PA with lipid trajectories was observed among women. (Badon et al. Citation2021) In recent years, the impact of PA combined with other factors on lipid metabolism has been scarcely explored. (Zhang et al. Citation2019; Hershey et al. Citation2020) For instance, there have additive effects of the co-occurrence of physical inactivity and obesity on blood lipid metabolism based on data collected from a physical examination center. (Zhang et al. Citation2019) Furthermore, it is necessary to explore associations of PA and obesity with blood lipids by different populations based on community residents. Therefore, this study was performed to assess the association between PA or its interaction with obesity and either lipid indices or dyslipidemia.
Methods
Study population
During 2015–2017, the Henan Rural Cohort Study was carried out in five counties of Henan province, China. (Liu et al. Citation2019) A total of 39,259 subjects were finally enrolled in this cohort study. The study finally excluded individuals lacking information on BMI (n = 128), WC (n = 16), WHtR (n = 4), and lipid abnormalities (n = 54), which eventually incorporated 39,959 subjects. It is required to provide information including personal features such as social demography, diet patterns, lifestyle habits, and medical history. The personal PA is evaluated through the international PA questionnaire described in the published study. (Tu et al. Citation2019) The PA-metabolic equivalent (MET) for each participant was computed by the duration (hour/time) multiplied by frequency per week multiplied by the corresponding MET coefficient for the different types of activity. (Hou et al. Citation2020b) The criteria for social-economic status and cigarette and alcohol use were the same as in previous studies. (Hou et al. Citation2020a, Citation2021) The present study gained approval from Zhengzhou University Life Science Ethics Committee. All individuals signed written informed consent before enrolling in this study.
Obesity measurement
Anthropometric indices (height, weight, hip circumference and WC) were measured based on our previous method. (Liu et al. Citation2019) Each individual’s BMI was computed as the corresponding body weight in kilogram (kg) divided by the corresponding square of height in metres (m2). Each individual’s WHtR or WHR was computed as WC in centimetres (cm) divided by height or hip circumference in cm. The criteria for total overweight/obese individuals classified by BMI or central obese individuals classified by WC, WHR, or WHtR are the same as the previously published document. (Zhou Citation2002; Liu et al. Citation2020)
Blood lipids indices and dyslipidemia
Each individual provided 5.0 ml venous blood without energy intake for at least 8 hours. The device of Cobas c501 (Roche, Switzerland) Automatic Chemistry Analyzer was applied to determine blood lipid indices (TC, TG, HDL, as well as LDL). Subjects with dyslipidemia were defined as those with over one abnormal lipid index (the abnormal cut-off point of TC, TG, HDL and LDL were no less than 6.22 mmol/L, 2.26 mmol/L, 1.04 mmol/L and 4.14 mmol/L, respectively); or use anti-dyslipidemia medications for recent two weeks (Liu et al. Citation2018)
Statistical analysis
Categorical variables were depicted by the number plus percentage, and their distributions between subjects with and without dyslipidemia groups were compared by Chi-square test. Normally continuous variables were exhibited by mean plus standard deviation (SD) and their distributions mean difference between subjects with and without dyslipidemia were computed by the student’s t test. Non-normally continuous variables were presented by the median plus interquartile range, and their medians different between subjects with and without dyslipidemia were examined by the Mann-Whitney U test. Individual associations between PA or obesity indices and either blood lipid indices or dyslipidemia were explored by generalized linear regression models.
To quantify the interaction of the co-occurrence of PA and each of the four obesity indices on prevalent dyslipidemia, PA (low and moderate-high PA) and obesity indices were classified into dichotomous variables. Furthermore, the relative excess risk (RERI) and attributable proportion (AP) due to interaction as well as synergy index (SI) were computed in accordance with the method which was depicted in the previous studies. (Skrondal Citation2003; Knol and VanderWeele Citation2012; Stoicescu et al. Citation2019) The confidence interval of RERI or AP included 0, and of S contained 1 suggesting no additive interaction. The RERI, AP and S represented the relative excess risks, additional risk and synergistic effect of additive effect between PA and obesity indices on prevalent dyslipidemia. The constructed models of this study were adjusted for continuous variables (age) and categorical variables (gender, social-economic indicators, lifestyles (cigarette and alcohol use), meal patterns), chronic diseases (type 2 diabetes mellitus (T2DM), and hypertension). R software was performed to analyze the data of this study. The statistical significance was assigned a P value<0.05 at two-tailed.
Results
Characteristics of non-dyslipidaemic and dyslipidaemic subjects
shows the descriptive characteristics of study participants between non-dyslipidaemia and dyslipidaemia groups. The significant differences in distributions of gender, marital status, cigarette and alcohol use status, PA, hypertension and T2DM were found between non-dyslipidaemia and dyslipidaemia groups; no difference in distributions of educational levels, average monthly income, meal diets (high-fat diet, fruit and vegetable intake) were found between non-dyslipidaemia and dyslipidaemia groups. The mean age of dyslipidaemic subjects was higher than that of non-dyslipidaemic participants (P < 0.01). The median PA in participants with dyslipidaemia was lower than that in participants without dyslipidaemia (P < 0.01). displays the differences in obesity measurements and lipid parameters between non-dyslipidaemia and dyslipidaemia groups. The mean of the four obesity indices and lipid parameters (TC, TG, HDL and LDL) among participants with dyslipidaemia were higher than that in the ones with dyslipidaemia (All P < 0.01). The differences in the distributions of the categorical obesity indices were observed between non-dyslipidaemic and dyslipidaemic participants (All P < 0.01).
Table 1. Descriptive analysis of study participants between dyslipidaemia and non-dyslipidaemia groups.
Table 2. Distributions of obesity measurements and lipid parameters of the participants.
Association of PA and obesity indices with blood lipid profiles
shows associations of blood lipid profiles with either PA or obesity indices. Results showed that the estimated percent change (95% confidence interval, 95%CI) in TC, TG, HDL and LDL in response to each one-unit (1 kg/m2) increased BMI values were 2.79(2.51, 3.08), 7.68(7.34, 8.02), −2.97(−3.05, −2.88) and 2.59(2.35, 2.83), respectively; the results were not substantial change for estimated change in blood lipid indices with each per-unit increased other obesity indices; the estimated percent changes (95%CI) in TC, TG, HDL and LDL in response to each 10 MET increased PA were associated with −3.60(−4.52, −2.68), −6.26(−7.28, −5.22), 2.48(2.15, 2.81) and 0.13(−0.67, 0.94), respectively. The overweight/obesity participants had a high risk for prevalent dyslipidaemia relative to nonobese counterparts. The estimated OR (95%CI) of prevalent dyslipidaemia in response to the obese individuals by WC, WHR or WHtR versus the counterpart ones was 2.8(2.67, 2.93), 2.90(2.77, 3.05) or 3.14(2.98, 3.31). The estimated OR (95%CI) of prevalent dyslipidaemia in response to each 10 MET increment in PA was 0.87(0.85, 0.89).
Table 3. Associations of obesity measurements, PA with abnormal lipid metabolism and dyslipidaemia.
Associations between combined PA with obesity indices and blood lipid profiles
shows associations between combined PA with obesity indices and blood lipid profiles. The estimated percentage change (95%CI) in LDL in response to individuals with low PA level plus non-obesity classified by BMI, moderate-high PA level plus overweight/obesity, low PA plus overweight/obesity versus moderate-high PA level plus non-obesity were 0.44(−2.19, 3.15), 17.09(14.78, 19.44) and 18.37(15.56, 21.24), respectively; the corresponding matched figures of HDL were −3.76(−4.75, −2.76), −16.93(−17.57, −16.28) and −20.15(−20.89, −19.4); of TC were 7.1(3.78, 10.54), 16.82(14.09, 19.61) and 29.78(26.13, 33.54), of TG were 4.57(0.9, 8.38), 56.02(51.89, 60.26) and 70.47(65.03, 76.08). Similar results of combined associations between PA and obesity classified by WC, WHR and WHtR with blood lipid profiles were observed.
Figure 1. Associations between combined obesity indices with physical activity and lipid metabolism indices.
shows associations of combined PA with obesity indices with prevalent dyslipidaemia. The estimated ORs (95%CI) of prevalent dyslipidaemia in response to individuals with low PA level plus non-obesity, moderate-high PA level plus overweight/obesity, low PA plus overweight/obesity versus moderate-high PA level plus non-obesity were 1.20(1.11, 1.29), 2.52(2.38, 2.66) and 3.16(2.97, 3.38), respectively. The results of combined PA with the other three obesity indices on prevalent dyslipidaemia were no substantial changes.
Figure 2. Associations between combined obesity indices with physical activity and prevalent dyslipidaemia.
shows the estimated values of RERI (95%CI) between PA and each obesity index (BMI, WC, WHR and WHtR) on prevalent dyslipidaemia, and the matched figures were 0.45(0.26, 0.63), 0.03(0.01, 0.05), 0.04(0.02, 0.07) and 0.05(0.03, 0.07); the estimated corresponding matched values of AP (95%CI) were 0.14(0.09, 0.20), 0.02(0.01, 0.04), 0.03(0.01, 0.05) and 0.04(0.02, 0.06), of SI (95%CI) were 1.26(1.14, 1.39), 1.11(1.03, 1.21), 1.16(1.06, 1.25) and 1.18(1.08, 1.28). Moreover, shows the positive associations of obesity indices with blood lipid profile as a function of physical activity. The results showed that increased PA was correlated with estimated β values of blood lipid profiles reduction accompanied by increased obesity indices values.
Figure 3. The quantified biological interactions of obesity indices and physical activity on prevalent dyslipidaemia.
Figure 4. The associations of obesity indices with lipid metabolism indices as a function of physical activity.
Discussions
The study findings indicated positive associations between obesity indices and lipid metabolism disorders and dyslipidaemia, and these associations were alleviated by increased PA in rural adults. Additionally, central obesity subjects had a high risk for lipid metabolism disorders and prevalent dyslipidaemia. A previous study conducted in Spain shows that central obesity reflected by WC and WHtR is better than BMI in distinguishing dyslipidemia. (Guasch-Ferré et al. Citation2012)
Synergistic effects between each obesity index and low PA on lipid metabolism and dyslipidaemia were found. Zhang et al. reported similar results, which revealed that the estimated ORs (95%CIs) of high TG and low HDL among physical inactivity of obese individuals were 2.23 (1.98–2.51) and 2.17 (1.91–2.47) rather than the adequate PA of non-obese counterpart, indicating that there have additive effects of PA and obesity on blood lipids alteration. (Zhang et al. Citation2019) Similar results were also reported for their additive effect on other adverse health outcomes. Results showed that obese older adults with physically inactive were at high risk for suffering from multimorbidity relative to obese elderly with physically active among Indian individuals. (Srivastava et al. Citation2021) Mao et al. reported that the adjusted OR for abnormal glucose tolerance among obese women with insufficient moderate-to-vigorous intensity PA was 4.49 (95% CI, 1.35–14.92) in comparison to non-obese ones with sufficient moderate-to-vigorous intensity PA. (Mao et al. Citation2020) The ARIC Study showed that PA might have a benefit effect on obesity-related subclinical myocardial damage. (Florido et al. Citation2017) As research mentioned above, we may infer that obesity synergized with PA to alter lipid metabolism and increase prevalent dyslipidaemia.
Although the underlying mechanism of obesity synergized with PA linked to lipid metabolism and dyslipidaemia were largely unknown, the reasons for these associations are as follows: obesity, as the excessive fat accumulated in the body, is involved in inducing inflammatory process and endothelial impairment. (Seravalle and Grassi Citation2017) Whilst PA might exert a beneficial effect on lipid metabolism by inducing decreased inflammation. (Ertek and Cicero Citation2012) Moreover, a multi-ancestry meta-analysis showed that BMI might affect the beneficial effects of PA on multiple diseases. (Wang et al. Citation2022) PA can improve the antioxidant capacity in the body; meanwhile, obesity-related oxidative stress generation may induce by pro-inflammatory response mediated toxic reactive oxygen species. (Dludla et al. Citation2018) Both abnormal inflammatory response and excessive reactive oxygen species production enhance the metabolic complications in an obese state. Furthermore, an experimental study showed that epigallocatechin gallate can extend lifespan by leading to improvement of free fatty acids metabolism, and reduction of inflammatory response and oxidative stress among obese rats. (Yuan et al. Citation2020) The plausible biological mechanism for this study’s findings of PA and obesity had a synergistic effect on lipid metabolism may via inducing inflammatory process and oxidative stress. Additionally, positive associations of obesity with abnormal lipid indices were consistent with previously published research. (Vekic et al. Citation2019; Zhang et al. Citation2019) Previous studies’ findings supported the current findings of the beneficial effect of PA on lipid metabolism. (Mitchell et al. Citation2019; Muscella et al. Citation2020a) A prospective study indicated that a greater amount of PA was closely related to improving lipids; however, the PA intensity was less important. (Stoicescu et al. Citation2019) Furthermore, a randomized, controlled study also showed that PA had beneficial effects on lipoprotein metabolism. (Slentz et al. Citation2007) However, Badon et al. conducted a study was reported no significant associations between PA and lipid trajectories. (Badon et al. Citation2021) The present study’s findings were different from the previous studies’ findings, ascribed to the sample size, ethnicity, and the assessment method of PA.
What does the current work add to the existing knowledge relative to the previous studies?
Previous literature suggested that obesity and low PA were linked to lipid metabolism disorders and increased dyslipidemia. (Zhu et al. Citation2022, Nguyen et al. Citation2008, Sangrós et al. Citation2018, Kraus et al. Citation2002; Hamasaki et al. Citation2015) Limited studies have suggested a joint effect of PA and obesity on the alteration of lipid levels and dyslipidemia based on a physical examination center; (Mora et al. Citation2006; Zhang et al. Citation2019) however, previous studies collected data from a physical examination center or were limited to women. This study’s findings were obtained from a well-designed cohort study carried out among general rural populations, which filled in gaps for low PA, and obesity might have a synergistic effect on dyslipidemia in rural regions.
Strengths and limitations
This study has several strengths that should mention: this cohort study was well-designed, and the questionnaire was used to collect individual information by well-trained investigators. This study’s findings were obtained from a large population in the rural regions of China, which may have a better generalization to other similar rural region’s populations. This study’s results have important implications for preventing patients with dyslipidemia and its-related disease burden. The blood lipid level can be improved by increased PA levels, hinting at the effective measures for maintaining normal lipid metabolism. However, several limitations should be neglected: First, this study’s findings were based on analysis of cross-sectional data; thus, the cause associations of obesity and PA with lipid indices and dyslipidemia could not be inferred. Secondly, a questionnaire was applied to collect data regarding PA types and its-related duration and frequency, which may lead to recall bias. In future studies, the behavioral diary and personal monitoring data on PA levels may provide the accuracy of the effect of PA on dyslipidemia. Finally, although several vital influencing factors were adjusted, there still neglected other factors, such as air pollutants, which may confound this study’s findings.
Conclusions
Positive associations of obesity indices with dyslipidemia were observed, and these associations were alleviated accompanied by increased PA levels, implicating that PA, as a healthy lifestyle, may be recognized as a costless method to reduce the disease burden-related lipid metabolism disorders. Therefore, the role of PA in the development of dyslipidemia should be addressed and may prevent or reduce the need for pharmaceutical treatment for maintaining lipids at normal levels. Furthermore, prospective studies are needed to explore their effects and potential mechanisms.
Abbreviation
95%CI: 95% confidence interval; AP: attributable proportion due to interaction; BMI: body mass index; DALYs: disability-adjusted life of years; HDL: high-density lipoprotein; IQR: interquartile range; LDL: low-density lipoprotein; MET: metabolic equivalent; PA: physical activity; RERI: relative excess risk due to interaction; SI: synergy index; SD: standard deviation; T2DM: type 2 diabetes mellitus; TC: total cholesterol; TG: triglyceride; WC: waist circumference; WHR: waist-to-hip ratio; WHtR: waist-to-height ratio.
Clinical trial registration
The Henan Rural Cohort study has been registered at Chinese Clinical Trial Register
(Registration number: ChiCTR-OOC-15006699, http://www.chictr.org.cn/showproj.aspx?proj=11375).
Acknowledgements
All the participants, coordinators as well as administrators were thanked for their supports in this study. Furthermore, Dr. Tanko Abdulai was thanked for his critical correction the grammar and syntax errors.
Disclosure statement
No potential conflict of interest was reported by the authors.
Additional information
Funding
References
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