Next Article in Journal
Norepinephrine Infusion in the Emergency Department in Septic Shock Patients: A Retrospective 2-Years Safety Report and Outcome Analysis
Previous Article in Journal
Bioremediation of Petroleum Hydrocarbons Using Acinetobacter sp. SCYY-5 Isolated from Contaminated Oil Sludge: Strategy and Effectiveness Study
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

No Interaction Effect between Interleukin-6 Polymorphisms and Acid Ash Diet with Bone Resorption Marker in Postmenopausal Women

by
Sook Yee Lim
1,
Yoke Mun Chan
1,2,3,*,
Vasudevan Ramachandran
3,*,
Zalilah Mohd Shariff
1,2,
Yit Siew Chin
1,2 and
Manohar Arumugam
4
1
Department of Nutrition and Dietetics, Faculty of Medicine and Health Sciences, University Putra Malaysia (UPM), UPM Serdang, Seri Kembangan 43400, Selangor, Malaysia
2
Research Center of Excellence Nutrition and Non-Communicable Diseases, Faculty of Medicine and Health Sciences, University Putra Malaysia (UPM), UPM Serdang, Seri Kembangan 43400, Selangor, Malaysia
3
Malaysian Research Institute on Ageing, University Putra Malaysia, UPM Serdang, Seri Kembangan 43400, Selangor, Malaysia
4
Department of Orthopedics, Faculty of Medicine and Health Sciences, University Putra Malaysia (UPM), UPM Serdang, Seri Kembangan 43400, Selangor, Malaysia
*
Authors to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2021, 18(2), 827; https://doi.org/10.3390/ijerph18020827
Submission received: 16 September 2020 / Revised: 13 October 2020 / Accepted: 29 October 2020 / Published: 19 January 2021
(This article belongs to the Section Women's Health)

Abstract

:
Background: Evidence is growing that a high-acid diet might accelerate the rate of bone loss, and gene polymorphisms such as Interleukin 6 (IL6) -174G/C and -572G/C are related to bone deterioration. However, no study of the interaction between diet and IL6 polymorphisms has been conducted among Asians. Thus, the objective of this study was to determine whether IL6 gene polymorphisms modified the association between dietary acidity and the rate of bone resorption. Methods: This cross-sectional study recruited 203 postmenopausal women (age ranged from 51 to 85 years old) in community settings. The dietary intakes of the participants were assessed using a validated interviewer-administered semi-quantitative food frequency questionnaire (FFQ), while dietary acid load (DAL) was estimated using net endogenous acid production (NEAP). Agena® MassARRAY genotyping analysis and serum collagen type 1 cross-linked C-telopeptide (CTX1) were used to identify the IL6 genotype and as a bone resorption marker, respectively. The interactions between diet and single-nucleotide polymorphisms (SNPs) were assessed using linear regressions. Results: A total of 203 healthy postmenopausal women aged between 51 and 85 years participated in this study. The mean BMI of the participants was 24.3 kg/m2. In IL6 -174 G/C, all the participants carried the GG genotype, while the C allele was absent. Approximately 40% of the participants had a high dietary acid load. Dietary acid load (B = 0.15, p = 0.031) and the IL6 -572 CC genotype group (B = 0.14, p = 0.044) were positively associated with a higher bone resorption. However, there was no moderating effect of the IL6 genetic polymorphism on the relationship between and acid ash diet and bone resorption markers among the postmenopausal women (p = 0.79). Conclusion: High consumption of an acid ash diet and the IL6 -572 C allele seem to attribute to high bone resorption among postmenopausal women. However, our finding does not support the interaction effect of dietary acidity and IL6 (-174G/C and -572G/C) polymorphisms on the rate of bone resorption. Taken together, these results have given scientific research other candidate genes to focus on which may interact with DAL on bone resorption, to enhance planning for preventing or delaying the onset of osteoporosis among postmenopausal women.

1. Introduction

A bourgeoning body of research has focused on osteoporotic fractures in light of their great impact on public health [1]. Osteoporotic-related fractures may lead to disability, comorbidity (e.g., functional intestinal disorder [2], heart failure, acute renal failure, thromboembolic and cirrhosis complications) [3], increased medical costs, decreased quality of life, and mortality [4]. In Asia, the incidence of osteoporotic fractures is estimated to rapidly increase and it is estimated that 50% of all osteoporotic hip fractures worldwide will occur in Asia by the year 2050 [5]. In a multiracial country such as Malaysia with three main ethnicities—namely, Malays, Chinese, and Indians—Malaysian Chinese females aged 50 years and above are more susceptible to osteoporotic fractures [6]. Nonetheless, the interplay between dietary factors and genetics factors, which is expected to play an important role in bones, has received little attention.
Despite being the gold standard for the quantitative assessment of bone and an important predictor of fracture risk [7], bone mineral density (BMD) measurement using dual-energy X-ray absorptiometry (DEXA) is not able to assess bone quality [8] and requires a long duration to detect BMD changes. On the other hand, biochemical bone turnover markers (BTMs), which are able to evaluate the dynamics of bone remodeling in terms of the rate of bone formation and resorption, are widely used to offer clinical information for predicting fracture risk and monitoring the efficacy of anti-osteoporosis therapy [9,10]. Among BTMs, serum-procollagen type I N-propeptide (PINP) and serum C-terminal telopeptide of type I collagen (CTX1) are the most promising reference markers to detect bone formation and bone resorption for use in clinical medicine, respectively [11,12].
Attributable to the complex predominance of osteoclastic activity, genetic polymorphism factors contributed up to 80% in determining BMD [13]. Interleukin 6 (IL6) single-nucleotide polymorphism (SNP) is one of the susceptible genes that is commonly used to detect bone deterioration [14]. IL6 is a key moderator of inflammation which has been found to play an important role in the pathogenesis of atherosclerosis, while IL6 gene -174G/C (rs 1800795) and -572G/C (rs 1800796) polymorphisms were related to bone metabolism and BMD among postmenopausal women [15]. Several studies identified IL6 -572G/C polymorphism as being common in the Asian population [16,17,18]. Among all, Pan et al. [19] reported that approximately 60% of the Han Chinese population carry the CC genotype, while 40% carry GG + CG genotypes. On the other hand, nearly all of the IL6 -174 G/C polymorphism studies were among Europeans, with a scarcity of data from Asians. Sun et al. [20] showed that nearly one third of their subjects had either CC or GC genotypes in their coronary artery disease study in China, indicating that the Chinese population may have the IL6 -174 G/C polymorphism too.
Dietary factors are other independent factors that may affect body acidity and the rate of bone loss. Generally, a highly acidic diet is recognized as a diet that is high in animal proteins and may generate endogenous acids. Although body acidity is generally tightly regulated, the habitual intake of a high-acid ash diet without adequate compensation by alkaline ions such as potassium and magnesium from fruit and vegetables may lead to a lower blood pH [21], which may affect the acid–base homeostasis in the body [22]. Despite the acid base balance theory hypothesized, the habitual consumption of a high acid-load diet and an inadequate consumption of potassium and bicarbonate-rich (base-load diet) is associated with an increase in urinary calcium, magnesium loss, and consequently a higher risk of osteoporosis. The evidence of the influence of DAL on bone health has been inconsistent [23,24,25,26,27]. The contradictory findings could be related to a lack of consideration of the links between DAL and genetic factors.
On the other hand, while there is growing evidence that IL6 (-174G/C and -572G/C) genetic polymorphisms with the presence of C alleles is associated with a higher risk of bone resorption, the potential association between dietary acidity and bone health remains inconclusive. Therefore, we hypothesized that the complex interaction between IL6 (-174G/C and -572G/C) genetic polymorphisms and dietary acidity may be implicated in lower bone mass. To date, this is the first study to examine how genetic factors may modify the association between diet and bone resorption among postmenopausal women. We aimed to explore whether IL6 SNPs modulate the effect of dietary acidity on bone resorption among postmenopausal women.

2. Materials and Methods

2.1. Study Population

A total of 203 postmenopausal women with ages ranging from 51 to 85 years old was randomly recruited from National Council of Senior Citizens Organizations Malaysia (NACSCOM). This was an analytical cross-sectional study conducted on a representative population (Chinese women that have been postmenopausal for five years or more, not on any medications that may affect bone health within the past one year). The exclusion criteria were the presence of any systemic diseases (e.g., cardiovascular diseases, cancer, stroke, impaired liver and renal function) and on medication (e.g., hormone replacement therapy, aluminum-containing antacids, anticonvulsants, aromatase inhibitors, immunosuppressant, glucocorticoids, proton pump inhibitors, selective serotonin reuptake inhibitors, and thiazolidinediones) that could affect bone health within the past one year. Figure 1 shows the flow diagram of the study. The sample size was determined using the Gpower software using a number of predictors set at nine (age, height, serum of 25(OH)D, education level, fasting blood glucose, waist circumference, NEAP, IL6, and bone resorption); the effect size f2 was 0.15, which gave a study power of 95%. The written informed consent of participants were obtained prior to study enrollment. The study protocol was approved by the Ethics Committee for Research Involving Human Subjects (project reference number FPSK (FR16) P019).

2.2. Measurements

The sociodemographic factors of participants were obtained using a pre-tested structured questionnaire. Anthropometric measurements including height, weight, and waist circumference were ascertained using standardized techniques [28,29]. A universal surrogate measure of body weight status, body mass index (BMI), was computed as the ratio of weight (kg) to height2 (m2), and was classified according to the WHO (2000) [30]. The percentage of body fat of the participants was measured using the body fat monitor HBF-306 (Omron Matsusaka Co. Ltd., Matsusaka, Japan) with an accuracy of up to 0.1%. Blood pressure was assessed using a Digital Automatic BP monitor (OMRON HEM-907, Omron, Kyoto, Japan), while the physical activity level was obtained using the validated Global Physical Activity Questionnaire (GPAQ) [31]. The adequacy of physical activity among the participants was ascertained according to the recommendation by the WHO whereby an individual should perform more than 600 Metabolic Equivalent (MET) minutes of total physical activity per week, with MET (Metabolic Equivalent) defined as “the ratio of a person’s working metabolic rate relative to the resting metabolic rate” in the GPAQ analysis guide [32].

2.3. Dietary Assessment

The habitual food intake of participants over the past one month was assessed using a validated 165-item semi-quantitative food frequency questionnaire (FFQ) [33], which was adapted from the Malaysian Adult Nutrition Survey (MANS) [34]. The food items were foods that are commonly consumed by the Malaysia population and were categorized according to 14 food groups: cereals and cereal products, fast food, meat and meat products, fish and seafood, eggs, legumes and legume products, milk and milk products, vegetables, fruits, drinks, alcoholic drinks, confectionaries, bread spread, and seasonings. The frequency intake of each food item on a daily, weekly, or monthly basis was converted to daily intakes; portion sizes were converted to grams, based on the household measurement listed in the Album Makanan Malaysia [35]. Nutrients were computed using the Nutritionist Pro™ Diet Analysis (Axxya Systems, Stafford, TX, USA) software, with the Nutrient Composition of Malaysia Foods [36] and Singapore Food Composition Database [8] as the primary databases. Several studies showed that the FFQ provides a good validity and reliability to assess the long-term nutrient intake for the estimation of dietary acidity [37,38,39]. Potential renal acid load (PRAL) and daily net endogenous acid production (NEAP) are the two common algorithm calculations used to estimate the acid load from dietary intake. Both NEAP and PRAL are highly correlated with acid load measured from 24 h urine samples in healthy men and women [40,41], making them suitable as surrogate measures for dietary acid load. In this study, dietary acidity was determined using the following equation: NEAP (mEq/d) = [54.5 × protein intake (g/d)/potassium intake (mEq/d)]−10.2 [40].

2.4. Biochemical Measurements

A total of 10 mL of fasting blood was collected between 9:00 and 10:30 h by certified phlebotomists and was then sent to a certified commercial laboratory for biochemical analysis. The fasting blood glucose was estimated by the Hexokinase method performed by the Olympus AU analyzer. The serum levels of 25(OH) vitamin D were determined using the Siemens ADVIA Centaur Vitamin D Total assay (Siemens, Tarrytown, NY, USA), with the analytical measuring range of 4.2 to 150 ng/mL (10.5 to 375 nmol/L). This assay has been standardized to the University of Ghent ID-LC/MS/MS reference measurement procedure and has achieved the Centers for Disease Control Vitamin D Standardization Certification program [35]. The vitamin D statuses of participants were classified into three subgroups according to the recommendation by the Institute of Medicine (2011) [36] as deficient (<30 nmol/L), inadequate (30–50 nmol/L), or adequate (>50 nmol/L), respectively. On the other hand, the serum CTX1 was assessed by a fully automated analyzer (Elecsys 2010, Roche Diagnostics, GmbH, Mannheim, Germany), which can reduce the variability and is suitable for routine use in clinical chemistry [37].

2.5. Genetic Analysis

Genomic DNA was extracted from non-coagulated whole blood samples (EDTA tube, Becton Dickinson, NJ, USA) using a commercially available DNA extraction kit (QIAamp DNA Blood Mini Kit Qiagen, Hilden, Germany) according to the manufacturer’s protocol. The quality of the extracted DNA was evaluated by means of electrophoresis, and the concentration of the extracted DNA was estimated using the spectrophotometer. For the SNP control, built-in controls were used to easily quantify amplifiable copies of DNA. The panel includes 5 DNA copy number control assays (albumin) to quantify from as little as 500 to 18,000 amplifiable copies (~1 ng to 60 ng) of DNA. Each genotyping was performed with the Agena® MassARRAY platform. After the SNP detection process, the Typer Analyzer was used to analyze the output data from the Agena® MassARRAY platform.

2.6. Statistical Analysis

The Statistical Package for Social Sciences for Windows version 22.0 (SPSS, Chicago, IL, USA) was used to perform the statistical analysis. The data normality was checked by a normal P-P plot of the standardized residuals. First, the descriptive data were expressed as mean ± SD or frequency and percentage. In light of the absence of established cut offs for NEAP and CTX1, the mean values of the participants were arbitrarily used to identify the overall dietary acid load and rate of bone resorption of the participants. Second, bivariate analyses of Pearson’s correlation were used to determine the relationships between CTX1 with NEAP, sociodemographic background (age and education level), anthropometry parameters (waist circumference and height), and biochemical indices (serum vitamin D and fasting blood glucose). Next, the Hardy–Weinberg equilibrium for genotypic distribution was examined by the Hardy–Weinberg equilibrium exact test. For the IL6 gene -572 G/C polymorphism, with a small number of GG homozygous (5 participants), GG and CG genotypes were collapsed into one single group. Comparisons of the study variables by the IL6 gene -572 G/C polymorphism were examined by the t test (continuous variables) or chi-square test (two categorical groups).
A hierarchical multiple linear regression model was used to determine the interaction effect between an acid ash diet and SNPs, and also to determine the contribution of adjusted variables and variables of interest (acid ash diet and IL6 SNPs) to bone resorption. Hierarchical regression is more flexible, as it allows the researcher to specify the order of entry of the independent variables in the regression equation [42]. Among the theoretic reasons, well-known risk factors such as age, height, serum of 25(OH)D, education level, fasting blood glucose, and waist circumference are the strongest predictors of the dependent variable (CTX1) [43]. Thus, they were dictated as a set of adjusted variables and were accorded priority of entry, and their total amount of variance was evaluated. Then, the interest of variables (NEAP and IL6 SNPs) were entered and evaluated in terms of what they added to the explanation power (total amount of variance). Lastly, the interaction effect was added and evaluated in terms of what it adds to the explanation of the dependent variable. Prior to analysis, the quality of the data was checked and an interaction term was added to the linear regression model for testing the SNP–diet interactions on bone resorption. The assumption of linearity, homoscedasticity, independence of error terms, normality of error distribution, and the absence of multicollinearity was made before hierarchical regression was performed. Figure 2 demonstrates the directed acyclic graph diagram of the study.

3. Results

3.1. Characteristics of Participants

As shown in Table 1, the mean age and duration of menopause of participants was 67 ± 7 years and 16 ± 8 years, respectively. Majority of the participants were married and had lower secondary education. The mean BMI of the participants was 24.3 kg/m2, with the majority of them normal or overweight, while approximately 5% were underweight. Less than two thirds of the participants met the recommended duration of physical activity. The mean score of NEAP was 72.8 ± 28.7, with approximately 45% having a high dietary acid load. The prevalence of vitamin D deficiency and inadequacy was extremely high (more than 80%). The mean serum CTX1 of the participants was 45% ± 0.2 ng/mL, and approximately 43% had an elevated rate of bone resorption. The IL6 -174G/C polymorphism was absent in this study population, with all the participants carrying the GG wild type. As the CC and CG genotypes were absent, the comparison and linear regression analysis were therefore not pursued for this SNP. The genotype frequencies for rs 1,800,796 were 2.5%, 41.4%, and 56.1% for GG, CG, and CC, respectively.

3.2. Correlations between Variables and CTX1

There was a significant negative correlation between CTX1 and age (r = −0.19, p = 0.007), while the NEAP, height, serum 25(OH) vitamin D, educational level, fasting blood glucose, and waist circumference of the participants were not associated with the CTX1 (Table 2).

3.3. Demographic, Anthropometrics, Lifestyle Factors, and Biochemical Analysis of Participants According to IL6 -572G/C Genotypes

There were no significant genotype-associated differences observed for any of the sociodemographic and anthropometric characteristics, lifestyle factors (physical activity and dietary acidity), biochemical measures (fasting blood glucose, serum 25(OH) vitamin D), and CTX1 (Table 3).

3.4. Interaction of IL6 -572G/C with NEAP in Relation to CTX1

The hierarchical regression results to determine the direct and interaction effects between the selected variables and the CTX1 are summarized in Table 4, with model 1 (control variables) accounting for 6.6% of the variance (R2 = 0.066). In model 2, the NEAP score (B = 0.15, p = 0.031) and IL6 CC genotype group (B = 0.14, p = 0.044) showed positive associations with the CTX1 rate. The inclusion of the NEAP and IL6 -572G/C increased the explained variance in CTX1 by 3.8%. However, inclusion of the interaction term (NEAP*IL6) in model 3 did not reflect any changes in R Square, and hence did not support the interaction term (NEAP*IL6) associated with CTX1 (R square = 0.000, p = 0.79) (Table 4).

4. Discussion

The present study was the first study on dietary acidity among Malaysians. The higher NEAP score as compared to studies in Hong Kong among an older Chinese population [44] and Caucasians [39,45,46,47] may be attributed to the nutrition transition of the dietary pattern among Malaysians. This finding corroborates the ideas of Soon and Tee [48], who suggested that dietary patterns in Southeast Asian nations have changed from traditional dietary patterns that are high in fresh, cooked, or pickled fruits and vegetables to Western dietary patterns that are generally high in animal products, wheat, sugars, fats, and salts. Several polymorphism studies have tried to establish the association between polymorphisms of certain cytokines with bone health. IL6 is a cytokine that involved inflammation and infection responses and was reported to play a role in the pathogenesis of osteoporosis [17,49]. In this study, all the participants carried the IL6 -174 G/C GG genotype. Our findings are consistent with other Asian studies [50,51,52,53,54,55]. For example, a study in South Korea reported that the CC genotype was absent among adolescent idiopathic scoliosis patients and healthy individuals, with only one participant carrying CG genotype [50]. Similar findings were reported among the Chinese [51,52] and Japanese population [54]. Our result was also supported by a local study conducted among the three major ethnicities (Malay, Chinese, and Indian), which showed that only Indians carried 4% of the CC genotype, while no CC genotype was found among Malay and Chinese people [55]. Therefore, we concluded that the IL6 polymorphism at −174 is rare and unlikely to contribute significantly to disease susceptibility in the Malaysian Chinese population.
We did not find any significant association between -572G/C genotypes and sociodemographics, anthropometrics, lifestyle factors, or biochemical measures. The results are in line with a Caucasian study conducted in Brazil, indicating that the -572G/C genetic polymorphism was not associated with the fasting blood glucose [56]. Moreover, previous studies [57,58,59] showed that there was no significant association between IL6 -572G/C with age, BMI, and serum 25(OH)D among Japanese postmenopausal women.
The present study shows that a higher acid ash diet as determined by NEAP score may accentuate the bone resorption marker. Prior studies have suggested a few mechanisms to interpret the influence of diet-induced acidosis on bone metabolisms [40,60,61]. Frick and Bushinsky [61] suggested that a slight decrease in the metabolic pH will result in the depletion of bone calcium by increases in urine calcium without increased calcium absorption in the intestine. It was estimated that the quantity of excess calcium excreted in the urine which associated with the acid ash diet over time could be as high as 24g per year or 480 g over 20 years, equivalent to almost half of an adult’s skeletal mass of calcium [62]. The habitual consumption of a high-acidity diet may lead to a consistent stimulation of osteoclast activity and subsequently osteoporosis. New et al. [63] demonstrated that high NEAP scores were associated with a lower bone mass of the femur, hip, and spine among 1056 women. Recently, Shariati-Bafghi et al. [64] showed that PRAL and NEAP scores were inversely correlated with bone mass, and such a relationship was independent of calcium intake. Another Korean study also reported that a high potassium intake was positively associated with a higher bone mineral density in the femur, lumbar spine, and hip, even with the subjects with a lower calcium intake [65]. Despite the positive evidence described, a systematic review and meta-analysis conducted by Fenton et al. [66] concluded that there was no causal association between an acidic diet and the risk of osteoporosis among adults. The inconclusive results may be influenced by the individual genetic makeup. However, since a long-term acidic diet may influence the osteoclast activity, it is believed that the excessive acid gains from a habitual diet and release into the bloodstream may have a significant impact on bone health. More studies are needed on this aspect.
In this study, the IL6 -174G/C and -572G/C SNPs were considered as candidate genes for bone resorption because of their potential involvement in regulating the proliferation, differentiation, and maturation of osteoblasts in bone turnover pathways [67]. Postmenopausal women are susceptible to higher bone resorption due to the decreased estrogen level caused by oophorectomy or natural menopause [68]. Estrogen plays a role in the bone protection effect inhibition of osteoclast activity, and reducing the estrogen level that may trigger the expression of IL6 and accelerate the bone resorption process. The findings of this study indicated that the participants with the CC genotype have a significant positive relationship with bone resorption. To the best of our knowledge, there is no other study that investigates the association between IL6 –572G/C SNP and bone resorption, despite earlier studies by Lee et al. [50] and Chung et al. [69] reporting that C carrier was associated with increased BMD. As BMD reflects the density of bones but is not a marker of bone resorption, a direct comparison with the above two studies cannot be made. More studies are warranted to delineate such association in future studies.
In this study, we provide the first IL6 genetic polymorphism and diet interaction data on bone resorption. The present study shows that higher dietary acidity, as determined by NEAP score and IL6 polymorphism at the -572 CC genotype, may accentuate the rate of bone resorption. Studies on the effects of dietary acidity on bone have yielded mixed and inconsistent findings. While some studies suggested that high dietary acidity are detrimental to bone [39,45,70], other studies reported no association between dietary acidity and the risk of osteoporosis [66,71,72]. With the contrasting results, we tried to reconcile the inconclusive findings of dietary acidity with the interaction of IL6 SNPs, but to no avail. Although the interaction between IL6 -572G/C and dietary acidity was not statistically significant in the analysis, the replication of this interaction in another large population study is highly warranted.
An important strength in our study was that it was population-based. Bone health is highly dependent on heritability, and a population-based study may minimize the bias of the result. There are several limitations in this study. Firstly, it was a cross-sectional study, and hence we were unable to determine the causality between the consumption of acid ash diet/IL6 SNP and CTX1. Future studies should also include the investigation of long-term changes in IL6 and the quantification of bone mineral. Secondly, the acid ash diet was estimated from dietary intake. The absorption of individual ions from food is variable and quite complex. It is highly dependent on an individual’s gastrointestinal absorption of the nutrients from food and also on kidney function to filter the net acid excretion. Thus, measurement bias in determining the acid ash diet’s effect on bone health was one of the major limitations that could not be avoided. In addition, although we have included the confounders in the analysis, other potential confounding factors such as gene–gene interactions due to other potential candidate genes (vitamin D receptor [73], estrogen receptor beta (ERβ) [74], transforming growth factor-β1 (TGF-β1) [75], C-reactive protein (CRP), adiponectin, and tumor necrosis factor-alpha (TNF-α) [76] were not included in this study due to financial and resource constraints. Last but not least, other biochemical parameters which may influence bone remodeling as suggested in several studies [77,78,79]—for example, estrogen, testosterone, parathyroid hormone (PTH), and blood PH—were not measured in this study, which limits our comprehensive interpretation of the results. Despite the fact that the current study used FFQ as a dietary assessment tool, and this is generally accepted as a better dietary assessment tool for reflecting habitual dietary intake as compared to food records, nevertheless, it is inevitable that food intake measurement is based on self-reported data which have inherent limitations and may limit the finding interpretation of the association between bone turnover markers such as CTX-1 and dietary acid load. Future studies should consider the use of biomarkers capable of objectively assessing food consumption without the bias of self-reported dietary assessment.

5. Conclusions

In summary, our study confirms that the IL6 polymorphism at -174 G/C is rare in the Chinese population and does not contribute significantly to CTX1. Besides this, our results also do not support the interaction effect of dietary acidity and IL6-572 G/C with CTX1. These data add to the limited literature regarding the interaction between DAL and the IL6 polymorphism associated with bone health, especially in Asian populations. It is suggested that future studies should include other bone biomarkers for bone resorption and formation, and should be accompanied by more time points of assessment for the better understanding of the role of genetic polymorphism and dietary acidity in bone health.

Author Contributions

The authors’ responsibilities were as follows: Y.M.C. designed the overall study. V.R. designed the genetic study. Y.M.C., V.R., Z.M.S., Y.S.C., and M.A. supervised the overall study. S.Y.L. performed the genotyping and statistical analysis. S.Y.L., Y.M.C., and V.R. prepared the manuscript, while Z.M.S., Y.S.C., and M.A. reviewed and edited the manuscript. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by Fundamental Research Grant Scheme, Ministry of Higher Education, Malaysia, grant number 04-01-15-1739FR and Putra Grant UPM. The funder had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Acknowledgments

We thank all of the study participants.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Cauley, J.A. Public health impact of osteoporosis. J. Gerontol. A Biol. Sci. Med. Sci. 2013, 68, 1243–1251. [Google Scholar] [CrossRef] [Green Version]
  2. Taguchi, Y.; Inoue, Y.; Kido, T.; Arai, N. Treatment costs and cost drivers among osteoporotic fracture patients in Japan: A retrospective database analysis. Arch. Osteoporos. 2018, 13, 45. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  3. Lloret, A.; Coiffier, G.; Couchouron, T.; Perdriger, A.; Guggenbuhl, P. Risk factors of mortality during the first year after low energy osteoporosis fracture: A retrospective case-control study. Clin. Cases Min. Bone Metab. 2016, 13, 123–126. [Google Scholar] [CrossRef] [PubMed]
  4. Pisani, P.; Renna, M.D.; Conversano, F.; Casciaro, E.; Di Paola, M.; Quarta, E.; Muratore, M.; Casciaro, S. Major osteoporotic fragility fractures: Risk factor updates and societal impact. World J. Orthop. 2016, 7, 171–181. [Google Scholar] [CrossRef] [PubMed]
  5. Gullberg, B.; Johnell, O.; Kanis, J.A. World-wide projections for hip fracture. Osteoporos. Int. 1997, 7, 407–413. [Google Scholar] [CrossRef] [PubMed]
  6. LEE, J.K.; KHIR, A.S. The incidence of hip fracture in Malaysians above 50 years of age: Variation in different ethnic groups. Aplar. J. Rheumatol. 2007, 10, 300–305. [Google Scholar] [CrossRef]
  7. Greenwood, C.; Clement, J.; Dicken, A.; Evans, J.P.; Lyburn, I.; Martin, R.M.; Rogers, K.; Stone, N.; Zioupos, P. Towards new material biomarkers for fracture risk. Bone 2016, 93, 55–63. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  8. Vasikaran, S.D. Utility of biochemical markers of bone turnover and bone mineral density in management of osteoporosis. Crit. Rev. Clin. Lab. Sci. 2008, 45, 221–258. [Google Scholar] [CrossRef]
  9. Morris, H.A.; Eastell, R.; Jorgensen, N.R.; Cavalier, E.; Vasikaran, S.; Chubb, S.A.P.; Kanis, J.A.; Cooper, C.; Makris, K. Clinical usefulness of bone turnover marker concentrations in osteoporosis. Clin. Chim. Acta 2017, 467, 34–41. [Google Scholar] [CrossRef]
  10. Glendenning, P. Markers of bone turnover for the prediction of fracture risk and monitoring of osteoporosis treatment: A need for international reference standards: Osteoporos int 2011;22:391-420. Clin. Biochem. Rev. 2011, 32, 45–47. [Google Scholar]
  11. Bauer, D.; Krege, J.; Lane, N.; Leary, E.; Libanati, C.; Miller, P.; Myers, G.; Silverman, S.; Vesper, H.W.; Lee, D.; et al. National Bone Health Alliance Bone Turnover Marker Project: Current practices and the need for US harmonization, standardization, and common reference ranges. Osteoporos. Int. A J. Establ. Result Coop. Eur. Found. Osteoporos. Natl. Osteoporos. Found. USA 2012, 23, 2425–2433. [Google Scholar] [CrossRef]
  12. Vasikaran, S.; Eastell, R.; Bruyere, O.; Foldes, A.J.; Garnero, P.; Griesmacher, A.; McClung, M.; Morris, H.A.; Silverman, S.; Trenti, T.; et al. Markers of bone turnover for the prediction of fracture risk and monitoring of osteoporosis treatment: A need for international reference standards. Osteoporos. Int. A J. Establ. Result Coop. Eur. Found. Osteoporos. Natl. Osteoporos. Found. USA 2011, 22, 391–420. [Google Scholar] [CrossRef]
  13. Peacock, M.; Turner, C.H.; Econs, M.J.; Foroud, T. Genetics of osteoporosis. Endocr. Rev. 2002, 23, 303–326. [Google Scholar] [CrossRef] [PubMed]
  14. Moura, K.F.; Haidar, M.; Bonduki, C.; Feldner, P.C., Jr.; Silva, I.; Soares, J.M., Jr.; Girao, M.J. Frequencies of interleukin-6, GST and progesterone receptor gene polymorphisms in postmenopausal women with low bone mineral density. Sao Paulo Med. J. 2014, 132, 36–40. [Google Scholar] [CrossRef] [Green Version]
  15. Ferrari, S.L.; Garnero, P.; Emond, S.; Montgomery, H.; Humphries, S.E.; Greenspan, S.L. A functional polymorphic variant in the interleukin-6 gene promoter associated with low bone resorption in postmenopausal women. Arthritis Rheum. 2001, 44, 196–201. [Google Scholar] [CrossRef]
  16. Ni, Y.; Li, H.; Zhang, Y.; Zhang, H.; Pan, Y.; Ma, J.; Wang, L. Association of IL-6 G-174C polymorphism with bone mineral density. J. Bone Min. Metab. 2014, 32, 167–173. [Google Scholar] [CrossRef]
  17. Wang, Z.; Yang, Y.; He, M.; Wang, R.; Ma, J.; Zhang, Y.; Zhao, L.; Yu, K. Association between interleukin-6 gene polymorphisms and bone mineral density: A meta-analysis. Genet. Test. Mol. Biomark. 2013, 17, 898–909. [Google Scholar] [CrossRef] [Green Version]
  18. Yan, L.; Hu, R.; Tu, S.; Cheng, W.J.; Zheng, Q.; Wang, J.W.; Kan, W.S.; Ren, Y.J. Meta-analysis of association between IL-6 -634C/G polymorphism and osteoporosis. Genet. Mol. Res. Gmr. 2015, 14, 19225–19232. [Google Scholar] [CrossRef]
  19. Pan, M.; Gao, S.P.; Jiang, M.H.; Guo, J.; Zheng, J.G.; Zhu, J.H. Interleukin 6 promoter polymorphisms in normal Han Chinese population: Frequencies and effects on inflammatory markers. J. Investig. Med. 2011, 59, 272–276. [Google Scholar] [CrossRef]
  20. Sun, G.Q.; Wu, G.D.; Meng, Y.; Du, B.; Li, Y.B. IL-6 gene promoter polymorphisms and risk of coronary artery disease in a Chinese population. Genet. Mol. Res. Gmr. 2014, 13, 7718–7724. [Google Scholar] [CrossRef]
  21. Welch, A.A.; MacGregor, A.J.; Skinner, J.; Spector, T.D.; Moayyeri, A.; Cassidy, A. A higher alkaline dietary load is associated with greater indexes of skeletal muscle mass in women. Osteoporos. Int. 2013, 24, 1899–1908. [Google Scholar] [CrossRef]
  22. Williams, R.S.; Kozan, P.; Samocha-Bonet, D. The role of dietary acid load and mild metabolic acidosis in insulin resistance in humans. Biochimie 2016, 124, 171–177. [Google Scholar] [CrossRef] [PubMed]
  23. De Jonge, E.A.L.; Koromani, F.; Hofman, A.; Uitterlinden, A.G.; Franco, O.H.; Rivadeneira, F.; Kiefte-de Jong, J.C. Dietary acid load, trabecular bone integrity, and mineral density in an ageing population: The Rotterdam study. Osteoporos. Int. A J. Establ. Result Coop. Eur. Found. Osteoporos. Natl. Osteoporos. Found. USA 2017, 28, 2357–2365. [Google Scholar] [CrossRef] [Green Version]
  24. Jehle, S.; Hulter, H.N.; Krapf, R. Effect of potassium citrate on bone density, microarchitecture, and fracture risk in healthy older adults without osteoporosis: A randomized controlled trial. J. Clin. Endocrinol. Metab. 2013, 98, 207–217. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  25. Jia, T.; Byberg, L.; Lindholm, B.; Larsson, T.; Lind, L.; Michaëlsson, K.; Carrero, J.J.O.I. Dietary acid load, kidney function, osteoporosis, and risk of fractures in elderly men and women. Osteoporos. Int. 2015, 26, 563–570. [Google Scholar] [CrossRef] [PubMed]
  26. McLean, R.R.; Qiao, N.; Broe, K.E.; Tucker, K.L.; Casey, V.; Cupples, L.A.; Kiel, D.P.; Hannan, M.T. Dietary acid load is not associated with lower bone mineral density except in older men. J. Nutr. 2011, 141, 588–594. [Google Scholar] [CrossRef] [Green Version]
  27. Shi, L.; Libuda, L.; Schonau, E.; Frassetto, L.; Remer, T. Long term higher urinary calcium excretion within the normal physiologic range predicts impaired bone status of the proximal radius in healthy children with higher potential renal acid load. Bone 2012, 50, 1026–1031. [Google Scholar] [CrossRef] [PubMed]
  28. Brozek, J.; Keys, A. The evaluation of leanness-fatness in man; norms and interrelationships. Br. J. Nutr. 1951, 5, 194–206. [Google Scholar] [CrossRef] [Green Version]
  29. Harrison, G.G.; Buskirk, E.R.; Lindsay Carter, J.E.; Johnston, F.E.; Lohman, T.G.; Pollock, M.L.; Roche, A.F.; Wilmore, J. Skinfold Thicknesses and Measurement Technique: Anthropometric Standardization Reference Manual; Lohman, T.G.R., Martorell, A.F.R., Eds.; Human Kinetics Books: Champaign, IL, USA, 1988. [Google Scholar]
  30. WHO/IOTF/IASO. International Obesity Task Force, International Association for the Study of Obesity. In The Asia-Pacific Perspective: Redefining Obesity and Its Treatment; World Health Organization: Hong Kong, China, 2000. [Google Scholar]
  31. Bull, F.C.; Maslin, T.S.; Armstrong, T. Global physical activity questionnaire (GPAQ): Nine country reliability and validity study. J. Phys. Act. Health 2009, 6, 790–804. [Google Scholar] [CrossRef] [Green Version]
  32. World Health Organization. Global Recommendations on Physical Activity for Health; World Health Organization: Geneva, Switzerland, 2010. [Google Scholar]
  33. Institute for Public Health Kuala. National Health and Morbidity Survey 2015—Report on Smoking Status Among Malaysian Adults; Institute for Public Health Kuala: Lumpur, Malaysia, 2015. [Google Scholar]
  34. Aris, T.; Ahmad, N.A.; Tee, G.H. National Health and Morbidity Survey 2014: Malaysian Adults Nutrition Survey (MANS); Institute for Public Health: Kuala Lumpur, Malaysia, 2014. [Google Scholar]
  35. Institut Kesihatan Umum. Album Makanan Malaysia; Tahir, A., Azli, S.A.B., Nadrah, M.H., Yuhanis Auri, A.K., Nurul Fatihah, H.G., Anim Zakiah, M., Eds.; Institut Kesihatan Umum: Kuala Lumpur, Malaysia, 2011. [Google Scholar]
  36. Tee, E.S. Nutrition Composition of Malaysian Foods, 4th ed.; Ministry of Health Malaysia: Kuala Lumpur, Malaysia, 1997. [Google Scholar]
  37. Abshirini, M.; Bagheri, F.; Mahaki, B.; Siassi, F.; Koohdani, F.; Safabakhsh, M.; Sotoudeh, G. The dietary acid load is higher in subjects with prediabetes who are at greater risk of diabetes: A case-control study. Diabetol. Metab. Syndr. 2019, 11, 52. [Google Scholar] [CrossRef]
  38. Ko, B.J.; Chang, Y.; Ryu, S.; Kim, E.M.; Lee, M.Y.; Hyun, Y.Y.; Lee, K.B. Dietary acid load and chronic kidney disease in elderly adults: Protein and potassium intake. PLoS ONE 2017, 12, e0185069. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  39. Wynn, E.; Lanham-New, S.A.; Krieg, M.A.; Whittamore, D.R.; Burckhardt, P. Low estimates of dietary acid load are positively associated with bone ultrasound in women older than 75 years of age with a lifetime fracture. J. Nutr. 2008, 138, 1349–1354. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  40. Frassetto, L.A.; Todd, K.M.; Morris, R.C., Jr.; Sebastian, A. Estimation of net endogenous noncarbonic acid production in humans from diet potassium and protein contents. Am. J. Clin. Nutr. 1998, 68, 576–583. [Google Scholar] [CrossRef] [Green Version]
  41. Remer, T.; Manz, F. Estimation of the renal net acid excretion by adults consuming diets containing variable amounts of protein. Am. J. Clin. Nutr. 1994, 59, 1356–1361. [Google Scholar] [CrossRef]
  42. Ho, R. Handbook of Univariate and Multivariate Data Analysis with IBM SPSS; Chapman and Hall/CRC: Boca Raton, FL, USA, 2013. [Google Scholar]
  43. Weaver, C.; Gordon, C.; Janz, K.; Kalkwarf, H.; Lappe, J.; Lewis, R.; O’Karma, M.; Wallace, T.; Zemel, B.J.O.I. The National Osteoporosis Foundation’s position statement on peak bone mass development and lifestyle factors: A systematic review and implementation recommendations. Osteoporos. Int. 2016, 27, 1281–1386. [Google Scholar] [CrossRef] [Green Version]
  44. Chan, R.; Leung, J.; Woo, J. Association Between Estimated Net Endogenous Acid Production and Subsequent Decline in Muscle Mass Over Four Years in Ambulatory Older Chinese People in Hong Kong: A Prospective Cohort Study. J. Gerontol. A Biol. Sci. Med. Sci. 2015, 70, 905–911. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  45. Macdonald, H.M.; New, S.A.; Fraser, W.D.; Campbell, M.K.; Reid, D.M. Low dietary potassium intakes and high dietary estimates of net endogenous acid production are associated with low bone mineral density in premenopausal women and increased markers of bone resorption in postmenopausal women. Am. J. Clin. Nutr. 2005, 81, 923–933. [Google Scholar] [CrossRef]
  46. Engberink, M.F.; Bakker, S.J.; Brink, E.J.; van Baak, M.A.; van Rooij, F.J.; Hofman, A.; Witteman, J.C.; Geleijnse, J.M. Dietary acid load and risk of hypertension: The Rotterdam Study. Am. J. Clin. Nutr. 2012, 95, 1438–1444. [Google Scholar] [CrossRef] [Green Version]
  47. Gannon, R.H.; Millward, D.J.; Brown, J.E.; Macdonald, H.M.; Lovell, D.P.; Frassetto, L.A.; Remer, T.; Lanham-New, S.A. Estimates of daily net endogenous acid production in the elderly UK population: Analysis of the National Diet and Nutrition Survey (NDNS) of British adults aged 65 years and over. Br. J. Nutr. 2008, 100, 615–623. [Google Scholar] [CrossRef] [Green Version]
  48. Soon, J.M.; Tee, E.S. Changing trends in dietary pattern and implications to food and nutrition security in Association of Southeast Asian Nations (ASEAN). Int. J. Nutr. Food Sci. 2014, 3, 259–269. [Google Scholar] [CrossRef] [Green Version]
  49. Fajar, J.K.; Azharuddin, A. The association between interleukin 6 -174 G/C gene polymorphism and the risk of osteoporosis: A meta-analysis. J. Taibah Univ. Med. Sci. 2017, 12, 212–220. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  50. Lee, J.S.; Suh, K.T.; Eun, I.S. Polymorphism in interleukin-6 gene is associated with bone mineral density in patients with adolescent idiopathic scoliosis. J. Bone Jt. Surg. Br. 2010, 92, 1118–1122. [Google Scholar] [CrossRef] [Green Version]
  51. Zhai, R.; Liu, G.; Yang, C.; Huang, C.; Wu, C.; Christiani, D.C. The G to C polymorphism at -174 of the interleukin-6 gene is rare in a Southern Chinese population. Pharmacogenetics 2001, 11, 699–701. [Google Scholar] [CrossRef] [PubMed]
  52. Tong, Y.; Wang, Z.; Geng, Y.; Liu, J.; Zhang, R.; Lin, Q.; Li, X.; Huang, D.; Gao, S.; Hu, D.; et al. The association of functional polymorphisms of IL-6 gene promoter with ischemic stroke: Analysis in two Chinese populations. Biochem. Biophys. Res. Commun. 2010, 391, 481–485. [Google Scholar] [CrossRef]
  53. Meenagh, A.; Williams, F.; Ross, O.A.; Patterson, C.; Gorodezky, C.; Hammond, M.; Leheny, W.A.; Middleton, D. Frequency of cytokine polymorphisms in populations from western Europe, Africa, Asia, the Middle East and South America. Hum. Immunol. 2002, 63, 1055–1061. [Google Scholar] [CrossRef]
  54. Migita, K.; Miyazoe, S.; Maeda, Y.; Daikoku, M.; Abiru, S.; Ueki, T.; Yano, K.; Nagaoka, S.; Matsumoto, T.; Nakao, K.; et al. Cytokine gene polymorphisms in Japanese patients with hepatitis B virus infection--association between TGF-beta1 polymorphisms and hepatocellular carcinoma. J. Hepatol. 2005, 42, 505–510. [Google Scholar] [CrossRef]
  55. Gan, G.G.; Subramaniam, R.; Lian, L.H.; Nadarajan, V. Ethnic variation in interleukin-6 -174 (g/c) polymorphism in the malaysian population. Balk. J. Med. Genet. 2013, 16, 53–58. [Google Scholar] [CrossRef] [Green Version]
  56. Ururahy, M.A.; de Souza, K.S.; Oliveira, Y.M.; Loureiro, M.B.; da Silva, H.P.; Freire-Neto, F.P.; Bezerra, J.F.; Luchessi, A.D.; Doi, S.Q.; Hirata, R.D.; et al. Association of polymorphisms in IL6 gene promoter region with type 1 diabetes and increased albumin-to-creatinine ratio. Diabetes/Metab. Res. Rev. 2015, 31, 500–506. [Google Scholar] [CrossRef]
  57. Hanai, Y.; Sugita, N.; Wang, Y.; Yoshihara, A.; Iwasaki, M.; Miyazaki, H.; Nakamura, K.; Yoshie, H. Relationships between IL-6 gene polymorphism, low BMD and periodontitis in postmenopausal women. Arch. Oral Biol. 2015, 60, 533–539. [Google Scholar] [CrossRef]
  58. Yamada, Y.; Ando, F.; Niino, N.; Miki, T.; Shimokata, H. Association of polymorphisms of paraoxonase 1 and 2 genes, alone or in combination, with bone mineral density in community-dwelling Japanese. J. Hum. Genet. 2003, 48, 469–475. [Google Scholar] [CrossRef] [PubMed]
  59. Ota, N.; Nakajima, T.; Nakazawa, I.; Suzuki, T.; Hosoi, T.; Orimo, H.; Inoue, S.; Shirai, Y.; Emi, M. A nucleotide variant in the promoter region of the interleukin-6 gene associated with decreased bone mineral density. J. Hum. Genet. 2001, 46, 267–272. [Google Scholar] [CrossRef] [Green Version]
  60. Krieger, N.S.; Sessler, N.E.; Bushinsky, D.A. Acidosis inhibits osteoblastic and stimulates osteoclastic activity in vitro. Am. J. Physiol. 1992, 262, F442–F448. [Google Scholar] [CrossRef]
  61. Frick, K.K.; Bushinsky, D.A. Effect of metabolic and respiratory acidosis on intracellular calcium in osteoblasts. Am. J. Physiol Ren. Physiol. 2010, 299, F418–F425. [Google Scholar] [CrossRef] [Green Version]
  62. Fenton, T.R.; Eliasziw, M.; Lyon, A.W.; Tough, S.C.; Hanley, D.A. Meta-analysis of the quantity of calcium excretion associated with the net acid excretion of the modern diet under the acid-ash diet hypothesis. Am. J. Clin. Nutr. 2008, 88, 1159–1166. [Google Scholar] [CrossRef] [Green Version]
  63. New, S.A.; MacDonald, H.M.; Campbell, M.K.; Martin, J.C.; Garton, M.J.; Robins, S.P.; Reid, D.M. Lower estimates of net endogenous non-carbonic acid production are positively associated with indexes of bone health in premenopausal and perimenopausal women. Am. J. Clin. Nutr. 2004, 79, 131–138. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  64. Shariati-Bafghi, S.E.; Nosrat-Mirshekarlou, E.; Karamati, M.; Rashidkhani, B. Higher Dietary Acidity is Associated with Lower Bone Mineral Density in Postmenopausal Iranian Women, Independent of Dietary Calcium Intake. Int. J. Vitam. Nutr. Res. 2014, 84, 206–217. [Google Scholar] [CrossRef]
  65. Kong, S.H.; Kim, J.H.; Hong, A.R.; Lee, J.H.; Kim, S.W.; Shin, C.S. Dietary potassium intake is beneficial to bone health in a low calcium intake population: The Korean National Health and Nutrition Examination Survey (KNHANES) (2008-2011). Osteoporos. Int. A J. Establ. Result Coop. Eur. Found. Osteoporos. Natl. Osteoporos. Found. USA 2017, 28, 1577–1585. [Google Scholar] [CrossRef]
  66. Fenton, T.R.; Tough, S.C.; Lyon, A.W.; Eliasziw, M.; Hanley, D.A. Causal assessment of dietary acid load and bone disease: A systematic review & meta-analysis applying Hill’s epidemiologic criteria for causality. Nutr. J. 2011, 10, 41. [Google Scholar] [CrossRef] [Green Version]
  67. Kaneshiro, S.; Ebina, K.; Shi, K.; Higuchi, C.; Hirao, M.; Okamoto, M.; Koizumi, K.; Morimoto, T.; Yoshikawa, H.; Hashimoto, J. IL-6 negatively regulates osteoblast differentiation through the SHP2/MEK2 and SHP2/Akt2 pathways in vitro. J. Bone Min. Metab. 2014, 32, 378–392. [Google Scholar] [CrossRef]
  68. Nguyen, H.T.; von Schoultz, B.; Nguyen, T.V.; Thang, T.X.; Chau, T.T.; Duc, P.T.; Hirschberg, A.L. Sex hormone levels as determinants of bone mineral density and osteoporosis in Vietnamese women and men. J. Bone Miner. Metab. 2015, 33, 658–665. [Google Scholar] [CrossRef] [PubMed]
  69. Chung, H.W.; Seo, J.S.; Hur, S.E.; Kim, H.L.; Kim, J.Y.; Jung, J.H.; Kim, L.H.; Park, B.L.; Shin, H.D. Association of interleukin-6 promoter variant with bone mineral density in pre-menopausal women. J. Hum. Genet. 2003, 48, 243–248. [Google Scholar] [CrossRef] [Green Version]
  70. Berardi, J.M.; Logan, A.C.; Rao, A.V. Plant based dietary supplement increases urinary pH. J. Int. Soc. Sports Nutr. 2008, 5, 20. [Google Scholar] [CrossRef] [Green Version]
  71. Dargent-Molina, P.; Sabia, S.; Touvier, M.; Kesse, E.; Breart, G.; Clavel-Chapelon, F.; Boutron-Ruault, M.C. Proteins, dietary acid load, and calcium and risk of postmenopausal fractures in the E3N French women prospective study. J. Bone Min. Res. 2008, 23, 1915–1922. [Google Scholar] [CrossRef]
  72. Garcia, A.H.; Franco, O.H.; Voortman, T.; de Jonge, E.A.; Gordillo, N.G.; Jaddoe, V.W.; Rivadeneira, F.; van den Hooven, E.H. Dietary acid load in early life and bone health in childhood: The Generation R Study. Am. J. Clin. Nutr. 2015, 102, 1595–1603. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  73. Ahmad, I.; Jafar, T.; Mahdi, F.; Arshad, M.; Das, S.K.; Waliullah, S.; Mahdi, A.A. Association of Vitamin D Receptor (FokI and BsmI) Gene Polymorphism with Bone Mineral Density and Their Effect on 25-Hydroxyvitamin D Level in North Indian Postmenopausal Women with Osteoporosis. Indian J. Clin. Biochem. 2018, 33, 429–437. [Google Scholar] [CrossRef]
  74. Geng, L.; Yao, Z.; Yang, H.; Luo, J.; Han, L.; Lu, Q. Association of CA repeat polymorphism in estrogen receptor beta gene with postmenopausal osteoporosis in Chinese. J. Genet. Genom. Yi Chuan Xue Bao 2007, 34, 868–876. [Google Scholar] [CrossRef]
  75. Sun, J.; Zhang, C.; Xu, L.; Yang, M.; Yang, H. The transforming growth factor-β1 (TGF-β1) gene polymorphisms (TGF-β1 T869C and TGF-β1 T29C) and susceptibility to postmenopausal osteoporosis: A meta-analysis. Medicine 2015, 94, e461. [Google Scholar] [CrossRef]
  76. Lim, H.S.; Park, Y.H.; Kim, S.K. Relationship between Serum Inflammatory Marker and Bone Mineral Density in Healthy Adults. J. Bone Metab. 2016, 23, 27–33. [Google Scholar] [CrossRef] [Green Version]
  77. Huang, J.; Lin, D.; Wei, Z.; Li, Q.; Zheng, J.; Zheng, Q.; Cai, L.; Li, X.; Yuan, Y.; Li, J. Parathyroid Hormone Derivative with Reduced Osteoclastic Activity Promoted Bone Regeneration via Synergistic Bone Remodeling and Angiogenesis. Small 2020, 16, e1905876. [Google Scholar] [CrossRef] [PubMed]
  78. Wein, M.N. Parathyroid Hormone Signaling in Osteocytes. JBMR Plus 2018, 2, 22–30. [Google Scholar] [CrossRef]
  79. Wein, M.N.; Kronenberg, H.M. Regulation of Bone Remodeling by Parathyroid Hormone. Cold Spring Harb. Perspect. Med. 2018, 8. [Google Scholar] [CrossRef] [Green Version]
Figure 1. Flow diagram of the study.
Figure 1. Flow diagram of the study.
Ijerph 18 00827 g001
Figure 2. Directed acyclic graph diagram of the hierarchical regression model.
Figure 2. Directed acyclic graph diagram of the hierarchical regression model.
Ijerph 18 00827 g002
Table 1. Characteristics of the study participants.
Table 1. Characteristics of the study participants.
n (%)Mean ± SD
Social demographics
Age (year) 67 ± 7
Years of menopause (year) 16 ± 8
Educational level (years) 8 ± 5
Anthropometrics
Weight (kg) 57.91 ± 9.6
Height (m) 1.54 ± 0.1
Waist circumference (cm) 80.3 ± 9.1
Body fat percentage (%) 35.1 ± 5.2
BMI (kg/m2) 24.3 ± 3.8
Underweight (<18.5)10 (5.0)
Normal (18.5–24.9)113 (55.9)
Overweight (25.0–29.9)64 (31.7)
Obese (≥ 30)15 (7.4)
Lifestyles
Physical activity (MET-min/week)
Below recommendation (<600 MET) 77 (37.9)
Meeting recommendation (≥600 MET) 126 (62.1)
NEAP score (mEq/day) 72.8 ± 28.7
Normal (<72.8)114 (56.2)
Elevated (≥72.8)89 (43.8)
Biochemical analysis
Fasting blood glucose (mmol/L) 5.89 ± 0.93
Serum of 25(OH)D (nmol/L)
Deficiency (<30)66 (32.5)
Inadequate (30–50)100 (49.3)
Adequate (>50)37 (18.2)
CTX-1 (ng/mL) 0.445 ± 0.198
Normal (<0.445)116 (57.1)
Elevated (≥0.445) 87 (42.9)
Genetic analysis
IL6 gene -174G/C (genotype)
CC0
CG0
GG203 (100)
IL6 gene -572G/C (genotype)
GG5 (2.5)
CG84 (41.4)
CC114 (56.1)
Data are presented as mean ± SD or frequency (percentage).
Table 2. Correlations between selected sociodemographic background, anthropometry parameters, biochemical indices, and NEAP with CTX1 among the participants.
Table 2. Correlations between selected sociodemographic background, anthropometry parameters, biochemical indices, and NEAP with CTX1 among the participants.
rp
NEAP0.0840.24
Age (year)−0.1890.01 *
Height (m) 0.1330.06
Serum of 25(OH)D (nmol/L)−0.1050.14
Educational level (year)0.1080.13
Fasting blood glucose (mmol/L)−0.1010.15
Waist circumference (cm)−0.0980.16
* p < 0.05.
Table 3. Demographic, anthropometrics, lifestyle factors, and biochemical analysis according to IL6 gene -572G/C.
Table 3. Demographic, anthropometrics, lifestyle factors, and biochemical analysis according to IL6 gene -572G/C.
IL6 rs1800796
GG + CG (n = 89)CC (n = 114)t-test p
Social demographics
Age (year)65.91 ± 5.767.17 ± 7.1−1.360.18
Years of menopause (year)15.47 ± 6.816.63 ± 8.4−1.080.28
Marital status
Single8 (9)10 (8.8)
Married67 (75.3)90 (78.9)
Divorced2 (2.2)4 (3.5)
Others (widow or widower)12 (13.5)10 (8.8)
Educational level (years) 8.54 ± 4.77.45 ± 4.51.240.22
Anthropometrics
Weight (kg) 57.23 ± 9.458.44 ± 9.8−0.8830.38
Height (m) 1.54 ± 0.11.54 ± 0.10.1550.88
Waist circumference (cm) 79.74 ± 8.780.72 ± 9.4−0.7530.45
Body fat percentage (%)34.96 ± 5.535.26 ± 5.04−0.4000.69
BMI (kg/m2) 24.1 ± 3.724.5 ± 3.9−0.6280.53
Lifestyles factors
Physical activity (MET-min/week)
Below recommendation (<600 MET) 30 (33.7)47 (41.2)
Meeting recommendation (≥600 MET) 59 (66.3)67 (58.8)
NEAP (mEq/day)74.97 ± 29.471.12 ± 28.20.9450.35
Biochemical analysis
Fasting blood glucose (mmol/L)5.92 ± 0.955.87 ± 0.90.3970.69
Serum of 25(OH) D (nmol/L)
Deficiency 28 (31.5)38 (33.3)
Inadequate 44 (49.4)56 (49.1)
Adequate 17 (19.1)20 (17.5)
CTX-1 (ng/mL)0.42 ± 0.190.46 ± 0.21−1.540.12
Data are presented as mean ± SD or frequency (percentage); difference between the means of two independent groups was measured by independent samples test.
Table 4. Hierarchical linear regression analyses for the association between the selected variables and CTX1.
Table 4. Hierarchical linear regression analyses for the association between the selected variables and CTX1.
Variables Step 1Step 2Step 3
Beta tpBeta tpBeta tp
Age (year)−0.143−1.910.057−0.168−2.260.025−0.166−2.210.028
Height (m)0.0831.160.250.0831.170.240.0841.190.24
Serum of 25(OH)D (nmol/L)−0.108−1.540.13−0.114−1.640.10−0.115−1.640.10
Educational level (years)0.0360.4940.620.0720.9780.330.0751.0040.32
Fasting blood glucose (mmol/L)−0.053−0.7280.47−0.058−0.7970.43−0.055−0.7560.45
Waist circumference (cm)−0.077−1.030.30−0.079−1.070.28−0.080−1.090.28
NEAP (mEq/day) 0.1532.180.0310.1731.660.098
IL6 gene -572G/C (GG + CG = 0, CC = 1) 0.1402.030.0440.1880.9770.33
NEAP x IL6 gene -572G/C −0.054−0.2660.79
Step 1: F (6, 196) = 2.32, p = 0.035, R2 = 0.066; Step 2: F (8, 194) = 2.81, p = 0.006, ΔR2 = 0.038, ΔF (2, 194) = 4.07, p (ΔF) = 0.018; Step 3: F (9, 193) = 2.49, p = 0.010, ΔR2 = 0.000, ΔF (1, 193) = 0.071, p (ΔF) = 0.79. 2 Step 1 included all the adjusted variables (age, height, serum of 25(OH)D, education level, fasting blood glucose, and waist circumference); Step 2 included the 2 independent variables of interest (NEAP and IL6); Step 3 added the interaction term of NEAP*IL6.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Lim, S.Y.; Chan, Y.M.; Ramachandran, V.; Shariff, Z.M.; Chin, Y.S.; Arumugam, M. No Interaction Effect between Interleukin-6 Polymorphisms and Acid Ash Diet with Bone Resorption Marker in Postmenopausal Women. Int. J. Environ. Res. Public Health 2021, 18, 827. https://doi.org/10.3390/ijerph18020827

AMA Style

Lim SY, Chan YM, Ramachandran V, Shariff ZM, Chin YS, Arumugam M. No Interaction Effect between Interleukin-6 Polymorphisms and Acid Ash Diet with Bone Resorption Marker in Postmenopausal Women. International Journal of Environmental Research and Public Health. 2021; 18(2):827. https://doi.org/10.3390/ijerph18020827

Chicago/Turabian Style

Lim, Sook Yee, Yoke Mun Chan, Vasudevan Ramachandran, Zalilah Mohd Shariff, Yit Siew Chin, and Manohar Arumugam. 2021. "No Interaction Effect between Interleukin-6 Polymorphisms and Acid Ash Diet with Bone Resorption Marker in Postmenopausal Women" International Journal of Environmental Research and Public Health 18, no. 2: 827. https://doi.org/10.3390/ijerph18020827

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop