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Article

Serum Folate Related to Five Measurements of Obesity and High-Sensitivity C-Reactive Protein in Korean Adults

by
Mee-Ri Lee
1 and
Sung Min Jung
2,*
1
Department of Preventive Medicine, Soonchunhyang University College of Medicine, Cheonan-si 31151, Korea
2
Department of Surgery, Inje University, Ilsan Paik Hospital, Goyang-si 10380, Korea
*
Author to whom correspondence should be addressed.
Nutrients 2022, 14(17), 3461; https://doi.org/10.3390/nu14173461
Submission received: 12 July 2022 / Revised: 14 August 2022 / Accepted: 18 August 2022 / Published: 24 August 2022
(This article belongs to the Special Issue Micronutrients Deficiency and Obesity)
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Abstract

:
This study investigated the effects of folic acid on obesity and high-sensitivity C-reactive protein (CRP) levels. Using data from the Korea National Health and Nutrition Examination Survey (KNHANES VII 2016–2018), 6394 adults (aged 19–80 years) who met the study criteria were identified and divided into young, middle-aged, and older adult groups. The analysis assessed associations using logistic regression for complex samples. Obesity was measured using body mass index (BMI), waist circumference (WC), waist-to-height ratio (WHtR), a body shape index (ABSI), and body roundness index (BRI). The odds ratio (OR) of obesity based on BMI were statistically significant for young adults and older participants with low levels of folic acid compared to those with high levels (OR: 1.33 and 1.58, respectively). The OR of obesity based on BMI, WC, WHtR, ABSI, and BRI was significant with low levels of folic acid in middle-aged individuals (OR: 1.36, 1.32, 1.41, 1.29, and 1.47, respectively). Low folate levels were related to higher high-sensitivity CRP levels in middle-aged patients. In conclusion, folate level showed a significant inverse association with obesity and inflammatory biomarkers, especially in the middle-aged group. Further longitudinal or randomized controlled trials are needed to confirm and expand our results.

1. Introduction

Obesity and being overweight are among the most important health burdens worldwide [1]. Global obesity has nearly tripled since 1975, with 39% of adults being overweight and 13% being obese in 2016 [2]. Obesity affects insulin resistance [3], cardiovascular diseases [4], ischemic stroke [5], cancers [6], and increases mortality.
Although high-carbohydrate and high-fat diets are associated with obesity [7], association between micronutrient status and obesity is unclear. A previous study revealed that vitamin B12 is inversely associated with obesity [8] and vitamin D supplementation in obese subjects could affect weight loss and decrease fat mass [9].
Folate is an essential water-soluble vitamin that is present in fruits, green leafy vegetables, and the liver [10] It reduces the risk of serous neural tube defects in babies, and some countries have mandatory folic acid fortification of flour [11].
Previous studies showed that lower folate associated with obesity [12,13]. However, a review article reported that there was no relationship between folate concentration and body mass index (BMI) [14]. A cross-sectional study showed no association between BMI and waist circumference (WC) [15]. Inconsistent evidence has revealed that folate levels may be associated with obesity.
BMI is a useful population-based measurement of general obesity and the well-known World Health Organization (WHO) categories [16,17]. However, BMI has limitations; thus, WC and waist-to-height ratio (WHtR) are preferred alternatives [18].
Two novel obesity indices were introduced. Krakauer and Krakauer developed a body shape index (ABSI) that correlates with abdominal adiposity deposition in 2012 [19]. In 2013, Thomas et al. (2013) developed the body roundness index (BRI) as a predictor of visceral adiposity tissue and body fat percentage [20]. In obesity, adipocytes are enlarged and the secretion of inflammatory factors such as high-sensitivity C-reactive protein (hs-CRP) is increased [21]. Cross-sectional paper for Asia found that central obesity was associated with hs-CRP [22,23]. Some studies showed subjects with a high BMI and the health metabolic profile was associated with increased hs-CRP compared to non-obese subjects [24]. However, another study found that hs-CRP was not related to future weight gain or increased waist circumference in Finnish adults [25].
Despite some studies on the association folic acid and obesity, to our knowledge, no study has used the ABSI and BRI. Few studies have used all five obesity measures. Therefore, we investigated the association between folic acid and five obesity measurements (BMI, WC, WHtR, ABSI, and BRI) and inflammatory biomarkers (hs-CRP) in Korean adults using data from the Korea National Health and Nutrition Examination Survey (KNHANES VII 2016–2018).

2. Materials and Methods

2.1. Study Design and Study Population

The KNHANES is a study that is representative of the Korean population and used a complex study design to randomly select subjects to collect data. It is an annual study conducted by the Ministry of Health and Welfare. The flow chart was shown in Figure 1 and 6394 adults were included in the analysis. Subjects were divided into young adult (19–39 years old), middle-aged (40–64 years old), and older adult (65–80 years old) age groups.

2.2. Measurement of Folic Acid and High-Sensitivity C-Reactive Protein

Folate was detected in the samples using ARCHITECT i4000Sr (Abbott Laboratories, Abbott Park, IL, USA) and ARCHITECT Folate-only reagent with chemiluminescent microparticle immunoassay methods.
Hs-CRP was measured using Cobas immunoturbidimetry (Roche, Berlin, Germany).

2.3. Obesity Assessment

BMI was calculated as weight divided by height squared (kg/m2). A high BMI was defined as 25 kg/m2 or higher. High WC was defined as WC ≥ 85 cm in women and ≥90 cm in men. WHtR was measured as waist circumference divided by height. A high WHtR was defined as 0.5 and higher. The calculations of ABSI and BRI are described below (WC (m), height (m), and BMI (kg/m2)) [19].
ABSI   = 1000 ∗ W C B M I 2 / 3     H e i g h t 1 / 2  
BRI = 364.2 − 365.5   ∗   1 − ( ( W C / ( 2 π ) 2 ( 0.5 h e i g h t ) 2 )
High ABSI was defined as a mean or higher in each age group (means of total subject, young adult, middle-aged, and older adults were 77.56, 74.94, 77.56, and 81.62, respectively).
High BRI was defined as a mean or higher in each age group (means of total subject, young adult, middle-aged, and older adults were 3.49, 2.94, 3.53, and 4.24, respectively).

2.4. Covariates

Education was defined as the period of education divided into three categories: less than 9 years, 10–12 years, and more than 12 years. Smoking status was divided into three groups: lifetime non-smokers, former smokers (those who had smoked in the past but not currently), and current smokers (those who had smoked more than 100 cigarettes in their lifetime and were still smoking). Drinkers were defined as those who drank at least one drink per month in the past year. Physical activity was defined as at least 150 min per week of moderate-intensity, 75 min or more per week of high-intensity, or a combination of moderate-intensity and high-intensity physical activity. All covariates were information obtained from the subject’s self-reported questionnaire.

2.5. Statistical Analysis

We analyzed KNHANES VII (2016–2018) data using complex sample analysis, according to the statistical guidelines provided by the Centers for Disease Control and Prevention.
Folic acid levels were divided into two groups according to the mean. The mean folic acid levels for the total subjects, young adults, middle-aged adults, and older adults were 7.39, 6.49, 7.76, and 7.91, respectively).
One-way ANOVA or chi-square test was used to compare differences in sociodemographic and clinical characteristics according to the three age groups, and the Student’s t-test or chi-square test was used for comparison according to the folate level.
Pearson’s correlation coefficient was used to test correlations between BMI and serum folate, and since folate did not have a normal distribution, it was analyzed by converting it to normal by log transform.
Odds ratios (OR) were calculated by dividing the odds of the occurrence of high BMI, WC, WHtR, ABSI, BRI, and hs-CRP (in those with lower-than-average folic acid levels) by the odds of the occurrence of high BMI, WC, WHtR, ABSI, BRI, and hs-CRP (in those with above-average folic acid levels). Multivariate logistic analysis was adjusted for age, sex, education, smoking status, alcohol consumption, and physical activity.
p-value of 0.05 or less is defined as statistically significant. All statistical analyses and graphs were performed using Stata version 17 (Stata Corp., College Station, TX, USA) and the R software, version 4.0.2 (The Comprehensive R Archive Network: http://cran.r-project.org (accessed on 13 April 2022).

3. Results

A total of 6394 participants (mean 49.3 years, range (19–80)) were included in the data, including young adults (2013, mean 30.4 years), middle-aged adults (3086, mean 51.9 years), and older adults (1295, 72.5 years old). The mean of folic acid for the subjects, young adults, middle aged, and the older adults, was 7.39 ng/mL, 6.49 ng/mL, 7.76 ng/mL, and 7.91 ng/mL, respectively).
Table 1 shows the differences in sex, education, smoking status, alcohol consumption, physical activity, BMI, WC, WHtR, ABSI, BRI, hs-CRP, and folic acid among the demographic and anthropometric characteristics of the three population groups.
Table 2 compares the variables between high- and low-serum folate level groups in young adults, middle-aged, and older adults. In young adults, the high-folate group had the following characteristics when compared to the low-folate group: women (70.9% vs. 43.3%), high education (84.8% vs. 80.6%), non-smoker (68.8% vs. 54.9%), non-drinker (36.8% vs. 30.0%), and lower level of BMI, WC, WHtR, ABSI, and BRI. In middle-aged adults, the high-folate group compared with the low-folate group showed more women (71.7% vs. 44.4%), low education (24.1% vs. 20.0%), non-smoker (72.7% vs. 48.2%), non-drinker (49.6% vs. 38.0%), physical activity (49.8% vs. 39.9%), and a lower level of BMI, WC, WHtR, ABSI, BRI, and hs-CRP. In older adults, the high-folate group was more women (63.8% vs. 45.6%), non-smoker (70.2% vs. 51.2%), non-drinker (70.3% vs. 59.8%), physical activity (37.9% vs. 29.9%), and lower level of WC and ABSI compared to the low-folate group.
Using Pearson’s correlation coefficient, significant negative correlation was found between log transform of folate and BMI in young adults and middle-aged adults. The scatter plot is presented in Supplementary Figure S1.
After adjustment for all covariates, among participants who had low levels of folic acid, the ORs of having high BMI, WC, WHtR, ABSI, and BRI were 1.30 (95% confidence interval (CI): 1.14–1.49), 1.21 (95% CI: 1.05–1.40), 1.23 (95% CI: 1.07–1.41), 1.17 (95% CI: 1.00-–1.36), and 1.25 (95%CI: 1.08–1.43) compared to those with high levels in all participants (Figure 2 and Figure 3). In middle-aged adults, lower folic acid levels had significant ORs of five obesity measurements (BMI, WC, WHtR, ABSI, and BRI) compared with high folic acid levels (OR (95% CI): 1.36 (1.13–1.65); 1.32 (1.1–1.62); 1.41 (1.18–1.7); 1.29 (1.06–1.56); 1.47 (1.22–1.77). Lower folate levels were related to high BMI in young adults and the older adults (OR (95% CI): 1.33 (1.03–1.73) and 1.58 (1.15–2.16), respectively), but not with other obesity variables (Figure 2 and Figure 3).
In middle-aged adults, lower levels of folate were significantly related with hs-CRP (OR (95%CI): 1.28 (1.05–1.57)), but not in the young adults or the older adults using multivariate logistic regression (Figure 4).

4. Discussion

This study examined the association of serum folate levels with all five measures of obesity (BMI, WC, WHtR, ABSI, and BRI) and hs-CRP levels in a nationally representative sample of Korean adults, especially middle-aged adults. However, in young adults and older adults, only obesity measured by BMI was statistically significant. In this study, the level of folic acid increased with age, which is similar to the results of previous studies that used representative US data [26].
Our findings are similar to those of a previous study. A study of 2695 Brazilian subjects showed that dietary folate intake was negatively associated with overweight (BMI ≥ 25 kg/m2) and obese (BMI ≥ 30 kg/m2) prevalence [27].
A case–control study of 421 healthy participants (aged 20–40) revealed that lower folate serum concentrations were negatively associated with BMI (≥ 25 kg/m2), waist-to-hip ratio (WHR), WC, and fat percentage [12]. Recent Korean study reported that serum folate levels were associated with WC (≥85 cm) in 1730 premenopausal women [13]. In the meta-analysis of a randomized controlled trial, folic acid supplementation significantly reduced serum levels of hs-CRP [28]. Dose–response analysis demonstrated a significant association between an elevated dosage of folic acid supplementation and lower CRP concentrations [29].
BMI, WC, WHtR, ABSI, and BRI have different aspects for obesity measurement. BMI refers to “total obesity” but did not account for an individual’s body composition and body fat distribution [30]. WC reflects abdominal obesity using an absolute measure and does not indicate whether fat is concentrated in the abdomen or spread throughout the body [31]. Although WC increases according to the body size, it can be classified as obese if a normal-weighted person has an extremely large waist [32]. Some studies used WHtR to adequately capture the distribution of body fat for assessing obesity and body composition and found that WHtR was a better predictive indicator of cardiovascular disease than other common indices of obesity (BMI, WHR, and WC) [33,34]. In a study in England, waist circumference indices (WHtR, WC) were consistently superior to BMI when explaining or predicting cardiometabolic risk (high-density lipoprotein cholesterol, glycated hemoglobin, systolic and diastolic blood pressure) [18]. ABSI was developed to compensate for the BMI and WC limitation and was obtained from allometric regression and designed to be minimally associated with weight, height, and BMI [31,32]. The BRI was used to predict the percentage of body fat and visceral fat, which are not clearly addressed by BMI [20].
The association of folic acid with all measures of obesity and hs-CRP, especially in middle-aged individuals, may be due to age-related changes in body composition. Previous studies explain that muscle mass increases until the age of 40, but when people reach the middle age of 40 or more, their skeletal muscle gradually decreases, fat increases and relocation occurs, and sarcopenic obesity occurs at an old age [35]. In addition, the measurement of BMI is more likely to include people with a lot of muscle mass as obese in the younger group, and people with very little muscle mass and a lot of fat in the older adults are more likely to be judged as normal.
Several possible mechanisms may underlie the inverse association between serum folate levels and obesity and hs-CRP levels, but these are still not fully understood. One possible explanation is homocysteine. Absorbed folate is metabolized to 5-methyltetrahydrofolate (5-methylTHF), which supplies a methyl group that converts homocysteine (Hcy) to methionine [36]. Folate deficiency induces the development of hyperhomocysteinemia [37], which is associated with suppressed lipolysis in adipocytes and adipose tissues [38]. Hcy also stimulates the expression of inflammatory cytokines by enhancing poly adenosine diphosphate (ADP) ribose polymerase activation and prompting nuclear factor kappa B (NF-kB) activation [29]. In vivo and in vitro studies have demonstrated that folate deficiency increases fat accumulation and leptin production in adipocytes, which may contribute to the increase in obesity prevalence and inflammatory disease [39]. Another explanation is that obesity is regarded as chronic low-grade inflammation with permanently increased oxidative stress [40]. A double-blind placebo-controlled study showed a reduction in oxidative stress after folate administration in 30 healthy postmenopausal Caucasian women [41].
US centers for disease control and prevention recommendations require that all women of the childbearing age consume 400 micrograms (mcg) of folic acid daily to prevent neural tube defects [42]. Typical dosages for folic acid supplementation in the United States range from 400 to 800 mcg folic acid for adults and 200 to 400 mcg folic acid for children’s multivitamins [43]. A study using the US data found that about one-third of men and women of all ages used folic acid supplements, and the average level of non-supplement users was 11% lower than overall [44]. They also found that more than half of women who did not take folic acid supplements consumed less folic acid than the WHO recommended [44].
This study has several important advantages. This is the first study to investigate serum folate levels related to ABSI and BRI using data representing the entire Korean population. In addition, the results were shown in three age groups: young adults, middle-aged adults, and older adults, rather than in a limited age group. Finally, based on recent cohort study for Chinese adults showing that BMI and hs-CRP were jointly related to metabolic health [45], our findings suggest that raising folic acid concentrations may be beneficial for metabolic health as well as obesity.
However, this study also has some limitations. Firstly, because of the cross-sectional nature of the study, the reverse relationship was not excluded; thus, it does not allow inferences about causality. Another study showed that obese participants had lower serum folate levels because of folate metabolic changes, reduced supplement use, and unhealthy diets [46]. Secondly, these data are only for Korean adults, so it may be difficult for other races and nationalities. Thirdly, since the KNHANES data do not have information about the folic acid supplements taken by participants, it is not possible to examine the effects of folic acid supplementation on obesity.

5. Conclusions

In middle-aged individuals, higher folate levels may improve obesity and inflammation. Future longitudinal or randomized controlled trials for the appropriate management of middle-aged individuals with lower folate levels are needed to confirm and expand our findings.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/nu14173461/s1, Figure S1: The scatter plot between log transform of serum folate and body mass index.

Author Contributions

M.-R.L., conceptualization, methodology, validation, formal analysis, investigation, resources, data curation, writing—original draft preparation, writing—review and editing, and visualization. S.M.J., conceptualization, supervision, and software. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the Soonchunhyang University Research Fund.

Institutional Review Board Statement

Our study was approved by the Institutional Review Board (IRB) (2022-06-007).

Informed Consent Statement

In the KNHANES study, informed consent was obtained from all participants.

Data Availability Statement

The data are freely available on the website (https://knhanes.kdca.go.kr/knhanes/main.do (accessed on 12 August 2022)).

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Kelly, T.; Yang, W.; Chen, C.S.; Reynolds, K.; He, J. Global burden of obesity in 2005 and projections to 2030. Int. J. Obes. 2008, 32, 1431–1437. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  2. Obesity and Overweight. Available online: https://www.who.int/news-room/fact-sheets/detail/obesity-and-overweight#:~:text=Worldwide%20obesity%20has%20nearly%20tripled,%2C%20and%2013%25%20were%20obese (accessed on 12 August 2022).
  3. Ye, J. Mechanisms of insulin resistance in obesity. Front. Med. 2013, 7, 14–24. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  4. Yatsuya, H.; Li, Y.; Hilawe, E.H.; Ota, A.; Wang, C.; Chiang, C.; Zhang, Y.; Uemura, M.; Osako, A.; Ozaki, Y.; et al. Global trend in overweight and obesity and its association with cardiovascular disease incidence. Circ. J. 2014, 78, 2807–2818. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  5. Horn, J.W.; Feng, T.; Mørkedal, B.; Strand, L.B.; Horn, J.; Mukamal, K.; Janszky, I. Obesity and Risk for First Ischemic Stroke Depends on Metabolic Syndrome: The HUNT Study. Stroke 2021, 52, 3555–3561. [Google Scholar] [CrossRef]
  6. Wolin, K.Y.; Carson, K.; Colditz, G.A. Obesity and cancer. Oncologist 2010, 15, 556–565. [Google Scholar] [CrossRef]
  7. Johnston, B.C.; Kanters, S.; Bandayrel, K.; Wu, P.; Naji, F.; Siemieniuk, R.A.; Ball, G.D.; Busse, J.W.; Thorlund, K.; Guyatt, G.; et al. Comparison of weight loss among named diet programs in overweight and obese adults: A meta-analysis. JAMA 2014, 312, 923–933. [Google Scholar] [CrossRef]
  8. Sun, Y.; Sun, M.; Liu, B.; Du, Y.; Rong, S.; Xu, G.; Snetselaar, L.G.; Bao, W. Inverse Association between Serum Vitamin B12 Concentration and Obesity among Adults in the United States. Front. Endocrinol. 2019, 10, 414. [Google Scholar] [CrossRef] [Green Version]
  9. Lotfi-Dizaji, L.; Mahboob, S.; Aliashrafi, S.; Vaghef-Mehrabany, E.; Ebrahimi-Mameghani, M.; Morovati, A. Effect of vitamin D supplementation along with weight loss diet on meta-inflammation and fat mass in obese subjects with vitamin D deficiency: A double-blind placebo-controlled randomized clinical trial. Clin. Endocrinol. 2019, 90, 94–101. [Google Scholar] [CrossRef] [Green Version]
  10. Khan, K.M.; Jialal, I. Folic Acid Deficiency. In StatPearls; StatPearls Publishing: Treasure Island, FL, USA, 2022. [Google Scholar]
  11. Haggarty, P. UK introduces folic acid fortification of flour to prevent neural tube defects. Lancet 2021, 398, 1199–1201. [Google Scholar] [CrossRef]
  12. Mlodzik-Czyzewska, M.A.; Malinowska, A.M.; Chmurzynska, A. Low folate intake and serum levels are associated with higher body mass index and abdominal fat accumulation: A case control study. Nutr. J. 2020, 19, 53. [Google Scholar] [CrossRef]
  13. Koo, Y.S.; Lee, Y.J.; Park, J.M. Inverse Association of Serum Folate Level with Metabolic Syndrome and Its Components in Korean Premenopausal Women: Findings of the 2016–2018 Korean National Health Nutrition Examination Survey. Nutrients 2022, 14, 880. [Google Scholar] [CrossRef]
  14. Wiebe, N.; Field, C.J.; Tonelli, M. A systematic review of the vitamin B12, folate and homocysteine triad across body mass index. Obes. Rev. 2018, 19, 1608–1618. [Google Scholar] [CrossRef] [PubMed]
  15. Navarrete-Muñoz, E.-M.; Vioque, J.; Toledo, E.; Oncina-Canovas, A.; Martínez-González, M.Á.; Salas-Salvadó, J.; Corella, D.; Fitó, M.; Romaguera, D.; Alonso-Gómez, Á.M.; et al. Dietary folate intake and metabolic syndrome in participants of PREDIMED-Plus study: A cross-sectional study. Eur. J. Nutr. 2021, 60, 1125–1136. [Google Scholar] [CrossRef] [PubMed]
  16. Donfrancesco, C.; Profumo, E.; Lo Noce, C.; Minutoli, D.; Di Lonardo, A.; Buttari, B.; Vespasiano, F.; Vannucchi, S.; Galletti, F.; Onder, G.; et al. Trends of overweight, obesity and anthropometric measurements among the adult population in Italy: The CUORE Project health examination surveys 1998, 2008, and 2018. PLoS ONE 2022, 17, e0264778. [Google Scholar] [CrossRef] [PubMed]
  17. Mohammadian Khonsari, N.; Khashayar, P.; Shahrestanaki, E.; Kelishadi, R.; Mohammadpoor Nami, S.; Heidari-Beni, M.; Esmaeili Abdar, Z.; Tabatabaei-Malazy, O.; Qorbani, M. Normal Weight Obesity and Cardiometabolic Risk Factors: A Systematic Review and Meta-Analysis. Front. Endocrinol. 2022, 13, 857930. [Google Scholar] [CrossRef]
  18. Nevill, A.M.; Duncan, M.J.; Myers, T. BMI is dead; long live waist-circumference indices: But which index should we choose to predict cardio-metabolic risk? Nutr. Metab. Cardiovasc. Dis. 2022, 32, 1642–1650. [Google Scholar] [CrossRef]
  19. Krakauer, N.Y.; Krakauer, J.C. A new body shape index predicts mortality hazard independently of body mass index. PLoS ONE 2012, 7, e39504. [Google Scholar]
  20. Thomas, D.M.; Bredlau, C.; Bosy-Westphal, A.; Mueller, M.; Shen, W.; Gallagher, D.; Maeda, Y.; McDougall, A.; Peterson, C.M.; Ravussin, E.; et al. Relationships between body roundness with body fat and visceral adipose tissue emerging from a new geometrical model. Obesity 2013, 21, 2264–2271. [Google Scholar] [CrossRef] [Green Version]
  21. Schlecht, I.; Fischer, B.; Behrens, G.; Leitzmann, M.F. Relations of Visceral and Abdominal Subcutaneous Adipose Tissue, Body Mass Index, and Waist Circumference to Serum Concentrations of Parameters of Chronic Inflammation. Obes. Facts 2016, 9, 144–157. [Google Scholar] [CrossRef]
  22. Mahwati, Y.; Nurrika, D. Obesity Indicators and C-Reactive Protein in Indonesian Adults (More than Equal to 40 Years Old): The Indonesian Family Life Survey 5. J. Kesehat. Masy. Nas. 2020, 15, 169–174. [Google Scholar] [CrossRef]
  23. Biswas, D.C.; Rahman, M.; Sharmin, F.; Jahan, I.; Roy, A.; Begum, S. Association of high-sensitivity C-reactive protein level with central obesity of the children in a tertiary care hospital of Bangladesh. Issues Dev. Health Res. 2021, 8, 17–25. [Google Scholar]
  24. Rasheed, A.; Acharya, S.; Shukla, S.; Kumar, S.; Yarappa, R.; Gupte, Y.; Hulkoti, V. High-Sensitivity C-Reactive Protein in Metabolic Healthy Obesity (MHO). J. Evol. Med. Dent. Sci. 2020, 9, 443–447. [Google Scholar] [CrossRef]
  25. Santa-Paavola, R.; Lehtinen-Jacks, S.; Jääskeläinen, T.; Männistö, S.; Lundqvist, A. The association of high-sensitivity C-reactive protein with future weight gain in adults. Int. J. Obes. 2022, 46, 1234–1240. [Google Scholar] [CrossRef]
  26. Pfeiffer, C.M.; Sternberg, M.R.; Zhang, M.; Fazili, Z.; Storandt, R.J.; Crider, K.S.; Yamini, S.; Gahche, J.J.; Juan, W.; Wang, C.-Y.; et al. Folate status in the US population 20 y after the introduction of folic acid fortification. Am. J. Clin. Nutr. 2019, 110, 1088–1097. [Google Scholar] [CrossRef] [PubMed]
  27. Pereira, G.A.; Bressan, J.; Oliveira, F.L.P.; Sant’Ana, H.M.P.; Pimenta, A.M.; Lopes, L.L.; Hermsdorff, H.H.M. Dietary Folate Intake Is Negatively Associated with Excess Body Weight in Brazilian Graduates and Postgraduates (CUME Project). Nutrients 2019, 11, 518. [Google Scholar] [CrossRef] [Green Version]
  28. Zargarzadeh, N.; Severo, J.S.; Pizarro, A.B.; Persad, E.; Mousavi, S.M. The Effects of Folic Acid Supplementation on Pro-inflammatory Mediators: A Systematic Review and Dose-Response Meta-Analysis of Randomized Controlled Trials. Clin. Ther. 2021, 43, e346–e363. [Google Scholar] [CrossRef]
  29. Asbaghi, O.; Ashtary-Larky, D.; Bagheri, R.; Moosavian, S.P.; Nazarian, B.; Afrisham, R.; Kelishadi, M.R.; Wong, A.; Dutheil, F.; Suzuki, K.; et al. Effects of Folic Acid Supplementation on Inflammatory Markers: A Grade-Assessed Systematic Review and Dose-Response Meta-Analysis of Randomized Controlled Trials. Nutrients 2021, 13, 2327. [Google Scholar] [CrossRef]
  30. Moltrer, M.; Pala, L.; Cosentino, C.; Mannucci, E.; Rotella, C.M.; Cresci, B. Body mass index (BMI), waist circumference (WC), waist-to-height ratio (WHtR) e waist body mass index (wBMI): Which is better? Endocrine 2022, 76, 578–583. [Google Scholar] [CrossRef]
  31. Christakoudi, S.; Tsilidis, K.K.; Evangelou, E.; Riboli, E. A Body Shape Index (ABSI), hip index, and risk of cancer in the UK Biobank cohort. Cancer Med. 2021, 10, 5614–5628. [Google Scholar] [CrossRef]
  32. Christakoudi, S.; Tsilidis, K.K.; Muller, D.C.; Freisling, H.; Weiderpass, E.; Overvad, K.; Söderberg, S.; Häggström, C.; Pischon, T.; Dahm, C.C.; et al. A Body Shape Index (ABSI) achieves better mortality risk stratification than alternative indices of abdominal obesity: Results from a large European cohort. Sci. Rep. 2020, 10, 14541. [Google Scholar] [CrossRef]
  33. Zhang, S.; Fu, X.; Du, Z.; Guo, X.; Li, Z.; Sun, G.; Zhou, Y.; Yang, H.; Yu, S.; Zheng, L.; et al. Is waist-to-height ratio the best predictive indicator of cardiovascular disease incidence in hypertensive adults? A cohort study. BMC Cardiovasc. Disord. 2022, 22, 214. [Google Scholar] [CrossRef] [PubMed]
  34. Mehran, L.; Amouzegar, A.; Fanaei, S.M.; Masoumi, S.; Azizi, F. Anthropometric measures and risk of all-cause and cardiovascular mortality: An 18 years follow-up. Obes. Res. Clin. Pract. 2022, 16, 63–71. [Google Scholar] [CrossRef] [PubMed]
  35. Kim, T.N. Elderly Obesity: Is It Harmful or Beneficial? J. Obes. Metab. Syndr. 2018, 27, 84–92. [Google Scholar] [CrossRef]
  36. Li, J.; Goh, C.E.; Demmer, R.T.; Whitcomb, B.W.; Du, P.; Liu, Z. Association between Serum Folate and Insulin Resistance among U.S. Nondiabetic Adults. Sci. Rep. 2017, 7, 9187. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  37. Klee, G.G. Cobalamin and folate evaluation: Measurement of methylmalonic acid and homocysteine vs. vitamin B(12) and folate. Clin. Chem. 2000, 46, 1277–1283. [Google Scholar] [CrossRef] [Green Version]
  38. Li, X.; Cheng, Y.; Zhong, X.; Zhang, B.; Bao, Z.; Zhang, Y.; Wang, Z. Nuclear factor erythroid 2-related factor 2 activation mediates hyperhomocysteinemia-associated lipolysis suppression in adipocytes. Exp. Biol. Med. 2018, 243, 926–933. [Google Scholar] [CrossRef]
  39. Chan, C.-W.; Chan, P.-H.; Lin, B.-F. Folate Deficiency Increased Lipid Accumulation and Leptin Production of Adipocytes. Front. Nutr. 2022, 9, 852451. [Google Scholar] [CrossRef]
  40. Marseglia, L.; Manti, S.; D’Angelo, G.; Nicotera, A.; Parisi, E.; Di Rosa, G.; Gitto, E.; Arrigo, T. Oxidative stress in obesity: A critical component in human diseases. Int. J. Mol. Sci. 2014, 16, 378–400. [Google Scholar] [CrossRef] [Green Version]
  41. Cagnacci, A.; Cannoletta, M.; Xholli, A.; Piacenti, I.; Palma, F.; Palmieri, B. Folate administration decreases oxidative status and blood pressure in postmenopausal women. Eur. J. Nutr. 2015, 54, 429–435. [Google Scholar] [CrossRef]
  42. Folic Acid Recommendations. Available online: https://www.cdc.gov/ncbddd/folicacid/recommendations.html#:~:text=All%20women%20of%20reproductive%20age,and%20spine%20(spina%20bifida) (accessed on 12 August 2022).
  43. Folate Fact Sheet for Health Professionals. Available online: https://ods.od.nih.gov/factsheets/Folate-HealthProfessional/#en18] (accessed on 12 August 2022).
  44. Adams, J.B.; Kirby, J.K.; Sorensen, J.C.; Pollard, E.L.; Audhya, T. Evidence based recommendations for an optimal prenatal supplement for women in the US: Vitamins and related nutrients. Matern. Health Neonatol. Perinatol. 2022, 8, 4. [Google Scholar] [CrossRef]
  45. Xu, R.; Shen, P.; Wu, C.; Wan, Y.; Fan, Z.; Gao, X. BMI, high-sensitivity C-reactive protein and the conversion from metabolically healthy to unhealthy phenotype in Chinese adults: A cohort study. Public Health Nutr. 2021, 24, 4124–4131. [Google Scholar] [CrossRef] [PubMed]
  46. Köse, S.; Sözlü, S.; Bölükbaşi, H.; Ünsal, N.; Gezmen-Karadağ, M. Obesity is associated with folate metabolism. Int. J. Vitam. Nutr. Res. 2020, 90, 353–364. [Google Scholar] [CrossRef] [PubMed]
Nutrients 14 03461 g001 550
Figure 1. Flow chart showing exclusion procedure of participants.
Figure 1. Flow chart showing exclusion procedure of participants.
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Nutrients 14 03461 g002 550
Figure 2. The association between binary of folic acid and BMI, WC, WHtR, and ABSI in each age group using multivariate logistic regression. Abbreviations: BMI, body mass index; WC, waist circumference; WHtR, waist-to-height ratio.
Figure 2. The association between binary of folic acid and BMI, WC, WHtR, and ABSI in each age group using multivariate logistic regression. Abbreviations: BMI, body mass index; WC, waist circumference; WHtR, waist-to-height ratio.
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Nutrients 14 03461 g003 550
Figure 3. The association between the binary of folic acid and new obesity indices (ABSI, BRI) in each age group using multivariate logistic regression. Abbreviations: ABSI, a body shape index; BRI, body roundness index.
Figure 3. The association between the binary of folic acid and new obesity indices (ABSI, BRI) in each age group using multivariate logistic regression. Abbreviations: ABSI, a body shape index; BRI, body roundness index.
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Figure 4. The association between the binary of folic acid and high–sensitivity C-reactive protein in each age group using multivariate logistic regression. Abbreviations: hs-CRP, high-sensitivity C-reactive protein.
Figure 4. The association between the binary of folic acid and high–sensitivity C-reactive protein in each age group using multivariate logistic regression. Abbreviations: hs-CRP, high-sensitivity C-reactive protein.
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Table
Table 1. Participants’ sociodemographic and clinical characteristics according to age group.
Table 1. Participants’ sociodemographic and clinical characteristics according to age group.
VariableTotal19–3940–6465+p Value
n6394201330861295
Age, y49.3 ± 16.330.4 ± 6.151.9 ± 7.072.5 ± 5.1<0.001
Women3514(50.3)1092(47.4)1733(51.0)689(54.1)0.004
Education
Low1496(20.0)23(0.8)639(18.8)834(65.3)<0.001
Medium1680(26.9)320(15.6)1113(37.7)247(20.3)
High2923(53.1)1589(83.6)1189(43.5)145(14.4)
No response2958114569
Smoking status
Non-smoker3752(57.7)1203(58.9)1798(55.8)751(60.6)<0.001
Former smoker1339(21.9)314(17.0)632(22.6)393(30.0)
Current smoker1230(20.4)476(24.1)630(21.6)124(9.4)
No response73202627
Alcohol consumption
Non-drinker2788(40.1)654(31.2)1317(40.4)817(64.3)<0.001
Alcohol drinker3545(58.9)1346(68.8)1744(59.6)455(35.7)
No response61132523
Physical activity
No3336(52.3)877(43.1)1644(54.4)815(66.1)<0.001
Yes2762(47.7)1054(56.9)1301(45.6)407(33.9)
No response2968214173
BMI
<254195(65.5)1412(69.5)1974(65.5)809(62.6)0.0002
≥252199(34.5)601(30.5)1112(36.5)486(37.4)
WC
Male < 90, Female < 854556(72.5)1577(79.0)2224(72.0)755(60.1)<0.001
Male ≥ 90, Female ≥ 851838(27.5)436(21.1)862(28.0)540(39.9)
WHtR
WHtR < 0.53147(52.1)1409(71.0)1433(47.5)305(24.7)<0.001
WHtR ≥ 0.53247(47.9)604(29.0)1653(52.5)990(75.3)
ABSI77.56(4.64)74.94(3.96)77.56(3.92)81.62(4.25)<0.001
BRI3.49(1.14)2.94(1.13)3.53(1.06)4.24(1.27)<0.001
hs-CRP, mg/L1.17 ± 2.001.10 ± 1.961.09 ± 1.701.49 ± 2.60<0.001
Folic acid, ng/mL7.39 ± 3.586.49 ± 3.407.76 ± 3.457.91 ± 3.88<0.001
Data were presented as mean ± standard deviation or number (percentage). χ2 test and one-way ANOVA were used for categorical and continuous variables, respectively. Bold numbers highlight the statistical significance. Abbreviations: ABSI, a body shape index; BMI, body mass index; BRI, body roundness index; hs-CRP, high-sensitivity C-reactive protein; SD, standard deviation; WC, waist circumference; WHtR, waist-to-height ratio.
Table
Table 2. Participants’ sociodemographic and clinical characteristics according to the level of serum folate.
Table 2. Participants’ sociodemographic and clinical characteristics according to the level of serum folate.
Total19–3940–6465+
LowHighpLowHighpLowHighpLowHighp
Age, y46.76 ± 16.9851.78 ± 15.19<0.00129.75 ± 6.2631.51 ± 5.63<0.00151.27 ± 6.9852.83 ± 6.84<0.00172.82 ± 5.1172.16 ± 4.990.021
women1296 (41.75)2218 (67.42)<0.001527 (43.3)565 (70.9)<0.001778 (44.4)955 (71.7)<0.001345 (45.6)344 (63.8)<0.001
Education
Low645 (22.0)851 (26.9)<0.00118 (1.5)5 (0.6)0.027333 (20.0)306 (24.1)0.020495 (70.5)339 (64.7)0.092
Medium787 (26.8)893 (28.2) 208 (17.9)112 (14.6) 656 (39.3)457 (35.9) 132 (18.8)115 (21.9)
High1505 (51.2)1418 (44.9) 937 (80.6)652 (84.8) 680 (40.7)509 (40.0) 75 (10.7)70 (13.4)
No response167128 5328 8560 5415
Smoking status
Non-smoker1502 (49.0)2250 (69.2)<0.001662 (54.9)541 (68.8)<0.001838 (48.2)960 (72.7)<0.001376 (51.2)375 (70.2)<0.001
Former smoker685 (22.3)654 (20.1) 173 (14.3)141 (18.0) 408 (23.5)224 (16.9) 258 (35.2)135 (25.3)
Current smoker880 (28.7)350 (10.8) 372 (30.8)104 (13.2) 493 (28.4)137 (10.4) 100 (13.6)24 (4.5)
No response3736 911 1511 225
Alcohol consumption
Non-drinker1176 (38.2)1612 (49.5)<0.001364 (30.0)290 (36.8)0.002661 (38.0)656 (49.6)<0.001440 (59.8)377 (70.3)<0.001
Drinker1899 (61.8)1646 (50.5) 848 (70.0)498 (63.2) 1078 (62.0)666 (50.4) 296 (40.2)159 (29.7)
No response2932 49 1510 203
Physical activity
No1666 (56.7)1670 (52.9)<0.001536 (46.1)341 (44.3)0.4411006 (60.1)638 (50.2)<0.001491 (70.1)324 (62.1)0.003
Yes1273 (43.3)1489 (47.1) 626 (53.9)428 (55.7) 667 (39.9)634 (49.8) 209 (29.9)198 (37.9)
No response165131 5428 8160 5617
BMI, kg/m224.15 ± 3.7523.69 ± 3.32<0.00123.83 ± 4.1922.92 ± 3.69<0.00124.44 ± 3.4223.65 ± 3.13<0.00124.19 ± 3.2624.09 ± 3.090.593
WC, cm 83.29 ± 10.5981.13 ± 9.75<0.00180.76 ± 11.7077.49 ± 10.61<0.00183.95 ± 9.4780.60 ± 8.95<0.00186.09 ± 8.9085.03 ± 9.020.036
WHtR0.50 ± 0.070.50 ± 0.060.4260.48 ± 0.060.47 ± 0.06<0.0010.51 ± 0.060.50 ± 0.05<0.0010.54 ± 0.060.54 ± 0.060.597
ABSI77.65 ± 4.777.47 ± 4.580.13975.09 ± 3.8774.71 ± 4.080.03777.87 ± 3.8877.14 ± 3.95<0.00181.83 ± 4.3081.34 ± 4.160.041
BRI3.51 ± 1.263.47 ± 1.180.2833.01 ± 1.182.84 ± 1.06<0.0013.62 ± 1.093.41 ± 1.00<0.0014.26 ± 1.274.22 ± 1.270.597
hs-CRP, mg/L1.24 ± 2.051.11 ± 1.950.0131.13 ± 1.921.04 ± 2.030.2871.17 ± 1.760.98 ± 1.610.0031.54 ± 2.701.41 ± 2.440.398
Data were presented as mean ± standard deviation or number (percentage). χ2 test and Student’s t test were conducted for categorical and continuous variables, respectively. Bold numbers highlight the statistical significance. Abbreviations: ABSI, a body shape index; BMI, body mass index; BRI, body roundness index; hs-CRP, high-sensitivity C-reactive protein; SD, standard deviation; WC, waist circumference; WHtR, waist-to-height ratio.
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Lee, M.-R.; Jung, S.M. Serum Folate Related to Five Measurements of Obesity and High-Sensitivity C-Reactive Protein in Korean Adults. Nutrients 2022, 14, 3461. https://doi.org/10.3390/nu14173461

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Lee M-R, Jung SM. Serum Folate Related to Five Measurements of Obesity and High-Sensitivity C-Reactive Protein in Korean Adults. Nutrients. 2022; 14(17):3461. https://doi.org/10.3390/nu14173461

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Lee, Mee-Ri, and Sung Min Jung. 2022. "Serum Folate Related to Five Measurements of Obesity and High-Sensitivity C-Reactive Protein in Korean Adults" Nutrients 14, no. 17: 3461. https://doi.org/10.3390/nu14173461

APA Style

Lee, M. -R., & Jung, S. M. (2022). Serum Folate Related to Five Measurements of Obesity and High-Sensitivity C-Reactive Protein in Korean Adults. Nutrients, 14(17), 3461. https://doi.org/10.3390/nu14173461

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