Version Changes
Revised. Amendments from Version 2
A sentence about sample size calculation was added; an exclusion criterion was also added, a reference 11 had been added, also a sentence was added to the limitations.
Abstract
Background
B vitamins play a crucial role in the balance and metabolism of energy. Energy metabolism mainly benefits from the B-complex vitamins. Specifically, decarboxylation, transamination, acylation, oxidation, and reduction of substrates that are ultimately employed in energy intake require thiamin, riboflavin, niacin, and vitamin B6. Vitamin deficiency could lead to chronic disease occurrence.
Objectives
To assess the impact of energy-releasing B-vitamins intake (B1, B2, B3, and B6) on selected indices of obesity and cardiac function.
Methods
A cross-sectional study was performed on 491 apparently healthy adults (18-64 years old) between January and May 2019 at Hashemite University, Jordan. Anthropometric measurements were taken, lipid profiles were analyzed, and indices of obesity and cardiac function were calculated. The typical dietary intake of B1, B2, B3, and B6 vitamins was calculated.
Results
Conicity index (CI) and abdominal volume index (AVI) scores significantly decreased with the increased adjusted vitamin B1 and B6 intake. Also, body roundness index (BRI), weight-adjusted-waist index (WWI), lipid accumulation product (LAP), and atherogenic index of plasma (AIP) scores were decreased with the increase of adjusted B6 intake ( p<0.05). The total sample showed a significant inverse weak correlation between energy-adjusted intake of B1 and AVI (r= -0.156, p=0.001) and BRI (r= 0.111, p=0.014). Similar correlations were detected among male participants between energy-adjusted B1 intake and BAI, AVI, and BRI. Female participants had a significant weak inverse correlation between BAI and energy-adjusted B2 (r= -0.180, p=0.029) and B6 intake (r= -0.212, p=0.010). Only B1, the vitamin, significantly explained 2.43 and 1.24% of changes observed in the AVI and BRI scores, respectively ( p<0.05).
Conclusions
Increasing the consumption of B1, B2, and B6 may significantly lower values of indices of obesity and cardiac function regardless of sex differences. Thus reducing the occurrence of obesity and related coronary heart diseases.
Keywords: energy-releasing vitamins, obesity indices, cardiac function indices, abdominal volume index, body roundness index
Introduction
Micronutrients work as coenzymes, cocatalysts, and buffers and are essential for various cellular and biochemical processes, such as releasing energy for synthesis, movement, and other processes. 1 Due to their impact on intracellular homocysteine concentration and oxidative stress, subclinical deficiencies in several micronutrients have the potential to significantly slow the development of chronic disease. 2 When the consumption of a micronutrient falls below the current recommended dietary amount, a significant chronic metabolic disturbance may result from the nutrient deficiencies such as polycystic ovarian syndrome (PCOS), insulin resistance, obesity, and metabolic syndrome. 3 – 5 In recent years, many studies approved that nutrients could play a vital role in obesity and metabolic syndrome prevention. Also, energy-dense foods with deficit nutrients contribute to the development of obesity, metabolic syndrome, and inflammation. 4 , 6
B vitamins play a crucial role in the balance and metabolism of energy. 7 Energy metabolism mainly benefits from the B-complex vitamins. Specifically, decarboxylation, transamination, acylation, oxidation, and reduction of substrates that are ultimately employed in energy intake require thiamin, riboflavin, niacin, and vitamin B6. One or more of these are also necessary for synthesizing steroids, fatty acids, cholesterol, amino acids, and glucose. 8 B-vitamin deficiency may impact energy metabolism, and cause insulin resistance, chronic inflammation, obesity, and elevated oxidative stress, which may contribute to the pathophysiology of metabolic syndrome. 9 – 11 B-vitamin deficiency has been approved in individuals with obesity and metabolic syndrome. 2 , 12 Moreover, thiamin (B1) and riboflavin (B2) intake are inversely associated with body mass index (BMI). 2 , 7 As energy metabolism may be affected by deficiencies in biotin, niacin (B3), folate, and vitamins B6 and B12, which may enhance the generation of reactive oxidants and inflammatory reactions, lipogenesis may also be promoted, which may increase obesity. 13 , 14 Additionally, B vitamins play a vital role (methyl group donor and act as a coenzyme) in DNA methylation, which is important for body adiposity regulation 15 and is associated with BMI. 16
Wu et al., 6 suggested that a higher intake of vitamins B1, B2, B3, B6, and dietary folate were associated with a reduced risk of metabolic syndrome. Also, numerous cardiovascular diseases (CVDs), and risk factors, such as type 1 and type 2 diabetes, obesity, chronic vascular inflammation, dyslipidemia, heart failure, myocardial infarction, and depression, have been linked to B1, B3, and B6 deficiency. 12 , 17
Dietary intake of B vitamins has been found to be inversely related to CVD, as their attribution in lowering CVD risk factors, such as plasma homocysteine, serum triglycerides, and C-reaction protein. 18 These risk factors have been linked to the atherosclerotic process 19 and endothelial dysfunction, 20 which engage in CVD development. B vitamins play a role in the metabolism of one carbon unit, therefore each of these nutrients may have an impact on the pathological development of CVD by reducing homocysteine concentrations, methylation processes, and oxidative stress. 21 , 22
Furthermore, nicotinamide adenine dinucleotide (NAD) and nicotinamide adenine dinucleotide phosphate (NADP) are redox mediator coenzymes that are precursors of B3, 23 which has also been demonstrated to enhance levels of high-density lipoprotein cholesterol (HDL), lower levels of triglycerides (TG), very low levels of low-density lipoprotein cholesterol (VLDL-C), and dramatically diminish the risk of CVD and the progression of atherosclerosis. 24 Based on the aforementioned, not enough data are available to cover the association between energy-releasing vitamins with indices of obesity and cardiac function. So, this study aimed to assess the impact of energy-releasing B-vitamins intake (B1, B2, B3, and B6) on selected indices of obesity and cardiac function.
Methods
Study design, participants, and ethical approval
A cross-sectional study design was used and conducted on 491 apparently healthy randomly selected male and female adults, university employees and students, aged between 18-64 years old (between January and May 2019) at The Hashemite University, Jordan. The participants were asked to sign informed consent before participation; an example of the consent form can be found as Extended data. 25 Exclusion criteria were pregnant or lactating women, subjects with incomplete anthropometric measurements and/or biochemical data. The Raosoft calculator (online) was used to calculate the sample size based on the Department of Statistics in 2020. Adults represented 52.2% (5,621,970) of Jordan's population (10,806,000). With a 5% error margin, 95% CI, and 50% response distribution, the minimum sample size was 385 participants.
The study was approved by The Hashemite University Ethics Committee, which authorized the protocols, tools, and procedures. All procedures were carried out according to the Declaration of Helsinki and the Institutional Board Review (IRB) (No.7/13/2018/2019; date approved 13/7/2018).
Data collection
Demographic data, including age, sex, marital status, education level, lifestyle data (such as smoking status and physical activity), medical history, and family history of diseases were gathered through conducting a face-to-face structured interview in a private room using closed-ended questions, which can be found as Extended data. 26 The interviewer asked the participants questions and then filled out the data form with the participants responses to ensure the accuracy of the filled data (took place over 15-18 minutes). The participants were asked to provide verbal and written consent before participation. Subjects who consumed nutritional supplements like multivitamins or subjects who adhered to restrictive diets such as weight loss diets or have food allergies, pregnant and lactating women were excluded.
Qualitative variables
Socio-demographic data, including age, sex, marital status, education level, lifestyle data (such as smoking status and physical activity), medical history, and family history of diseases, were gathered through professional staff face-to-face interviews. These data were collected using a designed list of questions created for the study and put through a reliability test, after completion by 25 participants in a pilot study (α=0.77). The first 24 hours of dietary intake were recorded during these interviews. Moreover, participants were asked to schedule another meeting for fasting blood sample testing and collecting the second 24-hour dietary recall.
Quantitative variables
Each participant had a fasting blood sample drawn after 8-12 hours of fasting, which was subsequently examined using a standardized procedure. A biochemical autoanalyzer (SEAL - AA3 HR - Colorimeter) was used to analyze the triglyceride (TG) and high-density lipoprotein (HDL). Using standardized techniques, well-trained examiners measured anthropometric indices. Participants were measured for body weight and height, wearing light clothing and bare feet, to the nearest 0.1 kg and 0.1 cm, respectively. With the individual standing, the waist circumference was measured using a flexible anthropometric tape on the horizontal plane halfway between the lowest rib and the iliac crest. At the place where the buttocks are at their widest comprehensive hip circumference was measured over thin clothing. The measurements of both circumferences were made to the nearest 0.1 cm. BMI was computed as weight divided by height square (kg)/(m 2). The waist-to-height ratio was calculated (waist measurement divided by height measurement). Furthermore, in this study, five obesity indices were considered and calculated using standard formulas as follows: conicity index (CI) = WC (m)/(0.109 × √ (Bodyweight (kg)/Height (m)) 27 ; body adiposity index (BAI) = hip circumference (cm)/height (m)1.5–18) 28 ; abdominal volume index (AVI) = (2 cm (waist) 2 + 0.7 cm (waist-hip)2)/1,000) 27 ; body roundness index (BRI0 = 365.2 − 365.5 × √(1 − ((WC/2π) 2)/((0.5 × height)) 2)) 29 ; and weight-adjusted-waist index (WWI) = WC (cm) divided by the square root of weight (kg). 30
Moreover, cardiac function indices were considered and calculated, including an atherogenic index of plasma (AIP), lipid accumulation product (LAP), and cardiometabolic index (CMI), based on the following equations: LAP for males = (waist (cm)−65) × TG concentration (mmol/l), and for female = (waist (cm)−58) × TG concentration (mmol/l); CMI = TG/HDL-C ×(waist-to-height); AIP = Log (TG/HDL-C). 31
Dietary assessment
Two nonconsecutive 24-hour day recalls measured dietary intake (weekday and weekend). The participants were instructed to recollect and list all foods and beverages they had from midnight to midnight the day before, along with their quantity, cooking technique, and the names of brands. To ascertain the typical dietary intake of B1, B2, B3, and B6 vitamins, the database of ingested items was connected to ESHA Food Processor Nutrition Analysis (RRID: SCR_022528) Software (version 11:0; ESHA Research). The average total intake of the selected vitamins after adjustment for energy intake was calculated, then using percentiles (33% and 66%); the subject intake was categorized as tertiles (T1 presents the lower intake, T3 presents medium intake, and T3 presents the higher intake). T1 was a value less than the 33 percentiles (B1: <1.6536; B2: <1.4200; B3: < 25.6736; B6: < 1.3700), T2 was the value between 33 and 66 percentiles (B1: 1.6536-2.2800; B2: 1.4200-1.9400; B3: 25.6736-36.6252; B6: 1.3700-1.9300), and T3 was the value above the 66 percentiles (B1: >2.2800; B2: >1.9400; B3: >36.6252; B6: >1.9300).
Bias
To prevent sample bias, we selected a representative sample size, which was calculated based on the total Jordanian population using online software Raosoft (Raosoft Inc., free online software, Seattle, WA, USA). Also, the incomplete anthropometric measurements and/or biochemical data were excluded to eliminate information bias. However, there were no self-reporting data to bypass recall bias, the interviewers were trained for data collection.
Study size
The sample size was calculated using the calculator Raosoft based on the Department of Statistics in 2020, adults represented 52.2% (5,621,970) of the total population (10,806,000) in Jordan. With a 5% error margin, 95% CI, and 50% response distribution, and after addition of 10%, the minimum sample size was 424 participants. The total number of final participants was 491.
Statistical analysis
Shapiro–Wilk test was used to verify the normality of variables. To describe the sample, descriptive statistical tests were used. Means and standard deviations (means± SD) were used to express continuous variables. Categorical variables, on the other hand, are displayed as frequencies and percentages (n (%)). Age groups stratified this analysis.
Means and standard deviation to present the continuous variables were calculated using an ANOVA test. Based on the sample distribution and test statistics, inferential statistics were applied, including the student’s t-test, chi-squared test, and independent samples t-test. The energy-adjusted intakes of B-vitamins were calculated by adding the expected mean nutrient intake of the study population to the residual derived from the regression analysis. 32 This energy adjustment method was computed from the residuals of the regression model with total energy intake as the independent variable and the nutrient as the dependent variable. Pearson correlation coefficient was performed to determine the correlation between obesity indices and macronutrient intake. The percentage change in the index explained by nutrient intake was calculated using r-square from linear regression analysis considering age and sex as confounding factors. The statistical significance was set at p < 0.05. IBM SPSS Statistics (RRID: SCR_016479) version 25 (IBM, Chicago, IL, USA) was used.
Results
Descriptive data
From the total sample, 198 participants were aged between 20-34 years old, 169 were aged 35-44 years old, and 124 were aged 45-65 years old. Participants aged between 20-34 years old had a mean weight of 74.04 ± 15.72 kg, WC 89.69 ± 13.15 cm, HC 102.98 ± 10.09 cm, and BMI 25.68 ± 4.96 kg/m 2. The participants aged between 35-44 years old had a mean weight of 79.48 ± 15.32 kg, WC 97.18 ± 12.65 cm, HC 106.26 ± 9.62 cm, and BMI 28.00 ± 4.58 kg/m 2. While participants aged between 45-65 years old had a mean weight of 84.40 ± 15.51 kg, WC 105.18 ± 15.59 cm, HC 108.22 ± 12.02 cm, and BMI 30.21 ± 5.94 kg/m 2. Most of the participants in the different age groups were male (66.2, 68.0 and 79.0%, respectively) ( Table 1). 33 A total of 51.5% of participants aged 20-34 years old had a school-level education, while 57.4% and 50.8% of participants aged 35-44 and 45-65 years old, respectively, had a university-level education. Most of the participants aged 35-44 and 45-65 years old were single, whereas 43.9% of participants aged 20-34 years old were married and 55.6% were single. Participants aged 20-34 years old were either normal or overweight (49.5% and 30.3%, respectively). Participants aged 35-44 and 45-65 years old were either overweight (40.2% and 42.7%, respectively) or obese (32.0% and 41.1%, respectively). In addition, a larger proportion of the participants of all ages were physically active and smokers. Almost all the participants of different ages were disease-free ( Table 1).
Table 1. The general characteristics of the study population (n=491).
| Variables | Mean ± SD | p-value * | ||
|---|---|---|---|---|
| 20-34 y (n = 198) | 35-44 y (n = 169) | 45-65 y (n = 124) | ||
| Weight (kg) | 74.04 ± 15.72 | 79.48 ± 15.32 | 84.40 ± 15.51 | <0.001 ** |
| Waist circumference (WC) (cm) | 89.69 ± 13.15 | 97.18 ± 12.65 | 105.18 ± 15.59 | <0.001 ** |
| Hip circumference (HC) (cm) | 102.98 ± 10.09 | 106.26 ± 9.62 | 108.22 ± 12.02 | <0.001 ** |
| Body mass index (BMI) (kg/m 2) | 25.68 ± 4.96 | 28.00 ± 4.58 | 30.21 ± 5.94 | <0.001 ** |
| n (%) | ||||
| Sex | ||||
| Male | 131 (66.2) | 115 (68.0) | 98 (79.0) | 0.038 |
| Female | 67 (33.8) | 54 (32.0) | 26 (21.0) | |
| Education level | ||||
| School education level | 102 (51.5) | 72 (42.6) | 61 (49.2) | 0.221 |
| University education level | 96 (48.5) | 97 (57.4) | 63 (50.8) | |
| Marital status | ||||
| Married | 87 (43.9) | 148 (87.6) | 118 (95.2) | <0.001 ** |
| Single | 110 (55.6) | 18 (10.7) | 4 (3.2) | |
| Divorced | 1 (0.5) | 2 (1.2) | 1 (0.8) | |
| Widow | 0 (0.0) | 1 (0.6) | 1 (0.8) | |
| Body mass index categories | ||||
| Underweight | 8 (4.0) | 1 (0.6) | 0 (0.0) | <0.001 ** |
| Normal weight | 98 (49.5) | 46 (27.2) | 20 (16.1) | |
| Overweight | 60 (30.3) | 68 (40.2) | 53 (42.7) | |
| Obese | 32 (16.2) | 54 (32.0) | 51 (41.1) | |
| Physical activity | ||||
| Active | 128 (65.6) | 90 (53.3) | 63 (50.8) | 0.022 |
| Inactive | 70 (35.4) | 79 (46.7) | 61 (49.2) | |
| Smoking | ||||
| Yes | 82 (41.4) | 61 (36.1) | 57 (46.0) | 0.002 ** |
| No | 63 (31.8) | 67 (39.6) | 32 (25.8) | |
| Ex-smoker | 9 (4.5) | 15 (8.9) | 19 (15.3) | |
| Passive smoking | 44 (22.2) | 26 (15.4) | 16 (12.9) | |
| Having diabetes multiuse | ||||
| Yes | 0 (0.0) | 6 (3.6) | 22 (17.7) | <0.001 ** |
| No | 198 (100.0) | 163 (96.4) | 102 (82.3) | |
| Having dyslipidemia | ||||
| Yes | 1 (0.5) | 11 (6.5) | 16 (12.9) | <0.001 ** |
| No | 197 (99.5) | 158 (93.5) | 108 (87.1) | |
| Having hypertension | ||||
| Yes | 2 (1.0) | 7 (4.1) | 18 (14.5) | <0.001 ** |
| No | 196 (99.0) | 162 (95.9) | 106 (85.5) | |
| Having heart disease | ||||
| Yes | 1 (0.5) | 3 (1.8) | 8 (6.5) | 0.003 ** |
| No | 197 (99.5) | 166 (98.2) | 116 (93.5) | |
p-value < 0.05 considered statistically significant (2-tailed).
p-value is significant at the 0.01 level (2-tailed).
Main results
The average total intake of the macronutrients and selected B vitamins were 3,050.09 ± 914.90 Kcal for energy, 364.32 ± 118.76 g for carbohydrate, 98.28 ± 41.29 g for protein, 134.89 ± 58.84 g for fat, 2.09 ± 0.82 mg for unadjusted B1 vitamin, 1.79 ± 0.83 mg for unadjusted B2 vitamin, 32.84 ± 14.16 mg for unadjusted B3 vitamin, and 2.02 ± 6.29 mg for unadjusted B6 vitamin. The mean intake and tertiles range for adjusted B vitamins were: 2.09 ± 0.59 mg (T1: <1.89; T2: 1.89-2.38; T3: >2.38) for energy-adjusted B1 vitamin, 1.79 ± 0.70 mg (T1: <1.54; T2: 1.54-1.88; T3: >1.88) for energy-adjusted B2 vitamin, 32.8 ± 12.34 mg (T1: <26.11; T2: 26.11-36.33; T3: >36.33) for energy-adjusted B3 vitamin, and 2.02 ± 6.22 mg (T1: <1.47; T2: 1.47-2.06; T3: >2.06) for energy-adjusted B6 vitamin.
The mean score of indices of obesity and cardiac function among the adjusted B vitamins tertiles are shown in Table 2. Among adjusted vitamin B1 intake tertiles, the CI and AVI index score significantly decreased as the intake of B1 intake increased (CI: T1 = 1.31 ± 0.01 vs. T3 = 1.28 ± 0.01, p = 0.031; AVI: T1=20.75 ± 0.64 vs. T3 = 18.14 ± 0.43, p = 0.02). Among adjusted B6 tertiles the score of the CI (T1 = 1.33 ± 0.01 vs. T3 = 1.27 ± 0.01, p < 0.001), AVI (T1 = 20.62 ± 0.57 vs. T3 = 17.43 ± 0.36, p < 0.001), BRI (T1 = 2.19 ± 0.09 vs. T3 = 1.60 ± 0.06, p < 0.001), WWI (T1 = 11.16 ± 0.09 vs. T3 = 10.71 ± 0.06, p < 0.001), LAP (T1 = 69.29 ± 4.48 vs. T3 = 50.31 ± 3.09, p = 0.048), AIP (T1 = 0.55 ± 0.03 vs. T3 = 0.46 ± 0.02, p = 0.034) decreased significantly as we moved from T1 to T3.
Table 2. The mean scores of indices of obesity and cardiac function among the adjusted B-vitamins tertiles.
| Nutrient | Mean ± SEM | p-value * | ||
|---|---|---|---|---|
| T1 (n = 165) | T2 (n = 163) | T3 (n = 163) | ||
| Vitamin B1 | ||||
| Conicity Index (CI) | 1.31 ± 0.01 | 1.31 ± 0.01 | 1.28 ± 0.01 | 0.031 * |
| Body adiposity index (BAI) | -17.49 ± 0.01 | -17.50 ± 0.01 | -17.50 ± 0.01 | 0.582 |
| Abdominal volume index (AVI) | 20.75 ± 0.64 | 19.14 ± 0.39 | 18.14 ± 0.43 | 0.002 ** |
| Body Roundness Index (BRI) | 2.01 ± 0.11 | 1.79 ± 0.07 | 1.72 ± 0.08 | 0.059 |
| Weight-adjusted-waist index (WWI) | 10.90 ± 0.10 | 10.94 ± 0.07 | 10.79 ± 0.07 | 0.275 |
| Cardiometabolic index (CMI) | 2.59 ± 0.20 | 2.44 ± 0.20 | 2.68 ± 0.28 | 0.734 |
| Lipid accumulation product (LAP) | 61.92 ± 4.78 | 56.83 ± 3.36 | 62.47 ± 7.78 | 0.728 |
| Atherogenic index of plasma (AIP) | 0.53 ± 0.03 | 0.48 ± 0.02 | 0.48 ± 0.03 | 0.428 |
| Vitamin B2 | ||||
| Conicity Index (CI) | 1.31 ± 0.01 | 1.29 ± 0.01 | 1.29 ± 0.01 | 0.442 |
| Body adiposity index (BAI) | -17.51 ± 0.01 | -17.49 ± 0.01 | -17.51 ± 0.01 | 0.259 |
| Abdominal volume index (AVI) | 19.51 ± 0.65 | 18.94 ± 0.37 | 19.02 ± 0.47 | 0.693 |
| Body Roundness Index (BRI) | 1.82 ± 0.11 | 1.83 ± 0.06 | 1.77 ± 0.08 | 0.816 |
| Weight-adjusted-waist index (WWI) | 10.91 ± 0.10 | 10.89 ± 0.06 | 10.83 ± 0.07 | 0.773 |
| Cardiometabolic index (CMI) | 2.90 ± 0.48 | 2.54 ± 0.15 | 2.34 ± 0.17 | 0.359 |
| Lipid accumulation product (LAP) | 67.85 ± 12.38 | 59.26 ± 3.21 | 55.29 ± 3.80 | 0.400 |
| Atherogenic index of plasma (AIP) | 0.48 ± 0.03 | 0.50 ± 0.02 | 0.48 ± 0.03 | 0.832 |
| Vitamin B3 | ||||
| Conicity Index (CI) | 1.30 ± 0.01 | 1.29 ± 0.01 | 1.30 ± 0.01 | 0.866 |
| Body adiposity index (BAI) | -17.50 ± 0.01 | -17.50 ± 0.01 | -17.51 ± 0.01 | 0.644 |
| Abdominal volume index (AVI) | 19.20 ± 0.44 | 18.92 ± 0.51 | 19.18 ± 0.44 | 0.890 |
| Body Roundness Index (BRI) | 1.82 ± 0.07 | 1.80 ± 0.09 | 1.80 ± 0.07 | 0.972 |
| Weight-adjusted-waist index (WWI) | 10.89 ± 0.06 | 10.86 ± 0.08 | 10.87 ± 0.07 | 0.943 |
| Cardiometabolic index (CMI) | 2.33 ± 0.23 | 2.48 ± 0.17 | 2.86 ± 0.31 | 0.292 |
| Lipid accumulation product (LAP) | 55.54 ± 4.09 | 58.57 ± 3.98 | 65.70 ± 8.41 | 0.460 |
| Atherogenic index of plasma (AIP) | 0.45 ± 0.03 | 0.50 ± 0.02 | 0.52 ± 0.03 | 0.209 |
| Vitamin B6 | ||||
| Conicity Index (CI) | 1.33 ± 0.01 | 1.30 ± 0.01 | 1.27 ± 0.01 | <0.001 ** |
| Body adiposity index (BAI) | -17.49 ± 0.01 | -17.50 ± 0.01 | -17.51 ± 0.01 | 0.051 |
| Abdominal volume index (AVI) | 20.62 ± 0.57 | 19.09 ± 0.45 | 17.43 ± 0.36 | <0.001 ** |
| Body Roundness Index (BRI) | 2.19 ± 0.09 | 1.76 ± 0.08 | 1.60 ± 0.06 | <0.001 ** |
| Weight-adjusted-waist index (WWI) | 11.16 ± 0.09 | 10.84 ± 0.07 | 10.71 ± 0.06 | <0.001 ** |
| Cardiometabolic index (CMI) | 2.78 ± 0.20 | 2.79 ± 0.36 | 2.24 ± 0.15 | 0.163 |
| Lipid accumulation product (LAP) | 69.29 ± 4.48 | 64.63 ± 9.13 | 50.31 ± 3.09 | 0.048 * |
| Atherogenic index of plasma (AIP) | 0.55 ± 0.03 | 0.48 ± 0.03 | 0.46 ± 0.02 | 0.034 * |
p-value < 0.05 considered statistically significant (2-tailed).
p-value is significant at the 0.01 level (2-tailed).
The association between indices of obesity and cardiac function with the adjusted energy intake of B vitamins among the total sample and based on sex are illustrated in Table 3. The energy-adjusted intake of B1 for the total sample had a significantly inverse weak correlation with AVI (r = -0.156, p = 0.001) and BRI (r = -0.111, p = 0.014). Among male participants, similar correlations were detected between energy adjusted B1 intake and BAI (r = -0.207, p < 0.001), AVI (r = -0.145, p = 0.007), and BRI (r = -0.163, p = 0.002). While, among female participants, there was a significant weak inverse correlation between BAI and energy adjusted B2 (r = -0.180, p = 0.029) and B6 intake (r = -0.212, p = 0.010). Moreover, there was a significant weak inverse association between energy adjusted B6 intake and AVI (r = -0.252, p = 0.002), BRI (r = -0.230, p = 0.005), and LAP (r = -0.179, p = 0.030) indices.
Table 3. The correlation between adjusted for energy vitamin B1, B2, B3, and B6 intake with obesity and cardiac function indices.
| B-vitamins | Statistical analysis | Indices of obesity and cardiac function | |||||||
|---|---|---|---|---|---|---|---|---|---|
| CI | BAI | AVI | BRI | WWI | CMI | LAP | AIP | ||
| Total participants | |||||||||
| B1 intake (mg) | Pearson r | -0.078 | -0.064 | -0.156 | -0.111 | -0.041 | 0.040 | 0.040 | -0.022 |
| p-value | 0.084 | 0.155 | 0.001 ** | 0.014 * | 0.362 | 0.372 | 0.371 | 0.631 | |
| B2 intake (mg) | Pearson r | -0.047 | -0.036 | -0.058 | -0.040 | -0.029 | -0.054 | -0.051 | -0.010 |
| p-value | 0.298 | 0.421 | 0.203 | 0.379 | 0.518 | 0.235 | 0.257 | 0.823 | |
| B3 intake (mg) | Pearson r | -0.001 | -0.031 | -0.001 | -0.010 | -0.007 | 0.049 | 0.034 | 0.071 |
| p-value | 0.989 | 0.500 | 0.979 | 0.829 | 0.875 | 0.276 | 0.450 | 0.115 | |
| B6 intake (mg) | Pearson r | -0.064 | -0.014 | -0.051 | -0.055 | -0.070 | -0.022 | -0.028 | -0.013 |
| p-value | 0.156 | 0.756 | 0.263 | 0.222 | 0.120 | 0.620 | 0.530 | 0.772 | |
| Male participants | |||||||||
| B1 intake (mg) | Pearson r | -0.020 | -0.207 | -0.145 | -0.163 | -0.040 | 0.078 | 0.079 | 0.030 |
| p-value | 0.716 | <0.001 ** | 0.007 ** | 0.002 ** | 0.465 | 0.149 | 0.141 | 0.583 | |
| B2 intake (mg) | Pearson r | -0.038 | 0.010 | -0.014 | -0.006 | -0.028 | -0.019 | -0.026 | 0.037 |
| p-value | 0.488 | 0.846 | 0.799 | 0.916 | 0.604 | 0.725 | 0.629 | 0.493 | |
| B3 intake (mg) | Pearson r | -0.020 | -0.042 | -0.025 | -0.036 | -0.027 | 0.095 | 0.058 | 0.093 |
| p-value | 0.706 | 0.441 | 0.650 | 0.502 | 0.619 | 0.077 | 0.281 | 0.085 | |
| B6 intake (mg) | Pearson r | -0.070 | -0.015 | -0.049 | -0.058 | -0.076 | -0.020 | -0.025 | -0.007 |
| p-value | 0.194 | 0.786 | 0.369 | 0.280 | 0.157 | 0.710 | 0.650 | 0.890 | |
| Female participants | |||||||||
| B1 intake (mg) | Pearson r | 0.137 | -0.127 | 0.042 | 0.048 | 0.147 | 0.073 | 0.080 | 0.105 |
| p-value | 0.097 | 0.126 | 0.618 | 0.565 | 0.076 | 0.381 | 0.336 | 0.206 | |
| B2 intake (mg) | Pearson r | 0.039 | -0.180 | -0.079 | -0.082 | 0.022 | -0.098 | -0.093 | -0.033 |
| p-value | 0.639 | 0.029 * | 0.339 | 0.322 | 0.788 | 0.239 | 0.263 | 0.690 | |
| B3 intake (mg) | Pearson r | 0.039 | -0.015 | 0.046 | 0.040 | 0.036 | -0.077 | -0.065 | 0.021 |
| p-value | 0.642 | 0.854 | 0.584 | 0.634 | 0.663 | 0.357 | 0.435 | 0.800 | |
| B6 intake (mg) | Pearson r | -0.136 | -0.212 | -0.252 | -0.230 | -0.131 | -0.111 | -0.179 | -0.112 |
| p-value | 0.100 | 0.010 ** | 0.002 ** | 0.005 ** | 0.112 | 0.179 | 0.030 * | 0.175 | |
CI: Conicity Index; BAI: Body adiposity index; AVI: Abdominal volume index; BRI: Body Roundness Index; WWI: Weight-adjusted-waist index; CMI: Cardiometabolic index; LAP: Lipid accumulation product; AIP: Atherogenic index of plasma.
Correlation is significant at the 0.01 level (2-tailed).
Correlation is significant at the 0.05 level (2-tailed).
For more details, the percentage changes in indices of obesity and cardiac function explained by energy-adjusted B vitamins intakes using the linear regression model are presented in Table 4. Among B vitamins, only vitamin B1 significantly explained 2.43% of changes observed in the AVI score and 1.24% of changes observed in the BRI score ( p < 0.05).
Table 4. Changes in obesity and cardiac function indices based on B vitamin intake using the linear regression model.
| Model | R | R Square change | % change | p-value for change * |
|---|---|---|---|---|
| Conicity Index (CI) | ||||
| Adjusted Vitamin B1 | 0.078 | 0.006 | 0.61% | 0.084 |
| Adjusted Vitamin B2 | 0.047 | 0.002 | 0.22% | 0.298 |
| Adjusted Vitamin B3 | 0.001 | 0.000 | 0.00% | 0.989 |
| Adjusted Vitamin B6 | 0.064 | 0.004 | 0.41% | 0.156 |
| Body adiposity index (BAI) | ||||
| Adjusted Vitamin B1 | 0.064 | 0.004 | 0.41% | 0.155 |
| Adjusted Vitamin B2 | 0.036 | 0.001 | 0.13% | 0.421 |
| Adjusted Vitamin B3 | 0.031 | 0.001 | 0.09% | 0.500 |
| Adjusted Vitamin B6 | 0.014 | 0.000 | 0.02% | 0.756 |
| Abdominal volume index (AVI) | ||||
| Adjusted Vitamin B1 | 0.156 | 0.024 | 2.43% | 0.001 * |
| Adjusted Vitamin B2 | 0.058 | 0.003 | 0.33% | 0.203 |
| Adjusted Vitamin B3 | 0.001 | 0.000 | 0.00% | 0.979 |
| Adjusted Vitamin B6 | 0.051 | 0.003 | 0.26% | 0.263 |
| Body Roundness Index (BRI) | ||||
| Adjusted Vitamin B1 | 0.111 | 0.012 | 1.24% | 0.014 * |
| Adjusted Vitamin B2 | 0.040 | 0.002 | 0.16% | 0.379 |
| Adjusted Vitamin B3 | 0.010 | 0.000 | 0.01% | 0.829 |
| Adjusted Vitamin B6 | 0.055 | 0.003 | 0.31% | 0.222 |
| Weight-adjusted-waist index (WWI) | ||||
| Adjusted Vitamin B1 | 0.041 | 0.002 | 0.17% | 0.362 |
| Adjusted Vitamin B2 | 0.029 | 0.001 | 0.09% | 0.518 |
| Adjusted Vitamin B3 | 0.007 | 0.000 | 0.01% | 0.875 |
| Adjusted Vitamin B6 | 0.070 | 0.005 | 0.49% | 0.120 |
| Cardiometabolic index (CMI) | ||||
| Adjusted Vitamin B1 | 0.040 | 0.002 | 0.16% | 0.372 |
| Adjusted Vitamin B2 | 0.054 | 0.003 | 0.29% | 0.235 |
| Adjusted Vitamin B3 | 0.049 | 0.002 | 0.24% | 0.276 |
| Adjusted Vitamin B6 | 0.022 | 0.001 | 0.05% | 0.620 |
| Lipid accumulation product (LAP) | ||||
| Adjusted Vitamin B1 | 0.040 | 0.002 | 0.16% | 0.371 |
| Adjusted Vitamin B2 | 0.051 | 0.003 | 0.26% | 0.257 |
| Adjusted Vitamin B3 | 0.034 | 0.001 | 0.12% | 0.450 |
| Adjusted Vitamin B6 | 0.028 | 0.001 | 0.08% | 0.530 |
| Atherogenic index of plasma (AIP) | ||||
| Adjusted Vitamin B1 | 0.022 | 0.000 | 0.05% | 0.631 |
| Adjusted Vitamin B2 | 0.010 | 0.000 | 0.01% | 0.823 |
| Adjusted Vitamin B3 | 0.071 | 0.005 | 0.51% | 0.115 |
| Adjusted Vitamin B6 | 0.013 | 0.000 | 0.02% | 0.772 |
p-value < 0.05 considered statistically significant (2-tailed).
Discussion
Obesity is considered to be one of the malnutrition forms, which develops individually and as a result of unbalanced nutrition. 2 Based on The World Health Organization (WHO), obesity is defined as the “double burden of malnutrition” and indicates that it may occur more frequently in socioeconomically underdeveloped and poorly nourished societies side by side with malnutrition, and in consequence, chronic diseases may develop. 34 Vitamins B1 (thiamine), B2 (Riboflavin), B3 (niacin), and B6 (pyridoxine), all of which are vital for the metabolism of energy, carbohydrate, lipid, and amino acid, as well as the regulation of the cellular redox state. 12 Some studies have revealed that obesity is associated with low levels of fruit intake, total diet quality, and micronutrient intake compared to normal BMI, which means that people living with obesity may consume fewer micronutrients due to the foods they eat and subsequently may lead to low levels of water-soluble vitamins. 12 , 35 Also, it has been found that the requirements of B vitamins has been increased for people with obesity, diabetes, and peripheral neuropathy. 36 , 37
The current study studied the association between dietary intake of B vitamins; specifically, B1, B2, B3, and B6, with indices of obesity and cardiac function were studied. Among these B vitamins, B1 and B6 had a significant association with some of the obesity indices; B2 with BAI indices, and B6 had a significant association with LAP (one of the cardiac function indices) The result revealed that the score of obesity indices CI and AVI decreased with an increase in the dietary intake of vitamin B1. Moreover, the energy-adjusted intake of B1 had a significantly inverse weak correlation with AVI and BRI among the total sample and male participants. Energy-adjusted B1 vitamin significantly explained 2.43% of changes observed in the AVI and 1.24% of changes observed in the BRI indices ( p < 0.05). In addition, based on this study result, increasing the intake of B6 led to reductions in the score of obesity (CI, AVI, BRI, WWI) and cardiac function indices (LAP and AIP).
By these results, Kerns et al., reported that 15.5–29% of patients with obesity who seek bariatric surgery have malnutrition disease, specifically, B1 deficiency. 38 In line with the present findings, B1 deficiency was found in children with obesity, and most of those cases were subclinical. 39 Kardas et al., observed significantly decreased levels of vitamin B2, vitamin B12, and B9 in the obesity and metabolic syndrome groups. 2 By contrast, they observed an increased level of B1 with a positive correlation with LDL-C and a negative correlation with HOMA-IR and body weight in the target group. 2 Studies show that vitamin B2 deficiency causes functional alterations in adipocytes, and thus has pro-inflammatory and proinsulin effects, resulting from increased adipocytes. 40 Moreover, in the subjects with obesity, an inverse correlation was found between vitamin B6 and body weight. 2 Gunanti et al. (2014) examined the associations between serum B vitamins and vitamin intake with body fat in Mexican American children aged 8–15 years. B1 and B2 intakes were inversely associated with BMI and trunk fat mass (b:21.26, p < 0.05; b: 21.37, p < 0.05). 7 Different suggested mechanisms have been introduced to describe the correlation between B-vitamin deficiency and obesity occurrence, all are summarized in Figure 1. The interaction mechanism between the reduced serum B-complex level and obesity includes the reduction in B1, B2, B3, and B6 levels associated with an increased risk of metabolic syndrome, which is considered one of the major cases of obesity. 6 Also, decreases in the levels of B1 and B2 are correlated with increased BMI, 7 and B6 regulates the expression of peroxisome proliferator-activated receptor gamma (PPARγ), which is a target gene that plays a key role in adipocyte gene expression and adipogenesis. 14 Additionally, decreased B1, B3, and B6 concentrations correlate significantly with an increased risk of insulin resistance, metabolic dysfunction, and obesity. 41 Moreover, generally, B vitamins have a fundamental role in the lipid and lipoprotein metabolism cascade. Therefore, quantitative or qualitative defects in B vitamins will negatively impact the metabolism of lipid derivatives. 42 Also, B2 plays a vital role in energy-expenditure adipocyte gene regulation at the epigenetic level. 43 The final suggested mechanism is that increased body weight leads to the reduction of vitamin B6 levels by inducing systemic oxidative stress, which leads to stimulating adipocytokines dysregulation. 7 , 24
Figure 1. Mechanism of interaction between reduction level of serum B-complex and obesity.
(1) Deficiencies in B7, B6, B9, and B12 lead to energy metabolism disruption, which induces the production of reactive oxygen species, inflammation, and lipogenesis. 7 (2) Reduced levels of B1, B2, B3, and B6 levels are associated with an increased risk of metabolic syndrome, which is considered one of the major cases of obesity. 6 (3) Decreased levels of B1 and B2 are correlated with increased body mass index (BMI). 7 (4) B6 regulates the expression of peroxisome proliferator-activated receptor gamma)PPARg(target genes, which play a key role in adipocyte gene expression and adipogenesis. 45 (5) Decreased B1, B3, and B6 concentrations are significantly correlated with increased risk of insulin resistance, metabolic dysfunction and obesity. 46 (6) B vitamins are fundamental in the lipid and lipoprotein metabolism cascade. Therefore, quantitative or qualitative defects in B vitamins will negatively impact the metabolism of lipid derivatives. 41 (7) B2 plays a vital role in energy-expenditure adipocyte gene regulation at the epigenetic level. 47 (8) Increased body weight leads to the reduction of vitamin B6 levels by inducing systemic oxidative stress, which leads to stimulating cytokines dysregulation. 7 , 42
Studies investigating the prevalence of B1 deficiency among patients with cardiovascular diseases (CVDs) concluded that B1 deficiency could have a role in the development of CVDs. 17 Odum et al. (2012) found that the mean plasma vitamins B1, B3, B6 and C concentrations of subjects with metabolic syndrome were significantly lower than that of controls ( p = 0.001, 0.05, 0.045, 0.001, respectively). Also, 14% and 32% of subjects had inadequate vitamin B1 and vitamin C status, respectively. 12 Additionally, Wu et al., found a linear inverse relationship between dietary vitamin B1, niacin, B6, and metabolic syndrome, and a non-linear inverse relationship was found between dietary vitamin B2 and metabolic syndrome. 6 It was noted that vitamin B6 supplementation is vital in preventing metabolic syndrome and pyridoxine levels were lower in patients with metabolic syndrome compared to those in healthy patients. 43 They suggested that a higher intake of vitamin B1, B2, niacin, B6, and dietary folate equivalent was associated with a reduced risk of metabolic syndrome. 6 Not like other results, Kardas et al., found that B1 levels were high in subjects with obesity and metabolic syndrome; they explained this difference by the fact that these individuals typically consumed large quantities of bread and cereals, which are considered major sources of B1. 2 Many suggested mechanisms have been introduced to describe the association between B-vitamin deficiency and metabolic syndrome and coronary heart diseases, all are summarized in Figure 2.
Figure 2. Relationship between B-vitamins intake and cardiovascular disease.
(1) B3 intake leads to decreased serum levels of low-density lipoprotein cholesterol (LDL), very low-density lipoprotein cholesterol (VLDL), and triglycerides (TG). Also, increased levels of high-density lipoprotein cholesterol (HDL). 21 (2) Vitamins B2, B6, B9, and B12 are vital in homocysteine demethylation into methionine, leading to decreased risk of atherosclerosis. 15 , 21 (3) Decreased levels of folate intake lead to decreased DNA-methylation, which has been detected in atherosclerotic lesions. 48 Also, B6 is associated with reduced risk of metabolic syndrome. 6 (4) Low B1, B3, and B6 levels were associated with increased insulin resistance, metabolic dysfunction, and increased risk of atherosclerosis, obesity, metabolic syndrome, diabetes, and cardiovascular disease. 12 , 44
B3 intake is suggested to decrease serum levels of LDL, VLDL, and TG, as well as, increase levels of HDL. 21 , 24 Also, vitamins B2 and B6 have a vital role in homocysteine demethylation into methionine, which leads to decreased risk of atherosclerosis. 15 , 21 Additionally, B6 is associated with a reduced risk of metabolic syndrome. 6 Lastly, low levels of B1, B3, and B6 are associated with increased insulin resistance, metabolic dysfunction, and increased risk of atherosclerosis, obesity, metabolic syndrome, diabetes, and cardiovascular disease. 12 , 44
This study is one of the early studies that placed importance on examining the relationship between inadequate dietary intake of energy-releasing B vitamins, and each of the indices of obesity and cardiac function in healthy individuals. There are not enough studies that clarify the correlation between B vitamins and indices of obesity and cardiac function. This provides the scope to highlight the significance of dietary consumption and the part that nutritional inadequacies play in the prevalence of obesity and heart disease.
The present study had certain limitations. First, because the study was cross-sectional, it was impossible to conclusively establish a causal link between obesity, cardiac function indices, and dietary intake of B vitamins. Second, data were collected from one place, which affects the generalizability of conclusions that cannot reflect the society’s situation. The availability of both conventional and innovative anthropometric measures as well as cardiac function indices is one of our study’s key strengths. Additionally, because the current data were gathered before the COVID-19 pandemic, they can be used to compare studies done before and after the pandemic by researchers in the same field. Another key benefit of the study is the originality of its investigation of the relationship between indicators of cardiac function and several chosen nutrients. We selected a representative sample size to prevent sample bias, which was calculated based on the total Jordanian population using online software. Also, incomplete anthropometric measurements and/or biochemical data have been excluded to eliminate information bias. However, there were no self-reporting data to bypass recall bias, the interviewers were trained for data collection.
Conclusions
In conclusion, after energy adjustment, obesity indices are inversely associated with B1, B2, and B6 vitamins. Specifically, AVI and BRI were inversely associated with B1 and B6 vitamins, whereas BAI was inversely associated with B2 vitamins among female participants. Only the B6 vitamin was inversely associated with LAP and AIP among cardiac function indices. Increasing the intake of these vitamins (B1, B2, and B6) results in reducing the score of obesity (CI, AVI, BRI, WWI) and cardiac function indices (LAP and AIP), which in consequence may help reduce the occurrence of obesity and related coronary diseases.
Acknowledgments
The authors thank the participants for their patience and great help. Grammarly software program has been used to enhance language editing.
Funding Statement
The author(s) declared that no grants were involved in supporting this work.
[version 3; peer review: 2 approved]
Data availability
Underlying data
Zenodo: The impact of energy releasing B-vitamin intake on indices of obesity and cardiac function: a cross-sectional study, https://doi.org/10.5281/zenodo.8159117. 33
The project contains the following underlying data:
-
•
Supplementary File 1-Energy releasing B vitamins 13-1-2023.sav (Anonymized participant data used in this study)
Extended data
Zenodo: The impact of energy releasing B-vitamin intake on indices of obesity and cardiac function: a cross-sectional study, https://doi.org/10.5281/zenodo.8167423. 26
This project contains the list of interview questions used with 24-hour recall form.
Zenodo: The impact of energy releasing B-vitamin intake on indices of obesity and cardiac function: a cross-sectional study, https://doi.org/10.5281/zenodo.8167442. 25
This project contains an example of the consent form used.
Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0).
References
- 1. Monteverde D, Gómez-Consarnau L, Suffridge C, et al. : Life’s utilization of B vitamins on early Earth. Geobiology. 2017;15(1):3–18. 10.1111/gbi.12202 [DOI] [PubMed] [Google Scholar]
- 2. Kardaş F, Yücel A, Kendirci M, et al. : Evaluation of micronutrient levels in children and adolescents with obesity and their correlation with the components of metabolic syndrome. Turk. J. Pediatr. 2021;63(1):48–68. 10.24953/turkjped.2021.01.006 [DOI] [PubMed] [Google Scholar]
- 3. Motamed S, Ebrahimi M, Safarian M, et al. : Micronutrient intake and the presence of the metabolic syndrome. N. Am. J. Med. Sci. 2013;5(6):377–385. 10.4103/1947-2714.114171 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4. Via M: The malnutrition of obesity: micronutrient deficiencies that promote diabetes. ISRN Endocrinol. 2012;2012:1–8. 10.5402/2012/103472 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5. Szczuko M, Hawryłkowicz V, Kikut J, et al. : The implications of vitamin content in the plasma in reference to the parameters of carbohydrate metabolism and hormone and lipid profiles in PCOS. J. Steroid Biochem. Mol. Biol. 2020;198: 105570. 10.1016/j.jsbmb.2019.105570 [DOI] [PubMed] [Google Scholar]
- 6. Wu Y, Li S, Wang W, et al. : Associations of dietary vitamin B1, vitamin B2, niacin, vitamin B6, vitamin B12 and folate equivalent intakes with metabolic syndrome. Int. J. Food Sci. Nutr. 2020;71(6):738–749. 10.1080/09637486.2020.1719390 [DOI] [PubMed] [Google Scholar]
- 7. Gunanti I, Marks G, Al-Mamun A, et al. : Low serum vitamin B-12 and folate concentrations and low thiamin and riboflavin intakes are inversely associated with greater adiposity in Mexican American children. J. Nutr. 2014;144(12):2027–2033. 10.3945/jn.114.201202 [DOI] [PubMed] [Google Scholar]
- 8. Yanni AE, Kokkinos A, Psychogiou G, et al. : Daily consumption of fruit-flavored yoghurt enriched with vitamins B contributes to lower energy intake and body weight reduction, in type 2 diabetic patients: a randomized clinical trial. Food Funct. 2019;10(11):7435–7443. 10.1039/C9FO01796H [DOI] [PubMed] [Google Scholar]
- 9. Barazzoni R, Silva V, Singer P: Clinical biomarkers in metabolic syndrome. Nutr. Clin. Pract. 2014;29(2):215–221. 10.1177/0884533613516168 [DOI] [PubMed] [Google Scholar]
- 10. McCracken E, Monaghan M, Sreenivasan S: Pathophysiology of the metabolic syndrome. Clin. Dermatol. 2018;36(1):14–20. 10.1016/j.clindermatol.2017.09.004 [DOI] [PubMed] [Google Scholar]
- 11. Ghanavati M, Rahmani J, Clark CCT, et al. : Pistachios and cardiometabolic risk factors: A systematic review and meta-analysis of randomized controlled clinical trials. Complement. Ther. Med. 2020;52:102513. PubMed Abstract. 10.1016/j.ctim.2020.102513 [DOI] [PubMed] [Google Scholar]
- 12. Odum E, Wakwe V: Plasma concentrations of water-soluble vitamins in metabolic syndrome subjects. Niger. J. Clin. Pract. 2012;15:442–447. 10.4103/1119-3077.104522 [DOI] [PubMed] [Google Scholar]
- 13. Ullegaddi R, Powers H, Gariballa S: B-group vitamin supplementation mitigates oxidative damage after acute ischaemic stroke. Clin. Sci. 2004;107:477–484. 10.1042/CS20040134 [DOI] [PubMed] [Google Scholar]
- 14. Folsom A, Desvarieux M, Nieto F, et al. : B vitamin status and inflammatory markers. Atherosclorosis. 2003;169:169–174. 10.1016/S0021-9150(03)00161-8 [DOI] [PubMed] [Google Scholar]
- 15. Huang T, Zheng Y, Qi Q, et al. : DNA Methylation Variants at HIF3A Locus, B-Vitamin Intake, and Long-term Weight Change: Gene-Diet Interactions in Two U.S. Cohorts. Diabetes. 2015;64(9):3146–3154. 10.2337/db15-0264 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16. Dick K, Nelson C, Tsaprouni L, et al. : DNA methylation and body-mass index: a genome-wide analysis. Lancet. 2014;383:1990–1998. 10.1016/S0140-6736(13)62674-4 [DOI] [PubMed] [Google Scholar]
- 17. Eshak E, Arafa A: Thiamine deficiency and cardiovascular disorders. Nutr. Metab. Cardiovasc. Dis. 2018;28(10):965–972. 10.1016/j.numecd.2018.06.013 [DOI] [PubMed] [Google Scholar]
- 18. Huang T, Li K, Ksimi S, et al. : Effect of vitamin B-12 and n-3 polyunsaturated fatty acids on plasma homocysteine, ferritin, C-reaction protein, and other cardiovascular risk factors: A randomized controlled trial. Asia Pac. J. Clin. Nutr. 2015;24:403–411. 10.6133/apjcn.2015.24.3.19 [DOI] [PubMed] [Google Scholar]
- 19. Blacher J, Czernichow S, Paillard F, et al. : Cardiovascular effects of B-vitamins and/or N-3 fatty acids: the SU. FOL. OM3 trial. Int. J. Cardiol. 2013;167(2):508–513. 10.1016/j.ijcard.2012.01.060 [DOI] [PubMed] [Google Scholar]
- 20. Valko M, Rhodes C, Moncol J, et al. : Free radicals, metals and antioxidants in oxidative stress-induced cancer. Chem. Biol. Interact. 2006;160(1):1–40. 10.1016/j.cbi.2005.12.009 [DOI] [PubMed] [Google Scholar]
- 21. Fiorito G, Guarrera S, Valle C, et al. : B-vitamins intake, DNA-methylation of one carbon metabolism and homocysteine pathway genes and myocardial infarction risk: the EPICOR study. Nutr. Metab. Cardiovasc. Dis. 2014;24(5):483–488. 10.1016/j.numecd.2013.10.026 [DOI] [PubMed] [Google Scholar]
- 22. Zhu J, Xun P, Kolencik M, et al. : Do B Vitamins Enhance the Effect of Omega-3 Polyunsaturated Fatty Acids on Cardiovascular Diseases? A Systematic Review of Clinical Trials. Nutrients. 2022;14(8):1608. 10.3390/nu14081608 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23. Wan P, Moat S, Anstey A: Pellagra: a review with mphasis on hotosensitivity. Br. J. Dermatol. 2011;164(6):1188–1200. 10.1111/j.1365-2133.2010.10163.x [DOI] [PubMed] [Google Scholar]
- 24. Villines TC, Kim AS, Gore RS, et al. : Niacin: The evidence, clinical use, and future directions. Curr. Atheroscler. Rep. 2011;14:49–59. [DOI] [PubMed] [Google Scholar]
- 25. Agraib LM, Al-Shami I, Alkhatib B, et al. : The impact of energy releasing B-vitamins intake on obesity indices and coronary heart indices: a cross-sectional study (v1, consent form).[Dataset]. Zenodo. 2023. 10.5281/zenodo.8167442 [DOI] [PMC free article] [PubMed]
- 26. Agraib LM, Al-Shami I, Alkhatib B, et al. : The impact of energy releasing B-vitamins intake on obesity indices and coronary heart indices: a cross-sectional study (v1, Study Questionnaire).[Dataset]. Zenodo. 2023. 10.5281/zenodo.8167423 [DOI] [PMC free article] [PubMed]
- 27. Gowda V, Philip K: Abdominal volume index and conicity index in predicting metabolic abnormalities in young women of different socioeconomic classes. Int. J. Med. Sci. Public Health. 2016;5(7):1452–1456. 10.5455/ijmsph.2016.13102015231 [DOI] [Google Scholar]
- 28. Wang H, Chen Y, Sun G, et al. : Validity of cardiometabolic index, lipid accumulation product, and body adiposity index in predicting the risk of hypertension in Chinese population. Postgrad. Med. 2018;130(3):325–333. 10.1080/00325481.2018.1444901 [DOI] [PubMed] [Google Scholar]
- 29. Chang Y, Guo X, Chen Y, et al. : A body shape index and body roundness index: Two new body indices to identify diabetes mellitus among rural populations in northeast China. BMC Public Health. 2015;15:794. 10.1186/s12889-015-2150-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30. Cai S, Zhou L, Zhang Y, et al. : Association of the Weight-Adjusted-Waist Index With Risk of All-Cause Mortality: A 10-Year Follow-Up Study. Front. Nutr. 2022;9. 10.3389/fnut.2022.894686 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31. Wakabayashi I: Associations of Smoking and Drinking with New Lipid-Related Indices in Women with Hyperglycemia. Women’s Health Reports. 2021;2(1):23–31. 10.1089/whr.2020.0100 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32. Willett W, Stampfer MJ: Total energy intake: implications for epidemiologic analyses. Am. J. Epidemiol. 1986;124(1):17–27. 10.1093/oxfordjournals.aje.a114366 [DOI] [PubMed] [Google Scholar]
- 33. Agraib LM, Al-Shami I, Alkhatib B, et al. : The impact of energy releasing B-vitamin intake on indices of obesity and cardiac function: a cross-sectional study.[Dataset]. Zenodo. 2023. 10.5281/zenodo.8159117 [DOI] [PMC free article] [PubMed]
- 34. WHO: besity and overweight 2020. Reference Source
- 35. Agarwal S, Reider C, Brooks JR, et al. : Comparison of prevalence of inadequate nutrient intake based on body weight status of adults in the United States: an analysis of NHANES 2001–2008. J. Am. Coll. Nutr. 2015;34(2):126–134. 10.1080/07315724.2014.901196 [DOI] [PubMed] [Google Scholar]
- 36. Nix WA, Zirwes R, Bangert V, et al. : Vitamin B status in patients with type 2 diabetes mellitus with and without incipient nephropathy. Diabetes Res. Clin. Pract. 2015;107(1):157–165. 10.1016/j.diabres.2014.09.058 [DOI] [PubMed] [Google Scholar]
- 37. Stein J, Geisel J, Obeid R: Association between neuropathy and B-vitamins: A systematic review and meta-analysis. Eur. J. Neurol. 2021;28(6):2054–2064. 10.1111/ene.14786 [DOI] [PubMed] [Google Scholar]
- 38. Kerns JC, Arundel C, Chawla LS: Thiamin deficiency in people with obesity. Adv. Nutr. 2015;6(2):147–153. 10.3945/an.114.007526 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39. Densupsoontorn N, Srisawat C, Chotipanang K, et al. : Prevalence of and factors associated with thiamin deficiency in obese Thai children. Asia Pac. J. Clin. Nutr. 2019;28(1):116–121. 10.6133/apjcn.201903_28(1).0016 [DOI] [PubMed] [Google Scholar]
- 40. Mazur-Bialy AI, Pocheć E: Vitamin B2 deficiency enhances the pro-inflammatory activity of adipocyte, consequences for insulin resistance and metabolic syndrome development. Life Sci. 2017;178:9–16. 10.1016/j.lfs.2017.04.010 [DOI] [PubMed] [Google Scholar]
- 41. Ibrahim GR, Shah I, Gariballa S, et al. : Significantly elevated levels of plasma nicotinamide, pyridoxal, and pyridoxamine phosphate levels in obese Emirati population: A cross-sectional study. Molecules. 2020;25(17):3932. 10.3390/molecules25173932 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 42. Furukawa S, Fujita T, Shimabukuro M, et al. : Increased oxidative stress in obesity and its impact on metabolic syndrome. J. Clin. Invest. 2017;114(12):1752–1761. 10.1172/JCI21625 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 43. Liu Z, Li P, Zhao Z-H, et al. : Vitamin b6 prevents endothelial dysfunction, insulin resistance, and hepatic lipid accumulation in apoe−/− mice fed with high-fat diet. J. Diabetes Res. 2016;2016:1–8. 10.1155/2016/1748065 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 44. Shen J, Lai C-Q, Mattei J, et al. : Association of vitamin B-6 status with inflammation, oxidative stress, and chronic inflammatory conditions: the Boston Puerto Rican Health Study. Am. J. Clin. Nutr. 2010;91(2):337–342. 10.3945/ajcn.2009.28571 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 45. Yanaka N, Kanda M, Toya K, et al. : Vitamin B6 regulates mRNA expression of peroxisome proliferator-activated receptor-γ target genes. Exp. Ther. Med. 2011;2(3):419–424. 10.3892/etm.2011.238 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 46. Patrini C, Griziotti A, Ricciardi L: Obese individuals as thiamin storers. Int. J. Obes. 2004;28(7):920–924. 10.1038/sj.ijo.0802638 [DOI] [PubMed] [Google Scholar]
- 47. Hino S, Sakamoto A, Nagaoka K, et al. : FAD-dependent lysine-specific demethylase-1 regulates cellular energy expenditure. Nat. Commun. 2012;3(1):1–12. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 48. Jiang Y, Zhang H, Sun T, et al. : The comprehensive effects of hyperlipidemia and hyperhomocysteinemia on pathogenesis of atherosclerosis and DNA hypomethylation in ApoE−/− mice. Acta Biochim. Biophys. Sin. 2012;44(10):866–875. 10.1093/abbs/gms075 [DOI] [PubMed] [Google Scholar]