JP is co-founder and CEO, and BI is co-founder, and both are currently employed at Laboratoris Sanifit S.L. This current commercial affiliation does not alter the authors' adherence to PLOS ONE policies on sharing data and materials. FG, RMP, ACB and JP are co-inventors of the patent WO2010018278. The other authors have declared that no competing interests exist.
Conceived and designed the experiments: FG CFP RMP. Performed the experiments: CFP AG JP BI PS. Analyzed the data: CFP AG PS. Contributed reagents/materials/analysis tools: ACB OC OB AB AGR. Wrote the paper: FG PS JP RMP.
Current Address: Laboratoris Sanifit S.L., Research and Development Department, 07121, Palma de Mallorca, Spain
Pathological calcification generally consists of the formation of solid deposits of hydroxyapatite (calcium phosphate) in soft tissues. Supersaturation is the thermodynamic driving force for crystallization, so it is believed that higher blood levels of calcium and phosphate increase the risk of cardiovascular calcification. However several factors can promote or inhibit the natural process of pathological calcification. This cross-sectional study evaluated the relationship between physiological levels of urinary phytate and heart valve calcification in a population of elderly out subjects. A population of 188 elderly subjects (mean age: 68 years) was studied. Valve calcification was measured by echocardiography. Phytate determination was performed from a urine sample and data on blood chemistry, end-systolic volume, concomitant diseases, cardiovascular risk factors, medication usage and food were obtained. The study population was classified in three tertiles according to level of urinary phytate: low (<0.610 μM), intermediate (0.61–1.21 μM), and high (>1.21 μM). Subjects with higher levels of urinary phytate had less mitral annulus calcification and were less likely to have diabetes and hypercholesterolemia. In the multivariate analysis, age, serum phosphorous, leukocytes total count and urinary phytate excretion appeared as independent factors predictive of presence of mitral annulus calcification. There was an inverse correlation between urinary phytate content and mitral annulus calcification in our population of elderly out subjects. These results suggest that consumption of phytate-rich foods may help to prevent cardiovascular calcification evolution.
Numerous mechanisms regulate calcium levels in the body, and blood levels of calcium in healthy individuals usually occur within a narrow range. Calcium absorption from the gut, elimination through the kidneys, and deposition into bones all affect the body’s level of calcium [
CVC is a pathological form of soft tissue calcification. Supersaturation is the thermodynamic driving force for crystallization, so it is believed that higher blood levels of calcium and phosphate increase the risk of CVC. However several factors can promote or inhibit the natural process of CVC; vitamin D, lipids, and inflammatorycytokines promote calcification, whereas fetuin-A, pyrophosphate, vitamin K, osteopontin, and matrix Gla protein inhibit CVC [
CVC may be classified as intimal or medial, depending on its location in the vessel. Medial CVC is more common in subjects with CKD or diabetes [
Phytate (myo-inositol hexaphosphate) is a naturally occurring substance that the FDA classifies as GRAS (Generally Recognized As Safe). This substance is a powerful inhibitor of crystallization that can block the formation and growth of hydroxyapatite deposits. Previous research indicated that phytate can inhibit the formation of kidney stones [
The study adhered to the Declaration of Helsinki. The Ethics Committee from the Balearic Islands approved the study protocol (Protocol IB 459/05 PI), and all subjects gave their written informed consent.
The study sample consisted of 188 consecutive out patients referred by cardiologists to the Echocardiography Laboratory of the Cardiology Department of Son Dureta Hospital (Palma de Mallorca, Spain). The study sample was classified according urinary phytate concentration tertiles. All individuals had unrestricted diets at the time of urine collection. Subjects with chronic kidney disease, end-stage renal disease, a prosthetic valve, aortic or mitral stenosis, or a terminal disease were excluded.
Phytate determination was performed from a urine sample collected 2 h after the first urination of the morning. Five millilitres of fresh urine (acidified 1:1 with HCl to pH 3–4) was transferred to a column containing 0.2 g of anion-exchange resin (inner diameter: 4 mm), and the first eluate was discarded. The column was then washed with 50 mL of 50 mM HCl, and the second eluate was also discarded. Then, the column was washed with 3 mL of 2 M HNO3. Phytate was determined by direct phosphorus analysis of this last eluate using inductively coupled plasma atomic emission spectroscopy (ICP-AES). Taking into account the sample treatment performed, the lower limit of detection of phytate was 64 μg/L, while the limit of quantification was 213 μg/L. The working linear range used was 0–7 mg/L phytate. The relative standard deviation (R.S.D.) corresponding to five measurements of 1.35 mg/L phytate was 2.4%, the accuracy of the method in spiked samples gave a recovery of 97–105%. [
Transthoracic echocardiography was performed with an echocardiograph (General Electric System Five, GE Healthcare, Buckinghamshire UK). The parasternal long axis, valvular and left ventricular short axis, apical four chamber, two chamber, and long axis projections were obtained. Left atrial volume and diameter and left ventricular end-diastolic and end-systolic diameters were measured. Hemodynamic parameters were obtained with pulsatile and continuous wave Doppler measurement. A mitral annulus calcification (MAC) was defined by the following features on cross-sectional echocardiography in the parasternal window: focal increased echogenicity at the base of the posterior leaflet when visualized in parasternal long axis, short axis, and four chamber views. Aortic valve calcification (AVC) was defined by the presence of irregular cusp thickening and focal hyperechogenicity at the base in the respective projections. The extent of valve calcification was estimated by the Rosenheck score [
Diseases known as risk factors for atherosclerosis were recorded from each medical history. This includes diabetes, high blood pressure, high plasma cholesterol levels, CKD, renal lithiasis, previous cerebrovascular disease, previous myocardial infarction, vascular disease, arthropathy, osteoporosis, gout, any type of cancer, colon cancer, obesity, and smoking. Use of the following medications was also recorded: acetylsalicylic acid, ticlopidine, statins, fibrate, ezetimibe, angiotensin converting enzyme (ACE) inhibitors, angiotensin receptor blockers (ARBs), diuretics, beta blockers, allopurinol, oral antidiabetics, insulin, calcium antagonists, calcitonin, oral calcium, and thyroid hormone.
Blood samples were collected from all subjects for measurement of the following parameters: calcium, chloride, phosphorus, magnesium, potassium, sodium, creatinine, glucose, intact PTH (iPTH), urea, alkaline phosphatase (ALP), alanine aminotransferase (ALT), aspartate aminotransferase (AST), total bilirubin, uric acid, triglycerides, total cholesterol, high-density lipoprotein (HDL), low-density lipoprotein cholesterol (LDL-C), total protein, albumin, fibrinogen, homocysteine, lipoprotein A (LPA), gamma glutamyl transferase (GGT), haemoglobin, haematocrit, erythrocytes, leukocytes, and the percentage of leucocytes as monocytes, lymphocytes, eosinophils, basophils, and neutrophils. End-systolic volume was also measured by 2-dimensional echocardiography.
Continuous variables are expressed as mean ± standard deviation and categorical variables are expressed as total number (percentage). All continuous variables were checked with normality plots and tests to show their distributions. Continuous variables with normal distributions were compared using one-way analysis of variance (ANOVA) and/or the t test for independent samples. Continuous variables with abnormal distributions were compared using Kruskal-Wallis one-way analysis of variance by ranks and/or Mann-Whitney U tests. For categorical variables, the chi-square test and/or Fisher’s exact test were used.
Univariate and multivariate binary logistic regressions were used to identify risk factors associated with presence of MAC. Multivariate analysis was performed using the stepwise backward method for all models.
A two-tailed
The statistical package SPSS (Statistical Package for the Social Sciences, version 17.0, SSPS Inc, Chicago, Ill, USA) was used for statistical analyses.
We studied a population of 188 elderly subjects from a single institution in Spain (mean age: 68± 11 years; 103 males and 85 females).
All subjects were classified into their urinary phytate tertiles: low (<0.61 μM), intermediate (0.61–1.21 μM), and high (>1.21 μM).
Urinary phytate levels (μM) | p-value | ||||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Low | Intermediate | High | All subjects | ||||||||||||||
T1: < 0.61 | T2: 0.61–1.21 | T3: > 1.21 | |||||||||||||||
(n = 63) | (n = 62) | (n = 63) | (n = 188) | ||||||||||||||
Age (years) | 70.5 | ± | 9.2 | 68.9 | ± | 11.4 | 65.0 | ± | 11.8 | a | 68.1 | ± | 11.0 | 0.017 | |||
BMI (kg/m2) | 27.7 | ± | 5.0 | 27.7 | ± | 4.7 | 27.9 | ± | 5.7 | 27.8 | ± | 5.1 | 0.960 | ||||
Sex (male) | 32 | ( | 50.8% | ) | 38 | ( | 61.3% | ) | 33 | ( | 52.4% | ) | 103 | ( | 54.8% | ) | 0.447 |
Diabetes | 23 | ( | 36.5% | ) | 18 | ( | 29.0% | ) | 11 | ( | 17.5% | )a | 52 | ( | 27.7% | ) | 0.048 |
Hypertension | 36 | ( | 57.1% | ) | 37 | ( | 59.7% | ) | 33 | ( | 52.4% | ) | 106 | ( | 56.4% | ) | 0.705 |
Hypercholesterolemia | 35 | ( | 55.6% | ) | 26 | ( | 41.9% | ) | 23 | ( | 36.5% | ) | 84 | ( | 44.7% | ) | 0.086 |
Chronic kidney disease | 12 | ( | 19.0% | ) | 5 | ( | 8.1% | ) | 8 | ( | 12.7% | ) | 25 | ( | 13.3% | ) | 0.192 |
Renal lithiasis | 7 | ( | 11.1% | ) | 11 | ( | 17.7% | ) | 15 | ( | 23.8% | ) | 33 | ( | 17.6% | ) | 0.173 |
Cerebrovascular disease | 11 | ( | 17.5% | ) | 10 | ( | 16.1% | ) | 8 | ( | 12.7% | ) | 29 | ( | 15.4% | ) | 0.747 |
Myocardial infarction | 9 | ( | 14.3% | ) | 12 | ( | 19.4% | ) | 7 | ( | 11.1% | ) | 28 | ( | 14.9% | ) | 0.427 |
Peripheral vascular disease | 6 | ( | 9.5% | ) | 2 | ( | 3.2% | ) | 4 | ( | 6.3% | ) | 12 | ( | 6.4% | ) | 0.354 |
Arthrosis | 31 | ( | 49.2% | ) | 30 | ( | 48.4% | ) | 27 | ( | 42.9% | ) | 88 | ( | 46.8% | ) | 0.740 |
Osteoporosis | 10 | ( | 15.9% | ) | 7 | ( | 11.3% | ) | 6 | ( | 9.5% | ) | 23 | ( | 12.2% | ) | 0.533 |
Gout | 7 | ( | 11.1% | ) | 10 | ( | 16.1% | ) | 6 | ( | 9.5% | ) | 23 | ( | 12.2% | ) | 0.501 |
Colon cancer | 1 | ( | 1.6% | ) | 0 | ( | 0.0% | ) | 1 | ( | 1.6% | ) | 2 | ( | 1.1% | ) | 0.608 |
Cancer | 8 | ( | 12.7% | ) | 3 | ( | 4.8% | ) | 6 | ( | 9.5% | ) | 17 | ( | 9.0% | ) | 0.305 |
Physical exercise | 17 | ( | 27.0% | ) | 20 | ( | 32.3% | ) | 18 | ( | 28.6% | ) | 55 | ( | 29.3% | ) | 0.802 |
Smoking (currently or past) | 20 | ( | 31.7% | ) | 15 | ( | 24.2% | ) | 18 | ( | 28.6% | ) | 53 | ( | 28.2% | ) | 0.642 |
Alcohol (currently or past) | 2 | ( | 3.2% | ) | 2 | ( | 3.2% | ) | 3 | ( | 4.8% | ) | 7 | ( | 3.7% | ) | 0.732 |
Statistics: Continuous variables are expressed as mean ± standard deviation and categorical variables are expressed as total number (percentage). Continuous variables were compared using one-way analysis of variance (ANOVA) and t-test for independent samples. Continuous variables with abnormal distributions were compared using the Kruskal-Wallis one-way analysis of variance by ranks and Mann-Whitney U test. For categorical variables, the chi-square test and Fisher’s exact test were used. The Bonferroni correction was used to account for multiple comparisons. The p-values correspond to the analysis of variance or chi-square test. a: p<0.05/3
Analysis of cardiovascular calcification indicated a trend for decreased aortic valve calcification as phytate urinary concentration increased (
Statistics. Values are expressed as percentage. Comparisons between groups were performed using chi-square test and Fisher’s exact test. *
Urinary phytate levels (μM) | p-value | ||||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Low | Intermediate | High | Allsubjects | ||||||||||||||
T1: < 0.61 | T2: 0.61–1.21 | T3: > 1.21 | |||||||||||||||
(n = 63) | (n = 62) | (n = 63) | (n = 188) | ||||||||||||||
Acetylsalicylic acid | 19 | ( | 30.2% | ) | 16 | ( | 25.8% | ) | 20 | ( | 31.7% | ) | 55 | ( | 29.3% | ) | 0.752 |
Ticlopidine | 4 | ( | 6.3% | ) | 7 | ( | 11.3% | ) | 5 | ( | 7.9% | ) | 16 | ( | 8.5% | ) | 0.601 |
Statins | 25 | ( | 39.7% | ) | 21 | ( | 33.9% | ) | 19 | ( | 30.2% | ) | 65 | ( | 34.6% | ) | 0.526 |
Fibrate | 1 | ( | 1.6% | ) | 2 | ( | 3.2% | ) | 3 | ( | 4.8% | ) | 6 | ( | 3.2% | ) | 0.598 |
Ezetimibe | 0 | ( | 0.0% | ) | 0 | ( | 0.0% | ) | 1 | ( | 1.6% | ) | 1 | ( | 0.5% | ) | 0.369 |
ACE inhibitors | 19 | ( | 30.2% | ) | 20 | ( | 32.3% | ) | 19 | ( | 30.2% | ) | 58 | ( | 30.9% | ) | 0.958 |
ARBs | 10 | ( | 15.9% | ) | 12 | ( | 19.4% | ) | 8 | ( | 12.7% | ) | 30 | ( | 16.0% | ) | 0.597 |
Proximal diuretics | 6 | ( | 9.5% | ) | 6 | ( | 9.7% | ) | 3 | ( | 4.8% | ) | 15 | ( | 8.0% | ) | 0.513 |
Distal diuretics | 16 | ( | 25.4% | ) | 14 | ( | 22.6% | ) | 16 | ( | 25.4% | ) | 46 | ( | 24.5% | ) | 0.915 |
Beta blockers | 17 | ( | 27.0% | ) | 19 | ( | 30.6% | ) | 17 | ( | 27.0% | ) | 53 | ( | 28.2% | ) | 0.871 |
Oral antidiabetics | 14 | ( | 22.2% | ) | 9 | ( | 14.5% | ) | 5 | ( | 7.9% | ) | 28 | ( | 14.9% | ) | 0.079 |
Insulin | 11 | ( | 17.5% | ) | 8 | ( | 12.9% | ) | 3 | ( | 4.8% | ) | 22 | ( | 11.7% | ) | 0.080 |
Calcium antagonist | 16 | ( | 25.4% | ) | 12 | ( | 19.4% | ) | 6 | ( | 9.5% | ) | 34 | ( | 18.1% | ) | 0.065 |
Calcitonin | 3 | ( | 4.8% | ) | 1 | ( | 1.6% | ) | 1 | ( | 1.6% | ) | 5 | ( | 2.7% | ) | 0.445 |
Statistics: Variables are expressed as total number (percentage). Comparisons between groups were performed by the chi-square test.
Urinary phytate levels (μM) | |||||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Low | Intermediate | High | All subjects | p-value | |||||||||||||
T1: < 0.61 | T2: 0.61–1.21 | T3: > 1.21 | |||||||||||||||
(n = 63) | (n = 62) | (n = 63) | (n = 188) | ||||||||||||||
Calcium (mg/dL) | 9.00 | ± | 0.70 | 8.92 | ± | 0.38 | 9.03 | ± | 0.54 | 8.98 | ± | 0.55 | 0.533 | ||||
Chloride (mEq/L) | 101 | ± | 4 | 102 | ± | 4 | 102 | ± | 4 | 102 | ± | 4 | 0.163 | ||||
Phosphorous (mg/dL) | 3.29 | ± | 0.81 | 3.18 | ± | 0.62 | 3.29 | ± | 0.86 | 3.25 | ± | 0.77 | 0.713 | ||||
Magnesium (mEq/dL) | 1.56 | ± | 0.50 | 1.62 | ± | 0.52 | 1.68 | ± | 0.47 | 1.62 | ± | 0.50 | 0.385 | ||||
Potassium (mEq/L) | 4.08 | ± | 0.42 | 3.98 | ± | 0.50 | 4.02 | ± | 0.43 | 4.03 | ± | 0.45 | 0.418 | ||||
Sodium (mEq/L) | 141.0 | ± | 2.5 | 142.1 | ± | 2.7 | a | 141.6 | ± | 2.9 | 141.5 | ± | 2.7 | 0.049 | |||
Creatinine (mg/dL) | 0.70 | ± | 0.87 | 0.44 | ± | 0.50 | 0.39 | ± | 0.66 | 0.51 | ± | 0.71 | 0.045 | ||||
Glucose (mg/dL) | 120 | ± | 37 | 113 | ± | 33 | 111 | ± | 34 | 115 | ± | 34 | 0.105 | ||||
Haemoglobin (g/dL) | 12.3 | ± | 1.9 | 13.3 | ± | 4.4 | 13.1 | ± | 2.1 | a | 12.9 | ± | 3.0 | 0.040 | |||
Urea(mg/dL) | 58 | ± | 31 | 46 | ± | 18 | a | 48 | ± | 38 | 50 | ± | 30 | 0.021 | |||
Uric acid (mg/dL) | 6.0 | ± | 2.2 | 5.7 | ± | 2.1 | 5.5 | ± | 1.7 | 5.7 | ± | 2.0 | 0.402 | ||||
Total cholesterol (mg/dL) | 182 | ± | 42 | 193 | ± | 34 | 199 | ± | 53 | 191 | ± | 44 | 0.041 | ||||
HDL (mg/dL) | 54 | ± | 16 | 53 | ± | 15 | 55 | ± | 17 | 54 | ± | 16 | 0.862 | ||||
LDL-C (mg/dL) | 100 | ± | 37 | 110 | ± | 33 | 114 | ± | 50 | a | 108 | ± | 41 | 0.049 | |||
Triglycerides (mg/dL) | 137 | ± | 66 | 129 | ± | 81 | 131 | ± | 63 | 133 | ± | 70 | 0.330 | ||||
Total protein (g/L) | 70 | ± | 8 | 71 | ± | 6 | 72 | ± | 5 | 71 | ± | 6 | 0.589 | ||||
Albumin (g/L) | 41.2 | ± | 5.3 | 42.7 | ± | 4.0 | 42.2 | ± | 3.47 | 42.0 | ± | 4.3 | 0.191 | ||||
ALP (U/L) | 81 | ± | 26 | 74 | ± | 24 | 76 | ± | 31 | 77 | ± | 27 | 0.174 | ||||
ALT (U/L) | 27 | ± | 24 | 20 | ± | 10 | 25 | ± | 23 | 24 | ± | 20 | 0.285 | ||||
AST (U/L) | 23 | ± | 11 | 20 | ± | 7 | 21 | ± | 16 | 22 | ± | 12 | 0.058 | ||||
Gamma glutamyltransferase (U/L) | 46 | ± | 56 | 30 | ± | 24 | 33 | ± | 30 | 37 | ± | 40 | 0.610 | ||||
Haematocrit (%) | 37.7 | ± | 5.4 | 39.3 | ± | 4.9 | 39.9 | ± | 5.5 | a | 39.0 | ± | 5.3 | 0.046 | |||
Erythrocytes (x106 /μL) | 3.87 | ± | 0.75 | 4.13 | ± | 0.59 | 4.19 | ± | 0.69 | a | 4.06 | ± | 0.69 | 0.033 | |||
Leukocytes (x103/μL) | 9 | ± | 16 | 7 | ± | 2 | 7 | ± | 2 | 8 | ± | 10 | 0.854 | ||||
Lymphocytes (%) | 29 | ± | 11 | 28 | ± | 8 | 29 | ± | 9 | 29 | ± | 9 | 0.850 | ||||
Neutrophils (%) | 59 | ± | 12 | 60 | ± | 10 | 58 | ± | 11 | 59 | ± | 11 | 0.867 | ||||
Basophils (%) | 0.54 | ± | 0.50 | 0.52 | ± | 0.27 | 0.53 | ± | 0.28 | 0.53 | ± | 0.37 | 0.643 | ||||
Eosinophils (%) | 2.5 | ± | 1.9 | 2.5 | ± | 1.8 | 2.2 | ± | 1.4 | 2.4 | ± | 1.7 | 0.574 | ||||
Monocytes (%) | 7.7 | ± | 1.9 | 7.5 | ± | 1.9 | 7.6 | ± | 2.1 | 7.6 | ± | 2.0 | 0.830 | ||||
Neutrophil/Lymphocyte ratio | 2.6 | ± | 1.7 | 2.6 | ± | 1.9 | 2.4 | ± | 2.0 | 2.5 | ± | 1.9 | 0.871 | ||||
iPTH (pg/mL) | 62 | ± | 45 | 57 | ± | 32 | 56 | ± | 35 | 59 | ± | 38 | 0.823 | ||||
Fibrinogen (mg/dL) | 410 | ± | 128 | 393 | ± | 109 | 407 | ± | 142 | 403 | ± | 127 | 0.661 | ||||
Homocysteine (μM) | 10.3 | ± | 6.3 | 11.8 | ± | 14.2 | 11.7 | ± | 15.5 | 11.3 | ± | 12.5 | 0.887 | ||||
Lipoprotein A(mg/dL) | 60 | ± | 57 | 45 | ± | 32 | 49 | ± | 46 | 51 | ± | 46 | 0.609 | ||||
End-systolic volume (mL) | 29 | ± | 31 | 19 | ± | 17 | 23 | ± | 29 | 24 | ± | 26 | 0.094 |
Statistics: Continuous variables are expressed as mean ± standard deviation and categorical variables are expressed as total number (percentage). Continuous variables with normal distributions were compared using one-way analysis of variance (ANOVA) and t-test for independent samples. Continuous variables with abnormal distributions were compared using the Kruskal-Wallis one-way analysis of variance by ranks and Mann-Whitney U test. For categorical variables, the chi-square test was used. The Bonferroni correction was used to account for multiple comparisons. The p-values correspond to the analysis of variance or chi-square test. a: p<0.05/3
Subjects were classified into 2 categories according to the presence or absence of MAC. Of whole sample, 32.1% presented MAC.
Demographics and risk factors, blood chemistry end-systolic volume of both groups are shown in
MAC | No-MAC | p-value | |||||||
---|---|---|---|---|---|---|---|---|---|
(n = 62) | (n = 126) | ||||||||
Age (years) | 73.1 | ± | 8.2 | 66.0 | ± | 11.4 | <0.001 | ||
BMI (kg/m2) | 28.0 | ± | 5.1 | 27.9 | ± | 5.1 | 0.578 | ||
Sex (male) | 30 | ( | 48.4% | ) | 73 | ( | 57.9% | ) | 0.275 |
Hypertension | 41 | ( | 66.1% | ) | 65 | ( | 51.6% | ) | 0.063 |
Diabetes type II | 23 | ( | 37.1% | ) | 29 | ( | 23.0% | ) | 0.039 |
Hypercholesterolemia | 29 | ( | 46.8% | ) | 55 | ( | 43.7% | ) | 0.756 |
Chronic kidney disease | 11 | ( | 17.7% | ) | 14 | ( | 11.1% | ) | 0.254 |
Cerebrovascular disease | 8 | ( | 12.9% | ) | 21 | ( | 16.7% | ) | 0.668 |
Myocardial infarction | 12 | ( | 19.4% | ) | 16 | ( | 12.7% | ) | 0.276 |
Peripheral vascular disease | 4 | ( | 6.5% | ) | 8 | ( | 6.3% | ) | 1.000 |
Smoking (currently or past) | 18 | ( | 29.0% | ) | 35 | ( | 27.8% | ) | 1.000 |
Physical exercise | 20 | ( | 32.3% | ) | 35 | ( | 27.8% | ) | 0.609 |
Alcohol (currently or past) | 9 | ( | 14.5% | ) | 30 | ( | 23.8% | ) | 0.248 |
Calcium (mg/dL) | 9.0 | 0.6 | 9.0 | 0.6 | 0.783 | ||||
Phosphorous (mg/dL) | 3.5 | 0.8 | 3.1 | 0.7 | 0.002 | ||||
Magnesium (mEq/dL) | 1.6 | 0.5 | 1.6 | 0.5 | 0.800 | ||||
Creatinine (mg/dL) | 0.6 | 0.8 | 0.5 | 0.7 | 0.165 | ||||
Glucose (mg/dL) | 124 | 42 | 111 | 31 | 0.046 | ||||
Uric acid (mg/dL) | 6 | 2 | 5 | 2 | 0.064 | ||||
Total cholesterol (mg/dL) | 182 | 33 | 194 | 46 | 0.071 | ||||
HDL (mg/dL) | 52 | 15 | 55 | 16 | 0.303 | ||||
LDL-C (mg/dL) | 101 | 32 | 111 | 44 | 0.220 | ||||
Triglycerides (mg/dL) | 132 | 72 | 135 | 73 | 0.843 | ||||
Haematocrit (%) | 37 | ± | 6 | 40 | ± | 5 | 0.001 | ||
Erythrocytes (x106/μl) | 3.9 | ± | 0.7 | 4.2 | ± | 0.7 | 0.006 | ||
Leukocytes (x103/μL) | 9.6 | 6.3 | 6.7 | 2.2 | 0.019 | ||||
Lymphocytes (%) | 28 | ± | 11 | 29 | ± | 8 | 0.029 | ||
Neutrophils (%) | 61 | 12 | 58 | 10 | 0.037 | ||||
Eosinophils (%) | 2.3 | ± | 1.6 | 2.5 | ± | 1.8 | 0.641 | ||
Basophils (%) | 0.55 | ± | 0.34 | 0.51 | ± | 0.42 | 0.076 | ||
Monocytes (%) | 7.6 | ± | 1.9 | 7.6 | ± | 2.1 | 0.836 | ||
Neutrophil/Lymphocyte ratio | 2.7 | 1.6 | 2.4 | 1.9 | 0.022 | ||||
iPTH (pg/mL) | 56 | 35 | 60 | 39 | 0.728 | ||||
Fibrinogen (mg/dL) | 416 | 138 | 396 | 120 | 0.186 | ||||
Homocystein (μM) | 11.5 | 8.5 | 11.1 | 9.1 | 0.341 | ||||
Lipoprotein A (mg/dL) | 48 | 37 | 59 | 60 | 0.737 | ||||
End-systolic volume (mL) | 20 | ± | 22 | 30 | ± | 31 | 0.011 |
Statistics. Values are expressed as mean ± SD or frequency (percentage). Continuous variables with normal distributions were compared using t test for independent samples. Continuous variables with abnormal distributions were compared using Mann-Whitney U test. For categorical variables, Fisher’s exact test was used.
Regarding urinary phytate levels, subjects without MAC had significantly higher levels of urinary phytate than subjects with MAC (
Statistics. Values are expressed as mean ± SE. Comparisons between groups were performed using Mann-Whitney U test. *
As can be seen in
Unadjusted Odds Ratio (O.R.) | (95% C.I.for O.R.) | p-value | Adjusted Odds Ratio (O.R.) | (95% C.I.for O.R.) | p-value | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Age (years) | 1.076 | ( | 1.038 | - | 1.115 | ) | <0.0001 | 1.083 | ( | 1.041 | - | 1.128 | ) | <0.0001 |
Phytate (μM) | 0.599 | ( | 0.387 | - | 0.929 | ) | 0.022 | 0.640 | ( | 0.414 | - | 0.990 | ) | 0.045 |
Leukocytes (x103/μL) | 1.138 | ( | 1.009 | - | 1.284 | ) | 0.035 | 1.152 | ( | 1.005 | - | 1.320 | ) | 0.042 |
Phosphorous (mg/dL) | 1.881 | ( | 1.224 | - | 2.892 | ) | 0.004 | 1.970 | ( | 1.199 | - | 3.239 | ) | 0.007 |
Diabetes (yes |
2.178 | ( | 1.127 | - | 4.208 | ) | 0.021 | |||||||
Hypertension (yes vs. no) | 1.832 | ( | 0.974 | - | 3.445 | ) | 0.060 | |||||||
Glucose (mg/dL) | 1.009 | ( | 1.000 | - | 1.018 | ) | 0.052 | |||||||
Total cholesterol (mg/dL) | 1.008 | ( | 0.999 | - | 1.016 | ) | 0.068 | |||||||
Neutrophil/Lymphocyte ratio | 1.079 | ( | 0.922 | - | 1.262 | ) | 0.342 |
Statistics: Univariate and multivariate binary logistic regressions were used to identify risk factors associated with presence of MAC. Multivariate analysis was performed using the stepwise backward method.
In this study we demonstrated a significantly inverse correlation between urinary phytate level and MAC. Phytate is a naturally occurring component in the diet and the FDA classifies it as GRAS. It is a highly polar substance, and its oral bioavailability is very low because of limited absorption [
However, bisphosphonates are non-hydrolysable pyrophosphate derivatives that have long physiological half-lives. Other research indicated that urinary phytate levels reflect the phytate status of an organism, and that there is a good correlation between plasma and urinary levels of phytate [
Phytate in humans and other mammals occurs in intracellular and extracellular compartments. Intracellular phytate (concentration range: 10–100 μM) occurs following sequential phosphorylation of lower phosphoinositides [
The major sources of dietary phytate are whole grains, legumes, beans, and vegetable seeds [
These previous studies and the results presented here support the view that dietary consumption of phytate is crucial for maintaining adequate physiological levels of this compound, and that a phytate-rich diet may help to protect against valve calcification. In fact, the reduced phytate consumption in many industrialized countries could be partially responsible for the increasing prevalence of vascular and valve calcification in these countries. The extent to which low phytate consumption is responsible for vascular and valve calcification is unclear, but dietary phytate deserves further attention as a potentially protective factor.
There is a clear tendency for improved cardiovascular health in subjects with higher levels of urinary phytate (
Regarding independent factors associated to MAC, previous studies have reported that age, serum phosphorus levels and leukocytes are strongly associated with vascular calcification [
Nevertheless, in our study, 67.0% of subjects present no MAC and among these subjects, 28.6% have more than 65 years. These finding are in line with previous observations [
According our results, phytate rich-food consumption can be one of these features that protect subjects from calcification. Dietary phytate treatment has demonstrated to reduce drastically age-related aortic calcification in rats [
To the best of our knowledge, the present study is the first prospective observational clinical data to identify a correlation between high urinary level of phytate and low cardiovascular (valve) calcification. These results suggest that increased consumption of phytate rich foods may help to prevent or minimize these dystrophic calcifications.
Relationship between Urinary Level of Phytate and Valvular Calcification in an Elderly Population: a Cross-Sectional Study.
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