Sage Journals: Discover world-class research

Abstract

Background

Serum high-sensitive C-reactive protein (hs-CRP) concentrations independently predict the development of diabetes, metabolic syndrome and cardiovascular disease. However, the impact of dietary factors on serum high-sensitive C-reactive protein concentrations in diabetic patients has received limited attention. We aimed to investigate the association between dietary factors and high-sensitive C-reactive protein , among diabetic patients with and without hypertension and healthy subjects.

Methods

In this cross-sectional study, diabetics with (n = 325) and without hypertension (n = 599) and healthy individuals (n = 1220) were recruited in Mashhad, Iran. Dietary intake was assessed by 24-h recall. Biochemical parameters including serum high-sensitive C-reactive protein were measured using standard protocols. Stepwise multiple regression analysis was used to predict whether serum high-sensitive C-reactive protein concentration was associated with dietary constituents.

Results

High-sensitive C-reactive protein was significantly higher among hypertensive and non-hypertensive diabetic patients compared with healthy subjects (P < 0.001). The dietary intake of zinc + 6.4% and calcium −3.4% and body mass index +3.9% explained approximately 13.7% of the variation in serum high-sensitive C-reactive protein among diabetic hypertensive patients. Approximately 9.7% of the variation in serum high-sensitive C-reactive protein in diabetic non-hypertensive patients could be explained by body mass index, and intake of sodium, iron and cholesterol. In the healthy subjects, approximately 4.4% of the total variation in serum high-sensitive C-reactive protein concentration could be explained by cholesterol consumption and waist circumference.

Conclusion

Serum high-sensitive C-reactive protein concentrations were found to be a significant predictor for hypertensive and non-hypertensive diabetic subjects. There was a significant association between dietary factors include zinc, iron, sodium and cholesterol and serum high-sensitive C-reactive protein, while there was an inverse association between dietary calcium and serum high-sensitive C-reactive protein in diabetic hypertensive individuals.

Keywords

High-sensitive C-reactive protein inflammation diabetes hypertension dietary intake

Introduction

The prevalence of diabetes mellitus is increasing globally and has become a challenging issue for public health in both developed and developing nations.^1,2 In 2015, the number of people with diabetes mellitus was estimated to be approximately 415 million by the International Diabetes Federation (IDF).³ Approximately 7.7% of Iranian adults (2 million adults) have diabetes mellitus.⁴

Raised serum inflammatory markers, that include high-sensitive C reactive protein (hs-CRP), are reported to be associated with several non-communicable conditions including obesity⁵ hypertension,⁶ metabolic syndrome,⁷ diabetes mellitus and cardiovascular disease.⁸ hs-CRP is a marker of systemic inflammation and is produced by the liver in response to pro-inflammatory cytokines such as interleukin 6 (IL-6).⁹

Inflammatory factors are thought to be involved in the pathogenesis of insulin resistance,¹⁰ diabetes mellitus¹¹ and metabolic syndrome,¹² and it has been suggested that an elevated hs-CRP should be added to the definition of metabolic syndrome.⁸ Although there is a positive association between several factors such as smoking,¹³ physical inactivity,¹⁴ waist circumferences (WCs),¹⁵ body mass index (BMI)¹⁵ and serum hs-CRP, the relationship between specific dietary components and serum hs-CRP is unclear. A significant inverse association has been shown between some dietary items including fruits and vegetables,¹⁶ fish and poultry,¹⁷ dietary fiber,¹⁸ oleic acid¹⁹ and serum hs-CRP, whereas a significant positive association has been reported between the consumption of red meat,²⁰ trans-fat,²¹ and saturated fat²² and serum hs-CRP. Other studies have reported no significant association between consumption of fiber,²³ carbohydrate, protein, total fat²² and trans fatty acid²⁴ and serum hs-CRP. In this study, we aimed to investigate the association between the intake of energy and macronutrients including carbohydrate, fat and protein, antioxidants including vitamins E and C, and the other specific dietary components such as potassium, calcium, magnesium, phosphorus, iron, copper, zinc, selenium, iodine, folate and sodium and the level of serum hs-CRP among diabetics with and without hypertension and within healthy subjects, derived from a large sample of Iranian adults.

Methods

Study design and subject selection

This cross-sectional study is a part of ongoing large cohort study entitled Mashhad stroke and heart atherosclerotic disorder (MASHAD) study.²⁵ As previously described in detail,²⁵ in the first phase of the MASHAD study, participants were drawn from three regions in Mashhad using stratified cluster randomized sampling. Each region was divided into nine sites centred upon Mashhad Healthcare Center divisions. Households with individuals of eligible age between 35 and 65 years were identified and contacted by telephone to arrange an appointment for the formal physical examination.²⁵ Finally, from a total of 2,427,117 residents of Mashhad city, 11,800 individuals were chosen to participate in the study, of whom a total of 9761 participants, mean age 48.1 years old agreed to participate. The demographic, anthropometric and lifestyle data were obtained by two expert health-care professionals and a nurse. Exclusion criteria were pregnancy and lactation, established cardiovascular disease, cancer or chronic kidney and consumption of dietary supplements. All diabetic subjects who were either hypertensive (n = 325) or non-hypertensive (n = 559) were selected for this sub-study. Healthy individuals (subjects without any history of cardiac disease, metabolic abnormalities or clinical disorders categorizes as healthy individuals), drawn from the same region were matched with the case participants as a healthy group (n = 1220) (control group).

Anthropometric and biochemical measurements

Anthropometric measurements, including weight, height, WC and hip circumference were measured by trained nurses. All of the anthropometric measurements were made in duplicate and the average value recorded. BMI was computed as weight in kilograms divided by the square of height in metres. Resting blood pressure was measured three times using a standardized protocol and the mean of these records was reported as subject’s blood pressure. Fasting blood samples were obtained early in morning after a 12 h fast and stored at −80℃. Enzymatic methods and auto-analyser (BT3000 biotechnical instruments company, Spain) were used to measure serum triglycerides (TG), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), fasting blood glucose (FBG) and hs-CRP. Low-density lipoprotein cholesterol (LDL-C) was calculated using Friedewald formula if serum TGs concentrations were lower than 400 mg/dL.²⁶ The intra- and inter-assay coefficients of variation of FBG were ≤1.72% and ≤1.16%, respectively. The intra- and inter-assay coefficients of variation of lipids profile were less than 5%. The intra- and inter-assay coefficients of variation of hs-CRP were ≤2.27% and ≤8.52%, respectively. The standard protocols for the anthropometric and biochemical assessment have been reported previously.²⁵

Assessments of dietary intakes

Data on the dietary intake of the study participants were collected using 24-h recall, by a trained interviewer. For the analysis of the dietary intake, Dietplan6 software was used (Forest field Software Ltd, UK).

Assessment of metabolic disorders

Blood pressure equal or greater than 140/90 mmHg was defined as hypertension.²⁷ Individuals with an FBG ≥7 mmol/L) or higher on two separate tests, or those under treatment with oral hypoglycaemic agents or insulin were considered to be diabetic.²⁸

Assessment of other variables

Demographic and lifestyle information including age, smoking status, medical history and drug use were obtained by expert interviewers using a structured questionnaire. Physical activity level (PAL) was measured using the James and Schofield human energy requirements equations²⁹ and was calculated as the total energy expenditure (TEE): basal metabolic rate (BMR) ratio over a 24-h period. Questions on physical activity were based on the James and Schofield equations and were selected from those used in the Scottish Heart Health Study (SHHS)/MONICA questionnaire. Questions were included to assess the time spent on activities during work (including housework), outside work and in bed (resting and sleeping).³⁰

Statistical methods

Statistical analysis was undertaken with SPSS software version 16.0 (SPSS® Inc., Chicago, IL). Normality of data was assessed by using the Kolomogorov-Smirnov test. We used parametric tests for normally distributed data and non-parametric test for non-normally distributed data. Between-group comparisons of continuous variables were assessed by one-way ANOVA for parametric data and Mann-Whitney for non-parametric data. Chi-square test was used for assessment of categorical data between groups. The dietary intakes were found to be non-normally distributed and were therefore compared using Mann-Whitney tests. Nutrient intakes adjusted for total energy intake through the residual method and expressed in grams, milligrams and micrograms. To predict the associations of serum hs-CRP concentrations and dietary intake, we used linear regression analysis. To enable adjustment for potential confounding factors, we entered into the equation the factors age, sex, smoking, PAL, BMI, WC, SBP, DBP, LDL-C, HDL-C, FBG, TC and TG. A P value of <0.05 was considered significant.

Results

General characteristics and anthropometric measurements in diabetic hypertensive, diabetic non-hypertensive patients and healthy subjects

The general characteristics and anthropometric measurements in diabetic hypertensive and non-hypertensive patients and healthy subjects are shown in Table 1. There were no significant differences between smoking status and the percentage of males and females between all three groups. However, participants in both diabetic groups were older than the healthy subjects (P < 0.001). In addition, diabetic hypertensive patients were older than diabetic non-hypertensive patients. BMI, WC, SBP and DBP were significantly higher subgroups of diabetic patients in comparison to the healthy subjects. Furthermore, hypertensive diabetic patients had a significantly higher BMI and WC, compared with diabetic non-hypertensive subjects. Moreover, patients in the diabetic hypertensive group were less active compared with diabetic non-hypertensive patients and healthy individuals (Table 1).

Table 1.

General characteristics and anthropometric measurements in patients and healthy subjects.^a

Groups
Variables	Diabetic hypertensive (n = 325)	Diabetic non-hypertensive (n = 559)	Healthy (n = 1220)	P ^b	P ^c	P ^d
Age (y)	54 ± 6.9^e	50.8 ± 7.4	46.5 ± 7.9	<0.001	<0.001	<0.001
Males (%)	40	37	40	0.94	0.23	0.33
Current smoking (%)	15.7	22	19.5	0.12	0.22	0.05
PAL (MET-h/d)	1.52 ± 0.3	1.58 ± 0.27	1.58 ± 0.26	0.001	0.97	0.002
BMI (kg/m²)	29.8 ± 4.6	28.3 ± 4.6	27.2 ± 4.5	<0.001	<0.001	<0.001
WC (cm)	101.6 ± 11.1	97.8 ± 11.7	93.5 ± 11.5	<0.001	<0.001	<0.001
SBP (mm Hg)	148 ± 16	118.4 ± 11.3	114.4 ± 11.7	<0.001	<0.001	<0.001
DBP (mmHg)	91.6 ± 8.4	75.9 ± 7.5	74.7 ± 8.2	<0.001	0.005	<0.001

PAL: physical activity level; BMI: body mass index; WC: waist circumference; SBP: systolic blood pressure; DBP: diastolic blood pressure.

Continuous data were compared by one-way ANOVA and categorical variables were compared by chi-square test.

Diabetic hypertensive in compared with the healthy.

Diabetic non-hypertensive in compared with the healthy.

Diabetic hypertensive in compared with the Diabetic non-hypertensive.

Mean ± standard deviation.

Clinical and biochemical parameters

Table 2 shows the clinical and biochemical measurements between patient group and healthy group. Serum hs-CRP, LDL, TC and TG were significantly higher among diabetic hypertensive and non-hypertensive patients, in comparison to the healthy subjects. Furthermore, TC and TG were significantly higher among diabetic hypertensive patients compared with non-hypertensive subjects. However, there were no significant differences between serum levels of HDL in all three groups (Table 2).

Table 2.

Serum biochemical measurements in patients and healthy subjects.^a

Groups
Variables	Diabetic hypertensive	Diabetic non-hypertensive	Healthy	P ^b	P ^c	P ^d
hs-CRP (nmol/L)	27.6 (7.1–48.1)^e	23.8 (4.5–43.1)	12.3 (4–20.6)	<0.001	<0.001	0.16
LDL-C (mmol/L)	3.25 ± 1.12^f	3.13 ± 1.03	3.01 ± 0.83	<0.001	0.01	0.3
HDL-C (mmol/L)	1.1 ± 0.25	1.08 ± 0.26	1.1 ± 0.25	0.93	0.23	0.62
FBG (mmol/L)	10.8 ± 3.31	10.8 ± 3.39	4.36 ± 0.55	<0.001	<0.001	0.77
TC (mmol/L)	5.52 ± 1.27	5.24 ± 1.19	4.84 ± 0.91	<0.001	<0.001	0.005
TG (mmol/L)	1.94 (1.26–2.62)	1.73 (1.03–2.42)	1.27 (0.82–1.72)	<0.001	<0.001	0.002

hs-CRP: high-sensitive C-reactive protein; LDL-C: low-density lipoprotein-cholesterol; HDL-C: high-density lipoprotein-cholesterol; FBG: fasting blood glucose; TC: total cholesterol; TG: triglyceride.

Continuous data were compared by one-way ANOVA or Mann-Whitney and categorical variables were compared by chi-square test.

Diabetic hypertensive in compared with the control.

Diabetic non-hypertensive in compared with the control.

Diabetic hypertensive in compared with the diabetic non-hypertensive.

Median (interquartile range).

Mean ± standard deviation.

Dietary intake of macro- and micronutrients in diabetic hypertensive, diabetic non-hypertensive patients and healthy subjects

There were no significant differences between the dietary energy, intake of fat and several dietary micronutrients including vitamin E, calcium, iron, copper, zinc, selenium, iodine and folate in diabetic hypertensive patients and healthy subjects (Table 3). In addition, there were no significant differences between dietary energy and dietary micronutrients including vitamins C and E, calcium, copper, selenium, iodine and folate in diabetic non-hypertensive patients and healthy individuals (Table 3). However, diabetic patients in both subgroups consumed significantly less carbohydrate and more protein, potassium, magnesium, phosphorus and sodium than healthy individuals. Moreover, a lower intake of vitamin C was found in the diabetic hypertensive patients compared with healthy subjects. Dietary intake of fat, iron and zinc were significantly higher among diabetic non-hypertensive patients (Table 3). The differences in dietary intake (including energy, macro and micronutrients) of diabetic hypertensive and non-hypertensive patients were not significant.

Table 3.

Comparison of macro- and micronutrient dietary intakes between patients and healthy subjects.^a

Groups
Dietary intakes	Diabetic hypertensive	Diabetic non-hypertensive	Healthy	P ^b	P ^c	P ^d
Macronutrients
Energy (kcal)	1966 (1555–2447)^e	1867 (1422–2310)	1820 (1443–2197)	0.14	0.54	0.42
Carbohydrate (g)	240.7 (201.7–279.7)	229.1 (196.9–261.2)	243.2 (212.6–273.8)	0.01	<0.001	0.33
Fat (g)	69 (55–83)	72.4 (58.7–86)	69.3 (257.3–281.3)	0.12	0.01	0.74
Protein (g)	69.3 (58.1–80.4)	72.4 (61.6–83.2)	68.3 (59–77.6)	0.04	<0.001	0.9
Antioxidants
Vitamin E (mg)	16.3 (9.2–23.3)	17.1 (9.9–24.3)	16.5 (10.8–22.2)	0.8	0.57	0.8
Vitamin C (mg)	61.6 (14.3–108.9)	78.6 (25.7–131.4)	77.5 (28.2–126.7)	0.03	0.56	0.4
Others
Potassium (mg)	2915 (2303–3527)	2898 (2238–3558)	2766 (2282–3250)	0.01	0.005	0.7
Calcium (mg)	859 (564–1154)	839 (576–1102)	841 (632–1049)	0.2	0.92	0.23
Magnesium (mg)	252.5 (190.1–314.7)	253.7 (210.5–296.8)	230.6 (150.6–310.6)	0.003	<0.001	0.78
Phosphorus (mg)	1377 (1120–1634)	1343 (1105–1581)	1286 (1101.3–1470.7)	0.001	0.005	0.24
Iron (mg)	9.5 (6–13)	10.8 (7.5–14)	9.5 (6.7–12.3)	0.2	<0.001	0.76
Cooper (mg)	1.8 (1.47–2.13)	1.8 (1.53–2.07)	1.7 (1.47–1.93)	0.34	0.3	0.41
Zinc (mg)	9.1 (7.1–11.1)	9.5 (7.5–11.5)	9 (7.4–10.6)	0.22	0.004	0.7
Selenium (mg)	29.4 (15.9–42.9)	31.3 (17.6–44.9)	34.2 (22.7–45.7)	0.07	0.4	0.65
Iodine (µg)	91.2 (32.4–149.9)	97.4 (35.5–159.2)	108.3 (50.3–166.3)	0.35	0.14	0.21
Folate (µg)	220.9 (144–297.7)	229.5 (166.3–292.6)	217.9 (159.8–276)	0.5	0.4	0.24
Sodium (mg)	2435 (1453–3416)	2425 (1510–3339)	1908 (1105–2710)	<0.001	<0.001	0.41

Obtained using Mann-Whitney test.

Diabetic hypertensive in compared with the healthy.

Diabetic non-hypertensive in compared with the healthy.

Diabetic hypertensive in compared with the diabetic non-hypertensive.

Median (interquartile range).

Multivariate analysis

The dietary intake of zinc (P = 0.001, +6.4%) and calcium (P = 0.02, −3.4%) and BMI values (P = 0.01, +3.9%) explained approximately 13.7% of the variation in serum hs-CRP among diabetic hypertensive patients using the best fitting models derived from stepwise multiple linear regression analysis (Table 4). Approximately, 9.7% of the variation in serum hs-CRP in diabetic non-hypertensive patients could be explained by BMI, and intake of sodium, iron and cholesterol (Table 5). In the healthy subjects, about 4.4% of total variation of hs-CRP concentration could be explained by cholesterol consumption and WC using a similar analysis (Table 6).

Table 4.

Multifactorial analysis of serum high sensitive C-reactive protein in diabetic hypertensive patients.^a

Variables	β	R² (%)	P
Zinc	0.914	6.4	0.001
BMI	0.526	3.9	0.01
Calcium	−0.005	3.4	0.02
Sodium	0.001	0.02	0.33
Iron	0.07	0.006	0.29
Cholesterol	0.006	0.01	0.13
WC	0.05	0.003	0.38

BMI: body mass index; WC: waist circumference.

Linear regression was used.

Table 5.

Multifactorial analysis of serum high sensitive C-reactive protein in diabetic non-hypertensive patients.^a

Variables	β	R² (%)	P
BMI	0.324	3.1	0.009
Sodium	0.001	2.9	0.005
Iron	0.226	2.1	0.025
Cholesterol	0.025	1.6	0.047
WC	0.06	0.006	0.19
Zinc	0.004	0.12	0.28
Calcium	−0.001	0.001	0.57

BMI: body mass index; WC: waist circumference.

Linear regression was used.

Table 6.

Multifactorial analysis of serum high sensitive C-reactive protein in healthy subjects.^a

Variables	β	R² (%)	P
Cholesterol	0.007	2.2	0.002
WC	0.104	2.2	0.002
BMI	−0.05	0.000	0.49
Sodium	0.001	0.000	0.88
Iron	0.04	0.001	0.36
Zinc	−0.01	0.000	0.81
Calcium	0.000	0.000	0.61

BMI: body mass index; WC: waist circumference.

Linear regression was used.

Discussion

The results of the current study showed that serum hs-CRP concentrations in diabetics with and without concomitant hypertension, were significantly higher than healthy individuals. In addition, some dietary items as well as some anthropometric parameters were significantly related to serum hs-CRP concentrations. To the best of our knowledge, this is the first study investigating the association between serum hs-CRP with dietary intake in healthy and diabetic hypertensive and diabetic non-hypertensive patients. It appears that hs-CRP concentrations increased in association with both diabetes mellitus⁸ and hypertension,⁶ and hypertension is highly prevalent among diabetic patients³¹; we therefore divided the diabetic patients into two subgroups.

While it has been shown previously that zinc supplementation reduces serum inflammatory markers such as hs-CRP and IL-6 among obese young women,³² in our study, zinc was positively associated with hs-CRP in the diabetic hypertensive patients, suggesting more studies particularly longitudinal studies might be needed to clarify this relationship.

We found a significant association between the intake of iron and hs-CRP among diabetic non-hypertensive patients. It has been shown previously that a high consumption of iron is associated with increased risk of coronary heart disease (CHD) and type 2 diabetes.^33,34 Indeed, it has been previously suggested that iron could cause an increased production of reactive oxygen species, oxidative stress and inflammation.³⁵ Results of a recent meta-analysis have shown that the intake of heme iron is positively associated with CHDs,³⁶ possibly related to the potential tissue-damaging inflammation which may be caused by heme iron in the oxidation of LDL.^36–38

We found that serum hs-CRP concentrations in both diabetic subgroup patients were approximately two-fold higher than healthy individuals. In addition, BMI was significantly associated with elevated serum hs-CRP in both diabetic groups. Earlier reports have indicated that obesity, BMI, insulin resistance and metabolic syndrome are positively associated with low-grade inflammation particularly elevated blood hs-CRP.^{12,15,39–42} It has been suggested that an increased production of inflammatory markers such as IL-6 and tumour necrosis factor-alpha (TNF-α) associated with obesity, result in an increased synthesis and secretion of hs-CRP by the liver, and this may also lead to insulin resistance and diabetes mellitus among obese individuals.^41,43 Following a healthy lifestyle, including a healthy diet and increased physical activity, both of which can normalize weight might be helpful to protect against elevated hs-CRP and several chronic diseases related to it.

We found an inverse association between dietary calcium and serum hs-CRP among diabetic hypertensive. There are limited data on the relationship between dietary calcium and serum hs-CRP concentrations. However, in a previous study examining the association between dairy products (highly source of calcium) intake and inflammatory markers such as hs-CRP and IL-6 and TNF-α, a significant reverse association was found between consumption of servings of dairy products per week and inflammatory markers.⁴⁴ In addition, in a study of Iranian women, a significant inverse association was found between the intake of low-fat dairy and hs-CRP though the relationship between intake of total dairy products and hs-CRP was not significant.⁴⁵ With regard to the inverse association between calcium intake and insulin resistance, obesity and CHD risk,^46–49 and the positive association between BMI and serum hs-CRP concnetration,^15,39 it is possible that our results on calcium intake and hs-CRP might be explained through an impact of dietary calcium on body weight.

Dietary intake of sodium was also associated with hs-CRP among diabetic non-hypertensive patients. A direct role of sodium in stimulation of inflammatory response and hypothesis that proposed sodium may increase the gene expression of inflammatory factors might be the possible mechanisms to explain the role of sodium in increasing inflammatory response.^50,51 An association between sodium intake and serum CRP, which may be influenced by BMI, was found in a previous observational study,⁵² which is consistent with our results. In their study, Zhu et al.⁵³ found a significant direct association between higher sodium intake and adipocyte dysfunction and inflammation among adolescents. However, in a recent randomized controlled clinical trial, investigating the effect of Dietary Approaches to Stop Hypertension (DASH diet), which contained restricted amount of sodium, on inflammation in gestational diabetes, there was no significant difference between serum hs-CRP among intervened and control groups.⁵⁴

We also found a significant positive association between cholesterol intake and hs-CRP concentration in the diabetic, non-hypertensive and healthy individuals. Hypercholesterolaemia and atherosclerosis are related to the consumption of high-fat, high-cholesterol diets, causing an accumulation of cholesterol in immune cells such as macrophages, which increases inflammatory response.⁵⁵ Furthermore, the results of a previous rodent study indicated that hepatic inflammation occurs by dietary cholesterol, rather than liver steatosis, suggesting dietary cholesterol should be considered as a salient factor of hepatic inflammation.⁵⁶

With respect to the significant positive association between WCs and plasma hs-CRP in healthy participants in this study, previous studies have investigated the relationship between component of metabolic syndrome and serum hs-CRP, and it has been shown that central obesity is a major determinant of increased hs-CRP in metabolic syndrome.⁵⁷ Similarly, other reports have indicated that WC is an important determinant of serum hs-CRP.^15,58 In a study conducted among healthy men, there was a significant association between abdominal adipose tissue and elevated hs-CRP, suggesting that although the best predictor of CRP levels is the amount of total body fat, abdominal fat deposition should be considered as a major determinant of an inflammatory metabolic state.⁵⁹

This study has some limitations. The cross-sectional study design cannot be used to impute causality. Furthermore, we obtained dietary intake of the study participants using self-report questionnaire, which indicated measurement bias. Finally, we used 24-h food recall to collect dietary intake. Although this method has been widely applied, it depends on memory and under- or over-reporting might lead to imprecision.

In conclusion, our findings highlight the significant association between serum hs-CRP concentration in diabetics with and without hypertension and demonstrate that BMI and some dietary constituents including zinc, iron, sodium and cholesterol have a significant association with serum hs-CRP. Dietary calcium might have a protective effect against elevated hs-CRP in diabetic patients. More longitudinal studies, particularly randomized clinical trial using more population will be required to clarify the exact association between serum hs-CRP and dietary intake in diabetic patients.

Footnotes

Acknowledgements

The participation of the staff of Cardiovascular Research Center of the Mashhad University of Medical Science is gratefully acknowledged. We thank all the participants in this study.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was approved and funded by Mashhad University of Medical Sciences (MUMS).

Ethical approval

This study was approved by the Mashhad University of Medical Sciences Ethics Committee; Reference number: 84135.

Guarantor

MGM.

Contributorship

MB and SSKH contributed equally to this work including study design, data management, data analysis and interpretation and writing the drafts of this project; AHB, ME, MS and MN: were involved in protocol development, gaining ethical approval, data collection and study conduction; GF: data interpretation and revision of the drafts. MGM and ME: Researched literature conceived the study and mentored all steps of the project. All authors reviewed and edited the manuscript and approved the final version of the manuscript.

References

Chen

Magliano

Zimmet

. The worldwide epidemiology of type 2 diabetes mellitus – present and future perspectives. Nat Rev Endocrinol 2012; 8: 228–236.

Zimmet

Alberti

Magliano

et al.

Diabetes mellitus statistics on prevalence and mortality: facts and fallacies. Nat Rev Endocrinol 2016; 12: 616–622.

Ogurtsova K, da Rocha Fernandes JD, Huang Y, et al. IDF Diabetes Atlas: Global estimates for the prevalence of diabetes for 2015 and 2040. Diabetes Research and Clinical Practice 2017; 128: 40–50.i.

Esteghamati

Gouya

Abbasi

et al.

prevalence of diabetes and impaired fasting glucose in the adult population of Iran national survey of risk factors for non-communicable diseases of Iran. Diabetes Care 2008; 31: 96–98.

Park

. Relationship of obesity and visceral adiposity with serum concentrations of CRP, TNF-α and IL-6. Diabetes Res Clin Pract 2005; 69: 29–35.

Mahmud

Feely

. Arterial stiffness is related to systemic inflammation in essential hypertension. Hypertension 2005; 46: 1118–1122.

Ridker

Wilson

Grundy

. Should C-reactive protein be added to metabolic syndrome and to assessment of global cardiovascular risk? Circulation 2004; 109: 2818–2825.

Haffner

. The metabolic syndrome: inflammation, diabetes mellitus, and cardiovascular disease. Am J Cardiol 2006; 97: 3–11.

Pepys

Hirschfield

. C-reactive protein: a critical update. J Clin Invest 2003; 111: 1805–1812.

10.

Shoelson

Lee

Goldfine

. Inflammation and insulin resistance. J Clin Invest 2006; 116: 1793–1801.

11.

Pickup

. Inflammation and activated innate immunity in the pathogenesis of type 2 diabetes. Diabetes Care 2004; 27: 813–823.

12.

Dandona

Aljada

Chaudhuri

et al.

Metabolic syndrome a comprehensive perspective based on interactions between obesity, diabetes, and inflammation. Circulation 2005; 111: 1448–1454.

13.

Bermudez

Rifai

Buring

et al.

Relation between markers of systemic vascular inflammation and smoking in women. Am J Cardiol 2002; 89: 1117–1119.

14.

Wang

et al.

Associations of physical activity with inflammatory factors, adipocytokines, and metabolic syndrome in middle-aged and older Chinese people. Circulation 2009; 119: 2969–2977.

15.

Huffman

Whisner

Zarini

et al.

Waist circumference and BMI in relation to serum high sensitivity C-reactive protein (hs-CRP) in Cuban Americans with and without type 2 diabetes. Int J Environ Res Public Health 2010; 7: 842–852.

16.

Kim

Han

C-J

et al.

Association between high sensitivity C-reactive protein and dietary intake in Vietnamese young women. Nutr Res Pract 2014; 8: 445–452.

17.

Lopez-Garcia

Schulze

Fung

et al.

Major dietary patterns are related to plasma concentrations of markers of inflammation and endothelial dysfunction. Am J Clin Nutr 2004; 80: 1029–1035.

18.

Griffith

Chasan-Taber

et al.

Association between dietary fiber and serum C-reactive protein. Am J Clin Nutr 2006; 83: 760–766.

19.

Yoneyama

Miura

Sasaki

et al.

Dietary intake of fatty acids and serum C-reactive protein in Japanese. J Epidemiol 2007; 17: 86–92.

20.

Montonen

Boeing

Fritsche

et al.

Consumption of red meat and whole-grain bread in relation to biomarkers of obesity, inflammation, glucose metabolism and oxidative stress. Eur J Nutr 2013; 52: 337–345.

21.

Lopez-Garcia

Schulze

Meigs

et al.

Consumption of trans fatty acids is related to plasma biomarkers of inflammation and endothelial dysfunction. J Nutr 2005; 135: 562–566.

22.

Arya

Isharwal

Misra

et al.

C-reactive protein and dietary nutrients in urban Asian Indian adolescents and young adults. Nutrition 2006; 22: 865–871.

23.

Hébert

et al.

Association between dietary fiber and markers of systemic inflammation in the Women's Health Initiative Observational Study. Nutrition 2008; 24: 941–949.

24.

Mozaffarian

Pischon

Hankinson

et al.

Dietary intake of trans fatty acids and systemic inflammation in women. Am J Clin Nutr 2004; 79: 606–612.

25.

Ghayour-Mobarhan

Moohebati

Esmaily

et al.

Mashhad stroke and heart atherosclerotic disorder (MASHAD) study: design, baseline characteristics and 10-year cardiovascular risk estimation. Int J Public Health 2015; 60: 1–12.

26.

Castelli

Garrison

Wilson

et al.

Incidence of coronary heart disease and lipoprotein cholesterol levels: the Framingham Study. JAMA 1986; 256: 2835–2838.

27.

Kearney

Whelton

Reynolds

et al.

Global burden of hypertension: analysis of worldwide data. Lancet 2005; 365: 217–223.

28.

Alberti

KGMM

Zimmet

. Definition, diagnosis and classification of diabetes mellitus and its complications. Part 1: diagnosis and classification of diabetes mellitus. Provisional report of a WHO consultation. Diabetic Med 1998; 15: 539–553.

29.

James

WPT

Schofield

. Human energy requirement, Oxford: Oxford University Press, 1990.

30.

James

WPT

Schofield

. Human energy requirements. A manual for planners and nutritionists, Oxford: Oxford University Press, 1990.

31.

Epstein

Sowers

. Diabetes mellitus and hypertension. Hypertension 1992; 19: 403–418.

32.

Kim

Ahn

. Effect of zinc supplementation on inflammatory markers and adipokines in young obese women. Biol Trace Element Res 2014; 157: 101–106.

33.

Bao

Rong

et al.

Dietary iron intake, body iron stores, and the risk of type 2 diabetes: a systematic review and meta-analysis. BMC Med 2012; 10: 119–119.

34.

Ascherio

Willett

Rimm

et al.

Dietary iron intake and risk of coronary disease among men. Circulation 1994; 89: 969–974.

35.

Wagener

Volk

H-D

Willis

et al.

Different faces of the heme-heme oxygenase system in inflammation. Pharmacol Rev 2003; 55: 551–571.

36.

Hunnicutt

Xun

. Dietary iron intake and body iron stores are associated with risk of coronary heart disease in a meta-analysis of prospective cohort studies. J Nutr 2014; 144: 359–366.

37.

Meyers

. The iron hypothesis: does iron play a role in atherosclerosis? Transfusion 2000; 40: 1023–1029.

38.

McCord

. Iron, free radicals, and oxidative injury. Semin Hematol 1998; 35: 5–12.

39.

Rawson

Freedson

Osganian

et al.

Body mass index, but not physical activity, is associated with C-reactive protein. Med Sci Sports Exerc 2003; 35: 1160–1166.

40.

Dandona

Aljada

Bandyopadhyay

. Inflammation: the link between insulin resistance, obesity and diabetes. Trends Immunol 2004; 25: 4–7.

41.

Bastard

J-P

Maachi

Lagathu

et al.

Recent advances in the relationship between obesity, inflammation, and insulin resistance. Eur Cytokine Netw 2006; 17: 4–12.

42.

Weisberg

McCann

Desai

et al.

Obesity is associated with macrophage accumulation in adipose tissue. J Clin Invest 2003; 112: 1796–1808.

43.

Barzilay

Abraham

Heckbert

et al.

The relation of markers of inflammation to the development of glucose disorders in the elderly the Cardiovascular Health Study. Diabetes 2001; 50: 2384–2389.

44.

Panagiotakos

Pitsavos

Zampelas

et al.

Dairy products consumption is associated with decreased levels of inflammatory markers related to cardiovascular disease in apparently healthy adults: the ATTICA study. J Am Coll Nutr 2010; 29: 357–364.

45.

Esmaillzadeh

Azadbakht

. Dairy consumption and circulating levels of inflammatory markers among Iranian women. Public Health Nutr 2010; 13: 1395–1402.

46.

Davies

Heaney

Recker

et al.

Calcium intake and body weight 1. J Clin Endocrinol Metab 2000; 85: 4635–4638.

47.

Jacqmain

Doucet

Després

J-P

et al.

Calcium intake, body composition, and lipoprotein-lipid concentrations in adults. Am J Clin Nutr 2003; 77: 1448–1452.

48.

Varenna

Binelli

Casari

et al.

Effects of dietary calcium intake on body weight and prevalence of osteoporosis in early postmenopausal women. Am J Clin Nutr 2007; 86: 639–644.

49.

Teegarden

. Calcium intake and reduction in weight or fat mass. J Nutr 2003; 133: 249S–251S.

50.

Telini

LSR

de Carvalho Beduschi

Caramori

JCT

et al.

Effect of dietary sodium restriction on body water, blood pressure, and inflammation in hemodialysis patients: a prospective randomized controlled study. Int Urol Nephrol 2014; 46: 91–97.

51.

Dinarello

. Hyperosmolar sodium chloride, p38 mitogen activated protein and cytokine-mediated inflammation. Semin Dial 2008; 22: 256–259.

52.

Fogarty

Lewis

McKeever

et al.

Is higher sodium intake associated with elevated systemic inflammation? A population-based study. Am J Clin Nutr 2009; 89: 1901–1904.

53.

Zhu

Pollock

Kotak

et al.

Dietary sodium, adiposity, and inflammation in healthy adolescents. Pediatrics 2014; 133: e635–e642.

54.

Asemi

Samimi

Tabassi

et al.

A randomized controlled clinical trial investigating the effect of DASH diet on insulin resistance, inflammation, and oxidative stress in gestational diabetes. Nutrition 2013; 29: 619–624.

55.

Tall

Yvan-Charvet

. Cholesterol, inflammation and innate immunity. Nat Rev Immunol 2015; 15: 104–116.

56.

Wouters

van Gorp

Bieghs

et al.

Dietary cholesterol, rather than liver steatosis, leads to hepatic inflammation in hyperlipidemic mouse models of nonalcoholic steatohepatitis. Hepatology 2008; 48: 474–486.

57.

Santos

Lopes

Guimaraes

et al.

Central obesity as a major determinant of increased high-sensitivity C-reactive protein in metabolic syndrome. Int J Obes 2005; 29: 1452–1456.

58.

Shemesh

Rowley

Jenkins

et al.

Differential association of C-reactive protein with adiposity in men and women in an Aboriginal community in northeast Arnhem Land of Australia. Int J Obes 2007; 31: 103–108.

59.

Lemieux

Pascot

Prud’homme

et al.

Elevated C-reactive protein another component of the atherothrombotic profile of abdominal obesity. Arterioscler Thromb Vasc Biol 2001; 21: 961–967.

Serum high-sensitive C-reactive protein is associated with dietary intakes in diabetic patients with and without hypertension: a cross-sectional study

Abstract

Background

Methods

Results

Conclusion

Keywords

Introduction

Methods

Study design and subject selection

Anthropometric and biochemical measurements

Assessments of dietary intakes

Assessment of metabolic disorders

Assessment of other variables

Statistical methods

Results

General characteristics and anthropometric measurements in diabetic hypertensive, diabetic non-hypertensive patients and healthy subjects

Clinical and biochemical parameters

Dietary intake of macro- and micronutrients in diabetic hypertensive, diabetic non-hypertensive patients and healthy subjects

Multivariate analysis

Discussion

Footnotes

Acknowledgements

Declaration of conflicting interests

Funding

Ethical approval

Guarantor

Contributorship

References