Literature DB >> 31827626

Zinc supplementation improves body weight management, inflammatory biomarkers and insulin resistance in individuals with obesity: a randomized, placebo-controlled, double-blind trial.

Hoda Khorsandi1, Omid Nikpayam2,3, Reyhaneh Yousefi1, Maryam Parandoosh1, Nima Hosseinzadeh4, Atoosa Saidpour1, Arman Ghorbani5.   

Abstract

BACKGROUND: The present study was designed to determine whether zinc supplementation would increase the effects of restricted calorie diet (RCD) on obesity. METHODS AND MATERIALS: A randomized, double-blind clinical trial was performed on 40 obese subjects who were randomly assigned to receive zinc supplements (30 mg/day) or placebo for a period of 15-weeks. Both groups were under a restricted calorie diet (~ 300 kcal lower than the estimated energy requirement). Anthropometric measurements, biochemical markers, appetite, and dietary intakes were determined during the study period.
RESULTS: The reductions of body weight, body mass index, waist circumference, and hip circumference were significantly higher in the zinc group compared to the placebo group (P = 0.032, 0.025, 0.003, and 0.0001, respectively). Lower levels of high sensitivity C-reactive protein, apelin, homeostatic model assessment of insulin resistance (HOMA-IR), and appetite score were observed in the zinc group in comparison with the placebo group (P = 0.0001, 0.001, 0.031 and 0.001 respectively).
CONCLUSION: This study indicates that Zn supplementation with a restricted calorie diet has favorable effects in reducing anthropometric measurements, inflammatory markers, insulin resistance and appetite in individuals with obesity, and may play an effective role in the treatment of obesity.Trial registration This clinical trial was registered at clinicaltrials.gov at the U.S. National Library of Medicine (NCT02516475).
© The Author(s) 2019.

Entities:  

Keywords:  Anthropometric measurements; Insulin resistance; Obesity; Zinc supplement; hs-CRP

Year:  2019        PMID: 31827626      PMCID: PMC6889702          DOI: 10.1186/s13098-019-0497-8

Source DB:  PubMed          Journal:  Diabetol Metab Syndr        ISSN: 1758-5996            Impact factor:   3.320


Background

As the etiology of obesity is complex [1], current interventions for weight management are only modestly successful [2]. Restricted calorie diets (RCD) are playing a fundamental role in prevention and treatment of obesity [3, 4]; but these diets often result in micronutrient deficiencies [5]. Furthermore, obesity and obesity-related inflammation are related to abnormal micronutrient status [5-7]. Among these micronutrients, zinc (Zn) deficiency is a common problem in obese individuals [8-10]. Furthermore, Zn has been reported as limiting nutrients in RDCs [11]. Previous studies have also demonstrated that plasma Zn level and dietary intake of Zn are insufficient in obese individuals [12-14]. So, it seems that further weight gain or development of obesity-related disorders may occur if the Zn deficiency is not corrected [15]. Payahoo et al. [16] also showed that daily intake of 30 mg Zn gluconate for 1 month decreased significantly body weight and body mass index (BMI). Two key assumptions about the possible mechanisms for the effects of Zn supplementation on weight loss are including appetite regulation [17] and improving insulin resistance (IR) [18, 19]. Another important aspect which worth to notice is the beneficial effects of dietary intake of Zn and plasma Zn level on inflammatory status [20, 21]. Zn has shown possible anti-inflammatory effects through cytokine signaling pathways [22] and the attenuation of plasma levels of Interleukin-6 (IL-6), tumor necrosis factor-α (TNF-α) and C-reactive protein (CRP) [23, 24]. Moreover, a growing body of literature has demonstrated that these inflammatory markers are directly or indirectly correlated with obesity-related IR through blocking the insulin signaling receptors activation in pancreatic β-cells [25]. More recently, apelin is also proposed as an adipokine mediator which might have an adaptive response to prevent chronic inflammation associated with obesity [26, 27]. Previous reports also imply that higher apelin levels are associated with both insulin resistance and chronic inflammation in individuals with obesity [28]. So, based on previous studies, low Zn concentration and high level of inflammatory markers maybe correlated to high BMI [29, 30], and it seems reasonable to assume that Zn supplementation may have favorable effects on weight loss or reversing obesity-related comorbidities such as IR. Therefore, this study was designed to evaluate the effects of daily intake of 30 mg Zn supplement along with RCD on anthropometric measurements, appetite, IR, and serum levels of inflammatory markers, apelin, and neuropeptide Y (NPY), in obese individuals.

Materials and methods

Study design and participants

This double-blind randomized clinical trial was conducted from December 2015 to April 2016. In order to detect a difference of 4.5 kg/m2 in the BMI and with respect to a pooled standard deviation of 26.21 kg/m2, obtaining from the study by Payahoo et al. [16], the sample size was calculated 20 subjects for each group. In this two-arm parallel study with two-tailed testing, a power (1–β) of 80% and α = 0.05 was used. Fifty healthy adults (men and women) with obesity and BMI more than 30 kg/m2 in the age range of 18–45 years were selected using convenience sampling from the Specialized Clinic of Nutrition & Diet Therapy located at the Faculty of Nutrition Sciences and Food Technology of Shahid Beheshti University of Medical Sciences in Tehran, Iran. In our study, exclusion criteria were the presence of pregnancy or lactation, chronic kidney or hepatic disease, autoimmune and infectious disease, chronic inflammatory diseases, recent surgery, smoking, having weight loss diets in the last 2 months, the use of Zn, calcium, or iron supplements in the last 2 months, and taking anticoagulant drugs, lipid-lowering or beta-blocker drugs. The primary outcomes were anthropometric measurements, and secondary outcome were appetite score, serum levels of inflammatory markers, apelin, NPY, glucose, Zn and insulin, and IR. The study protocol was approved by the Ethics Committee of the National Nutrition and Food Technology Research Institute of Iran (IR.SBMU.nntri.Rec.1394.407). The study was in adherence with the Declaration of Helsinki. Written informed consent was obtained from all subjects before initiating the study. This clinical trial was registered at clinicaltrials.gov at the U.S. National Library of Medicine (NCT02516475).

Randomization

The subjects were randomly allocated to either a Zn or placebo group by block randomization. A trained dietitian completed the block randomization with a block size of 4 and possible balanced combinations with 2 P (placebo) and 2 Z (Zn supplement) subjects, calculated as 6 blocks (ZZPP, PZPZ, PZZP, ZPZP, PPZZ, ZPPZ). Then, blocks were randomly chosen, using a simple random sampling method to determine the assignment of all the participants into the groups.

Intervention

During this study, subjects in the Zn group received 30 mg zinc sulfate as 1 capsule (between meals) while those in the placebo group received corresponding placebo capsules containing starch (also between meals). All capsules were produced by Dineh Iran Company, Tehran, Iran. According to the literature, zinc supplement is safe at a dose of 30 mg/day [31, 32]. Blinding was performed by a trained dietician, and the patients and researchers were kept blinded to the allocation. In addition, subjects in both Zn and placebo groups received a restricted calorie diet (RCD) with ~ 300 kcal lower than the estimated energy requirement based on the Mifflin-St Jeor equation in order to reduce their weight about 1 kg per month, and this RCD contained ~ 55% carbohydrate, ~ 15% protein and ~ 30% fat [33]. Adherence to the diet was monthly assessed by a registered dietitian. Participants were followed twice a month via telephone calls in order to ensure their compliance and were asked to maintain their usual physical activity level. They were also asked to return the remaining capsules, and based on the number of returned capsules by each subject and adherence to the diet, their degree of compliance was determined and the data of individuals with the degree of compliance more than 90% were analyzed at the end of the study.

Dietary intakes and appetite assessments

Dietary intakes of participations were assessed using a 3-day dietary recall (2 weekdays and 1 weekend day) at baseline and at the end of week 15. Individuals’ diets were analyzed by Nutritionist IV software (N Squared Computing, San Bruno, CA, USA). Basal metabolic rate (BMR) was calculated based on Mifflin and St Jeor et al. [34]. Underreporting was defined as a ratio reported energy intake by 3-day dietary recall/BMR < 1.1 [35]. Simplified nutritional appetite questionnaire (SNAQ), a valid 4-item questionnaire recommended for clinical purposes [36], were used to assess the appetite at baseline and week 15. The SNAQ items were as follows: #1, Appetite; #2, Feeling full; #3, Food tastes; #4, Feeling hunger, and the sum of the 4 items scores constitutes the total SNAQ score which ranges from 4 to 20. The total score of 4 to 14 and 15 to 20 indicates low and normal appetite, respectively [36].

Anthropometric assessments

Weight was measured with minimum clothes and without shoes using a calibrated scale (Seca, CA, USA) and precision of 100 g. Height was measured using a wall-mounted stadiometer with the precision of 0.5 cm. Hip and waist circumference were also measured using an inflexible tapeline with the precision of 0.5 cm, in the narrowest circumference below the rib cage and above the umbilicus and the largest circumference between the waist and knees, respectively [37]. BMI was calculated as the ratio of weight (kg)/height2 (m2). Anthropometric parameters were measured at baseline and at the end of weeks 7 and 15.

Physical activity assessment

Physical activity level was estimated using a valid and reliable physical activity questionnaire [38] and calculating metabolic equivalent (MET) at baseline and the end of the study.

Blood samples and biochemical assessments

A sample of 5 ml blood was collected from all participants after a 12 to 14 h fast, at baseline and at the end of week 15. These samples were centrifuged at 4000 rpm for 15 min. The samples of serum were separated into small aliquots and were frozen at − 80 °C. For Zn analysis, all tubes were washed by acid and rinsed with distilled water, then atomic absorption spectrometry (variant Chemthech Analytical 2000) was used to determine serum Zn concentration [39, 40]. Serum concentration of high-sensitivity C-reactive protein (hs-CRP) was determined by enzyme-linked immunosorbent assay (ELISA) kits (Diagnostics Biochem Canada, Ontario, Canada) with an intra-assay coefficient of variation (CV) of 7.2%. Serum TNF-α was measured by ELISA kits (Diaclone, Besancon, France). Intra-assay CV for serum TNF-αwas 6.5%. Serum apelin concentration was assessed by ELISA kits (ZellBio GmbH, Ulm, Germany), with an intra-assay CV of 7.2%. Serum insulin was determined by ELISA kits (Monobind, USA), with an intra-assay CV of 7.4%. Serum glucosewas measured by commercial kits (Pars Azemoon, Tehran, Iran) with the aid of a Selectra 2 Autoanalyzer (Vital Scientific, Spankeren, The Netherlands). Homeostatic Model Assessment of Insulin Resistance (HOMA-IR) was determined using the following equation:

Statistical analysis

Intention-to-treat principle was applied for anthropometric and dietary intake variables. Per-protocol analysis (PPA) was performed for analyzing the biochemical data. Data analysis was performed using SPSS version 20. The results are presented as mean (± SD) and frequency (percent) for quantitative and qualitative variables, respectively. The Kolmogorov–Smirnov test was used to assess normal distribution of data. None normal data distribution has been presented as 25/75 IQR. Natural log transformations on plasma Zn, insulin, TNF-α, NPY, apelin and HOMA-IR were transformed through Box-Cox transformation. To compare qualitative variables between the two groups, the Chi square test was used. We used a t test and paired t-test to compare quantitative parameters between and within groups, respectively. In addition, because anthropometric parameters were measured 3 times during the study, analysis of variance for repeated measurements was used to compare data between various times. Analysis of covariance was performed in order to remove the effect of confounding factors. In this study, P values of less than 0.05 were considered statistically significant.

Results

Of the 50 subjects initially enrolled, 10 subjects were eliminated because of non- compliance and medical treatment (Fig. 1). The baseline characteristics of the subjects did not differ significantly between the two groups (Table 1).
Fig. 1

Flow diagram of the study

Table 1

Baseline characteristics of subjects in the Zinc and Placebo groups

CharacteristicsZinc (n = 18)Placebo (n = 22)
Age (years)a35.63 ± 3.232.95 ± 1.7
Sex (n/%)
 Male6 (24%)8 (32%)
 Female19 (76%)17 (68%)
Past experiences with weight-reducing treatment (n/%)
 Yes15 (60%)13 (52%)
 No10 (40%)12 (48%)
Marital status (n/%)
 Single8 (32%)9 (36%)
 Married17 (68%)16 (64%)

aValues are mean ± SD

Flow diagram of the study Baseline characteristics of subjects in the Zinc and Placebo groups aValues are mean ± SD

Dietary intakes and physical activity

As is shown in Table 2, dietary intakes of energy, protein, carbohydrate, fat, saturated fatty acids (SAFA), monounsaturated fatty acids (MUFA), polyunsaturated fatty acids (PUFA), cholesterol, and Zn were not significantly different between the groups at baseline and the end of week 15. No significant changes were observed in physical activity levels between the two groups during the study.
Table 2

Dietary intakes and physical activity in the Zinc and Placebo groups

VariablesZinc groupPlacebo groupp-value
BaselineWeek 15Mean changeBaselineWeek 15Mean changeP1P2P3
Energy (kcal/d)1727.12 ± 378.761463.49  ±  424.07− 263.62 ± 514.361643.97 ± 476.191542.96 ± 500.29− 101.01 ±  481. 080.4980.5470.254
P40.0170.304
Protein (g/d)64.34  ± 11.4260.64 ± 7.16− 9.63 ± 6.9869.73 ± 11.7861.44 ± 19.38− 7.52 ± 18.990.1560.6880.453
P40.2090.057
Carbohydrate (g/d)237.99 ± 60.18201.79 ± 65.89− 36.27 ± 82.69223.19 ± 55.26208.57 ± 69.33− 14.62 ± 57.370.3700.7220.288
P40.0380.215
Fat (g/d)58.83 ± 15.4350.64 ± 15.36− 8.18 ± 22.2855.31 ± 19.7649.23 ± 15.93− 6.07 ± 20.930.4860.7520.731
P40.0790.160
SFA (g/d)14.01 ± 4.5512.64 ± 3.91− 1.37 ± 5.2516.34 ± 15.0614.84 ± 4.95− 1.50 ± 14.450.4830.0890.965
P40.2050.607
MUFA (g/d)18.41 ± 5.7817.69 ± 5.48− .71 ± 4.1017.79 ± 6.5218.98 ± 5.250.53 ± 8.000.7220.6830.491
P40.3910.742
PUFA (g/d)23.70 ± 8.8418.95 ± 8.68− 4.75 ± 10.6820.57 ± 8.9216.23 ± 8.58− 4.34  ± 10.040.2190.2720.88
P40.0360.041
Cholesterol (mg/d)179.86 ± 87.60184.76 ± 92.004.89 ± 110.06189.21 ± 67.111999.92 ± 86.2110.71  ± 77.500.6740.5500.830
P40.8260.496
Zinc (mg/d)14.01 ± 1.1414.02 ± 0.740.19 ± 0.9413.5 ± 1.1813.80 ± 1.340.30 ± 0.700.1270.2040.624
P40.3170.04
Physical activity (MET/day)32.87 ± 3.4032.77 ± 3.62− 0.09 ± 0.6732.23 ± 4.3832.44 ± 4.330.21 ± 0.650.5670.7700.107
P40.4810.119

All values are mean ± SD

SFA saturated fatty acids, MUFA monounsaturated fatty acid, PUFA polyunsaturated fatty acid

P1: p-values for comparison of variables between two group by independent T-test at baseline

P2: p-values for comparison of variables between two group by independent T-test at week 15

P3: p-values for comparison of mean change of variables between two group by independent T-test

P4: p-values for comparison of variables within groups by Paired T-test

Dietary intakes and physical activity in the Zinc and Placebo groups All values are mean ± SD SFA saturated fatty acids, MUFA monounsaturated fatty acid, PUFA polyunsaturated fatty acid P1: p-values for comparison of variables between two group by independent T-test at baseline P2: p-values for comparison of variables between two group by independent T-test at week 15 P3: p-values for comparison of mean change of variables between two group by independent T-test P4: p-values for comparison of variables within groups by Paired T-test

Effects on anthropometric measurements

Weight, BMI, waist circumference and hip circumference decreased in both groups compared to baseline. However, the reductions of weight (P = 0.032), BMI (P = 0.025), waist circumference (P = 0.003) and hip circumference (P = 0.0001) were significantly higher in the Zn group than in the placebo group (Table 3). No significant change was observed in WHR within each group during the study (Table 3).
Table 3

Anthropometric parameters in Zinc and Control groups

VariableTimeZinc groupPlacebo groupP*
Body weight (kg)Baseline89.59 ± 17.1088.41 ± 12.460.781
Week 783.16 ± 14.5287.09 ± 12.530.312
Week 1584.99 ± 13.4186.93 ± 12.340.597
P**0.0200.007
Mean changea− 4.60 ± 8.80− 1.48 ± 2.370.093
P***0.032
BMI (kg/m2)Baseline33.17  ± 6.3432.64 ± 2.370.701
Week 730.66 ± 4.1032.16 ± 2.630.129
Week 1531.50 ± 5.0832.09 ± 2.310.599
P**0.0240.007
Mean change− 1.66 ± 3.33− 0.55 ± .890.113
P***0.025
Waist circumference (cm)Baseline99.48 ± 10.1999.32 ± 9.420.954
Week 796.80  ± 10.0798.10 ± 9.440.639
Week 1594.36 ± 10.3197.82 ± 9.900.231
P***0.1050.023
Mean change− 5.12 ± 6.67− 1.49 ± 3.520.020
P***0.003
Hip circumference (cm)Baseline114.72 ± 8.77115.16 ± 5.490.833
Week 7111.83 ± 8.17114.68 ± 5.640.159
Week 15109.84 ± 7.53114.68 ± 5.680.013
P**0.00010.063
Mean change− 4.88 ± 3.58− 0.48 ± 1.040.0001
P***0.0001
WHRBaseline0.87 ± 0.090.86 ± 0.060.734
Week 70.86 ± 0.070.85 ± 0.060.597
Week 150.86 ± 0.110.85 ± 0.070.710
P**0.8640.1490.880
Mean change− 0.07 ± 0.053− 0.009 ± 0.028
P***0.682

All values are mean ± SD

aMean change for the 15-week period

P*: p-values for comparison of variables between two group by independent T-test

P**: p-values for comparison of variables within groups by analysis of variance for repeated measurement

P***: p-values for comparison between mean changes of variables by Analysis of covariance (adjusted for age, mean change of calorie intake, mean change of zinc intake)

Anthropometric parameters in Zinc and Control groups All values are mean ± SD aMean change for the 15-week period P*: p-values for comparison of variables between two group by independent T-test P**: p-values for comparison of variables within groups by analysis of variance for repeated measurement P***: p-values for comparison between mean changes of variables by Analysis of covariance (adjusted for age, mean change of calorie intake, mean change of zinc intake)

Effects on biochemical markers and appetite

Serum zinc concentration increased significantly in the Zn group at the end of week 15 compared with baseline (P = 0.0001), whereas no significant change was observed in the placebo group. The increment of serum zinc concentration in the Zn group was significant in comparison with the placebo group (P = 0.002; Table 4). Serum hs-CRP reduced significantly in the Zn group at the end of week 15 in comparison with baseline (P = 0.0001), whereas no significant change was observed in the placebo group. The reduction of serum hs-CRP in the Zn group was significant in comparison with the placebo group (P = .0001; Table 4). Serum TNF-α concentration did not significantly change within each group during the study (Table 4). Serum apelin reduced significantly in the Zn group at the end of week 15 in comparison with baseline (P = 0.042), whereas it increased significantly in the placebo group (P = 0.001). The reduction of serum apelin in the Zn group was significant in comparison with the placebo group (P = 0.001; Table 4). Serum glucose (P = 0.046) and insulin (P = 0.002) reduced significantly in the Zn group at the end of week 15 in comparison with baseline. However, these reductions in the Zn group were not significant in comparison with the placebo. In addition, HOMA-IR decreased significantly in the Zn group at the end of week 15 in comparison with baseline (P = 0.0001), whereas no significant change was observed in the placebo group. The reduction of HOMA-IR in the Zn group was significant in comparison with the placebo group (P = .031; Table 4). Serum NPY decreased in the Zn group and this reduction was significant in comparison with the placebo group (Table 4; P = 0.048); however, after statistical adjustment for age and calorie intake, the reduction of NPY in the Zn group was not significant in comparison with the placebo group. Appetite score decreased significantly in the Zn group at the end of week 15 in comparison with baseline (P = 0.004), whereas no significant change was observed in the placebo group. The reduction of appetite score in the Zn group was significant in comparison with the placebo group (P = .001; Table 4).
Table 4

Biochemical markers and appetite in the Zinc and Placebo groups

VariablesZinc groupPlacebo groupp-value
BaselineWeek 15Mean changeBaselineWeek 15Mean changeP2P3P4P5
Zinc (µg/dL)165.2 ± 5.975.4 ±  8.210.2 ±  6.871.15 ± 13.268.15 ± 10− 3  ± 13.10.0860.0180.00010.002
P10.00010.296
hs-CRP (mg/L)15.27 ± 2.93b3.37 ± 2.24− 1.89 ± 1.604.75 ± 2.283.98 ± 2.04− 0.07 ± 1.80.1240.0640.00010.0001
P10.00010.60
TNF-α (pg/ml) 232.41 (10.92, 708.48) a30.43 (11.79, 546.19)− 1.9826.07 (13.19, 67.47)25.07 (9.62, 94.65)− 1.00.1700.2380.2930.723
P10.4730.451
Apelin (pg/ml)21568.20 (1119, 3282)1245.13 (482, 2087)− 323.071493.45 (505, 4467)1683.32 (963, 3804)189.870.8050.0170.0020.001
P10.0420.001
FBS (mg/dL)186.83 ± 11.9483.50 ± 7.36− 3.33 ± 6.5686.90 ± 9.9386.90 ± 13.420.00 ± 6.000.9830.3430.1020.088
P10.0461.00
Insulin (microU/L)25.91 (1.7,18.60)4.05 (1,9.1)− 1.865.07 (1.8, 22.6)5.08 (1.7, 27.2)0.010.1350.330.0190.073
P10.0020.735
HOMA-IR21.35 (0.37, 2.81)0.83 (0.21, 2.27)− 0.521.02 (0.41, 5.88)1.08 (0.33, 9.27)0.060.1600.2940.0110.031
P10.00010.757
NPY (ng/l)2306.1 (178.8, 1540.5)273.8 (170.1, 1022.8)− 32.27400.9 (198.9, 2239.2)411.6 (180.6, 1900.9)10.720.1470.0370.0480.151
P10.1140.243
Appetite116.00 ± 2.1113.77 ± 2.36− 2.22 ± 2.8115.50 ± 1.8015.45 ± 1.59− 0.04 ± 1.590.4260.0110.0070.001
P10.0040.892

aValues are geometric mean (minimum, maximum)

bValues are mean ± SD

P1: p-values for comparison of variables within groups by Paired T-test

P2: p-values for comparison of variables between two groups by independent T-test at baseline

P3: p-values for comparison of variables between two group by independent T-test at week15

P4: p-values for comparison of mean change of variables between two group by independent T-test

P5: p-values for comparison between mean changes of variables by Analysis of covariance (adjusted for age, and mean change of calorie intake)

Biochemical markers and appetite in the Zinc and Placebo groups aValues are geometric mean (minimum, maximum) bValues are mean ± SD P1: p-values for comparison of variables within groups by Paired T-test P2: p-values for comparison of variables between two groups by independent T-test at baseline P3: p-values for comparison of variables between two group by independent T-test at week15 P4: p-values for comparison of mean change of variables between two group by independent T-test P5: p-values for comparison between mean changes of variables by Analysis of covariance (adjusted for age, and mean change of calorie intake)

Discussion

In our study, mean serum zinc in the Zn group (65.2 ± 5.9 µg/dL) was lower than normal range (70–120 µg/dL) at baseline [41]. At the end of week 15, mean serum zinc increased significantly in the Zn group (75.4 ± 8.2 µg/dL), whereas no significant change was observed in the placebo group. In the present study, weight, BMI, waist circumference and hip circumference decreased in both groups compared to baseline. However, the reductions of these anthropometric parameters were significantly higher in the Zn group than in the placebo group. To our knowledge, this is the first study to evaluate the co-administration of Zn supplement and RCD in individuals with obesity. In agreement with the present study, Payahoo et al. [16] showed that daily administration of 30 mg zinc gluconate for 1 month reduced body weight, BMI and waist circumferences in the healthy obese adults. It is documented that body weight management requires restricting energy intake, and increasing energy expenditure [42]. No significant changes were observed in physical activity levels between the two groups. In our study, although the difference in energy intake between the two groups was not statistically significant, the reduction of energy intake was higher in the Zn group than in the placebo group. Based on previous studies, it seems that improvement in Zn status could have beneficial effects on food intake regulation [43]. One of the suggested mechanisms may be related to the favorable effect of improvement in Zn status on leptin regulation for inhibiting eating behaviors through reduction in neuropeptide Y mRNA level [44]. Zn deficiency and obesity can lead to leptin resistance which may increase NPY levels in the hypothalamus of rodents and men [45]. Previous findings also report that Zn deficiency can cause a 50% increase in NPY levels [46], but despite the higher level of NPY in Zn deficient rats, their food intake is reduced because of NPY resistance [46, 47]. On the other hand, previous reports imply that Zn has an essential role in serotonin synthesis which stimulates satiety and reduce food intake [48]. Based on our knowledge, the functional role of zinc status in weight or appetite management of individuals with obesity has not been revealed. However, the role of lower plasma zinc level in inhibiting TSH secretion [49] and the involvement of zinc in the production, storage and release of insulin were also previously showed [50]. So it seems reasonable that zinc level may have an essential role in weight or appetite management of individuals with obesity. In our study, the baseline Zn level was below than the normal range (70–120 µg/dL) [51] in the Zn group; however, Zn levels turn to a normal status after the supplementation. In agreement with previous studies, serum NPY decreased in the Zn group and this reduction was significant in comparison with the placebo group. In addition, appetite score decreased significantly in the Zn group at the end of week 15 in comparison with baseline, and this reduction was significant in comparison with the placebo group. Welch et al. [52] also documented that NPY not only effect on food intake but also seems to be associated with macronutrient selection, such a way that increase carbohydrate intake. In agreement with Welch et al. study, carbohydrate and fat intakes were significantly reduced in the Zn group as compared to the placebo group in our study. In the present study, serum hs-CRP, an inflammatory marker, reduced significantly in the Zn group at the end of week 15 in comparison with baseline, and this reduction was significant in comparison with the placebo group. Inflammation is one of the main complications of obesity [53] and weight loss through dietary restriction may have a favorable effect on obesity-related inflammatory status [54]. Selvin et al. [55] suggested that a 1 kg weight loss through changes in diet and lifestyle will lead to a 0.13 mg/L reduction in serum CRP level. In our study, Serum TNF-α concentration did not significantly change in the Zn group. In agreement with this study, Kim et al. [30] did not find any significant reduction in serum TNF-α after a 8-week supplementation with Zn. In addition, serum apelin, an adipose tissue inflammatory biomarker [28], reduced significantly in the Zn group at the end of week 15 in comparison with baseline, and this reduction was significant in comparison with the placebo group. To our knowledge, no studies to date have evaluated the effects of Zn supplementation on apelin levels; however, some studies revealed that weight loss with RCD can cause a significant reduction in apelin level [28, 56] which seems this reduction has been largely attributed to decreased inflammation or increased insulin sensitivity [28, 55–62]. Serum glucose and HOMA-IR reduced significantly in the Zn group at the end of week 15 in comparison with baseline. Insulin sensitivity improvement is documented in previous weight loss interventions using calorie restriction [57-59]. It has been shown that a 5–10% weight loss increases insulin sensitivity [60, 61]. However, the effectiveness of the Zn supplementation on IR is controversial [18, 62, 63]. It seems that zinc supplementation with longer duration has more favorable effects on IR or glucose tolerance [64, 65]. One of the probable mechanisms for the beneficial effects of Zn on IR may be related to decreased inflammation [66]. Few studies have proposed that higher levels of hs-CRP are associated with insulin resistance and hyperinsulinemia [67-69]. Furthermore, the role of apelin in the development of insulin resistance has also attracted a lot of attention in the recent years [70, 71]. It has been shown that apelin level is higher in insulin-resistant individuals and it has also been suggested that apelin can inhibit the insulin secretion [70, 72, 73]. The proposed mechanisms for the role of apelin in insulin sensitivity include direct effects on glucose uptake or insulin signaling pathways and indirect effects on energy metabolism [28]. A limitation of our study was the small sample size.

Conclusion

This study indicates that Zn supplementation with a restricted calorie diet has favorable effects in reducing anthropometric measurements, inflammatory markers, insulin resistance and appetite in individuals with obesity, and may play an effective role in the treatment of obesity.
  63 in total

Review 1.  Critical evaluation of energy intake data using fundamental principles of energy physiology: 1. Derivation of cut-off limits to identify under-recording.

Authors:  G R Goldberg; A E Black; S A Jebb; T J Cole; P R Murgatroyd; W A Coward; A M Prentice
Journal:  Eur J Clin Nutr       Date:  1991-12       Impact factor: 4.016

Review 2.  The effect of weight loss on C-reactive protein: a systematic review.

Authors:  Elizabeth Selvin; Nina P Paynter; Thomas P Erlinger
Journal:  Arch Intern Med       Date:  2007-01-08

3.  Direct measurement of zinc in plasma by atomic absorption spectroscopy.

Authors:  J C Smith; G P Butrimovitz; W C Purdy
Journal:  Clin Chem       Date:  1979-08       Impact factor: 8.327

4.  Cyclic feeding behaviour and changes in hypothalamic galanin and neuropeptide Y gene expression induced by zinc deficiency in the rat.

Authors:  P L Selvais; C Labuche; X N Nguyen; J M Ketelslegers; J F Denef; D M Maiter
Journal:  J Neuroendocrinol       Date:  1997-01       Impact factor: 3.627

Review 5.  Zinc and its role in immunity and inflammation.

Authors:  Paola Bonaventura; Giulia Benedetti; Francis Albarède; Pierre Miossec
Journal:  Autoimmun Rev       Date:  2014-11-24       Impact factor: 9.754

Review 6.  Review: Apelin in disease.

Authors:  Hanna Antushevich; Maciej Wójcik
Journal:  Clin Chim Acta       Date:  2018-05-08       Impact factor: 3.786

Review 7.  Impact of micronutrient deficiencies on obesity.

Authors:  Olga P García; Kurt Z Long; Jorge L Rosado
Journal:  Nutr Rev       Date:  2009-10       Impact factor: 7.110

Review 8.  Adipokines as emerging mediators of immune response and inflammation.

Authors:  Francisca Lago; Carlos Dieguez; Juan Gómez-Reino; Oreste Gualillo
Journal:  Nat Clin Pract Rheumatol       Date:  2007-12

9.  Effect of zinc supplementation on markers of insulin resistance, oxidative stress, and inflammation among prepubescent children with metabolic syndrome.

Authors:  Roya Kelishadi; Mahin Hashemipour; Khosrow Adeli; Naser Tavakoli; Ahmad Movahedian-Attar; Javad Shapouri; Parinaz Poursafa; Akbar Rouzbahani
Journal:  Metab Syndr Relat Disord       Date:  2010-10-28       Impact factor: 1.894

10.  Opposite effects of zinc and melatonin on thyroid hormones in rats.

Authors:  Abdulkerim Kasim Baltaci; Rasim Mogulkoc; Aylin Kul; Cem Seref Bediz; Aysegul Ugur
Journal:  Toxicology       Date:  2004-01-15       Impact factor: 4.221

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  16 in total

1.  Total plasma magnesium, zinc, copper and selenium concentrations in obese patients before and after bariatric surgery.

Authors:  Stephen J Hierons; Anthony Catchpole; Kazim Abbas; Wingzou Wong; Mathew S Giles; Glenn V Miller; Ramzi A Ajjan; Alan J Stewart
Journal:  Biometals       Date:  2022-02-09       Impact factor: 2.949

2.  Impact of Zinc to Copper Ratio and Lipocalin 2 in Obese Patients Undergoing Sleeve Gastrectomy.

Authors:  Hala M Demerdash; Ahmed A Sabry; Omar E Arida
Journal:  Biomed Res Int       Date:  2022-06-10       Impact factor: 3.246

Review 3.  The Role of Nutrition on Meta-inflammation: Insights and Potential Targets in Communicable and Chronic Disease Management.

Authors:  Omar Ramos-Lopez; Diego Martinez-Urbistondo; Juan A Vargas-Nuñez; J Alfredo Martinez
Journal:  Curr Obes Rep       Date:  2022-10-18

4.  Zinc.

Authors:  Anatoly V Skalny; Michael Aschner; Alexey A Tinkov
Journal:  Adv Food Nutr Res       Date:  2021-05-24

Review 5.  The role of labile Zn2+ and Zn2+-transporters in the pathophysiology of mitochondria dysfunction in cardiomyocytes.

Authors:  Belma Turan; Erkan Tuncay
Journal:  Mol Cell Biochem       Date:  2020-11-22       Impact factor: 3.396

6.  Essential sufficiency of zinc, ω-3 polyunsaturated fatty acids, vitamin D and magnesium for prevention and treatment of COVID-19, diabetes, cardiovascular diseases, lung diseases and cancer.

Authors:  Michael J Story
Journal:  Biochimie       Date:  2021-05-31       Impact factor: 4.079

Review 7.  Zinc Deficiency-An Independent Risk Factor in the Pathogenesis of Haemorrhagic Stroke?

Authors:  Kurt Grüngreiff; Thomas Gottstein; Dirk Reinhold
Journal:  Nutrients       Date:  2020-11-19       Impact factor: 5.717

8.  Trace Element and Mineral Levels in Serum, Hair, and Urine of Obese Women in Relation to Body Composition, Blood Pressure, Lipid Profile, and Insulin Resistance.

Authors:  Alexey A Tinkov; Paweł Bogdański; Damian Skrypnik; Katarzyna Skrypnik; Anatoly V Skalny; Jan Aaseth; Margarita G Skalnaya; Joanna Suliburska
Journal:  Biomolecules       Date:  2021-05-04

9.  Association between dietary mineral nutrient intake, body mass index, and waist circumference in U.S. adults using quantile regression analysis NHANES 2007-2014.

Authors:  Shan Jiang; Xiaoyu Ma; Meng Li; Shoumeng Yan; Hantong Zhao; Yingan Pan; Changcong Wang; Yan Yao; Lina Jin; Bo Li
Journal:  PeerJ       Date:  2020-05-04       Impact factor: 2.984

10.  COVID-19 and nutriceutical therapies, especially using zinc to supplement antimicrobials.

Authors:  Desley Butters; Michael Whitehouse
Journal:  Inflammopharmacology       Date:  2020-11-16       Impact factor: 4.473

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