Research article Special Issues

Real Energy Payback Time and Carbon Footprint of a GCPVS

  • Received: 22 October 2016 Accepted: 02 January 2017 Published: 09 January 2017
  • Grid connected PV systems, or GCPVS, produce clean and renewable energy through the photovoltaic effect in the operation stage of the power plant. However, this is the penultimate stage of the facilities before its dismantlement. Before starting generating electricity with zero CO2 emissions, a negative energy balance exists mainly because of the embodied energy costs of the PV components manufacturing, transport and late dismantlement. First, a review of existing studies about energy life cycle assessment (LCA) and Carbon Footprint of PV systems has been carried out in this paper. Then, a new method to evaluate the Real Energy Payback Time (REPBT), which includes power looses due to PV panels degradation is proposed and differences with traditional Energy Payback Time are analysed. Finally, a typical PV grid connected plant (100 kW nominal power) located in Northern Spain is studied in these sustainability terms. This facility has been firstly completely modelled, including PV modules, inverters, structures and wiring. It has been also considerated the energy involved in the replacement of those components with shorter lifespan. The PV panels degradation has been analysed through the comparison of normalised flash test reports on a significant sample of the installed modules before and 5 years after installation. Results show that real PV degradation affect significantly to the Energy Payback Time of the installation increasing slightly a 4:2% more the EPBT value for the case study. However, along a lifespan of 30 years, the GCPVS under analysis will return only 5:6 times the inverted energy on components manufacturing, transport and installation, rather than the expected 9:1 times with the classical estimation.

    Citation: Miguel de Simón-Martín, Montserrat Díez-Mediavilla, Cristina Alonso-Tristán. Real Energy Payback Time and Carbon Footprint of a GCPVS[J]. AIMS Energy, 2017, 5(1): 77-95. doi: 10.3934/energy.2017.1.77

    Related Papers:

    [1] Hazhar M. Balaky, Parween Abdulsamad Ismail . Serum resistin, visfatin, IL-17 and IL-23 as novel diagnostic biomarkers for thyroid carcinoma. AIMS Allergy and Immunology, 2025, 9(3): 166-179. doi: 10.3934/Allergy.2025013
    [2] Kazheen Majeed, Hazhmat Ali . Association of low-grade inflammation and oxidative stress with metabolic dysfunction in healthy obese individuals. AIMS Allergy and Immunology, 2025, 9(2): 56-69. doi: 10.3934/Allergy.2025004
    [3] Catalina Marysol Carvajal Gonczi, Fadi Touma, Tina Daigneault, Chelsea Pozzebon, Kelly Burchell-Reyes, Peter J. Darlington . Modulation of IL-17A and IFNγ by β2-adrenergic agonist terbutaline and inverse-agonist nebivolol, influence of ADRB2 polymorphisms. AIMS Allergy and Immunology, 2021, 5(4): 222-239. doi: 10.3934/Allergy.2021017
    [4] Joseph J. Dolence, Hirohito Kita . Allergic sensitization to peanuts is enhanced in mice fed a high-fat diet. AIMS Allergy and Immunology, 2020, 4(4): 88-99. doi: 10.3934/Allergy.2020008
    [5] Ryuta Muromoto, Kenji Oritani, Tadashi Matsuda . Tyk2-mediated homeostatic control by regulating the PGE2-PKA-IL-10 axis. AIMS Allergy and Immunology, 2021, 5(3): 175-183. doi: 10.3934/Allergy.2021013
    [6] Dmitriy I. Sokolov, Anastasia R. Kozyreva, Kseniia L. Markova, Valentina A. Mikhailova, Andrey V. Korenevskii, Yulia P. Miliutina, Olga A. Balabas, Sergey V. Chepanov, Sergey A. Selkov . Microvesicles produced by monocytes affect the phenotype and functions of endothelial cells. AIMS Allergy and Immunology, 2021, 5(3): 135-159. doi: 10.3934/Allergy.2021011
    [7] Shancy Petsel Jacob, Chikkamenahalli Lakshminarayana Lakshmikanth, Thomas M. McIntyre, Gopal Kedihitlu Marathe . Platelet-activating factor and oxidized phosphatidylcholines do not suppress endotoxin-induced pro-inflammatory signaling among human myeloid and endothelial cells. AIMS Allergy and Immunology, 2017, 1(3): 108-123. doi: 10.3934/Allergy.2017.3.108
    [8] Ken S. Rosenthal, Daniel H. Zimmerman . J-LEAPS vaccines elicit antigen specific Th1 responses by promoting maturation of type 1 dendritic cells (DC1). AIMS Allergy and Immunology, 2017, 1(2): 89-100. doi: 10.3934/Allergy.2017.2.89
    [9] Robert Cote, Laura Lynn Eggink, J. Kenneth Hoober . CLEC receptors, endocytosis and calcium signaling. AIMS Allergy and Immunology, 2017, 1(4): 207-231. doi: 10.3934/Allergy.2017.4.207
    [10] Norio Kodaka, Chihiro Nakano, Takeshi Oshio, Hiroto Matsuse . The treatment of severe uncontrolled asthma using biologics. AIMS Allergy and Immunology, 2020, 4(1): 1-13. doi: 10.3934/Allergy.2020001
  • Grid connected PV systems, or GCPVS, produce clean and renewable energy through the photovoltaic effect in the operation stage of the power plant. However, this is the penultimate stage of the facilities before its dismantlement. Before starting generating electricity with zero CO2 emissions, a negative energy balance exists mainly because of the embodied energy costs of the PV components manufacturing, transport and late dismantlement. First, a review of existing studies about energy life cycle assessment (LCA) and Carbon Footprint of PV systems has been carried out in this paper. Then, a new method to evaluate the Real Energy Payback Time (REPBT), which includes power looses due to PV panels degradation is proposed and differences with traditional Energy Payback Time are analysed. Finally, a typical PV grid connected plant (100 kW nominal power) located in Northern Spain is studied in these sustainability terms. This facility has been firstly completely modelled, including PV modules, inverters, structures and wiring. It has been also considerated the energy involved in the replacement of those components with shorter lifespan. The PV panels degradation has been analysed through the comparison of normalised flash test reports on a significant sample of the installed modules before and 5 years after installation. Results show that real PV degradation affect significantly to the Energy Payback Time of the installation increasing slightly a 4:2% more the EPBT value for the case study. However, along a lifespan of 30 years, the GCPVS under analysis will return only 5:6 times the inverted energy on components manufacturing, transport and installation, rather than the expected 9:1 times with the classical estimation.


    Abbreviations

    AC fasting blood glucose
    BMI body mass index
    BUN blood urea nitrogen
    Con A concanavalin A
    CRE creatinine
    HDL-C high density lipoprotein-cholesterol
    MS metabolic syndrome
    PBMCs     peripheral blood mononuclear cells
    RELMs resistin-like molecules
    RETN human resistin gene
    SNPs single nucleotide polymorphisms
    SP systolic pressure
    T2DM type 2 diabetes mellitus
    TG triglyceride

    1. Introduction

    The etiology of type 2 diabetes mellitus (T2DM), which affects at least 200 million throughout the world, is not entirely disclosed. Though insulin resistance seems to be a central abnormality, the origin of the impaired insulin action and how it explains the many other clinical symptoms and complications of T2DM still await to be investigated. Obesity, primarily characterized by an increased mass of fat, is a major risk factor which leads to the development of hyperlipidaemia, metabolic syndrome (MS), and T2DM. The adipose tissue is traditionally considered to play a passive role in metabolism by acting as a fat storage reservoir. In addition to being the main energy storage organ, adipocytes are involved actively in maintaining metabolic balance [1,2] by secreting several hormones and cytokines [3]. These adipose derived signaling molecules exert potent metabolic effects to distant organs, which are likely to play a key role in the complex inter-organ communication network to modulate metabolism and energy homeostasis [4,5].

    Resistin is a 12.5 kDa cysteine-rich peptide that is secreted from adipocytes in rodents and from macrophages in human [6]. Resistin is a member of the resistin-like molecules (RELMs) with a conserved pattern of 11 cysteine residues at its C-terminus [7]. The postulated roles for resistin include the regulation of glucose homeostasis, adipose tissue mass and inflammation. In murine models, resistin is induced during adipocyte differentiation and its expression is reduced upon treatment with insulin sensitizers. Serum resistin levels are elevated in obese mice, and administration of resistin impairs glucose tolerance in normal mice. Several human studies report serum resistin levels are increased in patients with obesity, insulin resistance, and/or T2DM [8,9,10]. Inflammation is a hyper-resistinemic state in humans, and cytokine induction of resistin may contribute to insulin resistance, obesity, and other inflammatory states [11,12,13].However, the mechanism and importance of increased resistin levels in human MS and T2DM are still not fully illustrated.

    The human resistin gene (RETN) is located on chromosome 19p13.3, with several identified single-nucleotide polymorphisms (SNPs) associated with T2DM [14,15]. Two RETN 5'-flanking SNPs are associated with obesity among non-diabetic individuals [16,17]. The RETN -420C>G variants draw much attention and are most extensively investigated. This SNP is the major determinant of the resistin expression and associated with insulin resistance, and possibly with cellular oxidative stress [18]. Mattevi et al. reported that RETN -420C>G is associated with higher mRNA expression in human abdominal subcutaneous fat [19]. Nevertheless, epidemiological studies on the association between the RETN -420C>G SNPs and T2DM risk are conflicting and controversial. Most of the resistin-related studies focus on the investigation of the SNPs in Caucasian or Japanese subjects, relatively few corresponding data in Chinese/Taiwanese populations are reported. Therefore, it is tempting for us to investigate whether RETN SNPs are linked to T2DM in Taiwanese population for determining the ethnic-dependent variations for T2DM development.


    2. Materials and Methods


    2.1. Study subjects

    Fasting venous blood samples were taken from 244 T2DM patients attending the diabetic clinic in Department of Internal Medicine, Chung Shan Medical University Hospital. Fasting blood samples from 305 non-diabetic control subjects were collected from Physical Check Up Unit. Written informed consent was obtained from all the study subjects after the nature of the procedure was explained. The information of body height, weight, age, fasting blood sugar, renal function index (creatinine [CRE] and blood urea nitrogen [BUN]), etc., was collected and filed for further statistical analysis. The study protocol was approved by the Institutional Review Board of Chung Shan Medical University Hospital.


    2.2. Isolation of peripheral blood mononuclear cells (PBMCs)

    Peripheral blood was obtained from normal healthy donors, who were receiving no medications nor had any previous history of metabolic disorders, and type 2 diabetic subjects as well. The blood was collected from the antecubital vein in tubes containing EDTA. Samples were immediately processed after blood withdrawal. PBMCs were isolated from buffy coats by layering blood samples onto Ficoll-Paque (Pharmacia Biotech) gradient method in 50 mL conical centrifuge tubes as described [20,21].After centrifugation at 400 × g for 30 min at room temperature, the PBMCs were transferred to a new conical centrifuge tube where the cells were washed twice with phosphate buffered saline.


    2.3. Analysis of resistin secretion

    PBMCs were isolated from whole blood using Ficoll-Paque (Pharmacia Biotech) gradient centrifugation method. After isolation, 2 × 106 PBMCs were cultured in RPMI medium (Hyclone) containing 10% FBS (GIBCO). After 24 h of 10 μg/mL concanavalin A (Con A, Sigma) treatment, secreted resitin levels by the activated PBMCs were determined using ELISA kit (R & D).


    2.4. Analysis of RETN promoter genotype

    Genomic DNA was extracted from PBMCs using commercially available kit. The 5'-flanking region of the RETN gene was amplified by PCR reaction containing 100 ng of genomic DNA, 12.5 pmol of each primer (5'-TGTCATTCTCACCCAGAGACA-3' and 5'-TGGGCTCAGCTAACCAAATC-3'), 1 unit of Taq DNA polymerase and 125 μM dNTP in a total volume of 25 μL, using a PCR thermalcycler. DNA products after PCR amplification were digested using restriction enzyme Bpi I to detect the -420C>G polymorphism, followed by separation in 12% polyacrylamide gel.


    2.5. Statistical analysis

    Data analysis started with descriptive statistics, including mean and standard deviation for continuous variables, and frequency for categorical variables. If necessary, natural logarithm transformation was used to enhance normality for blood biochemistry parameters with skewed distribution. Student's t test was applied for comparisons of age, body mass index (BMI), and each of the blood biochemistry parameters between T2DM subjects and controls, and Chi-square test for comparing frequencies of different genotypes and sex between groups. Moreover, one-way analysis of variance was applied to compare means of respective blood biochemistry parameters among subjects with different RETN genotypes. Finally, multiple linear regression analysis was used to assess the associations between RETN genotypes and each of the biochemistry parameters, with adjustment for diabetes status, age, and sex. An alpha level of 0.05 was used for all statistical tests.


    3. Results

    Our study aimed at investigating the distribution of the RETN -420 SNPs among control and type 2 diabetic subjects to examine the possible correlation between RETN genetic polymorphisms and T2DM in Taiwanese population.


    3.1. Characteristics of the study subjects (Table 1)

    Table 1. Demographic and biochemical data of study subjects in this study.
    Item Normal range Study subjects P
    Control (n = 244) T2DM (n = 305)
    Male/female 156/88 159/146 0.003
    Age (years) 51.5 ± 13.3 57.6 ± 11.2 < 0.001
    BMI (kg/m2) 25.5 ± 5.5 25.4 ± 5.5 0.078
    Fasting plasma glucose (AC) 70–110 mg/dl 105.2 ± 40.3 176.4 ± 70.4 < 0.001
    Blood urea nitrogen (BUN) 7–21 mg/dl 15.0 ± 4.8 17.2 ± 7.8 < 0.001
    Creatinine 0.6–1.4 mg/dl 1.1 ± 0.3 1.0 ± 0.5 0.066
    Systolic pressure (SP) 120–140 mmHg 123.7 ± 17.6 133.5 ± 18.6 < 0.001
    Diastolic pressure (DP) 70–90 mmHg 78.4 ± 9.6 79.2 ± 11.5 0.521
    Cholesterol (CHO) 125–240 mg/dl 193.0 ± 37.5 198.8 ± 46.8 0.055
    High density lipoprotein-cholesterol (HDL-C) > 35 mg/dl 55.5 ± 38.2 47.8 ± 13.9 < 0.001
    Triglyceride (TG) 20–200 mg/dl 150.7 ± 129.6 183.6 ± 129.6 0.004
    *Student's t test. Data are presented as mean ± standard deviation.
     | Show Table
    DownLoad: CSV

    Data regarding biochemical parameters of recruited subjects, including the healthy control individuals and diabetic patients, were listed in Table 1. Among the biochemical parameters examined, significant differences were observed in fasting blood glucose (AC; 105.2 ± 40.3 v.s 176.4 ± 70.4 mg/dL, p < 0.001), BUN (15.0 ± 4.8 v.s 17.2 ± 7.8 mg/dL, p < 0.001), systolic pressure (SP, 123.7 ± 17.6 v.s 133.5 ± 18.6 mmHg, p < 0.001), high density lipoprotein-cholesterol (HDL-C; 55.5 ± 38.2 v.s 47.8 ± 13.9 mg/dL, p < 0.001) and triglyceride (TG; 150.7 ± 129.6 v.s 183.6 ± 129.6 mg/dL, p = 0.004) under fasting conditions between patients and controls.


    3.2. Significant association of RETN -420 genotypes and T2DM (Table 2)

    Table 2. Associations between RETN -420 SNPs and clinical parameters of study subjects.
    Genotype/allele Resistin -420C>G SNPs P
    Control (n = 244) T2DM (n = 305)
    C/C 70 (28.7%) 124 (40.6%) 0.01
    C/G 127 (52.0%) 139 (45.6%)
    G/G 47 (19.3%) 42 (13.8%)
    C 267 (54.7%) 387 (63.4%) 0.002
    G 221 (45.3%) 223 (36.6%)
     | Show Table
    DownLoad: CSV

    Results regarding the distribution of the RETN -420 SNPs in recruited subjects were summarized in Table 2. Among the 244 non-diabetic control individuals, 70 (28.7%), 127 (52.0%) and 47 (19.3%) subjects carried C/C, C/G and G/G genotype, respectively; while the corresponding number in T2DM patients was 124 (40.6%), 139 (45.6%) and 42 (13.8%). Significant difference in distribution of RETN -420C>G genotypes between T2DM and control subjects was observed (p = 0.01). In addition, the prevalence of RETN -420 C and G allele in control individuals was 54.7% and 45.3%, respectively; and that in T2DM counterpart was 63.4% and 36.6%. Significant difference in the distribution of RETN alleles between diabetic patients and control subjects was also observed (p = 0.002). The above observations demonstrate that the RETN -420 SNPs are associated with T2DM subjects in Taiwanese population.

    In addition to the significant association between RETN -420C>G genotypes with T2DM, we further investigated the correlation between the SNPs and biochemical parameters by stratifying the study subjects according to the criteria of whether their test results were within or beyond the normal range (Table 3). No significant association between the biochemical data and RETN -420 SNPs was found.

    Table 3. Associations between RETN -420 SNPs and clinical parameters of study subjects.
    Item Range Control T2DM
    n C/C n (%) C/G n (%) G/G n (%) P n C/C n (%) C/G n (%) G/G n (%) P
    BMI ≥25 97 34 (35.1%) 49 (50.5%) 14 (14.4%) 0.073 74 28 (37.8%) 38 (51.4%) 8 (10.8%) 0.280
    < 25 124 29 (23.4%) 65 (52.4%) 30 (24.2%) 54 23 (42.6%) 21 (38.9%) 10 (18.5%)
    SP ≥140 45 9 (20%) 29 (64.4%) 7 (15.6%) 0.162 37 13 (35.1%) 18 (48.7%) 6 (16.2%) 0.794
    < 140 175 53 (30.3%) 85 (48.6%) 37 (21.1%) 57 24 (42.1%) 25 (43.9%) 8 (14%)
    DP ≥90 33 8 (24.2%) 18 (54.6%) 7 (21.2%) 0.862 25 10 (40%) 13 (52%) 2 (8%) 0.501
    < 90 187 54 (28.9%) 96 (51.3%) 37 (19.8%) 69 27 (39.1%) 30 (43.5%) 12 (17.4%)
    AC ≥110 27 7 (26%) 12 (44.4%) 8 (29.6%) 0.399 186 77 (41.4%) 80 (43%) 29 (15.6%) 0.186
    < 110 194 56 (28.8%) 102 (52.6%) 36 (18.6%) 19 4 (21.1%) 10 (52.6%) 5 (26.3%)
    BUN ≥25 2 0 2 (100%) 0 0.388 21 7 (33.3%) 11 (52.4%) 3 (14.3%) 0.634
    < 25 219 63 (28.8%) 112 (51.1%) 44 (20.1%) 132 57 (43.2%) 55 (41.7%) 20 (15.1%)
    CRE ≥1.4 17 1 (5.9%) 12 (70.6%) 4 (23.5%) 0.092 27 11 (40.8%) 9 (33.3%) 7 (25.9%) 0.256
    < 1.4 203 62 (30.5%) 101 (49.8%) 40 (19.7%) 163 63 (38.7%) 76 (46.6%) 24 (14.7%)
    CHO ≥240 21 4 (19.1%) 14 (66.7%) 3 (14.2%) 0.358 24 13 (54.2%) 6 (25%) 5 (20.8%) 0.103
    < 240 199 58 (29.1%) 100 (50.3%) 41 (20.6%) 175 64 (36.6%) 84 (48%) 27 (15.4%)
    HDL-C > 35 196 60 (30.6%) 100 (51%) 36 (18.4%) 0.088 131 52 (39.7%) 58 (44.3%) 21 (16%) 0.390
    ≤35 25 3 (12%) 14 (56%) 8 (32%) 37 12 (32.4%) 21 (56.8%) 4 (10.8%)
    TG ≥200 51 14 (27.5%) 24 (47.1%) 13 (25.4%) 0.521 59 26 (44.1%) 24 (40.7%) 9 (15.2%) 0.557
    < 200 169 48 (28.4%) 90 (53.3%) 31 (18.3%) 139 50 (36%) 66 (47.5%) 23 (16.5%)
     | Show Table
    DownLoad: CSV

    3.3. RETN -420 genotypes and resistin levels (Table 4)

    Table 4. Associations between RETN -420 SNPs and resistin levels among study subjects.
    Genotype Control T2DM P
    n (107) Resistin (ng/mL) n (58) Resistin (ng/mL)
    C/C 16 2.50 ± 0.44 22 3.95 ± 0.80 0.044
    C/G 54 3.94 ± 0.53 24 4.31 ± 0.88
    G/G 37 2.56 ± 0.43 12 4.88 ± 2.31
    Resistin levels secreted from the ConA-activated PBMCs from 107 control and 58 diabetic subjects were analyzed.
     | Show Table
    DownLoad: CSV

    To further investigate the association of resistin and T2DM, resistin levels produced by ConA-activated PBMCs from 107 control and 58 T2DM study subjects were determined. The results showed that while no significant difference between the resistin levels and RETN -420 genotypes was observed, the secretory resisitn levels by Con A-stimulated PBMCs from T2DM were significantly higher than that from control subjects.


    4. Discussion

    Most of the identified RETN SNPs are mapped to the non-coding region, with rs1862513 at position -420 attracts much attention and thus is most widely studied SNPs among different ethnic populations. In addition to the association between RETN -420 SNPs with insulin resistance [15], obesity [15,17,19] and T2DM [22,23], the RETN -420 G allele is associated with stronger promoter activity [22], higher abdominal fat resistin mRNA levels and plasma resistin concentrations [6,14]. This G allele is also suggested to be an independent predictor for blood glucose deterioration in a Chinese population [24]. Despite the association between RETN SNPs with plasma resistin levels and insulin resistance [25], conflicting results are reported. While a meta-analysis indicates that individuals carrying homozygous -420 G allele have a 30% increased odds of developing T2DM [22], another concludes no significant association is observed between RETN -420C>G and T2DM risk [26]. Additionally, even varying results are identified in different subjects sampling within the same ethnic origin, possibly due to the difference of patients' clinical profiles or statistical strategies.

    Most of the recruited subjects regarding RETN genetic studies are populations from Japanese and Caucasian origins. As mentioned above, discrepancies are reported and the associations between RETN genotypes and diabetic onset are therefore controversial. To the best of our knowledge, only 1 study regarding the resistin +62G>A genotype in Taiwanese subjects is documented [27]. Therefore, it is tempting for us to investigate the most profoundly studied RETN -420C>G SNPs in Taiwanese diabetic patients for examining the putative correlation and involvement of this SNP in our population. Our results indicate that not only the distribution of RETN -420C>G genotypes but also G allele is significantly different between diabetic and control subjects. Nevertheless, no significant association between the subjects' biochemical data and RETN -420 SNPs is found. Although focusing on different RETN SNPs, our data support the report from Tan et al, in which resistin gene polymorphism is an independent factor associated with T2DM [27].

    It is intriguing to consider the contribution of RETN -420 SNPs from a general prospective by analyzing the distribution of the variants among different ethnic populations. Upon comparing the overall frequency of RETN -420C>G variants among populations with different ethnic origins, several interesting phenomena were observed. First of all, the frequencies of RETN -420 G allele in Asian population (>30%) are higher than Caucasian subjects (Table 5). The observation supports the finding from Chi et al that the frequency of RETN -420 G allele in Chinese population is significantly different from those in European population [28]. Secondly, among the Asian subjects with higher G allele frequency, this SNP is associated with T2DM onset in Japanese and Taiwanese population, but not in the Caucasian populations (Table 5). Thirdly, the RETN genotypes are reported to be associated with circulatory resistin levels in most of the studies from Asian countries (Table 6).

    Table 5. Prevalence of RETN -420 SNPs in populations with different ethnic background.
    Population/ Subjects n SNP allele Association Ref.
    C/C C/G G/G C G
    Finnish
    Control 409 599 (73.2%) 219 (26.7%) insulin resistance [17]
    T2DM 781 1141 (73.0%) 423 (27.0%)
    Canadian
    Control 411 212 (51.6%) 169 (41.1%) 30 (7.3%) 593 (72.1%) 229 (27.9%) BMI [15]
    T2DM 179 90 (50.3%) 78 (43.6%) 11 (6.1%) 258 (72.1%) 100 (27.9%)
    Scandinavian
    Control 433 236 (54.5%) 156 (36.0%) 41 (9.5%) 628 (72.5%) 238 (27.5%) [15]
    T2DM 452 238 (52.7%) 170 (37.6%) 44 (9.7%) 646 (71.5%) 258 (28.5%)
    European-derived Brazilian
    Control 251 125 (49.8%) 101 (40.2%) 25 (10.0%) 351 (69.9%) 151 (30.1%) BMI waist circumference [19]
    Overweight & obese 334 182 (54.5%) 122 (36.5%) 30 (9.0%) 486 (72.8%) 182 (27.2%)
    Korean
    Control 173 89 (51.5%) 63 (36.4%) 21 (12.1%) 241 (69.7%) 105 (30.3%) resistin level [6]
    T2DM 411 194 (47.2%) 163 (39.7%) 54 (13.1%) 551 (67.0%) 271 (33.0%)
    Japanese
    Control 564 247 (43.8%) 269 (47.7%) 48 (8.5%) 763 (67.6%) 365 (32.4%) T2DM [22]
    T2DM 546 216 (39.6%) 254 (46.5%) 76 (13.9%) 686 (62.8%) 406 (37.2%)
    Japanese
    Control 2,502 1,080 (43.2%) 1,123 (44.9%) 299 (11.9%) 3,283 (65.6%) 1,721 (34.4%) young T2DM [23]
    T2DM 2,610 1,169 (44.9%) 1,144 (43.8%) 297 (11.4%) 3,482 (66.7%) 1,738 (33.3%)
    Taiwanese
    Control 244 70 (28.7%) 127 (52.0%) 47 (19.3%) 267 (54.7%) 221 (45.3%) T2DM resistin level this study
    T2DM 305 124 (40.6%) 139 (45.6%) 42 (13.8%) 387 (63.4%) 223 (36.6%)
    Numbers in parenthesis of population indicated the number of study subjects in each study.
     | Show Table
    DownLoad: CSV
    Table 6. Prevalence of RETN genetic variants in Asian populations.
    No Population/Subjects n SNP Association Ref.
    Serum resistin T2DM others
    Meta-analysis
    1 Control 5,959 -420C>G [26]
    T2DM 5,935
    2 Control 529 -638G>A + [29]
    T2DM 529 -420C>G
    Chinese
    3 Control 370 -420C>G [28]
    T2DM 318 -394C>G
    4 Non-diabetic subjects 624 -420C>G + insulin resistance [24]
    +62G>A glycemia
    Japanese
    5 Control 286 -420C>G + stroke [30]
    T2DM 349
    6 Community subjects 2,077 -420C>G + synergistically with PPAR P12A [31]
    7 Control 2,502 -420C>G young T2DM onset [23]
    T2DM 2,610
    8 Community subjects 2,078 -420C>G + insulin resistance, low HDL high CRP [37]
    9 Obese 60 -638G>A + [38]
    -420C>G +
    10 controls 157 -420C>G + [32]
    T2DM 198
    11 Control 406 -420C>G + + [22]
    T2DM 397
    Thais
    12 Control 105 -420C>G [39]
    T2DM 95 +299G>A + +
    Korean
    13 Control 173 -537A>C + [6]
    T2DM 411 -420C>G +
     | Show Table
    DownLoad: CSV

    Several contradictory facts can be raised according to the above observations. First of all, several reports reveal that resistin levels are significantly increased in a genotype-dependent manner based on the RETN -420 polymorphisms. Subjects carrying RETN -420 G/G genotype had the highest circulating resistin levels, followed by C/G-and C/C-carrying individuals [29,30,31]. The RETN -420 G/G genotype increases T2DM susceptibility by enhancing its promoter activity [32]. On the contrary, no significant difference between the resistin levels and the RETN -420 genotypes is found in the present study. Nevertheless, our results show that the resistin levels from T2DM subjects are significantly higher than the control subgroup (Table 4). The reason behinds this discrepancy is perhaps that we examined secretory resistin from ConA-activated PBMCs, since we suggest that the plasma resistin levels would be affected by multiple unidentified factors which are possible to deviate the data and corresponding conclusions. In addition, although it is unlikely that RETN is the only gene carrying susceptibility for T2DM development, the other paradox is that the diabetic prevalence of Asian populations with higher frequency of the susceptible G allele is much lower than Caucasian [28]. We previously characterized that certain SNPs identified to be associated with insulin sensitivity in Caucasians are unlikely to be involved in Taiwanese T2DM development [33,34,35,36]. Taken together, unique genetic characteristics and distinct factors may play roles in the diabetic pathogenesis among subjects from different racial origins. A given population may have unique protective elements in the genetic reservoir despite the high prevalence of disease-susceptible alleles. Therefore, the possibility of differential protective genetic factors related to a particular ethnicity may lead to the conflicting results among studies.


    5. Conclusion

    This study reports the association between RETN -420 polymorphisms and diabetic incidence in Taiwanese population. Our results suggest that RETN -420C>G SNPs are associated with diabetic susceptibility, but not subjects' clinical profiles. Investigation of RETN SNPs in T2DM patients from various ethnic populations is crucial and will contribute to the understanding of this gene in the diabetic etiology. The present results provide clues to elucidate the contribution of genetic heterogeneity for diabetic development.


    Acknowledgments

    This work was supported in part by grants from Ministry of Science and Technology (MOST-105-2320-B-241-005 and 106-2314-B-010-032), Taiwan.


    Conflicts of interest

    All authors declare no conflicts of interest in this paper.


    [1] Perpiñán O, Lorenzo E, Castro M A, et al. (2009) Energy payback time of grid connected PV systems: comparison between tracking and fixed systems. Prog Photovoltaics 17(2): 137–147.
    [2] Fthenakis V, Alsema E A, de Wild-Scholten M (2005) Life cycle assessment of photovoltaics: perceptions, needs, and challenges, in: Conference Record of the Thirty-first IEEE Photovoltaic Specialists Conference, 1655–1658.
    [3] Keoleian G A, Lewis G M (1997) Application of life-cycle energy analysis to photovoltaic module design. Prog Photovoltaics 5(4): 287–300.
    [4] Sherwani A, Usmani J, Varun (2010) Life cycle assessment of solar PV based electricity generation systems: A review. Renew Sust Energ Rev 14(1): 540–544.
    [5] Tahara K, Kojima T, Inaba A (1997) Estimation of power plants by LCA. Kagaku Kogaku Ronbun 23(1): 93–94.
    [6] de Wild-Scholten M, Alsema E (2004) Towards cleaner solar PV: Environmental and health impacts of crystalline silicon photovoltaics. Refocus 5(5): 46–49.
    [7] Fthenakis V, Alsema E (2006) Photovoltaics energy payback times, greenhouse gas emissions and external costs: 2004 – early 2005 status. Prog Photovoltaics 14(3): 275–280.
    [8] Bayod-Rújula A A, Lorente-Lafuente A M, Cirez-Oto F (2011) Environmental assessment of grid connected photovoltaic plants with 2-axis tracking versus fixed modules systems. Energy 36(5): 3148–3158.
    [9] Mason J E, Fthenakis V M, Hansen T, et al. (2006) Energy payback and life-cycle CO2 emissions of the BOS in an optimized 3.5 MW PV installation. Prog Photovoltaics 14(2): 179–190.
    [10] Nawaz I, Tiwari G (2006) Embodied energy analysis of photovoltaic (PV) system based on macroand micro-level. Energ Policy 34(17): 3144–3152.
    [11] SoDa Team SoDa: HelioClim-3, 2016, Available from: http://www.soda-pro.com/web-services/radiation/helioclim-3-for-free.
    [12] Europan Commission: PVGIS- PV Potential Estimation Utility, 2016, Available from: http://re.jrc.ec.europa.eu/pvgis/apps4/pvest.php.
    [13] Hacke P, Smith R, Terwilliger K, et al. (2013) Testing and Analysis for Lifetime Prediction of Crystalline Silicon PV Modules Undergoing Degradation by System Voltage Stress. IEEE J Photovoltaics 3(1): 246–253.
    [14] Muñoz M A, Alonso-García M C, Vela N, et al. (2011) Early degradation of silicon PV modules and guaranty conditions. Sol Energy 85(9): 2264–2274.
    [15] Jordan D C, Kurtz S R (2012) Photovoltaic Degradation Rates-An Analytical Review. NREL/JA- 5200-51664 1(1): 1–32.
    [16] Osterwald C R, Anderberg A, Rummel S, et al. (2002) Degradation analysis of weathered crystalline-silicon PV modules, in: Conference Record of the Twenty-Ninth IEEE Photovoltaic Specialists Conference, 1392–1395.
    [17] University of Manchester: Carbon calculations over the life cycle of industrial activities, 2016, Available from:http://www.ccalc.org.uk/.
    [18] Postnote from the Parliamentary Office of Science and Technology: Carbon footprint of electricity generation, 2006, 268:1–4.
    [19] Díez-Mediavilla M, Alonso-Tristán C, Rodríguez-Amigo M, et al. (2012) Performance analysis of PV plants: Optimization for improving profitability. Energ Convers Manage 54(1): 17–23.
    [20] Khasawneh Q A, Damra Q A, Salman O H B Determining the Optimum Tilt Angle for Solar Applications in Northern Jordan 9(3): 187–193.
    [21] Skeiker K Optimum tilt angle and orientation for solar collectors in Syria 50(1): 2439–2448.
    [22] Government of Spain: Carbon emission factors and primary energy conversion coe cients for the different electrical energy sources in the Building Sector in Spain, 2014, IDAE, 1–32.
    [23] Alsema E (1998) Energy Requirements and CO2 Mitigation Potential of PV Systems, in: BNL/NREL Workshop PV and the Environment, 1–11.
    [24] Adelstein J, Sekulic B (2005) Performance and reliability of a 1-kW amorphous silicon photovoltaic roofing system, in: Conference Record of the Thirty-first IEEE Photovoltaic Specialists Conference, 1627–1630.
    [25] Chamberlin C E, Rocheleau M A, Marshall M W, et al. (2011) Comparison of PV module performance before and after 11 and 20 years of field exposure, in: 2011 37th IEEE Photovoltaic Specialists Conference (PVSC), 101–105.
    [26] Lorenzo E, Zilles R, Moretón R, et al. (2013) Performance analysis of a 7-kW crystalline silicon generator after 17 years of operation in Madrid. Prog Photovoltaics 22(12): 1273–1279.
    [27] Espinosa N, García-Valverde R, Urbina A, et al. (2011) A life cycle analysis of polymer solar cell modules prepared using roll-to-roll methods under ambient conditions. Sol Energ Mat Sol C 95(5): 1293–1302.
    [28] Fthenakis V M, Kim H C (2013) Life cycle assessment of high-concentration photovoltaic systems. Prog Photovoltaics 21(3): 379–388.
    [29] Hondo H (2005) Life cycle GHG emission analysis of power generation systems: Japanese case. Energy 30(11-12): 2042–2056.
    [30] Peng J, Lu L, Yang H (2013) Review on life cycle assessment of energy payback and greenhouse gas emission of solar photovoltaic systems. Renew Sust Energ Rev 19: 255–274. doi: 10.1016/j.rser.2012.11.035
    [31] Jiao Y, Salce A, Ben W, et al. (2011) Siemens and siemens-like processes for producing photovoltaics: Energy payback time and lifetime carbon emissions. JOM 63(1): 28–31.
    [32] Mann S A, de Wild-Scholten M J, Fthenakis V M, et al. (2013) The energy payback time of advanced crystalline silicon PV modules in 2020: A prospective study. Prog Photovoltaics 22(11): 1180–1194.
    [33] Mohr N J, Meijer A, Huijbregts M a J, et al. (2013) Environmental life cycle assessment of roof-integrated flexible amorphous silicon/nanocrystalline silicon solar cell laminate. Prog Photovoltaics 21(4): 802–815.
    [34] Yamada K, Komiyama H, Kato K, et al. (1995) Evaluation of photovoltaic energy systems in terms of economics, energy and CO2 emissions. Energ Convers Manage 36(6–9): 819–822.
    [35] Hammond G, Jones C (2008) Embodied energy and carbon in construction materials. P I Civil Eng Energ 161(2): 87–98.
    [36] Pacca S, Sivaraman D, Keoleian G A (2007) Parameters affecting the life cycle performance of PV technologies and systems. Energ Policy 35(6): 3316–3326.
    [37] European Comission: Eurostat database, 2016, Available from: http://ec.europa.eu/ eurostat/data/database.
    [38] Davis S C, Diegel S W, Boundy R G (2012) Transportation Energy Data Book, volume 1, 31st edition, Oak Laboratory: U.S. Department of Energy.
    [39] European Environment Agency E U (2016) Explaining road transport emissions, volume 1, 1st edition, Luxembourg: Publications O ce of the European Union.
    [40] Oficina Catalana del Canvi Climátic E S (2011) Practical Guide for the Carbon Emissions Calculation. Oficina Catalana del Canvi Climátic 1(1): 1–32.
    [41] Joint Research Centre I E T (Ed.) (2013) Well-to-wheels analysis of future automotive fuels and powertrains in the European context, volume 1, 1st edition, Luxembourg: Publications Office of the European Union.
    [42] Pucker N, Schappacher W (1994) Installation of new energy systems: Energy balances and installation times; application to a photovoltaic system. Renew Energ 5(1–4): 212–214.
    [43] Previ A, Iliceto A, Belli G, et al. The 3.3 MW-peak photovoltaic power station at Serre, in: Proceedings of 1994 IEEE 1st World Conference on Photovoltaic Energy Conversion - WCPEC (A Joint Conference of PVSC, PVSEC and PSEC), volume 1, 750–753.
    [44] Iliceto A, Vigotti R (1998) The largest PV installation in Europe: Perspectives of multimegawatt PV. Renew Energ 15(1): 48–53.
  • This article has been cited by:

    1. Fei Luo, Mingjie Shi, Junhao Guo, Yisen Cheng, Xusan Xu, Jieqing Zeng, Si Huang, Weijun Huang, Wenfeng Wei, Yajun Wang, Riling Chen, Guoda Ma, Association between the RETN -420C/G polymorphism and type 2 diabetes mellitus susceptibility: A meta-analysis of 23 studies, 2022, 13, 1664-2392, 10.3389/fendo.2022.1039919
  • Reader Comments
  • © 2017 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(7945) PDF downloads(1379) Cited by(5)

Article outline

Figures and Tables

Figures(7)  /  Tables(6)

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog