Download Microsoft Office 2016 and Microsoft Office 365 IMG/ISO Files Microsoft.NET Framework 4.7.2 Offline Installer Latest Service Packs Download List Of All Microsoft Office Products. The Volume Licensing Service Center is the single location for Microsoft Volume Licensing customers to view Licensing information, download Microsoft software and manage Volume Licensing benefits and subscriptions. So if your not a Volume Licensing customer, then you will not be able to download it from Volume Licensing Service Center. Just to verify, were you able to download the Microsoft Office 2016 Volume License Pack? P lease respond to this thread to let us know how the issue progresses. Similarly, if you have more questions, please inform us by replying to this post. Download page for Simple 1500 Series Vol.58 - The Sumou (J) ISO for Sony Playstation PSX PS1. ROMs, ISOs, Games. Most Popular Sections. PS2 ISOs (4078) PSP ISOs (2907) PSX. » Download Simple 1500 Series Vol.58 - The Sumou (J) ISO Download Links: Love this game? Write a review! Rating: 4/5, 1 vote. This game is unavailable. Download page for Simple 1500 Series Vol.58 - The Sumou (J) ISO for Sony Playstation PSX PS1.
- Arirang Volume 58 Iso Download Full
- Arirang Volume 58 Iso Download Youtube
- Arirang Volume 58 Iso Download Torrent
- Arirang Volume 58 Iso Download Pc
Office 2019 is finally out and is available for download. If you have Office 2016 installed, it is high time for you to upgrade to Office 2019 as it comes with some of the newest features of Microsoft Office. If you are an Office 365 user, you can continue using Office 365 as it already contains all the features and updates which have been released in Office 2019.
Quick Summary
Arirang Volume 58 Iso Download Full
- 5 Download Microsoft Office 2019 RTM ISOs
What is Office 2019?
Office 2019 is the stand alone version of Microsoft Office which comes with a perpetual license meaning that it is only one time cost while Office 365 users have to pay a monthly subscription cost. It is especially for those who do not prefer the subscription model and want to pay a one time cost for their Office needs.
Price and requirements
The price tag of Office Home & Business 2019 will be around $249 while you can get Office 365 for $99/year. Office 2019 will only run on supported versions of Windows 10, Windows Server 2019 and two most recent versions of MacOS.
Requirements include .NET Framework 4.6 or later to be installed on the system. Some components may also require .NET Framework 3.5. You can check which versions of .NET Framework are installed on your computer using command line.
Some Office search functionality requires Windows Search and an Internet connection, for example, Outlook search.
Installation and upgrade
Microsoft has removed the MSI installer from Office 2019. Only Click-to-Run installer is available. According to Microsoft:
With Office 2019, we’re moving the on-premises versions of Office to C2R to reduce costs and improve security. The advantages of C2R include predictable monthly security updates, up-to-date apps on installation, reduced network consumption through Windows 10 download optimization technology, and an easy upgrade path to Office 365 ProPlus.
In my opinion, Microsoft is slowly moving towards the subscription only model and may remove the perpetual license altogether.
When you run the click-to-run installer, it will automatically install Office 2019 in the default location. This installer does not ask the user anything. You can’t upgrade from Office 2016 to 2019. Office 2019 will install along side Office 2016 and even Office 365.
But there is one caveat, although the installation of Office 2019 will complete, it will not run along side Office 2016 and Office 365. So if you want to install and work on Office 2019, you have to uninstall all previous Office versions including Office 365.
New features
Here are some of the improved and few features introduced in Microsoft Office 2019:
- Two new dark themes are supported, dark grey theme and black theme. You can change your current theme by going to any Office application File menu –> Account –> Office theme.
- Improved inking support for all Office apps
- New Excel features like new chart types, 2D maps, timelines, PowerPivot and PowerQuery improvements.
- A Focus Mode to let you concentrate on your writing by hiding everything including menus in Microsoft word.
- Focused Inbox in Outlook to show important emails on top of everything else.
Microsoft Outlook 2019 does not include modern authentication protocols which I was expecting. I’m using Mailbird as my default mail application instead of Outlook.
Download Microsoft Office 2019 RTM ISOs
The following files are .IMG files which can be mounted like ISO files in Windows Explorer. Just double-click the IMG file to automatically mount and open the contents of the file. Run setup.exe to start the installation.
Since it is a Click-to-Run installer, it will automatically start installing without asking any question from the user. The Office suite includes Word, Excel, PowerPoint, Outlook, Publisher, Skype for Business, Publisher and Access.
English
Download Office 2019 ProPlus English [3.3 GB]
Download Office Project Pro 2019 English [3.3 GB]
Download Office Visio Pro 2019 English [3.3 GB]
Other languages
Download Office 2019 ProPlus Chinese [3.3 GB]
Download Office 2019 ProPlus Czech [3.3 GB]
Download Office 2019 ProPlus Dutch [3.3 GB]
Download Office 2019 ProPlus French [3.3 GB]
Download Office 2019 ProPlus German [3.3 GB]
Download Office 2019 ProPlus Italian [3.3 GB]
Download Office 2019 ProPlus Japanese [3.3 GB]
Download Office 2019 ProPlus Korean [3.3 GB]
Download Office 2019 ProPlus Polish [3.3 GB]
Download Office 2019 ProPlus Portuguese [3.3 GB]
Download Office 2019 ProPlus Russian [3.3 GB]
Download Office 2019 ProPlus Spanish [3.3 GB]
Download Office 2019 ProPlus Swedish [3.3 GB]
Microsoft wireless all-in-one media keyboard mac. Download Office 2019 ProPlus Turkish [3.2 GB]
Office 2019 for Mac
Download Office 2019 ProPlus for Mac [1.7 GB]
If we have missed anything, please let us know through comments below.
Must Read Articles:
Abstract
In this prospective study, we evaluated the association of retinoic acid (RA) with the metabolic syndrome (MetS) in the Chinese population.
Design and Participants:
A total of 1042 nondiabetic adults from the population-based Nutrition and Health of Aging Population were prospectively followed up for 4 years. Serum RA concentrations was determined and its relationship with the MetS and its component was investigated.
At baseline, higher RA levels were inversely associated with the presence of MetS (odds ratio 0.61; 95% confidence interval [CI] 0.44–0.74, P < .001) after adjustment for age, gender, body mass index, the homeostasis model assessment index for insulin resistance (HOMA-IR), and other confounding factors. Subjects with lower RA levels had a progressively worse cardiometabolic risk profile at baseline. Serum RA levels were inversely associated with 8-iso-prostaglandin F2α (P < .001), high-sensitivity C-reactive protein (P = .015), and IL-6 (P = .020) and positively correlated with high-density lipoprotein cholesterol (P = .038). Among 825 subjects without MetS at baseline, 146 had developed it at 4 years. Serum RA by quartiles was inversely correlated with the incident MetS (adjusted hazard ratio 0.67; 95% CI 0.48–0.81, P = .006). Apart from HOMA-IR (P < .001), the baseline RA level was the only independent predictor of the development of the MetS during the 4-year follow-up (odds ratio 0.53; 95% CI 0.40–0.69; P < .001) after adjustment for age, gender, body mass index, and HOMA-IR.
Conclusions:
The serum RA level is inversely associated with the development of MetS independently of adiposity and insulin resistance.
Metabolic syndrome (MetS), a cluster of multiple metabolic abnormalities including central obesity, dyslipidemia, hyperglycemia, insulin resistance, and hypertension, has become a major public health problem throughout the world. MetS plays an important role in the origin of cardiometabolic diseases, including cardiovascular disease and type 2 diabetes mellitus (1–3). The prevalence of MetS has increased rapidly in recent decades, accompanied by rapid economic growth, adoption of sedentary lifestyles, and dramatic changes in nutritional habits; it is now reaching epidemic proportions in China (4–6). The most recent national survey in 2014 reported that the prevalence of MetS in Chinese adults was approximately 20%–25% (7). Although dietary patterns have been proven to play an essential role in the development of this epidemic (8–11), it is of vital importance to identify the specific dietary nutrients and their metabolites in the prevention of MetS.
All-trans retinoic acid (RA) is the active metabolite of vitamin A (retinol) that mediates the physiological functions required for growth and development (11, 12). RA is synthesized intracellularly and primarily from retinaldehyde, which itself can be produced from retinol or from provitamin A carotenoids such as β-carotene (13). It exerts its broad range of biological effects in large part by controlling gene expression. RA binds to and activates members of the nuclear receptor family including retinoic acid receptor and retinoid X receptor (RXR), transcription factors that link vitamin A metabolism to the transcriptional regulation of specific gene cassettes (14–16). RXR also controls key metabolic pathways by serving as the obligate heterodimeric partner for several members of the steroid hormone nuclear receptor family, including peroxisome proliferator-activated receptors (17). Davinci 12.5 download mac. Adipogenesis is a differentiation process regulated by the complex interaction of some RXR heterodimeric partners (18). In 3T3-L1 adipocyte differentiation assays, retinoic acid effects vary as a function of the stage of adipogenesis and relative retinoic acid receptor, peroxisome proliferator-activated receptor-γ, and RXR expression (19). Inhibition of endogenous RA production by the inactivation of retinaldehyde dehydrogenase (the primary retinaldehyde metabolizing enzyme) increased energy dissipation and reduced abdominal fat accumulation, thus preventing and ameliorating diet-induced obesity (20). Although the role of RA in adipogenesis has been generally proven in vitro, little information on the relationship between serum RA level and MetS (as well as its components) is available for the Chinese population.
To further evaluate the association between serum RA and the incidence of MetS, we investigated prospectively the 4-year development of the MetS in relation to baseline serum RA levels in a population-based cohort comprising 1042 Chinese subjects recruited from the Guangzhou Nutrition and Health of Aging study population.
Materials and Methods
Study participants
The study subjects were recruited from the population-based Nutrition and Health of Aging Population in South China study, which investigated associations of dietary and genetic factors, as well as their interactions, with aging-related metabolic diseases. From March 2008 to March 2009, 2280 subjects aged 50–70 years participated in the cross-sectional study. Among these subjects, 1042 nondiabetic subjects (as defined by 2007 American Diabetes Association diagnostic criteria) (21) were subsequently invited to participate in the prospective cohort study to assess the progression of MetS and its component. Subjects receiving regular lipid-lowering drugs without an available pretreatment lipid profile or using multivitamin supplement were excluded from the further analysis. The study protocol was approved by the Institutional Review Board of the Sun Yat-sen University, conducted according to the principles expressed in the Declaration of Helsinki, and written informed consent was obtained from all participants.
Data collection
Baseline data were collected by trained interviewers via semistructured questionnaires during face-to-face interviews. The questionnaire was designed based on several pilot surveys given to this population. Information on sociodemographic factors, health status, and lifestyle practices were included in the questionnaire. Standing height, body weight, and waist circumference were measured with participants in light indoor clothing and without shoes. Body mass index (BMI) was calculated as weight in kilograms divided by height in meters squared. Two researchers independently entered the baseline data from the questionnaires, and the data were further checked by a third researcher when differences were found. All subjects were examined in the morning after an overnight fasting.
Laboratory measurements
Overnight fasting blood samples were collected in tubes containing liquid EDTA, centrifuged at 4°C, and stored at −80°C until analysis. The blood total cholesterol, high-density lipoprotein (HDL) cholesterol, triglycerides, and glucose were measured enzymatically on a Hitachi 7180 biochemistry automatic analyzer (Hitachi) using a commercial assay kit (Wako Pure Chemical Industries). Low-density lipoprotein (LDL) cholesterol was subsequently calculated using the Friedewald formula (22). Plasma insulin concentration was measured by RIA (Roche), which has less than 0.2% cross-reactivity with proinsulin. A homeostasis model assessment of insulin resistance (HOMA-IR) was calculated by the following formula: fasting insulin (in units per liter) × fasting glucose (in millimoles per liter)/22.5. The concentration of adiponectin was measured in stored frozen plasma by using a commercially available competitive ELISA kit (R&D Systems; catalog number DRP300), as previously described (23). Plasma 8-iso-prostaglandin F2α (8-iso PGF2α) levels were quantitatively determined using an ELISA kit (Enzo Life Sciences International). The assay sensitivity was 16.3 pg/mL. The intraassay coefficient of variation was 4.4%–11%, and the interassay coefficient of variation was 5.0%–11%. Plasma C-reactive protein (CRP) was measured by a particle-enhanced immunoturbidimetric assay (Ultrasensitive CRP kit; CRM Diagnostic Systems) using microparticles coated with antihuman CRP antibodies. The precision of the method in the cutoff value of decision (1.8–2 μg/mL) is less than 5.5%. Serum IL-6 levels were measured using an IL-6 ELISA kit (Abcam).
Determination of RA concentrations
Serum RA concentrations were measured using a commercially available ELISA kit according to the manufacturer's instructions (catalog number MBS705877; MyBioSource). This assay has high sensitivity and excellent specificity for the detection of human retinoic acid. No significant cross-reactivity or interference between human retinoic acid and analogs was observed. The detection range is 0.625–10 ng/mL. Serum RA levels were measured in duplicate in serum aliquots that had undergone one or two freeze-thaw cycles. The ELISA samples were run in duplicate. The coefficient of variation for the interassay replicate samples was less than 8%.
Definition of MetS
MetS was defined based on the updated National Cholesterol Education Program/Adult treatment Panel III criteria for Asian Americans as having at least three of the following components: 1) waist circumference of 90 cm or greater for men or 80 cm or greater for women; 2) triglycerides of 1.7 mmol/L or greater; 3) HDL cholesterol less than 1.03 mmol/L for men or less than 1.30 mmol/L for women; 4) blood pressure (BP) of 130/85 mm Hg or greater or the current use of antihypertensive medications; or 5) fasting glucose of 5.6 mmol/L or greater, previously diagnosed type 2 diabetes, or treatment with oral antidiabetic agents or insulin (24).
Statistical analysis
Normally distributed data were expressed as the mean ± SD, whereas variables with a skewed distribution were reported as the median (interquartile range) and log transformed to approximate normality before analysis. Categorical variables were expressed as frequency and percentage. Analysis of covariance for continuous variables and multivariate logistic regression analysis for categorical variables were applied for the comparison according to RA quartiles. Correlation coefficients between RA and metabolic features were calculated by partial correlation analysis on ranks (Spearman correlation). Serum RA levels were depicted according to the number of MetS components using a linear regression model. Multivariate logistic regression models were used to estimate the odds ratios (ORs) for MetS and its components. Potential confounding variables including age, BMI, LDL cholesterol, HDL cholesterol, adiponectin, 8-iso PGF2α, high-sensitivity CRP (hsCRP), IL-6, and RA in gender-specific quartiles. Data management and statistical analysis were performed with SPSS version 19.0 (SPSS, Inc). P < .05 was considered statistically significant.
Results
The baseline clinical characteristics of the cohort are summarized in Table 1. Among the 1042 subjects who were recruited for the 4-year follow-up assessment, 825 subjects did not have the MetS at baseline. More male subjects had the MetS compared with female individuals (P < .05). As expected, subjects with the MetS at baseline had more adverse risk factors than those without the MetS, including higher BMI, age, LDL cholesterol, and insulin resistance index (as determined by HOMA-IR) as well as MetS-defining parameters such as waist circumference, fasting glucose, triglycerides, hypertension, and low HDL cholesterol. MetS subjects also had higher hsCRP and lower serum adiponectin levels (both P < .001). In addition, the RA concentration was significantly lower in MetS subjects compared with the non-MetS subjects (P < .001, gender adjusted). The RA level decreased gradually with the number of MetS components (Figure 1). The RA levels decreased gradually from the subjects without MetS (2.33 ng/mL) to those with all five MetS components (0.81 ng/mL).
Serum RA concentration according to the number of MetS components at baseline.
P values were calculated from the multivariable-adjusted general linear regression model. The covariates were adjusted by age, gender, BMI, and HOMA-IR.
Serum RA concentration according to the number of MetS components at baseline.
P values were calculated from the multivariable-adjusted general linear regression model. The covariates were adjusted by age, gender, BMI, and HOMA-IR.
Baseline Characteristics of Subjects With or Without the MetS at Baseline
Men | 1.02 (0.68–1.37) | 1.92 (1.32–2.49) | <.001b |
Women | 1.06 (0.72–1.45) | 1.99 (1.38–2.55) |
Men | 1.02 (0.68–1.37) | 1.92 (1.32–2.49) | <.001b |
Women | 1.06 (0.72–1.45) | 1.99 (1.38–2.55) |
Data are mean ± SD or median (interquartile range) unless otherwise indicated.
Log transformed before analysis.
Sex-adjusted.
Baseline Characteristics of Subjects With or Without the MetS at Baseline
Men | 1.02 (0.68–1.37) | 1.92 (1.32–2.49) | <.001b |
Women | 1.06 (0.72–1.45) | 1.99 (1.38–2.55) |
Men | 1.02 (0.68–1.37) | 1.92 (1.32–2.49) | <.001b |
Women | 1.06 (0.72–1.45) | 1.99 (1.38–2.55) |
Data are mean ± SD or median (interquartile range) unless otherwise indicated.
Log transformed before analysis.
Sex-adjusted.
On multiple stepwise logistic regression analysis, the serum RA level in quartiles (P < .001) together with age (P < .001), BMI (P < .001) and HOMA-IR (P < .001) was independently associated with the presence of MetS at baseline. Subjects with the highest quartile of RA had a much decreased likelihood of having the MetS compared with those in the lowest quartile (OR 0.61; 95% confidence interval [CI] 0.49–0.74, P < .001, Table 2).
Multiple Stepwise Logistic Regression Analysis of Factors Independently Associated With the MetS at Baseline
Quartile 1 | 1.00 | |
Quartile 2 | 0.82 (0.65–0.97) | .047 |
Quartile 3 | 0.72 (0.57–0.86) | .015 |
Quartile 4 | 0.61 (0.44–0.74) | <.001 |
Quartile 1 | 1.00 | |
Quartile 2 | 0.82 (0.65–0.97) | .047 |
Quartile 3 | 0.72 (0.57–0.86) | .015 |
Quartile 4 | 0.61 (0.44–0.74) | <.001 |
Variables included in the original model are age, BMI, HOMA-IR, LDL cholesterol, HDL cholesterol, adiponectin, 8-iso PGF2α, hsCRP, IL-6, and RA in gender-specific quartiles.
Multiple Stepwise Logistic Regression Analysis of Factors Independently Associated With the MetS at Baseline
Quartile 1 | 1.00 | |
Quartile 2 | 0.82 (0.65–0.97) | .047 |
Quartile 3 | 0.72 (0.57–0.86) | .015 |
Quartile 4 | 0.61 (0.44–0.74) | <.001 |
Quartile 1 | 1.00 | |
Quartile 2 | 0.82 (0.65–0.97) | .047 |
Quartile 3 | 0.72 (0.57–0.86) | .015 |
Quartile 4 | 0.61 (0.44–0.74) | <.001 |
Variables included in the original model are age, BMI, HOMA-IR, LDL cholesterol, HDL cholesterol, adiponectin, 8-iso PGF2α, hsCRP, IL-6, and RA in gender-specific quartiles.
At baseline, the age- and gender-adjusted RA was found to have significant inverse correlations with multiple adverse metabolic parameters (Table 3). Serum RA was negatively correlated with fasting plasma glucose (r = −0.185, P < .001), insulin (r = −0.126, P = .014), and HOMA-IR (r = −0.208, P < .001). Furthermore, the serum RA level was inversely associated with total cholesterol (r = −0.102, P = .023), triglycerides (r = −0.194, P < .001), and LDL cholesterol (r = −0.152, P = .009). Participants with higher serum RA had a higher HDL cholesterol level (r = 0.177, P < .001). An inverse correlation between RA and systolic BP was observed (r = −0.131, P = .010), but no significant association was found between RA and diastolic BP (r = −0.054, P = .087). RA was positively associated with adiponectin level (r = 0.155, P = .008). In addition, a higher RA level was strongly correlated with lower oxidative markers such as 8-iso PGF2α (r = −0.191, P < .001) and reduced proinflammatory molecules including hsCRP (r = −0.167, P = .005).
Multivariable-Adjusted Spearman Correlation Coefficients of RA and Metabolic Risk Factors at Baselinea Os x mojave suppoort for nvidia cards.
P Value for Trenda | |||||
---|---|---|---|---|---|
Q1 (<0.94 ng/mL) | Q2 (0.94–1.45 ng/mL) | Q3 (1.46–2.08 ng/mL) | Q4 (≥2.08 ng/mL) | ||
n | 206 | 206 | 206 | 207 | |
MetS cases, %b | 59 (28.6) | 41 (19.9) | 28 (13.6) | 15 (7.3) | |
Model 1c | 1.00 | 0.55 (0.38–0.74) | 0.46 (0.28–0.62) | 0.37 (0.20–0.55) | <.001 |
Model 2d | 1.00 | 0.67 (0.48–0.84) | 0.57 (0.40–0.72) | 0.45 (0.29–0.63) | <.001 |
Model 3e | 1.00 | 0.75 (0.56–0.91) | 0.63 (0.47–0.80) | 0.54 (0.39–0.70) | <.001 |
Central obesity cases, % | 41 (19.8) | 34 (16.5) | 23 (11.2) | 14 (6.8) | |
Model 1 | 1.00 | 0.72 (0.52–0.94) | 0.63 (0.44–0.85) | 0.49 (0.31–0.69) | <.001 |
Model 2 | 1.00 | 0.81 (0.60–1.02) | 0.71 (0.51–0.92) | 0.58 (0.42–0.79) | .011 |
Model 3 | 1.00 | 0.92 (0.69–1.12) | 0.81 (0.62–1.03) | 0.68 (0.51–0.88) | .040 |
Hypertriglyceridemia cases, % | 54 (26.1) | 42 (20.4) | 29 (14.1) | 20 (9.7) | |
Model 1 | 1.00 | 0.57 (0.38–0.76) | 0.48 (0.29–0.66) | 0.39 (0.22–0.52) | <.001 |
Model 2 | 1.00 | 0.69 (0.52–0.87) | 0.60 (0.37–0.73) | 0.47 (0.30–0.64) | <.001 |
Model 3 | 1.00 | 0.78 (0.57–0.96) | 0.69 (0.42–0.78) | 0.58 (0.39–0.77) | .003 |
Reduced HDL cholesterol cases, % | 45 (21.7) | 32 (15.5) | 20 (9.7) | 12 (5.8) | |
Model 1 | 1.00 | 0.62 (0.43–0.81) | 0.53 (0.31–0.74) | 0.41 (0.23–0.62) | <.001 |
Model 2 | 1.00 | 0.73 (0.55–0.94) | 0.61 (0.41–0.82) | 0.50 (0.32–0.73) | .001 |
Model 3 | 1.00 | 0.82 (0.64–1.05) | 0.70 (0.52–0.91) | 0.61 (0.40–0.79) | .008 |
Elevated BP cases, % | 50 (24.3) | 40 (19.4) | 28 (13.6) | 19 (9.2) | |
Model 1 | 1.00 | 0.64 (0.42–0.83) | 0.55 (0.36–0.74) | 0.45 (0.28–0.63) | <.001 |
Model 2 | 1.00 | 0.76 (0.55–0.97) | 0.67 (0.45–0.84) | 0.56 (0.37–0.73) | .002 |
Model 3 | 1.00 | 0.88 (0.66–1.07) | 0.79 (0.58–0.98) | 0.66 (0.49–0.82) | .017 |
Hyperglycemia cases, % | 37 (18.0) | 32 (15.5) | 24 (11.7) | 15 (7.3) | |
Model 1 | 1.00 | 0.76 (0.54–0.95) | 0.64 (0.43–0.86) | 0.55 (0.32–0.74) | .004 |
Model 2 | 1.00 | 0.87 (0.65–1.04) | 0.77 (0.58–0.95) | 0.68 (0.48–0.89) | .023 |
Model 3 | 1.00 | 0.97 (0.75–1.16) | 0.85 (0.67–1.07) | 0.76 (0.60–0.95) | .045 |
P Value for Trenda | |||||
---|---|---|---|---|---|
Q1 (<0.94 ng/mL) | Q2 (0.94–1.45 ng/mL) | Q3 (1.46–2.08 ng/mL) | Q4 (≥2.08 ng/mL) | ||
n | 206 | 206 | 206 | 207 | |
MetS cases, %b | 59 (28.6) | 41 (19.9) | 28 (13.6) | 15 (7.3) | |
Model 1c | 1.00 | 0.55 (0.38–0.74) | 0.46 (0.28–0.62) | 0.37 (0.20–0.55) | <.001 |
Model 2d | 1.00 | 0.67 (0.48–0.84) | 0.57 (0.40–0.72) | 0.45 (0.29–0.63) | <.001 |
Model 3e | 1.00 | 0.75 (0.56–0.91) | 0.63 (0.47–0.80) | 0.54 (0.39–0.70) | <.001 |
Central obesity cases, % | 41 (19.8) | 34 (16.5) | 23 (11.2) | 14 (6.8) | |
Model 1 | 1.00 | 0.72 (0.52–0.94) | 0.63 (0.44–0.85) | 0.49 (0.31–0.69) | <.001 |
Model 2 | 1.00 | 0.81 (0.60–1.02) | 0.71 (0.51–0.92) | 0.58 (0.42–0.79) | .011 |
Model 3 | 1.00 | 0.92 (0.69–1.12) | 0.81 (0.62–1.03) | 0.68 (0.51–0.88) | .040 |
Hypertriglyceridemia cases, % | 54 (26.1) | 42 (20.4) | 29 (14.1) | 20 (9.7) | |
Model 1 | 1.00 | 0.57 (0.38–0.76) | 0.48 (0.29–0.66) | 0.39 (0.22–0.52) | <.001 |
Model 2 | 1.00 | 0.69 (0.52–0.87) | 0.60 (0.37–0.73) | 0.47 (0.30–0.64) | <.001 |
Model 3 | 1.00 | 0.78 (0.57–0.96) | 0.69 (0.42–0.78) | 0.58 (0.39–0.77) | .003 |
Reduced HDL cholesterol cases, % | 45 (21.7) | 32 (15.5) | 20 (9.7) | 12 (5.8) | |
Model 1 | 1.00 | 0.62 (0.43–0.81) | 0.53 (0.31–0.74) | 0.41 (0.23–0.62) | <.001 |
Model 2 | 1.00 | 0.73 (0.55–0.94) | 0.61 (0.41–0.82) | 0.50 (0.32–0.73) | .001 |
Model 3 | 1.00 | 0.82 (0.64–1.05) | 0.70 (0.52–0.91) | 0.61 (0.40–0.79) | .008 |
Elevated BP cases, % | 50 (24.3) | 40 (19.4) | 28 (13.6) | 19 (9.2) | |
Model 1 | 1.00 | 0.64 (0.42–0.83) | 0.55 (0.36–0.74) | 0.45 (0.28–0.63) | <.001 |
Model 2 | 1.00 | 0.76 (0.55–0.97) | 0.67 (0.45–0.84) | 0.56 (0.37–0.73) | .002 |
Model 3 | 1.00 | 0.88 (0.66–1.07) | 0.79 (0.58–0.98) | 0.66 (0.49–0.82) | .017 |
Hyperglycemia cases, % | 37 (18.0) | 32 (15.5) | 24 (11.7) | 15 (7.3) | |
Model 1 | 1.00 | 0.76 (0.54–0.95) | 0.64 (0.43–0.86) | 0.55 (0.32–0.74) | .004 |
Model 2 | 1.00 | 0.87 (0.65–1.04) | 0.77 (0.58–0.95) | 0.68 (0.48–0.89) | .023 |
Model 3 | 1.00 | 0.97 (0.75–1.16) | 0.85 (0.67–1.07) | 0.76 (0.60–0.95) | .045 |
Calculated by using multivariable logistic regression.
Izotope rx final mix video. MetS was defined according to the updated National Cholesterol Education Program Adult Treatment Panel III criteria for Asian Americans.
Adjusted for age and sex.
Adjusted as for model 1 plus BMI (except when modeling associations for central obesity), smoking, drinking, family history of cardiovascular diseases, and diabetes.
Adjusted as for model 2 plus adiponectin, 8-iso PGF2α, hsCRP. and IL-6.
Incidence of the MetS and Its Components by Quartiles of Serum RA After 4-Year Follow Up
P Value for Trenda | |||||
---|---|---|---|---|---|
Q1 (<0.94 ng/mL) | Q2 (0.94–1.45 ng/mL) | Q3 (1.46–2.08 ng/mL) | Q4 (≥2.08 ng/mL) | ||
n | 206 | 206 | 206 | 207 | |
MetS cases, %b | 59 (28.6) | 41 (19.9) | 28 (13.6) | 15 (7.3) | |
Model 1c | 1.00 | 0.55 (0.38–0.74) | 0.46 (0.28–0.62) | 0.37 (0.20–0.55) | <.001 |
Model 2d | 1.00 | 0.67 (0.48–0.84) | 0.57 (0.40–0.72) | 0.45 (0.29–0.63) | <.001 |
Model 3e | 1.00 | 0.75 (0.56–0.91) | 0.63 (0.47–0.80) | 0.54 (0.39–0.70) | <.001 |
Central obesity cases, % | 41 (19.8) | 34 (16.5) | 23 (11.2) | 14 (6.8) | |
Model 1 | 1.00 | 0.72 (0.52–0.94) | 0.63 (0.44–0.85) | 0.49 (0.31–0.69) | <.001 |
Model 2 | 1.00 | 0.81 (0.60–1.02) | 0.71 (0.51–0.92) | 0.58 (0.42–0.79) | .011 |
Model 3 | 1.00 | 0.92 (0.69–1.12) | 0.81 (0.62–1.03) | 0.68 (0.51–0.88) | .040 |
Hypertriglyceridemia cases, % | 54 (26.1) | 42 (20.4) | 29 (14.1) | 20 (9.7) | |
Model 1 | 1.00 | 0.57 (0.38–0.76) | 0.48 (0.29–0.66) | 0.39 (0.22–0.52) | <.001 |
Model 2 | 1.00 | 0.69 (0.52–0.87) | 0.60 (0.37–0.73) | 0.47 (0.30–0.64) | <.001 |
Model 3 | 1.00 | 0.78 (0.57–0.96) | 0.69 (0.42–0.78) | 0.58 (0.39–0.77) | .003 |
Reduced HDL cholesterol cases, % | 45 (21.7) | 32 (15.5) | 20 (9.7) | 12 (5.8) | |
Model 1 | 1.00 | 0.62 (0.43–0.81) | 0.53 (0.31–0.74) | 0.41 (0.23–0.62) | <.001 |
Model 2 | 1.00 | 0.73 (0.55–0.94) | 0.61 (0.41–0.82) | 0.50 (0.32–0.73) | .001 |
Model 3 | 1.00 | 0.82 (0.64–1.05) | 0.70 (0.52–0.91) | 0.61 (0.40–0.79) | .008 |
Elevated BP cases, % | 50 (24.3) | 40 (19.4) | 28 (13.6) | 19 (9.2) | |
Model 1 | 1.00 | 0.64 (0.42–0.83) | 0.55 (0.36–0.74) | 0.45 (0.28–0.63) | <.001 |
Model 2 | 1.00 | 0.76 (0.55–0.97) | 0.67 (0.45–0.84) | 0.56 (0.37–0.73) | .002 |
Model 3 | 1.00 | 0.88 (0.66–1.07) | 0.79 (0.58–0.98) | 0.66 (0.49–0.82) | .017 |
Hyperglycemia cases, % | 37 (18.0) | 32 (15.5) | 24 (11.7) | 15 (7.3) | |
Model 1 | 1.00 | 0.76 (0.54–0.95) | 0.64 (0.43–0.86) | 0.55 (0.32–0.74) | .004 |
Model 2 | 1.00 | 0.87 (0.65–1.04) | 0.77 (0.58–0.95) | 0.68 (0.48–0.89) | .023 |
Model 3 | 1.00 | 0.97 (0.75–1.16) | 0.85 (0.67–1.07) | 0.76 (0.60–0.95) | .045 |
P Value for Trenda | |||||
---|---|---|---|---|---|
Q1 (<0.94 ng/mL) | Q2 (0.94–1.45 ng/mL) | Q3 (1.46–2.08 ng/mL) | Q4 (≥2.08 ng/mL) | ||
n | 206 | 206 | 206 | 207 | |
MetS cases, %b | 59 (28.6) | 41 (19.9) | 28 (13.6) | 15 (7.3) | |
Model 1c | 1.00 | 0.55 (0.38–0.74) | 0.46 (0.28–0.62) | 0.37 (0.20–0.55) | <.001 |
Model 2d | 1.00 | 0.67 (0.48–0.84) | 0.57 (0.40–0.72) | 0.45 (0.29–0.63) | <.001 |
Model 3e | 1.00 | 0.75 (0.56–0.91) | 0.63 (0.47–0.80) | 0.54 (0.39–0.70) | <.001 |
Central obesity cases, % | 41 (19.8) | 34 (16.5) | 23 (11.2) | 14 (6.8) | |
Model 1 | 1.00 | 0.72 (0.52–0.94) | 0.63 (0.44–0.85) | 0.49 (0.31–0.69) | <.001 |
Model 2 | 1.00 | 0.81 (0.60–1.02) | 0.71 (0.51–0.92) | 0.58 (0.42–0.79) | .011 |
Model 3 | 1.00 | 0.92 (0.69–1.12) | 0.81 (0.62–1.03) | 0.68 (0.51–0.88) | .040 |
Hypertriglyceridemia cases, % | 54 (26.1) | 42 (20.4) | 29 (14.1) | 20 (9.7) | |
Model 1 | 1.00 | 0.57 (0.38–0.76) | 0.48 (0.29–0.66) | 0.39 (0.22–0.52) | <.001 |
Model 2 | 1.00 | 0.69 (0.52–0.87) | 0.60 (0.37–0.73) | 0.47 (0.30–0.64) | <.001 |
Model 3 | 1.00 | 0.78 (0.57–0.96) | 0.69 (0.42–0.78) | 0.58 (0.39–0.77) | .003 |
Reduced HDL cholesterol cases, % | 45 (21.7) | 32 (15.5) | 20 (9.7) | 12 (5.8) | |
Model 1 | 1.00 | 0.62 (0.43–0.81) | 0.53 (0.31–0.74) | 0.41 (0.23–0.62) | <.001 |
Model 2 | 1.00 | 0.73 (0.55–0.94) | 0.61 (0.41–0.82) | 0.50 (0.32–0.73) | .001 |
Model 3 | 1.00 | 0.82 (0.64–1.05) | 0.70 (0.52–0.91) | 0.61 (0.40–0.79) | .008 |
Elevated BP cases, % | 50 (24.3) | 40 (19.4) | 28 (13.6) | 19 (9.2) | |
Model 1 | 1.00 | 0.64 (0.42–0.83) | 0.55 (0.36–0.74) | 0.45 (0.28–0.63) | <.001 |
Model 2 | 1.00 | 0.76 (0.55–0.97) | 0.67 (0.45–0.84) | 0.56 (0.37–0.73) | .002 |
Model 3 | 1.00 | 0.88 (0.66–1.07) | 0.79 (0.58–0.98) | 0.66 (0.49–0.82) | .017 |
Hyperglycemia cases, % | 37 (18.0) | 32 (15.5) | 24 (11.7) | 15 (7.3) | |
Model 1 | 1.00 | 0.76 (0.54–0.95) | 0.64 (0.43–0.86) | 0.55 (0.32–0.74) | .004 |
Model 2 | 1.00 | 0.87 (0.65–1.04) | 0.77 (0.58–0.95) | 0.68 (0.48–0.89) | .023 |
Model 3 | 1.00 | 0.97 (0.75–1.16) | 0.85 (0.67–1.07) | 0.76 (0.60–0.95) | .045 |
Calculated by using multivariable logistic regression.
MetS was defined according to the updated National Cholesterol Education Program Adult Treatment Panel III criteria for Asian Americans.
Adjusted for age and sex.
Adjusted as for model 1 plus BMI (except when modeling associations for central obesity), smoking, drinking, family history of cardiovascular diseases, and diabetes.
Adjusted as for model 2 plus adiponectin, 8-iso PGF2α, hsCRP. and IL-6.
In multiple stepwise logistic regression analysis, baseline RA (P < .001), and HOMA-IR (P = .001) were the only independent predictors for the development of the MetS at year 4 (Table 5). HOMA-IR was positively correlated with the incident MetS, whereas RA was inversely correlated with the incident MetS. Subjects with the highest gender-specific quartile of baseline RA had a 46% lower risk of developing the MetS at year 4 compared with those with RA in the lowest quartile (OR 0.54, 95% CI 0.42–0.65, P < .001) after adjustment for BMI and HOMA-IR. Adjustment for alcohol drinking and smoking status did not alter any of the above results.
Multiple Logistic Regression Analysis of Factors Predicting the Development of MetS at 4-Year Follow Up
Quartile 1 | 1.00 | |
Quartile 2 | 0.79 (0.63–0.94) | .044 |
Quartile 3 | 0.67 (0.52–0.83) | .012 |
Quartile 4 | 0.53 (0.40–0.69) | <.001 |
Quartile 1 | 1.00 | |
Quartile 2 | 0.79 (0.63–0.94) | .044 |
Quartile 3 | 0.67 (0.52–0.83) | .012 |
Quartile 4 | 0.53 (0.40–0.69) | <.001 |
Variables included in the original model are adjusted for age, BMI, HOMA-IR, LDL cholesterol, HDL cholesterol, adiponectin, 8-iso PGF2α, hsCRP, IL-6, and RA in gender-specific quartiles.
Multiple Logistic Regression Analysis of Factors Predicting the Development of MetS at 4-Year Follow Up
Quartile 1 | 1.00 | |
Quartile 2 | 0.79 (0.63–0.94) | .044 |
Quartile 3 | 0.67 (0.52–0.83) | .012 |
Quartile 4 | 0.53 (0.40–0.69) | <.001 |
Quartile 1 | 1.00 | |
Quartile 2 | 0.79 (0.63–0.94) | .044 |
Quartile 3 | 0.67 (0.52–0.83) | .012 |
Quartile 4 | 0.53 (0.40–0.69) | <.001 |
Variables included in the original model are adjusted for age, BMI, HOMA-IR, LDL cholesterol, HDL cholesterol, adiponectin, 8-iso PGF2α, hsCRP, IL-6, and RA in gender-specific quartiles.
Discussion
RA, the most studied metabolite in the vitamin A pathway, has been previously reported to play a distinct metabolic role in adipocyte differentiation in vitro and in diet-induced insulin resistance and obesity in vivo (25). However, the evidence supporting the potential role of RA in the development of MetS in human populations is limited. In the present study, we provide the first clinical evidence demonstrating that serum RA is a significant protective factor for the development of MetS, independent of adiposity and insulin resistance. Both our cross-sectional and prospective studies have demonstrated an independent association of the circulating RA level with all the metabolic risk factors, including abdominal obesity, insulin resistance, atherogenic dyslipidemia, hyperglycemia, and hypertension. The baseline RA level predicted the development of the MetS during the 4-year follow-up, even after adjustment for age, gender, BMI, and other confounding factors. Notably, our multiple logistic regression analysis demonstrated that only the RA level and HOMA-IR were independent predictors of the MetS in this cohort, suggesting that a lower level of RA might be an important protector to this disease in humans. Furthermore, the increased risk for developing the MetS associated with a lower baseline RA was independent of the baseline status of the MetS components.
MetS is a complex of clinical features and the most important feature, which is an increased abdominal visceral fat depot (26). Although RA was shown to inhibit adipogenesis in preadipocytes (27) and in an obese animal model (28), the possible role of RA on body weight and fat mass in human populations is still unknown. Here we demonstrated that higher serum RA level was significantly associated with lower BMI in this population. Furthermore, RA was inversely associated with waist circumstance and waist to hip ratio, two anthropometric and simple measures of central adiposity that may predict cardiovascular and diabetes risk (29). Individuals who are obese and/or suffer from the MetS display a characteristic imbalance of their adipokine profile (30, 31). This altered adipokine profile leads to profound changes in insulin sensitivity and other biochemical alterations of metabolites, making an individual more prone to metabolic disorders (32, 33). In the current study, we found that a high RA level was positively associated with the level of adiponectin, a key negative modulator of insulin resistance. Thus, our data suggested that RA may improve the aberrant adipokine production and thus has therapeutic potential in the management of the MetS. However, the impact of RA on the cellular composition of adipose tissue, the altered production of adipokines, and the underlying molecular mechanisms need to be elucidated in the future study.
Furthermore, our findings demonstrated that high serum RA levels were associated with lower fasting glucose and HOMA index. Also, a positive association between serum RA and HDL cholesterol concentrations but an inverse correlation with total cholesterol, LDL cholesterol, and triglycerides in this population was observed in this population. We speculate that the effect of RA on blood lipid concentrations (especially cholesterol and triglycerides) may be mediated through its role in the metabolism of hepatic fatty acids, which regulates the expression of genes involved in lipid metabolism (34, 35).
Systemic inflammation, manifested by elevated plasma levels of hsCRP and IL-6, is an important predictor of MetS (36–38). The present study revealed an inverse correlation between the RA level and elevated inflammatory markers, including hsCRP and IL-6, even after adjustment for adiposity, which have been well documented to play an important role in the initiation and development of inflammatory effects in adipose tissue. Thus, our results identified proinflammatory markers as a critical link between RA and the incidence of MetS.
Increased oxidative stress plays an essential role in MetS-related disorders (39, 40). In this study, higher serum RA was significantly associated with lower serum concentrations of 8-iso PGF2-α, major products of the peroxidation of unsaturated fatty acids, and a reliable marker of oxidant stress (41). These results indicate that a higher RA status may prevent systemic oxidative damage. Therefore, oxidative stress may represent another mechanistic pathway through which the high RA status is protected against the metabolic disorders.
Nevertheless, several limitations of this study should be addressed. First, the number of subjects who developed the MetS was relatively small during the 4-year follow-up. The inverse correlation of RA with the risk of MetS needs to be validated in the larger study populations. https://feimiter.hatenablog.com/entry/2020/11/22/223905. Furthermore, our conclusions also need to be confirmed in other ethnic populations with different genetic and environmental backgrounds. Another limitation of the present study is that the statistical analysis was not hypothesis driven but exploratory in nature to find significant factors as a result of application of stepwise methods. Finally, serum RA concentration was measured only once at baseline of the study cohort. Therefore, we were not able to adjust for possible moderate fluctuations of serum RA during the follow-up, for example, due to increased intake of vitamin A-rich food.
In conclusion, our findings suggested that reduced RA levels are associated with an increased risk of having MetS and adverse values for MetS-related components. Moreover, high serum RA levels were negatively associated with inflammatory markers and oxidative stress. Our results suggest a potential link between RA and the incidence of MetS. Because there is evidence of ethnic variations in the RA effect and limited data on the association of RA with MetS in Asians, our data provide novel insights into the nature of this association among Asians. Nevertheless, the benefits of vitamin A on MetS and related diseases such as type 2 diabetes need to be confirmed in future prospective studies and clinical trials.
Acknowledgments
The authors made the following contributions: Y.L. and H.C. performed the research. Y.L., H.C., D.M., J.F., J.S., Y.Z., and D.L. reviewed/edited the manuscript. Y.L., H.C., D.M., J.F., J.S., Y.Z., and D.L. analyzed the data. M.X. contributed to the experimental design and the manuscript, managed the overall project, and wrote the manuscript.
This work was supported by the Guangzhou Science and Technology Innovation Committee (Grant 201510010220).
Disclosure Summary: The authors have nothing to disclose.
Abbreviations
- BMI
- BP
- CI
- CRP
- HDL
- HOMA-IRhomeostasis model assessment index for insulin resistance
- high-sensitivity CRP
- 8-iso-prostaglandin F2α
- low-density lipoprotein
- metabolic syndrome
- odds ratio
- retinoic acid
- retinoid X receptor.
References
ES
, WH
, WH
. Prevalence of the metabolic syndrome among US adults: findings from the third National Health and Nutrition Examination Survey
. . 2002
;:356
–.RH
, SM
, PZ
. . Lancet
. ;365
:–1428
.Expert Panel on the Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults
. Executive Summary of The Third Report of The National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol In Adults (Adult Treatment Panel III)
. . 2001
;:2486
–.L
, L
, F
, Y
, R
. . Lancet
. ;366
:–1824
.G
, Y
, Y
, et al. . Rapid health transition in China, 1990–2010: findings from the Global Burden of Disease Study 2010
. . 2013
;:1987
–.J
, D
, X
, et al. . . N Engl J Med
. ;353
:–1134
.Y
, L
, J
, et al. . 2010 China Noncommunicable Disease Surveillance Group. Prevalence and control of diabetes in Chinese adults
. . 2013
;:948
–.J
, M
, M
, J
. Nuts in the prevention and treatment of metabolic syndrome
. . 2014
;:399S
–.ME
, JB
, JT
, NM
, PF
. Mediterranean-style dietary pattern, reduced risk of metabolic syndrome traits, and incidence in the Framingham Offspring Cohort
. . 2009
;:1608
–.YA
, L
, WJ
. . Am J Clin Nutr
. ;83
:–1504S
.BH
, CJ
, MA
, GD
. Dairy components and risk factors for cardiometabolic syndrome: recent evidence and opportunities for future research
. . 2011
;:396
–.SA
, PJ
, UC
, LM
. . Physiol Rev
. ;80
:–1054
.PM
. . J Am Acad Dermatol
. ;45
:–S142
.R
, HK
. . J Neurobiol
. ;66
:–630
.AI
, DJ
. Retinoid X receptor heterodimers in the metabolic syndrome
. . 2005
;:604
–.RA
, DJ
, JA
, RB
, G
. Retinoic acid is a high affinity ligand for the retinoid X receptor
. . 1992
;:397
–.O
, J
. Retinoid metabolism and nuclear receptor responses: new insights into coordinated regulation of the PPAR-RXR complex
. . 2008
;:32
–.J
. The PPAR-RXR transcriptional complex in the vasculature: energy in the balance
. . 2011
;:1002
–.M
, T
, AL
, M
, RT
. . Mol Endocrinol
. ;19
:–2450
.T
, A
. Effects of citral, a naturally occurring antiadipogenic molecule, on an energy-intense diet model of obesity
. . 2011
;:300
–. Diagnosis and classification of diabetes mellitus. American Diabetes Association
. . 2007
;(suppl 1
):–S47
.WT
, RI
, DS
. Estimation of the concentration of low-density lipoprotein cholesterol in plasma, without use of the preparative ultracentrifuge
. . 1972
;:499
–.Y
, D
, Y
, R
, M
. Anthocyanin increases adiponectin secretion and protects against diabetes-related endothelial dysfunction
. . 2014
;:E975
–.SM
, JI
, SR
, KA
, RH
. Diagnosis and management of the metabolic syndrome: an American Heart Association/National Heart, Lung, and Blood Institute Scientific Statement
. . 2005
;:2735
–.DC
, D
, H
, CM
, N
. Retinoic acid upregulates preadipocyte genes to block adipogenesis and suppress diet-induced obesity
. . 2012
;:1112
–.S
, B
, H
, et al. . Distribution of abdominal visceral and subcutaneous adipose tissue and metabolic syndrome in Korean population
. . 2011
;:504
–.F
, ML
, J
, A
. Modulation of resistin expression by retinoic acid and vitamin A status
. . 2004
;:882
–.DC
, N
. All-trans-retinoic acid represses obesity and insulin resistance by activating both peroxisome proliferation-activated receptor β/δ and retinoic acid receptor
Arirang Volume 58 Iso Download Youtube
. .2009
;:3286
–.LG
, SS
, TA
, AH
, PR
. Anthropometric measurements of general and central obesity and the prediction of cardiovascular disease risk in women: a cross-sectional study
. . 2014
;:e004138
.KM
, M
, MS
, et al. . Serum adipocyte fatty acid-binding protein, retinol-binding protein 4, and adiponectin concentrations in relation to the development of the metabolic syndrome in Korean boys: a 3-y prospective cohort study
. . 2011
;:19
–.JY
, SV
, JH
, et al. . Prospective study of serum adiponectin and incident metabolic syndrome: the ARIRANG study
. . 2013
;:1547
–.JC
, TL
, SE
, J
, AM
. Adiponectin in childhood and adolescent obesity and its association with inflammatory markers and components of the metabolic syndrome
. . 2006
;:4415
–.A
, AW
, BM
, Y
, NM
. Circulating adipocyte-fatty acid protein levels predict the development of the metabolic syndrome: a 5-year prospective study
. . 2007
;:1537
–.DC
, A
, PV
. All-trans retinoic acid lowers serum retinol-binding protein 4 concentrations and increases insulin sensitivity in diabetic mice
. . 2010
;:311
–.ML
, J
, A
. Lipid metabolism in mammalian tissues and its control by retinoic acid
. . 2012
;:177
–.GR
, J
, SE
Arirang Volume 58 Iso Download Torrent
.Metabolic syndrome, insulin resistance, and roles of inflammation–mechanisms and therapeutic targets
. . 2012
;:1771
–.PM
, JE
, NR
, N
. C-reactive protein, the metabolic syndrome, and risk of incident cardiovascular events: an 8-year follow-up of 14719 initially healthy American women
. . 2003
;:391
–.SH
, CD
, I
, AJ
, A
. Metabolic syndrome, haemostatic and inflammatory markers, cerebrovascular and peripheral arterial disease: the Edinburgh Artery Study
. . 2009
;:604
–.S
Arirang Volume 58 Iso Download Pc
,T
, M
, M
,
Y
. Increased oxidative stress in obesity and its impact on metabolic syndrome
. . 2004
;:1752
–.AM
, Y
, A
, MP
. Mitochondrial oxidative stress and the metabolic syndrome
. . 2012
;:429
–.D
. F(2)-isoprostanes: sensitive and specific non-invasive indices of lipid peroxidation in vivo
. . 1999
;:1
–.