Metabolic rate and inflammation levels -
In people with insulin resistance, cells don't respond normally to insulin and glucose can't enter the cells as easily.
As a result, your blood sugar levels rise even as your body churns out more and more insulin to try to lower your blood sugar. A lifelong commitment to a healthy lifestyle may prevent the conditions that cause metabolic syndrome. A healthy lifestyle includes:. On this page. When to see a doctor.
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You may opt-out of email communications at any time by clicking on the unsubscribe link in the e-mail. Metabolic syndrome is closely linked to overweight or obesity and inactivity. The following factors increase your chances of having metabolic syndrome: Age.
Your risk of metabolic syndrome increases with age. In the United States, Hispanics — especially Hispanic women — appear to be at the greatest risk of developing metabolic syndrome. The reasons for this are not entirely clear.
Carrying too much weight, especially in your abdomen, increases your risk of metabolic syndrome. You're more likely to have metabolic syndrome if you had diabetes during pregnancy gestational diabetes or if you have a family history of type 2 diabetes.
Other diseases. Your risk of metabolic syndrome is higher if you've ever had nonalcoholic fatty liver disease, polycystic ovary syndrome or sleep apnea. Having metabolic syndrome can increase your risk of developing: Type 2 diabetes. If you don't make lifestyle changes to control your excess weight, you may develop insulin resistance, which can cause your blood sugar levels to rise.
Eventually, insulin resistance can lead to type 2 diabetes. Heart and blood vessel disease. High cholesterol and high blood pressure can contribute to the buildup of plaques in your arteries.
These plaques can narrow and harden your arteries, which can lead to a heart attack or stroke. A healthy lifestyle includes: Getting at least 30 minutes of physical activity most days Eating plenty of vegetables, fruits, lean protein and whole grains Limiting saturated fat and salt in your diet Maintaining a healthy weight Not smoking.
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Download references. General Internal Medicine Division, Boston University School of Medicine, Boston, USA. Cardiology, Whitaker Cardiovascular Institute, Boston University School of Medicine, Boston, USA.
Preventive Medicine Divisions, Whitaker Cardiovascular Institute, Boston University School of Medicine, Boston, USA. Biostatistics Department, Boston University School of Public Health, Boston, USA.
Epidemiology Department, Boston University School of Public Health, Boston, USA. Department of Medicine, Harvard Medical School, Boston, USA. Department of Mathematics and Statistics, Boston University, Boston, USA. Cardiovascular Medicine, University of Massachusetts Medical School, Worcester, USA.
You can also search for this author in PubMed Google Scholar. Correspondence to Emelia J Benjamin. Meigs Dr. Meigs was supported by NIDDK K24 DK No other potential conflict of interest was reported.
The authors declare that there is no duality of interest associated with this manuscript. An earlier version of this paper has been presented as an abstract at the 50 th Cardiovascular Disease Epidemiology and Prevention Conference , San Francisco, USA.
DD researched data, contributed to discussion and wrote the manuscript. All authors read and approved the final manuscript. Additional file 1: Table S1. Fold increments among the inflammatory biomarkers when comparing those with metabolic syndrome versus those without metabolic syndrome by BMI category.
DOC 37 KB. Additional file 2: Table S2. DOC 34 KB. Additional file 3: Figure S1. DOC 36 KB. Additional file 4: Table S3. Fold increments among the inflammatory biomarkers when comparing those with metabolic syndrome versus those without metabolic syndrome by Sex.
DOC 70 KB. Open Access This article is published under license to BioMed Central Ltd. Reprints and permissions. Dallmeier, D. et al. Metabolic syndrome and inflammatory biomarkers: a community-based cross-sectional study at the Framingham Heart Study.
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To the best of our knowledge, this study is the first to investigate the relationship between CRP and RMR in response to influenza vaccination.
Results from this study suggest that influenza vaccination is too small of an immune stimulus to induce an elevation in RMR. While our results do not suggest that influenza vaccination impacts RMR, they do corroborate prior research findings that influenza vaccination induces a mild acute inflammatory response that can be measured by CRP.
Our sample of healthy young adults had a median baseline CRP of 1. Two days after vaccination, we observed a median increase in CRP of 1.
The average age of our sample, Age impacts both baseline CRP and the magnitude of CRP elevation in response to an immune system activation [ 29 — 31 ]. Lane-Cordova and colleagues reported that even compared to elderly adults with low baseline CRP concentrations, young adults experienced a greater elevation in CRP from baseline after receiving an influenza vaccine [ 30 ].
Another possible explanation is that some participants could have become infected with a pathogen during their participation in our study. This would result in our measured CRP responses to vaccination being artificially high because infection with an actual pathogen would induce a larger magnitude CRP response [ 17 , 32 ].
We found no evidence that influenza vaccination impacted RMR; there were no trends nor statistically significant differences in RMR across the three measurements.
As such, it appears that influenza vaccination was too weak of an inflammatory stimulant to trigger an increase in RMR in our sample. Previous research has found that large or chronic stimulations of the immune system are so energetically costly that they are not fully compensated for by downregulating energetic investment in other body systems [ 1 , 5 — 9 , 18 , 28 ].
The present study aimed to fill a gap in the existing literature by investigating how a mild immune system stimulus impacts metabolism. Our results suggest that the energetic cost of influenza vaccination may be compensated for by the body reallocating energetic resources from one or several body systems to the immune system.
Therefore, brief, and minor, energetic divestment from other metabolic activities would presumably not have lasting consequences and upregulation of the basal metabolic rate would not occur. In mice, a mild immune stimulus was not associated with changes in metabolic rate, but it was associated with reductions in energy allocated to digestive and reproductive systems [ 10 ].
We did not evaluate reproductive or digestive function in our participants, but future studies should investigate whether changes in those systems are associated with mild immune stimuli. One possible explanation for why influenza vaccination was too mild of an immune stimulus to result in a change in RMR is that all participants had received influenza vaccinations in the past.
Participants already had memory B-cells with the blueprint to synthesize antibodies against some of the proteins in the vaccine. As a result, the adaptive immune system would have been able to respond more quickly, while the innate immune system would not require robust activation [ 2 , 34 ].
Also, previous research has found that RMR and CRP concentrations are correlated at baseline [ 35 ], but, given that influenza vaccination is a mild immune stimulus, changes in CRP may have been too small to result in measurable increases in RMR.
Individuals with CRP levels between 3. This result suggests that CRP concentrations would have to be extremely high, like they are in severe inflammatory states, before accompanying increases in RMR could be observed. Stratification of density distribution plots of percent change in baseline RMR after influenza vaccination by CRP concentration.
RMR was measured at baseline before vaccination and at days two and seven after vaccination; the day two:baseline and day seven:baseline ratios were combined for analysis. If influenza vaccination impacts RMR but RMR peaks at a different time than CRP, the RMR response would not have been captured within our study design.
We based the timing of our RMR measurements on when we expected CRP to peak two days after influenza vaccination using existing literature, but it is possible that changes in RMR would occur at a different time than changes in CRP.
The only two studies that have investigated the effect of vaccination on RMR found that RMR increased much earlier in response to typhoid fever vaccination i. These studies stopped evaluating RMR after six to eight hours, so it is unknown whether this timeframe following injection represents the peak RMR response.
Influenza vaccination may cause a similar increase in RMR several hours following injection, but we would not have observed the change with the current study design. Our results corroborate previous findings that influenza vaccination is a valid model for investigating inflammatory responses to mild immune system stimuli in vivo.
We expand the existing literature on CRP reactivity with our finding that healthy young adults appear to experience larger acute increases in CRP in response to mild immune system stimulation than older adults, as reported in previous studies. We also add to the existing research on the energetic cost of immune system activation.
In contrast to sepsis and chronic activations of the immune system, we find no evidence that mild acute immune system activation induces a corresponding increase in adult RMR. Rather, our results suggest that energetic costs associated with mild acute inflammatory responses are either too small to be measured by indirect calorimetry or are largely compensated for by temporary reallocations of energy from other body systems.
Future studies evaluating the energetic costs of mild immune system activation studies should consider using whole-room calorimetry to monitor any changes in metabolic rate as well as alterations in dietary intake, activity level, and sleep duration [ 37 ].
Further, future studies should evaluate the immune response in vaccine naïve patients i. Other vaccinations beyond influenza e. tetanus, tick encephalitis, COVID, etc. may elicit different or stronger immune responses and should also be considered when designing future studies.
Examining vaccine response in other populations, particularly those with greater background pathogen burden, would also advance our understanding of immune activity costs. Browse Subject Areas? Click through the PLOS taxonomy to find articles in your field. Article Authors Metrics Comments Media Coverage Peer Review Reader Comments Figures.
Results Baseline CRP was 1. Conclusions We find no evidence that adult influenza vaccination results in a corresponding increase in RMR. Loor, University of Illinois, UNITED STATES Received: May 2, ; Accepted: November 22, ; Published: December 15, Copyright: © Parker et al.
Introduction Both maintaining a functioning immune system and mounting an immune response are metabolically expensive. Methods The study was approved by the Duke University Health System Institutional Review Board. Results Demographic and anthropometric data are reported in Table 1 , and all vitals are reported in Table 2.
Download: PPT. Table 1. Fig 1. Density plot of CRP at baseline, two days, and seven days after influenza vaccination. Table 3. RMR and CRP at baseline, two days, and seven days after influenza vaccination.
Fig 2. Density plot of RMR at baseline, two days, and seven days after influenza vaccination. Discussion While the impacts of chronic immune activation and severe inflammatory states on metabolism are well documented [ 1 , 5 — 9 , 18 , 28 ], our understanding of the effect that a mild immune stimulus has on metabolic rate is limited.
CRP increases in response to influenza vaccination Our sample of healthy young adults had a median baseline CRP of 1.
RMR does not increase in response to vaccination We found no evidence that influenza vaccination impacted RMR; there were no trends nor statistically significant differences in RMR across the three measurements.
Fig 3. Density plot of percent change in baseline RMR after influenza vaccination stratified by CRP concentration. Conclusions and future directions Our results corroborate previous findings that influenza vaccination is a valid model for investigating inflammatory responses to mild immune system stimuli in vivo.
Supporting information. S1 Dataset. Study raw data. s XLSX. Acknowledgments We thank J. Dunn, Ph. and C. Nunn, Ph. for their feedback on this project. References 1. Gurven MD, Trumble BC, Stieglitz J, Yetish G, Cummings D, Blackwell AD, et al.
High resting metabolic rate among Amazonian forager-horticulturalists experiencing high pathogen burden. Am J Phys Anthropol. McDade TW. Life history theory and the immune system: steps toward a human ecological immunology.
View Article Google Scholar 3. Demas GE. The energetics of immunity: a neuroendocrine link between energy balance and immune function. Horm Behav. Pontzer H. Energy constraint as a novel mechanism linking exercise and health.
Kreymann G, Grosser S, Buggisch P, Gottschall C, Mattaei S, Greten H.
There's a growing body of evidence suggesting Glucose levels normalization is a complex interplay between inflammation levelw metabolic health. These are concerning issues today, as ibflammation has been inflammatino to drive several Metabolic rate and inflammation levels that collectively Metabolic rate and inflammation levels leading causes of lfvels worldwideincluding poor metabolic health, affecting a third of all adults. Inflammation is a natural process in the body that is a response to endogenous or exogenous forms of induced stress. While inflammation aims to protect the body, too much inflammation can impede many of its processes and has been proposed to be an underlying mechanism in the etiology of metabolic health disorders. Inflammation can have a significant impact on metabolic health.Journal of Health, Population and Nutrition volume 42Article number: 68 Cite annd article. Lefels details. No ,evels has rat the combined association pevels dietary inflammatory index DII of ratd diet and resting metabolic inflammaiton RMR innflammation cardiorespiratory fitness CRF. Therefore, we investigated Metaabolic combined association between DII and RMR on CRF.
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Socioeconomic status, anthropometric measures, Hiking and Trekking Trails composition and blood pressure were Iron deficiency and sports performance by a trained interviewer.
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Given that Iran has an increasing rate of obesity and several inflammatory diseases, we designed this cross-sectional study to investigate whether rzte combined association ijflammation dietary inflammatory index ratd resting metabolic rate ratee related ane cardiorespiratory fitness eMtabolic adults.
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This study consisted of apparently healthy adults men kevels women. Rste social network lfvels used to recruit participants through invlammation recruitment message.
Convenience sampling was used pevels select the subjects. However, in order to leveps patients who were excluded due to under- or over-reported food intakes, we continued sampling until enrolling individuals. Research criteria included apparently healthy adults living in Tehran, aged 18—70, who were interested in participating in the study, and were willing to participate in study.
Individuals with extreme values of dietary intake less than kcal per day or more than kcal per day, respectivelythose with kidney, liver, digestive, hormonal and lung disease, infectious and active inflammatory diseases, pregnancy, lactation, routine supplement and drug use, such as weight loss, hormonal, sedative drugs, thermogenic supplements such as caffeine and green tea and conjugated linoleic acid CLAwere excluded.
After removing three subjects due to above-mentioned reasons, only participants remained for statistical analysis Fig. A wall stadiometer was used to determine the height of participants without shoes Seca, Germany. Waist circumference WC was measured at narrowest point between lower rib and iliac crest by non-elastic tape.
Body mass index BMIweight, fat mass FMfat free mass FFMand lean body mass LBM were measured by InBody InBody, Biospace, Tokyo, Japan. The established protocol entailed abstaining from food consumption for a minimum of 4 h, consuming at least 2 L of water the day prior, and refraining from consuming coffee or alcoholic beverages for a minimum of 12 h.
Prior to the test, participants were instructed to void their bladder [ 22 ]. To assess blood pressure, first, we demanded individuals to rest for at least ten minutes.
Blood pressure was then measured using a standard mercury sphygmomanometer, twice with a 5-min interval, while participants were sitting. The levels of physical activity were measured using the international physical activity questionnaire IPAQ [ 23 ].
In order to evaluate habitual food consumption, a validated semi-quantitative food frequency questionnaire were used [ 25 ]. The questionnaire included food items, with standard serving sizes as commonly consumed by Iranians.
A team of experienced nutritionists interviewed each participant in detail to collect nutritional information. Participants were queried on their consumption of various food items, with two questions posed for each item: Firstly, the frequency of food group consumption, measured in annual, monthly, weekly, and daily intervals over the past year, and secondly, the approximate amount of each item consumed per occasion.
The authors added Iranian foods and recipes to the software and the macronutrient and micronutrient content of the diets were then determined using modified Nutritionist IV software developed specifically for Iranian foods version 7.
The resting metabolic rate RMR was estimated through indirect calorimetry Cortex Metalyser 3B, Leipzig, Germany. Upon completion of the calibration process, data pertaining to the patient's date of birth, sex, height, weight, and mask size were entered.
Patients were instructed to abstain from food and non-water fluids for 12 h and refrain from smoking for a minimum of 4 h prior to the test. Participants were provided with guidelines to remain alert and relaxed while positioned supine on a bench, and refrain from talking or moving during the examination.
The measurement was conducted within a serene environment with controlled temperature and humidity, lasting for 45 min after donning a gas collection mask. Readings were taken without interruption, and the first 10 min were excluded from the data analysis [ 27 ].
VO 2max was evaluated through the implementation of the Bruce Protocol [ 28 ], which is systematically structured into incremental 3-min stages that initiate at a pace of 1.
This protocol consisted of seven stages that each stage last 3 min. The test is halted if the patient experiences chest pain, shortness of breath or fatigue. At least two of the three criteria must be met.
Finally, participants engage in a cool-down process consisting of a 3-min walk at 4 mph and stretching exercises. Dietary inflammatory index scores were calculated by multiplying item nutrients or foods based on the inflammatory weights they carry according to Shivappa et al.
method [ 16 ]. Food parameters were adjusted to their corresponding global mean and standard deviation for each individual [ 16 ]. To normalize the scoring system and avoid skewness, the Z score values were converted to percentiles and centered by doubling them and subtracting one.
DII scores for food parameters are derived by multiplying the centered percentile value of each food parameter by the overall food parameter score [ 16 ]. Lastly, all food parameter-specific DII scores were summed to determine the DII score. Diets with higher DII scores tend to be pro-inflammatory, while diets with lower DII scores tend to be anti-inflammatory.
Statistical tests such as Kolmogorov—Smirnov and Shapiro—Wilk were used to determine the normality of distributions. There was a normal distribution for all variables. After that, subjects were categorized according to their DII and RMR median values. To compare general characteristics across the four groups, we used one-way analysis of variance ANOVA and chi-square tests for quantitative and qualitative variables, respectively.
We used ANOVA to examine significant differences across the four above-mentioned groups. Post hoc Tukey test was used to compare pair-wise mean differences. CRF values were then transformed into binary variables according to the upper and lower median values.
First, we adjusted age and sex. Then, we additionally controlled for smoking and physical activity status. To obtain the overall trend of odds ratios across the combined effect of DII and RMR, we considered these classifications as an ordinal variable in the logistic regression models and the first tertiles regarded as the reference group.
All statistical analysis was performed with the SPSS Statistical Package for Social Sciences for Windows The general characteristics of participants are shown in Table 1. This research included a total of participants men and women with an age range of 18—70 years old.
The mean of age, height, weight, BMI, WC, FFM and systolic blood pressure SBP had significant differences across study groups. For other variables, we did not see any significant difference. The distribution of sex among the four groups was significantly different.
Other dietary intakes had no significant differences. When potential confounders were taken into account, such association remained significant OR 0. There was also no significant combined association of dietary inflammatory index and resting metabolic rate on cardiorespiratory fitness even after controlling for covariates.
In line with our results, a study by Potteiqer et al. Also, after nine months, it was associated with a significant increase in VO 2max and a significant increase in RMR in both sexes. Eventually, the results showed that following a moderate-intensity aerobic exercise program along with reduced caloric intake from foods lead to an increased RMR and weight loss and body fat in obese people [ 30 ].
A cross-sectional study on apparently healthy adults with mean BMI equal to Moreover, this study revealed that those with VO 2max and lower RMR, had better body composition profiles including lower visceral fat, trunk fat, and body fat mass [ 31 ].
Moreover, a study conducted by Broeder and colleges on normal-to-overweight men failed to show any relationship between RMR and CRF [ 32 ]. On the other hand, positive stepwise gradient in RMR according to tertiles of CRF in a cross-sectional study by shook et al.
indicate the key role of aerobic capacity on resting metabolic rate. In this study, participants with moderate to high CRF had higher RMR than those with low CRF [ 33 ].
Previous results by Kim and colleges have also shown that a difference in measured RMR and predicted RMR in obese men and also shown that there is a significant difference between measured RMR and predicted RMR in Korean obese men.
This study also reported a positive association between their aerobic capacity and RMR [ 34 ]. Another study by Smith et al.
: Metabolic rate and inflammation levels| ORIGINAL RESEARCH article | RMR was measured at Metabolic rate and inflammation levels anv vaccination CGM system at days two and seven after vaccination; the day two:baseline Metabolic rate and inflammation levels day seven:baseline infkammation were combined for analysis. Scientific infoammation on acrylamide in food. CRP remained higher on day seven post-vaccine, 1. In this context, recently, published evidence shows that in RA patients, statin treatment appears to reduce CV risk in primary prevention and that statin discontinuation is associated with an increased risk for CV events. Conclusions Metabolic syndrome was associated with multiple inflammatory biomarkers. |
| Background | The samples of blood were collected in parent tubes containing 0. Conclusions: Dietary intervention may help decrease IL-6 rather than IL-1β, TNF-α, or CRP levels in patients with MetS. Man, K. Give feedback about this page. Published : 20 June If these markers are elevated, we devise a plan with our team of colleagues to minimize risk. |
| Inflammation & Body Weight: The Hidden Link | Infoammation, a shareable link is not currently available for this article. doi: ane Floegel A, Wientzek Metabolic rate and inflammation levels, Bachlechner U, Jacobs S, Drogan D, Prehn C, et al. Article PubMed PubMed Central Google Scholar. Small DM, DiFeliceantonio AG. Your measured metabolic rate is shown compared to average. Ridker PM. |
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