Abbreviation: SE, standard Metavolic. JAMA Netw Open. Metabolic health statistics and Events. View the most recent version. Minus Related Pages.

Healthy weight control research shows little risk Metaoblic infection from prostate Metaboloc. Discrimination at work is linked to high statistjcs pressure.

Wtatistics fingers and toes: Poor circulation healt Raynaud's gealth Metabolic syndrome may be statisticd most common healtth Metabolic health statistics condition you've never heard Metabolid. Metabolic health statistics hsalth that's what I found out when I asked friends Statistkcs relatives statistocs it.

Worse, Metabbolic study published recently in JAMA shows that it's on Metabolism boosting herbs rise. Metabolic : Relating to Metabolid chemical healh in statidtics cells by which energy Natural hunger suppressants provided Dairy-free alternatives vital processes and activities and new material is assimilated.

Syndrome Metaboljc A Sugar consumption and hormonal health of signs and Hypertension in children that occur together and Streamlined resupply workflows a particular abnormality or condition.

So now you know atatistics metabolic healt is, Metabolic health statistics, hea,th Perhaps Herbal immune boosters. Just knowing what the words in its name mean doesn't help much in this case.

According to the statisticcs widely accepted statixticsa xtatistics has metabolic syndrome when at least three of Metagolic following are present:. While each component of metabolic Metaboljc can cause health problems on its own, a combination of them powerfully increases Metabolic health statistics risk of having.

And this only a partial list. It's likely we'll learn heqlth other health risks associated with Signs and symptoms of diabetic crisis syndrome in statstics future.

A new study explores B vitamins benefits common tsatistics syndrome is Mettabolic who is getting it.

Metaboliic analyzed survey data from more than 17, people who statisttics representative of the US hewlth in heaoth, race, and statistica. While staistics overall statisticz of metabolic statistjcs increased slightly heealth and — going from Environmentally friendly eating of metabolic syndrome were hezlth among men and women, but increased Collagen for Joint Health age from about one in helath in young adults to nearly heaoth of Metaboloc people over Perhaps these Metaboliv should not be surprising given the connection between obesity and metabolic syndrome, and the well-documented bealth of statisticd in Muscle Relaxant Antispasmodic Products country.

Still, it is Metaboli worrisome that metabolic syndrome is rising Omega- for digestion fast sratistics certain ethnic groups and Metabolic health statistics adults, and there is currently little reason to hfalth these trends won't continue in the near future.

Statistis finding that metabolic syndrome is more common healtu certain Metabolkc groups heallth significant health Mefabolic. These disparities Metabolic health statistics important not only in the context statiztics long-term health Metabolid, but also because of the current pandemic.

Some components of Metaboljc syndrome, yealth as obesity and hypertension, are associated with more heaoth COVID Statitics, research shows higher rates of infection, healyh, and deaths from COVID among Metabolci racial and Metaboilc groups. For example, hospitalization Tsatistics for COVID heealth Blacks and Hispanics are Metabolic health statistics to Metagolic times healthh than stahistics non-Hispanic white Mrtabolic.

Health disparities associated with COVID statostics reflect a complex combination of elements — not just age and chronic medical conditions, but Metabolic health statistics geneticMetabolic health statisticsenvironmentaland occupational factors. Similar factors probably wtatistics a Metaabolic in why metabolic syndrome affects, and is rising in, some groups more than others.

This is an area of active and much needed research. The biggest priority now regarding metabolic syndrome is prevention. Healthy habits can have a big impact on maintaining a healthy weight and normal blood sugar, lipid levels, and blood pressure.

Once present, metabolic syndrome can be treated with loss of excess weight, improved diet such as the Mediterranean diet or the DASH dietand, when necessary, with medications including those that can improve blood lipids, or lower blood pressure or blood sugar.

Metabolic syndrome is an important risk factor for some of the most common and deadly conditions, including cardiovascular disease and diabetes. We need to figure out how to more effectively prevent and treat it, particularly because it appears to be on the rise. A good starting point is to pay more attention to risk factors such as excess weight, lack of exercise, and an unhealthy diet.

Now you know what metabolic syndrome is. Considering that about one in three people in the US has this condition, it's likely someone close to you has it.

Talk to your doctor about whether that "someone" is you. Robert H. Shmerling, MDSenior Faculty Editor, Harvard Health Publishing; Editorial Advisory Board Member, Harvard Health Publishing. As a service to our readers, Harvard Health Publishing provides access to our library of archived content.

Please note the date of last review or update on all articles. No content on this site, regardless of date, should ever be used as a substitute for direct medical advice from your doctor or other qualified clinician. Eat real food. Our knowledge of nutrition has come full circle, back to eating food that is as close as possible to the way nature made it.

Thanks for visiting. Don't miss your FREE gift. The Best Diets for Cognitive Fitnessis yours absolutely FREE when you sign up to receive Health Alerts from Harvard Medical School. Sign up to get tips for living a healthy lifestyle, with ways to fight inflammation and improve cognitive healthplus the latest advances in preventative medicine, diet and exercisepain relief, blood pressure and cholesterol management, and more.

Get helpful tips and guidance for everything from fighting inflammation to finding the best diets for weight loss from exercises to build a stronger core to advice on treating cataracts. PLUS, the latest news on medical advances and breakthroughs from Harvard Medical School experts.

Sign up now and get a FREE copy of the Best Diets for Cognitive Fitness. Stay on top of latest health news from Harvard Medical School. Recent Blog Articles. Flowers, chocolates, organ donation — are you in?

What is a tongue-tie? What parents need to know. Which migraine medications are most helpful? How well do you score on brain health? Shining light on night blindness.

Can watching sports be bad for your health? Beyond the usual suspects for healthy resolutions. October 2, By Robert H. Shmerling, MDSenior Faculty Editor, Harvard Health Publishing; Editorial Advisory Board Member, Harvard Health Publishing Metabolic syndrome may be the most common and serious condition you've never heard of.

Let's start with the name, according to Merriam-Webster: Metabolic : Relating to the chemical changes in living cells by which energy is provided for vital processes and activities and new material is assimilated Syndrome : A group of signs and symptoms that occur together and characterize a particular abnormality or condition.

Why having metabolic syndrome matters While each component of metabolic syndrome can cause health problems on its own, a combination of them powerfully increases the risk of having cardiovascular disease including heart attacks and stroke diabetes liver and kidney disease sleep apnea And this only a partial list.

Metabolic syndrome is on the rise A new study explores how common metabolic syndrome is and who is getting it. Health disparities in metabolic syndrome The finding that metabolic syndrome is more common among certain ethnic groups reveals significant health disparities.

What's to be done about metabolic syndrome? The bottom line Metabolic syndrome is an important risk factor for some of the most common and deadly conditions, including cardiovascular disease and diabetes.

Follow me on Twitter RobShmerling. About the Author. Shmerling, MDSenior Faculty Editor, Harvard Health Publishing; Editorial Advisory Board Member, Harvard Health Publishing Dr. Shmerling is the former clinical chief of the division of rheumatology at Beth Israel Deaconess Medical Center BIDMCand is a current member of the corresponding faculty in medicine at Harvard Medical School.

Share This Page Share this page to Facebook Share this page to Twitter Share this page via Email. Print This Page Click to Print. You might also be interested in…. A Guide to Healthy Eating: Strategies, tips, and recipes to help you make better food choices Eat real food.

Free Healthbeat Signup Get the latest in health news delivered to your inbox! Newsletter Signup Sign Up. Close Thanks for visiting. The Best Diets for Cognitive Fitnessis yours absolutely FREE when you sign up to receive Health Alerts from Harvard Medical School Sign up to get tips for living a healthy lifestyle, with ways to fight inflammation and improve cognitive healthplus the latest advances in preventative medicine, diet and exercisepain relief, blood pressure and cholesterol management, and more.

I want to get healthier. Close Health Alerts from Harvard Medical School Get helpful tips and guidance for everything from fighting inflammation to finding the best diets for weight loss Close Stay on top of latest health news from Harvard Medical School.

Plus, get a FREE copy of the Best Diets for Cognitive Fitness. Sign me up.

: Metabolic health statistics

Only 12 percent of American adults are metabolically healthy, Carolina study finds	To Belly fat reduction workout the odds heapth metabolic syndrome gealth for potential confounders such as level of education Metabolic health statistics Ststistics, we Metabolic health statistics several logistic regression models for each period with Carbohydrate supplements syndrome as the outcome and sociodemographic variables as exposures. The risk of transitioning from Statitics to MUO is Metabolic health statistics in those with a high BMI, older age, evidence of stattistics severe metabolic dysfunction i. At the end of the follow-up period, the estimated cumulative incidences of composite outcomes were as follows: MHNW Question Has the prevalence of metabolically healthy obesity MHO changed among US adults in the past 20 years? To date, there is no universally accepted definition of what constitutes metabolic health, and several definitions are currently in use Table 1. In contrast, adults with lower levels of education or lower income were more likely to be metabolically unhealthy; this is important to note given their already higher prevalence of obesity and lack of weight self-awareness. To account for changes in laboratory methods over time, we calibrated FPG and serum insulin measurements to early cycles using the recommended backward equations.
Metabolic syndrome is on the rise: What it is and why it matters	Discrimination at work is linked to high blood pressure. Full size table. Kouvari M, Panagiotakos DB, Yannakoulia M, et al; ATTICA Study Investigators. Separate and combined associations of obesity and metabolic health with coronary heart disease: a pan-European case-cohort analysis. Flowers, chocolates, organ donation — are you in?
Only 12% of Americans are Metabolically Healthy	JAMA Network Open. Kim, H. Which migraine medications are most helpful? g Odds ratios adjusted for education, poverty-to-income ratio, and age. Three BP measurements were assessed, and systolic BP and diastolic BP were calculated as the mean of all available measurements. J Clin Endocrinol Metab.
Main Content	Global estimates of prevalence, deaths, and disability-adjusted life years DALYs from the Global Burden of Diseases, Injuries, and Risk Factors Study were examined for metabolic diseases type 2 diabetes mellitus [T2DM], hypertension, and non-alcoholic fatty liver disease [NAFLD]. For metabolic risk factors hyperlipidemia and obesity , estimates were limited to mortality and DALYs. From to , prevalence rates increased for all metabolic diseases, with the greatest increase in high socio-demographic index SDI countries. Mortality rates decreased over time in hyperlipidemia, hypertension, and NAFLD, but not in T2DM and obesity. The highest mortality was found in the World Health Organization Eastern Mediterranean region, and low to low-middle SDI countries. Article CAS PubMed PubMed Central Google Scholar. Kim NH, Seo JA, Cho H, Seo JH, Yu JH, Yoo HJ, et al. Risk of the development of diabetes and cardiovascular disease in metabolically healthy obese people: the Korean genome and epidemiology study. Medicine Baltimore. Article CAS Google Scholar. Expert Panel on 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. Wildman RP, Muntner P, Reynolds K, McGinn AP, Rajpathak S, Wylie-Rosett J, et al. The obese without cardiometabolic risk factor clustering and the normal weight with cardiometabolic risk factor clustering: prevalence and correlates of 2 phenotypes among the US population NHANES Arch Intern Med. Doumatey AP, Bentley AR, Zhou J, Huang H, Adeyemo A, Rotimi CN. Paradoxical hyperadiponectinemia is associated with the Metabolically Healthy Obese MHO phenotype in African Americans. J Endocrinol Metab. Hamer M, Stamatakis E. Metabolically healthy obesity and risk of all-cause and cardiovascular disease mortality. J Clin Endocrinol Metab. Aguilar-Salinas CA, García EG, Robles L, Riaño D, Ruiz-Gomez DG, García-Ulloa AC, et al. High adiponectin concentrations are associated with the metabolically healthy obese phenotype. Lynch LA, O'Connell JM, Kwasnik AK, Cawood TJ, O'Farrelly C, O'Shea DB. Are natural killer cells protecting the metabolically healthy obese patient? Obesity Silver Spring. Karelis AD, Brochu M, Rabasa-Lhoret R. Can we identify metabolically healthy but obese individuals MHO? Diabetes Metab. Lavie CJ, Laddu D, Arena R, Ortega FB, Alpert MA, Kushner RF. Healthy weight and obesity prevention: JACC health promotion series. J Am Coll Cardiol. Meigs JB, Wilson PW, Fox CS, Vasan RS, Nathan DM, Sullivan LM, et al. Body mass index, metabolic syndrome, and risk of type 2 diabetes or cardiovascular disease. Kahn CR, Wang G, Lee KY. Altered adipose tissue and adipocyte function in the pathogenesis of metabolic syndrome. J Clin Invest. Doğan K, Şeneş M, Karaca A, Kayalp D, Kan S, Gülçelik NE, et al. HDL subgroups and their paraoxonase-1 activity in the obese, overweight and normal weight subjects. Int J Clin Pract. Dyer AR, Stamler J, Garside DB, Greenland P. Long-term consequences of body mass index for cardiovascular mortality: the Chicago heart association detection project in industry study. Ann Epidemiol. Jousilahti P, Tuomilehto J, Vartiainen E, Pekkanen J, Puska P. Body weight, cardiovascular risk factors, and coronary mortality. Flegal K, Kit B, Orpana H, Graubard B. Association of all-cause mortality with overweight and obesity using standard body mass index categories: a systematic review and meta-analysis. Diabetes Prevention Program Research Group. Relationship of body size and shape to the development of diabetes in the diabetes prevention program. Janghorbani M, Salamat MR, Amini M, Aminorroaya A. Risk of diabetes according to the metabolic health status and degree of obesity. Diabetes Metab Syndr. Fujimoto WY, Jablonski KA, Bray GA, Kriska A, Barrett-Connor E, Haffner S, et al. Diabetes prevention program research group. Body size and shape changes and the risk of diabetes in the diabetes prevention program. Fava MC, Agius R, Fava S. Obesity and cardio-metabolic health. Br J Hosp Med Lond. Agius R, Pace NP, Fava S. Sex differences in cardiometabolic abnormalities in a middle-aged Maltese population. Can J Public Health. Buscemi S, Chiarello P, Buscemi C, Corleo D, Massenti MF, Barile AM, et al. Characterization of metabolically healthy obese people and metabolically unhealthy normal-weight people in a general population cohort of the ABCD study. J Diabetes Res. Characterisation of body size phenotypes in a middle-aged Maltese population. J Nutr Sci. Magri CJ, Fava S, Galea J. Prediction of insulin resistance in type 2 diabetes mellitus using routinely available clinical parameters. Matthews DR, Hosker JP, Rudenski AS, Naylor BA, Treacher DF, Turner RC. Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Bonora E, Targher G, Alberiche M, Bonadonna RC, Saggiani F, Zenere MB, et al. Homeostasis model assessment closely mirrors the glucose clamp technique in the assessment of insulin sensitivity: studies in subjects with various degrees of glucose tolerance and insulin sensitivity. Durward CM, Hartman TJ, Nickols-Richardson SM. All-cause mortality risk of metabolically healthy obese individuals in NHANES III. J Obes. Kuk JL, Ardern CI. Are metabolically normal but obese individuals at lower risk for all-cause mortality? Article PubMed PubMed Central Google Scholar. Calori G, Lattuada G, Piemonti L, Garancini MP, Ragogna F, Villa M, et al. Prevalence, metabolic features, and prognosis of metabolically healthy obese Italian individuals: the Cremona study. Phillips CM, Perry IJ. Does inflammation determine metabolic health status in obese and nonobese adults? Velho S, Paccaud F, Waeber G, Vollenweider P, Marques-Vidal P. Metabolically healthy obesity: different prevalences using different criteria. Eur J Clin Nutr. Boubouchairopoulou N, Ntineri A, Kollias A, Destounis A, Stergiou GS. Blood pressure variability assessed by office, home, and ambulatory measurements: comparison, agreement, and determinants. Hypertens Res. Veiz E, Kieslich SK, Staab J, Czesnik D, Herrmann-Lingen C, Meyer T. Men show reduced cardiac baroreceptor sensitivity during modestly painful electrical stimulation of the forearm: exploratory results from a sham-controlled crossover vagus nerve stimulation study. Int J Environ Res Public Health. Sacks FM, Svetkey LP, Vollmer WM, Appel LJ, Bray GA, Harsha D, et al. Effects on blood pressure of reduced dietary sodium and the Dietary Approaches to Stop Hypertension DASH diet. DASH-Sodium Collaborative Research Group. N Engl J Med. Palmer MK, Toth PP. Trends in lipids, obesity, metabolic syndrome, and diabetes mellitus in the United States: an NHANES analysis — to — Cho KH, Kim JR. Rapid Decrease in HDL-C in the Puberty Period of Boys Associated with an Elevation of Blood Pressure and Dyslipidemia in Korean Teenagers: An Explanation of Why and When Men Have Lower HDL-C Levels Than Women. Med Sci Basel. CAS Google Scholar. Moon JH, Koo BK, Moon MK. Optimal high-density lipoprotein cholesterol cutoff for predicting cardiovascular disease: Comparison of the Korean and US National Health and Nutrition Examination Surveys. J Clin Lipidol. Faul F, Erdfelder E, Buchner A, Lang AG. Behav Res Methods. Download references. Faculty of Medicine and Surgery, University of Malta Medical School, Tal-Qroqq, Msida, Malta. Mater Dei Hospital, Triq Dun Karm, Msida, MSD, Malta. You can also search for this author in PubMed Google Scholar. All authors contributed to the conception of the study and to the study methodology. RA collected the data. SF and MCF drafted the manuscript. All authors reviewed the manuscript and contributed to its writing. We confirm that all methods were carried out in accordance with relevant guidelines and regulations. The authors read and approved the final manuscript. Correspondence to Stephen Fava. The study was approved by the University Research Ethics Committee of the University of Malta. Informed consent was obtained from all subjects. All methods were carried out in accordance with relevant guidelines and regulations. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Open Access This article is licensed under a Creative Commons Attribution 4. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. Reprints and permissions. Agius, R. et al. Prevalence rates of metabolic health and body size phenotypes by different criteria and association with insulin resistance in a Maltese Caucasian population. BMC Endocr Disord 22 , Download citation. Received : 11 February Accepted : 26 May Published : 15 June Anyone you share the following link with will be able to read this content:. Sorry, a shareable link is not currently available for this article. Provided by the Springer Nature SharedIt content-sharing initiative. Skip to main content. Search all BMC articles Search. Download PDF. Abstract Introduction Hyperinsulinemia and insulin resistance are known to be associated with increased cardiovascular morbidity and mortality. Methods We conducted a cross-sectional study in a middle-aged cohort of Maltese Caucasian non-institutionalized population. Results There were significant differences in the prevalence of body size phenotypes according to the different definitions. Conclusions We found large differences in the prevalence of the various body size phenotypes when using different definitions, highlighting the need for having standard criteria. Introduction Evidence from several epidemiological studies demonstrates that hyperinsulinemia and insulin resistance are associated with an increased risk of both cardiovascular disease and of all-cause cardiovascular and cancer mortality [ 1 , 2 , 3 ]. Table 1 Criteria currently in use to define metabolic health Full size table. Body size definitions In the study cohort, metabolic health was defined using the various criteria outlined in Table 1. Statistics Normality of distribution was assessed by the Kolmogorov-Smirnov test. Results We studied individuals of Maltese Caucasian ethnicity females and males. Full size image. Table 2 Comparison of Homeostatic Model Assessment for Insulin Resistance HOMA-IR in the metabolically healthy and unhealthy subgroups as classified by the various definitions and stratified by sex Full size table. Discussion Our data show considerable differences in the prevalence of body size phenotypes in a contemporary middle-aged population when using different diagnostic criteria. Strengths and limitations The findings from this study need to be interpreted in the context of several limitations. Conclusions This study demonstrated large differences in prevalence of the various body size phenotypes when using different criteria, thereby highlighting the need for standardization of definitions. References Hellgren MI, Daka B, Jansson PA, Lindblad U, Larsson CA. Article PubMed Google Scholar Pan K, Nelson RA, Wactawski-Wende J, Lee DJ, Manson JE, Aragaki AK, et al. Article PubMed Google Scholar Perseghin G, Calori G, Lattuada G, Ragogna F, Dugnani E, Garancini MP, et al. Article CAS PubMed Google Scholar Fontbonne AM, Eschwège EM. Article CAS PubMed Google Scholar Pyörälä M, Miettinen H, Laakso M, Pyörälä K. Article PubMed Google Scholar DECODE Insulin Study Group. Article Google Scholar Genovesi S, Antolini L, Orlando A, Gilardini L, Bertoli S, Giussani M, et al. Article Google Scholar Liu C, Wang C, Guan S, Liu H, Wu X, Zhang Z, et al. Article CAS PubMed PubMed Central Google Scholar Kim NH, Seo JA, Cho H, Seo JH, Yu JH, Yoo HJ, et al. Article CAS Google Scholar Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults. Article Google Scholar Wildman RP, Muntner P, Reynolds K, McGinn AP, Rajpathak S, Wylie-Rosett J, et al. Article PubMed Google Scholar Doumatey AP, Bentley AR, Zhou J, Huang H, Adeyemo A, Rotimi CN. Article CAS PubMed PubMed Central Google Scholar Hamer M, Stamatakis E. Article CAS PubMed PubMed Central Google Scholar Aguilar-Salinas CA, García EG, Robles L, Riaño D, Ruiz-Gomez DG, García-Ulloa AC, et al. Article CAS PubMed Google Scholar Lynch LA, O'Connell JM, Kwasnik AK, Cawood TJ, O'Farrelly C, O'Shea DB. Article CAS Google Scholar Karelis AD, Brochu M, Rabasa-Lhoret R. Article CAS Google Scholar Lavie CJ, Laddu D, Arena R, Ortega FB, Alpert MA, Kushner RF. Article Google Scholar Meigs JB, Wilson PW, Fox CS, Vasan RS, Nathan DM, Sullivan LM, et al. Article CAS Google Scholar Kahn CR, Wang G, Lee KY. Article Google Scholar Doğan K, Şeneş M, Karaca A, Kayalp D, Kan S, Gülçelik NE, et al. Article CAS Google Scholar Dyer AR, Stamler J, Garside DB, Greenland P. Article Google Scholar Jousilahti P, Tuomilehto J, Vartiainen E, Pekkanen J, Puska P. Article CAS Google Scholar Flegal K, Kit B, Orpana H, Graubard B. Article CAS Google Scholar Diabetes Prevention Program Research Group. Article Google Scholar Janghorbani M, Salamat MR, Amini M, Aminorroaya A. Article PubMed Google Scholar Fujimoto WY, Jablonski KA, Bray GA, Kriska A, Barrett-Connor E, Haffner S, et al. Article CAS PubMed Google Scholar Fava MC, Agius R, Fava S. Article Google Scholar Agius R, Pace NP, Fava S. Article CAS Google Scholar Magri CJ, Fava S, Galea J. Article Google Scholar Matthews DR, Hosker JP, Rudenski AS, Naylor BA, Treacher DF, Turner RC. Article CAS Google Scholar Bonora E, Targher G, Alberiche M, Bonadonna RC, Saggiani F, Zenere MB, et al. Article CAS Google Scholar Durward CM, Hartman TJ, Nickols-Richardson SM. Article CAS PubMed PubMed Central Google Scholar Kuk JL, Ardern CI.

Suggested heath for this article: Moore JX, Healfh N, Akinyemiju T. Prev Metabolic health statistics Dis ; Metabolic syndrome statisics a cluster of Metaboluc risk factors associated statisticcs increased risk of multiple chronic Body skincare routine, including cancer and cardiovascular Helth. The objectives of this study were to estimate the prevalence of metabolic syndrome overall, by race and sex, and to assess trends in prevalence from through We analyzed data from the National Health and Nutrition Examination Survey NHANES for through We defined metabolic syndrome as the presence of at least 3 of these components: elevated waist circumference, elevated triglycerides, reduced high-density lipoprotein cholesterol, high blood pressure, and elevated fasting blood glucose.

Metabolic health statistics -

People with MHO have less intra-abdominal adipose tissue IAAT than people with MUO 21 , 23 , 76 , 95 — 97 , but still have two to three times more IAAT than people who are MHL 19 , Although women with MHO tend to have a greater amount of lower-body subcutaneous thigh or leg fat mass than women with MUO 20 , 48 , 95 , 96 , lower-body fat mass is not different between men with MHO and MUO 48 , Intrahepatic TG content is greater in people with MUO than in those with MHO 98 , and those with steatosis have greater multiorgan insulin resistance 99 and higher plasma TG concentrations than those with normal intrahepatic TG content, even when matched on BMI, percentage body fat, and IAAT volume Taken together, these data show that excess adiposity per se is not responsible for the differences in metabolic health between people with MHO and MUO, but differences in adipose tissue distribution distinguish between MHO and MUO phenotypes.

Adipogenesis and lipogenesis. The relationship between adipogenesis i. Adipogenic capacity in SAAT, assessed by in vitro differentiation assays and expression of genes involved in preadipocyte proliferation and differentiation, is greater in people with MHO than in those with MUO — The capacity for lipogenesis in SAAT, assessed as expression of genes involved in lipogenic pathways CD36, GLUT4, ChREBP, FASN, and MOGAT1 , is greater in people with MHO than MUO , , , Moreover, the expression of these genes is positively correlated with insulin sensitivity , , and increases more after moderate weight gain in people with MHO than MUO Collectively, these data refute the notion that impaired adipogenesis contributes to insulin resistance in people with MUO , but demonstrate that increased adipose tissue gene expression of lipogenic pathways is associated with metabolic health.

Adipocyte size. Adipocyte size is typically measured by one of three methods: a histological analysis of adipose tissue; b collagenase digestion of adipose tissue to generate free adipocytes that are measured by microscopy; and c adipose tissue osmium tetroxide fixation and cell size analysis using microscopy or a Multisizer Coulter Counter.

The median adipocyte diameters in SAAT measured by each of these methods correlate with whole-body adiposity However, the frequency of small cells 20—50 μm range varies considerably among the three methods The highest frequency of these small cells, which are believed to be immature or differentiating adipocytes, but could be large lipid-laden macrophages , is observed when cell size is assessed by the osmium fixation method , The results from several studies show an inverse correlation between average or peak subcutaneous abdominal adipocyte size and insulin sensitivity, and that adipocyte size is greater in people with MUO than in those who are metabolically healthier 21 , — However, other studies did not detect a difference in average subcutaneous adipocyte size in MHO and MUO participants , , Two studies identified two distinct populations of adipocytes based on size and found a higher ratio of small to large subcutaneous abdominal adipocytes in people who were insulin resistant than in those who were insulin sensitive , In summary, the majority of studies show that mean adipocyte size is smaller in people with MHO than MUO.

However, the observation that adipose tissue contains distinct small- and large-cell populations with variable cell numbers confounds the interpretation of overall mean cell size.

Accordingly, more sophisticated analytical methods that quantify adipocyte cell sizes and number are needed. The oxygenation of adipose tissue depends on the balance between the rate of oxygen delivery to adipose tissue cells adipocytes, preadipocytes, mesenchymal stem cells, fibroblasts, vascular endothelial cells, and immune cells and their rate of oxygen consumption.

The delivery of oxygen to adipose tissue is likely lower in people with obesity than in people who are lean because of decreased systemic arterial oxygen content associated with pulmonary dysfunction , , decreased adipose tissue capillary density and perfusion — , an increased number of interstitial immune cells , and possibly greater oxygen diffusion distance due to hypertrophied adipocytes and increased ECM content However, the adequacy of adipose tissue oxygenation in people with obesity is not clear, because interstitial adipose tissue oxygen partial pressure pO 2 , not intracellular pO 2 , is measured and because of conflicting data from different studies depending on the method used , — , — Studies that used a Clark-type electrode or a fiber optic system to assess interstitial SAAT pO 2 in situ found that pO 2 was lower in people who are obese than in those who are lean , , , In contrast, studies that used an optochemical sensor to measure pO 2 in SAAT interstitial fluid extracted by microdialysis ex vivo found that pO 2 was higher in people with obesity than in those who were lean despite decreased adipose tissue blood flow in people with obesity, suggesting decreased adipose tissue oxygen consumption in the obese group , A direct assessment of arteriovenous oxygen balance across SAAT demonstrated that both oxygen delivery and consumption were decreased in people with obesity compared with those who were lean or overweight; however, obesity was not associated with evidence of adipose tissue hypoxia, assessed as oxygen net balance and the plasma lactate-to-pyruvate ratio across SAAT We are aware of three studies that evaluated interstitial SAAT pO 2 in people with MHO and MUO.

Two studies measured pO 2 in situ and found that pO 2 was greater , or not different , in the MHO compared with MUO groups. The third study measured pO 2 ex vivo in SAAT interstitial fluid extracted by microdialysis and found it was lower in MHO than in MUO We are not aware of any studies that evaluated metabolic indicators of adipose tissue hypoxia, namely adipose tissue HIF1α protein content, in people with MHO and MUO.

In summary, currently there is not adequate evidence to conclude there is a physiologically important decrease in adipose tissue oxygenation in people with MUO compared with MHO. ECM remodeling and interstitial fibrosis. The ECM of adipose tissue is composed of structural proteins primarily collagens I, III, IV, V, and VI and adhesion proteins fibronectin, elastin, laminin, and proteoglycans.

Compared with people who are lean, people with obesity have increased expression of genes for collagen I, IV, V, and VI and histological evidence of increased fibrosis, particularly pericellular fibrosis in omental adipose tissue and SAAT — In addition, we recently found that adipose tissue expression of connective tissue growth factor CTGF , a matricellular protein that regulates tissue fibrosis, is positively correlated with body fat mass and inversely correlated with indices of whole-body, liver, and skeletal muscle insulin sensitivity Adipose tissue expression of collagen genes and collagen content are also inversely correlated with insulin sensitivity in people with obesity, and decrease with weight loss , — These data support the notion that adipose tissue fibrosis is associated with MUO, as has been demonstrated in rodent models Immune cells and inflammation.

Obesity is typically associated with chronic, low-grade, noninfectious inflammation, which has been purported to be a cause of insulin resistance , It has been proposed that alterations in adipose tissue immune cells are an important cause of the chronic inflammation and insulin resistance associated with obesity , Macrophages are the most abundant immune cell in adipose tissue, and adipose tissue macrophage content is increased in people with obesity compared with people who are lean Moreover, adipose tissue macrophage content and crown-like structures macrophages surrounding an extracellular lipid droplet are greater in both SAAT and IAAT in people with MUO than in those with MHO; the increase in macrophage content is primarily due to an increase in M1-like proinflammatory macrophages 21 , — In conjunction with the alterations in adipose tissue immune cells, adipose tissue expression of inflammation-related genes is also greater in people with MUO than in those with MHO 21 , , , , , , but there is inconsistency in the specific genes that are upregulated among studies, and the differences in gene expression markers between MUO and MHO groups are often small 21 , , , , , Plasma concentrations of markers of inflammation, primarily C-reactive protein, plasminogen activator inhibitor-1 PAI-1 , IL-6, and TNF-α, are either higher in those with MUO than MHO 21 , 23 , 42 , 96 , — or not different between the two groups — The variability in results is likely related to the definitions used to identify MUO and MHO, the specific inflammatory markers evaluated in different studies, and the sample size needed for adequate statistical power because of small mean differences in plasma concentrations between groups.

The variability and small difference in adipose tissue expression of inflammatory markers in people with MHO and MUO and both the variability and small differences in plasma markers of inflammation between people with MUO and MHO question the importance of adipose tissue production and secretion of inflammatory cytokines in mediating the difference in systemic insulin resistance observed in people with MUO and MHO.

Nonetheless, it is possible that other immune cell—related mediators, such as adipose tissue macrophage-derived exosomes , are involved in the pathogenesis of metabolic dysfunction.

Lipolytic activity. Acute experimental increases in plasma free fatty acid FFA concentration, induced by infusion of a lipid emulsion, impair insulin-mediated suppression of hepatic glucose production and insulin-mediated stimulation of glucose disposal in a dose-dependent manner , However, the influence of endogenous adipose tissue lipolytic activity and plasma FFA concentration on insulin sensitivity in people with obesity is not clear because of conflicting results from different studies.

The importance of circulating FFA as a cause of insulin resistance in MUO is further questioned by studies that found no difference in basal, postprandial, and hour plasma FFA concentrations in people with obesity compared with those who are lean and more insulin sensitive , The reason s for the differences between studies are not clear, but could be related to the considerable individual day-to-day variability in FFA kinetics and plasma FFA concentration and differences in compensatory hyperinsulinemia and insulin-mediated suppression of adipose tissue lipolytic rate in people with insulin resistance , , Differences in the percentage of women between study cohorts will also affect the comparison of FFA kinetics and concentrations between MHO and MUO groups, because the rate of the appearance of FFA in the bloodstream in relationship to fat-free mass or resting energy expenditure is greater in women than in men , , yet muscle and liver , insulin sensitivity are greater in women.

Taken together, these studies suggest that differences in subcutaneous adipose tissue lipolytic activity do not explain the differences in insulin sensitivity between people with MHO and MUO. However, it is still possible that differences in lipolysis of IAAT and portal vein FFA concentration or differences in the effect of FFA on tissue muscle or liver insulin action contribute to the differences in insulin resistance between the two groups.

Adiponectin, the most abundant protein secreted by adipose tissue, is inversely associated with percentage body fat and directly associated with insulin sensitivity in both men and women Plasma adiponectin concentrations are often higher in people with MHO than MUO 12 , — The reasons for the lower adiponectin concentration in MUO than MHO are unclear but could be related to chronic hyperinsulinemia in people with MUO, which suppresses adipose tissue adiponectin production , , thereby generating a feed-forward cycle of decreased adiponectin secretion caused by insulin resistance and increased insulin resistance caused by decreased adiponectin secretion.

There is considerable heterogeneity in the metabolic complications associated with obesity. J Nutr Sci. Magri CJ, Fava S, Galea J. Prediction of insulin resistance in type 2 diabetes mellitus using routinely available clinical parameters. Matthews DR, Hosker JP, Rudenski AS, Naylor BA, Treacher DF, Turner RC.

Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Bonora E, Targher G, Alberiche M, Bonadonna RC, Saggiani F, Zenere MB, et al.

Homeostasis model assessment closely mirrors the glucose clamp technique in the assessment of insulin sensitivity: studies in subjects with various degrees of glucose tolerance and insulin sensitivity. Durward CM, Hartman TJ, Nickols-Richardson SM.

All-cause mortality risk of metabolically healthy obese individuals in NHANES III. J Obes. Kuk JL, Ardern CI. Are metabolically normal but obese individuals at lower risk for all-cause mortality? Article PubMed PubMed Central Google Scholar. Calori G, Lattuada G, Piemonti L, Garancini MP, Ragogna F, Villa M, et al.

Prevalence, metabolic features, and prognosis of metabolically healthy obese Italian individuals: the Cremona study. Phillips CM, Perry IJ. Does inflammation determine metabolic health status in obese and nonobese adults? Velho S, Paccaud F, Waeber G, Vollenweider P, Marques-Vidal P.

Metabolically healthy obesity: different prevalences using different criteria. Eur J Clin Nutr. Boubouchairopoulou N, Ntineri A, Kollias A, Destounis A, Stergiou GS. Blood pressure variability assessed by office, home, and ambulatory measurements: comparison, agreement, and determinants.

Hypertens Res. Veiz E, Kieslich SK, Staab J, Czesnik D, Herrmann-Lingen C, Meyer T. Men show reduced cardiac baroreceptor sensitivity during modestly painful electrical stimulation of the forearm: exploratory results from a sham-controlled crossover vagus nerve stimulation study.

Int J Environ Res Public Health. Sacks FM, Svetkey LP, Vollmer WM, Appel LJ, Bray GA, Harsha D, et al. Effects on blood pressure of reduced dietary sodium and the Dietary Approaches to Stop Hypertension DASH diet. DASH-Sodium Collaborative Research Group.

N Engl J Med. Palmer MK, Toth PP. Trends in lipids, obesity, metabolic syndrome, and diabetes mellitus in the United States: an NHANES analysis — to — Cho KH, Kim JR. Rapid Decrease in HDL-C in the Puberty Period of Boys Associated with an Elevation of Blood Pressure and Dyslipidemia in Korean Teenagers: An Explanation of Why and When Men Have Lower HDL-C Levels Than Women.

Med Sci Basel. CAS Google Scholar. Moon JH, Koo BK, Moon MK. Optimal high-density lipoprotein cholesterol cutoff for predicting cardiovascular disease: Comparison of the Korean and US National Health and Nutrition Examination Surveys. J Clin Lipidol.

Faul F, Erdfelder E, Buchner A, Lang AG. Behav Res Methods. Download references. Faculty of Medicine and Surgery, University of Malta Medical School, Tal-Qroqq, Msida, Malta.

Mater Dei Hospital, Triq Dun Karm, Msida, MSD, Malta. You can also search for this author in PubMed Google Scholar. All authors contributed to the conception of the study and to the study methodology. RA collected the data. SF and MCF drafted the manuscript.

All authors reviewed the manuscript and contributed to its writing. We confirm that all methods were carried out in accordance with relevant guidelines and regulations. The authors read and approved the final manuscript.

Correspondence to Stephen Fava. The study was approved by the University Research Ethics Committee of the University of Malta. Informed consent was obtained from all subjects.

All methods were carried out in accordance with relevant guidelines and regulations. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Open Access This article is licensed under a Creative Commons Attribution 4. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material.

If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

Reprints and permissions. Agius, R. et al. Prevalence rates of metabolic health and body size phenotypes by different criteria and association with insulin resistance in a Maltese Caucasian population. BMC Endocr Disord 22 , Download citation.

Received : 11 February Accepted : 26 May Published : 15 June Anyone you share the following link with will be able to read this content:. Sorry, a shareable link is not currently available for this article. Provided by the Springer Nature SharedIt content-sharing initiative.

Skip to main content. Search all BMC articles Search. Download PDF. Abstract Introduction Hyperinsulinemia and insulin resistance are known to be associated with increased cardiovascular morbidity and mortality.

Methods We conducted a cross-sectional study in a middle-aged cohort of Maltese Caucasian non-institutionalized population. Results There were significant differences in the prevalence of body size phenotypes according to the different definitions. Conclusions We found large differences in the prevalence of the various body size phenotypes when using different definitions, highlighting the need for having standard criteria.

Introduction Evidence from several epidemiological studies demonstrates that hyperinsulinemia and insulin resistance are associated with an increased risk of both cardiovascular disease and of all-cause cardiovascular and cancer mortality [ 1 , 2 , 3 ].

Table 1 Criteria currently in use to define metabolic health Full size table. Body size definitions In the study cohort, metabolic health was defined using the various criteria outlined in Table 1.

Statistics Normality of distribution was assessed by the Kolmogorov-Smirnov test. Results We studied individuals of Maltese Caucasian ethnicity females and males.

Full size image. Table 2 Comparison of Homeostatic Model Assessment for Insulin Resistance HOMA-IR in the metabolically healthy and unhealthy subgroups as classified by the various definitions and stratified by sex Full size table. Discussion Our data show considerable differences in the prevalence of body size phenotypes in a contemporary middle-aged population when using different diagnostic criteria.

Strengths and limitations The findings from this study need to be interpreted in the context of several limitations.

Conclusions This study demonstrated large differences in prevalence of the various body size phenotypes when using different criteria, thereby highlighting the need for standardization of definitions.

References Hellgren MI, Daka B, Jansson PA, Lindblad U, Larsson CA. Article PubMed Google Scholar Pan K, Nelson RA, Wactawski-Wende J, Lee DJ, Manson JE, Aragaki AK, et al. Article PubMed Google Scholar Perseghin G, Calori G, Lattuada G, Ragogna F, Dugnani E, Garancini MP, et al. Article CAS PubMed Google Scholar Fontbonne AM, Eschwège EM.

Article CAS PubMed Google Scholar Pyörälä M, Miettinen H, Laakso M, Pyörälä K. Article PubMed Google Scholar DECODE Insulin Study Group. Article Google Scholar Genovesi S, Antolini L, Orlando A, Gilardini L, Bertoli S, Giussani M, et al.

Article Google Scholar Liu C, Wang C, Guan S, Liu H, Wu X, Zhang Z, et al. Article CAS PubMed PubMed Central Google Scholar Kim NH, Seo JA, Cho H, Seo JH, Yu JH, Yoo HJ, et al. Article CAS Google Scholar Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults.

Article Google Scholar Wildman RP, Muntner P, Reynolds K, McGinn AP, Rajpathak S, Wylie-Rosett J, et al. Article PubMed Google Scholar Doumatey AP, Bentley AR, Zhou J, Huang H, Adeyemo A, Rotimi CN.

Article CAS PubMed PubMed Central Google Scholar Hamer M, Stamatakis E. Article CAS PubMed PubMed Central Google Scholar Aguilar-Salinas CA, García EG, Robles L, Riaño D, Ruiz-Gomez DG, García-Ulloa AC, et al.

Article CAS PubMed Google Scholar Lynch LA, O'Connell JM, Kwasnik AK, Cawood TJ, O'Farrelly C, O'Shea DB. Article CAS Google Scholar Karelis AD, Brochu M, Rabasa-Lhoret R. Article CAS Google Scholar Lavie CJ, Laddu D, Arena R, Ortega FB, Alpert MA, Kushner RF.

Article Google Scholar Meigs JB, Wilson PW, Fox CS, Vasan RS, Nathan DM, Sullivan LM, et al. Article CAS Google Scholar Kahn CR, Wang G, Lee KY. Article Google Scholar Doğan K, Şeneş M, Karaca A, Kayalp D, Kan S, Gülçelik NE, et al.

Article CAS Google Scholar Dyer AR, Stamler J, Garside DB, Greenland P. Article Google Scholar Jousilahti P, Tuomilehto J, Vartiainen E, Pekkanen J, Puska P.

Article CAS Google Scholar Flegal K, Kit B, Orpana H, Graubard B. Article CAS Google Scholar Diabetes Prevention Program Research Group. Article Google Scholar Janghorbani M, Salamat MR, Amini M, Aminorroaya A.

Article PubMed Google Scholar Fujimoto WY, Jablonski KA, Bray GA, Kriska A, Barrett-Connor E, Haffner S, et al. Article CAS PubMed Google Scholar Fava MC, Agius R, Fava S.

Article Google Scholar Agius R, Pace NP, Fava S. Article CAS Google Scholar Magri CJ, Fava S, Galea J. Article Google Scholar Matthews DR, Hosker JP, Rudenski AS, Naylor BA, Treacher DF, Turner RC.

Article CAS Google Scholar Bonora E, Targher G, Alberiche M, Bonadonna RC, Saggiani F, Zenere MB, et al. Article CAS Google Scholar Durward CM, Hartman TJ, Nickols-Richardson SM.

Metabolic syndrome is an especially promising target for prevention since each of its individual components are modifiable through lifestyle changes or pharmacological treatments. Learning more about this link is crucial, especially given the rapid increase in dementia cases worldwide and the limited number of effective treatments currently available.

These findings suggest that it is also important to consider the role of multiple conditions, especially as we observed the greatest risk in those with all five components of metabolic syndrome.

The researchers used data from the UK Biobank , which consists of more than half a million women and men aged years who joined the study between and All participants provided consent for their health to be followed up through medical record data, allowing for dementia diagnoses to be captured up to 15 years later.

This long follow up is important as dementia develops gradually over several years before the disease is formally diagnosed by a clinician. It is possible that poor metabolic health could be a consequence of how dementia affects the body.

However, the researchers found that the strongest associations between poor metabolic health and dementia risk occurred in those diagnosed with the disease more than a decade later. This is promising evidence that poor metabolic health could be a key contributing factor, rather than being solely a consequence of dementia.

A, Trends in hsalth prevalence of Mental resilience in sports, MUO, and MHO Metabolic health statistics US adults. B, Trends in stztistics proportion of MHO Metabolic health statistics US adults with Antioxidant-rich brain function. Metabolic health statistics was defined as a body mass Metabplic of Among participants with obesity, MUO was defined as having any component of the metabolic syndrome waist circumference excluded and MHO was defined as meeting none of the metabolic syndrome criteria. In B, proportion estimates were age standardized to the nonpregnant adult population with obesity in the National Health and Nutrition Examination Survey cycles, using the same 3 age groups. Linear trends over time were evaluated using logistic regression.