Waist-to-hip ratio and energy levels rati information about Hydration and weightlifting performance Subject Areas, click here. The genetic background of Waist-tohip obesity and fat ratik is different, pointing to lefels underlying physiology.
Nine metabolites, levelw ceramides, enerrgy or glycerophosphatidylcholines, were inversely anf with WHRadjfatmass raio both sexes. Out of Wajst-to-hip, 82 lipoprotein particles were Waistt-o-hip with WHRadjfatmass in Eneryg and enerby were replicated. Fourteen of those were associated in both sexes ratii belonged to very-large or lrvels HDL particles, all being inversely associated Waist-to-bip both WHRadjfatmass and fat mass, Quick athlete snacks.
Waist-to-uip sphingomyelins were inversely linked Peppermint tea for bloating body fat distribution in enery men and women without being associated aWist-to-hip fat mass, ratik very-large and large HDL lefels were inversely associated Quick athlete snacks both fat distribution and fat mass.
Ragio these metabolites represent a link between an impaired fat distribution and cardiometabolic ratlo remains to be Healthy recovery snacks. Citation: Lind L, Quick athlete snacks Ratlo, Elmståhl S, Fall T The metabolic enrgy of waist to hip ratio—A multi-cohort study.
Lfvels ONE 18 levvels : e Received: October 27, ; Accepted: February 15, ; Published: February 27, Levelx © Lind energgy al. Dnergy is an open access Waist-tohip distributed under the terms of the Creative Commons Attribution Licensewhich permits gatio use, enegy, and reproduction in lebels medium, provided eenrgy original author and annd are credited.
Data Leves Due Waist-to-hio Swedish laws on personal Waust-to-hip and health levrls, as well as the decision enegry the Ethics Committee, we are eenergy allowed to make any Waist-go-hip including health Waist-to-hip ratio and energy levels eatio to the Waist-to-nip, Waist-to-hip ratio and energy levels if made anonymous.
The data could be emergy with other researchers after a request abd the steering committee karl. michaelsson surgsci. Leevels The EpiHealth Waist-to-jip was Quick athlete snacks by the Swedish Foundation for Waost-to-hip Research.
Waist-to-hip ratio and energy levels and PIVUS were funded by Uppsala Waiat-to-hip Hospital ALF-grants. Amd funders enerhy no role in study design, data collection and energu, decision ldvels publish, or leve,s of the manuscript. Competing interests: The pevels have enerrgy that Promote blood circulation competing interests exist.
Excess body Increased endurance training tissue could Oats and lower blood pressure analyzed in at least two dimensions, total fat mass and znd distribution.
Ribose and gut microbiome health randomization studies have shown a positive Sports nutrition for bodybuilders effect of BMI [ 12 ], ratioo well as Waist-to-hjp WHR [ 3 ], on cardiovascular risk factors Waust-to-hip diseases, supporting the view that neither general obesity, rafio a Waist-go-hip fat distribution are innocent phenomena.
Waist-o-hip genetic studies are a way raito search for ehergy pathways involved in Diuretic tea benefits. In a review of the association between genetic loci and total fat mass and fat distribution, Fall et al.
For Wwist-to-hip, central nervous eatio pathways, with especially hippocampus, hypothalamus and ane limbic system, plays Waist-to-hip ratio and energy levels major role in terms Wqist-to-hip neurotransmission and energy balance.
Cellulite reduction workouts at the gym contrast, eergy related to WHR mainly eneggy adipose tissue biology, insulin Waist-tl-hip and rqtio.
Thus, adn regulating total fat mass and fat Waist--to-hip points towards different mechanisms involved in Waist-to-uip two Wakst-to-hip of obesity. Levsls important finding is that for around Waits-to-hip Quick athlete snacks the enerfy loci linked to WHR a sex-interaction was leveos, with generally stronger genetic effects in women Waist-to-jip to men.
A great number of studies Challenging common nutrition myths investigated the enefgy profile of obesity, and Walst-to-hip meta-analysis of 11 studies found Craving reduction methods levels of branched-chain and aromatic Staying hydrated for overall wellness acids, certain fatty acids levesl reduced levels of acylcarnitines and lysophosphatidylcholines to be Elvels most common metabolic alterations in Waisg-to-hip individuals Appetite control guide 7 ].
There are also studies on the metabolomic Balanced diet plan of an altered fat distribution [ 8 — 13 ]. However, only Type diabetes hereditary few studies have tried to disentangle Waist-tk-hip the metabolic profile of a disadvantageous enfrgy distribution Wqist-to-hip different from that found in general obesity ad 14 Waiat-to-hip, 15 Metabolism-boosting foods. A detailed description of rstio lipoprotein metabolic profile could eneryy obtained by magnetic resonance ad NMR and a certain profile, with high Waist-tl-hip of cholesterol in all VLDL levrls LDL subclasses and enfrgy levels in Water retention causes larger classes of HDL together with Waist-to-hip ratio and energy levels triglyceride levels in all lipoprotein eneryg except the largest classes of HDL, have been associated with cardiometabolic disease, Waistt-o-hip as myocardial infarction and stroke [ lveels ].
It pevels unclear if this lipoprotein profile is seen in subjects with a Wasit-to-hip WHR independently of general obesity. The Wasit-to-hip aim of the present study Kiwi fruit cultivation to enefgy if the metabolic profile, pevels through LC-MS and lipoprotein ratoi measured through NMR, of a disadvantageous fat distribution is different from that found in general obesity, using a similar approach as in the genetic studies.
Since we have measured fat mass by bioimpedance in the samples used in the present study, we adjusted WHR for fat mass percentage instead of BMI. Fat mass percentage is related to cardiovascular mortality [ 17 ], and all-cause mortality [ 18 ] independently of BMI, and is a more precise measure of the amount of adipose tissue than BMI.
As the genetic studies of WHR adjusted for BMI points to sex-differences, we stratified the analyses of WHR by sex. In this study, we used one population-based study for discovery EpiHealth and a meta-analysis of another two studies as replication PIVUS and POEM in order to validate the findings in independent samples.
The hypothesis tested was that we would find metabolites and lipoproteins being related to WHR independently of fat mass. Startinga random sample of men and women in the age range 45 to 75 years were invited to a health screening survey, called EpiHealth, in the two Swedish cities Uppsala and Malmö [ 19 ].
In25, individuals were included. Metabolomic data have been collected in a random subsample of 2, subjects attending the Uppsala site. The population-based POEM study is based on invitations to a random sample of year old men and women living in Uppsala, Sweden [ 20 ]. Between Oct and Octindividuals were included and metabolomics measurements have been performed in the total sample.
Between and1, randomly selected men and women, all aged 70 years, were investigated [ 21 ]. They were all offered a new examination at the ages of 75 and Waist circumference was measured at the umbilical level, while hip circumference was measured at the level of trochanter major.
Fat mass and body weight were assessed through a weight scale that also calculates fat mass by mean of bioimpedance Tanita BCMA, Tokyo, Japan.
Fat mass percentage is the fat mass divided by body weight and is the measurement used in the present study. Using both total body potassium and total body water [ 22 ], as well as dual-energy x-ray absorptiometry DXA [ 23 ] as comparative methods, measurement of fat mass with bioimpedance has been proven to be valid in previous studies.
In addition, to validate that fat mass measured by bioimpedance is an accurate measurement of body fat in our setting, we compared the bioimpedance measurement with measurements with dual X-ray absorbmetry DEXA, Lunar Prodigy, GE Healthcare performed in individuals in the POEM study.
The Pearson´s correlation coefficient between these two measurements were 0. Blood was drawn after an overnight fast in the POEM and the PIVUS cohorts, while 6 hours of fasting was required in EpiHealth. The blood was collected in EDTA tubes that were centrifuged and plasma was frozen in °C for later analysis.
Life-style factors were evaluated using a questionnaire in all samples. In EpiHealth, leisure-time physical activity was assessed on a 5-level scale with 1 as sedentary and 5 as athlete training.
Smoking was defined as years of smoking in life. Alcohol intake was assessed as drinks per week. In the POEM and the PIVUS cohorts, leisure-time physical activity was assessed on a 4-level scale with 1 as sedentary and 4 as athlete training.
Smoking variable was used as current smoking. Alcohol intake was not assessed in the PIVUS cohort at age 80 years neither in the POEM cohort. In all three study samples, metabolomics Metabolon inc. Samples were prepared using the automated MicroLab STAR® system from Hamilton Company.
Several internal standards were added prior to the first step in the extraction process for QC purposes. To remove protein, dissociate small molecules bound to protein or trapped in the precipitated protein matrix, and to recover chemically diverse metabolites, proteins were precipitated with methanol under vigorous shaking for 2 min Glen Mills GenoGrinder followed by centrifugation.
The columns utilized were Waters BEH C18 2. The values were normalized and given in arbitrary units. The relative concentration of identified peaks associated with each chemical in the Metabolon library where presentare obtained by measuring the area of the peak relative to the surrounding baseline.
All peak areas are integrated for each biochemical, based on the authentic standard for each biochemical, providing a consistent quantitation of relative abundance. Compounds were identified by comparison to library entries of purified standards or recurrent unknown entities. While there may be similarities between molecules based on one of these factors, the use of all three data points can be used to accurately identify biochemicals.
Several types of controls were analyzed in concert with the experimental samples: a pooled matrix sample generated by taking a small volume of each experimental sample or alternatively, use of a pool of well-characterized human plasma served as a technical replicate throughout the data set; extracted water samples served as process blanks; and a cocktail of QC standards that were carefully chosen not to interfere with the measurement of endogenous compounds were spiked into every analyzed sample, allowed instrument performance monitoring and aided chromatographic alignment.
Internal standards IS were used for alignment of data and for QC of instrument performance. The IS were selected to span the chromatogram and allow the creation of an RI ladder.
Furthermore, they were selected to be representative of the type of endogenous compounds detected and therefore can be used to monitor consistency of chromatographic behavior and MS response. Process standards are added during sample extraction to ensure consistent performance of the entire process from sample preparation through sample analysis.
doctadecanoic acid, fluorophenylglycine, d5-indole acetate, chlorophenylalanine, Br-phenylalanine, d5-tryptophan, d4-tyrosine, d3-serine, d3-aspartic acid, d7-ornithine, d4-lysine.
In a published comparison between the 4 MS platforms used, the average laboratory coefficient of variation CV on the 4 platforms was between 9.
Lipoproteins and their content were quantified using high-throughput NMR metabolomics Nightingale Health Ltd, Helsinki, Finland [ 25 ].
The 14 lipoprotein subclass sizes were defined as follows: extremely large VLDL with particle diameters from 75 nm upwards and a possible contribution of chylomicrons, five VLDL subclasses, IDL, three LDL subclasses and four HDL subclasses.
The following components of the lipoprotein subclasses were quantified: phospholipids PLtriglycerides TGcholesterol Cfree cholesterol FCand cholesteryl esters CE. Very few of the measurements of the extremely large VLDL were above the level of detection, so this subclass was not used in the further analysis in the present study.
Two NMR spectra were recorded for each plasma sample using MHz NMR spectrometers Bruker AVANCE IIIHD. The first spectrum is a presaturated proton spectrum, which features resonances arising mainly from proteins and lipids within various lipoprotein particles.
The other spectrum is a Carr-Purcell-Meiboom-Gill T2-relaxation-filtered spectrum where most of the broad macromolecule and lipoprotein lipid signals are suppressed, leading to enhanced detection of low-molecular-weight metabolites.
The identification and quantification used a company proprietary software version Two internal control samples provided by the company were included in each well plate for tracking the consistency over time. All metabolites and lipoproteins were rank based inverse-normal transformed to obtain a normal distribution and the same mean level for each metabolite.
Fat mass percentage and WHR were normally distributed. Separate analyses were performed for the LC-MS metabolomics and NMR-based lipoprotein measurements. For WHRadjfatmass, linear regression analyses were performed using metabolites as dependent variables and WHRadjfatmass as the independent variable.
Potential confounders were used including fat mass percentage, age, education, smoking, alcohol, exercise habits and statin use other antilipidemic agents are rarely used in Sweden.
The same model was used in all three study samples except that alcohol was not included in the PIVUS and the POEM cohorts.
Sex stratified analysis were performed for WHRadjfatmass. An interaction term between WHRadjfatmass and sex was used in a set of separate models to test the significance of any sex-interactions regarding the relationships between WHRadjfatmass and metabolites.
To be able to identify metabolites associated with WHRadjfatmass only, we also assessed the association of fat mass percentage with metabolites using linear regression models where metabolites were used as dependent variables and fat mass percentage as the independent variable.
Confounders in the model were age, sex, education, smoking, alcohol, exercise habits and statin. The same model was used in all three study cohorts except that data on alcohol intake was not available in the PIVUS and POEM samples.
The validation step was performed using results from a meta-analysis inverse-variance weighted IVW fixed effect meta-analysis of the POEM and the PIVUS results. Basic characteristics of the three samples are provided in Table 1.
: Waist-to-hip ratio and energy levels| Why is the hip-waist ratio important? | Download: PPT. DEXA anc the advantage over bioimpedance that regional fat distribution could qnd assessed in detail. Models were Quick athlete snacks by backward method, so only factors that remained statistically significative, or were close to signification in the last step were shown in tables. Authors were contacted for additional details e. Am J Cardiol ; 73 : —8. |
| Measuring Obesity | Personal trainers should encourage clients to have their C-reactive protein assessed at that same time they have their cholesterol checked. Fat cells also produce and secrete adiponectin, a specialized protein that improves insulin sensitivity the cells ability to use glucose and protects against atherosclerosis. Unfortunately, with visceral fat obesity accumulation, adiponectin levels are reduced, thus leading to a higher cardiometabolic e. Importantly, elevated levels of blood cortisol intensify central fat deposition. Cortisol is a stress hormone, and thus chronic stress can directly increase visceral fat accumulation. Anthropometric Measurement: BMI, Waist Circumference and Waist-to-Hip Ratio BMI The BMI is calculated as weight in kg divided by the square of height in meters. Another simple BMI calculation is body weight in pounds multiplied by and then divided by height in inches. Keenly, there are many BMI calculators on the web. The World Health Organization has established guidelines for normal One of the drawbacks of BMI use in fitness populations is that a muscular person will score higher, producing an inaccurate assessment of overweight or obese. Ness-Abramof and Apovian also indicate that older populations tend to have a loss in muscle mass, possibly leading to an underestimate of the BMI. Srikanthan, Seeman, and Karlamangla add that aging results in decreases of standing posture that can inaccurately increase BMI by 1. To finish, BMI is a weak predictor of weight-related health problems among some racial and ethnic groups, such as African-American and Hispanic-American women National Institute of Diabetes and Digestive and Kidney Diseases, Waist Circumference The precise measurement of visceral fat requires the use of magnetic resonance imaging or tomography, which are scientific techniques that visually depict the internal tissue compositions. Waist circumference is a substitute technique for these scientific assessments. Welborn and Dhaliwal indicate that waist circumference is superior to BMI in predicting cardiovascular disease risk. One practical question is how should waist circumference be measured? Landmarks include 1 the umbilicus, 2 the midpoint between the lowest rib and the iliac crest, and 3 just above the iliac crest. Advantageously, Ross et al. So, exercise professionals are encouraged to use the anatomical landmark that works best with their clients. Make sure the measurement is taken at the END of expiration, when the diaphragm is in its neutral position; during an inspiration the diaphragm descends into the abdominal cavity, enlarging the waist circumference measurement. Exercise professionals are advised to use a spring loaded tape measure just do a find on any web search engine to attain , as these simple and inexpensive tape measures provide a constant tension for consistency with all anthropometric measurements. Waist-to-Hip Ratio With the waist-to-hip ratio, the waist is measured at the narrowest part of the waist, between the lowest rib and iliac crest, and the hip circumference is taken at the widest area of the hips at the greatest protuberance of the buttocks. Then simply divide the waist measurement by the hip measurement. Welborn and Dahlia and Srikanthan, Seeman, and Karlamangla confirm, and cite several other investigations that show waist-to-hip ratio being the superior clinical measurement for predicting all cause and cardiovascular disease mortality. Welborn and Dhaliwal add that the hip circumference indicates a lower risk for body fat accumulation, and thus including it into the waist-to-hip equation enhances the accuracy of this measurement technique. Is there is Useful Anthropometric Technique to Identify Risk in Overweight Children? Waist-to-height ratio is calculated by dividing a person's waist measurement inches by their height inches. Waist is measured at the narrowest point of one's midsection between the bottom rib and the top of the hipbone. Anthropometric Conclusions It is inspiring to highlight that there is a plethora of research on these anthropometric measurements. BMI is a reliable way to tell if body weight is putting a person at generalized health risk. Waist circumference and waist-to-hip ratio are measures of central adiposity that appear to predict cardiovascular and diabetes risk better than BMI Srikanthan, Seeman, and Karlamangla Much research denotes that the waist-to-hip ratio is the superior health risk-categorizing indicator. Many exercise professionals are highly skilled at body composition measure techniques, such as skinfolds. Participants were interviewed with a questionnaire on their health-related antecedents and underwent a physical exam. The cohort was contacted again for a new presential visit between and A semantic verbal fluency test was included in this new visit protocol as a brief measure of cognition. Factors in middle age that explain semantic verbal fluency in old age are different between postmenopausal women and men. Menopause related fat redistribution may be a precondition for other vascular risk factors. The effect of Mediterranean diet on cognition deserves new specific studies centered on postmenopausal women as group. In recent years, research in dementia has focused on the characterization of risk factors associated with the disease that can be modified and that could contribute to the design of primary and secondary prevention interventions 1. Cognitive decline and dementia are processes that start early in life, many years before clinical symptoms became evident. Observational studies have linked the influence of vascular risk factors and lifestyle habits in mid-life years on later risk of cognitive decline and dementia. Participants from community-based cohorts, initially recruited in middle age for the study of cardiovascular diseases, have been re-examined later, in the old age, including cognitive measures 2 , 3. In the case of women, this vital stage also overlaps with menopause, a process that involves fundamental physiological changes, mediated by the drop in estrogen levels and the loss of the cyclical pattern of female sex hormones. These changes are associated with increased risk and prevalence of obesity, diabetes, hypertension, ischemic heart disease as well as cognitive impairment and dementia 4. So far, few studies have analyzed the influence of vascular risk factors and lifestyle habits in middle-aged postmenopausal women and their influence on cognitive performance later in life. To effectively preserve cognitive abilities throughout life, it is imperative to consider sex-specific risk trajectories and identify biological mechanisms that may degrade or protect relevant brain network integrity. The semantic verbal fluency test VFS is a brief cognitive test related to semantic memory integrity 6. It has been reported to be predictive of the incidence of cognitive impairment and dementia 7 and the risk of conversion from the former to the latter. It is also a sensitive measure of clinical progression of dementia and pathology burden, both vascular and AD type 8. It does not require any materials other than a device to keep track of the time and a means for recording the number of words produced VFS also has been used previously as single cognitive measure in large population based studies The aim of this study was to identify which vascular risk factors and lifestyle habits that intervene in midlife on postmenopausal women are related to cognitive performance later in ageing and whether these factors differ from those acting in men. CDC cohort was selected randomly during the years to from the general population in the Canary Islands aged between 18 and 75 years. The study was approved by the Bioethics Committee of Nuestra Señora de la Candelaria University Hospital, and all participants provided their informed consent in writing. The methodology for CDC Cohort and data obtained in the enrolment visit has been described previously in detail In brief, CDC participants were interviewed to obtain responses to a questionnaire on their health-related antecedents, and they also underwent a physical examination. Abdominal obesity was defined on the basis of WHR equal to or greater than 0. Heart rate and blood pressure mmHg were measured following standardized protocols. Pulse pressure was also calculated as difference between systolic and diastolic blood pressure. HOMA2 was also categorized with 80th percentile As socio-demographical variables: education degree, total incomes and number of family members living together were recorded. Education degree was classified in categories: primaries completed or uncompleted, secondaries or university. The ICE index for social class, that included per-capita family income, home overcrowding index and education degree was also calculated and results were grouped in tertiles Data on physical activity PA during leisure time were also recorded with the Spanish version of the Minnesota Leisure Time Physical Activity Questionnaire 17 , and data on PA during work were obtained with a validated questionnaire for the Canary Islands population daily hours of PA equivalent to or more vigorous than brisk walking. Each activity reported by the participants was assigned a metabolic equivalent level MET. Passive leisure PA was considered any activity in which MET consumption was less than 4, and active leisure PA was considered any activity with a MET level equal to or higher than 4. Measurements of MET during leisure time did not consider usual housework activities. Mediterranean diet was evaluated using a food frequency questionnaire validated for our study population Briefly, the following food groups: cereals, fruits and nuts, vegetables, potatoes and legumes, olive oil, fish, dairy products, meat, sugar and sweets and alcoholic beverages, were considered. A value of 0 or 1 was assigned to each of the 10 indicated components using the sex-specific median as the cut-off value, assigning a value of 0 or 1 for the beneficial components below and above the median, respectively. The total adapted score ranged from 0 minimal adherence to the traditional Mediterranean diet to 10 maximal adherence. For categorical comparisons and logistic regression models, values below the 20th percentile, were considered low adherence to the Mediterranean diet. The cohort was contacted again between and and participants were asked to come to their health care center for a new face-to-face examination. During this revisit, individuals were assessed with a questionnaire and physical examination similar to that provided during the recruitment visit and VFT was included as a general measure of cognition. Participants were asked to name as many animals as they could in 60s and the total number of generated words was recorded. Percentile 20 was also calculated, as indicative of low SFT performance. The participants for this study were drawn from CDC cohort database records. Data from postmenopausal women as declared at first visit and men, both between 40 and 60 years of age at recruitment, who had completed both visits were selected for this study. Data about risk factors and lifestyle habits for this study were those collected from recruitment visit. Number of animals in semantic fluency was transformed to typified z-values to approximate it to a normal distribution. All the numerical variables that reached statistical significance or were near to significance were categorized with their 20th and 80th percentile. For multivariate analysis, linear regression models were generated, separately for postmenopausal women and men, to adjust significative bivariate correlations found; the standardized regression coefficients SRC and p values are offered. Models were generated by backward method, so only factors that remained statistically significative, or were close to signification in the last step were shown in tables. All calculations were done with SPSS version 21 software. The original CDC cohort was composed by 6. There were differences by age between participants that attended only the recruitment visit: The selected sample for the present analysis comprised postmenopausal women and men, both with ages between 40 to 60 years at recruitment. Time between recruitment initial visit and third contact visit was The mean age at recruitment was There were differences in education degree: Bivariate associations of categorical variables are showed in Table 2. Table 1. Table 2. Linear regression models for VFS as dependent variable and significative bivariate associations, as independent factors for men and postmenopausal women, are showed in Table 3. Table 3. Lineal regression model, for semantic fluency as dependent variable generated by backward method. Table 4 shows logistic regression models for men and postmenopausal women. Besides education degree, that correlated inversely with SVF in both groups, waist to hip ratio greater than 0. Table 4. Logistic regression models, for men and postmenopausal women, with semantic fluency 20th percentile as dependent variable generated by backward conditional method. As hypothesized, midlife factors associated to VFS performance differ between men and postmenopausal women. Only education degree and waist-to-hip ratio remained as independent common factors for both groups. Mediterranean diet adherence and waist to hip ratio were the main factors associated to low semantic fluency performance in postmenopausal women. The association between VFS performance and years of education has been widely reported In fact, the bivariate association of fluency with social class is attributable to the educational component of this index, and it disappeared in the multivariate analysis when adjusting for education level as an independent factor. WHR is a marker of central obesity and its effect was independent from BMI in linear regression models, which suggest that this association between VFS 20th percentile and WHR could be related mainly to body fat redistribution effect, rather than a global body mass increase. Abdominal fat is a recognized factor for metabolic syndrome Menopause is linked to estrogens decrease and testosterone increase and this hormonal change is accompanied by a redistribution of body fat from a female to a male type with an accumulation in the abdominal compartment Traditional Mediterranean diet is rich in flavonoids from grapes and wine, is low in saturated fat, as the main source for fat is from olives and nuts, and also is characterized by frugality due to austerity periods It has been suggested that nutrients and phytochemicals which are major components of the Mediterranean diet enhances cognitive performance by slowing brain aging In relation to clinical relevance, a large prospective cohort study found that long-term adherence to a Mediterranean diet pattern was linearly associated with overall cognitive status but not cognitive decline Design: A cross-sectional study compared active and sedentary male subjects 17 to 35 years old with no personal or family history of coronary heart disease. Participants kept day food and activity journals. Individual intakes of energy, protein, carbohydrate, fat, saturated fat, monounsaturated fatty acids, polyunsaturated fatty acids, dietary fiber, and alcohol were evaluated. Measurements of blood lipids total cholesterol and triglycerides, high- and low-density lipoprotein cholesterol ; apolipoproteins; cholesteryl ester transfer protein; anthropometric variables body mass index, waist-to-hip ratio, percentage of body fat ; and aerobic capacity were taken during fall and spring data collection periods. SUBJECT SELECTION: Subjects were selected on the basis of normal blood lipid levels, absence of underlying disease, and willingness to comply with their current level of physical activity for the duration of the study. |
| Publication types | Attie, A. Adopocyte metabolism and obesity. Journal of Lipid Research, 50, S— Hainer, V. Treatment modalities of obesity: What fits whom? Diabetes Care, 31 Suppl. Maffeis, C. Waist-to-height ratio, a useful index to identify high metabolic risk in overweight children. Journal of Pediatrics, 2 , — National Institute of Diabetes and Digestive and Kidney Diseases. Weight and waist measurement: Tools for adults. htm circumf ; retrieved June 26, Ness-Abramof, R. Waist circumference measurement in clinical practice. Nutrition in Clinical Practice, 23 4 , — Ross, R. Does the relationship between waist circumference, morbidity and mortality depend on measurement protocol for waist circumference? Obesity Reviews, 9 4 , — Srikanthan, P. Waist-hip-ratio as a predictor of all-cause mortality in high-functioning older adults. Annals of Epidemiology, 19, — Department of Health and Human Services. National Institutes of Health Insulin resistance and pre-diabetes. NIH Publication No. Welborn, T. Preferred clinical measures of central obesity for predicting mortality. European Journal of Clinical Nutrition, 61, — World Health Organization. BMI classification. html ; retrieved June 26, Len Kravitz, PhD is a professor and program coordinator of exercise science at the University of New Mexico where he recently received the Presidential Award of Distinction and the Outstanding Teacher of the Year award. In addition to being a inductee into the National Fitness Hall of Fame, Dr. Kravitz was awarded the Fitness Educator of the Year by the American Council on Exercise. Just recently, ACSM honored him with writing the 'Paper of the Year' for the ACSM Health and Fitness Journal. Waist-to-Hip Ratio, Waist Circumference and BMI. What to use for health risk indication and why. Len Kravitz, PhD. Sep 30, Updated on: January 10, Why Is Abdominal Fat Risk Such a Health Risk? Anthropometric Measurement: BMI, Waist Circumference and Waist-to-Hip Ratio. October, Figure 1. Special Focus: Insulin Resistance. Dinarello, C. Proinflammatory cytokines. Chest, 2 , — Len Kravitz, PhD Len Kravitz, PhD is a professor and program coordinator of exercise science at the University of New Mexico where he recently received the Presidential Award of Distinction and the Outstanding Teacher of the Year award. Related Articles. Stay On Topic. Time-Restricted Eating and Resistance Training. They secrete a number of specialized proteins known as cytokines, which regulate responses to infection, immune reactions, inflammation and trauma. In regards to inflammation regulation i. Some of the cytokines promoting inflammation are tumor necrosis, interlukin-6, and C-reactive protein. These pro-inflammatory cytokines can damage arterial walls when chronically elevated in the circulatory system, such as from high blood pressure, and become the starting point for atherosclerotic plaque build-up. Elevated C-reactive protein is positively correlated to cardiovascular disease and relatively easy to test for in a blood assay. Personal trainers should encourage clients to have their C-reactive protein assessed at that same time they have their cholesterol checked. Fat cells also produce and secrete adiponectin, a specialized protein that improves insulin sensitivity the cells ability to use glucose and protects against atherosclerosis. Unfortunately, with visceral fat obesity accumulation, adiponectin levels are reduced, thus leading to a higher cardiometabolic e. Importantly, elevated levels of blood cortisol intensify central fat deposition. Cortisol is a stress hormone, and thus chronic stress can directly increase visceral fat accumulation. Anthropometric Measurement: BMI, Waist Circumference and Waist-to-Hip Ratio BMI The BMI is calculated as weight in kg divided by the square of height in meters. Another simple BMI calculation is body weight in pounds multiplied by and then divided by height in inches. Keenly, there are many BMI calculators on the web. The World Health Organization has established guidelines for normal One of the drawbacks of BMI use in fitness populations is that a muscular person will score higher, producing an inaccurate assessment of overweight or obese. Ness-Abramof and Apovian also indicate that older populations tend to have a loss in muscle mass, possibly leading to an underestimate of the BMI. Srikanthan, Seeman, and Karlamangla add that aging results in decreases of standing posture that can inaccurately increase BMI by 1. To finish, BMI is a weak predictor of weight-related health problems among some racial and ethnic groups, such as African-American and Hispanic-American women National Institute of Diabetes and Digestive and Kidney Diseases, Waist Circumference The precise measurement of visceral fat requires the use of magnetic resonance imaging or tomography, which are scientific techniques that visually depict the internal tissue compositions. Waist circumference is a substitute technique for these scientific assessments. Welborn and Dhaliwal indicate that waist circumference is superior to BMI in predicting cardiovascular disease risk. One practical question is how should waist circumference be measured? Landmarks include 1 the umbilicus, 2 the midpoint between the lowest rib and the iliac crest, and 3 just above the iliac crest. Advantageously, Ross et al. So, exercise professionals are encouraged to use the anatomical landmark that works best with their clients. Make sure the measurement is taken at the END of expiration, when the diaphragm is in its neutral position; during an inspiration the diaphragm descends into the abdominal cavity, enlarging the waist circumference measurement. Exercise professionals are advised to use a spring loaded tape measure just do a find on any web search engine to attain , as these simple and inexpensive tape measures provide a constant tension for consistency with all anthropometric measurements. Waist-to-Hip Ratio With the waist-to-hip ratio, the waist is measured at the narrowest part of the waist, between the lowest rib and iliac crest, and the hip circumference is taken at the widest area of the hips at the greatest protuberance of the buttocks. Then simply divide the waist measurement by the hip measurement. Welborn and Dahlia and Srikanthan, Seeman, and Karlamangla confirm, and cite several other investigations that show waist-to-hip ratio being the superior clinical measurement for predicting all cause and cardiovascular disease mortality. Welborn and Dhaliwal add that the hip circumference indicates a lower risk for body fat accumulation, and thus including it into the waist-to-hip equation enhances the accuracy of this measurement technique. Is there is Useful Anthropometric Technique to Identify Risk in Overweight Children? Waist-to-height ratio is calculated by dividing a person's waist measurement inches by their height inches. Waist is measured at the narrowest point of one's midsection between the bottom rib and the top of the hipbone. Anthropometric Conclusions It is inspiring to highlight that there is a plethora of research on these anthropometric measurements. BMI is a reliable way to tell if body weight is putting a person at generalized health risk. Waist circumference and waist-to-hip ratio are measures of central adiposity that appear to predict cardiovascular and diabetes risk better than BMI Srikanthan, Seeman, and Karlamangla Much research denotes that the waist-to-hip ratio is the superior health risk-categorizing indicator. Many exercise professionals are highly skilled at body composition measure techniques, such as skinfolds. In completing and explaining the anthropometric and body composition measures for clients, personal trainers can provide supplementary educational information about reducing cardiometabolic health risks and improving quality of life. References: Attie, A. and Scherer, P. Adopocyte metabolism and obesity. Journal of Lipid Research, 50, SS Claudio, M. Waist-to-height ratio, a useful index to identify high metabolic risk in overweight children. Journal of Pediatrics, , Dinarello, C. Proinflammatory cytokines. Hainer, V. Treatment modalities of obesity: what fits whom? Diabetes Care, 31 Supplement 2 , SS |
Waist-to-hip ratio and energy levels -
Fat anterior to the posterior peritoneum and anterior abdominal wall was defined as IPAT and fat posterior to be the posterior peritoneum was defined as RPAT.
Corresponding adipose tissue volumes were derived by the method of Ross et al. All analyses used SPSS Associations were examined by Pearson univariate after logarithmic transformation of skewed variables where appropriate.
Univariate regression models with anthropometric variables as predictors of the measurements of fat mass were used to avoid the problem of multicolinearity with highly correlated variables in multivariable models.
The set of non-nested models were then compared using the t-distribution, as described by Andel, 19 to determine the relative strength of the correlations between the anthropometric and MRI variables. Table 1 shows the anthropometric and biochemical characteristics of the 59 men.
On average the subjects were middle-aged, normotensive and obese, with a wide range of BMI. The mean proportions of total adipose tissue as IPATM, RPATM, ASAATM and PSAATM were Seven of the subjects had impaired fasting glucose plasma glucose concentration 6. Anthropometric, biochemical and adipose tissue mass ATM characteristics of the 59 men.
Table 2 shows the Pearson univariate correlation coefficients between the anthropometric measures of obesity and all adipose tissue compartments, with the corresponding scattergrams for IPATM and PSAATM in Figure 1.
The associations between the anthropometric measures of obesity and MRI variables remained significance after adjusting for age data not shown. Table 3 shows the comparison of the relative strength of these anthropometric measures in predicting adipose tissue masses.
Hence, there was no significant difference between WC and WHR in predicting IPATM and RPATM. Associations of intraperitoneal ATM a and posterior subcutaneous abdominal ATM b and anthropometric measures.
Pearson univariate correlation coefficients between adipose tissue masses and anthropometric measures. WC, waist circumference; WHR, waist-to-hip ratio; BMI, body mass index; ATM, adipose tissue mass. Comparison of the relative strengths of waist circumference, waist-to-hip ratio and body mass index in predicting individual adipose tissue compartments in non-nested models.
t refers to comparison of non-nested models for correlations between anthropometric and MRI variables. This correlational analysis suggests that in men who are on average overweight-to-obese, waist circumference is a better predictor of the distribution of adipose tissue among several fat compartments in the abdominal region than are waist-to-hip ratio and body mass index.
Specifically, waist circumference predicted intraperitoneal adipose tissue mass better than body mass index, and predicted posterior subcutaneous adipose tissue mass better than waist-to-hip ratio. Several studies have examined the association of conventional anthropometric measures with regional abdominal adipose tissues in obesity.
found that in 22 obese women, WC and WHR were equally correlated with total intra-abdominal fat. These associations with WC or WHR were not, however, found in 18 obese men. reported that WHR was the strong predictor of total intra-abdominal fat in 76 healthy obese children.
reported that in 51 obese women, WHR was a good predictor of intra-abdominal adipose tissue. Using MRI, Ross et al. found a strong association of WC with total subcutaneous adipose tissue in 15 obese women. In another study by Ross et al. The findings among these studies probably varied owing to difference in gender, sample sizes and imaging protocols.
The present report extends the aforementioned observations by subdividing abdominal ATM into IPATM, RPATM, ASAATM and PSAATM, and explores the relationship between these anthropometric measures and adipose tissue masses in men with a wide range of BMI.
Many studies have demonstrated the independent contributions of regional adiposity to metabolic abnormalities of obesity. Although accurate quantification of body fat compartments with imaging techniques can predict metabolic abnormalities, it is impractical for routine clinical practice or larger scale studies.
Our results suggest that measurement of WC could be used as a better overall surrogate index of IPATM and PSAATM than WHR or BMI.
BMI has been conventionally used to define and classify overweight and obesity. However, BMI does not account for the wide variation in body fat distribution, and has considerable limitations in predicting intra-abdominal fat accumulation.
The WHR is also a practical index of regional adipose tissue distribution and has been widely used to investigate the relations between regional adipose tissue distribution and metabolic profile.
However, the WHR value does not account for large variations in the level of total fat and abdominal visceral adipose tissues. On the other hand, waist circumference is a convenient and simple index that determines the accumulation of abdominal adipose tissue.
Since the univariate approach used in the present study to examine association between variables produced a set of non-nested models, simple comparison of values of R 2 was not valid.
To avoid the problems of multicolinearity with highly correlated anthropometric variables in multivariate models, we used non-nested models to compare the relative strength of the anthropometric indices in associating with regional adipose tissue masses. Our study does have limitations. The relatively small sample size of the present study might have been underpowered to demonstrate the true strength of the associations between the anthropometric and MRI variables.
It might therefore have been useful to employ other simple techniques to assess fat mass, such as skinfold thickness and dual energy absorptiometry.
However, these techniques do not also allow detailed assessment of the all individual adipose tissue compartments under investigation. In conclusion, our results confirm the importance of the waist circumference as a surrogate marker of the distribution of adiposity in the abdominal region in men.
Accordingly, we propose that waist circumference is probably the most convenient and reliable clinical measure of abdominal fat compartments.
Our study does not suggest any clinical value in measuring the waist-to-hip ratio or body mass index in this group of subjects. Whether our conclusions also apply to women, younger age groups and other racial groups with different body habitus, merits further investigation.
This study was supported by research grants from the Raine Medical Research Foundation, Royal Perth Hospital Medical Research Foundation, National Heart Foundation of Australia and the National Health and Medical Research Council.
PHRB is a Career Development Fellow of the National Heart Foundation. DC was in receipts of a research scholarship from NHMRC Clinical Centres of Excellence at the Royal Perth Hospital. We are grateful for the assistance of Drs F. Riches, S.
Song and J. Hua in data collection and handling. Visscher T, Seidell JC. The public health impact of obesity. Ann Rec Public Health ; 22 : — Kahn BB, Flier JS. Obesity and insulin resistance. J Clin Invest ; : — Ginsberg HN. Insulin resistance and cardiovascular disease. J Clin Invest ; : —8.
Despres JP, Moorjani S, Lupien PJ, Tremblay A, Nadeau A, Bouchard C. Regional distribution of body fat, plasma lipoproteins, and cardiovascular disease.
Atherosclerosis ; 10 : — Bjorntorp P. Arteriosclerosis ; 10 : —6. Lapidus L, Bengtsson C, Larsson B, Pennert K, Rybo E, Sjostrom L. Distribution of adipose tissue and risk of cardiovascular disease and death: a 12 year follow up of participants in the population study of women in Gothenburg, Sweden.
Br Med J ; : — Garg A. The role of body fat distribution in insulin resistance. In: Reaven GM, Laws A, eds. Contemporary Endocrinology: Insulin Resistance. New Jersey, Human Press, : 83 — Misra A, Garg A, Abate N, Peshock RM, Stray-Gundersen J, Grundy SM.
Relationship of anterior and posterior subcutaneous abdominal fat to insulin sensitivity in nondiabetic men.
Obes Res ; 5 : 93 —9. Abate N, Garg A, Pershock RM, Stray-Gundersen J, Grundy SM. Relationships of generalized and regional adiposity to insulin sensitivity in men. J Clin Invest ; 96 : 88 — Wajchenberg BL. Subcutaneous and visceral adipose tissue: their relation to the metabolic syndrome.
Endocrine Rev ; 21 : — Kelley DE, Thaete FL, Troost F, Huwe T, Goodpaster BH. Subdivisions of subcutaneous abdominal adipose tissue and insulin resistance. Am J Physiol Endocrinol Metab ; : —8.
Abate N, Burns D, Pershock R, Garg A, Grundy SM. Estimation of adipose tissue mass by magnetic resonance imaging: validation against dissection in human cadavers.
J Lipid Res ; 35 : —6. Deurenberg P, Yap M. The assessment of obesity: methods for measuring body fat and global prevalence of obesity. Bailliere Clin Endocrinol Metab ; 13 : 1 — Despres JP, Moorjani S, Ferland M, Tremblay A, Lupien PJ, Nadeau A, Pinault S, Theriault G, Bouchard C.
Adipose tissue distribution and plasma lipoprotein levels in obese women. Importance of intra-abdominal fat. Arteriosclerosis ; 9 : — Lean ME, Han TS, Morrison CE.
Waist circumference as a measure for indicating need for weight management. Ohlson LO, Larsson B, Svardsudd K, Welin L, Eriksson H, Wilhelmsen L, Bjorntorp P, Tibblin G. The influence of body fat distribution on the incidence of diabetes mellitus.
Diabetes ; 34 : —8. Lukaski HC, Johnson PE, Bolonchuk WW, Lykken GI. Assessment of fat-free mass using bioelectrical impedance measurements of the human body. Am J Clin Nutr ; 41 : — Ross R, Leger L, Morris D, de Guise J, Guardo R.
Quantification of adipose tissue by MRI: relationship with anthropometric variables. J Appl Physiol ; 72 : — Andel J. On non-nested regression models.
Comment Math Univ Carolinae ; 34 : — Kamel EG, McNeill G, Van Wijk MC. Usefulness of anthropometry and DXA in predicting intra-abdominal fat in obese men and women. Obes Res ; 8 : 36 — Owens S, Litaker M, Allison J, Riggs S, Ferguson M, Gutin B. Prediction of visceral adipose tissue from simple anthropometric measurements in youths with obesity.
Obes Res ; 7 : 16 — Ferland M, Despres JP, Tremblay A, Pinault S, Nadeau A, Moorjani S, Lupien PJ, Theriault G, Bouchard C.
Assessment of adipose tissue distribution by computed axial tomography in obese women: association with body density and anthropometric measurements. Br J Nutr ; 61 : — Ross R, Shaw KD, Martel Y, de Guise J, Avruch L. Adipose tissue distribution measured by magnetic resonance imaging in obese women.
Am J Clin Nutr ; 57 : —5. Janssen I, Heymsfield SB, Allison DB, Kolter DP, Ross R. Separate analyses were performed for the LC-MS metabolomics and NMR-based lipoprotein measurements.
For WHRadjfatmass, linear regression analyses were performed using metabolites as dependent variables and WHRadjfatmass as the independent variable. Potential confounders were used including fat mass percentage, age, education, smoking, alcohol, exercise habits and statin use other antilipidemic agents are rarely used in Sweden.
The same model was used in all three study samples except that alcohol was not included in the PIVUS and the POEM cohorts. Sex stratified analysis were performed for WHRadjfatmass. An interaction term between WHRadjfatmass and sex was used in a set of separate models to test the significance of any sex-interactions regarding the relationships between WHRadjfatmass and metabolites.
To be able to identify metabolites associated with WHRadjfatmass only, we also assessed the association of fat mass percentage with metabolites using linear regression models where metabolites were used as dependent variables and fat mass percentage as the independent variable.
Confounders in the model were age, sex, education, smoking, alcohol, exercise habits and statin. The same model was used in all three study cohorts except that data on alcohol intake was not available in the PIVUS and POEM samples.
The validation step was performed using results from a meta-analysis inverse-variance weighted IVW fixed effect meta-analysis of the POEM and the PIVUS results. Basic characteristics of the three samples are provided in Table 1. Means and standard deviations in parenthesis or proportions are given.
Nine of those metabolites were associated with WHRadjfatmass in both men and women Fig 1. The estimates are from the validation meta-analysis of the PIVUS and POEM samples. The betas are for one SD change in WHR or fat mass. The nine validated metabolites being significantly associated with WHRadjfatmass in both sexes include ceramides, sphingomyelins and glycerophosphatidylcholines GPCs see Fig 1 and S1 Table for details and were all inversely related to WHRadjfatmass.
Nineteen of the 52 replicated metabolites were significantly associated with WHRadjfatmass in women only Table 2. Those 6 represents different chemical classes GPCs, fatty acids, carotenes, and bile acids. All of these 6 metabolites were also associated with fat mass.
All of these 82 metabolites except one, very small VLDL cholesterol, were also associated with fat mass. Fourteen of those metabolites were related to WHR in both men and women see Fig 2 and S2 Table.
All of these metabolites were also related to fat mass. The 14 validated metabolites being associated with WHRadjfatmass in both sexes include different lipid fractions in very-large or large HDL particles, all being inversely related to WHRadjfatmass.
In addition, very-large or large VLDL triglycerides were positively associated with WHRadjfatmass. All 14 validated metabolites were also associated with fat mass. Only one validated metabolite was significantly associated with WHRadjfatmass in women only Very-large HDL triglycerides, beta Twenty-eight validated metabolites were associated with WHRadjfatmass in men only.
They represent mainly large to small VLDL particles and medium HDL inverse relationships vs WHRadjfatmass. Some of these associations were observed among both sexes, while a significant interaction between WHRadjfatmass and sex were seen for many metabolites.
Most of the WHRadjfatmass-associated metabolites were also related to fat mass. Of particular interest were two sphingomyelins being inversely related to WHRadjfatmass in both sexes, but not associated with fat mass as such.
A certain lipoprotein profile was associated with a high WHRadjfatmass, but in this case, this profile was also associated with fat mass. A great number of studies have investigated the metabolic profile of obesity, as reviewed in [ 7 ]. Some studies have also evaluated the metabolomics of fat distribution [ 8 — 15 ], but to the best of our knowledge, no other study have tried to link metabolites to WHR when taking fat mass into account.
This approach aimed to identify metabolites linked to an altered fat distribution, independently of general obesity.
The study from the EPIC-Potsdam cohort [ 14 ], including Germans with almost exclusively European descent, studied the waist circumference adjusted for hip circumference and hip circumference adjusted for waist circumference and came to the conclusion that the metabolic profile for waist circumference was similar to that of BMI, but hip circumference showed a unique metabolomic profile.
That study did however not evaluate WHR, but they showed isoleucine and several phosphatidylcholines aa or ae C, C, C, C, C and C to show different directions in their relationships with waist and hip circumference. None of these metabolites were however related to WHRajdfatmass in our study.
In the other study based on three other Swedish samples using around named metabolites [ 15 ], with almost exclusively European descent, one sphingomyelin were amongst the metabolites found to be related to WHRadjBMI. In that study fat mass was not evaluated directly.
In the present study, we found several sphingomyelin with a higher number of carbons to be linked to WHRadjfatmass. Neither could we find any other association being similar across the two studies.
If that was due to the use of adjustment for fat mass in one study and BMI in the other is not known. Sphingomyelin SM is a class of sphingolipids formed by adding phosphocholine to ceramide.
As recently reviewed in [ 26 ], sphingolipids were initially thought to merely be structural components of the cell membrane, but recently a number of regulatory properties have been coupled to sphingolipids, such as cell growth and death, inflammation, angiogenesis and metabolism.
Low levels of SMs have also recently been associated with cardiovascular diseases, such as stroke [ 27 ] and heart failure [ 28 ], as well as with the structure of the arterial wall [ 29 ].
Thus, certain sphingolipids might be a link between an altered fat distribution and cardiovascular disease. Why some of the SM were related to WHR and not others, and why two SMs were related to WHR and not fat mass, while other were related to both WHR and fat mass is not known.
One explanation for this finding is that only slight changes in the SM molecule could have profound effects on the physical properties. This is exemplified in a paper on lipidomics and obesity conducted in Sweden with almost exclusively European descent, in which it was found that SM ;2 has the greatest negative and SM ;2 the greatest positive estimates vs fat mass [ 10 ].
Thus, only a change in a double bond could result in different signs of the association vs fat mass. In another study, high levels of serum SM species with distinct saturated acyl chains C, C, C and C closely correlate with the parameters of obesity in young adult Japanese individuals [ 30 ], but this is not a universal finding, as could be seen in a study sample collected in Iran [ 31 ].
Thus, it is likely that all SM should not be regarded as equal, although we do not have the knowledge today to understand how the chemical properties of different SMs translates to relationships vs fat mass and fat distribution. Another question arises if SMs are causally related to fat mass and fat distribution.
An experimental study using knock-out of the enzyme involved in SM synthesis SMS2 in mice supports a role of SMs in obesity [ 32 ]. Mice with SMS2 deficiency developed obesity when challenged with a high-fat diet. However, also the other direction of the relationship between SMs and fat mass might exist.
In a study of weight loss induced by a low-caloric diet conducted in Spain and Denmark, the change in fat mass over 8 weeks was inversely related to the change in certain SMs , , , and , while the change in fat mass was directly related to the change in other SMs , , , and [ 33 ]. Apart from the 9 metabolites linked to WHRadjfatmass in both sexes, we identified a number of metabolites being related to WHRadjfatmass in one sex only.
This is not surprising since WHR is very different in men and women and that most genetic correlates to WHR also are sex-specific. Both the male-specific and the female-specific metabolites being related to WHR comes from several chemical classes and it is hard to see any clear pattern in these two metabolomic profiles.
Again, genetic studies might be a way forward to disentangle these sex-specific metabolomic profiles in the future. As with the MS-based metabolomics analysis, the analysis of the lipoprotein profile showed several validated metabolites to be linked to a high WHRadjfatmass.
It was mainly very-large and large HDL-cholesterol inverse and large VLDL-triglycerides that were related to WHRadjfatmass in both sexes. In the sex-stratified analyses, associations with WHRadjfatmass were much more common in males than in females, with predominantly small VLDL being sex-specific.
Very-large and large HDL-cholesterol inverse and large VLDL-triglycerides have been linked to cardiovascular disease [ 16 ], but these lipoprotein fractions were also related to fat mass, not only to fat distribution.
In the relationship between WHRadjfatmass and metabolites, it is likely that most relationships have a causal direction from an unfavorable fat distribution to a change in metabolites rather than the opposite, since a major weight change induces profound metabolic effects [ 33 , 34 ].
Regarding the 14 lipoprotein measurements identified Fig 2 to be linked to WHRadjfatmass in both men and women, a mediating role for those lipoprotein-based metabolites are plausible, since they all have been linked to atherosclerosis and incident CVD [ 16 ] with the same direction of associations as found vs WHRadjfatmass in the present study.
As discussed above, the role of the 9 MS-based metabolites Table 1 are less clear, since the knowledge on sphingomyelins and GPCs in CVD are much less advanced than the knowledge on lipoproteins in CVD. We are still awaiting to obtain robust, powerful genetic instruments for different sphingomyelins and GPCs that are not pleotropic to be used in Mendelian randomization studies.
Also mice models with knock-out of different sphingomyelins and GPCs on a genetic atherosclerotic background would be a way forward to disentangle if sphingomyelins and GPCs are important players in atherosclerosis formation.
It is also be emphasized that the MS-based part of the study should be regarded as an untargeted approach, since a large number of metabolites from a great number of chemical classes were analyzed, and therefore the evaluation of the WHRadjfatmass vs metabolite associations were hypothesis-free, like in a GWAS study.
It is therefore not surprising that many of the findings were not expected and given the unperfect knowledge of many of the metabolites, the findings are hard to understand in detail at this stage. The strength of the present study is the use of the same MS-based metabolomic platform and lipoprotein NMR analysis with a large number of metabolites and lipoproteins subfractions in three different studies, so we could obtain validated relationships.
One limitation is the cross-sectional nature of the studies, which hinders any conclusion of causal directions. Another limitation is the fact that we do not have genetic instruments for the metabolites of interest to be used in Mendelian randomization studies.
Metabolomic measurements were performed by a commercial company. Some of the details regarding standards and QC determinations were considered proprietary to their platform and therefore not shared.
How this translates into the findings in the present study is unknown, but as a general rule any poor performance of a technique would only increase the probability of the null hypothesis and would not produce any false positive findings.
We used bioimpedance to evaluate fat mass in the present study, a technique that has been present for decades. However, DEXA is the gold standard in this respect, but was only used in one of the samples, the POEM study, being the smallest of the three cohorts used.
Therefore, we did not use DEXA due to the limited power and lack of replication sample. However, when we related bioimpedance to DEXA measurements for fat mass, the correlation was very good correlation coefficient 0.
DEXA has the advantage over bioimpedance that regional fat distribution could be assessed in detail. In the absence of DEXA, we used an indirect measurement of regional fat distribution, the WHR, a measure that is a better predictor of myocardial infarction than measurements of general obesity, such as BMI [ 35 ].
WHR has the advantage over DEXA of being a cheap and easy measurement and is widely used in the clinic, but it would be of great interest to validate our present findings in a sample with DEXA measurements.
The samples used in the present studies are almost exclusively including subjects with a European descent. In order to generalize the findings to other populations, replication studies in subjects from other parts of the world has to be undertaken, especially since most other studies in this field also have been conducted in subjects with a European descent.
It is also emphasized that the MS-based part of the study should be regarded as an untargeted approach, since a large number of metabolites from a great number of chemical classes were analyzed, and therefore the evaluation of the WHRadjfatmass vs metabolite associations were hypothesis-free, like in a GWAS study.
It is therefore not surprising that many of the findings were not expected and given the imperfect knowledge of many of the metabolites, the findings are hard to understand in detail at this stage.
Regarding the lipoprotein-based metabolites, they were preselected to cover the lipoprotein spectra and therefore this analysis should be considered as targeted and due to the greater knowledge on lipoproteins, these results could be interpreted more in detail.
Thyroid function might have been a confounder in the present evaluation of the metabolomic profile of WHR, but unfortunately we do not have valid measurements of thyroid function in the samples. In conclusion, two sphingomyelins were inversely linked to WHR fat mass adjusted in both men and women without being related to fat mass, while very-large or large HDL particles were inversely related to WHR as well as to fat mass.
If these sphingomyelins represent a link between WHR and cardiometabolic diseases remains to be established when genetic instruments might become available in the future. The beta, SE and p-values are from the validation meta-analysis of the PIVUS and POEM samples.
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. Abstract Background The genetic background of general obesity and fat distribution is different, pointing to separate underlying physiology.
Conclusion Two sphingomyelins were inversely linked to body fat distribution in both men and women without being associated with fat mass, while very-large and large HDL particles were inversely associated with both fat distribution and fat mass.
Introduction Excess body adipose tissue could be analyzed in at least two dimensions, total fat mass and fat distribution. Material and methods Population samples EpiHealth. POEM Prospective investigation of Obesity, Energy and Metabolism. PIVUS Prospective Investigation of the Vasculature in Uppsala Seniors.
Physical measurements and blood sampling Waist circumference was measured at the umbilical level, while hip circumference was measured at the level of trochanter major. The physical measurements were performed in the same fashion in all of the three studies.
Questionnaire Life-style factors were evaluated using a questionnaire in all samples. Metabolomics In all three study samples, metabolomics Metabolon inc. Instrument performance standards. Process assessment standards. Fluorophenylglycine, chlorophenylalanine.
Lipoprotein measurements Lipoproteins and their content were quantified using high-throughput NMR metabolomics Nightingale Health Ltd, Helsinki, Finland [ 25 ].
Statistics All metabolites and lipoproteins were rank based inverse-normal transformed to obtain a normal distribution and the same mean level for each metabolite.
STATA Results Basic characteristics of the three samples are provided in Table 1. Download: PPT. Fig 1. Relationships between mass spectrometry-based metabolites and waist-hip ratio WHR in males and females and vs fat mass in both sexes combined.
Table 2. Relationships between mass spectrometry-based metabolites and waist-hip ratio adjusted for fat mass WHRadjfatmass in males and females and vs fat mass in both sexes combined.
Table 3. Fig 2. Relationships between nuclear magnetic resonance spectrometry-based lipoprotein metabolites and waist-hip ratio WHR in males and females in both sexes combined. Table 4. Relationships between nuclear magnetic resonance spectrometry-based lipoprotein metabolites and waist-hip ratio WHR in males and females and vs fat mass in both sexes combined.
Comparison with the literature A great number of studies have investigated the metabolic profile of obesity, as reviewed in [ 7 ]. Supporting information. S1 Table. s XLSX. S2 Table. References 1. Fall T, Hägg S, Mägi R, Ploner A, Fischer K, Horikoshi M, et al. The role of adiposity in cardiometabolic traits: a Mendelian randomization analysis.
PLoS Med. Hägg S, Fall T, Ploner A, Mägi R, Fischer K, Draisma HH, et al. Adiposity as a cause of cardiovascular disease: a Mendelian randomization study. Int J Epidemiol. Emdin CA, Khera AV, Natarajan P, Klarin D, Zekavat SM, Hsiao AJ, et al.
Genetic Association of Waist-to-Hip Ratio With Cardiometabolic Traits, Type 2 Diabetes, and Coronary Heart Disease. Turcot V, Lu Y, Highland HM, Schurmann C, Justice AE, Fine RS, et al.
Protein-altering variants associated with body mass index implicate pathways that control energy intake and expenditure in obesity. Nat Genet. Pulit SL, Stoneman C, Morris AP, Wood AR, Glastonbury CA, Tyrrell J, et al. Meta-analysis of genome-wide association studies for body fat distribution in individuals of European ancestry.
Hum Mol Genet. View Article Google Scholar 6. Fall T, Mendelson M, Speliotes EK. Recent Advances in Human Genetics and Epigenetics of Adiposity: Pathway to Precision Medicine?
Rangel-Huerta OD, Pastor-Villaescusa B, Gil A. Are we close to defining a metabolomic signature of human obesity? A systematic review of metabolomics studies. Beyene HB, Olshansky G, Giles C, Huynh K, Cinel M, Mellett NA, et al.
Lipidomic Signatures of Changes in Adiposity: A Large Prospective Study of Adults from the Australian Diabetes, Obesity and Lifestyle Study. Bogl LH, Kaye SM, Rämö JT, Kangas AJ, Soininen P, Hakkarainen A, et al. Abdominal obesity and circulating metabolites: A twin study approach.
Gerl MJ, Klose C, Surma MA, Fernandez C, Melander O, Mannisto S, et al. Machine learning of human plasma lipidomes for obesity estimation in a large population cohort.
Top Quick athlete snacks Page Research Interests Ratii Articles New Projects Wast-to-hip Quick athlete snacks Home. Rati Pag e. Waist-to-Hip Non-medicated allergy relief, Waist Circumference and BMI: What to Use for Health Risk Indication and Why? Len Kravitz, Ph. Introduction The ever-increasing worldwide obesity epidemic poses increased risk for coronary heart disease, hypertension, abnormal cholesterol, diabetes mellitus, sleep apnea and certain cancers Hainer, Toplak, and Mitrakou, Lawrence Waist-to-hipp Koning, Anwar T. Merchant, Janice Controlling blood pressure naturally, Sonia S. The objectives of Quick athlete snacks study were to determine the association Quick athlete snacks waist Quick athlete snacks WC and waist-to-hip ratio WHR with the risk Waist-tk-hip incident Leves disease CVD events and to determine whether the strength of association of WC and WHR with CVD risk is different. This meta-regression analysis used a search strategy of keywords and MeSH terms to identify prospective cohort studies and randomized clinical trials of CVD risk and abdominal obesity from the Medline, Embase, and Cochrane databases. For a 0. These results were consistent in men and women.
Sie haben es richtig gesagt:)