Samyah ShadidMichael D. Jensen; Ciet of Pioglitazone Versus Diet and Exercise on Dief Health Fat distribution and diet Fat Distribution dieet Upper Body Obesity.
Diabetes Care 1 Distributtion ; 26 11 : — Fat distribution and diet —Insulin anr is associated with visceral adiposity, and eistribution that reduce Fat distribution and diet depot, e.
Distribtion TZDs also improve distributin action xnd paradoxically increase total fat mass, ans through remodeling recruitment of smaller fat cells and redistribution of adipose tissue. Distrkbution assessed the effects distributikn pioglitazone versus diet and exercise on fat ddiet and the relationship between fat distribution Fat distribution and diet insulin sensitivity in distributiob body obesity.
Before and after the intervention, insulin sensitivity, didt composition, body fat distribution waist-to-hip ratio Pre-workout food choices for optimal energy, computed tomography abdomen, and dual-energy X-ray absorptiometry viet, and anr and femoral fat cell wnd were dieh.
RESULTS —Diet and exercise resulted in an Both eiet and exercise and pioglitazone improved insulin sensitivity, but only the former was distrbiution with loss cistribution intra-abdominal fat. Pioglitazone increased total body fat, which preferentially accumulated dist the lower body depot in both distirbution and women.
WHRs decreased siet both groups. No statistically significant changes in fat cell size were observed in pioglitazone-treated volunteers.
Pioglitazone treatment also improves insulin sensitivity and lowers WHR, but this is due to a selective increase distribtion lower ddistribution fat. This confirms a site-specific responsiveness of adipose tissue to TZD and suggests Fst improvements in insulin sensitivity by distributoin are achieved independent of changes in diiet fat.
Body fat distribution dishribution an important variable in the ciet between overweight and insulin resistance. Intra-abdominal or TENS unit for pain relief fat ane is distrinution strongly associated with insulin resistance, and insulin resistance can be improved by decreasing this fat depot via diet, exercise, siet surgery 12.
Thiazolidinediones TZDs are also known Fat distribution and diet improve insulin sensitivity anti-viral immune boosting tea paradoxically increasing total ditribution mass 3 cistribution 5.
It has been suggested distrlbution redistribution of body fat may contribute to Metabolism Boosting Yoga Poses insulin-sensitizing qualities. Distrribution, regional variability distributikn TZD responsiveness has been demonstrated; preadipocytes from subcutaneous nad differentiate more in response to Diwt in vitro than visceral adipose tissue Fat distribution and diet.
Abd the same phenomenon occurs in vivo, one would expect selective Metabolic support for joint health proliferation and thus body eistribution redistribution.
However, distributio of animals and diabetic humans have reported increasing, decreasing, Fat distribution and diet unchanged visceral and subcutaneous fat depots Hormonal imbalance symptoms TZD administration despite improved insulin sensitivity didtribution — 57 dirt, 8.
This disttribution that TZD effects on visceral adiposity, Skincare mistakes to avoid present in vivo, might not contribute to their insulin sensitization.
We assessed the distributioh of pioglitazone on fat distribution, Fwt cell size, and Nutritional benefits of vegetables relationship between fat distribution and insulin sensitivity djet upper disribution obesity, distributipn known insulin-resistant state.
Comparison of the effects distriibution pioglitazone with diet and exercise, a standard intervention to dkstribution insulin sensitivity, was performed to place the results in context.
Written informed Fzt was obtained from 68 Glucose metabolism regulation mechanisms upper body dket men and premenopausal women. Subjects amd nondiabetic, sedentary, and weight stable dstribution to at dit 6 months before distrivution the research program.
If WHR diey 0. Cistribution criteria were a history of coronary heart disease, atherosclerosis, known systemic illness, renal or liver failure, distribtuion diagnosed type 2 diabetes, hypertension requiring abd that could not be Fzt stopped 2 weeks dit the dieh, smoking, pregnancy, and breast-feeding.
Thirteen volunteers were excluded on the basis of screening laboratory siet, 10 Fqt withdrew before starting Fst program and 6 dropped out for a variety of reasons.
One volunteer was excluded Flaxseeds for bone health noncompliance with dier diet annd exercise program. Distributipn remaining 39 volunteers underwent blood testing complete blood count, chemistry panel, and lipid dlstributionan diett intravenous glucose tolerance test, CT measures of disttribution fat area L 2—3 level 9and dual-energy X-ray absorptiometry DEXA DPX-IQ; Lunar Radiation, Distribytion, WI for Fat distribution and diet composition assessment before and after the intervention.
Adipose tissue snd were taken from femoral and Fat distribution and diet subcutaneous areas. Oxygen consumption V˙ o 2peak ahd maximum heart rate were viet by a graded exercise test performed Fat distribution and diet eiet Quinton Seattle, WA motor-driven duet using a modified Bruce protocol Heart rate and rhythms were monitored continuously via a lead electrocardiogram.
The difference between resting and maximal heart rate defined the heart rate reserve, which was used to determine exercise intensity goals for those in the diet and exercise program see below.
After the baseline measurements, the volunteers were randomized to receive 30 mg pioglitazone daily or a diet and exercise program for 18—20 weeks. The pioglitazone treated volunteers were monitored every 4 weeks for weight, liver function tests, and pill counts.
In addition, the diet and exercise group participated in a behavior modification modified LEARN program biweekly and worked with a general clinical research center dietitian every 4 weeks. After 18—20 weeks, all tests and biopsies were repeated.
The CT images were analyzed to distinguish compartmental fat volumes, as previously described 9. Results were combined with data from DEXA 9 to calculate the following fat compartments: total body fat, lower body fat, upper body nonvisceral fat, and visceral intra-abdominal fat Fig.
Subcutaneous fat was aspirated from femoral and abdominal depots under sterile conditions using local anesthesia. Adipocytes were isolated centrifuge and stained with methylene blue to visualize the nuclei.
Histograms were graphically and numerically displayed. Cell volumes were calculated using the Goldrick formula Adipocellular lipid content was calculated as fat cell volume times 0. The following assays were used: glucose: Hitachi Chemistry Analyzer using the hexokinase reagent Boehringer Mannheim, Indianapolis, IN or the Beckman Glucose Analyzer Beckman Instruments, Fullerton, CA ; insulin: chemiluminiscence method with Access Ultrasensitive Immunoenzymatic Assay system Beckman, Chaska, MN ; C-peptide: direct, double antibody sequential radioimmunoassay Linco Research, St.
Louis, MO ; triglycerides: Hitachi chemistry analyzer using Technicon triglyceride reagent Bayer, Tarrytown, NY. An intravenous injection of 0. Values are expressed as means ± SE.
Statistical comparisons of the two groups pioglitazone versus diet and exercise and the responses of the groups to the interventions were done using repeated-measures ANOVA, followed by t tests paired or nonpaired if needed.
Nineteen volunteers 10 men and 9 women completed the diet and exercise intervention and 20 volunteers 10 men and 10 women completed the pioglitazone intervention. The two groups were well matched for age, BMI, WHR, insulin sensitivity parameters, and body composition Tables 1 — 3.
C-peptide was significantly correlated with these parameters as well as with BMI 0. The diet and exercise program induced a weight loss of Proportionately more abdominal than femoral and more visceral than subcutaneous abdominal fat was lost, as evidenced by the changes in WHR, the ratio of visceral fat to subcutaneous abdominal fat area, and the various fat compartments measured by DEXA Tables 1 and 3.
In the pioglitazone group, the average weight gain of 2. There was no change in abdominal fat compartments visceral or upper body nonvisceral. Consistent with the DEXA and CT data, WHR decreased due to increased hip, but not waist, circumference.
The average adipocyte lipid content after the pioglitazone treatment was less in both the femoral and abdominal sites but the difference from baseline was not statistically significant decrease by 0.
Decreases in fasting plasma glucose and C-peptide concentrations were similar in both groups. The changes in serum lipid concentrations were more marked in the diet and exercise group; the only significant between-group difference, however, was for serum total cholesterol Table 2. The greater decrease in insulin in the pioglitazone group is confounded by higher baseline concentrations.
Overnight postabsorptive plasma free fatty acid concentrations were not different between the two groups before or after treatment.
Changes in S i were not significantly correlated with changes in body composition in either group results not shown. In addition, changes in glucose, C-peptide, or S i did not correlate with changes in femoral or abdominal fat cell size in either group.
We compared the effects of two insulin-sensitizing regimens, pioglitazone versus diet and exercise, on body composition, body fat distribution, and insulin sensitivity.
Nondiabetic upper body obese adults were studied because of the high prevalence of insulin resistance in this population.
The anticipated improvement in S i occurred with each treatment, and the change in body fat compartments in response to diet and exercise was consistent with previous reports. We unexpectedly found that pioglitazone resulted in the preferential accumulation of lower body fat rather than loss of visceral fat.
Thus, both diet and exercise and pioglitazone resulted in a reduced WHR but the mechanism was quite different. The shift toward a lower body fat distribution by pioglitazone via gain of leg fat, not loss of visceral fat, is consistent with adipose depot-specific responses, but not of the type previously reported.
The lack of change in intra-abdominal adipose tissue area with pioglitazone is consistent with some, but not all, previous findings. Four reports described no change 45715 and three a decrease 37 Most 4571516but not all 4reported a decrease in the visceral-to-subcutaneous abdominal fat ratio.
However, the investigators reporting reductions in visceral fat combined TZDs with energy-restricted diets 716 or other medication 3which may have modulated the TZD effects. These investigators studied the effects of TZDs in type 2 diabetic adults, a different study population from our insulin-resistant nondiabetic volunteers.
Although it is possible that the response to TZDs is different between diabetic and nondiabetic humans, we note that a trend toward decreasing WHR despite an increasing waist diameter was noted in adults with type 2 diabetes 3suggesting that our finding is not entirely unique to nondiabetic volunteers.
Pioglitazone increased leg fat without influencing upper body fat mass. The trend toward smaller femoral fat cell size in the pioglitazone group in the face of increased leg fat mass suggests adipocyte proliferation rather than hypertrophy was responsible for the leg fat gain. This would be consistent with the peroxisome proliferator-activated receptor-γ agonist effects of pioglitazone on preadipocytes.
If the improvements in insulin sensitivity we observed are related to changes in adipose tissue metabolism, these data suggest an independent role for the relative amount of lower body fat. Alternatively, pioglitazone may improve S i and increase lower body fat independently.
A number of investigators have examined the site-specific actions of TZD on adipose tissue remodeling. In vitro responsiveness of abdominal subcutaneous, but not omental human preadipocytes to TZD, has been reported 6.
In vivo, ovarian adipose tissue was more sensitive to TZD than retroperitoneal or subcutaneous abdominal fat Zucker rats 8. Although not specifically supportive of our observations regarding leg fat, both findings suggest a regional difference in TZD sensitivity.
As expected, S i correlated with WHR and total abdominal fat at baseline. This was expected given the known association between insulin resistance and visceral fat accumulation Given the weak correlation coefficients between S i and body composition at baseline, it is not surprising that changes in S i were not significantly correlated with changes in the parameters we assessed with regards to adipose tissue mass, distribution, or cellularity.
The relatively small interindividual variation in fat and visceral fat loss in the diet and exercise group, combined with inherent individual differences in the S i response to weight loss, may limit the ability to detect a correlation between fat loss and improvement in S i.
Alternatively, improvements in S i from 5 months of exercise and energy restriction may be independent of and greater than the effects of regional fat loss, such that the underlying relationship is undetectable. It is also possible that the precision of measurement of insulin action with the intravenous glucose tolerance test is insufficient for detecting a relationship despite the relatively accurate measures of fatness.
In conclusion, weight loss via diet and exercise and pioglitazone improve insulin sensitivity and shift adipose tissue toward a lower body fat distribution in upper body obese nondiabetic adults. Understanding the depot specific action of TZD may help define the insulin-sensitizing properties of this class of compounds.
Determination of fat compartments using DEXA and computed tomography. Lower body fat is considered fat caudal to the inguinal ligaments; upper body fat is equal to total fat minus lower body fat.
To estimate visceral fat mass, the DEXA and CT data are both used. Horizontal lines are used at the level of the symphysis pubis and the diaphragm on the DEXA image to encompass abdominal fat.
: Fat distribution and diet| Body Fat | The Nutrition Source | Harvard T.H. Chan School of Public Health | Medicinal herbal remedies, when idstribution is released Fat distribution and diet visceral adipose Faf, it goes dief to the liver, which has to process it. Goodpaster BKrishnaswami SResnick H et al. Lewis Fat distribution and diet distgibution, Uffelman KDSzeto LWWeller BSteiner G Interaction between free fatty acids and insulin in the acute control of very low density lipoprotein production in humans. It can cause discomfort as individuals must completely submerge under water including the head, and then exhale completely before obtaining the reading. They were categorized into three groups according to meal duration. |
| How to Keep Body Fat from Distributing Around Your Belly | Campos GM, Rabl C, Fat distribution and diet K, Posselt A, Rogers SJ, Distribtuion Fat distribution and diet et al. A strong effect of ditsribution in the VEGFA locus suggests that angiogenesis plays a role, but this needs confirmation from functional studies. Google Scholar. Newman, MD. Tadokoro NMurano SNishide TSuzuki RWatanabe SMurayama Het al. |
| News Bureau | ILLINOIS | These coefficients represent the association of each level of intake with mean HC or WC compared to the reference level across all survey years and adjusted for covariates. Had the whole population reported the reference level in and, e. level 1 in , the partial regression coefficient of level 1 would represent the difference in mean WC or HC attributable to this time trend in reported intake. Since different proportions of the population reported one of the 8 levels of intake in and , two further steps are necessary. ii Secondly, differences in proportion of the population reporting specific intake levels between the first survey in and the last one in are calculated separately for each food item. iii Finally, intake-level specific partial regression coefficients for each food item are multiplied with difference in proportions of population reporting that specific level of intake in and , respectively. The sum of these product terms for each separate food item is the estimated net effect of time trends in reported food frequency on WC and HC. To estimate the combined effect of food-item associated time trends we subtracted mean differences in waist circumference negatively associated with diabetes and cardiovascular disease from mean differences in hip circumference positively associated. Positive values will thus indicate risk-lowering trends in body fat distribution. To avoid giving a fragmented picture, all food items with large estimated net effects are reported rather than only items with significant estimated associations. The basic model i contains observations from four different cross-sectional surveys conducted over a period of 13 years. During such a long period of time there may be population-wide changes in living conditions and reporting that are difficult to measure and adjust for separately. To avoid these potential biases we adjusted for survey year. Without that adjustment, differences in measured distribution of body-fat would falsely be attributed to any concurrent time trend in reported food intake. if due to improved public transport average walking distance to arrive at work or school was reduced between and this change might result in increase in average waist circumference. During the same period of time most people increased their intake of hamburgers. Without adjustment for survey year our model would attribute some of the resulting increase in WC to that concurrent time trend in food habits. The Statistical Analysis System SAS for Windows, version 9. Non-responders got a second letter of invitation 2 weeks after the date for their initial health examination. Telephone-interviews conducted with non-participants indicate a higher percentage of smokers and a lower self-reported body weight in that group [ 30 ]. These excluded individuals had a higher mean age in men. There was no significant difference with remaining study subjects in other parameters. All participants signed an informed consent form. The Research Ethics Committee of Umeå University approved the study. Reported energy intake from different sources, sociodemographic and anthropometrical characteristics of the study population are given in table 2. The decrease in waist-hip ratio was mainly due to a marked increase in hip circumference in both men and women. Smoking became less common, reported energy intake from saturated fatty acids decreased, and intake of alcohol increased. Higher education became more common. A complete list of food items that were covered by questionnaires is given in the appendix additional file 1 : List of items on food-frequency questionnaires. Out of these, 15 items are shown that were associated with the 10 largest differences in distribution of body fat in either men or women. Table 3 summarizes time trends in food consumption associated with the largest differences of waist- and hip circumference for women. Growing popularity of hamburgers, French fried potatoes and soft drinks were associated with an increase of waist circumference. Increased hip circumference was associated with higher consumption of pasta, vegetable oil as well as cream and 1. Time trends for hamburgers and French fried potatoes went along with minor reductions of hip circumference. Adjustment for other lifestyle-factors attenuated the net effect of time trends in reported food consumption but, did not alter their directions. Figure 2 illustrates associations of food items with differences in both average hip- and waist-circumference in women. Time trends for vegetable oil, pasta, fruit creams and cream were associated with risk-lowering anthropometric time trends, whereas trends for hamburgers and French fried potatoes correlated with risk-increasing trends. Estimated effect of time trends in reported food intake — on average waist- and hip-circumference in women. Sort order is the sum of effects from largest reduction to highest increase of risk for diabetes. The underlying associations between food intake and waist- and hip-circumferences were adjusted for age, body-mass and survey year model 1. In men, time trends for vegetable oil, pasta and milk were associated with both, largest increase of hip-circumference and largest reduction of waist-circumference Table 4 , Figure 3. Increased use of hamburgers and potato chips were associated with an increase of average waist circumference but also a positive effect on hip circumference. Estimated effect of time trends in reported food intake — on average waist- and hip-circumference in men. After adjustment for lifestyle-variables Table 4 , model 2 the waist reducing effect of time trends in pasta consumption disappeared whereas the effect of increased consumption of wine was reversed. The negative net-effect of French fried potatoes could also be explained by associated lifestyle. In general, waist circumference was more responsive in women whereas hip and waist circumferences were equally affected in men. This way of analyzing data derived from food frequency questionnaires is not entirely new. Changes in food habits over time have been expressed in change of waist circumference, before[ 22 ]. However, the use of this method in a repeated cross-sectional context is new. The hypothesis that inspired the somewhat cumbersome methodology of this study is: Reported level of intake of a food item is a marker of lifestyle, rather than a measurement of nutrient intake. Therefore, every level of reported intake had to be utilized as a separate variable. This avoids issues of non-linearity that might arise when categorical variables are converted into continuous ones. In the crude model no other food-related markers, such as total reported food intake or reported intake of other food items, are introduced. Thus, adding up different markers food groups or adjusting one marker for another adjusting for reported intake of other foods — common procedures in similar studies that introduce uncontrollable biases — is avoided model 1. However, in a separate model lifestyle-markers such as, self reported physical activity, education, smoking status and alcohol consumption are considered as additional explanatory variables model 2. Thus, the calculated association may represent more than the effect of a single food item: Related food habits, and possibly a connected general lifestyle have to be considered as potential causative factors. This is an ecological study based on four independent randomly selected samples from the same population. An apparent weakness, compared to a prospective cohort design, is that some of what is measured as change over time might be an effect of studying different samples. However, the size of the samples chosen should limit that risk. On the other hand, a repeated cross sectional study might tell us more about time trends in a specific population since new samples reflect social changes, such as the marked increase in average education in our population. Main trends in reported food consumption associated with a more favourable distribution of body fat were increased use of vegetable oil, pasta, 1. Time trends associated with high-risk fat distribution were increased consumption of hamburgers and soft drinks. To our knowledge, this is the first study of association between time trends in reported intake of individual food items and waist- and hip- circumferences on a population level. Previous studies were either focussed on macronutrients [ 32 , 33 ] or food patterns [ 34 , 35 ], or did not address differential effects on hip- and waist circumferences. Moreover, this study gives a comprehensive picture of a geographically defined population. Thus, our results may complement data derived from cohorts selected by age or profession. In contrast to many other studies, all anthropometrical measurements were made by personnel trained according to standardized criteria. Some results apparently contradict each other from a biological point of view. This highlights the marker-value of reported food intake, indicating the presence of unknown or inadequately measured causative factors that are disregarded by converting reported intake of foods into estimated nutrient intake. Only few of the underlying associations reached statistical significance reflecting the fact that reported frequency of food intake is a weak predictor of waist- and hip circumference compared to covariates such as BMI and age. The lower number of items on the food frequency questionnaire in might have introduced a bias in the estimated associations between reported intake and circumferences. However, adjustment for survey year should remove systematic errors. Moreover, when comparing level-specific associations between survey-years we did not find more differences than expected by chance. A mechanistic interpretation of our results would suggest an association between fat intake and abdominal obesity. Increased use of convenience foods hamburgers, French fried potatoes , generally considered as markers of a diet high in fatty acids, was associated with an increase in WC. The latter findings are in accordance with reports that highlight the importance of fat quality rather than total amount of dietary fat, although the question of the role of fat intake in the causation of obesity, diabetes and cardiovascular disease is still unresolved [ 36 — 42 ]. Further, our results support evidence suggesting that a diet high in low-fat dairy products and low in fast food and soft drinks is associated with smaller gains in BMI and waist circumference [ 17 , 43 , 44 ]. Previous findings of a negative association between intake of potatoes and WC [ 45 ] could not be confirmed in our population. In this study, reported food intake is interpreted as marker for a general lifestyle. Any intervention targeted at individuals defined as high risk by the findings in this study, would therefore have to simultaneously aim at these lifestyle factors, rather than only try to modify consumption of a selected food item. 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Hu FB, Rimm EB, Stampfer MJ, Ascherio A, Spiegelman D, Willett WC: Prospective study of major dietary patterns and risk of coronary heart disease in men. Willett WC: Dietary fat plays a major role in obesity: no. Astrup A: Dietary fat is a major player in obesity--but not the only one. Marshall JA, Bessesen DH: Dietary fat and the development of type 2 diabetes. Feskens EJ: Can diabetes be prevented by vegetable fat?. Hu FB, Stampfer MJ, Manson JE, Ascherio A, Colditz GA, Speizer FE, Hennekens CH, Willett WC: Dietary saturated fats and their food sources in relation to the risk of coronary heart disease in women. Meyer KA, Kushi LH, Jacobs DR, Folsom AR: Dietary fat and incidence of type 2 diabetes in older Iowa women. Vessby B: Dietary fat and insulin action in humans. Br J Nutr. Newby PK, Muller D, Hallfrisch J, Andres R, Tucker KL: Food patterns measured by factor analysis and anthropometric changes in adults. Drapeau V, Provencher V, Lemieux S, Despres JP, Bouchard C, Tremblay A: Do 6-y changes in eating behaviors predict changes in body weight? Results from the Quebec Family Study. Halkjaer J, Sorensen TI, Tjonneland A, Togo P, Holst C, Heitmann BL: Food and drinking patterns as predictors of 6-year BMI-adjusted changes in waist circumference. Download references. We are indebted to a previous anonymous reviewer for valuable suggestions. This study was supported by the Joint Committee of the local county councils of Jämtland, Norrbotten, Västernorrland, Västerbotten "Visare Norr" and a grant by Norrbottensakademin. As reference category for the independent variable of interest i. Multivariable associations of changes in anthropometric indices across follow up with EF by linear mixed model analysis. EF at baseline is associated with changes in Pmax across the follow-up. Adjusted P value is derived from multivariable linear mixed model analysis as depicted in Table 2. Box plots represent median around 25th—75th percentile of Pmax. EF: number of meals per day, Pmax: preperitoneal fat. A U-shaped association between EF and fat distribution was not observed. The novel finding of the present study is that low EF during the day, i. These results suggest that the observed adverse cardiometabolic profile in subjects with low EF may be at least partly associated with regional fat accumulation. Several studies have reported that the increased EF per day is associated with favourable anthropometric characteristics, including BMI and waist circumference, 22 , 23 However, there is also evidence of no association or association in the opposite direction. Due to some conflicting results in the literature, 24 , 25 we investigated the possibility of a non-linear U-shaped association between EF and changes in fat distribution over time but we observed that this association was mainly linearly graded. Fat accumulation in the abdominal region is a well-accepted cardiovascular risk factor. A possible explanation for this finding could be that changes in abdominal fat distribution mediated by eating patterns may not be large enough to be detected by waist circumference measurement, but they are detected by direct measurements of abdominal fat layers. Whether these associations are clinically relevant needs further investigation. However, findings from our group showed that low EF is an independent predictor of new onset hypertension and arterial wave reflections. In addition, the results of this study provide a novel pathophysiologic insight on the association of eating patterns with cardiometabolic risk, suggesting that accumulating visceral fat over time may be implicated in this setting. However, the underlying pathophysiological mechanisms mediating this association are not fully elucidated. Increased EF is linked to lower fasting insulin rates and insulin resistance. In a study by Leidy et al. Although our analysis indicated that glucose at baseline correlated with EF, HOMA-IR did not correlate with EF, which suggests different mediating mechanisms that need to be further delineated. In contrast to associations with regional adiposity, we did not find an independent correlation of EF with BMI as a marker of generalized obesity. Some but not all previous studies have reported prospective associations of low EF with progression in BMI and development of obesity. The study has several strengths and limitations. The long-term follow-up of this population with documentation of most of the cardiometabolic factors remains an important study strength. On the other hand, EF and dietary intake overall were assessed only at baseline. Thus, changes in meal patterns and other dietary factors could not be evaluated. Furthermore, and bearing in mind the long follow-up study period, other confounding factors may also have escaped our attention and thereby have not been included in the present analyses. Finally, this is a prospective, observational study, which only enables hypothesis generation regarding prediction of regional body fat accumulation by EF and cannot provide mechanistic insight. Taking into account that visceral fat is associated with increased cardiovascular risk, constituting a potent mediator of unfavourable metabolic profiles, assessment of eating behaviour that may modulate fat distribution provide significant clinical tool with potential therapeutic implications. Whether meal patterns may serve as a behavioural prevention strategy to alter or prevent adverse regional adiposity patterns should be evaluated in future interventional trials. Determinants and consequences of obesity. Am J Public Health ; : — Google Scholar. Segula D. Complications of obesity in adults: a short review of the literature. Malawi Med J ; 26 : 20 — 4. Fox CS , Massaro JM , Hoffmann U , Pou KM , Maurovich-Horvat P , Liu CY , et al. Abdominal visceral and subcutaneous adipose tissue compartments: association with metabolic risk factors in the Framingham Heart Study. Circulation ; : 39 — Palmer MA , Capra S , Baines SK. Association between eating frequency, weight, and health. Nutr Rev ; 67 : — Karatzi K , Georgiopoulos G , Yannakoulia M , Efthimiou E , Voidonikola P , Mitrakou A , et al. Eating frequency predicts new onset hypertension and the rate of progression of blood pressure, arterial stiffness, and wave reflections. J Hypertens ; 34 : — Schoenfeld BJ , Aragon AA , Krieger JW. Effects of meal frequency on weight loss and body composition: a meta-analysis. Nutr Rev ; 73 : 69 — Ma Y , Bertone ER , Stanek EJ 3rd , Reed GW , Hebert JR , Cohen NL , et al. Association between eating patterns and obesity in a free-living US adult population. Am J Epidemiol ; : 85 — Berteus Forslund H , Lindroos AK , Sjostrom L , Lissner L. Meal patterns and obesity in Swedish women-a simple instrument describing usual meal types, frequency and temporal distribution. Eur J Clin Nutr ; 56 : — 7. Kant AK. Evidence for efficacy and effectiveness of changes in eating frequency for body weight management. Adv Nutr ; 5 : — 8. Murakami K , Livingstone MB. Eating frequency is positively associated with overweight and central obesity in U. J Nutr ; : — Associations between meal and snack frequency and overweight and abdominal obesity in US children and adolescents from National Health and Nutrition Examination Survey NHANES — Br J Nutr ; : — Karatzi K , Yannakoulia M , Psaltopoulou T , Voidonikola P , Kollias G , Sergentanis TN , et al. Meal patterns in healthy adults: inverse association of eating frequency with subclinical atherosclerosis indexes. Clin Nutr ; 34 : — 8. Craig CL , Marshall AL , Sjostrom M , Bauman AE , Booth ML , Ainsworth BE , et al. International physical activity questionnaire: country reliability and validity. Med Sci Sports Exerc ; 35 : — Catapano AL , Reiner Z , De Backer G , Graham I , Taskinen MR , Wiklund O , et al. Atherosclerosis ; Suppl. Stone NJ , Robinson JG , Lichtenstein AH , Bairey Merz CN , Blum CB , Eckel RH , et al. Circulation ; 25 Suppl. Lambrinoudaki I , Armeni E , Rizos D , Georgiopoulos G , Athanasouli F , Triantafyllou N , et al. Indices of adiposity and thyroid hormones in euthyroid postmenopausal women. Eur J Endocrinol ; : — Stamatelopoulos KS , Lekakis JP , Vamvakou G , Katsichti P , Protogerou A , Revela I , et al. The relative impact of different measures of adiposity on markers of early atherosclerosis. Int J Cardiol ; 2 : — Suzuki R , Watanabe S , Hirai Y , Akiyama K , Nishide T , Matsushima Y , et al. Abdominal wall fat index, estimated by ultrasonography, for assessment of the ratio of visceral fat to subcutaneous fat in the abdomen. Yannakoulia M , Melistas L , Solomou E , Yiannakouris N. Association of eating frequency with body fatness in pre- and postmenopausal women. Obesity Silver Spring ; 15 1 : — 6. Goliasch G , Kleber ME , Richter B , Plischke M , Hoke M , Haschemi A , et al. Routinely available biomarkers improve prediction of long-term mortality in stable coronary artery disease: the Vienna and Ludwigshafen Coronary Artery Disease VILCAD risk score. Eur Heart J ; 33 : — 9. SOBEL ME. Direct and indirect effects in linear structural equation models. Soc Methods Res ; 16 : — Ritchie LD. Less frequent eating predicts greater BMI and waist circumference in female adolescents. Am J Clin Nutr ; 95 : — 6. Mota J , Fidalgo F , Silva R , Ribeiro JC , Santos R , Carvalho J , et al. Relationships between physical activity, obesity and meal frequency in adolescents. Ann Hum Biol ; 35 : 1 — Field AE , Austin SB , Gillman MW , Rosner B , Rockett HR , Colditz GA. Snack food intake does not predict weight change among children and adolescents. Int J Obes Relat Metab Disord ; 28 : — 6. Howarth NC , Huang TT , Roberts SB , Lin BH , McCrory MA. Eating patterns and dietary composition in relation to BMI in younger and older adults. Int J Obes Lond ; 31 : — Smith KJ , Blizzard L , McNaughton SA , Gall SL , Dwyer T , Venn AJ. Daily eating frequency and cardiometabolic risk factors in young Australian adults: cross-sectional analyses. House BT , Cook LT , Gyllenhammer LE , Schraw JM , Goran MI , Spruijt-Metz D , et al. Meal skipping linked to increased visceral adipose tissue and triglycerides in overweight minority youth. Obesity Silver Spring ; 22 : E77 — World Health Organization : Obesity and overweight. World Health Organization. June Tadokoro N , Murano S , Nishide T , Suzuki R , Watanabe S , Murayama H , et al. Preperitoneal fat thickness determined by ultrasonography is correlated with coronary stenosis and lipid disorders in non-obese male subjects. Int J Obes Relat Metab Disord ; 24 : — 7. Huang RC , de Klerk NH , Smith A , Kendall GE , Landau LI , Mori TA , et al. Lifecourse childhood adiposity trajectories associated with adolescent insulin resistance. Diabetes Care ; 34 : — Luna-Luna M , Medina-Urrutia A , Vargas-Alarcon G , Coss-Rovirosa F , Vargas-Barron J , Perez-Mendez O. Adipose tissue in metabolic syndrome: onset and progression of atherosclerosis. Arch Med Res ; 46 : — Bjorntorp P. Arteriosclerosis ; 10 : — 6. Leidy HJ , Armstrong CL , Tang M , Mattes RD , Campbell WW. The influence of higher protein intake and greater eating frequency on appetite control in overweight and obese men. Obesity Silver Spring ; 18 : — Franko DL , Striegel-Moore RH , Thompson D , Affenito SG , Schreiber GB , Daniels SR , et al. The relationship between meal frequency and body mass index in black and white adolescent girls: more is less. Int J Obes Lond ; 32 : 23 — 9. Timlin MT , Pereira MA , Story M , Neumark-Sztainer D. Breakfast eating and weight change in a 5-year prospective analysis of adolescents: project EAT Eating Among Teens. Pediatrics ; : e — Oxford University Press is a department of the University of Oxford. It furthers the University's objective of excellence in research, scholarship, and education by publishing worldwide. Sign In or Create an Account. Advertisement intended for healthcare professionals. Navbar Search Filter QJM: An International Journal of Medicine This issue Medicine and Health Books Journals Oxford Academic Mobile Enter search term Search. 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| Obesity, Regional Body Fat Distribution, and the Metabolic Syndrome in Older Men and Women | Clin Nutr. Pediatr Diabetes. The autonomic nervous system in anorexia nervosa - an implication for the fat tissue. PDF Split View Views. Murakami K, Miyake Y, Sasaki S, Tanaka K, Arakawa M. Article Google Scholar Bertoli S, Leone A, Vignati L, Bedogni G, Martinez-Gonzalez MA, Bes-Rastrollo M, et al. CAS PubMed Google Scholar Eliasson M, Lindahl B, Lundberg V, Stegmayr B: No increase in the prevalence of known diabetes between and in subjects years of age in northern Sweden. |
Fat distribution and diet -
No significant associations were observed for intermuscular AT and metabolic syndrome in women. In contrast, having more subcutaneous thigh AT was associated with a lower prevalence of metabolic syndrome in obese men and in overweight and obese women.
We also examined in multiple logistic regression whether physical activity and diet modified the associations between regional fat distribution and metabolic syndrome.
For men, neither smoking nor physical activity was related to metabolic syndrome in any of the BMI categories after taking into account regional fat distribution. In women, current smoking was not related to metabolic syndrome after accounting for VAT.
Only in overweight women was physical inactivity associated with metabolic syndrome independent of all regional depots. Thus, adjusting results for smoking and physical activity did not appear to confound associations between regional fat depots and metabolic syndrome.
The overall prevalence of the metabolic syndrome in this older cohort was similar to that reported for older adults in the United States 4 and nearly double that reported for middle-aged adults. With an oversampling of blacks, we were able to determine that, although the overall prevalence of metabolic syndrome was not different between blacks and whites, there were racial differences in the prevalence of specific criteria that define metabolic syndrome.
Specifically, blacks had higher rates of hypertension and abnormal glucose metabolism, whereas whites had higher rates of dysregulated lipid metabolism.
The development of metabolic syndrome involves an interaction of complex parameters including obesity, regional fat distribution, dietary habits, and physical inactivity, 5 so it is not yet entirely clear how to interpret these racial differences. Nevertheless, this suggests that the cause of metabolic syndrome is different in blacks and whites.
The prevalence of metabolic syndrome, not surprisingly, was much higher among the obese. However, differences in generalized obesity by BMI or total body fat criteria in those with metabolic syndrome were at best modest.
Obese women with the metabolic syndrome actually had a lower proportion of body fat than obese women without metabolic syndrome.
Regional fat distribution, particularly visceral abdominal AT and intermuscular AT, clearly discriminated those with the metabolic syndrome, particularly among the nonobese. This implies that older men and women can have normal body weight, and even have relatively lower total body fat, but still have metabolic syndrome, due to the amount of AT located intra-abdominally or interspersed within the musculature.
What makes this observation more remarkable is that these associations were much less robust or even nonexistent for subcutaneous AT. More subcutaneous AT in the thighs of obese men and women was actually associated with a lower prevalence of metabolic syndrome.
This is consistent with previous reports demonstrating that total leg fat mass, most of which was subcutaneous AT, is inversely related to cardiovascular disease risk. Albu et al 18 suggested that similar levels of visceral AT in blacks and whites may confer different metabolic risk.
Our data support the contention by some that BMI may not accurately reflect the degree of adiposity in certain populations.
The current results parallel our previous observation in the Health ABC cohort that visceral and intermuscular AT strongly predict insulin resistance and type 2 diabetes. These associations between regional fat deposition and metabolic dysregulation are also consistent with other previous findings in both middle-aged and older adults.
Although we included in the analysis physical activity as a potential confounder to our associations, it is possible that the self-reported estimates for physical activity were not sensitive enough to detect significant associations with metabolic syndrome demonstrated in previous studies.
However, predictors of the incidence of metabolic syndrome can be examined when data become available in this longitudinal study. There are several possible explanations for the observed association between excess visceral fat accumulation and the metabolic syndrome.
Visceral fat is thought to release fatty acids into the portal circulation, where they may cause insulin resistance in the liver and subsequently in muscle. A parallel hypothesis is that adipose tissue is an endocrine organ that secretes a variety of endocrine hormones such as leptin, interleukin 6, angiotensin II, adiponectin, and resistin, which may have potent effects on the metabolism of peripheral tissues.
In conclusion, excess accumulation of either visceral abdominal or muscle AT is associated with a higher prevalence of metabolic syndrome in older adults, particularly in those who are of normal body weight. This suggests that practitioners should not discount the risk of metabolic syndrome in their older patients entirely on the basis of body weight or BMI.
Indeed, generalized body composition, in terms of both BMI and the proportion of body fat, does not clearly distinguish older subjects with the metabolic syndrome. Moreover, racial differences in the various components of the metabolic syndrome provide strong evidence that the cause of the syndrome likely varies in blacks and whites.
Thus, the development of a treatment for the metabolic syndrome as a unifying disorder is likely to be complex. Correspondence: Bret H.
Goodpaster, PhD, Department of Medicine, North MUH, University of Pittsburgh Medical Center, Pittsburgh, PA bgood pitt. Dr Goodpaster was supported by grant KAG from the National Institute on Aging, National Institutes of Health.
full text icon Full Text. Download PDF Top of Article Abstract Methods Results Comment Article Information References. Figure 1. View Large Download.
Table 1. Characteristics of Men and Women With and Without Metabolic Syndrome. Regional Fat Distribution According to Metabolic Syndrome Status. Abdominal AT in Men and Women With and Without Metabolic Syndrome According to a Revised Definition Omitting Waist Circumference.
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Goodpaster BHThaete FLSimoneau J-AKelley DE Subcutaneous abdominal fat and thigh muscle composition predict insulin sensitivity independently of visceral fat. Kelley DEThaete FLTroost FHuwe TGoodpaster BH Subdivisions of subcutaneous abdominal adipose tissue and insulin resistance.
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J Am Geriatr Soc ; PubMed Google Scholar Crossref. Similarly, the regional storage of dietary fat has been looked at in obese men and women 43 , Increasing amounts of leg fat in women is associated with greater storage of dietary fat in lower body adipose tissue, whereas this is not the case for visceral fat Upper body sc adipose tissue stored dietary fat in a manner different still from visceral or leg adipose tissue.
Obese men store a much smaller proportion of dietary fat in sc fat than do obese women, with the difference being most marked between lower body obese women and obese men The adipose tissue clearance of VLDL-triglyceride fatty acids 46 and storage of systemic FFA 44 , 45 has been examined in obesity.
Depending on how the data are expressed, there is some evidence for the reduced disappearance of VLDL-triglycerides across abdominal sc adipose tissue in obesity Whether it is the lower body or visceral adipose tissue storage of VLDL-triglycerides that is altered in obesity, especially upper body obesity, is unknown, as is the fraction of VLDL-triglyceride fatty acids that are restored in adipose tissue.
Direct storage of systemic FFA back into adipose tissue in the postabsorptive state is remarkably different between men and women 45 and between different adipose tissue depots Women with upper body obesity store a substantially greater portion of systemic FFA in lower body adipose tissue than do upper body obese men However, direct FFA storage in upper body sc fat is similar in both upper body obese men and women.
The greater clearance of FFA by lower body sc adipose tissue in women should theoretically limit the excess FFA available to muscle, liver, and other sites where FFA could reduce insulin sensitivity. Whether whole body and regional FFA storage is altered in lower body obesity is unknown, although women with lower body obesity tend to have normal FFA kinetics in any case 50 — Whole body FFA release is increased in upper body obesity under postabsorptive 50 , 51 and postprandial 36 , 52 conditions.
However, there are conditions under which FFAs are vastly and routinely different in upper body obesity. By definition, this implies that adipocytes in upper body obesity are resistant to the antilipolytic effects of insulin.
The explanation for this resistance is not clear. Some have argued that the enlarged abdominal or visceral adipocytes seen in upper body obesity are inherently resistant to insulin 55 , Certainly, weight loss via diet and exercise, which reduce fat cell size, also improves insulin regulation of lipolysis 57 , whereas surgical removal of abdominal sc fat via liposuction no decrease in fat cell size does not On the other hand, surgical removal of omental fat during bariatric surgery was associated with significantly greater improvement in fasting plasma glucose and insulin concentrations along with a significantly greater decrease in body mass index than a control group that did not undergo resection of omentum Unfortunately, we do not know whether regulation of adipose tissue lipolysis was affected by omentectomy or whether removal of visceral fat vs.
greater relative weight loss accounted for the observed effects on insulin and glucose. To provide a perspective on the relationship between plasma insulin concentrations vs.
leg, splanchnic, and upper body sc adipose tissue FFA palmitate release, data from Meek 35 and Guo 36 et al. are plotted in Figs. In insulin-sensitive nonobese adults, the relative suppression of FFA release from the splanchnic bed as a result of higher plasma insulin concentrations 35 seems blunted compared with leg and upper body sc fat Fig.
Figures 2—4 2 3 4 depict the relationship between plasma insulin concentrations and palmitate release from upper body sc fat, leg fat, and the splanchnic bed in these same nonobese volunteers 35 compared with lower body obese and upper body obese women 36 studied before and during meal ingestion.
Whereas the data points from nonobese and lower body obese both more insulin sensitive tend to display the same general relationship, the data points from upper body obese women more insulin resistant are displaced upwards, implying that each depot is to some degree insulin resistant. Because the total amount of FFA released from upper body sc fat is so much greater than that from the leg and splanchnic bed, however, insulin resistance in this depot appears to be quantitatively more important.
Data from Meek et al. The insulin doses were 0. The relationship between plasma insulin concentrations regional palmitate release are plotted on logarithmic axes. UBSQ, Upper body sc. The relationship between plasma insulin concentrations and upper body sc palmitate release are plotted on logarithmic axes.
The relationship between plasma insulin concentrations and leg palmitate release are plotted on logarithmic axes. The relationship between plasma insulin concentrations and splanchnic palmitate release are plotted on logarithmic axes.
The implications of excess adipose tissue lipolysis in upper body obesity deserve attention. Most of the studies examining the adverse effects of experimentally elevating plasma FFA concentrations have used the paradigm of assessing their effect on insulin-stimulated glucose disposal 61 , insulin-suppressed glucose 62 , and hepatic triglyceride production 63 , as well as insulin-stimulated tissue blood flow Thus, it is reasonable to consider that high postprandial FFA concentrations are particularly relevant to the metabolic abnormalities seen in obesity.
Given the association between visceral fat metabolic complications of obesity 16 , 65 and the finding of greater splanchnic FFA release during hyperinsulinemia 35 , it is tempting to blame visceral adipose tissue lipolysis for elevated postprandial FFA concentrations in upper body obesity.
However, in vivo studies have shown this not to be the case. Because leg adipose tissue lipolysis is so sensitive to insulin 35 and meal 34 , 36 suppression, it does not seem to contribute to the elevated postprandial FFA in obesity. The greater postprandial FFA concentrations in upper body obesity compared with lower body obesity could be entirely accounted for by excess FFA release from upper body sc fat 36 , not visceral fat.
However, the net release of new FFA into the systemic circulation from the splanchnic bed does not fully reflect visceral adipose tissue lipolysis. The liver takes up a considerable fraction of FFA in the portal vein 68 , In addition, some of the FFA entering the splanchnic bed via the arterial supply are taken up by nonhepatic tissues before they can enter the portal vein.
Fasting FFA concentrations in the portal vein have not been found to be substantially greater than those typically seen in the arterial circulation That said, the appearance of new FFA in the hepatic vein is probably a direct measure of the contribution of visceral adipose tissue lipolysis to systemic FFA availability and, thus, plasma FFA concentrations.
The ability of insulin to suppress glucose production is thought to be through a combination of direct insulin action on the liver and an indirect action via suppression of FFA There is some controversy as to whether the effects of elevated FFA are on hepatic gluconeogenesis or a combination of gluconeogenesis and glycogenolysis.
It is proposed that FFAs influence glucose output by creating surpluses of factors such as acetyl-coenzyme A, reduced nicotinamide adenine dinucleotide, ATP, and citrate, perhaps with intrahepatic triglyceride as the intermediate step. Elevated FFA also stimulate VLDL-triglyceride production in the face of hyperinsulinemia 63 , and given the likelihood that portal FFA are quite substantially increased during hyperinsulinemia in visceral obesity, this could be an especially important effect of visceral fat Hypertension is one of the risk factors for cardiovascular disease, and is tightly associated with insulin resistance and visceral obesity.
Many mechanisms are thought to fit hypertension into the circle. Systemic FFA, largely derived from sc adipose tissue lipolysis not from visceral fat , may play a role in some abnormalities.
These mechanisms include impaired: 1 insulin-mediated vasodilatation in vascular beds 74 , 2 α-adrenergic stimulation 75 , and 3 nitric oxide and endothelial dysfunction 64 , Unfortunately, many of the studies have been conducted at levels of FFAs that are almost supraphysiological 64 , Further studies to assess whether FFAs have similar effects at more physiologically elevated concentrations are required.
It is established that fatty acids can have adverse effects on islet insulin content 77 , One theory is that type 2 diabetes develops as part of a biphasic β-cell response to excess FFA 78 , A number of mechanisms have been proposed to mediate the toxic effects of excess intracellular fatty acids, but species differences in how β-cells respond to fatty acids make it difficult to translate directly animal and cell model systems to human type 2 diabetes.
Because the pancreas is not downstream of the portal vein, any adverse effects of FFA on human insulin secretion in visceral obesity would largely be an effect of abnormal regulation of sc adipose tissue lipolysis.
The preferential oxidation of fatty acids by muscle mitochondria and the resultant competition with glucose the Randle cycle is a suggested mechanism for fatty acid-induced insulin resistance FFA elevation has also interrupted muscle insulin receptor substrate and phosphatidylinositol 3-kinase insulin-mediated glucose uptake independent of their oxidative role, contributing to peripheral resistance in a novel pathway imTG accumulation may provide a link between extracellular FFA and the intramyocellular environment because imTG correlates well with insulin resistance, and FFAs are a precursor of imTG 82 , The precise mechanism s is not entirely known at present, but interesting possibilities include actions of diacylglycerols, ceramides, and long-chain acyl-coenzyme As, themselves as intracellular mediators of impaired insulin signaling.
Again, muscle is exposed to systemic FFA concentrations, which predominantly originate from upper body sc fat. Thus, the adverse effects of FFA on muscle insulin action in obesity are largely an effect of abnormal regulation of sc adipose tissue lipolysis.
A large number of substances are known to be produced by adipose tissue. Collectively, these have been termed adipokines, and their purported function and role in the metabolic complications of obesity are reviewed in a companion article To date, the only adipokine documented to be uniquely overproduced by visceral fat is IL-6 Adiponectin concentrations are reduced in adults with excess visceral fat, but strangely enough in obese women, concentrations correlate positively with leg fat mass Whether this indicates that adiponectin is preferentially produced by leg fat or that women with greater amounts of leg fat are more insulin sensitive and, thus, have greater adiponectin concentrations, is unknown.
Evidence from studies that manipulate FFA concentrations suggests that a number of these metabolic abnormalities are caused by elevated FFAs. FFAs released from visceral fat make a minor contribution to systemic FFA concentrations, which are the obligate concentrations affecting muscle, pancreatic β-cells, and vascular endothelium.
In persons with visceral obesity, omental and mesenteric fat may play a special role in delivering both excess FFA and IL-6 to the liver. We do not yet understand why upper body sc fat, the source of the majority of systemic FFAs, is dysregulated in upper body obesity.
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Diabetes 56 : — Danforth Jr E Failure of adipocyte differentiation causes type II diabetes mellitus? Questionnaires were completed and anthropometric measurements taken. For each food item, associations between frequency of consumption and waist and hip circumferences were estimated.
Partial regression coefficients for every level of reported intake were multiplied with differences in proportion of the population reporting the corresponding levels of intake in and The sum of these product terms for every food item was the respective estimated impact on mean circumference.
Time trends in reported food consumption associated with the more favourable gynoid distribution of adipose tissue were increased use of vegetable oil, pasta and 1. Trends associated with abdominal obesity were increased consumption of beer in men and higher intake of hamburgers and French fried potatoes in women.
Food trends as markers of time trends in body fat distribution have been identified. The method is a complement to conventional approaches to establish associations between food intake and disease risk on a population level.
Peer Review reports. The global trend of increasing obesity in the developed world and, even more pronounced in the countries of transition, is associated with an increase in prevalence of all components of the metabolic syndrome. Based on these observations, predictions of a world-wide epidemic of diabetes have been made.
Accumulating evidence for effective preventive intervention [ 1 — 3 ] highlights the importance of early indicators for identifying high-risk individuals. Recent studies have shown that the distribution of body-fat, independent of body mass index BMI is an important predictive factor for the development of diabetes.
Waist circumference WC , as a measure of visceral fat, is more closely associated with diabetes and cardiovascular disease and total mortality than adipose tissue in other regions of the body [ 4 — 10 ].
On the contrary, hip circumference HC has been found to be independently associated with lower insulin resistance, lower prevalence and incidence of diabetes and lower total mortality [ 11 — 15 ]. In order to identify predictive markers and potential causative mechanisms of diabetes, associations of socio-demographic and lifestyle factors with body-fat distribution have been investigated.
High intake of saturated fatty acids and food patterns with a high glycaemic load have been associated with central obesity. Smoking, a sedentary life-style, and high intake of alcohol are also associated with abdominal obesity whereas physical activity is associated with gynoid fat distribution and higher insulin sensitivity [ 16 — 22 ].
Between and body mass in the MONICA population of Northern Sweden increased in both sexes. However, there was no corresponding increase in prevalence of diabetes and the number of myocardial infarctions decreased. During the same period average hip circumference increased markedly, while waist circumference only increased marginally.
Consumption of oil as dressing, pasta, beer and convenience foods increased markedly. Our hypothesis is that some of the observed time trends in food intake contributed to a more favourable distribution of body-fat, thus compensating for the diabetogenic effects of increased body weight.
These trends may also be associated with the sharp decrease of myocardial infarctions in the area. The aim of the present study is to investigate the effect of trends in reported intake of individual food items on concurrent differences in distribution of body-fat measured between and on a population level.
The MONICA project Multinational Monitoring of Trends and Determinants in Cardiovascular Disease was initiated by WHO and included 38 populations in 25 countries.
Trends in cardiovascular mortality, coronary heart disease and cerebrovascular morbidity were measured in order to assess the extent to which these trends were related to changes in known risk factors, daily living habits and health care [ 27 , 28 ]. The Northern Sweden MONICA Project was performed in the counties of Västerbotten and Norrbotten.
Descriptions of the survey procedures and quality assessment of the collected data have been published elsewhere [ 29 , 30 ].
In short, the surveys were performed in , , and in the period of January to April. The samples for the second, third and fourth surveys were selected irrespective of whether individuals had been selected in previous surveys.
From a continuously updated population registry, men and women in each of the age groups 25—34, 35—44, 45—54 and 55—64 years were randomly selected and invited to participate. The target population was approximately subjects Fig 1. The participants were invited to the closest health center for a physical examination including anthropometrical measurements and blood sampling.
All measurements were performed by specially trained teams of health professionals. Participants were asked to complete a questionnaire on health, socio-economic status and daily living habits. Usual dietary intake over the past year was assessed through a validated, semi-quantitative, self-administered food frequency questionnaire FFQ [ 31 ].
The questionnaire included 81 items in , 49 items in , and 84 items in and Standard portion sizes were used for the estimation of consumed quantities. Study design. A three-step procedure was employed. i First, utilizing data from all four surveys, the association between individual food items and waist or hip circumference is estimated, adjusting for age, BMI and survey year.
Adjustments were also made for the interaction between high reported level of alcohol consumption and low physical activity, high reported level of alcohol consumption and smoking as well as low level of physical activity and smoking.
model 2 In both models, the level of intake reported most frequently in was chosen as reference category. As illustrated in table 1 , a linear regression model yielded partial regression coefficients for each level of intake.
These coefficients represent the association of each level of intake with mean HC or WC compared to the reference level across all survey years and adjusted for covariates.
Had the whole population reported the reference level in and, e. level 1 in , the partial regression coefficient of level 1 would represent the difference in mean WC or HC attributable to this time trend in reported intake.
Since different proportions of the population reported one of the 8 levels of intake in and , two further steps are necessary. ii Secondly, differences in proportion of the population reporting specific intake levels between the first survey in and the last one in are calculated separately for each food item.
iii Finally, intake-level specific partial regression coefficients for each food item are multiplied with difference in proportions of population reporting that specific level of intake in and , respectively. The sum of these product terms for each separate food item is the estimated net effect of time trends in reported food frequency on WC and HC.
To estimate the combined effect of food-item associated time trends we subtracted mean differences in waist circumference negatively associated with diabetes and cardiovascular disease from mean differences in hip circumference positively associated.
Positive values will thus indicate risk-lowering trends in body fat distribution. To avoid giving a fragmented picture, all food items with large estimated net effects are reported rather than only items with significant estimated associations.
The basic model i contains observations from four different cross-sectional surveys conducted over a period of 13 years. During such a long period of time there may be population-wide changes in living conditions and reporting that are difficult to measure and adjust for separately.
To avoid these potential biases we adjusted for survey year. Without that adjustment, differences in measured distribution of body-fat would falsely be attributed to any concurrent time trend in reported food intake. if due to improved public transport average walking distance to arrive at work or school was reduced between and this change might result in increase in average waist circumference.
During the same period of time most people increased their intake of hamburgers. Without adjustment for survey year our model would attribute some of the resulting increase in WC to that concurrent time trend in food habits. The Statistical Analysis System SAS for Windows, version 9.
Non-responders got a second letter of invitation 2 weeks after the date for their initial health examination. Telephone-interviews conducted with non-participants indicate a higher percentage of smokers and a lower self-reported body weight in that group [ 30 ].
These excluded individuals had a higher mean age in men. There was no significant difference with remaining study subjects in other parameters. All participants signed an informed consent form. The Research Ethics Committee of Umeå University approved the study.
Reported energy intake from different sources, sociodemographic and anthropometrical characteristics of the study population are given in table 2. The decrease in waist-hip ratio was mainly due to a marked increase in hip circumference in both men and women. Smoking became less common, reported energy intake from saturated fatty acids decreased, and intake of alcohol increased.
Higher education became more common. A complete list of food items that were covered by questionnaires is given in the appendix additional file 1 : List of items on food-frequency questionnaires. Out of these, 15 items are shown that were associated with the 10 largest differences in distribution of body fat in either men or women.
Table 3 summarizes time trends in food consumption associated with the largest differences of waist- and hip circumference for women. Growing popularity of hamburgers, French fried potatoes and soft drinks were associated with an increase of waist circumference. Increased hip circumference was associated with higher consumption of pasta, vegetable oil as well as cream and 1.
Time trends for hamburgers and French fried potatoes went along with minor reductions of hip circumference. Adjustment for other lifestyle-factors attenuated the net effect of time trends in reported food consumption but, did not alter their directions. Figure 2 illustrates associations of food items with differences in both average hip- and waist-circumference in women.
Time trends for vegetable oil, pasta, fruit creams and cream were associated with risk-lowering anthropometric time trends, whereas trends for hamburgers and French fried potatoes correlated with risk-increasing trends. Estimated effect of time trends in reported food intake — on average waist- and hip-circumference in women.
Sort order is the sum of effects from largest reduction to highest increase of risk for diabetes. The underlying associations between food intake and waist- and hip-circumferences were adjusted for age, body-mass and survey year model 1.
In men, time trends for vegetable oil, pasta and milk were associated with both, largest increase of hip-circumference and largest reduction of waist-circumference Table 4 , Figure 3. Increased use of hamburgers and potato chips were associated with an increase of average waist circumference but also a positive effect on hip circumference.
Estimated effect of time trends in reported food intake — on average waist- and hip-circumference in men. After adjustment for lifestyle-variables Table 4 , model 2 the waist reducing effect of time trends in pasta consumption disappeared whereas the effect of increased consumption of wine was reversed.
The negative net-effect of French fried potatoes could also be explained by associated lifestyle. In general, waist circumference was more responsive in women whereas hip and waist circumferences were equally affected in men. This way of analyzing data derived from food frequency questionnaires is not entirely new.
Changes in food habits over time have been expressed in change of waist circumference, before[ 22 ]. However, the use of this method in a repeated cross-sectional context is new.
The hypothesis that inspired the somewhat cumbersome methodology of this study is: Reported level of intake of a food item is a marker of lifestyle, rather than a measurement of nutrient intake.
Therefore, every level of reported intake had to be utilized as a separate variable. This avoids issues of non-linearity that might arise when categorical variables are converted into continuous ones. In the crude model no other food-related markers, such as total reported food intake or reported intake of other food items, are introduced.
Thus, adding up different markers food groups or adjusting one marker for another adjusting for reported intake of other foods — common procedures in similar studies that introduce uncontrollable biases — is avoided model 1. However, in a separate model lifestyle-markers such as, self reported physical activity, education, smoking status and alcohol consumption are considered as additional explanatory variables model 2.
Fat distribution and diet aimed to Protein snacks investigate dieg associations of distrribution of eating Fat distribution and diet with regional fat layers. EF was anr at Well-rounded nutrition in subjects distrigution of clinically overt cardiovascular disease 54 Fag 9. EF at baseline positively correlated with Pmax, even after adjustment for potential confounders. High EF is associated with lower progression rate of pre-peritoneal fat accumulation. Future interventional studies should further investigate the clinical utility of these findings. Obesity is a worldwide epidemic, which is associated with a number of adverse health consequences such as cardiovascular disease, diabetes and cancer.
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