Merit Lagerpusch

Beyond the body mass index: tracking body composition in the pathogenesis of obesity and the metabolic syndrome

Body composition is related to various physiological and pathological states. Characterization of individual body components adds to understand metabolic, endocrine and genetic data on obesity and obesity‐related metabolic risks, e.g. insulin resistance. The obese phenotype is multifaceted and can be characterized by measures of body fat, leg fat, liver fat and skeletal muscle mass rather than by body mass index. The contribution of either whole body fat or fat distribution or individual fat depots to insulin resistance is moderate, but liver fat has a closer association with (hepatic) insulin resistance. Although liver fat is associated with visceral fat, its effect on insulin resistance is independent of visceral adipose tissue. In contrast to abdominal fat, appendicular or leg fat is inversely related to insulin resistance. The association between ‘high fat mass + low muscle mass’ (i.e. ‘sarcopenic adiposity’) and insulin resistance deserves further investigation and also attention in daily clinical practice. In addition to cross‐sectional data, longitudinal assessment of body composition during controlled under‐ and overfeeding of normal‐weight healthy young men shows that small decreases and increases in fat mass are associated with corresponding decreases and increases in insulin secretion as well as increases and decreases in insulin sensitivity. However, even under controlled conditions, there is a high intra‐ and inter‐individual variance in the changes of (i) body composition; (ii) the ‘body composition–glucose metabolism relationship’ and (iii) glucose metabolism itself. Combining individual body components with their related functional aspects (e.g. the endocrine, metabolic and inflammatory profiles) will provide a suitable basis for future definitions of a ‘metabolically healthy body composition’.

Association of Pericardial Fat With Liver Fat and Insulin Sensitivity After Diet‐Induced Weight Loss in Overweight Women

Pericardial adipose tissue (PAT) is positively associated with fatty liver and obesity‐related insulin resistance. Because PAT is a well‐known marker of visceral adiposity, we investigated the impact of weight loss on PAT and its relationship with liver fat and insulin sensitivity independently of body fat distribution. Thirty overweight nondiabetic women (BMI 28.2–46.8 kg/m2, 22–41 years) followed a 14.2 ± 4‐weeks low‐calorie diet. PAT, abdominal subcutaneous (SAT), and visceral fat volumes (VAT) were measured by magnetic resonance imaging (MRI), total fat mass, trunk, and leg fat by dual‐energy X‐ray absorptiometry and intrahepatocellular lipids (IHCL) by (1)H‐magnetic resonance spectroscopy. Euglycemic hyperinsulinemic clamp (M) and homeostasis model assessment of insulin resistance (HOMAIR) were used to assess insulin sensitivity or insulin resistance. At baseline, PAT correlated with VAT (r = 0.82; P < 0.001), IHCL (r = 0.46), HOMAIR (r = 0.46), and M value (r = −0.40; all P < 0.05). During intervention, body weight decreased by −8.5%, accompanied by decreases of −12% PAT, −13% VAT, −44% IHCL, −10% HOMA2‐%B, and +24% as well as +15% increases in HOMA2‐%S and M, respectively. Decreases in PAT were only correlated with baseline PAT and the loss in VAT (r = −0.56; P < 0.01; r = 0.42; P < 0.05) but no associations with liver fat or indexes of insulin sensitivity were observed. Improvements in HOMAIR and HOMA2‐%B were only related to the decrease in IHCL (r = 0.62, P < 0.01; r = 0.65, P = 0.002) and decreases in IHCL only correlated with the decrease in VAT (r = 0.61, P = 0.004). In conclusion, cross‐sectionally PAT is correlated with VAT, liver fat, and insulin resistance. Longitudinally, the association between PAT and insulin resistance was lost suggesting no causal relationship between the two.

Impact of Intra‐ and Extra‐Osseous Soft Tissue Composition on Changes in Bone Mineral Density With Weight Loss and Regain

Recent studies report a significant gain in bone mineral density (BMD) after diet‐induced weight loss. This might be explained by a measurement artefact. We therefore investigated the impact of intra‐ and extra‐osseous soft tissue composition on bone measurements by dual X‐ray absorptiometry (DXA) in a longitudinal study of diet‐induced weight loss and regain in 55 women and 17 men (19–46 years, BMI 28.2–46.8 kg/m2). Total and regional BMD were measured before and after 12.7 ± 2.2 week diet‐induced weight loss and 6 months after significant weight regain (≥30%). Hydration of fat free mass (FFM) was assessed by a 3‐compartment model. Skeletal muscle (SM) mass, extra‐osseous adipose tissue, and bone marrow were measured by whole body magnetic resonance imaging (MRI). Mean weight loss was −9.2 ± 4.4 kg (P < 0.001) and was followed by weight regain in a subgroup of 24 subjects (+6.3 ± 2.9 kg; P < 0.001). With weight loss, bone marrow and extra‐osseous adipose tissue decreased whereas BMD increased at the total body, lumbar spine, and the legs (women only) but decreased at the pelvis (men only, all P < 0.05). The decrease in BMDpelvis correlated with the loss in visceral adipose tissue (VAT) (P < 0.05). Increases in BMDlegs were reversed after weight regain and inversely correlated with BMDlegs decreases. No other associations between changes in BMD and intra‐ or extra‐osseous soft tissue composition were found. In conclusion, changes in extra‐osseous soft tissue composition had a minor contribution to changes in BMD with weight loss and decreases in bone marrow adipose tissue (BMAT) were not related to changes in BMD.

Influence of Changes in Body Composition and Adaptive Thermogenesis on the Difference between Measured and Predicted Weight Loss in Obese Women

Background: There is a difference between measured and predicted weight loss in obese patients. This might be explained by the composition of weight loss, adaptive thermogenesis, or poor compliance. Patients and Methods: 48 overweight and obese female patients (31.5 ± 6.1 years; BMI 35.4 ± 4.4 kg/m2) were investigated before and 13.9 ± 2.4 weeks after dietary treatment (1,000 kcal/day). Body composition was measured by air-displacement plethysmography and resting energy expenditure (REE) by indirect calorimetry. Physical activity was assessed using electronic pedometers in order to calculate total energy expenditure from REE and physical activity level (PAL). Fat mass (FM) and fat-free mass (FFM) were converted into caloric equivalents using 9.45 kcal/g FM and 1.13 kcal/g FFM. Predicted weight loss was calculated by Wishnofsky’s ‘7,700 kcal/kg rule’. Results: Weight (–8.4 ± 3.9 kg; p < 0.001), FM (–7.8 ± 3.6 kg; p < 0.001), and FFM (–0.6 ± 2.0 kg; p < 0.05) decreased with caloric restriction. Measured weight loss was only 44% of the predicted value. Since FM contributed to 87% of weight loss, the energy deficit/kg weight loss was considerably higher (9,098 ± 2,349 kcal/kg) than the assumed 7,700 kcal/kg. Adaptive thermogenesis after weight loss was significant in 26 of 48 women (–3.2 ± 1.2 kcal per kg FFM; p < 0.001). Conclusion: 14% of the difference between measured and predicted weight loss was explained by the higher proportion of FM in weight loss and 38% by adaptive thermogenesis (in 54% of the women). Thus, poor compliance was responsible for about 50% of the difference between measured and predicted weight loss only.

Carbohydrate Quality and Quantity Affect Glucose and Lipid Metabolism during Weight Regain in Healthy Men

In this controlled, parallel-group feeding trial, we examined the impact of carbohydrate (CHO) intake and glycemic index (GI) on glucose and lipid metabolism during refeeding after weight loss. Healthy men (n = 32 total, age: 25.5 ± 3.9 y, BMI: 23.5 ± 2.0 kg/m2) overconsumed diets containing either 50% or 65% CHO for 1 wk (+50% of energy requirements) and then underwent 3 wk of calorie restriction (CR; -50%) followed by 2 wk of overconsuming (refeeding, +50%) the same diet but with either a low or high GI (40 vs.70 during CR, 41 vs.74 during refeeding) so that glycemic load (GL; dietary CHO content x GI) differed between groups during all phases. Glucose profiles were assessed by continuous interstitial glucose monitoring, insulin sensitivity (IS) by fasting blood sampling, oral glucose tolerance test (OGTT) and hyperinsulinemic-euglycemic clamp, and liver fat by MRI. Daytime area under the curve-glucose during refeeding was higher with high compared with low GI (P = 0.01) and 65% compared with 50% CHO intake (P = 0.05) and correlated with dietary GL (r = 0.71; P < 0.001). IS increased with CR and decreased again with refeeding in all groups. The decrease in OGTT-derived IS was greater with high- than with low-GI diets (-41 vs. -15%; P-interaction = 0.01) and correlated with dietary GL during refeeding (r = -0.51; P < 0.01). Serum triglycerides (TGs) and liver fat also improved with CR (-17 ± 38 mg/dL and -1.1 ± 1.3%; P < 0.05 and <0.001) and increased again with refeeding (+48 ± 48 mg/dL and +2.2 ± 1.6%; P < 0.001). After refeeding, serum TGs and liver fat were elevated above baseline values with 65% CHO intake only (+59.9 ± 37.5 mg/dL and +1.1 ± 1.7%, P-interaction <0.001 and <0.05). In conclusion, a diet low in GI and moderate in CHO content (i.e., low GL) may have health benefits by positively affecting daylong glycemia, IS, and liver fat.

Impact of glycaemic index and dietary fibre on insulin sensitivity during the refeeding phase of a weight cycle in young healthy men

Previous studies suggest that a low-glycaemic index (LGI) diet may improve insulin sensitivity (IS). As IS has been shown to decrease during refeeding, we hypothesised that an LGI- v. high-GI (HGI) diet might have favourable effects during this phase. In a controlled nutritional intervention study, sixteen healthy men (aged 26·8 (SD 4·1) years, BMI 23·0 (SD 1·7) kg/m2) followed 1 week of overfeeding, 3 weeks of energy restriction and of 2 weeks refeeding at ^50% energy requirement (50% carbohydrates, 35% fat and 15% protein). During refeeding, subjects were divided into two matched groups receiving either high-fibre LGI or lower-fibre HGI foods (GI 40 v. 74, fibre intake 65 (SD 6) v. 27 (SD 4) g/d). Body weight was equally regained in both groups with refeeding (mean regain 70·5 (SD 28·0)% of loss). IS was improved by energy restriction and decreased with refeeding. The decreases in IS were greater in the HGI than in the LGIgroup (group £ time interactions for insulin, homeostasis model assessment of insulin resistance (HOMAIR), Matsuda IS index (MatsudaISI);all P,0·05). Mean interstitial glucose profiles during the day were also higher in the HGI group (DAUCHGI-LGI of continuous interstitial glucose monitoring: 6·6 mmol/l per 14 h, P¼0·04). At the end of refeeding, parameters of IS did not differ from baseline values in either diet group (adiponectin, insulin, HOMAIR, Matsuda ISI, M-value; all P.0·05). In conclusion, nutritional stress imposed by dietary restriction and refeeding reveals a GI/fibre effect in healthy non-obese subjects. LGI foods rich in fibre may improve glucose metabolism during the vulnerable refeeding phase of a weight cycle.

Use of Balance Methods for Assessment of Short-Term Changes in Body Composition

Balance methods reveal changes in body energy, nitrogen, macro‐ and micronutrients as well as fluid in response to different feeding regimens. Under metabolic ward conditions, where physical activity is restricted and activity and food intake are controlled, the errors of estimates of energy intake, energy expenditure, and energy losses are about 2, 4, and 2%, respectively. Balance techniques can be used to validate techniques of in vivo body composition analysis (BCA). This is necessary since immediate and transient changes in body composition in response to a change in diet adversely affect the validity of techniques by violating the assumptions underlying standard methods (i.e., a constant composition or hydration of lean mass). Using two compartment reference methods, like densitometry, dual X‐ray absorptiometry (DXA) or deuterium dilution, changes in fat mass with caloric restriction and overfeeding can be measured with a minimal detectable change (MDC) of 1.0–2.0 kg. However, when compared against balance data, the validity of these techniques to measure short‐term changes in body composition is poor. The noninvasive and rapid new quantitative magnetic resonance (QMR) technique has a high precision with a MDC of 0.18 kg of fat mass. The validity of QMR to assess short‐term changes in fat mass is challenged by comparison to balance data. Today, techniques used for in vivo BCA should be related to steady state conditions only, while in the nonsteady state, the use of balance methods is recommended to assess short‐term changes in body composition.

Deep body composition phenotyping during weight cycling: relevance to metabolic efficiency and metabolic risk

Weight cycling may lead to adverse effects on metabolic efficiency (i.e. adaptive thermogenesis or ‘metabolic slowing’) and metabolic risks (e.g. increased risk for insulin resistance and the metabolic syndrome). In order to investigate these topics, the partitioning of fat and lean mass (i.e. the change in the proportion of both compartments) needs to be extended to the organ and tissue level because metabolic risk differs between adipose tissue depots and lean mass is metabolically heterogeneous being composed of organs and tissues differing in metabolic rate. Contrary to data obtained with severe weight loss and regain in lean people, weight cycling most likely has no adverse effects on fat distribution and metabolic risk in obese patients. There is even evidence for an increased ability of fat storage in subcutaneous fat depots (at the trunk in men and at the limbs in women) with weight cycling that may provide a certain protection from ectopic lipid deposition and thus explain the preservation of a favourable metabolic profile despite weight regain. On the other hand, the mass‐specific metabolic rate of lean mass may increase with weight gain and decrease with weight loss mainly because of an increase and respective decrease in the proportion (and/or activity) of metabolically active organ mass. Obese people could therefore have a higher slope of the regression line between resting energy expenditure (REE) and fat‐free mass that leads to an overestimation of metabolic efficiency when applied to normalize REE data after weight loss. Furthermore, in addressing the impact of macronutrient composition of the diet on partitioning of lean and fat mass, and the old controversy about whether a calorie is a calorie, we discuss recent evidence in support of a low glycaemic weight maintenance diet in countering weight regain and challenge this concept for weight loss by proposing the opposite.

Endocrine Determinants of Changes in Insulin Sensitivity and Insulin Secretion during a Weight Cycle in Healthy Men

Objective Changes in insulin sensitivity (IS) and insulin secretion occur with perturbations in energy balance and glycemic load (GL) of the diet that may precede the development of insulin resistance and hyperinsulinemia. Determinants of changes in IS and insulin secretion with weight cycling in non-obese healthy subjects remain unclear. Methods In a 6wk controlled 2-stage randomized dietary intervention 32 healthy men (26±4y, BMI: 24±2kg/m2) followed 1wk of overfeeding (OF), 3wks of caloric restriction (CR) containing either 50% or 65% carbohydrate (CHO) and 2wks of refeeding (RF) with the same amount of CHO but either low or high glycaemic index at ±50% energy requirement. Measures of IS (basal: HOMA-index, postprandial: Matsuda-ISI), insulin secretion (early: Stumvoll-index, total: tAUC-insulin/tAUC-glucose) and potential endocrine determinants (ghrelin, leptin, adiponectin, thyroid hormone levels, 24h-urinary catecholamine excretion) were assessed. Results IS improved and insulin secretion decreased due to CR and normalized upon RF. Weight loss-induced improvements in basal and postprandial IS were associated with decreases in leptin and increases in ghrelin levels, respectively (r = 0.36 and r = 0.62, p<0.05). Weight regain-induced decrease in postprandial IS correlated with increases in adiponectin, fT3, TSH, GL of the diet and a decrease in ghrelin levels (r-values between -0.40 and 0.83, p<0.05) whereas increases in early and total insulin secretion were associated with a decrease in leptin/adiponectin-ratio (r = -0.52 and r = -0.46, p<0.05) and a decrease in fT4 (r = -0.38, p<0.05 for total insulin secretion only). After controlling for GL associations between RF-induced decrease in postprandial IS and increases in fT3 and TSH levels were no longer significant. Conclusion Weight cycling induced changes in IS and insulin secretion were associated with changes in all measured hormones, except for catecholamine excretion. While leptin, adiponectin and ghrelin seem to be the major endocrine determinants of IS, leptin/adiponectin-ratio and fT4 levels may impact changes in insulin secretion with weight cycling. Trial Registration ClinicalTrials.gov NCT01737034