Obesity is increasing, yet despite the necessity of maintaining muscle mass and function with age, the effect of obesity on muscle protein turnover in older adults remains unknown. Eleven obese (BMI 31.9 ± 1.1 kg · m−2) and 15 healthy-weight (BMI 23.4 ± 0.3 kg · m−2) older men (55–75 years old) participated in a study that determined muscle protein synthesis (MPS) and leg protein breakdown (LPB) under postabsorptive (hypoinsulinemic-euglycemic clamp) and postprandial (hyperinsulinemic hyperaminoacidemic-euglycemic clamp) conditions. Obesity was associated with systemic inflammation, greater leg fat mass, and patterns of mRNA expression consistent with muscle deconditioning, whereas leg lean mass, strength, and work done during maximal exercise were no different. Under postabsorptive conditions, MPS and LPB were equivalent between groups, whereas insulin and amino acid administration increased MPS in only healthy-weight subjects and was associated with lower leg glucose disposal (LGD) (63%) in obese men. Blunting of MPS in the obese men was offset by an apparent decline in LPB, which was absent in healthy-weight subjects. Lower postprandial LGD in obese subjects and blunting of MPS responses to amino acids suggest that obesity in older adults is associated with diminished muscle metabolic quality. This does not, however, appear to be associated with lower leg lean mass or strength.

Aging is strongly associated with a decline in muscle mass and strength (1). This is in part attributable to the failure of protein nutrition to increase the rate of muscle protein synthesis (MPS) to that observed in the young (2), as well as the inability of hyperinsulinemia to inhibit leg protein breakdown (LPB) in older people (3). Even after correcting for muscle mass loss, however, aging has been allied with a decline in muscle strength and metabolic quality (4), signifying that physiological drivers other than muscle mass must contribute to weakness and diminished quality of muscle in older adults (5).

It is widely reported that the prevalence of obesity is increasing in the developed world among people of all ages, including older adults (6). Of concern, a longitudinal study of older adults showed that the temporal decline in lean mass and leg extension strength relative to leg mass (the latter is used as an index of muscle quality) could be predicted by the degree of fat mass evident at the time of recruitment (7). Furthermore, this relationship remained even when differences in physical activity levels were taken into account (8). The implication that older adults in particular may be at risk for accelerated muscle deterioration from obesity is supported by a cross-sectional study showing that the combination of low muscle strength and obesity was associated with poorer physical function than either in isolation (9). Crucially, whether the derangements in MPS and LPB described above for normal-weight older adults (2,3) are further exacerbated in obese older adults is unknown and forms the focus of this study.

Several reports provide credence that MPS and LPB may be negatively affected in obese individuals. When visceral fat mass is enlarged, plasma concentrations of tumor necrosis factor (TNF)-α, interleukin-6 (IL-6), resistin, and leptin appear to be increased (see ref. 10). Furthermore, increased plasma IL-6, C-reactive protein (CRP), and TNF-α concentrations in older adults have been associated with the development of insulin resistance (11) and the decline of muscle mass and strength (12,13). Moreover, in preclinical models characterized by systemic increases in TNF-α and IL-6, cytokine mRNA expression in muscle is markedly elevated and processes involved in the promotion of protein synthesis are inhibited in parallel to enhanced proteolysis (14,15).

Therefore, the aim of this study was to investigate whether the response of MPS and LPB to experimentally induced postabsorptive and postprandial conditions was different in obese compared with healthy-weight older men and, if so, whether these responses could underlie any deficits in muscle mass and contractile function observed in obese older individuals.

Study Participants

Men between the ages of 55 and 75 years were recruited and assigned to the healthy-weight (control) group if they had a BMI <26 kg · m−2 and a serum CRP <1.35 µg · mL−1 (n = 15) or the obese group if they had a BMI >29 kg · m−2 and a serum CRP >1.35 µg · mL−1 (n = 11). These benchmarks were based on reports that serum CRP concentrations >1.35 µg · mL−1 are associated with increased levels of IL-6 and TNF-α in obese individuals (16). All individuals not meeting these criteria were excluded from participating. Subjects underwent a medical examination and were unable to participate if they had type 2 diabetes; a previous diagnosis of cancer; unresolved hypertension; cardiovascular disease; ongoing musculoskeletal complaints; frailty (underweight and self-report of exhaustion, weakness, slowness, and low physical activity levels [17]); or any other chronic condition. Furthermore, subjects were not allowed to participate if they smoked, were trying to lose weight, were participating in regular organized sports, or were taking anti-inflammatory, antidepressant, or statin medications. Characteristics of included subjects are presented in Table 1. Written informed consent was obtained from volunteers and the study was approved by the University of Nottingham Medical School Ethics Committee and conducted in accordance with the Declaration of Helsinki.

Table 1

Subject characteristics

Healthy-weight subjects (n = 15)Obese subjects (n = 11)
Age (years) 66.7 ± 1.1 66.4 ± 1.8 
Height (cm) 176 ± 2 175 ± 2 
Weight (kg) 72.8 ± 2.0 97.7 ± 3.6*** 
BMI (kg · m−223.4 ± 0.3 31.9 ± 1.1*** 
Body fat mass (%) 22.7 ± 1.5 36.4 ± 1.0*** 
Diastolic blood pressure (mmHg) 83 ± 1 84 ± 2 
Systolic blood pressure (mmHg) 131 ± 2 141 ± 2** 
Total blood cholesterol concentration (mmol · L−15.8 ± 0.3 (n = 12) 6.5 ± 0.4 (n = 8) 
Fasting blood glucose concentration (mmol · L−14.68 ± 0.15 4.81 ± 0.19 
Fasting serum insulin concentration (mU · L−15.2 ± 0.2 10.1 ± 1.8** 
Serum C-reactive protein concentration (µg · mL−10.69 ± 0.09 4.20 ± 0.83*** 
Plasma adiponectin concentration (ng · mL−111.6 ± 1.1 6.7 ± 1.2** 
Plasma IL-6 concentration (pg · mL−10.84 ± 0.16 1.98 ± 0.29** 
Plasma leptin concentration (ng · mL−12.35 ± 0.31 9.58 ± 1.15*** 
Plasma resistin concentration (ng · mL−14.93 ± 0.34 6.43 ± 0.49* 
Plasma TNF-α concentration (pg · mL−11.21 ± 0.20 2.28 ± 0.47* 
Medications Aspirin 75 mg daily (n = 1) Atenolol 50 mg daily (n = 1); aspirin 75 mg daily (n = 1); lisinopril 40 mg daily; Germoloids cream as required (n = 1) 
Healthy-weight subjects (n = 15)Obese subjects (n = 11)
Age (years) 66.7 ± 1.1 66.4 ± 1.8 
Height (cm) 176 ± 2 175 ± 2 
Weight (kg) 72.8 ± 2.0 97.7 ± 3.6*** 
BMI (kg · m−223.4 ± 0.3 31.9 ± 1.1*** 
Body fat mass (%) 22.7 ± 1.5 36.4 ± 1.0*** 
Diastolic blood pressure (mmHg) 83 ± 1 84 ± 2 
Systolic blood pressure (mmHg) 131 ± 2 141 ± 2** 
Total blood cholesterol concentration (mmol · L−15.8 ± 0.3 (n = 12) 6.5 ± 0.4 (n = 8) 
Fasting blood glucose concentration (mmol · L−14.68 ± 0.15 4.81 ± 0.19 
Fasting serum insulin concentration (mU · L−15.2 ± 0.2 10.1 ± 1.8** 
Serum C-reactive protein concentration (µg · mL−10.69 ± 0.09 4.20 ± 0.83*** 
Plasma adiponectin concentration (ng · mL−111.6 ± 1.1 6.7 ± 1.2** 
Plasma IL-6 concentration (pg · mL−10.84 ± 0.16 1.98 ± 0.29** 
Plasma leptin concentration (ng · mL−12.35 ± 0.31 9.58 ± 1.15*** 
Plasma resistin concentration (ng · mL−14.93 ± 0.34 6.43 ± 0.49* 
Plasma TNF-α concentration (pg · mL−11.21 ± 0.20 2.28 ± 0.47* 
Medications Aspirin 75 mg daily (n = 1) Atenolol 50 mg daily (n = 1); aspirin 75 mg daily (n = 1); lisinopril 40 mg daily; Germoloids cream as required (n = 1) 

Values are provided as mean ± SEM. ***P < 0.001, **P < 0.01, *P < 0.05, significantly different from healthy-weight subjects.

Muscle Function Protocol

Subjects were instructed to refrain from alcohol and exercise for 48 h before each visit and attend the laboratory at 0800 h in a fasted state. Upon arrival, a whole-body DXA scan was performed to assess lean and fat masses (Lunar Prodigy; GE Healthcare). Afterward, isometric and isokinetic leg muscle function was measured in a subset of volunteers (10 healthy-weight and 8 obese subjects), as described in detail elsewhere (18).

Metabolic Study Protocol

A minimum of 72 h was allowed to elapse from the muscle function assessment visit before subjects returned to the laboratory to undergo detailed assessment of multiple whole-body and muscle metabolic parameters using an insulin-clamp protocol described previously (18). Briefly, a 240-min euglycemic (4.5 mmol · L−1) insulin clamp began at t = 0 with an insulin infusion rate of 0.6 mU · m−2 · min−1 to maintain serum insulin concentrations similar to the fasting condition (∼5 mU · L−1); octreotide (30 ng · kg−1 · min−1) and glucagon (15 ng ·kg−1 · h−1) were administered to block endogenous insulin production and maintain serum glucagon concentrations, respectively (Fig. 1). At t = 120 the insulin infusion rate was increased to 15 mU · m−2 · min−1 to achieve a serum insulin concentration equivalent to the postprandial state (∼40 mU · L−1). In parallel, a mixed amino acid infusion (10 g · h−1; Glamin; Fresenius Kabi, Bad Homburg, Germany) commenced and was maintained for the remaining 120 min of the study period. Throughout the entire 240-min period of the insulin clamp, a primed constant infusion of the stable isotope–labeled amino acids [1,2-13C2]-leucine and [ring-D5]-phenylalanine was administered (Fig. 1).

Figure 1

Study protocol for the measurement of MPS and LPB in the postabsorptive (0–120 min) and postprandial (120–240 min) states. For clarity, a-v blood sampling is not indicated, but it occurred at regular intervals throughout the study period.

Figure 1

Study protocol for the measurement of MPS and LPB in the postabsorptive (0–120 min) and postprandial (120–240 min) states. For clarity, a-v blood sampling is not indicated, but it occurred at regular intervals throughout the study period.

Muscle biopsies were obtained from the vastus lateralis by percutaneous needle biopsy at t = 0, 120, and 240 min. Arterialized-venous (a-v) blood samples were obtained at 5-min intervals for the determination of blood glucose (YSI 2300 automated analyzer; YSI Inc., Yellow Springs, OH) and serum insulin concentrations by ELISA (DRG Diagnostics, Marburg, Germany). An additional sample taken at t = 0 was used to determine circulating TNF-α, IL-6, resistin, adiponectin, and leptin concentrations by ELISA (R&D Systems Inc., Abingdon, U.K.). Femoral venous blood samples taken at regular intervals in tandem with a-v blood samples were analyzed for blood glucose concentrations to determine leg glucose disposal (LGD) between the period of 210 to 240 min, reflecting a steady state under fed conditions. Blood samples (a-v and femoral venous) were obtained at t = 0, 75, 90, 105, 120, 195, 210, 225, and 240 min for the determination of [1,2-13C2]-leucine and [ring-D5]-phenylalanine enrichment, as previously described (19). Femoral artery blood flow in the contralateral limb was determined at these same time points using Doppler ultrasound (Aplio XV; Toshiba). Before the end of the 0.6 and 15 mU · m−2 · min−1 clamps, the respiratory exchange ratio (RER) was determined using a GEM indirect calorimetry system with a ventilated hood (GEMNutrition Ltd., Daresbury, U.K.).

Muscle Protein Synthesis

Myofibrillar proteins were extracted from ∼30 mg of muscle tissue, as previously described (19). Following extraction, proteins were hydrolyzed into their constituent amino acids via acid hydrolysis and purified using ion exchange chromatography (20). The resultant amino acids were derivatized as their N-acetyl-N-propyl esters, and the incorporation of [1,2-13C2]-leucine into protein was determined by capillary gas chromatography combustion isotope ratio mass spectrometry (DELTAplus XL; Thermo Fisher Scientific, U.K.) according to established methods (20). The rate of MPS was determined by measuring the incorporation of [1,2-13C2]-leucine in successive biopsies; the labeling of blood α-ketoisocaproate was used as a surrogate measure for the immediate precursor of protein synthesis, leucyl-tRNA (20).

Leg Protein Turnover

Whole blood was used to determine labeling (atom percent excess) and the concentration of a-v and venous phenylalanine. Blood samples were precipitated with perchloric acid and the supernatant was neutralized before purification by cation exchange chromatography. The subsequent eluent was dried under nitrogen before derivatizing the amino acids to their tert-butyldimethylsilyl derivatives using standard techniques (21). Concentrations and enrichments were determined by gas chromatography–mass spectrometry (Trace DSQ; Thermo Scientific, U.K.), with norleucine and d2-phenylalanine used as internal standards. For each 120-min postabsorptive and postprandial period, the values for enrichment and concentration were determined from the mean of four separately analyzed samples collected over the final 45 min. Leg protein synthesis (LPS) was calculated as the disappearance of phenylalanine into the leg and LPB as the a-v dilution of [ring-D5]-phenylalanine, as previously described (20).

Gene Expression Analysis

Total RNA was extracted from muscle biopsy specimens and cDNA was synthesized using standard protocols (18). mRNA expression levels for 90 genes spanning carbohydrate metabolism, fat metabolism, insulin signaling, and proteolysis were examined via the use of low-density microfluidic cards (Applied Biosystems; see Table 2 for a comprehensive list of the genes examined) and analyzed using an Applied Biosystems 7900 Real-Time PCR system. Data were normalized to the geometric average of the housekeeping genes: α-actin, β2-microglobulin, 18S, and RNA polymerase 2A. The suitability of the housekeeping genes was confirmed using RealTime StatMiner software (Integromics, Granada, Spain). Gene expression was calculated via use of the 2−ΔΔCt method, and significance was determined using the linear modeling approach (LIMMA). The false discovery rate was assessed using the method described by Benjamini and Hochberg (22).

Table 2

Changes in mRNA levels of several transcripts associated with metabolism or muscle growth in skeletal muscle of obese older men versus healthy-weight older men

PathwayGeneGene codeProbe identifierFold change*P valueFDR
Apoptosis Apoptotic peptidase activating factor 1 APAF1 Hs00559441 1.04 0.582 0.856 
B-cell CLL/lymphoma 2 BCL2 Hs00608023 1.19 0.055 0.291 
Caspase 8 CASP8 Hs01018151 1.04 0.727 0.894 
Caspase 9 CASP9 Hs00154261 0.97 0.637 0.861 
Diablo, IAP-binding mitochondrial protein DIABLO Hs00219876 0.98 0.693 0.890 
Autophagy BCL2/adenovirus E1B 19kda interacting protein 3 BNIP3 Hs00969291 1.07 0.269 0.676 
Dynamin 1-like DNM1L Hs00247147 0.91 0.027 0.223 
GABA(A) receptor-associated protein GABARAP Hs00925899 0.88 0.028 0.223 
Calcium release Ryanodine receptor 1 RYR1 Hs00166991 1.00 0.988 0.988 
Ryanodine receptor 3 RYR3 Hs00168821 0.65 0.033 0.223 
Carbohydrate metabolism Creatine kinase CKM Hs00176490 0.21 0.411 0.839 
Lactate dehydrogenase B LDHB Hs00929956 0.91 0.919 0.985 
NADH dehydrogenase (ubiquinone) 1 alpha subcomplex, subunit 5 NDUFA5 Hs00606273 0.83 0.008 0.102 
Pyruvate dehydrogenase kinase, isozyme 4 PDK4 Hs00176875 1.10 0.786 0.932 
Solute carrier family 22 (organic cation/ergothioneine transporter), member 4 GLUT4 Hs00268200 0.89 0.814 0.953 
Inflammation Interleukin-6 IL-6 Hs00985639 0.67 0.405 0.839 
Metallothionein 1A MT1A Hs00831826 0.84 0.533 0.856 
Tumor necrosis factor α TNFα Hs99999043 0.80 0.423 0.839 
Insulin signaling Eukaryotic translation initiation factor 2 beta, subunit 2 beta EIF2B2 Hs00204540 0.84 0.015 0.162 
Mitogen-activated protein kinase 3 MAPK3 Hs00385075 0.90 0.06 0.306 
Mitogen-activated protein kinase kinase 1 MAP2K1 Hs00605615 1.04 0.568 0.856 
Phosphoinositide-3-kinase, regulatory subunit 1 (alpha) PIK3R1 Hs00933163 0.59 0.148 0.489 
Protein kinase, AMP-activated, alpha 1 catalytic subunit PRKAA1 Hs01562308 0.94 0.141 0.482 
Ras homolog family member Q RHOQ Hs00817629 1.07 0.183 0.550 
RAS p21 protein activator (GTpase activating protein) 1 RASA1 Hs00243115 0.98 0.710 0.890 
Ras-related GTP binding A RRAGA Hs00538975 1.01 0.779 0.932 
V-AKT murine thymoma viral oncogene homolog 2 AKT2 Hs01086102 0.95 0.275 0.676 
Lipid metabolism Carnitine palmitoyltransferase 1B CPT1B Hs00993896 0.78 0.080 0.322 
Carnitine transporter OCTN2 SLC22A5 Hs00161895 0.69 0.164 0.524 
Farnesyl diphosphate synthase FDPS Hs00266635 0.95 0.666 0.876 
Fatty acid binding protein 3 FABP3 Hs00269758 0.79 0.107 0.411 
Fatty acid synthase FASN Hs00188012 1.12 0.489 0.856 
Fatty acid translocase/CD36 CD36 Hs00169627 0.93 0.413 0.839 
Heat shock 70kDa protein 5 HSPA5 Hs99999174 1.04 0.575 0.856 
NADH dehydrogenase (ubiquinone) Fe-S protein4 NDUFS4 Hs00942568 0.87 0.068 0.322 
Protein kinase C, alpha PRKCA Hs00925195 0.94 0.555 0.856 
Protein kinase C, epsilon PRKCE Hs00178455 1.02 0.859 0.963 
Protein kinase C, iota PRKCI Hs00702254 1.06 0.548 0.856 
Protein kinase C, theta PRKCQ Hs00989970 0.97 0.630 0.861 
Stearoyl-CoA desaturase 1 SCD Hs01682761 1.30 0.550 0.856 
Myogenesis CD34 molecule CD34 Hs00990732 0.79 0.004 0.068 
M-cadherin 15 CDH15 Hs00170504 1.09 0.292 0.700 
Met proto-oncogene MET Hs01565584 1.13 0.191 0.556 
Myogenic factor 5 MYF5 Hs00271574 1.21 0.029 0.223 
Ras homolog enriched in brain RHEB Hs00950800 1.00 0.971 0.985 
Ras homolog family member A RHOA Hs00236938 1.06 0.247 0.659 
Ras homolog family member B RHOB Hs00269660 1.09 0.367 0.820 
Rho-associated, coiled-coil containing protein kinase 1 ROCK1 Hs00178463 1.00 0.949 0.985 
V-Ha-ras Harvey rat sarcoma viral oncogene homolog HRAS Hs00610483 0.99 0.863 0.963 
Organic anion transporter Solute carrier organic anion transporter family, member 1B1 SLCO1B1 Hs00272374 0.05 0.004 0.068 
Oxidative phosphorylation/electronic transport chain Coenzyme Q10 homolog B COQ10B Hs00257861 1.24 0.497 0.856 
COX-I, cytochrome C oxidase 1 MT-CO1 Hs02596864 0.81 0.031 0.223 
COX-II, cytochrome C oxidase 2 MT-CO2 Hs00153133 0.71 0.472 0.856 
Cytochrome c CYCS Hs01588974 0.70 0.000 0.014 
Cytochrome c oxidase subunit via polypeptide 1 COX7A1 Hs00156989 0.85 0.074 0.322 
Succinate dehydrogenase complex II SDHA Hs00188166 0.89 0.077 0.322 
Ubiquinol-cytochrome c reductase, complex III UQCRQ Hs00416927 0.85 0.055 0.291 
Proteolysis Calpain 1 CAPN1 Hs00559804 1.03 0.513 0.856 
Calpain 2 CAPN2 Hs00965092 0.96 0.477 0.856 
Calpain 3 (p94) CAPN3 Hs00544975 1.03 0.593 0.856 
Calpastatin CAST Hs00156280 1.01 0.943 0.985 
Cathepsin L1 CTSL1 Hs00377632 0.92 0.201 0.568 
F-box protein 32 MAFbx Hs01041408 1.02 0.840 0.963 
Matrix metallopeptidase 2 MMP2 Hs01548727 1.06 0.595 0.856 
Proteasome subunit, beta 1 PSMB1 Hs00427357 1.04 0.666 0.876 
Proteasome subunit, beta 2 PSMB2 Hs01002946 1.03 0.604 0.856 
Proteasome subunit, beta 5 PSMB5 Hs00605652 1.02 0.714 0.890 
Tripartite motif containing 63, E3 ubiquitin protein ligase MuRF1 Hs00261590 0.85 0.310 0.726 
Ubiquitin B UBB Hs00430290 1.00 0.975 0.985 
Ubiquitin C UBC Hs00824723 1.06 0.548 0.856 
Ubiquitin specific peptidase 19 USP19 Hs00324123 1.06 0.226 0.619 
Regulator of cell growth Myostatin MSTN Hs00976237 1.8 0.004 0.068 
Transcription factor Activating transcription factor 3 ATF3 Hs00231069 0.89 0.606 0.856 
Forkhead box O1 FOXO1 Hs00231106 1.01 0.959 0.985 
Forkhead box O3 FOXO3 Hs00818121 0.96 0.704 0.890 
Inhibitor of kappa light polypeptide gene enhancer in B-cells, kinase beta IKBKB Hs00233287 0.95 0.532 0.856 
Jun proto-oncogene JUN Hs00277190 1.08 0.380 0.829 
Myocyte enhancer factor 2C MEF2C Hs00231149 1.00 0.972 0.985 
Myogenic differentiation 1 MYOD1 Hs00159528 1.01 0.924 0.985 
Myogenin (myogenic factor 4) MYOG Hs00231167 1.19 0.046 0.286 
Nuclear factor of kappa light polypeptide gene enhancer in B-cells 1 NFKB1 Hs00765730 1.12 0.048 0.286 
Nuclear factor of kappa light polypeptide gene enhancer in B-cells 2 (p49/p100) NFKB2 Hs00174517 1.06 0.519 0.856 
Peroxisome proliferator activated receptor alpha PPARA Hs00231882 0.77 0.002 0.068 
Peroxisome proliferator activated receptor delta PPARD Hs00602622 0.94 0.273 0.676 
Peroxisome proliferator activated receptor gamma PPARG Hs01115513 0.85 0.081 0.322 
Peroxisome proliferator activated receptor gamma, coactivator 1 alpha PPARGC1A Hs01016724 0.67 0.002 0.068 
Sterol regulatory element binding transcription factor 1 SREBF1 Hs01088691 1.11 0.428 0.839 
Sterol regulatory element binding transcription factor 2 SREBF2 Hs01081778 1.02 0.785 0.932 
Transcription factor A, mitochondrial TFAM Hs01082775 0.88 0.006 0.088 
V-rel reticuloendotheliosis viral oncogene homolog A RELA Hs00153294 1.00 0.96 0.985 
PathwayGeneGene codeProbe identifierFold change*P valueFDR
Apoptosis Apoptotic peptidase activating factor 1 APAF1 Hs00559441 1.04 0.582 0.856 
B-cell CLL/lymphoma 2 BCL2 Hs00608023 1.19 0.055 0.291 
Caspase 8 CASP8 Hs01018151 1.04 0.727 0.894 
Caspase 9 CASP9 Hs00154261 0.97 0.637 0.861 
Diablo, IAP-binding mitochondrial protein DIABLO Hs00219876 0.98 0.693 0.890 
Autophagy BCL2/adenovirus E1B 19kda interacting protein 3 BNIP3 Hs00969291 1.07 0.269 0.676 
Dynamin 1-like DNM1L Hs00247147 0.91 0.027 0.223 
GABA(A) receptor-associated protein GABARAP Hs00925899 0.88 0.028 0.223 
Calcium release Ryanodine receptor 1 RYR1 Hs00166991 1.00 0.988 0.988 
Ryanodine receptor 3 RYR3 Hs00168821 0.65 0.033 0.223 
Carbohydrate metabolism Creatine kinase CKM Hs00176490 0.21 0.411 0.839 
Lactate dehydrogenase B LDHB Hs00929956 0.91 0.919 0.985 
NADH dehydrogenase (ubiquinone) 1 alpha subcomplex, subunit 5 NDUFA5 Hs00606273 0.83 0.008 0.102 
Pyruvate dehydrogenase kinase, isozyme 4 PDK4 Hs00176875 1.10 0.786 0.932 
Solute carrier family 22 (organic cation/ergothioneine transporter), member 4 GLUT4 Hs00268200 0.89 0.814 0.953 
Inflammation Interleukin-6 IL-6 Hs00985639 0.67 0.405 0.839 
Metallothionein 1A MT1A Hs00831826 0.84 0.533 0.856 
Tumor necrosis factor α TNFα Hs99999043 0.80 0.423 0.839 
Insulin signaling Eukaryotic translation initiation factor 2 beta, subunit 2 beta EIF2B2 Hs00204540 0.84 0.015 0.162 
Mitogen-activated protein kinase 3 MAPK3 Hs00385075 0.90 0.06 0.306 
Mitogen-activated protein kinase kinase 1 MAP2K1 Hs00605615 1.04 0.568 0.856 
Phosphoinositide-3-kinase, regulatory subunit 1 (alpha) PIK3R1 Hs00933163 0.59 0.148 0.489 
Protein kinase, AMP-activated, alpha 1 catalytic subunit PRKAA1 Hs01562308 0.94 0.141 0.482 
Ras homolog family member Q RHOQ Hs00817629 1.07 0.183 0.550 
RAS p21 protein activator (GTpase activating protein) 1 RASA1 Hs00243115 0.98 0.710 0.890 
Ras-related GTP binding A RRAGA Hs00538975 1.01 0.779 0.932 
V-AKT murine thymoma viral oncogene homolog 2 AKT2 Hs01086102 0.95 0.275 0.676 
Lipid metabolism Carnitine palmitoyltransferase 1B CPT1B Hs00993896 0.78 0.080 0.322 
Carnitine transporter OCTN2 SLC22A5 Hs00161895 0.69 0.164 0.524 
Farnesyl diphosphate synthase FDPS Hs00266635 0.95 0.666 0.876 
Fatty acid binding protein 3 FABP3 Hs00269758 0.79 0.107 0.411 
Fatty acid synthase FASN Hs00188012 1.12 0.489 0.856 
Fatty acid translocase/CD36 CD36 Hs00169627 0.93 0.413 0.839 
Heat shock 70kDa protein 5 HSPA5 Hs99999174 1.04 0.575 0.856 
NADH dehydrogenase (ubiquinone) Fe-S protein4 NDUFS4 Hs00942568 0.87 0.068 0.322 
Protein kinase C, alpha PRKCA Hs00925195 0.94 0.555 0.856 
Protein kinase C, epsilon PRKCE Hs00178455 1.02 0.859 0.963 
Protein kinase C, iota PRKCI Hs00702254 1.06 0.548 0.856 
Protein kinase C, theta PRKCQ Hs00989970 0.97 0.630 0.861 
Stearoyl-CoA desaturase 1 SCD Hs01682761 1.30 0.550 0.856 
Myogenesis CD34 molecule CD34 Hs00990732 0.79 0.004 0.068 
M-cadherin 15 CDH15 Hs00170504 1.09 0.292 0.700 
Met proto-oncogene MET Hs01565584 1.13 0.191 0.556 
Myogenic factor 5 MYF5 Hs00271574 1.21 0.029 0.223 
Ras homolog enriched in brain RHEB Hs00950800 1.00 0.971 0.985 
Ras homolog family member A RHOA Hs00236938 1.06 0.247 0.659 
Ras homolog family member B RHOB Hs00269660 1.09 0.367 0.820 
Rho-associated, coiled-coil containing protein kinase 1 ROCK1 Hs00178463 1.00 0.949 0.985 
V-Ha-ras Harvey rat sarcoma viral oncogene homolog HRAS Hs00610483 0.99 0.863 0.963 
Organic anion transporter Solute carrier organic anion transporter family, member 1B1 SLCO1B1 Hs00272374 0.05 0.004 0.068 
Oxidative phosphorylation/electronic transport chain Coenzyme Q10 homolog B COQ10B Hs00257861 1.24 0.497 0.856 
COX-I, cytochrome C oxidase 1 MT-CO1 Hs02596864 0.81 0.031 0.223 
COX-II, cytochrome C oxidase 2 MT-CO2 Hs00153133 0.71 0.472 0.856 
Cytochrome c CYCS Hs01588974 0.70 0.000 0.014 
Cytochrome c oxidase subunit via polypeptide 1 COX7A1 Hs00156989 0.85 0.074 0.322 
Succinate dehydrogenase complex II SDHA Hs00188166 0.89 0.077 0.322 
Ubiquinol-cytochrome c reductase, complex III UQCRQ Hs00416927 0.85 0.055 0.291 
Proteolysis Calpain 1 CAPN1 Hs00559804 1.03 0.513 0.856 
Calpain 2 CAPN2 Hs00965092 0.96 0.477 0.856 
Calpain 3 (p94) CAPN3 Hs00544975 1.03 0.593 0.856 
Calpastatin CAST Hs00156280 1.01 0.943 0.985 
Cathepsin L1 CTSL1 Hs00377632 0.92 0.201 0.568 
F-box protein 32 MAFbx Hs01041408 1.02 0.840 0.963 
Matrix metallopeptidase 2 MMP2 Hs01548727 1.06 0.595 0.856 
Proteasome subunit, beta 1 PSMB1 Hs00427357 1.04 0.666 0.876 
Proteasome subunit, beta 2 PSMB2 Hs01002946 1.03 0.604 0.856 
Proteasome subunit, beta 5 PSMB5 Hs00605652 1.02 0.714 0.890 
Tripartite motif containing 63, E3 ubiquitin protein ligase MuRF1 Hs00261590 0.85 0.310 0.726 
Ubiquitin B UBB Hs00430290 1.00 0.975 0.985 
Ubiquitin C UBC Hs00824723 1.06 0.548 0.856 
Ubiquitin specific peptidase 19 USP19 Hs00324123 1.06 0.226 0.619 
Regulator of cell growth Myostatin MSTN Hs00976237 1.8 0.004 0.068 
Transcription factor Activating transcription factor 3 ATF3 Hs00231069 0.89 0.606 0.856 
Forkhead box O1 FOXO1 Hs00231106 1.01 0.959 0.985 
Forkhead box O3 FOXO3 Hs00818121 0.96 0.704 0.890 
Inhibitor of kappa light polypeptide gene enhancer in B-cells, kinase beta IKBKB Hs00233287 0.95 0.532 0.856 
Jun proto-oncogene JUN Hs00277190 1.08 0.380 0.829 
Myocyte enhancer factor 2C MEF2C Hs00231149 1.00 0.972 0.985 
Myogenic differentiation 1 MYOD1 Hs00159528 1.01 0.924 0.985 
Myogenin (myogenic factor 4) MYOG Hs00231167 1.19 0.046 0.286 
Nuclear factor of kappa light polypeptide gene enhancer in B-cells 1 NFKB1 Hs00765730 1.12 0.048 0.286 
Nuclear factor of kappa light polypeptide gene enhancer in B-cells 2 (p49/p100) NFKB2 Hs00174517 1.06 0.519 0.856 
Peroxisome proliferator activated receptor alpha PPARA Hs00231882 0.77 0.002 0.068 
Peroxisome proliferator activated receptor delta PPARD Hs00602622 0.94 0.273 0.676 
Peroxisome proliferator activated receptor gamma PPARG Hs01115513 0.85 0.081 0.322 
Peroxisome proliferator activated receptor gamma, coactivator 1 alpha PPARGC1A Hs01016724 0.67 0.002 0.068 
Sterol regulatory element binding transcription factor 1 SREBF1 Hs01088691 1.11 0.428 0.839 
Sterol regulatory element binding transcription factor 2 SREBF2 Hs01081778 1.02 0.785 0.932 
Transcription factor A, mitochondrial TFAM Hs01082775 0.88 0.006 0.088 
V-rel reticuloendotheliosis viral oncogene homolog A RELA Hs00153294 1.00 0.96 0.985 

Seven genes (bold) satisfied the criterion of false discovery rate (FDR) <0.1 and were considered differentially expressed.

*Fold change is expressed relative to healthy-weight subjects.

Immunoblotting

Cytosolic proteins were extracted from 30 mg of muscle tissue and analyzed by Western blot using established methods (23). Membranes were incubated overnight at 4°C with a polyclonal antibody for either total or phosphorylation-specific forms of AKT1 (Thr308) or mammalian target of rapamycin (mTOR) (Ser2448; Cell Signaling Technology Inc., Danvers, MA) or, as a loading control, β-actin (Sigma-Aldrich, Gillingham, U.K.). Following washing in a Tris-buffered saline–Tween 20 solution, membranes were probed with a fluorescently labeled antimouse or antirabbit secondary antibody (Dylight conjugate; Cell Signaling Technology Inc.), as appropriate, and imaged on an electronic image acquisition system (Odyssey CLx; LI-COR Biosciences, Lincoln, NE). Band densities were determined using proprietary software supplied with the imaging system and normalized to the loading control.

Statistics

Statistical differences in subject characteristics, body composition, muscle functional data, plasma adipokine concentrations, and area under the curve data for serum insulin and glucose disposal rate were determined using unpaired two-tailed Student t tests. Differences in RER, femoral blood flow, protein turnover measurements, and Western blot data were determined using a two-way ANOVA with repeated measures. When a significant interaction between main effects was observed, a Student t test with Šidák correction was performed to locate differences. Correlation between changes in MPS with feeding and leg fat mass were assessed by linear regression analysis. Statistical analysis was performed using Prism version 6.0 software (GraphPad Software Inc., La Jolla, CA). Data are reported as mean ± SEM, with significance accepted at the P < 0.05 level.

Adipokine Expression

Subjects were recruited based on both their BMI and basal serum CRP concentration. As a consequence, serum CRP concentration was greater in the obese versus healthy-weight individuals (P < 0.001; Table 1). This selection strategy was effective at recruiting obese individuals with chronic low-grade inflammation relative to their healthy-weight study counterparts, as evidenced by the greater plasma concentrations of leptin, resistin, TNF-α, and IL-6 in the obese individuals. Conversely, plasma concentrations of adiponectin were significantly lower in obese versus healthy-weight subjects (Table 1), as anticipated (10).

Body Composition, Muscle Strength, and Muscle Fatigue

As expected, BMI was greater in the obese than in the healthy-weight participants (31.9 ± 1.1 and 23.4 ± 0.3 kg · m−2, respectively; P < 0.001). Likewise, significantly greater fat masses for all five body compartments interrogated were observed in the obese compared with the healthy-weight subjects (P < 0.001; Fig. 2A). Lean mass was also significantly greater in the obese individuals in the trunk (18% higher), android (26% higher), and gynoidal (14% higher) regions compared with the healthy-weight subjects (Fig. 2B). Lean tissue mass in the arms and legs of the healthy-weight and obese subjects was no different between the two subject groups.

Figure 2

Regional fat and lean masses in healthy-weight and obese older men. Mean values ± SEM for fat mass (A) and lean mass (B) in healthy-weight and obese volunteers separated by anatomic region. C: Diagrammatic representation of the approximate trunk, android, and gynoid regions determined by the DXA imaging software. ***P < 0.001, **P < 0.01, significantly different from healthy-weight individuals.

Figure 2

Regional fat and lean masses in healthy-weight and obese older men. Mean values ± SEM for fat mass (A) and lean mass (B) in healthy-weight and obese volunteers separated by anatomic region. C: Diagrammatic representation of the approximate trunk, android, and gynoid regions determined by the DXA imaging software. ***P < 0.001, **P < 0.01, significantly different from healthy-weight individuals.

There was no significant difference in the isometric strength of the knee extensors between the healthy-weight and obese individuals (34.3 ± 2.5 versus 33.9 ± 3.0 kg, respectively; Fig. 3A). Similarly, the volume of work performed during 30 maximal isokinetic knee extensions was equivalent between subject groups (Fig. 3B), as was a measure of fatigue calculated from changes in torque output over time (Fig. 3C).

Figure 3

Isometric strength, total work output, and fatigue index during 30 maximal isokinetic knee extensions (at 90° · s−1) in older healthy-weight and obese men. Values represent the mean ± SEM for isometric strength (A), work done (B) and fatigue index ([peak torque − minimum torque]/peak torque) (C). No significant differences between healthy-weight and obese individuals were observed for any of the three parameters examined. AU, arbitrary units.

Figure 3

Isometric strength, total work output, and fatigue index during 30 maximal isokinetic knee extensions (at 90° · s−1) in older healthy-weight and obese men. Values represent the mean ± SEM for isometric strength (A), work done (B) and fatigue index ([peak torque − minimum torque]/peak torque) (C). No significant differences between healthy-weight and obese individuals were observed for any of the three parameters examined. AU, arbitrary units.

Gene Expression Analysis

mRNA expression levels for 90 genes spanning inflammation, carbohydrate and fat metabolism, insulin signaling, and proteolysis were examined. Using a false discovery rate <0.10, seven transcripts appeared to be differentially expressed in obese compared with healthy-weight volunteers (Table 2). Cytochrome c, peroxisome proliferator–activated receptor-α, peroxisome proliferator–activated receptor-γ coactivator 1-α, and transcription factor A mitochondrial—all of which are associated with either mitochondrial biogenesis or the control of mitochondrial oxidative phosphorylation—were expressed at lower levels in the muscle of obese individuals. Similarly, CD34, a marker of satellite cell quiescence, and the solute carrier organic anion transporter family, member 1B1, involved in hepatic drug metabolism, were both lower in the obese. By contrast, the expression of myostatin, which is a negative regulator of muscle growth, was greater in obese skeletal muscle (1.80-fold compared with control subjects; P < 0.01). These changes are consistent with a general deconditioning of muscle in the obese subjects relative to the healthy-weight volunteers. Interestingly, in spite of the systemic low-grade inflammation occurring in the obese volunteers, muscle mRNA levels of TNF-α and IL-6 were not elevated.

Carbohydrate Metabolism

Serum insulin concentrations under clamp conditions are presented in Fig. 4A. The insulin infusions rates of 0.6 and 15 mU · m−2 · min−1 produced steady-state serum insulin concentrations of 5.2 ± 1.2 and 69.7 ± 5.4 mU · L−1 in the healthy-weight subjects and 4.2 ± 0.3 and 78.7 ± 4.4 mU · L−1 in the obese subjects, respectively. There was no significant difference in either absolute serum insulin concentration or area under the curve during steady-state conditions between groups (Fig. 4A). The 0.6 mU · m−2 · min−1 insulin infusion resulted in negligible LGD in both subject groups (data not shown). By contrast, under steady-state conditions (210–240 min) the greater insulin infusion rate (15 mU · m−2 · min−1) resulted in an increased rate of LGD in both sets of individuals, but it was more marked in the healthy-weight subjects (3.7 ± 0.4 g · min−1) than in the obese subjects (1.3 ± 0.2 g · min−1) over the 30-min period examined (P < 0.001; Fig. 4B). The RER was no different between subject groups in the simulated postabsorptive state (0.72 ± 0.02 and 0.68 ± 0.01 in healthy-weight and obese subjects, respectively; Fig. 4C). As expected, the simulated postprandial state increased the RER in the healthy-weight individuals (0.82 ± 0.03; P < 0.001) but had no effect on obese volunteers, in whom the RER remained unchanged (0.71 ± 0.01).

Figure 4

Serum insulin concentration, leg glucose uptake, and RER in fasted and fed conditions in older healthy-weight and obese men. Mean ± SEM concentration of serum insulin (A) and LGD (B) in response to a hypoaminoacidemic/hypoinsulinemic clamp (0–120 min; only insulin data shown) and a hyperaminoacidemic/hyperinsulinemic–euglycemic clamp (120–240 min). The inset bar charts denote the area under the insulin curve (AUC) (A) or LGD rate (B) calculated over the last 30 min of the hyperaminoacidemic/hyperinsulinemic clamp (shaded regions in A and B). C: Mean ± SEM for RER in healthy-weight and obese older adults in both the postabsorptive and postprandial states. Where relevant, P values for each main effect and interaction between main effects (determined by two-way ANOVA) are displayed alongside the corresponding graph. ***P < 0.001, **P < 0.01, significantly different from healthy-weight individuals; †††P < 0.001, significantly different from fasted clamp conditions. AA, amino acids; GDR, glucose disposal rate; NS, not significant.

Figure 4

Serum insulin concentration, leg glucose uptake, and RER in fasted and fed conditions in older healthy-weight and obese men. Mean ± SEM concentration of serum insulin (A) and LGD (B) in response to a hypoaminoacidemic/hypoinsulinemic clamp (0–120 min; only insulin data shown) and a hyperaminoacidemic/hyperinsulinemic–euglycemic clamp (120–240 min). The inset bar charts denote the area under the insulin curve (AUC) (A) or LGD rate (B) calculated over the last 30 min of the hyperaminoacidemic/hyperinsulinemic clamp (shaded regions in A and B). C: Mean ± SEM for RER in healthy-weight and obese older adults in both the postabsorptive and postprandial states. Where relevant, P values for each main effect and interaction between main effects (determined by two-way ANOVA) are displayed alongside the corresponding graph. ***P < 0.001, **P < 0.01, significantly different from healthy-weight individuals; †††P < 0.001, significantly different from fasted clamp conditions. AA, amino acids; GDR, glucose disposal rate; NS, not significant.

Muscle Protein Turnover

During the 0.6 mU · m−2 · min−1 insulin infusion, when mixed amino acids were not being provided, the rates of MPS, LPS, and LPB were equivalent between the healthy-weight and obese volunteers (Fig. 5A, C, and D). Under these conditions, the rate of LPB exceeded LPS; as such, the net leg phenylalanine balance was negative but equivalent between groups (Fig. 5E).

Figure 5

Muscle protein turnover and associated signaling in postabsorptive and postprandial states in older healthy-weight and obese men. Myofibrillar FSR (A) was assessed in healthy-weight and obese individuals following simulated fasted or fed conditions. A negative correlation was observed between leg fat mass and change in MPS with feeding (B), which was significant by linear regression analysis (P = 0.05). Graphs also show the leg protein synthesis rate (C), leg protein breakdown rate (D), and phenylalanine balance across the leg (E). To delineate the processes underpinning the observed changes in MPS, total protein concentrations and the main phosphorylated forms of AKT and mTOR were determined by Western blotting (F). Bars represent mean values ± SEM. Where relevant, P values for each main effect and interaction between main effects (determined by two-way ANOVA) are displayed alongside corresponding graph. **P < 0.01, significantly different from healthy-weight individuals; †††P < 0.001, ††P < 0.01, significantly different from fasted clamp conditions. AA, amino acids; AU, arbitrary units; NS, not significant.

Figure 5

Muscle protein turnover and associated signaling in postabsorptive and postprandial states in older healthy-weight and obese men. Myofibrillar FSR (A) was assessed in healthy-weight and obese individuals following simulated fasted or fed conditions. A negative correlation was observed between leg fat mass and change in MPS with feeding (B), which was significant by linear regression analysis (P = 0.05). Graphs also show the leg protein synthesis rate (C), leg protein breakdown rate (D), and phenylalanine balance across the leg (E). To delineate the processes underpinning the observed changes in MPS, total protein concentrations and the main phosphorylated forms of AKT and mTOR were determined by Western blotting (F). Bars represent mean values ± SEM. Where relevant, P values for each main effect and interaction between main effects (determined by two-way ANOVA) are displayed alongside corresponding graph. **P < 0.01, significantly different from healthy-weight individuals; †††P < 0.001, ††P < 0.01, significantly different from fasted clamp conditions. AA, amino acids; AU, arbitrary units; NS, not significant.

When serum insulin concentrations were increased and mixed amino acids provided, a-v plasma phenylalanine concentrations doubled, from 60–80 to 130–150 μmol · L−1 in all volunteers (data not shown). Similarly, a doubling of the myofibrillar protein fractional synthetic rate (FSR) was observed in healthy-weight individuals (0.047 ± 0.004% · h−1 during fasted conditions versus 0.099 ± 0.011% · h−1 under fed conditions; P < 0.001), but no significant increase was observed in the obese volunteers (Fig. 5A). When LPS was assessed by calculating phenylalanine disappearance into the leg (accepted as a less sensitive approach than muscle FSR), this difference between groups was still apparent but not significant (Fig. 5C). While we observed a main effect of hyperinsulinemia and hyperaminoacidemia to decrease LPB rates, along with a trend toward an interaction between main effects (P = 0.10; Fig. 5D), proceeding with post hoc tests revealed the decrease in LPB rates was confined to the obese (48.5 ± 9.5 versus 29.9 ± 5.5 nmol · min−1 · 100 g leg mass−1 in fasted and fed states, respectively; P < 0.01). Importantly, femoral blood flow, which affects the determination of LPB rates, was equivalent between subject groups during both the fasted and fed clamps (Table 3). The culmination of these individual effects on LPS and LPB was that net leg phenylalanine balance was significantly enhanced in both subject groups (25.4 ± 6.7 versus 12.6 ± 5.1 nmol · min−1 · 100 g leg mass−1 in the healthy-weight and obese subjects, respectively; Fig. 5E). Comparison of leg fat mass with the net change in MPS rates in the simulated postprandial state revealed a weak but significant (P = 0.05) negative correlation between the two variables (Fig. 5B).

Table 3

Femoral blood flow in healthy-weight and obese volunteers as assessed by Doppler ultrasound at baseline and in the last 30 min of the fasted and fed clamps

Healthy-weight subjects 
(n = 15)Obese subjects 
(n = 11)
Baseline 385 ± 34 358 ± 30 
Insulin clamp   
 Fasted state (insulin 0.6 mU · m−2 · min−1512 ± 48 472 ± 56 
 Fed state (insulin 15 mU · m−2 · min−1 plus mixed amino acid infusion [10 g · h−1]) 529 ± 45 450 ± 51 
Healthy-weight subjects 
(n = 15)Obese subjects 
(n = 11)
Baseline 385 ± 34 358 ± 30 
Insulin clamp   
 Fasted state (insulin 0.6 mU · m−2 · min−1512 ± 48 472 ± 56 
 Fed state (insulin 15 mU · m−2 · min−1 plus mixed amino acid infusion [10 g · h−1]) 529 ± 45 450 ± 51 

Results are expressed as mL · min−1, and values represent the mean ± SEM. A significant main effect of insulin treatment was observed (P < 0.001). No significant main effect of BMI stratification or an interaction between main effects was observed.

Anabolic Signaling

Under simulated postabsorptive conditions, total and phosphorylated levels of the anabolic signaling intermediary proteins AKT and mTOR were of comparable magnitude between muscle samples of obese individuals and their healthy-weight counterparts (Fig. 5F). The simulated postprandial state resulted in a significant and equivalent increase in AKT Thr308 and mTOR Ser2448 phosphorylation in both groups.

Here, we present novel evidence to suggest that obesity in nonfrail older men is not associated with deficits in lean mass, quadriceps strength, or fatigability compared with healthy-weight men of comparable age, despite evident systemic (but not muscle) inflammation. Furthermore, to the best of our knowledge, we demonstrate for the first time that the ability of amino acids to increase MPS is blunted in obese older men compared with their healthy-weight counterparts, but that net leg phenylalanine balance is not affected because of a concomitant decrease in LPB in these individuals. In short, obesity appears to be associated with systemic inflammation and altered MPS and LPB responses to increased nutrient delivery in older, nonfrail men, but not reduced muscle mass or contractile function. Despite this, differences in whole-body RER, LGD, and muscle mRNA changes consistent with a decline in overall muscle metabolic quality in obese older men were evident. These findings represent an important contribution to our understanding of the impact and interaction of systemic inflammation, aging, and obesity on muscle health.

In this study, the lean masses of all body regions examined were equivalent or greater in obese men compared with healthy-weight counterparts of similar age. Moreover, isometric strength, work output, and fatigability during repeated maximal isokinetic contractions were identical between groups. This is in contrast to the suggestion that obesity in old age accelerates muscle mass loss and functional decline (7,8). Our lack of evidence to support the existence of increased sarcopenia or dynapenia in older, nonfrail, obese men is perhaps not surprising. In young individuals obesity is typically associated with a 36% greater lean mass compared with healthy-weight counterparts of similar stature (24), thought to be caused by the additional contractile work performed by the obese during locomotion and daily living. Furthermore, evidence shows dynapenia occurs in only a subset of obese older adults; the prevalence of the condition is largely equivalent between healthy-weight (36%) and obese (27%) individuals (9), suggesting dynapenia occurs independent of obesity. However, we acknowledge that our observations need to be confirmed in a larger cohort using more sensitive measures of lean tissue mass, such as MRI or D3-creatine dilution.

While previous studies have attempted to detail the effects of obesity on MPS in young adults, a consensus has not emerged. For example, obesity has been associated with lower (25) and elevated (26) MPS in the postabsorptive state. Furthermore, while greater rates of whole-body protein synthesis have been observed in obese younger volunteers in the fed state (27,28), the magnitude of the increase in MPS from the postabsorptive to the simulated fed state was comparable between nonobese and obese volunteers (25). By contrast, we report here that, under postabsorptive conditions in which the muscle FSR is at its lowest, the rate at which muscle proteins were being synthesized was comparable between healthy-weight and obese older men. More important, the stimulatory effect of increased amino acid provision during hyperinsulinemia on MPS in older obese adults was blunted when compared with their healthy-weight counterparts. The exact basis for this “anabolic resistance” to amino acid provision is unclear, but it is not likely to be related to muscle inflammation given we could find no evidence of this despite clear systemic inflammation. One potential contributor is the increased intracellular accumulation of lipids within the muscles of obese individuals. A negative correlation between leg fat mass and the degree of stimulation of LPS was observed (P < 0.05), suggesting that resistance to the anabolic actions of amino acids within the muscle tissue was as a direct result of the increased fat mass. Moreover, it was recently shown that acute intravenous administration of a lipid emulsion (intralipid, 100 mL · h−1) results in the blunting of MPS during a hyperinsulinemic-euglycemic clamp concomitant with amino acid feeding (29). However, whether this is a direct effect of lipid species on the mechanisms responsible for MPS or is mediated via changes in insulin sensitivity cannot be deduced from the studies performed to date.

The possible role of chronically reduced habitual physical activity levels in the anabolic resistance observed in this study cannot be underestimated. Indeed, a recent study demonstrated that reducing daily step count by ∼76% for 14 days in older individuals resulted in a 26% reduction in postprandial rates of MPS and a 43% reduction in insulin sensitivity, but it did not affect protein synthetic rates under postabsorptive conditions (30). Our own data demonstrate overt traits of muscle deconditioning evident in the obese volunteers of this study. For example, steady-state LGD and whole-body carbohydrate oxidation rates—both of which are known to accompany inactivity (31)—were blunted in the obese volunteers. Furthermore, between-group differences in muscle mRNA expression clearly indicate that mitochondrial biogenesis and oxidative metabolism were dampened. Importantly, while daily physical activity levels were not measured in this study, evidence suggests that even a 1-h period of daily vigorous exercise cannot compensate for the effects of inactivity on blood markers of poor musculoskeletal health if the remainder of the day is spent sitting (32). These findings support the assertion that greater muscle deconditioning had occurred in the obese individuals in this study.

Despite a failure of amino acids to stimulate MPS in the older obese men, the ability of insulin and amino acids to stimulate AKT and mTOR phosphorylation was unperturbed. A discord between AKT/mTOR signaling and MPS is not without precedent. Following stepwise increases in serum insulin concentration during conditions of hyperaminoacidemia in healthy, young volunteers, AKT phosphorylation paralleled the increase in insulin concentration but was not matched by further increases in MPS and mTOR phosphorylation (20), suggesting AKT phosphorylation reflects insulin concentration rather than any measure of protein synthesis. Likewise, with its suggested role as an amino acid sensor, mTOR phosphorylation may reflect extracellular amino acid availability rather than the commitment of the muscle cell to enhance MPS. As such, our results show clearly that the failure of amino acids to stimulate MPS in the older obese men is not due to an inability to phosphorylate AKT and mTOR.

Whole-body protein breakdown has been found to be inhibited less in the fed state in obese versus nonobese younger subjects (25). Given that muscle is reported to account for only 25% of whole-body proteolysis in the basal state (33), however, the implications of these findings remain unclear. Despite the heightened systemic inflammatory state of obese individuals in this study, this did not translate into increased LPB under postabsorptive conditions. Indeed, our results suggest that the rate of LPB in the postprandial state was lower in obese than in healthy-weight volunteers. Therefore, the inability of amino acids to stimulate MPS in the obese appeared to be offset largely by a concomitant decline in the rate of LPB, culminating in the magnitude of the change in net phenylalanine balance between the postabsorptive and postprandial states being equivalent among both healthy-weight and obese subjects. This represents one potential mechanism for the equivalent leg lean mass in both groups, although the consequence of assessing volunteers under acute conditions in the rested state is that the contribution of habitual physical activity levels and dietary behavior on chronic muscle protein turnover, and thereby muscle mass, remains unknown.

To conclude, obesity in older men is aligned with systemic, but not muscle, inflammation. We found no evidence that obese, nonfrail, older men are at increased risk of accelerated muscle mass loss or impaired contractile function (strength and fatigability) compared with their healthy-weight counterparts. However, our results highlight the negative effect that obesity has on the metabolic quality of skeletal muscle in older adults. The exact role that inactivity plays in the decline in muscle metabolic health in the older obese adult remains unclear, but it could prove to be the central causative feature and should be the focus of future work.

Acknowledgments. The authors are grateful to the volunteers who took part in the study and to Dr. Liz Simpson, Aline Nixon, and Sara Brown (Life Sciences, University of Nottingham) for their technical assistance.

Funding. Support for this study was provided by the Biotechnology and Biological Sciences Research Council, U.K. (BB/G011435/1).

Duality of Interest. No potential conflicts of interest relevant to this article were reported.

Author Contributions. A.J.M., K.M., J.E.M., and A.L.S. conducted the research. A.J.M., K.M., J.E.M., K.S., and P.L.G. edited and revised the manuscript. A.J.M., A.L.S., K.S., and P.L.G. analyzed the data. A.J.M., K.S., M.J.R., and P.L.G. designed the study. A.J.M. and P.L.G. drafted the manuscript. A.J.M. and P.L.G. are the guarantors of this work and, as such, had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

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