ORIGINAL ARTICLE Journal of Cachexia, Sarcopenia and Muscle 2019; 10: 974–984 Published online 29 May 2019 in Wiley Online Library (wileyonlinelibrary.com) DOI: 10.1002/jcsm.12442 Association of lifestyle factors and inflammation with sarcopenic obesity: data from the PREDIMED-Plus trial Itziar Abete1,2,3†, Jadwiga Konieczna2,4† , M. Angeles Zulet1,2,3* , Aina M. Galmés-Panades2,4, Idoia Ibero-Baraibar1, Nancy Babio2,5, Ramón Estruch2,6, Josep Vidal7,8, Estefanía Toledo2,3,9, Cristina Razquin3,9, Rafael Bartolomé10, Andrés Díaz-Lopez2,5, Miquel Fiol2,4, Rosa Casas2,6, Josep Vera11, Pilar Buil-Cosiales2,3,9,10, Xavier Pintó2,12, Emili Corbella2,12, Maria Puy Portillo2,13, Jose Antonio de Paz14, Vicente Martín15,16, Lidia Daimiel17, Albert Goday18, Nuria Rosique- Esteban2,5, Jordi Salas-Salvadó2,5, Dora Romaguera2,4, J. Alfredo Martínez1,2,3,17 & on behalf of PREDIMED-PLUS Investigators 1 Department of Nutrition, Food Sciences and Physiology, Center for Nutrition Research, University of Navarra (UNAV), Pamplona, Spain, 2CIBER Fisiopatología de la Obesidad y Nutrición (CIBERobn), Instituto de Salud Carlos III, Madrid, Spain, 3IdiSNA (Instituto de Investigación Sanitaria de Navarra), Pamplona, Spain, 4Instituto de Investigación Sanitaria Illes Balears (IdISBa), University Hospital Son Espases, Palma de Mallorca, Spain, 5Department of Biochemistry and Biotechnology, Human Nutrition Unit, IISPV, Rovira i Virgili University, Hospital Universitari Sant Joan de Reus, Reus, Spain, 6Department of Internal Medicine, IDIBAPS, Hospital Clinic, University of Barcelona, Barcelona, Spain, 7Department of Endocrinology, IDIBAPS, Hospital Clinic, University of Barcelona, Barcelona, Spain, 8CIBER Diabetes y enfermedades metabólicas (CIBERdem), Instituto de Salud Carlos III (ISCIII), Madrid, Spain, 9Department of Preventive Medicine and Public Health, University of Navarra, Pamplona, Spain, 10Atención Primaria, Servicio Navarro de Salud-Osasunbidea, Pamplona, Spain, 11Institut Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain, 12 Vascular Risk Unit, Internal Medicine Department, Bellvitge University Hospital—IDIBELL, Hospitalet de Llobregat, Barcelona, Spain, 13Nutrition and Obesity Group, Department of Nutrition and Food Science, University of the Basque Country (UPV/EHU) and Lucio Lascaray Research Institute, Vitoria, Spain, 14Instituto de Biomedicina (IBIOMED), University of León, León, Spain, 15Division of Preventive Medicine, University of León, León, Spain, 16CIBER Epidemiología y Salud Pública (CIBEResp), Instituto de Salud Carlos III (ISCIII), Madrid, Spain, 17Madrid Institute for Advanced Studies (IMDEA), Food Institute, Madrid, Spain, 18Lipids and Cardiovascular Epidemiology Research Unit, Institut Municipal d’Investigació Mèdica (IMIM), Endocrinology and Diabetes Unit, Department de Medicina, Hospital del Mar Barcelona, Universitat Autònoma de Barcelona, Barcelona, Spain Abstract Background Sarcopenia is a progressive age-related skeletal muscle disorder associated with increased likelihood of adverse outcomes. Muscle wasting is often accompanied by an increase in body fat, leading to ‘sarcopenic obesity’. The aim of the present study was to analyse the association of lifestyle variables such as diet, dietary components, physical activity (PA), body composition, and inflammatory markers, with the risk of sarcopenic obesity. Methods A cross-sectional analysis based on baseline data from the PREDIMED-Plus study was performed. A total of 1535 participants (48% women) with overweight/obesity (body mass index: 32.5 ± 3.3 kg/m2; age: 65.2 ± 4.9 years old) and metabolic syndrome were categorized according to sex-specific tertiles (T) of the sarcopenic index (SI) as assessed by dual-energy X-ray absorptiometry scanning. Anthropometrical measurements, biochemical markers, dietary intake, and PA information were collected. Linear regression analyses were carried out to evaluate the association between variables. Results Subjects in the first SI tertile were older, less physically active, showed higher frequency of abdominal obesity and diabetes, and consumed higher saturated fat and less vitamin C than subjects from the other two tertiles (all P < 0.05). Multiple adjusted linear regression models evidenced significant positive associations across tertiles of SI with adherence to the Mediterranean dietary score (P-trend < 0.05), PA (P-trend < 0.0001), and the 30 s chair stand test (P-trend < 0.0001), whereas significant negative associations were found with an inadequate vitamin C consumption (P-trend < 0.05), visceral fat and leucocyte count (all P-trend < 0.0001), and some white cell subtypes (neutrophils and monocytes), neutrophil-to- lymphocyte ratio, and platelet count (all P-trend < 0.05). When models were additionally adjusted by potential mediators (inflammatory markers, diabetes, and waist circumference), no relevant changes were observed, only dietary variables lost significance. © 2019 The Authors. Journal of Cachexia, Sarcopenia and Muscle published by John Wiley & Sons Ltd on behalf of the Society on Sarcopenia, Cachexia and Wasting Disorders This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any me- dium, provided the original work is properly cited and is not used for commercial purposes. Baseline data from the PREDIMED-Plus trial 975 Conclusions Diet and PA are important regulatory mediators of systemic inflammation, which is directly involved in the sarcopenic process. A healthy dietary pattern combined with exercise is a promising strategy to limit age-related sarcopenia. Keywords Sarcopenic index; Visceral fat; Leucocyte count; Mediterranean diet score; Systemic inflammation; Physical activity Received: 24 September 2018; Accepted: 5 April 2019 *Correspondence to: M. Angeles Zulet, Department of Nutrition, Food Sciences and Physiology, Center for Nutrition Research, University of Navarra (UNAV), Pamplona, Spain. Phone: +34 948425600 ext 806317/fax, Email: mazulet@unav.es †Itziar Abete and Jadwiga Konieczna shared the first authorship. Introduction decelerate diet-related and age-related diseases.17,18 There- fore, it could be hypothesized that lifestyle could play an Sarcopenia is an age-associated process characterized by a important role in the management and reversion of muscle progressive loss of skeletal muscle mass and strength.1 This mass loss and promote a healthy body composition (BC). In condition is a major health concern in older adults because fact, PA (resistance training) in combination with proper nu- it has been associated with metabolic impairments, cardio- trition (protein intake) is a promising strategy to limit age- vascular disease risk factors, and physical and functional dis- related sarcopenia.19 Nonetheless, little information is avail- ability and increases the likelihood/risk of early mortality.2 able comparing nutrient intake, BC, and lifestyle between All these metabolic and physical alterations cause important sarcopenic and non-sarcopenic obese older adults as well healthcare costs and significantly affect quality of life.3 as about the role of inflammatory processes associated with Sarcopenia often coexists with obesity leading to a specific sarcopenia. The aim of the present study was to analyse the condition named ‘sarcopenic obesity’.4 Current evidences association of lifestyle variables such as diet, dietary compo- suggest that sarcopenic obesity may be associated with a nents, PA, BC, and inflammatory markers, with the risk of larger number of metabolic disorders and an increased risk sarcopenic obesity. of mortality than obesity or sarcopenia alone.5 However, few studies have been carried out in this field and with con- tradictory results. Cross-sectional studies in sarcopenic obe- Materials and methods sity subjects have reported higher prevalence of cardiovascular risk factors and metabolic syndrome in those individuals.6,7 Strong associations with inflammatory markers compared with sarcopenia-only subjects have been found.8 Study design Conversely, some longitudinal studies have shown that sarcopenic obesity does apparently not confer any greater The current research is a cross-sectional analysis including mortality risk than sarcopenia alone.9–11 baseline data of the PREDIMED-Plus study, a 6 year There is evidence that the biological process of ageing is multicentre, randomized, parallel-group, primary prevention characterized by oxidative stress and mitochondrial dysfunc- clinical trial conducted in Spain to assess the effect on cardio- tion.12 Increased reactive oxygen species production and vascular disease morbi-mortality of a weight loss intervention decreased antioxidant defences in older people seem to programme based on an energy-restricted traditional be important factors contributing to muscle impairment.13 Mediterranean diet, PA promotion, and behavioural support, During ageing, in turn, chronic low-grade inflammation, in comparison with a usual care intervention only with called inflammaging, develops, which contributes to the energy-unrestricted Mediterranean diet (control group). A pathogenesis of age-related diseases.14 Inflammaging has more detailed description of the PREDIMED-Plus study has been described as a common biological component of main been recently published,20 and there is available study infor- age-related chronic diseases such as atherosclerosis and mation at http://predimedplus.com/. This study was regis- type 2 diabetes mellitus, and age-related conditions like tered at the International Standard Randomized Controlled sarcopenia, frailty, and disability.15 Another new concept Trial (http://www.isrctn.com/ISRCTN89898870) with Number that has emerged is ‘metaflammation’, the metabolic in- 89898870 (registration date 24 July 2014). flammation accompanying metabolic diseases driven by nu- trient excess or overnutrition; metaflammation is characterized by the same mechanisms underpinning Study subjects inflammaging. Lifestyle factors like diet and physical activity (PA) are able to modulate oxidative and inflammatory pro- A total of 6874 participants were recruited at 23 different cesses.16 Improving nutrition has been proposed as an ef- research sites in Spain and randomized to one of the two fective strategy to reduce inflammaging and to prevent or study groups (September 2013–December 2016). The eligible Journal of Cachexia, Sarcopenia and Muscle 2019; 10: 974–984 DOI: 10.1002/jcsm.12442 976 I. Abete et al. participants were community-dwelling adults (aged internal procedures. Body weight (kg) and height (cm) were 65.2 ± 4.9 years) with overweight/obesity [body mass index measured in light clothing and without shoes with the use (BMI) = 32.5 ± 3.3 kg/m2], who met at least three compo- of a calibrated scale and a wall-mounted stadiometer, nents of the metabolic syndrome according to the updated respectively. BMI was calculated as weight (kg) divided by harmonized criteria of the International Diabetes Federation the square of height (m). Waist circumference (cm) was and the American Heart Association and National Heart, measured midway between the lowest rib and the iliac Lung and Blood Institute.21 Information about exclusion crest using a measuring tape. Blood pressure and heart rate criteria has been previously published.20 Most of partici- were measured in triplicate with the use of a validated pants (97.5%) were of Caucasian origin. All participants semiautomatic oscillometer (Omron HEM-705CP, The provided written informed consent, and the study protocol Netherlands) according to World Health Organization and procedures were approved according to the ethical criteria. Hypertensive participants were defined as those standards of the Declaration of Helsinki by all the participat- that registered high blood pressure levels (≥130/85 mmHg) ing institutions. at baseline or were on blood pressure medication. The present work encompasses a subsample of partici- Blood samples were collected at baseline after 12 h pants who underwent dual-energy X-ray absorptiometry overnight fast and were used to perform biochemical anal- (DXA) scans for BC assessment (n = 1535) in seven out of yses by means of standard laboratory enzymatic methods. the 23 PREDIMED-Plus recruiting centres, as these were the These analyses included plasma glucose (mg/dL), glycated only centres with available DXA scanner for this research. haemoglobin (HbA1c, %), high-density lipoprotein choles- terol (HDL-c, mg/dL), and triglyceride (mg/dL) levels. Current diabetes was defined as previous diagnosis of Assessment of body composition with dual-energy diabetes or HbA1c ≥ 6.5%, use of antidiabetic medication, X-ray absorptiometry or fasting glucose >126 mg/dL in the screening visit plus fasting glucose >126 mg/dL at baseline visit. Participants Dual-energy X-ray absorptiometry scans (Lunar iDXA and DXA with low HDL-c concentration were those who had low Lunar Prodigy Primo, GE Healthcare) were performed by HDL-c levels, defined as <40 mg/dL in men and trained radiology technicians to assess BC following a vali- <50 mg/dL in women, or with HDL-c medication at base- dated standardized protocol and subject positioning provided line. Hypertriglyceridaemia was defined as presence of high by the manufacturer. The DXA was calibrated daily according triglyceride levels (≥150 mg/dL) or triglyceride-lowering to manufacturer guidelines. Thus, total bone mass, fat mass, drugs. A complete blood count was also performed follow- lean mass, fat-free mass, and regionally distributed fat and ing standardized procedures. Blood parameters assessed in lean mass (trunk, android, gynoid, arms, and legs) were de- this study were the white blood cell count, its subtypes termined. For visceral adipose tissue measures in the android (lymphocytes, monocytes, neutrophils, and eosinophils), region, scans were reanalysed using validated CoreScan soft- and platelet count. ware application.22 Appendicular skeletal muscle mass (ASM, kg) was calculated as the sum of the muscle mass from the four limbs as described elsewhere.23 Dietary variables Calculation of sarcopenic indexes At baseline, a trained dietitian administered a validated 2 Skeletal muscle mass index (kg/m ) was calculated with the 143-item semi-quantitative food frequency questionnaire equation ASM (kg)height2 (m). The sarcopenic index (SI) to determine dietary factors in a face-to-face visit.26 Partic- was obtained by dividing the amount of ASM (kg) by the body ipants were asked about their frequency consumption of weight (kg) × 100.24 Participants were categorized according each specific item during the preceding year. There were to the sex-specific SI tertiles (women: T1: <21.0%, T2: ≥21.0 nine possible answers ranging from never to more than to <22.7%, and T3: ≥22.7; men: T1: <26.3%, T2: ≥26.3 to six times per day, which were transformed to g/day, taking <28.5%, and T3: ≥28.5%), which provide a good estimate of into account the standard portion size of each item. Two the amount of skeletal muscle mass relative to body size.25 Spanish food composition tables were used to calculate Thus, the higher index, the better for health. total energy and nutrients intake.27,28 The Mediterranean dietary pattern was calculated according to a validated Mediterranean dietary score,29 Anthropometric, clinical, and biochemical variables considering the consumption of nine food groups or nutri- ents (cereals, fruits and nuts, vegetables, legumes, fish, The anthropometric variables were measured at the base- meat, dairy products, ratio of monounsaturated to satu- line visit by trained staff according to the PREDIMED-Plus rated fatty acids, and alcohol). Journal of Cachexia, Sarcopenia and Muscle 2019; 10: 974–984 DOI: 10.1002/jcsm.12442 Baseline data from the PREDIMED-Plus trial 977 Physical activity variables plus mediators (direct effect determined using DAGs) were run: diabetes, neutrophil-to-lymphocyte ratio (NLR) (in Leisure-time PA at baseline was evaluated with the validated models with dietary, PA, and BC variables as independent var- REGICOR questionnaire as detailed elsewhere,30 which in- iables), and waist circumference (in models with dietary and cluded questions about the activity type, frequency (number PA variables as independent variables). of days), and duration (min/day) performed during a repre- Lastly, we evaluated plausible effect modification by age sentative month. As described previously,31 time spent in group (<65 or ≥65 years of age), sex, diabetes (yes/no), obe- PA (min/day) was further obtained by assigning to each activ- sity prevalence (BMI > 30 kg/m2), and centre, by adding an ity their corresponding intensity according to the compen- interaction term between these variables and all exposures dium of PA.32,33 Time spent in total PA was computed as in a multiple adjusted linear regression model (Model 2). the sum of the time from all PA intensities. The 30 s chair When a significant interaction was detected, stratified analy- stand test was used as an indicator of the lower-limb muscle ses were conducted. strength. As reported, performance was based on the num- Statistical analyses were performed using Stata 12.0, and ber of times participants stand and sit in a chair in 30 s.34 the statistical significance was set at P < 0.05 for bilateral contrast. Other sociodemographic variables Participants self-reported baseline age, sex, smoking habit, Results and educational level. Smoking was categorized as current, former, and never smokers. Education was categorized as Subjects were categorized according to SI sex-specific tertiles. bachelor’s degree, primary/secondary school, and no An overview on clinical and sociodemographic data, BC and education/no data. inflammatory markers, and dietary characteristics, consider- ing SI tertiles, are given in Tables 1, 2, and 3, respectively. Participants from the first SI tertile were on average older, Statistical analyses shorter, and had higher BMI, heart rate, and blood glucose level than subjects from the upper two tertiles (all Normality of variables was initially studied by using the P < 0.05) (Table 1). Subjects in the first SI tertile also showed Shapiro–Wilk test. Baseline characteristics of the study higher frequency of abdominal obesity and diabetes but participants are presented as means ± standard deviation lower frequency of low HDL-c and hypertriglyceridaemia than for quantitative variables and numbers and percentages for subjects from the upper two tertiles (all P < 0.05) (Table 1). categorical variables. Differences in anthropometric, BC, and No differences in smoking habit and educational level were biochemical variables, dietary characteristics, and food group observed among groups. On the other hand, significant intake among the three SI sex-specific tertiles were tested by differences were found in PA among SI tertiles (P < 0.0001) analysis of variance and the χ 2 test for categorical variables. (Table 1). The 30 s chair test scoring was lower in participants Multiple adjusted linear regression models were used to from the first tertile as compared with subjects from the evaluate the association between the variables showing a second and third tertiles (P < 0.0009) (Table 1). strong relationship (in tertiles) with SI (as continuous vari- Body composition variables showed no differences in bone able). We run first a minimally adjusted Model 1 including mass between groups. Subjects in the first tertile had higher sex, age, and centre (all exposures). Model 2 was adjusted total fat mass, as well as higher trunk, android, gynoid, and by the minimally sufficient adjustment set, determined using visceral fat content than subjects from the second and third directed acyclic graphs (DAGs) implemented in DAGitty soft- tertiles (all P < 0.0001) (Table 2). Regarding lean muscle ware,35 available free on www.dagitty.net. The DAGs were mass, subjects in the first SI tertile had lower total lean mass built by identifying known factors affecting each of our expo- and lower skeletal muscle mass index and fat-free mass sures on SI (see DAGs in Supporting Information, Figure S1A– content than subjects from the other groups (all P < 0.001) D). Thus, assuming the total effect of our exposures on SI, the (Table 2). covariables used in Model 2 included sex, age, centre, PA (in No significant differences were observed in total energy models with dietary, BC, and inflammatory variables as inde- intake among SI tertiles. However, subjects from the first pendent variables), Mediterranean dietary score (in models tertile consumed less carbohydrate, fibre, and vitamin C and with PA, BC, and inflammatory variables as independent var- more saturated fat, than subjects from the other two tertiles iables), and waist circumference (in models with inflamma- (all P < 0.05) (Table 3). When dietary food groups were tory variables as independent variables). assessed, main differences were observed in fruits, cereals, Additionally, as sensitivity analyses, multivariable models and extra virgin olive oil, whose consumption was decreased (Model 3) adjusted for the same covariables as in Model 2 in participants with lower SI (all P < 0.05) (Table 3). Journal of Cachexia, Sarcopenia and Muscle 2019; 10: 974–984 DOI: 10.1002/jcsm.12442 978 I. Abete et al. Table 1 Clinical and sociodemographic characteristics of the study participants according to sex-specific SI tertiles (T) SI (%) P-value T1 T2 T3 Men: 801 <26.3% ≥26.3 to <28.5% ≥28.5% Women: 734 <21.0% ≥21.0 to <22.7% ≥22.7% n = 1535 512 513 510 — Men/women 267/245 268/245 266/244 — Clinical variables Age (years) 65.6 ± 5.0 65.3 ± 4.9 64.8 ± 5.0 0.036 Weight (kg) 90.2 ± 13.1 85.9 ± 12.6 82.8 ± 11.7 <0.0001 Height (cm) 162.0 ± 9.1 163.5 ± 9.5 163.6 ± 9.3 0.019 2 BMI (kg/m ) 34.2 ± 3.3 32.3 ± 3.0 30.8 ± 2.6 <0.0001 SBP (mmHg) 139.7 ± 16.9 140.1 ± 16.8 139.8 ± 16.8 0.940 DBP (mmHg) 79.9 ± 10.7 80.3 ± 9.8 80.3 ± 10.5 0.804 HR (b.p.m.) 70.9 ± 11.0 69.6 ± 10.8 67.6 ± 10.3 <0.0001 Glucose (mg/dL) 115.4 ± 32.6 112.1 ± 25.6 110.3 ± 23.9 0.010 Glycated haemoglobin (%) 6.10 ± 0.90 6.10 ± 0.70 6.00 ± 0.70 0.050 Metabolic syndrome criteria Abdominal obesity, n (%) 504 (98) 480 (94) 455 (89) <0.0001* Diabetes, n (%) 200 (39) 163 (32) 153 (30) 0.021* Hypertension, n (%) 476 (93) 479 (93) 468 (92) 0.539* Low HDL-c, n (%) 203 (40) 249 (49) 257 (50) 0.001* Hypertriglyceridaemia, n (%) 251 (49) 298 (58) 313 (61) <0.0001* Sociodemographic variables Smoking habit, n (%) Never 230 (45) 208 (41) 211 (41) 0.523* Current 64 (12) 72 (14) 56 (11) Former 218 (43) 231 (45) 243 (48) Family history of obesity (%) 289 (57) 272 (53.2) 243 (48.0) 0.016* Educational level, n (%) Bachelor’s degree 104 (20) 113 (22) 110 (22) 0.930* Primary/secondary 403 (79) 396 (77) 396 (77) No education/no data 6 (1) 4 (1) 5 (1) Physical activity (MET, min/day) Inactive 329 (64) 274 (53) 245 (48) <0.0001* Moderately active 85 (17) 100 (19) 130 (26) Active 98 (19) 139 (27) 134 (26) 30 s chair test (repeats) 13.5 ± 5.1 14.3 ± 5.3 14.7 ± 5.2 0.0009 BMI, body mass index; DBP, diastolic blood pressure; HR, heart rate; HDL-c, high-density lipoprotein cholesterol; MET, metabolic equiva- lent; SBP, systolic blood pressure; SI, sarcopenic index. SI was calculated as a percentage of the total body weight: SI = [appendicular skeletal muscle mass (kg)weight (kg)] × 100. Data are pre- sented as means ± standard deviations for quantitative variables and number and percentages for categorical variables. Differences in characteristics among the three SI sex-specific tertiles were tested by analysis of variance. Metabolic syndrome criteria: abdominal obesity: men: waist circumference ≥102 cm and women: waist circumference ≥88 cm; diabetes: fasting glucose ≥100 mg/dL or glycated haemoglobin ≥6.5% or taking treatment; hypertension: SBP ≥ 135 mmHg or DBP ≥ 85 mmHg or taking antihypertensive medication; low HDL-c levels: men ≤40 mg/dL and women ≤50 mg/dL or taking specific medication to regulate HDL-c; and hypertriglyceridaemia: fasting triglyceride concentration ≥150 mg/dL or taking medication to regulate triglycerides. 2 *Categorical variables: the P-value was obtained by means of the χ test. Moreover, lower adherence to a validated Mediterranean di- trend = 0.018), visceral fat (P-trend < 0.0001), leucocyte count etary score was observed in the first tertile group as com- (P-trend < 0.0001), and some white cell subtypes [neutrophils pared with the second and third tertile participants and monocytes (P-trend = 0.012)], NLR (P-trend = 0.010), and (P = 0.001) (Table 3). platelet count (P-trend = 0.020). In Supporting Information, Linear regression models were set up with SI as the Table S1 are shown other independent variables that were dependent variable and lifestyle variables such as diet, PA, significantly associated to SI in descriptive analyses. Carbo- BC, and inflammatory markers as independent factors hydrates, fruits intake, and lean mass were significantly (Table 4). Both minimally adjusted (Model 1) and multiple and positively associated with SI (all P-trend < 0.05), adjusted (Model 2) models showed significant positive whereas saturated fat consumption, total fat, gynoid fat, associations between the lowest to highest tertile of SI and ad- and eosinophil count were inversely associated with SI (all herence to the Mediterranean dietary score (P-trend = 0.045), P-trend ≤ 0.01). In order to estimate the magnitude of the PA (P-trend < 0.0001), and the 30 s chair stand test (P- association explained by the interrelation between lifestyle, trend < 0.0001), whereas significant negative associations obesity, inflammation, and sarcopenia, an additional Model were found with an inadequate vitamin C consumption (P- 3 including all covariates to estimate the direct effect of Journal of Cachexia, Sarcopenia and Muscle 2019; 10: 974–984 DOI: 10.1002/jcsm.12442 Baseline data from the PREDIMED-Plus trial 979 Table 2 Body composition by DXA scanning and inflammatory markers of study participants according to SI sex-specific tertiles (T) SI (%) P-value T1 T2 T3 Men <26.3% ≥26.3 to <28.5% ≥28.5% Women <21.0% ≥21.0 to <22.7% ≥22.7% Body composition variables Bone mass (kg) 2.6 ± 0.5 2.6 ± 0.5 2.6 ± 0.5 0.255 Total fat mass (%) 43.8 ± 6.0 40.3 ± 6.3 36.5 ± 6.5 <0.0001 Trunk fat (kg) 23.2 ± 4.3 20.1 ± 3.5 17.4 ± 2.9 <0.0001 Android fat (kg) 4.3 ± 0.9 3.6 ± 0.7 3.1 ± 0.6 <0.0001 Gynoid fat (kg) 5.7 ± 1.4 5.0 ± 1.3 4.2 ± 1.0 <0.0001 VAT (kg) 2.6 ± 0.9 2.3 ± 0.8 2.0 ± 0.6 <0.0001 Total lean mass (%) 53.1 ± 5.8 56.5 ± 6.0 60.1 ± 6.2 <0.0001 2 SMI (kg/m ) 7.6 ± 1.0 8.0 ± 1.1 8.4 ± 1.1 <0.0001 FFM (kg) 49.8 ± 9.8 50.8 ± 10.3 52.2 ± 10.6 0.0008 Inflammatory markers 9 WBC (× 10 /L) 6.9 ± 1.8 6.8 ± 2.0 6.5 ± 3.2 0.043 Lymphocytes 2.5 ± 3.5 2.6 ± 3.8 2.4 ± 3.3 0.844 Monocytes 0.6 ± 0.7 0.5 ± 0.7 0.5 ± 0.7 0.908 Neutrophils 4.8 ± 6.7 4.5 ± 6.3 4.2 ± 6.3 0.434 Eosinophils 0.2 ± 0.4 0.2 ± 0.2 0.2 ± 0.2 0.236 NLR 2.2 ± 3.4 2.0 ± 1.6 1.8 ± 1.6 0.072 9 Platelet count (× 10 /L) 230 ± 57 227 ± 54 226 ± 57 0.440 DXA, dual-energy X-ray absorptiometry; FFM, fat-free mass; NLR, neutrophil-to-lymphocyte ratio; VAT, visceral adipose tissue; SI, sarcopenic index; SMI, skeletal muscle mass index; WBC, white blood cell count. Differences in characteristics among the three SI sex-specific tertiles were tested by analysis of variance. Table 3 Dietary consumption of study participants according to SI sex-specific tertiles (T) SI (%) P-value T1 T2 T3 Men: 803 <26.3% ≥26.3 to <28.5% ≥28.5% Women: 734 <21.0% ≥21.0 to <22.7% ≥22.7% Nutrients Energy intake (kcal/day) 2402 ± 582 2389 ± 576 2443 ± 589 0.293 Carbohydrates (%) 39.6 ± 6.4 40.0 ± 6.7 40.9 ± 6.2 0.003 Proteins (%) 16.4 ± 2.6 16.4 ± 2.7 16.1 ± 2.4 0.094 Lipids (%) 40.6 ± 6.1 40.2 ± 6.2 39.7 ± 5.8 0.052 PUFA (%) 6.2 ± 1.8 6.3 ± 1.8 6.2 ± 1.7 0.553 SFA (%) 10.3 ± 1.9 10.0 ± 1.9 9.8 ± 1.9 0.0001 MUFA (%) 21.3 ± 4.5 21.0 ± 4.3 20.8 ± 4.0 0.238 Fibre (g/day) 25.2 ± 8.3 25.4 ± 9.0 26.8 ± 8.2 0.003 Alcohol intake (g/day) 11.6 ± 15.2 11.8 ± 15.5 11.7 ± 15.1 0.974 ω3 fatty acids (g/day) 0.85 ± 0.44 0.86 ± 0.45 0.88 ± 0.46 0.578 Fish ω3 fatty acids (g/day) 0.63 ± 0.33 0.63 ± 0.35 0.66 ± 0.36 0.312 Vitamin C (mg/day) 194 ± 81 202 ± 89 209 ± 84 0.016 Vitamin D (μg/day) 5.8 ± 3.1 5.8 ± 3.2 6.0 ± 3.3 0.610 Food groups Vegetables (g/day) 300 ± 116 308 ± 127 318 ± 122 0.054 Fruits (g/day) 321 ± 193 328 ± 192 366 ± 206 0.0004 Legumes (g/day) 20.0 ± 11.4 19.6 ± 10.4 19.5 ± 9.6 0.686 Cereals (g/day) 146 ± 79 153 ± 84 163 ± 83 0.004 Dairy products (g/day) 345 ± 215 336 ± 206 329 ± 193 0.418 Meats (g/day) 157 ± 59 155 ± 61 149 ± 56 0.087 Extra virgin olive oil (g/day) 31.5 ± 21.0 33.9 ± 19.9 34.6 ± 19.8 0.038 Fish (g/day) 95.9 ± 42.3 95.8 ± 45.2 101 ± 47 0.150 Nuts (g/day) 14.0 ± 17.9 14.2 ± 16.0 14.9 ± 17.1 0.649 Confectionery (g/day) 26.5 ± 33.5 26.3 ± 28.5 28.1 ± 34.1 0.606 Mediterranean diet score (points) 4.2 ± 1.5 4.3 ± 1.6 4.5 ± 1.6 0.001 MUFA, monounsaturated fatty acids; PUFA, polyunsaturated fatty acids; SFA, saturated fatty acids; SI, sarcopenic index. Differences in characteristics among the three SI sex-specific tertiles were tested by analysis of variance. Journal of Cachexia, Sarcopenia and Muscle 2019; 10: 974–984 DOI: 10.1002/jcsm.12442 980 I. Abete et al. Table 4 Linear regression models with SI as the dependent variable and lifestyle, body composition, and inflammatory variables as independent factors Model 1, β (95%CI) Model 2, β (95%CI) Model 3, β (95%CI) Dietary variables Mediterranean diet T1 Ref Ref Ref score (points) T2 0.02 (–0.25; 0.30) 0.01 ( 0.27; 0.26) 0.14 ( 0.39; 0.09) T3 0.39 (0.09; 0.69) 0.32 (0.02; 0.62) 0.16 ( 0.10; 0.42) P-trend 0.015 0.045 0.340 Vitamin C (mg/day) ≥RDA Ref Ref Ref