Associations between Mediterranean diet adherence and body composition and metabolic outcomes in older adults at risk of sarcopenic obesity

Associations between Mediterranean diet adherence and body composition and metabolic outcomes in older adults at risk of sarcopenic obesity | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Associations between Mediterranean diet adherence and body composition and metabolic outcomes in older adults at risk of sarcopenic obesity Surbhi Sood, Costas Glavas, Melkamu Tamir Hunegnaw, Paul Jansons, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8917541/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Background Sarcopenic obesity (SO) is prevalent in older adults and is associated with metabolic dysfunction and adverse health outcomes. Lifestyle factors including diet may influence SO development and associated metabolic perturbations. This study examined associations between MedDiet adherence and body composition and metabolic outcomes in community-dwelling older adults at risk of SO and explored links between individual food groups and these outcomes. Methods This cross-sectional study included adults aged ≥ 60 years at risk of SO (BMI ≥ 30 kg/m 2 , SARC-F score ≥ 2). Diet was assessed via 24-hour recall, and MedDiet measured using Mediterranean Diet Score (range 0–45). Body composition was measured by dual-energy X-ray absorptiometry. Results Among 116 participants (mean ± SD age 66.5 ± 4.1 years; MDS 23.1 ± 5.0), higher MedDiet adherence was not associated with body composition [fat mass (β= -0.01; 95%CI: -1.93, 1.90), percentage body fat (β= -0.39; 95%CI: -1.17, 0.40), lean soft tissue mass (β = 1.00; 95%CI: -0.28, 2.29), visceral fat (β = 5.56; 95%CI: -60.72, 71.84), appendicular lean mass (β= -0.01; 95%CI: -0.18, 0.17)]. In exploratory analysis, higher fish intake was associated with lower waist-to-hip ratio (β= -0.0053; 95%CI: -0.01, -0.0005), higher poultry intake with higher high-density lipoprotein (β = 0.05; 95%CI: 0.01, 0.08) and lower diastolic blood pressure (β= -1.39; 95%CI: -2.71, -0.08), and higher grains intake with lower low-density lipoprotein (β= -0.14; 95%CI: -0.28, -0.01) in fully adjusted models. Conclusions In older adults at risk of SO, MedDiet adherence showed no associations with body composition and limited associations with metabolic outcomes, though higher fish, poultry and grains intake had modest associations with anthropometric and metabolic outcomes. Trial registration (ANZCTR): ACTRN12621000236897. Date of registration: 05/03/2021. Digital health Sarcopenic obesity Body composition Mediterranean diet Background Sarcopenia refers to the progressive loss of skeletal muscle mass and strength associated with aging, which can lead to long-term functional decline and disability [ 1 ]. Depending on the diagnostic criteria used, the global prevalence of sarcopenia is estimated to affect between 10 and 29% of community-dwelling older adults [ 2 , 3 ], with some estimates as high as 55% [ 4 – 6 ]. Sarcopenia often co-exists with obesity, a chronic condition known as sarcopenic obesity (SO), which has an estimated prevalence ranging from 7.9% to 23% in clinical and 7.1% to 9.6% in community-dwelling settings [ 5 , 7 ], and is projected to increase due to our ageing population and global obesity epidemic [ 8 ]. SO is associated with metabolic dysfunction, insulin resistance and fractures, resulting in greater risk of disability, hospitalisation, and mortality [ 9 ]. Lifestyle interventions shown to be effective in managing SO include dietary strategies that achieve energy restriction with adequate protein intake and high quality diet (e.g. Mediterranean dietary pattern) and structured exercise [ 7 , 9 – 12 ]. Previous studies have reported on diet-induced weight loss strategies and interventions focusing on isolated nutrients including protein, vitamin D and omega-3 fatty acids due to their established roles in muscle function [ 13 , 14 ]. However, weight loss interventions with energy restriction, and with poor diet quality and exercise may potentially exacerbate age-related SO due to disproportionate loss of skeletal muscle mass and function [ 15 , 16 ]. Additionally, supplementation with protein, vitamin D or omega-3 fatty acids alone may be insufficient, as previous evidence suggests that single nutrients yield inconsistent effects on muscle mass when not combined with comprehensive lifestyle approaches [ 17 ]. Therefore, interventions combining whole dietary patterns with structured exercise are needed to promote fat loss and preserve muscle mass [ 18 ]. One such dietary pattern is the Mediterranean diet (MedDiet), which has demonstrated protective effects against chronic diseases such as metabolic dysfunction associated steatotic liver disease (MASLD), type 2 diabetes mellitus (T2DM) and cardiovascular disease (CVD) [ 19 ]. The MedDiet is characterised by a nutrient-dense, anti-inflammatory dietary pattern emphasising whole grains, fruits, vegetables, legumes, nuts and seeds, and olive oil, moderate intake of fish, poultry, and low-fat dairy and limited intake of red and processed meats and added sugars [ 20 – 22 ]. Higher MedDiet adherence has been associated with better cardiometabolic markers, including blood pressure, insulin sensitivity, and dyslipidaemia, as well as favourable weight outcomes [ 23 – 25 ], better muscle mass, function and strength in older adults [ 26 – 28 ]. Additionally, given the increased burden of T2DM in older adults and in individuals with SO [ 29 , 30 ], associations between MedDiet adherence and body composition (e.g. fat mass) and metabolic outcomes (e.g. insulin resistance) may differ depending on diabetes status [ 31 ]. Despite evidence supporting the benefits of individual and combined dietary quality and exercise, data examining the associations between MedDiet adherence and body composition and metabolic health in community-dwelling older adults at risk of SO remain limited [ 32 ]. The primary aim of this study was to investigate associations between MedDiet adherence and body composition and metabolic outcomes in community-dwelling older adults at risk of SO. Secondary aims were to investigate exploratory associations between individual food groups and body composition and metabolic outcomes, and to examine whether T2DM status modified the associations between MedDiet and these outcomes. Methods Study design and participants This cross-sectional secondary analysis was conducted using baseline data from a 24-week randomised controlled trial (RCT) of 116 community-dwelling older adults residing in Melbourne, Australia, aged ≥ 60 years at risk of SO (self-reported BMI ≥ 30 kg/m 2 and probable poor muscle health determined by a Strength, Assistance with walking, Rising from a chair, Climbing stairs and Falls (SARC-F) questionnaire score ≥ 2) [ 33 ]. Participants were English-speaking, capable of walking across a room unaided, and had no self-reported diagnosis of progressive neurological or psychotic disorders, severe knee or hip osteoarthritis (awaiting or having had a joint replacement), CVD or a life expectancy less than 12 months. Ethical approval was granted by Monash University Human Research Ethics Committee (HREC/72/MonH892) and the Deakin University Human Research Ethics Committee (HREC 2021 − 353) and was carried out in accordance with The Code of Ethics of the World Medical Association (Declaration of Helsinki). All participants provided informed consent to participate. Outcome measures Participants attended the Monash Health Translational Precinct (Monash Medical Centre, Clayton, Victoria) for all physical assessments and dietary assessments were conducted via telephone consultation at baseline. Protocols for each assessment relevant to this study are described below. Anthropometric measures Height and body weight were measured with minimal clothing and no footwear. Weight was measured by an electronic scale (Seca 804, Seca, Germany) with an accuracy of 0.1 kg. Height was measured using a wall mounted stadiometer (Seca 123, Seca, Germany) to the nearest 0.1 cm. Waist circumference (WC) was measured at the mid-point between the inferior margin of the last rib and the crest of the ilium in the mid-axillary plane, and hip circumference (HC) was measured at the level of the greatest posterior protuberance of the buttocks to the nearest 0.1 cm. If there was a difference of more than 2 cm between the first and second reading, a third measurement for WC and HC was performed. Dual-energy X-ray absorptiometry (DXA) Whole-body dual-energy X-ray absorptiometry (DXA) (Hologic Discovery A, Hologic, USA) assessed body composition, including total body fat mass (kg), percentage body fat, visceral fat mass (g), lean soft tissue mass (kg) and appendicular lean mass (kg) [ 33 ]. Short-term precision (%CVrms) for these measurements was based on our previously published data for total fat mass 0.96%, visceral fat mass 4.74%, total lean mass 0.57%, and ALM 0.90% [ 34 ]. Biochemical measures Participants attended Monash Pathology (Monash Medical Centre, Clayton, Victoria) with a coded referral form for blood collection. A 10mL fasting blood sample was taken by a qualified technician. Serum samples were analysed by Monash Pathology for triglycerides (TG), high-density lipoprotein (HDL), low-density lipoprotein (LDL), total cholesterol and glucose using coupled enzymatic analysis on the DxC 700 AU automated analyser (Beckman Coulter, Inc. CA, US). Insulin was measured separately using the Access Ultrasensitive Insulin Assay. Glycated haemoglobin (HbA1c) was analysed using high-resolution capillary electrophoresis on the Sebia Capillarys 3 Tera automated analyser (Abacus dx, QLD, Australia). Insulin resistance was estimated using the Homeostatic Model Assessment for Insulin Resistance (HOMA-IR), calculated as fasting insulin (µU/mL) x fasting glucose (mmol/L) divided by 22.5. Blood pressure assessment Blood pressure was measured in the right arm using a digital monitor (Welch Allyn Connex Pro BP 3400, Welch Allyn, USA) with the participant seated, arm supported at heart level, and legs uncrossed. Two readings were obtained 30 seconds apart, with a third if systolic blood pressure (SBP) differed by more than 25 mmHg or 15 mmHg for diastolic blood pressure (DBP) and the average recorded for analysis. Dietary assessment Dietary intake was assessed using the Automated Self-Administered 24-hour recall (ASA24)-Australia 2016 [ 35 ]. Participants completed three non-consecutive recalls (two weekdays, one weekend day) within 7 days at baseline to estimate habitual energy and nutrient intake, reporting all foods, beverages and portion sizes consumed. The Mediterranean Diet Score (MDS) was used to quantify adherence to the MedDiet [ 36 ]. To improve cultural relevance and quantifiability, modifications were made to align food groups with the Australian Guide to Healthy Eating [ 37 ] adjusting the score to the Australian cohort, following approaches used in prior Australian-based studies [ 38 , 39 ]. Mean dietary intake data for each participant using the three 24-hour recalls were used to convert into weekly consumption to calculate MDS. Nine food components were included in the score: non-refined grains, fruits, vegetables, legumes, potatoes, fish, meat and meat products, poultry, and dairy products (e.g., milk, cheese, yoghurt). Each component was scored from 0 to 5, based on frequency of consumption. For foods consistent with the MedDiet including non-refined grains, vegetables, fruits, legumes, fish, and potatoes, higher frequency of intake received higher scores (assigned MDS of 0 when participants reported no consumption and MDS of 1 to 5 on a scale for rare to daily consumption). In contrast, for food groups that were not considered to reflect the dietary pattern, including red meat, poultry, dairy, the scoring was reversed (assigned MDS of 5 when someone reported no consumption to MDS of 0 when daily consumption was reported). Olive oil and wine were excluded from the scoring due to insufficient data to quantify specific types (e.g. extra virgin olive oil, red wine), an approach consistent with previous studies presenting with these dietary data limitations [ 39 ]. Dietary data was missing for 3 participants who did not complete their food records. The final modified overall MDS had a possible score range from 0 to 45, with higher scores indicating greater adherence to the MedDiet pattern [ 36 ]. Objective physical activity assessment Participants were fitted with an ActiGraph GT9XLink accelerometer (Actigraph, FL, USA) that was worn on their non-dominant wrist (except when completing water-based activities i.e., swimming, showering) for the duration of seven days and given instructions on its use. Participants were required to keep a diary to record wear times and reasons for not wearing the device. A valid day was defined as ≥ 10 h of wear time in a 24-h period [ 40 ], and participants with fewer than 4 days were excluded from the analysis [ 41 , 42 ]. Wear time was classified using vector magnitude cut-points: sedentary (0-2859), light (2860–3940), moderate-to-vigorous (3941–6165), vigorous (≥ 6166) physical activity [ 43 ] and presented as average moderate-to-vigorous activity (MVPA) (mins per day). In this cohort, no participants recorded vigorous-intensity physical activity according to these cut-points, therefore MVPA reflects moderate-intensity activity only. Objectively assessed physical activity data was missing for 7 participants. Statistical analysis Data were analysed using STATA SE 18 (StataCorp LLC, TX, USA). Variables were inspected for data errors, and in the case of missing data, original records were consulted. Descriptive characteristics were summarised using mean ± standard deviation (SD) or median with interquartile range (IQR), while categorical variables were presented as frequencies and percentages (%). Continuous data were assessed for normality using Shapiro-Wilk tests and non-normally distributed variables were analysed through non-parametric testing. Multiple linear regression models were used to evaluate associations between the MDS and body composition, anthropometric and biochemical outcomes. Two models were specified: Model 1, adjusted for age and sex; and Model 2, adjusted for age, sex, MVPA, and diabetes status. To improve interpretability, continuous MDS was scaled to reflect a 5-point unit increase, consistent with a previous study [ 44 ]. Results from the regression models are presented as β-coefficients. Residual plots were examined to assess the assumptions of normality and homoscedasticity of residuals. Multicollinearity among independent variables was evaluated using Variance Inflation Factors (VIF), with a threshold of > 5 indicating potential multicollinearity. For all analyses, a p-value of < 0.05 and 95% confidence intervals (CI) not including the null point were considered statistically significant. Additionally, exploratory analysis was conducted to examine associations between individual food group scores contributing to the MDS and body composition, anthropometric, and biochemical outcomes. Each food group (vegetables, fruits, grains, dairy, fish, poultry, red meat, potatoes and legumes) was treated as a continuous independent variable. Multiple linear regression analyses were performed to assess these associations. Model 1 was adjusted for age and sex and model 2 was adjusted for age, sex, MVPA and diabetes status. Lastly, moderation analysis was conducted to examine whether diabetes status moderated the associations between MDS and body composition, anthropometric, and biochemical outcomes. Interaction models were fitted using linear regression by including the main effects of MDS and diabetes status, and the interaction term (MDS x diabetes status). Participants were classified as having T2DM if they had fasting plasma glucose ≥ 7.0 mmol/L; fasting HbA1c levels ≥ 6.5%; self-reported a T2DM diagnosis or reported current use of glucose-lowering medications (e.g. biguanides, sulfonylureas and DPP-4 inhibitors) [ 45 ]. Model 1 was adjusted for age and sex. Results Table 1 presents participant characteristics overall, stratified by sex including the prevalence of co-morbidities. Overall, 116 participants at risk of SO (self-reported BMI ≥ 30 kg/m 2 and SARC-F ≥ 2) were included with a mean age of 66.5 ± 4.1 years (range 60 to 84 years) and a mean BMI of 35.6 kg/m 2 , with 6% classified as overweight and 94% as obese. Overall, 40 (35%) had T2DM. The mean SARC-F score was 2.8 ± 1.1. Participants had a mean MDS score of 23.1 ± 5.0 out of 45.0 (range 11.0 to 36.0), with no differences between males and females (21.5 ± 5.2 vs 23.6 ± 4.8). On average, participants engaged in 81.8 ± 39.9 minutes of MVPA per day, with females reporting higher MVPA levels than males (86.5 ± 35.6 vs 67.6 ± 48.4 minutes/day, respectively). In this cohort, 83.6% of participants had elevated blood pressure (SBP ≥ 120 mmHg or DBP ≥ 80 mmHg) [ 46 ]. Elevated HbA1c (≥ 6.5%) [ 47 ] was present in 9.5% of participants. Regarding lipid profiles, 56.0% of participants had total cholesterol ≥ 5.5 mmol/L and 90.5% had LDL ≥ 2.0 mmol/L, exceeding the reference range. Table 1 Lifestyle, anthropometric, biochemical and body composition baseline characteristics of the study participants (n = 116). Characteristics Overall ( n 116) Males ( n 30) Females ( n 86) Age, mean (years) 66.5 ± 4.1 66.6 ± 3.6 66.5 ± 4.2 Parents birthplace n (%) * Australia 49 (42%) 13 (45%) 36 (42%) Overseas 64 (55%) 16 (55%) 48 (57%) Unknown/prefer not to say 1 (1%) 0 1 (1%) Education n (%) * Did not attend school 1 (1%) 0 1 (1%) Secondary or high school education 18 (16%) 1 (3%) 17 (20%) Technical or further educational institution 32 (28%) 8 (28%) 24 (28%) University or tertiary education 63 (54%) 20 (69%) 43 (51%) Marital status n (%) * Single 16 (14%) 2 (7%) 14 (17%) Widowed 14 (12%) 1 (3%) 13 (15%) Divorced 15 (13%) 5 (18%) 10 (12%) Separated not divorced 4 (3%) 1 (3%) 3 (4%) Married or de factor 65 (56%) 20 (69%) 45 (53%) Current employment status n (%) * Employed/self-employed full-time 25 (22%) 12 (41%) 13 (15%) Employed/self-employed part-time 25 (22%) 4 (14%) 21 (25%) Unemployed 3 (3%) 0 3 (4%) Retired 52 (45%) 12 (41%) 40 (47%) Student 1 (1%) 0 1 (1%) Home duties 1 (1%) 0 1 (1%) Pension 7 (6%) 1 (3%) 6 (7%) Diabetes status Yes n (%) 40 (34.5%) 15 (50%) 25 (29.1%) No n (%) 76 (65.5%) 15 (50%) 61 (70.9%) Smoking status n (%) * Current smoker 3 (3%) 0 3 (4%) Ex-smoker 58 (50%) 17 (59%) 41 (48%) Non-smoker 53 (46%) 12 (41%) 41 (48%) SARC-F n (%) 2 66 (56.9%) 18 (60.0%) 48 (55.8%) 3 26 (22.4%) 7 (23.3%) 19 (22.1%) 4 14 (12.1%) 2 (6.7%) 12 (13.9%) 5 10 (8.6%) 3 (10.0%) 7 (8.1%) Total MedDiet Score ** 23.1 ± 5.0 21.5 ± 5.2 23.6 ± 4.8 MVPA (minutes/day) *** 81.8 ± 39.9 67.6 ± 48.8 86.5 ± 35.6 Anthropometric Weight (kg) 98.4 ± 16.7 110.8 ± 15.1 94.1 ± 15.1 Height (cm) 165.9 ± 8.9 176.3 ± 7.1 162.4 ± 6.4 BMI category Overweight (25-29.9 kg/m 2 ) 7 (6%) 2 (7%) 5 (6%) Obese (≥ 30 kg/m 2 ) 109 (94%) 28 (93%) 81 (94%) Hip circumference (cm) 119.3 ± 11.3 116.1 ± 10.3 120.4 ± 11.5 Waist circumference (cm) 114.6 ± 12.8 120.6 ± 11.3 112.6 ± 12.7 Waist-to-hip ratio 1.0 ± 0.1 1.0 ± 0.0 0.9 ± 0.1 Haemodynamic SBP (mmHg) 131.2 ± 15.8 131.8 ± 16.2 131.0 ± 15.8 DBP (mmHg) 80.8 ± 10.7 80.0 ± 11.3 81.0 ± 10.6 Biochemical Glucose (mmol/L) 5.8 ± 1.4 6.2 ± 1.9 5.6 ± 1.1 HbA1c (%) 5.8 ± 0.7 5.9 ± 0.7 5.8 ± 0.7 Insulin (mU/L) 12.0 ± 7.3 15.2 ± 7.2 10.8 ± 7.0 HOMA-IR 3.3 ± 2.7 4.3 ± 2.7 2.9 ± 2.6 Total cholesterol (mmol/L) 5.5 ± 1.2 4.9 ± 1.2 5.7 ± 1.2 Triglycerides (mmol/L) 1.5 ± 0.6 1.6 ± 0.6 1.5 ± 0.6 HDL (mmol/L) 1.6 ± 0.4 1.3 ± 0.3 1.7 ± 0.3 LDL (mmol/L) 3.3 ± 1.0 3.0 ± 1.0 3.4 ± 1.0 Body composition Total body fat mass (kg) 47.1 ± 9.8 44.4 ± 9.8 48.0 ± 9.7 Percentage body fat (%) 47.6 ± 6.1 39.5 ± 4.8 50.4 ± 3.4 Visceral fat mass (g) 1093.8 ± 333.7 1258.5 ± 355.9 1036.4 ± 304.5 Lean soft tissue mass (kg) 52.0 ± 11.2 67.2 ± 7.9 46.7 ± 6.4 Appendicular lean mass (kg) 20.1 ± 4.6 26.1 ± 3.4 18.0 ± 2.8 1 Values are means ± SDs, medians (IQR) or frequencies (percentages). Abbreviations: MVPA, moderate-to-vigorous physical activity; BMI, Body Mass Index; SBP, systolic blood pressure; DBP, diastolic blood pressure, HbA1c, Haemoglobin A1c; HOMA-IR, Homeostatic Model Assessment of Insulin Resistance; LDL, Low-Density Lipoprotein; HDL, High-Density Lipoprotein; MedDiet, Mediterranean Diet; SARC-F, Strength, assistance with walking, rising from chair, climbing stairs and falls. * n = 114 for Parents birthplace, Education, Marital status, Current employment status, Smoking status ** n = 113 for MDS ***n = 109 for MVPA Multivariable regression models demonstrated no significant associations between MedDiet adherence, assessed using the MDS and body composition (Table 2 ). In biochemical outcomes, insulin (β = -1.48, 95%CI -2.85, -0.11) and HOMA-IR (β = -0.54, 95%CI -1.05, -0.02) were inversely associated with MDS, whereas HDL levels (β = 0.07 mmol/L, 95%CI 0.01, 0.13) were positively associated with MDS after adjustment for age and sex. However, these associations were not significant following further adjustment for MVPA and diabetes status. No significant associations were observed between MDS and anthropometric (i.e., weight, BMI, WC, waist-to-hip ratio) and other metabolic (i.e., glucose, HbA1c, cholesterol, TG, and LDL, SBP, and DBP) outcomes. Table 2 Multiple linear regression analysis evaluating the association between Mediterranean Diet Score (5-unit increase) and body composition, anthropometric, biochemical and haemodynamic outcomes. Outcomes Beta Coefficients, 95% Confidence Interval and p-value Models Model 1 a Model 2 b n β (95% CI) p-value n β (95% CI) p-value Body composition outcomes Total body fat mass (kg) 113 -0.25 (-2.12, 1.63) 0.794 106 -0.01 (-1.93, 1.90) 0.989 Percentage body fat (%) 113 -0.35 (-1.10, 0.39) 0.347 106 -0.39 (-1.17, 0.40) 0.331 Lean soft tissue mass (kg) 113 0.62 (-0.61, 1.86) 0.321 106 1.00 (-0.28, 2.29) 0.125 Visceral fat mass (g) 113 8.39 (-53.98, 70.76) 0.790 106 5.56 (-60.72, 71.84) 0.868 Appendicular lean mass (kg) 113 -0.03 (-0.20, 0.14) 0.719 106 -0.01 (-0.18, 0.17) 0.937 Anthropometric outcomes Weight (kg) 113 0.49 (-2.35, 3.33) 0.733 106 1.19 (-1.70, 4.08) 0.416 BMI (kg/m²) 113 -0.27 (-1.16, 0.63) 0.553 106 -0.20 (-1.12, 0.72) 0.668 Waist circumference (cm) 113 0.00 (-0.03, 0.02) 0.764 106 0.00 (-0.03, 0.03) 0.995 Waist-to-hip ratio 113 -0.01 (-0.02, 0.002) 0.132 106 -0.01 (-0.02, 0.004) 0.222 Biochemical outcomes Glucose (mmol/L) 113 -0.23 (-0.49, 0.03) 0.088 106 -0.16 (-0.41, 0.09) 0.208 HbA1c (%) 113 -0.05 (-0.18, 0.08) 0.430 106 -0.01 (-0.13, 0.11) 0.859 Insulin (µU/mL) 113 -1.48 (-2.85, -0.11) 0.034 106 -0.83 (-2.14, 0.47) 0.209 HOMA-IR 113 -0.54 (-1.05, -0.02) 0.040 106 -0.30 (-0.78, 0.18) 0.217 Cholesterol (mmol/L) 113 0.14 (-0.09, 0.37) 0.223 106 0.13 (-0.12, 0.37) 0.301 Triglycerides (mmol/L) 113 -0.05 (-0.18, 0.07) 0.406 106 -0.06 (-0.19, 0.08) 0.423 LDL (mmol/L) 113 0.08 (-0.13, 0.28) 0.457 106 0.08 (-0.13, 0.29) 0.465 HDL (mmol/L) 113 0.07 (0.01, 0.13) 0.024 106 0.05 (-0.01, 0.12) 0.103 Haemodynamic outcomes Systolic blood pressure (mmHg) 111 0.83 (-2.38, 4.05) 0.609 104 0.41 (-3.04, 3.87) 0.812 Diastolic blood pressure (mmHg) 111 0.72 (-1.39, 2.83) 0.501 104 0.72 (-1.58, 3.01) 0.537 Beta coefficients represent change in outcome measure per 5-unit increase in Mediterranean Diet Score; Glucose and HbA1c were log-transformed to approximate normality; a Model 1: adjusted for age and sex, b Model 2: adjusted for age, sex, moderate-to-vigorous physical activity, and diabetes status. Abbreviations: HbA1c, glycated haemoglobin A1C; HOMA-IR, homeostatic model assessment insulin resistance; HDL, high-density lipoprotein cholesterol; LDL, low-density lipoprotein cholesterol. Exploratory analysis of individual food groups showed no significant associations with body composition outcomes in fully adjusted models (Supplementary table 1 ). Higher fish intake was associated with lower waist-to-hip ratio (β = -0.0053, 95% CI -0.0101, -0.0005), increased grain intake with lower LDL levels (β = -0.14, 95% CI -0.28, -0.01), and higher poultry intake with lower DBP (β = -1.39, 95% CI -2.71, -0.08) and higher HDL levels (β = 0.05, 95% CI 0.01, 0.08) in fully adjusted models. Conversely, higher vegetable intake was associated with increased systolic blood pressure (β = 2.55, 95% CI 0.12, 4.99) in fully adjusted models. In models adjusted for age and sex only, higher vegetable intake was associated with lower insulin levels (β = -1.03, 95% CI -2.05, -0.02) and higher HDL levels (β = 0.05, 95% CI -0.001, 0.09). No other significant associations were observed between food groups and anthropometric or metabolic outcomes. Moderation analysis indicated that diabetes status did not significantly moderate the associations between MDS and body composition, anthropometric, and metabolic outcomes after adjustment for age and sex, with all interaction terms remaining non-significant (Supplementary table 2 ). Discussion In this cross-sectional study, MedDiet was not associated with body composition or anthropometric outcomes among community-dwelling older adults at risk of SO. In terms of biochemical outcomes, we observed that greater adherence to MedDiet was associated with lower insulin and HOMA-IR, and higher HDL levels. Similarly, individual MedDiet food groups showed no associations with body composition. However, some food groups were associated with metabolic outcomes, higher poultry intake was associated with lower DBP and higher HDL levels, increased fish intake with a lower waist-to-hip ratio and higher grain intake with lower LDL levels. Evidence examining MedDiet adherence and body composition in individuals with, or at risk of SO is scarce, with no RCTs conducted to date. A narrative review synthesising 13 observational and clinical studies reported that higher MedDiet adherence combined with physical activity may improve body composition and cardiometabolic health in older adults who are overweight/obese [ 48 ]. Studies on the MedDiet and body composition in older adults mainly focus on metabolic syndrome and overweight/obesity [ 49 , 50 ]. Observational data from broader older adult cohorts show positive associations, for example the PREDIMED-Plus cross-sectional study (n = 1,425, overweight/obese older adults with metabolic syndrome) reported significant associations between MedDiet and body composition (assessed via DXA) including lower fat mass (β=-0.06 kg/m² per 1-point increase, p = 0.01) and higher lean body mass (p < 0.05) [ 49 ]. Similarly, another cross-sectional study (n = 521 community dwelling older adults from the Mediterranean region) reported significant associations between MedDiet and fat-free mass percentage and skeletal muscle mass (both p < 0.05) measured via bioelectrical impedance [ 50 ]. However, these associations were not observed in this study, which may reflect the smaller sample size (n = 116) limiting statistical power, there was also lower MedDiet adherence in this Australian sample, compared to Mediterranean populations where adherence is higher, and participants herein reported higher physical activity levels exceeding recommended guidelines potentially attenuating associations between outcomes however adjustment for physical activity did not alter these findings. Differences in health status, including 35% of participants with T2DM, did not appear to influence dietary behaviours or metabolic responses in moderation analyses. These factors collectively explain the modest non-significant associations, highlighting the need for larger studies and RCTs in older adults residing in non-Mediterranean regions. We did not observe associations between MedDiet adherence and anthropometric outcomes, including weight, BMI, WC, and waist-to-hip ratio. Previous literature suggests that the MedDiet may influence central adiposity rather than overall body weight and BMI [ 51 ]. A review of 18 RCTs (n = 7,186 overweight/obese middle aged to older adults) reported significant reductions in WC and waist-to-hip ratio with MedDiet adherence, while weight and BMI changes were modest and non-significant [ 51 ]. These effects may be attributed to the low-energy density and fibre-rich foods (e.g. fruits, vegetables and legumes) found in MedDiet which promote satiety and help limit energy intake, as well as its high content of polyphenols and monounsaturated fatty acids, which may reduce fat accumulation through anti-inflammatory mechanisms [ 51 ]. Conversely, the lack of associations observed in our study may relate to the low MedDiet adherence typical of non-Mediterranean populations and our modest sample size. We observed associations between MedDiet adherence and lower insulin, HOMA-IR and higher HDL levels, however these associations were attenuated in fully adjusted models. A previous cross-sectional study (n = 2,956 community dwelling older adults who were overweight/obese from Mediterranean islands), reported higher MedDiet adherence was significantly associated with lower HOMA-IR and higher HDL levels, however they also reported lower fasting glucose (all p < 0.05) [ 52 ]. These effects may be explained by MedDiet components that improve glucose metabolism and insulin sensitivity [ 53 – 55 ] through synergistic effects of dietary fibres, which slows down glucose absorption rate; antioxidants, which protect beta-cells against oxidative stress; and oleic acid from olive oil, which increases adiponectin and may attenuate insulin resistance [ 54 , 56 , 57 ]. Additionally, improved insulin sensitivity may support higher HDL concentrations, while MedDiet components (i.e. nuts, legumes, fish) may enhance HDL particle size, composition and cholesterol efflux capacity [ 58 ]. These findings suggest that while MedDiet may support glycaemic regulation and lipid metabolism, its independent effects can be influenced by factors such as physical activity and comorbidities. Our results demonstrated that poultry intake was associated with higher HDL levels and lower DBP. This aligns with previous literature indicating that poultry consumption is associated with lower risk of metabolic syndrome, reflecting lower saturated fat content and essential B-vitamins that support lipid metabolism when it replaces red and processed meats in diet [ 59 ]. Observational studies examining poultry intake and blood pressure report mixed findings with some suggesting small associations with hypertension risk overall [ 60 ]. Additionally, increased fish intake was associated with lower waist-to-hip ratio in our study, consistent with evidence that fish consumption, rich in omega-3 fatty acids supports healthier fat distribution in older adults [ 61 – 63 ]. Higher grain intake was associated with lower LDL levels, consistent with a previous meta-analysis showing that whole grains reduce LDL by approximately 0.09–0.18 mmol/L potentially mediated by soluble fibre (e.g., β-glucan) increasing bile acid excretion and phytosterols reducing intestinal cholesterol absorption [ 64 ]. Despite limited individual food group associations with body composition, MedDiet components may enhance metabolic health and anthropometric outcomes through synergistic nutrient interactions. Strengths and limitations This present study had several notable strengths and limitations. Body composition was measured using DXA, a gold-standard method providing precise assessments of fat distribution. Physical activity was objectively measured and included as a covariate, improving the robustness of associations between diet and outcomes. Inclusion of chronic disease data (i.e., T2DM), allowed for meaningful subgroup analyses and adjustment, strengthening the applicability of findings to diverse populations. Overall, the study contributes to the growing body of evidence on the relationship between MedDiet and body composition and metabolic outcomes in multicultural, non-Mediterranean populations. Conversely, the cross-sectional design limits causal and temporal relationships between outcomes. Dietary intake was self-reported, which may be affected by misreporting, particularly in individuals who are living with overweight and obesity. The low MDS in this cohort may have limited our ability to detect benefits, reflecting challenges in adopting this dietary pattern in non-Mediterranean populations [ 38 ]. Physical activity levels may be overestimated particularly in older adults with higher body mass, with devices such as GT9XLink reporting limited sensitivity for distinguishing light-to-moderate activity [ 65 ]. Additionally, the over-representation of females in the study sample and lack of ethnicity data may limit generalisability. Finally, exploratory associations with individual food groups should be interpreted cautiously and warrant further investigation. Conclusion In this cohort of community-dwelling older adults at risk of SO, MedDiet adherence was low and demonstrated no association with body composition and limited association with metabolic outcomes. Exploratory analysis of individual MedDiet food groups suggested that increased fish, poultry and grains intake may be associated with favourable metabolic and anthropometric outcomes, but these findings are preliminary and warrant further investigation in clinical studies. This study provides novel evidence on the effects of MedDiet in community-dwelling older adults at risk of SO, highlighting the need for tailored dietary and physical activity interventions in diverse older populations. Declarations Supplementary Information Supplemental Table 1. Associations between individual food groups scores and body composition, anthropometric, haemodynamic, biochemical outcomes. Supplemental Table 2. Interaction between Mediterranean Diet Score and diabetes status on body composition, anthropometric and biochemical outcomes. Authors’ contributions SS: Conceptualization, formal analysis, methodology, investigation, writing – original draft. CG: Methodology, Writing – review & editing. MTH: Writing – review & editing. PJ: Writing – review & editing. JM: Writing – review & editing. JRR: Writing – review & editing. AZ: Writing – review & editing. PRE: Writing – review & editing. RMD: Methodology, writing – review & editing. ESG: Conceptualization, formal analysis, methodology, investigation, supervision, writing – review & editing. DS: Conceptualization, formal analysis, methodology, investigation, supervision, funding acquisition, writing – review & editing. Funding SS is supported by Deakin University, Faculty of Health, Doctor of Philosophy scholarship. RMD reports honoraria consulting fees and an educational grant from Abbott Australasia Pty Ltd and honoraria from Fresenius Kabi and Nutricia Australia. Availability of data and materials Data sharing is not applicable to this article as no datasets were generated or analysed during the current study. Ethics approval and consent to participate The study was reviewed and approved by the Monash University Research Ethics Committee with the reference number: HREC/72/MonH892 and the Deakin University Human Research Ethics Committee the reference number: HREC 2021-353. Consent for publication Not applicable. 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Effect of mediterranean diet in diabetes control and cardiovascular risk modification: a systematic review. Front public health. 2015;3:69. Khalil M, Shanmugam H, Abdallah H, John Britto JS, Galerati I, Gómez-Ambrosi J, et al. The potential of the Mediterranean diet to improve mitochondrial function in experimental models of obesity and metabolic syndrome. Nutrients. 2022;14(15):3112. Grao-Cruces E, Varela LM, Martin ME, Bermudez B. Montserrat-de la Paz S. High-density lipoproteins and mediterranean diet: A systematic review. Nutrients. 2021;13(3):955. Milićević D, Vranić D, Mašić Z, Parunović N, Trbović D, Nedeljković-Trailović J, et al. The role of total fats, saturated/unsaturated fatty acids and cholesterol content in chicken meat as cardiovascular risk factors. Lipids Health Dis. 2014;13(1):42. Connolly G, Campbell WW. Poultry consumption and human cardiometabolic health-related outcomes: a narrative review. Nutrients. 2023;15(16):3550. Alhussain MH, ALshammari MM. 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Barnett A, Van Den Hoek D, Barnett D, Cerin E. Measuring moderate-intensity walking in older adults using the ActiGraph accelerometer. BMC Geriatr. 2016;16(1):211. Additional Declarations No competing interests reported. Supplementary Files STable1.docx STable2.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 31 Mar, 2026 Reviews received at journal 26 Mar, 2026 Reviewers agreed at journal 24 Mar, 2026 Reviews received at journal 23 Mar, 2026 Reviewers agreed at journal 02 Mar, 2026 Reviewers invited by journal 24 Feb, 2026 Editor invited by journal 24 Feb, 2026 Editor assigned by journal 22 Feb, 2026 Submission checks completed at journal 22 Feb, 2026 First submitted to journal 19 Feb, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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progressive loss of skeletal muscle mass and strength associated with aging, which can lead to long-term functional decline and disability [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Depending on the diagnostic criteria used, the global prevalence of sarcopenia is estimated to affect between 10 and 29% of community-dwelling older adults [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], with some estimates as high as 55% [\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Sarcopenia often co-exists with obesity, a chronic condition known as sarcopenic obesity (SO), which has an estimated prevalence ranging from 7.9% to 23% in clinical and 7.1% to 9.6% in community-dwelling settings [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], and is projected to increase due to our ageing population and global obesity epidemic [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. SO is associated with metabolic dysfunction, insulin resistance and fractures, resulting in greater risk of disability, hospitalisation, and mortality [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eLifestyle interventions shown to be effective in managing SO include dietary strategies that achieve energy restriction with adequate protein intake and high quality diet (e.g. Mediterranean dietary pattern) and structured exercise [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan additionalcitationids=\"CR10 CR11\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Previous studies have reported on diet-induced weight loss strategies and interventions focusing on isolated nutrients including protein, vitamin D and omega-3 fatty acids due to their established roles in muscle function [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. However, weight loss interventions with energy restriction, and with poor diet quality and exercise may potentially exacerbate age-related SO due to disproportionate loss of skeletal muscle mass and function [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Additionally, supplementation with protein, vitamin D or omega-3 fatty acids alone may be insufficient, as previous evidence suggests that single nutrients yield inconsistent effects on muscle mass when not combined with comprehensive lifestyle approaches [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Therefore, interventions combining whole dietary patterns with structured exercise are needed to promote fat loss and preserve muscle mass [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOne such dietary pattern is the Mediterranean diet (MedDiet), which has demonstrated protective effects against chronic diseases such as metabolic dysfunction associated steatotic liver disease (MASLD), type 2 diabetes mellitus (T2DM) and cardiovascular disease (CVD) [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The MedDiet is characterised by a nutrient-dense, anti-inflammatory dietary pattern emphasising whole grains, fruits, vegetables, legumes, nuts and seeds, and olive oil, moderate intake of fish, poultry, and low-fat dairy and limited intake of red and processed meats and added sugars [\u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Higher MedDiet adherence has been associated with better cardiometabolic markers, including blood pressure, insulin sensitivity, and dyslipidaemia, as well as favourable weight outcomes [\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], better muscle mass, function and strength in older adults [\u003cspan additionalcitationids=\"CR27\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Additionally, given the increased burden of T2DM in older adults and in individuals with SO [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], associations between MedDiet adherence and body composition (e.g. fat mass) and metabolic outcomes (e.g. insulin resistance) may differ depending on diabetes status [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDespite evidence supporting the benefits of individual and combined dietary quality and exercise, data examining the associations between MedDiet adherence and body composition and metabolic health in community-dwelling older adults at risk of SO remain limited [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. The primary aim of this study was to investigate associations between MedDiet adherence and body composition and metabolic outcomes in community-dwelling older adults at risk of SO. Secondary aims were to investigate exploratory associations between individual food groups and body composition and metabolic outcomes, and to examine whether T2DM status modified the associations between MedDiet and these outcomes.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and participants\u003c/h2\u003e \u003cp\u003eThis cross-sectional secondary analysis was conducted using baseline data from a 24-week randomised controlled trial (RCT) of 116 community-dwelling older adults residing in Melbourne, Australia, aged\u0026thinsp;\u0026ge;\u0026thinsp;60 years at risk of SO (self-reported BMI\u0026thinsp;\u0026ge;\u0026thinsp;30 kg/m\u003csup\u003e2\u003c/sup\u003e and probable poor muscle health determined by a Strength, Assistance with walking, Rising from a chair, Climbing stairs and Falls (SARC-F) questionnaire score\u0026thinsp;\u0026ge;\u0026thinsp;2) [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Participants were English-speaking, capable of walking across a room unaided, and had no self-reported diagnosis of progressive neurological or psychotic disorders, severe knee or hip osteoarthritis (awaiting or having had a joint replacement), CVD or a life expectancy less than 12 months.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eEthical approval\u003c/strong\u003e \u003cp\u003e was granted by Monash University Human Research Ethics Committee (HREC/72/MonH892) and the Deakin University Human Research Ethics Committee (HREC 2021\u0026thinsp;\u0026minus;\u0026thinsp;353) and was carried out in accordance with The Code of Ethics of the World Medical Association (Declaration of Helsinki). All participants provided informed consent to participate.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eOutcome measures\u003c/h3\u003e\n\u003cp\u003eParticipants attended the Monash Health Translational Precinct (Monash Medical Centre, Clayton, Victoria) for all physical assessments and dietary assessments were conducted via telephone consultation at baseline. Protocols for each assessment relevant to this study are described below.\u003c/p\u003e\n\u003ch3\u003eAnthropometric measures\u003c/h3\u003e\n\u003cp\u003eHeight and body weight were measured with minimal clothing and no footwear. Weight was measured by an electronic scale (Seca 804, Seca, Germany) with an accuracy of 0.1 kg. Height was measured using a wall mounted stadiometer (Seca 123, Seca, Germany) to the nearest 0.1 cm. Waist circumference (WC) was measured at the mid-point between the inferior margin of the last rib and the crest of the ilium in the mid-axillary plane, and hip circumference (HC) was measured at the level of the greatest posterior protuberance of the buttocks to the nearest 0.1 cm. If there was a difference of more than 2 cm between the first and second reading, a third measurement for WC and HC was performed.\u003c/p\u003e\n\u003ch3\u003eDual-energy X-ray absorptiometry (DXA)\u003c/h3\u003e\n\u003cp\u003eWhole-body dual-energy X-ray absorptiometry (DXA) (Hologic Discovery A, Hologic, USA) assessed body composition, including total body fat mass (kg), percentage body fat, visceral fat mass (g), lean soft tissue mass (kg) and appendicular lean mass (kg) [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Short-term precision (%CVrms) for these measurements was based on our previously published data for total fat mass 0.96%, visceral fat mass 4.74%, total lean mass 0.57%, and ALM 0.90% [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eBiochemical measures\u003c/h3\u003e\n\u003cp\u003e Participants attended Monash Pathology (Monash Medical Centre, Clayton, Victoria) with a coded referral form for blood collection. A 10mL fasting blood sample was taken by a qualified technician. Serum samples were analysed by Monash Pathology for triglycerides (TG), high-density lipoprotein (HDL), low-density lipoprotein (LDL), total cholesterol and glucose using coupled enzymatic analysis on the DxC 700 AU automated analyser (Beckman Coulter, Inc. CA, US). Insulin was measured separately using the Access Ultrasensitive Insulin Assay. Glycated haemoglobin (HbA1c) was analysed using high-resolution capillary electrophoresis on the Sebia Capillarys 3 Tera automated analyser (Abacus dx, QLD, Australia). Insulin resistance was estimated using the Homeostatic Model Assessment for Insulin Resistance (HOMA-IR), calculated as fasting insulin (\u0026micro;U/mL) x fasting glucose (mmol/L) divided by 22.5.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eBlood pressure assessment\u003c/h2\u003e \u003cp\u003eBlood pressure was measured in the right arm using a digital monitor (Welch Allyn Connex Pro BP 3400, Welch Allyn, USA) with the participant seated, arm supported at heart level, and legs uncrossed. Two readings were obtained 30 seconds apart, with a third if systolic blood pressure (SBP) differed by more than 25 mmHg or 15 mmHg for diastolic blood pressure (DBP) and the average recorded for analysis.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDietary assessment\u003c/h3\u003e\n\u003cp\u003eDietary intake was assessed using the Automated Self-Administered 24-hour recall (ASA24)-Australia 2016 [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Participants completed three non-consecutive recalls (two weekdays, one weekend day) within 7 days at baseline to estimate habitual energy and nutrient intake, reporting all foods, beverages and portion sizes consumed. The Mediterranean Diet Score (MDS) was used to quantify adherence to the MedDiet [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. To improve cultural relevance and quantifiability, modifications were made to align food groups with the Australian Guide to Healthy Eating [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] adjusting the score to the Australian cohort, following approaches used in prior Australian-based studies [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Mean dietary intake data for each participant using the three 24-hour recalls were used to convert into weekly consumption to calculate MDS. Nine food components were included in the score: non-refined grains, fruits, vegetables, legumes, potatoes, fish, meat and meat products, poultry, and dairy products (e.g., milk, cheese, yoghurt). Each component was scored from 0 to 5, based on frequency of consumption. For foods consistent with the MedDiet including non-refined grains, vegetables, fruits, legumes, fish, and potatoes, higher frequency of intake received higher scores (assigned MDS of 0 when participants reported no consumption and MDS of 1 to 5 on a scale for rare to daily consumption). In contrast, for food groups that were not considered to reflect the dietary pattern, including red meat, poultry, dairy, the scoring was reversed (assigned MDS of 5 when someone reported no consumption to MDS of 0 when daily consumption was reported). Olive oil and wine were excluded from the scoring due to insufficient data to quantify specific types (e.g. extra virgin olive oil, red wine), an approach consistent with previous studies presenting with these dietary data limitations [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Dietary data was missing for 3 participants who did not complete their food records. The final modified overall MDS had a possible score range from 0 to 45, with higher scores indicating greater adherence to the MedDiet pattern [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eObjective physical activity assessment\u003c/h3\u003e\n\u003cp\u003eParticipants were fitted with an ActiGraph GT9XLink accelerometer (Actigraph, FL, USA) that was worn on their non-dominant wrist (except when completing water-based activities i.e., swimming, showering) for the duration of seven days and given instructions on its use. Participants were required to keep a diary to record wear times and reasons for not wearing the device. A valid day was defined as \u0026ge;\u0026thinsp;10 h of wear time in a 24-h period [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], and participants with fewer than 4 days were excluded from the analysis [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Wear time was classified using vector magnitude cut-points: sedentary (0-2859), light (2860\u0026ndash;3940), moderate-to-vigorous (3941\u0026ndash;6165), vigorous (\u0026ge;\u0026thinsp;6166) physical activity [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e] and presented as average moderate-to-vigorous activity (MVPA) (mins per day). In this cohort, no participants recorded vigorous-intensity physical activity according to these cut-points, therefore MVPA reflects moderate-intensity activity only. Objectively assessed physical activity data was missing for 7 participants.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eData were analysed using STATA SE 18 (StataCorp LLC, TX, USA). Variables were inspected for data errors, and in the case of missing data, original records were consulted. Descriptive characteristics were summarised using mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) or median with interquartile range (IQR), while categorical variables were presented as frequencies and percentages (%). Continuous data were assessed for normality using Shapiro-Wilk tests and non-normally distributed variables were analysed through non-parametric testing. Multiple linear regression models were used to evaluate associations between the MDS and body composition, anthropometric and biochemical outcomes. Two models were specified: Model 1, adjusted for age and sex; and Model 2, adjusted for age, sex, MVPA, and diabetes status. To improve interpretability, continuous MDS was scaled to reflect a 5-point unit increase, consistent with a previous study [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Results from the regression models are presented as β-coefficients. Residual plots were examined to assess the assumptions of normality and homoscedasticity of residuals. Multicollinearity among independent variables was evaluated using Variance Inflation Factors (VIF), with a threshold of \u0026gt;\u0026thinsp;5 indicating potential multicollinearity. For all analyses, a \u003cem\u003ep-value\u003c/em\u003e of \u0026lt;\u0026thinsp;0.05 and 95% confidence intervals (CI) not including the null point were considered statistically significant.\u003c/p\u003e \u003cp\u003eAdditionally, exploratory analysis was conducted to examine associations between individual food group scores contributing to the MDS and body composition, anthropometric, and biochemical outcomes. Each food group (vegetables, fruits, grains, dairy, fish, poultry, red meat, potatoes and legumes) was treated as a continuous independent variable. Multiple linear regression analyses were performed to assess these associations. Model 1 was adjusted for age and sex and model 2 was adjusted for age, sex, MVPA and diabetes status.\u003c/p\u003e \u003cp\u003eLastly, moderation analysis was conducted to examine whether diabetes status moderated the associations between MDS and body composition, anthropometric, and biochemical outcomes. Interaction models were fitted using linear regression by including the main effects of MDS and diabetes status, and the interaction term (MDS x diabetes status). Participants were classified as having T2DM if they had fasting plasma glucose\u0026thinsp;\u0026ge;\u0026thinsp;7.0 mmol/L; fasting HbA1c levels\u0026thinsp;\u0026ge;\u0026thinsp;6.5%; self-reported a T2DM diagnosis or reported current use of glucose-lowering medications (e.g. biguanides, sulfonylureas and DPP-4 inhibitors) [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Model 1 was adjusted for age and sex.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents participant characteristics overall, stratified by sex including the prevalence of co-morbidities. Overall, 116 participants at risk of SO (self-reported BMI\u0026thinsp;\u0026ge;\u0026thinsp;30 kg/m\u003csup\u003e2\u003c/sup\u003e and SARC-F\u0026thinsp;\u0026ge;\u0026thinsp;2) were included with a mean age of 66.5\u0026thinsp;\u0026plusmn;\u0026thinsp;4.1 years (range 60 to 84 years) and a mean BMI of 35.6 kg/m\u003csup\u003e2\u003c/sup\u003e, with 6% classified as overweight and 94% as obese. Overall, 40 (35%) had T2DM. The mean SARC-F score was 2.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1. Participants had a mean MDS score of 23.1\u0026thinsp;\u0026plusmn;\u0026thinsp;5.0 out of 45.0 (range 11.0 to 36.0), with no differences between males and females (21.5\u0026thinsp;\u0026plusmn;\u0026thinsp;5.2 vs 23.6\u0026thinsp;\u0026plusmn;\u0026thinsp;4.8). On average, participants engaged in 81.8\u0026thinsp;\u0026plusmn;\u0026thinsp;39.9 minutes of MVPA per day, with females reporting higher MVPA levels than males (86.5\u0026thinsp;\u0026plusmn;\u0026thinsp;35.6 vs 67.6\u0026thinsp;\u0026plusmn;\u0026thinsp;48.4 minutes/day, respectively). In this cohort, 83.6% of participants had elevated blood pressure (SBP\u0026thinsp;\u0026ge;\u0026thinsp;120 mmHg or DBP\u0026thinsp;\u0026ge;\u0026thinsp;80 mmHg) [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Elevated HbA1c (\u0026ge;\u0026thinsp;6.5%) [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e] was present in 9.5% of participants. Regarding lipid profiles, 56.0% of participants had total cholesterol\u0026thinsp;\u0026ge;\u0026thinsp;5.5 mmol/L and 90.5% had LDL\u0026thinsp;\u0026ge;\u0026thinsp;2.0 mmol/L, exceeding the reference range.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLifestyle, anthropometric, biochemical and body composition baseline characteristics of the study participants (n\u0026thinsp;=\u0026thinsp;116).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall (\u003cem\u003en\u003c/em\u003e 116)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMales (\u003cem\u003en\u003c/em\u003e 30)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFemales (\u003cem\u003en\u003c/em\u003e 86)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, mean (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e66.5\u0026thinsp;\u0026plusmn;\u0026thinsp;4.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66.6\u0026thinsp;\u0026plusmn;\u0026thinsp;3.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e66.5\u0026thinsp;\u0026plusmn;\u0026thinsp;4.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParents birthplace n (%) *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAustralia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49 (42%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13 (45%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36 (42%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverseas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64 (55%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16 (55%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48 (57%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown/prefer not to say\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation n (%) *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDid not attend school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary or high school education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18 (16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17 (20%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTechnical or further educational institution\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32 (28%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (28%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24 (28%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUniversity or tertiary education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63 (54%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20 (69%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43 (51%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital status n (%) *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSingle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16 (14%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14 (17%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWidowed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14 (12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13 (15%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDivorced\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15 (13%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (18%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10 (12%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeparated not divorced\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried or de factor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65 (56%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20 (69%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45 (53%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent employment status n (%) *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmployed/self-employed full-time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25 (22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (41%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13 (15%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmployed/self-employed part-time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25 (22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (14%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21 (25%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnemployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRetired\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52 (45%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (41%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40 (47%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStudent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHome duties\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40 (34.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25 (29.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e76 (65.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e61 (70.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking status n (%) *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEx-smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e58 (50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (59%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41 (48%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53 (46%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (41%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41 (48%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSARC-F n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e66 (56.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (60.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48 (55.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26 (22.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (23.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19 (22.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14 (12.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (6.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12 (13.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (8.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (10.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7 (8.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal MedDiet Score **\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.1\u0026thinsp;\u0026plusmn;\u0026thinsp;5.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.5\u0026thinsp;\u0026plusmn;\u0026thinsp;5.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.6\u0026thinsp;\u0026plusmn;\u0026thinsp;4.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMVPA (minutes/day) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e81.8\u0026thinsp;\u0026plusmn;\u0026thinsp;39.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67.6\u0026thinsp;\u0026plusmn;\u0026thinsp;48.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e86.5\u0026thinsp;\u0026plusmn;\u0026thinsp;35.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAnthropometric\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e98.4\u0026thinsp;\u0026plusmn;\u0026thinsp;16.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e110.8\u0026thinsp;\u0026plusmn;\u0026thinsp;15.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e94.1\u0026thinsp;\u0026plusmn;\u0026thinsp;15.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeight (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e165.9\u0026thinsp;\u0026plusmn;\u0026thinsp;8.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e176.3\u0026thinsp;\u0026plusmn;\u0026thinsp;7.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e162.4\u0026thinsp;\u0026plusmn;\u0026thinsp;6.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI category\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverweight (25-29.9 kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObese (\u0026ge;\u0026thinsp;30 kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e109 (94%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28 (93%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e81 (94%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHip circumference (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e119.3\u0026thinsp;\u0026plusmn;\u0026thinsp;11.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e116.1\u0026thinsp;\u0026plusmn;\u0026thinsp;10.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e120.4\u0026thinsp;\u0026plusmn;\u0026thinsp;11.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWaist circumference (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e114.6\u0026thinsp;\u0026plusmn;\u0026thinsp;12.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e120.6\u0026thinsp;\u0026plusmn;\u0026thinsp;11.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e112.6\u0026thinsp;\u0026plusmn;\u0026thinsp;12.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWaist-to-hip ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHaemodynamic\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSBP (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e131.2\u0026thinsp;\u0026plusmn;\u0026thinsp;15.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e131.8\u0026thinsp;\u0026plusmn;\u0026thinsp;16.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e131.0\u0026thinsp;\u0026plusmn;\u0026thinsp;15.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDBP (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80.8\u0026thinsp;\u0026plusmn;\u0026thinsp;10.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80.0\u0026thinsp;\u0026plusmn;\u0026thinsp;11.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e81.0\u0026thinsp;\u0026plusmn;\u0026thinsp;10.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBiochemical\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlucose (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHbA1c (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInsulin (mU/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.0\u0026thinsp;\u0026plusmn;\u0026thinsp;7.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.2\u0026thinsp;\u0026plusmn;\u0026thinsp;7.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.8\u0026thinsp;\u0026plusmn;\u0026thinsp;7.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHOMA-IR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.3\u0026thinsp;\u0026plusmn;\u0026thinsp;2.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.3\u0026thinsp;\u0026plusmn;\u0026thinsp;2.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.9\u0026thinsp;\u0026plusmn;\u0026thinsp;2.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal cholesterol (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriglycerides (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBody composition\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal body fat mass (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47.1\u0026thinsp;\u0026plusmn;\u0026thinsp;9.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44.4\u0026thinsp;\u0026plusmn;\u0026thinsp;9.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48.0\u0026thinsp;\u0026plusmn;\u0026thinsp;9.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePercentage body fat (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47.6\u0026thinsp;\u0026plusmn;\u0026thinsp;6.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39.5\u0026thinsp;\u0026plusmn;\u0026thinsp;4.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50.4\u0026thinsp;\u0026plusmn;\u0026thinsp;3.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVisceral fat mass (g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1093.8\u0026thinsp;\u0026plusmn;\u0026thinsp;333.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1258.5\u0026thinsp;\u0026plusmn;\u0026thinsp;355.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1036.4\u0026thinsp;\u0026plusmn;\u0026thinsp;304.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLean soft tissue mass (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52.0\u0026thinsp;\u0026plusmn;\u0026thinsp;11.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67.2\u0026thinsp;\u0026plusmn;\u0026thinsp;7.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e46.7\u0026thinsp;\u0026plusmn;\u0026thinsp;6.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAppendicular lean mass (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20.1\u0026thinsp;\u0026plusmn;\u0026thinsp;4.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.1\u0026thinsp;\u0026plusmn;\u0026thinsp;3.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.0\u0026thinsp;\u0026plusmn;\u0026thinsp;2.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003csup\u003e1\u003c/sup\u003eValues are means\u0026thinsp;\u0026plusmn;\u0026thinsp;SDs, medians (IQR) or frequencies (percentages). Abbreviations: MVPA, moderate-to-vigorous physical activity; BMI, Body Mass Index; SBP, systolic blood pressure; DBP, diastolic blood pressure, HbA1c, Haemoglobin A1c; HOMA-IR, Homeostatic Model Assessment of Insulin Resistance; LDL, Low-Density Lipoprotein; HDL, High-Density Lipoprotein; MedDiet, Mediterranean Diet; SARC-F, Strength, assistance with walking, rising from chair, climbing stairs and falls.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e*\u003cem\u003en\u0026thinsp;=\u0026thinsp;114\u003c/em\u003e for Parents birthplace, Education, Marital status, Current employment status, Smoking status\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e**\u003cem\u003en\u0026thinsp;=\u0026thinsp;113\u003c/em\u003e for MDS\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cem\u003e***n\u0026thinsp;=\u0026thinsp;109\u003c/em\u003e for MVPA\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eMultivariable regression models demonstrated no significant associations between MedDiet adherence, assessed using the MDS and body composition (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In biochemical outcomes, insulin (β = -1.48, 95%CI -2.85, -0.11) and HOMA-IR (β = -0.54, 95%CI -1.05, -0.02) were inversely associated with MDS, whereas HDL levels (β\u0026thinsp;=\u0026thinsp;0.07 mmol/L, 95%CI 0.01, 0.13) were positively associated with MDS after adjustment for age and sex. However, these associations were not significant following further adjustment for MVPA and diabetes status. No significant associations were observed between MDS and anthropometric (i.e., weight, BMI, WC, waist-to-hip ratio) and other metabolic (i.e., glucose, HbA1c, cholesterol, TG, and LDL, SBP, and DBP) outcomes.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultiple linear regression analysis evaluating the association between Mediterranean Diet Score (5-unit increase) and body composition, anthropometric, biochemical and haemodynamic outcomes.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutcomes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003eBeta Coefficients, 95% Confidence Interval and p-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModels\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eModel 1\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eModel 2\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eβ (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eβ (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBody composition outcomes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal body fat mass (kg)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.25 (-2.12, 1.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.794\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.01 (-1.93, 1.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.989\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePercentage body fat (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.35 (-1.10, 0.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.347\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.39 (-1.17, 0.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.331\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLean soft tissue mass (kg)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.62 (-0.61, 1.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.321\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00 (-0.28, 2.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.125\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVisceral fat mass (g)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.39 (-53.98, 70.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.790\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.56 (-60.72, 71.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.868\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAppendicular lean mass (kg)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.03 (-0.20, 0.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.719\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.01 (-0.18, 0.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.937\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAnthropometric outcomes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWeight (kg)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.49 (-2.35, 3.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.733\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.19 (-1.70, 4.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.416\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI (kg/m\u0026sup2;)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.27 (-1.16, 0.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.553\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.20 (-1.12, 0.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.668\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWaist circumference (cm)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00 (-0.03, 0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.764\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.00 (-0.03, 0.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.995\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWaist-to-hip ratio\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.01 (-0.02, 0.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.01 (-0.02, 0.004)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.222\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBiochemical outcomes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGlucose (mmol/L)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.23 (-0.49, 0.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.088\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.16 (-0.41, 0.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.208\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHbA1c (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.05 (-0.18, 0.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.430\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.01 (-0.13, 0.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.859\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eInsulin (\u0026micro;U/mL)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.48 (-2.85, -0.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.034\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.83 (-2.14, 0.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.209\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHOMA-IR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.54 (-1.05, -0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.040\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.30 (-0.78, 0.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.217\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCholesterol (mmol/L)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.14 (-0.09, 0.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.13 (-0.12, 0.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.301\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTriglycerides (mmol/L)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.05 (-0.18, 0.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.406\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.06 (-0.19, 0.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.423\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLDL (mmol/L)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.08 (-0.13, 0.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.457\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.08 (-0.13, 0.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.465\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHDL (mmol/L)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.07 (0.01, 0.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.024\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.05 (-0.01, 0.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.103\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHaemodynamic outcomes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSystolic blood pressure (mmHg)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.83 (-2.38, 4.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.609\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.41 (-3.04, 3.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.812\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDiastolic blood pressure (mmHg)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.72 (-1.39, 2.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.72 (-1.58, 3.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.537\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eBeta coefficients represent change in outcome measure per 5-unit increase in Mediterranean Diet Score; Glucose and HbA1c were log-transformed to approximate normality; \u003csup\u003ea\u003c/sup\u003e Model 1: adjusted for age and sex, \u003csup\u003eb\u003c/sup\u003e Model 2: adjusted for age, sex, moderate-to-vigorous physical activity, and diabetes status. Abbreviations: HbA1c, glycated haemoglobin A1C; HOMA-IR, homeostatic model assessment insulin resistance; HDL, high-density lipoprotein cholesterol; LDL, low-density lipoprotein cholesterol.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eExploratory analysis of individual food groups showed no significant associations with body composition outcomes in fully adjusted models (Supplementary table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Higher fish intake was associated with lower waist-to-hip ratio (β = -0.0053, 95% CI -0.0101, -0.0005), increased grain intake with lower LDL levels (β = -0.14, 95% CI -0.28, -0.01), and higher poultry intake with lower DBP (β = -1.39, 95% CI -2.71, -0.08) and higher HDL levels (β\u0026thinsp;=\u0026thinsp;0.05, 95% CI 0.01, 0.08) in fully adjusted models. Conversely, higher vegetable intake was associated with increased systolic blood pressure (β\u0026thinsp;=\u0026thinsp;2.55, 95% CI 0.12, 4.99) in fully adjusted models. In models adjusted for age and sex only, higher vegetable intake was associated with lower insulin levels (β = -1.03, 95% CI -2.05, -0.02) and higher HDL levels (β\u0026thinsp;=\u0026thinsp;0.05, 95% CI -0.001, 0.09). No other significant associations were observed between food groups and anthropometric or metabolic outcomes.\u003c/p\u003e \u003cp\u003eModeration analysis indicated that diabetes status did not significantly moderate the associations between MDS and body composition, anthropometric, and metabolic outcomes after adjustment for age and sex, with all interaction terms remaining non-significant (Supplementary table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this cross-sectional study, MedDiet was not associated with body composition or anthropometric outcomes among community-dwelling older adults at risk of SO. In terms of biochemical outcomes, we observed that greater adherence to MedDiet was associated with lower insulin and HOMA-IR, and higher HDL levels. Similarly, individual MedDiet food groups showed no associations with body composition. However, some food groups were associated with metabolic outcomes, higher poultry intake was associated with lower DBP and higher HDL levels, increased fish intake with a lower waist-to-hip ratio and higher grain intake with lower LDL levels.\u003c/p\u003e \u003cp\u003eEvidence examining MedDiet adherence and body composition in individuals with, or at risk of SO is scarce, with no RCTs conducted to date. A narrative review synthesising 13 observational and clinical studies reported that higher MedDiet adherence combined with physical activity may improve body composition and cardiometabolic health in older adults who are overweight/obese [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Studies on the MedDiet and body composition in older adults mainly focus on metabolic syndrome and overweight/obesity [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Observational data from broader older adult cohorts show positive associations, for example the PREDIMED-Plus cross-sectional study (n\u0026thinsp;=\u0026thinsp;1,425, overweight/obese older adults with metabolic syndrome) reported significant associations between MedDiet and body composition (assessed via DXA) including lower fat mass (β=-0.06 kg/m\u0026sup2; per 1-point increase, p\u0026thinsp;=\u0026thinsp;0.01) and higher lean body mass (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Similarly, another cross-sectional study (n\u0026thinsp;=\u0026thinsp;521 community dwelling older adults from the Mediterranean region) reported significant associations between MedDiet and fat-free mass percentage and skeletal muscle mass (both p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) measured via bioelectrical impedance [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. However, these associations were not observed in this study, which may reflect the smaller sample size (n\u0026thinsp;=\u0026thinsp;116) limiting statistical power, there was also lower MedDiet adherence in this Australian sample, compared to Mediterranean populations where adherence is higher, and participants herein reported higher physical activity levels exceeding recommended guidelines potentially attenuating associations between outcomes however adjustment for physical activity did not alter these findings. Differences in health status, including 35% of participants with T2DM, did not appear to influence dietary behaviours or metabolic responses in moderation analyses. These factors collectively explain the modest non-significant associations, highlighting the need for larger studies and RCTs in older adults residing in non-Mediterranean regions.\u003c/p\u003e \u003cp\u003eWe did not observe associations between MedDiet adherence and anthropometric outcomes, including weight, BMI, WC, and waist-to-hip ratio. Previous literature suggests that the MedDiet may influence central adiposity rather than overall body weight and BMI [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. A review of 18 RCTs (n\u0026thinsp;=\u0026thinsp;7,186 overweight/obese middle aged to older adults) reported significant reductions in WC and waist-to-hip ratio with MedDiet adherence, while weight and BMI changes were modest and non-significant [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. These effects may be attributed to the low-energy density and fibre-rich foods (e.g. fruits, vegetables and legumes) found in MedDiet which promote satiety and help limit energy intake, as well as its high content of polyphenols and monounsaturated fatty acids, which may reduce fat accumulation through anti-inflammatory mechanisms [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Conversely, the lack of associations observed in our study may relate to the low MedDiet adherence typical of non-Mediterranean populations and our modest sample size.\u003c/p\u003e \u003cp\u003eWe observed associations between MedDiet adherence and lower insulin, HOMA-IR and higher HDL levels, however these associations were attenuated in fully adjusted models. A previous cross-sectional study (n\u0026thinsp;=\u0026thinsp;2,956 community dwelling older adults who were overweight/obese from Mediterranean islands), reported higher MedDiet adherence was significantly associated with lower HOMA-IR and higher HDL levels, however they also reported lower fasting glucose (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. These effects may be explained by MedDiet components that improve glucose metabolism and insulin sensitivity [\u003cspan additionalcitationids=\"CR54\" citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e] through synergistic effects of dietary fibres, which slows down glucose absorption rate; antioxidants, which protect beta-cells against oxidative stress; and oleic acid from olive oil, which increases adiponectin and may attenuate insulin resistance [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. Additionally, improved insulin sensitivity may support higher HDL concentrations, while MedDiet components (i.e. nuts, legumes, fish) may enhance HDL particle size, composition and cholesterol efflux capacity [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. These findings suggest that while MedDiet may support glycaemic regulation and lipid metabolism, its independent effects can be influenced by factors such as physical activity and comorbidities.\u003c/p\u003e \u003cp\u003eOur results demonstrated that poultry intake was associated with higher HDL levels and lower DBP. This aligns with previous literature indicating that poultry consumption is associated with lower risk of metabolic syndrome, reflecting lower saturated fat content and essential B-vitamins that support lipid metabolism when it replaces red and processed meats in diet [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. Observational studies examining poultry intake and blood pressure report mixed findings with some suggesting small associations with hypertension risk overall [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. Additionally, increased fish intake was associated with lower waist-to-hip ratio in our study, consistent with evidence that fish consumption, rich in omega-3 fatty acids supports healthier fat distribution in older adults [\u003cspan additionalcitationids=\"CR62\" citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. Higher grain intake was associated with lower LDL levels, consistent with a previous meta-analysis showing that whole grains reduce LDL by approximately 0.09\u0026ndash;0.18 mmol/L potentially mediated by soluble fibre (e.g., β-glucan) increasing bile acid excretion and phytosterols reducing intestinal cholesterol absorption [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. Despite limited individual food group associations with body composition, MedDiet components may enhance metabolic health and anthropometric outcomes through synergistic nutrient interactions.\u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eStrengths and limitations\u003c/h2\u003e \u003cp\u003eThis present study had several notable strengths and limitations. Body composition was measured using DXA, a gold-standard method providing precise assessments of fat distribution. Physical activity was objectively measured and included as a covariate, improving the robustness of associations between diet and outcomes. Inclusion of chronic disease data (i.e., T2DM), allowed for meaningful subgroup analyses and adjustment, strengthening the applicability of findings to diverse populations. Overall, the study contributes to the growing body of evidence on the relationship between MedDiet and body composition and metabolic outcomes in multicultural, non-Mediterranean populations. Conversely, the cross-sectional design limits causal and temporal relationships between outcomes. Dietary intake was self-reported, which may be affected by misreporting, particularly in individuals who are living with overweight and obesity. The low MDS in this cohort may have limited our ability to detect benefits, reflecting challenges in adopting this dietary pattern in non-Mediterranean populations [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Physical activity levels may be overestimated particularly in older adults with higher body mass, with devices such as GT9XLink reporting limited sensitivity for distinguishing light-to-moderate activity [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. Additionally, the over-representation of females in the study sample and lack of ethnicity data may limit generalisability. Finally, exploratory associations with individual food groups should be interpreted cautiously and warrant further investigation.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn this cohort of community-dwelling older adults at risk of SO, MedDiet adherence was low and demonstrated no association with body composition and limited association with metabolic outcomes. Exploratory analysis of individual MedDiet food groups suggested that increased fish, poultry and grains intake may be associated with favourable metabolic and anthropometric outcomes, but these findings are preliminary and warrant further investigation in clinical studies. This study provides novel evidence on the effects of MedDiet in community-dwelling older adults at risk of SO, highlighting the need for tailored dietary and physical activity interventions in diverse older populations.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eSupplementary Information\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplemental Table 1.\u003c/strong\u003e Associations between individual food groups scores and body composition, anthropometric, haemodynamic, biochemical outcomes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplemental Table 2.\u003c/strong\u003e Interaction between Mediterranean Diet Score and diabetes status on body composition, anthropometric and biochemical outcomes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSS: Conceptualization, formal analysis, methodology, investigation, writing \u0026ndash; original draft.\u0026nbsp;CG: Methodology, Writing \u0026ndash; review \u0026amp; editing. MTH: Writing \u0026ndash; review \u0026amp; editing. PJ: Writing \u0026ndash; review \u0026amp; editing. JM: Writing \u0026ndash; review \u0026amp; editing. JRR: Writing \u0026ndash; review \u0026amp; editing. AZ: Writing \u0026ndash; review \u0026amp; editing. PRE: Writing \u0026ndash; review \u0026amp; editing. RMD: Methodology, writing \u0026ndash; review \u0026amp; editing. ESG: Conceptualization, formal analysis, methodology, investigation, supervision, writing \u0026ndash; review \u0026amp; editing. DS: Conceptualization, formal analysis, methodology, investigation, supervision, funding acquisition, writing \u0026ndash; review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSS is supported by Deakin University, Faculty of Health, Doctor of Philosophy scholarship. RMD reports honoraria consulting fees and an educational grant from Abbott Australasia Pty Ltd and honoraria from Fresenius Kabi and Nutricia Australia.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData sharing is not applicable to this article as no datasets were generated or analysed during the current study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was reviewed and approved by the Monash University Research Ethics Committee with the reference number: HREC/72/MonH892 and the Deakin University Human Research Ethics Committee the reference number: HREC 2021-353.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003ePrado CM, Batsis JA, Donini LM, Gonzalez MC, Siervo M. 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Front Nutr. 2021;8:746880.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRobinson SM, Jameson KA, Batelaan SF, Martin HJ, Syddall HE, Dennison EM, et al. Diet and its relationship with grip strength in community-dwelling older men and women: the Hertfordshire cohort study. J Am Geriatr Soc. 2008;56(1):84\u0026ndash;90.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSmith GI, Atherton P, Reeds DN, Mohammed BS, Rankin D, Rennie MJ, et al. Omega-3 polyunsaturated fatty acids augment the muscle protein anabolic response to hyperinsulinaemia\u0026ndash;hyperaminoacidaemia in healthy young and middle-aged men and women. Clin Sci. 2011;121(6):267\u0026ndash;78.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMarshall S, Petocz P, Duve E, Abbott K, Cassettari T, Blumfield M, et al. The effect of replacing refined grains with whole grains on cardiovascular risk factors: a systematic review and meta-analysis of randomized controlled trials with GRADE clinical recommendation. J Acad Nutr Dietetics. 2020;120(11):1859\u0026ndash;83. e31.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBarnett A, Van Den Hoek D, Barnett D, Cerin E. Measuring moderate-intensity walking in older adults using the ActiGraph accelerometer. BMC Geriatr. 2016;16(1):211.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-geriatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bgtc","sideBox":"Learn more about [BMC Geriatrics](http://bmcgeriatr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bgtc/default.aspx","title":"BMC Geriatrics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Digital health, Sarcopenic obesity, Body composition, Mediterranean diet","lastPublishedDoi":"10.21203/rs.3.rs-8917541/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8917541/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eSarcopenic obesity (SO) is prevalent in older adults and is associated with metabolic dysfunction and adverse health outcomes. Lifestyle factors including diet may influence SO development and associated metabolic perturbations. This study examined associations between MedDiet adherence and body composition and metabolic outcomes in community-dwelling older adults at risk of SO and explored links between individual food groups and these outcomes.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis cross-sectional study included adults aged\u0026thinsp;\u0026ge;\u0026thinsp;60 years at risk of SO (BMI\u0026thinsp;\u0026ge;\u0026thinsp;30 kg/m\u003csup\u003e2\u003c/sup\u003e, SARC-F score\u0026thinsp;\u0026ge;\u0026thinsp;2). Diet was assessed via 24-hour recall, and MedDiet measured using Mediterranean Diet Score (range 0\u0026ndash;45). Body composition was measured by dual-energy X-ray absorptiometry.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAmong 116 participants (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD age 66.5\u0026thinsp;\u0026plusmn;\u0026thinsp;4.1 years; MDS 23.1\u0026thinsp;\u0026plusmn;\u0026thinsp;5.0), higher MedDiet adherence was not associated with body composition [fat mass (β= -0.01; 95%CI: -1.93, 1.90), percentage body fat (β= -0.39; 95%CI: -1.17, 0.40), lean soft tissue mass (β\u0026thinsp;=\u0026thinsp;1.00; 95%CI: -0.28, 2.29), visceral fat (β\u0026thinsp;=\u0026thinsp;5.56; 95%CI: -60.72, 71.84), appendicular lean mass (β= -0.01; 95%CI: -0.18, 0.17)]. In exploratory analysis, higher fish intake was associated with lower waist-to-hip ratio (β= -0.0053; 95%CI: -0.01, -0.0005), higher poultry intake with higher high-density lipoprotein (β\u0026thinsp;=\u0026thinsp;0.05; 95%CI: 0.01, 0.08) and lower diastolic blood pressure (β= -1.39; 95%CI: -2.71, -0.08), and higher grains intake with lower low-density lipoprotein (β= -0.14; 95%CI: -0.28, -0.01) in fully adjusted models.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eIn older adults at risk of SO, MedDiet adherence showed no associations with body composition and limited associations with metabolic outcomes, though higher fish, poultry and grains intake had modest associations with anthropometric and metabolic outcomes.\u003c/p\u003e\u003ch2\u003eTrial registration (ANZCTR):\u003c/h2\u003e \u003cp\u003e \u003cem\u003eACTRN12621000236897. Date of registration: 05/03/2021.\u003c/em\u003e \u003c/p\u003e","manuscriptTitle":"Associations between Mediterranean diet adherence and body composition and metabolic outcomes in older adults at risk of sarcopenic obesity","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-25 23:06:48","doi":"10.21203/rs.3.rs-8917541/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-31T10:50:05+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-27T00:39:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"139372203992871412288095790070703364358","date":"2026-03-24T22:34:34+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-23T14:48:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"336608734049778479605385390503934036577","date":"2026-03-02T09:21:18+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-24T07:25:15+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-02-24T05:39:14+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-23T01:07:16+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-23T01:05:54+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Geriatrics","date":"2026-02-19T12:07:05+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-geriatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bgtc","sideBox":"Learn more about [BMC Geriatrics](http://bmcgeriatr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bgtc/default.aspx","title":"BMC Geriatrics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c8541a5c-86b9-445c-afc1-18be1d7a990d","owner":[],"postedDate":"February 25th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-19T04:53:21+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-25 23:06:48","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8917541","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8917541","identity":"rs-8917541","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}