Effects of 12 weeks of resistance and concurrent training with graded protein intakes on lipid profile, kidney and liver biomarkers in middle-aged to older women

Effects of 12 weeks of resistance and concurrent training with graded protein intakes on lipid profile, kidney and liver biomarkers in middle-aged to older women | 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 Effects of 12 weeks of resistance and concurrent training with graded protein intakes on lipid profile, kidney and liver biomarkers in middle-aged to older women Reza Bagheri, Hamid Ghobadi, Mehdi Kargarfard, Katsuhiko Suzuki This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9024829/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Purpose This study examined whether dietary protein dose modifies adaptations to resistance training (RT) versus concurrent training (CT) in middle-aged to older women, focusing on lipid profile and kidney and liver biomarkers. Methods A total of 108 middle-aged to older women (40–77 years) were randomized to 12 weeks of supervised RT or CT (3 sessions/week), combined with low (0.8 g·kg⁻¹·d⁻¹), moderate (1.6 g·kg⁻¹·d⁻¹), or high (2.2 g·kg⁻¹·d⁻¹) protein intake (six groups; n = 18/group). Only participants with complete pre-post data were included; the analyzed sample comprised n = 83 (CT1 n = 14, CT2 n = 15, CT3 n = 13, RT1 n = 13, RT2 n = 14, RT3 n = 14). Results TG, TC, LDL-C and ApoB decreased and HDL-C increased from pre to post (p FDR ≤ 0.05). These improvements were generally greater at 1.6 and/or 2.2 vs 0.8 g·kg⁻¹·d⁻¹ (Time × Protein, p FDR ≤ 0.05), and for several lipids the protein-related benefit differed by training mode (Time × Training × Protein, p FDR ≤ 0.05; primarily at 1.6 g·kg⁻¹·d⁻¹). Urea and BUN increased pre to post, with dose-dependent elevations at 1.6 and 2.2 vs 0.8 (Time × Protein, p FDR < 0.001), accompanied by small creatinine/cystatin C increases and modest eGFR reductions at higher protein intakes (p FDR ≤ 0.05). ALT, AST and GGT increased from pre to post with clear protein-dose effects (Time × Protein, p FDR < 0.001), and AST/GGT showed training-dependent protein responses (Time × Training × Protein, p FDR ≤ 0.05). For TG, the overall decrease was attenuated in RT relative to CT. Conclusions Twelve weeks of supervised RT or CT combined with controlled protein intakes elicited favorable lipid changes overall, while higher protein doses (particularly 1.6–2.2 g·kg⁻¹·d⁻¹) were associated with greater improvements in selected lipid outcomes but also with dose-dependent increases in urea/BUN and modest reductions in eGFR estimates, and increases in liver enzymes, some of which differed by training modality. These findings indicate that protein dose meaningfully modifies metabolic and clinical chemistry responses to training in middle-aged to older women, and that higher protein intake may involve trade-offs between lipid benefits and changes in kidney- and liver-related biomarkers. Renal failure Nutrition Metabolic function Metabolic dysfunction Introduction Midlife is a paramount transition in women’s health with consequential implications for both individuals and society. As global life expectancy rises, more women are spending extended periods in midlife and postmenopausal stages [1]. This period is marked by hormonal variation, lower physical activity, and unfavorable changes in body composition, all of which contribute to an increased prevalence of metabolic disorders. Epidemiological investigations demonstrate that middle-aged to older women have higher rates of dyslipidemia, insulin resistance, and low-grade systemic inflammation, which increase cardiovascular risk and may impose additional physiological demand on the kidneys and liver [2–4]. Because menopausal status and hormone replacement therapy (HRT) can influence cardiometabolic risk and clinical chemistry outcomes, and were not assessed using a standardized classification in the present study, we provide an age-based proxy descriptively and interpret biomarker responses within this context. Regular exercise training induces a wide range of metabolic and anti-inflammatory adaptations. Several studies have reported improvements in insulin sensitivity, enhanced mitochondrial function, and beneficial changes in lipid profile and kidney-related markers in midlife and older women, who often experience dyslipidemia and reduced metabolic flexibility due to age- and hormone-related changes [5–8]. For example, a network meta-analysis suggested that combining aerobic and resistance training (RT) may provide broad metabolic benefits, while different exercise modes may be superior for specific outcomes, supporting individualized exercise prescriptions despite limited evidence quality [6]. These findings underscore the relevance of structured exercise modalities, particularly resistance training (RT) and concurrent training (CT), that may address the complex metabolic challenges experienced by middle-aged to older women [9, 10]. Although RT is widely recognized as a fundamental modality for improving lipid profiles and markers related to liver and kidney function [10–13], emerging evidence suggests that CT may, in some contexts, elicit comparable or superior metabolic outcomes [14–17]. A key unresolved issue is whether dietary protein dose modifies these training-related adaptations in a manner that is clinically meaningful for lipid and organ-related biomarkers. In resistance-trained men, higher-protein diets combined with RT have generally not been associated with adverse changes in standard liver and kidney biomarkers, even when nitrogenous waste markers increase modestly [18]. However, evidence in women, particularly middle-aged to older women, is limited, and data are sparse for graded protein intakes in combination with CT. Thus, it remains unclear whether increasing protein intake from habitual levels alters the lipid benefits of RT or CT while also influencing renal nitrogen handling and liver enzyme activity in this population. CT is often discussed in the context of an “interference effect” for strength and hypertrophy adaptations [19–21], and higher protein intake has been proposed as a strategy to support training adaptations by enhancing muscle protein synthesis [18, 22]. Importantly, a higher-protein diet may also influence systemic metabolism and clinical chemistry outcomes through greater amino acid turnover, urea production, and changes in hepatic amino acid handling. Yet few studies have tested whether protein dose interacts with training modality to shape lipid profile alongside kidney and liver biomarkers in middle-aged to older women. Therefore, the primary aim of this study was to evaluate the effects of 12 weeks of supervised RT versus CT, combined with low (0.8), moderate (1.6), or high (2.2 g·kg⁻¹·d⁻¹) protein intake, on lipid profiles, liver enzymes, and kidney function markers in middle-aged to older women. We specifically tested whether pre-post changes differed by protein dose (time × protein) and whether protein-related changes differed by training modality (time × training × protein). Materials and methods Participants A total of 108 women between the ages of 40 and 77 years were enrolled in this study. Eligibility was determined through medical screening, with individuals excluded if they had Type 2 Diabetes, hypertension, cardiovascular conditions, clinically relevant sleep disturbances (defined as a self-reported physician-diagnosed sleep disorder [e.g., insomnia or obstructive sleep apnea] and/or current use of prescription sleep medication), or any other risk factors identified during examination. Menopausal status was not collected using a standardized classification (e.g., (Stages of Reproductive Aging Workshop [STRAW] criteria and/or hormonal confirmation) and therefore was not available for covariate adjustment; an age-based proxy is provided descriptively. HRT use was not recorded. Participants also completed self-report questionnaires regarding health and physical activity, confirming that they had engaged in <2 h/week over the past year, and reporting a habitual sleep duration of approximately 7-8 h/night; this item was used to describe the sample rather than as an exclusion criterion. Sleep quality was additionally assessed using the Pittsburgh Sleep Quality Index (PSQI) to characterize baseline sleep quality, and PSQI scores were not used to determine eligibility. Participants reported that they were not taking supplements or medications (including non-steroidal anti-inflammatory drugs [NSAIDs]). Reported dietary recalls indicated that habitual protein consumption was below approximately 0.8 g·kg⁻¹·d⁻¹, which was required for enrollment. Written informed consent was obtained from all eligible participants after study procedures were explained in detail. Those assigned to the RT plus protein intervention received nutritional guidance to achieve target intakes of 0.8, 1.6, or 2.2 g·kg⁻¹·d⁻¹, distributed across 3-6 meals per day. The study protocol received approval from the Research Ethics Committee of the University of Isfahan (approval code: IR.UI.REC.1404.174) and was carried out in accordance with the Declaration of Helsinki. Study Design Participants were randomized into six groups: RT + 0.8 g·kg⁻¹·d⁻¹ protein (n = 18; RT1), RT + 1.6 g·kg⁻¹·d⁻¹ protein (n = 18; RT2), RT + 2.2 g·kg⁻¹·d⁻¹ protein (n = 18; RT3), CT + 0.8 g·kg⁻¹·d⁻¹ protein (n = 18; CT1), CT + 1.6 g·kg⁻¹·d⁻¹ protein (n = 18; CT2), or CT + 2.2 g·kg⁻¹·d⁻¹ protein (n = 18; CT3). Randomization was performed using a computer-generated sequence with stratification by baseline skeletal muscle mass (SMM) to achieve balanced group distributions. Within each SMM stratum, allocation was generated using a restricted randomization approach (computer-generated permuted blocks) to maintain balance across the six groups; variable block sizes were used and were concealed from investigators involved in recruitment or enrollment. The randomization sequence was generated by an investigator not involved in recruitment, training supervision, or outcome assessments. Allocation was concealed using sequentially numbered, opaque, sealed envelopes opened after completion of baseline testing. Due to the nature of the exercise interventions, participants and exercise supervisors were not blinded to training modality (RT vs CT). To minimize bias, outcome assessments were performed by assessors blinded to group allocation, and participants were instructed not to disclose their intervention to assessors. Outcome measures were collected at baseline and after 12 weeks at consistent times of day (±1 h). All RT and CT sessions were supervised, scheduled by appointment, and logged to ensure compliance. Adherence was defined as completion of all prescribed supervised sessions; if a session was missed, a make-up session was scheduled and completed so that participants completed the full training dose. Baseline values were implicitly accounted for by including Time and its interactions in the mixed model; baseline comparability was evaluated descriptively. Other measures To assess sleep quality and health status, the Pittsburgh Sleep Quality Index (PSQI) and the General Health Questionnaire-28 (GHQ-28) were used, respectively [23]. Body composition Upon arrival at the laboratory, participants were instructed to void their bladder within 30 minutes prior to assessment to minimize variability related to acute fluid balance. Body mass was measured to the nearest 0.1 kg using a calibrated digital scale (Lumbar, China), and stature was recorded to the nearest 0.1 cm with a wall-mounted stadiometer (Race Industrialization, China). Body mass index (BMI) was calculated as body mass divided by height squared (kg·m⁻²). Whole-body composition, including body fat percentage (BFP) and SMM, was assessed using multi-frequency bioelectrical impedance analysis (BIA; InBody 270, South Korea) in accordance with standardized manufacturer protocols. To minimize confounding influences on impedance-derived estimates, participants were instructed to fast for ≥12 h, obtain at least 8 h of sleep, and refrain from strenuous exercise and alcohol consumption for 36-48 h prior to testing. BIA was selected as the primary method of body composition assessment due to its non-invasive nature, high reproducibility, and established validity for estimating fat mass and SMM in healthy adults when compared with dual-energy X-ray absorptiometry (DXA) [24]. Resistance training Participants in the three RT groups completed three supervised sessions per week (Saturday, Monday, and Wednesday) under the guidance of certified strength and conditioning specialists. Each session consisted of a full-body RT protocol. A standardized 10-minute warm-up, including general and specific dynamic movements, was performed before training. The exercise program included lat pulldown, machine chest press, leg press, forward lunge, standing calf raise (machine), dumbbell lateral raise, machine shoulder press (seated), machine abdominal crunch, back extension, cable biceps curl, and cable triceps pushdown. During the first six weeks, participants performed three sets per exercise, progressing to four sets in the final six weeks. Training intensity ranged from 50% to 75% of one-repetition maximum (1-RM), with repetitions between 6 and 16 and inter-set rest intervals of 30 to 80 seconds [25, 26]. Further program details are presented in Table 1. Concurrent training Participants in the three CT groups also completed three supervised sessions per week (Saturday, Monday, and Wednesday). In these sessions, the RT component was performed first, following the same protocol described above, and was immediately followed by ET to minimize potential interference effects [27, 28] The ET was performed on a cycle ergometer, with a duration ranging from 10 to 35 minutes and an intensity corresponding to 50% to 75% of age-predicted maximum heart rate (HRmax = 220 - age). Further details of the program are presented in Table 2. Biochemical markers Fasting blood samples (10 mL) were collected from the cubital vein using standard procedures following an 8-hour overnight fast, at the same time of day (8:00–9:00 a.m.) at pre- and post-intervention. Blood samples were centrifuged at 1000 × g at 4°C for 15 min, and aliquots of serum were frozen in liquid N₂ and stored at -80°C until analysis. Serum concentrations of alanine transaminase (ALT; intra-assay CV: 1.81%; inter-assay CV: 2.00%), aspartate aminotransferase (AST; intra-assay CV: 2.01%; inter-assay CV: 2.54%), gamma-glutamyl transferase (GGT; intra-assay CV: 1.56%; inter-assay CV: 0.92%), creatinine (serum creatinine; SCr; intra-assay CV: 1.60%; inter-assay CV: 2.24%), total cholesterol (TC; intra-assay CV: 1.11%; inter-assay CV: 1.18%), low-density lipoprotein cholesterol (LDL-C; intra-assay CV: 0.64%; inter-assay CV: 1.37%), high-density lipoprotein cholesterol (HDL-C; intra-assay CV: 0.77%; inter-assay CV: 1.80%), and triglycerides (TG; Pars Azmoon; intra-assay CV: 0.6%; inter-assay CV: 1.8%) were measured in duplicate using Pars Azmoon kits and enzymatic colorimetric (spectrophotometric) assays on the same analyzer. Apolipoprotein B (ApoB; Pars Azmoon; intra-assay CV: 2%; inter-assay CV: 2.5%) was measured in duplicate using an immunoturbidimetric assay on the same analyzer, according to the manufacturer’s instructions. Serum cystatin C was measured in duplicate using a latex-enhanced immunoturbidimetric (particle-enhanced) assay on the same analyzer, according to the manufacturer’s instructions. All blood samples were collected after 48 hours of rest. Laboratory technicians were blinded to group allocation and time point using coded sample identifiers. Serum urea was measured (mg/dL), and BUN (mg/dL) was calculated as BUN = Urea × 0.466. eGFR was calculated from serum creatinine and age using the CKD-EPI 2021 creatinine equation (race-free). Cystatin C was used to compute cystatin C-based eGFR (eGFRcys) via the CKD-EPI 2012 cystatin C equation, and a combined estimate (eGFRcr-cys) was calculated using the CKD-EPI 2021 creatinine–cystatin C equation (race-free). Absolute and relative changes were computed as Δ = post - pre and %Δ = 100 × (post - pre)/pre. The BUN-to-creatinine ratio was calculated as BUN/Cr = BUN (mg/dL)/SCr (mg/dL). Full equations, constants, and derived renal biomarkers are provided in Supplementary Methods S1 (CKD-EPI equations and derived renal biomarkers). Diet Participants completed six 24-h dietary logs (4 non-consecutive weekdays and 2 non-consecutive weekend days) to estimate baseline habitual protein intake. During the 12-week intervention, dietary protein intake was intentionally manipulated to achieve assigned targets (0.8, 1.6, or 2.2 g·kg⁻¹·d⁻¹). To support target attainment, participants consumed ~20-40 g of animal-based protein (e.g., chicken breast or comparable sources) immediately after each supervised training session, with remaining daily protein obtained from self-selected foods (animal- and plant-based). Dietary intake (energy and macronutrients) was monitored throughout the intervention using daily food records (mobile applications) and repeated 24-h dietary logs. The rationale for the 1.6 g·kg⁻¹·d⁻¹ protein condition was based on Morton et al. (2018), which reported this intake as sufficient to maximize fat-free mass gains with RT [29]. Because no studies have examined protein intakes above 2.0 g·kg⁻¹·d⁻¹ in the context of CT adaptations in middle-aged women, we included a higher-protein condition (2.2 g·kg⁻¹·d⁻¹) to ensure a clear separation between groups while remaining within a range considered tolerable and safe. Participants attended consultations with an registered dietitian/nutritionist every two weeks and were provided individualized guidance to meet protein and energy targets, including recommendations to distribute protein across the day (3-7 eating occasions; ~20-40 g protein per meal) to support MPS [30-32]. Macronutrient composition was supervised, with total energy intake [33] and protein intake prioritized. Carbohydrate and fat intakes were recommended to fall within the Acceptable Macronutrient Distribution Range (45-65% and 20-35% of total energy intake, respectively). Participants were instructed to avoid intentional energy restriction and to consume sufficient energy to support training and recovery, thereby minimizing the potential for energetic stress to interfere with anabolic adaptations [34, 35]. All dietary intake data were analyzed using Diet Analysis Plus (version 10; Cengage) to ensure use of a consistent food database across participants and time points. Statistical analysis A priori sample size estimation was performed using G*Power (version 3.1.9.2) within an F-test framework (to power the training modality × protein dose interaction for SMM, the primary outcome of the parent trial). Accordingly, the present analyses of lipid, kidney, and liver biomarkers should be considered secondary outcomes. The planned design comprised six groups (2 training modalities × 3 protein doses) and included baseline SMM as a covariate (1 covariate). Power was calculated for the training modality × protein dose interaction (numerator df = 2), using α = 0.05 and 1-β = 0.80. A moderate interaction effect size was specified (Cohen’s f = 0.35; ηp² ≈ 0.11), yielding a required total sample size of N = 82. To account for anticipated attrition and incomplete pre–post data, 108 participants (n = 18 per group) were recruited and randomized; analyses were conducted on participants with complete pre- and post-intervention data (analyzed sample: n = 83). Statistical analyses were performed using linear mixed-effects models. For each outcome, data were analyzed in long format with fixed effects for time (pre vs post), training modality (RT vs CT), protein dose (0.8, 1.6, 2.2 g·kg⁻¹·d⁻¹; modeled as a categorical factor), and all interactions (time × training, time × protein, and time × training × protein), and with a subject-specific random intercept to account for repeated measures. The primary inferential tests for intervention responses were the time × training interaction, time × protein interaction, and the time × training × protein interaction. Where an interaction was statistically meaningful, pre-specified contrasts were used to aid interpretation (pairwise comparisons between protein doses and/or within training modality as appropriate), and effect sizes (standardized mean change) were reported alongside model estimates. Baseline comparability across groups was evaluated descriptively and via factorial ANOVA on baseline values. To address multiplicity across biomarkers, outcomes were grouped into pre-specified families (lipids, kidney function, liver function) and p-values for the primary mixed-model interaction terms within each family were adjusted using the Benjamini-Hochberg false discovery rate (FDR) procedure; SMM remained the primary outcome in the parent paper and was interpreted at α = 0.05. Data are presented as mean ± standard deviation unless otherwise stated. Results Participant characteristics One hundred fifty participants were assessed for eligibility. Thirty did not meet the inclusion criteria, while 12 were not interested in participating after the first interview. Accordingly, 108 participants were randomized to the six groups (n = 18 per group). Four participants from CT1, RT2 and RT3, three from CT2, and five from CT3 and RT1 (due to lack of time or not being interested) withdrew from the study. The final analyzed sample with complete pre-post data was n = 83, distributed as follows: CT1 (n = 14), CT2 (n = 15), CT3 (n = 13), RT1 (n = 13), RT2 (n = 14), and RT3 (n = 14). There were no significant between-group differences in all baseline characteristics (Table 3). Training adherence was high: 100% of prescribed sessions were completed (missed sessions were rescheduled as make-up sessions), with no between-group differences. There were no differences between groups for PSQI (p = 0.494) or GHQ-28 (p = 0.230). Kidney function markers All pre and post data for kidney function markers are shown in Table 4, and linear mixed model results for kidney outcomes are shown in Table 5. In the kidney panel, there were consistent protein-dose-dependent increases in nitrogenous waste markers from pre to post: urea increased in CT1 (1.50 ± 0.26, p FDR < 0.001) and the pre–post rise was larger at 1.6 vs 0.8 (1.70 ± 0.36, p FDR < 0.001) and 2.2 vs 0.8 (3.50 ± 0.37, p FDR < 0.001); the same pattern was observed for BUN (time: 0.70 ± 0.12, p FDR < 0.001; time × protein 1.6: 0.79 ± 0.17, p FDR < 0.001; time × protein 2.2: 1.64 ± 0.17, p FDR < 0.001) and for BUN/Cr ratio (time: 0.56 ± 0.16, p FDR = 0.001; time × protein 2.2: 1.26 ± 0.23, p FDR < 0.001). Creatinine showed a small but significant time × protein effect (time × protein 1.6: 0.049 ± 0.011, p FDR < 0.001; time × protein 2.2: 0.049 ± 0.012, p FDR < 0.001), and measured cystatin C also showed significant time × protein effects (time × protein 1.6: 0.066 ± 0.013, p FDR < 0.001; time × protein 2.2: 0.052 ± 0.014, p FDR < 0.001). eGFRcr 2021 decreased over time (time: -2.13 ± 0.85, p FDR = 0.028) with larger decreases at higher protein (time × protein 1.6: -4.62 ± 1.18, p FDR < 0.001; time × protein 2.2: -3.76 ± 1.22, p FDR = 0.006). For eGFRcys 2012, the overall time effect was not FDR-significant (time: -0.89 ± 0.94, p FDR = 0.483), but the protein-dose-dependent declines were significant (time × protein 1.6: -6.33 ± 1.31, p FDR < 0.001; time × protein 2.2: -4.50 ± 1.36, p FDR = 0.003). Training mode contributed to the pre–post change for urea and BUN (time × training: -0.89 and -0.41, respectively; p FDR = 0.038), while no FDR-significant 3-way (time × training × protein) effects were detected for kidney outcomes. Liver function markers All pre and post data for liver function markers are shown in Table 4, and linear mixed model results for liver outcomes are shown in Table 5. For liver enzymes, the pre-post changes depended strongly on protein dose, with several training-dependent effects. ALT increased from pre to post in CT1 (1.04 ± 0.18, p FDR < 0.001) and showed additional increases at higher protein (time × protein 1.6 vs 0.8: 1.42 ± 0.24, p FDR < 0.001; 2.2 vs 0.8: 3.73 ± 0.25, p FDR < 0.001), without an FDR-significant 3-way interaction (Time × Training × Protein at 2.2: nominal p = 0.048, p FDR = 0.054). AST demonstrated significant overall time effects (0.57 ± 0.25, p FDR = 0.024) and a training modulation (time × training: 0.97 ± 0.36, p FDR = 0.010), alongside pronounced protein effects (time × protein 1.6: 2.30 ± 0.34, p FDR < 0.001; 2.2: 4.81 ± 0.36, p FDR < 0.001) and FDR-significant time × training × protein interactions at both doses (-1.19 ± 0.49 for 1.6 and -1.21 ± 0.50 for 2.2; p FDR = 0.021). GGT increased over time (1.43 ± 0.22, p FDR < 0.001), showed a strong training modulation (time × training: -1.66 ± 0.32, p FDR < 0.001), increased further with protein (time × protein 1.6: 1.97 ± 0.31, p FDR < 0.001; 2.2: 2.73 ± 0.32, p FDR < 0.001), and exhibited clear time × training × protein interactions (1.33 ± 0.44 for 1.6, p FDR = 0.004; +2.22 ± 0.45 for 2.2, p FDR < 0.001), indicating that protein-related enzyme responses differed between RT and CT for AST and GGT. Lipid profile All pre and post data for lipid outcomes are shown in Table 4, and linear mixed model results for lipid outcomes are shown in Table 5. Across the lipid panel, there were robust improvements over time in the reference group (CT1) and several protein- and training-dependent pre–post changes. TG decreased substantially (-9.57 ± 0.71, p FDR < 0.001), with additional reductions at higher protein (time × protein 1.6: -6.90 ± 0.99, p FDR < 0.001; 2.2: -3.51 ± 1.02, p FDR = 0.001) and a training modulation (time × training: 5.26 ± 1.02, p FDR < 0.001), indicating that the TG reduction was attenuated in RT relative to CT (i.e., less negative change in RT vs CT). TC and LDL both declined over time (TC: -8.43 ± 0.64, p FDR < 0.001; LDL: -7.64 ± 0.57, p FDR < 0.001) and showed additional protein-related improvements (TC time × protein 1.6: -4.44 ± 0.89, p FDR < 0.001; 2.2: -2.57 ± 0.93, p FDR = 0.008; LDL time × protein 1.6: -7.56 ± 0.80, p FDR < 0.001; 2.2: -5.36 ± 0.83, p FDR < 0.001), with significant training modulation (TC time × training: 4.04 ± 0.93, p FDR < 0.001; LDL time × training: 4.41 ± 0.83, p FDR < 0.001) and FDR-significant 3-way interactions at 1.6 g·kg⁻¹·d⁻¹ (TC: 4.32 ± 1.29, p FDR = 0.001; LDL: 4.86 ± 1.15, p FDR < 0.001), indicating that the protein-related change differed by training mode specifically at 1.6. HDL increased over time (3.14 ± 0.51, p FDR < 0.001), with a positive protein effect at 1.6 vs 0.8 (3.42 ± 0.71, p FDR < 0.001), a negative training modulation (-2.14 ± 0.73, p FDR = 0.006), and a time × training × protein (1.6) interaction (-2.92 ± 1.02, p FDR = 0.006). ApoB decreased over time (-8.18 ± 0.58, p FDR < 0.001) and showed a strong training modulation (6.55 ± 0.83, p FDR < 0.001), with no FDR-significant protein interaction terms. Dietary adherence, compliance, and nutrient intake All pre and post dietary intake data are shown in Table 6. Dietary intake was monitored throughout the intervention using daily food entries recorded by participants in Diet Analysis Plus (version 10; Cengage, Boston, MA, USA) and reviewed weekly by the research team. Weekly reviews verified attainment of assigned protein targets and included plausibility checks (e.g., unusually low total energy intake or inconsistent macronutrient totals), followed by feedback to resolve missing/implausible entries and reinforce adherence. Nutrient intake was quantified from these daily records and repeated 24-h dietary logs using Diet Analysis Plus, as described in the methods. Achieved protein intake (g·kg⁻¹·d⁻¹) is summarized in Table 6. At post-intervention, mean protein intake approximated prescribed targets and demonstrated clear separation between dietary conditions within both training modalities: 0.8 g·kg⁻¹·d⁻¹ (CT1: 0.80 ± 0.015; RT1: 0.80 ± 0.019), 1.6 g·kg⁻¹·d⁻¹ (CT2: 1.59 ± 0.018; RT2: 1.60 ± 0.020), and 2.2 g·kg⁻¹·d⁻¹ (CT3: 2.19 ± 0.023; RT3: 2.20 ± 0.012). Carbohydrate and fat intakes showed no meaningful pre-to-post changes across groups, indicating that group separation was achieved primarily through protein intake rather than broader macronutrient shifts. Total energy intake increased from pre- to post-intervention in RT2, RT3, and CT3, consistent with the higher protein dose; there was no evidence of systematic between-group energy restriction that could explain the main outcomes. Discussion This study examined whether dietary protein dose (0.8, 1.6, or 2.2 g·kg⁻¹·d⁻¹) modifies the metabolic and clinical chemistry responses to 12 weeks of supervised RT versus CT in middle-aged to older women, focusing on lipid profile and kidney and liver biomarkers. Overall, training produced a favorable lipid response (TG, TC, LDL-C and ApoB decreased and HDL-C increased), and several lipid improvements were enhanced at moderate-to-high protein intakes, with the clearest training-dependent protein effects occurring mainly at 1.6 g·kg⁻¹·d⁻¹. In contrast, higher protein intake was the dominant driver of dose-dependent increases in renal nitrogen markers (urea and BUN, with increases also evident in BUN/Cr), accompanied by small rises in creatinine and cystatin C and modest reductions in eGFR estimates at higher protein doses. Liver enzymes (ALT, AST and GGT) also increased in a protein-dose–dependent manner, and AST/GGT showed training-modality-specific protein responses, indicating that diet–training interactions were more evident for selected hepatic enzymes, whereas renal responses were primarily protein-driven. Kidney function markers The renal results indicate that higher protein intake is associated with increased renal metabolic load rather than overt dysfunction. However, because we did not formally evaluate whether values exceeded clinical reference ranges, the clinical significance of these short-term biomarker shifts remains uncertain over 12 weeks. Over 12 weeks of training, protein intake elicited a clear dose-dependent rise in markers of renal nitrogen handling, with urea, BUN, and the BUN/creatinine ratio increasing more at 1.6 and especially 2.2 g·kg⁻¹·day⁻¹ compared with 0.8 g·kg⁻¹·day⁻¹. Conversely, the small increases in creatinine and cystatin C, together with the modest reductions in eGFR observed at higher protein intakes, likely reflect physiological adaptations to increased protein turnover and urea production rather than clear evidence of renal injury. This pattern is consistent with a protein dose-dependent response that would be expected under high-protein dietary conditions. Training modality showed small but significant effects on urea and BUN; however, protein intake produced the clearest dose-dependent elevations in renal nitrogen load markers, with only modest parallel shifts in estimated filtration, suggesting effects were primarily protein-driven. These changes likely reflect heightened protein turnover and metabolic adaptations in middle-aged and older women, and our findings suggest that prolonged adherence to higher-protein diets may amplify renal nitrogen responses [4]. However, several studies have reported that high-protein diets, when combined with RT or CT, do not adversely affect renal function markers [18, 28]. For example, we previously reported that resistance-trained males consuming 3.2 g·kg⁻¹·day⁻¹ for 16 weeks showed modest increases in urea, and creatinine, mostly within normal ranges and without significant differences from lower-protein groups. These results suggest that very high protein intake may slightly elevate renal markers, warranting periodic monitoring for long-term adherence [28]. In addition, we also reported that a high-protein diet, combined with RT, increased SMM and performance without adversely affecting renal function [18]. In contrast, our findings extend this literature by showing dose-dependent increases in nitrogen handling markers (urea and BUN) and modest reductions in eGFR estimates at higher protein intakes in middle-aged to older women, a population that remains underrepresented in prior work. The small but significant increase in cystatin C compared to creatinine reflects its higher sensitivity and independence from age, sex, or SMM [36–38]. This suggests that high-protein diets exceeding 1.6 g·kg⁻¹·day⁻¹ may impose a greater renal metabolic load during the initial phase, highlighting the importance of monitoring. Long-term assessment, particularly in middle-aged and older women, is necessary to fully understand the physiological consequences of protein intake at different doses. Although different training modalities (RT vs. CT) did not independently alter renal function markers in our intervention, physical exercise per se may transiently increase renal metabolic load in the early phases of adaptation [39]. This response likely reflects broader physiological adaptations rather than training-specific stress. In middle‑aged to older women, age‑related differences in hormone status, SMM, and metabolic flexibility could further influence these renal adaptations and should be considered when interpreting kidney responses to combined exercise and dietary interventions. Liver function markers Liver enzymes (ALT, AST, GGT) increased over time, with larger rises at higher protein doses, particularly 2.2 g·kg⁻¹·day⁻¹. AST and GGT responses depended on training type, showing greater increases in one mode versus the other, while ALT was less affected by training. Overall, higher protein intake raised liver enzymes, with exercise context influencing AST and GGT, likely reflecting both hepatobiliary and muscle-related contributions. Because creatine kinase (CK) and enzyme isoforms were not measured, we cannot determine the extent to which AST (and potentially GGT) changes reflected skeletal muscle stress versus hepatic sources. Therefore, training effects on liver enzymes vary with protein intake, with CT showing slightly higher increases at low protein and RT at moderate to high protein, and no mode consistently causing the largest changes. Although high-protein diets have gained widespread popularity in the general population due to their effectiveness in weight control and fat loss [40, 41], such diets may also attenuate lipid absorption mediated by Lactobacillus, thereby limiting fat mass accumulation following a dieting phase [40, 42]. However, elevated protein intake may increase intraglomerular pressure, potentially leading to renal hyperfiltration, glomerular injury, and the development of proteinuria, as reported in some contexts [43]. Moreover, experimental studies demonstrate that very high–protein diets, particularly when combined with low carbohydrate intake, can activate inflammatory pathways and induce increases in liver enzymes as well as cellular and histopathological liver damage in animal models, reflecting an elevated risk of hepatic injury [44, 45]. In line with these findings, our results revealed dose-dependent alterations in liver-related parameters with increasing protein intake, whereas lower-protein diets were associated with smaller increases in liver enzymes [46]. Importantly, the source of dietary protein (plant versus animal) should be considered, as specific amino acid compositions, such as higher levels of branched-chain amino acids and glutamine, may confer protective effects on liver health [47]. Therefore, high-protein intake appears to act as an independent determinant of changes in liver enzyme activity, with dose-dependent effects that are most pronounced at higher intakes (particularly 2.2 g·kg⁻¹·day⁻¹). These findings underscore the importance of considering individual characteristics, protein source, and total protein consumption, as the interaction among these factors can substantially modulate hepatic responses. Accordingly, further investigation is warranted to clarify the long-term implications of high-protein diets on liver health. Additional results showed that RT was associated with moderately greater increases in liver enzyme activity than CT at higher daily protein intakes. The somewhat greater elevations in AST and GGT seen with RT under higher protein intakes may be attributed to increased muscle involvement, greater amino acid turnover, and physiological hepatic adaptation to higher nitrogen and oxidative demands, rather than to direct hepatocellular damage [48, 49]. This interpretation is further supported by the limited effect of training modality on ALT, which is considered more specific to liver injury. However, without CK/isoenzyme data, mechanistic attribution should be considered tentative. In contrast, the smaller changes in liver enzymes may result from adaptations in amino acid metabolism induced by CT, whereby a portion of amino acids is directed toward energy production, thereby reducing the nitrogen load on the liver [34]. Therefore, RT and CT appear to promote metabolic adaptations that can help maintain stable liver enzyme levels over time. Enzyme alterations were minor at lower protein intake (0.8 g·kg⁻¹·day⁻¹), but the progressive rise in liver enzyme activity with increasing protein doses emphasizes the significant role of dietary protein. Lipid profile Overall, lipid profiles improved, with decreases in TG, TC, LDL-C, and ApoB, and increases in HDL. Higher protein intake (1.6–2.2 kg⁻¹·day⁻¹) further enhanced some lipid responses, particularly TG, TC, LDL-C, and HDL. Training mode also influenced changes, with the clearest protein × training interactions at 1.6 kg⁻¹·day⁻¹, indicating that the added benefit of moderate protein depended on whether participants performed RT or CT. ApoB improved over time and varied by training type but showed little protein-dose effect, suggesting training had a stronger role than protein for this marker. Overall, training improved lipids, with additional benefits from moderate/high protein for several outcomes, especially when considering exercise type. Importantly, for TG the overall pre–post change was a decrease (negative), and the positive time × training coefficient means the decrease was smaller in RT than in CT (i.e., RT showed a less negative change, or a weaker reduction, than CT). A substantial body of evidence indicates that RT, alone or combined with aerobic exercise, improves lipid profiles by reducing both subcutaneous and visceral fat. Mechanisms include enhanced muscle uptake of triglycerides and glucose, increased lipoprotein lipase activity, and improved reverse cholesterol transport, while combined training further promotes efficient fat oxidation through mitochondrial biogenesis [9, 50, 51]. A study found that a high-protein, energy-restricted diet combined with RT promotes greater weight and fat loss while similarly improving cardiometabolic risk markers. [52]. Our results indicate that a high-protein diet combined with RT or CT can improve lipid profiles. ApoB levels also improved over time and differed by training modality, but showed minimal response to protein intake, suggesting that exercise plays a more significant role than dietary protein in modulating this lipoprotein [53]. Indeed, CT combined with a high-protein diet may enhance its effectiveness in middle-aged and older women; however, further studies are needed to clarify the underlying mechanisms. Additionally, the beneficial effects on lipid metabolism were further augmented by higher protein intake, particularly at 1.6 g·kg⁻¹·day⁻¹. The previous findings showed that high-protein diets have been associated with reductions in triglycerides and LDL-cholesterol [54–56]. Several mechanisms may underlie the beneficial effects of a high-protein (HP) diet on lipid metabolism. HP feeding could increase hepatic energy demands, although whole-body resting energy expenditure [8] was unchanged in the present study, and postprandial or 24‑h energy expenditure was not assessed. HP diets also reduce de novo lipogenesis (DNL) by providing amino acid-derived carbon skeletons that are poorly converted to fatty acids, accompanied by downregulation of key lipogenic enzymes such as Fasn and SREBP-1c, which modulate insulin signaling and limit hepatic lipid accumulation. The lower carbohydrate content of HP diets may further suppress DNL, contributing to reduced blood triglycerides [57, 58]. Overall, high-protein diets, particularly at 1.6 g·kg⁻¹·day⁻¹, combined with RT or CT, improved lipid profiles by reducing TG, TC, LDL-C, and ApoB and increasing HDL. These effects may be mediated through enhanced fat oxidation, reduced de novo lipogenesis, improved muscle lipid uptake, and favorable modulation of lipogenic enzymes. Strengths and limitations This study’s strengths include a randomized 2 × 3 factorial design, supervised RT and CT protocols, and a clear separation of achieved protein intake across dietary conditions. Several limitations should also be considered. Analyses were restricted to participants with complete pre–post data, which may introduce attrition-related bias. Menopausal status and HRT use were not assessed using standardized classification, limiting adjustment for hormonal factors that can influence lipid and clinical chemistry outcomes. Dietary intake was monitored but relied on self-report, and protein source (plant vs animal) was not tightly controlled beyond post-exercise provision. Finally, the intervention duration was 12 weeks and outcomes were biomarker-based; longer follow-up and clinical endpoints (e.g., albuminuria/proteinuria, imaging, or physician-diagnosed liver/kidney outcomes) are needed to clarify long-term safety and clinical relevance. Conclusion In conclusion, a moderate-to-high protein diet combined with RT or CT improves lipid profiles, including reductions in TG, TC, LDL-C, and ApoB, and increases HDL. These benefits occur alongside dose-dependent elevations in renal nitrogen markers and liver enzymes, which likely reflect physiological adaptations but whose clinical significance remains uncertain over 12 weeks. Exercise modality influenced ApoB and modulated AST and GGT responses, whereas protein dose was the dominant driver of liver enzyme and renal nitrogen marker changes. Overall, combining moderate-to-high protein intake with structured training appears to improve cardiometabolic risk markers in middle-aged to older women, though long-term monitoring and further research are warranted to clarify underlying mechanisms and safety. Perspective In middle aged to older women, a group with increased cardiometabolic risk across midlife and postmenopause, these findings add important context to current sports medicine recommendations on combining structured exercise with higher protein intakes. Consistent with previous reports that RT and CT can improve lipid related risk markers, 12 weeks of supervised training produced overall improvements in the lipid profile, including reductions in TG, TC, LDL-C and ApoB, alongside increases in HDL-C. Importantly, dietary protein dose meaningfully modified these adaptations, with moderate to high intakes, particularly around 1.6 g·kg⁻¹·day⁻¹, generally producing greater improvements in several lipid outcomes, and with some responses differing by training modality. At the same time, higher protein intakes were associated with dose dependent increases in urea and BUN, small increases in creatinine and cystatin C, modest reductions in eGFR, and increases in ALT, AST and GGT, with AST and GGT showing training related differences. From a clinical perspective, these results suggest that higher protein prescriptions may enhance lipid benefits during training in aging women, but may also warrant monitoring of kidney and liver related biomarkers, especially when intakes exceed habitual levels. Declarations Conflict of Interest: None. Funding: This work is based upon research funded by Iran National Science Foundation (INSF) under project No.4036777. Author Contribution Conceptualization: [Reza Bagheri]; Methodology: [Reza Bagheri]; Formal analysis and investigation: [Reza Bagheri]; Writing - original draft preparation: [Reza Bagheri and Hamid Ghobadi]; Writing - review and editing: [Reza Bagheri]; Supervision: [Mehdi Kargarfard]. All authors read and approved the final version. Acknowledgments: The authors sincerely thank all participants for their time, commitment, and effort throughout the study. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9024829","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":610990720,"identity":"40f4ac3c-b216-4797-ba09-0765ed510266","order_by":0,"name":"Reza Bagheri","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzElEQVRIiWNgGAWjYDACZubGAww2EgwG7A0MzERqYWw4wJAG1MJzgFgtDGAtDAwGEglEajE4DtKSYJG4XfKN4eeCChsG/vbuBPxaDoO1SCTunJ1jLD3jTBqDxJmzG/BqMQNpYfwhYWxwO8dAmrftMNCFuURoAdpibHDzjPFvkrTIGdzgMSPOFnuQlgSgFsuetDJrnjNpPAT9Itl/+OCDDwl1PObshzff5qmwkeNv78WvBQwSwCSHAYjkIawcAdgfkKJ6FIyCUTAKRhAAAOWERWRNYGENAAAAAElFTkSuQmCC","orcid":"","institution":"University of Isfahan","correspondingAuthor":true,"prefix":"","firstName":"Reza","middleName":"","lastName":"Bagheri","suffix":""},{"id":610990721,"identity":"2051d22c-871f-4692-b099-7d6d67745b4b","order_by":1,"name":"Hamid Ghobadi","email":"","orcid":"","institution":"Ferdowsi University of Mashhad","correspondingAuthor":false,"prefix":"","firstName":"Hamid","middleName":"","lastName":"Ghobadi","suffix":""},{"id":610990722,"identity":"86c4a089-db5e-43cc-a23e-7efe1596c617","order_by":2,"name":"Mehdi Kargarfard","email":"","orcid":"","institution":"University of Isfahan","correspondingAuthor":false,"prefix":"","firstName":"Mehdi","middleName":"","lastName":"Kargarfard","suffix":""},{"id":610990723,"identity":"67aa853a-a513-4694-b2b2-e6d6eee2aea9","order_by":3,"name":"Katsuhiko Suzuki","email":"","orcid":"","institution":"Waseda University","correspondingAuthor":false,"prefix":"","firstName":"Katsuhiko","middleName":"","lastName":"Suzuki","suffix":""}],"badges":[],"createdAt":"2026-03-04 02:23:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9024829/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9024829/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105565248,"identity":"c632baaf-7365-4db3-912c-5ca32c9bcc9c","added_by":"auto","created_at":"2026-03-27 12:52:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":655731,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9024829/v1/9060a12e-459e-48d5-b1e7-c2dcb402c8d5.pdf"},{"id":105405591,"identity":"0aa59848-ba41-4ac5-84f5-0b93e1d8e090","added_by":"auto","created_at":"2026-03-25 16:19:46","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":15100,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymethods.docx","url":"https://assets-eu.researchsquare.com/files/rs-9024829/v1/4c01c5be2ae388513d9ba0cb.docx"},{"id":105405590,"identity":"0e8a26e4-771a-4523-a8c8-74194b45f100","added_by":"auto","created_at":"2026-03-25 16:19:46","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":75768,"visible":true,"origin":"","legend":"","description":"","filename":"TablesReza.docx","url":"https://assets-eu.researchsquare.com/files/rs-9024829/v1/bc27bbf11cfee33fd5d41f1a.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Effects of 12 weeks of resistance and concurrent training with graded protein intakes on lipid profile, kidney and liver biomarkers in middle-aged to older women","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMidlife is a paramount transition in women\u0026rsquo;s health with consequential implications for both individuals and society. As global life expectancy rises, more women are spending extended periods in midlife and postmenopausal stages [1]. This period is marked by hormonal variation, lower physical activity, and unfavorable changes in body composition, all of which contribute to an increased prevalence of metabolic disorders. Epidemiological investigations demonstrate that middle-aged to older women have higher rates of dyslipidemia, insulin resistance, and low-grade systemic inflammation, which increase cardiovascular risk and may impose additional physiological demand on the kidneys and liver [2\u0026ndash;4]. Because menopausal status and hormone replacement therapy (HRT) can influence cardiometabolic risk and clinical chemistry outcomes, and were not assessed using a standardized classification in the present study, we provide an age-based proxy descriptively and interpret biomarker responses within this context.\u003c/p\u003e \u003cp\u003eRegular exercise training induces a wide range of metabolic and anti-inflammatory adaptations. Several studies have reported improvements in insulin sensitivity, enhanced mitochondrial function, and beneficial changes in lipid profile and kidney-related markers in midlife and older women, who often experience dyslipidemia and reduced metabolic flexibility due to age- and hormone-related changes [5\u0026ndash;8]. For example, a network meta-analysis suggested that combining aerobic and resistance training (RT) may provide broad metabolic benefits, while different exercise modes may be superior for specific outcomes, supporting individualized exercise prescriptions despite limited evidence quality [6]. These findings underscore the relevance of structured exercise modalities, particularly resistance training (RT) and concurrent training (CT), that may address the complex metabolic challenges experienced by middle-aged to older women [9, 10]. Although RT is widely recognized as a fundamental modality for improving lipid profiles and markers related to liver and kidney function [10\u0026ndash;13], emerging evidence suggests that CT may, in some contexts, elicit comparable or superior metabolic outcomes [14\u0026ndash;17].\u003c/p\u003e \u003cp\u003eA key unresolved issue is whether dietary protein dose modifies these training-related adaptations in a manner that is clinically meaningful for lipid and organ-related biomarkers. In resistance-trained men, higher-protein diets combined with RT have generally not been associated with adverse changes in standard liver and kidney biomarkers, even when nitrogenous waste markers increase modestly [18]. However, evidence in women, particularly middle-aged to older women, is limited, and data are sparse for graded protein intakes in combination with CT. Thus, it remains unclear whether increasing protein intake from habitual levels alters the lipid benefits of RT or CT while also influencing renal nitrogen handling and liver enzyme activity in this population.\u003c/p\u003e \u003cp\u003eCT is often discussed in the context of an \u0026ldquo;interference effect\u0026rdquo; for strength and hypertrophy adaptations [19\u0026ndash;21], and higher protein intake has been proposed as a strategy to support training adaptations by enhancing muscle protein synthesis [18, 22]. Importantly, a higher-protein diet may also influence systemic metabolism and clinical chemistry outcomes through greater amino acid turnover, urea production, and changes in hepatic amino acid handling. Yet few studies have tested whether protein dose interacts with training modality to shape lipid profile alongside kidney and liver biomarkers in middle-aged to older women. Therefore, the primary aim of this study was to evaluate the effects of 12 weeks of supervised RT versus CT, combined with low (0.8), moderate (1.6), or high (2.2 g\u0026middot;kg⁻\u0026sup1;\u0026middot;d⁻\u0026sup1;) protein intake, on lipid profiles, liver enzymes, and kidney function markers in middle-aged to older women. We specifically tested whether pre-post changes differed by protein dose (time \u0026times; protein) and whether protein-related changes differed by training modality (time \u0026times; training \u0026times; protein).\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003e\u003cstrong\u003eParticipants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 108 women between the ages of 40 and 77 years were enrolled in this study. Eligibility was determined through medical screening, with individuals excluded if they had Type 2 Diabetes, hypertension, cardiovascular conditions, clinically relevant sleep disturbances (defined as a self-reported physician-diagnosed sleep disorder [e.g., insomnia or obstructive sleep apnea] and/or current use of prescription sleep medication), or any other risk factors identified during examination. Menopausal status was not collected using a standardized classification (e.g., (Stages of Reproductive Aging Workshop [STRAW] criteria and/or hormonal confirmation) and therefore was not available for covariate adjustment; an age-based proxy is provided descriptively. HRT use was not recorded. Participants also completed self-report questionnaires regarding health and physical activity, confirming that they had engaged in \u0026lt;2 h/week over the past year, and reporting a habitual sleep duration of approximately 7-8 h/night; this item was used to describe the sample rather than as an exclusion criterion. Sleep quality was additionally assessed using the Pittsburgh Sleep Quality Index (PSQI) to characterize baseline sleep quality, and PSQI scores were not used to determine eligibility. Participants reported that they were not taking supplements or medications (including non-steroidal anti-inflammatory drugs [NSAIDs]). Reported dietary recalls indicated that habitual protein consumption was below approximately 0.8 g\u0026middot;kg⁻\u0026sup1;\u0026middot;d⁻\u0026sup1;, which was required for enrollment. Written informed consent was obtained from all eligible participants after study procedures were explained in detail. Those assigned to the RT plus protein intervention received nutritional guidance to achieve target intakes of 0.8, 1.6, or 2.2 g\u0026middot;kg⁻\u0026sup1;\u0026middot;d⁻\u0026sup1;, distributed across 3-6 meals per day. The study protocol received approval from the Research Ethics Committee of the University of Isfahan (approval code: IR.UI.REC.1404.174) and was carried out in accordance with the Declaration of Helsinki.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy Design\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eParticipants were randomized into six groups: RT + 0.8 g\u0026middot;kg⁻\u0026sup1;\u0026middot;d⁻\u0026sup1; protein (n = 18; RT1), RT + 1.6 g\u0026middot;kg⁻\u0026sup1;\u0026middot;d⁻\u0026sup1; protein (n = 18; RT2), RT + 2.2 g\u0026middot;kg⁻\u0026sup1;\u0026middot;d⁻\u0026sup1; protein (n = 18; RT3), CT + 0.8 g\u0026middot;kg⁻\u0026sup1;\u0026middot;d⁻\u0026sup1; protein (n = 18; CT1), CT + 1.6 g\u0026middot;kg⁻\u0026sup1;\u0026middot;d⁻\u0026sup1; protein (n = 18; CT2), or CT + 2.2 g\u0026middot;kg⁻\u0026sup1;\u0026middot;d⁻\u0026sup1; protein (n = 18; CT3). Randomization was performed using a computer-generated sequence with stratification by baseline skeletal muscle mass (SMM) to achieve balanced group distributions. Within each SMM stratum, allocation was generated using a restricted randomization approach (computer-generated permuted blocks) to maintain balance across the six groups; variable block sizes were used and were concealed from investigators involved in recruitment or enrollment. The randomization sequence was generated by an investigator not involved in recruitment, training supervision, or outcome assessments. Allocation was concealed using sequentially numbered, opaque, sealed envelopes opened after completion of baseline testing. Due to the nature of the exercise interventions, participants and exercise supervisors were not blinded to training modality (RT vs CT). To minimize bias, outcome assessments were performed by assessors blinded to group allocation, and participants were instructed not to disclose their intervention to assessors. Outcome measures were collected at baseline and after 12 weeks at consistent times of day (\u0026plusmn;1 h). All RT and CT sessions were supervised, scheduled by appointment, and logged to ensure compliance. Adherence was defined as completion of all prescribed supervised sessions; if a session was missed, a make-up session was scheduled and completed so that participants completed the full training dose. Baseline values were implicitly accounted for by including Time and its interactions in the mixed model; baseline comparability was evaluated descriptively.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOther measures\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo assess sleep quality and health status, the Pittsburgh Sleep Quality Index (PSQI) and the General Health Questionnaire-28 (GHQ-28) were used, respectively [23].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBody composition\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUpon arrival at the laboratory, participants were instructed to void their bladder within 30 minutes prior to assessment to minimize variability related to acute fluid balance. Body mass was measured to the nearest 0.1 kg using a calibrated digital scale (Lumbar, China), and stature was recorded to the nearest 0.1 cm with a wall-mounted stadiometer (Race Industrialization, China). Body mass index (BMI) was calculated as body mass divided by height squared (kg\u0026middot;m⁻\u0026sup2;). Whole-body composition, including body fat percentage (BFP) and SMM, was assessed using multi-frequency bioelectrical impedance analysis (BIA; InBody 270, South Korea) in accordance with standardized manufacturer protocols. To minimize confounding influences on impedance-derived estimates, participants were instructed to fast for \u0026ge;12 h, obtain at least 8 h of sleep, and refrain from strenuous exercise and alcohol consumption for 36-48 h prior to testing. BIA was selected as the primary method of body composition assessment due to its non-invasive nature, high reproducibility, and established validity for estimating fat mass and SMM in healthy adults when compared with dual-energy X-ray absorptiometry (DXA) [24]. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResistance training\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eParticipants in the three RT groups completed three supervised sessions per week (Saturday, Monday, and Wednesday) under the guidance of certified strength and conditioning specialists. Each session consisted of a full-body RT protocol. A standardized 10-minute warm-up, including general and specific dynamic movements, was performed before training. The exercise program included lat pulldown, machine chest press, leg press, forward lunge, standing calf raise (machine), dumbbell lateral raise, machine shoulder press (seated), machine abdominal crunch, back extension, cable biceps curl, and cable triceps pushdown. During the first six weeks, participants performed three sets per exercise, progressing to four sets in the final six weeks. Training intensity ranged from 50% to 75% of one-repetition maximum (1-RM), with repetitions between 6 and 16 and inter-set rest intervals of 30 to 80 seconds [25, 26]. Further program details are presented in Table 1.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConcurrent training\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eParticipants in the three CT groups also completed three supervised sessions per week (Saturday, Monday, and Wednesday). In these sessions, the RT component was performed first, following the same protocol described above, and was immediately followed by ET to minimize potential interference effects [27, 28] \u0026nbsp;The ET was performed on a cycle ergometer, with a duration ranging from 10 to 35 minutes and an intensity corresponding to 50% to 75% of age-predicted maximum heart rate (HRmax = 220 - age). Further details of the program are presented in Table 2.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBiochemical markers\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFasting blood samples (10 mL) were collected from the cubital vein using standard procedures following an 8-hour overnight fast, at the same time of day (8:00\u0026ndash;9:00 a.m.) at pre- and post-intervention. Blood samples were centrifuged at 1000 \u0026times; g at 4\u0026deg;C for 15 min, and aliquots of serum were frozen in liquid N₂ and stored at -80\u0026deg;C until analysis. Serum concentrations of alanine transaminase (ALT; intra-assay CV: 1.81%; inter-assay CV: 2.00%), aspartate aminotransferase (AST; intra-assay CV: 2.01%; inter-assay CV: 2.54%), gamma-glutamyl transferase (GGT; intra-assay CV: 1.56%; inter-assay CV: 0.92%), creatinine (serum creatinine; SCr; intra-assay CV: 1.60%; inter-assay CV: 2.24%), total cholesterol (TC; intra-assay CV: 1.11%; inter-assay CV: 1.18%), low-density lipoprotein cholesterol (LDL-C; intra-assay CV: 0.64%; inter-assay CV: 1.37%), high-density lipoprotein cholesterol (HDL-C; intra-assay CV: 0.77%; inter-assay CV: 1.80%), and triglycerides (TG; Pars Azmoon; intra-assay CV: 0.6%; inter-assay CV: 1.8%) were measured in duplicate using Pars Azmoon kits and enzymatic colorimetric (spectrophotometric) assays on the same analyzer. Apolipoprotein B (ApoB; Pars Azmoon; intra-assay CV: 2%; inter-assay CV: 2.5%) was measured in duplicate using an immunoturbidimetric assay on the same analyzer, according to the manufacturer\u0026rsquo;s instructions. Serum cystatin C was measured in duplicate using a latex-enhanced immunoturbidimetric (particle-enhanced) assay on the same analyzer, according to the manufacturer\u0026rsquo;s instructions. All blood samples were collected after 48 hours of rest. Laboratory technicians were blinded to group allocation and time point using coded sample identifiers. Serum urea was measured (mg/dL), and BUN (mg/dL) was calculated as BUN = Urea \u0026times; 0.466.\u003c/p\u003e\n\u003cp\u003eeGFR was calculated from serum creatinine and age using the CKD-EPI 2021 creatinine equation (race-free). Cystatin C was used to compute cystatin C-based eGFR (eGFRcys) via the CKD-EPI 2012 cystatin C equation, and a combined estimate (eGFRcr-cys) was calculated using the CKD-EPI 2021 creatinine\u0026ndash;cystatin C equation (race-free). Absolute and relative changes were computed as \u0026Delta; = post - pre and %\u0026Delta; = 100 \u0026times; (post - pre)/pre. The BUN-to-creatinine ratio was calculated as BUN/Cr = BUN (mg/dL)/SCr (mg/dL). Full equations, constants, and derived renal biomarkers are provided in Supplementary Methods S1 (CKD-EPI equations and derived renal biomarkers).\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDiet\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eParticipants completed six 24-h dietary logs (4 non-consecutive weekdays and 2 non-consecutive weekend days) to estimate baseline habitual protein intake. During the 12-week intervention, dietary protein intake was intentionally manipulated to achieve assigned targets (0.8, 1.6, or 2.2 g\u0026middot;kg⁻\u0026sup1;\u0026middot;d⁻\u0026sup1;). To support target attainment, participants consumed ~20-40 g of animal-based protein (e.g., chicken breast or comparable sources) immediately after each supervised training session, with remaining daily protein obtained from self-selected foods (animal- and plant-based). Dietary intake (energy and macronutrients) was monitored throughout the intervention using daily food records (mobile applications) and repeated 24-h dietary logs.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe rationale for the 1.6 g\u0026middot;kg⁻\u0026sup1;\u0026middot;d⁻\u0026sup1; protein condition was based on Morton et al. (2018), which reported this intake as sufficient to maximize fat-free mass gains with RT\u0026nbsp;[29]. Because no studies have examined protein intakes above 2.0 g\u0026middot;kg⁻\u0026sup1;\u0026middot;d⁻\u0026sup1; in the context of\u0026nbsp;CT\u0026nbsp;adaptations in middle-aged women, we included a higher-protein condition (2.2 g\u0026middot;kg⁻\u0026sup1;\u0026middot;d⁻\u0026sup1;) to ensure a clear separation between groups while remaining within a range considered tolerable and safe.\u003c/p\u003e\n\u003cp\u003eParticipants attended consultations with an registered dietitian/nutritionist every two weeks and were provided individualized guidance to meet protein and energy targets, including recommendations to distribute protein across the day (3-7 eating occasions; ~20-40 g protein per meal) to support MPS [30-32]. Macronutrient composition was supervised, with total energy intake [33] and protein intake prioritized. Carbohydrate and fat intakes were recommended to fall within the Acceptable Macronutrient Distribution Range (45-65% and 20-35% of total energy intake, respectively). Participants were instructed to avoid intentional energy restriction and to consume sufficient energy to support training and recovery, thereby minimizing the potential for energetic stress to interfere with anabolic adaptations [34, 35]. All dietary intake data were analyzed using Diet Analysis Plus (version 10; Cengage) to ensure use of a consistent food database across participants and time points.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA priori sample size estimation was performed using G*Power (version 3.1.9.2) within an F-test framework (to power the training modality \u0026times; protein dose interaction for SMM, the primary outcome of the parent trial). Accordingly, the present analyses of lipid, kidney, and liver biomarkers should be considered secondary outcomes.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eThe planned design comprised six groups (2 training modalities \u0026times; 3 protein doses) and included baseline SMM as a covariate (1 covariate). Power was calculated for the training modality \u0026times; protein dose interaction (numerator df = 2), using \u0026alpha; = 0.05 and 1-\u0026beta; = 0.80. A moderate interaction effect size was specified (Cohen\u0026rsquo;s f = 0.35; \u0026eta;p\u0026sup2; \u0026asymp; 0.11), yielding a required total sample size of N = 82. To account for anticipated attrition and incomplete pre\u0026ndash;post data, 108 participants (n = 18 per group) were recruited and randomized; analyses were conducted on participants with complete pre- and post-intervention data (analyzed sample: n = 83). Statistical analyses were performed using linear mixed-effects models. For each outcome, data were analyzed in long format with fixed effects for time (pre vs post), training modality (RT vs CT), protein dose (0.8, 1.6, 2.2 g\u0026middot;kg⁻\u0026sup1;\u0026middot;d⁻\u0026sup1;; modeled as a categorical factor), and all interactions (time \u0026times; training, time \u0026times; protein, and time \u0026times; training \u0026times; protein), and with a subject-specific random intercept to account for repeated measures. The primary inferential tests for intervention responses were the time \u0026times; training interaction, time \u0026times; protein interaction, and the time \u0026times; training \u0026times; protein interaction. Where an interaction was statistically meaningful, pre-specified contrasts were used to aid interpretation (pairwise comparisons between protein doses and/or within training modality as appropriate), and effect sizes (standardized mean change) were reported alongside model estimates. Baseline comparability across groups was evaluated descriptively and via factorial ANOVA on baseline values. To address multiplicity across biomarkers, outcomes were grouped into pre-specified families (lipids, kidney function, liver function) and p-values for the primary mixed-model interaction terms within each family were adjusted using the Benjamini-Hochberg false discovery rate (FDR) procedure; SMM remained the primary outcome in the parent paper and was interpreted at \u0026alpha; = 0.05. Data are presented as mean \u0026plusmn; standard deviation unless otherwise stated.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eParticipant characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOne hundred fifty participants were assessed for eligibility. Thirty did not meet the inclusion criteria, while 12 were not interested in participating after the first interview. Accordingly, 108 participants were randomized to the six groups (n = 18 per group). Four participants from CT1, RT2 and RT3, three from CT2, and five from CT3 and RT1 (due to lack of time or not being interested) withdrew from the study. The final analyzed sample with complete pre-post data was n = 83, distributed as follows: CT1 (n = 14), CT2 (n = 15), CT3 (n = 13), RT1 (n = 13), RT2 (n = 14), and RT3 (n = 14). There were no significant between-group differences in all baseline characteristics (Table 3). Training adherence was high: 100% of prescribed sessions were completed (missed sessions were rescheduled as make-up sessions), with no between-group differences. There were no differences between groups for PSQI (p = 0.494) or GHQ-28 (p = 0.230).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eKidney function markers\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll pre and post data for kidney function markers are shown in Table 4, and linear mixed model results for kidney outcomes are shown in Table 5.\u003c/p\u003e\n\u003cp\u003eIn the kidney panel, there were consistent protein-dose-dependent increases in nitrogenous waste markers from pre to post: urea increased in CT1 (1.50 \u0026plusmn; 0.26, p FDR \u0026lt; 0.001) and the pre\u0026ndash;post rise was larger at 1.6 vs 0.8 (1.70 \u0026plusmn; 0.36, p FDR \u0026lt; 0.001) and 2.2 vs 0.8 (3.50 \u0026plusmn; 0.37, p FDR \u0026lt; 0.001); the same pattern was observed for BUN (time: 0.70 \u0026plusmn; 0.12, p FDR \u0026lt; 0.001; time \u0026times; protein 1.6: 0.79 \u0026plusmn; 0.17, p FDR \u0026lt; 0.001; time \u0026times; protein 2.2: 1.64 \u0026plusmn; 0.17, p FDR \u0026lt; 0.001) and for BUN/Cr ratio (time: 0.56 \u0026plusmn; 0.16, p FDR = 0.001; time \u0026times; protein 2.2: 1.26 \u0026plusmn; 0.23, p FDR \u0026lt; 0.001). Creatinine showed a small but significant time \u0026times; protein effect (time \u0026times; protein 1.6: 0.049 \u0026plusmn; 0.011, p FDR \u0026lt; 0.001; time \u0026times; protein 2.2: 0.049 \u0026plusmn; 0.012, p FDR \u0026lt; 0.001), and measured cystatin C also showed significant time \u0026times; protein effects (time \u0026times; protein 1.6: 0.066 \u0026plusmn; 0.013, p FDR \u0026lt; 0.001; time \u0026times; protein 2.2: 0.052 \u0026plusmn; 0.014, p FDR \u0026lt; 0.001). eGFRcr 2021 decreased over time (time: -2.13 \u0026plusmn; 0.85, p FDR = 0.028) with larger decreases at higher protein (time \u0026times; protein 1.6: -4.62 \u0026plusmn; 1.18, p FDR \u0026lt; 0.001; time \u0026times; protein 2.2: -3.76 \u0026plusmn; 1.22, p FDR = 0.006). For eGFRcys 2012, the overall time effect was not FDR-significant (time: -0.89 \u0026plusmn; 0.94, p FDR = 0.483), but the protein-dose-dependent declines were significant (time \u0026times; protein 1.6: -6.33 \u0026plusmn; 1.31, p FDR \u0026lt; 0.001; time \u0026times; protein 2.2: -4.50 \u0026plusmn; 1.36, p FDR = 0.003). Training mode contributed to the pre\u0026ndash;post change for urea and BUN (time \u0026times; training: -0.89 and -0.41, respectively; p FDR = 0.038), while no FDR-significant 3-way (time \u0026times; training \u0026times; protein) effects were detected for kidney outcomes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLiver function markers\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll pre and post data for liver function markers are shown in Table 4, and linear mixed model results for liver outcomes are shown in Table 5.\u003c/p\u003e\n\u003cp\u003eFor liver enzymes, the pre-post changes depended strongly on protein dose, with several training-dependent effects. ALT increased from pre to post in CT1 (1.04 \u0026plusmn; 0.18, p FDR \u0026lt; 0.001) and showed additional increases at higher protein (time \u0026times; protein 1.6 vs 0.8: 1.42 \u0026plusmn; 0.24, p FDR \u0026lt; 0.001; 2.2 vs 0.8: 3.73 \u0026plusmn; 0.25, p FDR \u0026lt; 0.001), without an FDR-significant 3-way interaction (Time \u0026times; Training \u0026times; Protein at 2.2: nominal p = 0.048, p FDR = 0.054). AST demonstrated significant overall time effects (0.57 \u0026plusmn; 0.25, p FDR = 0.024) and a training modulation (time \u0026times; training: 0.97 \u0026plusmn; 0.36, p FDR = 0.010), alongside pronounced protein effects (time \u0026times; protein 1.6: 2.30 \u0026plusmn; 0.34, p FDR \u0026lt; 0.001; 2.2: 4.81 \u0026plusmn; 0.36, p FDR \u0026lt; 0.001) and FDR-significant time \u0026times; training \u0026times; protein interactions at both doses (-1.19 \u0026plusmn; 0.49 for 1.6 and -1.21 \u0026plusmn; 0.50 for 2.2; p FDR = 0.021). GGT increased over time (1.43 \u0026plusmn; 0.22, p FDR \u0026lt; 0.001), showed a strong training modulation (time \u0026times; training: -1.66 \u0026plusmn; 0.32, p FDR \u0026lt; 0.001), increased further with protein (time \u0026times; protein 1.6: 1.97 \u0026plusmn; 0.31, p FDR \u0026lt; 0.001; 2.2: 2.73 \u0026plusmn; 0.32, p FDR \u0026lt; 0.001), and exhibited clear time \u0026times; training \u0026times; protein interactions (1.33 \u0026plusmn; 0.44 for 1.6, p FDR = 0.004; +2.22 \u0026plusmn; 0.45 for 2.2, p FDR \u0026lt; 0.001), indicating that protein-related enzyme responses differed between RT and CT for AST and GGT.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLipid profile\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll pre and post data for lipid outcomes are shown in Table 4, and linear mixed model results for lipid outcomes are shown in Table 5.\u003c/p\u003e\n\u003cp\u003eAcross the lipid panel, there were robust improvements over time in the reference group (CT1) and several protein- and training-dependent pre\u0026ndash;post changes. TG decreased substantially (-9.57 \u0026plusmn; 0.71, p FDR \u0026lt; 0.001), with additional reductions at higher protein (time \u0026times; protein 1.6: -6.90 \u0026plusmn; 0.99, p FDR \u0026lt; 0.001; 2.2: -3.51 \u0026plusmn; 1.02, p FDR = 0.001) and a training modulation (time \u0026times; training: 5.26 \u0026plusmn; 1.02, p FDR \u0026lt; 0.001), indicating that the TG reduction was attenuated in RT relative to CT (i.e., less negative change in RT vs CT). TC and LDL both declined over time (TC: -8.43 \u0026plusmn; 0.64, p FDR \u0026lt; 0.001; LDL: -7.64 \u0026plusmn; 0.57, p FDR \u0026lt; 0.001) and showed additional protein-related improvements (TC time \u0026times; protein 1.6: -4.44 \u0026plusmn; 0.89, p FDR \u0026lt; 0.001; 2.2: -2.57 \u0026plusmn; 0.93, p FDR = 0.008; LDL time \u0026times; protein 1.6: -7.56 \u0026plusmn; 0.80, p FDR \u0026lt; 0.001; 2.2: -5.36 \u0026plusmn; 0.83, p FDR \u0026lt; 0.001), with significant training modulation (TC time \u0026times; training: 4.04 \u0026plusmn; 0.93, p FDR \u0026lt; 0.001; LDL time \u0026times; training: 4.41 \u0026plusmn; 0.83, p FDR \u0026lt; 0.001) and FDR-significant 3-way interactions at 1.6 g\u0026middot;kg⁻\u0026sup1;\u0026middot;d⁻\u0026sup1; (TC: 4.32 \u0026plusmn; 1.29, p FDR = 0.001; LDL: 4.86 \u0026plusmn; 1.15, p FDR \u0026lt; 0.001), indicating that the protein-related change differed by training mode specifically at 1.6. HDL increased over time (3.14 \u0026plusmn; 0.51, p FDR \u0026lt; 0.001), with a positive protein effect at 1.6 vs 0.8 (3.42 \u0026plusmn; 0.71, p FDR \u0026lt; 0.001), a negative training modulation (-2.14 \u0026plusmn; 0.73, p FDR = 0.006), and a time \u0026times; training \u0026times; protein (1.6) interaction (-2.92 \u0026plusmn; 1.02, p FDR = 0.006). ApoB decreased over time (-8.18 \u0026plusmn; 0.58, p FDR \u0026lt; 0.001) and showed a strong training modulation (6.55 \u0026plusmn; 0.83, p FDR \u0026lt; 0.001), with no FDR-significant protein interaction terms.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDietary adherence, compliance, and nutrient intake\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll pre and post dietary intake data are shown in Table 6.\u003c/p\u003e\n\u003cp\u003eDietary intake was monitored throughout the intervention using daily food entries recorded by participants in Diet Analysis Plus (version 10; Cengage, Boston, MA, USA) and reviewed weekly by the research team. Weekly reviews verified attainment of assigned protein targets and included plausibility checks (e.g., unusually low total energy intake or inconsistent macronutrient totals), followed by feedback to resolve missing/implausible entries and reinforce adherence. Nutrient intake was quantified from these daily records and repeated 24-h dietary logs using Diet Analysis Plus, as described in the methods. Achieved protein intake (g\u0026middot;kg⁻\u0026sup1;\u0026middot;d⁻\u0026sup1;) is summarized in Table 6. At post-intervention, mean protein intake approximated prescribed targets and demonstrated clear separation between dietary conditions within both training modalities: 0.8 g\u0026middot;kg⁻\u0026sup1;\u0026middot;d⁻\u0026sup1; (CT1: 0.80 \u0026plusmn; 0.015; RT1: 0.80 \u0026plusmn; 0.019), 1.6 g\u0026middot;kg⁻\u0026sup1;\u0026middot;d⁻\u0026sup1; (CT2: 1.59 \u0026plusmn; 0.018; RT2: 1.60 \u0026plusmn; 0.020), and 2.2 g\u0026middot;kg⁻\u0026sup1;\u0026middot;d⁻\u0026sup1; (CT3: 2.19 \u0026plusmn; 0.023; RT3: 2.20 \u0026plusmn; 0.012). Carbohydrate and fat intakes showed no meaningful pre-to-post changes across groups, indicating that group separation was achieved primarily through protein intake rather than broader macronutrient shifts. Total energy intake increased from pre- to post-intervention in RT2, RT3, and CT3, consistent with the higher protein dose; there was no evidence of systematic between-group energy restriction that could explain the main outcomes.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study examined whether dietary protein dose (0.8, 1.6, or 2.2 g\u0026middot;kg⁻\u0026sup1;\u0026middot;d⁻\u0026sup1;) modifies the metabolic and clinical chemistry responses to 12 weeks of supervised RT versus CT in middle-aged to older women, focusing on lipid profile and kidney and liver biomarkers. Overall, training produced a favorable lipid response (TG, TC, LDL-C and ApoB decreased and HDL-C increased), and several lipid improvements were enhanced at moderate-to-high protein intakes, with the clearest training-dependent protein effects occurring mainly at 1.6 g\u0026middot;kg⁻\u0026sup1;\u0026middot;d⁻\u0026sup1;. In contrast, higher protein intake was the dominant driver of dose-dependent increases in renal nitrogen markers (urea and BUN, with increases also evident in BUN/Cr), accompanied by small rises in creatinine and cystatin C and modest reductions in eGFR estimates at higher protein doses. Liver enzymes (ALT, AST and GGT) also increased in a protein-dose\u0026ndash;dependent manner, and AST/GGT showed training-modality-specific protein responses, indicating that diet\u0026ndash;training interactions were more evident for selected hepatic enzymes, whereas renal responses were primarily protein-driven.\u003c/p\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eKidney function markers\u003c/h2\u003e \u003cp\u003eThe renal results indicate that higher protein intake is associated with increased renal metabolic load rather than overt dysfunction. However, because we did not formally evaluate whether values exceeded clinical reference ranges, the clinical significance of these short-term biomarker shifts remains uncertain over 12 weeks. Over 12 weeks of training, protein intake elicited a clear dose-dependent rise in markers of renal nitrogen handling, with urea, BUN, and the BUN/creatinine ratio increasing more at 1.6 and especially 2.2 g\u0026middot;kg⁻\u0026sup1;\u0026middot;day⁻\u0026sup1; compared with 0.8 g\u0026middot;kg⁻\u0026sup1;\u0026middot;day⁻\u0026sup1;. Conversely, the small increases in creatinine and cystatin C, together with the modest reductions in eGFR observed at higher protein intakes, likely reflect physiological adaptations to increased protein turnover and urea production rather than clear evidence of renal injury. This pattern is consistent with a protein dose-dependent response that would be expected under high-protein dietary conditions. Training modality showed small but significant effects on urea and BUN; however, protein intake produced the clearest dose-dependent elevations in renal nitrogen load markers, with only modest parallel shifts in estimated filtration, suggesting effects were primarily protein-driven.\u003c/p\u003e \u003cp\u003eThese changes likely reflect heightened protein turnover and metabolic adaptations in middle-aged and older women, and our findings suggest that prolonged adherence to higher-protein diets may amplify renal nitrogen responses [4]. However, several studies have reported that high-protein diets, when combined with RT or CT, do not adversely affect renal function markers [18, 28]. For example, we previously reported that resistance-trained males consuming 3.2 g\u0026middot;kg⁻\u0026sup1;\u0026middot;day⁻\u0026sup1; for 16 weeks showed modest increases in urea, and creatinine, mostly within normal ranges and without significant differences from lower-protein groups. These results suggest that very high protein intake may slightly elevate renal markers, warranting periodic monitoring for long-term adherence [28]. In addition, we also reported that a high-protein diet, combined with RT, increased SMM and performance without adversely affecting renal function [18]. In contrast, our findings extend this literature by showing dose-dependent increases in nitrogen handling markers (urea and BUN) and modest reductions in eGFR estimates at higher protein intakes in middle-aged to older women, a population that remains underrepresented in prior work. The small but significant increase in cystatin C compared to creatinine reflects its higher sensitivity and independence from age, sex, or SMM [36\u0026ndash;38]. This suggests that high-protein diets exceeding 1.6 g\u0026middot;kg⁻\u0026sup1;\u0026middot;day⁻\u0026sup1; may impose a greater renal metabolic load during the initial phase, highlighting the importance of monitoring. Long-term assessment, particularly in middle-aged and older women, is necessary to fully understand the physiological consequences of protein intake at different doses.\u003c/p\u003e \u003cp\u003eAlthough different training modalities (RT vs. CT) did not independently alter renal function markers in our intervention, physical exercise per se may transiently increase renal metabolic load in the early phases of adaptation [39]. This response likely reflects broader physiological adaptations rather than training-specific stress. In middle‑aged to older women, age‑related differences in hormone status, SMM, and metabolic flexibility could further influence these renal adaptations and should be considered when interpreting kidney responses to combined exercise and dietary interventions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eLiver function markers\u003c/h2\u003e \u003cp\u003eLiver enzymes (ALT, AST, GGT) increased over time, with larger rises at higher protein doses, particularly 2.2 g\u0026middot;kg⁻\u0026sup1;\u0026middot;day⁻\u0026sup1;. AST and GGT responses depended on training type, showing greater increases in one mode versus the other, while ALT was less affected by training. Overall, higher protein intake raised liver enzymes, with exercise context influencing AST and GGT, likely reflecting both hepatobiliary and muscle-related contributions. Because creatine kinase (CK) and enzyme isoforms were not measured, we cannot determine the extent to which AST (and potentially GGT) changes reflected skeletal muscle stress versus hepatic sources. Therefore, training effects on liver enzymes vary with protein intake, with CT showing slightly higher increases at low protein and RT at moderate to high protein, and no mode consistently causing the largest changes.\u003c/p\u003e \u003cp\u003eAlthough high-protein diets have gained widespread popularity in the general population due to their effectiveness in weight control and fat loss [40, 41], such diets may also attenuate lipid absorption mediated by Lactobacillus, thereby limiting fat mass accumulation following a dieting phase [40, 42]. However, elevated protein intake may increase intraglomerular pressure, potentially leading to renal hyperfiltration, glomerular injury, and the development of proteinuria, as reported in some contexts [43]. Moreover, experimental studies demonstrate that very high\u0026ndash;protein diets, particularly when combined with low carbohydrate intake, can activate inflammatory pathways and induce increases in liver enzymes as well as cellular and histopathological liver damage in animal models, reflecting an elevated risk of hepatic injury [44, 45]. In line with these findings, our results revealed dose-dependent alterations in liver-related parameters with increasing protein intake, whereas lower-protein diets were associated with smaller increases in liver enzymes [46]. Importantly, the source of dietary protein (plant versus animal) should be considered, as specific amino acid compositions, such as higher levels of branched-chain amino acids and glutamine, may confer protective effects on liver health [47]. Therefore, high-protein intake appears to act as an independent determinant of changes in liver enzyme activity, with dose-dependent effects that are most pronounced at higher intakes (particularly 2.2 g\u0026middot;kg⁻\u0026sup1;\u0026middot;day⁻\u0026sup1;). These findings underscore the importance of considering individual characteristics, protein source, and total protein consumption, as the interaction among these factors can substantially modulate hepatic responses. Accordingly, further investigation is warranted to clarify the long-term implications of high-protein diets on liver health.\u003c/p\u003e \u003cp\u003eAdditional results showed that RT was associated with moderately greater increases in liver enzyme activity than CT at higher daily protein intakes.\u003c/p\u003e \u003cp\u003eThe somewhat greater elevations in AST and GGT seen with RT under higher protein intakes may be attributed to increased muscle involvement, greater amino acid turnover, and physiological hepatic adaptation to higher nitrogen and oxidative demands, rather than to direct hepatocellular damage [48, 49]. This interpretation is further supported by the limited effect of training modality on ALT, which is considered more specific to liver injury. However, without CK/isoenzyme data, mechanistic attribution should be considered tentative. In contrast, the smaller changes in liver enzymes may result from adaptations in amino acid metabolism induced by CT, whereby a portion of amino acids is directed toward energy production, thereby reducing the nitrogen load on the liver [34]. Therefore, RT and CT appear to promote metabolic adaptations that can help maintain stable liver enzyme levels over time. Enzyme alterations were minor at lower protein intake (0.8 g\u0026middot;kg⁻\u0026sup1;\u0026middot;day⁻\u0026sup1;), but the progressive rise in liver enzyme activity with increasing protein doses emphasizes the significant role of dietary protein.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eLipid profile\u003c/h2\u003e \u003cp\u003eOverall, lipid profiles improved, with decreases in TG, TC, LDL-C, and ApoB, and increases in HDL. Higher protein intake (1.6\u0026ndash;2.2 kg⁻\u0026sup1;\u0026middot;day⁻\u0026sup1;) further enhanced some lipid responses, particularly TG, TC, LDL-C, and HDL. Training mode also influenced changes, with the clearest protein \u0026times; training interactions at 1.6 kg⁻\u0026sup1;\u0026middot;day⁻\u0026sup1;, indicating that the added benefit of moderate protein depended on whether participants performed RT or CT. ApoB improved over time and varied by training type but showed little protein-dose effect, suggesting training had a stronger role than protein for this marker. Overall, training improved lipids, with additional benefits from moderate/high protein for several outcomes, especially when considering exercise type. Importantly, for TG the overall pre\u0026ndash;post change was a decrease (negative), and the positive time \u0026times; training coefficient means the decrease was smaller in RT than in CT (i.e., RT showed a less negative change, or a weaker reduction, than CT).\u003c/p\u003e \u003cp\u003eA substantial body of evidence indicates that RT, alone or combined with aerobic exercise, improves lipid profiles by reducing both subcutaneous and visceral fat. Mechanisms include enhanced muscle uptake of triglycerides and glucose, increased lipoprotein lipase activity, and improved reverse cholesterol transport, while combined training further promotes efficient fat oxidation through mitochondrial biogenesis [9, 50, 51]. A study found that a high-protein, energy-restricted diet combined with RT promotes greater weight and fat loss while similarly improving cardiometabolic risk markers. [52]. Our results indicate that a high-protein diet combined with RT or CT can improve lipid profiles. ApoB levels also improved over time and differed by training modality, but showed minimal response to protein intake, suggesting that exercise plays a more significant role than dietary protein in modulating this lipoprotein [53]. Indeed, CT combined with a high-protein diet may enhance its effectiveness in middle-aged and older women; however, further studies are needed to clarify the underlying mechanisms.\u003c/p\u003e \u003cp\u003eAdditionally, the beneficial effects on lipid metabolism were further augmented by higher protein intake, particularly at 1.6 g\u0026middot;kg⁻\u0026sup1;\u0026middot;day⁻\u0026sup1;. The previous findings showed that high-protein diets have been associated with reductions in triglycerides and LDL-cholesterol [54\u0026ndash;56]. Several mechanisms may underlie the beneficial effects of a high-protein (HP) diet on lipid metabolism. HP feeding could increase hepatic energy demands, although whole-body resting energy expenditure [8] was unchanged in the present study, and postprandial or 24‑h energy expenditure was not assessed. HP diets also reduce de novo lipogenesis (DNL) by providing amino acid-derived carbon skeletons that are poorly converted to fatty acids, accompanied by downregulation of key lipogenic enzymes such as Fasn and SREBP-1c, which modulate insulin signaling and limit hepatic lipid accumulation. The lower carbohydrate content of HP diets may further suppress DNL, contributing to reduced blood triglycerides [57, 58]. Overall, high-protein diets, particularly at 1.6 g\u0026middot;kg⁻\u0026sup1;\u0026middot;day⁻\u0026sup1;, combined with RT or CT, improved lipid profiles by reducing TG, TC, LDL-C, and ApoB and increasing HDL. These effects may be mediated through enhanced fat oxidation, reduced de novo lipogenesis, improved muscle lipid uptake, and favorable modulation of lipogenic enzymes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eStrengths and limitations\u003c/h2\u003e \u003cp\u003eThis study\u0026rsquo;s strengths include a randomized 2 \u0026times; 3 factorial design, supervised RT and CT protocols, and a clear separation of achieved protein intake across dietary conditions. Several limitations should also be considered. Analyses were restricted to participants with complete pre\u0026ndash;post data, which may introduce attrition-related bias. Menopausal status and HRT use were not assessed using standardized classification, limiting adjustment for hormonal factors that can influence lipid and clinical chemistry outcomes. Dietary intake was monitored but relied on self-report, and protein source (plant vs animal) was not tightly controlled beyond post-exercise provision. Finally, the intervention duration was 12 weeks and outcomes were biomarker-based; longer follow-up and clinical endpoints (e.g., albuminuria/proteinuria, imaging, or physician-diagnosed liver/kidney outcomes) are needed to clarify long-term safety and clinical relevance.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, a moderate-to-high protein diet combined with RT or CT improves lipid profiles, including reductions in TG, TC, LDL-C, and ApoB, and increases HDL. These benefits occur alongside dose-dependent elevations in renal nitrogen markers and liver enzymes, which likely reflect physiological adaptations but whose clinical significance remains uncertain over 12 weeks. Exercise modality influenced ApoB and modulated AST and GGT responses, whereas protein dose was the dominant driver of liver enzyme and renal nitrogen marker changes. Overall, combining moderate-to-high protein intake with structured training appears to improve cardiometabolic risk markers in middle-aged to older women, though long-term monitoring and further research are warranted to clarify underlying mechanisms and safety.\u003c/p\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003ePerspective\u003c/h2\u003e \u003cp\u003eIn middle aged to older women, a group with increased cardiometabolic risk across midlife and postmenopause, these findings add important context to current sports medicine recommendations on combining structured exercise with higher protein intakes. Consistent with previous reports that RT and CT can improve lipid related risk markers, 12 weeks of supervised training produced overall improvements in the lipid profile, including reductions in TG, TC, LDL-C and ApoB, alongside increases in HDL-C. Importantly, dietary protein dose meaningfully modified these adaptations, with moderate to high intakes, particularly around 1.6 g\u0026middot;kg⁻\u0026sup1;\u0026middot;day⁻\u0026sup1;, generally producing greater improvements in several lipid outcomes, and with some responses differing by training modality. At the same time, higher protein intakes were associated with dose dependent increases in urea and BUN, small increases in creatinine and cystatin C, modest reductions in eGFR, and increases in ALT, AST and GGT, with AST and GGT showing training related differences. From a clinical perspective, these results suggest that higher protein prescriptions may enhance lipid benefits during training in aging women, but may also warrant monitoring of kidney and liver related biomarkers, especially when intakes exceed habitual levels.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eConflict of Interest:\u003c/h2\u003e \u003cp\u003eNone.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eThis work is based upon research funded by Iran National Science Foundation (INSF) under project No.4036777.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualization: [Reza Bagheri]; Methodology: [Reza Bagheri]; Formal analysis and investigation: [Reza Bagheri]; Writing - original draft preparation: [Reza Bagheri and Hamid Ghobadi]; Writing - review and editing: [Reza Bagheri]; Supervision: [Mehdi Kargarfard]. All authors read and approved the final version.\u003c/p\u003e\u003ch2\u003eAcknowledgments:\u003c/h2\u003e \u003cp\u003eThe authors sincerely thank all participants for their time, commitment, and effort throughout the study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eKilpi F et al (2022) \u003cem\u003eChanges in women\u0026rsquo;s physical function in mid-life by reproductive age and hormones: a longitudinal study.\u003c/em\u003e BMC Women's Health, 22(1): p. 473\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTorosyan N et al (2022) \u003cem\u003eDyslipidemia in midlife women: Approach and considerations during the menopausal transition.\u003c/em\u003e Maturitas, 166: pp. 14\u0026ndash;20\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoradi L et al (2024) \u003cem\u003eComparison of metabolic risk factors, lipid indices, healthy eating index, and physical activity among premenopausal, menopausal, and postmenopausal women.\u003c/em\u003e Rom J Intern Med, 62(3): pp. 260\u0026thinsp;\u0026ndash;\u0026thinsp;71\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e4 Kurniawan AL et al (2019) Association of kidney function-related dietary pattern, weight status, and cardiovascular risk factors with severity of impaired kidney function in middle-aged and older adults with chronic kidney disease: a cross-sectional population study. 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Br J Sports Med 52(6):376\u0026ndash;384\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoore DR et al (2009) \u003cem\u003eIngested protein dose response of muscle and albumin protein synthesis after resistance exercise in young men.\u003c/em\u003e The American journal of clinical nutrition, 89(1): pp. 161\u0026ndash;168\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSnijders T et al (2015) \u003cem\u003eProtein ingestion before sleep increases muscle mass and strength gains during prolonged resistance-type exercise training in healthy young men.\u003c/em\u003e The Journal of nutrition, 145(6): pp. 1178\u0026ndash;1184\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLarsson L et al (2018) \u003cem\u003eSarcopenia: aging-related loss of muscle mass and function.\u003c/em\u003e Physiological reviews\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWeinstein JR, Anderson S (2010) \u003cem\u003eThe aging kidney: physiological changes.\u003c/em\u003e Advances in chronic kidney disease, 17(4): pp. 302\u0026ndash;307\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePerez-Schindler J et al (2015) \u003cem\u003eNutritional strategies to support concurrent training.\u003c/em\u003e European journal of sport science, 15(1): pp. 41\u0026ndash;52\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHamilton DL, Philp A (2013) \u003cem\u003eCan AMPK mediated suppression of mTORC1 explain the concurrent training effect?\u003c/em\u003e Cellular and Molecular Exercise Physiology, 2(1): p. e4\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMussap M et al (2002) \u003cem\u003eCystatin C is a more sensitive marker than creatinine for the estimation of GFR in type 2 diabetic patients.\u003c/em\u003e Kidney international, 61(4): pp. 1453\u0026ndash;1461\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStevens LA et al (2008) \u003cem\u003eEstimating GFR using serum cystatin C alone and in combination with serum creatinine: a pooled analysis of 3,418 individuals with CKD.\u003c/em\u003e American journal of kidney diseases, 51(3): pp. 395\u0026ndash;406\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eToffaletti JG (2017) \u003cem\u003eRelationships and clinical utility of creatinine, cystatin C, eGFRs, GFRs, and clearances.\u003c/em\u003e The Journal of Applied Laboratory Medicine, 2(3): pp. 413\u0026ndash;422\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu Q et al (2025) \u003cem\u003eEffect of exercise on kidney-relevant biomarkers in the general population: a systematic review and meta-analysis.\u003c/em\u003e BMJ open, 15(1): p. e093017\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoon J, Koh G (2020) \u003cem\u003eClinical evidence and mechanisms of high-protein diet-induced weight loss.\u003c/em\u003e Journal of obesity \u0026amp; metabolic syndrome, 29(3): p. 166\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCheng Y et al (2024) \u003cem\u003eAssociation between dietary protein intake and risk of chronic kidney disease: a systematic review and meta-analysis.\u003c/em\u003e Frontiers in Nutrition, 11: p. 1408424\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCava E, Yeat NC, Mittendorfer B (2017) \u003cem\u003ePreserving healthy muscle during weight loss.\u003c/em\u003e Advances in nutrition, 8(3): pp. 511\u0026ndash;519\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKalantar-Zadeh K, Kramer HM, Fouque D (2020) \u003cem\u003eHigh-protein diet is bad for kidney health: unleashing the taboo\u003c/em\u003e. Oxford University Press. pp. 1\u0026ndash;4\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eD\u0026iacute;az-R\u0026uacute;a R et al (2017) \u003cem\u003eLong-term intake of a high-protein diet increases liver triacylglycerol deposition pathways and hepatic signs of injury in rats.\u003c/em\u003e The Journal of nutritional biochemistry, 46: pp. 39\u0026ndash;48\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMonteiro MEL et al (2016) \u003cem\u003eApoptosis induced by a low-carbohydrate and high-protein diet in rat livers.\u003c/em\u003e World journal of gastroenterology, 22(22): p. 5165\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYin H-Y et al (2026) \u003cem\u003eHigh-protein diets and metabolic dysfunction-associated steatotic liver disease: A double-edged sword in liver health.\u003c/em\u003e World Journal of Gastroenterology, 32(6)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAimutis WR (2022) \u003cem\u003ePlant-based proteins: the good, bad, and ugly.\u003c/em\u003e Annual Review of Food Science and Technology, 13(1): pp. 1\u0026ndash;17\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBiolo G et al (1995) \u003cem\u003eIncreased rates of muscle protein turnover and amino acid transport after resistance exercise in humans.\u003c/em\u003e American Journal of Physiology-Endocrinology and Metabolism, 268(3): pp. E514-E520\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePhillips SM et al (1997) \u003cem\u003eMixed muscle protein synthesis and breakdown after resistance exercise in humans.\u003c/em\u003e American journal of physiology-endocrinology and metabolism, 273(1): pp. E99-E107\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTambalis K et al (2009) \u003cem\u003eResponses of blood lipids to aerobic, resistance, and combined aerobic with resistance exercise training: a systematic review of current evidence.\u003c/em\u003e Angiology, 60(5): pp. 614\u0026ndash;632\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePitsavos C et al (2009) \u003cem\u003eResistance exercise plus to aerobic activities is associated with better lipids\u0026rsquo; profile among healthy individuals: the ATTICA study.\u003c/em\u003e QJM: An International Journal of Medicine, 102(9): pp. 609\u0026ndash;616\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWycherley TP et al (2010) \u003cem\u003eA high-protein diet with resistance exercise training improves weight loss and body composition in overweight and obese patients with type 2 diabetes.\u003c/em\u003e Diabetes care, 33(5): pp. 969\u0026ndash;976\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShojah-anzabi B et al (2026) \u003cem\u003eImpact of Resistance, Endurance, and Combined Exercise Training on Lipid Profile, Apolipoprotein A-1, and Nitric Oxide Synthase Expression in Women with Type 2 Diabetes: A Randomized Controlled Trial.\u003c/em\u003e Human Nutrition \u0026amp; Metabolism, : p. 200360\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFarnsworth E et al (2003) \u003cem\u003eEffect of a high-protein, energy-restricted diet on body composition, glycemic control, and lipid concentrations in overweight and obese hyperinsulinemic men and women.\u003c/em\u003e The American journal of clinical nutrition, 78(1): pp. 31\u0026ndash;39\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSkov 55R (1999) Changes in renal function during weight loss induced by high vs low-protein low-fat diets in overweight subjects. Int J Obes 23(11):1170\u0026ndash;1177\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eParker B et al (2002) \u003cem\u003eEffect of a high-protein, high\u0026ndash;monounsaturated fat weight loss diet on glycemic control and lipid levels in type 2 diabetes.\u003c/em\u003e Diabetes care, 25(3): pp. 425\u0026ndash;430\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRietman A et al (2014) \u003cem\u003eIncreasing protein intake modulates lipid metabolism in healthy young men and women consuming a high-fat hypercaloric diet.\u003c/em\u003e The Journal of nutrition, 144(8): pp. 1174\u0026ndash;1180\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBahadoran Z et al (2013) \u003cem\u003eDietary protein, protein to carbohydrate ratio and subsequent changes in lipid profile after a 3-year follow-up: Tehran lipid and glucose study.\u003c/em\u003e Iranian Journal of Public Health, 42(11): p. 1232\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 1 to 6 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Renal failure, Nutrition, Metabolic function, Metabolic dysfunction","lastPublishedDoi":"10.21203/rs.3.rs-9024829/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9024829/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eThis study examined whether dietary protein dose modifies adaptations to resistance training (RT) versus concurrent training (CT) in middle-aged to older women, focusing on lipid profile and kidney and liver biomarkers.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA total of 108 middle-aged to older women (40\u0026ndash;77 years) were randomized to 12 weeks of supervised RT or CT (3 sessions/week), combined with low (0.8 g\u0026middot;kg⁻\u0026sup1;\u0026middot;d⁻\u0026sup1;), moderate (1.6 g\u0026middot;kg⁻\u0026sup1;\u0026middot;d⁻\u0026sup1;), or high (2.2 g\u0026middot;kg⁻\u0026sup1;\u0026middot;d⁻\u0026sup1;) protein intake (six groups; n\u0026thinsp;=\u0026thinsp;18/group). Only participants with complete pre-post data were included; the analyzed sample comprised n\u0026thinsp;=\u0026thinsp;83 (CT1 n\u0026thinsp;=\u0026thinsp;14, CT2 n\u0026thinsp;=\u0026thinsp;15, CT3 n\u0026thinsp;=\u0026thinsp;13, RT1 n\u0026thinsp;=\u0026thinsp;13, RT2 n\u0026thinsp;=\u0026thinsp;14, RT3 n\u0026thinsp;=\u0026thinsp;14).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eTG, TC, LDL-C and ApoB decreased and HDL-C increased from pre to post (p FDR\u0026thinsp;\u0026le;\u0026thinsp;0.05). These improvements were generally greater at 1.6 and/or 2.2 vs 0.8 g\u0026middot;kg⁻\u0026sup1;\u0026middot;d⁻\u0026sup1; (Time \u0026times; Protein, p FDR\u0026thinsp;\u0026le;\u0026thinsp;0.05), and for several lipids the protein-related benefit differed by training mode (Time \u0026times; Training \u0026times; Protein, p FDR\u0026thinsp;\u0026le;\u0026thinsp;0.05; primarily at 1.6 g\u0026middot;kg⁻\u0026sup1;\u0026middot;d⁻\u0026sup1;). Urea and BUN increased pre to post, with dose-dependent elevations at 1.6 and 2.2 vs 0.8 (Time \u0026times; Protein, p FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.001), accompanied by small creatinine/cystatin C increases and modest eGFR reductions at higher protein intakes (p FDR\u0026thinsp;\u0026le;\u0026thinsp;0.05). ALT, AST and GGT increased from pre to post with clear protein-dose effects (Time \u0026times; Protein, p FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and AST/GGT showed training-dependent protein responses (Time \u0026times; Training \u0026times; Protein, p FDR\u0026thinsp;\u0026le;\u0026thinsp;0.05). For TG, the overall decrease was attenuated in RT relative to CT.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eTwelve weeks of supervised RT or CT combined with controlled protein intakes elicited favorable lipid changes overall, while higher protein doses (particularly 1.6\u0026ndash;2.2 g\u0026middot;kg⁻\u0026sup1;\u0026middot;d⁻\u0026sup1;) were associated with greater improvements in selected lipid outcomes but also with dose-dependent increases in urea/BUN and modest reductions in eGFR estimates, and increases in liver enzymes, some of which differed by training modality. These findings indicate that protein dose meaningfully modifies metabolic and clinical chemistry responses to training in middle-aged to older women, and that higher protein intake may involve trade-offs between lipid benefits and changes in kidney- and liver-related biomarkers.\u003c/p\u003e","manuscriptTitle":"Effects of 12 weeks of resistance and concurrent training with graded protein intakes on lipid profile, kidney and liver biomarkers in middle-aged to older women","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-25 16:19:41","doi":"10.21203/rs.3.rs-9024829/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3210ce2c-b0e5-482c-aad9-9f1e85dc02cd","owner":[],"postedDate":"March 25th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-25T16:19:41+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-25 16:19:41","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9024829","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9024829","identity":"rs-9024829","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}