Role of NAFLD on the Health Related QoL Response to Lifestyle in Patients With Metabolic Syndrome: The PREDIMED Plus Cohort

Role of NAFLD on the Health Related QoL Response to Lifestyle in Patients With Metabolic Syndrome: The PREDIMED Plus Cohort Diego Martı ´nez-Urbistondo 1 *, RodrigoSan San Cristo ´bal 2 , Paula Villares 1 , Miguel A ´ ngel Martı ´ nez-Gonza ´lez 3,4,5 , Nancy Babio 4,6,7 , Dolores Corella 4,8 ,Jose ´Luis del Val 4,9 , Jose ´M. Ordova ´s 10,11 ,A ´ ngel M. Alonso-Go ´mez 4,12 , Julia Wärnberg 4,13 , Jesu ´s Vioque 14,15 , Dora Romaguera 4,16 , Jose ´Lo ´pez-Miranda 4,17 ,RamonEstruch 4,18 , Francisco J. Tinahones 4,19 , Jose ´Lapetra 4,20 ,J.Luı ´s Serra-Majem 4,21 , Aurora Bueno-Cavanillas 13,22 ,JosepA.Tur 4,23 , Alba Marcos 14,24 , Xavier Pinto ´ 4,25 ,MiguelDelgado-Rodrı ´ guez 13,26 ,PilarMatı ´ a-Martı ´n 27 , Josep Vidal 28,29 , Clotilde Va ´zquez 4,30 , Emilio Ros 4,29 ,Marı ´a Vanessa Bullo ´nVela 3,4 , Antoni Palau 4,6,7 ,MartaMasague ´ 4,9 ,ItziarAbete 4,31 , Anai Moreno-Rodrı ´ guez 4,12 , Inma Candela-Garcı ´a 32 ,JadwigaKonieczna 4,16 ,AntonioGarcı ´a-Rı ´os 4,17 , Oscar Lecea Jua ´rez 3,4 ,PacoMartı ´ n 4,6,7 ,AlbertGoday 4,9,33 ,MA ´ ngeles Zulet 4,31 , Jessica Vaquero-Luna 4,12 ,Marı ´a del Carmen Sayo ´nOrea 3,4 , Isabel Megı ´ as 4,6,7 , Enric Baltasar 4,9 ,J.AlfredoMartı ´nez 2,4,31 and Lidia Daimiel 34 1 Internal Medicine Department, Hospital HM Sanchinarro, HM Hospitales, Madrid, Spain, 2 Cardiometabolic Nutrition Group, Precision Nutrition and Cardiometabolic Health Program, Instituto Madrileño de Estudios Avanzados (IMDEA) Food, Centro de Excelencia en Investigacio ´n (CEI) Universidad Auto ´noma de Madrid (UAM) + Centro Superior de Investigaciones Científicas (CSIC), Madrid, Spain, 3 Department of Preventive Medicine and Public Health, University of Navarra, IdiSNA, Pamplona, Spain, 4 Consorcio CIBER, M.P. Fisiopatologı ´ a de la Obesidad y Nutricio ´n (CIBERObn), Instituto de Salud Carlos III (ISCIII). Madrid, Spain, 5 Department of Nutrition, Harvard T. H. Chan School of Public Health, Boston, MA, United States, 6 Universitat Rovira i Virgili, Departament de Bioquı ´ mica i biotecnologia, Unitat de Nutricio ´Humana, Reus, Spain, 7 Institut d’Investigacio ´Sanitària Pere Virgili (IISPV), Hospital Universitari San Joan de Reus. Human Nutrition unit, Reus, Spain, 8 Department of Preventive Medicine, University of Valencia, Valencia, Spain, 9 Cardiovascular Risk and Nutrition Research Group (CARIN), Hospital del Mar Medical Research Institute (IMIM), Barcelona, Spain, 10 Nutritional Genomics and Epigenomics Group, Precision Nutrition and Obesity Program. Instituto Madrileño de Estudios Avanzados (IMDEA) Food, Centro de Excelencia en Investigacio ´n(CEI)Universidad Auto ´noma de Madrid (UAM) + Centro Superior de Investigaciones Científicas (CSIC), Madrid, Spain, 11 Nutrition and Genomics Laboratory, Jean Mayer United States Department of Agriculture (JM_USDA) Human Nutrition Research Center on Aging, Tufts University, Boston, MA, United States, 12 Bioaraba Health Research Institute, Osakidetza Basque Health Service, Araba University Hospital, University of the Basque Country Universidad del Paı ´ s Vasco/Euskal Herriko Unibertsitatea (UPV/EHU), Vitoria-Gasteiz, Spain, 13 Department of Nursing, School of Health Sciences, University of Ma ´laga, Instituto de Investigacio ´n Biome ´dica de Ma ´laga (IBIMA), Ma ´laga, Spain, 14 Centro de Investigacio ´n Biome ´dica en Red de Epidemiologı ´ aySaludPu ´blica (CIBERESP), Instituto de Salud Carlos III, Madrid, Spain, 15 Instituto de Investigacio ´nSanitariayBiome ´dica de Alicante. Universidad Miguel Herna ´ndez (ISABIAL-UMH). Alicante, Spain, 16 Research Group on Nutritional Epidemiology & Cardiovascular Physiopathology (NUTRECOR). Health Research Institute of the Balearic Islands (IdISBa), University Hospital Son Espases (HUSE), Palma de Mallorca, Spain, 17 Lipids and Atherosclerosis Unit, Department of Internal Medicine, Maimonides Biomedical Research Institute of Cordoba (IMIBIC), Reina Sofia University Hospital, University of Cordoba, Co ´rdoba, Spain, 18 Department of Internal Medicine, Instituto de Investigaciones Biome ´dicas August Pi i Sunyer (IDIBAPS), Hospital Clinic, University of Barcelona, Barcelona, Spain, 19 Department of Endocrinology, Instituto de Investigacio ´nBiome ´dica de Ma ´laga (IBIMA), Virgen de la Victoria Hospital, University of Ma ´laga, Ma ´laga, Spain, 20 Department of Family Medicine, Research Unit, Distrito Sanitario Atencio ´n Primaria Sevilla, Sevilla, Spain, 21 Research Institute of Biomedical and Health Sciences Instituto Universitario de Investigaciones Biome ´dicas y Sanitarias (IUIBS), University of Las Palmas de Gran Canaria, Preventive Medicine Service, Centro Hospitalario Universitario Insular Materno Infantil (CHUIMI), Canarian Health Service, Las Palmas, Spain, 22 Department of Preventive Medicine and Public Health, University of Granada, Granada, Spain, 23 Research Group on Community Nutrition & Oxidative Stress, University of Balearic Islands, Palma de Mallorca, Spain, 24 Institute of Biomedicine (IBIOMED), University of Leo ´n, Leo ´n, Spain, 25 Lipids and Vascular Risk Unit, Internal Medicine, Hospital Universitario de Bellvitge, Hospitalet de Llobregat, Barcelona, Spain, 26 Departamento de Ciencias de la Salud, Centro de Estudios Avanzados en Olivar y Aceites de Oliva, Universidad de Jae ´n, Jae ´n, Spain, 27 Department of Endocrinology and Nutrition, Instituto de Investigacio ´n Sanitaria Hospital Clı ´ nico San Carlos Frontiers in Endocrinology | www.frontiersin.org June 2022 | Volume 13 | Article 8687951 Edited by: Marwan El Ghoch, Beirut Arab University, Lebanon Reviewed by: Ferdinando Carlo Sasso, Università della Campania Luigi Vanvitelli, Italy Mario Luca Morieri, University of Padua, Italy *Correspondence: Diego Martı ´nez-Urbistondo [email protected] Specialty section: This article was submitted to Obesity, a section of the journal Frontiers in Endocrinology Received: 08 February 2022 Accepted: 26 April 2022 Published: 29 June 2022 Citation: Martı ´nez-Urbistondo D, San Cristo ´bal R, Villares P, Martı ´nez-Gonza ´lez MA ´ , Babio N, Corella D, del Val JL, Ordova ´s JM, Alonso-Go ´mez A ´ M, Wärnberg J, Vioque J, Romaguera D, Lo ´pez-Miranda J, Estruch R, Tinahones FJ, Lapetra J, Serra-Majem JL, Bueno-Cavanillas A, Tur JA, Marcos A, Pinto ´X, Delgado-Rodrı ´guez M, Matı ´a-Martı ´nP, Vidal J, Va ´zquez C, Ros E, Bullo ´n Vela MV, Palau A, Masague ´M, Abete I, Moreno-Rodrı ´guez A, Candela-Garcı ´aI,Konieczna J, Garcı ´a-Rı ´os A, Jua ´rez OL, Martı ´nP, Goday A, Zulet MA ´ , Vaquero-Luna J, Orea MdCS, Megı ´as I, Baltasar E, Martı ´nez JA and Daimiel L (2022) Role of NAFLD on the Health Related QoL Response to Lifestyle in Patients With Metabolic Syndrome: The PREDIMED Plus Cohort. Front. Endocrinol. 13:868795. doi: 10.3389/fendo.2022.868795 ORIGINAL RESEARCH published: 29 June 2022 doi: 10.3389/fendo.2022.868795 (IdISSC), Madrid, Spain, 28 Biomedical Research Centre for Diabetes and Metabolic Diseases Network (CIBERDEM), Instituto de Salud Carlos III (ISCIII), Madrid, Spain, 29 Endocrinology and Nutrition Service, Instituto de Investigaciones Biome ´dicas August Pi i Sunyer (IDIBAPS), Hospital Clinic, University of Barcelona, Barcelona, Spain, 30 Department of Endocrinology and Nutrition, Hospital Fundacio ´nJimenezDı ´ az, Instituto de Investigaciones Biome ´dicas IISFJD. University Auto ´noma, Madrid, Spain, 31 Department of Nutrition, Food Sciences and Physiology, University of Navarra, Pamplona, Spain, 32 Centro de Salud San Pola, Alicante, Spain, 33 Departament de Medicina, Universitat Autònoma de Barcelona, Barcelona, Spain, 34 Nutritional Control of the Epigenome Group. Precision Nutrition and Obesity Program. Instituto Madrileño de Estudios Avanzados (IMDEA) Food, Centro de Excelencia en Investigacio ´n (CEI) Universidad Auto ´noma de Madrid (UAM) + Centro Superior de Investigaciones Científicas (CSIC), Madrid, Spain Objective: To evaluate the effect of Non-alcoholic fatty liver disease (NAFLD) status in the impact of lifestyle over Health-related quality of life (HRQoL) in patients with metabolic syndrome (MetS). Methods: Baseline and 1 year follow up data from the PREDIMED-plus cohort (men and women, 55-75 years old with overweight/obesity and MetS) were studied. Adherence to an energy-restricted Mediterranean Diet (er-MeDiet) and Physical Activity (PA) were assessed with a validated screeners. Hepatic steatosis index (HSI) was implemented to evaluate NAFLD while the SF-36 questionnaire provided HRQoL evaluation. Statistical analyses were performed to evaluate the influence of baseline NAFLD on HRQoL as affected by lifestyle during 1 year of follow up. Results: Data from 5205 patients with mean age of 65 years and a 48% of female participants. Adjusted linear multivariate mixed regression models showed that patients with lower probability of NAFLD (HSI < 36 points) were more responsive to er-MeDiet (b 0.64 vs b0.05 per er-MeDiet adherence point, p< 0.01) and PA (b0.05 vs b0.01 per MET-h/week, p = 0.001) than those with high probability for NAFLD in terms Physical SF36 summary in the 1 year follow up. 10 points of er-MeDiet adherence and 50 MET-h/ week were thresholds for a beneficial effect of lifestyle on HRQoL physical domain in patients with lower probability of NAFLD. Conclusion: The evaluation of NAFLD by the HSI index in patients with MetS might identify subjects with different prospective sensitivity to lifestyle changes in terms of physical HRQoL (http://www.isrctn.com/ISRCTN89898870). Keywords: NAFLD, metabolic syndrome, mediterranean diet, physical activity, HRQoL INTRODUCTION The prevalence of Non-alcoholic Fatty Liver Disease (NAFLD) is raising worldwide (1). Indeed, lipid metabolism associated disorders are the most rapidly increasing cause of morbidity and mortality among hepatic patients in Western countries (2). NAFLD can be considered a manifestation of the metabolic syndrome (MetS) burden in the liver (3,4). In terms of pathogenesis, fatty liver disease is the result of the clustering of genetic inheritance, lifestyle factors, ageing, hepatotoxic drugs, co-morbidities and gut microbiota (5–11). In this context, non-invasive assessment of liver steatosis using indexes, such as Hepatic Steatosis Index (HSI) were developed to select patients at a higher risk of NAFLD in population studies (12). These scales could provide a non-invasive longitudinal monitoring of patients and an objective quantitation of the effect of therapeutic measures on NAFLD status. Weight loss is a major target in patients with fatty liver disease (13). In this context, changes in lifestyle have an impact on NAFLD status (7). Among dietary patterns, the Mediterranean Diet (MedDiet) provides a balanced nutrient composition, which should be strongly deemed in treating this disease (14). Furthermore, moderate physical activity (PA) has shown a healthy potential in the reduction of NAFLD morbid severity through the improvement of insulin resistance, anti-inflammatory effects and antioxidant mechanisms. Likewise, both MedDiet and PA are closely related to quality of life (QoL) in patients with MetS (15,16). The SF-36 index is widely used, being sensitive to changes and has been validated for the Spanish population, separating information through mental and physical components of QoL and providing a precision medicine approach to Health-Related Quality of life (HRQoL). The physical aggregated component of SF-36 (PCS) mainly accounts for categories related to physical function, physical Martı ´nez-Urbistondo et al. NAFLD Impact Over Lifestyle Changes Frontiers in Endocrinology | www.frontiersin.org June 2022 | Volume 13 | Article 8687952 role, vitality, body pain and general health perceptions. Thus, the PCS is relevant in the clinical setting for the screening of physical factors that influence medical issues in QoL (17,18). The personalization of indications and the linkage between lifestyle intervention and a short-term benefit in terms of quality of life could improve the adherence to healthy habits, which may benefit NAFLD patients. In this context, the objective of the present study is the longitudinal evaluation of the impact of MedDiet and Physical activity on Health-related Quality of Life according to a baseline non-invasive evaluation of the NAFLD status based on the HSI score in patients with MetS from the PREDIMED-Plus trial. METHODS Study Design and Participants This analysis was based on baseline and 1 year data of the multicenter PREDIMED-Plus trial. The study protocol, including study design and data collection, has been published (19) and can be found at the PREDIMED-Plus website (https:// www.predimedplus.com/en/). The detailed description of design and aims of the study could be found in Appendix 1. All authors had access to the study data and had reviewed and approved the final manuscript. Hepatic Steatosis Index The liver status in terms of risk of NAFLD was measured with the Hepatic Steatosis Index (HSI), which was calculated using the described formula: HSI =8xALT AST +BMI +2if type 2diabetes yes,+2if femaleðÞ (1) A cut-off value of 36 points was used to select patients with a higher risk of NAFLD as described in previous reports (20). Health-Related Quality of Life An adapted version of previously published 36-items HRQL questionnaire (SF36-HRQL) was used. This scale is validated for the Spanish population and extensively used to measure the subjective awareness of health and capability or physical constraints to manage with daily tasks. The PCS SF-36 index was calculated according to previously published coefficients (18). Statistical Analyses Data from 5021 participants were included (Figure 1). The patient’s characteristics were defined as mean ± SD for quantitative variables and as proportions for qualitative variables. Participants were stratified according to the HSI quartiles and regarding the 36-point cut-off at baseline. The score for each category of the SF36-HRQL tests as well as for the physical component dimension were evaluated as continuous variables. Data among quartiles were compared using ANOVA. For longitudinal analyses, data from volunteers with missing or implausible values for the defined variables at 1 year of follow-up were excluded, including 3902 participants (Figure 1). Longitudinal analysis for the variation of the SF-36 components were analyzed with correlation panels. Afterwards, linear mixed model with individual random effect adjusted by age, sex, recruitment center, daily alcohol consumption, total caloric intake, waist circumference and number of metabolic syndrome features were applied to evaluate and compare the response of SF36 PCS depending on er-MeDiet and PA adherence in both HSI < 36 and HSI ≥36 subgroups. Contrasts were performed including the interaction between modifiable factors (Mediterranean diet adherence and physical activity) as an independent variable in the model. Two-tailed level of 0.05 were considered as a threshold for statistical significance. All analyses were conducted with data from database version 202012220958_PREDIMEDplus_2anys_202012-22. “R-Studio1.4.17”(RStudio Team. PBC, Boston, MA) was used for analysis. RESULTS Population Description and Liver Status Assessment at Baseline A total of 5,021 patients fulfilled the inclusion criteria and were analyzed in the study (Figure 1). At baseline, the mean age of the study sample was 65.10 ± 4.91 years with a 48.1% of female participants. Demographic, anthropometric, lifestyle characteristics and MetS features are described by HSI quartiles (Table 1)andby HSI validated cut-off values HSI >36 and HSI < 36 (Supplementary Table 1). As expected, participants with higher HSI index showed higher levels of glucose, tryglicerides and liver enzymes. They also showed higher BMI and waist perimeter and lower HDL levels. They also showed a worse lifestyle indicated by a lower adherence to a MedDiet, lower PA and higher sedentarism. A poor control of the riskfactorsincludedinMetSwasfoundtobedirectlyrelatedtothe HSI score with statistical signification, except for hypertension (p < 0.05) (Supplementary Figure 1). Health Related Quality of Life Distribution at Baseline The levels of HRQoL were described as the result of the 8 categories of SF-36 and the 2 validated physical and mental summaries among HSI quartiles. These results are shown divided by HSI quartiles (Table 2). Quality of life showed a statistically significant inverse association to HSI score quartile among all categories and both summaries (p< 0,001). Longitudinal Evaluation of NAFLD Status in the Influence of Lifestyle on HRQoL For a longitudinal evaluation of the influence of NAFLD in the effect of Lifestyle on HRQoL, a raw correlation analysis was performed featuring the increase in HRQoL and both the erMeDiet and PA adherence after one year follow-up, dividing patients between those with a HSI score below 36 (lower probability of liver steatosis) and those with 36 points or more in the HSI score (higher probability of liver steatosis). Both models Martı ´nez-Urbistondo et al. NAFLD Impact Over Lifestyle Changes Frontiers in Endocrinology | www.frontiersin.org June 2022 | Volume 13 | Article 8687953 showed a different response in terms of HRQoL to lifestyle changes depending on liver status in both er-MeDiet and PA adherence (p < 0.05 respectively). This data are shown in Figure 2. According to these results, mixed multivariate regression models were developed to evaluate the consequences of erMeDiet adherence and PA in patients with lower probability of liver steatosis according to HSI < 36 points and those with a high probability of NAFLD (HSI ≥36 points) including baseline and 1 year of follow up data. In the evaluation of er-MeDiet, differences were found between both subgroups in physical role, and PCS after adjustment by age, sex, recruitment node, total calory intake, alcohol consumption, waist circumference, number of items of the MetS and PA. The PCS response to er-MeDiet adherence was found to be higher in patients with lower HSI (b0.58 CI 95% 0.27-1.00) in comparison with those with a HSI ≥36 points (b 0.07 CI 95% -0.06-0.16) as shown (Table 3A). These results were not replicated when stratifying patients in the different components of the HSI (ALT/AST ratio, BMI, fasting blood glucose > 100 mg/dl and sex) or by age (Supplementary Tables 1, 2). Data were plotted to show the influence of erMeDiet adherence in terms of HRQoL variation (Figure 3A). FIGURE 1 | Flowchart of the PREDIMED-Plus participants. Martı ´nez-Urbistondo et al. NAFLD Impact Over Lifestyle Changes Frontiers in Endocrinology | www.frontiersin.org June 2022 | Volume 13 | Article 8687954 In this figure, the linear mixed model predicts the annual increase of HRQoL depending on the er-MeDiet score providing that patients with high probability of NAFLD tend to be resistant to increases in er-MeDiet adherence, while patients with lower NAFLD risk are very sensitive to dietary changes in terms of HRQoL aggregated physical domain. Beyond, patients below a 10-point adherence to er-MeDiet, tended to lose HRQoL PCS while those with a score of 10 points or higher, improved HRQoL PCS in the 1-year followup (Figure 3A). In the evaluation of PA, patients with a higher improvement in physical role, body pain and vitality scores in response to increases in PA were associated to a lower HSI after previously described adjustments (Table 3B). In the evaluation of PA, the TABLE 2 | Score of the SF-36 Quality of Life categories among HSI baseline levels. Variable (0-100 points) HSI p < 25 HSI p26 - p50 HSI p51 –p75 HSI > p75 p HSI interval [min - max] [30.95 - 39.62] [39.62 - 42.68] [42.69 - 46.31] [46.31 - 192.54] NA Physical status, (SD) 82.01 (16.78) 77.43 (18.84) 75.57 (18.75) 69.12 (21.64) <0.001 Physical role, points, (SD) 81.28 (32.79) 77.23 (35.09) 76.71 (35.23) 70.81 (37.94) <0.001 Body pain, points, (SD) 69.69 (25.25) 65.20 (25.95) 62.46 (26.07) 57.47 (27.48) <0.001 General health, points, (SD) 65.74 (17.01) 63.51 (18.67) 61.89 (18.38) 58.62 (19.63) <0.001 Vitality, points, (SD) 68.53 (19.95) 65.41 (21.13) 63.51 (21.16) 58.64 (22.57) <0.001 Social role, points, (SD) 89.30 (17.88) 87.19 (20.01) 86.22 (21.20) 82.44 (23.54) <0.001 Emotional role, points, (SD) 89.74 (27.37) 88.19 (28.58) 87.56 (29.74) 83.67 (33.24) <0.001 Mental health, points, (SD) 77.00 (18.10) 76.08 (18.74) 74.62 (19.48) 71.58 (20.75) <0.001 Physical SF-36 summary, points, (SD) 47.39 (8.16) 45.51 (8.62) 44.79 (8.67) 42.60 (9.61) <0.001 Mental SF-36 summary, points, (SD) 52.11 (9.59) 51.91 (9.86) 51.47 (10.45) 50.23 (11.49) <0.001 HSI, Hepatic steatosis index; SD, Standard deviation; MeDiet, Mediterranean Diet. Bold for p values < 0.05. NA, Not applicable. TABLE 1 | Population characteristics at baseline. Variable Global HSI p < 25 HSI p26 - p50 HSI p51 –p75 HSI > p75 p HSI interval [min - max] [30.95 - 39.62] [39.62 - 42.68] [42.69 - 46.31] [46.31 - 192.54] NA n5021 1256 1255 1255 1255 Age, years (SD) 65.10 (4.91) 65.48 (5.08) 65.63 (4.88) 64.76 (4.91) 64.53 (4.71) <0.001 Sex, female (%) 2417 (48.1) 455 (36.2) 565 (45.0) 657 (52.4) 740 (59.0) <0.001 Civil status (%) 0.052 Single 248 (4.9) 61 (4.9) 56 (4.5) 61 (4.9) 70 (5.6) Married 3857 (77.0) 989 (78.9) 970 (77.5) 977 (78.0) 921 (73.6) Widowed/divorced 906 (18.1) 204 (16.3) 226 (18.1) 215 (17.2) 261 (20.8) Education level (%) 0.001 Primary 2479 (49.4) 570 (45.4) 606 (48.3) 657 (52.4) 646 (51.5) Secondary 1461 (29.1) 369 (29.4) 373 (29.7) 352 (28.0) 367 (29.2) College 1081 (21.5) 317 (25.2) 276 (22.0) 246 (19.6) 242 (19.3) Working status, inactive (%) 4005 (80.1) 1005 (80.3) 1017 (81.2) 974 (78.0) 1009 (80.7) 0.192 Body mass index, kg/m 2 , (SD) 32.51 (3.42) 29.20 (1.58) 31.23 (1.98) 33.26 (2.27) 36.36 (2.71) <0.001 Waist circumference, cm, (SD) 107.48 (9.55) 101.21 (7.36) 105.08 (7.85) 108.91 (8.45) 114.71 (8.83) <0.001 Adherence to MeDiet, 0-17, (SD) 8.48 (2.64) 8.57 (2.73) 8.60 (2.64) 8.45 (2.62) 8.33 (2.58) 0,042 Physical activity, METs-min/week, (SD) 2519.35 (2322.82) 2972.26 (2457.73) 2625.55 (2299.09) 2470.96 (2392.26) 2008.27 (2016.13) <0.001 Sedentarism, Yes (%) 2193 (43.7) 462 (36.8) 495 (39.5) 581 (46.3) 655 (52.2) <0.001 Energy intake reported, Kcal/day, (SD) 2351.48 (552.16) 2383.52 (550.77) 2354.77 (541.46) 2322.94 (557.01) 2344.64 (558.17) 0.05 Alcohol intake, g/day, (SD) 10.90 (14.86) 12.23 (15.08) 11.19 (14.89) 10.92 (15.25) 9.27 (14.08) <0.001 Fasting blood glucosa, mg/dl, (SD) 113.44 (27.30) 105.11 (19.19) 110.90 (25.18) 115.80 (28.37) 121.97 (31.98) <0.001 Fasting blood tryglicerides, mg/dl, (SD) 140.88 (52.93) 132.94 (52.38) 138.51 (51.98) 145.23 (53.09) 146.83 (53.15) <0.001 Fasting High-density lipoprotein, mg/dl, (SD) 48.01 (11.63) 48.86 (12.49) 47.86 (10.95) 47.67 (11.12) 47.65 (11.88) 0,027 Alanin aminotransferase, UI/L, (SD) 26.71 (15.05) 20.36 (8.19) 24.39 (12.83) 28.24 (14.35) 33.86 (19.24) <0.001 Aspartate aminotransferase, UI/L, (SD) 23.10 (9.52) 22.72 (8.54) 22.68 (9.05) 23.57 (10.06) 23.42 (10.30) 0,029 Hepatic steatosis index, points (SD) 43.28 (5.80) 37.41 (1.63) 41.15 (0.88) 44.42 (1.05) 50.15 (6.53) <0.001 Systolic blood pressure (mmHg) 139.72 (16.87) 138.51 (16.59) 140.00 (17.23) 139.65 (16.70) 140.74 (16.91) 0.01 Diastolic blood pressure (mmHg) 80.64 (9.86) 80.46 (9.05) 80.54 (9.99) 80.94 (10.06) 80.62 (10.30) 0.644 Diagnosed hypertension (%) 4187 (84.0) 1037 (83.3) 1037 (83.0) 1036 (83.5) 1077 (86.3) 0.085 Diagnosed diabetes (%) 1358 (27.1) 176 (14.1) 289 (23.1) 388 (31.0) 505 (40.3) <0.001 Liver esteatosis HSI > 36 points (%) 4777 (95.1) 1012 (80.6) 1255 (100.0) 1255 (100.0) 1255 (100.0) <0.001 HSI, Hepatic steatosis index; SD, Standard deviation; MeDiet, Mediterranean Diet. Bold for p values < 0.05. NA, Not applicable. Martı ´nez-Urbistondo et al. NAFLD Impact Over Lifestyle Changes Frontiers in Endocrinology | www.frontiersin.org June 2022 | Volume 13 | Article 8687955 HSI influence on HRQoL evolution was not replicated when stratifying patients in the different components of the HSI (ALT/ AST ratio, BMI, diabetes and sex) or by age (Supplementary Tables 2, 3). The PCS response to PA was found to improve more in patients with lower HSI (b0.05 CI 95% 0.03-0.07) in comparison with those with a HSI ≥36 points (b0.03 CI 95% 0.01-0.02) as described (Table 3B and Figure 3B). Besides, patients performing less than 50 MET-h/week of PA, tended to lose HRQoL while those accomplishing more PA, improved HRQoL in the 1-year follow-up (Figure 3B). Additional information for the interpretation of this results about the predicted PCS year increase depending on different levels of PA and er-MeDiet are also provided (Supplementary Figure 2). After this analysis, the regression model was replicated among quartiles of HSI to provide information about progressive response of HRQoL depending on lifestyle. Although no important statistically significant results were found in this approach, a trend towards statistical significance was found in the Physical Activity adherence group (Supplementary Tables 4, 5). DISCUSSION The results from the current research evidence the capacity of a non-invasive evaluation of NAFLD to modify the effect of lifestyle over Health-related Quality of Life. The increase in the adherence to a er-MedDiet pattern and PA might lead to more than a 5-fold increase in the aggregated physical domain of HRQoL depending on the presence of NAFLD, using previously validated and easy to fulfill scores such as SF-36 and HSI (18,20). In this context, different methods of detection of liver steatosis have been previously validated, such as ultrasound, controlled attenuation parameters of FibroScan © . Among them, HSI has A B FIGURE 2 |(A) Correlation of HRQoL increase and er-MeDiet adherence modification in patients with higher NAFLD probability and lower NAFLD probability. (B) Correlation of HRQoL increase and Physical activity modification in patients with higher NAFLD probability and lower NAFLD probability. Martı ´nez-Urbistondo et al. NAFLD Impact Over Lifestyle Changes Frontiers in Endocrinology | www.frontiersin.org June 2022 | Volume 13 | Article 8687956 demonstrated adequacy in the epidemiological study of liver disease (21), while demonstrating a high similar discrimination power than other more expensive or less available detection methods (22). Besides, our results provide a clinically relevant objectives in terms of er-MeDiet adherence and PA to improve HRQoL in patients with MetS and lower probability of NAFLD. To our knowledge, this is the first investigation to proof the differential effect of lifestyle measures in patients with MetS depending on a non-invasive assessment of liver steatosis. Interestingly, in our cohort, the HSI score was associated to a lower adherence to er-MeDiet and PA performance and to a higher prevalence and of the components of MetS at baseline. These findings are endorsed by indirect findings and provide further evidence on the association of HSI score and the MetS features (3). Furthermore, these results reinforce the impact of liver steatosis in QoL (20) and might support previous evidence on the use of non-invasive methods for the individualization of patients with MetS in the clinical setting (23–25). Thus, the apparent resistance of patients with NAFLD to lifestyle measures in terms of HRQoL in the longitudinal analysis could adequate the prescription of the therapeutic armamentarium in these patients, bringing forward more aggressive treatments in selected subjects, such as GLP-1 analogs and bariatric surgery (26,27). Beyond, the 10 point er-MeDiet adherence and 50 MET-h/week thresholds for the improvement of HRQoL in patients with lower probability of NAFLD may provide a goal of lifestyle change in these patients as well as an objective evaluation and counselling in the clinical setting. Therefore, our study could contribute to the development of new lifestylebased intervention guidelines for patients with MetS according to the liver status which in fact, may also provide further benefits in the cardiovascular disease scenario, due to the association of NAFLD and atherosclerotic disease development (28,29). With regard of the impact of Mediterranean diet and PA in HRQoL depending on the liver status, some aspects should be emphasized. The effect of Mediterranean diet has been previously related to the MetS prevention and control after 1 year follow up (30). Besides, MedDiet pattern has also been TABLE 3B | Physical activity performance. Lower probability of NAFLD (HSI < 36) Higher probability of NAFLDHSI > 36 p for non-NAFLD vs. NAFLD patients b95% Conf. Interval b95% Conf. Interval Pyisical status, points, (SD) 0.030 -0.027 0.086 0.015 -0.001 0.031 0.619 Physical role, points, (SD) 0.179 0.054 0.304 0.034 -0.002 0.070 0.030 Body pain, points, (SD) 0.123 0.035 0.211 0.021 -0.004 0.046 0.031 General health, points, (SD) 0.012 -0.046 0.069 0.015 -0.001 0.031 0.907 Vitality, points, (SD) 0.050 -0.014 0.114 -0.003 -0.021 0.015 0.126 Social role, points, (SD) -0.001 -0.073 0.071 0.007 -0.014 0.028 0.845 Emotional role, points, (SD) -0.035 -0.144 0.073 -0.0004 -0.032 0.031 0.545 Mental health, points, (SD) -0.033 -0.092 0.026 -0.002 -0.018 0.015 0.323 Physical SF-36 summary, points, (SD) 0.046 0.018 0.073 0.011 0.003 0.019 0.019 Mental SF-36 summary, points, (SD) -0.026 -0.061 0.008 -0.004 -0.014 0.005 0.229 Adjusted by: Age (years), sex, recruitment node, Mediterranean diet score, daily alcohol consumption (g/day), total caloric intake (kcal/day), waist circumference and number of Metabolic Syndrome components. HSI, Hepatic steatosis index; SD, Standard deviation. Bold for p values < 0.05. TABLE 3A | Mixed multivariate model on the effect of Mediterranean diet adherence on SF-36 components after 1-year follow up. Indeterminate probability of NAFLD (HSI < 36) High probability of NAFLDHSI > 36 p for non-NAFLD vs. NAFLD patients b95% Conf. Interval b95% Conf. Interval Pyisical status, points, (SD) 0.70 -0.15 1.55 -0.12 -0.35 0.11 0.067 Physical role, points, (SD) 2.63 0.77 4.49 0.34 -0.17 0.85 0.020 Body pain, points, (SD) 1.14 -0.18 2.46 -0.02 -0.37 0.34 0.097 General health, points, (SD) 0.96 0.10 1.83 0.44 0.21 0.67 0.255 Vitality, points, (SD) 0.46 -0.51 1.42 0.18 -0.08 0.44 0.588 Social role, points, (SD) 0.03 -1.05 1.10 0.21 -0.08 0.50 0.744 Emotional role, points, (SD) 0.41 -1.20 2.02 0.30 -0.15 0.74 0.893 Mental health, points, (SD) 0.22 -0.66 1.11 0.13 -0.11 0.37 0.845 Physical SF-36 summary, points, (SD) 0.58 0.16 1.00 0.04 -0.07 0.15 0.014 Mental SF-36 summary, points, (SD) -0.11 -0.62 0.41 0.11 -0.03 0.25 0.426 er-MeDiet adherence. Adjusted by: Age (years), sex, recruitment node, physical activity (METs/h/week), daily alcohol consumption (g/day), total caloric intake (kcal/day), waist circumference and number of Metabolic Syndrome components. HSI, Hepatic steatosis index; SD, Standard deviation. Bold for p values < 0.05. Martı ´nez-Urbistondo et al. NAFLD Impact Over Lifestyle Changes Frontiers in Endocrinology | www.frontiersin.org June 2022 | Volume 13 | Article 8687957 related to HRQoL in previous series and in the present cohort (31). Similar results were found on PA relationship to HRQoL, with a direct relationship to the control of MetS and HRQoL. However, the concept of a modulation of NAFLD status in the impact of er-MeDiet and PA on HRQoL is new. In fact, interventions tend to be maximized in patients at higher risk (32). Thus, the capacity of HSI to identify patients with a better response to lifestyle as measured with HRQoL parameters might be related to the NAFLD morbidity burden (33). Other factor that could contribute to our results is the severity of the MetS among progressive HSI values. This fact might be related to a poorer metabolic flexibility of patients with NAFLD in terms of body composition (34), insulin resistance (35), microbioma (9) and redox/equilibrium (36)andthus,areductiononthe sensitivity to lifestyle change of this population due to a reduction in homeostasis capability (37). In the methodological arena, the design quality of PREDIMED plus provides strength to the present results in A B FIGURE 3 |(A) Prediction of effect of Mediterranean diet adherence on SF-36 components after 1-year follow up adjusted by age, sex, recruitment node, daily alcohol consumption, total caloric intake, waist circumference and aggregated MetS features. a: percentile 20 for er-MEDAS MedDiet score (8 points) of participants with HSI < 36; b: percentile 40 for er-MEDAS MedDiet score (10 points) of participants with HSI < 36; c: percentile 60 for er-MEDAS MedDiet score (12 points) of participants with HSI < 36; d: percentile 80 for er-MEDAS MedDiet score (15 points) of participants with HSI < 36; * cutoff point (10 points) with the x axis (DPhysical SF-36 summary = 0) from which patients with HSI < 36 benefit from er-MeDiet adherence in terms of physical HRQoL. (B) Prediction of effect of Physical activity on SF-36 components after 1-year follow up adjusted by age, sex, recruitment node, daily alcohol consumption, total caloric intake, hip-waist circumference and aggregated MetS features. a: percentile 20 for METs*hour/week (25 METs*hour/week) of participants with HSI < 36; b: percentile 40 for METs*hour/week (40 METs*hour/week) of participants with HSI ;< 36 c: percentile 60 for METs*hour/week (62 METs*hour/week) of participants with HSI < 36; d: percentile 80 for METs*hour/week (94 METs*hour/week) of participants with HSI < 36; * cutoff point (50 METs*hour/week) with the x axis (DPhysical SF-36 summary = 0) from which patients with HSI < 36 benefit from PA changes in terms of physical HRQoL. Martı ´nez-Urbistondo et al. NAFLD Impact Over Lifestyle Changes Frontiers in Endocrinology | www.frontiersin.org June 2022 | Volume 13 | Article 8687958 terms of inclusion criteria fulfillment, anthropometric and laboratory tests and adherence to er-MeDiet and PA patterns assessment (19). Besides, the SF-36 Physical summary and the HSI index are well validated and universally used indexes in the evaluation of both HRQoL and liver steatosis (18,20). Even though, some limitations should be addressed. First, the cohort recruitment was designed to evaluate the effect of the intervention on dietary patterns and PA in patients with MetS. Then, the present findings should be externally validated in a different cohort. In this context, the present analysis does not follow the original strategy of the cohort, resembling to a perprotocol evaluation by using patient information about erMeDiet and PA adherence according to patient declaration and not due to the distribution between the control and intervention branches. Although the characteristics of the full PREDIMED plus trial do not allow to uncover the intervention and control groups, no differences were found in the proportion of patients in the intervention and control groups among HSI < 36 and HSI > 36 (p > 0.05). Besides, although HSI is a remarkable non-invasive tool in the detection of NAFLD, this index is not designed for NAFLD staging. In fact, biopsy directed studies have demonstrated a different distribution of NASH in patients with MetS than in those with type 2 diabetes mellitus (38). Although the NAFLD staging may exceed the objective of the present study, this feature could be addressed in future studies. Yet, the present results are valuable from the clinical point of view, providing remarkably similar assessment of the influence of adherence to healthy lifestyle patterns in patients’health as that of the consultation room. The longitudinal multivariate analysis and adjustments, using features related to HRQoL such as waist circumference obesity, MetS features (39), alcohol consumption (40) and total caloric intake (41,42)provide plausibility to the present results. In summary, the demonstration of the influence of NAFLD in theresponsetolifestylemodifications might represent an interesting tool for personalization and precision medicine in the cardiovascular risk epidemiological and clinical scenarios emphasizing the mediation of liver status in the therapeutical response. The present findings could encourage future research in the individualization of treatment of patients with MetS based on liver condition. CONCLUSION The detection of NAFLD by the HSI index in patients with MetS might identify subjects with lower sensitivity to lifestyle changes in terms of HRQoL. Patients with lower NAFLD probability begun to obtain improvement on HRQoL at 10 points of erMeDiet adherence and 50 MET-h/week, providing precision medicine prescriptions depending on liver condition status. DATA AVAILABILITY STATEMENT There are restrictions on data availability for the PREDIMEDPlus trial due to the signed consent agreements around data sharing, which only allow access to external researchers for studies following the project purposes. Requestors wishing to access the PREDIMED-Plus trial data used in this study can make a request to the PREDIMED-Plus trial Steering Committee chair: [email protected]. The request will then be passed to members of the PREDIMED-Plus Steering Committee for deliberation. ETHICS STATEMENT The studies involving human participants were reviewed and approved by Predimed Plus trial. The patients/participants provided their written informed consent to participate in this study. AUTHOR CONTRIBUTIONS MM-G, DC, JM, AA-G, JW, JV (11th author), DR, JL-M, RE, FT, JV (7th author), JS-M, AB-C, JT, VMS, XP, MD-R, PM-M, JV (11th author), CV, ER, JO and LD designed and conducted the research. DM-U, RC, JV (24th author), JM and LD conceived the study idea and the analysis design. LD supervised the research and DM-U and RC carried out the analysis procedures, bibliographic research, data preparation, statistical analysis and wrote initial drafts. RC assisted with statistical analysis and R programming. PV and JO participated in the scientific discussion of experimental results. All the authors assisted in manuscript revision for intellectual content and approved it. FUNDING The present study received funding from coordinated FIS project official Spanish institutions for funding scientific biomedical research, CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN) and Instituto de Salud Carlos III (ISCIII) through the Fondo de Investigación para la Salud (FIS) which is co-funded by the European Regional Development Fund (including the following projects: PI13/00673, PI13/00492, PI13/ 00272, PI13/01123, PI13/00462, PI13/00233, PI13/02184, PI13/00728, PI13/01090, PI13/01056, PI14/01722, PI14/00636, PI14/00618, PI14/00696, PI14/01206, PI14/01919, PI14/ 00853, PI14/01374, PI14/00972, PI14/00728, PI14/01471, PI16/00473, PI16/00662, PI16/01873, PI16/01094, PI16/00501, PI16/00533, PI16/00381, PI16/00366, PI16/01522, PI16/ 01120, PI17/00764, PI17/01183, PI17/00855, PI17/01347, PI17/00525, PI17/01827, PI17/00532, PI17/00215, PI17/ 01441, PI17/00508, PI17/01732, PI17/00926, PI19/00957, PI19/00386, PI19/00309, PI19/01032, PI19/00576, PI19/ 00017, PI19/01226, PI19/00781, PI19/01560, PI19/01332, PI20/01802, PI20/00138, PI20/01532, PI20/00456, PI20/00339, PI20/00557, PI20/00886, PI20/01158), the Special Action Project “Implementación y evaluación de una intervención intensiva Martı ´nez-Urbistondo et al. NAFLD Impact Over Lifestyle Changes Frontiers in Endocrinology | www.frontiersin.org June 2022 | Volume 13 | Article 8687959